## 3 Experiments and Results  
### Experiments with BEE1  
In the first set of our experiments, we focused on our auto-designed ConvNets. The actual training, testing, and validation procedures were implemented in Python 3.4 with tflearn [60] and executed on an Ubuntu 16.04.S LTS computer with an AMD Ryzen 7 1700X Eight-Core Processor with 16 GiB of DDR4 RAM and a GeForce GTX 1080 Ti GPU with 11 GB of onboard memory. Each model was trained for 50 epochs with a batch size of 50. Each image was normalized to have the pixel values on each channel to be in the range [0, 1]. Table 2 gives a performance summary of the best auto-designed ConvNets with the number of hidden layers in the range [1, 5] on the validation dataset of BEE1. The best one-hidden-layer model had a filter size of 9, 29 hidden nodes, and a validation accuracy of 48.52%. The best performing two-hidden-layer model had 31 nodes with a filter size of 12 in the first hidden layer and 40 notes with a filter size of 10 in the second hidden layer. This model had a validation accuracy of 98.66%. The best performing three-hidden-layer model had 27 hidden nodes in Layer 1 with a filter size of 9, 42 hidden  
nodes in Layer 2 with a filter size of 11, 105 hidden nodes with a filter size of 11 in Layer 3, and a validation accuracy of 99.09%. The best performing four-hidden-layer model had 27 hidden nodes with a filter size of 9 in layer 1, 38 hidden nodes with a filter size of 9 in Layer 2, 85 hidden nodes with a filter size of 9 in Layer 3, 220 hidden nodes with a filter size of 10 in Layer 4, and a validation accuracy of 98.63%. The best performing five-hidden-layer model had 27 hidden nodes with a filter size of 9 in layer 1, 38 nodes with a filter size of 9 in Layer 2, 85 nodes with a filter size of 9 in Layer 3, 260 nodes with a filter size of 8 in Layer 4, 240 nodes with a filter size of 9 in Layer 5, a testing loss of 0.03, and a validation accuracy of 99.03%. In the ConvNet architectures with six hidden layers (not shown in Table 2), for the first, second, third, fourth, and fifth hidden layers, the numbers of hidden nodes and filter sizes ranged from the Top 5 best performing five-hidden-layer ConvNets. The number of hidden nodes in Layer 6 ranged from 600 to 1240 and the filter size from 7 to 12. The best performing six-hidden-layer model had 27 hidden nodes with a filter size of 9 in layer 1, 38 nodes with a filter size of 9 in Layer 2, 85 nodes with a filter size of 9 in Layer 3, 260 nodes with a filter size of 8 in Layer 4, 240 nodes with a filter size of 9 in Layer 5, and a validation accuracy of 99.03%, which was not better than the performance results of ConvNetGS-5 from the previous generation. Adding more hidden layers (seven and eight) did not improve the validation performance of the auto-designed ConvNets in that the average validation accuracy varied from 98.90% to 99.09%. We next compared the performance of our auto-designed ConvNets with hand-designed ConvNets on the same dataset. Toward that end, we organized a deep learning competition on BEE1. The competition was organized as a longitudinal class project in a graduate Computer Science course taught by the first author at Utah State University in Fall 2018. There were 52 graduate and upper-level undergraduate students enrolled in this course. The students received detailed instruction on the best ConvNet design practices [12; 29; 61; 62] and the ConvNet design and training tools (e.g., tflearn [60] and TensorFlow [63]) and were asked to design, train, and test ConvNets on the training and testing images of BEE1. Each ConvNet was evaluated on the validation dataset of BEE1, which was withheld from the students during the design, training, and testing phases. Only the networks with a validation accuracy of 97% or above were chosen, which gave us nine ConvNets. The architecture of the best performing hand-designed ConvNet (ConvNet 1) with a validation accuracy of 99.45% is given in Figure 9. In addition to the input and output layers, this ConvNet has six hidden layers.