# Optimizer

class `paddle.v2.fluid.optimizer.``Optimizer`(global_step=None)

  Optimizer Base class.

  Define the common interface of an optimizer. User should not use this class directly, but need to use one of it’s implementation.

  `create_optimization_pass`(parameters_and_grads, loss, startup_program=None)

    Add optimization operators to update gradients to variables.

    参数:

    - loss – the target that this optimization is for.
    - parameters_and_grads – a list of (variable, gradient) pair to update.

    返回: | a list of operators that will complete one step of optimization. This will include parameter update ops, global step update ops and any other custom ops required by subclasses to manage their internal state. :param startup_program:
    返回类型: | return_op_list

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

    Add operations to minimize loss by updating parameter_list.

    This method combines interface append_backward_ops() and create_optimization_pass() into one.

## SGDOptimizer

class `paddle.v2.fluid.optimizer.``SGDOptimizer`(learning_rate, global_step=None)

  Simple SGD optimizer without any state.

## MomentumOptimizer

class `paddle.v2.fluid.optimizer.``MomentumOptimizer`(learning_rate, momentum, use_nesterov=False, global_step=None)

  Simple Momentum optimizer with velocity state

## AdagradOptimizer

class `paddle.v2.fluid.optimizer.``AdagradOptimizer`(learning_rate, epsilon=1e-06, global_step=None)

  Simple Adagrad optimizer with moment state

## AdamOptimizer

class `paddle.v2.fluid.optimizer.``AdamOptimizer`(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, global_step=None)

  Implements the Adam Optimizer

## AdamaxOptimizer

class `paddle.v2.fluid.optimizer.``AdamaxOptimizer`(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, global_step=None)

  Implements the Adamax Optimizer

## DecayedAdagradOptimizer

class `paddle.v2.fluid.optimizer.``DecayedAdagradOptimizer`(learning_rate, decay=0.95, epsilon=1e-06, global_step=None)

  Simple Decayed Adagrad optimizer with moment state