Module ops

Expand description

This module provides the core operations for tensors, including filling, padding, reshaping, and tensor manipulation.

Modules§

fill
mask: this module implements various masks often used in neural networks and other machine learning applications to prevent overfitting or to control the flow of information through the network.
norm: this module implements various normalization operations for tensors
pad
reshape

DecrementAxis: The DecrementAxis trait defines a method enabling an axis to decrement itself,
DropOut: [Dropout] randomly zeroizes elements with a given probability (p).
IncrementAxis: The IncrementAxis trait defines a method enabling an axis to increment itself, effectively adding a new axis to the array.
IsSquare: IsSquare is a trait for checking if the layout, or dimensionality, of a tensor is square.
L1Norm: a trait for computing the L1 norm of a tensor or array
L2Norm: a trait for computing the L2 norm of a tensor or array
MaskFill: This trait is used to fill an array with a value based on a mask. The mask is a boolean array of the same shape as the array.
Norm: The Norm trait serves as a unified interface for various normalization routnines. At the moment, the trait provides L1 and L2 techniques.
Pad: The Pad trait defines a padding operation for tensors.
Unsqueeze: The Unsqueeze trait establishes an interface for a routine that unsqueezes an array, by inserting a new axis at a specified position. This is useful for reshaping arrays to meet specific dimensional requirements.