질문이있다? TensorFlow 포럼 에서 커뮤니티와 연결

so as to have a consistent variance.

It implements the Rectified Adam (a.k.a. RAdam) proposed by Liyuan Liu et al. in On The Variance Of The Adaptive Learning Rate And Beyond.

#### Example of usage:

``````opt = tfa.optimizers.RectifiedAdam(lr=1e-3)
``````

RAdam is not a placement of the heuristic warmup, the settings should be kept if warmup has already been employed and tuned in the baseline method. You can enable warmup by setting `total_steps` and `warmup_proportion`:

``````opt = tfa.optimizers.RectifiedAdam(
lr=1e-3,
total_steps=10000,
warmup_proportion=0.1,
min_lr=1e-5,
)
``````

In the above example, the learning rate will increase linearly from 0 to `lr` in 1000 steps, then decrease linearly from `lr` to `min_lr` in 9000 steps.

Lookahead, proposed by Michael R. Zhang et.al in the paper Lookahead Optimizer: k steps forward, 1 step back, can be integrated with RAdam, which is announced by Less Wright and the new combined optimizer can also be called "Ranger". The mechanism can be enabled by using the lookahead wrapper. For example:

``````radam = tfa.optimizers.RectifiedAdam()
``````

`learning_rate` A `Tensor` or a floating point value, or a schedule that is a `tf.keras.optimizers.schedules.LearningRateSchedule`. The learning rate.
`beta_1` A float value or a constant float tensor. The exponential decay rate for the 1st moment estimates.
`beta_2` A float value or a constant float tensor. The exponential decay rate for the 2nd moment estimates.
`epsilon` A small constant for numerical stability.
`weight_decay` A `Tensor` or a floating point value, or a schedule that is a `tf.keras.optimizers.schedules.LearningRateSchedule`. Weight decay for each parameter.
`amsgrad` boolean. Whether to apply AMSGrad variant of this algorithm from the paper "On the Convergence of Adam and beyond". sma_threshold. A float value. The threshold for simple mean average.
`total_steps` An integer value. Total number of training steps. Enable warmup by setting a positive value.
`warmup_proportion` A floating point value. The proportion of increasing steps.
`min_lr` A floating point value. Minimum learning rate after warmup.
`name` Optional name for the operations created when applying gradients. Defaults to "RectifiedAdam".
`**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.
`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`

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

### `from_config`

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`

View source

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.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"필요한 정보가 없음" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"너무 복잡함/단계 수가 너무 많음" },{ "type": "thumb-down", "id": "outOfDate", "label":"오래됨" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"기타" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"이해하기 쉬움" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"문제가 해결됨" },{ "type": "thumb-up", "id": "otherUp", "label":"기타" }]