टाइप एरर: Seq2Seq

Question

में ऑब्जेक्ट्स _thread.lock ऑब्जेक्ट नहीं कर सकता है मुझे अपने टेन्सफोर्लो मॉडल में बाल्टी का उपयोग करने में परेशानी हो रही है। जब मैं इसे buckets = [(100, 100)] के साथ चलाता हूं, तो यह ठीक काम करता है। जब मैं इसे buckets = [(100, 100), (200, 200)] के साथ चलाता हूं तो यह बिल्कुल भी काम नहीं करता है (नीचे stacktrace)।

दिलचस्प बात यह है कि टेंसफोर्लो के सेक 2 सेक ट्यूटोरियल को चलाना लगभग समान स्टैकट्रैस के साथ एक ही तरह का मुद्दा देता है। परीक्षण उद्देश्यों के लिए, भंडार का लिंक here है।

मुझे यकीन नहीं है कि समस्या क्या है, लेकिन एक से अधिक बाल्टी हमेशा इसे ट्रिगर करने लगती है।

इस कोड को एक स्टैंडअलोन के रूप में काम नहीं करेगा, लेकिन इस समारोह में जहां यह क्रैश हो रहा है - याद रखें कि [(100, 100)] से buckets बदलते [(100, 100), (200, 200)] को दुर्घटना से चलाता है।

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()) 

स्टैकट्रेस: ​​

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 

Answer 1

समस्या seq2seq.py में नवीनतम परिवर्तनों के साथ है। इसे अपनी स्क्रिप्ट में जोड़ें और यह कोशिकाओं की गहरी प्रतिलिपि से बच जाएगा:

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)