View source on GitHub |
Initializer that generates a truncated normal distribution.
Inherits From: Initializer
tf.initializers.truncated_normal(
mean=0.0, stddev=1.0, seed=None, dtype=tf.dtypes.float32
)
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.
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.
|
dtype
|
Default data type, used if no dtype argument is provided when
calling the initializer. Only floating point types are supported.
|
Methods
from_config
@classmethod
from_config( 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
get_config()
Returns the configuration of the initializer as a JSON-serializable dict.
Returns | |
---|---|
A JSON-serializable Python dict. |
__call__
__call__(
shape, dtype=None, partition_info=None
)
Returns a tensor object initialized as specified by the initializer.
Args | |
---|---|
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. |