From 08c7cf9c88befd845f00c00d85e40a9eead4b1b3 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Sun, 19 Jun 2016 21:28:15 +0000
Subject: [PATCH] no mean on input binarization

---
 src/softmax_layer.c |   72 ++++++++++++++++++++----------------
 1 files changed, 40 insertions(+), 32 deletions(-)

diff --git a/src/softmax_layer.c b/src/softmax_layer.c
index 79375de..e189701 100644
--- a/src/softmax_layer.c
+++ b/src/softmax_layer.c
@@ -1,56 +1,64 @@
 #include "softmax_layer.h"
+#include "blas.h"
+#include "cuda.h"
+#include <float.h>
 #include <math.h>
 #include <stdlib.h>
 #include <stdio.h>
+#include <assert.h>
 
-softmax_layer *make_softmax_layer(int inputs)
+softmax_layer make_softmax_layer(int batch, int inputs, int groups)
 {
+    assert(inputs%groups == 0);
     fprintf(stderr, "Softmax Layer: %d inputs\n", inputs);
-    softmax_layer *layer = calloc(1, sizeof(softmax_layer));
-    layer->inputs = inputs;
-    layer->output = calloc(inputs, sizeof(float));
-    layer->delta = calloc(inputs, sizeof(float));
-    return layer;
+    softmax_layer l = {0};
+    l.type = SOFTMAX;
+    l.batch = batch;
+    l.groups = groups;
+    l.inputs = inputs;
+    l.outputs = inputs;
+    l.output = calloc(inputs*batch, sizeof(float));
+    l.delta = calloc(inputs*batch, sizeof(float));
+    #ifdef GPU
+    l.output_gpu = cuda_make_array(l.output, inputs*batch); 
+    l.delta_gpu = cuda_make_array(l.delta, inputs*batch); 
+    #endif
+    return l;
 }
 
-/* UNSTABLE!
-void forward_softmax_layer(const softmax_layer layer, float *input)
+void softmax_array(float *input, int n, float temp, float *output)
 {
     int i;
     float sum = 0;
-    for(i = 0; i < layer.inputs; ++i){
-        sum += exp(input[i]);
-    }
-    for(i = 0; i < layer.inputs; ++i){
-        layer.output[i] = exp(input[i])/sum;
-    }
-}
-*/
-void forward_softmax_layer(const softmax_layer layer, float *input)
-{
-    int i;
-    float sum = 0;
-    float largest = 0;
-    for(i = 0; i < layer.inputs; ++i){
+    float largest = -FLT_MAX;
+    for(i = 0; i < n; ++i){
         if(input[i] > largest) largest = input[i];
     }
-    for(i = 0; i < layer.inputs; ++i){
-        sum += exp(input[i]-largest);
-        printf("%f, ", input[i]);
+    for(i = 0; i < n; ++i){
+        sum += exp(input[i]/temp-largest/temp);
     }
-    printf("\n");
-    if(sum) sum = largest+log(sum);
+    if(sum) sum = largest/temp+log(sum);
     else sum = largest-100;
-    for(i = 0; i < layer.inputs; ++i){
-        layer.output[i] = exp(input[i]-sum);
+    for(i = 0; i < n; ++i){
+        output[i] = exp(input[i]/temp-sum);
     }
 }
 
-void backward_softmax_layer(const softmax_layer layer, float *input, float *delta)
+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);
+    }
+}
+
+void backward_softmax_layer(const softmax_layer l, network_state state)
 {
     int i;
-    for(i = 0; i < layer.inputs; ++i){
-        delta[i] = layer.delta[i];
+    for(i = 0; i < l.inputs*l.batch; ++i){
+        state.delta[i] += l.delta[i];
     }
 }
 

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