如何解决训练时使用 BERT 模型进行文本情感分析时出现错误
我正在使用 BERT 对推文进行情感分析。在训练模型时出现错误。我无法理解此错误的来源，更不用说修复它了。
完整代码如下：
import os
import shutil
import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
import tensorflow_text as text
from official.nlp import optimization # to create AdamW optimizer
import matplotlib.pyplot as plt
tf.get_logger().setLevel('ERROR')
main_file = 'C:\\Users\\meena\\Desktop\\Meenal\\Studies\\College\\Third Year\\Capstone Project\\Execution\\FYP-II-master\\data\\Annotated4.csv'
test_file = "C:\\Users\\meena\\Desktop\\Meenal\\Studies\\College\\Third Year\\Capstone Project\\Execution\\FYP-II-master\\data\\AnnotatedData2.csv"
df = pd.read_csv(main_file)
#BERT cannot work with 'no' or 'yes' so using 1 = yes,0 = undefined,-1 = no
for i in range(len(df)):
if(df.loc[i,'class'] == "yes" or df.loc[i,'class'] == "PI" or df.loc[i,'class'] == "Yes"):
df.loc[i,'class'] = '1'
elif(df.loc[i,'class'] == "no" or df.loc[i,'class'] == "no PI" or df.loc[i,'class'] == "No"):
df.loc[i,'class'] = '-1'
elif(df.loc[i,'class'] == "Undefined" or df.loc[i,'class'] == "undefined"):
df.loc[i,'class'] = '0'
#print(df.head(5))
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test = train_test_split(df['text'],df['class'])
x_train,x_val,y_val = train_test_split(x_train,y_train)
bert_model_name = 'albert_en_base'
map_name_to_handle = {
'bert_en_uncased_L-12_H-768_A-12':
'https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/3','bert_en_cased_L-12_H-768_A-12':
'https://tfhub.dev/tensorflow/bert_en_cased_L-12_H-768_A-12/3','bert_multi_cased_L-12_H-768_A-12':
'https://tfhub.dev/tensorflow/bert_multi_cased_L-12_H-768_A-12/3','small_bert/bert_en_uncased_L-2_H-128_A-2':
'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-128_A-2/1','small_bert/bert_en_uncased_L-2_H-256_A-4':
'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-256_A-4/1','small_bert/bert_en_uncased_L-2_H-512_A-8':
'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-512_A-8/1','small_bert/bert_en_uncased_L-2_H-768_A-12':
'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-768_A-12/1','small_bert/bert_en_uncased_L-4_H-128_A-2':
'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-128_A-2/1','small_bert/bert_en_uncased_L-4_H-256_A-4':
'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-256_A-4/1','small_bert/bert_en_uncased_L-4_H-512_A-8':
'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-512_A-8/1','small_bert/bert_en_uncased_L-4_H-768_A-12':
'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-768_A-12/1','small_bert/bert_en_uncased_L-6_H-128_A-2':
'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-6_H-128_A-2/1','small_bert/bert_en_uncased_L-6_H-256_A-4':
'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-6_H-256_A-4/1','small_bert/bert_en_uncased_L-6_H-512_A-8':
'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-6_H-512_A-8/1','small_bert/bert_en_uncased_L-6_H-768_A-12':
'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-6_H-768_A-12/1','small_bert/bert_en_uncased_L-8_H-128_A-2':
'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-8_H-128_A-2/1','small_bert/bert_en_uncased_L-8_H-256_A-4':
'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-8_H-256_A-4/1','small_bert/bert_en_uncased_L-8_H-512_A-8':
'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-8_H-512_A-8/1','small_bert/bert_en_uncased_L-8_H-768_A-12':
'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-8_H-768_A-12/1','small_bert/bert_en_uncased_L-10_H-128_A-2':
'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-10_H-128_A-2/1','small_bert/bert_en_uncased_L-10_H-256_A-4':
'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-10_H-256_A-4/1','small_bert/bert_en_uncased_L-10_H-512_A-8':
'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-10_H-512_A-8/1','small_bert/bert_en_uncased_L-10_H-768_A-12':
'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-10_H-768_A-12/1','small_bert/bert_en_uncased_L-12_H-128_A-2':
'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-12_H-128_A-2/1','small_bert/bert_en_uncased_L-12_H-256_A-4':
'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-12_H-256_A-4/1','small_bert/bert_en_uncased_L-12_H-512_A-8':
'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-12_H-512_A-8/1','small_bert/bert_en_uncased_L-12_H-768_A-12':
'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-12_H-768_A-12/1','albert_en_base':
'https://tfhub.dev/tensorflow/albert_en_base/2','electra_small':
'https://tfhub.dev/google/electra_small/2','electra_base':
'https://tfhub.dev/google/electra_base/2','experts_pubmed':
'https://tfhub.dev/google/experts/bert/pubmed/2','experts_wiki_books':
'https://tfhub.dev/google/experts/bert/wiki_books/2','talking-heads_base':
'https://tfhub.dev/tensorflow/talkheads_ggelu_bert_en_base/1',}
map_model_to_preprocess = {
'bert_en_uncased_L-12_H-768_A-12':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','bert_en_cased_L-12_H-768_A-12':
'https://tfhub.dev/tensorflow/bert_en_cased_preprocess/3','small_bert/bert_en_uncased_L-2_H-128_A-2':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','small_bert/bert_en_uncased_L-2_H-256_A-4':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','small_bert/bert_en_uncased_L-2_H-512_A-8':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','small_bert/bert_en_uncased_L-2_H-768_A-12':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','small_bert/bert_en_uncased_L-4_H-128_A-2':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','small_bert/bert_en_uncased_L-4_H-256_A-4':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','small_bert/bert_en_uncased_L-4_H-512_A-8':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','small_bert/bert_en_uncased_L-4_H-768_A-12':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','small_bert/bert_en_uncased_L-6_H-128_A-2':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','small_bert/bert_en_uncased_L-6_H-256_A-4':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','small_bert/bert_en_uncased_L-6_H-512_A-8':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','small_bert/bert_en_uncased_L-6_H-768_A-12':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','small_bert/bert_en_uncased_L-8_H-128_A-2':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','small_bert/bert_en_uncased_L-8_H-256_A-4':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','small_bert/bert_en_uncased_L-8_H-512_A-8':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','small_bert/bert_en_uncased_L-8_H-768_A-12':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','small_bert/bert_en_uncased_L-10_H-128_A-2':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','small_bert/bert_en_uncased_L-10_H-256_A-4':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','small_bert/bert_en_uncased_L-10_H-512_A-8':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','small_bert/bert_en_uncased_L-10_H-768_A-12':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','small_bert/bert_en_uncased_L-12_H-128_A-2':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','small_bert/bert_en_uncased_L-12_H-256_A-4':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','small_bert/bert_en_uncased_L-12_H-512_A-8':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','small_bert/bert_en_uncased_L-12_H-768_A-12':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','bert_multi_cased_L-12_H-768_A-12':
'https://tfhub.dev/tensorflow/bert_multi_cased_preprocess/3','albert_en_base':
'https://tfhub.dev/tensorflow/albert_en_preprocess/3','electra_small':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','electra_base':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','experts_pubmed':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','experts_wiki_books':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3','talking-heads_base':
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3',}
tfhub_handle_encoder = map_name_to_handle[bert_model_name]
tfhub_handle_preprocess = map_model_to_preprocess[bert_model_name]
print(f'BERT model selected : {tfhub_handle_encoder}')
print(f'Preprocess model auto-selected: {tfhub_handle_preprocess}')
def build_classifier_model():
text_input = tf.keras.layers.Input(shape=(),dtype=tf.string,name='text')
preprocessing_layer = hub.KerasLayer(tfhub_handle_preprocess,name='preprocessing')
encoder_inputs = preprocessing_layer(text_input)
encoder = hub.KerasLayer(tfhub_handle_encoder,trainable=True,name='BERT_encoder')
outputs = encoder(encoder_inputs)
net = outputs['pooled_output']
net = tf.keras.layers.Dropout(0.1)(net)
net = tf.keras.layers.Dense(1,activation=None,name='classifier')(net)
return tf.keras.Model(text_input,net)
classifier_model = build_classifier_model()
loss = tf.keras.losses.CategoricalCrossentropy(from_logits=True)
metrics = tf.metrics.CategoricalAccuracy()
import numpy as np
y_train = np.asarray(y_train).astype('float32').reshape((-1,1))
y_val = np.asarray(y_val).astype('float32').reshape((-1,1))
y_test = np.asarray(y_test).astype('float32').reshape((-1,1))
train_ds = tf.data.Dataset.from_tensor_slices((x_train,y_train))
val_ds = tf.data.Dataset.from_tensor_slices((x_val,y_val))
test_ds = tf.data.Dataset.from_tensor_slices((x_test,y_test))
epochs = 5
steps_per_epoch = tf.data.experimental.cardinality(train_ds).numpy()
num_train_steps = steps_per_epoch * epochs
num_warmup_steps = int(0.1*num_train_steps)
init_lr = 3e-5
optimizer = optimization.create_optimizer(init_lr=init_lr,num_train_steps=num_train_steps,num_warmup_steps=num_warmup_steps,optimizer_type='adamw')
classifier_model.compile(optimizer=optimizer,loss=loss,metrics=metrics)
print(f'Training model with {tfhub_handle_encoder}')
history = classifier_model.fit(x=train_ds,validation_data=val_ds,epochs=epochs)
我使用的数据库有两列类和文本：
我得到的完整错误是：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-108-71a9fbf2d907> in <module>
2 history = classifier_model.fit(x=train_ds,3 validation_data=val_ds,----> 4 epochs=epochs)
~\AppData\Roaming\Python\python36\site-packages\tensorflow\python\keras\engine\training.py in fit(self,x,y,batch_size,epochs,verbose,callbacks,validation_split,validation_data,shuffle,class_weight,sample_weight,initial_epoch,steps_per_epoch,validation_steps,validation_batch_size,validation_freq,max_queue_size,workers,use_multiprocessing)
1181 _r=1):
1182 callbacks.on_train_batch_begin(step)
-> 1183 tmp_logs = self.train_function(iterator)
1184 if data_handler.should_sync:
1185 context.async_wait()
~\AppData\Roaming\Python\python36\site-packages\tensorflow\python\eager\def_function.py in __call__(self,*args,**kwds)
887
888 with OptionalXlaContext(self._jit_compile):
--> 889 result = self._call(*args,**kwds)
890
891 new_tracing_count = self.experimental_get_tracing_count()
~\AppData\Roaming\Python\python36\site-packages\tensorflow\python\eager\def_function.py in _call(self,**kwds)
931 # This is the first call of __call__,so we have to initialize.
932 initializers = []
--> 933 self._initialize(args,kwds,add_initializers_to=initializers)
934 finally:
935 # At this point we kNow that the initialization is complete (or less
~\AppData\Roaming\Python\python36\site-packages\tensorflow\python\eager\def_function.py in _initialize(self,args,add_initializers_to)
762 self._concrete_stateful_fn = (
763 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 764 *args,**kwds))
765
766 def invalid_creator_scope(*unused_args,**unused_kwds):
~\AppData\Roaming\Python\python36\site-packages\tensorflow\python\eager\function.py in _get_concrete_function_internal_garbage_collected(self,**kwargs)
3048 args,kwargs = None,None
3049 with self._lock:
-> 3050 graph_function,_ = self._maybe_define_function(args,kwargs)
3051 return graph_function
3052
~\AppData\Roaming\Python\python36\site-packages\tensorflow\python\eager\function.py in _maybe_define_function(self,kwargs)
3442
3443 self._function_cache.missed.add(call_context_key)
-> 3444 graph_function = self._create_graph_function(args,kwargs)
3445 self._function_cache.primary[cache_key] = graph_function
3446
~\AppData\Roaming\Python\python36\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self,kwargs,override_flat_arg_shapes)
3287 arg_names=arg_names,3288 override_flat_arg_shapes=override_flat_arg_shapes,-> 3289 capture_by_value=self._capture_by_value),3290 self._function_attributes,3291 function_spec=self.function_spec,~\AppData\Roaming\Python\python36\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)
997 _,original_func = tf_decorator.unwrap(python_func)
998
--> 999 func_outputs = python_func(*func_args,**func_kwargs)
1000
1001 # invariant: `func_outputs` contains only Tensors,CompositeTensors,~\AppData\Roaming\Python\python36\site-packages\tensorflow\python\eager\def_function.py in wrapped_fn(*args,**kwds)
670 # the function a weak reference to itself to avoid a reference cycle.
671 with OptionalXlaContext(compile_with_xla):
--> 672 out = weak_wrapped_fn().__wrapped__(*args,**kwds)
673 return out
674
~\AppData\Roaming\Python\python36\site-packages\tensorflow\python\framework\func_graph.py in wrapper(*args,**kwargs)
984 except Exception as e: # pylint:disable=broad-except
985 if hasattr(e,"ag_error_Metadata"):
--> 986 raise e.ag_error_Metadata.to_exception(e)
987 else:
988 raise
ValueError: in user code:
C:\Users\meena\AppData\Roaming\Python\python36\site-packages\tensorflow\python\keras\engine\training.py:855 train_function *
return step_function(self,iterator)
C:\Users\meena\AppData\Roaming\Python\python36\site-packages\tensorflow_hub\keras_layer.py:237 call *
result = smart_cond.smart_cond(training,C:\Users\meena\AppData\Roaming\Python\python36\site-packages\tensorflow\python\saved_model\load.py:670 _call_attribute **
return instance.__call__(*args,**kwargs)
C:\Users\meena\AppData\Roaming\Python\python36\site-packages\tensorflow\python\eager\def_function.py:889 __call__
result = self._call(*args,**kwds)
C:\Users\meena\AppData\Roaming\Python\python36\site-packages\tensorflow\python\eager\def_function.py:924 _call
results = self._stateful_fn(*args,**kwds)
C:\Users\meena\AppData\Roaming\Python\python36\site-packages\tensorflow\python\eager\function.py:3022 __call__
filtered_flat_args) = self._maybe_define_function(args,kwargs)
C:\Users\meena\AppData\Roaming\Python\python36\site-packages\tensorflow\python\eager\function.py:3444 _maybe_define_function
graph_function = self._create_graph_function(args,kwargs)
C:\Users\meena\AppData\Roaming\Python\python36\site-packages\tensorflow\python\eager\function.py:3289 _create_graph_function
capture_by_value=self._capture_by_value),C:\Users\meena\AppData\Roaming\Python\python36\site-packages\tensorflow\python\framework\func_graph.py:999 func_graph_from_py_func
func_outputs = python_func(*func_args,**func_kwargs)
C:\Users\meena\AppData\Roaming\Python\python36\site-packages\tensorflow\python\eager\def_function.py:672 wrapped_fn
out = weak_wrapped_fn().__wrapped__(*args,**kwds)
C:\Users\meena\AppData\Roaming\Python\python36\site-packages\tensorflow\python\saved_model\function_deserialization.py:291 restored_function_body
"\n\n".join(signature_descriptions)))
ValueError: Could not find matching function to call loaded from the SavedModel. Got:
Positional arguments (3 total):
* Tensor("inputs:0",shape=(),dtype=string)
* False
* None
Keyword arguments: {}
Expected these arguments to match one of the following 4 option(s):
Option 1:
Positional arguments (3 total):
* TensorSpec(shape=(None,),name='sentences')
* True
* None
Keyword arguments: {}
Option 2:
Positional arguments (3 total):
* TensorSpec(shape=(None,name='sentences')
* False
* None
Keyword arguments: {}
Option 3:
Positional arguments (3 total):
* TensorSpec(shape=(None,name='inputs')
* True
* None
Keyword arguments: {}
Option 4:
Positional arguments (3 total):
* TensorSpec(shape=(None,name='inputs')
* False
* None
Keyword arguments: {}
我在 Jupyter Notebook 上运行它，并且我使用 GPU 来加快速度。我正在尝试训练模型，但它不断出现此错误。 我使用的是 Python 3.6。
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