Skip to content

README.md

Latest commit

 

History

History
146 lines (107 loc) · 3.96 KB

README.md

File metadata and controls

146 lines (107 loc) · 3.96 KB

ScratchNet 🔗

Overview

ScratchNet is a demonstration of building and training Artificial Neural Networks (ANNs) from scratch in Python using only NumPy - no deep learning frameworks. The models are trained on classic datasets and this app showcases their performance and predictions interactively.

ScratchNet.mp4

Datasets

Breast Cancer (Binary Classification)

Classifies tumors as benign or malignant using 30 features from digitized images of fine needle aspirate (FNA) of breast mass.
Target labels: Benign (0), Malignant (1).
Dataset: scikit-learn's Breast Cancer Wisconsin.

California Housing (Regression)

Predicts the median house value in a California block group using 8 features such as median income, house age, average rooms, and location.
Target label: MedHouseVal (Median House Value).
Dataset: scikit-learn's California Housing.

Iris Flower Species (Multiclass Classification)

Predicts the species of an iris flower using four features: sepal length, sepal width, petal length, and petal width.
Target labels: Setosa, Versicolor, Virginica.
Dataset: scikit-learn's Iris.

Models

Model Architecture

Breast Cancer
  • 4 layers:
    • Input: 30 features
    • Hidden: 30, 60, 15 neurons
    • Output: 1 (Sigmoid)
  • Activations: ReLU, Sigmoid
  • Loss: Binary Cross Entropy
  • Accuracy: 89%
    View Model Code
California Housing
  • 5 layers:
    • Input: 8 features
    • Hidden: 10, 16, 32, 16 neurons
    • Output: 1 (Linear)
  • Activations: ReLU, Linear
  • Loss: MSE
    View Model Code
Iris
  • 5 layers:
    • Input: 4 features
    • Hidden: 8, 16, 64, 10 neurons
    • Output: 3 (Softmax)
  • Activations: ReLU, Softmax
  • Loss: Sparse Categorical Cross Entropy
  • Accuracy: 97%
    View Model Code

Neural Network Module

The neural network, dense layers, loss, and activation functions are implemented from scratch in the NN/ directory:

  • network.py: Neural network class, training loop, prediction, summary, save/load.
  • layers.py: Dense layer implementation.
  • activations.py: ReLU, Sigmoid, Softmax, Linear, Tanh.
  • loss.py: MSE, Binary Cross Entropy, Sparse Categorical Cross Entropy.

View NN Module

Model Notebook

All model development, training, and analysis are documented in
ANN_from_Scratch.ipynb.

Steps to Run

Requires:

  • Python 3.10.16
  • Streamlit 1.46.0

  1. Clone the repository

    git clone https://github.com/Param302/ScratchNet.git
cd ScratchNet

  2. Create a virtual environment

    • Windows 
      python -m venv venv
venv\Scripts\activate
    • Linux/Mac 
      python3 -m venv venv
source venv/bin/activate

  3. Install requirements

    pip install -r requirements.txt

  4. Run the Streamlit app

    streamlit run app/main.py

    OR

    Run each model script directly to train the model

    python models/code/ANN_Breast_cancer.py
python models/code/ANN_California_housing.py
python models/code/ANN_iris.py

Contact

For any questions or contributions, feel free to reach out:
Parampreet Singh Email: connectwithparam.30@gmail.com