View source on GitHub |

Updated base class for optimizers.

Inherits From: `CheckpointableBase`

tf.keras.optimizers.Optimizer( name, **kwargs )

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 `tf.keras.optimizers.SGD`

, `tf.keras.optimizers.Adam`

.

# Create an optimizer with the desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is a callable that takes no argument and returns the value # to minimize. loss = lambda: 3 * var1 * var1 + 2 * var2 * var2 # In graph mode, returns op that minimizes the loss by updating the listed # variables. opt_op = opt.minimize(loss, var_list=[var1, var2]) opt_op.run() # In eager mode, simply call minimize to update the list of variables. opt.minimize(loss, var_list=[var1, var2])

In Keras models, sometimes variables are created when the model is first called, instead of construction time. Examples include 1) sequential models without input shape pre-defined, or 2) subclassed models. Pass var_list as callable in these cases.

opt = tf.keras.optimizers.SGD(learning_rate=0.1) model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden, activation='relu')) model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid')) loss_fn = lambda: tf.keras.losses.mse(model(input), output) var_list_fn = lambda: model.trainable_weights for input, output in data: opt.minimize(loss_fn, var_list_fn)

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:

- Compute the gradients with
`tf.GradientTape`

. - Process the gradients as you wish.
- Apply the processed gradients with
`apply_gradients()`

.

# Create an optimizer. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # Compute the gradients for a list of variables. with tf.GradientTape() as tape: loss = <call_loss_function> vars = <list_of_variables> grads = tape.gradient(loss, vars) # Process the gradients, for example cap them, etc. # capped_grads = [MyCapper(g) for g in grads] processed_grads = [process_gradient(g) for g in grads] # Ask the optimizer to apply the processed gradients. opt.apply_gradients(zip(processed_grads, var_list))

`tf.distribute.Strategy`

.This optimizer class is `tf.distribute.Strategy`

aware, which means it automatically sums gradients across all replicas. To average gradients, you divide your loss by the global batch size, which is done automatically if you use `tf.keras`

built-in training or evaluation loops. See the `reduction`

argument of your loss which should be set to `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE`

for averaging or `tf.keras.losses.Reduction.SUM`

for not.

If you are not using these and you want to average gradients, you should use `tf.math.reduce_sum`

to add up your per-example losses and then divide by the global batch size. Note that when using `tf.distribute.Strategy`

, the first component of a tensor's shape is the *replica-local* batch size, which is off by a factor equal to the number of replicas being used to compute a single step. As a result, using `tf.math.reduce_mean`

will give the wrong answer, resulting in gradients that can be many times too big.

All Keras optimizers respect variable constraints. If constraint function is passed to any variable, the constraint will be applied to the variable after the gradient has been applied to the variable. Important: If gradient is sparse tensor, variable constraint is not supported.

The entire optimizer is currently thread compatible, not thread-safe. The user needs to perform synchronization if necessary.

Many optimizer subclasses, such as `Adam`

and `Adagrad`

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.

These are arguments passed to the optimizer subclass constructor (the `__init__`

method), and then passed to `self._set_hyper()`

. They can be either regular Python values (like 1.0), tensors, or callables. If they are callable, the callable will be called during `apply_gradients()`

to get the value for the hyper parameter.

Hyper parameters can be overwritten through user code:

# Create an optimizer with the desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is a callable that takes no argument and returns the value # to minimize. loss = lambda: 3 * var1 + 2 * var2 # In eager mode, simply call minimize to update the list of variables. opt.minimize(loss, var_list=[var1, var2]) # update learning rate opt.learning_rate = 0.05 opt.minimize(loss, var_list=[var1, var2])

If you intend to create your own optimization algorithm, simply inherit from this class and override the following methods:

- resource_apply_dense (update variable given gradient tensor is dense)
- resource_apply_sparse (update variable given gradient tensor is sparse)
- create_slots (if your optimizer algorithm requires additional variables)
- get_config (serialization of the optimizer, include all hyper parameters)

Args | |
---|---|

`name` | A non-empty string. The name to use for accumulators created for the optimizer. |

`**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. |

Raises | |
---|---|

`ValueError` | If name is malformed. |

`RuntimeError` | If _create_slots has been overridden instead of _create_vars. |

Attributes | |
---|---|

`iterations` | Variable. The number of training steps this Optimizer has run. |

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

`add_slot`

add_slot( var, slot_name, initializer='zeros' )

Add a new slot variable for `var`

.

`add_weight`

add_weight( name, shape, dtype=None, initializer='zeros', trainable=None, synchronization=tf.VariableSynchronization.AUTO, aggregation=tf.VariableAggregation.NONE )

`apply_gradients`

apply_gradients( grads_and_vars, name=None )

Apply gradients to variables.

This is the second part of `minimize()`

. It returns an `Operation`

that applies gradients.

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. |

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`

@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.

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`

@abc.abstractmethod get_config()

Returns the config of the optimimizer.

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`

get_gradients( loss, params )

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_slot`

get_slot( var, slot_name )

`get_slot_names`

get_slot_names()

A list of names for this optimizer's slots.

`get_updates`

get_updates( loss, params )

`get_weights`

get_weights()

`minimize`

minimize( loss, var_list, grad_loss=None, name=None )

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` . If `global_step` was not `None` , that operation also increments `global_step` . |

Raises | |
---|---|

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

`set_weights`

set_weights( weights )

`variables`

variables()

Returns variables of this Optimizer based on the order created.

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/optimizers/Optimizer