How to fix this Problem ?
I am trying to apply deep learning MODEL to my data but facing the below problem, The process and Problem snapshot are below. Please guide me in this regard.
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="9.5.001" 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="9.5.001" expanded="true" height="68" name="Retrieve Retroreflectivity_data" width="90" x="45" y="136">
<parameter key="repository_entry" value="//Temporary Repository/Retroreflectivity_data"/>
</operator>
<operator activated="true" class="select_attributes" compatibility="9.5.001" expanded="true" height="82" name="Select Attributes" width="90" x="179" y="136">
<parameter key="attribute_filter_type" value="subset"/>
<parameter key="attribute" value=""/>
<parameter key="attributes" value="Brand|Color|Age (years)|Installation Year|Observation Angle (Degrees)|Orientation (Degrees)|RA-values(Cd/lx/m2)|Sheeting Type"/>
<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="9.5.001" expanded="true" height="82" name="Set Role" width="90" x="313" y="136">
<parameter key="attribute_name" value="RA-values(Cd/lx/m2)"/>
<parameter key="target_role" value="label"/>
<list key="set_additional_roles"/>
</operator>
<operator activated="true" class="nominal_to_numerical" compatibility="9.5.001" expanded="true" height="103" name="Nominal to Numerical" width="90" x="447" y="136">
<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="Sheeting Type|Color|Brand"/>
<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="concurrency:cross_validation" compatibility="9.5.001" expanded="true" height="145" name="Cross Validation" width="90" x="581" y="136">
<parameter key="split_on_batch_attribute" value="false"/>
<parameter key="leave_one_out" value="false"/>
<parameter key="number_of_folds" value="10"/>
<parameter key="sampling_type" value="shuffled sampling"/>
<parameter key="use_local_random_seed" value="false"/>
<parameter key="local_random_seed" value="1992"/>
<parameter key="enable_parallel_execution" value="true"/>
<process expanded="true">
<operator activated="true" class="deeplearning:dl4j_sequential_neural_network" compatibility="0.9.001" expanded="true" height="103" name="Deep Learning" width="90" x="179" y="34">
<parameter key="loss_function" value="Mean Squared Error (Linear Regression)"/>
<parameter key="epochs" value="100"/>
<parameter key="use_miniBatch" value="false"/>
<parameter key="batch_size" value="32"/>
<parameter key="updater" value="Adam"/>
<parameter key="learning_rate" value="0.01"/>
<parameter key="momentum" value="0.9"/>
<parameter key="rho" value="0.95"/>
<parameter key="epsilon" value="1.0E-6"/>
<parameter key="beta1" value="0.9"/>
<parameter key="beta2" value="0.999"/>
<parameter key="RMSdecay" value="0.95"/>
<parameter key="weight_initialization" value="Normal"/>
<parameter key="bias_initialization" value="0.0"/>
<parameter key="use_regularization" value="false"/>
<parameter key="l1_strength" value="0.1"/>
<parameter key="l2_strength" value="0.1"/>
<parameter key="optimization_method" value="Stochastic Gradient Descent"/>
<parameter key="backpropagation" value="Standard"/>
<parameter key="backpropagation_length" value="50"/>
<parameter key="infer_input_shape" value="true"/>
<parameter key="network_type" value="Simple Neural Network"/>
<parameter key="log_each_epoch" value="true"/>
<parameter key="epochs_per_log" value="10"/>
<parameter key="use_local_random_seed" value="false"/>
<parameter key="local_random_seed" value="1992"/>
<process expanded="true">
<operator activated="true" class="deeplearning:dl4j_lstm_layer" compatibility="0.9.001" expanded="true" height="68" name="Add LSTM Layer" width="90" x="246" y="34">
<parameter key="neurons" value="50"/>
<parameter key="gate_activation" value="Softmax"/>
<parameter key="forget_gate_bias_initialization" value="1.0"/>
</operator>
<operator activated="true" class="deeplearning:dl4j_dense_layer" compatibility="0.9.001" expanded="true" height="68" name="Add Fully-Connected Layer" width="90" x="514" y="34">
<parameter key="number_of_neurons" value="100"/>
<parameter key="activation_function" value="ReLU (Rectified Linear Unit)"/>
<parameter key="use_dropout" value="false"/>
<parameter key="dropout_rate" value="0.25"/>
<parameter key="overwrite_networks_weight_initialization" value="false"/>
<parameter key="weight_initialization" value="Normal"/>
<parameter key="overwrite_networks_bias_initialization" value="false"/>
<parameter key="bias_initialization" value="0.0"/>
</operator>
<connect from_port="layerArchitecture" to_op="Add LSTM Layer" to_port="layerArchitecture"/>
<connect from_op="Add LSTM Layer" from_port="layerArchitecture" to_op="Add Fully-Connected Layer" to_port="layerArchitecture"/>
<connect from_op="Add Fully-Connected Layer" from_port="layerArchitecture" to_port="layerArchitecture"/>
<portSpacing port="source_layerArchitecture" spacing="0"/>
<portSpacing port="sink_layerArchitecture" spacing="0"/>
</process>
</operator>
<connect from_port="training set" to_op="Deep Learning" to_port="training set"/>
<connect from_op="Deep Learning" from_port="model" to_port="model"/>
<portSpacing port="source_training set" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
</process>
<process expanded="true">
<operator activated="true" class="apply_model" compatibility="9.5.001" expanded="true" height="82" name="Apply Model" width="90" x="45" y="34">
<list key="application_parameters"/>
<parameter key="create_view" value="false"/>
</operator>
<operator activated="true" class="performance_regression" compatibility="9.5.001" expanded="true" height="82" name="Performance" width="90" x="179" y="34">
<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="false"/>
<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="true"/>
<parameter key="correlation" value="true"/>
<parameter key="squared_correlation" value="true"/>
<parameter key="prediction_average" value="true"/>
<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>
<connect from_port="model" to_op="Apply Model" to_port="model"/>
<connect from_port="test set" to_op="Apply Model" to_port="unlabelled data"/>
<connect from_op="Apply Model" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
<connect from_op="Performance" from_port="performance" to_port="performance 1"/>
<portSpacing port="source_model" spacing="0"/>
<portSpacing port="source_test set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="sink_test set results" spacing="0"/>
<portSpacing port="sink_performance 1" spacing="0"/>
<portSpacing port="sink_performance 2" spacing="0"/>
</process>
</operator>
<connect from_op="Retrieve Retroreflectivity_data" 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="Cross Validation" to_port="example set"/>
<connect from_op="Cross Validation" from_port="model" to_port="result 1"/>
<connect from_op="Cross Validation" from_port="example set" to_port="result 2"/>
<connect from_op="Cross Validation" from_port="test result set" to_port="result 3"/>
<connect from_op="Cross Validation" from_port="performance 1" to_port="result 4"/>
<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"/>
<portSpacing port="sink_result 5" spacing="0"/>
</process>
</operator>
</process>
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Hi @MunchCrunch19,
it looks like your network configuration is missing a layer. Please try it with an added "Fully-connected layer" as the last layer, that contains 1 neuron (since it seems you're doing a prediction of one attribute) and as an activation function you can choose for example "none (identity)".
Hope this helps,
Philipp
Scott