# A Novel Batch Streaming Pipeline for Radar Emitter Classification
## 4 Experiment
### Pattern Recognition Model Setup
However, in the case of the fixed pattern (above), the original minmax normalization failed to preserve the pattern’s fixed shape within the 0 to 1 scale, as noise amplification occurred. On the other hand, the modified minmax normalization successfully maintained the fixed pattern’s shape. The optimizer used was Adam, with a learning rate of 0.00001, and the batch size was set to 128. We chose the loss function of the model as the categorical cross-entropy loss, defined as  
\[\text{Categorical Cross-Entropy}=-\sum_{i=1}^{C}y_{i}\log(p_{i}).\]  
The training was conducted with the train set for 1000 epochs. At each epoch, the accuracy on the validation set was computed, and if it surpassed the previous best accuracy, the model was saved. After training, the model with the highest validation accuracy was selected as the final model. This allowed us to select a final model that was not overfitted to the train set. The model validation accuracy for each RNN encoder type is presented in Table 4. In the experimental results, both LSTM and GRU outperformed the vanilla RNN, achieving an impressive accuracy of approximately 97%. This superiority can be attributed to the advanced architecture, such as the memory cells and gates of the LSTM and GRU models, as discussed in Section 2.2. Unlike the vanilla RNN, LSTM and GRU were equipped with mechanisms such as memory cells and gates, allowing them to capture and retain long-term dependencies in the input sequence. We compared the experimental results between the LSTM and GRU models in Figure 7 and Table 5. Figure 7 shows the accuracy and loss graphs on the validation set during the training process. As evident from the graph, the GRU model demonstrated faster learning compared to the LSTM model. This can be attributed to the fact that the GRU model was a simplified version of the LSTM model, with fewer parameters to learn, resulting in faster training. Table  
As shown in Section 4.2, the pattern recognition model could be further improved by optimizing the hyperparameters. The hyperparameters used in the pattern recognition model were the context vector dimension (context_vec_dim), the number of RNN layers (rnn_layers), the number of fully connected layer nodes (fc_1, fc_2, fc_3), and the learning rate (lr). For the LSTM and GRU units, we found 16 sets of hyperparameters in the search space in Table 6, using Bayesian optimization. The scores in Bayesian optimization for the pattern recognition model were computed based on pattern recognition accuracy and on validation data. The results of the Bayesian optimization for the deinterleaving model are shown in Tables 7 and 8. It is shown that the pattern recognition accuracy of the model with the hyperparameter setting that scored the best in each Bayesian optimization was higher than the result of the model with initial hyperparameter values in Table 4. Therefore, we selected the model with the best hyperparameter values from the Bayesian optimization as our final pattern recognition model. \begin{table}
\begin{tabular}{c c} \hline
**Hyperparameter** & **Search Space** \\ \hline context\_vec\_dim & [2; 32] \\ rnn\_layers & [1; 8] \\ fc\_1 & [256, 1024] \\ fc\_2 & [256, 1024] \\ fc\_3 & [256, 1024] \\ lr & [0.000005, 0.00005] \\ \hline \end{tabular} Hyperparameters in integer form were used after rounding up the searched values and changing them to integer form.  
\end{table} 
Table 6: Search space for Bayesian optimization in pattern recognition model.