From 2db9fbef2bd7d35a547d0018a9850f6b249c524f Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Wed, 13 Nov 2013 18:50:38 +0000
Subject: [PATCH] Parsing, image loading, lots of stuff
---
src/tests.c | 233 ++++++++++++++++++++++++++++++++++-----------------------
1 files changed, 139 insertions(+), 94 deletions(-)
diff --git a/src/tests.c b/src/tests.c
index 7e2539a..65811e9 100644
--- a/src/tests.c
+++ b/src/tests.c
@@ -3,6 +3,9 @@
#include "maxpool_layer.h"
#include "network.h"
#include "image.h"
+#include "parser.h"
+#include "data.h"
+#include "matrix.h"
#include <time.h>
#include <stdlib.h>
@@ -34,23 +37,24 @@
void test_convolutional_layer()
{
srand(0);
- image dog = load_image("test_dog.jpg");
+ image dog = load_image("dog.jpg");
int i;
- int n = 5;
+ int n = 3;
int stride = 1;
- int size = 8;
- convolutional_layer layer = make_convolutional_layer(dog.h, dog.w, dog.c, n, size, stride);
+ int size = 3;
+ convolutional_layer layer = *make_convolutional_layer(dog.h, dog.w, dog.c, n, size, stride, RELU);
char buff[256];
for(i = 0; i < n; ++i) {
sprintf(buff, "Kernel %d", i);
show_image(layer.kernels[i], buff);
}
- run_convolutional_layer(dog, layer);
+ forward_convolutional_layer(layer, dog.data);
- maxpool_layer mlayer = make_maxpool_layer(layer.output.h, layer.output.w, layer.output.c, 3);
- run_maxpool_layer(layer.output,mlayer);
+ image output = get_convolutional_image(layer);
+ maxpool_layer mlayer = *make_maxpool_layer(output.h, output.w, output.c, 2);
+ forward_maxpool_layer(mlayer, layer.output);
- show_image_layers(mlayer.output, "Test Maxpool Layer");
+ show_image_layers(get_maxpool_image(mlayer), "Test Maxpool Layer");
}
void test_load()
@@ -89,110 +93,151 @@
show_image(random, "Test Rotate Random");
}
-void test_network()
+void test_parser()
{
- network net;
- net.n = 11;
- net.layers = calloc(net.n, sizeof(void *));
- net.types = calloc(net.n, sizeof(LAYER_TYPE));
- net.types[0] = CONVOLUTIONAL;
- net.types[1] = MAXPOOL;
- net.types[2] = CONVOLUTIONAL;
- net.types[3] = MAXPOOL;
- net.types[4] = CONVOLUTIONAL;
- net.types[5] = CONVOLUTIONAL;
- net.types[6] = CONVOLUTIONAL;
- net.types[7] = MAXPOOL;
- net.types[8] = CONNECTED;
- net.types[9] = CONNECTED;
- net.types[10] = CONNECTED;
-
- image dog = load_image("test_hinton.jpg");
-
- int n = 48;
- int stride = 4;
- int size = 11;
- convolutional_layer cl = make_convolutional_layer(dog.h, dog.w, dog.c, n, size, stride);
- maxpool_layer ml = make_maxpool_layer(cl.output.h, cl.output.w, cl.output.c, 2);
-
- n = 128;
- size = 5;
- stride = 1;
- convolutional_layer cl2 = make_convolutional_layer(ml.output.h, ml.output.w, ml.output.c, n, size, stride);
- maxpool_layer ml2 = make_maxpool_layer(cl2.output.h, cl2.output.w, cl2.output.c, 2);
-
- n = 192;
- size = 3;
- convolutional_layer cl3 = make_convolutional_layer(ml2.output.h, ml2.output.w, ml2.output.c, n, size, stride);
- convolutional_layer cl4 = make_convolutional_layer(cl3.output.h, cl3.output.w, cl3.output.c, n, size, stride);
- n = 128;
- convolutional_layer cl5 = make_convolutional_layer(cl4.output.h, cl4.output.w, cl4.output.c, n, size, stride);
- maxpool_layer ml3 = make_maxpool_layer(cl5.output.h, cl5.output.w, cl5.output.c, 4);
- connected_layer nl = make_connected_layer(ml3.output.h*ml3.output.w*ml3.output.c, 4096);
- connected_layer nl2 = make_connected_layer(4096, 4096);
- connected_layer nl3 = make_connected_layer(4096, 1000);
-
- net.layers[0] = &cl;
- net.layers[1] = &ml;
- net.layers[2] = &cl2;
- net.layers[3] = &ml2;
- net.layers[4] = &cl3;
- net.layers[5] = &cl4;
- net.layers[6] = &cl5;
- net.layers[7] = &ml3;
- net.layers[8] = &nl;
- net.layers[9] = &nl2;
- net.layers[10] = &nl3;
-
- int i;
- clock_t start = clock(), end;
- for(i = 0; i < 10; ++i){
- run_network(dog, net);
- rotate_image(dog);
+ network net = parse_network_cfg("test_parser.cfg");
+ double input[1];
+ int count = 0;
+
+ double avgerr = 0;
+ while(1){
+ double v = ((double)rand()/RAND_MAX);
+ double truth = v*v;
+ input[0] = v;
+ forward_network(net, input);
+ double *out = get_network_output(net);
+ 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);
+ delta[0] = truth - out[0];
+ learn_network(net, input);
+ update_network(net, .001);
}
- end = clock();
- printf("Ran %lf second per iteration\n", (double)(end-start)/CLOCKS_PER_SEC/10);
-
- show_image_layers(get_network_image(net), "Test Network Layer");
}
-void test_backpropagate()
+
+void test_data()
{
- int n = 3;
- int size = 4;
- int stride = 10;
- image dog = load_image("dog.jpg");
- show_image(dog, "Test Backpropagate Input");
- image dog_copy = copy_image(dog);
- convolutional_layer cl = make_convolutional_layer(dog.h, dog.w, dog.c, n, size, stride);
- run_convolutional_layer(dog, cl);
- show_image(cl.output, "Test Backpropagate Output");
+ batch train = random_batch("train_paths.txt", 101);
+ 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()
+{
+ network net = parse_network_cfg("test.cfg");
+ srand(0);
+ //visualize_network(net);
+ int i = 1000;
+ //while(1){
+ while(i > 0){
+ batch train = random_batch("train_paths.txt", 100);
+ train_network_batch(net, train);
+ //show_image_layers(get_network_image(net), "hey");
+ //visualize_network(net);
+ //cvWaitKey(0);
+ free_batch(train);
+ --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;
+}
+
+void classify_random_filters()
+{
+ network net = parse_network_cfg("random_filter_finish.cfg");
+ matrix m = csv_to_matrix("train.csv");
+ matrix ho = hold_out_matrix(&m, 2500);
+ double *truth = pop_column(&m, 0);
+ double *ho_truth = pop_column(&ho, 0);
int i;
clock_t start = clock(), end;
- for(i = 0; i < 100; ++i){
- backpropagate_layer(dog_copy, cl);
+ int count = 0;
+ while(++count <= 300){
+ for(i = 0; i < m.rows; ++i){
+ int index = rand()%m.rows;
+ //image p = double_to_image(1690,1,1,m.vals[index]);
+ //normalize_image(p);
+ forward_network(net, m.vals[index]);
+ double *out = get_network_output(net);
+ double *delta = get_network_delta(net);
+ //printf("%f\n", out[0]);
+ delta[0] = truth[index] - out[0];
+ // printf("%f\n", delta[0]);
+ //printf("%f %f\n", truth[index], out[0]);
+ learn_network(net, m.vals[index]);
+ update_network(net, .000005);
+ }
+ 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();
- printf("Backpropagate: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
- start = clock();
- for(i = 0; i < 100; ++i){
- backpropagate_layer_convolve(dog, cl);
+ FILE *fp = fopen("submission/out.txt", "w");
+ matrix test = csv_to_matrix("test.csv");
+ truth = pop_column(&test, 0);
+ for(i = 0; i < test.rows; ++i){
+ forward_network(net, test.vals[i]);
+ double *out = get_network_output(net);
+ if(fabs(out[0]) < .5) fprintf(fp, "0\n");
+ else fprintf(fp, "1\n");
}
- end = clock();
- printf("Backpropagate Using Convolutions: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
- show_image(dog_copy, "Test Backpropagate 1");
- show_image(dog, "Test Backpropagate 2");
- subtract_image(dog, dog_copy);
- show_image(dog, "Test Backpropagate Difference");
+ fclose(fp);
+ printf("Neural Net Learning: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
+}
+
+void test_random_filters()
+{
+ FILE *file = fopen("test.csv", "w");
+ int i,j,k;
+ srand(0);
+ network net = parse_network_cfg("test_random_filter.cfg");
+ for(i = 0; i < 100; ++i){
+ printf("%d\n", i);
+ batch part = get_batch("test_paths.txt", i, 100);
+ 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);
+ }
}
int main()
{
+ //classify_random_filters();
+ //test_random_filters();
+ test_train();
+ //test_parser();
//test_backpropagate();
+ //test_ann();
//test_convolve();
//test_upsample();
//test_rotate();
//test_load();
- test_network();
+ //test_network();
//test_convolutional_layer();
//test_color();
cvWaitKey(0);
--
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