Initializer that generates tensors with a normal distribution.

Used in the notebooks

Used in the guide Used in the tutorials

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.


def make_variables(k, initializer):
  return (tf.Variable(initializer(shape=[k], dtype=tf.float32)),
          tf.Variable(initializer(shape=[k, k], dtype=tf.float32)))
v1, v2 = make_variables(3,
                        tf.random_normal_initializer(mean=1., stddev=2.))
<tf.Variable ... shape=(3,) ... numpy=array([...], dtype=float32)>
<tf.Variable ... shape=(3, 3) ... numpy=

make_variables(4, tf.random_uniform_initializer(minval=-1., maxval=1.))
(<tf.Variable...shape=(4,) dtype=float32...>, <tf.Variable...shape=(4, 4) ...

mean a python scalar or a scalar tensor. Mean of the random values to generate.
stddev a python scalar or a scalar tensor. Standard deviation of the random values to generate.
seed A Python integer. Used to create random seeds. See tf.random.set_seed for behavior.



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