#include "convolutional_layer.h"
|
#include <stdio.h>
|
|
image get_convolutional_image(convolutional_layer layer)
|
{
|
int h = (layer.h-1)/layer.stride + 1;
|
int w = (layer.w-1)/layer.stride + 1;
|
int c = layer.n;
|
return double_to_image(h,w,c,layer.output);
|
}
|
|
image get_convolutional_delta(convolutional_layer layer)
|
{
|
int h = (layer.h-1)/layer.stride + 1;
|
int w = (layer.w-1)/layer.stride + 1;
|
int c = layer.n;
|
return double_to_image(h,w,c,layer.delta);
|
}
|
|
convolutional_layer *make_convolutional_layer(int h, int w, int c, int n, int size, int stride, ACTIVATION activator)
|
{
|
printf("Convolutional Layer: %d x %d x %d image, %d filters\n", h,w,c,n);
|
int i;
|
convolutional_layer *layer = calloc(1, sizeof(convolutional_layer));
|
layer->h = h;
|
layer->w = w;
|
layer->c = c;
|
layer->n = n;
|
layer->stride = stride;
|
layer->kernels = calloc(n, sizeof(image));
|
layer->kernel_updates = calloc(n, sizeof(image));
|
layer->biases = calloc(n, sizeof(double));
|
layer->bias_updates = calloc(n, sizeof(double));
|
for(i = 0; i < n; ++i){
|
layer->biases[i] = .005;
|
layer->kernels[i] = make_random_kernel(size, c);
|
layer->kernel_updates[i] = make_random_kernel(size, c);
|
}
|
layer->output = calloc(((h-1)/stride+1) * ((w-1)/stride+1) * n, sizeof(double));
|
layer->delta = calloc(((h-1)/stride+1) * ((w-1)/stride+1) * n, sizeof(double));
|
layer->upsampled = make_image(h,w,n);
|
|
if(activator == SIGMOID){
|
layer->activation = sigmoid_activation;
|
layer->gradient = sigmoid_gradient;
|
}else if(activator == RELU){
|
layer->activation = relu_activation;
|
layer->gradient = relu_gradient;
|
}else if(activator == IDENTITY){
|
layer->activation = identity_activation;
|
layer->gradient = identity_gradient;
|
}
|
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);
|
}
|
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] = layer.activation(output.data[index]);
|
}
|
}
|
}
|
|
void backward_convolutional_layer(convolutional_layer layer, double *input, double *delta)
|
{
|
int i;
|
|
image in_image = double_to_image(layer.h, layer.w, layer.c, input);
|
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);
|
}
|
for(i = 0; i < layer.h*layer.w*layer.c; ++i){
|
in_delta.data[i] *= layer.gradient(in_image.data[i]);
|
}
|
}
|
|
/*
|
void backpropagate_convolutional_layer_convolve(image input, convolutional_layer layer)
|
{
|
int i,j;
|
for(i = 0; i < layer.n; ++i){
|
rotate_image(layer.kernels[i]);
|
}
|
|
zero_image(input);
|
upsample_image(layer.output, layer.stride, layer.upsampled);
|
for(j = 0; j < input.c; ++j){
|
for(i = 0; i < layer.n; ++i){
|
two_d_convolve(layer.upsampled, i, layer.kernels[i], j, 1, input, j);
|
}
|
}
|
|
for(i = 0; i < layer.n; ++i){
|
rotate_image(layer.kernels[i]);
|
}
|
}
|
*/
|
|
void learn_convolutional_layer(convolutional_layer layer, double *input)
|
{
|
int i;
|
image in_image = double_to_image(layer.h, layer.w, layer.c, input);
|
image out_delta = get_convolutional_delta(layer);
|
for(i = 0; i < layer.n; ++i){
|
kernel_update(in_image, layer.kernel_updates[i], layer.stride, i, out_delta);
|
layer.bias_updates[i] += avg_image_layer(out_delta, i);
|
}
|
}
|
|
void update_convolutional_layer(convolutional_layer layer, double step)
|
{
|
return;
|
int i,j;
|
for(i = 0; i < layer.n; ++i){
|
layer.biases[i] += step*layer.bias_updates[i];
|
layer.bias_updates[i] = 0;
|
int pixels = layer.kernels[i].h*layer.kernels[i].w*layer.kernels[i].c;
|
for(j = 0; j < pixels; ++j){
|
layer.kernels[i].data[j] += step*layer.kernel_updates[i].data[j];
|
}
|
zero_image(layer.kernel_updates[i]);
|
}
|
}
|
|
void visualize_convolutional_layer(convolutional_layer layer)
|
{
|
int i;
|
char buff[256];
|
//image vis = make_image(layer.n*layer.size, layer.size*layer.kernels[0].c, 3);
|
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);
|
}
|
}
|
