基于pytorch的mnist手写数字识别步骤

以下是基于PyTorch的MNIST手写数字识别的步骤：

1.导入必要的库

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms

2.定义数据预处理操作

transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))
])

3.加载数据集

train_data = datasets.MNIST(root='data', train=True, download=True, transform=transform)
test_data = datasets.MNIST(root='data', train=False, download=True, transform=transform)

4.定义数据加载器

batch_size = 64

train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=True)

5.定义模型

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.dropout1 = nn.Dropout2d(0.25)
        self.dropout2 = nn.Dropout2d(0.5)
        self.fc1 = nn.Linear(64*7*7, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = nn.functional.relu(self.conv1(x))
        x = nn.functional.relu(self.conv2(x))
        x = nn.functional.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = nn.functional.relu(self.fc1(x))
        x = self.dropout2(x)
        x = self.fc2(x)
        return nn.functional.log_softmax(x, dim=1)

model = Net()

6.定义优化器和损失函数

learning_rate = 0.01

optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=0.5)
criterion = nn.CrossEntropyLoss()

7.训练模型

epochs = 10

for epoch in range(epochs):
    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()))

8.测试模型

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.data.max(1, keepdim=True)[1]
        correct += pred.eq(target.data.view_as(pred)).sum()

test_loss /= len(test_loader.dataset)
accuracy = 100. * correct / len(test_loader.dataset)

print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
    test_loss, correct, len(test_loader.dataset), accuracy))

这就是基于PyTorch的MNIST手写数字识别的步骤。