第二次作业：卷积神经网络 part 2
生成式对抗网络
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GAN-生成式对抗网络
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生成式对抗网络-代码练习
import torch.nn as nn z_dim = 32 hidden_dim = 128 # 定义生成器 net_G = nn.Sequential( nn.Linear(z_dim,hidden_dim), nn.ReLU(), nn.Linear(hidden_dim, 2)) # 定义判别器 net_D = nn.Sequential( nn.Linear(2,hidden_dim), nn.ReLU(), nn.Linear(hidden_dim,1), nn.Sigmoid()) # 网络放到 GPU 上 net_G = net_G.to(device) net_D = net_D.to(device) # 定义网络的优化器 optimizer_G = torch.optim.Adam(net_G.parameters(),lr=0.0001) optimizer_D = torch.optim.Adam(net_D.parameters(),lr=0.0001) batch_size = 50 nb_epochs = 1000 loss_D_epoch = [] loss_G_epoch = [] for e in range(nb_epochs): np.random.shuffle(X) real_samples = torch.from_numpy(X).type(torch.FloatTensor) loss_G = 0 loss_D = 0 for t, real_batch in enumerate(real_samples.split(batch_size)): # 固定生成器G，改进判别器D # 使用normal_()函数生成一组随机噪声，输入G得到一组样本 z = torch.empty(batch_size,z_dim).normal_().to(device) fake_batch = net_G(z) # 将真、假样本分别输入判别器，得到结果 D_scores_on_real = net_D(real_batch.to(device)) D_scores_on_fake = net_D(fake_batch) # 优化过程中，假样本的score会越来越小，真样本的score会越来越大，下面 loss 的定义刚好符合这一规律， # 要保证loss越来越小，真样本的score前面要加负号 # 要保证loss越来越小，假样本的score前面是正号（负负得正） loss = -torch.mean(torch.log(1-D_scores_on_fake) + torch.log(D_scores_on_real)) # 梯度清零 optimizer_D.zero_grad() # 反向传播优化 loss.backward() # 更新全部参数 optimizer_D.step() loss_D += loss # 固定判别器，改进生成器 # 生成一组随机噪声，输入生成器得到一组假样本 z = torch.empty(batch_size,z_dim).normal_().to(device) fake_batch = net_G(z) # 假样本输入判别器得到 score D_scores_on_fake = net_D(fake_batch) # 我们希望假样本能够骗过生成器，得到较高的分数，下面的 loss 定义也符合这一规律 # 要保证 loss 越来越小，假样本的前面要加负号 loss = -torch.mean(torch.log(D_scores_on_fake)) optimizer_G.zero_grad() loss.backward() optimizer_G.step() loss_G += loss if e % 50 ==0: print(f'\n Epoch {e} , D loss: {loss_D}, G loss: {loss_G}') loss_D_epoch.append(loss_D) loss_G_epoch.append(loss_G) #显示loss变化 plt.plot(loss_D_epoch) plt.plot(loss_G_epoch)
训练的结果不是很理想
z = torch.empty(n_samples,z_dim).normal_().to(device)
fake_samples = net_G(z)
fake_data = fake_samples.cpu().data.numpy()
fig, ax = plt.subplots(1, 1, facecolor='#4B6EA9')
all_data = np.concatenate((X,fake_data),axis=0)
Y2 = np.concatenate((np.ones(n_samples),np.zeros(n_samples)))
plot_data(ax, all_data, Y2)
plt.show()
# 定义网络的优化器
optimizer_G = torch.optim.Adam(net_G.parameters(),lr=0.001)
optimizer_D = torch.optim.Adam(net_D.parameters(),lr=0.001)
batch_size = 250
loss_D_epoch = []
loss_G_epoch = []
for e in range(nb_epochs):
np.random.shuffle(X)
real_samples = torch.from_numpy(X).type(torch.FloatTensor)
loss_G = 0
loss_D = 0
for t, real_batch in enumerate(real_samples.split(batch_size)):
# 固定生成器G，改进判别器D
# 使用normal_()函数生成一组随机噪声，输入G得到一组样本
z = torch.empty(batch_size,z_dim).normal_().to(device)
fake_batch = net_G(z)
# 将真、假样本分别输入判别器，得到结果
D_scores_on_real = net_D(real_batch.to(device))
D_scores_on_fake = net_D(fake_batch)
# 要保证loss越来越小，真样本的score前面要加负号
# 要保证loss越来越小，假样本的score前面是正号（负负得正）
loss = -torch.mean(torch.log(1-D_scores_on_fake) + torch.log(D_scores_on_real))
# 梯度清零
optimizer_D.zero_grad()
# 反向传播优化
loss.backward()
# 更新全部参数
optimizer_D.step()
loss_D += loss
# 固定判别器，改进生成器
# 生成一组随机噪声，输入生成器得到一组假样本
z = torch.empty(batch_size,z_dim).normal_().to(device)
fake_batch = net_G(z)
# 假样本输入判别器得到 score
D_scores_on_fake = net_D(fake_batch)
# 我们希望假样本能够骗过生成器，得到较高的分数，下面的 loss 定义也符合这一规律
# 要保证 loss 越来越小，假样本的前面要加负号
loss = -torch.mean(torch.log(D_scores_on_fake))
optimizer_G.zero_grad()
loss.backward()
optimizer_G.step()
loss_G += loss
if e % 50 ==0:
print(f'\n Epoch {e} , D loss: {loss_D}, G loss: {loss_G}')
loss_D_epoch.append(loss_D)
loss_G_epoch.append(loss_G)
z = torch.empty(n_samples,z_dim).normal_().to(device)
fake_samples = net_G(z)
fake_data = fake_samples.cpu().data.numpy()
fig, ax = plt.subplots(1, 1, facecolor='#4B6EA9')
all_data = np.concatenate((X,fake_data),axis=0)
Y2 = np.concatenate((np.ones(n_samples),np.zeros(n_samples)))
plot_data(ax, all_data, Y2)
plt.show()
z = torch.empty(10*n_samples,z_dim).normal_().to(device)
fake_samples = net_G(z)
fake_data = fake_samples.cpu().data.numpy()
fig, ax = plt.subplots(1, 1, facecolor='#4B6EA9')
all_data = np.concatenate((X,fake_data),axis=0)
Y2 = np.concatenate((np.ones(n_samples),np.zeros(10*n_samples)))
plot_data(ax, all_data, Y2)
plt.show()
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CGAN 和 DCGAN-代码练习
CGAN：
#加载数据集。。。 #实现CGAN #生成器：(784 + 10) ==> 512 ==> 256 ==> 1 #判别器：(100 + 10) ==> 128 ==> 256 ==> 512 ==> 784 class Discriminator(nn.Module): '''全连接判别器，用于1x28x28的MNIST数据,输出是数据和类别''' def __init__(self): super(Discriminator, self).__init__() self.model = nn.Sequential( nn.Linear(28*28+10, 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, x, c): x = x.view(x.size(0), -1) validity = self.model(torch.cat([x, c], -1)) return validity class Generator(nn.Module): '''全连接生成器，用于1x28x28的MNIST数据，输入是噪声和类别''' def __init__(self, z_dim): super(Generator, self).__init__() self.model = nn.Sequential( nn.Linear(z_dim+10, 128), nn.LeakyReLU(0.2, inplace=True), nn.Linear(128, 256), nn.BatchNorm1d(256, 0.8), nn.LeakyReLU(0.2, inplace=True), nn.Linear(256, 512), nn.BatchNorm1d(512, 0.8), nn.LeakyReLU(0.2, inplace=True), nn.Linear(in_features=512, out_features=28*28), nn.Tanh() ) def forward(self, z, c): x = self.model(torch.cat([z, c], dim=1)) x = x.view(-1, 1, 28, 28) return x # 初始化构建判别器和生成器 discriminator = Discriminator().to(device) generator = Generator(z_dim=z_dim).to(device) # 初始化二值交叉熵损失 bce = torch.nn.BCELoss().to(device) ones = torch.ones(batch_size).to(device) zeros = torch.zeros(batch_size).to(device) # 初始化优化器，使用Adam优化器 g_optimizer = optim.Adam(generator.parameters(), lr=learning_rate) d_optimizer = optim.Adam(discriminator.parameters(), lr=learning_rate) # 开始训练，一共训练total_epochs for epoch in range(total_epochs): # torch.nn.Module.train() 指的是模型启用 BatchNormalization 和 Dropout # torch.nn.Module.eval() 指的是模型不启用 BatchNormalization 和 Dropout # 因此，train()一般在训练时用到， eval() 一般在测试时用到 generator = generator.train() # 训练一个epoch for i, data in enumerate(dataloader): # 加载真实数据 real_images, real_labels = data real_images = real_images.to(device) # 把对应的标签转化成 one-hot 类型 tmp = torch.FloatTensor(real_labels.size(0), 10).zero_() real_labels = tmp.scatter_(dim=1, index=torch.LongTensor(real_labels.view(-1, 1)), value=1) real_labels = real_labels.to(device) # 生成数据 # 用正态分布中采样batch_size个随机噪声 z = torch.randn([batch_size, z_dim]).to(device) # 生成 batch_size 个 ont-hot 标签 c = torch.FloatTensor(batch_size, 10).zero_() c = c.scatter_(dim=1, index=torch.LongTensor(np.random.choice(10, batch_size).reshape([batch_size, 1])), value=1) c = c.to(device) # 生成数据 fake_images = generator(z,c) # 计算判别器损失，并优化判别器 real_loss = bce(discriminator(real_images, real_labels), ones) fake_loss = bce(discriminator(fake_images.detach(), c), zeros) d_loss = real_loss + fake_loss d_optimizer.zero_grad() d_loss.backward() d_optimizer.step() # 计算生成器损失，并优化生成器 g_loss = bce(discriminator(fake_images, c), ones) g_optimizer.zero_grad() g_loss.backward() g_optimizer.step() # 输出损失 print("[Epoch %d/%d] [D loss: %f] [G loss: %f]" % (epoch, total_epochs, d_loss.item(), g_loss.item()))训练结果：
测试结果：
DCGAN：
class D_dcgan(nn.Module):
'''滑动卷积判别器'''
def __init__(self):
super(D_dcgan, self).__init__()
self.conv = nn.Sequential(
# 第一个滑动卷积层，不使用BN，LRelu激活函数
nn.Conv2d(in_channels=1, out_channels=16, kernel_size=3, stride=2, padding=1),
nn.LeakyReLU(0.2, inplace=True),
# 第二个滑动卷积层，包含BN，LRelu激活函数
nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(32),
nn.LeakyReLU(0.2, inplace=True),
# 第三个滑动卷积层，包含BN，LRelu激活函数
nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(64),
nn.LeakyReLU(0.2, inplace=True),
# 第四个滑动卷积层，包含BN，LRelu激活函数
nn.Conv2d(in_channels=64, out_channels=128, kernel_size=4, stride=1),
nn.BatchNorm2d(128),
nn.LeakyReLU(0.2, inplace=True)
)
# 全连接层+Sigmoid激活函数
self.linear = nn.Sequential(nn.Linear(in_features=128, out_features=1), nn.Sigmoid())
def forward(self, x):
x = self.conv(x)
x = x.view(x.size(0), -1)
validity = self.linear(x)
return validity
class G_dcgan(nn.Module):
'''反滑动卷积生成器'''
def __init__(self, z_dim):
super(G_dcgan, self).__init__()
self.z_dim = z_dim
# 第一层：把输入线性变换成256x4x4的矩阵，并在这个基础上做反卷机操作
self.linear = nn.Linear(self.z_dim, 4*4*256)
self.model = nn.Sequential(
# 第二层：bn+relu
nn.ConvTranspose2d(in_channels=256, out_channels=128, kernel_size=3, stride=2, padding=0),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
# 第三层：bn+relu
nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
# 第四层:不使用BN，使用tanh激活函数
nn.ConvTranspose2d(in_channels=64, out_channels=1, kernel_size=4, stride=2, padding=2),
nn.Tanh()
)
def forward(self, z):
# 把随机噪声经过线性变换，resize成256x4x4的大小
x = self.linear(z)
x = x.view([x.size(0), 256, 4, 4])
# 生成图片
x = self.model(x)
return x
# 构建判别器和生成器
d_dcgan = D_dcgan().to(device)
g_dcgan = G_dcgan(z_dim=z_dim).to(device)
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('BatchNorm2d') != -1:
torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
torch.nn.init.constant_(m.bias.data, 0.0)
# 使用均值为0，方差为0.02的正态分布初始化神经网络
d_dcgan.apply(weights_init_normal)
g_dcgan.apply(weights_init_normal)
# 初始化优化器，使用Adam优化器
g_dcgan_optim = optim.Adam(g_dcgan.parameters(), lr=learning_rate)
d_dcgan_optim = optim.Adam(d_dcgan.parameters(), lr=learning_rate)
# 加载MNIST数据集，和之前不同的是，DCGAN输入的图像被 resize 成 32*32 像素
dcgan_dataloader = torch.utils.data.DataLoader(
datasets.MNIST('./data', train=True, download=True,
transform=transforms.Compose([transforms.Resize(32), transforms.ToTensor(),transforms.Normalize((0.5,), (0.5,))])
), batch_size, shuffle=True, drop_last=True)
# 开始训练，一共训练 total_epochs
for e in range(total_epochs):
# 给generator启用 BatchNormalization
g_dcgan = g_dcgan.train()
# 训练一个epoch
for i, data in enumerate(dcgan_dataloader):
# 加载真实数据，不加载标签
real_images, _ = data
real_images = real_images.to(device)
# 用正态分布中采样batch_size个噪声，然后生成对应的图片
z = torch.randn([batch_size, z_dim]).to(device)
fake_images = g_dcgan(z)
# 计算判别器损失，并优化判别器
real_loss = bce(d_dcgan(real_images), ones)
fake_loss = bce(d_dcgan(fake_images.detach()), zeros)
d_loss = real_loss + fake_loss
d_dcgan_optim.zero_grad()
d_loss.backward()
d_dcgan_optim.step()
# 计算生成器损失，并优化生成器
g_loss = bce(d_dcgan(fake_images), ones)
g_dcgan_optim.zero_grad()
g_loss.backward()
g_dcgan_optim.step()
# 输出损失
print ("[Epoch %d/%d] [D loss: %f] [G loss: %f]" % (e, total_epochs, d_loss.item(), g_loss.item()))
#用于生成效果图
# 生成100个随机噪声向量
fixed_z = torch.randn([100, z_dim]).to(device)
g_dcgan = g_dcgan.eval()
fixed_fake_images = g_dcgan(fixed_z)
plt.figure(figsize=(8, 8))
for j in range(10):
for i in range(10):
img = fixed_fake_images[j*10+i, 0, :, :].detach().cpu().numpy()
img = img.reshape([32, 32])
plt.subplot(10, 10, j*10+i+1)
plt.imshow(img, 'gray')
