From f7a17f82eb43de864a4f980f235055da9685eef8 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Wed, 29 Jan 2014 00:28:42 +0000
Subject: [PATCH] Convolutional layers working w/ matrices
---
src/tests.c | 129 ++++++++++++++++++------------------------
1 files changed, 55 insertions(+), 74 deletions(-)
diff --git a/src/tests.c b/src/tests.c
index af22ddb..00cd1a1 100644
--- a/src/tests.c
+++ b/src/tests.c
@@ -14,6 +14,9 @@
#include <stdlib.h>
#include <stdio.h>
+#define _GNU_SOURCE
+#include <fenv.h>
+
void test_convolve()
{
image dog = load_image("dog.jpg");
@@ -26,7 +29,7 @@
convolve(dog, kernel, 1, 0, edge, 1);
}
end = clock();
- printf("Convolutions: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
+ printf("Convolutions: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
show_image_layers(edge, "Test Convolve");
}
@@ -38,11 +41,11 @@
int size = 11;
int stride = 4;
int n = 40;
- double *filters = make_random_image(size, size, dog.c*n).data;
+ float *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));
+ float *matrix = calloc(mh*mw, sizeof(float));
image edge = make_image((dog.h-size)/stride+1, (dog.w-size)/stride+1, n);
@@ -54,7 +57,7 @@
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);
+ printf("Convolutions: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
show_image_layers(edge, "Test Convolve");
cvWaitKey(0);
}
@@ -72,11 +75,11 @@
int n = 1;
int stride = 1;
int size = 3;
- double eps = .00000001;
+ float 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));
+ float **jacobian = calloc(test.h*test.w*test.c, sizeof(float));
forward_convolutional_layer(layer, test.data);
image base = copy_image(out);
@@ -90,19 +93,19 @@
jacobian[i] = partial.data;
test.data[i] -= eps;
}
- double **jacobian2 = calloc(out.h*out.w*out.c, sizeof(double));
+ float **jacobian2 = calloc(out.h*out.w*out.c, sizeof(float));
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);
+ backward_convolutional_layer(layer, 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));
+ float *j1 = calloc(test.h*test.w*test.c*out.h*out.w*out.c, sizeof(float));
+ float *j2 = calloc(test.h*test.w*test.c*out.h*out.w*out.c, sizeof(float));
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];
@@ -112,12 +115,11 @@
}
- 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);
+ image mj1 = float_to_image(test.w*test.h*test.c, out.w*out.h*out.c, 1, j1);
+ image mj2 = float_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()
@@ -145,7 +147,7 @@
rotate_image(dog);
}
end = clock();
- printf("Rotations: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
+ printf("Rotations: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
show_image(dog, "Test Rotate");
image random = make_random_image(3,3,3);
@@ -159,18 +161,18 @@
void test_parser()
{
network net = parse_network_cfg("test_parser.cfg");
- double input[1];
+ float input[1];
int count = 0;
- double avgerr = 0;
+ float avgerr = 0;
while(++count < 100000000){
- double v = ((double)rand()/RAND_MAX);
- double truth = v*v;
+ float v = ((float)rand()/RAND_MAX);
+ float 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.);
+ float *out = get_network_output(net);
+ float *delta = get_network_delta(net);
+ float err = pow((out[0]-truth),2.);
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];
@@ -192,9 +194,9 @@
srand(0);
int i = 0;
char *labels[] = {"cat","dog"};
- double lr = .00001;
- double momentum = .9;
- double decay = 0.01;
+ float lr = .00001;
+ float momentum = .9;
+ float 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);
@@ -207,32 +209,33 @@
{
srand(444444);
srand(888888);
- network net = parse_network_cfg("nist_basic.cfg");
+ network net = parse_network_cfg("nist.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;
+ float lr = .0005;
+ float momentum = .9;
+ float decay = 0.01;
clock_t start = clock(), end;
while(++count <= 100){
- visualize_network(net);
- double loss = train_network_sgd(net, train, 10000, lr, momentum, decay);
+ //visualize_network(net);
+ float loss = train_network_sgd(net, train, 1000, lr, momentum, decay);
printf("%5d Training Loss: %lf, Params: %f %f %f, ",count*100, loss, lr, momentum, decay);
end = clock();
- printf("Time: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
+ printf("Time: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
start=end;
cvWaitKey(100);
//lr /= 2;
if(count%5 == 0){
- double train_acc = network_accuracy(net, train);
+ float train_acc = network_accuracy(net, train);
fprintf(stderr, "\nTRAIN: %f\n", train_acc);
- double test_acc = network_accuracy(net, test);
+ float test_acc = network_accuracy(net, test);
fprintf(stderr, "TEST: %f\n\n", test_acc);
printf("%d, %f, %f\n", count, train_acc, test_acc);
+ lr *= .5;
}
}
}
@@ -253,24 +256,24 @@
int n = 30;
for(i = 0; i < n; ++i){
int count = 0;
- double lr = .0005;
- double momentum = .9;
- double decay = .01;
+ float lr = .0005;
+ float momentum = .9;
+ float 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);
+ float 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);
+ float 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);
+ float acc = matrix_accuracy(test.y, prediction);
printf("Full Ensemble Accuracy: %lf\n", acc);
}
@@ -279,19 +282,19 @@
network net = parse_network_cfg("connected.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);
+ float *truth = pop_column(&m, 0);
+ //float *ho_truth = pop_column(&ho, 0);
int i;
clock_t start = clock(), end;
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]);
+ //image p = float_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);
+ float *out = get_network_output(net);
+ float *delta = get_network_delta(net);
//printf("%f\n", out[0]);
delta[0] = truth[index] - out[0];
// printf("%f\n", delta[0]);
@@ -299,8 +302,8 @@
//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);
+ //float test_acc = error_network(net, m, truth);
+ //float 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;
@@ -311,12 +314,12 @@
truth = pop_column(&test, 0);
for(i = 0; i < test.rows; ++i){
forward_network(net, test.vals[i]);
- double *out = get_network_output(net);
+ float *out = get_network_output(net);
if(fabs(out[0]) < .5) fprintf(fp, "0\n");
else fprintf(fp, "1\n");
}
fclose(fp);
- printf("Neural Net Learning: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
+ printf("Neural Net Learning: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
}
void test_split()
@@ -326,30 +329,6 @@
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;
- }
- }
- return m;
-}
-
-void test_blas()
-{
- int m = 1000, n = 1000, k = 1000;
- 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;
@@ -362,16 +341,18 @@
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));
+ float *matrix = calloc(msize, sizeof(float));
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);
+ image render = float_to_image(mh, mw, mc, matrix);
}
}
int main()
{
+ //feenableexcept(FE_DIVBYZERO | FE_INVALID | FE_OVERFLOW);
+
//test_blas();
//test_convolve_matrix();
// test_im2row();
--
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