old_contents: import keras 
  from keras.datasets import mnist
  from keras.layers import BatchNormalization
  from keras.layers import Activation, Dense, Flatten
  from keras.layers import Conv2D, SeparableConv2D, MaxPooling2D
  from keras.models import Sequential
  from keras.optimizers import SGD
   
  batch_size = 128
  epochs = 20
  num_classes = 10
   
  # input image dimensions
  img_rows, img_cols = 28, 28
   
  (x_train, y_train), (x_test, y_test) = mnist.load_data()
   
  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)
   
  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(Conv2D(24,
                   kernel_initializer="he_normal",
                   kernel_size=(5, 5),
                   input_shape=(img_rows, img_cols, 1)))
  model.add(BatchNormalization())
  model.add(Activation('relu'))
  model.add(MaxPooling2D())
  model.add(SeparableConv2D(48,
                            depthwise_initializer="he_normal",
                            pointwise_initializer="he_normal",
                            kernel_size=(5, 5)))
  model.add(BatchNormalization())
  model.add(Activation('relu'))
  model.add(MaxPooling2D())
  model.add(Flatten())
  model.add(Dense(512,
                  kernel_initializer="he_normal"))
  model.add(BatchNormalization())
  model.add(Activation('relu'))
  model.add(Dense(num_classes,
                  activation='softmax',
                  kernel_initializer="he_normal"))
  model.summary()
  model.compile(loss='categorical_crossentropy',
                optimizer=SGD(lr=0.5),
                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 BatchNormalization
  from keras.layers import Activation, Dense, Flatten
  from keras.layers import Conv2D, SeparableConv2D, MaxPooling2D
  from keras.models import Sequential
  from keras.optimizers import SGD
   
  batch_size = 128
  epochs = 20
  learning_rate = 0.5
  num_classes = 10
   
  # input image dimensions
  img_rows, img_cols = 28, 28
   
  (x_train, y_train), (x_test, y_test) = mnist.load_data()
   
  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)
   
  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(Conv2D(24,
                   kernel_initializer="he_normal",
                   kernel_size=(5, 5),
                   input_shape=(img_rows, img_cols, 1)))
  model.add(BatchNormalization())
  model.add(Activation('relu'))
  model.add(MaxPooling2D())
  model.add(SeparableConv2D(48,
                            depthwise_initializer="he_normal",
                            pointwise_initializer="he_normal",
                            kernel_size=(5, 5)))
  model.add(BatchNormalization())
  model.add(Activation('relu'))
  model.add(MaxPooling2D())
  model.add(Flatten())
  model.add(Dense(512,
                  kernel_initializer="he_normal"))
  model.add(BatchNormalization())
  model.add(Activation('relu'))
  model.add(Dense(num_classes,
                  activation='softmax',
                  kernel_initializer="he_normal"))
  model.summary()
  model.compile(loss='categorical_crossentropy',
                optimizer=SGD(lr=learning_rate),
                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