Update.
authorFrancois Fleuret <francois@fleuret.org>
Sun, 14 Aug 2022 08:11:10 +0000 (10:11 +0200)
committerFrancois Fleuret <francois@fleuret.org>
Sun, 14 Aug 2022 08:11:10 +0000 (10:11 +0200)
minidiffusion.py

index 6fd8564..42dff7c 100755 (executable)
@@ -272,6 +272,8 @@ if train_input.dim() == 2:
         x = generate((10000, 1), model)
 
         ax.set_xlim(-1.25, 1.25)
+        ax.spines.right.set_visible(False)
+        ax.spines.top.set_visible(False)
 
         d = train_input.flatten().detach().to('cpu').numpy()
         ax.hist(d, 25, (-1, 1),
@@ -297,11 +299,11 @@ if train_input.dim() == 2:
 
         d = x.detach().to('cpu').numpy()
         ax.scatter(d[:, 0], d[:, 1],
-                   facecolors = 'none', color = 'red', label = 'Synthesis')
+                   s = 2.0, color = 'red', label = 'Synthesis')
 
         d = train_input[:x.size(0)].detach().to('cpu').numpy()
         ax.scatter(d[:, 0], d[:, 1],
-                   s = 1.0, color = 'blue', label = 'Train')
+                   s = 2.0, color = 'gray', label = 'Train')
 
         ax.legend(frameon = False, loc = 2)