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).
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)
Methods
from_config
View source
@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
View source
get_config()
Returns the configuration of the initializer as a JSON-serializable dict.
Returns |
A JSON-serializable Python dict.
|
__call__
View source
__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.
|