深度学习ACGan学习笔记，keras版

对抗生成网络Gan变体集合 keras版本

一.ACGAN(Auxiliary Classifier GAN)
https://arxiv.org/abs/1610.09585
依旧有Generator,Discriminator,可以使用MNSIT训练生成图片。

和DCGAN的不一样:
1.增长了class类别标签参与训练，能够生成指定类别的图片

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-acgan.py

生成器G代码:

def build_generator(latent_size):

    cnn = Sequential()
    cnn.add(Dense(1024, input_dim=latent_size, activation='relu'))
    cnn.add(Dense(128 * 7 * 7, activation='relu'))
    cnn.add(Reshape((128, 7, 7)))
    cnn.add(UpSampling2D(size=(2, 2)))
    cnn.add(Conv2D(256, (5, 5), padding="same", kernel_initializer="glorot_normal", activation="relu"))
    cnn.add(UpSampling2D(size=(2, 2)))
    cnn.add(Conv2D(128, (5, 5), padding="same", kernel_initializer="glorot_normal", activation="relu"))
    cnn.add(Conv2D(1, (2, 2), padding="same", kernel_initializer="glorot_normal", activation="tanh"))
    latent = Input(shape=(latent_size, ))
    image_class = Input(shape=(1,), dtype='int32')
    cls = Flatten()(Embedding(10, 100, embeddings_initializer="glorot_normal")(image_class))
    #h = merge([latent, cls], mode='mul')
    h=add([latent, cls])
    fake_image = cnn(h)
    return Model(inputs=[latent, image_class], outputs=[fake_image])

判别器D代码:

def build_discriminator():

    cnn = Sequential()
    cnn.add(Conv2D(32, (3, 3), padding="same", strides=(2, 2), input_shape=(1, 28, 28) ))
    cnn.add(LeakyReLU())
    cnn.add(Dropout(0.3))
    cnn.add(Conv2D(64, (3, 3), padding="same", strides=(1, 1)))
    cnn.add(LeakyReLU())
    cnn.add(Dropout(0.3))
    cnn.add(Conv2D(128, (3, 3), padding="same", strides=(2, 2)))
    cnn.add(LeakyReLU())
    cnn.add(Dropout(0.3))
    cnn.add(Conv2D(256, (3, 3), padding="same", strides=(1, 1)))
    cnn.add(LeakyReLU())
    cnn.add(Dropout(0.3))
    cnn.add(Flatten())
    image = Input(shape=(1, 28, 28))
    features = cnn(image)
    fake = Dense(1, activation='sigmoid', name='generation')(features)
    aux = Dense(10, activation='softmax', name='auxiliary')(features)
    return Model(inputs=[image], outputs=[fake, aux])

训练图: