MultiplicativeDecay¶

class paddle.optimizer.lr. MultiplicativeDecay ( learning_rate, lr_lambda, last_epoch=- 1, verbose=False ) [source]

Multiply the learning rate of optimizer by the factor given in function lr_lambda .

The algorithm can be described as the code below.

           learning_rate = 0.5        # init learning_rate
lr_lambda = lambda epoch: 0.95

learning_rate = 0.5        # epoch 0,
learning_rate = 0.475      # epoch 1, 0.5*0.95
learning_rate = 0.45125    # epoch 2, 0.475*0.95

          

Parameters

learning_rate (float) – The initial learning rate. It is a python float number.
lr_lambda (function) – A function which computes a factor by epoch , and then multiply the last learning rate by this factor.
last_epoch (int, optional) – The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
verbose (bool, optional) – If True, prints a message to stdout for each update. Default: False .

Returns

MultiplicativeDecay instance to schedule learning rate.

Examples

           >>> import paddle

>>> # train on default dynamic graph mode
>>> linear = paddle.nn.Linear(10, 10)
>>> scheduler = paddle.optimizer.lr.MultiplicativeDecay(learning_rate=0.5, lr_lambda=lambda x:0.95, verbose=True)
>>> sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameters=linear.parameters())
>>> for epoch in range(20):
...     for batch_id in range(5):
...         x = paddle.uniform([10, 10])
...         out = linear(x)
...         loss = paddle.mean(out)
...         loss.backward()
...         sgd.step()
...         sgd.clear_gradients()
...         scheduler.step()    # If you update learning rate each step
...     # scheduler.step()        # If you update learning rate each epoch
...

          

get_lr ( ) get_lr¶

For those subclass who overload LRScheduler (Base Class), User should have a custom implementation of get_lr() .

Otherwise, an NotImplementedError exception will be thrown.

set_dict ( state_dict ) set_dict¶: Loads the schedulers state.

set_state_dict ( state_dict ) set_state_dict¶: Loads the schedulers state.

state_dict ( ) state_dict¶

Returns the state of the scheduler as a dict.

It is a subset of self.__dict__ .

state_keys ( ) state_keys¶

For those subclass who overload LRScheduler (Base Class). Acquiescently, “last_epoch, last_lr” will be saved by self.keys = ['last_epoch', 'last_lr'] .

last_epoch is the current epoch num, and last_lr is the current learning rate.

If you want to change the default behavior, you should have a custom implementation of _state_keys() to redefine self.keys .

step ( epoch=None ) step¶

step should be called after optimizer.step . It will update the learning rate in optimizer according to current epoch . The new learning rate will take effect on next optimizer.step .

Parameters: epoch (int, None) – specify current epoch. Default: None. Auto-increment from last_epoch=-1.
Returns: None

Examples

             >>> import paddle
>>> value = paddle.arange(26, dtype='float32')
>>> a = paddle.reshape(value, [2, 13])
>>> linear = paddle.nn.Linear(13, 5)
>>> adadelta = paddle.optimizer.Adadelta(learning_rate=0.0003, epsilon=1e-06, rho=0.95,
...                             parameters = linear.parameters())
>>> out = linear(a)
>>> out.backward()
>>> adadelta.step()
>>> adadelta.clear_grad()

            

             >>> import paddle
>>> value = paddle.arange(26, dtype='float32')
>>> a = paddle.reshape(value, [2, 13])
>>> linear = paddle.nn.Linear(13, 5)
>>> adadelta = paddle.optimizer.Adadelta(learning_rate=0.0003, epsilon=1e-06, rho=0.95,
...                             parameters = linear.parameters())
>>> out = linear(a)
>>> out.backward()
>>> adadelta.step()
>>> adadelta.clear_grad()

            

MultiplicativeDecay¶

get_lr¶

set_dict¶

set_state_dict¶

state_dict¶

state_keys¶

step¶