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建设局网站投诉开发商,怎样创建自己的电商平台,免费简历制作app,巨鹿网站建设设计PyTorch 实现 MNIST 手写数字识别 MNIST 是一个经典的手写数字数据集#xff0c;包含 60000 张训练图像和 10000 张测试图像。使用 PyTorch 实现 MNIST 分类通常包括数据加载、模型构建、训练和评估几个部分。 数据加载与预处理 使用 torchvision 加载 MNIST 数据集#x…PyTorch 实现 MNIST 手写数字识别 MNIST 是一个经典的手写数字数据集包含 60000 张训练图像和 10000 张测试图像。使用 PyTorch 实现 MNIST 分类通常包括数据加载、模型构建、训练和评估几个部分。 数据加载与预处理 使用 torchvision 加载 MNIST 数据集并进行归一化和数据增强可选。以下是数据加载的示例代码 import torch from torchvision import datasets, transformstransform transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.1307,), (0.3081,)) ])train_dataset datasets.MNIST(data, trainTrue, downloadTrue, transformtransform) test_dataset datasets.MNIST(data, trainFalse, transformtransform)train_loader torch.utils.data.DataLoader(train_dataset, batch_size64, shuffleTrue) test_loader torch.utils.data.DataLoader(test_dataset, batch_size1000, shuffleFalse)构建模型 定义一个简单的卷积神经网络CNN模型 import torch.nn as nn import torch.nn.functional as Fclass Net(nn.Module):def init(self):super(Net, self).init()self.conv1 nn.Conv2d(1, 32, 3, 1)self.conv2 nn.Conv2d(32, 64, 3, 1)self.fc1 nn.Linear(1024, 128)self.fc2 nn.Linear(128, 10)def forward(self, x):x F.relu(self.conv1(x))x F.max_pool2d(x, 2)x F.relu(self.conv2(x))x F.max_pool2d(x, 2)x torch.flatten(x, 1)x F.relu(self.fc1(x))x self.fc2(x)return F.log_softmax(x, dim1)训练模型 定义优化器和损失函数并进行训练 model Net() optimizer torch.optim.Adam(model.parameters(), lr0.001) criterion nn.CrossEntropyLoss()def train(model, device, train_loader, optimizer, epoch):model.train()for batch_idx, (data, target) in enumerate(train_loader):data, target data.to(device), target.to(device)optimizer.zero_grad()output model(data)loss criterion(output, target)loss.backward()optimizer.step()if batch_idx % 100 0:print(fEpoch: {epoch}, Loss: {loss.item():.4f})测试模型 在测试集上评估模型性能 def test(model, device, test_loader):model.eval()test_loss 0correct 0with torch.no_grad():for data, target in test_loader:data, target data.to(device), target.to(device)output model(data)test_loss criterion(output, target).item()pred output.argmax(dim1, keepdimTrue)correct pred.eq(target.view_as(pred)).sum().item()test_loss / len(test_loader.dataset)print(fTest Accuracy: {correct}/{len(test_loader.dataset)} ({100. * correct / len(test_loader.dataset):.2f}%))完整训练循环 将训练和测试整合到一个完整的循环中进行10次训练 device torch.device(cuda if torch.cuda.is_available() else cpu) model.to(device)for epoch in range(1, 10):train(model, device, train_loader, optimizer, epoch)test(model, device, test_loader)模型保存与加载 训练完成后可以保存模型 torch.save(model.state_dict(), mnist_cnn.pt)加载模型 加载模块 model Net() model.load_state_dict(torch.load(mnist_cnn.pt)) model.eval()或者是Mnist其他源代码 Mnist main.py import argparse import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transforms from torch.optim.lr_scheduler import StepLRclass Net(nn.Module):def init(self):super(Net, self).init()self.conv1 nn.Conv2d(1, 32, 3, 1)self.conv2 nn.Conv2d(32, 64, 3, 1)self.dropout1 nn.Dropout(0.25)self.dropout2 nn.Dropout(0.5)self.fc1 nn.Linear(9216, 128)self.fc2 nn.Linear(128, 10)def forward(self, x):x self.conv1(x)x F.relu(x)x self.conv2(x)x F.relu(x)x F.max_pool2d(x, 2)x self.dropout1(x)x torch.flatten(x, 1)x self.fc1(x)x F.relu(x)x self.dropout2(x)x self.fc2(x)output F.log_softmax(x, dim1)return output#训练模型 def train(args, model, device, train_loader, optimizer, epoch):model.train()for batch_idx, (data, target) in enumerate(train_loader):data, target data.to(device), target.to(device)optimizer.zero_grad()output model(data)loss F.nll_loss(output, target)loss.backward()optimizer.step()if batch_idx % args.log_interval 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()))if args.dry_run:break#测试模型 def test(model, device, test_loader):model.eval()test_loss 0correct 0with torch.no_grad():for data, target in test_loader:data, target data.to(device), target.to(device)output model(data)test_loss F.nll_loss(output, target, reductionsum).item() # sum up batch losspred output.argmax(dim1, keepdimTrue) # get the index of the max log-probabilitycorrect 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)))#主函数 MNIST Example的主函数 def main():# Training settingsparser argparse.ArgumentParser(descriptionPyTorch MNIST Example)parser.add_argument(–batch-size, typeint, default64, metavarN,helpinput batch size for training (default: 64))parser.add_argument(–test-batch-size, typeint, default1000, metavarN,helpinput batch size for testing (default: 1000))parser.add_argument(–epochs, typeint, default14, metavarN,helpnumber of epochs to train (default: 14))parser.add_argument(–lr, typefloat, default1.0, metavarLR,helplearning rate (default: 1.0))parser.add_argument(–gamma, typefloat, default0.7, metavarM,helpLearning rate step gamma (default: 0.7))parser.add_argument(–no-accel, actionstore_true,helpdisables accelerator)parser.add_argument(–dry-run, actionstore_true,helpquickly check a single pass)parser.add_argument(–seed, typeint, default1, metavarS,helprandom seed (default: 1))parser.add_argument(–log-interval, typeint, default10, metavarN,helphow many batches to wait before logging training status)parser.add_argument(–save-model, actionstore_true, helpFor Saving the current Model)args parser.parse_args()use_accel not args.no_accel and torch.accelerator.is_available()torch.manual_seed(args.seed)if use_accel:device torch.accelerator.current_accelerator()else:device torch.device(cpu)train_kwargs {batch_size: args.batch_size}test_kwargs {batch_size: args.test_batch_size}if use_accel:accel_kwargs {num_workers: 1,pin_memory: True,shuffle: True}train_kwargs.update(accel_kwargs)test_kwargs.update(accel_kwargs)transformtransforms.Compose([transforms.ToTensor(),transforms.Normalize((0.1307,), (0.3081,))])dataset1 datasets.MNIST(../data, trainTrue, downloadTrue,transformtransform)dataset2 datasets.MNIST(../data, trainFalse,transformtransform)train_loader torch.utils.data.DataLoader(dataset1,**train_kwargs)test_loader torch.utils.data.DataLoader(dataset2, **test_kwargs)model Net().to(device)optimizer optim.Adadelta(model.parameters(), lrargs.lr)scheduler StepLR(optimizer, step_size1, gammaargs.gamma)for epoch in range(1, args.epochs 1):train(args, model, device, train_loader, optimizer, epoch)test(model, device, test_loader)scheduler.step()if args.save_model:torch.save(model.state_dict(), mnist_cnn.pt)if name main:main() 带命令的Mnist函数 python main.py –help usage: main.py [-h] [–batch-size N] [–test-batch-size N] [–epochs N] [–lr LR] [–gamma M] [–no-accel][–dry-run] [–seed S] [–log-interval N] [–save-model]PyTorch MNIST Exampleoptional arguments:-h, –help show this help message and exit–batch-size N input batch size for training (default: 64)–test-batch-size N input batch size for testing (default: 1000)–epochs N number of epochs to train (default: 14)–lr LR learning rate (default: 1.0)–gamma M Learning rate step gamma (default: 0.7)–no-accel disables accelerator–dry-run quickly check a single pass–seed S random seed (default: 1)–log-interval N how many batches to wait before logging training status–save-model For Saving the current Model 数据目录 注意事项 确保安装了 PyTorch 和 torchvision 库。可以根据硬件条件调整 batch_size。模型结构和超参数如学习率可以根据需求调整。使用 GPU 可以显著加速训练。