From b36512ea2e7e6d2ec36a3241c1bf751e4e074fe1 Mon Sep 17 00:00:00 2001
From: vinjn <vinjn.z@gmail.com>
Date: Sat, 26 May 2018 13:42:53 +0000
Subject: [PATCH] classifier.c - add the awesome training chart and make sure "top" is not bigger than "classes" in datacfg file.
---
src/softmax_layer.c | 125 +++++++++++++++++++++++++++--------------
1 files changed, 83 insertions(+), 42 deletions(-)
diff --git a/src/softmax_layer.c b/src/softmax_layer.c
index 1268423..27f73fd 100644
--- a/src/softmax_layer.c
+++ b/src/softmax_layer.c
@@ -1,59 +1,100 @@
#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 batch, int inputs)
+softmax_layer make_softmax_layer(int batch, int inputs, int groups)
{
- fprintf(stderr, "Softmax Layer: %d inputs\n", inputs);
- softmax_layer *layer = calloc(1, sizeof(softmax_layer));
- layer->batch = batch;
- layer->inputs = inputs;
- layer->output = calloc(inputs*batch, sizeof(float));
- layer->delta = calloc(inputs*batch, sizeof(float));
- return layer;
+ assert(inputs%groups == 0);
+ fprintf(stderr, "softmax %4d\n", inputs);
+ 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));
+
+ l.forward = forward_softmax_layer;
+ l.backward = backward_softmax_layer;
+ #ifdef GPU
+ l.forward_gpu = forward_softmax_layer_gpu;
+ l.backward_gpu = backward_softmax_layer_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_tree(float *input, int batch, int inputs, float temp, tree *hierarchy, float *output)
+{
+ int b;
+ for(b = 0; b < batch; ++b){
+ int i;
+ int count = 0;
+ for(i = 0; i < hierarchy->groups; ++i){
+ int group_size = hierarchy->group_size[i];
+ softmax(input+b*inputs + count, group_size, temp, output+b*inputs + count, 1);
+ count += group_size;
+ }
+ }
+}
+
+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;
+ if(l.softmax_tree){
+ softmax_tree(state.input, batch, inputs, l.temperature, l.softmax_tree, l.output);
+ } else {
+ for(b = 0; b < batch; ++b){
+ softmax(state.input+b*inputs, inputs, l.temperature, l.output+b*inputs, 1);
+ }
+ }
+}
+
+void backward_softmax_layer(const softmax_layer l, network_state state)
{
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,b;
- for(b = 0; b < layer.batch; ++b){
- float sum = 0;
- float largest = 0;
- for(i = 0; i < layer.inputs; ++i){
- if(input[i+b*layer.inputs] > largest) largest = input[i+b*layer.inputs];
- }
- for(i = 0; i < layer.inputs; ++i){
- sum += exp(input[i+b*layer.inputs]-largest);
- //printf("%f, ", input[i]);
- }
- //printf("\n");
- if(sum) sum = largest+log(sum);
- else sum = largest-100;
- for(i = 0; i < layer.inputs; ++i){
- layer.output[i+b*layer.inputs] = exp(input[i+b*layer.inputs]-sum);
- }
+ for(i = 0; i < l.inputs*l.batch; ++i){
+ state.delta[i] += l.delta[i];
}
}
-void backward_softmax_layer(const softmax_layer layer, float *input, float *delta)
+#ifdef GPU
+
+void pull_softmax_layer_output(const softmax_layer layer)
{
- int i;
- for(i = 0; i < layer.inputs*layer.batch; ++i){
- delta[i] = layer.delta[i];
+ 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
--
Gitblit v1.10.0