在多分类任务实验中用torch.nn实现dropout

1 导入需要的包

import torch
import torch.nn as nn
import numpy as np
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt

2 下载MNIST数据集以及读取数据

mnist_train = torchvision.datasets.MNIST(root='../Datasets/MNIST', train=True, download=True, transform=transforms.ToTensor())  
mnist_test = torchvision.datasets.MNIST(root='../Datasets/MNIST', train=False,download=True, transform=transforms.ToTensor())  
batch_size = 256 
train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True,num_workers=0)  
test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False,num_workers=0)  

3 定义模型

class LinearNet(nn.Module):
    def __init__(self,num_inputs, num_outputs, num_hiddens1, num_hiddens2, drop_prob1,drop_prob2):
        super(LinearNet,self).__init__()
        self.linear1 = nn.Linear(num_inputs,num_hiddens1)
        self.relu = nn.ReLU()
        self.drop1 = nn.Dropout(drop_prob1)
        self.linear2 = nn.Linear(num_hiddens1,num_hiddens2)
        self.drop2 = nn.Dropout(drop_prob2)
        self.linear3 = nn.Linear(num_hiddens2,num_outputs)
        self.flatten  = nn.Flatten()
    
    def forward(self,x):
        x = self.flatten(x)
        x = self.linear1(x)
        x = self.relu(x)
        x = self.drop1(x)
        x = self.linear2(x)
        x = self.relu(x)
        x = self.drop2(x)
        x = self.linear3(x)
        y = self.relu(x)
        return y

4 定义训练模型

def train(net,train_iter,test_iter,loss,num_epochs,batch_size,params=None,lr=None,optimizer=None):
    train_ls, test_ls = [], []
    for epoch in range(num_epochs):
        ls, count = 0, 0
        for X,y in train_iter:
            l=loss(net(X),y)
            optimizer.zero_grad()
            l.backward()
            optimizer.step()
            ls += l.item()
            count += y.shape[0]
        train_ls.append(ls)
        ls, count = 0, 0
        for X,y in test_iter:
            l=loss(net(X),y)
            ls += l.item()
            count += y.shape[0]
        test_ls.append(ls)
        if(epoch+1)%5==0:
            print('epoch: %d, train loss: %f, test loss: %f'%(epoch+1,train_ls[-1],test_ls[-1]))
    return train_ls,test_ls

5 比较不同dropout的影响

num_inputs,num_hiddens1,num_hiddens2,num_outputs =784, 256,256,10
num_epochs=20
lr = 0.001
drop_probs = np.arange(0,1.1,0.1)
Train_ls, Test_ls = [], []
for drop_prob in drop_probs:
    net = LinearNet(num_inputs, num_outputs, num_hiddens1, num_hiddens2, drop_prob,drop_prob)
    for param in net.parameters():
        nn.init.normal_(param,mean=0, std= 0.01)
    loss = nn.CrossEntropyLoss()
    optimizer = torch.optim.SGD(net.parameters(),lr)
    train_ls, test_ls = train(net,train_iter,test_iter,loss,num_epochs,batch_size,net.parameters,lr,optimizer)
    Train_ls.append(train_ls)
    Test_ls.append(test_ls)

6 绘制不同dropout损失图

x = np.linspace(0,len(train_ls),len(train_ls))
plt.figure(figsize=(10,8))
for i in range(0,len(drop_probs)):
    plt.plot(x,Train_ls[i],label= 'drop_prob=%.1f'%(drop_probs[i]),linewidth=1.5)
    plt.xlabel('epoch')
    plt.ylabel('loss')
plt.legend(loc=2, bbox_to_anchor=(1.05,1.0),borderaxespad = 0.)
plt.title('train loss with dropout')
plt.show()

nn.Flatten() demo

input = torch.randn(2, 5, 5)
m = nn.Sequential(
nn.Flatten()
)
output = m(input)
output.size()