tf.raw_ops.ParallelFilterDataset
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Creates a dataset containing elements of input_dataset matching predicate.
tf.raw_ops.ParallelFilterDataset(
input_dataset,
other_arguments,
num_parallel_calls,
predicate,
output_types,
output_shapes,
deterministic='default',
metadata='',
name=None
)
The predicate function must return a scalar boolean and accept the
following arguments:
- One tensor for each component of an element of
input_dataset.
- One tensor for each value in
other_arguments.
Unlike a "FilterDataset", which applies predicate sequentially, this dataset
invokes up to num_parallel_calls copies of predicate in parallel.
Args |
|---|
input_dataset
|
A Tensor of type variant.
|
other_arguments
|
A list of Tensor objects.
A list of tensors, typically values that were captured when
building a closure for predicate.
|
num_parallel_calls
|
A Tensor of type int64.
The number of concurrent invocations of predicate that process
elements from input_dataset in parallel.
|
predicate
|
A function decorated with @Defun.
A function returning a scalar boolean.
|
output_types
|
A list of tf.DTypes that has length >= 1.
|
output_shapes
|
A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
|
deterministic
|
An optional string. Defaults to "default".
A string indicating the op-level determinism to use. Deterministic controls
whether the interleave is allowed to return elements out of order if the next
element to be returned isn't available, but a later element is. Options are
"true", "false", and "default". "default" indicates that determinism should be
decided by the experimental_deterministic parameter of tf.data.Options.
|
metadata
|
An optional string. Defaults to "".
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A Tensor of type variant.
|
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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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26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,["# tf.raw_ops.ParallelFilterDataset\n\n\u003cbr /\u003e\n\nCreates a dataset containing elements of `input_dataset` matching `predicate`.\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.raw_ops.ParallelFilterDataset`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/ParallelFilterDataset)\n\n\u003cbr /\u003e\n\n tf.raw_ops.ParallelFilterDataset(\n input_dataset,\n other_arguments,\n num_parallel_calls,\n predicate,\n output_types,\n output_shapes,\n deterministic='default',\n metadata='',\n name=None\n )\n\nThe `predicate` function must return a scalar boolean and accept the\nfollowing arguments:\n\n- One tensor for each component of an element of `input_dataset`.\n- One tensor for each value in `other_arguments`.\n\nUnlike a \"FilterDataset\", which applies `predicate` sequentially, this dataset\ninvokes up to `num_parallel_calls` copies of `predicate` in parallel.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `input_dataset` | A `Tensor` of type `variant`. |\n| `other_arguments` | A list of `Tensor` objects. A list of tensors, typically values that were captured when building a closure for `predicate`. |\n| `num_parallel_calls` | A `Tensor` of type `int64`. The number of concurrent invocations of `predicate` that process elements from `input_dataset` in parallel. |\n| `predicate` | A function decorated with @Defun. A function returning a scalar boolean. |\n| `output_types` | A list of `tf.DTypes` that has length `\u003e= 1`. |\n| `output_shapes` | A list of shapes (each a [`tf.TensorShape`](../../tf/TensorShape) or list of `ints`) that has length `\u003e= 1`. |\n| `deterministic` | An optional `string`. Defaults to `\"default\"`. A string indicating the op-level determinism to use. Deterministic controls whether the interleave is allowed to return elements out of order if the next element to be returned isn't available, but a later element is. Options are \"true\", \"false\", and \"default\". \"default\" indicates that determinism should be decided by the `experimental_deterministic` parameter of [`tf.data.Options`](../../tf/data/Options). |\n| `metadata` | An optional `string`. Defaults to `\"\"`. |\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 `variant`. ||\n\n\u003cbr /\u003e"]]
