Class glorot_uniform_initializer
Inherits From: VarianceScaling
Aliases:
- Class
tf.glorot_uniform_initializer
- Class
tf.initializers.glorot_uniform
- Class
tf.keras.initializers.glorot_uniform
Defined in tensorflow/python/ops/init_ops.py
.
The Glorot uniform initializer, also called Xavier uniform initializer.
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.set_random_seed
for behavior.dtype
: Default data type, used if nodtype
argument is provided when calling the initializer. Only floating point types are supported.
References: Glorot et al., 2010 (pdf)
__init__
__init__(
seed=None,
dtype=tf.dtypes.float32
)
DEPRECATED FUNCTION ARGUMENT VALUES
Methods
tf.glorot_uniform_initializer.__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.
tf.glorot_uniform_initializer.from_config
from_config(
cls,
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.
tf.glorot_uniform_initializer.get_config
get_config()
Returns the configuration of the initializer as a JSON-serializable dict.
Returns:
A JSON-serializable Python dict.