tinkerbell 0.1.0

Tinkerbell is a lightweight, educational deep learning framework built entirely from scratch

Tinkerbell

Tinkerbell is a lightweight, educational deep learning framework built entirely from scratch using NumPy. It is designed to demonstrate the fundamental building blocks of neural networks, including forward and backward propagation, gradient descent, and modular layer architecture.

Features

• Modular Architecture: Built with FunctionBlock and Layer abstractions.
• Automated Backward Pass: Manual implementation of gradients for all blocks.
• Layers:
  • DenseLayer: Standard fully connected layer.
  • SigmoidLayer: Sigmoid activation function.
• Optimizers:
  • SGD: Stochastic Gradient Descent.
• Loss Functions:
  • MSELoss: Mean Squared Error.
• Additional Tools:
  • SimpleLinearRegression: A simplified implementation for linear problems.
  • Progress tracking with tqdm.

🛠️ Installation

1. Clone the repository:

   git clone https://github.com/yourusername/tinkerbell.git
   cd tinkerbell
   

2. Install dependencies:

   pip install -e .
   

Quick Start

Building and training a model in Tinkerbell is straightforward:

import numpy as np
from tinkerbell.models import Model
from tinkerbell.layers import DenseLayer, SigmoidLayer
from tinkerbell.loss import MSELoss
from tinkerbell.optimizer import SGD

# 1. Create dummy data
x = np.random.random((100, 3))
y = np.dot(x, np.array([[2.0], [3.0], [4.0]])) + 5.0 

# 2. Initialize the Model
model = Model(layers=[
    DenseLayer(output_dim=4),
    SigmoidLayer(),
    DenseLayer(output_dim=1)
])

# 3. Setup Optimizer and Loss
optimizer = SGD(model.get_params(), learning_rate=0.01)
model.trainer(loss=MSELoss(), optimizer=optimizer)

# 4. Fit the Model
model.fit(x, y, epochs=100, batch_size=10)

# 5. Make Predictions
predictions = model(x)

Project Structure

src/
└── tinkerbell/
    ├── core.py        # Base classes for blocks (WeightMatrix, Bias)
    ├── functions.py   # Activation functions (Sigmoid)
    ├── layers.py      # Layer implementations (Dense, Sigmoid)
    ├── loss.py        # Loss functions (MSE)
    ├── models.py      # Model class and training logic
    └── optimizer.py   # Optimization algorithms (SGD)

Authors

• Aaiswarya Mishra - [e-mail]