未验证 提交 9fad436b 编写于 作者: D daminglu 提交者: GitHub

Simplify fluid api for fit a line (#10301)

上级 6ab935f8
......@@ -6,4 +6,5 @@ foreach(src ${TEST_OPS})
py_test(${src} SRCS ${src}.py)
endforeach()
add_subdirectory(fit_a_line)
add_subdirectory(recognize_digits)
file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
# default test
foreach(src ${TEST_OPS})
py_test(${src} SRCS ${src}.py)
endforeach()
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle
import paddle.fluid as fluid
import contextlib
import numpy
import unittest
# train reader
BATCH_SIZE = 20
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.uci_housing.train(), buf_size=500),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.uci_housing.test(), buf_size=500),
batch_size=BATCH_SIZE)
def inference_program():
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
return y_predict
def linear():
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
y_predict = inference_program()
loss = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_loss = fluid.layers.mean(loss)
return avg_loss
def train(use_cuda, save_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
trainer = fluid.Trainer(
train_func=linear,
infer_func=inference_program,
place=place,
optimizer=fluid.optimizer.SGD(learning_rate=0.001))
def event_handler(event):
if isinstance(event, fluid.EndEpochEvent):
test_metrics = trainer.test(
reader=test_reader, feed_order=['x', 'y'])
print test_metrics
'''
...
['25.768919467926025']
['15.343549569447836']
...
'''
if float(test_metrics[0]) < 20.0:
if save_dirname is not None:
# NOT clear yet
# fluid.io.save_inference_model(save_dirname, ['x'], [y_predict])
# trainer.save_params(save_dirname)
# https://github.com/PaddlePaddle/Paddle/pull/10445
trainer.save_inference_model(save_dirname)
return
trainer.train(
reader=train_reader,
num_epochs=100,
event_handler=event_handler,
feed_order=['x', 'y'])
# infer
def infer(use_cuda, save_dirname=None):
if save_dirname is None:
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
inferencer = fluid.Inferencer(param_path=save_dirname, place=place)
batch_size = 10
tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
results = inferencer.infer({'x': tensor_x})
print("infer results: ", results[0])
def main(use_cuda):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
# Directory for saving the trained model
save_dirname = "fit_a_line.inference.model"
train(use_cuda, save_dirname)
infer(use_cuda, save_dirname)
class TestFitALine(unittest.TestCase):
def test_cpu(self):
with self.program_scope_guard():
with fluid.unique_name.guard():
main(use_cuda=False)
def test_cuda(self):
with self.program_scope_guard():
with fluid.unique_name.guard():
main(use_cuda=True)
@contextlib.contextmanager
def program_scope_guard(self):
prog = fluid.Program()
startup_prog = fluid.Program()
scope = fluid.core.Scope()
with fluid.scope_guard(scope):
with fluid.program_guard(prog, startup_prog):
yield
if __name__ == '__main__':
unittest.main()
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