title: Why Conv2D has different number of filters in each layer
body: <p>Learning from this <a href="https://keras.io/examples/cifar10_cnn/" rel="nofollow noreferrer">Keras document example</a></p>
  <pre><code>model = Sequential()
  model.add(Conv2D(32, (3, 3), padding='same',    # why filter is 32?
                   input_shape=x_train.shape[1:]))
  model.add(Activation('relu'))
  model.add(Conv2D(32, (3, 3)))      # why filter is not changed?
  model.add(Activation('relu'))
  model.add(MaxPooling2D(pool_size=(2, 2)))
  model.add(Dropout(0.25))
  
  model.add(Conv2D(64, (3, 3), padding='same'))     # why filter is changed to 64?
  model.add(Activation('relu'))
  model.add(Conv2D(64, (3, 3)))
  model.add(Activation('relu'))
  model.add(MaxPooling2D(pool_size=(2, 2)))
  model.add(Dropout(0.25))
  
  model.add(Flatten())
  model.add(Dense(512))     # why Dense neurons is 512? not 1024? what's the rule to set the number?
  </code></pre>
  <p>Here are my qeustions:</p>
  <ol>
  <li><p>why in the 1st layer filter is 32 and not changed in the 2nd place but still in 1st layer?</p>
  </li>
  <li><p>Why in the 2nd layer filter is changed to 64? What is the rule to set the number?</p>
  </li>
  <li><p>why Dense neurons are 512? not 1024? what's the rule to set the number?</p>
  </li>
  </ol>