new_contents: import numpy as np
  
  from keras.models import Sequential
  from keras.layers import Dense, Dropout, Activation, Flatten
  from keras.utils import np_utils
  from keras.optimizers import Adam
  
  from keras.datasets import mnist
  from matplotlib import pyplot as plt
  
  np.random.seed(666)
  (X_train, y_train), (X_test, y_test) = mnist.load_data()
  
  # Input data proprocessing
  X_train = X_train.reshape(X_train.shape[0], 1, 28, 28)
  X_test = X_test.reshape(X_test.shape[0], 1, 28, 28)
  X_train = X_train.astype('float32')
  X_test = X_test.astype('float32')
  X_train /= 255
  X_test /= 255
  
  # Class data preprocessing
  Y_train = np_utils.to_categorical(y_train, 10)
  Y_test = np_utils.to_categorical(y_test, 10)
  
  model = Sequential()
  
  model.add(Flatten(input_shape=(1,28,28)))
  model.add(Dense(128, activation='relu'))
  model.add(Dense(128, activation='relu'))
  model.add(Dense(128, activation='relu'))
  model.add(Dense(128, activation='relu'))
  model.add(Dense(128, activation='relu'))
  model.add(Dense(128, activation='relu'))
  model.add(Dense(10, activation='softmax'))
  
  model.compile(loss='categorical_crossentropy',
                optimizer=Adam(),
                metrics=['accuracy'])
  
  model.fit(X_train, Y_train, batch_size=32, epochs=10, verbose=1)
  score = model.evaluate(X_test, Y_test)
  print(score)
  
  
subject: Add keras mnist code