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This repository has been archived by the owner on Mar 6, 2021. It is now read-only.
You are setting alpha=0, which means that the activations of the MLP are ignored, as described in the docstring of ELMClassifier: https://github.com/dclambert/Python-ELM/blob/master/elm.py#L357 activation = alpha*mlp_activation + (1-alpha)*rbf_width*rbf_activation
With alpha=1, I get train acc is 1, test acc is 0.07
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from elm import ELMClassifier
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
train = mnist.train.next_batch(100)
elmc = ELMClassifier(n_hidden=1000, activation_func='gaussian', alpha=0.0, random_state=0)
elmc.fit(train[0], train[1])
test = mnist.test.next_batch(100)
print('train acc is %g, test acc is %g ' %( elmc.score(train[0], train[1]), elmc.score(test[0], test[1])))
run and get this,
train acc is 0, test acc is 0
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