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diff --git a/docs/posts/2019-12-08-Image-Classifier-Tensorflow.html b/docs/posts/2019-12-08-Image-Classifier-Tensorflow.html
index a8491ee..c9ee1e0 100644
--- a/docs/posts/2019-12-08-Image-Classifier-Tensorflow.html
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- <h1>Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria</h1>
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+ <h1 id="creating-a-custom-image-classifier-using-tensorflow-2x-and-keras-for-detecting-malaria">Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria</h1>
<p><strong>Done during Google Code-In. Org: Tensorflow.</strong></p>
-<h2>Imports</h2>
+<h2 id="imports">Imports</h2>
<div class="codehilite">
<pre><span></span><code><span class="o">%</span><span class="n">tensorflow_version</span> <span class="mf">2.</span><span class="n">x</span> <span class="c1">#This is for telling Colab that you want to use TF 2.0, ignore if running on local machine</span>
@@ -65,9 +97,9 @@
</code></pre>
</div>
-<h2>Dataset</h2>
+<h2 id="dataset">Dataset</h2>
-<h3>Fetching the Data</h3>
+<h3 id="fetching-the-data">Fetching the Data</h3>
<div class="codehilite">
<pre><span></span><code><span class="err">!</span><span class="n">wget</span> <span class="n">ftp</span><span class="p">:</span><span class="o">//</span><span class="n">lhcftp</span><span class="o">.</span><span class="n">nlm</span><span class="o">.</span><span class="n">nih</span><span class="o">.</span><span class="n">gov</span><span class="o">/</span><span class="n">Open</span><span class="o">-</span><span class="n">Access</span><span class="o">-</span><span class="n">Datasets</span><span class="o">/</span><span class="n">Malaria</span><span class="o">/</span><span class="n">cell_images</span><span class="o">.</span><span class="n">zip</span>
@@ -75,7 +107,7 @@
</code></pre>
</div>
-<h3>Processing the Data</h3>
+<h3 id="processing-the-data">Processing the Data</h3>
<p>We resize all the images as 50x50 and add the numpy array of that image as well as their label names (Infected or Not) to common arrays.</p>
@@ -107,7 +139,7 @@
</code></pre>
</div>
-<h3>Splitting Data</h3>
+<h3 id="splitting-data">Splitting Data</h3>
<div class="codehilite">
<pre><span></span><code><span class="n">df</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
@@ -124,9 +156,9 @@ y_train=y_train[s]
X_train = X_train/255.0
</code></pre>
-<h2>Model</h2>
+<h2 id="model">Model</h2>
-<h3>Creating Model</h3>
+<h3 id="creating-model">Creating Model</h3>
<p>By creating a sequential model, we create a linear stack of layers.</p>
@@ -149,7 +181,7 @@ X_train = X_train/255.0
</code></pre>
</div>
-<h3>Compiling Model</h3>
+<h3 id="compiling-model">Compiling Model</h3>
<p>We use the Adam optimiser as it is an adaptive learning rate optimisation algorithm that's been designed specifically for <em>training</em> deep neural networks, which means it changes its learning rate automatically to get the best results</p>
@@ -160,7 +192,7 @@ X_train = X_train/255.0
</code></pre>
</div>
-<h3>Training Model</h3>
+<h3 id="training-model">Training Model</h3>
<p>We train the model for 10 epochs on the training data and then validate it using the testing data</p>
@@ -194,7 +226,7 @@ X_train = X_train/255.0
</code></pre>
</div>
-<h3>Results</h3>
+<h3 id="results">Results</h3>
<div class="codehilite">
<pre><span></span><code><span class="n">accuracy</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span>
@@ -223,14 +255,15 @@ X_train = X_train/255.0
<p><a rel="noopener" target="_blank" href="https://colab.research.google.com/drive/1ZswDsxLwYZEnev89MzlL5Lwt6ut7iwp-" title="Colab Notebook">Link to Colab Notebook</a></p>
+ </div>
<blockquote>If you have scrolled this far, consider subscribing to my mailing list <a href="https://listmonk.navan.dev/subscription/form">here.</a> You can subscribe to either a specific type of post you are interested in, or subscribe to everything with the "Everything" list.</blockquote>
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