# Description for `caffe2op-filler`

## Filler Operators for Initializing Neural
Network Parameters

This Rust crate provides a collection of
mathematical operators, called filler operators,
used in deep learning and machine learning
applications to initialize the weights and biases
of neural network parameters. The initialization
of neural network parameters is an essential step
in the training process of deep learning models,
as it sets the initial values for the weights and
biases which will be gradually updated during the
training process.

The crate includes various types of filler
operators, such as `UniformFillOp`,
`UniformIntFill`, `DiagonalFillOp`, `Xavier`,
`RandGaussian`, `RangeFillOp`, and
`UniqueUniformFillOp`, among others. Each of these
operators initializes the weights and biases in
a different way, based on different mathematical
concepts and strategies.

For instance, the `UniformFillOp` operator
initializes the weights and biases with values
drawn uniformly from a specified range, while the
`Xavier` operator initializes the weights with
values drawn from a normal distribution with zero
mean and a variance calculated based on the fan-in
and fan-out of the layer. The `RandGaussian`
operator initializes the weights with values drawn
from a Gaussian distribution with a specified mean
and standard deviation, and the `RangeFillOp`
operator initializes the weights and biases with
values generated from a specified range of
numbers.

The crate also includes several helper functions,
such as `GetStepSize`, `CheckRange`, and
`VerifyOutputShape`, which assist in the
initialization of neural network parameters.

Overall, the `caffe2op-filler` crate provides
a useful collection of mathematical operators and
helper functions for initializing neural network
parameters in deep learning and machine learning
applications.

## `caffe2op-filltensor`

Crate for filling tensors with values using the
`FillerOp` operator, a mathematical operation used
in DSP and machine learning computations.

The `FillerOp` takes a tensor as input and fills
it with values according to a specified template,
indicated by the `FillerTensorInference`
parameter. This operator can be useful in various
applications where it is necessary to fill tensors
with specific values, such as in neural network
initialization or data augmentation.

The `caffe2op-filltensor` crate provides
a convenient implementation of the `FillerOp`
using the `FetchBlob` function to input the tensor
and the `FillWithType` function to fill it with
values. The `ResetWorkspace` function can be used
to reset the state of the workspace between calls
to the operator.

The `Desired` parameter can be used to specify the
desired size of the tensor to be filled, while the
`ExtractValues` parameter can be used to extract
the filled tensor from the workspace. The
`Strictly` parameter can be used to indicate
whether the size of the filled tensor should
strictly match the desired size, or if it should
be allowed to have a slightly different size.

Overall, the `caffe2op-filltensor` crate provides
a powerful set of tools for efficiently filling
tensors with desired values, with applications in
machine learning, neural network initialization,
and data augmentation.