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An optimizer that applies loss scaling.

Inherits From: `Optimizer`

tf.keras.mixed_precision.experimental.LossScaleOptimizer( opt, loss_scale )

Loss scaling is a process that multiplies the loss by a multiplier called the loss scale, and divides each gradient by the same multiplier. The pseudocode for this process is:

loss = ... loss *= loss_scale grads = gradients(loss, vars) grads /= loss_scale

Mathematically, loss scaling has no effect, but can help avoid numerical underflow in intermediate gradients when float16 tensors are used. By multiplying the loss, each intermediate gradient will have the same multiplier applied.

The loss scale can either be a fixed constant, chosen by the user, or be dynamically determined. Dynamically determining the loss scale is convenient as a loss scale does not have to be explicitly chosen. However it reduces performance.

This optimizer wraps another optimizer and applies loss scaling to it via a `LossScale`

. Loss scaling is applied whenever gradients are computed, either through `minimize()`

or `get_gradients()`

. The loss scale is updated via `LossScale.update()`

whenever gradients are applied, either through `minimize()`

or `apply_gradients()`

. For example:

opt = tf.keras.optimizers.SGD(0.1) opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer(opt, "dynamic") # 'minimize' applies loss scaling to the loss and updates the loss sale. opt.minimize(loss_fn)

If a `tf.GradientTape`

is used to compute gradients instead of `LossScaleOptimizer.minimize`

or `LossScaleOptimizer.get_gradients`

, the loss and gradients must be scaled manually. This can be done by calling `LossScaleOptimizer.get_scaled_loss`

before passing the loss to `tf.GradientTape`

, and `LossScaleOptimizer.get_unscaled_gradients`

after computing the gradients with `tf.GradientTape`

. For example:

opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer(...) vars = ... with tf.GradientTape() as tape: loss = ... scaled_loss = opt.get_scaled_loss(loss) scaled_grads = tape.gradient(scaled_loss, vars) grads = opt.get_unscaled_gradients(scaled_grads) opt.apply_gradients(zip(grads, vars)) # Loss scale will be updated here

Args | |
---|---|

`opt` | The Optimizer instance to wrap. |

`loss_scale` | The loss scale to scale the loss and gradients. This can either be an int/float to use a fixed loss scale, the string "dynamic" to use dynamic loss scaling, or an instance of a LossScale. The string "dynamic" equivalent to passing `DynamicLossScale()` , and passing an int/float is equivalent to passing a FixedLossScale with the given loss scale. |

Attributes | |
---|---|

`iterations` | Variable. The number of training steps this Optimizer has run. |

`learning_rate` | |

`loss_scale` | The `LossScale` instance associated with this optimizer. |

`lr` | |

`weights` | Returns variables of this Optimizer based on the order created. |

`add_slot`

add_slot( var, slot_name, initializer='zeros' )

Add a new slot variable for `var`

.

`add_weight`

add_weight( name, shape, dtype=None, initializer='zeros', trainable=None, synchronization=tf.VariableSynchronization.AUTO, aggregation=tf.VariableAggregation.NONE )

`apply_gradients`

apply_gradients( grads_and_vars, name=None )

Apply gradients to variables.

This is the second part of `minimize()`

. It returns an `Operation`

that applies gradients.

Args | |
---|---|

`grads_and_vars` | List of (gradient, variable) pairs. |

`name` | Optional name for the returned operation. Default to the name passed to the `Optimizer` constructor. |

Returns | |
---|---|

An `Operation` that applies the specified gradients. The `iterations` will be automatically increased by 1. |

Raises | |
---|---|

`TypeError` | If `grads_and_vars` is malformed. |

`ValueError` | If none of the variables have gradients. |

`from_config`

@classmethod from_config( config, custom_objects=None )

Creates an optimizer from its config.

This method is the reverse of `get_config`

, capable of instantiating the same optimizer from the config dictionary.

Arguments | |
---|---|

`config` | A Python dictionary, typically the output of get_config. |

`custom_objects` | A Python dictionary mapping names to additional Python objects used to create this optimizer, such as a function used for a hyperparameter. |

Returns | |
---|---|

An optimizer instance. |

`get_config`

get_config()

Returns the config of the optimimizer.

An optimizer config is a Python dictionary (serializable) containing the configuration of an optimizer. The same optimizer can be reinstantiated later (without any saved state) from this configuration.

Returns | |
---|---|

Python dictionary. |

`get_gradients`

get_gradients( loss, params )

Returns gradients of `loss`

with respect to `params`

.

Arguments | |
---|---|

`loss` | Loss tensor. |

`params` | List of variables. |

Returns | |
---|---|

List of gradient tensors. |

Raises | |
---|---|

`ValueError` | In case any gradient cannot be computed (e.g. if gradient function not implemented). |

`get_scaled_loss`

get_scaled_loss( loss )

Scales the loss by the loss scale.

This method is only needed if you compute gradients manually, e.g. with `tf.GradientTape`

. In that case, call this method to scale the loss before passing the loss to `tf.GradientTape`

. If you use `LossScaleOptimizer.minimize`

or `LossScaleOptimizer.get_gradients`

, loss scaling is automatically applied and this method is unneeded.

If this method is called, `get_unscaled_gradients`

should also be called. See the `tf.keras.mixed_precision.experimental.LossScaleOptimizer`

doc for an example.

Args | |
---|---|

`loss` | The loss, which will be multiplied by the loss scale. Can either be a tensor or a callable returning a tensor. |

Returns | |
---|---|

`loss` multiplied by `LossScaleOptimizer.loss_scale()` . |

`get_slot`

get_slot( var, slot_name )

`get_slot_names`

get_slot_names()

A list of names for this optimizer's slots.

`get_unscaled_gradients`

get_unscaled_gradients( grads )

Unscales the gradients by the loss scale.

This method is only needed if you compute gradients manually, e.g. with `tf.GradientTape`

. In that case, call this method to unscale the gradients after computing them with `tf.GradientTape`

. If you use `LossScaleOptimizer.minimize`

or `LossScaleOptimizer.get_gradients`

, loss scaling is automatically applied and this method is unneeded.

If this method is called, `get_scaled_loss`

should also be called. See the `tf.keras.mixed_precision.experimental.LossScaleOptimizer`

doc for an example.

Args | |
---|---|

`grads` | A list of tensors, each which will be divided by the loss scale. Can have None values, which are ignored. |

Returns | |
---|---|

A new list the same size as `grads` , where every non-None value in `grads` is divided by `LossScaleOptimizer.loss_scale()` . |

`get_updates`

get_updates( loss, params )

`get_weights`

get_weights()

`minimize`

minimize( loss, var_list, grad_loss=None, name=None )

Minimize `loss`

by updating `var_list`

.

This method simply computes gradient using `tf.GradientTape`

and calls `apply_gradients()`

. If you want to process the gradient before applying then call `tf.GradientTape`

and `apply_gradients()`

explicitly instead of using this function.

Args | |
---|---|

`loss` | A callable taking no arguments which returns the value to minimize. |

`var_list` | list or tuple of `Variable` objects to update to minimize `loss` , or a callable returning the list or tuple of `Variable` objects. Use callable when the variable list would otherwise be incomplete before `minimize` since the variables are created at the first time `loss` is called. |

`grad_loss` | Optional. A `Tensor` holding the gradient computed for `loss` . |

`name` | Optional name for the returned operation. |

Returns | |
---|---|

An Operation that updates the variables in `var_list` . If `global_step` was not `None` , that operation also increments `global_step` . |

Raises | |
---|---|

`ValueError` | If some of the variables are not `Variable` objects. |

`set_weights`

set_weights( weights )

`variables`

variables()

Returns variables of this Optimizer based on the order created.

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/mixed_precision/experimental/LossScaleOptimizer