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60 lines
2.7 KiB
60 lines
2.7 KiB
Python Prediction
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==================
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PaddlePaddle offers a set of clean prediction interfaces for python with the help of
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SWIG. The main steps of predict values in python are:
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* Parse training configurations
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* Construct GradientMachine
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* Prepare data
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* Predict
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Here is a sample python script that shows the typical prediction process for the
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MNIST classification problem. A complete sample code could be found at
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:code:`src_root/doc/ui/predict/predict_sample.py`.
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.. literalinclude:: src/predict_sample.py
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:language: python
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:lines: 15-18,90-100,101-104
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The module that does the most of the job is py_paddle.swig_paddle, it's
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generated by SWIG and has complete documents, for more details you can use
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python's :code:`help()` function. Let's walk through the above python script:
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* At the beginning, use :code:`swig_paddle.initPaddle()` to initialize
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PaddlePaddle with command line arguments, for more about command line arguments
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see :ref:`cmd_detail_introduction` .
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* Parse the configuration file that is used in training with :code:`parse_config()`.
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Because data to predict with always have no label, and output of prediction work
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normally is the output layer rather than the cost layer, so you should modify
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the configuration file accordingly before using it in the prediction work.
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* Create a neural network with
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:code:`swig_paddle.GradientMachine.createFromConfigproto()`, which takes the
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parsed configuration :code:`conf.model_config` as argument. Then load the
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trained parameters from the model with :code:`network.loadParameters()`.
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* Create a data converter object of utility class :code:`DataProviderConverter`.
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- Note: As swig_paddle can only accept C++ matrices, we offer a utility
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class DataProviderConverter that can accept the same input data with
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PyDataProvider2, for more information please refer to document
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of :ref:`api_pydataprovider2` .
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* Do the prediction with :code:`forwardTest()`, which takes the converted
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input data and outputs the activations of the output layer.
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Here is a typical output:
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.. code-block:: text
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[{'id': None, 'value': array([[ 5.53018653e-09, 1.12194102e-05, 1.96644767e-09,
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1.43630644e-02, 1.51111044e-13, 9.85625684e-01,
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2.08823112e-10, 2.32777140e-08, 2.00186201e-09,
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1.15501715e-08],
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[ 9.99982715e-01, 1.27787406e-10, 1.72296313e-05,
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1.49316648e-09, 1.36540484e-11, 6.93137714e-10,
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2.70634608e-08, 3.48565123e-08, 5.25639710e-09,
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4.48684503e-08]], dtype=float32)}]
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:code:`value` is the output of the output layer, each row represents result of
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the corresponding row in the input data, each element represents activation of
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the corresponding neuron in the output layer.
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