Overview
Loss Functions are the functions used to measure the error of a neural network model against an input/output pair or set of pairs.API Examples
The following shows calculating the total loss or error using pre-defined loss functions from the api. The error function takes four parameters.- layers - this is the array that represents the model
- inputs - this is an array of column vectors
- outputs - this is an array of column vectors
- loss function
- meanSquaredError
let error = ba.error(layers, inputs, outputs, ba.meanSquaredError());
- crossEntropyLoss
let error = ba.error(layers, inputs, outputs, ba.crossEntropyLoss());
Implementation
An error function must implement two functions:- evaluate - evaluates the loss for the two values, one being the evaluated value, and the other being the target (or real) value.
- inputGradient - the inputGradient function calculates the gradient of the layer given an input and corresponding output.
The following code demonstrates implementing
function meanSquaredError(){
let layer = {
type:'meanSquaredError',
evaluate:function(val1, val2){
let diff = la.subtract(val1, val2);
let result = la.multiply(la.transpose(diff), diff);
result[0][0] = 0.5 * result[0][0];
return result;
},
inputGradient:function(input, output){
let result = [];
for(let i=0;i<input.length;i++){
result.push([input[i][0] - output[i][0]]);
}
return result;
},
};
return layer;
}