tf.keras.initializers.GlorotNormal
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The Glorot normal initializer, also called Xavier normal initializer.
Inherits From: VarianceScaling, Initializer
tf.keras.initializers.GlorotNormal(
seed=None
)
Also available via the shortcut function
tf.keras.initializers.glorot_normal.
Draws samples from a truncated normal distribution centered on 0 with
stddev = sqrt(2 / (fan_in + fan_out)) where fan_in is the number of
input units in the weight tensor and fan_out is the number of output units
in the weight tensor.
Examples:
# Standalone usage:
initializer = tf.keras.initializers.GlorotNormal()
values = initializer(shape=(2, 2))
# Usage in a Keras layer:
initializer = tf.keras.initializers.GlorotNormal()
layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)
Args |
|---|
seed
|
A Python integer. Used to make the behavior of the initializer
deterministic. Note that a seeded initializer will not produce the same
random values across multiple calls, but multiple initializers will
produce the same sequence when constructed with the 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().
|
| 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 as specified by the initializer.
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Last updated 2023-10-06 UTC.
[null,null,["Last updated 2023-10-06 UTC."],[],[],null,["# tf.keras.initializers.GlorotNormal\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#L892-L935) |\n\nThe Glorot normal initializer, also called Xavier normal initializer.\n\nInherits From: [`VarianceScaling`](../../../tf/keras/initializers/VarianceScaling), [`Initializer`](../../../tf/keras/initializers/Initializer)\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tf.initializers.GlorotNormal`](https://www.tensorflow.org/api_docs/python/tf/keras/initializers/GlorotNormal), [`tf.initializers.glorot_normal`](https://www.tensorflow.org/api_docs/python/tf/keras/initializers/GlorotNormal), [`tf.keras.initializers.glorot_normal`](https://www.tensorflow.org/api_docs/python/tf/keras/initializers/GlorotNormal)\n\n\u003cbr /\u003e\n\n tf.keras.initializers.GlorotNormal(\n seed=None\n )\n\nAlso available via the shortcut function\n[`tf.keras.initializers.glorot_normal`](../../../tf/keras/initializers/GlorotNormal).\n\nDraws samples from a truncated normal distribution centered on 0 with\n`stddev = sqrt(2 / (fan_in + fan_out))` where `fan_in` is the number of\ninput units in the weight tensor and `fan_out` is the number of output units\nin the weight tensor.\n\n#### Examples:\n\n # Standalone usage:\n initializer = tf.keras.initializers.GlorotNormal()\n values = initializer(shape=(2, 2))\n\n # Usage in a Keras layer:\n initializer = tf.keras.initializers.GlorotNormal()\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| `seed` | A Python integer. Used to make the behavior of the initializer deterministic. Note that a seeded initializer will not produce the same random values across multiple calls, but multiple initializers will produce the same sequence when constructed with the same seed value. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| References ---------- ||\n|---|---|\n| \u003cbr /\u003e - [Glorot et al., 2010](http://proceedings.mlr.press/v9/glorot10a.html) ||\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#L934-L935) \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#L616-L647) \n\n __call__(\n shape, dtype=None, **kwargs\n )\n\nReturns a tensor object initialized as specified by the initializer.\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