Python 张量转换为数据类型为int64的张量请求了数据类型为int32的数据-而estimator.export\u savedmodel

尝试导出使用以下方法生成的模型：

def serving_input_fn():
    with tf.variable_scope("bert_model"):
      feature_spec = {
          "input_ids": tf.FixedLenFeature([MAX_SEQ_LENGTH], tf.int64),
          "input_mask": tf.FixedLenFeature([MAX_SEQ_LENGTH], tf.int64),
          "segment_ids": tf.FixedLenFeature([MAX_SEQ_LENGTH], tf.int64),
          "label_ids": tf.FixedLenFeature([], tf.int64),
        }
      serialized_tf_example = tf.placeholder(dtype=tf.string,
                                             shape=[None],
                                             name='input_example_tensor')
      receiver_tensors = {'examples': serialized_tf_example}
      features = tf.parse_example(serialized_tf_example, feature_spec)
      return tf.estimator.export.ServingInputReceiver(features, receiver_tensors)

MODEL_DIR = 'gs://{}/bert/models_servable/{}'.format(BUCKET,'bert')
tf.gfile.MakeDirs(MODEL_DIR)
estimator._export_to_tpu = False
model_file = os.path.join(MODEL_DIR, "bert_model")
path = estimator.export_savedmodel(model_file, serving_input_fn)
print(path)

它给出了以下错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-106-aaf5ee490ed7> in <module>()
     21 model_file = os.path.join(MODEL_DIR, "bert_model")
     22 print(model_file)
---> 23 path = estimator.export_savedmodel(model_file, serving_input_fn)
     24 print(path)

/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py in export_savedmodel(self, export_dir_base, serving_input_receiver_fn, assets_extra, as_text, checkpoint_path, strip_default_attrs)
   1643         as_text=as_text,
   1644         checkpoint_path=checkpoint_path,
-> 1645         experimental_mode=model_fn_lib.ModeKeys.PREDICT)
   1646 
   1647   def export_saved_model(

/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py in export_saved_model(self, export_dir_base, serving_input_receiver_fn, assets_extra, as_text, checkpoint_path, experimental_mode)
    721         assets_extra=assets_extra,
    722         as_text=as_text,
--> 723         checkpoint_path=checkpoint_path)
    724 
    725   def experimental_export_all_saved_models(

/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py in experimental_export_all_saved_models(self, export_dir_base, input_receiver_fn_map, assets_extra, as_text, checkpoint_path)
    825         self._add_meta_graph_for_mode(
    826             builder, input_receiver_fn_map, checkpoint_path,
--> 827             save_variables, mode=model_fn_lib.ModeKeys.PREDICT)
    828         save_variables = False
    829 

/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py in _add_meta_graph_for_mode(self, builder, input_receiver_fn_map, checkpoint_path, save_variables, mode, export_tags, check_variables)
    895           labels=getattr(input_receiver, 'labels', None),
    896           mode=mode,
--> 897           config=self.config)
    898 
    899       export_outputs = model_fn_lib.export_outputs_for_mode(

/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py in _call_model_fn(self, features, labels, mode, config)
   1110 
   1111     logging.info('Calling model_fn.')
-> 1112     model_fn_results = self._model_fn(features=features, **kwargs)
   1113     logging.info('Done calling model_fn.')
   1114 

<ipython-input-90-119a3167bf33> in model_fn(features, labels, mode, params)
     72     else:
     73       (predicted_labels, log_probs) = create_model(
---> 74         is_predicting, input_ids, input_mask, segment_ids, label_ids, num_labels)
     75 
     76       predictions = {

<ipython-input-89-0f7bd7d1be35> in create_model(is_predicting, input_ids, input_mask, segment_ids, labels, num_labels)
     13       inputs=bert_inputs,
     14       signature="tokens",
---> 15       as_dict=True)
     16 
     17   # Use "pooled_output" for classification tasks on an entire sentence.

/usr/local/lib/python3.6/dist-packages/tensorflow_hub/module.py in __call__(self, inputs, _sentinel, signature, as_dict)
    240     dict_inputs = _convert_dict_inputs(
    241         inputs, self._spec.get_input_info_dict(signature=signature,
--> 242                                                tags=self._tags))
    243 
    244     dict_outputs = self._impl.create_apply_graph(

/usr/local/lib/python3.6/dist-packages/tensorflow_hub/module.py in _convert_dict_inputs(inputs, tensor_info_map)
    442   dict_inputs = _prepare_dict_inputs(inputs, tensor_info_map)
    443   return tensor_info.convert_dict_to_compatible_tensor(dict_inputs,
--> 444                                                        tensor_info_map)
    445 
    446 

/usr/local/lib/python3.6/dist-packages/tensorflow_hub/tensor_info.py in convert_dict_to_compatible_tensor(values, targets)
    146   for key, value in sorted(values.items()):
    147     result[key] = _convert_to_compatible_tensor(
--> 148         value, targets[key], error_prefix="Can't convert %r" % key)
    149   return result
    150 

/usr/local/lib/python3.6/dist-packages/tensorflow_hub/tensor_info.py in _convert_to_compatible_tensor(value, target, error_prefix)
    115   """
    116   try:
--> 117     tensor = tf.convert_to_tensor_or_indexed_slices(value, target.dtype)
    118   except TypeError as e:
    119     raise TypeError("%s: %s" % (error_prefix, e))

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in convert_to_tensor_or_indexed_slices(value, dtype, name)
   1296   """
   1297   return internal_convert_to_tensor_or_indexed_slices(
-> 1298       value=value, dtype=dtype, name=name, as_ref=False)
   1299 
   1300 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in internal_convert_to_tensor_or_indexed_slices(value, dtype, name, as_ref)
   1330       raise ValueError(
   1331           "Tensor conversion requested dtype %s for Tensor with dtype %s: %r" %
-> 1332           (dtypes.as_dtype(dtype).name, value.dtype.name, str(value)))
   1333     return value
   1334   else:

ValueError: Tensor conversion requested dtype int32 for Tensor with dtype string: 'Tensor("bert_model/ParseExample/ParseExample:0", shape=(?, 128), dtype=string)'

---

int32强制在哪里发生以及如何修复？提前谢谢

bert模型需要输入\ ID、输入\掩码和段\ ID作为tf.int32的类型。为了修复这个bug，您必须将它们从tf.int64转换为tf.int32，如下所示

def create_model(is_predicting, input_ids, input_mask, segment_ids, labels, num_labels):
  """Creates a classification model."""
  input_ids = tf.cast(input_ids, tf.int32)
  input_mask = tf.cast(input_mask, tf.int32)
  segment_ids = tf.cast(segment_ids, tf.int32)

  bert_module = hub.Module(
      BERT_MODEL_HUB,
      trainable=True)

def create_model(is_predicting, input_ids, input_mask, segment_ids, labels, num_labels):
  """Creates a classification model."""
  input_ids = tf.cast(input_ids, tf.int32)
  input_mask = tf.cast(input_mask, tf.int32)
  segment_ids = tf.cast(segment_ids, tf.int32)

  bert_module = hub.Module(
      BERT_MODEL_HUB,
      trainable=True)