temp

    def build_model(self):
        # Build the network of vgg for 10 classes with massive dropout and weight decay as described in the paper.

        model = Sequential()
        weight_decay = self.weight_decay

        model.add(Conv2D(64, (3, 3), padding='same',input_shape=self.x_shape,kernel_regularizer=regularizers.l2(weight_decay)))
        #kernel_regularizer表示施加在权重上的正则项
        model.add(Activation('relu'))
        model.add(BatchNormalization())
        #该层在每个batch（批次）上将前一层的激活值重新规范化，即使得其输出数据的均值接近0，其标准差接近1
        model.add(Dropout(0.3))

        model.add(Conv2D(64, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
        model.add(Activation('relu'))
        model.add(BatchNormalization())

        model.add(MaxPooling2D(pool_size=(2, 2)))

        model.add(Conv2D(128, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
        model.add(Activation('relu'))
        model.add(BatchNormalization())
        model.add(Dropout(0.4))

        model.add(Conv2D(128, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
        model.add(Activation('relu'))
        model.add(BatchNormalization())

        model.add(MaxPooling2D(pool_size=(2, 2)))

        model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
        model.add(Activation('relu'))
        model.add(BatchNormalization())
        model.add(Dropout(0.4))

        model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
        model.add(Activation('relu'))
        model.add(BatchNormalization())
        model.add(Dropout(0.4))

        model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
        model.add(Activation('relu'))
        model.add(BatchNormalization())

        model.add(MaxPooling2D(pool_size=(2, 2)))


        model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
        model.add(Activation('relu'))
        model.add(BatchNormalization())
        model.add(Dropout(0.4))

        model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
        model.add(Activation('relu'))
        model.add(BatchNormalization())
        model.add(Dropout(0.4))

        model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
        model.add(Activation('relu'))
        model.add(BatchNormalization())

        model.add(MaxPooling2D(pool_size=(2, 2)))


        model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
        model.add(Activation('relu'))
        model.add(BatchNormalization())
        model.add(Dropout(0.4))

        model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
        model.add(Activation('relu'))
        model.add(BatchNormalization())
        model.add(Dropout(0.4))

        model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
        model.add(Activation('relu'))
        model.add(BatchNormalization())

        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.5))

        model.add(Flatten())  #即把多维的输入一维化，常用于卷积层到全连接层的过渡
        model.add(Dense(512,kernel_regularizer=regularizers.l2(weight_decay)))
        model.add(Activation('relu'))
        model.add(BatchNormalization())

        model.add(Dropout(0.5))
        model.add(Dense(self.num_classes))  #该全连接层的输出维度为3
        model.add(Activation('softmax'))
        return model