From b715671988a4f3e476586df52fa3bf052cce7f80 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 05 Dec 2013 21:17:16 +0000
Subject: [PATCH] Works well on MNIST
---
src/convolutional_layer.c | 172 +++++++++++++++++++++++++++++++++++++++++++--------------
1 files changed, 130 insertions(+), 42 deletions(-)
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index d4aff73..45b55b8 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -1,55 +1,74 @@
#include "convolutional_layer.h"
+#include "utils.h"
#include <stdio.h>
image get_convolutional_image(convolutional_layer layer)
{
- int h = (layer.h-1)/layer.stride + 1;
- int w = (layer.w-1)/layer.stride + 1;
- int c = layer.n;
+ int h,w,c;
+ if(layer.edge){
+ h = (layer.h-1)/layer.stride + 1;
+ w = (layer.w-1)/layer.stride + 1;
+ }else{
+ h = (layer.h - layer.size)/layer.stride+1;
+ w = (layer.h - layer.size)/layer.stride+1;
+ }
+ c = layer.n;
return double_to_image(h,w,c,layer.output);
}
image get_convolutional_delta(convolutional_layer layer)
{
- int h = (layer.h-1)/layer.stride + 1;
- int w = (layer.w-1)/layer.stride + 1;
- int c = layer.n;
+ int h,w,c;
+ if(layer.edge){
+ h = (layer.h-1)/layer.stride + 1;
+ w = (layer.w-1)/layer.stride + 1;
+ }else{
+ h = (layer.h - layer.size)/layer.stride+1;
+ w = (layer.h - layer.size)/layer.stride+1;
+ }
+ c = layer.n;
return double_to_image(h,w,c,layer.delta);
}
-convolutional_layer *make_convolutional_layer(int h, int w, int c, int n, int size, int stride, ACTIVATION activator)
+convolutional_layer *make_convolutional_layer(int h, int w, int c, int n, int size, int stride, ACTIVATION activation)
{
- printf("Convolutional Layer: %d x %d x %d image, %d filters\n", h,w,c,n);
int i;
+ int out_h,out_w;
convolutional_layer *layer = calloc(1, sizeof(convolutional_layer));
layer->h = h;
layer->w = w;
layer->c = c;
layer->n = n;
+ layer->edge = 1;
layer->stride = stride;
layer->kernels = calloc(n, sizeof(image));
layer->kernel_updates = calloc(n, sizeof(image));
+ layer->kernel_momentum = calloc(n, sizeof(image));
layer->biases = calloc(n, sizeof(double));
layer->bias_updates = calloc(n, sizeof(double));
+ layer->bias_momentum = calloc(n, sizeof(double));
+ double scale = 2./(size*size);
for(i = 0; i < n; ++i){
- layer->biases[i] = .005;
- layer->kernels[i] = make_random_kernel(size, c);
- layer->kernel_updates[i] = make_random_kernel(size, c);
+ //layer->biases[i] = rand_normal()*scale + scale;
+ layer->biases[i] = 0;
+ layer->kernels[i] = make_random_kernel(size, c, scale);
+ layer->kernel_updates[i] = make_random_kernel(size, c, 0);
+ layer->kernel_momentum[i] = make_random_kernel(size, c, 0);
}
- layer->output = calloc(((h-1)/stride+1) * ((w-1)/stride+1) * n, sizeof(double));
- layer->delta = calloc(((h-1)/stride+1) * ((w-1)/stride+1) * n, sizeof(double));
+ layer->size = 2*(size/2)+1;
+ if(layer->edge){
+ out_h = (layer->h-1)/layer->stride + 1;
+ out_w = (layer->w-1)/layer->stride + 1;
+ }else{
+ out_h = (layer->h - layer->size)/layer->stride+1;
+ out_w = (layer->h - layer->size)/layer->stride+1;
+ }
+ 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);
+ layer->output = calloc(out_h * out_w * n, sizeof(double));
+ layer->delta = calloc(out_h * out_w * n, sizeof(double));
layer->upsampled = make_image(h,w,n);
+ layer->activation = activation;
- if(activator == SIGMOID){
- layer->activation = sigmoid_activation;
- layer->gradient = sigmoid_gradient;
- }else if(activator == RELU){
- layer->activation = relu_activation;
- layer->gradient = relu_gradient;
- }else if(activator == IDENTITY){
- layer->activation = identity_activation;
- layer->gradient = identity_gradient;
- }
return layer;
}
@@ -59,13 +78,13 @@
image output = get_convolutional_image(layer);
int i,j;
for(i = 0; i < layer.n; ++i){
- convolve(input, layer.kernels[i], layer.stride, i, output);
+ convolve(input, layer.kernels[i], layer.stride, i, output, layer.edge);
}
for(i = 0; i < output.c; ++i){
for(j = 0; j < output.h*output.w; ++j){
int index = i*output.h*output.w + j;
output.data[index] += layer.biases[i];
- output.data[index] = layer.activation(output.data[index]);
+ output.data[index] = activate(output.data[index], layer.activation);
}
}
}
@@ -74,32 +93,29 @@
{
int i;
- image in_image = double_to_image(layer.h, layer.w, layer.c, input);
image in_delta = double_to_image(layer.h, layer.w, layer.c, delta);
image out_delta = get_convolutional_delta(layer);
zero_image(in_delta);
for(i = 0; i < layer.n; ++i){
- back_convolve(in_delta, layer.kernels[i], layer.stride, i, out_delta);
- }
- for(i = 0; i < layer.h*layer.w*layer.c; ++i){
- in_delta.data[i] *= layer.gradient(in_image.data[i]);
+ back_convolve(in_delta, layer.kernels[i], layer.stride, i, out_delta, layer.edge);
}
}
-/*
-void backpropagate_convolutional_layer_convolve(image input, convolutional_layer layer)
+void backward_convolutional_layer2(convolutional_layer layer, double *input, double *delta)
{
+ image in_delta = double_to_image(layer.h, layer.w, layer.c, delta);
+ image out_delta = get_convolutional_delta(layer);
int i,j;
for(i = 0; i < layer.n; ++i){
rotate_image(layer.kernels[i]);
}
- zero_image(input);
- upsample_image(layer.output, layer.stride, layer.upsampled);
- for(j = 0; j < input.c; ++j){
+ zero_image(in_delta);
+ upsample_image(out_delta, layer.stride, layer.upsampled);
+ for(j = 0; j < in_delta.c; ++j){
for(i = 0; i < layer.n; ++i){
- two_d_convolve(layer.upsampled, i, layer.kernels[i], j, 1, input, j);
+ two_d_convolve(layer.upsampled, i, layer.kernels[i], j, 1, in_delta, j, layer.edge);
}
}
@@ -107,34 +123,106 @@
rotate_image(layer.kernels[i]);
}
}
-*/
+
+void gradient_delta_convolutional_layer(convolutional_layer layer)
+{
+ int i;
+ image out_delta = get_convolutional_delta(layer);
+ image out_image = get_convolutional_image(layer);
+ for(i = 0; i < out_image.h*out_image.w*out_image.c; ++i){
+ out_delta.data[i] *= gradient(out_image.data[i], layer.activation);
+ }
+}
void learn_convolutional_layer(convolutional_layer layer, double *input)
{
int i;
image in_image = double_to_image(layer.h, layer.w, layer.c, input);
image out_delta = get_convolutional_delta(layer);
+ gradient_delta_convolutional_layer(layer);
for(i = 0; i < layer.n; ++i){
- kernel_update(in_image, layer.kernel_updates[i], layer.stride, i, out_delta);
+ kernel_update(in_image, layer.kernel_updates[i], layer.stride, i, out_delta, layer.edge);
layer.bias_updates[i] += avg_image_layer(out_delta, i);
+ //printf("%30.20lf\n", layer.bias_updates[i]);
}
}
-void update_convolutional_layer(convolutional_layer layer, double step)
+void update_convolutional_layer(convolutional_layer layer, double step, double momentum, double decay)
{
- return;
+ //step = .01;
int i,j;
for(i = 0; i < layer.n; ++i){
- layer.biases[i] += step*layer.bias_updates[i];
+ layer.bias_momentum[i] = step*(layer.bias_updates[i])
+ + momentum*layer.bias_momentum[i];
+ layer.biases[i] += layer.bias_momentum[i];
+ //layer.biases[i] = constrain(layer.biases[i],1.);
layer.bias_updates[i] = 0;
int pixels = layer.kernels[i].h*layer.kernels[i].w*layer.kernels[i].c;
for(j = 0; j < pixels; ++j){
- layer.kernels[i].data[j] += step*layer.kernel_updates[i].data[j];
+ layer.kernel_momentum[i].data[j] = step*(layer.kernel_updates[i].data[j] - decay*layer.kernels[i].data[j])
+ + momentum*layer.kernel_momentum[i].data[j];
+ layer.kernels[i].data[j] += layer.kernel_momentum[i].data[j];
+ //layer.kernels[i].data[j] = constrain(layer.kernels[i].data[j], 1.);
}
zero_image(layer.kernel_updates[i]);
}
}
+void visualize_convolutional_filters(convolutional_layer layer, char *window)
+{
+ int color = 1;
+ int border = 1;
+ int h,w,c;
+ int size = layer.size;
+ h = size;
+ w = (size + border) * layer.n - border;
+ c = layer.kernels[0].c;
+ if(c != 3 || !color){
+ h = (h+border)*c - border;
+ c = 1;
+ }
+
+ image filters = make_image(h,w,c);
+ int i,j;
+ for(i = 0; i < layer.n; ++i){
+ int w_offset = i*(size+border);
+ image k = layer.kernels[i];
+ image copy = copy_image(k);
+ /*
+ printf("Kernel %d - Bias: %f, Channels:",i,layer.biases[i]);
+ for(j = 0; j < k.c; ++j){
+ double a = avg_image_layer(k, j);
+ printf("%f, ", a);
+ }
+ printf("\n");
+ */
+ normalize_image(copy);
+ for(j = 0; j < k.c; ++j){
+ set_pixel(copy,0,0,j,layer.biases[i]);
+ }
+ if(c == 3 && color){
+ embed_image(copy, filters, 0, w_offset);
+ }
+ else{
+ for(j = 0; j < k.c; ++j){
+ int h_offset = j*(size+border);
+ image layer = get_image_layer(k, j);
+ embed_image(layer, filters, h_offset, w_offset);
+ free_image(layer);
+ }
+ }
+ free_image(copy);
+ }
+ image delta = get_convolutional_delta(layer);
+ image dc = collapse_image_layers(delta, 1);
+ char buff[256];
+ sprintf(buff, "%s: Delta", window);
+ show_image(dc, buff);
+ free_image(dc);
+ show_image(filters, window);
+ free_image(filters);
+}
+
void visualize_convolutional_layer(convolutional_layer layer)
{
int i;
--
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