981e4266d4ea816b08a762193bd52f40cd1a3242,examples/mnist/keras/mnist_inference.py,,inference,#Any#Any#Any#,26

Before Change

  ds = tf.data.Dataset.list_files("{}/part-*".format(args.images_labels))
  ds = ds.shard(num_workers, worker_num)
  ds = ds.interleave(tf.data.TFRecordDataset, cycle_length=1)
  ds = ds.map(parse_tfr).batch(10)
  iterator = ds.make_one_shot_iterator()
  image_label = iterator.get_next(name="inf_image")

  // create an output file per spark worker for the predictions
  tf.gfile.MakeDirs(args.output)
  output_file = tf.gfile.GFile("{}/part-{:05d}".format(args.output, worker_num), mode="w")

After Change

  output_file = tf.io.gfile.GFile("{}/part-{:05d}".format(args.output, worker_num), mode="w")

  for batch in ds:
    predictions = predict(conv2d_input=batch[0])
    labels = np.reshape(batch[1], -1).astype(np.int)
    preds = np.argmax(predictions["dense_1"], axis=1)
    for x in zip(labels, preds):
      output_file.write("{} {}\n".format(x[0], x[1]))

  output_file.close()

Instances