Received this message while running a small neural net example. Did not get a stack trace message.
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.0">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="5.0.10" expanded="true" name="Process">
<parameter key="parallelize_main_process" value="true"/>
<process expanded="true" height="691" width="1090">
<operator activated="true" class="retrieve" compatibility="5.0.10" expanded="true" height="60" name="Retrieve" width="90" x="45" y="30">
<parameter key="repository_entry" value="//Samples/data/Sonar"/>
</operator>
<operator activated="true" class="generate_id" compatibility="5.0.10" expanded="true" height="76" name="Generate ID" width="90" x="45" y="165">
<parameter key="create_nominal_ids" value="true"/>
</operator>
<operator activated="true" class="subprocess" compatibility="5.0.10" expanded="true" height="76" name="Make Imbalance" width="90" x="45" y="300">
<process expanded="true" height="556" width="1139">
<operator activated="true" class="nominal_to_numerical" compatibility="5.0.10" expanded="true" height="94" name="Nominal to Numerical (2)" width="90" x="112" y="120">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="class"/>
<parameter key="include_special_attributes" value="true"/>
</operator>
<operator activated="true" class="split_data" compatibility="5.0.10" expanded="true" height="94" name="Split Data (3)" width="90" x="246" y="165">
<enumeration key="partitions">
<parameter key="ratio" value="0.1"/>
<parameter key="ratio" value="0.9"/>
</enumeration>
<parameter key="sampling_type" value="linear sampling"/>
</operator>
<operator activated="true" class="filter_examples" compatibility="5.0.10" expanded="true" height="76" name="Filter Examples (4)" width="90" x="380" y="255">
<parameter key="condition_class" value="attribute_value_filter"/>
<parameter key="parameter_string" value="class=1"/>
</operator>
<operator activated="true" class="filter_examples" compatibility="5.0.10" expanded="true" height="76" name="Filter Examples (3)" width="90" x="380" y="75">
<parameter key="condition_class" value="attribute_value_filter"/>
<parameter key="parameter_string" value="class<1"/>
</operator>
<operator activated="true" class="append" compatibility="5.0.10" expanded="true" height="94" name="Append (2)" width="90" x="658" y="179"/>
<connect from_port="in 1" to_op="Nominal to Numerical (2)" to_port="example set input"/>
<connect from_op="Nominal to Numerical (2)" from_port="example set output" to_op="Split Data (3)" to_port="example set"/>
<connect from_op="Split Data (3)" from_port="partition 1" to_op="Filter Examples (3)" to_port="example set input"/>
<connect from_op="Split Data (3)" from_port="partition 2" to_op="Filter Examples (4)" to_port="example set input"/>
<connect from_op="Filter Examples (4)" from_port="example set output" to_op="Append (2)" to_port="example set 1"/>
<connect from_op="Filter Examples (3)" from_port="example set output" to_op="Append (2)" to_port="example set 2"/>
<connect from_op="Append (2)" from_port="merged set" to_port="out 1"/>
<portSpacing port="source_in 1" spacing="0"/>
<portSpacing port="source_in 2" spacing="0"/>
<portSpacing port="sink_out 1" spacing="0"/>
<portSpacing port="sink_out 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="multiply" compatibility="5.0.10" expanded="true" height="94" name="Multiply" width="90" x="45" y="390"/>
<operator activated="true" class="split_data" compatibility="5.0.10" expanded="true" height="94" name="Split Data" width="90" x="45" y="525">
<enumeration key="partitions">
<parameter key="ratio" value="0.5"/>
<parameter key="ratio" value="0.5"/>
</enumeration>
<parameter key="sampling_type" value="stratified sampling"/>
</operator>
<operator activated="true" class="numerical_to_polynominal" compatibility="5.0.10" expanded="true" height="76" name="Numerical to Polynominal" width="90" x="246" y="210">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="class"/>
<parameter key="include_special_attributes" value="true"/>
</operator>
<operator activated="true" class="generate_weight_stratification" compatibility="5.0.10" expanded="true" height="76" name="Generate Weight (Stratification)" width="90" x="380" y="210"/>
<operator activated="true" class="nominal_to_numerical" compatibility="5.0.10" expanded="true" height="94" name="Nominal to Numerical (3)" width="90" x="514" y="210">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="class"/>
<parameter key="include_special_attributes" value="true"/>
</operator>
<operator activated="true" class="neural_net" compatibility="5.0.10" expanded="true" height="76" name="NNModel" width="90" x="715" y="165">
<list key="hidden_layers">
<parameter key="1" value="-1"/>
<parameter key="2" value="-1"/>
<parameter key="3" value="-1"/>
</list>
<parameter key="training_cycles" value="5000"/>
<parameter key="learning_rate" value="0.49999999999999994"/>
<parameter key="momentum" value="0.59"/>
</operator>
<operator activated="true" class="numerical_to_polynominal" compatibility="5.0.10" expanded="true" height="76" name="Numerical to Polynominal (3)" width="90" x="313" y="345">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="class"/>
<parameter key="include_special_attributes" value="true"/>
</operator>
<operator activated="true" class="generate_weight_stratification" compatibility="5.0.10" expanded="true" height="76" name="Generate Weight (3)" width="90" x="447" y="345"/>
<operator activated="true" class="nominal_to_numerical" compatibility="5.0.10" expanded="true" height="94" name="Nominal to Numerical (5)" width="90" x="583" y="345">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="class"/>
<parameter key="include_special_attributes" value="true"/>
</operator>
<operator activated="true" class="apply_model" compatibility="5.0.10" expanded="true" height="76" name="Apply Model" width="90" x="782" y="300">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="numerical_to_binominal" compatibility="5.0.10" expanded="true" height="76" name="Numerical to Binominal" width="90" x="916" y="210">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="prediction(class)"/>
<parameter key="include_special_attributes" value="true"/>
<parameter key="min" value="-1000.0"/>
<parameter key="max" value="0.5"/>
</operator>
<operator activated="true" class="numerical_to_polynominal" compatibility="5.0.10" expanded="true" height="76" name="Numerical to Polynominal (2)" width="90" x="246" y="30">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="class"/>
<parameter key="include_special_attributes" value="true"/>
</operator>
<operator activated="true" class="generate_weight_stratification" compatibility="5.0.10" expanded="true" height="76" name="Generate Weight (2)" width="90" x="380" y="30"/>
<operator activated="true" class="nominal_to_numerical" compatibility="5.0.10" expanded="true" height="94" name="Nominal to Numerical (4)" width="90" x="514" y="30">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="class"/>
<parameter key="include_special_attributes" value="true"/>
</operator>
<operator activated="true" class="optimize_parameters_grid" compatibility="5.0.10" expanded="true" height="94" name="Optimize Parameters (Grid)" width="90" x="648" y="30">
<list key="parameters">
<parameter key="NNTrain.learning_rate" value="[.1;.9;3;linear]"/>
<parameter key="NNTrain.momentum" value="[0.0;1.0;4;linear]"/>
</list>
<parameter key="parallelize_optimization_process" value="true"/>
<process expanded="true" height="619" width="1139">
<operator activated="true" class="x_validation" compatibility="5.0.10" expanded="true" height="112" name="Validation" width="90" x="45" y="30">
<parameter key="number_of_validations" value="3"/>
<parameter key="parallelize_training" value="true"/>
<parameter key="parallelize_testing" value="true"/>
<process expanded="true" height="637" width="553">
<operator activated="true" class="neural_net" compatibility="5.0.10" expanded="true" height="76" name="NNTrain" width="90" x="246" y="30">
<list key="hidden_layers"/>
<parameter key="learning_rate" value="0.6333333333333333"/>
<parameter key="momentum" value="1.0"/>
<parameter key="use_local_random_seed" value="true"/>
</operator>
<connect from_port="training" to_op="NNTrain" to_port="training set"/>
<connect from_op="NNTrain" from_port="model" to_port="model"/>
<portSpacing port="source_training" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
</process>
<process expanded="true" height="637" width="553">
<operator activated="true" class="apply_model" compatibility="5.0.10" expanded="true" height="76" name="Apply Model (2)" width="90" x="45" y="30">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance_regression" compatibility="5.0.10" expanded="true" height="76" name="Performance" width="90" x="299" y="30">
<parameter key="absolute_error" value="true"/>
<parameter key="normalized_absolute_error" value="true"/>
<parameter key="squared_error" value="true"/>
</operator>
<connect from_port="model" to_op="Apply Model (2)" to_port="model"/>
<connect from_port="test set" to_op="Apply Model (2)" to_port="unlabelled data"/>
<connect from_op="Apply Model (2)" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
<connect from_op="Performance" from_port="performance" to_port="averagable 1"/>
<portSpacing port="source_model" spacing="0"/>
<portSpacing port="source_test set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="sink_averagable 1" spacing="0"/>
<portSpacing port="sink_averagable 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="log" compatibility="5.0.10" expanded="true" height="76" name="Log" width="90" x="581" y="30">
<parameter key="filename" value="R:\DataRMDep\churntest.log"/>
<list key="log">
<parameter key="train_learn_rate" value="operator.NNTrain.parameter.learning_rate"/>
<parameter key="train_momentum" value="operator.NNTrain.parameter.momentum"/>
<parameter key="performance" value="operator.Validation.value.performance"/>
</list>
</operator>
<connect from_port="input 1" to_op="Validation" to_port="training"/>
<connect from_op="Validation" from_port="averagable 1" to_op="Log" to_port="through 1"/>
<connect from_op="Log" from_port="through 1" to_port="performance"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="source_input 2" spacing="0"/>
<portSpacing port="sink_performance" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
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</operator>
<operator activated="true" class="set_parameters" compatibility="5.0.10" expanded="true" height="60" name="Set Parameters" width="90" x="782" y="30">
<list key="name_map">
<parameter key="NNTrain" value="NNModel"/>
</list>
</operator>
<connect from_op="Retrieve" from_port="output" to_op="Generate ID" to_port="example set input"/>
<connect from_op="Generate ID" from_port="example set output" to_op="Make Imbalance" to_port="in 1"/>
<connect from_op="Make Imbalance" from_port="out 1" to_op="Multiply" to_port="input"/>
<connect from_op="Multiply" from_port="output 1" to_op="Split Data" to_port="example set"/>
<connect from_op="Multiply" from_port="output 2" to_op="Numerical to Polynominal (2)" to_port="example set input"/>
<connect from_op="Split Data" from_port="partition 1" to_op="Numerical to Polynominal" to_port="example set input"/>
<connect from_op="Split Data" from_port="partition 2" to_op="Numerical to Polynominal (3)" to_port="example set input"/>
<connect from_op="Numerical to Polynominal" from_port="example set output" to_op="Generate Weight (Stratification)" to_port="example set input"/>
<connect from_op="Generate Weight (Stratification)" from_port="example set output" to_op="Nominal to Numerical (3)" to_port="example set input"/>
<connect from_op="Nominal to Numerical (3)" from_port="example set output" to_op="NNModel" to_port="training set"/>
<connect from_op="NNModel" from_port="model" to_op="Apply Model" to_port="model"/>
<connect from_op="Numerical to Polynominal (3)" from_port="example set output" to_op="Generate Weight (3)" to_port="example set input"/>
<connect from_op="Generate Weight (3)" from_port="example set output" to_op="Nominal to Numerical (5)" to_port="example set input"/>
<connect from_op="Nominal to Numerical (5)" from_port="example set output" to_op="Apply Model" to_port="unlabelled data"/>
<connect from_op="Apply Model" from_port="labelled data" to_op="Numerical to Binominal" to_port="example set input"/>
<connect from_op="Apply Model" from_port="model" to_port="result 2"/>
<connect from_op="Numerical to Binominal" from_port="example set output" to_port="result 1"/>
<connect from_op="Numerical to Polynominal (2)" from_port="example set output" to_op="Generate Weight (2)" to_port="example set input"/>
<connect from_op="Generate Weight (2)" from_port="example set output" to_op="Nominal to Numerical (4)" to_port="example set input"/>
<connect from_op="Nominal to Numerical (4)" from_port="example set output" to_op="Optimize Parameters (Grid)" to_port="input 1"/>
<connect from_op="Optimize Parameters (Grid)" from_port="parameter" to_op="Set Parameters" to_port="parameter set"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
<portSpacing port="sink_result 3" spacing="0"/>
</process>
</operator>
</process>
