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<!DOCTYPE html>
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<title>Chapter 5 Reccurent Neural Networks (RNN) | Natural Language Processing with R</title>
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<li><a href="./">NLP with R</a></li>
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<li class="chapter" data-level="1" data-path="index.html"><a href="index.html"><i class="fa fa-check"></i><b>1</b> Introduction</a></li>
<li class="chapter" data-level="2" data-path="text-processing.html"><a href="text-processing.html"><i class="fa fa-check"></i><b>2</b> Text processing</a><ul>
<li class="chapter" data-level="2.1" data-path="text-processing.html"><a href="text-processing.html#text-data"><i class="fa fa-check"></i><b>2.1</b> Text data</a></li>
<li class="chapter" data-level="2.2" data-path="text-processing.html"><a href="text-processing.html#nlp-applications"><i class="fa fa-check"></i><b>2.2</b> NLP applications</a></li>
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<li class="chapter" data-level="2.5" data-path="text-processing.html"><a href="text-processing.html#words-frequencies"><i class="fa fa-check"></i><b>2.5</b> Words frequencies</a></li>
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<li class="chapter" data-level="3" data-path="Word-embeddings.html"><a href="Word-embeddings.html"><i class="fa fa-check"></i><b>3</b> Word embeddings</a><ul>
<li class="chapter" data-level="3.1" data-path="Word-embeddings.html"><a href="Word-embeddings.html#vectorizing-text"><i class="fa fa-check"></i><b>3.1</b> Vectorizing text</a></li>
<li class="chapter" data-level="3.2" data-path="Word-embeddings.html"><a href="Word-embeddings.html#one-hot-encoding"><i class="fa fa-check"></i><b>3.2</b> One-hot encoding</a></li>
<li class="chapter" data-level="3.3" data-path="Word-embeddings.html"><a href="Word-embeddings.html#word-embeddings-methods"><i class="fa fa-check"></i><b>3.3</b> Word embeddings methods</a><ul>
<li class="chapter" data-level="3.3.1" data-path="Word-embeddings.html"><a href="Word-embeddings.html#learn-world-embeddings"><i class="fa fa-check"></i><b>3.3.1</b> Learn world embeddings</a></li>
<li class="chapter" data-level="3.3.2" data-path="Word-embeddings.html"><a href="Word-embeddings.html#pre-trained-word-embeddings"><i class="fa fa-check"></i><b>3.3.2</b> Pre-trained word embeddings</a></li>
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<li class="chapter" data-level="3.4" data-path="Word-embeddings.html"><a href="Word-embeddings.html#applications"><i class="fa fa-check"></i><b>3.4</b> Applications</a><ul>
<li class="chapter" data-level="3.4.1" data-path="Word-embeddings.html"><a href="Word-embeddings.html#using-skip-gram"><i class="fa fa-check"></i><b>3.4.1</b> Using Skip-Gram</a></li>
<li class="chapter" data-level="3.4.2" data-path="Word-embeddings.html"><a href="Word-embeddings.html#using-glove"><i class="fa fa-check"></i><b>3.4.2</b> Using GloVe</a></li>
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<li class="chapter" data-level="3.5" data-path="Word-embeddings.html"><a href="Word-embeddings.html#references"><i class="fa fa-check"></i><b>3.5</b> references</a></li>
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<li class="chapter" data-level="4" data-path="text-classification.html"><a href="text-classification.html"><i class="fa fa-check"></i><b>4</b> Text classification</a><ul>
<li class="chapter" data-level="4.1" data-path="text-classification.html"><a href="text-classification.html#load-the-data"><i class="fa fa-check"></i><b>4.1</b> Load the data</a></li>
<li class="chapter" data-level="4.2" data-path="text-classification.html"><a href="text-classification.html#prepare-the-data-for-neural-network"><i class="fa fa-check"></i><b>4.2</b> Prepare the data for neural network</a></li>
<li class="chapter" data-level="4.3" data-path="text-classification.html"><a href="text-classification.html#building-the-model"><i class="fa fa-check"></i><b>4.3</b> Building the model</a></li>
<li class="chapter" data-level="4.4" data-path="text-classification.html"><a href="text-classification.html#testing-the-model"><i class="fa fa-check"></i><b>4.4</b> Testing the model</a></li>
<li class="chapter" data-level="4.5" data-path="text-classification.html"><a href="text-classification.html#reference"><i class="fa fa-check"></i><b>4.5</b> Reference</a></li>
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<li class="chapter" data-level="5" data-path="RNN.html"><a href="RNN.html"><i class="fa fa-check"></i><b>5</b> Reccurent Neural Networks (RNN)</a><ul>
<li class="chapter" data-level="5.1" data-path="RNN.html"><a href="RNN.html#understanding-recurrent-neural-network"><i class="fa fa-check"></i><b>5.1</b> Understanding Recurrent Neural Network</a></li>
<li class="chapter" data-level="5.2" data-path="RNN.html"><a href="RNN.html#rnn-with-keras"><i class="fa fa-check"></i><b>5.2</b> RNN with Keras</a></li>
<li class="chapter" data-level="5.3" data-path="RNN.html"><a href="RNN.html#lstm-with-keras"><i class="fa fa-check"></i><b>5.3</b> LSTM with Keras</a></li>
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<li class="chapter" data-level="6" data-path="sentiment-analysis.html"><a href="sentiment-analysis.html"><i class="fa fa-check"></i><b>6</b> Sentiment Analysis</a><ul>
<li class="chapter" data-level="6.1" data-path="sentiment-analysis.html"><a href="sentiment-analysis.html#the-sentiments-dataset"><i class="fa fa-check"></i><b>6.1</b> The “Sentiments” dataset</a></li>
<li class="chapter" data-level="6.2" data-path="sentiment-analysis.html"><a href="sentiment-analysis.html#application"><i class="fa fa-check"></i><b>6.2</b> Application</a></li>
<li class="chapter" data-level="6.3" data-path="sentiment-analysis.html"><a href="sentiment-analysis.html#references-1"><i class="fa fa-check"></i><b>6.3</b> References:</a></li>
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<li class="chapter" data-level="7" data-path="word-and-document-frequency-tf-idf.html"><a href="word-and-document-frequency-tf-idf.html"><i class="fa fa-check"></i><b>7</b> Word and document frequency (TF-IDF)</a><ul>
<li class="chapter" data-level="7.1" data-path="word-and-document-frequency-tf-idf.html"><a href="word-and-document-frequency-tf-idf.html#term-frequency-application"><i class="fa fa-check"></i><b>7.1</b> Term frequency application</a></li>
<li class="chapter" data-level="7.2" data-path="word-and-document-frequency-tf-idf.html"><a href="word-and-document-frequency-tf-idf.html#zipfs-law"><i class="fa fa-check"></i><b>7.2</b> Zipf’s law</a></li>
<li class="chapter" data-level="7.3" data-path="word-and-document-frequency-tf-idf.html"><a href="word-and-document-frequency-tf-idf.html#tf_idf-metric"><i class="fa fa-check"></i><b>7.3</b> TF_IDF metric</a></li>
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<li class="chapter" data-level="8" data-path="topic-modeling.html"><a href="topic-modeling.html"><i class="fa fa-check"></i><b>8</b> Topic modeling</a><ul>
<li class="chapter" data-level="8.1" data-path="topic-modeling.html"><a href="topic-modeling.html#latent-dirichlet-allocation"><i class="fa fa-check"></i><b>8.1</b> Latent Dirichlet allocation</a></li>
<li class="chapter" data-level="8.2" data-path="topic-modeling.html"><a href="topic-modeling.html#document-topic-probabilities"><i class="fa fa-check"></i><b>8.2</b> Document-topic probabilities</a></li>
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<li class="chapter" data-level="9" data-path="words-relationships-analysis.html"><a href="words-relationships-analysis.html"><i class="fa fa-check"></i><b>9</b> Words’ relationships analysis</a><ul>
<li class="chapter" data-level="9.1" data-path="words-relationships-analysis.html"><a href="words-relationships-analysis.html#extracting-bi-grams"><i class="fa fa-check"></i><b>9.1</b> Extracting bi-grams</a></li>
<li class="chapter" data-level="9.2" data-path="words-relationships-analysis.html"><a href="words-relationships-analysis.html#analyzing-bi-grams"><i class="fa fa-check"></i><b>9.2</b> Analyzing bi-grams</a></li>
<li class="chapter" data-level="9.3" data-path="words-relationships-analysis.html"><a href="words-relationships-analysis.html#visualizing-a-network-of-bigrams"><i class="fa fa-check"></i><b>9.3</b> Visualizing a network of bigrams</a></li>
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<li class="chapter" data-level="10" data-path="document-term-matrix.html"><a href="document-term-matrix.html"><i class="fa fa-check"></i><b>10</b> Document-term matrix</a><ul>
<li class="chapter" data-level="10.1" data-path="document-term-matrix.html"><a href="document-term-matrix.html#converting-dtm-into-dataframe"><i class="fa fa-check"></i><b>10.1</b> COnverting DTM into dataframe</a></li>
<li class="chapter" data-level="10.2" data-path="document-term-matrix.html"><a href="document-term-matrix.html#generating-document-term-matrix"><i class="fa fa-check"></i><b>10.2</b> Generating Document-term matrix</a></li>
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<section class="normal" id="section-">
<div id="RNN" class="section level1">
<h1><span class="header-section-number">Chapter 5</span> Reccurent Neural Networks (RNN)</h1>
<div id="understanding-recurrent-neural-network" class="section level2">
<h2><span class="header-section-number">5.1</span> Understanding Recurrent Neural Network</h2>
</div>
<div id="rnn-with-keras" class="section level2">
<h2><span class="header-section-number">5.2</span> RNN with Keras</h2>
<div class="sourceCode" id="cb93"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb93-1" title="1"><span class="kw">library</span>(keras)</a>
<a class="sourceLine" id="cb93-2" title="2">max_features <-<span class="st"> </span><span class="dv">10000</span> <span class="co"># Number of words to consider as features</span></a>
<a class="sourceLine" id="cb93-3" title="3">maxlen <-<span class="st"> </span><span class="dv">500</span> <span class="co"># Cuts off texts after this many words (among the max_features most common words)</span></a>
<a class="sourceLine" id="cb93-4" title="4">batch_size <-<span class="st"> </span><span class="dv">32</span></a>
<a class="sourceLine" id="cb93-5" title="5"><span class="kw">cat</span>(<span class="st">"Loading data...</span><span class="ch">\n</span><span class="st">"</span>)</a></code></pre></div>
<pre><code>## Loading data...</code></pre>
<div class="sourceCode" id="cb95"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb95-1" title="1"><span class="co"># load data</span></a>
<a class="sourceLine" id="cb95-2" title="2">imdb <-<span class="st"> </span><span class="kw">dataset_imdb</span>(<span class="dt">num_words =</span> max_features)</a>
<a class="sourceLine" id="cb95-3" title="3"><span class="kw">c</span>(<span class="kw">c</span>(input_train, y_train), <span class="kw">c</span>(input_test, y_test)) <span class="op">%<-%</span><span class="st"> </span>imdb </a>
<a class="sourceLine" id="cb95-4" title="4"><span class="kw">cat</span>(<span class="kw">length</span>(input_train), <span class="st">"train sequences</span><span class="ch">\n</span><span class="st">"</span>)</a></code></pre></div>
<pre><code>## 25000 train sequences</code></pre>
<div class="sourceCode" id="cb97"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb97-1" title="1"><span class="kw">cat</span>(<span class="kw">length</span>(input_test), <span class="st">"test sequences"</span>)</a></code></pre></div>
<pre><code>## 25000 test sequences</code></pre>
<div class="sourceCode" id="cb99"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb99-1" title="1"><span class="co"># pad sequences</span></a>
<a class="sourceLine" id="cb99-2" title="2">input_train <-<span class="st"> </span><span class="kw">pad_sequences</span>(input_train, <span class="dt">maxlen =</span> maxlen)</a>
<a class="sourceLine" id="cb99-3" title="3">input_test <-<span class="st"> </span><span class="kw">pad_sequences</span>(input_test, <span class="dt">maxlen =</span> maxlen)</a>
<a class="sourceLine" id="cb99-4" title="4"><span class="kw">cat</span>(<span class="st">"input_train shape:"</span>, <span class="kw">dim</span>(input_train), <span class="st">"</span><span class="ch">\n</span><span class="st">"</span>)</a></code></pre></div>
<pre><code>## input_train shape: 25000 500</code></pre>
<p>let’s train the model</p>
<div class="sourceCode" id="cb101"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb101-1" title="1">model <-<span class="st"> </span><span class="kw">keras_model_sequential</span>() <span class="op">%>%</span></a>
<a class="sourceLine" id="cb101-2" title="2"><span class="st"> </span><span class="kw">layer_embedding</span>(<span class="dt">input_dim =</span> max_features, <span class="dt">output_dim =</span> <span class="dv">32</span>) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb101-3" title="3"><span class="st"> </span><span class="kw">layer_simple_rnn</span>(<span class="dt">units =</span> <span class="dv">32</span>) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb101-4" title="4"><span class="st"> </span><span class="kw">layer_dense</span>(<span class="dt">units =</span> <span class="dv">1</span>, <span class="dt">activation =</span> <span class="st">"sigmoid"</span>)</a>
<a class="sourceLine" id="cb101-5" title="5">model <span class="op">%>%</span><span class="st"> </span><span class="kw">compile</span>(</a>
<a class="sourceLine" id="cb101-6" title="6"> <span class="dt">optimizer =</span> <span class="st">"rmsprop"</span>,</a>
<a class="sourceLine" id="cb101-7" title="7"> <span class="dt">loss =</span> <span class="st">"binary_crossentropy"</span>,</a>
<a class="sourceLine" id="cb101-8" title="8"> <span class="dt">metrics =</span> <span class="kw">c</span>(<span class="st">"acc"</span>)</a>
<a class="sourceLine" id="cb101-9" title="9">)</a>
<a class="sourceLine" id="cb101-10" title="10">history <-<span class="st"> </span>model <span class="op">%>%</span><span class="st"> </span>keras<span class="op">::</span><span class="kw">fit</span>(</a>
<a class="sourceLine" id="cb101-11" title="11"> input_train, y_train,</a>
<a class="sourceLine" id="cb101-12" title="12"> <span class="dt">epochs =</span> <span class="dv">10</span>,</a>
<a class="sourceLine" id="cb101-13" title="13"> <span class="dt">batch_size =</span> <span class="dv">128</span>,</a>
<a class="sourceLine" id="cb101-14" title="14"> <span class="dt">validation_split =</span> <span class="fl">0.2</span></a>
<a class="sourceLine" id="cb101-15" title="15">)</a>
<a class="sourceLine" id="cb101-16" title="16"></a>
<a class="sourceLine" id="cb101-17" title="17"><span class="kw">plot</span>(history)</a></code></pre></div>
<pre><code>## `geom_smooth()` using formula 'y ~ x'</code></pre>
<p><img src="NLP-book_files/figure-html/RNN_keras_model_sequential%20-1.png" width="672" /></p>
</div>
<div id="lstm-with-keras" class="section level2">
<h2><span class="header-section-number">5.3</span> LSTM with Keras</h2>
<div class="sourceCode" id="cb103"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb103-1" title="1">model <-<span class="st"> </span><span class="kw">keras_model_sequential</span>() <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb103-2" title="2"><span class="st"> </span><span class="kw">layer_embedding</span>(<span class="dt">input_dim =</span> max_features, <span class="dt">output_dim =</span> <span class="dv">32</span>) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb103-3" title="3"><span class="st"> </span><span class="kw">layer_lstm</span>(<span class="dt">units =</span> <span class="dv">32</span>) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb103-4" title="4"><span class="st"> </span><span class="kw">layer_dense</span>(<span class="dt">units =</span> <span class="dv">1</span>, <span class="dt">activation =</span> <span class="st">"sigmoid"</span>)</a>
<a class="sourceLine" id="cb103-5" title="5"></a>
<a class="sourceLine" id="cb103-6" title="6">model <span class="op">%>%</span><span class="st"> </span><span class="kw">compile</span>(</a>
<a class="sourceLine" id="cb103-7" title="7"> <span class="dt">optimizer =</span> <span class="st">"rmsprop"</span>, </a>
<a class="sourceLine" id="cb103-8" title="8"> <span class="dt">loss =</span> <span class="st">"binary_crossentropy"</span>, </a>
<a class="sourceLine" id="cb103-9" title="9"> <span class="dt">metrics =</span> <span class="kw">c</span>(<span class="st">"acc"</span>)</a>
<a class="sourceLine" id="cb103-10" title="10">)</a>
<a class="sourceLine" id="cb103-11" title="11"></a>
<a class="sourceLine" id="cb103-12" title="12">history <-<span class="st"> </span>model <span class="op">%>%</span><span class="st"> </span>keras<span class="op">::</span><span class="kw">fit</span>(</a>
<a class="sourceLine" id="cb103-13" title="13"> input_train, y_train,</a>
<a class="sourceLine" id="cb103-14" title="14"> <span class="dt">epochs =</span> <span class="dv">5</span>,</a>
<a class="sourceLine" id="cb103-15" title="15"> <span class="dt">batch_size =</span> <span class="dv">128</span>,</a>
<a class="sourceLine" id="cb103-16" title="16"> <span class="dt">validation_split =</span> <span class="fl">0.2</span></a>
<a class="sourceLine" id="cb103-17" title="17">)</a>
<a class="sourceLine" id="cb103-18" title="18"><span class="kw">plot</span>(history)</a></code></pre></div>
<p><img src="NLP-book_files/figure-html/LSTM_keras_model_sequential%20-1.png" width="672" /></p>
<ul>
<li><a href="https://jjallaire.github.io/deep-learning-with-r-notebooks/notebooks/6.3-advanced-usage-of-recurrent-neural-networks.nb.html" class="uri">https://jjallaire.github.io/deep-learning-with-r-notebooks/notebooks/6.3-advanced-usage-of-recurrent-neural-networks.nb.html</a></li>
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