tf.keras.initializers.RandomNormal

Class RandomNormal

Inherits From: random_normal

Aliases:

• Class tf.keras.initializers.RandomNormal
• Class tf.keras.initializers.normal
• Class tf.keras.initializers.random_normal

Defined in tensorflow/python/keras/initializers.py.

Initializer that generates tensors with a normal distribution.

Args:

• mean: a python scalar or a scalar tensor. Mean of the random values to generate. Defaults to 0.
• stddev: a python scalar or a scalar tensor. Standard deviation of the random values to generate. Defaults to 0.05.
• seed: A Python integer. Used to create random seeds. See tf.set_random_seed for behavior.
• dtype: The data type. Only floating point types are supported.

Returns:

RandomNormal instance.

__init__

__init__(
    mean=0.0,
    stddev=0.05,
    seed=None,
    dtype=tf.dtypes.float32
)

Initialize self. See help(type(self)) for accurate signature.

Methods

tf.keras.initializers.RandomNormal.__call__

__call__(
    shape,
    dtype=None,
    partition_info=None
)

Returns a tensor object initialized as specified by the initializer.

Args:

• shape: Shape of the tensor.
• dtype: Optional dtype of the tensor. If not provided use the initializer dtype.
• partition_info: Optional information about the possible partitioning of a tensor.

tf.keras.initializers.RandomNormal.from_config

from_config(
    cls,
    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. It will typically be the output of get_config.

Returns:

An Initializer instance.

tf.keras.initializers.RandomNormal.get_config

get_config()

Returns the configuration of the initializer as a JSON-serializable dict.

Returns:

A JSON-serializable Python dict.