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Neural Net LR error

Hello

I am getting the following error when i try using Neural Net model for a classification problem.

Exception: com.rapidminer.operator.OperatorException
Message: Cannot reset network to a smaller learning rate.
Stack trace:
com.rapidminer.operator.learner.functions.neuralnet.ImprovedNeuralNetModel.train(ImprovedNeuralNetModel.java:179)
com.rapidminer.operator.learner.functions.neuralnet.ImprovedNeuralNetModel.train(ImprovedNeuralNetModel.java:182)
com.rapidminer.operator.learner.functions.neuralnet.ImprovedNeuralNetModel.train(ImprovedNeuralNetModel.java:182)

Any clue how to fix this?

Thannks a lot.

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Comments

Hi @ZAM

Can you provide XML if possible (View --> Show Panel --> XML )?

Thanks

Hello @ZAM,

Do you have any null labels in your dataset? I saw this issue was discussed earlier, this happens when the learning rate goes to zero. Here are the earlier threads that discuss this. You need to set decay in NN parameters and also tune your learning rate. If you could provide XML and dataset we can deep dive.

https://community.rapidminer.com/discussion/44187/warning-caught-exception-cannot-reset-network-to-a-smaller-learning-rate
https://community.rapidminer.com/discussion/48146/optimize-operator-failure-cannot-reset-network-to-a-smaller-learning-rate

@varunm1 Sorry for the late reply.

<?xml version="1.0" encoding="UTF-8"?><process version="9.2.001">

<context>

<input/>

<output/>

<macros/>

</context>

<operator activated="true" class="process" compatibility="9.2.001" 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="9.2.001" expanded="true" height="68" name="Retrieve" width="90" x="45" y="238">

<parameter key="repository_entry" value="//Local Repository/data/newresultsCoded9999_2"/>

</operator>

<operator activated="true" class="select_attributes" compatibility="9.2.001" expanded="true" height="82" name="Select Attributes" width="90" x="45" y="340">

<parameter key="attribute_filter_type" value="subset"/>

<parameter key="attribute" value=""/>

<parameter key="attributes" value="AGE|CUS_GENDER|Governate|LENGTH_OF_SERVICE|MARITAL_ST|Monthly_Income|NUM_DEP_PEOPLE|PRODUCT_CODE|PRODUCT_CODE_2|PROFFESSION_SECTOR|SUM(ABS(EQU_BAL))"/>

<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>

<operator activated="true" class="set_role" compatibility="9.2.001" expanded="true" height="82" name="Set Role" width="90" x="112" y="493">

<parameter key="attribute_name" value="PRODUCT_CODE_2"/>

<parameter key="target_role" value="label"/>

<list key="set_additional_roles"/>

</operator>

<operator activated="true" class="nominal_to_numerical" compatibility="9.2.001" expanded="true" height="103" name="Nominal to Numerical" width="90" x="246" y="391">

<parameter key="return_preprocessing_model" value="false"/>

<parameter key="create_view" value="false"/>

<parameter key="attribute_filter_type" value="subset"/>

<parameter key="attribute" value=""/>

<parameter key="attributes" value="CUS_GENDER|CUS_SHO_NAME|MARITAL_ST"/>

<parameter key="use_except_expression" value="false"/>

<parameter key="value_type" value="nominal"/>

<parameter key="use_value_type_exception" value="false"/>

<parameter key="except_value_type" value="file_path"/>

<parameter key="block_type" value="single_value"/>

<parameter key="use_block_type_exception" value="false"/>

<parameter key="except_block_type" value="single_value"/>

<parameter key="invert_selection" value="false"/>

<parameter key="include_special_attributes" value="false"/>

<parameter key="coding_type" value="dummy coding"/>

<parameter key="use_comparison_groups" value="false"/>

<list key="comparison_groups"/>

<parameter key="unexpected_value_handling" value="all 0 and warning"/>

<parameter key="use_underscore_in_name" value="false"/>

</operator>

<operator activated="true" class="split_data" compatibility="9.2.001" expanded="true" height="103" name="Split Data" width="90" x="246" y="187">

<enumeration key="partitions">

<parameter key="ratio" value="0.7"/>

<parameter key="ratio" value="0.3"/>

</enumeration>

<parameter key="sampling_type" value="automatic"/>

<parameter key="use_local_random_seed" value="false"/>

<parameter key="local_random_seed" value="1992"/>

</operator>

<operator activated="true" class="multiply" compatibility="9.2.001" expanded="true" height="103" name="Multiply" width="90" x="380" y="136"/>

<operator activated="true" class="neural_net" compatibility="9.2.001" expanded="true" height="82" name="Neural Net" width="90" x="514" y="34">

<list key="hidden_layers">

<parameter key="1" value="10"/>

<parameter key="2" value="10"/>

<parameter key="3" value="10"/>

</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="multiply" compatibility="9.2.001" expanded="true" height="103" name="Multiply (2)" width="90" x="380" y="493"/>

<operator activated="true" class="apply_model" compatibility="9.2.001" expanded="true" height="82" name="Apply Model" width="90" x="648" y="136">

<list key="application_parameters"/>

<parameter key="create_view" value="false"/>

</operator>

<operator activated="true" class="model_simulator:explain_predictions" compatibility="9.2.001" expanded="true" height="103" name="Explain Predictions" width="90" x="782" y="289">

<parameter key="maximal explaining attributes" value="3"/>

<parameter key="local sample size" value="500"/>

<parameter key="only create predictions" value="false"/>

</operator>

<connect from_op="Retrieve" from_port="output" to_op="Select Attributes" to_port="example set input"/>

<connect from_op="Select Attributes" from_port="example set output" to_op="Set Role" to_port="example set input"/>

<connect from_op="Set Role" from_port="example set output" to_op="Nominal to Numerical" to_port="example set input"/>

<connect from_op="Nominal to Numerical" from_port="example set output" to_op="Split Data" to_port="example set"/>

<connect from_op="Split Data" from_port="partition 1" to_op="Multiply" to_port="input"/>

<connect from_op="Split Data" from_port="partition 2" to_op="Multiply (2)" to_port="input"/>

<connect from_op="Multiply" from_port="output 1" to_op="Neural Net" to_port="training set"/>

<connect from_op="Multiply" from_port="output 2" to_op="Explain Predictions" to_port="training data"/>

<connect from_op="Neural Net" from_port="model" to_op="Apply Model" to_port="model"/>

<connect from_op="Multiply (2)" from_port="output 1" to_op="Apply Model" to_port="unlabelled data"/>

<connect from_op="Multiply (2)" from_port="output 2" to_op="Explain Predictions" to_port="test data"/>

<connect from_op="Apply Model" from_port="labelled data" to_port="result 1"/>

<connect from_op="Apply Model" from_port="model" to_op="Explain Predictions" to_port="model"/>

<connect from_op="Explain Predictions" from_port="visualization output" to_port="result 2"/>

<connect from_op="Explain Predictions" from_port="example set output" to_port="result 3"/>

<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"/>

<portSpacing port="sink_result 4" spacing="0"/>

</process>

</operator>

</process>

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