"Can I get multiple predictions using Neural Network?"

Metalik

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>

varunm1

Hi @Metalik

Can you post you XML code and dataset  so that we can see what you are tring to do? Sorry, i am bit confused, what do you mean by having more than one output at same time? Can you provide simple example what you are trying to get? Are you trying to look at the probability of each class for the same sample?

In case if you are new, you can find XML code in (View --> Show Panel --> XML) in menu bar. Copy the whole code and paste here by selecting as shown below.




Thanks
Varun