CosineAnnealingDecay¶

class paddle.optimizer.lr. CosineAnnealingDecay ( learning_rate, T_max, eta_min=0, last_epoch=- 1, verbose=False ) [source]

Set the learning rate using a cosine annealing schedule, where \(\eta_{max}\) is set to the initial learning_rate. \(T_{cur}\) is the number of epochs since the last restart in SGDR.

The algorithm can be described as following.

\[ \begin{align}\begin{aligned}\eta_t & = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 + \cos\left(\frac{T_{cur}}{T_{max}}\pi\right)\right), & T_{cur} \neq (2k+1)T_{max};\\\eta_{t+1} & = \eta_{t} + \frac{1}{2}(\eta_{max} - \eta_{min}) \left(1 - \cos\left(\frac{1}{T_{max}}\pi\right)\right), & T_{cur} = (2k+1)T_{max}.\end{aligned}\end{align} \]

It has been proposed in SGDR: Stochastic Gradient Descent with Warm Restarts. Note that this only implements the cosine annealing part of SGDR, and not the restarts.

Parameters

learning_rate (float) – The initial learning rate, that is \(\eta_{max}\) . It can be set to python float or int number.
T_max (int) – Maximum number of iterations. It is half of the decay cycle of learning rate. It must be a positive integer.
eta_min (float|int, optional) – Minimum learning rate, that is \(\eta_{min}\) . Default: 0.
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

CosineAnnealingDecay instance to schedule learning rate.

Examples

           >>> # Example1: train on default dynamic graph mode
>>> import paddle
>>> import numpy as np

>>> # train on default dynamic graph mode
>>> linear = paddle.nn.Linear(10, 10)
>>> scheduler = paddle.optimizer.lr.CosineAnnealingDecay(learning_rate=0.5, T_max=10, 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

          

           >>> # Example2: train on static graph mode
>>> import paddle
>>> import numpy as np
>>> paddle.enable_static()
>>> main_prog = paddle.static.Program()
>>> start_prog = paddle.static.Program()
>>> with paddle.static.program_guard(main_prog, start_prog):
...     x = paddle.static.data(name='x', shape=[None, 4, 5])
...     y = paddle.static.data(name='y', shape=[None, 4, 5])
...     z = paddle.static.nn.fc(x, 100)
...     loss = paddle.mean(z)
...     scheduler = paddle.optimizer.lr.CosineAnnealingDecay(learning_rate=0.5, T_max=10, verbose=True)
...     sgd = paddle.optimizer.SGD(learning_rate=scheduler)
...     sgd.minimize(loss)
...
>>> exe = paddle.static.Executor()
>>> exe.run(start_prog)
>>> for epoch in range(20):
...     for batch_id in range(5):
...         out = exe.run(
...             main_prog,
...             feed={
...                 'x': np.random.randn(3, 4, 5).astype('float32'),
...                 'y': np.random.randn(3, 4, 5).astype('float32')
...             },
...             fetch_list=loss.name)
...         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()

            

CosineAnnealingDecay¶

get_lr¶

set_dict¶

set_state_dict¶

state_dict¶

state_keys¶

step¶