Refine doc of Python Prediction API, replace

DataProviderWrapperConverter with DataProviderConverter.

doc/ui/predict/predict_sample.py

@@ -12,8 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from py_paddle import swig_paddle, DataProviderWrapperConverter
-from paddle.trainer.PyDataProviderWrapper import DenseSlot
+from py_paddle import swig_paddle, DataProviderConverter
+from paddle.trainer.PyDataProvider2 import dense_vector
 from paddle.trainer.config_parser import parse_config
 
 TEST_DATA = [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
@@ -89,12 +89,12 @@ TEST_DATA = [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
 
 
 def main():
-    conf = parse_config("./mnist_model/trainer_config.conf.norm", "")
+    conf = parse_config("./mnist_model/trainer_config.py", "")
     print conf.data_config.load_data_args
     network = swig_paddle.GradientMachine.createFromConfigProto(conf.model_config)
     assert isinstance(network, swig_paddle.GradientMachine)  # For code hint.
     network.loadParameters("./mnist_model/")
-    converter = DataProviderWrapperConverter(False, [DenseSlot(784)])
+    converter = DataProviderConverter([dense_vector(784)])
     inArg = converter(TEST_DATA)
     print network.forwardTest(inArg)
 

doc/ui/predict/swig_py_paddle_en.rst

@@ -10,27 +10,35 @@ SWIG. The main steps of predict values in python are:
 * Predict
 
 Here is a sample python script that shows the typical prediction process for the
-MNIST classification problem.
+MNIST classification problem. A complete sample code could be found at
+:code:`src_root/doc/ui/predict/predict_sample.py`.
 
 ..  literalinclude:: ./predict_sample.py
     :language: python
-    :linenos:
+    :lines: 15-18,90-100,101-104
 
 The module that does the most of the job is py_paddle.swig_paddle, it's
 generated by SWIG and has complete documents, for more details you can use
 python's :code:`help()` function. Let's walk through the above python script:
 
-* At the beginning, initialize PaddlePaddle with command line arguments(line 90).
-* Parse the configuration file that is used in training(line 93).
-* Create a neural network at line 95 according the parsed configuration, then
-  load the trained parameters from model at line 97.
-* A utility class for data transformation is created at line 98.
+* At the beginning, use :code:`swig_paddle.initPaddle()` to initialize
+  PaddlePaddle with command line arguments, for more about command line arguments
+  see `Command Line Arguments <../cmd_argument/detail_introduction.html>`_.
+* Parse the configuration file that is used in training with :code:`parse_config()`.
+  Because data to predict with always have no label, and output of prediction work
+  normally is the output layer rather than the cost layer, so you should modify
+  the configuration file accordingly before using it in the prediction work.
+* Create a neural network with
+  :code:`swig_paddle.GradientMachine.createFromConfigproto()`, which takes the
+  parsed configuration :code:`conf.model_config` as argument. Then load the
+  trained parameters from the model with :code:`network.loadParameters()`.
+* Create a data converter object of utility class :code:`DataProviderConverter`.
     - Note: As swig_paddle can only accept C++ matrices, we offer a utility
-      class DataProviderWraaperConverter that can accept the same input data with
-      PyDataProviderWrapper, for more information please refer to document
+      class DataProviderConverter that can accept the same input data with
+      PyDataProvider2, for more information please refer to document
       of `PyDataProvider2 <../data_provider/pydataprovider2.html>`_.
-* Do the prediction and output the result at line 100, forwardTest is another
-  utility class that directly takes the activations of the output layer.
+* Do the prediction with :code:`forwardTest()`, which takes the converted
+  input data and outputs the activations of the output layer.
 
 Here is a typical output: