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Problem with Write Model Operator
Contributor II
Hi, i have a simple process
Read csv-> Optimize(a neural net)->write parameter
->write performance
->write model
I don't understand why but (and this is happen again) i have this message of error:
Oct 6, 2011 1:29:22 PM SEVERE: Process failed: No data was deliverd at port Write Model.input (connected).
Oct 6, 2011 1:29:22 PM SEVERE: Here: Process[1] (Process)
subprocess 'Main Process'
+- Read CSV (2)[1] (Read CSV)
+- Optimize Neural[1] (Optimize Parameters (Evolutionary))
subprocess 'Optimization Process'
| +- Read CSV SVMX (2)[114] (Read CSV)
| +- Neural Net[114] (Neural Net)
| +- Apply Model SVMX (2)[114] (Apply Model)
| +- Performance (2)[114] (Performance (Classification))
==> +- Write Model[1] (Write Model)
+- Write Performance (2)[0] (Write Performance)
+- Write Parameters (2)[0] (Write Parameters)
Everyone can help me?
Regards
Nello
Read csv-> Optimize(a neural net)->write parameter
->write performance
->write model
I don't understand why but (and this is happen again) i have this message of error:
Oct 6, 2011 1:29:22 PM SEVERE: Process failed: No data was deliverd at port Write Model.input (connected).
Oct 6, 2011 1:29:22 PM SEVERE: Here: Process[1] (Process)
subprocess 'Main Process'
+- Read CSV (2)[1] (Read CSV)
+- Optimize Neural[1] (Optimize Parameters (Evolutionary))
subprocess 'Optimization Process'
| +- Read CSV SVMX (2)[114] (Read CSV)
| +- Neural Net[114] (Neural Net)
| +- Apply Model SVMX (2)[114] (Apply Model)
| +- Performance (2)[114] (Performance (Classification))
==> +- Write Model[1] (Write Model)
+- Write Performance (2)[0] (Write Performance)
+- Write Parameters (2)[0] (Write Parameters)
Everyone can help me?
Regards
Nello
0
Answers
the write model operator needs a model at its input port, i.e. it has to be connected (directly or indirectly) to the output of the Neural Net operator. Please post your process setup if you need further help. Just go to the "xml" tab of the process and copy the contents here (using the insert code above).
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.1.011">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="5.1.011" expanded="true" name="Process">
<parameter key="parallelize_main_process" value="true"/>
<process expanded="true" height="737" width="1217">
<operator activated="true" class="read_csv" compatibility="5.1.011" expanded="true" height="60" name="Read CSV (2)" width="90" x="45" y="30">
<parameter key="csv_file" value="/home/leno/EsperimentosugiuRegolandoNumero/Regolo18/trainingRegoloNumClass18.csv"/>
<parameter key="column_separators" value=","/>
<parameter key="first_row_as_names" value="false"/>
<list key="annotations"/>
<parameter key="locale" value="Italian (Italy)"/>
<parameter key="encoding" value="MacRoman"/>
<list key="data_set_meta_data_information">
<parameter key="0" value="att1.true.real.attribute"/>
<parameter key="1" value="att2.true.real.attribute"/>
<parameter key="2" value="att3.true.real.attribute"/>
<parameter key="3" value="att4.true.real.attribute"/>
<parameter key="4" value="att5.true.real.attribute"/>
<parameter key="5" value="att6.true.real.attribute"/>
<parameter key="6" value="att7.true.real.attribute"/>
<parameter key="7" value="att8.true.real.attribute"/>
<parameter key="8" value="att9.true.real.attribute"/>
<parameter key="9" value="att10.true.real.attribute"/>
<parameter key="10" value="att11.true.nominal.label"/>
</list>
</operator>
<operator activated="true" class="optimize_parameters_evolutionary" compatibility="5.1.011" expanded="true" height="112" name="Optimize Neural" width="90" x="246" y="30">
<list key="parameters">
<parameter key="Neural Net.learning_rate" value="[0.01;1.0]"/>
<parameter key="Neural Net.momentum" value="[0.0;1.0]"/>
</list>
<parameter key="use_early_stopping" value="true"/>
<parameter key="specify_population_size" value="false"/>
<parameter key="tournament_fraction" value="0.75"/>
<process expanded="true" height="377" width="681">
<operator activated="true" class="read_csv" compatibility="5.1.011" expanded="true" height="60" name="Read CSV SVMX (2)" width="90" x="45" y="255">
<parameter key="csv_file" value="/home/leno/EsperimentosugiuRegolandoNumero/Regolo18/testRegoloNumClass18.csv"/>
<parameter key="column_separators" value=","/>
<parameter key="first_row_as_names" value="false"/>
<list key="annotations"/>
<parameter key="encoding" value="MacRoman"/>
<list key="data_set_meta_data_information">
<parameter key="0" value="att1.true.real.attribute"/>
<parameter key="1" value="att2.true.real.attribute"/>
<parameter key="2" value="att3.true.real.attribute"/>
<parameter key="3" value="att4.true.real.attribute"/>
<parameter key="4" value="att5.true.real.attribute"/>
<parameter key="5" value="att6.true.real.attribute"/>
<parameter key="6" value="att7.true.real.attribute"/>
<parameter key="7" value="att8.true.real.attribute"/>
<parameter key="8" value="att9.true.real.attribute"/>
<parameter key="9" value="att10.true.real.attribute"/>
<parameter key="10" value="att11.true.nominal.label"/>
</list>
</operator>
<operator activated="true" class="neural_net" compatibility="5.1.011" expanded="true" height="76" name="Neural Net" width="90" x="112" y="30">
<list key="hidden_layers"/>
<parameter key="training_cycles" value="1000"/>
</operator>
<operator activated="true" class="apply_model" compatibility="5.1.011" expanded="true" height="76" name="Apply Model SVMX (2)" width="90" x="313" y="120">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance_classification" compatibility="5.1.011" expanded="true" height="76" name="Performance (2)" width="90" x="447" y="30">
<parameter key="classification_error" value="true"/>
<parameter key="weighted_mean_recall" value="true"/>
<parameter key="weighted_mean_precision" value="true"/>
<parameter key="root_mean_squared_error" value="true"/>
<list key="class_weights"/>
</operator>
<connect from_port="input 1" to_op="Neural Net" to_port="training set"/>
<connect from_op="Read CSV SVMX (2)" from_port="output" to_op="Apply Model SVMX (2)" to_port="unlabelled data"/>
<connect from_op="Neural Net" from_port="model" to_op="Apply Model SVMX (2)" to_port="model"/>
<connect from_op="Apply Model SVMX (2)" from_port="labelled data" to_op="Performance (2)" to_port="labelled data"/>
<connect from_op="Apply Model SVMX (2)" from_port="model" to_port="result 1"/>
<connect from_op="Performance (2)" from_port="performance" 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"/>
<portSpacing port="sink_result 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="write_model" compatibility="5.1.011" expanded="true" height="60" name="Write Model" width="90" x="514" y="210">
<parameter key="model_file" value="/home/leno/EsperimentosugiuRegolandoNumero/Regolo18/RisultatiSoloNeural/ModelNeuralClassRegoloNum18"/>
<parameter key="output_type" value="XML"/>
</operator>
<operator activated="true" class="write_performance" compatibility="5.1.011" expanded="true" height="60" name="Write Performance (2)" width="90" x="514" y="30">
<parameter key="performance_file" value="/home/leno/EsperimentosugiuRegolandoNumero/Regolo18/RisultatiSoloNeural/PerformanceNeuralClassRegoloNum18"/>
</operator>
<operator activated="true" class="write_parameters" compatibility="5.1.011" expanded="true" height="60" name="Write Parameters (2)" width="90" x="514" y="120">
<parameter key="parameter_file" value="/home/leno/EsperimentosugiuRegolandoNumero/Regolo18/RisultatiSoloNeural/ParametriNeuralClassRegoloNum18"/>
</operator>
<connect from_op="Read CSV (2)" from_port="output" to_op="Optimize Neural" to_port="input 1"/>
<connect from_op="Optimize Neural" from_port="performance" to_op="Write Performance (2)" to_port="input"/>
<connect from_op="Optimize Neural" from_port="parameter" to_op="Write Parameters (2)" to_port="input"/>
<connect from_op="Optimize Neural" from_port="result 1" to_op="Write Model" to_port="input"/>
<connect from_op="Write Model" from_port="through" 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>