tf.keras.initializers.RandomNormal

Initializer that generates tensors with a normal distribution.

Inherits From: Initializer

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

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

Examples:

# Standalone usage:
initializer = tf.keras.initializers.RandomNormal(mean=0., stddev=1.)
values = initializer(shape=(2, 2))
 
# Usage in a Keras layer:
initializer = tf.keras.initializers.RandomNormal(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. Used to make the behavior of the initializer deterministic. Note that a seeded initializer will produce the same random values across multiple calls.

Methods

from_config

View source

@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

View source

get_config()

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

Returns
A JSON-serializable Python dict.

__call__

View source

__call__(
    shape, dtype=None, **kwargs
)

Returns a tensor object initialized to random normal values.

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))
**kwargs Additional keyword arguments.