Created
May 29, 2018 18:16
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################################################################################################# | |
## TRAIN PAST ENCODER | |
################################################################################################# | |
x_train_past = data[hold_out:,0:2400] | |
print("training past on: " + str(x_train_past.shape)) | |
input_past = Input(shape=(x_train_past.shape[1],)) | |
encoded_past = Dense(encoding_dim, activation='relu')(input_past) | |
decoded_past = Dense(x_train_past.shape[1], activation='linear')(encoded_past) | |
autoencoder_past = Model(input_past, decoded_past) | |
autoencoder_past.compile(optimizer='adadelta', loss='mean_squared_error', metrics=['accuracy']) | |
modelPath_past= savePath+"/models/autoencoder-past-"+str(encoding_dim)+".hdf5" | |
checkpoint_past = ModelCheckpoint(modelPath_past, monitor='acc', verbose=2, save_best_only=True, mode='max') | |
history_past = autoencoder_past.fit(x_train_past, x_train_past, | |
batch_size=batch_size, | |
epochs=epochs, | |
verbose=2, | |
callbacks=[checkpoint_past], | |
) |
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