# FtrlOptimizer¶

class paddle.fluid.optimizer.FtrlOptimizer(learning_rate, l1=0.0, l2=0.0, lr_power=-0.5, regularization=None, name=None)[source]

\begin{align}\begin{aligned}&new\_accum = squared\_accum + grad^2\\&if (lr\_power == -0.5):\\&\quad linear\_accum += grad - \frac{\sqrt{new\_accum} - \sqrt{squared\_accum}}{learning\_rate * param}\\&else:\\&\quad linear\_accum += grad - \frac{new\_accum^{-lr\_power} - accum^{-lr\_power}}{learning\_rate * param}\\ &x = l1 * sign(linear\_accum) - linear\_accum\\&if (lr\_power == -0.5):\\&\quad y = \frac{\sqrt{new\_accum}}{learning\_rate} + (2 * l2)\\&\quad pre\_shrink = \frac{x}{y}\\&\quad param = (abs(linear\_accum) > l1).select(pre\_shrink, 0.0)\\&else:\\&\quad y = \frac{new\_accum^{-lr\_power}}{learning\_rate} + (2 * l2)\\&\quad pre\_shrink = \frac{x}{y}\\&\quad param = (abs(linear\_accum) > l1).select(pre\_shrink, 0.0)\\&squared\_accum += grad^2\end{aligned}\end{align}
Parameters
• learning_rate (float|Variable) – Global learning rate.

• l1 (float) – L1 regularization strength, default is 0.0.

• l2 (float) – L2 regularization strength, default is 0.0.

• lr_power (float) – Learning Rate Power, default is -0.5.

• regularization – A Regularizer, such as L2DecayRegularizer. Optional, default is None.

• name (str, optional) – This parameter is used by developers to print debugging information. For details, please refer to Name. Default is None.

Raises

ValueError – If learning_rate, rho, epsilon, momentum are None.

Examples

import paddle
import numpy as np

place = fluid.CPUPlace()
main = fluid.Program()
with fluid.program_guard(main):
x = fluid.layers.data(name='x', shape=, dtype='float32')
y = fluid.layers.data(name='y', shape=, dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)

ftrl_optimizer = fluid.optimizer.Ftrl(learning_rate=0.1)
ftrl_optimizer.minimize(avg_cost)

fetch_list = [avg_cost]
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list)


Note

Currently, FtrlOptimizer doesn’t support sparse parameter optimization.

minimize(loss, startup_program=None, parameter_list=None, no_grad_set=None, grad_clip=None)

Add operations to minimize loss by updating parameter_list.

Parameters
• loss (Variable) – A Variable containing the value to minimize.

• startup_program (Program, optional) – Program for initializing parameters in parameter_list. The default value is None, at this time default_startup_program will be used.

• parameter_list (list, optional) – List of Variable names to update to minimize loss. The default value is None, at this time all parameters will be updated.

• no_grad_set (set, optional) – Set of Variable objects that don’t need to be updated. The default value is None.

• grad_clip (GradClipBase, optional) – Gradient clipping strategy, static graph mode does not need to use this argument. Currently, this argument only supports gradient clipping in dygraph mode. In the future, this argument my be adjusted. The default value is None.

Returns

tuple (optimize_ops, params_grads), A list of operators appended by minimize and a list of (param, grad) variable pairs, param is Parameter, grad is the gradient value corresponding to the parameter.

Return type

tuple

Examples

Please refer to the example of current Optimizer.

set_dict(state_dict)

Load optimizer state dict. For Adam opimizer, contains beta1, beta2, momentum etc. If LearningRateDecay have been used, global_step will be changed.

Parameters

state_dict (dict) – Dict contains all the Variable needed by optimizer

Returns

None

Examples

with fluid.dygraph.guard():
emb = fluid.dygraph.Embedding( "emb", [10, 10])

state_dict = emb.state_dict()


state_dict()
import paddle.fluid as fluid