tf.compat.v1.glorot_uniform_initializer | TensorFlow v2.16.1 (original) (raw)
tf.compat.v1.glorot_uniform_initializer
Stay organized with collections Save and categorize content based on your preferences.
The Glorot uniform initializer, also called Xavier uniform initializer.
Inherits From: variance_scaling_initializer
View aliases
Compat aliases for migration
SeeMigration guide for more details.
tf.compat.v1.initializers.glorot_uniform
tf.compat.v1.glorot_uniform_initializer(
seed=None,
dtype=tf.dtypes.float32
)
It draws samples from a uniform distribution within [-limit, limit] where limit is sqrt(6 / (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. Seetf.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. |
| References |
|---|
| Glorot et al., 2010(pdf) |
Methods
from_config
@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 ofget_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. |