From 5ef74c2031a040f30a670dc7d60790fc6a9ec720 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 02 May 2014 22:20:34 +0000
Subject: [PATCH] Slowly refactoring and pushing to GPU
---
src/connected_layer.c | 79 +++++----------------------------------
1 files changed, 11 insertions(+), 68 deletions(-)
diff --git a/src/connected_layer.c b/src/connected_layer.c
index 07fad69..792f20b 100644
--- a/src/connected_layer.c
+++ b/src/connected_layer.c
@@ -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));
@@ -38,27 +39,6 @@
return layer;
}
-/*
-void update_connected_layer(connected_layer layer, float step, float momentum, float decay)
-{
- int i;
- for(i = 0; i < layer.outputs; ++i){
- float delta = layer.bias_updates[i];
- layer.bias_adapt[i] += delta*delta;
- layer.bias_momentum[i] = step/sqrt(layer.bias_adapt[i])*(layer.bias_updates[i]) + momentum*layer.bias_momentum[i];
- layer.biases[i] += layer.bias_momentum[i];
- }
- for(i = 0; i < layer.outputs*layer.inputs; ++i){
- float delta = layer.weight_updates[i];
- layer.weight_adapt[i] += delta*delta;
- layer.weight_momentum[i] = step/sqrt(layer.weight_adapt[i])*(layer.weight_updates[i] - decay*layer.weights[i]) + momentum*layer.weight_momentum[i];
- layer.weights[i] += layer.weight_momentum[i];
- }
- memset(layer.bias_updates, 0, layer.outputs*sizeof(float));
- memset(layer.weight_updates, 0, layer.outputs*layer.inputs*sizeof(float));
-}
-*/
-
void update_connected_layer(connected_layer layer, float step, float momentum, float decay)
{
int i;
@@ -78,28 +58,27 @@
{
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");
}
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;
@@ -109,50 +88,14 @@
void backward_connected_layer(connected_layer layer, float *input, float *delta)
{
- memset(delta, 0, layer.inputs*sizeof(float));
-
int m = layer.inputs;
int k = layer.outputs;
- int n = 1;
+ int n = layer.batch;
float *a = layer.weights;
float *b = layer.delta;
float *c = delta;
- gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
+ gemm(0,0,m,n,k,1,a,k,b,n,0,c,n);
}
-/*
- void forward_connected_layer(connected_layer layer, float *input)
- {
- int i, j;
- for(i = 0; i < layer.outputs; ++i){
- layer.output[i] = layer.biases[i];
- for(j = 0; j < layer.inputs; ++j){
- layer.output[i] += input[j]*layer.weights[i*layer.inputs + j];
- }
- layer.output[i] = activate(layer.output[i], layer.activation);
- }
- }
- void learn_connected_layer(connected_layer layer, float *input)
- {
- int i, j;
- for(i = 0; i < layer.outputs; ++i){
- layer.delta[i] *= gradient(layer.output[i], layer.activation);
- layer.bias_updates[i] += layer.delta[i];
- for(j = 0; j < layer.inputs; ++j){
- layer.weight_updates[i*layer.inputs + j] += layer.delta[i]*input[j];
- }
- }
- }
- void backward_connected_layer(connected_layer layer, float *input, float *delta)
- {
- int i, j;
- for(j = 0; j < layer.inputs; ++j){
- delta[j] = 0;
- for(i = 0; i < layer.outputs; ++i){
- delta[j] += layer.delta[i]*layer.weights[i*layer.inputs + j];
- }
- }
- }
- */
--
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