From 3a3ff62e20d813051e52aa71cd4a5b42a8734dcf Mon Sep 17 00:00:00 2001 From: tangwei12 Date: Tue, 10 Apr 2018 16:06:47 +0800 Subject: [PATCH] fix quick start for fluid #9660 --- doc/fluid/getstarted/quickstart_cn.rst | 119 +++++++++++++++++++++++- doc/fluid/getstarted/quickstart_en.rst | 121 ++++++++++++++++++++++++- 2 files changed, 238 insertions(+), 2 deletions(-) mode change 120000 => 100644 doc/fluid/getstarted/quickstart_cn.rst mode change 120000 => 100644 doc/fluid/getstarted/quickstart_en.rst diff --git a/doc/fluid/getstarted/quickstart_cn.rst b/doc/fluid/getstarted/quickstart_cn.rst deleted file mode 120000 index 93a9e4e37a..0000000000 --- a/doc/fluid/getstarted/quickstart_cn.rst +++ /dev/null @@ -1 +0,0 @@ -../../v2/getstarted/quickstart_cn.rst \ No newline at end of file diff --git a/doc/fluid/getstarted/quickstart_cn.rst b/doc/fluid/getstarted/quickstart_cn.rst new file mode 100644 index 0000000000..102ce803f2 --- /dev/null +++ b/doc/fluid/getstarted/quickstart_cn.rst @@ -0,0 +1,118 @@ +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。 diff --git a/doc/fluid/getstarted/quickstart_en.rst b/doc/fluid/getstarted/quickstart_en.rst deleted file mode 120000 index 6e1894faa1..0000000000 --- a/doc/fluid/getstarted/quickstart_en.rst +++ /dev/null @@ -1 +0,0 @@ -../../v2/getstarted/quickstart_en.rst \ No newline at end of file diff --git a/doc/fluid/getstarted/quickstart_en.rst b/doc/fluid/getstarted/quickstart_en.rst new file mode 100644 index 0000000000..a5b9e977c3 --- /dev/null +++ b/doc/fluid/getstarted/quickstart_en.rst @@ -0,0 +1,120 @@ +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. -- GitLab