from _data_process import asMinutesUnit
import numpy  as np

f = np.loadtxt('D:/xinze/Documents/GitHub/Stochastic_CNN/FasionMNIST_pytorch_mT/Result_npz/C32F10/Time_Log.txt')

f[:,2]=f[:,1]*60+f[:,2]
for k , t in zip(f[:,0],f[:,2]):
    m=asMinutesUnit(t)
    print("%d\t%s" % (k,m) )

with open("./Time_Log.txt", "w+") as n:
    for k , t in zip(f[:,0],f[:,2]):
        m=asMinutesUnit(t)
        print("%d\t%s" % (k,m) , file=n)

a0b8f92ad0add66c6c3bd190f8add3b54ee69e28
<mask token>

<mask token>
for k, t in zip(f[:, 0], f[:, 2]):
    m = asMinutesUnit(t)
    print('%d\t%s' % (k, m))
with open('./Time_Log.txt', 'w+') as n:
    for k, t in zip(f[:, 0], f[:, 2]):
        m = asMinutesUnit(t)
        print('%d\t%s' % (k, m), file=n)

<mask token>
f = np.loadtxt(
    'D:/xinze/Documents/GitHub/Stochastic_CNN/FasionMNIST_pytorch_mT/Result_npz/C32F10/Time_Log.txt'
    )
f[:, 2] = f[:, 1] * 60 + f[:, 2]
for k, t in zip(f[:, 0], f[:, 2]):
    m = asMinutesUnit(t)
    print('%d\t%s' % (k, m))
with open('./Time_Log.txt', 'w+') as n:
    for k, t in zip(f[:, 0], f[:, 2]):
        m = asMinutesUnit(t)
        print('%d\t%s' % (k, m), file=n)

from _data_process import asMinutesUnit
import numpy as np
f = np.loadtxt(
    'D:/xinze/Documents/GitHub/Stochastic_CNN/FasionMNIST_pytorch_mT/Result_npz/C32F10/Time_Log.txt'
    )
f[:, 2] = f[:, 1] * 60 + f[:, 2]
for k, t in zip(f[:, 0], f[:, 2]):
    m = asMinutesUnit(t)
    print('%d\t%s' % (k, m))
with open('./Time_Log.txt', 'w+') as n:
    for k, t in zip(f[:, 0], f[:, 2]):
        m = asMinutesUnit(t)
        print('%d\t%s' % (k, m), file=n)

from _data_process import asMinutesUnit
import numpy  as np

f = np.loadtxt('D:/xinze/Documents/GitHub/Stochastic_CNN/FasionMNIST_pytorch_mT/Result_npz/C32F10/Time_Log.txt')

f[:,2]=f[:,1]*60+f[:,2]
for k , t in zip(f[:,0],f[:,2]):
    m=asMinutesUnit(t)
    print("%d\t%s" % (k,m) )

with open("./Time_Log.txt", "w+") as n:
    for k , t in zip(f[:,0],f[:,2]):
        m=asMinutesUnit(t)
        print("%d\t%s" % (k,m) , file=n)
