tf.nn.sampled_softmax_loss API parameter error about stanford-tensorflow-tutorials

[ade1963]

Try such code:
` if config.NUM_SAMPLES > 0 and config.NUM_SAMPLES < config.DEC_VOCAB:
w_t = tf.get_variable('proj_w', [config.DEC_VOCAB, config.HIDDEN_SIZE])
w = tf.transpose(w_t)
b = tf.get_variable('proj_b', [config.DEC_VOCAB])
self.output_projection = (w, b)

    def sampled_loss(labels, logits):
      labels = tf.reshape(labels, [-1, 1])
      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.nn.sampled_softmax_loss(
               weights=local_w_t,
               biases=local_b,
               labels=labels,
               inputs=local_inputs,
               num_sampled=config.NUM_SAMPLES,
               num_classes=config.DEC_VOCAB)
    self.softmax_loss_function = sampled_loss

`

[tongda]

In the new version of model_with_bucket, the signature of argument softmax_loss_function has been changed:

softmax_loss_function: Function (labels-batch, inputs-batch) -> loss-batch to be used instead of the standard softmax (the default if this is None).

So you have to change the order of sampled_loss arguments to def sampled_loss(labels, inputs):

[crazyofapple]

@ade1963 after change the code, I get this.
Traceback (most recent call last): File "chatbot.py", line 261, in <module> main() File "chatbot.py", line 256, in main train() File "chatbot.py", line 137, in train model.build_graph() File "/home/ngly/tf-stanford-tutorials/assignments/chatbot/model.py", line 143, in build_graph self._create_loss() File "/home/ngly/tf-stanford-tutorials/assignments/chatbot/model.py", line 111, in _create_loss softmax_loss_function=self.softmax_loss_function) File "/home/ngly/ngly/local/lib/python2.7/site-packages/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py", line 1206, in model_with_buckets decoder_inputs[:bucket[1]]) File "/home/ngly/tf-stanford-tutorials/assignments/chatbot/model.py", line 86, in _seq2seq_f feed_previous=False) File "/home/ngly/ngly/local/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 "/usr/lib/python2.7/copy.py", line 174, in deepcopy y = copier(memo) File "/home/ngly/ngly/local/lib/python2.7/site-packages/tensorflow/python/layers/base.py", line 476, in __deepcopy__ setattr(result, k, copy.deepcopy(v, memo)) File "/usr/lib/python2.7/copy.py", line 163, in deepcopy y = copier(x, memo) File "/usr/lib/python2.7/copy.py", line 230, in _deepcopy_list y.append(deepcopy(a, memo)) File "/usr/lib/python2.7/copy.py", line 190, in deepcopy y = _reconstruct(x, rv, 1, memo) File "/usr/lib/python2.7/copy.py", line 334, in _reconstruct state = deepcopy(state, memo) File "/usr/lib/python2.7/copy.py", line 163, in deepcopy y = copier(x, memo) File "/usr/lib/python2.7/copy.py", line 257, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "/usr/lib/python2.7/copy.py", line 190, in deepcopy y = _reconstruct(x, rv, 1, memo) File "/usr/lib/python2.7/copy.py", line 334, in _reconstruct state = deepcopy(state, memo) File "/usr/lib/python2.7/copy.py", line 163, in deepcopy y = copier(x, memo) File "/usr/lib/python2.7/copy.py", line 257, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "/usr/lib/python2.7/copy.py", line 190, in deepcopy y = _reconstruct(x, rv, 1, memo) File "/usr/lib/python2.7/copy.py", line 334, in _reconstruct state = deepcopy(state, memo) File "/usr/lib/python2.7/copy.py", line 163, in deepcopy y = copier(x, memo) File "/usr/lib/python2.7/copy.py", line 257, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "/usr/lib/python2.7/copy.py", line 190, in deepcopy y = _reconstruct(x, rv, 1, memo) File "/usr/lib/python2.7/copy.py", line 334, in _reconstruct state = deepcopy(state, memo) File "/usr/lib/python2.7/copy.py", line 163, in deepcopy y = copier(x, memo) File "/usr/lib/python2.7/copy.py", line 257, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "/usr/lib/python2.7/copy.py", line 163, in deepcopy y = copier(x, memo) File "/usr/lib/python2.7/copy.py", line 257, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "/usr/lib/python2.7/copy.py", line 163, in deepcopy y = copier(x, memo) File "/usr/lib/python2.7/copy.py", line 230, in _deepcopy_list y.append(deepcopy(a, memo)) File "/usr/lib/python2.7/copy.py", line 190, in deepcopy y = _reconstruct(x, rv, 1, memo) File "/usr/lib/python2.7/copy.py", line 334, in _reconstruct state = deepcopy(state, memo) File "/usr/lib/python2.7/copy.py", line 163, in deepcopy y = copier(x, memo) File "/usr/lib/python2.7/copy.py", line 257, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "/usr/lib/python2.7/copy.py", line 190, in deepcopy y = _reconstruct(x, rv, 1, memo) File "/usr/lib/python2.7/copy.py", line 334, in _reconstruct state = deepcopy(state, memo) File "/usr/lib/python2.7/copy.py", line 163, in deepcopy y = copier(x, memo) File "/usr/lib/python2.7/copy.py", line 257, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "/usr/lib/python2.7/copy.py", line 190, in deepcopy y = _reconstruct(x, rv, 1, memo) File "/usr/lib/python2.7/copy.py", line 334, in _reconstruct state = deepcopy(state, memo) File "/usr/lib/python2.7/copy.py", line 163, in deepcopy y = copier(x, memo) File "/usr/lib/python2.7/copy.py", line 257, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "/usr/lib/python2.7/copy.py", line 163, in deepcopy y = copier(x, memo) File "/usr/lib/python2.7/copy.py", line 230, in _deepcopy_list y.append(deepcopy(a, memo)) File "/usr/lib/python2.7/copy.py", line 163, in deepcopy y = copier(x, memo) File "/usr/lib/python2.7/copy.py", line 237, in _deepcopy_tuple y.append(deepcopy(a, memo)) File "/usr/lib/python2.7/copy.py", line 163, in deepcopy y = copier(x, memo) File "/usr/lib/python2.7/copy.py", line 257, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "/usr/lib/python2.7/copy.py", line 190, in deepcopy y = _reconstruct(x, rv, 1, memo) File "/usr/lib/python2.7/copy.py", line 334, in _reconstruct state = deepcopy(state, memo) File "/usr/lib/python2.7/copy.py", line 163, in deepcopy y = copier(x, memo) File "/usr/lib/python2.7/copy.py", line 257, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "/usr/lib/python2.7/copy.py", line 190, in deepcopy y = _reconstruct(x, rv, 1, memo) File "/usr/lib/python2.7/copy.py", line 343, in _reconstruct y.__dict__.update(state) AttributeError: 'NoneType' object has no attribute 'update'
how to fix it?

[chiphuyen]

chatbot currently incompatible with tf 1. will update it soon.

[Pankajchandan]

Initialize new model Create placeholders Create inference Creating loss... It might take a couple of minutes depending on how many buckets you have. Traceback (most recent call last): File "chatbot.py", line 262, in <module> main() File "chatbot.py", line 257, in main train() File "chatbot.py", line 138, in train model.build_graph() File "/home/pankaj/git/s2s/model.py", line 133, in build_graph self._create_loss() File "/home/pankaj/git/s2s/model.py", line 101, in _create_loss softmax_loss_function=self.softmax_loss_function) File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py", line 1221, in model_with_buckets softmax_loss_function=softmax_loss_function)) File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py", line 1134, in sequence_loss softmax_loss_function=softmax_loss_function)) File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py", line 1089, in sequence_loss_by_example crossent = softmax_loss_function(labels=target, logits=logit) TypeError: sampled_loss() got an unexpected keyword argument 'logits'

This is the error i get while building the graph