text: from __future__ import print_function
  import keras
  from keras.datasets import mnist
  from keras.models import Sequential
  from keras.layers import Dense, Dropout, Flatten
  from keras.layers import Conv2D, MaxPooling2D
  from keras import backend as K
  from keras.models import load_model
  import matplotlib.pyplot as plt
  import tensorflow as tf
  import numpy as np
  import os
  
  '''
  Train and save a neural network model on the MNIST data
  '''
  
  num_classes = 10
  
  
  def preprocessing(x_train, y_train, x_test, y_test):
      # input image dimensions
      img_rows, img_cols = 28, 28
  
      if K.image_data_format() == "channels_first":
          x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
          x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
          input_shape = (1, img_rows, img_cols)
      else:
          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)
          input_shape = (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)
  
      return x_train, y_train, x_test, y_test, input_shape
  
  
  def generate_model(input_shape):
      model = Sequential()
      model.add(
          Conv2D(32, kernel_size=(3, 3), activation="relu", input_shape=input_shape)
      )
      model.add(Conv2D(64, (3, 3), activation="relu"))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout(0.25))
      model.add(Flatten())
      model.add(Dense(128, activation="relu"))
      model.add(Dropout(0.5))
      model.add(Dense(num_classes, activation="softmax"))
  
      model.compile(
          loss=keras.losses.categorical_crossentropy,
          optimizer=keras.optimizers.Adadelta(),
          metrics=["accuracy"],
      )
  
      return model
  
  
  # from https://keras.io/examples/mnist_cnn/
  def main(batch_size=128, epochs=2):
      # the data, split between train and test sets
      (x_train, y_train), (x_test, y_test) = mnist.load_data()
  
      x_train, y_train, x_test, y_test, input_shape = preprocessing(
          x_train, y_train, x_test, y_test
      )
  
      print("x_train shape:", x_train.shape)
      print(x_train.shape[0], "train samples")
      print(x_test.shape[0], "test samples")
  
      model = generate_model(input_shape)
  
      model.fit(
          x_train, y_train, epochs=epochs, verbose=1, validation_data=(x_test, y_test)
      )
      score = model.evaluate(x_test, y_test, verbose=0)
      print("Test loss:", score[0])
      print("Test accuracy:", score[1])
  
      model.save("mnist_model2.h5")
  
  
  if __name__ == "__main__":
      main(batch_size=128, epochs=2)
  