https://github.com/hyouteki/malpractice
A lightweight C library for creating, training, and evaluating simple neural networks
https://github.com/hyouteki/malpractice
c library neural-network
Last synced: 3 months ago
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A lightweight C library for creating, training, and evaluating simple neural networks
- Host: GitHub
- URL: https://github.com/hyouteki/malpractice
- Owner: hyouteki
- Created: 2024-10-28T10:10:46.000Z (7 months ago)
- Default Branch: master
- Last Pushed: 2024-12-10T08:11:13.000Z (6 months ago)
- Last Synced: 2025-02-13T06:01:43.267Z (3 months ago)
- Topics: c, library, neural-network
- Language: C
- Homepage:
- Size: 43.9 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
> A lightweight C library for creating, training, and evaluating simple neural networks with customizable initialization methods.
> This library supports basic forward and backward propagation, sigmoid activation, data handling, and model saving/loading.
---
## Features
1. **Custom Initialization Techniques**: Supports zero, random, and Xavier initialization.
2. **Forward and Backward Propagation**: Implements core functions for feedforward and backpropagation.
3. **Data Handling**: Functions to initialize, normalize, partition, and describe data.
4. **Model Serialization**: Save and load model configurations to/from files.
## Usage
1. **Model Initialization**: Set input, hidden, and output sizes with your preferred initialization method.
2. **Training and Testing**: Use train and test functions with Data and Parameters structures to train the model.
3. **Model Persistence**: Save and load models using save_model and load_model.
## API Overview
### Core Structures
1. **Model**: Represents the neural network with layer sizes and weight initialization.
2. **Data**: Holds data samples and labels for training/testing.
3. **Parameters**: Configurable training parameters.
### Key Functions
1. **Model Initialization**: `initialize_model()`
2. **Forward and Backward Propagation**: `forward()`, `backward()`
3. **Data Manipulation**: `normalize_data()`, `partition_data()`, `describe_data()`
4. **Model Persistence**: `save_model()`, `load_model()`
## Getting Started
```bash
git clone https://github.com/hyouteki/malpractice --recursive --depth=1
cd malpractice
./build.sh
```
## Example
```c
#include "malpractice.h"
int main() {
// Model parameters
size_t input_size = 784, hidden_size = 128, output_size = 10;
Model_InitTechnique init_tech = Model_Init_Xavier;
Model *model = initialize_model(input_size, hidden_size, output_size, init_tech);
// Load data (replace with actual loading logic)
Data *data = zero_initialize_data(input_size, 100);
// Set training parameters
Parameters params = {.learning_rate = 0.01, .epochs = 1000, .log_train_metrics = 1};
// Train and evaluate
train(data, params, model);
test(data, model);
// Save model
save_model(model, "model.bin");
// Cleanup
deinitialize_data(data);
deinitialize_model(model);
return 0;
}
```