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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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| | | |
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| | | typedef enum { |
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| | | CONVOLUTIONAL, |
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| | | CONNECTED, |
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| | | MAXPOOL |
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| | | } LAYER_TYPE; |
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| | | CONSTANT, STEP, EXP, POLY, STEPS, SIG |
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| | | } learning_rate_policy; |
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| | | |
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| | | typedef struct { |
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| | | typedef struct network{ |
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| | | int n; |
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| | | void **layers; |
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| | | LAYER_TYPE *types; |
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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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| | | |
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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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| | | |
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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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| | | |
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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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| | | |
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| | | void run_network(image input, network net); |
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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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| | | int train; |
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| | | int index; |
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| | | network net; |
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| | | } network_state; |
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| | | |
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| | | #ifdef GPU |
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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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| | | |
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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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| | | |
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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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| | | |
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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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| | | |
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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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| | | |
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| | | int get_network_nuisance(network net); |
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| | | int get_network_background(network net); |
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| | | |
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| | | #endif |
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