Question

我使用传统的序列到序列框架在TensorFlow 1.0.1中构建编码器 - 解码器模型。当我在编码器和解码器中有一层LSTM时，一切正常。但是，当我尝试使用MultiRNNCell中包含的＆gt; 1层LSTM时，我在调用tf.contrib.legacy_seq2seq.rnn_decoder时出错。

完整的错误是在这篇文章的最后，但简而言之，它是由一行引起的

(c_prev, m_prev) = state

抛出TypeError: 'Tensor' object is not iterable.的TensorFlow中的

。我对此感到困惑，因为我传递给rnn_decoder的初始状态确实是一个应该存在的元组。据我所知，使用1或> 1层之间的唯一区别是后者涉及使用MultiRNNCell。使用它时是否有一些我应该知道的API怪癖？

这是我的代码（基于this GitHub repo中的示例）。道歉的长度;这是我能做到的最小化，同时仍然是完整和可验证的。

import tensorflow as tf
import tensorflow.contrib.legacy_seq2seq as seq2seq
import tensorflow.contrib.rnn as rnn

seq_len = 50
input_dim = 300
output_dim = 12
num_layers = 2
hidden_units = 100

sess = tf.Session()

encoder_inputs = []
decoder_inputs = []

for i in range(seq_len):
    encoder_inputs.append(tf.placeholder(tf.float32, shape=(None, input_dim),
                                         name="encoder_{0}".format(i)))

for i in range(seq_len + 1):
    decoder_inputs.append(tf.placeholder(tf.float32, shape=(None, output_dim),
                                         name="decoder_{0}".format(i)))

if num_layers > 1:
    # Encoder cells (bidirectional)
    # Forward
    enc_cells_fw = [rnn.LSTMCell(hidden_units)
                    for _ in range(num_layers)]
    enc_cell_fw = rnn.MultiRNNCell(enc_cells_fw)
    # Backward
    enc_cells_bw = [rnn.LSTMCell(hidden_units)
                    for _ in range(num_layers)]
    enc_cell_bw = rnn.MultiRNNCell(enc_cells_bw)
    # Decoder cell
    dec_cells = [rnn.LSTMCell(2*hidden_units)
                 for _ in range(num_layers)]
    dec_cell = rnn.MultiRNNCell(dec_cells)
else:
    # Encoder
    enc_cell_fw = rnn.LSTMCell(hidden_units)
    enc_cell_bw = rnn.LSTMCell(hidden_units)
    # Decoder
    dec_cell = rnn.LSTMCell(2*hidden_units)

# Make sure input and output are the correct dimensions
enc_cell_fw = rnn.InputProjectionWrapper(enc_cell_fw, input_dim)
enc_cell_bw = rnn.InputProjectionWrapper(enc_cell_bw, input_dim)
dec_cell = rnn.OutputProjectionWrapper(dec_cell, output_dim)

_, final_fw_state, final_bw_state = \
     rnn.static_bidirectional_rnn(enc_cell_fw,
                                  enc_cell_bw,
                                  encoder_inputs,
                                  dtype=tf.float32)

# Concatenate forward and backward cell states
# (The state is a tuple of previous output and cell state)
if num_layers == 1:
    initial_dec_state = tuple([tf.concat([final_fw_state[i],
                                          final_bw_state[i]], 1) 
                               for i in range(2)])
else:
    initial_dec_state = tuple([tf.concat([final_fw_state[-1][i],
                                          final_bw_state[-1][i]], 1) 
                               for i in range(2)])

decoder = seq2seq.rnn_decoder(decoder_inputs, initial_dec_state, dec_cell)

tf.global_variables_initializer().run(session=sess)

这就是错误：

Traceback (most recent call last):
  File "example.py", line 67, in <module>
    decoder = seq2seq.rnn_decoder(decoder_inputs, initial_dec_state, dec_cell)
  File "/home/tao/.virtualenvs/example/lib/python2.7/site-packages/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py", line 150, in rnn_decoder
    output, state = cell(inp, state)
  File "/home/tao/.virtualenvs/example/lib/python2.7/site-packages/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py", line 426, in __call__
    output, res_state = self._cell(inputs, state)
  File "/home/tao/.virtualenvs/example/lib/python2.7/site-packages/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py", line 655, in __call__
    cur_inp, new_state = cell(cur_inp, cur_state)
  File "/home/tao/.virtualenvs/example/lib/python2.7/site-packages/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py", line 321, in __call__
    (c_prev, m_prev) = state
  File "/home/tao/.virtualenvs/example/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 502, in __iter__
    raise TypeError("'Tensor' object is not iterable.")
TypeError: 'Tensor' object is not iterable.

谢谢！

Answer 1

问题在于传递给initial_dec_state的初始状态（seq2seq.rnn_decoder）的格式。

当您使用rnn.MultiRNNCell时，您构建了一个多层循环网络，因此您需要为每个这些图层提供初始状态。

因此，您应该提供元组的列表作为初始状态，其中列表的每个元素都是来自周期性网络的相应层的先前状态。

所以你的initial_dec_state初始化如下：

    initial_dec_state = tuple([tf.concat([final_fw_state[-1][i],
                                      final_bw_state[-1][i]], 1) 
                           for i in range(2)])

应该是这样的：

    initial_dec_state = [
                    tuple([tf.concat([final_fw_state[j][i],final_bw_state[j][i]], 1) 
                           for i in range(2)]) for j in range(len(final_fw_state))
                        ]

以下列格式创建元组列表：

    [(state_c1, state_m1), (state_c2, state_m2) ...]

更详细地说，发生'Tensor' object is not iterable.错误是因为seq2seq.rnn_decoder在内部调用您的rnn.MultiRNNCell（dec_cell）将初始状态（initial_dec_state）传递给它

rnn.MultiRNNCell.__call__遍历初始状态列表，并为每个状态提取元组(c_prev, m_prev)（在语句(c_prev, m_prev) = state中）。

因此，如果您只传递一个元组，rnn.MultiRNNCell.__call__将迭代它，并且一旦到达(c_prev, m_prev) = state，它将找到一个张量（应该是一个元组）为{{1}并抛出state错误。

了解'Tensor' object is not iterable.期望的初始状态格式的一种好方法是调用seq2seq.rnn_decoder。此方法以初始化您正在使用的循环模块所需的确切格式返回零填充状态张量。

TensorFlow仅在使用MultiRNNCell时抛出错误

1 个答案: