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SequenceMostLayer[]

represents a net that takes a sequence of inputs and removes its last element.

Details and Options
Details and Options Details and Options
Examples  
Basic Examples  
Scope  
Ports  
Possible Issues  
See Also
Tech Notes
Related Guides
History
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SequenceMostLayer

SequenceMostLayer[]

represents a net that takes a sequence of inputs and removes its last element.

Details and Options

  • SequenceMostLayer[] represents a net that takes a sequence of arrays and outputs a sequence of arrays of length one less than the input sequence length.
  • SequenceMostLayer[…][input] explicitly computes the output NumericArray object from applying the layer.
  • SequenceMostLayer[…][{input1,input2,…}] explicitly computes an output for each of the inputi.
  • When given a NumericArray as input, the output will be a NumericArray.
  • SequenceMostLayer is typically used inside NetChain, NetGraph, etc.
  • The input and output ports of the net represented by SequenceMostLayer[] are:
  • "Input"a sequence of n arrays
    "Output"a sequence of n-1 arrays
  • SequenceMostLayer["Input"shape] allows the shape of the input to be specified. Possible forms for shape are:
  • na sequence of n numbers
    "Varying"a sequence of numbers of varying length
    {len,d1,d2,…}sequence of len arrays of dimensions d1×d2×…
    {"Varying",d1,d2,…}varying number of arrays of dimensions d1×d2×…
  • Options[SequenceMostLayer] gives the list of default options to construct the layer. Options[SequenceMostLayer[…]] gives the list of default options to evaluate the layer on some data.
  • Information[SequenceMostLayer[…]] gives a report about the layer.
  • Information[SequenceMostLayer[…],prop] gives the value of the property prop of SequenceMostLayer[…]. Possible properties are the same as for NetGraph.

Examples

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Basic Examples  (2)

Create a SequenceMostLayer:

Wolfram Language code: SequenceMostLayer[]

Create a SequenceMostLayer that expects a sequence of vectors of size 3:

Wolfram Language code: most = SequenceMostLayer["Input" -> {"Varying", 3}]

Apply the layer to a sequence:

Wolfram Language code: most[{{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}]

Scope  (3)

Ports  (3)

Create a SequenceMostLayer that expects a length-3 sequence of 2x2 matrices:

Wolfram Language code: most = SequenceMostLayer["Input" -> {3, 2, 2}]

Apply the layer to a sequence:

Wolfram Language code: most[{{{1, 2}, {4, 5}}, {{7, 8}, {10, 11}}, {{12, 13}, {14, 15}}}]

Create a SequenceMostLayer that expects a sequence of real numbers:

Wolfram Language code: most = SequenceMostLayer["Input" -> "Varying"]

Thread the layer across a batch of inputs:

Wolfram Language code: most[{{1, 2, 3}, {4, 5}, {6, 7, 8, 9}}]

Create a SequenceMostLayer that drops the last color channel from an RGB image, turning it into a two-channel image:

Wolfram Language code: most = SequenceMostLayer["Input" -> NetEncoder["Image"], "Output" -> NetDecoder["Image"]]

Apply it to an image:

Wolfram Language code: most[[image]]

Possible Issues  (1)

SequenceMostLayer cannot accept symbolic inputs:

Wolfram Language code: most = SequenceMostLayer[]; most[{1, 2}]
Wolfram Language code: most[{1, z}]
Wolfram Research (2017), SequenceMostLayer, Wolfram Language function, https://reference.wolfram.com/language/ref/SequenceMostLayer.html.

Text

Wolfram Research (2017), SequenceMostLayer, Wolfram Language function, https://reference.wolfram.com/language/ref/SequenceMostLayer.html.

CMS

Wolfram Language. 2017. "SequenceMostLayer." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/SequenceMostLayer.html.

APA

Wolfram Language. (2017). SequenceMostLayer. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/SequenceMostLayer.html

BibTeX

@misc{reference.wolfram_2026_sequencemostlayer, author="Wolfram Research", title="{SequenceMostLayer}", year="2017", howpublished="\url{https://reference.wolfram.com/language/ref/SequenceMostLayer.html}", note=[Accessed: 13-June-2026]}

BibLaTeX

@online{reference.wolfram_2026_sequencemostlayer, organization={Wolfram Research}, title={SequenceMostLayer}, year={2017}, url={https://reference.wolfram.com/language/ref/SequenceMostLayer.html}, note=[Accessed: 13-June-2026]}