# tf.contrib.mixed_precision.FixedLossScaleManager

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/mixed_precision/python/loss_scale_manager.py#L73-L101) |

Loss scale manager with a fixed loss scale.

Inherits From: [`LossScaleManager`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/contrib/mixed_precision/LossScaleManager)

    tf.contrib.mixed_precision.FixedLossScaleManager(
        loss_scale
    )

The loss scale is not updated for the lifetime of the class.

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| ## Args ||
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| `loss_scale` | A Python float. Its ideal value varies depending on models to run. Choosing a too small loss_scale might affect model quality; a too big loss_scale might cause inf or nan. There is no single right loss_scale to apply. There is no harm choosing a relatively big number as long as no nan or inf is encountered in training. |

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| ## Raises ||
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| `ValueError` | If loss_scale is less than 1. |

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## Methods

### `get_loss_scale`

[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/mixed_precision/python/loss_scale_manager.py#L96-L97)

    get_loss_scale()

Returns the loss scale as a scalar `float32` tensor.

### `update_loss_scale`

[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/mixed_precision/python/loss_scale_manager.py#L99-L101)

    update_loss_scale(
        finite_grads
    )

Updates loss scale based on if gradients are finite in current step.

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| Args ||
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| `finite_grads` | bool scalar tensor indicating if all gradients are finite (i.e., not inf or nan). |

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| Returns ||
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| An op, when executed updates the loss scale. If eager execution is enabled, does not return anything. ||

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