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
)
Used in the notebooks
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 = GlorotNormal()
values = initializer(shape=(2, 2))
# Usage in a Keras layer:
initializer = GlorotNormal()
layer = Dense(3, kernel_initializer=initializer)
Args |
|---|
seed
|
A Python integer or instance of
keras.backend.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.backend.SeedGenerator.
|
Reference:
Methods
clone
View source
clone()
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
)
Returns a tensor object initialized as specified by the initializer.
| Args |
|---|
shape
|
Shape of the tensor.
|
dtype
|
Optional dtype of the tensor.
|
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Last updated 2024-06-07 UTC.
[null,null,["Last updated 2024-06-07 UTC."],[],[],null,["# tf.keras.initializers.GlorotNormal\n\n\u003cbr /\u003e\n\n|----------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/initializers/random_initializers.py#L357-L407) |\n\nThe Glorot normal initializer, also called Xavier normal initializer.\n\nInherits From: [`VarianceScaling`](../../../tf/keras/initializers/VarianceScaling), [`Initializer`](../../../tf/keras/Initializer)\n\n#### View aliases\n\n\n**Main aliases**\n\n[`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\n### Used in the notebooks\n\n| Used in the tutorials |\n|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| - [Building Your Own Federated Learning Algorithm](https://www.tensorflow.org/federated/tutorials/building_your_own_federated_learning_algorithm) - [Composing Learning Algorithms](https://www.tensorflow.org/federated/tutorials/composing_learning_algorithms) |\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 = GlorotNormal()\n values = initializer(shape=(2, 2))\n\n # Usage in a Keras layer:\n initializer = GlorotNormal()\n layer = 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 or instance of `keras.backend.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.backend.SeedGenerator`. |\n\n\u003cbr /\u003e\n\n#### Reference:\n\n- [Glorot et al., 2010](http://proceedings.mlr.press/v9/glorot10a.html)\n\nMethods\n-------\n\n### `clone`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/initializers/initializer.py#L83-L84) \n\n clone()\n\n### `from_config`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/initializers/initializer.py#L63-L81) \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/v3.3.3/keras/src/initializers/random_initializers.py#L404-L407) \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/v3.3.3/keras/src/initializers/random_initializers.py#L273-L296) \n\n __call__(\n shape, dtype=None\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. |\n\n\u003cbr /\u003e"]]
View source on GitHub