buildNetwork(inputSize, outputSize) {
            var size = 2 + (this.hiddenLayers.length - 1);
            this.model = new Array(size);

            // input layer
            this.model[0] = new Layer({
                inputSize: inputSize,
                outputSize: this.hiddenLayers[0],
                activation: this.activation,
                activationParam: this.activationParam,
                regularization: this.regularization,
                epsilon: this.learningRate
            });

            // hidden layers
            for (var i = 1; i < this.hiddenLayers.length; ++i) {
                this.model[i] = new Layer({
                    inputSize: this.hiddenLayers[i - 1],
                    outputSize: this.hiddenLayers[i],
                    activation: this.activation,
                    activationParam: this.activationParam,
                    regularization: this.regularization,
                    epsilon: this.learningRate
                });
            }

            // output layer
            this.model[size - 1] = new OutputLayer({
                inputSize: this.hiddenLayers[this.hiddenLayers.length - 1],
                outputSize: outputSize,
                activation: this.activation,
                activationParam: this.activationParam,
                regularization: this.regularization,
                epsilon: this.learningRate
            });
        }
@private
Function that build and initialize the neural net.
@param {number} inputSize - total of features to fit.
@param {number} outputSize - total of labels of the prediction set.