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Update.
author
Francois Fleuret
<francois@fleuret.org>
Sun, 14 Aug 2022 08:11:10 +0000
(10:11 +0200)
committer
Francois Fleuret
<francois@fleuret.org>
Sun, 14 Aug 2022 08:11:10 +0000
(10:11 +0200)
minidiffusion.py
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diff --git
a/minidiffusion.py
b/minidiffusion.py
index
6fd8564
..
42dff7c
100755
(executable)
--- a/
minidiffusion.py
+++ b/
minidiffusion.py
@@
-272,6
+272,8
@@
if train_input.dim() == 2:
x = generate((10000, 1), model)
ax.set_xlim(-1.25, 1.25)
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),
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],
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],
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)
ax.legend(frameon = False, loc = 2)