好像还挺好玩的GAN4——Keras搭建ACGAN利用卷积给生成结果贴上标签

╰半夏微凉° 2023-06-25 10:27 12阅读 0赞

### 好像还挺好玩的GAN4——Keras搭建ACGAN利用卷积给生成结果贴上标签 ###

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

# 学习前言 #

请各位发粪图强！  
![在这里插入图片描述][20190723165901974.jpg_pic_center]

# 什么是ACGAN #

ACGAN一种带条件约束的DCGAN，在生成模型（D）和判别模型（G）的建模中均引入条件变量y（conditional variable y）。

**ACGAN相当于是DCGAN和CGAN的结合，将深度卷积网络和标签带入到GAN当中。**

使用额外信息y对模型增加条件，可以指导数据生成过程。这些条件变量y可以基于多种信息，例如类别标签，用于图像修复的部分数据，来自不同模态（modality）的数据。

在存在类别标签的情况下，将深度卷积网络带入到GAN当中，提高图片的生成质量。

这个简单直接的改进被证明非常有效。

**简单来讲，普通的GAN输入的是一个N维的正态分布随机数，而ACGAN会为这个随机数添上标签，其利用Embedding层将正整数（索引值）转换为固定尺寸的稠密向量，并将这个稠密向量与N维的正态分布随机数相乘，从而获得一个有标签的随机数。**

**与此同时，ACGAN将深度卷积网络带入到存在标签的GAN中，可以生成更加高质量的图片。**

# 神经网络构建 #

## 1、Generator ##

生成网络的输入是一个带标签的随机数，具体操作方式是**生成一个N维的正态分布随机数，再利用Embedding层将正整数（索引值）转换为N维的稠密向量，并将这个稠密向量与N维的正态分布随机数相乘。**

输入的数进行reshape后利用上采样与卷积生成图像。

def build_generator(self):
    
        model = Sequential()
    
        # 先全连接到64*7*7的维度上
        model.add(Dense(32 * 7 * 7, activation="relu", input_dim=self.latent_dim))
        # reshape成特征层的样式
        model.add(Reshape((7, 7, 32)))
    
        # 7, 7, 64
        model.add(Conv2D(64, kernel_size=3, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Activation("relu"))
        # 上采样
        # 7, 7, 64 -> 14, 14, 64
        model.add(UpSampling2D())
        model.add(Conv2D(128, kernel_size=3, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Activation("relu"))
        # 上采样
        # 14, 14, 128 -> 28, 28, 64
        model.add(UpSampling2D())
        model.add(Conv2D(64, kernel_size=3, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Activation("relu"))
    
        # 上采样
        # 28, 28, 64 -> 28, 28, 1
        model.add(Conv2D(self.channels, kernel_size=3, padding="same"))
        model.add(Activation("tanh"))
    
    
        model.summary()
    
        noise = Input(shape=(self.latent_dim,))
        label = Input(shape=(1,), dtype='int32')
        label_embedding = Flatten()(Embedding(self.num_classes, self.latent_dim)(label))
    
        model_input = multiply([noise, label_embedding])
        img = model(model_input)
    
        return Model([noise, label], img)

## 2、Discriminator ##

普通GAN的判别模型的目的是根据输入的图片**判断出真伪**。  
在ACGAN中，其不仅要判断出真伪，还要判断出种类，主干网络利用卷积构成。。

因此它的输入一个28,28,1维的图片，输出有两个：  
**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。与普通GAN不同的是，它使用的是卷积神经网络。  
另一个是一个向量，用于判断这张图片属于什么类。**

def build_discriminator(self):
    
        model = Sequential()
        # 28,28,1 -> 14,14,16
        model.add(Conv2D(16, kernel_size=3, strides=2, input_shape=self.img_shape, padding="same"))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dropout(0.25))
        # 14,14,16 -> 8,8,32
        model.add(Conv2D(32, kernel_size=3, strides=2, padding="same"))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dropout(0.25))
        model.add(BatchNormalization(momentum=0.8))
        # 8,8,32 -> 4,4,64
        model.add(ZeroPadding2D(padding=((0,1),(0,1))))
        model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dropout(0.25))
        model.add(BatchNormalization(momentum=0.8))
        # 4,4,64 -> 4,4,128
        model.add(Conv2D(128, kernel_size=3, strides=1, padding="same"))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dropout(0.25))
        model.add(GlobalAveragePooling2D())
    
        img = Input(shape=self.img_shape)
    
        features = model(img)
    
        validity = Dense(1, activation="sigmoid")(features)
        label = Dense(self.num_classes, activation="softmax")(features)
    
        return Model(img, [validity, label])

# 训练思路 #

ACGAN的训练思路分为如下几个步骤：  
**1、随机选取batch\_size个真实的图片和它的标签。  
2、随机生成batch\_size个N维向量和其对应的标签label，利用Embedding层进行组合，传入到Generator中生成batch\_size个虚假图片。  
3、Discriminator的loss函数由两部分组成，一部分是真伪的判断结果与真实情况的对比，一部分是图片所属标签的判断结果与真实情况的对比。  
4、Generator的loss函数也由两部分组成，一部分是生成的图片是否被Discriminator判断为1，另一部分是生成的图片是否被分成了正确的类。**

# 实现全部代码 #

from __future__ import print_function, division
    import tensorflow as tf
    from keras.datasets import mnist
    from keras.backend.tensorflow_backend import set_session
    from keras.layers import Input, Dense, Reshape, Flatten, Dropout, multiply
    from keras.layers import BatchNormalization, Activation, Embedding, 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 matplotlib.pyplot as plt
    import os
    import numpy as np
    
    class ACGAN():
        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)
            # 判别模型
            losses = ['binary_crossentropy', 'sparse_categorical_crossentropy']
            self.discriminator = self.build_discriminator()
            self.discriminator.compile(loss=losses,
                optimizer=optimizer,
                metrics=['accuracy'])
    
            # 生成模型
            self.generator = self.build_generator()
    
            # conbine是生成模型和判别模型的结合
            # 判别模型的trainable为False
            # 用于训练生成模型
            noise = Input(shape=(self.latent_dim,))
            label = Input(shape=(1,))
            img = self.generator([noise, label])
    
            self.discriminator.trainable = False
    
            valid, target_label = self.discriminator(img)
    
            self.combined = Model([noise, label], [valid, target_label])
            self.combined.compile(loss=losses,
                optimizer=optimizer)
    
        def build_generator(self):
    
            model = Sequential()
    
            # 先全连接到64*7*7的维度上
            model.add(Dense(32 * 7 * 7, activation="relu", input_dim=self.latent_dim))
            # reshape成特征层的样式
            model.add(Reshape((7, 7, 32)))
    
            # 7, 7, 64
            model.add(Conv2D(64, kernel_size=3, padding="same"))
            model.add(BatchNormalization(momentum=0.8))
            model.add(Activation("relu"))
            # 上采样
            # 7, 7, 64 -> 14, 14, 64
            model.add(UpSampling2D())
            model.add(Conv2D(128, kernel_size=3, padding="same"))
            model.add(BatchNormalization(momentum=0.8))
            model.add(Activation("relu"))
            # 上采样
            # 14, 14, 128 -> 28, 28, 64
            model.add(UpSampling2D())
            model.add(Conv2D(64, kernel_size=3, padding="same"))
            model.add(BatchNormalization(momentum=0.8))
            model.add(Activation("relu"))
    
            # 上采样
            # 28, 28, 64 -> 28, 28, 1
            model.add(Conv2D(self.channels, kernel_size=3, padding="same"))
            model.add(Activation("tanh"))
    
    
            model.summary()
    
            noise = Input(shape=(self.latent_dim,))
            label = Input(shape=(1,), dtype='int32')
            label_embedding = Flatten()(Embedding(self.num_classes, self.latent_dim)(label))
    
            model_input = multiply([noise, label_embedding])
            img = model(model_input)
    
            return Model([noise, label], img)
    
        def build_discriminator(self):
    
            model = Sequential()
            # 28,28,1 -> 14,14,16
            model.add(Conv2D(16, kernel_size=3, strides=2, input_shape=self.img_shape, padding="same"))
            model.add(LeakyReLU(alpha=0.2))
            model.add(Dropout(0.25))
            # 14,14,16 -> 8,8,32
            model.add(Conv2D(32, kernel_size=3, strides=2, padding="same"))
            model.add(LeakyReLU(alpha=0.2))
            model.add(Dropout(0.25))
            model.add(BatchNormalization(momentum=0.8))
            # 8,8,32 -> 4,4,64
            model.add(ZeroPadding2D(padding=((0,1),(0,1))))
            model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
            model.add(LeakyReLU(alpha=0.2))
            model.add(Dropout(0.25))
            model.add(BatchNormalization(momentum=0.8))
            # 4,4,64 -> 4,4,128
            model.add(Conv2D(128, kernel_size=3, strides=1, padding="same"))
            model.add(LeakyReLU(alpha=0.2))
            model.add(Dropout(0.25))
            model.add(GlobalAveragePooling2D())
    
            img = Input(shape=self.img_shape)
    
            features = model(img)
    
            validity = Dense(1, activation="sigmoid")(features)
            label = Dense(self.num_classes, activation="softmax")(features)
    
            return Model(img, [validity, label])
    
        def train(self, epochs, batch_size=128, sample_interval=50):
    
            # 载入数据库
            (X_train, y_train), (_,  _) = mnist.load_data()
    
            # 归一化
            X_train = (X_train.astype(np.float32) - 127.5) / 127.5
            X_train = np.expand_dims(X_train, axis=3)
            y_train = y_train.reshape(-1, 1)
    
            valid = np.ones((batch_size, 1))
            fake = np.zeros((batch_size, 1))
    
            for epoch in range(epochs):
    
                # --------------------- #
                #  训练鉴别模型
                # --------------------- #
                idx = np.random.randint(0, X_train.shape[0], batch_size)
                imgs, labels = X_train[idx], y_train[idx]
    
                # ---------------------- # 
                #   生成正态分布的输入
                # ---------------------- #
                noise = np.random.normal(0, 1, (batch_size, self.latent_dim))
                sampled_labels = np.random.randint(0, 10, (batch_size, 1))
                gen_imgs = self.generator.predict([noise, sampled_labels])
    
                d_loss_real = self.discriminator.train_on_batch(imgs, [valid, labels])
                d_loss_fake = self.discriminator.train_on_batch(gen_imgs, [fake, sampled_labels])
                d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
    
                # --------------------- #
                #  训练生成模型
                # --------------------- #
                g_loss = self.combined.train_on_batch([noise, sampled_labels], [valid, sampled_labels])
    
                print ("%d [D loss: %f, acc.: %.2f%%, op_acc: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[3], 100*d_loss[4], g_loss[0]))
    
                if epoch % sample_interval == 0:
                    self.sample_images(epoch)
    
    
        def sample_images(self, epoch):
            r, c = 2, 5
            noise = np.random.normal(0, 1, (r * c, 100))
            sampled_labels = np.arange(0, 10).reshape(-1, 1)
    
            gen_imgs = self.generator.predict([noise, sampled_labels])
            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].set_title("Digit: %d" % sampled_labels[cnt])
                    axs[i,j].axis('off')
                    cnt += 1
            fig.savefig("images/%d.png" % epoch)
            plt.close()
            
        def save_model(self):
    
            def save(model, model_name):
                model_path = "saved_model/%s.json" % model_name
                weights_path = "saved_model/%s_weights.hdf5" % model_name
                options = {
        "file_arch": model_path,
                            "file_weight": weights_path}
                json_string = model.to_json()
                open(options['file_arch'], 'w').write(json_string)
                model.save_weights(options['file_weight'])
    
            save(self.generator, "generator")
            save(self.discriminator, "discriminator")
    
    if __name__ == '__main__':
        if not os.path.exists("./images"):
            os.makedirs("./images")
        acgan = ACGAN()
        acgan.train(epochs=20000, 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/20191229123217699.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA==,size_16,color_FFFFFF,t_70#pic_center