LookAhead

class paddle.incubate. LookAhead ( inner_optimizer, alpha=0.5, k=5, name=None ) [源代码]

此 API 为论文 Lookahead Optimizer: k steps forward, 1 step back 中 Lookahead 优化器的实现。 Lookahead 保留两组参数:fast_params 和 slow_params。每次训练迭代中 inner_optimizer 更新 fast_params。 Lookahead 每 k 次训练迭代更新 slow_params 和 fast_params,如下所示:

\[ \begin{align}\begin{aligned}slow\_param_t & = slow\_param_{t-1} + alpha * (fast\_param_{t-1} - slow\_param_{t-1})\\fast\_param_t & = slow\_param_t\end{aligned}\end{align} \]

参数

  • inner_optimizer (inner_optimizer) - 每次迭代更新 fast params 的优化器。

  • alpha (float,可选) - Lookahead 的学习率。默认值为 0.5。

  • k (int,可选) - slow params 每 k 次迭代更新一次。默认值为 5。

  • name (str,可选) - 具体用法请参见 Name,一般无需设置,默认值为 None。

代码示例

>>> import numpy as np
>>> import paddle
>>> import paddle.nn as nn

>>> BATCH_SIZE = 16
>>> BATCH_NUM = 4
>>> EPOCH_NUM = 4

>>> IMAGE_SIZE = 784
>>> CLASS_NUM = 10
>>> # define a random dataset
>>> class RandomDataset(paddle.io.Dataset):
...     def __init__(self, num_samples):
...         self.num_samples = num_samples
...     def __getitem__(self, idx):
...         image = np.random.random([IMAGE_SIZE]).astype('float32')
...         label = np.random.randint(0, CLASS_NUM - 1,
...                                 (1, )).astype('int64')
...         return image, label
...     def __len__(self):
...         return self.num_samples

>>> class LinearNet(nn.Layer):
...     def __init__(self):
...         super().__init__()
...         self._linear = nn.Linear(IMAGE_SIZE, CLASS_NUM)
...         self.bias = self._linear.bias
...     @paddle.jit.to_static
...     def forward(self, x):
...         return self._linear(x)

>>> def train(layer, loader, loss_fn, opt):
...     for epoch_id in range(EPOCH_NUM):
...         for batch_id, (image, label) in enumerate(loader()):
...             out = layer(image)
...             loss = loss_fn(out, label)
...             loss.backward()
...             opt.step()
...             opt.clear_grad()
...             print("Train Epoch {} batch {}: loss = {}".format(
...                 epoch_id, batch_id, np.mean(loss.numpy())))
>>> layer = LinearNet()
>>> loss_fn = nn.CrossEntropyLoss()
>>> optimizer = paddle.optimizer.SGD(learning_rate=0.1, parameters=layer.parameters())
>>> lookahead = paddle.incubate.LookAhead(optimizer, alpha=0.2, k=5)

>>> # create data loader
>>> dataset = RandomDataset(BATCH_NUM * BATCH_SIZE)
>>> loader = paddle.io.DataLoader(
...     dataset,
...     batch_size=BATCH_SIZE,
...     shuffle=True,
...     drop_last=True,
...     num_workers=2)

>>> train(layer, loader, loss_fn, lookahead)

方法

step()

执行优化器并更新参数一次。

返回

None。

代码示例

>>> import paddle
>>> inp = paddle.rand([1,10], dtype="float32")
>>> linear = paddle.nn.Linear(10, 1)
>>> out = linear(inp)
>>> loss = paddle.mean(out)
>>> sgd = paddle.optimizer.SGD(learning_rate=0.1,parameters=linear.parameters())
>>> lookahead = paddle.incubate.LookAhead(sgd, alpha=0.2, k=5)
>>> loss.backward()
>>> lookahead.step()
>>> lookahead.clear_grad()

minimize(loss, startup_program=None, parameters=None, no_grad_set=None)

增加操作以通过更新参数来最小化损失。

参数

  • loss (Tensor) - 包含要最小化的值的 Tensor。

  • startup_program (Program,可选) - Program。在 parameters 中初始化参数。默认值为 None,此时将使用 default_startup_program

  • parameters (list,可选) - 列出更新最小化 lossTensorTensor.name。默认值为 None,此时所有参数都会被更新。

  • no_grad_set (set,可选) - 不需要更新的 TensorTensor.name 的集合。默认值为 None。

返回

tuple: tuple (optimize_ops, params_grads),由 minimize 添加的操作列表和 (param, grad) Tensor 对的列表,其中 param 是参数,grad 参数对应的梯度值。在静态图模式中,返回的元组可以传给 Executor.run() 中的 fetch_list 来表示程序剪枝。这样程序在运行之前会通过 feedfetch_list 被剪枝,详情请参考 Executor

代码示例

>>> import paddle

>>> inp = paddle.rand([1, 10], dtype="float32")
>>> linear = paddle.nn.Linear(10, 1)
>>> out = linear(inp)
>>> loss = paddle.mean(out)
>>> sgd = paddle.optimizer.SGD(learning_rate=0.1,parameters=linear.parameters())
>>> lookahead = paddle.incubate.LookAhead(sgd, alpha=0.2, k=5)
>>> loss.backward()
>>> lookahead.minimize(loss)
>>> lookahead.clear_grad()