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tf.compat.v2.keras.initializers.Orthogonal

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Class Orthogonal

Initializer that generates an orthogonal matrix.

Inherits From: Initializer

Aliases:

  • Class tf.compat.v2.initializers.Orthogonal
  • Class tf.compat.v2.initializers.orthogonal
  • Class tf.compat.v2.keras.initializers.orthogonal

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.compat.v1.set_random_seed for behavior.

References:

Saxe et al., 2014 (pdf)

__init__

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__init__(
    gain=1.0,
    seed=None
)

Methods

__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 or the input shape is not valid.

from_config

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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

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get_config()