set_model()缺少1个必需的位置参数:'model'

Question

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.这个stackoverflow问题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, (5, 5), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Dropout(0.2))

model.add(Conv2D(128, (5, 5), activation='relu'))
model.add(Conv2D(128, (5, 5), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Dropout(0.2))

model.add(Conv2D(256, (5, 5), activation='relu'))
model.add(Conv2D(256, (5, 5), activation='relu'))
model.add(MaxPooling2D((2, 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', 
                                            patience=3, 
                                            verbose=1, 
                                            factor=0.4, 
                                            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),verbose=1, 
                           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, y_train, batch_size=75), 
    129                            epochs=10, validation_data=(x_validation, y_test),verbose=1,
--> 130                            steps_per_epoch=x_train.shape[0], callbacks=[csvlogger, checkpoint, 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)
     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/engine/training.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)
   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, epoch, 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.关于Adam Optimizer的这个tensorflow链接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？

Answer 1

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 class

model.fit_generator(datagen.flow(x_train, y_train, batch_size=75), 
                    epochs=10, validation_data=(x_validation, y_test),
                    verbose=1, steps_per_epoch=x_train.shape[0],
                    callbacks=[csvlogger, checkpoint, my_callback_object])

Answer 2

该错误意味着您没有为位置 1（从 0 开始）处的参数提供值（称为“模型”）。调用者是 Tensorflow 本身，因此故障很可能不存在。

此错误表明您正在调用静态方法而不是对象上的方法，因此仅传递 1 个参数而不是 2 个。这是因为当您调用对象上的方法时，该对象作为第一个参数传递，并且“模型” " 将作为第二个参数传递。

简而言之，您的错误在于您的回调是“类”而不是“对象”。确保您提供的是回调类的实例，而不是类本身。

像这样（注意“MyCallback”后面的括号）：

model.fit_generator(datagen.flow(x_train, y_train, batch_size=75), epochs=10, validation_data=(x_validation, y_test),verbose=1, steps_per_epoch=x_train.shape[0], callbacks=[csvlogger, checkpoint, MyCallback()])

Answer 3

这更像是一条评论，只是不合适。

这很奇怪。set_model继承的默认实现MyCallback是：

def set_model(self, model):
    self.model = model

根据堆栈跟踪，这正是它的调用方式：

/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)

我目前唯一的猜测是您的系统上存在一些版本不匹配。您可能还有一些旧的 .pyc 。我会尝试直接编辑 /usr/local/lib/python3.6/dist-packages/keras/callbacks.py 来调试它。例如，在第 52 行之前添加一条 print 语句以确保此代码确实运行。然后，进入pdb(add import pdb; pdb.set_trace) 并检查它抱怨的原因。这是一个基本的Python问题。