Pytorch学习笔记DCGAN极简入门教程 Pytorch学习笔记DCGAN极简入门教

1.图片分类网络
2.图片生成网络

首先是图片分类网络：
重点是生成网络

每一个step分为三个步骤：

1.图片分类网络这是一个二分类网络，可以是alxnet ,vgg,resnet任何一个，负责对图片进行二分类，区分图片是真实图片还是生成的图片

2.图片生成网络输入是一个随机噪声，输出是一张图片，使用的是反卷积层
相信学过深度学习的都能写出这两个网络，当然如果你写不出来，没关系，有人替你写好了

首先是图片分类网络：
简单来说就是cnn+relu+sogmid，可以换成任何一个分类网络，比如bgg,resnet等

class Discriminator(nn.Module):def __init__(self, ngpu):super(Discriminator, self).__init__()self.ngpu = ngpuself.main = nn.Sequential(# input is (nc) x 64 x 64nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),nn.LeakyReLU(0.2, inplace=True),# state size. (ndf) x 32 x 32nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),nn.BatchNorm2d(ndf * 2),nn.LeakyReLU(0.2, inplace=True),# state size. (ndf*2) x 16 x 16nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),nn.BatchNorm2d(ndf * 4),nn.LeakyReLU(0.2, inplace=True),# state size. (ndf*4) x 8 x 8nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),nn.BatchNorm2d(ndf * 8),nn.LeakyReLU(0.2, inplace=True),# state size. (ndf*8) x 4 x 4nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),nn.Sigmoid())def forward(self, input):return self.main(input)

重点是生成网络
代码如下,其实就是反卷积+bn+relu

class Generator(nn.Module):def __init__(self, ngpu):super(Generator, self).__init__()self.ngpu = ngpuself.main = nn.Sequential(# input is Z, going into a convolutionnn.ConvTranspose2d( nz, ngf * 8, 4, 1, 0, bias=False),nn.BatchNorm2d(ngf * 8),nn.ReLU(True),# state size. (ngf*8) x 4 x 4nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),nn.BatchNorm2d(ngf * 4),nn.ReLU(True),# state size. (ngf*4) x 8 x 8nn.ConvTranspose2d( ngf * 4, ngf * 2, 4, 2, 1, bias=False),nn.BatchNorm2d(ngf * 2),nn.ReLU(True),# state size. (ngf*2) x 16 x 16nn.ConvTranspose2d( ngf * 2, ngf, 4, 2, 1, bias=False),nn.BatchNorm2d(ngf),nn.ReLU(True),# state size. (ngf) x 32 x 32nn.ConvTranspose2d( ngf, nc, 4, 2, 1, bias=False),nn.Tanh()# state size. (nc) x 64 x 64)def forward(self, input):return self.main(input)

讲道理，以上两个网络都挺简单。
真正的重点到了，怎么训练

每一个step分为三个步骤：

训练二分类网络
1.输入真实图片，经过二分类，希望判定为真实图片，更新二分类网络
2.输入噪声，进过生成网络，生成一张图片，输入二分类网络，希望判定为虚假图片，更新二分类网络
训练生成网络
3.输入噪声，进过生成网络，生成一张图片，输入二分类网络，希望判定为真实图片，更新生成网络

不多说直接上代码

for epoch in range(num_epochs):# For each batch in the dataloaderfor i, data in enumerate(dataloader, 0):############################# (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))############################# Train with all-real batchnetD.zero_grad()# Format batchreal_cpu = data[0].to(device)b_size = real_cpu.size(0)label = torch.full((b_size,), real_label, device=device)# Forward pass real batch through Doutput = netD(real_cpu).view(-1)# Calculate loss on all-real batcherrD_real = criterion(output, label)# Calculate gradients for D in backward passerrD_real.backward()D_x = output.mean().item()## Train with all-fake batch# Generate batch of latent vectorsnoise = torch.randn(b_size, nz, 1, 1, device=device)# Generate fake image batch with Gfake = netG(noise)label.fill_(fake_label)# Classify all fake batch with Doutput = netD(fake.detach()).view(-1)# Calculate D's loss on the all-fake batcherrD_fake = criterion(output, label)# Calculate the gradients for this batcherrD_fake.backward()D_G_z1 = output.mean().item()# Add the gradients from the all-real and all-fake batcheserrD = errD_real + errD_fake# Update DoptimizerD.step()############################# (2) Update G network: maximize log(D(G(z)))###########################netG.zero_grad()label.fill_(real_label)# fake labels are real for generator cost# Since we just updated D, perform another forward pass of all-fake batch through Doutput = netD(fake).view(-1)# Calculate G's loss based on this outputerrG = criterion(output, label)# Calculate gradients for GerrG.backward()D_G_z2 = output.mean().item()# Update GoptimizerG.step()# Output training statsif i % 50 == 0:print('[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f\tD(x): %.4f\tD(G(z)): %.4f / %.4f'% (epoch, num_epochs, i, len(dataloader),errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))# Save Losses for plotting laterG_losses.append(errG.item())D_losses.append(errD.item())# Check how the generator is doing by saving G's output on fixed_noiseif (iters % 500 == 0) or ((epoch == num_epochs-1) and (i == len(dataloader)-1)):with torch.no_grad():fake = netG(fixed_noise).detach().cpu()img_list.append(vutils.make_grid(fake, padding=2, normalize=True))iters += 1

【Pytorch学习笔记DCGAN极简入门教程】以上就是Pytorch学习笔记DCGAN极简入门教程的详细内容，更多关于Pytorch学习DCGAN入门教程的资料请关注脚本之家其它相关文章！