tf.truncated_normal_initializer

Class truncated_normal_initializer

Inherits From: Initializer

Aliases:

• Class tf.initializers.truncated_normal
• Class tf.keras.initializers.TruncatedNormal
• Class tf.truncated_normal_initializer

Defined in tensorflow/python/ops/init_ops.py.

See the guide: Variables > Sharing Variables

Initializer that generates a truncated normal distribution.

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.set_random_seed for behavior.
• dtype: The data type. Only floating point types are supported.

Methods

__init__

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

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

__call__

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

Call self as a function.

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.

get_config

get_config()

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

Returns:

A JSON-serializable Python dict.