提交 3a3ff62e 编写于 作者: T tangwei12

fix quick start for fluid #9660

上级 e0babe7c
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../../v2/getstarted/quickstart_cn.rst
\ No newline at end of file
快速开始
========
快速安装
--------
PaddlePaddle支持使用pip快速安装,目前支持CentOS 6以上, Ubuntu 14.04以及MacOS 10.12,并安装有Python2.7。
执行下面的命令完成快速安装,版本为cpu_avx_openblas:
.. code-block:: bash
pip install paddlepaddle
如果需要安装支持GPU的版本(cuda7.5_cudnn5_avx_openblas),需要执行:
.. code-block:: bash
pip install paddlepaddle-gpu
更详细的安装和编译方法参考::ref:`install_steps` 。
快速使用
--------
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
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()
执行 :code:`python housing.py` 瞧! 它应该打印出预测住房数据的清单。
../../v2/getstarted/quickstart_en.rst
\ No newline at end of file
Quick Start
============
Quick Install
-------------
You can use pip to install PaddlePaddle with a single command, supports
CentOS 6 above, Ubuntu 14.04 above or MacOS 10.12, with Python 2.7 installed.
Simply run the following command to install, the version is cpu_avx_openblas:
.. code-block:: bash
pip install paddlepaddle
If you need to install GPU version (cuda7.5_cudnn5_avx_openblas), run:
.. code-block:: bash
pip install paddlepaddle-gpu
For more details about installation and build: :ref:`install_steps` .
Quick Use
---------
Create a new file called housing.py, and paste this Python
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()
Run :code:`python housing.py` and voila! It should print out a list of predictions
for the test housing data.
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