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 intializer applied to unpacked weights. num_hidden : int should be the same with arguments passed to FusedRNNCell. num_layers : int should be the same with arguments passed to FusedRNNCell. mode : str should be the same with arguments passed to FusedRNNCell. bidirectional : bool should be the same with arguments passed to FusedRNNCell. forget_bias : float should be the same with arguments passed to FusedRNNCell.