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/*
* Copyright (C) 2010-2019 Arm Limited or its affiliates. All rights reserved.
*
* SPDX-License-Identifier: Apache-2.0
*
* 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
*
* 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.
*/
/* ----------------------------------------------------------------------
* Project: CMSIS NN Library
* Title: arm_softmax_with_batch_q7.c
* Description: Q7 softmax function
*
* $Date: 09. October 2020
* $Revision: V.1.0.1
*
* Target Processor: Cortex-M and Cortex-A cores
*
* -------------------------------------------------------------------- */
#include "arm_nnfunctions.h"
/**
* @ingroup groupNN
*/
/**
* @addtogroup Softmax
* @{
*/
/**
* @brief Q7 softmax function with batch parameter
* @param[in] vec_in pointer to input vector
* @param[in] nb_batches number of batches
* @param[in] dim_vec input vector dimention
* @param[out] p_out pointer to output vector
*
* @details
*
* Here, instead of typical natural logarithm e based softmax, we use
* 2-based softmax here, i.e.,:
*
* y_i = 2^(x_i) / sum(2^x_j)
*
* The relative output will be different here.
* But mathematically, the gradient will be the same
* with a log(2) scaling factor.
*
*/
void arm_softmax_with_batch_q7(const q7_t *vec_in, const uint16_t nb_batches, const uint16_t dim_vec, q7_t *p_out)
{
for (int i = 0; i < nb_batches; i++)
{
arm_softmax_q7(vec_in, dim_vec, p_out);
vec_in += dim_vec;
p_out += dim_vec;
}
}
/**
* @} end of Softmax group
*/
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