银行官方网站,WordPress维护模式退出,做网站效果图是用ps还是ai,搭建个人博客网站文章目录 DCGAN介绍代码结果参考 DCGAN介绍
将CNN和GAN结合起来#xff0c;把监督学习和无监督学习结合起来。具体解释可以参见 深度卷积对抗生成网络(DCGAN)
DCGAN的生成器结构#xff1a; 图片来源#xff1a;https://arxiv.org/abs/1511.06434
代码
model.py
impor… 文章目录 DCGAN介绍代码结果参考 DCGAN介绍
将CNN和GAN结合起来把监督学习和无监督学习结合起来。具体解释可以参见 深度卷积对抗生成网络(DCGAN)
DCGAN的生成器结构 图片来源https://arxiv.org/abs/1511.06434
代码
model.py
import torch
import torch.nn as nnclass Discriminator(nn.Module):def __init__(self, channels_img, features_d):super(Discriminator, self).__init__()self.disc nn.Sequential(# Input: N x channels_img x 64 x 64nn.Conv2d(channels_img, features_d, kernel_size4, stride2, padding1), # 32 x 32nn.LeakyReLU(0.2),self._block(features_d, features_d*2, 4, 2, 1), # 16 x 16self._block(features_d*2, features_d*4, 4, 2, 1), # 8 x 8self._block(features_d*4, features_d*8, 4, 2, 1), # 4 x 4nn.Conv2d(features_d*8, 1, kernel_size4, stride2, padding0), # 1 x 1nn.Sigmoid(),)def _block(self, in_channels, out_channels, kernel_size, stride, padding):return nn.Sequential(nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, biasFalse),nn.BatchNorm2d(out_channels),nn.LeakyReLU(0.2),)def forward(self, x):return self.disc(x)class Generator(nn.Module):def __init__(self, z_dim, channels_img, features_g):super(Generator, self).__init__()self.gen nn.Sequential(# Input: N x z_dim x 1 x 1self._block(z_dim, features_g*16, 4, 1, 0), # N x f_g*16 x 4 x 4self._block(features_g*16, features_g*8, 4, 2, 1), # 8x8self._block(features_g*8, features_g*4, 4, 2, 1), # 16x16self._block(features_g*4, features_g*2, 4, 2, 1), # 32x32nn.ConvTranspose2d(features_g*2, channels_img, kernel_size4, stride2, padding1,),nn.Tanh(),)def _block(self, in_channels, out_channels, kernel_size, stride, padding):return nn.Sequential(nn.ConvTranspose2d(in_channels,out_channels,kernel_size,stride,padding,biasFalse,),nn.BatchNorm2d(out_channels),nn.ReLU(),)def forward(self, x):return self.gen(x)def initialize_weights(model):for m in model.modules():if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d, nn.BatchNorm2d)):nn.init.normal_(m.weight.data, 0.0, 0.02)def test():N, in_channels, H, W 8, 3, 64, 64z_dim 100x torch.randn((N, in_channels, H, W))disc Discriminator(in_channels, 8)initialize_weights(disc)assert disc(x).shape (N, 1, 1, 1)gen Generator(z_dim, in_channels, 8)initialize_weights(gen)z torch.randn((N, z_dim, 1, 1))assert gen(z).shape (N, in_channels, H, W)print(success)if __name__ __main__:test()
训练使用的数据集CelebA dataset (Images Only) 总共1.3GB的图片使用方法将其解压到当前目录
图片如下图所示
train.py
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from model import Discriminator, Generator, initialize_weights# Hyperparameters etc.
device torch.device(cuda if torch.cuda.is_available() else cpu)
LEARNING_RATE 2e-4 # could also use two lrs, one for gen and one for disc
BATCH_SIZE 128
IMAGE_SIZE 64
CHANNELS_IMG 3 # 1 if MNIST dataset; 3 if celeb dataset
NOISE_DIM 100
NUM_EPOCHS 5
FEATURES_DISC 64
FEATURES_GEN 64transforms transforms.Compose([transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)),transforms.ToTensor(),transforms.Normalize([0.5 for _ in range(CHANNELS_IMG)], [0.5 for _ in range(CHANNELS_IMG)]),]
)# If you train on MNIST, remember to set channels_img to 1
# dataset datasets.MNIST(
# rootdataset/, trainTrue, transformtransforms, downloadTrue
# )# comment mnist above and uncomment below if train on CelebA# If you train on celeb dataset, remember to set channels_img to 3
dataset datasets.ImageFolder(rootceleb_dataset, transformtransforms)
dataloader DataLoader(dataset, batch_sizeBATCH_SIZE, shuffleTrue)
gen Generator(NOISE_DIM, CHANNELS_IMG, FEATURES_GEN).to(device)
disc Discriminator(CHANNELS_IMG, FEATURES_DISC).to(device)
initialize_weights(gen)
initialize_weights(disc)opt_gen optim.Adam(gen.parameters(), lrLEARNING_RATE, betas(0.5, 0.999))
opt_disc optim.Adam(disc.parameters(), lrLEARNING_RATE, betas(0.5, 0.999))
criterion nn.BCELoss()fixed_noise torch.randn(32, NOISE_DIM, 1, 1).to(device)
writer_real SummaryWriter(flogs/real)
writer_fake SummaryWriter(flogs/fake)
step 0gen.train()
disc.train()for epoch in range(NUM_EPOCHS):# Target labels not needed! 3 unsupervisedfor batch_idx, (real, _) in enumerate(dataloader):real real.to(device)noise torch.randn(BATCH_SIZE, NOISE_DIM, 1, 1).to(device)fake gen(noise)### Train Discriminator: max log(D(x)) log(1 - D(G(z)))disc_real disc(real).reshape(-1)loss_disc_real criterion(disc_real, torch.ones_like(disc_real))disc_fake disc(fake.detach()).reshape(-1)loss_disc_fake criterion(disc_fake, torch.zeros_like(disc_fake))loss_disc (loss_disc_real loss_disc_fake) / 2disc.zero_grad()loss_disc.backward()opt_disc.step()### Train Generator: min log(1 - D(G(z))) - max log(D(G(z))output disc(fake).reshape(-1)loss_gen criterion(output, torch.ones_like(output))gen.zero_grad()loss_gen.backward()opt_gen.step()# Print losses occasionally and print to tensorboardif batch_idx % 100 0:print(fEpoch [{epoch}/{NUM_EPOCHS}] Batch {batch_idx}/{len(dataloader)} \Loss D: {loss_disc:.4f}, loss G: {loss_gen:.4f})with torch.no_grad():fake gen(fixed_noise)# take out (up to) 32 examplesimg_grid_real torchvision.utils.make_grid(real[:32], normalizeTrue)img_grid_fake torchvision.utils.make_grid(fake[:32], normalizeTrue)writer_real.add_image(Real, img_grid_real, global_stepstep)writer_fake.add_image(Fake, img_grid_fake, global_stepstep)step 1结果
训练5个epoch部分结果如下
Epoch [3/5] Batch 1500/1583 Loss D: 0.4996, loss G: 1.1738
Epoch [4/5] Batch 0/1583 Loss D: 0.4268, loss G: 1.6633
Epoch [4/5] Batch 100/1583 Loss D: 0.4841, loss G: 1.7475
Epoch [4/5] Batch 200/1583 Loss D: 0.5094, loss G: 1.2376
Epoch [4/5] Batch 300/1583 Loss D: 0.4376, loss G: 2.1271
Epoch [4/5] Batch 400/1583 Loss D: 0.4173, loss G: 1.4380
Epoch [4/5] Batch 500/1583 Loss D: 0.5213, loss G: 2.1665
Epoch [4/5] Batch 600/1583 Loss D: 0.5036, loss G: 2.1079
Epoch [4/5] Batch 700/1583 Loss D: 0.5158, loss G: 1.0579
Epoch [4/5] Batch 800/1583 Loss D: 0.5426, loss G: 1.9427
Epoch [4/5] Batch 900/1583 Loss D: 0.4721, loss G: 1.2659
Epoch [4/5] Batch 1000/1583 Loss D: 0.5662, loss G: 2.4537
Epoch [4/5] Batch 1100/1583 Loss D: 0.5604, loss G: 0.8978
Epoch [4/5] Batch 1200/1583 Loss D: 0.4085, loss G: 2.0747
Epoch [4/5] Batch 1300/1583 Loss D: 1.1894, loss G: 0.1825
Epoch [4/5] Batch 1400/1583 Loss D: 0.4518, loss G: 2.1509
Epoch [4/5] Batch 1500/1583 Loss D: 0.3814, loss G: 1.9391使用
tensorboard --logdirlogs打开tensorboard 参考
[1] DCGAN implementation from scratch [2] https://arxiv.org/abs/1511.06434
