distillchain_lstm_memory_deep_combine_chain_model.py 文件源码

def sub_lstm(self, model_input, num_frames, lstm_size, number_of_layers, sub_scope=""):
    stacked_lstm = tf.contrib.rnn.MultiRNNCell(
            [
                tf.contrib.rnn.BasicLSTMCell(
                    lstm_size, forget_bias=1.0, state_is_tuple=True)
                for _ in range(number_of_layers)
                ],
            state_is_tuple=True)

    loss = 0.0
    with tf.variable_scope(sub_scope+"-RNN"):
      outputs, state = tf.nn.dynamic_rnn(stacked_lstm, model_input,
                                         sequence_length=num_frames, 
                                         swap_memory=FLAGS.rnn_swap_memory,
                                         dtype=tf.float32)
      final_state = tf.concat(map(lambda x: x.c, state), axis = 1)
    return final_state