# tf.keras.initializers.TruncatedNormal

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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#L442-L529) |

Initializer that generates a truncated normal distribution.

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

#### View aliases


**Main aliases**

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

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    tf.keras.initializers.TruncatedNormal(
        mean=0.0, stddev=0.05, seed=None
    )

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

The values generated are similar to values from a
[`tf.keras.initializers.RandomNormal`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/initializers/RandomNormal) initializer except that values more
than two standard deviations from the mean are
discarded and re-drawn.

#### Examples:

    # Standalone usage:
    initializer = tf.keras.initializers.TruncatedNormal(mean=0., stddev=1.)
    values = initializer(shape=(2, 2))

    # Usage in a Keras layer:
    initializer = tf.keras.initializers.TruncatedNormal(mean=0., stddev=1.)
    layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)

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| ## Args ||
|---|---|
| `mean` | a python scalar or a scalar tensor. Mean of the random values to generate. |
| `stddev` | a python scalar or a scalar tensor. Standard deviation of the random values to generate before truncation. |
| `seed` | A Python integer. Used to make the behavior of the initializer deterministic. Note that a seeded initializer will produce the same random values across multiple calls. |

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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#L528-L529)

    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#L486-L526)

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

Returns a tensor initialized to random normal values (truncated).

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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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