我正在尝试在 Tensorflow 中构建一个序列到序列模型，我已经遵循了几个教程，一切都很好。直到我决定在我的模型中删除教师强迫。下面是我正在使用的解码器网络示例：
def decoding_layer_train(encoder_state, dec_cell, dec_embed_input,
target_sequence_length, max_summary_length,
output_layer, keep_prob):
"""
Create a decoding layer for training
:param encoder_state: Encoder State
:param dec_cell: Decoder RNN Cell
:param dec_embed_input: Decoder embedded input
:param target_sequence_length: The lengths of each sequence in the target batch
:param max_summary_length: The length of the longest sequence in the batch
:param output_layer: Function to apply the output layer
:param keep_prob: Dropout keep probability
:return: BasicDecoderOutput containing training logits and sample_id
"""
training_helper = tf.contrib.seq2seq.TrainingHelper(inputs=dec_embed_input,
sequence_length=target_sequence_length,
time_major=False)
training_decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell, training_helper, encoder_state, output_layer)
training_decoder_output = tf.contrib.seq2seq.dynamic_decode(training_decoder,
impute_finished=True,
maximum_iterations=max_summary_length)[0]
return training_decoder_output
据我了解，TrainingHelper 正在强迫教师。尤其是将真实输出作为其参数的一部分。我尝试在没有培训帮助的情况下使用解码器，但这似乎是强制性的。我试图将真实输出设置为 0，但显然 TrainingHelper 需要输出。我也尝试用谷歌搜索解决方案，但没有找到任何相关内容。
===================更新=============
我很抱歉之前没有提到这一点，但我也尝试使用 GreedyEmbeddingHelper。该模型运行良好几次迭代，然后开始抛出运行时错误。似乎 GreedyEmbeddingHelper 开始预测与预期形状不同的输出。下面是我使用 GreedyEmbeddingHelper 时的功能
def decoding_layer_train(encoder_state, dec_cell, dec_embeddings,
target_sequence_length, max_summary_length,
output_layer, keep_prob):
"""
Create a decoding layer for training
:param encoder_state: Encoder State
:param dec_cell: Decoder RNN Cell
:param dec_embed_input: Decoder embedded input
:param target_sequence_length: The lengths of each sequence in the target batch
:param max_summary_length: The length of the longest sequence in the batch
:param output_layer: Function to apply the output layer
:param keep_prob: Dropout keep probability
:return: BasicDecoderOutput containing training logits and sample_id
"""
start_tokens = tf.tile(tf.constant([target_vocab_to_int['<GO>']], dtype=tf.int32), [batch_size], name='start_tokens')
training_helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(dec_embeddings,
start_tokens,
target_vocab_to_int['<EOS>'])
training_decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell, training_helper, encoder_state, output_layer)
training_decoder_output = tf.contrib.seq2seq.dynamic_decode(training_decoder,
impute_finished=True,
maximum_iterations=max_summary_length)[0]
return training_decoder_output
这是经过多次训练迭代后引发的错误示例：
Ok
Epoch 0 Batch 5/91 - Train Accuracy: 0.4347, Validation Accuracy: 0.3557, Loss: 2.8656
++++Epoch 0 Batch 5/91 - Train WER: 1.0000, Validation WER: 1.0000
Epoch 0 Batch 10/91 - Train Accuracy: 0.4050, Validation Accuracy: 0.3864, Loss: 2.6347
++++Epoch 0 Batch 10/91 - Train WER: 1.0000, Validation WER: 1.0000
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-115-1d2a9495ad42> in <module>()
57 target_sequence_length: targets_lengths,
58 source_sequence_length: sources_lengths,
---> 59 keep_prob: keep_probability})
60
61
/Users/alsulaimi/Documents/AI/Tensorflow-make/workspace/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict, options, run_metadata)
887 try:
888 result = self._run(None, fetches, feed_dict, options_ptr,
--> 889 run_metadata_ptr)
890 if run_metadata:
891 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/Users/alsulaimi/Documents/AI/Tensorflow-make/workspace/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _run(self, handle, fetches, feed_dict, options, run_metadata)
1116 if final_fetches or final_targets or (handle and feed_dict_tensor):
1117 results = self._do_run(handle, final_targets, final_fetches,
-> 1118 feed_dict_tensor, options, run_metadata)
1119 else:
1120 results = []
/Users/alsulaimi/Documents/AI/Tensorflow-make/workspace/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1313 if handle is None:
1314 return self._do_call(_run_fn, self._session, feeds, fetches, targets,
-> 1315 options, run_metadata)
1316 else:
1317 return self._do_call(_prun_fn, self._session, handle, feeds, fetches)
/Users/alsulaimi/Documents/AI/Tensorflow-make/workspace/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _do_call(self, fn, *args)
1332 except KeyError:
1333 pass
-> 1334 raise type(e)(node_def, op, message)
1335
1336 def _extend_graph(self):
InvalidArgumentError: logits and labels must have the same first dimension, got logits shape [1100,78] and labels shape [1400]
我不确定，但我猜 GreedyEmbeddingHepler 不应该用于训练。，我将感谢您对如何阻止老师强迫的帮助和想法。
谢谢你。