mirror of https://github.com/glouw/tinn
cleanup
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parent
4b3a45af8d
commit
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20
Tinn.c
20
Tinn.c
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@ -18,7 +18,7 @@ static float pderr(const float a, const float b)
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}
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// Total error.
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static float terr(const float* const tg, const float* const o, const int size)
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static float toterr(const float* const tg, const float* const o, const int size)
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{
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float sum = 0.0f;
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for(int i = 0; i < size; i++)
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@ -45,7 +45,7 @@ static float frand()
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}
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// Back propagation.
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static void backwards(const Tinn t, const float* const in, const float* const tg, float rate)
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static void bprop(const Tinn t, const float* const in, const float* const tg, float rate)
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{
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for(int i = 0; i < t.nhid; i++)
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{
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@ -66,7 +66,7 @@ static void backwards(const Tinn t, const float* const in, const float* const tg
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}
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// Forward propagation.
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static void forewards(const Tinn t, const float* const in)
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static void fprop(const Tinn t, const float* const in)
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{
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// Calculate hidden layer neuron values.
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for(int i = 0; i < t.nhid; i++)
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@ -121,17 +121,17 @@ static void* ecalloc(const size_t nmemb, const size_t size)
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return mem;
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}
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float* xpredict(const Tinn t, const float* const in)
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float* xtpredict(const Tinn t, const float* const in)
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{
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forewards(t, in);
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fprop(t, in);
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return t.o;
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}
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float xttrain(const Tinn t, const float* const in, const float* const tg, float rate)
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{
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forewards(t, in);
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backwards(t, in, tg, rate);
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return terr(tg, t.o, t.nops);
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fprop(t, in);
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bprop(t, in, tg, rate);
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return toterr(tg, t.o, t.nops);
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}
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Tinn xtbuild(const int nips, const int nhid, const int nops)
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@ -154,7 +154,7 @@ Tinn xtbuild(const int nips, const int nhid, const int nops)
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void xtsave(const Tinn t, const char* const path)
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{
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FILE* const file = efopen(path, "w");
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FILE* const file = efopen(path, "wb");
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// Header.
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fprintf(file, "%d %d %d\n", t.nips, t.nhid, t.nops);
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// Biases and weights.
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@ -165,7 +165,7 @@ void xtsave(const Tinn t, const char* const path)
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Tinn xtload(const char* const path)
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{
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FILE* const file = efopen(path, "r");
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FILE* const file = efopen(path, "rb");
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int nips = 0;
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int nhid = 0;
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int nops = 0;
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4
Tinn.h
4
Tinn.h
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@ -19,7 +19,7 @@ Tinn;
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// Trains a tinn with an input and target output with a learning rate.
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// Returns error rate of the neural network.
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float xttrain(const Tinn, const float* in, const float* tg, float rate);
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float xttrain(Tinn, const float* in, const float* tg, float rate);
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// Builds a new tinn object given number of inputs (nips),
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// number of hidden neurons for the hidden layer (nhid),
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@ -27,7 +27,7 @@ float xttrain(const Tinn, const float* in, const float* tg, float rate);
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Tinn xtbuild(int nips, int nhid, int nops);
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// Returns an output prediction given an input.
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float* xpredict(const Tinn, const float* in);
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float* xtpredict(Tinn, const float* in);
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// Saves the tinn to disk.
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void xtsave(Tinn, const char* path);
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6
test.c
6
test.c
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@ -154,7 +154,9 @@ int main()
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const float* const tg = data.tg[j];
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error += xttrain(tinn, in, tg, rate);
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}
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printf("error %.12f :: rate %f\n", (double) error / data.rows, (double) rate);
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printf("error %.12f :: learning rate %f\n",
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(double) error / data.rows,
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(double) rate);
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rate *= anneal;
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}
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// This is how you save the neural network to disk.
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@ -167,7 +169,7 @@ int main()
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// but for the sake of brevity here we just reuse the training set from earlier.
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const float* const in = data.in[0];
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const float* const tg = data.tg[0];
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const float* const pd = xpredict(loaded, in);
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const float* const pd = xtpredict(loaded, in);
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for(int i = 0; i < data.nops; i++) { printf("%f ", (double) tg[i]); } printf("\n");
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for(int i = 0; i < data.nops; i++) { printf("%f ", (double) pd[i]); } printf("\n");
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// All done. Let's clean up.
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