| ## based on https://github.com/dmlc/mxnet/issues/1302 |
| ## Parses the model fit log file and generates a train/val vs epoch plot |
| 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="log_tr_va", |
| 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() |
