tf.random_normal_initializer

Initializer that generates tensors with a normal distribution.

Inherits From: Initializer

Main aliases

tf.initializers.random_normal

Compat aliases for migration

See Migration guide for more details.

tf.random_normal_initializer(
    mean=0.0, stddev=1.0, seed=None, dtype=tf.dtypes.float32
)

Args
`mean`	a python scalar or a scalar tensor. Mean of the random values to generate.
`stddev`	a python scalar or a scalar tensor. Standard deviation of the random values to generate.
`seed`	A Python integer. Used to create random seeds. See `tf.compat.v1.set_random_seed` for behavior.
`dtype`	Default data type, used if no `dtype` argument is provided when calling the initializer. Only floating point types are supported.

Methods

@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. It will typically be the output of `get_config`.

Returns
An Initializer instance.

get_config()

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

Returns
A JSON-serializable Python dict.

__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.