# tf.compat.v1.keras.initializers.Orthogonal

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/init_ops.py#L895-L963) |

Initializer that generates an orthogonal matrix.

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.initializers.orthogonal`](https://www.tensorflow.org/api_docs/python/tf/compat/v1/orthogonal_initializer), \`tf.compat.v1.keras.initializers.orthogonal\`, [`tf.compat.v1.orthogonal_initializer`](https://www.tensorflow.org/api_docs/python/tf/compat/v1/orthogonal_initializer)

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    tf.compat.v1.keras.initializers.Orthogonal(
        gain=1.0,
        seed=None,
        dtype=https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/dtypes#float32
    )

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[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.

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| ## Args ||
|---|---|
| `gain` | multiplicative factor to apply to the orthogonal matrix |
| `seed` | A Python integer. Used to create random seeds. See [`tf.compat.v1.set_random_seed`](https://www.tensorflow.org/versions/r2.13/api_docs/python/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. |

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| ## References ||
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| [Saxe et al., 2014](https://openreview.net/forum?id=_wzZwKpTDF_9C) ([pdf](https://arxiv.org/pdf/1312.6120.pdf)) ||

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## Methods

### `from_config`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/init_ops.py#L75-L94)

    @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)

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| Args ||
|---|---|
| `config` | A Python dictionary. It will typically be the output of `get_config`. |

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| Returns ||
|---|---|
| An Initializer instance. ||

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### `get_config`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/init_ops.py#L962-L963)

    get_config()

Returns the configuration of the initializer as a JSON-serializable dict.

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| Returns ||
|---|---|
| A JSON-serializable Python dict. ||

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### `__call__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/init_ops.py#L931-L960)

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

Returns a tensor object initialized as specified by the initializer.

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| 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. |

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