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LeCun normal initializer.
tf.keras.initializers.lecun_normal(
seed=None
)
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:
def make_variables(k, initializer):return (tf.Variable(initializer(shape=[k, k], dtype=tf.float32)),tf.Variable(initializer(shape=[k, k, k], dtype=tf.float32)))v1, v2 = make_variables(3, tf.initializers.lecun_normal())v1<tf.Variable ... shape=(3, 3) ...v2<tf.Variable ... shape=(3, 3, 3) ...make_variables(4, tf.initializers.RandomNormal())(<tf.Variable ... shape=(4, 4) dtype=float32...<tf.Variable ... shape=(4, 4, 4) dtype=float32...
Arguments | |
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seed
|
A Python integer. Used to seed the random generator. |
Returns | |
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A callable Initializer with shape and dtype arguments which generates a
tensor.
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References:
- Self-Normalizing Neural Networks, Klambauer et al., 2017 (pdf)
- Efficient Backprop, Lecun et al., 1998