结果
训练好的模型呢
训练过程中，不断变化
官网：
是https://github.com/pytorch/examples/blob/main/mnist/main.py
test改个名字
如何使用训练好的模型，来识别我手写的
| from __future__ import print_function | |
| 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 StepLR | |
| class 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, dim=1) | |
| return output | |
| 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 | |
| def test(model, device, test_loader): | |
| model.eval() | |
| test_loss = 0 | |
| correct = 0 | |
| with 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, reduction='sum').item() # sum up batch loss | |
| pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability | |
| 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))) | |
| def main(): | |
| # Training settings | |
| parser = argparse.ArgumentParser(description='PyTorch MNIST Example') | |
| parser.add_argument('--batch-size', type=int, default=64, metavar='N', | |
| help='input batch size for training (default: 64)') | |
| parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', | |
| help='input batch size for testing (default: 1000)') | |
| parser.add_argument('--epochs', type=int, default=14, metavar='N', | |
| help='number of epochs to train (default: 14)') | |
| parser.add_argument('--lr', type=float, default=1.0, metavar='LR', | |
| help='learning rate (default: 1.0)') | |
| parser.add_argument('--gamma', type=float, default=0.7, metavar='M', | |
| help='Learning rate step gamma (default: 0.7)') | |
| parser.add_argument('--no-cuda', action='store_true', default=False, | |
| help='disables CUDA training') | |
| parser.add_argument('--no-mps', action='store_true', default=False, | |
| help='disables macOS GPU training') | |
| parser.add_argument('--dry-run', action='store_true', default=False, | |
| help='quickly check a single pass') | |
| parser.add_argument('--seed', type=int, default=1, metavar='S', | |
| help='random seed (default: 1)') | |
| parser.add_argument('--log-interval', type=int, default=10, metavar='N', | |
| help='how many batches to wait before logging training status') | |
| parser.add_argument('--save-model', action='store_true', default=False, | |
| help='For Saving the current Model') | |
| args = parser.parse_args() | |
| use_cuda = not args.no_cuda and torch.cuda.is_available() | |
| use_mps = not args.no_mps and torch.backends.mps.is_available() | |
| torch.manual_seed(args.seed) | |
| if use_cuda: | |
| device = torch.device("cuda") | |
| elif use_mps: | |
| device = torch.device("mps") | |
| else: | |
| device = torch.device("cpu") | |
| train_kwargs = {'batch_size': args.batch_size} | |
| test_kwargs = {'batch_size': args.test_batch_size} | |
| if use_cuda: | |
| cuda_kwargs = {'num_workers': 1, | |
| 'pin_memory': True, | |
| 'shuffle': True} | |
| train_kwargs.update(cuda_kwargs) | |
| test_kwargs.update(cuda_kwargs) | |
| transform=transforms.Compose([ | |
| transforms.ToTensor(), | |
| transforms.Normalize((0.1307,), (0.3081,)) | |
| ]) | |
| dataset1 = datasets.MNIST('../data', train=True, download=True, | |
| transform=transform) | |
| dataset2 = datasets.MNIST('../data', train=False, | |
| transform=transform) | |
| 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(), lr=args.lr) | |
| scheduler = StepLR(optimizer, step_size=1, gamma=args.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() |