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/**
* ml - a neural network processor written with C
* Copyright (C) 2023 jvech
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <https://www.gnu.org/licenses/>.
*/
#ifndef __NN__
#define __NN__
#include <stdbool.h>
#include <stddef.h>
struct Cost {
double (*func)(double labels[], double net_out[], size_t shape);
double (*dfunc_out)(double labels, double net_out);
};
struct Activation {
double (*func)(double);
double (*dfunc)(double);
};
typedef struct Layer {
double *weights, *bias;
struct Activation activation;
size_t neurons, input_nodes;
} Layer;
void nn_network_write_weights(char *filepath, Layer *network, size_t network_size);
void nn_network_read_weights(char *filepath, Layer *network, size_t network_size);
void nn_network_init_weights(Layer *network, size_t nmemb, size_t input_cols, bool fill_random);
void nn_network_free_weights(Layer *network, size_t nmemb);
void nn_network_predict(
double *out, size_t out_shape[2],
double *input, size_t input_shape[2],
Layer network[], size_t network_size);
void nn_network_train(
Layer network[], size_t network_size,
double *input, size_t input_shape[2],
double *labels, size_t labels_shape[2],
struct Cost cost, size_t epochs,
size_t batch_size, double alpha);
void nn_layer_map_activation(
double (*activation)(double),
double *aout, size_t aout_shape[2],
double *zout, size_t zout_shape[2]);
double sigmoid(double x);
double relu(double x);
double identity(double x);
void nn_forward(
double **aout, double **zout,
double *input, size_t input_shape[2],
Layer network[], size_t network_size);
void nn_backward(
double **weights, double **bias,
double **zout, double **outs,
double *input, size_t input_shape[2],
double *labels, size_t labels_shape[2],
Layer network[], size_t network_size,
double (cost_derivative)(double, double),
double alpha);
void nn_layer_forward(
Layer layer,
double *out, size_t out_shape[2],
double *input, size_t input_shape[2]);
void nn_layer_backward(
double *weights, double *bias, size_t weigths_shape[2],
double *delta, double *out_prev,
Layer layer, double alpha);
void nn_layer_out_delta(
double *delta, double *dcost_out, double *zout, size_t cols,
double (*activation_derivative)(double));
void nn_layer_hidden_delta(
double *delta, double *delta_next, double *zout,
double *weights_next, size_t weights_next_shape[2],
double (*activation_derivative)(double));
#endif
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