tf.keras.initializers.TruncatedNormal

Initializer that generates a truncated normal distribution.

Inherits From: Initializer

View aliases

Main aliases

tf.keras.initializers.truncated_normal

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

Used in the notebooks

Used in the tutorials
Customizing a Transformer Encoder Fine-tuning a BERT model

The values generated are similar to values from a RandomNormal initializer, except that values more than two standard deviations from the mean are discarded and re-drawn.

Examples:

# Standalone usage:
initializer = TruncatedNormal(mean=0., stddev=1.)
values = initializer(shape=(2, 2))

# Usage in a Keras layer:
initializer = TruncatedNormal(mean=0., stddev=1.)
layer = Dense(3, kernel_initializer=initializer)

Args
mean	A python scalar or a scalar keras tensor. Mean of the random values to generate.
stddev	A python scalar or a scalar keras tensor. Standard deviation of the random values to generate.
seed	A Python integer or instance of keras.backend.SeedGenerator. Used to make the behavior of the initializer deterministic. Note that an initializer seeded with an integer or None (unseeded) will produce the same random values across multiple calls. To get different random values across multiple calls, use as seed an instance of keras.backend.SeedGenerator.

Methods

clone

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clone()

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, the output of get_config().

Returns
An Initializer instance.

get_config

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get_config()

Returns the initializer's configuration as a JSON-serializable dict.

Returns
A JSON-serializable Python dict.

__call__

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__call__(
    shape, dtype=None
)

Returns a tensor object initialized as specified by the initializer.

Args
shape	Shape of the tensor.
dtype	Optional dtype of the tensor.