pytorch构建一个卷积神经网络进行手写题图像分类
时间: 2023-07-18 19:45:20 浏览: 158
利用tensorflow实现的卷积神经网络来进行MNIST手写数字图像的分类.py
好的,我可以为您介绍如何使用PyTorch构建一个卷积神经网络进行手写数字图像分类。
和之前一样,您需要一个数据集来训练您的模型,可以使用MNIST数据集。首先,需要导入相关的库:
```
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
```
接下来,定义一个卷积神经网络模型:
```
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=3)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
self.dropout1 = nn.Dropout2d(0.25)
self.dropout2 = nn.Dropout2d(0.5)
self.fc1 = nn.Linear(1600, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.relu(self.conv2(x))
x = F.max_pool2d(x, 2)
x = self.dropout1(x)
x = torch.flatten(x, 1)
x = F.relu(self.fc1(x))
x = self.dropout2(x)
x = self.fc2(x)
return F.log_softmax(x, dim=1)
```
该模型包括两个卷积层和一个池化层,以及两个Dropout层和两个全连接层。其中,第一个全连接层的输入维度为1600,是因为前面的池化层和卷积层的输出维度为64x5x5=1600。
然后,您可以使用以下代码来定义优化器和损失函数:
```
model = Net()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
criterion = nn.CrossEntropyLoss()
```
接下来,使用以下代码来加载数据集、训练和测试模型:
```
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
train_dataset = datasets.MNIST('../data', train=True, download=True, transform=transform)
test_dataset = datasets.MNIST('../data', train=False, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=True)
epochs = 10
for epoch in range(1, epochs + 1):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
if batch_idx % 100 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
output = model(data)
test_loss += criterion(output, target).item()
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
```
在训练过程中,使用测试集来验证模型的性能。
希望这个简单的卷积神经网络模型能够帮助您进行手写数字图像分类任务。
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