Initializer capable of adapting its scale to the shape of weights tensors.
tf.compat.v1.keras.initializers.VarianceScaling(
scale=1.0, mode='fan_in', distribution='truncated_normal',
seed=None, dtype=tf.dtypes.float32
)
With distribution="truncated_normal" or "untruncated_normal"
,
samples are drawn from a truncated/untruncated normal
distribution with a mean of zero and a standard deviation (after truncation,
if used) stddev = sqrt(scale / n)
where n is:
- number of input units in the weight tensor, if mode = "fan_in"
- number of output units, if mode = "fan_out"
- average of the numbers of input and output units, if mode = "fan_avg"
With distribution="uniform"
, samples are drawn from a uniform distribution
within [-limit, limit], with limit = sqrt(3 * scale / n)
.
Args |
scale
|
Scaling factor (positive float).
|
mode
|
One of "fan_in", "fan_out", "fan_avg".
|
distribution
|
Random distribution to use. One of "normal", "uniform".
|
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.
|
Raises |
ValueError
|
In case of an invalid value for the "scale", mode" or
"distribution" arguments.
|
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.
|