什么是GAN
实现代码
import tensorflow as tffrom tensorflow import kerasfrom tensorflow.keras import layersimport matplotlib.pyplot as plt%matplotlib inlineimport numpy as npimport globimport os
# 显存自适应分配gpus = tf.config.experimental.list_physical_devices(device_type='GPU')for gpu in gpus:tf.config.experimental.set_memory_growth(gpu,True)
gpu_ok = tf.test.is_gpu_available()print("tf version:", tf.__version__)print("use GPU", gpu_ok) # 判断是否使用gpu进行训练
# 手写数据集(train_images,train_labels),(test_images,test_labels) = tf.keras.datasets.mnist.load_data()
train_images = train_images.reshape(train_images.shape[0],28,28,1).astype("float32")
# 归一化train_images = (train_images-127.5)/127.5
BATCH_SIZE = 256BUFFER_SIZE = 60000
# 创建数据集datasets = tf.data.Dataset.from_tensor_slices(train_images)
# 乱序datasets = datasets.shuffle(BUFFER_SIZE).batch(BATCH_SIZE)
编写模型
# 生成器模型def generator_model():model = keras.Sequential() # 顺序模型model.add(layers.Dense(256,input_shape=(100,),use_bias=False)) # 输出256个单元，随机数输入数据形状长度100的向量model.add(layers.BatchNormalization()) model.add(layers.LeakyReLU())# LeakyReLU()激活model.add(layers.Dense(512,use_bias=False))model.add(layers.BatchNormalization())model.add(layers.LeakyReLU())# 激活model.add(layers.Dense(28*28*1,use_bias=False,activation="tanh")) # 输出28*28*1形状 使用tanh激活得到-1 到1 的值model.add(layers.BatchNormalization())model.add(layers.Reshape((28,28,1))) # reshape成28*28*1的形状return model
# 判别模型def discriminator_model():model = keras.Sequential()model.add(layers.Flatten())model.add(layers.Dense(512,use_bias=False))model.add(layers.BatchNormalization())model.add(layers.LeakyReLU())# 激活model.add(layers.Dense(256,use_bias=False))model.add(layers.BatchNormalization())model.add(layers.LeakyReLU())# 激活model.add(layers.Dense(1))return model
# 编写loss binary_crossentropy（对数损失函数）即 log loss，与 sigmoid 相对应的损失函数，针对于二分类问题。cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)
# 辨别器lossdef discriminator_loss(real_out,fake_out):read_loss = cross_entropy(tf.ones_like(real_out),real_out) # 使用binary_crossentropy 对真实图片判别为1fake_loss = cross_entropy(tf.zeros_like(fake_out),fake_out) # 生成的图片 判别为0return read_loss + fake_loss
# 生成器lossdef generator_loss(fake_out):return cross_entropy(tf.ones_like(fake_out),fake_out) # 希望对生成的图片返回为1
# 优化器generator_opt = tf.keras.optimizers.Adam(1e-4) # 学习速率1e-4 0.0001discriminator_opt = tf.keras.optimizers.Adam(1e-4)
EPOCHS = 100 # 训练步数noise_dim = 100 num_exp_to_generate = 16seed = tf.random.normal([num_exp_to_generate,noise_dim]) # 16，100 # 生成16个样本，长度为100的随机数
generator = generator_model()
discriminator = discriminator_model()
# 训练一个epochdef train_step(images):noise = tf.random.normal([BATCH_SIZE,noise_dim])with tf.GradientTape() as gen_tape,tf.GradientTape() as disc_tape: # 梯度real_out = discriminator(images,training=True)gen_image = generator(noise,training=True)fake_out = discriminator(gen_image,training=True)gen_loss = generator_loss(fake_out)disc_loss = discriminator_loss(real_out,fake_out)gradient_gen = gen_tape.gradient(gen_loss,generator.trainable_variables)gradient_disc = disc_tape.gradient(disc_loss,discriminator.trainable_variables)generator_opt.apply_gradients(zip(gradient_gen,generator.trainable_variables))discriminator_opt.apply_gradients(zip(gradient_disc,discriminator.trainable_variables))
def genrate_plot_image(gen_model,test_noise):pre_images = gen_model(test_noise,training=False)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()
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)
# 训练模型train(datasets,EPOCHS)
