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The Glorot uniform initializer, also called Xavier uniform initializer.
Inherits From: VarianceScaling
tf.compat.v1.keras.initializers.glorot_uniform(
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. 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.
|
References:
Methods
from_config
@classmethodfrom_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
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. |