Awesome

##ConvolutionalNeuralNetwork

A general purpose convolutional neural network.

Under construction. This network it currently under development and expected to be finished soon. When finished I will write a blog post on how to write the thing from scratch yourself.

###About

This project is a simple to use general purpose convolutional neural network framework. It features several types of layers which can be linked together as they are needed. Written in C++ which allows it to run blazingly fast (not just yet) and stay extremely portable (it has no dependencies).

###Usage

The network has a layered design which allows different configurations of how many layers and what layer types should be stacked together.

There are four types of layers:

Convolution. The convolution layer will perform the main feature extraction for the provided sample.
Pooling. The pooling layer performs a downsampling of the input by the factor of filterSize (defaults to 2) and with a stride provided in stride (defaults to 2). So the default downsampling is equal to 2x2.
Hidden. The hidden layer is a layer of a regular multi layer perceptron. It's only difference from the output layer is the way it computes its backpropagation gradients.
Output layer. The final layer. There are no hyperparameters to specify, just make sure you end the network with an output layer.

A fancy ASCII art of the network structure:

+-------------------+---------------+-----+--------------+-----+--------------+
| Convolution layer | Pooling layer | ... | Hidden layer | ... | Output layer |
+-------------------+---------------+-----+--------------+-----+--------------+

The convolution & pooling layer combinations are optional and there are no restrictions on how many there can be. If no conv & pooling layers are present the netowrk behaves line a regular multilayer perceptron. While the pooling layer itself is also optional it is recommended since the network yields better results when used (spatial invariance).

#####Code

Here's what an example might look like (so far only the MLP part is working):

using namespace sf;

//Size of our input data
const unsigned long inputWidth = 3;
const unsigned long inputHeight = 1;

//A bunch of samples. The 1 & 2 are similar so are 3 & 4 and 5 & 6.
double sample1[] = {1.0, 0.2, 0.1};     //Cow
double sample2[] = {0.8, 0.1, 0.25};    //Cow
double sample3[] = {0.2, 0.95, 0.1};    //Chicken
double sample4[] = {0.11, 0.9, 0.13};   //Chicken
double sample5[] = {0.0, 0.2, 0.91};    //Car
double sample6[] = {0.21, 0.12, 1.0};   //Car

//A new network with the given data width and height
Net *net = new Net(inputWidth, inputHeight);

sf::LayerDescriptor descriptor;
descriptor.type = kLayerTypeHiddenNeuron;
descriptor.neuronCount = 4;

net->addLayer(descriptor); //A hidden neural layer with 4 neurons
net->addLayer(descriptor); //A hidden neural layer with 4 neurons

descriptor.type = kLayerTypeOutputNeuron;

net->addLayer(descriptor); //Finish it off by adding an output layer

//Add all the samples with their corresponding labels
net->addTrainingSample(sample1, "cow");
net->addTrainingSample(sample2, "cow");
net->addTrainingSample(sample3, "chicken");
net->addTrainingSample(sample4, "chicken");
net->addTrainingSample(sample5, "car");
net->addTrainingSample(sample6, "car");

//And now we play the waiting game
net->train();

//This example is similar to "chicken" so we expect the chicken probability to be close to 1 and car and cow to be close to 0
double example[] = {0.1, 0.98, 0.01};
auto output = net->classifySample(example);

//Let's see what we get
for (auto &tuple : output)
    std::cout << std::get<1>(tuple) << ": " << std::get<0>(tuple) << std::endl;

std::cout << std::endl;

return 0;

####Building it

You can open it using Xcode or just make build. To toggle debug mode (exposes all private properties, ...) just update the #define DEBUG flag in the helpers.h.

###TODO

Things to come (in order):