Dann Object

Import

const Dann = require('dannjs').dann;

Contructor( inputSize , outputSize )

When you create a neural network, you need to specify the size of the input & output layers.

const nn = new Dann(2,2);

Object Properties

• arch
  

This value represents the architecture of the model in the form of an array.

• lr
  

This defines the learning rate of the model. This value is set to 0.001 by default.

• epoch
  

This is an empty value. This is meant for you to increase whenever you have completed one epoch. This serves as a way to save the number of epochs along with the weights in the dannData.json file.

loss

This is the most recent loss value of the model. If the model has never been trained before, this value will be set to 0.

Function properties

Model Creation

• addHiddenLayer
  
• outputActivation
  
• makeWeights
  
• setLossFunction

Model Interaction

• feedForward
  
• backpropagate

Weight Mutations

• mutateAdd
  
• mutateRandom

Saving & Loading

• save (Browser)
  
• save (Nodejs)
  
• load (Browser)
  
• load (Nodejs)

Debug

• log

Example

Here is a neural network training to solve XOR:

const Dann = require('dannjs').dann;  //nodejs only

// XOR neural network
const nn =  new Dann(2,1);
nn.addHiddenLayer(4,'tanH');
nn.outputActivation('sigmoid');
nn.makeWeights();
nn.lr = 0.1;

// feeding data to the model before training
nn.feedForward([0,0],{log:true});

// training the model
const epoch = 10000;
for (let e = 0; e < epoch; e++) {
    nn.backpropagate([0,0],[0]);
    nn.backpropagate([1,1],[0]);
    nn.backpropagate([0,1],[1]);
    nn.backpropagate([1,0],[1]);
}

// feeding data to the model after training
nn.feedForward([0,0],{log:true});