update

tutorial-contents/504_batch_normalization.py

@@ -58,7 +58,7 @@ class Net(nn.Module):
         self.bns = []
         self.bn_input = nn.BatchNorm1d(1, momentum=0.5)   # for input data
 
-        for i in range(N_HIDDEN):              # build hidden layers and BN layers
+        for i in range(N_HIDDEN):               # build hidden layers and BN layers
             input_size = 1 if i == 0 else 10
             fc = nn.Linear(input_size, 10)
             setattr(self, 'fc%i' % i, fc)       # IMPORTANT set layer to the Module
@@ -83,7 +83,7 @@ class Net(nn.Module):
         for i in range(N_HIDDEN):
             x = self.fcs[i](x)
             pre_activation.append(x)
-            if self.do_bn: x = self.bns[i](x)  # batch normalization
+            if self.do_bn: x = self.bns[i](x)   # batch normalization
             x = ACTIVATION(x)
             layer_input.append(x)
         out = self.predict(x)
@@ -147,7 +147,7 @@ for epoch in range(EPOCH):
             loss = loss_func(pred, b_y)
             opt.zero_grad()
             loss.backward()
-            opt.step()    # it will also learn the parameters in Batch Normalization
+            opt.step()    # it will also learns the parameters in Batch Normalization
 
 
 plt.ioff()