TypeError:无法在Seq2Seq中腌制_thread.lock对象 [英] TypeError: can't pickle _thread.lock objects in Seq2Seq

问题描述

我在Tensorflow模型中使用存储桶时遇到问题.当我用buckets = [(100, 100)]运行它时，它可以正常工作.当我用buckets = [(100, 100), (200, 200)]运行它根本不起作用(底部的stacktrace).

I'm having trouble using buckets in my Tensorflow model. When I run it with buckets = [(100, 100)], it works fine. When I run it with buckets = [(100, 100), (200, 200)] it doesn't work at all (stacktrace at bottom).

有趣的是，运行Tensorflow的Seq2Seq教程会产生几乎相同的堆栈跟踪问题.出于测试目的，在此处的链接.

Interestingly, running Tensorflow's Seq2Seq tutorial gives the same kind of issue with a nearly identical stacktrace. For testing purposes, the link to the repository is here.

我不确定是什么问题，但是总是有多个存储桶似乎总是会触发该问题.

此代码无法独立运行，但这是崩溃的功能-请记住，将buckets从[(100, 100)]更改为[(100, 100), (200, 200)]会触发崩溃.

This code won't work as a standalone, but this is the function where it is crashing - remember that changing buckets from [(100, 100)] to [(100, 100), (200, 200)] triggers the crash.

class MySeq2Seq(object):
    def __init__(self, source_vocab_size, target_vocab_size, buckets, size, num_layers, batch_size, learning_rate):
        self.source_vocab_size = source_vocab_size
        self.target_vocab_size = target_vocab_size
        self.buckets = buckets
        self.batch_size = batch_size

        cell = single_cell = tf.nn.rnn_cell.GRUCell(size)
        if num_layers > 1:
            cell = tf.nn.rnn_cell.MultiRNNCell([single_cell] * num_layers)

        # The seq2seq function: we use embedding for the input and attention
        def seq2seq_f(encoder_inputs, decoder_inputs, do_decode):
            return tf.contrib.legacy_seq2seq.embedding_attention_seq2seq(
                encoder_inputs, decoder_inputs, cell,
                num_encoder_symbols=source_vocab_size,
                num_decoder_symbols=target_vocab_size,
                embedding_size=size,
                feed_previous=do_decode)

        # Feeds for inputs
        self.encoder_inputs = []
        self.decoder_inputs = []
        self.target_weights = []
        for i in range(buckets[-1][0]):
            self.encoder_inputs.append(tf.placeholder(tf.int32, shape=[None], name="encoder{0}".format(i)))
        for i in range(buckets[-1][1] + 1):
            self.decoder_inputs.append(tf.placeholder(tf.int32, shape=[None], name="decoder{0}".format(i)))
            self.target_weights.append(tf.placeholder(tf.float32, shape=[None], name="weight{0}".format(i)))

        # Our targets are decoder inputs shifted by one
        targets = [self.decoder_inputs[i + 1] for i in range(len(self.decoder_inputs) - 1)]
        self.outputs, self.losses = tf.contrib.legacy_seq2seq.model_with_buckets(
            self.encoder_inputs, self.decoder_inputs, targets,
            self.target_weights, [(100, 100)],
            lambda x, y: seq2seq_f(x, y, False))

        # Gradients update operation for training the model
        params = tf.trainable_variables()
        self.updates = []
        for b in range(len(buckets)):
            self.updates.append(tf.train.AdamOptimizer(learning_rate).minimize(self.losses[b]))

        self.saver = tf.train.Saver(tf.global_variables())

Stacktrace:

Stacktrace:

    Traceback (most recent call last):
  File "D:/Stuff/IdeaProjects/myproject/src/main.py", line 38, in <module>
    model = predict.make_model(input_vocab_size, output_vocab_size, buckets, cell_size, model_layers, batch_size, learning_rate)
  File "D:\Stuff\IdeaProjects\myproject\src\predictor.py", line 88, in make_model
    size=cell_size, num_layers=model_layers, batch_size=batch_size, learning_rate=learning_rate)
  File "D:\Stuff\IdeaProjects\myproject\src\predictor.py", line 45, in __init__
    lambda x, y: seq2seq_f(x, y, False))
  File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\contrib\legacy_seq2seq\python\ops\seq2seq.py", line 1206, in model_with_buckets
    decoder_inputs[:bucket[1]])
  File "D:\Stuff\IdeaProjects\myproject\src\predictor.py", line 45, in <lambda>
    lambda x, y: seq2seq_f(x, y, False))
  File "D:\Stuff\IdeaProjects\myproject\src\predictor.py", line 28, in seq2seq_f
    feed_previous=do_decode)
  File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\contrib\legacy_seq2seq\python\ops\seq2seq.py", line 848, in embedding_attention_seq2seq
    encoder_cell = copy.deepcopy(cell)
  File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 161, in deepcopy
    y = copier(memo)
  File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\layers\base.py", line 476, in __deepcopy__
    setattr(result, k, copy.deepcopy(v, memo))
  File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 215, in _deepcopy_list
    append(deepcopy(a, memo))
  File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 180, in deepcopy
    y = _reconstruct(x, memo, *rv)
  File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 280, in _reconstruct
    state = deepcopy(state, memo)
  File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 240, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 180, in deepcopy
    y = _reconstruct(x, memo, *rv)
  File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 280, in _reconstruct
    state = deepcopy(state, memo)
  File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 240, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 180, in deepcopy
    y = _reconstruct(x, memo, *rv)
  File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 280, in _reconstruct
    state = deepcopy(state, memo)
  File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 240, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 180, in deepcopy
    y = _reconstruct(x, memo, *rv)
  File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 280, in _reconstruct
    state = deepcopy(state, memo)
  File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 240, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\copy.py", line 169, in deepcopy
    rv = reductor(4)
TypeError: can't pickle _thread.lock objects

推荐答案

问题出在seq2seq.py中的最新更改.将此添加到您的脚本中，它将避免对单元格进行深层处理:

The problem is with latest changes in seq2seq.py. Add this to your script and it will avoid deep-coping of the cells:

setattr(tf.contrib.rnn.GRUCell, '__deepcopy__', lambda self, _: self)
setattr(tf.contrib.rnn.BasicLSTMCell, '__deepcopy__', lambda self, _: self)
setattr(tf.contrib.rnn.MultiRNNCell, '__deepcopy__', lambda self, _: self)

这篇关于TypeError:无法在Seq2Seq中腌制_thread.lock对象的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！