title: what is the difference between binary, sparse and categorical in class_mode
body: <blockquote>
  <p>below the code for understanding,</p>
  </blockquote>
  <pre><code>#Fitting the CNN to the images
  from keras.preprocessing.image import ImageDataGenerator
  train_datagen = ImageDataGenerator(
      rescale = 1.0 / 255,
      shear_range = 0.2,
      zoom_range = 0.2,
      horizontal_flip = True
  )
  
  test_datagen = ImageDataGenerator(rescale= 1.0 / 255)
  
  training_set = train_datagen.flow_from_directory(
      'dataset/training_set',
      target_size = (64,64),
      batch_size = 32,
      class_mode = 'binary'
  )
  
  test_set = test_datagen.flow_from_directory(
      'dataset/test_set',
      target_size = (64,64),
      batch_size = 32,
      class_mode = 'binary'
  )
  </code></pre>
  <blockquote>
  <p>When I used  <strong>sparse</strong> / <strong>categorical</strong>. It show the same output,</p>
  </blockquote>
  <p><strong>Below the output,</strong></p>
  <pre><code>Found 214 images belonging to 5 classes.
  Found 20 images belonging to 5 classes.
  </code></pre>
  <p><strong>Note:</strong> This code is for training datasets but unfortunately it doesn't train</p>
  <p><strong>So need help</strong></p>