train_cifar10.py

import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt

# define NN
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

if __name__ == '__main__':
    torch.multiprocessing.freeze_support()

    # load data
    # transform PILImages of range [0, 1] to Tensors of normalized range [-1, 1]
    # transforms.Normalize(means for each channel, standard deviations for each
    # channel): output[channel] = (input[channel] - mean[channel]) / std[channel]
    transform = transforms.Compose(
        [transforms.ToTensor(),
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

    trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
    trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)

    testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
    testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2)

    classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

    net = Net()

    # define loss function and optimizer
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

    # train the NN
    for epoch in range(2):
        running_loss = 0.0
        for i, data in enumerate(trainloader, 0):
            # get inputs; data is list of [inputs, labels]
            inputs, labels = data

            optimizer.zero_grad()

            # forward + backward + optimize
            outputs = net(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            # print statistics
            running_loss += loss.item()
            if i % 2000 == 1999: # print every 2000 mini-batches
                print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 2000))
                running_loss = 0.0

    print("Finished training")

    # save trained model
    PATH = './eyelang/cifar_net.pth'
    torch.save(net.state_dict(), PATH)

    # test the NN on test data
    # re-load saved model
    net = Net()
    net.load_state_dict(torch.load(PATH))

    dataiter = iter(testloader)
    images, labels = dataiter.next()

    # print images
    # permute to have channels as last dimension
    plt.imshow(torchvision.utils.make_grid(images).permute(1, 2, 0))
    plt.show()
    print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))
    outputs = net(images) # neural net's classifications
    _, predicted = torch.max(outputs, 1) # get highest energy/most likely label
    print('Predicted: ', ' '.join('%5s' % classes[predicted[j]] for j in range(4)))

    correct = 0
    total = 0
    with torch.no_grad():
        for data in testloader:
            images, labels = data
            outputs = net(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))