121 lines
3.1 KiB
C
121 lines
3.1 KiB
C
/*
|
|
* Copyright (C) 2010-2018 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_q15.c
|
|
* Description: Q15 softmax function
|
|
*
|
|
* $Date: 20. February 2018
|
|
* $Revision: V.1.0.0
|
|
*
|
|
* Target Processor: Cortex-M cores
|
|
*
|
|
* -------------------------------------------------------------------- */
|
|
|
|
#include "arm_math.h"
|
|
#include "arm_nnfunctions.h"
|
|
|
|
/**
|
|
* @ingroup groupNN
|
|
*/
|
|
|
|
/**
|
|
* @addtogroup Softmax
|
|
* @{
|
|
*/
|
|
|
|
/**
|
|
* @brief Q15 softmax function
|
|
* @param[in] vec_in pointer to input vector
|
|
* @param[in] dim_vec input vector dimention
|
|
* @param[out] p_out pointer to output vector
|
|
* @return none.
|
|
*
|
|
* @details
|
|
*
|
|
* Here, instead of typical e based softmax, we use
|
|
* 2-based softmax, 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_q15(const q15_t * vec_in, const uint16_t dim_vec, q15_t * p_out)
|
|
{
|
|
q31_t sum;
|
|
int16_t i;
|
|
uint8_t shift;
|
|
q31_t base;
|
|
base = -1 * 0x100000;
|
|
for (i = 0; i < dim_vec; i++)
|
|
{
|
|
if (vec_in[i] > base)
|
|
{
|
|
base = vec_in[i];
|
|
}
|
|
}
|
|
|
|
/* we ignore really small values
|
|
* anyway, they will be 0 after shrinking
|
|
* to q15_t
|
|
*/
|
|
base = base - 16;
|
|
|
|
sum = 0;
|
|
|
|
for (i = 0; i < dim_vec; i++)
|
|
{
|
|
if (vec_in[i] > base)
|
|
{
|
|
shift = (uint8_t)__USAT(vec_in[i] - base, 5);
|
|
sum += 0x1 << shift;
|
|
}
|
|
}
|
|
|
|
/* This is effectively (0x1 << 32) / sum */
|
|
int64_t div_base = 0x100000000LL;
|
|
int output_base = (int32_t)(div_base / sum);
|
|
|
|
/* Final confidence will be output_base >> ( 17 - (vec_in[i] - base) )
|
|
* so 32768 (0x1<<15) -> 100% confidence when sum = 0x1 << 16, output_base = 0x1 << 16
|
|
* and vec_in[i]-base = 16
|
|
*/
|
|
for (i = 0; i < dim_vec; i++)
|
|
{
|
|
if (vec_in[i] > base)
|
|
{
|
|
/* Here minimum value of 17+base-vec[i] will be 1 */
|
|
shift = (uint8_t)__USAT(17+base-vec_in[i], 5);
|
|
p_out[i] = (q15_t) __SSAT((output_base >> shift), 16);
|
|
} else
|
|
{
|
|
p_out[i] = 0;
|
|
}
|
|
}
|
|
|
|
}
|
|
|
|
/**
|
|
* @} end of Softmax group
|
|
*/
|