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Initializer that generates a truncated normal distribution.

Inherits From: Initializer

Used in the notebooks

Used in the guide 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. Used to create random seeds. See tf.compat.v1.set_random_seed for behavior. Note that seeded initializer will not produce same random values across multiple calls, but multiple initializers will produce same sequence when constructed with same seed value.



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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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Returns a tensor object initialized to random normal values (truncated).

shape Shape of the tensor.
dtype Optional dtype of the tensor. Only floating point types are supported. If not specified, tf.keras.backend.floatx() is used, which default to float32 unless you configured it otherwise (via tf.keras.backend.set_floatx(float_dtype))
**kwargs Additional keyword arguments.