https://github.com/LiuXiaolong19920720/simple_net
A simple deep neural network implemented in C++，based with OpenCV Mat matrix class
https://github.com/LiuXiaolong19920720/simple_net
cpp neural-network opencv
Last synced: 8 months ago
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A simple deep neural network implemented in C++，based with OpenCV Mat matrix class
- Host: GitHub
- URL: https://github.com/LiuXiaolong19920720/simple_net
- Owner: LiuXiaolong19920720
- License: mit
- Created: 2016-12-15T10:50:02.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2019-04-20T07:59:11.000Z (over 6 years ago)
- Last Synced: 2024-10-27T23:25:05.757Z (about 1 year ago)
- Topics: cpp, neural-network, opencv
- Language: C++
- Size: 2.1 MB
- Stars: 311
- Watchers: 17
- Forks: 128
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Simple Net
**Simple net** is a simple deep neural network implemented in C++，based with OpenCV Mat matrix class
---
## Examples
You can initialize a neural network just like bellow:
```cpp
//Set neuron number of every layer
vector layer_neuron_num = { 784,100,10 };
// Initialise Net and weights
Net net;
net.initNet(layer_neuron_num);
net.initWeights(0, 0., 0.01);
net.initBias(Scalar(0.5));
```
It is very easy to train:
```cpp
#include"../include/Net.h"
//
using namespace std;
using namespace cv;
using namespace liu;
int main(int argc, char *argv[])
{
//Set neuron number of every layer
vector layer_neuron_num = { 784,100,10 };
// Initialise Net and weights
Net net;
net.initNet(layer_neuron_num);
net.initWeights(0, 0., 0.01);
net.initBias(Scalar(0.5));
//Get test samples and test samples
Mat input, label, test_input, test_label;
int sample_number = 800;
get_input_label("data/input_label_1000.xml", input, label, sample_number);
get_input_label("data/input_label_1000.xml", test_input, test_label, 200, 800);
//Set loss threshold,learning rate and activation function
float loss_threshold = 0.5;
net.learning_rate = 0.3;
net.output_interval = 2;
net.activation_function = "sigmoid";
//Train,and draw the loss curve(cause the last parameter is ture) and test the trained net
net.train(input, label, loss_threshold, true);
net.test(test_input, test_label);
//Save the model
net.save("models/model_sigmoid_800_200.xml");
getchar();
return 0;
}
```
It is easier to load a trained net and use:
```cpp
#include"../include/Net.h"
//
using namespace std;
using namespace cv;
using namespace liu;
int main(int argc, char *argv[])
{
//Get test samples and the label is 0--1
Mat test_input, test_label;
int sample_number = 200;
int start_position = 800;
get_input_label("data/input_label_1000.xml", test_input, test_label, sample_number, start_position);
//Load the trained net and test.
Net net;
net.load("models/model_sigmoid_800_200.xml");
net.test(test_input, test_label);
getchar();
return 0;
}
```