Save the date! Google I/O returns May 18-20 Register now


View source on GitHub

Initializer that generates a truncated normal distribution.

Inherits From: Initializer

These values are similar to values from a random_normal_initializer except that values more than two standard deviations from the mean are discarded and re-drawn. This is the recommended initializer for neural network weights and filters.

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.
dtype Default data type, used if no dtype argument is provided when calling the initializer. Only floating point types are supported.



View source

Instantiates an initializer from a configuration dictionary.


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

config A Python dictionary. It will typically be the output of get_config.

An Initializer instance.


View source

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

A JSON-serializable Python dict.


View source

Returns a tensor object initialized as specified by the initializer.

shape Shape of the tensor.
dtype Optional dtype of the tensor. If not provided use the initializer dtype.
partition_info Optional information about the possible partitioning of a tensor.