minst机器学习pytorch

Minst是一个手写数字识别数据集，包含60000张训练图片和10000张测试图片。PyTorch是一个基于Python的机器学习框架，它可以帮助我们快速构建、训练和测试深度神经网络。

以下是使用PyTorch进行Minst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
import torchvision.datasets as datasets
import torchvision.transforms as transforms

2. 加载数据集

train_dataset = datasets.MNIST(root='data/', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.MNIST(root='data/', train=False, transform=transforms.ToTensor(), download=True)

train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=64, shuffle=False)

3. 定义模型

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = nn.functional.relu(nn.functional.max_pool2d(self.conv1(x), 2))
        x = nn.functional.relu(nn.functional.max_pool2d(self.conv2(x), 2))
        x = x.view(-1, 320)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return nn.functional.log_softmax(x, dim=1)

model = Net()

4. 定义损失函数和优化器

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)

5. 训练模型

for epoch in range(10):
    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()))

6. 测试模型

correct = 0
total = 0

with torch.no_grad():
    for data, target in test_loader:
        output = model(data)
        _, predicted = torch.max(output.data, 1)
        total += target.size(0)
        correct += (predicted == target).sum().item()

print('Accuracy of the model on the test images: {}%'.format(100 * correct / total))

通过以上步骤，我们可以使用PyTorch构建一个简单的卷积神经网络进行Minst手写数字识别。