activation_relu

Activation functions

Description

relu(...): Applies the rectified linear unit activation function. elu(...): Exponential Linear Unit. selu(...): Scaled Exponential Linear Unit (SELU). hard_sigmoid(...): Hard sigmoid activation function. linear(...): Linear activation function (pass-through). sigmoid(...): Sigmoid activation function, sigmoid(x) = 1 / (1 + exp(-x)). softmax(...): Softmax converts a vector of values to a probability distribution. softplus(...): Softplus activation function, softplus(x) = log(exp(x) + 1). softsign(...): Softsign activation function, softsign(x) = x / (abs(x) + 1). tanh(...): Hyperbolic tangent activation function. exponential(...): Exponential activation function. gelu(...): Applies the Gaussian error linear unit (GELU) activation function. swish(...): Swish activation function, swish(x) = x * sigmoid(x).

Usage

 
activation_relu(x, alpha = 0, max_value = NULL, threshold = 0) 
 
activation_elu(x, alpha = 1) 
 
activation_selu(x) 
 
activation_hard_sigmoid(x) 
 
activation_linear(x) 
 
activation_sigmoid(x) 
 
activation_softmax(x, axis = -1) 
 
activation_softplus(x) 
 
activation_softsign(x) 
 
activation_tanh(x) 
 
activation_exponential(x) 
 
activation_gelu(x, approximate = FALSE) 
 
activation_swish(x)

Arguments

Arguments	Description
x	Tensor
alpha	Alpha value
max_value	Max value
threshold	Threshold value for thresholded activation.
axis	Integer, axis along which the softmax normalization is applied
approximate	A bool, whether to enable approximation.

Details

Activations functions can either be used through layer_activation(), or through the activation argument supported by all forward layers.

activation_selu() to be used together with the initialization “lecun_normal”.
activation_selu() to be used together with the dropout variant “AlphaDropout”.

Section

References

activation_swish(): Searching for Activation Functions
activation_gelu(): Gaussian Error Linear Units (GELUs)
activation_selu(): Self-Normalizing Neural Networks
activation_elu(): Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

Value

Tensor with the same shape and dtype as x.