From 0d6bb5d44d8e815ebf6ccce1dae2f83178780e7b Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 03 Dec 2013 00:41:40 +0000
Subject: [PATCH] Working?
---
src/tests.c | 187 ++++++++++++++++++++++++++++++++++++++++------
1 files changed, 162 insertions(+), 25 deletions(-)
diff --git a/src/tests.c b/src/tests.c
index 65811e9..722de1a 100644
--- a/src/tests.c
+++ b/src/tests.c
@@ -6,6 +6,7 @@
#include "parser.h"
#include "data.h"
#include "matrix.h"
+#include "utils.h"
#include <time.h>
#include <stdlib.h>
@@ -21,7 +22,7 @@
int i;
clock_t start = clock(), end;
for(i = 0; i < 1000; ++i){
- convolve(dog, kernel, 1, 0, edge);
+ convolve(dog, kernel, 1, 0, edge, 1);
}
end = clock();
printf("Convolutions: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
@@ -57,6 +58,61 @@
show_image_layers(get_maxpool_image(mlayer), "Test Maxpool Layer");
}
+void verify_convolutional_layer()
+{
+ srand(0);
+ int i;
+ int n = 1;
+ int stride = 1;
+ int size = 3;
+ double eps = .00000001;
+ image test = make_random_image(5,5, 1);
+ convolutional_layer layer = *make_convolutional_layer(test.h,test.w,test.c, n, size, stride, RELU);
+ image out = get_convolutional_image(layer);
+ double **jacobian = calloc(test.h*test.w*test.c, sizeof(double));
+
+ forward_convolutional_layer(layer, test.data);
+ image base = copy_image(out);
+
+ for(i = 0; i < test.h*test.w*test.c; ++i){
+ test.data[i] += eps;
+ forward_convolutional_layer(layer, test.data);
+ image partial = copy_image(out);
+ subtract_image(partial, base);
+ scale_image(partial, 1/eps);
+ jacobian[i] = partial.data;
+ test.data[i] -= eps;
+ }
+ double **jacobian2 = calloc(out.h*out.w*out.c, sizeof(double));
+ image in_delta = make_image(test.h, test.w, test.c);
+ image out_delta = get_convolutional_delta(layer);
+ for(i = 0; i < out.h*out.w*out.c; ++i){
+ out_delta.data[i] = 1;
+ backward_convolutional_layer2(layer, test.data, in_delta.data);
+ image partial = copy_image(in_delta);
+ jacobian2[i] = partial.data;
+ out_delta.data[i] = 0;
+ }
+ int j;
+ double *j1 = calloc(test.h*test.w*test.c*out.h*out.w*out.c, sizeof(double));
+ double *j2 = calloc(test.h*test.w*test.c*out.h*out.w*out.c, sizeof(double));
+ for(i = 0; i < test.h*test.w*test.c; ++i){
+ for(j =0 ; j < out.h*out.w*out.c; ++j){
+ j1[i*out.h*out.w*out.c + j] = jacobian[i][j];
+ j2[i*out.h*out.w*out.c + j] = jacobian2[j][i];
+ printf("%f %f\n", jacobian[i][j], jacobian2[j][i]);
+ }
+ }
+
+
+ image mj1 = double_to_image(test.w*test.h*test.c, out.w*out.h*out.c, 1, j1);
+ image mj2 = double_to_image(test.w*test.h*test.c, out.w*out.h*out.c, 1, j2);
+ printf("%f %f\n", avg_image_layer(mj1,0), avg_image_layer(mj2,0));
+ show_image(mj1, "forward jacobian");
+ show_image(mj2, "backward jacobian");
+
+}
+
void test_load()
{
image dog = load_image("dog.jpg");
@@ -119,30 +175,26 @@
void test_data()
{
- batch train = random_batch("train_paths.txt", 101);
+ char *labels[] = {"cat","dog"};
+ batch train = random_batch("train_paths.txt", 101,labels, 2);
show_image(train.images[0], "Test Data Loading");
show_image(train.images[100], "Test Data Loading");
show_image(train.images[10], "Test Data Loading");
free_batch(train);
}
-void test_train()
+void test_full()
{
- network net = parse_network_cfg("test.cfg");
+ network net = parse_network_cfg("full.cfg");
srand(0);
- //visualize_network(net);
- int i = 1000;
- //while(1){
- while(i > 0){
- batch train = random_batch("train_paths.txt", 100);
+ int i = 0;
+ char *labels[] = {"cat","dog"};
+ while(i++ < 1000 || 1){
+ batch train = random_batch("train_paths.txt", 1000, labels, 2);
train_network_batch(net, train);
- //show_image_layers(get_network_image(net), "hey");
- //visualize_network(net);
- //cvWaitKey(0);
free_batch(train);
- --i;
- }
- //}
+ printf("Round %d\n", i);
+ }
}
double error_network(network net, matrix m, double *truth)
@@ -158,9 +210,90 @@
return (double)correct/m.rows;
}
-void classify_random_filters()
+double **one_hot(double *a, int n, int k)
{
- network net = parse_network_cfg("random_filter_finish.cfg");
+ int i;
+ double **t = calloc(n, sizeof(double*));
+ for(i = 0; i < n; ++i){
+ t[i] = calloc(k, sizeof(double));
+ int index = (int)a[i];
+ t[i][index] = 1;
+ }
+ return t;
+}
+
+void test_nist()
+{
+ network net = parse_network_cfg("nist.cfg");
+ matrix m = csv_to_matrix("images/nist_train.csv");
+ matrix ho = hold_out_matrix(&m, 3000);
+ double *truth_1d = pop_column(&m, 0);
+ double **truth = one_hot(truth_1d, m.rows, 10);
+ double *ho_truth_1d = pop_column(&ho, 0);
+ double **ho_truth = one_hot(ho_truth_1d, ho.rows, 10);
+ int i,j;
+ clock_t start = clock(), end;
+ int count = 0;
+ double lr = .0001;
+ while(++count <= 3000000){
+ //lr *= .99;
+ int index = 0;
+ int correct = 0;
+ for(i = 0; i < 1000; ++i){
+ index = rand()%m.rows;
+ normalize_array(m.vals[index], 28*28);
+ forward_network(net, m.vals[index]);
+ double *out = get_network_output(net);
+ double *delta = get_network_delta(net);
+ int max_i = 0;
+ double max = out[0];
+ for(j = 0; j < 10; ++j){
+ delta[j] = truth[index][j]-out[j];
+ if(out[j] > max){
+ max = out[j];
+ max_i = j;
+ }
+ }
+ if(truth[index][max_i]) ++correct;
+ learn_network(net, m.vals[index]);
+ update_network(net, lr);
+ }
+ print_network(net);
+ image input = double_to_image(28,28,1, m.vals[index]);
+ show_image(input, "Input");
+ image o = get_network_image(net);
+ show_image_collapsed(o, "Output");
+ visualize_network(net);
+ cvWaitKey(100);
+ //double test_acc = error_network(net, m, truth);
+ //double valid_acc = error_network(net, ho, ho_truth);
+ //printf("%f, %f\n", test_acc, valid_acc);
+ fprintf(stderr, "%5d: %f %f\n",count, (double)correct/1000, lr);
+ //if(valid_acc > .70) break;
+ }
+ end = clock();
+ printf("Neural Net Learning: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
+}
+
+void test_kernel_update()
+{
+ srand(0);
+ double delta[] = {.1};
+ double input[] = {.3, .5, .3, .5, .5, .5, .5, .0, .5};
+ double kernel[] = {1,2,3,4,5,6,7,8,9};
+ convolutional_layer layer = *make_convolutional_layer(3, 3, 1, 1, 3, 1, IDENTITY);
+ layer.kernels[0].data = kernel;
+ layer.delta = delta;
+ learn_convolutional_layer(layer, input);
+ print_image(layer.kernels[0]);
+ print_image(get_convolutional_delta(layer));
+ print_image(layer.kernel_updates[0]);
+
+}
+
+void test_random_classify()
+{
+ network net = parse_network_cfg("connected.cfg");
matrix m = csv_to_matrix("train.csv");
matrix ho = hold_out_matrix(&m, 2500);
double *truth = pop_column(&m, 0);
@@ -181,7 +314,7 @@
// printf("%f\n", delta[0]);
//printf("%f %f\n", truth[index], out[0]);
learn_network(net, m.vals[index]);
- update_network(net, .000005);
+ update_network(net, .00001);
}
double test_acc = error_network(net, m, truth);
double valid_acc = error_network(net, ho, ho_truth);
@@ -203,15 +336,16 @@
printf("Neural Net Learning: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
}
-void test_random_filters()
+void test_random_preprocess()
{
- FILE *file = fopen("test.csv", "w");
+ FILE *file = fopen("train.csv", "w");
+ char *labels[] = {"cat","dog"};
int i,j,k;
srand(0);
- network net = parse_network_cfg("test_random_filter.cfg");
+ network net = parse_network_cfg("convolutional.cfg");
for(i = 0; i < 100; ++i){
printf("%d\n", i);
- batch part = get_batch("test_paths.txt", i, 100);
+ batch part = get_batch("train_paths.txt", i, 100, labels, 2);
for(j = 0; j < part.n; ++j){
forward_network(net, part.images[j].data);
double *out = get_network_output(net);
@@ -227,9 +361,11 @@
int main()
{
- //classify_random_filters();
- //test_random_filters();
- test_train();
+ //test_kernel_update();
+ //test_nist();
+ test_full();
+ //test_random_preprocess();
+ //test_random_classify();
//test_parser();
//test_backpropagate();
//test_ann();
@@ -239,6 +375,7 @@
//test_load();
//test_network();
//test_convolutional_layer();
+ //verify_convolutional_layer();
//test_color();
cvWaitKey(0);
return 0;
--
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