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 Datatrain_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 outmodel = 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))))