doc update

This commit is contained in:
Gustav Louw 2018-04-10 19:12:35 -07:00
parent 63ba88bbb8
commit 10eb871ca7
3 changed files with 53 additions and 23 deletions

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@ -84,6 +84,26 @@ This gives 10 outputs to the neural network. The test program will output the
accuracy for each digit. Expect above 99% accuracy for the correct digit, and
less that 1% accuracy for the other digits.
# Tips
* Tinn will never use more than the C standard library.
* Tinn is great for embedded systems. Train a model on your powerful desktop and load
it onto a microcontroller and use the analog to digital converter to predict real time events.
* The Tinn source code will always be less than 200 lines. Functions externed in the Tinn header
are protected with the _xt_ namespace standing for _externed tinn_.
* Tinn can easily be multi-threaded with a bit of ingenuity but the master branch will remain
single threaded to aid development for embedded systems.
* Tinn does not seed the random number generator. Do not forget to do so yourself.
* Always shuffle your input data. Shuffle again after every training iteration.
* Get greater training accuracy by annealing your learning rate. For instance, multiply
your learning rate by 0.99 every training iteration. This will zero in on a good learning minima.
# Disclaimer
Tinn is not a fully featured neural network C library like Kann, or Genann:

9
Tinn.c
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@ -121,12 +121,15 @@ static void* ecalloc(const size_t nmemb, const size_t size)
return mem;
}
// Returns an output prediction given an input.
float* xtpredict(const Tinn t, const float* const in)
{
fprop(t, in);
return t.o;
}
// Trains a tinn with an input and target output with a learning rate.
// Returns error rate of the neural network.
float xttrain(const Tinn t, const float* const in, const float* const tg, float rate)
{
fprop(t, in);
@ -134,6 +137,9 @@ float xttrain(const Tinn t, const float* const in, const float* const tg, float
return toterr(tg, t.o, t.nops);
}
// Builds a new tinn object given number of inputs (nips),
// number of hidden neurons for the hidden layer (nhid),
// and number of outputs (nops).
Tinn xtbuild(const int nips, const int nhid, const int nops)
{
Tinn t;
@ -152,6 +158,7 @@ Tinn xtbuild(const int nips, const int nhid, const int nops)
return t;
}
// Saves the tinn to disk.
void xtsave(const Tinn t, const char* const path)
{
FILE* const file = efopen(path, "w");
@ -163,6 +170,7 @@ void xtsave(const Tinn t, const char* const path)
fclose(file);
}
// Loads a new tinn from disk.
Tinn xtload(const char* const path)
{
FILE* const file = efopen(path, "r");
@ -180,6 +188,7 @@ Tinn xtload(const char* const path)
return t;
}
// Frees a tinn from the heap.
void xtfree(const Tinn t)
{
free(t.w);

47
Tinn.h
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@ -2,38 +2,39 @@
typedef struct
{
float* w; // All the weights.
float* x; // Hidden to output layer weights.
float* b; // Biases.
float* h; // Hidden layer.
float* o; // Output layer.
// All the weights.
float* w;
// Hidden to output layer weights.
float* x;
// Biases.
float* b;
// Hidden layer.
float* h;
// Output layer.
float* o;
int nb; // Number of biases - always two - Tinn only supports a single hidden layer.
int nw; // Number of weights.
// Number of biases - always two - Tinn only supports a single hidden layer.
int nb;
// Number of weights.
int nw;
int nips; // Number of inputs.
int nhid; // Number of hidden neurons.
int nops; // Number of outputs.
// Number of inputs.
int nips;
// Number of hidden neurons.
int nhid;
// Number of outputs.
int nops;
}
Tinn;
// Trains a tinn with an input and target output with a learning rate.
// Returns error rate of the neural network.
float xttrain(Tinn, const float* in, const float* tg, float rate);
// Builds a new tinn object given number of inputs (nips),
// number of hidden neurons for the hidden layer (nhid),
// and number of outputs (nops).
Tinn xtbuild(int nips, int nhid, int nops);
// Returns an output prediction given an input.
float* xtpredict(Tinn, const float* in);
// Saves the tinn to disk.
float xttrain(Tinn, const float* in, const float* tg, float rate);
Tinn xtbuild(int nips, int nhid, int nops);
void xtsave(Tinn, const char* path);
// Loads a new tinn from disk.
Tinn xtload(const char* path);
// Frees a tinn from the heap.
void xtfree(Tinn);