new Dann

(

• [input]
• [output]

)

• [input] Number optional

  the number of input neurons.

• [output] Number optional

  the number of output neurons.

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


// 784 input, 2 output model
const nn = new Dann(784, 2);
nn.log();

addHiddenLayer

(

• size
• [act]

)

• size Number

  Layer size, the number of neurons in the layer.

• [act] String optional

  Takes a string of the activation function's name. If left empty, the activation function will be set to 'sigmoid' by default. See available activation functions below.

  Name	Desmos
  Sigmoid	See graph
  leakyReLU	See graph
  reLU	See graph
  siLU	See graph
  tanH	See graph
  binary	See graph
  softsign	See graph
  sinc	See graph
  softplus	See graph

  See how to add more

const nn = new Dann(10, 2);
//Add a layer
nn.addHiddenLayer(8, 'sigmoid');
//console log
console.log('Added first hidden layer: ');
nn.log({struct:true});
//Add a layer
nn.addHiddenLayer(4, 'tanH');
//console log
console.log('Added a second hidden layer: ');
nn.log({struct:true});

backpropagate

(

• inputs
• target
• [options]

)

• inputs Array

  Array of input data.

• target Array

  Array of expected output.

• [options] Object optional

  Object including specific properties.

  Property	Type	Function
  log	Boolean	If set to true, it will log a report in the console.
  table	Boolean	If the 'log' option is set to true, setting this value to true will print the arrays of this function in tables.
  dropout	Number	A value ranging from 0 to 1 determining the chance of a neuron being idle during a backward pass.

const nn = new Dann(2, 1);
nn.addHiddenLayer(8);
nn.makeWeights();
// Train 1000 epoch
for (let i = 0; i < 1000; i++) {
   nn.backpropagate([0,0],[0]);
   nn.backpropagate([1,0],[1]);
   nn.backpropagate([0,1],[1]);
   nn.backpropagate([1,1],[0]);
}

createFromJSON

(

• data

)

• data Object

  model data json object, you can get this object from a yourmodel.toJSON(); See docs here.

const nn = new Dann(24, 4);
nn.addHiddenLayer(12, 'sigmoid');
nn.makeWeights();
const modelData = nn.toJSON();
const newNN = Dann.createFromJSON(modelData);
newNN.log();

feedForward

(

• inputs
• [options]

)

• inputs Array

  Array of input data.

• [options] Object optional

  Object including specific properties.

  Property	Type	Function
  log	Boolean	If set to true, it will log a report in the console.
  table	Boolean	If the 'log' option is set to true, setting this value to true will print the arrays of this function in tables.
  decimals	Integer	If used, the output of this function will be rounded to the number of decimals specified.

const nn = new Dann(4, 2);
nn.makeWeights();
let prediction = nn.feedForward([0,0,0,1], {log:true});
//outputs an array of length 2
console.log(prediction);

fromJSON

(

• data

)

• data Object

  model data json object, you can get this object from a yourmodel.toJSON(); See docs here.

const nn = new Dann(24,4);
nn.addHiddenLayer(18,'tanH');
nn.addHiddenLayer(12,'sigmoid');
nn.makeWeights();
const modelData = nn.toJSON();
const newNN = new Dann();
newNN.fromJSON(modelData);
newNN.log();

log

(

• [options]

)

• [options] Object optional

  An object including specific properties.

  Property	Type	Function
  details	Boolean	If set to true, the function will log more advanced details about the model.
  decimals	integer	The number of decimals the logged data is going to have. It is set to 3 by default.
  table	Boolean	Whether or not we want to print our matrices in the form of a table or Matrix object log.
  gradients	Boolean	If this is set to true, the the function will log the gradients of the model.
  biases	Boolean	If this is set to true, the the function will log the biases of the model.
  weights	Boolean	If this is set to true, the the function will log the weights of the model.
  struct	Boolean	If this is set to true, the the function will log the structure of the model.
  errors	Boolean	If this is set to true, the the function will log the errors of the model.
  misc	Boolean	If this is set to true, the the function will log the loss of the model, the learning rate of the model and the loss function.

const nn = new Dann(24, 2);
nn.log();

makeWeights

(

• [arg1]
• [arg2]

)

• [arg1] Number optional

  The minimum range value.

• [arg2] Number optional

  The maximum range value.

const nn = new Dann(2, 2);
// initiate the Weights
nn.makeWeights();
// log weights
nn.log({weights:true, table:true});
// add a layer & re-initiate weights in a range of (-0.1, 0.1)
nn.addHiddenLayer(4, 'sigmoid');
nn.makeWeights(-0.1, 0.1);
// log weights
console.log('New re-initiated weights:');
nn.log({weights:true, table:true});

mapWeights

(

• f

)

• f Function

  the function to map the weights with.

const nn = new Dann(2, 2);
nn.makeWeights(-1, 1);
nn.log({weights:true});
nn.mapWeights((x)=>{
  return (Math.random()*0.1)+x;
});
nn.log({weights:true})

mutateAdd

(

• randomFactor

)

• randomFactor Number

  Percentage to add to each weight. Generally in 0 and 1.

const nn = new Dann(4, 2);
nn.makeWeights();
nn.log({weights:true, table:true})
// weights add 5% of themselves.
nn.mutateAdd(0.05);
nn.log({weights:true,table:true});

mutateRandom

(

• range
• [probability]

)

• range Number

  This will multiply with a random number from -range to range and add to each weight.

• [probability] Number optional

  The probability of a weight being affected by a random mutation. Ranging from 0 to 1. Setting this value to 1 would mutate all the model's weights.

const nn = new Dann(4, 2);
nn.makeWeights();
nn.log({weights:true, table:true});
// adding (weight*random(-0.1, 0.1)) to 50% of the weights.
nn.mutateRandom(0.1, 0.5);
nn.log({weights:true, table:true});

outputActivation

(

• act

)

• act String

  Takes a string of the activation function's name. If this function is not called, the activation function will be set to 'sigmoid' by default. See available activation functions here.

  Name	Desmos
  Sigmoid	See graph
  leakyReLU	See graph
  reLU	See graph
  siLU	See graph
  tanH	See graph
  binary	See graph
  softsign	See graph
  sinc	See graph
  softplus	See graph

  See how to add more

const nn = new Dann(4, 2);
nn.addHiddenLayer(8, 'sigmoid');
nn.makeWeights();
console.log('Before changing the output activation');
nn.log({struct:true});
nn.outputActivation('tanH');
console.log('After changing the output activation');
nn.log({struct:true});

setLossFunction

(

• name
• [percentile]

)

• name String

  Takes a string of the loss function's name. If this function is not called, the loss function will be set to 'mse' by default. See available loss functions Here.

• [percentile] Number optional

  Some loss functions like the Quantile loss will need a percentile value. Ranges between 0 and 1.

  Name	Desmos
  mse	See graph
  mae	See graph
  lcl	See graph
  mbe	See graph
  mael	See graph
  rmse	See graph
  mce	See graph
  bce	See graph
  quantile	See graph

  See how to add more

const nn = new Dann(4, 2);
nn.addHiddenLayer(8, 'sigmoid');
nn.makeWeights();
//Before changing the loss function
console.log(nn.lossfunc);
nn.setLossFunction('mael');
//After changing the loss function
console.log(nn.lossfunc);

const nn = new Dann(4, 4);
nn.addHiddenLayer(16, 'sigmoid');
nn.makeWeights();
//Before changing the loss function
console.log(nn.lossfunc);
// Quantile loss with 40 percentile
nn.setLossFunction('quantile', 0.4);
//After changing the loss function
console.log(nn.lossfunc);

toFunction

(

• [name]

)

• [name] String optional

  the name of the function, set to 'myDannFunction' by default.

const nn = new Dann(4, 4);
nn.addHiddenLayer(8);
nn.makeWeights();
let stringFunction = nn.toFunction();
// Copy & paste the string function!
console.log(stringFunction);

toJSON ( )

const nn = new Dann(24, 4);
nn.addHiddenLayer(12, 'sigmoid');
nn.makeWeights();
// Getting json
const modelData = nn.toJSON();
const newNN = new Dann();
// Setting
newNN.fromJSON(modelData);
newNN.log();