mirror/darknet
1
0
Fork 0
mirror of https://github.com/pjreddie/darknet.git synced 2023-08-10 21:13:14 +03:00
Code Issues Projects Releases Wiki Activity

Better VOC handling and resizing

Browse Source
This commit is contained in:
Joseph Redmon
2014-03-12 21:57:34 -07:00
parent 15e86996d6
commit 2ea63c0e99
21 changed files with 288 additions and 173 deletions
Download Patch File Download Diff File Expand all files Collapse all files

19
src/connected_layer.c
View File

@ -7,16 +7,17 @@
#include <stdlib.h>
#include <string.h>
connected_layer *make_connected_layer(int inputs, int outputs, ACTIVATION activation)
connected_layer *make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation)
{
fprintf(stderr, "Connected Layer: %d inputs, %d outputs\n", inputs, outputs);
int i;
connected_layer *layer = calloc(1, sizeof(connected_layer));
layer->inputs = inputs;
layer->outputs = outputs;
layer->batch=batch;
layer->output = calloc(outputs, sizeof(float*));
layer->delta = calloc(outputs, sizeof(float*));
layer->output = calloc(batch*outputs, sizeof(float*));
layer->delta = calloc(batch*outputs, sizeof(float*));
layer->weight_updates = calloc(inputs*outputs, sizeof(float));
layer->weight_adapt = calloc(inputs*outputs, sizeof(float));
@ -78,14 +79,14 @@ void forward_connected_layer(connected_layer layer, float *input)
{
int i;
memcpy(layer.output, layer.biases, layer.outputs*sizeof(float));
int m = 1;
int m = layer.batch;
int k = layer.inputs;
int n = layer.outputs;
float *a = input;
float *b = layer.weights;
float *c = layer.output;
gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
for(i = 0; i < layer.outputs; ++i){
for(i = 0; i < layer.outputs*layer.batch; ++i){
layer.output[i] = activate(layer.output[i], layer.activation);
}
//for(i = 0; i < layer.outputs; ++i) if(i%(layer.outputs/10+1)==0) printf("%f, ", layer.output[i]); printf("\n");
@ -94,12 +95,12 @@ void forward_connected_layer(connected_layer layer, float *input)
void learn_connected_layer(connected_layer layer, float *input)
{
int i;
for(i = 0; i < layer.outputs; ++i){
for(i = 0; i < layer.outputs*layer.batch; ++i){
layer.delta[i] *= gradient(layer.output[i], layer.activation);
layer.bias_updates[i] += layer.delta[i];
layer.bias_updates[i%layer.batch] += layer.delta[i]/layer.batch;
}
int m = layer.inputs;
int k = 1;
int k = layer.batch;
int n = layer.outputs;
float *a = input;
float *b = layer.delta;
@ -113,7 +114,7 @@ void backward_connected_layer(connected_layer layer, float *input, float *delta)
int m = layer.inputs;
int k = layer.outputs;
int n = 1;
int n = layer.batch;
float *a = layer.weights;
float *b = layer.delta;