tf.keras.initializers.VarianceScaling

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Initializer capable of adapting its scale to the shape of weights tensors.

Inherits From: Initializer

tf.keras.initializers.VarianceScaling(
    scale=1.0, mode='fan_in', distribution='truncated_normal', 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.

With distribution="truncated_normal" or "untruncated_normal", samples are drawn from a truncated/untruncated normal distribution with a mean of zero and a standard deviation (after truncation, if used) stddev = sqrt(scale / n) where n is:

  • number of input units in the weight tensor, if mode = "fan_in"
  • number of output units, if mode = "fan_out"
  • average of the numbers of input and output units, if mode = "fan_avg"

With distribution="uniform", samples are drawn from a uniform distribution within [-limit, limit], with limit = sqrt(3 * scale / n).

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.initializers.VarianceScaling(scale=1.))
v1
<tf.Variable ... shape=(3,) ... numpy=array([...], dtype=float32)>
v2
<tf.Variable ... shape=(3, 3) ... numpy=

make_variables(4, tf.initializers.VarianceScaling(distribution='uniform'))
(<tf.Variable...shape=(4,) dtype=float32...>, <tf.Variable...shape=(4, 4) ...

scale Scaling factor (positive float).
mode One of "fan_in", "fan_out", "fan_avg".
distribution Random distribution to use. One of "truncated_normal", "untruncated_normal" and "uniform".
seed A Python integer. Used to create random seeds. See tf.random.set_seed for behavior.

ValueError In case of an invalid value for the "scale", mode" or "distribution" arguments.

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