time sqlite3 u.sqlite 'create table t (d text, i text)'
time sqlite3 av.sqlite -cmd "attach 'u.sqlite' as u" "insert into u.t select min(d) as d, min(i) as i from t where d not like '%.%.%' group by i having count(distinct d) = 1"

time sqlite3 av.sqlite -cmd "attach 'u.sqlite' as u" "insert into u.t select min(d) as d, min(i) as i from t where i glob '208.*' and d not like '%.%.%' and (d like '%.com' or d like '%.net') group by i having count(distinct d) = 1"

8,chrisjmcgregor.com
11,80end.com
28,fine5.net
38,bestarabictv.com
49,xy005.com
50,cmsasoccer.com
80,museemontpellier.net
100,newtiger.com
108,lps-promptservice.com
111,bridesmaiddressesshow.com

#!/usr/bin/env bash
bin=$((2**24))
sqlite3 2013-dns-census-a-novirt.sqlite -cmd '.mode csv' >2013-dns-census-a-novirt-hist.csv <<EOF
select i, sum(cnt) from (
  select floor(i/${bin}) as i,
         count(*) as cnt
    from t
    group by 1
  union
  select *, 0 as cnt from generate_series(0, 255)
)
group by i
EOF
gnuplot \
  -e 'set terminal svg size 1200, 800' \
  -e 'set output "2013-dns-census-a-novirt-hist.svg"' \
  -e 'set datafile separator ","' \
  -e 'set tics scale 0' \
  -e 'unset key' \
  -e 'set xrange[0:255]' \
  -e 'set title "Counts of IPs with a single hostname"' \
  -e 'set xlabel "IPv4 first byte"' \
  -e 'set ylabel "count"' \
  -e 'plot "2013-dns-census-a-novirt-hist.csv" using 1:2:1 with labels' \
;

cat << EOF | patch
diff --git a/run.py b/run.py
index 104d363..20072d1 100644
--- a/run.py
+++ b/run.py
@@ -24,7 +24,8 @@ data_test = MNIST('./data/mnist',
 data_train_loader = DataLoader(data_train, batch_size=256, shuffle=True, num_workers=8)
 data_test_loader = DataLoader(data_test, batch_size=1024, num_workers=8)

-net = LeNet5()
+device = 'cuda'
+net = LeNet5().to(device)
 criterion = nn.CrossEntropyLoss()
 optimizer = optim.Adam(net.parameters(), lr=2e-3)

@@ -43,6 +44,8 @@ def train(epoch):
     net.train()
     loss_list, batch_list = [], []
     for i, (images, labels) in enumerate(data_train_loader):
+        labels = labels.to(device)
+        images = images.to(device)
         optimizer.zero_grad()

         output = net(images)
@@ -71,6 +74,8 @@ def test():
     total_correct = 0
     avg_loss = 0.0
     for i, (images, labels) in enumerate(data_test_loader):
+        labels = labels.to(device)
+        images = images.to(device)
         output = net(images)
         avg_loss += criterion(output, labels).sum()
         pred = output.detach().max(1)[1]
@@ -84,7 +89,7 @@ def train_and_test(epoch):
     train(epoch)
     test()

-    dummy_input = torch.randn(1, 1, 32, 32, requires_grad=True)
+    dummy_input = torch.randn(1, 1, 32, 32, requires_grad=True).to(device)
     torch.onnx.export(net, dummy_input, "lenet.onnx")

     onnx_model = onnx.load("lenet.onnx")
EOF

real    1m27.829s
user    4m37.266s
sys     0m27.562s

cd lenet
cp ~/git/LeNet-5/lenet.onnx .
wget -O 9.png https://raw.githubusercontent.com/cirosantilli/media/master/Digit_9_hand_drawn_by_Ciro_Santilli_on_GIMP_with_mouse_white_on_black.png
./infer.py 9.png

9

infer_mnist.py lenet.onnx mnist_png/out/testing/1/*.png