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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

import torchvision.models as models

from pytorch_lightning.callbacks import EarlyStopping

from sklearn.metrics import accuracy_score

from torch.utils.data import DataLoader, random_split

class TransferCNN(pl.LightningModule):

    def __init__(self):

        super(TransferCNN, 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):

        avg_loss = torch.stack([x["test_loss"] for x in outputs]).mean()

        avg_test_acc = torch.stack([x["test_acc"] for x in outputs]).mean()

        logs = {"test_loss": avg_loss, "test_acc": avg_test_acc}

        results = {

            "avg_test_loss": avg_loss,

            "avg_test_acc": avg_test_acc,

            "log": logs,

            "progress_bar": logs,

        }

        self.log_dict(results)

        return results

if __name__ == "__main__":

    # Main function of script

    # TODO: Implement data loading, learning and prediction

    # data path:

    # train "/home/jovyan/data/IntroML/Chapter8_data/Assignment_4/train_crop_images"

    # test "/home/jovyan/data/IntroML/Chapter8_data/Assignment_4/test_crop_images"

```