Merge branch 'master' of github.com:CodingTrain/Toy-Neural-Network-JS

Author: shiffman

README.md

@@ -4,6 +4,8 @@ Neural Network JavaScript library for Coding Train tutorials
 
 ## Getting Started
 
+If you're looking for the original source code to match the videos [visit this repo] (https://github.com/CodingTrain/Rainbow-Code/tree/master/Courses/natureofcode/10.18-toy_neural_network)
+
 TODO
 
 ### Prerequisites
@@ -22,6 +24,12 @@ npm install
 
 This Project doesn't require any additional Installing steps
 
+### Documentation
+
+* `NeuralNetwork` - The neural network class
+  * `predict(input_array)` - Returns the output of a neural network
+  * `train(input_array, target_array)` - Trains a neural network
+
 ## Running the tests
 
 The Tests can either be checked via the automaticly running CircleCI Tests or you can also run `npm test` on your PC after you have done the Step "Prerequisites"
@@ -38,7 +46,7 @@ Please send PullRequests. These need to pass a automated Test first and after it
 
 ## Versioning
 
-We use [SemVer](http://semver.org/) for versioning. For the versions available, see the [tags on this repository](https://github.com/CodingTrain/Toy-Neural-Network-JS/tags). 
+We use [SemVer](http://semver.org/) for versioning. For the versions available, see the [tags on this repository](https://github.com/CodingTrain/Toy-Neural-Network-JS/tags).
 
 ## Authors
 

TODO.md

@@ -0,0 +1,10 @@
+# To-Do List
+
+* [x] Redo gradient descent video about
+* [x] delta weight formulas, connect to "mathematics of gradient" video
+* [x] Impelment gradient descent in library / with code
+* [ ] XOR coding challenge
+* [ ] MNIST coding challenge
+* [ ] Support for multiple hidden layers
+* [ ] Support for different activation functions
+* [ ] Combine with ml5 / deeplearnjs.

examples/xor/sketch.js

@@ -41,7 +41,7 @@ function draw() {
       let y = j * resolution;
       let input_1 = i / (cols - 1);
       let input_2 = j / (rows - 1);
-      let output = nn.feedforward([input_1, input_2]);
+      let output = nn.predict([input_1, input_2]);
       let col = output[0] * 255;
       fill(col);
       noStroke();

lib/matrix.js

@@ -5,33 +5,17 @@ class Matrix {
   constructor(rows, cols) {
     this.rows = rows;
     this.cols = cols;
-    this.data = [];
-
-    for (let i = 0; i < this.rows; i++) {
-      this.data[i] = [];
-      for (let j = 0; j < this.cols; j++) {
-        this.data[i][j] = 0;
-      }
-    }
+    this.data = Array(this.rows).fill().map(() => Array(this.cols).fill(0));
   }
 
   static fromArray(arr) {
-    let m = new Matrix(arr.length, 1);
-    for (let i = 0; i < arr.length; i++) {
-      m.data[i][0] = arr[i];
-    }
-    return m;
+    return new Matrix(arr.length, 1).map((e, i) => arr[i]);
   }
 
   static subtract(a, b) {
     // Return a new Matrix a-b
-    let result = new Matrix(a.rows, a.cols);
-    for (let i = 0; i < result.rows; i++) {
-      for (let j = 0; j < result.cols; j++) {
-        result.data[i][j] = a.data[i][j] - b.data[i][j];
-      }
-    }
-    return result;
+    return new Matrix(a.rows, a.cols)
+      .map((_, i, j) => a.data[i][j] - b.data[i][j]);
   }
 
   toArray() {
@@ -45,74 +29,47 @@ class Matrix {
   }
 
   randomize() {
-    for (let i = 0; i < this.rows; i++) {
-      for (let j = 0; j < this.cols; j++) {
-        this.data[i][j] = Math.random() * 2 - 1;
-      }
-    }
+    return this.map(e => Math.random() * 2 - 1);
   }
 
   add(n) {
     if (n instanceof Matrix) {
-      for (let i = 0; i < this.rows; i++) {
-        for (let j = 0; j < this.cols; j++) {
-          this.data[i][j] += n.data[i][j];
-        }
-      }
+      return this.map((e, i, j) => e + n.data[i][j]);
     } else {
-      for (let i = 0; i < this.rows; i++) {
-        for (let j = 0; j < this.cols; j++) {
-          this.data[i][j] += n;
-        }
-      }
+      return this.map(e => e + n);
     }
   }
 
   static transpose(matrix) {
-    let result = new Matrix(matrix.cols, matrix.rows);
-    for (let i = 0; i < matrix.rows; i++) {
-      for (let j = 0; j < matrix.cols; j++) {
-        result.data[j][i] = matrix.data[i][j];
-      }
-    }
-    return result;
+    return new Matrix(matrix.cols, matrix.rows)
+      .map((_, i, j) => matrix.data[j][i]);
   }
 
   static multiply(a, b) {
     // Matrix product
     if (a.cols !== b.rows) {
       console.log('Columns of A must match rows of B.')
-      return undefined;
+      return;
     }
-    let result = new Matrix(a.rows, b.cols);
-    for (let i = 0; i < result.rows; i++) {
-      for (let j = 0; j < result.cols; j++) {
+
+    return new Matrix(a.rows, b.cols)
+      .map((e, i, j) => {
         // Dot product of values in col
         let sum = 0;
         for (let k = 0; k < a.cols; k++) {
           sum += a.data[i][k] * b.data[k][j];
         }
-        result.data[i][j] = sum;
-      }
-    }
-    return result;
+        return sum;
+      });
   }
 
   multiply(n) {
     if (n instanceof Matrix) {
       // hadamard product
-      for (let i = 0; i < this.rows; i++) {
-        for (let j = 0; j < this.cols; j++) {
-          this.data[i][j] *= n.data[i][j];
-        }
-      }
+      return this.map((e, i, j) => e * n.data[i][j]);
     } else {
       // Scalar product
-      for (let i = 0; i < this.rows; i++) {
-        for (let j = 0; j < this.cols; j++) {
-          this.data[i][j] *= n;
-        }
-      }
+      return this.map(e => e * n);
     }
   }
 
@@ -121,29 +78,24 @@ class Matrix {
     for (let i = 0; i < this.rows; i++) {
       for (let j = 0; j < this.cols; j++) {
         let val = this.data[i][j];
-        this.data[i][j] = func(val);
+        this.data[i][j] = func(val, i, j);
       }
     }
+    return this;
   }
 
   static map(matrix, func) {
-    let result = new Matrix(matrix.rows, matrix.cols);
     // Apply a function to every element of matrix
-    for (let i = 0; i < matrix.rows; i++) {
-      for (let j = 0; j < matrix.cols; j++) {
-        let val = matrix.data[i][j];
-        result.data[i][j] = func(val);
-      }
-    }
-    return result;
+    return new Matrix(matrix.rows, matrix.cols)
+      .map((e, i, j) => func(matrix.data[i][j], i, j));
   }
 
   print() {
     console.table(this.data);
+    return this;
   }
 }
 
-
 if (typeof module !== 'undefined') {
   module.exports = Matrix;
-}
+}