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torch教程——使用GPU运行神经网络

花匠小妙招
2024-12-18 09:00

CPU运行神经网络较慢，可以使用GPU加速。

安装cuda版本的to

安装教程

demo

导入torch库以后，调用下面的函数，如果打印True就说明安装成功。

import torch print(torch.cuda.is_available()) 12

定义一个cuda设备

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") print(device) 12

在这里插入图片描述

将网络的参数设置为cuda张量

net = Net().to(device) 1

将输入和输出设置为cuda张量

input, label = input.to(device), label.to(device) 1

完整代码

这是一个简易神经网络训练的代码，是一份通用代码，如果cuda设备是可用的，则使用cuda加速，否则使用cpu运算。

import torch #张量：不初始化（都是0） print("构造张量：不初始化") x = torch.empty((5,3)) print(x) #随机初始化 print("构造张量：随机初始化") x = torch.rand((5,3)) print(x) #全0矩阵 print("构造张量：全0") x = torch.zeros((5,3), dtype=torch.long) print(x) #从列表构造张量 print("构造张量：从列表构造张量") x = torch.tensor([1, 2, 3]) print(x) #从已有张量构造张量：y会继承x的属性 print("构造张量：从已有张量构造张量") y = x.new_ones((5, 3), dtype=torch.long) print(y) y = torch.rand_like(x, dtype=torch.float) print(y) #获取维度信息 print("获取维度信息") x = torch.empty((5,3)) print(x.shape) #基本运算 print("基本运算") x = torch.rand((5,3)) y = torch.rand_like(x) print(x+y) print(x.add(y)) #带有_的函数会改变调用此函数的对象 x.add_(y) print(x) #改变形状 print("改变形状") x = torch.rand(4,4) y = x.view(-1, 8) print(x.size(), y.size()) #如果只有一个元素，获取元素 print("获取元素") x = torch.rand((1,1)) print(x) print(x.item()) #%% #自动微分 import torch x = torch.ones(2, 2, requires_grad=True) y = x + 2 z = y * y * 3 out = z.mean() #out是标量，因此反向传播无需指定参数 out.backward() #打印梯度 print("打印梯度") print(x.grad) #雅克比向量积 print("雅克比向量积") x = torch.rand(3, requires_grad=True) y = x * 2 print(y) #反向传播的参数是权重 #I = w1*y1 + w2*y2 + w3 * y3 #这里是I对x的各个分量求导 w = torch.tensor([1,2,3], dtype=torch.float) y.backward(w) print(x.grad) #%% #神经网络 import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import torchvision import torchvision.transforms as transforms import torch.utils.data as Data #神经网络需要定义两个函数 #分别是构造函数，前向传播 #自定义的神经网络需要继承nn.Module class Net(nn.Module): #构造函数 def __init__(self): super(Net, self).__init__() #卷积层三个参数：in_channel, out_channels, 5*5 kernal self.con1 = nn.Conv2d(3, 116, 5) self.con2 = nn.Conv2d(116, 100, 5) #全连接层两个参数：in_channels, out_channels self.fc1 = nn.Linear(100 * 5 * 5, 500) self.fc2 = nn.Linear(500, 84) self.fc3 = nn.Linear(84, 10) #前向传播 def forward(self, input): #卷积 --> 激活函数（Relu) --> 池化 x = self.con1(input) x = F.relu(x) x = F.max_pool2d(x, (2, 2)) #重复上述过程 x = self.con2(x) x = F.relu(x) x = F.max_pool2d(x, (2, 2)) #展平 x = x.view(-1, self.num_flat_features(x)) #全连接层 x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) x = F.softmax(x) return x #展平 def num_flat_features(self, x): size = x.size()[1:] num_features = 1 for i in size: num_features = num_features * i return num_features #测试神经网络的输入与输出 # net = Net() # input = torch.randn((1, 3, 32, 32)) # out = net(input) # print(out.size()) #%% #训练一个神经网络的步骤 #1. 定义函数集：确定神经网络的结构 #2. 定义损失函数 #3. 寻找最优函数 #3.1 设置优化器种类（随机梯度下降，Adam等） #3.2 前向传播 #3.3 计算梯度：反向传播 #3.4 更新参数 #3.5 梯度清零 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") print("*********************") print(device) #开始训练 net = Net().to(device) #损失函数 criterion = nn.CrossEntropyLoss() #优化器 optimizer = optim.Adam(net.parameters(), lr = 0.001) #定义变换 transform = transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))] ) #训练集和测试集 transet = torchvision.datasets.CIFAR10(root = './data', train = True, download=True, transform = transform) testset = torchvision.datasets.CIFAR10(root = './data', train=False, download=True, transform = transform) trainLoader = Data.DataLoader(dataset = transet, batch_size = 400, shuffle = True) testLoader = Data.DataLoader(dataset = testset, batch_size = 400, shuffle = False) num_epoches = 15 #开始训练 for epoch in range(num_epoches): correct = 0 total = 0 run_loss = 0.0 for i, data in enumerate(trainLoader): input, label = data input, label = input.to(device), label.to(device) optimizer.zero_grad() outputs = net(input) lossValue = criterion(outputs, label) lossValue.backward() optimizer.step() run_loss += lossValue.item() num = 20 if i % num == num - 1: print('[%d, %5d] loss : %.3f' % (epoch + 1, i + 1, run_loss / num)) run_loss = 0 #训练集准确率 _, pred = outputs.max(1) correct += (pred == label).sum().item() total += label.size()[0] print("训练集准确率：", correct / total) print("finished training!") #预测 correct = 0 total = 0 #预测的时候，无需梯度更新 with torch.no_grad(): for data in testLoader: input, label = data input, label = input.to(device), label.to(device) outputs = net(input) _, pre = torch.max(outputs, 1) total += label.size(0) correct += (pre == label).sum().item() print("准确率：", correct / total)

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