SNNPy

This is a Python module with api's to a C implemented configurable FFN neural network model.

This NeuralNetwork modeling software was made for learnings-sake and is not made for performance in the first hand. It does not feature parallelism, hardware accelaration, its memory ineffective and its a naive implementation.

For more info about features check release.

Dependencies

CMake is recommended as the build tool for this source code. No other external dependencies.

Installation/Building

• Download source code.
• Build the shared libraries for the python module using CMake.
• Start using python module!

This project was developed and tested using:

• Tested OS: Microsoft Windows 11 version 23H2, MacOs Sonoma version 14
• Tested compilers: GCC 13.10.0, Clang 15.0.0
• Build System: CMake 3.28.1

Usage

Python example of a neural network that classifies binary numbers into integers

  from snnpy import snn_py
  
  # Define the dataset inputs
  inputs: list[list[float]] = [[0,0,0,0],
                               [0,0,0,1],
                               [0,0,1,0],
                               [0,0,1,1],
                               [0,1,0,0],
                               [0,1,0,1],
                               [0,1,1,0],
                               [0,1,1,1],
                               [1,0,0,0],
                               [1,0,0,1],
                               [1,0,1,0],
                               [1,0,1,1],
                               [1,1,0,0],
                               [1,1,0,1],
                               [1,1,1,0],
                               [1,1,1,1]]
  
  # Define the dataset labels
  outputs: list[list[float]] = [[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
                                [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
                                [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0],
                                [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0],
                                [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],
                                [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0],
                                [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0],
                                [0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0],
                                [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],
                                [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0],
                                [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0],
                                [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0],
                                [0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],
                                [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0],
                                [0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0],
                                [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]]
  
  snn_py.set_rng_seed(0) # Set the seed for the random number generator
  
  neural_network = snn_py.create_neural_network(4,1,4,16) # Create a neural network model
  
  snn_py.set_activation_functions(neural_network, 'linear', 'relu', 'sigmoid') # Set the activation functions for each layer (layers* in the case of hidden layers)
  snn_py.set_cost_function(neural_network, 'cross_entropy') # Set the cost function for the model
  snn_py.set_regularization(neural_network, 'no_reg') # Set the regularization for the model
  snn_py.init_weights_xavier_normal(neural_network) # Initialize the weights of the model
  snn_py.init_biases_constant(neural_network, 0.1) # Initialize the biases of the model
  
  # Train the model on a batch from the dataset
  snn_py.train_neural_network(neural_network, inputs, outputs, 16, 250000, 0.08, 0.01)
  
  # Test the model on a batch from the dataset
  result = snn_py.predict(neural_network, inputs[5])
  result = [int(num) for num in result]
  print(f"Input: {inputs[5]} Prediction: {result} Expected: {outputs[5]}")
  input("Press Enter to continue...")