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<li class="toctree-l3"><a class="reference internal" href="../beginner/nlp/word_embeddings_tutorial.html#exercise-computing-word-embeddings-continuous-bag-of-words">Exercise: Computing Word Embeddings: Continuous Bag-of-Words</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../beginner/nlp/sequence_models_tutorial.html#exercise-augmenting-the-lstm-part-of-speech-tagger-with-character-level-features">Exercise: Augmenting the LSTM part-of-speech tagger with character-level features</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../beginner/nlp/advanced_tutorial.html">Advanced: Making Dynamic Decisions and the Bi-LSTM CRF</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../beginner/nlp/advanced_tutorial.html#dynamic-versus-static-deep-learning-toolkits">Dynamic versus Static Deep Learning Toolkits</a></li>
<li class="toctree-l3"><a class="reference internal" href="../beginner/nlp/advanced_tutorial.html#bi-lstm-conditional-random-field-discussion">Bi-LSTM Conditional Random Field Discussion</a></li>
<li class="toctree-l3"><a class="reference internal" href="../beginner/nlp/advanced_tutorial.html#implementation-notes">Implementation Notes</a></li>
<li class="toctree-l3"><a class="reference internal" href="../beginner/nlp/advanced_tutorial.html#exercise-a-new-loss-function-for-discriminative-tagging">Exercise: A new loss function for discriminative tagging</a></li>
</ul>
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</ul>
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<p class="caption"><span class="caption-text">Intermediate Tutorials</span></p>
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="char_rnn_classification_tutorial.html">Classifying Names with a Character-Level RNN</a><ul>
<li class="toctree-l2"><a class="reference internal" href="char_rnn_classification_tutorial.html#preparing-the-data">Preparing the Data</a><ul>
<li class="toctree-l3"><a class="reference internal" href="char_rnn_classification_tutorial.html#turning-names-into-tensors">Turning Names into Tensors</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="char_rnn_classification_tutorial.html#creating-the-network">Creating the Network</a></li>
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<li class="toctree-l1 current"><a class="current reference internal" href="#">Generating Names with a Character-Level RNN</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#preparing-the-data">Preparing the Data</a></li>
<li class="toctree-l2"><a class="reference internal" href="#creating-the-network">Creating the Network</a></li>
<li class="toctree-l2"><a class="reference internal" href="#training">Training</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="#plotting-the-losses">Plotting the Losses</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#sampling-the-network">Sampling the Network</a></li>
<li class="toctree-l2"><a class="reference internal" href="#exercises">Exercises</a></li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="seq2seq_translation_tutorial.html">Translation with a Sequence to Sequence Network and Attention</a><ul>
<li class="toctree-l2"><a class="reference internal" href="seq2seq_translation_tutorial.html#loading-data-files">Loading data files</a></li>
<li class="toctree-l2"><a class="reference internal" href="seq2seq_translation_tutorial.html#the-seq2seq-model">The Seq2Seq Model</a><ul>
<li class="toctree-l3"><a class="reference internal" href="seq2seq_translation_tutorial.html#the-encoder">The Encoder</a></li>
<li class="toctree-l3"><a class="reference internal" href="seq2seq_translation_tutorial.html#the-decoder">The Decoder</a><ul>
<li class="toctree-l4"><a class="reference internal" href="seq2seq_translation_tutorial.html#simple-decoder">Simple Decoder</a></li>
<li class="toctree-l4"><a class="reference internal" href="seq2seq_translation_tutorial.html#attention-decoder">Attention Decoder</a></li>
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<div class="section" id="generating-names-with-a-character-level-rnn">
<span id="sphx-glr-intermediate-char-rnn-generation-tutorial-py"></span><h1>Generating Names with a Character-Level RNN<a class="headerlink" href="#generating-names-with-a-character-level-rnn" title="Permalink to this headline">¶</a></h1>
<p><strong>Author</strong>: <a class="reference external" href="https://github.com/spro/practical-pytorch">Sean Robertson</a></p>
<p>In the <a class="reference internal" href="char_rnn_classification_tutorial.html"><span class="doc">last tutorial</span></a>
we used a RNN to classify names into their language of origin. This time
we’ll turn around and generate names from languages.</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">></span> <span class="n">python</span> <span class="n">sample</span><span class="o">.</span><span class="n">py</span> <span class="n">Russian</span> <span class="n">RUS</span>
<span class="n">Rovakov</span>
<span class="n">Uantov</span>
<span class="n">Shavakov</span>
<span class="o">></span> <span class="n">python</span> <span class="n">sample</span><span class="o">.</span><span class="n">py</span> <span class="n">German</span> <span class="n">GER</span>
<span class="n">Gerren</span>
<span class="n">Ereng</span>
<span class="n">Rosher</span>
<span class="o">></span> <span class="n">python</span> <span class="n">sample</span><span class="o">.</span><span class="n">py</span> <span class="n">Spanish</span> <span class="n">SPA</span>
<span class="n">Salla</span>
<span class="n">Parer</span>
<span class="n">Allan</span>
<span class="o">></span> <span class="n">python</span> <span class="n">sample</span><span class="o">.</span><span class="n">py</span> <span class="n">Chinese</span> <span class="n">CHI</span>
<span class="n">Chan</span>
<span class="n">Hang</span>
<span class="n">Iun</span>
</pre></div>
</div>
<p>We are still hand-crafting a small RNN with a few linear layers. The big
difference is instead of predicting a category after reading in all the
letters of a name, we input a category and output one letter at a time.
Recurrently predicting characters to form language (this could also be
done with words or other higher order constructs) is often referred to
as a “language model”.</p>
<p><strong>Recommended Reading:</strong></p>
<p>I assume you have at least installed PyTorch, know Python, and
understand Tensors:</p>
<ul class="simple">
<li><a class="reference external" href="http://pytorch.org/">http://pytorch.org/</a> For installation instructions</li>
<li><a class="reference internal" href="../beginner/deep_learning_60min_blitz.html"><span class="doc">Deep Learning with PyTorch: A 60 Minute Blitz</span></a> to get started with PyTorch in general</li>
<li><a class="reference internal" href="../beginner/pytorch_with_examples.html"><span class="doc">Learning PyTorch with Examples</span></a> for a wide and deep overview</li>
<li><a class="reference internal" href="../beginner/former_torchies_tutorial.html"><span class="doc">PyTorch for former Torch users</span></a> if you are former Lua Torch user</li>
</ul>
<p>It would also be useful to know about RNNs and how they work:</p>
<ul class="simple">
<li><a class="reference external" href="http://karpathy.github.io/2015/05/21/rnn-effectiveness/">The Unreasonable Effectiveness of Recurrent Neural
Networks</a>
shows a bunch of real life examples</li>
<li><a class="reference external" href="http://colah.github.io/posts/2015-08-Understanding-LSTMs/">Understanding LSTM
Networks</a>
is about LSTMs specifically but also informative about RNNs in
general</li>
</ul>
<p>I also suggest the previous tutorial, <a class="reference internal" href="char_rnn_classification_tutorial.html"><span class="doc">Classifying Names with a Character-Level RNN</span></a></p>
<div class="section" id="preparing-the-data">
<h2>Preparing the Data<a class="headerlink" href="#preparing-the-data" title="Permalink to this headline">¶</a></h2>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Download the data from
<a class="reference external" href="https://download.pytorch.org/tutorial/data.zip">here</a>
and extract it to the current directory.</p>
</div>
<p>See the last tutorial for more detail of this process. In short, there
are a bunch of plain text files <code class="docutils literal"><span class="pre">data/names/[Language].txt</span></code> with a
name per line. We split lines into an array, convert Unicode to ASCII,
and end up with a dictionary <code class="docutils literal"><span class="pre">{language:</span> <span class="pre">[names</span> <span class="pre">...]}</span></code>.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">unicode_literals</span><span class="p">,</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">division</span>
<span class="kn">from</span> <span class="nn">io</span> <span class="kn">import</span> <span class="nb">open</span>
<span class="kn">import</span> <span class="nn">glob</span>
<span class="kn">import</span> <span class="nn">unicodedata</span>
<span class="kn">import</span> <span class="nn">string</span>
<span class="n">all_letters</span> <span class="o">=</span> <span class="n">string</span><span class="o">.</span><span class="n">ascii_letters</span> <span class="o">+</span> <span class="s2">" .,;'-"</span>
<span class="n">n_letters</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_letters</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span> <span class="c1"># Plus EOS marker</span>
<span class="k">def</span> <span class="nf">findFiles</span><span class="p">(</span><span class="n">path</span><span class="p">):</span> <span class="k">return</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
<span class="c1"># Turn a Unicode string to plain ASCII, thanks to http://stackoverflow.com/a/518232/2809427</span>
<span class="k">def</span> <span class="nf">unicodeToAscii</span><span class="p">(</span><span class="n">s</span><span class="p">):</span>
<span class="k">return</span> <span class="s1">''</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
<span class="n">c</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">unicodedata</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="s1">'NFD'</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span>
<span class="k">if</span> <span class="n">unicodedata</span><span class="o">.</span><span class="n">category</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> <span class="o">!=</span> <span class="s1">'Mn'</span>
<span class="ow">and</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">all_letters</span>
<span class="p">)</span>
<span class="c1"># Read a file and split into lines</span>
<span class="k">def</span> <span class="nf">readLines</span><span class="p">(</span><span class="n">filename</span><span class="p">):</span>
<span class="n">lines</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">'utf-8'</span><span class="p">)</span><span class="o">.</span><span class="n">read</span><span class="p">()</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="p">)</span>
<span class="k">return</span> <span class="p">[</span><span class="n">unicodeToAscii</span><span class="p">(</span><span class="n">line</span><span class="p">)</span> <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">lines</span><span class="p">]</span>
<span class="c1"># Build the category_lines dictionary, a list of lines per category</span>
<span class="n">category_lines</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">all_categories</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">filename</span> <span class="ow">in</span> <span class="n">findFiles</span><span class="p">(</span><span class="s1">'data/names/*.txt'</span><span class="p">):</span>
<span class="n">category</span> <span class="o">=</span> <span class="n">filename</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">'/'</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">'.'</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">all_categories</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">category</span><span class="p">)</span>
<span class="n">lines</span> <span class="o">=</span> <span class="n">readLines</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span>
<span class="n">category_lines</span><span class="p">[</span><span class="n">category</span><span class="p">]</span> <span class="o">=</span> <span class="n">lines</span>
<span class="n">n_categories</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_categories</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s1">'# categories:'</span><span class="p">,</span> <span class="n">n_categories</span><span class="p">,</span> <span class="n">all_categories</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">unicodeToAscii</span><span class="p">(</span><span class="s2">"O'Néàl"</span><span class="p">))</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-default"><div class="highlight"><pre><span></span><span class="c1"># categories: 18 ['Arabic', 'Chinese', 'Czech', 'Dutch', 'English', 'French', 'German', 'Greek', 'Irish', 'Italian', 'Japanese', 'Korean', 'Polish', 'Portuguese', 'Russian', 'Scottish', 'Spanish', 'Vietnamese']</span>
<span class="n">O</span><span class="s1">'Neal</span>
</pre></div>
</div>
</div>
<div class="section" id="creating-the-network">
<h2>Creating the Network<a class="headerlink" href="#creating-the-network" title="Permalink to this headline">¶</a></h2>
<p>This network extends <a class="reference external" href="#Creating-the-Network">the last tutorial’s RNN</a>
with an extra argument for the category tensor, which is concatenated
along with the others. The category tensor is a one-hot vector just like
the letter input.</p>
<p>We will interpret the output as the probability of the next letter. When
sampling, the most likely output letter is used as the next input
letter.</p>
<p>I added a second linear layer <code class="docutils literal"><span class="pre">o2o</span></code> (after combining hidden and
output) to give it more muscle to work with. There’s also a dropout
layer, which <a class="reference external" href="https://arxiv.org/abs/1207.0580">randomly zeros parts of its
input</a> with a given probability
(here 0.1) and is usually used to fuzz inputs to prevent overfitting.
Here we’re using it towards the end of the network to purposely add some
chaos and increase sampling variety.</p>
<div class="figure">
<img alt="" src="https://i.imgur.com/jzVrf7f.png" />
</div>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="kn">as</span> <span class="nn">nn</span>
<span class="kn">from</span> <span class="nn">torch.autograd</span> <span class="kn">import</span> <span class="n">Variable</span>
<span class="k">class</span> <span class="nc">RNN</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_size</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">output_size</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">RNN</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span> <span class="o">=</span> <span class="n">hidden_size</span>
<span class="bp">self</span><span class="o">.</span><span class="n">i2h</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">n_categories</span> <span class="o">+</span> <span class="n">input_size</span> <span class="o">+</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">i2o</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">n_categories</span> <span class="o">+</span> <span class="n">input_size</span> <span class="o">+</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">output_size</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">o2o</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">hidden_size</span> <span class="o">+</span> <span class="n">output_size</span><span class="p">,</span> <span class="n">output_size</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.1</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">softmax</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">LogSoftmax</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">category</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">hidden</span><span class="p">):</span>
<span class="n">input_combined</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">category</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">hidden</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">hidden</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">i2h</span><span class="p">(</span><span class="n">input_combined</span><span class="p">)</span>
<span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">i2o</span><span class="p">(</span><span class="n">input_combined</span><span class="p">)</span>
<span class="n">output_combined</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">hidden</span><span class="p">,</span> <span class="n">output</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">o2o</span><span class="p">(</span><span class="n">output_combined</span><span class="p">)</span>
<span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
<span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
<span class="k">return</span> <span class="n">output</span><span class="p">,</span> <span class="n">hidden</span>
<span class="k">def</span> <span class="nf">initHidden</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">Variable</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span><span class="p">))</span>
</pre></div>
</div>
</div>
<div class="section" id="training">
<h2>Training<a class="headerlink" href="#training" title="Permalink to this headline">¶</a></h2>
<div class="section" id="preparing-for-training">
<h3>Preparing for Training<a class="headerlink" href="#preparing-for-training" title="Permalink to this headline">¶</a></h3>
<p>First of all, helper functions to get random pairs of (category, line):</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">random</span>
<span class="c1"># Random item from a list</span>
<span class="k">def</span> <span class="nf">randomChoice</span><span class="p">(</span><span class="n">l</span><span class="p">):</span>
<span class="k">return</span> <span class="n">l</span><span class="p">[</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">l</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)]</span>
<span class="c1"># Get a random category and random line from that category</span>
<span class="k">def</span> <span class="nf">randomTrainingPair</span><span class="p">():</span>
<span class="n">category</span> <span class="o">=</span> <span class="n">randomChoice</span><span class="p">(</span><span class="n">all_categories</span><span class="p">)</span>
<span class="n">line</span> <span class="o">=</span> <span class="n">randomChoice</span><span class="p">(</span><span class="n">category_lines</span><span class="p">[</span><span class="n">category</span><span class="p">])</span>
<span class="k">return</span> <span class="n">category</span><span class="p">,</span> <span class="n">line</span>
</pre></div>
</div>
<p>For each timestep (that is, for each letter in a training word) the
inputs of the network will be
<code class="docutils literal"><span class="pre">(category,</span> <span class="pre">current</span> <span class="pre">letter,</span> <span class="pre">hidden</span> <span class="pre">state)</span></code> and the outputs will be
<code class="docutils literal"><span class="pre">(next</span> <span class="pre">letter,</span> <span class="pre">next</span> <span class="pre">hidden</span> <span class="pre">state)</span></code>. So for each training set, we’ll
need the category, a set of input letters, and a set of output/target
letters.</p>
<p>Since we are predicting the next letter from the current letter for each
timestep, the letter pairs are groups of consecutive letters from the
line - e.g. for <code class="docutils literal"><span class="pre">"ABCD<EOS>"</span></code> we would create (“A”, “B”), (“B”, “C”),
(“C”, “D”), (“D”, “EOS”).</p>
<div class="figure">
<img alt="" src="https://i.imgur.com/JH58tXY.png" />
</div>
<p>The category tensor is a <a class="reference external" href="https://en.wikipedia.org/wiki/One-hot">one-hot
tensor</a> of size
<code class="docutils literal"><span class="pre"><1</span> <span class="pre">x</span> <span class="pre">n_categories></span></code>. When training we feed it to the network at every
timestep - this is a design choice, it could have been included as part
of initial hidden state or some other strategy.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># One-hot vector for category</span>
<span class="k">def</span> <span class="nf">categoryTensor</span><span class="p">(</span><span class="n">category</span><span class="p">):</span>
<span class="n">li</span> <span class="o">=</span> <span class="n">all_categories</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">category</span><span class="p">)</span>
<span class="n">tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_categories</span><span class="p">)</span>
<span class="n">tensor</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">li</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">return</span> <span class="n">tensor</span>
<span class="c1"># One-hot matrix of first to last letters (not including EOS) for input</span>
<span class="k">def</span> <span class="nf">inputTensor</span><span class="p">(</span><span class="n">line</span><span class="p">):</span>
<span class="n">tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">line</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="n">n_letters</span><span class="p">)</span>
<span class="k">for</span> <span class="n">li</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">line</span><span class="p">)):</span>
<span class="n">letter</span> <span class="o">=</span> <span class="n">line</span><span class="p">[</span><span class="n">li</span><span class="p">]</span>
<span class="n">tensor</span><span class="p">[</span><span class="n">li</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="n">all_letters</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="n">letter</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">return</span> <span class="n">tensor</span>
<span class="c1"># LongTensor of second letter to end (EOS) for target</span>
<span class="k">def</span> <span class="nf">targetTensor</span><span class="p">(</span><span class="n">line</span><span class="p">):</span>
<span class="n">letter_indexes</span> <span class="o">=</span> <span class="p">[</span><span class="n">all_letters</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="n">li</span><span class="p">])</span> <span class="k">for</span> <span class="n">li</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">line</span><span class="p">))]</span>
<span class="n">letter_indexes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">n_letters</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># EOS</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">(</span><span class="n">letter_indexes</span><span class="p">)</span>
</pre></div>
</div>
<p>For convenience during training we’ll make a <code class="docutils literal"><span class="pre">randomTrainingExample</span></code>
function that fetches a random (category, line) pair and turns them into
the required (category, input, target) tensors.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># Make category, input, and target tensors from a random category, line pair</span>
<span class="k">def</span> <span class="nf">randomTrainingExample</span><span class="p">():</span>
<span class="n">category</span><span class="p">,</span> <span class="n">line</span> <span class="o">=</span> <span class="n">randomTrainingPair</span><span class="p">()</span>
<span class="n">category_tensor</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">categoryTensor</span><span class="p">(</span><span class="n">category</span><span class="p">))</span>
<span class="n">input_line_tensor</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">inputTensor</span><span class="p">(</span><span class="n">line</span><span class="p">))</span>
<span class="n">target_line_tensor</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">targetTensor</span><span class="p">(</span><span class="n">line</span><span class="p">))</span>
<span class="k">return</span> <span class="n">category_tensor</span><span class="p">,</span> <span class="n">input_line_tensor</span><span class="p">,</span> <span class="n">target_line_tensor</span>
</pre></div>
</div>
</div>
<div class="section" id="training-the-network">
<h3>Training the Network<a class="headerlink" href="#training-the-network" title="Permalink to this headline">¶</a></h3>
<p>In contrast to classification, where only the last output is used, we
are making a prediction at every step, so we are calculating loss at
every step.</p>
<p>The magic of autograd allows you to simply sum these losses at each step
and call backward at the end.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">criterion</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span><span class="p">()</span>
<span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">0.0005</span>
<span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="n">category_tensor</span><span class="p">,</span> <span class="n">input_line_tensor</span><span class="p">,</span> <span class="n">target_line_tensor</span><span class="p">):</span>
<span class="n">hidden</span> <span class="o">=</span> <span class="n">rnn</span><span class="o">.</span><span class="n">initHidden</span><span class="p">()</span>
<span class="n">rnn</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
<span class="n">loss</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">input_line_tensor</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">0</span><span class="p">]):</span>
<span class="n">output</span><span class="p">,</span> <span class="n">hidden</span> <span class="o">=</span> <span class="n">rnn</span><span class="p">(</span><span class="n">category_tensor</span><span class="p">,</span> <span class="n">input_line_tensor</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">hidden</span><span class="p">)</span>
<span class="n">loss</span> <span class="o">+=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target_line_tensor</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">rnn</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
<span class="n">p</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">add_</span><span class="p">(</span><span class="o">-</span><span class="n">learning_rate</span><span class="p">,</span> <span class="n">p</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
<span class="k">return</span> <span class="n">output</span><span class="p">,</span> <span class="n">loss</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="n">input_line_tensor</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
</pre></div>
</div>
<p>To keep track of how long training takes I am adding a
<code class="docutils literal"><span class="pre">timeSince(timestamp)</span></code> function which returns a human readable string:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">time</span>
<span class="kn">import</span> <span class="nn">math</span>
<span class="k">def</span> <span class="nf">timeSince</span><span class="p">(</span><span class="n">since</span><span class="p">):</span>
<span class="n">now</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">s</span> <span class="o">=</span> <span class="n">now</span> <span class="o">-</span> <span class="n">since</span>
<span class="n">m</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">s</span> <span class="o">/</span> <span class="mi">60</span><span class="p">)</span>
<span class="n">s</span> <span class="o">-=</span> <span class="n">m</span> <span class="o">*</span> <span class="mi">60</span>
<span class="k">return</span> <span class="s1">'</span><span class="si">%d</span><span class="s1">m </span><span class="si">%d</span><span class="s1">s'</span> <span class="o">%</span> <span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span>
</pre></div>
</div>
<p>Training is business as usual - call train a bunch of times and wait a
few minutes, printing the current time and loss every <code class="docutils literal"><span class="pre">print_every</span></code>
examples, and keeping store of an average loss per <code class="docutils literal"><span class="pre">plot_every</span></code> examples
in <code class="docutils literal"><span class="pre">all_losses</span></code> for plotting later.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">rnn</span> <span class="o">=</span> <span class="n">RNN</span><span class="p">(</span><span class="n">n_letters</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="n">n_letters</span><span class="p">)</span>
<span class="n">n_iters</span> <span class="o">=</span> <span class="mi">100000</span>
<span class="n">print_every</span> <span class="o">=</span> <span class="mi">5000</span>
<span class="n">plot_every</span> <span class="o">=</span> <span class="mi">500</span>
<span class="n">all_losses</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">total_loss</span> <span class="o">=</span> <span class="mi">0</span> <span class="c1"># Reset every plot_every iters</span>
<span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="k">for</span> <span class="nb">iter</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_iters</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
<span class="n">output</span><span class="p">,</span> <span class="n">loss</span> <span class="o">=</span> <span class="n">train</span><span class="p">(</span><span class="o">*</span><span class="n">randomTrainingExample</span><span class="p">())</span>
<span class="n">total_loss</span> <span class="o">+=</span> <span class="n">loss</span>
<span class="k">if</span> <span class="nb">iter</span> <span class="o">%</span> <span class="n">print_every</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">print</span><span class="p">(</span><span class="s1">'</span><span class="si">%s</span><span class="s1"> (</span><span class="si">%d</span><span class="s1"> </span><span class="si">%d%%</span><span class="s1">) </span><span class="si">%.4f</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="n">timeSince</span><span class="p">(</span><span class="n">start</span><span class="p">),</span> <span class="nb">iter</span><span class="p">,</span> <span class="nb">iter</span> <span class="o">/</span> <span class="n">n_iters</span> <span class="o">*</span> <span class="mi">100</span><span class="p">,</span> <span class="n">loss</span><span class="p">))</span>
<span class="k">if</span> <span class="nb">iter</span> <span class="o">%</span> <span class="n">plot_every</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">all_losses</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">total_loss</span> <span class="o">/</span> <span class="n">plot_every</span><span class="p">)</span>
<span class="n">total_loss</span> <span class="o">=</span> <span class="mi">0</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-default"><div class="highlight"><pre><span></span><span class="mi">0</span><span class="n">m</span> <span class="mi">32</span><span class="n">s</span> <span class="p">(</span><span class="mi">5000</span> <span class="mi">5</span><span class="o">%</span><span class="p">)</span> <span class="mf">2.6438</span>
<span class="mi">1</span><span class="n">m</span> <span class="mi">3</span><span class="n">s</span> <span class="p">(</span><span class="mi">10000</span> <span class="mi">10</span><span class="o">%</span><span class="p">)</span> <span class="mf">2.6677</span>
<span class="mi">1</span><span class="n">m</span> <span class="mi">36</span><span class="n">s</span> <span class="p">(</span><span class="mi">15000</span> <span class="mi">15</span><span class="o">%</span><span class="p">)</span> <span class="mf">2.8923</span>
<span class="mi">2</span><span class="n">m</span> <span class="mi">8</span><span class="n">s</span> <span class="p">(</span><span class="mi">20000</span> <span class="mi">20</span><span class="o">%</span><span class="p">)</span> <span class="mf">2.1252</span>
<span class="mi">2</span><span class="n">m</span> <span class="mi">41</span><span class="n">s</span> <span class="p">(</span><span class="mi">25000</span> <span class="mi">25</span><span class="o">%</span><span class="p">)</span> <span class="mf">2.5617</span>
<span class="mi">3</span><span class="n">m</span> <span class="mi">11</span><span class="n">s</span> <span class="p">(</span><span class="mi">30000</span> <span class="mi">30</span><span class="o">%</span><span class="p">)</span> <span class="mf">2.6809</span>
<span class="mi">3</span><span class="n">m</span> <span class="mi">43</span><span class="n">s</span> <span class="p">(</span><span class="mi">35000</span> <span class="mi">35</span><span class="o">%</span><span class="p">)</span> <span class="mf">2.1847</span>
<span class="mi">4</span><span class="n">m</span> <span class="mi">14</span><span class="n">s</span> <span class="p">(</span><span class="mi">40000</span> <span class="mi">40</span><span class="o">%</span><span class="p">)</span> <span class="mf">1.8300</span>
<span class="mi">4</span><span class="n">m</span> <span class="mi">45</span><span class="n">s</span> <span class="p">(</span><span class="mi">45000</span> <span class="mi">45</span><span class="o">%</span><span class="p">)</span> <span class="mf">2.2488</span>
<span class="mi">5</span><span class="n">m</span> <span class="mi">16</span><span class="n">s</span> <span class="p">(</span><span class="mi">50000</span> <span class="mi">50</span><span class="o">%</span><span class="p">)</span> <span class="mf">2.8388</span>
<span class="mi">5</span><span class="n">m</span> <span class="mi">47</span><span class="n">s</span> <span class="p">(</span><span class="mi">55000</span> <span class="mi">55</span><span class="o">%</span><span class="p">)</span> <span class="mf">2.6692</span>
<span class="mi">6</span><span class="n">m</span> <span class="mi">21</span><span class="n">s</span> <span class="p">(</span><span class="mi">60000</span> <span class="mi">60</span><span class="o">%</span><span class="p">)</span> <span class="mf">1.6389</span>
<span class="mi">6</span><span class="n">m</span> <span class="mi">53</span><span class="n">s</span> <span class="p">(</span><span class="mi">65000</span> <span class="mi">65</span><span class="o">%</span><span class="p">)</span> <span class="mf">2.8467</span>
<span class="mi">7</span><span class="n">m</span> <span class="mi">26</span><span class="n">s</span> <span class="p">(</span><span class="mi">70000</span> <span class="mi">70</span><span class="o">%</span><span class="p">)</span> <span class="mf">1.2306</span>
<span class="mi">7</span><span class="n">m</span> <span class="mi">56</span><span class="n">s</span> <span class="p">(</span><span class="mi">75000</span> <span class="mi">75</span><span class="o">%</span><span class="p">)</span> <span class="mf">2.8025</span>
<span class="mi">8</span><span class="n">m</span> <span class="mi">27</span><span class="n">s</span> <span class="p">(</span><span class="mi">80000</span> <span class="mi">80</span><span class="o">%</span><span class="p">)</span> <span class="mf">2.3902</span>
<span class="mi">8</span><span class="n">m</span> <span class="mi">57</span><span class="n">s</span> <span class="p">(</span><span class="mi">85000</span> <span class="mi">85</span><span class="o">%</span><span class="p">)</span> <span class="mf">2.1619</span>
<span class="mi">9</span><span class="n">m</span> <span class="mi">31</span><span class="n">s</span> <span class="p">(</span><span class="mi">90000</span> <span class="mi">90</span><span class="o">%</span><span class="p">)</span> <span class="mf">2.4279</span>
<span class="mi">10</span><span class="n">m</span> <span class="mi">7</span><span class="n">s</span> <span class="p">(</span><span class="mi">95000</span> <span class="mi">95</span><span class="o">%</span><span class="p">)</span> <span class="mf">2.3626</span>
<span class="mi">10</span><span class="n">m</span> <span class="mi">40</span><span class="n">s</span> <span class="p">(</span><span class="mi">100000</span> <span class="mi">100</span><span class="o">%</span><span class="p">)</span> <span class="mf">2.9210</span>
</pre></div>
</div>
</div>
<div class="section" id="plotting-the-losses">
<h3>Plotting the Losses<a class="headerlink" href="#plotting-the-losses" title="Permalink to this headline">¶</a></h3>
<p>Plotting the historical loss from all_losses shows the network
learning:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">matplotlib.ticker</span> <span class="kn">as</span> <span class="nn">ticker</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">all_losses</span><span class="p">)</span>
</pre></div>
</div>
<img alt="../_images/sphx_glr_char_rnn_generation_tutorial_001.png" class="align-center" src="../_images/sphx_glr_char_rnn_generation_tutorial_001.png" />
</div>
</div>
<div class="section" id="sampling-the-network">
<h2>Sampling the Network<a class="headerlink" href="#sampling-the-network" title="Permalink to this headline">¶</a></h2>
<p>To sample we give the network a letter and ask what the next one is,
feed that in as the next letter, and repeat until the EOS token.</p>
<ul class="simple">
<li>Create tensors for input category, starting letter, and empty hidden
state</li>
<li>Create a string <code class="docutils literal"><span class="pre">output_name</span></code> with the starting letter</li>
<li>Up to a maximum output length,<ul>
<li>Feed the current letter to the network</li>
<li>Get the next letter from highest output, and next hidden state</li>
<li>If the letter is EOS, stop here</li>
<li>If a regular letter, add to <code class="docutils literal"><span class="pre">output_name</span></code> and continue</li>
</ul>
</li>
<li>Return the final name</li>
</ul>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Rather than having to give it a starting letter, another
strategy would have been to include a “start of string” token in
training and have the network choose its own starting letter.</p>
</div>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">max_length</span> <span class="o">=</span> <span class="mi">20</span>
<span class="c1"># Sample from a category and starting letter</span>
<span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="n">category</span><span class="p">,</span> <span class="n">start_letter</span><span class="o">=</span><span class="s1">'A'</span><span class="p">):</span>
<span class="n">category_tensor</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">categoryTensor</span><span class="p">(</span><span class="n">category</span><span class="p">))</span>
<span class="nb">input</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">inputTensor</span><span class="p">(</span><span class="n">start_letter</span><span class="p">))</span>
<span class="n">hidden</span> <span class="o">=</span> <span class="n">rnn</span><span class="o">.</span><span class="n">initHidden</span><span class="p">()</span>
<span class="n">output_name</span> <span class="o">=</span> <span class="n">start_letter</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">max_length</span><span class="p">):</span>
<span class="n">output</span><span class="p">,</span> <span class="n">hidden</span> <span class="o">=</span> <span class="n">rnn</span><span class="p">(</span><span class="n">category_tensor</span><span class="p">,</span> <span class="nb">input</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">hidden</span><span class="p">)</span>
<span class="n">topv</span><span class="p">,</span> <span class="n">topi</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">topk</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="n">topi</span> <span class="o">=</span> <span class="n">topi</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
<span class="k">if</span> <span class="n">topi</span> <span class="o">==</span> <span class="n">n_letters</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
<span class="k">break</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">letter</span> <span class="o">=</span> <span class="n">all_letters</span><span class="p">[</span><span class="n">topi</span><span class="p">]</span>
<span class="n">output_name</span> <span class="o">+=</span> <span class="n">letter</span>
<span class="nb">input</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">inputTensor</span><span class="p">(</span><span class="n">letter</span><span class="p">))</span>
<span class="k">return</span> <span class="n">output_name</span>
<span class="c1"># Get multiple samples from one category and multiple starting letters</span>
<span class="k">def</span> <span class="nf">samples</span><span class="p">(</span><span class="n">category</span><span class="p">,</span> <span class="n">start_letters</span><span class="o">=</span><span class="s1">'ABC'</span><span class="p">):</span>
<span class="k">for</span> <span class="n">start_letter</span> <span class="ow">in</span> <span class="n">start_letters</span><span class="p">:</span>
<span class="k">print</span><span class="p">(</span><span class="n">sample</span><span class="p">(</span><span class="n">category</span><span class="p">,</span> <span class="n">start_letter</span><span class="p">))</span>
<span class="n">samples</span><span class="p">(</span><span class="s1">'Russian'</span><span class="p">,</span> <span class="s1">'RUS'</span><span class="p">)</span>
<span class="n">samples</span><span class="p">(</span><span class="s1">'German'</span><span class="p">,</span> <span class="s1">'GER'</span><span class="p">)</span>
<span class="n">samples</span><span class="p">(</span><span class="s1">'Spanish'</span><span class="p">,</span> <span class="s1">'SPA'</span><span class="p">)</span>
<span class="n">samples</span><span class="p">(</span><span class="s1">'Chinese'</span><span class="p">,</span> <span class="s1">'CHI'</span><span class="p">)</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-default"><div class="highlight"><pre><span></span><span class="n">Rovanov</span>
<span class="n">Uantonovo</span>
<span class="n">Sharan</span>
<span class="n">Gerter</span>
<span class="n">Eren</span>
<span class="n">Ronger</span>
<span class="n">Sallan</span>
<span class="n">Pore</span>
<span class="n">Allan</span>
<span class="n">Chan</span>
<span class="n">Ha</span>
<span class="n">Iun</span>
</pre></div>
</div>
</div>
<div class="section" id="exercises">
<h2>Exercises<a class="headerlink" href="#exercises" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>Try with a different dataset of category -> line, for example:<ul>
<li>Fictional series -> Character name</li>
<li>Part of speech -> Word</li>
<li>Country -> City</li>
</ul>
</li>
<li>Use a “start of sentence” token so that sampling can be done without
choosing a start letter</li>
<li>Get better results with a bigger and/or better shaped network<ul>
<li>Try the nn.LSTM and nn.GRU layers</li>
<li>Combine multiple of these RNNs as a higher level network</li>
</ul>
</li>
</ul>
<p><strong>Total running time of the script:</strong> ( 10 minutes 40.884 seconds)</p>
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