loss_visualization.py

# -*- coding: utf-8 -*-
"""
@File    : loss_visualization.py
@Time    : 11/6/20 10:49 AM
@Author  : Mingqiang Ning
@Email   : ningmq_cv@foxmail.com
@Modify Time        @Version    @Description
------------        --------    -----------
11/6/20 10:49 AM      1.0         None
# @Software: PyCharm
"""

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# %matplotlib inline

lines = 750  # 改为自己生成的train_log_loss.txt中的行数
result = pd.read_csv('train_log_loss.txt', skiprows=[x for x in range(lines) if ((x % 10 != 9) | (x < 1000))],
                     error_bad_lines=False, names=['total_loss',  'lr', 'seconds', 'images'])
# error_bad_lines = False, names = ['total_loss', 'avg', 'lr', 'seconds', 'images'])
result.head()

result['loss'] = result['loss'].str.split(' ').str.get(1)
result['avg'] = result['avg'].str.split(' ').str.get(1)
result['rate'] = result['rate'].str.split(' ').str.get(1)
result['seconds'] = result['seconds'].str.split(' ').str.get(1)
result['images'] = result['images'].str.split(' ').str.get(1)
result.head()
result.tail()

# print(result.head())
# print(result.tail())
# print(result.dtypes)

print(result['loss'])
print(result['avg'])
print(result['rate'])
print(result['seconds'])
print(result['images'])

result['loss'] = pd.to_numeric(result['loss'])
result['avg'] = pd.to_numeric(result['avg'])
result['rate'] = pd.to_numeric(result['rate'])
result['seconds'] = pd.to_numeric(result['seconds'])
result['images'] = pd.to_numeric(result['images'])
result.dtypes

fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(result['avg'].values, label='avg_loss')
# ax.plot(result['loss'].values,label='loss')
ax.legend(loc='best')  # 图列自适应位置
ax.set_title('The loss curves')
ax.set_xlabel('batches')
fig.savefig('avg_loss')
# fig.savefig('loss')