## 3 Results
### Recognition Results and Performance Analysis
#### 3.1.4 Visual Analysis of MCNN  
At present, the deep convolutional neural network has been widely used in classification and recognition based on massive data in various fields. However, the processing process of input data and results in CNN is similar to that in a \"black box\". When we apply each model, we tend to ignore the explanation of the feature training process between the layers within the network, which leads to the questioning of the reliability of the network model after training. Therefore, it is necessary to further clarify whether the model can truly identify the target object. We use four visualization algorithms to show the distribution of the eigenvalues extracted by MCNN.  
After many times of convolution and pooling (feature extraction layer), the deep convolution neural network often contains the most abundant spatial and semantic information in the last convolution layer. The last convolution layer can extract the features contained in the input image to the greatest extent, and it has the most direct impact on the final classification results. Therefore, to verify the function of the model and more vividly understand the classification process of the MCNN model for PD pattern recognition, the Guided-Backpropagation (Guided-BP) algorithm is used to visually calculate the feature map of the convolution output of the last layer of the MCNN model. The CAM method can show the decision basis of the model in the form of a class activation map to further analyze the interpretability of the model. To fully explain the classification results, we further explored which region of the model is used to judge the category of the input, and the CAM method is used for convolution feature up-sampling processing and a gradient backpropagation. Guided-BP, original CAM, Grad-CAM based on gradient, and Guided  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Parameter/Million** & **Weight Storage/MB** \\ \hline AlexNet & 57.02 & 217.51 \\ ResNet-18 & 11.18 & 42.65 \\ VGG16 & 134.29 & 512.28 \\ SqueezeNet1.0 & 0.74 & 2.82 \\ DenseNet121 & 6.96 & 26.55 \\ MobileNetsV2 & 2.23 & 8.51 \\ OurModel & 0.80 & 3.05 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Core module construction of MOBILENETS.  
Grad-CAM based on gradient guidance are used to visualize the features of MCNN. The results are shown in Table 5.  
The visualization graphs in Table 5 show all the features that can be extracted from the MCNN. The feature maps obtained by Guided-BP are noise-free, and it can be seen that the features extracted by the model are concentrated on the discharge data of all kinds of PRPD spectra. Through the reverse visualization calculation, the pixels used to judge the network are marked. The figures in Table 5 show that the eigenvalue regions selected by MCNN in the training process are consistent in the four visualization algorithms, and the eigenvalues are all in the discharge pulse aggregation region. Therefore, the training process and recognition results of MCNN are reliable.  
The weighted sum of the feature maps contained in the last convolutional layer in the MCNN model and their corresponding weights are obtained as the result of CAM. The Grad-Cam thermal diagram is obtained by superimposing the original image with the up-sampling processing of these features. On this basis, the back-propagation gradient is used as the weight of the feature map; it can be concluded that the visualization results obtained by the Guided-Grad-CAM method are highly consistent with those obtained by the Guided-BP. Therefore, through the above four methods to visualize the recognition process of all types of PD data in the MCNN, it is very intuitive to explain the classification results of the model. The visualization graphs in Table 5 further show that the MCNN designed in this paper can extract the image features of different discharge types and has strong feasibility for the realization of PD pattern recognition.