目錄
- 1.圖片分類(lèi)網(wǎng)絡(luò)
- 2.圖片生成網(wǎng)絡(luò)
- 首先是圖片分類(lèi)網(wǎng)絡(luò)：
- 重點(diǎn)是生成網(wǎng)絡(luò)
- 每一個(gè)step分為三個(gè)步驟：
1.圖片分類(lèi)網(wǎng)絡(luò)
這是一個(gè)二分類(lèi)網(wǎng)絡(luò)，可以是alxnet ,vgg,resnet任何一個(gè)，負(fù)責(zé)對(duì)圖片進(jìn)行二分類(lèi)，區(qū)分圖片是真實(shí)圖片還是生成的圖片
2.圖片生成網(wǎng)絡(luò)
輸入是一個(gè)隨機(jī)噪聲，輸出是一張圖片，使用的是反卷積層
相信學(xué)過(guò)深度學(xué)習(xí)的都能寫(xiě)出這兩個(gè)網(wǎng)絡(luò)，當(dāng)然如果你寫(xiě)不出來(lái)，沒(méi)關(guān)系，有人替你寫(xiě)好了
首先是圖片分類(lèi)網(wǎng)絡(luò)：
簡(jiǎn)單來(lái)說(shuō)就是cnn+relu+sogmid，可以換成任何一個(gè)分類(lèi)網(wǎng)絡(luò)，比如bgg,resnet等
class Discriminator(nn.Module):
def __init__(self, ngpu):
super(Discriminator, self).__init__()
self.ngpu = ngpu
self.main = nn.Sequential(
# input is (nc) x 64 x 64
nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndf) x 32 x 32
nn.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 16
nn.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 8
nn.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 4
nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),
nn.Sigmoid()
)
def forward(self, input):
return self.main(input)
重點(diǎn)是生成網(wǎng)絡(luò)
代碼如下,其實(shí)就是反卷積+bn+relu
class Generator(nn.Module):
def __init__(self, ngpu):
super(Generator, self).__init__()
self.ngpu = ngpu
self.main = nn.Sequential(
# input is Z, going into a convolution
nn.ConvTranspose2d( nz, ngf * 8, 4, 1, 0, bias=False),
nn.BatchNorm2d(ngf * 8),
nn.ReLU(True),
# state size. (ngf*8) x 4 x 4
nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 4),
nn.ReLU(True),
# state size. (ngf*4) x 8 x 8
nn.ConvTranspose2d( ngf * 4, ngf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 2),
nn.ReLU(True),
# state size. (ngf*2) x 16 x 16
nn.ConvTranspose2d( ngf * 2, ngf, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf),
nn.ReLU(True),
# state size. (ngf) x 32 x 32
nn.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)
講道理，以上兩個(gè)網(wǎng)絡(luò)都挺簡(jiǎn)單。
真正的重點(diǎn)到了，怎么訓(xùn)練
每一個(gè)step分為三個(gè)步驟：
- 訓(xùn)練二分類(lèi)網(wǎng)絡(luò)
1.輸入真實(shí)圖片，經(jīng)過(guò)二分類(lèi)，希望判定為真實(shí)圖片，更新二分類(lèi)網(wǎng)絡(luò)
2.輸入噪聲，進(jìn)過(guò)生成網(wǎng)絡(luò)，生成一張圖片，輸入二分類(lèi)網(wǎng)絡(luò)，希望判定為虛假圖片，更新二分類(lèi)網(wǎng)絡(luò)
- 訓(xùn)練生成網(wǎng)絡(luò)
3.輸入噪聲，進(jìn)過(guò)生成網(wǎng)絡(luò)，生成一張圖片，輸入二分類(lèi)網(wǎng)絡(luò)，希望判定為真實(shí)圖片，更新生成網(wǎng)絡(luò)
不多說(shuō)直接上代碼
for epoch in range(num_epochs):
# For each batch in the dataloader
for i, data in enumerate(dataloader, 0):
############################
# (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))
###########################
## Train with all-real batch
netD.zero_grad()
# Format batch
real_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 D
output = netD(real_cpu).view(-1)
# Calculate loss on all-real batch
errD_real = criterion(output, label)
# Calculate gradients for D in backward pass
errD_real.backward()
D_x = output.mean().item()
## Train with all-fake batch
# Generate batch of latent vectors
noise = torch.randn(b_size, nz, 1, 1, device=device)
# Generate fake image batch with G
fake = netG(noise)
label.fill_(fake_label)
# Classify all fake batch with D
output = netD(fake.detach()).view(-1)
# Calculate D's loss on the all-fake batch
errD_fake = criterion(output, label)
# Calculate the gradients for this batch
errD_fake.backward()
D_G_z1 = output.mean().item()
# Add the gradients from the all-real and all-fake batches
errD = errD_real + errD_fake
# Update D
optimizerD.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 D
output = netD(fake).view(-1)
# Calculate G's loss based on this output
errG = criterion(output, label)
# Calculate gradients for G
errG.backward()
D_G_z2 = output.mean().item()
# Update G
optimizerG.step()
# Output training stats
if 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 later
G_losses.append(errG.item())
D_losses.append(errD.item())
# Check how the generator is doing by saving G's output on fixed_noise
if (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
以上就是Pytorch學(xué)習(xí)筆記DCGAN極簡(jiǎn)入門(mén)教程的詳細(xì)內(nèi)容，更多關(guān)于Pytorch學(xué)習(xí)DCGAN入門(mén)教程的資料請(qǐng)關(guān)注腳本之家其它相關(guān)文章！
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