Tukukkk

I would like to implement an autoencoder model that acts as following:

for epoch in xrange(100):

  for X_batch in batch_list:

     model.train_on_batch(X_batch, X_batch)

     training_error = model.evaluate(X_batch, X_batch, verbose=0)

  average the training error by the number of the batches considered

  save it as the epoch training error

  call the function to get the validation error in the same fashion over  the validation data

  compare the two errors and decide whether go on training or stopping

I have looked on Internet and already asked something and it has been suggested me to use fit_generator, but I have not understand how to implement it. Or should I use the method train_on_batch or the fit one with number of epochs equal to 1 to proper fit the model?

Which is the best practice in this case? Do you have an example or a similar question to link me?

From what I can understand, you want to use validation error as early stopping criteria. Good news is that keras already has early stopping callback. SO all you need is to create a callback and call it during training after some epochs/iterations.

keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=0, verbose=0, mode='auto', baseline=None, restore_best_weights=False)

Let us look at train_on_batch and fit()

train_on_batch(x, y, sample_weight=None, class_weight=None)





fit(x=None, y=None, batch_size=None, epochs=1, verbose=1, callbacks=None, validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None)

You can see that train_on_batch doesn't take any callback as input, so good choice is to use fit here, unless you want to implement it by yourself.

Now you can call fit as following

callbacks = [EarlyStopping(monitor='val_loss', patience=2),

         ModelCheckpoint(filepath='path to latest ckpt', monitor='val_loss', save_best_only=True)]



history = model.fit(train_features,train_target, epochs=num_epochs, callbacks=callbacks, verbose=0, batch_size=your_choice, validation_data)