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https://github.com/glouw/tinn
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making some good progress
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Gustav Louw
parent
758a0bcbbd
commit
756e383bb5
131
test.c
131
test.c
@ -2,72 +2,93 @@
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#include <stdlib.h>
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#include <math.h>
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// [i1] [h1] [o1]
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//
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// [i2] [h2] [o2]
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//
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// [b1] [b2]
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static double act(const double in)
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{
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return 1.0 / (1.0 + exp(-in));
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}
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static double shid(const double W[], const double I[], const int neuron, const int inputs)
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{
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double sum = 0.0;
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int i;
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for(i = 0; i < inputs; i++)
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sum += I[i] * W[i + neuron * inputs];
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return sum;
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}
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static double sout(const double W[], const double I[], const int neuron, const int inputs, const int hidden)
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{
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double sum = 0.0;
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int i;
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for(i = 0; i < inputs; i++)
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sum += I[i] * W[i + hidden * (neuron + inputs)];
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return sum;
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}
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static double cerr(const double T[], const double O[], const int count)
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{
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double ssqr = 0.0;
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int i;
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for(i = 0; i < count; i++)
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{
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const double sub = T[i] - O[i];
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ssqr += sub * sub;
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}
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return 0.5 * ssqr;
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}
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static void bprop(double W[], const double I[], const double H[], const double O[], const double T[], const double rate)
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{
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const double a = -(T[0] - O[0]) * O[0] * (1.0 - O[0]);
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const double b = -(T[1] - O[1]) * O[1] * (1.0 - O[1]);
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const double c = (W[4] * a + W[6] * b) * (1.0 - H[0]);
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const double d = (W[5] * a + W[7] * b) * (1.0 - H[1]);
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/* Hidden layer */
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W[0] -= rate * H[0] * c * I[0];
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W[1] -= rate * H[0] * c * I[1];
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W[2] -= rate * H[1] * d * I[0];
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W[3] -= rate * H[1] * d * I[1];
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/* Output layer */
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W[4] -= rate * H[0] * a;
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W[5] -= rate * H[1] * a;
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W[6] -= rate * H[0] * b;
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W[7] -= rate * H[1] * b;
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}
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/* Single layer feed forward neural network with back propogation error correction */
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static double train(const double I[], const double T[], const int nips, const int nops, const double rate, const int iters)
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{
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const double B[] = { 0.35, 0.60 };
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const int nhid = sizeof(B) / sizeof(*B);
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double W[] = { 0.15, 0.20, 0.25, 0.30, 0.40, 0.45, 0.50, 0.55 };
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double* H = (double*) malloc(sizeof(*H) * nhid);
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double* O = (double*) malloc(sizeof(*O) * nops);
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double error;
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int iter;
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for(iter = 0; iter < iters; iter++)
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{
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int i;
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for(i = 0; i < nhid; i++) H[i] = act(B[0] + shid(W, I, i, nips));
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for(i = 0; i < nops; i++) O[i] = act(B[1] + sout(W, H, i, nips, nhid));
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bprop(W, I, H, O, T, rate);
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}
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error = cerr(T, O, nops);
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free(H);
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free(O);
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return error;
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}
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int main()
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{
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const double rate = 0.5;
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// Input.
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const double i1 = 0.05;
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const double i2 = 0.10;
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// Output.
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const double t1 = 0.01;
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const double t2 = 0.99;
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// Weights and biases.
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double w[] = { 0.15, 0.20, 0.25, 0.30, 0.40, 0.45, 0.50, 0.55 };
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double b[] = { 0.35, 0.60 };
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double et = 0;
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const double I[] = { 0.05, 0.10 };
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for(int i = 0; i < 10000; i++)
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{
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// Compute.
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const double h1 = act(w[0] * i1 + w[1] * i2 + b[0]);
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const double h2 = act(w[2] * i1 + w[3] * i2 + b[0]);
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const double o1 = act(w[4] * h1 + w[5] * h2 + b[1]);
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const double o2 = act(w[6] * h1 + w[7] * h2 + b[1]);
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const double T[] = { 0.01, 0.99 };
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// Error calculation.
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const double to1 = t1 - o1;
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const double to2 = t2 - o2;
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const double e1 = 0.5 * to1 * to1;
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const double e2 = 0.5 * to2 * to2;
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et = e1 + e2;
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const double error = train(I, T, sizeof(I) / sizeof(*I), sizeof(T) / sizeof(*T), rate, 10000);
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const double a = -to1 * o1 * (1.0 - o1);
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const double b = -to2 * o2 * (1.0 - o2);
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const double c = (w[4] * a + w[6] * b) * (1.0 - h1);
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const double d = (w[5] * a + w[7] * b) * (1.0 - h2);
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printf("%f\n", error);
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// Back Propogation.
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w[0] -= rate * h1 * c * i1;
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w[1] -= rate * h1 * c * i2;
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w[2] -= rate * h2 * d * i1;
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w[3] -= rate * h2 * d * i2;
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w[4] -= rate * h1 * a;
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w[5] -= rate * h2 * a;
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w[6] -= rate * h1 * b;
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w[7] -= rate * h2 * b;
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#if 0
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printf("w1 %.9f\n", w[0]);
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printf("w2 %.9f\n", w[1]);
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printf("w3 %.9f\n", w[2]);
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printf("w4 %.9f\n", w[3]);
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printf("w5 %.9f\n", w[4]);
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printf("w6 %.9f\n", w[5]);
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printf("w7 %.9f\n", w[6]);
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printf("w8 %.9f\n", w[7]);
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#endif
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}
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printf("%0.12f\n", et);
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return 0;
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}
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112
test2.c
Normal file
112
test2.c
Normal file
@ -0,0 +1,112 @@
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#include <stdlib.h>
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#include <stdio.h>
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#include <math.h>
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static double act(double net)
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{
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return 1.0 / (1.0 + exp(-net));
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}
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static void forepass(double* I, double* O, double* H, double* W, double* B, const int inputs, const int output, const int hidden)
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{
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double* X = W + hidden * inputs;
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for(int i = 0; i < hidden; i++) { for(int j = 0; j < inputs; j++) H[i] += I[j] * W[i * inputs + j]; H[i] = act(H[i] + B[0]); }
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for(int i = 0; i < output; i++) { for(int j = 0; j < hidden; j++) O[i] += H[j] * X[i * hidden + j]; O[i] = act(O[i] + B[1]); }
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}
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static void backpass(double* I, double* O, double* H, double* W, double* T, const int inputs, const int output, const int hidden, const double rate)
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{
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double* X = W + hidden * inputs;
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for(int i = 0; i < output; i++)
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for(int j = 0; j < hidden; j++)
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X[2 * i + j] -= rate * ((O[i] - T[i]) * (O[i] * (1 - O[i])) * H[j]);
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//W[4] -= rate * ((T[0] - O[0]) * (T[0] * (1 - T[0])) * H[0]);
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//W[5] -= rate * ((T[0] - O[0]) * (T[0] * (1 - T[0])) * H[1]);
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//W[6] -= rate * ((T[1] - O[1]) * (T[1] * (1 - T[1])) * H[0]);
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//W[7] -= rate * ((T[1] - O[1]) * (T[1] * (1 - T[1])) * H[1]);
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}
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static double cerror(double *O, double* T, const int output)
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{
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double error = 0.0;
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for(int i = 0; i < output; i++)
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error += 0.5 * pow(T[i] - O[i], 2.0);
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return error;
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}
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static double* train(double* I, double* T, const int inputs, const int output, const int hidden)
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{
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// Weights.
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double* W = (double*) calloc(hidden * (inputs + output), sizeof(*W));
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W[0] = 0.15;
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W[1] = 0.20;
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W[2] = 0.25;
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W[3] = 0.30;
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W[4] = 0.40;
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W[5] = 0.45;
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W[6] = 0.50;
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W[7] = 0.55;
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// Fixed at single hidden layer - only two biases are needed.
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double B[] = { 0.35, 0.60 };
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// Hidden layer.
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double* H = (double*) calloc(hidden, sizeof(*H));
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// Output layer. Will eventually converge to output with enough iterations.
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double* O = (double*) calloc(output, sizeof(*O));
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// Computes hidden and target nodes.
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forepass(I, O, H, W, B, inputs, output, hidden);
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// Computes output to target error.
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double err = cerror(O, O, output);
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printf("error: %f\n", err);
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// Updates weights based on target error.
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backpass(I, O, H, W, T, inputs, output, hidden, 0.5);
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printf("W5: %f\n", W[4]);
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printf("W6: %f\n", W[5]);
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printf("W7: %f\n", W[6]);
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printf("W8: %f\n", W[7]);
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printf("%f\n", H[0]);
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printf("%f\n", H[1]);
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printf("%f\n", O[0]);
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printf("%f\n", O[1]);
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free(H);
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return W;
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}
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double* predict(double* I, double* W, const int inputs, const int output)
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{
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double* O = NULL;
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// ...
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return O;
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}
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int main()
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{
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const int inputs = 2, output = 2, hidden = 2;
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// Input.
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double* I = (double*) calloc(inputs, sizeof(*I));
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I[0] = 0.05;
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I[1] = 0.10;
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// Target.
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double* T = (double*) calloc(output, sizeof(*I));
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T[0] = 0.01;
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T[1] = 0.99;
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train(I, T, inputs, output, hidden);
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return 0;
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}
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