From c26de33fde016c6890208305a7c16109fb9530a1 Mon Sep 17 00:00:00 2001 From: Samuel Friedman Date: Wed, 25 Jan 2023 15:52:57 -0500 Subject: [PATCH] Push to GitHub container registry GHCR (#507) * dataflow cleanups --- docker/vm_boot_images/build.sh | 10 +- .../config/tensorflow-requirements.txt | 6 +- env/requirements_ml4cvd_dataflow.txt | 45 - ml4h/TensorMap.py | 24 +- ml4h/__init__.py | 1 - ml4h/arguments.py | 6 +- ml4h/defines.py | 2 +- ml4h/explorations.py | 361 +-- ml4h/hyperparameters.py | 415 ---- ml4h/hypertuning.py | 8 +- ml4h/metrics.py | 8 +- ml4h/ml4ht_integration/tensor_generator.py | 14 +- ml4h/ml4ht_integration/tensor_map.py | 25 +- ml4h/models/basic_blocks.py | 50 +- ml4h/models/conv_blocks.py | 112 +- ml4h/models/legacy_models.py | 6 +- ml4h/models/merge_blocks.py | 20 +- ml4h/models/model_factory.py | 17 +- ml4h/models/pretrained_blocks.py | 30 +- ml4h/models/transformer_blocks.py | 3 + ml4h/plots.py | 81 +- ml4h/recipes.py | 42 +- .../{database => dataflow}/__init__.py | 0 .../bigquery_ukb_queries.py} | 0 .../{database => dataflow}/fieldids.json | 0 .../{database => dataflow}/load_fieldids.sh | 0 .../tensorize/dataflow/ml4h_dataflow.yml | 11 +- .../dataflow/requirements_ml4h_dataflow.txt | 194 ++ ml4h/tensorize/merge_hd5s.py | 2 +- ml4h/tensorize/tensorize_dataflow.py | 10 +- ml4h/tensormap/tensor_map_maker.py | 11 +- ml4h/tensormap/ukb/categorical.py | 359 +++ ml4h/tensormap/ukb/continuous.py | 2172 +++++++++++++++++ ml4h/tensormap/ukb/ecg.py | 4 +- ml4h/tensormap/ukb/mri.py | 4 + scripts/merge_hd5s.sh | 2 +- setup.py | 5 +- tests/ml4ht_integration/test_tensor_map.py | 37 +- tests/test_models.py | 44 +- tests/test_recipes.py | 4 +- 40 files changed, 3093 insertions(+), 1052 deletions(-) delete mode 100644 env/requirements_ml4cvd_dataflow.txt delete mode 100644 ml4h/hyperparameters.py rename ml4h/tensorize/{database => dataflow}/__init__.py (100%) rename ml4h/tensorize/{database/tensorize.py => dataflow/bigquery_ukb_queries.py} (100%) rename ml4h/tensorize/{database => dataflow}/fieldids.json (100%) rename ml4h/tensorize/{database => dataflow}/load_fieldids.sh (100%) rename env/ml4cvd_dataflow.yml => ml4h/tensorize/dataflow/ml4h_dataflow.yml (65%) create mode 100644 ml4h/tensorize/dataflow/requirements_ml4h_dataflow.txt create mode 100644 ml4h/tensormap/ukb/categorical.py create mode 100644 ml4h/tensormap/ukb/continuous.py diff --git a/docker/vm_boot_images/build.sh b/docker/vm_boot_images/build.sh index d266ec54d..8e078ec07 100755 --- a/docker/vm_boot_images/build.sh +++ b/docker/vm_boot_images/build.sh @@ -12,6 +12,7 @@ set -e ################### VARIABLES ############################################ REPO="gcr.io/broad-ml4cvd/deeplearning" +GITHUB_REPO="ghcr.io/broadinstitute/ml4h" TAG=$( git rev-parse --short HEAD ) CONTEXT="docker/vm_boot_images/" CPU_ONLY="false" @@ -54,7 +55,7 @@ usage() Default: Build image to run on GPU-enabled machines and tag image also as '${LATEST_TAG_GPU}'. -p Push to Google Container Register - -P Push to latest tag + -P Push to latest tag and to the Github GHCR repository -h Print this help text @@ -116,12 +117,17 @@ docker build ${CONTEXT} \ --build-arg BASE_IMAGE=${BASE_IMAGE} \ --tag "${REPO}:${TAG}" \ --tag "${REPO}:${LATEST_TAG}" \ + --tag "${GITHUB_REPO}:${TAG}" \ + --tag "${GITHUB_REPO}:${LATEST_TAG}" \ --network host \ if [[ ${PUSH_TO_LATEST} == "true" ]]; then - echo -e "${BLUE}Pushing the image '${REPO}' to Google Container Registry with tags '${TAG}' and '${LATEST_TAG}'...${NC}" + echo -e "${BLUE}Pushing the image '${REPO}' to Github GHCR and Google Container Registry with tags '${TAG}' and '${LATEST_TAG}'...${NC}" docker push ${REPO}:${TAG} docker push ${REPO}:${LATEST_TAG} + + docker push ${GITHUB_REPO}:${TAG} + docker push ${GITHUB_REPO}:${LATEST_TAG} fi if [[ ${PUSH_TO_GCR} == "true" ]]; then diff --git a/docker/vm_boot_images/config/tensorflow-requirements.txt b/docker/vm_boot_images/config/tensorflow-requirements.txt index 55ff8c3c4..d517a6e47 100644 --- a/docker/vm_boot_images/config/tensorflow-requirements.txt +++ b/docker/vm_boot_images/config/tensorflow-requirements.txt @@ -15,8 +15,10 @@ pytest pysam tensorflow==2.9.1 tensorflow-addons==0.17.1 -tensorflow_probability==0.17.0 tensorflow_hub +tensorflow_probability==0.17.0 +tensorflow-text==2.9 +tf-models-official==2.9 keras-tuner numcodecs beautifulsoup4 @@ -36,4 +38,4 @@ torch opencv-python blosc boto3 -ml4ht +ml4ht==0.0.9 diff --git a/env/requirements_ml4cvd_dataflow.txt b/env/requirements_ml4cvd_dataflow.txt deleted file mode 100644 index 971d20dcf..000000000 --- a/env/requirements_ml4cvd_dataflow.txt +++ /dev/null @@ -1,45 +0,0 @@ -apache-beam==2.12.0 -avro-python3==1.8.2 -cachetools==3.1.0 -certifi==2019.3.9 -chardet==3.0.4 -crcmod==1.7 -dill==0.2.9 -docopt==0.6.2 -fastavro==0.21.22 -fasteners==0.14.1 -future==0.17.1 -google-api-core==1.10.0 -google-apitools==0.5.26 -google-auth==1.6.3 -google-cloud-bigquery==1.6.1 -google-cloud-bigtable==0.31.1 -google-cloud-core==0.28.1 -google-cloud-pubsub==0.39.0 -google-cloud-storage==1.13.0 -google-resumable-media==0.3.2 -googleapis-common-protos==1.5.10 -grpc-google-iam-v1==0.11.4 -grpcio==1.20.1 -h5py==2.9.0 -hdfs==2.5.2 -httplib2==0.11.3 -idna==2.8 -mock==2.0.0 -monotonic==1.5 -numpy==1.16.3 -oauth2client==3.0.0 -pbr==5.2.0 -proto-google-cloud-datastore-v1==0.90.4 -protobuf==3.7.1 -pyarrow==0.11.1 -pyasn1==0.4.5 -pyasn1-modules==0.2.5 -pydot==1.2.4 -pyparsing==2.4.0 -pytz==2019.1 -PyYAML==3.13 -requests==2.21.0 -rsa==4.0 -six==1.12.0 -urllib3==1.24.2 diff --git a/ml4h/TensorMap.py b/ml4h/TensorMap.py index 7574a44db..bd4e505f2 100755 --- a/ml4h/TensorMap.py +++ b/ml4h/TensorMap.py @@ -46,6 +46,7 @@ class Interpretation(Enum): SURVIVAL_CURVE = auto() DISCRETIZED = auto() MESH = auto() + TEXT = auto() def __str__(self): """class Interpretation.FLOAT_ARRAY becomes float_array""" @@ -170,8 +171,17 @@ def __init__( self.time_series_lookup = time_series_lookup self.discretization_bounds = discretization_bounds + # Infer shape from channel map or interpretation + if self.shape is None: + self.shape = (2,) if self.is_time_to_event() else (len(channel_map),) + # Setting time_series_limit indicates dynamic shaping which is always accompanied by 1st dim of None + if self.time_series_limit is not None: + self.shape = (None,) + self.shape + # Infer loss from interpretation - if self.loss is None and self.is_categorical(): + if self.loss is None and self.is_categorical() and self.shape[0] == 1: + self.loss = 'sparse_categorical_crossentropy' + elif self.loss is None and self.is_categorical(): self.loss = 'categorical_crossentropy' elif self.loss is None and self.is_continuous() and self.sentinel is not None: self.loss = sentinel_logcosh_loss(self.sentinel) @@ -194,12 +204,7 @@ def __init__( elif self.activation is None and (self.is_survival_curve() or self.is_time_to_event()): self.activation = 'sigmoid' - # Infer shape from channel map or interpretation - if self.shape is None: - self.shape = (2,) if self.is_time_to_event() else (len(channel_map),) - # Setting time_series_limit indicates dynamic shaping which is always accompanied by 1st dim of None - if self.time_series_limit is not None: - self.shape = (None,) + self.shape + if self.channel_map is None and self.is_time_to_event(): self.channel_map = DEFAULT_TIME_TO_EVENT_CHANNELS @@ -274,6 +279,9 @@ def is_survival_curve(self): def is_discretized(self): return self.interpretation == Interpretation.DISCRETIZED + def is_text(self): + return self.interpretation == Interpretation.TEXT + def axes(self): return len(self.shape) @@ -450,7 +458,7 @@ def _default_tensor_from_file(tm, hd5, dependents={}): if tm.hd5_key_guess() in hd5: data = tm.hd5_first_dataset_in_group(hd5, tm.hd5_key_guess()) if tm.storage_type == StorageType.CATEGORICAL_INDEX or tm.storage_type == StorageType.CATEGORICAL_FLAG: - index = int(data[0]) + index = min(int(data[0]), categorical_data.shape[0]-1) categorical_data[index] = 1.0 else: categorical_data = np.array(data) diff --git a/ml4h/__init__.py b/ml4h/__init__.py index 14d641875..a60871898 100644 --- a/ml4h/__init__.py +++ b/ml4h/__init__.py @@ -1,2 +1 @@ from . import defines -#from .tensorize.database.tensorize import tensorize_sql_fields, write_tensor_from_sql diff --git a/ml4h/arguments.py b/ml4h/arguments.py index 4c139f419..391ea87ef 100644 --- a/ml4h/arguments.py +++ b/ml4h/arguments.py @@ -69,8 +69,6 @@ def parse_args(): parser.add_argument('--bigquery_dataset', default='broad-ml4cvd.ukbb7089_r10data', help='BigQuery dataset containing tables we want to query.') parser.add_argument('--xml_folder', default='/mnt/disks/ecg-rest-xml/', help='Path to folder of XMLs of ECG data.') parser.add_argument('--zip_folder', default='/mnt/disks/sax-mri-zip/', help='Path to folder of zipped dicom images.') - parser.add_argument('--phenos_folder', default='gs://ml4cvd/phenotypes/', help='Path to folder of phenotype defining CSVs.') - parser.add_argument('--phecode_definitions', default='/mnt/ml4cvd/projects/jamesp/data/phecode_definitions1.2.csv', help='CSV of phecode definitions') parser.add_argument('--dicoms', default='./dicoms/', help='Path to folder of dicoms.') parser.add_argument('--sample_csv', default=None, help='Path to CSV with Sample IDs to restrict tensor paths') parser.add_argument('--tsv_style', default='standard', choices=['standard', 'genetics'], help='Format choice for the TSV file produced in output by infer and explore modes.') @@ -452,7 +450,7 @@ def generate_tensormap_id(tm): return hashlib.sha256(str(tm).encode("utf-8")).hexdigest() -def generate_model_id(tensor_maps_in, tensor_maps_out): +def generate_model_id(model_name: str, tensor_maps_in: List[TensorMap], tensor_maps_out: List[TensorMap]): str_i = '_'.join([str(tmi) for tmi in tensor_maps_in]) str_o = '_'.join([str(tmo) for tmo in tensor_maps_out]) model_str = f'{str_i}&{str_o}' @@ -526,7 +524,7 @@ def _process_args(args): logging.info(f"Command Line was: {command_line}") logging.info(f'Input SHA256s: {[(tm.name, generate_tensormap_id(tm)) for tm in args.tensor_maps_in]}') logging.info(f'Output SHA256s: {[(tm.name, generate_tensormap_id(tm)) for tm in args.tensor_maps_out]}') - logging.info(f'Model SHA256: {generate_model_id(args.tensor_maps_in, args.tensor_maps_out)}') + logging.info(f'Model SHA256: {generate_model_id(args.id, args.tensor_maps_in, args.tensor_maps_out)}') logging.info(f"Arguments are {args}\n") if args.eager: diff --git a/ml4h/defines.py b/ml4h/defines.py index ee9c90884..2ce170462 100755 --- a/ml4h/defines.py +++ b/ml4h/defines.py @@ -173,7 +173,7 @@ def __str__(self): TENSOR_MAPS_FILE_NAME = 'by_script' # BigQuery tables -SQL_DATASET = "ukbb7089_201904" +SQL_DATASET = "ukbb7089_202006" DICTIONARY_TABLE = SQL_DATASET+".dictionary" CODING_TABLE = SQL_DATASET+".coding" PHENOTYPE_TABLE = SQL_DATASET+".phenotype" diff --git a/ml4h/explorations.py b/ml4h/explorations.py index b3bfd2a56..d21bb789d 100644 --- a/ml4h/explorations.py +++ b/ml4h/explorations.py @@ -24,7 +24,7 @@ matplotlib.use('Agg') # Need this to write images from the GSA servers. Order matters: import matplotlib.pyplot as plt # First import matplotlib, then use Agg, then import plt -from ml4h.models.legacy_models import make_multimodal_multitask_model +from ml4h.models.legacy_models import legacy_multimodal_multitask_model from ml4h.TensorMap import TensorMap, Interpretation, decompress_data from ml4h.tensor_generators import TensorGenerator, test_train_valid_tensor_generators from ml4h.tensor_generators import BATCH_INPUT_INDEX, BATCH_OUTPUT_INDEX, BATCH_PATHS_INDEX @@ -321,68 +321,23 @@ def sample_from_language_model( model, test_data, max_samples=16, heat=0.7, ): burn_in = np.zeros((1,) + language_input.shape, dtype=np.float32) - index_2_token = {v: k for k, v in language_output.channel_map.items()} + index_2_token = {v: str(k) for k, v in language_output.channel_map.items()} for i in range(min(max_samples, test_data[language_input.input_name()].shape[0])): # iterate over the batch burn_in[0] = test_data[language_input.input_name()][i] - sentence = ''.join([index_2_token[index] for index in burn_in[0]]) - logging.info(f' Batch sentence start:{sentence} ------ {i}') + sentence = ''.join([index_2_token[index] for index in burn_in[0] if index in index_2_token]) + logging.info(f'Input:{sentence} ------ {i}') for j in range(max_samples): burn_in = np.zeros((1,) + language_input.shape, dtype=np.float32) for k, c in enumerate(sentence[j:]): - burn_in[0, k] = language_output.channel_map[c] + if c in language_output.channel_map: + burn_in[0, k] = language_output.channel_map[c] cur_test = {language_input.input_name(): burn_in} prediction = model.predict(cur_test) + if isinstance(prediction, list): # handle multitask + prediction = prediction[0] next_token = index_2_token[_sample_with_heat(prediction[0, -1, :], heat)] sentence += next_token - logging.info(f'Model completed sentence:{sentence}') - - -def sample_from_char_embed_model(tensor_maps_in: List[TensorMap], char_model: Model, test_batch: Dict[str, np.ndarray], test_paths: List[str]) -> None: - for tm in tensor_maps_in: - if tm.interpretation == Interpretation.LANGUAGE: - language_map = tm - if PARTNERS_READ_TEXT in tm.name: - index_map = PARTNERS_IDX_2_CHAR - char_map = PARTNERS_CHAR_2_IDX - else: - index_map = ECG_IDX_2_CHAR - char_map = ECG_CHAR_2_IDX - elif tm.interpretation == Interpretation.EMBEDDING: - embed_map = tm - - try: - embed_map - except NameError: - raise ValueError(f'Sampling from a character level model requires an embedding tmap.') - - window_size = test_batch[language_map.input_name()].shape[1] - alphabet_size = test_batch[language_map.input_name()].shape[2] - for i in range(test_batch[embed_map.input_name()].shape[0]): - count = 0 - sentence = '' - next_char = '' - embed_in = test_batch[embed_map.input_name()][i:i+1, :] - burn_in = np.zeros((1, window_size, alphabet_size), dtype=np.float32) - window_size = burn_in.shape[1] - with h5py.File(test_paths[i], 'r') as hd5: - logging.info(f"\n") - if 'read_' in language_map.name: - caption = decompress_data(data_compressed=hd5[tm.name][()], dtype=hd5[tm.name].attrs['dtype']) - else: - caption = str(tm.hd5_first_dataset_in_group(hd5, tm.hd5_key_guess())[()]).strip() - logging.info(f"Real text: {caption}") - while next_char != '!' and count < 400: - cur_test = {embed_map.input_name(): embed_in, language_map.input_name(): burn_in} - y_pred = char_model.predict(cur_test) - next_char = index_map[_sample_with_heat(y_pred[0, :], 0.7)] - sentence += next_char - burn_in = np.zeros((1,) + test_batch[language_map.input_name()].shape[1:], dtype=np.float32) - for j, c in enumerate(reversed(sentence)): - if j == window_size: - break - burn_in[0, window_size-j-1, char_map[c]] = 1.0 - count += 1 - logging.info(f"Model text:{sentence}") + logging.info(f'Model:{sentence}') def tensors_to_label_dictionary( @@ -435,7 +390,7 @@ def infer_with_pixels(args): inference_tsv = os.path.join(args.output_folder, args.id, 'pixel_inference_' + args.id + '.tsv') tensor_paths = [args.tensors + tp for tp in sorted(os.listdir(args.tensors)) if os.path.splitext(tp)[-1].lower() == TENSOR_EXT] # hard code batch size to 1 so we can iterate over file names and generated tensors together in the tensor_paths for loop - model = make_multimodal_multitask_model(**args.__dict__) + model = legacy_multimodal_multitask_model(**args.__dict__) generate_test = TensorGenerator( 1, args.tensor_maps_in, args.tensor_maps_out, tensor_paths, num_workers=args.num_workers, cache_size=args.cache_size, keep_paths=True, mixup_alpha=args.mixup_alpha, @@ -778,7 +733,7 @@ def _hd5_to_disk(self, path, gen_name): dict_of_tensor_dicts[0][f'error_type_{tm.name}'] = type(e).__name__ for i in dict_of_tensor_dicts: - dict_of_tensor_dicts[i]['fpath'] = os.path.basename(path).split('.')[0] + dict_of_tensor_dicts[i]['sample_id'] = os.path.basename(path).split('.')[0] dict_of_tensor_dicts[i]['generator'] = gen_name # write tdicts to disk @@ -819,7 +774,7 @@ def _tensors_to_df(args): ExploreParallelWrapper(tmaps, paths, args.num_workers, args.output_folder, args.id).run() # get columns that should have dtype 'string' instead of dtype 'O' - str_cols = ['fpath', 'generator'] + str_cols = ['sample_id', 'generator'] for tm in tmaps: if tm.interpretation == Interpretation.LANGUAGE: str_cols.extend([f'{tm.name} {cm}' for cm in tm.channel_map] if tm.channel_map else [tm.name]) @@ -835,7 +790,8 @@ def _tensors_to_df(args): fpath = os.path.join(base, name) _df = pd.read_csv(fpath, dtype=str_cols) logging.debug(f'Loaded {fpath} into memory') - df = df.append(_df, ignore_index=True) + #df = df.append(_df, ignore_index=True) + df = pd.concat([df, _df], ignore_index=True) logging.debug(f'Appended {fpath} to overall dataframe') temp_files.append(fpath) @@ -894,12 +850,15 @@ def explore(args): df = df[cols] if tsv_style_is_genetics: - fid = df['fpath'].str.split('/').str[-1].str.split('.').str[0] + fid = df['sample_id'].str.split('/').str[-1].str.split('.').str[0] df.insert(0, 'FID', fid) df.insert(1, 'IID', fid) # Save dataframe to CSV fpath = os.path.join(args.output_folder, args.id, f"tensors_all_union.{out_ext}") df.to_csv(fpath, index=False, sep=out_sep) + df[df.generator=='train'][['sample_id']].to_csv(os.path.join(args.output_folder, args.id, f"train.{out_ext}"), index=False, sep=out_sep) + df[df.generator=='test'][['sample_id']].to_csv(os.path.join(args.output_folder, args.id, f"test.{out_ext}"), index=False, sep=out_sep) + df[df.generator=='valid'][['sample_id']].to_csv(os.path.join(args.output_folder, args.id, f"valid.{out_ext}"), index=False, sep=out_sep) fpath = os.path.join(args.output_folder, args.id, f"tensors_all_intersect.{out_ext}") df.dropna().to_csv(fpath, index=False, sep=out_sep) logging.info(f"Saved dataframe of tensors (union and intersect) to {fpath}") @@ -1084,292 +1043,12 @@ def explore(args): logging.info(f"Saved {name} histogram plot at: {figure_path}") - -def cross_reference(args): - """Cross reference a source cohort with a reference cohort.""" - cohort_counts = OrderedDict() - - src_path = args.tensors_source - src_name = args.tensors_name - src_join = args.join_tensors - src_time = args.time_tensor - ref_path = args.reference_tensors - ref_name = args.reference_name - ref_join = args.reference_join_tensors - ref_start = args.reference_start_time_tensor - ref_end = args.reference_end_time_tensor - ref_labels = args.reference_labels - number_in_window = args.number_per_window - order_in_window = args.order_in_window - window_names = args.window_name - match_exact_window = order_in_window is not None - match_min_window = not match_exact_window - match_any_window = args.match_any_window - match_every_window = not match_any_window - - # parse options - src_cols = list(src_join) - ref_cols = list(ref_join) - if ref_labels is not None: - ref_cols.extend(ref_labels) - - def _cols_from_time_windows(time_windows): - return {time_point[0] for time_window in time_windows for time_point in time_window} - - use_time = not any(arg is None for arg in [src_time, ref_start, ref_end]) - if use_time: - if len(ref_start) != len(ref_end): - raise ValueError(f"Invalid time windows, got {len(ref_start)} starts and {len(ref_end)} ends") - - if order_in_window is None: - # if not matching exactly N in time window, order_in_window is None - # make array of blanks so zip doesnt break later - order_in_window = [''] * len(ref_start) - elif len(order_in_window) != len(ref_start): - raise ValueError(f"Ambiguous time selection in time windows, got {len(order_in_window)} order_in_window for {len(ref_start)} windows") - - if window_names is None: - window_names = [str(i) for i in range(len(ref_start))] - elif len(window_names) != len(ref_start): - raise ValueError(f"Ambiguous time window names, got {len(window_names)} names for {len(ref_start)} windows") - - # get time columns and ensure time windows are defined - src_cols.append(src_time) - - # ref start and end are lists of lists, defining time windows - time_windows = list(zip(ref_start, ref_end)) - - # each time window is defined by a tuples of two lists, - # where the first list of each tuple defines the start point of the time window - # and the second list of each tuple defines the end point of the time window - for start, end in time_windows: - # each start/end point list is two elements, - # where the first element in the list is the name of the time tensor - # and the second element is the offset to the value of the time tensor - - # add day offset of 0 for time points without explicit offset - [time_point.append(0) for time_point in [start, end] if len(time_point) == 1] - - # parse day offset as int - start[1] = int(start[1]) - end[1] = int(end[1]) - - # add unique column names to ref_cols - ref_cols.extend(_cols_from_time_windows(time_windows)) - - # load data into dataframes - def _load_data(name, path, cols): - if os.path.isdir(path): - logging.debug(f'Assuming {name} is directory of hd5 at {path}') - from ml4h.arguments import tensormap_lookup - # TODO: check if this works! - args.tensor_maps_in = [tensormap_lookup(it) for it in cols] - df = _tensors_to_df(args)[cols] - else: - logging.debug(f'Assuming {name} is a csv at {path}') - df = pd.read_csv(path, usecols=cols, low_memory=False) - return df - - src_df = _load_data(src_name, src_path, src_cols) - logging.info(f'Loaded {src_name} into dataframe') - ref_df = _load_data(ref_name, ref_path, ref_cols) - logging.info(f'Loaded {ref_name} into dataframe') - - # cleanup time col - if use_time: - src_df[src_time] = pd.to_datetime(src_df[src_time], errors='coerce', infer_datetime_format=True) - src_df.dropna(subset=[src_time], inplace=True) - - for ref_time in _cols_from_time_windows(time_windows): - ref_df[ref_time] = pd.to_datetime(ref_df[ref_time], errors='coerce', infer_datetime_format=True) - ref_df.dropna(subset=_cols_from_time_windows(time_windows), inplace=True) - - def _add_offset_time(ref_time): - offset = ref_time[1] - ref_time = ref_time[0] - if offset == 0: - return ref_time - ref_time_col = f'{ref_time}_{offset:+}_days' - if ref_time_col not in ref_df: - ref_df[ref_time_col] = ref_df[ref_time].apply(lambda x: x + datetime.timedelta(days=offset)) - ref_cols.append(ref_time_col) - return ref_time_col - - # convert time windows to tuples of cleaned and parsed column names - time_windows = [(_add_offset_time(start), _add_offset_time(end)) for start, end in time_windows] - logging.info('Cleaned data columns and removed rows that could not be parsed') - - # drop duplicates based on cols - src_df.drop_duplicates(subset=src_cols, inplace=True) - ref_df.drop_duplicates(subset=ref_cols, inplace=True) - logging.info('Removed duplicates from dataframes, based on join, time, and label') - - cohort_counts[f'{src_name} (total)'] = len(src_df) - cohort_counts[f'{src_name} (unique {" + ".join(src_join)})'] = len(src_df.drop_duplicates(subset=src_join)) - cohort_counts[f'{ref_name} (total)'] = len(ref_df) - cohort_counts[f'{ref_name} (unique {" + ".join(ref_join)})'] = len(ref_df.drop_duplicates(subset=ref_join)) - - # merging on join columns duplicates rows in source if there are duplicate join values in both source and reference - # this is fine, each row in reference needs all associated rows in source - cross_df = src_df.merge(ref_df, how='inner', left_on=src_join, right_on=ref_join).sort_values(src_cols) - logging.info('Cross referenced based on join tensors') - - cohort_counts[f'{src_name} in {ref_name} (unique {" + ".join(src_cols)})'] = len(cross_df.drop_duplicates(subset=src_cols)) - cohort_counts[f'{src_name} in {ref_name} (unique {" + ".join(src_join)})'] = len(cross_df.drop_duplicates(subset=src_join)) - cohort_counts[f'{ref_name} in {src_name} (unique joins + times + labels)'] = len(cross_df.drop_duplicates(subset=ref_cols)) - cohort_counts[f'{ref_name} in {src_name} (unique {" + ".join(ref_join)})'] = len(cross_df.drop_duplicates(subset=ref_join)) - - # dump results and report label distribution - def _report_cross_reference(df, title): - title = title.replace(' ', '_') - if ref_labels is not None: - series = df[ref_labels].astype(str).apply(lambda x: '<>'.join(x), axis=1, raw=True) - label_values, counts = np.unique(series, return_counts=True) - label_values = np.array(list(map(lambda val: val.split('<>'), label_values))) - label_values = np.append(label_values, [['Total']*len(ref_labels)], axis=0) - total = sum(counts) - counts = np.append(counts, [total]) - fracs = list(map(lambda f: f'{f:0.5f}', counts / total)) - - res = pd.DataFrame(data=label_values, columns=ref_labels) - res['count'] = counts - res['fraction total'] = fracs - - # save label counts to csv - fpath = os.path.join(args.output_folder, args.id, f'label_counts_{title}.csv') - res.to_csv(fpath, index=False) - logging.info(f'Saved distribution of labels in cross reference to {fpath}') - - # save cross reference to csv - fpath = os.path.join(args.output_folder, args.id, f'list_{title}.csv') - df.set_index(src_join, drop=True).to_csv(fpath) - logging.info(f'Saved cross reference to {fpath}') - - if use_time: - # count rows across time windows - def _count_time_windows(dfs, title, exact_or_min): - if type(dfs) is list: - # Number of pre-op (surgdt -180 days; surgdt) ECG from patients with 1+ ECG in all windows - # Number of distinct pre-op (surgdt -180 days; surgdt) ECG from patients with 1+ ECG in all windows - # Number of distinct pre-op (surgdt -180 days; surgdt) partners_ecg_patientid_clean from patients with 1+ ECG in all windows - - # Number of newest pre-op (surgdt -180 days; surgdt) ECG from patients with 1 ECG in all windows - # Number of distinct newest pre-op (surgdt -180 days; surgdt) ECG from patients with 1 ECG in all windows - # Number of distinct newest pre-op (surgdt -180 days; surgdt) partners_ecg_patientid_clean from patients with 1 ECG in all windows - for df, window_name, order, (start, end) in zip(dfs, window_names, order_in_window, time_windows): - order = f'{order} ' if exact_or_min == 'exactly' else '' - start = start.replace('_', ' ') - end = end.replace('_', ' ') - cohort_counts[f'Number of {order}{window_name} ({start}; {end}) {src_name} from patients with {title}'] = len(df) - cohort_counts[f'Number of distinct {order}{window_name} ({start}; {end}) {src_name} from patients with {title}'] = len(df.drop_duplicates(subset=src_cols)) - cohort_counts[f'Number of distinct {order}{window_name} ({start}; {end}) {" + ".join(src_join)} from patients with {title}'] = len(df.drop_duplicates(subset=src_join)) - else: - # Number of ECGs from patients with 1+ ECG in all windows - # Number of distinct ECGs from patients with 1+ ECG in all windows - # Number of distinct partners_ecg_patientid_clean from patients with 1+ ECG in all windows - df = dfs - cohort_counts[f'Number of {src_name} from patients with {title}'] = len(df) - cohort_counts[f'Number of distinct {src_name} from patients with {title}'] = len(df.drop_duplicates(subset=src_cols)) - cohort_counts[f'Number of distinct {" + ".join(src_join)} from patients with {title}'] = len(df.drop_duplicates(subset=src_join)) - - # aggregate all time windows back into one dataframe with indicator for time window index - def _aggregate_time_windows(time_window_dfs, window_names): - for df, window_name in zip(time_window_dfs, window_names): - if 'time_window' not in df: - df['time_window'] = window_name - aggregated_df = pd.concat(time_window_dfs, ignore_index=True).sort_values(by=src_cols + ['time_window'], ignore_index=True) - return aggregated_df - - # get only occurrences for join_tensors that appear in every time window - def _intersect_time_windows(time_window_dfs): - # find the intersection of join_tensors that appear in all time_window_dfs - join_tensor_intersect = reduce(lambda a, b: a.merge(b), [pd.DataFrame(df[src_join].drop_duplicates()) for df in time_window_dfs]) - - # filter time_window_dfs to only the rows that have join_tensors across all time windows - time_window_dfs_intersect = [df.merge(join_tensor_intersect) for df in time_window_dfs] - return time_window_dfs_intersect - - # 1. get data with at least N (default 1) occurrences in all time windows - # 2. within each time window, get only data for join_tensors that have N rows in the time window - # 3. across all time windows, get only data for join_tensors that have data in all time windows - - # get df for each time window - dfs_min_in_any_time_window = [ - cross_df[(cross_df[start] < cross_df[src_time]) & (cross_df[src_time] < cross_df[end])] - for start, end in time_windows - ] - - # get at least N occurrences in any time window - dfs_min_in_any_time_window = [df.groupby(src_join+[start, end]).filter(lambda g: len(g) >= number_in_window) for df, (start, end) in zip(dfs_min_in_any_time_window, time_windows)] - if match_min_window and match_any_window: - min_in_any_time_window = _aggregate_time_windows(dfs_min_in_any_time_window, window_names) - logging.info(f"Cross referenced so unique event occurs {number_in_window}+ times in any time window") - title = f'{number_in_window}+ in any window' - _report_cross_reference(min_in_any_time_window, title) - _count_time_windows(dfs_min_in_any_time_window, title, 'at least') - if len(dfs_min_in_any_time_window) > 1: - _count_time_windows(min_in_any_time_window, title, 'at least') - - # get at least N occurrences in every time window - if match_min_window and match_every_window: - dfs_min_in_every_time_window = _intersect_time_windows(dfs_min_in_any_time_window) - min_in_every_time_window = _aggregate_time_windows(dfs_min_in_every_time_window, window_names) - logging.info(f"Cross referenced so unique event occurs {number_in_window}+ times in all time windows") - title = f'{number_in_window}+ in all windows' - _report_cross_reference(min_in_every_time_window, title) - _count_time_windows(dfs_min_in_every_time_window, title, 'at least') - if len(dfs_min_in_every_time_window) > 1: - _count_time_windows(min_in_every_time_window, title, 'at least') - - # get exactly N occurrences, select based on ordering - def _get_occurrences(df, order, start, end): - if order == 'newest': - df = df.groupby(src_join+[start, end]).tail(number_in_window) - elif order == 'oldest': - df = df.groupby(src_join+[start, end]).head(number_in_window) - elif order == 'random': - df = df.groupby(src_join+[start, end]).apply(lambda g: g.sample(number_in_window)) - else: - raise NotImplementedError(f"Ordering for which rows to use in time window unknown: '{order}'") - return df.reset_index(drop=True) - - # get exactly N occurrences in any time window - if match_exact_window: - dfs_exact_in_any_time_window = [_get_occurrences(df, order, start, end) for df, order, (start, end) in zip(dfs_min_in_any_time_window, order_in_window, time_windows)] - if match_exact_window and match_any_window: - exact_in_any_time_window = _aggregate_time_windows(dfs_exact_in_any_time_window, window_names) - logging.info(f"Cross referenced so unique event occurs exactly {number_in_window} times in any time window") - title = f'{number_in_window} in any window' - _report_cross_reference(exact_in_any_time_window, title) - _count_time_windows(dfs_exact_in_any_time_window, title, 'exactly') - if len(dfs_exact_in_any_time_window) > 1: - _count_time_windows(exact_in_any_time_window, title, 'exactly') - - # get exactly N occurrences in every time window - if match_exact_window and match_every_window: - dfs_exact_in_every_time_window = _intersect_time_windows(dfs_exact_in_any_time_window) - exact_in_every_time_window = _aggregate_time_windows(dfs_exact_in_every_time_window, window_names) - logging.info(f"Cross referenced so unique event occurs exactly {number_in_window} times in all time windows") - title = f'{number_in_window} in all windows' - _report_cross_reference(exact_in_every_time_window, title) - _count_time_windows(dfs_exact_in_every_time_window, title, 'exactly') - if len(dfs_exact_in_every_time_window) > 1: - _count_time_windows(exact_in_every_time_window, title, 'exactly') - else: - _report_cross_reference(cross_df, f'all {src_name} in {ref_name}') - - # report counts - fpath = os.path.join(args.output_folder, args.id, 'summary_cohort_counts.csv') - pd.DataFrame.from_dict(cohort_counts, orient='index', columns=['count']).rename_axis('description').to_csv(fpath) - logging.info(f'Saved cohort counts to {fpath}') - - def latent_space_dataframe(infer_hidden_tsv, explore_csv): df = pd.read_csv(explore_csv) - df['fpath'] = pd.to_numeric(df['fpath'], errors='coerce') + df['sample_id'] = pd.to_numeric(df['sample_id'], errors='coerce') df2 = pd.read_csv(infer_hidden_tsv, sep='\t', engine='python') df2['sample_id'] = pd.to_numeric(df2['sample_id'], errors='coerce') - latent_df = pd.merge(df, df2, left_on='fpath', right_on='sample_id', how='inner') + latent_df = pd.merge(df, df2, on='sample_id', how='inner') return latent_df diff --git a/ml4h/hyperparameters.py b/ml4h/hyperparameters.py deleted file mode 100644 index 01509a931..000000000 --- a/ml4h/hyperparameters.py +++ /dev/null @@ -1,415 +0,0 @@ -# hyperparameters.py - -# Imports -import gc -import os -import logging -import numpy as np -from timeit import default_timer as timer - -import hyperopt -from hyperopt import fmin, tpe, hp - -import matplotlib -matplotlib.use('Agg') # Need this to write images from the GSA servers. Order matters: -import matplotlib.pyplot as plt # First import matplotlib, then use Agg, then import plt - -from skimage.filters import threshold_otsu - - -from ml4h.arguments import parse_args -from ml4h.plots import plot_metric_history -from ml4h.tensor_maps_by_script import TMAPS -from ml4h.defines import IMAGE_EXT, MODEL_EXT -from ml4h.models.legacy_models import train_model_from_generators, make_multimodal_multitask_model -from ml4h.tensor_generators import test_train_valid_tensor_generators, big_batch_from_minibatch_generator - -MAX_LOSS = 9e9 - - -def run(args): - # Keep track of elapsed execution time - start_time = timer() - try: - if 'conv' == args.mode: - optimize_conv_layers_multimodal_multitask(args) - elif 'dense_layers' == args.mode: - optimize_dense_layers_multimodal_multitask(args) - elif 'lr' == args.mode: - optimize_lr_multimodal_multitask(args) - elif 'inputs' == args.mode: - optimize_input_tensor_maps(args) - elif 'optimizer' == args.mode: - optimize_optimizer(args) - elif 'architecture' == args.mode: - optimize_architecture(args) - elif 'ecg_rest' == args.mode: - optimize_ecg_rest_architecture(args) - elif 'ecg_rest_unet' == args.mode: - optimize_ecg_rest_unet_architecture(args) - elif 'mri_sax' == args.mode: - optimize_mri_sax_architecture(args) - elif 'conv_x' == args.mode: - optimize_conv_x(args) - else: - raise ValueError('Unknown hyper-parameter optimization mode:', args.mode) - - except Exception as e: - logging.exception(e) - - end_time = timer() - elapsed_time = end_time - start_time - logging.info("Executed the '{}' operation in {:.2f} seconds".format(args.mode, elapsed_time)) - - -def hyperparameter_optimizer(args, space, param_lists={}): - args.keep_paths = False - args.keep_paths_test = False - _, _, generate_test = test_train_valid_tensor_generators(**args.__dict__) - test_data, test_labels = big_batch_from_minibatch_generator(generate_test, args.test_steps) - histories = [] - fig_path = os.path.join(args.output_folder, args.id, 'plots') - i = 0 - - def loss_from_multimodal_multitask(x): - model = None - history = None - nonlocal i - i += 1 - try: - set_args_from_x(args, x) - model = make_multimodal_multitask_model(**args.__dict__) - - if model.count_params() > args.max_parameters: - logging.info(f"Model too big, max parameters is:{args.max_parameters}, model has:{model.count_params()}. Return max loss.") - return MAX_LOSS - generate_train, generate_valid, _ = test_train_valid_tensor_generators(**args.__dict__) - model, history = train_model_from_generators( - model, generate_train, generate_valid, args.training_steps, args.validation_steps, - args.batch_size, args.epochs, args.patience, args.output_folder, args.id, - args.inspect_model, args.inspect_show_labels, True, False, - ) - history.history['parameter_count'] = [model.count_params()] - histories.append(history.history) - title = f'trial_{i}' # refer to loss_by_params.txt to find the params for this trial - plot_metric_history(history, args.training_steps, title, fig_path) - model.load_weights(os.path.join(args.output_folder, args.id, args.id + MODEL_EXT)) - loss_and_metrics = model.evaluate(test_data, test_labels, batch_size=args.batch_size) - logging.info(f'Current architecture:\n{string_from_arch_dict(x)}\nCurrent model size: {model.count_params()}.') - logging.info(f"Iteration {i} out of maximum {args.max_models}\nTest Loss: {loss_and_metrics[0]}") - return loss_and_metrics[0] - - except ValueError: - logging.exception('ValueError trying to make a model for hyperparameter optimization. Returning max loss.') - return MAX_LOSS - except: - logging.exception('Error trying hyperparameter optimization. Returning max loss.') - return MAX_LOSS - finally: - del model - gc.collect() - if history is None: - histories.append({'loss': [MAX_LOSS], 'val_loss': [MAX_LOSS], 'parameter_count': [0]}) - - trials = hyperopt.Trials() - fmin(loss_from_multimodal_multitask, space=space, algo=tpe.suggest, max_evals=args.max_models, trials=trials) - plot_trials(trials, histories, fig_path, param_lists) - logging.info('Saved learning plot to:{}'.format(fig_path)) - - -def optimize_architecture(args): - dense_blocks_sets = [[16], [32], [48], [32, 16], [32, 32], [32, 24, 16], [48, 32, 24], [48, 48, 48]] - conv_layers_sets = [[64], [48], [32], [24]] - dense_layers_sets = [[16, 64], [8, 128], [48], [32], [24], [16]] - u_connect = [True, False] - conv_dilate = [True, False] - activation = ['leaky', 'prelu', 'elu', 'thresh_relu', 'relu'] - conv_bn = [True, False] - pool_type = ['max', 'average'] - space = { - 'pool_x': hp.quniform('pool_x', 1, 4, 1), - 'conv_layers': hp.choice('conv_layers', conv_layers_sets), - 'dense_blocks': hp.choice('dense_blocks', dense_blocks_sets), - 'dense_layers': hp.choice('dense_layers', dense_layers_sets), - 'u_connect': hp.choice('u_connect', u_connect), - 'conv_dilate': hp.choice('conv_dilate', conv_dilate), - 'activation': hp.choice('activation', activation), - 'conv_bn': hp.choice('conv_bn', conv_bn), - 'pool_type': hp.choice('pool_type', pool_type), - 'dropout': hp.uniform('dropout', 0, .2), - 'conv_dropout': hp.uniform('conv_dropout', 0, .2), - 'conv_width': hp.quniform('conv_width', 2, 128, 1), - 'block_size': hp.quniform('block_size', 1, 4, 1), - } - param_lists = { - 'conv_layers': conv_layers_sets, - 'dense_blocks': dense_blocks_sets, - 'dense_layers': dense_layers_sets, - 'u_connect': u_connect, - 'conv_dilate': conv_dilate, - 'activation': activation, - 'conv_bn': conv_bn, - 'pool_type': pool_type, - } - hyperparameter_optimizer(args, space, param_lists) - - -def optimize_ecg_rest_architecture(args): - dense_blocks_sets = [[], [32], [32, 24], [32, 24, 16], [48, 48, 48], [48, 48, 48, 48]] - conv_layers_sets = [[], [32], [48], [32, 32], [48, 48], [48, 32, 24], [48, 48, 48], [32, 32, 32, 32], [48, 48, 48, 48]] - dense_layers_sets = [[8], [16], [16, 64], [32, 128]] - conv_dilate = [True, False] - activation = ['leaky', 'prelu', 'relu'] - conv_normalize = ['', 'batch_norm'] - pool_type = ['max', 'average'] - space = { - 'pool_x': hp.quniform('pool_x', 1, 8, 1), - 'conv_layers': hp.choice('conv_layers', conv_layers_sets), - 'dense_blocks': hp.choice('dense_blocks', dense_blocks_sets), - 'dense_layers': hp.choice('dense_layers', dense_layers_sets), - 'conv_dilate': hp.choice('conv_dilate', conv_dilate), - 'activation': hp.choice('activation', activation), - #'conv_normalize': hp.choice('conv_normalize', conv_normalize), - 'pool_type': hp.choice('pool_type', pool_type), - 'conv_width': hp.loguniform('conv_width', 1, 5), - 'block_size': hp.quniform('block_size', 1, 6, 1), - } - param_lists = { - 'conv_layers': conv_layers_sets, - 'dense_blocks': dense_blocks_sets, - 'dense_layers': dense_layers_sets, - 'conv_dilate': conv_dilate, - 'activation': activation, - #'conv_normalize': conv_normalize, - 'pool_type': pool_type, - } - hyperparameter_optimizer(args, space, param_lists) - - -def optimize_ecg_rest_unet_architecture(args): - dense_blocks_sets = [[32], [48], [32, 16], [32, 32], [32, 24, 16], [48, 32, 24], [48, 48, 48], [32, 32, 32, 32], [48, 48, 48, 48]] - conv_layers_sets = [[16], [32], [48], [32, 32], [48, 32], [48, 32, 24], [48, 48, 48], [32, 32, 32, 32], [48, 48, 48, 48]] - dense_layers_sets = [[32, 32], [16, 64], [8, 128], [16, 16], [8, 8], [48], [32], [24], [16], [8]] - conv_dilate = [True, False] - activation = ['leaky', 'prelu', 'relu'] - conv_normalize = ['', 'batch_norm'] - pool_type = ['max', 'average'] - pool_xs = [1, 5, 10, 25] - space = { - 'pool_x': hp.choice('pool_x', pool_xs), - 'conv_layers': hp.choice('conv_layers', conv_layers_sets), - 'dense_blocks': hp.choice('dense_blocks', dense_blocks_sets), - 'dense_layers': hp.choice('dense_layers', dense_layers_sets), - 'conv_dilate': hp.choice('conv_dilate', conv_dilate), - 'activation': hp.choice('activation', activation), - 'conv_normalize': hp.choice('conv_normalize', conv_normalize), - 'pool_type': hp.choice('pool_type', pool_type), - 'conv_width': hp.loguniform('conv_width', 1, 5), - 'block_size': hp.quniform('block_size', 1, 6, 1), - } - param_lists = { - 'conv_layers': conv_layers_sets, - 'dense_blocks': dense_blocks_sets, - 'dense_layers': dense_layers_sets, - 'conv_dilate': conv_dilate, - 'activation': activation, - 'conv_normalize': conv_normalize, - 'pool_type': pool_type, - } - hyperparameter_optimizer(args, space, param_lists) - - -def optimize_mri_sax_architecture(args): - dense_blocks_sets = [[], [16], [32], [48], [32, 16], [32, 32], [32, 24, 16], [48, 32, 24], [48, 48, 48]] - conv_layers_sets = [[], [16], [32], [48], [32, 32], [48, 32], [48, 32, 24]] - dense_layers_sets = [[16], [24], [32], [48], [16, 64], [8, 128], [16, 64, 128]] - activation = ['leaky', 'prelu', 'relu', 'elu'] - conv_dilate = [True, False] - conv_normalize = ['', 'batch_norm'] - conv_type = ['conv', 'separable', 'depth'] - pool_type = ['max', 'average'] - space = { - 'pool_x': hp.quniform('pool_x', 2, 8, 2), - 'pool_y': hp.quniform('pool_y', 2, 8, 2), - 'pool_z': hp.quniform('pool_z', 1, 2, 1), - 'conv_layers': hp.choice('conv_layers', conv_layers_sets), - 'dense_blocks': hp.choice('dense_blocks', dense_blocks_sets), - 'dense_layers': hp.choice('dense_layers', dense_layers_sets), - 'conv_dilate': hp.choice('conv_dilate', conv_dilate), - 'conv_normalize': hp.choice('conv_normalize', conv_normalize), - 'conv_type': hp.choice('conv_type', conv_type), - 'activation': hp.choice('activation', activation), - 'pool_type': hp.choice('pool_type', pool_type), - 'block_size': hp.quniform('block_size', 1, 6, 1), - } - param_lists = { - 'conv_layers': conv_layers_sets, - 'dense_blocks': dense_blocks_sets, - 'dense_layers': dense_layers_sets, - 'conv_dilate': conv_dilate, - 'conv_normalize': conv_normalize, - 'conv_type': conv_type, - 'activation': activation, - 'pool_type': pool_type, - } - hyperparameter_optimizer(args, space, param_lists) - - -def optimize_conv_x(args): - space = { - 'conv_x': hp.loguniform('conv_x', 1, 6), - } - hyperparameter_optimizer(args, space, {}) - - -def optimize_conv_layers_multimodal_multitask(args): - dense_blocks_sets = [[16], [32], [48], [32, 16], [32, 32], [32, 24, 16], [48, 32, 24], [48, 48, 48]] - conv_layers_sets = [[64], [48], [32], [24]] - dense_layers_sets = [[16, 64], [8, 128], [48], [32], [24], [16]] - space = { - 'pool_x': hp.choice('pool_x', list(range(1, 5))), - 'conv_layers': hp.choice('conv_layers', conv_layers_sets), - 'dense_blocks': hp.choice('dense_blocks', dense_blocks_sets), - 'dense_layers': hp.choice('dense_layers', dense_layers_sets), - } - param_lists = {'conv_layers': conv_layers_sets, 'dense_blocks': dense_blocks_sets, 'dense_layers': dense_layers_sets} - hyperparameter_optimizer(args, space, param_lists) - - -def optimize_dense_layers_multimodal_multitask(args): - space = {'num_layers': hp.choice(list(range(2, 42)))} - hyperparameter_optimizer(args, space) - - -def optimize_lr_multimodal_multitask(args): - space = {'learning_rate': hp.loguniform('learning_rate', -10, -2)} - hyperparameter_optimizer(args, space) - - -def optimize_input_tensor_maps(args): - input_tensor_map_sets = [['categorical-phenotypes-72'], ['mri-slice'], ['sax_inlinevf_zoom'], ['cine_segmented_sax_inlinevf'], ['ekg-leads']] - space = {'input_tensor_maps': hp.choice('input_tensor_maps', input_tensor_map_sets)} - param_lists = {'input_tensor_maps': input_tensor_map_sets} - hyperparameter_optimizer(args, space, param_lists) - - -def optimize_optimizer(args): - optimizers = [ - 'adam', - 'radam', - 'sgd', - ] - space = { - 'learning_rate': hp.loguniform('learning_rate', -10, -2), - 'optimizer': hp.choice('optimizer', optimizers), - } - hyperparameter_optimizer(args, space, {'optimizer': optimizers}) - - -def set_args_from_x(args, x): - for k in args.__dict__: - if k in x: - print(k, x[k], args.__dict__[k]) - if isinstance(args.__dict__[k], int): - args.__dict__[k] = int(x[k]) - elif isinstance(args.__dict__[k], float): - v = float(x[k]) - if v == int(v): - v = int(v) - args.__dict__[k] = v - else: - args.__dict__[k] = x[k] - logging.info(f"Set arguments to: {args}") - args.tensor_maps_in = [TMAPS[it] for it in args.input_tensors] - args.tensor_maps_out = [TMAPS[ot] for ot in args.output_tensors] - - -def string_from_arch_dict(x): - return '\n'.join([f'{k} = {x[k]}' for k in x]) - - -def _string_from_trials(trials, index, param_lists={}): - s = '' - x = trials.trials[index]['misc']['vals'] - for k in x: - s += '\n' + k + ' = ' - v = x[k][0] - if k in param_lists: - s += str(param_lists[k][int(v)]) - elif k in ['num_layers', 'layer_width']: - s += str(int(v)) - elif v < 1: - s += f'{v:.2E}' - else: - s += f'{v:.2f}' - return s - - -def _model_label_from_losses_and_histories(i, all_losses, histories, trials, param_lists): - label = f'Trial {i}: \nTest Loss:{all_losses[i]:.3f}\nTrain Loss:{histories[i]["loss"][-1]:.3f}\nValidation Loss:{histories[i]["val_loss"][-1]:.3f}' - label += f'\nModel parameter count: {histories[i]["parameter_count"][-1]}' - label += f'{_string_from_trials(trials, i, param_lists)}' - return label - - -def plot_trials(trials, histories, figure_path, param_lists={}): - all_losses = np.array(trials.losses()) # the losses we will put in the text - real_losses = all_losses[all_losses != MAX_LOSS] - cutoff = MAX_LOSS - try: - cutoff = threshold_otsu(real_losses) - except ValueError: - logging.info('Otsu thresholding failed. Using MAX_LOSS for threshold.') - lplot = np.clip(all_losses, a_min=-np.inf, a_max=cutoff) # the losses we will plot - plt.figure(figsize=(64, 64)) - matplotlib.rcParams.update({'font.size': 9}) - colors = ['r' if x == cutoff else 'b' for x in lplot] - plt.plot(lplot) - with open(os.path.join(figure_path, 'loss_by_params.txt'), 'w') as f: - for i in range(len(trials.trials)): - label = _model_label_from_losses_and_histories(i, all_losses, histories, trials, param_lists) - plt.text(i, lplot[i], label, color=colors[i]) - f.write(label.replace('\n', ',') + '\n') - plt.xlabel('Iterations') - plt.ylabel('Losses') - plt.ylim(min(lplot) * .95, max(lplot) * 1.05) - plt.title(f'Hyperparameter Optimization\n') - if not os.path.exists(os.path.dirname(figure_path)): - os.makedirs(os.path.dirname(figure_path)) - plt.axhline(cutoff, label=f'Loss display cutoff at {cutoff:.3f}', color='r', linestyle='--') - loss_path = os.path.join(figure_path, 'loss_per_iteration' + IMAGE_EXT) - plt.legend() - plt.savefig(loss_path) - logging.info('Saved loss plot to: {}'.format(loss_path)) - - fig, [ax1, ax3, ax2] = plt.subplots(nrows=1, ncols=3, figsize=(60, 20), sharey='all', gridspec_kw={'width_ratios': [2, 1, 2]}) - cm = plt.get_cmap('gist_rainbow') - ax1.set_xlabel('Epoch') - ax1.set_ylabel('Training Loss') - ax2.set_xlabel('Epoch') - ax2.set_ylabel('Validation Loss') - linestyles = 'solid', 'dotted', 'dashed', 'dashdot' - for i, history in enumerate(histories): - color = cm(i / len(histories)) - training_loss = np.clip(history['loss'], a_min=-np.inf, a_max=cutoff) - val_loss = np.clip(history['val_loss'], a_min=-np.inf, a_max=cutoff) - label = _model_label_from_losses_and_histories(i, all_losses, histories, trials, param_lists) - ax1.plot(training_loss, label=label, linestyle=linestyles[i % 4], color=color) - ax1.text(len(training_loss) - 1, training_loss[-1], str(i)) - ax2.plot(val_loss, label=label, linestyle=linestyles[i % 4], color=color) - ax2.text(len(val_loss) - 1, val_loss[-1], str(i)) - ax1.axhline(cutoff, label=f'Loss display cutoff at {cutoff:.3f}', color='k', linestyle='--') - ax1.set_title('Training Loss') - ax2.axhline(cutoff, label=f'Loss display cutoff at {cutoff:.3f}', color='k', linestyle='--') - ax2.set_title('Validation Loss') - ax3.legend(*ax2.get_legend_handles_labels(), loc='upper center', fontsize='x-small', mode='expand', ncol=5) - ax3.axis('off') - learning_path = os.path.join(figure_path, 'learning_curves' + IMAGE_EXT) - plt.tight_layout() - plt.savefig(learning_path) - logging.info('Saved learning curve plot to: {}'.format(learning_path)) - - -if __name__ == '__main__': - args = parse_args() - run(args) # back to the top diff --git a/ml4h/hypertuning.py b/ml4h/hypertuning.py index 89b28f6a5..2e433a61b 100644 --- a/ml4h/hypertuning.py +++ b/ml4h/hypertuning.py @@ -13,7 +13,7 @@ from ml4h.arguments import parse_args from ml4h.models.layer_wrappers import NORMALIZATION_CLASSES -from ml4h.models.model_factory import block_make_multimodal_multitask_model +from ml4h.models.model_factory import make_multimodal_multitask_model from ml4h.tensor_generators import test_train_valid_tensor_generators tuner_type = 'bayes' @@ -94,7 +94,7 @@ def model_builder(hp): conv_normalize = hp.Choice('conv_normalize', ['layer_norm', 'instance_norm', 'poincare_norm', 'None']) args.__dict__['conv_normalize'] = None if conv_normalize == 'None' else conv_normalize args.__dict__['pool_type'] = 'max' if hp.Boolean('pool_type_is_max') else 'average' - model, _, _, _ = block_make_multimodal_multitask_model(**args.__dict__) + model, _, _, _ = make_multimodal_multitask_model(**args.__dict__) nonlocal model_count logging.info( f'Hyper-tuner is {100.0*(model_count / (args.max_models*args.min_samples)):0.1f}% complete. ' @@ -108,7 +108,7 @@ def model_builder(hp): def make_model_builder_activation(args): def model_builder(hp): args.__dict__['activation'] = hp.Choice('activation', ['leaky', 'swish', 'gelu', 'lisht', 'mish', 'relu', 'selu']) - model, _, _, _ = block_make_multimodal_multitask_model(**args.__dict__) + model, _, _, _ = make_multimodal_multitask_model(**args.__dict__) return model return model_builder @@ -119,7 +119,7 @@ def model_builder(hp): args.__dict__['dense_normalize'] = None if dense_normalize == 'None' else dense_normalize conv_normalize = hp.Choice('conv_normalize', list(NORMALIZATION_CLASSES.keys()) + ['None']) args.__dict__['conv_normalize'] = None if conv_normalize == 'None' else conv_normalize - model, _, _, _ = block_make_multimodal_multitask_model(**args.__dict__) + model, _, _, _ = make_multimodal_multitask_model(**args.__dict__) return model return model_builder diff --git a/ml4h/metrics.py b/ml4h/metrics.py index e62398ef5..34e594617 100755 --- a/ml4h/metrics.py +++ b/ml4h/metrics.py @@ -12,7 +12,7 @@ STRING_METRICS = [ 'categorical_crossentropy','binary_crossentropy','mean_absolute_error','mae', - 'mean_squared_error', 'mse', 'cosine_similarity', 'logcosh', + 'mean_squared_error', 'mse', 'cosine_similarity', 'logcosh', 'sparse_categorical_crossentropy', ] @@ -417,7 +417,7 @@ def get_metric_dict(output_tensor_maps): for m in tm.metrics: if isinstance(m, str): metrics[m] = m - else: + elif hasattr(m, '__name__'): metrics[m.__name__] = m if tm.loss == 'categorical_crossentropy': @@ -436,9 +436,11 @@ def get_metric_dict(output_tensor_maps): losses.append(logcosh) elif tm.loss == 'mape': losses.append(mean_absolute_percentage_error) - else: + elif hasattr(tm.loss, '__name__'): metrics[tm.loss.__name__] = tm.loss losses.append(tm.loss) + else: + losses.append(tm.loss) def loss(y_true, y_pred): my_loss = 0 diff --git a/ml4h/ml4ht_integration/tensor_generator.py b/ml4h/ml4ht_integration/tensor_generator.py index e67499c74..1fad57f29 100644 --- a/ml4h/ml4ht_integration/tensor_generator.py +++ b/ml4h/ml4ht_integration/tensor_generator.py @@ -2,7 +2,7 @@ from typing import List from torch.utils.data import DataLoader -from ml4ht.data.data_loader import SampleGetterIterableDataset, numpy_collate_fn +from ml4ht.data.data_loader import SampleGetterIterableDataset, numpy_collate_fn, shuffle_get_epoch from ml4h.TensorMap import TensorMap from ml4h.defines import TensorGeneratorABC @@ -28,13 +28,14 @@ def __init__( ) self.dset = SampleGetterIterableDataset( paths, self.sample_getter, - get_epoch=SampleGetterIterableDataset.shuffle_get_epoch, + get_epoch=shuffle_get_epoch, ) self.data_loader = DataLoader( self.dset, batch_size=batch_size, num_workers=num_workers, collate_fn=self._collate_fn, drop_last=drop_last, ) self.iter_loader = iter(self.data_loader) + self.true_epochs = 0 def _collate_fn(self, batches): @@ -67,7 +68,8 @@ def __next__(self): return next(self.iter_loader) except StopIteration: self.iter_loader = iter(self.data_loader) - logging.info("Completed one epoch.") + self.true_epochs += 1 + logging.info(f"Completed {self.true_epochs} true epochs.") return next(self.iter_loader) def __call__(self): @@ -96,7 +98,7 @@ def __init__( collate_fn=self._collate_fn, drop_last=drop_last, ) self.iter_loader = iter(self.data_loader) - self.true_iterations = 0 + self.true_epochs = 0 def _collate_fn(self, batches): return numpy_collate_fn(batches) @@ -110,8 +112,8 @@ def __next__(self): return next(self.iter_loader) except StopIteration: self.iter_loader = iter(self.data_loader) - self.true_iterations += 1 - print(f"Completed {self.true_iterations} true epochs.") + self.true_epochs += 1 + logging.info(f"Completed {self.true_epochs} true epochs.") return next(self.iter_loader) def __call__(self): diff --git a/ml4h/ml4ht_integration/tensor_map.py b/ml4h/ml4ht_integration/tensor_map.py index 1d630ae90..620d479ab 100644 --- a/ml4h/ml4ht_integration/tensor_map.py +++ b/ml4h/ml4ht_integration/tensor_map.py @@ -1,10 +1,14 @@ from typing import Callable, List, Tuple import h5py +import numpy as np +import pandas as pd -from ml4h.TensorMap import TensorMap, Interpretation from ml4ht.data.data_description import DataDescription from ml4ht.data.defines import SampleID, LoadingOption, Tensor, Batch +from ml4ht.data.util.data_frame_data_description import DataFrameDataDescription + +from ml4h.TensorMap import TensorMap, Interpretation class TensorMapSampleGetter: @@ -73,6 +77,7 @@ def __call__(self, path: str) -> Batch: ) out_batch = {} for tm in self.tensor_maps_out: + # TODO: Check for dependents here out_batch[tm.output_name()] = tm.postprocess_tensor( tm.tensor_from_file(tm, hd5, dependents), augment=self.augment, hd5=hd5, @@ -135,3 +140,21 @@ def tensor_map_from_data_description( **tensor_map_kwargs, ) return tmap + + +def _one_hot(x): + return np.array([1, 0], dtype=np.float32) if x == 0 else np.array([0, 1], dtype=np.float32) + + +def dataframe_data_description_from_tensor_map( + tensor_map: TensorMap, + dataframe: pd.DataFrame, + is_input: bool = False, +) -> DataDescription: + return DataFrameDataDescription( + dataframe, + col=tensor_map.name, + name=tensor_map.input_name() if is_input else tensor_map.output_name(), + value_to_tensor = _one_hot if tensor_map.is_categorical() else None, + + ) diff --git a/ml4h/models/basic_blocks.py b/ml4h/models/basic_blocks.py index 6c10d8d5f..25456a26a 100644 --- a/ml4h/models/basic_blocks.py +++ b/ml4h/models/basic_blocks.py @@ -2,7 +2,7 @@ import tensorflow as tf from tensorflow.keras.models import Model -from tensorflow.keras.layers import Dense, LSTM, Concatenate +from tensorflow.keras.layers import Dense, LSTM, Concatenate, Flatten from ml4h.models.Block import Block from ml4h.TensorMap import TensorMap @@ -40,11 +40,11 @@ def __init__( self, *, tensor_map: TensorMap, - dense_layers: List[int], - activation: str, - dense_normalize: str, - dense_regularize: str, - dense_regularize_rate: float, + dense_layers: List[int] = [32], + activation: str = 'swish', + dense_normalize: str = None, + dense_regularize: str = None, + dense_regularize_rate: float = 0.0, **kwargs, ): self.tensor_map = tensor_map @@ -74,7 +74,7 @@ class DenseDecoder(Block): def __init__( self, tensor_map: TensorMap, - activation: str, + activation: str = 'swish', parents: List[TensorMap] = None, **kwargs, ): @@ -96,7 +96,8 @@ def __call__(self, x: Tensor, intermediates: Dict[TensorMap, List[Tensor]]) -> T x = Concatenate()([x] + [intermediates[parent][-1] for parent in self.parents]) x = Dense(units=self.units)(x) x = self.dense(self.activation(x)) - x = self.dense(x) + else: + x = self.dense(x) intermediates[self.tensor_map].append(x) return x @@ -175,6 +176,7 @@ class LSTMEncoderBlock(Block): def __init__( self, tensor_map, + **kwargs, ): self.tensor_map = tensor_map if not self.can_apply(): @@ -184,7 +186,7 @@ def __init__( def can_apply(self): return self.tensor_map.is_language() - def __call__(self, x: Tensor, intermediates: Dict[TensorMap, List[Tensor]]) -> Tensor: + def __call__(self, x: Tensor, intermediates: Dict[TensorMap, List[Tensor]] = None) -> Tensor: if not self.can_apply(): return x return self.lstm(x) @@ -194,6 +196,7 @@ class LanguageDecoderBlock(Block): def __init__( self, tensor_map: TensorMap, + **kwargs, ): self.tensor_map = tensor_map if not self.can_apply(): @@ -203,7 +206,34 @@ def __init__( def can_apply(self): return self.tensor_map.is_language() - def __call__(self, x: Tensor, intermediates: Dict[TensorMap, List[Tensor]]) -> Tensor: + def __call__(self, x: Tensor, intermediates: Dict[TensorMap, List[Tensor]] = None) -> Tensor: if not self.can_apply(): return x return self.dense(x) + + +class LanguagePredictionBlock(Block): + def __init__( + self, + tensor_map: TensorMap, + dropout_rate = 0.25, + units=64, + activation='swish', + **kwargs, + ): + self.tensor_map = tensor_map + self.dropout_rate = dropout_rate + self.units = units + self.activation = activation + self.drop = tf.keras.layers.Dropout(dropout_rate) + self.dense = Dense(units, activation=activation) + self.final_layer = Dense(units=tensor_map.shape[0], name=tensor_map.output_name(), activation=tensor_map.activation) + + + def __call__(self, x: Tensor, intermediates: Dict[TensorMap, List[Tensor]] = None) -> Tensor: + x = self.dense(self.drop(x)) + if self.tensor_map.is_continuous(): + x = tf.keras.layers.Dense(self.units, activation=self.activation)(x) + x = tf.keras.layers.Dropout(self.dropout_rate)(x) + x = self.final_layer(x) + return x diff --git a/ml4h/models/conv_blocks.py b/ml4h/models/conv_blocks.py index 3375d6c2d..2003bfbb1 100644 --- a/ml4h/models/conv_blocks.py +++ b/ml4h/models/conv_blocks.py @@ -19,27 +19,27 @@ def __init__( self, *, tensor_map: TensorMap, - dense_blocks: List[int], - dense_layers: List[int], - dense_normalize: str, - dense_regularize: str, - dense_regularize_rate: float, - conv_layers: List[int], - conv_type: str, - conv_width: List[int], - conv_x: List[int], - conv_y: List[int], - conv_z: List[int], - block_size: int, - activation: str, - conv_normalize: str, - conv_regularize: str, - conv_regularize_rate: float, - conv_dilate: bool, - pool_type: str, - pool_x: int, - pool_y: int, - pool_z: int, + dense_blocks: List[int] = [32, 32, 32], + dense_layers: List[int] = [32], + dense_normalize: str = None, + dense_regularize: str = None, + dense_regularize_rate: float = 0.0, + conv_layers: List[int] = [32], + conv_type: str = 'conv', + conv_width: List[int] = [71], + conv_x: List[int] = [3], + conv_y: List[int] = [3], + conv_z: List[int] = [3], + block_size: int = 3, + activation: str = 'swish', + conv_normalize: str = None, + conv_regularize: str = None, + conv_regularize_rate: float = 0.0, + conv_dilate: bool = False, + pool_type: str = 'max', + pool_x: int = 2, + pool_y: int = 2, + pool_z: int = 1, **kwargs, ): self.tensor_map = tensor_map @@ -79,7 +79,7 @@ def __init__( def can_apply(self): return self.tensor_map.axes() > 1 - def __call__(self, x: Tensor, intermediates: Dict[TensorMap, List[Tensor]]) -> Tensor: + def __call__(self, x: Tensor, intermediates: Dict[TensorMap, List[Tensor]] = None) -> Tensor: if not self.can_apply(): return x #x = self.preprocess_block(x) # TODO: upgrade to tensorflow 2.3+ @@ -101,20 +101,20 @@ def __init__( self, *, tensor_map: TensorMap, - dense_blocks: List[int], - conv_type: str, - conv_width: List[int], - conv_x: List[int], - conv_y: List[int], - conv_z: List[int], - block_size: int, - activation: str, - conv_normalize: str, - conv_regularize: str, - conv_regularize_rate: float, - pool_x: int, - pool_y: int, - pool_z: int, + dense_blocks: List[int] = [32, 32, 32], + conv_type: str = 'conv', + conv_width: List[int] = [71], + conv_x: List[int] = [3], + conv_y: List[int] = [3], + conv_z: List[int] = [3], + block_size: int = 3, + activation: str = 'swish', + conv_normalize: str = None, + conv_regularize: str = None, + conv_regularize_rate: float = 0.0, + pool_x: int = 2, + pool_y: int = 2, + pool_z: int = 1, u_connect_parents: List[TensorMap] = None, **kwargs, ): @@ -147,7 +147,7 @@ def __init__( def can_apply(self): return self.tensor_map.axes() > 1 - def __call__(self, x: Tensor, intermediates: Dict[TensorMap, List[Tensor]]) -> Tensor: + def __call__(self, x: Tensor, intermediates: Dict[TensorMap, List[Tensor]] = None) -> Tensor: if not self.can_apply(): return x if x.shape != self.start_shape: @@ -203,7 +203,7 @@ def __init__( def can_apply(self): return self.tensor_map.axes() > 1 - def __call__(self, x: Tensor, intermediates: Dict[TensorMap, List[Tensor]]) -> Tensor: + def __call__(self, x: Tensor, intermediates: Dict[TensorMap, List[Tensor]] = None) -> Tensor: if not self.can_apply(): return x previous = x @@ -238,7 +238,7 @@ def __init__( def can_apply(self): return self.tensor_map.axes() > 1 - def __call__(self, x: Tensor, intermediates: Dict[TensorMap, List[Tensor]]) -> Tensor: + def __call__(self, x: Tensor, intermediates: Dict[TensorMap, List[Tensor]] = None) -> Tensor: if not self.can_apply(): return x x = self.pool(x) @@ -323,23 +323,23 @@ def __init__( self, *, tensor_map: TensorMap, - conv_layers: List[int], - conv_type: str, - conv_width: List[int], - conv_x: List[int], - conv_y: List[int], - conv_z: List[int], - pool_x: int, - pool_y: int, - pool_z: int, - activation: str, - conv_dilate: bool, - conv_normalize: str, - conv_regularize: str, - conv_regularize_rate: float, - conv_without_bias: bool, - conv_bias_initializer: str, - conv_kernel_initializer: str, + conv_layers: List[int] = [32], + conv_type: str = 'conv', + conv_width: List[int] = [71], + conv_x: List[int] = [3], + conv_y: List[int] = [3], + conv_z: List[int] = [3], + pool_x: int = 2, + pool_y: int = 2, + pool_z: int = 1, + activation: str = 'swish', + conv_dilate: bool = False, + conv_normalize: str = None, + conv_regularize: str = None, + conv_regularize_rate: float = 0.2, + conv_without_bias: bool = False, + conv_bias_initializer: str = 'zeros', + conv_kernel_initializer: str = 'glorot_uniform', **kwargs, ): self.tensor_map = tensor_map diff --git a/ml4h/models/legacy_models.py b/ml4h/models/legacy_models.py index cdb38cf8f..956b8ba4a 100755 --- a/ml4h/models/legacy_models.py +++ b/ml4h/models/legacy_models.py @@ -907,7 +907,7 @@ def _repeat_dimension(filters: List[int], num_filters_needed: int) -> List[int]: return filters -def make_multimodal_multitask_model( +def legacy_multimodal_multitask_model( tensor_maps_in: List[TensorMap], tensor_maps_out: List[TensorMap], activation: str = 'relu', @@ -1246,7 +1246,7 @@ def make_paired_autoencoder_model( encode_left = encoders[left] else: kwargs['tensor_maps_in'] = [left] - left_model = make_multimodal_multitask_model(**kwargs) + left_model = legacy_multimodal_multitask_model(**kwargs) encode_left = make_hidden_layer_model(left_model, [left], kwargs['hidden_layer']) h_left = encode_left(inputs[left]) @@ -1254,7 +1254,7 @@ def make_paired_autoencoder_model( encode_right = encoders[right] else: kwargs['tensor_maps_in'] = [right] - right_model = make_multimodal_multitask_model(**kwargs) + right_model = legacy_multimodal_multitask_model(**kwargs) encode_right = make_hidden_layer_model(right_model, [right], kwargs['hidden_layer']) h_right = encode_right(inputs[right]) diff --git a/ml4h/models/merge_blocks.py b/ml4h/models/merge_blocks.py index a2f274ef8..d69169417 100644 --- a/ml4h/models/merge_blocks.py +++ b/ml4h/models/merge_blocks.py @@ -38,11 +38,11 @@ class FlatConcatDenseBlock(Block): """ def __init__( self, - activation: str, - dense_layers: List[int], - dense_normalize: str, - dense_regularize: str, - dense_regularize_rate: float, + activation: str = 'swish', + dense_layers: List[int] = [32], + dense_normalize: str = None, + dense_regularize: str = None, + dense_regularize_rate: float = 0.0, **kwargs, ): self.fully_connected = DenseBlock( @@ -67,11 +67,11 @@ class GlobalAveragePoolBlock(Block): """ def __init__( self, - activation: str, - dense_layers: List[int], - dense_normalize: str, - dense_regularize: str, - dense_regularize_rate: float, + activation: str = 'swish', + dense_layers: List[int] = [32], + dense_normalize: str = None, + dense_regularize: str = None, + dense_regularize_rate: float = 0.0, **kwargs, ): self.fully_connected = DenseBlock( diff --git a/ml4h/models/model_factory.py b/ml4h/models/model_factory.py index 1886e1476..b6d602446 100644 --- a/ml4h/models/model_factory.py +++ b/ml4h/models/model_factory.py @@ -15,14 +15,14 @@ from ml4h.models.Block import Block from ml4h.TensorMap import TensorMap from ml4h.metrics import get_metric_dict -from ml4h.models.pretrained_blocks import ResNetEncoder, MoviNetEncoder from ml4h.optimizers import NON_KERAS_OPTIMIZERS, get_optimizer from ml4h.models.layer_wrappers import ACTIVATION_FUNCTIONS, NORMALIZATION_CLASSES +from ml4h.models.pretrained_blocks import ResNetEncoder, MoviNetEncoder, BertEncoder from ml4h.models.conv_blocks import ConvEncoderBlock, ConvDecoderBlock, ResidualBlock, PoolBlock, ConvUp, ConvDown from ml4h.models.transformer_blocks import TransformerDecoder, TransformerEncoder, PositionalEncoding, MultiHeadAttention -from ml4h.models.basic_blocks import ModelAsBlock, LSTMEncoderBlock, LanguageDecoderBlock, DenseEncoder, DenseDecoder, LinearDecoder, PartitionedLinearDecoder from ml4h.models.merge_blocks import GlobalAveragePoolBlock, EncodeIdentityBlock, L2LossLayer, CosineLossLayer, VariationalDiagNormal from ml4h.models.merge_blocks import FlatConcatDenseBlock, FlatConcatBlock, AverageBlock, PairLossBlock, ReduceMean, ContrastiveLossLayer +from ml4h.models.basic_blocks import ModelAsBlock, LSTMEncoderBlock, LanguageDecoderBlock, DenseEncoder, DenseDecoder, LinearDecoder, PartitionedLinearDecoder, LanguagePredictionBlock BLOCK_CLASSES = { @@ -40,6 +40,7 @@ 'gap': GlobalAveragePoolBlock, 'lstm_encode': LSTMEncoderBlock, 'language_decode': LanguageDecoderBlock, + 'language_predict': LanguagePredictionBlock, 'dense_encode': DenseEncoder, 'dense_decode': DenseDecoder, 'linear_decode': LinearDecoder, @@ -49,6 +50,8 @@ 'transformer_decoder': TransformerDecoder, 'resnet_encoder': ResNetEncoder, 'movinet_encoder': MoviNetEncoder, + 'bert_encoder': BertEncoder, + } @@ -60,7 +63,7 @@ def identity(x, _): return x -def block_make_multimodal_multitask_model( +def make_multimodal_multitask_model( tensor_maps_in: List[TensorMap], tensor_maps_out: List[TensorMap], encoder_blocks: List[str], @@ -230,7 +233,11 @@ def make_multimodal_multitask_model_block( intermediates: Dict[TensorMap, List[Layer]] = defaultdict(list) for tm, encoder_block in encoder_block_functions.items(): - inputs[tm] = Input(shape=tm.shape, name=tm.input_name()) + if tm.is_text(): + inputs[tm] = Input(shape=(), dtype=tf.string, name=tm.name) + else: + inputs[tm] = Input(shape=tm.shape, name=tm.input_name()) + encoding = encoder_block(inputs[tm], intermediates) encoders[tm] = Model(inputs[tm], encoding, name=f'encode_{tm.name}') encodings.append(encoders[tm](inputs[tm])) @@ -242,7 +249,9 @@ def make_multimodal_multitask_model_block( latent_inputs = Input(shape=(multimodal_activation[0].shape[-1],), name='input_multimodal_space') else: latent_inputs = Input(shape=(multimodal_activation.shape[-1],), name='input_multimodal_space') + logging.info(f'multimodal_activation.shapes: {multimodal_activation.shape}') logging.info(f'Graph from input TensorMaps has intermediates: {[(tm, [ti.shape for ti in t]) for tm, t in intermediates.items()]}') + decoders: Dict[TensorMap, Model] = {} decoder_outputs = [] for tm, decoder_block in decoder_block_functions.items(): # TODO this needs to be a topological sorted according to parents hierarchy diff --git a/ml4h/models/pretrained_blocks.py b/ml4h/models/pretrained_blocks.py index 879ea200b..1112e2a99 100644 --- a/ml4h/models/pretrained_blocks.py +++ b/ml4h/models/pretrained_blocks.py @@ -38,7 +38,7 @@ def __init__( def can_apply(self): return self.tensor_map.axes() == 3 and self.tensor_map.shape[-1] == 3 - def __call__(self, x: Tensor, intermediates: Dict[TensorMap, List[Tensor]]) -> Tensor: + def __call__(self, x: Tensor, intermediates: Dict[TensorMap, List[Tensor]] = None) -> Tensor: if not self.can_apply(): return x x = self.base_model(x, training=False) @@ -65,10 +65,36 @@ def __init__( def can_apply(self): return self.tensor_map.axes() == 4 - def __call__(self, x: Tensor, intermediates: Dict[TensorMap, List[Tensor]]) -> Tensor: + def __call__(self, x: Tensor, intermediates: Dict[TensorMap, List[Tensor]] = None) -> Tensor: if not self.can_apply(): return x x = self.base_model({'image': x}, training=True) #x = keras.layers.GlobalAveragePooling2D()(x) intermediates[self.tensor_map].append(x) return x + + +class BertEncoder(Block): + def __init__( + self, + *, + tensor_map: TensorMap, + pretrain_trainable: bool, + base_model = "https://tfhub.dev/jeongukjae/roberta_en_cased_L-24_H-1024_A-16/1", + preprocess_model="https://tfhub.dev/jeongukjae/roberta_en_cased_preprocess/1", + **kwargs, + ): + self.tensor_map = tensor_map + if not self.can_apply(): + return + self.preprocess_model = hub.KerasLayer(preprocess_model, name='preprocessing') + self.encoder = hub.KerasLayer(base_model, trainable=pretrain_trainable, name='BERT_encoder') + + def can_apply(self): + return self.tensor_map.is_text() + + def __call__(self, x: Tensor, intermediates: Dict[TensorMap, List[Tensor]] = None) -> Tensor: + encoder_inputs = self.preprocess_model(x) + outputs = self.encoder(encoder_inputs) + intermediates[self.tensor_map].append(outputs['pooled_output']) + return outputs['pooled_output'] diff --git a/ml4h/models/transformer_blocks.py b/ml4h/models/transformer_blocks.py index 169ad6534..9022c8030 100644 --- a/ml4h/models/transformer_blocks.py +++ b/ml4h/models/transformer_blocks.py @@ -76,10 +76,13 @@ def __init__( ) def __call__(self, x: Tensor, intermediates: Dict[TensorMap, List[Tensor]] = None) -> Tensor: + encoder_input = None for tm in intermediates: if self.tensor_map == tm: encoder_input = intermediates[self.tensor_map][0] encoder_outputs = intermediates[self.tensor_map][-1] + if encoder_input is None: + return x look_ahead = self.look_ahead_mask(encoder_input) # Decoder does not need to use same domain as encoder/ can have different inputs pad = self.decoder_padding_mask(encoder_input) decoder_outputs = self.decoder_layers(inputs=[encoder_input, encoder_outputs, look_ahead, pad]) diff --git a/ml4h/plots.py b/ml4h/plots.py index b2645b2fa..5bb667699 100644 --- a/ml4h/plots.py +++ b/ml4h/plots.py @@ -420,7 +420,7 @@ def make_one_hot(y, num_labels): return ohy -def plot_metric_history(history, training_steps: int, title: str, prefix="./figures/", dpi=300, width=7, height=7): +def plot_metric_history(history, training_steps: int, title: str, prefix="./figures/", dpi=300, width=3, height=3): row = 0 col = 0 total_plots = int( @@ -483,10 +483,11 @@ def plot_rocs( dpi: int = 300, width: int = 7, height: int = 7, + line_width = 2, ): """Plot Receiver Operating Characteristic (ROC) curves from a dictionary of predictions - Typically this function is used to compare several models predictions across multiple labels. + Typically, this function is used to compare several models predictions across multiple labels. As a hack to avoid repetitive ROC curves for binary classification label string containing 'no_' are skipped. :param predictions: The keys are strings identifying the model the values are numpy arrays @@ -495,20 +496,23 @@ def plot_rocs( :param labels: Dictionary mapping strings describing each class to their corresponding index in the arrays :param title: The name of this plot :param prefix: Optional path prefix where the plot will be saved + :param dpi: Dots per inch of the figure + :param width: Width in inches of the figure + :param height: Height in inches of the figure + :param line_width: Line width in pixels """ - lw = 2 + true_sums = np.sum(truth, axis=0) plt.figure(figsize=(width, height), dpi=dpi) - for p in predictions: fpr, tpr, roc_auc = get_fpr_tpr_roc_pred(predictions[p], truth, labels) for key in labels: - if "no_" in key and len(labels) == 2: + if "no_" in str(key) and len(labels) == 2: continue - color = _hash_string_to_color(p + key) + color = _hash_string_to_color(p + str(key)) label_text = f"{p}_{key} area:{roc_auc[labels[key]]:.3f} n={true_sums[labels[key]]:.0f}" plt.plot( - fpr[labels[key]], tpr[labels[key]], color=color, lw=lw, label=label_text, + fpr[labels[key]], tpr[labels[key]], color=color, lw=line_width, label=label_text, ) logging.info(f"ROC Label {label_text}") @@ -549,6 +553,9 @@ def plot_prediction_calibrations( :param title: The name of this plot :param prefix: Optional path prefix where the plot will be saved :param n_bins: Number of bins to quantize predictions into + :param dpi: Dots per inch of the figure + :param width: Width in inches of the figure + :param height: Height in inches of the figure """ _ = plt.figure(figsize=(width, height), dpi=dpi) ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan=2) @@ -621,8 +628,11 @@ def plot_prediction_calibration( :param title: The name of this plot :param prefix: Optional path prefix where the plot will be saved :param n_bins: Number of bins to quantize predictions into + :param dpi: Dots per inch of the figure + :param width: Width in inches of the figure + :param height: Height in inches of the figure """ - _, (ax1, ax3, ax2) = plt.subplots(3, figsize=(width, 2 * height), dpi=dpi) + _, (ax1, ax3, ax2) = plt.subplots(3, figsize=(width, height), dpi=dpi) true_sums = np.sum(truth, axis=0) ax1.plot([0, 1], [0, 1], "k:", label="Perfectly calibrated Brier score: 0.0") @@ -703,7 +713,7 @@ def bootstrap_confidence_interval( def plot_scatter( prediction, truth, title, prefix="./figures/", paths=None, top_k=3, alpha=0.5, - bootstrap=True, dpi=300, width=7, height=7, + bootstrap=True, dpi=300, width=4, height=4, ): margin = float((np.max(truth) - np.min(truth)) / 100) fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(width, 2 * height), dpi=dpi) @@ -722,7 +732,7 @@ def plot_scatter( if bootstrap: pearson, ci = bootstrap_confidence_interval(prediction, truth) - label = f'Pearson:{pearson:0.4f} $R^2$:{big_r_squared:0.4f}, 95% Confidence:({ci[0]:0.4f}, {ci[1]:0.4f})' + label = f'Pearson:{pearson:0.4f} $R^2$:{big_r_squared:0.4f}\n 95% Confidence:({ci[0]:0.4f}, {ci[1]:0.4f})' else: label = f"Pearson:{pearson:0.3f} $r^2$:{pearson * pearson:0.3f} $R^2$:{big_r_squared:0.3f}" logging.info(f"{label}") @@ -754,7 +764,7 @@ def plot_scatter( ax1.set_xlabel("Predictions") ax1.set_ylabel("Actual") - ax1.set_title(title + "\n") + ax1.set_title(title) ax1.legend(loc="lower right") sns.distplot(prediction, label="Predicted", color="r", ax=ax2) @@ -764,13 +774,13 @@ def plot_scatter( figure_path = os.path.join(prefix, "scatter_" + title + IMAGE_EXT) if not os.path.exists(os.path.dirname(figure_path)): os.makedirs(os.path.dirname(figure_path)) - logging.info("Try to save scatter plot at: {}".format(figure_path)) + logging.info(f"Try to save scatter plot at: {figure_path}") plt.savefig(figure_path) return {title + "_pearson": pearson} def plot_scatters( - predictions, truth, title, prefix="./figures/", paths=None, top_k=3, alpha=0.5, dpi=300, width=7, height=7, + predictions, truth, title, prefix="./figures/", paths=None, top_k=3, alpha=0.5, dpi=300, width=4, height=4, ): margin = float((np.max(truth) - np.min(truth)) / 100) plt.figure(figsize=(width, height), dpi=dpi) @@ -813,14 +823,14 @@ def plot_scatters( ) plt.xlabel("Predictions") plt.ylabel("Actual") - plt.title(title + "\n") - plt.legend(loc="upper left") + plt.title(title) + plt.legend() figure_path = os.path.join(prefix, "scatters_" + title + IMAGE_EXT) if not os.path.exists(os.path.dirname(figure_path)): os.makedirs(os.path.dirname(figure_path)) plt.savefig(figure_path) - logging.info("Saved scatter plot at: {}".format(figure_path)) + logging.info(f"Saved scatter plot at: {figure_path}") def subplot_pearson_per_class( @@ -2533,9 +2543,9 @@ def plot_roc(prediction, truth, labels, title, prefix="./figures/", dpi=300, wid fpr, tpr, roc_auc = get_fpr_tpr_roc_pred(prediction, truth, labels) for key in labels: - if "no_" in key and len(labels) == 2: + if "no_" in str(key) and len(labels) == 2: continue - color = _hash_string_to_color(key) + color = _hash_string_to_color(str(key)) labels_to_areas[key] = roc_auc[labels[key]] label_text = f"{key} area:{roc_auc[labels[key]]:.3f} n={true_sums[labels[key]]:.0f}" plt.plot( @@ -2559,37 +2569,6 @@ def plot_roc(prediction, truth, labels, title, prefix="./figures/", dpi=300, wid return labels_to_areas -def plot_rocs(predictions, truth, labels, title, prefix="./figures/", dpi=300, width: int = 6, height: int = 6): - lw = 2 - true_sums = np.sum(truth, axis=0) - plt.figure(figsize=(width, height), dpi=dpi) - for p in predictions: - fpr, tpr, roc_auc = get_fpr_tpr_roc_pred(predictions[p], truth, labels) - for key in labels: - if "no_" in key and len(labels) == 2: - continue - color = _hash_string_to_color(p + key) - label_text = f"{p}_{key} area:{roc_auc[labels[key]]:.3f} n={true_sums[labels[key]]:.0f}" - plt.plot( - fpr[labels[key]], tpr[labels[key]], color=color, lw=lw, label=label_text, - ) - logging.info(f"ROC Label {label_text}") - - plt.xlim([0.0, 1.0]) - plt.ylim([-0.02, 1.03]) - plt.ylabel(RECALL_LABEL) - plt.xlabel(FALLOUT_LABEL) - plt.legend(loc="lower right") - plt.plot([0, 1], [0, 1], "k:", lw=0.5) - plt.title(f"ROC {title} n={np.sum(true_sums):.0f}") - - figure_path = os.path.join(prefix, "per_class_roc_" + title + IMAGE_EXT) - if not os.path.exists(os.path.dirname(figure_path)): - os.makedirs(os.path.dirname(figure_path)) - plt.savefig(figure_path) - logging.info(f"Saved ROC curve at: {figure_path}") - - def _figure_and_subplot_axes_from_total(total_plots: int, dpi: int = 300, width: int = 6, height: int = 6): cols = max(2, int(math.ceil(math.sqrt(total_plots)))) rows = max(2, int(math.ceil(total_plots / cols))) @@ -2685,7 +2664,7 @@ def plot_precision_recall_per_class( plt.figure(figsize=(width, height), dpi=dpi) for k in labels: - c = _hash_string_to_color(k) + c = _hash_string_to_color(str(k)) precision, recall, _ = precision_recall_curve( truth[:, labels[k]], prediction[:, labels[k]], ) @@ -2703,7 +2682,7 @@ def plot_precision_recall_per_class( plt.ylim([-0.02, 1.03]) plt.xlabel(RECALL_LABEL) plt.ylabel(PRECISION_LABEL) - plt.legend(loc="lower left") + plt.legend() plt.title(f"{title} n={np.sum(true_sums):.0f}") figure_path = os.path.join(prefix, "precision_recall_" + title + IMAGE_EXT) diff --git a/ml4h/recipes.py b/ml4h/recipes.py index 7cd6b1999..a913c6911 100755 --- a/ml4h/recipes.py +++ b/ml4h/recipes.py @@ -19,17 +19,17 @@ from ml4h.models.train import train_model_from_generators from ml4h.tensormap.tensor_map_maker import write_tensor_maps from ml4h.tensorize.tensor_writer_mgb import write_tensors_mgb -from ml4h.models.model_factory import block_make_multimodal_multitask_model +from ml4h.models.model_factory import make_multimodal_multitask_model from ml4h.tensor_generators import BATCH_INPUT_INDEX, BATCH_OUTPUT_INDEX, BATCH_PATHS_INDEX -from ml4h.explorations import mri_dates, ecg_dates, predictions_to_pngs, sample_from_language_model, pca_on_tsv -from ml4h.explorations import plot_while_learning, plot_histograms_of_tensors_in_pdf, cross_reference -from ml4h.explorations import test_labels_to_label_map, infer_with_pixels, explore, latent_space_dataframe +from ml4h.explorations import test_labels_to_label_map, infer_with_pixels, latent_space_dataframe +from ml4h.explorations import mri_dates, ecg_dates, predictions_to_pngs, sample_from_language_model +from ml4h.explorations import plot_while_learning, plot_histograms_of_tensors_in_pdf, explore, pca_on_tsv from ml4h.tensor_generators import TensorGenerator, test_train_valid_tensor_generators, big_batch_from_minibatch_generator from ml4h.metrics import get_roc_aucs, get_precision_recall_aucs, get_pearson_coefficients, log_aucs, log_pearson_coefficients from ml4h.plots import evaluate_predictions, plot_scatters, plot_rocs, plot_precision_recalls, subplot_roc_per_class, plot_tsne, plot_survival from ml4h.plots import plot_reconstruction, plot_hit_to_miss_transforms, plot_saliency_maps, plot_partners_ecgs, plot_ecg_rest_mp from ml4h.plots import subplot_rocs, subplot_comparison_rocs, subplot_scatters, subplot_comparison_scatters, plot_prediction_calibrations -from ml4h.models.legacy_models import make_character_model_plus, embed_model_predict, make_siamese_model, make_multimodal_multitask_model +from ml4h.models.legacy_models import make_character_model_plus, embed_model_predict, make_siamese_model, legacy_multimodal_multitask_model from ml4h.models.legacy_models import get_model_inputs_outputs, make_shallow_model, make_hidden_layer_model, make_paired_autoencoder_model from ml4h.tensorize.tensor_writer_ukbb import write_tensors, append_fields_from_csv, append_gene_csv, write_tensors_from_dicom_pngs, write_tensors_from_ecg_pngs @@ -50,8 +50,6 @@ def run(args): write_tensors_mgb(args.xml_folder, args.tensors, args.num_workers) elif 'explore' == args.mode: explore(args) - elif 'cross_reference' == args.mode: - cross_reference(args) elif 'train' == args.mode: train_multimodal_multitask(args) elif 'train_legacy' == args.mode: @@ -130,7 +128,7 @@ def _find_learning_rate(args) -> float: schedule = args.learning_rate_schedule args.learning_rate_schedule = None # learning rate schedule interferes with setting lr done by find_learning_rate generate_train, _, _ = test_train_valid_tensor_generators(**args.__dict__) - model = make_multimodal_multitask_model(**args.__dict__) + model = legacy_multimodal_multitask_model(**args.__dict__) lr = find_learning_rate(model, generate_train, args.training_steps, os.path.join(args.output_folder, args.id)) args.learning_rate_schedule = schedule return lr @@ -138,7 +136,7 @@ def _find_learning_rate(args) -> float: def train_legacy(args): generate_train, generate_valid, generate_test = test_train_valid_tensor_generators(**args.__dict__) - model = make_multimodal_multitask_model(**args.__dict__) + model = legacy_multimodal_multitask_model(**args.__dict__) model = train_model_from_generators( model, generate_train, generate_valid, args.training_steps, args.validation_steps, args.batch_size, args.epochs, args.patience, args.output_folder, args.id, args.inspect_model, args.inspect_show_labels, args.tensor_maps_out, @@ -156,7 +154,7 @@ def train_legacy(args): def train_multimodal_multitask(args): generate_train, generate_valid, generate_test = test_train_valid_tensor_generators(**args.__dict__) - model, encoders, decoders, merger = block_make_multimodal_multitask_model(**args.__dict__) + model, encoders, decoders, merger = make_multimodal_multitask_model(**args.__dict__) model = train_model_from_generators( model, generate_train, generate_valid, args.training_steps, args.validation_steps, args.batch_size, args.epochs, args.patience, args.output_folder, args.id, args.inspect_model, args.inspect_show_labels, args.tensor_maps_out, @@ -205,7 +203,7 @@ def train_multimodal_multitask(args): def test_multimodal_multitask(args): _, _, generate_test = test_train_valid_tensor_generators(**args.__dict__) - model = make_multimodal_multitask_model(**args.__dict__) + model = legacy_multimodal_multitask_model(**args.__dict__) out_path = os.path.join(args.output_folder, args.id + '/') data, labels, paths = big_batch_from_minibatch_generator(generate_test, args.test_steps) return _predict_and_evaluate( @@ -217,7 +215,7 @@ def test_multimodal_multitask(args): def test_multimodal_scalar_tasks(args): _, _, generate_test = test_train_valid_tensor_generators(**args.__dict__) - model = make_multimodal_multitask_model(**args.__dict__) + model = legacy_multimodal_multitask_model(**args.__dict__) p = os.path.join(args.output_folder, args.id + '/') return _predict_scalars_and_evaluate_from_generator( model, generate_test, args.tensor_maps_in, args.tensor_maps_out, args.tensor_maps_protected, args.test_steps, @@ -269,7 +267,7 @@ def infer_multimodal_multitask(args): inference_tsv = inference_file_name(args.output_folder, args.id) tsv_style_is_genetics = 'genetics' in args.tsv_style - model = make_multimodal_multitask_model(**args.__dict__) + model = legacy_multimodal_multitask_model(**args.__dict__) no_fail_tmaps_out = [_make_tmap_nan_on_fail(tmap) for tmap in args.tensor_maps_out] tensor_paths = _tensor_paths_from_sample_csv(args.tensors, args.sample_csv) # hard code batch size to 1 so we can iterate over file names and generated tensors together in the tensor_paths for loop @@ -386,7 +384,7 @@ def infer_hidden_layer_multimodal_multitask(args): cache_size=args.cache_size, keep_paths=True, mixup_alpha=args.mixup_alpha, ) generate_test.set_worker_paths(tensor_paths) - full_model = make_multimodal_multitask_model(**args.__dict__) + full_model = legacy_multimodal_multitask_model(**args.__dict__) embed_model = make_hidden_layer_model(full_model, args.tensor_maps_in, args.hidden_layer) embed_model.save(_hidden_file_name(args.output_folder, f'{args.hidden_layer}_encoder_', args.id, '.h5')) dummy_input = {tm.input_name(): np.zeros((1,) + full_model.get_layer(tm.input_name()).input_shape[0][1:]) for tm in args.tensor_maps_in} @@ -439,7 +437,7 @@ def train_shallow_model(args): def train_char_model(args): args.num_workers = 0 logging.info(f'Number of workers forced to 0 for character emitting LSTM model.') - base_model = make_multimodal_multitask_model(**args.__dict__) + base_model = legacy_multimodal_multitask_model(**args.__dict__) model, char_model = make_character_model_plus( args.tensor_maps_in, args.tensor_maps_out, args.learning_rate, base_model, args.language_layer, args.language_prefix, args.model_layers, @@ -464,7 +462,7 @@ def train_char_model(args): def train_siamese_model(args): - base_model = make_multimodal_multitask_model(**args.__dict__) + base_model = legacy_multimodal_multitask_model(**args.__dict__) siamese_model = make_siamese_model(base_model, **args.__dict__) generate_train, generate_valid, generate_test = test_train_valid_tensor_generators(**args.__dict__, siamese=True) siamese_model = train_model_from_generators( @@ -488,7 +486,7 @@ def infer_encoders_block_multimodal_multitask(args): with open(args.sample_csv, 'r') as csv_file: sample_ids = [row[0] for row in csv.reader(csv_file)] sample_set = set(sample_ids[1:]) - _, encoders, _, _ = block_make_multimodal_multitask_model(**args.__dict__) + _, encoders, _, _ = make_multimodal_multitask_model(**args.__dict__) latent_dimensions = args.dense_layers[-1] for e in encoders: stats = Counter() @@ -538,7 +536,7 @@ def pca_on_hidden_inference(args): def plot_predictions(args): _, _, generate_test = test_train_valid_tensor_generators(**args.__dict__) - model, _, _, _ = block_make_multimodal_multitask_model(**args.__dict__) + model, _, _, _ = make_multimodal_multitask_model(**args.__dict__) data, labels, paths = big_batch_from_minibatch_generator(generate_test, args.test_steps) predictions = model.predict(data, batch_size=args.batch_size) if len(args.tensor_maps_out) == 1: @@ -550,7 +548,7 @@ def plot_predictions(args): def plot_while_training(args): generate_train, _, generate_test = test_train_valid_tensor_generators(**args.__dict__) test_data, test_labels, test_paths = big_batch_from_minibatch_generator(generate_test, args.test_steps) - model = make_multimodal_multitask_model(**args.__dict__) + model = legacy_multimodal_multitask_model(**args.__dict__) plot_folder = os.path.join(args.output_folder, args.id, 'training_frames/') plot_while_learning( @@ -563,7 +561,7 @@ def saliency_maps(args): import tensorflow as tf tf.compat.v1.disable_eager_execution() _, _, generate_test = test_train_valid_tensor_generators(**args.__dict__) - model = make_multimodal_multitask_model(**args.__dict__) + model = legacy_multimodal_multitask_model(**args.__dict__) data, labels, paths = big_batch_from_minibatch_generator(generate_test, args.test_steps) in_tensor = data[args.tensor_maps_in[0].input_name()] for tm in args.tensor_maps_out: @@ -714,7 +712,7 @@ def _get_predictions(args, models_inputs_outputs, input_data, outputs, input_pre args.tensor_maps_out = models_inputs_outputs[model_file][output_prefix] args.tensor_maps_in = models_inputs_outputs[model_file][input_prefix] args.model_file = model_file - model = make_multimodal_multitask_model(**args.__dict__) + model = legacy_multimodal_multitask_model(**args.__dict__) model_name = os.path.basename(model_file).replace(MODEL_EXT, '_') # We can feed 'model.predict()' the entire input data because it knows what subset to use @@ -758,7 +756,7 @@ def _scalar_predictions_from_generator(args, models_inputs_outputs, generator, s args.model_file = model_file args.tensor_maps_in = models_inputs_outputs[model_file][input_prefix] args.tensor_maps_out = models_inputs_outputs[model_file][output_prefix] - model = make_multimodal_multitask_model(**args.__dict__) + model = legacy_multimodal_multitask_model(**args.__dict__) model_name = os.path.basename(model_file).replace(MODEL_EXT, '') models[model_name] = model scalar_predictions[model_name] = [tm for tm in models_inputs_outputs[model_file][output_prefix] if len(tm.shape) == 1] diff --git a/ml4h/tensorize/database/__init__.py b/ml4h/tensorize/dataflow/__init__.py similarity index 100% rename from ml4h/tensorize/database/__init__.py rename to ml4h/tensorize/dataflow/__init__.py diff --git a/ml4h/tensorize/database/tensorize.py b/ml4h/tensorize/dataflow/bigquery_ukb_queries.py similarity index 100% rename from ml4h/tensorize/database/tensorize.py rename to ml4h/tensorize/dataflow/bigquery_ukb_queries.py diff --git a/ml4h/tensorize/database/fieldids.json b/ml4h/tensorize/dataflow/fieldids.json similarity index 100% rename from ml4h/tensorize/database/fieldids.json rename to ml4h/tensorize/dataflow/fieldids.json diff --git a/ml4h/tensorize/database/load_fieldids.sh b/ml4h/tensorize/dataflow/load_fieldids.sh similarity index 100% rename from ml4h/tensorize/database/load_fieldids.sh rename to ml4h/tensorize/dataflow/load_fieldids.sh diff --git a/env/ml4cvd_dataflow.yml b/ml4h/tensorize/dataflow/ml4h_dataflow.yml similarity index 65% rename from env/ml4cvd_dataflow.yml rename to ml4h/tensorize/dataflow/ml4h_dataflow.yml index 67e444136..05883f0e3 100644 --- a/env/ml4cvd_dataflow.yml +++ b/ml4h/tensorize/dataflow/ml4h_dataflow.yml @@ -6,8 +6,9 @@ channels: - defaults - anaconda dependencies: - - python==3.6.2 - - pip: - - apache-beam[gcp]==2.12.0 - - google-cloud-storage==1.13.0 - - h5py==2.9.0 + - python==3.8.10 +# - pip==22.3.1 +# - pip: +# - apache-beam[gcp]==2.12.0 +# - google-cloud-storage==1.13.0 +# - h5py==2.9.0 diff --git a/ml4h/tensorize/dataflow/requirements_ml4h_dataflow.txt b/ml4h/tensorize/dataflow/requirements_ml4h_dataflow.txt new file mode 100644 index 000000000..df3399d75 --- /dev/null +++ b/ml4h/tensorize/dataflow/requirements_ml4h_dataflow.txt @@ -0,0 +1,194 @@ +absl-py==1.4.0 +aiohttp==3.8.3 +aiosignal==1.3.1 +altair==4.2.0 +anyio==3.6.2 +apache-beam==2.43.0 +appnope==0.1.3 +argon2-cffi==21.3.0 +argon2-cffi-bindings==21.2.0 +arrow==1.2.3 +asttokens==2.2.1 +astunparse==1.6.3 +async-timeout==4.0.2 +attrs==22.2.0 +autobahn==22.12.1 +automat==22.10.0 +backcall==0.2.0 +backports-zoneinfo==0.2.1 +beautifulsoup4==4.11.1 +bidict==0.22.1 +bleach==5.0.1 +blosc==1.11.1 +boto3==1.26.49 +botocore==1.29.49 +cachetools==4.2.4 +cffi==1.15.1 +charset-normalizer==2.1.1 +cloudpickle==2.2.0 +comm==0.1.2 +constantly==15.1.0 +contourpy==1.0.7 +crcmod==1.7 +cryptography==39.0.0 +cycler==0.11.0 +db-dtypes==1.0.5 +debugpy==1.6.5 +decorator==5.1.1 +defusedxml==0.7.1 +dill==0.3.1.1 +dm-tree==0.1.8 +dnspython==2.2.1 +docopt==0.6.2 +entrypoints==0.4 +exceptiongroup==1.1.0 +executing==1.2.0 +fasteners==0.18 +fastjsonschema==2.16.2 +flatbuffers==1.12 +fonttools==4.38.0 +fqdn==1.5.1 +frozenlist==1.3.3 +future==0.18.3 +gast==0.4.0 +google-api-core==2.11.0 +google-apitools==0.5.31 +google-auth==2.16.0 +google-auth-httplib2==0.1.0 +google-auth-oauthlib==0.4.6 +google-cloud-bigquery==3.4.1 +google-cloud-bigquery-storage==2.13.2 +google-cloud-bigtable==1.7.3 +google-cloud-core==2.3.2 +google-cloud-datastore==1.15.5 +google-cloud-dlp==3.11.0 +google-cloud-language==1.3.2 +google-cloud-pubsub==2.13.12 +google-cloud-pubsublite==1.6.0 +google-cloud-recommendations-ai==0.7.1 +google-cloud-spanner==3.27.0 +google-cloud-storage==2.7.0 +google-cloud-videointelligence==1.16.3 +google-cloud-vision==1.0.2 +google-crc32c==1.5.0 +google-pasta==0.2.0 +google-resumable-media==2.4.0 +googleapis-common-protos==1.58.0 +grpc-google-iam-v1==0.12.6 +grpcio==1.51.1 +grpcio-status==1.48.2 +h5py==3.7.0 +hdfs==2.7.0 +httplib2==0.20.4 +hyperlink==21.0.0 +hyperopt==0.1.2 +imageio==2.6.1 +importlib-metadata==6.0.0 +importlib-resources==5.10.2 +incremental==22.10.0 +iniconfig==2.0.0 +isoduration==20.11.0 +jedi==0.18.2 +jinja2==3.1.2 +jmespath==1.0.1 +joblib==1.2.0 +jsonpointer==2.3 +jsonschema==4.17.3 +kiwisolver==1.4.4 +kt-legacy==1.0.4 +libclang==15.0.6.1 +lxml==4.9.2 +markdown==3.4.1 +markupsafe==2.1.1 +mistune==2.0.4 +mizani==0.8.1 +multidict==6.0.4 +nest-asyncio==1.5.6 +networkx==3.0 +numcodecs==0.11.0 +oauth2client==4.1.3 +oauthlib==3.2.2 +objsize==0.5.2 +opt-einsum==3.3.0 +orjson==3.8.5 +overrides==6.5.0 +packaging==21.3 +palettable==3.3.0 +pandas==1.5.2 +pandas-gbq==0.17.9 +pandocfilters==1.5.0 +parso==0.8.3 +patsy==0.5.3 +pexpect==4.8.0 +pickleshare==0.7.5 +pkgutil-resolve-name==1.3.10 +platformdirs==2.6.2 +plotnine==0.10.1 +pluggy==1.0.0 +prometheus-client==0.15.0 +prompt-toolkit==3.0.36 +proto-plus==1.22.2 +protobuf==3.19.6 +psutil==5.9.4 +ptyprocess==0.7.0 +pure-eval==0.2.2 +pyarrow==9.0.0 +pyasn1==0.4.8 +pyasn1-modules==0.2.8 +pycparser==2.21 +pydata-google-auth==1.5.0 +pygments==2.14.0 +pymongo==3.13.0 +pyparsing==3.0.9 +pyrsistent==0.19.3 +pytest==7.2.0 +python-dateutil==2.8.2 +python-json-logger==2.0.4 +pytz==2022.7 +pywavelets==1.4.1 +pyyaml==6.0 +pyzmq==25.0.0 +qtconsole==5.4.0 +qtpy==2.3.0 +regex==2022.10.31 +requests==2.28.2 +requests-oauthlib==1.3.1 +rsa==4.9 +s3transfer==0.6.0 +send2trash==1.8.0 +shortuuid==1.0.11 +six==1.16.0 +sniffio==1.3.0 +soupsieve==2.3.2.post1 +sqlparse==0.4.3 +stack-data==0.6.2 +termcolor==2.2.0 +terminado==0.17.1 +threadpoolctl==3.1.0 +tifffile==2022.10.10 +tinycss2==1.2.1 +tomli==2.0.1 +toolz==0.12.0 +tornado==6.2 +tqdm==4.64.1 +traitlets==5.8.1 +twisted==22.10.0 +txaio==22.2.1 +typeguard==2.13.3 +typing-extensions==4.4.0 +uri-template==1.2.0 +urllib3==1.26.14 +vega==3.6.0 +vtk==9.0.3 +wcwidth==0.2.5 +webencodings==0.5.1 +websocket-client==1.4.2 +werkzeug==2.2.2 +wrapt==1.14.1 +wslink==1.10.0 +xmltodict==0.13.0 +xyzservices==2022.9.0 +yarl==1.8.2 +zipp==3.11.0 +zope-interface==5.5.2 +zstandard==0.19.0 \ No newline at end of file diff --git a/ml4h/tensorize/merge_hd5s.py b/ml4h/tensorize/merge_hd5s.py index 836e84fc6..7c9b454bd 100644 --- a/ml4h/tensorize/merge_hd5s.py +++ b/ml4h/tensorize/merge_hd5s.py @@ -63,7 +63,7 @@ def _copy_hd5_datasets(source_hd5, destination_hd5, group_path=HD5_GROUP_CHAR, s destination_hd5.create_dataset(group_path + k, data=source_hd5[group_path][k], compression='gzip') stats[group_path + k] += 1 except (OSError, KeyError, RuntimeError, ValueError) as e: - logging.warning(f"Error trying to write:{k} at group path:{group_path} error:{e}\n{traceback.format_exc()}\n") + logging.debug(f"Error trying to write:{k} at group path:{group_path} error:{e}\n{traceback.format_exc()}\n") else: logging.debug(f"copying group {group_path + k}") _copy_hd5_datasets(source_hd5, destination_hd5, group_path=group_path + k + HD5_GROUP_CHAR, stats=stats) diff --git a/ml4h/tensorize/tensorize_dataflow.py b/ml4h/tensorize/tensorize_dataflow.py index 11b2762bb..8c7e15941 100644 --- a/ml4h/tensorize/tensorize_dataflow.py +++ b/ml4h/tensorize/tensorize_dataflow.py @@ -6,7 +6,7 @@ from apache_beam.options.pipeline_options import PipelineOptions, GoogleCloudOptions, StandardOptions from ml4h.defines import GCS_BUCKET -from ml4h.tensorize.database import tensorize +from ml4h.tensorize.dataflow import bigquery_ukb_queries def parse_args(): @@ -43,7 +43,7 @@ def parse_args(): help='Name of the Google Cloud Platform project', ) parser.add_argument( - '--gcp_region', default='us-east1', + '--gcp_region', default='us-central1', help='Google Cloud Platform region', ) # parser.add_argument('--gcs_bucket', default='ml4h', @@ -67,8 +67,8 @@ def parse_args(): logging.getLogger().setLevel(args.logging_level) packaging_args = [ - '--requirements_file={}'.format(f"{args.repo_root}/env/requirements_ml4h_dataflow.txt"), - '--setup_file={}'.format(f"{args.repo_root}/setup.py"), + f'--requirements_file={args.repo_root}/ml4h/tensorize/dataflow/requirements_ml4h_dataflow.txt', + f'--setup_file={args.repo_root}/setup.py', ] pipeline_opts = PipelineOptions(flags=packaging_args) @@ -82,7 +82,7 @@ def parse_args(): pipeline = beam.Pipeline(options=pipeline_opts) - tensorize.tensorize_sql_fields( + bigquery_ukb_queries.tensorize_sql_fields( pipeline, args.gcs_output_path, args.bigquery_dataset, diff --git a/ml4h/tensormap/tensor_map_maker.py b/ml4h/tensormap/tensor_map_maker.py index 78d4019e9..078e6c012 100644 --- a/ml4h/tensormap/tensor_map_maker.py +++ b/ml4h/tensormap/tensor_map_maker.py @@ -38,9 +38,9 @@ def write_tensor_maps(args) -> None: db_client = BigQueryDatabaseClient(credentials_file=args.bigquery_credentials_file) with open(tensor_maps_file, 'w') as f: f.write(_get_tensor_map_file_imports()) - _write_disease_tensor_maps(args.phenos_folder, f) - _write_disease_tensor_maps_incident_prevalent(args.phenos_folder, f) - _write_phecode_tensor_maps(f, args.phecode_definitions, db_client) + #_write_disease_tensor_maps(args.phenos_folder, f) + #_write_disease_tensor_maps_incident_prevalent(args.phenos_folder, f) + #_write_phecode_tensor_maps(f, args.phecode_definitions, db_client) _write_continuous_tensor_maps(f, db_client) f.write('\n') @@ -205,7 +205,7 @@ def _write_continuous_tensor_maps(f: TextIO, db_client: DatabaseClient): channel_map += f"'{name}{JOIN_CHAR}{i}': {i}, " channel_map += "}" f.write(f"ukb_{row.FieldID}_{row.instance} = TensorMap('{name}{JOIN_CHAR}{i}', loss='logcosh', path_prefix='continuous', ") - f.write(f"normalization={{'mean': {row.mean}, 'std': {row.std}}}, annotation_units={row.max_array+1}, {channel_map})\n") + f.write(f"normalization={{'mean': {row.mean}, 'std': {row.std}}}, {channel_map})\n") def _get_pkl_path_for_field(field_id: int, pyukbb_data_path: str): @@ -286,7 +286,8 @@ def generate_random_text_tensor_maps(text_file: str, window_size: int) -> Tuple[ def generate_random_pixel_as_text_tensor_maps( - tensors: str, path_prefix: str, + tensors: str, + path_prefix: str, window_shape: Tuple[int], ) -> Tuple[TensorMap, TensorMap]: name = path_prefix.split('/')[-1] diff --git a/ml4h/tensormap/ukb/categorical.py b/ml4h/tensormap/ukb/categorical.py new file mode 100644 index 000000000..4858b6d4a --- /dev/null +++ b/ml4h/tensormap/ukb/categorical.py @@ -0,0 +1,359 @@ +from ml4h.defines import StorageType +from ml4h.TensorMap import TensorMap, Interpretation + +adopted_as_a_child_no_0_0 = TensorMap('Adopted-as-a-child_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Adopted-as-a-child_No_0_0': 0, 'Adopted-as-a-child_No_0_0': 1}) +alcohol_drinker_status_current_0_0 = TensorMap('Alcohol-drinker-status_Current_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Alcohol-drinker-status_Current_0_0': 0, 'Alcohol-drinker-status_Current_0_0': 1}) +alcohol_intake_frequency_once_or_twice_a_week_0_0 = TensorMap('Alcohol-intake-frequency_Once-or-twice-a-week_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Alcohol-intake-frequency_Once-or-twice-a-week_0_0': 0, 'Alcohol-intake-frequency_Once-or-twice-a-week_0_0': 1}) +alcohol_intake_versus_10_years_previously_less_nowadays_0_0 = TensorMap('Alcohol-intake-versus-10-years-previously_Less-nowadays_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Alcohol-intake-versus-10-years-previously_Less-nowadays_0_0': 0, 'Alcohol-intake-versus-10-years-previously_Less-nowadays_0_0': 1}) +alcohol_usually_taken_with_meals_no_0_0 = TensorMap('Alcohol-usually-taken-with-meals_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Alcohol-usually-taken-with-meals_No_0_0': 0, 'Alcohol-usually-taken-with-meals_No_0_0': 1}) +average_total_household_income_before_tax_52000_to_100000_0_0 = TensorMap('Average-total-household-income-before-tax_52000-to-100000_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Average-total-household-income-before-tax_52000-to-100000_0_0': 0, 'Average-total-household-income-before-tax_52000-to-100000_0_0': 1}) +beef_intake_once_a_week_0_0 = TensorMap('Beef-intake_Once-a-week_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Beef-intake_Once-a-week_0_0': 0, 'Beef-intake_Once-a-week_0_0': 1}) +birth_weight_known_no_0_0 = TensorMap('Birth-weight-known_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Birth-weight-known_No_0_0': 0, 'Birth-weight-known_No_0_0': 1}) +blood_clot_dvt_bronchitis_emphysema_asthma_rhinitis_eczema_allergy_diagnosed_by_doctor_none_of_the_above_0_0 = TensorMap('Blood-clot-DVT-bronchitis-emphysema-asthma-rhinitis-eczema-allergy-diagnosed-by-doctor_None-of-the-above_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Blood-clot-DVT-bronchitis-emphysema-asthma-rhinitis-eczema-allergy-diagnosed-by-doctor_None-of-the-above_0_0': 0, 'Blood-clot-DVT-bronchitis-emphysema-asthma-rhinitis-eczema-allergy-diagnosed-by-doctor_None-of-the-above_0_0': 1}) +bread_type_wholemeal_or_wholegrain_0_0 = TensorMap('Bread-type_Wholemeal-or-wholegrain_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Bread-type_Wholemeal-or-wholegrain_0_0': 0, 'Bread-type_Wholemeal-or-wholegrain_0_0': 1}) +breastfed_as_a_baby_yes_0_0 = TensorMap('Breastfed-as-a-baby_Yes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Breastfed-as-a-baby_Yes_0_0': 0, 'Breastfed-as-a-baby_Yes_0_0': 1}) +breathing_problems_during_period_of_job_no_0_0 = TensorMap('Breathing-problems-during-period-of-job_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Breathing-problems-during-period-of-job_No_0_0': 0, 'Breathing-problems-during-period-of-job_No_0_0': 1}) +breathing_problems_during_period_of_job_no_0_1 = TensorMap('Breathing-problems-during-period-of-job_No_0_1', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Breathing-problems-during-period-of-job_No_0_1': 0, 'Breathing-problems-during-period-of-job_No_0_1': 1}) +caffeine_drink_within_last_hour_no_0_0 = TensorMap('Caffeine-drink-within-last-hour_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Caffeine-drink-within-last-hour_No_0_0': 0, 'Caffeine-drink-within-last-hour_No_0_0': 1}) +cancer_diagnosed_by_doctor_no_0_0 = TensorMap('Cancer-diagnosed-by-doctor_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Cancer-diagnosed-by-doctor_No_0_0': 0, 'Cancer-diagnosed-by-doctor_No_0_0': 1}) +cheese_intake_24_times_a_week_0_0 = TensorMap('Cheese-intake_24-times-a-week_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Cheese-intake_24-times-a-week_0_0': 0, 'Cheese-intake_24-times-a-week_0_0': 1}) +chest_pain_or_discomfort_no_0_0 = TensorMap('Chest-pain-or-discomfort_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Chest-pain-or-discomfort_No_0_0': 0, 'Chest-pain-or-discomfort_No_0_0': 1}) +coffee_type_instant_coffee_0_0 = TensorMap('Coffee-type_Instant-coffee_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Coffee-type_Instant-coffee_0_0': 0, 'Coffee-type_Instant-coffee_0_0': 1}) +comparative_body_size_at_age_10_about_average_0_0 = TensorMap('Comparative-body-size-at-age-10_About-average_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Comparative-body-size-at-age-10_About-average_0_0': 0, 'Comparative-body-size-at-age-10_About-average_0_0': 1}) +comparative_height_size_at_age_10_shorter_0_0 = TensorMap('Comparative-height-size-at-age-10_Shorter_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Comparative-height-size-at-age-10_Shorter_0_0': 0, 'Comparative-height-size-at-age-10_Shorter_0_0': 1}) +contraindications_for_spirometry_no_0_0 = TensorMap('Contraindications-for-spirometry_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Contraindications-for-spirometry_No_0_0': 0, 'Contraindications-for-spirometry_No_0_0': 1}) +country_of_birth_ukelsewhere_england_0_0 = TensorMap('Country-of-birth-UKelsewhere_England_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Country-of-birth-UKelsewhere_England_0_0': 0, 'Country-of-birth-UKelsewhere_England_0_0': 1}) +current_employment_status_in_paid_employment_or_selfemployed_0_0 = TensorMap('Current-employment-status_In-paid-employment-or-selfemployed_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Current-employment-status_In-paid-employment-or-selfemployed_0_0': 0, 'Current-employment-status_In-paid-employment-or-selfemployed_0_0': 1}) +current_tobacco_smoking_no_0_0 = TensorMap('Current-tobacco-smoking_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Current-tobacco-smoking_No_0_0': 0, 'Current-tobacco-smoking_No_0_0': 1}) +diabetes_diagnosed_by_doctor_no_0_0 = TensorMap('Diabetes-diagnosed-by-doctor_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Diabetes-diagnosed-by-doctor_No_0_0': 0, 'Diabetes-diagnosed-by-doctor_No_0_0': 1}) +drive_faster_than_motorway_speed_limit_sometimes_0_0 = TensorMap('Drive-faster-than-motorway-speed-limit_Sometimes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Drive-faster-than-motorway-speed-limit_Sometimes_0_0': 0, 'Drive-faster-than-motorway-speed-limit_Sometimes_0_0': 1}) +ease_of_skin_tanning_get_moderately_tanned_0_0 = TensorMap('Ease-of-skin-tanning_Get-moderately-tanned_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Ease-of-skin-tanning_Get-moderately-tanned_0_0': 0, 'Ease-of-skin-tanning_Get-moderately-tanned_0_0': 1}) +ethnic_background_british_0_0 = TensorMap('Ethnic-background_British_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Ethnic-background_British_0_0': 0, 'Ethnic-background_British_0_0': 1}) +ever_had_bowel_cancer_screening_no_0_0 = TensorMap('Ever-had-bowel-cancer-screening_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Ever-had-bowel-cancer-screening_No_0_0': 0, 'Ever-had-bowel-cancer-screening_No_0_0': 1}) +ever_had_prostate_specific_antigen_psa_test_no_0_0 = TensorMap('Ever-had-prostate-specific-antigen-PSA-test_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Ever-had-prostate-specific-antigen-PSA-test_No_0_0': 0, 'Ever-had-prostate-specific-antigen-PSA-test_No_0_0': 1}) +ever_smoked_yes_0_0 = TensorMap('Ever-smoked_Yes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Ever-smoked_Yes_0_0': 0, 'Ever-smoked_Yes_0_0': 1}) +fi1_numeric_addition_test_15_0_0 = TensorMap('FI1-numeric-addition-test_15_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_FI1-numeric-addition-test_15_0_0': 0, 'FI1-numeric-addition-test_15_0_0': 1}) +fi2_identify_largest_number_987_0_0 = TensorMap('FI2-identify-largest-number_987_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_FI2-identify-largest-number_987_0_0': 0, 'FI2-identify-largest-number_987_0_0': 1}) +fi3_word_interpolation_adult_0_0 = TensorMap('FI3-word-interpolation_adult_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_FI3-word-interpolation_adult_0_0': 0, 'FI3-word-interpolation_adult_0_0': 1}) +fi4_positional_arithmetic_6_0_0 = TensorMap('FI4-positional-arithmetic_6_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_FI4-positional-arithmetic_6_0_0': 0, 'FI4-positional-arithmetic_6_0_0': 1}) +facial_ageing_younger_than_you_are_0_0 = TensorMap('Facial-ageing_Younger-than-you-are_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Facial-ageing_Younger-than-you-are_0_0': 0, 'Facial-ageing_Younger-than-you-are_0_0': 1}) +falls_in_the_last_year_no_falls_0_0 = TensorMap('Falls-in-the-last-year_No-falls_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Falls-in-the-last-year_No-falls_0_0': 0, 'Falls-in-the-last-year_No-falls_0_0': 1}) +frequency_of_stair_climbing_in_last_4_weeks_1115_times_a_day_0_0 = TensorMap('Frequency-of-stair-climbing-in-last-4-weeks_1115-times-a-day_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Frequency-of-stair-climbing-in-last-4-weeks_1115-times-a-day_0_0': 0, 'Frequency-of-stair-climbing-in-last-4-weeks_1115-times-a-day_0_0': 1}) +frequency_of_walking_for_pleasure_in_last_4_weeks_23_times_in_the_last_4_weeks_0_0 = TensorMap('Frequency-of-walking-for-pleasure-in-last-4-weeks_23-times-in-the-last-4-weeks_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Frequency-of-walking-for-pleasure-in-last-4-weeks_23-times-in-the-last-4-weeks_0_0': 0, 'Frequency-of-walking-for-pleasure-in-last-4-weeks_23-times-in-the-last-4-weeks_0_0': 1}) +genetic_ethnic_grouping_caucasian_0_0 = TensorMap('Genetic-ethnic-grouping_Caucasian_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Genetic-ethnic-grouping_Caucasian_0_0': 0, 'Genetic-ethnic-grouping_Caucasian_0_0': 1}) +genetic_sex_male_0_0 = TensorMap('Genetic-sex_Male_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Genetic-sex_Male_0_0': 0, 'Genetic-sex_Male_0_0': 1}) +had_major_operations_no_0_0 = TensorMap('Had-major-operations_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Had-major-operations_No_0_0': 0, 'Had-major-operations_No_0_0': 1}) +hair_colour_natural_before_greying_dark_brown_0_0 = TensorMap('Hair-colour-natural-before-greying_Dark-brown_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Hair-colour-natural-before-greying_Dark-brown_0_0': 0, 'Hair-colour-natural-before-greying_Dark-brown_0_0': 1}) +handedness_chiralitylaterality_righthanded_0_0 = TensorMap('Handedness-chiralitylaterality_Righthanded_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Handedness-chiralitylaterality_Righthanded_0_0': 0, 'Handedness-chiralitylaterality_Righthanded_0_0': 1}) +hearing_aid_user_no_0_0 = TensorMap('Hearing-aid-user_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Hearing-aid-user_No_0_0': 0, 'Hearing-aid-user_No_0_0': 1}) +hearing_difficultyproblems_with_background_noise_yes_0_0 = TensorMap('Hearing-difficultyproblems-with-background-noise_Yes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Hearing-difficultyproblems-with-background-noise_Yes_0_0': 0, 'Hearing-difficultyproblems-with-background-noise_Yes_0_0': 1}) +hearing_difficultyproblems_no_0_0 = TensorMap('Hearing-difficultyproblems_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Hearing-difficultyproblems_No_0_0': 0, 'Hearing-difficultyproblems_No_0_0': 1}) +hot_drink_temperature_hot_0_0 = TensorMap('Hot-drink-temperature_Hot_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Hot-drink-temperature_Hot_0_0': 0, 'Hot-drink-temperature_Hot_0_0': 1}) +illnesses_of_mother_high_blood_pressure_0_0 = TensorMap('Illnesses-of-mother_High-blood-pressure_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Illnesses-of-mother_High-blood-pressure_0_0': 0, 'Illnesses-of-mother_High-blood-pressure_0_0': 1}) +illnesses_of_mother_none_of_the_above_group_2_0_1 = TensorMap('Illnesses-of-mother_None-of-the-above-group-2_0_1', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Illnesses-of-mother_None-of-the-above-group-2_0_1': 0, 'Illnesses-of-mother_None-of-the-above-group-2_0_1': 1}) +illnesses_of_siblings_none_of_the_above_group_2_0_1 = TensorMap('Illnesses-of-siblings_None-of-the-above-group-2_0_1', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Illnesses-of-siblings_None-of-the-above-group-2_0_1': 0, 'Illnesses-of-siblings_None-of-the-above-group-2_0_1': 1}) +job_involved_shift_work_no_0_0 = TensorMap('Job-involved-shift-work_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Job-involved-shift-work_No_0_0': 0, 'Job-involved-shift-work_No_0_0': 1}) +job_involved_shift_work_no_0_1 = TensorMap('Job-involved-shift-work_No_0_1', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Job-involved-shift-work_No_0_1': 0, 'Job-involved-shift-work_No_0_1': 1}) +lambmutton_intake_once_a_week_0_0 = TensorMap('Lambmutton-intake_Once-a-week_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Lambmutton-intake_Once-a-week_0_0': 0, 'Lambmutton-intake_Once-a-week_0_0': 1}) +light_smokers_at_least_100_smokes_in_lifetime_no_0_0 = TensorMap('Light-smokers-at-least-100-smokes-in-lifetime_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Light-smokers-at-least-100-smokes-in-lifetime_No_0_0': 0, 'Light-smokers-at-least-100-smokes-in-lifetime_No_0_0': 1}) +longstanding_illness_disability_or_infirmity_no_0_0 = TensorMap('Longstanding-illness-disability-or-infirmity_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Longstanding-illness-disability-or-infirmity_No_0_0': 0, 'Longstanding-illness-disability-or-infirmity_No_0_0': 1}) +major_dietary_changes_in_the_last_5_years_no_0_0 = TensorMap('Major-dietary-changes-in-the-last-5-years_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Major-dietary-changes-in-the-last-5-years_No_0_0': 0, 'Major-dietary-changes-in-the-last-5-years_No_0_0': 1}) +maternal_smoking_around_birth_yes_0_0 = TensorMap('Maternal-smoking-around-birth_Yes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Maternal-smoking-around-birth_Yes_0_0': 0, 'Maternal-smoking-around-birth_Yes_0_0': 1}) +medication_for_cholesterol_blood_pressure_or_diabetes_none_of_the_above_0_0 = TensorMap('Medication-for-cholesterol-blood-pressure-or-diabetes_None-of-the-above_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Medication-for-cholesterol-blood-pressure-or-diabetes_None-of-the-above_0_0': 0, 'Medication-for-cholesterol-blood-pressure-or-diabetes_None-of-the-above_0_0': 1}) +medication_for_pain_relief_constipation_heartburn_none_of_the_above_0_0 = TensorMap('Medication-for-pain-relief-constipation-heartburn_None-of-the-above_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Medication-for-pain-relief-constipation-heartburn_None-of-the-above_0_0': 0, 'Medication-for-pain-relief-constipation-heartburn_None-of-the-above_0_0': 1}) +milk_type_used_semiskimmed_0_0 = TensorMap('Milk-type-used_Semiskimmed_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Milk-type-used_Semiskimmed_0_0': 0, 'Milk-type-used_Semiskimmed_0_0': 1}) +mineral_and_other_dietary_supplements_none_of_the_above_0_0 = TensorMap('Mineral-and-other-dietary-supplements_None-of-the-above_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Mineral-and-other-dietary-supplements_None-of-the-above_0_0': 0, 'Mineral-and-other-dietary-supplements_None-of-the-above_0_0': 1}) +mother_still_alive_yes_0_0 = TensorMap('Mother-still-alive_Yes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Mother-still-alive_Yes_0_0': 0, 'Mother-still-alive_Yes_0_0': 1}) +mouthteeth_dental_problems_none_of_the_above_0_0 = TensorMap('Mouthteeth-dental-problems_None-of-the-above_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Mouthteeth-dental-problems_None-of-the-above_0_0': 0, 'Mouthteeth-dental-problems_None-of-the-above_0_0': 1}) +never_eat_eggs_dairy_wheat_sugar_i_eat_all_of_the_above_0_0 = TensorMap('Never-eat-eggs-dairy-wheat-sugar_I-eat-all-of-the-above_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Never-eat-eggs-dairy-wheat-sugar_I-eat-all-of-the-above_0_0': 0, 'Never-eat-eggs-dairy-wheat-sugar_I-eat-all-of-the-above_0_0': 1}) +nonoily_fish_intake_24_times_a_week_0_0 = TensorMap('Nonoily-fish-intake_24-times-a-week_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Nonoily-fish-intake_24-times-a-week_0_0': 0, 'Nonoily-fish-intake_24-times-a-week_0_0': 1}) +oily_fish_intake_less_than_once_a_week_0_0 = TensorMap('Oily-fish-intake_Less-than-once-a-week_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Oily-fish-intake_Less-than-once-a-week_0_0': 0, 'Oily-fish-intake_Less-than-once-a-week_0_0': 1}) +other_eye_problems_no_0_0 = TensorMap('Other-eye-problems_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Other-eye-problems_No_0_0': 0, 'Other-eye-problems_No_0_0': 1}) +other_serious_medical_conditiondisability_diagnosed_by_doctor_no_0_0 = TensorMap('Other-serious-medical-conditiondisability-diagnosed-by-doctor_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Other-serious-medical-conditiondisability-diagnosed-by-doctor_No_0_0': 0, 'Other-serious-medical-conditiondisability-diagnosed-by-doctor_No_0_0': 1}) +overall_health_rating_excellent_0_0 = TensorMap('Overall-health-rating_Excellent_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Overall-health-rating_Excellent_0_0': 0, 'Overall-health-rating_Excellent_0_0': 1}) +own_or_rent_accommodation_lived_in_own_with_a_mortgage_0_0 = TensorMap('Own-or-rent-accommodation-lived-in_Own-with-a-mortgage_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Own-or-rent-accommodation-lived-in_Own-with-a-mortgage_0_0': 0, 'Own-or-rent-accommodation-lived-in_Own-with-a-mortgage_0_0': 1}) +pacemaker_no_0_0 = TensorMap('Pacemaker_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Pacemaker_No_0_0': 0, 'Pacemaker_No_0_0': 1}) +part_of_a_multiple_birth_no_0_0 = TensorMap('Part-of-a-multiple-birth_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Part-of-a-multiple-birth_No_0_0': 0, 'Part-of-a-multiple-birth_No_0_0': 1}) +past_tobacco_smoking_just_tried_once_or_twice_0_0 = TensorMap('Past-tobacco-smoking_Just-tried-once-or-twice_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Past-tobacco-smoking_Just-tried-once-or-twice_0_0': 0, 'Past-tobacco-smoking_Just-tried-once-or-twice_0_0': 1}) +pork_intake_once_a_week_0_0 = TensorMap('Pork-intake_Once-a-week_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Pork-intake_Once-a-week_0_0': 0, 'Pork-intake_Once-a-week_0_0': 1}) +poultry_intake_24_times_a_week_0_0 = TensorMap('Poultry-intake_24-times-a-week_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Poultry-intake_24-times-a-week_0_0': 0, 'Poultry-intake_24-times-a-week_0_0': 1}) +processed_meat_intake_24_times_a_week_0_0 = TensorMap('Processed-meat-intake_24-times-a-week_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Processed-meat-intake_24-times-a-week_0_0': 0, 'Processed-meat-intake_24-times-a-week_0_0': 1}) +qualifications_o_levelsgcses_or_equivalent_0_0 = TensorMap('Qualifications_O-levelsGCSEs-or-equivalent_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Qualifications_O-levelsGCSEs-or-equivalent_0_0': 0, 'Qualifications_O-levelsGCSEs-or-equivalent_0_0': 1}) +reason_for_reducing_amount_of_alcohol_drunk_other_reason_0_0 = TensorMap('Reason-for-reducing-amount-of-alcohol-drunk_Other-reason_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Reason-for-reducing-amount-of-alcohol-drunk_Other-reason_0_0': 0, 'Reason-for-reducing-amount-of-alcohol-drunk_Other-reason_0_0': 1}) +relative_age_voice_broke_about_average_age_0_0 = TensorMap('Relative-age-voice-broke_About-average-age_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Relative-age-voice-broke_About-average-age_0_0': 0, 'Relative-age-voice-broke_About-average-age_0_0': 1}) +result_ranking_best_0_0 = TensorMap('Result-ranking_best_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Result-ranking_best_0_0': 0, 'Result-ranking_best_0_0': 1}) +result_ranking_second_best_0_1 = TensorMap('Result-ranking_second-best_0_1', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Result-ranking_second-best_0_1': 0, 'Result-ranking_second-best_0_1': 1}) +result_ranking_third_best_ie_worst_0_2 = TensorMap('Result-ranking_third-best-ie-worst_0_2', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Result-ranking_third-best-ie-worst_0_2': 0, 'Result-ranking_third-best-ie-worst_0_2': 1}) +salt_added_to_food_neverrarely_0_0 = TensorMap('Salt-added-to-food_Neverrarely_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Salt-added-to-food_Neverrarely_0_0': 0, 'Salt-added-to-food_Neverrarely_0_0': 1}) +sex_male_0_0 = TensorMap('Sex_Male_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Sex_Male_0_0': 0, 'Sex_Male_0_0': 1}) +skin_colour_fair_0_0 = TensorMap('Skin-colour_Fair_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Skin-colour_Fair_0_0': 0, 'Skin-colour_Fair_0_0': 1}) +smoking_status_never_0_0 = TensorMap('Smoking-status_Never_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Smoking-status_Never_0_0': 0, 'Smoking-status_Never_0_0': 1}) +spread_type_butterspreadable_butter_0_0 = TensorMap('Spread-type_Butterspreadable-butter_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Spread-type_Butterspreadable-butter_0_0': 0, 'Spread-type_Butterspreadable-butter_0_0': 1}) +taking_other_prescription_medications_no_0_0 = TensorMap('Taking-other-prescription-medications_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Taking-other-prescription-medications_No_0_0': 0, 'Taking-other-prescription-medications_No_0_0': 1}) +type_of_accommodation_lived_in_a_house_or_bungalow_0_0 = TensorMap('Type-of-accommodation-lived-in_A-house-or-bungalow_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Type-of-accommodation-lived-in_A-house-or-bungalow_0_0': 0, 'Type-of-accommodation-lived-in_A-house-or-bungalow_0_0': 1}) +types_of_physical_activity_in_last_4_weeks_heavy_diy_eg_weeding_lawn_mowing_carpentry_digging_0_2 = TensorMap('Types-of-physical-activity-in-last-4-weeks_Heavy-DIY-eg-weeding-lawn-mowing-carpentry-digging_0_2', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Types-of-physical-activity-in-last-4-weeks_Heavy-DIY-eg-weeding-lawn-mowing-carpentry-digging_0_2': 0, 'Types-of-physical-activity-in-last-4-weeks_Heavy-DIY-eg-weeding-lawn-mowing-carpentry-digging_0_2': 1}) +types_of_physical_activity_in_last_4_weeks_walking_for_pleasure_not_as_a_means_of_transport_0_0 = TensorMap('Types-of-physical-activity-in-last-4-weeks_Walking-for-pleasure-not-as-a-means-of-transport_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Types-of-physical-activity-in-last-4-weeks_Walking-for-pleasure-not-as-a-means-of-transport_0_0': 0, 'Types-of-physical-activity-in-last-4-weeks_Walking-for-pleasure-not-as-a-means-of-transport_0_0': 1}) +types_of_transport_used_excluding_work_carmotor_vehicle_0_0 = TensorMap('Types-of-transport-used-excluding-work_Carmotor-vehicle_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Types-of-transport-used-excluding-work_Carmotor-vehicle_0_0': 0, 'Types-of-transport-used-excluding-work_Carmotor-vehicle_0_0': 1}) +types_of_transport_used_excluding_work_walk_0_1 = TensorMap('Types-of-transport-used-excluding-work_Walk_0_1', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Types-of-transport-used-excluding-work_Walk_0_1': 0, 'Types-of-transport-used-excluding-work_Walk_0_1': 1}) +used_an_inhaler_for_chest_within_last_hour_no_0_0 = TensorMap('Used-an-inhaler-for-chest-within-last-hour_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Used-an-inhaler-for-chest-within-last-hour_No_0_0': 0, 'Used-an-inhaler-for-chest-within-last-hour_No_0_0': 1}) +usual_walking_pace_brisk_pace_0_0 = TensorMap('Usual-walking-pace_Brisk-pace_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Usual-walking-pace_Brisk-pace_0_0': 0, 'Usual-walking-pace_Brisk-pace_0_0': 1}) +variation_in_diet_sometimes_0_0 = TensorMap('Variation-in-diet_Sometimes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Variation-in-diet_Sometimes_0_0': 0, 'Variation-in-diet_Sometimes_0_0': 1}) +vascularheart_problems_diagnosed_by_doctor_none_of_the_above_0_0 = TensorMap('Vascularheart-problems-diagnosed-by-doctor_None-of-the-above_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Vascularheart-problems-diagnosed-by-doctor_None-of-the-above_0_0': 0, 'Vascularheart-problems-diagnosed-by-doctor_None-of-the-above_0_0': 1}) +vitamin_and_mineral_supplements_none_of_the_above_0_0 = TensorMap('Vitamin-and-mineral-supplements_None-of-the-above_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Vitamin-and-mineral-supplements_None-of-the-above_0_0': 0, 'Vitamin-and-mineral-supplements_None-of-the-above_0_0': 1}) +weight_change_compared_with_1_year_ago_yes_gained_weight_0_0 = TensorMap('Weight-change-compared-with-1-year-ago_Yes-gained-weight_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Weight-change-compared-with-1-year-ago_Yes-gained-weight_0_0': 0, 'Weight-change-compared-with-1-year-ago_Yes-gained-weight_0_0': 1}) +wheeze_or_whistling_in_the_chest_in_last_year_no_0_0 = TensorMap('Wheeze-or-whistling-in-the-chest-in-last-year_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Wheeze-or-whistling-in-the-chest-in-last-year_No_0_0': 0, 'Wheeze-or-whistling-in-the-chest-in-last-year_No_0_0': 1}) +worked_with_materials_containing_asbestos_rarelynever_0_1 = TensorMap('Worked-with-materials-containing-asbestos_Rarelynever_0_1', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Worked-with-materials-containing-asbestos_Rarelynever_0_1': 0, 'Worked-with-materials-containing-asbestos_Rarelynever_0_1': 1}) +worked_with_pesticides_rarelynever_0_0 = TensorMap('Worked-with-pesticides_Rarelynever_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Worked-with-pesticides_Rarelynever_0_0': 0, 'Worked-with-pesticides_Rarelynever_0_0': 1}) +worked_with_pesticides_rarelynever_0_1 = TensorMap('Worked-with-pesticides_Rarelynever_0_1', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Worked-with-pesticides_Rarelynever_0_1': 0, 'Worked-with-pesticides_Rarelynever_0_1': 1}) +workplace_full_of_chemical_or_other_fumes_rarelynever_0_0 = TensorMap('Workplace-full-of-chemical-or-other-fumes_Rarelynever_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Workplace-full-of-chemical-or-other-fumes_Rarelynever_0_0': 0, 'Workplace-full-of-chemical-or-other-fumes_Rarelynever_0_0': 1}) +workplace_had_a_lot_of_diesel_exhaust_rarelynever_0_0 = TensorMap('Workplace-had-a-lot-of-diesel-exhaust_Rarelynever_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Workplace-had-a-lot-of-diesel-exhaust_Rarelynever_0_0': 0, 'Workplace-had-a-lot-of-diesel-exhaust_Rarelynever_0_0': 1}) +workplace_had_a_lot_of_diesel_exhaust_rarelynever_0_1 = TensorMap('Workplace-had-a-lot-of-diesel-exhaust_Rarelynever_0_1', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Workplace-had-a-lot-of-diesel-exhaust_Rarelynever_0_1': 0, 'Workplace-had-a-lot-of-diesel-exhaust_Rarelynever_0_1': 1}) +death = TensorMap('death', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_death': 0, 'death': 1}) +osteoarthritis = TensorMap('osteoarthritis', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_osteoarthritis': 0, 'osteoarthritis': 1}) +alcohol_intake_frequency_daily_or_almost_daily_0_0 = TensorMap('Alcohol-intake-frequency_Daily-or-almost-daily_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Alcohol-intake-frequency_Daily-or-almost-daily_0_0': 0, 'Alcohol-intake-frequency_Daily-or-almost-daily_0_0': 1}) +alcohol_intake_versus_10_years_previously_about_the_same_0_0 = TensorMap('Alcohol-intake-versus-10-years-previously_About-the-same_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Alcohol-intake-versus-10-years-previously_About-the-same_0_0': 0, 'Alcohol-intake-versus-10-years-previously_About-the-same_0_0': 1}) +alcohol_usually_taken_with_meals_yes_0_0 = TensorMap('Alcohol-usually-taken-with-meals_Yes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Alcohol-usually-taken-with-meals_Yes_0_0': 0, 'Alcohol-usually-taken-with-meals_Yes_0_0': 1}) +average_total_household_income_before_tax_31000_to_51999_0_0 = TensorMap('Average-total-household-income-before-tax_31000-to-51999_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Average-total-household-income-before-tax_31000-to-51999_0_0': 0, 'Average-total-household-income-before-tax_31000-to-51999_0_0': 1}) +birth_weight_known_yes_pounds_and_ounces_0_0 = TensorMap('Birth-weight-known_Yes-pounds-and-ounces_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Birth-weight-known_Yes-pounds-and-ounces_0_0': 0, 'Birth-weight-known_Yes-pounds-and-ounces_0_0': 1}) +cereal_type_biscuit_cereal_eg_weetabix_0_0 = TensorMap('Cereal-type_Biscuit-cereal-eg-Weetabix_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Cereal-type_Biscuit-cereal-eg-Weetabix_0_0': 0, 'Cereal-type_Biscuit-cereal-eg-Weetabix_0_0': 1}) +comparative_body_size_at_age_10_thinner_0_0 = TensorMap('Comparative-body-size-at-age-10_Thinner_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Comparative-body-size-at-age-10_Thinner_0_0': 0, 'Comparative-body-size-at-age-10_Thinner_0_0': 1}) +duration_of_other_exercises_between_30_minutes_and_1_hour_0_0 = TensorMap('Duration-of-other-exercises_Between-30-minutes-and-1-hour_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Duration-of-other-exercises_Between-30-minutes-and-1-hour_0_0': 0, 'Duration-of-other-exercises_Between-30-minutes-and-1-hour_0_0': 1}) +duration_walking_for_pleasure_between_30_minutes_and_1_hour_0_0 = TensorMap('Duration-walking-for-pleasure_Between-30-minutes-and-1-hour_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Duration-walking-for-pleasure_Between-30-minutes-and-1-hour_0_0': 0, 'Duration-walking-for-pleasure_Between-30-minutes-and-1-hour_0_0': 1}) +ease_of_skin_tanning_get_very_tanned_0_0 = TensorMap('Ease-of-skin-tanning_Get-very-tanned_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Ease-of-skin-tanning_Get-very-tanned_0_0': 0, 'Ease-of-skin-tanning_Get-very-tanned_0_0': 1}) +ever_smoked_no_0_0 = TensorMap('Ever-smoked_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Ever-smoked_No_0_0': 0, 'Ever-smoked_No_0_0': 1}) +facial_ageing_about_your_age_0_0 = TensorMap('Facial-ageing_About-your-age_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Facial-ageing_About-your-age_0_0': 0, 'Facial-ageing_About-your-age_0_0': 1}) +father_still_alive_yes_0_0 = TensorMap('Father-still-alive_Yes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Father-still-alive_Yes_0_0': 0, 'Father-still-alive_Yes_0_0': 1}) +fracturedbroken_bones_in_last_5_years_no_0_0 = TensorMap('Fracturedbroken-bones-in-last-5-years_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Fracturedbroken-bones-in-last-5-years_No_0_0': 0, 'Fracturedbroken-bones-in-last-5-years_No_0_0': 1}) +frequency_of_stair_climbing_in_last_4_weeks_15_times_a_day_0_0 = TensorMap('Frequency-of-stair-climbing-in-last-4-weeks_15-times-a-day_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Frequency-of-stair-climbing-in-last-4-weeks_15-times-a-day_0_0': 0, 'Frequency-of-stair-climbing-in-last-4-weeks_15-times-a-day_0_0': 1}) +frequency_of_walking_for_pleasure_in_last_4_weeks_23_times_a_week_0_0 = TensorMap('Frequency-of-walking-for-pleasure-in-last-4-weeks_23-times-a-week_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Frequency-of-walking-for-pleasure-in-last-4-weeks_23-times-a-week_0_0': 0, 'Frequency-of-walking-for-pleasure-in-last-4-weeks_23-times-a-week_0_0': 1}) +had_major_operations_yes_you_will_be_asked_about_this_later_by_an_interviewer_0_0 = TensorMap('Had-major-operations_Yes-you-will-be-asked-about-this-later-by-an-interviewer_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Had-major-operations_Yes-you-will-be-asked-about-this-later-by-an-interviewer_0_0': 0, 'Had-major-operations_Yes-you-will-be-asked-about-this-later-by-an-interviewer_0_0': 1}) +hot_drink_temperature_very_hot_0_0 = TensorMap('Hot-drink-temperature_Very-hot_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Hot-drink-temperature_Very-hot_0_0': 0, 'Hot-drink-temperature_Very-hot_0_0': 1}) +illnesses_of_father_none_of_the_above_group_1_0_0 = TensorMap('Illnesses-of-father_None-of-the-above-group-1_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Illnesses-of-father_None-of-the-above-group-1_0_0': 0, 'Illnesses-of-father_None-of-the-above-group-1_0_0': 1}) +illnesses_of_father_none_of_the_above_group_2_0_1 = TensorMap('Illnesses-of-father_None-of-the-above-group-2_0_1', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Illnesses-of-father_None-of-the-above-group-2_0_1': 0, 'Illnesses-of-father_None-of-the-above-group-2_0_1': 1}) +illnesses_of_mother_none_of_the_above_group_1_0_0 = TensorMap('Illnesses-of-mother_None-of-the-above-group-1_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Illnesses-of-mother_None-of-the-above-group-1_0_0': 0, 'Illnesses-of-mother_None-of-the-above-group-1_0_0': 1}) +illnesses_of_siblings_none_of_the_above_group_1_0_0 = TensorMap('Illnesses-of-siblings_None-of-the-above-group-1_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Illnesses-of-siblings_None-of-the-above-group-1_0_0': 0, 'Illnesses-of-siblings_None-of-the-above-group-1_0_0': 1}) +lambmutton_intake_less_than_once_a_week_0_0 = TensorMap('Lambmutton-intake_Less-than-once-a-week_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Lambmutton-intake_Less-than-once-a-week_0_0': 0, 'Lambmutton-intake_Less-than-once-a-week_0_0': 1}) +maternal_smoking_around_birth_no_0_0 = TensorMap('Maternal-smoking-around-birth_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Maternal-smoking-around-birth_No_0_0': 0, 'Maternal-smoking-around-birth_No_0_0': 1}) +oily_fish_intake_once_a_week_0_0 = TensorMap('Oily-fish-intake_Once-a-week_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Oily-fish-intake_Once-a-week_0_0': 0, 'Oily-fish-intake_Once-a-week_0_0': 1}) +own_or_rent_accommodation_lived_in_own_outright_by_you_or_someone_in_your_household_0_0 = TensorMap('Own-or-rent-accommodation-lived-in_Own-outright-by-you-or-someone-in-your-household_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Own-or-rent-accommodation-lived-in_Own-outright-by-you-or-someone-in-your-household_0_0': 0, 'Own-or-rent-accommodation-lived-in_Own-outright-by-you-or-someone-in-your-household_0_0': 1}) +pain_types_experienced_in_last_month_none_of_the_above_0_0 = TensorMap('Pain-types-experienced-in-last-month_None-of-the-above_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Pain-types-experienced-in-last-month_None-of-the-above_0_0': 0, 'Pain-types-experienced-in-last-month_None-of-the-above_0_0': 1}) +past_tobacco_smoking_i_have_never_smoked_0_0 = TensorMap('Past-tobacco-smoking_I-have-never-smoked_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Past-tobacco-smoking_I-have-never-smoked_0_0': 0, 'Past-tobacco-smoking_I-have-never-smoked_0_0': 1}) +pork_intake_less_than_once_a_week_0_0 = TensorMap('Pork-intake_Less-than-once-a-week_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Pork-intake_Less-than-once-a-week_0_0': 0, 'Pork-intake_Less-than-once-a-week_0_0': 1}) +qualifications_a_levelsas_levels_or_equivalent_0_1 = TensorMap('Qualifications_A-levelsAS-levels-or-equivalent_0_1', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Qualifications_A-levelsAS-levels-or-equivalent_0_1': 0, 'Qualifications_A-levelsAS-levels-or-equivalent_0_1': 1}) +qualifications_college_or_university_degree_0_0 = TensorMap('Qualifications_College-or-University-degree_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Qualifications_College-or-University-degree_0_0': 0, 'Qualifications_College-or-University-degree_0_0': 1}) +qualifications_o_levelsgcses_or_equivalent_0_2 = TensorMap('Qualifications_O-levelsGCSEs-or-equivalent_0_2', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Qualifications_O-levelsGCSEs-or-equivalent_0_2': 0, 'Qualifications_O-levelsGCSEs-or-equivalent_0_2': 1}) +skin_colour_light_olive_0_0 = TensorMap('Skin-colour_Light-olive_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Skin-colour_Light-olive_0_0': 0, 'Skin-colour_Light-olive_0_0': 1}) +smokingsmokers_in_household_no_0_0 = TensorMap('Smokingsmokers-in-household_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Smokingsmokers-in-household_No_0_0': 0, 'Smokingsmokers-in-household_No_0_0': 1}) +types_of_physical_activity_in_last_4_weeks_other_exercises_eg_swimming_cycling_keep_fit_bowling_0_1 = TensorMap('Types-of-physical-activity-in-last-4-weeks_Other-exercises-eg-swimming-cycling-keep-fit-bowling_0_1', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Types-of-physical-activity-in-last-4-weeks_Other-exercises-eg-swimming-cycling-keep-fit-bowling_0_1': 0, 'Types-of-physical-activity-in-last-4-weeks_Other-exercises-eg-swimming-cycling-keep-fit-bowling_0_1': 1}) +usual_walking_pace_steady_average_pace_0_0 = TensorMap('Usual-walking-pace_Steady-average-pace_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Usual-walking-pace_Steady-average-pace_0_0': 0, 'Usual-walking-pace_Steady-average-pace_0_0': 1}) +wears_glasses_or_contact_lenses_yes_0_0 = TensorMap('Wears-glasses-or-contact-lenses_Yes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Wears-glasses-or-contact-lenses_Yes_0_0': 0, 'Wears-glasses-or-contact-lenses_Yes_0_0': 1}) +weight_change_compared_with_1_year_ago_no_weigh_about_the_same_0_0 = TensorMap('Weight-change-compared-with-1-year-ago_No-weigh-about-the-same_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Weight-change-compared-with-1-year-ago_No-weigh-about-the-same_0_0': 0, 'Weight-change-compared-with-1-year-ago_No-weigh-about-the-same_0_0': 1}) +worked_with_materials_containing_asbestos_rarelynever_0_0 = TensorMap('Worked-with-materials-containing-asbestos_Rarelynever_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Worked-with-materials-containing-asbestos_Rarelynever_0_0': 0, 'Worked-with-materials-containing-asbestos_Rarelynever_0_0': 1}) +worked_with_paints_thinners_or_glues_rarelynever_0_0 = TensorMap('Worked-with-paints-thinners-or-glues_Rarelynever_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Worked-with-paints-thinners-or-glues_Rarelynever_0_0': 0, 'Worked-with-paints-thinners-or-glues_Rarelynever_0_0': 1}) +worked_with_paints_thinners_or_glues_rarelynever_0_1 = TensorMap('Worked-with-paints-thinners-or-glues_Rarelynever_0_1', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Worked-with-paints-thinners-or-glues_Rarelynever_0_1': 0, 'Worked-with-paints-thinners-or-glues_Rarelynever_0_1': 1}) +workplace_full_of_chemical_or_other_fumes_rarelynever_0_1 = TensorMap('Workplace-full-of-chemical-or-other-fumes_Rarelynever_0_1', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Workplace-full-of-chemical-or-other-fumes_Rarelynever_0_1': 0, 'Workplace-full-of-chemical-or-other-fumes_Rarelynever_0_1': 1}) +workplace_very_cold_rarelynever_0_0 = TensorMap('Workplace-very-cold_Rarelynever_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Workplace-very-cold_Rarelynever_0_0': 0, 'Workplace-very-cold_Rarelynever_0_0': 1}) +workplace_very_dusty_rarelynever_0_0 = TensorMap('Workplace-very-dusty_Rarelynever_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Workplace-very-dusty_Rarelynever_0_0': 0, 'Workplace-very-dusty_Rarelynever_0_0': 1}) +workplace_very_dusty_rarelynever_0_1 = TensorMap('Workplace-very-dusty_Rarelynever_0_1', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Workplace-very-dusty_Rarelynever_0_1': 0, 'Workplace-very-dusty_Rarelynever_0_1': 1}) +able_to_walk_or_cycle_unaided_for_10_minutes_yes_0_0 = TensorMap('Able-to-walk-or-cycle-unaided-for-10-minutes_Yes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Able-to-walk-or-cycle-unaided-for-10-minutes_Yes_0_0': 0, 'Able-to-walk-or-cycle-unaided-for-10-minutes_Yes_0_0': 1}) +alcohol_usually_taken_with_meals_it_varies_0_0 = TensorMap('Alcohol-usually-taken-with-meals_It-varies_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Alcohol-usually-taken-with-meals_It-varies_0_0': 0, 'Alcohol-usually-taken-with-meals_It-varies_0_0': 1}) +attempted_fluid_intelligence_fi_test_begin_puzzles_0_0 = TensorMap('Attempted-fluid-intelligence-FI-test_Begin-puzzles_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Attempted-fluid-intelligence-FI-test_Begin-puzzles_0_0': 0, 'Attempted-fluid-intelligence-FI-test_Begin-puzzles_0_0': 1}) +autorefraction_method_left_measurable_0_0 = TensorMap('Autorefraction-method-left_Measurable_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Autorefraction-method-left_Measurable_0_0': 0, 'Autorefraction-method-left_Measurable_0_0': 1}) +autorefraction_method_right_measurable_0_0 = TensorMap('Autorefraction-method-right_Measurable_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Autorefraction-method-right_Measurable_0_0': 0, 'Autorefraction-method-right_Measurable_0_0': 1}) +average_total_household_income_before_tax_less_than_18000_0_0 = TensorMap('Average-total-household-income-before-tax_Less-than-18000_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Average-total-household-income-before-tax_Less-than-18000_0_0': 0, 'Average-total-household-income-before-tax_Less-than-18000_0_0': 1}) +both_eyes_present_yes_0_0 = TensorMap('Both-eyes-present_Yes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Both-eyes-present_Yes_0_0': 0, 'Both-eyes-present_Yes_0_0': 1}) +cereal_type_oat_cereal_eg_ready_brek_porridge_0_0 = TensorMap('Cereal-type_Oat-cereal-eg-Ready-Brek-porridge_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Cereal-type_Oat-cereal-eg-Ready-Brek-porridge_0_0': 0, 'Cereal-type_Oat-cereal-eg-Ready-Brek-porridge_0_0': 1}) +cheese_intake_once_a_week_0_0 = TensorMap('Cheese-intake_Once-a-week_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Cheese-intake_Once-a-week_0_0': 0, 'Cheese-intake_Once-a-week_0_0': 1}) +chest_pain_felt_during_physical_activity_no_0_0 = TensorMap('Chest-pain-felt-during-physical-activity_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Chest-pain-felt-during-physical-activity_No_0_0': 0, 'Chest-pain-felt-during-physical-activity_No_0_0': 1}) +chest_pain_or_discomfort_yes_0_0 = TensorMap('Chest-pain-or-discomfort_Yes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Chest-pain-or-discomfort_Yes_0_0': 0, 'Chest-pain-or-discomfort_Yes_0_0': 1}) +cochlear_implant_no_0_0 = TensorMap('Cochlear-implant_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Cochlear-implant_No_0_0': 0, 'Cochlear-implant_No_0_0': 1}) +coffee_type_ground_coffee_include_espresso_filter_etc_0_0 = TensorMap('Coffee-type_Ground-coffee-include-espresso-filter-etc_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Coffee-type_Ground-coffee-include-espresso-filter-etc_0_0': 0, 'Coffee-type_Ground-coffee-include-espresso-filter-etc_0_0': 1}) +comparative_height_size_at_age_10_about_average_0_0 = TensorMap('Comparative-height-size-at-age-10_About-average_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Comparative-height-size-at-age-10_About-average_0_0': 0, 'Comparative-height-size-at-age-10_About-average_0_0': 1}) +completion_status_left_completed_0_0 = TensorMap('Completion-status-left_completed_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Completion-status-left_completed_0_0': 0, 'Completion-status-left_completed_0_0': 1}) +completion_status_of_test_fully_completed_0_0 = TensorMap('Completion-status-of-test_Fully-completed_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Completion-status-of-test_Fully-completed_0_0': 0, 'Completion-status-of-test_Fully-completed_0_0': 1}) +completion_status_right_completed_0_0 = TensorMap('Completion-status-right_completed_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Completion-status-right_completed_0_0': 0, 'Completion-status-right_completed_0_0': 1}) +current_eye_infection_no_0_0 = TensorMap('Current-eye-infection_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Current-eye-infection_No_0_0': 0, 'Current-eye-infection_No_0_0': 1}) +direct_or_mirror_view_left_plain_0_0 = TensorMap('Direct-or-mirror-view-left_plain_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Direct-or-mirror-view-left_plain_0_0': 0, 'Direct-or-mirror-view-left_plain_0_0': 1}) +direct_or_mirror_view_right_plain_0_0 = TensorMap('Direct-or-mirror-view-right_plain_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Direct-or-mirror-view-right_plain_0_0': 0, 'Direct-or-mirror-view-right_plain_0_0': 1}) +doctor_restricts_physical_activity_due_to_heart_condition_no_0_0 = TensorMap('Doctor-restricts-physical-activity-due-to-heart-condition_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Doctor-restricts-physical-activity-due-to-heart-condition_No_0_0': 0, 'Doctor-restricts-physical-activity-due-to-heart-condition_No_0_0': 1}) +drive_faster_than_motorway_speed_limit_neverrarely_0_0 = TensorMap('Drive-faster-than-motorway-speed-limit_Neverrarely_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Drive-faster-than-motorway-speed-limit_Neverrarely_0_0': 0, 'Drive-faster-than-motorway-speed-limit_Neverrarely_0_0': 1}) +duration_of_light_diy_between_30_minutes_and_1_hour_0_0 = TensorMap('Duration-of-light-DIY_Between-30-minutes-and-1-hour_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Duration-of-light-DIY_Between-30-minutes-and-1-hour_0_0': 0, 'Duration-of-light-DIY_Between-30-minutes-and-1-hour_0_0': 1}) +duration_walking_for_pleasure_between_15_and_30_minutes_0_0 = TensorMap('Duration-walking-for-pleasure_Between-15-and-30-minutes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Duration-walking-for-pleasure_Between-15-and-30-minutes_0_0': 0, 'Duration-walking-for-pleasure_Between-15-and-30-minutes_0_0': 1}) +ever_had_eye_surgery_no_0_0 = TensorMap('Ever-had-eye-surgery_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Ever-had-eye-surgery_No_0_0': 0, 'Ever-had-eye-surgery_No_0_0': 1}) +eye_problemsdisorders_none_of_the_above_0_0 = TensorMap('Eye-problemsdisorders_None-of-the-above_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Eye-problemsdisorders_None-of-the-above_0_0': 0, 'Eye-problemsdisorders_None-of-the-above_0_0': 1}) +final_attempt_correct_yes_0_0 = TensorMap('Final-attempt-correct_yes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Final-attempt-correct_yes_0_0': 0, 'Final-attempt-correct_yes_0_0': 1}) +fractured_heel_left_no_0_0 = TensorMap('Fractured-heel-left_no_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Fractured-heel-left_no_0_0': 0, 'Fractured-heel-left_no_0_0': 1}) +fractured_heel_right_no_0_0 = TensorMap('Fractured-heel-right_no_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Fractured-heel-right_no_0_0': 0, 'Fractured-heel-right_no_0_0': 1}) +frequency_of_stair_climbing_in_last_4_weeks_610_times_a_day_0_0 = TensorMap('Frequency-of-stair-climbing-in-last-4-weeks_610-times-a-day_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Frequency-of-stair-climbing-in-last-4-weeks_610-times-a-day_0_0': 0, 'Frequency-of-stair-climbing-in-last-4-weeks_610-times-a-day_0_0': 1}) +hearing_difficultyproblems_with_background_noise_no_0_0 = TensorMap('Hearing-difficultyproblems-with-background-noise_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Hearing-difficultyproblems-with-background-noise_No_0_0': 0, 'Hearing-difficultyproblems-with-background-noise_No_0_0': 1}) +hearing_test_done_yes_0_0 = TensorMap('Hearing-test-done_Yes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Hearing-test-done_Yes_0_0': 0, 'Hearing-test-done_Yes_0_0': 1}) +heel_ultrasound_method_left_direct_entry_0_0 = TensorMap('Heel-ultrasound-method-left_Direct-entry_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Heel-ultrasound-method-left_Direct-entry_0_0': 0, 'Heel-ultrasound-method-left_Direct-entry_0_0': 1}) +heel_ultrasound_method_right_direct_entry_0_0 = TensorMap('Heel-ultrasound-method-right_Direct-entry_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Heel-ultrasound-method-right_Direct-entry_0_0': 0, 'Heel-ultrasound-method-right_Direct-entry_0_0': 1}) +hot_drink_temperature_warm_0_0 = TensorMap('Hot-drink-temperature_Warm_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Hot-drink-temperature_Warm_0_0': 0, 'Hot-drink-temperature_Warm_0_0': 1}) +illnesses_of_father_high_blood_pressure_0_0 = TensorMap('Illnesses-of-father_High-blood-pressure_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Illnesses-of-father_High-blood-pressure_0_0': 0, 'Illnesses-of-father_High-blood-pressure_0_0': 1}) +intraocular_pressure_iop_method_left_measurable_0_0 = TensorMap('Intraocular-pressure-IOP-method-left_Measurable_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Intraocular-pressure-IOP-method-left_Measurable_0_0': 0, 'Intraocular-pressure-IOP-method-left_Measurable_0_0': 1}) +intraocular_pressure_iop_method_right_measurable_0_0 = TensorMap('Intraocular-pressure-IOP-method-right_Measurable_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Intraocular-pressure-IOP-method-right_Measurable_0_0': 0, 'Intraocular-pressure-IOP-method-right_Measurable_0_0': 1}) +leg_pain_on_walking_no_0_0 = TensorMap('Leg-pain-on-walking_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Leg-pain-on-walking_No_0_0': 0, 'Leg-pain-on-walking_No_0_0': 1}) +likelihood_of_resuming_smoking_no_definitely_not_0_0 = TensorMap('Likelihood-of-resuming-smoking_No-definitely-not_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Likelihood-of-resuming-smoking_No-definitely-not_0_0': 0, 'Likelihood-of-resuming-smoking_No-definitely-not_0_0': 1}) +major_dietary_changes_in_the_last_5_years_yes_because_of_other_reasons_0_0 = TensorMap('Major-dietary-changes-in-the-last-5-years_Yes-because-of-other-reasons_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Major-dietary-changes-in-the-last-5-years_Yes-because-of-other-reasons_0_0': 0, 'Major-dietary-changes-in-the-last-5-years_Yes-because-of-other-reasons_0_0': 1}) +mineral_and_other_dietary_supplements_fish_oil_including_cod_liver_oil_0_0 = TensorMap('Mineral-and-other-dietary-supplements_Fish-oil-including-cod-liver-oil_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Mineral-and-other-dietary-supplements_Fish-oil-including-cod-liver-oil_0_0': 0, 'Mineral-and-other-dietary-supplements_Fish-oil-including-cod-liver-oil_0_0': 1}) +nonaccidental_death_in_close_genetic_family_no_0_0 = TensorMap('Nonaccidental-death-in-close-genetic-family_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Nonaccidental-death-in-close-genetic-family_No_0_0': 0, 'Nonaccidental-death-in-close-genetic-family_No_0_0': 1}) +nonoily_fish_intake_less_than_once_a_week_0_0 = TensorMap('Nonoily-fish-intake_Less-than-once-a-week_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Nonoily-fish-intake_Less-than-once-a-week_0_0': 0, 'Nonoily-fish-intake_Less-than-once-a-week_0_0': 1}) +oily_fish_intake_24_times_a_week_0_0 = TensorMap('Oily-fish-intake_24-times-a-week_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Oily-fish-intake_24-times-a-week_0_0': 0, 'Oily-fish-intake_24-times-a-week_0_0': 1}) +other_eye_problems_yes_0_0 = TensorMap('Other-eye-problems_Yes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Other-eye-problems_Yes_0_0': 0, 'Other-eye-problems_Yes_0_0': 1}) +overall_health_rating_fair_0_0 = TensorMap('Overall-health-rating_Fair_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Overall-health-rating_Fair_0_0': 0, 'Overall-health-rating_Fair_0_0': 1}) +pm_final_answer_orange_circle_0_0 = TensorMap('PM-final-answer_Orange-circle_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_PM-final-answer_Orange-circle_0_0': 0, 'PM-final-answer_Orange-circle_0_0': 1}) +pm_initial_answer_orange_circle_0_0 = TensorMap('PM-initial-answer_Orange-circle_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_PM-initial-answer_Orange-circle_0_0': 0, 'PM-initial-answer_Orange-circle_0_0': 1}) +past_tobacco_smoking_smoked_on_most_or_all_days_0_0 = TensorMap('Past-tobacco-smoking_Smoked-on-most-or-all-days_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Past-tobacco-smoking_Smoked-on-most-or-all-days_0_0': 0, 'Past-tobacco-smoking_Smoked-on-most-or-all-days_0_0': 1}) +poultry_intake_once_a_week_0_0 = TensorMap('Poultry-intake_Once-a-week_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Poultry-intake_Once-a-week_0_0': 0, 'Poultry-intake_Once-a-week_0_0': 1}) +processed_meat_intake_once_a_week_0_0 = TensorMap('Processed-meat-intake_Once-a-week_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Processed-meat-intake_Once-a-week_0_0': 0, 'Processed-meat-intake_Once-a-week_0_0': 1}) +prospective_memory_result_correct_recall_on_first_attempt_0_0 = TensorMap('Prospective-memory-result_Correct-recall-on-first-attempt_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Prospective-memory-result_Correct-recall-on-first-attempt_0_0': 0, 'Prospective-memory-result_Correct-recall-on-first-attempt_0_0': 1}) +relative_age_of_first_facial_hair_about_average_age_0_0 = TensorMap('Relative-age-of-first-facial-hair_About-average-age_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Relative-age-of-first-facial-hair_About-average-age_0_0': 0, 'Relative-age-of-first-facial-hair_About-average-age_0_0': 1}) +salt_added_to_food_sometimes_0_0 = TensorMap('Salt-added-to-food_Sometimes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Salt-added-to-food_Sometimes_0_0': 0, 'Salt-added-to-food_Sometimes_0_0': 1}) +shortness_of_breath_walking_on_level_ground_no_0_0 = TensorMap('Shortness-of-breath-walking-on-level-ground_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Shortness-of-breath-walking-on-level-ground_No_0_0': 0, 'Shortness-of-breath-walking-on-level-ground_No_0_0': 1}) +smoking_status_previous_0_0 = TensorMap('Smoking-status_Previous_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Smoking-status_Previous_0_0': 0, 'Smoking-status_Previous_0_0': 1}) +spread_type_other_type_of_spreadmargarine_0_0 = TensorMap('Spread-type_Other-type-of-spreadmargarine_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Spread-type_Other-type-of-spreadmargarine_0_0': 0, 'Spread-type_Other-type-of-spreadmargarine_0_0': 1}) +stiffness_method_direct_entry_0_0 = TensorMap('Stiffness-method_Direct-entry_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Stiffness-method_Direct-entry_0_0': 0, 'Stiffness-method_Direct-entry_0_0': 1}) +test_completion_status_complete_0_0 = TensorMap('Test-completion-status_complete_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Test-completion-status_complete_0_0': 0, 'Test-completion-status_complete_0_0': 1}) +tinnitus_no_never_0_0 = TensorMap('Tinnitus_No-never_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Tinnitus_No-never_0_0': 0, 'Tinnitus_No-never_0_0': 1}) +triplet_correct_left_no_0_12 = TensorMap('Triplet-correct-left_no_0_12', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-left_no_0_12': 0, 'Triplet-correct-left_no_0_12': 1}) +triplet_correct_left_no_0_13 = TensorMap('Triplet-correct-left_no_0_13', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-left_no_0_13': 0, 'Triplet-correct-left_no_0_13': 1}) +triplet_correct_left_no_0_14 = TensorMap('Triplet-correct-left_no_0_14', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-left_no_0_14': 0, 'Triplet-correct-left_no_0_14': 1}) +triplet_correct_left_no_0_5 = TensorMap('Triplet-correct-left_no_0_5', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-left_no_0_5': 0, 'Triplet-correct-left_no_0_5': 1}) +triplet_correct_left_no_0_6 = TensorMap('Triplet-correct-left_no_0_6', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-left_no_0_6': 0, 'Triplet-correct-left_no_0_6': 1}) +triplet_correct_left_no_0_7 = TensorMap('Triplet-correct-left_no_0_7', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-left_no_0_7': 0, 'Triplet-correct-left_no_0_7': 1}) +triplet_correct_left_no_0_8 = TensorMap('Triplet-correct-left_no_0_8', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-left_no_0_8': 0, 'Triplet-correct-left_no_0_8': 1}) +triplet_correct_left_yes_0_1 = TensorMap('Triplet-correct-left_yes_0_1', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-left_yes_0_1': 0, 'Triplet-correct-left_yes_0_1': 1}) +triplet_correct_left_yes_0_10 = TensorMap('Triplet-correct-left_yes_0_10', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-left_yes_0_10': 0, 'Triplet-correct-left_yes_0_10': 1}) +triplet_correct_left_yes_0_11 = TensorMap('Triplet-correct-left_yes_0_11', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-left_yes_0_11': 0, 'Triplet-correct-left_yes_0_11': 1}) +triplet_correct_left_yes_0_15 = TensorMap('Triplet-correct-left_yes_0_15', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-left_yes_0_15': 0, 'Triplet-correct-left_yes_0_15': 1}) +triplet_correct_left_yes_0_2 = TensorMap('Triplet-correct-left_yes_0_2', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-left_yes_0_2': 0, 'Triplet-correct-left_yes_0_2': 1}) +triplet_correct_left_yes_0_3 = TensorMap('Triplet-correct-left_yes_0_3', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-left_yes_0_3': 0, 'Triplet-correct-left_yes_0_3': 1}) +triplet_correct_left_yes_0_4 = TensorMap('Triplet-correct-left_yes_0_4', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-left_yes_0_4': 0, 'Triplet-correct-left_yes_0_4': 1}) +triplet_correct_left_yes_0_9 = TensorMap('Triplet-correct-left_yes_0_9', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-left_yes_0_9': 0, 'Triplet-correct-left_yes_0_9': 1}) +triplet_correct_right_no_0_11 = TensorMap('Triplet-correct-right_no_0_11', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-right_no_0_11': 0, 'Triplet-correct-right_no_0_11': 1}) +triplet_correct_right_no_0_12 = TensorMap('Triplet-correct-right_no_0_12', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-right_no_0_12': 0, 'Triplet-correct-right_no_0_12': 1}) +triplet_correct_right_no_0_15 = TensorMap('Triplet-correct-right_no_0_15', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-right_no_0_15': 0, 'Triplet-correct-right_no_0_15': 1}) +triplet_correct_right_no_0_5 = TensorMap('Triplet-correct-right_no_0_5', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-right_no_0_5': 0, 'Triplet-correct-right_no_0_5': 1}) +triplet_correct_right_no_0_6 = TensorMap('Triplet-correct-right_no_0_6', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-right_no_0_6': 0, 'Triplet-correct-right_no_0_6': 1}) +triplet_correct_right_no_0_8 = TensorMap('Triplet-correct-right_no_0_8', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-right_no_0_8': 0, 'Triplet-correct-right_no_0_8': 1}) +triplet_correct_right_no_0_9 = TensorMap('Triplet-correct-right_no_0_9', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-right_no_0_9': 0, 'Triplet-correct-right_no_0_9': 1}) +triplet_correct_right_yes_0_1 = TensorMap('Triplet-correct-right_yes_0_1', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-right_yes_0_1': 0, 'Triplet-correct-right_yes_0_1': 1}) +triplet_correct_right_yes_0_10 = TensorMap('Triplet-correct-right_yes_0_10', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-right_yes_0_10': 0, 'Triplet-correct-right_yes_0_10': 1}) +triplet_correct_right_yes_0_13 = TensorMap('Triplet-correct-right_yes_0_13', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-right_yes_0_13': 0, 'Triplet-correct-right_yes_0_13': 1}) +triplet_correct_right_yes_0_14 = TensorMap('Triplet-correct-right_yes_0_14', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-right_yes_0_14': 0, 'Triplet-correct-right_yes_0_14': 1}) +triplet_correct_right_yes_0_2 = TensorMap('Triplet-correct-right_yes_0_2', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-right_yes_0_2': 0, 'Triplet-correct-right_yes_0_2': 1}) +triplet_correct_right_yes_0_3 = TensorMap('Triplet-correct-right_yes_0_3', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-right_yes_0_3': 0, 'Triplet-correct-right_yes_0_3': 1}) +triplet_correct_right_yes_0_4 = TensorMap('Triplet-correct-right_yes_0_4', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-right_yes_0_4': 0, 'Triplet-correct-right_yes_0_4': 1}) +triplet_correct_right_yes_0_7 = TensorMap('Triplet-correct-right_yes_0_7', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-right_yes_0_7': 0, 'Triplet-correct-right_yes_0_7': 1}) +type_of_tobacco_previously_smoked_manufactured_cigarettes_0_0 = TensorMap('Type-of-tobacco-previously-smoked_Manufactured-cigarettes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Type-of-tobacco-previously-smoked_Manufactured-cigarettes_0_0': 0, 'Type-of-tobacco-previously-smoked_Manufactured-cigarettes_0_0': 1}) +types_of_physical_activity_in_last_4_weeks_light_diy_eg_pruning_watering_the_lawn_0_2 = TensorMap('Types-of-physical-activity-in-last-4-weeks_Light-DIY-eg-pruning-watering-the-lawn_0_2', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Types-of-physical-activity-in-last-4-weeks_Light-DIY-eg-pruning-watering-the-lawn_0_2': 0, 'Types-of-physical-activity-in-last-4-weeks_Light-DIY-eg-pruning-watering-the-lawn_0_2': 1}) +types_of_transport_used_excluding_work_walk_0_0 = TensorMap('Types-of-transport-used-excluding-work_Walk_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Types-of-transport-used-excluding-work_Walk_0_0': 0, 'Types-of-transport-used-excluding-work_Walk_0_0': 1}) +use_of_sunuv_protection_sometimes_0_0 = TensorMap('Use-of-sunuv-protection_Sometimes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Use-of-sunuv-protection_Sometimes_0_0': 0, 'Use-of-sunuv-protection_Sometimes_0_0': 1}) +visual_acuity_measured_left_measurable_0_0 = TensorMap('Visual-acuity-measured-left_Measurable_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Visual-acuity-measured-left_Measurable_0_0': 0, 'Visual-acuity-measured-left_Measurable_0_0': 1}) +visual_acuity_measured_right_measurable_0_0 = TensorMap('Visual-acuity-measured-right_Measurable_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Visual-acuity-measured-right_Measurable_0_0': 0, 'Visual-acuity-measured-right_Measurable_0_0': 1}) +vitamin_and_mineral_supplements_multivitamins_minerals_0_0 = TensorMap('Vitamin-and-mineral-supplements_Multivitamins-minerals_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Vitamin-and-mineral-supplements_Multivitamins-minerals_0_0': 0, 'Vitamin-and-mineral-supplements_Multivitamins-minerals_0_0': 1}) +wheeze_or_whistling_in_the_chest_in_last_year_yes_0_0 = TensorMap('Wheeze-or-whistling-in-the-chest-in-last-year_Yes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Wheeze-or-whistling-in-the-chest-in-last-year_Yes_0_0': 0, 'Wheeze-or-whistling-in-the-chest-in-last-year_Yes_0_0': 1}) +alcohol_intake_versus_10_years_previously_more_nowadays_0_0 = TensorMap('Alcohol-intake-versus-10-years-previously_More-nowadays_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Alcohol-intake-versus-10-years-previously_More-nowadays_0_0': 0, 'Alcohol-intake-versus-10-years-previously_More-nowadays_0_0': 1}) +beef_intake_less_than_once_a_week_0_0 = TensorMap('Beef-intake_Less-than-once-a-week_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Beef-intake_Less-than-once-a-week_0_0': 0, 'Beef-intake_Less-than-once-a-week_0_0': 1}) +bilateral_oophorectomy_both_ovaries_removed_no_0_0 = TensorMap('Bilateral-oophorectomy-both-ovaries-removed_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Bilateral-oophorectomy-both-ovaries-removed_No_0_0': 0, 'Bilateral-oophorectomy-both-ovaries-removed_No_0_0': 1}) +cereal_type_muesli_0_0 = TensorMap('Cereal-type_Muesli_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Cereal-type_Muesli_0_0': 0, 'Cereal-type_Muesli_0_0': 1}) +ease_of_skin_tanning_never_tan_only_burn_0_0 = TensorMap('Ease-of-skin-tanning_Never-tan-only-burn_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Ease-of-skin-tanning_Never-tan-only-burn_0_0': 0, 'Ease-of-skin-tanning_Never-tan-only-burn_0_0': 1}) +ever_had_bowel_cancer_screening_yes_0_0 = TensorMap('Ever-had-bowel-cancer-screening_Yes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Ever-had-bowel-cancer-screening_Yes_0_0': 0, 'Ever-had-bowel-cancer-screening_Yes_0_0': 1}) +ever_had_breast_cancer_screening_mammogram_yes_0_0 = TensorMap('Ever-had-breast-cancer-screening-mammogram_Yes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Ever-had-breast-cancer-screening-mammogram_Yes_0_0': 0, 'Ever-had-breast-cancer-screening-mammogram_Yes_0_0': 1}) +ever_had_cervical_smear_test_yes_0_0 = TensorMap('Ever-had-cervical-smear-test_Yes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Ever-had-cervical-smear-test_Yes_0_0': 0, 'Ever-had-cervical-smear-test_Yes_0_0': 1}) +ever_had_hysterectomy_womb_removed_no_0_0 = TensorMap('Ever-had-hysterectomy-womb-removed_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Ever-had-hysterectomy-womb-removed_No_0_0': 0, 'Ever-had-hysterectomy-womb-removed_No_0_0': 1}) +ever_had_stillbirth_spontaneous_miscarriage_or_termination_no_0_0 = TensorMap('Ever-had-stillbirth-spontaneous-miscarriage-or-termination_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Ever-had-stillbirth-spontaneous-miscarriage-or-termination_No_0_0': 0, 'Ever-had-stillbirth-spontaneous-miscarriage-or-termination_No_0_0': 1}) +ever_taken_oral_contraceptive_pill_yes_0_0 = TensorMap('Ever-taken-oral-contraceptive-pill_Yes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Ever-taken-oral-contraceptive-pill_Yes_0_0': 0, 'Ever-taken-oral-contraceptive-pill_Yes_0_0': 1}) +ever_used_hormonereplacement_therapy_hrt_no_0_0 = TensorMap('Ever-used-hormonereplacement-therapy-HRT_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Ever-used-hormonereplacement-therapy-HRT_No_0_0': 0, 'Ever-used-hormonereplacement-therapy-HRT_No_0_0': 1}) +fi6_conditional_arithmetic_69_0_0 = TensorMap('FI6-conditional-arithmetic_69_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_FI6-conditional-arithmetic_69_0_0': 0, 'FI6-conditional-arithmetic_69_0_0': 1}) +fi7_synonym_cease_0_0 = TensorMap('FI7-synonym_cease_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_FI7-synonym_cease_0_0': 0, 'FI7-synonym_cease_0_0': 1}) +father_still_alive_no_0_0 = TensorMap('Father-still-alive_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Father-still-alive_No_0_0': 0, 'Father-still-alive_No_0_0': 1}) +genetic_sex_female_0_0 = TensorMap('Genetic-sex_Female_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Genetic-sex_Female_0_0': 0, 'Genetic-sex_Female_0_0': 1}) +had_menopause_yes_0_0 = TensorMap('Had-menopause_Yes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Had-menopause_Yes_0_0': 0, 'Had-menopause_Yes_0_0': 1}) +had_other_major_operations_no_0_0 = TensorMap('Had-other-major-operations_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Had-other-major-operations_No_0_0': 0, 'Had-other-major-operations_No_0_0': 1}) +hair_colour_natural_before_greying_light_brown_0_0 = TensorMap('Hair-colour-natural-before-greying_Light-brown_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Hair-colour-natural-before-greying_Light-brown_0_0': 0, 'Hair-colour-natural-before-greying_Light-brown_0_0': 1}) +loud_music_exposure_frequency_no_0_0 = TensorMap('Loud-music-exposure-frequency_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Loud-music-exposure-frequency_No_0_0': 0, 'Loud-music-exposure-frequency_No_0_0': 1}) +medication_for_cholesterol_blood_pressure_diabetes_or_take_exogenous_hormones_none_of_the_above_0_0 = TensorMap('Medication-for-cholesterol-blood-pressure-diabetes-or-take-exogenous-hormones_None-of-the-above_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Medication-for-cholesterol-blood-pressure-diabetes-or-take-exogenous-hormones_None-of-the-above_0_0': 0, 'Medication-for-cholesterol-blood-pressure-diabetes-or-take-exogenous-hormones_None-of-the-above_0_0': 1}) +mother_still_alive_no_0_0 = TensorMap('Mother-still-alive_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Mother-still-alive_No_0_0': 0, 'Mother-still-alive_No_0_0': 1}) +noisy_workplace_no_0_0 = TensorMap('Noisy-workplace_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Noisy-workplace_No_0_0': 0, 'Noisy-workplace_No_0_0': 1}) +nonoily_fish_intake_once_a_week_0_0 = TensorMap('Nonoily-fish-intake_Once-a-week_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Nonoily-fish-intake_Once-a-week_0_0': 0, 'Nonoily-fish-intake_Once-a-week_0_0': 1}) +overall_health_rating_good_0_0 = TensorMap('Overall-health-rating_Good_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Overall-health-rating_Good_0_0': 0, 'Overall-health-rating_Good_0_0': 1}) +pregnant_no_0_0 = TensorMap('Pregnant_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Pregnant_No_0_0': 0, 'Pregnant_No_0_0': 1}) +sex_female_0_0 = TensorMap('Sex_Female_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Sex_Female_0_0': 0, 'Sex_Female_0_0': 1}) +triplet_correct_left_no_0_10 = TensorMap('Triplet-correct-left_no_0_10', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-left_no_0_10': 0, 'Triplet-correct-left_no_0_10': 1}) +triplet_correct_left_no_0_15 = TensorMap('Triplet-correct-left_no_0_15', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-left_no_0_15': 0, 'Triplet-correct-left_no_0_15': 1}) +triplet_correct_left_no_0_4 = TensorMap('Triplet-correct-left_no_0_4', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-left_no_0_4': 0, 'Triplet-correct-left_no_0_4': 1}) +triplet_correct_left_no_0_9 = TensorMap('Triplet-correct-left_no_0_9', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-left_no_0_9': 0, 'Triplet-correct-left_no_0_9': 1}) +triplet_correct_left_yes_0_14 = TensorMap('Triplet-correct-left_yes_0_14', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-left_yes_0_14': 0, 'Triplet-correct-left_yes_0_14': 1}) +triplet_correct_left_yes_0_5 = TensorMap('Triplet-correct-left_yes_0_5', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-left_yes_0_5': 0, 'Triplet-correct-left_yes_0_5': 1}) +triplet_correct_left_yes_0_7 = TensorMap('Triplet-correct-left_yes_0_7', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-left_yes_0_7': 0, 'Triplet-correct-left_yes_0_7': 1}) +triplet_correct_left_yes_0_8 = TensorMap('Triplet-correct-left_yes_0_8', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-left_yes_0_8': 0, 'Triplet-correct-left_yes_0_8': 1}) +triplet_correct_right_no_0_14 = TensorMap('Triplet-correct-right_no_0_14', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-right_no_0_14': 0, 'Triplet-correct-right_no_0_14': 1}) +triplet_correct_right_yes_0_12 = TensorMap('Triplet-correct-right_yes_0_12', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-right_yes_0_12': 0, 'Triplet-correct-right_yes_0_12': 1}) +triplet_correct_right_yes_0_6 = TensorMap('Triplet-correct-right_yes_0_6', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-right_yes_0_6': 0, 'Triplet-correct-right_yes_0_6': 1}) +triplet_correct_right_yes_0_9 = TensorMap('Triplet-correct-right_yes_0_9', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-right_yes_0_9': 0, 'Triplet-correct-right_yes_0_9': 1}) +use_of_sunuv_protection_most_of_the_time_0_0 = TensorMap('Use-of-sunuv-protection_Most-of-the-time_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Use-of-sunuv-protection_Most-of-the-time_0_0': 0, 'Use-of-sunuv-protection_Most-of-the-time_0_0': 1}) +hernia = TensorMap('hernia', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_hernia': 0, 'hernia': 1}) +hypertension = TensorMap('hypertension', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_hypertension': 0, 'hypertension': 1}) +average_total_household_income_before_tax_18000_to_30999_0_0 = TensorMap('Average-total-household-income-before-tax_18000-to-30999_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Average-total-household-income-before-tax_18000-to-30999_0_0': 0, 'Average-total-household-income-before-tax_18000-to-30999_0_0': 1}) +hairbalding_pattern_pattern_1_0_0 = TensorMap('Hairbalding-pattern_Pattern-1_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Hairbalding-pattern_Pattern-1_0_0': 0, 'Hairbalding-pattern_Pattern-1_0_0': 1}) +current_employment_status_retired_0_0 = TensorMap('Current-employment-status_Retired_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Current-employment-status_Retired_0_0': 0, 'Current-employment-status_Retired_0_0': 1}) +ever_used_hormonereplacement_therapy_hrt_yes_0_0 = TensorMap('Ever-used-hormonereplacement-therapy-HRT_Yes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Ever-used-hormonereplacement-therapy-HRT_Yes_0_0': 0, 'Ever-used-hormonereplacement-therapy-HRT_Yes_0_0': 1}) +hearing_difficultyproblems_yes_0_0 = TensorMap('Hearing-difficultyproblems_Yes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Hearing-difficultyproblems_Yes_0_0': 0, 'Hearing-difficultyproblems_Yes_0_0': 1}) +qualifications_none_of_the_above_0_0 = TensorMap('Qualifications_None-of-the-above_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Qualifications_None-of-the-above_0_0': 0, 'Qualifications_None-of-the-above_0_0': 1}) +taking_other_prescription_medications_yes_you_will_be_asked_about_this_later_by_an_interviewer_0_0 = TensorMap('Taking-other-prescription-medications_Yes-you-will-be-asked-about-this-later-by-an-interviewer_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Taking-other-prescription-medications_Yes-you-will-be-asked-about-this-later-by-an-interviewer_0_0': 0, 'Taking-other-prescription-medications_Yes-you-will-be-asked-about-this-later-by-an-interviewer_0_0': 1}) +types_of_physical_activity_in_last_4_weeks_light_diy_eg_pruning_watering_the_lawn_0_1 = TensorMap('Types-of-physical-activity-in-last-4-weeks_Light-DIY-eg-pruning-watering-the-lawn_0_1', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Types-of-physical-activity-in-last-4-weeks_Light-DIY-eg-pruning-watering-the-lawn_0_1': 0, 'Types-of-physical-activity-in-last-4-weeks_Light-DIY-eg-pruning-watering-the-lawn_0_1': 1}) +hypercholesterolemia = TensorMap('hypercholesterolemia', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_hypercholesterolemia': 0, 'hypercholesterolemia': 1}) +breastfed_as_a_baby_no_0_0 = TensorMap('Breastfed-as-a-baby_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Breastfed-as-a-baby_No_0_0': 0, 'Breastfed-as-a-baby_No_0_0': 1}) +coffee_type_decaffeinated_coffee_any_type_0_0 = TensorMap('Coffee-type_Decaffeinated-coffee-any-type_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Coffee-type_Decaffeinated-coffee-any-type_0_0': 0, 'Coffee-type_Decaffeinated-coffee-any-type_0_0': 1}) +use_of_sunuv_protection_always_0_0 = TensorMap('Use-of-sunuv-protection_Always_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Use-of-sunuv-protection_Always_0_0': 0, 'Use-of-sunuv-protection_Always_0_0': 1}) +variation_in_diet_neverrarely_0_0 = TensorMap('Variation-in-diet_Neverrarely_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Variation-in-diet_Neverrarely_0_0': 0, 'Variation-in-diet_Neverrarely_0_0': 1}) +alcohol_intake_frequency_three_or_four_times_a_week_0_0 = TensorMap('Alcohol-intake-frequency_Three-or-four-times-a-week_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Alcohol-intake-frequency_Three-or-four-times-a-week_0_0': 0, 'Alcohol-intake-frequency_Three-or-four-times-a-week_0_0': 1}) +back_pain_for_3_months_yes_0_0 = TensorMap('Back-pain-for-3-months_Yes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Back-pain-for-3-months_Yes_0_0': 0, 'Back-pain-for-3-months_Yes_0_0': 1}) +comparative_height_size_at_age_10_taller_0_0 = TensorMap('Comparative-height-size-at-age-10_Taller_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Comparative-height-size-at-age-10_Taller_0_0': 0, 'Comparative-height-size-at-age-10_Taller_0_0': 1}) +frequency_of_other_exercises_in_last_4_weeks_23_times_a_week_0_0 = TensorMap('Frequency-of-other-exercises-in-last-4-weeks_23-times-a-week_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Frequency-of-other-exercises-in-last-4-weeks_23-times-a-week_0_0': 0, 'Frequency-of-other-exercises-in-last-4-weeks_23-times-a-week_0_0': 1}) +other_serious_medical_conditiondisability_diagnosed_by_doctor_yes_you_will_be_asked_about_this_later_by_an_interviewer_0_0 = TensorMap('Other-serious-medical-conditiondisability-diagnosed-by-doctor_Yes-you-will-be-asked-about-this-later-by-an-interviewer_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Other-serious-medical-conditiondisability-diagnosed-by-doctor_Yes-you-will-be-asked-about-this-later-by-an-interviewer_0_0': 0, 'Other-serious-medical-conditiondisability-diagnosed-by-doctor_Yes-you-will-be-asked-about-this-later-by-an-interviewer_0_0': 1}) +asthma = TensorMap('asthma', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_asthma': 0, 'asthma': 1}) +absence_of_notch_position_in_the_pulse_waveform_false_0_0 = TensorMap('Absence-of-notch-position-in-the-pulse-waveform_False_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Absence-of-notch-position-in-the-pulse-waveform_False_0_0': 0, 'Absence-of-notch-position-in-the-pulse-waveform_False_0_0': 1}) +cheese_intake_less_than_once_a_week_0_0 = TensorMap('Cheese-intake_Less-than-once-a-week_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Cheese-intake_Less-than-once-a-week_0_0': 0, 'Cheese-intake_Less-than-once-a-week_0_0': 1}) +chest_pain_felt_outside_physical_activity_no_0_0 = TensorMap('Chest-pain-felt-outside-physical-activity_No_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Chest-pain-felt-outside-physical-activity_No_0_0': 0, 'Chest-pain-felt-outside-physical-activity_No_0_0': 1}) +comparative_body_size_at_age_10_plumper_0_0 = TensorMap('Comparative-body-size-at-age-10_Plumper_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Comparative-body-size-at-age-10_Plumper_0_0': 0, 'Comparative-body-size-at-age-10_Plumper_0_0': 1}) +ease_of_skin_tanning_get_mildly_or_occasionally_tanned_0_0 = TensorMap('Ease-of-skin-tanning_Get-mildly-or-occasionally-tanned_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Ease-of-skin-tanning_Get-mildly-or-occasionally-tanned_0_0': 0, 'Ease-of-skin-tanning_Get-mildly-or-occasionally-tanned_0_0': 1}) +had_other_major_operations_yes_you_will_be_asked_about_this_later_by_an_interviewer_0_0 = TensorMap('Had-other-major-operations_Yes-you-will-be-asked-about-this-later-by-an-interviewer_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Had-other-major-operations_Yes-you-will-be-asked-about-this-later-by-an-interviewer_0_0': 0, 'Had-other-major-operations_Yes-you-will-be-asked-about-this-later-by-an-interviewer_0_0': 1}) +processed_meat_intake_less_than_once_a_week_0_0 = TensorMap('Processed-meat-intake_Less-than-once-a-week_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Processed-meat-intake_Less-than-once-a-week_0_0': 0, 'Processed-meat-intake_Less-than-once-a-week_0_0': 1}) +triplet_correct_left_no_0_11 = TensorMap('Triplet-correct-left_no_0_11', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-left_no_0_11': 0, 'Triplet-correct-left_no_0_11': 1}) +triplet_correct_left_yes_0_13 = TensorMap('Triplet-correct-left_yes_0_13', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-left_yes_0_13': 0, 'Triplet-correct-left_yes_0_13': 1}) +triplet_correct_left_yes_0_6 = TensorMap('Triplet-correct-left_yes_0_6', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-left_yes_0_6': 0, 'Triplet-correct-left_yes_0_6': 1}) +triplet_correct_right_no_0_10 = TensorMap('Triplet-correct-right_no_0_10', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-right_no_0_10': 0, 'Triplet-correct-right_no_0_10': 1}) +triplet_correct_right_no_0_13 = TensorMap('Triplet-correct-right_no_0_13', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-right_no_0_13': 0, 'Triplet-correct-right_no_0_13': 1}) +triplet_correct_right_no_0_4 = TensorMap('Triplet-correct-right_no_0_4', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-right_no_0_4': 0, 'Triplet-correct-right_no_0_4': 1}) +triplet_correct_right_yes_0_15 = TensorMap('Triplet-correct-right_yes_0_15', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-right_yes_0_15': 0, 'Triplet-correct-right_yes_0_15': 1}) +triplet_correct_right_yes_0_5 = TensorMap('Triplet-correct-right_yes_0_5', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-right_yes_0_5': 0, 'Triplet-correct-right_yes_0_5': 1}) +triplet_correct_right_yes_0_8 = TensorMap('Triplet-correct-right_yes_0_8', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-right_yes_0_8': 0, 'Triplet-correct-right_yes_0_8': 1}) +bread_type_white_0_0 = TensorMap('Bread-type_White_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Bread-type_White_0_0': 0, 'Bread-type_White_0_0': 1}) +breastfed_as_a_baby_do_not_know_0_0 = TensorMap('Breastfed-as-a-baby_Do-not-know_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Breastfed-as-a-baby_Do-not-know_0_0': 0, 'Breastfed-as-a-baby_Do-not-know_0_0': 1}) +never_eat_eggs_dairy_wheat_sugar_sugar_or_foodsdrinks_containing_sugar_0_0 = TensorMap('Never-eat-eggs-dairy-wheat-sugar_Sugar-or-foodsdrinks-containing-sugar_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Never-eat-eggs-dairy-wheat-sugar_Sugar-or-foodsdrinks-containing-sugar_0_0': 0, 'Never-eat-eggs-dairy-wheat-sugar_Sugar-or-foodsdrinks-containing-sugar_0_0': 1}) +weight_change_compared_with_1_year_ago_yes_lost_weight_0_0 = TensorMap('Weight-change-compared-with-1-year-ago_Yes-lost-weight_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Weight-change-compared-with-1-year-ago_Yes-lost-weight_0_0': 0, 'Weight-change-compared-with-1-year-ago_Yes-lost-weight_0_0': 1}) +neckshoulder_pain_for_3_months_yes_0_0 = TensorMap('Neckshoulder-pain-for-3-months_Yes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Neckshoulder-pain-for-3-months_Yes_0_0': 0, 'Neckshoulder-pain-for-3-months_Yes_0_0': 1}) +pain_types_experienced_in_last_month_neck_or_shoulder_pain_0_0 = TensorMap('Pain-types-experienced-in-last-month_Neck-or-shoulder-pain_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Pain-types-experienced-in-last-month_Neck-or-shoulder-pain_0_0': 0, 'Pain-types-experienced-in-last-month_Neck-or-shoulder-pain_0_0': 1}) +anycancer = TensorMap('anycancer', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_anycancer': 0, 'anycancer': 1}) +composite_mi_cad_stroke_death = TensorMap('composite_mi_cad_stroke_death', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_composite_mi_cad_stroke_death': 0, 'composite_mi_cad_stroke_death': 1}) +composite_mi_cad_stroke_death_exclude_aml = TensorMap('composite_mi_cad_stroke_death_exclude_aml', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_composite_mi_cad_stroke_death_exclude_aml': 0, 'composite_mi_cad_stroke_death_exclude_aml': 1}) +composite_mi_cad_stroke_death_exclude_heme = TensorMap('composite_mi_cad_stroke_death_exclude_heme', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_composite_mi_cad_stroke_death_exclude_heme': 0, 'composite_mi_cad_stroke_death_exclude_heme': 1}) +knee_pain_for_3_months_yes_0_0 = TensorMap('Knee-pain-for-3-months_Yes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Knee-pain-for-3-months_Yes_0_0': 0, 'Knee-pain-for-3-months_Yes_0_0': 1}) +longstanding_illness_disability_or_infirmity_yes_0_0 = TensorMap('Longstanding-illness-disability-or-infirmity_Yes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Longstanding-illness-disability-or-infirmity_Yes_0_0': 0, 'Longstanding-illness-disability-or-infirmity_Yes_0_0': 1}) +noncancer_illness_code_selfreported_hypertension_0_0 = TensorMap('Noncancer-illness-code-selfreported_hypertension_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Noncancer-illness-code-selfreported_hypertension_0_0': 0, 'Noncancer-illness-code-selfreported_hypertension_0_0': 1}) +pain_types_experienced_in_last_month_headache_0_0 = TensorMap('Pain-types-experienced-in-last-month_Headache_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Pain-types-experienced-in-last-month_Headache_0_0': 0, 'Pain-types-experienced-in-last-month_Headache_0_0': 1}) +triplet_correct_left_yes_0_12 = TensorMap('Triplet-correct-left_yes_0_12', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-left_yes_0_12': 0, 'Triplet-correct-left_yes_0_12': 1}) +vascularheart_problems_diagnosed_by_doctor_high_blood_pressure_0_0 = TensorMap('Vascularheart-problems-diagnosed-by-doctor_High-blood-pressure_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Vascularheart-problems-diagnosed-by-doctor_High-blood-pressure_0_0': 0, 'Vascularheart-problems-diagnosed-by-doctor_High-blood-pressure_0_0': 1}) +cereal_type_other_eg_cornflakes_frosties_0_0 = TensorMap('Cereal-type_Other-eg-Cornflakes-Frosties_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Cereal-type_Other-eg-Cornflakes-Frosties_0_0': 0, 'Cereal-type_Other-eg-Cornflakes-Frosties_0_0': 1}) +milk_type_used_skimmed_0_0 = TensorMap('Milk-type-used_Skimmed_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Milk-type-used_Skimmed_0_0': 0, 'Milk-type-used_Skimmed_0_0': 1}) +nonbutter_spread_type_details_polyunsaturatedsunflower_oil_based_spread_eg_flora_0_0 = TensorMap('Nonbutter-spread-type-details_Polyunsaturatedsunflower-oil-based-spread-eg-Flora_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Nonbutter-spread-type-details_Polyunsaturatedsunflower-oil-based-spread-eg-Flora_0_0': 0, 'Nonbutter-spread-type-details_Polyunsaturatedsunflower-oil-based-spread-eg-Flora_0_0': 1}) +imaging = TensorMap('imaging', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_imaging': 0, 'imaging': 1}) +blood_clot_dvt_bronchitis_emphysema_asthma_rhinitis_eczema_allergy_diagnosed_by_doctor_hayfever_allergic_rhinitis_or_eczema_0_0 = TensorMap('Blood-clot-DVT-bronchitis-emphysema-asthma-rhinitis-eczema-allergy-diagnosed-by-doctor_Hayfever-allergic-rhinitis-or-eczema_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Blood-clot-DVT-bronchitis-emphysema-asthma-rhinitis-eczema-allergy-diagnosed-by-doctor_Hayfever-allergic-rhinitis-or-eczema_0_0': 0, 'Blood-clot-DVT-bronchitis-emphysema-asthma-rhinitis-eczema-allergy-diagnosed-by-doctor_Hayfever-allergic-rhinitis-or-eczema_0_0': 1}) +lambmutton_intake_never_0_0 = TensorMap('Lambmutton-intake_Never_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Lambmutton-intake_Never_0_0': 0, 'Lambmutton-intake_Never_0_0': 1}) +pork_intake_never_0_0 = TensorMap('Pork-intake_Never_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Pork-intake_Never_0_0': 0, 'Pork-intake_Never_0_0': 1}) +triplet_correct_right_no_0_7 = TensorMap('Triplet-correct-right_no_0_7', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-right_no_0_7': 0, 'Triplet-correct-right_no_0_7': 1}) +triplet_correct_right_yes_0_11 = TensorMap('Triplet-correct-right_yes_0_11', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Triplet-correct-right_yes_0_11': 0, 'Triplet-correct-right_yes_0_11': 1}) +ever_had_stillbirth_spontaneous_miscarriage_or_termination_yes_0_0 = TensorMap('Ever-had-stillbirth-spontaneous-miscarriage-or-termination_Yes_0_0', Interpretation.CATEGORICAL, storage_type=StorageType.CATEGORICAL_INDEX, loss='categorical_crossentropy', channel_map={'no_Ever-had-stillbirth-spontaneous-miscarriage-or-termination_Yes_0_0': 0, 'Ever-had-stillbirth-spontaneous-miscarriage-or-termination_Yes_0_0': 1}) diff --git a/ml4h/tensormap/ukb/continuous.py b/ml4h/tensormap/ukb/continuous.py new file mode 100644 index 000000000..d230e7da0 --- /dev/null +++ b/ml4h/tensormap/ukb/continuous.py @@ -0,0 +1,2172 @@ +from ml4h.TensorMap import TensorMap + +field_id_102_pulse_rate_automated_reading_0_0 = TensorMap('102_Pulse-rate-automated-reading_0_0', loss='logcosh', channel_map={'no_102_Pulse-rate-automated-reading_0_0': 0}) +field_id_102_pulse_rate_automated_reading_0_1 = TensorMap('102_Pulse-rate-automated-reading_0_1', loss='logcosh', channel_map={'no_102_Pulse-rate-automated-reading_0_1': 0}) +field_id_1050_time_spend_outdoors_in_summer_0_0 = TensorMap('1050_Time-spend-outdoors-in-summer_0_0', loss='logcosh', channel_map={'no_1050_Time-spend-outdoors-in-summer_0_0': 0}) +field_id_1060_time_spent_outdoors_in_winter_0_0 = TensorMap('1060_Time-spent-outdoors-in-winter_0_0', loss='logcosh', channel_map={'no_1060_Time-spent-outdoors-in-winter_0_0': 0}) +field_id_1070_time_spent_watching_television_tv_0_0 = TensorMap('1070_Time-spent-watching-television-TV_0_0', loss='logcosh', channel_map={'no_1070_Time-spent-watching-television-TV_0_0': 0}) +field_id_1080_time_spent_using_computer_0_0 = TensorMap('1080_Time-spent-using-computer_0_0', loss='logcosh', channel_map={'no_1080_Time-spent-using-computer_0_0': 0}) +field_id_1090_time_spent_driving_0_0 = TensorMap('1090_Time-spent-driving_0_0', loss='logcosh', channel_map={'no_1090_Time-spent-driving_0_0': 0}) +field_id_1269_exposure_to_tobacco_smoke_at_home_0_0 = TensorMap('1269_Exposure-to-tobacco-smoke-at-home_0_0', loss='logcosh', channel_map={'no_1269_Exposure-to-tobacco-smoke-at-home_0_0': 0}) +field_id_1279_exposure_to_tobacco_smoke_outside_home_0_0 = TensorMap('1279_Exposure-to-tobacco-smoke-outside-home_0_0', loss='logcosh', channel_map={'no_1279_Exposure-to-tobacco-smoke-outside-home_0_0': 0}) +field_id_1289_cooked_vegetable_intake_0_0 = TensorMap('1289_Cooked-vegetable-intake_0_0', loss='logcosh', channel_map={'no_1289_Cooked-vegetable-intake_0_0': 0}) +field_id_1299_salad_raw_vegetable_intake_0_0 = TensorMap('1299_Salad-raw-vegetable-intake_0_0', loss='logcosh', channel_map={'no_1299_Salad-raw-vegetable-intake_0_0': 0}) +field_id_129_place_of_birth_in_uk_north_coordinate_0_0 = TensorMap('129_Place-of-birth-in-UK-north-coordinate_0_0', loss='logcosh', channel_map={'no_129_Place-of-birth-in-UK-north-coordinate_0_0': 0}) +field_id_1309_fresh_fruit_intake_0_0 = TensorMap('1309_Fresh-fruit-intake_0_0', loss='logcosh', channel_map={'no_1309_Fresh-fruit-intake_0_0': 0}) +field_id_130_place_of_birth_in_uk_east_coordinate_0_0 = TensorMap('130_Place-of-birth-in-UK-east-coordinate_0_0', loss='logcosh', channel_map={'no_130_Place-of-birth-in-UK-east-coordinate_0_0': 0}) +field_id_1319_dried_fruit_intake_0_0 = TensorMap('1319_Dried-fruit-intake_0_0', loss='logcosh', channel_map={'no_1319_Dried-fruit-intake_0_0': 0}) +field_id_134_number_of_selfreported_cancers_0_0 = TensorMap('134_Number-of-selfreported-cancers_0_0', loss='logcosh', channel_map={'no_134_Number-of-selfreported-cancers_0_0': 0}) +field_id_135_number_of_selfreported_noncancer_illnesses_0_0 = TensorMap('135_Number-of-selfreported-noncancer-illnesses_0_0', loss='logcosh', channel_map={'no_135_Number-of-selfreported-noncancer-illnesses_0_0': 0}) +field_id_136_number_of_operations_selfreported_0_0 = TensorMap('136_Number-of-operations-selfreported_0_0', loss='logcosh', channel_map={'no_136_Number-of-operations-selfreported_0_0': 0}) +field_id_137_number_of_treatmentsmedications_taken_0_0 = TensorMap('137_Number-of-treatmentsmedications-taken_0_0', loss='logcosh', channel_map={'no_137_Number-of-treatmentsmedications-taken_0_0': 0}) +field_id_1438_bread_intake_0_0 = TensorMap('1438_Bread-intake_0_0', loss='logcosh', channel_map={'no_1438_Bread-intake_0_0': 0}) +field_id_1458_cereal_intake_0_0 = TensorMap('1458_Cereal-intake_0_0', loss='logcosh', channel_map={'no_1458_Cereal-intake_0_0': 0}) +field_id_1488_tea_intake_0_0 = TensorMap('1488_Tea-intake_0_0', loss='logcosh', channel_map={'no_1488_Tea-intake_0_0': 0}) +field_id_1498_coffee_intake_0_0 = TensorMap('1498_Coffee-intake_0_0', loss='logcosh', channel_map={'no_1498_Coffee-intake_0_0': 0}) +field_id_1528_water_intake_0_0 = TensorMap('1528_Water-intake_0_0', loss='logcosh', channel_map={'no_1528_Water-intake_0_0': 0}) +field_id_1568_average_weekly_red_wine_intake_0_0 = TensorMap('1568_Average-weekly-red-wine-intake_0_0', loss='logcosh', channel_map={'no_1568_Average-weekly-red-wine-intake_0_0': 0}) +field_id_1578_average_weekly_champagne_plus_white_wine_intake_0_0 = TensorMap('1578_Average-weekly-champagne-plus-white-wine-intake_0_0', loss='logcosh', channel_map={'no_1578_Average-weekly-champagne-plus-white-wine-intake_0_0': 0}) +field_id_1588_average_weekly_beer_plus_cider_intake_0_0 = TensorMap('1588_Average-weekly-beer-plus-cider-intake_0_0', loss='logcosh', channel_map={'no_1588_Average-weekly-beer-plus-cider-intake_0_0': 0}) +field_id_1598_average_weekly_spirits_intake_0_0 = TensorMap('1598_Average-weekly-spirits-intake_0_0', loss='logcosh', channel_map={'no_1598_Average-weekly-spirits-intake_0_0': 0}) +field_id_1608_average_weekly_fortified_wine_intake_0_0 = TensorMap('1608_Average-weekly-fortified-wine-intake_0_0', loss='logcosh', channel_map={'no_1608_Average-weekly-fortified-wine-intake_0_0': 0}) +field_id_1737_childhood_sunburn_occasions_0_0 = TensorMap('1737_Childhood-sunburn-occasions_0_0', loss='logcosh', channel_map={'no_1737_Childhood-sunburn-occasions_0_0': 0}) +field_id_1845_mothers_age_0_0 = TensorMap('1845_Mothers-age_0_0', loss='logcosh', channel_map={'no_1845_Mothers-age_0_0': 0}) +field_id_1873_number_of_full_brothers_0_0 = TensorMap('1873_Number-of-full-brothers_0_0', loss='logcosh', channel_map={'no_1873_Number-of-full-brothers_0_0': 0}) +field_id_1883_number_of_full_sisters_0_0 = TensorMap('1883_Number-of-full-sisters_0_0', loss='logcosh', channel_map={'no_1883_Number-of-full-sisters_0_0': 0}) +field_id_189_townsend_deprivation_index_at_recruitment_0_0 = TensorMap('189_Townsend-deprivation-index-at-recruitment_0_0', loss='logcosh', channel_map={'no_189_Townsend-deprivation-index-at-recruitment_0_0': 0}) +field_id_20015_sitting_height_0_0 = TensorMap('20015_Sitting-height_0_0', loss='logcosh', channel_map={'no_20015_Sitting-height_0_0': 0}) +field_id_20023_mean_time_to_correctly_identify_matches_0_0 = TensorMap('20023_Mean-time-to-correctly-identify-matches_0_0', loss='logcosh', channel_map={'no_20023_Mean-time-to-correctly-identify-matches_0_0': 0}) +field_id_20159_number_of_symbol_digit_matches_made_correctly_0_0 = TensorMap('20159_Number-of-symbol-digit-matches-made-correctly_0_0', loss='logcosh', channel_map={'no_20159_Number-of-symbol-digit-matches-made-correctly_0_0': 0}) +field_id_20191_fluid_intelligence_score_0_0 = TensorMap('20191_Fluid-intelligence-score_0_0', loss='logcosh', channel_map={'no_20191_Fluid-intelligence-score_0_0': 0}) +field_id_21001_body_mass_index_bmi_0_0 = TensorMap('21001_Body-mass-index-BMI_0_0', loss='logcosh', channel_map={'no_21001_Body-mass-index-BMI_0_0': 0}) +field_id_21002_weight_0_0 = TensorMap('21002_Weight_0_0', loss='logcosh', channel_map={'no_21002_Weight_0_0': 0}) +field_id_21003_age_when_attended_assessment_centre_0_0 = TensorMap('21003_Age-when-attended-assessment-centre_0_0', loss='logcosh', channel_map={'no_21003_Age-when-attended-assessment-centre_0_0': 0}) +field_id_22003_heterozygosity_0_0 = TensorMap('22003_Heterozygosity_0_0', loss='logcosh', channel_map={'no_22003_Heterozygosity_0_0': 0}) +field_id_22004_heterozygosity_pca_corrected_0_0 = TensorMap('22004_Heterozygosity-PCA-corrected_0_0', loss='logcosh', channel_map={'no_22004_Heterozygosity-PCA-corrected_0_0': 0}) +field_id_22005_missingness_0_0 = TensorMap('22005_Missingness_0_0', loss='logcosh', channel_map={'no_22005_Missingness_0_0': 0}) +field_id_22009_genetic_principal_components_0_1 = TensorMap('22009_Genetic-principal-components_0_1', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_1': 0}) +field_id_22009_genetic_principal_components_0_10 = TensorMap('22009_Genetic-principal-components_0_10', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_10': 0}) +field_id_22009_genetic_principal_components_0_11 = TensorMap('22009_Genetic-principal-components_0_11', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_11': 0}) +field_id_22009_genetic_principal_components_0_12 = TensorMap('22009_Genetic-principal-components_0_12', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_12': 0}) +field_id_22009_genetic_principal_components_0_13 = TensorMap('22009_Genetic-principal-components_0_13', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_13': 0}) +field_id_22009_genetic_principal_components_0_14 = TensorMap('22009_Genetic-principal-components_0_14', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_14': 0}) +field_id_22009_genetic_principal_components_0_15 = TensorMap('22009_Genetic-principal-components_0_15', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_15': 0}) +field_id_22009_genetic_principal_components_0_16 = TensorMap('22009_Genetic-principal-components_0_16', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_16': 0}) +field_id_22009_genetic_principal_components_0_17 = TensorMap('22009_Genetic-principal-components_0_17', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_17': 0}) +field_id_22009_genetic_principal_components_0_18 = TensorMap('22009_Genetic-principal-components_0_18', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_18': 0}) +field_id_22009_genetic_principal_components_0_19 = TensorMap('22009_Genetic-principal-components_0_19', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_19': 0}) +field_id_22009_genetic_principal_components_0_2 = TensorMap('22009_Genetic-principal-components_0_2', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_2': 0}) +field_id_22009_genetic_principal_components_0_20 = TensorMap('22009_Genetic-principal-components_0_20', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_20': 0}) +field_id_22009_genetic_principal_components_0_21 = TensorMap('22009_Genetic-principal-components_0_21', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_21': 0}) +field_id_22009_genetic_principal_components_0_22 = TensorMap('22009_Genetic-principal-components_0_22', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_22': 0}) +field_id_22009_genetic_principal_components_0_23 = TensorMap('22009_Genetic-principal-components_0_23', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_23': 0}) +field_id_22009_genetic_principal_components_0_24 = TensorMap('22009_Genetic-principal-components_0_24', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_24': 0}) +field_id_22009_genetic_principal_components_0_25 = TensorMap('22009_Genetic-principal-components_0_25', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_25': 0}) +field_id_22009_genetic_principal_components_0_26 = TensorMap('22009_Genetic-principal-components_0_26', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_26': 0}) +field_id_22009_genetic_principal_components_0_27 = TensorMap('22009_Genetic-principal-components_0_27', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_27': 0}) +field_id_22009_genetic_principal_components_0_28 = TensorMap('22009_Genetic-principal-components_0_28', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_28': 0}) +field_id_22009_genetic_principal_components_0_29 = TensorMap('22009_Genetic-principal-components_0_29', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_29': 0}) +field_id_22009_genetic_principal_components_0_3 = TensorMap('22009_Genetic-principal-components_0_3', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_3': 0}) +field_id_22009_genetic_principal_components_0_30 = TensorMap('22009_Genetic-principal-components_0_30', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_30': 0}) +field_id_22009_genetic_principal_components_0_31 = TensorMap('22009_Genetic-principal-components_0_31', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_31': 0}) +field_id_22009_genetic_principal_components_0_32 = TensorMap('22009_Genetic-principal-components_0_32', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_32': 0}) +field_id_22009_genetic_principal_components_0_33 = TensorMap('22009_Genetic-principal-components_0_33', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_33': 0}) +field_id_22009_genetic_principal_components_0_34 = TensorMap('22009_Genetic-principal-components_0_34', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_34': 0}) +field_id_22009_genetic_principal_components_0_35 = TensorMap('22009_Genetic-principal-components_0_35', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_35': 0}) +field_id_22009_genetic_principal_components_0_36 = TensorMap('22009_Genetic-principal-components_0_36', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_36': 0}) +field_id_22009_genetic_principal_components_0_37 = TensorMap('22009_Genetic-principal-components_0_37', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_37': 0}) +field_id_22009_genetic_principal_components_0_38 = TensorMap('22009_Genetic-principal-components_0_38', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_38': 0}) +field_id_22009_genetic_principal_components_0_39 = TensorMap('22009_Genetic-principal-components_0_39', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_39': 0}) +field_id_22009_genetic_principal_components_0_4 = TensorMap('22009_Genetic-principal-components_0_4', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_4': 0}) +field_id_22009_genetic_principal_components_0_40 = TensorMap('22009_Genetic-principal-components_0_40', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_40': 0}) +field_id_22009_genetic_principal_components_0_5 = TensorMap('22009_Genetic-principal-components_0_5', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_5': 0}) +field_id_22009_genetic_principal_components_0_6 = TensorMap('22009_Genetic-principal-components_0_6', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_6': 0}) +field_id_22009_genetic_principal_components_0_7 = TensorMap('22009_Genetic-principal-components_0_7', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_7': 0}) +field_id_22009_genetic_principal_components_0_8 = TensorMap('22009_Genetic-principal-components_0_8', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_8': 0}) +field_id_22009_genetic_principal_components_0_9 = TensorMap('22009_Genetic-principal-components_0_9', loss='logcosh', channel_map={'no_22009_Genetic-principal-components_0_9': 0}) +field_id_22200_year_of_birth_0_0 = TensorMap('22200_Year-of-birth_0_0', loss='logcosh', channel_map={'no_22200_Year-of-birth_0_0': 0}) +field_id_22599_number_of_jobs_held_0_0 = TensorMap('22599_Number-of-jobs-held_0_0', loss='logcosh', channel_map={'no_22599_Number-of-jobs-held_0_0': 0}) +field_id_22602_year_job_started_0_0 = TensorMap('22602_Year-job-started_0_0', loss='logcosh', channel_map={'no_22602_Year-job-started_0_0': 0}) +field_id_22602_year_job_started_0_1 = TensorMap('22602_Year-job-started_0_1', loss='logcosh', channel_map={'no_22602_Year-job-started_0_1': 0}) +field_id_22603_year_job_ended_0_0 = TensorMap('22603_Year-job-ended_0_0', loss='logcosh', channel_map={'no_22603_Year-job-ended_0_0': 0}) +field_id_22603_year_job_ended_0_1 = TensorMap('22603_Year-job-ended_0_1', loss='logcosh', channel_map={'no_22603_Year-job-ended_0_1': 0}) +field_id_22605_work_hours_per_week_exact_value_0_1 = TensorMap('22605_Work-hours-per-week-exact-value_0_1', loss='logcosh', channel_map={'no_22605_Work-hours-per-week-exact-value_0_1': 0}) +field_id_22661_number_of_gap_periods_0_0 = TensorMap('22661_Number-of-gap-periods_0_0', loss='logcosh', channel_map={'no_22661_Number-of-gap-periods_0_0': 0}) +field_id_2277_frequency_of_solariumsunlamp_use_0_0 = TensorMap('2277_Frequency-of-solariumsunlamp-use_0_0', loss='logcosh', channel_map={'no_2277_Frequency-of-solariumsunlamp-use_0_0': 0}) +field_id_23098_weight_0_0 = TensorMap('23098_Weight_0_0', loss='logcosh', channel_map={'no_23098_Weight_0_0': 0}) +field_id_23099_body_fat_percentage_0_0 = TensorMap('23099_Body-fat-percentage_0_0', loss='logcosh', channel_map={'no_23099_Body-fat-percentage_0_0': 0}) +field_id_23100_whole_body_fat_mass_0_0 = TensorMap('23100_Whole-body-fat-mass_0_0', loss='logcosh', channel_map={'no_23100_Whole-body-fat-mass_0_0': 0}) +field_id_23101_whole_body_fatfree_mass_0_0 = TensorMap('23101_Whole-body-fatfree-mass_0_0', loss='logcosh', channel_map={'no_23101_Whole-body-fatfree-mass_0_0': 0}) +field_id_23102_whole_body_water_mass_0_0 = TensorMap('23102_Whole-body-water-mass_0_0', loss='logcosh', channel_map={'no_23102_Whole-body-water-mass_0_0': 0}) +field_id_23104_body_mass_index_bmi_0_0 = TensorMap('23104_Body-mass-index-BMI_0_0', loss='logcosh', channel_map={'no_23104_Body-mass-index-BMI_0_0': 0}) +field_id_23105_basal_metabolic_rate_0_0 = TensorMap('23105_Basal-metabolic-rate_0_0', loss='logcosh', channel_map={'no_23105_Basal-metabolic-rate_0_0': 0}) +field_id_23106_impedance_of_whole_body_0_0 = TensorMap('23106_Impedance-of-whole-body_0_0', loss='logcosh', channel_map={'no_23106_Impedance-of-whole-body_0_0': 0}) +field_id_23107_impedance_of_leg_right_0_0 = TensorMap('23107_Impedance-of-leg-right_0_0', loss='logcosh', channel_map={'no_23107_Impedance-of-leg-right_0_0': 0}) +field_id_23108_impedance_of_leg_left_0_0 = TensorMap('23108_Impedance-of-leg-left_0_0', loss='logcosh', channel_map={'no_23108_Impedance-of-leg-left_0_0': 0}) +field_id_23109_impedance_of_arm_right_0_0 = TensorMap('23109_Impedance-of-arm-right_0_0', loss='logcosh', channel_map={'no_23109_Impedance-of-arm-right_0_0': 0}) +field_id_23110_impedance_of_arm_left_0_0 = TensorMap('23110_Impedance-of-arm-left_0_0', loss='logcosh', channel_map={'no_23110_Impedance-of-arm-left_0_0': 0}) +field_id_23111_leg_fat_percentage_right_0_0 = TensorMap('23111_Leg-fat-percentage-right_0_0', loss='logcosh', channel_map={'no_23111_Leg-fat-percentage-right_0_0': 0}) +field_id_23112_leg_fat_mass_right_0_0 = TensorMap('23112_Leg-fat-mass-right_0_0', loss='logcosh', channel_map={'no_23112_Leg-fat-mass-right_0_0': 0}) +field_id_23113_leg_fatfree_mass_right_0_0 = TensorMap('23113_Leg-fatfree-mass-right_0_0', loss='logcosh', channel_map={'no_23113_Leg-fatfree-mass-right_0_0': 0}) +field_id_23114_leg_predicted_mass_right_0_0 = TensorMap('23114_Leg-predicted-mass-right_0_0', loss='logcosh', channel_map={'no_23114_Leg-predicted-mass-right_0_0': 0}) +field_id_23115_leg_fat_percentage_left_0_0 = TensorMap('23115_Leg-fat-percentage-left_0_0', loss='logcosh', channel_map={'no_23115_Leg-fat-percentage-left_0_0': 0}) +field_id_23116_leg_fat_mass_left_0_0 = TensorMap('23116_Leg-fat-mass-left_0_0', loss='logcosh', channel_map={'no_23116_Leg-fat-mass-left_0_0': 0}) +field_id_23117_leg_fatfree_mass_left_0_0 = TensorMap('23117_Leg-fatfree-mass-left_0_0', loss='logcosh', channel_map={'no_23117_Leg-fatfree-mass-left_0_0': 0}) +field_id_23118_leg_predicted_mass_left_0_0 = TensorMap('23118_Leg-predicted-mass-left_0_0', loss='logcosh', channel_map={'no_23118_Leg-predicted-mass-left_0_0': 0}) +field_id_23119_arm_fat_percentage_right_0_0 = TensorMap('23119_Arm-fat-percentage-right_0_0', loss='logcosh', channel_map={'no_23119_Arm-fat-percentage-right_0_0': 0}) +field_id_23120_arm_fat_mass_right_0_0 = TensorMap('23120_Arm-fat-mass-right_0_0', loss='logcosh', channel_map={'no_23120_Arm-fat-mass-right_0_0': 0}) +field_id_23121_arm_fatfree_mass_right_0_0 = TensorMap('23121_Arm-fatfree-mass-right_0_0', loss='logcosh', channel_map={'no_23121_Arm-fatfree-mass-right_0_0': 0}) +field_id_23122_arm_predicted_mass_right_0_0 = TensorMap('23122_Arm-predicted-mass-right_0_0', loss='logcosh', channel_map={'no_23122_Arm-predicted-mass-right_0_0': 0}) +field_id_23123_arm_fat_percentage_left_0_0 = TensorMap('23123_Arm-fat-percentage-left_0_0', loss='logcosh', channel_map={'no_23123_Arm-fat-percentage-left_0_0': 0}) +field_id_23124_arm_fat_mass_left_0_0 = TensorMap('23124_Arm-fat-mass-left_0_0', loss='logcosh', channel_map={'no_23124_Arm-fat-mass-left_0_0': 0}) +field_id_23125_arm_fatfree_mass_left_0_0 = TensorMap('23125_Arm-fatfree-mass-left_0_0', loss='logcosh', channel_map={'no_23125_Arm-fatfree-mass-left_0_0': 0}) +field_id_23126_arm_predicted_mass_left_0_0 = TensorMap('23126_Arm-predicted-mass-left_0_0', loss='logcosh', channel_map={'no_23126_Arm-predicted-mass-left_0_0': 0}) +field_id_23127_trunk_fat_percentage_0_0 = TensorMap('23127_Trunk-fat-percentage_0_0', loss='logcosh', channel_map={'no_23127_Trunk-fat-percentage_0_0': 0}) +field_id_23128_trunk_fat_mass_0_0 = TensorMap('23128_Trunk-fat-mass_0_0', loss='logcosh', channel_map={'no_23128_Trunk-fat-mass_0_0': 0}) +field_id_23129_trunk_fatfree_mass_0_0 = TensorMap('23129_Trunk-fatfree-mass_0_0', loss='logcosh', channel_map={'no_23129_Trunk-fatfree-mass_0_0': 0}) +field_id_23130_trunk_predicted_mass_0_0 = TensorMap('23130_Trunk-predicted-mass_0_0', loss='logcosh', channel_map={'no_23130_Trunk-predicted-mass_0_0': 0}) +field_id_2405_number_of_children_fathered_0_0 = TensorMap('2405_Number-of-children-fathered_0_0', loss='logcosh', channel_map={'no_2405_Number-of-children-fathered_0_0': 0}) +field_id_30000_white_blood_cell_leukocyte_count_0_0 = TensorMap('30000_White-blood-cell-leukocyte-count_0_0', loss='logcosh', channel_map={'no_30000_White-blood-cell-leukocyte-count_0_0': 0}) +field_id_30010_red_blood_cell_erythrocyte_count_0_0 = TensorMap('30010_Red-blood-cell-erythrocyte-count_0_0', loss='logcosh', channel_map={'no_30010_Red-blood-cell-erythrocyte-count_0_0': 0}) +field_id_30020_haemoglobin_concentration_0_0 = TensorMap('30020_Haemoglobin-concentration_0_0', loss='logcosh', channel_map={'no_30020_Haemoglobin-concentration_0_0': 0}) +field_id_30030_haematocrit_percentage_0_0 = TensorMap('30030_Haematocrit-percentage_0_0', loss='logcosh', channel_map={'no_30030_Haematocrit-percentage_0_0': 0}) +field_id_30040_mean_corpuscular_volume_0_0 = TensorMap('30040_Mean-corpuscular-volume_0_0', loss='logcosh', channel_map={'no_30040_Mean-corpuscular-volume_0_0': 0}) +field_id_30050_mean_corpuscular_haemoglobin_0_0 = TensorMap('30050_Mean-corpuscular-haemoglobin_0_0', loss='logcosh', channel_map={'no_30050_Mean-corpuscular-haemoglobin_0_0': 0}) +field_id_30060_mean_corpuscular_haemoglobin_concentration_0_0 = TensorMap('30060_Mean-corpuscular-haemoglobin-concentration_0_0', loss='logcosh', channel_map={'no_30060_Mean-corpuscular-haemoglobin-concentration_0_0': 0}) +field_id_30070_red_blood_cell_erythrocyte_distribution_width_0_0 = TensorMap('30070_Red-blood-cell-erythrocyte-distribution-width_0_0', loss='logcosh', channel_map={'no_30070_Red-blood-cell-erythrocyte-distribution-width_0_0': 0}) +field_id_30080_platelet_count_0_0 = TensorMap('30080_Platelet-count_0_0', loss='logcosh', channel_map={'no_30080_Platelet-count_0_0': 0}) +field_id_30090_platelet_crit_0_0 = TensorMap('30090_Platelet-crit_0_0', loss='logcosh', channel_map={'no_30090_Platelet-crit_0_0': 0}) +field_id_30100_mean_platelet_thrombocyte_volume_0_0 = TensorMap('30100_Mean-platelet-thrombocyte-volume_0_0', loss='logcosh', channel_map={'no_30100_Mean-platelet-thrombocyte-volume_0_0': 0}) +field_id_30110_platelet_distribution_width_0_0 = TensorMap('30110_Platelet-distribution-width_0_0', loss='logcosh', channel_map={'no_30110_Platelet-distribution-width_0_0': 0}) +field_id_30120_lymphocyte_count_0_0 = TensorMap('30120_Lymphocyte-count_0_0', loss='logcosh', channel_map={'no_30120_Lymphocyte-count_0_0': 0}) +field_id_30130_monocyte_count_0_0 = TensorMap('30130_Monocyte-count_0_0', loss='logcosh', channel_map={'no_30130_Monocyte-count_0_0': 0}) +field_id_30140_neutrophill_count_0_0 = TensorMap('30140_Neutrophill-count_0_0', loss='logcosh', channel_map={'no_30140_Neutrophill-count_0_0': 0}) +field_id_30150_eosinophill_count_0_0 = TensorMap('30150_Eosinophill-count_0_0', loss='logcosh', channel_map={'no_30150_Eosinophill-count_0_0': 0}) +field_id_30160_basophill_count_0_0 = TensorMap('30160_Basophill-count_0_0', loss='logcosh', channel_map={'no_30160_Basophill-count_0_0': 0}) +field_id_30170_nucleated_red_blood_cell_count_0_0 = TensorMap('30170_Nucleated-red-blood-cell-count_0_0', loss='logcosh', channel_map={'no_30170_Nucleated-red-blood-cell-count_0_0': 0}) +field_id_30180_lymphocyte_percentage_0_0 = TensorMap('30180_Lymphocyte-percentage_0_0', loss='logcosh', channel_map={'no_30180_Lymphocyte-percentage_0_0': 0}) +field_id_30190_monocyte_percentage_0_0 = TensorMap('30190_Monocyte-percentage_0_0', loss='logcosh', channel_map={'no_30190_Monocyte-percentage_0_0': 0}) +field_id_30200_neutrophill_percentage_0_0 = TensorMap('30200_Neutrophill-percentage_0_0', loss='logcosh', channel_map={'no_30200_Neutrophill-percentage_0_0': 0}) +field_id_30210_eosinophill_percentage_0_0 = TensorMap('30210_Eosinophill-percentage_0_0', loss='logcosh', channel_map={'no_30210_Eosinophill-percentage_0_0': 0}) +field_id_30220_basophill_percentage_0_0 = TensorMap('30220_Basophill-percentage_0_0', loss='logcosh', channel_map={'no_30220_Basophill-percentage_0_0': 0}) +field_id_30230_nucleated_red_blood_cell_percentage_0_0 = TensorMap('30230_Nucleated-red-blood-cell-percentage_0_0', loss='logcosh', channel_map={'no_30230_Nucleated-red-blood-cell-percentage_0_0': 0}) +field_id_30240_reticulocyte_percentage_0_0 = TensorMap('30240_Reticulocyte-percentage_0_0', loss='logcosh', channel_map={'no_30240_Reticulocyte-percentage_0_0': 0}) +field_id_30250_reticulocyte_count_0_0 = TensorMap('30250_Reticulocyte-count_0_0', loss='logcosh', channel_map={'no_30250_Reticulocyte-count_0_0': 0}) +field_id_30260_mean_reticulocyte_volume_0_0 = TensorMap('30260_Mean-reticulocyte-volume_0_0', loss='logcosh', channel_map={'no_30260_Mean-reticulocyte-volume_0_0': 0}) +field_id_30270_mean_sphered_cell_volume_0_0 = TensorMap('30270_Mean-sphered-cell-volume_0_0', loss='logcosh', channel_map={'no_30270_Mean-sphered-cell-volume_0_0': 0}) +field_id_30280_immature_reticulocyte_fraction_0_0 = TensorMap('30280_Immature-reticulocyte-fraction_0_0', loss='logcosh', channel_map={'no_30280_Immature-reticulocyte-fraction_0_0': 0}) +field_id_30290_high_light_scatter_reticulocyte_percentage_0_0 = TensorMap('30290_High-light-scatter-reticulocyte-percentage_0_0', loss='logcosh', channel_map={'no_30290_High-light-scatter-reticulocyte-percentage_0_0': 0}) +field_id_30300_high_light_scatter_reticulocyte_count_0_0 = TensorMap('30300_High-light-scatter-reticulocyte-count_0_0', loss='logcosh', channel_map={'no_30300_High-light-scatter-reticulocyte-count_0_0': 0}) +field_id_30314_volume_of_edta1_plasma_held_by_ukb_0_0 = TensorMap('30314_Volume-of-EDTA1-plasma-held-by-UKB_0_0', loss='logcosh', channel_map={'no_30314_Volume-of-EDTA1-plasma-held-by-UKB_0_0': 0}) +field_id_30324_volume_of_edta1_red_cells_held_by_ukb_0_0 = TensorMap('30324_Volume-of-EDTA1-red-cells-held-by-UKB_0_0', loss='logcosh', channel_map={'no_30324_Volume-of-EDTA1-red-cells-held-by-UKB_0_0': 0}) +field_id_30334_volume_of_edta1_buffy_held_by_ukb_0_0 = TensorMap('30334_Volume-of-EDTA1-buffy-held-by-UKB_0_0', loss='logcosh', channel_map={'no_30334_Volume-of-EDTA1-buffy-held-by-UKB_0_0': 0}) +field_id_30344_volume_of_edta2_plasma_held_by_ukb_0_0 = TensorMap('30344_Volume-of-EDTA2-plasma-held-by-UKB_0_0', loss='logcosh', channel_map={'no_30344_Volume-of-EDTA2-plasma-held-by-UKB_0_0': 0}) +field_id_30354_volume_of_edta2_buffy_held_by_ukb_0_0 = TensorMap('30354_Volume-of-EDTA2-buffy-held-by-UKB_0_0', loss='logcosh', channel_map={'no_30354_Volume-of-EDTA2-buffy-held-by-UKB_0_0': 0}) +field_id_30364_volume_of_edta2_red_cells_held_by_ukb_0_0 = TensorMap('30364_Volume-of-EDTA2-red-cells-held-by-UKB_0_0', loss='logcosh', channel_map={'no_30364_Volume-of-EDTA2-red-cells-held-by-UKB_0_0': 0}) +field_id_30374_volume_of_lihep_plasma_held_by_ukb_0_0 = TensorMap('30374_Volume-of-LiHep-plasma-held-by-UKB_0_0', loss='logcosh', channel_map={'no_30374_Volume-of-LiHep-plasma-held-by-UKB_0_0': 0}) +field_id_30384_volume_of_serum_held_by_ukb_0_0 = TensorMap('30384_Volume-of-serum-held-by-UKB_0_0', loss='logcosh', channel_map={'no_30384_Volume-of-serum-held-by-UKB_0_0': 0}) +field_id_30404_volume_of_acd_held_by_ukb_0_0 = TensorMap('30404_Volume-of-ACD-held-by-UKB_0_0', loss='logcosh', channel_map={'no_30404_Volume-of-ACD-held-by-UKB_0_0': 0}) +field_id_30510_creatinine_enzymatic_in_urine_0_0 = TensorMap('30510_Creatinine-enzymatic-in-urine_0_0', loss='logcosh', channel_map={'no_30510_Creatinine-enzymatic-in-urine_0_0': 0}) +field_id_30520_potassium_in_urine_0_0 = TensorMap('30520_Potassium-in-urine_0_0', loss='logcosh', channel_map={'no_30520_Potassium-in-urine_0_0': 0}) +field_id_30530_sodium_in_urine_0_0 = TensorMap('30530_Sodium-in-urine_0_0', loss='logcosh', channel_map={'no_30530_Sodium-in-urine_0_0': 0}) +field_id_3062_forced_vital_capacity_fvc_0_0 = TensorMap('3062_Forced-vital-capacity-FVC_0_0', loss='logcosh', channel_map={'no_3062_Forced-vital-capacity-FVC_0_0': 0}) +field_id_3062_forced_vital_capacity_fvc_0_1 = TensorMap('3062_Forced-vital-capacity-FVC_0_1', loss='logcosh', channel_map={'no_3062_Forced-vital-capacity-FVC_0_1': 0}) +field_id_3062_forced_vital_capacity_fvc_0_2 = TensorMap('3062_Forced-vital-capacity-FVC_0_2', loss='logcosh', channel_map={'no_3062_Forced-vital-capacity-FVC_0_2': 0}) +field_id_30630_apolipoprotein_a_0_0 = TensorMap('30630_Apolipoprotein-A_0_0', loss='logcosh', channel_map={'no_30630_Apolipoprotein-A_0_0': 0}) +field_id_3063_forced_expiratory_volume_in_1second_fev1_0_0 = TensorMap('3063_Forced-expiratory-volume-in-1second-FEV1_0_0', loss='logcosh', channel_map={'no_3063_Forced-expiratory-volume-in-1second-FEV1_0_0': 0}) +field_id_3063_forced_expiratory_volume_in_1second_fev1_0_1 = TensorMap('3063_Forced-expiratory-volume-in-1second-FEV1_0_1', loss='logcosh', channel_map={'no_3063_Forced-expiratory-volume-in-1second-FEV1_0_1': 0}) +field_id_3063_forced_expiratory_volume_in_1second_fev1_0_2 = TensorMap('3063_Forced-expiratory-volume-in-1second-FEV1_0_2', loss='logcosh', channel_map={'no_3063_Forced-expiratory-volume-in-1second-FEV1_0_2': 0}) +field_id_30640_apolipoprotein_b_0_0 = TensorMap('30640_Apolipoprotein-B_0_0', loss='logcosh', channel_map={'no_30640_Apolipoprotein-B_0_0': 0}) +field_id_3064_peak_expiratory_flow_pef_0_0 = TensorMap('3064_Peak-expiratory-flow-PEF_0_0', loss='logcosh', channel_map={'no_3064_Peak-expiratory-flow-PEF_0_0': 0}) +field_id_3064_peak_expiratory_flow_pef_0_1 = TensorMap('3064_Peak-expiratory-flow-PEF_0_1', loss='logcosh', channel_map={'no_3064_Peak-expiratory-flow-PEF_0_1': 0}) +field_id_3064_peak_expiratory_flow_pef_0_2 = TensorMap('3064_Peak-expiratory-flow-PEF_0_2', loss='logcosh', channel_map={'no_3064_Peak-expiratory-flow-PEF_0_2': 0}) +field_id_30660_direct_bilirubin_0_0 = TensorMap('30660_Direct-bilirubin_0_0', loss='logcosh', channel_map={'no_30660_Direct-bilirubin_0_0': 0}) +field_id_30670_urea_0_0 = TensorMap('30670_Urea_0_0', loss='logcosh', channel_map={'no_30670_Urea_0_0': 0}) +field_id_30680_calcium_0_0 = TensorMap('30680_Calcium_0_0', loss='logcosh', channel_map={'no_30680_Calcium_0_0': 0}) +field_id_30690_cholesterol_0_0 = TensorMap('30690_Cholesterol_0_0', loss='logcosh', channel_map={'no_30690_Cholesterol_0_0': 0}) +field_id_30710_creactive_protein_0_0 = TensorMap('30710_Creactive-protein_0_0', loss='logcosh', channel_map={'no_30710_Creactive-protein_0_0': 0}) +field_id_30740_glucose_0_0 = TensorMap('30740_Glucose_0_0', loss='logcosh', channel_map={'no_30740_Glucose_0_0': 0}) +field_id_30760_hdl_cholesterol_0_0 = TensorMap('30760_HDL-cholesterol_0_0', loss='logcosh', channel_map={'no_30760_HDL-cholesterol_0_0': 0}) +field_id_30770_igf1_0_0 = TensorMap('30770_IGF1_0_0', loss='logcosh', channel_map={'no_30770_IGF1_0_0': 0}) +field_id_30790_lipoprotein_a_0_0 = TensorMap('30790_Lipoprotein-A_0_0', loss='logcosh', channel_map={'no_30790_Lipoprotein-A_0_0': 0}) +field_id_30810_phosphate_0_0 = TensorMap('30810_Phosphate_0_0', loss='logcosh', channel_map={'no_30810_Phosphate_0_0': 0}) +field_id_30840_total_bilirubin_0_0 = TensorMap('30840_Total-bilirubin_0_0', loss='logcosh', channel_map={'no_30840_Total-bilirubin_0_0': 0}) +field_id_30850_testosterone_0_0 = TensorMap('30850_Testosterone_0_0', loss='logcosh', channel_map={'no_30850_Testosterone_0_0': 0}) +field_id_30860_total_protein_0_0 = TensorMap('30860_Total-protein_0_0', loss='logcosh', channel_map={'no_30860_Total-protein_0_0': 0}) +field_id_30870_triglycerides_0_0 = TensorMap('30870_Triglycerides_0_0', loss='logcosh', channel_map={'no_30870_Triglycerides_0_0': 0}) +field_id_30880_urate_0_0 = TensorMap('30880_Urate_0_0', loss='logcosh', channel_map={'no_30880_Urate_0_0': 0}) +field_id_30890_vitamin_d_0_0 = TensorMap('30890_Vitamin-D_0_0', loss='logcosh', channel_map={'no_30890_Vitamin-D_0_0': 0}) +field_id_3143_ankle_spacing_width_0_0 = TensorMap('3143_Ankle-spacing-width_0_0', loss='logcosh', channel_map={'no_3143_Ankle-spacing-width_0_0': 0}) +field_id_3144_heel_broadband_ultrasound_attenuation_direct_entry_0_0 = TensorMap('3144_Heel-Broadband-ultrasound-attenuation-direct-entry_0_0', loss='logcosh', channel_map={'no_3144_Heel-Broadband-ultrasound-attenuation-direct-entry_0_0': 0}) +field_id_3146_speed_of_sound_through_heel_0_0 = TensorMap('3146_Speed-of-sound-through-heel_0_0', loss='logcosh', channel_map={'no_3146_Speed-of-sound-through-heel_0_0': 0}) +field_id_3147_heel_quantitative_ultrasound_index_qui_direct_entry_0_0 = TensorMap('3147_Heel-quantitative-ultrasound-index-QUI-direct-entry_0_0', loss='logcosh', channel_map={'no_3147_Heel-quantitative-ultrasound-index-QUI-direct-entry_0_0': 0}) +field_id_3148_heel_bone_mineral_density_bmd_0_0 = TensorMap('3148_Heel-bone-mineral-density-BMD_0_0', loss='logcosh', channel_map={'no_3148_Heel-bone-mineral-density-BMD_0_0': 0}) +field_id_34_year_of_birth_0_0 = TensorMap('34_Year-of-birth_0_0', loss='logcosh', channel_map={'no_34_Year-of-birth_0_0': 0}) +field_id_396_number_of_columns_displayed_in_round_0_1 = TensorMap('396_Number-of-columns-displayed-in-round_0_1', loss='logcosh', channel_map={'no_396_Number-of-columns-displayed-in-round_0_1': 0}) +field_id_396_number_of_columns_displayed_in_round_0_2 = TensorMap('396_Number-of-columns-displayed-in-round_0_2', loss='logcosh', channel_map={'no_396_Number-of-columns-displayed-in-round_0_2': 0}) +field_id_397_number_of_rows_displayed_in_round_0_1 = TensorMap('397_Number-of-rows-displayed-in-round_0_1', loss='logcosh', channel_map={'no_397_Number-of-rows-displayed-in-round_0_1': 0}) +field_id_397_number_of_rows_displayed_in_round_0_2 = TensorMap('397_Number-of-rows-displayed-in-round_0_2', loss='logcosh', channel_map={'no_397_Number-of-rows-displayed-in-round_0_2': 0}) +field_id_398_number_of_correct_matches_in_round_0_1 = TensorMap('398_Number-of-correct-matches-in-round_0_1', loss='logcosh', channel_map={'no_398_Number-of-correct-matches-in-round_0_1': 0}) +field_id_398_number_of_correct_matches_in_round_0_2 = TensorMap('398_Number-of-correct-matches-in-round_0_2', loss='logcosh', channel_map={'no_398_Number-of-correct-matches-in-round_0_2': 0}) +field_id_399_number_of_incorrect_matches_in_round_0_1 = TensorMap('399_Number-of-incorrect-matches-in-round_0_1', loss='logcosh', channel_map={'no_399_Number-of-incorrect-matches-in-round_0_1': 0}) +field_id_399_number_of_incorrect_matches_in_round_0_2 = TensorMap('399_Number-of-incorrect-matches-in-round_0_2', loss='logcosh', channel_map={'no_399_Number-of-incorrect-matches-in-round_0_2': 0}) +field_id_400_time_to_complete_round_0_1 = TensorMap('400_Time-to-complete-round_0_1', loss='logcosh', channel_map={'no_400_Time-to-complete-round_0_1': 0}) +field_id_400_time_to_complete_round_0_2 = TensorMap('400_Time-to-complete-round_0_2', loss='logcosh', channel_map={'no_400_Time-to-complete-round_0_2': 0}) +field_id_404_duration_to_first_press_of_snapbutton_in_each_round_0_1 = TensorMap('404_Duration-to-first-press-of-snapbutton-in-each-round_0_1', loss='logcosh', channel_map={'no_404_Duration-to-first-press-of-snapbutton-in-each-round_0_1': 0}) +field_id_404_duration_to_first_press_of_snapbutton_in_each_round_0_10 = TensorMap('404_Duration-to-first-press-of-snapbutton-in-each-round_0_10', loss='logcosh', channel_map={'no_404_Duration-to-first-press-of-snapbutton-in-each-round_0_10': 0}) +field_id_404_duration_to_first_press_of_snapbutton_in_each_round_0_11 = TensorMap('404_Duration-to-first-press-of-snapbutton-in-each-round_0_11', loss='logcosh', channel_map={'no_404_Duration-to-first-press-of-snapbutton-in-each-round_0_11': 0}) +field_id_404_duration_to_first_press_of_snapbutton_in_each_round_0_4 = TensorMap('404_Duration-to-first-press-of-snapbutton-in-each-round_0_4', loss='logcosh', channel_map={'no_404_Duration-to-first-press-of-snapbutton-in-each-round_0_4': 0}) +field_id_404_duration_to_first_press_of_snapbutton_in_each_round_0_5 = TensorMap('404_Duration-to-first-press-of-snapbutton-in-each-round_0_5', loss='logcosh', channel_map={'no_404_Duration-to-first-press-of-snapbutton-in-each-round_0_5': 0}) +field_id_404_duration_to_first_press_of_snapbutton_in_each_round_0_7 = TensorMap('404_Duration-to-first-press-of-snapbutton-in-each-round_0_7', loss='logcosh', channel_map={'no_404_Duration-to-first-press-of-snapbutton-in-each-round_0_7': 0}) +field_id_4079_diastolic_blood_pressure_automated_reading_0_0 = TensorMap('4079_Diastolic-blood-pressure-automated-reading_0_0', loss='logcosh', channel_map={'no_4079_Diastolic-blood-pressure-automated-reading_0_0': 0}) +field_id_4079_diastolic_blood_pressure_automated_reading_0_1 = TensorMap('4079_Diastolic-blood-pressure-automated-reading_0_1', loss='logcosh', channel_map={'no_4079_Diastolic-blood-pressure-automated-reading_0_1': 0}) +field_id_4080_systolic_blood_pressure_automated_reading_0_0 = TensorMap('4080_Systolic-blood-pressure-automated-reading_0_0', loss='logcosh', channel_map={'no_4080_Systolic-blood-pressure-automated-reading_0_0': 0}) +field_id_4080_systolic_blood_pressure_automated_reading_0_1 = TensorMap('4080_Systolic-blood-pressure-automated-reading_0_1', loss='logcosh', channel_map={'no_4080_Systolic-blood-pressure-automated-reading_0_1': 0}) +field_id_46_hand_grip_strength_left_0_0 = TensorMap('46_Hand-grip-strength-left_0_0', loss='logcosh', channel_map={'no_46_Hand-grip-strength-left_0_0': 0}) +field_id_47_hand_grip_strength_right_0_0 = TensorMap('47_Hand-grip-strength-right_0_0', loss='logcosh', channel_map={'no_47_Hand-grip-strength-right_0_0': 0}) +field_id_48_waist_circumference_0_0 = TensorMap('48_Waist-circumference_0_0', loss='logcosh', channel_map={'no_48_Waist-circumference_0_0': 0}) +field_id_49_hip_circumference_0_0 = TensorMap('49_Hip-circumference_0_0', loss='logcosh', channel_map={'no_49_Hip-circumference_0_0': 0}) +field_id_50_standing_height_0_0 = TensorMap('50_Standing-height_0_0', loss='logcosh', channel_map={'no_50_Standing-height_0_0': 0}) +field_id_51_seated_height_0_0 = TensorMap('51_Seated-height_0_0', loss='logcosh', channel_map={'no_51_Seated-height_0_0': 0}) +field_id_699_length_of_time_at_current_address_0_0 = TensorMap('699_Length-of-time-at-current-address_0_0', loss='logcosh', channel_map={'no_699_Length-of-time-at-current-address_0_0': 0}) +field_id_709_number_in_household_0_0 = TensorMap('709_Number-in-household_0_0', loss='logcosh', channel_map={'no_709_Number-in-household_0_0': 0}) +field_id_845_age_completed_full_time_education_0_0 = TensorMap('845_Age-completed-full-time-education_0_0', loss='logcosh', channel_map={'no_845_Age-completed-full-time-education_0_0': 0}) +field_id_864_number_of_daysweek_walked_10_minutes_0_0 = TensorMap('864_Number-of-daysweek-walked-10-minutes_0_0', loss='logcosh', channel_map={'no_864_Number-of-daysweek-walked-10-minutes_0_0': 0}) +field_id_874_duration_of_walks_0_0 = TensorMap('874_Duration-of-walks_0_0', loss='logcosh', channel_map={'no_874_Duration-of-walks_0_0': 0}) +field_id_884_number_of_daysweek_of_moderate_physical_activity_10_minutes_0_0 = TensorMap('884_Number-of-daysweek-of-moderate-physical-activity-10-minutes_0_0', loss='logcosh', channel_map={'no_884_Number-of-daysweek-of-moderate-physical-activity-10-minutes_0_0': 0}) +field_id_894_duration_of_moderate_activity_0_0 = TensorMap('894_Duration-of-moderate-activity_0_0', loss='logcosh', channel_map={'no_894_Duration-of-moderate-activity_0_0': 0}) +field_id_904_number_of_daysweek_of_vigorous_physical_activity_10_minutes_0_0 = TensorMap('904_Number-of-daysweek-of-vigorous-physical-activity-10-minutes_0_0', loss='logcosh', channel_map={'no_904_Number-of-daysweek-of-vigorous-physical-activity-10-minutes_0_0': 0}) +field_id_914_duration_of_vigorous_activity_0_0 = TensorMap('914_Duration-of-vigorous-activity_0_0', loss='logcosh', channel_map={'no_914_Duration-of-vigorous-activity_0_0': 0}) +field_id_20011_interpolated_age_of_participant_when_operation_took_place_0_0 = TensorMap('20011_Interpolated-Age-of-participant-when-operation-took-place_0_0', loss='logcosh', channel_map={'no_20011_Interpolated-Age-of-participant-when-operation-took-place_0_0': 0}) +field_id_20022_birth_weight_0_0 = TensorMap('20022_Birth-weight_0_0', loss='logcosh', channel_map={'no_20022_Birth-weight_0_0': 0}) +field_id_20150_forced_expiratory_volume_in_1second_fev1_best_measure_0_0 = TensorMap('20150_Forced-expiratory-volume-in-1second-FEV1-Best-measure_0_0', loss='logcosh', channel_map={'no_20150_Forced-expiratory-volume-in-1second-FEV1-Best-measure_0_0': 0}) +field_id_20153_forced_expiratory_volume_in_1second_fev1_predicted_0_0 = TensorMap('20153_Forced-expiratory-volume-in-1second-FEV1-predicted_0_0', loss='logcosh', channel_map={'no_20153_Forced-expiratory-volume-in-1second-FEV1-predicted_0_0': 0}) +field_id_2217_age_started_wearing_glasses_or_contact_lenses_0_0 = TensorMap('2217_Age-started-wearing-glasses-or-contact-lenses_0_0', loss='logcosh', channel_map={'no_2217_Age-started-wearing-glasses-or-contact-lenses_0_0': 0}) +field_id_22602_year_job_started_0_2 = TensorMap('22602_Year-job-started_0_2', loss='logcosh', channel_map={'no_22602_Year-job-started_0_2': 0}) +field_id_22602_year_job_started_0_3 = TensorMap('22602_Year-job-started_0_3', loss='logcosh', channel_map={'no_22602_Year-job-started_0_3': 0}) +field_id_22603_year_job_ended_0_2 = TensorMap('22603_Year-job-ended_0_2', loss='logcosh', channel_map={'no_22603_Year-job-ended_0_2': 0}) +field_id_22603_year_job_ended_0_3 = TensorMap('22603_Year-job-ended_0_3', loss='logcosh', channel_map={'no_22603_Year-job-ended_0_3': 0}) +field_id_22605_work_hours_per_week_exact_value_0_0 = TensorMap('22605_Work-hours-per-week-exact-value_0_0', loss='logcosh', channel_map={'no_22605_Work-hours-per-week-exact-value_0_0': 0}) +field_id_22605_work_hours_per_week_exact_value_0_2 = TensorMap('22605_Work-hours-per-week-exact-value_0_2', loss='logcosh', channel_map={'no_22605_Work-hours-per-week-exact-value_0_2': 0}) +field_id_22605_work_hours_per_week_exact_value_0_3 = TensorMap('22605_Work-hours-per-week-exact-value_0_3', loss='logcosh', channel_map={'no_22605_Work-hours-per-week-exact-value_0_3': 0}) +field_id_22663_year_gap_started_0_0 = TensorMap('22663_Year-gap-started_0_0', loss='logcosh', channel_map={'no_22663_Year-gap-started_0_0': 0}) +field_id_22663_year_gap_started_0_1 = TensorMap('22663_Year-gap-started_0_1', loss='logcosh', channel_map={'no_22663_Year-gap-started_0_1': 0}) +field_id_22664_year_gap_ended_0_0 = TensorMap('22664_Year-gap-ended_0_0', loss='logcosh', channel_map={'no_22664_Year-gap-ended_0_0': 0}) +field_id_22664_year_gap_ended_0_1 = TensorMap('22664_Year-gap-ended_0_1', loss='logcosh', channel_map={'no_22664_Year-gap-ended_0_1': 0}) +field_id_2946_fathers_age_0_0 = TensorMap('2946_Fathers-age_0_0', loss='logcosh', channel_map={'no_2946_Fathers-age_0_0': 0}) +field_id_404_duration_to_first_press_of_snapbutton_in_each_round_0_2 = TensorMap('404_Duration-to-first-press-of-snapbutton-in-each-round_0_2', loss='logcosh', channel_map={'no_404_Duration-to-first-press-of-snapbutton-in-each-round_0_2': 0}) +field_id_92_operation_yearage_first_occurred_0_0 = TensorMap('92_Operation-yearage-first-occurred_0_0', loss='logcosh', channel_map={'no_92_Operation-yearage-first-occurred_0_0': 0}) +field_id_20011_interpolated_age_of_participant_when_operation_took_place_0_1 = TensorMap('20011_Interpolated-Age-of-participant-when-operation-took-place_0_1', loss='logcosh', channel_map={'no_20011_Interpolated-Age-of-participant-when-operation-took-place_0_1': 0}) +field_id_20016_fluid_intelligence_score_0_0 = TensorMap('20016_Fluid-intelligence-score_0_0', loss='logcosh', channel_map={'no_20016_Fluid-intelligence-score_0_0': 0}) +field_id_20019_speechreceptionthreshold_srt_estimate_left_0_0 = TensorMap('20019_Speechreceptionthreshold-SRT-estimate-left_0_0', loss='logcosh', channel_map={'no_20019_Speechreceptionthreshold-SRT-estimate-left_0_0': 0}) +field_id_20021_speechreceptionthreshold_srt_estimate_right_0_0 = TensorMap('20021_Speechreceptionthreshold-SRT-estimate-right_0_0', loss='logcosh', channel_map={'no_20021_Speechreceptionthreshold-SRT-estimate-right_0_0': 0}) +field_id_20128_number_of_fluid_intelligence_questions_attempted_within_time_limit_0_0 = TensorMap('20128_Number-of-fluid-intelligence-questions-attempted-within-time-limit_0_0', loss='logcosh', channel_map={'no_20128_Number-of-fluid-intelligence-questions-attempted-within-time-limit_0_0': 0}) +field_id_20161_pack_years_of_smoking_0_0 = TensorMap('20161_Pack-years-of-smoking_0_0', loss='logcosh', channel_map={'no_20161_Pack-years-of-smoking_0_0': 0}) +field_id_20162_pack_years_adult_smoking_as_proportion_of_life_span_exposed_to_smoking_0_0 = TensorMap('20162_Pack-years-adult-smoking-as-proportion-of-life-span-exposed-to-smoking_0_0', loss='logcosh', channel_map={'no_20162_Pack-years-adult-smoking-as-proportion-of-life-span-exposed-to-smoking_0_0': 0}) +field_id_21021_pulse_wave_arterial_stiffness_index_0_0 = TensorMap('21021_Pulse-wave-Arterial-Stiffness-index_0_0', loss='logcosh', channel_map={'no_21021_Pulse-wave-Arterial-Stiffness-index_0_0': 0}) +field_id_2867_age_started_smoking_in_former_smokers_0_0 = TensorMap('2867_Age-started-smoking-in-former-smokers_0_0', loss='logcosh', channel_map={'no_2867_Age-started-smoking-in-former-smokers_0_0': 0}) +field_id_2887_number_of_cigarettes_previously_smoked_daily_0_0 = TensorMap('2887_Number-of-cigarettes-previously-smoked-daily_0_0', loss='logcosh', channel_map={'no_2887_Number-of-cigarettes-previously-smoked-daily_0_0': 0}) +field_id_2897_age_stopped_smoking_0_0 = TensorMap('2897_Age-stopped-smoking_0_0', loss='logcosh', channel_map={'no_2897_Age-stopped-smoking_0_0': 0}) +field_id_2926_number_of_unsuccessful_stopsmoking_attempts_0_0 = TensorMap('2926_Number-of-unsuccessful-stopsmoking-attempts_0_0', loss='logcosh', channel_map={'no_2926_Number-of-unsuccessful-stopsmoking-attempts_0_0': 0}) +field_id_30414_volume_of_rna_held_by_ukb_0_0 = TensorMap('30414_Volume-of-RNA-held-by-UKB_0_0', loss='logcosh', channel_map={'no_30414_Volume-of-RNA-held-by-UKB_0_0': 0}) +field_id_30500_microalbumin_in_urine_0_0 = TensorMap('30500_Microalbumin-in-urine_0_0', loss='logcosh', channel_map={'no_30500_Microalbumin-in-urine_0_0': 0}) +field_id_3809_time_since_last_prostate_specific_antigen_psa_test_0_0 = TensorMap('3809_Time-since-last-prostate-specific-antigen-PSA-test_0_0', loss='logcosh', channel_map={'no_3809_Time-since-last-prostate-specific-antigen-PSA-test_0_0': 0}) +field_id_404_duration_to_first_press_of_snapbutton_in_each_round_0_3 = TensorMap('404_Duration-to-first-press-of-snapbutton-in-each-round_0_3', loss='logcosh', channel_map={'no_404_Duration-to-first-press-of-snapbutton-in-each-round_0_3': 0}) +field_id_4100_ankle_spacing_width_left_0_0 = TensorMap('4100_Ankle-spacing-width-left_0_0', loss='logcosh', channel_map={'no_4100_Ankle-spacing-width-left_0_0': 0}) +field_id_4101_heel_broadband_ultrasound_attenuation_left_0_0 = TensorMap('4101_Heel-broadband-ultrasound-attenuation-left_0_0', loss='logcosh', channel_map={'no_4101_Heel-broadband-ultrasound-attenuation-left_0_0': 0}) +field_id_4103_speed_of_sound_through_heel_left_0_0 = TensorMap('4103_Speed-of-sound-through-heel-left_0_0', loss='logcosh', channel_map={'no_4103_Speed-of-sound-through-heel-left_0_0': 0}) +field_id_4104_heel_quantitative_ultrasound_index_qui_direct_entry_left_0_0 = TensorMap('4104_Heel-quantitative-ultrasound-index-QUI-direct-entry-left_0_0', loss='logcosh', channel_map={'no_4104_Heel-quantitative-ultrasound-index-QUI-direct-entry-left_0_0': 0}) +field_id_4105_heel_bone_mineral_density_bmd_left_0_0 = TensorMap('4105_Heel-bone-mineral-density-BMD-left_0_0', loss='logcosh', channel_map={'no_4105_Heel-bone-mineral-density-BMD-left_0_0': 0}) +field_id_4106_heel_bone_mineral_density_bmd_tscore_automated_left_0_0 = TensorMap('4106_Heel-bone-mineral-density-BMD-Tscore-automated-left_0_0', loss='logcosh', channel_map={'no_4106_Heel-bone-mineral-density-BMD-Tscore-automated-left_0_0': 0}) +field_id_4119_ankle_spacing_width_right_0_0 = TensorMap('4119_Ankle-spacing-width-right_0_0', loss='logcosh', channel_map={'no_4119_Ankle-spacing-width-right_0_0': 0}) +field_id_4120_heel_broadband_ultrasound_attenuation_right_0_0 = TensorMap('4120_Heel-broadband-ultrasound-attenuation-right_0_0', loss='logcosh', channel_map={'no_4120_Heel-broadband-ultrasound-attenuation-right_0_0': 0}) +field_id_4122_speed_of_sound_through_heel_right_0_0 = TensorMap('4122_Speed-of-sound-through-heel-right_0_0', loss='logcosh', channel_map={'no_4122_Speed-of-sound-through-heel-right_0_0': 0}) +field_id_4123_heel_quantitative_ultrasound_index_qui_direct_entry_right_0_0 = TensorMap('4123_Heel-quantitative-ultrasound-index-QUI-direct-entry-right_0_0', loss='logcosh', channel_map={'no_4123_Heel-quantitative-ultrasound-index-QUI-direct-entry-right_0_0': 0}) +field_id_4124_heel_bone_mineral_density_bmd_right_0_0 = TensorMap('4124_Heel-bone-mineral-density-BMD-right_0_0', loss='logcosh', channel_map={'no_4124_Heel-bone-mineral-density-BMD-right_0_0': 0}) +field_id_4125_heel_bone_mineral_density_bmd_tscore_automated_right_0_0 = TensorMap('4125_Heel-bone-mineral-density-BMD-Tscore-automated-right_0_0', loss='logcosh', channel_map={'no_4125_Heel-bone-mineral-density-BMD-Tscore-automated-right_0_0': 0}) +field_id_4194_pulse_rate_0_0 = TensorMap('4194_Pulse-rate_0_0', loss='logcosh', channel_map={'no_4194_Pulse-rate_0_0': 0}) +field_id_4195_pulse_wave_reflection_index_0_0 = TensorMap('4195_Pulse-wave-reflection-index_0_0', loss='logcosh', channel_map={'no_4195_Pulse-wave-reflection-index_0_0': 0}) +field_id_4196_pulse_wave_peak_to_peak_time_0_0 = TensorMap('4196_Pulse-wave-peak-to-peak-time_0_0', loss='logcosh', channel_map={'no_4196_Pulse-wave-peak-to-peak-time_0_0': 0}) +field_id_4198_position_of_the_pulse_wave_peak_0_0 = TensorMap('4198_Position-of-the-pulse-wave-peak_0_0', loss='logcosh', channel_map={'no_4198_Position-of-the-pulse-wave-peak_0_0': 0}) +field_id_4199_position_of_pulse_wave_notch_0_0 = TensorMap('4199_Position-of-pulse-wave-notch_0_0', loss='logcosh', channel_map={'no_4199_Position-of-pulse-wave-notch_0_0': 0}) +field_id_4200_position_of_the_shoulder_on_the_pulse_waveform_0_0 = TensorMap('4200_Position-of-the-shoulder-on-the-pulse-waveform_0_0', loss='logcosh', channel_map={'no_4200_Position-of-the-shoulder-on-the-pulse-waveform_0_0': 0}) +field_id_4269_number_of_triplets_attempted_left_0_0 = TensorMap('4269_Number-of-triplets-attempted-left_0_0', loss='logcosh', channel_map={'no_4269_Number-of-triplets-attempted-left_0_0': 0}) +field_id_4272_duration_of_hearing_test_left_0_0 = TensorMap('4272_Duration-of-hearing-test-left_0_0', loss='logcosh', channel_map={'no_4272_Duration-of-hearing-test-left_0_0': 0}) +field_id_4276_number_of_triplets_attempted_right_0_0 = TensorMap('4276_Number-of-triplets-attempted-right_0_0', loss='logcosh', channel_map={'no_4276_Number-of-triplets-attempted-right_0_0': 0}) +field_id_4279_duration_of_hearing_test_right_0_0 = TensorMap('4279_Duration-of-hearing-test-right_0_0', loss='logcosh', channel_map={'no_4279_Duration-of-hearing-test-right_0_0': 0}) +field_id_4288_time_to_answer_0_0 = TensorMap('4288_Time-to-answer_0_0', loss='logcosh', channel_map={'no_4288_Time-to-answer_0_0': 0}) +field_id_4290_duration_screen_displayed_0_0 = TensorMap('4290_Duration-screen-displayed_0_0', loss='logcosh', channel_map={'no_4290_Duration-screen-displayed_0_0': 0}) +field_id_4291_number_of_attempts_0_0 = TensorMap('4291_Number-of-attempts_0_0', loss='logcosh', channel_map={'no_4291_Number-of-attempts_0_0': 0}) +field_id_5057_number_of_older_siblings_0_0 = TensorMap('5057_Number-of-older-siblings_0_0', loss='logcosh', channel_map={'no_5057_Number-of-older-siblings_0_0': 0}) +field_id_5074_number_of_letters_shown_in_round_left_0_0 = TensorMap('5074_Number-of-letters-shown-in-round-left_0_0', loss='logcosh', channel_map={'no_5074_Number-of-letters-shown-in-round-left_0_0': 0}) +field_id_5074_number_of_letters_shown_in_round_left_0_1 = TensorMap('5074_Number-of-letters-shown-in-round-left_0_1', loss='logcosh', channel_map={'no_5074_Number-of-letters-shown-in-round-left_0_1': 0}) +field_id_5075_number_of_letters_shown_in_round_right_0_0 = TensorMap('5075_Number-of-letters-shown-in-round-right_0_0', loss='logcosh', channel_map={'no_5075_Number-of-letters-shown-in-round-right_0_0': 0}) +field_id_5076_number_of_letters_correct_in_round_right_0_0 = TensorMap('5076_Number-of-letters-correct-in-round-right_0_0', loss='logcosh', channel_map={'no_5076_Number-of-letters-correct-in-round-right_0_0': 0}) +field_id_5077_number_of_letters_correct_in_round_left_0_0 = TensorMap('5077_Number-of-letters-correct-in-round-left_0_0', loss='logcosh', channel_map={'no_5077_Number-of-letters-correct-in-round-left_0_0': 0}) +field_id_5077_number_of_letters_correct_in_round_left_0_1 = TensorMap('5077_Number-of-letters-correct-in-round-left_0_1', loss='logcosh', channel_map={'no_5077_Number-of-letters-correct-in-round-left_0_1': 0}) +field_id_5078_logmar_in_round_left_0_0 = TensorMap('5078_logMAR-in-round-left_0_0', loss='logcosh', channel_map={'no_5078_logMAR-in-round-left_0_0': 0}) +field_id_5078_logmar_in_round_left_0_1 = TensorMap('5078_logMAR-in-round-left_0_1', loss='logcosh', channel_map={'no_5078_logMAR-in-round-left_0_1': 0}) +field_id_5079_logmar_in_round_right_0_0 = TensorMap('5079_logMAR-in-round-right_0_0', loss='logcosh', channel_map={'no_5079_logMAR-in-round-right_0_0': 0}) +field_id_5084_spherical_power_right_0_0 = TensorMap('5084_Spherical-power-right_0_0', loss='logcosh', channel_map={'no_5084_Spherical-power-right_0_0': 0}) +field_id_5084_spherical_power_right_0_1 = TensorMap('5084_Spherical-power-right_0_1', loss='logcosh', channel_map={'no_5084_Spherical-power-right_0_1': 0}) +field_id_5084_spherical_power_right_0_2 = TensorMap('5084_Spherical-power-right_0_2', loss='logcosh', channel_map={'no_5084_Spherical-power-right_0_2': 0}) +field_id_5085_spherical_power_left_0_0 = TensorMap('5085_Spherical-power-left_0_0', loss='logcosh', channel_map={'no_5085_Spherical-power-left_0_0': 0}) +field_id_5085_spherical_power_left_0_1 = TensorMap('5085_Spherical-power-left_0_1', loss='logcosh', channel_map={'no_5085_Spherical-power-left_0_1': 0}) +field_id_5085_spherical_power_left_0_2 = TensorMap('5085_Spherical-power-left_0_2', loss='logcosh', channel_map={'no_5085_Spherical-power-left_0_2': 0}) +field_id_5086_cylindrical_power_left_0_0 = TensorMap('5086_Cylindrical-power-left_0_0', loss='logcosh', channel_map={'no_5086_Cylindrical-power-left_0_0': 0}) +field_id_5086_cylindrical_power_left_0_1 = TensorMap('5086_Cylindrical-power-left_0_1', loss='logcosh', channel_map={'no_5086_Cylindrical-power-left_0_1': 0}) +field_id_5086_cylindrical_power_left_0_2 = TensorMap('5086_Cylindrical-power-left_0_2', loss='logcosh', channel_map={'no_5086_Cylindrical-power-left_0_2': 0}) +field_id_5087_cylindrical_power_right_0_0 = TensorMap('5087_Cylindrical-power-right_0_0', loss='logcosh', channel_map={'no_5087_Cylindrical-power-right_0_0': 0}) +field_id_5087_cylindrical_power_right_0_1 = TensorMap('5087_Cylindrical-power-right_0_1', loss='logcosh', channel_map={'no_5087_Cylindrical-power-right_0_1': 0}) +field_id_5087_cylindrical_power_right_0_2 = TensorMap('5087_Cylindrical-power-right_0_2', loss='logcosh', channel_map={'no_5087_Cylindrical-power-right_0_2': 0}) +field_id_5088_astigmatism_angle_right_0_0 = TensorMap('5088_Astigmatism-angle-right_0_0', loss='logcosh', channel_map={'no_5088_Astigmatism-angle-right_0_0': 0}) +field_id_5088_astigmatism_angle_right_0_1 = TensorMap('5088_Astigmatism-angle-right_0_1', loss='logcosh', channel_map={'no_5088_Astigmatism-angle-right_0_1': 0}) +field_id_5088_astigmatism_angle_right_0_2 = TensorMap('5088_Astigmatism-angle-right_0_2', loss='logcosh', channel_map={'no_5088_Astigmatism-angle-right_0_2': 0}) +field_id_5089_astigmatism_angle_left_0_0 = TensorMap('5089_Astigmatism-angle-left_0_0', loss='logcosh', channel_map={'no_5089_Astigmatism-angle-left_0_0': 0}) +field_id_5089_astigmatism_angle_left_0_1 = TensorMap('5089_Astigmatism-angle-left_0_1', loss='logcosh', channel_map={'no_5089_Astigmatism-angle-left_0_1': 0}) +field_id_5089_astigmatism_angle_left_0_2 = TensorMap('5089_Astigmatism-angle-left_0_2', loss='logcosh', channel_map={'no_5089_Astigmatism-angle-left_0_2': 0}) +field_id_5096_3mm_weak_meridian_left_0_0 = TensorMap('5096_3mm-weak-meridian-left_0_0', loss='logcosh', channel_map={'no_5096_3mm-weak-meridian-left_0_0': 0}) +field_id_5096_3mm_weak_meridian_left_0_1 = TensorMap('5096_3mm-weak-meridian-left_0_1', loss='logcosh', channel_map={'no_5096_3mm-weak-meridian-left_0_1': 0}) +field_id_5096_3mm_weak_meridian_left_0_2 = TensorMap('5096_3mm-weak-meridian-left_0_2', loss='logcosh', channel_map={'no_5096_3mm-weak-meridian-left_0_2': 0}) +field_id_5097_6mm_weak_meridian_left_0_0 = TensorMap('5097_6mm-weak-meridian-left_0_0', loss='logcosh', channel_map={'no_5097_6mm-weak-meridian-left_0_0': 0}) +field_id_5097_6mm_weak_meridian_left_0_1 = TensorMap('5097_6mm-weak-meridian-left_0_1', loss='logcosh', channel_map={'no_5097_6mm-weak-meridian-left_0_1': 0}) +field_id_5097_6mm_weak_meridian_left_0_2 = TensorMap('5097_6mm-weak-meridian-left_0_2', loss='logcosh', channel_map={'no_5097_6mm-weak-meridian-left_0_2': 0}) +field_id_5098_6mm_weak_meridian_right_0_0 = TensorMap('5098_6mm-weak-meridian-right_0_0', loss='logcosh', channel_map={'no_5098_6mm-weak-meridian-right_0_0': 0}) +field_id_5098_6mm_weak_meridian_right_0_1 = TensorMap('5098_6mm-weak-meridian-right_0_1', loss='logcosh', channel_map={'no_5098_6mm-weak-meridian-right_0_1': 0}) +field_id_5099_3mm_weak_meridian_right_0_0 = TensorMap('5099_3mm-weak-meridian-right_0_0', loss='logcosh', channel_map={'no_5099_3mm-weak-meridian-right_0_0': 0}) +field_id_5099_3mm_weak_meridian_right_0_1 = TensorMap('5099_3mm-weak-meridian-right_0_1', loss='logcosh', channel_map={'no_5099_3mm-weak-meridian-right_0_1': 0}) +field_id_5100_3mm_weak_meridian_angle_right_0_0 = TensorMap('5100_3mm-weak-meridian-angle-right_0_0', loss='logcosh', channel_map={'no_5100_3mm-weak-meridian-angle-right_0_0': 0}) +field_id_5100_3mm_weak_meridian_angle_right_0_1 = TensorMap('5100_3mm-weak-meridian-angle-right_0_1', loss='logcosh', channel_map={'no_5100_3mm-weak-meridian-angle-right_0_1': 0}) +field_id_5101_6mm_weak_meridian_angle_right_0_0 = TensorMap('5101_6mm-weak-meridian-angle-right_0_0', loss='logcosh', channel_map={'no_5101_6mm-weak-meridian-angle-right_0_0': 0}) +field_id_5101_6mm_weak_meridian_angle_right_0_1 = TensorMap('5101_6mm-weak-meridian-angle-right_0_1', loss='logcosh', channel_map={'no_5101_6mm-weak-meridian-angle-right_0_1': 0}) +field_id_5102_6mm_weak_meridian_angle_left_0_0 = TensorMap('5102_6mm-weak-meridian-angle-left_0_0', loss='logcosh', channel_map={'no_5102_6mm-weak-meridian-angle-left_0_0': 0}) +field_id_5102_6mm_weak_meridian_angle_left_0_1 = TensorMap('5102_6mm-weak-meridian-angle-left_0_1', loss='logcosh', channel_map={'no_5102_6mm-weak-meridian-angle-left_0_1': 0}) +field_id_5102_6mm_weak_meridian_angle_left_0_2 = TensorMap('5102_6mm-weak-meridian-angle-left_0_2', loss='logcosh', channel_map={'no_5102_6mm-weak-meridian-angle-left_0_2': 0}) +field_id_5103_3mm_weak_meridian_angle_left_0_0 = TensorMap('5103_3mm-weak-meridian-angle-left_0_0', loss='logcosh', channel_map={'no_5103_3mm-weak-meridian-angle-left_0_0': 0}) +field_id_5103_3mm_weak_meridian_angle_left_0_1 = TensorMap('5103_3mm-weak-meridian-angle-left_0_1', loss='logcosh', channel_map={'no_5103_3mm-weak-meridian-angle-left_0_1': 0}) +field_id_5103_3mm_weak_meridian_angle_left_0_2 = TensorMap('5103_3mm-weak-meridian-angle-left_0_2', loss='logcosh', channel_map={'no_5103_3mm-weak-meridian-angle-left_0_2': 0}) +field_id_5104_3mm_strong_meridian_angle_left_0_0 = TensorMap('5104_3mm-strong-meridian-angle-left_0_0', loss='logcosh', channel_map={'no_5104_3mm-strong-meridian-angle-left_0_0': 0}) +field_id_5104_3mm_strong_meridian_angle_left_0_1 = TensorMap('5104_3mm-strong-meridian-angle-left_0_1', loss='logcosh', channel_map={'no_5104_3mm-strong-meridian-angle-left_0_1': 0}) +field_id_5104_3mm_strong_meridian_angle_left_0_2 = TensorMap('5104_3mm-strong-meridian-angle-left_0_2', loss='logcosh', channel_map={'no_5104_3mm-strong-meridian-angle-left_0_2': 0}) +field_id_5105_6mm_strong_meridian_angle_left_0_0 = TensorMap('5105_6mm-strong-meridian-angle-left_0_0', loss='logcosh', channel_map={'no_5105_6mm-strong-meridian-angle-left_0_0': 0}) +field_id_5105_6mm_strong_meridian_angle_left_0_1 = TensorMap('5105_6mm-strong-meridian-angle-left_0_1', loss='logcosh', channel_map={'no_5105_6mm-strong-meridian-angle-left_0_1': 0}) +field_id_5105_6mm_strong_meridian_angle_left_0_2 = TensorMap('5105_6mm-strong-meridian-angle-left_0_2', loss='logcosh', channel_map={'no_5105_6mm-strong-meridian-angle-left_0_2': 0}) +field_id_5106_6mm_strong_meridian_angle_right_0_0 = TensorMap('5106_6mm-strong-meridian-angle-right_0_0', loss='logcosh', channel_map={'no_5106_6mm-strong-meridian-angle-right_0_0': 0}) +field_id_5106_6mm_strong_meridian_angle_right_0_1 = TensorMap('5106_6mm-strong-meridian-angle-right_0_1', loss='logcosh', channel_map={'no_5106_6mm-strong-meridian-angle-right_0_1': 0}) +field_id_5107_3mm_strong_meridian_angle_right_0_0 = TensorMap('5107_3mm-strong-meridian-angle-right_0_0', loss='logcosh', channel_map={'no_5107_3mm-strong-meridian-angle-right_0_0': 0}) +field_id_5107_3mm_strong_meridian_angle_right_0_1 = TensorMap('5107_3mm-strong-meridian-angle-right_0_1', loss='logcosh', channel_map={'no_5107_3mm-strong-meridian-angle-right_0_1': 0}) +field_id_5108_3mm_asymmetry_angle_right_0_0 = TensorMap('5108_3mm-asymmetry-angle-right_0_0', loss='logcosh', channel_map={'no_5108_3mm-asymmetry-angle-right_0_0': 0}) +field_id_5108_3mm_asymmetry_angle_right_0_1 = TensorMap('5108_3mm-asymmetry-angle-right_0_1', loss='logcosh', channel_map={'no_5108_3mm-asymmetry-angle-right_0_1': 0}) +field_id_5109_6mm_asymmetry_angle_right_0_0 = TensorMap('5109_6mm-asymmetry-angle-right_0_0', loss='logcosh', channel_map={'no_5109_6mm-asymmetry-angle-right_0_0': 0}) +field_id_5109_6mm_asymmetry_angle_right_0_1 = TensorMap('5109_6mm-asymmetry-angle-right_0_1', loss='logcosh', channel_map={'no_5109_6mm-asymmetry-angle-right_0_1': 0}) +field_id_5110_6mm_asymmetry_angle_left_0_2 = TensorMap('5110_6mm-asymmetry-angle-left_0_2', loss='logcosh', channel_map={'no_5110_6mm-asymmetry-angle-left_0_2': 0}) +field_id_5111_3mm_asymmetry_angle_left_0_0 = TensorMap('5111_3mm-asymmetry-angle-left_0_0', loss='logcosh', channel_map={'no_5111_3mm-asymmetry-angle-left_0_0': 0}) +field_id_5111_3mm_asymmetry_angle_left_0_1 = TensorMap('5111_3mm-asymmetry-angle-left_0_1', loss='logcosh', channel_map={'no_5111_3mm-asymmetry-angle-left_0_1': 0}) +field_id_5111_3mm_asymmetry_angle_left_0_2 = TensorMap('5111_3mm-asymmetry-angle-left_0_2', loss='logcosh', channel_map={'no_5111_3mm-asymmetry-angle-left_0_2': 0}) +field_id_5112_3mm_cylindrical_power_angle_left_0_0 = TensorMap('5112_3mm-cylindrical-power-angle-left_0_0', loss='logcosh', channel_map={'no_5112_3mm-cylindrical-power-angle-left_0_0': 0}) +field_id_5112_3mm_cylindrical_power_angle_left_0_1 = TensorMap('5112_3mm-cylindrical-power-angle-left_0_1', loss='logcosh', channel_map={'no_5112_3mm-cylindrical-power-angle-left_0_1': 0}) +field_id_5112_3mm_cylindrical_power_angle_left_0_2 = TensorMap('5112_3mm-cylindrical-power-angle-left_0_2', loss='logcosh', channel_map={'no_5112_3mm-cylindrical-power-angle-left_0_2': 0}) +field_id_5113_6mm_cylindrical_power_angle_left_0_0 = TensorMap('5113_6mm-cylindrical-power-angle-left_0_0', loss='logcosh', channel_map={'no_5113_6mm-cylindrical-power-angle-left_0_0': 0}) +field_id_5113_6mm_cylindrical_power_angle_left_0_1 = TensorMap('5113_6mm-cylindrical-power-angle-left_0_1', loss='logcosh', channel_map={'no_5113_6mm-cylindrical-power-angle-left_0_1': 0}) +field_id_5113_6mm_cylindrical_power_angle_left_0_2 = TensorMap('5113_6mm-cylindrical-power-angle-left_0_2', loss='logcosh', channel_map={'no_5113_6mm-cylindrical-power-angle-left_0_2': 0}) +field_id_5114_6mm_cylindrical_power_angle_right_0_0 = TensorMap('5114_6mm-cylindrical-power-angle-right_0_0', loss='logcosh', channel_map={'no_5114_6mm-cylindrical-power-angle-right_0_0': 0}) +field_id_5114_6mm_cylindrical_power_angle_right_0_1 = TensorMap('5114_6mm-cylindrical-power-angle-right_0_1', loss='logcosh', channel_map={'no_5114_6mm-cylindrical-power-angle-right_0_1': 0}) +field_id_5115_3mm_cylindrical_power_angle_right_0_0 = TensorMap('5115_3mm-cylindrical-power-angle-right_0_0', loss='logcosh', channel_map={'no_5115_3mm-cylindrical-power-angle-right_0_0': 0}) +field_id_5115_3mm_cylindrical_power_angle_right_0_1 = TensorMap('5115_3mm-cylindrical-power-angle-right_0_1', loss='logcosh', channel_map={'no_5115_3mm-cylindrical-power-angle-right_0_1': 0}) +field_id_5116_3mm_cylindrical_power_right_0_0 = TensorMap('5116_3mm-cylindrical-power-right_0_0', loss='logcosh', channel_map={'no_5116_3mm-cylindrical-power-right_0_0': 0}) +field_id_5116_3mm_cylindrical_power_right_0_1 = TensorMap('5116_3mm-cylindrical-power-right_0_1', loss='logcosh', channel_map={'no_5116_3mm-cylindrical-power-right_0_1': 0}) +field_id_5117_6mm_cylindrical_power_right_0_0 = TensorMap('5117_6mm-cylindrical-power-right_0_0', loss='logcosh', channel_map={'no_5117_6mm-cylindrical-power-right_0_0': 0}) +field_id_5117_6mm_cylindrical_power_right_0_1 = TensorMap('5117_6mm-cylindrical-power-right_0_1', loss='logcosh', channel_map={'no_5117_6mm-cylindrical-power-right_0_1': 0}) +field_id_5118_6mm_cylindrical_power_left_0_0 = TensorMap('5118_6mm-cylindrical-power-left_0_0', loss='logcosh', channel_map={'no_5118_6mm-cylindrical-power-left_0_0': 0}) +field_id_5118_6mm_cylindrical_power_left_0_1 = TensorMap('5118_6mm-cylindrical-power-left_0_1', loss='logcosh', channel_map={'no_5118_6mm-cylindrical-power-left_0_1': 0}) +field_id_5118_6mm_cylindrical_power_left_0_2 = TensorMap('5118_6mm-cylindrical-power-left_0_2', loss='logcosh', channel_map={'no_5118_6mm-cylindrical-power-left_0_2': 0}) +field_id_5119_3mm_cylindrical_power_left_0_0 = TensorMap('5119_3mm-cylindrical-power-left_0_0', loss='logcosh', channel_map={'no_5119_3mm-cylindrical-power-left_0_0': 0}) +field_id_5119_3mm_cylindrical_power_left_0_1 = TensorMap('5119_3mm-cylindrical-power-left_0_1', loss='logcosh', channel_map={'no_5119_3mm-cylindrical-power-left_0_1': 0}) +field_id_5119_3mm_cylindrical_power_left_0_2 = TensorMap('5119_3mm-cylindrical-power-left_0_2', loss='logcosh', channel_map={'no_5119_3mm-cylindrical-power-left_0_2': 0}) +field_id_5132_3mm_strong_meridian_right_0_0 = TensorMap('5132_3mm-strong-meridian-right_0_0', loss='logcosh', channel_map={'no_5132_3mm-strong-meridian-right_0_0': 0}) +field_id_5132_3mm_strong_meridian_right_0_1 = TensorMap('5132_3mm-strong-meridian-right_0_1', loss='logcosh', channel_map={'no_5132_3mm-strong-meridian-right_0_1': 0}) +field_id_5133_6mm_strong_meridian_right_0_0 = TensorMap('5133_6mm-strong-meridian-right_0_0', loss='logcosh', channel_map={'no_5133_6mm-strong-meridian-right_0_0': 0}) +field_id_5133_6mm_strong_meridian_right_0_1 = TensorMap('5133_6mm-strong-meridian-right_0_1', loss='logcosh', channel_map={'no_5133_6mm-strong-meridian-right_0_1': 0}) +field_id_5134_6mm_strong_meridian_left_0_0 = TensorMap('5134_6mm-strong-meridian-left_0_0', loss='logcosh', channel_map={'no_5134_6mm-strong-meridian-left_0_0': 0}) +field_id_5134_6mm_strong_meridian_left_0_1 = TensorMap('5134_6mm-strong-meridian-left_0_1', loss='logcosh', channel_map={'no_5134_6mm-strong-meridian-left_0_1': 0}) +field_id_5134_6mm_strong_meridian_left_0_2 = TensorMap('5134_6mm-strong-meridian-left_0_2', loss='logcosh', channel_map={'no_5134_6mm-strong-meridian-left_0_2': 0}) +field_id_5135_3mm_strong_meridian_left_0_0 = TensorMap('5135_3mm-strong-meridian-left_0_0', loss='logcosh', channel_map={'no_5135_3mm-strong-meridian-left_0_0': 0}) +field_id_5135_3mm_strong_meridian_left_0_1 = TensorMap('5135_3mm-strong-meridian-left_0_1', loss='logcosh', channel_map={'no_5135_3mm-strong-meridian-left_0_1': 0}) +field_id_5135_3mm_strong_meridian_left_0_2 = TensorMap('5135_3mm-strong-meridian-left_0_2', loss='logcosh', channel_map={'no_5135_3mm-strong-meridian-left_0_2': 0}) +field_id_5156_3mm_asymmetry_index_left_0_0 = TensorMap('5156_3mm-asymmetry-index-left_0_0', loss='logcosh', channel_map={'no_5156_3mm-asymmetry-index-left_0_0': 0}) +field_id_5156_3mm_asymmetry_index_left_0_1 = TensorMap('5156_3mm-asymmetry-index-left_0_1', loss='logcosh', channel_map={'no_5156_3mm-asymmetry-index-left_0_1': 0}) +field_id_5156_3mm_asymmetry_index_left_0_2 = TensorMap('5156_3mm-asymmetry-index-left_0_2', loss='logcosh', channel_map={'no_5156_3mm-asymmetry-index-left_0_2': 0}) +field_id_5157_6mm_asymmetry_index_left_0_2 = TensorMap('5157_6mm-asymmetry-index-left_0_2', loss='logcosh', channel_map={'no_5157_6mm-asymmetry-index-left_0_2': 0}) +field_id_5158_6mm_asymmetry_index_right_0_0 = TensorMap('5158_6mm-asymmetry-index-right_0_0', loss='logcosh', channel_map={'no_5158_6mm-asymmetry-index-right_0_0': 0}) +field_id_5158_6mm_asymmetry_index_right_0_1 = TensorMap('5158_6mm-asymmetry-index-right_0_1', loss='logcosh', channel_map={'no_5158_6mm-asymmetry-index-right_0_1': 0}) +field_id_5159_3mm_asymmetry_index_right_0_0 = TensorMap('5159_3mm-asymmetry-index-right_0_0', loss='logcosh', channel_map={'no_5159_3mm-asymmetry-index-right_0_0': 0}) +field_id_5159_3mm_asymmetry_index_right_0_1 = TensorMap('5159_3mm-asymmetry-index-right_0_1', loss='logcosh', channel_map={'no_5159_3mm-asymmetry-index-right_0_1': 0}) +field_id_5160_3mm_regularity_index_right_0_0 = TensorMap('5160_3mm-regularity-index-right_0_0', loss='logcosh', channel_map={'no_5160_3mm-regularity-index-right_0_0': 0}) +field_id_5160_3mm_regularity_index_right_0_1 = TensorMap('5160_3mm-regularity-index-right_0_1', loss='logcosh', channel_map={'no_5160_3mm-regularity-index-right_0_1': 0}) +field_id_5161_6mm_regularity_index_right_0_0 = TensorMap('5161_6mm-regularity-index-right_0_0', loss='logcosh', channel_map={'no_5161_6mm-regularity-index-right_0_0': 0}) +field_id_5161_6mm_regularity_index_right_0_1 = TensorMap('5161_6mm-regularity-index-right_0_1', loss='logcosh', channel_map={'no_5161_6mm-regularity-index-right_0_1': 0}) +field_id_5162_6mm_regularity_index_left_0_2 = TensorMap('5162_6mm-regularity-index-left_0_2', loss='logcosh', channel_map={'no_5162_6mm-regularity-index-left_0_2': 0}) +field_id_5163_3mm_regularity_index_left_0_0 = TensorMap('5163_3mm-regularity-index-left_0_0', loss='logcosh', channel_map={'no_5163_3mm-regularity-index-left_0_0': 0}) +field_id_5163_3mm_regularity_index_left_0_1 = TensorMap('5163_3mm-regularity-index-left_0_1', loss='logcosh', channel_map={'no_5163_3mm-regularity-index-left_0_1': 0}) +field_id_5163_3mm_regularity_index_left_0_2 = TensorMap('5163_3mm-regularity-index-left_0_2', loss='logcosh', channel_map={'no_5163_3mm-regularity-index-left_0_2': 0}) +field_id_5186_duration_visualacuity_screen_displayed_right_0_0 = TensorMap('5186_Duration-visualacuity-screen-displayed-right_0_0', loss='logcosh', channel_map={'no_5186_Duration-visualacuity-screen-displayed-right_0_0': 0}) +field_id_5188_duration_visualacuity_screen_displayed_left_0_0 = TensorMap('5188_Duration-visualacuity-screen-displayed-left_0_0', loss='logcosh', channel_map={'no_5188_Duration-visualacuity-screen-displayed-left_0_0': 0}) +field_id_5190_duration_at_which_refractometer_first_shown_right_0_0 = TensorMap('5190_Duration-at-which-refractometer-first-shown-right_0_0', loss='logcosh', channel_map={'no_5190_Duration-at-which-refractometer-first-shown-right_0_0': 0}) +field_id_5193_duration_at_which_refractometer_first_shown_left_0_0 = TensorMap('5193_Duration-at-which-refractometer-first-shown-left_0_0', loss='logcosh', channel_map={'no_5193_Duration-at-which-refractometer-first-shown-left_0_0': 0}) +field_id_5199_logmar_initial_right_0_0 = TensorMap('5199_logMAR-initial-right_0_0', loss='logcosh', channel_map={'no_5199_logMAR-initial-right_0_0': 0}) +field_id_5200_final_number_of_letters_displayed_right_0_0 = TensorMap('5200_Final-number-of-letters-displayed-right_0_0', loss='logcosh', channel_map={'no_5200_Final-number-of-letters-displayed-right_0_0': 0}) +field_id_5201_logmar_final_right_0_0 = TensorMap('5201_logMAR-final-right_0_0', loss='logcosh', channel_map={'no_5201_logMAR-final-right_0_0': 0}) +field_id_5202_number_of_rounds_to_result_right_0_0 = TensorMap('5202_Number-of-rounds-to-result-right_0_0', loss='logcosh', channel_map={'no_5202_Number-of-rounds-to-result-right_0_0': 0}) +field_id_5204_distance_of_viewer_to_screen_right_0_0 = TensorMap('5204_Distance-of-viewer-to-screen-right_0_0', loss='logcosh', channel_map={'no_5204_Distance-of-viewer-to-screen-right_0_0': 0}) +field_id_5206_logmar_initial_left_0_0 = TensorMap('5206_logMAR-initial-left_0_0', loss='logcosh', channel_map={'no_5206_logMAR-initial-left_0_0': 0}) +field_id_5207_final_number_of_letters_displayed_left_0_0 = TensorMap('5207_Final-number-of-letters-displayed-left_0_0', loss='logcosh', channel_map={'no_5207_Final-number-of-letters-displayed-left_0_0': 0}) +field_id_5208_logmar_final_left_0_0 = TensorMap('5208_logMAR-final-left_0_0', loss='logcosh', channel_map={'no_5208_logMAR-final-left_0_0': 0}) +field_id_5209_number_of_rounds_to_result_left_0_0 = TensorMap('5209_Number-of-rounds-to-result-left_0_0', loss='logcosh', channel_map={'no_5209_Number-of-rounds-to-result-left_0_0': 0}) +field_id_5211_distance_of_viewer_to_screen_left_0_0 = TensorMap('5211_Distance-of-viewer-to-screen-left_0_0', loss='logcosh', channel_map={'no_5211_Distance-of-viewer-to-screen-left_0_0': 0}) +field_id_5215_vertex_distance_right_0_0 = TensorMap('5215_Vertex-distance-right_0_0', loss='logcosh', channel_map={'no_5215_Vertex-distance-right_0_0': 0}) +field_id_5221_index_of_best_refractometry_result_right_0_0 = TensorMap('5221_Index-of-best-refractometry-result-right_0_0', loss='logcosh', channel_map={'no_5221_Index-of-best-refractometry-result-right_0_0': 0}) +field_id_5237_3mm_index_of_best_keratometry_results_right_0_0 = TensorMap('5237_3mm-index-of-best-keratometry-results-right_0_0', loss='logcosh', channel_map={'no_5237_3mm-index-of-best-keratometry-results-right_0_0': 0}) +field_id_5251_6mm_index_of_best_keratometry_results_right_0_0 = TensorMap('5251_6mm-index-of-best-keratometry-results-right_0_0', loss='logcosh', channel_map={'no_5251_6mm-index-of-best-keratometry-results-right_0_0': 0}) +field_id_5254_intraocular_pressure_cornealcompensated_right_0_0 = TensorMap('5254_Intraocular-pressure-cornealcompensated-right_0_0', loss='logcosh', channel_map={'no_5254_Intraocular-pressure-cornealcompensated-right_0_0': 0}) +field_id_5255_intraocular_pressure_goldmanncorrelated_right_0_0 = TensorMap('5255_Intraocular-pressure-Goldmanncorrelated-right_0_0', loss='logcosh', channel_map={'no_5255_Intraocular-pressure-Goldmanncorrelated-right_0_0': 0}) +field_id_5256_corneal_hysteresis_right_0_0 = TensorMap('5256_Corneal-hysteresis-right_0_0', loss='logcosh', channel_map={'no_5256_Corneal-hysteresis-right_0_0': 0}) +field_id_5257_corneal_resistance_factor_right_0_0 = TensorMap('5257_Corneal-resistance-factor-right_0_0', loss='logcosh', channel_map={'no_5257_Corneal-resistance-factor-right_0_0': 0}) +field_id_5262_intraocular_pressure_cornealcompensated_left_0_0 = TensorMap('5262_Intraocular-pressure-cornealcompensated-left_0_0', loss='logcosh', channel_map={'no_5262_Intraocular-pressure-cornealcompensated-left_0_0': 0}) +field_id_5263_intraocular_pressure_goldmanncorrelated_left_0_0 = TensorMap('5263_Intraocular-pressure-Goldmanncorrelated-left_0_0', loss='logcosh', channel_map={'no_5263_Intraocular-pressure-Goldmanncorrelated-left_0_0': 0}) +field_id_5264_corneal_hysteresis_left_0_0 = TensorMap('5264_Corneal-hysteresis-left_0_0', loss='logcosh', channel_map={'no_5264_Corneal-hysteresis-left_0_0': 0}) +field_id_5265_corneal_resistance_factor_left_0_0 = TensorMap('5265_Corneal-resistance-factor-left_0_0', loss='logcosh', channel_map={'no_5265_Corneal-resistance-factor-left_0_0': 0}) +field_id_5274_vertex_distance_left_0_0 = TensorMap('5274_Vertex-distance-left_0_0', loss='logcosh', channel_map={'no_5274_Vertex-distance-left_0_0': 0}) +field_id_5276_index_of_best_refractometry_result_left_0_0 = TensorMap('5276_Index-of-best-refractometry-result-left_0_0', loss='logcosh', channel_map={'no_5276_Index-of-best-refractometry-result-left_0_0': 0}) +field_id_5292_3mm_index_of_best_keratometry_results_left_0_0 = TensorMap('5292_3mm-index-of-best-keratometry-results-left_0_0', loss='logcosh', channel_map={'no_5292_3mm-index-of-best-keratometry-results-left_0_0': 0}) +field_id_5306_6mm_index_of_best_keratometry_results_left_0_0 = TensorMap('5306_6mm-index-of-best-keratometry-results-left_0_0', loss='logcosh', channel_map={'no_5306_6mm-index-of-best-keratometry-results-left_0_0': 0}) +field_id_5364_average_weekly_intake_of_other_alcoholic_drinks_0_0 = TensorMap('5364_Average-weekly-intake-of-other-alcoholic-drinks_0_0', loss='logcosh', channel_map={'no_5364_Average-weekly-intake-of-other-alcoholic-drinks_0_0': 0}) +field_id_6032_maximum_workload_during_fitness_test_0_0 = TensorMap('6032_Maximum-workload-during-fitness-test_0_0', loss='logcosh', channel_map={'no_6032_Maximum-workload-during-fitness-test_0_0': 0}) +field_id_6033_maximum_heart_rate_during_fitness_test_0_0 = TensorMap('6033_Maximum-heart-rate-during-fitness-test_0_0', loss='logcosh', channel_map={'no_6033_Maximum-heart-rate-during-fitness-test_0_0': 0}) +field_id_6038_number_of_trend_entries_0_0 = TensorMap('6038_Number-of-trend-entries_0_0', loss='logcosh', channel_map={'no_6038_Number-of-trend-entries_0_0': 0}) +field_id_6039_duration_of_fitness_test_0_0 = TensorMap('6039_Duration-of-fitness-test_0_0', loss='logcosh', channel_map={'no_6039_Duration-of-fitness-test_0_0': 0}) +field_id_6071_duration_at_which_oct_screen_shown_right_0_0 = TensorMap('6071_Duration-at-which-OCT-screen-shown-right_0_0', loss='logcosh', channel_map={'no_6071_Duration-at-which-OCT-screen-shown-right_0_0': 0}) +field_id_6073_duration_at_which_oct_screen_shown_left_0_0 = TensorMap('6073_Duration-at-which-OCT-screen-shown-left_0_0', loss='logcosh', channel_map={'no_6073_Duration-at-which-OCT-screen-shown-left_0_0': 0}) +field_id_92_operation_yearage_first_occurred_0_1 = TensorMap('92_Operation-yearage-first-occurred_0_1', loss='logcosh', channel_map={'no_92_Operation-yearage-first-occurred_0_1': 0}) +field_id_102_pulse_rate_automated_reading_2_0 = TensorMap('102_Pulse-rate-automated-reading_2_0', loss='logcosh', channel_map={'no_102_Pulse-rate-automated-reading_2_0': 0}) +field_id_102_pulse_rate_automated_reading_2_1 = TensorMap('102_Pulse-rate-automated-reading_2_1', loss='logcosh', channel_map={'no_102_Pulse-rate-automated-reading_2_1': 0}) +field_id_1050_time_spend_outdoors_in_summer_2_0 = TensorMap('1050_Time-spend-outdoors-in-summer_2_0', loss='logcosh', channel_map={'no_1050_Time-spend-outdoors-in-summer_2_0': 0}) +field_id_1060_time_spent_outdoors_in_winter_2_0 = TensorMap('1060_Time-spent-outdoors-in-winter_2_0', loss='logcosh', channel_map={'no_1060_Time-spent-outdoors-in-winter_2_0': 0}) +field_id_1070_time_spent_watching_television_tv_2_0 = TensorMap('1070_Time-spent-watching-television-TV_2_0', loss='logcosh', channel_map={'no_1070_Time-spent-watching-television-TV_2_0': 0}) +field_id_1080_time_spent_using_computer_2_0 = TensorMap('1080_Time-spent-using-computer_2_0', loss='logcosh', channel_map={'no_1080_Time-spent-using-computer_2_0': 0}) +field_id_1090_time_spent_driving_2_0 = TensorMap('1090_Time-spent-driving_2_0', loss='logcosh', channel_map={'no_1090_Time-spent-driving_2_0': 0}) +field_id_1269_exposure_to_tobacco_smoke_at_home_2_0 = TensorMap('1269_Exposure-to-tobacco-smoke-at-home_2_0', loss='logcosh', channel_map={'no_1269_Exposure-to-tobacco-smoke-at-home_2_0': 0}) +field_id_1279_exposure_to_tobacco_smoke_outside_home_2_0 = TensorMap('1279_Exposure-to-tobacco-smoke-outside-home_2_0', loss='logcosh', channel_map={'no_1279_Exposure-to-tobacco-smoke-outside-home_2_0': 0}) +field_id_1289_cooked_vegetable_intake_2_0 = TensorMap('1289_Cooked-vegetable-intake_2_0', loss='logcosh', channel_map={'no_1289_Cooked-vegetable-intake_2_0': 0}) +field_id_1299_salad_raw_vegetable_intake_2_0 = TensorMap('1299_Salad-raw-vegetable-intake_2_0', loss='logcosh', channel_map={'no_1299_Salad-raw-vegetable-intake_2_0': 0}) +field_id_1309_fresh_fruit_intake_2_0 = TensorMap('1309_Fresh-fruit-intake_2_0', loss='logcosh', channel_map={'no_1309_Fresh-fruit-intake_2_0': 0}) +field_id_1319_dried_fruit_intake_2_0 = TensorMap('1319_Dried-fruit-intake_2_0', loss='logcosh', channel_map={'no_1319_Dried-fruit-intake_2_0': 0}) +field_id_134_number_of_selfreported_cancers_2_0 = TensorMap('134_Number-of-selfreported-cancers_2_0', loss='logcosh', channel_map={'no_134_Number-of-selfreported-cancers_2_0': 0}) +field_id_135_number_of_selfreported_noncancer_illnesses_2_0 = TensorMap('135_Number-of-selfreported-noncancer-illnesses_2_0', loss='logcosh', channel_map={'no_135_Number-of-selfreported-noncancer-illnesses_2_0': 0}) +field_id_136_number_of_operations_selfreported_2_0 = TensorMap('136_Number-of-operations-selfreported_2_0', loss='logcosh', channel_map={'no_136_Number-of-operations-selfreported_2_0': 0}) +field_id_137_number_of_treatmentsmedications_taken_2_0 = TensorMap('137_Number-of-treatmentsmedications-taken_2_0', loss='logcosh', channel_map={'no_137_Number-of-treatmentsmedications-taken_2_0': 0}) +field_id_1438_bread_intake_2_0 = TensorMap('1438_Bread-intake_2_0', loss='logcosh', channel_map={'no_1438_Bread-intake_2_0': 0}) +field_id_1458_cereal_intake_2_0 = TensorMap('1458_Cereal-intake_2_0', loss='logcosh', channel_map={'no_1458_Cereal-intake_2_0': 0}) +field_id_1488_tea_intake_2_0 = TensorMap('1488_Tea-intake_2_0', loss='logcosh', channel_map={'no_1488_Tea-intake_2_0': 0}) +field_id_1498_coffee_intake_2_0 = TensorMap('1498_Coffee-intake_2_0', loss='logcosh', channel_map={'no_1498_Coffee-intake_2_0': 0}) +field_id_1528_water_intake_2_0 = TensorMap('1528_Water-intake_2_0', loss='logcosh', channel_map={'no_1528_Water-intake_2_0': 0}) +field_id_1568_average_weekly_red_wine_intake_2_0 = TensorMap('1568_Average-weekly-red-wine-intake_2_0', loss='logcosh', channel_map={'no_1568_Average-weekly-red-wine-intake_2_0': 0}) +field_id_1578_average_weekly_champagne_plus_white_wine_intake_2_0 = TensorMap('1578_Average-weekly-champagne-plus-white-wine-intake_2_0', loss='logcosh', channel_map={'no_1578_Average-weekly-champagne-plus-white-wine-intake_2_0': 0}) +field_id_1588_average_weekly_beer_plus_cider_intake_2_0 = TensorMap('1588_Average-weekly-beer-plus-cider-intake_2_0', loss='logcosh', channel_map={'no_1588_Average-weekly-beer-plus-cider-intake_2_0': 0}) +field_id_1598_average_weekly_spirits_intake_2_0 = TensorMap('1598_Average-weekly-spirits-intake_2_0', loss='logcosh', channel_map={'no_1598_Average-weekly-spirits-intake_2_0': 0}) +field_id_1608_average_weekly_fortified_wine_intake_2_0 = TensorMap('1608_Average-weekly-fortified-wine-intake_2_0', loss='logcosh', channel_map={'no_1608_Average-weekly-fortified-wine-intake_2_0': 0}) +field_id_1807_fathers_age_at_death_0_0 = TensorMap('1807_Fathers-age-at-death_0_0', loss='logcosh', channel_map={'no_1807_Fathers-age-at-death_0_0': 0}) +field_id_1807_fathers_age_at_death_2_0 = TensorMap('1807_Fathers-age-at-death_2_0', loss='logcosh', channel_map={'no_1807_Fathers-age-at-death_2_0': 0}) +field_id_1873_number_of_full_brothers_2_0 = TensorMap('1873_Number-of-full-brothers_2_0', loss='logcosh', channel_map={'no_1873_Number-of-full-brothers_2_0': 0}) +field_id_1883_number_of_full_sisters_2_0 = TensorMap('1883_Number-of-full-sisters_2_0', loss='logcosh', channel_map={'no_1883_Number-of-full-sisters_2_0': 0}) +field_id_20009_interpolated_age_of_participant_when_noncancer_illness_first_diagnosed_0_0 = TensorMap('20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_0_0', loss='logcosh', channel_map={'no_20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_0_0': 0}) +field_id_20009_interpolated_age_of_participant_when_noncancer_illness_first_diagnosed_2_0 = TensorMap('20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_2_0', loss='logcosh', channel_map={'no_20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_2_0': 0}) +field_id_20009_interpolated_age_of_participant_when_noncancer_illness_first_diagnosed_2_1 = TensorMap('20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_2_1', loss='logcosh', channel_map={'no_20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_2_1': 0}) +field_id_20009_interpolated_age_of_participant_when_noncancer_illness_first_diagnosed_2_2 = TensorMap('20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_2_2', loss='logcosh', channel_map={'no_20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_2_2': 0}) +field_id_20009_interpolated_age_of_participant_when_noncancer_illness_first_diagnosed_2_3 = TensorMap('20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_2_3', loss='logcosh', channel_map={'no_20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_2_3': 0}) +field_id_20009_interpolated_age_of_participant_when_noncancer_illness_first_diagnosed_2_4 = TensorMap('20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_2_4', loss='logcosh', channel_map={'no_20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_2_4': 0}) +field_id_20011_interpolated_age_of_participant_when_operation_took_place_2_0 = TensorMap('20011_Interpolated-Age-of-participant-when-operation-took-place_2_0', loss='logcosh', channel_map={'no_20011_Interpolated-Age-of-participant-when-operation-took-place_2_0': 0}) +field_id_20011_interpolated_age_of_participant_when_operation_took_place_2_1 = TensorMap('20011_Interpolated-Age-of-participant-when-operation-took-place_2_1', loss='logcosh', channel_map={'no_20011_Interpolated-Age-of-participant-when-operation-took-place_2_1': 0}) +field_id_20015_sitting_height_2_0 = TensorMap('20015_Sitting-height_2_0', loss='logcosh', channel_map={'no_20015_Sitting-height_2_0': 0}) +field_id_20016_fluid_intelligence_score_2_0 = TensorMap('20016_Fluid-intelligence-score_2_0', loss='logcosh', channel_map={'no_20016_Fluid-intelligence-score_2_0': 0}) +field_id_20019_speechreceptionthreshold_srt_estimate_left_2_0 = TensorMap('20019_Speechreceptionthreshold-SRT-estimate-left_2_0', loss='logcosh', channel_map={'no_20019_Speechreceptionthreshold-SRT-estimate-left_2_0': 0}) +field_id_20021_speechreceptionthreshold_srt_estimate_right_2_0 = TensorMap('20021_Speechreceptionthreshold-SRT-estimate-right_2_0', loss='logcosh', channel_map={'no_20021_Speechreceptionthreshold-SRT-estimate-right_2_0': 0}) +field_id_20023_mean_time_to_correctly_identify_matches_2_0 = TensorMap('20023_Mean-time-to-correctly-identify-matches_2_0', loss='logcosh', channel_map={'no_20023_Mean-time-to-correctly-identify-matches_2_0': 0}) +field_id_20128_number_of_fluid_intelligence_questions_attempted_within_time_limit_2_0 = TensorMap('20128_Number-of-fluid-intelligence-questions-attempted-within-time-limit_2_0', loss='logcosh', channel_map={'no_20128_Number-of-fluid-intelligence-questions-attempted-within-time-limit_2_0': 0}) +field_id_21001_body_mass_index_bmi_2_0 = TensorMap('21001_Body-mass-index-BMI_2_0', loss='logcosh', channel_map={'no_21001_Body-mass-index-BMI_2_0': 0}) +field_id_21002_weight_2_0 = TensorMap('21002_Weight_2_0', loss='logcosh', channel_map={'no_21002_Weight_2_0': 0}) +field_id_21003_age_when_attended_assessment_centre_2_0 = TensorMap('21003_Age-when-attended-assessment-centre_2_0', loss='logcosh', channel_map={'no_21003_Age-when-attended-assessment-centre_2_0': 0}) +field_id_21021_pulse_wave_arterial_stiffness_index_2_0 = TensorMap('21021_Pulse-wave-Arterial-Stiffness-index_2_0', loss='logcosh', channel_map={'no_21021_Pulse-wave-Arterial-Stiffness-index_2_0': 0}) +field_id_2217_age_started_wearing_glasses_or_contact_lenses_2_0 = TensorMap('2217_Age-started-wearing-glasses-or-contact-lenses_2_0', loss='logcosh', channel_map={'no_2217_Age-started-wearing-glasses-or-contact-lenses_2_0': 0}) +field_id_22602_year_job_started_0_4 = TensorMap('22602_Year-job-started_0_4', loss='logcosh', channel_map={'no_22602_Year-job-started_0_4': 0}) +field_id_22602_year_job_started_0_5 = TensorMap('22602_Year-job-started_0_5', loss='logcosh', channel_map={'no_22602_Year-job-started_0_5': 0}) +field_id_22603_year_job_ended_0_4 = TensorMap('22603_Year-job-ended_0_4', loss='logcosh', channel_map={'no_22603_Year-job-ended_0_4': 0}) +field_id_22603_year_job_ended_0_5 = TensorMap('22603_Year-job-ended_0_5', loss='logcosh', channel_map={'no_22603_Year-job-ended_0_5': 0}) +field_id_2277_frequency_of_solariumsunlamp_use_2_0 = TensorMap('2277_Frequency-of-solariumsunlamp-use_2_0', loss='logcosh', channel_map={'no_2277_Frequency-of-solariumsunlamp-use_2_0': 0}) +field_id_23098_weight_2_0 = TensorMap('23098_Weight_2_0', loss='logcosh', channel_map={'no_23098_Weight_2_0': 0}) +field_id_23099_body_fat_percentage_2_0 = TensorMap('23099_Body-fat-percentage_2_0', loss='logcosh', channel_map={'no_23099_Body-fat-percentage_2_0': 0}) +field_id_23100_whole_body_fat_mass_2_0 = TensorMap('23100_Whole-body-fat-mass_2_0', loss='logcosh', channel_map={'no_23100_Whole-body-fat-mass_2_0': 0}) +field_id_23101_whole_body_fatfree_mass_2_0 = TensorMap('23101_Whole-body-fatfree-mass_2_0', loss='logcosh', channel_map={'no_23101_Whole-body-fatfree-mass_2_0': 0}) +field_id_23102_whole_body_water_mass_2_0 = TensorMap('23102_Whole-body-water-mass_2_0', loss='logcosh', channel_map={'no_23102_Whole-body-water-mass_2_0': 0}) +field_id_23104_body_mass_index_bmi_2_0 = TensorMap('23104_Body-mass-index-BMI_2_0', loss='logcosh', channel_map={'no_23104_Body-mass-index-BMI_2_0': 0}) +field_id_23105_basal_metabolic_rate_2_0 = TensorMap('23105_Basal-metabolic-rate_2_0', loss='logcosh', channel_map={'no_23105_Basal-metabolic-rate_2_0': 0}) +field_id_23106_impedance_of_whole_body_2_0 = TensorMap('23106_Impedance-of-whole-body_2_0', loss='logcosh', channel_map={'no_23106_Impedance-of-whole-body_2_0': 0}) +field_id_23107_impedance_of_leg_right_2_0 = TensorMap('23107_Impedance-of-leg-right_2_0', loss='logcosh', channel_map={'no_23107_Impedance-of-leg-right_2_0': 0}) +field_id_23108_impedance_of_leg_left_2_0 = TensorMap('23108_Impedance-of-leg-left_2_0', loss='logcosh', channel_map={'no_23108_Impedance-of-leg-left_2_0': 0}) +field_id_23109_impedance_of_arm_right_2_0 = TensorMap('23109_Impedance-of-arm-right_2_0', loss='logcosh', channel_map={'no_23109_Impedance-of-arm-right_2_0': 0}) +field_id_23110_impedance_of_arm_left_2_0 = TensorMap('23110_Impedance-of-arm-left_2_0', loss='logcosh', channel_map={'no_23110_Impedance-of-arm-left_2_0': 0}) +field_id_23111_leg_fat_percentage_right_2_0 = TensorMap('23111_Leg-fat-percentage-right_2_0', loss='logcosh', channel_map={'no_23111_Leg-fat-percentage-right_2_0': 0}) +field_id_23112_leg_fat_mass_right_2_0 = TensorMap('23112_Leg-fat-mass-right_2_0', loss='logcosh', channel_map={'no_23112_Leg-fat-mass-right_2_0': 0}) +field_id_23113_leg_fatfree_mass_right_2_0 = TensorMap('23113_Leg-fatfree-mass-right_2_0', loss='logcosh', channel_map={'no_23113_Leg-fatfree-mass-right_2_0': 0}) +field_id_23114_leg_predicted_mass_right_2_0 = TensorMap('23114_Leg-predicted-mass-right_2_0', loss='logcosh', channel_map={'no_23114_Leg-predicted-mass-right_2_0': 0}) +field_id_23115_leg_fat_percentage_left_2_0 = TensorMap('23115_Leg-fat-percentage-left_2_0', loss='logcosh', channel_map={'no_23115_Leg-fat-percentage-left_2_0': 0}) +field_id_23116_leg_fat_mass_left_2_0 = TensorMap('23116_Leg-fat-mass-left_2_0', loss='logcosh', channel_map={'no_23116_Leg-fat-mass-left_2_0': 0}) +field_id_23117_leg_fatfree_mass_left_2_0 = TensorMap('23117_Leg-fatfree-mass-left_2_0', loss='logcosh', channel_map={'no_23117_Leg-fatfree-mass-left_2_0': 0}) +field_id_23118_leg_predicted_mass_left_2_0 = TensorMap('23118_Leg-predicted-mass-left_2_0', loss='logcosh', channel_map={'no_23118_Leg-predicted-mass-left_2_0': 0}) +field_id_23119_arm_fat_percentage_right_2_0 = TensorMap('23119_Arm-fat-percentage-right_2_0', loss='logcosh', channel_map={'no_23119_Arm-fat-percentage-right_2_0': 0}) +field_id_23120_arm_fat_mass_right_2_0 = TensorMap('23120_Arm-fat-mass-right_2_0', loss='logcosh', channel_map={'no_23120_Arm-fat-mass-right_2_0': 0}) +field_id_23121_arm_fatfree_mass_right_2_0 = TensorMap('23121_Arm-fatfree-mass-right_2_0', loss='logcosh', channel_map={'no_23121_Arm-fatfree-mass-right_2_0': 0}) +field_id_23122_arm_predicted_mass_right_2_0 = TensorMap('23122_Arm-predicted-mass-right_2_0', loss='logcosh', channel_map={'no_23122_Arm-predicted-mass-right_2_0': 0}) +field_id_23123_arm_fat_percentage_left_2_0 = TensorMap('23123_Arm-fat-percentage-left_2_0', loss='logcosh', channel_map={'no_23123_Arm-fat-percentage-left_2_0': 0}) +field_id_23124_arm_fat_mass_left_2_0 = TensorMap('23124_Arm-fat-mass-left_2_0', loss='logcosh', channel_map={'no_23124_Arm-fat-mass-left_2_0': 0}) +field_id_23125_arm_fatfree_mass_left_2_0 = TensorMap('23125_Arm-fatfree-mass-left_2_0', loss='logcosh', channel_map={'no_23125_Arm-fatfree-mass-left_2_0': 0}) +field_id_23126_arm_predicted_mass_left_2_0 = TensorMap('23126_Arm-predicted-mass-left_2_0', loss='logcosh', channel_map={'no_23126_Arm-predicted-mass-left_2_0': 0}) +field_id_23127_trunk_fat_percentage_2_0 = TensorMap('23127_Trunk-fat-percentage_2_0', loss='logcosh', channel_map={'no_23127_Trunk-fat-percentage_2_0': 0}) +field_id_23128_trunk_fat_mass_2_0 = TensorMap('23128_Trunk-fat-mass_2_0', loss='logcosh', channel_map={'no_23128_Trunk-fat-mass_2_0': 0}) +field_id_23129_trunk_fatfree_mass_2_0 = TensorMap('23129_Trunk-fatfree-mass_2_0', loss='logcosh', channel_map={'no_23129_Trunk-fatfree-mass_2_0': 0}) +field_id_23130_trunk_predicted_mass_2_0 = TensorMap('23130_Trunk-predicted-mass_2_0', loss='logcosh', channel_map={'no_23130_Trunk-predicted-mass_2_0': 0}) +field_id_2355_most_recent_bowel_cancer_screening_0_0 = TensorMap('2355_Most-recent-bowel-cancer-screening_0_0', loss='logcosh', channel_map={'no_2355_Most-recent-bowel-cancer-screening_0_0': 0}) +field_id_2355_most_recent_bowel_cancer_screening_2_0 = TensorMap('2355_Most-recent-bowel-cancer-screening_2_0', loss='logcosh', channel_map={'no_2355_Most-recent-bowel-cancer-screening_2_0': 0}) +field_id_25000_volumetric_scaling_from_t1_head_image_to_standard_space_2_0 = TensorMap('25000_Volumetric-scaling-from-T1-head-image-to-standard-space_2_0', loss='logcosh', channel_map={'no_25000_Volumetric-scaling-from-T1-head-image-to-standard-space_2_0': 0}) +field_id_25001_volume_of_peripheral_cortical_grey_matter_normalised_for_head_size_2_0 = TensorMap('25001_Volume-of-peripheral-cortical-grey-matter-normalised-for-head-size_2_0', loss='logcosh', channel_map={'no_25001_Volume-of-peripheral-cortical-grey-matter-normalised-for-head-size_2_0': 0}) +field_id_25002_volume_of_peripheral_cortical_grey_matter_2_0 = TensorMap('25002_Volume-of-peripheral-cortical-grey-matter_2_0', loss='logcosh', channel_map={'no_25002_Volume-of-peripheral-cortical-grey-matter_2_0': 0}) +field_id_25003_volume_of_ventricular_cerebrospinal_fluid_normalised_for_head_size_2_0 = TensorMap('25003_Volume-of-ventricular-cerebrospinal-fluid-normalised-for-head-size_2_0', loss='logcosh', channel_map={'no_25003_Volume-of-ventricular-cerebrospinal-fluid-normalised-for-head-size_2_0': 0}) +field_id_25004_volume_of_ventricular_cerebrospinal_fluid_2_0 = TensorMap('25004_Volume-of-ventricular-cerebrospinal-fluid_2_0', loss='logcosh', channel_map={'no_25004_Volume-of-ventricular-cerebrospinal-fluid_2_0': 0}) +field_id_25005_volume_of_grey_matter_normalised_for_head_size_2_0 = TensorMap('25005_Volume-of-grey-matter-normalised-for-head-size_2_0', loss='logcosh', channel_map={'no_25005_Volume-of-grey-matter-normalised-for-head-size_2_0': 0}) +field_id_25006_volume_of_grey_matter_2_0 = TensorMap('25006_Volume-of-grey-matter_2_0', loss='logcosh', channel_map={'no_25006_Volume-of-grey-matter_2_0': 0}) +field_id_25007_volume_of_white_matter_normalised_for_head_size_2_0 = TensorMap('25007_Volume-of-white-matter-normalised-for-head-size_2_0', loss='logcosh', channel_map={'no_25007_Volume-of-white-matter-normalised-for-head-size_2_0': 0}) +field_id_25008_volume_of_white_matter_2_0 = TensorMap('25008_Volume-of-white-matter_2_0', loss='logcosh', channel_map={'no_25008_Volume-of-white-matter_2_0': 0}) +field_id_25009_volume_of_brain_greywhite_matter_normalised_for_head_size_2_0 = TensorMap('25009_Volume-of-brain-greywhite-matter-normalised-for-head-size_2_0', loss='logcosh', channel_map={'no_25009_Volume-of-brain-greywhite-matter-normalised-for-head-size_2_0': 0}) +field_id_25010_volume_of_brain_greywhite_matter_2_0 = TensorMap('25010_Volume-of-brain-greywhite-matter_2_0', loss='logcosh', channel_map={'no_25010_Volume-of-brain-greywhite-matter_2_0': 0}) +field_id_25011_volume_of_thalamus_left_2_0 = TensorMap('25011_Volume-of-thalamus-left_2_0', loss='logcosh', channel_map={'no_25011_Volume-of-thalamus-left_2_0': 0}) +field_id_25012_volume_of_thalamus_right_2_0 = TensorMap('25012_Volume-of-thalamus-right_2_0', loss='logcosh', channel_map={'no_25012_Volume-of-thalamus-right_2_0': 0}) +field_id_25013_volume_of_caudate_left_2_0 = TensorMap('25013_Volume-of-caudate-left_2_0', loss='logcosh', channel_map={'no_25013_Volume-of-caudate-left_2_0': 0}) +field_id_25014_volume_of_caudate_right_2_0 = TensorMap('25014_Volume-of-caudate-right_2_0', loss='logcosh', channel_map={'no_25014_Volume-of-caudate-right_2_0': 0}) +field_id_25015_volume_of_putamen_left_2_0 = TensorMap('25015_Volume-of-putamen-left_2_0', loss='logcosh', channel_map={'no_25015_Volume-of-putamen-left_2_0': 0}) +field_id_25016_volume_of_putamen_right_2_0 = TensorMap('25016_Volume-of-putamen-right_2_0', loss='logcosh', channel_map={'no_25016_Volume-of-putamen-right_2_0': 0}) +field_id_25017_volume_of_pallidum_left_2_0 = TensorMap('25017_Volume-of-pallidum-left_2_0', loss='logcosh', channel_map={'no_25017_Volume-of-pallidum-left_2_0': 0}) +field_id_25018_volume_of_pallidum_right_2_0 = TensorMap('25018_Volume-of-pallidum-right_2_0', loss='logcosh', channel_map={'no_25018_Volume-of-pallidum-right_2_0': 0}) +field_id_25019_volume_of_hippocampus_left_2_0 = TensorMap('25019_Volume-of-hippocampus-left_2_0', loss='logcosh', channel_map={'no_25019_Volume-of-hippocampus-left_2_0': 0}) +field_id_25020_volume_of_hippocampus_right_2_0 = TensorMap('25020_Volume-of-hippocampus-right_2_0', loss='logcosh', channel_map={'no_25020_Volume-of-hippocampus-right_2_0': 0}) +field_id_25021_volume_of_amygdala_left_2_0 = TensorMap('25021_Volume-of-amygdala-left_2_0', loss='logcosh', channel_map={'no_25021_Volume-of-amygdala-left_2_0': 0}) +field_id_25022_volume_of_amygdala_right_2_0 = TensorMap('25022_Volume-of-amygdala-right_2_0', loss='logcosh', channel_map={'no_25022_Volume-of-amygdala-right_2_0': 0}) +field_id_25023_volume_of_accumbens_left_2_0 = TensorMap('25023_Volume-of-accumbens-left_2_0', loss='logcosh', channel_map={'no_25023_Volume-of-accumbens-left_2_0': 0}) +field_id_25024_volume_of_accumbens_right_2_0 = TensorMap('25024_Volume-of-accumbens-right_2_0', loss='logcosh', channel_map={'no_25024_Volume-of-accumbens-right_2_0': 0}) +field_id_25025_volume_of_brain_stem_4th_ventricle_2_0 = TensorMap('25025_Volume-of-brain-stem-4th-ventricle_2_0', loss='logcosh', channel_map={'no_25025_Volume-of-brain-stem-4th-ventricle_2_0': 0}) +field_id_25026_median_t2star_in_thalamus_left_2_0 = TensorMap('25026_Median-T2star-in-thalamus-left_2_0', loss='logcosh', channel_map={'no_25026_Median-T2star-in-thalamus-left_2_0': 0}) +field_id_25027_median_t2star_in_thalamus_right_2_0 = TensorMap('25027_Median-T2star-in-thalamus-right_2_0', loss='logcosh', channel_map={'no_25027_Median-T2star-in-thalamus-right_2_0': 0}) +field_id_25028_median_t2star_in_caudate_left_2_0 = TensorMap('25028_Median-T2star-in-caudate-left_2_0', loss='logcosh', channel_map={'no_25028_Median-T2star-in-caudate-left_2_0': 0}) +field_id_25029_median_t2star_in_caudate_right_2_0 = TensorMap('25029_Median-T2star-in-caudate-right_2_0', loss='logcosh', channel_map={'no_25029_Median-T2star-in-caudate-right_2_0': 0}) +field_id_25030_median_t2star_in_putamen_left_2_0 = TensorMap('25030_Median-T2star-in-putamen-left_2_0', loss='logcosh', channel_map={'no_25030_Median-T2star-in-putamen-left_2_0': 0}) +field_id_25031_median_t2star_in_putamen_right_2_0 = TensorMap('25031_Median-T2star-in-putamen-right_2_0', loss='logcosh', channel_map={'no_25031_Median-T2star-in-putamen-right_2_0': 0}) +field_id_25032_median_t2star_in_pallidum_left_2_0 = TensorMap('25032_Median-T2star-in-pallidum-left_2_0', loss='logcosh', channel_map={'no_25032_Median-T2star-in-pallidum-left_2_0': 0}) +field_id_25033_median_t2star_in_pallidum_right_2_0 = TensorMap('25033_Median-T2star-in-pallidum-right_2_0', loss='logcosh', channel_map={'no_25033_Median-T2star-in-pallidum-right_2_0': 0}) +field_id_25034_median_t2star_in_hippocampus_left_2_0 = TensorMap('25034_Median-T2star-in-hippocampus-left_2_0', loss='logcosh', channel_map={'no_25034_Median-T2star-in-hippocampus-left_2_0': 0}) +field_id_25035_median_t2star_in_hippocampus_right_2_0 = TensorMap('25035_Median-T2star-in-hippocampus-right_2_0', loss='logcosh', channel_map={'no_25035_Median-T2star-in-hippocampus-right_2_0': 0}) +field_id_25036_median_t2star_in_amygdala_left_2_0 = TensorMap('25036_Median-T2star-in-amygdala-left_2_0', loss='logcosh', channel_map={'no_25036_Median-T2star-in-amygdala-left_2_0': 0}) +field_id_25037_median_t2star_in_amygdala_right_2_0 = TensorMap('25037_Median-T2star-in-amygdala-right_2_0', loss='logcosh', channel_map={'no_25037_Median-T2star-in-amygdala-right_2_0': 0}) +field_id_25038_median_t2star_in_accumbens_left_2_0 = TensorMap('25038_Median-T2star-in-accumbens-left_2_0', loss='logcosh', channel_map={'no_25038_Median-T2star-in-accumbens-left_2_0': 0}) +field_id_25039_median_t2star_in_accumbens_right_2_0 = TensorMap('25039_Median-T2star-in-accumbens-right_2_0', loss='logcosh', channel_map={'no_25039_Median-T2star-in-accumbens-right_2_0': 0}) +field_id_25056_mean_fa_in_middle_cerebellar_peduncle_on_fa_skeleton_2_0 = TensorMap('25056_Mean-FA-in-middle-cerebellar-peduncle-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25056_Mean-FA-in-middle-cerebellar-peduncle-on-FA-skeleton_2_0': 0}) +field_id_25057_mean_fa_in_pontine_crossing_tract_on_fa_skeleton_2_0 = TensorMap('25057_Mean-FA-in-pontine-crossing-tract-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25057_Mean-FA-in-pontine-crossing-tract-on-FA-skeleton_2_0': 0}) +field_id_25058_mean_fa_in_genu_of_corpus_callosum_on_fa_skeleton_2_0 = TensorMap('25058_Mean-FA-in-genu-of-corpus-callosum-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25058_Mean-FA-in-genu-of-corpus-callosum-on-FA-skeleton_2_0': 0}) +field_id_25059_mean_fa_in_body_of_corpus_callosum_on_fa_skeleton_2_0 = TensorMap('25059_Mean-FA-in-body-of-corpus-callosum-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25059_Mean-FA-in-body-of-corpus-callosum-on-FA-skeleton_2_0': 0}) +field_id_25060_mean_fa_in_splenium_of_corpus_callosum_on_fa_skeleton_2_0 = TensorMap('25060_Mean-FA-in-splenium-of-corpus-callosum-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25060_Mean-FA-in-splenium-of-corpus-callosum-on-FA-skeleton_2_0': 0}) +field_id_25061_mean_fa_in_fornix_on_fa_skeleton_2_0 = TensorMap('25061_Mean-FA-in-fornix-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25061_Mean-FA-in-fornix-on-FA-skeleton_2_0': 0}) +field_id_25062_mean_fa_in_corticospinal_tract_on_fa_skeleton_right_2_0 = TensorMap('25062_Mean-FA-in-corticospinal-tract-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25062_Mean-FA-in-corticospinal-tract-on-FA-skeleton-right_2_0': 0}) +field_id_25063_mean_fa_in_corticospinal_tract_on_fa_skeleton_left_2_0 = TensorMap('25063_Mean-FA-in-corticospinal-tract-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25063_Mean-FA-in-corticospinal-tract-on-FA-skeleton-left_2_0': 0}) +field_id_25064_mean_fa_in_medial_lemniscus_on_fa_skeleton_right_2_0 = TensorMap('25064_Mean-FA-in-medial-lemniscus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25064_Mean-FA-in-medial-lemniscus-on-FA-skeleton-right_2_0': 0}) +field_id_25065_mean_fa_in_medial_lemniscus_on_fa_skeleton_left_2_0 = TensorMap('25065_Mean-FA-in-medial-lemniscus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25065_Mean-FA-in-medial-lemniscus-on-FA-skeleton-left_2_0': 0}) +field_id_25066_mean_fa_in_inferior_cerebellar_peduncle_on_fa_skeleton_right_2_0 = TensorMap('25066_Mean-FA-in-inferior-cerebellar-peduncle-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25066_Mean-FA-in-inferior-cerebellar-peduncle-on-FA-skeleton-right_2_0': 0}) +field_id_25067_mean_fa_in_inferior_cerebellar_peduncle_on_fa_skeleton_left_2_0 = TensorMap('25067_Mean-FA-in-inferior-cerebellar-peduncle-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25067_Mean-FA-in-inferior-cerebellar-peduncle-on-FA-skeleton-left_2_0': 0}) +field_id_25068_mean_fa_in_superior_cerebellar_peduncle_on_fa_skeleton_right_2_0 = TensorMap('25068_Mean-FA-in-superior-cerebellar-peduncle-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25068_Mean-FA-in-superior-cerebellar-peduncle-on-FA-skeleton-right_2_0': 0}) +field_id_25069_mean_fa_in_superior_cerebellar_peduncle_on_fa_skeleton_left_2_0 = TensorMap('25069_Mean-FA-in-superior-cerebellar-peduncle-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25069_Mean-FA-in-superior-cerebellar-peduncle-on-FA-skeleton-left_2_0': 0}) +field_id_25070_mean_fa_in_cerebral_peduncle_on_fa_skeleton_right_2_0 = TensorMap('25070_Mean-FA-in-cerebral-peduncle-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25070_Mean-FA-in-cerebral-peduncle-on-FA-skeleton-right_2_0': 0}) +field_id_25071_mean_fa_in_cerebral_peduncle_on_fa_skeleton_left_2_0 = TensorMap('25071_Mean-FA-in-cerebral-peduncle-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25071_Mean-FA-in-cerebral-peduncle-on-FA-skeleton-left_2_0': 0}) +field_id_25072_mean_fa_in_anterior_limb_of_internal_capsule_on_fa_skeleton_right_2_0 = TensorMap('25072_Mean-FA-in-anterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25072_Mean-FA-in-anterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25073_mean_fa_in_anterior_limb_of_internal_capsule_on_fa_skeleton_left_2_0 = TensorMap('25073_Mean-FA-in-anterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25073_Mean-FA-in-anterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25074_mean_fa_in_posterior_limb_of_internal_capsule_on_fa_skeleton_right_2_0 = TensorMap('25074_Mean-FA-in-posterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25074_Mean-FA-in-posterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25075_mean_fa_in_posterior_limb_of_internal_capsule_on_fa_skeleton_left_2_0 = TensorMap('25075_Mean-FA-in-posterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25075_Mean-FA-in-posterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25076_mean_fa_in_retrolenticular_part_of_internal_capsule_on_fa_skeleton_right_2_0 = TensorMap('25076_Mean-FA-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25076_Mean-FA-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25077_mean_fa_in_retrolenticular_part_of_internal_capsule_on_fa_skeleton_left_2_0 = TensorMap('25077_Mean-FA-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25077_Mean-FA-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25078_mean_fa_in_anterior_corona_radiata_on_fa_skeleton_right_2_0 = TensorMap('25078_Mean-FA-in-anterior-corona-radiata-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25078_Mean-FA-in-anterior-corona-radiata-on-FA-skeleton-right_2_0': 0}) +field_id_25079_mean_fa_in_anterior_corona_radiata_on_fa_skeleton_left_2_0 = TensorMap('25079_Mean-FA-in-anterior-corona-radiata-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25079_Mean-FA-in-anterior-corona-radiata-on-FA-skeleton-left_2_0': 0}) +field_id_25080_mean_fa_in_superior_corona_radiata_on_fa_skeleton_right_2_0 = TensorMap('25080_Mean-FA-in-superior-corona-radiata-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25080_Mean-FA-in-superior-corona-radiata-on-FA-skeleton-right_2_0': 0}) +field_id_25081_mean_fa_in_superior_corona_radiata_on_fa_skeleton_left_2_0 = TensorMap('25081_Mean-FA-in-superior-corona-radiata-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25081_Mean-FA-in-superior-corona-radiata-on-FA-skeleton-left_2_0': 0}) +field_id_25082_mean_fa_in_posterior_corona_radiata_on_fa_skeleton_right_2_0 = TensorMap('25082_Mean-FA-in-posterior-corona-radiata-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25082_Mean-FA-in-posterior-corona-radiata-on-FA-skeleton-right_2_0': 0}) +field_id_25083_mean_fa_in_posterior_corona_radiata_on_fa_skeleton_left_2_0 = TensorMap('25083_Mean-FA-in-posterior-corona-radiata-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25083_Mean-FA-in-posterior-corona-radiata-on-FA-skeleton-left_2_0': 0}) +field_id_25084_mean_fa_in_posterior_thalamic_radiation_on_fa_skeleton_right_2_0 = TensorMap('25084_Mean-FA-in-posterior-thalamic-radiation-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25084_Mean-FA-in-posterior-thalamic-radiation-on-FA-skeleton-right_2_0': 0}) +field_id_25085_mean_fa_in_posterior_thalamic_radiation_on_fa_skeleton_left_2_0 = TensorMap('25085_Mean-FA-in-posterior-thalamic-radiation-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25085_Mean-FA-in-posterior-thalamic-radiation-on-FA-skeleton-left_2_0': 0}) +field_id_25086_mean_fa_in_sagittal_stratum_on_fa_skeleton_right_2_0 = TensorMap('25086_Mean-FA-in-sagittal-stratum-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25086_Mean-FA-in-sagittal-stratum-on-FA-skeleton-right_2_0': 0}) +field_id_25087_mean_fa_in_sagittal_stratum_on_fa_skeleton_left_2_0 = TensorMap('25087_Mean-FA-in-sagittal-stratum-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25087_Mean-FA-in-sagittal-stratum-on-FA-skeleton-left_2_0': 0}) +field_id_25088_mean_fa_in_external_capsule_on_fa_skeleton_right_2_0 = TensorMap('25088_Mean-FA-in-external-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25088_Mean-FA-in-external-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25089_mean_fa_in_external_capsule_on_fa_skeleton_left_2_0 = TensorMap('25089_Mean-FA-in-external-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25089_Mean-FA-in-external-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25090_mean_fa_in_cingulum_cingulate_gyrus_on_fa_skeleton_right_2_0 = TensorMap('25090_Mean-FA-in-cingulum-cingulate-gyrus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25090_Mean-FA-in-cingulum-cingulate-gyrus-on-FA-skeleton-right_2_0': 0}) +field_id_25091_mean_fa_in_cingulum_cingulate_gyrus_on_fa_skeleton_left_2_0 = TensorMap('25091_Mean-FA-in-cingulum-cingulate-gyrus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25091_Mean-FA-in-cingulum-cingulate-gyrus-on-FA-skeleton-left_2_0': 0}) +field_id_25092_mean_fa_in_cingulum_hippocampus_on_fa_skeleton_right_2_0 = TensorMap('25092_Mean-FA-in-cingulum-hippocampus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25092_Mean-FA-in-cingulum-hippocampus-on-FA-skeleton-right_2_0': 0}) +field_id_25093_mean_fa_in_cingulum_hippocampus_on_fa_skeleton_left_2_0 = TensorMap('25093_Mean-FA-in-cingulum-hippocampus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25093_Mean-FA-in-cingulum-hippocampus-on-FA-skeleton-left_2_0': 0}) +field_id_25094_mean_fa_in_fornix_cresstria_terminalis_on_fa_skeleton_right_2_0 = TensorMap('25094_Mean-FA-in-fornix-cresstria-terminalis-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25094_Mean-FA-in-fornix-cresstria-terminalis-on-FA-skeleton-right_2_0': 0}) +field_id_25095_mean_fa_in_fornix_cresstria_terminalis_on_fa_skeleton_left_2_0 = TensorMap('25095_Mean-FA-in-fornix-cresstria-terminalis-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25095_Mean-FA-in-fornix-cresstria-terminalis-on-FA-skeleton-left_2_0': 0}) +field_id_25096_mean_fa_in_superior_longitudinal_fasciculus_on_fa_skeleton_right_2_0 = TensorMap('25096_Mean-FA-in-superior-longitudinal-fasciculus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25096_Mean-FA-in-superior-longitudinal-fasciculus-on-FA-skeleton-right_2_0': 0}) +field_id_25097_mean_fa_in_superior_longitudinal_fasciculus_on_fa_skeleton_left_2_0 = TensorMap('25097_Mean-FA-in-superior-longitudinal-fasciculus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25097_Mean-FA-in-superior-longitudinal-fasciculus-on-FA-skeleton-left_2_0': 0}) +field_id_25098_mean_fa_in_superior_frontooccipital_fasciculus_on_fa_skeleton_right_2_0 = TensorMap('25098_Mean-FA-in-superior-frontooccipital-fasciculus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25098_Mean-FA-in-superior-frontooccipital-fasciculus-on-FA-skeleton-right_2_0': 0}) +field_id_25099_mean_fa_in_superior_frontooccipital_fasciculus_on_fa_skeleton_left_2_0 = TensorMap('25099_Mean-FA-in-superior-frontooccipital-fasciculus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25099_Mean-FA-in-superior-frontooccipital-fasciculus-on-FA-skeleton-left_2_0': 0}) +field_id_25100_mean_fa_in_uncinate_fasciculus_on_fa_skeleton_right_2_0 = TensorMap('25100_Mean-FA-in-uncinate-fasciculus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25100_Mean-FA-in-uncinate-fasciculus-on-FA-skeleton-right_2_0': 0}) +field_id_25101_mean_fa_in_uncinate_fasciculus_on_fa_skeleton_left_2_0 = TensorMap('25101_Mean-FA-in-uncinate-fasciculus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25101_Mean-FA-in-uncinate-fasciculus-on-FA-skeleton-left_2_0': 0}) +field_id_25102_mean_fa_in_tapetum_on_fa_skeleton_right_2_0 = TensorMap('25102_Mean-FA-in-tapetum-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25102_Mean-FA-in-tapetum-on-FA-skeleton-right_2_0': 0}) +field_id_25103_mean_fa_in_tapetum_on_fa_skeleton_left_2_0 = TensorMap('25103_Mean-FA-in-tapetum-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25103_Mean-FA-in-tapetum-on-FA-skeleton-left_2_0': 0}) +field_id_25104_mean_md_in_middle_cerebellar_peduncle_on_fa_skeleton_2_0 = TensorMap('25104_Mean-MD-in-middle-cerebellar-peduncle-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25104_Mean-MD-in-middle-cerebellar-peduncle-on-FA-skeleton_2_0': 0}) +field_id_25105_mean_md_in_pontine_crossing_tract_on_fa_skeleton_2_0 = TensorMap('25105_Mean-MD-in-pontine-crossing-tract-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25105_Mean-MD-in-pontine-crossing-tract-on-FA-skeleton_2_0': 0}) +field_id_25106_mean_md_in_genu_of_corpus_callosum_on_fa_skeleton_2_0 = TensorMap('25106_Mean-MD-in-genu-of-corpus-callosum-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25106_Mean-MD-in-genu-of-corpus-callosum-on-FA-skeleton_2_0': 0}) +field_id_25107_mean_md_in_body_of_corpus_callosum_on_fa_skeleton_2_0 = TensorMap('25107_Mean-MD-in-body-of-corpus-callosum-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25107_Mean-MD-in-body-of-corpus-callosum-on-FA-skeleton_2_0': 0}) +field_id_25108_mean_md_in_splenium_of_corpus_callosum_on_fa_skeleton_2_0 = TensorMap('25108_Mean-MD-in-splenium-of-corpus-callosum-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25108_Mean-MD-in-splenium-of-corpus-callosum-on-FA-skeleton_2_0': 0}) +field_id_25109_mean_md_in_fornix_on_fa_skeleton_2_0 = TensorMap('25109_Mean-MD-in-fornix-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25109_Mean-MD-in-fornix-on-FA-skeleton_2_0': 0}) +field_id_25110_mean_md_in_corticospinal_tract_on_fa_skeleton_right_2_0 = TensorMap('25110_Mean-MD-in-corticospinal-tract-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25110_Mean-MD-in-corticospinal-tract-on-FA-skeleton-right_2_0': 0}) +field_id_25111_mean_md_in_corticospinal_tract_on_fa_skeleton_left_2_0 = TensorMap('25111_Mean-MD-in-corticospinal-tract-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25111_Mean-MD-in-corticospinal-tract-on-FA-skeleton-left_2_0': 0}) +field_id_25112_mean_md_in_medial_lemniscus_on_fa_skeleton_right_2_0 = TensorMap('25112_Mean-MD-in-medial-lemniscus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25112_Mean-MD-in-medial-lemniscus-on-FA-skeleton-right_2_0': 0}) +field_id_25113_mean_md_in_medial_lemniscus_on_fa_skeleton_left_2_0 = TensorMap('25113_Mean-MD-in-medial-lemniscus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25113_Mean-MD-in-medial-lemniscus-on-FA-skeleton-left_2_0': 0}) +field_id_25114_mean_md_in_inferior_cerebellar_peduncle_on_fa_skeleton_right_2_0 = TensorMap('25114_Mean-MD-in-inferior-cerebellar-peduncle-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25114_Mean-MD-in-inferior-cerebellar-peduncle-on-FA-skeleton-right_2_0': 0}) +field_id_25115_mean_md_in_inferior_cerebellar_peduncle_on_fa_skeleton_left_2_0 = TensorMap('25115_Mean-MD-in-inferior-cerebellar-peduncle-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25115_Mean-MD-in-inferior-cerebellar-peduncle-on-FA-skeleton-left_2_0': 0}) +field_id_25116_mean_md_in_superior_cerebellar_peduncle_on_fa_skeleton_right_2_0 = TensorMap('25116_Mean-MD-in-superior-cerebellar-peduncle-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25116_Mean-MD-in-superior-cerebellar-peduncle-on-FA-skeleton-right_2_0': 0}) +field_id_25117_mean_md_in_superior_cerebellar_peduncle_on_fa_skeleton_left_2_0 = TensorMap('25117_Mean-MD-in-superior-cerebellar-peduncle-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25117_Mean-MD-in-superior-cerebellar-peduncle-on-FA-skeleton-left_2_0': 0}) +field_id_25118_mean_md_in_cerebral_peduncle_on_fa_skeleton_right_2_0 = TensorMap('25118_Mean-MD-in-cerebral-peduncle-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25118_Mean-MD-in-cerebral-peduncle-on-FA-skeleton-right_2_0': 0}) +field_id_25119_mean_md_in_cerebral_peduncle_on_fa_skeleton_left_2_0 = TensorMap('25119_Mean-MD-in-cerebral-peduncle-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25119_Mean-MD-in-cerebral-peduncle-on-FA-skeleton-left_2_0': 0}) +field_id_25120_mean_md_in_anterior_limb_of_internal_capsule_on_fa_skeleton_right_2_0 = TensorMap('25120_Mean-MD-in-anterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25120_Mean-MD-in-anterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25121_mean_md_in_anterior_limb_of_internal_capsule_on_fa_skeleton_left_2_0 = TensorMap('25121_Mean-MD-in-anterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25121_Mean-MD-in-anterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25122_mean_md_in_posterior_limb_of_internal_capsule_on_fa_skeleton_right_2_0 = TensorMap('25122_Mean-MD-in-posterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25122_Mean-MD-in-posterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25123_mean_md_in_posterior_limb_of_internal_capsule_on_fa_skeleton_left_2_0 = TensorMap('25123_Mean-MD-in-posterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25123_Mean-MD-in-posterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25124_mean_md_in_retrolenticular_part_of_internal_capsule_on_fa_skeleton_right_2_0 = TensorMap('25124_Mean-MD-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25124_Mean-MD-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25125_mean_md_in_retrolenticular_part_of_internal_capsule_on_fa_skeleton_left_2_0 = TensorMap('25125_Mean-MD-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25125_Mean-MD-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25126_mean_md_in_anterior_corona_radiata_on_fa_skeleton_right_2_0 = TensorMap('25126_Mean-MD-in-anterior-corona-radiata-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25126_Mean-MD-in-anterior-corona-radiata-on-FA-skeleton-right_2_0': 0}) +field_id_25127_mean_md_in_anterior_corona_radiata_on_fa_skeleton_left_2_0 = TensorMap('25127_Mean-MD-in-anterior-corona-radiata-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25127_Mean-MD-in-anterior-corona-radiata-on-FA-skeleton-left_2_0': 0}) +field_id_25128_mean_md_in_superior_corona_radiata_on_fa_skeleton_right_2_0 = TensorMap('25128_Mean-MD-in-superior-corona-radiata-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25128_Mean-MD-in-superior-corona-radiata-on-FA-skeleton-right_2_0': 0}) +field_id_25129_mean_md_in_superior_corona_radiata_on_fa_skeleton_left_2_0 = TensorMap('25129_Mean-MD-in-superior-corona-radiata-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25129_Mean-MD-in-superior-corona-radiata-on-FA-skeleton-left_2_0': 0}) +field_id_25130_mean_md_in_posterior_corona_radiata_on_fa_skeleton_right_2_0 = TensorMap('25130_Mean-MD-in-posterior-corona-radiata-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25130_Mean-MD-in-posterior-corona-radiata-on-FA-skeleton-right_2_0': 0}) +field_id_25131_mean_md_in_posterior_corona_radiata_on_fa_skeleton_left_2_0 = TensorMap('25131_Mean-MD-in-posterior-corona-radiata-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25131_Mean-MD-in-posterior-corona-radiata-on-FA-skeleton-left_2_0': 0}) +field_id_25132_mean_md_in_posterior_thalamic_radiation_on_fa_skeleton_right_2_0 = TensorMap('25132_Mean-MD-in-posterior-thalamic-radiation-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25132_Mean-MD-in-posterior-thalamic-radiation-on-FA-skeleton-right_2_0': 0}) +field_id_25133_mean_md_in_posterior_thalamic_radiation_on_fa_skeleton_left_2_0 = TensorMap('25133_Mean-MD-in-posterior-thalamic-radiation-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25133_Mean-MD-in-posterior-thalamic-radiation-on-FA-skeleton-left_2_0': 0}) +field_id_25134_mean_md_in_sagittal_stratum_on_fa_skeleton_right_2_0 = TensorMap('25134_Mean-MD-in-sagittal-stratum-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25134_Mean-MD-in-sagittal-stratum-on-FA-skeleton-right_2_0': 0}) +field_id_25135_mean_md_in_sagittal_stratum_on_fa_skeleton_left_2_0 = TensorMap('25135_Mean-MD-in-sagittal-stratum-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25135_Mean-MD-in-sagittal-stratum-on-FA-skeleton-left_2_0': 0}) +field_id_25136_mean_md_in_external_capsule_on_fa_skeleton_right_2_0 = TensorMap('25136_Mean-MD-in-external-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25136_Mean-MD-in-external-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25137_mean_md_in_external_capsule_on_fa_skeleton_left_2_0 = TensorMap('25137_Mean-MD-in-external-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25137_Mean-MD-in-external-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25138_mean_md_in_cingulum_cingulate_gyrus_on_fa_skeleton_right_2_0 = TensorMap('25138_Mean-MD-in-cingulum-cingulate-gyrus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25138_Mean-MD-in-cingulum-cingulate-gyrus-on-FA-skeleton-right_2_0': 0}) +field_id_25139_mean_md_in_cingulum_cingulate_gyrus_on_fa_skeleton_left_2_0 = TensorMap('25139_Mean-MD-in-cingulum-cingulate-gyrus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25139_Mean-MD-in-cingulum-cingulate-gyrus-on-FA-skeleton-left_2_0': 0}) +field_id_25140_mean_md_in_cingulum_hippocampus_on_fa_skeleton_right_2_0 = TensorMap('25140_Mean-MD-in-cingulum-hippocampus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25140_Mean-MD-in-cingulum-hippocampus-on-FA-skeleton-right_2_0': 0}) +field_id_25141_mean_md_in_cingulum_hippocampus_on_fa_skeleton_left_2_0 = TensorMap('25141_Mean-MD-in-cingulum-hippocampus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25141_Mean-MD-in-cingulum-hippocampus-on-FA-skeleton-left_2_0': 0}) +field_id_25142_mean_md_in_fornix_cresstria_terminalis_on_fa_skeleton_right_2_0 = TensorMap('25142_Mean-MD-in-fornix-cresstria-terminalis-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25142_Mean-MD-in-fornix-cresstria-terminalis-on-FA-skeleton-right_2_0': 0}) +field_id_25143_mean_md_in_fornix_cresstria_terminalis_on_fa_skeleton_left_2_0 = TensorMap('25143_Mean-MD-in-fornix-cresstria-terminalis-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25143_Mean-MD-in-fornix-cresstria-terminalis-on-FA-skeleton-left_2_0': 0}) +field_id_25144_mean_md_in_superior_longitudinal_fasciculus_on_fa_skeleton_right_2_0 = TensorMap('25144_Mean-MD-in-superior-longitudinal-fasciculus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25144_Mean-MD-in-superior-longitudinal-fasciculus-on-FA-skeleton-right_2_0': 0}) +field_id_25145_mean_md_in_superior_longitudinal_fasciculus_on_fa_skeleton_left_2_0 = TensorMap('25145_Mean-MD-in-superior-longitudinal-fasciculus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25145_Mean-MD-in-superior-longitudinal-fasciculus-on-FA-skeleton-left_2_0': 0}) +field_id_25146_mean_md_in_superior_frontooccipital_fasciculus_on_fa_skeleton_right_2_0 = TensorMap('25146_Mean-MD-in-superior-frontooccipital-fasciculus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25146_Mean-MD-in-superior-frontooccipital-fasciculus-on-FA-skeleton-right_2_0': 0}) +field_id_25147_mean_md_in_superior_frontooccipital_fasciculus_on_fa_skeleton_left_2_0 = TensorMap('25147_Mean-MD-in-superior-frontooccipital-fasciculus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25147_Mean-MD-in-superior-frontooccipital-fasciculus-on-FA-skeleton-left_2_0': 0}) +field_id_25148_mean_md_in_uncinate_fasciculus_on_fa_skeleton_right_2_0 = TensorMap('25148_Mean-MD-in-uncinate-fasciculus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25148_Mean-MD-in-uncinate-fasciculus-on-FA-skeleton-right_2_0': 0}) +field_id_25149_mean_md_in_uncinate_fasciculus_on_fa_skeleton_left_2_0 = TensorMap('25149_Mean-MD-in-uncinate-fasciculus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25149_Mean-MD-in-uncinate-fasciculus-on-FA-skeleton-left_2_0': 0}) +field_id_25150_mean_md_in_tapetum_on_fa_skeleton_right_2_0 = TensorMap('25150_Mean-MD-in-tapetum-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25150_Mean-MD-in-tapetum-on-FA-skeleton-right_2_0': 0}) +field_id_25151_mean_md_in_tapetum_on_fa_skeleton_left_2_0 = TensorMap('25151_Mean-MD-in-tapetum-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25151_Mean-MD-in-tapetum-on-FA-skeleton-left_2_0': 0}) +field_id_25152_mean_mo_in_middle_cerebellar_peduncle_on_fa_skeleton_2_0 = TensorMap('25152_Mean-MO-in-middle-cerebellar-peduncle-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25152_Mean-MO-in-middle-cerebellar-peduncle-on-FA-skeleton_2_0': 0}) +field_id_25153_mean_mo_in_pontine_crossing_tract_on_fa_skeleton_2_0 = TensorMap('25153_Mean-MO-in-pontine-crossing-tract-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25153_Mean-MO-in-pontine-crossing-tract-on-FA-skeleton_2_0': 0}) +field_id_25154_mean_mo_in_genu_of_corpus_callosum_on_fa_skeleton_2_0 = TensorMap('25154_Mean-MO-in-genu-of-corpus-callosum-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25154_Mean-MO-in-genu-of-corpus-callosum-on-FA-skeleton_2_0': 0}) +field_id_25155_mean_mo_in_body_of_corpus_callosum_on_fa_skeleton_2_0 = TensorMap('25155_Mean-MO-in-body-of-corpus-callosum-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25155_Mean-MO-in-body-of-corpus-callosum-on-FA-skeleton_2_0': 0}) +field_id_25156_mean_mo_in_splenium_of_corpus_callosum_on_fa_skeleton_2_0 = TensorMap('25156_Mean-MO-in-splenium-of-corpus-callosum-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25156_Mean-MO-in-splenium-of-corpus-callosum-on-FA-skeleton_2_0': 0}) +field_id_25157_mean_mo_in_fornix_on_fa_skeleton_2_0 = TensorMap('25157_Mean-MO-in-fornix-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25157_Mean-MO-in-fornix-on-FA-skeleton_2_0': 0}) +field_id_25158_mean_mo_in_corticospinal_tract_on_fa_skeleton_right_2_0 = TensorMap('25158_Mean-MO-in-corticospinal-tract-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25158_Mean-MO-in-corticospinal-tract-on-FA-skeleton-right_2_0': 0}) +field_id_25159_mean_mo_in_corticospinal_tract_on_fa_skeleton_left_2_0 = TensorMap('25159_Mean-MO-in-corticospinal-tract-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25159_Mean-MO-in-corticospinal-tract-on-FA-skeleton-left_2_0': 0}) +field_id_25160_mean_mo_in_medial_lemniscus_on_fa_skeleton_right_2_0 = TensorMap('25160_Mean-MO-in-medial-lemniscus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25160_Mean-MO-in-medial-lemniscus-on-FA-skeleton-right_2_0': 0}) +field_id_25161_mean_mo_in_medial_lemniscus_on_fa_skeleton_left_2_0 = TensorMap('25161_Mean-MO-in-medial-lemniscus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25161_Mean-MO-in-medial-lemniscus-on-FA-skeleton-left_2_0': 0}) +field_id_25162_mean_mo_in_inferior_cerebellar_peduncle_on_fa_skeleton_right_2_0 = TensorMap('25162_Mean-MO-in-inferior-cerebellar-peduncle-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25162_Mean-MO-in-inferior-cerebellar-peduncle-on-FA-skeleton-right_2_0': 0}) +field_id_25163_mean_mo_in_inferior_cerebellar_peduncle_on_fa_skeleton_left_2_0 = TensorMap('25163_Mean-MO-in-inferior-cerebellar-peduncle-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25163_Mean-MO-in-inferior-cerebellar-peduncle-on-FA-skeleton-left_2_0': 0}) +field_id_25164_mean_mo_in_superior_cerebellar_peduncle_on_fa_skeleton_right_2_0 = TensorMap('25164_Mean-MO-in-superior-cerebellar-peduncle-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25164_Mean-MO-in-superior-cerebellar-peduncle-on-FA-skeleton-right_2_0': 0}) +field_id_25165_mean_mo_in_superior_cerebellar_peduncle_on_fa_skeleton_left_2_0 = TensorMap('25165_Mean-MO-in-superior-cerebellar-peduncle-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25165_Mean-MO-in-superior-cerebellar-peduncle-on-FA-skeleton-left_2_0': 0}) +field_id_25166_mean_mo_in_cerebral_peduncle_on_fa_skeleton_right_2_0 = TensorMap('25166_Mean-MO-in-cerebral-peduncle-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25166_Mean-MO-in-cerebral-peduncle-on-FA-skeleton-right_2_0': 0}) +field_id_25167_mean_mo_in_cerebral_peduncle_on_fa_skeleton_left_2_0 = TensorMap('25167_Mean-MO-in-cerebral-peduncle-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25167_Mean-MO-in-cerebral-peduncle-on-FA-skeleton-left_2_0': 0}) +field_id_25168_mean_mo_in_anterior_limb_of_internal_capsule_on_fa_skeleton_right_2_0 = TensorMap('25168_Mean-MO-in-anterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25168_Mean-MO-in-anterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25169_mean_mo_in_anterior_limb_of_internal_capsule_on_fa_skeleton_left_2_0 = TensorMap('25169_Mean-MO-in-anterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25169_Mean-MO-in-anterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25170_mean_mo_in_posterior_limb_of_internal_capsule_on_fa_skeleton_right_2_0 = TensorMap('25170_Mean-MO-in-posterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25170_Mean-MO-in-posterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25171_mean_mo_in_posterior_limb_of_internal_capsule_on_fa_skeleton_left_2_0 = TensorMap('25171_Mean-MO-in-posterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25171_Mean-MO-in-posterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25172_mean_mo_in_retrolenticular_part_of_internal_capsule_on_fa_skeleton_right_2_0 = TensorMap('25172_Mean-MO-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25172_Mean-MO-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25173_mean_mo_in_retrolenticular_part_of_internal_capsule_on_fa_skeleton_left_2_0 = TensorMap('25173_Mean-MO-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25173_Mean-MO-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25174_mean_mo_in_anterior_corona_radiata_on_fa_skeleton_right_2_0 = TensorMap('25174_Mean-MO-in-anterior-corona-radiata-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25174_Mean-MO-in-anterior-corona-radiata-on-FA-skeleton-right_2_0': 0}) +field_id_25175_mean_mo_in_anterior_corona_radiata_on_fa_skeleton_left_2_0 = TensorMap('25175_Mean-MO-in-anterior-corona-radiata-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25175_Mean-MO-in-anterior-corona-radiata-on-FA-skeleton-left_2_0': 0}) +field_id_25176_mean_mo_in_superior_corona_radiata_on_fa_skeleton_right_2_0 = TensorMap('25176_Mean-MO-in-superior-corona-radiata-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25176_Mean-MO-in-superior-corona-radiata-on-FA-skeleton-right_2_0': 0}) +field_id_25177_mean_mo_in_superior_corona_radiata_on_fa_skeleton_left_2_0 = TensorMap('25177_Mean-MO-in-superior-corona-radiata-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25177_Mean-MO-in-superior-corona-radiata-on-FA-skeleton-left_2_0': 0}) +field_id_25178_mean_mo_in_posterior_corona_radiata_on_fa_skeleton_right_2_0 = TensorMap('25178_Mean-MO-in-posterior-corona-radiata-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25178_Mean-MO-in-posterior-corona-radiata-on-FA-skeleton-right_2_0': 0}) +field_id_25179_mean_mo_in_posterior_corona_radiata_on_fa_skeleton_left_2_0 = TensorMap('25179_Mean-MO-in-posterior-corona-radiata-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25179_Mean-MO-in-posterior-corona-radiata-on-FA-skeleton-left_2_0': 0}) +field_id_25180_mean_mo_in_posterior_thalamic_radiation_on_fa_skeleton_right_2_0 = TensorMap('25180_Mean-MO-in-posterior-thalamic-radiation-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25180_Mean-MO-in-posterior-thalamic-radiation-on-FA-skeleton-right_2_0': 0}) +field_id_25181_mean_mo_in_posterior_thalamic_radiation_on_fa_skeleton_left_2_0 = TensorMap('25181_Mean-MO-in-posterior-thalamic-radiation-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25181_Mean-MO-in-posterior-thalamic-radiation-on-FA-skeleton-left_2_0': 0}) +field_id_25182_mean_mo_in_sagittal_stratum_on_fa_skeleton_right_2_0 = TensorMap('25182_Mean-MO-in-sagittal-stratum-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25182_Mean-MO-in-sagittal-stratum-on-FA-skeleton-right_2_0': 0}) +field_id_25183_mean_mo_in_sagittal_stratum_on_fa_skeleton_left_2_0 = TensorMap('25183_Mean-MO-in-sagittal-stratum-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25183_Mean-MO-in-sagittal-stratum-on-FA-skeleton-left_2_0': 0}) +field_id_25184_mean_mo_in_external_capsule_on_fa_skeleton_right_2_0 = TensorMap('25184_Mean-MO-in-external-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25184_Mean-MO-in-external-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25185_mean_mo_in_external_capsule_on_fa_skeleton_left_2_0 = TensorMap('25185_Mean-MO-in-external-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25185_Mean-MO-in-external-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25186_mean_mo_in_cingulum_cingulate_gyrus_on_fa_skeleton_right_2_0 = TensorMap('25186_Mean-MO-in-cingulum-cingulate-gyrus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25186_Mean-MO-in-cingulum-cingulate-gyrus-on-FA-skeleton-right_2_0': 0}) +field_id_25187_mean_mo_in_cingulum_cingulate_gyrus_on_fa_skeleton_left_2_0 = TensorMap('25187_Mean-MO-in-cingulum-cingulate-gyrus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25187_Mean-MO-in-cingulum-cingulate-gyrus-on-FA-skeleton-left_2_0': 0}) +field_id_25188_mean_mo_in_cingulum_hippocampus_on_fa_skeleton_right_2_0 = TensorMap('25188_Mean-MO-in-cingulum-hippocampus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25188_Mean-MO-in-cingulum-hippocampus-on-FA-skeleton-right_2_0': 0}) +field_id_25189_mean_mo_in_cingulum_hippocampus_on_fa_skeleton_left_2_0 = TensorMap('25189_Mean-MO-in-cingulum-hippocampus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25189_Mean-MO-in-cingulum-hippocampus-on-FA-skeleton-left_2_0': 0}) +field_id_25190_mean_mo_in_fornix_cresstria_terminalis_on_fa_skeleton_right_2_0 = TensorMap('25190_Mean-MO-in-fornix-cresstria-terminalis-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25190_Mean-MO-in-fornix-cresstria-terminalis-on-FA-skeleton-right_2_0': 0}) +field_id_25191_mean_mo_in_fornix_cresstria_terminalis_on_fa_skeleton_left_2_0 = TensorMap('25191_Mean-MO-in-fornix-cresstria-terminalis-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25191_Mean-MO-in-fornix-cresstria-terminalis-on-FA-skeleton-left_2_0': 0}) +field_id_25192_mean_mo_in_superior_longitudinal_fasciculus_on_fa_skeleton_right_2_0 = TensorMap('25192_Mean-MO-in-superior-longitudinal-fasciculus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25192_Mean-MO-in-superior-longitudinal-fasciculus-on-FA-skeleton-right_2_0': 0}) +field_id_25193_mean_mo_in_superior_longitudinal_fasciculus_on_fa_skeleton_left_2_0 = TensorMap('25193_Mean-MO-in-superior-longitudinal-fasciculus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25193_Mean-MO-in-superior-longitudinal-fasciculus-on-FA-skeleton-left_2_0': 0}) +field_id_25194_mean_mo_in_superior_frontooccipital_fasciculus_on_fa_skeleton_right_2_0 = TensorMap('25194_Mean-MO-in-superior-frontooccipital-fasciculus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25194_Mean-MO-in-superior-frontooccipital-fasciculus-on-FA-skeleton-right_2_0': 0}) +field_id_25195_mean_mo_in_superior_frontooccipital_fasciculus_on_fa_skeleton_left_2_0 = TensorMap('25195_Mean-MO-in-superior-frontooccipital-fasciculus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25195_Mean-MO-in-superior-frontooccipital-fasciculus-on-FA-skeleton-left_2_0': 0}) +field_id_25196_mean_mo_in_uncinate_fasciculus_on_fa_skeleton_right_2_0 = TensorMap('25196_Mean-MO-in-uncinate-fasciculus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25196_Mean-MO-in-uncinate-fasciculus-on-FA-skeleton-right_2_0': 0}) +field_id_25197_mean_mo_in_uncinate_fasciculus_on_fa_skeleton_left_2_0 = TensorMap('25197_Mean-MO-in-uncinate-fasciculus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25197_Mean-MO-in-uncinate-fasciculus-on-FA-skeleton-left_2_0': 0}) +field_id_25198_mean_mo_in_tapetum_on_fa_skeleton_right_2_0 = TensorMap('25198_Mean-MO-in-tapetum-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25198_Mean-MO-in-tapetum-on-FA-skeleton-right_2_0': 0}) +field_id_25199_mean_mo_in_tapetum_on_fa_skeleton_left_2_0 = TensorMap('25199_Mean-MO-in-tapetum-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25199_Mean-MO-in-tapetum-on-FA-skeleton-left_2_0': 0}) +field_id_25200_mean_l1_in_middle_cerebellar_peduncle_on_fa_skeleton_2_0 = TensorMap('25200_Mean-L1-in-middle-cerebellar-peduncle-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25200_Mean-L1-in-middle-cerebellar-peduncle-on-FA-skeleton_2_0': 0}) +field_id_25201_mean_l1_in_pontine_crossing_tract_on_fa_skeleton_2_0 = TensorMap('25201_Mean-L1-in-pontine-crossing-tract-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25201_Mean-L1-in-pontine-crossing-tract-on-FA-skeleton_2_0': 0}) +field_id_25202_mean_l1_in_genu_of_corpus_callosum_on_fa_skeleton_2_0 = TensorMap('25202_Mean-L1-in-genu-of-corpus-callosum-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25202_Mean-L1-in-genu-of-corpus-callosum-on-FA-skeleton_2_0': 0}) +field_id_25203_mean_l1_in_body_of_corpus_callosum_on_fa_skeleton_2_0 = TensorMap('25203_Mean-L1-in-body-of-corpus-callosum-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25203_Mean-L1-in-body-of-corpus-callosum-on-FA-skeleton_2_0': 0}) +field_id_25204_mean_l1_in_splenium_of_corpus_callosum_on_fa_skeleton_2_0 = TensorMap('25204_Mean-L1-in-splenium-of-corpus-callosum-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25204_Mean-L1-in-splenium-of-corpus-callosum-on-FA-skeleton_2_0': 0}) +field_id_25205_mean_l1_in_fornix_on_fa_skeleton_2_0 = TensorMap('25205_Mean-L1-in-fornix-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25205_Mean-L1-in-fornix-on-FA-skeleton_2_0': 0}) +field_id_25206_mean_l1_in_corticospinal_tract_on_fa_skeleton_right_2_0 = TensorMap('25206_Mean-L1-in-corticospinal-tract-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25206_Mean-L1-in-corticospinal-tract-on-FA-skeleton-right_2_0': 0}) +field_id_25207_mean_l1_in_corticospinal_tract_on_fa_skeleton_left_2_0 = TensorMap('25207_Mean-L1-in-corticospinal-tract-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25207_Mean-L1-in-corticospinal-tract-on-FA-skeleton-left_2_0': 0}) +field_id_25208_mean_l1_in_medial_lemniscus_on_fa_skeleton_right_2_0 = TensorMap('25208_Mean-L1-in-medial-lemniscus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25208_Mean-L1-in-medial-lemniscus-on-FA-skeleton-right_2_0': 0}) +field_id_25209_mean_l1_in_medial_lemniscus_on_fa_skeleton_left_2_0 = TensorMap('25209_Mean-L1-in-medial-lemniscus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25209_Mean-L1-in-medial-lemniscus-on-FA-skeleton-left_2_0': 0}) +field_id_25210_mean_l1_in_inferior_cerebellar_peduncle_on_fa_skeleton_right_2_0 = TensorMap('25210_Mean-L1-in-inferior-cerebellar-peduncle-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25210_Mean-L1-in-inferior-cerebellar-peduncle-on-FA-skeleton-right_2_0': 0}) +field_id_25211_mean_l1_in_inferior_cerebellar_peduncle_on_fa_skeleton_left_2_0 = TensorMap('25211_Mean-L1-in-inferior-cerebellar-peduncle-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25211_Mean-L1-in-inferior-cerebellar-peduncle-on-FA-skeleton-left_2_0': 0}) +field_id_25212_mean_l1_in_superior_cerebellar_peduncle_on_fa_skeleton_right_2_0 = TensorMap('25212_Mean-L1-in-superior-cerebellar-peduncle-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25212_Mean-L1-in-superior-cerebellar-peduncle-on-FA-skeleton-right_2_0': 0}) +field_id_25213_mean_l1_in_superior_cerebellar_peduncle_on_fa_skeleton_left_2_0 = TensorMap('25213_Mean-L1-in-superior-cerebellar-peduncle-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25213_Mean-L1-in-superior-cerebellar-peduncle-on-FA-skeleton-left_2_0': 0}) +field_id_25214_mean_l1_in_cerebral_peduncle_on_fa_skeleton_right_2_0 = TensorMap('25214_Mean-L1-in-cerebral-peduncle-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25214_Mean-L1-in-cerebral-peduncle-on-FA-skeleton-right_2_0': 0}) +field_id_25215_mean_l1_in_cerebral_peduncle_on_fa_skeleton_left_2_0 = TensorMap('25215_Mean-L1-in-cerebral-peduncle-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25215_Mean-L1-in-cerebral-peduncle-on-FA-skeleton-left_2_0': 0}) +field_id_25216_mean_l1_in_anterior_limb_of_internal_capsule_on_fa_skeleton_right_2_0 = TensorMap('25216_Mean-L1-in-anterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25216_Mean-L1-in-anterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25217_mean_l1_in_anterior_limb_of_internal_capsule_on_fa_skeleton_left_2_0 = TensorMap('25217_Mean-L1-in-anterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25217_Mean-L1-in-anterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25218_mean_l1_in_posterior_limb_of_internal_capsule_on_fa_skeleton_right_2_0 = TensorMap('25218_Mean-L1-in-posterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25218_Mean-L1-in-posterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25219_mean_l1_in_posterior_limb_of_internal_capsule_on_fa_skeleton_left_2_0 = TensorMap('25219_Mean-L1-in-posterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25219_Mean-L1-in-posterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25220_mean_l1_in_retrolenticular_part_of_internal_capsule_on_fa_skeleton_right_2_0 = TensorMap('25220_Mean-L1-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25220_Mean-L1-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25221_mean_l1_in_retrolenticular_part_of_internal_capsule_on_fa_skeleton_left_2_0 = TensorMap('25221_Mean-L1-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25221_Mean-L1-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25222_mean_l1_in_anterior_corona_radiata_on_fa_skeleton_right_2_0 = TensorMap('25222_Mean-L1-in-anterior-corona-radiata-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25222_Mean-L1-in-anterior-corona-radiata-on-FA-skeleton-right_2_0': 0}) +field_id_25223_mean_l1_in_anterior_corona_radiata_on_fa_skeleton_left_2_0 = TensorMap('25223_Mean-L1-in-anterior-corona-radiata-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25223_Mean-L1-in-anterior-corona-radiata-on-FA-skeleton-left_2_0': 0}) +field_id_25224_mean_l1_in_superior_corona_radiata_on_fa_skeleton_right_2_0 = TensorMap('25224_Mean-L1-in-superior-corona-radiata-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25224_Mean-L1-in-superior-corona-radiata-on-FA-skeleton-right_2_0': 0}) +field_id_25225_mean_l1_in_superior_corona_radiata_on_fa_skeleton_left_2_0 = TensorMap('25225_Mean-L1-in-superior-corona-radiata-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25225_Mean-L1-in-superior-corona-radiata-on-FA-skeleton-left_2_0': 0}) +field_id_25226_mean_l1_in_posterior_corona_radiata_on_fa_skeleton_right_2_0 = TensorMap('25226_Mean-L1-in-posterior-corona-radiata-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25226_Mean-L1-in-posterior-corona-radiata-on-FA-skeleton-right_2_0': 0}) +field_id_25227_mean_l1_in_posterior_corona_radiata_on_fa_skeleton_left_2_0 = TensorMap('25227_Mean-L1-in-posterior-corona-radiata-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25227_Mean-L1-in-posterior-corona-radiata-on-FA-skeleton-left_2_0': 0}) +field_id_25228_mean_l1_in_posterior_thalamic_radiation_on_fa_skeleton_right_2_0 = TensorMap('25228_Mean-L1-in-posterior-thalamic-radiation-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25228_Mean-L1-in-posterior-thalamic-radiation-on-FA-skeleton-right_2_0': 0}) +field_id_25229_mean_l1_in_posterior_thalamic_radiation_on_fa_skeleton_left_2_0 = TensorMap('25229_Mean-L1-in-posterior-thalamic-radiation-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25229_Mean-L1-in-posterior-thalamic-radiation-on-FA-skeleton-left_2_0': 0}) +field_id_25230_mean_l1_in_sagittal_stratum_on_fa_skeleton_right_2_0 = TensorMap('25230_Mean-L1-in-sagittal-stratum-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25230_Mean-L1-in-sagittal-stratum-on-FA-skeleton-right_2_0': 0}) +field_id_25231_mean_l1_in_sagittal_stratum_on_fa_skeleton_left_2_0 = TensorMap('25231_Mean-L1-in-sagittal-stratum-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25231_Mean-L1-in-sagittal-stratum-on-FA-skeleton-left_2_0': 0}) +field_id_25232_mean_l1_in_external_capsule_on_fa_skeleton_right_2_0 = TensorMap('25232_Mean-L1-in-external-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25232_Mean-L1-in-external-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25233_mean_l1_in_external_capsule_on_fa_skeleton_left_2_0 = TensorMap('25233_Mean-L1-in-external-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25233_Mean-L1-in-external-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25234_mean_l1_in_cingulum_cingulate_gyrus_on_fa_skeleton_right_2_0 = TensorMap('25234_Mean-L1-in-cingulum-cingulate-gyrus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25234_Mean-L1-in-cingulum-cingulate-gyrus-on-FA-skeleton-right_2_0': 0}) +field_id_25235_mean_l1_in_cingulum_cingulate_gyrus_on_fa_skeleton_left_2_0 = TensorMap('25235_Mean-L1-in-cingulum-cingulate-gyrus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25235_Mean-L1-in-cingulum-cingulate-gyrus-on-FA-skeleton-left_2_0': 0}) +field_id_25236_mean_l1_in_cingulum_hippocampus_on_fa_skeleton_right_2_0 = TensorMap('25236_Mean-L1-in-cingulum-hippocampus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25236_Mean-L1-in-cingulum-hippocampus-on-FA-skeleton-right_2_0': 0}) +field_id_25237_mean_l1_in_cingulum_hippocampus_on_fa_skeleton_left_2_0 = TensorMap('25237_Mean-L1-in-cingulum-hippocampus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25237_Mean-L1-in-cingulum-hippocampus-on-FA-skeleton-left_2_0': 0}) +field_id_25238_mean_l1_in_fornix_cresstria_terminalis_on_fa_skeleton_right_2_0 = TensorMap('25238_Mean-L1-in-fornix-cresstria-terminalis-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25238_Mean-L1-in-fornix-cresstria-terminalis-on-FA-skeleton-right_2_0': 0}) +field_id_25239_mean_l1_in_fornix_cresstria_terminalis_on_fa_skeleton_left_2_0 = TensorMap('25239_Mean-L1-in-fornix-cresstria-terminalis-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25239_Mean-L1-in-fornix-cresstria-terminalis-on-FA-skeleton-left_2_0': 0}) +field_id_25240_mean_l1_in_superior_longitudinal_fasciculus_on_fa_skeleton_right_2_0 = TensorMap('25240_Mean-L1-in-superior-longitudinal-fasciculus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25240_Mean-L1-in-superior-longitudinal-fasciculus-on-FA-skeleton-right_2_0': 0}) +field_id_25241_mean_l1_in_superior_longitudinal_fasciculus_on_fa_skeleton_left_2_0 = TensorMap('25241_Mean-L1-in-superior-longitudinal-fasciculus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25241_Mean-L1-in-superior-longitudinal-fasciculus-on-FA-skeleton-left_2_0': 0}) +field_id_25242_mean_l1_in_superior_frontooccipital_fasciculus_on_fa_skeleton_right_2_0 = TensorMap('25242_Mean-L1-in-superior-frontooccipital-fasciculus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25242_Mean-L1-in-superior-frontooccipital-fasciculus-on-FA-skeleton-right_2_0': 0}) +field_id_25243_mean_l1_in_superior_frontooccipital_fasciculus_on_fa_skeleton_left_2_0 = TensorMap('25243_Mean-L1-in-superior-frontooccipital-fasciculus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25243_Mean-L1-in-superior-frontooccipital-fasciculus-on-FA-skeleton-left_2_0': 0}) +field_id_25244_mean_l1_in_uncinate_fasciculus_on_fa_skeleton_right_2_0 = TensorMap('25244_Mean-L1-in-uncinate-fasciculus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25244_Mean-L1-in-uncinate-fasciculus-on-FA-skeleton-right_2_0': 0}) +field_id_25245_mean_l1_in_uncinate_fasciculus_on_fa_skeleton_left_2_0 = TensorMap('25245_Mean-L1-in-uncinate-fasciculus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25245_Mean-L1-in-uncinate-fasciculus-on-FA-skeleton-left_2_0': 0}) +field_id_25246_mean_l1_in_tapetum_on_fa_skeleton_right_2_0 = TensorMap('25246_Mean-L1-in-tapetum-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25246_Mean-L1-in-tapetum-on-FA-skeleton-right_2_0': 0}) +field_id_25247_mean_l1_in_tapetum_on_fa_skeleton_left_2_0 = TensorMap('25247_Mean-L1-in-tapetum-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25247_Mean-L1-in-tapetum-on-FA-skeleton-left_2_0': 0}) +field_id_25248_mean_l2_in_middle_cerebellar_peduncle_on_fa_skeleton_2_0 = TensorMap('25248_Mean-L2-in-middle-cerebellar-peduncle-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25248_Mean-L2-in-middle-cerebellar-peduncle-on-FA-skeleton_2_0': 0}) +field_id_25249_mean_l2_in_pontine_crossing_tract_on_fa_skeleton_2_0 = TensorMap('25249_Mean-L2-in-pontine-crossing-tract-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25249_Mean-L2-in-pontine-crossing-tract-on-FA-skeleton_2_0': 0}) +field_id_25250_mean_l2_in_genu_of_corpus_callosum_on_fa_skeleton_2_0 = TensorMap('25250_Mean-L2-in-genu-of-corpus-callosum-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25250_Mean-L2-in-genu-of-corpus-callosum-on-FA-skeleton_2_0': 0}) +field_id_25251_mean_l2_in_body_of_corpus_callosum_on_fa_skeleton_2_0 = TensorMap('25251_Mean-L2-in-body-of-corpus-callosum-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25251_Mean-L2-in-body-of-corpus-callosum-on-FA-skeleton_2_0': 0}) +field_id_25252_mean_l2_in_splenium_of_corpus_callosum_on_fa_skeleton_2_0 = TensorMap('25252_Mean-L2-in-splenium-of-corpus-callosum-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25252_Mean-L2-in-splenium-of-corpus-callosum-on-FA-skeleton_2_0': 0}) +field_id_25253_mean_l2_in_fornix_on_fa_skeleton_2_0 = TensorMap('25253_Mean-L2-in-fornix-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25253_Mean-L2-in-fornix-on-FA-skeleton_2_0': 0}) +field_id_25254_mean_l2_in_corticospinal_tract_on_fa_skeleton_right_2_0 = TensorMap('25254_Mean-L2-in-corticospinal-tract-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25254_Mean-L2-in-corticospinal-tract-on-FA-skeleton-right_2_0': 0}) +field_id_25255_mean_l2_in_corticospinal_tract_on_fa_skeleton_left_2_0 = TensorMap('25255_Mean-L2-in-corticospinal-tract-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25255_Mean-L2-in-corticospinal-tract-on-FA-skeleton-left_2_0': 0}) +field_id_25256_mean_l2_in_medial_lemniscus_on_fa_skeleton_right_2_0 = TensorMap('25256_Mean-L2-in-medial-lemniscus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25256_Mean-L2-in-medial-lemniscus-on-FA-skeleton-right_2_0': 0}) +field_id_25257_mean_l2_in_medial_lemniscus_on_fa_skeleton_left_2_0 = TensorMap('25257_Mean-L2-in-medial-lemniscus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25257_Mean-L2-in-medial-lemniscus-on-FA-skeleton-left_2_0': 0}) +field_id_25258_mean_l2_in_inferior_cerebellar_peduncle_on_fa_skeleton_right_2_0 = TensorMap('25258_Mean-L2-in-inferior-cerebellar-peduncle-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25258_Mean-L2-in-inferior-cerebellar-peduncle-on-FA-skeleton-right_2_0': 0}) +field_id_25259_mean_l2_in_inferior_cerebellar_peduncle_on_fa_skeleton_left_2_0 = TensorMap('25259_Mean-L2-in-inferior-cerebellar-peduncle-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25259_Mean-L2-in-inferior-cerebellar-peduncle-on-FA-skeleton-left_2_0': 0}) +field_id_25260_mean_l2_in_superior_cerebellar_peduncle_on_fa_skeleton_right_2_0 = TensorMap('25260_Mean-L2-in-superior-cerebellar-peduncle-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25260_Mean-L2-in-superior-cerebellar-peduncle-on-FA-skeleton-right_2_0': 0}) +field_id_25261_mean_l2_in_superior_cerebellar_peduncle_on_fa_skeleton_left_2_0 = TensorMap('25261_Mean-L2-in-superior-cerebellar-peduncle-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25261_Mean-L2-in-superior-cerebellar-peduncle-on-FA-skeleton-left_2_0': 0}) +field_id_25262_mean_l2_in_cerebral_peduncle_on_fa_skeleton_right_2_0 = TensorMap('25262_Mean-L2-in-cerebral-peduncle-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25262_Mean-L2-in-cerebral-peduncle-on-FA-skeleton-right_2_0': 0}) +field_id_25263_mean_l2_in_cerebral_peduncle_on_fa_skeleton_left_2_0 = TensorMap('25263_Mean-L2-in-cerebral-peduncle-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25263_Mean-L2-in-cerebral-peduncle-on-FA-skeleton-left_2_0': 0}) +field_id_25264_mean_l2_in_anterior_limb_of_internal_capsule_on_fa_skeleton_right_2_0 = TensorMap('25264_Mean-L2-in-anterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25264_Mean-L2-in-anterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25265_mean_l2_in_anterior_limb_of_internal_capsule_on_fa_skeleton_left_2_0 = TensorMap('25265_Mean-L2-in-anterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25265_Mean-L2-in-anterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25266_mean_l2_in_posterior_limb_of_internal_capsule_on_fa_skeleton_right_2_0 = TensorMap('25266_Mean-L2-in-posterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25266_Mean-L2-in-posterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25267_mean_l2_in_posterior_limb_of_internal_capsule_on_fa_skeleton_left_2_0 = TensorMap('25267_Mean-L2-in-posterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25267_Mean-L2-in-posterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25268_mean_l2_in_retrolenticular_part_of_internal_capsule_on_fa_skeleton_right_2_0 = TensorMap('25268_Mean-L2-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25268_Mean-L2-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25269_mean_l2_in_retrolenticular_part_of_internal_capsule_on_fa_skeleton_left_2_0 = TensorMap('25269_Mean-L2-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25269_Mean-L2-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25270_mean_l2_in_anterior_corona_radiata_on_fa_skeleton_right_2_0 = TensorMap('25270_Mean-L2-in-anterior-corona-radiata-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25270_Mean-L2-in-anterior-corona-radiata-on-FA-skeleton-right_2_0': 0}) +field_id_25271_mean_l2_in_anterior_corona_radiata_on_fa_skeleton_left_2_0 = TensorMap('25271_Mean-L2-in-anterior-corona-radiata-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25271_Mean-L2-in-anterior-corona-radiata-on-FA-skeleton-left_2_0': 0}) +field_id_25272_mean_l2_in_superior_corona_radiata_on_fa_skeleton_right_2_0 = TensorMap('25272_Mean-L2-in-superior-corona-radiata-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25272_Mean-L2-in-superior-corona-radiata-on-FA-skeleton-right_2_0': 0}) +field_id_25273_mean_l2_in_superior_corona_radiata_on_fa_skeleton_left_2_0 = TensorMap('25273_Mean-L2-in-superior-corona-radiata-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25273_Mean-L2-in-superior-corona-radiata-on-FA-skeleton-left_2_0': 0}) +field_id_25274_mean_l2_in_posterior_corona_radiata_on_fa_skeleton_right_2_0 = TensorMap('25274_Mean-L2-in-posterior-corona-radiata-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25274_Mean-L2-in-posterior-corona-radiata-on-FA-skeleton-right_2_0': 0}) +field_id_25275_mean_l2_in_posterior_corona_radiata_on_fa_skeleton_left_2_0 = TensorMap('25275_Mean-L2-in-posterior-corona-radiata-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25275_Mean-L2-in-posterior-corona-radiata-on-FA-skeleton-left_2_0': 0}) +field_id_25276_mean_l2_in_posterior_thalamic_radiation_on_fa_skeleton_right_2_0 = TensorMap('25276_Mean-L2-in-posterior-thalamic-radiation-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25276_Mean-L2-in-posterior-thalamic-radiation-on-FA-skeleton-right_2_0': 0}) +field_id_25277_mean_l2_in_posterior_thalamic_radiation_on_fa_skeleton_left_2_0 = TensorMap('25277_Mean-L2-in-posterior-thalamic-radiation-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25277_Mean-L2-in-posterior-thalamic-radiation-on-FA-skeleton-left_2_0': 0}) +field_id_25278_mean_l2_in_sagittal_stratum_on_fa_skeleton_right_2_0 = TensorMap('25278_Mean-L2-in-sagittal-stratum-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25278_Mean-L2-in-sagittal-stratum-on-FA-skeleton-right_2_0': 0}) +field_id_25279_mean_l2_in_sagittal_stratum_on_fa_skeleton_left_2_0 = TensorMap('25279_Mean-L2-in-sagittal-stratum-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25279_Mean-L2-in-sagittal-stratum-on-FA-skeleton-left_2_0': 0}) +field_id_25280_mean_l2_in_external_capsule_on_fa_skeleton_right_2_0 = TensorMap('25280_Mean-L2-in-external-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25280_Mean-L2-in-external-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25281_mean_l2_in_external_capsule_on_fa_skeleton_left_2_0 = TensorMap('25281_Mean-L2-in-external-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25281_Mean-L2-in-external-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25282_mean_l2_in_cingulum_cingulate_gyrus_on_fa_skeleton_right_2_0 = TensorMap('25282_Mean-L2-in-cingulum-cingulate-gyrus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25282_Mean-L2-in-cingulum-cingulate-gyrus-on-FA-skeleton-right_2_0': 0}) +field_id_25283_mean_l2_in_cingulum_cingulate_gyrus_on_fa_skeleton_left_2_0 = TensorMap('25283_Mean-L2-in-cingulum-cingulate-gyrus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25283_Mean-L2-in-cingulum-cingulate-gyrus-on-FA-skeleton-left_2_0': 0}) +field_id_25284_mean_l2_in_cingulum_hippocampus_on_fa_skeleton_right_2_0 = TensorMap('25284_Mean-L2-in-cingulum-hippocampus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25284_Mean-L2-in-cingulum-hippocampus-on-FA-skeleton-right_2_0': 0}) +field_id_25285_mean_l2_in_cingulum_hippocampus_on_fa_skeleton_left_2_0 = TensorMap('25285_Mean-L2-in-cingulum-hippocampus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25285_Mean-L2-in-cingulum-hippocampus-on-FA-skeleton-left_2_0': 0}) +field_id_25286_mean_l2_in_fornix_cresstria_terminalis_on_fa_skeleton_right_2_0 = TensorMap('25286_Mean-L2-in-fornix-cresstria-terminalis-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25286_Mean-L2-in-fornix-cresstria-terminalis-on-FA-skeleton-right_2_0': 0}) +field_id_25287_mean_l2_in_fornix_cresstria_terminalis_on_fa_skeleton_left_2_0 = TensorMap('25287_Mean-L2-in-fornix-cresstria-terminalis-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25287_Mean-L2-in-fornix-cresstria-terminalis-on-FA-skeleton-left_2_0': 0}) +field_id_25288_mean_l2_in_superior_longitudinal_fasciculus_on_fa_skeleton_right_2_0 = TensorMap('25288_Mean-L2-in-superior-longitudinal-fasciculus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25288_Mean-L2-in-superior-longitudinal-fasciculus-on-FA-skeleton-right_2_0': 0}) +field_id_25289_mean_l2_in_superior_longitudinal_fasciculus_on_fa_skeleton_left_2_0 = TensorMap('25289_Mean-L2-in-superior-longitudinal-fasciculus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25289_Mean-L2-in-superior-longitudinal-fasciculus-on-FA-skeleton-left_2_0': 0}) +field_id_25290_mean_l2_in_superior_frontooccipital_fasciculus_on_fa_skeleton_right_2_0 = TensorMap('25290_Mean-L2-in-superior-frontooccipital-fasciculus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25290_Mean-L2-in-superior-frontooccipital-fasciculus-on-FA-skeleton-right_2_0': 0}) +field_id_25291_mean_l2_in_superior_frontooccipital_fasciculus_on_fa_skeleton_left_2_0 = TensorMap('25291_Mean-L2-in-superior-frontooccipital-fasciculus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25291_Mean-L2-in-superior-frontooccipital-fasciculus-on-FA-skeleton-left_2_0': 0}) +field_id_25292_mean_l2_in_uncinate_fasciculus_on_fa_skeleton_right_2_0 = TensorMap('25292_Mean-L2-in-uncinate-fasciculus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25292_Mean-L2-in-uncinate-fasciculus-on-FA-skeleton-right_2_0': 0}) +field_id_25293_mean_l2_in_uncinate_fasciculus_on_fa_skeleton_left_2_0 = TensorMap('25293_Mean-L2-in-uncinate-fasciculus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25293_Mean-L2-in-uncinate-fasciculus-on-FA-skeleton-left_2_0': 0}) +field_id_25294_mean_l2_in_tapetum_on_fa_skeleton_right_2_0 = TensorMap('25294_Mean-L2-in-tapetum-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25294_Mean-L2-in-tapetum-on-FA-skeleton-right_2_0': 0}) +field_id_25295_mean_l2_in_tapetum_on_fa_skeleton_left_2_0 = TensorMap('25295_Mean-L2-in-tapetum-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25295_Mean-L2-in-tapetum-on-FA-skeleton-left_2_0': 0}) +field_id_25296_mean_l3_in_middle_cerebellar_peduncle_on_fa_skeleton_2_0 = TensorMap('25296_Mean-L3-in-middle-cerebellar-peduncle-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25296_Mean-L3-in-middle-cerebellar-peduncle-on-FA-skeleton_2_0': 0}) +field_id_25297_mean_l3_in_pontine_crossing_tract_on_fa_skeleton_2_0 = TensorMap('25297_Mean-L3-in-pontine-crossing-tract-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25297_Mean-L3-in-pontine-crossing-tract-on-FA-skeleton_2_0': 0}) +field_id_25298_mean_l3_in_genu_of_corpus_callosum_on_fa_skeleton_2_0 = TensorMap('25298_Mean-L3-in-genu-of-corpus-callosum-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25298_Mean-L3-in-genu-of-corpus-callosum-on-FA-skeleton_2_0': 0}) +field_id_25299_mean_l3_in_body_of_corpus_callosum_on_fa_skeleton_2_0 = TensorMap('25299_Mean-L3-in-body-of-corpus-callosum-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25299_Mean-L3-in-body-of-corpus-callosum-on-FA-skeleton_2_0': 0}) +field_id_25300_mean_l3_in_splenium_of_corpus_callosum_on_fa_skeleton_2_0 = TensorMap('25300_Mean-L3-in-splenium-of-corpus-callosum-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25300_Mean-L3-in-splenium-of-corpus-callosum-on-FA-skeleton_2_0': 0}) +field_id_25301_mean_l3_in_fornix_on_fa_skeleton_2_0 = TensorMap('25301_Mean-L3-in-fornix-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25301_Mean-L3-in-fornix-on-FA-skeleton_2_0': 0}) +field_id_25302_mean_l3_in_corticospinal_tract_on_fa_skeleton_right_2_0 = TensorMap('25302_Mean-L3-in-corticospinal-tract-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25302_Mean-L3-in-corticospinal-tract-on-FA-skeleton-right_2_0': 0}) +field_id_25303_mean_l3_in_corticospinal_tract_on_fa_skeleton_left_2_0 = TensorMap('25303_Mean-L3-in-corticospinal-tract-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25303_Mean-L3-in-corticospinal-tract-on-FA-skeleton-left_2_0': 0}) +field_id_25304_mean_l3_in_medial_lemniscus_on_fa_skeleton_right_2_0 = TensorMap('25304_Mean-L3-in-medial-lemniscus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25304_Mean-L3-in-medial-lemniscus-on-FA-skeleton-right_2_0': 0}) +field_id_25305_mean_l3_in_medial_lemniscus_on_fa_skeleton_left_2_0 = TensorMap('25305_Mean-L3-in-medial-lemniscus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25305_Mean-L3-in-medial-lemniscus-on-FA-skeleton-left_2_0': 0}) +field_id_25306_mean_l3_in_inferior_cerebellar_peduncle_on_fa_skeleton_right_2_0 = TensorMap('25306_Mean-L3-in-inferior-cerebellar-peduncle-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25306_Mean-L3-in-inferior-cerebellar-peduncle-on-FA-skeleton-right_2_0': 0}) +field_id_25307_mean_l3_in_inferior_cerebellar_peduncle_on_fa_skeleton_left_2_0 = TensorMap('25307_Mean-L3-in-inferior-cerebellar-peduncle-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25307_Mean-L3-in-inferior-cerebellar-peduncle-on-FA-skeleton-left_2_0': 0}) +field_id_25308_mean_l3_in_superior_cerebellar_peduncle_on_fa_skeleton_right_2_0 = TensorMap('25308_Mean-L3-in-superior-cerebellar-peduncle-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25308_Mean-L3-in-superior-cerebellar-peduncle-on-FA-skeleton-right_2_0': 0}) +field_id_25309_mean_l3_in_superior_cerebellar_peduncle_on_fa_skeleton_left_2_0 = TensorMap('25309_Mean-L3-in-superior-cerebellar-peduncle-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25309_Mean-L3-in-superior-cerebellar-peduncle-on-FA-skeleton-left_2_0': 0}) +field_id_25310_mean_l3_in_cerebral_peduncle_on_fa_skeleton_right_2_0 = TensorMap('25310_Mean-L3-in-cerebral-peduncle-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25310_Mean-L3-in-cerebral-peduncle-on-FA-skeleton-right_2_0': 0}) +field_id_25311_mean_l3_in_cerebral_peduncle_on_fa_skeleton_left_2_0 = TensorMap('25311_Mean-L3-in-cerebral-peduncle-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25311_Mean-L3-in-cerebral-peduncle-on-FA-skeleton-left_2_0': 0}) +field_id_25312_mean_l3_in_anterior_limb_of_internal_capsule_on_fa_skeleton_right_2_0 = TensorMap('25312_Mean-L3-in-anterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25312_Mean-L3-in-anterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25313_mean_l3_in_anterior_limb_of_internal_capsule_on_fa_skeleton_left_2_0 = TensorMap('25313_Mean-L3-in-anterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25313_Mean-L3-in-anterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25314_mean_l3_in_posterior_limb_of_internal_capsule_on_fa_skeleton_right_2_0 = TensorMap('25314_Mean-L3-in-posterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25314_Mean-L3-in-posterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25315_mean_l3_in_posterior_limb_of_internal_capsule_on_fa_skeleton_left_2_0 = TensorMap('25315_Mean-L3-in-posterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25315_Mean-L3-in-posterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25316_mean_l3_in_retrolenticular_part_of_internal_capsule_on_fa_skeleton_right_2_0 = TensorMap('25316_Mean-L3-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25316_Mean-L3-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25317_mean_l3_in_retrolenticular_part_of_internal_capsule_on_fa_skeleton_left_2_0 = TensorMap('25317_Mean-L3-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25317_Mean-L3-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25318_mean_l3_in_anterior_corona_radiata_on_fa_skeleton_right_2_0 = TensorMap('25318_Mean-L3-in-anterior-corona-radiata-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25318_Mean-L3-in-anterior-corona-radiata-on-FA-skeleton-right_2_0': 0}) +field_id_25319_mean_l3_in_anterior_corona_radiata_on_fa_skeleton_left_2_0 = TensorMap('25319_Mean-L3-in-anterior-corona-radiata-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25319_Mean-L3-in-anterior-corona-radiata-on-FA-skeleton-left_2_0': 0}) +field_id_25320_mean_l3_in_superior_corona_radiata_on_fa_skeleton_right_2_0 = TensorMap('25320_Mean-L3-in-superior-corona-radiata-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25320_Mean-L3-in-superior-corona-radiata-on-FA-skeleton-right_2_0': 0}) +field_id_25321_mean_l3_in_superior_corona_radiata_on_fa_skeleton_left_2_0 = TensorMap('25321_Mean-L3-in-superior-corona-radiata-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25321_Mean-L3-in-superior-corona-radiata-on-FA-skeleton-left_2_0': 0}) +field_id_25322_mean_l3_in_posterior_corona_radiata_on_fa_skeleton_right_2_0 = TensorMap('25322_Mean-L3-in-posterior-corona-radiata-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25322_Mean-L3-in-posterior-corona-radiata-on-FA-skeleton-right_2_0': 0}) +field_id_25323_mean_l3_in_posterior_corona_radiata_on_fa_skeleton_left_2_0 = TensorMap('25323_Mean-L3-in-posterior-corona-radiata-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25323_Mean-L3-in-posterior-corona-radiata-on-FA-skeleton-left_2_0': 0}) +field_id_25324_mean_l3_in_posterior_thalamic_radiation_on_fa_skeleton_right_2_0 = TensorMap('25324_Mean-L3-in-posterior-thalamic-radiation-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25324_Mean-L3-in-posterior-thalamic-radiation-on-FA-skeleton-right_2_0': 0}) +field_id_25325_mean_l3_in_posterior_thalamic_radiation_on_fa_skeleton_left_2_0 = TensorMap('25325_Mean-L3-in-posterior-thalamic-radiation-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25325_Mean-L3-in-posterior-thalamic-radiation-on-FA-skeleton-left_2_0': 0}) +field_id_25326_mean_l3_in_sagittal_stratum_on_fa_skeleton_right_2_0 = TensorMap('25326_Mean-L3-in-sagittal-stratum-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25326_Mean-L3-in-sagittal-stratum-on-FA-skeleton-right_2_0': 0}) +field_id_25327_mean_l3_in_sagittal_stratum_on_fa_skeleton_left_2_0 = TensorMap('25327_Mean-L3-in-sagittal-stratum-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25327_Mean-L3-in-sagittal-stratum-on-FA-skeleton-left_2_0': 0}) +field_id_25328_mean_l3_in_external_capsule_on_fa_skeleton_right_2_0 = TensorMap('25328_Mean-L3-in-external-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25328_Mean-L3-in-external-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25329_mean_l3_in_external_capsule_on_fa_skeleton_left_2_0 = TensorMap('25329_Mean-L3-in-external-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25329_Mean-L3-in-external-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25330_mean_l3_in_cingulum_cingulate_gyrus_on_fa_skeleton_right_2_0 = TensorMap('25330_Mean-L3-in-cingulum-cingulate-gyrus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25330_Mean-L3-in-cingulum-cingulate-gyrus-on-FA-skeleton-right_2_0': 0}) +field_id_25331_mean_l3_in_cingulum_cingulate_gyrus_on_fa_skeleton_left_2_0 = TensorMap('25331_Mean-L3-in-cingulum-cingulate-gyrus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25331_Mean-L3-in-cingulum-cingulate-gyrus-on-FA-skeleton-left_2_0': 0}) +field_id_25332_mean_l3_in_cingulum_hippocampus_on_fa_skeleton_right_2_0 = TensorMap('25332_Mean-L3-in-cingulum-hippocampus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25332_Mean-L3-in-cingulum-hippocampus-on-FA-skeleton-right_2_0': 0}) +field_id_25333_mean_l3_in_cingulum_hippocampus_on_fa_skeleton_left_2_0 = TensorMap('25333_Mean-L3-in-cingulum-hippocampus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25333_Mean-L3-in-cingulum-hippocampus-on-FA-skeleton-left_2_0': 0}) +field_id_25334_mean_l3_in_fornix_cresstria_terminalis_on_fa_skeleton_right_2_0 = TensorMap('25334_Mean-L3-in-fornix-cresstria-terminalis-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25334_Mean-L3-in-fornix-cresstria-terminalis-on-FA-skeleton-right_2_0': 0}) +field_id_25335_mean_l3_in_fornix_cresstria_terminalis_on_fa_skeleton_left_2_0 = TensorMap('25335_Mean-L3-in-fornix-cresstria-terminalis-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25335_Mean-L3-in-fornix-cresstria-terminalis-on-FA-skeleton-left_2_0': 0}) +field_id_25336_mean_l3_in_superior_longitudinal_fasciculus_on_fa_skeleton_right_2_0 = TensorMap('25336_Mean-L3-in-superior-longitudinal-fasciculus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25336_Mean-L3-in-superior-longitudinal-fasciculus-on-FA-skeleton-right_2_0': 0}) +field_id_25337_mean_l3_in_superior_longitudinal_fasciculus_on_fa_skeleton_left_2_0 = TensorMap('25337_Mean-L3-in-superior-longitudinal-fasciculus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25337_Mean-L3-in-superior-longitudinal-fasciculus-on-FA-skeleton-left_2_0': 0}) +field_id_25338_mean_l3_in_superior_frontooccipital_fasciculus_on_fa_skeleton_right_2_0 = TensorMap('25338_Mean-L3-in-superior-frontooccipital-fasciculus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25338_Mean-L3-in-superior-frontooccipital-fasciculus-on-FA-skeleton-right_2_0': 0}) +field_id_25339_mean_l3_in_superior_frontooccipital_fasciculus_on_fa_skeleton_left_2_0 = TensorMap('25339_Mean-L3-in-superior-frontooccipital-fasciculus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25339_Mean-L3-in-superior-frontooccipital-fasciculus-on-FA-skeleton-left_2_0': 0}) +field_id_25340_mean_l3_in_uncinate_fasciculus_on_fa_skeleton_right_2_0 = TensorMap('25340_Mean-L3-in-uncinate-fasciculus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25340_Mean-L3-in-uncinate-fasciculus-on-FA-skeleton-right_2_0': 0}) +field_id_25341_mean_l3_in_uncinate_fasciculus_on_fa_skeleton_left_2_0 = TensorMap('25341_Mean-L3-in-uncinate-fasciculus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25341_Mean-L3-in-uncinate-fasciculus-on-FA-skeleton-left_2_0': 0}) +field_id_25342_mean_l3_in_tapetum_on_fa_skeleton_right_2_0 = TensorMap('25342_Mean-L3-in-tapetum-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25342_Mean-L3-in-tapetum-on-FA-skeleton-right_2_0': 0}) +field_id_25343_mean_l3_in_tapetum_on_fa_skeleton_left_2_0 = TensorMap('25343_Mean-L3-in-tapetum-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25343_Mean-L3-in-tapetum-on-FA-skeleton-left_2_0': 0}) +field_id_25344_mean_icvf_in_middle_cerebellar_peduncle_on_fa_skeleton_2_0 = TensorMap('25344_Mean-ICVF-in-middle-cerebellar-peduncle-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25344_Mean-ICVF-in-middle-cerebellar-peduncle-on-FA-skeleton_2_0': 0}) +field_id_25345_mean_icvf_in_pontine_crossing_tract_on_fa_skeleton_2_0 = TensorMap('25345_Mean-ICVF-in-pontine-crossing-tract-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25345_Mean-ICVF-in-pontine-crossing-tract-on-FA-skeleton_2_0': 0}) +field_id_25346_mean_icvf_in_genu_of_corpus_callosum_on_fa_skeleton_2_0 = TensorMap('25346_Mean-ICVF-in-genu-of-corpus-callosum-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25346_Mean-ICVF-in-genu-of-corpus-callosum-on-FA-skeleton_2_0': 0}) +field_id_25347_mean_icvf_in_body_of_corpus_callosum_on_fa_skeleton_2_0 = TensorMap('25347_Mean-ICVF-in-body-of-corpus-callosum-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25347_Mean-ICVF-in-body-of-corpus-callosum-on-FA-skeleton_2_0': 0}) +field_id_25348_mean_icvf_in_splenium_of_corpus_callosum_on_fa_skeleton_2_0 = TensorMap('25348_Mean-ICVF-in-splenium-of-corpus-callosum-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25348_Mean-ICVF-in-splenium-of-corpus-callosum-on-FA-skeleton_2_0': 0}) +field_id_25349_mean_icvf_in_fornix_on_fa_skeleton_2_0 = TensorMap('25349_Mean-ICVF-in-fornix-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25349_Mean-ICVF-in-fornix-on-FA-skeleton_2_0': 0}) +field_id_25350_mean_icvf_in_corticospinal_tract_on_fa_skeleton_right_2_0 = TensorMap('25350_Mean-ICVF-in-corticospinal-tract-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25350_Mean-ICVF-in-corticospinal-tract-on-FA-skeleton-right_2_0': 0}) +field_id_25351_mean_icvf_in_corticospinal_tract_on_fa_skeleton_left_2_0 = TensorMap('25351_Mean-ICVF-in-corticospinal-tract-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25351_Mean-ICVF-in-corticospinal-tract-on-FA-skeleton-left_2_0': 0}) +field_id_25352_mean_icvf_in_medial_lemniscus_on_fa_skeleton_right_2_0 = TensorMap('25352_Mean-ICVF-in-medial-lemniscus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25352_Mean-ICVF-in-medial-lemniscus-on-FA-skeleton-right_2_0': 0}) +field_id_25353_mean_icvf_in_medial_lemniscus_on_fa_skeleton_left_2_0 = TensorMap('25353_Mean-ICVF-in-medial-lemniscus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25353_Mean-ICVF-in-medial-lemniscus-on-FA-skeleton-left_2_0': 0}) +field_id_25354_mean_icvf_in_inferior_cerebellar_peduncle_on_fa_skeleton_right_2_0 = TensorMap('25354_Mean-ICVF-in-inferior-cerebellar-peduncle-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25354_Mean-ICVF-in-inferior-cerebellar-peduncle-on-FA-skeleton-right_2_0': 0}) +field_id_25355_mean_icvf_in_inferior_cerebellar_peduncle_on_fa_skeleton_left_2_0 = TensorMap('25355_Mean-ICVF-in-inferior-cerebellar-peduncle-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25355_Mean-ICVF-in-inferior-cerebellar-peduncle-on-FA-skeleton-left_2_0': 0}) +field_id_25356_mean_icvf_in_superior_cerebellar_peduncle_on_fa_skeleton_right_2_0 = TensorMap('25356_Mean-ICVF-in-superior-cerebellar-peduncle-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25356_Mean-ICVF-in-superior-cerebellar-peduncle-on-FA-skeleton-right_2_0': 0}) +field_id_25357_mean_icvf_in_superior_cerebellar_peduncle_on_fa_skeleton_left_2_0 = TensorMap('25357_Mean-ICVF-in-superior-cerebellar-peduncle-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25357_Mean-ICVF-in-superior-cerebellar-peduncle-on-FA-skeleton-left_2_0': 0}) +field_id_25358_mean_icvf_in_cerebral_peduncle_on_fa_skeleton_right_2_0 = TensorMap('25358_Mean-ICVF-in-cerebral-peduncle-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25358_Mean-ICVF-in-cerebral-peduncle-on-FA-skeleton-right_2_0': 0}) +field_id_25359_mean_icvf_in_cerebral_peduncle_on_fa_skeleton_left_2_0 = TensorMap('25359_Mean-ICVF-in-cerebral-peduncle-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25359_Mean-ICVF-in-cerebral-peduncle-on-FA-skeleton-left_2_0': 0}) +field_id_25360_mean_icvf_in_anterior_limb_of_internal_capsule_on_fa_skeleton_right_2_0 = TensorMap('25360_Mean-ICVF-in-anterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25360_Mean-ICVF-in-anterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25361_mean_icvf_in_anterior_limb_of_internal_capsule_on_fa_skeleton_left_2_0 = TensorMap('25361_Mean-ICVF-in-anterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25361_Mean-ICVF-in-anterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25362_mean_icvf_in_posterior_limb_of_internal_capsule_on_fa_skeleton_right_2_0 = TensorMap('25362_Mean-ICVF-in-posterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25362_Mean-ICVF-in-posterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25363_mean_icvf_in_posterior_limb_of_internal_capsule_on_fa_skeleton_left_2_0 = TensorMap('25363_Mean-ICVF-in-posterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25363_Mean-ICVF-in-posterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25364_mean_icvf_in_retrolenticular_part_of_internal_capsule_on_fa_skeleton_right_2_0 = TensorMap('25364_Mean-ICVF-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25364_Mean-ICVF-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25365_mean_icvf_in_retrolenticular_part_of_internal_capsule_on_fa_skeleton_left_2_0 = TensorMap('25365_Mean-ICVF-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25365_Mean-ICVF-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25366_mean_icvf_in_anterior_corona_radiata_on_fa_skeleton_right_2_0 = TensorMap('25366_Mean-ICVF-in-anterior-corona-radiata-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25366_Mean-ICVF-in-anterior-corona-radiata-on-FA-skeleton-right_2_0': 0}) +field_id_25367_mean_icvf_in_anterior_corona_radiata_on_fa_skeleton_left_2_0 = TensorMap('25367_Mean-ICVF-in-anterior-corona-radiata-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25367_Mean-ICVF-in-anterior-corona-radiata-on-FA-skeleton-left_2_0': 0}) +field_id_25368_mean_icvf_in_superior_corona_radiata_on_fa_skeleton_right_2_0 = TensorMap('25368_Mean-ICVF-in-superior-corona-radiata-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25368_Mean-ICVF-in-superior-corona-radiata-on-FA-skeleton-right_2_0': 0}) +field_id_25369_mean_icvf_in_superior_corona_radiata_on_fa_skeleton_left_2_0 = TensorMap('25369_Mean-ICVF-in-superior-corona-radiata-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25369_Mean-ICVF-in-superior-corona-radiata-on-FA-skeleton-left_2_0': 0}) +field_id_25370_mean_icvf_in_posterior_corona_radiata_on_fa_skeleton_right_2_0 = TensorMap('25370_Mean-ICVF-in-posterior-corona-radiata-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25370_Mean-ICVF-in-posterior-corona-radiata-on-FA-skeleton-right_2_0': 0}) +field_id_25371_mean_icvf_in_posterior_corona_radiata_on_fa_skeleton_left_2_0 = TensorMap('25371_Mean-ICVF-in-posterior-corona-radiata-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25371_Mean-ICVF-in-posterior-corona-radiata-on-FA-skeleton-left_2_0': 0}) +field_id_25372_mean_icvf_in_posterior_thalamic_radiation_on_fa_skeleton_right_2_0 = TensorMap('25372_Mean-ICVF-in-posterior-thalamic-radiation-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25372_Mean-ICVF-in-posterior-thalamic-radiation-on-FA-skeleton-right_2_0': 0}) +field_id_25373_mean_icvf_in_posterior_thalamic_radiation_on_fa_skeleton_left_2_0 = TensorMap('25373_Mean-ICVF-in-posterior-thalamic-radiation-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25373_Mean-ICVF-in-posterior-thalamic-radiation-on-FA-skeleton-left_2_0': 0}) +field_id_25374_mean_icvf_in_sagittal_stratum_on_fa_skeleton_right_2_0 = TensorMap('25374_Mean-ICVF-in-sagittal-stratum-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25374_Mean-ICVF-in-sagittal-stratum-on-FA-skeleton-right_2_0': 0}) +field_id_25375_mean_icvf_in_sagittal_stratum_on_fa_skeleton_left_2_0 = TensorMap('25375_Mean-ICVF-in-sagittal-stratum-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25375_Mean-ICVF-in-sagittal-stratum-on-FA-skeleton-left_2_0': 0}) +field_id_25376_mean_icvf_in_external_capsule_on_fa_skeleton_right_2_0 = TensorMap('25376_Mean-ICVF-in-external-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25376_Mean-ICVF-in-external-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25377_mean_icvf_in_external_capsule_on_fa_skeleton_left_2_0 = TensorMap('25377_Mean-ICVF-in-external-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25377_Mean-ICVF-in-external-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25378_mean_icvf_in_cingulum_cingulate_gyrus_on_fa_skeleton_right_2_0 = TensorMap('25378_Mean-ICVF-in-cingulum-cingulate-gyrus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25378_Mean-ICVF-in-cingulum-cingulate-gyrus-on-FA-skeleton-right_2_0': 0}) +field_id_25379_mean_icvf_in_cingulum_cingulate_gyrus_on_fa_skeleton_left_2_0 = TensorMap('25379_Mean-ICVF-in-cingulum-cingulate-gyrus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25379_Mean-ICVF-in-cingulum-cingulate-gyrus-on-FA-skeleton-left_2_0': 0}) +field_id_25380_mean_icvf_in_cingulum_hippocampus_on_fa_skeleton_right_2_0 = TensorMap('25380_Mean-ICVF-in-cingulum-hippocampus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25380_Mean-ICVF-in-cingulum-hippocampus-on-FA-skeleton-right_2_0': 0}) +field_id_25381_mean_icvf_in_cingulum_hippocampus_on_fa_skeleton_left_2_0 = TensorMap('25381_Mean-ICVF-in-cingulum-hippocampus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25381_Mean-ICVF-in-cingulum-hippocampus-on-FA-skeleton-left_2_0': 0}) +field_id_25382_mean_icvf_in_fornix_cresstria_terminalis_on_fa_skeleton_right_2_0 = TensorMap('25382_Mean-ICVF-in-fornix-cresstria-terminalis-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25382_Mean-ICVF-in-fornix-cresstria-terminalis-on-FA-skeleton-right_2_0': 0}) +field_id_25383_mean_icvf_in_fornix_cresstria_terminalis_on_fa_skeleton_left_2_0 = TensorMap('25383_Mean-ICVF-in-fornix-cresstria-terminalis-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25383_Mean-ICVF-in-fornix-cresstria-terminalis-on-FA-skeleton-left_2_0': 0}) +field_id_25384_mean_icvf_in_superior_longitudinal_fasciculus_on_fa_skeleton_right_2_0 = TensorMap('25384_Mean-ICVF-in-superior-longitudinal-fasciculus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25384_Mean-ICVF-in-superior-longitudinal-fasciculus-on-FA-skeleton-right_2_0': 0}) +field_id_25385_mean_icvf_in_superior_longitudinal_fasciculus_on_fa_skeleton_left_2_0 = TensorMap('25385_Mean-ICVF-in-superior-longitudinal-fasciculus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25385_Mean-ICVF-in-superior-longitudinal-fasciculus-on-FA-skeleton-left_2_0': 0}) +field_id_25386_mean_icvf_in_superior_frontooccipital_fasciculus_on_fa_skeleton_right_2_0 = TensorMap('25386_Mean-ICVF-in-superior-frontooccipital-fasciculus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25386_Mean-ICVF-in-superior-frontooccipital-fasciculus-on-FA-skeleton-right_2_0': 0}) +field_id_25387_mean_icvf_in_superior_frontooccipital_fasciculus_on_fa_skeleton_left_2_0 = TensorMap('25387_Mean-ICVF-in-superior-frontooccipital-fasciculus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25387_Mean-ICVF-in-superior-frontooccipital-fasciculus-on-FA-skeleton-left_2_0': 0}) +field_id_25388_mean_icvf_in_uncinate_fasciculus_on_fa_skeleton_right_2_0 = TensorMap('25388_Mean-ICVF-in-uncinate-fasciculus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25388_Mean-ICVF-in-uncinate-fasciculus-on-FA-skeleton-right_2_0': 0}) +field_id_25389_mean_icvf_in_uncinate_fasciculus_on_fa_skeleton_left_2_0 = TensorMap('25389_Mean-ICVF-in-uncinate-fasciculus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25389_Mean-ICVF-in-uncinate-fasciculus-on-FA-skeleton-left_2_0': 0}) +field_id_25390_mean_icvf_in_tapetum_on_fa_skeleton_right_2_0 = TensorMap('25390_Mean-ICVF-in-tapetum-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25390_Mean-ICVF-in-tapetum-on-FA-skeleton-right_2_0': 0}) +field_id_25391_mean_icvf_in_tapetum_on_fa_skeleton_left_2_0 = TensorMap('25391_Mean-ICVF-in-tapetum-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25391_Mean-ICVF-in-tapetum-on-FA-skeleton-left_2_0': 0}) +field_id_25392_mean_od_in_middle_cerebellar_peduncle_on_fa_skeleton_2_0 = TensorMap('25392_Mean-OD-in-middle-cerebellar-peduncle-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25392_Mean-OD-in-middle-cerebellar-peduncle-on-FA-skeleton_2_0': 0}) +field_id_25393_mean_od_in_pontine_crossing_tract_on_fa_skeleton_2_0 = TensorMap('25393_Mean-OD-in-pontine-crossing-tract-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25393_Mean-OD-in-pontine-crossing-tract-on-FA-skeleton_2_0': 0}) +field_id_25394_mean_od_in_genu_of_corpus_callosum_on_fa_skeleton_2_0 = TensorMap('25394_Mean-OD-in-genu-of-corpus-callosum-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25394_Mean-OD-in-genu-of-corpus-callosum-on-FA-skeleton_2_0': 0}) +field_id_25395_mean_od_in_body_of_corpus_callosum_on_fa_skeleton_2_0 = TensorMap('25395_Mean-OD-in-body-of-corpus-callosum-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25395_Mean-OD-in-body-of-corpus-callosum-on-FA-skeleton_2_0': 0}) +field_id_25396_mean_od_in_splenium_of_corpus_callosum_on_fa_skeleton_2_0 = TensorMap('25396_Mean-OD-in-splenium-of-corpus-callosum-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25396_Mean-OD-in-splenium-of-corpus-callosum-on-FA-skeleton_2_0': 0}) +field_id_25397_mean_od_in_fornix_on_fa_skeleton_2_0 = TensorMap('25397_Mean-OD-in-fornix-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25397_Mean-OD-in-fornix-on-FA-skeleton_2_0': 0}) +field_id_25398_mean_od_in_corticospinal_tract_on_fa_skeleton_right_2_0 = TensorMap('25398_Mean-OD-in-corticospinal-tract-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25398_Mean-OD-in-corticospinal-tract-on-FA-skeleton-right_2_0': 0}) +field_id_25399_mean_od_in_corticospinal_tract_on_fa_skeleton_left_2_0 = TensorMap('25399_Mean-OD-in-corticospinal-tract-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25399_Mean-OD-in-corticospinal-tract-on-FA-skeleton-left_2_0': 0}) +field_id_25400_mean_od_in_medial_lemniscus_on_fa_skeleton_right_2_0 = TensorMap('25400_Mean-OD-in-medial-lemniscus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25400_Mean-OD-in-medial-lemniscus-on-FA-skeleton-right_2_0': 0}) +field_id_25401_mean_od_in_medial_lemniscus_on_fa_skeleton_left_2_0 = TensorMap('25401_Mean-OD-in-medial-lemniscus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25401_Mean-OD-in-medial-lemniscus-on-FA-skeleton-left_2_0': 0}) +field_id_25402_mean_od_in_inferior_cerebellar_peduncle_on_fa_skeleton_right_2_0 = TensorMap('25402_Mean-OD-in-inferior-cerebellar-peduncle-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25402_Mean-OD-in-inferior-cerebellar-peduncle-on-FA-skeleton-right_2_0': 0}) +field_id_25403_mean_od_in_inferior_cerebellar_peduncle_on_fa_skeleton_left_2_0 = TensorMap('25403_Mean-OD-in-inferior-cerebellar-peduncle-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25403_Mean-OD-in-inferior-cerebellar-peduncle-on-FA-skeleton-left_2_0': 0}) +field_id_25404_mean_od_in_superior_cerebellar_peduncle_on_fa_skeleton_right_2_0 = TensorMap('25404_Mean-OD-in-superior-cerebellar-peduncle-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25404_Mean-OD-in-superior-cerebellar-peduncle-on-FA-skeleton-right_2_0': 0}) +field_id_25405_mean_od_in_superior_cerebellar_peduncle_on_fa_skeleton_left_2_0 = TensorMap('25405_Mean-OD-in-superior-cerebellar-peduncle-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25405_Mean-OD-in-superior-cerebellar-peduncle-on-FA-skeleton-left_2_0': 0}) +field_id_25406_mean_od_in_cerebral_peduncle_on_fa_skeleton_right_2_0 = TensorMap('25406_Mean-OD-in-cerebral-peduncle-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25406_Mean-OD-in-cerebral-peduncle-on-FA-skeleton-right_2_0': 0}) +field_id_25407_mean_od_in_cerebral_peduncle_on_fa_skeleton_left_2_0 = TensorMap('25407_Mean-OD-in-cerebral-peduncle-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25407_Mean-OD-in-cerebral-peduncle-on-FA-skeleton-left_2_0': 0}) +field_id_25408_mean_od_in_anterior_limb_of_internal_capsule_on_fa_skeleton_right_2_0 = TensorMap('25408_Mean-OD-in-anterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25408_Mean-OD-in-anterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25409_mean_od_in_anterior_limb_of_internal_capsule_on_fa_skeleton_left_2_0 = TensorMap('25409_Mean-OD-in-anterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25409_Mean-OD-in-anterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25410_mean_od_in_posterior_limb_of_internal_capsule_on_fa_skeleton_right_2_0 = TensorMap('25410_Mean-OD-in-posterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25410_Mean-OD-in-posterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25411_mean_od_in_posterior_limb_of_internal_capsule_on_fa_skeleton_left_2_0 = TensorMap('25411_Mean-OD-in-posterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25411_Mean-OD-in-posterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25412_mean_od_in_retrolenticular_part_of_internal_capsule_on_fa_skeleton_right_2_0 = TensorMap('25412_Mean-OD-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25412_Mean-OD-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25413_mean_od_in_retrolenticular_part_of_internal_capsule_on_fa_skeleton_left_2_0 = TensorMap('25413_Mean-OD-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25413_Mean-OD-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25414_mean_od_in_anterior_corona_radiata_on_fa_skeleton_right_2_0 = TensorMap('25414_Mean-OD-in-anterior-corona-radiata-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25414_Mean-OD-in-anterior-corona-radiata-on-FA-skeleton-right_2_0': 0}) +field_id_25415_mean_od_in_anterior_corona_radiata_on_fa_skeleton_left_2_0 = TensorMap('25415_Mean-OD-in-anterior-corona-radiata-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25415_Mean-OD-in-anterior-corona-radiata-on-FA-skeleton-left_2_0': 0}) +field_id_25416_mean_od_in_superior_corona_radiata_on_fa_skeleton_right_2_0 = TensorMap('25416_Mean-OD-in-superior-corona-radiata-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25416_Mean-OD-in-superior-corona-radiata-on-FA-skeleton-right_2_0': 0}) +field_id_25417_mean_od_in_superior_corona_radiata_on_fa_skeleton_left_2_0 = TensorMap('25417_Mean-OD-in-superior-corona-radiata-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25417_Mean-OD-in-superior-corona-radiata-on-FA-skeleton-left_2_0': 0}) +field_id_25418_mean_od_in_posterior_corona_radiata_on_fa_skeleton_right_2_0 = TensorMap('25418_Mean-OD-in-posterior-corona-radiata-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25418_Mean-OD-in-posterior-corona-radiata-on-FA-skeleton-right_2_0': 0}) +field_id_25419_mean_od_in_posterior_corona_radiata_on_fa_skeleton_left_2_0 = TensorMap('25419_Mean-OD-in-posterior-corona-radiata-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25419_Mean-OD-in-posterior-corona-radiata-on-FA-skeleton-left_2_0': 0}) +field_id_25420_mean_od_in_posterior_thalamic_radiation_on_fa_skeleton_right_2_0 = TensorMap('25420_Mean-OD-in-posterior-thalamic-radiation-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25420_Mean-OD-in-posterior-thalamic-radiation-on-FA-skeleton-right_2_0': 0}) +field_id_25421_mean_od_in_posterior_thalamic_radiation_on_fa_skeleton_left_2_0 = TensorMap('25421_Mean-OD-in-posterior-thalamic-radiation-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25421_Mean-OD-in-posterior-thalamic-radiation-on-FA-skeleton-left_2_0': 0}) +field_id_25422_mean_od_in_sagittal_stratum_on_fa_skeleton_right_2_0 = TensorMap('25422_Mean-OD-in-sagittal-stratum-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25422_Mean-OD-in-sagittal-stratum-on-FA-skeleton-right_2_0': 0}) +field_id_25423_mean_od_in_sagittal_stratum_on_fa_skeleton_left_2_0 = TensorMap('25423_Mean-OD-in-sagittal-stratum-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25423_Mean-OD-in-sagittal-stratum-on-FA-skeleton-left_2_0': 0}) +field_id_25424_mean_od_in_external_capsule_on_fa_skeleton_right_2_0 = TensorMap('25424_Mean-OD-in-external-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25424_Mean-OD-in-external-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25425_mean_od_in_external_capsule_on_fa_skeleton_left_2_0 = TensorMap('25425_Mean-OD-in-external-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25425_Mean-OD-in-external-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25426_mean_od_in_cingulum_cingulate_gyrus_on_fa_skeleton_right_2_0 = TensorMap('25426_Mean-OD-in-cingulum-cingulate-gyrus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25426_Mean-OD-in-cingulum-cingulate-gyrus-on-FA-skeleton-right_2_0': 0}) +field_id_25427_mean_od_in_cingulum_cingulate_gyrus_on_fa_skeleton_left_2_0 = TensorMap('25427_Mean-OD-in-cingulum-cingulate-gyrus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25427_Mean-OD-in-cingulum-cingulate-gyrus-on-FA-skeleton-left_2_0': 0}) +field_id_25428_mean_od_in_cingulum_hippocampus_on_fa_skeleton_right_2_0 = TensorMap('25428_Mean-OD-in-cingulum-hippocampus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25428_Mean-OD-in-cingulum-hippocampus-on-FA-skeleton-right_2_0': 0}) +field_id_25429_mean_od_in_cingulum_hippocampus_on_fa_skeleton_left_2_0 = TensorMap('25429_Mean-OD-in-cingulum-hippocampus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25429_Mean-OD-in-cingulum-hippocampus-on-FA-skeleton-left_2_0': 0}) +field_id_25430_mean_od_in_fornix_cresstria_terminalis_on_fa_skeleton_right_2_0 = TensorMap('25430_Mean-OD-in-fornix-cresstria-terminalis-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25430_Mean-OD-in-fornix-cresstria-terminalis-on-FA-skeleton-right_2_0': 0}) +field_id_25431_mean_od_in_fornix_cresstria_terminalis_on_fa_skeleton_left_2_0 = TensorMap('25431_Mean-OD-in-fornix-cresstria-terminalis-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25431_Mean-OD-in-fornix-cresstria-terminalis-on-FA-skeleton-left_2_0': 0}) +field_id_25432_mean_od_in_superior_longitudinal_fasciculus_on_fa_skeleton_right_2_0 = TensorMap('25432_Mean-OD-in-superior-longitudinal-fasciculus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25432_Mean-OD-in-superior-longitudinal-fasciculus-on-FA-skeleton-right_2_0': 0}) +field_id_25433_mean_od_in_superior_longitudinal_fasciculus_on_fa_skeleton_left_2_0 = TensorMap('25433_Mean-OD-in-superior-longitudinal-fasciculus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25433_Mean-OD-in-superior-longitudinal-fasciculus-on-FA-skeleton-left_2_0': 0}) +field_id_25434_mean_od_in_superior_frontooccipital_fasciculus_on_fa_skeleton_right_2_0 = TensorMap('25434_Mean-OD-in-superior-frontooccipital-fasciculus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25434_Mean-OD-in-superior-frontooccipital-fasciculus-on-FA-skeleton-right_2_0': 0}) +field_id_25435_mean_od_in_superior_frontooccipital_fasciculus_on_fa_skeleton_left_2_0 = TensorMap('25435_Mean-OD-in-superior-frontooccipital-fasciculus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25435_Mean-OD-in-superior-frontooccipital-fasciculus-on-FA-skeleton-left_2_0': 0}) +field_id_25436_mean_od_in_uncinate_fasciculus_on_fa_skeleton_right_2_0 = TensorMap('25436_Mean-OD-in-uncinate-fasciculus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25436_Mean-OD-in-uncinate-fasciculus-on-FA-skeleton-right_2_0': 0}) +field_id_25437_mean_od_in_uncinate_fasciculus_on_fa_skeleton_left_2_0 = TensorMap('25437_Mean-OD-in-uncinate-fasciculus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25437_Mean-OD-in-uncinate-fasciculus-on-FA-skeleton-left_2_0': 0}) +field_id_25438_mean_od_in_tapetum_on_fa_skeleton_right_2_0 = TensorMap('25438_Mean-OD-in-tapetum-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25438_Mean-OD-in-tapetum-on-FA-skeleton-right_2_0': 0}) +field_id_25439_mean_od_in_tapetum_on_fa_skeleton_left_2_0 = TensorMap('25439_Mean-OD-in-tapetum-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25439_Mean-OD-in-tapetum-on-FA-skeleton-left_2_0': 0}) +field_id_25440_mean_isovf_in_middle_cerebellar_peduncle_on_fa_skeleton_2_0 = TensorMap('25440_Mean-ISOVF-in-middle-cerebellar-peduncle-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25440_Mean-ISOVF-in-middle-cerebellar-peduncle-on-FA-skeleton_2_0': 0}) +field_id_25441_mean_isovf_in_pontine_crossing_tract_on_fa_skeleton_2_0 = TensorMap('25441_Mean-ISOVF-in-pontine-crossing-tract-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25441_Mean-ISOVF-in-pontine-crossing-tract-on-FA-skeleton_2_0': 0}) +field_id_25442_mean_isovf_in_genu_of_corpus_callosum_on_fa_skeleton_2_0 = TensorMap('25442_Mean-ISOVF-in-genu-of-corpus-callosum-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25442_Mean-ISOVF-in-genu-of-corpus-callosum-on-FA-skeleton_2_0': 0}) +field_id_25443_mean_isovf_in_body_of_corpus_callosum_on_fa_skeleton_2_0 = TensorMap('25443_Mean-ISOVF-in-body-of-corpus-callosum-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25443_Mean-ISOVF-in-body-of-corpus-callosum-on-FA-skeleton_2_0': 0}) +field_id_25444_mean_isovf_in_splenium_of_corpus_callosum_on_fa_skeleton_2_0 = TensorMap('25444_Mean-ISOVF-in-splenium-of-corpus-callosum-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25444_Mean-ISOVF-in-splenium-of-corpus-callosum-on-FA-skeleton_2_0': 0}) +field_id_25445_mean_isovf_in_fornix_on_fa_skeleton_2_0 = TensorMap('25445_Mean-ISOVF-in-fornix-on-FA-skeleton_2_0', loss='logcosh', channel_map={'no_25445_Mean-ISOVF-in-fornix-on-FA-skeleton_2_0': 0}) +field_id_25446_mean_isovf_in_corticospinal_tract_on_fa_skeleton_right_2_0 = TensorMap('25446_Mean-ISOVF-in-corticospinal-tract-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25446_Mean-ISOVF-in-corticospinal-tract-on-FA-skeleton-right_2_0': 0}) +field_id_25447_mean_isovf_in_corticospinal_tract_on_fa_skeleton_left_2_0 = TensorMap('25447_Mean-ISOVF-in-corticospinal-tract-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25447_Mean-ISOVF-in-corticospinal-tract-on-FA-skeleton-left_2_0': 0}) +field_id_25448_mean_isovf_in_medial_lemniscus_on_fa_skeleton_right_2_0 = TensorMap('25448_Mean-ISOVF-in-medial-lemniscus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25448_Mean-ISOVF-in-medial-lemniscus-on-FA-skeleton-right_2_0': 0}) +field_id_25449_mean_isovf_in_medial_lemniscus_on_fa_skeleton_left_2_0 = TensorMap('25449_Mean-ISOVF-in-medial-lemniscus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25449_Mean-ISOVF-in-medial-lemniscus-on-FA-skeleton-left_2_0': 0}) +field_id_25450_mean_isovf_in_inferior_cerebellar_peduncle_on_fa_skeleton_right_2_0 = TensorMap('25450_Mean-ISOVF-in-inferior-cerebellar-peduncle-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25450_Mean-ISOVF-in-inferior-cerebellar-peduncle-on-FA-skeleton-right_2_0': 0}) +field_id_25451_mean_isovf_in_inferior_cerebellar_peduncle_on_fa_skeleton_left_2_0 = TensorMap('25451_Mean-ISOVF-in-inferior-cerebellar-peduncle-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25451_Mean-ISOVF-in-inferior-cerebellar-peduncle-on-FA-skeleton-left_2_0': 0}) +field_id_25452_mean_isovf_in_superior_cerebellar_peduncle_on_fa_skeleton_right_2_0 = TensorMap('25452_Mean-ISOVF-in-superior-cerebellar-peduncle-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25452_Mean-ISOVF-in-superior-cerebellar-peduncle-on-FA-skeleton-right_2_0': 0}) +field_id_25453_mean_isovf_in_superior_cerebellar_peduncle_on_fa_skeleton_left_2_0 = TensorMap('25453_Mean-ISOVF-in-superior-cerebellar-peduncle-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25453_Mean-ISOVF-in-superior-cerebellar-peduncle-on-FA-skeleton-left_2_0': 0}) +field_id_25454_mean_isovf_in_cerebral_peduncle_on_fa_skeleton_right_2_0 = TensorMap('25454_Mean-ISOVF-in-cerebral-peduncle-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25454_Mean-ISOVF-in-cerebral-peduncle-on-FA-skeleton-right_2_0': 0}) +field_id_25455_mean_isovf_in_cerebral_peduncle_on_fa_skeleton_left_2_0 = TensorMap('25455_Mean-ISOVF-in-cerebral-peduncle-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25455_Mean-ISOVF-in-cerebral-peduncle-on-FA-skeleton-left_2_0': 0}) +field_id_25456_mean_isovf_in_anterior_limb_of_internal_capsule_on_fa_skeleton_right_2_0 = TensorMap('25456_Mean-ISOVF-in-anterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25456_Mean-ISOVF-in-anterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25457_mean_isovf_in_anterior_limb_of_internal_capsule_on_fa_skeleton_left_2_0 = TensorMap('25457_Mean-ISOVF-in-anterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25457_Mean-ISOVF-in-anterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25458_mean_isovf_in_posterior_limb_of_internal_capsule_on_fa_skeleton_right_2_0 = TensorMap('25458_Mean-ISOVF-in-posterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25458_Mean-ISOVF-in-posterior-limb-of-internal-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25459_mean_isovf_in_posterior_limb_of_internal_capsule_on_fa_skeleton_left_2_0 = TensorMap('25459_Mean-ISOVF-in-posterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25459_Mean-ISOVF-in-posterior-limb-of-internal-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25460_mean_isovf_in_retrolenticular_part_of_internal_capsule_on_fa_skeleton_right_2_0 = TensorMap('25460_Mean-ISOVF-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25460_Mean-ISOVF-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25461_mean_isovf_in_retrolenticular_part_of_internal_capsule_on_fa_skeleton_left_2_0 = TensorMap('25461_Mean-ISOVF-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25461_Mean-ISOVF-in-retrolenticular-part-of-internal-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25462_mean_isovf_in_anterior_corona_radiata_on_fa_skeleton_right_2_0 = TensorMap('25462_Mean-ISOVF-in-anterior-corona-radiata-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25462_Mean-ISOVF-in-anterior-corona-radiata-on-FA-skeleton-right_2_0': 0}) +field_id_25463_mean_isovf_in_anterior_corona_radiata_on_fa_skeleton_left_2_0 = TensorMap('25463_Mean-ISOVF-in-anterior-corona-radiata-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25463_Mean-ISOVF-in-anterior-corona-radiata-on-FA-skeleton-left_2_0': 0}) +field_id_25464_mean_isovf_in_superior_corona_radiata_on_fa_skeleton_right_2_0 = TensorMap('25464_Mean-ISOVF-in-superior-corona-radiata-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25464_Mean-ISOVF-in-superior-corona-radiata-on-FA-skeleton-right_2_0': 0}) +field_id_25465_mean_isovf_in_superior_corona_radiata_on_fa_skeleton_left_2_0 = TensorMap('25465_Mean-ISOVF-in-superior-corona-radiata-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25465_Mean-ISOVF-in-superior-corona-radiata-on-FA-skeleton-left_2_0': 0}) +field_id_25466_mean_isovf_in_posterior_corona_radiata_on_fa_skeleton_right_2_0 = TensorMap('25466_Mean-ISOVF-in-posterior-corona-radiata-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25466_Mean-ISOVF-in-posterior-corona-radiata-on-FA-skeleton-right_2_0': 0}) +field_id_25467_mean_isovf_in_posterior_corona_radiata_on_fa_skeleton_left_2_0 = TensorMap('25467_Mean-ISOVF-in-posterior-corona-radiata-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25467_Mean-ISOVF-in-posterior-corona-radiata-on-FA-skeleton-left_2_0': 0}) +field_id_25468_mean_isovf_in_posterior_thalamic_radiation_on_fa_skeleton_right_2_0 = TensorMap('25468_Mean-ISOVF-in-posterior-thalamic-radiation-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25468_Mean-ISOVF-in-posterior-thalamic-radiation-on-FA-skeleton-right_2_0': 0}) +field_id_25469_mean_isovf_in_posterior_thalamic_radiation_on_fa_skeleton_left_2_0 = TensorMap('25469_Mean-ISOVF-in-posterior-thalamic-radiation-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25469_Mean-ISOVF-in-posterior-thalamic-radiation-on-FA-skeleton-left_2_0': 0}) +field_id_25470_mean_isovf_in_sagittal_stratum_on_fa_skeleton_right_2_0 = TensorMap('25470_Mean-ISOVF-in-sagittal-stratum-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25470_Mean-ISOVF-in-sagittal-stratum-on-FA-skeleton-right_2_0': 0}) +field_id_25471_mean_isovf_in_sagittal_stratum_on_fa_skeleton_left_2_0 = TensorMap('25471_Mean-ISOVF-in-sagittal-stratum-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25471_Mean-ISOVF-in-sagittal-stratum-on-FA-skeleton-left_2_0': 0}) +field_id_25472_mean_isovf_in_external_capsule_on_fa_skeleton_right_2_0 = TensorMap('25472_Mean-ISOVF-in-external-capsule-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25472_Mean-ISOVF-in-external-capsule-on-FA-skeleton-right_2_0': 0}) +field_id_25473_mean_isovf_in_external_capsule_on_fa_skeleton_left_2_0 = TensorMap('25473_Mean-ISOVF-in-external-capsule-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25473_Mean-ISOVF-in-external-capsule-on-FA-skeleton-left_2_0': 0}) +field_id_25474_mean_isovf_in_cingulum_cingulate_gyrus_on_fa_skeleton_right_2_0 = TensorMap('25474_Mean-ISOVF-in-cingulum-cingulate-gyrus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25474_Mean-ISOVF-in-cingulum-cingulate-gyrus-on-FA-skeleton-right_2_0': 0}) +field_id_25475_mean_isovf_in_cingulum_cingulate_gyrus_on_fa_skeleton_left_2_0 = TensorMap('25475_Mean-ISOVF-in-cingulum-cingulate-gyrus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25475_Mean-ISOVF-in-cingulum-cingulate-gyrus-on-FA-skeleton-left_2_0': 0}) +field_id_25476_mean_isovf_in_cingulum_hippocampus_on_fa_skeleton_right_2_0 = TensorMap('25476_Mean-ISOVF-in-cingulum-hippocampus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25476_Mean-ISOVF-in-cingulum-hippocampus-on-FA-skeleton-right_2_0': 0}) +field_id_25477_mean_isovf_in_cingulum_hippocampus_on_fa_skeleton_left_2_0 = TensorMap('25477_Mean-ISOVF-in-cingulum-hippocampus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25477_Mean-ISOVF-in-cingulum-hippocampus-on-FA-skeleton-left_2_0': 0}) +field_id_25478_mean_isovf_in_fornix_cresstria_terminalis_on_fa_skeleton_right_2_0 = TensorMap('25478_Mean-ISOVF-in-fornix-cresstria-terminalis-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25478_Mean-ISOVF-in-fornix-cresstria-terminalis-on-FA-skeleton-right_2_0': 0}) +field_id_25479_mean_isovf_in_fornix_cresstria_terminalis_on_fa_skeleton_left_2_0 = TensorMap('25479_Mean-ISOVF-in-fornix-cresstria-terminalis-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25479_Mean-ISOVF-in-fornix-cresstria-terminalis-on-FA-skeleton-left_2_0': 0}) +field_id_25480_mean_isovf_in_superior_longitudinal_fasciculus_on_fa_skeleton_right_2_0 = TensorMap('25480_Mean-ISOVF-in-superior-longitudinal-fasciculus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25480_Mean-ISOVF-in-superior-longitudinal-fasciculus-on-FA-skeleton-right_2_0': 0}) +field_id_25481_mean_isovf_in_superior_longitudinal_fasciculus_on_fa_skeleton_left_2_0 = TensorMap('25481_Mean-ISOVF-in-superior-longitudinal-fasciculus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25481_Mean-ISOVF-in-superior-longitudinal-fasciculus-on-FA-skeleton-left_2_0': 0}) +field_id_25482_mean_isovf_in_superior_frontooccipital_fasciculus_on_fa_skeleton_right_2_0 = TensorMap('25482_Mean-ISOVF-in-superior-frontooccipital-fasciculus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25482_Mean-ISOVF-in-superior-frontooccipital-fasciculus-on-FA-skeleton-right_2_0': 0}) +field_id_25483_mean_isovf_in_superior_frontooccipital_fasciculus_on_fa_skeleton_left_2_0 = TensorMap('25483_Mean-ISOVF-in-superior-frontooccipital-fasciculus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25483_Mean-ISOVF-in-superior-frontooccipital-fasciculus-on-FA-skeleton-left_2_0': 0}) +field_id_25484_mean_isovf_in_uncinate_fasciculus_on_fa_skeleton_right_2_0 = TensorMap('25484_Mean-ISOVF-in-uncinate-fasciculus-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25484_Mean-ISOVF-in-uncinate-fasciculus-on-FA-skeleton-right_2_0': 0}) +field_id_25485_mean_isovf_in_uncinate_fasciculus_on_fa_skeleton_left_2_0 = TensorMap('25485_Mean-ISOVF-in-uncinate-fasciculus-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25485_Mean-ISOVF-in-uncinate-fasciculus-on-FA-skeleton-left_2_0': 0}) +field_id_25486_mean_isovf_in_tapetum_on_fa_skeleton_right_2_0 = TensorMap('25486_Mean-ISOVF-in-tapetum-on-FA-skeleton-right_2_0', loss='logcosh', channel_map={'no_25486_Mean-ISOVF-in-tapetum-on-FA-skeleton-right_2_0': 0}) +field_id_25487_mean_isovf_in_tapetum_on_fa_skeleton_left_2_0 = TensorMap('25487_Mean-ISOVF-in-tapetum-on-FA-skeleton-left_2_0', loss='logcosh', channel_map={'no_25487_Mean-ISOVF-in-tapetum-on-FA-skeleton-left_2_0': 0}) +field_id_25488_weightedmean_fa_in_tract_acoustic_radiation_left_2_0 = TensorMap('25488_Weightedmean-FA-in-tract-acoustic-radiation-left_2_0', loss='logcosh', channel_map={'no_25488_Weightedmean-FA-in-tract-acoustic-radiation-left_2_0': 0}) +field_id_25489_weightedmean_fa_in_tract_acoustic_radiation_right_2_0 = TensorMap('25489_Weightedmean-FA-in-tract-acoustic-radiation-right_2_0', loss='logcosh', channel_map={'no_25489_Weightedmean-FA-in-tract-acoustic-radiation-right_2_0': 0}) +field_id_25490_weightedmean_fa_in_tract_anterior_thalamic_radiation_left_2_0 = TensorMap('25490_Weightedmean-FA-in-tract-anterior-thalamic-radiation-left_2_0', loss='logcosh', channel_map={'no_25490_Weightedmean-FA-in-tract-anterior-thalamic-radiation-left_2_0': 0}) +field_id_25491_weightedmean_fa_in_tract_anterior_thalamic_radiation_right_2_0 = TensorMap('25491_Weightedmean-FA-in-tract-anterior-thalamic-radiation-right_2_0', loss='logcosh', channel_map={'no_25491_Weightedmean-FA-in-tract-anterior-thalamic-radiation-right_2_0': 0}) +field_id_25492_weightedmean_fa_in_tract_cingulate_gyrus_part_of_cingulum_left_2_0 = TensorMap('25492_Weightedmean-FA-in-tract-cingulate-gyrus-part-of-cingulum-left_2_0', loss='logcosh', channel_map={'no_25492_Weightedmean-FA-in-tract-cingulate-gyrus-part-of-cingulum-left_2_0': 0}) +field_id_25493_weightedmean_fa_in_tract_cingulate_gyrus_part_of_cingulum_right_2_0 = TensorMap('25493_Weightedmean-FA-in-tract-cingulate-gyrus-part-of-cingulum-right_2_0', loss='logcosh', channel_map={'no_25493_Weightedmean-FA-in-tract-cingulate-gyrus-part-of-cingulum-right_2_0': 0}) +field_id_25494_weightedmean_fa_in_tract_parahippocampal_part_of_cingulum_left_2_0 = TensorMap('25494_Weightedmean-FA-in-tract-parahippocampal-part-of-cingulum-left_2_0', loss='logcosh', channel_map={'no_25494_Weightedmean-FA-in-tract-parahippocampal-part-of-cingulum-left_2_0': 0}) +field_id_25495_weightedmean_fa_in_tract_parahippocampal_part_of_cingulum_right_2_0 = TensorMap('25495_Weightedmean-FA-in-tract-parahippocampal-part-of-cingulum-right_2_0', loss='logcosh', channel_map={'no_25495_Weightedmean-FA-in-tract-parahippocampal-part-of-cingulum-right_2_0': 0}) +field_id_25496_weightedmean_fa_in_tract_corticospinal_tract_left_2_0 = TensorMap('25496_Weightedmean-FA-in-tract-corticospinal-tract-left_2_0', loss='logcosh', channel_map={'no_25496_Weightedmean-FA-in-tract-corticospinal-tract-left_2_0': 0}) +field_id_25497_weightedmean_fa_in_tract_corticospinal_tract_right_2_0 = TensorMap('25497_Weightedmean-FA-in-tract-corticospinal-tract-right_2_0', loss='logcosh', channel_map={'no_25497_Weightedmean-FA-in-tract-corticospinal-tract-right_2_0': 0}) +field_id_25498_weightedmean_fa_in_tract_forceps_major_2_0 = TensorMap('25498_Weightedmean-FA-in-tract-forceps-major_2_0', loss='logcosh', channel_map={'no_25498_Weightedmean-FA-in-tract-forceps-major_2_0': 0}) +field_id_25499_weightedmean_fa_in_tract_forceps_minor_2_0 = TensorMap('25499_Weightedmean-FA-in-tract-forceps-minor_2_0', loss='logcosh', channel_map={'no_25499_Weightedmean-FA-in-tract-forceps-minor_2_0': 0}) +field_id_25500_weightedmean_fa_in_tract_inferior_frontooccipital_fasciculus_left_2_0 = TensorMap('25500_Weightedmean-FA-in-tract-inferior-frontooccipital-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25500_Weightedmean-FA-in-tract-inferior-frontooccipital-fasciculus-left_2_0': 0}) +field_id_25501_weightedmean_fa_in_tract_inferior_frontooccipital_fasciculus_right_2_0 = TensorMap('25501_Weightedmean-FA-in-tract-inferior-frontooccipital-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25501_Weightedmean-FA-in-tract-inferior-frontooccipital-fasciculus-right_2_0': 0}) +field_id_25502_weightedmean_fa_in_tract_inferior_longitudinal_fasciculus_left_2_0 = TensorMap('25502_Weightedmean-FA-in-tract-inferior-longitudinal-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25502_Weightedmean-FA-in-tract-inferior-longitudinal-fasciculus-left_2_0': 0}) +field_id_25503_weightedmean_fa_in_tract_inferior_longitudinal_fasciculus_right_2_0 = TensorMap('25503_Weightedmean-FA-in-tract-inferior-longitudinal-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25503_Weightedmean-FA-in-tract-inferior-longitudinal-fasciculus-right_2_0': 0}) +field_id_25504_weightedmean_fa_in_tract_middle_cerebellar_peduncle_2_0 = TensorMap('25504_Weightedmean-FA-in-tract-middle-cerebellar-peduncle_2_0', loss='logcosh', channel_map={'no_25504_Weightedmean-FA-in-tract-middle-cerebellar-peduncle_2_0': 0}) +field_id_25505_weightedmean_fa_in_tract_medial_lemniscus_left_2_0 = TensorMap('25505_Weightedmean-FA-in-tract-medial-lemniscus-left_2_0', loss='logcosh', channel_map={'no_25505_Weightedmean-FA-in-tract-medial-lemniscus-left_2_0': 0}) +field_id_25506_weightedmean_fa_in_tract_medial_lemniscus_right_2_0 = TensorMap('25506_Weightedmean-FA-in-tract-medial-lemniscus-right_2_0', loss='logcosh', channel_map={'no_25506_Weightedmean-FA-in-tract-medial-lemniscus-right_2_0': 0}) +field_id_25507_weightedmean_fa_in_tract_posterior_thalamic_radiation_left_2_0 = TensorMap('25507_Weightedmean-FA-in-tract-posterior-thalamic-radiation-left_2_0', loss='logcosh', channel_map={'no_25507_Weightedmean-FA-in-tract-posterior-thalamic-radiation-left_2_0': 0}) +field_id_25508_weightedmean_fa_in_tract_posterior_thalamic_radiation_right_2_0 = TensorMap('25508_Weightedmean-FA-in-tract-posterior-thalamic-radiation-right_2_0', loss='logcosh', channel_map={'no_25508_Weightedmean-FA-in-tract-posterior-thalamic-radiation-right_2_0': 0}) +field_id_25509_weightedmean_fa_in_tract_superior_longitudinal_fasciculus_left_2_0 = TensorMap('25509_Weightedmean-FA-in-tract-superior-longitudinal-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25509_Weightedmean-FA-in-tract-superior-longitudinal-fasciculus-left_2_0': 0}) +field_id_25510_weightedmean_fa_in_tract_superior_longitudinal_fasciculus_right_2_0 = TensorMap('25510_Weightedmean-FA-in-tract-superior-longitudinal-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25510_Weightedmean-FA-in-tract-superior-longitudinal-fasciculus-right_2_0': 0}) +field_id_25511_weightedmean_fa_in_tract_superior_thalamic_radiation_left_2_0 = TensorMap('25511_Weightedmean-FA-in-tract-superior-thalamic-radiation-left_2_0', loss='logcosh', channel_map={'no_25511_Weightedmean-FA-in-tract-superior-thalamic-radiation-left_2_0': 0}) +field_id_25512_weightedmean_fa_in_tract_superior_thalamic_radiation_right_2_0 = TensorMap('25512_Weightedmean-FA-in-tract-superior-thalamic-radiation-right_2_0', loss='logcosh', channel_map={'no_25512_Weightedmean-FA-in-tract-superior-thalamic-radiation-right_2_0': 0}) +field_id_25513_weightedmean_fa_in_tract_uncinate_fasciculus_left_2_0 = TensorMap('25513_Weightedmean-FA-in-tract-uncinate-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25513_Weightedmean-FA-in-tract-uncinate-fasciculus-left_2_0': 0}) +field_id_25514_weightedmean_fa_in_tract_uncinate_fasciculus_right_2_0 = TensorMap('25514_Weightedmean-FA-in-tract-uncinate-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25514_Weightedmean-FA-in-tract-uncinate-fasciculus-right_2_0': 0}) +field_id_25515_weightedmean_md_in_tract_acoustic_radiation_left_2_0 = TensorMap('25515_Weightedmean-MD-in-tract-acoustic-radiation-left_2_0', loss='logcosh', channel_map={'no_25515_Weightedmean-MD-in-tract-acoustic-radiation-left_2_0': 0}) +field_id_25516_weightedmean_md_in_tract_acoustic_radiation_right_2_0 = TensorMap('25516_Weightedmean-MD-in-tract-acoustic-radiation-right_2_0', loss='logcosh', channel_map={'no_25516_Weightedmean-MD-in-tract-acoustic-radiation-right_2_0': 0}) +field_id_25517_weightedmean_md_in_tract_anterior_thalamic_radiation_left_2_0 = TensorMap('25517_Weightedmean-MD-in-tract-anterior-thalamic-radiation-left_2_0', loss='logcosh', channel_map={'no_25517_Weightedmean-MD-in-tract-anterior-thalamic-radiation-left_2_0': 0}) +field_id_25518_weightedmean_md_in_tract_anterior_thalamic_radiation_right_2_0 = TensorMap('25518_Weightedmean-MD-in-tract-anterior-thalamic-radiation-right_2_0', loss='logcosh', channel_map={'no_25518_Weightedmean-MD-in-tract-anterior-thalamic-radiation-right_2_0': 0}) +field_id_25519_weightedmean_md_in_tract_cingulate_gyrus_part_of_cingulum_left_2_0 = TensorMap('25519_Weightedmean-MD-in-tract-cingulate-gyrus-part-of-cingulum-left_2_0', loss='logcosh', channel_map={'no_25519_Weightedmean-MD-in-tract-cingulate-gyrus-part-of-cingulum-left_2_0': 0}) +field_id_25520_weightedmean_md_in_tract_cingulate_gyrus_part_of_cingulum_right_2_0 = TensorMap('25520_Weightedmean-MD-in-tract-cingulate-gyrus-part-of-cingulum-right_2_0', loss='logcosh', channel_map={'no_25520_Weightedmean-MD-in-tract-cingulate-gyrus-part-of-cingulum-right_2_0': 0}) +field_id_25521_weightedmean_md_in_tract_parahippocampal_part_of_cingulum_left_2_0 = TensorMap('25521_Weightedmean-MD-in-tract-parahippocampal-part-of-cingulum-left_2_0', loss='logcosh', channel_map={'no_25521_Weightedmean-MD-in-tract-parahippocampal-part-of-cingulum-left_2_0': 0}) +field_id_25522_weightedmean_md_in_tract_parahippocampal_part_of_cingulum_right_2_0 = TensorMap('25522_Weightedmean-MD-in-tract-parahippocampal-part-of-cingulum-right_2_0', loss='logcosh', channel_map={'no_25522_Weightedmean-MD-in-tract-parahippocampal-part-of-cingulum-right_2_0': 0}) +field_id_25523_weightedmean_md_in_tract_corticospinal_tract_left_2_0 = TensorMap('25523_Weightedmean-MD-in-tract-corticospinal-tract-left_2_0', loss='logcosh', channel_map={'no_25523_Weightedmean-MD-in-tract-corticospinal-tract-left_2_0': 0}) +field_id_25524_weightedmean_md_in_tract_corticospinal_tract_right_2_0 = TensorMap('25524_Weightedmean-MD-in-tract-corticospinal-tract-right_2_0', loss='logcosh', channel_map={'no_25524_Weightedmean-MD-in-tract-corticospinal-tract-right_2_0': 0}) +field_id_25525_weightedmean_md_in_tract_forceps_major_2_0 = TensorMap('25525_Weightedmean-MD-in-tract-forceps-major_2_0', loss='logcosh', channel_map={'no_25525_Weightedmean-MD-in-tract-forceps-major_2_0': 0}) +field_id_25526_weightedmean_md_in_tract_forceps_minor_2_0 = TensorMap('25526_Weightedmean-MD-in-tract-forceps-minor_2_0', loss='logcosh', channel_map={'no_25526_Weightedmean-MD-in-tract-forceps-minor_2_0': 0}) +field_id_25527_weightedmean_md_in_tract_inferior_frontooccipital_fasciculus_left_2_0 = TensorMap('25527_Weightedmean-MD-in-tract-inferior-frontooccipital-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25527_Weightedmean-MD-in-tract-inferior-frontooccipital-fasciculus-left_2_0': 0}) +field_id_25528_weightedmean_md_in_tract_inferior_frontooccipital_fasciculus_right_2_0 = TensorMap('25528_Weightedmean-MD-in-tract-inferior-frontooccipital-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25528_Weightedmean-MD-in-tract-inferior-frontooccipital-fasciculus-right_2_0': 0}) +field_id_25529_weightedmean_md_in_tract_inferior_longitudinal_fasciculus_left_2_0 = TensorMap('25529_Weightedmean-MD-in-tract-inferior-longitudinal-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25529_Weightedmean-MD-in-tract-inferior-longitudinal-fasciculus-left_2_0': 0}) +field_id_25530_weightedmean_md_in_tract_inferior_longitudinal_fasciculus_right_2_0 = TensorMap('25530_Weightedmean-MD-in-tract-inferior-longitudinal-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25530_Weightedmean-MD-in-tract-inferior-longitudinal-fasciculus-right_2_0': 0}) +field_id_25531_weightedmean_md_in_tract_middle_cerebellar_peduncle_2_0 = TensorMap('25531_Weightedmean-MD-in-tract-middle-cerebellar-peduncle_2_0', loss='logcosh', channel_map={'no_25531_Weightedmean-MD-in-tract-middle-cerebellar-peduncle_2_0': 0}) +field_id_25532_weightedmean_md_in_tract_medial_lemniscus_left_2_0 = TensorMap('25532_Weightedmean-MD-in-tract-medial-lemniscus-left_2_0', loss='logcosh', channel_map={'no_25532_Weightedmean-MD-in-tract-medial-lemniscus-left_2_0': 0}) +field_id_25533_weightedmean_md_in_tract_medial_lemniscus_right_2_0 = TensorMap('25533_Weightedmean-MD-in-tract-medial-lemniscus-right_2_0', loss='logcosh', channel_map={'no_25533_Weightedmean-MD-in-tract-medial-lemniscus-right_2_0': 0}) +field_id_25534_weightedmean_md_in_tract_posterior_thalamic_radiation_left_2_0 = TensorMap('25534_Weightedmean-MD-in-tract-posterior-thalamic-radiation-left_2_0', loss='logcosh', channel_map={'no_25534_Weightedmean-MD-in-tract-posterior-thalamic-radiation-left_2_0': 0}) +field_id_25535_weightedmean_md_in_tract_posterior_thalamic_radiation_right_2_0 = TensorMap('25535_Weightedmean-MD-in-tract-posterior-thalamic-radiation-right_2_0', loss='logcosh', channel_map={'no_25535_Weightedmean-MD-in-tract-posterior-thalamic-radiation-right_2_0': 0}) +field_id_25536_weightedmean_md_in_tract_superior_longitudinal_fasciculus_left_2_0 = TensorMap('25536_Weightedmean-MD-in-tract-superior-longitudinal-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25536_Weightedmean-MD-in-tract-superior-longitudinal-fasciculus-left_2_0': 0}) +field_id_25537_weightedmean_md_in_tract_superior_longitudinal_fasciculus_right_2_0 = TensorMap('25537_Weightedmean-MD-in-tract-superior-longitudinal-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25537_Weightedmean-MD-in-tract-superior-longitudinal-fasciculus-right_2_0': 0}) +field_id_25538_weightedmean_md_in_tract_superior_thalamic_radiation_left_2_0 = TensorMap('25538_Weightedmean-MD-in-tract-superior-thalamic-radiation-left_2_0', loss='logcosh', channel_map={'no_25538_Weightedmean-MD-in-tract-superior-thalamic-radiation-left_2_0': 0}) +field_id_25539_weightedmean_md_in_tract_superior_thalamic_radiation_right_2_0 = TensorMap('25539_Weightedmean-MD-in-tract-superior-thalamic-radiation-right_2_0', loss='logcosh', channel_map={'no_25539_Weightedmean-MD-in-tract-superior-thalamic-radiation-right_2_0': 0}) +field_id_25540_weightedmean_md_in_tract_uncinate_fasciculus_left_2_0 = TensorMap('25540_Weightedmean-MD-in-tract-uncinate-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25540_Weightedmean-MD-in-tract-uncinate-fasciculus-left_2_0': 0}) +field_id_25541_weightedmean_md_in_tract_uncinate_fasciculus_right_2_0 = TensorMap('25541_Weightedmean-MD-in-tract-uncinate-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25541_Weightedmean-MD-in-tract-uncinate-fasciculus-right_2_0': 0}) +field_id_25542_weightedmean_mo_in_tract_acoustic_radiation_left_2_0 = TensorMap('25542_Weightedmean-MO-in-tract-acoustic-radiation-left_2_0', loss='logcosh', channel_map={'no_25542_Weightedmean-MO-in-tract-acoustic-radiation-left_2_0': 0}) +field_id_25543_weightedmean_mo_in_tract_acoustic_radiation_right_2_0 = TensorMap('25543_Weightedmean-MO-in-tract-acoustic-radiation-right_2_0', loss='logcosh', channel_map={'no_25543_Weightedmean-MO-in-tract-acoustic-radiation-right_2_0': 0}) +field_id_25544_weightedmean_mo_in_tract_anterior_thalamic_radiation_left_2_0 = TensorMap('25544_Weightedmean-MO-in-tract-anterior-thalamic-radiation-left_2_0', loss='logcosh', channel_map={'no_25544_Weightedmean-MO-in-tract-anterior-thalamic-radiation-left_2_0': 0}) +field_id_25545_weightedmean_mo_in_tract_anterior_thalamic_radiation_right_2_0 = TensorMap('25545_Weightedmean-MO-in-tract-anterior-thalamic-radiation-right_2_0', loss='logcosh', channel_map={'no_25545_Weightedmean-MO-in-tract-anterior-thalamic-radiation-right_2_0': 0}) +field_id_25546_weightedmean_mo_in_tract_cingulate_gyrus_part_of_cingulum_left_2_0 = TensorMap('25546_Weightedmean-MO-in-tract-cingulate-gyrus-part-of-cingulum-left_2_0', loss='logcosh', channel_map={'no_25546_Weightedmean-MO-in-tract-cingulate-gyrus-part-of-cingulum-left_2_0': 0}) +field_id_25547_weightedmean_mo_in_tract_cingulate_gyrus_part_of_cingulum_right_2_0 = TensorMap('25547_Weightedmean-MO-in-tract-cingulate-gyrus-part-of-cingulum-right_2_0', loss='logcosh', channel_map={'no_25547_Weightedmean-MO-in-tract-cingulate-gyrus-part-of-cingulum-right_2_0': 0}) +field_id_25548_weightedmean_mo_in_tract_parahippocampal_part_of_cingulum_left_2_0 = TensorMap('25548_Weightedmean-MO-in-tract-parahippocampal-part-of-cingulum-left_2_0', loss='logcosh', channel_map={'no_25548_Weightedmean-MO-in-tract-parahippocampal-part-of-cingulum-left_2_0': 0}) +field_id_25549_weightedmean_mo_in_tract_parahippocampal_part_of_cingulum_right_2_0 = TensorMap('25549_Weightedmean-MO-in-tract-parahippocampal-part-of-cingulum-right_2_0', loss='logcosh', channel_map={'no_25549_Weightedmean-MO-in-tract-parahippocampal-part-of-cingulum-right_2_0': 0}) +field_id_25550_weightedmean_mo_in_tract_corticospinal_tract_left_2_0 = TensorMap('25550_Weightedmean-MO-in-tract-corticospinal-tract-left_2_0', loss='logcosh', channel_map={'no_25550_Weightedmean-MO-in-tract-corticospinal-tract-left_2_0': 0}) +field_id_25551_weightedmean_mo_in_tract_corticospinal_tract_right_2_0 = TensorMap('25551_Weightedmean-MO-in-tract-corticospinal-tract-right_2_0', loss='logcosh', channel_map={'no_25551_Weightedmean-MO-in-tract-corticospinal-tract-right_2_0': 0}) +field_id_25552_weightedmean_mo_in_tract_forceps_major_2_0 = TensorMap('25552_Weightedmean-MO-in-tract-forceps-major_2_0', loss='logcosh', channel_map={'no_25552_Weightedmean-MO-in-tract-forceps-major_2_0': 0}) +field_id_25553_weightedmean_mo_in_tract_forceps_minor_2_0 = TensorMap('25553_Weightedmean-MO-in-tract-forceps-minor_2_0', loss='logcosh', channel_map={'no_25553_Weightedmean-MO-in-tract-forceps-minor_2_0': 0}) +field_id_25554_weightedmean_mo_in_tract_inferior_frontooccipital_fasciculus_left_2_0 = TensorMap('25554_Weightedmean-MO-in-tract-inferior-frontooccipital-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25554_Weightedmean-MO-in-tract-inferior-frontooccipital-fasciculus-left_2_0': 0}) +field_id_25555_weightedmean_mo_in_tract_inferior_frontooccipital_fasciculus_right_2_0 = TensorMap('25555_Weightedmean-MO-in-tract-inferior-frontooccipital-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25555_Weightedmean-MO-in-tract-inferior-frontooccipital-fasciculus-right_2_0': 0}) +field_id_25556_weightedmean_mo_in_tract_inferior_longitudinal_fasciculus_left_2_0 = TensorMap('25556_Weightedmean-MO-in-tract-inferior-longitudinal-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25556_Weightedmean-MO-in-tract-inferior-longitudinal-fasciculus-left_2_0': 0}) +field_id_25557_weightedmean_mo_in_tract_inferior_longitudinal_fasciculus_right_2_0 = TensorMap('25557_Weightedmean-MO-in-tract-inferior-longitudinal-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25557_Weightedmean-MO-in-tract-inferior-longitudinal-fasciculus-right_2_0': 0}) +field_id_25558_weightedmean_mo_in_tract_middle_cerebellar_peduncle_2_0 = TensorMap('25558_Weightedmean-MO-in-tract-middle-cerebellar-peduncle_2_0', loss='logcosh', channel_map={'no_25558_Weightedmean-MO-in-tract-middle-cerebellar-peduncle_2_0': 0}) +field_id_25559_weightedmean_mo_in_tract_medial_lemniscus_left_2_0 = TensorMap('25559_Weightedmean-MO-in-tract-medial-lemniscus-left_2_0', loss='logcosh', channel_map={'no_25559_Weightedmean-MO-in-tract-medial-lemniscus-left_2_0': 0}) +field_id_25560_weightedmean_mo_in_tract_medial_lemniscus_right_2_0 = TensorMap('25560_Weightedmean-MO-in-tract-medial-lemniscus-right_2_0', loss='logcosh', channel_map={'no_25560_Weightedmean-MO-in-tract-medial-lemniscus-right_2_0': 0}) +field_id_25561_weightedmean_mo_in_tract_posterior_thalamic_radiation_left_2_0 = TensorMap('25561_Weightedmean-MO-in-tract-posterior-thalamic-radiation-left_2_0', loss='logcosh', channel_map={'no_25561_Weightedmean-MO-in-tract-posterior-thalamic-radiation-left_2_0': 0}) +field_id_25562_weightedmean_mo_in_tract_posterior_thalamic_radiation_right_2_0 = TensorMap('25562_Weightedmean-MO-in-tract-posterior-thalamic-radiation-right_2_0', loss='logcosh', channel_map={'no_25562_Weightedmean-MO-in-tract-posterior-thalamic-radiation-right_2_0': 0}) +field_id_25563_weightedmean_mo_in_tract_superior_longitudinal_fasciculus_left_2_0 = TensorMap('25563_Weightedmean-MO-in-tract-superior-longitudinal-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25563_Weightedmean-MO-in-tract-superior-longitudinal-fasciculus-left_2_0': 0}) +field_id_25564_weightedmean_mo_in_tract_superior_longitudinal_fasciculus_right_2_0 = TensorMap('25564_Weightedmean-MO-in-tract-superior-longitudinal-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25564_Weightedmean-MO-in-tract-superior-longitudinal-fasciculus-right_2_0': 0}) +field_id_25565_weightedmean_mo_in_tract_superior_thalamic_radiation_left_2_0 = TensorMap('25565_Weightedmean-MO-in-tract-superior-thalamic-radiation-left_2_0', loss='logcosh', channel_map={'no_25565_Weightedmean-MO-in-tract-superior-thalamic-radiation-left_2_0': 0}) +field_id_25566_weightedmean_mo_in_tract_superior_thalamic_radiation_right_2_0 = TensorMap('25566_Weightedmean-MO-in-tract-superior-thalamic-radiation-right_2_0', loss='logcosh', channel_map={'no_25566_Weightedmean-MO-in-tract-superior-thalamic-radiation-right_2_0': 0}) +field_id_25567_weightedmean_mo_in_tract_uncinate_fasciculus_left_2_0 = TensorMap('25567_Weightedmean-MO-in-tract-uncinate-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25567_Weightedmean-MO-in-tract-uncinate-fasciculus-left_2_0': 0}) +field_id_25568_weightedmean_mo_in_tract_uncinate_fasciculus_right_2_0 = TensorMap('25568_Weightedmean-MO-in-tract-uncinate-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25568_Weightedmean-MO-in-tract-uncinate-fasciculus-right_2_0': 0}) +field_id_25569_weightedmean_l1_in_tract_acoustic_radiation_left_2_0 = TensorMap('25569_Weightedmean-L1-in-tract-acoustic-radiation-left_2_0', loss='logcosh', channel_map={'no_25569_Weightedmean-L1-in-tract-acoustic-radiation-left_2_0': 0}) +field_id_25570_weightedmean_l1_in_tract_acoustic_radiation_right_2_0 = TensorMap('25570_Weightedmean-L1-in-tract-acoustic-radiation-right_2_0', loss='logcosh', channel_map={'no_25570_Weightedmean-L1-in-tract-acoustic-radiation-right_2_0': 0}) +field_id_25571_weightedmean_l1_in_tract_anterior_thalamic_radiation_left_2_0 = TensorMap('25571_Weightedmean-L1-in-tract-anterior-thalamic-radiation-left_2_0', loss='logcosh', channel_map={'no_25571_Weightedmean-L1-in-tract-anterior-thalamic-radiation-left_2_0': 0}) +field_id_25572_weightedmean_l1_in_tract_anterior_thalamic_radiation_right_2_0 = TensorMap('25572_Weightedmean-L1-in-tract-anterior-thalamic-radiation-right_2_0', loss='logcosh', channel_map={'no_25572_Weightedmean-L1-in-tract-anterior-thalamic-radiation-right_2_0': 0}) +field_id_25573_weightedmean_l1_in_tract_cingulate_gyrus_part_of_cingulum_left_2_0 = TensorMap('25573_Weightedmean-L1-in-tract-cingulate-gyrus-part-of-cingulum-left_2_0', loss='logcosh', channel_map={'no_25573_Weightedmean-L1-in-tract-cingulate-gyrus-part-of-cingulum-left_2_0': 0}) +field_id_25574_weightedmean_l1_in_tract_cingulate_gyrus_part_of_cingulum_right_2_0 = TensorMap('25574_Weightedmean-L1-in-tract-cingulate-gyrus-part-of-cingulum-right_2_0', loss='logcosh', channel_map={'no_25574_Weightedmean-L1-in-tract-cingulate-gyrus-part-of-cingulum-right_2_0': 0}) +field_id_25575_weightedmean_l1_in_tract_parahippocampal_part_of_cingulum_left_2_0 = TensorMap('25575_Weightedmean-L1-in-tract-parahippocampal-part-of-cingulum-left_2_0', loss='logcosh', channel_map={'no_25575_Weightedmean-L1-in-tract-parahippocampal-part-of-cingulum-left_2_0': 0}) +field_id_25576_weightedmean_l1_in_tract_parahippocampal_part_of_cingulum_right_2_0 = TensorMap('25576_Weightedmean-L1-in-tract-parahippocampal-part-of-cingulum-right_2_0', loss='logcosh', channel_map={'no_25576_Weightedmean-L1-in-tract-parahippocampal-part-of-cingulum-right_2_0': 0}) +field_id_25577_weightedmean_l1_in_tract_corticospinal_tract_left_2_0 = TensorMap('25577_Weightedmean-L1-in-tract-corticospinal-tract-left_2_0', loss='logcosh', channel_map={'no_25577_Weightedmean-L1-in-tract-corticospinal-tract-left_2_0': 0}) +field_id_25578_weightedmean_l1_in_tract_corticospinal_tract_right_2_0 = TensorMap('25578_Weightedmean-L1-in-tract-corticospinal-tract-right_2_0', loss='logcosh', channel_map={'no_25578_Weightedmean-L1-in-tract-corticospinal-tract-right_2_0': 0}) +field_id_25579_weightedmean_l1_in_tract_forceps_major_2_0 = TensorMap('25579_Weightedmean-L1-in-tract-forceps-major_2_0', loss='logcosh', channel_map={'no_25579_Weightedmean-L1-in-tract-forceps-major_2_0': 0}) +field_id_25580_weightedmean_l1_in_tract_forceps_minor_2_0 = TensorMap('25580_Weightedmean-L1-in-tract-forceps-minor_2_0', loss='logcosh', channel_map={'no_25580_Weightedmean-L1-in-tract-forceps-minor_2_0': 0}) +field_id_25581_weightedmean_l1_in_tract_inferior_frontooccipital_fasciculus_left_2_0 = TensorMap('25581_Weightedmean-L1-in-tract-inferior-frontooccipital-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25581_Weightedmean-L1-in-tract-inferior-frontooccipital-fasciculus-left_2_0': 0}) +field_id_25582_weightedmean_l1_in_tract_inferior_frontooccipital_fasciculus_right_2_0 = TensorMap('25582_Weightedmean-L1-in-tract-inferior-frontooccipital-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25582_Weightedmean-L1-in-tract-inferior-frontooccipital-fasciculus-right_2_0': 0}) +field_id_25583_weightedmean_l1_in_tract_inferior_longitudinal_fasciculus_left_2_0 = TensorMap('25583_Weightedmean-L1-in-tract-inferior-longitudinal-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25583_Weightedmean-L1-in-tract-inferior-longitudinal-fasciculus-left_2_0': 0}) +field_id_25584_weightedmean_l1_in_tract_inferior_longitudinal_fasciculus_right_2_0 = TensorMap('25584_Weightedmean-L1-in-tract-inferior-longitudinal-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25584_Weightedmean-L1-in-tract-inferior-longitudinal-fasciculus-right_2_0': 0}) +field_id_25585_weightedmean_l1_in_tract_middle_cerebellar_peduncle_2_0 = TensorMap('25585_Weightedmean-L1-in-tract-middle-cerebellar-peduncle_2_0', loss='logcosh', channel_map={'no_25585_Weightedmean-L1-in-tract-middle-cerebellar-peduncle_2_0': 0}) +field_id_25586_weightedmean_l1_in_tract_medial_lemniscus_left_2_0 = TensorMap('25586_Weightedmean-L1-in-tract-medial-lemniscus-left_2_0', loss='logcosh', channel_map={'no_25586_Weightedmean-L1-in-tract-medial-lemniscus-left_2_0': 0}) +field_id_25587_weightedmean_l1_in_tract_medial_lemniscus_right_2_0 = TensorMap('25587_Weightedmean-L1-in-tract-medial-lemniscus-right_2_0', loss='logcosh', channel_map={'no_25587_Weightedmean-L1-in-tract-medial-lemniscus-right_2_0': 0}) +field_id_25588_weightedmean_l1_in_tract_posterior_thalamic_radiation_left_2_0 = TensorMap('25588_Weightedmean-L1-in-tract-posterior-thalamic-radiation-left_2_0', loss='logcosh', channel_map={'no_25588_Weightedmean-L1-in-tract-posterior-thalamic-radiation-left_2_0': 0}) +field_id_25589_weightedmean_l1_in_tract_posterior_thalamic_radiation_right_2_0 = TensorMap('25589_Weightedmean-L1-in-tract-posterior-thalamic-radiation-right_2_0', loss='logcosh', channel_map={'no_25589_Weightedmean-L1-in-tract-posterior-thalamic-radiation-right_2_0': 0}) +field_id_25590_weightedmean_l1_in_tract_superior_longitudinal_fasciculus_left_2_0 = TensorMap('25590_Weightedmean-L1-in-tract-superior-longitudinal-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25590_Weightedmean-L1-in-tract-superior-longitudinal-fasciculus-left_2_0': 0}) +field_id_25591_weightedmean_l1_in_tract_superior_longitudinal_fasciculus_right_2_0 = TensorMap('25591_Weightedmean-L1-in-tract-superior-longitudinal-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25591_Weightedmean-L1-in-tract-superior-longitudinal-fasciculus-right_2_0': 0}) +field_id_25592_weightedmean_l1_in_tract_superior_thalamic_radiation_left_2_0 = TensorMap('25592_Weightedmean-L1-in-tract-superior-thalamic-radiation-left_2_0', loss='logcosh', channel_map={'no_25592_Weightedmean-L1-in-tract-superior-thalamic-radiation-left_2_0': 0}) +field_id_25593_weightedmean_l1_in_tract_superior_thalamic_radiation_right_2_0 = TensorMap('25593_Weightedmean-L1-in-tract-superior-thalamic-radiation-right_2_0', loss='logcosh', channel_map={'no_25593_Weightedmean-L1-in-tract-superior-thalamic-radiation-right_2_0': 0}) +field_id_25594_weightedmean_l1_in_tract_uncinate_fasciculus_left_2_0 = TensorMap('25594_Weightedmean-L1-in-tract-uncinate-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25594_Weightedmean-L1-in-tract-uncinate-fasciculus-left_2_0': 0}) +field_id_25595_weightedmean_l1_in_tract_uncinate_fasciculus_right_2_0 = TensorMap('25595_Weightedmean-L1-in-tract-uncinate-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25595_Weightedmean-L1-in-tract-uncinate-fasciculus-right_2_0': 0}) +field_id_25596_weightedmean_l2_in_tract_acoustic_radiation_left_2_0 = TensorMap('25596_Weightedmean-L2-in-tract-acoustic-radiation-left_2_0', loss='logcosh', channel_map={'no_25596_Weightedmean-L2-in-tract-acoustic-radiation-left_2_0': 0}) +field_id_25597_weightedmean_l2_in_tract_acoustic_radiation_right_2_0 = TensorMap('25597_Weightedmean-L2-in-tract-acoustic-radiation-right_2_0', loss='logcosh', channel_map={'no_25597_Weightedmean-L2-in-tract-acoustic-radiation-right_2_0': 0}) +field_id_25598_weightedmean_l2_in_tract_anterior_thalamic_radiation_left_2_0 = TensorMap('25598_Weightedmean-L2-in-tract-anterior-thalamic-radiation-left_2_0', loss='logcosh', channel_map={'no_25598_Weightedmean-L2-in-tract-anterior-thalamic-radiation-left_2_0': 0}) +field_id_25599_weightedmean_l2_in_tract_anterior_thalamic_radiation_right_2_0 = TensorMap('25599_Weightedmean-L2-in-tract-anterior-thalamic-radiation-right_2_0', loss='logcosh', channel_map={'no_25599_Weightedmean-L2-in-tract-anterior-thalamic-radiation-right_2_0': 0}) +field_id_25600_weightedmean_l2_in_tract_cingulate_gyrus_part_of_cingulum_left_2_0 = TensorMap('25600_Weightedmean-L2-in-tract-cingulate-gyrus-part-of-cingulum-left_2_0', loss='logcosh', channel_map={'no_25600_Weightedmean-L2-in-tract-cingulate-gyrus-part-of-cingulum-left_2_0': 0}) +field_id_25601_weightedmean_l2_in_tract_cingulate_gyrus_part_of_cingulum_right_2_0 = TensorMap('25601_Weightedmean-L2-in-tract-cingulate-gyrus-part-of-cingulum-right_2_0', loss='logcosh', channel_map={'no_25601_Weightedmean-L2-in-tract-cingulate-gyrus-part-of-cingulum-right_2_0': 0}) +field_id_25602_weightedmean_l2_in_tract_parahippocampal_part_of_cingulum_left_2_0 = TensorMap('25602_Weightedmean-L2-in-tract-parahippocampal-part-of-cingulum-left_2_0', loss='logcosh', channel_map={'no_25602_Weightedmean-L2-in-tract-parahippocampal-part-of-cingulum-left_2_0': 0}) +field_id_25603_weightedmean_l2_in_tract_parahippocampal_part_of_cingulum_right_2_0 = TensorMap('25603_Weightedmean-L2-in-tract-parahippocampal-part-of-cingulum-right_2_0', loss='logcosh', channel_map={'no_25603_Weightedmean-L2-in-tract-parahippocampal-part-of-cingulum-right_2_0': 0}) +field_id_25604_weightedmean_l2_in_tract_corticospinal_tract_left_2_0 = TensorMap('25604_Weightedmean-L2-in-tract-corticospinal-tract-left_2_0', loss='logcosh', channel_map={'no_25604_Weightedmean-L2-in-tract-corticospinal-tract-left_2_0': 0}) +field_id_25605_weightedmean_l2_in_tract_corticospinal_tract_right_2_0 = TensorMap('25605_Weightedmean-L2-in-tract-corticospinal-tract-right_2_0', loss='logcosh', channel_map={'no_25605_Weightedmean-L2-in-tract-corticospinal-tract-right_2_0': 0}) +field_id_25606_weightedmean_l2_in_tract_forceps_major_2_0 = TensorMap('25606_Weightedmean-L2-in-tract-forceps-major_2_0', loss='logcosh', channel_map={'no_25606_Weightedmean-L2-in-tract-forceps-major_2_0': 0}) +field_id_25607_weightedmean_l2_in_tract_forceps_minor_2_0 = TensorMap('25607_Weightedmean-L2-in-tract-forceps-minor_2_0', loss='logcosh', channel_map={'no_25607_Weightedmean-L2-in-tract-forceps-minor_2_0': 0}) +field_id_25608_weightedmean_l2_in_tract_inferior_frontooccipital_fasciculus_left_2_0 = TensorMap('25608_Weightedmean-L2-in-tract-inferior-frontooccipital-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25608_Weightedmean-L2-in-tract-inferior-frontooccipital-fasciculus-left_2_0': 0}) +field_id_25609_weightedmean_l2_in_tract_inferior_frontooccipital_fasciculus_right_2_0 = TensorMap('25609_Weightedmean-L2-in-tract-inferior-frontooccipital-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25609_Weightedmean-L2-in-tract-inferior-frontooccipital-fasciculus-right_2_0': 0}) +field_id_25610_weightedmean_l2_in_tract_inferior_longitudinal_fasciculus_left_2_0 = TensorMap('25610_Weightedmean-L2-in-tract-inferior-longitudinal-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25610_Weightedmean-L2-in-tract-inferior-longitudinal-fasciculus-left_2_0': 0}) +field_id_25611_weightedmean_l2_in_tract_inferior_longitudinal_fasciculus_right_2_0 = TensorMap('25611_Weightedmean-L2-in-tract-inferior-longitudinal-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25611_Weightedmean-L2-in-tract-inferior-longitudinal-fasciculus-right_2_0': 0}) +field_id_25612_weightedmean_l2_in_tract_middle_cerebellar_peduncle_2_0 = TensorMap('25612_Weightedmean-L2-in-tract-middle-cerebellar-peduncle_2_0', loss='logcosh', channel_map={'no_25612_Weightedmean-L2-in-tract-middle-cerebellar-peduncle_2_0': 0}) +field_id_25613_weightedmean_l2_in_tract_medial_lemniscus_left_2_0 = TensorMap('25613_Weightedmean-L2-in-tract-medial-lemniscus-left_2_0', loss='logcosh', channel_map={'no_25613_Weightedmean-L2-in-tract-medial-lemniscus-left_2_0': 0}) +field_id_25614_weightedmean_l2_in_tract_medial_lemniscus_right_2_0 = TensorMap('25614_Weightedmean-L2-in-tract-medial-lemniscus-right_2_0', loss='logcosh', channel_map={'no_25614_Weightedmean-L2-in-tract-medial-lemniscus-right_2_0': 0}) +field_id_25615_weightedmean_l2_in_tract_posterior_thalamic_radiation_left_2_0 = TensorMap('25615_Weightedmean-L2-in-tract-posterior-thalamic-radiation-left_2_0', loss='logcosh', channel_map={'no_25615_Weightedmean-L2-in-tract-posterior-thalamic-radiation-left_2_0': 0}) +field_id_25616_weightedmean_l2_in_tract_posterior_thalamic_radiation_right_2_0 = TensorMap('25616_Weightedmean-L2-in-tract-posterior-thalamic-radiation-right_2_0', loss='logcosh', channel_map={'no_25616_Weightedmean-L2-in-tract-posterior-thalamic-radiation-right_2_0': 0}) +field_id_25617_weightedmean_l2_in_tract_superior_longitudinal_fasciculus_left_2_0 = TensorMap('25617_Weightedmean-L2-in-tract-superior-longitudinal-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25617_Weightedmean-L2-in-tract-superior-longitudinal-fasciculus-left_2_0': 0}) +field_id_25618_weightedmean_l2_in_tract_superior_longitudinal_fasciculus_right_2_0 = TensorMap('25618_Weightedmean-L2-in-tract-superior-longitudinal-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25618_Weightedmean-L2-in-tract-superior-longitudinal-fasciculus-right_2_0': 0}) +field_id_25619_weightedmean_l2_in_tract_superior_thalamic_radiation_left_2_0 = TensorMap('25619_Weightedmean-L2-in-tract-superior-thalamic-radiation-left_2_0', loss='logcosh', channel_map={'no_25619_Weightedmean-L2-in-tract-superior-thalamic-radiation-left_2_0': 0}) +field_id_25620_weightedmean_l2_in_tract_superior_thalamic_radiation_right_2_0 = TensorMap('25620_Weightedmean-L2-in-tract-superior-thalamic-radiation-right_2_0', loss='logcosh', channel_map={'no_25620_Weightedmean-L2-in-tract-superior-thalamic-radiation-right_2_0': 0}) +field_id_25621_weightedmean_l2_in_tract_uncinate_fasciculus_left_2_0 = TensorMap('25621_Weightedmean-L2-in-tract-uncinate-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25621_Weightedmean-L2-in-tract-uncinate-fasciculus-left_2_0': 0}) +field_id_25622_weightedmean_l2_in_tract_uncinate_fasciculus_right_2_0 = TensorMap('25622_Weightedmean-L2-in-tract-uncinate-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25622_Weightedmean-L2-in-tract-uncinate-fasciculus-right_2_0': 0}) +field_id_25623_weightedmean_l3_in_tract_acoustic_radiation_left_2_0 = TensorMap('25623_Weightedmean-L3-in-tract-acoustic-radiation-left_2_0', loss='logcosh', channel_map={'no_25623_Weightedmean-L3-in-tract-acoustic-radiation-left_2_0': 0}) +field_id_25624_weightedmean_l3_in_tract_acoustic_radiation_right_2_0 = TensorMap('25624_Weightedmean-L3-in-tract-acoustic-radiation-right_2_0', loss='logcosh', channel_map={'no_25624_Weightedmean-L3-in-tract-acoustic-radiation-right_2_0': 0}) +field_id_25625_weightedmean_l3_in_tract_anterior_thalamic_radiation_left_2_0 = TensorMap('25625_Weightedmean-L3-in-tract-anterior-thalamic-radiation-left_2_0', loss='logcosh', channel_map={'no_25625_Weightedmean-L3-in-tract-anterior-thalamic-radiation-left_2_0': 0}) +field_id_25626_weightedmean_l3_in_tract_anterior_thalamic_radiation_right_2_0 = TensorMap('25626_Weightedmean-L3-in-tract-anterior-thalamic-radiation-right_2_0', loss='logcosh', channel_map={'no_25626_Weightedmean-L3-in-tract-anterior-thalamic-radiation-right_2_0': 0}) +field_id_25627_weightedmean_l3_in_tract_cingulate_gyrus_part_of_cingulum_left_2_0 = TensorMap('25627_Weightedmean-L3-in-tract-cingulate-gyrus-part-of-cingulum-left_2_0', loss='logcosh', channel_map={'no_25627_Weightedmean-L3-in-tract-cingulate-gyrus-part-of-cingulum-left_2_0': 0}) +field_id_25628_weightedmean_l3_in_tract_cingulate_gyrus_part_of_cingulum_right_2_0 = TensorMap('25628_Weightedmean-L3-in-tract-cingulate-gyrus-part-of-cingulum-right_2_0', loss='logcosh', channel_map={'no_25628_Weightedmean-L3-in-tract-cingulate-gyrus-part-of-cingulum-right_2_0': 0}) +field_id_25629_weightedmean_l3_in_tract_parahippocampal_part_of_cingulum_left_2_0 = TensorMap('25629_Weightedmean-L3-in-tract-parahippocampal-part-of-cingulum-left_2_0', loss='logcosh', channel_map={'no_25629_Weightedmean-L3-in-tract-parahippocampal-part-of-cingulum-left_2_0': 0}) +field_id_25630_weightedmean_l3_in_tract_parahippocampal_part_of_cingulum_right_2_0 = TensorMap('25630_Weightedmean-L3-in-tract-parahippocampal-part-of-cingulum-right_2_0', loss='logcosh', channel_map={'no_25630_Weightedmean-L3-in-tract-parahippocampal-part-of-cingulum-right_2_0': 0}) +field_id_25631_weightedmean_l3_in_tract_corticospinal_tract_left_2_0 = TensorMap('25631_Weightedmean-L3-in-tract-corticospinal-tract-left_2_0', loss='logcosh', channel_map={'no_25631_Weightedmean-L3-in-tract-corticospinal-tract-left_2_0': 0}) +field_id_25632_weightedmean_l3_in_tract_corticospinal_tract_right_2_0 = TensorMap('25632_Weightedmean-L3-in-tract-corticospinal-tract-right_2_0', loss='logcosh', channel_map={'no_25632_Weightedmean-L3-in-tract-corticospinal-tract-right_2_0': 0}) +field_id_25633_weightedmean_l3_in_tract_forceps_major_2_0 = TensorMap('25633_Weightedmean-L3-in-tract-forceps-major_2_0', loss='logcosh', channel_map={'no_25633_Weightedmean-L3-in-tract-forceps-major_2_0': 0}) +field_id_25634_weightedmean_l3_in_tract_forceps_minor_2_0 = TensorMap('25634_Weightedmean-L3-in-tract-forceps-minor_2_0', loss='logcosh', channel_map={'no_25634_Weightedmean-L3-in-tract-forceps-minor_2_0': 0}) +field_id_25635_weightedmean_l3_in_tract_inferior_frontooccipital_fasciculus_left_2_0 = TensorMap('25635_Weightedmean-L3-in-tract-inferior-frontooccipital-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25635_Weightedmean-L3-in-tract-inferior-frontooccipital-fasciculus-left_2_0': 0}) +field_id_25636_weightedmean_l3_in_tract_inferior_frontooccipital_fasciculus_right_2_0 = TensorMap('25636_Weightedmean-L3-in-tract-inferior-frontooccipital-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25636_Weightedmean-L3-in-tract-inferior-frontooccipital-fasciculus-right_2_0': 0}) +field_id_25637_weightedmean_l3_in_tract_inferior_longitudinal_fasciculus_left_2_0 = TensorMap('25637_Weightedmean-L3-in-tract-inferior-longitudinal-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25637_Weightedmean-L3-in-tract-inferior-longitudinal-fasciculus-left_2_0': 0}) +field_id_25638_weightedmean_l3_in_tract_inferior_longitudinal_fasciculus_right_2_0 = TensorMap('25638_Weightedmean-L3-in-tract-inferior-longitudinal-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25638_Weightedmean-L3-in-tract-inferior-longitudinal-fasciculus-right_2_0': 0}) +field_id_25639_weightedmean_l3_in_tract_middle_cerebellar_peduncle_2_0 = TensorMap('25639_Weightedmean-L3-in-tract-middle-cerebellar-peduncle_2_0', loss='logcosh', channel_map={'no_25639_Weightedmean-L3-in-tract-middle-cerebellar-peduncle_2_0': 0}) +field_id_25640_weightedmean_l3_in_tract_medial_lemniscus_left_2_0 = TensorMap('25640_Weightedmean-L3-in-tract-medial-lemniscus-left_2_0', loss='logcosh', channel_map={'no_25640_Weightedmean-L3-in-tract-medial-lemniscus-left_2_0': 0}) +field_id_25641_weightedmean_l3_in_tract_medial_lemniscus_right_2_0 = TensorMap('25641_Weightedmean-L3-in-tract-medial-lemniscus-right_2_0', loss='logcosh', channel_map={'no_25641_Weightedmean-L3-in-tract-medial-lemniscus-right_2_0': 0}) +field_id_25642_weightedmean_l3_in_tract_posterior_thalamic_radiation_left_2_0 = TensorMap('25642_Weightedmean-L3-in-tract-posterior-thalamic-radiation-left_2_0', loss='logcosh', channel_map={'no_25642_Weightedmean-L3-in-tract-posterior-thalamic-radiation-left_2_0': 0}) +field_id_25643_weightedmean_l3_in_tract_posterior_thalamic_radiation_right_2_0 = TensorMap('25643_Weightedmean-L3-in-tract-posterior-thalamic-radiation-right_2_0', loss='logcosh', channel_map={'no_25643_Weightedmean-L3-in-tract-posterior-thalamic-radiation-right_2_0': 0}) +field_id_25644_weightedmean_l3_in_tract_superior_longitudinal_fasciculus_left_2_0 = TensorMap('25644_Weightedmean-L3-in-tract-superior-longitudinal-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25644_Weightedmean-L3-in-tract-superior-longitudinal-fasciculus-left_2_0': 0}) +field_id_25645_weightedmean_l3_in_tract_superior_longitudinal_fasciculus_right_2_0 = TensorMap('25645_Weightedmean-L3-in-tract-superior-longitudinal-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25645_Weightedmean-L3-in-tract-superior-longitudinal-fasciculus-right_2_0': 0}) +field_id_25646_weightedmean_l3_in_tract_superior_thalamic_radiation_left_2_0 = TensorMap('25646_Weightedmean-L3-in-tract-superior-thalamic-radiation-left_2_0', loss='logcosh', channel_map={'no_25646_Weightedmean-L3-in-tract-superior-thalamic-radiation-left_2_0': 0}) +field_id_25647_weightedmean_l3_in_tract_superior_thalamic_radiation_right_2_0 = TensorMap('25647_Weightedmean-L3-in-tract-superior-thalamic-radiation-right_2_0', loss='logcosh', channel_map={'no_25647_Weightedmean-L3-in-tract-superior-thalamic-radiation-right_2_0': 0}) +field_id_25648_weightedmean_l3_in_tract_uncinate_fasciculus_left_2_0 = TensorMap('25648_Weightedmean-L3-in-tract-uncinate-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25648_Weightedmean-L3-in-tract-uncinate-fasciculus-left_2_0': 0}) +field_id_25649_weightedmean_l3_in_tract_uncinate_fasciculus_right_2_0 = TensorMap('25649_Weightedmean-L3-in-tract-uncinate-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25649_Weightedmean-L3-in-tract-uncinate-fasciculus-right_2_0': 0}) +field_id_25650_weightedmean_icvf_in_tract_acoustic_radiation_left_2_0 = TensorMap('25650_Weightedmean-ICVF-in-tract-acoustic-radiation-left_2_0', loss='logcosh', channel_map={'no_25650_Weightedmean-ICVF-in-tract-acoustic-radiation-left_2_0': 0}) +field_id_25651_weightedmean_icvf_in_tract_acoustic_radiation_right_2_0 = TensorMap('25651_Weightedmean-ICVF-in-tract-acoustic-radiation-right_2_0', loss='logcosh', channel_map={'no_25651_Weightedmean-ICVF-in-tract-acoustic-radiation-right_2_0': 0}) +field_id_25652_weightedmean_icvf_in_tract_anterior_thalamic_radiation_left_2_0 = TensorMap('25652_Weightedmean-ICVF-in-tract-anterior-thalamic-radiation-left_2_0', loss='logcosh', channel_map={'no_25652_Weightedmean-ICVF-in-tract-anterior-thalamic-radiation-left_2_0': 0}) +field_id_25653_weightedmean_icvf_in_tract_anterior_thalamic_radiation_right_2_0 = TensorMap('25653_Weightedmean-ICVF-in-tract-anterior-thalamic-radiation-right_2_0', loss='logcosh', channel_map={'no_25653_Weightedmean-ICVF-in-tract-anterior-thalamic-radiation-right_2_0': 0}) +field_id_25654_weightedmean_icvf_in_tract_cingulate_gyrus_part_of_cingulum_left_2_0 = TensorMap('25654_Weightedmean-ICVF-in-tract-cingulate-gyrus-part-of-cingulum-left_2_0', loss='logcosh', channel_map={'no_25654_Weightedmean-ICVF-in-tract-cingulate-gyrus-part-of-cingulum-left_2_0': 0}) +field_id_25655_weightedmean_icvf_in_tract_cingulate_gyrus_part_of_cingulum_right_2_0 = TensorMap('25655_Weightedmean-ICVF-in-tract-cingulate-gyrus-part-of-cingulum-right_2_0', loss='logcosh', channel_map={'no_25655_Weightedmean-ICVF-in-tract-cingulate-gyrus-part-of-cingulum-right_2_0': 0}) +field_id_25656_weightedmean_icvf_in_tract_parahippocampal_part_of_cingulum_left_2_0 = TensorMap('25656_Weightedmean-ICVF-in-tract-parahippocampal-part-of-cingulum-left_2_0', loss='logcosh', channel_map={'no_25656_Weightedmean-ICVF-in-tract-parahippocampal-part-of-cingulum-left_2_0': 0}) +field_id_25657_weightedmean_icvf_in_tract_parahippocampal_part_of_cingulum_right_2_0 = TensorMap('25657_Weightedmean-ICVF-in-tract-parahippocampal-part-of-cingulum-right_2_0', loss='logcosh', channel_map={'no_25657_Weightedmean-ICVF-in-tract-parahippocampal-part-of-cingulum-right_2_0': 0}) +field_id_25658_weightedmean_icvf_in_tract_corticospinal_tract_left_2_0 = TensorMap('25658_Weightedmean-ICVF-in-tract-corticospinal-tract-left_2_0', loss='logcosh', channel_map={'no_25658_Weightedmean-ICVF-in-tract-corticospinal-tract-left_2_0': 0}) +field_id_25659_weightedmean_icvf_in_tract_corticospinal_tract_right_2_0 = TensorMap('25659_Weightedmean-ICVF-in-tract-corticospinal-tract-right_2_0', loss='logcosh', channel_map={'no_25659_Weightedmean-ICVF-in-tract-corticospinal-tract-right_2_0': 0}) +field_id_25660_weightedmean_icvf_in_tract_forceps_major_2_0 = TensorMap('25660_Weightedmean-ICVF-in-tract-forceps-major_2_0', loss='logcosh', channel_map={'no_25660_Weightedmean-ICVF-in-tract-forceps-major_2_0': 0}) +field_id_25661_weightedmean_icvf_in_tract_forceps_minor_2_0 = TensorMap('25661_Weightedmean-ICVF-in-tract-forceps-minor_2_0', loss='logcosh', channel_map={'no_25661_Weightedmean-ICVF-in-tract-forceps-minor_2_0': 0}) +field_id_25662_weightedmean_icvf_in_tract_inferior_frontooccipital_fasciculus_left_2_0 = TensorMap('25662_Weightedmean-ICVF-in-tract-inferior-frontooccipital-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25662_Weightedmean-ICVF-in-tract-inferior-frontooccipital-fasciculus-left_2_0': 0}) +field_id_25663_weightedmean_icvf_in_tract_inferior_frontooccipital_fasciculus_right_2_0 = TensorMap('25663_Weightedmean-ICVF-in-tract-inferior-frontooccipital-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25663_Weightedmean-ICVF-in-tract-inferior-frontooccipital-fasciculus-right_2_0': 0}) +field_id_25664_weightedmean_icvf_in_tract_inferior_longitudinal_fasciculus_left_2_0 = TensorMap('25664_Weightedmean-ICVF-in-tract-inferior-longitudinal-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25664_Weightedmean-ICVF-in-tract-inferior-longitudinal-fasciculus-left_2_0': 0}) +field_id_25665_weightedmean_icvf_in_tract_inferior_longitudinal_fasciculus_right_2_0 = TensorMap('25665_Weightedmean-ICVF-in-tract-inferior-longitudinal-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25665_Weightedmean-ICVF-in-tract-inferior-longitudinal-fasciculus-right_2_0': 0}) +field_id_25666_weightedmean_icvf_in_tract_middle_cerebellar_peduncle_2_0 = TensorMap('25666_Weightedmean-ICVF-in-tract-middle-cerebellar-peduncle_2_0', loss='logcosh', channel_map={'no_25666_Weightedmean-ICVF-in-tract-middle-cerebellar-peduncle_2_0': 0}) +field_id_25667_weightedmean_icvf_in_tract_medial_lemniscus_left_2_0 = TensorMap('25667_Weightedmean-ICVF-in-tract-medial-lemniscus-left_2_0', loss='logcosh', channel_map={'no_25667_Weightedmean-ICVF-in-tract-medial-lemniscus-left_2_0': 0}) +field_id_25668_weightedmean_icvf_in_tract_medial_lemniscus_right_2_0 = TensorMap('25668_Weightedmean-ICVF-in-tract-medial-lemniscus-right_2_0', loss='logcosh', channel_map={'no_25668_Weightedmean-ICVF-in-tract-medial-lemniscus-right_2_0': 0}) +field_id_25669_weightedmean_icvf_in_tract_posterior_thalamic_radiation_left_2_0 = TensorMap('25669_Weightedmean-ICVF-in-tract-posterior-thalamic-radiation-left_2_0', loss='logcosh', channel_map={'no_25669_Weightedmean-ICVF-in-tract-posterior-thalamic-radiation-left_2_0': 0}) +field_id_25670_weightedmean_icvf_in_tract_posterior_thalamic_radiation_right_2_0 = TensorMap('25670_Weightedmean-ICVF-in-tract-posterior-thalamic-radiation-right_2_0', loss='logcosh', channel_map={'no_25670_Weightedmean-ICVF-in-tract-posterior-thalamic-radiation-right_2_0': 0}) +field_id_25671_weightedmean_icvf_in_tract_superior_longitudinal_fasciculus_left_2_0 = TensorMap('25671_Weightedmean-ICVF-in-tract-superior-longitudinal-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25671_Weightedmean-ICVF-in-tract-superior-longitudinal-fasciculus-left_2_0': 0}) +field_id_25672_weightedmean_icvf_in_tract_superior_longitudinal_fasciculus_right_2_0 = TensorMap('25672_Weightedmean-ICVF-in-tract-superior-longitudinal-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25672_Weightedmean-ICVF-in-tract-superior-longitudinal-fasciculus-right_2_0': 0}) +field_id_25673_weightedmean_icvf_in_tract_superior_thalamic_radiation_left_2_0 = TensorMap('25673_Weightedmean-ICVF-in-tract-superior-thalamic-radiation-left_2_0', loss='logcosh', channel_map={'no_25673_Weightedmean-ICVF-in-tract-superior-thalamic-radiation-left_2_0': 0}) +field_id_25674_weightedmean_icvf_in_tract_superior_thalamic_radiation_right_2_0 = TensorMap('25674_Weightedmean-ICVF-in-tract-superior-thalamic-radiation-right_2_0', loss='logcosh', channel_map={'no_25674_Weightedmean-ICVF-in-tract-superior-thalamic-radiation-right_2_0': 0}) +field_id_25675_weightedmean_icvf_in_tract_uncinate_fasciculus_left_2_0 = TensorMap('25675_Weightedmean-ICVF-in-tract-uncinate-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25675_Weightedmean-ICVF-in-tract-uncinate-fasciculus-left_2_0': 0}) +field_id_25676_weightedmean_icvf_in_tract_uncinate_fasciculus_right_2_0 = TensorMap('25676_Weightedmean-ICVF-in-tract-uncinate-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25676_Weightedmean-ICVF-in-tract-uncinate-fasciculus-right_2_0': 0}) +field_id_25677_weightedmean_od_in_tract_acoustic_radiation_left_2_0 = TensorMap('25677_Weightedmean-OD-in-tract-acoustic-radiation-left_2_0', loss='logcosh', channel_map={'no_25677_Weightedmean-OD-in-tract-acoustic-radiation-left_2_0': 0}) +field_id_25678_weightedmean_od_in_tract_acoustic_radiation_right_2_0 = TensorMap('25678_Weightedmean-OD-in-tract-acoustic-radiation-right_2_0', loss='logcosh', channel_map={'no_25678_Weightedmean-OD-in-tract-acoustic-radiation-right_2_0': 0}) +field_id_25679_weightedmean_od_in_tract_anterior_thalamic_radiation_left_2_0 = TensorMap('25679_Weightedmean-OD-in-tract-anterior-thalamic-radiation-left_2_0', loss='logcosh', channel_map={'no_25679_Weightedmean-OD-in-tract-anterior-thalamic-radiation-left_2_0': 0}) +field_id_25680_weightedmean_od_in_tract_anterior_thalamic_radiation_right_2_0 = TensorMap('25680_Weightedmean-OD-in-tract-anterior-thalamic-radiation-right_2_0', loss='logcosh', channel_map={'no_25680_Weightedmean-OD-in-tract-anterior-thalamic-radiation-right_2_0': 0}) +field_id_25681_weightedmean_od_in_tract_cingulate_gyrus_part_of_cingulum_left_2_0 = TensorMap('25681_Weightedmean-OD-in-tract-cingulate-gyrus-part-of-cingulum-left_2_0', loss='logcosh', channel_map={'no_25681_Weightedmean-OD-in-tract-cingulate-gyrus-part-of-cingulum-left_2_0': 0}) +field_id_25682_weightedmean_od_in_tract_cingulate_gyrus_part_of_cingulum_right_2_0 = TensorMap('25682_Weightedmean-OD-in-tract-cingulate-gyrus-part-of-cingulum-right_2_0', loss='logcosh', channel_map={'no_25682_Weightedmean-OD-in-tract-cingulate-gyrus-part-of-cingulum-right_2_0': 0}) +field_id_25683_weightedmean_od_in_tract_parahippocampal_part_of_cingulum_left_2_0 = TensorMap('25683_Weightedmean-OD-in-tract-parahippocampal-part-of-cingulum-left_2_0', loss='logcosh', channel_map={'no_25683_Weightedmean-OD-in-tract-parahippocampal-part-of-cingulum-left_2_0': 0}) +field_id_25684_weightedmean_od_in_tract_parahippocampal_part_of_cingulum_right_2_0 = TensorMap('25684_Weightedmean-OD-in-tract-parahippocampal-part-of-cingulum-right_2_0', loss='logcosh', channel_map={'no_25684_Weightedmean-OD-in-tract-parahippocampal-part-of-cingulum-right_2_0': 0}) +field_id_25685_weightedmean_od_in_tract_corticospinal_tract_left_2_0 = TensorMap('25685_Weightedmean-OD-in-tract-corticospinal-tract-left_2_0', loss='logcosh', channel_map={'no_25685_Weightedmean-OD-in-tract-corticospinal-tract-left_2_0': 0}) +field_id_25686_weightedmean_od_in_tract_corticospinal_tract_right_2_0 = TensorMap('25686_Weightedmean-OD-in-tract-corticospinal-tract-right_2_0', loss='logcosh', channel_map={'no_25686_Weightedmean-OD-in-tract-corticospinal-tract-right_2_0': 0}) +field_id_25687_weightedmean_od_in_tract_forceps_major_2_0 = TensorMap('25687_Weightedmean-OD-in-tract-forceps-major_2_0', loss='logcosh', channel_map={'no_25687_Weightedmean-OD-in-tract-forceps-major_2_0': 0}) +field_id_25688_weightedmean_od_in_tract_forceps_minor_2_0 = TensorMap('25688_Weightedmean-OD-in-tract-forceps-minor_2_0', loss='logcosh', channel_map={'no_25688_Weightedmean-OD-in-tract-forceps-minor_2_0': 0}) +field_id_25689_weightedmean_od_in_tract_inferior_frontooccipital_fasciculus_left_2_0 = TensorMap('25689_Weightedmean-OD-in-tract-inferior-frontooccipital-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25689_Weightedmean-OD-in-tract-inferior-frontooccipital-fasciculus-left_2_0': 0}) +field_id_25690_weightedmean_od_in_tract_inferior_frontooccipital_fasciculus_right_2_0 = TensorMap('25690_Weightedmean-OD-in-tract-inferior-frontooccipital-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25690_Weightedmean-OD-in-tract-inferior-frontooccipital-fasciculus-right_2_0': 0}) +field_id_25691_weightedmean_od_in_tract_inferior_longitudinal_fasciculus_left_2_0 = TensorMap('25691_Weightedmean-OD-in-tract-inferior-longitudinal-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25691_Weightedmean-OD-in-tract-inferior-longitudinal-fasciculus-left_2_0': 0}) +field_id_25692_weightedmean_od_in_tract_inferior_longitudinal_fasciculus_right_2_0 = TensorMap('25692_Weightedmean-OD-in-tract-inferior-longitudinal-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25692_Weightedmean-OD-in-tract-inferior-longitudinal-fasciculus-right_2_0': 0}) +field_id_25693_weightedmean_od_in_tract_middle_cerebellar_peduncle_2_0 = TensorMap('25693_Weightedmean-OD-in-tract-middle-cerebellar-peduncle_2_0', loss='logcosh', channel_map={'no_25693_Weightedmean-OD-in-tract-middle-cerebellar-peduncle_2_0': 0}) +field_id_25694_weightedmean_od_in_tract_medial_lemniscus_left_2_0 = TensorMap('25694_Weightedmean-OD-in-tract-medial-lemniscus-left_2_0', loss='logcosh', channel_map={'no_25694_Weightedmean-OD-in-tract-medial-lemniscus-left_2_0': 0}) +field_id_25695_weightedmean_od_in_tract_medial_lemniscus_right_2_0 = TensorMap('25695_Weightedmean-OD-in-tract-medial-lemniscus-right_2_0', loss='logcosh', channel_map={'no_25695_Weightedmean-OD-in-tract-medial-lemniscus-right_2_0': 0}) +field_id_25696_weightedmean_od_in_tract_posterior_thalamic_radiation_left_2_0 = TensorMap('25696_Weightedmean-OD-in-tract-posterior-thalamic-radiation-left_2_0', loss='logcosh', channel_map={'no_25696_Weightedmean-OD-in-tract-posterior-thalamic-radiation-left_2_0': 0}) +field_id_25697_weightedmean_od_in_tract_posterior_thalamic_radiation_right_2_0 = TensorMap('25697_Weightedmean-OD-in-tract-posterior-thalamic-radiation-right_2_0', loss='logcosh', channel_map={'no_25697_Weightedmean-OD-in-tract-posterior-thalamic-radiation-right_2_0': 0}) +field_id_25698_weightedmean_od_in_tract_superior_longitudinal_fasciculus_left_2_0 = TensorMap('25698_Weightedmean-OD-in-tract-superior-longitudinal-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25698_Weightedmean-OD-in-tract-superior-longitudinal-fasciculus-left_2_0': 0}) +field_id_25699_weightedmean_od_in_tract_superior_longitudinal_fasciculus_right_2_0 = TensorMap('25699_Weightedmean-OD-in-tract-superior-longitudinal-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25699_Weightedmean-OD-in-tract-superior-longitudinal-fasciculus-right_2_0': 0}) +field_id_25700_weightedmean_od_in_tract_superior_thalamic_radiation_left_2_0 = TensorMap('25700_Weightedmean-OD-in-tract-superior-thalamic-radiation-left_2_0', loss='logcosh', channel_map={'no_25700_Weightedmean-OD-in-tract-superior-thalamic-radiation-left_2_0': 0}) +field_id_25701_weightedmean_od_in_tract_superior_thalamic_radiation_right_2_0 = TensorMap('25701_Weightedmean-OD-in-tract-superior-thalamic-radiation-right_2_0', loss='logcosh', channel_map={'no_25701_Weightedmean-OD-in-tract-superior-thalamic-radiation-right_2_0': 0}) +field_id_25702_weightedmean_od_in_tract_uncinate_fasciculus_left_2_0 = TensorMap('25702_Weightedmean-OD-in-tract-uncinate-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25702_Weightedmean-OD-in-tract-uncinate-fasciculus-left_2_0': 0}) +field_id_25703_weightedmean_od_in_tract_uncinate_fasciculus_right_2_0 = TensorMap('25703_Weightedmean-OD-in-tract-uncinate-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25703_Weightedmean-OD-in-tract-uncinate-fasciculus-right_2_0': 0}) +field_id_25704_weightedmean_isovf_in_tract_acoustic_radiation_left_2_0 = TensorMap('25704_Weightedmean-ISOVF-in-tract-acoustic-radiation-left_2_0', loss='logcosh', channel_map={'no_25704_Weightedmean-ISOVF-in-tract-acoustic-radiation-left_2_0': 0}) +field_id_25705_weightedmean_isovf_in_tract_acoustic_radiation_right_2_0 = TensorMap('25705_Weightedmean-ISOVF-in-tract-acoustic-radiation-right_2_0', loss='logcosh', channel_map={'no_25705_Weightedmean-ISOVF-in-tract-acoustic-radiation-right_2_0': 0}) +field_id_25706_weightedmean_isovf_in_tract_anterior_thalamic_radiation_left_2_0 = TensorMap('25706_Weightedmean-ISOVF-in-tract-anterior-thalamic-radiation-left_2_0', loss='logcosh', channel_map={'no_25706_Weightedmean-ISOVF-in-tract-anterior-thalamic-radiation-left_2_0': 0}) +field_id_25707_weightedmean_isovf_in_tract_anterior_thalamic_radiation_right_2_0 = TensorMap('25707_Weightedmean-ISOVF-in-tract-anterior-thalamic-radiation-right_2_0', loss='logcosh', channel_map={'no_25707_Weightedmean-ISOVF-in-tract-anterior-thalamic-radiation-right_2_0': 0}) +field_id_25708_weightedmean_isovf_in_tract_cingulate_gyrus_part_of_cingulum_left_2_0 = TensorMap('25708_Weightedmean-ISOVF-in-tract-cingulate-gyrus-part-of-cingulum-left_2_0', loss='logcosh', channel_map={'no_25708_Weightedmean-ISOVF-in-tract-cingulate-gyrus-part-of-cingulum-left_2_0': 0}) +field_id_25709_weightedmean_isovf_in_tract_cingulate_gyrus_part_of_cingulum_right_2_0 = TensorMap('25709_Weightedmean-ISOVF-in-tract-cingulate-gyrus-part-of-cingulum-right_2_0', loss='logcosh', channel_map={'no_25709_Weightedmean-ISOVF-in-tract-cingulate-gyrus-part-of-cingulum-right_2_0': 0}) +field_id_25710_weightedmean_isovf_in_tract_parahippocampal_part_of_cingulum_left_2_0 = TensorMap('25710_Weightedmean-ISOVF-in-tract-parahippocampal-part-of-cingulum-left_2_0', loss='logcosh', channel_map={'no_25710_Weightedmean-ISOVF-in-tract-parahippocampal-part-of-cingulum-left_2_0': 0}) +field_id_25711_weightedmean_isovf_in_tract_parahippocampal_part_of_cingulum_right_2_0 = TensorMap('25711_Weightedmean-ISOVF-in-tract-parahippocampal-part-of-cingulum-right_2_0', loss='logcosh', channel_map={'no_25711_Weightedmean-ISOVF-in-tract-parahippocampal-part-of-cingulum-right_2_0': 0}) +field_id_25712_weightedmean_isovf_in_tract_corticospinal_tract_left_2_0 = TensorMap('25712_Weightedmean-ISOVF-in-tract-corticospinal-tract-left_2_0', loss='logcosh', channel_map={'no_25712_Weightedmean-ISOVF-in-tract-corticospinal-tract-left_2_0': 0}) +field_id_25713_weightedmean_isovf_in_tract_corticospinal_tract_right_2_0 = TensorMap('25713_Weightedmean-ISOVF-in-tract-corticospinal-tract-right_2_0', loss='logcosh', channel_map={'no_25713_Weightedmean-ISOVF-in-tract-corticospinal-tract-right_2_0': 0}) +field_id_25714_weightedmean_isovf_in_tract_forceps_major_2_0 = TensorMap('25714_Weightedmean-ISOVF-in-tract-forceps-major_2_0', loss='logcosh', channel_map={'no_25714_Weightedmean-ISOVF-in-tract-forceps-major_2_0': 0}) +field_id_25715_weightedmean_isovf_in_tract_forceps_minor_2_0 = TensorMap('25715_Weightedmean-ISOVF-in-tract-forceps-minor_2_0', loss='logcosh', channel_map={'no_25715_Weightedmean-ISOVF-in-tract-forceps-minor_2_0': 0}) +field_id_25716_weightedmean_isovf_in_tract_inferior_frontooccipital_fasciculus_left_2_0 = TensorMap('25716_Weightedmean-ISOVF-in-tract-inferior-frontooccipital-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25716_Weightedmean-ISOVF-in-tract-inferior-frontooccipital-fasciculus-left_2_0': 0}) +field_id_25717_weightedmean_isovf_in_tract_inferior_frontooccipital_fasciculus_right_2_0 = TensorMap('25717_Weightedmean-ISOVF-in-tract-inferior-frontooccipital-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25717_Weightedmean-ISOVF-in-tract-inferior-frontooccipital-fasciculus-right_2_0': 0}) +field_id_25718_weightedmean_isovf_in_tract_inferior_longitudinal_fasciculus_left_2_0 = TensorMap('25718_Weightedmean-ISOVF-in-tract-inferior-longitudinal-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25718_Weightedmean-ISOVF-in-tract-inferior-longitudinal-fasciculus-left_2_0': 0}) +field_id_25719_weightedmean_isovf_in_tract_inferior_longitudinal_fasciculus_right_2_0 = TensorMap('25719_Weightedmean-ISOVF-in-tract-inferior-longitudinal-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25719_Weightedmean-ISOVF-in-tract-inferior-longitudinal-fasciculus-right_2_0': 0}) +field_id_25720_weightedmean_isovf_in_tract_middle_cerebellar_peduncle_2_0 = TensorMap('25720_Weightedmean-ISOVF-in-tract-middle-cerebellar-peduncle_2_0', loss='logcosh', channel_map={'no_25720_Weightedmean-ISOVF-in-tract-middle-cerebellar-peduncle_2_0': 0}) +field_id_25721_weightedmean_isovf_in_tract_medial_lemniscus_left_2_0 = TensorMap('25721_Weightedmean-ISOVF-in-tract-medial-lemniscus-left_2_0', loss='logcosh', channel_map={'no_25721_Weightedmean-ISOVF-in-tract-medial-lemniscus-left_2_0': 0}) +field_id_25722_weightedmean_isovf_in_tract_medial_lemniscus_right_2_0 = TensorMap('25722_Weightedmean-ISOVF-in-tract-medial-lemniscus-right_2_0', loss='logcosh', channel_map={'no_25722_Weightedmean-ISOVF-in-tract-medial-lemniscus-right_2_0': 0}) +field_id_25723_weightedmean_isovf_in_tract_posterior_thalamic_radiation_left_2_0 = TensorMap('25723_Weightedmean-ISOVF-in-tract-posterior-thalamic-radiation-left_2_0', loss='logcosh', channel_map={'no_25723_Weightedmean-ISOVF-in-tract-posterior-thalamic-radiation-left_2_0': 0}) +field_id_25724_weightedmean_isovf_in_tract_posterior_thalamic_radiation_right_2_0 = TensorMap('25724_Weightedmean-ISOVF-in-tract-posterior-thalamic-radiation-right_2_0', loss='logcosh', channel_map={'no_25724_Weightedmean-ISOVF-in-tract-posterior-thalamic-radiation-right_2_0': 0}) +field_id_25725_weightedmean_isovf_in_tract_superior_longitudinal_fasciculus_left_2_0 = TensorMap('25725_Weightedmean-ISOVF-in-tract-superior-longitudinal-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25725_Weightedmean-ISOVF-in-tract-superior-longitudinal-fasciculus-left_2_0': 0}) +field_id_25726_weightedmean_isovf_in_tract_superior_longitudinal_fasciculus_right_2_0 = TensorMap('25726_Weightedmean-ISOVF-in-tract-superior-longitudinal-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25726_Weightedmean-ISOVF-in-tract-superior-longitudinal-fasciculus-right_2_0': 0}) +field_id_25727_weightedmean_isovf_in_tract_superior_thalamic_radiation_left_2_0 = TensorMap('25727_Weightedmean-ISOVF-in-tract-superior-thalamic-radiation-left_2_0', loss='logcosh', channel_map={'no_25727_Weightedmean-ISOVF-in-tract-superior-thalamic-radiation-left_2_0': 0}) +field_id_25728_weightedmean_isovf_in_tract_superior_thalamic_radiation_right_2_0 = TensorMap('25728_Weightedmean-ISOVF-in-tract-superior-thalamic-radiation-right_2_0', loss='logcosh', channel_map={'no_25728_Weightedmean-ISOVF-in-tract-superior-thalamic-radiation-right_2_0': 0}) +field_id_25729_weightedmean_isovf_in_tract_uncinate_fasciculus_left_2_0 = TensorMap('25729_Weightedmean-ISOVF-in-tract-uncinate-fasciculus-left_2_0', loss='logcosh', channel_map={'no_25729_Weightedmean-ISOVF-in-tract-uncinate-fasciculus-left_2_0': 0}) +field_id_25730_weightedmean_isovf_in_tract_uncinate_fasciculus_right_2_0 = TensorMap('25730_Weightedmean-ISOVF-in-tract-uncinate-fasciculus-right_2_0', loss='logcosh', channel_map={'no_25730_Weightedmean-ISOVF-in-tract-uncinate-fasciculus-right_2_0': 0}) +field_id_25731_discrepancy_between_t1_brain_image_and_standardspace_brain_template_linearlyaligned_2_0 = TensorMap('25731_Discrepancy-between-T1-brain-image-and-standardspace-brain-template-linearlyaligned_2_0', loss='logcosh', channel_map={'no_25731_Discrepancy-between-T1-brain-image-and-standardspace-brain-template-linearlyaligned_2_0': 0}) +field_id_25732_discrepancy_between_t1_brain_image_and_standardspace_brain_template_nonlinearlyaligned_2_0 = TensorMap('25732_Discrepancy-between-T1-brain-image-and-standardspace-brain-template-nonlinearlyaligned_2_0', loss='logcosh', channel_map={'no_25732_Discrepancy-between-T1-brain-image-and-standardspace-brain-template-nonlinearlyaligned_2_0': 0}) +field_id_25734_inverted_signaltonoise_ratio_in_t1_2_0 = TensorMap('25734_Inverted-signaltonoise-ratio-in-T1_2_0', loss='logcosh', channel_map={'no_25734_Inverted-signaltonoise-ratio-in-T1_2_0': 0}) +field_id_25735_inverted_contrasttonoise_ratio_in_t1_2_0 = TensorMap('25735_Inverted-contrasttonoise-ratio-in-T1_2_0', loss='logcosh', channel_map={'no_25735_Inverted-contrasttonoise-ratio-in-T1_2_0': 0}) +field_id_25736_discrepancy_between_t2_flair_brain_image_and_t1_brain_image_2_0 = TensorMap('25736_Discrepancy-between-T2-FLAIR-brain-image-and-T1-brain-image_2_0', loss='logcosh', channel_map={'no_25736_Discrepancy-between-T2-FLAIR-brain-image-and-T1-brain-image_2_0': 0}) +field_id_25737_discrepancy_between_dmri_brain_image_and_t1_brain_image_2_0 = TensorMap('25737_Discrepancy-between-dMRI-brain-image-and-T1-brain-image_2_0', loss='logcosh', channel_map={'no_25737_Discrepancy-between-dMRI-brain-image-and-T1-brain-image_2_0': 0}) +field_id_25738_discrepancy_between_swi_brain_image_and_t1_brain_image_2_0 = TensorMap('25738_Discrepancy-between-SWI-brain-image-and-T1-brain-image_2_0', loss='logcosh', channel_map={'no_25738_Discrepancy-between-SWI-brain-image-and-T1-brain-image_2_0': 0}) +field_id_25739_discrepancy_between_rfmri_brain_image_and_t1_brain_image_2_0 = TensorMap('25739_Discrepancy-between-rfMRI-brain-image-and-T1-brain-image_2_0', loss='logcosh', channel_map={'no_25739_Discrepancy-between-rfMRI-brain-image-and-T1-brain-image_2_0': 0}) +field_id_25781_total_volume_of_white_matter_hyperintensities_from_t1_and_t2flair_images_2_0 = TensorMap('25781_Total-volume-of-white-matter-hyperintensities-from-T1-and-T2FLAIR-images_2_0', loss='logcosh', channel_map={'no_25781_Total-volume-of-white-matter-hyperintensities-from-T1-and-T2FLAIR-images_2_0': 0}) +field_id_25782_volume_of_grey_matter_in_frontal_pole_left_2_0 = TensorMap('25782_Volume-of-grey-matter-in-Frontal-Pole-left_2_0', loss='logcosh', channel_map={'no_25782_Volume-of-grey-matter-in-Frontal-Pole-left_2_0': 0}) +field_id_25783_volume_of_grey_matter_in_frontal_pole_right_2_0 = TensorMap('25783_Volume-of-grey-matter-in-Frontal-Pole-right_2_0', loss='logcosh', channel_map={'no_25783_Volume-of-grey-matter-in-Frontal-Pole-right_2_0': 0}) +field_id_25784_volume_of_grey_matter_in_insular_cortex_left_2_0 = TensorMap('25784_Volume-of-grey-matter-in-Insular-Cortex-left_2_0', loss='logcosh', channel_map={'no_25784_Volume-of-grey-matter-in-Insular-Cortex-left_2_0': 0}) +field_id_25785_volume_of_grey_matter_in_insular_cortex_right_2_0 = TensorMap('25785_Volume-of-grey-matter-in-Insular-Cortex-right_2_0', loss='logcosh', channel_map={'no_25785_Volume-of-grey-matter-in-Insular-Cortex-right_2_0': 0}) +field_id_25786_volume_of_grey_matter_in_superior_frontal_gyrus_left_2_0 = TensorMap('25786_Volume-of-grey-matter-in-Superior-Frontal-Gyrus-left_2_0', loss='logcosh', channel_map={'no_25786_Volume-of-grey-matter-in-Superior-Frontal-Gyrus-left_2_0': 0}) +field_id_25787_volume_of_grey_matter_in_superior_frontal_gyrus_right_2_0 = TensorMap('25787_Volume-of-grey-matter-in-Superior-Frontal-Gyrus-right_2_0', loss='logcosh', channel_map={'no_25787_Volume-of-grey-matter-in-Superior-Frontal-Gyrus-right_2_0': 0}) +field_id_25788_volume_of_grey_matter_in_middle_frontal_gyrus_left_2_0 = TensorMap('25788_Volume-of-grey-matter-in-Middle-Frontal-Gyrus-left_2_0', loss='logcosh', channel_map={'no_25788_Volume-of-grey-matter-in-Middle-Frontal-Gyrus-left_2_0': 0}) +field_id_25789_volume_of_grey_matter_in_middle_frontal_gyrus_right_2_0 = TensorMap('25789_Volume-of-grey-matter-in-Middle-Frontal-Gyrus-right_2_0', loss='logcosh', channel_map={'no_25789_Volume-of-grey-matter-in-Middle-Frontal-Gyrus-right_2_0': 0}) +field_id_25790_volume_of_grey_matter_in_inferior_frontal_gyrus_pars_triangularis_left_2_0 = TensorMap('25790_Volume-of-grey-matter-in-Inferior-Frontal-Gyrus-pars-triangularis-left_2_0', loss='logcosh', channel_map={'no_25790_Volume-of-grey-matter-in-Inferior-Frontal-Gyrus-pars-triangularis-left_2_0': 0}) +field_id_25791_volume_of_grey_matter_in_inferior_frontal_gyrus_pars_triangularis_right_2_0 = TensorMap('25791_Volume-of-grey-matter-in-Inferior-Frontal-Gyrus-pars-triangularis-right_2_0', loss='logcosh', channel_map={'no_25791_Volume-of-grey-matter-in-Inferior-Frontal-Gyrus-pars-triangularis-right_2_0': 0}) +field_id_25792_volume_of_grey_matter_in_inferior_frontal_gyrus_pars_opercularis_left_2_0 = TensorMap('25792_Volume-of-grey-matter-in-Inferior-Frontal-Gyrus-pars-opercularis-left_2_0', loss='logcosh', channel_map={'no_25792_Volume-of-grey-matter-in-Inferior-Frontal-Gyrus-pars-opercularis-left_2_0': 0}) +field_id_25793_volume_of_grey_matter_in_inferior_frontal_gyrus_pars_opercularis_right_2_0 = TensorMap('25793_Volume-of-grey-matter-in-Inferior-Frontal-Gyrus-pars-opercularis-right_2_0', loss='logcosh', channel_map={'no_25793_Volume-of-grey-matter-in-Inferior-Frontal-Gyrus-pars-opercularis-right_2_0': 0}) +field_id_25794_volume_of_grey_matter_in_precentral_gyrus_left_2_0 = TensorMap('25794_Volume-of-grey-matter-in-Precentral-Gyrus-left_2_0', loss='logcosh', channel_map={'no_25794_Volume-of-grey-matter-in-Precentral-Gyrus-left_2_0': 0}) +field_id_25795_volume_of_grey_matter_in_precentral_gyrus_right_2_0 = TensorMap('25795_Volume-of-grey-matter-in-Precentral-Gyrus-right_2_0', loss='logcosh', channel_map={'no_25795_Volume-of-grey-matter-in-Precentral-Gyrus-right_2_0': 0}) +field_id_25796_volume_of_grey_matter_in_temporal_pole_left_2_0 = TensorMap('25796_Volume-of-grey-matter-in-Temporal-Pole-left_2_0', loss='logcosh', channel_map={'no_25796_Volume-of-grey-matter-in-Temporal-Pole-left_2_0': 0}) +field_id_25797_volume_of_grey_matter_in_temporal_pole_right_2_0 = TensorMap('25797_Volume-of-grey-matter-in-Temporal-Pole-right_2_0', loss='logcosh', channel_map={'no_25797_Volume-of-grey-matter-in-Temporal-Pole-right_2_0': 0}) +field_id_25798_volume_of_grey_matter_in_superior_temporal_gyrus_anterior_division_left_2_0 = TensorMap('25798_Volume-of-grey-matter-in-Superior-Temporal-Gyrus-anterior-division-left_2_0', loss='logcosh', channel_map={'no_25798_Volume-of-grey-matter-in-Superior-Temporal-Gyrus-anterior-division-left_2_0': 0}) +field_id_25799_volume_of_grey_matter_in_superior_temporal_gyrus_anterior_division_right_2_0 = TensorMap('25799_Volume-of-grey-matter-in-Superior-Temporal-Gyrus-anterior-division-right_2_0', loss='logcosh', channel_map={'no_25799_Volume-of-grey-matter-in-Superior-Temporal-Gyrus-anterior-division-right_2_0': 0}) +field_id_25800_volume_of_grey_matter_in_superior_temporal_gyrus_posterior_division_left_2_0 = TensorMap('25800_Volume-of-grey-matter-in-Superior-Temporal-Gyrus-posterior-division-left_2_0', loss='logcosh', channel_map={'no_25800_Volume-of-grey-matter-in-Superior-Temporal-Gyrus-posterior-division-left_2_0': 0}) +field_id_25801_volume_of_grey_matter_in_superior_temporal_gyrus_posterior_division_right_2_0 = TensorMap('25801_Volume-of-grey-matter-in-Superior-Temporal-Gyrus-posterior-division-right_2_0', loss='logcosh', channel_map={'no_25801_Volume-of-grey-matter-in-Superior-Temporal-Gyrus-posterior-division-right_2_0': 0}) +field_id_25802_volume_of_grey_matter_in_middle_temporal_gyrus_anterior_division_left_2_0 = TensorMap('25802_Volume-of-grey-matter-in-Middle-Temporal-Gyrus-anterior-division-left_2_0', loss='logcosh', channel_map={'no_25802_Volume-of-grey-matter-in-Middle-Temporal-Gyrus-anterior-division-left_2_0': 0}) +field_id_25803_volume_of_grey_matter_in_middle_temporal_gyrus_anterior_division_right_2_0 = TensorMap('25803_Volume-of-grey-matter-in-Middle-Temporal-Gyrus-anterior-division-right_2_0', loss='logcosh', channel_map={'no_25803_Volume-of-grey-matter-in-Middle-Temporal-Gyrus-anterior-division-right_2_0': 0}) +field_id_25804_volume_of_grey_matter_in_middle_temporal_gyrus_posterior_division_left_2_0 = TensorMap('25804_Volume-of-grey-matter-in-Middle-Temporal-Gyrus-posterior-division-left_2_0', loss='logcosh', channel_map={'no_25804_Volume-of-grey-matter-in-Middle-Temporal-Gyrus-posterior-division-left_2_0': 0}) +field_id_25805_volume_of_grey_matter_in_middle_temporal_gyrus_posterior_division_right_2_0 = TensorMap('25805_Volume-of-grey-matter-in-Middle-Temporal-Gyrus-posterior-division-right_2_0', loss='logcosh', channel_map={'no_25805_Volume-of-grey-matter-in-Middle-Temporal-Gyrus-posterior-division-right_2_0': 0}) +field_id_25806_volume_of_grey_matter_in_middle_temporal_gyrus_temporooccipital_part_left_2_0 = TensorMap('25806_Volume-of-grey-matter-in-Middle-Temporal-Gyrus-temporooccipital-part-left_2_0', loss='logcosh', channel_map={'no_25806_Volume-of-grey-matter-in-Middle-Temporal-Gyrus-temporooccipital-part-left_2_0': 0}) +field_id_25807_volume_of_grey_matter_in_middle_temporal_gyrus_temporooccipital_part_right_2_0 = TensorMap('25807_Volume-of-grey-matter-in-Middle-Temporal-Gyrus-temporooccipital-part-right_2_0', loss='logcosh', channel_map={'no_25807_Volume-of-grey-matter-in-Middle-Temporal-Gyrus-temporooccipital-part-right_2_0': 0}) +field_id_25808_volume_of_grey_matter_in_inferior_temporal_gyrus_anterior_division_left_2_0 = TensorMap('25808_Volume-of-grey-matter-in-Inferior-Temporal-Gyrus-anterior-division-left_2_0', loss='logcosh', channel_map={'no_25808_Volume-of-grey-matter-in-Inferior-Temporal-Gyrus-anterior-division-left_2_0': 0}) +field_id_25809_volume_of_grey_matter_in_inferior_temporal_gyrus_anterior_division_right_2_0 = TensorMap('25809_Volume-of-grey-matter-in-Inferior-Temporal-Gyrus-anterior-division-right_2_0', loss='logcosh', channel_map={'no_25809_Volume-of-grey-matter-in-Inferior-Temporal-Gyrus-anterior-division-right_2_0': 0}) +field_id_25810_volume_of_grey_matter_in_inferior_temporal_gyrus_posterior_division_left_2_0 = TensorMap('25810_Volume-of-grey-matter-in-Inferior-Temporal-Gyrus-posterior-division-left_2_0', loss='logcosh', channel_map={'no_25810_Volume-of-grey-matter-in-Inferior-Temporal-Gyrus-posterior-division-left_2_0': 0}) +field_id_25811_volume_of_grey_matter_in_inferior_temporal_gyrus_posterior_division_right_2_0 = TensorMap('25811_Volume-of-grey-matter-in-Inferior-Temporal-Gyrus-posterior-division-right_2_0', loss='logcosh', channel_map={'no_25811_Volume-of-grey-matter-in-Inferior-Temporal-Gyrus-posterior-division-right_2_0': 0}) +field_id_25812_volume_of_grey_matter_in_inferior_temporal_gyrus_temporooccipital_part_left_2_0 = TensorMap('25812_Volume-of-grey-matter-in-Inferior-Temporal-Gyrus-temporooccipital-part-left_2_0', loss='logcosh', channel_map={'no_25812_Volume-of-grey-matter-in-Inferior-Temporal-Gyrus-temporooccipital-part-left_2_0': 0}) +field_id_25813_volume_of_grey_matter_in_inferior_temporal_gyrus_temporooccipital_part_right_2_0 = TensorMap('25813_Volume-of-grey-matter-in-Inferior-Temporal-Gyrus-temporooccipital-part-right_2_0', loss='logcosh', channel_map={'no_25813_Volume-of-grey-matter-in-Inferior-Temporal-Gyrus-temporooccipital-part-right_2_0': 0}) +field_id_25814_volume_of_grey_matter_in_postcentral_gyrus_left_2_0 = TensorMap('25814_Volume-of-grey-matter-in-Postcentral-Gyrus-left_2_0', loss='logcosh', channel_map={'no_25814_Volume-of-grey-matter-in-Postcentral-Gyrus-left_2_0': 0}) +field_id_25815_volume_of_grey_matter_in_postcentral_gyrus_right_2_0 = TensorMap('25815_Volume-of-grey-matter-in-Postcentral-Gyrus-right_2_0', loss='logcosh', channel_map={'no_25815_Volume-of-grey-matter-in-Postcentral-Gyrus-right_2_0': 0}) +field_id_25816_volume_of_grey_matter_in_superior_parietal_lobule_left_2_0 = TensorMap('25816_Volume-of-grey-matter-in-Superior-Parietal-Lobule-left_2_0', loss='logcosh', channel_map={'no_25816_Volume-of-grey-matter-in-Superior-Parietal-Lobule-left_2_0': 0}) +field_id_25817_volume_of_grey_matter_in_superior_parietal_lobule_right_2_0 = TensorMap('25817_Volume-of-grey-matter-in-Superior-Parietal-Lobule-right_2_0', loss='logcosh', channel_map={'no_25817_Volume-of-grey-matter-in-Superior-Parietal-Lobule-right_2_0': 0}) +field_id_25818_volume_of_grey_matter_in_supramarginal_gyrus_anterior_division_left_2_0 = TensorMap('25818_Volume-of-grey-matter-in-Supramarginal-Gyrus-anterior-division-left_2_0', loss='logcosh', channel_map={'no_25818_Volume-of-grey-matter-in-Supramarginal-Gyrus-anterior-division-left_2_0': 0}) +field_id_25819_volume_of_grey_matter_in_supramarginal_gyrus_anterior_division_right_2_0 = TensorMap('25819_Volume-of-grey-matter-in-Supramarginal-Gyrus-anterior-division-right_2_0', loss='logcosh', channel_map={'no_25819_Volume-of-grey-matter-in-Supramarginal-Gyrus-anterior-division-right_2_0': 0}) +field_id_25820_volume_of_grey_matter_in_supramarginal_gyrus_posterior_division_left_2_0 = TensorMap('25820_Volume-of-grey-matter-in-Supramarginal-Gyrus-posterior-division-left_2_0', loss='logcosh', channel_map={'no_25820_Volume-of-grey-matter-in-Supramarginal-Gyrus-posterior-division-left_2_0': 0}) +field_id_25821_volume_of_grey_matter_in_supramarginal_gyrus_posterior_division_right_2_0 = TensorMap('25821_Volume-of-grey-matter-in-Supramarginal-Gyrus-posterior-division-right_2_0', loss='logcosh', channel_map={'no_25821_Volume-of-grey-matter-in-Supramarginal-Gyrus-posterior-division-right_2_0': 0}) +field_id_25822_volume_of_grey_matter_in_angular_gyrus_left_2_0 = TensorMap('25822_Volume-of-grey-matter-in-Angular-Gyrus-left_2_0', loss='logcosh', channel_map={'no_25822_Volume-of-grey-matter-in-Angular-Gyrus-left_2_0': 0}) +field_id_25823_volume_of_grey_matter_in_angular_gyrus_right_2_0 = TensorMap('25823_Volume-of-grey-matter-in-Angular-Gyrus-right_2_0', loss='logcosh', channel_map={'no_25823_Volume-of-grey-matter-in-Angular-Gyrus-right_2_0': 0}) +field_id_25824_volume_of_grey_matter_in_lateral_occipital_cortex_superior_division_left_2_0 = TensorMap('25824_Volume-of-grey-matter-in-Lateral-Occipital-Cortex-superior-division-left_2_0', loss='logcosh', channel_map={'no_25824_Volume-of-grey-matter-in-Lateral-Occipital-Cortex-superior-division-left_2_0': 0}) +field_id_25825_volume_of_grey_matter_in_lateral_occipital_cortex_superior_division_right_2_0 = TensorMap('25825_Volume-of-grey-matter-in-Lateral-Occipital-Cortex-superior-division-right_2_0', loss='logcosh', channel_map={'no_25825_Volume-of-grey-matter-in-Lateral-Occipital-Cortex-superior-division-right_2_0': 0}) +field_id_25826_volume_of_grey_matter_in_lateral_occipital_cortex_inferior_division_left_2_0 = TensorMap('25826_Volume-of-grey-matter-in-Lateral-Occipital-Cortex-inferior-division-left_2_0', loss='logcosh', channel_map={'no_25826_Volume-of-grey-matter-in-Lateral-Occipital-Cortex-inferior-division-left_2_0': 0}) +field_id_25827_volume_of_grey_matter_in_lateral_occipital_cortex_inferior_division_right_2_0 = TensorMap('25827_Volume-of-grey-matter-in-Lateral-Occipital-Cortex-inferior-division-right_2_0', loss='logcosh', channel_map={'no_25827_Volume-of-grey-matter-in-Lateral-Occipital-Cortex-inferior-division-right_2_0': 0}) +field_id_25828_volume_of_grey_matter_in_intracalcarine_cortex_left_2_0 = TensorMap('25828_Volume-of-grey-matter-in-Intracalcarine-Cortex-left_2_0', loss='logcosh', channel_map={'no_25828_Volume-of-grey-matter-in-Intracalcarine-Cortex-left_2_0': 0}) +field_id_25829_volume_of_grey_matter_in_intracalcarine_cortex_right_2_0 = TensorMap('25829_Volume-of-grey-matter-in-Intracalcarine-Cortex-right_2_0', loss='logcosh', channel_map={'no_25829_Volume-of-grey-matter-in-Intracalcarine-Cortex-right_2_0': 0}) +field_id_25830_volume_of_grey_matter_in_frontal_medial_cortex_left_2_0 = TensorMap('25830_Volume-of-grey-matter-in-Frontal-Medial-Cortex-left_2_0', loss='logcosh', channel_map={'no_25830_Volume-of-grey-matter-in-Frontal-Medial-Cortex-left_2_0': 0}) +field_id_25831_volume_of_grey_matter_in_frontal_medial_cortex_right_2_0 = TensorMap('25831_Volume-of-grey-matter-in-Frontal-Medial-Cortex-right_2_0', loss='logcosh', channel_map={'no_25831_Volume-of-grey-matter-in-Frontal-Medial-Cortex-right_2_0': 0}) +field_id_25832_volume_of_grey_matter_in_juxtapositional_lobule_cortex_formerly_supplementary_motor_cortex_left_2_0 = TensorMap('25832_Volume-of-grey-matter-in-Juxtapositional-Lobule-Cortex-formerly-Supplementary-Motor-Cortex-left_2_0', loss='logcosh', channel_map={'no_25832_Volume-of-grey-matter-in-Juxtapositional-Lobule-Cortex-formerly-Supplementary-Motor-Cortex-left_2_0': 0}) +field_id_25833_volume_of_grey_matter_in_juxtapositional_lobule_cortex_formerly_supplementary_motor_cortex_right_2_0 = TensorMap('25833_Volume-of-grey-matter-in-Juxtapositional-Lobule-Cortex-formerly-Supplementary-Motor-Cortex-right_2_0', loss='logcosh', channel_map={'no_25833_Volume-of-grey-matter-in-Juxtapositional-Lobule-Cortex-formerly-Supplementary-Motor-Cortex-right_2_0': 0}) +field_id_25834_volume_of_grey_matter_in_subcallosal_cortex_left_2_0 = TensorMap('25834_Volume-of-grey-matter-in-Subcallosal-Cortex-left_2_0', loss='logcosh', channel_map={'no_25834_Volume-of-grey-matter-in-Subcallosal-Cortex-left_2_0': 0}) +field_id_25835_volume_of_grey_matter_in_subcallosal_cortex_right_2_0 = TensorMap('25835_Volume-of-grey-matter-in-Subcallosal-Cortex-right_2_0', loss='logcosh', channel_map={'no_25835_Volume-of-grey-matter-in-Subcallosal-Cortex-right_2_0': 0}) +field_id_25836_volume_of_grey_matter_in_paracingulate_gyrus_left_2_0 = TensorMap('25836_Volume-of-grey-matter-in-Paracingulate-Gyrus-left_2_0', loss='logcosh', channel_map={'no_25836_Volume-of-grey-matter-in-Paracingulate-Gyrus-left_2_0': 0}) +field_id_25837_volume_of_grey_matter_in_paracingulate_gyrus_right_2_0 = TensorMap('25837_Volume-of-grey-matter-in-Paracingulate-Gyrus-right_2_0', loss='logcosh', channel_map={'no_25837_Volume-of-grey-matter-in-Paracingulate-Gyrus-right_2_0': 0}) +field_id_25838_volume_of_grey_matter_in_cingulate_gyrus_anterior_division_left_2_0 = TensorMap('25838_Volume-of-grey-matter-in-Cingulate-Gyrus-anterior-division-left_2_0', loss='logcosh', channel_map={'no_25838_Volume-of-grey-matter-in-Cingulate-Gyrus-anterior-division-left_2_0': 0}) +field_id_25839_volume_of_grey_matter_in_cingulate_gyrus_anterior_division_right_2_0 = TensorMap('25839_Volume-of-grey-matter-in-Cingulate-Gyrus-anterior-division-right_2_0', loss='logcosh', channel_map={'no_25839_Volume-of-grey-matter-in-Cingulate-Gyrus-anterior-division-right_2_0': 0}) +field_id_25840_volume_of_grey_matter_in_cingulate_gyrus_posterior_division_left_2_0 = TensorMap('25840_Volume-of-grey-matter-in-Cingulate-Gyrus-posterior-division-left_2_0', loss='logcosh', channel_map={'no_25840_Volume-of-grey-matter-in-Cingulate-Gyrus-posterior-division-left_2_0': 0}) +field_id_25841_volume_of_grey_matter_in_cingulate_gyrus_posterior_division_right_2_0 = TensorMap('25841_Volume-of-grey-matter-in-Cingulate-Gyrus-posterior-division-right_2_0', loss='logcosh', channel_map={'no_25841_Volume-of-grey-matter-in-Cingulate-Gyrus-posterior-division-right_2_0': 0}) +field_id_25842_volume_of_grey_matter_in_precuneous_cortex_left_2_0 = TensorMap('25842_Volume-of-grey-matter-in-Precuneous-Cortex-left_2_0', loss='logcosh', channel_map={'no_25842_Volume-of-grey-matter-in-Precuneous-Cortex-left_2_0': 0}) +field_id_25843_volume_of_grey_matter_in_precuneous_cortex_right_2_0 = TensorMap('25843_Volume-of-grey-matter-in-Precuneous-Cortex-right_2_0', loss='logcosh', channel_map={'no_25843_Volume-of-grey-matter-in-Precuneous-Cortex-right_2_0': 0}) +field_id_25844_volume_of_grey_matter_in_cuneal_cortex_left_2_0 = TensorMap('25844_Volume-of-grey-matter-in-Cuneal-Cortex-left_2_0', loss='logcosh', channel_map={'no_25844_Volume-of-grey-matter-in-Cuneal-Cortex-left_2_0': 0}) +field_id_25845_volume_of_grey_matter_in_cuneal_cortex_right_2_0 = TensorMap('25845_Volume-of-grey-matter-in-Cuneal-Cortex-right_2_0', loss='logcosh', channel_map={'no_25845_Volume-of-grey-matter-in-Cuneal-Cortex-right_2_0': 0}) +field_id_25846_volume_of_grey_matter_in_frontal_orbital_cortex_left_2_0 = TensorMap('25846_Volume-of-grey-matter-in-Frontal-Orbital-Cortex-left_2_0', loss='logcosh', channel_map={'no_25846_Volume-of-grey-matter-in-Frontal-Orbital-Cortex-left_2_0': 0}) +field_id_25847_volume_of_grey_matter_in_frontal_orbital_cortex_right_2_0 = TensorMap('25847_Volume-of-grey-matter-in-Frontal-Orbital-Cortex-right_2_0', loss='logcosh', channel_map={'no_25847_Volume-of-grey-matter-in-Frontal-Orbital-Cortex-right_2_0': 0}) +field_id_25848_volume_of_grey_matter_in_parahippocampal_gyrus_anterior_division_left_2_0 = TensorMap('25848_Volume-of-grey-matter-in-Parahippocampal-Gyrus-anterior-division-left_2_0', loss='logcosh', channel_map={'no_25848_Volume-of-grey-matter-in-Parahippocampal-Gyrus-anterior-division-left_2_0': 0}) +field_id_25849_volume_of_grey_matter_in_parahippocampal_gyrus_anterior_division_right_2_0 = TensorMap('25849_Volume-of-grey-matter-in-Parahippocampal-Gyrus-anterior-division-right_2_0', loss='logcosh', channel_map={'no_25849_Volume-of-grey-matter-in-Parahippocampal-Gyrus-anterior-division-right_2_0': 0}) +field_id_25850_volume_of_grey_matter_in_parahippocampal_gyrus_posterior_division_left_2_0 = TensorMap('25850_Volume-of-grey-matter-in-Parahippocampal-Gyrus-posterior-division-left_2_0', loss='logcosh', channel_map={'no_25850_Volume-of-grey-matter-in-Parahippocampal-Gyrus-posterior-division-left_2_0': 0}) +field_id_25851_volume_of_grey_matter_in_parahippocampal_gyrus_posterior_division_right_2_0 = TensorMap('25851_Volume-of-grey-matter-in-Parahippocampal-Gyrus-posterior-division-right_2_0', loss='logcosh', channel_map={'no_25851_Volume-of-grey-matter-in-Parahippocampal-Gyrus-posterior-division-right_2_0': 0}) +field_id_25852_volume_of_grey_matter_in_lingual_gyrus_left_2_0 = TensorMap('25852_Volume-of-grey-matter-in-Lingual-Gyrus-left_2_0', loss='logcosh', channel_map={'no_25852_Volume-of-grey-matter-in-Lingual-Gyrus-left_2_0': 0}) +field_id_25853_volume_of_grey_matter_in_lingual_gyrus_right_2_0 = TensorMap('25853_Volume-of-grey-matter-in-Lingual-Gyrus-right_2_0', loss='logcosh', channel_map={'no_25853_Volume-of-grey-matter-in-Lingual-Gyrus-right_2_0': 0}) +field_id_25854_volume_of_grey_matter_in_temporal_fusiform_cortex_anterior_division_left_2_0 = TensorMap('25854_Volume-of-grey-matter-in-Temporal-Fusiform-Cortex-anterior-division-left_2_0', loss='logcosh', channel_map={'no_25854_Volume-of-grey-matter-in-Temporal-Fusiform-Cortex-anterior-division-left_2_0': 0}) +field_id_25855_volume_of_grey_matter_in_temporal_fusiform_cortex_anterior_division_right_2_0 = TensorMap('25855_Volume-of-grey-matter-in-Temporal-Fusiform-Cortex-anterior-division-right_2_0', loss='logcosh', channel_map={'no_25855_Volume-of-grey-matter-in-Temporal-Fusiform-Cortex-anterior-division-right_2_0': 0}) +field_id_25856_volume_of_grey_matter_in_temporal_fusiform_cortex_posterior_division_left_2_0 = TensorMap('25856_Volume-of-grey-matter-in-Temporal-Fusiform-Cortex-posterior-division-left_2_0', loss='logcosh', channel_map={'no_25856_Volume-of-grey-matter-in-Temporal-Fusiform-Cortex-posterior-division-left_2_0': 0}) +field_id_25857_volume_of_grey_matter_in_temporal_fusiform_cortex_posterior_division_right_2_0 = TensorMap('25857_Volume-of-grey-matter-in-Temporal-Fusiform-Cortex-posterior-division-right_2_0', loss='logcosh', channel_map={'no_25857_Volume-of-grey-matter-in-Temporal-Fusiform-Cortex-posterior-division-right_2_0': 0}) +field_id_25858_volume_of_grey_matter_in_temporal_occipital_fusiform_cortex_left_2_0 = TensorMap('25858_Volume-of-grey-matter-in-Temporal-Occipital-Fusiform-Cortex-left_2_0', loss='logcosh', channel_map={'no_25858_Volume-of-grey-matter-in-Temporal-Occipital-Fusiform-Cortex-left_2_0': 0}) +field_id_25859_volume_of_grey_matter_in_temporal_occipital_fusiform_cortex_right_2_0 = TensorMap('25859_Volume-of-grey-matter-in-Temporal-Occipital-Fusiform-Cortex-right_2_0', loss='logcosh', channel_map={'no_25859_Volume-of-grey-matter-in-Temporal-Occipital-Fusiform-Cortex-right_2_0': 0}) +field_id_25860_volume_of_grey_matter_in_occipital_fusiform_gyrus_left_2_0 = TensorMap('25860_Volume-of-grey-matter-in-Occipital-Fusiform-Gyrus-left_2_0', loss='logcosh', channel_map={'no_25860_Volume-of-grey-matter-in-Occipital-Fusiform-Gyrus-left_2_0': 0}) +field_id_25861_volume_of_grey_matter_in_occipital_fusiform_gyrus_right_2_0 = TensorMap('25861_Volume-of-grey-matter-in-Occipital-Fusiform-Gyrus-right_2_0', loss='logcosh', channel_map={'no_25861_Volume-of-grey-matter-in-Occipital-Fusiform-Gyrus-right_2_0': 0}) +field_id_25862_volume_of_grey_matter_in_frontal_operculum_cortex_left_2_0 = TensorMap('25862_Volume-of-grey-matter-in-Frontal-Operculum-Cortex-left_2_0', loss='logcosh', channel_map={'no_25862_Volume-of-grey-matter-in-Frontal-Operculum-Cortex-left_2_0': 0}) +field_id_25863_volume_of_grey_matter_in_frontal_operculum_cortex_right_2_0 = TensorMap('25863_Volume-of-grey-matter-in-Frontal-Operculum-Cortex-right_2_0', loss='logcosh', channel_map={'no_25863_Volume-of-grey-matter-in-Frontal-Operculum-Cortex-right_2_0': 0}) +field_id_25864_volume_of_grey_matter_in_central_opercular_cortex_left_2_0 = TensorMap('25864_Volume-of-grey-matter-in-Central-Opercular-Cortex-left_2_0', loss='logcosh', channel_map={'no_25864_Volume-of-grey-matter-in-Central-Opercular-Cortex-left_2_0': 0}) +field_id_25865_volume_of_grey_matter_in_central_opercular_cortex_right_2_0 = TensorMap('25865_Volume-of-grey-matter-in-Central-Opercular-Cortex-right_2_0', loss='logcosh', channel_map={'no_25865_Volume-of-grey-matter-in-Central-Opercular-Cortex-right_2_0': 0}) +field_id_25866_volume_of_grey_matter_in_parietal_operculum_cortex_left_2_0 = TensorMap('25866_Volume-of-grey-matter-in-Parietal-Operculum-Cortex-left_2_0', loss='logcosh', channel_map={'no_25866_Volume-of-grey-matter-in-Parietal-Operculum-Cortex-left_2_0': 0}) +field_id_25867_volume_of_grey_matter_in_parietal_operculum_cortex_right_2_0 = TensorMap('25867_Volume-of-grey-matter-in-Parietal-Operculum-Cortex-right_2_0', loss='logcosh', channel_map={'no_25867_Volume-of-grey-matter-in-Parietal-Operculum-Cortex-right_2_0': 0}) +field_id_25868_volume_of_grey_matter_in_planum_polare_left_2_0 = TensorMap('25868_Volume-of-grey-matter-in-Planum-Polare-left_2_0', loss='logcosh', channel_map={'no_25868_Volume-of-grey-matter-in-Planum-Polare-left_2_0': 0}) +field_id_25869_volume_of_grey_matter_in_planum_polare_right_2_0 = TensorMap('25869_Volume-of-grey-matter-in-Planum-Polare-right_2_0', loss='logcosh', channel_map={'no_25869_Volume-of-grey-matter-in-Planum-Polare-right_2_0': 0}) +field_id_25870_volume_of_grey_matter_in_heschls_gyrus_includes_h1_and_h2_left_2_0 = TensorMap('25870_Volume-of-grey-matter-in-Heschls-Gyrus-includes-H1-and-H2-left_2_0', loss='logcosh', channel_map={'no_25870_Volume-of-grey-matter-in-Heschls-Gyrus-includes-H1-and-H2-left_2_0': 0}) +field_id_25871_volume_of_grey_matter_in_heschls_gyrus_includes_h1_and_h2_right_2_0 = TensorMap('25871_Volume-of-grey-matter-in-Heschls-Gyrus-includes-H1-and-H2-right_2_0', loss='logcosh', channel_map={'no_25871_Volume-of-grey-matter-in-Heschls-Gyrus-includes-H1-and-H2-right_2_0': 0}) +field_id_25872_volume_of_grey_matter_in_planum_temporale_left_2_0 = TensorMap('25872_Volume-of-grey-matter-in-Planum-Temporale-left_2_0', loss='logcosh', channel_map={'no_25872_Volume-of-grey-matter-in-Planum-Temporale-left_2_0': 0}) +field_id_25873_volume_of_grey_matter_in_planum_temporale_right_2_0 = TensorMap('25873_Volume-of-grey-matter-in-Planum-Temporale-right_2_0', loss='logcosh', channel_map={'no_25873_Volume-of-grey-matter-in-Planum-Temporale-right_2_0': 0}) +field_id_25874_volume_of_grey_matter_in_supracalcarine_cortex_left_2_0 = TensorMap('25874_Volume-of-grey-matter-in-Supracalcarine-Cortex-left_2_0', loss='logcosh', channel_map={'no_25874_Volume-of-grey-matter-in-Supracalcarine-Cortex-left_2_0': 0}) +field_id_25875_volume_of_grey_matter_in_supracalcarine_cortex_right_2_0 = TensorMap('25875_Volume-of-grey-matter-in-Supracalcarine-Cortex-right_2_0', loss='logcosh', channel_map={'no_25875_Volume-of-grey-matter-in-Supracalcarine-Cortex-right_2_0': 0}) +field_id_25876_volume_of_grey_matter_in_occipital_pole_left_2_0 = TensorMap('25876_Volume-of-grey-matter-in-Occipital-Pole-left_2_0', loss='logcosh', channel_map={'no_25876_Volume-of-grey-matter-in-Occipital-Pole-left_2_0': 0}) +field_id_25877_volume_of_grey_matter_in_occipital_pole_right_2_0 = TensorMap('25877_Volume-of-grey-matter-in-Occipital-Pole-right_2_0', loss='logcosh', channel_map={'no_25877_Volume-of-grey-matter-in-Occipital-Pole-right_2_0': 0}) +field_id_25878_volume_of_grey_matter_in_thalamus_left_2_0 = TensorMap('25878_Volume-of-grey-matter-in-Thalamus-left_2_0', loss='logcosh', channel_map={'no_25878_Volume-of-grey-matter-in-Thalamus-left_2_0': 0}) +field_id_25879_volume_of_grey_matter_in_thalamus_right_2_0 = TensorMap('25879_Volume-of-grey-matter-in-Thalamus-right_2_0', loss='logcosh', channel_map={'no_25879_Volume-of-grey-matter-in-Thalamus-right_2_0': 0}) +field_id_25880_volume_of_grey_matter_in_caudate_left_2_0 = TensorMap('25880_Volume-of-grey-matter-in-Caudate-left_2_0', loss='logcosh', channel_map={'no_25880_Volume-of-grey-matter-in-Caudate-left_2_0': 0}) +field_id_25881_volume_of_grey_matter_in_caudate_right_2_0 = TensorMap('25881_Volume-of-grey-matter-in-Caudate-right_2_0', loss='logcosh', channel_map={'no_25881_Volume-of-grey-matter-in-Caudate-right_2_0': 0}) +field_id_25882_volume_of_grey_matter_in_putamen_left_2_0 = TensorMap('25882_Volume-of-grey-matter-in-Putamen-left_2_0', loss='logcosh', channel_map={'no_25882_Volume-of-grey-matter-in-Putamen-left_2_0': 0}) +field_id_25883_volume_of_grey_matter_in_putamen_right_2_0 = TensorMap('25883_Volume-of-grey-matter-in-Putamen-right_2_0', loss='logcosh', channel_map={'no_25883_Volume-of-grey-matter-in-Putamen-right_2_0': 0}) +field_id_25884_volume_of_grey_matter_in_pallidum_left_2_0 = TensorMap('25884_Volume-of-grey-matter-in-Pallidum-left_2_0', loss='logcosh', channel_map={'no_25884_Volume-of-grey-matter-in-Pallidum-left_2_0': 0}) +field_id_25885_volume_of_grey_matter_in_pallidum_right_2_0 = TensorMap('25885_Volume-of-grey-matter-in-Pallidum-right_2_0', loss='logcosh', channel_map={'no_25885_Volume-of-grey-matter-in-Pallidum-right_2_0': 0}) +field_id_25886_volume_of_grey_matter_in_hippocampus_left_2_0 = TensorMap('25886_Volume-of-grey-matter-in-Hippocampus-left_2_0', loss='logcosh', channel_map={'no_25886_Volume-of-grey-matter-in-Hippocampus-left_2_0': 0}) +field_id_25887_volume_of_grey_matter_in_hippocampus_right_2_0 = TensorMap('25887_Volume-of-grey-matter-in-Hippocampus-right_2_0', loss='logcosh', channel_map={'no_25887_Volume-of-grey-matter-in-Hippocampus-right_2_0': 0}) +field_id_25888_volume_of_grey_matter_in_amygdala_left_2_0 = TensorMap('25888_Volume-of-grey-matter-in-Amygdala-left_2_0', loss='logcosh', channel_map={'no_25888_Volume-of-grey-matter-in-Amygdala-left_2_0': 0}) +field_id_25889_volume_of_grey_matter_in_amygdala_right_2_0 = TensorMap('25889_Volume-of-grey-matter-in-Amygdala-right_2_0', loss='logcosh', channel_map={'no_25889_Volume-of-grey-matter-in-Amygdala-right_2_0': 0}) +field_id_25890_volume_of_grey_matter_in_ventral_striatum_left_2_0 = TensorMap('25890_Volume-of-grey-matter-in-Ventral-Striatum-left_2_0', loss='logcosh', channel_map={'no_25890_Volume-of-grey-matter-in-Ventral-Striatum-left_2_0': 0}) +field_id_25891_volume_of_grey_matter_in_ventral_striatum_right_2_0 = TensorMap('25891_Volume-of-grey-matter-in-Ventral-Striatum-right_2_0', loss='logcosh', channel_map={'no_25891_Volume-of-grey-matter-in-Ventral-Striatum-right_2_0': 0}) +field_id_25892_volume_of_grey_matter_in_brainstem_2_0 = TensorMap('25892_Volume-of-grey-matter-in-BrainStem_2_0', loss='logcosh', channel_map={'no_25892_Volume-of-grey-matter-in-BrainStem_2_0': 0}) +field_id_25893_volume_of_grey_matter_in_iiv_cerebellum_left_2_0 = TensorMap('25893_Volume-of-grey-matter-in-IIV-Cerebellum-left_2_0', loss='logcosh', channel_map={'no_25893_Volume-of-grey-matter-in-IIV-Cerebellum-left_2_0': 0}) +field_id_25894_volume_of_grey_matter_in_iiv_cerebellum_right_2_0 = TensorMap('25894_Volume-of-grey-matter-in-IIV-Cerebellum-right_2_0', loss='logcosh', channel_map={'no_25894_Volume-of-grey-matter-in-IIV-Cerebellum-right_2_0': 0}) +field_id_25895_volume_of_grey_matter_in_v_cerebellum_left_2_0 = TensorMap('25895_Volume-of-grey-matter-in-V-Cerebellum-left_2_0', loss='logcosh', channel_map={'no_25895_Volume-of-grey-matter-in-V-Cerebellum-left_2_0': 0}) +field_id_25896_volume_of_grey_matter_in_v_cerebellum_right_2_0 = TensorMap('25896_Volume-of-grey-matter-in-V-Cerebellum-right_2_0', loss='logcosh', channel_map={'no_25896_Volume-of-grey-matter-in-V-Cerebellum-right_2_0': 0}) +field_id_25897_volume_of_grey_matter_in_vi_cerebellum_left_2_0 = TensorMap('25897_Volume-of-grey-matter-in-VI-Cerebellum-left_2_0', loss='logcosh', channel_map={'no_25897_Volume-of-grey-matter-in-VI-Cerebellum-left_2_0': 0}) +field_id_25898_volume_of_grey_matter_in_vi_cerebellum_vermis_2_0 = TensorMap('25898_Volume-of-grey-matter-in-VI-Cerebellum-vermis_2_0', loss='logcosh', channel_map={'no_25898_Volume-of-grey-matter-in-VI-Cerebellum-vermis_2_0': 0}) +field_id_25899_volume_of_grey_matter_in_vi_cerebellum_right_2_0 = TensorMap('25899_Volume-of-grey-matter-in-VI-Cerebellum-right_2_0', loss='logcosh', channel_map={'no_25899_Volume-of-grey-matter-in-VI-Cerebellum-right_2_0': 0}) +field_id_25900_volume_of_grey_matter_in_crus_i_cerebellum_left_2_0 = TensorMap('25900_Volume-of-grey-matter-in-Crus-I-Cerebellum-left_2_0', loss='logcosh', channel_map={'no_25900_Volume-of-grey-matter-in-Crus-I-Cerebellum-left_2_0': 0}) +field_id_25901_volume_of_grey_matter_in_crus_i_cerebellum_vermis_2_0 = TensorMap('25901_Volume-of-grey-matter-in-Crus-I-Cerebellum-vermis_2_0', loss='logcosh', channel_map={'no_25901_Volume-of-grey-matter-in-Crus-I-Cerebellum-vermis_2_0': 0}) +field_id_25902_volume_of_grey_matter_in_crus_i_cerebellum_right_2_0 = TensorMap('25902_Volume-of-grey-matter-in-Crus-I-Cerebellum-right_2_0', loss='logcosh', channel_map={'no_25902_Volume-of-grey-matter-in-Crus-I-Cerebellum-right_2_0': 0}) +field_id_25903_volume_of_grey_matter_in_crus_ii_cerebellum_left_2_0 = TensorMap('25903_Volume-of-grey-matter-in-Crus-II-Cerebellum-left_2_0', loss='logcosh', channel_map={'no_25903_Volume-of-grey-matter-in-Crus-II-Cerebellum-left_2_0': 0}) +field_id_25904_volume_of_grey_matter_in_crus_ii_cerebellum_vermis_2_0 = TensorMap('25904_Volume-of-grey-matter-in-Crus-II-Cerebellum-vermis_2_0', loss='logcosh', channel_map={'no_25904_Volume-of-grey-matter-in-Crus-II-Cerebellum-vermis_2_0': 0}) +field_id_25905_volume_of_grey_matter_in_crus_ii_cerebellum_right_2_0 = TensorMap('25905_Volume-of-grey-matter-in-Crus-II-Cerebellum-right_2_0', loss='logcosh', channel_map={'no_25905_Volume-of-grey-matter-in-Crus-II-Cerebellum-right_2_0': 0}) +field_id_25906_volume_of_grey_matter_in_viib_cerebellum_left_2_0 = TensorMap('25906_Volume-of-grey-matter-in-VIIb-Cerebellum-left_2_0', loss='logcosh', channel_map={'no_25906_Volume-of-grey-matter-in-VIIb-Cerebellum-left_2_0': 0}) +field_id_25907_volume_of_grey_matter_in_viib_cerebellum_vermis_2_0 = TensorMap('25907_Volume-of-grey-matter-in-VIIb-Cerebellum-vermis_2_0', loss='logcosh', channel_map={'no_25907_Volume-of-grey-matter-in-VIIb-Cerebellum-vermis_2_0': 0}) +field_id_25908_volume_of_grey_matter_in_viib_cerebellum_right_2_0 = TensorMap('25908_Volume-of-grey-matter-in-VIIb-Cerebellum-right_2_0', loss='logcosh', channel_map={'no_25908_Volume-of-grey-matter-in-VIIb-Cerebellum-right_2_0': 0}) +field_id_25909_volume_of_grey_matter_in_viiia_cerebellum_left_2_0 = TensorMap('25909_Volume-of-grey-matter-in-VIIIa-Cerebellum-left_2_0', loss='logcosh', channel_map={'no_25909_Volume-of-grey-matter-in-VIIIa-Cerebellum-left_2_0': 0}) +field_id_25910_volume_of_grey_matter_in_viiia_cerebellum_vermis_2_0 = TensorMap('25910_Volume-of-grey-matter-in-VIIIa-Cerebellum-vermis_2_0', loss='logcosh', channel_map={'no_25910_Volume-of-grey-matter-in-VIIIa-Cerebellum-vermis_2_0': 0}) +field_id_25911_volume_of_grey_matter_in_viiia_cerebellum_right_2_0 = TensorMap('25911_Volume-of-grey-matter-in-VIIIa-Cerebellum-right_2_0', loss='logcosh', channel_map={'no_25911_Volume-of-grey-matter-in-VIIIa-Cerebellum-right_2_0': 0}) +field_id_25912_volume_of_grey_matter_in_viiib_cerebellum_left_2_0 = TensorMap('25912_Volume-of-grey-matter-in-VIIIb-Cerebellum-left_2_0', loss='logcosh', channel_map={'no_25912_Volume-of-grey-matter-in-VIIIb-Cerebellum-left_2_0': 0}) +field_id_25913_volume_of_grey_matter_in_viiib_cerebellum_vermis_2_0 = TensorMap('25913_Volume-of-grey-matter-in-VIIIb-Cerebellum-vermis_2_0', loss='logcosh', channel_map={'no_25913_Volume-of-grey-matter-in-VIIIb-Cerebellum-vermis_2_0': 0}) +field_id_25914_volume_of_grey_matter_in_viiib_cerebellum_right_2_0 = TensorMap('25914_Volume-of-grey-matter-in-VIIIb-Cerebellum-right_2_0', loss='logcosh', channel_map={'no_25914_Volume-of-grey-matter-in-VIIIb-Cerebellum-right_2_0': 0}) +field_id_25915_volume_of_grey_matter_in_ix_cerebellum_left_2_0 = TensorMap('25915_Volume-of-grey-matter-in-IX-Cerebellum-left_2_0', loss='logcosh', channel_map={'no_25915_Volume-of-grey-matter-in-IX-Cerebellum-left_2_0': 0}) +field_id_25916_volume_of_grey_matter_in_ix_cerebellum_vermis_2_0 = TensorMap('25916_Volume-of-grey-matter-in-IX-Cerebellum-vermis_2_0', loss='logcosh', channel_map={'no_25916_Volume-of-grey-matter-in-IX-Cerebellum-vermis_2_0': 0}) +field_id_25917_volume_of_grey_matter_in_ix_cerebellum_right_2_0 = TensorMap('25917_Volume-of-grey-matter-in-IX-Cerebellum-right_2_0', loss='logcosh', channel_map={'no_25917_Volume-of-grey-matter-in-IX-Cerebellum-right_2_0': 0}) +field_id_25918_volume_of_grey_matter_in_x_cerebellum_left_2_0 = TensorMap('25918_Volume-of-grey-matter-in-X-Cerebellum-left_2_0', loss='logcosh', channel_map={'no_25918_Volume-of-grey-matter-in-X-Cerebellum-left_2_0': 0}) +field_id_25919_volume_of_grey_matter_in_x_cerebellum_vermis_2_0 = TensorMap('25919_Volume-of-grey-matter-in-X-Cerebellum-vermis_2_0', loss='logcosh', channel_map={'no_25919_Volume-of-grey-matter-in-X-Cerebellum-vermis_2_0': 0}) +field_id_25920_volume_of_grey_matter_in_x_cerebellum_right_2_0 = TensorMap('25920_Volume-of-grey-matter-in-X-Cerebellum-right_2_0', loss='logcosh', channel_map={'no_25920_Volume-of-grey-matter-in-X-Cerebellum-right_2_0': 0}) +field_id_2684_years_since_last_breast_cancer_screening_mammogram_0_0 = TensorMap('2684_Years-since-last-breast-cancer-screening-mammogram_0_0', loss='logcosh', channel_map={'no_2684_Years-since-last-breast-cancer-screening-mammogram_0_0': 0}) +field_id_2684_years_since_last_breast_cancer_screening_mammogram_2_0 = TensorMap('2684_Years-since-last-breast-cancer-screening-mammogram_2_0', loss='logcosh', channel_map={'no_2684_Years-since-last-breast-cancer-screening-mammogram_2_0': 0}) +field_id_2704_years_since_last_cervical_smear_test_0_0 = TensorMap('2704_Years-since-last-cervical-smear-test_0_0', loss='logcosh', channel_map={'no_2704_Years-since-last-cervical-smear-test_0_0': 0}) +field_id_2704_years_since_last_cervical_smear_test_2_0 = TensorMap('2704_Years-since-last-cervical-smear-test_2_0', loss='logcosh', channel_map={'no_2704_Years-since-last-cervical-smear-test_2_0': 0}) +field_id_2714_age_when_periods_started_menarche_0_0 = TensorMap('2714_Age-when-periods-started-menarche_0_0', loss='logcosh', channel_map={'no_2714_Age-when-periods-started-menarche_0_0': 0}) +field_id_2714_age_when_periods_started_menarche_2_0 = TensorMap('2714_Age-when-periods-started-menarche_2_0', loss='logcosh', channel_map={'no_2714_Age-when-periods-started-menarche_2_0': 0}) +field_id_2734_number_of_live_births_0_0 = TensorMap('2734_Number-of-live-births_0_0', loss='logcosh', channel_map={'no_2734_Number-of-live-births_0_0': 0}) +field_id_2734_number_of_live_births_2_0 = TensorMap('2734_Number-of-live-births_2_0', loss='logcosh', channel_map={'no_2734_Number-of-live-births_2_0': 0}) +field_id_2744_birth_weight_of_first_child_0_0 = TensorMap('2744_Birth-weight-of-first-child_0_0', loss='logcosh', channel_map={'no_2744_Birth-weight-of-first-child_0_0': 0}) +field_id_2744_birth_weight_of_first_child_2_0 = TensorMap('2744_Birth-weight-of-first-child_2_0', loss='logcosh', channel_map={'no_2744_Birth-weight-of-first-child_2_0': 0}) +field_id_2754_age_at_first_live_birth_0_0 = TensorMap('2754_Age-at-first-live-birth_0_0', loss='logcosh', channel_map={'no_2754_Age-at-first-live-birth_0_0': 0}) +field_id_2754_age_at_first_live_birth_2_0 = TensorMap('2754_Age-at-first-live-birth_2_0', loss='logcosh', channel_map={'no_2754_Age-at-first-live-birth_2_0': 0}) +field_id_2764_age_at_last_live_birth_0_0 = TensorMap('2764_Age-at-last-live-birth_0_0', loss='logcosh', channel_map={'no_2764_Age-at-last-live-birth_0_0': 0}) +field_id_2764_age_at_last_live_birth_2_0 = TensorMap('2764_Age-at-last-live-birth_2_0', loss='logcosh', channel_map={'no_2764_Age-at-last-live-birth_2_0': 0}) +field_id_2794_age_started_oral_contraceptive_pill_0_0 = TensorMap('2794_Age-started-oral-contraceptive-pill_0_0', loss='logcosh', channel_map={'no_2794_Age-started-oral-contraceptive-pill_0_0': 0}) +field_id_2794_age_started_oral_contraceptive_pill_2_0 = TensorMap('2794_Age-started-oral-contraceptive-pill_2_0', loss='logcosh', channel_map={'no_2794_Age-started-oral-contraceptive-pill_2_0': 0}) +field_id_2804_age_when_last_used_oral_contraceptive_pill_0_0 = TensorMap('2804_Age-when-last-used-oral-contraceptive-pill_0_0', loss='logcosh', channel_map={'no_2804_Age-when-last-used-oral-contraceptive-pill_0_0': 0}) +field_id_2804_age_when_last_used_oral_contraceptive_pill_2_0 = TensorMap('2804_Age-when-last-used-oral-contraceptive-pill_2_0', loss='logcosh', channel_map={'no_2804_Age-when-last-used-oral-contraceptive-pill_2_0': 0}) +field_id_2966_age_high_blood_pressure_diagnosed_2_0 = TensorMap('2966_Age-high-blood-pressure-diagnosed_2_0', loss='logcosh', channel_map={'no_2966_Age-high-blood-pressure-diagnosed_2_0': 0}) +field_id_3062_forced_vital_capacity_fvc_2_0 = TensorMap('3062_Forced-vital-capacity-FVC_2_0', loss='logcosh', channel_map={'no_3062_Forced-vital-capacity-FVC_2_0': 0}) +field_id_3062_forced_vital_capacity_fvc_2_1 = TensorMap('3062_Forced-vital-capacity-FVC_2_1', loss='logcosh', channel_map={'no_3062_Forced-vital-capacity-FVC_2_1': 0}) +field_id_3062_forced_vital_capacity_fvc_2_2 = TensorMap('3062_Forced-vital-capacity-FVC_2_2', loss='logcosh', channel_map={'no_3062_Forced-vital-capacity-FVC_2_2': 0}) +field_id_3063_forced_expiratory_volume_in_1second_fev1_2_0 = TensorMap('3063_Forced-expiratory-volume-in-1second-FEV1_2_0', loss='logcosh', channel_map={'no_3063_Forced-expiratory-volume-in-1second-FEV1_2_0': 0}) +field_id_3063_forced_expiratory_volume_in_1second_fev1_2_1 = TensorMap('3063_Forced-expiratory-volume-in-1second-FEV1_2_1', loss='logcosh', channel_map={'no_3063_Forced-expiratory-volume-in-1second-FEV1_2_1': 0}) +field_id_3063_forced_expiratory_volume_in_1second_fev1_2_2 = TensorMap('3063_Forced-expiratory-volume-in-1second-FEV1_2_2', loss='logcosh', channel_map={'no_3063_Forced-expiratory-volume-in-1second-FEV1_2_2': 0}) +field_id_3064_peak_expiratory_flow_pef_2_0 = TensorMap('3064_Peak-expiratory-flow-PEF_2_0', loss='logcosh', channel_map={'no_3064_Peak-expiratory-flow-PEF_2_0': 0}) +field_id_3064_peak_expiratory_flow_pef_2_1 = TensorMap('3064_Peak-expiratory-flow-PEF_2_1', loss='logcosh', channel_map={'no_3064_Peak-expiratory-flow-PEF_2_1': 0}) +field_id_3064_peak_expiratory_flow_pef_2_2 = TensorMap('3064_Peak-expiratory-flow-PEF_2_2', loss='logcosh', channel_map={'no_3064_Peak-expiratory-flow-PEF_2_2': 0}) +field_id_3526_mothers_age_at_death_0_0 = TensorMap('3526_Mothers-age-at-death_0_0', loss='logcosh', channel_map={'no_3526_Mothers-age-at-death_0_0': 0}) +field_id_3526_mothers_age_at_death_2_0 = TensorMap('3526_Mothers-age-at-death_2_0', loss='logcosh', channel_map={'no_3526_Mothers-age-at-death_2_0': 0}) +field_id_3581_age_at_menopause_last_menstrual_period_0_0 = TensorMap('3581_Age-at-menopause-last-menstrual-period_0_0', loss='logcosh', channel_map={'no_3581_Age-at-menopause-last-menstrual-period_0_0': 0}) +field_id_3581_age_at_menopause_last_menstrual_period_2_0 = TensorMap('3581_Age-at-menopause-last-menstrual-period_2_0', loss='logcosh', channel_map={'no_3581_Age-at-menopause-last-menstrual-period_2_0': 0}) +field_id_396_number_of_columns_displayed_in_round_2_1 = TensorMap('396_Number-of-columns-displayed-in-round_2_1', loss='logcosh', channel_map={'no_396_Number-of-columns-displayed-in-round_2_1': 0}) +field_id_396_number_of_columns_displayed_in_round_2_2 = TensorMap('396_Number-of-columns-displayed-in-round_2_2', loss='logcosh', channel_map={'no_396_Number-of-columns-displayed-in-round_2_2': 0}) +field_id_396_number_of_columns_displayed_in_round_2_3 = TensorMap('396_Number-of-columns-displayed-in-round_2_3', loss='logcosh', channel_map={'no_396_Number-of-columns-displayed-in-round_2_3': 0}) +field_id_397_number_of_rows_displayed_in_round_2_1 = TensorMap('397_Number-of-rows-displayed-in-round_2_1', loss='logcosh', channel_map={'no_397_Number-of-rows-displayed-in-round_2_1': 0}) +field_id_397_number_of_rows_displayed_in_round_2_2 = TensorMap('397_Number-of-rows-displayed-in-round_2_2', loss='logcosh', channel_map={'no_397_Number-of-rows-displayed-in-round_2_2': 0}) +field_id_398_number_of_correct_matches_in_round_2_1 = TensorMap('398_Number-of-correct-matches-in-round_2_1', loss='logcosh', channel_map={'no_398_Number-of-correct-matches-in-round_2_1': 0}) +field_id_398_number_of_correct_matches_in_round_2_2 = TensorMap('398_Number-of-correct-matches-in-round_2_2', loss='logcosh', channel_map={'no_398_Number-of-correct-matches-in-round_2_2': 0}) +field_id_398_number_of_correct_matches_in_round_2_3 = TensorMap('398_Number-of-correct-matches-in-round_2_3', loss='logcosh', channel_map={'no_398_Number-of-correct-matches-in-round_2_3': 0}) +field_id_399_number_of_incorrect_matches_in_round_2_1 = TensorMap('399_Number-of-incorrect-matches-in-round_2_1', loss='logcosh', channel_map={'no_399_Number-of-incorrect-matches-in-round_2_1': 0}) +field_id_399_number_of_incorrect_matches_in_round_2_2 = TensorMap('399_Number-of-incorrect-matches-in-round_2_2', loss='logcosh', channel_map={'no_399_Number-of-incorrect-matches-in-round_2_2': 0}) +field_id_399_number_of_incorrect_matches_in_round_2_3 = TensorMap('399_Number-of-incorrect-matches-in-round_2_3', loss='logcosh', channel_map={'no_399_Number-of-incorrect-matches-in-round_2_3': 0}) +field_id_400_time_to_complete_round_2_1 = TensorMap('400_Time-to-complete-round_2_1', loss='logcosh', channel_map={'no_400_Time-to-complete-round_2_1': 0}) +field_id_400_time_to_complete_round_2_2 = TensorMap('400_Time-to-complete-round_2_2', loss='logcosh', channel_map={'no_400_Time-to-complete-round_2_2': 0}) +field_id_400_time_to_complete_round_2_3 = TensorMap('400_Time-to-complete-round_2_3', loss='logcosh', channel_map={'no_400_Time-to-complete-round_2_3': 0}) +field_id_404_duration_to_first_press_of_snapbutton_in_each_round_2_1 = TensorMap('404_Duration-to-first-press-of-snapbutton-in-each-round_2_1', loss='logcosh', channel_map={'no_404_Duration-to-first-press-of-snapbutton-in-each-round_2_1': 0}) +field_id_404_duration_to_first_press_of_snapbutton_in_each_round_2_10 = TensorMap('404_Duration-to-first-press-of-snapbutton-in-each-round_2_10', loss='logcosh', channel_map={'no_404_Duration-to-first-press-of-snapbutton-in-each-round_2_10': 0}) +field_id_404_duration_to_first_press_of_snapbutton_in_each_round_2_11 = TensorMap('404_Duration-to-first-press-of-snapbutton-in-each-round_2_11', loss='logcosh', channel_map={'no_404_Duration-to-first-press-of-snapbutton-in-each-round_2_11': 0}) +field_id_404_duration_to_first_press_of_snapbutton_in_each_round_2_2 = TensorMap('404_Duration-to-first-press-of-snapbutton-in-each-round_2_2', loss='logcosh', channel_map={'no_404_Duration-to-first-press-of-snapbutton-in-each-round_2_2': 0}) +field_id_404_duration_to_first_press_of_snapbutton_in_each_round_2_5 = TensorMap('404_Duration-to-first-press-of-snapbutton-in-each-round_2_5', loss='logcosh', channel_map={'no_404_Duration-to-first-press-of-snapbutton-in-each-round_2_5': 0}) +field_id_404_duration_to_first_press_of_snapbutton_in_each_round_2_7 = TensorMap('404_Duration-to-first-press-of-snapbutton-in-each-round_2_7', loss='logcosh', channel_map={'no_404_Duration-to-first-press-of-snapbutton-in-each-round_2_7': 0}) +field_id_4079_diastolic_blood_pressure_automated_reading_2_0 = TensorMap('4079_Diastolic-blood-pressure-automated-reading_2_0', loss='logcosh', channel_map={'no_4079_Diastolic-blood-pressure-automated-reading_2_0': 0}) +field_id_4079_diastolic_blood_pressure_automated_reading_2_1 = TensorMap('4079_Diastolic-blood-pressure-automated-reading_2_1', loss='logcosh', channel_map={'no_4079_Diastolic-blood-pressure-automated-reading_2_1': 0}) +field_id_4080_systolic_blood_pressure_automated_reading_2_0 = TensorMap('4080_Systolic-blood-pressure-automated-reading_2_0', loss='logcosh', channel_map={'no_4080_Systolic-blood-pressure-automated-reading_2_0': 0}) +field_id_4080_systolic_blood_pressure_automated_reading_2_1 = TensorMap('4080_Systolic-blood-pressure-automated-reading_2_1', loss='logcosh', channel_map={'no_4080_Systolic-blood-pressure-automated-reading_2_1': 0}) +field_id_4194_pulse_rate_2_0 = TensorMap('4194_Pulse-rate_2_0', loss='logcosh', channel_map={'no_4194_Pulse-rate_2_0': 0}) +field_id_4195_pulse_wave_reflection_index_2_0 = TensorMap('4195_Pulse-wave-reflection-index_2_0', loss='logcosh', channel_map={'no_4195_Pulse-wave-reflection-index_2_0': 0}) +field_id_4196_pulse_wave_peak_to_peak_time_2_0 = TensorMap('4196_Pulse-wave-peak-to-peak-time_2_0', loss='logcosh', channel_map={'no_4196_Pulse-wave-peak-to-peak-time_2_0': 0}) +field_id_4198_position_of_the_pulse_wave_peak_2_0 = TensorMap('4198_Position-of-the-pulse-wave-peak_2_0', loss='logcosh', channel_map={'no_4198_Position-of-the-pulse-wave-peak_2_0': 0}) +field_id_4199_position_of_pulse_wave_notch_2_0 = TensorMap('4199_Position-of-pulse-wave-notch_2_0', loss='logcosh', channel_map={'no_4199_Position-of-pulse-wave-notch_2_0': 0}) +field_id_4200_position_of_the_shoulder_on_the_pulse_waveform_2_0 = TensorMap('4200_Position-of-the-shoulder-on-the-pulse-waveform_2_0', loss='logcosh', channel_map={'no_4200_Position-of-the-shoulder-on-the-pulse-waveform_2_0': 0}) +field_id_4256_time_elapsed_2_0 = TensorMap('4256_Time-elapsed_2_0', loss='logcosh', channel_map={'no_4256_Time-elapsed_2_0': 0}) +field_id_4256_time_elapsed_2_1 = TensorMap('4256_Time-elapsed_2_1', loss='logcosh', channel_map={'no_4256_Time-elapsed_2_1': 0}) +field_id_4256_time_elapsed_2_2 = TensorMap('4256_Time-elapsed_2_2', loss='logcosh', channel_map={'no_4256_Time-elapsed_2_2': 0}) +field_id_4256_time_elapsed_2_3 = TensorMap('4256_Time-elapsed_2_3', loss='logcosh', channel_map={'no_4256_Time-elapsed_2_3': 0}) +field_id_4256_time_elapsed_2_4 = TensorMap('4256_Time-elapsed_2_4', loss='logcosh', channel_map={'no_4256_Time-elapsed_2_4': 0}) +field_id_4256_time_elapsed_2_5 = TensorMap('4256_Time-elapsed_2_5', loss='logcosh', channel_map={'no_4256_Time-elapsed_2_5': 0}) +field_id_4256_time_elapsed_2_6 = TensorMap('4256_Time-elapsed_2_6', loss='logcosh', channel_map={'no_4256_Time-elapsed_2_6': 0}) +field_id_4260_round_of_numeric_memory_test_2_0 = TensorMap('4260_Round-of-numeric-memory-test_2_0', loss='logcosh', channel_map={'no_4260_Round-of-numeric-memory-test_2_0': 0}) +field_id_4260_round_of_numeric_memory_test_2_1 = TensorMap('4260_Round-of-numeric-memory-test_2_1', loss='logcosh', channel_map={'no_4260_Round-of-numeric-memory-test_2_1': 0}) +field_id_4260_round_of_numeric_memory_test_2_2 = TensorMap('4260_Round-of-numeric-memory-test_2_2', loss='logcosh', channel_map={'no_4260_Round-of-numeric-memory-test_2_2': 0}) +field_id_4260_round_of_numeric_memory_test_2_3 = TensorMap('4260_Round-of-numeric-memory-test_2_3', loss='logcosh', channel_map={'no_4260_Round-of-numeric-memory-test_2_3': 0}) +field_id_4260_round_of_numeric_memory_test_2_4 = TensorMap('4260_Round-of-numeric-memory-test_2_4', loss='logcosh', channel_map={'no_4260_Round-of-numeric-memory-test_2_4': 0}) +field_id_4260_round_of_numeric_memory_test_2_5 = TensorMap('4260_Round-of-numeric-memory-test_2_5', loss='logcosh', channel_map={'no_4260_Round-of-numeric-memory-test_2_5': 0}) +field_id_4260_round_of_numeric_memory_test_2_6 = TensorMap('4260_Round-of-numeric-memory-test_2_6', loss='logcosh', channel_map={'no_4260_Round-of-numeric-memory-test_2_6': 0}) +field_id_4269_number_of_triplets_attempted_left_2_0 = TensorMap('4269_Number-of-triplets-attempted-left_2_0', loss='logcosh', channel_map={'no_4269_Number-of-triplets-attempted-left_2_0': 0}) +field_id_4272_duration_of_hearing_test_left_2_0 = TensorMap('4272_Duration-of-hearing-test-left_2_0', loss='logcosh', channel_map={'no_4272_Duration-of-hearing-test-left_2_0': 0}) +field_id_4276_number_of_triplets_attempted_right_2_0 = TensorMap('4276_Number-of-triplets-attempted-right_2_0', loss='logcosh', channel_map={'no_4276_Number-of-triplets-attempted-right_2_0': 0}) +field_id_4279_duration_of_hearing_test_right_2_0 = TensorMap('4279_Duration-of-hearing-test-right_2_0', loss='logcosh', channel_map={'no_4279_Duration-of-hearing-test-right_2_0': 0}) +field_id_4282_maximum_digits_remembered_correctly_2_0 = TensorMap('4282_Maximum-digits-remembered-correctly_2_0', loss='logcosh', channel_map={'no_4282_Maximum-digits-remembered-correctly_2_0': 0}) +field_id_4283_number_of_rounds_of_numeric_memory_test_performed_2_0 = TensorMap('4283_Number-of-rounds-of-numeric-memory-test-performed_2_0', loss='logcosh', channel_map={'no_4283_Number-of-rounds-of-numeric-memory-test-performed_2_0': 0}) +field_id_4285_time_to_complete_test_2_0 = TensorMap('4285_Time-to-complete-test_2_0', loss='logcosh', channel_map={'no_4285_Time-to-complete-test_2_0': 0}) +field_id_4288_time_to_answer_2_0 = TensorMap('4288_Time-to-answer_2_0', loss='logcosh', channel_map={'no_4288_Time-to-answer_2_0': 0}) +field_id_4290_duration_screen_displayed_2_0 = TensorMap('4290_Duration-screen-displayed_2_0', loss='logcosh', channel_map={'no_4290_Duration-screen-displayed_2_0': 0}) +field_id_4291_number_of_attempts_2_0 = TensorMap('4291_Number-of-attempts_2_0', loss='logcosh', channel_map={'no_4291_Number-of-attempts_2_0': 0}) +field_id_46_hand_grip_strength_left_2_0 = TensorMap('46_Hand-grip-strength-left_2_0', loss='logcosh', channel_map={'no_46_Hand-grip-strength-left_2_0': 0}) +field_id_47_hand_grip_strength_right_2_0 = TensorMap('47_Hand-grip-strength-right_2_0', loss='logcosh', channel_map={'no_47_Hand-grip-strength-right_2_0': 0}) +field_id_48_waist_circumference_2_0 = TensorMap('48_Waist-circumference_2_0', loss='logcosh', channel_map={'no_48_Waist-circumference_2_0': 0}) +field_id_49_hip_circumference_2_0 = TensorMap('49_Hip-circumference_2_0', loss='logcosh', channel_map={'no_49_Hip-circumference_2_0': 0}) +field_id_5057_number_of_older_siblings_2_0 = TensorMap('5057_Number-of-older-siblings_2_0', loss='logcosh', channel_map={'no_5057_Number-of-older-siblings_2_0': 0}) +field_id_5074_number_of_letters_shown_in_round_left_0_2 = TensorMap('5074_Number-of-letters-shown-in-round-left_0_2', loss='logcosh', channel_map={'no_5074_Number-of-letters-shown-in-round-left_0_2': 0}) +field_id_5074_number_of_letters_shown_in_round_left_0_3 = TensorMap('5074_Number-of-letters-shown-in-round-left_0_3', loss='logcosh', channel_map={'no_5074_Number-of-letters-shown-in-round-left_0_3': 0}) +field_id_5074_number_of_letters_shown_in_round_left_0_4 = TensorMap('5074_Number-of-letters-shown-in-round-left_0_4', loss='logcosh', channel_map={'no_5074_Number-of-letters-shown-in-round-left_0_4': 0}) +field_id_5075_number_of_letters_shown_in_round_right_0_1 = TensorMap('5075_Number-of-letters-shown-in-round-right_0_1', loss='logcosh', channel_map={'no_5075_Number-of-letters-shown-in-round-right_0_1': 0}) +field_id_5075_number_of_letters_shown_in_round_right_0_2 = TensorMap('5075_Number-of-letters-shown-in-round-right_0_2', loss='logcosh', channel_map={'no_5075_Number-of-letters-shown-in-round-right_0_2': 0}) +field_id_5076_number_of_letters_correct_in_round_right_0_1 = TensorMap('5076_Number-of-letters-correct-in-round-right_0_1', loss='logcosh', channel_map={'no_5076_Number-of-letters-correct-in-round-right_0_1': 0}) +field_id_5076_number_of_letters_correct_in_round_right_0_2 = TensorMap('5076_Number-of-letters-correct-in-round-right_0_2', loss='logcosh', channel_map={'no_5076_Number-of-letters-correct-in-round-right_0_2': 0}) +field_id_5077_number_of_letters_correct_in_round_left_0_2 = TensorMap('5077_Number-of-letters-correct-in-round-left_0_2', loss='logcosh', channel_map={'no_5077_Number-of-letters-correct-in-round-left_0_2': 0}) +field_id_5077_number_of_letters_correct_in_round_left_0_3 = TensorMap('5077_Number-of-letters-correct-in-round-left_0_3', loss='logcosh', channel_map={'no_5077_Number-of-letters-correct-in-round-left_0_3': 0}) +field_id_5077_number_of_letters_correct_in_round_left_0_4 = TensorMap('5077_Number-of-letters-correct-in-round-left_0_4', loss='logcosh', channel_map={'no_5077_Number-of-letters-correct-in-round-left_0_4': 0}) +field_id_5078_logmar_in_round_left_0_2 = TensorMap('5078_logMAR-in-round-left_0_2', loss='logcosh', channel_map={'no_5078_logMAR-in-round-left_0_2': 0}) +field_id_5078_logmar_in_round_left_0_3 = TensorMap('5078_logMAR-in-round-left_0_3', loss='logcosh', channel_map={'no_5078_logMAR-in-round-left_0_3': 0}) +field_id_5078_logmar_in_round_left_0_4 = TensorMap('5078_logMAR-in-round-left_0_4', loss='logcosh', channel_map={'no_5078_logMAR-in-round-left_0_4': 0}) +field_id_5079_logmar_in_round_right_0_1 = TensorMap('5079_logMAR-in-round-right_0_1', loss='logcosh', channel_map={'no_5079_logMAR-in-round-right_0_1': 0}) +field_id_5079_logmar_in_round_right_0_2 = TensorMap('5079_logMAR-in-round-right_0_2', loss='logcosh', channel_map={'no_5079_logMAR-in-round-right_0_2': 0}) +field_id_5099_3mm_weak_meridian_right_0_2 = TensorMap('5099_3mm-weak-meridian-right_0_2', loss='logcosh', channel_map={'no_5099_3mm-weak-meridian-right_0_2': 0}) +field_id_50_standing_height_2_0 = TensorMap('50_Standing-height_2_0', loss='logcosh', channel_map={'no_50_Standing-height_2_0': 0}) +field_id_5100_3mm_weak_meridian_angle_right_0_2 = TensorMap('5100_3mm-weak-meridian-angle-right_0_2', loss='logcosh', channel_map={'no_5100_3mm-weak-meridian-angle-right_0_2': 0}) +field_id_5107_3mm_strong_meridian_angle_right_0_2 = TensorMap('5107_3mm-strong-meridian-angle-right_0_2', loss='logcosh', channel_map={'no_5107_3mm-strong-meridian-angle-right_0_2': 0}) +field_id_5108_3mm_asymmetry_angle_right_0_2 = TensorMap('5108_3mm-asymmetry-angle-right_0_2', loss='logcosh', channel_map={'no_5108_3mm-asymmetry-angle-right_0_2': 0}) +field_id_5115_3mm_cylindrical_power_angle_right_0_2 = TensorMap('5115_3mm-cylindrical-power-angle-right_0_2', loss='logcosh', channel_map={'no_5115_3mm-cylindrical-power-angle-right_0_2': 0}) +field_id_5116_3mm_cylindrical_power_right_0_2 = TensorMap('5116_3mm-cylindrical-power-right_0_2', loss='logcosh', channel_map={'no_5116_3mm-cylindrical-power-right_0_2': 0}) +field_id_5132_3mm_strong_meridian_right_0_2 = TensorMap('5132_3mm-strong-meridian-right_0_2', loss='logcosh', channel_map={'no_5132_3mm-strong-meridian-right_0_2': 0}) +field_id_5159_3mm_asymmetry_index_right_0_2 = TensorMap('5159_3mm-asymmetry-index-right_0_2', loss='logcosh', channel_map={'no_5159_3mm-asymmetry-index-right_0_2': 0}) +field_id_5160_3mm_regularity_index_right_0_2 = TensorMap('5160_3mm-regularity-index-right_0_2', loss='logcosh', channel_map={'no_5160_3mm-regularity-index-right_0_2': 0}) +field_id_51_seated_height_2_0 = TensorMap('51_Seated-height_2_0', loss='logcosh', channel_map={'no_51_Seated-height_2_0': 0}) +field_id_5364_average_weekly_intake_of_other_alcoholic_drinks_2_0 = TensorMap('5364_Average-weekly-intake-of-other-alcoholic-drinks_2_0', loss='logcosh', channel_map={'no_5364_Average-weekly-intake-of-other-alcoholic-drinks_2_0': 0}) +field_id_699_length_of_time_at_current_address_2_0 = TensorMap('699_Length-of-time-at-current-address_2_0', loss='logcosh', channel_map={'no_699_Length-of-time-at-current-address_2_0': 0}) +field_id_709_number_in_household_2_0 = TensorMap('709_Number-in-household_2_0', loss='logcosh', channel_map={'no_709_Number-in-household_2_0': 0}) +field_id_864_number_of_daysweek_walked_10_minutes_2_0 = TensorMap('864_Number-of-daysweek-walked-10-minutes_2_0', loss='logcosh', channel_map={'no_864_Number-of-daysweek-walked-10-minutes_2_0': 0}) +field_id_874_duration_of_walks_2_0 = TensorMap('874_Duration-of-walks_2_0', loss='logcosh', channel_map={'no_874_Duration-of-walks_2_0': 0}) +field_id_87_noncancer_illness_yearage_first_occurred_0_0 = TensorMap('87_Noncancer-illness-yearage-first-occurred_0_0', loss='logcosh', channel_map={'no_87_Noncancer-illness-yearage-first-occurred_0_0': 0}) +field_id_87_noncancer_illness_yearage_first_occurred_2_0 = TensorMap('87_Noncancer-illness-yearage-first-occurred_2_0', loss='logcosh', channel_map={'no_87_Noncancer-illness-yearage-first-occurred_2_0': 0}) +field_id_87_noncancer_illness_yearage_first_occurred_2_1 = TensorMap('87_Noncancer-illness-yearage-first-occurred_2_1', loss='logcosh', channel_map={'no_87_Noncancer-illness-yearage-first-occurred_2_1': 0}) +field_id_87_noncancer_illness_yearage_first_occurred_2_2 = TensorMap('87_Noncancer-illness-yearage-first-occurred_2_2', loss='logcosh', channel_map={'no_87_Noncancer-illness-yearage-first-occurred_2_2': 0}) +field_id_87_noncancer_illness_yearage_first_occurred_2_3 = TensorMap('87_Noncancer-illness-yearage-first-occurred_2_3', loss='logcosh', channel_map={'no_87_Noncancer-illness-yearage-first-occurred_2_3': 0}) +field_id_87_noncancer_illness_yearage_first_occurred_2_4 = TensorMap('87_Noncancer-illness-yearage-first-occurred_2_4', loss='logcosh', channel_map={'no_87_Noncancer-illness-yearage-first-occurred_2_4': 0}) +field_id_884_number_of_daysweek_of_moderate_physical_activity_10_minutes_2_0 = TensorMap('884_Number-of-daysweek-of-moderate-physical-activity-10-minutes_2_0', loss='logcosh', channel_map={'no_884_Number-of-daysweek-of-moderate-physical-activity-10-minutes_2_0': 0}) +field_id_90012_overall_acceleration_average_0_0 = TensorMap('90012_Overall-acceleration-average_0_0', loss='logcosh', channel_map={'no_90012_Overall-acceleration-average_0_0': 0}) +field_id_90013_standard_deviation_of_acceleration_0_0 = TensorMap('90013_Standard-deviation-of-acceleration_0_0', loss='logcosh', channel_map={'no_90013_Standard-deviation-of-acceleration_0_0': 0}) +field_id_904_number_of_daysweek_of_vigorous_physical_activity_10_minutes_2_0 = TensorMap('904_Number-of-daysweek-of-vigorous-physical-activity-10-minutes_2_0', loss='logcosh', channel_map={'no_904_Number-of-daysweek-of-vigorous-physical-activity-10-minutes_2_0': 0}) +field_id_92_operation_yearage_first_occurred_2_0 = TensorMap('92_Operation-yearage-first-occurred_2_0', loss='logcosh', channel_map={'no_92_Operation-yearage-first-occurred_2_0': 0}) +field_id_92_operation_yearage_first_occurred_2_1 = TensorMap('92_Operation-yearage-first-occurred_2_1', loss='logcosh', channel_map={'no_92_Operation-yearage-first-occurred_2_1': 0}) +field_id_3659_year_immigrated_to_uk_united_kingdom_0_0 = TensorMap('3659_Year-immigrated-to-UK-United-Kingdom_0_0', loss='logcosh', channel_map={'no_3659_Year-immigrated-to-UK-United-Kingdom_0_0': 0}) +field_id_20009_interpolated_age_of_participant_when_noncancer_illness_first_diagnosed_0_1 = TensorMap('20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_0_1', loss='logcosh', channel_map={'no_20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_0_1': 0}) +field_id_20009_interpolated_age_of_participant_when_noncancer_illness_first_diagnosed_0_2 = TensorMap('20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_0_2', loss='logcosh', channel_map={'no_20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_0_2': 0}) +field_id_3436_age_started_smoking_in_current_smokers_0_0 = TensorMap('3436_Age-started-smoking-in-current-smokers_0_0', loss='logcosh', channel_map={'no_3436_Age-started-smoking-in-current-smokers_0_0': 0}) +field_id_3456_number_of_cigarettes_currently_smoked_daily_current_cigarette_smokers_0_0 = TensorMap('3456_Number-of-cigarettes-currently-smoked-daily-current-cigarette-smokers_0_0', loss='logcosh', channel_map={'no_3456_Number-of-cigarettes-currently-smoked-daily-current-cigarette-smokers_0_0': 0}) +field_id_3536_age_started_hormonereplacement_therapy_hrt_0_0 = TensorMap('3536_Age-started-hormonereplacement-therapy-HRT_0_0', loss='logcosh', channel_map={'no_3536_Age-started-hormonereplacement-therapy-HRT_0_0': 0}) +field_id_3546_age_last_used_hormonereplacement_therapy_hrt_0_0 = TensorMap('3546_Age-last-used-hormonereplacement-therapy-HRT_0_0', loss='logcosh', channel_map={'no_3546_Age-last-used-hormonereplacement-therapy-HRT_0_0': 0}) +field_id_3992_age_emphysemachronic_bronchitis_diagnosed_0_0 = TensorMap('3992_Age-emphysemachronic-bronchitis-diagnosed_0_0', loss='logcosh', channel_map={'no_3992_Age-emphysemachronic-bronchitis-diagnosed_0_0': 0}) +field_id_87_noncancer_illness_yearage_first_occurred_0_1 = TensorMap('87_Noncancer-illness-yearage-first-occurred_0_1', loss='logcosh', channel_map={'no_87_Noncancer-illness-yearage-first-occurred_0_1': 0}) +field_id_87_noncancer_illness_yearage_first_occurred_0_2 = TensorMap('87_Noncancer-illness-yearage-first-occurred_0_2', loss='logcosh', channel_map={'no_87_Noncancer-illness-yearage-first-occurred_0_2': 0}) +field_id_404_duration_to_first_press_of_snapbutton_in_each_round_0_0 = TensorMap('404_Duration-to-first-press-of-snapbutton-in-each-round_0_0', loss='logcosh', channel_map={'no_404_Duration-to-first-press-of-snapbutton-in-each-round_0_0': 0}) +field_id_20009_interpolated_age_of_participant_when_noncancer_illness_first_diagnosed_0_3 = TensorMap('20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_0_3', loss='logcosh', channel_map={'no_20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_0_3': 0}) +field_id_3761_age_hay_fever_rhinitis_or_eczema_diagnosed_0_0 = TensorMap('3761_Age-hay-fever-rhinitis-or-eczema-diagnosed_0_0', loss='logcosh', channel_map={'no_3761_Age-hay-fever-rhinitis-or-eczema-diagnosed_0_0': 0}) +field_id_3786_age_asthma_diagnosed_0_0 = TensorMap('3786_Age-asthma-diagnosed_0_0', loss='logcosh', channel_map={'no_3786_Age-asthma-diagnosed_0_0': 0}) +field_id_87_noncancer_illness_yearage_first_occurred_0_3 = TensorMap('87_Noncancer-illness-yearage-first-occurred_0_3', loss='logcosh', channel_map={'no_87_Noncancer-illness-yearage-first-occurred_0_3': 0}) +field_id_5075_number_of_letters_shown_in_round_right_0_3 = TensorMap('5075_Number-of-letters-shown-in-round-right_0_3', loss='logcosh', channel_map={'no_5075_Number-of-letters-shown-in-round-right_0_3': 0}) +field_id_5075_number_of_letters_shown_in_round_right_0_4 = TensorMap('5075_Number-of-letters-shown-in-round-right_0_4', loss='logcosh', channel_map={'no_5075_Number-of-letters-shown-in-round-right_0_4': 0}) +field_id_5076_number_of_letters_correct_in_round_right_0_3 = TensorMap('5076_Number-of-letters-correct-in-round-right_0_3', loss='logcosh', channel_map={'no_5076_Number-of-letters-correct-in-round-right_0_3': 0}) +field_id_5076_number_of_letters_correct_in_round_right_0_4 = TensorMap('5076_Number-of-letters-correct-in-round-right_0_4', loss='logcosh', channel_map={'no_5076_Number-of-letters-correct-in-round-right_0_4': 0}) +field_id_5079_logmar_in_round_right_0_3 = TensorMap('5079_logMAR-in-round-right_0_3', loss='logcosh', channel_map={'no_5079_logMAR-in-round-right_0_3': 0}) +field_id_5079_logmar_in_round_right_0_4 = TensorMap('5079_logMAR-in-round-right_0_4', loss='logcosh', channel_map={'no_5079_logMAR-in-round-right_0_4': 0}) +field_id_102_pulse_rate_automated_reading_1_0 = TensorMap('102_Pulse-rate-automated-reading_1_0', loss='logcosh', channel_map={'no_102_Pulse-rate-automated-reading_1_0': 0}) +field_id_102_pulse_rate_automated_reading_1_1 = TensorMap('102_Pulse-rate-automated-reading_1_1', loss='logcosh', channel_map={'no_102_Pulse-rate-automated-reading_1_1': 0}) +field_id_1050_time_spend_outdoors_in_summer_1_0 = TensorMap('1050_Time-spend-outdoors-in-summer_1_0', loss='logcosh', channel_map={'no_1050_Time-spend-outdoors-in-summer_1_0': 0}) +field_id_1060_time_spent_outdoors_in_winter_1_0 = TensorMap('1060_Time-spent-outdoors-in-winter_1_0', loss='logcosh', channel_map={'no_1060_Time-spent-outdoors-in-winter_1_0': 0}) +field_id_1070_time_spent_watching_television_tv_1_0 = TensorMap('1070_Time-spent-watching-television-TV_1_0', loss='logcosh', channel_map={'no_1070_Time-spent-watching-television-TV_1_0': 0}) +field_id_1080_time_spent_using_computer_1_0 = TensorMap('1080_Time-spent-using-computer_1_0', loss='logcosh', channel_map={'no_1080_Time-spent-using-computer_1_0': 0}) +field_id_1090_time_spent_driving_1_0 = TensorMap('1090_Time-spent-driving_1_0', loss='logcosh', channel_map={'no_1090_Time-spent-driving_1_0': 0}) +field_id_1269_exposure_to_tobacco_smoke_at_home_1_0 = TensorMap('1269_Exposure-to-tobacco-smoke-at-home_1_0', loss='logcosh', channel_map={'no_1269_Exposure-to-tobacco-smoke-at-home_1_0': 0}) +field_id_1279_exposure_to_tobacco_smoke_outside_home_1_0 = TensorMap('1279_Exposure-to-tobacco-smoke-outside-home_1_0', loss='logcosh', channel_map={'no_1279_Exposure-to-tobacco-smoke-outside-home_1_0': 0}) +field_id_1289_cooked_vegetable_intake_1_0 = TensorMap('1289_Cooked-vegetable-intake_1_0', loss='logcosh', channel_map={'no_1289_Cooked-vegetable-intake_1_0': 0}) +field_id_1299_salad_raw_vegetable_intake_1_0 = TensorMap('1299_Salad-raw-vegetable-intake_1_0', loss='logcosh', channel_map={'no_1299_Salad-raw-vegetable-intake_1_0': 0}) +field_id_129_place_of_birth_in_uk_north_coordinate_1_0 = TensorMap('129_Place-of-birth-in-UK-north-coordinate_1_0', loss='logcosh', channel_map={'no_129_Place-of-birth-in-UK-north-coordinate_1_0': 0}) +field_id_1309_fresh_fruit_intake_1_0 = TensorMap('1309_Fresh-fruit-intake_1_0', loss='logcosh', channel_map={'no_1309_Fresh-fruit-intake_1_0': 0}) +field_id_130_place_of_birth_in_uk_east_coordinate_1_0 = TensorMap('130_Place-of-birth-in-UK-east-coordinate_1_0', loss='logcosh', channel_map={'no_130_Place-of-birth-in-UK-east-coordinate_1_0': 0}) +field_id_1319_dried_fruit_intake_1_0 = TensorMap('1319_Dried-fruit-intake_1_0', loss='logcosh', channel_map={'no_1319_Dried-fruit-intake_1_0': 0}) +field_id_134_number_of_selfreported_cancers_1_0 = TensorMap('134_Number-of-selfreported-cancers_1_0', loss='logcosh', channel_map={'no_134_Number-of-selfreported-cancers_1_0': 0}) +field_id_135_number_of_selfreported_noncancer_illnesses_1_0 = TensorMap('135_Number-of-selfreported-noncancer-illnesses_1_0', loss='logcosh', channel_map={'no_135_Number-of-selfreported-noncancer-illnesses_1_0': 0}) +field_id_136_number_of_operations_selfreported_1_0 = TensorMap('136_Number-of-operations-selfreported_1_0', loss='logcosh', channel_map={'no_136_Number-of-operations-selfreported_1_0': 0}) +field_id_137_number_of_treatmentsmedications_taken_1_0 = TensorMap('137_Number-of-treatmentsmedications-taken_1_0', loss='logcosh', channel_map={'no_137_Number-of-treatmentsmedications-taken_1_0': 0}) +field_id_1438_bread_intake_1_0 = TensorMap('1438_Bread-intake_1_0', loss='logcosh', channel_map={'no_1438_Bread-intake_1_0': 0}) +field_id_1458_cereal_intake_1_0 = TensorMap('1458_Cereal-intake_1_0', loss='logcosh', channel_map={'no_1458_Cereal-intake_1_0': 0}) +field_id_1488_tea_intake_1_0 = TensorMap('1488_Tea-intake_1_0', loss='logcosh', channel_map={'no_1488_Tea-intake_1_0': 0}) +field_id_1498_coffee_intake_1_0 = TensorMap('1498_Coffee-intake_1_0', loss='logcosh', channel_map={'no_1498_Coffee-intake_1_0': 0}) +field_id_1528_water_intake_1_0 = TensorMap('1528_Water-intake_1_0', loss='logcosh', channel_map={'no_1528_Water-intake_1_0': 0}) +field_id_1568_average_weekly_red_wine_intake_1_0 = TensorMap('1568_Average-weekly-red-wine-intake_1_0', loss='logcosh', channel_map={'no_1568_Average-weekly-red-wine-intake_1_0': 0}) +field_id_1578_average_weekly_champagne_plus_white_wine_intake_1_0 = TensorMap('1578_Average-weekly-champagne-plus-white-wine-intake_1_0', loss='logcosh', channel_map={'no_1578_Average-weekly-champagne-plus-white-wine-intake_1_0': 0}) +field_id_1588_average_weekly_beer_plus_cider_intake_1_0 = TensorMap('1588_Average-weekly-beer-plus-cider-intake_1_0', loss='logcosh', channel_map={'no_1588_Average-weekly-beer-plus-cider-intake_1_0': 0}) +field_id_1598_average_weekly_spirits_intake_1_0 = TensorMap('1598_Average-weekly-spirits-intake_1_0', loss='logcosh', channel_map={'no_1598_Average-weekly-spirits-intake_1_0': 0}) +field_id_1608_average_weekly_fortified_wine_intake_1_0 = TensorMap('1608_Average-weekly-fortified-wine-intake_1_0', loss='logcosh', channel_map={'no_1608_Average-weekly-fortified-wine-intake_1_0': 0}) +field_id_1737_childhood_sunburn_occasions_1_0 = TensorMap('1737_Childhood-sunburn-occasions_1_0', loss='logcosh', channel_map={'no_1737_Childhood-sunburn-occasions_1_0': 0}) +field_id_1807_fathers_age_at_death_1_0 = TensorMap('1807_Fathers-age-at-death_1_0', loss='logcosh', channel_map={'no_1807_Fathers-age-at-death_1_0': 0}) +field_id_1873_number_of_full_brothers_1_0 = TensorMap('1873_Number-of-full-brothers_1_0', loss='logcosh', channel_map={'no_1873_Number-of-full-brothers_1_0': 0}) +field_id_1883_number_of_full_sisters_1_0 = TensorMap('1883_Number-of-full-sisters_1_0', loss='logcosh', channel_map={'no_1883_Number-of-full-sisters_1_0': 0}) +field_id_20009_interpolated_age_of_participant_when_noncancer_illness_first_diagnosed_0_4 = TensorMap('20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_0_4', loss='logcosh', channel_map={'no_20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_0_4': 0}) +field_id_20009_interpolated_age_of_participant_when_noncancer_illness_first_diagnosed_1_0 = TensorMap('20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_1_0', loss='logcosh', channel_map={'no_20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_1_0': 0}) +field_id_20009_interpolated_age_of_participant_when_noncancer_illness_first_diagnosed_1_1 = TensorMap('20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_1_1', loss='logcosh', channel_map={'no_20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_1_1': 0}) +field_id_20009_interpolated_age_of_participant_when_noncancer_illness_first_diagnosed_1_2 = TensorMap('20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_1_2', loss='logcosh', channel_map={'no_20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_1_2': 0}) +field_id_20011_interpolated_age_of_participant_when_operation_took_place_1_0 = TensorMap('20011_Interpolated-Age-of-participant-when-operation-took-place_1_0', loss='logcosh', channel_map={'no_20011_Interpolated-Age-of-participant-when-operation-took-place_1_0': 0}) +field_id_20015_sitting_height_1_0 = TensorMap('20015_Sitting-height_1_0', loss='logcosh', channel_map={'no_20015_Sitting-height_1_0': 0}) +field_id_20022_birth_weight_1_0 = TensorMap('20022_Birth-weight_1_0', loss='logcosh', channel_map={'no_20022_Birth-weight_1_0': 0}) +field_id_20023_mean_time_to_correctly_identify_matches_1_0 = TensorMap('20023_Mean-time-to-correctly-identify-matches_1_0', loss='logcosh', channel_map={'no_20023_Mean-time-to-correctly-identify-matches_1_0': 0}) +field_id_21001_body_mass_index_bmi_1_0 = TensorMap('21001_Body-mass-index-BMI_1_0', loss='logcosh', channel_map={'no_21001_Body-mass-index-BMI_1_0': 0}) +field_id_21002_weight_1_0 = TensorMap('21002_Weight_1_0', loss='logcosh', channel_map={'no_21002_Weight_1_0': 0}) +field_id_21003_age_when_attended_assessment_centre_1_0 = TensorMap('21003_Age-when-attended-assessment-centre_1_0', loss='logcosh', channel_map={'no_21003_Age-when-attended-assessment-centre_1_0': 0}) +field_id_21021_pulse_wave_arterial_stiffness_index_1_0 = TensorMap('21021_Pulse-wave-Arterial-Stiffness-index_1_0', loss='logcosh', channel_map={'no_21021_Pulse-wave-Arterial-Stiffness-index_1_0': 0}) +field_id_2217_age_started_wearing_glasses_or_contact_lenses_1_0 = TensorMap('2217_Age-started-wearing-glasses-or-contact-lenses_1_0', loss='logcosh', channel_map={'no_2217_Age-started-wearing-glasses-or-contact-lenses_1_0': 0}) +field_id_2277_frequency_of_solariumsunlamp_use_1_0 = TensorMap('2277_Frequency-of-solariumsunlamp-use_1_0', loss='logcosh', channel_map={'no_2277_Frequency-of-solariumsunlamp-use_1_0': 0}) +field_id_23098_weight_1_0 = TensorMap('23098_Weight_1_0', loss='logcosh', channel_map={'no_23098_Weight_1_0': 0}) +field_id_23099_body_fat_percentage_1_0 = TensorMap('23099_Body-fat-percentage_1_0', loss='logcosh', channel_map={'no_23099_Body-fat-percentage_1_0': 0}) +field_id_23100_whole_body_fat_mass_1_0 = TensorMap('23100_Whole-body-fat-mass_1_0', loss='logcosh', channel_map={'no_23100_Whole-body-fat-mass_1_0': 0}) +field_id_23101_whole_body_fatfree_mass_1_0 = TensorMap('23101_Whole-body-fatfree-mass_1_0', loss='logcosh', channel_map={'no_23101_Whole-body-fatfree-mass_1_0': 0}) +field_id_23102_whole_body_water_mass_1_0 = TensorMap('23102_Whole-body-water-mass_1_0', loss='logcosh', channel_map={'no_23102_Whole-body-water-mass_1_0': 0}) +field_id_23104_body_mass_index_bmi_1_0 = TensorMap('23104_Body-mass-index-BMI_1_0', loss='logcosh', channel_map={'no_23104_Body-mass-index-BMI_1_0': 0}) +field_id_23105_basal_metabolic_rate_1_0 = TensorMap('23105_Basal-metabolic-rate_1_0', loss='logcosh', channel_map={'no_23105_Basal-metabolic-rate_1_0': 0}) +field_id_23106_impedance_of_whole_body_1_0 = TensorMap('23106_Impedance-of-whole-body_1_0', loss='logcosh', channel_map={'no_23106_Impedance-of-whole-body_1_0': 0}) +field_id_23107_impedance_of_leg_right_1_0 = TensorMap('23107_Impedance-of-leg-right_1_0', loss='logcosh', channel_map={'no_23107_Impedance-of-leg-right_1_0': 0}) +field_id_23108_impedance_of_leg_left_1_0 = TensorMap('23108_Impedance-of-leg-left_1_0', loss='logcosh', channel_map={'no_23108_Impedance-of-leg-left_1_0': 0}) +field_id_23109_impedance_of_arm_right_1_0 = TensorMap('23109_Impedance-of-arm-right_1_0', loss='logcosh', channel_map={'no_23109_Impedance-of-arm-right_1_0': 0}) +field_id_23110_impedance_of_arm_left_1_0 = TensorMap('23110_Impedance-of-arm-left_1_0', loss='logcosh', channel_map={'no_23110_Impedance-of-arm-left_1_0': 0}) +field_id_23111_leg_fat_percentage_right_1_0 = TensorMap('23111_Leg-fat-percentage-right_1_0', loss='logcosh', channel_map={'no_23111_Leg-fat-percentage-right_1_0': 0}) +field_id_23112_leg_fat_mass_right_1_0 = TensorMap('23112_Leg-fat-mass-right_1_0', loss='logcosh', channel_map={'no_23112_Leg-fat-mass-right_1_0': 0}) +field_id_23113_leg_fatfree_mass_right_1_0 = TensorMap('23113_Leg-fatfree-mass-right_1_0', loss='logcosh', channel_map={'no_23113_Leg-fatfree-mass-right_1_0': 0}) +field_id_23114_leg_predicted_mass_right_1_0 = TensorMap('23114_Leg-predicted-mass-right_1_0', loss='logcosh', channel_map={'no_23114_Leg-predicted-mass-right_1_0': 0}) +field_id_23115_leg_fat_percentage_left_1_0 = TensorMap('23115_Leg-fat-percentage-left_1_0', loss='logcosh', channel_map={'no_23115_Leg-fat-percentage-left_1_0': 0}) +field_id_23116_leg_fat_mass_left_1_0 = TensorMap('23116_Leg-fat-mass-left_1_0', loss='logcosh', channel_map={'no_23116_Leg-fat-mass-left_1_0': 0}) +field_id_23117_leg_fatfree_mass_left_1_0 = TensorMap('23117_Leg-fatfree-mass-left_1_0', loss='logcosh', channel_map={'no_23117_Leg-fatfree-mass-left_1_0': 0}) +field_id_23118_leg_predicted_mass_left_1_0 = TensorMap('23118_Leg-predicted-mass-left_1_0', loss='logcosh', channel_map={'no_23118_Leg-predicted-mass-left_1_0': 0}) +field_id_23119_arm_fat_percentage_right_1_0 = TensorMap('23119_Arm-fat-percentage-right_1_0', loss='logcosh', channel_map={'no_23119_Arm-fat-percentage-right_1_0': 0}) +field_id_23120_arm_fat_mass_right_1_0 = TensorMap('23120_Arm-fat-mass-right_1_0', loss='logcosh', channel_map={'no_23120_Arm-fat-mass-right_1_0': 0}) +field_id_23121_arm_fatfree_mass_right_1_0 = TensorMap('23121_Arm-fatfree-mass-right_1_0', loss='logcosh', channel_map={'no_23121_Arm-fatfree-mass-right_1_0': 0}) +field_id_23122_arm_predicted_mass_right_1_0 = TensorMap('23122_Arm-predicted-mass-right_1_0', loss='logcosh', channel_map={'no_23122_Arm-predicted-mass-right_1_0': 0}) +field_id_23123_arm_fat_percentage_left_1_0 = TensorMap('23123_Arm-fat-percentage-left_1_0', loss='logcosh', channel_map={'no_23123_Arm-fat-percentage-left_1_0': 0}) +field_id_23124_arm_fat_mass_left_1_0 = TensorMap('23124_Arm-fat-mass-left_1_0', loss='logcosh', channel_map={'no_23124_Arm-fat-mass-left_1_0': 0}) +field_id_23125_arm_fatfree_mass_left_1_0 = TensorMap('23125_Arm-fatfree-mass-left_1_0', loss='logcosh', channel_map={'no_23125_Arm-fatfree-mass-left_1_0': 0}) +field_id_23126_arm_predicted_mass_left_1_0 = TensorMap('23126_Arm-predicted-mass-left_1_0', loss='logcosh', channel_map={'no_23126_Arm-predicted-mass-left_1_0': 0}) +field_id_23127_trunk_fat_percentage_1_0 = TensorMap('23127_Trunk-fat-percentage_1_0', loss='logcosh', channel_map={'no_23127_Trunk-fat-percentage_1_0': 0}) +field_id_23128_trunk_fat_mass_1_0 = TensorMap('23128_Trunk-fat-mass_1_0', loss='logcosh', channel_map={'no_23128_Trunk-fat-mass_1_0': 0}) +field_id_23129_trunk_fatfree_mass_1_0 = TensorMap('23129_Trunk-fatfree-mass_1_0', loss='logcosh', channel_map={'no_23129_Trunk-fatfree-mass_1_0': 0}) +field_id_23130_trunk_predicted_mass_1_0 = TensorMap('23130_Trunk-predicted-mass_1_0', loss='logcosh', channel_map={'no_23130_Trunk-predicted-mass_1_0': 0}) +field_id_2355_most_recent_bowel_cancer_screening_1_0 = TensorMap('2355_Most-recent-bowel-cancer-screening_1_0', loss='logcosh', channel_map={'no_2355_Most-recent-bowel-cancer-screening_1_0': 0}) +field_id_2405_number_of_children_fathered_1_0 = TensorMap('2405_Number-of-children-fathered_1_0', loss='logcosh', channel_map={'no_2405_Number-of-children-fathered_1_0': 0}) +field_id_2966_age_high_blood_pressure_diagnosed_0_0 = TensorMap('2966_Age-high-blood-pressure-diagnosed_0_0', loss='logcosh', channel_map={'no_2966_Age-high-blood-pressure-diagnosed_0_0': 0}) +field_id_30000_white_blood_cell_leukocyte_count_1_0 = TensorMap('30000_White-blood-cell-leukocyte-count_1_0', loss='logcosh', channel_map={'no_30000_White-blood-cell-leukocyte-count_1_0': 0}) +field_id_30010_red_blood_cell_erythrocyte_count_1_0 = TensorMap('30010_Red-blood-cell-erythrocyte-count_1_0', loss='logcosh', channel_map={'no_30010_Red-blood-cell-erythrocyte-count_1_0': 0}) +field_id_30020_haemoglobin_concentration_1_0 = TensorMap('30020_Haemoglobin-concentration_1_0', loss='logcosh', channel_map={'no_30020_Haemoglobin-concentration_1_0': 0}) +field_id_30030_haematocrit_percentage_1_0 = TensorMap('30030_Haematocrit-percentage_1_0', loss='logcosh', channel_map={'no_30030_Haematocrit-percentage_1_0': 0}) +field_id_30040_mean_corpuscular_volume_1_0 = TensorMap('30040_Mean-corpuscular-volume_1_0', loss='logcosh', channel_map={'no_30040_Mean-corpuscular-volume_1_0': 0}) +field_id_30050_mean_corpuscular_haemoglobin_1_0 = TensorMap('30050_Mean-corpuscular-haemoglobin_1_0', loss='logcosh', channel_map={'no_30050_Mean-corpuscular-haemoglobin_1_0': 0}) +field_id_30060_mean_corpuscular_haemoglobin_concentration_1_0 = TensorMap('30060_Mean-corpuscular-haemoglobin-concentration_1_0', loss='logcosh', channel_map={'no_30060_Mean-corpuscular-haemoglobin-concentration_1_0': 0}) +field_id_30070_red_blood_cell_erythrocyte_distribution_width_1_0 = TensorMap('30070_Red-blood-cell-erythrocyte-distribution-width_1_0', loss='logcosh', channel_map={'no_30070_Red-blood-cell-erythrocyte-distribution-width_1_0': 0}) +field_id_30080_platelet_count_1_0 = TensorMap('30080_Platelet-count_1_0', loss='logcosh', channel_map={'no_30080_Platelet-count_1_0': 0}) +field_id_30090_platelet_crit_1_0 = TensorMap('30090_Platelet-crit_1_0', loss='logcosh', channel_map={'no_30090_Platelet-crit_1_0': 0}) +field_id_30100_mean_platelet_thrombocyte_volume_1_0 = TensorMap('30100_Mean-platelet-thrombocyte-volume_1_0', loss='logcosh', channel_map={'no_30100_Mean-platelet-thrombocyte-volume_1_0': 0}) +field_id_30110_platelet_distribution_width_1_0 = TensorMap('30110_Platelet-distribution-width_1_0', loss='logcosh', channel_map={'no_30110_Platelet-distribution-width_1_0': 0}) +field_id_30120_lymphocyte_count_1_0 = TensorMap('30120_Lymphocyte-count_1_0', loss='logcosh', channel_map={'no_30120_Lymphocyte-count_1_0': 0}) +field_id_30130_monocyte_count_1_0 = TensorMap('30130_Monocyte-count_1_0', loss='logcosh', channel_map={'no_30130_Monocyte-count_1_0': 0}) +field_id_30140_neutrophill_count_1_0 = TensorMap('30140_Neutrophill-count_1_0', loss='logcosh', channel_map={'no_30140_Neutrophill-count_1_0': 0}) +field_id_30150_eosinophill_count_1_0 = TensorMap('30150_Eosinophill-count_1_0', loss='logcosh', channel_map={'no_30150_Eosinophill-count_1_0': 0}) +field_id_30160_basophill_count_1_0 = TensorMap('30160_Basophill-count_1_0', loss='logcosh', channel_map={'no_30160_Basophill-count_1_0': 0}) +field_id_30170_nucleated_red_blood_cell_count_1_0 = TensorMap('30170_Nucleated-red-blood-cell-count_1_0', loss='logcosh', channel_map={'no_30170_Nucleated-red-blood-cell-count_1_0': 0}) +field_id_30180_lymphocyte_percentage_1_0 = TensorMap('30180_Lymphocyte-percentage_1_0', loss='logcosh', channel_map={'no_30180_Lymphocyte-percentage_1_0': 0}) +field_id_30190_monocyte_percentage_1_0 = TensorMap('30190_Monocyte-percentage_1_0', loss='logcosh', channel_map={'no_30190_Monocyte-percentage_1_0': 0}) +field_id_30200_neutrophill_percentage_1_0 = TensorMap('30200_Neutrophill-percentage_1_0', loss='logcosh', channel_map={'no_30200_Neutrophill-percentage_1_0': 0}) +field_id_30210_eosinophill_percentage_1_0 = TensorMap('30210_Eosinophill-percentage_1_0', loss='logcosh', channel_map={'no_30210_Eosinophill-percentage_1_0': 0}) +field_id_30220_basophill_percentage_1_0 = TensorMap('30220_Basophill-percentage_1_0', loss='logcosh', channel_map={'no_30220_Basophill-percentage_1_0': 0}) +field_id_30230_nucleated_red_blood_cell_percentage_1_0 = TensorMap('30230_Nucleated-red-blood-cell-percentage_1_0', loss='logcosh', channel_map={'no_30230_Nucleated-red-blood-cell-percentage_1_0': 0}) +field_id_30240_reticulocyte_percentage_1_0 = TensorMap('30240_Reticulocyte-percentage_1_0', loss='logcosh', channel_map={'no_30240_Reticulocyte-percentage_1_0': 0}) +field_id_30250_reticulocyte_count_1_0 = TensorMap('30250_Reticulocyte-count_1_0', loss='logcosh', channel_map={'no_30250_Reticulocyte-count_1_0': 0}) +field_id_30260_mean_reticulocyte_volume_1_0 = TensorMap('30260_Mean-reticulocyte-volume_1_0', loss='logcosh', channel_map={'no_30260_Mean-reticulocyte-volume_1_0': 0}) +field_id_30270_mean_sphered_cell_volume_1_0 = TensorMap('30270_Mean-sphered-cell-volume_1_0', loss='logcosh', channel_map={'no_30270_Mean-sphered-cell-volume_1_0': 0}) +field_id_30280_immature_reticulocyte_fraction_1_0 = TensorMap('30280_Immature-reticulocyte-fraction_1_0', loss='logcosh', channel_map={'no_30280_Immature-reticulocyte-fraction_1_0': 0}) +field_id_30290_high_light_scatter_reticulocyte_percentage_1_0 = TensorMap('30290_High-light-scatter-reticulocyte-percentage_1_0', loss='logcosh', channel_map={'no_30290_High-light-scatter-reticulocyte-percentage_1_0': 0}) +field_id_30300_high_light_scatter_reticulocyte_count_1_0 = TensorMap('30300_High-light-scatter-reticulocyte-count_1_0', loss='logcosh', channel_map={'no_30300_High-light-scatter-reticulocyte-count_1_0': 0}) +field_id_30314_volume_of_edta1_plasma_held_by_ukb_1_0 = TensorMap('30314_Volume-of-EDTA1-plasma-held-by-UKB_1_0', loss='logcosh', channel_map={'no_30314_Volume-of-EDTA1-plasma-held-by-UKB_1_0': 0}) +field_id_30324_volume_of_edta1_red_cells_held_by_ukb_1_0 = TensorMap('30324_Volume-of-EDTA1-red-cells-held-by-UKB_1_0', loss='logcosh', channel_map={'no_30324_Volume-of-EDTA1-red-cells-held-by-UKB_1_0': 0}) +field_id_30334_volume_of_edta1_buffy_held_by_ukb_1_0 = TensorMap('30334_Volume-of-EDTA1-buffy-held-by-UKB_1_0', loss='logcosh', channel_map={'no_30334_Volume-of-EDTA1-buffy-held-by-UKB_1_0': 0}) +field_id_30344_volume_of_edta2_plasma_held_by_ukb_1_0 = TensorMap('30344_Volume-of-EDTA2-plasma-held-by-UKB_1_0', loss='logcosh', channel_map={'no_30344_Volume-of-EDTA2-plasma-held-by-UKB_1_0': 0}) +field_id_30354_volume_of_edta2_buffy_held_by_ukb_1_0 = TensorMap('30354_Volume-of-EDTA2-buffy-held-by-UKB_1_0', loss='logcosh', channel_map={'no_30354_Volume-of-EDTA2-buffy-held-by-UKB_1_0': 0}) +field_id_30364_volume_of_edta2_red_cells_held_by_ukb_1_0 = TensorMap('30364_Volume-of-EDTA2-red-cells-held-by-UKB_1_0', loss='logcosh', channel_map={'no_30364_Volume-of-EDTA2-red-cells-held-by-UKB_1_0': 0}) +field_id_30374_volume_of_lihep_plasma_held_by_ukb_1_0 = TensorMap('30374_Volume-of-LiHep-plasma-held-by-UKB_1_0', loss='logcosh', channel_map={'no_30374_Volume-of-LiHep-plasma-held-by-UKB_1_0': 0}) +field_id_30384_volume_of_serum_held_by_ukb_1_0 = TensorMap('30384_Volume-of-serum-held-by-UKB_1_0', loss='logcosh', channel_map={'no_30384_Volume-of-serum-held-by-UKB_1_0': 0}) +field_id_30404_volume_of_acd_held_by_ukb_1_0 = TensorMap('30404_Volume-of-ACD-held-by-UKB_1_0', loss='logcosh', channel_map={'no_30404_Volume-of-ACD-held-by-UKB_1_0': 0}) +field_id_30414_volume_of_rna_held_by_ukb_1_0 = TensorMap('30414_Volume-of-RNA-held-by-UKB_1_0', loss='logcosh', channel_map={'no_30414_Volume-of-RNA-held-by-UKB_1_0': 0}) +field_id_30510_creatinine_enzymatic_in_urine_1_0 = TensorMap('30510_Creatinine-enzymatic-in-urine_1_0', loss='logcosh', channel_map={'no_30510_Creatinine-enzymatic-in-urine_1_0': 0}) +field_id_30520_potassium_in_urine_1_0 = TensorMap('30520_Potassium-in-urine_1_0', loss='logcosh', channel_map={'no_30520_Potassium-in-urine_1_0': 0}) +field_id_30530_sodium_in_urine_1_0 = TensorMap('30530_Sodium-in-urine_1_0', loss='logcosh', channel_map={'no_30530_Sodium-in-urine_1_0': 0}) +field_id_3062_forced_vital_capacity_fvc_1_0 = TensorMap('3062_Forced-vital-capacity-FVC_1_0', loss='logcosh', channel_map={'no_3062_Forced-vital-capacity-FVC_1_0': 0}) +field_id_3062_forced_vital_capacity_fvc_1_1 = TensorMap('3062_Forced-vital-capacity-FVC_1_1', loss='logcosh', channel_map={'no_3062_Forced-vital-capacity-FVC_1_1': 0}) +field_id_30630_apolipoprotein_a_1_0 = TensorMap('30630_Apolipoprotein-A_1_0', loss='logcosh', channel_map={'no_30630_Apolipoprotein-A_1_0': 0}) +field_id_3063_forced_expiratory_volume_in_1second_fev1_1_0 = TensorMap('3063_Forced-expiratory-volume-in-1second-FEV1_1_0', loss='logcosh', channel_map={'no_3063_Forced-expiratory-volume-in-1second-FEV1_1_0': 0}) +field_id_3063_forced_expiratory_volume_in_1second_fev1_1_1 = TensorMap('3063_Forced-expiratory-volume-in-1second-FEV1_1_1', loss='logcosh', channel_map={'no_3063_Forced-expiratory-volume-in-1second-FEV1_1_1': 0}) +field_id_30640_apolipoprotein_b_1_0 = TensorMap('30640_Apolipoprotein-B_1_0', loss='logcosh', channel_map={'no_30640_Apolipoprotein-B_1_0': 0}) +field_id_3064_peak_expiratory_flow_pef_1_0 = TensorMap('3064_Peak-expiratory-flow-PEF_1_0', loss='logcosh', channel_map={'no_3064_Peak-expiratory-flow-PEF_1_0': 0}) +field_id_3064_peak_expiratory_flow_pef_1_1 = TensorMap('3064_Peak-expiratory-flow-PEF_1_1', loss='logcosh', channel_map={'no_3064_Peak-expiratory-flow-PEF_1_1': 0}) +field_id_30660_direct_bilirubin_1_0 = TensorMap('30660_Direct-bilirubin_1_0', loss='logcosh', channel_map={'no_30660_Direct-bilirubin_1_0': 0}) +field_id_30670_urea_1_0 = TensorMap('30670_Urea_1_0', loss='logcosh', channel_map={'no_30670_Urea_1_0': 0}) +field_id_30680_calcium_1_0 = TensorMap('30680_Calcium_1_0', loss='logcosh', channel_map={'no_30680_Calcium_1_0': 0}) +field_id_30690_cholesterol_1_0 = TensorMap('30690_Cholesterol_1_0', loss='logcosh', channel_map={'no_30690_Cholesterol_1_0': 0}) +field_id_30710_creactive_protein_1_0 = TensorMap('30710_Creactive-protein_1_0', loss='logcosh', channel_map={'no_30710_Creactive-protein_1_0': 0}) +field_id_30740_glucose_1_0 = TensorMap('30740_Glucose_1_0', loss='logcosh', channel_map={'no_30740_Glucose_1_0': 0}) +field_id_30760_hdl_cholesterol_1_0 = TensorMap('30760_HDL-cholesterol_1_0', loss='logcosh', channel_map={'no_30760_HDL-cholesterol_1_0': 0}) +field_id_30770_igf1_1_0 = TensorMap('30770_IGF1_1_0', loss='logcosh', channel_map={'no_30770_IGF1_1_0': 0}) +field_id_30790_lipoprotein_a_1_0 = TensorMap('30790_Lipoprotein-A_1_0', loss='logcosh', channel_map={'no_30790_Lipoprotein-A_1_0': 0}) +field_id_30810_phosphate_1_0 = TensorMap('30810_Phosphate_1_0', loss='logcosh', channel_map={'no_30810_Phosphate_1_0': 0}) +field_id_3083_heel_quantitative_ultrasound_index_qui_manual_entry_0_0 = TensorMap('3083_Heel-quantitative-ultrasound-index-QUI-manual-entry_0_0', loss='logcosh', channel_map={'no_3083_Heel-quantitative-ultrasound-index-QUI-manual-entry_0_0': 0}) +field_id_30840_total_bilirubin_1_0 = TensorMap('30840_Total-bilirubin_1_0', loss='logcosh', channel_map={'no_30840_Total-bilirubin_1_0': 0}) +field_id_3084_heel_bone_mineral_density_bmd_manual_entry_0_0 = TensorMap('3084_Heel-bone-mineral-density-BMD-manual-entry_0_0', loss='logcosh', channel_map={'no_3084_Heel-bone-mineral-density-BMD-manual-entry_0_0': 0}) +field_id_30850_testosterone_1_0 = TensorMap('30850_Testosterone_1_0', loss='logcosh', channel_map={'no_30850_Testosterone_1_0': 0}) +field_id_3085_heel_broadband_ultrasound_attenuation_bua_manual_entry_0_0 = TensorMap('3085_Heel-Broadband-ultrasound-attenuation-BUA-manual-entry_0_0', loss='logcosh', channel_map={'no_3085_Heel-Broadband-ultrasound-attenuation-BUA-manual-entry_0_0': 0}) +field_id_30860_total_protein_1_0 = TensorMap('30860_Total-protein_1_0', loss='logcosh', channel_map={'no_30860_Total-protein_1_0': 0}) +field_id_3086_speed_of_sound_through_heel_manual_entry_0_0 = TensorMap('3086_Speed-of-sound-through-heel-manual-entry_0_0', loss='logcosh', channel_map={'no_3086_Speed-of-sound-through-heel-manual-entry_0_0': 0}) +field_id_30870_triglycerides_1_0 = TensorMap('30870_Triglycerides_1_0', loss='logcosh', channel_map={'no_30870_Triglycerides_1_0': 0}) +field_id_30880_urate_1_0 = TensorMap('30880_Urate_1_0', loss='logcosh', channel_map={'no_30880_Urate_1_0': 0}) +field_id_30890_vitamin_d_1_0 = TensorMap('30890_Vitamin-D_1_0', loss='logcosh', channel_map={'no_30890_Vitamin-D_1_0': 0}) +field_id_3526_mothers_age_at_death_1_0 = TensorMap('3526_Mothers-age-at-death_1_0', loss='logcosh', channel_map={'no_3526_Mothers-age-at-death_1_0': 0}) +field_id_3627_age_angina_diagnosed_0_0 = TensorMap('3627_Age-angina-diagnosed_0_0', loss='logcosh', channel_map={'no_3627_Age-angina-diagnosed_0_0': 0}) +field_id_396_number_of_columns_displayed_in_round_1_1 = TensorMap('396_Number-of-columns-displayed-in-round_1_1', loss='logcosh', channel_map={'no_396_Number-of-columns-displayed-in-round_1_1': 0}) +field_id_396_number_of_columns_displayed_in_round_1_2 = TensorMap('396_Number-of-columns-displayed-in-round_1_2', loss='logcosh', channel_map={'no_396_Number-of-columns-displayed-in-round_1_2': 0}) +field_id_397_number_of_rows_displayed_in_round_1_1 = TensorMap('397_Number-of-rows-displayed-in-round_1_1', loss='logcosh', channel_map={'no_397_Number-of-rows-displayed-in-round_1_1': 0}) +field_id_397_number_of_rows_displayed_in_round_1_2 = TensorMap('397_Number-of-rows-displayed-in-round_1_2', loss='logcosh', channel_map={'no_397_Number-of-rows-displayed-in-round_1_2': 0}) +field_id_398_number_of_correct_matches_in_round_1_1 = TensorMap('398_Number-of-correct-matches-in-round_1_1', loss='logcosh', channel_map={'no_398_Number-of-correct-matches-in-round_1_1': 0}) +field_id_398_number_of_correct_matches_in_round_1_2 = TensorMap('398_Number-of-correct-matches-in-round_1_2', loss='logcosh', channel_map={'no_398_Number-of-correct-matches-in-round_1_2': 0}) +field_id_399_number_of_incorrect_matches_in_round_1_1 = TensorMap('399_Number-of-incorrect-matches-in-round_1_1', loss='logcosh', channel_map={'no_399_Number-of-incorrect-matches-in-round_1_1': 0}) +field_id_399_number_of_incorrect_matches_in_round_1_2 = TensorMap('399_Number-of-incorrect-matches-in-round_1_2', loss='logcosh', channel_map={'no_399_Number-of-incorrect-matches-in-round_1_2': 0}) +field_id_40008_age_at_cancer_diagnosis_0_0 = TensorMap('40008_Age-at-cancer-diagnosis_0_0', loss='logcosh', channel_map={'no_40008_Age-at-cancer-diagnosis_0_0': 0}) +field_id_40009_reported_occurrences_of_cancer_0_0 = TensorMap('40009_Reported-occurrences-of-cancer_0_0', loss='logcosh', channel_map={'no_40009_Reported-occurrences-of-cancer_0_0': 0}) +field_id_400_time_to_complete_round_1_1 = TensorMap('400_Time-to-complete-round_1_1', loss='logcosh', channel_map={'no_400_Time-to-complete-round_1_1': 0}) +field_id_400_time_to_complete_round_1_2 = TensorMap('400_Time-to-complete-round_1_2', loss='logcosh', channel_map={'no_400_Time-to-complete-round_1_2': 0}) +field_id_404_duration_to_first_press_of_snapbutton_in_each_round_1_0 = TensorMap('404_Duration-to-first-press-of-snapbutton-in-each-round_1_0', loss='logcosh', channel_map={'no_404_Duration-to-first-press-of-snapbutton-in-each-round_1_0': 0}) +field_id_404_duration_to_first_press_of_snapbutton_in_each_round_1_1 = TensorMap('404_Duration-to-first-press-of-snapbutton-in-each-round_1_1', loss='logcosh', channel_map={'no_404_Duration-to-first-press-of-snapbutton-in-each-round_1_1': 0}) +field_id_404_duration_to_first_press_of_snapbutton_in_each_round_1_10 = TensorMap('404_Duration-to-first-press-of-snapbutton-in-each-round_1_10', loss='logcosh', channel_map={'no_404_Duration-to-first-press-of-snapbutton-in-each-round_1_10': 0}) +field_id_404_duration_to_first_press_of_snapbutton_in_each_round_1_11 = TensorMap('404_Duration-to-first-press-of-snapbutton-in-each-round_1_11', loss='logcosh', channel_map={'no_404_Duration-to-first-press-of-snapbutton-in-each-round_1_11': 0}) +field_id_404_duration_to_first_press_of_snapbutton_in_each_round_1_5 = TensorMap('404_Duration-to-first-press-of-snapbutton-in-each-round_1_5', loss='logcosh', channel_map={'no_404_Duration-to-first-press-of-snapbutton-in-each-round_1_5': 0}) +field_id_404_duration_to_first_press_of_snapbutton_in_each_round_1_7 = TensorMap('404_Duration-to-first-press-of-snapbutton-in-each-round_1_7', loss='logcosh', channel_map={'no_404_Duration-to-first-press-of-snapbutton-in-each-round_1_7': 0}) +field_id_4079_diastolic_blood_pressure_automated_reading_1_0 = TensorMap('4079_Diastolic-blood-pressure-automated-reading_1_0', loss='logcosh', channel_map={'no_4079_Diastolic-blood-pressure-automated-reading_1_0': 0}) +field_id_4079_diastolic_blood_pressure_automated_reading_1_1 = TensorMap('4079_Diastolic-blood-pressure-automated-reading_1_1', loss='logcosh', channel_map={'no_4079_Diastolic-blood-pressure-automated-reading_1_1': 0}) +field_id_4080_systolic_blood_pressure_automated_reading_1_0 = TensorMap('4080_Systolic-blood-pressure-automated-reading_1_0', loss='logcosh', channel_map={'no_4080_Systolic-blood-pressure-automated-reading_1_0': 0}) +field_id_4080_systolic_blood_pressure_automated_reading_1_1 = TensorMap('4080_Systolic-blood-pressure-automated-reading_1_1', loss='logcosh', channel_map={'no_4080_Systolic-blood-pressure-automated-reading_1_1': 0}) +field_id_4100_ankle_spacing_width_left_1_0 = TensorMap('4100_Ankle-spacing-width-left_1_0', loss='logcosh', channel_map={'no_4100_Ankle-spacing-width-left_1_0': 0}) +field_id_4101_heel_broadband_ultrasound_attenuation_left_1_0 = TensorMap('4101_Heel-broadband-ultrasound-attenuation-left_1_0', loss='logcosh', channel_map={'no_4101_Heel-broadband-ultrasound-attenuation-left_1_0': 0}) +field_id_4103_speed_of_sound_through_heel_left_1_0 = TensorMap('4103_Speed-of-sound-through-heel-left_1_0', loss='logcosh', channel_map={'no_4103_Speed-of-sound-through-heel-left_1_0': 0}) +field_id_4104_heel_quantitative_ultrasound_index_qui_direct_entry_left_1_0 = TensorMap('4104_Heel-quantitative-ultrasound-index-QUI-direct-entry-left_1_0', loss='logcosh', channel_map={'no_4104_Heel-quantitative-ultrasound-index-QUI-direct-entry-left_1_0': 0}) +field_id_4105_heel_bone_mineral_density_bmd_left_1_0 = TensorMap('4105_Heel-bone-mineral-density-BMD-left_1_0', loss='logcosh', channel_map={'no_4105_Heel-bone-mineral-density-BMD-left_1_0': 0}) +field_id_4106_heel_bone_mineral_density_bmd_tscore_automated_left_1_0 = TensorMap('4106_Heel-bone-mineral-density-BMD-Tscore-automated-left_1_0', loss='logcosh', channel_map={'no_4106_Heel-bone-mineral-density-BMD-Tscore-automated-left_1_0': 0}) +field_id_4119_ankle_spacing_width_right_1_0 = TensorMap('4119_Ankle-spacing-width-right_1_0', loss='logcosh', channel_map={'no_4119_Ankle-spacing-width-right_1_0': 0}) +field_id_4120_heel_broadband_ultrasound_attenuation_right_1_0 = TensorMap('4120_Heel-broadband-ultrasound-attenuation-right_1_0', loss='logcosh', channel_map={'no_4120_Heel-broadband-ultrasound-attenuation-right_1_0': 0}) +field_id_4122_speed_of_sound_through_heel_right_1_0 = TensorMap('4122_Speed-of-sound-through-heel-right_1_0', loss='logcosh', channel_map={'no_4122_Speed-of-sound-through-heel-right_1_0': 0}) +field_id_4123_heel_quantitative_ultrasound_index_qui_direct_entry_right_1_0 = TensorMap('4123_Heel-quantitative-ultrasound-index-QUI-direct-entry-right_1_0', loss='logcosh', channel_map={'no_4123_Heel-quantitative-ultrasound-index-QUI-direct-entry-right_1_0': 0}) +field_id_4124_heel_bone_mineral_density_bmd_right_1_0 = TensorMap('4124_Heel-bone-mineral-density-BMD-right_1_0', loss='logcosh', channel_map={'no_4124_Heel-bone-mineral-density-BMD-right_1_0': 0}) +field_id_4125_heel_bone_mineral_density_bmd_tscore_automated_right_1_0 = TensorMap('4125_Heel-bone-mineral-density-BMD-Tscore-automated-right_1_0', loss='logcosh', channel_map={'no_4125_Heel-bone-mineral-density-BMD-Tscore-automated-right_1_0': 0}) +field_id_4194_pulse_rate_1_0 = TensorMap('4194_Pulse-rate_1_0', loss='logcosh', channel_map={'no_4194_Pulse-rate_1_0': 0}) +field_id_4195_pulse_wave_reflection_index_1_0 = TensorMap('4195_Pulse-wave-reflection-index_1_0', loss='logcosh', channel_map={'no_4195_Pulse-wave-reflection-index_1_0': 0}) +field_id_4196_pulse_wave_peak_to_peak_time_1_0 = TensorMap('4196_Pulse-wave-peak-to-peak-time_1_0', loss='logcosh', channel_map={'no_4196_Pulse-wave-peak-to-peak-time_1_0': 0}) +field_id_4198_position_of_the_pulse_wave_peak_1_0 = TensorMap('4198_Position-of-the-pulse-wave-peak_1_0', loss='logcosh', channel_map={'no_4198_Position-of-the-pulse-wave-peak_1_0': 0}) +field_id_4199_position_of_pulse_wave_notch_1_0 = TensorMap('4199_Position-of-pulse-wave-notch_1_0', loss='logcosh', channel_map={'no_4199_Position-of-pulse-wave-notch_1_0': 0}) +field_id_4200_position_of_the_shoulder_on_the_pulse_waveform_1_0 = TensorMap('4200_Position-of-the-shoulder-on-the-pulse-waveform_1_0', loss='logcosh', channel_map={'no_4200_Position-of-the-shoulder-on-the-pulse-waveform_1_0': 0}) +field_id_4288_time_to_answer_1_0 = TensorMap('4288_Time-to-answer_1_0', loss='logcosh', channel_map={'no_4288_Time-to-answer_1_0': 0}) +field_id_4290_duration_screen_displayed_1_0 = TensorMap('4290_Duration-screen-displayed_1_0', loss='logcosh', channel_map={'no_4290_Duration-screen-displayed_1_0': 0}) +field_id_4291_number_of_attempts_1_0 = TensorMap('4291_Number-of-attempts_1_0', loss='logcosh', channel_map={'no_4291_Number-of-attempts_1_0': 0}) +field_id_46_hand_grip_strength_left_1_0 = TensorMap('46_Hand-grip-strength-left_1_0', loss='logcosh', channel_map={'no_46_Hand-grip-strength-left_1_0': 0}) +field_id_47_hand_grip_strength_right_1_0 = TensorMap('47_Hand-grip-strength-right_1_0', loss='logcosh', channel_map={'no_47_Hand-grip-strength-right_1_0': 0}) +field_id_48_waist_circumference_1_0 = TensorMap('48_Waist-circumference_1_0', loss='logcosh', channel_map={'no_48_Waist-circumference_1_0': 0}) +field_id_49_hip_circumference_1_0 = TensorMap('49_Hip-circumference_1_0', loss='logcosh', channel_map={'no_49_Hip-circumference_1_0': 0}) +field_id_5057_number_of_older_siblings_1_0 = TensorMap('5057_Number-of-older-siblings_1_0', loss='logcosh', channel_map={'no_5057_Number-of-older-siblings_1_0': 0}) +field_id_5074_number_of_letters_shown_in_round_left_1_0 = TensorMap('5074_Number-of-letters-shown-in-round-left_1_0', loss='logcosh', channel_map={'no_5074_Number-of-letters-shown-in-round-left_1_0': 0}) +field_id_5074_number_of_letters_shown_in_round_left_1_1 = TensorMap('5074_Number-of-letters-shown-in-round-left_1_1', loss='logcosh', channel_map={'no_5074_Number-of-letters-shown-in-round-left_1_1': 0}) +field_id_5074_number_of_letters_shown_in_round_left_1_2 = TensorMap('5074_Number-of-letters-shown-in-round-left_1_2', loss='logcosh', channel_map={'no_5074_Number-of-letters-shown-in-round-left_1_2': 0}) +field_id_5075_number_of_letters_shown_in_round_right_1_0 = TensorMap('5075_Number-of-letters-shown-in-round-right_1_0', loss='logcosh', channel_map={'no_5075_Number-of-letters-shown-in-round-right_1_0': 0}) +field_id_5075_number_of_letters_shown_in_round_right_1_1 = TensorMap('5075_Number-of-letters-shown-in-round-right_1_1', loss='logcosh', channel_map={'no_5075_Number-of-letters-shown-in-round-right_1_1': 0}) +field_id_5075_number_of_letters_shown_in_round_right_1_2 = TensorMap('5075_Number-of-letters-shown-in-round-right_1_2', loss='logcosh', channel_map={'no_5075_Number-of-letters-shown-in-round-right_1_2': 0}) +field_id_5076_number_of_letters_correct_in_round_right_1_0 = TensorMap('5076_Number-of-letters-correct-in-round-right_1_0', loss='logcosh', channel_map={'no_5076_Number-of-letters-correct-in-round-right_1_0': 0}) +field_id_5076_number_of_letters_correct_in_round_right_1_1 = TensorMap('5076_Number-of-letters-correct-in-round-right_1_1', loss='logcosh', channel_map={'no_5076_Number-of-letters-correct-in-round-right_1_1': 0}) +field_id_5076_number_of_letters_correct_in_round_right_1_2 = TensorMap('5076_Number-of-letters-correct-in-round-right_1_2', loss='logcosh', channel_map={'no_5076_Number-of-letters-correct-in-round-right_1_2': 0}) +field_id_5077_number_of_letters_correct_in_round_left_1_0 = TensorMap('5077_Number-of-letters-correct-in-round-left_1_0', loss='logcosh', channel_map={'no_5077_Number-of-letters-correct-in-round-left_1_0': 0}) +field_id_5077_number_of_letters_correct_in_round_left_1_1 = TensorMap('5077_Number-of-letters-correct-in-round-left_1_1', loss='logcosh', channel_map={'no_5077_Number-of-letters-correct-in-round-left_1_1': 0}) +field_id_5077_number_of_letters_correct_in_round_left_1_2 = TensorMap('5077_Number-of-letters-correct-in-round-left_1_2', loss='logcosh', channel_map={'no_5077_Number-of-letters-correct-in-round-left_1_2': 0}) +field_id_5078_logmar_in_round_left_1_0 = TensorMap('5078_logMAR-in-round-left_1_0', loss='logcosh', channel_map={'no_5078_logMAR-in-round-left_1_0': 0}) +field_id_5078_logmar_in_round_left_1_1 = TensorMap('5078_logMAR-in-round-left_1_1', loss='logcosh', channel_map={'no_5078_logMAR-in-round-left_1_1': 0}) +field_id_5078_logmar_in_round_left_1_2 = TensorMap('5078_logMAR-in-round-left_1_2', loss='logcosh', channel_map={'no_5078_logMAR-in-round-left_1_2': 0}) +field_id_5079_logmar_in_round_right_1_0 = TensorMap('5079_logMAR-in-round-right_1_0', loss='logcosh', channel_map={'no_5079_logMAR-in-round-right_1_0': 0}) +field_id_5079_logmar_in_round_right_1_1 = TensorMap('5079_logMAR-in-round-right_1_1', loss='logcosh', channel_map={'no_5079_logMAR-in-round-right_1_1': 0}) +field_id_5079_logmar_in_round_right_1_2 = TensorMap('5079_logMAR-in-round-right_1_2', loss='logcosh', channel_map={'no_5079_logMAR-in-round-right_1_2': 0}) +field_id_5084_spherical_power_right_1_0 = TensorMap('5084_Spherical-power-right_1_0', loss='logcosh', channel_map={'no_5084_Spherical-power-right_1_0': 0}) +field_id_5084_spherical_power_right_1_1 = TensorMap('5084_Spherical-power-right_1_1', loss='logcosh', channel_map={'no_5084_Spherical-power-right_1_1': 0}) +field_id_5084_spherical_power_right_1_2 = TensorMap('5084_Spherical-power-right_1_2', loss='logcosh', channel_map={'no_5084_Spherical-power-right_1_2': 0}) +field_id_5085_spherical_power_left_1_0 = TensorMap('5085_Spherical-power-left_1_0', loss='logcosh', channel_map={'no_5085_Spherical-power-left_1_0': 0}) +field_id_5085_spherical_power_left_1_1 = TensorMap('5085_Spherical-power-left_1_1', loss='logcosh', channel_map={'no_5085_Spherical-power-left_1_1': 0}) +field_id_5085_spherical_power_left_1_2 = TensorMap('5085_Spherical-power-left_1_2', loss='logcosh', channel_map={'no_5085_Spherical-power-left_1_2': 0}) +field_id_5086_cylindrical_power_left_1_0 = TensorMap('5086_Cylindrical-power-left_1_0', loss='logcosh', channel_map={'no_5086_Cylindrical-power-left_1_0': 0}) +field_id_5086_cylindrical_power_left_1_1 = TensorMap('5086_Cylindrical-power-left_1_1', loss='logcosh', channel_map={'no_5086_Cylindrical-power-left_1_1': 0}) +field_id_5086_cylindrical_power_left_1_2 = TensorMap('5086_Cylindrical-power-left_1_2', loss='logcosh', channel_map={'no_5086_Cylindrical-power-left_1_2': 0}) +field_id_5087_cylindrical_power_right_1_0 = TensorMap('5087_Cylindrical-power-right_1_0', loss='logcosh', channel_map={'no_5087_Cylindrical-power-right_1_0': 0}) +field_id_5087_cylindrical_power_right_1_1 = TensorMap('5087_Cylindrical-power-right_1_1', loss='logcosh', channel_map={'no_5087_Cylindrical-power-right_1_1': 0}) +field_id_5087_cylindrical_power_right_1_2 = TensorMap('5087_Cylindrical-power-right_1_2', loss='logcosh', channel_map={'no_5087_Cylindrical-power-right_1_2': 0}) +field_id_5088_astigmatism_angle_right_1_0 = TensorMap('5088_Astigmatism-angle-right_1_0', loss='logcosh', channel_map={'no_5088_Astigmatism-angle-right_1_0': 0}) +field_id_5088_astigmatism_angle_right_1_1 = TensorMap('5088_Astigmatism-angle-right_1_1', loss='logcosh', channel_map={'no_5088_Astigmatism-angle-right_1_1': 0}) +field_id_5088_astigmatism_angle_right_1_2 = TensorMap('5088_Astigmatism-angle-right_1_2', loss='logcosh', channel_map={'no_5088_Astigmatism-angle-right_1_2': 0}) +field_id_5089_astigmatism_angle_left_1_0 = TensorMap('5089_Astigmatism-angle-left_1_0', loss='logcosh', channel_map={'no_5089_Astigmatism-angle-left_1_0': 0}) +field_id_5089_astigmatism_angle_left_1_1 = TensorMap('5089_Astigmatism-angle-left_1_1', loss='logcosh', channel_map={'no_5089_Astigmatism-angle-left_1_1': 0}) +field_id_5089_astigmatism_angle_left_1_2 = TensorMap('5089_Astigmatism-angle-left_1_2', loss='logcosh', channel_map={'no_5089_Astigmatism-angle-left_1_2': 0}) +field_id_5096_3mm_weak_meridian_left_1_0 = TensorMap('5096_3mm-weak-meridian-left_1_0', loss='logcosh', channel_map={'no_5096_3mm-weak-meridian-left_1_0': 0}) +field_id_5096_3mm_weak_meridian_left_1_1 = TensorMap('5096_3mm-weak-meridian-left_1_1', loss='logcosh', channel_map={'no_5096_3mm-weak-meridian-left_1_1': 0}) +field_id_5096_3mm_weak_meridian_left_1_2 = TensorMap('5096_3mm-weak-meridian-left_1_2', loss='logcosh', channel_map={'no_5096_3mm-weak-meridian-left_1_2': 0}) +field_id_5097_6mm_weak_meridian_left_1_0 = TensorMap('5097_6mm-weak-meridian-left_1_0', loss='logcosh', channel_map={'no_5097_6mm-weak-meridian-left_1_0': 0}) +field_id_5097_6mm_weak_meridian_left_1_1 = TensorMap('5097_6mm-weak-meridian-left_1_1', loss='logcosh', channel_map={'no_5097_6mm-weak-meridian-left_1_1': 0}) +field_id_5097_6mm_weak_meridian_left_1_2 = TensorMap('5097_6mm-weak-meridian-left_1_2', loss='logcosh', channel_map={'no_5097_6mm-weak-meridian-left_1_2': 0}) +field_id_5099_3mm_weak_meridian_right_1_0 = TensorMap('5099_3mm-weak-meridian-right_1_0', loss='logcosh', channel_map={'no_5099_3mm-weak-meridian-right_1_0': 0}) +field_id_5099_3mm_weak_meridian_right_1_1 = TensorMap('5099_3mm-weak-meridian-right_1_1', loss='logcosh', channel_map={'no_5099_3mm-weak-meridian-right_1_1': 0}) +field_id_50_standing_height_1_0 = TensorMap('50_Standing-height_1_0', loss='logcosh', channel_map={'no_50_Standing-height_1_0': 0}) +field_id_5100_3mm_weak_meridian_angle_right_1_0 = TensorMap('5100_3mm-weak-meridian-angle-right_1_0', loss='logcosh', channel_map={'no_5100_3mm-weak-meridian-angle-right_1_0': 0}) +field_id_5100_3mm_weak_meridian_angle_right_1_1 = TensorMap('5100_3mm-weak-meridian-angle-right_1_1', loss='logcosh', channel_map={'no_5100_3mm-weak-meridian-angle-right_1_1': 0}) +field_id_5102_6mm_weak_meridian_angle_left_1_0 = TensorMap('5102_6mm-weak-meridian-angle-left_1_0', loss='logcosh', channel_map={'no_5102_6mm-weak-meridian-angle-left_1_0': 0}) +field_id_5102_6mm_weak_meridian_angle_left_1_1 = TensorMap('5102_6mm-weak-meridian-angle-left_1_1', loss='logcosh', channel_map={'no_5102_6mm-weak-meridian-angle-left_1_1': 0}) +field_id_5102_6mm_weak_meridian_angle_left_1_2 = TensorMap('5102_6mm-weak-meridian-angle-left_1_2', loss='logcosh', channel_map={'no_5102_6mm-weak-meridian-angle-left_1_2': 0}) +field_id_5103_3mm_weak_meridian_angle_left_1_0 = TensorMap('5103_3mm-weak-meridian-angle-left_1_0', loss='logcosh', channel_map={'no_5103_3mm-weak-meridian-angle-left_1_0': 0}) +field_id_5103_3mm_weak_meridian_angle_left_1_1 = TensorMap('5103_3mm-weak-meridian-angle-left_1_1', loss='logcosh', channel_map={'no_5103_3mm-weak-meridian-angle-left_1_1': 0}) +field_id_5103_3mm_weak_meridian_angle_left_1_2 = TensorMap('5103_3mm-weak-meridian-angle-left_1_2', loss='logcosh', channel_map={'no_5103_3mm-weak-meridian-angle-left_1_2': 0}) +field_id_5104_3mm_strong_meridian_angle_left_1_0 = TensorMap('5104_3mm-strong-meridian-angle-left_1_0', loss='logcosh', channel_map={'no_5104_3mm-strong-meridian-angle-left_1_0': 0}) +field_id_5104_3mm_strong_meridian_angle_left_1_1 = TensorMap('5104_3mm-strong-meridian-angle-left_1_1', loss='logcosh', channel_map={'no_5104_3mm-strong-meridian-angle-left_1_1': 0}) +field_id_5104_3mm_strong_meridian_angle_left_1_2 = TensorMap('5104_3mm-strong-meridian-angle-left_1_2', loss='logcosh', channel_map={'no_5104_3mm-strong-meridian-angle-left_1_2': 0}) +field_id_5105_6mm_strong_meridian_angle_left_1_0 = TensorMap('5105_6mm-strong-meridian-angle-left_1_0', loss='logcosh', channel_map={'no_5105_6mm-strong-meridian-angle-left_1_0': 0}) +field_id_5105_6mm_strong_meridian_angle_left_1_1 = TensorMap('5105_6mm-strong-meridian-angle-left_1_1', loss='logcosh', channel_map={'no_5105_6mm-strong-meridian-angle-left_1_1': 0}) +field_id_5105_6mm_strong_meridian_angle_left_1_2 = TensorMap('5105_6mm-strong-meridian-angle-left_1_2', loss='logcosh', channel_map={'no_5105_6mm-strong-meridian-angle-left_1_2': 0}) +field_id_5107_3mm_strong_meridian_angle_right_1_0 = TensorMap('5107_3mm-strong-meridian-angle-right_1_0', loss='logcosh', channel_map={'no_5107_3mm-strong-meridian-angle-right_1_0': 0}) +field_id_5107_3mm_strong_meridian_angle_right_1_1 = TensorMap('5107_3mm-strong-meridian-angle-right_1_1', loss='logcosh', channel_map={'no_5107_3mm-strong-meridian-angle-right_1_1': 0}) +field_id_5108_3mm_asymmetry_angle_right_1_0 = TensorMap('5108_3mm-asymmetry-angle-right_1_0', loss='logcosh', channel_map={'no_5108_3mm-asymmetry-angle-right_1_0': 0}) +field_id_5108_3mm_asymmetry_angle_right_1_1 = TensorMap('5108_3mm-asymmetry-angle-right_1_1', loss='logcosh', channel_map={'no_5108_3mm-asymmetry-angle-right_1_1': 0}) +field_id_5110_6mm_asymmetry_angle_left_1_1 = TensorMap('5110_6mm-asymmetry-angle-left_1_1', loss='logcosh', channel_map={'no_5110_6mm-asymmetry-angle-left_1_1': 0}) +field_id_5110_6mm_asymmetry_angle_left_1_2 = TensorMap('5110_6mm-asymmetry-angle-left_1_2', loss='logcosh', channel_map={'no_5110_6mm-asymmetry-angle-left_1_2': 0}) +field_id_5111_3mm_asymmetry_angle_left_1_0 = TensorMap('5111_3mm-asymmetry-angle-left_1_0', loss='logcosh', channel_map={'no_5111_3mm-asymmetry-angle-left_1_0': 0}) +field_id_5111_3mm_asymmetry_angle_left_1_1 = TensorMap('5111_3mm-asymmetry-angle-left_1_1', loss='logcosh', channel_map={'no_5111_3mm-asymmetry-angle-left_1_1': 0}) +field_id_5111_3mm_asymmetry_angle_left_1_2 = TensorMap('5111_3mm-asymmetry-angle-left_1_2', loss='logcosh', channel_map={'no_5111_3mm-asymmetry-angle-left_1_2': 0}) +field_id_5112_3mm_cylindrical_power_angle_left_1_0 = TensorMap('5112_3mm-cylindrical-power-angle-left_1_0', loss='logcosh', channel_map={'no_5112_3mm-cylindrical-power-angle-left_1_0': 0}) +field_id_5112_3mm_cylindrical_power_angle_left_1_1 = TensorMap('5112_3mm-cylindrical-power-angle-left_1_1', loss='logcosh', channel_map={'no_5112_3mm-cylindrical-power-angle-left_1_1': 0}) +field_id_5112_3mm_cylindrical_power_angle_left_1_2 = TensorMap('5112_3mm-cylindrical-power-angle-left_1_2', loss='logcosh', channel_map={'no_5112_3mm-cylindrical-power-angle-left_1_2': 0}) +field_id_5113_6mm_cylindrical_power_angle_left_1_0 = TensorMap('5113_6mm-cylindrical-power-angle-left_1_0', loss='logcosh', channel_map={'no_5113_6mm-cylindrical-power-angle-left_1_0': 0}) +field_id_5113_6mm_cylindrical_power_angle_left_1_1 = TensorMap('5113_6mm-cylindrical-power-angle-left_1_1', loss='logcosh', channel_map={'no_5113_6mm-cylindrical-power-angle-left_1_1': 0}) +field_id_5113_6mm_cylindrical_power_angle_left_1_2 = TensorMap('5113_6mm-cylindrical-power-angle-left_1_2', loss='logcosh', channel_map={'no_5113_6mm-cylindrical-power-angle-left_1_2': 0}) +field_id_5115_3mm_cylindrical_power_angle_right_1_0 = TensorMap('5115_3mm-cylindrical-power-angle-right_1_0', loss='logcosh', channel_map={'no_5115_3mm-cylindrical-power-angle-right_1_0': 0}) +field_id_5115_3mm_cylindrical_power_angle_right_1_1 = TensorMap('5115_3mm-cylindrical-power-angle-right_1_1', loss='logcosh', channel_map={'no_5115_3mm-cylindrical-power-angle-right_1_1': 0}) +field_id_5116_3mm_cylindrical_power_right_1_0 = TensorMap('5116_3mm-cylindrical-power-right_1_0', loss='logcosh', channel_map={'no_5116_3mm-cylindrical-power-right_1_0': 0}) +field_id_5116_3mm_cylindrical_power_right_1_1 = TensorMap('5116_3mm-cylindrical-power-right_1_1', loss='logcosh', channel_map={'no_5116_3mm-cylindrical-power-right_1_1': 0}) +field_id_5118_6mm_cylindrical_power_left_1_0 = TensorMap('5118_6mm-cylindrical-power-left_1_0', loss='logcosh', channel_map={'no_5118_6mm-cylindrical-power-left_1_0': 0}) +field_id_5118_6mm_cylindrical_power_left_1_1 = TensorMap('5118_6mm-cylindrical-power-left_1_1', loss='logcosh', channel_map={'no_5118_6mm-cylindrical-power-left_1_1': 0}) +field_id_5118_6mm_cylindrical_power_left_1_2 = TensorMap('5118_6mm-cylindrical-power-left_1_2', loss='logcosh', channel_map={'no_5118_6mm-cylindrical-power-left_1_2': 0}) +field_id_5119_3mm_cylindrical_power_left_1_0 = TensorMap('5119_3mm-cylindrical-power-left_1_0', loss='logcosh', channel_map={'no_5119_3mm-cylindrical-power-left_1_0': 0}) +field_id_5119_3mm_cylindrical_power_left_1_1 = TensorMap('5119_3mm-cylindrical-power-left_1_1', loss='logcosh', channel_map={'no_5119_3mm-cylindrical-power-left_1_1': 0}) +field_id_5119_3mm_cylindrical_power_left_1_2 = TensorMap('5119_3mm-cylindrical-power-left_1_2', loss='logcosh', channel_map={'no_5119_3mm-cylindrical-power-left_1_2': 0}) +field_id_5132_3mm_strong_meridian_right_1_0 = TensorMap('5132_3mm-strong-meridian-right_1_0', loss='logcosh', channel_map={'no_5132_3mm-strong-meridian-right_1_0': 0}) +field_id_5132_3mm_strong_meridian_right_1_1 = TensorMap('5132_3mm-strong-meridian-right_1_1', loss='logcosh', channel_map={'no_5132_3mm-strong-meridian-right_1_1': 0}) +field_id_5134_6mm_strong_meridian_left_1_0 = TensorMap('5134_6mm-strong-meridian-left_1_0', loss='logcosh', channel_map={'no_5134_6mm-strong-meridian-left_1_0': 0}) +field_id_5134_6mm_strong_meridian_left_1_1 = TensorMap('5134_6mm-strong-meridian-left_1_1', loss='logcosh', channel_map={'no_5134_6mm-strong-meridian-left_1_1': 0}) +field_id_5134_6mm_strong_meridian_left_1_2 = TensorMap('5134_6mm-strong-meridian-left_1_2', loss='logcosh', channel_map={'no_5134_6mm-strong-meridian-left_1_2': 0}) +field_id_5135_3mm_strong_meridian_left_1_0 = TensorMap('5135_3mm-strong-meridian-left_1_0', loss='logcosh', channel_map={'no_5135_3mm-strong-meridian-left_1_0': 0}) +field_id_5135_3mm_strong_meridian_left_1_1 = TensorMap('5135_3mm-strong-meridian-left_1_1', loss='logcosh', channel_map={'no_5135_3mm-strong-meridian-left_1_1': 0}) +field_id_5135_3mm_strong_meridian_left_1_2 = TensorMap('5135_3mm-strong-meridian-left_1_2', loss='logcosh', channel_map={'no_5135_3mm-strong-meridian-left_1_2': 0}) +field_id_5156_3mm_asymmetry_index_left_1_0 = TensorMap('5156_3mm-asymmetry-index-left_1_0', loss='logcosh', channel_map={'no_5156_3mm-asymmetry-index-left_1_0': 0}) +field_id_5156_3mm_asymmetry_index_left_1_1 = TensorMap('5156_3mm-asymmetry-index-left_1_1', loss='logcosh', channel_map={'no_5156_3mm-asymmetry-index-left_1_1': 0}) +field_id_5156_3mm_asymmetry_index_left_1_2 = TensorMap('5156_3mm-asymmetry-index-left_1_2', loss='logcosh', channel_map={'no_5156_3mm-asymmetry-index-left_1_2': 0}) +field_id_5157_6mm_asymmetry_index_left_1_1 = TensorMap('5157_6mm-asymmetry-index-left_1_1', loss='logcosh', channel_map={'no_5157_6mm-asymmetry-index-left_1_1': 0}) +field_id_5157_6mm_asymmetry_index_left_1_2 = TensorMap('5157_6mm-asymmetry-index-left_1_2', loss='logcosh', channel_map={'no_5157_6mm-asymmetry-index-left_1_2': 0}) +field_id_5159_3mm_asymmetry_index_right_1_0 = TensorMap('5159_3mm-asymmetry-index-right_1_0', loss='logcosh', channel_map={'no_5159_3mm-asymmetry-index-right_1_0': 0}) +field_id_5159_3mm_asymmetry_index_right_1_1 = TensorMap('5159_3mm-asymmetry-index-right_1_1', loss='logcosh', channel_map={'no_5159_3mm-asymmetry-index-right_1_1': 0}) +field_id_5160_3mm_regularity_index_right_1_0 = TensorMap('5160_3mm-regularity-index-right_1_0', loss='logcosh', channel_map={'no_5160_3mm-regularity-index-right_1_0': 0}) +field_id_5160_3mm_regularity_index_right_1_1 = TensorMap('5160_3mm-regularity-index-right_1_1', loss='logcosh', channel_map={'no_5160_3mm-regularity-index-right_1_1': 0}) +field_id_5162_6mm_regularity_index_left_1_1 = TensorMap('5162_6mm-regularity-index-left_1_1', loss='logcosh', channel_map={'no_5162_6mm-regularity-index-left_1_1': 0}) +field_id_5162_6mm_regularity_index_left_1_2 = TensorMap('5162_6mm-regularity-index-left_1_2', loss='logcosh', channel_map={'no_5162_6mm-regularity-index-left_1_2': 0}) +field_id_5163_3mm_regularity_index_left_1_0 = TensorMap('5163_3mm-regularity-index-left_1_0', loss='logcosh', channel_map={'no_5163_3mm-regularity-index-left_1_0': 0}) +field_id_5163_3mm_regularity_index_left_1_1 = TensorMap('5163_3mm-regularity-index-left_1_1', loss='logcosh', channel_map={'no_5163_3mm-regularity-index-left_1_1': 0}) +field_id_5163_3mm_regularity_index_left_1_2 = TensorMap('5163_3mm-regularity-index-left_1_2', loss='logcosh', channel_map={'no_5163_3mm-regularity-index-left_1_2': 0}) +field_id_5186_duration_visualacuity_screen_displayed_right_1_0 = TensorMap('5186_Duration-visualacuity-screen-displayed-right_1_0', loss='logcosh', channel_map={'no_5186_Duration-visualacuity-screen-displayed-right_1_0': 0}) +field_id_5188_duration_visualacuity_screen_displayed_left_1_0 = TensorMap('5188_Duration-visualacuity-screen-displayed-left_1_0', loss='logcosh', channel_map={'no_5188_Duration-visualacuity-screen-displayed-left_1_0': 0}) +field_id_5190_duration_at_which_refractometer_first_shown_right_1_0 = TensorMap('5190_Duration-at-which-refractometer-first-shown-right_1_0', loss='logcosh', channel_map={'no_5190_Duration-at-which-refractometer-first-shown-right_1_0': 0}) +field_id_5193_duration_at_which_refractometer_first_shown_left_1_0 = TensorMap('5193_Duration-at-which-refractometer-first-shown-left_1_0', loss='logcosh', channel_map={'no_5193_Duration-at-which-refractometer-first-shown-left_1_0': 0}) +field_id_5199_logmar_initial_right_1_0 = TensorMap('5199_logMAR-initial-right_1_0', loss='logcosh', channel_map={'no_5199_logMAR-initial-right_1_0': 0}) +field_id_51_seated_height_1_0 = TensorMap('51_Seated-height_1_0', loss='logcosh', channel_map={'no_51_Seated-height_1_0': 0}) +field_id_5200_final_number_of_letters_displayed_right_1_0 = TensorMap('5200_Final-number-of-letters-displayed-right_1_0', loss='logcosh', channel_map={'no_5200_Final-number-of-letters-displayed-right_1_0': 0}) +field_id_5201_logmar_final_right_1_0 = TensorMap('5201_logMAR-final-right_1_0', loss='logcosh', channel_map={'no_5201_logMAR-final-right_1_0': 0}) +field_id_5202_number_of_rounds_to_result_right_1_0 = TensorMap('5202_Number-of-rounds-to-result-right_1_0', loss='logcosh', channel_map={'no_5202_Number-of-rounds-to-result-right_1_0': 0}) +field_id_5204_distance_of_viewer_to_screen_right_1_0 = TensorMap('5204_Distance-of-viewer-to-screen-right_1_0', loss='logcosh', channel_map={'no_5204_Distance-of-viewer-to-screen-right_1_0': 0}) +field_id_5206_logmar_initial_left_1_0 = TensorMap('5206_logMAR-initial-left_1_0', loss='logcosh', channel_map={'no_5206_logMAR-initial-left_1_0': 0}) +field_id_5207_final_number_of_letters_displayed_left_1_0 = TensorMap('5207_Final-number-of-letters-displayed-left_1_0', loss='logcosh', channel_map={'no_5207_Final-number-of-letters-displayed-left_1_0': 0}) +field_id_5208_logmar_final_left_1_0 = TensorMap('5208_logMAR-final-left_1_0', loss='logcosh', channel_map={'no_5208_logMAR-final-left_1_0': 0}) +field_id_5209_number_of_rounds_to_result_left_1_0 = TensorMap('5209_Number-of-rounds-to-result-left_1_0', loss='logcosh', channel_map={'no_5209_Number-of-rounds-to-result-left_1_0': 0}) +field_id_5211_distance_of_viewer_to_screen_left_1_0 = TensorMap('5211_Distance-of-viewer-to-screen-left_1_0', loss='logcosh', channel_map={'no_5211_Distance-of-viewer-to-screen-left_1_0': 0}) +field_id_5215_vertex_distance_right_1_0 = TensorMap('5215_Vertex-distance-right_1_0', loss='logcosh', channel_map={'no_5215_Vertex-distance-right_1_0': 0}) +field_id_5221_index_of_best_refractometry_result_right_1_0 = TensorMap('5221_Index-of-best-refractometry-result-right_1_0', loss='logcosh', channel_map={'no_5221_Index-of-best-refractometry-result-right_1_0': 0}) +field_id_5237_3mm_index_of_best_keratometry_results_right_1_0 = TensorMap('5237_3mm-index-of-best-keratometry-results-right_1_0', loss='logcosh', channel_map={'no_5237_3mm-index-of-best-keratometry-results-right_1_0': 0}) +field_id_5254_intraocular_pressure_cornealcompensated_right_1_0 = TensorMap('5254_Intraocular-pressure-cornealcompensated-right_1_0', loss='logcosh', channel_map={'no_5254_Intraocular-pressure-cornealcompensated-right_1_0': 0}) +field_id_5255_intraocular_pressure_goldmanncorrelated_right_1_0 = TensorMap('5255_Intraocular-pressure-Goldmanncorrelated-right_1_0', loss='logcosh', channel_map={'no_5255_Intraocular-pressure-Goldmanncorrelated-right_1_0': 0}) +field_id_5256_corneal_hysteresis_right_1_0 = TensorMap('5256_Corneal-hysteresis-right_1_0', loss='logcosh', channel_map={'no_5256_Corneal-hysteresis-right_1_0': 0}) +field_id_5257_corneal_resistance_factor_right_1_0 = TensorMap('5257_Corneal-resistance-factor-right_1_0', loss='logcosh', channel_map={'no_5257_Corneal-resistance-factor-right_1_0': 0}) +field_id_5262_intraocular_pressure_cornealcompensated_left_1_0 = TensorMap('5262_Intraocular-pressure-cornealcompensated-left_1_0', loss='logcosh', channel_map={'no_5262_Intraocular-pressure-cornealcompensated-left_1_0': 0}) +field_id_5263_intraocular_pressure_goldmanncorrelated_left_1_0 = TensorMap('5263_Intraocular-pressure-Goldmanncorrelated-left_1_0', loss='logcosh', channel_map={'no_5263_Intraocular-pressure-Goldmanncorrelated-left_1_0': 0}) +field_id_5264_corneal_hysteresis_left_1_0 = TensorMap('5264_Corneal-hysteresis-left_1_0', loss='logcosh', channel_map={'no_5264_Corneal-hysteresis-left_1_0': 0}) +field_id_5265_corneal_resistance_factor_left_1_0 = TensorMap('5265_Corneal-resistance-factor-left_1_0', loss='logcosh', channel_map={'no_5265_Corneal-resistance-factor-left_1_0': 0}) +field_id_5274_vertex_distance_left_1_0 = TensorMap('5274_Vertex-distance-left_1_0', loss='logcosh', channel_map={'no_5274_Vertex-distance-left_1_0': 0}) +field_id_5276_index_of_best_refractometry_result_left_1_0 = TensorMap('5276_Index-of-best-refractometry-result-left_1_0', loss='logcosh', channel_map={'no_5276_Index-of-best-refractometry-result-left_1_0': 0}) +field_id_5292_3mm_index_of_best_keratometry_results_left_1_0 = TensorMap('5292_3mm-index-of-best-keratometry-results-left_1_0', loss='logcosh', channel_map={'no_5292_3mm-index-of-best-keratometry-results-left_1_0': 0}) +field_id_5306_6mm_index_of_best_keratometry_results_left_1_0 = TensorMap('5306_6mm-index-of-best-keratometry-results-left_1_0', loss='logcosh', channel_map={'no_5306_6mm-index-of-best-keratometry-results-left_1_0': 0}) +field_id_5364_average_weekly_intake_of_other_alcoholic_drinks_1_0 = TensorMap('5364_Average-weekly-intake-of-other-alcoholic-drinks_1_0', loss='logcosh', channel_map={'no_5364_Average-weekly-intake-of-other-alcoholic-drinks_1_0': 0}) +field_id_6032_maximum_workload_during_fitness_test_1_0 = TensorMap('6032_Maximum-workload-during-fitness-test_1_0', loss='logcosh', channel_map={'no_6032_Maximum-workload-during-fitness-test_1_0': 0}) +field_id_6033_maximum_heart_rate_during_fitness_test_1_0 = TensorMap('6033_Maximum-heart-rate-during-fitness-test_1_0', loss='logcosh', channel_map={'no_6033_Maximum-heart-rate-during-fitness-test_1_0': 0}) +field_id_6038_number_of_trend_entries_1_0 = TensorMap('6038_Number-of-trend-entries_1_0', loss='logcosh', channel_map={'no_6038_Number-of-trend-entries_1_0': 0}) +field_id_6039_duration_of_fitness_test_1_0 = TensorMap('6039_Duration-of-fitness-test_1_0', loss='logcosh', channel_map={'no_6039_Duration-of-fitness-test_1_0': 0}) +field_id_6071_duration_at_which_oct_screen_shown_right_1_0 = TensorMap('6071_Duration-at-which-OCT-screen-shown-right_1_0', loss='logcosh', channel_map={'no_6071_Duration-at-which-OCT-screen-shown-right_1_0': 0}) +field_id_6073_duration_at_which_oct_screen_shown_left_1_0 = TensorMap('6073_Duration-at-which-OCT-screen-shown-left_1_0', loss='logcosh', channel_map={'no_6073_Duration-at-which-OCT-screen-shown-left_1_0': 0}) +field_id_699_length_of_time_at_current_address_1_0 = TensorMap('699_Length-of-time-at-current-address_1_0', loss='logcosh', channel_map={'no_699_Length-of-time-at-current-address_1_0': 0}) +field_id_709_number_in_household_1_0 = TensorMap('709_Number-in-household_1_0', loss='logcosh', channel_map={'no_709_Number-in-household_1_0': 0}) +field_id_845_age_completed_full_time_education_1_0 = TensorMap('845_Age-completed-full-time-education_1_0', loss='logcosh', channel_map={'no_845_Age-completed-full-time-education_1_0': 0}) +field_id_864_number_of_daysweek_walked_10_minutes_1_0 = TensorMap('864_Number-of-daysweek-walked-10-minutes_1_0', loss='logcosh', channel_map={'no_864_Number-of-daysweek-walked-10-minutes_1_0': 0}) +field_id_874_duration_of_walks_1_0 = TensorMap('874_Duration-of-walks_1_0', loss='logcosh', channel_map={'no_874_Duration-of-walks_1_0': 0}) +field_id_87_noncancer_illness_yearage_first_occurred_0_4 = TensorMap('87_Noncancer-illness-yearage-first-occurred_0_4', loss='logcosh', channel_map={'no_87_Noncancer-illness-yearage-first-occurred_0_4': 0}) +field_id_87_noncancer_illness_yearage_first_occurred_1_0 = TensorMap('87_Noncancer-illness-yearage-first-occurred_1_0', loss='logcosh', channel_map={'no_87_Noncancer-illness-yearage-first-occurred_1_0': 0}) +field_id_87_noncancer_illness_yearage_first_occurred_1_1 = TensorMap('87_Noncancer-illness-yearage-first-occurred_1_1', loss='logcosh', channel_map={'no_87_Noncancer-illness-yearage-first-occurred_1_1': 0}) +field_id_87_noncancer_illness_yearage_first_occurred_1_2 = TensorMap('87_Noncancer-illness-yearage-first-occurred_1_2', loss='logcosh', channel_map={'no_87_Noncancer-illness-yearage-first-occurred_1_2': 0}) +field_id_884_number_of_daysweek_of_moderate_physical_activity_10_minutes_1_0 = TensorMap('884_Number-of-daysweek-of-moderate-physical-activity-10-minutes_1_0', loss='logcosh', channel_map={'no_884_Number-of-daysweek-of-moderate-physical-activity-10-minutes_1_0': 0}) +field_id_894_duration_of_moderate_activity_1_0 = TensorMap('894_Duration-of-moderate-activity_1_0', loss='logcosh', channel_map={'no_894_Duration-of-moderate-activity_1_0': 0}) +field_id_904_number_of_daysweek_of_vigorous_physical_activity_10_minutes_1_0 = TensorMap('904_Number-of-daysweek-of-vigorous-physical-activity-10-minutes_1_0', loss='logcosh', channel_map={'no_904_Number-of-daysweek-of-vigorous-physical-activity-10-minutes_1_0': 0}) +field_id_92_operation_yearage_first_occurred_1_0 = TensorMap('92_Operation-yearage-first-occurred_1_0', loss='logcosh', channel_map={'no_92_Operation-yearage-first-occurred_1_0': 0}) +field_id_20009_interpolated_age_of_participant_when_noncancer_illness_first_diagnosed_0_5 = TensorMap('20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_0_5', loss='logcosh', channel_map={'no_20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_0_5': 0}) +field_id_20009_interpolated_age_of_participant_when_noncancer_illness_first_diagnosed_0_6 = TensorMap('20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_0_6', loss='logcosh', channel_map={'no_20009_Interpolated-Age-of-participant-when-noncancer-illness-first-diagnosed_0_6': 0}) +field_id_20011_interpolated_age_of_participant_when_operation_took_place_0_2 = TensorMap('20011_Interpolated-Age-of-participant-when-operation-took-place_0_2', loss='logcosh', channel_map={'no_20011_Interpolated-Age-of-participant-when-operation-took-place_0_2': 0}) +field_id_20011_interpolated_age_of_participant_when_operation_took_place_0_3 = TensorMap('20011_Interpolated-Age-of-participant-when-operation-took-place_0_3', loss='logcosh', channel_map={'no_20011_Interpolated-Age-of-participant-when-operation-took-place_0_3': 0}) +field_id_22605_work_hours_per_week_exact_value_0_4 = TensorMap('22605_Work-hours-per-week-exact-value_0_4', loss='logcosh', channel_map={'no_22605_Work-hours-per-week-exact-value_0_4': 0}) +field_id_22642_usual_length_of_each_night_shift_during_mixed_shift_periods_0_0 = TensorMap('22642_Usual-length-of-each-night-shift-during-mixed-shift-periods_0_0', loss='logcosh', channel_map={'no_22642_Usual-length-of-each-night-shift-during-mixed-shift-periods_0_0': 0}) +field_id_22643_number_of_night_shifts_worked_monthly_during_mixed_shift_periods_0_0 = TensorMap('22643_Number-of-night-shifts-worked-monthly-during-mixed-shift-periods_0_0', loss='logcosh', channel_map={'no_22643_Number-of-night-shifts-worked-monthly-during-mixed-shift-periods_0_0': 0}) +field_id_22644_consecutive_night_shifts_during_mixed_shift_periods_0_0 = TensorMap('22644_Consecutive-night-shifts-during-mixed-shift-periods_0_0', loss='logcosh', channel_map={'no_22644_Consecutive-night-shifts-during-mixed-shift-periods_0_0': 0}) +field_id_22645_rest_days_during_mixed_shift_periods_0_0 = TensorMap('22645_Rest-days-during-mixed-shift-periods_0_0', loss='logcosh', channel_map={'no_22645_Rest-days-during-mixed-shift-periods_0_0': 0}) +field_id_4256_time_elapsed_0_0 = TensorMap('4256_Time-elapsed_0_0', loss='logcosh', channel_map={'no_4256_Time-elapsed_0_0': 0}) +field_id_4256_time_elapsed_0_1 = TensorMap('4256_Time-elapsed_0_1', loss='logcosh', channel_map={'no_4256_Time-elapsed_0_1': 0}) +field_id_4256_time_elapsed_0_2 = TensorMap('4256_Time-elapsed_0_2', loss='logcosh', channel_map={'no_4256_Time-elapsed_0_2': 0}) +field_id_4256_time_elapsed_0_3 = TensorMap('4256_Time-elapsed_0_3', loss='logcosh', channel_map={'no_4256_Time-elapsed_0_3': 0}) +field_id_4256_time_elapsed_0_4 = TensorMap('4256_Time-elapsed_0_4', loss='logcosh', channel_map={'no_4256_Time-elapsed_0_4': 0}) +field_id_4260_round_of_numeric_memory_test_0_0 = TensorMap('4260_Round-of-numeric-memory-test_0_0', loss='logcosh', channel_map={'no_4260_Round-of-numeric-memory-test_0_0': 0}) +field_id_4260_round_of_numeric_memory_test_0_1 = TensorMap('4260_Round-of-numeric-memory-test_0_1', loss='logcosh', channel_map={'no_4260_Round-of-numeric-memory-test_0_1': 0}) +field_id_4260_round_of_numeric_memory_test_0_2 = TensorMap('4260_Round-of-numeric-memory-test_0_2', loss='logcosh', channel_map={'no_4260_Round-of-numeric-memory-test_0_2': 0}) +field_id_4260_round_of_numeric_memory_test_0_3 = TensorMap('4260_Round-of-numeric-memory-test_0_3', loss='logcosh', channel_map={'no_4260_Round-of-numeric-memory-test_0_3': 0}) +field_id_4260_round_of_numeric_memory_test_0_4 = TensorMap('4260_Round-of-numeric-memory-test_0_4', loss='logcosh', channel_map={'no_4260_Round-of-numeric-memory-test_0_4': 0}) +field_id_4282_maximum_digits_remembered_correctly_0_0 = TensorMap('4282_Maximum-digits-remembered-correctly_0_0', loss='logcosh', channel_map={'no_4282_Maximum-digits-remembered-correctly_0_0': 0}) +field_id_4283_number_of_rounds_of_numeric_memory_test_performed_0_0 = TensorMap('4283_Number-of-rounds-of-numeric-memory-test-performed_0_0', loss='logcosh', channel_map={'no_4283_Number-of-rounds-of-numeric-memory-test-performed_0_0': 0}) +field_id_4285_time_to_complete_test_0_0 = TensorMap('4285_Time-to-complete-test_0_0', loss='logcosh', channel_map={'no_4285_Time-to-complete-test_0_0': 0}) +field_id_87_noncancer_illness_yearage_first_occurred_0_5 = TensorMap('87_Noncancer-illness-yearage-first-occurred_0_5', loss='logcosh', channel_map={'no_87_Noncancer-illness-yearage-first-occurred_0_5': 0}) +field_id_87_noncancer_illness_yearage_first_occurred_0_6 = TensorMap('87_Noncancer-illness-yearage-first-occurred_0_6', loss='logcosh', channel_map={'no_87_Noncancer-illness-yearage-first-occurred_0_6': 0}) +field_id_92_operation_yearage_first_occurred_0_2 = TensorMap('92_Operation-yearage-first-occurred_0_2', loss='logcosh', channel_map={'no_92_Operation-yearage-first-occurred_0_2': 0}) +field_id_92_operation_yearage_first_occurred_0_3 = TensorMap('92_Operation-yearage-first-occurred_0_3', loss='logcosh', channel_map={'no_92_Operation-yearage-first-occurred_0_3': 0}) +field_id_20007_interpolated_age_of_participant_when_cancer_first_diagnosed_0_0 = TensorMap('20007_Interpolated-Age-of-participant-when-cancer-first-diagnosed_0_0', loss='logcosh', channel_map={'no_20007_Interpolated-Age-of-participant-when-cancer-first-diagnosed_0_0': 0}) +field_id_20011_interpolated_age_of_participant_when_operation_took_place_0_4 = TensorMap('20011_Interpolated-Age-of-participant-when-operation-took-place_0_4', loss='logcosh', channel_map={'no_20011_Interpolated-Age-of-participant-when-operation-took-place_0_4': 0}) +field_id_40008_age_at_cancer_diagnosis_1_0 = TensorMap('40008_Age-at-cancer-diagnosis_1_0', loss='logcosh', channel_map={'no_40008_Age-at-cancer-diagnosis_1_0': 0}) +field_id_4256_time_elapsed_0_5 = TensorMap('4256_Time-elapsed_0_5', loss='logcosh', channel_map={'no_4256_Time-elapsed_0_5': 0}) +field_id_4256_time_elapsed_0_6 = TensorMap('4256_Time-elapsed_0_6', loss='logcosh', channel_map={'no_4256_Time-elapsed_0_6': 0}) +field_id_4256_time_elapsed_0_7 = TensorMap('4256_Time-elapsed_0_7', loss='logcosh', channel_map={'no_4256_Time-elapsed_0_7': 0}) +field_id_4256_time_elapsed_0_8 = TensorMap('4256_Time-elapsed_0_8', loss='logcosh', channel_map={'no_4256_Time-elapsed_0_8': 0}) +field_id_4260_round_of_numeric_memory_test_0_5 = TensorMap('4260_Round-of-numeric-memory-test_0_5', loss='logcosh', channel_map={'no_4260_Round-of-numeric-memory-test_0_5': 0}) +field_id_4260_round_of_numeric_memory_test_0_6 = TensorMap('4260_Round-of-numeric-memory-test_0_6', loss='logcosh', channel_map={'no_4260_Round-of-numeric-memory-test_0_6': 0}) +field_id_4260_round_of_numeric_memory_test_0_7 = TensorMap('4260_Round-of-numeric-memory-test_0_7', loss='logcosh', channel_map={'no_4260_Round-of-numeric-memory-test_0_7': 0}) +field_id_4260_round_of_numeric_memory_test_0_8 = TensorMap('4260_Round-of-numeric-memory-test_0_8', loss='logcosh', channel_map={'no_4260_Round-of-numeric-memory-test_0_8': 0}) +field_id_4700_age_cataract_diagnosed_0_0 = TensorMap('4700_Age-cataract-diagnosed_0_0', loss='logcosh', channel_map={'no_4700_Age-cataract-diagnosed_0_0': 0}) +field_id_84_cancer_yearage_first_occurred_0_0 = TensorMap('84_Cancer-yearage-first-occurred_0_0', loss='logcosh', channel_map={'no_84_Cancer-yearage-first-occurred_0_0': 0}) +field_id_92_operation_yearage_first_occurred_0_4 = TensorMap('92_Operation-yearage-first-occurred_0_4', loss='logcosh', channel_map={'no_92_Operation-yearage-first-occurred_0_4': 0}) +field_id_2976_age_diabetes_diagnosed_0_0 = TensorMap('2976_Age-diabetes-diagnosed_0_0', loss='logcosh', channel_map={'no_2976_Age-diabetes-diagnosed_0_0': 0}) +field_id_5098_6mm_weak_meridian_right_0_2 = TensorMap('5098_6mm-weak-meridian-right_0_2', loss='logcosh', channel_map={'no_5098_6mm-weak-meridian-right_0_2': 0}) +field_id_5101_6mm_weak_meridian_angle_right_0_2 = TensorMap('5101_6mm-weak-meridian-angle-right_0_2', loss='logcosh', channel_map={'no_5101_6mm-weak-meridian-angle-right_0_2': 0}) +field_id_5106_6mm_strong_meridian_angle_right_0_2 = TensorMap('5106_6mm-strong-meridian-angle-right_0_2', loss='logcosh', channel_map={'no_5106_6mm-strong-meridian-angle-right_0_2': 0}) +field_id_5109_6mm_asymmetry_angle_right_0_2 = TensorMap('5109_6mm-asymmetry-angle-right_0_2', loss='logcosh', channel_map={'no_5109_6mm-asymmetry-angle-right_0_2': 0}) +field_id_5110_6mm_asymmetry_angle_left_0_0 = TensorMap('5110_6mm-asymmetry-angle-left_0_0', loss='logcosh', channel_map={'no_5110_6mm-asymmetry-angle-left_0_0': 0}) +field_id_5110_6mm_asymmetry_angle_left_0_1 = TensorMap('5110_6mm-asymmetry-angle-left_0_1', loss='logcosh', channel_map={'no_5110_6mm-asymmetry-angle-left_0_1': 0}) +field_id_5114_6mm_cylindrical_power_angle_right_0_2 = TensorMap('5114_6mm-cylindrical-power-angle-right_0_2', loss='logcosh', channel_map={'no_5114_6mm-cylindrical-power-angle-right_0_2': 0}) +field_id_5117_6mm_cylindrical_power_right_0_2 = TensorMap('5117_6mm-cylindrical-power-right_0_2', loss='logcosh', channel_map={'no_5117_6mm-cylindrical-power-right_0_2': 0}) +field_id_5133_6mm_strong_meridian_right_0_2 = TensorMap('5133_6mm-strong-meridian-right_0_2', loss='logcosh', channel_map={'no_5133_6mm-strong-meridian-right_0_2': 0}) +field_id_5157_6mm_asymmetry_index_left_0_0 = TensorMap('5157_6mm-asymmetry-index-left_0_0', loss='logcosh', channel_map={'no_5157_6mm-asymmetry-index-left_0_0': 0}) +field_id_5157_6mm_asymmetry_index_left_0_1 = TensorMap('5157_6mm-asymmetry-index-left_0_1', loss='logcosh', channel_map={'no_5157_6mm-asymmetry-index-left_0_1': 0}) +field_id_5158_6mm_asymmetry_index_right_0_2 = TensorMap('5158_6mm-asymmetry-index-right_0_2', loss='logcosh', channel_map={'no_5158_6mm-asymmetry-index-right_0_2': 0}) +field_id_5161_6mm_regularity_index_right_0_2 = TensorMap('5161_6mm-regularity-index-right_0_2', loss='logcosh', channel_map={'no_5161_6mm-regularity-index-right_0_2': 0}) +field_id_5162_6mm_regularity_index_left_0_0 = TensorMap('5162_6mm-regularity-index-left_0_0', loss='logcosh', channel_map={'no_5162_6mm-regularity-index-left_0_0': 0}) +field_id_5162_6mm_regularity_index_left_0_1 = TensorMap('5162_6mm-regularity-index-left_0_1', loss='logcosh', channel_map={'no_5162_6mm-regularity-index-left_0_1': 0}) +field_id_30800_oestradiol_0_0 = TensorMap('30800_Oestradiol_0_0', loss='logcosh', channel_map={'no_30800_Oestradiol_0_0': 0}) +field_id_3700_time_since_last_menstrual_period_0_0 = TensorMap('3700_Time-since-last-menstrual-period_0_0', loss='logcosh', channel_map={'no_3700_Time-since-last-menstrual-period_0_0': 0}) +field_id_3710_length_of_menstrual_cycle_0_0 = TensorMap('3710_Length-of-menstrual-cycle_0_0', loss='logcosh', channel_map={'no_3710_Length-of-menstrual-cycle_0_0': 0}) +field_id_20011_interpolated_age_of_participant_when_operation_took_place_0_5 = TensorMap('20011_Interpolated-Age-of-participant-when-operation-took-place_0_5', loss='logcosh', channel_map={'no_20011_Interpolated-Age-of-participant-when-operation-took-place_0_5': 0}) +field_id_92_operation_yearage_first_occurred_0_5 = TensorMap('92_Operation-yearage-first-occurred_0_5', loss='logcosh', channel_map={'no_92_Operation-yearage-first-occurred_0_5': 0}) +field_id_4256_time_elapsed_0_9 = TensorMap('4256_Time-elapsed_0_9', loss='logcosh', channel_map={'no_4256_Time-elapsed_0_9': 0}) +field_id_4260_round_of_numeric_memory_test_0_9 = TensorMap('4260_Round-of-numeric-memory-test_0_9', loss='logcosh', channel_map={'no_4260_Round-of-numeric-memory-test_0_9': 0}) +field_id_12673_heart_rate_during_pwa_2_0 = TensorMap('12673_Heart-rate-during-PWA_2_0', loss='logcosh', channel_map={'no_12673_Heart-rate-during-PWA_2_0': 0}) +field_id_12673_heart_rate_during_pwa_2_1 = TensorMap('12673_Heart-rate-during-PWA_2_1', loss='logcosh', channel_map={'no_12673_Heart-rate-during-PWA_2_1': 0}) +field_id_12674_systolic_brachial_blood_pressure_during_pwa_2_0 = TensorMap('12674_Systolic-brachial-blood-pressure-during-PWA_2_0', loss='logcosh', channel_map={'no_12674_Systolic-brachial-blood-pressure-during-PWA_2_0': 0}) +field_id_12675_diastolic_brachial_blood_pressure_during_pwa_2_0 = TensorMap('12675_Diastolic-brachial-blood-pressure-during-PWA_2_0', loss='logcosh', channel_map={'no_12675_Diastolic-brachial-blood-pressure-during-PWA_2_0': 0}) +field_id_12676_peripheral_pulse_pressure_during_pwa_2_0 = TensorMap('12676_Peripheral-pulse-pressure-during-PWA_2_0', loss='logcosh', channel_map={'no_12676_Peripheral-pulse-pressure-during-PWA_2_0': 0}) +field_id_12677_central_systolic_blood_pressure_during_pwa_2_0 = TensorMap('12677_Central-systolic-blood-pressure-during-PWA_2_0', loss='logcosh', channel_map={'no_12677_Central-systolic-blood-pressure-during-PWA_2_0': 0}) +field_id_12678_central_pulse_pressure_during_pwa_2_0 = TensorMap('12678_Central-pulse-pressure-during-PWA_2_0', loss='logcosh', channel_map={'no_12678_Central-pulse-pressure-during-PWA_2_0': 0}) +field_id_12679_number_of_beats_in_waveform_average_for_pwa_2_0 = TensorMap('12679_Number-of-beats-in-waveform-average-for-PWA_2_0', loss='logcosh', channel_map={'no_12679_Number-of-beats-in-waveform-average-for-PWA_2_0': 0}) +field_id_12679_number_of_beats_in_waveform_average_for_pwa_2_1 = TensorMap('12679_Number-of-beats-in-waveform-average-for-PWA_2_1', loss='logcosh', channel_map={'no_12679_Number-of-beats-in-waveform-average-for-PWA_2_1': 0}) +field_id_12680_central_augmentation_pressure_during_pwa_2_0 = TensorMap('12680_Central-augmentation-pressure-during-PWA_2_0', loss='logcosh', channel_map={'no_12680_Central-augmentation-pressure-during-PWA_2_0': 0}) +field_id_12681_augmentation_index_for_pwa_2_0 = TensorMap('12681_Augmentation-index-for-PWA_2_0', loss='logcosh', channel_map={'no_12681_Augmentation-index-for-PWA_2_0': 0}) +field_id_12681_augmentation_index_for_pwa_2_1 = TensorMap('12681_Augmentation-index-for-PWA_2_1', loss='logcosh', channel_map={'no_12681_Augmentation-index-for-PWA_2_1': 0}) +field_id_12682_cardiac_output_during_pwa_2_0 = TensorMap('12682_Cardiac-output-during-PWA_2_0', loss='logcosh', channel_map={'no_12682_Cardiac-output-during-PWA_2_0': 0}) +field_id_12683_end_systolic_pressure_during_pwa_2_0 = TensorMap('12683_End-systolic-pressure-during-PWA_2_0', loss='logcosh', channel_map={'no_12683_End-systolic-pressure-during-PWA_2_0': 0}) +field_id_12684_end_systolic_pressure_index_during_pwa_2_0 = TensorMap('12684_End-systolic-pressure-index-during-PWA_2_0', loss='logcosh', channel_map={'no_12684_End-systolic-pressure-index-during-PWA_2_0': 0}) +field_id_12685_total_peripheral_resistance_during_pwa_2_0 = TensorMap('12685_Total-peripheral-resistance-during-PWA_2_0', loss='logcosh', channel_map={'no_12685_Total-peripheral-resistance-during-PWA_2_0': 0}) +field_id_12686_stroke_volume_during_pwa_2_0 = TensorMap('12686_Stroke-volume-during-PWA_2_0', loss='logcosh', channel_map={'no_12686_Stroke-volume-during-PWA_2_0': 0}) +field_id_12687_mean_arterial_pressure_during_pwa_2_0 = TensorMap('12687_Mean-arterial-pressure-during-PWA_2_0', loss='logcosh', channel_map={'no_12687_Mean-arterial-pressure-during-PWA_2_0': 0}) +field_id_12697_systolic_brachial_blood_pressure_2_0 = TensorMap('12697_Systolic-brachial-blood-pressure_2_0', loss='logcosh', channel_map={'no_12697_Systolic-brachial-blood-pressure_2_0': 0}) +field_id_12698_diastolic_brachial_blood_pressure_2_0 = TensorMap('12698_Diastolic-brachial-blood-pressure_2_0', loss='logcosh', channel_map={'no_12698_Diastolic-brachial-blood-pressure_2_0': 0}) +field_id_12699_number_of_pwa_tests_performed_2_0 = TensorMap('12699_Number-of-PWA-tests-performed_2_0', loss='logcosh', channel_map={'no_12699_Number-of-PWA-tests-performed_2_0': 0}) +field_id_12702_cardiac_index_during_pwa_2_0 = TensorMap('12702_Cardiac-index-during-PWA_2_0', loss='logcosh', channel_map={'no_12702_Cardiac-index-during-PWA_2_0': 0}) +field_id_129_place_of_birth_in_uk_north_coordinate_2_0 = TensorMap('129_Place-of-birth-in-UK-north-coordinate_2_0', loss='logcosh', channel_map={'no_129_Place-of-birth-in-UK-north-coordinate_2_0': 0}) +field_id_130_place_of_birth_in_uk_east_coordinate_2_0 = TensorMap('130_Place-of-birth-in-UK-east-coordinate_2_0', loss='logcosh', channel_map={'no_130_Place-of-birth-in-UK-east-coordinate_2_0': 0}) +field_id_1737_childhood_sunburn_occasions_2_0 = TensorMap('1737_Childhood-sunburn-occasions_2_0', loss='logcosh', channel_map={'no_1737_Childhood-sunburn-occasions_2_0': 0}) +field_id_2405_number_of_children_fathered_2_0 = TensorMap('2405_Number-of-children-fathered_2_0', loss='logcosh', channel_map={'no_2405_Number-of-children-fathered_2_0': 0}) +field_id_3761_age_hay_fever_rhinitis_or_eczema_diagnosed_2_0 = TensorMap('3761_Age-hay-fever-rhinitis-or-eczema-diagnosed_2_0', loss='logcosh', channel_map={'no_3761_Age-hay-fever-rhinitis-or-eczema-diagnosed_2_0': 0}) +field_id_404_duration_to_first_press_of_snapbutton_in_each_round_2_0 = TensorMap('404_Duration-to-first-press-of-snapbutton-in-each-round_2_0', loss='logcosh', channel_map={'no_404_Duration-to-first-press-of-snapbutton-in-each-round_2_0': 0}) +field_id_404_duration_to_first_press_of_snapbutton_in_each_round_2_3 = TensorMap('404_Duration-to-first-press-of-snapbutton-in-each-round_2_3', loss='logcosh', channel_map={'no_404_Duration-to-first-press-of-snapbutton-in-each-round_2_3': 0}) +field_id_404_duration_to_first_press_of_snapbutton_in_each_round_2_4 = TensorMap('404_Duration-to-first-press-of-snapbutton-in-each-round_2_4', loss='logcosh', channel_map={'no_404_Duration-to-first-press-of-snapbutton-in-each-round_2_4': 0}) +field_id_4100_ankle_spacing_width_left_2_0 = TensorMap('4100_Ankle-spacing-width-left_2_0', loss='logcosh', channel_map={'no_4100_Ankle-spacing-width-left_2_0': 0}) +field_id_4101_heel_broadband_ultrasound_attenuation_left_2_0 = TensorMap('4101_Heel-broadband-ultrasound-attenuation-left_2_0', loss='logcosh', channel_map={'no_4101_Heel-broadband-ultrasound-attenuation-left_2_0': 0}) +field_id_4103_speed_of_sound_through_heel_left_2_0 = TensorMap('4103_Speed-of-sound-through-heel-left_2_0', loss='logcosh', channel_map={'no_4103_Speed-of-sound-through-heel-left_2_0': 0}) +field_id_4104_heel_quantitative_ultrasound_index_qui_direct_entry_left_2_0 = TensorMap('4104_Heel-quantitative-ultrasound-index-QUI-direct-entry-left_2_0', loss='logcosh', channel_map={'no_4104_Heel-quantitative-ultrasound-index-QUI-direct-entry-left_2_0': 0}) +field_id_4105_heel_bone_mineral_density_bmd_left_2_0 = TensorMap('4105_Heel-bone-mineral-density-BMD-left_2_0', loss='logcosh', channel_map={'no_4105_Heel-bone-mineral-density-BMD-left_2_0': 0}) +field_id_4106_heel_bone_mineral_density_bmd_tscore_automated_left_2_0 = TensorMap('4106_Heel-bone-mineral-density-BMD-Tscore-automated-left_2_0', loss='logcosh', channel_map={'no_4106_Heel-bone-mineral-density-BMD-Tscore-automated-left_2_0': 0}) +field_id_4119_ankle_spacing_width_right_2_0 = TensorMap('4119_Ankle-spacing-width-right_2_0', loss='logcosh', channel_map={'no_4119_Ankle-spacing-width-right_2_0': 0}) +field_id_4120_heel_broadband_ultrasound_attenuation_right_2_0 = TensorMap('4120_Heel-broadband-ultrasound-attenuation-right_2_0', loss='logcosh', channel_map={'no_4120_Heel-broadband-ultrasound-attenuation-right_2_0': 0}) +field_id_4122_speed_of_sound_through_heel_right_2_0 = TensorMap('4122_Speed-of-sound-through-heel-right_2_0', loss='logcosh', channel_map={'no_4122_Speed-of-sound-through-heel-right_2_0': 0}) +field_id_4123_heel_quantitative_ultrasound_index_qui_direct_entry_right_2_0 = TensorMap('4123_Heel-quantitative-ultrasound-index-QUI-direct-entry-right_2_0', loss='logcosh', channel_map={'no_4123_Heel-quantitative-ultrasound-index-QUI-direct-entry-right_2_0': 0}) +field_id_4124_heel_bone_mineral_density_bmd_right_2_0 = TensorMap('4124_Heel-bone-mineral-density-BMD-right_2_0', loss='logcosh', channel_map={'no_4124_Heel-bone-mineral-density-BMD-right_2_0': 0}) +field_id_4125_heel_bone_mineral_density_bmd_tscore_automated_right_2_0 = TensorMap('4125_Heel-bone-mineral-density-BMD-Tscore-automated-right_2_0', loss='logcosh', channel_map={'no_4125_Heel-bone-mineral-density-BMD-Tscore-automated-right_2_0': 0}) +field_id_4407_average_monthly_red_wine_intake_0_0 = TensorMap('4407_Average-monthly-red-wine-intake_0_0', loss='logcosh', channel_map={'no_4407_Average-monthly-red-wine-intake_0_0': 0}) +field_id_4407_average_monthly_red_wine_intake_2_0 = TensorMap('4407_Average-monthly-red-wine-intake_2_0', loss='logcosh', channel_map={'no_4407_Average-monthly-red-wine-intake_2_0': 0}) +field_id_4418_average_monthly_champagne_plus_white_wine_intake_0_0 = TensorMap('4418_Average-monthly-champagne-plus-white-wine-intake_0_0', loss='logcosh', channel_map={'no_4418_Average-monthly-champagne-plus-white-wine-intake_0_0': 0}) +field_id_4418_average_monthly_champagne_plus_white_wine_intake_2_0 = TensorMap('4418_Average-monthly-champagne-plus-white-wine-intake_2_0', loss='logcosh', channel_map={'no_4418_Average-monthly-champagne-plus-white-wine-intake_2_0': 0}) +field_id_4429_average_monthly_beer_plus_cider_intake_0_0 = TensorMap('4429_Average-monthly-beer-plus-cider-intake_0_0', loss='logcosh', channel_map={'no_4429_Average-monthly-beer-plus-cider-intake_0_0': 0}) +field_id_4429_average_monthly_beer_plus_cider_intake_2_0 = TensorMap('4429_Average-monthly-beer-plus-cider-intake_2_0', loss='logcosh', channel_map={'no_4429_Average-monthly-beer-plus-cider-intake_2_0': 0}) +field_id_4440_average_monthly_spirits_intake_0_0 = TensorMap('4440_Average-monthly-spirits-intake_0_0', loss='logcosh', channel_map={'no_4440_Average-monthly-spirits-intake_0_0': 0}) +field_id_4440_average_monthly_spirits_intake_2_0 = TensorMap('4440_Average-monthly-spirits-intake_2_0', loss='logcosh', channel_map={'no_4440_Average-monthly-spirits-intake_2_0': 0}) +field_id_4451_average_monthly_fortified_wine_intake_0_0 = TensorMap('4451_Average-monthly-fortified-wine-intake_0_0', loss='logcosh', channel_map={'no_4451_Average-monthly-fortified-wine-intake_0_0': 0}) +field_id_4451_average_monthly_fortified_wine_intake_2_0 = TensorMap('4451_Average-monthly-fortified-wine-intake_2_0', loss='logcosh', channel_map={'no_4451_Average-monthly-fortified-wine-intake_2_0': 0}) +field_id_4462_average_monthly_intake_of_other_alcoholic_drinks_0_0 = TensorMap('4462_Average-monthly-intake-of-other-alcoholic-drinks_0_0', loss='logcosh', channel_map={'no_4462_Average-monthly-intake-of-other-alcoholic-drinks_0_0': 0}) +field_id_4462_average_monthly_intake_of_other_alcoholic_drinks_2_0 = TensorMap('4462_Average-monthly-intake-of-other-alcoholic-drinks_2_0', loss='logcosh', channel_map={'no_4462_Average-monthly-intake-of-other-alcoholic-drinks_2_0': 0}) +field_id_894_duration_of_moderate_activity_2_0 = TensorMap('894_Duration-of-moderate-activity_2_0', loss='logcosh', channel_map={'no_894_Duration-of-moderate-activity_2_0': 0}) +field_id_914_duration_of_vigorous_activity_2_0 = TensorMap('914_Duration-of-vigorous-activity_2_0', loss='logcosh', channel_map={'no_914_Duration-of-vigorous-activity_2_0': 0}) +field_id_3829_number_of_stillbirths_0_0 = TensorMap('3829_Number-of-stillbirths_0_0', loss='logcosh', channel_map={'no_3829_Number-of-stillbirths_0_0': 0}) +field_id_3839_number_of_spontaneous_miscarriages_0_0 = TensorMap('3839_Number-of-spontaneous-miscarriages_0_0', loss='logcosh', channel_map={'no_3839_Number-of-spontaneous-miscarriages_0_0': 0}) +field_id_3849_number_of_pregnancy_terminations_0_0 = TensorMap('3849_Number-of-pregnancy-terminations_0_0', loss='logcosh', channel_map={'no_3849_Number-of-pregnancy-terminations_0_0': 0}) +field_id_3872_age_of_primiparous_women_at_birth_of_child_0_0 = TensorMap('3872_Age-of-primiparous-women-at-birth-of-child_0_0', loss='logcosh', channel_map={'no_3872_Age-of-primiparous-women-at-birth-of-child_0_0': 0}) +field_id_3972_number_of_adopted_brothers_0_0 = TensorMap('3972_Number-of-adopted-brothers_0_0', loss='logcosh', channel_map={'no_3972_Number-of-adopted-brothers_0_0': 0}) +field_id_3982_number_of_adopted_sisters_0_0 = TensorMap('3982_Number-of-adopted-sisters_0_0', loss='logcosh', channel_map={'no_3982_Number-of-adopted-sisters_0_0': 0}) +field_id_12651_duration_of_eprime_test_2_0 = TensorMap('12651_Duration-of-eprime-test_2_0', loss='logcosh', channel_map={'no_12651_Duration-of-eprime-test_2_0': 0}) +field_id_1845_mothers_age_2_0 = TensorMap('1845_Mothers-age_2_0', loss='logcosh', channel_map={'no_1845_Mothers-age_2_0': 0}) +field_id_25040_median_bold_effect_in_groupdefined_mask_for_shapes_activation_2_0 = TensorMap('25040_Median-BOLD-effect-in-groupdefined-mask-for-shapes-activation_2_0', loss='logcosh', channel_map={'no_25040_Median-BOLD-effect-in-groupdefined-mask-for-shapes-activation_2_0': 0}) +field_id_25042_median_zstatistic_in_groupdefined_mask_for_shapes_activation_2_0 = TensorMap('25042_Median-zstatistic-in-groupdefined-mask-for-shapes-activation_2_0', loss='logcosh', channel_map={'no_25042_Median-zstatistic-in-groupdefined-mask-for-shapes-activation_2_0': 0}) +field_id_25044_median_bold_effect_in_groupdefined_mask_for_faces_activation_2_0 = TensorMap('25044_Median-BOLD-effect-in-groupdefined-mask-for-faces-activation_2_0', loss='logcosh', channel_map={'no_25044_Median-BOLD-effect-in-groupdefined-mask-for-faces-activation_2_0': 0}) +field_id_25046_median_zstatistic_in_groupdefined_mask_for_faces_activation_2_0 = TensorMap('25046_Median-zstatistic-in-groupdefined-mask-for-faces-activation_2_0', loss='logcosh', channel_map={'no_25046_Median-zstatistic-in-groupdefined-mask-for-faces-activation_2_0': 0}) +field_id_25048_median_bold_effect_in_groupdefined_mask_for_facesshapes_contrast_2_0 = TensorMap('25048_Median-BOLD-effect-in-groupdefined-mask-for-facesshapes-contrast_2_0', loss='logcosh', channel_map={'no_25048_Median-BOLD-effect-in-groupdefined-mask-for-facesshapes-contrast_2_0': 0}) +field_id_25050_median_zstatistic_in_groupdefined_mask_for_facesshapes_contrast_2_0 = TensorMap('25050_Median-zstatistic-in-groupdefined-mask-for-facesshapes-contrast_2_0', loss='logcosh', channel_map={'no_25050_Median-zstatistic-in-groupdefined-mask-for-facesshapes-contrast_2_0': 0}) +field_id_25052_median_bold_effect_in_groupdefined_amygdala_activation_mask_for_facesshapes_contrast_2_0 = TensorMap('25052_Median-BOLD-effect-in-groupdefined-amygdala-activation-mask-for-facesshapes-contrast_2_0', loss='logcosh', channel_map={'no_25052_Median-BOLD-effect-in-groupdefined-amygdala-activation-mask-for-facesshapes-contrast_2_0': 0}) +field_id_25054_median_zstatistic_in_groupdefined_amygdala_activation_mask_for_facesshapes_contrast_2_0 = TensorMap('25054_Median-zstatistic-in-groupdefined-amygdala-activation-mask-for-facesshapes-contrast_2_0', loss='logcosh', channel_map={'no_25054_Median-zstatistic-in-groupdefined-amygdala-activation-mask-for-facesshapes-contrast_2_0': 0}) +field_id_25740_discrepancy_between_tfmri_brain_image_and_t1_brain_image_2_0 = TensorMap('25740_Discrepancy-between-tfMRI-brain-image-and-T1-brain-image_2_0', loss='logcosh', channel_map={'no_25740_Discrepancy-between-tfMRI-brain-image-and-T1-brain-image_2_0': 0}) +field_id_4256_time_elapsed_2_7 = TensorMap('4256_Time-elapsed_2_7', loss='logcosh', channel_map={'no_4256_Time-elapsed_2_7': 0}) +field_id_4256_time_elapsed_2_8 = TensorMap('4256_Time-elapsed_2_8', loss='logcosh', channel_map={'no_4256_Time-elapsed_2_8': 0}) +field_id_4260_round_of_numeric_memory_test_2_7 = TensorMap('4260_Round-of-numeric-memory-test_2_7', loss='logcosh', channel_map={'no_4260_Round-of-numeric-memory-test_2_7': 0}) +field_id_4260_round_of_numeric_memory_test_2_8 = TensorMap('4260_Round-of-numeric-memory-test_2_8', loss='logcosh', channel_map={'no_4260_Round-of-numeric-memory-test_2_8': 0}) +field_id_22663_year_gap_started_0_2 = TensorMap('22663_Year-gap-started_0_2', loss='logcosh', channel_map={'no_22663_Year-gap-started_0_2': 0}) +field_id_22663_year_gap_started_0_3 = TensorMap('22663_Year-gap-started_0_3', loss='logcosh', channel_map={'no_22663_Year-gap-started_0_3': 0}) +field_id_22664_year_gap_ended_0_2 = TensorMap('22664_Year-gap-ended_0_2', loss='logcosh', channel_map={'no_22664_Year-gap-ended_0_2': 0}) +field_id_22664_year_gap_ended_0_3 = TensorMap('22664_Year-gap-ended_0_3', loss='logcosh', channel_map={'no_22664_Year-gap-ended_0_3': 0}) +field_id_2824_age_at_hysterectomy_0_0 = TensorMap('2824_Age-at-hysterectomy_0_0', loss='logcosh', channel_map={'no_2824_Age-at-hysterectomy_0_0': 0}) +field_id_93_systolic_blood_pressure_manual_reading_0_1 = TensorMap('93_Systolic-blood-pressure-manual-reading_0_1', loss='logcosh', channel_map={'no_93_Systolic-blood-pressure-manual-reading_0_1': 0}) +field_id_94_diastolic_blood_pressure_manual_reading_0_1 = TensorMap('94_Diastolic-blood-pressure-manual-reading_0_1', loss='logcosh', channel_map={'no_94_Diastolic-blood-pressure-manual-reading_0_1': 0}) +field_id_95_pulse_rate_during_bloodpressure_measurement_0_1 = TensorMap('95_Pulse-rate-during-bloodpressure-measurement_0_1', loss='logcosh', channel_map={'no_95_Pulse-rate-during-bloodpressure-measurement_0_1': 0}) +field_id_40007_age_at_death_0_0 = TensorMap('40007_Age-at-death_0_0', loss='logcosh', channel_map={'no_40007_Age-at-death_0_0': 0}) +field_id_12674_systolic_brachial_blood_pressure_during_pwa_2_1 = TensorMap('12674_Systolic-brachial-blood-pressure-during-PWA_2_1', loss='logcosh', channel_map={'no_12674_Systolic-brachial-blood-pressure-during-PWA_2_1': 0}) +field_id_12675_diastolic_brachial_blood_pressure_during_pwa_2_1 = TensorMap('12675_Diastolic-brachial-blood-pressure-during-PWA_2_1', loss='logcosh', channel_map={'no_12675_Diastolic-brachial-blood-pressure-during-PWA_2_1': 0}) +field_id_12676_peripheral_pulse_pressure_during_pwa_2_1 = TensorMap('12676_Peripheral-pulse-pressure-during-PWA_2_1', loss='logcosh', channel_map={'no_12676_Peripheral-pulse-pressure-during-PWA_2_1': 0}) +field_id_12677_central_systolic_blood_pressure_during_pwa_2_1 = TensorMap('12677_Central-systolic-blood-pressure-during-PWA_2_1', loss='logcosh', channel_map={'no_12677_Central-systolic-blood-pressure-during-PWA_2_1': 0}) +field_id_12678_central_pulse_pressure_during_pwa_2_1 = TensorMap('12678_Central-pulse-pressure-during-PWA_2_1', loss='logcosh', channel_map={'no_12678_Central-pulse-pressure-during-PWA_2_1': 0}) +field_id_12680_central_augmentation_pressure_during_pwa_2_1 = TensorMap('12680_Central-augmentation-pressure-during-PWA_2_1', loss='logcosh', channel_map={'no_12680_Central-augmentation-pressure-during-PWA_2_1': 0}) +field_id_12682_cardiac_output_during_pwa_2_1 = TensorMap('12682_Cardiac-output-during-PWA_2_1', loss='logcosh', channel_map={'no_12682_Cardiac-output-during-PWA_2_1': 0}) +field_id_12683_end_systolic_pressure_during_pwa_2_1 = TensorMap('12683_End-systolic-pressure-during-PWA_2_1', loss='logcosh', channel_map={'no_12683_End-systolic-pressure-during-PWA_2_1': 0}) +field_id_12684_end_systolic_pressure_index_during_pwa_2_1 = TensorMap('12684_End-systolic-pressure-index-during-PWA_2_1', loss='logcosh', channel_map={'no_12684_End-systolic-pressure-index-during-PWA_2_1': 0}) +field_id_12685_total_peripheral_resistance_during_pwa_2_1 = TensorMap('12685_Total-peripheral-resistance-during-PWA_2_1', loss='logcosh', channel_map={'no_12685_Total-peripheral-resistance-during-PWA_2_1': 0}) +field_id_12686_stroke_volume_during_pwa_2_1 = TensorMap('12686_Stroke-volume-during-PWA_2_1', loss='logcosh', channel_map={'no_12686_Stroke-volume-during-PWA_2_1': 0}) +field_id_12687_mean_arterial_pressure_during_pwa_2_1 = TensorMap('12687_Mean-arterial-pressure-during-PWA_2_1', loss='logcosh', channel_map={'no_12687_Mean-arterial-pressure-during-PWA_2_1': 0}) +field_id_12702_cardiac_index_during_pwa_2_1 = TensorMap('12702_Cardiac-index-during-PWA_2_1', loss='logcosh', channel_map={'no_12702_Cardiac-index-during-PWA_2_1': 0}) +field_id_20011_interpolated_age_of_participant_when_operation_took_place_2_2 = TensorMap('20011_Interpolated-Age-of-participant-when-operation-took-place_2_2', loss='logcosh', channel_map={'no_20011_Interpolated-Age-of-participant-when-operation-took-place_2_2': 0}) +field_id_3536_age_started_hormonereplacement_therapy_hrt_2_0 = TensorMap('3536_Age-started-hormonereplacement-therapy-HRT_2_0', loss='logcosh', channel_map={'no_3536_Age-started-hormonereplacement-therapy-HRT_2_0': 0}) +field_id_3546_age_last_used_hormonereplacement_therapy_hrt_2_0 = TensorMap('3546_Age-last-used-hormonereplacement-therapy-HRT_2_0', loss='logcosh', channel_map={'no_3546_Age-last-used-hormonereplacement-therapy-HRT_2_0': 0}) +field_id_3882_age_at_bilateral_oophorectomy_both_ovaries_removed_0_0 = TensorMap('3882_Age-at-bilateral-oophorectomy-both-ovaries-removed_0_0', loss='logcosh', channel_map={'no_3882_Age-at-bilateral-oophorectomy-both-ovaries-removed_0_0': 0}) +field_id_92_operation_yearage_first_occurred_2_2 = TensorMap('92_Operation-yearage-first-occurred_2_2', loss='logcosh', channel_map={'no_92_Operation-yearage-first-occurred_2_2': 0}) +field_id_3894_age_heart_attack_diagnosed_0_0 = TensorMap('3894_Age-heart-attack-diagnosed_0_0', loss='logcosh', channel_map={'no_3894_Age-heart-attack-diagnosed_0_0': 0}) +field_id_3809_time_since_last_prostate_specific_antigen_psa_test_2_0 = TensorMap('3809_Time-since-last-prostate-specific-antigen-PSA-test_2_0', loss='logcosh', channel_map={'no_3809_Time-since-last-prostate-specific-antigen-PSA-test_2_0': 0}) +field_id_20161_pack_years_of_smoking_2_0 = TensorMap('20161_Pack-years-of-smoking_2_0', loss='logcosh', channel_map={'no_20161_Pack-years-of-smoking_2_0': 0}) +field_id_20162_pack_years_adult_smoking_as_proportion_of_life_span_exposed_to_smoking_2_0 = TensorMap('20162_Pack-years-adult-smoking-as-proportion-of-life-span-exposed-to-smoking_2_0', loss='logcosh', channel_map={'no_20162_Pack-years-adult-smoking-as-proportion-of-life-span-exposed-to-smoking_2_0': 0}) +field_id_2867_age_started_smoking_in_former_smokers_2_0 = TensorMap('2867_Age-started-smoking-in-former-smokers_2_0', loss='logcosh', channel_map={'no_2867_Age-started-smoking-in-former-smokers_2_0': 0}) +field_id_2887_number_of_cigarettes_previously_smoked_daily_2_0 = TensorMap('2887_Number-of-cigarettes-previously-smoked-daily_2_0', loss='logcosh', channel_map={'no_2887_Number-of-cigarettes-previously-smoked-daily_2_0': 0}) +field_id_2897_age_stopped_smoking_2_0 = TensorMap('2897_Age-stopped-smoking_2_0', loss='logcosh', channel_map={'no_2897_Age-stopped-smoking_2_0': 0}) +field_id_2926_number_of_unsuccessful_stopsmoking_attempts_2_0 = TensorMap('2926_Number-of-unsuccessful-stopsmoking-attempts_2_0', loss='logcosh', channel_map={'no_2926_Number-of-unsuccessful-stopsmoking-attempts_2_0': 0}) +field_id_93_systolic_blood_pressure_manual_reading_0_0 = TensorMap('93_Systolic-blood-pressure-manual-reading_0_0', loss='logcosh', channel_map={'no_93_Systolic-blood-pressure-manual-reading_0_0': 0}) +field_id_94_diastolic_blood_pressure_manual_reading_0_0 = TensorMap('94_Diastolic-blood-pressure-manual-reading_0_0', loss='logcosh', channel_map={'no_94_Diastolic-blood-pressure-manual-reading_0_0': 0}) +field_id_95_pulse_rate_during_bloodpressure_measurement_0_0 = TensorMap('95_Pulse-rate-during-bloodpressure-measurement_0_0', loss='logcosh', channel_map={'no_95_Pulse-rate-during-bloodpressure-measurement_0_0': 0}) +field_id_22602_year_job_started_0_6 = TensorMap('22602_Year-job-started_0_6', loss='logcosh', channel_map={'no_22602_Year-job-started_0_6': 0}) +field_id_22603_year_job_ended_0_6 = TensorMap('22603_Year-job-ended_0_6', loss='logcosh', channel_map={'no_22603_Year-job-ended_0_6': 0}) +field_id_3829_number_of_stillbirths_2_0 = TensorMap('3829_Number-of-stillbirths_2_0', loss='logcosh', channel_map={'no_3829_Number-of-stillbirths_2_0': 0}) +field_id_3839_number_of_spontaneous_miscarriages_2_0 = TensorMap('3839_Number-of-spontaneous-miscarriages_2_0', loss='logcosh', channel_map={'no_3839_Number-of-spontaneous-miscarriages_2_0': 0}) +field_id_3849_number_of_pregnancy_terminations_2_0 = TensorMap('3849_Number-of-pregnancy-terminations_2_0', loss='logcosh', channel_map={'no_3849_Number-of-pregnancy-terminations_2_0': 0}) +field_id_3680_age_when_last_ate_meat_0_0 = TensorMap('3680_Age-when-last-ate-meat_0_0', loss='logcosh', channel_map={'no_3680_Age-when-last-ate-meat_0_0': 0}) +field_id_22642_usual_length_of_each_night_shift_during_mixed_shift_periods_0_1 = TensorMap('22642_Usual-length-of-each-night-shift-during-mixed-shift-periods_0_1', loss='logcosh', channel_map={'no_22642_Usual-length-of-each-night-shift-during-mixed-shift-periods_0_1': 0}) +field_id_22643_number_of_night_shifts_worked_monthly_during_mixed_shift_periods_0_1 = TensorMap('22643_Number-of-night-shifts-worked-monthly-during-mixed-shift-periods_0_1', loss='logcosh', channel_map={'no_22643_Number-of-night-shifts-worked-monthly-during-mixed-shift-periods_0_1': 0}) +field_id_22644_consecutive_night_shifts_during_mixed_shift_periods_0_1 = TensorMap('22644_Consecutive-night-shifts-during-mixed-shift-periods_0_1', loss='logcosh', channel_map={'no_22644_Consecutive-night-shifts-during-mixed-shift-periods_0_1': 0}) +field_id_22645_rest_days_during_mixed_shift_periods_0_1 = TensorMap('22645_Rest-days-during-mixed-shift-periods_0_1', loss='logcosh', channel_map={'no_22645_Rest-days-during-mixed-shift-periods_0_1': 0}) +field_id_20011_interpolated_age_of_participant_when_operation_took_place_1_1 = TensorMap('20011_Interpolated-Age-of-participant-when-operation-took-place_1_1', loss='logcosh', channel_map={'no_20011_Interpolated-Age-of-participant-when-operation-took-place_1_1': 0}) +field_id_20016_fluid_intelligence_score_1_0 = TensorMap('20016_Fluid-intelligence-score_1_0', loss='logcosh', channel_map={'no_20016_Fluid-intelligence-score_1_0': 0}) +field_id_20019_speechreceptionthreshold_srt_estimate_left_1_0 = TensorMap('20019_Speechreceptionthreshold-SRT-estimate-left_1_0', loss='logcosh', channel_map={'no_20019_Speechreceptionthreshold-SRT-estimate-left_1_0': 0}) +field_id_20021_speechreceptionthreshold_srt_estimate_right_1_0 = TensorMap('20021_Speechreceptionthreshold-SRT-estimate-right_1_0', loss='logcosh', channel_map={'no_20021_Speechreceptionthreshold-SRT-estimate-right_1_0': 0}) +field_id_20128_number_of_fluid_intelligence_questions_attempted_within_time_limit_1_0 = TensorMap('20128_Number-of-fluid-intelligence-questions-attempted-within-time-limit_1_0', loss='logcosh', channel_map={'no_20128_Number-of-fluid-intelligence-questions-attempted-within-time-limit_1_0': 0}) +field_id_3062_forced_vital_capacity_fvc_1_2 = TensorMap('3062_Forced-vital-capacity-FVC_1_2', loss='logcosh', channel_map={'no_3062_Forced-vital-capacity-FVC_1_2': 0}) +field_id_3063_forced_expiratory_volume_in_1second_fev1_1_2 = TensorMap('3063_Forced-expiratory-volume-in-1second-FEV1_1_2', loss='logcosh', channel_map={'no_3063_Forced-expiratory-volume-in-1second-FEV1_1_2': 0}) +field_id_3064_peak_expiratory_flow_pef_1_2 = TensorMap('3064_Peak-expiratory-flow-PEF_1_2', loss='logcosh', channel_map={'no_3064_Peak-expiratory-flow-PEF_1_2': 0}) +field_id_404_duration_to_first_press_of_snapbutton_in_each_round_1_2 = TensorMap('404_Duration-to-first-press-of-snapbutton-in-each-round_1_2', loss='logcosh', channel_map={'no_404_Duration-to-first-press-of-snapbutton-in-each-round_1_2': 0}) +field_id_404_duration_to_first_press_of_snapbutton_in_each_round_1_4 = TensorMap('404_Duration-to-first-press-of-snapbutton-in-each-round_1_4', loss='logcosh', channel_map={'no_404_Duration-to-first-press-of-snapbutton-in-each-round_1_4': 0}) +field_id_4269_number_of_triplets_attempted_left_1_0 = TensorMap('4269_Number-of-triplets-attempted-left_1_0', loss='logcosh', channel_map={'no_4269_Number-of-triplets-attempted-left_1_0': 0}) +field_id_4272_duration_of_hearing_test_left_1_0 = TensorMap('4272_Duration-of-hearing-test-left_1_0', loss='logcosh', channel_map={'no_4272_Duration-of-hearing-test-left_1_0': 0}) +field_id_4276_number_of_triplets_attempted_right_1_0 = TensorMap('4276_Number-of-triplets-attempted-right_1_0', loss='logcosh', channel_map={'no_4276_Number-of-triplets-attempted-right_1_0': 0}) +field_id_4279_duration_of_hearing_test_right_1_0 = TensorMap('4279_Duration-of-hearing-test-right_1_0', loss='logcosh', channel_map={'no_4279_Duration-of-hearing-test-right_1_0': 0}) +field_id_5098_6mm_weak_meridian_right_1_0 = TensorMap('5098_6mm-weak-meridian-right_1_0', loss='logcosh', channel_map={'no_5098_6mm-weak-meridian-right_1_0': 0}) +field_id_5098_6mm_weak_meridian_right_1_1 = TensorMap('5098_6mm-weak-meridian-right_1_1', loss='logcosh', channel_map={'no_5098_6mm-weak-meridian-right_1_1': 0}) +field_id_5098_6mm_weak_meridian_right_1_2 = TensorMap('5098_6mm-weak-meridian-right_1_2', loss='logcosh', channel_map={'no_5098_6mm-weak-meridian-right_1_2': 0}) +field_id_5099_3mm_weak_meridian_right_1_2 = TensorMap('5099_3mm-weak-meridian-right_1_2', loss='logcosh', channel_map={'no_5099_3mm-weak-meridian-right_1_2': 0}) +field_id_5100_3mm_weak_meridian_angle_right_1_2 = TensorMap('5100_3mm-weak-meridian-angle-right_1_2', loss='logcosh', channel_map={'no_5100_3mm-weak-meridian-angle-right_1_2': 0}) +field_id_5101_6mm_weak_meridian_angle_right_1_0 = TensorMap('5101_6mm-weak-meridian-angle-right_1_0', loss='logcosh', channel_map={'no_5101_6mm-weak-meridian-angle-right_1_0': 0}) +field_id_5101_6mm_weak_meridian_angle_right_1_1 = TensorMap('5101_6mm-weak-meridian-angle-right_1_1', loss='logcosh', channel_map={'no_5101_6mm-weak-meridian-angle-right_1_1': 0}) +field_id_5101_6mm_weak_meridian_angle_right_1_2 = TensorMap('5101_6mm-weak-meridian-angle-right_1_2', loss='logcosh', channel_map={'no_5101_6mm-weak-meridian-angle-right_1_2': 0}) +field_id_5106_6mm_strong_meridian_angle_right_1_0 = TensorMap('5106_6mm-strong-meridian-angle-right_1_0', loss='logcosh', channel_map={'no_5106_6mm-strong-meridian-angle-right_1_0': 0}) +field_id_5106_6mm_strong_meridian_angle_right_1_1 = TensorMap('5106_6mm-strong-meridian-angle-right_1_1', loss='logcosh', channel_map={'no_5106_6mm-strong-meridian-angle-right_1_1': 0}) +field_id_5106_6mm_strong_meridian_angle_right_1_2 = TensorMap('5106_6mm-strong-meridian-angle-right_1_2', loss='logcosh', channel_map={'no_5106_6mm-strong-meridian-angle-right_1_2': 0}) +field_id_5107_3mm_strong_meridian_angle_right_1_2 = TensorMap('5107_3mm-strong-meridian-angle-right_1_2', loss='logcosh', channel_map={'no_5107_3mm-strong-meridian-angle-right_1_2': 0}) +field_id_5108_3mm_asymmetry_angle_right_1_2 = TensorMap('5108_3mm-asymmetry-angle-right_1_2', loss='logcosh', channel_map={'no_5108_3mm-asymmetry-angle-right_1_2': 0}) +field_id_5109_6mm_asymmetry_angle_right_1_0 = TensorMap('5109_6mm-asymmetry-angle-right_1_0', loss='logcosh', channel_map={'no_5109_6mm-asymmetry-angle-right_1_0': 0}) +field_id_5109_6mm_asymmetry_angle_right_1_1 = TensorMap('5109_6mm-asymmetry-angle-right_1_1', loss='logcosh', channel_map={'no_5109_6mm-asymmetry-angle-right_1_1': 0}) +field_id_5109_6mm_asymmetry_angle_right_1_2 = TensorMap('5109_6mm-asymmetry-angle-right_1_2', loss='logcosh', channel_map={'no_5109_6mm-asymmetry-angle-right_1_2': 0}) +field_id_5110_6mm_asymmetry_angle_left_1_0 = TensorMap('5110_6mm-asymmetry-angle-left_1_0', loss='logcosh', channel_map={'no_5110_6mm-asymmetry-angle-left_1_0': 0}) +field_id_5114_6mm_cylindrical_power_angle_right_1_0 = TensorMap('5114_6mm-cylindrical-power-angle-right_1_0', loss='logcosh', channel_map={'no_5114_6mm-cylindrical-power-angle-right_1_0': 0}) +field_id_5114_6mm_cylindrical_power_angle_right_1_1 = TensorMap('5114_6mm-cylindrical-power-angle-right_1_1', loss='logcosh', channel_map={'no_5114_6mm-cylindrical-power-angle-right_1_1': 0}) +field_id_5114_6mm_cylindrical_power_angle_right_1_2 = TensorMap('5114_6mm-cylindrical-power-angle-right_1_2', loss='logcosh', channel_map={'no_5114_6mm-cylindrical-power-angle-right_1_2': 0}) +field_id_5115_3mm_cylindrical_power_angle_right_1_2 = TensorMap('5115_3mm-cylindrical-power-angle-right_1_2', loss='logcosh', channel_map={'no_5115_3mm-cylindrical-power-angle-right_1_2': 0}) +field_id_5116_3mm_cylindrical_power_right_1_2 = TensorMap('5116_3mm-cylindrical-power-right_1_2', loss='logcosh', channel_map={'no_5116_3mm-cylindrical-power-right_1_2': 0}) +field_id_5117_6mm_cylindrical_power_right_1_0 = TensorMap('5117_6mm-cylindrical-power-right_1_0', loss='logcosh', channel_map={'no_5117_6mm-cylindrical-power-right_1_0': 0}) +field_id_5117_6mm_cylindrical_power_right_1_1 = TensorMap('5117_6mm-cylindrical-power-right_1_1', loss='logcosh', channel_map={'no_5117_6mm-cylindrical-power-right_1_1': 0}) +field_id_5117_6mm_cylindrical_power_right_1_2 = TensorMap('5117_6mm-cylindrical-power-right_1_2', loss='logcosh', channel_map={'no_5117_6mm-cylindrical-power-right_1_2': 0}) +field_id_5132_3mm_strong_meridian_right_1_2 = TensorMap('5132_3mm-strong-meridian-right_1_2', loss='logcosh', channel_map={'no_5132_3mm-strong-meridian-right_1_2': 0}) +field_id_5133_6mm_strong_meridian_right_1_0 = TensorMap('5133_6mm-strong-meridian-right_1_0', loss='logcosh', channel_map={'no_5133_6mm-strong-meridian-right_1_0': 0}) +field_id_5133_6mm_strong_meridian_right_1_1 = TensorMap('5133_6mm-strong-meridian-right_1_1', loss='logcosh', channel_map={'no_5133_6mm-strong-meridian-right_1_1': 0}) +field_id_5133_6mm_strong_meridian_right_1_2 = TensorMap('5133_6mm-strong-meridian-right_1_2', loss='logcosh', channel_map={'no_5133_6mm-strong-meridian-right_1_2': 0}) +field_id_5157_6mm_asymmetry_index_left_1_0 = TensorMap('5157_6mm-asymmetry-index-left_1_0', loss='logcosh', channel_map={'no_5157_6mm-asymmetry-index-left_1_0': 0}) +field_id_5158_6mm_asymmetry_index_right_1_0 = TensorMap('5158_6mm-asymmetry-index-right_1_0', loss='logcosh', channel_map={'no_5158_6mm-asymmetry-index-right_1_0': 0}) +field_id_5158_6mm_asymmetry_index_right_1_1 = TensorMap('5158_6mm-asymmetry-index-right_1_1', loss='logcosh', channel_map={'no_5158_6mm-asymmetry-index-right_1_1': 0}) +field_id_5158_6mm_asymmetry_index_right_1_2 = TensorMap('5158_6mm-asymmetry-index-right_1_2', loss='logcosh', channel_map={'no_5158_6mm-asymmetry-index-right_1_2': 0}) +field_id_5159_3mm_asymmetry_index_right_1_2 = TensorMap('5159_3mm-asymmetry-index-right_1_2', loss='logcosh', channel_map={'no_5159_3mm-asymmetry-index-right_1_2': 0}) +field_id_5160_3mm_regularity_index_right_1_2 = TensorMap('5160_3mm-regularity-index-right_1_2', loss='logcosh', channel_map={'no_5160_3mm-regularity-index-right_1_2': 0}) +field_id_5161_6mm_regularity_index_right_1_0 = TensorMap('5161_6mm-regularity-index-right_1_0', loss='logcosh', channel_map={'no_5161_6mm-regularity-index-right_1_0': 0}) +field_id_5161_6mm_regularity_index_right_1_1 = TensorMap('5161_6mm-regularity-index-right_1_1', loss='logcosh', channel_map={'no_5161_6mm-regularity-index-right_1_1': 0}) +field_id_5161_6mm_regularity_index_right_1_2 = TensorMap('5161_6mm-regularity-index-right_1_2', loss='logcosh', channel_map={'no_5161_6mm-regularity-index-right_1_2': 0}) +field_id_5162_6mm_regularity_index_left_1_0 = TensorMap('5162_6mm-regularity-index-left_1_0', loss='logcosh', channel_map={'no_5162_6mm-regularity-index-left_1_0': 0}) +field_id_5251_6mm_index_of_best_keratometry_results_right_1_0 = TensorMap('5251_6mm-index-of-best-keratometry-results-right_1_0', loss='logcosh', channel_map={'no_5251_6mm-index-of-best-keratometry-results-right_1_0': 0}) +field_id_914_duration_of_vigorous_activity_1_0 = TensorMap('914_Duration-of-vigorous-activity_1_0', loss='logcosh', channel_map={'no_914_Duration-of-vigorous-activity_1_0': 0}) +field_id_92_operation_yearage_first_occurred_1_1 = TensorMap('92_Operation-yearage-first-occurred_1_1', loss='logcosh', channel_map={'no_92_Operation-yearage-first-occurred_1_1': 0}) +field_id_3160_weight_manual_entry_0_0 = TensorMap('3160_Weight-manual-entry_0_0', loss='logcosh', channel_map={'no_3160_Weight-manual-entry_0_0': 0}) +field_id_4012_age_deepvein_thrombosis_dvt_blood_clot_in_leg_diagnosed_0_0 = TensorMap('4012_Age-deepvein-thrombosis-DVT-blood-clot-in-leg-diagnosed_0_0', loss='logcosh', channel_map={'no_4012_Age-deepvein-thrombosis-DVT-blood-clot-in-leg-diagnosed_0_0': 0}) +field_id_2684_years_since_last_breast_cancer_screening_mammogram_1_0 = TensorMap('2684_Years-since-last-breast-cancer-screening-mammogram_1_0', loss='logcosh', channel_map={'no_2684_Years-since-last-breast-cancer-screening-mammogram_1_0': 0}) +field_id_2704_years_since_last_cervical_smear_test_1_0 = TensorMap('2704_Years-since-last-cervical-smear-test_1_0', loss='logcosh', channel_map={'no_2704_Years-since-last-cervical-smear-test_1_0': 0}) +field_id_2714_age_when_periods_started_menarche_1_0 = TensorMap('2714_Age-when-periods-started-menarche_1_0', loss='logcosh', channel_map={'no_2714_Age-when-periods-started-menarche_1_0': 0}) +field_id_2734_number_of_live_births_1_0 = TensorMap('2734_Number-of-live-births_1_0', loss='logcosh', channel_map={'no_2734_Number-of-live-births_1_0': 0}) +field_id_2744_birth_weight_of_first_child_1_0 = TensorMap('2744_Birth-weight-of-first-child_1_0', loss='logcosh', channel_map={'no_2744_Birth-weight-of-first-child_1_0': 0}) +field_id_2794_age_started_oral_contraceptive_pill_1_0 = TensorMap('2794_Age-started-oral-contraceptive-pill_1_0', loss='logcosh', channel_map={'no_2794_Age-started-oral-contraceptive-pill_1_0': 0}) +field_id_2804_age_when_last_used_oral_contraceptive_pill_1_0 = TensorMap('2804_Age-when-last-used-oral-contraceptive-pill_1_0', loss='logcosh', channel_map={'no_2804_Age-when-last-used-oral-contraceptive-pill_1_0': 0}) +field_id_3581_age_at_menopause_last_menstrual_period_1_0 = TensorMap('3581_Age-at-menopause-last-menstrual-period_1_0', loss='logcosh', channel_map={'no_3581_Age-at-menopause-last-menstrual-period_1_0': 0}) +field_id_4056_age_stroke_diagnosed_0_0 = TensorMap('4056_Age-stroke-diagnosed_0_0', loss='logcosh', channel_map={'no_4056_Age-stroke-diagnosed_0_0': 0}) +field_id_404_duration_to_first_press_of_snapbutton_in_each_round_1_3 = TensorMap('404_Duration-to-first-press-of-snapbutton-in-each-round_1_3', loss='logcosh', channel_map={'no_404_Duration-to-first-press-of-snapbutton-in-each-round_1_3': 0}) diff --git a/ml4h/tensormap/ukb/ecg.py b/ml4h/tensormap/ukb/ecg.py index 2def5466e..2b031f0b7 100644 --- a/ml4h/tensormap/ukb/ecg.py +++ b/ml4h/tensormap/ukb/ecg.py @@ -1039,8 +1039,8 @@ def ecg_prediction_lead_from_hd5(tm: TensorMap, hd5: h5py.File, dependents: Dict normalization={'mean': 25.7, 'std': 36.6}, ) rr_interval = TensorMap( - 'RRInterval', Interpretation.CONTINUOUS, path_prefix='ukb_ecg_rest', channel_map={'RRInterval': 0}, loss='logcosh', validator=make_range_validator(400, 2000), - normalization={'mean': 1040.61, 'std': 175.5}, + 'RRInterval', Interpretation.CONTINUOUS, path_prefix='ukb_ecg_rest', channel_map={'RRInterval': 0}, loss='logcosh', + validator=make_range_validator(400, 2000), normalization={'mean': 1040.61, 'std': 175.5}, metrics=[], ) ventricular_rate = TensorMap( 'VentricularRate', Interpretation.CONTINUOUS, path_prefix='ukb_ecg_rest', channel_map={'VentricularRate': 0}, validator=make_range_validator(30, 150), diff --git a/ml4h/tensormap/ukb/mri.py b/ml4h/tensormap/ukb/mri.py index a27522aee..7a07a1f1c 100644 --- a/ml4h/tensormap/ukb/mri.py +++ b/ml4h/tensormap/ukb/mri.py @@ -802,6 +802,10 @@ def sax_tensor_from_file(tm, hd5, dependents={}): 'sax_all_systole_3d', shape=(224, 224, 13), tensor_from_file=sax_tensor('cine_segmented_sax_inlinevf/2', 18), path_prefix='ukb_cardiac_mri', normalization=ZeroMeanStd1(), ) +sax_all_diastole_3d_3slice = TensorMap( + 'sax_all_diastole', shape=(224, 224, 3), tensor_from_file=sax_tensor('cine_segmented_sax_inlinevf/2'), + path_prefix='ukb_cardiac_mri', normalization=ZeroMeanStd1(), +) def sax_random_slice_tensor_maker(b_series_prefix, b_segmented_prefix, lv_tsv=None): diff --git a/scripts/merge_hd5s.sh b/scripts/merge_hd5s.sh index cc9e1148a..7da8dc8b0 100755 --- a/scripts/merge_hd5s.sh +++ b/scripts/merge_hd5s.sh @@ -147,7 +147,7 @@ LAUNCH_CMDLINE_MESSAGE let MIN_SAMPLE_ID=MIN_SAMPLE_ID+INCREMENT let MAX_SAMPLE_ID=MAX_SAMPLE_ID+INCREMENT - sleep 1s + sleep 3s done ################### DISPLAY TIME ######################################### diff --git a/setup.py b/setup.py index a22dfd195..3a67a5a3a 100644 --- a/setup.py +++ b/setup.py @@ -3,16 +3,17 @@ here = pathlib.Path(__file__).parent.resolve() # Get the requirements from the requirements file -requirements = (here / 'docker/vm_boot_images/config/tensorflow-requirements.txt').read_text(encoding='utf-8') +requirements = (here / 'docker/vm_boot_images/config/tensorflow-requirements.txt').read_text(encoding='utf-8') long_description = (here / 'README.md').read_text(encoding='utf-8') setup( name='ml4h', - version='0.0.2', + version='0.0.4', description='Machine Learning for Health python package', long_description=long_description, # Optional long_description_content_type='text/markdown', url='https://github.com/broadinstitute/ml4h', python_requires='>=3.6', install_requires=requirements, + #install_requires=["ml4ht", "tensorflow"], packages=find_packages(), ) diff --git a/tests/ml4ht_integration/test_tensor_map.py b/tests/ml4ht_integration/test_tensor_map.py index 40713d1d7..1f29bcce2 100644 --- a/tests/ml4ht_integration/test_tensor_map.py +++ b/tests/ml4ht_integration/test_tensor_map.py @@ -5,18 +5,15 @@ import numpy as np from torch.utils.data import DataLoader -from ml4h.test_utils import TMAPS_UP_TO_4D, build_hdf5s -from ml4h.ml4ht_integration.tensor_map import TensorMapSampleGetter, tensor_map_from_data_description -from ml4h.models.legacy_models import make_multimodal_multitask_model, BottleneckType -from ml4h.TensorMap import Interpretation -from ml4ht.data.data_loader import numpy_collate_fn, SampleGetterIterableDataset from ml4ht.data.data_description import DataDescription -from ml4ht.data.util.date_selector import ( - DateRangeOptionPicker, - first_dt, - DATE_OPTION_KEY, -) from ml4ht.data.sample_getter import DataDescriptionSampleGetter +from ml4ht.data.data_loader import numpy_collate_fn, SampleGetterIterableDataset +from ml4ht.data.util.date_selector import DateRangeOptionPicker, first_dt, DATE_OPTION_KEY +from ml4h.ml4ht_integration.tensor_map import TensorMapSampleGetter, tensor_map_from_data_description + +from ml4h.TensorMap import Interpretation +from ml4h.test_utils import TMAPS_UP_TO_4D, build_hdf5s +from ml4h.models.model_factory import make_multimodal_multitask_model # Tests @@ -29,8 +26,13 @@ def test_tensor_map_from_data_description(): loss='logcosh', metrics=['mae', 'mse'], ) - model = make_multimodal_multitask_model( + model, _, _, _ = make_multimodal_multitask_model( [tmap_in], [tmap_out], + encoder_blocks=['conv_encode'], + decoder_blocks=['conv_decode'], + merge_blocks=[], + learning_rate=1e-4, + optimizer='sgd', ) data_set = SampleGetterIterableDataset( sample_ids=list(RAW_DATA), @@ -108,14 +110,17 @@ def model(): return make_multimodal_multitask_model( tensor_maps_in=TMAPS_UP_TO_4D, tensor_maps_out=TMAPS_UP_TO_4D, + encoder_blocks=['conv_encode', 'dense_encode'], + decoder_blocks=['conv_decode', 'dense_decode'], + merge_blocks=['concat'], + learning_rate=1e-4, + optimizer='sgd', conv_x=[3], conv_y=[3], conv_z=[1], pool_x=1, pool_y=1, pool_z=1, dense_blocks=[4], dense_layers=[4], - block_size=1, - activation='relu', learning_rate=1e-5, - bottleneck_type=BottleneckType.FlattenRestructure, - optimizer='sgd', conv_layers=[8], conv_type='conv', - ) + block_size=3, + activation='relu', conv_layers=[8], conv_type='conv', + )[0] # Only return the full model, not encoders decoders and merger RAW_DATA = { diff --git a/tests/test_models.py b/tests/test_models.py index 2802f01c5..4f6d58f4a 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -7,9 +7,9 @@ from typing import List, Optional, Dict, Tuple, Iterator from ml4h.TensorMap import TensorMap -from ml4h.models.model_factory import block_make_multimodal_multitask_model +from ml4h.models.model_factory import make_multimodal_multitask_model from ml4h.models.train import train_model_from_generators -from ml4h.models.legacy_models import make_multimodal_multitask_model, parent_sort, BottleneckType +from ml4h.models.legacy_models import legacy_multimodal_multitask_model, parent_sort, BottleneckType from ml4h.models.legacy_models import ACTIVATION_FUNCTIONS, MODEL_EXT, check_no_bottleneck, make_paired_autoencoder_model from ml4h.test_utils import TMAPS_UP_TO_4D, MULTIMODAL_UP_TO_4D, CATEGORICAL_TMAPS, CONTINUOUS_TMAPS, SEGMENT_IN, SEGMENT_OUT, PARENT_TMAPS, CYCLE_PARENTS from ml4h.test_utils import LANGUAGE_TMAP_1HOT_WINDOW, LANGUAGE_TMAP_1HOT_SOFTMAX @@ -80,13 +80,13 @@ def assert_model_trains( use_legacy_model: bool = False, ): if m is None and use_legacy_model: - m = make_multimodal_multitask_model( + m = legacy_multimodal_multitask_model( input_tmaps, output_tmaps, **DEFAULT_PARAMS, ) elif m is None: - m, _, _, _ = block_make_multimodal_multitask_model( + m, _, _, _ = make_multimodal_multitask_model( input_tmaps, output_tmaps, **DEFAULT_PARAMS, @@ -160,7 +160,7 @@ def test_unimodal_md_to_nd(self, input_tmap: TensorMap, output_tmap: TensorMap): ) def test_load_unimodal(self, tmpdir, input_tmap, output_tmap): params = DEFAULT_PARAMS.copy() - m, _, _, _ = block_make_multimodal_multitask_model( + m, _, _, _ = make_multimodal_multitask_model( [input_tmap], [output_tmap], **params, @@ -168,7 +168,7 @@ def test_load_unimodal(self, tmpdir, input_tmap, output_tmap): path = os.path.join(tmpdir, f'm{MODEL_EXT}') m.save(path) params['model_file'] = path - block_make_multimodal_multitask_model( + make_multimodal_multitask_model( [input_tmap], [output_tmap], **DEFAULT_PARAMS, @@ -183,7 +183,7 @@ def test_load_custom_activations(self, tmpdir, activation): inp, out = CONTINUOUS_TMAPS[:2], CATEGORICAL_TMAPS[:2] params = DEFAULT_PARAMS.copy() params['activation'] = activation - m, _, _, _ = block_make_multimodal_multitask_model( + m, _, _, _ = make_multimodal_multitask_model( inp, out, **params, @@ -191,7 +191,7 @@ def test_load_custom_activations(self, tmpdir, activation): path = os.path.join(tmpdir, f'm{MODEL_EXT}') m.save(path) params['model_file'] = path - block_make_multimodal_multitask_model( + make_multimodal_multitask_model( inp, out, **params, @@ -207,7 +207,7 @@ def test_load_custom_activations(self, tmpdir, activation): MULTIMODAL_UP_TO_4D, ) def test_load_multimodal(self, tmpdir, input_tmaps: List[TensorMap], output_tmaps: List[TensorMap]): - m, _, _, _ = block_make_multimodal_multitask_model( + m, _, _, _ = make_multimodal_multitask_model( input_tmaps, output_tmaps, **DEFAULT_PARAMS, @@ -216,7 +216,7 @@ def test_load_multimodal(self, tmpdir, input_tmaps: List[TensorMap], output_tmap m.save(path) params = DEFAULT_PARAMS.copy() params['model_file'] = path - block_make_multimodal_multitask_model( + make_multimodal_multitask_model( input_tmaps, output_tmaps, **params, @@ -228,7 +228,7 @@ def test_u_connect_auto_encode(self): params['conv_layers'] = [8, 8] params['dense_blocks'] = [4, 4, 2] params['u_connect'] = defaultdict(set, {SEGMENT_IN: {SEGMENT_IN}}) - m, _, _, _ = block_make_multimodal_multitask_model( + m, _, _, _ = make_multimodal_multitask_model( [SEGMENT_IN], [SEGMENT_IN], **params, @@ -239,7 +239,7 @@ def test_u_connect_segment(self): params = DEFAULT_PARAMS.copy() params['pool_x'] = params['pool_y'] = 2 params['u_connect'] = defaultdict(set, {SEGMENT_IN: {SEGMENT_OUT}}) - m, _, _, _ = block_make_multimodal_multitask_model( + m, _, _, _ = make_multimodal_multitask_model( [SEGMENT_IN], [SEGMENT_OUT], **params, @@ -260,7 +260,7 @@ def test_multimodal_multitask_variational(self, input_output_tmaps, tmpdir): params = DEFAULT_PARAMS.copy() params['bottleneck_type'] = BottleneckType.Variational params['pool_x'] = params['pool_y'] = 2 - m, _, _, _ = block_make_multimodal_multitask_model( + m, _, _, _ = make_multimodal_multitask_model( input_output_tmaps[0], input_output_tmaps[1], **params @@ -270,7 +270,7 @@ def test_multimodal_multitask_variational(self, input_output_tmaps, tmpdir): path = os.path.join(tmpdir, f'm{MODEL_EXT}') m.save(path) params['model_file'] = path - block_make_multimodal_multitask_model( + make_multimodal_multitask_model( input_output_tmaps[0], input_output_tmaps[1], **params, @@ -281,7 +281,7 @@ def test_u_connect_adaptive_normalization(self): params['pool_x'] = params['pool_y'] = 2 params['bottleneck_type'] = BottleneckType.GlobalAveragePoolStructured params['u_connect'] = defaultdict(set, {SEGMENT_IN: {SEGMENT_OUT}}) - m, _, _, _ = block_make_multimodal_multitask_model( + m, _, _, _ = make_multimodal_multitask_model( [SEGMENT_IN, TMAPS_UP_TO_4D[0]], [SEGMENT_OUT], **params, @@ -293,7 +293,7 @@ def test_u_connect_no_bottleneck(self): params['pool_x'] = params['pool_y'] = 2 params['bottleneck_type'] = BottleneckType.NoBottleNeck params['u_connect'] = defaultdict(set, {SEGMENT_IN: {SEGMENT_OUT}}) - m, _, _, _ = block_make_multimodal_multitask_model( + m, _, _, _ = make_multimodal_multitask_model( [SEGMENT_IN, TMAPS_UP_TO_4D[0]], [SEGMENT_OUT], **params, @@ -304,7 +304,7 @@ def test_no_dense_layers(self): params = DEFAULT_PARAMS.copy() params['dense_layers'] = [] inp, out = CONTINUOUS_TMAPS[:2], CATEGORICAL_TMAPS[:2] - m, _, _, _ = block_make_multimodal_multitask_model( + m, _, _, _ = make_multimodal_multitask_model( inp, out, **DEFAULT_PARAMS, @@ -326,7 +326,7 @@ def test_parents(self, output_tmaps): ) def test_language_models(self, input_output_tmaps, tmpdir): params = DEFAULT_PARAMS.copy() - m, _, _, _ = block_make_multimodal_multitask_model( + m, _, _, _ = make_multimodal_multitask_model( tensor_maps_in=input_output_tmaps[0], tensor_maps_out=input_output_tmaps[1], **params @@ -335,7 +335,7 @@ def test_language_models(self, input_output_tmaps, tmpdir): path = os.path.join(tmpdir, f'lstm{MODEL_EXT}') m.save(path) params['model_file'] = path - block_make_multimodal_multitask_model( + make_multimodal_multitask_model( input_output_tmaps[0], input_output_tmaps[1], **params, @@ -354,7 +354,7 @@ def test_paired_models(self, pairs, tmpdir): pair_list = list(set([p[0] for p in pairs] + [p[1] for p in pairs])) params['u_connect'] = {tm: [] for tm in pair_list} params['merge_blocks'] = ['pair'] - m, encoders, decoders, merger = block_make_multimodal_multitask_model( + m, encoders, decoders, merger = make_multimodal_multitask_model( pairs=pairs, tensor_maps_in=pair_list, tensor_maps_out=pair_list, @@ -392,7 +392,7 @@ def test_semi_supervised_paired_models(self, pairs, output_tmaps, tmpdir): pair_list = list(set([p[0] for p in pairs] + [p[1] for p in pairs])) params['u_connect'] = {tm: [] for tm in pair_list} params['merge_blocks'] = ['pair'] - m, encoders, decoders, merger = block_make_multimodal_multitask_model( + m, encoders, decoders, merger = make_multimodal_multitask_model( pairs=pairs, tensor_maps_in=pair_list, tensor_maps_out=pair_list+output_tmaps, @@ -462,7 +462,7 @@ def test_brain_seg(self, tmpdir): tmaps_in = [TMAPS['t1_30_slices_4d']] tmaps_out = [TMAPS['t1_seg_30_slices']] - m, _, _, _ = block_make_multimodal_multitask_model( + m, _, _, _ = make_multimodal_multitask_model( tensor_maps_in=tmaps_in, tensor_maps_out=tmaps_out, activation='relu', learning_rate=1e-3, diff --git a/tests/test_recipes.py b/tests/test_recipes.py index 43a42a468..0ba0eb0d7 100644 --- a/tests/test_recipes.py +++ b/tests/test_recipes.py @@ -68,11 +68,11 @@ def test_explore(self, default_arguments, tmpdir_factory): ) explore_result = pd.read_csv(csv_path) logging.info(f'Tested explore {[c for c in explore_expected]}') - logging.info(f'Tested explore_result {[c for c in explore_result["fpath"]]}') + logging.info(f'Tested explore_result {[c for c in explore_result["sample_id"]]}') for row in explore_result.iterrows(): row = row[1] for tm in tmaps: - row_expected = explore_expected[(row['fpath'], tm.name)] + row_expected = explore_expected[(row['sample_id'], tm.name)] if _should_error_detect(tm): actual = getattr(row, tm.name) assert not np.isnan(actual)