And predict the resulting loss of accuracy of each epoch, the drawing can be seen that the convergence of the training results and the visual pleasure.
Coding #:. 8 UTF-
Import matplotlib.pyplot AS PLT
data_dir = "D: \\ the result.txt"
Train_Loss_list = []
Train_Accuracy_list = []
Valid_Loss_list = []
Valid_Accuracy_list = []
F1 = Open (data_dir, 'R & lt')
Data = []
# outputs the result to the training result.txt, the stupid way, pick up the digital results byte bits
for F1 in Line:
IF (line.find ( 'train')> = 0):
# IF (to string.find has (Line, 'Train') = -1!):
Train_Loss_list.append (Line [12:18])
Train_Accuracy_list.append (Line [24:30])
IF (line.find ( 'Valid')> 0 =):
# IF (to string.find has (Line, 'Valid') = -1):!
Valid_Loss_list.append (Line [12:18])
Valid_Accuracy_list.append(line[24:30])
f1.close ()
# iterated 30 times, the range of x (0,30), and then each time and the corresponding loss of accuracy on the x attached
X1 = Range (0, 30)
X2 Range = (0, 30)
Y1 = Train_Accuracy_list
Y2 = Train_Loss_list
Y3 = Valid_Accuracy_list
Y4 = Valid_Loss_list
plt.subplot (2,. 1,. 1)
# plt.plot (X1, Y1, 'O -', Color = 'R & lt')
PLT .plot (X1, Y1, 'O -', label = "Train_Accuracy")
plt.plot (X1, Y3, 'O -', label = "Valid_Accuracy")
plt.title ( 'the Test Accuracy FC vs. Epoches')
PLT .ylabel ( 'the Test Accuracy')
plt.legend (LOC = 'Best')
plt.subplot (2,. 1, 2)
plt.plot (X2, Y2, '.-', label = "Train_Loss")
plt.plot (X2, Y4, '.-', label = "Valid_Loss")
PLT.xlabel('Test loss vs. epoches')
plt.ylabel('Test loss')
plt.legend (loc = 'best')
plt.show ()
