TwoDimTable createSummaryTable ( ) { TwoDimTable table = new TwoDimTable ( "Status of Deep Learning Model" , ( get_params ( ) . _network == null ? ( "MLP: " + Arrays . toString ( get_params ( ) . _hidden ) ) : get_params ( ) . _network . toString ( ) ) + ", " + PrettyPrint . bytes ( size ( ) ) + ", " + ( ! get_params ( ) . _autoencoder ? ( "predicting " + get_params ( ) . _response_column + ", " ) : "" ) + ( get_params ( ) . _autoencoder ? "auto-encoder" : _classification ? ( _classes + "-class classification" ) : "regression" ) + ", " + String . format ( "%,d" , get_processed_global ( ) ) + " training samples, " + "mini-batch size " + String . format ( "%,d" , get_params ( ) . _mini_batch_size ) , new String [ 1 ] , //rows new String [ ] { "Input Neurons" , "Rate" , "Momentum" } , new String [ ] { "int" , "double" , "double" } , new String [ ] { "%d" , "%5f" , "%5f" } , "" ) ; table . set ( 0 , 0 , _dataInfo != null ? _dataInfo . fullN ( ) : _width * _height * _channels ) ; table . set ( 0 , 1 , get_params ( ) . learningRate ( get_processed_global ( ) ) ) ; table . set ( 0 , 2 , get_params ( ) . momentum ( get_processed_global ( ) ) ) ; summaryTable = table ; return summaryTable ; }
Create a summary table