Inputs to Encoder-Decoder LSTMCell/RNN Network

Question

I'm creating an LSTM Encoder-Decoder Network, using Keras, following the code provided here: https://github.com/LukeTonin/keras-seq-2-seq-signal-prediction. The only change I made is to replace the GRUCell with an LSTMCell. Basically both the encoder and decoder consists of 2 layers, of 35 LSTMCells. The layers are stacked over (and combined with) each other using an RNN Layer.

The LSTMCell returns 2 states whereas the GRUCell returns 1 state. This is where I am encountering an error, as I do not know how to code for the 2 returned states of the LSTMCell.

I have created two models: first, an encoder-decoder model. Second, a prediction model. I am not encountering any problems in the encoder-decoder model, but a encountering problems in the decoder of the prediction model.

The error I am getting is: ValueError: Layer rnn_4 expects 9 inputs, but it received 3 input tensors. Input received: [<tf.Tensor 'input_4:0' shape=(?, ?, 1) dtype=float32>, <tf.Tensor 'input_11:0' shape=(?, 35) dtype=float32>, <tf.Tensor 'input_12:0' shape=(?, 35) dtype=float32>]

This error happens when this line below, in the prediction model, is run:

decoder_outputs_and_states = decoder(
        decoder_inputs, initial_state=decoder_states_inputs)

The section of code this fits into is:

encoder_predict_model = keras.models.Model(encoder_inputs,
                                           encoder_states)

decoder_states_inputs = []

# Read layers backwards to fit the format of initial_state
# For some reason, the states of the model are order backwards (state of the first layer at the end of the list)
# If instead of a GRU you were using an LSTM Cell, you would have to append two Input tensors since the LSTM has 2 states.
for hidden_neurons in layers[::-1]:
    # One state for GRU, but two states for LSTMCell
    decoder_states_inputs.append(keras.layers.Input(shape=(hidden_neurons,)))
decoder_outputs_and_states = decoder(
        decoder_inputs, initial_state=decoder_states_inputs)

decoder_outputs = decoder_outputs_and_states[0]
decoder_states = decoder_outputs_and_states[1:]

decoder_outputs = decoder_dense(decoder_outputs)

decoder_predict_model = keras.models.Model(
        [decoder_inputs] + decoder_states_inputs,
        [decoder_outputs] + decoder_states)

Could somebody help me with the for loop above, and initial states I should be passing the decoder after that?

Daniel Street Daniel Street · Accepted Answer · 2020-10-16T12:27:22

I had an similar error and i solved just doing what he says, adding another input tensor:

# If instead of a GRU you were using an LSTM Cell, you would have to append two Input tensors since the LSTM has 2 states.
for hidden_neurons in layers[::-1]:
    # One state for GRU
    decoder_states_inputs.append(keras.layers.Input(shape=(hidden_neurons,)))
    decoder_states_inputs.append(keras.layers.Input(shape=(hidden_neurons,)))

here it solved the prolem...

Inputs to Encoder-Decoder LSTMCell/RNN Network

1 Answers