pytorch实现mnist分类

本文详细介绍了如何使用PyTorch框架训练一个简单的卷积神经网络,对MNIST数据集的手写数字进行分类。从数据预处理、模型构建到训练与评估,一步步解析整个过程。

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jun  9 19:40:53 2020

@author: 
"""

import torchvision
from matplotlib import pyplot as plt
import torch
from torchvision import datasets, transforms
from torch.autograd import Variable

#%%
transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean=[0.5], std=[0.5])])
#transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean=[0.5,0.5], std=[0.5,0.5])])
data_train=datasets.MNIST(root="/Users/didi/Documents/MNIST_master/",transform=transform,train=True,download=True)
data_test=datasets.MNIST(root="/Users/didi/Documents/MNIST_master/",transform=transform,train=False)
#%%
data_loader_train=torch.utils.data.DataLoader(dataset=data_train,batch_size=64,shuffle=True)
data_loader_test=torch.utils.data.DataLoader(dataset=data_test,batch_size=64,shuf
MNIST是一个非常经典的手写数字识别数据集,使用PyTorch实现MNIST分类可以分为以下几个步骤: 1. 导入必要的库和数据集 ```python import torch import torch.nn as nn import torch.optim as optim import torchvision.datasets as datasets import torchvision.transforms as transforms train_data = datasets.MNIST(root='data', train=True, transform=transforms.ToTensor(), download=True) test_data = datasets.MNIST(root='data', train=False, transform=transforms.ToTensor(), download=True) train_loader = torch.utils.data.DataLoader(train_data, batch_size=64, shuffle=True) test_loader = torch.utils.data.DataLoader(test_data, batch_size=64, shuffle=False) ``` 2. 定义模型 我们可以使用一个简单的卷积神经网络来实现MNIST分类。这里我们定义了一个包含两个卷积层和两个全连接层的模型。 ```python class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 32, 3, padding=1) self.conv2 = nn.Conv2d(32, 64, 3, padding=1) self.fc1 = nn.Linear(64 * 7 * 7, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = nn.functional.relu(self.conv1(x)) x = nn.functional.max_pool2d(x, 2) x = nn.functional.relu(self.conv2(x)) x = nn.functional.max_pool2d(x, 2) x = x.view(-1, 64 * 7 * 7) x = nn.functional.relu(self.fc1(x)) x = self.fc2(x) return x model = Net() ``` 3. 定义损失函数和优化器 ```python criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) ``` 4. 训练模型 ```python for epoch in range(10): for i, (images, labels) in enumerate(train_loader): optimizer.zero_grad() outputs = model(images) loss = criterion(outputs, labels) loss.backward() optimizer.step() if (i+1) % 100 == 0: print('Epoch [%d/%d], Step [%d/%d], Loss: %.4f' % (epoch+1, 10, i+1, len(train_loader), loss.item())) ``` 5. 测试模型 ```python correct = 0 total = 0 with torch.no_grad(): for images, labels in test_loader: outputs = model(images) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total)) ``` 这样就完成了使用PyTorch实现MNIST分类的过程。
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