tfc.ops.entropy_decode_channel
Stay organized with collections
Save and categorize content based on your preferences.
Decodes the encoded stream inside handle.
tfc.ops.entropy_decode_channel(
handle, shape, Tdecoded, name=None
)
The output shape is defined as handle.shape + MakeShape(shape), and therefore
both handle and shape arguments determine how many symbols are decoded.
Like encoders, decoders in handle reference multiple distributions. The last
(innermost) dimension of value determines which distribution is used to decode
each value in the output. For example, if decoded is a 3-D array, then
output(i,j,k) is decoded using the k-th distribution.
handle controls the number of coding streams. Suppose that index has the
shape [2, 3, 4] and that handle has shape [2]. Then the first output slice
[0, :, :] of shape [3, 4] is decoded from handle[0] and the second output
slice [1, :, :] is decoded from handle[1]. If handle has shape [], then
there is only one handle, and the entire output is decoded from a single stream.
The input handle may be produced by the CreateRangeDecoder op, or may be
passed through from a different EntropyDecode* op.
This op modifies the input handle. The handle input edge to the op nodes of
this type should not have other consumers in the graph.
Args |
|---|
handle
|
A Tensor of type variant.
|
shape
|
A Tensor of type int32.
|
Tdecoded
|
A tf.DType from: tf.int32.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A tuple of Tensor objects (aliased_handle, decoded).
|
aliased_handle
|
A Tensor of type variant.
|
decoded
|
A Tensor of type Tdecoded.
|
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates. Some content is licensed under the numpy license.
Last updated 2024-04-26 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[],null,["# tfc.ops.entropy_decode_channel\n\n\u003cbr /\u003e\n\nDecodes the encoded stream inside `handle`.\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tfc.entropy_decode_channel`](https://www.tensorflow.org/api_docs/python/tfc/ops/entropy_decode_channel)\n\n\u003cbr /\u003e\n\n tfc.ops.entropy_decode_channel(\n handle, shape, Tdecoded, name=None\n )\n\nThe output shape is defined as `handle.shape + MakeShape(shape)`, and therefore\nboth `handle` and `shape` arguments determine how many symbols are decoded.\n\nLike encoders, decoders in `handle` reference multiple distributions. The last\n(innermost) dimension of `value` determines which distribution is used to decode\neach value in the output. For example, if `decoded` is a 3-D array, then\n`output(i,j,k)` is decoded using the `k`-th distribution.\n\n`handle` controls the number of coding streams. Suppose that `index` has the\nshape `[2, 3, 4]` and that `handle` has shape `[2]`. Then the first output slice\n`[0, :, :]` of shape `[3, 4]` is decoded from `handle[0]` and the second output\nslice `[1, :, :]` is decoded from `handle[1]`. If `handle` has shape `[]`, then\nthere is only one handle, and the entire output is decoded from a single stream.\n\nThe input handle may be produced by the `CreateRangeDecoder` op, or may be\npassed through from a different `EntropyDecode*` op.\n\nThis op modifies the input `handle`. The handle input edge to the op nodes of\nthis type should not have other consumers in the graph.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|------------|-----------------------------------------------------------------------------------------------------------------------------------------------------|\n| `handle` | A `Tensor` of type `variant`. |\n| `shape` | A `Tensor` of type `int32`. |\n| `Tdecoded` | A [`tf.DType`](https://www.tensorflow.org/api_docs/python/tf/dtypes/DType) from: [`tf.int32`](https://www.tensorflow.org/api_docs/python/tf#int32). |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|------------------|--------------------------------|\n| A tuple of `Tensor` objects (aliased_handle, decoded). ||\n| `aliased_handle` | A `Tensor` of type `variant`. |\n| `decoded` | A `Tensor` of type `Tdecoded`. |\n\n\u003cbr /\u003e"]]
