我正在构建一个神经机器翻译器，我必须使用两个不同的 LSTM 单元(一个用于编码器，一个用于解码器)。
两个单元有不同的形状:
- 编码器(第一个)输入句子的标记并产生状态向量
- 解码器(第二个)输入先前的状态向量，以及它自己生成的标记
我在 Tensorflow 中编写了这个，当我运行脚本时，出现了以下错误(在解码器阶段出现):
outputs, states = tf.nn.rnn(cell_backward, inputs, initial_state=initial_state)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn.py", line 158, in rnn
(output, state) = call_cell()
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn.py", line 145, in <lambda>
call_cell = lambda: cell(input_, state)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn_cell.py", line 520, in __call__
dtype, self._num_unit_shards)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn_cell.py", line 357, in _get_concat_variable
sharded_variable = _get_sharded_variable(name, shape, dtype, num_shards)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn_cell.py", line 387, in _get_sharded_variable
dtype=dtype))
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 732, in get_variable
partitioner=partitioner, validate_shape=validate_shape)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 596, in get_variable
partitioner=partitioner, validate_shape=validate_shape)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 161, in get_variable
caching_device=caching_device, validate_shape=validate_shape)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 437, in _get_single_variable
name, "".join(traceback.format_list(tb))))
ValueError: Variable backward/RNN/LSTMCell/W_0 already exists, disallowed. Did you mean to set reuse=True in VarScope? Originally defined at:
File "/home/alexis/Documents/NMT/NMT.py", line 88, in dense_to_vector_state
outputs, states = tf.nn.rnn(cell_forward, inputs, initial_state=initial_state)
我如何明确指定我想要创建一个全新的 LSTM 单元？
提前致谢!
亚历克西斯
请您参考如下方法:
使用变量作用域
with tf.variable_scope('enc'):
cell_enc = LSTMCell(hidden_size)
with tf.variable_scope('dec'):
cell_dec = LSTMCell(hidden_size)
