好像还挺好玩的GAN5——Keras搭建COGAN耦合生成式对抗网络

拼搏现实的明天。 2023-06-25 13:28 5阅读 0赞

### 好像还挺好玩的GAN5——Keras搭建COGAN耦合生成式对抗网络 ###

*  学习前言
 *  什么是COGAN
 *  神经网络构建
 *   *  1、Generator
     *  2、Discriminator
 *  训练思路
 *  实现全部代码

# 学习前言 #

发现一个挺有意思的结构，可以通过同一个输入，生成不同的内容。  
![在这里插入图片描述][20190723165901974.jpg_pic_center]

# 什么是COGAN #

COGAN是一种耦合生成式对抗网络，其内部具有一定的耦合，可以对同一个输入有不同的输出。

其具体实现方式就是：  
**1、建立两个生成模型，两个判别模型。  
2、两个生成模型的特征提取部分有一定的重合，在最后生成图片的部分分开，以生成不同类型的图片。  
3、两个判别模型的特征提取部分有一定的重合，在最后判别真伪的部分分开，以判别不同类型的图片。**

# 神经网络构建 #

## 1、Generator ##

生成模型的输入是一个N维度的符合正太分布的随机数，输出是一个28,28,1的Mnist手写体。  
**一共存在两个生成模型，两个生成模型的特征提取部分有一定的重合，在最后生成图片的部分分开，以生成不同类型的图片。**

即：权值部分有一定的共享。

def build_generators(self):
        # 共享权值部分
        noise = Input(shape=(self.latent_dim,))
        x = Dense(32 * 7 * 7, activation="relu", input_dim=self.latent_dim)(noise)
        x = Reshape((7, 7, 32))(x)
        
        x = Conv2D(64, kernel_size=3, padding="same")(x)
        x = BatchNormalization(momentum=0.8)(x)
        x = Activation("relu")(x)
    
        x = UpSampling2D()(x)
        x = Conv2D(128, kernel_size=3, padding="same")(x)
        x = BatchNormalization(momentum=0.8)(x)
        x = Activation("relu")(x)
    
        x = UpSampling2D()(x)
        x = Conv2D(128, kernel_size=3, padding="same")(x)
        x = BatchNormalization(momentum=0.8)(x)
        feature_repr = Activation("relu")(x)
        model = Model(noise,feature_repr)
    
        noise = Input(shape=(self.latent_dim,))
        feature_repr = model(noise)
        # 生成模型1
        g1 = Conv2D(64, kernel_size=1, padding="same")(feature_repr)
        g1 = BatchNormalization(momentum=0.8)(g1)
        g1 = Activation("relu")(g1)
    
        g1 = Conv2D(64, kernel_size=3, padding="same")(g1)
        g1 = BatchNormalization(momentum=0.8)(g1)
        g1 = Activation("relu")(g1)
    
        g1 = Conv2D(64, kernel_size=1, padding="same")(g1)
        g1 = BatchNormalization(momentum=0.8)(g1)
        g1 = Activation("relu")(g1)
    
        g1 = Conv2D(self.channels, kernel_size=1, padding="same")(g1)
        img1 = Activation("tanh")(g1)
    
        # 生成模型2
        g2 = Conv2D(64, kernel_size=1, padding="same")(feature_repr)
        g2 = BatchNormalization(momentum=0.8)(g2)
        g2 = Activation("relu")(g2)
        
        g2 = Conv2D(64, kernel_size=3, padding="same")(g2)
        g2 = BatchNormalization(momentum=0.8)(g2)
        g2 = Activation("relu")(g2)
    
        g2 = Conv2D(64, kernel_size=1, padding="same")(g2)
        g2 = BatchNormalization(momentum=0.8)(g2)
        g2 = Activation("relu")(g2)
    
        g2 = Conv2D(self.channels, kernel_size=1, padding="same")(g2)
        img2 = Activation("tanh")(g2)
    
        return Model(noise, img1), Model(noise, img2)

## 2、Discriminator ##

判别模型的输入一个28,28,1维的图片，**输出一个是0到1之间的数，1代表判断这个图片是真的，0代表判断这个图片是假的。**

**一共存在两个判别模型，两个生成模型的特征提取部分有一定的重合，在最后判别真伪的部分分开，以判别不同类型的图片。**

def build_discriminators(self):
    
        # 共享权值部分
        img = Input(shape=self.img_shape)
        x = Conv2D(64, kernel_size=3, strides=2, padding="same")(img)
        x = BatchNormalization(momentum=0.8)(x)
        x = Activation("relu")(x)
    
        x = Conv2D(128, kernel_size=3, strides=2, padding="same")(x)
        x = BatchNormalization(momentum=0.8)(x)
        x = Activation("relu")(x)
    
        x = Conv2D(64, kernel_size=3, strides=2, padding="same")(x)
        x = BatchNormalization(momentum=0.8)(x)
        x = GlobalAveragePooling2D()(x)
        feature_repr = Activation("relu")(x)
    
        model = Model(img,feature_repr)
    
        img1 = Input(shape=self.img_shape)
        img2 = Input(shape=self.img_shape)
        img1_embedding = model(img1)
        img2_embedding = model(img2)
        # 生成评价模型1
        validity1 = Dense(1, activation='sigmoid')(img1_embedding)
        # 生成评价模型2
        validity2 = Dense(1, activation='sigmoid')(img2_embedding)
    
        return Model(img1, validity1), Model(img2, validity2)

# 训练思路 #

COGAN的训练思路分为如下几个步骤：  
**1、创建两个风格不同的数据集。  
2、随机生成batch\_size个N维向量，利用两个不同的生成模型生成图片。  
3、利用两个判别模型分别对两个不同的生成模型的生成图片进行判别、对两个风格不同的数据集进行随机选取并进行判别。  
4、根据两个判别模型的结果与1对比，对两个生成模型进行训练。**

# 实现全部代码 #

from __future__ import print_function, division
    import scipy
    
    from keras.datasets import mnist
    from keras.layers import Input, Dense, Reshape, Flatten, Dropout
    from keras.layers import BatchNormalization, Activation, ZeroPadding2D, GlobalAveragePooling2D
    from keras.layers.advanced_activations import LeakyReLU
    from keras.layers.convolutional import UpSampling2D, Conv2D
    from keras.models import Sequential, Model
    from keras.optimizers import Adam
    import os
    import matplotlib.pyplot as plt
    import numpy as np
    
    class COGAN():
        def __init__(self):
            # 输入shape
            self.img_rows = 28
            self.img_cols = 28
            self.channels = 1
            self.img_shape = (self.img_rows, self.img_cols, self.channels)
            # 分十类
            self.num_classes = 10
            self.latent_dim = 100
            # adam优化器
            optimizer = Adam(0.0002, 0.5)
            # 生成两个判别器
            self.d1, self.d2 = self.build_discriminators()
            self.d1.compile(loss='binary_crossentropy',
                optimizer=optimizer,
                metrics=['accuracy'])
            self.d2.compile(loss='binary_crossentropy',
                optimizer=optimizer,
                metrics=['accuracy'])
            # 建立生成器
            self.g1, self.g2 = self.build_generators()
    
            z = Input(shape=(self.latent_dim,))
            img1 = self.g1(z)
            img2 = self.g2(z)
    
            self.d1.trainable = False
            self.d2.trainable = False
    
            valid1 = self.d1(img1)
            valid2 = self.d2(img2)
    
            self.combined = Model(z, [valid1, valid2])
            self.combined.compile(loss=['binary_crossentropy', 'binary_crossentropy'],
                                        optimizer=optimizer)
    
        def build_generators(self):
            # 共享权值部分
            noise = Input(shape=(self.latent_dim,))
            x = Dense(32 * 7 * 7, activation="relu", input_dim=self.latent_dim)(noise)
            x = Reshape((7, 7, 32))(x)
            
            x = Conv2D(64, kernel_size=3, padding="same")(x)
            x = BatchNormalization(momentum=0.8)(x)
            x = Activation("relu")(x)
    
            x = UpSampling2D()(x)
            x = Conv2D(128, kernel_size=3, padding="same")(x)
            x = BatchNormalization(momentum=0.8)(x)
            x = Activation("relu")(x)
    
            x = UpSampling2D()(x)
            x = Conv2D(128, kernel_size=3, padding="same")(x)
            x = BatchNormalization(momentum=0.8)(x)
            feature_repr = Activation("relu")(x)
            model = Model(noise,feature_repr)
    
            noise = Input(shape=(self.latent_dim,))
            feature_repr = model(noise)
            # 生成模型1
            g1 = Conv2D(64, kernel_size=1, padding="same")(feature_repr)
            g1 = BatchNormalization(momentum=0.8)(g1)
            g1 = Activation("relu")(g1)
    
            g1 = Conv2D(64, kernel_size=3, padding="same")(g1)
            g1 = BatchNormalization(momentum=0.8)(g1)
            g1 = Activation("relu")(g1)
    
            g1 = Conv2D(64, kernel_size=1, padding="same")(g1)
            g1 = BatchNormalization(momentum=0.8)(g1)
            g1 = Activation("relu")(g1)
    
            g1 = Conv2D(self.channels, kernel_size=1, padding="same")(g1)
            img1 = Activation("tanh")(g1)
    
            # 生成模型2
            g2 = Conv2D(64, kernel_size=1, padding="same")(feature_repr)
            g2 = BatchNormalization(momentum=0.8)(g2)
            g2 = Activation("relu")(g2)
            
            g2 = Conv2D(64, kernel_size=3, padding="same")(g2)
            g2 = BatchNormalization(momentum=0.8)(g2)
            g2 = Activation("relu")(g2)
    
            g2 = Conv2D(64, kernel_size=1, padding="same")(g2)
            g2 = BatchNormalization(momentum=0.8)(g2)
            g2 = Activation("relu")(g2)
    
            g2 = Conv2D(self.channels, kernel_size=1, padding="same")(g2)
            img2 = Activation("tanh")(g2)
    
            return Model(noise, img1), Model(noise, img2)
    
    
        def build_discriminators(self):
    
            # 共享权值部分
            img = Input(shape=self.img_shape)
            x = Conv2D(64, kernel_size=3, strides=2, padding="same")(img)
            x = BatchNormalization(momentum=0.8)(x)
            x = Activation("relu")(x)
    
            x = Conv2D(128, kernel_size=3, strides=2, padding="same")(x)
            x = BatchNormalization(momentum=0.8)(x)
            x = Activation("relu")(x)
    
            x = Conv2D(64, kernel_size=3, strides=2, padding="same")(x)
            x = BatchNormalization(momentum=0.8)(x)
            x = GlobalAveragePooling2D()(x)
            feature_repr = Activation("relu")(x)
    
            model = Model(img,feature_repr)
    
            img1 = Input(shape=self.img_shape)
            img2 = Input(shape=self.img_shape)
            img1_embedding = model(img1)
            img2_embedding = model(img2)
            # 生成评价模型1
            validity1 = Dense(1, activation='sigmoid')(img1_embedding)
            # 生成评价模型2
            validity2 = Dense(1, activation='sigmoid')(img2_embedding)
    
            return Model(img1, validity1), Model(img2, validity2)
    
        def train(self, epochs, batch_size=128, sample_interval=50):
            (X_train, _), (_, _) = mnist.load_data()
    
            X_train = (X_train.astype(np.float32) - 127.5) / 127.5
            X_train = np.expand_dims(X_train, axis=3)
    
            X1 = X_train[:int(X_train.shape[0]/2)]
            X2 = X_train[int(X_train.shape[0]/2):]
            X2 = scipy.ndimage.interpolation.rotate(X2, 90, axes=(1, 2))
    
            valid = np.ones((batch_size, 1))
            fake = np.zeros((batch_size, 1))
    
            for epoch in range(epochs):
    
                # ---------------------- #
                #  训练评价者
                # ---------------------- #
    
                idx = np.random.randint(0, X1.shape[0], batch_size)
                imgs1 = X1[idx]
                imgs2 = X2[idx]
    
                noise = np.random.normal(0, 1, (batch_size, 100))
    
                gen_imgs1 = self.g1.predict(noise)
                gen_imgs2 = self.g2.predict(noise)
    
                d1_loss_real = self.d1.train_on_batch(imgs1, valid)
                d2_loss_real = self.d2.train_on_batch(imgs2, valid)
                d1_loss_fake = self.d1.train_on_batch(gen_imgs1, fake)
                d2_loss_fake = self.d2.train_on_batch(gen_imgs2, fake)
                d1_loss = 0.5 * np.add(d1_loss_real, d1_loss_fake)
                d2_loss = 0.5 * np.add(d2_loss_real, d2_loss_fake)
    
    
                # ------------------ #
                #  训练生成模型
                # ------------------ #
    
                g_loss = self.combined.train_on_batch(noise, [valid, valid])
    
                print ("%d [D1 loss: %f, acc.: %.2f%%] [D2 loss: %f, acc.: %.2f%%] [G loss: %f]" \
                    % (epoch, d1_loss[0], 100*d1_loss[1], d2_loss[0], 100*d2_loss[1], g_loss[0]))
    
                if epoch % sample_interval == 0:
                    self.sample_images(epoch)
    
        def sample_images(self, epoch):
            r, c = 4, 4
            noise = np.random.normal(0, 1, (r * int(c/2), 100))
            gen_imgs1 = self.g1.predict(noise)
            gen_imgs2 = self.g2.predict(noise)
            gen_imgs = np.concatenate([gen_imgs1, gen_imgs2])
    
            gen_imgs = 0.5 * gen_imgs + 0.5
    
            fig, axs = plt.subplots(r, c)
            cnt = 0
            for i in range(r):
                for j in range(c):
                    axs[i,j].imshow(gen_imgs[cnt, :,:,0], cmap='gray')
                    axs[i,j].axis('off')
                    cnt += 1
            fig.savefig("images/mnist_%d.png" % epoch)
            plt.close()
    
    
    if __name__ == '__main__':
        if not os.path.exists("./images"):
            os.makedirs("./images")
        gan = COGAN()
        gan.train(epochs=30000, batch_size=256, sample_interval=200)

实现效果为：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center]

[20190723165901974.jpg_pic_center]: https://img-blog.csdnimg.cn/20190723165901974.jpg#pic_center
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center]: https://img-blog.csdnimg.cn/20191229160517250.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA==,size_16,color_FFFFFF,t_70#pic_center