python多层感知机的从零开始实现

多层感知机的从零开始实现可以通过以下步骤进行：

1. 导入所需的库和模块：

   import torch
   from torch import nn

2. 定义模型类：

   class MLP(nn.Module):
       def __init__(self, input_size, hidden_size, output_size):
           super(MLP, self).__init__()
           self.hidden = nn.Linear(input_size, hidden_size)
           self.relu = nn.ReLU()
           self.output = nn.Linear(hidden_size, output_size)

       def forward(self, x):
           x = self.hidden(x)
           x = self.relu(x)
           x = self.output(x)
           return x

3. 初始化模型参数：

   input_size = 784
   hidden_size = 256
   output_size = 10

   model = MLP(input_size, hidden_size, output_size)

4. 定义损失函数和优化器：

   loss_fn = nn.CrossEntropyLoss()
   optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

5. 训练模型：

   num_epochs = 10

   for epoch in range(num_epochs):
       for inputs, labels in train_iter:
           # 前向传播
           outputs = model(inputs)
           loss = loss_fn(outputs, labels)

           # 反向传播和优化
           optimizer.zero_grad()
           loss.backward()
           optimizer.step()

6. 在测试集上评估模型：

   with torch.no_grad():
       correct = 0
       total = 0
       for inputs, labels in test_iter:
           outputs = model(inputs)
           _, predicted = torch.max(outputs.data, 1)
           total += labels.size(0)
           correct += (predicted == labels).sum().item()

       accuracy = correct / total
       print('Test Accuracy: {:.2f}%'.format(accuracy * 100))