层和块学习

1.生成一个网络，其中包含一个具有256个单元和ReLU激活函数的全连接隐藏层，然后是一个具有10个隐藏单元且不带激活函数的全连接输出层。

import torch
from torch import nn
from torch.nn import functional as Fnet = nn.Sequential(nn.Linear(20, 256), nn.ReLU(), nn.Linear(256, 10))X = torch.rand(2, 20)
net(X)

2.从零开始编写一个块。它包含一个多层感知机，具有256个隐藏单元的隐藏层和一个10维输出层。

class MLP(nn.Module):def __init__(self):super().__init__()self.hidden = nn.Linear(20, 256)self.out = nn.Linear(256, 10)def forward(self, X):return self.out(F.relu(self.hidden(X)))

3.实例化多层感知机的层，在每次调用前向传播函数时调用这些层

net = MLP()
net(X)

4. 使用MySequential类重新实现多层感知机

class MySequential(nn.Module):def __init__(self, *args):super().__init__()for idx, module in enumerate(args):self._modules[str(idx)] = moduledef forward(self, X):for block in self._modules.values():X = block(X)return Xnet = MySequential(nn.Linear(20, 256), nn.ReLU(), nn.Linear(256, 10))
net(X)

5.在前向传播函数中执行Python的控制流

class FixedHiddenMLP(nn.Module):def __init__(self):super().__init__()self.rand_weight = torch.rand((20, 20), requires_grad=False)self.linear = nn.Linear(20, 20)def forward(self, X):X = self.linear(X)X = F.relu(torch.mm(X, self.rand_weight) + 1)X = self.linear(X)while X.abs().sum() > 1:X /= 2return X.sum()

6.混合搭配各种组合块

class NestMLP(nn.Module):def __init__(self):super().__init__()self.net = nn.Sequential(nn.Linear(20, 64), nn.ReLU(),nn.Linear(64, 32), nn.ReLU())self.linear = nn.Linear(32, 16)def forward(self, X):return self.linear(self.net(X))chimera = nn.Sequential(NestMLP(), nn.Linear(16, 20), FixedHiddenMLP())
chimera(X)