tf.keras.initializers.RandomNormal

Random normal initializer.

Inherits From: Initializer

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)

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 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

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@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__

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__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.