## 3 Experiments and Results
### Experiments with BEE1
Specifically, it consists of three convolutional layers, each of which is followed by a maxpooling layer. Each convolutional layer doubles the number of filters in the convolutional layer: the first convolutional layer has 64 3 \(\times\) 3 filters, the second one 128 3 \(\times\) 3 filters, and the third one 256 3 \(\times\) 3 filters. All convolutional layers have the ReLU activation function and a weight decay of 0.001. The convolutional and maxpooling layers are followed by four FC layers of 512 units, 256 units, 128 units, and 64 units. Each of these layers has the ReLU activation function and a dropout of 0.5. The last FC layer has the softmax activation function, the adam optimizer, a learning rate of 0.0001, and the categorical cross entropy as its loss function. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **BEE Acc.** & **NO-BEE Acc.** & **Valid Accuracy** \\ \hline ConvNetGS-3 & 98.48\% & 99.66\% & 99.09\% \\ ConvNetGS-5 & 98.37\% & 99.66\% & 99.03\% \\ ConvNetGS-2 & 98.25\% & 99.06\% & 98.66\% \\ ConvNetGS-4 & 97.72\% & 99.50\% & 98.63\% \\ ConvNetGS-1 & 98.71\% & 0.88\% & 48.52\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of best auto-designed ConvNet models on BEE1. Each network was trained for 50 epochs. The number following the model type (ConvNetGS—GS stands for greedy search) shows the number of hidden layers in each ConvNet. A hidden layer consists of a convolutional layer followed by a maxpooling layer. The validation accuracies are sorted from highest to lowest. The architecture of ConvNetGS-3 is given in Figure 1 in Appendix A. To obtain the standard machine learning (ML) benchmarks, we trained and validated RFs and SVMs 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. We varied the number of trees in RFs from 20 to 100 in increments of 20. All RFs were trained with the gini entropy function. The minimum number of samples required to split and the minimum number of samples required to be at a leaf node were both set to 2. The best performing RF on the validation dataset of BEE1 (see Table 3) was the RF with 40 trees whose accuracy was 93.67%. All SVMs used the linear kernel with the max-iter parameter varying from 10 to 1000, an L2 penalty, a squared hinge loss function, and a tolerance of 0.0001. The best accuracy of 63.66% on the validation dataset was achieved by the linear SVM with a max-iter of 1000. For more thorough comparison, we also trained, tested, and validated ResNet32 [64] and VGG16 [14], two popular ConvNet architectures frequently referenced in the DL literature on image classification. We did not use any pre-trained weights in ResNet32 or VGG16. Both ConvNets were trained from scratch with no internal layer optimization. Table 3 summarizes the performance results of the ConvNets, RFs, and SVMs and compares them with the results of the two top performing auto-designed ConvNets (ConvNetGS-3 and ConvNetGS-5) on BEE1. The results in Tables 2 and 3 show that, on BEE1, ResNet32 and ConvNet 1 tied for first place \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **BEE Acc.** & **NO-BEE Acc.** & **Valid Accuracy** \\ \hline ConvNet 1 & 99.06\% & 99.89\% & 99.48\% \\ ResNet32 & 99.12\% & 99.83\% & 99.48\% \\ ConvNetGS-3 & 98.48\% & 99.66\% & 99.09\% \\ ConvNet 2 & 99.07\% & 99.12\% & 99.09\% \\ ConvNetGS-5 & 98.37\% & 99.66\% & 99.03\% \\ ConvNet 3 & 99.07\% & 98.95\% & 99.01\% \\ VGG16 & 97.90\% & 99.83\% & 98.87\% \\ ConvNet 4 & 98.95\% & 98.78\% & 98.87\% \\ ConvNet 5 & 98.37\% & 99.17\% & 98.77\% \\ ConvNet 6 & 98.60\% & 98.84\% & 98.72\% \\ ConvNet 7 & 97.96\% & 98.28\% & 98.13\% \\ ConvNet 8 & 98.43\% & 96.19\% & 97.31\% \\ ConvNet 9 & 97.67\% & 96.85\% & 97.26\% \\ RF (40) & 90.55\% & 96.80\% & 93.67\% \\ RF (80) & 90.88\% & 96.10\% & 93.49\% \\ RF (100) & 90.33\% & 96.16\% & 93.24\% \\ RF (20) & 89.89\% & 96.16\% & 93.02\% \\ RF (60) & 89.89\% & 95.98\% & 92.94\% \\ SVM & 58.29\% & 69.03\% & 63.66\% \\ \hline \hline \end{tabular}
\end{table}