/ 
AI-MXNet-1.32
River stage one • 2 direct dependents • 2 total dependents
/ AI::MXNet::InitDesc

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

DESCRIPTION

The base class AI::MXNet::Initializer defines the default behaviors to initialize various parameters,
such as set bias to 1, except for the weight. Other classes then define how to initialize the weights.
Currently following classes are available:
mx->init->Uniform    Initializes weights with random values uniformly sampled from a given range.
mx->init->Normal     Initializes weights with random values sampled from a normal distribution with a mean of zero and standard deviation of sigma.
mx->init->Load       Initializes variables by loading data from file or dict.
mx->init->Mixed      Initialize parameters using multiple initializers.
mx->init->Zero       Initializes weights to zero.
mx->init->One        Initializes weights to one.
mx->init->Constant   Initializes the weights to a given value.
mx->init->Orthogonal Initialize weight as orthogonal matrix.
mx->init->Xavier     Returns an initializer performing “Xavier” initialization for weights.
mx->init->MSRAPrelu  Initialize the weight according to a MSRA paper.
mx->init->Bilinear   Initialize weight for upsampling layers.
mx->init->FusedRNN   Initialize parameters for fused rnn layers.

register

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

set_verbosity

Switch on/off verbose mode

Parameters
----------
$verbose : bool
    switch on/off verbose mode
$print_func : CodeRef
    A function that computes statistics of initialized arrays.
    Takes an AI::MXNet::NDArray and returns a scalar. Defaults to mean
    absolute value |x|/size(x)

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 with 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

1 POD Error

The following errors were encountered while parsing the POD:

Around line 89:

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