tf.orthogonal_initializer

Class orthogonal_initializer

Inherits From: Initializer

Aliases:

  • Class tf.initializers.orthogonal
  • Class tf.keras.initializers.Orthogonal
  • Class tf.keras.initializers.orthogonal
  • Class tf.orthogonal_initializer

Defined in tensorflow/python/ops/init_ops.py.

See the guide: Variables > Sharing Variables

Initializer that generates an orthogonal matrix.

If the shape of the tensor to initialize is two-dimensional, it is initialized with an orthogonal matrix obtained from the QR decomposition of a matrix of random numbers drawn from a normal distribution. If the matrix has fewer rows than columns then the output will have orthogonal rows. Otherwise, the output will have orthogonal columns.

If the shape of the tensor to initialize is more than two-dimensional, a matrix of shape (shape[0] * ... * shape[n - 2], shape[n - 1]) is initialized, where n is the length of the shape vector. The matrix is subsequently reshaped to give a tensor of the desired shape.

Args:

  • gain: multiplicative factor to apply to the orthogonal matrix
  • seed: A Python integer. Used to create random seeds. See tf.set_random_seed for behavior.
  • dtype: The data type.

Methods

__init__

__init__(
    gain=1.0,
    seed=None,
    dtype=tf.float32
)

__call__

__call__(
    shape,
    dtype=None,
    partition_info=None
)

from_config

from_config(
    cls,
    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

get_config()