orthogonal_initializer
Inherits From: Initializer
tf.initializers.orthogonal
tf.keras.initializers.Orthogonal
tf.orthogonal_initializer
Defined in tensorflow/python/ops/init_ops.py
.
See the guide: Variables > Sharing Variables
Initializer that generates an orthogonal matrix.
If the shape of the tensor to initialize is two-dimensional, it is initialized with an orthogonal matrix obtained from the QR decomposition of a matrix of uniform random numbers. If the matrix has fewer rows than columns then the output will have orthogonal rows. Otherwise, the output will have orthogonal columns.
If the shape of the tensor to initialize is more than two-dimensional, a matrix of shape (shape[0] * ... * shape[n - 2], shape[n - 1])
is initialized, where n
is the length of the shape vector. The matrix is subsequently reshaped to give a tensor of the desired shape.
gain
: multiplicative factor to apply to the orthogonal matrixdtype
: The type of the output.seed
: A Python integer. Used to create random seeds. See tf.set_random_seed
for behavior.__init__
__init__( gain=1.0, seed=None, dtype=tf.float32 )
Initialize self. See help(type(self)) for accurate signature.
__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)
config
: A Python dictionary. It will typically be the output of get_config
.An Initializer instance.
get_config
get_config()
Returns the configuration of the initializer as a JSON-serializable dict.
A JSON-serializable Python dict.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/orthogonal_initializer