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<li><a class="reference internal" href="#data-preparation">Data Preparation</a><ul>
<li><a class="reference internal" href="#download-and-extract-dataset">Download and extract dataset</a></li>
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<li><a class="reference internal" href="#extract-movie-user-features-to-python-object">Extract Movie/User features to python object</a><ul>
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  <div class="section" id="regression-movielens-ratting">
<h1>Regression MovieLens Ratting<a class="headerlink" href="#regression-movielens-ratting" title="Permalink to this headline"></a></h1>
<p>Here we demonstrate a <strong>Cosine Similarity Regression</strong> job in movie lens dataset.
This demo will show how paddle does (word) embedding job,
handles the similarity regression,
the character-level convolutional networks for text, and how does paddle handle
multiple types of inputs.
Note that the model structure is not fine-tuned and just a demo to show how paddle works.</p>
<p>YOU ARE WELCOME TO BUILD A BETTER DEMO
BY USING PADDLEPADDLE, AND LET US KNOW TO MAKE THIS DEMO BETTER.</p>
<div class="section" id="data-preparation">
<h2>Data Preparation<a class="headerlink" href="#data-preparation" title="Permalink to this headline"></a></h2>
<div class="section" id="download-and-extract-dataset">
<h3>Download and extract dataset<a class="headerlink" href="#download-and-extract-dataset" title="Permalink to this headline"></a></h3>
<p>We use <a class="reference internal" href="ml_dataset_en.html#demo-ml-dataset"><span class="std std-ref">MovieLens Dataset</span></a> here.
To download and unzip the dataset, simply run the following commands.</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="nb">cd</span> demo/recommendation/data
./ml_data.sh
</pre></div>
</div>
<p>And the directory structure of <code class="code docutils literal"><span class="pre">demo/recommendation/data/ml-1m</span></code> is:</p>
<div class="highlight-text"><div class="highlight"><pre><span></span>+--ml-1m
     +--- movies.dat    # movie features
     +--- ratings.dat   # ratings
     +--- users.dat     # user features
     +--- README        # dataset description
</pre></div>
</div>
</div>
<div class="section" id="field-config-file">
<h3>Field config file<a class="headerlink" href="#field-config-file" title="Permalink to this headline"></a></h3>
<p><strong>Field config file</strong> is used to specify the fields of the dataset and the file format,
i.e, specific <strong>WHAT</strong> type it is in each feature file.</p>
<p>The field config file of ml-1m shows in <code class="code docutils literal"><span class="pre">demo/recommendation/data/config.json</span></code>.
It specifics the field types and file names: 1) there are four types of field for user file: id, gender, age and occupation;
2) the filename is &#8220;users.dat&#8221;, and the delimiter of file is &#8221;::&#8221;.</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="p">{</span>
  <span class="s2">&quot;user&quot;</span><span class="p">:</span> <span class="p">{</span>
    <span class="s2">&quot;file&quot;</span><span class="p">:</span> <span class="p">{</span>
      <span class="s2">&quot;name&quot;</span><span class="p">:</span> <span class="s2">&quot;users.dat&quot;</span><span class="p">,</span>
      <span class="s2">&quot;delimiter&quot;</span><span class="p">:</span> <span class="s2">&quot;::&quot;</span>
    <span class="p">},</span>
    <span class="s2">&quot;fields&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;id&quot;</span><span class="p">,</span> <span class="s2">&quot;gender&quot;</span><span class="p">,</span> <span class="s2">&quot;age&quot;</span><span class="p">,</span> <span class="s2">&quot;occupation&quot;</span><span class="p">]</span>
  <span class="p">},</span>
  <span class="s2">&quot;movie&quot;</span><span class="p">:</span> <span class="p">{</span>
    <span class="s2">&quot;file&quot;</span><span class="p">:</span> <span class="p">{</span>
      <span class="s2">&quot;name&quot;</span><span class="p">:</span> <span class="s2">&quot;movies.dat&quot;</span><span class="p">,</span>
      <span class="s2">&quot;delimiter&quot;</span><span class="p">:</span> <span class="s2">&quot;::&quot;</span>
    <span class="p">},</span>
    <span class="s2">&quot;fields&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;id&quot;</span><span class="p">,</span> <span class="s2">&quot;title&quot;</span><span class="p">,</span> <span class="s2">&quot;genres&quot;</span><span class="p">]</span>
  <span class="p">}</span>
<span class="p">}</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="preprocess-data">
<h2>Preprocess Data<a class="headerlink" href="#preprocess-data" title="Permalink to this headline"></a></h2>
<p>You need to install python 3rd party libraries.
IT IS HIGHLY RECOMMEND TO USE VIRTUALENV MAKE A CLEAN PYTHON ENVIRONMENT.</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>pip install -r requirements.txt
</pre></div>
</div>
<p>The general command for preprocessing the dataset is:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="nb">cd</span> demo/recommendation
./preprocess.sh
</pre></div>
</div>
<p>And the detail steps are introduced as follows.</p>
<div class="section" id="extract-movie-user-features-to-python-object">
<h3>Extract Movie/User features to python object<a class="headerlink" href="#extract-movie-user-features-to-python-object" title="Permalink to this headline"></a></h3>
<p>There are many features in movie or user in movielens 1m dataset.
Each line of rating file just provides a Movie/User id to refer each movie or user.
We process the movie/user feature file first, and pickle the feature (<strong>Meta</strong>) object as a file.</p>
<div class="section" id="meta-config-file">
<h4>Meta config file<a class="headerlink" href="#meta-config-file" title="Permalink to this headline"></a></h4>
<p><strong>Meta config file</strong> is used to specific <strong>HOW</strong> to parse each field in dataset.
It could be translated from field config file, or written by hand.
Its file format could be either json or yaml syntax file. Parser will automatically choose the file format by extension name.</p>
<p>To convert Field config file to meta config file, just run:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="nb">cd</span> demo/recommendation/data
python config_generator.py config.json &gt; meta_config.json
</pre></div>
</div>
<p>The meta config file shows below:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="p">{</span>
  <span class="s2">&quot;meta&quot;</span><span class="p">:</span> <span class="p">{</span>
    <span class="s2">&quot;movie&quot;</span><span class="p">:</span> <span class="p">{</span>
      <span class="s2">&quot;fields&quot;</span><span class="p">:</span> <span class="p">[</span>
        <span class="p">{</span>
          <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;id&quot;</span><span class="p">,</span> 
          <span class="s2">&quot;pos&quot;</span><span class="p">:</span> <span class="mi">0</span>
        <span class="p">},</span> 
        <span class="p">{</span>
          <span class="s2">&quot;regex&quot;</span><span class="p">:</span> <span class="p">{</span>
            <span class="s2">&quot;pattern&quot;</span><span class="p">:</span> <span class="s2">&quot;^(.*)</span><span class="se">\\</span><span class="s2">((</span><span class="se">\\</span><span class="s2">d+)</span><span class="se">\\</span><span class="s2">)$&quot;</span><span class="p">,</span> 
            <span class="s2">&quot;group_id&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> 
            <span class="s2">&quot;strip&quot;</span><span class="p">:</span> <span class="n">true</span>
          <span class="p">},</span> 
          <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="p">{</span>
            <span class="s2">&quot;seq_type&quot;</span><span class="p">:</span> <span class="s2">&quot;sequence&quot;</span><span class="p">,</span> 
            <span class="s2">&quot;name&quot;</span><span class="p">:</span> <span class="s2">&quot;embedding&quot;</span>
          <span class="p">},</span> 
          <span class="s2">&quot;dict&quot;</span><span class="p">:</span> <span class="p">{</span>
            <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;char_based&quot;</span>
          <span class="p">},</span> 
          <span class="s2">&quot;name&quot;</span><span class="p">:</span> <span class="s2">&quot;title&quot;</span><span class="p">,</span> 
          <span class="s2">&quot;pos&quot;</span><span class="p">:</span> <span class="mi">1</span>
        <span class="p">},</span> 
        <span class="p">{</span>
          <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;one_hot_dense&quot;</span><span class="p">,</span> 
          <span class="s2">&quot;dict&quot;</span><span class="p">:</span> <span class="p">{</span>
            <span class="s2">&quot;delimiter&quot;</span><span class="p">:</span> <span class="s2">&quot;|&quot;</span><span class="p">,</span> 
            <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;split&quot;</span>
          <span class="p">},</span> 
          <span class="s2">&quot;name&quot;</span><span class="p">:</span> <span class="s2">&quot;genres&quot;</span><span class="p">,</span> 
          <span class="s2">&quot;pos&quot;</span><span class="p">:</span> <span class="mi">2</span>
        <span class="p">}</span>
      <span class="p">],</span> 
      <span class="s2">&quot;file&quot;</span><span class="p">:</span> <span class="p">{</span>
        <span class="s2">&quot;delimiter&quot;</span><span class="p">:</span> <span class="s2">&quot;::&quot;</span><span class="p">,</span> 
        <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;split&quot;</span><span class="p">,</span> 
        <span class="s2">&quot;name&quot;</span><span class="p">:</span> <span class="s2">&quot;movies.dat&quot;</span>
      <span class="p">}</span>
    <span class="p">},</span> 
    <span class="s2">&quot;user&quot;</span><span class="p">:</span> <span class="p">{</span>
      <span class="s2">&quot;fields&quot;</span><span class="p">:</span> <span class="p">[</span>
        <span class="p">{</span>
          <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;id&quot;</span><span class="p">,</span> 
          <span class="s2">&quot;pos&quot;</span><span class="p">:</span> <span class="mi">0</span>
        <span class="p">},</span> 
        <span class="p">{</span>
          <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;embedding&quot;</span><span class="p">,</span> 
          <span class="s2">&quot;dict&quot;</span><span class="p">:</span> <span class="p">{</span>
            <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;char_based&quot;</span>
          <span class="p">},</span> 
          <span class="s2">&quot;name&quot;</span><span class="p">:</span> <span class="s2">&quot;gender&quot;</span><span class="p">,</span> 
          <span class="s2">&quot;pos&quot;</span><span class="p">:</span> <span class="mi">1</span>
        <span class="p">},</span> 
        <span class="p">{</span>
          <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;embedding&quot;</span><span class="p">,</span> 
          <span class="s2">&quot;dict&quot;</span><span class="p">:</span> <span class="p">{</span>
            <span class="s2">&quot;sort&quot;</span><span class="p">:</span> <span class="n">true</span><span class="p">,</span> 
            <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;whole_content&quot;</span>
          <span class="p">},</span> 
          <span class="s2">&quot;name&quot;</span><span class="p">:</span> <span class="s2">&quot;age&quot;</span><span class="p">,</span> 
          <span class="s2">&quot;pos&quot;</span><span class="p">:</span> <span class="mi">2</span>
        <span class="p">},</span> 
        <span class="p">{</span>
          <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;embedding&quot;</span><span class="p">,</span> 
          <span class="s2">&quot;dict&quot;</span><span class="p">:</span> <span class="p">{</span>
            <span class="s2">&quot;sort&quot;</span><span class="p">:</span> <span class="s2">&quot;true&quot;</span><span class="p">,</span> 
            <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;whole_content&quot;</span>
          <span class="p">},</span> 
          <span class="s2">&quot;name&quot;</span><span class="p">:</span> <span class="s2">&quot;occupation&quot;</span><span class="p">,</span> 
          <span class="s2">&quot;pos&quot;</span><span class="p">:</span> <span class="mi">3</span>
        <span class="p">}</span>
      <span class="p">],</span> 
      <span class="s2">&quot;file&quot;</span><span class="p">:</span> <span class="p">{</span>
        <span class="s2">&quot;delimiter&quot;</span><span class="p">:</span> <span class="s2">&quot;::&quot;</span><span class="p">,</span> 
        <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;split&quot;</span><span class="p">,</span> 
        <span class="s2">&quot;name&quot;</span><span class="p">:</span> <span class="s2">&quot;users.dat&quot;</span>
      <span class="p">}</span>
    <span class="p">}</span>
  <span class="p">}</span>
<span class="p">}</span>
</pre></div>
</div>
<p>There are two kinds of features in meta: movie and user.</p>
<ul class="simple">
<li><dl class="first docutils">
<dt>in movie file, whose name is movies.dat</dt>
<dd><ul class="first last">
<li>we just split each line by &#8221;::&#8221;</li>
<li>pos 0 is id.</li>
<li><dl class="first docutils">
<dt>pos 1 feature:</dt>
<dd><ul class="first last">
<li>name is title.</li>
<li>it uses regex to parse this feature.</li>
<li>it is a char based word embedding feature.</li>
<li>it is a sequence.</li>
</ul>
</dd>
</dl>
</li>
<li><dl class="first docutils">
<dt>pos 2 feature:</dt>
<dd><ul class="first last">
<li>name is genres.</li>
<li>type is one hot dense vector.</li>
<li>dictionary is auto generated by parsing, each key is split by &#8216;|&#8217;</li>
</ul>
</dd>
</dl>
</li>
</ul>
</dd>
</dl>
</li>
<li><dl class="first docutils">
<dt>in user file, whose name is users.dat</dt>
<dd><ul class="first last">
<li>we just split each line by &#8221;::&#8221;</li>
<li>pos 0 is id.</li>
<li><dl class="first docutils">
<dt>pos 1 feature:</dt>
<dd><ul class="first last">
<li>name is gender</li>
<li>just simple char based embedding.</li>
</ul>
</dd>
</dl>
</li>
<li><dl class="first docutils">
<dt>pos 2 feature:</dt>
<dd><ul class="first last">
<li>name is age</li>
<li>just whole word embedding.</li>
<li>embedding id will be sort by word.</li>
</ul>
</dd>
</dl>
</li>
<li><dl class="first docutils">
<dt>pos 3 feature:</dt>
<dd><ul class="first last">
<li>name is occupation.</li>
<li>just simple whole word embedding.</li>
</ul>
</dd>
</dl>
</li>
</ul>
</dd>
</dl>
</li>
</ul>
</div>
</div>
<div class="section" id="meta-file">
<h3>Meta file<a class="headerlink" href="#meta-file" title="Permalink to this headline"></a></h3>
<p>After having meta config file, we can generate <strong>Meta file</strong>, a python pickle object which stores movie/user information.
The following commands could be run to generate it.</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>python meta_generator.py ml-1m meta.bin --config<span class="o">=</span>meta_config.json
</pre></div>
</div>
<p>And the structure of the meta file <code class="code docutils literal"><span class="pre">meta.bin</span></code> is:</p>
<div class="highlight-text"><div class="highlight"><pre><span></span>+--+ movie
|      +--+ __meta__
|      |       +--+ raw_meta  # each feature meta config. list
|      |       |       +
|      |       |       |     # ID Field, we use id as key
|      |       |       +--+ {&#39;count&#39;: 3883, &#39;max&#39;: 3952, &#39;is_key&#39;: True, &#39;type&#39;: &#39;id&#39;, &#39;min&#39;: 1}
|      |       |       |
|      |       |       |     # Titile field, the dictionary list of embedding.
|      |       |       +--+ {&#39;dict&#39;: [ ... ], &#39;type&#39;: &#39;embedding&#39;, &#39;name&#39;: &#39;title&#39;, &#39;seq&#39;: &#39;sequence&#39;}
|      |       |       |
|      |       |       |     # Genres field, the genres dictionary
|      |       |       +--+ {&#39;dict&#39;: [ ... ], &#39;type&#39;: &#39;one_hot_dense&#39;, &#39;name&#39;: &#39;genres&#39;}
|      |       |
|      |       +--+ feature_map [1, 2] # a list for raw_meta index for feature field.
|      |                               # it means there are 2 features for each key.
|      |                               #    * 0 offset of feature is raw_meta[1], Title.
|      |                               #    * 1 offset of feature is raw_meta[2], Genres.
|      |
|      +--+ 1 # movie 1 features
|      |    +
|      |    +---+ [[...], [...]] # title ids, genres dense vector
|      |
|      +--+ 2
|      |
|      +--+ ...
|
+--- user
       +--+ __meta__
       |       +
       |       +--+ raw_meta
       |       |       +
       |       |       +--+ id field as user
       |       |       |
       |       |       +--+ {&#39;dict&#39;: [&#39;F&#39;, &#39;M&#39;], &#39;type&#39;: &#39;embedding&#39;, &#39;name&#39;: &#39;gender&#39;, &#39;seq&#39;: &#39;no_sequence&#39;}
       |       |       |
       |       |       +--+ {&#39;dict&#39;: [&#39;1&#39;, &#39;18&#39;, &#39;25&#39;, &#39;35&#39;, &#39;45&#39;, &#39;50&#39;, &#39;56&#39;], &#39;type&#39;: &#39;embedding&#39;, &#39;name&#39;: &#39;age&#39;, &#39;seq&#39;: &#39;no_sequence&#39;}
       |       |       |
       |       |       +--+ {&#39;dict&#39;: [...], &#39;type&#39;: &#39;embedding&#39;, &#39;name&#39;: &#39;occupation&#39;, &#39;seq&#39;: &#39;no_sequence&#39;}
       |       |
       |       +--+ feature_map [1, 2, 3]
       |
       +--+ 1 # user 1 features
       |
       +--+ 2
       +--+ ...
</pre></div>
</div>
</div>
<div class="section" id="split-training-testing-files">
<h3>Split Training/Testing files<a class="headerlink" href="#split-training-testing-files" title="Permalink to this headline"></a></h3>
<p>We split <code class="code docutils literal"><span class="pre">ml-1m/ratings.dat</span></code> into a training and testing file. The way to split file is for each user, we split the
rating by two parts. So each user in testing file will have some rating information in training file.</p>
<p>Use <code class="code docutils literal"><span class="pre">separate.py</span></code> to separate the training and testing file.</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>python split.py ml-1m/ratings.dat --delimiter<span class="o">=</span><span class="s2">&quot;::&quot;</span> --test_ratio<span class="o">=</span><span class="m">0</span>.1
</pre></div>
</div>
<p>Then two files will be generated: <code class="code docutils literal"><span class="pre">ml-1m/ratings.dat.train</span></code> and <code class="code docutils literal"><span class="pre">ml-1m/rating.data.test</span></code>.
Move them to workspace <code class="code docutils literal"><span class="pre">data</span></code>, shuffle the train file, and prepare the file list for paddle train.</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>shuf ml-1m/ratings.dat.train &gt; ratings.dat.train
cp ml-1m/ratings.dat.test .
<span class="nb">echo</span> <span class="s2">&quot;./data/ratings.dat.train&quot;</span> &gt; train.list
<span class="nb">echo</span> <span class="s2">&quot;./data/ratings.dat.test&quot;</span> &gt; test.list
</pre></div>
</div>
</div>
</div>
<div class="section" id="neural-network-configuration">
<h2>Neural Network Configuration<a class="headerlink" href="#neural-network-configuration" title="Permalink to this headline"></a></h2>
<div class="section" id="trainer-config-file">
<h3>Trainer Config File<a class="headerlink" href="#trainer-config-file" title="Permalink to this headline"></a></h3>
<p>The network structure shows below.</p>
<img alt="rec_regression_network" class="align-center" src="../../_images/rec_regression_network.png" />
<p>The demo&#8217;s neural network config file <code class="code docutils literal"><span class="pre">trainer_config.py</span></code> show as below.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">paddle.trainer_config_helpers</span> <span class="kn">import</span> <span class="o">*</span>

<span class="k">try</span><span class="p">:</span>
    <span class="kn">import</span> <span class="nn">cPickle</span> <span class="kn">as</span> <span class="nn">pickle</span>
<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
    <span class="kn">import</span> <span class="nn">pickle</span>

<span class="n">is_predict</span> <span class="o">=</span> <span class="n">get_config_arg</span><span class="p">(</span><span class="s1">&#39;is_predict&#39;</span><span class="p">,</span> <span class="nb">bool</span><span class="p">,</span> <span class="bp">False</span><span class="p">)</span>

<span class="n">META_FILE</span> <span class="o">=</span> <span class="s1">&#39;data/meta.bin&#39;</span>

<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">META_FILE</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="c1"># load meta file</span>
    <span class="n">meta</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>

<span class="n">settings</span><span class="p">(</span>
    <span class="n">batch_size</span><span class="o">=</span><span class="mi">1600</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">,</span> <span class="n">learning_method</span><span class="o">=</span><span class="n">RMSPropOptimizer</span><span class="p">())</span>


<span class="k">def</span> <span class="nf">construct_feature</span><span class="p">(</span><span class="n">name</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Construct movie/user features.</span>

<span class="sd">    This method read from meta data. Then convert feature to neural network due</span>
<span class="sd">    to feature type. The map relation as follow.</span>

<span class="sd">    * id: embedding =&gt; fc</span>
<span class="sd">    * embedding:</span>
<span class="sd">        is_sequence:  embedding =&gt; context_projection =&gt; fc =&gt; pool</span>
<span class="sd">        not sequence: embedding =&gt; fc</span>
<span class="sd">    * one_hot_dense:  fc =&gt; fc</span>

<span class="sd">    Then gather all features vector, and use a fc layer to combined them as</span>
<span class="sd">    return.</span>

<span class="sd">    :param name: &#39;movie&#39; or &#39;user&#39;</span>
<span class="sd">    :type name: basestring</span>
<span class="sd">    :return: combined feature output</span>
<span class="sd">    :rtype: LayerOutput</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">__meta__</span> <span class="o">=</span> <span class="n">meta</span><span class="p">[</span><span class="n">name</span><span class="p">][</span><span class="s1">&#39;__meta__&#39;</span><span class="p">][</span><span class="s1">&#39;raw_meta&#39;</span><span class="p">]</span>
    <span class="n">fusion</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">each_meta</span> <span class="ow">in</span> <span class="n">__meta__</span><span class="p">:</span>
        <span class="n">type_name</span> <span class="o">=</span> <span class="n">each_meta</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span>
        <span class="n">slot_name</span> <span class="o">=</span> <span class="n">each_meta</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;name&#39;</span><span class="p">,</span> <span class="s1">&#39;</span><span class="si">%s</span><span class="s1">_id&#39;</span> <span class="o">%</span> <span class="n">name</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">type_name</span> <span class="o">==</span> <span class="s1">&#39;id&#39;</span><span class="p">:</span>
            <span class="n">slot_dim</span> <span class="o">=</span> <span class="n">each_meta</span><span class="p">[</span><span class="s1">&#39;max&#39;</span><span class="p">]</span>
            <span class="n">embedding</span> <span class="o">=</span> <span class="n">embedding_layer</span><span class="p">(</span>
                <span class="nb">input</span><span class="o">=</span><span class="n">data_layer</span><span class="p">(</span>
                    <span class="n">slot_name</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">slot_dim</span><span class="p">),</span> <span class="n">size</span><span class="o">=</span><span class="mi">256</span><span class="p">)</span>
            <span class="n">fusion</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fc_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">embedding</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">256</span><span class="p">))</span>
        <span class="k">elif</span> <span class="n">type_name</span> <span class="o">==</span> <span class="s1">&#39;embedding&#39;</span><span class="p">:</span>
            <span class="n">is_seq</span> <span class="o">=</span> <span class="n">each_meta</span><span class="p">[</span><span class="s1">&#39;seq&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;sequence&#39;</span>
            <span class="n">slot_dim</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">each_meta</span><span class="p">[</span><span class="s1">&#39;dict&#39;</span><span class="p">])</span>
            <span class="n">din</span> <span class="o">=</span> <span class="n">data_layer</span><span class="p">(</span><span class="n">slot_name</span><span class="p">,</span> <span class="n">slot_dim</span><span class="p">)</span>
            <span class="n">embedding</span> <span class="o">=</span> <span class="n">embedding_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">din</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">256</span><span class="p">)</span>
            <span class="k">if</span> <span class="n">is_seq</span><span class="p">:</span>
                <span class="n">fusion</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
                    <span class="n">text_conv_pool</span><span class="p">(</span>
                        <span class="nb">input</span><span class="o">=</span><span class="n">embedding</span><span class="p">,</span> <span class="n">context_len</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">hidden_size</span><span class="o">=</span><span class="mi">256</span><span class="p">))</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">fusion</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fc_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">embedding</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">256</span><span class="p">))</span>
        <span class="k">elif</span> <span class="n">type_name</span> <span class="o">==</span> <span class="s1">&#39;one_hot_dense&#39;</span><span class="p">:</span>
            <span class="n">slot_dim</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">each_meta</span><span class="p">[</span><span class="s1">&#39;dict&#39;</span><span class="p">])</span>
            <span class="n">hidden</span> <span class="o">=</span> <span class="n">fc_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">data_layer</span><span class="p">(</span><span class="n">slot_name</span><span class="p">,</span> <span class="n">slot_dim</span><span class="p">),</span> <span class="n">size</span><span class="o">=</span><span class="mi">256</span><span class="p">)</span>
            <span class="n">fusion</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fc_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">hidden</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">256</span><span class="p">))</span>

    <span class="k">return</span> <span class="n">fc_layer</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2">_fusion&quot;</span> <span class="o">%</span> <span class="n">name</span><span class="p">,</span> <span class="nb">input</span><span class="o">=</span><span class="n">fusion</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">256</span><span class="p">)</span>


<span class="n">movie_feature</span> <span class="o">=</span> <span class="n">construct_feature</span><span class="p">(</span><span class="s2">&quot;movie&quot;</span><span class="p">)</span>
<span class="n">user_feature</span> <span class="o">=</span> <span class="n">construct_feature</span><span class="p">(</span><span class="s2">&quot;user&quot;</span><span class="p">)</span>
<span class="n">similarity</span> <span class="o">=</span> <span class="n">cos_sim</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="n">movie_feature</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">user_feature</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">is_predict</span><span class="p">:</span>
    <span class="n">outputs</span><span class="p">(</span>
        <span class="n">regression_cost</span><span class="p">(</span>
            <span class="nb">input</span><span class="o">=</span><span class="n">similarity</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">data_layer</span><span class="p">(</span>
                <span class="s1">&#39;rating&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">1</span><span class="p">)))</span>

    <span class="n">define_py_data_sources2</span><span class="p">(</span>
        <span class="s1">&#39;data/train.list&#39;</span><span class="p">,</span>
        <span class="s1">&#39;data/test.list&#39;</span><span class="p">,</span>
        <span class="n">module</span><span class="o">=</span><span class="s1">&#39;dataprovider&#39;</span><span class="p">,</span>
        <span class="n">obj</span><span class="o">=</span><span class="s1">&#39;process&#39;</span><span class="p">,</span>
        <span class="n">args</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;meta&#39;</span><span class="p">:</span> <span class="n">meta</span><span class="p">})</span>
<span class="k">else</span><span class="p">:</span>
    <span class="n">outputs</span><span class="p">(</span><span class="n">similarity</span><span class="p">)</span>
</pre></div>
</div>
<p>In this <code class="code docutils literal"><span class="pre">trainer_config.py</span></code>, we just map each feature type to
a feature vector, following shows how to map each feature to a vector shows below.</p>
<ul class="simple">
<li><code class="code docutils literal"><span class="pre">id</span></code>: Just simple embedding, and then add to fully connected layer.</li>
<li><dl class="first docutils">
<dt><code class="code docutils literal"><span class="pre">embedding</span></code>:</dt>
<dd><ul class="first last">
<li>if is_sequence, get the embedding and do a text convolutional operation,
get the average pooling result.</li>
<li>if not sequence, get the embedding and add to fully connected layer.</li>
</ul>
</dd>
</dl>
</li>
<li><dl class="first docutils">
<dt><code class="code docutils literal"><span class="pre">one_host_dense</span></code>:</dt>
<dd><ul class="first last">
<li>just two fully connected layer.</li>
</ul>
</dd>
</dl>
</li>
</ul>
<p>Then we combine each features of movie into one movie feature by a
<code class="code docutils literal"><span class="pre">fc_layer</span></code> with multiple inputs, and do the same thing to user features,
get one user feature. Then we calculate the cosine similarity of these two
features.</p>
<p>In these networks, we use several APIs in <a class="reference internal" href="../../api/v1/index_en.html#api-trainer-config"><span class="std std-ref">Model Config API</span></a> . There are</p>
<ul class="simple">
<li>Data Layer, <a class="reference internal" href="../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-data-layer"><span class="std std-ref">data_layer</span></a></li>
<li>Fully Connected Layer, <a class="reference internal" href="../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-fc-layer"><span class="std std-ref">fc_layer</span></a></li>
<li>Embedding Layer, <a class="reference internal" href="../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-embedding-layer"><span class="std std-ref">embedding_layer</span></a></li>
<li>Context Projection Layer, <a class="reference internal" href="../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-context-projection"><span class="std std-ref">context_projection</span></a></li>
<li>Pooling Layer, <a class="reference internal" href="../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-pooling-layer"><span class="std std-ref">pooling_layer</span></a></li>
<li>Cosine Similarity Layer, <a class="reference internal" href="../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-cos-sim"><span class="std std-ref">cos_sim</span></a></li>
T
Deploy to GitHub Pages: 362a14d777ec795f1f6b397c1039ebb594050a10  
Travis CI 已提交
676 
<li>Text Convolution Pooling Layer, <a class="reference internal" href="../../api/v2/config/networks.html#api-trainer-config-helpers-network-text-conv-pool"><span class="std std-ref">text_conv_pool</span></a></li>
T
Deploy to GitHub Pages: 2c54905c3e3154327a8eb57ad96d0770dcc5f435  
Travis CI 已提交
677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 
<li>Declare Python Data Sources <a class="reference internal" href="../../api/v1/trainer_config_helpers/data_sources.html#api-trainer-config-helpers-data-sources"><span class="std std-ref">DataSources</span></a>.</li>
</ul>
</div>
<div class="section" id="data-provider">
<h3>Data Provider<a class="headerlink" href="#data-provider" title="Permalink to this headline"></a></h3>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">paddle.trainer.PyDataProvider2</span> <span class="kn">import</span> <span class="o">*</span>
<span class="kn">import</span> <span class="nn">common_utils</span>  <span class="c1"># parse</span>


<span class="k">def</span> <span class="nf">__list_to_map__</span><span class="p">(</span><span class="n">lst</span><span class="p">):</span>
    <span class="n">ret_val</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
    <span class="k">for</span> <span class="n">each</span> <span class="ow">in</span> <span class="n">lst</span><span class="p">:</span>
        <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">each</span>
        <span class="n">ret_val</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
    <span class="k">return</span> <span class="n">ret_val</span>


<span class="k">def</span> <span class="nf">hook</span><span class="p">(</span><span class="n">settings</span><span class="p">,</span> <span class="n">meta</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Init hook is invoked before process data. It will set obj.slots and store</span>
<span class="sd">    data meta.</span>

<span class="sd">    :param obj: global object. It will passed to process routine.</span>
<span class="sd">    :type obj: object</span>
<span class="sd">    :param meta: the meta file object, which passed from trainer_config. Meta</span>
<span class="sd">                 file record movie/user features.</span>
<span class="sd">    :param kwargs: unused other arguments.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">del</span> <span class="n">kwargs</span>  <span class="c1"># unused kwargs</span>

    <span class="c1"># Header define slots that used for paddle.</span>
    <span class="c1">#    first part is movie features.</span>
    <span class="c1">#    second part is user features.</span>
    <span class="c1">#    final part is rating score.</span>
    <span class="c1"># header is a list of [USE_SEQ_OR_NOT?, SlotType]</span>
    <span class="n">movie_headers</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">common_utils</span><span class="o">.</span><span class="n">meta_to_header</span><span class="p">(</span><span class="n">meta</span><span class="p">,</span> <span class="s1">&#39;movie&#39;</span><span class="p">))</span>
    <span class="n">settings</span><span class="o">.</span><span class="n">movie_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">h</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">h</span> <span class="ow">in</span> <span class="n">movie_headers</span><span class="p">]</span>
    <span class="n">headers</span> <span class="o">=</span> <span class="n">movie_headers</span>
    <span class="n">user_headers</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">common_utils</span><span class="o">.</span><span class="n">meta_to_header</span><span class="p">(</span><span class="n">meta</span><span class="p">,</span> <span class="s1">&#39;user&#39;</span><span class="p">))</span>
    <span class="n">settings</span><span class="o">.</span><span class="n">user_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">h</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">h</span> <span class="ow">in</span> <span class="n">user_headers</span><span class="p">]</span>
    <span class="n">headers</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">user_headers</span><span class="p">)</span>
    <span class="n">headers</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="s2">&quot;rating&quot;</span><span class="p">,</span> <span class="n">dense_vector</span><span class="p">(</span><span class="mi">1</span><span class="p">)))</span>  <span class="c1"># Score</span>

    <span class="c1"># slot types.</span>
    <span class="n">settings</span><span class="o">.</span><span class="n">input_types</span> <span class="o">=</span> <span class="n">__list_to_map__</span><span class="p">(</span><span class="n">headers</span><span class="p">)</span>
    <span class="n">settings</span><span class="o">.</span><span class="n">meta</span> <span class="o">=</span> <span class="n">meta</span>


<span class="nd">@provider</span><span class="p">(</span><span class="n">init_hook</span><span class="o">=</span><span class="n">hook</span><span class="p">,</span> <span class="n">cache</span><span class="o">=</span><span class="n">CacheType</span><span class="o">.</span><span class="n">CACHE_PASS_IN_MEM</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="n">settings</span><span class="p">,</span> <span class="n">filename</span><span class="p">):</span>
    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
        <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">f</span><span class="p">:</span>
            <span class="c1"># Get a rating from file.</span>
            <span class="n">user_id</span><span class="p">,</span> <span class="n">movie_id</span><span class="p">,</span> <span class="n">score</span> <span class="o">=</span> <span class="nb">map</span><span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;::&#39;</span><span class="p">)[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>

            <span class="c1"># Scale score to [-5, +5]</span>
            <span class="n">score</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">score</span><span class="p">)</span> <span class="o">*</span> <span class="mi">2</span> <span class="o">-</span> <span class="mf">5.0</span>

            <span class="c1"># Get movie/user features by movie_id, user_id</span>
            <span class="n">movie_meta</span> <span class="o">=</span> <span class="n">settings</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;movie&#39;</span><span class="p">][</span><span class="n">movie_id</span><span class="p">]</span>
            <span class="n">user_meta</span> <span class="o">=</span> <span class="n">settings</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s1">&#39;user&#39;</span><span class="p">][</span><span class="n">user_id</span><span class="p">]</span>

            <span class="n">outputs</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">&#39;movie_id&#39;</span><span class="p">,</span> <span class="n">movie_id</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)]</span>

            <span class="c1"># Then add movie features</span>
            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">each_meta</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">movie_meta</span><span class="p">):</span>
                <span class="n">outputs</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">settings</span><span class="o">.</span><span class="n">movie_names</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">],</span> <span class="n">each_meta</span><span class="p">))</span>

            <span class="c1"># Then add user id.</span>
            <span class="n">outputs</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="s1">&#39;user_id&#39;</span><span class="p">,</span> <span class="n">user_id</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>

            <span class="c1"># Then add user features.</span>
            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">each_meta</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">user_meta</span><span class="p">):</span>
                <span class="n">outputs</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">settings</span><span class="o">.</span><span class="n">user_names</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">],</span> <span class="n">each_meta</span><span class="p">))</span>

            <span class="c1"># Finally, add score</span>
            <span class="n">outputs</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="s1">&#39;rating&#39;</span><span class="p">,</span> <span class="p">[</span><span class="n">score</span><span class="p">]))</span>
            <span class="c1"># Return data to paddle</span>
            <span class="k">yield</span> <span class="n">__list_to_map__</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span>
</pre></div>
</div>
<p>The data provider just read the meta.bin and rating file, yield each sample for training.
In this <code class="code docutils literal"><span class="pre">dataprovider.py</span></code>, we should set:</p>
<ul class="simple">
<li>obj.slots: The feature types and dimension.</li>
<li>use_seq: Whether this <code class="code docutils literal"><span class="pre">dataprovider.py</span></code> in sequence mode or not.</li>
<li>process: Return each sample of data to <code class="code docutils literal"><span class="pre">paddle</span></code>.</li>
</ul>
<p>The data provider details document see <a class="reference internal" href="../../api/v1/data_provider/pydataprovider2_en.html#api-pydataprovider2"><span class="std std-ref">PyDataProvider2</span></a>.</p>
</div>
</div>
<div class="section" id="train">
<h2>Train<a class="headerlink" href="#train" title="Permalink to this headline"></a></h2>
<p>After prepare data, config network, writting data provider, now we can run paddle training.</p>
<p>The <code class="code docutils literal"><span class="pre">run.sh</span></code> is shown as follow:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>paddle train <span class="se">\</span>
    --config<span class="o">=</span>trainer_config.py <span class="se">\</span>
    --save_dir<span class="o">=</span>./output <span class="se">\</span>
    --use_gpu<span class="o">=</span><span class="nb">false</span> <span class="se">\</span>
    --trainer_count<span class="o">=</span><span class="m">4</span><span class="se">\</span>
    --test_all_data_in_one_period<span class="o">=</span><span class="nb">true</span> <span class="se">\</span>
    --log_period<span class="o">=</span><span class="m">100</span> <span class="se">\</span>
    --dot_period<span class="o">=</span><span class="m">1</span> <span class="se">\</span>
    --num_passes<span class="o">=</span><span class="m">50</span>  <span class="m">2</span>&gt;<span class="p">&amp;</span><span class="m">1</span> <span class="p">|</span> tee <span class="s1">&#39;log.txt&#39;</span>
paddle usage -l log.txt -e <span class="nv">$?</span> -n <span class="s2">&quot;recommendation&quot;</span> &gt;/dev/null <span class="m">2</span>&gt;<span class="p">&amp;</span><span class="m">1</span>
</pre></div>
</div>
<p>It just start a paddle training process, write the log to <code class="code docutils literal"><span class="pre">log.txt</span></code>,
then print it on screen.</p>
<p>Each command line argument in <code class="code docutils literal"><span class="pre">run.sh</span></code>, please refer to the <a class="reference internal" href="../../howto/usage/cmd_parameter/index_en.html#cmd-line-index"><span class="std std-ref">Set Command-line Parameters</span></a> page. The short description of these arguments is shown as follow.</p>
<ul class="simple">
<li>config: Tell paddle which file is neural network configuration.</li>
<li>save_dir: Tell paddle save model into <code class="code docutils literal"><span class="pre">./output</span></code>.</li>
<li>use_gpu: Use gpu or not. Default is false.</li>
<li>trainer_count: The compute thread in one machine.</li>
<li>test_all_data_in_one_period: Test All Data during one test period. Otherwise,
will test a <code class="code docutils literal"><span class="pre">batch_size</span></code> data in one test period.</li>
<li>log_period: Print log after train <code class="code docutils literal"><span class="pre">log_period</span></code> batches.</li>
<li>dot_period: Print a <code class="code docutils literal"><span class="pre">.</span></code> after train <code class="code docutils literal"><span class="pre">dot_period</span></code> batches.</li>
<li>num_passes: Train at most <code class="code docutils literal"><span class="pre">num_passes</span></code>.</li>
</ul>
<p>If training process starts successfully, the output likes follow:</p>
<div class="highlight-text"><div class="highlight"><pre><span></span>I0601 08:07:22.832059 10549 TrainerInternal.cpp:157]  Batch=100 samples=160000 AvgCost=4.13494 CurrentCost=4.13494 Eval:  CurrentEval:

I0601 08:07:50.672627 10549 TrainerInternal.cpp:157]  Batch=200 samples=320000 AvgCost=3.80957 CurrentCost=3.48421 Eval:  CurrentEval:

I0601 08:08:18.877369 10549 TrainerInternal.cpp:157]  Batch=300 samples=480000 AvgCost=3.68145 CurrentCost=3.42519 Eval:  CurrentEval:

I0601 08:08:46.863963 10549 TrainerInternal.cpp:157]  Batch=400 samples=640000 AvgCost=3.6007 CurrentCost=3.35847 Eval:  CurrentEval:

I0601 08:09:15.413025 10549 TrainerInternal.cpp:157]  Batch=500 samples=800000 AvgCost=3.54811 CurrentCost=3.33773 Eval:  CurrentEval:
I0601 08:09:36.058670 10549 TrainerInternal.cpp:181]  Pass=0 Batch=565 samples=902826 AvgCost=3.52368 Eval:
I0601 08:09:46.215489 10549 Tester.cpp:101]  Test samples=97383 cost=3.32155 Eval:
I0601 08:09:46.215966 10549 GradientMachine.cpp:132] Saving parameters to ./output/model/pass-00000
I0601 08:09:46.233397 10549 ParamUtil.cpp:99] save dir ./output/model/pass-00000
I0601 08:09:46.233438 10549 Util.cpp:209] copy trainer_config.py to ./output/model/pass-00000
I0601 08:09:46.233541 10549 ParamUtil.cpp:147] fileName trainer_config.py
</pre></div>
</div>
<p>The model is saved in <code class="code docutils literal"><span class="pre">output/</span></code> directory. You can use <code class="code docutils literal"><span class="pre">Ctrl-C</span></code> to stop training whenever you want.</p>
</div>
<div class="section" id="evaluate-and-predict">
<h2>Evaluate and Predict<a class="headerlink" href="#evaluate-and-predict" title="Permalink to this headline"></a></h2>
<p>After training several passes, you can evaluate them and get the best pass. Just run</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>./evaluate.sh
</pre></div>
</div>
<p>You will see messages like this:</p>
<div class="highlight-text"><div class="highlight"><pre><span></span>Best pass is 00009,  error is 3.06949, which means predict get error as 0.875998002281
evaluating from pass output/pass-00009
</pre></div>
</div>
<p>Then, you can predict what any user will rate a movie. Just run</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>python prediction.py <span class="s1">&#39;output/pass-00009/&#39;</span>
</pre></div>
</div>
<p>Predictor will read user input, and predict scores. It has a command-line user interface as follows:</p>
<div class="highlight-text"><div class="highlight"><pre><span></span>Input movie_id: 9
Input user_id: 4
Prediction Score is 2.56
Input movie_id: 8
Input user_id: 2
Prediction Score is 3.13
</pre></div>
</div>
</div>
</div>


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