tf.initializers.truncated_normal

Initializer that generates a truncated normal distribution.

Inherits From: Initializer

View aliases

Main aliases

`tf.truncated_normal_initializer`

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.initializers.truncated_normal, tf.compat.v1.truncated_normal_initializer

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

These values are similar to values from a random_normal_initializer except that values more than two standard deviations from the mean are discarded and re-drawn. This is the recommended initializer for neural network weights and filters.

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

from_config

View source

@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. It will typically be the output of get_config.

Returns
An Initializer instance.

get_config

View source

get_config()

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

Returns
A JSON-serializable Python dict.

__call__

View source

__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.