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tf.random_normal_initializer

class tf.contrib.keras.initializers.RandomNormal

class tf.random_normal_initializer

Defined in tensorflow/python/ops/init_ops.py.

See the guide: Variables > Sharing Variables

Initializer that generates tensors with a normal distribution.

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.set_random_seed for behavior.
  • dtype: The data type. Only floating point types are supported.

Methods

__init__

__init__(
    mean=0.0,
    stddev=1.0,
    seed=None,
    dtype=tf.float32
)

__call__

__call__(
    shape,
    dtype=None,
    partition_info=None
)

from_config

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)

Arguments:

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

Returns:

An Initializer instance.

get_config

get_config()

© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/random_normal_initializer