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/tests.c | 86 ++++++++++++++++++++++++++----------------
1 files changed, 53 insertions(+), 33 deletions(-)
diff --git a/src/tests.c b/src/tests.c
index 722de1a..c221042 100644
--- a/src/tests.c
+++ b/src/tests.c
@@ -15,7 +15,6 @@
void test_convolve()
{
image dog = load_image("dog.jpg");
- //show_image_layers(dog, "Dog");
printf("dog channels %d\n", dog.c);
image kernel = make_random_image(3,3,dog.c);
image edge = make_image(dog.h, dog.w, 1);
@@ -88,7 +87,7 @@
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);
+ backward_convolutional_layer(layer, test.data, in_delta.data);
image partial = copy_image(in_delta);
jacobian2[i] = partial.data;
out_delta.data[i] = 0;
@@ -156,7 +155,7 @@
int count = 0;
double avgerr = 0;
- while(1){
+ while(++count < 100000000){
double v = ((double)rand()/RAND_MAX);
double truth = v*v;
input[0] = v;
@@ -165,8 +164,7 @@
double *delta = get_network_delta(net);
double err = pow((out[0]-truth),2.);
avgerr = .99 * avgerr + .01 * err;
- //if(++count % 100000 == 0) printf("%f\n", avgerr);
- if(++count % 1000000 == 0) printf("%f %f :%f AVG %f \n", truth, out[0], err, avgerr);
+ if(count % 1000000 == 0) printf("%f %f :%f AVG %f \n", truth, out[0], err, avgerr);
delta[0] = truth - out[0];
learn_network(net, input);
update_network(net, .001);
@@ -197,15 +195,16 @@
}
}
-double error_network(network net, matrix m, double *truth)
+double error_network(network net, matrix m, double **truth)
{
int i;
int correct = 0;
+ int k = get_network_output_size(net);
for(i = 0; i < m.rows; ++i){
forward_network(net, m.vals[i]);
double *out = get_network_output(net);
- double err = truth[i] - out[0];
- if(fabs(err) < .5) ++correct;
+ int guess = max_index(out, k);
+ if(truth[i][guess]) ++correct;
}
return (double)correct/m.rows;
}
@@ -224,24 +223,35 @@
void test_nist()
{
+ srand(999999);
network net = parse_network_cfg("nist.cfg");
- matrix m = csv_to_matrix("images/nist_train.csv");
- matrix ho = hold_out_matrix(&m, 3000);
+ matrix m = csv_to_matrix("mnist/mnist_train.csv");
+ matrix test = csv_to_matrix("mnist/mnist_test.csv");
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);
+ double *test_truth_1d = pop_column(&test, 0);
+ double **test_truth = one_hot(test_truth_1d, test.rows, 10);
int i,j;
clock_t start = clock(), end;
+ for(i = 0; i < test.rows; ++i){
+ normalize_array(test.vals[i], 28*28);
+ //scale_array(m.vals[i], 28*28, 1./255.);
+ //translate_array(m.vals[i], 28*28, -.1);
+ }
+ for(i = 0; i < m.rows; ++i){
+ normalize_array(m.vals[i], 28*28);
+ //scale_array(m.vals[i], 28*28, 1./255.);
+ //translate_array(m.vals[i], 28*28, -.1);
+ }
int count = 0;
- double lr = .0001;
- while(++count <= 3000000){
+ double lr = .0005;
+ while(++count <= 300){
//lr *= .99;
int index = 0;
int correct = 0;
- for(i = 0; i < 1000; ++i){
+ int number = 1000;
+ for(i = 0; i < number; ++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);
@@ -260,19 +270,29 @@
}
print_network(net);
image input = double_to_image(28,28,1, m.vals[index]);
- show_image(input, "Input");
+ //show_image(input, "Input");
image o = get_network_image(net);
- show_image_collapsed(o, "Output");
+ //show_image_collapsed(o, "Output");
visualize_network(net);
- cvWaitKey(100);
+ cvWaitKey(10);
//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;
+ fprintf(stderr, "\n%5d: %f %f\n\n",count, (double)correct/number, lr);
+ if(count % 10 == 0 && 0){
+ double train_acc = error_network(net, m, truth);
+ fprintf(stderr, "\nTRAIN: %f\n", train_acc);
+ double test_acc = error_network(net, test, test_truth);
+ fprintf(stderr, "TEST: %f\n\n", test_acc);
+ printf("%d, %f, %f\n", count, train_acc, test_acc);
+ }
+ if(count % (m.rows/number) == 0) lr /= 2;
}
+ double train_acc = error_network(net, m, truth);
+ fprintf(stderr, "\nTRAIN: %f\n", train_acc);
+ double test_acc = error_network(net, test, test_truth);
+ fprintf(stderr, "TEST: %f\n\n", test_acc);
+ printf("%d, %f, %f\n", count, train_acc, test_acc);
end = clock();
- printf("Neural Net Learning: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
+ //printf("Neural Net Learning: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
}
void test_kernel_update()
@@ -281,14 +301,14 @@
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);
+ convolutional_layer layer = *make_convolutional_layer(3, 3, 1, 1, 3, 1, LINEAR);
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()
@@ -311,15 +331,15 @@
double *delta = get_network_delta(net);
//printf("%f\n", out[0]);
delta[0] = truth[index] - out[0];
- // printf("%f\n", delta[0]);
+ // printf("%f\n", delta[0]);
//printf("%f %f\n", truth[index], out[0]);
learn_network(net, m.vals[index]);
update_network(net, .00001);
}
- 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 Valid: %f\n",count, test_acc, valid_acc);
+ //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 Valid: %f\n",count, test_acc, valid_acc);
//if(valid_acc > .70) break;
}
end = clock();
@@ -362,8 +382,8 @@
int main()
{
//test_kernel_update();
- //test_nist();
- test_full();
+ test_nist();
+ //test_full();
//test_random_preprocess();
//test_random_classify();
//test_parser();
--
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