tf.signal.rfft2d
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2D real-valued fast Fourier transform.
tf.signal.rfft2d(
input_tensor, fft_length=None, name=None
)
Used in the notebooks
Computes the 2-dimensional discrete Fourier transform of a real-valued signal
over the inner-most 2 dimensions of input.
Since the DFT of a real signal is Hermitian-symmetric, RFFT2D only returns the
fft_length / 2 + 1 unique components of the FFT for the inner-most dimension
of output: the zero-frequency term, followed by the fft_length / 2
positive-frequency terms.
Along each axis RFFT2D is computed on, if fft_length is smaller than the
corresponding dimension of input, the dimension is cropped. If it is larger,
the dimension is padded with zeros.
Args |
|---|
input
|
A Tensor. Must be one of the following types: float32, float64.
A float32 tensor.
|
fft_length
|
A Tensor of type int32.
An int32 tensor of shape [2]. The FFT length for each dimension.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A Tensor of type Tcomplex.
|
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Last updated 2024-04-26 UTC.
[null,null,["Last updated 2024-04-26 UTC."],[],[],null,["# tf.signal.rfft2d\n\n\u003cbr /\u003e\n\n|----------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/ops/signal/fft_ops.py#L140-L166) |\n\n2D real-valued fast Fourier transform.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.signal.rfft2d`](https://www.tensorflow.org/api_docs/python/tf/signal/rfft2d), [`tf.compat.v1.spectral.rfft2d`](https://www.tensorflow.org/api_docs/python/tf/signal/rfft2d)\n\n\u003cbr /\u003e\n\n tf.signal.rfft2d(\n input_tensor, fft_length=None, name=None\n )\n\n### Used in the notebooks\n\n| Used in the tutorials |\n|-----------------------------------------------------------------------------------------------|\n| - [Scalable model compression](https://www.tensorflow.org/tutorials/optimization/compression) |\n\nComputes the 2-dimensional discrete Fourier transform of a real-valued signal\nover the inner-most 2 dimensions of `input`.\n\nSince the DFT of a real signal is Hermitian-symmetric, `RFFT2D` only returns the\n`fft_length / 2 + 1` unique components of the FFT for the inner-most dimension\nof `output`: the zero-frequency term, followed by the `fft_length / 2`\npositive-frequency terms.\n\nAlong each axis `RFFT2D` is computed on, if `fft_length` is smaller than the\ncorresponding dimension of `input`, the dimension is cropped. If it is larger,\nthe dimension is padded with zeros.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|--------------|------------------------------------------------------------------------------------------------|\n| `input` | A `Tensor`. Must be one of the following types: `float32`, `float64`. A float32 tensor. |\n| `fft_length` | A `Tensor` of type `int32`. An int32 tensor of shape \\[2\\]. The FFT length for each dimension. |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A `Tensor` of type `Tcomplex`. ||\n\n\u003cbr /\u003e"]]
View source on GitHub