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122 lines
3.3 KiB
122 lines
3.3 KiB
// Oh boy, why am I about to do this.... |
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#ifndef NETWORK_H |
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#define NETWORK_H |
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#include "image.h" |
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#include "layer.h" |
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#include "data.h" |
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typedef enum { |
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CONSTANT, STEP, EXP, POLY, STEPS, SIG, RANDOM |
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} learning_rate_policy; |
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typedef struct network{ |
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float *workspace; |
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int n; |
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int batch; |
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int *seen; |
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float epoch; |
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int subdivisions; |
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float momentum; |
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float decay; |
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layer *layers; |
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int outputs; |
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float *output; |
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learning_rate_policy policy; |
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float learning_rate; |
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float gamma; |
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float scale; |
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float power; |
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int time_steps; |
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int step; |
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int max_batches; |
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float *scales; |
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int *steps; |
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int num_steps; |
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int burn_in; |
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int inputs; |
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int h, w, c; |
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int max_crop; |
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int min_crop; |
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float angle; |
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float aspect; |
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float exposure; |
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float saturation; |
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float hue; |
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int gpu_index; |
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#ifdef GPU |
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float **input_gpu; |
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float **truth_gpu; |
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#endif |
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} network; |
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typedef struct network_state { |
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float *truth; |
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float *input; |
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float *delta; |
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float *workspace; |
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int train; |
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int index; |
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network net; |
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} network_state; |
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#ifdef GPU |
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float train_networks(network *nets, int n, data d, int interval); |
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void sync_nets(network *nets, int n, int interval); |
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float train_network_datum_gpu(network net, float *x, float *y); |
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float *network_predict_gpu(network net, float *input); |
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float * get_network_output_gpu_layer(network net, int i); |
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float * get_network_delta_gpu_layer(network net, int i); |
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float *get_network_output_gpu(network net); |
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void forward_network_gpu(network net, network_state state); |
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void backward_network_gpu(network net, network_state state); |
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void update_network_gpu(network net); |
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#endif |
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float get_current_rate(network net); |
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int get_current_batch(network net); |
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void free_network(network net); |
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void compare_networks(network n1, network n2, data d); |
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char *get_layer_string(LAYER_TYPE a); |
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network make_network(int n); |
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void forward_network(network net, network_state state); |
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void backward_network(network net, network_state state); |
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void update_network(network net); |
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float train_network(network net, data d); |
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float train_network_batch(network net, data d, int n); |
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float train_network_sgd(network net, data d, int n); |
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float train_network_datum(network net, float *x, float *y); |
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matrix network_predict_data(network net, data test); |
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float *network_predict(network net, float *input); |
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float network_accuracy(network net, data d); |
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float *network_accuracies(network net, data d, int n); |
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float network_accuracy_multi(network net, data d, int n); |
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void top_predictions(network net, int n, int *index); |
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float *get_network_output(network net); |
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float *get_network_output_layer(network net, int i); |
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float *get_network_delta_layer(network net, int i); |
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float *get_network_delta(network net); |
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int get_network_output_size_layer(network net, int i); |
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int get_network_output_size(network net); |
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image get_network_image(network net); |
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image get_network_image_layer(network net, int i); |
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int get_predicted_class_network(network net); |
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void print_network(network net); |
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void visualize_network(network net); |
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int resize_network(network *net, int w, int h); |
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void set_batch_network(network *net, int b); |
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int get_network_input_size(network net); |
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float get_network_cost(network net); |
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int get_network_nuisance(network net); |
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int get_network_background(network net); |
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#endif |
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