Initializer that generates a truncated normal distribution.
Inherits From: Initializer
View aliases
Compat aliases for migration
See
Migration guide for
more details.
`tf.compat.v2.initializers.TruncatedNormal`
tf.compat.v2.keras.initializers.TruncatedNormal(
mean=0.0, stddev=0.05, seed=None
)
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.
|
Methods
from_config
View source
@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
View source
get_config()
Returns the configuration of the initializer as a JSON-serializable dict.
Returns |
A JSON-serializable Python dict.
|
__call__
View source
__call__(
shape, dtype=tf.dtypes.float32
)
Returns a tensor object initialized as specified by the initializer.
Args |
shape
|
Shape of the tensor.
|
dtype
|
Optional dtype of the tensor. Only floating point types are
supported.
|
Raises |
ValueError
|
If the dtype is not floating point
|