From 91f95c715bff84094fc18bad6a8f938291b9b0f5 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Mon, 24 Oct 2016 20:32:49 +0000
Subject: [PATCH] tree things, tree stuff
---
src/softmax_layer.c | 64 ++++++++++++++++++++++----------
1 files changed, 44 insertions(+), 20 deletions(-)
diff --git a/src/softmax_layer.c b/src/softmax_layer.c
index 20bc07f..31f3e03 100644
--- a/src/softmax_layer.c
+++ b/src/softmax_layer.c
@@ -32,31 +32,25 @@
return l;
}
-void softmax_array(float *input, int n, float temp, float *output)
-{
- int i;
- float sum = 0;
- float largest = -FLT_MAX;
- for(i = 0; i < n; ++i){
- if(input[i] > largest) largest = input[i];
- }
- for(i = 0; i < n; ++i){
- sum += exp(input[i]/temp-largest/temp);
- }
- if(sum) sum = largest/temp+log(sum);
- else sum = largest-100;
- for(i = 0; i < n; ++i){
- output[i] = exp(input[i]/temp-sum);
- }
-}
-
void forward_softmax_layer(const softmax_layer l, network_state state)
{
int b;
int inputs = l.inputs / l.groups;
int batch = l.batch * l.groups;
- for(b = 0; b < batch; ++b){
- softmax_array(state.input+b*inputs, inputs, l.temperature, l.output+b*inputs);
+ if(l.softmax_tree){
+ for(b = 0; b < batch; ++b){
+ int i;
+ int count = 0;
+ for(i = 0; i < l.softmax_tree->groups; ++i){
+ int group_size = l.softmax_tree->group_size[i];
+ softmax(state.input+b*inputs + count, group_size, l.temperature, l.output+b*inputs + count);
+ count += group_size;
+ }
+ }
+ } else {
+ for(b = 0; b < batch; ++b){
+ softmax(state.input+b*inputs, inputs, l.temperature, l.output+b*inputs);
+ }
}
}
@@ -68,3 +62,33 @@
}
}
+#ifdef GPU
+
+void pull_softmax_layer_output(const softmax_layer layer)
+{
+ cuda_pull_array(layer.output_gpu, layer.output, layer.inputs*layer.batch);
+}
+
+void forward_softmax_layer_gpu(const softmax_layer l, network_state state)
+{
+ int inputs = l.inputs / l.groups;
+ int batch = l.batch * l.groups;
+ if(l.softmax_tree){
+ int i;
+ int count = 0;
+ for (i = 0; i < l.softmax_tree->groups; ++i) {
+ int group_size = l.softmax_tree->group_size[i];
+ softmax_gpu(state.input+count, group_size, inputs, batch, l.temperature, l.output_gpu + count);
+ count += group_size;
+ }
+ } else {
+ softmax_gpu(state.input, inputs, inputs, batch, l.temperature, l.output_gpu);
+ }
+}
+
+void backward_softmax_layer_gpu(const softmax_layer layer, network_state state)
+{
+ axpy_ongpu(layer.batch*layer.inputs, 1, layer.delta_gpu, 1, state.delta, 1);
+}
+
+#endif
--
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