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# tf.keras.mixed_precision.experimental.LossScaleOptimizer

An optimizer that applies loss scaling.

Inherits From: `Optimizer`

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(0.25)`
`opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer(opt,`
`                                                               "dynamic")`
`var = tf.Variable(1.)`
`loss_fn = lambda: var ** 2`
`# 'minimize' applies loss scaling to the loss and updates the loss sale.`
`opt.minimize(loss_fn, var_list=var)`
`var.numpy()`
`0.5`
```

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:

````with tf.GradientTape() as tape:`
`  loss = loss_fn()`
`  scaled_loss = opt.get_scaled_loss(loss)`
`scaled_grad = tape.gradient(scaled_loss, var)`
`(grad,) = opt.get_unscaled_gradients([scaled_grad])`
`opt.apply_gradients([(grad, var)])  # Loss scale is updated here`
`var.numpy()`
`0.25`
```

`optimizer` 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.

`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.

## Methods

### `add_slot`

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Add a new slot variable for `var`.

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### `apply_gradients`

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Apply gradients to variables.

This is the second part of `minimize()`. It returns an `Operation` that applies gradients.

The method sums gradients from all replicas in the presence of `tf.distribute.Strategy` by default. You can aggregate gradients yourself by passing `experimental_aggregate_gradients=False`.

#### Example:

``````grads = tape.gradient(loss, vars)
grads = tf.distribute.get_replica_context().all_reduce('sum', grads)
# Processing aggregated gradients.
optimizer.apply_gradients(zip(grads, vars),
experimental_aggregate_gradients=False)

``````

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.
`experimental_aggregate_gradients` Whether to sum gradients from different replicas in the presense of `tf.distribute.Strategy`. If False, it's user responsibility to aggregate the gradients. Default to True.

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`

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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`

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Returns the config of the optimizer.

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`

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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`

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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()`.

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### `get_slot_names`

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A list of names for this optimizer's slots.

### `get_unscaled_gradients`

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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()`.

View source

### `get_weights`

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Returns the current weights of the optimizer.

The weights of an optimizer are its state (ie, variables). This function returns the weight values associated with this optimizer as a list of Numpy arrays. The first value is always the iterations count of the optimizer, followed by the optimizer's state variables in the order they were created. The returned list can in turn be used to load state into similarly parameterized optimizers.

For example, the RMSprop optimizer for this simple model returns a list of three values-- the iteration count, followed by the root-mean-square value of the kernel and bias of the single Dense layer:

````opt = tf.keras.optimizers.RMSprop()`
`m = tf.keras.models.Sequential([tf.keras.layers.Dense(10)])`
`m.compile(opt, loss='mse')`
`data = np.arange(100).reshape(5, 20)`
`labels = np.zeros(5)`
`print('Training'); results = m.fit(data, labels)`
`Training ...`
`len(opt.get_weights())`
`3`
```

Returns
Weights values as a list of numpy arrays.

### `minimize`

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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`. The `iterations` will be automatically increased by 1.

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

### `set_weights`

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Set the weights of the optimizer.

The weights of an optimizer are its state (ie, variables). This function takes the weight values associated with this optimizer as a list of Numpy arrays. The first value is always the iterations count of the optimizer, followed by the optimizer's state variables in the order they are created. The passed values are used to set the new state of the optimizer.

For example, the RMSprop optimizer for this simple model takes a list of three values-- the iteration count, followed by the root-mean-square value of the kernel and bias of the single Dense layer:

````opt = tf.keras.optimizers.RMSprop()`
`m = tf.keras.models.Sequential([tf.keras.layers.Dense(10)])`
`m.compile(opt, loss='mse')`
`data = np.arange(100).reshape(5, 20)`
`labels = np.zeros(5)`
`print('Training'); results = m.fit(data, labels)`
`Training ...`
`new_weights = [np.array(10), np.ones([20, 10]), np.zeros([10])]`
`opt.set_weights(new_weights)`
`opt.iterations`
`<tf.Variable 'RMSprop/iter:0' shape=() dtype=int64, numpy=10>`
```

Arguments
`weights` weight values as a list of numpy arrays.

### `variables`

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Returns variables of this Optimizer based on the order created.

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