Generative Adversarial Nets（小白学GAN系列 一）

原论文链接：https://arxiv.org/abs/1406.2661

简介

核心思想：借用了“零一”博弈的方式来训练生成网络。从而使得网络可以从已有的数据中学到其分布的概率。

上图中黑色点是真实的数据分布，绿色实线是G(生成器)学习到的数据分布，蓝色虚线是判别器的划分界限，即在蓝虚线之下的判断为真实数据，在蓝虚线之上的判断为虚拟数据。由此可见，在训练刚开始之时（a）蓝线可以分部分真假的数据，到（b）时D(判别器)训练了一次后划分真假数据更为明显，再到（c）训练G使得G的数据分布向着真实数据在靠近，如此重复n次直到（d）G的数据分布已经完全拟合真实数据分布，D也无力再区分数据真假。

核心过程：

通过优化V来完成D与G的博弈过程。

基础结构

判别器训练：

判别器训练和普通的训练过程相同，自己为真实数据和生成数据打上标签，然后将数据经过判别器后的结果与标签求loss来优化判别器，而此时V的值是在增大的

 

                                    

可知D结果的值域为（0，1），当G与真是数据分布相同时，D的结果为0.5。

生成器训练：

生成器的目标是尽可能地向实际数据分布拟合，从而使得判别器无法判断真伪，基于此来设计loss。

由上式展开可得：

从上式感性的认识上来说，当D全判断为真或全判断为假时，其值都为零，其他情况时值都为负，且最小值正是当G完全拟合了实际数据时，D的结果为0.5，其取到最小值：

                             

引入散度的概念后：

                                

实际中去掉 -log4 ，则当G与Data完全拟合时，loss刚好等于零。

总体上看

单一的优化D时，V的值变大，优化G时，V的值变小，形成相互对抗的局面。

代码实践与结果

代码实现（参考：https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/gan/gan.py）

自己做了少许修改，即训练两次G，再训练一次D:

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", 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=8, 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("--img_size", type=int, default=28, 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 betwen image samples")
opt = parser.parse_args()
print(opt)

img_shape = (opt.channels, opt.img_size, opt.img_size)

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)), 512),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(512, 256),
            nn.LeakyReLU(0.2, inplace=True),
            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(), lr=opt.lr, betas=(opt.b1, opt.b2))

Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor

# ----------
#  Training
# ----------

for epoch in range(opt.n_epochs):
    for i, (imgs, _) in enumerate(dataloader):

        # Adversarial ground truths
        valid = Variable(Tensor(imgs.size(0), 1).fill_(1.0), requires_grad=False)
        #真实数据打上标签
        fake = Variable(Tensor(imgs.size(0), 1).fill_(0.0), requires_grad=False)
        #生成数据打上标签

        # Configure input
        real_imgs = Variable(imgs.type(Tensor))

        # -----------------
        #  Train Generator 1
        # -----------------

        optimizer_G.zero_grad()

        # Sample noise as generator input
        z = Variable(Tensor(np.random.normal(0, 1, (imgs.shape[0], opt.latent_dim))))

        # 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)
        #看G有多厉害，可以用假的来蒙混过D

        g_loss.backward()
        optimizer_G.step()
        
        
        # -----------------
        #  Train Generator 2
        # -----------------

        optimizer_G.zero_grad()

        # Sample noise as generator input
        z = Variable(Tensor(np.random.normal(0, 1, (imgs.shape[0], opt.latent_dim))))

        # 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()
        optimizer_G.step()



        # ---------------------
        #  Train Discriminator
        # ---------------------

        optimizer_D.zero_grad()

        # Measure discriminator's ability to classify real from generated samples
        real_loss = adversarial_loss(discriminator(real_imgs), valid)
        #真判为真的能力
        fake_loss = adversarial_loss(discriminator(gen_imgs.detach()), fake)
        #假判为假的能力
        d_loss = (real_loss + fake_loss) / 2


        d_loss.backward()
        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)

用minist测试的结果