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


Following <a href="https://blog.keras.io/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras.html" rel="nofollow">this article</a>, 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.


Since your model has two inputs and one output, the generator should return a tuple with two elements where the first element is a <strong>list</strong> 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]


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