From b2b7137b6f185ce2f01664d782a09b08d50d5a07 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 28 Jan 2014 07:16:56 +0000
Subject: [PATCH] About to do something stupid...
---
nist_basic.cfg | 12 +
src/network.c | 31 ++-
src/mini_blas.c | 38 ++++
src/network.h | 2
Makefile | 11
src/convolutional_layer.c | 195 ++++++++++++++++-----------
src/mini_blas.h | 1
src/convolutional_layer.h | 26 ++-
src/tests.c | 75 ++--------
9 files changed, 218 insertions(+), 173 deletions(-)
diff --git a/Makefile b/Makefile
index 415c522..dc08b46 100644
--- a/Makefile
+++ b/Makefile
@@ -1,6 +1,6 @@
CC=gcc
COMMON=-Wall `pkg-config --cflags opencv`
-CFLAGS= $(COMMON) -O3 -ffast-math -flto
+CFLAGS= $(COMMON) -Ofast -ffast-math -flto
UNAME = $(shell uname)
ifeq ($(UNAME), Darwin)
COMMON += -isystem /usr/local/Cellar/opencv/2.4.6.1/include/opencv -isystem /usr/local/Cellar/opencv/2.4.6.1/include
@@ -10,12 +10,13 @@
#CFLAGS= $(COMMON) -O0 -g
LDFLAGS=`pkg-config --libs opencv` -lm
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 $@
%.o: %.c
@@ -24,5 +25,5 @@
.PHONY: clean
clean:
- rm -rf $(OBJ) cnn
+ rm -rf $(OBJ) $(EXEC)
diff --git a/nist_basic.cfg b/nist_basic.cfg
index 3b55166..f5ea0a3 100644
--- a/nist_basic.cfg
+++ b/nist_basic.cfg
@@ -1,7 +1,11 @@
-[conn]
-input=784
-output = 100
-activation=ramp
+[conv]
+width=28
+height=28
+channels=1
+filters=20
+size=5
+stride=1
+activation=linear
[conn]
output = 10
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index ef48120..53eb7bf 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -1,17 +1,13 @@
#include "convolutional_layer.h"
#include "utils.h"
+#include "mini_blas.h"
#include <stdio.h>
image get_convolutional_image(convolutional_layer layer)
{
int h,w,c;
- if(layer.edge){
- h = (layer.h-1)/layer.stride + 1;
- w = (layer.w-1)/layer.stride + 1;
- }else{
- h = (layer.h - layer.size)/layer.stride+1;
- w = (layer.h - layer.size)/layer.stride+1;
- }
+ h = layer.out_h;
+ w = layer.out_w;
c = layer.n;
return double_to_image(h,w,c,layer.output);
}
@@ -19,13 +15,8 @@
image get_convolutional_delta(convolutional_layer layer)
{
int h,w,c;
- if(layer.edge){
- h = (layer.h-1)/layer.stride + 1;
- w = (layer.w-1)/layer.stride + 1;
- }else{
- h = (layer.h - layer.size)/layer.stride+1;
- w = (layer.h - layer.size)/layer.stride+1;
- }
+ h = layer.out_h;
+ w = layer.out_w;
c = layer.n;
return double_to_image(h,w,c,layer.delta);
}
@@ -34,74 +25,114 @@
{
int i;
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));
layer->h = h;
layer->w = w;
layer->c = c;
layer->n = n;
- layer->edge = 0;
layer->stride = stride;
- layer->kernels = calloc(n, sizeof(image));
- layer->kernel_updates = calloc(n, sizeof(image));
- layer->kernel_momentum = calloc(n, sizeof(image));
+ layer->size = size;
+
+ 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->bias_updates = calloc(n, sizeof(double));
layer->bias_momentum = calloc(n, sizeof(double));
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){
//layer->biases[i] = rand_normal()*scale + scale;
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;
- if(layer->edge){
- out_h = (layer->h-1)/layer->stride + 1;
- out_w = (layer->w-1)/layer->stride + 1;
- }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);
+ out_h = (h-size)/stride + 1;
+ out_w = (w-size)/stride + 1;
+
+ layer->col_image = calloc(out_h*out_w*size*size*c, sizeof(double));
layer->output = 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->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;
}
void forward_convolutional_layer(const convolutional_layer layer, double *in)
{
- image input = double_to_image(layer.h, layer.w, layer.c, in);
- image output = get_convolutional_image(layer);
- int i,j;
- for(i = 0; i < layer.n; ++i){
- convolve(input, layer.kernels[i], layer.stride, i, output, layer.edge);
- }
- for(i = 0; i < output.c; ++i){
- for(j = 0; j < output.h*output.w; ++j){
- int index = i*output.h*output.w + j;
- output.data[index] += layer.biases[i];
- output.data[index] = activate(output.data[index], layer.activation);
- }
- }
+ int m = layer.n;
+ int k = layer.size*layer.size*layer.c;
+ int n = ((layer.h-layer.size)/layer.stride + 1)*
+ ((layer.w-layer.size)/layer.stride + 1);
+
+ memset(layer.output, 0, m*n*sizeof(double));
+
+ double *a = layer.filters;
+ double *b = layer.col_image;
+ double *c = layer.output;
+
+ 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;
-
- image in_delta = double_to_image(layer.h, layer.w, layer.c, delta);
- 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);
+ for(i = 0; i < layer.out_h*layer.out_w*layer.n; ++i){
+ layer.delta[i] *= gradient(layer.output[i], layer.activation);
}
}
+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)
{
image in_delta = double_to_image(layer.h, layer.w, layer.c, delta);
@@ -124,15 +155,6 @@
}
}
-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)
{
@@ -163,8 +185,37 @@
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 border = 1;
@@ -172,7 +223,7 @@
int size = layer.size;
h = size;
w = (size + border) * layer.n - border;
- c = layer.kernels[0].c;
+ c = layer.c;
if(c != 3 || !color){
h = (h+border)*c - border;
c = 1;
@@ -182,11 +233,13 @@
int i,j;
for(i = 0; i < layer.n; ++i){
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);
normalize_image(copy);
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){
embed_image(copy, filters, 0, w_offset);
@@ -211,15 +264,3 @@
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);
- }
-}
-
diff --git a/src/convolutional_layer.h b/src/convolutional_layer.h
index 135d983..e2e6cdc 100644
--- a/src/convolutional_layer.h
+++ b/src/convolutional_layer.h
@@ -6,36 +6,40 @@
typedef struct {
int h,w,c;
+ int out_h, out_w, out_c;
int n;
int size;
int stride;
- image *kernels;
- image *kernel_updates;
- image *kernel_momentum;
+ double *filters;
+ double *filter_updates;
+ double *filter_momentum;
+
double *biases;
double *bias_updates;
double *bias_momentum;
- image upsampled;
+
+ double *col_image;
double *delta;
double *output;
ACTIVATION activation;
- int edge;
} convolutional_layer;
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 backward_convolutional_layer(convolutional_layer layer, double *input, double *delta);
-void learn_convolutional_layer(convolutional_layer layer, double *input);
-
+void learn_convolutional_layer(convolutional_layer layer);
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 visualize_convolutional_filters(convolutional_layer layer, char *window);
-void visualize_convolutional_layer(convolutional_layer layer);
+//void backward_convolutional_layer(convolutional_layer layer, double *input, double *delta);
+
+//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_delta(convolutional_layer layer);
+image get_convolutional_filter(convolutional_layer layer, int i);
#endif
diff --git a/src/mini_blas.c b/src/mini_blas.c
index b15ba8e..3af36e5 100644
--- a/src/mini_blas.c
+++ b/src/mini_blas.c
@@ -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,
double *A, int lda,
double *B, int ldb,
double BETA,
double *C, int ldc)
{
- // Assume TA = TB = 0, beta = 1 LULZ
+ // Assume TA = 0, beta = 1 LULZ
int i,j,k;
- for(i = 0; i < M; ++i){
- for(k = 0; k < K; ++k){
+ if(TB && !TA){
+ for(i = 0; i < M; ++i){
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_cpu(double* data_im, const int channels,
const int height, const int width, const int ksize, const int stride,
double* data_col)
- {
+{
int c,h,w;
int height_col = (height - ksize) / stride + 1;
int width_col = (width - ksize) / stride + 1;
diff --git a/src/mini_blas.h b/src/mini_blas.h
index cdf3a86..46a37d3 100644
--- a/src/mini_blas.h
+++ b/src/mini_blas.h
@@ -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,
double *A, int lda,
double *B, int ldb,
diff --git a/src/network.c b/src/network.c
index 07ac621..2ce13d8 100644
--- a/src/network.c
+++ b/src/network.c
@@ -6,6 +6,7 @@
#include "connected_layer.h"
#include "convolutional_layer.h"
+//#include "old_conv.h"
#include "maxpool_layer.h"
#include "softmax_layer.h"
@@ -113,14 +114,17 @@
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 *out = get_network_output(net);
int i, k = get_network_output_size(net);
for(i = 0; i < k; ++i){
delta[i] = truth[i] - out[i];
+ sum += delta[i]*delta[i];
}
+ return sum;
}
int get_predicted_class_network(network net)
@@ -130,9 +134,9 @@
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;
double *prev_input;
double *prev_delta;
@@ -146,8 +150,9 @@
}
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- learn_convolutional_layer(layer, prev_input);
- if(i != 0) backward_convolutional_layer(layer, prev_input, prev_delta);
+ learn_convolutional_layer(layer);
+ //learn_convolutional_layer(layer);
+ //if(i != 0) backward_convolutional_layer(layer, prev_input, prev_delta);
}
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
@@ -163,29 +168,31 @@
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);
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);
- 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)
{
int i;
- int correct = 0;
+ double error = 0;
for(i = 0; i < n; ++i){
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){
// 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)
{
@@ -282,7 +289,7 @@
sprintf(buff, "Layer %d", i);
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- visualize_convolutional_filters(layer, buff);
+ visualize_convolutional_layer(layer, buff);
}
}
}
diff --git a/src/network.h b/src/network.h
index 975c3dd..fa109dd 100644
--- a/src/network.h
+++ b/src/network.h
@@ -22,7 +22,7 @@
network make_network(int n);
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);
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);
diff --git a/src/tests.c b/src/tests.c
index c459a36..af22ddb 100644
--- a/src/tests.c
+++ b/src/tests.c
@@ -1,4 +1,5 @@
#include "connected_layer.h"
+//#include "old_conv.h"
#include "convolutional_layer.h"
#include "maxpool_layer.h"
#include "network.h"
@@ -35,7 +36,7 @@
printf("dog channels %d\n", dog.c);
int size = 11;
- int stride = 1;
+ int stride = 4;
int n = 40;
double *filters = make_random_image(size, size, dog.c*n).data;
@@ -64,29 +65,6 @@
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()
{
srand(0);
@@ -117,7 +95,7 @@
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, test.data, in_delta.data);
image partial = copy_image(in_delta);
jacobian2[i] = partial.data;
out_delta.data[i] = 0;
@@ -240,16 +218,16 @@
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);
+ while(++count <= 100){
+ visualize_network(net);
+ 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();
printf("Time: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
start=end;
- //visualize_network(net);
- //cvWaitKey(100);
+ cvWaitKey(100);
//lr /= 2;
- if(count%5 == 0 && 0){
+ if(count%5 == 0){
double train_acc = network_accuracy(net, train);
fprintf(stderr, "\nTRAIN: %f\n", train_acc);
double test_acc = network_accuracy(net, test);
@@ -268,11 +246,9 @@
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];
- */
+ // 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){
@@ -298,22 +274,6 @@
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()
{
network net = parse_network_cfg("connected.cfg");
@@ -380,7 +340,7 @@
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 *b = random_matrix(k,n);
double *c = random_matrix(m,n);
@@ -405,17 +365,16 @@
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);
+ 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_convolve_matrix();
+ // test_im2row();
//test_split();
//test_ensemble();
test_nist();
--
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