old_contents: '''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 numpy as np
  np.random.seed(1337)  # for reproducibility
  
  from keras.datasets import mnist
  from keras.models import Sequential
  from keras.layers import Dense, Dropout, Activation, Flatten
  from keras.layers import Convolution2D, MaxPooling2D
  from keras.utils import np_utils
  
  batch_size = 128
  nb_classes = 10
  nb_epoch = 12
  
  # 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
  
  # the data, shuffled and split between train and test sets
  (X_train, y_train), (X_test, y_test) = mnist.load_data()
  
  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)
  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(y_train, nb_classes)
  Y_test = np_utils.to_categorical(y_test, nb_classes)
  
  model = Sequential()
  
  model.add(Convolution2D(nb_filters, nb_conv, nb_conv,
                          border_mode='valid',
                          input_shape=(1, img_rows, img_cols)))
  model.add(Activation('relu'))
  model.add(Convolution2D(nb_filters, nb_conv, nb_conv))
  model.add(Activation('relu'))
  model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
  model.add(Dropout(0.25))
  
  model.add(Flatten())
  model.add(Dense(128))
  model.add(Activation('relu'))
  model.add(Dropout(0.5))
  model.add(Dense(nb_classes))
  model.add(Activation('softmax'))
  
  model.compile(loss='categorical_crossentropy',
                optimizer='adadelta',
                metrics=['accuracy'])
  
  model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
            verbose=1, validation_data=(X_test, Y_test))
  score = model.evaluate(X_test, Y_test, verbose=0)
  print('Test score:', score[0])
  print('Test accuracy:', score[1])
  
new_contents: '''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 numpy as np
  np.random.seed(1337)  # for reproducibility
  
  from keras.datasets import mnist
  from keras.models import Sequential
  from keras.layers import Dense, Dropout, Activation, Flatten
  from keras.layers import Convolution2D, MaxPooling2D
  from keras.utils import np_utils
  
  batch_size = 128
  nb_classes = 10
  nb_epoch = 12
  
  # 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
  kernel_size = (3, 3)
  
  # the data, shuffled and split between train and test sets
  (X_train, y_train), (X_test, y_test) = mnist.load_data()
  
  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)
  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(y_train, nb_classes)
  Y_test = np_utils.to_categorical(y_test, nb_classes)
  
  model = Sequential()
  
  model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1],
                          border_mode='valid',
                          input_shape=(1, img_rows, img_cols)))
  model.add(Activation('relu'))
  model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1]))
  model.add(Activation('relu'))
  model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
  model.add(Dropout(0.25))
  
  model.add(Flatten())
  model.add(Dense(128))
  model.add(Activation('relu'))
  model.add(Dropout(0.5))
  model.add(Dense(nb_classes))
  model.add(Activation('softmax'))
  
  model.compile(loss='categorical_crossentropy',
                optimizer='adadelta',
                metrics=['accuracy'])
  
  model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
            verbose=1, validation_data=(X_test, Y_test))
  score = model.evaluate(X_test, Y_test, verbose=0)
  print('Test score:', score[0])
  print('Test accuracy:', score[1])
  
subject: Fix style in example