From a99050f0c8cb0315fa31e3d1fa3e38594fe5e40a Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Mon, 08 Dec 2014 04:16:21 +0000
Subject: [PATCH] Some fixes to momentum
---
src/convolutional_layer.c | 8 ++++----
src/connected_layer.c | 28 +++++++++++++---------------
src/cnn.c | 8 ++++----
src/server.c | 4 +++-
src/utils.c | 5 +++--
5 files changed, 27 insertions(+), 26 deletions(-)
diff --git a/src/cnn.c b/src/cnn.c
index 7971b95..620126d 100644
--- a/src/cnn.c
+++ b/src/cnn.c
@@ -374,7 +374,7 @@
void train_imagenet_distributed(char *address)
{
float avg_loss = 1;
- srand(0);
+ srand(time(0));
network net = parse_network_cfg("cfg/alexnet.client");
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = 1000/net.batch+1;
@@ -412,11 +412,11 @@
{
float avg_loss = 1;
//network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
- srand(0);
+ srand(time(0));
network net = parse_network_cfg("cfg/alexnet.cfg");
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = 1000/net.batch+1;
- imgs=1;
+ //imgs=1;
int i = 0;
char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
list *plist = get_paths("/data/imagenet/cls.train.list");
@@ -872,7 +872,7 @@
void run_server()
{
- srand(0);
+ srand(time(0));
network net = parse_network_cfg("cfg/alexnet.server");
server_update(net);
}
diff --git a/src/connected_layer.c b/src/connected_layer.c
index bcca631..85bf5c8 100644
--- a/src/connected_layer.c
+++ b/src/connected_layer.c
@@ -24,22 +24,20 @@
layer->delta = calloc(batch*outputs, sizeof(float*));
layer->weight_updates = calloc(inputs*outputs, sizeof(float));
- //layer->weight_adapt = calloc(inputs*outputs, sizeof(float));
layer->weights = calloc(inputs*outputs, sizeof(float));
float scale = 1./inputs;
scale = .01;
- for(i = 0; i < inputs*outputs; ++i)
- layer->weights[i] = scale*2*(rand_uniform()-.5);
-
- layer->bias_updates = calloc(outputs, sizeof(float));
- //layer->bias_adapt = calloc(outputs, sizeof(float));
- layer->biases = calloc(outputs, sizeof(float));
- for(i = 0; i < outputs; ++i){
- //layer->biases[i] = rand_normal()*scale + scale;
- layer->biases[i] = 1;
+ for(i = 0; i < inputs*outputs; ++i){
+ layer->weights[i] = scale*rand_normal();
}
- #ifdef GPU
+ layer->bias_updates = calloc(outputs, sizeof(float));
+ layer->biases = calloc(outputs, sizeof(float));
+ for(i = 0; i < outputs; ++i){
+ layer->biases[i] = .01;
+ }
+
+#ifdef GPU
layer->weights_cl = cl_make_array(layer->weights, inputs*outputs);
layer->biases_cl = cl_make_array(layer->biases, outputs);
@@ -48,7 +46,7 @@
layer->output_cl = cl_make_array(layer->output, outputs*batch);
layer->delta_cl = cl_make_array(layer->delta, outputs*batch);
- #endif
+#endif
layer->activation = activation;
fprintf(stderr, "Connected Layer: %d inputs, %d outputs\n", inputs, outputs);
return layer;
@@ -59,7 +57,7 @@
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.decay, layer.weights, 1, layer.weight_updates, 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);
}
@@ -129,7 +127,7 @@
axpy_ongpu(layer.outputs, layer.learning_rate, layer.bias_updates_cl, 1, layer.biases_cl, 1);
scal_ongpu(layer.outputs, layer.momentum, layer.bias_updates_cl, 1);
- scal_ongpu(layer.inputs*layer.outputs, 1.-layer.learning_rate*layer.decay, layer.weights_cl, 1);
+ axpy_ongpu(layer.inputs*layer.outputs, -layer.decay, layer.weights_cl, 1, layer.weight_updates_cl, 1);
axpy_ongpu(layer.inputs*layer.outputs, layer.learning_rate, layer.weight_updates_cl, 1, layer.weights_cl, 1);
scal_ongpu(layer.inputs*layer.outputs, layer.momentum, layer.weight_updates_cl, 1);
pull_connected_layer(layer);
@@ -176,4 +174,4 @@
if(c) gemm_ongpu(0,1,m,n,k,1,a,k,b,k,0,c,n);
}
- #endif
+#endif
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index 4ca6104..5b4e0b5 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -64,10 +64,10 @@
layer->bias_updates = calloc(n, sizeof(float));
float scale = 1./(size*size*c);
scale = .01;
- for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*2*(rand_uniform()-.5);
+ for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*rand_normal();
for(i = 0; i < n; ++i){
//layer->biases[i] = rand_normal()*scale + scale;
- layer->biases[i] = .5;
+ layer->biases[i] = .01;
}
int out_h = convolutional_out_height(*layer);
int out_w = convolutional_out_width(*layer);
@@ -204,7 +204,7 @@
axpy_cpu(layer.n, layer.learning_rate, layer.bias_updates, 1, layer.biases, 1);
scal_cpu(layer.n, layer.momentum, layer.bias_updates, 1);
- scal_cpu(size, 1.-layer.learning_rate*layer.decay, layer.filters, 1);
+ axpy_cpu(size, -layer.decay, layer.filters, 1, layer.filter_updates, 1);
axpy_cpu(size, layer.learning_rate, layer.filter_updates, 1, layer.filters, 1);
scal_cpu(size, layer.momentum, layer.filter_updates, 1);
}
@@ -409,7 +409,7 @@
axpy_ongpu(layer.n, layer.learning_rate, layer.bias_updates_cl, 1, layer.biases_cl, 1);
scal_ongpu(layer.n,layer.momentum, layer.bias_updates_cl, 1);
- scal_ongpu(size, 1.-layer.learning_rate*layer.decay, layer.filters_cl, 1);
+ axpy_ongpu(size, -layer.decay, layer.filters_cl, 1, layer.filter_updates_cl, 1);
axpy_ongpu(size, layer.learning_rate, layer.filter_updates_cl, 1, layer.filters_cl, 1);
scal_ongpu(size, layer.momentum, layer.filter_updates_cl, 1);
pull_convolutional_layer(layer);
diff --git a/src/server.c b/src/server.c
index c802f84..657ea7c 100644
--- a/src/server.c
+++ b/src/server.c
@@ -9,6 +9,7 @@
#include "mini_blas.h"
#include "utils.h"
+#include "parser.h"
#include "server.h"
#include "connected_layer.h"
#include "convolutional_layer.h"
@@ -82,7 +83,6 @@
connection_info info = *(connection_info *) pointer;
int fd = info.fd;
network net = info.net;
- ++*(info.counter);
int i;
for(i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
@@ -117,6 +117,8 @@
}
printf("Received updates\n");
close(fd);
+ ++*(info.counter);
+ if(*(info.counter)%10==0) save_network(net, "/home/pjreddie/imagenet_backup/alexnet.part");
}
void server_update(network net)
diff --git a/src/utils.c b/src/utils.c
index 20cde39..e100069 100644
--- a/src/utils.c
+++ b/src/utils.c
@@ -262,10 +262,11 @@
float rand_normal()
{
+ int n = 12;
int i;
float sum= 0;
- for(i = 0; i < 12; ++i) sum += (float)rand()/RAND_MAX;
- return sum-6.;
+ for(i = 0; i < n; ++i) sum += (float)rand()/RAND_MAX;
+ return sum-n/2.;
}
float rand_uniform()
{
--
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