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layer.h
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#ifndef BASE_LAYER_H
#define BASE_LAYER_H
#include "activations.h"
#include "stddef.h"
struct layer;
typedef struct layer layer;
typedef enum {
CONVOLUTIONAL,
DECONVOLUTIONAL,
CONNECTED,
MAXPOOL,
SOFTMAX,
DETECTION,
DROPOUT,
CROP,
ROUTE,
COST,
NORMALIZATION,
AVGPOOL,
LOCAL,
SHORTCUT,
ACTIVE,
RNN,
GRU,
CRNN,
BATCHNORM,
NETWORK,
BLANK
} LAYER_TYPE;
typedef enum{
SSE, MASKED, SMOOTH
} COST_TYPE;
struct layer{
LAYER_TYPE type;
ACTIVATION activation;
COST_TYPE cost_type;
int batch_normalize;
int shortcut;
int batch;
int forced;
int flipped;
int inputs;
int outputs;
int truths;
int h,w,c;
int out_h, out_w, out_c;
int n;
int groups;
int size;
int side;
int stride;
int pad;
int sqrt;
int flip;
int index;
int binary;
int xnor;
int steps;
int hidden;
float dot;
float angle;
float jitter;
float saturation;
float exposure;
float shift;
int softmax;
int classes;
int coords;
int background;
int rescore;
int objectness;
int does_cost;
int joint;
int noadjust;
float alpha;
float beta;
float kappa;
float coord_scale;
float object_scale;
float noobject_scale;
float class_scale;
int dontload;
int dontloadscales;
float temperature;
float probability;
float scale;
int *indexes;
float *rand;
float *cost;
float *filters;
char *cfilters;
float *filter_updates;
float *state;
float *state_delta;
float *concat;
float *concat_delta;
float *binary_filters;
float *biases;
float *bias_updates;
float *scales;
float *scale_updates;
float *weights;
float *weight_updates;
float *col_image;
int * input_layers;
int * input_sizes;
float * delta;
float * output;
float * squared;
float * norms;
float * spatial_mean;
float * mean;
float * variance;
float * mean_delta;
float * variance_delta;
float * rolling_mean;
float * rolling_variance;
float * x;
float * x_norm;
struct layer *input_layer;
struct layer *self_layer;
struct layer *output_layer;
struct layer *input_gate_layer;
struct layer *state_gate_layer;
struct layer *input_save_layer;
struct layer *state_save_layer;
struct layer *input_state_layer;
struct layer *state_state_layer;
struct layer *input_z_layer;
struct layer *state_z_layer;
struct layer *input_r_layer;
struct layer *state_r_layer;
struct layer *input_h_layer;
struct layer *state_h_layer;
size_t workspace_size;
#ifdef GPU
float *z_gpu;
float *r_gpu;
float *h_gpu;
int *indexes_gpu;
float * prev_state_gpu;
float * forgot_state_gpu;
float * forgot_delta_gpu;
float * state_gpu;
float * state_delta_gpu;
float * gate_gpu;
float * gate_delta_gpu;
float * save_gpu;
float * save_delta_gpu;
float * concat_gpu;
float * concat_delta_gpu;
float * filters_gpu;
float * filter_updates_gpu;
float *binary_input_gpu;
float *binary_filters_gpu;
float * mean_gpu;
float * variance_gpu;
float * rolling_mean_gpu;
float * rolling_variance_gpu;
float * variance_delta_gpu;
float * mean_delta_gpu;
float * col_image_gpu;
float * x_gpu;
float * x_norm_gpu;
float * weights_gpu;
float * weight_updates_gpu;
float * biases_gpu;
float * bias_updates_gpu;
float * scales_gpu;
float * scale_updates_gpu;
float * output_gpu;
float * delta_gpu;
float * rand_gpu;
float * squared_gpu;
float * norms_gpu;
#ifdef CUDNN
cudnnTensorDescriptor_t srcTensorDesc, dstTensorDesc;
cudnnTensorDescriptor_t dsrcTensorDesc, ddstTensorDesc;
cudnnFilterDescriptor_t filterDesc;
cudnnFilterDescriptor_t dfilterDesc;
cudnnConvolutionDescriptor_t convDesc;
cudnnConvolutionFwdAlgo_t fw_algo;
cudnnConvolutionBwdDataAlgo_t bd_algo;
cudnnConvolutionBwdFilterAlgo_t bf_algo;
#endif
#endif
};
void free_layer(layer);
#endif