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fix api doc: Optimizer.ModelAverage (#17395)

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dependabot/pip/python/requests-2.20.0
lujun 6 years ago committed by GitHub
parent 90eae0b39a
commit ed9d603a8a
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3 changed files with 44 additions and 14 deletions
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4
paddle/fluid/API.spec
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@ -500,13 +500,13 @@ paddle.fluid.optimizer.AdadeltaOptimizer.backward (ArgSpec(args=['self', 'loss',
paddle.fluid.optimizer.AdadeltaOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.AdadeltaOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b'))
paddle.fluid.optimizer.ModelAverage.__init__ (ArgSpec(args=['self', 'average_window_rate', 'min_average_window', 'max_average_window', 'regularization', 'name'], varargs=None, keywords=None, defaults=(10000, 10000, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.ModelAverage.apply (ArgSpec(args=['self', 'executor', 'need_restore'], varargs=None, keywords=None, defaults=(True,)), ('document', '46234a5470590feb336346f70a3db715'))
paddle.fluid.optimizer.ModelAverage.apply (ArgSpec(args=['self', 'executor', 'need_restore'], varargs=None, keywords=None, defaults=(True,)), ('document', '648010d0ac1fa707dac0b89f74b0e35c'))
paddle.fluid.optimizer.ModelAverage.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
paddle.fluid.optimizer.ModelAverage.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.ModelAverage.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
paddle.fluid.optimizer.ModelAverage.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.ModelAverage.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b'))
paddle.fluid.optimizer.ModelAverage.restore (ArgSpec(args=['self', 'executor'], varargs=None, keywords=None, defaults=None), ('document', '18db9c70be9c4dd466f9844457b21bfe'))
paddle.fluid.optimizer.ModelAverage.restore (ArgSpec(args=['self', 'executor'], varargs=None, keywords=None, defaults=None), ('document', '5f14ea4adda2791e1c3b37ff327f6a83'))
paddle.fluid.optimizer.LarsMomentumOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'momentum', 'lars_coeff', 'lars_weight_decay', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.0005, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.LarsMomentumOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
paddle.fluid.optimizer.LarsMomentumOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))

4
python/paddle/dataset/mnist.py
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@ -90,7 +90,7 @@ def train():
MNIST training set creator.
It returns a reader creator, each sample in the reader is image pixels in
[0, 1] and label in [0, 9].
[-1, 1] and label in [0, 9].
:return: Training reader creator
:rtype: callable
@ -107,7 +107,7 @@ def test():
MNIST test set creator.
It returns a reader creator, each sample in the reader is image pixels in
[0, 1] and label in [0, 9].
[-1, 1] and label in [0, 9].
:return: Test reader creator.
:rtype: callable

50
python/paddle/fluid/optimizer.py
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@ -2145,22 +2145,45 @@ class ModelAverage(Optimizer):
regularization: A Regularizer, such as
fluid.regularizer.L2DecayRegularizer.
name: A optional name prefix.
Examples:
.. code-block:: python
optimizer = fluid.optimizer.Momentum()
optimizer.minimize(cost)
model_average = fluid.optimizer.ModelAverage(0.15,
min_average_window=10000,
max_average_window=20000)
for pass_id in range(args.pass_num):
for data in train_reader():
exe.run(fluid.default_main_program()...)
import paddle.fluid as fluid
import numpy
# First create the Executor.
place = fluid.CPUPlace() # fluid.CUDAPlace(0)
exe = fluid.Executor(place)
train_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(train_program, startup_program):
# build net
data = fluid.layers.data(name='X', shape=[1], dtype='float32')
hidden = fluid.layers.fc(input=data, size=10)
loss = fluid.layers.mean(hidden)
optimizer = fluid.optimizer.Momentum(learning_rate=0.2, momentum=0.1)
optimizer.minimize(loss)
# build ModelAverage optimizer
model_average = fluid.optimizer.ModelAverage(0.15,
min_average_window=10000,
max_average_window=20000)
exe.run(startup_program)
x = numpy.random.random(size=(10, 1)).astype('float32')
outs = exe.run(program=train_program,
feed={'X': x},
fetch_list=[loss.name])
# apply ModelAverage
with model_average.apply(exe):
for data in test_reader():
exe.run(inference_program...)
x = numpy.random.random(size=(10, 1)).astype('float32')
exe.run(program=train_program,
feed={'X': x},
fetch_list=[loss.name])
"""
def __init__(self,
@ -2275,6 +2298,10 @@ class ModelAverage(Optimizer):
@signature_safe_contextmanager
def apply(self, executor, need_restore=True):
"""Apply average values to parameters of current model.
Args:
executor(fluid.Executor): current executor.
need_restore(bool): If you finally need to do restore, set it to True. Default is True.
"""
executor.run(self.apply_program)
try:
@ -2285,6 +2312,9 @@ class ModelAverage(Optimizer):
def restore(self, executor):
"""Restore parameter values of current model.
Args:
executor(fluid.Executor): current executor.
"""
executor.run(self.restore_program)

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