tf.random_normal_initializer

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Initializer that generates tensors with a normal distribution.

tf.random_normal_initializer(
    mean=0.0, stddev=0.05, 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.

Examples:

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.))
v1
<tf.Variable ... shape=(3,) ... numpy=array([...], dtype=float32)>
v2
<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) ...

Args
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.

Methods

from_config

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@classmethod
from_config(
    config
)

Instantiates an initializer from a configuration dictionary.

Example:

initializer = RandomUniform(-1, 1)
config = initializer.get_config()
initializer = RandomUniform.from_config(config)

Args
config	A Python dictionary. It will typically be the output of get_config.

Returns
An Initializer instance.

get_config

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get_config()

Returns the configuration of the initializer as a JSON-serializable dict.

Returns
A JSON-serializable Python dict.

__call__

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__call__(
    shape, dtype=tf.dtypes.float32
)

Returns a tensor object initialized as specified by the initializer.

Args
shape	Shape of the tensor.
dtype	Optional dtype of the tensor. Only floating point types are supported.

Raises
ValueError	If the dtype is not floating point