From d94f61bb772ecb25a8fc239f35e61d70616ac191 Mon Sep 17 00:00:00 2001
From: julienChemillier <chemillier.julien@gmail.com>
Date: Tue, 15 Nov 2022 12:50:38 +0100
Subject: [PATCH] First results in backprop ?
---
src/cnn/make.c | 3 +--
src/cnn/train.c | 13 ++++++++++---
src/cnn/update.c | 23 ++++++++++++-----------
3 files changed, 23 insertions(+), 16 deletions(-)
diff --git a/src/cnn/make.c b/src/cnn/make.c
index 855cac7..b403d3c 100644
--- a/src/cnn/make.c
+++ b/src/cnn/make.c
@@ -10,7 +10,6 @@ void make_average_pooling(float*** input, float*** output, int size, int output_
// output[output_depth][output_dim][output_dim]
float average;
int n = size*size;
-
for (int i=0; i < output_depth; i++) {
for (int j=0; j < output_dim; j++) {
for (int k=0; k < output_dim; k++) {
@@ -20,7 +19,7 @@ void make_average_pooling(float*** input, float*** output, int size, int output_
average += input[i][size*j +a][size*k +b];
}
}
- output[i][j][k] = average/n;
+ output[i][j][k] = average/(float)n;
}
}
}
diff --git a/src/cnn/train.c b/src/cnn/train.c
index b4aad18..afdcd98 100644
--- a/src/cnn/train.c
+++ b/src/cnn/train.c
@@ -10,6 +10,7 @@
#include "../include/colors.h"
#include "include/function.h"
#include "include/creation.h"
+#include "include/update.h"
#include "include/utils.h"
#include "include/free.h"
#include "include/cnn.h"
@@ -45,14 +46,20 @@ void* train_thread(void* parameters) {
int start = param->start;
int nb_images = param->nb_images;
float accuracy = 0.;
-
+ int cpt=1;
for (int i=start; i < start+nb_images; i++) {
if (dataset_type == 0) {
write_image_in_network_32(images[i], height, width, network->input[0][0]);
forward_propagation(network);
+ maxi = indice_max(network, 10);
backward_propagation(network, labels[i]);
-
- maxi = indice_max(network->input[network->size-1][0][0], network->width[network->size-1]);
+ if (cpt==16) { // Update the network
+ printf("a\n");
+ update_weights(network);
+ update_bias(network);
+ cpt = 0;
+ }
+ cpt++;
if (maxi == labels[i]) {
accuracy += 1.;
}
diff --git a/src/cnn/update.c b/src/cnn/update.c
index f3ab286..0e2e2bf 100644
--- a/src/cnn/update.c
+++ b/src/cnn/update.c
@@ -2,22 +2,25 @@
#include "include/update.h"
#include "include/struct.h"
+
+#include <stdio.h>
+
+
+
void update_weights(Network* network) {
int n = network->size;
- int input_depth, input_width, output_depth, output_width;
+ int input_depth, input_width, output_depth, output_width, k_size;
Kernel* k_i;
- Kernel* k_i_1;
for (int i=0; i<(n-1); i++) {
k_i = network->kernel[i];
- k_i_1 = network->kernel[i+1];
input_depth = network->depth[i];
input_width = network->width[i];
output_depth = network->depth[i+1];
output_width = network->width[i+1];
if (k_i->cnn) { // Convolution
- Kernel_cnn* cnn = k_i_1->cnn;
- int k_size = cnn->k_size;
+ Kernel_cnn* cnn = k_i->cnn; // ERRORS
+ k_size = cnn->k_size;
for (int a=0; a<input_depth; a++) {
for (int b=0; b<output_depth; b++) {
for (int c=0; c<k_size; c++) {
@@ -30,7 +33,7 @@ void update_weights(Network* network) {
}
} else if (k_i->nn) { // Full connection
if (input_depth==1) { // Vecteur -> Vecteur
- Kernel_nn* nn = k_i_1->nn;
+ Kernel_nn* nn = k_i->nn;
for (int a=0; a<input_width; a++) {
for (int b=0; b<output_width; b++) {
nn->weights[a][b] += network->learning_rate * nn->d_weights[a][b];
@@ -38,7 +41,7 @@ void update_weights(Network* network) {
}
}
} else { // Matrice -> vecteur
- Kernel_nn* nn = k_i_1->nn;
+ Kernel_nn* nn = k_i->nn;
int input_size = input_width*input_width*input_depth;
for (int a=0; a<input_size; a++) {
for (int b=0; b<output_width; b++) {
@@ -57,15 +60,13 @@ void update_bias(Network* network) {
int n = network->size;
int output_width, output_depth;
Kernel* k_i;
- Kernel* k_i_1;
for (int i=0; i<(n-1); i++) {
k_i = network->kernel[i];
- k_i_1 = network->kernel[i+1];
output_width = network->width[i+1];
output_depth = network->depth[i+1];
if (k_i->cnn) { // Convolution
- Kernel_cnn* cnn = k_i_1->cnn;
+ Kernel_cnn* cnn = k_i->cnn;
for (int a=0; a<output_depth; a++) {
for (int b=0; b<output_width; b++) {
for (int c=0; c<output_width; c++) {
@@ -75,7 +76,7 @@ void update_bias(Network* network) {
}
}
} else if (k_i->nn) { // Full connection
- Kernel_nn* nn = k_i_1->nn;
+ Kernel_nn* nn = k_i->nn;
for (int a=0; a<output_width; a++) {
nn->bias[a] += network->learning_rate * nn->d_bias[a];
nn->d_bias[a] = 0;