ParametricRampLayer

ParametricRampLayer[]

represents a net layer that computes a leaky ReLU activation with a slope that can be learned.

ParametricRampLayer[levels]

specifies the levels on which each dimension has a specific slope.

Details and Options

Examples

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Basic Examples  (1)

Create a ParametricRampLayer:

Scope  (3)

Initialize a ParametricRampLayer that takes a vector of length 3, having a slope coefficient for each dimension:

Get the values of the slopes:

Apply the layer to some inputs:

Learn the slopes on some data:

Initialize a ParametricRampLayer that takes a length-3 vector and uses a unique slope coefficient:

Initialize a ParametricRampLayer that takes a sequence of length-3 vectors:

Options  (3)

LearningRateMultipliers  (1)

Create a leaky ReLU with a fixed slope of 0.1:

Train a network with this nonlinearity:

The leaky ReLU still has the same slope value after training:

"Slope"  (2)

Create a ParametricRampLayer already initialized with a given slope value:

Apply the layer to some data:

Create a leaky ReLU with a unique slope, fixing the value of that slope:

Apply the layer to some data:

Wolfram Research (2020), ParametricRampLayer, Wolfram Language function, https://reference.wolfram.com/language/ref/ParametricRampLayer.html.

Text

Wolfram Research (2020), ParametricRampLayer, Wolfram Language function, https://reference.wolfram.com/language/ref/ParametricRampLayer.html.

CMS

Wolfram Language. 2020. "ParametricRampLayer." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/ParametricRampLayer.html.

APA

Wolfram Language. (2020). ParametricRampLayer. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/ParametricRampLayer.html

BibTeX

@misc{reference.wolfram_2024_parametricramplayer, author="Wolfram Research", title="{ParametricRampLayer}", year="2020", howpublished="\url{https://reference.wolfram.com/language/ref/ParametricRampLayer.html}", note=[Accessed: 21-November-2024 ]}

BibLaTeX

@online{reference.wolfram_2024_parametricramplayer, organization={Wolfram Research}, title={ParametricRampLayer}, year={2020}, url={https://reference.wolfram.com/language/ref/ParametricRampLayer.html}, note=[Accessed: 21-November-2024 ]}