diff --git a/convolutional-neural-networks/mnist-mlp/mnist_mlp_exercise.ipynb b/convolutional-neural-networks/mnist-mlp/mnist_mlp_exercise.ipynb
index 895dfc4f4d..8203501f36 100644
--- a/convolutional-neural-networks/mnist-mlp/mnist_mlp_exercise.ipynb
+++ b/convolutional-neural-networks/mnist-mlp/mnist_mlp_exercise.ipynb
@@ -309,7 +309,7 @@
     "    # compare predictions to true label\n",
     "    correct = np.squeeze(pred.eq(target.data.view_as(pred)))\n",
     "    # calculate test accuracy for each object class\n",
-    "    for i in range(batch_size):\n",
+    "    for i in range(len(target)):\n",
     "        label = target.data[i]\n",
     "        class_correct[label] += correct[i].item()\n",
     "        class_total[label] += 1\n",
diff --git a/convolutional-neural-networks/mnist-mlp/mnist_mlp_solution.ipynb b/convolutional-neural-networks/mnist-mlp/mnist_mlp_solution.ipynb
index eed4e46e98..5657e2ab2d 100644
--- a/convolutional-neural-networks/mnist-mlp/mnist_mlp_solution.ipynb
+++ b/convolutional-neural-networks/mnist-mlp/mnist_mlp_solution.ipynb
@@ -424,7 +424,7 @@
     "    # compare predictions to true label\n",
     "    correct = np.squeeze(pred.eq(target.data.view_as(pred)))\n",
     "    # calculate test accuracy for each object class\n",
-    "    for i in range(batch_size):\n",
+    "    for i in range(len(target)):\n",
     "        label = target.data[i]\n",
     "        class_correct[label] += correct[i].item()\n",
     "        class_total[label] += 1\n",
diff --git a/convolutional-neural-networks/mnist-mlp/mnist_mlp_solution_with_validation.ipynb b/convolutional-neural-networks/mnist-mlp/mnist_mlp_solution_with_validation.ipynb
index 47def3170a..50b322946c 100755
--- a/convolutional-neural-networks/mnist-mlp/mnist_mlp_solution_with_validation.ipynb
+++ b/convolutional-neural-networks/mnist-mlp/mnist_mlp_solution_with_validation.ipynb
@@ -509,7 +509,7 @@
     "    # compare predictions to true label\n",
     "    correct = np.squeeze(pred.eq(target.data.view_as(pred)))\n",
     "    # calculate test accuracy for each object class\n",
-    "    for i in range(batch_size):\n",
+    "    for i in range(len(target)):\n",
     "        label = target.data[i]\n",
     "        class_correct[label] += correct[i].item()\n",
     "        class_total[label] += 1\n",