matplot读取文本文件画图

# -*- coding: utf-8 -*-
"""
Created on Fri Sep  7 18:38:35 2018
 
@author: manuel
"""
import matplotlib.pyplot as plt
#from mpl_toolkits.axisartist.axislines import SubplotZero
import numpy as np
plt.rcParams['font.sans-serif']=['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus']=False # 用来正常显示负号
 
SAVE_LOSS0='HG_loss.txt'
#SAVE_LOSS1='hotrolledsteel1800_20_20_2000_10_loss.txt'
#SAVE_LOSS2='hotrolledsteel1800_20_20_2000_50_loss.txt'
#SAVE_LOSS3='hotrolledsteel1800_20_20_2000_100_loss.txt'
SAVE_VALIDATION_ACCURACY='HG_validation_accuracy.txt'
#x = np.linspace(0, 2, 100)
 
#plt.plot(x, x, label='linear')
#plt.plot(x, x**2, label='quadratic')
#plt.plot(x, x**3, label='cubic')
with open(SAVE_LOSS0, 'r') as open_file0:
    file_string0 = open_file0.read()
file_values0 = [float(x) for x in file_string0.split(',')]
 
with open(SAVE_VALIDATION_ACCURACY, 'r') as open_file1:
    file_string1 = open_file1.read()
file_values1 = [float(x) for x in file_string1.split(',')]
 
#with open(SAVE_LOSS2, 'r') as open_file2:
#    file_string2 = open_file2.read()
#file_values2 = [float(x) for x in file_string2.split(',')]
#
#with open(SAVE_LOSS3, 'r') as open_file3:
#    file_string3 = open_file3.read()
#file_values3 = [float(x) for x in file_string3.split(',')]
 
epoches=[i for i in range(len(file_values0))]
 
#plt.plot(epoches, file_values0, label='2000X5',color='black')#darkgray
#plt.plot(epoches, file_values1, label='2000X10',color='black')
#plt.plot(epoches, file_values2, label='2000X50',color='black')#darkgray
plt.plot(epoches, file_values0,'r-',markersize=1,linewidth=1,label="loss")
plt.plot(epoches, file_values1,'b-',markersize=1,linewidth=1,label="accuracy")
plt.plot(np.linspace(1,1,1000),color='black',linestyle='--')
plt.xlabel("epoches（迭代次数）")
plt.ylabel("Loss/Accuracy（损失值/准确率）")
#plt.xticks(range(0,2000,100))
 
#fig,ax_y2=plt.subplot()
#ax_c=ax_y2.twiny()
#ax_c.set_ylabel('第二Ｙ轴', color='b')
#ax_c.set_yticklabels(["$0$", r"$\frac{1}{2}\pi$", r"$\pi$", r"$\frac{3}{2}\pi$", r"$2\pi$"])
#plt.ylabel("Validation Accuracy")
#plt.axis([0, 2000, 0, 100])
plt.title("Training dataset(训练集)/Learning Rate=0.015 Batch=64")
 
plt.legend()
 
plt.show()
 
#!!!间隔描点未解决