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I am using Keras 2.0.0 with Theano.

I would like to update the training data between each epoch. I can do it in a for loop using nb_epochs=1 but it would be much more elegant using the on_epoch_end callback.

Here is my tentative code, based on a Keras 1 example(blog post):

class callback_change_X_train(keras.callbacks.Callback):

def on_epoch_end(self, epoch, logs={}):
    X_train = my_function_to_update_X_train(...)
    self.model.training_data[0] = X_train

Unfortunately, it seems that self.model.training_data does not exist anymore.

Any help much appreciated!

topel
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1 Answers1

1

Just ran into this problem myself and solved it using the model.fit_generator method instead of the standard fit method. You can perform some modification to the data at each epoch this way. I'm not sure if you can get access to the model object itself this way, but for my problem I just needed to shuffle the data along one of the inner axes.

Example code:

def shuffle_gen(x_train, y_train):
    while True:
        shuffle_inner_axis(x_train, y_train)
        for b_i in range(steps_per_epoch):
            yield(x_train[b_i * batch_size:(b_i + 1) * batch_size],
                  y_train[b_i * batch_size:(b_i + 1) * batch_size])
    return

model.fit_generator(shuffle_gen(x_train, y_train), steps_per_epoch, n_epochs)
cr1msonB1ade
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