What should the generator return if it is used in a multi-model functional API?

问题

Following this article, I'm trying to implement a generative RNN. In the mentioned article, the training and validation data are passed as fully loaded np.arrays. But I'm trying to use the model.fit_generator method and provide a generator instead.

I know that if it was a straightforward model, the generator should return:

def generator():
    ...
    yield (samples, targets)

But this is a generative model which means there are two models involved:

encoder_inputs = Input(shape=(None,))
x = Embedding(num_encoder_tokens, embedding_dim)(encoder_inputs)
x.set_weights([embedding_matrix])
x.trainable = False
x, state_h, state_c = LSTM(embedding_dim, return_state=True)(x)
encoder_states = [state_h, state_c]

decoder_inputs = Input(shape=(None,))
x = Embedding(num_decoder_tokens, embedding_dim)(decoder_inputs)
x.set_weights([embedding_matrix])
x.trainable = False
x = LSTM(embedding_dim, return_sequences=True)(x, initial_state=encoder_states)
decoder_outputs = Dense(num_decoder_tokens, activation='softmax')(x)

model = Model([encoder_inputs, decoder_inputs], decoder_outputs)

model.fit([encoder_input_data, decoder_input_data], decoder_target_data,
          batch_size=batch_size,
          epochs=epochs,
          validation_split=0.2)

As mentioned before, I'm trying to use a generator:

model.fit_generator(generator(),
                   steps_per_epoch=500,
                   epochs=20,
                   validation_data=generator(),
                   validation_steps=val_steps)

But what should the generator() return? I'm a little confused since there are two input collections and one target.

回答1:

Since your model has two inputs and one output, the generator should return a tuple with two elements where the first element is a list containing two arrays, which corresponds to two input layers, and the second element is an array corresponding to output layer:

def generator():
    ...
    yield [input_samples1, input_samples2], targets

Generally, in a model with M inputs and N outputs, the generator should return a tuple of two lists where the first one has M arrays and the second one has N arrays:

def generator():
        ...
        yield [in1, in2, ..., inM], [out1, out2, ..., outN]

来源：https://stackoverflow.com/questions/53474497/what-should-the-generator-return-if-it-is-used-in-a-multi-model-functional-api

标签

python

machine-learning

keras

generator

rnn