using multiple inputdef

[kiminh]

Hi,stormy-ua
Many thanks to the project.I've run the example successfuly. Here is my question:
I have two inputs as following python script.

import tensorflow as tf
import numpy as np

export_dir = 'tmp/saved_model_2'

builder = tf.saved_model.builder.SavedModelBuilder(export_dir=export_dir)

with tf.Graph().as_default(), tf.Session().as_default() as sess:
    x1 = tf.placeholder(shape=(2, 3), dtype=tf.float32, name='x1')
    x2 = tf.placeholder(shape=(2, 3), dtype=tf.float32, name='x2')
    y = tf.Variable(np.identity(3), dtype=tf.float32)

    z = tf.add(tf.matmul(x1, y, name='z'),tf.matmul(x2, y, name='z') )

    tf.global_variables_initializer().run()

    zval = z.eval(feed_dict={x1: np.random.randn(2, 3),x2: np.random.randn(2, 3)})

    print(zval)

    x1_proto_info = tf.saved_model.utils.build_tensor_info(x1)
    x2_proto_info = tf.saved_model.utils.build_tensor_info(x2)
    z_proto_info = tf.saved_model.utils.build_tensor_info(z)

    prediction_signature = (
        tf.saved_model.signature_def_utils.build_signature_def(
            inputs={'x1': x1_proto_info,'x2': x2_proto_info},
            outputs={'z': z_proto_info},
            method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME))

    builder.add_meta_graph_and_variables(sess, [tf.saved_model.tag_constants.SERVING],
                                         signature_def_map={
                                             tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: prediction_signature
                                         })
builder.save()

Such usage is wrong. Would you please check it? Thanks a lot.

          input1Array  <- Try { Array.range(0, 6).map(_.toFloat) }
          _           =  println(s"input1 array = ${shows(input1Array)}")

          input2Array  <- Try { Array.range(0, 6).map(_.toFloat) }
          _           =  println(s"input2 array = ${shows(input2Array)}")

          inputArray  <- Try {input1Array ++ input2Array}
          _           =  println(s"input array = ${shows(inputArray)}")

          _ <- (use(serving.tensor(inputArray.slice(0,5),shape = List(2,3))),use(serving.tensor(inputArray.slice(0,5),shape = List(2,3)))){ (input1Tensor,input2Tensor) =>
            for {
              input1Def    <- Try { signature.inputs("x1") }
              input2Def    <- Try { signature.inputs("x2") }
              output1Def   <- Try { signature.outputs("z") }

              output1Array <- serving.eval[Array[Array[Float]]](model, output1Def, Map(input1Def -> input1Tensor,input2Def->input2Tensor))
              _           =  println(s"output: ${shows(output1Array)}")

            } yield ()
          }