我使用 Roberta 模型编写了包含两个类的文本分类代码,现在我想绘制混淆矩阵。如何根据 Roberta 模型绘制混淆矩阵?
RobertaTokenizer = RobertaTokenizer.from_pretrained('roberta-base',do_lower_case=False)
roberta_model = TFRobertaForSequenceClassification.from_pretrained('roberta-base',num_labels=2)
input_ids=[]
attention_masks=[]
for sent in sentences:
bert_inp=RobertaTokenizer.encode_plus(sent,add_special_tokens = True,max_length =128,pad_to_max_length = True,return_attention_mask = True)
input_ids.append(bert_inp['input_ids'])
attention_masks.append(bert_inp['attention_mask'])
input_ids=np.asarray(input_ids)
attention_masks=np.array(attention_masks)
labels=np.array(labels)
#split
train_inp,val_inp,train_label,val_label,train_mask,val_mask=train_test_split(input_ids,labels,attention_masks,test_size=0.5)
print('Train inp shape {} Val input shape {}\nTrain label shape {} Val label shape {}\nTrain attention mask shape {} Val attention mask shape {}'.format(train_inp.shape,val_inp.shape,train_label.shape,val_label.shape,train_mask.shape,val_mask.shape))
log_dir='tensorboard_data/tb_roberta'
model_save_path='/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/callbacks.py'
callbacks = [tf.keras.callbacks.ModelCheckpoint(filepath=model_save_path,save_weights_only=True,monitor='val_loss',mode='min',save_best_only=True),keras.callbacks.TensorBoard(log_dir=log_dir)]
print('\nBert Model',roberta_model.summary())
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')
optimizer = tf.keras.optimizers.Adam(learning_rate=2e-5,epsilon=1e-08)
roberta_model.compile(loss=loss,optimizer=optimizer,metrics=[metric]) history=roberta_model.fit([train_inp,train_mask],train_label,batch_size=16,epochs=2,validation_data=([val_inp,val_mask],val_label),callbacks=callbacks)
preds = roberta_model.predict([val_inp,val_mask],batch_size=16)
pred_labels = preds.argmax(axis=1)
f1 = f1_score(val_label,pred_labels)
print('F1 score',f1)
print('Classification Report')
print(classification_report(val_label,pred_labels,target_names=target_names))
c1 = confusion_matrix(val_label,pred_labels)
print('confusion_matrix ',c1)
我现在遇到以下错误:
AttributeError Traceback (most recent call last)
<ipython-input-13-abcbb1d223b8> in <module>()
106
107 preds = trained_model.predict([val_inp,val_mask],batch_size=16)
--> 108 pred_labels = preds.argmax(axis=1)
109 f1 = f1_score(val_label,pred_labels)
110 print('F1 score',f1)
AttributeError: 'TFSequenceClassifierOutput' object has no attribute 'argmax'
最佳答案
替换以下代码,而不是 pred_labels = preds.argmax (axis = 1)
:
pred_labels = np.argmax(preds.logits, axis=1)
关于tensorflow - 属性错误: 'TFSequenceClassifierOutput' object has no attribute 'argmax' ,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/68254796/