[转]MXNet 怎样画出训练图？

版权声明：belongs to tony2278 https://blog.csdn.net/tony2278/article/details/87833810

train acc可以在99.9%水平，val acc 稳定在80%左右  

利用 log 画 training和val曲线

import matplotlib.pyplot as plt
import numpy as np
import re
import argparse
 
parser = argparse.ArgumentParser(description='Parses log file and generates train/val curves')
parser.add_argument('--log-file', type=str,default="/home/panda/Ureserch/mxnet_panda/UCM_EXP/UCM_128_log_4",
                    help='the path of log file')
args = parser.parse_args()
 
 
TR_RE = re.compile('.*?]\sTrain-accuracy=([\d\.]+)')
VA_RE = re.compile('.*?]\sValidation-accuracy=([\d\.]+)')
 
log = open(args.log_file).read()
 
log_tr = [float(x) for x in TR_RE.findall(log)]
log_va = [float(x) for x in VA_RE.findall(log)]
idx = np.arange(len(log_tr))
 
plt.figure(figsize=(8, 6))
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.plot(idx, log_tr, 'o', linestyle='-', color="r",
         label="Train accuracy")
 
plt.plot(idx, log_va, 'o', linestyle='-', color="b",
         label="Validation accuracy")
 
plt.legend(loc="best")
plt.xticks(np.arange(min(idx), max(idx)+1, 5))
plt.yticks(np.arange(0, 1, 0.2))
plt.ylim([0,1])
plt.show()

https://www.cnblogs.com/TreeDream/p/10102626.html

https://blog.csdn.net/panda1942/article/details/50923006