AVE相当于把图片压缩在解压的感觉。
自编码器 x ——编码器——z——解码器——
变分自编码器 对z有一个正态分布的约束
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from torchvision import transforms
from torchvision.utils import save_image
sample_dir = 'samples'
image_size = 784
h_dim = 400
z_dim = 20
num_epochs = 15
batch_size = 128
learning_rate = 0.001
dataset = torchvision.datasets.MNIST(root='./data',train=True,transform=transforms.ToTensor(),download=True)
data_loader = torch.utils.data.DataLoader(dataset=dataset,batch_size=batch_size,shuffle=True)
class VAE(nn.Module):
def __init__(self, image_size=784, h_dim=400, z_dim=20):
super(VAE, self).__init__()
self.fc1 = nn.Linear(image_size, h_dim)
self.fc2 = nn.Linear(h_dim, z_dim)
self.fc3 = nn.Linear(h_dim, z_dim)
self.fc4 = nn.Linear(z_dim, h_dim)
self.fc5 = nn.Linear(h_dim, image_size)
# 编码 学习高斯分布均值与方差
def encode(self, x):
h = F.relu(self.fc1(x))
return self.fc2(h), self.fc3(h)
# 将高斯分布均值与方差参数重表示，生成隐变量z 若x~N(mu, var*var)分布,则(x-mu)/var=z~N(0, 1)分布
# 用mu,log_var生成一个潜在空间点z,mu,log_var为两个统计参数假设分布能生成图像
def reparameterize(self, mu, log_var):
std = torch.exp(log_var / 2)
eps = torch.randn_like(std)
return mu + eps * std
# 解码隐变量z
def decode(self, z):
h = F.relu(self.fc4(z))
return F.sigmoid(self.fc5(h))
# 计算重构值和隐变量z的分布参数
def forward(self, x):
mu, log_var = self.encode(x) # 从原始样本x中学习隐变量z的分布，即学习服从高斯分布均值与方差
z = self.reparameterize(mu, log_var) # 将高斯分布均值与方差参数重表示，生成隐变量z
x_reconst = self.decode(z) # 解码隐变量z，生成重构x’
return x_reconst, mu, log_var # 返回重构值和隐变量的分布参数
model = VAE()
print(model)
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# 开始训练
for epoch in range(num_epochs):
for i, (x, _) in enumerate(data_loader):
x = x.view(-1,image_size) # 将batch_size*1*28*28 ---->batch_size*image_size 其中，image_size=1*28*28=784
x_reconst, mu, log_var = model(x) # 将batch_size*748的x输入模型进行前向传播计算,重构值和服从高斯分布的隐变量z的分布参数（均值和方差）
reconst_loss = F.binary_cross_entropy(x_reconst, x, size_average=False) # 计算重构损失和KL散度
kl_div = - 0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp()) # KL散度
loss = reconst_loss + kl_div # 计算误差(重构误差和KL散度值)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i + 1) % 10 == 0:
print("Epoch[{}/{}], Step [{}/{}], Reconst Loss: {:.4f}, KL Div: {:.4f}"
.format(epoch + 1, num_epochs, i + 1, len(data_loader), reconst_loss.item(), kl_div.item()))
with torch.no_grad():
# 保存采样图像，即潜在向量z通过解码器生成的图像
z = torch.randn(batch_size, z_dim) # z的大小为batch_size * z_dim = 128*20
out = model.decode(z).view(-1, 1, 28, 28) # 对随机数 z 进行解码decode输出
save_image(out, os.path.join(sample_dir, 'sampled-{}.png'.format(epoch + 1))) # 保存结果值
# 保存重构图像，即原图像通过解码器生成的图像
out, _, _ = model(x) # 将batch_size*748的x输入模型进行前向传播计算，获取重构值out
# 将输入与输出拼接在一起输出保存 batch_size*1*28*（28+28）=batch_size*1*28*56
x_concat = torch.cat([x.view(-1, 1, 28, 28), out.view(-1, 1, 28, 28)], dim=3)
save_image(x_concat, os.path.join(sample_dir, 'reconst-{}.png'.format(epoch + 1)))GAN是基于博弈论的，所以又称生成式对抗网络。他要解决的问题是如何从训练样本中学习出新样本，训练样本是图像就生成新图像，训练样本是文章就生成新文章。
GAN既不依赖标签来优化，也不根据对结果的奖惩来调整参数，他是依据生成器和判别器之间的博弈来不断优化。
import os
import torch
import torch.nn as nn
import torchvision
from torchvision import transforms
from torchvision.utils import save_image
sample_dir = 'samples'
image_size = 784
hidden_size = 256
latent_size = 64
num_epochs = 5
batch_size = 100
learning_rate = 0.001
dataset = torchvision.datasets.MNIST(root='./data',train=True,transform=transforms.ToTensor(),download=True)
data_loader = torch.utils.data.DataLoader(dataset=dataset,batch_size=batch_size,shuffle=True)
# 构建判断器
D = nn.Sequential(nn.Linear(image_size,hidden_size),
nn.LeakyReLU(0.2),
nn.Linear(hidden_size,hidden_size),
nn.LeakyReLU(0.2),
nn.Linear(hidden_size,1),
nn.Sigmoid())
# 构建生成器
G = nn.Sequential(nn.Linear(latent_size,hidden_size),
nn.ReLU(),
nn.Linear(hidden_size,hidden_size),
nn.ReLU(),
nn.Linear(hidden_size,image_size),
nn.Tanh())
criterion = nn.BCELoss() #用于二分类
optimizerG = torch.optim.Adam(G.parameters(), lr=0.0002) #生成器的优化器
optimizerD = torch.optim.Adam(D.parameters(), lr=0.0002) #判别器的优化器
def denorm(x):
out = (x + 1) / 2
return out.clamp(0, 1) # 将out张量每个元素的范围限制到区间 [min,max]
def reset_grad():
optimizerG.zero_grad() # 清空鉴别器的梯度器上一步的残余更新参数值
optimizerD.zero_grad() # 清空生成器的梯度器上一步的残余更新参数值
for epoch in range(num_epochs):
for i,(images,_) in enumerate(data_loader):
images = images.reshape(batch_size,-1)
# 定义图像真假的标签
real_labels = torch.ones(batch_size,1)
fake_labels = torch.zeros(batch_size,1)
# =========================================#
# 训练判别器 #
# =========================================#
# 定义判断器对真图像的损失函数
outputs = D(images)
d_loss_real = criterion(outputs,real_labels)
real_score = outputs
# 定义判别器对加图像的损失函数
z = torch.randn(batch_size,latent_size)
fake_image = G(z)
outputs = D(fake_image)
d_loss_fake = criterion(outputs,fake_labels)
fake_score = outputs
d_loss = d_loss_real + d_loss_fake # 判别器总的损失函数
# 对生成器判别器梯度清零
reset_grad()
d_loss.backward()
optimizerD.step()
# =========================================#
# 训练生成器 #
# =========================================#
# 定义生成器对假图像的损失函数
# 我们要求判别器生成的图像越来越像真图片，故损失函数中的标签改为真图像的标签，即希望生成的加图像越来越靠近真图像
z = torch.randn(batch_size,latent_size)
fake_image = G(z)
outputs = D(fake_image)
g_loss = criterion(outputs,real_labels)
reset_grad()
g_loss.backward()
optimizerG.step()
if (i + 1) % 200 == 0:
print('Epoch [{}/{}], Step [{}/{}], d_loss: {:.4f}, g_loss: {:.4f}, D(x): {:.2f}, D(G(z)): {:.2f}'
.format(epoch, num_epochs, i + 1, len(data_loader), d_loss.item(), g_loss.item(),
real_score.mean().item(), fake_score.mean().item()))
# 保存真图像
if(epoch+1) == 1:
images = images.reshape(images.size(0),1,28,28)
save_image(denorm(images),os.path.join(sample_dir,'real_images.png'))
# 保存假图像
fake_images = images.reshape(images.size(0),1,28,28)
save_image(denorm(fake_images),os.path.join(sample_dir,'fake_images-{}.png'.format(epoch+1)))
# 保存模型
torch.save(G.state_dict(),'G.ckpt')
torch.save(D.state_dict('D.ckpt')) fake
real