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About to do something stupid...

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This commit is contained in:
Joseph Redmon 2014-01-27 23:16:56 -08:00
parent ace5aeb0f5
commit b2b7137b6f
9 changed files with 218 additions and 173 deletions
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11
Makefile
View File

@ -1,6 +1,6 @@
CC=gcc CC=gcc
COMMON=-Wall `pkg-config --cflags opencv` COMMON=-Wall `pkg-config --cflags opencv`
CFLAGS= $(COMMON) -O3 -ffast-math -flto CFLAGS= $(COMMON) -Ofast -ffast-math -flto
UNAME = $(shell uname) UNAME = $(shell uname)
ifeq ($(UNAME), Darwin) ifeq ($(UNAME), Darwin)
COMMON += -isystem /usr/local/Cellar/opencv/2.4.6.1/include/opencv -isystem /usr/local/Cellar/opencv/2.4.6.1/include COMMON += -isystem /usr/local/Cellar/opencv/2.4.6.1/include/opencv -isystem /usr/local/Cellar/opencv/2.4.6.1/include
@ -10,12 +10,13 @@ endif
#CFLAGS= $(COMMON) -O0 -g #CFLAGS= $(COMMON) -O0 -g
LDFLAGS=`pkg-config --libs opencv` -lm LDFLAGS=`pkg-config --libs opencv` -lm
VPATH=./src/ VPATH=./src/
EXEC=cnn
OBJ=network.o image.o tests.o convolutional_layer.o connected_layer.o maxpool_layer.o activations.o list.o option_list.o parser.o utils.o data.o matrix.o softmax_layer.o mini_blas.o OBJ=network.o image.o tests.o connected_layer.o maxpool_layer.o activations.o list.o option_list.o parser.o utils.o data.o matrix.o softmax_layer.o mini_blas.o convolutional_layer.o
all: cnn all: $(EXEC)
cnn: $(OBJ) $(EXEC): $(OBJ)
$(CC) $(CFLAGS) $(LDFLAGS) $^ -o $@ $(CC) $(CFLAGS) $(LDFLAGS) $^ -o $@
%.o: %.c %.o: %.c
@ -24,5 +25,5 @@ cnn: $(OBJ)
.PHONY: clean .PHONY: clean
clean: clean:
rm -rf $(OBJ) cnn rm -rf $(OBJ) $(EXEC)

12
nist_basic.cfg
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@ -1,7 +1,11 @@
[conn] [conv]
input=784 width=28
output = 100 height=28
activation=ramp channels=1
filters=20
size=5
stride=1
activation=linear
[conn] [conn]
output = 10 output = 10

195
src/convolutional_layer.c
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@ -1,17 +1,13 @@
#include "convolutional_layer.h" #include "convolutional_layer.h"
#include "utils.h" #include "utils.h"
#include "mini_blas.h"
#include <stdio.h> #include <stdio.h>
image get_convolutional_image(convolutional_layer layer) image get_convolutional_image(convolutional_layer layer)
{ {
int h,w,c; int h,w,c;
if(layer.edge){ h = layer.out_h;
h = (layer.h-1)/layer.stride + 1; w = layer.out_w;
w = (layer.w-1)/layer.stride + 1;
}else{
h = (layer.h - layer.size)/layer.stride+1;
w = (layer.h - layer.size)/layer.stride+1;
}
c = layer.n; c = layer.n;
return double_to_image(h,w,c,layer.output); return double_to_image(h,w,c,layer.output);
} }
@ -19,13 +15,8 @@ image get_convolutional_image(convolutional_layer layer)
image get_convolutional_delta(convolutional_layer layer) image get_convolutional_delta(convolutional_layer layer)
{ {
int h,w,c; int h,w,c;
if(layer.edge){ h = layer.out_h;
h = (layer.h-1)/layer.stride + 1; w = layer.out_w;
w = (layer.w-1)/layer.stride + 1;
}else{
h = (layer.h - layer.size)/layer.stride+1;
w = (layer.h - layer.size)/layer.stride+1;
}
c = layer.n; c = layer.n;
return double_to_image(h,w,c,layer.delta); return double_to_image(h,w,c,layer.delta);
} }
@ -34,74 +25,114 @@ convolutional_layer *make_convolutional_layer(int h, int w, int c, int n, int si
{ {
int i; int i;
int out_h,out_w; int out_h,out_w;
size = 2*(size/2)+1; //HA! And you thought you'd use an even sized filter...
convolutional_layer *layer = calloc(1, sizeof(convolutional_layer)); convolutional_layer *layer = calloc(1, sizeof(convolutional_layer));
layer->h = h; layer->h = h;
layer->w = w; layer->w = w;
layer->c = c; layer->c = c;
layer->n = n; layer->n = n;
layer->edge = 0;
layer->stride = stride; layer->stride = stride;
layer->kernels = calloc(n, sizeof(image)); layer->size = size;
layer->kernel_updates = calloc(n, sizeof(image));
layer->kernel_momentum = calloc(n, sizeof(image)); layer->filters = calloc(c*n*size*size, sizeof(double));
layer->filter_updates = calloc(c*n*size*size, sizeof(double));
layer->filter_momentum = calloc(c*n*size*size, sizeof(double));
layer->biases = calloc(n, sizeof(double)); layer->biases = calloc(n, sizeof(double));
layer->bias_updates = calloc(n, sizeof(double)); layer->bias_updates = calloc(n, sizeof(double));
layer->bias_momentum = calloc(n, sizeof(double)); layer->bias_momentum = calloc(n, sizeof(double));
double scale = 2./(size*size); double scale = 2./(size*size);
for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = rand_normal()*scale;
for(i = 0; i < n; ++i){ for(i = 0; i < n; ++i){
//layer->biases[i] = rand_normal()*scale + scale; //layer->biases[i] = rand_normal()*scale + scale;
layer->biases[i] = 0; layer->biases[i] = 0;
layer->kernels[i] = make_random_kernel(size, c, scale);
layer->kernel_updates[i] = make_random_kernel(size, c, 0);
layer->kernel_momentum[i] = make_random_kernel(size, c, 0);
} }
layer->size = 2*(size/2)+1; out_h = (h-size)/stride + 1;
if(layer->edge){ out_w = (w-size)/stride + 1;
out_h = (layer->h-1)/layer->stride + 1;
out_w = (layer->w-1)/layer->stride + 1; layer->col_image = calloc(out_h*out_w*size*size*c, sizeof(double));
}else{
out_h = (layer->h - layer->size)/layer->stride+1;
out_w = (layer->h - layer->size)/layer->stride+1;
}
fprintf(stderr, "Convolutional Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n);
layer->output = calloc(out_h * out_w * n, sizeof(double)); layer->output = calloc(out_h * out_w * n, sizeof(double));
layer->delta = calloc(out_h * out_w * n, sizeof(double)); layer->delta = calloc(out_h * out_w * n, sizeof(double));
layer->upsampled = make_image(h,w,n);
layer->activation = activation; layer->activation = activation;
layer->out_h = out_h;
layer->out_w = out_w;
fprintf(stderr, "Convolutional Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n);
srand(0);
return layer; return layer;
} }
void forward_convolutional_layer(const convolutional_layer layer, double *in) void forward_convolutional_layer(const convolutional_layer layer, double *in)
{ {
image input = double_to_image(layer.h, layer.w, layer.c, in); int m = layer.n;
image output = get_convolutional_image(layer); int k = layer.size*layer.size*layer.c;
int i,j; int n = ((layer.h-layer.size)/layer.stride + 1)*
for(i = 0; i < layer.n; ++i){ ((layer.w-layer.size)/layer.stride + 1);
convolve(input, layer.kernels[i], layer.stride, i, output, layer.edge);
} memset(layer.output, 0, m*n*sizeof(double));
for(i = 0; i < output.c; ++i){
for(j = 0; j < output.h*output.w; ++j){ double *a = layer.filters;
int index = i*output.h*output.w + j; double *b = layer.col_image;
output.data[index] += layer.biases[i]; double *c = layer.output;
output.data[index] = activate(output.data[index], layer.activation);
} im2col_cpu(in, layer.c, layer.h, layer.w, layer.size, layer.stride, b);
} gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
} }
void backward_convolutional_layer(convolutional_layer layer, double *input, double *delta) void gradient_delta_convolutional_layer(convolutional_layer layer)
{ {
int i; int i;
for(i = 0; i < layer.out_h*layer.out_w*layer.n; ++i){
image in_delta = double_to_image(layer.h, layer.w, layer.c, delta); layer.delta[i] *= gradient(layer.output[i], layer.activation);
image out_delta = get_convolutional_delta(layer);
zero_image(in_delta);
for(i = 0; i < layer.n; ++i){
back_convolve(in_delta, layer.kernels[i], layer.stride, i, out_delta, layer.edge);
} }
} }
void learn_bias_convolutional_layer(convolutional_layer layer)
{
int i,j;
int size = layer.out_h*layer.out_w;
for(i = 0; i < layer.n; ++i){
double sum = 0;
for(j = 0; j < size; ++j){
sum += layer.delta[j+i*size];
}
layer.bias_updates[i] += sum/size;
}
}
void learn_convolutional_layer(convolutional_layer layer)
{
gradient_delta_convolutional_layer(layer);
learn_bias_convolutional_layer(layer);
int m = layer.n;
int n = layer.size*layer.size*layer.c;
int k = ((layer.h-layer.size)/layer.stride + 1)*
((layer.w-layer.size)/layer.stride + 1);
double *a = layer.delta;
double *b = layer.col_image;
double *c = layer.filter_updates;
gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
}
void update_convolutional_layer(convolutional_layer layer, double step, double momentum, double decay)
{
int i;
int size = layer.size*layer.size*layer.c*layer.n;
for(i = 0; i < layer.n; ++i){
layer.biases[i] += step*layer.bias_updates[i];
layer.bias_updates[i] *= momentum;
}
for(i = 0; i < size; ++i){
layer.filters[i] += step*(layer.filter_updates[i] - decay*layer.filters[i]);
layer.filter_updates[i] *= momentum;
}
}
/*
void backward_convolutional_layer2(convolutional_layer layer, double *input, double *delta) void backward_convolutional_layer2(convolutional_layer layer, double *input, double *delta)
{ {
image in_delta = double_to_image(layer.h, layer.w, layer.c, delta); image in_delta = double_to_image(layer.h, layer.w, layer.c, delta);
@ -124,15 +155,6 @@ void backward_convolutional_layer2(convolutional_layer layer, double *input, dou
} }
} }
void gradient_delta_convolutional_layer(convolutional_layer layer)
{
int i;
image out_delta = get_convolutional_delta(layer);
image out_image = get_convolutional_image(layer);
for(i = 0; i < out_image.h*out_image.w*out_image.c; ++i){
out_delta.data[i] *= gradient(out_image.data[i], layer.activation);
}
}
void learn_convolutional_layer(convolutional_layer layer, double *input) void learn_convolutional_layer(convolutional_layer layer, double *input)
{ {
@ -163,8 +185,37 @@ void update_convolutional_layer(convolutional_layer layer, double step, double m
zero_image(layer.kernel_updates[i]); zero_image(layer.kernel_updates[i]);
} }
} }
*/
void visualize_convolutional_filters(convolutional_layer layer, char *window) void test_convolutional_layer()
{
convolutional_layer l = *make_convolutional_layer(4,4,1,1,3,1,LINEAR);
double input[] = {1,2,3,4,
5,6,7,8,
9,10,11,12,
13,14,15,16};
double filter[] = {.5, 0, .3,
0 , 1, 0,
.2 , 0, 1};
double delta[] = {1, 2,
3, 4};
l.filters = filter;
forward_convolutional_layer(l, input);
l.delta = delta;
learn_convolutional_layer(l);
image filter_updates = double_to_image(3,3,1,l.filter_updates);
print_image(filter_updates);
}
image get_convolutional_filter(convolutional_layer layer, int i)
{
int h = layer.size;
int w = layer.size;
int c = layer.c;
return double_to_image(h,w,c,layer.filters+i*h*w*c);
}
void visualize_convolutional_layer(convolutional_layer layer, char *window)
{ {
int color = 1; int color = 1;
int border = 1; int border = 1;
@ -172,7 +223,7 @@ void visualize_convolutional_filters(convolutional_layer layer, char *window)
int size = layer.size; int size = layer.size;
h = size; h = size;
w = (size + border) * layer.n - border; w = (size + border) * layer.n - border;
c = layer.kernels[0].c; c = layer.c;
if(c != 3 || !color){ if(c != 3 || !color){
h = (h+border)*c - border; h = (h+border)*c - border;
c = 1; c = 1;
@ -182,11 +233,13 @@ void visualize_convolutional_filters(convolutional_layer layer, char *window)
int i,j; int i,j;
for(i = 0; i < layer.n; ++i){ for(i = 0; i < layer.n; ++i){
int w_offset = i*(size+border); int w_offset = i*(size+border);
image k = layer.kernels[i]; image k = get_convolutional_filter(layer, i);
//printf("%f ** ", layer.biases[i]);
//print_image(k);
image copy = copy_image(k); image copy = copy_image(k);
normalize_image(copy); normalize_image(copy);
for(j = 0; j < k.c; ++j){ for(j = 0; j < k.c; ++j){
set_pixel(copy,0,0,j,layer.biases[i]); //set_pixel(copy,0,0,j,layer.biases[i]);
} }
if(c == 3 && color){ if(c == 3 && color){
embed_image(copy, filters, 0, w_offset); embed_image(copy, filters, 0, w_offset);
@ -211,15 +264,3 @@ void visualize_convolutional_filters(convolutional_layer layer, char *window)
free_image(filters); free_image(filters);
} }
void visualize_convolutional_layer(convolutional_layer layer)
{
int i;
char buff[256];
for(i = 0; i < layer.n; ++i){
image k = layer.kernels[i];
sprintf(buff, "Kernel %d", i);
if(k.c <= 3) show_image(k, buff);
else show_image_layers(k, buff);
}
}

26
src/convolutional_layer.h
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@ -6,36 +6,40 @@
typedef struct { typedef struct {
int h,w,c; int h,w,c;
int out_h, out_w, out_c;
int n; int n;
int size; int size;
int stride; int stride;
image *kernels; double *filters;
image *kernel_updates; double *filter_updates;
image *kernel_momentum; double *filter_momentum;
double *biases; double *biases;
double *bias_updates; double *bias_updates;
double *bias_momentum; double *bias_momentum;
image upsampled;
double *col_image;
double *delta; double *delta;
double *output; double *output;
ACTIVATION activation; ACTIVATION activation;
int edge;
} convolutional_layer; } convolutional_layer;
convolutional_layer *make_convolutional_layer(int h, int w, int c, int n, int size, int stride, ACTIVATION activation); convolutional_layer *make_convolutional_layer(int h, int w, int c, int n, int size, int stride, ACTIVATION activation);
void forward_convolutional_layer(const convolutional_layer layer, double *in); void forward_convolutional_layer(const convolutional_layer layer, double *in);
void backward_convolutional_layer(convolutional_layer layer, double *input, double *delta); void learn_convolutional_layer(convolutional_layer layer);
void learn_convolutional_layer(convolutional_layer layer, double *input);
void update_convolutional_layer(convolutional_layer layer, double step, double momentum, double decay); void update_convolutional_layer(convolutional_layer layer, double step, double momentum, double decay);
void visualize_convolutional_layer(convolutional_layer layer, char *window);
void backpropagate_convolutional_layer_convolve(image input, convolutional_layer layer); //void backward_convolutional_layer(convolutional_layer layer, double *input, double *delta);
void visualize_convolutional_filters(convolutional_layer layer, char *window);
void visualize_convolutional_layer(convolutional_layer layer); //void backpropagate_convolutional_layer_convolve(image input, convolutional_layer layer);
//void visualize_convolutional_filters(convolutional_layer layer, char *window);
//void visualize_convolutional_layer(convolutional_layer layer);
image get_convolutional_image(convolutional_layer layer); image get_convolutional_image(convolutional_layer layer);
image get_convolutional_delta(convolutional_layer layer); image get_convolutional_delta(convolutional_layer layer);
image get_convolutional_filter(convolutional_layer layer, int i);
#endif #endif

38
src/mini_blas.c
View File

@ -1,16 +1,44 @@
#include <stdlib.h>
#include <math.h>
void pm(int M, int N, double *A)
{
int i,j;
for(i =0 ; i < M; ++i){
for(j = 0; j < N; ++j){
printf("%10.6f, ", A[i*N+j]);
}
printf("\n");
}
printf("\n");
}
void gemm(int TA, int TB, int M, int N, int K, double ALPHA, void gemm(int TA, int TB, int M, int N, int K, double ALPHA,
double *A, int lda, double *A, int lda,
double *B, int ldb, double *B, int ldb,
double BETA, double BETA,
double *C, int ldc) double *C, int ldc)
{ {
// Assume TA = TB = 0, beta = 1 LULZ // Assume TA = 0, beta = 1 LULZ
int i,j,k; int i,j,k;
for(i = 0; i < M; ++i){ if(TB && !TA){
for(k = 0; k < K; ++k){ for(i = 0; i < M; ++i){
for(j = 0; j < N; ++j){ for(j = 0; j < N; ++j){
C[i*ldc+j] += ALPHA*A[i*lda+k]*B[k*ldb+j]; register double sum = 0;
for(k = 0; k < K; ++k){
sum += ALPHA*A[i*lda+k]*B[k+j*ldb];
}
C[i*ldc+j] += sum;
}
}
}else{
for(i = 0; i < M; ++i){
for(k = 0; k < K; ++k){
register double A_PART = ALPHA*A[i*lda+k];
for(j = 0; j < N; ++j){
C[i*ldc+j] += A_PART*B[k*ldb+j];
}
} }
} }
} }
@ -59,7 +87,7 @@ void im2col(double *image, int h, int w, int c, int size, int stride, double *ma
void im2col_cpu(double* data_im, const int channels, void im2col_cpu(double* data_im, const int channels,
const int height, const int width, const int ksize, const int stride, const int height, const int width, const int ksize, const int stride,
double* data_col) double* data_col)
{ {
int c,h,w; int c,h,w;
int height_col = (height - ksize) / stride + 1; int height_col = (height - ksize) / stride + 1;
int width_col = (width - ksize) / stride + 1; int width_col = (width - ksize) / stride + 1;

1
src/mini_blas.h
View File

@ -1,3 +1,4 @@
void pm(int M, int N, double *A);
void gemm(int TA, int TB, int M, int N, int K, double ALPHA, void gemm(int TA, int TB, int M, int N, int K, double ALPHA,
double *A, int lda, double *A, int lda,
double *B, int ldb, double *B, int ldb,

31
src/network.c
View File

@ -6,6 +6,7 @@
#include "connected_layer.h" #include "connected_layer.h"
#include "convolutional_layer.h" #include "convolutional_layer.h"
//#include "old_conv.h"
#include "maxpool_layer.h" #include "maxpool_layer.h"
#include "softmax_layer.h" #include "softmax_layer.h"
@ -113,14 +114,17 @@ double *get_network_delta(network net)
return get_network_delta_layer(net, net.n-1); return get_network_delta_layer(net, net.n-1);
} }
void calculate_error_network(network net, double *truth) double calculate_error_network(network net, double *truth)
{ {
double sum = 0;
double *delta = get_network_delta(net); double *delta = get_network_delta(net);
double *out = get_network_output(net); double *out = get_network_output(net);
int i, k = get_network_output_size(net); int i, k = get_network_output_size(net);
for(i = 0; i < k; ++i){ for(i = 0; i < k; ++i){
delta[i] = truth[i] - out[i]; delta[i] = truth[i] - out[i];
sum += delta[i]*delta[i];
} }
return sum;
} }
int get_predicted_class_network(network net) int get_predicted_class_network(network net)
@ -130,9 +134,9 @@ int get_predicted_class_network(network net)
return max_index(out, k); return max_index(out, k);
} }
void backward_network(network net, double *input, double *truth) double backward_network(network net, double *input, double *truth)
{ {
calculate_error_network(net, truth); double error = calculate_error_network(net, truth);
int i; int i;
double *prev_input; double *prev_input;
double *prev_delta; double *prev_delta;
@ -146,8 +150,9 @@ void backward_network(network net, double *input, double *truth)
} }
if(net.types[i] == CONVOLUTIONAL){ if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i]; convolutional_layer layer = *(convolutional_layer *)net.layers[i];
learn_convolutional_layer(layer, prev_input); learn_convolutional_layer(layer);
if(i != 0) backward_convolutional_layer(layer, prev_input, prev_delta); //learn_convolutional_layer(layer);
//if(i != 0) backward_convolutional_layer(layer, prev_input, prev_delta);
} }
else if(net.types[i] == MAXPOOL){ else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i]; maxpool_layer layer = *(maxpool_layer *)net.layers[i];
@ -163,29 +168,31 @@ void backward_network(network net, double *input, double *truth)
if(i != 0) backward_connected_layer(layer, prev_input, prev_delta); if(i != 0) backward_connected_layer(layer, prev_input, prev_delta);
} }
} }
return error;
} }
int train_network_datum(network net, double *x, double *y, double step, double momentum, double decay) double train_network_datum(network net, double *x, double *y, double step, double momentum, double decay)
{ {
forward_network(net, x); forward_network(net, x);
int class = get_predicted_class_network(net); int class = get_predicted_class_network(net);
backward_network(net, x, y); double error = backward_network(net, x, y);
update_network(net, step, momentum, decay); update_network(net, step, momentum, decay);
return (y[class]?1:0); //return (y[class]?1:0);
return error;
} }
double train_network_sgd(network net, data d, int n, double step, double momentum,double decay) double train_network_sgd(network net, data d, int n, double step, double momentum,double decay)
{ {
int i; int i;
int correct = 0; double error = 0;
for(i = 0; i < n; ++i){ for(i = 0; i < n; ++i){
int index = rand()%d.X.rows; int index = rand()%d.X.rows;
correct += train_network_datum(net, d.X.vals[index], d.y.vals[index], step, momentum, decay); error += train_network_datum(net, d.X.vals[index], d.y.vals[index], step, momentum, decay);
//if((i+1)%10 == 0){ //if((i+1)%10 == 0){
// printf("%d: %f\n", (i+1), (double)correct/(i+1)); // printf("%d: %f\n", (i+1), (double)correct/(i+1));
//} //}
} }
return (double)correct/n; return error/n;
} }
double train_network_batch(network net, data d, int n, double step, double momentum,double decay) double train_network_batch(network net, data d, int n, double step, double momentum,double decay)
{ {
@ -282,7 +289,7 @@ void visualize_network(network net)
sprintf(buff, "Layer %d", i); sprintf(buff, "Layer %d", i);
if(net.types[i] == CONVOLUTIONAL){ if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i]; convolutional_layer layer = *(convolutional_layer *)net.layers[i];
visualize_convolutional_filters(layer, buff); visualize_convolutional_layer(layer, buff);
} }
} }
} }

2
src/network.h
View File

@ -22,7 +22,7 @@ typedef struct {
network make_network(int n); network make_network(int n);
void forward_network(network net, double *input); void forward_network(network net, double *input);
void backward_network(network net, double *input, double *truth); double backward_network(network net, double *input, double *truth);
void update_network(network net, double step, double momentum, double decay); void update_network(network net, double step, double momentum, double decay);
double train_network_sgd(network net, data d, int n, double step, double momentum,double decay); double train_network_sgd(network net, data d, int n, double step, double momentum,double decay);
double train_network_batch(network net, data d, int n, double step, double momentum,double decay); double train_network_batch(network net, data d, int n, double step, double momentum,double decay);

75
src/tests.c
View File

@ -1,4 +1,5 @@
#include "connected_layer.h" #include "connected_layer.h"
//#include "old_conv.h"
#include "convolutional_layer.h" #include "convolutional_layer.h"
#include "maxpool_layer.h" #include "maxpool_layer.h"
#include "network.h" #include "network.h"
@ -35,7 +36,7 @@ void test_convolve_matrix()
printf("dog channels %d\n", dog.c); printf("dog channels %d\n", dog.c);
int size = 11; int size = 11;
int stride = 1; int stride = 4;
int n = 40; int n = 40;
double *filters = make_random_image(size, size, dog.c*n).data; double *filters = make_random_image(size, size, dog.c*n).data;
@ -64,29 +65,6 @@ void test_color()
show_image_layers(dog, "Test Color"); show_image_layers(dog, "Test Color");
} }
void test_convolutional_layer()
{
srand(0);
image dog = load_image("dog.jpg");
int i;
int n = 3;
int stride = 1;
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);
}
forward_convolutional_layer(layer, dog.data);
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(get_maxpool_image(mlayer), "Test Maxpool Layer");
}
void verify_convolutional_layer() void verify_convolutional_layer()
{ {
srand(0); srand(0);
@ -117,7 +95,7 @@ void verify_convolutional_layer()
image out_delta = get_convolutional_delta(layer); image out_delta = get_convolutional_delta(layer);
for(i = 0; i < out.h*out.w*out.c; ++i){ for(i = 0; i < out.h*out.w*out.c; ++i){
out_delta.data[i] = 1; out_delta.data[i] = 1;
backward_convolutional_layer(layer, test.data, in_delta.data); //backward_convolutional_layer(layer, test.data, in_delta.data);
image partial = copy_image(in_delta); image partial = copy_image(in_delta);
jacobian2[i] = partial.data; jacobian2[i] = partial.data;
out_delta.data[i] = 0; out_delta.data[i] = 0;
@ -240,16 +218,16 @@ void test_nist()
double momentum = .9; double momentum = .9;
double decay = 0.01; double decay = 0.01;
clock_t start = clock(), end; clock_t start = clock(), end;
while(++count <= 1000){ while(++count <= 100){
double acc = train_network_sgd(net, train, 6400, lr, momentum, decay); visualize_network(net);
printf("%5d Training Loss: %lf, Params: %f %f %f, ",count*100, 1.-acc, lr, momentum, decay); double loss = train_network_sgd(net, train, 10000, lr, momentum, decay);
printf("%5d Training Loss: %lf, Params: %f %f %f, ",count*100, loss, lr, momentum, decay);
end = clock(); end = clock();
printf("Time: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC); printf("Time: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
start=end; start=end;
//visualize_network(net); cvWaitKey(100);
//cvWaitKey(100);
//lr /= 2; //lr /= 2;
if(count%5 == 0 && 0){ if(count%5 == 0){
double train_acc = network_accuracy(net, train); double train_acc = network_accuracy(net, train);
fprintf(stderr, "\nTRAIN: %f\n", train_acc); fprintf(stderr, "\nTRAIN: %f\n", train_acc);
double test_acc = network_accuracy(net, test); double test_acc = network_accuracy(net, test);
@ -268,11 +246,9 @@ void test_ensemble()
data test = load_categorical_data_csv("mnist/mnist_test.csv", 0,10); data test = load_categorical_data_csv("mnist/mnist_test.csv", 0,10);
normalize_data_rows(test); normalize_data_rows(test);
data train = d; data train = d;
/* // data *split = split_data(d, 1, 10);
data *split = split_data(d, 1, 10); // data train = split[0];
data train = split[0]; // data test = split[1];
data test = split[1];
*/
matrix prediction = make_matrix(test.y.rows, test.y.cols); matrix prediction = make_matrix(test.y.rows, test.y.cols);
int n = 30; int n = 30;
for(i = 0; i < n; ++i){ for(i = 0; i < n; ++i){
@ -298,22 +274,6 @@ void test_ensemble()
printf("Full Ensemble Accuracy: %lf\n", acc); printf("Full Ensemble Accuracy: %lf\n", acc);
} }
void test_kernel_update()
{
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() void test_random_classify()
{ {
network net = parse_network_cfg("connected.cfg"); network net = parse_network_cfg("connected.cfg");
@ -380,7 +340,7 @@ double *random_matrix(int rows, int cols)
void test_blas() void test_blas()
{ {
int m = 6025, n = 20, k = 11*11*3; int m = 1000, n = 1000, k = 1000;
double *a = random_matrix(m,k); double *a = random_matrix(m,k);
double *b = random_matrix(k,n); double *b = random_matrix(k,n);
double *c = random_matrix(m,n); double *c = random_matrix(m,n);
@ -405,17 +365,16 @@ void test_im2row()
double *matrix = calloc(msize, sizeof(double)); double *matrix = calloc(msize, sizeof(double));
int i; int i;
for(i = 0; i < 1000; ++i){ for(i = 0; i < 1000; ++i){
im2col_cpu(test.data, c, h, w, size, stride, matrix); im2col_cpu(test.data, c, h, w, size, stride, matrix);
image render = double_to_image(mh, mw, mc, matrix); image render = double_to_image(mh, mw, mc, matrix);
} }
} }
int main() int main()
{ {
//test_blas(); //test_blas();
//test_convolve_matrix(); //test_convolve_matrix();
// test_im2row(); // test_im2row();
//test_kernel_update();
//test_split(); //test_split();
//test_ensemble(); //test_ensemble();
test_nist(); test_nist();