From ace5aeb0f59fdceb99e607af9780added20da37c Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 24 Jan 2014 22:51:17 +0000
Subject: [PATCH] MNIST connected network showing off matrices
---
src/tests.c | 280 +++++++++++++++++++++++++++++++------------------------
1 files changed, 158 insertions(+), 122 deletions(-)
diff --git a/src/tests.c b/src/tests.c
index c221042..c459a36 100644
--- a/src/tests.c
+++ b/src/tests.c
@@ -7,6 +7,7 @@
#include "data.h"
#include "matrix.h"
#include "utils.h"
+#include "mini_blas.h"
#include <time.h>
#include <stdlib.h>
@@ -28,6 +29,35 @@
show_image_layers(edge, "Test Convolve");
}
+void test_convolve_matrix()
+{
+ image dog = load_image("dog.jpg");
+ printf("dog channels %d\n", dog.c);
+
+ int size = 11;
+ int stride = 1;
+ int n = 40;
+ double *filters = make_random_image(size, size, dog.c*n).data;
+
+ int mw = ((dog.h-size)/stride+1)*((dog.w-size)/stride+1);
+ int mh = (size*size*dog.c);
+ double *matrix = calloc(mh*mw, sizeof(double));
+
+ image edge = make_image((dog.h-size)/stride+1, (dog.w-size)/stride+1, n);
+
+
+ int i;
+ clock_t start = clock(), end;
+ for(i = 0; i < 1000; ++i){
+ im2col_cpu(dog.data, dog.c, dog.h, dog.w, size, stride, matrix);
+ gemm(0,0,n,mw,mh,1,filters,mh,matrix,mw,1,edge.data,mw);
+ }
+ end = clock();
+ printf("Convolutions: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
+ show_image_layers(edge, "Test Convolve");
+ cvWaitKey(0);
+}
+
void test_color()
{
image dog = load_image("test_color.png");
@@ -166,19 +196,16 @@
avgerr = .99 * avgerr + .01 * err;
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()
@@ -187,112 +214,88 @@
srand(0);
int i = 0;
char *labels[] = {"cat","dog"};
+ double lr = .00001;
+ double momentum = .9;
+ double decay = 0.01;
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, lr, momentum, decay);
+ free_data(train);
printf("Round %d\n", i);
}
}
-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);
- int guess = max_index(out, k);
- if(truth[i][guess]) ++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(999999);
- network net = parse_network_cfg("nist.cfg");
- 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 *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);
- }
+ srand(444444);
+ srand(888888);
+ network net = parse_network_cfg("nist_basic.cfg");
+ 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 = .0005;
- while(++count <= 300){
- //lr *= .99;
- int index = 0;
- int correct = 0;
- int number = 1000;
- for(i = 0; i < number; ++i){
- index = rand()%m.rows;
- 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(10);
- //double test_acc = error_network(net, m, truth);
- 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);
+ double momentum = .9;
+ double decay = 0.01;
+ clock_t start = clock(), end;
+ while(++count <= 1000){
+ double acc = train_network_sgd(net, train, 6400, lr, momentum, decay);
+ printf("%5d Training Loss: %lf, Params: %f %f %f, ",count*100, 1.-acc, lr, momentum, decay);
+ end = clock();
+ printf("Time: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
+ start=end;
+ //visualize_network(net);
+ //cvWaitKey(100);
+ //lr /= 2;
+ if(count%5 == 0 && 0){
+ double train_acc = network_accuracy(net, train);
fprintf(stderr, "\nTRAIN: %f\n", train_acc);
- double test_acc = error_network(net, test, test_truth);
+ 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);
}
- 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);
+}
+
+void test_ensemble()
+{
+ int i;
+ srand(888888);
+ data d = load_categorical_data_csv("mnist/mnist_train.csv", 0, 10);
+ normalize_data_rows(d);
+ data test = load_categorical_data_csv("mnist/mnist_test.csv", 0,10);
+ normalize_data_rows(test);
+ data train = d;
+ /*
+ data *split = split_data(d, 1, 10);
+ data train = split[0];
+ data test = split[1];
+ */
+ matrix prediction = make_matrix(test.y.rows, test.y.cols);
+ int n = 30;
+ for(i = 0; i < n; ++i){
+ int count = 0;
+ double lr = .0005;
+ double momentum = .9;
+ double decay = .01;
+ network net = parse_network_cfg("nist.cfg");
+ while(++count <= 15){
+ double acc = train_network_sgd(net, train, train.X.rows, lr, momentum, decay);
+ printf("Training Accuracy: %lf Learning Rate: %f Momentum: %f Decay: %f\n", acc, lr, momentum, decay );
+ lr /= 2;
+ }
+ matrix partial = network_predict_data(net, test);
+ double acc = matrix_accuracy(test.y, partial);
+ printf("Model Accuracy: %lf\n", acc);
+ matrix_add_matrix(partial, prediction);
+ acc = matrix_accuracy(test.y, prediction);
+ printf("Current Ensemble Accuracy: %lf\n", acc);
+ free_matrix(partial);
+ }
+ double acc = matrix_accuracy(test.y, prediction);
+ printf("Full Ensemble Accuracy: %lf\n", acc);
}
void test_kernel_update()
@@ -315,9 +318,9 @@
{
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;
@@ -333,8 +336,8 @@
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, .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);
@@ -356,32 +359,65 @@
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");
+ data train = load_categorical_data_csv("mnist/mnist_train.csv", 0, 10);
+ data *split = split_data(train, 0, 13);
+ printf("%d, %d, %d\n", train.X.rows, split[0].X.rows, split[1].X.rows);
+}
+
+double *random_matrix(int rows, int cols)
+{
+ int i, j;
+ double *m = calloc(rows*cols, sizeof(double));
+ for(i = 0; i < rows; ++i){
+ for(j = 0; j < cols; ++j){
+ m[i*cols+j] = (double)rand()/RAND_MAX;
}
- free_batch(part);
+ }
+ return m;
+}
+
+void test_blas()
+{
+ int m = 6025, n = 20, k = 11*11*3;
+ double *a = random_matrix(m,k);
+ double *b = random_matrix(k,n);
+ double *c = random_matrix(m,n);
+ int i;
+ for(i = 0; i<1000; ++i){
+ gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
+ }
+}
+
+void test_im2row()
+{
+ int h = 20;
+ int w = 20;
+ int c = 3;
+ int stride = 1;
+ int size = 11;
+ image test = make_random_image(h,w,c);
+ int mc = 1;
+ int mw = ((h-size)/stride+1)*((w-size)/stride+1);
+ int mh = (size*size*c);
+ int msize = mc*mw*mh;
+ double *matrix = calloc(msize, sizeof(double));
+ int i;
+ for(i = 0; i < 1000; ++i){
+ im2col_cpu(test.data, c, h, w, size, stride, matrix);
+ image render = double_to_image(mh, mw, mc, matrix);
}
}
int main()
{
+ //test_blas();
+ //test_convolve_matrix();
+// test_im2row();
//test_kernel_update();
+ //test_split();
+ //test_ensemble();
test_nist();
//test_full();
//test_random_preprocess();
@@ -397,6 +433,6 @@
//test_convolutional_layer();
//verify_convolutional_layer();
//test_color();
- cvWaitKey(0);
+ //cvWaitKey(0);
return 0;
}
--
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