1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
|
// Copyright 2014 Aravindh Mahendran
// TanH neuron activation function layer.
// Adapted from ReLU layer code written by Yangqing Jia
#include <algorithm>
#include <vector>
#include "caffe/layer.hpp"
#include "caffe/vision_layers.hpp"
namespace caffe {
template <typename Dtype>
__global__ void TanHForward(const int n, const Dtype* in, Dtype* out) {
CUDA_KERNEL_LOOP(index, n) {
Dtype exp2x = exp(2*in[index]);
out[index] = (exp2x - Dtype(1))/(exp2x + Dtype(1));
}
}
template <typename Dtype>
Dtype TanHLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top) {
const Dtype* bottom_data = bottom[0]->gpu_data();
Dtype* top_data = (*top)[0]->mutable_gpu_data();
const int count = bottom[0]->count();
// NOLINT_NEXT_LINE(whitespace/operators)
TanHForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
count, bottom_data, top_data);
CUDA_POST_KERNEL_CHECK;
// << " count: " << count << " bottom_data: "
// << (unsigned long)bottom_data
// << " top_data: " << (unsigned long)top_data
// << " blocks: " << CAFFE_GET_BLOCKS(count)
// << " threads: " << CAFFE_CUDA_NUM_THREADS;
return Dtype(0);
}
template <typename Dtype>
__global__ void TanHBackward(const int n, const Dtype* in_diff,
const Dtype* in_data, Dtype* out_diff) {
CUDA_KERNEL_LOOP(index, n) {
Dtype exp2x = exp(2*in_data[index]);
Dtype tanhx = (exp2x - Dtype(1))/(exp2x + Dtype(1));
out_diff[index] = in_diff[index] * (1 - tanhx*tanhx);
}
}
template <typename Dtype>
void TanHLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down,
vector<Blob<Dtype>*>* bottom) {
if (propagate_down) {
const Dtype* bottom_data = (*bottom)[0]->gpu_data();
const Dtype* top_diff = top[0]->gpu_diff();
Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
const int count = (*bottom)[0]->count();
// NOLINT_NEXT_LINE(whitespace/operators)
TanHBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
count, top_diff, bottom_data, bottom_diff);
CUDA_POST_KERNEL_CHECK;
}
}
INSTANTIATE_CLASS(TanHLayer);
} // namespace caffe
|
