Bert DL模型错误：要重塑的输入是具有3200个值的张量，但请求的形状有3328个

如何解决Bert DL模型错误：要重塑的输入是具有3200个值的张量，但请求的形状有3328个

我正在重复

中的代码

https://towardsdatascience.com/text-classification-with-nlp-tf-idf-vs-word2vec-vs-bert-41ff868d1794

这是BERT分类器的代码。错误代码在此问题的结尾：

## distil-bert tokenizer
tokenizer = transformers.AutoTokenizer.from_pretrained('distilbert-base-uncased',do_lower_case=True)
dtf_train,dtf_test = train_test_split(all_ct_df_twoyear_v4,test_size=0.2,random_state=101)

y_train = dtf_train['isChange'].values
y_test = dtf_test['isChange'].values


corpus = dtf_train["comment"]
maxlen = 50

## add special tokens
maxqnans = np.int((maxlen-20)/2)
corpus_tokenized = ["[CLS] "+
             " ".join(tokenizer.tokenize(re.sub(r'[^\w\s]+|\n','',str(txt).lower().strip()))[:maxqnans])+
             " [SEP] " for txt in corpus]

## generate masks
masks = [[1]*len(txt.split(" ")) + [0]*(maxlen - len(
           txt.split(" "))) for txt in corpus_tokenized]
    
## padding
txt2seq = [txt + " [PAD]"*(maxlen-len(txt.split(" "))) if len(txt.split(" ")) != maxlen else txt for txt in corpus_tokenized]
    
## generate idx
idx = [tokenizer.encode(seq.split(" ")) for seq in txt2seq]
    
X_train = [np.asarray(idx,dtype='int32'),np.asarray(masks,dtype='int32')] 
           #np.asarray(segments,dtype='int32')]
corpus = dtf_test["comment"]
maxlen = 50

## add special tokens
maxqnans = np.int((maxlen-20)/2)
corpus_tokenized = ["[CLS] "+
             " ".join(tokenizer.tokenize(re.sub(r'[^\w\s]+|\n',str(txt).lower().strip()))[:maxqnans])+
             " [SEP] " for txt in corpus]

## generate masks
masks = [[1]*len(txt.split(" ")) + [0]*(maxlen - len(
           txt.split(" "))) for txt in corpus_tokenized]
    
## padding
txt2seq = [txt + " [PAD]"*(maxlen-len(txt.split(" "))) if len(txt.split(" ")) != maxlen else txt for txt in corpus_tokenized]
    
## generate idx
idx = [tokenizer.encode(seq.split(" ")) for seq in txt2seq]
    

## feature matrix
X_test = [np.asarray(idx,dtype='int32')]
           #np.asarray(segments,dtype='int32')]
## inputs
idx = layers.Input((50),dtype="int32",name="input_idx")
masks = layers.Input((50),name="input_masks")
## pre-trained bert with config
config = transformers.DistilBertConfig(dropout=0.2,attention_dropout=0.2)
config.output_hidden_states = False
nlp = transformers.TFDistilBertModel.from_pretrained('distilbert-base-uncased',config=config)
bert_out = nlp(idx,attention_mask=masks)[0]
## fine-tuning
x = layers.GlobalAveragePooling1D()(bert_out)
x = layers.Dense(64,activation="relu")(x)
y_out = layers.Dense(len(np.unique(y_train)),activation='softmax')(x)
## compile
model = models.Model([idx,masks],y_out)
for layer in model.layers[:3]:
    layer.trainable = False
model.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
model.summary()
## encode y
dic_y_mapping = {n:label for n,label in 
                 enumerate(np.unique(y_train))}
inverse_dic = {v:k for k,v in dic_y_mapping.items()}
y_train = np.array([inverse_dic[y] for y in y_train])
## train
training = model.fit(x=X_train,y=y_train,batch_size=64,epochs=1,shuffle=True,verbose=1,validation_split=0.3)
## test
predicted_prob = model.predict(X_test)
predicted = [dic_y_mapping[np.argmax(pred)] for pred in 
             predicted_prob]

错误：

    InvalidArgumentError:  Input to reshape is a tensor with 3200 values,but the requested shape has 3328
    [[node functional_45/tf_distil_bert_model_22/distilbert/transformer/layer_._0/attention/Reshape_3 (defined at X:\Users\xuanyu\Anaconda3\lib\site-packages\transformers\modeling_tf_distilbert.py:237) ]] [Op:__inference_train_function_287881]

    Errors may have originated from an input operation.
    Input Source operations connected to node 
    functional_45/tf_distil_bert_model_22/distilbert/transformer/layer_._0/attention/Reshape_3:
    functional_45/tf_distil_bert_model_22/distilbert/Cast (defined at 
    X:\Users\xuanyu\Anaconda3\lib\site- 
    packages\transformers\modeling_tf_distilbert.py:466)

    Function call stack:
    train_function

我一直在搜索，搜索和调整参数，但仍然收到此错误。我没有发现任何地方可以更改重塑大小的修改方式。

解决方法

我希望我的回答还不算太晚，但对我来说，它适用于2.1转换器版本。执行

Map<String,String> templatedata = eData(response); 
/*Map<String,String> templatedata = new HashMap<>();
 templatedata.put("address",response.getAddress());
 templatedata.put("photo",response.getBase64Photo()); //iVBORw0KGgoAAAANSUhEUgAAAAUA
    AAAFCAYAAACNbyblAAAAHElEQVQI12P4//8/w38GIAXDIBKE0DHxgljNBAAO
        9TXL0Y4OHwAAAABJRU5ErkJggg==
[![enter image description here][1]][1]*/

 public String createContent(EmailTemplate template,Map<String,String> data) {
 return VelocityEngineUtils.mergeTemplateIntoString(velocityEngine,template.getFileName(),objectMap);
}