我正在研究文本摘要网络,需要实现一个编码器来使用tf.nn.seq2seq.embedding_attention_decoder.作为其中的一部分,我需要将不同批次的序列编码为表示向量,但最内层编码不会通过.
这是一个简化的片段,给出了同样的错误:
import tensorflow as tf
single_cell = tf.nn.rnn_cell.GRUCell(1024)
sentence_cell = tf.nn.rnn_cell.EmbeddingWrapper(single_cell,
embedding_classes = 40000)
batch = [tf.placeholder(tf.int32, [1,1]) for _ in range(250)]
(_ , state) = tf.nn.rnn(sentence_cell, batch, dtype= tf.int32)
这会因以下堆栈跟踪而失败:
Traceback (most recent call last):
File "/home/ubuntu/workspace/example.py", line 6, in <module>
(_ , state) = tf.nn.rnn(sentence_cell, batch, dtype= tf.int32)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn.py", line 126, in rnn
(output, state) = call_cell()
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn.py", line 119, in <lambda>
call_cell = lambda: cell(input_, state)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn_cell.py", line 616, in __call__ …