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tf.contrib.framework.convolutional_delta_orthogonal

Class convolutional_delta_orthogonal

Inherits From: Initializer

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

Initializer that generates a delta orthogonal kernel for ConvNets.

The shape of the tensor must have length 3, 4 or 5. The number of input filters must not exceed the number of output filters. The center pixels of the tensor form an orthogonal matrix. Other pixels are set to be zero. See algorithm 2 in (Xiao et al., 2018).

Args:

  • gain: Multiplicative factor to apply to the orthogonal matrix. Default is 1. The 2-norm of an input is multiplied by a factor of gain after applying this convolution.
  • seed: A Python integer. Used to create random seeds. See tf.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: Xiao et al., 2018 (pdf)

__init__

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

Initialize self. See help(type(self)) for accurate signature.

Methods

__call__

__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

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()

Returns the configuration of the initializer as a JSON-serializable dict.

Returns:

A JSON-serializable Python dict.