Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN)
0.13
Performance library for Deep Learning
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enum mkldnn_alg_kind_t |
Kinds of algorithms.
Flags for batch-normalization primititve.
Enumerator | |
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mkldnn_use_global_stats | Use global statistics. If specified
If not specified:
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mkldnn_use_scaleshift | Use scale and shift parameters. If specified:
If no specified:
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mkldnn_omit_stats | Omit statistics.
For time being had an affect on backward propagation only which allowed skipping some computations (the same semantics as mkldnn_use_global_stats) |
mkldnn_fuse_bn_relu | Fuse with ReLU. If specified:
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enum mkldnn_data_type_t |
Memory format specification.
Intel(R) MKL-DNN uses the following notation for memory format names:
'n'
denotes the mini-batch dimension'c'
denotes a channels dimension'i'
and 'o'
denote dimensions of input and output channels'h'
and 'w'
denote spatial width and height'mkldnn_nChw8c'
describes a format where the outermost dimension is mini-batch, followed by the channel block number, followed by the spatial height and width, and finally followed by 8-element channel blocks.'mkldnn_nc'
and 'mkldnn_io'
formats can be used to describe a 2D tensor. Enumerator | |
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mkldnn_format_undef | Undefined memory format, used for empty memory descriptors. |
mkldnn_any | Unspecified format. The primitive selects a format automatically. |
mkldnn_blocked | A tensor in a generic format described by the stride and blocking values in each dimension. See mkldnn_blocking_desc_t for more information. |
mkldnn_x | 1D data tensor. |
mkldnn_nc | 2D data tensor. |
mkldnn_nchw | 4D data tensor in the |
mkldnn_nhwc | 4D data tensor in the |
mkldnn_chwn | 4D data tensor in the |
mkldnn_nChw8c | 4D data tensor in the |
mkldnn_nChw16c | 4D data tensor in the |
mkldnn_oi | 2D weights tensor in the format (input channels, output channels). |
mkldnn_io | 2D weights tensor in the format (input channels, output channels). |
mkldnn_oihw | 4D weights tensor in the format (input channels, output channels, width, height). |
mkldnn_ihwo | 4D weights tensor in the format (input channels, height, width, output channels). |
mkldnn_hwio | 4D weights tensor in the format (height, width, input channels, output channels). |
mkldnn_OIhw8i8o | 4D weights tensor in the |
mkldnn_OIhw16i16o | 4D weights tensor in the |
mkldnn_OIhw4i16o4i | 4D weights tensor in the |
mkldnn_OIhw8i16o2i | 4D weights tensor in the |
mkldnn_OIhw8o16i2o | 4D weights tensor in the |
mkldnn_OIhw8o8i | 4D weights tensor in the |
mkldnn_OIhw16o16i | 4D weights tensor in the |
mkldnn_IOhw16o16i | 4D weights tensor in the |
mkldnn_Oihw8o | 4D weights tensor in the format (output channels, input channels, height, width) with output channels data laid out in memory in 8-element blocks. |
mkldnn_Oihw16o | 4D weights tensor in the format (output channels, input channels, height, width) with output channels data laid out in memory in 16-element blocks. |
mkldnn_Ohwi8o | 4D weights tensor in the format (output channels, width, height, input channels) with output channels data laid out in memory in 8-element blocks. |
mkldnn_Ohwi16o | 4D weights tensor in the format (output channels, width, height, input channels) with output channels data laid out in memory in 16-element blocks. |
mkldnn_OhIw16o4i | 4D weights tensor in the |
mkldnn_goihw | 5D weights tensor in the |
mkldnn_hwigo | 5D weights tensor in the |
mkldnn_gOIhw8i8o | 5D weights tensor in the blocked version of |
mkldnn_gOIhw16i16o | 5D weights tensor in the blocked version of |
mkldnn_gOIhw4i16o4i | 5D weights tensor in the |
mkldnn_gOIhw8i16o2i | 5D weights tensor in the |
mkldnn_gOIhw8o16i2o | 5D weights tensor in the |
mkldnn_gOIhw8o8i | 5D weights tensor in the blocked version of |
mkldnn_gOIhw16o16i | 5D weights tensor in the blocked version of |
mkldnn_gIOhw16o16i | 5D weights tensor in the blocked version of |
mkldnn_gOihw8o | 5D weights tensor in the blocked version of |
mkldnn_gOihw16o | 5D weights tensor in the blocked version of |
mkldnn_gOhwi8o | 5D weights tensor in the blocked version of |
mkldnn_gOhwi16o | 5D weights tensor in the blocked version of |
mkldnn_Goihw8g | 5D weights tensor in the blocked version of |
mkldnn_gOhIw16o4i | 5D weights tensor in the |
mkldnn_oIhw8i | 4D weights tensor in the oihw format with input channels data laid out in memory in 8-element blocks. |
mkldnn_oIhw16i | 4D weights tensor in the oihw format with input channels data laid out in memory in 16-element blocks. |
Kinds of primitives.
Used to implement a way to extend the library with new primitives without changing the ABI.
enum mkldnn_prop_kind_t |
Kinds of propagation.
enum mkldnn_round_mode_t |
enum mkldnn_status_t |
Status values returned by Intel(R) MKL-DNN functions.