Tensorflow seq2seq教程：NoneType對象沒有屬性'更新'

Question

我想要遵循https://www.tensorflow.org/tutorials/seq2seq的tensorflow教程。

的數據似乎加載罰款，但是當我初始化模式，我得到以下錯誤：

Traceback (most recent call last): 
    File "/Users/<username>/PycharmProjects/tensorflow_chatbot/execute.py", line 334, in <module> 
    train() 
    File "/Users/<username>/PycharmProjects/tensorflow_chatbot/execute.py", line 151, in train 
    model = create_model(sess, False) 
    File "/Users/<username>/PycharmProjects/tensorflow_chatbot/execute.py", line 113, in create_model 
    forward_only=forward_only) 
    File "/Users/<username>/PycharmProjects/tensorflow_chatbot/seq2seq_model_tf.py", line 181, in __init__ 
    softmax_loss_function=softmax_loss_function) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/site-packages/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py", line 1206, in model_with_buckets 
    decoder_inputs[:bucket[1]]) 
    File "/Users/<username>/PycharmProjects/tensorflow_chatbot/seq2seq_model_tf.py", line 180, in <lambda> 
    lambda x, y: seq2seq_f(x, y, False), 
    File "/Users/<username>/PycharmProjects/tensorflow_chatbot/seq2seq_model_tf.py", line 144, in seq2seq_f 
    dtype=dtype) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/site-packages/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py", line 848, in embedding_attention_seq2seq 
    encoder_cell = copy.deepcopy(cell) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 174, in deepcopy 
    y = copier(memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/site-packages/tensorflow/python/layers/base.py", line 476, in __deepcopy__ 
    setattr(result, k, copy.deepcopy(v, memo)) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 163, in deepcopy 
    y = copier(x, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 230, in _deepcopy_list 
    y.append(deepcopy(a, memo)) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 190, in deepcopy 
    y = _reconstruct(x, rv, 1, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 334, in _reconstruct 
    state = deepcopy(state, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 163, in deepcopy 
    y = copier(x, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 257, in _deepcopy_dict 
    y[deepcopy(key, memo)] = deepcopy(value, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 190, in deepcopy 
    y = _reconstruct(x, rv, 1, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 334, in _reconstruct 
    state = deepcopy(state, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 163, in deepcopy 
    y = copier(x, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 257, in _deepcopy_dict 
    y[deepcopy(key, memo)] = deepcopy(value, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 190, in deepcopy 
    y = _reconstruct(x, rv, 1, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 334, in _reconstruct 
    state = deepcopy(state, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 163, in deepcopy 
    y = copier(x, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 257, in _deepcopy_dict 
    y[deepcopy(key, memo)] = deepcopy(value, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 190, in deepcopy 
    y = _reconstruct(x, rv, 1, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 334, in _reconstruct 
    state = deepcopy(state, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 163, in deepcopy 
    y = copier(x, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 257, in _deepcopy_dict 
    y[deepcopy(key, memo)] = deepcopy(value, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 163, in deepcopy 
    y = copier(x, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 257, in _deepcopy_dict 
    y[deepcopy(key, memo)] = deepcopy(value, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 163, in deepcopy 
    y = copier(x, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 230, in _deepcopy_list 
    y.append(deepcopy(a, memo)) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 190, in deepcopy 
    y = _reconstruct(x, rv, 1, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 334, in _reconstruct 
    state = deepcopy(state, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 163, in deepcopy 
    y = copier(x, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 257, in _deepcopy_dict 
    y[deepcopy(key, memo)] = deepcopy(value, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 190, in deepcopy 
    y = _reconstruct(x, rv, 1, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 334, in _reconstruct 
    state = deepcopy(state, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 163, in deepcopy 
    y = copier(x, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 257, in _deepcopy_dict 
    y[deepcopy(key, memo)] = deepcopy(value, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 190, in deepcopy 
    y = _reconstruct(x, rv, 1, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 334, in _reconstruct 
    state = deepcopy(state, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 163, in deepcopy 
    y = copier(x, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 257, in _deepcopy_dict 
    y[deepcopy(key, memo)] = deepcopy(value, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 163, in deepcopy 
    y = copier(x, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 230, in _deepcopy_list 
    y.append(deepcopy(a, memo)) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 163, in deepcopy 
    y = copier(x, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 237, in _deepcopy_tuple 
    y.append(deepcopy(a, memo)) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 163, in deepcopy 
    y = copier(x, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 257, in _deepcopy_dict 
    y[deepcopy(key, memo)] = deepcopy(value, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 190, in deepcopy 
    y = _reconstruct(x, rv, 1, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 334, in _reconstruct 
    state = deepcopy(state, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 163, in deepcopy 
    y = copier(x, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 257, in _deepcopy_dict 
    y[deepcopy(key, memo)] = deepcopy(value, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 190, in deepcopy 
    y = _reconstruct(x, rv, 1, memo) 
    File "/Users/<username>/anaconda/envs/tensorflow_source_gpu/lib/python2.7/copy.py", line 343, in _reconstruct 
    y.__dict__.update(state) 
AttributeError: 'NoneType' object has no attribute 'update' 

如果我改變_buckets變量translate.py到只有1元組（也不要緊哪一個），沒有問題，但培訓不能很好地發揮作用。任何超過1個元組都會導致此錯誤。除了更改我的本地項目的文件名之外，沒有對github上的文件進行更改（https://github.com/tensorflow/models/tree/master/tutorials/rnn/translate）。

的__init__的功能，導致了該地區的開頭是這樣的：

def __init__(self, 
      source_vocab_size, 
      target_vocab_size, 
      buckets, 
      size, 
      num_layers, 
      max_gradient_norm, 
      batch_size, 
      learning_rate, 
      learning_rate_decay_factor, 
      use_lstm=False, 
      num_samples=512, 
      forward_only=False, 
      dtype=tf.float32): 
    """Create the model. 

Args: 
    source_vocab_size: size of the source vocabulary. 
    target_vocab_size: size of the target vocabulary. 
    buckets: a list of pairs (I, O), where I specifies maximum input length 
    that will be processed in that bucket, and O specifies maximum output 
    length. Training instances that have inputs longer than I or outputs 
    longer than O will be pushed to the next bucket and padded accordingly. 
    We assume that the list is sorted, e.g., [(2, 4), (8, 16)]. 
    size: number of units in each layer of the model. 
    num_layers: number of layers in the model. 
    max_gradient_norm: gradients will be clipped to maximally this norm. 
    batch_size: the size of the batches used during training; 
    the model construction is independent of batch_size, so it can be 
    changed after initialization if this is convenient, e.g., for decoding. 
    learning_rate: learning rate to start with. 
    learning_rate_decay_factor: decay learning rate by this much when needed. 
    use_lstm: if true, we use LSTM cells instead of GRU cells. 
    num_samples: number of samples for sampled softmax. 
    forward_only: if set, we do not construct the backward pass in the model. 
    dtype: the data type to use to store internal variables. 
""" 
    self.source_vocab_size = source_vocab_size 
    self.target_vocab_size = target_vocab_size 
    self.buckets = buckets 
    self.batch_size = batch_size 
    self.learning_rate = tf.Variable(
     float(learning_rate), trainable=False, dtype=dtype) 
    self.learning_rate_decay_op = self.learning_rate.assign(
     self.learning_rate * learning_rate_decay_factor) 
    self.global_step = tf.Variable(0, trainable=False) 

    # If we use sampled softmax, we need an output projection. 
    output_projection = None 
    softmax_loss_function = None 
    # Sampled softmax only makes sense if we sample less than vocabulary size. 
    if 0 < num_samples < self.target_vocab_size: 
     w_t = tf.get_variable("proj_w", [self.target_vocab_size, size], dtype=dtype) 
     w = tf.transpose(w_t) 
     b = tf.get_variable("proj_b", [self.target_vocab_size], dtype=dtype) 
     output_projection = (w, b) 

     def sampled_loss(labels, logits): 
      labels = tf.reshape(labels, [-1, 1]) 
      # We need to compute the sampled_softmax_loss using 32bit floats to 
      # avoid numerical instabilities. 
      local_w_t = tf.cast(w_t, tf.float32) 
      local_b = tf.cast(b, tf.float32) 
      local_inputs = tf.cast(logits, tf.float32) 
      return tf.cast(
       tf.nn.sampled_softmax_loss(
        weights=local_w_t, 
        biases=local_b, 
        labels=labels, 
        inputs=local_inputs, 
        num_sampled=num_samples, 
        num_classes=self.target_vocab_size), 
       dtype) 

     softmax_loss_function = sampled_loss 

    # Create the internal multi-layer cell for our RNN. 
    def single_cell(): 
     return tf.contrib.rnn.GRUCell(size) 

    if use_lstm: 
     def single_cell(): 
      return tf.contrib.rnn.BasicLSTMCell(size) 
    cell = single_cell() 
    if num_layers > 1: 
     cell = tf.contrib.rnn.MultiRNNCell([single_cell() for _ in range(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, 
      output_projection=output_projection, 
      feed_previous=do_decode, 
      dtype=dtype) 

    # Feeds for inputs. 
    self.encoder_inputs = [] 
    self.decoder_inputs = [] 
    self.target_weights = [] 
    for i in xrange(buckets[-1][0]): # Last bucket is the biggest one. 
     self.encoder_inputs.append(tf.placeholder(tf.int32, shape=[None], 
                name="encoder{0}".format(i))) 
    for i in xrange(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(dtype, shape=[None], 
                name="weight{0}".format(i))) 

    # Our targets are decoder inputs shifted by one. 
    targets = [self.decoder_inputs[i + 1] 
       for i in xrange(len(self.decoder_inputs) - 1)] 

    # Training outputs and losses. 
    if forward_only: 
     self.outputs, self.losses = tf.contrib.legacy_seq2seq.model_with_buckets(
      self.encoder_inputs, self.decoder_inputs, targets, 
      self.target_weights, buckets, lambda x, y: seq2seq_f(x, y, True), 
      softmax_loss_function=softmax_loss_function) 
     # If we use output projection, we need to project outputs for decoding. 
     if output_projection is not None: 
      for b in xrange(len(buckets)): 
       self.outputs[b] = [ 
        tf.matmul(output, output_projection[0]) + output_projection[1] 
        for output in self.outputs[b] 
       ] 
    else: 
     self.outputs, self.losses = tf.contrib.legacy_seq2seq.model_with_buckets(
      self.encoder_inputs, self.decoder_inputs, targets, 
      self.target_weights, buckets, 
      lambda x, y: seq2seq_f(x, y, False), 
      softmax_loss_function=softmax_loss_function) # this is where the error occurrs 

什麼我需要改變，以得到這個工作？使用tensorflow 1.2版

UPDATE：這已經與兩個tensorflow從源代碼構建和在Mac OSX塞拉利昂和相同問題的PIP包建發生

Answer 1

測試，因爲我已經評論here型號爲你試圖實施已被棄用。如果你想讓它工作，請檢查我在問題中粘貼的代碼。 從tensorflow 1.1和1.2開始，您可以使用動態解碼功能，如tf.nn.bidirectional_dynamic_rnn。它允許您免費考慮動態大小的序列。

我創建了一些例子，我會用新的api發佈一個工作示例。