Question

我正在尝试创建一种由4个双向lstm组成的网络体系结构，我想将所有这些求和并传递给一个线性层投影。

所以我的网络结构是：

Lstm 
Lstm
Lstm
Lstm 

Sum(All_four_lstm_last_state)

Linear_layer ( Sum ) 

Softmax( Linear_layer )

我的困惑是我为Lstm单元创建了一个函数，我想对所有四个rnn重用，但是我对variable_scope感到困惑。

所以我的代码如下：

首先，我创建了lstm单元函数：

  with tf.variable_scope('encoder'):
        # forward cell of Bi-directional lstm network



        def fr_cell():
            fr_cell_lstm = rnn.LSTMCell(num_units=rnn_num_units, forget_bias=forget_bias_)

            return rnn.DropoutWrapper(cell=fr_cell_lstm, output_keep_prob=1. - dropout, dtype=tf.float32)

        fr_cell_m = rnn.MultiRNNCell([fr_cell() for _ in range(1)], state_is_tuple=True)



    with tf.variable_scope('encoder'):



        def bw_cell():
            bw_cell_lstm = rnn.LSTMCell(num_units=rnn_num_units, forget_bias=forget_bias_)

            return rnn.DropoutWrapper(cell=bw_cell_lstm, output_keep_prob=1. - dropout, dtype=tf.float32)

        bw_cell_m = rnn.MultiRNNCell([bw_cell() for _ in range(1)], state_is_tuple=True)

现在我有四个RNN：

   # Bi-directional lstm network
    with tf.variable_scope('encoder') as scope:
        model, (state_c, state_h) = tf.nn.bidirectional_dynamic_rnn(
            cell_fw=fr_cell_m,  # forward cell
            cell_bw=bw_cell_m,  # backward cell
            inputs=long_word_embedding_lookup,  # 3 dim embedding input for rnn
            sequence_length= l_w_sequence_len,  # sequence len == batch_size

            dtype=tf.float32

        )

    transpose = tf.transpose(tf.concat(model, 2), [1, 0, 2])

    long_word_state_output = tf.concat([state_c[0].c, state_h[0].c], axis=-1)



    # Bi-directional lstm network
    with tf.variable_scope('encoder') as scope:
        model, (state_c, state_h) = tf.nn.bidirectional_dynamic_rnn(
            cell_fw=fr_cell_m,  # forward cell
            cell_bw=bw_cell_m,  # backward cell
            inputs=short_word_embedding_lookup,  # 3 dim embedding input for rnn
            sequence_length=s_w_sequence_len,  # sequence len == batch_size


            dtype=tf.float32

        )

    transpose = tf.transpose(tf.concat(model, 2), [1, 0, 2])

    short_word_state_output = tf.concat([state_c[0].c, state_h[0].c], axis=-1)



    with tf.variable_scope('encoder') as scope:
        model, (state_c, state_h) = tf.nn.bidirectional_dynamic_rnn(
            cell_fw=fr_cell_m,  # forward cell
            cell_bw=bw_cell_m,  # backward cell
            inputs=long_char_embedding_lookup,  # 3 dim embedding input for rnn
            sequence_length=l_c_sequence_len,  # sequence len == batch_size

            dtype=tf.float32

        )

    transpose = tf.transpose(tf.concat(model, 2), [1, 0, 2])

    long_char_state_output = tf.concat([state_c[0].c, state_h[0].c], axis=-1)



    with tf.variable_scope('encoder') as scope:
        model, (state_c, state_h) = tf.nn.bidirectional_dynamic_rnn(
            cell_fw=fr_cell_m,  # forward cell
            cell_bw=bw_cell_m,  # backward cell
            inputs=short_char_embedding_lookup,  # 3 dim embedding input for rnn
            sequence_length=s_c_sequence_len,  # sequence len == batch_size

            dtype=tf.float32

        )

    transpose = tf.transpose(tf.concat(model, 2), [1, 0, 2])

    short_char_state_output = tf.concat([state_c[0].c, state_h[0].c], axis=-1)

您可以看到我为lstm和所有四个Rnn使用了相同的name_scope（'encode'）。

如果有人能给我一些建议是正确的方法，还是如果我的四个输入都不同并且我要总结一下，我应该修改什么，我将不胜感激。

谢谢。

Tensorflow：我们可以对同一个lstm单元使用不同的name_scope和rnn吗？

0 个答案: