pytorch CIFAR10 分类 - 教程 demo 改写

目录

主要修改了来的数据加载部分，目前还没有归一化

网络定义（和教程一致）

import torch
import torch.nn as nn
import torch.nn.functional as F


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


net = Net()

数据加载和训练

import pickle
import matplotlib.pyplot as plt
import numpy as np

def unpickle(file):
    with open(file, 'rb') as fo:
        dict = pickle.load(fo, encoding='bytes')
    return dict

import torch.optim as optim

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

for i in range(1, 6):
    d = unpickle('data_batch_%d' % i)
    epoch = i
    running_loss = 0
    for i in range(len(d[b'data'])):
        raw = torch.tensor(d[b'data'][i], dtype=torch.float)
        label = d[b'labels'][i]
        inputx = raw.reshape(1, 3, 32, 32)
        outputs = net(inputx)
        loss = criterion(outputs, torch.tensor([label,]))
        loss.backward()
        optimizer.step()

        # print statistics
        running_loss += loss.item()
        if i % 2000 == 1999:    # print every 2000 mini-batches
                print('[%d, %5d] loss: %.3f' %
                      (epoch + 1, i + 1, running_loss / 2000))
                running_loss = 0.0

测试

correct = 0
total = 0
d = unpickle('test_batch')
with torch.no_grad():
    for i in range(len(d[b'data'])):
        raw = torch.tensor(d[b'data'][i], dtype=torch.float)
        label = d[b'labels'][i]
        inputx = raw.reshape(1, 3, 32, 32)
        outputs = net(inputx)
        outputs = net(inputx)
        _, predicted = torch.max(outputs.data, 1)
        total += 1
        if predicted.item() == label:
            correct += 1

print('Accuracy of the network on the 10000 test images: %d %%' % (
    100 * correct / total))

输出：Accuracy of the network on the 10000 test images: 10 %