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Split

Description

Setup and add the split layer into the model during the definition graph step. Type : polymorphic.

 

Input parameters

 

Model in : model architecture.

Parameters : layer parameters.

axis : integer, axis along which to concatenate.
Default value “-1”.
training? : boolean, whether the layer is in training mode (can store data for backward).
Default value “True”.
lda coeff : 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 “1”.

name (optional) : string, name of the layer.

 

Output parameters

 

First Half : contains the first part of the input along the split axis.
Second Half : contains the second part of the input along the same axis.

Dimension

Input shape

The input tensor must have a known and even size along the split axis.
The operation splits this axis into two equal parts.

 

Output shape

Two output tensors are produced :

• First Half contains the first part of the input along the split axis.
• Second Half contains the second part of the input along the same axis.

All other dimensions remain unchanged.

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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