SequenceConstruct

Public technical reference documentation from Graiphic. For current SOTA installation and toolkit guides, visit the documentation center.

Description

Construct a tensor sequence containing ‘inputs’ tensors. All tensors in ‘inputs’ must have the same data type.

Documentation illustration (illustration unavailable in the archive).

Input parameters

specified_outputs_name : array, this parameter lets you manually assign custom names to the output tensors of a node.
Documentation illustration (illustration unavailable in the archive). inputs (variadic, heterogeneous) – T : object, tensors.

Parameters : cluster,

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 node.

Output parameters

output_sequence (heterogeneous) – S : object, sequence enclosing the input tensors.

Type Constraints

T in (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 types to any tensor type.

S in (seq(tensor(bool)), seq(tensor(complex128)), seq(tensor(complex64)), seq(tensor(double)), seq(tensor(float)),
seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))) : Constrain output types to any tensor type.

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).