tf.contrib.framework.convolutional_orthogonal_3d

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Initializer that generates a 3D orthogonal kernel for ConvNets.

tf.contrib.framework.convolutional_orthogonal_3d(
    gain=1.0, seed=None, dtype=tf.dtypes.float32
)

The shape of the tensor must have length 5. The number of input filters must not exceed the number of output filters. The orthogonality(==isometry) is exact when the inputs are circular padded. There are finite-width effects with non-circular padding (e.g. zero padding). See algorithm 1 (Xiao et al., 2018).

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.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:

Xiao et al., 2018 (pdf)

Methods

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.

__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.