自定义纪元循环中的 Keras 回调

Question

I use keras to train an LSTM. The input sequences are of different length. Lets say the sequences have lengths between 1 and num_seq . Therefore, I group the sequences by length in each epoch in order to use a batch size > 1:

for epoch in xrange(nb_epochs):
 for i in range(1,num_seq):
  X,y = get_sequences(length=i)
  model.fit(X,y,batch_size=100,epochs=1, validation_split=0.1, callbacks=None)

Because I use a custom loop over the epochs, callbacks which use the epoch information do not work properly (eg the tensorboard, history, etc). What would be a way around this problem? Is there a way to tell the fit function, which epoch it currently does?

Answer 1

When doing manipulation on your training data during training you should use model.train_on_batch incrementally or - better yet - use fit_generator which lets you define a python generator that produces (x,y) tuples for each batch. This then takes care of the proper invocation of callbacks as well.

For example:

def train_gen():
   while True:
       for i in range(1,num_seq):
           X,y = get_sequences(length=i)
           yield X, y
model.fit_generator(train_gen, steps_per_epoch=num_seq)

The downside of this is that you have to do the batching yourself and also have to supply the validation split yourself which you can do with a generator as well (therefore you can reuse most of the code).