old_contents: """
  To know more or get code samples, please visit my website:
  https://morvanzhou.github.io/tutorials/
  Or search: 莫烦Python
  Thank you for supporting!
  """
  
  # please note, all tutorial code are running under python3.5.
  # If you use the version like python2.7, please modify the code accordingly
  
  # 8 - RNN Classifier example
  
  # to try tensorflow, un-comment following two lines
  # import os
  # os.environ['KERAS_BACKEND']='tensorflow'
  
  import numpy as np
  np.random.seed(1337)  # for reproducibility
  
  from keras.datasets import mnist
  from keras.utils import np_utils
  from keras.models import Sequential
  from keras.layers import SimpleRNN, Activation, Dense
  from keras.optimizers import Adam
  
  TIME_STEPS = 28     # same as the height of the image
  INPUT_SIZE = 28     # same as the width of the image
  BATCH_SIZE = 50
  BATCH_INDEX = 0
  OUTPUT_SIZE = 10
  CELL_SIZE = 50
  LR = 0.001
  
  
  # download the mnist to the path '~/.keras/datasets/' if it is the first time to be called
  # X shape (60,000 28x28), y shape (10,000, )
  (X_train, y_train), (X_test, y_test) = mnist.load_data()
  
  # data pre-processing
  X_train = X_train.reshape(-1, 28, 28) / 256.      # normalize
  X_test = X_test.reshape(-1, 28, 28) / 256.        # normalize
  y_train = np_utils.to_categorical(y_train, nb_classes=10)
  y_test = np_utils.to_categorical(y_test, nb_classes=10)
  
  # build RNN model
  model = Sequential()
  
  # RNN cell
  model.add(SimpleRNN(
      # for batch_input_shape, if using tensorflow as the backend, we have to put None for the batch_size.
      # Otherwise, model.evaluate() will get error.
      batch_input_shape=(None, TIME_STEPS, INPUT_SIZE),       # Or: input_dim=INPUT_SIZE, input_length=TIME_STEPS,
      output_dim=CELL_SIZE,
      unroll=True,
  ))
  
  # output layer
  model.add(Dense(OUTPUT_SIZE))
  model.add(Activation('softmax'))
  
  # optimizer
  adam = Adam(LR)
  model.compile(optimizer=adam,
                loss='categorical_crossentropy',
                metrics=['accuracy'])
  
  # training
  for step in range(4001):
      # data shape = (batch_num, steps, inputs/outputs)
      X_batch = X_train[BATCH_INDEX: BATCH_INDEX+BATCH_SIZE, :, :]
      Y_batch = y_train[BATCH_INDEX: BATCH_INDEX+BATCH_SIZE, :]
      cost = model.train_on_batch(X_batch, Y_batch)
      BATCH_INDEX += BATCH_SIZE
      BATCH_INDEX = 0 if BATCH_INDEX >= X_train.shape[0] else BATCH_INDEX
  
      if step % 500 == 0:
          cost, accuracy = model.evaluate(X_test, y_test, batch_size=y_test.shape[0], verbose=False)
          print('test cost: ', cost, 'test accuracy: ', accuracy)
  
  
  
  
  
new_contents: """
  To know more or get code samples, please visit my website:
  https://morvanzhou.github.io/tutorials/
  Or search: 莫烦Python
  Thank you for supporting!
  """
  
  # please note, all tutorial code are running under python3.5.
  # If you use the version like python2.7, please modify the code accordingly
  
  # 8 - RNN Classifier example
  
  # to try tensorflow, un-comment following two lines
  # import os
  # os.environ['KERAS_BACKEND']='tensorflow'
  
  import numpy as np
  np.random.seed(1337)  # for reproducibility
  
  from keras.datasets import mnist
  from keras.utils import np_utils
  from keras.models import Sequential
  from keras.layers import SimpleRNN, Activation, Dense
  from keras.optimizers import Adam
  
  TIME_STEPS = 28     # same as the height of the image
  INPUT_SIZE = 28     # same as the width of the image
  BATCH_SIZE = 50
  BATCH_INDEX = 0
  OUTPUT_SIZE = 10
  CELL_SIZE = 50
  LR = 0.001
  
  
  # download the mnist to the path '~/.keras/datasets/' if it is the first time to be called
  # X shape (60,000 28x28), y shape (10,000, )
  (X_train, y_train), (X_test, y_test) = mnist.load_data()
  
  # data pre-processing
  X_train = X_train.reshape(-1, 28, 28) / 255.      # normalize
  X_test = X_test.reshape(-1, 28, 28) / 255.        # normalize
  y_train = np_utils.to_categorical(y_train, nb_classes=10)
  y_test = np_utils.to_categorical(y_test, nb_classes=10)
  
  # build RNN model
  model = Sequential()
  
  # RNN cell
  model.add(SimpleRNN(
      # for batch_input_shape, if using tensorflow as the backend, we have to put None for the batch_size.
      # Otherwise, model.evaluate() will get error.
      batch_input_shape=(None, TIME_STEPS, INPUT_SIZE),       # Or: input_dim=INPUT_SIZE, input_length=TIME_STEPS,
      output_dim=CELL_SIZE,
      unroll=True,
  ))
  
  # output layer
  model.add(Dense(OUTPUT_SIZE))
  model.add(Activation('softmax'))
  
  # optimizer
  adam = Adam(LR)
  model.compile(optimizer=adam,
                loss='categorical_crossentropy',
                metrics=['accuracy'])
  
  # training
  for step in range(4001):
      # data shape = (batch_num, steps, inputs/outputs)
      X_batch = X_train[BATCH_INDEX: BATCH_INDEX+BATCH_SIZE, :, :]
      Y_batch = y_train[BATCH_INDEX: BATCH_INDEX+BATCH_SIZE, :]
      cost = model.train_on_batch(X_batch, Y_batch)
      BATCH_INDEX += BATCH_SIZE
      BATCH_INDEX = 0 if BATCH_INDEX >= X_train.shape[0] else BATCH_INDEX
  
      if step % 500 == 0:
          cost, accuracy = model.evaluate(X_test, y_test, batch_size=y_test.shape[0], verbose=False)
          print('test cost: ', cost, 'test accuracy: ', accuracy)
  
  
  
  
  
subject: change to /255.