tf.keras.initializers.RandomNormal

Random normal initializer.

Inherits From: Initializer

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.

# 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 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()

@classmethod from_config( config )

Instantiates an initializer from a configuration dictionary.

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()

Returns the initializer's configuration as a JSON-serializable dict.

Returns
A JSON-serializable Python dict.

__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.