#include "convolutional_layer.h"
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#include "utils.h"
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#include <stdio.h>
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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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if(layer.edge){
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h = (layer.h-1)/layer.stride + 1;
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w = (layer.w-1)/layer.stride + 1;
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}else{
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h = (layer.h - layer.size)/layer.stride+1;
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w = (layer.h - layer.size)/layer.stride+1;
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}
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c = layer.n;
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return double_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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if(layer.edge){
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h = (layer.h-1)/layer.stride + 1;
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w = (layer.w-1)/layer.stride + 1;
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}else{
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h = (layer.h - layer.size)/layer.stride+1;
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w = (layer.h - layer.size)/layer.stride+1;
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}
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c = layer.n;
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return double_to_image(h,w,c,layer.delta);
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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 activation)
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{
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int i;
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int out_h,out_w;
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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->edge = 1;
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layer->stride = stride;
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layer->kernels = calloc(n, sizeof(image));
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layer->kernel_updates = calloc(n, sizeof(image));
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layer->kernel_momentum = calloc(n, sizeof(image));
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layer->biases = calloc(n, sizeof(double));
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layer->bias_updates = calloc(n, sizeof(double));
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layer->bias_momentum = calloc(n, sizeof(double));
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double scale = 2./(size*size);
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for(i = 0; i < n; ++i){
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//layer->biases[i] = rand_normal()*scale + scale;
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layer->biases[i] = 0;
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layer->kernels[i] = make_random_kernel(size, c, scale);
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layer->kernel_updates[i] = make_random_kernel(size, c, 0);
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layer->kernel_momentum[i] = make_random_kernel(size, c, 0);
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}
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layer->size = 2*(size/2)+1;
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if(layer->edge){
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out_h = (layer->h-1)/layer->stride + 1;
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out_w = (layer->w-1)/layer->stride + 1;
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}else{
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out_h = (layer->h - layer->size)/layer->stride+1;
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out_w = (layer->h - layer->size)/layer->stride+1;
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}
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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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layer->output = calloc(out_h * out_w * n, sizeof(double));
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layer->delta = calloc(out_h * out_w * n, sizeof(double));
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layer->upsampled = make_image(h,w,n);
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layer->activation = activation;
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return layer;
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}
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void forward_convolutional_layer(const convolutional_layer layer, double *in)
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{
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image input = double_to_image(layer.h, layer.w, layer.c, in);
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image output = get_convolutional_image(layer);
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int i,j;
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for(i = 0; i < layer.n; ++i){
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convolve(input, layer.kernels[i], layer.stride, i, output, layer.edge);
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}
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for(i = 0; i < output.c; ++i){
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for(j = 0; j < output.h*output.w; ++j){
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int index = i*output.h*output.w + j;
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output.data[index] += layer.biases[i];
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output.data[index] = activate(output.data[index], layer.activation);
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}
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}
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}
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void backward_convolutional_layer(convolutional_layer layer, double *input, double *delta)
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{
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int i;
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image in_delta = double_to_image(layer.h, layer.w, layer.c, delta);
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image out_delta = get_convolutional_delta(layer);
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zero_image(in_delta);
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for(i = 0; i < layer.n; ++i){
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back_convolve(in_delta, layer.kernels[i], layer.stride, i, out_delta, layer.edge);
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}
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}
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void backward_convolutional_layer2(convolutional_layer layer, double *input, double *delta)
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{
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image in_delta = double_to_image(layer.h, layer.w, layer.c, delta);
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image out_delta = get_convolutional_delta(layer);
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int i,j;
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for(i = 0; i < layer.n; ++i){
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rotate_image(layer.kernels[i]);
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}
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zero_image(in_delta);
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upsample_image(out_delta, layer.stride, layer.upsampled);
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for(j = 0; j < in_delta.c; ++j){
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for(i = 0; i < layer.n; ++i){
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two_d_convolve(layer.upsampled, i, layer.kernels[i], j, 1, in_delta, j, layer.edge);
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}
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}
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for(i = 0; i < layer.n; ++i){
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rotate_image(layer.kernels[i]);
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}
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}
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void gradient_delta_convolutional_layer(convolutional_layer layer)
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{
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int i;
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image out_delta = get_convolutional_delta(layer);
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image out_image = get_convolutional_image(layer);
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for(i = 0; i < out_image.h*out_image.w*out_image.c; ++i){
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out_delta.data[i] *= gradient(out_image.data[i], layer.activation);
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}
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}
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void learn_convolutional_layer(convolutional_layer layer, double *input)
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{
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int i;
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image in_image = double_to_image(layer.h, layer.w, layer.c, input);
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image out_delta = get_convolutional_delta(layer);
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gradient_delta_convolutional_layer(layer);
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for(i = 0; i < layer.n; ++i){
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kernel_update(in_image, layer.kernel_updates[i], layer.stride, i, out_delta, layer.edge);
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layer.bias_updates[i] += avg_image_layer(out_delta, i);
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}
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}
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void update_convolutional_layer(convolutional_layer layer, double step, double momentum, double decay)
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{
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int i,j;
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for(i = 0; i < layer.n; ++i){
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layer.bias_momentum[i] = step*(layer.bias_updates[i])
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+ momentum*layer.bias_momentum[i];
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layer.biases[i] += layer.bias_momentum[i];
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layer.bias_updates[i] = 0;
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int pixels = layer.kernels[i].h*layer.kernels[i].w*layer.kernels[i].c;
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for(j = 0; j < pixels; ++j){
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layer.kernel_momentum[i].data[j] = step*(layer.kernel_updates[i].data[j] - decay*layer.kernels[i].data[j])
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+ momentum*layer.kernel_momentum[i].data[j];
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layer.kernels[i].data[j] += layer.kernel_momentum[i].data[j];
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}
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zero_image(layer.kernel_updates[i]);
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}
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}
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void visualize_convolutional_filters(convolutional_layer layer, char *window)
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{
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int color = 1;
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int border = 1;
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int h,w,c;
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int size = layer.size;
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h = size;
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w = (size + border) * layer.n - border;
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c = layer.kernels[0].c;
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if(c != 3 || !color){
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h = (h+border)*c - border;
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c = 1;
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}
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image filters = make_image(h,w,c);
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int i,j;
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for(i = 0; i < layer.n; ++i){
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int w_offset = i*(size+border);
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image k = layer.kernels[i];
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image copy = copy_image(k);
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normalize_image(copy);
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for(j = 0; j < k.c; ++j){
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set_pixel(copy,0,0,j,layer.biases[i]);
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}
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if(c == 3 && color){
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embed_image(copy, filters, 0, w_offset);
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}
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else{
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for(j = 0; j < k.c; ++j){
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int h_offset = j*(size+border);
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image layer = get_image_layer(k, j);
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embed_image(layer, filters, h_offset, w_offset);
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free_image(layer);
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}
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}
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free_image(copy);
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}
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image delta = get_convolutional_delta(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: Delta", window);
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show_image(dc, buff);
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free_image(dc);
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show_image(filters, window);
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free_image(filters);
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}
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void visualize_convolutional_layer(convolutional_layer layer)
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{
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int i;
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char buff[256];
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for(i = 0; i < layer.n; ++i){
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image k = layer.kernels[i];
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sprintf(buff, "Kernel %d", i);
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if(k.c <= 3) show_image(k, buff);
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else show_image_layers(k, buff);
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}
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}
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