NAME

AI::MXNet::InitDesc - A container for the initialization pattern serialization.

new

Parameters
---------
name : str
    name of variable
attrs : hash ref of str to str
    attributes of this variable taken from AI::MXNet::Symbol->attr_dict

NAME

AI::MXNet::Initializer - Base class for all Initializers

register

Register an initializer class to the AI::MXNet::Initializer factory.

init

Parameters
----------
$desc : AI::MXNet::InitDesc|str
    a name of corresponding ndarray
    or the object that describes the initializer.

$arr : AI::MXNet::NDArray
    an ndarray to be initialized.

NAME

AI::MXNet::Load  - Initialize by loading a pretrained param from a hash ref.

new

Parameters
----------
param: HashRef[AI::MXNet::NDArray]
default_init: Initializer
    default initializer when a name is not found in the param hash ref.
verbose: bool
log the names when initializing.

NAME

AI::MXNet::Mixed - A container for multiple initializer patterns.

new

patterns: array ref of str
    array ref of regular expression patterns to match parameter names.
initializers: array ref of AI::MXNet::Initializer objects.
    array ref of Initializers corresponding to the patterns.

NAME

AI::MXNet::Uniform - Initialize the weight with uniform random values.

DESCRIPTION

Initialize the weight with uniform random values contained within of [-scale, scale]

Parameters
----------
scale : float, optional
    The scale of the uniform distribution.

NAME

AI::MXNet::Normal - Initialize the weight with gaussian random values.

DESCRIPTION

Initialize the weight with gaussian random values contained within of [0, sigma]

Parameters
----------
sigma : float, optional
    Standard deviation for the gaussian distribution.

NAME

AI::MXNet::Orthogonal - Intialize the weight as an Orthogonal matrix.

DESCRIPTION

Intialize weight as Orthogonal matrix

Parameters
----------
scale : float, optional
    scaling factor of weight

rand_type: string optional
    use "uniform" or "normal" random number to initialize weight

Reference
---------
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
arXiv preprint arXiv:1312.6120 (2013).

NAME

AI::MXNet::Xavier - Initialize the weight with Xavier or similar initialization scheme.

DESCRIPTION

Parameters
----------
rnd_type: str, optional
    Use gaussian or uniform.
factor_type: str, optional
    Use avg, in, or out.
magnitude: float, optional
    The scale of the random number range.

NAME

AI::MXNet::MSRAPrelu - Custom initialization scheme.

DESCRIPTION

Initialize the weight with initialization scheme from
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification.

Parameters
----------
factor_type: str, optional
    Use avg, in, or out.
slope: float, optional
    initial slope of any PReLU (or similar) nonlinearities.

NAME

AI::MXNet::LSTMBias - Custom initializer for LSTM cells.

DESCRIPTION

Initializes all biases of an LSTMCell to 0.0 except for
the forget gate's bias that is set to a custom value.

Parameters
----------
forget_bias: float,a bias for the forget gate.
Jozefowicz et al. 2015 recommends setting this to 1.0.

NAME

AI::MXNet::FusedRNN - Custom initializer for fused RNN cells.

DESCRIPTION

Initializes parameters for fused rnn layer.

Parameters
----------
init : Initializer
    initializer applied to unpacked weights.
All parameters below must be exactly the same as ones passed to the
FusedRNNCell constructor.

num_hidden : int
num_layers : int
mode : str
bidirectional : bool
forget_bias : float