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Rfft

Description

This function computes the n-point one dimensional Fourier transform for a real-valued input where n is an even number.

 

Input parameters

 

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

 Parameters : cluster,

normalized : integer, must be 0, normalization currently not supported.
Default value “0”.
onesided : boolean, must be true, only one sided FFTs supported.
Default value “True”.
signal_ndim : integer, number of dimensions comprising the signal, collected in reverse order (e.g. 1 = last dimension is the signal).
Default value “0”.
 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

 

 Y (heterogeneous) – T : object, output tensor of size (n//2 + 1) in the signal dim and 2 in the last dimension for the real and complex parts.

Type Constraints

T in (tensor(float), tensor(double), tensor(float16)) : Constrain input and output types to float or half 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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