pytorch GAN伪造手写体mnist数据集方式

 import argparse import os import numpy as np import math import torchvision.transforms as transforms from torchvision.utils import save_image from torch.utils.data import DataLoader from torchvision import datasets from torch.autograd import Variable import torch.nn as nn import torch.nn.functional as F import torch os.makedirs("images", exist_ok=True) parser = argparse.ArgumentParser() parser.add_argument("--n_epochs", type=int, default=200, help="number of epochs of training") parser.add_argument("--batch_size", type=int, default=64, help="size of the batches") parser.add_argument("--lr", type=float, default=0.0002, help="adam: learning rate") parser.add_argument("--b1", type=float, default=0.5, help="adam: decay of first order momentum of gradient") parser.add_argument("--b2", type=float, default=0.999, help="adam: decay of first order momentum of gradient") parser.add_argument("--n_cpu", type=int, default=8, help="number of cpu threads to use during batch generation") parser.add_argument("--latent_dim", type=int, default=100, help="dimensionality of the latent space") parser.add_argument("--img_size", type=int, default=28, help="size of each image dimension") parser.add_argument("--channels", type=int, default=1, help="number of image channels") parser.add_argument("--sample_interval", type=int, default=400, help="interval betwen image samples") opt = parser.parse_args() print(opt) img_shape = (opt.channels, opt.img_size, opt.img_size) # 确定图片输入的格式为(1，28，28)，由于mnist数据集是灰度图所以通道为1 cuda = True if torch.cuda.is_available() else False class Generator(nn.Module): def __init__(self): super(Generator, self).__init__() def block(in_feat, out_feat, normalize=True): layers = [nn.Linear(in_feat, out_feat)] if normalize: layers.append(nn.BatchNorm1d(out_feat, 0.8)) layers.append(nn.LeakyReLU(0.2, inplace=True)) return layers self.model = nn.Sequential( *block(opt.latent_dim, 128, normalize=False), *block(128, 256), *block(256, 512), *block(512, 1024), nn.Linear(1024, int(np.prod(img_shape))), nn.Tanh() ) def forward(self, z): img = self.model(z) img = img.view(img.size(0), *img_shape) return img class Discriminator(nn.Module): def __init__(self): super(Discriminator, self).__init__() self.model = nn.Sequential( nn.Linear(int(np.prod(img_shape)), 512), nn.LeakyReLU(0.2, inplace=True), nn.Linear(512, 256), nn.LeakyReLU(0.2, inplace=True), nn.Linear(256, 1), nn.Sigmoid(), ) def forward(self, img): img_flat = img.view(img.size(0), -1) validity = self.model(img_flat) return validity # Loss function adversarial_loss = torch.nn.BCELoss() # Initialize generator and discriminator generator = Generator() discriminator = Discriminator() if cuda: generator.cuda() discriminator.cuda() adversarial_loss.cuda() # Configure data loader os.makedirs("../../data/mnist", exist_ok=True) dataloader = torch.utils.data.DataLoader( datasets.MNIST( "../../data/mnist", train=True, download=True, transform=transforms.Compose( [transforms.Resize(opt.img_size), transforms.ToTensor(), transforms.Normalize([0.5], [0.5])] ), ), batch_size=opt.batch_size, shuffle=True, ) # Optimizers optimizer_G = torch.optim.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2)) optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2)) Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor # ---------- # Training # ---------- if __name__ == '__main__': for epoch in range(opt.n_epochs): for i, (imgs, _) in enumerate(dataloader): # print(imgs.shape) # Adversarial ground truths valid = Variable(Tensor(imgs.size(0), 1).fill_(1.0), requires_grad=False) # 全1 fake = Variable(Tensor(imgs.size(0), 1).fill_(0.0), requires_grad=False) # 全0 # Configure input real_imgs = Variable(imgs.type(Tensor)) # ----------------- # Train Generator # ----------------- optimizer_G.zero_grad() # 清空G网络 上一个batch的梯度 # Sample noise as generator input z = Variable(Tensor(np.random.normal(0, 1, (imgs.shape[0], opt.latent_dim)))) # 生成的噪音，均值为0方差为1维度为(64，100)的噪音 # Generate a batch of images gen_imgs = generator(z) # Loss measures generator's ability to fool the discriminator g_loss = adversarial_loss(discriminator(gen_imgs), valid) g_loss.backward() # g_loss用于更新G网络的权值，g_loss于D网络的判断结果 有关 optimizer_G.step() # --------------------- # Train Discriminator # --------------------- optimizer_D.zero_grad() # 清空D网络 上一个batch的梯度 # Measure discriminator's ability to classify real from generated samples real_loss = adversarial_loss(discriminator(real_imgs), valid) fake_loss = adversarial_loss(discriminator(gen_imgs.detach()), fake) d_loss = (real_loss + fake_loss) / 2 d_loss.backward() # d_loss用于更新D网络的权值 optimizer_D.step() print( "[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]" % (epoch, opt.n_epochs, i, len(dataloader), d_loss.item(), g_loss.item()) ) batches_done = epoch * len(dataloader) + i if batches_done % opt.sample_interval == 0: save_image(gen_imgs.data[:25], "images/%d.png-600" % batches_done, nrow=5, normalize=True) # 保存一个batchsize中的25张 if (epoch+1) %2 ==0: print('save..') torch.save(generator,'g%d.pth' % epoch) torch.save(discriminator,'d%d.pth' % epoch)

 from gan import Generator,Discriminator import torch import matplotlib.pyplot as plt from torch.autograd import Variable import numpy as np from torchvision.utils import save_image device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') Tensor = torch.cuda.FloatTensor g = torch.load('g199.pth') #导入生成器Generator模型 #d = torch.load('d.pth') g = g.to(device) #d = d.to(device) z = Variable(Tensor(np.random.normal(0, 1, (64, 100)))) #输入的噪音 gen_imgs =g(z) #生产图片 save_image(gen_imgs.data[:25], "images.png-600" , nrow=5, normalize=True)