tf.contrib.framework.convolutional_delta_orthogonal

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Class convolutional_delta_orthogonal

Initializer that generates a delta orthogonal kernel for ConvNets.

Inherits From: Initializer

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.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)

__init__

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

Methods

__call__

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

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()