亲宝软件园·资讯

展开

python多分类

心️升明月 人气:0

一、实现过程

本文对经典手写数字数据集进行多分类,损失函数采用交叉熵,激活函数采用ReLU,优化器采用带有动量的mini-batchSGD算法。

所有代码如下:

0、导包

import torch
from torchvision import transforms,datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim

1、准备数据

batch_size = 64
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307,),(0.3081,))
])

# 训练集
train_dataset = datasets.MNIST(root='G:/datasets/mnist',train=True,download=False,transform=transform)
train_loader = DataLoader(train_dataset,shuffle=True,batch_size=batch_size)
# 测试集
test_dataset = datasets.MNIST(root='G:/datasets/mnist',train=False,download=False,transform=transform)
test_loader = DataLoader(test_dataset,shuffle=False,batch_size=batch_size)

2、设计模型

class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.l1 = torch.nn.Linear(784, 512)
        self.l2 = torch.nn.Linear(512, 256)
        self.l3 = torch.nn.Linear(256, 128)
        self.l4 = torch.nn.Linear(128, 64)
        self.l5 = torch.nn.Linear(64, 10)

    def forward(self, x):
        x = x.view(-1, 784)
        x = F.relu(self.l1(x))
        x = F.relu(self.l2(x))
        x = F.relu(self.l3(x))
        x = F.relu(self.l4(x))
        return self.l5(x)
model = Net()
# 模型加载到GPU上
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)

3、构造损失函数和优化器

criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(),lr=0.01,momentum=0.5)

4、训练和测试

def train(epoch):
    running_loss = 0.0
    for batch_idx, data in enumerate(train_loader, 0):
        inputs, target = data
        optimizer.zero_grad()

        # forward+backward+update
        outputs = model(inputs.to(device))
        loss = criterion(outputs, target.to(device))
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if batch_idx % 300 == 299:
            print('[%d,%d] loss: %.3f' % (epoch + 1, batch_idx + 1, running_loss / 300))
            running_loss = 0.0
def test():
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            images, labels = data
            outputs = model(images.to(device))
            _, predicted = torch.max(outputs.data, dim=1)
            total += labels.size(0)
            correct += (predicted.cpu() == labels).sum().item()
    print('Accuracy on test set: %d %%' % (100 * correct / total))

for epoch in range(10):
    train(epoch)
    test()

运行结果如下:

[1,300] loss: 2.166
[1,600] loss: 0.797
[1,900] loss: 0.405
Accuracy on test set: 90 %
[2,300] loss: 0.303
[2,600] loss: 0.252
[2,900] loss: 0.218
Accuracy on test set: 94 %
[3,300] loss: 0.178
[3,600] loss: 0.168
[3,900] loss: 0.142
Accuracy on test set: 95 %
[4,300] loss: 0.129
[4,600] loss: 0.119
[4,900] loss: 0.110
Accuracy on test set: 96 %
[5,300] loss: 0.094
[5,600] loss: 0.092
[5,900] loss: 0.091
Accuracy on test set: 96 %
[6,300] loss: 0.077
[6,600] loss: 0.070
[6,900] loss: 0.075
Accuracy on test set: 97 %
[7,300] loss: 0.061
[7,600] loss: 0.058
[7,900] loss: 0.058
Accuracy on test set: 97 %
[8,300] loss: 0.043
[8,600] loss: 0.051
[8,900] loss: 0.050
Accuracy on test set: 97 %
[9,300] loss: 0.041
[9,600] loss: 0.038
[9,900] loss: 0.043
Accuracy on test set: 97 %
[10,300] loss: 0.030
[10,600] loss: 0.032
[10,900] loss: 0.033
Accuracy on test set: 97 %

二、参考文献

 到此这篇关于PyTorch手写数字数据集进行多分类的文章就介绍到这了,更多相关python多分类内容请搜索以前的文章或继续浏览下面的相关文章希望大家以后多多支持!

加载全部内容

相关教程
猜你喜欢
用户评论