python - 无法在Python TensorFlow中将LSTMBlockCell替换为LSTMBlockFusedCell

将LSTMBlockCell替换为LSTMBlockFusedCell将在static\u rnn'中引发typeerror。我使用的是从源代码编译的tensorflow 1.2.0-rc1。
完整错误消息：

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-3-2986e054cb6b> in <module>()
     19     enc_cell = tf.contrib.rnn.LSTMBlockFusedCell(rnn_size)
     20     enc_layers = tf.contrib.rnn.MultiRNNCell([enc_cell] * num_layers, state_is_tuple=True)
---> 21     _, enc_state = tf.contrib.rnn.static_rnn(enc_layers, enc_input_unstacked, dtype=dtype)
     22
     23 with tf.variable_scope('decoder'):

~/Virtualenvs/scikit/lib/python3.6/site-packages/tensorflow/python/ops/rnn.py in static_rnn(cell, inputs, initial_state, dtype, sequence_length, scope)
   1139
   1140   if not _like_rnncell(cell):
-> 1141     raise TypeError("cell must be an instance of RNNCell")
   1142   if not nest.is_sequence(inputs):
   1143     raise TypeError("inputs must be a sequence")

TypeError: cell must be an instance of RNNCell

要复制的代码：

import tensorflow as tf

batch_size = 8
enc_input_length = 1000

dtype = tf.float32
rnn_size = 8
num_layers = 2

enc_input = tf.placeholder(dtype, shape=[batch_size, enc_input_length, 1])
enc_input_unstacked = tf.unstack(enc_input, axis=1)

with tf.variable_scope('encoder'):
    enc_cell = tf.contrib.rnn.LSTMBlockFusedCell(rnn_size)
    enc_layers = tf.contrib.rnn.MultiRNNCell([enc_cell] * num_layers)
    _, enc_state = tf.contrib.rnn.static_rnn(enc_layers, enc_input_unstacked, dtype=dtype)

_like_rnncell看起来像：

def _like_rnncell(cell):
  """Checks that a given object is an RNNCell by using duck typing."""
  conditions = [hasattr(cell, "output_size"), hasattr(cell, "state_size"),
                hasattr(cell, "zero_state"), callable(cell)]
  return all(conditions)

原来LSTMBlockFusedCell没有output_size实现的state_size和LSTMBlockCell属性。
这是一个bug，还是有一种方法可以使用我缺少的LSTMBlockFusedCell。

最佳答案

LSTMBlockFusedCell继承自FusedRNNCell而不是RNNCell，因此不能使用标准的tf.nn.static_rnn或tf.nn.dynamic_rnn实例（如错误消息所示）。
但是，在documentation中，可以直接调用单元格以获取完整的输出和状态。

inputs = tf.placeholder(tf.float32, [time_len, batch_size, input_size])
fused_rnn_cell = tf.contrib.rnn.LSTMBlockFusedCell(num_units)

outputs, state = fused_rnn_cell(inputs, dtype=tf.float32)

# outputs shape is (time_len, batch_size, num_units)
# state: LSTMStateTuple where c shape is (batch_size, num_units)
#  and h shape is also (batch_size, num_units).

RNNCell对象调用LSTMBlockFusedCellinternally，这应该相当于正常的lstm循环。
另外，请注意，任何gen_lstm_ops.block_lstm实例的输入都应该是time-major，这可以通过在调用单元格之前转置张量来完成。