tf.raw_ops.StatelessTruncatedNormalV2
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Outputs deterministic pseudorandom values from a truncated normal distribution.
View aliases
Compat aliases for migration
See
Migration guide for
more details.
tf.compat.v1.raw_ops.StatelessTruncatedNormalV2
tf.raw_ops.StatelessTruncatedNormalV2(
shape,
key,
counter,
alg,
dtype=tf.dtypes.float32,
name=None
)
The generated values follow a normal distribution with mean 0 and standard
deviation 1, except that values whose magnitude is more than 2 standard
deviations from the mean are dropped and re-picked.
The outputs are a deterministic function of shape, key, counter and alg.
Args |
|---|
shape
|
A Tensor. Must be one of the following types: int32, int64.
The shape of the output tensor.
|
key
|
A Tensor of type uint64.
Key for the counter-based RNG algorithm (shape uint64[1]).
|
counter
|
A Tensor of type uint64.
Initial counter for the counter-based RNG algorithm (shape uint64[2] or uint64[1] depending on the algorithm). If a larger vector is given, only the needed portion on the left (i.e. [:N]) will be used.
|
alg
|
A Tensor of type int32. The RNG algorithm (shape int32[]).
|
dtype
|
An optional tf.DType from: tf.half, tf.bfloat16, tf.float32, tf.float64. Defaults to tf.float32.
The type of the output.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A Tensor of type dtype.
|
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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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26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.StatelessTruncatedNormalV2\n\n\u003cbr /\u003e\n\nOutputs deterministic pseudorandom values from a truncated normal distribution.\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.StatelessTruncatedNormalV2`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/StatelessTruncatedNormalV2)\n\n\u003cbr /\u003e\n\n tf.raw_ops.StatelessTruncatedNormalV2(\n shape,\n key,\n counter,\n alg,\n dtype=../../tf/dtypes#float32,\n name=None\n )\n\nThe generated values follow a normal distribution with mean 0 and standard\ndeviation 1, except that values whose magnitude is more than 2 standard\ndeviations from the mean are dropped and re-picked.\n\nThe outputs are a deterministic function of `shape`, `key`, `counter` and `alg`.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-----------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `shape` | A `Tensor`. Must be one of the following types: `int32`, `int64`. The shape of the output tensor. |\n| `key` | A `Tensor` of type `uint64`. Key for the counter-based RNG algorithm (shape uint64\\[1\\]). |\n| `counter` | A `Tensor` of type `uint64`. Initial counter for the counter-based RNG algorithm (shape uint64\\[2\\] or uint64\\[1\\] depending on the algorithm). If a larger vector is given, only the needed portion on the left (i.e. \\[:N\\]) will be used. |\n| `alg` | A `Tensor` of type `int32`. The RNG algorithm (shape int32\\[\\]). |\n| `dtype` | An optional [`tf.DType`](../../tf/dtypes/DType) from: `tf.half, tf.bfloat16, tf.float32, tf.float64`. Defaults to [`tf.float32`](../../tf#float32). The type of the output. |\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 `dtype`. ||\n\n\u003cbr /\u003e"]]
