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UnfoldTensor

Description

Returns a tensor which contains all slices of sizeΒ sizeΒ from input tensor in the dimensionΒ dim. Step between two slices is given byΒ step. IfΒ sizedimΒ is the size of dimensionΒ dimΒ for input tensor, the size of dimensionΒ dimΒ in the returned tensor will beΒ (sizedim - size) / step + 1. An additional dimension of sizeΒ sizeΒ is appended in the returned tensor.

 

Input parameters

 

specified_outputs_name :Β array, this parameter lets you manually assign custom names to the output tensors of a node.
input (heterogeneous) – T : object, input tensor.

Β Parameters :Β cluster,

dim : integer, specify the dimension to unfold.
Default value β€œ0”.
size : integer, specify the size.
Default value β€œ0”.
step : integer, specify the step.
Default value β€œ0”.
Β training?Β :Β boolean, whether the layer is in training mode (can store data for backward).
Default value β€œTrue”.
Β lda coeff :Β float, defines the coefficient by which the loss derivative will be multiplied before being sent to the previous layer (since during the backward run we go backwards).
Default value β€œ1”.

Β name (optional) :Β string, name of the node.

Output parameters

 

output (heterogeneous) – T : object, output tensor.

Type Constraints

T in (tensor(uint8),Β tensor(uint16),Β tensor(uint32), tensor(uint64),Β tensor(int8),Β tensor(int16), tensor(int32),Β tensor(int64),Β tensor(bfloat16), tensor(float16),Β tensor(float),Β tensor(double), tensor(string), tensor(bool),Β tensor(complex64),Β tensor(complex128)) : Allow inputs and outputs to be any kind of tensor.

Example

All these exemples are snippets PNG, you can drop these Snippet onto the block diagram and get the depicted code added to your VI (Do not forget to install Deep Learning library to run it).
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