Found some missing text so I fixed it. Also change data directories.

Author: mcleonard

Image Classifier Project.ipynb

@@ -53,9 +53,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "train_dir = 'train'\n",
-    "valid_dir = 'valid'\n",
-    "test_dir = 'test'"
+    "data_dir = 'flowers'\n",
+    "train_dir = data_dir + '/train'\n",
+    "valid_dir = data_dir + '/valid'\n",
+    "test_dir = data_dir + '/test'"
    ]
   },
   {
@@ -270,9 +271,9 @@
    "source": [
     "## Class Prediction\n",
     "\n",
-    "Once you can get images in the correct format \n",
+    "Once you can get images in the correct format, it's time to write a function for making predictions with your model. A common practice is to predict the top 5 or so (usually called top-$K$) most probable classes. You'll want to calculate the class probabilities then find the $K$ largest values.\n",
     "\n",
-    "To get the top $K$ largest values in a tensor use [`x.topk(k)`](http://pytorch.org/docs/master/torch.html#torch.topk). This method returns both the highest `k` probabilities and the indices of those probabilities corresponding to the classes. You need to convert from these indices to the actual class labels using `class_to_idx` which hopefully you added to the model or from an `ImageFolder` you used to load the data ([see here](#Save-the-checkpoint)). Make sure to invert the dictio\n",
+    "To get the top $K$ largest values in a tensor use [`x.topk(k)`](http://pytorch.org/docs/master/torch.html#torch.topk). This method returns both the highest `k` probabilities and the indices of those probabilities corresponding to the classes. You need to convert from these indices to the actual class labels using `class_to_idx` which hopefully you added to the model or from an `ImageFolder` you used to load the data ([see here](#Save-the-checkpoint)). Make sure to invert the dictionary so you get a mapping from index to class as well.\n",
     "\n",
     "Again, this method should take a path to an image and a model checkpoint, then return the probabilities and classes.\n",
     "\n",