tf.keras.initializers.RandomNormal
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Initializer that generates tensors with a normal distribution.
Inherits From: Initializer
tf.keras.initializers.RandomNormal(
mean=0.0, stddev=0.05, seed=None
)
Also available via the shortcut function
tf.keras.initializers.random_normal.
Examples:
# Standalone usage:
initializer = tf.keras.initializers.RandomNormal(mean=0., stddev=1.)
values = initializer(shape=(2, 2))
# Usage in a Keras layer:
initializer = tf.keras.initializers.RandomNormal(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.
|
seed
|
A Python integer. Used to make the behavior of the initializer
deterministic. Note that a seeded initializer will produce the same
random values across multiple calls.
|
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().
|
| Returns |
|---|
An Initializer instance.
|
get_config
View source
get_config()
Returns the initializer's configuration 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.
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 2023-10-06 UTC.
[null,null,["Last updated 2023-10-06 UTC."],[],[],null,["# tf.keras.initializers.RandomNormal\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/keras-team/keras/tree/v2.14.0/keras/initializers/initializers.py#L364-L439) |\n\nInitializer that generates tensors with a normal distribution.\n\nInherits From: [`Initializer`](../../../tf/keras/initializers/Initializer)\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tf.initializers.RandomNormal`](https://www.tensorflow.org/api_docs/python/tf/keras/initializers/RandomNormal), [`tf.initializers.random_normal`](https://www.tensorflow.org/api_docs/python/tf/keras/initializers/RandomNormal), [`tf.keras.initializers.random_normal`](https://www.tensorflow.org/api_docs/python/tf/keras/initializers/RandomNormal)\n\n\u003cbr /\u003e\n\n tf.keras.initializers.RandomNormal(\n mean=0.0, stddev=0.05, seed=None\n )\n\nAlso available via the shortcut function\n[`tf.keras.initializers.random_normal`](../../../tf/keras/initializers/RandomNormal).\n\n#### Examples:\n\n # Standalone usage:\n initializer = tf.keras.initializers.RandomNormal(mean=0., stddev=1.)\n values = initializer(shape=(2, 2))\n\n # Usage in a Keras layer:\n initializer = tf.keras.initializers.RandomNormal(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. |\n| `seed` | A Python integer. Used to make the behavior of the initializer deterministic. Note that a seeded initializer will produce the same random values across multiple calls. |\n\n\u003cbr /\u003e\n\nMethods\n-------\n\n### `from_config`\n\n[View source](https://github.com/keras-team/keras/tree/v2.14.0/keras/initializers/initializers.py#L96-L115) \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| An `Initializer` instance. ||\n\n\u003cbr /\u003e\n\n### `get_config`\n\n[View source](https://github.com/keras-team/keras/tree/v2.14.0/keras/initializers/initializers.py#L438-L439) \n\n get_config()\n\nReturns the initializer's configuration 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/v2.14.0/keras/initializers/initializers.py#L401-L436) \n\n __call__(\n shape, dtype=None, **kwargs\n )\n\nReturns a tensor object initialized to random normal values.\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