多分类任务中不同隐藏单元个数对实验结果的影响

1 导入实验所需要的包

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数据集和读取数据

#下载MNIST手写数字数据集
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())

#读取数据
batch_size = 32
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 定义模型参数

#训练次数和学习率
num_epochs ,lr = 50, 0.01

4 定义模型

class LinearNet(nn.Module):
    def __init__(self,num_inputs, num_outputs, num_hiddens):
        super(LinearNet,self).__init__()
        self.linear1 = nn.Linear(num_inputs,num_hiddens)
        self.relu = nn.ReLU()
        self.linear2 = nn.Linear(num_hiddens,num_outputs)
    
    def forward(self,x):
        x = self.linear1(x)
        x = self.relu(x)
        x = self.linear2(x)
        y = self.relu(x)
        return y

5 定义训练函数

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:
            X = X.reshape(-1,num_inputs)
            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:
            X = X.reshape(-1,num_inputs)
            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

6 模型训练

different_hiddens = [100,200,300,400,500,600,700]

#定义输入层神经元个数和输出层神经元个数
num_inputs, num_outputs = 784, 10

#定义损失函数
loss = nn.CrossEntropyLoss()
Train_loss, Test_loss = [], []
for cur_hiddens in different_hiddens:
    net = LinearNet(num_inputs, num_outputs, cur_hiddens)
    optimizer = torch.optim.SGD(net.parameters(),lr = 0.001)
    for param in net.parameters():
        nn.init.normal_(param,mean=0, std= 0.01)
    train_ls, test_ls = train(net,train_iter,test_iter,loss,num_epochs,batch_size,net.parameters,lr,optimizer)
    Train_loss.append(train_ls)
    Test_loss.append(test_ls)

7 绘制不同隐藏单元损失图

x = np.linspace(0,len(train_ls),len(train_ls))

plt.figure(figsize=(10,8))
for i in range(0,len(different_hiddens)):
    plt.plot(x,Train_loss[i],label= f'Neuronss:{different_hiddens[i]}',linewidth=1.5)
    plt.xlabel('epoch')
    plt.ylabel('loss')
plt.legend()
plt.title('Train loss vs different hiddens')
plt.show()