tf.keras.initializers.lecun_normal

LeCun normal initializer.

View aliases

Main aliases

tf.initializers.lecun_normal

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

Returns
A callable Initializer with shape and dtype arguments which generates a tensor.

References:

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