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]: https://arxiv.org/abs/1806.05393

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 'sqrt(gain)' after applying this convolution.
  • seed: A Python integer. Used to create random seeds. See tf.set_random_seed for behavior.
  • dtype: The data type.

__init__

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

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

Methods

__call__

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

Call self as a function.

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.