Initializer that generates tensors with a normal distribution.
Inherits From: random_normal_initializer
tf.compat.v1.keras.initializers.RandomNormal(
mean=0.0, stddev=0.05, seed=None, dtype=tf.dtypes.float32
)
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
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=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.
|
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