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)