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[xdoctest] reformat example code with google style in No. 300 #56598

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Aug 31, 2023
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[xdoctest] reformat example code with google style in No. 300 #56598

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Update communicator.py
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Merge branch 'PaddlePaddle:develop' into xdoc7
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159 changes: 78 additions & 81 deletions python/paddle/incubate/optimizer/lookahead.py
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Original file line number Diff line number Diff line change
Expand Up @@ -50,66 +50,63 @@ class LookAhead(Optimizer):

.. code-block:: python

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

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

>>> # doctest: +SKIP('The run time will too long to pass the CI check.')
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>>> train(layer, loader, loss_fn, lookahead)
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"""
_slow_str = "slow"
Expand Down Expand Up @@ -161,16 +158,16 @@ def step(self):

.. code-block:: python

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()
>>> 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()

"""
self.inner_optimizer.step()
Expand Down Expand Up @@ -274,17 +271,17 @@ def minimize(

.. code-block:: python

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()
>>> 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()

"""
assert isinstance(loss, Variable), "The loss should be an Tensor."
Expand Down