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

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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.

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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

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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

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Model Notebook

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

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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
   

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Contact

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

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