Join TensorFlow at Google I/O, May 11-12

# tfa.optimizers.SWA

This class extends optimizers with Stochastic Weight Averaging (SWA).

Inherits From: `AveragedOptimizerWrapper`

### Used in the notebooks

Used in the tutorials

The Stochastic Weight Averaging mechanism was proposed by Pavel Izmailov et. al in the paper Averaging Weights Leads to Wider Optima and Better Generalization. The optimizer implements averaging of multiple points along the trajectory of SGD. The optimizer expects an inner optimizer which will be used to apply the gradients to the variables and itself computes a running average of the variables every `k` steps (which generally corresponds to the end of a cycle when a cyclic learning rate is employed).

We also allow the specification of the number of steps averaging should first happen after. Let's say, we want averaging to happen every `k` steps after the first `m` steps. After step `m` we'd take a snapshot of the variables and then average the weights appropriately at step `m + k`, `m + 2k` and so on. The assign_average_vars function can be called at the end of training to obtain the averaged_weights from the optimizer.

#### Example of usage:

``````opt = tf.keras.optimizers.SGD(learning_rate)
opt = tfa.optimizers.SWA(opt, start_averaging=m, average_period=k)
``````

`optimizer` The original optimizer that will be used to compute and apply the gradients.
`start_averaging` An integer. Threshold to start averaging using SWA. Averaging only occurs at `start_averaging` iters, must be >= 0. If start_averaging = m, the first snapshot will be taken after the mth application of gradients (where the first iteration is iteration 0).
`average_period` An integer. The synchronization period of SWA. The averaging occurs every average_period steps. Averaging period needs to be >= 1.
`name` Optional name for the operations created when applying gradients. Defaults to 'SWA'.
`**kwargs` keyword arguments. Allowed to be {`clipnorm`, `clipvalue`, `lr`, `decay`}. `clipnorm` is clip gradients by norm; `clipvalue` is clip gradients by value, `decay` is included for backward compatibility to allow time inverse decay of learning rate. `lr` is included for backward compatibility, recommended to use `learning_rate` instead.

`clipnorm` `float` or `None`. If set, clips gradients to a maximum norm.
`clipvalue` `float` or `None`. If set, clips gradients to a maximum value.
`global_clipnorm` `float` or `None`. If set, clips gradients to a maximum norm.
`iterations` Variable. The number of training steps this Optimizer has run.
`learning_rate`

`lr`

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

## Methods

### `add_slot`

Add a new slot variable for `var`.

A slot variable is an additional variable associated with `var` to train. It is allocated and managed by optimizers, e.g. `Adam`.

Args
`var` a `Variable` object.
`slot_name` name of the slot variable.
`initializer` initializer of the slot variable
`shape` (Optional) shape of the slot variable. If not set, it will default to the shape of `var`.

Returns
A slot variable.

### `apply_gradients`

View source

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)

``````

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 presence 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.
`RuntimeError` If called in a cross-replica context.

### `assign_average_vars`

View source

Assign variables in var_list with their respective averages.

Args
`var_list` List of model variables to be assigned to their average.

Returns
`assign_op` The op corresponding to the assignment operation of variables to their average.

#### Example:

``````model = tf.Sequential([...])
opt = tfa.optimizers.SWA(
tf.keras.optimizers.SGD(lr=2.0), 100, 10)
model.compile(opt, ...)
model.fit(x, y, ...)

# Update the weights to their mean before saving
opt.assign_average_vars(model.variables)

model.save('model.h5')
``````

View source

### `from_config`

View source

Creates an optimizer from its config.

This method is the reverse of `get_config`, capable of instantiating the same optimizer from the config dictionary.

Args
`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`

View source

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`

Returns gradients of `loss` with respect to `params`.

Should be used only in legacy v1 graph mode.

Args
`loss` Loss tensor.
`params` List of variables.

Returns

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

### `get_slot_names`

A list of names for this optimizer's slots.

### `get_weights`

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`

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` `Tensor` or callable. If a callable, `loss` should take no arguments and return the value to minimize. If a `Tensor`, the `tape` argument must be passed.
`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) str. Name for the returned operation.
`tape` (Optional) `tf.GradientTape`. If `loss` is provided as a `Tensor`, the tape that computed the `loss` must be provided.

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`

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>`
```

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

### `variables`

Returns variables of this Optimizer based on the order created.

[]
[]