6、利用torch.nn实现前馈神经网络解决多分类问题
#导入必要的包
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
#读取数据
mnist_train = datasets.MNIST(root = './data',train = True,download = False,transform =transforms.ToTensor())
mnist_test = datasets.MNIST(root ='./data',train = False,download = False,transform = transforms.ToTensor())
#训练集
batch_size = 256
train_iter = DataLoader(
dataset = mnist_train,
shuffle = True,
batch_size = batch_size,
num_workers = 0
)
#测试集
test_iter = DataLoader(
dataset = mnist_test,
shuffle =False,
batch_size = batch_size,
num_workers = 0
)
#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
#参数初始化
net = DNN(num_input,num_hidden1,num_hidden2,num_output)
for param in net.parameters():
nn.init.normal_(param,mean=0,std=0.001)
#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
#训练次数和学习率
num_epochs = 20
lr = 0.1
loss = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(),lr=lr)
#训练模型
train_loss,test_loss,train_acc,test_acc = train(net,train_iter,test_iter,loss,num_epochs)
#结果可视化
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()
原文地址:http://www.cnblogs.com/cyberbase/p/16821145.html
1. 本站所有资源来源于用户上传和网络,如有侵权请邮件联系站长!
2. 分享目的仅供大家学习和交流,请务用于商业用途!
3. 如果你也有好源码或者教程,可以到用户中心发布,分享有积分奖励和额外收入!
4. 本站提供的源码、模板、插件等等其他资源,都不包含技术服务请大家谅解!
5. 如有链接无法下载、失效或广告,请联系管理员处理!
6. 本站资源售价只是赞助,收取费用仅维持本站的日常运营所需!
7. 如遇到加密压缩包,默认解压密码为"gltf",如遇到无法解压的请联系管理员!
8. 因为资源和程序源码均为可复制品,所以不支持任何理由的退款兑现,请斟酌后支付下载
声明:如果标题没有注明"已测试"或者"测试可用"等字样的资源源码均未经过站长测试.特别注意没有标注的源码不保证任何可用性