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class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()

        # 28x28x1 => 26x26x32
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3)
        self.d1 = nn.Linear(26 * 26 * 32, 128)
        self.d2 = nn.Linear(128, 10)

    def forward(self, x):

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## test the model with 1 batch
model = MyModel()
for images, labels in trainloader:
    print("batch size:", images.shape)
    out = model(images)
    print(out.shape)
    break

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learning_rate = 0.001
num_epochs = 5

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = MyModel()
model = model.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

## compute accuracy

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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)

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

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plt.imshow(image)

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

dummy_result = dummy_transform(image)

plt.imshow(dummy_result)