AI::MXNet::InitDesc - A container for the initialization pattern serialization.
Parameters --------- name : str name of variable attrs : hash ref of str to str attributes of this variable taken from AI::MXNet::Symbol->attr_dict
AI::MXNet::Initializer - Base class for all Initializers
Register an initializer class to the AI::MXNet::Initializer factory.
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)
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.
AI::MXNet::Load - Initialize by loading a pretrained param from a hash ref.
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.
AI::MXNet::Mixed - A container for multiple initializer patterns.
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.
AI::MXNet::Uniform - Initialize the weight with uniform random values.
Initialize the weight with uniform random values contained within of [-scale, scale] Parameters ---------- scale : float, optional The scale of the uniform distribution.
AI::MXNet::Normal - Initialize the weight with gaussian random values.
Initialize the weight with gaussian random values contained within of [0, sigma] Parameters ---------- sigma : float, optional Standard deviation for the gaussian distribution.
AI::MXNet::Orthogonal - Intialize the weight as an Orthogonal matrix.
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).
AI::MXNet::Xavier - Initialize the weight with Xavier or similar initialization scheme.
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.
AI::MXNet::MSRAPrelu - Custom initialization scheme.
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.
AI::MXNet::LSTMBias - Custom initializer for LSTM cells.
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.
AI::MXNet::FusedRNN - Custom initializer for fused RNN cells.
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
To install AI::MXNet, copy and paste the appropriate command in to your terminal.
cpanm
cpanm AI::MXNet
CPAN shell
perl -MCPAN -e shell install AI::MXNet
For more information on module installation, please visit the detailed CPAN module installation guide.