title: Setting y-limit for plot with logarithmic axis (using plt.semilogy)
body: <p>I'd like to limit the y-scale for my plot with a logarithmic axis. However, adding <code>plt.ylim((10^(-1),10^(0)))</code> doesn't seem to change anything. Should I use a different command seeing as I'm using <code>plt.semilogy</code>? Below is the code and the data.</p>
  
  <pre><code># 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()
  </code></pre>
  
  <p><strong>loss.csv</strong></p>
  
  <pre><code>   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
  </code></pre>