Shape Network

An experimental neural network that showcases some of my own architecture. Additionally, this network makes use of sophisticated feature extraction techniques, enabling reliable and precise classifications such as face recognition.

The network performs well with single-class/label training using the cosine similarity measure, which removes the need for weight balancing to provide accurate predictions.

Build Network

ShapingNetwork model = new ShapingNetworkBuilder()
  .connectInRange(0, 1, // adds the defined layers in this range of targets (0 -> 1)
    new LayerBuilder()
      .addLayer(Shapes.MEAN, 2) // shape function, amount of nodes
      // more functions
    )
    .build();

Initialize Network

Initialize the network with the amount of input nodes, we will use 3 in this case

model.initialize(3);

Randomizing weights

model.randomize();

Training Data

The model will learn to classify the labels of these samples

Map<Integer, List<ContextVector>> trainingData = new HashMap<>();
List<ContextVector> zeros = Arrays.asList(
  ContextVector.newVector().addValuesToVector(0.001, 2.531, 1.523),
  ContextVector.newVector().addValuesToVector(0.009, 2.231, 1.241)
);

List<ContextVector> ones = Arrays.asList(
  ContextVector.newVector().addValuesToVector(1.8, 0.001, 4.9),
  ContextVector.newVector().addValuesToVector(2.5, 0.002, 4.5)
);

trainingData.put(0, zeros); // the model will aim to classify these samples as 0
trainingData.put(1, ones); // target 1

Training

int epochs = 100;
for (int i = 0; i < epochs; i++) {
  for (Map.Entry<Integer, List<ContextVector>> entry : trainingData.entrySet()) {
    for (ContextVector vector : entry.getValue())
      model.feed(vector, entry.getKey()); // input data & target  
  }
}

Predict

Returns an array of probabilities, whereas you can get the index of the highest probability

double[] prediction = model.predict(vector);

Example:

Accuracy: 0.996959560291407
Class 0: 
Index=0, Probability=0.9998734666758533
Index=0, Probability=0.9998261862004088

Class 1: 
Index=1, Probability=0.9970322786809951
Index=1, Probability=0.996959560291407