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    <a class="external-link dropdown-item" href="https://colab.research.google.com/drive/10xpRzGd3JeBAbqQYSCxzQUMctt01sx9D?usp=sharing">Full metagenome level: Colorectal cancer prediction</a>





  
  

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    <a class="external-link dropdown-item" href="https://colab.research.google.com/drive/1kyYK7IU7GSfdpDzO_a8U3_qD4i3zTu6w?usp=sharing">BERT with deepG</a>





  
  

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      <img src="../logo.png" class="logo" alt=""><h1>Using Tensorboard</h1>





  
  

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      <small class="dont-index">Source: <a href="https://github.com/GenomeNet/deepG/blob/HEAD/vignettes/using_tb.Rmd" class="external-link"><code>vignettes/using_tb.Rmd</code></a></small>





  
  

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<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">





  
  

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<code class="sourceCode R"><span><span class="co"># devtools::install_github("GenomeNet/deepG")</span></span>





  
  

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<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/GenomeNet/deepG" class="external-link">deepG</a></span><span class="op">)</span></span>





  
  

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<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://magrittr.tidyverse.org" class="external-link">magrittr</a></span><span class="op">)</span></span></code></pre></div>





  
  

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<style type="text/css">





  
  

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mark.in {





  
  

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<p>Tensorflow offers the





  
  

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<a href="https://www.tensorflow.org/tensorboard" class="external-link">Tensorboard</a>





  
  

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application to visualize the training process of our networks. DeepG





  
  

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expands on some of the default Tensorboard options and implements some





  
  

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custom settings. We train again a model that can differentiate sequences





  
  

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based on the GC content, as described in the





  
  

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<a href="getting_started.html">Getting started tutorial</a>.</p>





  
  

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<p>We start by creating our data. To show the difference between





  
  

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accuracy and balanced accuracy, we create 3 times more data with high GC





  
  

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content.</p>





  
  

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<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">





  
  

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<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123</span><span class="op">)</span></span>





  
  

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<span><span class="va">vocabulary</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"A"</span>, <span class="st">"C"</span>, <span class="st">"G"</span>, <span class="st">"T"</span><span class="op">)</span></span>





  
  

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<span></span>





  
  

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<span><span class="va">data_type</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"train"</span>, <span class="st">"validation"</span><span class="op">)</span></span>





  
  

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<span></span>





  
  

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<span><span class="kw">for</span> <span class="op">(</span><span class="va">i</span> <span class="kw">in</span> <span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">data_type</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>





  
  

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<span>  </span>





  
  

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<span>  <span class="va">temp_file</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/tempfile.html" class="external-link">tempfile</a></span><span class="op">(</span><span class="op">)</span></span>





  
  

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<span>  <span class="fu"><a href="https://rdrr.io/r/base/assign.html" class="external-link">assign</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">data_type</span><span class="op">[</span><span class="va">i</span><span class="op">]</span>, <span class="st">"_dir"</span><span class="op">)</span>, <span class="va">temp_file</span><span class="op">)</span></span>





  
  

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<span>  <span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.create</a></span><span class="op">(</span><span class="va">temp_file</span><span class="op">)</span></span>





  
  

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<span>  </span>





  
  

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<span>  <span class="kw">for</span> <span class="op">(</span><span class="va">j</span> <span class="kw">in</span> <span class="fl">1</span><span class="op">:</span><span class="fl">6</span><span class="op">)</span> <span class="op">{</span></span>





  
  

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<span>    </span>





  
  

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<span>    <span class="kw">if</span> <span class="op">(</span><span class="va">j</span> <span class="op"><a href="https://rdrr.io/r/base/Arithmetic.html" class="external-link">%%</a></span> <span class="fl">2</span> <span class="op">==</span> <span class="fl">1</span><span class="op">)</span> <span class="op">{</span></span>





  
  

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<span>      <span class="va">header</span> <span class="op">&lt;-</span> <span class="st">"high_gc"</span></span>





  
  

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<span>      <span class="va">prob</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.1</span>, <span class="fl">0.4</span>, <span class="fl">0.4</span>, <span class="fl">0.1</span><span class="op">)</span></span>





  
  

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<span>    <span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span>





  
  

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<span>      <span class="va">header</span> <span class="op">&lt;-</span> <span class="st">"equal_dist"</span></span>





  
  

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<span>      <span class="va">prob</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fl">0.25</span>, <span class="fl">4</span><span class="op">)</span></span>





  
  

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<span>    <span class="op">}</span></span>





  
  

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<span>    <span class="va">fasta_name_start</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">header</span>, <span class="st">"_"</span>, <span class="va">data_type</span><span class="op">[</span><span class="va">i</span><span class="op">]</span><span class="op">)</span></span>





  
  

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<span>    </span>





  
  

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<span>    <span class="fu"><a href="../reference/create_dummy_data.html">create_dummy_data</a></span><span class="op">(</span>file_path <span class="op">=</span> <span class="va">temp_file</span>,</span>





  
  

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<span>                      num_files <span class="op">=</span> <span class="fl">2</span>,</span>





  
  

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<span>                      seq_length <span class="op">=</span> <span class="fl">100</span>, </span>





  
  

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<span>                      num_seq <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">j</span> <span class="op"><a href="https://rdrr.io/r/base/Arithmetic.html" class="external-link">%%</a></span> <span class="fl">2</span> <span class="op">==</span> <span class="fl">1</span>, <span class="fl">6</span>, <span class="fl">2</span><span class="op">)</span>, <span class="co"># create more sequences for high GC content</span></span>





  
  

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<span>                      header <span class="op">=</span> <span class="va">header</span>,</span>





  
  

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<span>                      prob <span class="op">=</span> <span class="va">prob</span>,</span>





  
  

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<span>                      fasta_name_start <span class="op">=</span> <span class="va">fasta_name_start</span>,</span>





  
  

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<span>                      vocabulary <span class="op">=</span> <span class="va">vocabulary</span><span class="op">)</span></span>





  
  

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<span>  <span class="op">}</span></span>





  
  

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<span>  </span>





  
  

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<span><span class="op">}</span></span></code></pre></div>





  
  

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<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">





  
  

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<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/list.files.html" class="external-link">list.files</a></span><span class="op">(</span><span class="va">train_dir</span><span class="op">)</span></span></code></pre></div>





  
  

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<pre><code><span><span class="co">## [1] "equal_dist_train_1.fasta" "equal_dist_train_2.fasta"</span></span>





  
  

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<span><span class="co">## [3] "high_gc_train_1.fasta"    "high_gc_train_2.fasta"</span></span></code></pre>





  
  

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<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">





  
  

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<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/list.files.html" class="external-link">list.files</a></span><span class="op">(</span><span class="va">validation_dir</span><span class="op">)</span></span></code></pre></div>





  
  

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<pre><code><span><span class="co">## [1] "equal_dist_validation_1.fasta" "equal_dist_validation_2.fasta"</span></span>





  
  

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<span><span class="co">## [3] "high_gc_validation_1.fasta"    "high_gc_validation_2.fasta"</span></span></code></pre>





  
  

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<p>To use tensorboard, we first need to create a folder where we store





  
  

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our tensorboard logs.</p>





  
  

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<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">





  
  

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<code class="sourceCode R"><span><span class="co"># create folder for tensorboard logs</span></span>





  
  

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<span><span class="va">tb_dir</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/tempfile.html" class="external-link">tempfile</a></span><span class="op">(</span><span class="op">)</span></span>





  
  

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<span><span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.create</a></span><span class="op">(</span><span class="va">tb_dir</span><span class="op">)</span></span></code></pre></div>





  
  

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<p>When creating our model, we can add some additional metrics to





  
  

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observe like AUC, F1 and balanced accuracy.</p>





  
  

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<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">





  
  

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<code class="sourceCode R"><span><span class="va">maxlen</span> <span class="op">&lt;-</span> <span class="fl">50</span></span>





  
  

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<span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/create_model_lstm_cnn.html">create_model_lstm_cnn</a></span><span class="op">(</span>maxlen <span class="op">=</span> <span class="va">maxlen</span>,</span>





  
  

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<span>                               filters <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">8</span><span class="op">)</span>,</span>





  
  

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<span>                               kernel_size <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">12</span><span class="op">)</span>,</span>





  
  

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<span>                               pool_size <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span><span class="op">)</span>,</span>





  
  

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<span>                               layer_lstm <span class="op">=</span> <span class="fl">8</span>,</span>





  
  

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<span>                               auc_metric <span class="op">=</span> <span class="cn">TRUE</span>,</span>





  
  

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<span>                               f1_metric <span class="op">=</span> <span class="cn">TRUE</span>,</span>





  
  

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<span>                               bal_acc <span class="op">=</span> <span class="cn">TRUE</span>,</span>





  
  

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<span>                               layer_dense <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">2</span><span class="op">)</span>,</span>





  
  

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<span>                               model_seed <span class="op">=</span> <span class="fl">3</span><span class="op">)</span></span></code></pre></div>





  
  

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<pre><code><span><span class="co">## Will flatten y_true and y_pred matrices to a single vector for evaluation</span></span>





  
  

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<span><span class="co">##             rather than computing separate F1 scores for each class and taking the mean.</span></span></code></pre>





  
  

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<pre><code><span><span class="co">## Model: "model"</span></span>





  
  

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<span><span class="co">## _________________________________________________________________</span></span>





  
  

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<span><span class="co">##  Layer (type)                Output Shape              Param #   </span></span>





  
  

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<span><span class="co">## =================================================================</span></span>





  
  

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<span><span class="co">##  input_1 (InputLayer)        [(None, 50, 4)]           0         </span></span>





  
  

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<span><span class="co">##                                                                  </span></span>





  
  

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<span><span class="co">##  conv1d (Conv1D)             (None, 50, 8)             392       </span></span>





  
  

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<span><span class="co">##                                                                  </span></span>





  
  

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<span><span class="co">##  max_pooling1d (MaxPooling1  (None, 16, 8)             0         </span></span>





  
  

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<span><span class="co">##  D)                                                              </span></span>





  
  

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<span><span class="co">##                                                                  </span></span>





  
  

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<span><span class="co">##  batch_normalization (Batch  (None, 16, 8)             32        </span></span>





  
  

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<span><span class="co">##  Normalization)                                                  </span></span>





  
  

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<span><span class="co">##                                                                  </span></span>





  
  

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<span><span class="co">##  lstm (LSTM)                 (None, 8)                 544       </span></span>





  
  

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<span><span class="co">##                                                                  </span></span>





  
  

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<span><span class="co">##  dense (Dense)               (None, 4)                 36        </span></span>





  
  

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<span><span class="co">##                                                                  </span></span>





  
  

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<span><span class="co">##  dense_1 (Dense)             (None, 2)                 10        </span></span>





  
  

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<span><span class="co">##                                                                  </span></span>





  
  

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<span><span class="co">## =================================================================</span></span>





  
  

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<span><span class="co">## Total params: 1014 (3.96 KB)</span></span>





  
  

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<span><span class="co">## Trainable params: 998 (3.90 KB)</span></span>





  
  

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<span><span class="co">## Non-trainable params: 16 (64.00 Byte)</span></span>





  
  

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<span><span class="co">## _________________________________________________________________</span></span></code></pre>





  
  

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<p>Finally we can train the model.</p>





  
  

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<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">





  
  

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<code class="sourceCode R"><span><span class="va">hist</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/train_model.html">train_model</a></span><span class="op">(</span><span class="va">model</span>,</span>





  
  

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<span>                    train_type <span class="op">=</span> <span class="st">"label_header"</span>,</span>





  
  

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<span>                    run_name <span class="op">=</span> <span class="st">"gc_model_1"</span>,</span>





  
  

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<span>                    path <span class="op">=</span> <span class="va">train_dir</span>,</span>





  
  

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<span>                    path_val <span class="op">=</span> <span class="va">validation_dir</span>,</span>





  
  

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<span>                    epochs <span class="op">=</span> <span class="fl">5</span>,</span>





  
  

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<span>                    steps_per_epoch <span class="op">=</span> <span class="fl">2</span>, <span class="co"># 20</span></span>





  
  

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<span>                    batch_size <span class="op">=</span> <span class="fl">64</span>,</span>





  
  

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<span>                    step <span class="op">=</span> <span class="fl">50</span>, </span>





  
  

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<span>                    path_tensorboard <span class="op">=</span> <span class="va">tb_dir</span>, <span class="co"># path to tensorboard logs</span></span>





  
  

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<span>                    tb_images <span class="op">=</span> <span class="cn">F</span>, <span class="co"># show confusion matrix in tensorboard</span></span>





  
  

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<span>                    vocabulary_label <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"high_gc"</span>, <span class="st">"equal_dist"</span><span class="op">)</span><span class="op">)</span></span>





  
  

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<span>                    </span>





  
  

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<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">hist</span><span class="op">)</span></span></code></pre></div>





  
  

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<p>Use the following command to open tensorboard in a browser:</p>





  
  

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<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">





  
  

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<code class="sourceCode R"><span><span class="fu">keras</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/tensorflow/man/tensorboard.html" class="external-link">tensorboard</a></span><span class="op">(</span><span class="va">tb_dir</span><span class="op">)</span></span></code></pre></div>





  
  

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<p>We can observe the scores for loss, accuracy, balanced accuracy, F1





  
  

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and AUC under the “SCALARS” tab.</p>





  
  

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<p><img src="tb_images/loss.png" height="300"></p>





  
  

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<p><img src="tb_images/acc.png" height="300"></p>





  
  

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<p><img src="tb_images/bal_acc.png" height="300"></p>





  
  

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<p><img src="tb_images/f1.png"></p>





  
  

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<p><img src="tb_images/auc.png" height="300"></p>





  
  

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<p>We can also observe how the learning rate might have changed</p>





  
  

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<p><img src="tb_images/lr.png" height="300"></p>





  
  

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<p>In the “training files seen” window, we can observe how often we





  
  

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iterated over the training files.</p>





  
  

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<p><img src="tb_images/files_seen.png" height="300"></p>





  
  

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<p>In the “IMAGES” tab we can see a confusion matrix for the train and





  
  

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validation scores for every epoch</p>





  
  

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<p><img src="tb_images/cm_train.png" height="300"></p>





  
  

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<p><img src="tb_images/cm_val.png" height="300"></p>





  
  

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<p>In the “HPARAM” tab you can see hyper parameters of each run</p>





  
  

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<p><img src="tb_images/hparam.png" height="300"></p>





  
  

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  </main>





  
  

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</div>





  
  

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