pytorch:实现简单的GAN示例(MNIST数据集)

Python  /  管理员 发布于 5年前   703

我就废话不多说了，直接上代码吧！

# -*- coding: utf-8 -*-"""Created on Sat Oct 13 10:22:45 2018@author: www""" import torchfrom torch import nnfrom torch.autograd import Variable import torchvision.transforms as tfsfrom torch.utils.data import DataLoader, samplerfrom torchvision.datasets import MNIST import numpy as np import matplotlib.pyplot as pltimport matplotlib.gridspec as gridspec plt.rcParams['figure.figsize'] = (10.0, 8.0) # 设置画图的尺寸plt.rcParams['image.interpolation'] = 'nearest'plt.rcParams['image.cmap'] = 'gray' def show_images(images): # 定义画图工具  images = np.reshape(images, [images.shape[0], -1])  sqrtn = int(np.ceil(np.sqrt(images.shape[0])))  sqrtimg = int(np.ceil(np.sqrt(images.shape[1])))   fig = plt.figure(figsize=(sqrtn, sqrtn))  gs = gridspec.GridSpec(sqrtn, sqrtn)  gs.update(wspace=0.05, hspace=0.05)   for i, img in enumerate(images):    ax = plt.subplot(gs[i])    plt.axis('off')    ax.set_xticklabels([])    ax.set_yticklabels([])    ax.set_aspect('equal')    plt.imshow(img.reshape([sqrtimg,sqrtimg]))  return   def preprocess_img(x):  x = tfs.ToTensor()(x)  return (x - 0.5) / 0.5 def deprocess_img(x):  return (x + 1.0) / 2.0 class ChunkSampler(sampler.Sampler): # 定义一个取样的函数  """Samples elements sequentially from some offset.   Arguments:    num_samples: # of desired datapoints    start: offset where we should start selecting from  """  def __init__(self, num_samples, start=0):    self.num_samples = num_samples    self.start = start   def __iter__(self):    return iter(range(self.start, self.start + self.num_samples))   def __len__(self):    return self.num_samples    NUM_TRAIN = 50000NUM_VAL = 5000 NOISE_DIM = 96batch_size = 128 train_set = MNIST('E:/data', train=True, transform=preprocess_img) train_data = DataLoader(train_set, batch_size=batch_size, sampler=ChunkSampler(NUM_TRAIN, 0)) val_set = MNIST('E:/data', train=True, transform=preprocess_img) val_data = DataLoader(val_set, batch_size=batch_size, sampler=ChunkSampler(NUM_VAL, NUM_TRAIN)) imgs = deprocess_img(train_data.__iter__().next()[0].view(batch_size, 784)).numpy().squeeze() # 可视化图片效果show_images(imgs) #判别网络def discriminator():  net = nn.Sequential(          nn.Linear(784, 256),      nn.LeakyReLU(0.2),      nn.Linear(256, 256),      nn.LeakyReLU(0.2),      nn.Linear(256, 1)    )  return net  #生成网络def generator(noise_dim=NOISE_DIM):    net = nn.Sequential(    nn.Linear(noise_dim, 1024),    nn.ReLU(True),    nn.Linear(1024, 1024),    nn.ReLU(True),    nn.Linear(1024, 784),    nn.Tanh()  )  return net  #判别器的 loss 就是将真实数据的得分判断为 1，假的数据的得分判断为 0，而生成器的 loss 就是将假的数据判断为 1 bce_loss = nn.BCEWithLogitsLoss()#交叉熵损失函数 def discriminator_loss(logits_real, logits_fake): # 判别器的 loss  size = logits_real.shape[0]  true_labels = Variable(torch.ones(size, 1)).float()  false_labels = Variable(torch.zeros(size, 1)).float()  loss = bce_loss(logits_real, true_labels) + bce_loss(logits_fake, false_labels)  return loss  def generator_loss(logits_fake): # 生成器的 loss   size = logits_fake.shape[0]  true_labels = Variable(torch.ones(size, 1)).float()  loss = bce_loss(logits_fake, true_labels)  return loss  # 使用 adam 来进行训练，学习率是 3e-4, beta1 是 0.5, beta2 是 0.999def get_optimizer(net):  optimizer = torch.optim.Adam(net.parameters(), lr=3e-4, betas=(0.5, 0.999))  return optimizer  def train_a_gan(D_net, G_net, D_optimizer, G_optimizer, discriminator_loss, generator_loss, show_every=250,         noise_size=96, num_epochs=10):  iter_count = 0  for epoch in range(num_epochs):    for x, _ in train_data:      bs = x.shape[0]      # 判别网络      real_data = Variable(x).view(bs, -1) # 真实数据      logits_real = D_net(real_data) # 判别网络得分sample_noise = (torch.rand(bs, noise_size) - 0.5) / 0.5 # -1 ~ 1 的均匀分布      g_fake_seed = Variable(sample_noise)      fake_images = G_net(g_fake_seed) # 生成的假的数据      logits_fake = D_net(fake_images) # 判别网络得分       d_total_error = discriminator_loss(logits_real, logits_fake) # 判别器的 loss      D_optimizer.zero_grad()      d_total_error.backward()      D_optimizer.step() # 优化判别网络# 生成网络      g_fake_seed = Variable(sample_noise)      fake_images = G_net(g_fake_seed) # 生成的假的数据       gen_logits_fake = D_net(fake_images)      g_error = generator_loss(gen_logits_fake) # 生成网络的 loss      G_optimizer.zero_grad()      g_error.backward()      G_optimizer.step() # 优化生成网络       if (iter_count % show_every == 0):        print('Iter: {}, D: {:.4}, G:{:.4}'.format(iter_count, d_total_error.item(), g_error.item()))        imgs_numpy = deprocess_img(fake_images.data.cpu().numpy())        show_images(imgs_numpy[0:16])        plt.show()        print()      iter_count += 1 D = discriminator()G = generator() D_optim = get_optimizer(D)G_optim = get_optimizer(G) train_a_gan(D, G, D_optim, G_optim, discriminator_loss, generator_loss)      

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