网络结构

在DCGAN(本文)或者普通GAN的基础上，将交叉熵损失改为均方误差损失，就得到了LSGAN

LSGAN是对之前两种GAN的优化，因为当生成器生成的数据分布$P_G$与数据的真实分布$P_{data}$不重叠时，JS散度永远都是log2，从而导致生成器难以更新，见下图



导入相关函数

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import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import matplotlib.pyplot as plt
import numpy as np

准备数据

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(train_images,_),(_,_)=tf.keras.datasets.mnist.load_data()
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train_images.shape
(60000, 28, 28)

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train_images.dtype
dtype('uint8')

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train_images=train_images.reshape(train_images.shape[0],28,28,1).astype('float32')
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train_images.shape
(60000, 28, 28, 1)

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train_images.dtype
dtype('float32')

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train_images=(train_images-127.5)/127.1#归一化
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BATCH_SIZE=256
BUFFER_SIZE=60000
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datasets=tf.data.Dataset.from_tensor_slices(train_images)
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datasets
<TensorSliceDataset shapes: (28, 28, 1), types: tf.float32>

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datasets=datasets.shuffle(BUFFER_SIZE).batch(BATCH_SIZE)
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datasets
<BatchDataset shapes: (None, 28, 28, 1), types: tf.float32>

搭建生成器网络和判别器网络

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def generator_model():
    model=tf.keras.Sequential()
    
    model.add(layers.Dense(7*7*256,use_bias=False,input_shape=(100,)))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())
    
    model.add(layers.Reshape((7,7,256)))
    
    model.add(layers.Conv2DTranspose(128,(5,5),strides=(1,1),padding='same',use_bias=False))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())
    
    model.add(layers.Conv2DTranspose(64,(5,5),strides=(2,2),padding='same',use_bias=False))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())
    
    model.add(layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))
    
    return model
    
    
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def discriminator_model():
    model=tf.keras.Sequential()
    
    model.add(layers.Conv2D(64,(5,5),strides=(2,2),padding='same'))
    model.add(layers.LeakyReLU())
    model.add(layers.Dropout(0.3))
    
    model.add(layers.Conv2D(128,(5,5),strides=(2,2),padding='same'))
    model.add(layers.LeakyReLU())
    model.add(layers.Dropout(0.3))

    model.add(layers.Flatten())    
    model.add(layers.Dense(1))#判断真假，二分类
    
    return model

定义损失函数

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#real_out和fake_out都是回归预测值
def discriminator_loss(real_out,fake_out):
    real_loss=tf.reduce_sum(tf.square(real_out-1))/2
    fake_loss=tf.reduce_sum(tf.square(fake_out-0))/2
    return real_loss+fake_loss
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def generator_loss(fake_out):
    fake_loss=tf.reduce_sum(tf.square(fake_out-1))/2
    return fake_loss

定义优化器

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generator_opt=tf.keras.optimizers.Adam(1e-4)
discriminator_opt=tf.keras.optimizers.Adam(1e-4)

设置超参数，实例化生成器和判别器

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EPOCHS=100
noise_dim=100
num_example_to_generate=16
seed=tf.random.normal([num_example_to_generate,noise_dim])

generator=generator_model()
discriminator=discriminator_model()

定义每个batch训练的过程

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def train_step(images_one_batch):
    noise=tf.random.normal([num_example_to_generate,noise_dim])#noise=seed
    with tf.GradientTape() as gen_tape,tf.GradientTape() as disc_tape:
        real_out=discriminator(images_one_batch,training=True)#真实图片送入判别器之后得到的预测标签
        
        gen_image=generator(noise,training=True)#生成的假图片
        fake_out=discriminator(gen_image,training=True)#生成的假图片送入判别器之后得到的预测标签
        
        #分别计算两者的损失
        gen_loss=generator_loss(fake_out)
        disc_loss=discriminator_loss(real_out,fake_out)
    
    #求可训练参数的梯度
    gradient_gen=gen_tape.gradient(gen_loss,generator.trainable_variables)
    gradient_disc=disc_tape.gradient(disc_loss,discriminator.trainable_variables)
    
    #使用优化器更新可训练参数的权值
    generator_opt.apply_gradients(zip(gradient_gen,generator.trainable_variables))
    discriminator_opt.apply_gradients(zip(gradient_disc,discriminator.trainable_variables))

定义生成图片展示的函数

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#将test_noise送入gen_model，以产生假图片
def generate_plot_image(gen_model,test_noise):
    pre_images=gen_model(test_noise,training=False)#此时无需训练生成器网络
    fig=plt.figure(figsize=(4,4))
    for i in range(pre_images.shape[0]):
        plt.subplot(4,4,i+1)
        plt.imshow((pre_images[i,:,:,0]+1)/2,cmap='gray')
        plt.axis('off')
    plt.show()

定义训练函数

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def train(dataset,epochs):
    for epoch in range(epochs):
        for image_batch in dataset:
            train_step(image_batch)
        generate_plot_image(generator,seed)

开始训练

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train(datasets,EPOCHS)

漫长的等待过后，最终的生成图片如下：