text: 
  '''Trains a simple convnet on the MNIST dataset.
  Gets to 99.25% test accuracy after 12 epochs
  (there is still a lot of margin for parameter tuning).
  16 seconds per epoch on a GRID K520 GPU.
  '''
  
  from __future__ import print_function
  import keras
  from keras.datasets import mnist
  from keras.models import Sequential
  from keras.layers import Dense, Dropout, Flatten
  from keras.layers import Conv2D, MaxPooling2D
  from keras import backend as K
  
  batch_size = 128
  num_classes = 10
  epochs = 12
  
  # input image dimensions
  img_rows, img_cols = 28, 28
  
  # the data, split between train and test sets
  (x_train, y_train), (x_test, y_test) = mnist.load_data()
  
  if K.image_data_format() == 'channels_first':
      x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
      x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
      input_shape = (1, img_rows, img_cols)
  else:
      x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
      x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
      input_shape = (img_rows, img_cols, 1)
  
  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 = keras.utils.to_categorical(y_train, num_classes)
  y_test = keras.utils.to_categorical(y_test, num_classes)
  
  model = Sequential()
  model.add(Conv2D(32, kernel_size=(3, 3),
                   activation='relu',
                   input_shape=input_shape))
  model.add(Conv2D(64, (3, 3), activation='relu'))
  model.add(MaxPooling2D(pool_size=(2, 2)))
  model.add(Dropout(0.25))
  model.add(Flatten())
  model.add(Dense(128, activation='relu'))
  model.add(Dropout(0.5))
  model.add(Dense(num_classes, activation='softmax'))
  
  model.compile(loss=keras.losses.categorical_crossentropy,
                optimizer=keras.optimizers.Adadelta(),
                metrics=['accuracy'])
  
  model.fit(x_train, y_train,
            batch_size=batch_size,
            epochs=epochs,
            verbose=1,
            validation_data=(x_test, y_test))
  score = model.evaluate(x_test, y_test, verbose=0)
  model.save('keras-mnist.model')
  print('Test loss:', score[0])
  print('Test accuracy:', score[1])