feat: Sanity Checking: Use a trained model for predictions

Use matplotlib to plot the probabilities and the input image.

Author: silviomori

Image Classifier Project.ipynb

@@ -1414,7 +1414,34 @@
       },
       "cell_type": "code",
       "source": [
-        "# TODO: Display an image along with the top 5 classes"
+        "# Display an image along with the top 5 classes\n",
+        "probs, classes = predict(image_path, model)\n",
+        "\n",
+        "probs = probs.data.numpy().squeeze()\n",
+        "classes = classes.data.numpy().squeeze()\n",
+        "classes = [cat_label_to_name[clazz].title() for clazz in classes]\n",
+        "\n",
+        "label = class_to_idx[str(category)]\n",
+        "title = f'{cat_label_to_name[label].title()}'\n",
+        "\n",
+        "fig = plt.figure(figsize=(4, 10))\n",
+        "\n",
+        "ax1 = fig.add_subplot(2, 1, 1, xticks=[], yticks=[])\n",
+        "\n",
+        "image = Image.open(image_path)\n",
+        "image = process_image(image)\n",
+        "imshow(image, ax1, title)\n",
+        "\n",
+        "ax2 = fig.add_subplot(2, 1, 2, xticks=[], yticks=[])\n",
+        "ax2.barh(np.arange(5), probs)\n",
+        "ax2.set_yticks(np.arange(5))\n",
+        "ax2.set_yticklabels(classes)\n",
+        "ax2.set_ylim(-1, 5)\n",
+        "ax2.invert_yaxis()\n",
+        "ax2.set_xlim(0, 1.1)\n",
+        "ax2.set_title('Class Probability')\n",
+        "\n",
+        "plt.show()"
       ],
       "execution_count": 0,
       "outputs": []