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## functions to show an image
def imshow(img):
    #img = img / 2 + 0.5     # unnormalize
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))

## get some random training images
dataiter = iter(trainloader)
images, labels = dataiter.next()

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## functions to show an image
def imshow(img):
    #img = img / 2 + 0.5     # unnormalize
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))

## get some random training images
dataiter = iter(trainloader)
images, labels = dataiter.next()

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## dummy transform 
dummy2_transform = transforms.Compose(
    [transforms.RandomRotation(45), transforms.RandomVerticalFlip()])

dummy2_result = dummy2_transform(image)

plt.imshow(dummy2_result)

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## dummy transformation
dummy_transform = transforms.Compose(
    [transforms.RandomRotation(45)])

dummy_result = dummy_transform(image)

plt.imshow(dummy_result)

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plt.imshow(image)

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image = transforms.ToPILImage(mode='L')(torch.randn(1, 96, 96))

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## parameter denoting the batch size
BATCH_SIZE = 32

## transformations
transform = transforms.Compose(
    [transforms.ToTensor()])

## download and load training dataset
trainset = torchvision.datasets.MNIST(root='./data', train=True,
                                        download=True, transform=transform)

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## The usual imports
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms

## for printing image
import matplotlib.pyplot as plt
import numpy as np

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test_acc = 0.0
for i, (images, labels) in enumerate(testloader, 0):
    images = images.to(device)
    labels = labels.to(device)
    outputs = model(images)
    test_acc += get_accuracy(outputs, labels, BATCH_SIZE)
        
print('Test Accuracy: %.2f'%( test_acc/i))

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for epoch in range(num_epochs):
    train_running_loss = 0.0
    train_acc = 0.0

    model = model.train()

    ## training step
    for i, (images, labels) in enumerate(trainloader):
        
        images = images.to(device)