李沐笔记（丢弃法）

 x是到下一层的输出；p是丢弃当前数据的概率

import torch
from torch import nn
from d2l import torch as d2l

def dropout_layer(X, dropout):
    assert 0 <= dropout <= 1
    # 在本情况中，所有元素都被丢弃。
    if dropout == 1:
        return torch.zeros_like(X)
    # 在本情况中，所有元素都被保留。
    if dropout == 0:
        return X
    # mask =(torch.randn(X.shape) > dropout).float()
    mask = (torch.Tensor(X.shape).uniform_(0, 1) > dropout).float()
    return mask * X / (1.0 - dropout)

# 测试dropout_layer函数
X = torch.arange(16, dtype = torch.float32).reshape((2, 8))
print(X)
print(dropout_layer(X,0.))
print(dropout_layer(X,0.5))
print(dropout_layer(X,1.))

# 定义具有两层隐藏层的多层感知机，每个隐藏层256个单元
# 定义模型参数
num_inputs,num_outputs,num_hiddens1,num_hidden2=784,10,256,256
# 定义模型
dropout1,dropout2 = 0.2,0.5

class Net(nn.Module):
    def __init__(self,num_inputs,num_outputs,num_hidden1,num_hidden2,is_training=True):
        super(Net,self).__init__()
        self.num_inputs = num_inputs
        self.training = is_training
        self.lin1 = nn.Linear(num_inputs,num_hidden1)
        self.lin2 = nn.Linear(num_hidden1,num_hidden2)
        self.lin3 = nn.Linear(num_hidden2,num_outputs)
        self.relu = nn.ReLU()

    def forward(self,X):
        H1 = self.relu(self.lin1(X.reshape((-1,self.num_inputs))))    # 第一个隐藏层的输出
         # 只有在训练模型时才使用dropout
        if self.training == True:
            # 在第一个全连接层之后添加一个dropout层
            H1 = dropout_layer(H1, dropout1)
        H2 = self.relu(self.lin2(H1))     # 第二个隐藏层
        if self.training == True:
            # 在第二个全连接层之后添加一个dropout层
                H2 = dropout_layer(H2, dropout2)
        out = self.lin3(H2)     # 输出
        return out


net = Net(num_inputs, num_outputs, num_hiddens1, num_hiddens2)

# 训练和测试
num_epochs, lr, batch_size = 10, 0.5, 256
loss = nn.CrossEntropyLoss()
train_iter,test_iter = d2l.load_data_fashion_mnist(batch_size)
trainer = torch.optim.SGD(net.parameters(),lr=lr)
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)

# 简洁实现
net = nn.Sequential(nn.Flatten(),
        nn.Linear(784, 256),
        nn.ReLU(),
        # 在第一个全连接层之后添加一个dropout层
        nn.Dropout(dropout1),
        nn.Linear(256, 256),
        nn.ReLU(),
        # 在第二个全连接层之后添加一个dropout层
        nn.Dropout(dropout2),
        nn.Linear(256, 10))

def init_weights(m):
    if type(m) == nn.Linear:
        nn.init.normal_(m.weight, std=0.01)

net.apply(init_weights);
#训练和测试
trainer = torch.optim.SGD(net.parameters(), lr=lr)
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)