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move most codes to uci_housing #9660

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release/0.12.0
tangwei12 7 years ago
parent 3a3ff62e20
commit 554cea6839
3 changed files with 80 additions and 171 deletions
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99
doc/fluid/getstarted/quickstart_cn.rst
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@ -25,94 +25,31 @@ PaddlePaddle支持使用pip快速安装，目前支持CentOS 6以上, Ubuntu 14.
创建一个 housing.py 并粘贴此Python代码：
.. code-block:: python
import sys
import math
import numpy
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle
import paddle.fluid as fluid
def train(save_dirname):
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
BATCH_SIZE = 20
train_reader = paddle.batch(
paddle.reader.shuffle(paddle.dataset.uci_housing.train(), buf_size=500), batch_size=BATCH_SIZE)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe.run(fluid.default_startup_program())
main_program = fluid.default_main_program()
PASS_NUM = 100
for pass_id in range(PASS_NUM):
for data in train_reader():
avg_loss_value, = exe.run(main_program,
feed=feeder.feed(data),
fetch_list=[avg_cost])
if avg_loss_value[0] < 10.0:
if save_dirname is not None:
fluid.io.save_inference_model(save_dirname, ['x'],
[y_predict], exe)
return
if math.isnan(float(avg_loss_value)):
sys.exit("got NaN loss, training failed.")
raise AssertionError("Fit a line cost is too large, {0:2.2}".format(
avg_loss_value[0]))
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
place = fluid.CPUPlace()
exe = fluid.Executor(place=place)
feeder = fluid.DataFeeder(place=place, feed_list=[x])
def infer(save_dirname):
place = fluid.CPUPlace()
exe = fluid.Executor(place)
with fluid.scope_guard(fluid.core.Scope()):
parameter_model = paddle.dataset.uci_housing.fluid_model()
probs = []
[inference_program, feed_target_names,fetch_targets] = \
fluid.io.load_inference_model(parameter_model, exe)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
predict_reader = paddle.batch(paddle.dataset.uci_housing.predict_reader(), batch_size=20)
# The input's dimension should be 2-D and the second dim is 13
# The input data should be >= 0
batch_size = 10
tensor_x = numpy.random.uniform(0, 10,
[batch_size, 13]).astype("float32")
assert feed_target_names[0] == 'x'
results = exe.run(inference_program,
feed={feed_target_names[0]: tensor_x},
results = []
for data in predict_reader():
result = exe.run(inference_program,
feed=feeder.feed(data),
fetch_list=fetch_targets)
probs.append(results)
results.append(result)
for i in xrange(len(probs)):
print(probs[i][0] * 1000)
print('Predicted price: ${0}'.format(probs[i][0] * 1000))
def main():
# Directory for saving the trained model
save_dirname = "fit_a_line.inference.model"
train(save_dirname)
infer(save_dirname)
if __name__=="__main__":
main()
for res in results:
for i in xrange(len(res[0])):
print 'Predicted price: ${:,.2f}'.format(res[0][i][0] * 1000)
执行 :code:`python housing.py` 瞧！ 它应该打印出预测住房数据的清单。

116
doc/fluid/getstarted/quickstart_en.rst
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@ -28,93 +28,33 @@ code:
.. code-block:: python
import sys
import math
import numpy
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle
def train(save_dirname):
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
BATCH_SIZE = 20
train_reader = paddle.batch(
paddle.reader.shuffle(paddle.dataset.uci_housing.train(), buf_size=500), batch_size=BATCH_SIZE)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe.run(fluid.default_startup_program())
main_program = fluid.default_main_program()
PASS_NUM = 100
for pass_id in range(PASS_NUM):
for data in train_reader():
avg_loss_value, = exe.run(main_program,
feed=feeder.feed(data),
fetch_list=[avg_cost])
if avg_loss_value[0] < 10.0:
if save_dirname is not None:
fluid.io.save_inference_model(save_dirname, ['x'],
[y_predict], exe)
return
if math.isnan(float(avg_loss_value)):
sys.exit("got NaN loss, training failed.")
raise AssertionError("Fit a line cost is too large, {0:2.2}".format(
avg_loss_value[0]))
def infer(save_dirname):
place = fluid.CPUPlace()
exe = fluid.Executor(place)
probs = []
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
# The input's dimension should be 2-D and the second dim is 13
# The input data should be >= 0
batch_size = 10
tensor_x = numpy.random.uniform(0, 10,
[batch_size, 13]).astype("float32")
assert feed_target_names[0] == 'x'
results = exe.run(inference_program,
feed={feed_target_names[0]: tensor_x},
fetch_list=fetch_targets)
probs.append(results)
for i in xrange(len(probs)):
print(probs[i][0] * 1000)
print('Predicted price: ${0}'.format(probs[i][0] * 1000))
def main():
# Directory for saving the trained model
save_dirname = "fit_a_line.inference.model"
train(save_dirname)
infer(save_dirname)
if __name__=="__main__":
main()
import paddle
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
place = fluid.CPUPlace()
exe = fluid.Executor(place=place)
feeder = fluid.DataFeeder(place=place, feed_list=[x])
with fluid.scope_guard(fluid.core.Scope()):
parameter_model = paddle.dataset.uci_housing.fluid_model()
[inference_program, feed_target_names,fetch_targets] = \
fluid.io.load_inference_model(parameter_model, exe)
predict_reader = paddle.batch(paddle.dataset.uci_housing.predict_reader(), batch_size=20)
results = []
for data in predict_reader():
result = exe.run(inference_program,
feed=feeder.feed(data),
fetch_list=fetch_targets)
results.append(result)
for res in results:
for i in xrange(len(res[0])):
print 'Predicted price: ${:,.2f}'.format(res[0][i][0] * 1000)
Run :code:`python housing.py` and voila! It should print out a list of predictions
for the test housing data.

36
python/paddle/dataset/uci_housing.py
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@ -19,7 +19,11 @@ https://archive.ics.uci.edu/ml/machine-learning-databases/housing/ and
parse training set and test set into paddle reader creators.
"""
import os
import numpy as np
import tempfile
import tarfile
import os
import paddle.dataset.common
@ -34,8 +38,9 @@ feature_names = [
UCI_TRAIN_DATA = None
UCI_TEST_DATA = None
URL_MODEL = 'https://github.com/PaddlePaddle/book/raw/develop/01.fit_a_line/fit_a_line.tar'
MD5_MODEL = '52fc3da8ef3937822fcdd87ee05c0c9b'
FLUID_URL_MODEL = 'https://github.com/PaddlePaddle/book/raw/develop/01.fit_a_line/fluid/fit_a_line.fluid.tar'
FLUID_MD5_MODEL = '6e6dd637ccd5993961f68bfbde46090b'
def feature_range(maximums, minimums):
@ -112,6 +117,33 @@ def test():
return reader
def fluid_model():
parameter_tar = paddle.dataset.common.download(FLUID_URL_MODEL, 'uci_housing', FLUID_MD5_MODEL, 'fit_a_line.fluid.tar')
tar = tarfile.TarFile(parameter_tar, mode='r')
dirpath = tempfile.mkdtemp()
tar.extractall(path=dirpath)
return dirpath
def predict_reader():
"""
UCI_HOUSING test set creator.
It returns a reader creator, each sample in the reader is features after
normalization and price number.
:return: Test reader creator
:rtype: callable
"""
global UCI_TEST_DATA
load_data(paddle.dataset.common.download(URL, 'uci_housing', MD5))
def reader():
for d in UCI_TEST_DATA:
yield (d[:-1],)
return reader
def fetch():
paddle.dataset.common.download(URL, 'uci_housing', MD5)

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