[ti-machine-learning/ti-machine-learning.git] / src / common / cnn / timlCNNPoolingBackPropagation.c
1 /******************************************************************************/\r
2 /*!\r
3 * \file timlCNNPoolingBackPropagation.c\r
4 */\r
5 /* Copyright (C) 2015 Texas Instruments Incorporated - http://www.ti.com/\r
6 *\r
7 * Redistribution and use in source and binary forms, with or without\r
8 * modification, are permitted provided that the following conditions\r
9 * are met:\r
10 *\r
11 * Redistributions of source code must retain the above copyright\r
12 * notice, this list of conditions and the following disclaimer.\r
13 *\r
14 * Redistributions in binary form must reproduce the above copyright\r
15 * notice, this list of conditions and the following disclaimer in the\r
16 * documentation and/or other materials provided with the\r
17 * distribution.\r
18 *\r
19 * Neither the name of Texas Instruments Incorporated nor the names of\r
20 * its contributors may be used to endorse or promote products derived\r
21 * from this software without specific prior written permission.\r
22 *\r
23 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\r
24 * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\r
25 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR\r
26 * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT\r
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34 *\r
35 ******************************************************************************/\r
36 \r
37 \r
38 /*******************************************************************************\r
39 *\r
40 * INCLUDES\r
41 *\r
42 ******************************************************************************/\r
43 \r
44 #include "../api/timl.h"\r
45 \r
46 \r
47 /******************************************************************************/\r
48 /*!\r
49 * \ingroup cnn\r
50 * \brief Back propagate the gradient from the pooling layer to the previous layer\r
51 * \param[in] layer Layer ptr\r
52 * \return Error code\r
53 */\r
54 /******************************************************************************/\r
55 \r
56 int timlCNNPoolingBackPropagation(timlCNNLayer *layer)\r
57 {\r
58 \r
59 switch (layer->poolingParams.type) {\r
60 case CNN_MaxPooling:\r
61 return timlCNNMaxPoolingBackPropagation(layer);\r
62 break;\r
63 case CNN_MeanPooling:\r
64 return timlCNNMeanPoolingBackPropagation(layer);\r
65 break;\r
66 }\r
67 return 0;\r
68 }\r