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我想用對數座標軸限制我的座標軸的y軸座標。但是，添加plt.ylim((10^(-1),10^(0)))似乎沒有任何改變。我是否應該使用不同的命令，因爲我正在使用plt.semilogy？以下是代碼和數據。設置對數座標軸的y軸極限（使用plt.semilogy）
# Generate loss plots
# --------------- Latex Plot Beautification --------------------------
fig_width_pt = 492.0 #246.0 # Get this from LaTeX using \showthe\columnwidth
inches_per_pt = 1.0/72.27 # Convert pt to inch
golden_mean = (np.sqrt(5)-1.0)/2.0 # Aesthetic ratio
fig_width = fig_width_pt*inches_per_pt # width in inches
fig_height = fig_width*golden_mean # height in inches
fig_size = [fig_width+1,fig_height+1]
params = {'backend': 'ps',
'axes.labelsize': 12,
'font.size': 12,
'legend.fontsize': 10,
'xtick.labelsize': 10,
'ytick.labelsize': 10,
'text.usetex': False,
'figure.figsize': fig_size}
plt.rcParams.update(params)
# --------------- Latex Plot Beautification --------------------------
train = {}
tmp = list()
with open('loss.csv', 'rb') as csv_file:
reader = csv.reader(csv_file)
for i, row in enumerate(reader):
if i != 0:
tmp.append(row)
tmp = np.array(tmp)
train['iters'], train['seconds'], train['loss'], train['learn_rate'] = tmp[:,0], tmp[:,1], tmp[:,2], tmp[:,3]
plt.subplot(211)
plt.semilogy(train['iters'],train['loss'],'b',lw=2)
plt.ylabel('loss')
plt.ylim((10^(-1),10^(0)))
plt.subplot(212)
plt.semilogy(train['iters'],train['learn_rate'],'b',lw=2)
plt.xlabel('iterations')
plt.ylabel('learning rate')
plt.show()
loss.csv
NumIters,Seconds,TrainingLoss,LearningRate
0.0,0.486213,0.693148,nan
1000.0,7.557165,0.0961085,0.05
2000.0,14.041684,0.00384812,0.05
3000.0,20.410506,7.34072,0.05
4000.0,26.772446,4.78843,0.05
5000.0,34.117291,2.45869,0.05
6000.0,40.249146,0.179548,0.05
7000.0,46.377004,0.0033729,0.05
8000.0,52.499923,0.00020626,0.05
9000.0,59.317026,2.0962,0.05
10000.0,66.679739,1.20523,0.05
11000.0,72.846874,0.00894074,0.05
12000.0,78.87727,2.37395,0.05
13000.0,84.950737,0.00172985,0.05
14000.0,91.036988,8.13143,0.05
15000.0,98.153062,2.90689,0.05
16000.0,104.252995,1.78791,0.05
17000.0,110.286827,5.10336,0.05
18000.0,116.47252,3.34482,0.05
19000.0,122.683825,0.00838974,0.05
20000.0,129.637347,0.00341582,0.05
21000.0,135.640689,1.66777,0.05
22000.0,141.66995,3.30503,0.05
23000.0,147.721727,2.53775,0.05
24000.0,154.084407,1.35748,0.05
25000.0,161.426044,2.28748,0.05
26000.0,168.492162,0.00397386,0.05
27000.0,174.669545,0.000113542,0.05
28000.0,180.803535,2.5192,0.05
29000.0,187.004627,0.0019179,0.05
30000.0,194.150244,4.36825,0.05
31000.0,200.404565,1.38513,0.05
32000.0,206.412659,0.0108084,0.05
33000.0,212.437014,6.41096,0.05
34000.0,218.56177,0.000235395,0.05
35000.0,225.853988,7.88834,0.05
36000.0,231.888062,0.00109338,0.05
37000.0,238.976116,4.46498,0.05
38000.0,246.112036,0.00246135,0.05
39000.0,252.92424,0.00154073,0.05
40000.0,261.114472,1.49658,0.05
41000.0,268.695987,3.09471,0.05
42000.0,275.331985,0.000266829,0.05
43000.0,282.34568,1.06778,0.05
44000.0,290.059307,5.98044,0.05
45000.0,299.376506,0.00154176,0.05
46000.0,306.722876,9.46019,0.05
47000.0,314.33918,1.1353,0.05
48000.0,321.358202,7.14507,0.05
49000.0,328.710997,1.00035,0.05
50000.0,335.206681,4.40056,0.05