## 2 Materials and Methods
### Greedy Auto-Design of Convolutional Networks  
A recent trend in the DL literature is the automation of designing DL architectures (e.g., [28; 58]). The proponents of this approach argue that since successful DL models require considerable architecture engineering, it is worth it to automate DL model design for specific datasets. A disadvantage of this approach for low budget research projects or citizen scientist initiatives is the requirement of continuous access to considerable computational resources. For example, [PERSON] et al. [58] used a Q-learning training procedure to develop constrained ConvNet architectures for a variety of image datasets on four GPUs for 8-10 days. [PERSON] et al. [28] used their Neural Architecture Search (NAS) framework to learn transferable DL architectures for CIFAR-10 and ImageNet on 500 GPUs over four days resulting in 2000 GPU-hours. Since our research group had access to only one GPU for this investigation, we designed a multi-generational greedy grid search (GS) algorithm to automatically look for best-performing ConvNets by restricting the number of the DL architectural parameters and their value ranges and taking the hyperparameter values from the top \(N\) ConvNets, for different values of \(N\) given below, from the previous generation, where the generation number is the number of hidden layers (i.e., the number of convolutional and maxpooling layer pairs). Instead of exhaustively searching through each possible parameter value, as is done in standard grid search, our algorithm searches the space of ConvNet architectures constrained by the number of convolutional and maxpooling layer pairs (i.e., hidden layers), filter sizes, and the number of nodes in each hidden layer. An addition of a convolutional layer is always followed by an addition of a maxpooling layer with a kernel size of 2. All hidden layers use the ReLU activation function and the Adam optimizer [59]. The learning rate is set to 0.001; the loss function is the categorical cross-entropy that measures the probability \begin{table}
\begin{tabular}{l c c c c c} \hline  & **Train/Test (BEE)** & **Train/Test (NO-BEE)** & **Validate (BEE)** & **Validate (NO-BEE)** & **Total** \\ \hline BEE2\_1S & 11,094 & 36,143 & 8298 & 2666 & 58,201 \\ BEE2\_2S & 17,176 & 21,310 & 6823 & 9369 & 54,678 \\ \hline Total & 28,270 & 57,453 & 15,121 & 12,035 & 112,879 \\ \hline \end{tabular}
\end{table} 
Table 1: Numbers of images in BEE2_1S and BEE2_2S (collectively referred to as BEE2) used for training, testing, and validation. Figure 8: A sample of 2-super images from BEE2 (BEE2_2S). The first four rows include images classified as BEE. The last three rows consist of images classified as NO-BEE. error in classification tasks with mutually exclusive classes. Henceforth we refer to each ConvNet auto-designed through our greedy GS algorithm as ConvNetGS-\(x\), where \(x\) denotes the number of hidden layers. In searching the ConvNet architectures with one hidden layer, the number of nodes in the convolutional layer of the hidden layer varied from 1 to 32 and the filter size from 1 to 16. Algorithm 2 gives the pseudocode of our procedure for auto-designing ConvNets with a single hidden layer that consists of one convolutional layer followed by one maxpooling layer. On Line 3, an input layer of a specific size is created. On Lines 4-6, convolutional, maxpooling, and output layers are created. On Line 7, a ConvNet is created that consists of the input layer, the hidden layer (i.e., the convolutional layer followed by the maxpooling layer), and the output layer. The activation function in all layers of the newly created ConvNet is set to ReLU. The learning rate of the ConvNet is set to 0.001. The ConvNet's optimizer function is set to adam. Each created ConvNet is then persisted on the disk. After the ConvNets are created and persisted on the disk, each of them is inflated from the disk, trained, evaluated, and persisted along with its validation performance, which is shown on Lines 13-15 in Algorithm 2.