深度学习BiGan学习笔记,keras版

Love The Way You Lie 2022-04-13 02:29 583阅读 0赞

[对抗生成网络Gan变体集合 keras版本][Gan_ keras]  
bigan(Bidirectional GAN 双向Gan)  
[http://link.zhihu.com/?target=https://arxiv.org/abs/1605.09782][http_link.zhihu.com_target_https_arxiv.org_abs_1605.09782]  
具有生成器G，编码器encoder,判别器D  
作用:  
1.可以使用noise经过生成器G生成图片(这一点和GAN类似)  
2.随便给一张图片，经过Encoder,再经过生气G，可以生成相似的图片(新特点)

代码位置:  
[https://github.com/eriklindernoren/Keras-GAN/tree/master/bigan][https_github.com_eriklindernoren_Keras-GAN_tree_master_bigan]

生成器:

def build_generator(self):
    	model = Sequential()
    	model.add(Dense(512, input_dim=self.latent_dim))
    	model.add(LeakyReLU(alpha=0.2))
    	model.add(BatchNormalization(momentum=0.8))
    	model.add(Dense(512))
    	model.add(LeakyReLU(alpha=0.2))
    	model.add(BatchNormalization(momentum=0.8))
    	model.add(Dense(np.prod(self.img_shape), activation='tanh'))
    	model.add(Reshape(self.img_shape))
    	model.summary()
    	z = Input(shape=(self.latent_dim,))
    	gen_img = model(z)
    	
    	return Model(z, gen_img)

编码器:

def build_encoder(self):
    	model = Sequential()
    	model.add(Flatten(input_shape=self.img_shape))
    	model.add(Dense(512))
    	model.add(LeakyReLU(alpha=0.2))
    	model.add(BatchNormalization(momentum=0.8))
    	model.add(Dense(512))
    	model.add(LeakyReLU(alpha=0.2))
    	model.add(BatchNormalization(momentum=0.8))
    	model.add(Dense(self.latent_dim))
    	model.summary()
    	
    	img = Input(shape=self.img_shape)
    	z = model(img)
    	
    	return Model(img, z)

判别器:

def build_discriminator(self):
    	z = Input(shape=(self.latent_dim, ))
    	img = Input(shape=self.img_shape)
    	d_in = concatenate([z, Flatten()(img)])
    	
    	model = Dense(1024)(d_in)
    	model = LeakyReLU(alpha=0.2)(model)
    	model = Dropout(0.5)(model)
    	model = Dense(1024)(model)
    	model = LeakyReLU(alpha=0.2)(model)
    	model = Dropout(0.5)(model)
    	model = Dense(1024)(model)
    	model = LeakyReLU(alpha=0.2)(model)
    	model = Dropout(0.5)(model)
    	validity = Dense(1, activation="sigmoid")(model)
    	
    	return Model([z, img], validity)

训练图:  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTEzMTEyOTE_size_16_color_FFFFFF_t_70]

[Gan_ keras]: https://blog.csdn.net/u011311291/article/details/82428620
[http_link.zhihu.com_target_https_arxiv.org_abs_1605.09782]: http://link.zhihu.com/?target=https://arxiv.org/abs/1605.09782
[https_github.com_eriklindernoren_Keras-GAN_tree_master_bigan]: https://github.com/eriklindernoren/Keras-GAN/tree/master/bigan
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTEzMTEyOTE_size_16_color_FFFFFF_t_70]: /images/20220413/e14a34a535a54404a00eaacb5561729d.png