test passing but still bad labels
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6c186bc6e4
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574c670482
@ -136,6 +136,15 @@ Deno.test("Batch.Scale", ()=>
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assertEquals(t[0].length, 2, "correct dimensions");
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assertEquals(t[0].length, 2, "correct dimensions");
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assertEquals(t[1][0], 1.5, "correct placement");
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assertEquals(t[1][0], 1.5, "correct placement");
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});
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});
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Deno.test("Batch.Subtract", ()=>
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{
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const c = [[1, 2], [3, 4]];
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const s = [[0.5, 0.5], [0.5, 0.5]];
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const t = M.Batch.Subtract(c, s);
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assertEquals(t.length, 2, "correct count");
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assertEquals(t[0].length, 2, "correct dimensions");
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assertEquals(t[1][0], 2.5, "correct placement");
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});
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Deno.test("Batch.Sigmoid", ()=>
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Deno.test("Batch.Sigmoid", ()=>
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{
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{
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const m = [[-1000, 1000]];
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const m = [[-1000, 1000]];
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14
nn.test.js
14
nn.test.js
@ -31,8 +31,8 @@ Deno.test("NN.Label", ()=>
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Deno.test("NN.Backward", ()=>
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Deno.test("NN.Backward", ()=>
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{
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{
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let layer1 = M.Create.Box([0, 0, 0], [1, 1, 1], 4);
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let layer1 = M.Create.Box([0, 0, 0], [1, 1, 1], 2);
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let layer2 = M.Create.Box([0, 0, 0, 0, 0], [1, 1, 1, 1, 1], 1);
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let layer2 = M.Create.Box([0, 0, 0], [1, 1, 1], 1);
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let copy1 = M.Create.Clone(layer1);
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let copy1 = M.Create.Clone(layer1);
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let copy2 = M.Create.Clone(layer2);
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let copy2 = M.Create.Clone(layer2);
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@ -41,16 +41,18 @@ Deno.test("NN.Backward", ()=>
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for(let i=0; i<100; i++)
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for(let i=0; i<100; i++)
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{
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{
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Backward(stages, layers, training[1], 0.01);
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Backward(stages, layers, training[1], 0.1);
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}
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}
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console.log(layer1, copy1);
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assert(layer1[0][0] != copy1[0][0], "first matrix has changed");
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assert(layers[0][0][0] != copy1[0][0], "first matrix has changed");
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assert(layer1[1][0] != copy2[1][0], "second matrix has changed");
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assert(layers[1][0][0] != copy2[0][0], "second matrix has changed");
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});
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});
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Deno.test("NN.Forward", ()=>
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Deno.test("NN.Forward", ()=>
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{
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{
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console.log(Forward(stages, layers));
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console.log(Forward(stages, layers));
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console.log(training[1]);
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console.log(training[1]);
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});
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});
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10
nn.ts
10
nn.ts
@ -28,24 +28,26 @@ const Forward = (inStages:N, inLayers:N):Cloud.M =>
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inStages[i+1] = process(i);
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inStages[i+1] = process(i);
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return inStages[i+1];
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return inStages[i+1];
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};
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};
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const Backward = (inStages:N, inLayers:N, inGoals:Cloud.M, inRate:number):void =>
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const Backward = (inStages:N, inLayers:N, inGoals:Cloud.M, inRate:number):N =>
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{
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{
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let i:number;
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let i:number;
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let errorBack:Cloud.M = M.Batch.Subtract(Forward(inStages, inLayers), inGoals);
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let errorBack:Cloud.M = M.Batch.Subtract(Forward(inStages, inLayers), inGoals);
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for(i=inLayers.length-1; i>=0; i--)
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for(i=inLayers.length-1; i>=0; i--)
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{
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{
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let layerMatrix:Cloud.M = inLayers[i];
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let layerInput:Cloud.M = inStages[i];
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let layerInput:Cloud.M = inStages[i];
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let layerOutput:Cloud.M = inStages[i+1];
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let layerOutput:Cloud.M = inStages[i+1];
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let errorScaled:Cloud.M = M.Batch.Multiply(errorBack, M.Batch.Derivative(layerOutput));
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let errorScaled:Cloud.M = M.Batch.Multiply(errorBack, M.Batch.Derivative(layerOutput));
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errorBack = M.Batch.Affine(errorScaled, M.Create.Transpose(layerMatrix));
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errorBack = M.Batch.Affine(errorScaled, M.Create.Transpose(inLayers[i]));
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errorScaled.forEach((inScaledError:Cloud.V, inIndex:number)=> {
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errorScaled.forEach((inScaledError:Cloud.V, inIndex:number)=> {
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const deltas = M.Batch.Scale(M.Create.Outer(layerInput[inIndex], inScaledError), inRate);
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const deltas = M.Batch.Scale(M.Create.Outer(layerInput[inIndex], inScaledError), inRate);
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layerMatrix = M.Batch.Subtract(layerMatrix, deltas);
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inLayers[i] = M.Batch.Subtract(inLayers[i], deltas);
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});
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});
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}
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}
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return inLayers;
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};
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};
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export { Label, Forward, Backward };
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export { Label, Forward, Backward };
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