Manage activation function as a class

lib/nn.js

@@ -1,13 +1,21 @@
 // Other techniques for learning
 
-function sigmoid(x) {
-  return 1 / (1 + Math.exp(-x));
+class ActivationFunction{
+  constructor(func, dfunc){
+    this.func = func;
+    this.dfunc = dfunc;
+  }
 }
 
-function dsigmoid(y) {
-  // return sigmoid(x) * (1 - sigmoid(x));
-  return y * (1 - y);
-}
+let sigmoid = new ActivationFunction(
+  x => 1 / (1 + Math.exp(-x)),
+  y => y * (1- y)
+);
+
+let tanh = new ActivationFunction(
+  x => Math.tanh(x),
+  y => 1-(y*y)
+);
 
 
 class NeuralNetwork {
@@ -26,10 +34,9 @@ class NeuralNetwork {
     this.bias_h.randomize();
     this.bias_o.randomize();
     this.setLearningRate();
-    
+
     this.setActivationFunction();
-    this.setDActivationFunction();
-
+
   }
 
   predict(input_array) {
@@ -39,41 +46,37 @@ class NeuralNetwork {
     let hidden = Matrix.multiply(this.weights_ih, inputs);
     hidden.add(this.bias_h);
     // activation function!
-    hidden.map(this.activation_function);
+    hidden.map(this.activation_function.func);
 
     // Generating the output's output!
     let output = Matrix.multiply(this.weights_ho, hidden);
     output.add(this.bias_o);
-    output.map(this.activation_function);
+    output.map(this.activation_function.func);
 
     // Sending back to the caller!
     return output.toArray();
   }
-  
+
   setLearningRate(learning_rate = 0.1) {
-    this.learning_rate = learning_rate;    
+    this.learning_rate = learning_rate;
   }
-
-  setActivationFunction(Fun = sigmoid) {
-    this.activation_function = Fun;
-  }
-
-  setDActivationFunction(dFun = dsigmoid) {
-    this.d_activation_function = dFun;
+
+  setActivationFunction(func = sigmoid) {
+    this.activation_function = func;
   }
 
-  train(input_array, target_array) {    
+  train(input_array, target_array) {
     // Generating the Hidden Outputs
     let inputs = Matrix.fromArray(input_array);
     let hidden = Matrix.multiply(this.weights_ih, inputs);
     hidden.add(this.bias_h);
     // activation function!
-    hidden.map(this.activation_function);
+    hidden.map(this.activation_function.func);
 
     // Generating the output's output!
     let outputs = Matrix.multiply(this.weights_ho, hidden);
     outputs.add(this.bias_o);
-    outputs.map(this.activation_function);
+    outputs.map(this.activation_function.func);
 
     // Convert array to matrix object
     let targets = Matrix.fromArray(target_array);
@@ -84,7 +87,7 @@ class NeuralNetwork {
 
     // let gradient = outputs * (1 - outputs);
     // Calculate gradient
-    let gradients = Matrix.map(outputs, this.d_activation_function);
+    let gradients = Matrix.map(outputs, this.activation_function.dfunc);
     gradients.multiply(output_errors);
     gradients.multiply(this.learning_rate);
 
@@ -103,7 +106,7 @@ class NeuralNetwork {
     let hidden_errors = Matrix.multiply(who_t, output_errors);
 
     // Calculate hidden gradient
-    let hidden_gradient = Matrix.map(hidden, this.d_activation_function);
+    let hidden_gradient = Matrix.map(hidden, this.activation_function.dfunc);
     hidden_gradient.multiply(hidden_errors);
     hidden_gradient.multiply(this.learning_rate);