rnn_model.py

def  model_final (input_shape, output_sequence_length, english_vocab_size, french_vocab_size):
    """
    Build and train a model that incorporates embedding, encoder-decoder, and bidirectional RNN
    :param input_shape: Tuple of input shape
    :param output_sequence_length: Length of output sequence
    :param english_vocab_size: Number of unique English words in the dataset
    :param french_vocab_size: Number of unique French words in the dataset
    :return: Keras model built, but not trained
    """
    # Hyperparameters
    learning_rate = 0.003

    # Build the layers    
    model = Sequential()
    # Embedding
    model.add(Embedding(english_vocab_size, 128, input_length=input_shape[1],
                         input_shape=input_shape[1:]))
    # Encoder
    model.add(Bidirectional(GRU(128)))
    model.add(RepeatVector(output_sequence_length))
    # Decoder
    model.add(Bidirectional(GRU(128, return_sequences=True)))
    model.add(TimeDistributed(Dense(512, activation='relu')))
    model.add(Dropout(0.5))
    model.add(TimeDistributed(Dense(french_vocab_size, activation='softmax')))
    model.compile(loss=sparse_categorical_crossentropy,
                  optimizer=Adam(learning_rate),
                  metrics=['accuracy'])
    return model