neural-nets/nn.h at main · Theo607/neural-nets · GitHub

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#ifndef NN_H
#define NN_H

typedef struct {
  int input_width;
  int output_width;

  float *weights;
  float *biases;

  float *weight_grads;
  float *bias_grads;

  float *input_cache;
  float *z_cache;
  float *output_cache;

  float (*activation)(float);
  float (*activation_prime)(float);
} Layer;

Layer *layer_create(int input_width, int output_width,
                    float (*activation)(float),
                    float (*activation_prime)(float));
void layer_free(Layer *layer);

typedef struct {
  Layer **layers;
  int n;
  float (*loss)(const float *pred, const float *target, int size);
  void (*loss_prime)(const float *pred, const float *target, int size,
                     float *d_out);
  void (*optimizer)(Layer *layer, float learning_rate);
} Network;

Network *network_create(float (*loss)(const float *, const float *, int),
                        void (*loss_prime)(const float *, const float *, int,
                                           float *),
                        void (*optimizer)(Layer *, float));

void network_free(Network *net);

void optimizer_sgd(Layer *layer, float learning_rate);

float activation_sigmoid(float x);
float activation_sigmoid_prime(float x);
float activation_relu(float x);
float activation_relu_prime(float x);

float loss_mse(const float *pred, const float *target, int size);
void loss_mse_prime(const float *pred, const float *target, int size,
                    float *d_out);
int network_save_weights(const Network *net, const char *filename);
int network_load_weights(Network *net, const char *filename);
int network_add(Network *net, Layer *layer);
int network_forward(Network *net, const float *input, float **out_output);
int network_train_step(Network *net, const float *input, const float *target,
                       float learning_rate);

int forward(Layer *layer, const float *input, float **out_output);

#endif