From 664c5dd2f2d1c4ad177d5122df6ce3e2900c6648 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Sun, 22 Mar 2015 16:56:40 +0000
Subject: [PATCH] Subdivisions for batches
---
src/connected_layer.c | 26 ++++++++++++--------------
1 files changed, 12 insertions(+), 14 deletions(-)
diff --git a/src/connected_layer.c b/src/connected_layer.c
index 9df0e8f..1466ca4 100644
--- a/src/connected_layer.c
+++ b/src/connected_layer.c
@@ -55,13 +55,13 @@
return layer;
}
-void update_connected_layer(connected_layer layer, float learning_rate, float momentum, float decay)
+void update_connected_layer(connected_layer layer, int batch, float learning_rate, float momentum, float decay)
{
- axpy_cpu(layer.outputs, learning_rate, layer.bias_updates, 1, layer.biases, 1);
+ axpy_cpu(layer.outputs, learning_rate/batch, layer.bias_updates, 1, layer.biases, 1);
scal_cpu(layer.outputs, momentum, layer.bias_updates, 1);
- axpy_cpu(layer.inputs*layer.outputs, -decay, layer.weights, 1, layer.weight_updates, 1);
- axpy_cpu(layer.inputs*layer.outputs, learning_rate, layer.weight_updates, 1, layer.weights, 1);
+ axpy_cpu(layer.inputs*layer.outputs, -decay*batch, layer.weights, 1, layer.weight_updates, 1);
+ axpy_cpu(layer.inputs*layer.outputs, learning_rate/batch, layer.weight_updates, 1, layer.weights, 1);
scal_cpu(layer.inputs*layer.outputs, momentum, layer.weight_updates, 1);
}
@@ -84,10 +84,9 @@
void backward_connected_layer(connected_layer layer, network_state state)
{
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, alpha, layer.delta + i*layer.outputs, 1, layer.bias_updates, 1);
+ axpy_cpu(layer.outputs, 1, layer.delta + i*layer.outputs, 1, layer.bias_updates, 1);
}
int m = layer.inputs;
int k = layer.batch;
@@ -95,7 +94,7 @@
float *a = state.input;
float *b = layer.delta;
float *c = layer.weight_updates;
- gemm(1,0,m,n,k,alpha,a,m,b,n,1,c,n);
+ gemm(1,0,m,n,k,1,a,m,b,n,1,c,n);
m = layer.batch;
k = layer.outputs;
@@ -126,13 +125,13 @@
cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.outputs);
}
-void update_connected_layer_gpu(connected_layer layer, float learning_rate, float momentum, float decay)
+void update_connected_layer_gpu(connected_layer layer, int batch, float learning_rate, float momentum, float decay)
{
- axpy_ongpu(layer.outputs, learning_rate, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
+ axpy_ongpu(layer.outputs, learning_rate/batch, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
scal_ongpu(layer.outputs, momentum, layer.bias_updates_gpu, 1);
- axpy_ongpu(layer.inputs*layer.outputs, -decay, layer.weights_gpu, 1, layer.weight_updates_gpu, 1);
- axpy_ongpu(layer.inputs*layer.outputs, learning_rate, layer.weight_updates_gpu, 1, layer.weights_gpu, 1);
+ axpy_ongpu(layer.inputs*layer.outputs, -decay*batch, layer.weights_gpu, 1, layer.weight_updates_gpu, 1);
+ axpy_ongpu(layer.inputs*layer.outputs, learning_rate/batch, layer.weight_updates_gpu, 1, layer.weights_gpu, 1);
scal_ongpu(layer.inputs*layer.outputs, momentum, layer.weight_updates_gpu, 1);
}
@@ -154,11 +153,10 @@
void backward_connected_layer_gpu(connected_layer layer, network_state state)
{
- 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, alpha, layer.delta_gpu, i*layer.outputs, 1, layer.bias_updates_gpu, 0, 1);
+ axpy_ongpu_offset(layer.outputs, 1, layer.delta_gpu, i*layer.outputs, 1, layer.bias_updates_gpu, 0, 1);
}
int m = layer.inputs;
int k = layer.batch;
@@ -166,7 +164,7 @@
float * a = state.input;
float * b = layer.delta_gpu;
float * c = layer.weight_updates_gpu;
- gemm_ongpu(1,0,m,n,k,alpha,a,m,b,n,1,c,n);
+ gemm_ongpu(1,0,m,n,k,1,a,m,b,n,1,c,n);
m = layer.batch;
k = layer.outputs;
--
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