pytorch cnn 手写数字识别

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()