如何在Tensorflow中使用多层双向LSTM？

Question

我想知道如何在Tensorflow中使用多层双向LSTM。

我已经实现了双向LSTM的内容，但我想将此模型与添加的多层模型进行比较。

我该如何在这部分中添加一些代码？

x = tf.unstack(tf.transpose(x, perm=[1, 0, 2]))
#print(x[0].get_shape())

# Define lstm cells with tensorflow
# Forward direction cell
lstm_fw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)
# Backward direction cell
lstm_bw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)

# Get lstm cell output
try:
    outputs, _, _ = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,
                                          dtype=tf.float32)
except Exception: # Old TensorFlow version only returns outputs not states
    outputs = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,
                                    dtype=tf.float32)

# Linear activation, using rnn inner loop last output
outputs = tf.stack(outputs, axis=1)
outputs = tf.reshape(outputs, (batch_size*n_steps, n_hidden*2))
outputs = tf.matmul(outputs, weights['out']) + biases['out']
outputs = tf.reshape(outputs, (batch_size, n_steps, n_classes))

Answer 1

您可以使用两种不同的方法来应用多层bilstm模型：

1）使用先前的bilstm层作为下一个bilstm的输入。在开始时，您应该使用长度为num_layers的前向和后向单元格创建数组。和

for n in range(num_layers):
        cell_fw = cell_forw[n]
        cell_bw = cell_back[n]

        state_fw = cell_fw.zero_state(batch_size, tf.float32)
        state_bw = cell_bw.zero_state(batch_size, tf.float32)

        (output_fw, output_bw), last_state = tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, output,
                                                                             initial_state_fw=state_fw,
                                                                             initial_state_bw=state_bw,
                                                                             scope='BLSTM_'+ str(n),
                                                                             dtype=tf.float32)

        output = tf.concat([output_fw, output_bw], axis=2)

2）另外值得一看另一种方法stacked bilstm。

Answer 2

这与第一个答案大致相同，但是使用范围名称和添加的dropout包装器稍有不同。它还会处理第一个答案给出的关于变量范围的错误。

def bidirectional_lstm(input_data, num_layers, rnn_size, keep_prob):

    output = input_data
    for layer in range(num_layers):
        with tf.variable_scope('encoder_{}'.format(layer),reuse=tf.AUTO_REUSE):

            # By giving a different variable scope to each layer, I've ensured that
            # the weights are not shared among the layers. If you want to share the
            # weights, you can do that by giving variable_scope as "encoder" but do
            # make sure first that reuse is set to tf.AUTO_REUSE

            cell_fw = tf.contrib.rnn.LSTMCell(rnn_size, initializer=tf.truncated_normal_initializer(-0.1, 0.1, seed=2))
            cell_fw = tf.contrib.rnn.DropoutWrapper(cell_fw, input_keep_prob = keep_prob)

            cell_bw = tf.contrib.rnn.LSTMCell(rnn_size, initializer=tf.truncated_normal_initializer(-0.1, 0.1, seed=2))
            cell_bw = tf.contrib.rnn.DropoutWrapper(cell_bw, input_keep_prob = keep_prob)

            outputs, states = tf.nn.bidirectional_dynamic_rnn(cell_fw, 
                                                              cell_bw, 
                                                              output,
                                                              dtype=tf.float32)

            # Concat the forward and backward outputs
            output = tf.concat(outputs,2)

    return output

Answer 3

在塔拉斯的回答之上。这是使用仅具有GRU单元的2层双向RNN的另一示例

    embedding_weights = tf.Variable(tf.random_uniform([vocabulary_size, state_size], -1.0, 1.0))
    embedding_vectors = tf.nn.embedding_lookup(embedding_weights, tokens)

    #First BLSTM
    cell = tf.nn.rnn_cell.GRUCell(state_size)
    cell = tf.nn.rnn_cell.DropoutWrapper(cell, output_keep_prob=1-dropout)
    (forward_output, backward_output), _ = \
        tf.nn.bidirectional_dynamic_rnn(cell, cell, inputs=embedding_vectors,
                                        sequence_length=lengths, dtype=tf.float32,scope='BLSTM_1')
    outputs = tf.concat([forward_output, backward_output], axis=2)

    #Second BLSTM using the output of previous layer as an input.
    cell2 = tf.nn.rnn_cell.GRUCell(state_size)
    cell2 = tf.nn.rnn_cell.DropoutWrapper(cell2, output_keep_prob=1-dropout)
    (forward_output, backward_output), _ = \
        tf.nn.bidirectional_dynamic_rnn(cell2, cell2, inputs=outputs,
                                        sequence_length=lengths, dtype=tf.float32,scope='BLSTM_2')
    outputs = tf.concat([forward_output, backward_output], axis=2)

顺便说一下，别忘了添加不同的范围名称。希望这有帮助。

Answer 4

正如@Taras所指出的，你可以使用：

（1）tf.nn.bidirectional_dynamic_rnn()

（2）tf.contrib.rnn.stack_bidirectional_dynamic_rnn()。

所有以前的答案只捕获（1），所以我给出了（2）的一些细节，特别是因为它通常优于（1）。关于see here的不同连接性的直觉。

假设您要创建一个包含3个BLSTM图层的堆栈，每个图层包含64个节点：

num_layers = 3
num_nodes = 64


# Define LSTM cells
enc_fw_cells = [LSTMCell(num_nodes)for layer in range(num_layers)]
enc_bw_cells = [LSTMCell(num_nodes) for layer in range(num_layers)]

# Connect LSTM cells bidirectionally and stack
(all_states, fw_state, bw_state) = tf.contrib.rnn.stack_bidirectional_dynamic_rnn(
        cells_fw=enc_fw_cells, cells_bw=enc_bw_cells, inputs=input_embed, dtype=tf.float32)

# Concatenate results
for k in range(num_layers):
    if k == 0:
        con_c = tf.concat((fw_state[k].c, bw_state[k].c), 1)
        con_h = tf.concat((fw_state[k].h, bw_state[k].h), 1)
    else:
        con_c = tf.concat((con_c, fw_state[k].c, bw_state[k].c), 1)
        con_h = tf.concat((con_h, fw_state[k].h, bw_state[k].h), 1)

output = tf.contrib.rnn.LSTMStateTuple(c=con_c, h=con_h)

在这种情况下，我使用堆叠的biRNN的最终状态而不是所有时间步的状态（保存在all_states中），因为我使用的是编码解码方案，其中上面的代码只是编码器。