Diff: NN/source/SoftmaxFunctions/arm_softmax_q15.c

Revision:

0:eedb7d567a5d

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/NN/source/SoftmaxFunctions/arm_softmax_q15.c	Thu Apr 12 01:31:58 2018 +0000
@@ -0,0 +1,109 @@
+/*
+ * 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:        17. January 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;
+    q31_t     min, max;
+    max = -1 * 0x100000;
+    min = 0x100000;
+    for (i = 0; i < dim_vec; i++)
+    {
+        if (vec_in[i] > max)
+        {
+            max = vec_in[i];
+        }
+        if (vec_in[i] < min)
+        {
+            min = vec_in[i];
+        }
+    }
+
+    /* we ignore really small values  
+     * anyway, they will be 0 after shrinking
+     * to q7_t
+     */
+    if (max - min > 16)
+    {
+        min = max - 16;
+    }
+
+    sum = 0;
+
+    for (i = 0; i < dim_vec; i++)
+    {
+        sum += 0x1 << (vec_in[i] - min);
+    }
+
+    for (i = 0; i < dim_vec; i++)
+    {
+        /* we leave 7-bit dynamic range, so that 128 -> 100% confidence */
+        p_out[i] = (q15_t) __SSAT(((0x1 << (vec_in[i] - min + 14)) / sum), 16);
+    }
+
+}
+
+/**
+ * @} end of Softmax group
+ */
+