View source on GitHub
|
LeCun normal initializer.
tf.compat.v1.keras.initializers.lecun_normal(
seed=None
)
It draws samples from a truncated normal distribution centered on 0
with standard deviation (after truncation) given by
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