title: Keras tuner: mismatch between number of layers used and number of layers reported
body: <p>Using example from Keras Tuner website, I wrote simple tuning code</p>
  
  <pre><code>base_model = tf.keras.applications.vgg16.VGG16(input_shape=IMG_SHAPE,
                                                include_top=False, 
                                                weights='imagenet')
  base_model.trainable = False
  
  def build_model(hp):
      model = tf.keras.Sequential();
      model.add(base_model);
  
      for i in range(hp.Int('num_layers', 1, 2)):
          model.add(tf.keras.layers.Conv2D(filters=hp.Int('Conv2D_' + str(i),
              min_value=32,
              max_value=512,
              step=32),
              kernel_size=3, activation='relu'));
          model.add(tf.keras.layers.Dropout(hp.Choice('rate', [0.3, 0.5])));
  
      model.add(tf.keras.layers.GlobalAveragePooling2D());
      model.add(tf.keras.layers.Flatten());
      model.add(tf.keras.layers.Dropout(0.2));
      model.add(tf.keras.layers.Dense(5, activation='softmax'));
  
      model.compile(optimizer=tf.keras.optimizers.RMSprop(hp.Choice('learning_rate', [1e-4, 1e-5])),
          loss='categorical_crossentropy',
          metrics=['accuracy']);
  
      return model
  
  
  epochs = 2
  callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=3)
  
  tuner = RandomSearch(
      build_model,
      objective='val_accuracy',
      max_trials=24,
      executions_per_trial=1,
      directory=LOG_DIR);
  
  tuner.search_space_summary();
  
  tuner.search(train_generator,
               callbacks=[callback],
               epochs = epochs,
               steps_per_epoch = train_generator.samples // BATCH_SIZE,
               validation_data = valid_generator,
               validation_steps = valid_generator.samples // BATCH_SIZE,
               verbose = 1);
  
  tuner.results_summary();
  models = tuner.get_best_models(num_models=2);
  </code></pre>
  
  <p>However, when I run it with varying number of layers, but it shows mismatch between number of layers reported and value of num_layers. For example it reports three Conv2D layers and yet it shows num_layers as 1. Why ?</p>
  
  <pre><code>[Trial summary]
   |-Trial ID: 79cd7bb6146b4c243eb2bc51f19985de
   |-Score: 0.8444444537162781
   |-Best step: 0
   &gt; Hyperparameters:
   |-Conv2D_0: 448
   |-Conv2D_1: 448
   |-Conv2D_2: 512
   |-learning_rate: 0.0001
   |-num_layers: 1
   |-rate: 0.5
  </code></pre>