用python写一个LSTM模型pytorch基于minist数据集完成手写字符识别

好的，我可以帮你回答这个问题。以下是用Python编写LSTM模型并使用PyTorch在MNIST数据集上完成手写字符识别的代码示例：

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

# 定义数据预处理
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))])

# 加载MNIST数据集
train_dataset = datasets.MNIST(root='./data', train=True, transform=transform, download=True)
test_dataset = datasets.MNIST(root='./data', train=False, transform=transform, download=True)

# 定义模型
class LSTMModel(nn.Module):
    def __init__(self):
        super(LSTMModel, self).__init__()
        self.lstm = nn.LSTM(28, 64, 2, batch_first=True)
        self.fc = nn.Linear(64, 10)
        
    def forward(self, x):
        out, _ = self.lstm(x)
        out = self.fc(out[:, -1, :])
        return out
        
model = LSTMModel()

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.01)

# 定义训练函数
def train(model, train_dataloader, optimizer, criterion):
    model.train()
    for i, data in enumerate(train_dataloader):
        x, y = data
        x, y = Variable(x), Variable(y)
        optimizer.zero_grad()
        output = model(x)
        loss = criterion(output, y)
        loss.backward()
        optimizer.step()
        
# 定义测试函数
def test(model, test_dataloader, criterion):
    model.eval()
    total_loss = 0.0
    correct = 0
    for i, data in enumerate(test_dataloader):
        x, y = data
        x, y = Variable(x), Variable(y)
        output = model(x)
        loss = criterion(output, y)
        total_loss += loss.data[0]
        _, predicted = torch.max(output.data, 1)
        correct += (predicted == y.data).sum()
    avg_loss = total_loss / len(test_dataset)
    accuracy = float(correct) / len(test_dataset)
    return avg_loss, accuracy

# 开始训练模型
train_dataloader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_dataloader = DataLoader(test_dataset, batch_size=64, shuffle=False)

for epoch in range(10):
    train(model, train_dataloader, optimizer, criterion)
    avg_loss, accuracy = test(model, test_dataloader, criterion)
    print('Epoch [{}/{}], Loss: {:.4f}, Accuracy: {:.2f}%'.format(epoch+1, 10, avg_loss, accuracy*100))

这个代码会输出每个epoch的平均损失和准确率。你可以根据需要修改代码中的一些参数，如学习率，训练轮数等。