"Can I get multiple predictions using Neural Network?"

I am trying to create a Neural Net that can predict more than one output. The training set is [0,0,0,0,a,a] and the predicted output must come from another input such as [0,0,0,0] and predict [a,a]. My program is currently working but it will only show the predicted output of the labeled column of choice. I wanted to know if there is a possibility of there been more than 1 output at the same time.

I already tried to use loop labels but to no success. Am I missing something? Thanks.

edit:This is the code

<?xml version="1.0" encoding="UTF-8"?><process version="9.1.000">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="9.1.000" expanded="true" name="Process">
    <parameter key="logverbosity" value="init"/>
    <parameter key="random_seed" value="2001"/>
    <parameter key="send_mail" value="never"/>
    <parameter key="notification_email" value=""/>
    <parameter key="process_duration_for_mail" value="30"/>
    <parameter key="encoding" value="SYSTEM"/>
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="9.1.000" expanded="true" height="68" name="Retrieve Testing Pls" width="90" x="45" y="34">
        <parameter key="repository_entry" value="//Local Repository/data/Testing Pls"/>
      </operator>
      <operator activated="true" class="set_role" compatibility="9.1.000" expanded="true" height="82" name="Set Role" width="90" x="112" y="187">
        <parameter key="attribute_name" value="b1"/>
        <parameter key="target_role" value="label"/>
        <list key="set_additional_roles">
          <parameter key="b2" value="label"/>
          <parameter key="b3" value="label"/>
        </list>
      </operator>
      <operator activated="true" class="multiply" compatibility="9.1.000" expanded="true" height="103" name="Multiply" width="90" x="313" y="187"/>
      <operator activated="true" class="filter_examples" compatibility="9.1.000" expanded="true" height="103" name="Filter Examples (2)" width="90" x="514" y="238">
        <parameter key="parameter_expression" value=""/>
        <parameter key="condition_class" value="custom_filters"/>
        <parameter key="invert_filter" value="false"/>
        <list key="filters_list">
          <parameter key="filters_entry_key" value="b1.is_missing."/>
          <parameter key="filters_entry_key" value="b2.is_missing."/>
          <parameter key="filters_entry_key" value="b3.is_missing."/>
        </list>
        <parameter key="filters_logic_and" value="true"/>
        <parameter key="filters_check_metadata" value="true"/>
      </operator>
      <operator activated="true" class="filter_examples" compatibility="9.1.000" expanded="true" height="103" name="Filter Examples" width="90" x="447" y="34">
        <parameter key="parameter_expression" value=""/>
        <parameter key="condition_class" value="custom_filters"/>
        <parameter key="invert_filter" value="false"/>
        <list key="filters_list">
          <parameter key="filters_entry_key" value="b1.is_not_missing."/>
          <parameter key="filters_entry_key" value="b2.is_not_missing."/>
          <parameter key="filters_entry_key" value="b3.is_not_missing."/>
        </list>
        <parameter key="filters_logic_and" value="true"/>
        <parameter key="filters_check_metadata" value="true"/>
      </operator>
      <operator activated="true" class="neural_net" compatibility="9.1.000" expanded="true" height="82" name="Neural Net" width="90" x="581" y="34">
        <list key="hidden_layers">
          <parameter key="h1" value="4"/>
        </list>
        <parameter key="training_cycles" value="200"/>
        <parameter key="learning_rate" value="0.01"/>
        <parameter key="momentum" value="0.9"/>
        <parameter key="decay" value="false"/>
        <parameter key="shuffle" value="true"/>
        <parameter key="normalize" value="true"/>
        <parameter key="error_epsilon" value="1.0E-4"/>
        <parameter key="use_local_random_seed" value="false"/>
        <parameter key="local_random_seed" value="1992"/>
      </operator>
      <operator activated="true" class="apply_model" compatibility="9.1.000" expanded="true" height="82" name="Apply Model" width="90" x="715" y="136">
        <list key="application_parameters"/>
        <parameter key="create_view" value="false"/>
      </operator>
      <connect from_op="Retrieve Testing Pls" from_port="output" to_op="Set Role" to_port="example set input"/>
      <connect from_op="Set Role" from_port="example set output" to_op="Multiply" to_port="input"/>
      <connect from_op="Multiply" from_port="output 1" to_op="Filter Examples" to_port="example set input"/>
      <connect from_op="Multiply" from_port="output 2" to_op="Filter Examples (2)" to_port="example set input"/>
      <connect from_op="Filter Examples (2)" from_port="example set output" to_op="Apply Model" to_port="unlabelled data"/>
      <connect from_op="Filter Examples" from_port="example set output" to_op="Neural Net" to_port="training set"/>
      <connect from_op="Neural Net" from_port="model" to_op="Apply Model" to_port="model"/>
      <connect from_op="Apply Model" from_port="labelled data" to_port="result 1"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="0"/>
      <portSpacing port="sink_result 2" spacing="0"/>
    </process>
  </operator>
</process>