Update energy theory example notebook.

examples/linear_net_theoretical_energy.ipynb

@@ -314,16 +314,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Train iter 10, train loss=0.084618, avg test accuracy=45.022034\n",
-      "Train iter 20, train loss=0.062942, avg test accuracy=59.715546\n",
-      "Train iter 30, train loss=0.058635, avg test accuracy=72.215546\n",
-      "Train iter 40, train loss=0.058973, avg test accuracy=70.983574\n",
-      "Train iter 50, train loss=0.053510, avg test accuracy=77.163460\n",
-      "Train iter 60, train loss=0.052320, avg test accuracy=76.101761\n",
-      "Train iter 70, train loss=0.063362, avg test accuracy=76.352165\n",
-      "Train iter 80, train loss=0.057726, avg test accuracy=76.382210\n",
-      "Train iter 90, train loss=0.054073, avg test accuracy=75.801285\n",
-      "Train iter 100, train loss=0.053406, avg test accuracy=78.225159\n"
+      "Train iter 10, train loss=0.067622, avg test accuracy=59.535255\n",
+      "Train iter 20, train loss=0.054068, avg test accuracy=75.230370\n",
+      "Train iter 30, train loss=0.063356, avg test accuracy=77.453926\n",
+      "Train iter 40, train loss=0.051848, avg test accuracy=80.048080\n",
+      "Train iter 50, train loss=0.061488, avg test accuracy=82.061295\n",
+      "Train iter 60, train loss=0.044830, avg test accuracy=80.789261\n",
+      "Train iter 70, train loss=0.045716, avg test accuracy=84.174683\n",
+      "Train iter 80, train loss=0.053921, avg test accuracy=82.041267\n",
+      "Train iter 90, train loss=0.040125, avg test accuracy=83.072914\n",
+      "Train iter 100, train loss=0.050980, avg test accuracy=83.974358\n"
      ]
     },
     {