第G7周：Semi

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第G7周：Semi

 🍨 本文为🔗365天深度学习训练营 中的学习记录博客

  🍦 参考文章：365天深度学习训练营-第G7周：Semi-Supervised GAN 理论与实战（训练营内部成员可读）

  🍖 原作者：K同学啊|接辅导、项目定制

 🏡 运行环境：
电脑系统：Windows 10
语言环境：python 3.10
编译器：Pycharm 2022.1.1
深度学习环境：Pytorch  

目录

一、理论知识讲解

二、代码实现

1、配置代码 

 2、初始化权重

3、定义算法模型

4、配置模型

 5、训练模型

一、理论知识讲解

该算法将产生式对抗网络(GAN) 拓展到半监督学习，通过强制判别器D来输出类别标签。我们
在一个数据集上训练一个生成器G以及一个判别器D，输入是N类当中的一个。在训练的时候，判别器D被用于预测输入是属于N+1类中的哪一个,这个N+1是对应了生成器G的输出，这里的判别器
D同时也充当起了分类器C的效果。这种方法可以用于训练效果更好的判别器D，并且可以比普通的GAN产性更加高质量的样本。Semi-Supervised GAN有如下优点:
（1）作者对GANs做了一个新的扩展，允许它同时学习一个生成模型和一个分类器。我们把这个 扩展叫做半监督GAN或SGAN
（2）论文实验结果表明，SGAN在有限数据集比没有生成部分的基准分类器提升了分类性能。
（3）论文实验结果表明，SGAN可以显著地提升生成样本的质量并降低生成器的训练时间。 

二、代码实现

1、配置代码 

import argparse
import os
import numpy as np
import mathimport torchvision.transforms as transforms
from torchvision.utils import save_imagefrom torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variableimport torch.nn as nn
import torch.nn.functional as F
import torchos.makedirs("images", exist_ok=True)parser = argparse.ArgumentParser()
parser.add_argument("--n_epochs", type=int, default=2, help="number of epochs of training")
parser.add_argument("--batch_size", type=int, default=64, help="size of the batches")
parser.add_argument("--lr", type=float, default=0.0002, help="adam: learning rate")
parser.add_argument("--b1", type=float, default=0.5, help="adam: decay of first order momentum of gradient")
parser.add_argument("--b2", type=float, default=0.999, help="adam: decay of first order momentum of gradient")
parser.add_argument("--n_cpu", type=int, default=2, help="number of cpu threads to use during batch generation")
parser.add_argument("--latent_dim", type=int, default=100, help="dimensionality of the latent space")
parser.add_argument("--num_classes", type=int, default=10, help="number of classes for dataset")
parser.add_argument("--img_size", type=int, default=32, help="size of each image dimension")
parser.add_argument("--channels", type=int, default=1, help="number of image channels")
parser.add_argument("--sample_interval", type=int, default=400, help="interval between image sampling")
opt = parser.parse_args(args=[])
print(opt)cuda = True if torch.cuda.is_available() else False

Namespace(n_epochs=2, batch_size=64, lr=0.0002, b1=0.5, b2=0.999, n_cpu=2, latent_dim=100, num_classes=10, img_size=32, channels=1, sample_interval=400)

 2、初始化权重

def weights_init_normal(m):classname = m.__class__.__name__if classname.find("Conv") != -1:torch.nn.init.normal_(m.weight.data, 0.0, 0.02)elif classname.find("BatchNorm") != -1:torch.nn.init.normal_(m.weight.data, 1.0, 0.02)torch.nn.init.constant_(m.bias.data, 0.0)

3、定义算法模型

class Generator(nn.Module):def __init__(self):super(Generator, self).__init__()self.label_emb = nn.Embedding(opt.num_classes, opt.latent_dim)self.init_size = opt.img_size // 4  # Initial size before upsamplingself.l1 = nn.Sequential(nn.Linear(opt.latent_dim, 128 * self.init_size ** 2))self.conv_blocks = nn.Sequential(nn.BatchNorm2d(128),nn.Upsample(scale_factor=2),nn.Conv2d(128, 128, 3, stride=1, padding=1),nn.BatchNorm2d(128, 0.8),nn.LeakyReLU(0.2, inplace=True),nn.Upsample(scale_factor=2),nn.Conv2d(128, 64, 3, stride=1, padding=1),nn.BatchNorm2d(64, 0.8),nn.LeakyReLU(0.2, inplace=True),nn.Conv2d(64, opt.channels, 3, stride=1, padding=1),nn.Tanh(),)def forward(self, noise):out = self.l1(noise)out = out.view(out.shape[0], 128, self.init_size, self.init_size)img = self.conv_blocks(out)return imgclass Discriminator(nn.Module):def __init__(self):super(Discriminator, self).__init__()def discriminator_block(in_filters, out_filters, bn=True):"""Returns layers of each discriminator block"""block = [nn.Conv2d(in_filters, out_filters, 3, 2, 1), nn.LeakyReLU(0.2, inplace=True), nn.Dropout2d(0.25)]if bn:block.append(nn.BatchNorm2d(out_filters, 0.8))return blockself.conv_blocks = nn.Sequential(*discriminator_block(opt.channels, 16, bn=False),*discriminator_block(16, 32),*discriminator_block(32, 64),*discriminator_block(64, 128),)# The height and width of downsampled imageds_size = opt.img_size // 2 ** 4# Output layersself.adv_layer = nn.Sequential(nn.Linear(128 * ds_size ** 2, 1), nn.Sigmoid())self.aux_layer = nn.Sequential(nn.Linear(128 * ds_size ** 2, opt.num_classes + 1), nn.Softmax())def forward(self, img):out = self.conv_blocks(img)out = out.view(out.shape[0], -1)validity = self.adv_layer(out)label = self.aux_layer(out)return validity, label

4、配置模型

# Loss functions
adversarial_loss = torch.nn.BCELoss()
auxiliary_loss = torch.nn.CrossEntropyLoss()# Initialize generator and discriminator
generator = Generator()
discriminator = Discriminator()if cuda:generator.cuda()discriminator.cuda()adversarial_loss.cuda()auxiliary_loss.cuda()# Initialize weights
generator.apply(weights_init_normal)
discriminator.apply(weights_init_normal)# 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(), lr=opt.lr, betas=(opt.b1, opt.b2))FloatTensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
LongTensor = torch.cuda.LongTensor if cuda else torch.LongTensor

Downloading .gz
Downloading .gz to ../../data/mnist\MNIST\raw\train-images-idx3-ubyte.gz

Extracting ../../data/mnist\MNIST\raw\train-images-idx3-ubyte.gz to ../../data/mnist\MNIST\rawDownloading .gz
Downloading .gz to ../../data/mnist\MNIST\raw\train-labels-idx1-ubyte.gz

Extracting ../../data/mnist\MNIST\raw\train-labels-idx1-ubyte.gz to ../../data/mnist\MNIST\rawDownloading .gz
Downloading .gz to ../../data/mnist\MNIST\raw\t10k-images-idx3-ubyte.gz

Extracting ../../data/mnist\MNIST\raw\t10k-images-idx3-ubyte.gz to ../../data/mnist\MNIST\rawDownloading .gz
Downloading .gz to ../../data/mnist\MNIST\raw\t10k-labels-idx1-ubyte.gz

Extracting ../../data/mnist\MNIST\raw\t10k-labels-idx1-ubyte.gz to ../../data/mnist\MNIST\raw

 5、训练模型

# ----------
#  Training
# ----------for epoch in range(opt.n_epochs):for i, (imgs, labels) in enumerate(dataloader):batch_size = imgs.shape[0]# Adversarial ground truthsvalid = Variable(FloatTensor(batch_size, 1).fill_(1.0), requires_grad=False)fake = Variable(FloatTensor(batch_size, 1).fill_(0.0), requires_grad=False)fake_aux_gt = Variable(LongTensor(batch_size).fill_(opt.num_classes), requires_grad=False)# Configure inputreal_imgs = Variable(imgs.type(FloatTensor))labels = Variable(labels.type(LongTensor))# -----------------#  Train Generator# -----------------optimizer_G.zero_grad()# Sample noise and labels as generator inputz = Variable(FloatTensor(np.random.normal(0, 1, (batch_size, opt.latent_dim))))# Generate a batch of imagesgen_imgs = generator(z)# Loss measures generator's ability to fool the discriminatorvalidity, _ = discriminator(gen_imgs)g_loss = adversarial_loss(validity, valid)g_loss.backward()optimizer_G.step()# ---------------------#  Train Discriminator# ---------------------optimizer_D.zero_grad()# Loss for real imagesreal_pred, real_aux = discriminator(real_imgs)d_real_loss = (adversarial_loss(real_pred, valid) + auxiliary_loss(real_aux, labels)) / 2# Loss for fake imagesfake_pred, fake_aux = discriminator(gen_imgs.detach())d_fake_loss = (adversarial_loss(fake_pred, fake) + auxiliary_loss(fake_aux, fake_aux_gt)) / 2# Total discriminator lossd_loss = (d_real_loss + d_fake_loss) / 2# Calculate discriminator accuracypred = np.concatenate([real_aux.data.cpu().numpy(), fake_aux.data.cpu().numpy()], axis=0)gt = np.concatenate([labels.data.cpu().numpy(), fake_aux_gt.data.cpu().numpy()], axis=0)d_acc = np.mean(np.argmax(pred, axis=1) == gt)d_loss.backward()optimizer_D.step()batches_done = epoch * len(dataloader) + iif batches_done % opt.sample_interval == 0:save_image(gen_imgs.data[:25], "images/%d.png" % batches_done, nrow=5, normalize=True)print("[Epoch %d/%d] [Batch %d/%d] [D loss: %f, acc: %d%%] [G loss: %f]"% (epoch, opt.n_epochs, i, len(dataloader), d_loss.item(), 100 * d_acc, g_loss.item()))

[Epoch 0/2] [Batch 937/938] [D loss: 1.358861, acc: 50%] [G loss: 0.671799]
[Epoch 1/2] [Batch 937/938] [D loss: 1.343094, acc: 50%] [G loss: 0.681119]