tf.keras.initializers.lecun_normal

View source on GitHub

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:

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

Returns:

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

References: