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Softmax Functions
[Neural Network Functions]
EXP(2) based softmax function. More...
Functions | |
| void | arm_softmax_q15 (const q15_t *vec_in, const uint16_t dim_vec, q15_t *p_out) |
| Q15 softmax function. | |
| void | arm_softmax_q7 (const q7_t *vec_in, const uint16_t dim_vec, q7_t *p_out) |
| Q7 softmax function. | |
Detailed Description
EXP(2) based softmax function.
Function Documentation
| void arm_softmax_q15 | ( | const q15_t * | vec_in, |
| const uint16_t | dim_vec, | ||
| q15_t * | p_out | ||
| ) |
Q15 softmax function.
- Parameters:
-
[in] vec_in pointer to input vector [in] dim_vec input vector dimention [out] p_out pointer to output vector
- Returns:
- none.
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.
Definition at line 63 of file arm_softmax_q15.c.
| void arm_softmax_q7 | ( | const q7_t * | vec_in, |
| const uint16_t | dim_vec, | ||
| q7_t * | p_out | ||
| ) |
Q7 softmax function.
- Parameters:
-
[in] vec_in pointer to input vector [in] dim_vec input vector dimention [out] p_out pointer to output vector
- Returns:
- none.
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.
Definition at line 63 of file arm_softmax_q7.c.
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