tf.keras.initializers.RandomNormal | TensorFlow v2.16.1 (original) (raw)
tf.keras.initializers.RandomNormal
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Random normal initializer.
Inherits From: Initializer
View aliases
Main aliases
tf.keras.initializers.random_normal
tf.keras.initializers.RandomNormal(
mean=0.0, stddev=0.05, seed=None
)
Draws samples from a normal distribution for given parameters.
Examples:
# Standalone usage:
initializer = RandomNormal(mean=0.0, stddev=1.0)
values = initializer(shape=(2, 2))
# Usage in a Keras layer:
initializer = RandomNormal(mean=0.0, stddev=1.0)
layer = Dense(3, kernel_initializer=initializer)
| Args | |
|---|---|
| mean | A python scalar or a scalar keras tensor. Mean of the random values to generate. |
| stddev | A python scalar or a scalar keras tensor. Standard deviation of the random values to generate. |
| seed | A Python integer or instance ofkeras.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
clone()
from_config
@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
get_config()
Returns the initializer's configuration as a JSON-serializable dict.
| Returns |
|---|
| A JSON-serializable Python dict. |
__call__
__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. |