Initializer capable of adapting its scale to the shape of weights tensors.
Inherits From: VarianceScaling
tf.compat.v1.keras.initializers.he_uniform( seed=None )
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:
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. |
from_config
@classmethod from_config( config )
Instantiates an initializer from a configuration dictionary.
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. |
© 2020 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/versions/r2.4/api_docs/python/tf/compat/v1/keras/initializers/he_uniform