tf.orthogonal_initializer

tf.orthogonal_initializer

class tf.contrib.keras.initializers.Orthogonal

class 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, i is initialized with an orthogonal matrix obtained from the singular value decomposition of a matrix of uniform random numbers.

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.

Args:

• gain: multiplicative factor to apply to the orthogonal matrix
• dtype: The type of the output.
• seed: A Python integer. Used to create random seeds. See tf.set_random_seed for behavior.

Methods

__init__

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

__call__

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

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)

Arguments:

• config: A Python dictionary. It will typically be the output of get_config.

Returns:

An Initializer instance.

get_config

get_config()