View source on GitHub |
LeCun normal initializer.
tf.compat.v2.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 | |
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An initializer. |
References:
- Self-Normalizing Neural Networks, Klambauer et al., 2017 (pdf)
- Efficient Backprop, Lecun et al., 1998