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Initializer that generates a truncated normal distribution.

Inherits From: Initializer

Used in the notebooks

Used in the tutorials

Also available via the shortcut function tf.keras.initializers.truncated_normal.

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


# Standalone usage:
initializer = tf.keras.initializers.TruncatedNormal(mean=0., stddev=1.)
values = initializer(shape=(2, 2))
# Usage in a Keras layer:
initializer = tf.keras.initializers.TruncatedNormal(mean=0., stddev=1.)
layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)

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 before truncation.
seed A Python integer. An initializer created with a given seed will always produce the same random tensor for a given shape and dtype.



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Instantiates an initializer from a configuration dictionary.


initializer = RandomUniform(-1, 1)
config = initializer.get_config()
initializer = RandomUniform.from_config(config)

config A Python dictionary, the output of get_config.

A tf.keras.initializers.Initializer instance.


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

A JSON-serializable Python dict.


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