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.
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 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