Primitives

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.

- See also
- Basic Concepts