import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers import matplotlib.pyplot as plt %matplotlib inline import numpy as np import glob import os

查看tensorflow版本

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tf.__version__

查看是否支持GPU

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tf.test.is_gpu_available()

加载mnist手写数据集

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(train_image,train_labels),(_,_) = tf.keras.datasets.mnist.load_data()

查看数据

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train_image.shape
(60000, 28, 28)
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train_image.dtype
dtype('uint8')

数据转换

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

查看数据

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train_image.shape
(60000, 28, 28, 1)
train_image.dtype
dtype('float32')

归一化

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train_image = (train_image-127.5)/127.5 #[-1~1]

创建输入管道

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BATCH_SIZE = 256
BUFFER_SIZE = 60000
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datasets = tf.data.Dataset.from_tensor_slices(train_image)
datasets = datasets.shuffle(BUFFER_SIZE).batch(BATCH_SIZE)

生成器

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def generator_model():#生成器
    model = tf.keras.Sequential()
    model.add(layers.Dense(7*7*256,input_shape=(100,),use_bias=False))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())
    
    model.add(layers.Reshape((7,7,256)))#7*7*256
    
    model.add(layers.Conv2DTranspose(128,5,strides=(1,1),padding='same',use_bias=False))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())  #7*7*128
    
    model.add(layers.Conv2DTranspose(64,5,strides=(2,2),padding='same',use_bias=False))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())  #14*14*64
    
    model.add(layers.Conv2DTranspose(1,5,strides=(2,2),padding='same',use_bias=False,activation='tanh'))#28,28,1

    
    return model

辨别器

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def discriminator_model():#辨别器
    model = tf.keras.Sequential()
    model.add(layers.Conv2D(64,(5,5),strides=(2,2),padding='same',input_shape=(28,28,1)))
    model.add(layers.LeakyReLU())#14,14,64
    model.add(layers.Dropout(0.3))
    
    model.add(layers.Conv2D(128,(5,5),strides=(2,2),padding='same'))
    model.add(layers.LeakyReLU())#7,7,128
    model.add(layers.Dropout(0.3))

    model.add(layers.Conv2D(256,(5,5),strides=(2,2),padding='same'))
    model.add(layers.LeakyReLU())#3,3,256
    
    model.add(layers.Flatten())
    
    model.add(layers.Dense(1))
    return model

定义loss计算指标

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# 计算loss
cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)

定义辨别器loss

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def discriminator_loss(real_out,fake_out):#辨别器loss    实际图片、生成图片
    real_loss = cross_entropy(tf.ones_like(real_out),real_out)#我们希望真实的图片判定为1（ones）
    fake_loss = cross_entropy(tf.zeros_like(fake_out),fake_out)#我们希望生成的图片判定为0（zeros）
    return real_loss+fake_loss

定义生成器loss

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def generator_loss(fake_out):#生成器loss    实际图片、生成图片
    return cross_entropy(tf.ones_like(fake_out),fake_out)#对生成器我们希望生成的图片判定为0（zeros）

设置优化器

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generator_opt = tf.keras.optimizers.Adam(1e-4)#10的负4次放
discriminator_opt = tf.keras.optimizers.Adam(1e-4)#10的负4次放

设置参数

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EPOCHS = 100
noise_dim = 100

num_exp_to_genertor = 16#每个rpoch生成样本数量

seed = tf.random.normal([num_exp_to_genertor,noise_dim])# 正态分布随机数
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generator = generator_model()
discriminator = discriminator_model()

设置每步运算

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@tf.function
def train_step(images):
    noise = tf.random.normal([BATCH_SIZE,noise_dim])
    
    with tf.GradientTape() as gen_type,tf.GradientTape() as disc_type:
        real_out = discriminator(images,training=True)#真实输出  辨别器
        
        gen_image = generator(noise,training=True)#生成图片输出
        fake_out = discriminator(gen_image,training=True)
        
        gen_loss = generator_loss(fake_out)
        disc_loss = discriminator_loss(real_out,fake_out)
    gradient_gen = gen_type.gradient(gen_loss,generator.trainable_variables)#计算生成器损失与生成器变量之间的梯度
    gradient_disc = disc_type.gradient(disc_loss,discriminator.trainable_variables)#计算辨别器损失与辨别器变量之间的梯度
    
    generator_opt.apply_gradients(zip(gradient_gen,generator.trainable_variables))
    discriminator_opt.apply_gradients(zip(gradient_disc,discriminator.trainable_variables))

定义绘制函数

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def genrate_plot_image(gen_model,test_noise):#将生成的图片打印出来
    pre_images = gen_model(test_noise,training=False)#training=False 不需要训练
    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()

模型训练

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def train(dataset,epochs):
    for epoch in range(epochs):
        for image_batch in dataset:
            train_step(image_batch)
            print("*",end='')
        genrate_plot_image(generator,seed)