Update.

diff --git a/mine_mnist.py b/mine_mnist.py
index c6dc287..82f6530 100755 (executable)
--- a/mine_mnist.py
+++ b/mine_mnist.py
@@ -94,20 +94,21 @@ for e in range(nb_epochs):
 
     input_br = input_b[torch.randperm(input_b.size(0))]
 
-    mi = 0.0
+    acc_mi = 0.0
 
     for batch_a, batch_b, batch_br in zip(input_a.split(batch_size),
                                           input_b.split(batch_size),
                                           input_br.split(batch_size)):
-        loss = - (model(batch_a, batch_b).mean() - model(batch_a, batch_br).exp().mean().log())
-        mi -= loss.item()
+        mi = model(batch_a, batch_b).mean() - model(batch_a, batch_br).exp().mean().log()
+        loss = - mi
+        acc_mi += mi.item()
         optimizer.zero_grad()
         loss.backward()
         optimizer.step()
 
-    mi /= (input_a.size(0) // batch_size)
+    acc_mi /= (input_a.size(0) // batch_size)
 
-    print('%d %.04f %.04f'%(e, mi / math.log(2), class_entropy / math.log(2)))
+    print('%d %.04f %.04f'%(e, acc_mi / math.log(2), class_entropy / math.log(2)))
 
     sys.stdout.flush()
 
@@ -122,16 +123,16 @@ for e in range(nb_epochs):
 
     input_br = input_b[torch.randperm(input_b.size(0))]
 
-    mi = 0.0
+    acc_mi = 0.0
 
     for batch_a, batch_b, batch_br in zip(input_a.split(batch_size),
                                           input_b.split(batch_size),
                                           input_br.split(batch_size)):
         loss = - (model(batch_a, batch_b).mean() - model(batch_a, batch_br).exp().mean().log())
-        mi -= loss.item()
+        acc_mi -= loss.item()
 
-    mi /= (input_a.size(0) // batch_size)
+    acc_mi /= (input_a.size(0) // batch_size)
 
-print('test %.04f %.04f'%(mi / math.log(2), class_entropy / math.log(2)))
+print('test %.04f %.04f'%(acc_mi / math.log(2), class_entropy / math.log(2)))
 
 ######################################################################