Dann Object

Back to Home

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

Save & load

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

Save & load JSON

• applyToModel
  
• dataObject
  

Debug

• log

Static Function properties

• Dann.createFromObject
  

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});
nn.feedForward([1,1],{log:true});
nn.feedForward([0,1],{log:true});
nn.feedForward([1,0],{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});
nn.feedForward([1,1],{log:true});
nn.feedForward([0,1],{log:true});
nn.feedForward([1,0],{log:true});