constructor(options) {
            this.inputSize = options.inputSize;
            this.outputSize = options.outputSize;
            this.regularization = options.regularization;
            this.epsilon = options.epsilon;
            this.activation = options.activation;
            this.activationParam = options.activationParam;

            var selectedFunction = ACTIVATION_FUNCTIONS[options.activation];
            var params = selectedFunction.activation.length;

            var actFunction = params > 1 ? val => selectedFunction.activation(val, options.activationParam) : selectedFunction.activation;
            var derFunction = params > 1 ? val => selectedFunction.derivate(val, options.activationParam) : selectedFunction.derivate;

            this.activationFunction = function (i, j) {
                this[i][j] = actFunction(this[i][j]);
            };
            this.derivate = function (i, j) {
                this[i][j] = derFunction(this[i][j]);
            };

            if (options.model) {
                // load model
                this.W = Matrix.checkMatrix(options.W);
                this.b = Matrix.checkMatrix(options.b);

            } else {
                // default constructor

                this.W = Matrix.rand(this.inputSize, this.outputSize);
                this.b = Matrix.zeros(1, this.outputSize);

                this.W.apply(function (i, j) {
                    this[i][j] /= Math.sqrt(options.inputSize);
                });
            }
        }
@private
Create a new layer with the given options
@param {object} options
@param {number} [options.inputSize] - Number of conections that enter the neurons.
@param {number} [options.outputSize] - Number of conections that leave the neurons.
@param {number} [options.regularization] - Regularization parameter.
@param {number} [options.epsilon] - Learning rate parameter.
@param {string} [options.activation] - Activation function parameter from the FeedForwardNeuralNetwork class.
@param {number} [options.activationParam] - Activation parameter if needed.