tf.keras.random.truncated_normal
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Draw samples from a truncated normal distribution.
tf.keras.random.truncated_normal(
shape, mean=0.0, stddev=1.0, dtype=None, seed=None
)
The values are drawn from a normal distribution with specified mean and
standard deviation, discarding and re-drawing any samples that are more
than two standard deviations from the mean.
Args |
|---|
shape
|
The shape of the random values to generate.
|
mean
|
Float, defaults to 0. Mean of the random values to generate.
|
stddev
|
Float, defaults to 1. Standard deviation of the random values
to generate.
|
dtype
|
Optional dtype of the tensor. Only floating point types are
supported. If not specified, keras.config.floatx() is used,
which defaults to float32 unless you configured it otherwise (via
keras.config.set_floatx(float_dtype))
|
seed
|
A Python integer or instance of
keras.random.SeedGenerator.
Used to make the behavior of the initializer
deterministic. Note that an initializer seeded with an integer
or None (unseeded) will produce the same random values
across multiple calls. To get different random values
across multiple calls, use as seed an instance
of keras.random.SeedGenerator.
|
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Last updated 2024-06-07 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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-06-07 UTC."],[],[],null,["# tf.keras.random.truncated_normal\n\n\u003cbr /\u003e\n\n|---------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/random/random.py#L155-L183) |\n\nDraw samples from a truncated normal distribution. \n\n tf.keras.random.truncated_normal(\n shape, mean=0.0, stddev=1.0, dtype=None, seed=None\n )\n\nThe values are drawn from a normal distribution with specified mean and\nstandard deviation, discarding and re-drawing any samples that are more\nthan two standard deviations from the mean.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `shape` | The shape of the random values to generate. |\n| `mean` | Float, defaults to 0. Mean of the random values to generate. |\n| `stddev` | Float, defaults to 1. Standard deviation of the random values to generate. |\n| `dtype` | Optional dtype of the tensor. Only floating point types are supported. If not specified, [`keras.config.floatx()`](../../../tf/keras/backend/floatx) is used, which defaults to `float32` unless you configured it otherwise (via [`keras.config.set_floatx(float_dtype)`](../../../tf/keras/backend/set_floatx)) |\n| `seed` | A Python integer or instance of [`keras.random.SeedGenerator`](../../../tf/keras/random/SeedGenerator). Used to make the behavior of the initializer deterministic. Note that an initializer seeded with an integer or None (unseeded) will produce the same random values across multiple calls. To get different random values across multiple calls, use as seed an instance of [`keras.random.SeedGenerator`](../../../tf/keras/random/SeedGenerator). |\n\n\u003cbr /\u003e"]]
View source on GitHub