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Initializer capable of adapting its scale to the shape of weights tensors.

Inherits From: Initializer

With distribution="truncated_normal" or "untruncated_normal", samples are drawn from a truncated/untruncated normal distribution with a mean of zero and a standard deviation (after truncation, if used) stddev = sqrt(scale / n) where n is:

  • number of input units in the weight tensor, if mode = "fan_in"
  • number of output units, if mode = "fan_out"
  • average of the numbers of input and output units, if mode = "fan_avg"

With distribution="uniform", samples are drawn from a uniform distribution within [-limit, limit], with limit = sqrt(3 * scale / n).

scale Scaling factor (positive float).
mode One of "fan_in", "fan_out", "fan_avg".
distribution Random distribution to use. One of "truncated_normal", "untruncated_normal" and "uniform".
seed A Python integer. Used to create random seeds. See tf.compat.v1.set_random_seed for behavior.

ValueError In case of an invalid value for the "scale", mode" or "distribution" arguments.



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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. It will typically be the output of get_config.

An 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 as specified by the initializer.

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

ValueError If the dtype is not floating point