## 3 Algorithm Overview
The total number of images is 12,031. In the overall experimentation with CSSA, images are scaled to \(224\times 224\) resolution for training and testing. #### 5.2.2 Optimization

All proposed CNN models are developed, trained and tested through the Berkeley-Vision deep learning framework Caffe. The proposed network is trained on a single NVIDIA Tesla K40c GPU with 12 G of memory. Training for reduced BTCM models takes approximately six hours. The training of network is performed until the loss is converged, and there is no considerable increase or decrease in loss and accuracy. Figure 2: An illustration of the proposed CSSA architecture. [PERSON] et al. 's [45] proposed method for network initialization is used for the proposed encoder-decoder networks. Stochastic Gradient Descent (SGD) with the momentum of 0.9 is employed to initiate the training of network. The network is trained with a fixed learning rate of 0.1 [19], while the weight decay is 0.0005. BTCM is trained for 10,000 iterations. Whereas the CSSA network is fixed to train no more than 40,000 iterations, as this limit is set to ensure that the network is trained enough and training loss is converging. To avoid the memory overflow problems while training, the training batch is set to 12 images. In each epoch, the image set is shuffled to ensure that no image is used more than once in any epoch. The cross-entropy loss [59] is employed as the objective function for training the whole network. It sums up the overall loss of the pixels in a mini-batch and helps to assess the final loss. ## 6 Results and Analysis

This section will present the overall assessment for trained models and testing results by well-known segmentation and classification performance training measures, such as Global Accuracy (GA) and Class Average Accuracy (CAA). The GA offers an overall percentage of pixels properly classified in the dataset, while CAA is used to assess the predictive accuracy mean of the entire classes. Proposed tests were performed on BTCM with comparatively less training iterations. The proposed network is limited to run for a maximum of 10,000 training transactions to test the ultimate reduction in loss and enhancement in accuracy; whereas the final version of the proposed CNN is trained for 40,000 iterations. Test 1: Different kinds of tests are performed on BTCM. The proposed model is used for the training efficiency of different activation functions. We have tested for ReLU, ELU, PReLU, Maxout and Tanh. The final outcomes showed that ELU and PReLU are performing better and offer enhanced outcomes. It is assessed that the PReLU performs well throughout the training process, but accuracy declines at the end of the training process, as shown in Figure 3. The ELU converged to the ultimate peak till the end of the training process. The Exponential Linear Unit (ELU) with \(0<\alpha\) is:

\[f(x)=\left\{ \begin{array}{ll}x&\text{if }x>0\\ \alpha(\exp(x)-1)&\text{if }x\leq 0\end{array} \right., \tag{4}\]

\[f^{\prime}(x)=\left\{ \begin{array}{ll}1&\text{if }x>0\\ f(x)+\alpha&\text{if }x\leq 0\end{array} \right..\]

Figure 3: Comparison of different activation functions. The ELU hyperparameter \(\alpha\) manages the given value to which an ELU saturates for negative net-inputs. ELUs reduce the chances of vanishing gradient effect as Leaky ReLUs (LReLUs) and ReLUs do. The problem of the vanishing gradient is eased because the positive part of these functions is the identity; hence, their derivative is one and not contractive. However, sigmoid and tanh activation functions are contractive nearly everywhere. Contrary to ReLUs, ELUs offer negative values, which can push the mean of the activations very near to zero. The closer to zero, the faster the learning by the activation function, as this brings the gradient closer to the natural gradient. ELUs saturate to a negative value when the argument value gets smaller. Saturation means a small derivative that decreases the variation, as well as the information that is transferred to the next layer.