The Glorot normal initializer, also called Xavier normal initializer.
Inherits From: VarianceScaling
tf.compat.v1.keras.initializers.glorot_normal( seed=None, dtype=tf.dtypes.float32 )
It draws samples from a truncated normal distribution centered on 0 with standard deviation (after truncation) given by stddev = sqrt(2 / (fan_in + fan_out))
where fan_in
is the number of input units in the weight tensor and fan_out
is the number of output units in the weight tensor.
Args | |
---|---|
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. |
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/r2.3/api_docs/python/tf/compat/v1/keras/initializers/glorot_normal