tf.keras.initializers.TruncatedNormal

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Initializer that generates a truncated normal distribution.

Inherits From: Initializer

tf.keras.initializers.TruncatedNormal(
    mean=0.0, stddev=0.05, seed=None
)

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.

Methods

__call__

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__call__(
    shape, dtype=tf.dtypes.float32
)

Returns a tensor object initialized as specified by the initializer.

Args:

• shape: Shape of the tensor.
• dtype: Optional dtype of the tensor. Only floating point types are supported.

Raises:

• ValueError: If the dtype is not floating point

from_config

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@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

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get_config()

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

Returns:

A JSON-serializable Python dict.