From 956cfcaec993111426d91bcd61676b5fe0ebfd16 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Mon, 24 Feb 2014 21:02:53 +0000
Subject: [PATCH] Feature extraction using Imagenet
---
src/convolutional_layer.c | 43 ++++++++++++++++++++++++++++++-------------
1 files changed, 30 insertions(+), 13 deletions(-)
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index cdfe9e1..8d8efc1 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -3,11 +3,21 @@
#include "mini_blas.h"
#include <stdio.h>
+int convolutional_out_height(convolutional_layer layer)
+{
+ return (layer.h-layer.size)/layer.stride + 1;
+}
+
+int convolutional_out_width(convolutional_layer layer)
+{
+ return (layer.w-layer.size)/layer.stride + 1;
+}
+
image get_convolutional_image(convolutional_layer layer)
{
int h,w,c;
- h = layer.out_h;
- w = layer.out_w;
+ h = convolutional_out_height(layer);
+ w = convolutional_out_width(layer);
c = layer.n;
return float_to_image(h,w,c,layer.output);
}
@@ -15,8 +25,8 @@
image get_convolutional_delta(convolutional_layer layer)
{
int h,w,c;
- h = layer.out_h;
- w = layer.out_w;
+ h = convolutional_out_height(layer);
+ w = convolutional_out_width(layer);
c = layer.n;
return float_to_image(h,w,c,layer.delta);
}
@@ -24,7 +34,6 @@
convolutional_layer *make_convolutional_layer(int h, int w, int c, int n, int size, int stride, ACTIVATION activation)
{
int i;
- int out_h,out_w;
size = 2*(size/2)+1; //HA! And you thought you'd use an even sized filter...
convolutional_layer *layer = calloc(1, sizeof(convolutional_layer));
layer->h = h;
@@ -41,21 +50,19 @@
layer->biases = calloc(n, sizeof(float));
layer->bias_updates = calloc(n, sizeof(float));
layer->bias_momentum = calloc(n, sizeof(float));
- float scale = 2./(size*size);
- for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = rand_normal()*scale;
+ float scale = 1./(size*size*c);
+ for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*(rand_uniform());
for(i = 0; i < n; ++i){
//layer->biases[i] = rand_normal()*scale + scale;
layer->biases[i] = 0;
}
- out_h = (h-size)/stride + 1;
- out_w = (w-size)/stride + 1;
+ int out_h = (h-size)/stride + 1;
+ int out_w = (w-size)/stride + 1;
layer->col_image = calloc(out_h*out_w*size*size*c, sizeof(float));
layer->output = calloc(out_h * out_w * n, sizeof(float));
layer->delta = calloc(out_h * out_w * n, sizeof(float));
layer->activation = activation;
- layer->out_h = out_h;
- layer->out_w = out_w;
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);
srand(0);
@@ -65,6 +72,7 @@
void forward_convolutional_layer(const convolutional_layer layer, float *in)
{
+ int i;
int m = layer.n;
int k = layer.size*layer.size*layer.c;
int n = ((layer.h-layer.size)/layer.stride + 1)*
@@ -79,12 +87,20 @@
im2col_cpu(in, layer.c, layer.h, layer.w, layer.size, layer.stride, b);
gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
+ for(i = 0; i < m*n; ++i){
+ layer.output[i] = activate(layer.output[i], layer.activation);
+ }
+ //for(i = 0; i < m*n; ++i) if(i%(m*n/10+1)==0) printf("%f, ", layer.output[i]); printf("\n");
+
}
void gradient_delta_convolutional_layer(convolutional_layer layer)
{
int i;
- for(i = 0; i < layer.out_h*layer.out_w*layer.n; ++i){
+ int size = convolutional_out_height(layer)
+ *convolutional_out_width(layer)
+ *layer.n;
+ for(i = 0; i < size; ++i){
layer.delta[i] *= gradient(layer.output[i], layer.activation);
}
}
@@ -92,7 +108,8 @@
void learn_bias_convolutional_layer(convolutional_layer layer)
{
int i,j;
- int size = layer.out_h*layer.out_w;
+ int size = convolutional_out_height(layer)
+ *convolutional_out_width(layer);
for(i = 0; i < layer.n; ++i){
float sum = 0;
for(j = 0; j < size; ++j){
--
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