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| | | #ifndef CONVOLUTIONAL_LAYER_H |
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| | | #define CONVOLUTIONAL_LAYER_H |
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| | | #include "cuda.h" |
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| | | #include "params.h" |
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| | | #include "image.h" |
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| | | #include "activations.h" |
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| | | |
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| | | typedef struct { |
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| | | int batch; |
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| | | int h,w,c; |
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| | | int n; |
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| | | int size; |
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| | | int stride; |
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| | | image *kernels; |
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| | | image *kernel_updates; |
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| | | double *biases; |
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| | | double *bias_updates; |
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| | | image upsampled; |
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| | | double *delta; |
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| | | double *output; |
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| | | int pad; |
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| | | float *filters; |
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| | | float *filter_updates; |
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| | | |
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| | | double (* activation)(); |
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| | | double (* gradient)(); |
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| | | float *biases; |
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| | | float *bias_updates; |
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| | | |
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| | | float *col_image; |
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| | | float *delta; |
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| | | float *output; |
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| | | |
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| | | #ifdef GPU |
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| | | float * filters_gpu; |
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| | | float * filter_updates_gpu; |
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| | | |
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| | | float * biases_gpu; |
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| | | float * bias_updates_gpu; |
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| | | |
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| | | float * col_image_gpu; |
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| | | float * delta_gpu; |
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| | | float * output_gpu; |
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| | | #endif |
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| | | |
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| | | ACTIVATION activation; |
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| | | } convolutional_layer; |
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| | | |
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| | | convolutional_layer *make_convolutional_layer(int h, int w, int c, int n, int size, int stride, ACTIVATION activator); |
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| | | void forward_convolutional_layer(const convolutional_layer layer, double *in); |
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| | | void backward_convolutional_layer(convolutional_layer layer, double *input, double *delta); |
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| | | void learn_convolutional_layer(convolutional_layer layer, double *input); |
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| | | #ifdef GPU |
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| | | void forward_convolutional_layer_gpu(convolutional_layer layer, network_state state); |
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| | | void backward_convolutional_layer_gpu(convolutional_layer layer, network_state state); |
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| | | void update_convolutional_layer_gpu(convolutional_layer layer, float learning_rate, float momentum, float decay); |
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| | | void update_convolutional_layer(convolutional_layer layer, double step); |
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| | | void push_convolutional_layer(convolutional_layer layer); |
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| | | void pull_convolutional_layer(convolutional_layer layer); |
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| | | |
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| | | void backpropagate_convolutional_layer_convolve(image input, convolutional_layer layer); |
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| | | void visualize_convolutional_layer(convolutional_layer layer); |
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| | | void bias_output_gpu(float *output, float *biases, int batch, int n, int size); |
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| | | void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size); |
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| | | #endif |
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| | | |
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| | | convolutional_layer *make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation); |
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| | | void resize_convolutional_layer(convolutional_layer *layer, int h, int w); |
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| | | void forward_convolutional_layer(const convolutional_layer layer, network_state state); |
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| | | void update_convolutional_layer(convolutional_layer layer, float learning_rate, float momentum, float decay); |
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| | | image *visualize_convolutional_layer(convolutional_layer layer, char *window, image *prev_filters); |
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| | | void backward_convolutional_layer(convolutional_layer layer, network_state state); |
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| | | void bias_output(float *output, float *biases, int batch, int n, int size); |
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| | | void backward_bias(float *bias_updates, float *delta, int batch, int n, int size); |
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| | | |
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| | | image get_convolutional_image(convolutional_layer layer); |
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| | | image get_convolutional_delta(convolutional_layer layer); |
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| | | image get_convolutional_filter(convolutional_layer layer, int i); |
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| | | |
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| | | int convolutional_out_height(convolutional_layer layer); |
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| | | int convolutional_out_width(convolutional_layer layer); |
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| | | |
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| | | #endif |
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