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tf.keras.initializers.he_uniform

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He uniform variance scaling initializer.

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 uniform distribution within [-limit, limit] where limit is sqrt(6 / 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.he_uniform())
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...

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

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

References:

He et al., 2015 (pdf)