一，mnist資料集

形如上圖的數字手寫體就是mnist資料集。

二，GAN原理(生成對抗網路)

GAN網路一共由兩部分組成：一個是偽造器(Generator，簡稱G)，一個是判別器(Discrimniator，簡稱D)

一開始，G由服從某幾個分佈(如高斯分佈)的噪音組成，生成的圖片不斷送給D判斷是否正確，直到G生成的圖片連D都判斷以為是真的。D每一輪除了看過G生成的假圖片以外，還要見資料集中的真圖片，以前者和後者得到的損失函式值為依據更新D網路中的權值。因此G和D都在不停地更新權值。以下圖為例：

在v1時的G只不過是 一堆噪聲，見過資料集(real images)的D肯定能判斷出G所生成的是假的。當然G也能知道D判斷它是假的這個結果，因此G就會更新權值，到v2的時候，G就能生成更逼真的圖片來讓D判斷，當然在v2時D也是會先看一次真圖片，再去判斷G所生成的圖片。以此類推，不斷迴圈就是GAN的思想。

三，訓練程式碼

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) # 確定圖片輸入的格式為(1，28，28)，由於mnist資料集是灰度圖所以通道為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網路 上一個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，均值為0方差為1維度為(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用於更新G網路的權值，g_loss於D網路的判斷結果 有關
   optimizer_G.step()
 
   # ---------------------
   # Train Discriminator
   # ---------------------
 
   optimizer_D.zero_grad() # 清空D網路 上一個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用於更新D網路的權值
   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) # 儲存一個batchsize中的25張
   if (epoch+1) %2 ==0:
    print('save..')
    torch.save(generator,'g%d.pth' % epoch)
    torch.save(discriminator,'d%d.pth' % epoch)

執行結果：

一開始時，G生成的全是雜音：

然後逐漸呈現數字的雛形：

最後一次生成的結果：

四，測試程式碼：

匯入最後儲存生成器的模型：

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') #匯入生成器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)))) #輸入的噪音
gen_imgs =g(z) #生產圖片
save_image(gen_imgs.data[:25],"images.png",normalize=True)

生成結果：

以上這篇pytorch GAN偽造手寫體mnist資料集方式就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支援我們。