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

Before Change

  util.single_node_env()

  // load saved_model using default tag and signature
  sess = tf.Session()
  tf.saved_model.loader.load(sess, ["serve"], args.export)

  // parse function for TFRecords
  def parse_tfr(example_proto):
    feature_def = {"label": tf.FixedLenFeature(10, tf.int64),
                   "image": tf.FixedLenFeature(IMAGE_PIXELS * IMAGE_PIXELS, tf.int64)}
    features = tf.parse_single_example(example_proto, feature_def)
    norm = tf.constant(255, dtype=tf.float32, shape=(784,))
    image = tf.div(tf.to_float(features["image"]), norm)
    label = tf.to_float(features["label"])
    return (image, label)

  // define a new tf.data.Dataset (for inferencing)
  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")

  while True:
    try:
      // get images and labels from tf.data.Dataset
      img, lbl = sess.run(["inf_image:0", "inf_image:1"])

      // inference by feeding these images and labels into the input tensors
      // you can view the exported model signatures via:
      //     saved_model_cli show --dir <export_dir> --all

      // note that we feed directly into the graph tensors (bypassing the exported signatures)
      // these tensors will be shown in the "name" field of the signature definitions

      outputs = sess.run(["dense_2/Softmax:0"], feed_dict={"Placeholder:0": img})
      for p in outputs[0]:
        output_file.write("{}\n".format(np.argmax(p)))
    except tf.errors.OutOfRangeError:
      break

After Change

  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()


if __name__ == "__main__":

Instances