mirror of
https://github.com/pjreddie/darknet.git
synced 2023-08-10 21:13:14 +03:00
About to do something stupid...
This commit is contained in:
Joseph Redmon
parent
ace5aeb0f5
commit
b2b7137b6f
11
Makefile
11
Makefile
@ -1,6 +1,6 @@
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CC=gcc
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COMMON=-Wall `pkg-config --cflags opencv`
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CFLAGS= $(COMMON) -O3 -ffast-math -flto
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CFLAGS= $(COMMON) -Ofast -ffast-math -flto
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UNAME = $(shell uname)
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ifeq ($(UNAME), Darwin)
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COMMON += -isystem /usr/local/Cellar/opencv/2.4.6.1/include/opencv -isystem /usr/local/Cellar/opencv/2.4.6.1/include
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@ -10,12 +10,13 @@ endif
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#CFLAGS= $(COMMON) -O0 -g
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LDFLAGS=`pkg-config --libs opencv` -lm
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VPATH=./src/
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EXEC=cnn
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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
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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
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all: cnn
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all: $(EXEC)
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cnn: $(OBJ)
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$(EXEC): $(OBJ)
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$(CC) $(CFLAGS) $(LDFLAGS) $^ -o $@
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%.o: %.c
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@ -24,5 +25,5 @@ cnn: $(OBJ)
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.PHONY: clean
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clean:
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rm -rf $(OBJ) cnn
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rm -rf $(OBJ) $(EXEC)
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@ -1,7 +1,11 @@
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[conn]
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input=784
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output = 100
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activation=ramp
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[conv]
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width=28
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height=28
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channels=1
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filters=20
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size=5
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stride=1
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activation=linear
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[conn]
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output = 10
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@ -1,17 +1,13 @@
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#include "convolutional_layer.h"
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#include "utils.h"
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#include "mini_blas.h"
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#include <stdio.h>
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image get_convolutional_image(convolutional_layer layer)
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{
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int h,w,c;
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if(layer.edge){
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h = (layer.h-1)/layer.stride + 1;
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w = (layer.w-1)/layer.stride + 1;
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}else{
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h = (layer.h - layer.size)/layer.stride+1;
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w = (layer.h - layer.size)/layer.stride+1;
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}
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h = layer.out_h;
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w = layer.out_w;
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c = layer.n;
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return double_to_image(h,w,c,layer.output);
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}
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@ -19,13 +15,8 @@ image get_convolutional_image(convolutional_layer layer)
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image get_convolutional_delta(convolutional_layer layer)
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{
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int h,w,c;
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if(layer.edge){
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h = (layer.h-1)/layer.stride + 1;
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w = (layer.w-1)/layer.stride + 1;
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}else{
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h = (layer.h - layer.size)/layer.stride+1;
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w = (layer.h - layer.size)/layer.stride+1;
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}
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h = layer.out_h;
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w = layer.out_w;
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c = layer.n;
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return double_to_image(h,w,c,layer.delta);
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}
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@ -34,74 +25,114 @@ convolutional_layer *make_convolutional_layer(int h, int w, int c, int n, int si
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{
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int i;
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int out_h,out_w;
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size = 2*(size/2)+1; //HA! And you thought you'd use an even sized filter...
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convolutional_layer *layer = calloc(1, sizeof(convolutional_layer));
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layer->h = h;
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layer->w = w;
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layer->c = c;
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layer->n = n;
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layer->edge = 0;
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layer->stride = stride;
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layer->kernels = calloc(n, sizeof(image));
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layer->kernel_updates = calloc(n, sizeof(image));
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layer->kernel_momentum = calloc(n, sizeof(image));
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layer->size = size;
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layer->filters = calloc(c*n*size*size, sizeof(double));
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layer->filter_updates = calloc(c*n*size*size, sizeof(double));
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layer->filter_momentum = calloc(c*n*size*size, sizeof(double));
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layer->biases = calloc(n, sizeof(double));
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layer->bias_updates = calloc(n, sizeof(double));
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layer->bias_momentum = calloc(n, sizeof(double));
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double scale = 2./(size*size);
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for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = rand_normal()*scale;
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for(i = 0; i < n; ++i){
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//layer->biases[i] = rand_normal()*scale + scale;
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layer->biases[i] = 0;
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layer->kernels[i] = make_random_kernel(size, c, scale);
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layer->kernel_updates[i] = make_random_kernel(size, c, 0);
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layer->kernel_momentum[i] = make_random_kernel(size, c, 0);
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}
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layer->size = 2*(size/2)+1;
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if(layer->edge){
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out_h = (layer->h-1)/layer->stride + 1;
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out_w = (layer->w-1)/layer->stride + 1;
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}else{
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out_h = (layer->h - layer->size)/layer->stride+1;
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out_w = (layer->h - layer->size)/layer->stride+1;
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}
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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);
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out_h = (h-size)/stride + 1;
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out_w = (w-size)/stride + 1;
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layer->col_image = calloc(out_h*out_w*size*size*c, sizeof(double));
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layer->output = calloc(out_h * out_w * n, sizeof(double));
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layer->delta = calloc(out_h * out_w * n, sizeof(double));
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layer->upsampled = make_image(h,w,n);
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layer->activation = activation;
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layer->out_h = out_h;
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layer->out_w = out_w;
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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);
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srand(0);
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return layer;
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}
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void forward_convolutional_layer(const convolutional_layer layer, double *in)
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{
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image input = double_to_image(layer.h, layer.w, layer.c, in);
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image output = get_convolutional_image(layer);
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int i,j;
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for(i = 0; i < layer.n; ++i){
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convolve(input, layer.kernels[i], layer.stride, i, output, layer.edge);
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}
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for(i = 0; i < output.c; ++i){
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for(j = 0; j < output.h*output.w; ++j){
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int index = i*output.h*output.w + j;
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output.data[index] += layer.biases[i];
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output.data[index] = activate(output.data[index], layer.activation);
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}
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}
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int m = layer.n;
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int k = layer.size*layer.size*layer.c;
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int n = ((layer.h-layer.size)/layer.stride + 1)*
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((layer.w-layer.size)/layer.stride + 1);
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memset(layer.output, 0, m*n*sizeof(double));
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double *a = layer.filters;
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double *b = layer.col_image;
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double *c = layer.output;
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im2col_cpu(in, layer.c, layer.h, layer.w, layer.size, layer.stride, b);
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gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
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}
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void backward_convolutional_layer(convolutional_layer layer, double *input, double *delta)
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void gradient_delta_convolutional_layer(convolutional_layer layer)
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{
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int i;
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image in_delta = double_to_image(layer.h, layer.w, layer.c, delta);
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image out_delta = get_convolutional_delta(layer);
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zero_image(in_delta);
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for(i = 0; i < layer.n; ++i){
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back_convolve(in_delta, layer.kernels[i], layer.stride, i, out_delta, layer.edge);
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for(i = 0; i < layer.out_h*layer.out_w*layer.n; ++i){
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layer.delta[i] *= gradient(layer.output[i], layer.activation);
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}
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}
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void learn_bias_convolutional_layer(convolutional_layer layer)
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{
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int i,j;
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int size = layer.out_h*layer.out_w;
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for(i = 0; i < layer.n; ++i){
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double sum = 0;
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for(j = 0; j < size; ++j){
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sum += layer.delta[j+i*size];
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}
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layer.bias_updates[i] += sum/size;
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}
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}
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void learn_convolutional_layer(convolutional_layer layer)
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{
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gradient_delta_convolutional_layer(layer);
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learn_bias_convolutional_layer(layer);
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int m = layer.n;
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int n = layer.size*layer.size*layer.c;
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int k = ((layer.h-layer.size)/layer.stride + 1)*
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((layer.w-layer.size)/layer.stride + 1);
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double *a = layer.delta;
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double *b = layer.col_image;
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double *c = layer.filter_updates;
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gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
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}
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void update_convolutional_layer(convolutional_layer layer, double step, double momentum, double decay)
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{
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int i;
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int size = layer.size*layer.size*layer.c*layer.n;
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for(i = 0; i < layer.n; ++i){
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layer.biases[i] += step*layer.bias_updates[i];
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layer.bias_updates[i] *= momentum;
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}
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for(i = 0; i < size; ++i){
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layer.filters[i] += step*(layer.filter_updates[i] - decay*layer.filters[i]);
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layer.filter_updates[i] *= momentum;
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}
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}
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/*
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void backward_convolutional_layer2(convolutional_layer layer, double *input, double *delta)
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{
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image in_delta = double_to_image(layer.h, layer.w, layer.c, delta);
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@ -124,15 +155,6 @@ void backward_convolutional_layer2(convolutional_layer layer, double *input, dou
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}
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}
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void gradient_delta_convolutional_layer(convolutional_layer layer)
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{
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int i;
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image out_delta = get_convolutional_delta(layer);
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image out_image = get_convolutional_image(layer);
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for(i = 0; i < out_image.h*out_image.w*out_image.c; ++i){
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out_delta.data[i] *= gradient(out_image.data[i], layer.activation);
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}
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}
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void learn_convolutional_layer(convolutional_layer layer, double *input)
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{
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@ -163,8 +185,37 @@ void update_convolutional_layer(convolutional_layer layer, double step, double m
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zero_image(layer.kernel_updates[i]);
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}
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}
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*/
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void visualize_convolutional_filters(convolutional_layer layer, char *window)
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void test_convolutional_layer()
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{
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convolutional_layer l = *make_convolutional_layer(4,4,1,1,3,1,LINEAR);
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double input[] = {1,2,3,4,
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5,6,7,8,
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9,10,11,12,
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13,14,15,16};
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double filter[] = {.5, 0, .3,
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0 , 1, 0,
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.2 , 0, 1};
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double delta[] = {1, 2,
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3, 4};
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l.filters = filter;
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forward_convolutional_layer(l, input);
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l.delta = delta;
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learn_convolutional_layer(l);
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image filter_updates = double_to_image(3,3,1,l.filter_updates);
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print_image(filter_updates);
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}
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image get_convolutional_filter(convolutional_layer layer, int i)
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{
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int h = layer.size;
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int w = layer.size;
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int c = layer.c;
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return double_to_image(h,w,c,layer.filters+i*h*w*c);
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}
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void visualize_convolutional_layer(convolutional_layer layer, char *window)
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{
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int color = 1;
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int border = 1;
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@ -172,7 +223,7 @@ void visualize_convolutional_filters(convolutional_layer layer, char *window)
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int size = layer.size;
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h = size;
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w = (size + border) * layer.n - border;
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c = layer.kernels[0].c;
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c = layer.c;
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if(c != 3 || !color){
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h = (h+border)*c - border;
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c = 1;
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@ -182,11 +233,13 @@ void visualize_convolutional_filters(convolutional_layer layer, char *window)
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int i,j;
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for(i = 0; i < layer.n; ++i){
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int w_offset = i*(size+border);
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image k = layer.kernels[i];
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image k = get_convolutional_filter(layer, i);
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//printf("%f ** ", layer.biases[i]);
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//print_image(k);
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image copy = copy_image(k);
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normalize_image(copy);
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for(j = 0; j < k.c; ++j){
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set_pixel(copy,0,0,j,layer.biases[i]);
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//set_pixel(copy,0,0,j,layer.biases[i]);
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}
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if(c == 3 && color){
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embed_image(copy, filters, 0, w_offset);
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@ -211,15 +264,3 @@ void visualize_convolutional_filters(convolutional_layer layer, char *window)
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free_image(filters);
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}
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void visualize_convolutional_layer(convolutional_layer layer)
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{
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int i;
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char buff[256];
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for(i = 0; i < layer.n; ++i){
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image k = layer.kernels[i];
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sprintf(buff, "Kernel %d", i);
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if(k.c <= 3) show_image(k, buff);
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else show_image_layers(k, buff);
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}
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}
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@ -6,36 +6,40 @@
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typedef struct {
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int h,w,c;
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int out_h, out_w, out_c;
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int n;
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int size;
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int stride;
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image *kernels;
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image *kernel_updates;
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image *kernel_momentum;
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double *filters;
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double *filter_updates;
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double *filter_momentum;
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double *biases;
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double *bias_updates;
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double *bias_momentum;
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image upsampled;
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double *col_image;
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double *delta;
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double *output;
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ACTIVATION activation;
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int edge;
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} convolutional_layer;
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convolutional_layer *make_convolutional_layer(int h, int w, int c, int n, int size, int stride, ACTIVATION activation);
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void forward_convolutional_layer(const convolutional_layer layer, double *in);
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void backward_convolutional_layer(convolutional_layer layer, double *input, double *delta);
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void learn_convolutional_layer(convolutional_layer layer, double *input);
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void learn_convolutional_layer(convolutional_layer layer);
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void update_convolutional_layer(convolutional_layer layer, double step, double momentum, double decay);
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void visualize_convolutional_layer(convolutional_layer layer, char *window);
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void backpropagate_convolutional_layer_convolve(image input, convolutional_layer layer);
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void visualize_convolutional_filters(convolutional_layer layer, char *window);
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void visualize_convolutional_layer(convolutional_layer layer);
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//void backward_convolutional_layer(convolutional_layer layer, double *input, double *delta);
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//void backpropagate_convolutional_layer_convolve(image input, convolutional_layer layer);
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//void visualize_convolutional_filters(convolutional_layer layer, char *window);
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//void visualize_convolutional_layer(convolutional_layer layer);
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image get_convolutional_image(convolutional_layer layer);
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image get_convolutional_delta(convolutional_layer layer);
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image get_convolutional_filter(convolutional_layer layer, int i);
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#endif
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@ -1,16 +1,44 @@
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#include <stdlib.h>
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#include <math.h>
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void pm(int M, int N, double *A)
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{
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int i,j;
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for(i =0 ; i < M; ++i){
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for(j = 0; j < N; ++j){
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printf("%10.6f, ", A[i*N+j]);
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}
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printf("\n");
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}
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printf("\n");
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}
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void gemm(int TA, int TB, int M, int N, int K, double ALPHA,
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double *A, int lda,
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double *B, int ldb,
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double BETA,
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double *C, int ldc)
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{
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// Assume TA = TB = 0, beta = 1 LULZ
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// Assume TA = 0, beta = 1 LULZ
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int i,j,k;
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for(i = 0; i < M; ++i){
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for(k = 0; k < K; ++k){
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if(TB && !TA){
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for(i = 0; i < M; ++i){
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for(j = 0; j < N; ++j){
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C[i*ldc+j] += ALPHA*A[i*lda+k]*B[k*ldb+j];
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register double sum = 0;
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for(k = 0; k < K; ++k){
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sum += ALPHA*A[i*lda+k]*B[k+j*ldb];
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}
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C[i*ldc+j] += sum;
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}
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}
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}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,
|
||||
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;
|
||||
|
||||
@ -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,
|
||||
|
||||
@ -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 @@ double *get_network_delta(network net)
|
||||
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 @@ int get_predicted_class_network(network net)
|
||||
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 @@ void backward_network(network net, double *input, double *truth)
|
||||
}
|
||||
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 @@ void backward_network(network net, double *input, double *truth)
|
||||
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 @@ void visualize_network(network net)
|
||||
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);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -22,7 +22,7 @@ typedef struct {
|
||||
|
||||
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);
|
||||
|
||||
75
src/tests.c
75
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 @@ void test_convolve_matrix()
|
||||
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 @@ void 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()
|
||||
{
|
||||
srand(0);
|
||||
@ -117,7 +95,7 @@ void verify_convolutional_layer()
|
||||
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 @@ void test_nist()
|
||||
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 @@ void test_ensemble()
|
||||
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 @@ void test_ensemble()
|
||||
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 @@ double *random_matrix(int rows, int cols)
|
||||
|
||||
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 @@ void test_im2row()
|
||||
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();
|
||||
|
||||
Loading…
Reference in New Issue
Block a user