old_contents: import keras 
  from keras.datasets import mnist
  from keras.layers import Dense
  from keras.layers import Bidirectional, SimpleRNN
  from keras.models import Sequential
  from keras.optimizers import SGD
   
  batch_size = 128
  epochs = 100
  num_classes = 10
  time_step = 28
   
  (x_train, y_train), (x_test, y_test) = mnist.load_data()
  
  x_train = x_train.reshape(60000, time_step, 784 // time_step)
  x_test = x_test.reshape(10000, time_step, 784 // time_step)
  x_train = x_train.astype('float32')
  x_test = x_test.astype('float32')
  x_train /= 255
  x_test /= 255
  
  # 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(Bidirectional(SimpleRNN(128,
                                    activation='relu'),
                          input_shape=(time_step, 784 // time_step)))
  model.add(Dense(num_classes,
                  activation='softmax'))
  
  model.summary()
  model.compile(loss='categorical_crossentropy',
                optimizer=SGD(lr=0.03, decay=1e-6),
                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
  from keras.layers import Bidirectional, SimpleRNN
  from keras.models import Sequential
  from keras.optimizers import SGD
   
  batch_size = 128
  decay = 1e-6
  learning_rate = 0.03
  epochs = 100
  num_classes = 10
  time_step = 28
   
  (x_train, y_train), (x_test, y_test) = mnist.load_data()
   
  x_train = x_train.reshape(60000, time_step, 784 // time_step)
  x_test = x_test.reshape(10000, time_step, 784 // time_step)
  x_train = x_train.astype('float32')
  x_test = x_test.astype('float32')
  x_train /= 255
  x_test /= 255
   
  # 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(Bidirectional(SimpleRNN(128,
                                    activation='relu'),
                          input_shape=(time_step, 784 // time_step)))
  model.add(Dense(num_classes,
                  activation='softmax'))
   
  model.summary()
  model.compile(loss='categorical_crossentropy',
                optimizer=SGD(lr=learning_rate, decay=decay),
                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