01569e73dffe4315bee627906abf2f01b1d860e5,examples/mnist/keras/mnist_tf_ds.py,,main_fun,#Any#Any#,6

Before Change

  // tfos: /path/to/mnist/tfr/train/part-r-*
  image_pattern = ctx.absolute_path(args.images_labels)

  options = tf.data.Options()
  options.experimental_distribute.auto_shard = False

  ds = tf.data.Dataset.list_files(image_pattern)
  ds = ds.with_options(options)
  ds = ds.repeat(args.epochs).shuffle(BUFFER_SIZE)
  ds = ds.interleave(tf.data.TFRecordDataset)
  train_datasets_unbatched = ds.map(parse_tfos)

  def build_and_compile_cnn_model():
    model = tf.keras.Sequential([
        tf.keras.layers.Conv2D(32, 3, activation="relu", input_shape=(28, 28, 1)),

After Change

  ds = ds.repeat(args.epochs).shuffle(BUFFER_SIZE)
  ds = ds.interleave(tf.data.TFRecordDataset)

  if args.data_format == "tfds":
    train_datasets_unbatched = ds.map(parse_tfds)
  else:  // "tfos"
    train_datasets_unbatched = ds.map(parse_tfos)

  def build_and_compile_cnn_model():
    model = tf.keras.Sequential([
        tf.keras.layers.Conv2D(32, 3, activation="relu", input_shape=(28, 28, 1)),
        tf.keras.layers.MaxPooling2D(),

Instances