tf.compat.v1.keras.initializers.glorot_uniform

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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:

Glorot et al., 2010 (pdf)

Methods

__call__

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__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.

from_config

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@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 of get_config.

Returns:

An Initializer instance.

get_config

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get_config()

Returns the configuration of the initializer as a JSON-serializable dict.

Returns:

A JSON-serializable Python dict.