From bfffadc75502cadb5d05909435a2167db5204325 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Wed, 04 Feb 2015 20:41:20 +0000
Subject: [PATCH] Stable place to commit

---
 src/connected_layer.c |   18 +++++++++++++-----
 1 files changed, 13 insertions(+), 5 deletions(-)

diff --git a/src/connected_layer.c b/src/connected_layer.c
index 514dff0..1a5fc2b 100644
--- a/src/connected_layer.c
+++ b/src/connected_layer.c
@@ -43,6 +43,7 @@
 
     for(i = 0; i < outputs; ++i){
         layer->biases[i] = scale;
+       // layer->biases[i] = 1;
     }
 
 #ifdef GPU
@@ -113,9 +114,10 @@
 void backward_connected_layer(connected_layer layer, float *input, float *delta)
 {
     int i;
+    float alpha = 1./layer.batch;
     gradient_array(layer.output, layer.outputs*layer.batch, layer.activation, layer.delta);
     for(i = 0; i < layer.batch; ++i){
-        axpy_cpu(layer.outputs, 1, layer.delta + i*layer.outputs, 1, layer.bias_updates, 1);
+        axpy_cpu(layer.outputs, alpha, layer.delta + i*layer.outputs, 1, layer.bias_updates, 1);
     }
     int m = layer.inputs;
     int k = layer.batch;
@@ -123,7 +125,7 @@
     float *a = input;
     float *b = layer.delta;
     float *c = layer.weight_updates;
-    gemm(1,0,m,n,k,1,a,m,b,n,1,c,n);
+    gemm(1,0,m,n,k,alpha,a,m,b,n,1,c,n);
 
     m = layer.batch;
     k = layer.outputs;
@@ -156,13 +158,18 @@
 
 void update_connected_layer_gpu(connected_layer layer)
 {
+/*
+    cuda_pull_array(layer.weights_gpu, layer.weights, layer.inputs*layer.outputs);
+    cuda_pull_array(layer.weight_updates_gpu, layer.weight_updates, layer.inputs*layer.outputs);
+    printf("Weights: %f updates: %f\n", mag_array(layer.weights, layer.inputs*layer.outputs), layer.learning_rate*mag_array(layer.weight_updates, layer.inputs*layer.outputs));
+*/
+
     axpy_ongpu(layer.outputs, layer.learning_rate, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
     scal_ongpu(layer.outputs, layer.momentum, layer.bias_updates_gpu, 1);
 
     axpy_ongpu(layer.inputs*layer.outputs, -layer.decay, layer.weights_gpu, 1, layer.weight_updates_gpu, 1);
     axpy_ongpu(layer.inputs*layer.outputs, layer.learning_rate, layer.weight_updates_gpu, 1, layer.weights_gpu, 1);
     scal_ongpu(layer.inputs*layer.outputs, layer.momentum, layer.weight_updates_gpu, 1);
-    //pull_connected_layer(layer);
 }
 
 void forward_connected_layer_gpu(connected_layer layer, float * input)
@@ -183,10 +190,11 @@
 
 void backward_connected_layer_gpu(connected_layer layer, float * input, float * delta)
 {
+    float alpha = 1./layer.batch;
     int i;
     gradient_array_ongpu(layer.output_gpu, layer.outputs*layer.batch, layer.activation, layer.delta_gpu);
     for(i = 0; i < layer.batch; ++i){
-        axpy_ongpu_offset(layer.outputs, 1, layer.delta_gpu, i*layer.outputs, 1, layer.bias_updates_gpu, 0, 1);
+        axpy_ongpu_offset(layer.outputs, alpha, layer.delta_gpu, i*layer.outputs, 1, layer.bias_updates_gpu, 0, 1);
     }
     int m = layer.inputs;
     int k = layer.batch;
@@ -194,7 +202,7 @@
     float * a = input;
     float * b = layer.delta_gpu;
     float * c = layer.weight_updates_gpu;
-    gemm_ongpu(1,0,m,n,k,1,a,m,b,n,1,c,n);
+    gemm_ongpu(1,0,m,n,k,alpha,a,m,b,n,1,c,n);
 
     m = layer.batch;
     k = layer.outputs;

--
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