diff --git a/lib/nn.js b/lib/nn.js
index f678930..c6e3ad2 100644
--- a/lib/nn.js
+++ b/lib/nn.js
@@ -26,6 +26,10 @@ class NeuralNetwork {
     this.bias_h.randomize();
     this.bias_o.randomize();
     this.setLearningRate();
+    
+    this.setActivationFunction();
+    this.setDActivationFunction();
+    
   }
 
   predict(input_array) {
@@ -35,12 +39,12 @@ class NeuralNetwork {
     let hidden = Matrix.multiply(this.weights_ih, inputs);
     hidden.add(this.bias_h);
     // activation function!
-    hidden.map(sigmoid);
+    hidden.map(this.activation_function);
 
     // Generating the output's output!
     let output = Matrix.multiply(this.weights_ho, hidden);
     output.add(this.bias_o);
-    output.map(sigmoid);
+    output.map(this.activation_function);
 
     // Sending back to the caller!
     return output.toArray();
@@ -49,6 +53,14 @@ class NeuralNetwork {
   setLearningRate(learning_rate = 0.1) {
     this.learning_rate = learning_rate;    
   }
+  
+  setActivationFunction(Fun = sigmoid) {
+    this.activation_function = Fun;
+  }
+  
+  setDActivationFunction(dFun = dsigmoid) {
+    this.d_activation_function = dFun;
+  }
 
   train(input_array, target_array) {    
     // Generating the Hidden Outputs
@@ -56,12 +68,12 @@ class NeuralNetwork {
     let hidden = Matrix.multiply(this.weights_ih, inputs);
     hidden.add(this.bias_h);
     // activation function!
-    hidden.map(sigmoid);
+    hidden.map(this.activation_function);
 
     // Generating the output's output!
     let outputs = Matrix.multiply(this.weights_ho, hidden);
     outputs.add(this.bias_o);
-    outputs.map(sigmoid);
+    outputs.map(this.activation_function);
 
     // Convert array to matrix object
     let targets = Matrix.fromArray(target_array);
@@ -72,7 +84,7 @@ class NeuralNetwork {
 
     // let gradient = outputs * (1 - outputs);
     // Calculate gradient
-    let gradients = Matrix.map(outputs, dsigmoid);
+    let gradients = Matrix.map(outputs, this.d_activation_function);
     gradients.multiply(output_errors);
     gradients.multiply(this.learning_rate);
 
@@ -91,7 +103,7 @@ class NeuralNetwork {
     let hidden_errors = Matrix.multiply(who_t, output_errors);
 
     // Calculate hidden gradient
-    let hidden_gradient = Matrix.map(hidden, dsigmoid);
+    let hidden_gradient = Matrix.map(hidden, this.d_activation_function);
     hidden_gradient.multiply(hidden_errors);
     hidden_gradient.multiply(this.learning_rate);