text: # adapted from https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html
  # for a high level explanation of this, check out https://github.com/SchoolofAI-Vancouver/learn_image_classification_2/blob/master/keras_colab_demo/Image_Classification_II_Demo.ipynb
  # for a more detailed explanation, look at the original source at the top
  from keras.preprocessing.image import ImageDataGenerator
  from keras.models import Sequential
  from keras.layers import Conv2D, MaxPooling2D
  from keras.layers import Activation, Dropout, Flatten, Dense
  from keras import backend as K
  
  # dimensions of our images
  img_width, img_height = 64, 64
  
  # data dirs
  train_data_dir = '../../data/train'
  validation_data_dir = '../../data/validate'
  
  # basic training params
  nb_train_samples = 2048
  nb_validation_samples = 686
  epochs = 2
  batch_size = 12
  
  if K.image_data_format() == 'channels_first':
      input_shape = (3, img_width, img_height)
  else:
      input_shape = (img_width, img_height, 3)
  
  model = Sequential()
  model.add(Conv2D(32, (3, 3), input_shape=input_shape))
  model.add(Activation('relu'))
  model.add(MaxPooling2D(pool_size=(2, 2)))
  
  model.add(Conv2D(32, (3, 3)))
  model.add(Activation('relu'))
  model.add(MaxPooling2D(pool_size=(2, 2)))
  
  model.add(Conv2D(64, (3, 3)))
  model.add(Activation('relu'))
  model.add(MaxPooling2D(pool_size=(2, 2)))
  
  model.add(Flatten())
  model.add(Dense(64))
  model.add(Activation('relu'))
  model.add(Dropout(0.5))
  model.add(Dense(1))
  model.add(Activation('sigmoid'))
  
  model.compile(loss='binary_crossentropy',
                optimizer='rmsprop',
                metrics=['accuracy'])
  
  # this is the augmentation configuration we will use for training
  train_datagen = ImageDataGenerator(
      rescale=1. / 255,
      shear_range=0.2,
      zoom_range=0.2,
      horizontal_flip=True)
  
  # this is the augmentation configuration we will use for testing:
  # only rescaling
  test_datagen = ImageDataGenerator(rescale=1. / 255)
  
  # generate training data
  train_generator = train_datagen.flow_from_directory(
      train_data_dir,
      target_size=(img_width, img_height),
      batch_size=batch_size,
      class_mode='binary')
  
  # generate validation data
  validation_generator = test_datagen.flow_from_directory(
      validation_data_dir,
      target_size=(img_width, img_height),
      batch_size=batch_size,
      class_mode='binary')
  
  # fit model
  model.fit_generator(
      train_generator,
      steps_per_epoch=nb_train_samples // batch_size,
      epochs=epochs,
      validation_data=validation_generator,
      validation_steps=nb_validation_samples // batch_size)
  
  # save model
  model.save('distraction_model.hdf5')