基于pytorch实现transformer分类minist

以下是基于Pytorch实现Transformer分类MNIST的代码：

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

# 定义超参数
batch_size = 64
num_epochs = 10
lr = 0.001
num_classes = 10
d_model = 256
num_heads = 8
num_layers = 6
dropout = 0.1

# 定义Transformer模型
class Transformer(nn.Module):
    def __init__(self, d_model, num_heads, num_layers, num_classes, dropout):
        super(Transformer, self).__init__()
        self.d_model = d_model
        self.num_heads = num_heads
        self.num_layers = num_layers
        
        self.embedding = nn.Embedding(784, d_model)
        
        self.pos_encoding = nn.Parameter(torch.zeros(1, 784, d_model))
        
        self.dropout = nn.Dropout(dropout)
        
        self.transformer = nn.Transformer(d_model, num_heads, num_layers)
        
        self.fc = nn.Linear(d_model, num_classes)
        
    def forward(self, x):
        x = self.embedding(x) * (self.d_model ** 0.5)
        x = x + self.pos_encoding[:, :x.size(1), :]
        x = self.dropout(x)
        x = x.permute(1, 0, 2)
        output = self.transformer(x)
        output = output.permute(1, 0, 2)
        output = output[:, -1, :]
        output = self.fc(output)
        return output

# 加载数据集
train_dataset = datasets.MNIST(root="dataset/", train=True, transform=transforms.ToTensor(), download=True)
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)

test_dataset = datasets.MNIST(root="dataset/", train=False, transform=transforms.ToTensor(), download=True)
test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True)

# 初始化模型和优化器
model = Transformer(d_model, num_heads, num_layers, num_classes, dropout)
optimizer = optim.Adam(model.parameters(), lr=lr)

# 训练模型
for epoch in range(num_epochs):
    for batch_idx, (data, targets) in enumerate(train_loader):
        data = data.reshape(data.shape[0], -1)
        
        # 前向传播
        output = model(data)
        loss = nn.CrossEntropyLoss()(output, targets)
        
        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        if batch_idx % 100 == 0:
            print("Epoch [{}/{}], Batch [{}/{}], Loss: {:.4f}".format(epoch+1, num_epochs, batch_idx+1, len(train_loader), loss.item()))

# 测试模型
with torch.no_grad():
    correct = 0
    total = 0
    
    for data, targets in test_loader:
        data = data.reshape(data.shape[0], -1)
        
        output = model(data)
        _, predicted = torch.max(output.data, 1)
        
        total += targets.size(0)
        correct += (predicted == targets).sum().item()
        
    print("Accuracy on test set: {:.2f}%".format(correct / total * 100))

在这个例子中，我们使用了Pytorch内置的Transformer模型，同时也自定义了一些层和参数。我们将784维的MNIST图像像素值作为输入，经过一个嵌入层转换成d_model维的向量，然后加上位置编码。接着，我们将数据传入Transformer模型，最后输出一个10维的向量，代表每个类别的概率。在训练过程中，我们使用交叉熵损失函数并使用Adam优化器进行优化。在测试过程中，我们计算了模型在测试集上的准确率。