import torch
import torchvision
import torchvision.transforms as transforms
# import test_images_show as tis
# from multiprocessing import Process, freeze_support

"""
Training an image classifier

We will do the following steps in order:

1. Load and normalizing the CIFAR10 training and test datasets using torchvision
2. Define a Convolutional Neural Network
3. Define a loss function
4. Train the network on the training data
5. Test the network on the test data

"""

# Loading and normalizing CIFAR10 with torchvision

# The output of torchvision datasets are PILImage images of range [0, 1].
# We transform them to Tensors of normalized range [-1, 1].


def loader():
    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'
    )

    return classes, testset, testloader, trainloader, transform

# if __name__ == '__main__':
#     freeze_support()
#     p = Process(tis.peek(trainloader, torchvision, classes))
#     p.start()

a75de73823465e643397106da14b21d8fa01f62f
<mask token>

<mask token>


def loader():
    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')
    return classes, testset, testloader, trainloader, transform

import torch
import torchvision
import torchvision.transforms as transforms
<mask token>


def loader():
    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')
    return classes, testset, testloader, trainloader, transform

import torch
import torchvision
import torchvision.transforms as transforms
# import test_images_show as tis
# from multiprocessing import Process, freeze_support

"""
Training an image classifier

We will do the following steps in order:

1. Load and normalizing the CIFAR10 training and test datasets using torchvision
2. Define a Convolutional Neural Network
3. Define a loss function
4. Train the network on the training data
5. Test the network on the test data

"""

# Loading and normalizing CIFAR10 with torchvision

# The output of torchvision datasets are PILImage images of range [0, 1].
# We transform them to Tensors of normalized range [-1, 1].


def loader():
    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'
    )

    return classes, testset, testloader, trainloader, transform

# if __name__ == '__main__':
#     freeze_support()
#     p = Process(tis.peek(trainloader, torchvision, classes))
#     p.start()
