| /* |
| * 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_q7.c |
| * Description: Q7 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 Q7 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 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_q7(const q7_t * vec_in, const uint16_t dim_vec, q7_t * p_out) |
| { |
| q31_t sum; |
| int16_t i; |
| uint8_t shift; |
| q15_t base; |
| base = -257; |
| |
| /* We first search for the maximum */ |
| for (i = 0; i < dim_vec; i++) |
| { |
| if (vec_in[i] > base) |
| { |
| base = vec_in[i]; |
| } |
| } |
| |
| /* |
| * So the base is set to max-8, meaning |
| * that we ignore really small values. |
| * anyway, they will be 0 after shrinking to q7_t. |
| */ |
| base = base - 8; |
| |
| 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 << 20) / sum */ |
| int output_base = 0x100000 / sum; |
| |
| /* |
| * Final confidence will be output_base >> ( 13 - (vec_in[i] - base) ) |
| * so 128 (0x1<<7) -> 100% confidence when sum = 0x1 << 8, output_base = 0x1 << 12 |
| * and vec_in[i]-base = 8 |
| */ |
| for (i = 0; i < dim_vec; i++) |
| { |
| if (vec_in[i] > base) |
| { |
| /* Here minimum value of 13+base-vec_in[i] will be 5 */ |
| shift = (uint8_t)__USAT(13+base-vec_in[i], 5); |
| p_out[i] = (q7_t) __SSAT((output_base >> shift), 8); |
| } else { |
| p_out[i] = 0; |
| } |
| } |
| } |
| |
| /** |
| * @} end of Softmax group |
| */ |