From c8bd92ac0dab30d86f66930ad8733326eaf91f91 Mon Sep 17 00:00:00 2001 From: Piotr Szmelczynski Date: Tue, 3 Nov 2020 16:16:16 +0100 Subject: [PATCH] Sigmoid revise (#2942) * remove sigmoid_backprop * Update Sigmoid spec * Update Sigmoid spec --- docs/ops/activation/Sigmoid_1.md | 36 +++++++++++++++---- .../ngraph/runtime/reference/sigmoid.hpp | 13 ------- 2 files changed, 29 insertions(+), 20 deletions(-) diff --git a/docs/ops/activation/Sigmoid_1.md b/docs/ops/activation/Sigmoid_1.md index f4a70faaff0..f14e58e53b3 100644 --- a/docs/ops/activation/Sigmoid_1.md +++ b/docs/ops/activation/Sigmoid_1.md @@ -6,7 +6,17 @@ **Short description**: Sigmoid element-wise activation function. -**Attributes**: operations has no attributes. +**Detailed description**: [Reference](https://deepai.org/machine-learning-glossary-and-terms/sigmoid-function) + +**Attributes**: *Sigmoid* operation has no attributes. + +**Mathematical Formulation** + + For each element from the input tensor calculates corresponding + element in the output tensor with the following formula: + \f[ + sigmoid( x ) = \frac{1}{1+e^{-x}} + \f] **Inputs**: @@ -16,10 +26,22 @@ * **1**: Result of Sigmoid function applied to the input tensor *x*. Floating point tensor with shape and type matching the input tensor. Required. -**Mathematical Formulation** +**Example** - For each element from the input tensor calculates corresponding - element in the output tensor with the following formula: - \f[ - sigmoid( x ) = \frac{1}{1+e^{-x}} - \f] \ No newline at end of file +```xml + + + + 256 + 56 + + + + + 256 + 56 + + + + +``` \ No newline at end of file diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/sigmoid.hpp b/ngraph/core/reference/include/ngraph/runtime/reference/sigmoid.hpp index 5c10b5fb9e9..d9f60a290cb 100644 --- a/ngraph/core/reference/include/ngraph/runtime/reference/sigmoid.hpp +++ b/ngraph/core/reference/include/ngraph/runtime/reference/sigmoid.hpp @@ -35,19 +35,6 @@ namespace ngraph out[i] = 1 / (1 + exp_value); } } - - template - void sigmoid_backprop(const T* arg, const T* delta_arg, T* out, size_t count) - { - T exp_value; - T func_x; - for (size_t i = 0; i < count; i++) - { - exp_value = std::exp(-arg[i]); - func_x = 1 / (1 + exp_value); - out[i] = delta_arg[i] * func_x * (1 - func_x); - } - } } } }