tf.keras.initializers.TruncatedNormal
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Initializer that generates a truncated normal distribution.
Inherits From: Initializer
tf.keras.initializers.TruncatedNormal(
mean=0.0, stddev=0.05, seed=None
)
Also available via the shortcut function
tf.keras.initializers.truncated_normal.
The values generated are similar to values from a
tf.keras.initializers.RandomNormal initializer except that values more
than two standard deviations from the mean are
discarded and re-drawn.
Examples:
# Standalone usage:
initializer = tf.keras.initializers.TruncatedNormal(mean=0., stddev=1.)
values = initializer(shape=(2, 2))
# Usage in a Keras layer:
initializer = tf.keras.initializers.TruncatedNormal(mean=0., stddev=1.)
layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)
Args |
|---|
mean
|
a python scalar or a scalar tensor. Mean of the random values
to generate.
|
stddev
|
a python scalar or a scalar tensor. Standard deviation of the
random values to generate before truncation.
|
seed
|
A Python integer. Used to create random seeds. See
tf.compat.v1.set_random_seed for behavior. Note that seeded
initializer will not produce same random values across multiple calls,
but multiple initializers will produce same sequence when constructed with
same seed value.
|
Methods
from_config
View source
@classmethod
from_config(
config
)
Instantiates an initializer from a configuration dictionary.
Example:
initializer = RandomUniform(-1, 1)
config = initializer.get_config()
initializer = RandomUniform.from_config(config)
| Args |
|---|
config
|
A Python dictionary, the output of get_config.
|
get_config
View source
get_config()
Returns the configuration of the initializer as a JSON-serializable dict.
| Returns |
|---|
|
A JSON-serializable Python dict.
|
__call__
View source
__call__(
shape, dtype=None, **kwargs
)
Returns a tensor object initialized to random normal values (truncated).
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 2022-10-27 UTC.
[null,null,["Last updated 2022-10-27 UTC."],[],[],null,["# tf.keras.initializers.TruncatedNormal\n\n\u003cbr /\u003e\n\n|--------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/keras-team/keras/tree/v2.8.0/keras/initializers/initializers_v2.py#L362-L427) |\n\nInitializer that generates a truncated normal distribution.\n\nInherits From: [`Initializer`](../../../tf/keras/initializers/Initializer)\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tf.initializers.TruncatedNormal`](https://www.tensorflow.org/api_docs/python/tf/keras/initializers/TruncatedNormal), [`tf.initializers.truncated_normal`](https://www.tensorflow.org/api_docs/python/tf/keras/initializers/TruncatedNormal), [`tf.keras.initializers.truncated_normal`](https://www.tensorflow.org/api_docs/python/tf/keras/initializers/TruncatedNormal)\n\n\u003cbr /\u003e\n\n tf.keras.initializers.TruncatedNormal(\n mean=0.0, stddev=0.05, seed=None\n )\n\nAlso available via the shortcut function\n[`tf.keras.initializers.truncated_normal`](../../../tf/keras/initializers/TruncatedNormal).\n\nThe values generated are similar to values from a\n[`tf.keras.initializers.RandomNormal`](../../../tf/keras/initializers/RandomNormal) initializer except that values more\nthan two standard deviations from the mean are\ndiscarded and re-drawn.\n\n#### Examples:\n\n # Standalone usage:\n initializer = tf.keras.initializers.TruncatedNormal(mean=0., stddev=1.)\n values = initializer(shape=(2, 2))\n\n # Usage in a Keras layer:\n initializer = tf.keras.initializers.TruncatedNormal(mean=0., stddev=1.)\n layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `mean` | a python scalar or a scalar tensor. Mean of the random values to generate. |\n| `stddev` | a python scalar or a scalar tensor. Standard deviation of the random values to generate before truncation. |\n| `seed` | A Python integer. Used to create random seeds. See [`tf.compat.v1.set_random_seed`](../../../tf/compat/v1/set_random_seed) for behavior. Note that seeded initializer will not produce same random values across multiple calls, but multiple initializers will produce same sequence when constructed with same seed value. |\n\n\u003cbr /\u003e\n\nMethods\n-------\n\n### `from_config`\n\n[View source](https://github.com/keras-team/keras/tree/v2.8.0/keras/initializers/initializers_v2.py#L89-L108) \n\n @classmethod\n from_config(\n config\n )\n\nInstantiates an initializer from a configuration dictionary.\n\n#### Example:\n\n initializer = RandomUniform(-1, 1)\n config = initializer.get_config()\n initializer = RandomUniform.from_config(config)\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ||\n|----------|--------------------------------------------------|\n| `config` | A Python dictionary, the output of `get_config`. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ||\n|---|---|\n| A [`tf.keras.initializers.Initializer`](../../../tf/keras/initializers/Initializer) instance. ||\n\n\u003cbr /\u003e\n\n### `get_config`\n\n[View source](https://github.com/keras-team/keras/tree/v2.8.0/keras/initializers/initializers_v2.py#L422-L427) \n\n get_config()\n\nReturns the configuration of the initializer as a JSON-serializable dict.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ||\n|---|---|\n| A JSON-serializable Python dict. ||\n\n\u003cbr /\u003e\n\n### `__call__`\n\n[View source](https://github.com/keras-team/keras/tree/v2.8.0/keras/initializers/initializers_v2.py#L404-L420) \n\n __call__(\n shape, dtype=None, **kwargs\n )\n\nReturns a tensor object initialized to random normal values (truncated).\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ||\n|------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `shape` | Shape of the tensor. |\n| `dtype` | Optional dtype of the tensor. Only floating point types are supported. If not specified, [`tf.keras.backend.floatx()`](../../../tf/keras/backend/floatx) is used, which default to `float32` unless you configured it otherwise (via [`tf.keras.backend.set_floatx(float_dtype)`](../../../tf/keras/backend/set_floatx)) |\n| `**kwargs` | Additional keyword arguments. |\n\n\u003cbr /\u003e"]]
View source on GitHub