# tf.compat.v1.keras.initializers.VarianceScaling

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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#L741-L892) |

Initializer capable of adapting its scale to the shape of weights tensors.

#### View aliases


**Compat aliases for migration**

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

[`tf.compat.v1.initializers.variance_scaling`](https://www.tensorflow.org/api_docs/python/tf/compat/v1/variance_scaling_initializer), [`tf.compat.v1.variance_scaling_initializer`](https://www.tensorflow.org/api_docs/python/tf/compat/v1/variance_scaling_initializer)

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    tf.compat.v1.keras.initializers.VarianceScaling(
        scale=1.0,
        mode='fan_in',
        distribution='truncated_normal',
        seed=None,
        dtype=https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/dtypes#float32
    )

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## Migrate to TF2

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> [!CAUTION]
> **Caution:** This API was designed for TensorFlow v1. Continue reading for details on how to migrate from this API to a native TensorFlow v2 equivalent. See the [TensorFlow v1 to TensorFlow v2 migration guide](https://www.tensorflow.org/guide/migrate) for instructions on how to migrate the rest of your code.

Although it is a legacy [`compat.v1`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/compat/v1) API, this symbol is compatible with eager
execution and [`tf.function`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/function).

To switch to TF2 APIs, move to using either
[`tf.initializers.variance_scaling`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/initializers/VarianceScaling) or [`tf.keras.initializers.VarianceScaling`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/initializers/VarianceScaling)
(neither from [`compat.v1`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/compat/v1)) and
pass the dtype when calling the initializer.

#### Structural Mapping to TF2

Before:

    initializer = tf.compat.v1.variance_scaling_initializer(
      scale=scale,
      mode=mode,
      distribution=distribution
      seed=seed,
      dtype=dtype)

    weight_one = tf.Variable(initializer(shape_one))
    weight_two = tf.Variable(initializer(shape_two))

After:

    initializer = tf.keras.initializers.VarianceScaling(
      scale=scale,
      mode=mode,
      distribution=distribution
      seed=seed)

    weight_one = tf.Variable(initializer(shape_one, dtype=dtype))
    weight_two = tf.Variable(initializer(shape_two, dtype=dtype))

#### How to Map Arguments

| TF1 Arg Name | TF2 Arg Name | Note |
|---|---|---|
| `scale` | `scale` | No change to defaults |
| `mode` | `mode` | No change to defaults |
| `distribution` | `distribution` | No change to defaults. 'normal' maps to 'truncated_normal' |
| `seed` | `seed` |   |
| `dtype` | `dtype` | The TF2 api only takes it as a `__call__` arg, not a constructor arg. |
| `partition_info` | - | (`__call__` arg in TF1) Not supported |

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## Description

With `distribution="truncated_normal" or "untruncated_normal"`,
samples are drawn from a truncated/untruncated normal
distribution with a mean of zero and a standard deviation (after truncation,
if used) `stddev = sqrt(scale / n)`
where n is:

- number of input units in the weight tensor, if mode = "fan_in"
- number of output units, if mode = "fan_out"
- average of the numbers of input and output units, if mode = "fan_avg"

With `distribution="uniform"`, samples are drawn from a uniform distribution
within \[-limit, limit\], with `limit = sqrt(3 * scale / n)`.

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| ## Args ||
|---|---|
| `scale` | Scaling factor (positive float). |
| `mode` | One of "fan_in", "fan_out", "fan_avg". |
| `distribution` | Random distribution to use. One of "normal", "uniform". |
| `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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| ## Raises ||
|---|---|
| `ValueError` | In case of an invalid value for the "scale", mode" or "distribution" arguments. |

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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#L885-L892)

    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#L858-L883)

    __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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