#将baseline程序is_train改成True就是启动训练,此版本训练是多gpu的,如果但gpu训练可以稍微改下
python baseline就能启动训练
#模型预测就是is_train改成False然后把gpu指定改成单卡就可以进行推理了。
python baseline能启动推理
自动调用model_predict函数生成result.tsv文件,支持修改推理不同的文件。
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baseline问题~
def build_model():
token_ids = Input(shape=(None,))
segment_ids = Input(shape=(None,))
bert = build_transformer_model(
config_path=config_path,
checkpoint_path=checkpoint_path,
model='bert',
return_keras_model=False
)
output = Lambda(lambda x: x[:, 0])(bert.model([token_ids, segment_ids])) # 取出output中的第一个值,代表[cls]的值
output_love = Dense(units=emotion_classes,activation='softmax',kernel_initializer=bert.initializer,name='love_classes')(output) # (?,4)
output_happy = Dense(units=emotion_classes,activation='softmax',kernel_initializer=bert.initializer,name='happy_classes')(output) # (?,4)
output_scare = Dense(units=emotion_classes,activation='softmax',kernel_initializer=bert.initializer,name='scare_classes')(output) # (?,4)
output_angry = Dense(units=emotion_classes,activation='softmax',kernel_initializer=bert.initializer,name='angry_classes')(output) # (?,4)
output_afraid = Dense(units=emotion_classes,activation='softmax',kernel_initializer=bert.initializer,name='afraid_classes')(output) # (?,4)
output_sad = Dense(units=emotion_classes,activation='softmax',kernel_initializer=bert.initializer,name='sad_classes')(output) # (?,4)
model = keras.models.Model([token_ids, segment_ids], [output_love, output_happy,output_scare,output_angry,output_afraid,output_sad])
model.summary()
model.compile(
loss='categorical_crossentropy',
optimizer=Adam(lr),
metrics=['categorical_accuracy']
)
return model
代码如上,使用的也是苏神的bert4keras。想实现简单的多标签分类baseline,模型共享一个bert和输入,然后构造6个输出,但是很奇怪:①模型输出的是全0;②6个输出的值一模一样。请问是哪里出问题了呢?万分感谢!
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