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Problem with Neural Net "use local random seed"

GonzaloADGonzaloAD Member Posts: 4 Contributor I
edited December 2018 in Help

Hi guys, I have a small trouble.

 

How is it possible that when the "use local random seed" of Neural Net block is not enabled (unchecked) the same NN process does NOT provide the same results?

 

What is the purpose of "use local random seed"?

 

This is my XML process;

 

<?xml version="1.0" encoding="UTF-8"?><process version="8.1.001">
 <operator activated="true" class="read_csv" compatibility="8.1.001" expanded="true" height="68" name="Read CSV" width="90" x="112" y="187">
 <parameter key="csv_file" value="C:\Users\Admin\Desktop\data Example.csv"/>
 <parameter key="column_separators" value=";"/>
 <parameter key="trim_lines" value="false"/>
 <parameter key="use_quotes" value="true"/>
 <parameter key="quotes_character" value="&quot;"/>
 <parameter key="escape_character" value="\"/>
 <parameter key="skip_comments" value="false"/>
 <parameter key="comment_characters" value="#"/>
 <parameter key="parse_numbers" value="true"/>
 <parameter key="decimal_character" value="."/>
 <parameter key="grouped_digits" value="false"/>
 <parameter key="grouping_character" value=","/>
 <parameter key="date_format" value=""/>
 <parameter key="first_row_as_names" value="true"/>
 <list key="annotations"/>
 <parameter key="time_zone" value="SYSTEM"/>
 <parameter key="locale" value="English (United States)"/>
 <parameter key="encoding" value="SYSTEM"/>
 <parameter key="read_all_values_as_polynominal" value="false"/>
 <list key="data_set_meta_data_information"/>
 <parameter key="read_not_matching_values_as_missings" value="true"/>
 <parameter key="datamanagement" value="double_array"/>
 <parameter key="data_management" value="auto"/>
 </operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="8.1.001">
 <operator activated="true" class="select_attributes" compatibility="8.1.001" expanded="true" height="82" name="Select Attributes T" width="90" x="246" y="187">
 <parameter key="attribute_filter_type" value="subset"/>
 <parameter key="attribute" value=""/>
 <parameter key="attributes" value="F|D|C|B|A"/>
 <parameter key="use_except_expression" value="false"/>
 <parameter key="value_type" value="attribute_value"/>
 <parameter key="use_value_type_exception" value="false"/>
 <parameter key="except_value_type" value="time"/>
 <parameter key="block_type" value="attribute_block"/>
 <parameter key="use_block_type_exception" value="false"/>
 <parameter key="except_block_type" value="value_matrix_row_start"/>
 <parameter key="invert_selection" value="false"/>
 <parameter key="include_special_attributes" value="false"/>
 </operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="8.1.001">
 <operator activated="true" class="set_role" compatibility="8.1.001" expanded="true" height="82" name="Set Role T" width="90" x="380" y="187">
 <parameter key="attribute_name" value="F"/>
 <parameter key="target_role" value="label"/>
 <list key="set_additional_roles"/>
 </operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="8.1.001">
 <operator activated="true" class="multiply" compatibility="8.1.001" expanded="true" height="145" name="Multiply Data T" width="90" x="514" y="187"/>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="8.1.001">
 <operator activated="true" class="neural_net" compatibility="8.1.001" expanded="true" height="82" name="Neural Net" width="90" x="715" y="136">
 <list key="hidden_layers">
 <parameter key="Hidden" value="21"/>
 </list>
 <parameter key="training_cycles" value="32000"/>
 <parameter key="learning_rate" value="0.1"/>
 <parameter key="momentum" value="0.1"/>
 <parameter key="decay" value="false"/>
 <parameter key="shuffle" value="true"/>
 <parameter key="normalize" value="true"/>
 <parameter key="error_epsilon" value="1.0E-5"/>
 <parameter key="use_local_random_seed" value="false"/>
 <parameter key="local_random_seed" value="1992"/>
 </operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="8.1.001">
 <operator activated="true" class="apply_model" compatibility="8.1.001" expanded="true" height="82" name="Apply Model NN" width="90" x="849" y="136">
 <list key="application_parameters"/>
 <parameter key="create_view" value="false"/>
 </operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="8.1.001">
 <operator activated="true" class="performance_regression" compatibility="8.1.001" expanded="true" height="82" name="Performance NN" width="90" x="983" y="136">
 <parameter key="main_criterion" value="first"/>
 <parameter key="root_mean_squared_error" value="true"/>
 <parameter key="absolute_error" value="false"/>
 <parameter key="relative_error" value="true"/>
 <parameter key="relative_error_lenient" value="false"/>
 <parameter key="relative_error_strict" value="false"/>
 <parameter key="normalized_absolute_error" value="false"/>
 <parameter key="root_relative_squared_error" value="false"/>
 <parameter key="squared_error" value="false"/>
 <parameter key="correlation" value="true"/>
 <parameter key="squared_correlation" value="true"/>
 <parameter key="prediction_average" value="false"/>
 <parameter key="spearman_rho" value="false"/>
 <parameter key="kendall_tau" value="false"/>
 <parameter key="skip_undefined_labels" value="true"/>
 <parameter key="use_example_weights" value="true"/>
 </operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="8.1.001">
 <operator activated="true" class="neural_net" compatibility="8.1.001" expanded="true" height="82" name="Neural Net (2)" width="90" x="715" y="238">
 <list key="hidden_layers">
 <parameter key="Hidden" value="21"/>
 </list>
 <parameter key="training_cycles" value="32000"/>
 <parameter key="learning_rate" value="0.1"/>
 <parameter key="momentum" value="0.1"/>
 <parameter key="decay" value="false"/>
 <parameter key="shuffle" value="true"/>
 <parameter key="normalize" value="true"/>
 <parameter key="error_epsilon" value="1.0E-5"/>
 <parameter key="use_local_random_seed" value="false"/>
 <parameter key="local_random_seed" value="1992"/>
 </operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="8.1.001">
 <operator activated="true" class="apply_model" compatibility="8.1.001" expanded="true" height="82" name="Apply Model NN (2)" width="90" x="849" y="238">
 <list key="application_parameters"/>
 <parameter key="create_view" value="false"/>
 </operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="8.1.001">
 <operator activated="true" class="performance_regression" compatibility="8.1.001" expanded="true" height="82" name="Performance NN (2)" width="90" x="983" y="238">
 <parameter key="main_criterion" value="first"/>
 <parameter key="root_mean_squared_error" value="true"/>
 <parameter key="absolute_error" value="false"/>
 <parameter key="relative_error" value="true"/>
 <parameter key="relative_error_lenient" value="false"/>
 <parameter key="relative_error_strict" value="false"/>
 <parameter key="normalized_absolute_error" value="false"/>
 <parameter key="root_relative_squared_error" value="false"/>
 <parameter key="squared_error" value="false"/>
 <parameter key="correlation" value="true"/>
 <parameter key="squared_correlation" value="true"/>
 <parameter key="prediction_average" value="false"/>
 <parameter key="spearman_rho" value="false"/>
 <parameter key="kendall_tau" value="false"/>
 <parameter key="skip_undefined_labels" value="true"/>
 <parameter key="use_example_weights" value="true"/>
 </operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="8.1.001">
 <operator activated="true" class="neural_net" compatibility="8.1.001" expanded="true" height="82" name="Neural Net (3)" width="90" x="715" y="340">
 <list key="hidden_layers">
 <parameter key="Hidden" value="21"/>
 </list>
 <parameter key="training_cycles" value="32000"/>
 <parameter key="learning_rate" value="0.1"/>
 <parameter key="momentum" value="0.1"/>
 <parameter key="decay" value="false"/>
 <parameter key="shuffle" value="true"/>
 <parameter key="normalize" value="true"/>
 <parameter key="error_epsilon" value="1.0E-5"/>
 <parameter key="use_local_random_seed" value="false"/>
 <parameter key="local_random_seed" value="1992"/>
 </operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="8.1.001">
 <operator activated="true" class="apply_model" compatibility="8.1.001" expanded="true" height="82" name="Apply Model NN (3)" width="90" x="849" y="340">
 <list key="application_parameters"/>
 <parameter key="create_view" value="false"/>
 </operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="8.1.001">
 <operator activated="true" class="performance_regression" compatibility="8.1.001" expanded="true" height="82" name="Performance NN (3)" width="90" x="983" y="340">
 <parameter key="main_criterion" value="first"/>
 <parameter key="root_mean_squared_error" value="true"/>
 <parameter key="absolute_error" value="false"/>
 <parameter key="relative_error" value="true"/>
 <parameter key="relative_error_lenient" value="false"/>
 <parameter key="relative_error_strict" value="false"/>
 <parameter key="normalized_absolute_error" value="false"/>
 <parameter key="root_relative_squared_error" value="false"/>
 <parameter key="squared_error" value="false"/>
 <parameter key="correlation" value="true"/>
 <parameter key="squared_correlation" value="true"/>
 <parameter key="prediction_average" value="false"/>
 <parameter key="spearman_rho" value="false"/>
 <parameter key="kendall_tau" value="false"/>
 <parameter key="skip_undefined_labels" value="true"/>
 <parameter key="use_example_weights" value="true"/>
 </operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="8.1.001">
 <operator activated="true" class="neural_net" compatibility="8.1.001" expanded="true" height="82" name="Neural Net 21" width="90" x="715" y="442">
 <list key="hidden_layers">
 <parameter key="Hidden" value="21"/>
 </list>
 <parameter key="training_cycles" value="32000"/>
 <parameter key="learning_rate" value="0.1"/>
 <parameter key="momentum" value="0.1"/>
 <parameter key="decay" value="false"/>
 <parameter key="shuffle" value="true"/>
 <parameter key="normalize" value="true"/>
 <parameter key="error_epsilon" value="1.0E-5"/>
 <parameter key="use_local_random_seed" value="true"/>
 <parameter key="local_random_seed" value="1992"/>
 </operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="8.1.001">
 <operator activated="true" class="apply_model" compatibility="8.1.001" expanded="true" height="82" name="Apply Model NN (4)" width="90" x="849" y="442">
 <list key="application_parameters"/>
 <parameter key="create_view" value="false"/>
 </operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="8.1.001">
 <operator activated="true" class="performance_regression" compatibility="8.1.001" expanded="true" height="82" name="Performance NN (4)" width="90" x="983" y="442">
 <parameter key="main_criterion" value="first"/>
 <parameter key="root_mean_squared_error" value="true"/>
 <parameter key="absolute_error" value="false"/>
 <parameter key="relative_error" value="true"/>
 <parameter key="relative_error_lenient" value="false"/>
 <parameter key="relative_error_strict" value="false"/>
 <parameter key="normalized_absolute_error" value="false"/>
 <parameter key="root_relative_squared_error" value="false"/>
 <parameter key="squared_error" value="false"/>
 <parameter key="correlation" value="true"/>
 <parameter key="squared_correlation" value="true"/>
 <parameter key="prediction_average" value="false"/>
 <parameter key="spearman_rho" value="false"/>
 <parameter key="kendall_tau" value="false"/>
 <parameter key="skip_undefined_labels" value="true"/>
 <parameter key="use_example_weights" value="true"/>
 </operator>
</process>

 

 Can anyone help me?
Thanks!!!!

Tagged:

Answers

  • yyhuangyyhuang Administrator, Employee-RapidMiner, RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 364 RM Data Scientist

    Hi @GonzaloAD,

     

    You xml code is somehow broken but I get your point.

     

    The NN operator in RapidMiner learns a model by means of a feed-forward neural network trained by a back propagation algorithm (multi-layer perceptron).

    pseudocode.PNG

    The neural network corresponds to a function {\displaystyle y=f_{N}(w,x)} which, given a weight w,, maps an input x to an output y.produces a sequence of weights {\displaystyle w_{0},w_{1},\dots ,w_{p}}starting from some initial weight w0 , usually chosen at random. 

     

    Who is controlling the 'randomness' of the initial weight? The random seed. As you may know, we have no real random number generator in computer science. A seed is to be used in a pseudorandom number generator. 

     

    HTH,

    YY

     
  • GonzaloADGonzaloAD Member Posts: 4 Contributor I

    I got it.

    The reason was that without a specific random seed the initialization value is random.

    Thank you very much!!!

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