https://github.com/willayy/snnpy
Simple neural network modeling framework implemented in C with a Python api
https://github.com/willayy/snnpy
c cmake neural-networks python
Last synced: 2 months ago
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Simple neural network modeling framework implemented in C with a Python api
- Host: GitHub
- URL: https://github.com/willayy/snnpy
- Owner: willayy
- Created: 2023-10-30T20:19:18.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-23T12:14:59.000Z (4 months ago)
- Last Synced: 2025-02-23T13:24:18.219Z (4 months ago)
- Topics: c, cmake, neural-networks, python
- Language: C
- Homepage:
- Size: 659 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
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README
# 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
```Python
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...")
```