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HammingWindow

Description

Generates a Hamming window as described in the paper https://ieeexplore.ieee.org/document/1455106.

 

Input parameters

 

specified_outputs_name : array, this parameter lets you manually assign custom names to the output tensors of a node.
size (heterogeneous) – T1 : object, a scalar value indicating the length of the window.

 Parameters : cluster,

output_datatype : enum, the data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T2.
Default value “FLOAT”.
 periodic : boolean, if true, returns a window to be used as periodic function. If false, return a symmetric window. When ‘periodic’ is specified, hann computes a window of length size + 1 and returns the first size points.
Default value “True”.
 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 (heterogeneous) – T2 : object, a Hamming window with length: size. The output has the shape: .

Type Constraints

T1 in (tensor(int32), tensor(int64)) : Constrain the input size to int64_t.

T2 in (tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64),
tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)) : Constrain output types to numeric tensors.

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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