## 4 Experiments and Results
### Computational Analysis  
While achieving the state-of-the-art results, we have significant gains on the computational efficiency of the proposed algorithm. To evaluate the computational efficiency, we have considered and compared three key characteristics from each model. The considered characteristics are (i) number of FLOPs (floating-point operations), (ii) model parameters, and (iii) memory requirement. Table 4 (Tensorflow or Keras model's computational information is obtained using model profiler function available at [[https://pypi.org/project/model-profiler/](https://pypi.org/project/model-profiler/)]([https://pypi.org/project/model-profiler/](https://pypi.org/project/model-profiler/)), accessed on 7 October 2021) presents the computational gains achieved by PrimeNet and compares them with different considered baseline results, depending on the information available in the original paper. The majority of related research results are obtained using ResNet architecture as a base model, so we also considered ResNet50 computational results as the first baseline to be compared. RestNet50 with pre-trained Imagenet weights has been recreated for our task data classifier with the same settings for each experiment. Here, we have considered the combined computational information from the shallow dynamic deep neural structure and its classifier for computational analysis. Hence, the number of epochs for our algorithm is \(100+50\), which means \(100\) iterations for classifier network using shallow PrimeNet structure. Once the learned feature representation is extracted from the classifier, another \(50\) epochs are used for retraining. ResNet is trained for \(50\) epochs for the same purpose, and other baselines take \(300\) minimum epochs. Since we have used the same architecture for PrimeNet and ResNet baseline, we obtained the same number of parameters and FLOPs for all datasets. ResNet is one sizeable deep network that involves the highest computational cost. ONE [27] has produced the computational information for the experiment on SVHN, which has a precise, reduced number of FLOPs, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Dataset** & **Params (Millions)** & **FLOPs (Millions)** & **Epochs** \\ \hline ResNet [34] & All Three & 23.6 & 409 & 50 \\ PrimeNet (Ours) & All Three & 0.41 & 2.7 & 100 + 50 \\ Condensenet\({}^{\text{light}}\)[18] & CIFAR-10 & 0.33 & 122 & 300 \\ Condensenet\({}^{\text{86}}\)[18] & CIFAR-10 & 0.52 & 65 & 300 \\ ONE [27] & SVHN & 0.5 & 2.28 & 300, 40 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the computational results for MNIST, CIFAR-10, and SVHN experiments. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**PrimeNet Test Error (\%)**} & **Model** & **Baseline Error (\%)** & **Baseline Method** \\  & **Top-1** & **Top-5 (\(\mu\))** & \(\sigma\) & & & \\ \hline MNIST & 0.59 & 0.706 & 0.14 & DT-RAM [12] & 1.46, 1.12 & Conditional Computation \\ \hline \multirow{4}{*}{CIFAR-10} & \multirow{4}{*}{6.21} & \multirow{4}{*}{7.34} & \multirow{4}{*}{3.67} & Condensenet [18] & 3.46, 3.76 & Conditional Computation \\ \cline{3-5}  & & & & ITADN [17] & 5.9 & Conditional Computation \\ \cline{3-5}  & & & & & RNP [7] & 15.05 & Network Pruning \\ \cline{3-5}  & & & & & SFP [21] & 7.74, 6.32 & Network Pruning \\ \cline{3-5}  & & & & ITADN [17] & 3.13, 5.99 & Knowledge Distillation \\ \hline SVHN & 1.71 & 2.2 & 0.42 & ONE [27] & 1.63 & Knowledge Distillation \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of classification error rate on evaluation model (%). The symbols \((\mu)\) and \((\sigma)\) represent the mean and standard deviation, respectively, for the top 5 scores from each dataset. whereas PrimeNet is just 15% costlier, but, on the other side, PrimeNet can reduce to number of parameters by 18%. Similarly, for Cifar-10, _Condensenet\({}^{light}\)_[18] can reduce the parameter size by 20% from PrimeNet, but PrimeNet has a tremendous gain of almost 99% reduced number of FLOPs.