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%% Cell type:code id: tags:

``` python

# You are encouraged to follow this template as a guide, but feel free to make reasonable modifications as needed.

# The key requirement is that your code runs successfully and produces the expected results.

```

%% Cell type:code id: tags:

``` python

# NOTE:

# You may choose to use ChatGPT (or any AI-based tool) to assist with your assignment,

# but you must ensure that you fully understand the entire code.

# You are solely responsible for the work you submit.

# Please keep in mind: ChatGPT will not be available during the exam.

```

%% Cell type:code id: tags:

``` python

import pytorch_lightning as pl

import torch

import torch.nn as nn

import torchvision as torchvision

from pytorch_lightning.callbacks import EarlyStopping

from sklearn.metrics import accuracy_score

from torch.utils.data import DataLoader, random_split

class MNISTClassification(pl.LightningModule):

    def __init__(self):

        super(MNISTClassification, self).__init__()

        # TODO: Implement this function

    def forward(self, x):

        # TODO: Implement this function

        return 0

    def configure_optimizers(self):

        # TODO: Implement this function

        return 0

    def training_step(self, batch, batch_idx):

        # TODO: Implement this function

        return 0

    def validation_step(self, batch, batch_idx):

        # TODO: Implement this function

        return 0

    def test_step(self, batch, batch_idx):

        # TODO: Implement this function

        return 0

    def test_epoch_end(self, outputs):

        # TODO: Implement this function

        return 0

if __name__ == "__main__":

    # Main function of script

    train_set = torchvision.datasets.MNIST(

        "./files/",

        train=True,

        download=True,

        transform=torchvision.transforms.Compose(

            [

                torchvision.transforms.ToTensor(),

                torchvision.transforms.Normalize((0.1307,), (0.3081,)),

                torchvision.transforms.Lambda(lambda x: torch.reshape(x, (784,))),

            ]

        ),

    )

    test_set = torchvision.datasets.MNIST(

        "./files/",

        train=False,

        download=True,

        transform=torchvision.transforms.Compose(

            [

                torchvision.transforms.ToTensor(),

                torchvision.transforms.Normalize((0.1307,), (0.3081,)),

                torchvision.transforms.Lambda(lambda x: torch.reshape(x, (784,))),

            ]

        ),

    )

    train_set, val_set = random_split(train_set, [54000, 6000])

    # TODO: Implement data loader, learning and prediction

```