#include "convolutional_layer.h"
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#include "utils.h"
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#include "mini_blas.h"
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#include <stdio.h>
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int convolutional_out_height(convolutional_layer layer)
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{
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return (layer.h-layer.size)/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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return (layer.w-layer.size)/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, ACTIVATION activation)
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{
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int i;
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size = 2*(size/2)+1; //HA! And you thought you'd use an even sized filter...
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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->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->filter_momentum = 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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layer->bias_momentum = calloc(n, sizeof(float));
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float scale = 1./(size*size*c);
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for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*(rand_uniform());
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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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}
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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(layer->batch*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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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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srand(0);
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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, int c)
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{
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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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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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layer->batch*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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}
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void forward_convolutional_layer(const convolutional_layer layer, float *in)
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{
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int i;
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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 = convolutional_out_height(layer)*
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convolutional_out_width(layer)*
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layer.batch;
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memset(layer.output, 0, m*n*sizeof(float));
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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(in+i*(n/layer.batch), layer.c, layer.h, layer.w, layer.size, layer.stride, b+i*(n/layer.batch));
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}
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gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
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for(i = 0; i < m*n; ++i){
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layer.output[i] = activate(layer.output[i], layer.activation);
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}
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//for(i = 0; i < m*n; ++i) if(i%(m*n/10+1)==0) printf("%f, ", layer.output[i]); printf("\n");
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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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int size = convolutional_out_height(layer)*
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convolutional_out_width(layer)*
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layer.n*
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layer.batch;
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for(i = 0; i < size; ++i){
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layer.delta[i] *= gradient(layer.output[i], layer.activation);
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}
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}
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void learn_bias_convolutional_layer(convolutional_layer layer)
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{
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int i,j,b;
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int size = convolutional_out_height(layer)
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*convolutional_out_width(layer);
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for(b = 0; b < layer.batch; ++b){
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for(i = 0; i < layer.n; ++i){
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float sum = 0;
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for(j = 0; j < size; ++j){
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sum += layer.delta[j+size*(i+b*layer.n)];
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}
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layer.bias_updates[i] += sum/size;
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}
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}
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}
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void learn_convolutional_layer(convolutional_layer layer)
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{
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gradient_delta_convolutional_layer(layer);
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learn_bias_convolutional_layer(layer);
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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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layer.batch;
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float *a = layer.delta;
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float *b = layer.col_image;
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float *c = layer.filter_updates;
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gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
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}
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void backward_convolutional_layer(convolutional_layer layer, float *delta)
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{
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int i;
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int m = layer.size*layer.size*layer.c;
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int k = layer.n;
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int n = convolutional_out_height(layer)*
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convolutional_out_width(layer)*
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layer.batch;
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float *a = layer.filters;
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float *b = layer.delta;
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float *c = layer.col_image;
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memset(c, 0, m*n*sizeof(float));
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gemm(1,0,m,n,k,1,a,m,b,n,1,c,n);
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memset(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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col2im_cpu(c+i*n/layer.batch, layer.c, layer.h, layer.w, layer.size, layer.stride, delta+i*n/layer.batch);
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}
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}
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void update_convolutional_layer(convolutional_layer layer, float step, float momentum, float decay)
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{
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int i;
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int size = layer.size*layer.size*layer.c*layer.n;
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for(i = 0; i < layer.n; ++i){
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layer.biases[i] += step*layer.bias_updates[i];
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layer.bias_updates[i] *= momentum;
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}
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for(i = 0; i < size; ++i){
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layer.filters[i] += step*(layer.filter_updates[i] - decay*layer.filters[i]);
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layer.filter_updates[i] *= momentum;
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}
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}
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/*
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void backward_convolutional_layer2(convolutional_layer layer, float *input, float *delta)
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{
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image in_delta = float_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 learn_convolutional_layer(convolutional_layer layer, float *input)
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{
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int i;
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image in_image = float_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, float step, float momentum, float 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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*/
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void test_convolutional_layer()
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{
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convolutional_layer l = *make_convolutional_layer(1,4,4,1,1,3,1,LINEAR);
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float input[] = {1,2,3,4,
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5,6,7,8,
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9,10,11,12,
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13,14,15,16};
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float filter[] = {.5, 0, .3,
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0 , 1, 0,
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.2 , 0, 1};
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float delta[] = {1, 2,
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3, 4};
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float in_delta[] = {.5,1,.3,.6,
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5,6,7,8,
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9,10,11,12,
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13,14,15,16};
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l.filters = filter;
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forward_convolutional_layer(l, input);
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l.delta = delta;
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learn_convolutional_layer(l);
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image filter_updates = float_to_image(3,3,1,l.filter_updates);
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print_image(filter_updates);
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printf("Delta:\n");
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backward_convolutional_layer(l, in_delta);
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pm(4,4,in_delta);
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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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