tf.random_normal_initializer

TensorFlow 1 version View source on GitHub

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) ...

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

View source

@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

View source

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