7a34628edd88326e04d405e6a079f0b63c6197b7,samples/cookbook/regression/dnn_regression.py,,main,#Any#,39

Before Change

  test_y /= args.price_norm_factor

  // Build the training dataset.
  train = (
      automobile_data.make_dataset(train_x, train_y)
      // Shuffling with a buffer larger than the data set ensures
      // that the examples are well mixed.
      .shuffle(1000).batch(args.batch_size)
      // Repeat forever
      .repeat())

  // Build the validation dataset.
  test = automobile_data.make_dataset(test_x, test_y).batch(args.batch_size)

  // Use the same categorical columns as in `linear_regression_categorical`
  body_style_vocab = ["hardtop", "wagon", "sedan", "hatchback", "convertible"]
  body_style_column = tf.feature_column.categorical_column_with_vocabulary_list(

After Change

  (train_x,train_y), (test_x, test_y) = automobile_data.load_data()

  train_y /= args.price_norm_factor
  test_y /= args.price_norm_factor

  // Build the training dataset.
  train = automobile_data.make_dataset(args.batch_size, train_x, train_y, True, 1000)

  // Build the validation dataset.
  test = automobile_data.make_dataset(args.batch_size, test_x, test_y)

  // Use the same categorical columns as in `linear_regression_categorical`
  body_style_vocab = ["hardtop", "wagon", "sedan", "hatchback", "convertible"]
  body_style_column = tf.feature_column.categorical_column_with_vocabulary_list(

Instances