text: """
  COPYRIGHT
  
  All contributions by François Chollet:
  Copyright (c) 2015, François Chollet.
  All rights reserved.
  
  All contributions by Google:
  Copyright (c) 2015, Google, Inc.
  All rights reserved.
  
  All contributions by Microsoft:
  Copyright (c) 2017, Microsoft, Inc.
  All rights reserved.
  
  All other contributions:
  Copyright (c) 2015 - 2017, the respective contributors.
  All rights reserved.
  
  Each contributor holds copyright over their respective contributions.
  The project versioning (Git) records all such contribution source information.
  
  LICENSE
  
  The MIT License (MIT)
  
  Permission is hereby granted, free of charge, to any person obtaining a copy
  of this software and associated documentation files (the "Software"), to deal
  in the Software without restriction, including without limitation the rights
  to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  copies of the Software, and to permit persons to whom the Software is
  furnished to do so, subject to the following conditions:
  
  The above copyright notice and this permission notice shall be included in all
  copies or substantial portions of the Software.
  
  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  SOFTWARE.
  
  ---
  
  This is a modification of a Keras example CNN script [1] for the purposes of the
  'How to Run a Keras Model in the Browser with Keras.js' article [2] published on the Intoli blog.
  
  [1]: https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py
  [2]: https://intoli.com/blog/keras-weight-transfer/
  """
  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 = 1  # Note that this script uses only one epoch.
  
  # Load, restrict, and prepare data.
  img_rows, img_cols = 28, 28
  (x_train, y_train), (x_test, y_test) = mnist.load_data()
  
  x_train = x_train[:1280]
  y_train = y_train[:1280]
  
  x_test = x_test[:512]
  y_test = y_test[:512]
  
  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')
  print(epochs, 'epochs')
  
  y_train = keras.utils.to_categorical(y_train, num_classes)
  y_test = keras.utils.to_categorical(y_test, num_classes)
  
  # Define, compile, and train model.
  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)
  
  print('Test loss:', score[0])
  print('Test accuracy:', score[1])
  print('-' * 80)
  
  # Export the trained model.
  model.save('model.h5')
  