feat: expose leakyReluAlpha

very small feature, large consequences

README.md

@@ -3,8 +3,8 @@
 
 <img src="https://cdn.rawgit.com/harthur-org/brain.js/ff595242/logo.svg" alt="Logo" width=200px/>
 
-[![npm](https://img.shields.io/npm/dt/brain.js.svg?style=flat-square)](https://npmjs.com/package/brain.js) 
-[![Backers on Open Collective](https://opencollective.com/brainjs/backers/badge.svg)](#backers) [![Sponsors on Open Collective](https://opencollective.com/brainjs/sponsors/badge.svg)](#sponsors) 
+[![npm](https://img.shields.io/npm/dt/brain.js.svg?style=flat-square)](https://npmjs.com/package/brain.js)
+[![Backers on Open Collective](https://opencollective.com/brainjs/backers/badge.svg)](#backers) [![Sponsors on Open Collective](https://opencollective.com/brainjs/sponsors/badge.svg)](#sponsors)
 
 [![Gitter](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/brain-js/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) [![Slack](https://slack.bri.im/badge.svg)](https://slack.bri.im)
 
@@ -45,7 +45,7 @@
     + [`likely`](#likely)
 - [Neural Network Types](#neural-network-types)
     + [Why different Neural Network Types?](#why-different-neural-network-types)
-    
+
 # Examples
 Here's an example showcasing how to approximate the XOR function using `brain.js`:
 more info on config [here](https://github.com/BrainJS/brain.js/blob/develop/src/neural-network.js#L31).
@@ -55,7 +55,8 @@ more info on config [here](https://github.com/BrainJS/brain.js/blob/develop/src/
 const config = {
     binaryThresh: 0.5,
     hiddenLayers: [3],     // array of ints for the sizes of the hidden layers in the network
-    activation: 'sigmoid'  // supported activation types: ['sigmoid', 'relu', 'leaky-relu', 'tanh']
+    activation: 'sigmoid'  // supported activation types: ['sigmoid', 'relu', 'leaky-relu', 'tanh'],
+    leakyReluAlpha: 0.01   // supported for activation type 'leaky-relu'
 };
 
 // create a simple feed forward neural network with backpropagation
@@ -293,7 +294,7 @@ const net = crossValidate.fromJSON(json);
 An example of using cross validate can be found in [examples/cross-validate.js](examples/cross-validate.js)
 
 ### Train Stream
-Streams are a very powerful tool in node for massive data spread across processes and are provided via the brain.js api in the following way: 
+Streams are a very powerful tool in node for massive data spread across processes and are provided via the brain.js api in the following way:
 ```js
 const net = new brain.NeuralNetwork();
 const trainStream = new brain.TrainStream({
@@ -364,11 +365,12 @@ const net = new brain.NeuralNetwork({
 ```
 
 ### activation
-This parameter lets you specify which activation function your neural network should use. There are currently four supported activation functions, **sigmoid** being the default: 
+This parameter lets you specify which activation function your neural network should use. There are currently four supported activation functions, **sigmoid** being the default:
 
 - [sigmoid](https://www.wikiwand.com/en/Sigmoid_function)
 - [relu](https://www.wikiwand.com/en/Rectifier_(neural_networks))
 - [leaky-relu](https://www.wikiwand.com/en/Rectifier_(neural_networks))
+  * related option - 'leakyReluAlpha' optional number, defaults to 0.01
 - [tanh](https://theclevermachine.wordpress.com/tag/tanh-function/)
 
 Here's a table (thanks, Wikipedia!) summarizing a plethora of activation functions — [Activation Function](https://www.wikiwand.com/en/Activation_function)

browser.js

@@ -6,7 +6,7 @@
  *   license: MIT (http://opensource.org/licenses/MIT)
  *   author: Heather Arthur <fayearthur@gmail.com>
  *   homepage: https://github.com/brainjs/brain.js#readme
- *   version: 1.4.9
+ *   version: 1.4.10
  *
  * acorn:
  *   license: MIT (http://opensource.org/licenses/MIT)
@@ -1108,6 +1108,7 @@ var NeuralNetwork = function () {
     key: 'defaults',
     get: function get() {
       return {
+        leakyReluAlpha: 0.01,
         binaryThresh: 0.5,
         hiddenLayers: [3], // array of ints for the sizes of the hidden layers in the network
         activation: 'sigmoid' // Supported activation types ['sigmoid', 'relu', 'leaky-relu', 'tanh']
@@ -1298,7 +1299,7 @@ var NeuralNetwork = function () {
     key: '_runInputLeakyRelu',
     value: function _runInputLeakyRelu(input) {
       this.outputs[0] = input; // set output state of input layer
-
+      var alpha = this.leakyReluAlpha;
       var output = null;
       for (var layer = 1; layer <= this.outputLayer; layer++) {
         for (var node = 0; node < this.sizes[layer]; node++) {
@@ -1309,7 +1310,7 @@ var NeuralNetwork = function () {
             sum += weights[k] * input[k];
           }
           //leaky relu
-          this.outputs[layer][node] = sum < 0 ? 0 : 0.01 * sum;
+          this.outputs[layer][node] = sum < 0 ? 0 : alpha * sum;
         }
         output = input = this.outputs[layer];
       }
@@ -1678,6 +1679,7 @@ var NeuralNetwork = function () {
   }, {
     key: '_calculateDeltasLeakyRelu',
     value: function _calculateDeltasLeakyRelu(target) {
+      var alpha = this.leakyReluAlpha;
       for (var layer = this.outputLayer; layer >= 0; layer--) {
         for (var node = 0; node < this.sizes[layer]; node++) {
           var output = this.outputs[layer][node];
@@ -1692,7 +1694,7 @@ var NeuralNetwork = function () {
             }
           }
           this.errors[layer][node] = error;
-          this.deltas[layer][node] = output > 0 ? error : 0.01 * error;
+          this.deltas[layer][node] = output > 0 ? error : alpha * error;
         }
       }
     }
@@ -2085,6 +2087,7 @@ var NeuralNetwork = function () {
     key: 'toFunction',
     value: function toFunction() {
       var activation = this.activation;
+      var leakyReluAlpha = this.leakyReluAlpha;
       var needsVar = false;
       function nodeHandle(layers, layerNumber, nodeKey) {
         if (layerNumber === 0) {
@@ -2114,7 +2117,7 @@ var NeuralNetwork = function () {
           case 'leaky-relu':
             {
               needsVar = true;
-              return '((v=' + result.join('') + ')<0?0:0.01*v)';
+              return '((v=' + result.join('') + ')<0?0:' + leakyReluAlpha + '*v)';
             }
           case 'tanh':
             return 'Math.tanh(' + result.join('') + ')';

index.d.ts

@@ -34,7 +34,8 @@ export interface INeuralNetworkJSON {
   outputLookup: any;
   inputLookup: any;
   activation: NeuralNetworkActivation,
-  trainOpts: INeuralNetworkTrainingOptions
+  trainOpts: INeuralNetworkTrainingOptions,
+  leakyReluAlpha?: number,
 }
 
 export interface INeuralNetworkTrainingData {

package.json

@@ -1,7 +1,7 @@
 {
   "name": "brain.js",
   "description": "Neural network library",
-  "version": "1.4.9",
+  "version": "1.4.10",
   "author": "Heather Arthur <fayearthur@gmail.com>",
   "repository": {
     "type": "git",