debug work
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index.html
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index.html
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<script>
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/* Vector Library */
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/*
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Works with n-dimensional vectors: represented as arrays of numbers
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*/
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var V = {};
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V.Subtract = function(inV1, inV2)
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{
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var out = [];
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for(var i=0; i<inV1.length; i++)
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{
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out[i] = inV1[i] - inV2[i];
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}
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return out;
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};
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V.Add = function(inV1, inV2)
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{
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var out = [];
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for(var i=0; i<inV1.length; i++)
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{
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out[i] = inV1[i] + inV2[i];
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}
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return out;
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};
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V.Distance = function(inV1, inV2)
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{
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return V.Length(V.Subtract(inV1, inV2))
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};
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V.Dot = function(inV1, inV2)
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{
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var out = 0;
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for(var i=0; i<inV1.length; i++)
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{
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out += inV1[i] * inV2[i];
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}
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return out;
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};
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V.Multiply = function(inV1, inV2)
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{
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var out = [];
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for(var i=0; i<inV1.length; i++)
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{
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out[i] = inV1[i] * inV2[i];
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}
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return out;
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};
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V.Length = function(inV1)
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{
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return Math.sqrt(V.Dot(inV1, inV1));
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};
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V.Scale = function(inV1, inScalar)
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{
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var out = [];
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for(var i=0; i<inV1.length; i++)
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{
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out[i] = inV1[i] * inScalar;
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}
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return out;
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};
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V.Normalize = function(inV1)
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{
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return V.Scale(inV1, 1/V.Length(inV1));
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};
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V.Clone = function(inV1)
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{
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var out = [];
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var i;
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for(i=0; i<inV1.length; i++)
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{
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out[i] = inV1[i];
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}
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return out;
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};
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var M = {};
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/**************************
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M A T R I X
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*/
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// transform inC with inM
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// returns the transformed inC
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M.Transform = function(inM, inC)
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{
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var outM = [];
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var outV = [];
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var i, j;
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for(i=0; i<inC.length; i++)
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{
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outV = [];
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for(j=0; j<inM.length; j++)
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{
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outV[j] = V.Dot(inM[j], inC[i]);
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}
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outM.push(outV);
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}
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return outM;
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};
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// flip rows for columns in inM
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// returns the modified Matrix
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M.Transpose = function(inM)
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{
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var dimensions = inM[0].length;
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var i, j;
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var outM = [];
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var outV = [];
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for(i=0; i<dimensions; i++)
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{
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outV = [];
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for(j=0; j<inM.length; j++)
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{
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//the Ith componenth of the Jth member
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outV[j] = inM[j][i];
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}
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outM.push(outV);
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}
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return outM;
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}
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// returns a matrix that is the result of the outer product of inV1 and inV2
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// where the Nth member of outM is a copy of V1, scaled by the Nth component of V2
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M.Outer = function(inV1, inV2)
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{
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var outM = [];
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var i;
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for(i=0; i<inV2.length; i++)
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{
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outM.push(V.Scale(inV1, inV2[i]));
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}
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return outM;
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};
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/**************************
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B A T C H
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*/
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//smash the members of inM with a softmax
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M.Sigmoid = function(inM)
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{
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var i, j;
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var outM = [];
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var outV = [];
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for(i=0; i<inM.length; i++)
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{
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outV = [];
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for(j=0; j<inM[i].length; j++)
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{
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outV[j] = 1/(1 + Math.pow(Math.E, -inM[i][j]));
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}
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outM.push(outV);
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}
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return outM;
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};
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// return the derivatives of the members of inM (that have been run through the softmax)
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M.Derivative = function(inM)
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{
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var i, j;
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var component;
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var outM = [];
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var outV = [];
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for(i=0; i<inM.length; i++)
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{
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outV = [];
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for(j=0; j<inM[i].length; j++)
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{
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component = inM[i][j];
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outV[j] = component*(1 - component);
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}
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outM.push(outV);
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}
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return outM;
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};
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// batch multiply these pairs of vectors
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M.Multiply = function(inCloud1, inCloud2)
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{
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var i;
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var outM = [];
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for(i=0; i<inCloud1.length; i++)
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{
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outM.push(V.Multiply(inCloud1[i], inCloud2[i]));
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};
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return outM;
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};
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// batch add
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M.Add = function(inCloud1, inCloud2)
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{
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var outM = [];
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var i;
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for(i=0; i<inCloud1.length; i++)
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{
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outM.push(V.Add(inCloud1[i], inCloud2[i]));
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}
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return outM;
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};
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M.Subtract = function(inCloud1, inCloud2)
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{
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var outM = [];
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var i;
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for(i=0; i<inCloud1.length; i++)
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{
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outM.push(V.Subtract(inCloud1[i], inCloud2[i]));
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}
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return outM;
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};
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M.Scale = function(inCloud1, inScalar)
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{
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var outM = [];
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var i;
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for(i=0; i<inCloud1.length; i++)
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{
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outM.push(V.Scale(inCloud1[i], inScalar));
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}
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return outM;
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};
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M.Clone = function(inM)
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{
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var i;
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var outM;
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var outV;
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outM =[];
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for(i=0; i<inM.length; i++)
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{
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outM.push(V.Clone(inM[i]));
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}
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return outM;
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};
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/**************************
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B O U N D S
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*/
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// return the bounding box of inM as a two-member Matrix
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M.Bounds = function(inM)
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{
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var dimensions = inM[0].length;
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var i, j;
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var min = [];
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var max = [];
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for(i=0; i<dimensions; i++)
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{
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min[i] = 9999999;
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max[i] = -999999;
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}
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for(i=0; i<inM.length; i++)
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{
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for(j=0; j<dimensions; j++)
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{
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if(inM[i][j] < min[j])
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{
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min[j] = inM[i][j];
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}
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if(inM[i][j] > max[j])
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{
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max[j] = inM[i][j];
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}
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}
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}
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return [min, max];
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};
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// find the local coordinates for all the members of inM, within the bounding box inB
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// returns a new Matrix of relative vectors
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M.GlobalToLocal = function(inM, inB)
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{
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var dimensions = inB[0].length;
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var i, j;
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var outM = [];
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var outV = [];
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var size;
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var min;
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var denominator;
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for(i=0; i<inM.length; i++)
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{
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outV = [];
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for(j=0; j<dimensions; j++)
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{
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denominator = inB[1][j] - inB[0][j];
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if(denominator == 0)
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{
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outV[j] = inB[1][j];// if min and max are the same, just output max
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}
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else
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{
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outV[j] = (inM[i][j] - inB[0][j])/denominator;
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}
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}
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outM.push(outV);
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}
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return outM;
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};
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// find the global coordinates for all the members of inM, within the bounding box inB
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// returns a new Matrix of global vectors
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M.LocalToGlobal = function(inM, inB)
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{
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var dimensions = inB[0].length;
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var i, j;
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var outM = [];
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var outV = [];
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var size;
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var min;
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for(i=0; i<inM.length; i++)
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{
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outV = [];
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for(j=0; j<dimensions; j++)
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{
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outV[j] = inB[0][j] + inM[i][j] * (inB[1][j] - inB[0][j]);
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}
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outM.push(outV);
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}
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return outM;
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};
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/**************************
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C L O U D
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*/
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// return some number of points from inM as a new Matrix
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M.Reduce = function(inM, inCount)
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{
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var largeGroupSize;
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var largeGroupCount;
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var smallGroupSize;
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var outM = [];
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largeGroupSize = Math.floor(inM.length/inM);
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smallGroupSize = inM.length%inCount
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for(i=0; i<inM-1; i++)
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{
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index = i*largeGroupSize + Math.floor(Math.random()*largeGroupSize);
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outM.push( V.Clone(inM[index]) );
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}
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if(smallGroupSize != 0)
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{
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index = i*largeGroupSize + Math.floor(Math.random()*smallGroupSize)
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outM.push( V.Clone(inM[index]) );
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}
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return outM;
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};
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// return a Matrix of length inCount, where all the members fall within the circle paramemters, including a bias
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M.Circle = function(inCenter, inRadius, inBias, inCount)
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{
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var i, j;
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var vector;
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var length;
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var outM = [];
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for(i=0; i<inCount; i++)
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{
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//generate a random vector
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vector = [];
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for(j=0; j<inCenter.length; j++)
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{
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vector[j] = (Math.random() - 0.5);
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}
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//normalize the vector
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vector = V.Scale(vector, 1/V.Length(vector));
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//set a random length (with a bias)
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length = Math.pow(Math.random(), Math.log(inBias)/Math.log(0.5))*inRadius;
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vector = V.Scale(vector, length);
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//move the vector to the center
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vector = V.Add(vector, inCenter);
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outM.push(vector);
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}
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return outM;
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};
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// return a Matrix of length inCount, where all the members fall within inBounds
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M.Box = function(inBounds, inCount)
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{
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var vector;
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var dimensions = inBounds[0].length;
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var i, j;
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var min, max;
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var outM = [];
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for(i=0; i<inCount; i++)
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{
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vector = [];
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for(j=0; j<dimensions; j++)
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{
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min = inBounds[0][j];
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max = inBounds[1][j];
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vector[j] = min + Math.random()*(max - min);
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}
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outM.push(vector);
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}
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return outM;
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};
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//combine all the matricies in inList into one long Matrix
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M.Combine = function(inList)
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{
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var i, j;
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var outM = [];
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for(i=0; i<inList.length; i++)
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{
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for(j=0; j<inList[i].length; j++)
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{
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outM.push(V.Clone(inList[i][j]));
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}
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}
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return outM;
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};
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/*
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PLEASE NOTE: These padding routines are unique to this library in that they
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actually modify the input object(s) rather than returning modified copies!
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*/
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// add a new component (set to '1') to each member of inM
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M.Pad = function(inM)
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{
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var i;
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for(i=0; i<inM.length; i++)
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{
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inM[i].push(1);
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}
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return inM;
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};
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// remove the last component of each memeber of inM
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M.Unpad = function(inM)
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{
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var i;
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for(i=0; i<inM.length; i++)
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{
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inM[i].pop();
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}
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return inM;
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};
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// set the last component of each member of inM to 1
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M.Repad = function(inM)
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{
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var i;
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var last = inM[0].length-1;
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for(i=0; i<inM.length; i++)
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{
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inM[i][last] = 1;
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}
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return inM;
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};
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</script>
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<script>
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var NN = {};
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NN.TrainingSet = {};
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NN.TrainingSet.Instances = [];
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NN.TrainingSet.Create = function()
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{
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var obj = {};
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obj.Input = [];
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obj.Output = [];
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obj.Order = [];
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NN.TrainingSet.Instances.push(obj);
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return obj;
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};
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NN.TrainingSet.AddPoint = function(inTrainingSet, inType, inData)
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{
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inTrainingSet.Input.push(inData);
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inTrainingSet.Output.push(inType);
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inTrainingSet.Order.push(inTrainingSet.Order.length);
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};
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NN.TrainingSet.AddCloud = function(inTrainingSet, inLabel, inCloud)
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{
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var i;
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for(i=0; i<inCloud.length; i++)
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{
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NN.TrainingSet.AddPoint(inTrainingSet, inLabel, inCloud[i]);
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}
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};
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NN.TrainingSet.Randomize = function(inTrainingSet)
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{
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var newOrder = [];
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var selection;
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while(inTrainingSet.Order.length != 0)
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{
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selection = Math.floor(inTrainingSet.Order.length * Math.random());
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inTrainingSet.Order.splice(selection, 1);
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newOrder.push(selection);
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}
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inTrainingSet.Order = newOrder;
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};
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NN.Layer = {};
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NN.Layer.Create = function(sizeIn, sizeOut)
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{
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var i;
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var min = [];
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var max = [];
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var obj = {};
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sizeIn++;
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obj.Forward = {};
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for(i=0; i<sizeIn; i++)
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{
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min.push(-1);
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max.push(1);
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}
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obj.Forward.Matrix = M.Box([min, max], sizeOut);
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obj.Forward.StageInput = [];
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obj.Forward.StageAffine = [];
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obj.Forward.StageSigmoid = [];
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obj.Forward.StageDerivative = [];
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obj.Backward = {};
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obj.Backward.Matrix = M.Transpose(obj.Forward.Matrix);
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obj.Backward.StageInput = [];
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obj.Backward.StageDerivative = [];
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obj.Backward.StageAffine = [];
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return obj;
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};
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NN.Layer.Forward = function(inLayer, inInput)
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{
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inLayer.Forward.StageInput = M.Pad(inInput); // Pad the input
|
||||
inLayer.Forward.StageAffine = M.Transform(inLayer.Forward.Matrix, inLayer.Forward.StageInput);
|
||||
inLayer.Forward.StageSigmoid = M.Sigmoid(inLayer.Forward.StageAffine);
|
||||
|
||||
return inLayer.Forward.StageSigmoid;
|
||||
};
|
||||
NN.Layer.Error = function(inLayer, inTarget)
|
||||
{
|
||||
return M.Subtract(inLayer.Forward.StageSigmoid, inTarget);
|
||||
};
|
||||
NN.Layer.Backward = function(inLayer, inInput)
|
||||
{
|
||||
/* We need the derivative of the forward pass, but only during the backward pass.
|
||||
That's why-- even though it "belongs" to the forward pass-- it is being calculated here. */
|
||||
inLayer.Forward.StageDerivative = M.Derivative(inLayer.Forward.StageSigmoid);
|
||||
|
||||
/* This transpose matrix is for sending the error back to a previous layer.
|
||||
And again, even though it is derived directly from the forward matrix, it is only needed during the backward pass so we calculate it here.*/
|
||||
inLayer.Backward.Matrix = M.Transpose(inLayer.Forward.Matrix);
|
||||
|
||||
/* When the error vector arrives at a layer, it always needs to be multiplied (read 'supressed') by the derivative of
|
||||
what the layer output earlier during the forward pass.
|
||||
So despite its name, Backward.StageDerivative contains the result of this *multiplication* and not some new derivative calculation.*/
|
||||
inLayer.Backward.StageInput = inInput;
|
||||
inLayer.Backward.StageDerivative = M.Multiply(inLayer.Backward.StageInput, inLayer.Forward.StageDerivative);
|
||||
inLayer.Backward.StageAffine = M.Transform(inLayer.Backward.Matrix, inLayer.Backward.StageDerivative);
|
||||
|
||||
return M.Unpad(inLayer.Backward.StageAffine);// Unpad the output
|
||||
};
|
||||
NN.Layer.Adjust = function(inLayer, inLearningRate)
|
||||
{
|
||||
var deltas;
|
||||
var vector;
|
||||
var scalar;
|
||||
var i, j;
|
||||
|
||||
for(i=0; i<inLayer.Forward.StageInput.length; i++)
|
||||
{
|
||||
deltas = M.Outer(inLayer.Forward.StageInput[i], inLayer.Backward.StageDerivative[i]);
|
||||
deltas = M.Scale(deltas, inLearningRate);
|
||||
|
||||
inLayer.Forward.Matrix = M.Subtract(inLayer.Forward.Matrix, deltas);
|
||||
}
|
||||
};
|
||||
NN.Layer.Stochastic = function(inLayer, inTrainingSet, inIterations)
|
||||
{
|
||||
/* this method is ONLY for testing individual layers, and does not translate to network-level training */
|
||||
var i, j;
|
||||
var current;
|
||||
var error;
|
||||
for(i=0; i<inIterations; i++)
|
||||
{
|
||||
NN.TrainingSet.Randomize(inTrainingSet);
|
||||
for(j=0; j<inTrainingSet.Order.length; j++)
|
||||
{
|
||||
current = inTrainingSet.Order[j];
|
||||
NN.Layer.Forward(inLayer, [inTrainingSet.Input[current]]);
|
||||
error = M.Subtract(inLayer.Forward.StageSigmoid, [inTrainingSet.Output[current]]);
|
||||
NN.Layer.Backward(inLayer, error);
|
||||
NN.Layer.Adjust(inLayer, 0.1);
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
NN.Network = {};
|
||||
NN.Network.Instances = [];
|
||||
NN.Network.Create = function()
|
||||
{
|
||||
var obj = {};
|
||||
var i;
|
||||
|
||||
obj.Layers = [];
|
||||
obj.LearningRate = 0.8;
|
||||
obj.Error = [];
|
||||
|
||||
for(i=0; i<arguments.length-1; i++)
|
||||
{
|
||||
obj.Layers.push(NN.Layer.Create(arguments[i], arguments[i+1]));
|
||||
}
|
||||
|
||||
NN.Network.Instances.push(obj);
|
||||
return obj;
|
||||
};
|
||||
NN.Network.Observe = function(inNetwork, inBatch)
|
||||
{
|
||||
var input = M.Clone(inBatch);
|
||||
var i;
|
||||
for(i=0; i<inNetwork.Layers.length; i++)
|
||||
{
|
||||
input = NN.Layer.Forward(inNetwork.Layers[i], input);
|
||||
}
|
||||
return inNetwork.Layers[inNetwork.Layers.length-1].Forward.StageSigmoid;
|
||||
};
|
||||
NN.Network.Error = function(inNetwork, inTraining)
|
||||
{
|
||||
return M.Subtract(inNetwork.Layers[inNetwork.Layers.length-1].Forward.StageSigmoid, inTraining);
|
||||
};
|
||||
NN.Network.Learn = function(inNetwork, inError)
|
||||
{
|
||||
var input = inError;
|
||||
var i;
|
||||
for(i=inNetwork.Layers.length-1; i>=0; i--)
|
||||
{
|
||||
input = NN.Layer.Backward(inNetwork.Layers[i], input);
|
||||
NN.Layer.Adjust(inNetwork.Layers[i], inNetwork.LearningRate);
|
||||
}
|
||||
};
|
||||
|
||||
|
||||
NN.Network.Batch = function(inNetwork, inTrainingSet, inIterations)
|
||||
{
|
||||
var i;
|
||||
for(i=0; i<inIterations; i++)
|
||||
{
|
||||
NN.Network.Observe(inNetwork, inTrainingSet.Input);
|
||||
inNetwork.Error = NN.Network.Error(inNetwork, inTrainingSet.Output)
|
||||
NN.Network.Learn(inNetwork, inNetwork.Error);
|
||||
}
|
||||
};
|
||||
NN.Network.Stochastic = function(inNetwork, inTrainingSet, inIterations)
|
||||
{
|
||||
var i, j;
|
||||
var current;
|
||||
|
||||
for(i=0; i<inIterations; i++)
|
||||
{
|
||||
NN.TrainingSet.Randomize(inTrainingSet);
|
||||
for(j=0; j<inTrainingSet.Order.length; j++)
|
||||
{
|
||||
current = inTrainingSet.Order[j];
|
||||
NN.Network.Observe(inNetwork, [inTrainingSet.Input[current]]);
|
||||
inNetwork.Error = NN.Network.Error(inNetwork, [inTrainingSet.Output[current]]);
|
||||
NN.Network.Learn(inNetwork, inNetwork.Error);
|
||||
}
|
||||
}
|
||||
};
|
||||
</script>
|
||||
|
||||
<script>
|
||||
let matrix1 = [
|
||||
[-0.43662948305036675, -0.368590640707799, -0.23227179558890843],
|
||||
[-0.004292653969505622, 0.38670055222186317, -0.2478421495365568],
|
||||
[0.738181366836224, 0.3389203747353555, 0.4920200816404332]
|
||||
];
|
||||
|
||||
let matrix2 = [
|
||||
[0.5793881115472015, 0.9732593374796092, 0.15207639877016987, -0.5356575655337803]
|
||||
];
|
||||
|
||||
let typeA = [
|
||||
[ 0.1, 0.05],
|
||||
[ 0.0, -0.06]
|
||||
];
|
||||
let typeB = [
|
||||
[ 0.99, 0.85],
|
||||
[ 1.2, 1.05]
|
||||
];
|
||||
|
||||
var layer1 = NN.Layer.Create(1, 1);
|
||||
layer1.Forward.Matrix = matrix1;
|
||||
|
||||
var layer2 = NN.Layer.Create(1, 1);
|
||||
layer2.Forward.Matrix = matrix2;
|
||||
|
||||
let stage1 = NN.Layer.Forward(layer1, typeA);
|
||||
|
||||
console.log(stage1);
|
||||
|
||||
</script>
|
||||
@ -98,6 +98,7 @@ Deno.test("Single.Affine", ()=>
|
||||
assertEquals(t.length, 2, "correct dimensions");
|
||||
assertEquals(t[0], 0.5)
|
||||
assertEquals(t[1], 1.1, "correct placement");
|
||||
console.log(t);
|
||||
});
|
||||
Deno.test("Single.Subtract", ()=>
|
||||
{
|
||||
|
||||
9
m.ts
9
m.ts
@ -38,11 +38,18 @@ const Methods = {
|
||||
Pad: (inCloud:Cloud.M):Cloud.M=> {inCloud.forEach((row:Cloud.V)=> row.push(1)); return inCloud; },
|
||||
Unpad: (inCloud:Cloud.M):Cloud.M=> {inCloud.forEach((row:Cloud.V)=> row.pop()); return inCloud; }
|
||||
},
|
||||
Test:
|
||||
{
|
||||
Dot:(v1:Cloud.V, v2:Cloud.V):number=>
|
||||
{
|
||||
return v1.reduce((sum, current, index)=> sum + current*v2[index]);
|
||||
}
|
||||
},
|
||||
Single:
|
||||
{
|
||||
Subtract: (inV1:Cloud.V, inV2:Cloud.V):Cloud.V=> inV1.map((component, i)=> component-inV2[i]),
|
||||
Multiply: (inV1:Cloud.V, inV2:Cloud.V):Cloud.V=> inV1.map((component, i)=> component*inV2[i]),
|
||||
Affine: (inV:Cloud.V, inMatrix:Cloud.M):Cloud.V=> inMatrix.map((row:Cloud.V)=> row.reduce((sum, current, index)=> sum + current*inV[index]))
|
||||
Affine: (inV:Cloud.V, inMatrix:Cloud.M):Cloud.V=> inMatrix.map((row:Cloud.V)=> row.reduce((sum, current, index)=> sum + current*inV[index], 0))
|
||||
},
|
||||
Batch:
|
||||
{
|
||||
|
||||
57
nn.test.js
57
nn.test.js
@ -1,25 +1,57 @@
|
||||
import { assert, assertEquals } from "https://deno.land/std@0.102.0/testing/asserts.ts";
|
||||
import { Label, Forward, Backward } from "./nn.ts";
|
||||
import { default as M } from "./m.ts";
|
||||
import { default as Methods } from "./m.ts";
|
||||
|
||||
let training = [];
|
||||
let stages = [];
|
||||
let layers = [];
|
||||
|
||||
Deno.test("NN.Label", ()=>
|
||||
let typeA = [
|
||||
[ 0.1, 0.05],
|
||||
[ 0.0, -0.06]
|
||||
];
|
||||
let typeB = [
|
||||
[ 0.99, 0.85],
|
||||
[ 1.2, 1.05]
|
||||
];
|
||||
|
||||
|
||||
Deno.test("check.forward", ()=>
|
||||
{
|
||||
Label(training,
|
||||
[
|
||||
let training = [];
|
||||
let stages = [];
|
||||
let layers = [
|
||||
[
|
||||
[-0.43662948305036675, -0.368590640707799, -0.23227179558890843],
|
||||
[-0.004292653969505622, 0.38670055222186317, -0.2478421495365568],
|
||||
[0.738181366836224, 0.3389203747353555, 0.4920200816404332]
|
||||
],
|
||||
[
|
||||
[0.5793881115472015, 0.9732593374796092, 0.15207639877016987, -0.5356575655337803]
|
||||
]
|
||||
];
|
||||
|
||||
let typeA = [
|
||||
[ 0.1, 0.05],
|
||||
[ 0.0, -0.06]
|
||||
],
|
||||
[1]);
|
||||
Label(training,
|
||||
[
|
||||
];
|
||||
let typeB = [
|
||||
[ 0.99, 0.85],
|
||||
[ 1.2, 1.05]
|
||||
],
|
||||
[0]);
|
||||
];
|
||||
|
||||
Label(training, typeA, [1]);
|
||||
stages.push(training[0]);
|
||||
Forward(stages, layers);
|
||||
console.log(stages);
|
||||
});
|
||||
|
||||
/*
|
||||
Deno.test("NN.Label", ()=>
|
||||
{
|
||||
Label(training, typeA, [1]);
|
||||
Label(training, typeB, [0]);
|
||||
stages.push(training[0]);
|
||||
console.log(training);
|
||||
assertEquals(training.length, 2, "input and output sets created");
|
||||
@ -30,12 +62,10 @@ Deno.test("NN.Label", ()=>
|
||||
|
||||
Deno.test("NN.Backward", ()=>
|
||||
{
|
||||
|
||||
let layer1 = M.Create.Box([0, 0, 0], [1, 1, 1], 2);
|
||||
let layer2 = M.Create.Box([0, 0, 0], [1, 1, 1], 1);
|
||||
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);
|
||||
|
||||
@ -56,3 +86,4 @@ Deno.test("NN.Forward", ()=>
|
||||
});
|
||||
|
||||
|
||||
*/
|
||||
Loading…
Reference in New Issue
Block a user