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Split

Description

Split a tensor into a list of tensors, along the specified ‘axis’. Either input ‘split’ or the attribute ‘num_outputs’ should be specified, but not both. If the attribute ‘num_outputs’ is specified, then the tensor is split into equal sized parts. If the tensor is not evenly splittable into num_outputs, the last chunk will be smaller. If the input ‘split’ is specified, it indicates the sizes of each output in the split.

 

Input parameters

 

specified_outputs_name : array, this parameter lets you manually assign custom names to the output tensors of a node.

 Graphs in : cluster, ONNX model architecture.

input (heterogeneous) – T : object, the tensor to split.
split (optional, heterogeneous) – tensor(int64) : object, optional length of each output. Values should be >= 0.Sum of the values must be equal to the dim value at ‘axis’ specified.

 Parameters : cluster,

axis: integer, which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).
Default value “0”.
num_outputs : integer, number of outputs to split parts of the tensor into. If the tensor is not evenly splittable the last chunk will be smaller.
Default value “0”.
 training? : boolean, whether B should be transposed on the last two dimensions before doing multiplication.
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 node.

Output parameters

 

outputs (variadic, heterogeneous) – T : object, one or more outputs forming list of tensors after splitting.

Type Constraints

T in (tensor(bfloat16), tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16),
tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)) : Constrain input and output types to all tensor types.

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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