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LSTM in Deeplearning errors
Learner III
Hello, Rapidminer community!
I recently started using Rapidminer and drawing inspiration from an LSTM network from Kaggle I have set out to try and make my own. Whenever I run the model it throws me the error "java.lang.IllegalArgumentException: J: Index [0] must not be >= shape[0]=1." at the apply model process.
The second problem I have (which might be the root to the evil) is the fact that my model keeps saying "Couldn't update network in epoch 1", "Couldn't update network in epoch n". This shows up in the logs view of the process. I have tried adjusting the biases and weights but I haven't been able to remove the said error.
Any help would be greatly appreciated.
I recently started using Rapidminer and drawing inspiration from an LSTM network from Kaggle I have set out to try and make my own. Whenever I run the model it throws me the error "java.lang.IllegalArgumentException: J: Index [0] must not be >= shape[0]=1." at the apply model process.
The second problem I have (which might be the root to the evil) is the fact that my model keeps saying "Couldn't update network in epoch 1", "Couldn't update network in epoch n". This shows up in the logs view of the process. I have tried adjusting the biases and weights but I haven't been able to remove the said error.
Any help would be greatly appreciated.
<div><?xml version="1.0" encoding="UTF-8"?><process version="9.6.000"></div><div> <context></div><div> <input/></div><div> <output/></div><div> <macros/></div><div> </context></div><div> <operator activated="true" class="process" compatibility="9.6.000" expanded="true" name="Process"></div><div> <parameter key="logverbosity" value="init"/></div><div> <parameter key="random_seed" value="2001"/></div><div> <parameter key="send_mail" value="never"/></div><div> <parameter key="notification_email" value=""/></div><div> <parameter key="process_duration_for_mail" value="30"/></div><div> <parameter key="encoding" value="SYSTEM"/></div><div> <process expanded="true"></div><div> <operator activated="true" class="retrieve" compatibility="9.6.000" expanded="true" height="68" name="Retrieve DIA Basics" width="90" x="45" y="34"></div><div> <parameter key="repository_entry" value="//Local Repository/Stock data/DIA Basics"/></div><div> </operator></div><div> <operator activated="true" class="replace_missing_values" compatibility="9.6.000" expanded="true" height="103" name="Replace Missing Values" width="90" x="112" y="187"></div><div> <parameter key="return_preprocessing_model" value="false"/></div><div> <parameter key="create_view" value="false"/></div><div> <parameter key="attribute_filter_type" value="all"/></div><div> <parameter key="attribute" value=""/></div><div> <parameter key="attributes" value="ACCBL_20|ACCBM_20|ACCBU_20|AD|ADOSC_3_10|ADX_14|AMAT_LR_2|AMAT_SR_2|AO_5_34|AOBV_LR_2|AOBV_SR_2|APO_12_26|AROOND_14|AROONU_14|ATR_14|BBL_20|BBM_20|BBU_20|BOP|CCI_20_0.015|CG_10|close|CMF_20|CMO_14|COPC_11_14_10|DCL_10_20|DCM_10_20|DCU_10_20|DEC_1|DEMA_10|DMN_14|DMP_14|DPO_1|EFI_13|EMA_10|EOM_14_100000000|FISHERT_5|FWMA_10|high|HL2|HLC3|HMA_10|INC_1|KAMA_10_2_30|KCB_20|KCL_20|KCU_20|KST_10_15_20_30_10_10_10_15|KSTS_9|KURT_30|LDECAY_5|LOGRET_1|low|LR_14|MACD_12_26_9|MACDH_12_26_9|MACDS_12_26_9|MAD_30|MASSI_9_25|MEDIAN_30|MFI_14|MIDPOINT_2|MIDPRICE_2|MOM_10|NATR_14|NVI_1|OBV|OBV_EMA_2|OBV_EMA_4|OBV_max_2|OBV_min_2|OHLC4|open|PCTRET_1|PPO_12_26_9|PPOH_12_26_9|PPOS_12_26_9|PVI_1|PVOL|PVT|PWMA_10|QS_10|QTL_30_0.5|RMA_10|ROC_10|RSI_14|RVI_14_4|RVIS_14_4|SINWMA_14|SKEW_30|SLOPE_1|SMA_10|STDEV_30|STOCH_3|STOCH_5|STOCHF_3|STOCHF_14|SWMA_10|TEMA_10|TRIMA_10|TRUERANGE_1|TSI_13_25|UO_7_14_28|VAR_30|volume|VTXM_14|VTXP_14|VWAP|VWMA_10|WILLR_14|WMA_10|Z_30|ZLEMA_10"/></div><div> <parameter key="use_except_expression" value="false"/></div><div> <parameter key="value_type" value="attribute_value"/></div><div> <parameter key="use_value_type_exception" value="false"/></div><div> <parameter key="except_value_type" value="time"/></div><div> <parameter key="block_type" value="attribute_block"/></div><div> <parameter key="use_block_type_exception" value="false"/></div><div> <parameter key="except_block_type" value="value_matrix_row_start"/></div><div> <parameter key="invert_selection" value="false"/></div><div> <parameter key="include_special_attributes" value="false"/></div><div> <parameter key="default" value="average"/></div><div> <list key="columns"/></div><div> </operator></div><div> <operator activated="true" class="set_role" compatibility="9.6.000" expanded="true" height="82" name="Set Role" width="90" x="246" y="187"></div><div> <parameter key="attribute_name" value="close"/></div><div> <parameter key="target_role" value="label"/></div><div> <list key="set_additional_roles"/></div><div> </operator></div><div> <operator activated="true" class="time_series:windowing" compatibility="9.6.000" expanded="true" height="82" name="Windowing" width="90" x="313" y="340"></div><div> <parameter key="attribute_filter_type" value="subset"/></div><div> <parameter key="attribute" value=""/></div><div> <parameter key="attributes" value="close"/></div><div> <parameter key="use_except_expression" value="false"/></div><div> <parameter key="value_type" value="attribute_value"/></div><div> <parameter key="use_value_type_exception" value="false"/></div><div> <parameter key="except_value_type" value="time"/></div><div> <parameter key="block_type" value="attribute_block"/></div><div> <parameter key="use_block_type_exception" value="false"/></div><div> <parameter key="except_block_type" value="value_matrix_row_start"/></div><div> <parameter key="invert_selection" value="false"/></div><div> <parameter key="include_special_attributes" value="true"/></div><div> <parameter key="has_indices" value="true"/></div><div> <parameter key="indices_attribute" value="timestamp"/></div><div> <parameter key="window_size" value="30"/></div><div> <parameter key="no_overlapping_windows" value="false"/></div><div> <parameter key="step_size" value="1"/></div><div> <parameter key="create_horizon_(labels)" value="true"/></div><div> <parameter key="horizon_attribute" value="close"/></div><div> <parameter key="horizon_size" value="1"/></div><div> <parameter key="horizon_offset" value="0"/></div><div> </operator></div><div> <operator activated="true" class="set_role" compatibility="9.6.000" expanded="true" height="82" name="Set Role (3)" width="90" x="447" y="340"></div><div> <parameter key="attribute_name" value="timestamp"/></div><div> <parameter key="target_role" value="id"/></div><div> <list key="set_additional_roles"/></div><div> </operator></div><div> <operator activated="true" class="concurrency:join" compatibility="9.6.000" expanded="true" height="82" name="Join" width="90" x="447" y="238"></div><div> <parameter key="remove_double_attributes" value="true"/></div><div> <parameter key="join_type" value="outer"/></div><div> <parameter key="use_id_attribute_as_key" value="true"/></div><div> <list key="key_attributes"/></div><div> <parameter key="keep_both_join_attributes" value="false"/></div><div> </operator></div><div> <operator activated="true" class="split_data" compatibility="9.6.000" expanded="true" height="124" name="Split Data" width="90" x="514" y="34"></div><div> <enumeration key="partitions"></div><div> <parameter key="ratio" value="0.7"/></div><div> <parameter key="ratio" value="0.2"/></div><div> <parameter key="ratio" value="0.1"/></div><div> </enumeration></div><div> <parameter key="sampling_type" value="linear sampling"/></div><div> <parameter key="use_local_random_seed" value="false"/></div><div> <parameter key="local_random_seed" value="1992"/></div><div> </operator></div><div> <operator activated="true" class="set_role" compatibility="9.6.000" expanded="true" height="82" name="Set Role (2)" width="90" x="648" y="187"></div><div> <parameter key="attribute_name" value="close + 1 (horizon)"/></div><div> <parameter key="target_role" value="regular"/></div><div> <list key="set_additional_roles"/></div><div> </operator></div><div> <operator activated="true" class="deeplearning:dl4j_sequential_neural_network" compatibility="0.9.003" expanded="true" height="103" name="Deep Learning" width="90" x="648" y="34"></div><div> <parameter key="loss_function" value="Mean Squared Error (Linear Regression)"/></div><div> <parameter key="epochs" value="50"/></div><div> <parameter key="use_miniBatch" value="false"/></div><div> <parameter key="batch_size" value="32"/></div><div> <parameter key="updater" value="Adam"/></div><div> <parameter key="learning_rate" value="0.01"/></div><div> <parameter key="momentum" value="0.9"/></div><div> <parameter key="rho" value="0.95"/></div><div> <parameter key="epsilon" value="1.0E-6"/></div><div> <parameter key="beta1" value="0.9"/></div><div> <parameter key="beta2" value="0.999"/></div><div> <parameter key="RMSdecay" value="0.95"/></div><div> <parameter key="weight_initialization" value="Xavier"/></div><div> <parameter key="bias_initialization" value="143.0"/></div><div> <parameter key="use_regularization" value="false"/></div><div> <parameter key="l1_strength" value="0.1"/></div><div> <parameter key="l2_strength" value="0.1"/></div><div> <parameter key="optimization_method" value="Conjugate Gradient Line Search"/></div><div> <parameter key="backpropagation" value="Standard"/></div><div> <parameter key="backpropagation_length" value="50"/></div><div> <parameter key="infer_input_shape" value="true"/></div><div> <parameter key="network_type" value="Simple Neural Network"/></div><div> <parameter key="log_each_epoch" value="true"/></div><div> <parameter key="epochs_per_log" value="10"/></div><div> <parameter key="use_local_random_seed" value="false"/></div><div> <parameter key="local_random_seed" value="1992"/></div><div> <process expanded="true"></div><div> <operator activated="true" class="deeplearning:dl4j_lstm_layer" compatibility="0.9.003" expanded="true" height="68" name="Add LSTM Layer" width="90" x="179" y="85"></div><div> <parameter key="neurons" value="142"/></div><div> <parameter key="gate_activation" value="ReLU (Rectified Linear Unit)"/></div><div> <parameter key="forget_gate_bias_initialization" value="1.0"/></div><div> </operator></div><div> <operator activated="true" class="deeplearning:dl4j_dense_layer" compatibility="0.9.003" expanded="true" height="68" name="Add Fully-Connected Layer (2)" width="90" x="648" y="85"></div><div> <parameter key="number_of_neurons" value="2"/></div><div> <parameter key="activation_function" value="Softmax"/></div><div> <parameter key="use_dropout" value="false"/></div><div> <parameter key="dropout_rate" value="0.25"/></div><div> <parameter key="overwrite_networks_weight_initialization" value="false"/></div><div> <parameter key="weight_initialization" value="Normal"/></div><div> <parameter key="overwrite_networks_bias_initialization" value="false"/></div><div> <parameter key="bias_initialization" value="0.0"/></div><div> </operator></div><div> <connect from_port="layerArchitecture" to_op="Add LSTM Layer" to_port="layerArchitecture"/></div><div> <connect from_op="Add LSTM Layer" from_port="layerArchitecture" to_op="Add Fully-Connected Layer (2)" to_port="layerArchitecture"/></div><div> <connect from_op="Add Fully-Connected Layer (2)" from_port="layerArchitecture" to_port="layerArchitecture"/></div><div> <portSpacing port="source_layerArchitecture" spacing="0"/></div><div> <portSpacing port="sink_layerArchitecture" spacing="0"/></div><div> </process></div><div> </operator></div><div> <operator activated="true" class="apply_model" compatibility="9.6.000" expanded="true" height="82" name="Apply Model" width="90" x="782" y="136"></div><div> <list key="application_parameters"/></div><div> <parameter key="create_view" value="false"/></div><div> </operator></div><div> <operator activated="true" class="performance_regression" compatibility="9.6.000" expanded="true" height="82" name="Performance" width="90" x="916" y="136"></div><div> <parameter key="main_criterion" value="first"/></div><div> <parameter key="root_mean_squared_error" value="false"/></div><div> <parameter key="absolute_error" value="false"/></div><div> <parameter key="relative_error" value="true"/></div><div> <parameter key="relative_error_lenient" value="false"/></div><div> <parameter key="relative_error_strict" value="false"/></div><div> <parameter key="normalized_absolute_error" value="false"/></div><div> <parameter key="root_relative_squared_error" value="false"/></div><div> <parameter key="squared_error" value="false"/></div><div> <parameter key="correlation" value="false"/></div><div> <parameter key="squared_correlation" value="false"/></div><div> <parameter key="prediction_average" value="false"/></div><div> <parameter key="spearman_rho" value="false"/></div><div> <parameter key="kendall_tau" value="false"/></div><div> <parameter key="skip_undefined_labels" value="true"/></div><div> <parameter key="use_example_weights" value="true"/></div><div> </operator></div><div> <connect from_op="Retrieve DIA Basics" from_port="output" to_op="Replace Missing Values" to_port="example set input"/></div><div> <connect from_op="Replace Missing Values" from_port="example set output" to_op="Set Role" to_port="example set input"/></div><div> <connect from_op="Set Role" from_port="example set output" to_op="Windowing" to_port="example set"/></div><div> <connect from_op="Windowing" from_port="windowed example set" to_op="Join" to_port="left"/></div><div> <connect from_op="Windowing" from_port="original" to_op="Set Role (3)" to_port="example set input"/></div><div> <connect from_op="Set Role (3)" from_port="example set output" to_op="Join" to_port="right"/></div><div> <connect from_op="Join" from_port="join" to_op="Split Data" to_port="example set"/></div><div> <connect from_op="Split Data" from_port="partition 1" to_op="Deep Learning" to_port="training set"/></div><div> <connect from_op="Split Data" from_port="partition 2" to_op="Deep Learning" to_port="test set"/></div><div> <connect from_op="Split Data" from_port="partition 3" to_op="Set Role (2)" to_port="example set input"/></div><div> <connect from_op="Set Role (2)" from_port="example set output" to_op="Apply Model" to_port="unlabelled data"/></div><div> <connect from_op="Deep Learning" from_port="model" to_op="Apply Model" to_port="model"/></div><div> <connect from_op="Deep Learning" from_port="history" to_port="result 1"/></div><div> <connect from_op="Apply Model" from_port="labelled data" to_op="Performance" to_port="labelled data"/></div><div> <connect from_op="Performance" from_port="performance" to_port="result 2"/></div><div> <connect from_op="Performance" from_port="example set" to_port="result 3"/></div><div> <portSpacing port="source_input 1" spacing="0"/></div><div> <portSpacing port="sink_result 1" spacing="0"/></div><div> <portSpacing port="sink_result 2" spacing="0"/></div><div> <portSpacing port="sink_result 3" spacing="0"/></div><div> <portSpacing port="sink_result 4" spacing="0"/></div><div> </process></div><div> </operator></div><div></process></div><div></div>
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Best Answer
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hughesfleming68
Member Posts: 323 
Unicorn
This one!. I had switched off some attributes to save time. The site is unusable. Lot of problems.5
Answers
Everything is made as in the in the sample however it still throws that error
It also gives the following output when training which is concerning (and possible root to the select process problem): "Epoch: 1, training score: NaN, testing score: NaN"
<?xml version="1.0" encoding="UTF-8"?><process version="9.6.000"> <context> <input/> <output/> <macros/> </context> <operator activated="true" class="process" compatibility="9.6.000" 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="9.6.000" expanded="true" height="68" name="Retrieve DIA Basics" width="90" x="45" y="34"> <parameter key="repository_entry" value="//Local Repository/Stock data/DIA Basics"/> </operator> <operator activated="true" class="replace_missing_values" compatibility="9.6.000" expanded="true" height="103" name="Replace Missing Values" width="90" x="45" y="187"> <parameter key="return_preprocessing_model" value="false"/> <parameter key="create_view" value="false"/> <parameter key="attribute_filter_type" value="all"/> <parameter key="attribute" value=""/> <parameter key="attributes" value="ACCBL_20|ACCBM_20|ACCBU_20|AD|ADOSC_3_10|ADX_14|AMAT_LR_2|AMAT_SR_2|AO_5_34|AOBV_LR_2|AOBV_SR_2|APO_12_26|AROOND_14|AROONU_14|ATR_14|BBL_20|BBM_20|BBU_20|BOP|CCI_20_0.015|CG_10|close|CMF_20|CMO_14|COPC_11_14_10|DCL_10_20|DCM_10_20|DCU_10_20|DEC_1|DEMA_10|DMN_14|DMP_14|DPO_1|EFI_13|EMA_10|EOM_14_100000000|FISHERT_5|FWMA_10|high|HL2|HLC3|HMA_10|INC_1|KAMA_10_2_30|KCB_20|KCL_20|KCU_20|KST_10_15_20_30_10_10_10_15|KSTS_9|KURT_30|LDECAY_5|LOGRET_1|low|LR_14|MACD_12_26_9|MACDH_12_26_9|MACDS_12_26_9|MAD_30|MASSI_9_25|MEDIAN_30|MFI_14|MIDPOINT_2|MIDPRICE_2|MOM_10|NATR_14|NVI_1|OBV|OBV_EMA_2|OBV_EMA_4|OBV_max_2|OBV_min_2|OHLC4|open|PCTRET_1|PPO_12_26_9|PPOH_12_26_9|PPOS_12_26_9|PVI_1|PVOL|PVT|PWMA_10|QS_10|QTL_30_0.5|RMA_10|ROC_10|RSI_14|RVI_14_4|RVIS_14_4|SINWMA_14|SKEW_30|SLOPE_1|SMA_10|STDEV_30|STOCH_3|STOCH_5|STOCHF_3|STOCHF_14|SWMA_10|TEMA_10|TRIMA_10|TRUERANGE_1|TSI_13_25|UO_7_14_28|VAR_30|volume|VTXM_14|VTXP_14|VWAP|VWMA_10|WILLR_14|WMA_10|Z_30|ZLEMA_10"/> <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"/> <parameter key="default" value="average"/> <list key="columns"/> </operator> <operator activated="true" class="set_role" compatibility="9.6.000" expanded="true" height="82" name="Set Role" width="90" x="45" y="340"> <parameter key="attribute_name" value="close"/> <parameter key="target_role" value="label"/> <list key="set_additional_roles"/> </operator> <operator activated="true" class="time_series:windowing" compatibility="9.6.000" expanded="true" height="82" name="Windowing" width="90" x="179" y="340"> <parameter key="attribute_filter_type" value="subset"/> <parameter key="attribute" value=""/> <parameter key="attributes" value="close"/> <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="true"/> <parameter key="has_indices" value="true"/> <parameter key="indices_attribute" value="timestamp"/> <parameter key="window_size" value="30"/> <parameter key="no_overlapping_windows" value="false"/> <parameter key="step_size" value="1"/> <parameter key="create_horizon_(labels)" value="true"/> <parameter key="horizon_attribute" value="close"/> <parameter key="horizon_size" value="1"/> <parameter key="horizon_offset" value="0"/> </operator> <operator activated="true" class="set_role" compatibility="9.6.000" expanded="true" height="82" name="Set Role (3)" width="90" x="447" y="340"> <parameter key="attribute_name" value="timestamp"/> <parameter key="target_role" value="id"/> <list key="set_additional_roles"/> </operator> <operator activated="true" class="concurrency:join" compatibility="9.6.000" expanded="true" height="82" name="Join" width="90" x="313" y="238"> <parameter key="remove_double_attributes" value="true"/> <parameter key="join_type" value="outer"/> <parameter key="use_id_attribute_as_key" value="true"/> <list key="key_attributes"/> <parameter key="keep_both_join_attributes" value="false"/> </operator> <operator activated="true" class="split_data" compatibility="9.6.000" expanded="true" height="124" name="Split Data" width="90" x="313" y="34"> <enumeration key="partitions"> <parameter key="ratio" value="0.7"/> <parameter key="ratio" value="0.2"/> <parameter key="ratio" value="0.1"/> </enumeration> <parameter key="sampling_type" value="linear sampling"/> <parameter key="use_local_random_seed" value="false"/> <parameter key="local_random_seed" value="1992"/> </operator> <operator activated="true" class="set_role" compatibility="9.6.000" expanded="true" height="82" name="Set Role (2)" width="90" x="648" y="289"> <parameter key="attribute_name" value="close + 1 (horizon)"/> <parameter key="target_role" value="regular"/> <list key="set_additional_roles"/> </operator> <operator activated="true" class="collect" compatibility="9.6.000" expanded="true" height="82" name="Collect" width="90" x="849" y="34"> <parameter key="unfold" value="false"/> </operator> <operator activated="true" class="deeplearning:dl4j_timeseries_converter" compatibility="0.9.003" expanded="true" height="82" name="TimeSeries to Tensor" width="90" x="983" y="34"/> <operator activated="true" class="collect" compatibility="9.6.000" expanded="true" height="82" name="Collect (2)" width="90" x="849" y="187"> <parameter key="unfold" value="false"/> </operator> <operator activated="true" class="deeplearning:dl4j_timeseries_converter" compatibility="0.9.003" expanded="true" height="82" name="TimeSeries to Tensor (2)" width="90" x="983" y="187"/> <operator activated="true" class="deeplearning:dl4j_tensor_sequential_neural_network" compatibility="0.9.003" expanded="true" height="103" name="Deep Learning (Tensor)" width="90" x="1251" y="34"> <parameter key="loss_function" value="Mean Squared Error (Linear Regression)"/> <parameter key="epochs" value="50"/> <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="Xavier"/> <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="0.9.003" expanded="true" height="68" name="Add LSTM Layer" width="90" x="112" y="136"> <parameter key="neurons" value="142"/> <parameter key="gate_activation" value="ReLU (Rectified Linear Unit)"/> <parameter key="forget_gate_bias_initialization" value="1.0"/> </operator> <operator activated="true" class="deeplearning:dl4j_dense_layer" compatibility="0.9.003" expanded="true" height="68" name="Add Fully-Connected Layer" width="90" x="380" y="136"> <parameter key="number_of_neurons" value="1"/> <parameter key="activation_function" value="None (identity)"/> <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> <operator activated="true" class="collect" compatibility="9.6.000" expanded="true" height="82" name="Collect (3)" width="90" x="916" y="289"> <parameter key="unfold" value="false"/> </operator> <operator activated="true" class="deeplearning:dl4j_timeseries_converter" compatibility="0.9.003" expanded="true" height="82" name="TimeSeries to Tensor (3)" width="90" x="1050" y="289"/> <operator activated="true" class="deeplearning:dl4j_apply_tensor_model" compatibility="0.9.003" expanded="true" height="82" name="Apply Model (Tensor)" width="90" x="1385" y="136"/> <operator activated="true" class="select" compatibility="9.6.000" expanded="true" height="68" name="Select" width="90" x="1519" y="136"> <parameter key="index" value="1"/> <parameter key="unfold" value="false"/> </operator> <operator activated="true" class="performance_regression" compatibility="9.6.000" expanded="true" height="82" name="Performance" width="90" x="1653" 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="true"/> <parameter key="correlation" value="true"/> <parameter key="squared_correlation" value="false"/> <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_op="Retrieve DIA Basics" from_port="output" to_op="Replace Missing Values" to_port="example set input"/> <connect from_op="Replace Missing Values" 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="Windowing" to_port="example set"/> <connect from_op="Windowing" from_port="windowed example set" to_op="Join" to_port="left"/> <connect from_op="Windowing" from_port="original" to_op="Set Role (3)" to_port="example set input"/> <connect from_op="Set Role (3)" from_port="example set output" to_op="Join" to_port="right"/> <connect from_op="Join" from_port="join" to_op="Split Data" to_port="example set"/> <connect from_op="Split Data" from_port="partition 1" to_op="Collect" to_port="input 1"/> <connect from_op="Split Data" from_port="partition 2" to_op="Collect (2)" to_port="input 1"/> <connect from_op="Split Data" from_port="partition 3" to_op="Set Role (2)" to_port="example set input"/> <connect from_op="Set Role (2)" from_port="example set output" to_op="Collect (3)" to_port="input 1"/> <connect from_op="Collect" from_port="collection" to_op="TimeSeries to Tensor" to_port="collection"/> <connect from_op="TimeSeries to Tensor" from_port="tensor" to_op="Deep Learning (Tensor)" to_port="training set"/> <connect from_op="Collect (2)" from_port="collection" to_op="TimeSeries to Tensor (2)" to_port="collection"/> <connect from_op="TimeSeries to Tensor (2)" from_port="tensor" to_op="Deep Learning (Tensor)" to_port="test set"/> <connect from_op="Deep Learning (Tensor)" from_port="model" to_op="Apply Model (Tensor)" to_port="model"/> <connect from_op="Collect (3)" from_port="collection" to_op="TimeSeries to Tensor (3)" to_port="collection"/> <connect from_op="TimeSeries to Tensor (3)" from_port="tensor" to_op="Apply Model (Tensor)" to_port="unlabelled tensor"/> <connect from_op="Apply Model (Tensor)" from_port="labeled data" to_op="Select" to_port="collection"/> <connect from_op="Select" from_port="selected" to_op="Performance" to_port="labelled data"/> <connect from_op="Performance" from_port="performance" 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>What is the optimal amount of tensors to use? 1 tensor for every variable?
What is most effective to use in a deep learning model, minibatches, or no minibatches? If no, then when is it a good idea to use minibatches?
Again thank you for the help, much appriciated!