tinn/Tinn.h

40 lines
1.1 KiB
C
Raw Normal View History

2018-03-30 23:04:37 +03:00
#pragma once
2018-03-29 06:41:08 +03:00
typedef struct
{
2018-03-31 14:29:03 +03:00
float* w; // All the weights.
float* x; // Hidden to output layer weights.
float* b; // Biases.
float* h; // Hidden layer.
float* o; // Output layer.
2018-03-30 23:04:37 +03:00
2018-04-02 03:01:42 +03:00
int nb; // Number of biases - always two - Tinn only supports a single hidden layer.
int nw; // Number of weights.
2018-03-31 01:42:20 +03:00
2018-03-30 23:04:37 +03:00
int nips; // Number of inputs.
int nhid; // Number of hidden neurons.
int nops; // Number of outputs.
2018-03-29 06:41:08 +03:00
}
Tinn;
2018-03-31 01:42:20 +03:00
// Trains a tinn with an input and target output with a learning rate.
// Returns error rate of the neural network.
2018-04-04 01:12:36 +03:00
float xttrain(Tinn, const float* in, const float* tg, float rate);
2018-03-29 06:41:08 +03:00
2018-03-31 01:42:20 +03:00
// Builds a new tinn object given number of inputs (nips),
// number of hidden neurons for the hidden layer (nhid),
// and number of outputs (nops).
2018-03-30 23:04:37 +03:00
Tinn xtbuild(int nips, int nhid, int nops);
2018-03-29 06:41:08 +03:00
2018-03-31 01:42:20 +03:00
// Returns an output prediction given an input.
2018-04-04 01:12:36 +03:00
float* xtpredict(Tinn, const float* in);
2018-03-31 01:42:20 +03:00
// Saves the tinn to disk.
2018-04-03 23:08:27 +03:00
void xtsave(Tinn, const char* path);
2018-03-31 01:42:20 +03:00
// Loads a new tinn from disk.
Tinn xtload(const char* path);
// Frees a tinn from the heap.
2018-04-03 23:08:27 +03:00
void xtfree(Tinn);