一个gpu实例

import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
import torch.nn.functional as F
from torchvision import transforms,datasets


class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = torch.nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = torch.nn.Conv2d(10, 20, kernel_size=5)
        self.pooling = torch.nn.MaxPool2d(2)
        self.fc = torch.nn.Linear(320, 10)

    def forward(self, x):
        # Flatten data from (n, 1, 28, 28) to (n, 784)
        batch_size = x.size(0)
        x = F.relu(self.pooling(self.conv1(x)))
        x = F.relu(self.pooling(self.conv2(x)))
        x = x.view(batch_size, -1) # flatten
        x = self.fc(x)
        return x


class DiabetesDataset(Dataset):
    def __init__(self, filepath):
        xy = np.loadtxt(filepath, delimiter=',', dtype=np.float32)
        self.len = xy.shape[0]
        self.x_data = torch.from_numpy(xy[:, :-1])
        self.y_data = torch.from_numpy(xy[:, [-1]])

    def __getitem__(self, index):
        return self.x_data[index], self.y_data[index]

    def __len__(self):
        return self.len




# dataset = DiabetesDataset('diabetes.csv.gz')
# train_loader = DataLoader(dataset=dataset,
#                             batch_size=32,
#                             shuffle=True,
#                             num_workers=7)
transform = transforms.Compose([
    transforms.ToTensor(), # 先调用，转变成CWH，取值变成0-1
    transforms.Normalize((0.1307,),(0.3081)) # 归一化，均值和标准差，所有像素
])
batch_size = 128
train_dataset = datasets.MNIST(root='../dataset/mnist/',
    train=True,
    download=True,
    transform=transform)
train_loader = DataLoader(train_dataset,
    shuffle=True,
    batch_size=batch_size)
test_dataset = datasets.MNIST(root='../dataset/mnist/',
    train=False,
    download=True,
    transform=transform)
test_loader = DataLoader(test_dataset,
    shuffle=False,
    batch_size=batch_size)

model = Net()
device = torch.device("cuda")
model = model.to(device)

criterion = torch.nn.CrossEntropyLoss(reduction='mean')
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

def train(epoch):
    running_loss = 0.0
    for batch_idx, data in enumerate(train_loader, 0):
        inputs, target = data
        inputs, target = inputs.to(device), target.to(device)
        optimizer.zero_grad()
        # forward + backward + update
        outputs = model(inputs)
        loss = criterion(outputs, target)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if batch_idx % 300 == 299:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, batch_idx + 1, running_loss / 2000))
            running_loss = 0.0

def test():
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            inputs, target = data
            inputs, target = inputs.to(device), target.to(device)
            outputs = model(inputs)
            _, predicted = torch.max(outputs.data, dim=1)
            total += target.size(0)
            correct += (predicted == target).sum().item()
    print('Accuracy on test set: %d %% [%d/%d]' % (100 * correct / total, correct, total))




if __name__ == '__main__':
    for epoch in range(20):
        print(epoch)
        train(epoch)
        test()