pytorch实现手写字符识别

以下是使用PyTorch实现手写字符识别的步骤：

1. 导入必要的库和模块

import os
import numpy as np
import torch
import torch.nn.functional as F
import torch.optim as optim
from torch import nn
from torch.utils import data
import torchvision.transforms as transforms
from tensorboardX import SummaryWriter
import skimage.io as io
import matplotlib.pyplot as plt

2. 定义常量

train_batch_size = 64

3. 加载数据集

class TrainDataset(data.Dataset):
    def __init__(self, array, label):
        self.data = np.asarray(array)
        self.label = np.asarray(label)
        self.transform = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize([0.5], [0.5])
        ])

    def __getitem__(self, index):
        txt = torch.from_numpy(self.data[index])
        label = torch.tensor(self.label[index])
        return txt, label

    def __len__(self):
        return len(self.data)

train_data = np.load('train_data.npy')
train_label = np.load('train_label.npy')
train_dataset = TrainDataset(train_data, train_label)
train_loader = data.DataLoader(train_dataset, batch_size=train_batch_size, shuffle=True)

4. 定义模型

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=5)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=5)
        self.fc1 = nn.Linear(1024, 128)
        self.fc2 = nn.Linear(128, 47)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2(x), 2))
        x = x.view(-1, 1024)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

model = Net()

5. 定义损失函数和优化器

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

6. 训练模型

def train(epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 10 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))

for epoch in range(1, 11):
    train(epoch)

7. 测试模型

test_data = np.load('test_data.npy')
test_label = np.load('test_label.npy')
test_dataset = TrainDataset(test_data, test_label)
test_loader = data.DataLoader(test_dataset, batch_size=train_batch_size, shuffle=True)

def test():
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            output = model(data)
            test_loss += criterion(output, target).item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)

    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))

test()