title: Simple Logistic Regression with Neural Networks Mindset
body: <p>Got this simple exercise where I have to build a NN with the help of Logistic Regression. My dataset is built in this way :</p>
  
  <blockquote>
    <p>You are given a dataset ("data.h5") containing:</p>
    
    <ul>
    <li>a training set of m_train images labeled as cat (y=1) or non-cat (y=0)</li>
    <li>a test set of m_test images labeled as cat or non-cat</li>
    <li>each image is of shape (num_px, num_px, 3) where 3 is for the 3 channels (RGB). Thus, each image is square (height = num_px) and
    (width = num_px).</li>
    </ul>
  </blockquote>
  
  <p>To show an image from the database the text gives me an example :</p>
  
  <pre><code># Example of a picture# Examp 
  index = 25
  plt.imshow(train_set_x_orig[index])
  print ("y = " + str(train_set_y[:,index]) + ", it's a '" + classes[np.squeeze(train_set_y[:,index])].decode("utf-8") +  "' picture.")
  </code></pre>
  
  <p>I have 2 questions :</p>
  
  <p>1) I did not understand how this works : <code>str(train_set_y[:,index])</code></p>
  
  <p>2) The big problem is that , due to site problem , I can not download this database and in order to do the exercise I would like to understand how is it built. Can someone intuitively tell me how it could be structured?</p>