tf.keras.initializers.lecun_normal

View source on GitHub

LeCun normal initializer.

tf.keras.initializers.lecun_normal(
    seed=None
)

It 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.

Arguments:

• seed: A Python integer. Used to seed the random generator.

Returns:

An initializer.

References:

• Self-Normalizing Neural Networks, Klambauer et al., 2017 (pdf)
• Efficient Backprop, Lecun et al., 1998