quickstart_cn.rst

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。