This module contains primitive Neural Net (NN) operations.
use orion::operators::nn;
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
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. |