问题描述
我正在按照一个教程使用BERT执行句子嵌入。 这里的代码：
import tensorflow as tf
import tensorflow_hub as hub
import bert
from tensorflow.keras.models import Model
import math
from tensorflow.keras.layers import Input
import numpy as np
def get_masks(tokens,max_seq_length):
"""Mask for padding"""
if len(tokens)>max_seq_length:
raise IndexError("Token length more than max seq length!")
return [1]*len(tokens) + [0] * (max_seq_length - len(tokens))
def get_segments(tokens,max_seq_length):
"""Segments: 0 for the first sequence,1 for the second"""
if len(tokens)>max_seq_length:
raise IndexError("Token length more than max seq length!")
segments = []
current_segment_id = 0
for token in tokens:
segments.append(current_segment_id)
if token == "[SEP]":
current_segment_id = 1
return segments + [0] * (max_seq_length - len(tokens))
def get_ids(tokens,tokenizer,max_seq_length):
"""Token ids from Tokenizer vocab"""
token_ids = tokenizer.convert_tokens_to_ids(tokens)
input_ids = token_ids + [0] * (max_seq_length-len(token_ids))
return input_ids
modelPath = "tfhub_modules/bert"
max_seq_length = 128
input_word_ids = tf.keras.layers.Input(shape=(max_seq_length,),dtype=tf.int32,name="input_word_ids")
input_mask = tf.keras.layers.Input(shape=(max_seq_length,name="input_mask")
segment_ids = tf.keras.layers.Input(shape=(max_seq_length,name="segment_ids")
bert_layer = hub.KerasLayer(modelPath,trainable=True)
pooled_output,sequence_output = bert_layer([input_word_ids,input_mask,segment_ids])
model = Model(inputs=[input_word_ids,segment_ids],outputs=[pooled_output,sequence_output])
fullTokenizer = bert.bert_tokenization.FullTokenizer
vocab_file = bert_layer.resolved_object.vocab_file.asset_path.numpy()
do_lower_case = bert_layer.resolved_object.do_lower_case.numpy()
tokenizer = fullTokenizer(vocab_file,do_lower_case)
s = "This is a beautiful sentence"
stokens = tokenizer.tokenize(s)
stokens = ["[CLS]"] + stokens + ["[SEP]"]
input_ids = get_ids(stokens,max_seq_length)
input_masks = get_masks(stokens,max_seq_length)
input_segments = get_segments(stokens,max_seq_length)
pool_embs,all_embs = model.predict([[input_ids],[input_masks],[input_segments]])
引发的错误如下：
WARNING:tensorflow:Model was constructed with shape (None,128) for input Tensor("input_word_ids:0",shape=(None,128),dtype=int32),but it was called on an input with incompatible shape (None,1,128).
WARNING:tensorflow:Model was constructed with shape (None,128).
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-52-82ba7e20d77c> in <module>
----> 1 pool_embs,[input_segments]])
~/.conda/envs/python3_7/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self,*args,**kwargs)
128 raise ValueError('{} is not supported in multi-worker mode.'.format(
129 method.__name__))
--> 130 return method(self,**kwargs)
131
132 return tf_decorator.make_decorator(
~/.conda/envs/python3_7/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in predict(self,x,batch_size,verbose,steps,callbacks,max_queue_size,workers,use_multiprocessing)
1597 for step in data_handler.steps():
1598 callbacks.on_predict_batch_begin(step)
-> 1599 tmp_batch_outputs = predict_function(iterator)
1600 if data_handler.should_sync:
1601 context.async_wait()
~/.conda/envs/python3_7/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in __call__(self,**kwds)
778 else:
779 compiler = "nonXla"
--> 780 result = self._call(*args,**kwds)
781
782 new_tracing_count = self._get_tracing_count()
~/.conda/envs/python3_7/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self,**kwds)
821 # This is the first call of __call__,so we have to initialize.
822 initializers = []
--> 823 self._initialize(args,kwds,add_initializers_to=initializers)
824 finally:
825 # At this point we know that the initialization is complete (or less
~/.conda/envs/python3_7/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _initialize(self,args,add_initializers_to)
695 self._concrete_stateful_fn = (
696 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 697 *args,**kwds))
698
699 def invalid_creator_scope(*unused_args,**unused_kwds):
~/.conda/envs/python3_7/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self,**kwargs)
2853 args,kwargs = None,None
2854 with self._lock:
-> 2855 graph_function,_,_ = self._maybe_define_function(args,kwargs)
2856 return graph_function
2857
~/.conda/envs/python3_7/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self,kwargs)
3211
3212 self._function_cache.missed.add(call_context_key)
-> 3213 graph_function = self._create_graph_function(args,kwargs)
3214 self._function_cache.primary[cache_key] = graph_function
3215 return graph_function,kwargs
~/.conda/envs/python3_7/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self,kwargs,override_flat_arg_shapes)
3073 arg_names=arg_names,3074 override_flat_arg_shapes=override_flat_arg_shapes,-> 3075 capture_by_value=self._capture_by_value),3076 self._function_attributes,3077 function_spec=self.function_spec,~/.conda/envs/python3_7/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name,python_func,signature,func_graph,autograph,autograph_options,add_control_dependencies,arg_names,op_return_value,collections,capture_by_value,override_flat_arg_shapes)
984 _,original_func = tf_decorator.unwrap(python_func)
985
--> 986 func_outputs = python_func(*func_args,**func_kwargs)
987
988 # invariant: `func_outputs` contains only Tensors,CompositeTensors,~/.conda/envs/python3_7/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args,**kwds)
598 # __wrapped__ allows AutoGraph to swap in a converted function. We give
599 # the function a weak reference to itself to avoid a reference cycle.
--> 600 return weak_wrapped_fn().__wrapped__(*args,**kwds)
601 weak_wrapped_fn = weakref.ref(wrapped_fn)
602
~/.conda/envs/python3_7/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args,**kwargs)
971 except Exception as e: # pylint:disable=broad-except
972 if hasattr(e,"ag_error_metadata"):
--> 973 raise e.ag_error_metadata.to_exception(e)
974 else:
975 raise
AssertionError: in user code:
/home/zast/.conda/envs/python3_7/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:1462 predict_function *
return step_function(self,iterator)
/home/zast/.conda/envs/python3_7/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:1452 step_function **
outputs = model.distribute_strategy.run(run_step,args=(data,))
/home/zast/.conda/envs/python3_7/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
return self._extended.call_for_each_replica(fn,args=args,kwargs=kwargs)
/home/zast/.conda/envs/python3_7/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
return self._call_for_each_replica(fn,kwargs)
/home/zast/.conda/envs/python3_7/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
return fn(*args,**kwargs)
/home/zast/.conda/envs/python3_7/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:1445 run_step **
outputs = model.predict_step(data)
/home/zast/.conda/envs/python3_7/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:1418 predict_step
return self(x,training=False)
/home/zast/.conda/envs/python3_7/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py:985 __call__
outputs = call_fn(inputs,**kwargs)
/home/zast/.conda/envs/python3_7/lib/python3.7/site-packages/tensorflow/python/keras/engine/functional.py:386 call
inputs,training=training,mask=mask)
/home/zast/.conda/envs/python3_7/lib/python3.7/site-packages/tensorflow/python/keras/engine/functional.py:517 _run_internal_graph
assert x_id in tensor_dict,'Could not compute output ' + str(x)
AssertionError: Could not compute output Tensor("keras_layer/cond/Identity:0",768),dtype=float32)
从警告中，我认为输入层（，128）的形状与列表input_ids（1,128）的形状不匹配，但我不明白为什么input_ids具有这种形状。 在本教程中，不会显示此错误。 区别在于tensorflow版本库（2.3.0的mine和2.0.0的教程）和BERT模型（多语言的mine，以教程为基础）。
解决方法
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