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| | | #ifndef NETWORK_H |
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| | | #define NETWORK_H |
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| | | |
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| | | #include "opencl.h" |
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| | | #include "image.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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| | | MAXPOOL, |
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| | | SOFTMAX, |
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| | | NORMALIZATION, |
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| | | DROPOUT, |
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| | | FREEWEIGHT, |
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| | | CROP, |
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| | | COST |
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| | | } LAYER_TYPE; |
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| | | |
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| | | typedef struct { |
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| | | int n; |
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| | | int batch; |
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| | | float learning_rate; |
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| | | float momentum; |
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| | | float decay; |
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| | | void **layers; |
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| | | LAYER_TYPE *types; |
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| | | int outputs; |
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| | | float *output; |
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| | | |
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| | | #ifdef GPU |
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| | | cl_mem *input_cl; |
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| | | cl_mem *truth_cl; |
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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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| | | double *get_network_output(network net); |
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| | | void learn_network(image input, network net); |
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| | | void update_network(network net, double step); |
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| | | #ifdef GPU |
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| | | void forward_network_gpu(network net, cl_mem input, cl_mem truth, int train); |
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| | | void backward_network_gpu(network net, cl_mem input); |
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| | | void update_network_gpu(network net); |
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| | | cl_mem get_network_output_cl_layer(network net, int i); |
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| | | cl_mem get_network_delta_cl_layer(network net, int i); |
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| | | float train_network_sgd_gpu(network net, data d, int n); |
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| | | float train_network_data_gpu(network net, data d, int n); |
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| | | float *network_predict_gpu(network net, float *input); |
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| | | float network_accuracy_gpu(network net, data d); |
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| | | #endif |
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| | | |
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| | | network make_network(int n, int batch); |
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| | | void forward_network(network net, float *input, float *truth, int train); |
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| | | void backward_network(network net, float *input); |
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| | | void update_network(network net); |
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| | | float train_network_sgd(network net, data d, int n); |
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| | | float train_network_batch(network net, data d, int n); |
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| | | float train_network_data_cpu(network net, data d, int n); |
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| | | void train_network(network net, data d); |
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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_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 h, int w, int c); |
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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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| | | #endif |
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