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<li class="toctree-l3"><a class="reference internal" href="../beginner/transfer_learning_tutorial.html#visualizing-the-model-predictions">Visualizing the model predictions</a></li>
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</li>
<li class="toctree-l2"><a class="reference internal" href="../beginner/transfer_learning_tutorial.html#finetuning-the-convnet">Finetuning the convnet</a><ul>
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</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../beginner/transfer_learning_tutorial.html#convnet-as-fixed-feature-extractor">ConvNet as fixed feature extractor</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../beginner/transfer_learning_tutorial.html#id1">Train and evaluate</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../beginner/data_loading_tutorial.html#iterating-through-the-dataset">Iterating through the dataset</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../beginner/deep_learning_nlp_tutorial.html">Deep Learning for NLP with Pytorch</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="../beginner/nlp/pytorch_tutorial.html#introduction-to-torch-s-tensor-library">Introduction to Torch’s tensor library</a><ul>
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<li class="toctree-l4"><a class="reference internal" href="../beginner/nlp/pytorch_tutorial.html#reshaping-tensors">Reshaping Tensors</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../beginner/nlp/pytorch_tutorial.html#computation-graphs-and-automatic-differentiation">Computation Graphs and Automatic Differentiation</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../beginner/nlp/deep_learning_tutorial.html">Deep Learning with PyTorch</a><ul>
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<li class="toctree-l4"><a class="reference internal" href="../beginner/nlp/deep_learning_tutorial.html#affine-maps">Affine Maps</a></li>
<li class="toctree-l4"><a class="reference internal" href="../beginner/nlp/deep_learning_tutorial.html#non-linearities">Non-Linearities</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../beginner/nlp/deep_learning_tutorial.html#optimization-and-training">Optimization and Training</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../beginner/nlp/deep_learning_tutorial.html#example-logistic-regression-bag-of-words-classifier">Example: Logistic Regression Bag-of-Words classifier</a></li>
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</ul>
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<li class="toctree-l2"><a class="reference internal" href="../beginner/nlp/word_embeddings_tutorial.html">Word Embeddings: Encoding Lexical Semantics</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../beginner/nlp/word_embeddings_tutorial.html#getting-dense-word-embeddings">Getting Dense Word Embeddings</a></li>
<li class="toctree-l3"><a class="reference internal" href="../beginner/nlp/word_embeddings_tutorial.html#word-embeddings-in-pytorch">Word Embeddings in Pytorch</a></li>
<li class="toctree-l3"><a class="reference internal" href="../beginner/nlp/word_embeddings_tutorial.html#an-example-n-gram-language-modeling">An Example: N-Gram Language Modeling</a></li>
<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>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../beginner/nlp/sequence_models_tutorial.html">Sequence Models and Long-Short Term Memory Networks</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../beginner/nlp/sequence_models_tutorial.html#lstm-s-in-pytorch">LSTM’s in Pytorch</a></li>
<li class="toctree-l3"><a class="reference internal" href="../beginner/nlp/sequence_models_tutorial.html#example-an-lstm-for-part-of-speech-tagging">Example: An LSTM for Part-of-Speech Tagging</a></li>
<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>
</ul>
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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>
</li>
</ul>
</li>
</ul>
<p class="caption"><span class="caption-text">Intermediate Tutorials</span></p>
<ul class="current">
<li class="toctree-l1 current"><a class="current reference internal" href="#">Classifying Names with a Character-Level RNN</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#preparing-the-data">Preparing the Data</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#turning-names-into-tensors">Turning Names into Tensors</a></li>
</ul>
</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>
<li class="toctree-l3"><a class="reference internal" href="#preparing-for-training">Preparing for Training</a></li>
<li class="toctree-l3"><a class="reference internal" href="#training-the-network">Training the Network</a></li>
<li class="toctree-l3"><a class="reference internal" href="#plotting-the-results">Plotting the Results</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#evaluating-the-results">Evaluating the Results</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#running-on-user-input">Running on User Input</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#exercises">Exercises</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="char_rnn_generation_tutorial.html">Generating Names with a Character-Level RNN</a><ul>
<li class="toctree-l2"><a class="reference internal" href="char_rnn_generation_tutorial.html#preparing-the-data">Preparing the Data</a></li>
<li class="toctree-l2"><a class="reference internal" href="char_rnn_generation_tutorial.html#creating-the-network">Creating the Network</a></li>
<li class="toctree-l2"><a class="reference internal" href="char_rnn_generation_tutorial.html#training">Training</a><ul>
<li class="toctree-l3"><a class="reference internal" href="char_rnn_generation_tutorial.html#preparing-for-training">Preparing for Training</a></li>
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<li class="toctree-l3"><a class="reference internal" href="char_rnn_generation_tutorial.html#plotting-the-losses">Plotting the Losses</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="char_rnn_generation_tutorial.html#sampling-the-network">Sampling the Network</a></li>
<li class="toctree-l2"><a class="reference internal" href="char_rnn_generation_tutorial.html#exercises">Exercises</a></li>
</ul>
</li>
<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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</li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="seq2seq_translation_tutorial.html#training">Training</a><ul>
<li class="toctree-l3"><a class="reference internal" href="seq2seq_translation_tutorial.html#preparing-training-data">Preparing Training Data</a></li>
<li class="toctree-l3"><a class="reference internal" href="seq2seq_translation_tutorial.html#training-the-model">Training the Model</a></li>
<li class="toctree-l3"><a class="reference internal" href="seq2seq_translation_tutorial.html#plotting-results">Plotting results</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="seq2seq_translation_tutorial.html#evaluation">Evaluation</a></li>
<li class="toctree-l2"><a class="reference internal" href="seq2seq_translation_tutorial.html#training-and-evaluating">Training and Evaluating</a><ul>
<li class="toctree-l3"><a class="reference internal" href="seq2seq_translation_tutorial.html#visualizing-attention">Visualizing Attention</a></li>
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<li class="toctree-l2"><a class="reference internal" href="seq2seq_translation_tutorial.html#exercises">Exercises</a></li>
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<p class="caption"><span class="caption-text">Advanced Tutorials</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../advanced/neural_style_tutorial.html">Neural Transfer with PyTorch</a><ul>
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<div class="section" id="classifying-names-with-a-character-level-rnn">
<span id="sphx-glr-intermediate-char-rnn-classification-tutorial-py"></span><h1>Classifying Names with a Character-Level RNN<a class="headerlink" href="#classifying-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>We will be building and training a basic character-level RNN to classify
words. A character-level RNN reads words as a series of characters -
outputting a prediction and “hidden state” at each step, feeding its
previous hidden state into each next step. We take the final prediction
to be the output, i.e. which class the word belongs to.</p>
<p>Specifically, we’ll train on a few thousand surnames from 18 languages
of origin, and predict which language a name is from based on the
spelling:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span>$ python predict.py Hinton
(-0.47) Scottish
(-1.52) English
(-3.57) Irish
$ python predict.py Schmidhuber
(-0.19) German
(-2.48) Czech
(-2.68) Dutch
</pre></div>
</div>
<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>
<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>Included in the <code class="docutils literal"><span class="pre">data/names</span></code> directory are 18 text files named as
“[Language].txt”. Each file contains a bunch of names, one name per
line, mostly romanized (but we still need to convert from Unicode to
ASCII).</p>
<p>We’ll end up with a dictionary of lists of names per language,
<code class="docutils literal"><span class="pre">{language:</span> <span class="pre">[names</span> <span class="pre">...]}</span></code>. The generic variables “category” and “line”
(for language and name in our case) are used for later extensibility.</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="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="k">print</span><span class="p">(</span><span class="n">findFiles</span><span class="p">(</span><span class="s1">'data/names/*.txt'</span><span class="p">))</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="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="k">print</span><span class="p">(</span><span class="n">unicodeToAscii</span><span class="p">(</span><span class="s1">'Ślusàrski'</span><span class="p">))</span>
<span class="c1"># Build the category_lines dictionary, a list of names per language</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="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="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>
</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="p">[</span><span class="s1">'data/names/Arabic.txt'</span><span class="p">,</span> <span class="s1">'data/names/Chinese.txt'</span><span class="p">,</span> <span class="s1">'data/names/Czech.txt'</span><span class="p">,</span> <span class="s1">'data/names/Dutch.txt'</span><span class="p">,</span> <span class="s1">'data/names/English.txt'</span><span class="p">,</span> <span class="s1">'data/names/French.txt'</span><span class="p">,</span> <span class="s1">'data/names/German.txt'</span><span class="p">,</span> <span class="s1">'data/names/Greek.txt'</span><span class="p">,</span> <span class="s1">'data/names/Irish.txt'</span><span class="p">,</span> <span class="s1">'data/names/Italian.txt'</span><span class="p">,</span> <span class="s1">'data/names/Japanese.txt'</span><span class="p">,</span> <span class="s1">'data/names/Korean.txt'</span><span class="p">,</span> <span class="s1">'data/names/Polish.txt'</span><span class="p">,</span> <span class="s1">'data/names/Portuguese.txt'</span><span class="p">,</span> <span class="s1">'data/names/Russian.txt'</span><span class="p">,</span> <span class="s1">'data/names/Scottish.txt'</span><span class="p">,</span> <span class="s1">'data/names/Spanish.txt'</span><span class="p">,</span> <span class="s1">'data/names/Vietnamese.txt'</span><span class="p">]</span>
<span class="n">Slusarski</span>
</pre></div>
</div>
<p>Now we have <code class="docutils literal"><span class="pre">category_lines</span></code>, a dictionary mapping each category
(language) to a list of lines (names). We also kept track of
<code class="docutils literal"><span class="pre">all_categories</span></code> (just a list of languages) and <code class="docutils literal"><span class="pre">n_categories</span></code> for
later reference.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">print</span><span class="p">(</span><span class="n">category_lines</span><span class="p">[</span><span class="s1">'Italian'</span><span class="p">][:</span><span class="mi">5</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="p">[</span><span class="s1">'Abandonato'</span><span class="p">,</span> <span class="s1">'Abatangelo'</span><span class="p">,</span> <span class="s1">'Abatantuono'</span><span class="p">,</span> <span class="s1">'Abate'</span><span class="p">,</span> <span class="s1">'Abategiovanni'</span><span class="p">]</span>
</pre></div>
</div>
<div class="section" id="turning-names-into-tensors">
<h3>Turning Names into Tensors<a class="headerlink" href="#turning-names-into-tensors" title="Permalink to this headline">¶</a></h3>
<p>Now that we have all the names organized, we need to turn them into
Tensors to make any use of them.</p>
<p>To represent a single letter, we use a “one-hot vector” of size
<code class="docutils literal"><span class="pre"><1</span> <span class="pre">x</span> <span class="pre">n_letters></span></code>. A one-hot vector is filled with 0s except for a 1
at index of the current letter, e.g. <code class="docutils literal"><span class="pre">"b"</span> <span class="pre">=</span> <span class="pre"><0</span> <span class="pre">1</span> <span class="pre">0</span> <span class="pre">0</span> <span class="pre">0</span> <span class="pre">...></span></code>.</p>
<p>To make a word we join a bunch of those into a 2D matrix
<code class="docutils literal"><span class="pre"><line_length</span> <span class="pre">x</span> <span class="pre">1</span> <span class="pre">x</span> <span class="pre">n_letters></span></code>.</p>
<p>That extra 1 dimension is because PyTorch assumes everything is in
batches - we’re just using a batch size of 1 here.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="c1"># Find letter index from all_letters, e.g. "a" = 0</span>
<span class="k">def</span> <span class="nf">letterToIndex</span><span class="p">(</span><span class="n">letter</span><span class="p">):</span>
<span class="k">return</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="c1"># Just for demonstration, turn a letter into a <1 x n_letters> Tensor</span>
<span class="k">def</span> <span class="nf">letterToTensor</span><span class="p">(</span><span class="n">letter</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_letters</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">letterToIndex</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"># Turn a line into a <line_length x 1 x n_letters>,</span>
<span class="c1"># or an array of one-hot letter vectors</span>
<span class="k">def</span> <span class="nf">lineToTensor</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="p">,</span> <span class="n">letter</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">line</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">letterToIndex</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="k">print</span><span class="p">(</span><span class="n">letterToTensor</span><span class="p">(</span><span class="s1">'J'</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="n">lineToTensor</span><span class="p">(</span><span class="s1">'Jones'</span><span class="p">)</span><span class="o">.</span><span class="n">size</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">Columns</span> <span class="mi">0</span> <span class="n">to</span> <span class="mi">12</span>
<span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span>
<span class="n">Columns</span> <span class="mi">13</span> <span class="n">to</span> <span class="mi">25</span>
<span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span>
<span class="n">Columns</span> <span class="mi">26</span> <span class="n">to</span> <span class="mi">38</span>
<span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span>
<span class="n">Columns</span> <span class="mi">39</span> <span class="n">to</span> <span class="mi">51</span>
<span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span>
<span class="n">Columns</span> <span class="mi">52</span> <span class="n">to</span> <span class="mi">56</span>
<span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span>
<span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span> <span class="n">of</span> <span class="n">size</span> <span class="mi">1</span><span class="n">x57</span><span class="p">]</span>
<span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">([</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">57</span><span class="p">])</span>
</pre></div>
</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>Before autograd, creating a recurrent neural network in Torch involved
cloning the parameters of a layer over several timesteps. The layers
held hidden state and gradients which are now entirely handled by the
graph itself. This means you can implement a RNN in a very “pure” way,
as regular feed-forward layers.</p>
<p>This RNN module (mostly copied from <a class="reference external" href="https://github.com/pytorch/tutorials/blob/master/Introduction%20to%20PyTorch%20for%20former%20Torchies.ipynb">the PyTorch for Torch users
tutorial</a>)
is just 2 linear layers which operate on an input and hidden state, with
a LogSoftmax layer after the output.</p>
<div class="figure">
<img alt="" src="https://i.imgur.com/Z2xbySO.png" />
</div>
<div class="highlight-python"><div class="highlight"><pre><span></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">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">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">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="nb">input</span><span class="p">,</span> <span class="n">hidden</span><span class="p">):</span>
<span class="n">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="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">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">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">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>
<span class="n">n_hidden</span> <span class="o">=</span> <span class="mi">128</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="n">n_hidden</span><span class="p">,</span> <span class="n">n_categories</span><span class="p">)</span>
</pre></div>
</div>
<p>To run a step of this network we need to pass an input (in our case, the
Tensor for the current letter) and a previous hidden state (which we
initialize as zeros at first). We’ll get back the output (probability of
each language) and a next hidden state (which we keep for the next
step).</p>
<p>Remember that PyTorch modules operate on Variables rather than straight
up Tensors.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="nb">input</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">letterToTensor</span><span class="p">(</span><span class="s1">'A'</span><span class="p">))</span>
<span class="n">hidden</span> <span class="o">=</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="n">n_hidden</span><span class="p">))</span>
<span class="n">output</span><span class="p">,</span> <span class="n">next_hidden</span> <span class="o">=</span> <span class="n">rnn</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">hidden</span><span class="p">)</span>
</pre></div>
</div>
<p>For the sake of efficiency we don’t want to be creating a new Tensor for
every step, so we will use <code class="docutils literal"><span class="pre">lineToTensor</span></code> instead of
<code class="docutils literal"><span class="pre">letterToTensor</span></code> and use slices. This could be further optimized by
pre-computing batches of Tensors.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="nb">input</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">lineToTensor</span><span class="p">(</span><span class="s1">'Albert'</span><span class="p">))</span>
<span class="n">hidden</span> <span class="o">=</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="n">n_hidden</span><span class="p">))</span>
<span class="n">output</span><span class="p">,</span> <span class="n">next_hidden</span> <span class="o">=</span> <span class="n">rnn</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="k">print</span><span class="p">(</span><span class="n">output</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">Variable</span> <span class="n">containing</span><span class="p">:</span>
<span class="n">Columns</span> <span class="mi">0</span> <span class="n">to</span> <span class="mi">9</span>
<span class="o">-</span><span class="mf">2.9427</span> <span class="o">-</span><span class="mf">2.8834</span> <span class="o">-</span><span class="mf">2.8105</span> <span class="o">-</span><span class="mf">2.9505</span> <span class="o">-</span><span class="mf">2.9144</span> <span class="o">-</span><span class="mf">3.0235</span> <span class="o">-</span><span class="mf">2.8622</span> <span class="o">-</span><span class="mf">2.9749</span> <span class="o">-</span><span class="mf">2.9168</span> <span class="o">-</span><span class="mf">2.8478</span>
<span class="n">Columns</span> <span class="mi">10</span> <span class="n">to</span> <span class="mi">17</span>
<span class="o">-</span><span class="mf">2.8252</span> <span class="o">-</span><span class="mf">2.9027</span> <span class="o">-</span><span class="mf">2.8036</span> <span class="o">-</span><span class="mf">2.8963</span> <span class="o">-</span><span class="mf">2.8294</span> <span class="o">-</span><span class="mf">2.9595</span> <span class="o">-</span><span class="mf">2.8678</span> <span class="o">-</span><span class="mf">2.8471</span>
<span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span> <span class="n">of</span> <span class="n">size</span> <span class="mi">1</span><span class="n">x18</span><span class="p">]</span>
</pre></div>
</div>
<p>As you can see the output is a <code class="docutils literal"><span class="pre"><1</span> <span class="pre">x</span> <span class="pre">n_categories></span></code> Tensor, where
every item is the likelihood of that category (higher is more likely).</p>
</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>Before going into training we should make a few helper functions. The
first is to interpret the output of the network, which we know to be a
likelihood of each category. We can use <code class="docutils literal"><span class="pre">Tensor.topk</span></code> to get the index
of the greatest value:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">categoryFromOutput</span><span class="p">(</span><span class="n">output</span><span class="p">):</span>
<span class="n">top_n</span><span class="p">,</span> <span class="n">top_i</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="c1"># Tensor out of Variable with .data</span>
<span class="n">category_i</span> <span class="o">=</span> <span class="n">top_i</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">return</span> <span class="n">all_categories</span><span class="p">[</span><span class="n">category_i</span><span class="p">],</span> <span class="n">category_i</span>
<span class="k">print</span><span class="p">(</span><span class="n">categoryFromOutput</span><span class="p">(</span><span class="n">output</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="p">(</span><span class="s1">'Polish'</span><span class="p">,</span> <span class="mi">12</span><span class="p">)</span>
</pre></div>
</div>
<p>We will also want a quick way to get a training example (a name and its
language):</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">random</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="k">def</span> <span class="nf">randomTrainingExample</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="n">category_tensor</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">([</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">line_tensor</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">lineToTensor</span><span class="p">(</span><span class="n">line</span><span class="p">))</span>
<span class="k">return</span> <span class="n">category</span><span class="p">,</span> <span class="n">line</span><span class="p">,</span> <span class="n">category_tensor</span><span class="p">,</span> <span class="n">line_tensor</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="mi">10</span><span class="p">):</span>
<span class="n">category</span><span class="p">,</span> <span class="n">line</span><span class="p">,</span> <span class="n">category_tensor</span><span class="p">,</span> <span class="n">line_tensor</span> <span class="o">=</span> <span class="n">randomTrainingExample</span><span class="p">()</span>
<span class="k">print</span><span class="p">(</span><span class="s1">'category ='</span><span class="p">,</span> <span class="n">category</span><span class="p">,</span> <span class="s1">'/ line ='</span><span class="p">,</span> <span class="n">line</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">category</span> <span class="o">=</span> <span class="n">Russian</span> <span class="o">/</span> <span class="n">line</span> <span class="o">=</span> <span class="n">Zavodskoi</span>
<span class="n">category</span> <span class="o">=</span> <span class="n">French</span> <span class="o">/</span> <span class="n">line</span> <span class="o">=</span> <span class="n">Moreau</span>
<span class="n">category</span> <span class="o">=</span> <span class="n">Scottish</span> <span class="o">/</span> <span class="n">line</span> <span class="o">=</span> <span class="n">King</span>
<span class="n">category</span> <span class="o">=</span> <span class="n">Japanese</span> <span class="o">/</span> <span class="n">line</span> <span class="o">=</span> <span class="n">Toyotomi</span>
<span class="n">category</span> <span class="o">=</span> <span class="n">Vietnamese</span> <span class="o">/</span> <span class="n">line</span> <span class="o">=</span> <span class="n">Thao</span>
<span class="n">category</span> <span class="o">=</span> <span class="n">Arabic</span> <span class="o">/</span> <span class="n">line</span> <span class="o">=</span> <span class="n">Amari</span>
<span class="n">category</span> <span class="o">=</span> <span class="n">Japanese</span> <span class="o">/</span> <span class="n">line</span> <span class="o">=</span> <span class="n">Toshitala</span>
<span class="n">category</span> <span class="o">=</span> <span class="n">Arabic</span> <span class="o">/</span> <span class="n">line</span> <span class="o">=</span> <span class="n">Attia</span>
<span class="n">category</span> <span class="o">=</span> <span class="n">Dutch</span> <span class="o">/</span> <span class="n">line</span> <span class="o">=</span> <span class="n">Silje</span>
<span class="n">category</span> <span class="o">=</span> <span class="n">Korean</span> <span class="o">/</span> <span class="n">line</span> <span class="o">=</span> <span class="n">Li</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>Now all it takes to train this network is show it a bunch of examples,
have it make guesses, and tell it if it’s wrong.</p>
<p>For the loss function <code class="docutils literal"><span class="pre">nn.NLLLoss</span></code> is appropriate, since the last
layer of the RNN is <code class="docutils literal"><span class="pre">nn.LogSoftmax</span></code>.</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>
</pre></div>
</div>
<p>Each loop of training will:</p>
<ul class="simple">
<li>Create input and target tensors</li>
<li>Create a zeroed initial hidden state</li>
<li>Read each letter in and<ul>
<li>Keep hidden state for next letter</li>
</ul>
</li>
<li>Compare final output to target</li>
<li>Back-propagate</li>
<li>Return the output and loss</li>
</ul>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">0.005</span> <span class="c1"># If you set this too high, it might explode. If too low, it might not learn</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">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="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">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">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">category_tensor</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="c1"># Add parameters' gradients to their values, multiplied by learning rate</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>
</pre></div>
</div>
<p>Now we just have to run that with a bunch of examples. Since the
<code class="docutils literal"><span class="pre">train</span></code> function returns both the output and loss we can print its
guesses and also keep track of loss for plotting. Since there are 1000s
of examples we print only every <code class="docutils literal"><span class="pre">print_every</span></code> examples, and take an
average of the loss.</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="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">1000</span>
<span class="c1"># Keep track of losses for plotting</span>
<span class="n">current_loss</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">all_losses</span> <span class="o">=</span> <span class="p">[]</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>
<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">category</span><span class="p">,</span> <span class="n">line</span><span class="p">,</span> <span class="n">category_tensor</span><span class="p">,</span> <span class="n">line_tensor</span> <span class="o">=</span> <span class="n">randomTrainingExample</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="n">category_tensor</span><span class="p">,</span> <span class="n">line_tensor</span><span class="p">)</span>
<span class="n">current_loss</span> <span class="o">+=</span> <span class="n">loss</span>
<span class="c1"># Print iter number, loss, name and guess</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="n">guess</span><span class="p">,</span> <span class="n">guess_i</span> <span class="o">=</span> <span class="n">categoryFromOutput</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
<span class="n">correct</span> <span class="o">=</span> <span class="s1">'✓'</span> <span class="k">if</span> <span class="n">guess</span> <span class="o">==</span> <span class="n">category</span> <span class="k">else</span> <span class="s1">'✗ (</span><span class="si">%s</span><span class="s1">)'</span> <span class="o">%</span> <span class="n">category</span>
<span class="k">print</span><span class="p">(</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">%s</span><span class="s1">) </span><span class="si">%.4f</span><span class="s1"> </span><span class="si">%s</span><span class="s1"> / </span><span class="si">%s</span><span class="s1"> </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</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">timeSince</span><span class="p">(</span><span class="n">start</span><span class="p">),</span> <span class="n">loss</span><span class="p">,</span> <span class="n">line</span><span class="p">,</span> <span class="n">guess</span><span class="p">,</span> <span class="n">correct</span><span class="p">))</span>
<span class="c1"># Add current loss avg to list of losses</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">current_loss</span> <span class="o">/</span> <span class="n">plot_every</span><span class="p">)</span>
<span class="n">current_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>5000 5% (0m 13s) 1.9382 Adelardi / Italian ✓
10000 10% (0m 26s) 2.2294 Hanzlik / Russian ✗ (Czech)
15000 15% (0m 38s) 1.3864 Hanari / Japanese ✓
20000 20% (0m 51s) 2.8817 Sedmikova / Italian ✗ (Czech)
25000 25% (1m 6s) 2.6150 Ryan / Irish ✗ (English)
30000 30% (1m 20s) 2.7960 Abascal / Irish ✗ (Spanish)
35000 35% (1m 33s) 1.1576 Bilinsky / Polish ✗ (Russian)
40000 40% (1m 47s) 1.4279 Larenz / German ✓
45000 45% (2m 0s) 0.8948 Chavez / Spanish ✓
50000 50% (2m 13s) 1.2643 Bian / Vietnamese ✗ (Chinese)
55000 55% (2m 26s) 0.2171 Hentov / Russian ✓
60000 60% (2m 39s) 1.6691 Santos / Greek ✗ (Portuguese)
65000 65% (2m 51s) 2.1761 Richelieu / Scottish ✗ (French)
70000 70% (3m 4s) 1.3368 Chang / Chinese ✗ (Korean)
75000 75% (3m 18s) 1.7949 Zambrano / Vietnamese ✗ (Italian)
80000 80% (3m 31s) 1.7938 Felton / Scottish ✗ (English)
85000 85% (3m 46s) 1.1180 Klimek / Czech ✗ (Polish)
90000 90% (4m 0s) 3.0799 Kudrna / Italian ✗ (Czech)
95000 95% (4m 15s) 0.3723 Nowak / Polish ✓
100000 100% (4m 28s) 0.4955 Macdonald / Scottish ✓
</pre></div>
</div>
</div>
<div class="section" id="plotting-the-results">
<h3>Plotting the Results<a class="headerlink" href="#plotting-the-results" title="Permalink to this headline">¶</a></h3>
<p>Plotting the historical loss from <code class="docutils literal"><span class="pre">all_losses</span></code> 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_classification_tutorial_001.png" class="align-center" src="../_images/sphx_glr_char_rnn_classification_tutorial_001.png" />
</div>
</div>
<div class="section" id="evaluating-the-results">
<h2>Evaluating the Results<a class="headerlink" href="#evaluating-the-results" title="Permalink to this headline">¶</a></h2>
<p>To see how well the network performs on different categories, we will
create a confusion matrix, indicating for every actual language (rows)
which language the network guesses (columns). To calculate the confusion
matrix a bunch of samples are run through the network with
<code class="docutils literal"><span class="pre">evaluate()</span></code>, which is the same as <code class="docutils literal"><span class="pre">train()</span></code> minus the backprop.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># Keep track of correct guesses in a confusion matrix</span>
<span class="n">confusion</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="n">n_categories</span><span class="p">,</span> <span class="n">n_categories</span><span class="p">)</span>
<span class="n">n_confusion</span> <span class="o">=</span> <span class="mi">10000</span>
<span class="c1"># Just return an output given a line</span>
<span class="k">def</span> <span class="nf">evaluate</span><span class="p">(</span><span class="n">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="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">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">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="k">return</span> <span class="n">output</span>
<span class="c1"># Go through a bunch of examples and record which are correctly guessed</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">n_confusion</span><span class="p">):</span>
<span class="n">category</span><span class="p">,</span> <span class="n">line</span><span class="p">,</span> <span class="n">category_tensor</span><span class="p">,</span> <span class="n">line_tensor</span> <span class="o">=</span> <span class="n">randomTrainingExample</span><span class="p">()</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">line_tensor</span><span class="p">)</span>
<span class="n">guess</span><span class="p">,</span> <span class="n">guess_i</span> <span class="o">=</span> <span class="n">categoryFromOutput</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
<span class="n">category_i</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">confusion</span><span class="p">[</span><span class="n">category_i</span><span class="p">][</span><span class="n">guess_i</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="c1"># Normalize by dividing every row by its sum</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">n_categories</span><span class="p">):</span>
<span class="n">confusion</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">confusion</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/</span> <span class="n">confusion</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="c1"># Set up plot</span>
<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">)</span>
<span class="n">cax</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">matshow</span><span class="p">(</span><span class="n">confusion</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
<span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cax</span><span class="p">)</span>
<span class="c1"># Set up axes</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">([</span><span class="s1">''</span><span class="p">]</span> <span class="o">+</span> <span class="n">all_categories</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">90</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">([</span><span class="s1">''</span><span class="p">]</span> <span class="o">+</span> <span class="n">all_categories</span><span class="p">)</span>
<span class="c1"># Force label at every tick</span>
<span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span><span class="n">ticker</span><span class="o">.</span><span class="n">MultipleLocator</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span><span class="n">ticker</span><span class="o">.</span><span class="n">MultipleLocator</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
<span class="c1"># sphinx_gallery_thumbnail_number = 2</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
<img alt="../_images/sphx_glr_char_rnn_classification_tutorial_002.png" class="align-center" src="../_images/sphx_glr_char_rnn_classification_tutorial_002.png" />
<p>You can pick out bright spots off the main axis that show which
languages it guesses incorrectly, e.g. Chinese for Korean, and Spanish
for Italian. It seems to do very well with Greek, and very poorly with
English (perhaps because of overlap with other languages).</p>
<div class="section" id="running-on-user-input">
<h3>Running on User Input<a class="headerlink" href="#running-on-user-input" title="Permalink to this headline">¶</a></h3>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="n">input_line</span><span class="p">,</span> <span class="n">n_predictions</span><span class="o">=</span><span class="mi">3</span><span class="p">):</span>
<span class="k">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">> </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="n">input_line</span><span class="p">)</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">Variable</span><span class="p">(</span><span class="n">lineToTensor</span><span class="p">(</span><span class="n">input_line</span><span class="p">)))</span>
<span class="c1"># Get top N categories</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="n">n_predictions</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="bp">True</span><span class="p">)</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="p">[]</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">n_predictions</span><span class="p">):</span>
<span class="n">value</span> <span class="o">=</span> <span class="n">topv</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">]</span>
<span class="n">category_index</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="n">i</span><span class="p">]</span>
<span class="k">print</span><span class="p">(</span><span class="s1">'(</span><span class="si">%.2f</span><span class="s1">) </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">all_categories</span><span class="p">[</span><span class="n">category_index</span><span class="p">]))</span>
<span class="n">predictions</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">value</span><span class="p">,</span> <span class="n">all_categories</span><span class="p">[</span><span class="n">category_index</span><span class="p">]])</span>
<span class="n">predict</span><span class="p">(</span><span class="s1">'Dovesky'</span><span class="p">)</span>
<span class="n">predict</span><span class="p">(</span><span class="s1">'Jackson'</span><span class="p">)</span>
<span class="n">predict</span><span class="p">(</span><span class="s1">'Satoshi'</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="o">></span> <span class="n">Dovesky</span>
<span class="p">(</span><span class="o">-</span><span class="mf">0.62</span><span class="p">)</span> <span class="n">Russian</span>
<span class="p">(</span><span class="o">-</span><span class="mf">1.20</span><span class="p">)</span> <span class="n">Czech</span>
<span class="p">(</span><span class="o">-</span><span class="mf">2.80</span><span class="p">)</span> <span class="n">Polish</span>
<span class="o">></span> <span class="n">Jackson</span>
<span class="p">(</span><span class="o">-</span><span class="mf">0.77</span><span class="p">)</span> <span class="n">Scottish</span>
<span class="p">(</span><span class="o">-</span><span class="mf">1.09</span><span class="p">)</span> <span class="n">English</span>
<span class="p">(</span><span class="o">-</span><span class="mf">2.33</span><span class="p">)</span> <span class="n">Russian</span>
<span class="o">></span> <span class="n">Satoshi</span>
<span class="p">(</span><span class="o">-</span><span class="mf">1.28</span><span class="p">)</span> <span class="n">Italian</span>
<span class="p">(</span><span class="o">-</span><span class="mf">1.58</span><span class="p">)</span> <span class="n">Japanese</span>
<span class="p">(</span><span class="o">-</span><span class="mf">1.98</span><span class="p">)</span> <span class="n">Arabic</span>
</pre></div>
</div>
<p>The final versions of the scripts <a class="reference external" href="https://github.com/spro/practical-pytorch/tree/master/char-rnn-classification">in the Practical PyTorch
repo</a>
split the above code into a few files:</p>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">data.py</span></code> (loads files)</li>
<li><code class="docutils literal"><span class="pre">model.py</span></code> (defines the RNN)</li>
<li><code class="docutils literal"><span class="pre">train.py</span></code> (runs training)</li>
<li><code class="docutils literal"><span class="pre">predict.py</span></code> (runs <code class="docutils literal"><span class="pre">predict()</span></code> with command line arguments)</li>
<li><code class="docutils literal"><span class="pre">server.py</span></code> (serve prediction as a JSON API with bottle.py)</li>
</ul>
<p>Run <code class="docutils literal"><span class="pre">train.py</span></code> to train and save the network.</p>
<p>Run <code class="docutils literal"><span class="pre">predict.py</span></code> with a name to view predictions:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span>$ python predict.py Hazaki
(-0.42) Japanese
(-1.39) Polish
(-3.51) Czech
</pre></div>
</div>
<p>Run <code class="docutils literal"><span class="pre">server.py</span></code> and visit <a class="reference external" href="http://localhost:5533/Yourname">http://localhost:5533/Yourname</a> to get JSON
output of predictions.</p>
</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 line -> category, for example:<ul>
<li>Any word -> language</li>
<li>First name -> gender</li>
<li>Character name -> writer</li>
<li>Page title -> blog or subreddit</li>
</ul>
</li>
<li>Get better results with a bigger and/or better shaped network<ul>
<li>Add more linear layers</li>
<li>Try the <code class="docutils literal"><span class="pre">nn.LSTM</span></code> and <code class="docutils literal"><span class="pre">nn.GRU</span></code> layers</li>