super(type, obj): obj 必须是 Keras 中 type 的实例或子类型

Question

我实现了以下使用带有 Tensorflow 后端的 Keras 从头开始​​构建微型 yolo v2

我的代码在 Keras 2.1.5 中运行良好但是当我更新到 Keras 2.1.6 时我遇到了错误

""kernel_constraint=无，

TypeError: super(type, obj): obj must be an instance or subtype of type "" 请帮帮我，非常感谢

import tensorflow as tf
import keras
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten, 
Reshape, LeakyReLU, BatchNormalization 

def yolo():
    model = Sequential()
    model.add(Conv2D(16,(3,3), padding='same',input_shape=(416,416,3),data_format='channels_last'))
    model.add(LeakyReLU(alpha=0.1))
    model.add(MaxPooling2D(pool_size=(2,2)))

    model.add(Conv2D(32,(3,3), padding='same'))
    model.add(BatchNormalization(axis=-1))
    model.add(LeakyReLU(alpha=0.1))
    model.add(MaxPooling2D(pool_size=(2,2)))

    model.add(Conv2D(64,(3,3), padding='same'))
    model.add(BatchNormalization(axis=-1))
    model.add(LeakyReLU(alpha=0.1))
    model.add(MaxPooling2D(pool_size=(2,2)))

    model.add(Conv2D(128,(3,3), padding='same'))
    model.add(BatchNormalization(axis=-1))
    model.add(LeakyReLU(alpha=0.1))
    model.add(MaxPooling2D(pool_size=(2,2)))

    model.add(Conv2D(128,(3,3), padding='same'))
    model.add(BatchNormalization(axis=-1))
    model.add(LeakyReLU(alpha=0.1))
    model.add(MaxPooling2D(pool_size=(2,2)))

    model.add(Conv2D(12,(1,1), padding='same'))
    model.add(BatchNormalization(axis=-1))
    model.add(LeakyReLU(alpha=0.1))

    model.add(Reshape((13,13,2,6)))
    return model

model = yolo()
model.summary()

Answer 1

这可能是由于更新后没有重新启动python内核而导致的。