import torch
import torch.nn as nn

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.layers = nn.ModuleList([
            nn.Linear(10, 20),
            nn.Linear(20, 20),
            nn.Linear(20, 5)
        ])

    def forward(self, x):
        for layer in self.layers:
            x = layer(x)
        return x

model = Net()
x = torch.randn(4, 10)
output = model(x)

print("输入:", x.shape)
print("输出:", output.shape)
print("参数数量:", sum(p.numel() for p in model.parameters()))

import torch
import torch.nn as nn

class DynamicNet(nn.Module):
    def __init__(self, num_layers, dim):
        super(DynamicNet, self).__init__()
        self.layers = nn.ModuleList()
        for i in range(num_layers):
            in_dim = dim if i == 0 else dim
            self.layers.append(nn.Linear(in_dim, dim))

    def forward(self, x):
        for i, layer in enumerate(self.layers):
            x = layer(x)
            if i < len(self.layers) - 1:
                x = torch.relu(x)
        return x

model = DynamicNet(num_layers=5, dim=64)
print("层数:", len(model.layers))
print("总参数:", sum(p.numel() for p in model.parameters()))

import torch
import torch.nn as nn

# ModuleList - 灵活
mlist = nn.ModuleList([nn.Linear(10, 20), nn.ReLU(), nn.Linear(20, 5)])

# Sequential - 固定顺序
seq = nn.Sequential(nn.Linear(10, 20), nn.ReLU(), nn.Linear(20, 5))

print("ModuleList 索引访问:", mlist[0])
print("Sequential 索引访问:", seq[0])
print("nModuleList 可遍历但无 forward")
print("Sequential 直接调用")