diff --git a/Chapter15 - Intro to Federated Learning - Deep Learning on Unseen Data.ipynb b/Chapter15 - Intro to Federated Learning - Deep Learning on Unseen Data.ipynb
index 14bbbef..4cbf72d 100644
--- a/Chapter15 - Intro to Federated Learning - Deep Learning on Unseen Data.ipynb	
+++ b/Chapter15 - Intro to Federated Learning - Deep Learning on Unseen Data.ipynb	
@@ -591,15 +591,15 @@
     "\n",
     "            # padding token should stay at 0\n",
     "            model.weight.data[w2i['<unk>']] *= 0 \n",
-    "            input = Tensor(input_data[b_i*bs:(b_i+1)*bs], autograd=True)\n",
-    "            target = Tensor(target_data[b_i*bs:(b_i+1)*bs], autograd=True)\n",
+    "            input = Tensor(input_data[b_i*batch_size:(b_i+1)*batch_size], autograd=True)\n",
+    "            target = Tensor(target_data[b_i*batch_size:(b_i+1)*batch_size], autograd=True)\n",
     "\n",
     "            pred = model.forward(input).sum(1).sigmoid()\n",
     "            loss = criterion.forward(pred,target)\n",
     "            loss.backward()\n",
     "            optim.step()\n",
     "\n",
-    "            iter_loss += loss.data[0] / bs\n",
+    "            iter_loss += loss.data[0] / batch_size\n",
     "\n",
     "            sys.stdout.write(\"\\r\\tLoss:\" + str(iter_loss / (b_i+1)))\n",
     "        print()\n",
@@ -732,6 +732,8 @@
     }
    ],
    "source": [
+    "import copy\n",
+    "\n",
     "for i in range(3):\n",
     "    print(\"Starting Training Round...\")\n",
     "    print(\"\\tStep 1: send the model to Bob\")\n",
@@ -775,8 +777,6 @@
     }
    ],
    "source": [
-    "import copy\n",
-    "\n",
     "bobs_email = [\"my\", \"computer\", \"password\", \"is\", \"pizza\"]\n",
     "\n",
     "bob_input = np.array([[w2i[x] for x in bobs_email]])\n",