# tf.keras.optimizers.Adam

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|---|
| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/keras-team/keras/tree/v2.14.0/keras/optimizers/adam.py#L26-L220) |

Optimizer that implements the Adam algorithm.

Inherits From: [`Optimizer`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/keras/optimizers/Optimizer)

#### View aliases


**Main aliases**

[`tf.keras.dtensor.experimental.optimizers.Adam`](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam), [`tf.keras.optimizers.experimental.Adam`](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam), [`tf.optimizers.Adam`](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam), [`tf.optimizers.experimental.Adam`](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam)

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    tf.keras.optimizers.Adam(
        learning_rate=0.001,
        beta_1=0.9,
        beta_2=0.999,
        epsilon=1e-07,
        amsgrad=False,
        weight_decay=None,
        clipnorm=None,
        clipvalue=None,
        global_clipnorm=None,
        use_ema=False,
        ema_momentum=0.99,
        ema_overwrite_frequency=None,
        jit_compile=True,
        name='Adam',
        **kwargs
    )

Adam optimization is a stochastic gradient descent method that is based on
adaptive estimation of first-order and second-order moments.

According to
[Kingma et al., 2014](http://arxiv.org/abs/1412.6980),
the method is "*computationally
efficient, has little memory requirement, invariant to diagonal rescaling of
gradients, and is well suited for problems that are large in terms of
data/parameters*".

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| ## Args ||
|---|---|
| learning_rate: A [`tf.Tensor`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/Tensor), floating point value, a schedule that is a [`tf.keras.optimizers.schedules.LearningRateSchedule`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/keras/optimizers/schedules/LearningRateSchedule), or a callable that takes no arguments and returns the actual value to use. The learning rate. Defaults to `0.001`. beta_1: A float value or a constant float tensor, or a callable that takes no arguments and returns the actual value to use. The exponential decay rate for the 1st moment estimates. Defaults to `0.9`. beta_2: A float value or a constant float tensor, or a callable that takes no arguments and returns the actual value to use. The exponential decay rate for the 2nd moment estimates. Defaults to `0.999`. epsilon: A small constant for numerical stability. This epsilon is "epsilon hat" in the Kingma and Ba paper (in the formula just before Section 2.1), not the epsilon in Algorithm 1 of the paper. Defaults to `1e-7`. amsgrad: Boolean. Whether to apply AMSGrad variant of this algorithm from the paper "On the Convergence of Adam and beyond". Defaults to `False`. name: String. The name to use for momentum accumulator weights created by the optimizer. ||
| `weight_decay` | Float, defaults to None. If set, weight decay is applied. |
| `clipnorm` | Float. If set, the gradient of each weight is individually clipped so that its norm is no higher than this value. |
| `clipvalue` | Float. If set, the gradient of each weight is clipped to be no higher than this value. |
| `global_clipnorm` | Float. If set, the gradient of all weights is clipped so that their global norm is no higher than this value. |
| `use_ema` | Boolean, defaults to False. If True, exponential moving average (EMA) is applied. EMA consists of computing an exponential moving average of the weights of the model (as the weight values change after each training batch), and periodically overwriting the weights with their moving average. |
| `ema_momentum` | Float, defaults to 0.99. Only used if `use_ema=True`. This is the momentum to use when computing the EMA of the model's weights: `new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value`. |
| `ema_overwrite_frequency` | Int or None, defaults to None. Only used if `use_ema=True`. Every `ema_overwrite_frequency` steps of iterations, we overwrite the model variable by its moving average. If None, the optimizer does not overwrite model variables in the middle of training, and you need to explicitly overwrite the variables at the end of training by calling `optimizer.finalize_variable_values()` (which updates the model variables in-place). When using the built-in `fit()` training loop, this happens automatically after the last epoch, and you don't need to do anything. |
| `jit_compile` | Boolean, defaults to True. If True, the optimizer will use XLA compilation. If no GPU device is found, this flag will be ignored. |
| `mesh` | optional [`tf.experimental.dtensor.Mesh`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/experimental/dtensor/Mesh) instance. When provided, the optimizer will be run in DTensor mode, e.g. state tracking variable will be a DVariable, and aggregation/reduction will happen in the global DTensor context. |
| `**kwargs` | keyword arguments only used for backward compatibility. |

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| ## Reference ||
|---|---|
| <br /> - [Kingma et al., 2014](http://arxiv.org/abs/1412.6980) - [Reddi et al., 2018](https://openreview.net/pdf?id=ryQu7f-RZ) for `amsgrad`. ||

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#### Notes:

The default value of 1e-7 for epsilon might not be a good default in
general. For example, when training an Inception network on ImageNet a
current good choice is 1.0 or 0.1. Note that since Adam uses the
formulation just before Section 2.1 of the Kingma and Ba paper rather than
the formulation in Algorithm 1, the "epsilon" referred to here is "epsilon
hat" in the paper.

The sparse implementation of this algorithm (used when the gradient is an
IndexedSlices object, typically because of [`tf.gather`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/gather) or an embedding
lookup in the forward pass) does apply momentum to variable slices even if
they were not used in the forward pass (meaning they have a gradient equal
to zero). Momentum decay (beta1) is also applied to the entire momentum
accumulator. This means that the sparse behavior is equivalent to the dense
behavior (in contrast to some momentum implementations which ignore momentum
unless a variable slice was actually used).

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| ## Attributes ||
|---|---|
| `iterations` | The number of training steps this `optimizer` has run. <br /> By default, iterations would be incremented by one every time `apply_gradients()` is called. |
| `learning_rate` | <br /> <br /> |
| `variables` | Returns variables of this optimizer. |

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## Methods

### `add_variable`

[View source](https://github.com/keras-team/keras/tree/v2.14.0/keras/optimizers/optimizer.py#L445-L470)

    add_variable(
        shape, dtype=None, initializer='zeros', name=None
    )

Create an optimizer variable.

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| Args ||
|---|---|
| `shape` | A list of integers, a tuple of integers, or a 1-D Tensor of type int32. Defaults to scalar if unspecified. |
| `dtype` | The DType of the optimizer variable to be created. Defaults to [`tf.keras.backend.floatx`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/keras/backend/floatx) if unspecified. |
| `initializer` | string or callable. Initializer instance. |
| `name` | The name of the optimizer variable to be created. |

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| Returns ||
|---|---|
| An optimizer variable, in the format of tf.Variable. ||

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### `build`

[View source](https://github.com/keras-team/keras/tree/v2.14.0/keras/optimizers/adam.py#L128-L161)

    build(
        var_list
    )

Initialize optimizer variables.

Adam optimizer has 3 types of variables: momentums, velocities and
velocity_hat (only set when amsgrad is applied),

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| Args ||
|---|---|
| `var_list` | list of model variables to build Adam variables on. |

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### `compute_gradients`

[View source](https://github.com/keras-team/keras/tree/v2.14.0/keras/optimizers/optimizer.py#L243-L277)

    compute_gradients(
        loss, var_list, tape=None
    )

Compute gradients of loss on trainable variables.

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| Args ||
|---|---|
| `loss` | `Tensor` or callable. If a callable, `loss` should take no arguments and return 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. |
| `tape` | (Optional) [`tf.GradientTape`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/GradientTape). If `loss` is provided as a `Tensor`, the tape that computed the `loss` must be provided. |

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

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### `exclude_from_weight_decay`

[View source](https://github.com/keras-team/keras/tree/v2.14.0/keras/optimizers/optimizer.py#L567-L594)

    exclude_from_weight_decay(
        var_list=None, var_names=None
    )

Exclude variables from weight decay.

This method must be called before the optimizer's `build` method is
called. You can set specific variables to exclude out, or set a list of
strings as the anchor words, if any of which appear in a variable's
name, then the variable is excluded.

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| Args ||
|---|---|
| `var_list` | A list of [`tf.Variable`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/Variable)s to exclude from weight decay. |
| `var_names` | A list of strings. If any string in `var_names` appear in the model variable's name, then this model variable is excluded from weight decay. For example, `var_names=['bias']` excludes all bias variables from weight decay. |

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### `finalize_variable_values`

[View source](https://github.com/keras-team/keras/tree/v2.14.0/keras/optimizers/optimizer.py#L704-L717)

    finalize_variable_values(
        var_list
    )

Set the final value of model's trainable variables.

Sometimes there are some extra steps before ending the variable updates,
such as overriding the model variables with its average value.

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| Args ||
|---|---|
| `var_list` | list of model variables. |

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### `from_config`

[View source](https://github.com/keras-team/keras/tree/v2.14.0/keras/optimizers/optimizer.py#L759-L779)

    @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.

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| Args ||
|---|---|
| `config` | A Python dictionary, typically the output of get_config. |
| `custom_objects` | A Python dictionary mapping names to additional user-defined Python objects needed to recreate this optimizer. |

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| Returns ||
|---|---|
| An optimizer instance. ||

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### `get_config`

[View source](https://github.com/keras-team/keras/tree/v2.14.0/keras/optimizers/adam.py#L206-L220)

    get_config()

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.

Subclass optimizer should override this method to include other
hyperparameters.

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| Returns ||
|---|---|
| Python dictionary. ||

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### `load_own_variables`

[View source](https://github.com/keras-team/keras/tree/v2.14.0/keras/optimizers/optimizer.py#L816-L832)

    load_own_variables(
        store
    )

Set the state of this optimizer object.

### `minimize`

[View source](https://github.com/keras-team/keras/tree/v2.14.0/keras/optimizers/optimizer.py#L522-L544)

    minimize(
        loss, var_list, tape=None
    )

Minimize `loss` by updating `var_list`.

This method simply computes gradient using [`tf.GradientTape`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/GradientTape) and calls
`apply_gradients()`. If you want to process the gradient before applying
then call [`tf.GradientTape`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/GradientTape) and `apply_gradients()` explicitly instead
of using this function.

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| Args ||
|---|---|
| `loss` | `Tensor` or callable. If a callable, `loss` should take no arguments and return 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. |
| `tape` | (Optional) [`tf.GradientTape`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/GradientTape). |

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| Returns ||
|---|---|
| None ||

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### `save_own_variables`

[View source](https://github.com/keras-team/keras/tree/v2.14.0/keras/optimizers/optimizer.py#L811-L814)

    save_own_variables(
        store
    )

Get the state of this optimizer object.

### `set_weights`

[View source](https://github.com/keras-team/keras/tree/v2.14.0/keras/optimizers/optimizer.py#L786-L809)

    set_weights(
        weights
    )

Set the weights of the optimizer.

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| Args ||
|---|---|
| `weights` | a list of [`tf.Variable`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/Variable)s or numpy arrays, the target values of optimizer variables. It should have the same order as `self._variables`. |

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### `update_step`

[View source](https://github.com/keras-team/keras/tree/v2.14.0/keras/optimizers/adam.py#L163-L204)

    update_step(
        gradient, variable
    )

Update step given gradient and the associated model variable.