pytorch GAN伪造手写体mnist数据集方式

一，mnist数据集

形如上图的数字手写体就是mnist数据集。

二，GAN原理(生成对抗网络)

GAN网络一共由两部分组成：一个是伪造器(Generator，简称G)，一个是判别器(Discrimniator，简称D)

一开始，G由服从某几个分布(如高斯分布)的噪音组成，生成的图片不断送给D判断是否正确，直到G生成的图片连D都判断以为是真的。D每一轮除了看过G生成的假图片以外，还要见数据集中的真图片，以前者和后者得到的损失函数值为依据更新D网络中的权值。因此G和D都在不停地更新权值。以下图为例：

在v1时的G只不过是 一堆噪声，见过数据集(real images)的D肯定能判断出G所生成的是假的。当然G也能知道D判断它是假的这个结果，因此G就会更新权值，到v2的时候，G就能生成更逼真的图片来让D判断，当然在v2时D也是会先看一次真图片，再去判断G所生成的图片。以此类推，不断循环就是GAN的思想。

三，训练代码

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",default=64,help="size of the batches")
parser.add_argument("--lr",type=float,default=0.0002,help="adam: learning rate")
parser.add_argument("--b1",default=0.5,help="adam: decay of first order momentum of gradient")
parser.add_argument("--b2",default=0.999,help="adam: decay of first order momentum of gradient")
parser.add_argument("--n_cpu",default=8,help="number of cpu threads to use during batch generation")
parser.add_argument("--latent_dim",default=100,help="dimensionality of the latent space")
parser.add_argument("--img_size",default=28,help="size of each image dimension")
parser.add_argument("--channels",default=1,help="number of image channels")
parser.add_argument("--sample_interval",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)),nn.LeakyReLU(0.2,inplace=True),nn.Linear(512,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(),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" % 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)

运行结果：

一开始时，G生成的全是杂音：

然后逐渐呈现数字的雏形：

最后一次生成的结果：

四，测试代码：

导入最后保存生成器的模型：

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,(64,100)))) #输入的噪音
gen_imgs =g(z) #生产图片
save_image(gen_imgs.data[:25],"images.png",normalize=True)

生成结果：

以上这篇pytorch GAN伪造手写体mnist数据集方式就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持我们。

（编辑：李大同）

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