深度学习DCGan学习笔记，keras版

太过爱你忘了你带给我的痛 2022-05-14 02:07 288阅读 0赞

[对抗生成网络Gan变体集合 keras版本][Gan_ keras]

一.DCGAN(Deep Convolutional GAN)

依旧有Generator,Discriminator,可使用MNSIT训练生成图片。

和GAN的不同:  
1.增加了反卷积，能生成更好的图片，但依旧还是2分类，判断是不是手写数字。  
2.采用了BatchNormalization 防止梯度消失和过拟合

代码引用的《Web安全之强化学习与GAN》，位置:  
[https://github.com/duoergun0729/3book/tree/master/code/keras-dcgan.py][https_github.com_duoergun0729_3book_tree_master_code_keras-dcgan.py]

生成器G代码:

def generator_model():
        model = Sequential()
        model.add(Dense(input_dim=100, units=1024))
        model.add(Activation('tanh'))
        model.add(Dense(128*7*7))
        model.add(BatchNormalization())
        model.add(Activation('tanh'))
        model.add(Reshape((7, 7, 128), input_shape=(128*7*7,)))
        model.add(UpSampling2D(size=(2, 2)))
        model.add(Conv2D(64, (5, 5), padding='same'))
        model.add(Activation('tanh'))
        model.add(UpSampling2D(size=(2, 2)))
        model.add(Conv2D(1, (5, 5), padding='same'))
        model.add(Activation('tanh'))
        return model

判别器D代码:

def discriminator_model():
        model = Sequential()
        model.add(
                Conv2D(64, (5, 5),
                padding='same',
                input_shape=(28, 28, 1))
                )
        model.add(Activation('tanh'))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Conv2D(128, (5, 5)))
        model.add(Activation('tanh'))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Flatten())
        model.add(Dense(1024))
        model.add(Activation('tanh'))
        model.add(Dense(1))
        model.add(Activation('sigmoid'))
        return model

训练图:  
![这里写图片描述][70]

[Gan_ keras]: https://blog.csdn.net/u011311291/article/details/82428620
[https_github.com_duoergun0729_3book_tree_master_code_keras-dcgan.py]: https://github.com/duoergun0729/3book/tree/master/code/keras-dcgan.py
[70]: /images/20220514/9551265dc56c4d82a30e43754804f8e5.png