DCGAN训练mnist数据集
时间: 2023-11-27 07:48:26 浏览: 52
以下是使用DCGAN训练MNIST数据集的步骤:
1.导入必要的库和模块
```python
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import numpy as np
import matplotlib.pyplot as plt
```
2.加载数据集
```python
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=0.5, std=0.5)])
train_ds = torchvision.datasets.MNIST('data/', train=True, transform=transform, download=True)
dataloader = torch.utils.data.DataLoader(train_ds, batch_size=64, shuffle=True)
```
3.定义生成器Generator
```python
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
self.fc1 = nn.Linear(100, 256)
self.fc2 = nn.Linear(256, 512)
self.fc3 = nn.Linear(512, 1024)
self.fc4 = nn.Linear(1024, 784)
self.relu = nn.ReLU()
self.tanh = nn.Tanh()
def forward(self, x):
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
x = self.relu(self.fc3(x))
x = self.tanh(self.fc4(x))
return x
```
4.定义判别器Discriminator
```python
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
self.fc1 = nn.Linear(784, 512)
self.fc2 = nn.Linear(512, 256)
self.fc3 = nn.Linear(256, 1)
self.leaky_relu = nn.LeakyReLU(0.2)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
x = x.view(x.size(0), -1)
x = self.leaky_relu(self.fc1(x))
x = self.leaky_relu(self.fc2(x))
x = self.sigmoid(self.fc3(x))
return x
```
5.初始化生成器和判别器
```python
generator = Generator()
discriminator = Discriminator()
```
6.定义损失函数和优化器
```python
criterion = nn.BCELoss()
lr = 0.0002
optimizer_g = torch.optim.Adam(generator.parameters(), lr=lr)
optimizer_d = torch.optim.Adam(discriminator.parameters(), lr=lr)
```
7.训练模型
```python
num_epochs = 50
for epoch in range(num_epochs):
for i, (images, _) in enumerate(dataloader):
# 训练判别器
discriminator.zero_grad()
real_images = images.view(-1, 784)
real_labels = torch.ones(images.size(0), 1)
fake_labels = torch.zeros(images.size(0), 1)
z = torch.randn(images.size(0), 100)
fake_images = generator(z)
outputs_real = discriminator(real_images)
outputs_fake = discriminator(fake_images)
loss_d_real = criterion(outputs_real, real_labels)
loss_d_fake = criterion(outputs_fake, fake_labels)
loss_d = loss_d_real + loss_d_fake
loss_d.backward()
optimizer_d.step()
# 训练生成器
generator.zero_grad()
z = torch.randn(images.size(0), 100)
fake_images = generator(z)
outputs = discriminator(fake_images)
loss_g = criterion(outputs, real_labels)
loss_g.backward()
optimizer_g.step()
# 打印损失
if (i + 1) % 100 == 0:
print('Epoch [{}/{}], Step [{}/{}], d_loss: {:.4f}, g_loss: {:.4f}'
.format(epoch, num_epochs, i + 1, len(dataloader), loss_d.item(), loss_g.item()))
```
8.生成图片
```python
# 生成随机噪声
z = torch.randn(64, 100)
# 生成图片
fake_images = generator(z)
# 将图片转换为numpy数组
fake_images = fake_images.detach().numpy()
# 将图片可视化
fig, axs = plt.subplots(8, 8, figsize=(10, 10))
cnt = 0
for i in range(8):
for j in range(8):
axs[i, j].imshow(fake_images[cnt].reshape(28, 28), cmap='gray')
axs[i, j].axis('off')
cnt += 1
plt.show()
```
相关推荐
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)