Deep RNN Model Architecture

deepRNN.py

def deep_rnn_model(input_dim, units, recur_layers, output_dim=29):
    """ Build a deep recurrent network for speech 
    """
    # Main acoustic input
    input_data = Input(name='the_input', shape=(None, input_dim))
    # TODO: Add recurrent layers, each with batch normalization
    prev_layer = input_data
    for layer in range(recur_layers):
        prev_layer = GRU(units, activation='relu',
            return_sequences=True, implementation=2, name=f'rnn_{layer+1}')(prev_layer)
        # TODO: Add batch normalization
        prev_layer = BatchNormalization()(prev_layer)
    
    # TODO: Add a TimeDistributed(Dense(output_dim)) layer
    time_dense = TimeDistributed(Dense(output_dim))(prev_layer)
    # Add softmax activation layer
    y_pred = Activation('softmax', name='softmax')(time_dense)
    # Specify the model
    model = Model(inputs=input_data, outputs=y_pred)
    model.output_length = lambda x: x
    print(model.summary())
    return model