提交 b148f065 编写于 作者: Y Yang Yu

Make Fit a line a normal unittest

上级 c091dbdf
...@@ -12,44 +12,74 @@ ...@@ -12,44 +12,74 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
import numpy as np
import paddle.v2 as paddle import paddle.v2 as paddle
import paddle.v2.fluid as fluid import paddle.v2.fluid as fluid
import contextlib
import unittest
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None) def main(use_cuda):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
y = fluid.layers.data(name='y', shape=[1], dtype='float32') x = fluid.layers.data(name='x', shape=[13], dtype='float32')
cost = fluid.layers.square_error_cost(input=y_predict, label=y) y_predict = fluid.layers.fc(input=x, size=1, act=None)
avg_cost = fluid.layers.mean(x=cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001) y = fluid.layers.data(name='y', shape=[1], dtype='float32')
sgd_optimizer.minimize(avg_cost)
BATCH_SIZE = 20 cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(x=cost)
train_reader = paddle.batch( sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
paddle.reader.shuffle( sgd_optimizer.minimize(avg_cost)
paddle.dataset.uci_housing.train(), buf_size=500),
batch_size=BATCH_SIZE)
place = fluid.CPUPlace() BATCH_SIZE = 20
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program()) train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.uci_housing.train(), buf_size=500),
batch_size=BATCH_SIZE)
PASS_NUM = 100 place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
for pass_id in range(PASS_NUM): feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
fluid.io.save_persistables(exe, "./fit_a_line.model/") exe = fluid.Executor(place)
fluid.io.load_persistables(exe, "./fit_a_line.model/")
for data in train_reader(): exe.run(fluid.default_startup_program())
avg_loss_value, = exe.run(fluid.default_main_program(),
feed=feeder.feed(data), PASS_NUM = 100
fetch_list=[avg_cost]) for pass_id in range(PASS_NUM):
print(avg_loss_value) fluid.io.save_persistables(exe, "./fit_a_line.model/")
if avg_loss_value[0] < 10.0: fluid.io.load_persistables(exe, "./fit_a_line.model/")
exit(0) # if avg cost less than 10.0, we think our code is good. for data in train_reader():
exit(1) avg_loss_value, = exe.run(fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[avg_cost])
print(avg_loss_value)
if avg_loss_value[0] < 10.0:
return
raise AssertionError("Fit a line cost is too large, {0:2.2}".format(
avg_loss_value[0]))
class TestFitALine(unittest.TestCase):
def test_cpu(self):
with self.program_scope_guard():
main(use_cuda=False)
def test_cuda(self):
with self.program_scope_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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