s_autoencoder2.py

optimizer = optimizers.Adam(lr=1e-2)

autoencoder.compile(optimizer=optimizer, 
                    loss='mean_squared_error', 
                    metrics=['accuracy'])

checkpointer = ModelCheckpoint(filepath="model_bin.h5",
                               verbose=0,
                               save_best_only=True)

tensorboard = TensorBoard(log_dir='./logs',
                          histogram_freq=0,
                          write_graph=True,
                          write_images=True)

history = autoencoder.fit(X_train, X_train,
                    epochs=nb_epoch,
                    batch_size=batch_size,
                    shuffle=True,
                    validation_data=(X_test, X_test),
                    verbose=1,
                    callbacks=[checkpointer, tensorboard]).history