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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