Compute primitives. More...
Modules | |
| Common | |
| Common operations to create, destroy and inspect primitives. | |
| Attributes | |
| A container for parameters that extend primitives behavior. | |
| Reorder | |
| A primitive to copy data between two memory objects. | |
| Concat | |
| A primitive to concatenate data by arbitrary dimension. | |
| Sum | |
| A primitive to sum multiple tensors. | |
| Binary | |
| A primitive to perform tensor operations over two tensors. | |
| Convolution | |
| A primitive to perform 1D, 2D or 3D convolution. | |
| Deconvolution | |
| A primitive to perform 1D, 2D or 3D deconvolution. | |
| Shuffle | |
| A primitive to shuffle tensor data along an axis. | |
| Eltwise | |
| A primitive to perform elementwise operations such as the rectifier linear unit (ReLU). | |
| Softmax | |
| A primitive to perform softmax. | |
| LogSoftmax | |
| A primitive to perform logsoftmax. | |
| Pooling | |
| A primitive to perform max or average pooling. | |
| PReLU | |
| PReLU primitive A primitive to perform PReLU (leaky ReLU with trainable alpha parameter) | |
| LRN | |
| A primitive to perform local response normalization (LRN) across or within channels. | |
| Batch Normalization | |
| A primitive to perform batch normalization. | |
| Layer Normalization | |
| A primitive to perform layer normalization. | |
| Inner Product | |
| A primitive to compute an inner product. | |
| RNN | |
| A primitive to compute recurrent neural network layers. | |
| Matrix Multiplication | |
| A primitive to perform matrix-matrix multiplication. | |
| Resampling | |
| A primitive to compute resampling operation on 1D, 2D or 3D data tensor using Nearest Neighbor, or Linear (Bilinear, Trilinear) interpolation method. | |
| Reduction | |
| A primitive to compute reduction operation on data tensor using min, max, mul, sum, mean and norm_lp operations. | |
| Dnnl_api_pooling_v2 | |
Compute primitives.
