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The Glorot uniform initializer, also called Xavier uniform initializer.

Inherits From: VarianceScaling, Initializer

Also available via the shortcut function tf.keras.initializers.glorot_uniform.

Draws samples from a uniform distribution within [-limit, limit], where limit = sqrt(6 / (fan_in + fan_out)) (fan_in is the number of input units in the weight tensor and fan_out is the number of output units).


# Standalone usage:
initializer = tf.keras.initializers.GlorotUniform()
values = initializer(shape=(2, 2))
# Usage in a Keras layer:
initializer = tf.keras.initializers.GlorotUniform()
layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)

seed A Python integer. Used to create random seeds. See tf.compat.v1.set_random_seed for behavior. Note that seeded initializer will not produce same random values across multiple calls, but multiple initializers will produce same sequence when constructed with same seed value.




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Instantiates an initializer from a configuration dictionary.


initializer = RandomUniform(-1, 1)
config = initializer.get_config()
initializer = RandomUniform.from_config(config)

config A Python dictionary, the output of get_config.

A tf.keras.initializers.Initializer instance.


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

A JSON-serializable Python dict.


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Returns a tensor object initialized as specified by the initializer.

shape Shape of the tensor.
dtype Optional dtype of the tensor. Only floating point types are supported. If not specified, tf.keras.backend.floatx() is used, which default to float32 unless you configured it otherwise (via tf.keras.backend.set_floatx(float_dtype))
**kwargs Additional keyword arguments.