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我想用对数坐标轴限制我的坐标轴的y轴坐标。但是，添加plt.ylim((10^(-1),10^(0)))似乎没有任何改变。我是否应该使用不同的命令，因为我正在使用plt.semilogy？以下是代码和数据。设置对数坐标轴的y轴极限（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