# Class random_normal_initializer

Initializer that generates tensors with a normal distribution. InitializerInherits From:

# Aliases:

• Class tf.compat.v2.initializers.RandomNormal
• Class tf.compat.v2.keras.initializers.RandomNormal
• Class tf.compat.v2.random_normal_initializer
• Class tf.initializers.RandomNormal
• Class tf.keras.initializers.RandomNormal

# Used in the guide:

• Writing customlayers and modelswith Keras

# Used in the tutorials:

• Pix2Pix

# Args:

• 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 create random seeds. See tf.compat.v1.set_random_seed for behavior.

# init

View source

 __init__(
    mean=0.0,
    stddev=0.05,
    seed=None
)

Initialize self. See help(type(self)) for accurate signature.

# Methods

# call

View source

 __call__(
    shape,
    dtype=tf.dtypes.float32
)

Returns a tensor object initialized as specified by the initializer.

# Args:

• shape: Shape of the tensor.
• dtype: Optional dtype of the tensor. Only floating point types are supported.

# Raises:

• ValueError: If the dtype is not floating point

# from_config

View source

 from_config(
    cls,
    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. It will typically be the output of get_config.

# Returns:

An Initializer instance.

# get_config

View source

 get_config()

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

# Returns:

A JSON-serializable Python dict.