### Experiment Details
#### 3.4.2 The Implementation Process  
Our model was trained on one NVIDIA 4 GTX2080 GPUs with 16 GB memory for roughly 10 h. Experiments were implemented based on the deep learning framework TensorFlow. The operating system was Windows 10. The input image size of the spectrum is \(224\times 224\), which is cropped from the original 256 random jitter. The hopping characteristics identification network is a total convolutional neural network, which identifies the hopping features of 30 spectral graphs as high-dimensional features of 128 dimensions and sends them into the time network. The time network consists of five convolution layers and three FC layers, and the final softmax score is calculated by using the average score. The spatial CNN network proved its powerful ability in image detection with the spatial relationships, while the structure of spatial CNN is very complex and has complex directional convolution, which is a kind of black-box operation for us. In order to prevent overfitting, we enhanced the training data and, with random horizontal flipping and RGB jittering, adjusted the exposure curve. Similar results were also achieved by Alex-net and Resnet-101 [40]. We speculate that the reason for the failure is that with multiple feature extraction, the network extracts more representative information and naturally loses detail. However, in some cases, the differences between bad products and good products in our data lie in small changes. For some pictures, even people can be wrong. Therefore, we only use artifacts that can clearly distinguish features, and the fusion of multiple defect features is given to the neural network to fit by itself. According to the appeal, we conducted pre-training for the spatial network and run-out signal detection network. The difference is that the spatial network adopts supervised pre-training and the run-out signals detection network adopts unsupervised pre-training to increase the normality and sensitivity of signal recognition. The method did work and the accuracy was improved to 96.1%, but it was still too low for our demand. In this case, we used the continuous finetuning mechanism to further improve the accuracy, but only the action categorical loss function was computed. In spatial net training, a \(224\times 224\) sub-image is randomly cropped from the selected frame, then undergoes random horizontal flipping and RGB jittering. The sub-image is sampled from the whole frame, not just from its \(256\times 256\) center. In temporal net training, we computed an optical flow volume I for the selected training frame, as described in Section 3. From that volume, a fixed-size \(224\times 224\times 2\) L input is randomly cropped and flipped. For FC layers, the weight parameters are initialized from a truncated random normal distribution subject to N \(\sim\) (0, \(\frac{2}{n}\)), where n denotes the number of connections between two layers. In order to prevent the network from overfitting, we adopted a mirror to augment the data. The data sample was expanded fivefold by using the above method. In our case, a network architecture of two tributaries were modified so that it had two softmax classification layers on top of the last fully connected fusion layer. One softmax layer computes global feature scores, and the other one computes the local hopping characteristic scores. Each layer is equipped with its own loss function coming from the respective dataset. For each workpiece, we made the corresponding time-frequency spectrum graph and 15 hopping characteristic graphs generated by 30 frames of the spectrum. The overall training loss is computed as the sum of the individual tasks' losses, and the network weight derivatives can be found by back-propagation. We selected the cross-entropy function as the overall loss function of our model. During the training process, the stochastic gradient descent (SGD, momentum = 0.9) with mini-batches of weight samples was applied to update the weight parameters.