adjust ok
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09053ebf97
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@ -709,7 +709,10 @@ NN.Network.Stochastic = function(inNetwork, inTrainingSet, inIterations)
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let stage1 = NN.Layer.Forward(layer1, typeA);
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let stage1Error = NN.Layer.Error(layer1, goals);
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let stage1Back = NN.Layer.Backward(layer1, stage1Error);
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console.log(stage1Error);
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console.log("matrix before", layer1.Forward.Matrix);
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NN.Layer.Adjust(layer1, 0.1);
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console.log("matrix after", layer1.Forward.Matrix);
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</script>
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5
nn.ts
5
nn.ts
@ -34,8 +34,6 @@ const Backward = (inStages:N, inLayers:N, inGoals:Cloud.M, inRate:number):N =>
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let i:number;
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let errorBack:Cloud.M = M.Batch.Subtract(inStages[inStages.length-1], inGoals);
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console.log(errorBack);
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for(i=inLayers.length-1; i>=0; i--)
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{
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let layerInput:Cloud.M = inStages[i];
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@ -43,12 +41,15 @@ const Backward = (inStages:N, inLayers:N, inGoals:Cloud.M, inRate:number):N =>
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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(inLayers[i]));
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console.log("matrix before:", inLayers[i]);
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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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inLayers[i] = M.Batch.Subtract(inLayers[i], deltas);
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});
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console.log("matrix after:", inLayers[i]);
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}
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return inLayers;
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};
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