tfmri.initializers.GlorotNormal

class GlorotNormal(seed=None)[source]

Bases: keras.initializers.initializers_v2.GlorotNormal

The Glorot normal initializer, also called Xavier normal initializer.

Note

This initializer can be used as a drop-in replacement for tf.keras.initializers.GlorotNormal. However, this one also supports initialization of complex-valued weights. Simply pass dtype='complex64' or dtype='complex128' to its __call__ method.

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

Draws samples from a truncated normal distribution centered on 0 with stddev = sqrt(2 / (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.

Examples:

>>> # Standalone usage:
>>> initializer = tf.keras.initializers.GlorotNormal()
>>> values = initializer(shape=(2, 2))

>>> # Usage in a Keras layer:
>>> initializer = tf.keras.initializers.GlorotNormal()
>>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)
Args:
seed: A Python integer. Used to make the behavior of the initializer

deterministic. Note that a seeded initializer will not produce the same random values across multiple calls, but multiple initializers will produce the same sequence when constructed with the same seed value.

References: