text: from keras.models import Sequential
  from keras.layers import Dense, Activation
  from keras.optimizers import RMSprop
  from keras.utils import plot_model
  
  batch_size =    10
  epochs     =    100
  learn_rate =    0.001
  
  model = Sequential()
  
  model.add(Dense(12, activation='relu', input_dim=6))
  model.add(Dense(6, activation='relu'))
  model.add(Dense(3, activation='relu'))
  model.add(Dense(2, activation='sigmoid'))
  
  model.compile(RMSprop(lr=learn_rate), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
  
  model.summary()
  plot_model(model, to_file='model-sdn-dnn.png', show_layer_names=True, show_shapes=True)
  
  history = model.fit(kdd_train, kdd_train_labels, 
                      epochs=epochs, 
                      batch_size=batch_size,
                      verbose=1,
                      validation_data=(kdd_test, kdd_test_labels),
                      callbacks=callbacks)