DL — Deep Learning Practicals 1–9 (pip package)

All nine practicals refactored into one importable, pip-installable package named DL. After installing, you can import DL from any folder on any machine and run every practical — no more cd-ing into folders or broken relative CSV paths.

Installation

From PyPI:

pip install opdl

The install name is opdl (the name DL was already taken on PyPI), but the import name is exactly DL as used throughout this README.

For local development, from this folder:

pip install .          # normal install
# or, for development (edits take effect immediately):
pip install -e .

Dependencies installed automatically: none. This is a solo package — pip install opdl installs only the DL import package and the dl-prac* commands. It does not pull in TensorFlow, PyTorch, scikit-learn, etc.

Important: Because there are zero dependencies, pip install opdl will succeed on a bare Python install, but individual practicals still need their heavy libraries present when you actually run them (for example TensorFlow for prac 1, 2, 4, 5, 7; PyTorch for prac 3, 6, 8, 9). Install only the ones you need, e.g.::

pip install tensorflow torch scikit-learn pandas matplotlib

Everything else stays the same: the import name is DL, the install name is opdl, and nothing runs at import time — every practical is main() + helper functions.

What's inside

Usage

As commands

dl-prac1
dl-prac8
dl-prac5 --train-dir C:\data\hoousenumbers\train --test-dir C:\data\hoousenumbers\test
dl-prac6 --data-dir C:\data\cifar-10-batches-py
dl-prac7 --epochs 2 --max-samples 5000

As a library

from DL import prac8_recursive
model, probs = prac8_recursive.main()

from DL import prac2_regularization
cfg = prac2_regularization.Config(epochs=20)   # tweak any hyper-parameter
prac2_regularization.main(cfg=cfg)

# Use the bundled datasets with your own code too:
from DL import data_path
import pandas as pd
df = pd.read_csv(data_path("Employee.csv"))

Every main() also accepts your own data / output paths, e.g. prac1_feedforward.main(csv_path="my.csv"), prac3_fixmatch.main(data_path="my_data.csv", output_dir="out").

External datasets (prac 5 & 6)

These are too large to bundle in a wheel:

• SVHN (prac 5): get the HDF5-format SVHN (digitStruct.mat + PNGs) and pass --train-dir / --test-dir.
• CIFAR-10 (prac 6): download https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz, extract, and pass --data-dir <path to cifar-10-batches-py>.

Both raise a clear FileNotFoundError with instructions if the data is missing.

Notes on the refactor

• Nothing runs at import time; every practical is main() + helper functions.
• Dataset paths are resolved with importlib-friendly helpers (DL.data_path(...)) instead of __file__/cwd tricks, so they work in any installed location (including zipped wheels).
• Original scripts are untouched in their original folders; the package lives under src/DL/.
• Practical 3's dataset was missing from the original folder, so a replacement sample dataset (10 labeled + 10 unlabeled movie reviews) is bundled as semi_supervised_dataset.csv. Drop in your own CSV with the same id,text,label schema to use the real one.