200字范文 > Pytorch:GAN生成对抗网络实现MNIST手写数字的生成

Pytorch:GAN生成对抗网络实现MNIST手写数字的生成

时间：2019-07-24 01:11:15

github：/SPECTRELWF/pytorch-GAN-study

个人主页：liuweifeng.top:8090

网络结构

最近在疯狂补深度学习一些基本架构的基础，看了一下大佬的GAN的原始论文，说实话一头雾水，不是能看的很懂。推荐B站李宏毅老师的机器学习的课程，听完以后明白多了。原始论文中就说了一个generator和一个discriminator的结构，并没有细节的说具体是怎么去定义的，对新手不太友好，参考了Github的Pytorch-Gan-master仓库的代码，做了一下照搬吧，照着敲一边代码就明白了GAN的思想了。网上找了一张稍微好点的网络结构图：

因为生成对抗网络需要去度量两个分布之间的距离，原始的GAN并没有一个很好的度量，具体细节可以看李宏毅老师的课。导致GAN的训练会比较困难，而且整个LOSS是基本无变化的，但从肉眼上还是能清楚的看到生成的结果在变好。

数据集介绍

使用的是经典的MNIST数据集，后期会拿一些人脸数据集来做实验。

generator

# 定义生成器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 layersself.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(image_shape))),nn.Tanh())def forward(self,z):img = self.model(z)img = img.view(img.size(0),*image_shape)return img

discriminator

class Discriminator(nn.Module):def __init__(self):super(Discriminator,self).__init__()self.model = nn.Sequential(nn.Linear(int(np.prod(image_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

完整代码：

# !/usr/bin/python3# -*- coding:utf-8 -*-# Author:WeiFeng Liu# @Time: /11/14 下午3:05import argparseimport osimport numpy as npimport mathimport torchvision.transforms as transformsfrom torchvision.utils import save_imagefrom torch.utils.data import DataLoaderfrom torchvision import datasetsfrom torch.autograd import Variableimport torch.nn as nnimport torch.nn.functional as Fimport torchos.makedirs('new_images', exist_ok=True)parser = argparse.ArgumentParser() # 添加参数parser.add_argument("--n_epochs", type=int, default=200, help="number of epochs of training")parser.add_argument("--batch_size", type=int, default=1024, 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)image_shape = (opt.channels, opt.img_size, opt.img_size)cuda = True if torch.cuda.is_available() else False# 定义生成器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 layersself.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(image_shape))),nn.Tanh())def forward(self,z):img = self.model(z)img = img.view(img.size(0),*image_shape)return imgclass Discriminator(nn.Module):def __init__(self):super(Discriminator,self).__init__()self.model = nn.Sequential(nn.Linear(int(np.prod(image_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# lossadversarial_loss = torch.nn.BCELoss()#初始化G和Dgenerator = Generator()discriminator = Discriminator()if cuda:generator.cuda()discriminator.cuda()adversarial_loss.cuda()# loaddataos.makedirs("data/mnist",exist_ok=True)dataloader = torch.utils.data.DataLoader(datasets.MNIST("data/mnist",train = True,download=True,transform = pose([transforms.Resize(opt.img_size),transforms.ToTensor(),transforms.Normalize([0.5],[0.5]),])),batch_size=opt.batch_size,shuffle = True)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#trainfor epoch in range(opt.n_epochs):for i ,(imgs,_) in enumerate(dataloader):valid = Variable(Tensor(imgs.size(0),1).fill_(1.0),requires_grad = False)fake = Variable(Tensor(imgs.size(0), 1).fill_(0.0), requires_grad=False)real_imgs = Variable(imgs.type(Tensor))optimizer_G.zero_grad()z = Variable(Tensor(np.random.normal(0,1,(imgs.shape[0],opt.latent_dim))))gen_imgs = generator(z)g_loss = adversarial_loss(discriminator(gen_imgs),valid)g_loss.backward()optimizer_G.step()#train Discriminatoroptimizer_D.zero_grad()real_loss = adversarial_loss(discriminator(real_imgs),valid)fake_loss = adversarial_loss(discriminator(gen_imgs.detach()),fake)d_loss = (real_loss+fake_loss)/2d_loss.backward()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) + iif batches_done % opt.sample_interval == 0:save_image(gen_imgs.data[:1024], "new_images/%d.png" % batches_done, nrow=32, normalize=True)torch.save(generator.state_dict(),"G.pth")torch.save(discriminator.state_dict(),"D.pth")