From 4bdf96bd6aafbec6bc3f0eab8739d6652878fd24 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 06 Dec 2013 21:26:09 +0000
Subject: [PATCH] New data format
---
src/tests.c | 168 ++++++++++++++++---------------------------------------
1 files changed, 49 insertions(+), 119 deletions(-)
diff --git a/src/tests.c b/src/tests.c
index 722de1a..d7d9389 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;
@@ -165,22 +164,18 @@
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);
+ backward_network(net, input, &truth);
+ update_network(net, .001,0,0);
}
}
void test_data()
{
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);
+ data train = load_data_image_pathfile_random("train_paths.txt", 101,labels, 2);
+ free_data(train);
}
void test_full()
@@ -190,89 +185,38 @@
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);
- free_batch(train);
+ data train = load_data_image_pathfile_random("train_paths.txt", 1000, labels, 2);
+ train_network(net, train, .0005, 0, 0);
+ free_data(train);
printf("Round %d\n", i);
}
}
-double error_network(network net, matrix m, double *truth)
-{
- int i;
- int correct = 0;
- 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;
- }
- return (double)correct/m.rows;
-}
-
-double **one_hot(double *a, int n, int k)
-{
- 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()
{
+ srand(444444);
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;
+ data train = load_categorical_data_csv("mnist/mnist_train.csv", 0, 10);
+ data test = load_categorical_data_csv("mnist/mnist_test.csv",0,10);
+ normalize_data_rows(train);
+ normalize_data_rows(test);
+ randomize_data(train);
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;
+ double lr = .0005;
+ while(++count <= 1){
+ double acc = train_network_sgd(net, train, lr, .9, .001);
+ printf("Training Accuracy: %lf", acc);
+ lr /= 2;
}
- end = clock();
- printf("Neural Net Learning: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
+ /*
+ double train_acc = network_accuracy(net, train);
+ fprintf(stderr, "\nTRAIN: %f\n", train_acc);
+ double test_acc = network_accuracy(net, test);
+ 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);
}
void test_kernel_update()
@@ -281,23 +225,23 @@
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()
{
network net = parse_network_cfg("connected.cfg");
matrix m = csv_to_matrix("train.csv");
- matrix ho = hold_out_matrix(&m, 2500);
+ //matrix ho = hold_out_matrix(&m, 2500);
double *truth = pop_column(&m, 0);
- double *ho_truth = pop_column(&ho, 0);
+ //double *ho_truth = pop_column(&ho, 0);
int i;
clock_t start = clock(), end;
int count = 0;
@@ -311,15 +255,15 @@
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);
+ //backward_network(net, m.vals[index], );
+ update_network(net, .00001, 0,0);
}
- 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();
@@ -336,34 +280,20 @@
printf("Neural Net Learning: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
}
-void test_random_preprocess()
+void test_split()
{
- FILE *file = fopen("train.csv", "w");
- char *labels[] = {"cat","dog"};
- int i,j,k;
- srand(0);
- network net = parse_network_cfg("convolutional.cfg");
- for(i = 0; i < 100; ++i){
- printf("%d\n", i);
- 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);
- fprintf(file, "%f", part.truth[j][0]);
- for(k = 0; k < get_network_output_size(net); ++k){
- fprintf(file, ",%f", out[k]);
- }
- fprintf(file, "\n");
- }
- free_batch(part);
- }
+ data train = load_categorical_data_csv("mnist/mnist_train.csv", 0, 10);
+ data *split = cv_split_data(train, 0, 13);
+ printf("%d, %d, %d\n", train.X.rows, split[0].X.rows, split[1].X.rows);
}
+
int main()
{
//test_kernel_update();
- //test_nist();
- test_full();
+ test_split();
+ // test_nist();
+ //test_full();
//test_random_preprocess();
//test_random_classify();
//test_parser();
--
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