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Optimizer that implements the FTRL algorithm.

Inherits From: `Optimizer`

tf.keras.optimizers.Ftrl( learning_rate=0.001, learning_rate_power=-0.5, initial_accumulator_value=0.1, l1_regularization_strength=0.0, l2_regularization_strength=0.0, name='Ftrl', l2_shrinkage_regularization_strength=0.0, **kwargs )

See Algorithm 1 of this paper. This version has support for both online L2 (the L2 penalty given in the paper above) and shrinkage-type L2 (which is the addition of an L2 penalty to the loss function).

Args | |
---|---|

`learning_rate` | A `Tensor` , floating point value, or a schedule that is a `tf.keras.optimizers.schedules.LearningRateSchedule` . The learning rate. |

`learning_rate_power` | A float value, must be less or equal to zero. Controls how the learning rate decreases during training. Use zero for a fixed learning rate. |

`initial_accumulator_value` | The starting value for accumulators. Only zero or positive values are allowed. |

`l1_regularization_strength` | A float value, must be greater than or equal to zero. |

`l2_regularization_strength` | A float value, must be greater than or equal to zero. |

`name` | Optional name prefix for the operations created when applying gradients. Defaults to `"Ftrl"` . |

`l2_shrinkage_regularization_strength` | A float value, must be greater than or equal to zero. This differs from L2 above in that the L2 above is a stabilization penalty, whereas this L2 shrinkage is a magnitude penalty. When input is sparse shrinkage will only happen on the active weights. |

`**kwargs` | Keyword arguments. Allowed to be one of `"clipnorm"` or `"clipvalue"` . `"clipnorm"` (float) clips gradients by norm; `"clipvalue"` (float) clips gradients by value. |

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. |

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.compat.v1.VariableAggregation.NONE )

`apply_gradients`

apply_gradients( grads_and_vars, name=None, experimental_aggregate_gradients=True )

Apply gradients to variables.

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`

.

grads = tape.gradient(loss, vars) grads = tf.distribute.get_replica_context().all_reduce('sum', grads) # Processing aggregated gradients. optimizer.apply_gradients(zip(grads, vars), experimental_aggregate_gradients=False)

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. |

`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`

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.

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()

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, 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` . The `iterations` will be automatically increased by 1. |

Raises | |
---|---|

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

`set_weights`

set_weights( 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>

Arguments | |
---|---|

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

`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/r2.3/api_docs/python/tf/keras/optimizers/Ftrl