tf.random_normal_initializer
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. Seetf.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 ofget_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