<br />

|---|
| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/training/optimizer.py#L241-L1406) |

Base class for optimizers.

    tf.compat.v1.train.Optimizer(
        use_locking, name, use_own_namescope_for_non_slot_vars=False
    )

<br />

## Migrate to TF2

<br />

> [!CAUTION]
> **Caution:** This API was designed for TensorFlow v1. Continue reading for details on how to migrate from this API to a native TensorFlow v2 equivalent. See the [TensorFlow v1 to TensorFlow v2 migration guide](https://www.tensorflow.org/guide/migrate) for instructions on how to migrate the rest of your code.

[`tf.compat.v1.train.Optimizer`](https://www.tensorflow.org/api_docs/python/tf/compat/v1/train/Optimizer) can be used in eager mode and [`tf.function`](https://www.tensorflow.org/api_docs/python/tf/function),
but it is not recommended. Please use the subclasses of
[`tf.keras.optimizers.Optimizer`](https://www.tensorflow.org/api_docs/python/tf/keras/Optimizer) instead in TF2. Please see [Basic training
loops](https://www.tensorflow.org/guide/basic_training_loops) or
[Writing a training loop from scratch](https://www.tensorflow.org/guide/keras/writing_a_training_loop_from_scratch)
for examples.

If your TF1 code contains a [`tf.compat.v1.train.Optimizer`](https://www.tensorflow.org/api_docs/python/tf/compat/v1/train/Optimizer) symbol, you cannot
simply replace that with the corresponding [`tf.keras.optimizers.Optimizer`](https://www.tensorflow.org/api_docs/python/tf/keras/Optimizer)s.
To migrate to TF2, it is advised the whole training program to be migrated to
Keras [`Model.fit`](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit) based, or TF2 custom training loops.

#### Structural Mapping to Native TF2

Before:

    sgd_op = tf.compat.v1.train.GradientDescentOptimizer(3.0)
    opt_op = sgd_op.minimize(cost, global_step, [var0, var1])
    opt_op.run(session=session)

After:

    sgd = tf.keras.optimizers.SGD(3.0)
    sgd.minimize(cost_fn, [var0, var1])

#### How to Map Arguments

| TF1 Arg Name | TF2 Arg Name | Note |
|---|---|---|
| `use_locking` | Not supported | - |
| `name` | `name.` | - |

#### Before \& After Usage Example

Before:

    g = tf.compat.v1.Graph()
    with g.as_default():
      var0 = tf.compat.v1.Variable([1.0, 2.0])
      var1 = tf.compat.v1.Variable([3.0, 4.0])
      cost = 5 * var0 + 3 * var1
      global_step = tf.compat.v1.Variable(
          tf.compat.v1.zeros([], tf.compat.v1.int64), name='global_step')
      init_op = tf.compat.v1.initialize_all_variables()
      sgd_op = tf.compat.v1.train.GradientDescentOptimizer(3.0)
      opt_op = sgd_op.minimize(cost, global_step, [var0, var1])
    session = tf.compat.v1.Session(graph=g)
    session.run(init_op)
    opt_op.run(session=session)
    print(session.run(var0))
    [-14. -13.]

After:

    >>> var0 = tf.Variable([1.0, 2.0])
    >>> var1 = tf.Variable([3.0, 4.0])
    >>> cost_fn = lambda: 5 * var0 + 3 * var1
    >>> sgd = tf.keras.optimizers.SGD(3.0)
    >>> sgd.minimize(cost_fn, [var0, var1])
    >>> print(var0.numpy())
    [-14. -13.]

<br />

<br />

## Description

This class defines the API to add Ops to train a model. You never use this
class directly, but instead instantiate one of its subclasses such as
`GradientDescentOptimizer`, `AdagradOptimizer`, or `MomentumOptimizer`.

### Usage

    # Create an optimizer with the desired parameters.
    opt = GradientDescentOptimizer(learning_rate=0.1)
    # Add Ops to the graph to minimize a cost by updating a list of variables.
    # "cost" is a Tensor, and the list of variables contains tf.Variable
    # objects.
    opt_op = opt.minimize(cost, var_list=<list of variables>)

In the training program you will just have to run the returned Op.

    # Execute opt_op to do one step of training:
    opt_op.run()

### Processing gradients before applying them.

Calling `minimize()` takes care of both computing the gradients and
applying them to the variables. If you want to process the gradients
before applying them you can instead use the optimizer in three steps:

1. Compute the gradients with `compute_gradients()`.
2. Process the gradients as you wish.
3. Apply the processed gradients with `apply_gradients()`.

#### Example:

    # Create an optimizer.
    opt = GradientDescentOptimizer(learning_rate=0.1)

    # Compute the gradients for a list of variables.
    grads_and_vars = opt.compute_gradients(loss, <list of variables>)

    # grads_and_vars is a list of tuples (gradient, variable).  Do whatever you
    # need to the 'gradient' part, for example cap them, etc.
    capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for gv in grads_and_vars]

    # Ask the optimizer to apply the capped gradients.
    opt.apply_gradients(capped_grads_and_vars)

### Gating Gradients

Both `minimize()` and `compute_gradients()` accept a `gate_gradients`
argument that controls the degree of parallelism during the application of
the gradients.

The possible values are: `GATE_NONE`, `GATE_OP`, and `GATE_GRAPH`.

**`GATE_NONE`** : Compute and apply gradients in parallel. This provides
the maximum parallelism in execution, at the cost of some non-reproducibility
in the results. For example the two gradients of `matmul` depend on the input
values: With `GATE_NONE` one of the gradients could be applied to one of the
inputs *before* the other gradient is computed resulting in non-reproducible
results.

**`GATE_OP`**: For each Op, make sure all gradients are computed before
they are used. This prevents race conditions for Ops that generate gradients
for multiple inputs where the gradients depend on the inputs.

**`GATE_GRAPH`**: Make sure all gradients for all variables are computed
before any one of them is used. This provides the least parallelism but can
be useful if you want to process all gradients before applying any of them.

### Slots

Some optimizer subclasses, such as `MomentumOptimizer` and `AdagradOptimizer`
allocate and manage additional variables associated with the variables to
train. These are called *Slots*. Slots have names and you can ask the
optimizer for the names of the slots that it uses. Once you have a slot name
you can ask the optimizer for the variable it created to hold the slot value.

This can be useful if you want to log debug a training algorithm, report stats
about the slots, etc.

<br />

<br />

<br />

| ## Args ||
|---|---|
| `use_locking` | Bool. If True apply use locks to prevent concurrent updates to variables. |
| `name` | A non-empty string. The name to use for accumulators created for the optimizer. |
| `use_own_namescope_for_non_slot_vars` | If True, use a root namescope under self._name for non-slot variables. |

<br />

<br />

<br />

<br />

| ## Raises ||
|---|---|
| `ValueError` | If name is malformed. |

<br />

## Methods

### `apply_gradients`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/training/optimizer.py#L656-L783)

    apply_gradients(
        grads_and_vars,
        global_step=None,
        name=None,
        skip_gradients_aggregation=False
    )

Apply gradients to variables.

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

@compatibility(TF2)

#### How to Map Arguments

| TF1 Arg Name | TF2 Arg Name | Note |
|---|---|---|
| `grads_and_vars` | `grads_and_vars` | - |
| `global_step` | Not supported. | Use `optimizer.iterations` |
| `name` | `name.` | - |

<br />

<br />

<br />

| Args ||
|---|---|
| `grads_and_vars` | List of (gradient, variable) pairs as returned by `compute_gradients()`. |
| `global_step` | Optional `Variable` to increment by one after the variables have been updated. |
| `name` | Optional name for the returned operation. Default to the name passed to the `Optimizer` constructor. |
| `skip_gradients_aggregation` | If true, gradients aggregation will not be performed inside optimizer. Usually this arg is set to True when you write custom code aggregating gradients outside the optimizer. |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| An `Operation` that applies the specified gradients. If `global_step` was not None, that operation also increments `global_step`. ||

<br />

<br />

<br />

<br />

| Raises ||
|---|---|
| `TypeError` | If `grads_and_vars` is malformed. |
| `ValueError` | If none of the variables have gradients. |
| `RuntimeError` | If you should use `_distributed_apply()` instead. |

<br />

### `compute_gradients`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/training/optimizer.py#L523-L644)

    compute_gradients(
        loss,
        var_list=None,
        gate_gradients=GATE_OP,
        aggregation_method=None,
        colocate_gradients_with_ops=False,
        grad_loss=None
    )

Compute gradients of `loss` for the variables in `var_list`.

<br />

#### Migrate to TF2

<br />

> [!CAUTION]
> **Caution:** This API was designed for TensorFlow v1. Continue reading for details on how to migrate from this API to a native TensorFlow v2 equivalent. See the [TensorFlow v1 to TensorFlow v2 migration guide](https://www.tensorflow.org/guide/migrate) for instructions on how to migrate the rest of your code.

[`tf.keras.optimizers.Optimizer`](https://www.tensorflow.org/api_docs/python/tf/keras/Optimizer) in TF2 does not provide a
`compute_gradients` method, and you should use a [`tf.GradientTape`](https://www.tensorflow.org/api_docs/python/tf/GradientTape) to
obtain the gradients:

    @tf.function
    def train step(inputs):
      batch_data, labels = inputs
      with tf.GradientTape() as tape:
        predictions = model(batch_data, training=True)
        loss = tf.keras.losses.CategoricalCrossentropy(
            reduction=tf.keras.losses.Reduction.NONE)(labels, predictions)
      gradients = tape.gradient(loss, model.trainable_variables)
      optimizer.apply_gradients(zip(gradients, model.trainable_variables))

Args:
loss: A Tensor containing the value to minimize or a callable taking
no arguments which returns the value to minimize. When eager execution
is enabled it must be a callable.
var_list: Optional list or tuple of [`tf.Variable`](https://www.tensorflow.org/api_docs/python/tf/Variable) to update to minimize
`loss`. Defaults to the list of variables collected in the graph
under the key `GraphKeys.TRAINABLE_VARIABLES`.
gate_gradients: How to gate the computation of gradients. Can be
`GATE_NONE`, `GATE_OP`, or `GATE_GRAPH`.
aggregation_method: Specifies the method used to combine gradient terms.
Valid values are defined in the class `AggregationMethod`.
colocate_gradients_with_ops: If True, try colocating gradients with
the corresponding op.
grad_loss: Optional. A `Tensor` holding the gradient computed for `loss`.

Returns:
A list of (gradient, variable) pairs. Variable is always present, but
gradient can be `None`.

Raises:
TypeError: If `var_list` contains anything else than `Variable` objects.
ValueError: If some arguments are invalid.
RuntimeError: If called with eager execution enabled and `loss` is
not callable.

@compatibility(eager)
When eager execution is enabled, `gate_gradients`, `aggregation_method`,
and `colocate_gradients_with_ops` are ignored.

<br />

<br />

#### Description

This is the first part of `minimize()`. It returns a list
of (gradient, variable) pairs where "gradient" is the gradient
for "variable". Note that "gradient" can be a `Tensor`, an
`IndexedSlices`, or `None` if there is no gradient for the
given variable.

### `get_name`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/training/optimizer.py#L460-L461)

    get_name()

### `get_slot`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/training/optimizer.py#L880-L906)

    get_slot(
        var, name
    )

Return a slot named `name` created for `var` by the Optimizer.

Some `Optimizer` subclasses use additional variables. For example
`Momentum` and `Adagrad` use variables to accumulate updates. This method
gives access to these `Variable` objects if for some reason you need them.

Use `get_slot_names()` to get the list of slot names created by the
`Optimizer`.

<br />

<br />

<br />

| Args ||
|---|---|
| `var` | A variable passed to `minimize()` or `apply_gradients()`. |
| `name` | A string. |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| The `Variable` for the slot if it was created, `None` otherwise. ||

<br />

### `get_slot_names`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/training/optimizer.py#L908-L916)

    get_slot_names()

Return a list of the names of slots created by the `Optimizer`.

See `get_slot()`.

<br />

<br />

<br />

| Returns ||
|---|---|
| A list of strings. ||

<br />

### `minimize`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/training/optimizer.py#L463-L521)

    minimize(
        loss,
        global_step=None,
        var_list=None,
        gate_gradients=GATE_OP,
        aggregation_method=None,
        colocate_gradients_with_ops=False,
        name=None,
        grad_loss=None
    )

Add operations to minimize `loss` by updating `var_list`.

This method simply combines calls `compute_gradients()` and
`apply_gradients()`. If you want to process the gradient before applying
them call `compute_gradients()` and `apply_gradients()` explicitly instead
of using this function.

<br />

<br />

<br />

| Args ||
|---|---|
| `loss` | A `Tensor` containing the value to minimize. |
| `global_step` | Optional `Variable` to increment by one after the variables have been updated. |
| `var_list` | Optional list or tuple of `Variable` objects to update to minimize `loss`. Defaults to the list of variables collected in the graph under the key `GraphKeys.TRAINABLE_VARIABLES`. |
| `gate_gradients` | How to gate the computation of gradients. Can be `GATE_NONE`, `GATE_OP`, or `GATE_GRAPH`. |
| `aggregation_method` | Specifies the method used to combine gradient terms. Valid values are defined in the class `AggregationMethod`. |
| `colocate_gradients_with_ops` | If True, try colocating gradients with the corresponding op. |
| `name` | Optional name for the returned operation. |
| `grad_loss` | Optional. A `Tensor` holding the gradient computed for `loss`. |

<br />

<br />

<br />

<br />

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

<br />

<br />

<br />

<br />

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

<br />

<br />

#### eager compatibility

<br />

When eager execution is enabled, `loss` should be a Python function that
takes no arguments and computes the value to be minimized. Minimization (and
gradient computation) is done with respect to the elements of `var_list` if
not None, else with respect to any trainable variables created during the
execution of the `loss` function. `gate_gradients`, `aggregation_method`,
`colocate_gradients_with_ops` and `grad_loss` are ignored when eager
execution is enabled.

<br />

<br />

### `variables`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/training/optimizer.py#L918-L944)

    variables()

A list of variables which encode the current state of `Optimizer`.

Includes slot variables and additional global variables created by the
optimizer in the current default graph.

<br />

<br />

<br />

| Returns ||
|---|---|
| A list of variables. ||

<br />

<br />

<br />

<br />

| ## Class Variables ||
|---|---|
| GATE_GRAPH | `2` |
| GATE_NONE | `0` |
| GATE_OP | `1` |

<br />