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  • Pytorch入门实例:mnist分类训练

    #!/usr/bin/env python
    # -*- coding: utf-8 -*-
    __author__ = 'denny'
    __time__ = '2017-9-9 9:03'
    
    import torch
    import torchvision
    from torch.autograd import Variable
    import torch.utils.data.dataloader as Data
    
    train_data = torchvision.datasets.MNIST(
        './mnist', train=True, transform=torchvision.transforms.ToTensor(), download=True
    )
    test_data = torchvision.datasets.MNIST(
        './mnist', train=False, transform=torchvision.transforms.ToTensor()
    )
    print("train_data:", train_data.train_data.size())
    print("train_labels:", train_data.train_labels.size())
    print("test_data:", test_data.test_data.size())
    
    train_loader = Data.DataLoader(dataset=train_data, batch_size=64, shuffle=True)
    test_loader = Data.DataLoader(dataset=test_data, batch_size=64)
    
    
    class Net(torch.nn.Module):
        def __init__(self):
            super(Net, self).__init__()
            self.conv1 = torch.nn.Sequential(
                torch.nn.Conv2d(1, 32, 3, 1, 1),
                torch.nn.ReLU(),
                torch.nn.MaxPool2d(2))
            self.conv2 = torch.nn.Sequential(
                torch.nn.Conv2d(32, 64, 3, 1, 1),
                torch.nn.ReLU(),
                torch.nn.MaxPool2d(2)
            )
            self.conv3 = torch.nn.Sequential(
                torch.nn.Conv2d(64, 64, 3, 1, 1),
                torch.nn.ReLU(),
                torch.nn.MaxPool2d(2)
            )
            self.dense = torch.nn.Sequential(
                torch.nn.Linear(64 * 3 * 3, 128),
                torch.nn.ReLU(),
                torch.nn.Linear(128, 10)
            )
    
        def forward(self, x):
            conv1_out = self.conv1(x)
            conv2_out = self.conv2(conv1_out)
            conv3_out = self.conv3(conv2_out)
            res = conv3_out.view(conv3_out.size(0), -1)
            out = self.dense(res)
            return out
    
    
    model = Net()
    print(model)
    
    optimizer = torch.optim.Adam(model.parameters())
    loss_func = torch.nn.CrossEntropyLoss()
    
    for epoch in range(10):
        print('epoch {}'.format(epoch + 1))
        # training-----------------------------
        train_loss = 0.
        train_acc = 0.
        for batch_x, batch_y in train_loader:
            batch_x, batch_y = Variable(batch_x), Variable(batch_y)
            out = model(batch_x)
            loss = loss_func(out, batch_y)
            train_loss += loss.data[0]
            pred = torch.max(out, 1)[1]
            train_correct = (pred == batch_y).sum()
            train_acc += train_correct.data[0]
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
        print('Train Loss: {:.6f}, Acc: {:.6f}'.format(train_loss / (len(
            train_data)), train_acc / (len(train_data))))
    
        # evaluation--------------------------------
        model.eval()
        eval_loss = 0.
        eval_acc = 0.
        for batch_x, batch_y in test_loader:
            batch_x, batch_y = Variable(batch_x, volatile=True), Variable(batch_y, volatile=True)
            out = model(batch_x)
            loss = loss_func(out, batch_y)
            eval_loss += loss.data[0]
            pred = torch.max(out, 1)[1]
            num_correct = (pred == batch_y).sum()
            eval_acc += num_correct.data[0]
        print('Test Loss: {:.6f}, Acc: {:.6f}'.format(eval_loss / (len(
            test_data)), eval_acc / (len(test_data))))
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  • 原文地址:https://www.cnblogs.com/denny402/p/7506523.html
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