tf.random_normal_initializer

Initializer that generates tensors with a normal distribution.

tf.random_normal_initializer(
    mean=0.0, stddev=0.05, seed=None
)

Used in the notebooks

Used in the guide	Used in the tutorials
Making new Layers and Models via subclassing	Pix2Pix

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`

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`

View source

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

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.
`**kwargs`	Additional keyword arguments.

Raises
`ValueError`	If the dtype is not floating point

TensorFlow

tf / random_normal_initializer

Used in the notebooks

Examples:

Args

Methods

`from_config`

Example:

`get_config`

`call`

TensorFlow

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tf / random_normal_initializer

Used in the notebooks

Examples:

Args

Methods

from_config

Example:

get_config

__call__

`from_config`

`get_config`

`call`