Initializer that generates an orthogonal matrix.
tf.keras.initializers.Orthogonal( gain=1.0, seed=None )
Also available via the shortcut function
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 random numbers drawn from a normal distribution. 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 * ... * 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.
# Standalone usage: initializer = tf.keras.initializers.Orthogonal() values = initializer(shape=(2, 2))
# Usage in a Keras layer: initializer = tf.keras.initializers.Orthogonal() layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)
| ||multiplicative factor to apply to the orthogonal matrix|
| ||A Python integer. An initializer created with a given seed will always produce the same random tensor for a given shape and dtype.|
@classmethod from_config( config )
Instantiates an initializer from a configuration dictionary.
initializer = RandomUniform(-1, 1) config = initializer.get_config() initializer = RandomUniform.from_config(config)
| || A Python dictionary. It will typically be the output of |
|An Initializer instance.|
Returns the configuration of the initializer as a JSON-serializable dict.
|A JSON-serializable Python dict.|
__call__( shape, dtype=None, **kwargs )
Returns a tensor object initialized to an orthogonal matrix.
| ||Shape of the tensor.|
| || Optional dtype of the tensor. Only floating point types are supported. If not specified, |
| ||Additional keyword arguments.|
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Code samples licensed under the Apache 2.0 License.