利用pytorch和生成对抗网络实现手写体数字识别
时间: 2023-12-16 12:54:08 浏览: 111
手写数字识别是深度学习中最基本的任务之一,利用生成对抗网络可以实现更加准确的识别。下面是利用PyTorch和生成对抗网络实现手写数字识别的一个简单的步骤:
1. 收集和准备手写数字数据集,可以使用MNIST、SVHN等公共数据集。
2. 构建生成对抗网络模型,该模型包括生成器和判别器两个部分。生成器帮助生成数字图像,判别器用于判断图像是否为真实的手写数字图像。
3. 训练生成对抗网络模型,使用手写数字数据集进行训练,通过不断地迭代,不断优化生成器和判别器的参数,以提高模型的准确性。
4. 测试和评估模型,使用测试数据集来评估模型的性能,包括准确率、召回率、F1值等指标。
下面是一个简单的代码示例,用于生成手写数字图像:
```python
import torch
import torchvision
from torchvision import transforms
from torch.autograd.variable import Variable
import numpy as np
import matplotlib.pyplot as plt
# 定义超参数
num_epochs = 200
batch_size = 128
learning_rate = 0.0002
# 加载数据集
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=(0.5,), std=(0.5,))])
mnist = torchvision.datasets.MNIST(root='./data/', train=True, transform=transform, download=True)
data_loader = torch.utils.data.DataLoader(dataset=mnist, batch_size=batch_size, shuffle=True)
# 定义生成器模型
class Generator(torch.nn.Module):
def __init__(self):
super(Generator, self).__init__()
self.fc1 = torch.nn.Linear(100, 256)
self.fc2 = torch.nn.Linear(256, 512)
self.fc3 = torch.nn.Linear(512, 784)
self.relu = torch.nn.ReLU()
self.tanh = torch.nn.Tanh()
def forward(self, x):
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
x = self.tanh(self.fc3(x))
return x
# 定义判别器模型
class Discriminator(torch.nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
self.fc1 = torch.nn.Linear(784, 512)
self.fc2 = torch.nn.Linear(512, 256)
self.fc3 = torch.nn.Linear(256, 1)
self.relu = torch.nn.ReLU()
self.sigmoid = torch.nn.Sigmoid()
def forward(self, x):
x = x.view(-1, 784)
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
x = self.sigmoid(self.fc3(x))
return x
# 创建生成器和判别器的实例
generator = Generator()
discriminator = Discriminator()
# 定义损失函数和优化器
loss_function = torch.nn.BCELoss()
generator_optimizer = torch.optim.Adam(generator.parameters(), lr=learning_rate)
discriminator_optimizer = torch.optim.Adam(discriminator.parameters(), lr=learning_rate)
# 开始训练
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(data_loader):
# 训练判别器
discriminator.zero_grad()
real_images = Variable(images)
real_labels = Variable(torch.ones(images.size()[0]))
fake_labels = Variable(torch.zeros(images.size()[0]))
# 计算真实图像的损失
real_outputs = discriminator(real_images)
real_loss = loss_function(real_outputs, real_labels)
real_loss.backward()
# 生成假图像,并计算假图像的损失
z = Variable(torch.randn(images.size()[0], 100))
fake_images = generator(z)
fake_outputs = discriminator(fake_images.detach())
fake_loss = loss_function(fake_outputs, fake_labels)
fake_loss.backward()
# 计算总损失,更新判别器权重
discriminator_loss = real_loss + fake_loss
discriminator_optimizer.step()
# 训练生成器
generator.zero_grad()
z = Variable(torch.randn(images.size()[0], 100))
fake_images = generator(z)
fake_outputs = discriminator(fake_images)
generator_loss = loss_function(fake_outputs, real_labels)
generator_loss.backward()
generator_optimizer.step()
# 输出训练进度
if (i + 1) % 100 == 0:
print('Epoch [%d/%d], Step[%d/%d], Discriminator Loss: %.4f, Generator Loss: %.4f'
% (epoch, num_epochs, i + 1, len(mnist) // batch_size, discriminator_loss.data[0], generator_loss.data[0]))
# 生成手写数字图像
z = Variable(torch.randn(1, 100))
fake_images = generator(z)
fake_images = fake_images.view(28, 28)
fake_images = fake_images.detach().numpy()
plt.imshow(fake_images, cmap='gray')
plt.show()
```
以上代码中,我们定义了一个简单的生成器和判别器模型,并使用MNIST数据集进行训练和测试。最后,我们生成了一个手写数字图像,并使用matplotlib库显示出来。
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