python – set_model()缺少1个必需的位置参数：’model’

概述我已经创建了一个Keras顺序模型并使用了Adam优化器.我想在每个时代之后获得学习率.这 stackoverflow question似乎回答了我的问题.但是,当我按照上面提到的解决方案时,我收到以下错误 set_model() missing 1 required positional argument: 'model' 这是我创建模型的代码： model = Sequential()mo 我已经创建了一个Keras顺序模型并使用了Adam优化器.我想在每个时代之后获得学习率.这 stackoverflow question似乎回答了我的问题.但是,当我按照上面提到的解决方案时,我收到以下错误

set_model() missing 1 required positional argument: 'model'

这是我创建模型的代码：

model = Sequential()model.add(Conv2D(64,(5,5),input_shape=(img_HEIGHT,img_WIDTH,3),activation='relu'))model.add(Conv2D(64,activation='relu'))model.add(MaxPooling2D((2,2)))model.add(Dropout(0.2))model.add(Conv2D(128,activation='relu'))model.add(Conv2D(128,2)))model.add(Dropout(0.2))model.add(Conv2D(256,activation='relu'))model.add(Conv2D(256,2)))model.add(Batchnormalization(axis=3))model.add(Dropout(0.2))model.add(Flatten())model.add(Dense(256,activation='relu'))model.add(Dropout(0.5))model.add(Dense(256,activation='relu'))model.add(Dropout(0.5))model.add(Dense(10,activation='softmax'))model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc',patIEnce=3,verbose=1,factor=0.4,min_lr=0.0001)csvlogger = CSVLogger("solution.csv",separator='\t')checkpoint = ModelCheckpoint("models/best_model5.h5",monitor="val_acc",save_best_only=True,mode='max')learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc',min_lr=0.00001)class MyCallback(keras.callbacks.Callback):    def on_epoch_end(self,epoch,logs=None):        lr = self.model.optimizer.lr        decay = self.model.optimizer.decay        iterations = self.model.optimizer.iterations        lr_with_decay = lr / (1. + decay * K.cast(iterations,K.dtype(decay)))        print(K.eval(lr_with_decay))model.fit_generator(datagen.flow(x_train,y_train,batch_size=75),epochs=10,valIDation_data=(x_valIDation,y_test),steps_per_epoch=x_train.shape[0],callbacks=[csvlogger,checkpoint,MyCallback])

如何通过此错误“set_model()缺少1个必需的位置参数：’model’
“
下面是堆栈跟踪

TypeError                                 Traceback (most recent call last)<ipython-input-12-1826a19039cd> in <module>()    128 model.fit_generator(datagen.flow(x_train,129                            epochs=10,--> 130                            steps_per_epoch=x_train.shape[0],MyCallback])    131 model.save('trained_model5.h5')    132 /usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py in wrapper(*args,**kwargs)     89                 warnings.warn('Update your `' + object_name +     90                               '` call to the Keras 2 API: ' + signature,stacklevel=2)---> 91             return func(*args,**kwargs)     92         wrapper._original_function = func     93         return wrapper/usr/local/lib/python3.6/dist-packages/keras/models.py in fit_generator(self,generator,steps_per_epoch,epochs,verbose,callbacks,valIDation_data,valIDation_steps,class_weight,max_queue_size,workers,use_multiprocessing,shuffle,initial_epoch)   1274                                         use_multiprocessing=use_multiprocessing,1275                                         shuffle=shuffle,-> 1276                                         initial_epoch=initial_epoch)   1277    1278     @interfaces.legacy_generator_methods_support/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py in wrapper(*args,**kwargs)     92         wrapper._original_function = func     93         return wrapper/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in fit_generator(self,initial_epoch)   2131         else:   2132             callback_model = self-> 2133         callbacks.set_model(callback_model)   2134         callbacks.set_params({   2135             'epochs': epochs,/usr/local/lib/python3.6/dist-packages/keras/callbacks.py in set_model(self,model)     50     def set_model(self,model):     51         for callback in self.callbacks:---> 52             callback.set_model(model)     53      54     def on_epoch_begin(self,logs=None):TypeError: set_model() missing 1 required positional argument: 'model'

另外,我的另一个问题是,上述解决方案是否正确.This tensorflow link about Adam Optimizer建议学习率计算如下：

lr_t <- learning_rate * sqrt(1 – beta2^t) / (1 – beta1^t)

这似乎与其他链接中提到的解决方案完全不同.我错过了什么？

解决方法 实际上,在model.fit_generator方法的callbacks参数中,您传递的是类而不是该类的对象.

它应该是

my_calback_object = MyCallback() # create an object of the MyCallback classmodel.fit_generator(datagen.flow(x_train,my_callback_object])

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