From 7756cccb793bb4950c241f2804195ea859d1b407 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 14 Oct 2014 05:31:48 +0000
Subject: [PATCH] Refactored connected to use blas
---
src/network.c | 12 ++++++++++++
Makefile | 3 ++-
src/connected_layer.c | 23 ++++++++---------------
src/connected_layer.h | 3 ---
src/cnn.c | 4 ++--
5 files changed, 24 insertions(+), 21 deletions(-)
diff --git a/Makefile b/Makefile
index b5ad1eb..c4abedd 100644
--- a/Makefile
+++ b/Makefile
@@ -1,5 +1,5 @@
CC=gcc
-GPU=1
+GPU=0
COMMON=-Wall -Wfatal-errors `pkg-config --cflags opencv` -I/usr/local/cuda/include/
ifeq ($(GPU), 1)
COMMON+=-DGPU
@@ -7,6 +7,7 @@
endif
UNAME = $(shell uname)
OPTS=-Ofast -flto
+OPTS=-Ofast -flto
ifeq ($(UNAME), Darwin)
COMMON+= -isystem /usr/local/Cellar/opencv/2.4.6.1/include/opencv -isystem /usr/local/Cellar/opencv/2.4.6.1/include
ifeq ($(GPU), 1)
diff --git a/src/cnn.c b/src/cnn.c
index 472aa03..df3efa6 100644
--- a/src/cnn.c
+++ b/src/cnn.c
@@ -916,8 +916,8 @@
//test_ensemble();
//test_nist_single();
//test_nist();
- //train_nist();
- test_convolutional_layer();
+ train_nist();
+ //test_convolutional_layer();
//test_col2im();
//test_cifar10();
//train_cifar10();
diff --git a/src/connected_layer.c b/src/connected_layer.c
index 95db5d5..03590d6 100644
--- a/src/connected_layer.c
+++ b/src/connected_layer.c
@@ -26,7 +26,6 @@
layer->weight_updates = calloc(inputs*outputs, sizeof(float));
//layer->weight_adapt = calloc(inputs*outputs, sizeof(float));
- layer->weight_momentum = calloc(inputs*outputs, sizeof(float));
layer->weights = calloc(inputs*outputs, sizeof(float));
float scale = 1./inputs;
scale = .05;
@@ -35,7 +34,6 @@
layer->bias_updates = calloc(outputs, sizeof(float));
//layer->bias_adapt = calloc(outputs, sizeof(float));
- layer->bias_momentum = calloc(outputs, sizeof(float));
layer->biases = calloc(outputs, sizeof(float));
for(i = 0; i < outputs; ++i){
//layer->biases[i] = rand_normal()*scale + scale;
@@ -50,24 +48,19 @@
void update_connected_layer(connected_layer layer)
{
- int i;
- for(i = 0; i < layer.outputs; ++i){
- layer.bias_momentum[i] = layer.learning_rate*(layer.bias_updates[i]) + layer.momentum*layer.bias_momentum[i];
- layer.biases[i] += layer.bias_momentum[i];
- }
- for(i = 0; i < layer.outputs*layer.inputs; ++i){
- layer.weight_momentum[i] = layer.learning_rate*(layer.weight_updates[i] - layer.decay*layer.weights[i]) + layer.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));
+ axpy_cpu(layer.outputs, layer.learning_rate, layer.bias_updates, 1, layer.biases, 1);
+ scal_cpu(layer.outputs, layer.momentum, layer.bias_updates, 1);
+
+ scal_cpu(layer.inputs*layer.outputs, 1.-layer.learning_rate*layer.decay, layer.weights, 1);
+ axpy_cpu(layer.inputs*layer.outputs, layer.learning_rate, layer.weight_updates, 1, layer.weights, 1);
+ scal_cpu(layer.inputs*layer.outputs, layer.momentum, layer.weight_updates, 1);
}
void forward_connected_layer(connected_layer layer, float *input)
{
int i;
for(i = 0; i < layer.batch; ++i){
- memcpy(layer.output+i*layer.outputs, layer.biases, layer.outputs*sizeof(float));
+ copy_cpu(layer.outputs, layer.biases, 1, layer.output + i*layer.outputs, 1);
}
int m = layer.batch;
int k = layer.inputs;
@@ -82,8 +75,8 @@
void backward_connected_layer(connected_layer layer, float *input, float *delta)
{
int i;
+ gradient_array(layer.output, layer.outputs*layer.batch, layer.activation, layer.delta);
for(i = 0; i < layer.outputs*layer.batch; ++i){
- layer.delta[i] *= gradient(layer.output[i], layer.activation);
layer.bias_updates[i%layer.outputs] += layer.delta[i];
}
int m = layer.inputs;
diff --git a/src/connected_layer.h b/src/connected_layer.h
index 4322659..9181fe2 100644
--- a/src/connected_layer.h
+++ b/src/connected_layer.h
@@ -21,9 +21,6 @@
float *weight_adapt;
float *bias_adapt;
- float *weight_momentum;
- float *bias_momentum;
-
float *output;
float *delta;
diff --git a/src/network.c b/src/network.c
index 5833166..e4e4c8e 100644
--- a/src/network.c
+++ b/src/network.c
@@ -229,6 +229,8 @@
return layer.output;
} else if(net.types[i] == DROPOUT){
return get_network_output_layer(net, i-1);
+ } else if(net.types[i] == FREEWEIGHT){
+ return get_network_output_layer(net, i-1);
} else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.output;
@@ -258,6 +260,8 @@
return layer.delta;
} else if(net.types[i] == DROPOUT){
return get_network_delta_layer(net, i-1);
+ } else if(net.types[i] == FREEWEIGHT){
+ return get_network_delta_layer(net, i-1);
} else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.delta;
@@ -424,6 +428,10 @@
dropout_layer layer = *(dropout_layer *) net.layers[i];
return layer.inputs;
}
+ else if(net.types[i] == FREEWEIGHT){
+ freeweight_layer layer = *(freeweight_layer *) net.layers[i];
+ return layer.inputs;
+ }
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
return layer.inputs;
@@ -451,6 +459,10 @@
dropout_layer layer = *(dropout_layer *) net.layers[i];
return layer.inputs;
}
+ else if(net.types[i] == FREEWEIGHT){
+ freeweight_layer layer = *(freeweight_layer *) net.layers[i];
+ return layer.inputs;
+ }
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
return layer.inputs;
--
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