tf.keras.initializers.TruncatedNormal

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.

Examples:

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

Methods

from_config

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

Returns
A JSON-serializable Python dict.

__call__

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

Args
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))