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/*
* Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef __NNFW_CKER_OPTIMIZED_SOFTMAX_H__
#define __NNFW_CKER_OPTIMIZED_SOFTMAX_H__
#if defined(CKER_OPTIMIZED_EIGEN)
#include "cker/eigen/Utils.h"
#include "cker/Shape.h"
#include "cker/Types.h"
#include <Eigen/Core>
namespace nnfw
{
namespace cker
{
namespace optimized
{
inline void Softmax(const SoftmaxParams ¶ms, const Shape &input_shape, const float *input_data,
const Shape &output_shape, float *output_data)
{
// Validate whether if shapes of input and output are the same
MatchingFlatSize(input_shape, output_shape);
const auto in_mat = MapAsMatrixWithLastDimAsRows(input_data, input_shape);
auto out_mat = MapAsMatrixWithLastDimAsRows(output_data, output_shape);
// Compute the exponential first, removing the max coefficient for numerical
// stability.
out_mat = (in_mat.rowwise() - in_mat.colwise().maxCoeff()).array() * params.beta;
// We are separating out the exp function so that exp can be vectorized.
out_mat = out_mat.array().exp();
// Normalize to get the activations.
Eigen::Array<float, 1, Eigen::Dynamic> scale = out_mat.array().colwise().sum().inverse();
out_mat.array().rowwise() *= scale;
}
} // namespace optimized
} // namespace cker
} // namespace nnfw
#endif // defined(CKER_OPTIMIZED_EIGEN)
#endif // __NNFW_CKER_OPTIMIZED_SOFTMAX_H__
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