python - Keras autoencoder accuracy/loss doesn't change -

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here code:

ae_0 = sequential()  encoder = sequential([dense(output_dim=100, input_dim=256, activation='sigmoid')]) decoder = sequential([dense(output_dim=256, input_dim=100, activation='linear')])  ae_0.add(autoencoder(encoder=encoder, decoder=decoder, output_reconstruction=true)) ae_0.compile(loss='mse', optimizer=sgd(lr=0.03, momentum=0.9, decay=0.001, nesterov=true)) ae_0.fit(x, x, batch_size=21, nb_epoch=500, show_accuracy=true)

x has shape (537621, 256). i'm trying find way compress vectors of size 256 100, 70, 50. have done lasagne in keras seems easier work w/ autoencoders.

here output:

epoch 1/500 537621/537621 [==============================] - 27s - loss: 0.1339 - acc: 0.0036
epoch 2/500 537621/537621 [==============================] - 32s - loss: 0.1339 - acc: 0.0036
epoch 3/500 252336/537621 [=============>................] - eta: 14s - loss: 0.1339 - acc: 0.0035

and continues on , on..

it's fixed on master:) openning issues best choice https://github.com/fchollet/keras/issues/1604


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