/ 
AI-MXNet-1.6
River stage one • 2 direct dependents • 2 total dependents
/ AI::MXNet::Gluon::NN::Activation 

AI::MXNet::Gluon::NN::Activation

DESCRIPTION

Applies an activation function to input.

Parameters
----------
activation : str
    Name of activation function to use.
    See mxnet.ndarray.Activation for available choices.

Input shape:
    Arbitrary.

Output shape:
    Same shape as input.

AI::MXNet::Gluon::NN::LeakyReLU - Leaky version of a Rectified Linear Unit.

DESCRIPTION

Leaky version of a Rectified Linear Unit.

It allows a small gradient when the unit is not active

Parameters
----------
alpha : float
    slope coefficient for the negative half axis. Must be >= 0.

AI::MXNet::Gluon::NN::PReLU - Parametric leaky version of a Rectified Linear Unit.

DESCRIPTION

Parametric leaky version of a Rectified Linear Unit.
https://arxiv.org/abs/1502.01852

It learns a gradient when the unit is not active

Parameters
----------
alpha_initializer : Initializer
    Initializer for the embeddings matrix.

AI::MXNet::Gluon::NN::ELU - Exponential Linear Unit (ELU)

DESCRIPTION

Exponential Linear Unit (ELU)
    "Fast and Accurate Deep Network Learning by Exponential Linear Units", Clevert et al, 2016
    https://arxiv.org/abs/1511.07289
    Published as a conference paper at ICLR 2016

Parameters
----------
alpha : float
    The alpha parameter as described by Clevert et al, 2016

AI::MXNet::Gluon::NN::SELU - Scaled Exponential Linear Unit (SELU)

DESCRIPTION

Scaled Exponential Linear Unit (SELU)
"Self-Normalizing Neural Networks", Klambauer et al, 2017
https://arxiv.org/abs/1706.02515

AI::MXNet::Gluon::NN::Swish - Swish Activation function

DESCRIPTION

Swish Activation function
    https://arxiv.org/pdf/1710.05941.pdf

Parameters
----------
beta : float
    swish(x) = x * sigmoid(beta*x)