Can't load the LSTM model pre-trained in Python to R

[gjfreitas]

I have the following model architecture in Python

# Define the LSTM model
model = Sequential()
model.add(LSTM(256, input_shape=(X_train.shape[1], X_train.shape[2])))
model.add(Dropout(0.2))  # Add dropout to prevent overfitting
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.2))  # Add dropout to prevent overfitting
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.2))  # Add dropout to prevent overfitting
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.2))  # Add dropout to prevent overfitting
model.add(Dense(2))  # Output layer with 2 neurons for p and q
#model.compile(optimizer=RMSprop(), loss='mae')
model.compile(optimizer='adam', loss='mse')

# Define early stopping to prevent overfitting
early_stopping = EarlyStopping(monitor='val_loss', patience=10)

# Train the model
history = model.fit(X_train, y_train, epochs=100, batch_size=16, validation_data=(X_test, y_test), verbose=1, callbacks=[early_stopping])

I have tried saving the model after training in .keras, .h5, .json and then import into R to test there.

Nothing seems to work.

Anyone got an idea on how to do it?

This is one of the errors I'm getting

Error in py_call_impl(callable, call_args$unnamed, call_args$named) : 
  TypeError: Could not locate function 'mae'. Make sure custom classes are decorated with `@keras.saving.register_keras_serializable()`. Full object config: {'module': 'keras.metrics', 'class_name': 'function', 'config': 'mae', 'registered_name': 'mae'}
Run `reticulate::py_last_error()` for details.

[t-kalinowski]

Hi, can you please post a reproducible example? Something self-contained I can run locally. Please add necessary import statements to the Python script, define X_train = np.ones((3, 4, 5)), call model.save(). Similarly, how are you loading in R to see the error.