深度学习 对抗生成网络 使用生成对抗网络生成图片

 这是最新找到的  对抗生成网络的代码，亲测可以跑通。前几天也上传了一个网上找到的代码，但是这回这个代码中判别网络的假数据中加入了 detach() 函数， 网上查找说这个函数可以切断神经网络的反向传导，虽然不是很理解，但总是感觉这个更对一些。对于  detach 这个函数在这里面的作用网上怎么说的都有，不过个人感觉最有说服力的说法是 减少没有必要的运算，毕竟在判别网络中我们是不需要修改生成网络的参数的，也就是说这个时候求解生成网络的梯度，对其进行反向求导是没有必要的，而这个说法和代码中的注释部分相合。

#encoding:UTF-8


#读入CIFAR-10数据
from torch.utils.data import DataLoader
from torchvision.datasets import CIFAR10
import torchvision.transforms as transforms
from torchvision.utils import save_image

dataset = CIFAR10(root='./data', download=True,
        transform=transforms.ToTensor())
dataloader = DataLoader(dataset, batch_size=64, shuffle=True)

for batch_idx, data in enumerate(dataloader):
    real_images, _ = data
    batch_size = real_images.size(0)
    print ('#{} has {} images.'.format(batch_idx, batch_size))
    if batch_idx % 100 == 0:
        path = './data/CIFAR10_shuffled_batch{:03d}.png'.format(batch_idx)
        save_image(real_images, path, normalize=True)


#搭建生成网络和鉴别网络
import torch.nn as nn

# 搭建生成网络
latent_size = 64 # 潜在大小
n_channel = 3 # 输出通道数
n_g_feature = 64 # 生成网络隐藏层大小
gnet = nn.Sequential( 
        # 输入大小 = (64, 1, 1)
        nn.ConvTranspose2d(latent_size, 4 * n_g_feature, kernel_size=4,
                bias=False),
        nn.BatchNorm2d(4 * n_g_feature),
        nn.ReLU(),
        # 大小 = (256, 4, 4)
        nn.ConvTranspose2d(4 * n_g_feature, 2 * n_g_feature, kernel_size=4,
                stride=2, padding=1, bias=False),
        nn.BatchNorm2d(2 * n_g_feature),
        nn.ReLU(),
        # 大小 = (128, 8, 8)
        nn.ConvTranspose2d(2 * n_g_feature, n_g_feature, kernel_size=4,
                stride=2, padding=1, bias=False),
        nn.BatchNorm2d(n_g_feature),
        nn.ReLU(),
        # 大小 = (64, 16, 16)
        nn.ConvTranspose2d(n_g_feature, n_channel, kernel_size=4,
                stride=2, padding=1),
        nn.Sigmoid(),
        # 图片大小 = (3, 32, 32)
        )
print (gnet)


# 搭建鉴别网络
n_d_feature = 64 # 鉴别网络隐藏层大小
dnet = nn.Sequential( 
        # 图片大小 = (3, 32, 32)
        nn.Conv2d(n_channel, n_d_feature, kernel_size=4,
                stride=2, padding=1),
        nn.LeakyReLU(0.2),
        # 大小 = (64, 16, 16)
        nn.Conv2d(n_d_feature, 2 * n_d_feature, kernel_size=4,
                stride=2, padding=1, bias=False),
        nn.BatchNorm2d(2 * n_d_feature),
        nn.LeakyReLU(0.2),
        # 大小 = (128, 8, 8)
        nn.Conv2d(2 * n_d_feature, 4 * n_d_feature, kernel_size=4,
                stride=2, padding=1, bias=False),
        nn.BatchNorm2d(4 * n_d_feature),
        nn.LeakyReLU(0.2),
        # 大小 = (256, 4, 4)
        nn.Conv2d(4 * n_d_feature, 1, kernel_size=4),
        # 对数赔率张量大小 = (1, 1, 1)
        )
print(dnet)        


import torch.nn.init as init
#初始化权重值
def weights_init(m): # 用于初始化权重值的函数
    if type(m) in [nn.ConvTranspose2d, nn.Conv2d]:
        init.xavier_normal_(m.weight)
    elif type(m) == nn.BatchNorm2d:
        init.normal_(m.weight, 1.0, 0.02)
        init.constant_(m.bias, 0)

gnet.apply(weights_init)
dnet.apply(weights_init)



#主程序
import torch
import torch.optim

# 损失
criterion = nn.BCEWithLogitsLoss()

# 优化器
goptimizer = torch.optim.Adam(gnet.parameters(),
        lr=0.0002, betas=(0.5, 0.999))
doptimizer = torch.optim.Adam(dnet.parameters(),
        lr=0.0002, betas=(0.5, 0.999))

# 用于测试的固定噪声,用来查看相同的潜在张量在训练过程中生成图片的变换
batch_size = 64
fixed_noises = torch.randn(batch_size, latent_size, 1, 1)

# 训练过程
epoch_num = 10
for epoch in range(epoch_num):
    for batch_idx, data in enumerate(dataloader):
        # 载入本批次数据
        real_images, _ = data
        batch_size = real_images.size(0)

        # 训练鉴别网络
        labels = torch.ones(batch_size) # 真实数据对应标签为1
        preds = dnet(real_images) # 对真实数据进行判别
        outputs = preds.reshape(-1)
        dloss_real = criterion(outputs, labels) # 真实数据的鉴别器损失
        dmean_real = outputs.sigmoid().mean()
                # 计算鉴别器将多少比例的真数据判定为真,仅用于输出显示

        noises = torch.randn(batch_size, latent_size, 1, 1) # 潜在噪声
        fake_images = gnet(noises) # 生成假数据
        labels = torch.zeros(batch_size) # 假数据对应标签为0
        fake = fake_images.detach()
                # 使得梯度的计算不回溯到生成网络,可用于加快训练速度.删去此步结果不变
        preds = dnet(fake) # 对假数据进行鉴别
        outputs = preds.view(-1)
        dloss_fake = criterion(outputs, labels) # 假数据的鉴别器损失
        dmean_fake = outputs.sigmoid().mean()
                # 计算鉴别器将多少比例的假数据判定为真,仅用于输出显示

        dloss = dloss_real + dloss_fake # 总的鉴别器损失
        dnet.zero_grad()
        dloss.backward()
        doptimizer.step()

        # 训练生成网络
        labels = torch.ones(batch_size)
                # 生成网络希望所有生成的数据都被认为是真数据
        preds = dnet(fake_images) # 把假数据通过鉴别网络
        outputs = preds.view(-1)
        gloss = criterion(outputs, labels) # 真数据看到的损失
        gmean_fake = outputs.sigmoid().mean()
                # 计算鉴别器将多少比例的假数据判定为真,仅用于输出显示
        gnet.zero_grad()
        gloss.backward()
        goptimizer.step()

        # 输出本步训练结果
        print('[{}/{}]'.format(epoch, epoch_num) +
                '[{}/{}]'.format(batch_idx, len(dataloader)) +
                '鉴别网络损失:{:g} 生成网络损失:{:g}'.format(dloss, gloss) +
                '真数据判真比例:{:g} 假数据判真比例:{:g}/{:g}'.format(
                dmean_real, dmean_fake, gmean_fake))
        if batch_idx % 100 == 0:
            fake = gnet(fixed_noises) # 由固定潜在张量生成假数据
            save_image(fake, # 保存假数据
                    './data/images_epoch{:02d}_batch{:03d}.png'.format(
                    epoch, batch_idx))