TensorNN is a Python machine learning library built from scratch. It provides fundamental building blocks for creating and training neural networks.

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📋 Table of Contents

• Installation
• Features
• Quick Start
• Documentation
• Running Tests
• License
• Author

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⚙️ Installation

You can install TensorNN via pip (once it's published):

pip install tensornn

Dependencies

TensorNN requires the following Python packages:

• numpy
• tqdm

For documentation, additional packages are required:

• sphinx
• sphinx_rtd_theme

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✨ Features

TensorNN provides a range of modules to facilitate the creation and training of neural networks:

• Tensor Operations (tensornn.tensor): Custom Tensor class based on NumPy for multi-dimensional array operations.
• Neural Network Core (tensornn.nn): The NeuralNetwork class to define and manage network architecture.
• Layers (tensornn.layers):
  • Input: Specifies the input shape to the network.
  • Dense: Fully connected layer.
• Activation Functions (tensornn.activation): A variety of activation functions to introduce non-linearity, including:
  • ReLU
  • Sigmoid
  • Softmax
  • Tanh
  • LeakyReLU
  • ELU
  • Swish
  • LecunTanh
  • NoActivation (Linear)
• Loss Functions (tensornn.loss): Various functions to evaluate model performance:
  • MSE (Mean Squared Error)
  • CategoricalCrossEntropy
  • BinaryCrossEntropy
  • RMSE (Root Mean Squared Error)
  • MAE (Mean Absolute Error)
  • MSLE (Mean Squared Logarithmic Error)
  • Poisson
  • SquaredHinge
  • RSS (Residual Sum of Squares)
• Optimizers (tensornn.optimizers): Algorithms to update network weights:
  • SGD (Stochastic Gradient Descent)
  • Adam
  • RMSProp
• Utilities (tensornn.utils): Helper functions like one_hot encoding, data normalize, set_seed for reproducibility, flatten, and atleast_2d.
• Debugging (tensornn.debug): Tools to help inspect and debug your network's behavior, including progress bars and logging.
• Custom Errors (tensornn.errors): Specific error types for easier debugging (e.g., NotRegisteredError, InitializationError, InputDimError).

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🚀 Quick Start

Here's a simple example of how to define and train a neural network to solve the XOR problem:

from tensornn import Tensor, NeuralNetwork, Input, Dense, Tanh, Softmax, MSE, CategoricalCrossEntropy, SGD, Adam, set_seed

# For reproducibility
set_seed(0)

# 1. Define the data
inputs = Tensor([
    [0, 0],
    [0, 1],
    [1, 0],
    [1, 1],
])
# For regression (outputting a single value)
# outputs_reg = Tensor([[0], [1], [1], [0]])

# For classification (outputting probabilities for each class)
outputs_class = Tensor([
    [1, 0],  # Class 0
    [0, 1],  # Class 1
    [0, 1],  # Class 1
    [1, 0],  # Class 0
])

# 2. Create the Neural Network
# Example for classification using Softmax and CategoricalCrossEntropy
nn_classifier = NeuralNetwork([
    Input(2),                             # Input layer with 2 features
    Dense(2, activation=Tanh()),        # Hidden layer with 2 neurons and Tanh activation
    Dense(2, activation=Softmax())      # Output layer with 2 neurons (for 2 classes) and Softmax activation
])

# 3. Register the network with a loss function and an optimizer
nn_classifier.register(CategoricalCrossEntropy(), Adam(learning_rate=0.01))

# 4. Train the network
print("Training XOR classifier...")
nn_classifier.train(inputs, outputs_class, epochs=2000, batch_size=4)

# 5. Make predictions
predictions = nn_classifier.forward(inputs)
predicted_classes = predictions.argmax(axis=1)

print("\nClassifier Predictions:")
for i in range(len(inputs)):
    print(f"Input: {inputs[i]}, Predicted probabilities: {predictions[i]}, Predicted class: {predicted_classes[i]}, Expected class: {outputs_class[i].argmax()}")

You can find more examples in the examples/ directory, such as sine_vis.py which demonstrates training a network to learn the sine function with real-time visualization.

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📚 Documentation

The official documentation for TensorNN is hosted on ReadTheDocs:

https://tensornn.readthedocs.io/en/latest/

The documentation is built using Sphinx from the files in the docs/ directory. Key configuration files include .readthedocs.yaml and docs/conf.py.

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🧪 Running Tests

TensorNN uses pytest for testing. Tests are located in the tests/ directory. Examples include:

• test_and.py: Tests the AND gate logic.
• test_xor_class.py: Tests XOR classification.
• test_xor_reg.py: Tests XOR regression.
• test_linear_regression.py: Tests simple linear regression.
• test_sine.py: Tests learning the sine function.

To run the tests, you would typically navigate to the root directory of the project and run:

pytest

(Note: Ensure you have pytest installed: pip install pytest)

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📜 License

This project is licensed under the GNU General Public License v3.0. See the LICENSE file for more details.

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👨‍💻 Author

• Arjun Sahlot (iarjun.sahlot@gmail.com)
• GitHub: ArjunSahlot