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#include "caffe2/operators/quantized/int8_sigmoid_op.h"
namespace caffe2 {
REGISTER_CPU_OPERATOR(Int8Sigmoid, int8::Int8SigmoidOp);
OPERATOR_SCHEMA(Int8Sigmoid)
.NumInputs(1)
.NumOutputs(1)
.Arg("Y_scale", "Output tensor quantization scale")
.Arg("Y_zero_point", "Output tensor quantization offset")
.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
)DOC")
.Input(
0,
"input",
"The input tensor that's coerced into a 2D matrix of size (NxD) "
"as described above.")
.Output(
0,
"output",
"The sigmoid normalized output values with the same "
"shape as input tensor.");
} // namespace caffe2
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