Update week16.ipynb

Author: mhjensen

doc/pub/week16/ipynb/week16.ipynb

@@ -807,7 +807,35 @@
      "outputs_hidden": false
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading MNIST...\n",
+      "Epoch 1: Reconstruction error = 44.0517\n",
+      "Epoch 2: Reconstruction error = 30.6171\n",
+      "Epoch 3: Reconstruction error = 27.6941\n",
+      "Epoch 4: Reconstruction error = 26.0923\n",
+      "Epoch 5: Reconstruction error = 25.0179\n",
+      "Epoch 6: Reconstruction error = 24.1942\n",
+      "Epoch 7: Reconstruction error = 23.5854\n",
+      "Epoch 8: Reconstruction error = 23.1028\n",
+      "Epoch 9: Reconstruction error = 22.7083\n",
+      "Epoch 10: Reconstruction error = 22.3759\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "%matplotlib inline\n",
     "\n",