tf.keras.initializers.RandomUniform
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Random uniform initializer.
Inherits From: Initializer
tf.keras.initializers.RandomUniform(
minval=-0.05, maxval=0.05, seed=None
)
Used in the notebooks
Draws samples from a uniform distribution for given parameters.
Examples:
# Standalone usage:
initializer = RandomUniform(minval=0.0, maxval=1.0)
values = initializer(shape=(2, 2))
# Usage in a Keras layer:
initializer = RandomUniform(minval=0.0, maxval=1.0)
layer = Dense(3, kernel_initializer=initializer)
Args |
|---|
minval
|
A python scalar or a scalar keras tensor. Lower bound of the
range of random values to generate (inclusive).
|
maxval
|
A python scalar or a scalar keras tensor. Upper bound of the
range of random values to generate (exclusive).
|
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.
|
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.RandomUniform\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#L127-L185) |\n\nRandom uniform initializer.\n\nInherits From: [`Initializer`](../../../tf/keras/Initializer)\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tf.keras.initializers.random_uniform`](https://www.tensorflow.org/api_docs/python/tf/keras/initializers/RandomUniform)\n\n\u003cbr /\u003e\n\n tf.keras.initializers.RandomUniform(\n minval=-0.05, maxval=0.05, seed=None\n )\n\n### Used in the notebooks\n\n| Used in the tutorials |\n|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| - [Train a Deep Q Network with TF-Agents](https://www.tensorflow.org/agents/tutorials/1_dqn_tutorial) - [Networks](https://www.tensorflow.org/agents/tutorials/8_networks_tutorial) |\n\nDraws samples from a uniform distribution for given parameters.\n\n#### Examples:\n\n # Standalone usage:\n initializer = RandomUniform(minval=0.0, maxval=1.0)\n values = initializer(shape=(2, 2))\n\n # Usage in a Keras layer:\n initializer = RandomUniform(minval=0.0, maxval=1.0)\n layer = Dense(3, kernel_initializer=initializer)\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `minval` | A python scalar or a scalar keras tensor. Lower bound of the range of random values to generate (inclusive). |\n| `maxval` | A python scalar or a scalar keras tensor. Upper bound of the range of random values to generate (exclusive). |\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\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#L179-L185) \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#L170-L177) \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