text: 
  '''
  Forked from:
  https://github.com/keras-team/keras/blob/master/examples/cifar10_cnn.py
  '''
  
  '''Train a simple deep CNN on the CIFAR10 small images dataset.
  
  It gets to 75% validation accuracy in 25 epochs, and 79% after 50 epochs.
  (it's still underfitting at that point, though).
  '''
  
  from __future__ import print_function
  import keras
  from keras.datasets import cifar10
  from keras.preprocessing.image import ImageDataGenerator
  from keras.models import Sequential
  from keras.layers import Dense, Dropout, Activation, Flatten
  from keras.layers import Conv2D, MaxPooling2D
  import os
  
  batch_size = 32
  num_classes = 10
  epochs = 10
  data_augmentation = True
  num_predictions = 20
  save_dir = os.path.join(os.getcwd(), 'saved_models')
  model_name = 'keras_cifar10_trained_model.h5'
  
  # The data, split between train and test sets:
  (x_train, y_train), (x_test, y_test) = cifar10.load_data()
  print('x_train shape:', x_train.shape)
  print(x_train.shape[0], 'train samples')
  print(x_test.shape[0], 'test samples')
  
  # Convert class vectors to binary class matrices.
  y_train = keras.utils.to_categorical(y_train, num_classes)
  y_test = keras.utils.to_categorical(y_test, num_classes)
  
  model = Sequential()
  model.add(Conv2D(32, (3, 3), padding='same',
                   input_shape=x_train.shape[1:]))
  model.add(Activation('relu'))
  model.add(Conv2D(32, (3, 3)))
  model.add(Activation('relu'))
  model.add(MaxPooling2D(pool_size=(2, 2)))
  model.add(Dropout(0.25))
  
  model.add(Conv2D(64, (3, 3), padding='same'))
  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))
  model.add(Activation('relu'))
  model.add(Dropout(0.5))
  model.add(Dense(num_classes))
  model.add(Activation('softmax'))
  
  # initiate RMSprop optimizer
  opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)
  
  # Let's train the model using RMSprop
  model.compile(loss='categorical_crossentropy',
                optimizer=opt,
                metrics=['accuracy'])
  
  x_train = x_train.astype('float32')
  x_test = x_test.astype('float32')
  x_train /= 255
  x_test /= 255
  
  if not data_augmentation:
      print('Not using data augmentation.')
      model.fit(x_train, y_train,
                batch_size=batch_size,
                epochs=epochs,
                validation_data=(x_test, y_test),
                shuffle=True)
  else:
      print('Using real-time data augmentation.')
      # This will do preprocessing and realtime data augmentation:
      datagen = ImageDataGenerator(
          featurewise_center=False,  # set input mean to 0 over the dataset
          samplewise_center=False,  # set each sample mean to 0
          featurewise_std_normalization=False,  # divide inputs by std of the dataset
          samplewise_std_normalization=False,  # divide each input by its std
          zca_whitening=False,  # apply ZCA whitening
          rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)
          width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
          height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)
          horizontal_flip=True,  # randomly flip images
          vertical_flip=False)  # randomly flip images
  
      # Compute quantities required for feature-wise normalization
      # (std, mean, and principal components if ZCA whitening is applied).
      datagen.fit(x_train)
  
      # Fit the model on the batches generated by datagen.flow().
      model.fit_generator(datagen.flow(x_train, y_train,
                                       batch_size=batch_size),
                          epochs=epochs,
                          validation_data=(x_test, y_test),
                          workers=4)
  
  # Save model and weights
  if not os.path.isdir(save_dir):
      os.makedirs(save_dir)
  model_path = os.path.join(save_dir, model_name)
  model.save(model_path)
  print('Saved trained model at %s ' % model_path)
  
  # Score trained model.
  scores = model.evaluate(x_test, y_test, verbose=1)
  print('Test loss:', scores[0])
  print('Test accuracy:', scores[1])