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 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.

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

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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

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, **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