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
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
|
#include "caffe2/operators/sigmoid_op.h"
#include "caffe2/utils/eigen_utils.h"
namespace caffe2 {
template <>
template <typename T>
bool SigmoidFunctor<CPUContext>::
operator()(const int N, const T* X, T* Y, CPUContext* /* context */) const {
EigenVectorArrayMap<T>(Y, N) =
T(1) / (T(1) + (-ConstEigenVectorArrayMap<T>(X, N)).exp());
return true;
}
REGISTER_CPU_OPERATOR(
Sigmoid,
UnaryElementwiseOp<
TensorTypes<float>,
CPUContext,
SigmoidFunctor<CPUContext>>);
// Input: X, output: Y
OPERATOR_SCHEMA(Sigmoid)
.NumInputs(1)
.NumOutputs(1)
.AllowInplace({{0, 0}})
.IdenticalTypeAndShape()
.SetDoc(R"DOC(
Apply the Sigmoid function element-wise to the input tensor. This is often used
as a non-linear activation function in a neural network. The sigmoid function is
defined as:
$$Sigmoid(x) = \frac{1}{1+\exp(-x)}$$
Github Links:
- https://github.com/pytorch/pytorch/blob/master/caffe2/operators/sigmoid_op.cc
<details>
<summary> <b>Example</b> </summary>
**Code**
```
workspace.ResetWorkspace()
op = core.CreateOperator(
"Sigmoid",
["X"],
["Y"]
)
workspace.FeedBlob("X", np.random.randn(5).astype(np.float32))
print("input:", workspace.FetchBlob("X"))
workspace.RunOperatorOnce(op)
print("sigmoid:", workspace.FetchBlob("Y"))
```
**Result**
```
input: [ 1.5744036 0.31632107 1.7842269 1.4450722 -2.1726978 ]
sigmoid: [0.8284105 0.57842743 0.85621804 0.80923885 0.10222916]
```
</details>
)DOC")
.Input(0, "X", "*(type: Tensor`<float>`)* Input tensor.")
.Output(0, "Y", "*(type: Tensor`<float>`)* Output tensor.")
.InheritOnnxSchema();
// Input: Y, dY, output: dX
OPERATOR_SCHEMA(SigmoidGradient)
.NumInputs(2)
.NumOutputs(1)
.AllowInplace({{1, 0}})
.IdenticalTypeAndShapeOfInput(1)
.SetDoc(R"DOC(
SigmoidGradient takes both Y and dY and uses this to update dX according to the
chain rule and derivatives of the sigmoid function.
)DOC");
} // namespace caffe2
|
