# tf.keras.initializers.LecunUniform

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/keras-team/keras/tree/v2.13.1/keras/initializers/initializers.py#L984-L1023) |

Lecun uniform initializer.

Inherits From: [`VarianceScaling`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/initializers/VarianceScaling), [`Initializer`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/initializers/Initializer)

#### View aliases


**Main aliases**

[`tf.initializers.LecunUniform`](https://www.tensorflow.org/api_docs/python/tf/keras/initializers/LecunUniform), [`tf.initializers.lecun_uniform`](https://www.tensorflow.org/api_docs/python/tf/keras/initializers/LecunUniform), [`tf.keras.initializers.lecun_uniform`](https://www.tensorflow.org/api_docs/python/tf/keras/initializers/LecunUniform)

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    tf.keras.initializers.LecunUniform(
        seed=None
    )

Also available via the shortcut function
[`tf.keras.initializers.lecun_uniform`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/initializers/LecunUniform).

Draws samples from a uniform distribution within `[-limit, limit]`, where
`limit = sqrt(3 / fan_in)` (`fan_in` is the number of input units in the
weight tensor).

#### Examples:

    # Standalone usage:
    initializer = tf.keras.initializers.LecunUniform()
    values = initializer(shape=(2, 2))

    # Usage in a Keras layer:
    initializer = tf.keras.initializers.LecunUniform()
    layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)

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| ## Args ||
|---|---|
| `seed` | A Python integer. Used to make the behavior of the initializer deterministic. Note that a seeded initializer will not produce the same random values across multiple calls, but multiple initializers will produce the same sequence when constructed with the same seed value. |

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| ## References ||
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| <br /> - [Klambauer et al., 2017](https://arxiv.org/abs/1706.02515) ||

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## Methods

### `from_config`

[View source](https://github.com/keras-team/keras/tree/v2.13.1/keras/initializers/initializers.py#L96-L115)

    @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, the output of `get_config()`. |

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| Returns ||
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| An `Initializer` instance. ||

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### `get_config`

[View source](https://github.com/keras-team/keras/tree/v2.13.1/keras/initializers/initializers.py#L1022-L1023)

    get_config()

Returns the initializer's configuration 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/keras-team/keras/tree/v2.13.1/keras/initializers/initializers.py#L616-L647)

    __call__(
        shape, dtype=None, **kwargs
    )

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. Only floating point types are supported. If not specified, [`tf.keras.backend.floatx()`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/backend/floatx) is used, which default to `float32` unless you configured it otherwise (via [`tf.keras.backend.set_floatx(float_dtype)`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/backend/set_floatx)) |
| `**kwargs` | Additional keyword arguments. |

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