tfmri.initializers.LecunNormal

class LecunNormal(seed=None)[source]

Bases: keras.initializers.initializers_v2.LecunNormal

Lecun normal initializer.

Note

This initializer can be used as a drop-in replacement for tf.keras.initializers.LecunNormal. 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.lecun_normal.

Initializers allow you to pre-specify an initialization strategy, encoded in the Initializer object, without knowing the shape and dtype of the variable being initialized.

Draws samples from a truncated normal distribution centered on 0 with stddev = sqrt(1 / fan_in) where fan_in is the number of input units in the weight tensor.

Examples:

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

>>> # Usage in a Keras layer:
>>> initializer = tf.keras.initializers.LecunNormal()
>>> 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: