| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, |
| # software distributed under the License is distributed on an |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| # KIND, either express or implied. See the License for the |
| # specific language governing permissions and limitations |
| # under the License. |
| |
| ## 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() |
