Progress of dbnn experiment

1. Precsion,Recall,f1_scorem

precision of mbnn is 0.8756 recall of mbnn is 0.8670 f1_score of mbnn is 0.8689 

precision of cnn is 0.8952 recall of cnn is 0.8943 f1_score of cnn is 0.8946

precision of mbnnst is 0.8835 recall of mbnnst is 0.8847 f1_score of mbnnst is 0.8835 

2. Confusion matrix

Confusion matrix of each model

CNN	MBNN	MBNNST

3. Accuracy of each category

Precission of each category when using CNN: 0 : 0.8748 1 : 0.8121 2 : 0.9960

Precission of each category when using MBNN: 0 : 0.8856 1 : 0.7306 2 : 0.9848

Precission of each category when using MBNNST: 0 : 0.8661 1 : 0.7888 2 : 0.9992

When the accuracy is almost the same ,MBNN It can reduce the cost of reasoning time

4. Efficiency verification

Average Time of MBNN using cpu for block is 0.4906

Max Time of MBNN using cpu for block is 0.6025

Min Time of MBNN using cpu for block is 0.5275

Average Time of CNN using cpu for block is 0.8985

Max Time of CNN using cpu for block is 1.0449

Min Time of CNN using cpu for block is 0.9649

Average Time of MBNN using cpu for block75 is 0.8868

Max Time of MBNN using cpu for block75 is 1.0955

Min Time of MBNN using cpu for block75 is 0.9644 

 

Average Time of CNN using cpu for block75 is 0.9319

Max Time of CNN using cpu for block75 is 1.1225

Min Time of CNN using cpu for block75 is 0.9826

Average Time of MBNN using cpu for common is 0.8949

Max Time of MBNN using cpu for common is 1.0635

Min Time of MBNN using cpu for common is 0.9661 

 

Average Time of CNN using cpu for common is 0.9518

Max Time of CNN using cpu for common is 1.1473

Min Time of CNN using cpu for common is 0.9678

Look at the , On Model MBNN for , exactly block A simple branch of case reasoning time , Comparison common、block0.75 The situation of , In terms of reasoning time , Naturally, it decreases a lot

How to explain the reduction of reasoning time under the second branch ？ Communicate with Tianfang , Communication completed , It could be an error , Because the time difference is not big

The rectangle represents the average , The above I-type represents the interval between the maximum and minimum values

 

 

Experiments are mainly done

Comparison of reasoning speed of different models

Improvement of model accuracy / Confusion matrix

Comparison of reasoning speed under different platforms

Problems in the code have not been added Other Logic