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#include "caffe2/operators/tanh_op.h"
#include "caffe2/utils/eigen_utils.h"
namespace caffe2 {
#ifdef CAFFE2_USE_ACCELERATE
template <>
template <>
bool TanhFunctor<CPUContext>::operator()<float>(
const int N,
const float* X,
float* Y,
CPUContext* /* context */) const {
vvtanhf(Y, X, &N);
return true;
}
#endif // CAFFE2_USE_ACCELERATE
REGISTER_CPU_OPERATOR(
Tanh,
UnaryElementwiseOp<
TensorTypes<float>,
CPUContext,
TanhFunctor<CPUContext>>);
OPERATOR_SCHEMA(Tanh)
.NumInputs(1)
.NumOutputs(1)
.AllowInplace({{0, 0}})
.IdenticalTypeAndShape()
.SetDoc(R"DOC(
Calculates the hyperbolic tangent of the given input tensor element-wise. This
operation can be done in an in-place fashion too, by providing the same input
and output blobs.
Github Links:
- https://github.com/pytorch/pytorch/blob/master/caffe2/operators/tanh_op.cc
<details>
<summary> <b>Example</b> </summary>
**Code**
```
workspace.ResetWorkspace()
op = core.CreateOperator(
"Tanh",
["X"],
["X"],
)
workspace.FeedBlob("X", np.random.randn(3, 3).astype(np.float32))
print("X:\n", workspace.FetchBlob("X"), "\n")
workspace.RunOperatorOnce(op)
print("X:\n", workspace.FetchBlob("X"))
```
**Result**
```
X:
[[ 2.032603 -2.3556721 -0.14955314]
[ 0.39309832 -1.1020128 -0.92951244]
[-0.62815386 0.21342885 1.4002231 ]]
X:
[[ 0.9662601 -0.982175 -0.14844811]
[ 0.3740282 -0.8012209 -0.73036647]
[-0.55677974 0.21024609 0.8853999 ]]
```
</details>
)DOC")
.Input(0, "input", "1-D input tensor")
.Output(
0,
"output",
"The hyperbolic tangent values of the input tensor, computed "
"element-wise")
.InheritOnnxSchema();
OPERATOR_SCHEMA(TanhGradient)
.NumInputs(2)
.NumOutputs(1)
.IdenticalTypeAndShapeOfInput(1)
.AllowInplace({{1, 0}});
} // namespace caffe2
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