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 | 327 +++++++++++++++++++++++++++++++++++++++++++-----------
1 files changed, 260 insertions(+), 67 deletions(-)
diff --git a/src/tests.c b/src/tests.c
index 65811e9..c459a36 100644
--- a/src/tests.c
+++ b/src/tests.c
@@ -6,6 +6,8 @@
#include "parser.h"
#include "data.h"
#include "matrix.h"
+#include "utils.h"
+#include "mini_blas.h"
#include <time.h>
#include <stdlib.h>
@@ -14,20 +16,48 @@
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);
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);
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");
@@ -57,6 +87,61 @@
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_layer(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");
@@ -100,7 +185,7 @@
int count = 0;
double avgerr = 0;
- while(1){
+ while(++count < 100000000){
double v = ((double)rand()/RAND_MAX);
double truth = v*v;
input[0] = v;
@@ -109,62 +194,133 @@
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()
{
- 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);
+ char *labels[] = {"cat","dog"};
+ data train = load_data_image_pathfile_random("train_paths.txt", 101,labels, 2);
+ free_data(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);
- train_network_batch(net, train);
- //show_image_layers(get_network_image(net), "hey");
- //visualize_network(net);
- //cvWaitKey(0);
- free_batch(train);
- --i;
- }
- //}
+ int i = 0;
+ char *labels[] = {"cat","dog"};
+ double lr = .00001;
+ double momentum = .9;
+ double decay = 0.01;
+ while(i++ < 1000 || 1){
+ 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)
+void test_nist()
+{
+ 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;
+ 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 = network_accuracy(net, test);
+ fprintf(stderr, "TEST: %f\n\n", test_acc);
+ printf("%d, %f, %f\n", count, train_acc, test_acc);
+ }
+ }
+}
+
+void test_ensemble()
{
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;
+ 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);
}
- return (double)correct/m.rows;
+ double acc = matrix_accuracy(test.y, prediction);
+ printf("Full Ensemble Accuracy: %lf\n", acc);
}
-void classify_random_filters()
+void test_kernel_update()
{
- network net = parse_network_cfg("random_filter_finish.cfg");
+ 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, 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;
@@ -178,15 +334,15 @@
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, .000005);
+ //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();
@@ -203,33 +359,69 @@
printf("Neural Net Learning: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
}
-void test_random_filters()
+void test_split()
{
- 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");
+ 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()
{
- //classify_random_filters();
- //test_random_filters();
- test_train();
+ //test_blas();
+ //test_convolve_matrix();
+// test_im2row();
+ //test_kernel_update();
+ //test_split();
+ //test_ensemble();
+ test_nist();
+ //test_full();
+ //test_random_preprocess();
+ //test_random_classify();
//test_parser();
//test_backpropagate();
//test_ann();
@@ -239,7 +431,8 @@
//test_load();
//test_network();
//test_convolutional_layer();
+ //verify_convolutional_layer();
//test_color();
- cvWaitKey(0);
+ //cvWaitKey(0);
return 0;
}
--
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