tf.compat.v1.keras.initializers.Orthogonal

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Initializer that generates an orthogonal matrix.

Inherits From: Initializer

tf.compat.v1.keras.initializers.Orthogonal(
    gain=1.0, seed=None, dtype=tf.dtypes.float32
)

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.
• dtype: Default data type, used if no dtype argument is provided when calling the initializer. Only floating point types are supported.

References:

Saxe et al., 2014 (pdf)

Methods

__call__

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

Returns a tensor object initialized as specified by the initializer.

Args:

• shape: Shape of the tensor.
• dtype: Optional dtype of the tensor. If not provided use the initializer dtype.
• partition_info: Optional information about the possible partitioning of a tensor.

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.