keras: activation_relu – R documentation

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activation_relu

Activation functions

Description

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

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)

Arguments

`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

Details

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

Value

Tensor with the same shape and dtype as x.

References

activation_selu(): Self-Normalizing Neural Networks

keras

R Interface to 'Keras'

v2.4.0

MIT + file LICENSE

Authors

Daniel Falbel [ctb, cph, cre], JJ Allaire [aut, cph], François Chollet [aut, cph], RStudio [ctb, cph, fnd], Google [ctb, cph, fnd], Yuan Tang [ctb, cph] (<https://orcid.org/0000-0001-5243-233X>), Wouter Van Der Bijl [ctb, cph], Martin Studer [ctb, cph], Sigrid Keydana [ctb]

Initial release