fix quick start for fluid #9660

doc/fluid/getstarted/quickstart_cn.rst

-../../v2/getstarted/quickstart_cn.rst
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doc/fluid/getstarted/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。

doc/fluid/getstarted/quickstart_en.rst

-../../v2/getstarted/quickstart_en.rst
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doc/fluid/getstarted/quickstart_en.rst

+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.