## 3 Experiments  
The system consisted of a computer equipped with an i-7 Intel processor, Nvidia RTX 2080 Super GPU with 384 Tensor cores, the interferometer, a microscope objective, and a CMOS camera. The hologram-classifier uses the same set of hyperparameters of the EDL-IOHC reported in [6]. The new optical parameters for the digital holographic interferometric system are shown in Table 2.  
As illustrations, Figures 4-8 show examples from the human chromosome and the house fly wing datasets. Figures 4 and 6 are the corresponding images. Figures 5 and 7 are 3D plots of the spectrums and complex wavefront as a better means of visualization as they are not typical interpretable visual images but are a 2D array of complex phasors. The chromosome is close to transparent, and it is one of the most challenging samples to be separated if not using a hologram-classifier. The house fly wing is semitransparent, and the phase information can complement the magnitude information to build better decision boundaries for the deep learning network.  
From the full dataset of size 5000 capture raw holograms, 5000 digital holograms (complex object wavefront) are extracted. Then 4000 out of the 5000 are taken as the in-training dataset, while the remaining out-training dataset is used as a test set. The ensemble CNN is trained with 3200 in-training set data, and the remaining 800 are used as a validation set to stop the training process by an early stopping mechanism. Finally, both the in-training dataset and the out-training dataset are used to evaluate the hologram-classifier. Training is stopped by the validation set when the change of the validating accuracy is less than 0.01%.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Optical Parameters** & **Values** \\ \hline Wavelength of light & 632.8 nm \\ Pixel size & 3.45 μm \\ Size of hologram & 2056 rows \(\times\) 2546 columns \\ Off-axis angle & 1.5 degrees \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optical parameters of the digital holographic interferometer system.  
Figure 4: Sample image and experimental results for human chromosome sample.  
Figure 5: Frequency spectrum and reconstructed wavefront images of human chromosome sample.  
Figure 6: Sample image and experimental results for house fly wing sample.  
The ensemble CNN is trained by the dataset with cosine smoothing on the phase components, the validating set stops the training epoch, and the actual epoch run is 16. In each epoch, the holograms in the in-training set of the dataset are used to train the deep learning structure. The trained structure is then applied to classify the datasets. The  
Figure 8: The classification confusion matrix labels 0–9 correspond to the ten specimen classes as shown in Table 1 ‘Class Label’ column.  
Figure 7: Frequency spectrum and reconstructed wavefront images of house fly wing sample.  
following confusion matrix in Figure 8 shows that eight out of the ten classes are correct with very high overall classification accuracy, as shown in Table 3.  
The results in Table 3 reflect that in classifying the object, EDL-IOHC can maintain a high success rate of 99.60% for the out-training test set and 99.82% for the entire dataset. The performance is better than the benchmarking experiment conducted in [6] on partially occluded digit objects with speckle noise contamination.