## 3 Experiments and Results
### Experiments with BEE2  
To transfer the representations learned on BEE1 over to BEE2, we started our experiments on BEE2 with the ConvNets automatically designed with greedy grid search on BEE1. Specifically, we took the five ConvNetGS models (see Table 2) and trained and validated them on BEE2_1S and BEE2_2S (see Table 1). Each ConvNetGS was trained for 50 epochs. Tables 4 and 5 summarize the performance results of these auto-designed ConvNets on both validation datasets. We took the Top 9 performing hand-designed ConvNets from BEE1 (see Table 3) and trained and validated them on both BEE2_1S and BEE2_2S. We also trained, tested, and validated ResNet32 [64] and VGG16 [14] on these datasets. To obtain the standard ML benchmarks, we again trained, tested, and validated on BEE2_1S and BEE2_2S the same linear SVMs and RFs that we trained, tested, and validated on BEE1. Each image was normalized and flattened so that the inputs to the SVMs and RFs were the same as the inputs to the ConvNets. Tables 6 and 7 give the performance results of the hand-designed ConvNets, ResNet32, VGG16, the two top ConvNetGS models, and the ML models on BEE2_1S and BEE2_2S, respectively. In both tables, the parenthesized numbers following the RF abbreviation specify numbers of trees. The architecture of the best performing hand-designed ConvNet for BEE2_1S (ConvNet 1) is the same as the architecture of the best performing ConvNet for BEE1 (see Figure 9), except the input layer is 64 \(\times\) 64, as shown in Figure 10. The architecture of the best performing hand-designed ConvNet on BEE2_2S (ConvNet 3) is shown in Figure 11. On BEE2_1S, ConvNetGS-4 was the best-performing auto-designed model; on BEE2_2S, it was outperformed by ConvNetGS-5. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **BEE Acc.** & **NO-BEE Acc.** & **Overall Acc.** \\ \hline ConvNetGS-4 & 84.86\% & 91.03\% & 86.36\% \\ ConvNetGS-3 & 82.18\% & 90.73\% & 84.26\% \\ ConvNetGS-1 & 82.09\% & 80.23\% & 81.63\% \\ ConvNetGS-5 & 76.37\% & 95.76\% & 81.09\% \\ ConvNetGS-2 & 76.21\% & 95.04\% & 80.79\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of five auto-designed ConvNets on BEE2_1S validation dataset. Each ConvNet was trained for 50 epochs. The number in the first column following the abbreviation ConvNetGS specifies the number of hidden layers; validation accuracies are sorted from highest to lowest. The architecture of ConvNetGS-4 is given in Figure 10 in Appendix A. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **BEE Acc.** & **NO-BEE Acc.** & **Overall Acc.** \\ \hline ConvNetGS-5 & 74.52\% & 77.42\% & 76.20\% \\ ConvNetGS-4 & 64.82\% & 83.39\% & 75.56\% \\ ConvNetGS-3 & 64.94\% & 79.93\% & 73.61\% \\ ConvNetGS-2 & 65.70\% & 76.77\% & 72.10\% \\ ConvNetGS-1 & 66.04\% & 68.36\% & 67.38\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of five auto-designed ConvNets on BEE2_2S validation dataset. Each ConvNet was trained for 50 epochs. The number in the first column following the abbreviation ConvNetGS specifies the number of hidden layers. The validation accuracies are sorted from highest to lowest. The architecture of ConvNetGS-5 is given in Figure 10 in Appendix A.