Neural Network

This module contains primitive Neural Net (NN) operations.

use orion::operators::nn;

Data types

Orion supports currently these NN types.

Data type	dtype
32-bit integer (signed)	Tensor<i32>
8-bit integer (signed)	Tensor<i8>
32-bit integer (unsigned)	Tensor<u32>
Fixed point (signed)	Tensor<FP8x23 | FP16x16 | FP32x32 | FP64x64>

NNTrait

NNTrait contains the primitive functions to build a Neural Network.

function	description
nn.relu	Applies the rectified linear unit function element-wise.
nn.leaky_relu	Applies the leaky rectified linear unit (Leaky ReLU) activation function element-wise.
nn.sigmoid	Applies the Sigmoid function to an n-dimensional input tensor.
nn.softmax	Computes softmax activations.
nn.softmax_zero	Computes softmax zero.
nn.logsoftmax	Applies the natural log to Softmax function to an n-dimensional input Tensor.
nn.softsign	Applies the Softsign function element-wise.
nn.softplus	Applies the Softplus function element-wise.
nn.linear	Performs a linear transformation of the input tensor using the provided weights and bias.
nn.hard_sigmoid	Applies the Hard Sigmoid function to an n-dimensional input tensor.
nn.thresholded_relu	Performs the thresholded relu activation function element-wise.
nn.gemm	Performs General Matrix multiplication.
nn.grid_sample	Computes the grid sample of the input tensor and input grid.
nn.col2im	Rearranges column blocks back into a multidimensional image
nn.conv_transpose	Performs the convolution transpose of the input data tensor and weight tensor.
nn.conv	Performs the convolution of the input data tensor and weight tensor.
nn.global_average_pool	GlobalAveragePool consumes an input tensor X and applies average pooling across the values in the same channel.
nn.conv_integer	Performs integer convolution