minist手写数字识别pytoch完整代码，点一张手写数字，可以识别出来

抱歉，作为AI语言模型，我无法提供完整的Python代码。但是，以下是一个示例代码，可以使用PyTorch实现手写数字识别：

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

# 定义神经网络模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=5)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=5)
        self.pool = nn.MaxPool2d(2, 2)
        self.dropout1 = nn.Dropout2d()
        self.fc1 = nn.Linear(64 * 4 * 4, 128)
        self.dropout2 = nn.Dropout()
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.pool(torch.relu(self.conv1(x)))
        x = self.dropout1(x)
        x = self.pool(torch.relu(self.conv2(x)))
        x = self.dropout1(x)
        x = x.view(-1, 64 * 4 * 4)
        x = torch.relu(self.fc1(x))
        x = self.dropout2(x)
        x = self.fc2(x)
        return x

# 加载MNIST数据集
train_transform = transforms.Compose([
    transforms.RandomRotation(10),
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))
])
test_transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))
])
train_dataset = MNIST(root='./data', train=True, download=True, transform=train_transform)
test_dataset = MNIST(root='./data', train=False, download=True, transform=test_transform)

# 定义数据集加载器
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)

# 初始化模型和优化器
model = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
epochs = 10
for epoch in range(epochs):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

# 测试模型
correct = 0
total = 0
with torch.no_grad():
    for data in test_loader:
        images, labels = data
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))

# 使用模型进行预测
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image

# 加载手写数字图片
img = Image.open("test.png").convert('L')
img = img.resize((28, 28))
img = np.array(img)
img = img / 255.0
plt.imshow(img, cmap='gray')
plt.show()

# 将图片转换为张量并输入模型
img_tensor = torch.tensor(img, dtype=torch.float).unsqueeze(0).unsqueeze(0)
output = model(img_tensor)
_, predicted = torch.max(output.data, 1)
print("Predicted digit:", predicted.item())

该代码将使用PyTorch构建一个卷积神经网络，用于手写数字识别。代码将加载MNIST数据集，并使用数据集加载器进行训练和测试。最后，代码将使用模型进行预测，可以通过指定路径加载一张手写数字图片，并输出预测结果。