old_contents: import keras 
  from keras.datasets import mnist
  from keras.layers import Dense
  from keras.layers import SimpleRNN
  from keras.models import Sequential
  from keras.optimizers import SGD
   
  batch_size = 128
  epochs = 100
  learning_rate = 0.005
  momentum = 0.9
  time_step = 28
   
  (x_train, y_train), (x_test, y_test) = mnist.load_data()
  
  x_train = x_train.reshape(x_train.shape[0], time_step, 784 // time_step).astype('float32') / 255
  y_train = keras.utils.to_categorical(y_train, num_classes=10)
  
  x_test = x_test.reshape(x_test.shape[0], time_step, 784 // time_step).astype('float32') / 255
  y_test = keras.utils.to_categorical(y_test, num_classes=10)
  
  model = Sequential()
  model.add(SimpleRNN(128,
                      activation='relu',
                      kernel_initializer='he_normal',
                      input_shape=(time_step, 784 // time_step)))
  model.add(Dense(10,
                  activation='softmax',
                  kernel_initializer='glorot_uniform'))
  model.summary()
  model.compile(loss='categorical_crossentropy',
                optimizer=SGD(lr=learning_rate, momentum=momentum, nesterov=True),
                metrics=['accuracy'])
   
  history = model.fit(x_train,
                      y_train,
                      batch_size=batch_size,
                      epochs=epochs,
                      validation_data=(x_test, y_test))
  
new_contents: import keras 
  from keras.datasets import mnist
  from keras.layers import Dense, SimpleRNN
  from keras.models import Sequential
  from keras.optimizers import SGD
   
  batch_size = 128
  epochs = 100
  learning_rate = 0.005
  momentum = 0.9
  time_step = 28
   
  (x_train, y_train), (x_test, y_test) = mnist.load_data()
  
  x_train = x_train.reshape(x_train.shape[0], time_step, 784 // time_step).astype('float32') / 255
  y_train = keras.utils.to_categorical(y_train, num_classes=10)
  
  x_test = x_test.reshape(x_test.shape[0], time_step, 784 // time_step).astype('float32') / 255
  y_test = keras.utils.to_categorical(y_test, num_classes=10)
  
  model = Sequential()
  model.add(SimpleRNN(128,
                      activation='relu',
                      kernel_initializer='he_normal',
                      input_shape=(time_step, 784 // time_step)))
  model.add(Dense(10,
                  activation='softmax',
                  kernel_initializer='glorot_uniform'))
  model.summary()
  model.compile(loss='categorical_crossentropy',
                optimizer=SGD(lr=learning_rate, momentum=momentum, nesterov=True),
                metrics=['accuracy'])
   
  history = model.fit(x_train,
                      y_train,
                      batch_size=batch_size,
                      epochs=epochs,
                      validation_data=(x_test, y_test))
  
subject: Update Keras.py