text: from __future__ import absolute_import
  from __future__ import print_function
  import numpy as np
  import datetime
  
  np.random.seed(1337)  # for reproducibility
  
  from keras.datasets import mnist
  from keras.models import Sequential
  from keras.layers.core import Dense, Dropout, Activation, Flatten
  from keras.layers.convolutional import Convolution2D, MaxPooling2D
  from keras.utils import np_utils
  
  '''
      Transfer learning toy example:
          1- Train a simple convnet on the MNIST dataset the first 5 digits [0..4].
          2- Freeze convolutional layers and fine-tune dense layers for the classification of digits [5..9].
  
      Run on GPU: THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python mnist_cnn.py
  
      Get to 99.8% test accuracy after 5 epochs for the first five digits classifier
      and 99.2% for the last five digits after transfer + fine-tuning.
  '''
  
  now = datetime.datetime.now
  
  batch_size = 128
  nb_classes = 5
  nb_epoch = 5
  
  # input image dimensions
  img_rows, img_cols = 28, 28
  # number of convolutional filters to use
  nb_filters = 32
  # size of pooling area for max pooling
  nb_pool = 2
  # convolution kernel size
  nb_conv = 3
  
  
  def train_model(model, train, test, nb_classes):
      X_train = train[0].reshape(train[0].shape[0], 1, img_rows, img_cols)
      X_test = test[0].reshape(test[0].shape[0], 1, img_rows, img_cols)
      X_train = X_train.astype("float32")
      X_test = X_test.astype("float32")
      X_train /= 255
      X_test /= 255
      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 = np_utils.to_categorical(train[1], nb_classes)
      Y_test = np_utils.to_categorical(test[1], nb_classes)
  
      model.compile(loss='categorical_crossentropy', optimizer='adadelta')
  
      t = now()
      model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=1,
                validation_data=(X_test, Y_test))
      print('Training time: %s' % (now() - t))
      score = model.evaluate(X_test, Y_test, show_accuracy=True, verbose=0)
      print('Test score:', score[0])
      print('Test accuracy:', score[1])
  
  
  # the data, shuffled and split between train and test sets
  (X_train, y_train), (X_test, y_test) = mnist.load_data()
  
  # create two datasets one with digits below 5 and one with 5 and above
  X_train_lt5 = X_train[y_train < 5]
  y_train_lt5 = y_train[y_train < 5]
  X_test_lt5 = X_test[y_test < 5]
  y_test_lt5 = y_test[y_test < 5]
  
  X_train_gte5 = X_train[y_train >= 5]
  y_train_gte5 = y_train[y_train >= 5] - 5  # make classes start at 0 for
  X_test_gte5 = X_test[y_test >= 5]         # np_utils.to_categorical
  y_test_gte5 = y_test[y_test >= 5] - 5
  
  # define two groups of layers: feature (convolutions) and classification (dense)
  feature_layers = [
      Convolution2D(nb_filters, nb_conv, nb_conv,
                    border_mode='full',
                    input_shape=(1, img_rows, img_cols)),
      Activation('relu'),
      Convolution2D(nb_filters, nb_conv, nb_conv),
      Activation('relu'),
      MaxPooling2D(pool_size=(nb_pool, nb_pool)),
      Dropout(0.25),
      Flatten(),
  ]
  classification_layers = [
      Dense(128),
      Activation('relu'),
      Dropout(0.5),
      Dense(nb_classes),
      Activation('softmax')
  ]
  
  # create complete model
  model = Sequential()
  for l in feature_layers + classification_layers:
      model.add(l)
  
  # train model for 5-digit classification [0..4]
  train_model(model, (X_train_lt5, y_train_lt5), (X_test_lt5, y_test_lt5), nb_classes)
  
  # freeze feature layers and rebuild model
  for l in feature_layers:
      l.trainable = False
  
  # transfer: train dense layers for new classification task [5..9]
  train_model(model, (X_train_gte5, y_train_gte5), (X_test_gte5, y_test_gte5), nb_classes)
  