#include "convolutional_layer.h"
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#include "utils.h"
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#include "im2col.h"
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#include "col2im.h"
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#include "blas.h"
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#include "gemm.h"
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#include <stdio.h>
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#include <time.h>
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int convolutional_out_height(convolutional_layer layer)
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{
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int h = layer.h;
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if (!layer.pad) h -= layer.size;
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else h -= 1;
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return h/layer.stride + 1;
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}
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int convolutional_out_width(convolutional_layer layer)
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{
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int w = layer.w;
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if (!layer.pad) w -= layer.size;
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else w -= 1;
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return w/layer.stride + 1;
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}
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image get_convolutional_image(convolutional_layer layer)
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{
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int h,w,c;
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h = convolutional_out_height(layer);
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w = convolutional_out_width(layer);
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c = layer.n;
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return float_to_image(h,w,c,layer.output);
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}
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image get_convolutional_delta(convolutional_layer layer)
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{
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int h,w,c;
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h = convolutional_out_height(layer);
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w = convolutional_out_width(layer);
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c = layer.n;
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return float_to_image(h,w,c,layer.delta);
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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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{
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int i;
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convolutional_layer *layer = calloc(1, sizeof(convolutional_layer));
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layer->h = h;
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layer->w = w;
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layer->c = c;
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layer->n = n;
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layer->batch = batch;
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layer->stride = stride;
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layer->size = size;
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layer->pad = pad;
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layer->filters = calloc(c*n*size*size, sizeof(float));
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layer->filter_updates = calloc(c*n*size*size, sizeof(float));
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layer->biases = calloc(n, sizeof(float));
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layer->bias_updates = calloc(n, sizeof(float));
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float scale = 1./sqrt(size*size*c);
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for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*rand_normal();
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for(i = 0; i < n; ++i){
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layer->biases[i] = scale;
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}
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int out_h = convolutional_out_height(*layer);
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int out_w = convolutional_out_width(*layer);
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layer->col_image = calloc(out_h*out_w*size*size*c, sizeof(float));
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layer->output = calloc(layer->batch*out_h * out_w * n, sizeof(float));
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layer->delta = calloc(layer->batch*out_h * out_w * n, sizeof(float));
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#ifdef GPU
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layer->filters_gpu = cuda_make_array(layer->filters, c*n*size*size);
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layer->filter_updates_gpu = cuda_make_array(layer->filter_updates, c*n*size*size);
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layer->biases_gpu = cuda_make_array(layer->biases, n);
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layer->bias_updates_gpu = cuda_make_array(layer->bias_updates, n);
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layer->col_image_gpu = cuda_make_array(layer->col_image, out_h*out_w*size*size*c);
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layer->delta_gpu = cuda_make_array(layer->delta, layer->batch*out_h*out_w*n);
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layer->output_gpu = cuda_make_array(layer->output, layer->batch*out_h*out_w*n);
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#endif
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layer->activation = activation;
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fprintf(stderr, "Convolutional Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n);
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return layer;
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}
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void resize_convolutional_layer(convolutional_layer *layer, int h, int w)
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{
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layer->h = h;
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layer->w = w;
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int out_h = convolutional_out_height(*layer);
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int out_w = convolutional_out_width(*layer);
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layer->col_image = realloc(layer->col_image,
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out_h*out_w*layer->size*layer->size*layer->c*sizeof(float));
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layer->output = realloc(layer->output,
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layer->batch*out_h * out_w * layer->n*sizeof(float));
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layer->delta = realloc(layer->delta,
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layer->batch*out_h * out_w * layer->n*sizeof(float));
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#ifdef GPU
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cuda_free(layer->col_image_gpu);
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cuda_free(layer->delta_gpu);
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cuda_free(layer->output_gpu);
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layer->col_image_gpu = cuda_make_array(layer->col_image, out_h*out_w*layer->size*layer->size*layer->c);
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layer->delta_gpu = cuda_make_array(layer->delta, layer->batch*out_h*out_w*layer->n);
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layer->output_gpu = cuda_make_array(layer->output, layer->batch*out_h*out_w*layer->n);
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#endif
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}
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void bias_output(float *output, float *biases, int batch, int n, int size)
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{
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int i,j,b;
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for(b = 0; b < batch; ++b){
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for(i = 0; i < n; ++i){
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for(j = 0; j < size; ++j){
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output[(b*n + i)*size + j] = biases[i];
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}
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}
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}
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}
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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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int i,b;
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for(b = 0; b < batch; ++b){
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for(i = 0; i < n; ++i){
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bias_updates[i] += sum_array(delta+size*(i+b*n), size);
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}
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}
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}
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void forward_convolutional_layer(const convolutional_layer layer, network_state state)
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{
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int out_h = convolutional_out_height(layer);
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int out_w = convolutional_out_width(layer);
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int i;
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bias_output(layer.output, layer.biases, layer.batch, layer.n, out_h*out_w);
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int m = layer.n;
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int k = layer.size*layer.size*layer.c;
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int n = out_h*out_w;
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float *a = layer.filters;
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float *b = layer.col_image;
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float *c = layer.output;
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for(i = 0; i < layer.batch; ++i){
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im2col_cpu(state.input, layer.c, layer.h, layer.w,
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layer.size, layer.stride, layer.pad, b);
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gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
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c += n*m;
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state.input += layer.c*layer.h*layer.w;
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}
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activate_array(layer.output, m*n*layer.batch, layer.activation);
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}
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void backward_convolutional_layer(convolutional_layer layer, network_state state)
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{
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int i;
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int m = layer.n;
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int n = layer.size*layer.size*layer.c;
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int k = convolutional_out_height(layer)*
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convolutional_out_width(layer);
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gradient_array(layer.output, m*k*layer.batch, layer.activation, layer.delta);
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backward_bias(layer.bias_updates, layer.delta, layer.batch, layer.n, k);
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if(state.delta) memset(state.delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
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for(i = 0; i < layer.batch; ++i){
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float *a = layer.delta + i*m*k;
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float *b = layer.col_image;
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float *c = layer.filter_updates;
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float *im = state.input+i*layer.c*layer.h*layer.w;
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im2col_cpu(im, layer.c, layer.h, layer.w,
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layer.size, layer.stride, layer.pad, b);
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gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
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if(state.delta){
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a = layer.filters;
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b = layer.delta + i*m*k;
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c = layer.col_image;
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gemm(1,0,n,k,m,1,a,n,b,k,0,c,k);
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col2im_cpu(layer.col_image, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, state.delta+i*layer.c*layer.h*layer.w);
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}
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}
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}
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void update_convolutional_layer(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay)
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{
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int size = layer.size*layer.size*layer.c*layer.n;
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axpy_cpu(layer.n, learning_rate/batch, layer.bias_updates, 1, layer.biases, 1);
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scal_cpu(layer.n, momentum, layer.bias_updates, 1);
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axpy_cpu(size, -decay*batch, layer.filters, 1, layer.filter_updates, 1);
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axpy_cpu(size, learning_rate/batch, layer.filter_updates, 1, layer.filters, 1);
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scal_cpu(size, momentum, layer.filter_updates, 1);
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}
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image get_convolutional_filter(convolutional_layer layer, int i)
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{
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int h = layer.size;
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int w = layer.size;
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int c = layer.c;
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return float_to_image(h,w,c,layer.filters+i*h*w*c);
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}
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image *weighted_sum_filters(convolutional_layer layer, image *prev_filters)
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{
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image *filters = calloc(layer.n, sizeof(image));
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int i,j,k,c;
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if(!prev_filters){
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for(i = 0; i < layer.n; ++i){
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filters[i] = copy_image(get_convolutional_filter(layer, i));
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}
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}
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else{
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image base = prev_filters[0];
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for(i = 0; i < layer.n; ++i){
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image filter = get_convolutional_filter(layer, i);
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filters[i] = make_image(base.h, base.w, base.c);
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for(j = 0; j < layer.size; ++j){
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for(k = 0; k < layer.size; ++k){
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for(c = 0; c < layer.c; ++c){
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float weight = get_pixel(filter, j, k, c);
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image prev_filter = copy_image(prev_filters[c]);
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scale_image(prev_filter, weight);
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add_into_image(prev_filter, filters[i], 0,0);
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free_image(prev_filter);
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}
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}
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}
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}
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}
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return filters;
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}
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image *visualize_convolutional_layer(convolutional_layer layer, char *window, image *prev_filters)
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{
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image *single_filters = weighted_sum_filters(layer, 0);
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show_images(single_filters, layer.n, window);
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image delta = get_convolutional_image(layer);
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image dc = collapse_image_layers(delta, 1);
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char buff[256];
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sprintf(buff, "%s: Output", window);
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//show_image(dc, buff);
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//save_image(dc, buff);
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free_image(dc);
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return single_filters;
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}
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