index.html

@@ -614,7 +614,7 @@ NN.Network.Create = function()
     var i;    
     
     obj.Layers = [];
-    obj.LearningRate = 0.8;
+    obj.LearningRate = 0.1;
     obj.Error = [];
     
     for(i=0; i<arguments.length-1; i++)
@@ -688,31 +688,33 @@ NN.Network.Stochastic = function(inNetwork, inTrainingSet, inIterations)
     ];
 
     let matrix2 = [
-    [0.5793881115472015, 0.9732593374796092, 0.15207639877016987, -0.5356575655337803]
+    [0.7098703863463034, 0.35485944251238033, 0.7642849892333241, 0.03046174288491077],
+	[-0.30655426258144347, 0.45509633551425077, -0.5013795222004322, -0.3421292736637427]
     ];
 
-    let typeA = [
-        [ 0.1,  0.05],
-        [ 0.0, -0.06]
+    let input = [
+    [ 0.1,  0.05],
+    [ 0.0, -0.06],
+    [ 0.99, 0.85],
+    [ 1.2,  1.05]
     ];
-    let typeB = [
-        [ 0.99, 0.85],
-        [ 1.2,  1.05]
-    ];
-	let goals = [
-        [1, 1, 0],
-        [0, 0, 1]
+    let output = [
+    [1, 0],
+    [1, 0],
+    [0, 1],
+    [0, 1]
     ];
 
-    var layer1 = NN.Layer.Create(1, 1);
-    layer1.Forward.Matrix = matrix1;
+	let nn1 = NN.Network.Create(2, 3, 2);
+	nn1.Layers[0].Forward.Matrix = matrix1;
+	nn1.Layers[1].Forward.Matrix = matrix2;
 
-    let stage1 = NN.Layer.Forward(layer1, typeA);
-	let stage1Error = NN.Layer.Error(layer1, goals);
-	let stage1Back = NN.Layer.Backward(layer1, stage1Error);
+	let logLayers = inNN => inNN.Layers.forEach(L=>console.log(L.Forward.Matrix));
 
-	console.log("matrix before", layer1.Forward.Matrix);
-	NN.Layer.Adjust(layer1, 0.1);
-	console.log("matrix after", layer1.Forward.Matrix);
+	logLayers(nn1);
+
+	NN.Network.Batch(nn1, {Input:input, Output:output}, 100);
+
+	logLayers(nn1);
 
 </script>

nn.test.js

@@ -26,48 +26,62 @@ Deno.test("check.forward", ()=>
     ];
 
     let matrix2 = [
-    [0.5793881115472015, 0.9732593374796092, 0.15207639877016987, -0.5356575655337803]
+    [0.7098703863463034, 0.35485944251238033, 0.7642849892333241, 0.03046174288491077],
+    [-0.30655426258144347, 0.45509633551425077, -0.5013795222004322, -0.3421292736637427]
     ];
 
-    let typeA = [
-        [ 0.1,  0.05],
-        [ 0.0, -0.06]
+    let input = [
+    [ 0.1,  0.05],
+    [ 0.0, -0.06],
+    [ 0.99, 0.85],
+    [ 1.2,  1.05]
     ];
-    let typeB = [
-        [ 0.99, 0.85],
-        [ 1.2,  1.05]
-    ];
-    let goals = [
-        [1, 1, 0],
-        [0, 0, 1]
+    let output = [
+    [1, 0],
+    [1, 0],
+    [0, 1],
+    [0, 1]
     ];
 
-    let layers = [matrix1];
-    let stages = Forward(Methods.Mutate.Pad(typeA), layers);
-    Backward(stages, layers, goals, 0.1);
+    let layers = [matrix1, matrix2];
+    console.log("BEFORE", layers);
+    for(let i=0; i<100; i++)
+    {
+        let stages = Forward(Methods.Mutate.Pad(input), layers);
+        Backward(stages, layers, output, 0.1);
+    }
+
+    console.log("AFTER", layers);
 });
 
+
 /*
 Deno.test("NN.Label", ()=>
 {
-    Label(training, typeA, [1]);
-    Label(training, typeB, [0]);
-    stages.push(training[0]);
-    console.log(training);
+    Label(training, typeA, [1, 0]);
+    Label(training, typeB, [0, 1]);
     assertEquals(training.length, 2, "input and output sets created");
     assertEquals(training[0].length, training[1].length, "both sets have same length");
     assertEquals(training[0][0].length, 3, "padded input component");
-    assertEquals(training[1][0].length, 1, "unchanged label vector");
+    assertEquals(training[1][0].length, 2, "unchanged label vector");
+});
+
+Deno.test("NN.Forward", ()=>
+{
+    let layer1 = M.Create.Box([-1, -1, -1], [1, 1, 1], 2);
+    let layer2 = M.Create.Box([-1, -1, -1], [1, 1, 1], 1);
+    layers.push(layer1);
+    layers.push(layer2);
+
+    console.log(training[0]);
+    stages = Forward(training[0], layers);
+    console.log(stages);
 });
 
 Deno.test("NN.Backward", ()=>
 {
-    let layer1 = M.Create.Box([-1, -1, -1], [1, 1, 1], 2);
-    let layer2 = M.Create.Box([-1, -1, -1], [1, 1, 1], 1);
-    let copy1 = M.Create.Clone(layer1);
-    let copy2 = M.Create.Clone(layer2);
-    layers.push(layer1);
-    layers.push(layer2);
+    let copy1 = M.Create.Clone(layers[0]);
+    let copy2 = M.Create.Clone(layers[1]);
 
     for(let i=0; i<100; i++)
     {
@@ -79,11 +93,10 @@ Deno.test("NN.Backward", ()=>
 });
 
 
-Deno.test("NN.Forward", ()=>
+Deno.test("NN.Label", ()=>
 {
-    console.log(Forward(stages, layers));
-    console.log(training[1]);
+    let stages = Forward(training[0], layers);
+    console.log(stages[stages.length-1]);
 });
 
-
 */

nn.ts

@@ -41,15 +41,12 @@ const Backward = (inStages:N, inLayers:N, inGoals:Cloud.M, inRate:number):N =>
         let errorScaled:Cloud.M = M.Batch.Multiply(errorBack, M.Batch.Derivative(layerOutput));
 
         errorBack = M.Batch.Affine(errorScaled, M.Create.Transpose(inLayers[i]));
-        console.log("matrix before:", inLayers[i]);
 
         errorScaled.forEach((inScaledError:Cloud.V, inIndex:number)=> {
             const deltas = M.Batch.Scale(M.Create.Outer(layerInput[inIndex], inScaledError), inRate);
             inLayers[i] = M.Batch.Subtract(inLayers[i], deltas);
         });
 
-        console.log("matrix after:", inLayers[i]);
-
     }
     return inLayers;
 };