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利用 torch.nn 实现前馈神经网络解决 多分类 任务

2023-02-18 16:32:56 时间

1 导入实验需要的包

import torch
import numpy as np
from torch import nn
from torchvision.datasets import MNIST
import torchvision.transforms  as transforms
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from torch.utils.data import DataLoader
from torch import nn

2 导入 MNIST 数据

mnist_train = MNIST(
    root='./datasets/MNIST',
    train = True,
    download =True,
    transform=transforms.ToTensor())

mnist_test = MNIST(root='./datasets/MNIST',
                   train = False,
                   download =True,
                   transform=transforms.ToTensor())

3 加载数据

batch_size =64
train_iter = DataLoader(
    dataset = mnist_train,
    batch_size = batch_size,
    shuffle = True,
)
test_iter = DataLoader(
    dataset = mnist_test,
    batch_size = batch_size,
    shuffle = True,
)

4 定义模型

num_input,num_hidden1,num_hidden2,num_output = 28*28,512,256,10

class DNN(nn.Module):
    def __init__(self,num_input,num_hidden1,num_hidden2,num_output):
        super(DNN,self).__init__()
        self.linear1 = nn.Linear(num_input,num_hidden1)
        self.linear2 = nn.Linear(num_hidden1,num_hidden2)
        self.linear3 = nn.Linear(num_hidden2,num_output)
    def forward(self,input):
        input = input.view(-1,784)
        out = self.linear1(input)
        out = self.linear2(out)
        out = self.linear3(out)
        return out

5 模型初始化

net = DNN(num_input,num_hidden1,num_hidden2,num_output)
for param in net.parameters():
    nn.init.normal_(param,mean=0,std=0.001)

6 定义训练函数

def train(net,train_iter,test_iter,loss,num_epochs):
    train_ls,test_ls,train_acc,test_acc = [],[],[],[]
    for epoch in range(num_epochs):
        train_ls_sum,train_acc_sum,n = 0,0,0
        for x,y in train_iter:
            y_pred = net(x)
            l = loss(y_pred,y)
            optimizer.zero_grad()
            l.backward()
            optimizer.step()
            train_ls_sum +=l.item()
            train_acc_sum += (y_pred.argmax(dim = 1)==y).sum().item()
            n += y_pred.shape[0]
        train_ls.append(train_ls_sum)
        train_acc.append(train_acc_sum/n)
        
        test_ls_sum,test_acc_sum,n = 0,0,0
        for x,y in test_iter:
            y_pred = net(x)
            l = loss(y_pred,y)
            test_ls_sum +=l.item()
            test_acc_sum += (y_pred.argmax(dim = 1)==y).sum().item()
            n += y_pred.shape[0]
        test_ls.append(test_ls_sum)
        test_acc.append(test_acc_sum/n)
        print('epoch %d, train_loss %.6f,test_loss %f, train_acc %.6f,test_acc %f'
              %(epoch+1, train_ls[epoch],test_ls[epoch], train_acc[epoch],test_acc[epoch]))
    return train_ls,test_ls,train_acc,test_acc

7 优化器和损失函数定义

#训练次数和学习率
num_epochs = 20
lr = 0.01
loss  = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(),lr=lr)

8 训练

train_loss,test_loss,train_acc,test_acc = train(net,train_iter,test_iter,loss,num_epochs)

9 可视化

x = np.linspace(0,len(train_loss),len(train_loss))
plt.plot(x,train_loss,label="train_loss",linewidth=1.5)
plt.plot(x,test_loss,label="test_loss",linewidth=1.5)
plt.xlabel("epoch")
plt.ylabel("loss")
plt.legend()
plt.show()

 

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