animalClassifier.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "4e835eceb0e74dbca2433126991960d6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_06b3df2af4a9401c95a47ea82f4a61d7",
              "IPY_MODEL_d7f43ad422c84452bd4c86ba24965a26",
              "IPY_MODEL_93f3e754dfe649f38e094aa016a9d355"
            ],
            "layout": "IPY_MODEL_231ef276e2074cfe9facbf25653d67d1"
          }
        },
        "06b3df2af4a9401c95a47ea82f4a61d7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_116d9b293f4c46089ad2c6824d4109cf",
            "placeholder": "​",
            "style": "IPY_MODEL_09319c10b21e402c9c8b41ba89e8ca05",
            "value": "100%"
          }
        },
        "d7f43ad422c84452bd4c86ba24965a26": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_5b1da2a605774cc08ba455457b4a0368",
            "max": 125,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_52e8008befac41dd804581844762a747",
            "value": 125
          }
        },
        "93f3e754dfe649f38e094aa016a9d355": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_b8f9fe9f7a30433bbe1a805bc547272c",
            "placeholder": "​",
            "style": "IPY_MODEL_d2aec0e782d44c9ab3203c6ad0f6ac47",
            "value": " 125/125 [03:50&lt;00:00,  1.76s/it]"
          }
        },
        "231ef276e2074cfe9facbf25653d67d1": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "116d9b293f4c46089ad2c6824d4109cf": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "09319c10b21e402c9c8b41ba89e8ca05": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "5b1da2a605774cc08ba455457b4a0368": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "52e8008befac41dd804581844762a747": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "b8f9fe9f7a30433bbe1a805bc547272c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "d2aec0e782d44c9ab3203c6ad0f6ac47": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install selenium"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YsDbAtLjCane",
        "outputId": "9efbb592-9813-4d81-a345-ed078c090668"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting selenium\n",
            "  Downloading selenium-4.24.0-py3-none-any.whl.metadata (7.1 kB)\n",
            "Requirement already satisfied: urllib3<3,>=1.26 in /usr/local/lib/python3.10/dist-packages (from urllib3[socks]<3,>=1.26->selenium) (2.0.7)\n",
            "Collecting trio~=0.17 (from selenium)\n",
            "  Downloading trio-0.26.2-py3-none-any.whl.metadata (8.6 kB)\n",
            "Collecting trio-websocket~=0.9 (from selenium)\n",
            "  Downloading trio_websocket-0.11.1-py3-none-any.whl.metadata (4.7 kB)\n",
            "Requirement already satisfied: certifi>=2021.10.8 in /usr/local/lib/python3.10/dist-packages (from selenium) (2024.7.4)\n",
            "Requirement already satisfied: typing_extensions~=4.9 in /usr/local/lib/python3.10/dist-packages (from selenium) (4.12.2)\n",
            "Requirement already satisfied: websocket-client~=1.8 in /usr/local/lib/python3.10/dist-packages (from selenium) (1.8.0)\n",
            "Requirement already satisfied: attrs>=23.2.0 in /usr/local/lib/python3.10/dist-packages (from trio~=0.17->selenium) (24.2.0)\n",
            "Requirement already satisfied: sortedcontainers in /usr/local/lib/python3.10/dist-packages (from trio~=0.17->selenium) (2.4.0)\n",
            "Requirement already satisfied: idna in /usr/local/lib/python3.10/dist-packages (from trio~=0.17->selenium) (3.8)\n",
            "Collecting outcome (from trio~=0.17->selenium)\n",
            "  Downloading outcome-1.3.0.post0-py2.py3-none-any.whl.metadata (2.6 kB)\n",
            "Requirement already satisfied: sniffio>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from trio~=0.17->selenium) (1.3.1)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from trio~=0.17->selenium) (1.2.2)\n",
            "Collecting wsproto>=0.14 (from trio-websocket~=0.9->selenium)\n",
            "  Downloading wsproto-1.2.0-py3-none-any.whl.metadata (5.6 kB)\n",
            "Requirement already satisfied: pysocks!=1.5.7,<2.0,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from urllib3[socks]<3,>=1.26->selenium) (1.7.1)\n",
            "Collecting h11<1,>=0.9.0 (from wsproto>=0.14->trio-websocket~=0.9->selenium)\n",
            "  Downloading h11-0.14.0-py3-none-any.whl.metadata (8.2 kB)\n",
            "Downloading selenium-4.24.0-py3-none-any.whl (9.6 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.6/9.6 MB\u001b[0m \u001b[31m62.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading trio-0.26.2-py3-none-any.whl (475 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m476.0/476.0 kB\u001b[0m \u001b[31m23.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading trio_websocket-0.11.1-py3-none-any.whl (17 kB)\n",
            "Downloading wsproto-1.2.0-py3-none-any.whl (24 kB)\n",
            "Downloading outcome-1.3.0.post0-py2.py3-none-any.whl (10 kB)\n",
            "Downloading h11-0.14.0-py3-none-any.whl (58 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: outcome, h11, wsproto, trio, trio-websocket, selenium\n",
            "Successfully installed h11-0.14.0 outcome-1.3.0.post0 selenium-4.24.0 trio-0.26.2 trio-websocket-0.11.1 wsproto-1.2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from selenium import webdriver\n",
        "from selenium.webdriver.common.by import By\n",
        "from pathlib import Path\n",
        "from bs4 import BeautifulSoup\n",
        "from urllib.parse import urljoin\n",
        "\n",
        "import os\n",
        "import requests\n",
        "import time\n",
        "\n",
        "# Configure Selenium options for running in Colab\n",
        "options = webdriver.ChromeOptions()\n",
        "options.add_argument('--headless')\n",
        "options.add_argument('--no-sandbox')\n",
        "options.add_argument('--disable-dev-shm-usage')\n",
        "\n",
        "driver = webdriver.Chrome(options=options)\n",
        "\n",
        "def search_and_download_duckduckgo(query, num=100):\n",
        "    # URL for DDG image search\n",
        "    url = f\"https://duckduckgo.com/?q={query}&t=h_&iax=images&ia=images\"\n",
        "\n",
        "    # Loads webpage at specified URL\n",
        "    driver.get(url)\n",
        "\n",
        "    # Wait for the page to load\n",
        "    time.sleep(5)\n",
        "\n",
        "    # Parse the page source\n",
        "    soup = BeautifulSoup(driver.page_source, 'html.parser')\n",
        "\n",
        "    # Finds all images\n",
        "    image_elements = soup.find_all('img', limit=num)\n",
        "\n",
        "    # Setup path to a data folder\n",
        "    image_dir = \"data/animals\"\n",
        "\n",
        "    # Create new directory contingent on query\n",
        "    os.makedirs(os.path.join(image_dir, query), exist_ok=True)\n",
        "\n",
        "    page_num = 1\n",
        "    while len(image_elements) < num:\n",
        "        url = f\"https://duckduckgo.com/?q={query}&t=h_&iax=images&ia=images&page={page_num}\"\n",
        "        driver.get(url)\n",
        "        time.sleep(5)\n",
        "        soup = BeautifulSoup(driver.page_source, 'html.parser')\n",
        "        new_image_elements = soup.find_all('img', limit=num - len(image_elements))\n",
        "        image_elements.extend(new_image_elements)\n",
        "        page_num += 1\n",
        "\n",
        "    print(f\"\\nFound {len(image_elements)} images of {query}\")\n",
        "\n",
        "    for i, img in enumerate(image_elements):\n",
        "      # Extract the image URL via attributes\n",
        "      image_url = img.get('src') or img.get('data-src')\n",
        "\n",
        "      # Handle relative URLs by converting them to absolute URLs\n",
        "      if image_url and not image_url.startswith('http'):\n",
        "        image_url = urljoin(url, image_url)\n",
        "\n",
        "      # Check if the URL is a direct link to the image\n",
        "      if image_url and image_url.startswith(\"http\"):\n",
        "        # Get the image URL\n",
        "        try:\n",
        "          response = requests.get(image_url)\n",
        "\n",
        "          # Renames image based on query & position\n",
        "          filename = f\"{query}_{i}.jpg\"\n",
        "\n",
        "          # Saves image to proper directory\n",
        "          with open(os.path.join(image_dir, query, filename), 'wb') as f:\n",
        "              f.write(response.content)\n",
        "          print(f\"Downloaded {filename}\")\n",
        "        except Exception as e:\n",
        "          print(f\"Error downloading {image_url}: {e}\")\n",
        "      else:\n",
        "        print(f\"Skipping non-direct URL: {image_url}\")"
      ],
      "metadata": {
        "id": "eKPT8Y931djS"
      },
      "execution_count": 146,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "search_and_download_duckduckgo(\"cats\", 250)\n",
        "search_and_download_duckduckgo(\"dogs\", 250)\n",
        "search_and_download_duckduckgo(\"birds\", 250)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XwqwyCks-TRP",
        "outputId": "4873c7ed-b579-4097-a016-f5287fce758d"
      },
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Found 250 images of cats\n",
            "Downloaded cats_0.jpg\n",
            "Downloaded cats_1.jpg\n",
            "Downloaded cats_2.jpg\n",
            "Downloaded cats_3.jpg\n",
            "Downloaded cats_4.jpg\n",
            "Downloaded cats_5.jpg\n",
            "Downloaded cats_6.jpg\n",
            "Downloaded cats_7.jpg\n",
            "Downloaded cats_8.jpg\n",
            "Downloaded cats_9.jpg\n",
            "Downloaded cats_10.jpg\n",
            "Downloaded cats_11.jpg\n",
            "Downloaded cats_12.jpg\n",
            "Downloaded cats_13.jpg\n",
            "Downloaded cats_14.jpg\n",
            "Downloaded cats_15.jpg\n",
            "Downloaded cats_16.jpg\n",
            "Downloaded cats_17.jpg\n",
            "Downloaded cats_18.jpg\n",
            "Downloaded cats_19.jpg\n",
            "Downloaded cats_20.jpg\n",
            "Downloaded cats_21.jpg\n",
            "Downloaded cats_22.jpg\n",
            "Downloaded cats_23.jpg\n",
            "Downloaded cats_24.jpg\n",
            "Downloaded cats_25.jpg\n",
            "Downloaded cats_26.jpg\n",
            "Downloaded cats_27.jpg\n",
            "Downloaded cats_28.jpg\n",
            "Downloaded cats_29.jpg\n",
            "Downloaded cats_30.jpg\n",
            "Downloaded cats_31.jpg\n",
            "Downloaded cats_32.jpg\n",
            "Downloaded cats_33.jpg\n",
            "Downloaded cats_34.jpg\n",
            "Downloaded cats_35.jpg\n",
            "Downloaded cats_36.jpg\n",
            "Downloaded cats_37.jpg\n",
            "Downloaded cats_38.jpg\n",
            "Downloaded cats_39.jpg\n",
            "Downloaded cats_40.jpg\n",
            "Downloaded cats_41.jpg\n",
            "Downloaded cats_42.jpg\n",
            "Downloaded cats_43.jpg\n",
            "Downloaded cats_44.jpg\n",
            "Downloaded cats_45.jpg\n",
            "Downloaded cats_46.jpg\n",
            "Downloaded cats_47.jpg\n",
            "Downloaded cats_48.jpg\n",
            "Downloaded cats_49.jpg\n",
            "Downloaded cats_50.jpg\n",
            "Downloaded cats_51.jpg\n",
            "Downloaded cats_52.jpg\n",
            "Downloaded cats_53.jpg\n",
            "Downloaded cats_54.jpg\n",
            "Downloaded cats_55.jpg\n",
            "Downloaded cats_56.jpg\n",
            "Downloaded cats_57.jpg\n",
            "Downloaded cats_58.jpg\n",
            "Downloaded cats_59.jpg\n",
            "Downloaded cats_60.jpg\n",
            "Downloaded cats_61.jpg\n",
            "Downloaded cats_62.jpg\n",
            "Downloaded cats_63.jpg\n",
            "Downloaded cats_64.jpg\n",
            "Downloaded cats_65.jpg\n",
            "Downloaded cats_66.jpg\n",
            "Downloaded cats_67.jpg\n",
            "Downloaded cats_68.jpg\n",
            "Downloaded cats_69.jpg\n",
            "Downloaded cats_70.jpg\n",
            "Downloaded cats_71.jpg\n",
            "Downloaded cats_72.jpg\n",
            "Downloaded cats_73.jpg\n",
            "Downloaded cats_74.jpg\n",
            "Downloaded cats_75.jpg\n",
            "Downloaded cats_76.jpg\n",
            "Downloaded cats_77.jpg\n",
            "Downloaded cats_78.jpg\n",
            "Downloaded cats_79.jpg\n",
            "Downloaded cats_80.jpg\n",
            "Downloaded cats_81.jpg\n",
            "Downloaded cats_82.jpg\n",
            "Downloaded cats_83.jpg\n",
            "Downloaded cats_84.jpg\n",
            "Downloaded cats_85.jpg\n",
            "Downloaded cats_86.jpg\n",
            "Downloaded cats_87.jpg\n",
            "Downloaded cats_88.jpg\n",
            "Downloaded cats_89.jpg\n",
            "Downloaded cats_90.jpg\n",
            "Downloaded cats_91.jpg\n",
            "Downloaded cats_92.jpg\n",
            "Downloaded cats_93.jpg\n",
            "Downloaded cats_94.jpg\n",
            "Downloaded cats_95.jpg\n",
            "Downloaded cats_96.jpg\n",
            "Downloaded cats_97.jpg\n",
            "Downloaded cats_98.jpg\n",
            "Downloaded cats_99.jpg\n",
            "Downloaded cats_100.jpg\n",
            "Downloaded cats_101.jpg\n",
            "Downloaded cats_102.jpg\n",
            "Downloaded cats_103.jpg\n",
            "Downloaded cats_104.jpg\n",
            "Downloaded cats_105.jpg\n",
            "Downloaded cats_106.jpg\n",
            "Downloaded cats_107.jpg\n",
            "Downloaded cats_108.jpg\n",
            "Downloaded cats_109.jpg\n",
            "Downloaded cats_110.jpg\n",
            "Downloaded cats_111.jpg\n",
            "Downloaded cats_112.jpg\n",
            "Downloaded cats_113.jpg\n",
            "Downloaded cats_114.jpg\n",
            "Downloaded cats_115.jpg\n",
            "Downloaded cats_116.jpg\n",
            "Downloaded cats_117.jpg\n",
            "Downloaded cats_118.jpg\n",
            "Downloaded cats_119.jpg\n",
            "Downloaded cats_120.jpg\n",
            "Downloaded cats_121.jpg\n",
            "Downloaded cats_122.jpg\n",
            "Downloaded cats_123.jpg\n",
            "Downloaded cats_124.jpg\n",
            "Downloaded cats_125.jpg\n",
            "Downloaded cats_126.jpg\n",
            "Downloaded cats_127.jpg\n",
            "Downloaded cats_128.jpg\n",
            "Downloaded cats_129.jpg\n",
            "Downloaded cats_130.jpg\n",
            "Downloaded cats_131.jpg\n",
            "Downloaded cats_132.jpg\n",
            "Downloaded cats_133.jpg\n",
            "Downloaded cats_134.jpg\n",
            "Downloaded cats_135.jpg\n",
            "Downloaded cats_136.jpg\n",
            "Downloaded cats_137.jpg\n",
            "Downloaded cats_138.jpg\n",
            "Downloaded cats_139.jpg\n",
            "Downloaded cats_140.jpg\n",
            "Downloaded cats_141.jpg\n",
            "Downloaded cats_142.jpg\n",
            "Downloaded cats_143.jpg\n",
            "Downloaded cats_144.jpg\n",
            "Downloaded cats_145.jpg\n",
            "Downloaded cats_146.jpg\n",
            "Downloaded cats_147.jpg\n",
            "Downloaded cats_148.jpg\n",
            "Downloaded cats_149.jpg\n",
            "Downloaded cats_150.jpg\n",
            "Downloaded cats_151.jpg\n",
            "Downloaded cats_152.jpg\n",
            "Downloaded cats_153.jpg\n",
            "Downloaded cats_154.jpg\n",
            "Downloaded cats_155.jpg\n",
            "Downloaded cats_156.jpg\n",
            "Downloaded cats_157.jpg\n",
            "Downloaded cats_158.jpg\n",
            "Downloaded cats_159.jpg\n",
            "Downloaded cats_160.jpg\n",
            "Downloaded cats_161.jpg\n",
            "Downloaded cats_162.jpg\n",
            "Downloaded cats_163.jpg\n",
            "Downloaded cats_164.jpg\n",
            "Downloaded cats_165.jpg\n",
            "Downloaded cats_166.jpg\n",
            "Downloaded cats_167.jpg\n",
            "Downloaded cats_168.jpg\n",
            "Downloaded cats_169.jpg\n",
            "Downloaded cats_170.jpg\n",
            "Downloaded cats_171.jpg\n",
            "Downloaded cats_172.jpg\n",
            "Downloaded cats_173.jpg\n",
            "Downloaded cats_174.jpg\n",
            "Downloaded cats_175.jpg\n",
            "Downloaded cats_176.jpg\n",
            "Downloaded cats_177.jpg\n",
            "Downloaded cats_178.jpg\n",
            "Downloaded cats_179.jpg\n",
            "Downloaded cats_180.jpg\n",
            "Downloaded cats_181.jpg\n",
            "Downloaded cats_182.jpg\n",
            "Downloaded cats_183.jpg\n",
            "Downloaded cats_184.jpg\n",
            "Downloaded cats_185.jpg\n",
            "Downloaded cats_186.jpg\n",
            "Downloaded cats_187.jpg\n",
            "Downloaded cats_188.jpg\n",
            "Downloaded cats_189.jpg\n",
            "Downloaded cats_190.jpg\n",
            "Downloaded cats_191.jpg\n",
            "Downloaded cats_192.jpg\n",
            "Downloaded cats_193.jpg\n",
            "Downloaded cats_194.jpg\n",
            "Downloaded cats_195.jpg\n",
            "Downloaded cats_196.jpg\n",
            "Downloaded cats_197.jpg\n",
            "Downloaded cats_198.jpg\n",
            "Downloaded cats_199.jpg\n",
            "Downloaded cats_200.jpg\n",
            "Downloaded cats_201.jpg\n",
            "Downloaded cats_202.jpg\n",
            "Downloaded cats_203.jpg\n",
            "Downloaded cats_204.jpg\n",
            "Downloaded cats_205.jpg\n",
            "Downloaded cats_206.jpg\n",
            "Downloaded cats_207.jpg\n",
            "Downloaded cats_208.jpg\n",
            "Downloaded cats_209.jpg\n",
            "Downloaded cats_210.jpg\n",
            "Downloaded cats_211.jpg\n",
            "Downloaded cats_212.jpg\n",
            "Downloaded cats_213.jpg\n",
            "Downloaded cats_214.jpg\n",
            "Downloaded cats_215.jpg\n",
            "Downloaded cats_216.jpg\n",
            "Downloaded cats_217.jpg\n",
            "Downloaded cats_218.jpg\n",
            "Downloaded cats_219.jpg\n",
            "Downloaded cats_220.jpg\n",
            "Downloaded cats_221.jpg\n",
            "Downloaded cats_222.jpg\n",
            "Downloaded cats_223.jpg\n",
            "Downloaded cats_224.jpg\n",
            "Downloaded cats_225.jpg\n",
            "Downloaded cats_226.jpg\n",
            "Downloaded cats_227.jpg\n",
            "Downloaded cats_228.jpg\n",
            "Downloaded cats_229.jpg\n",
            "Downloaded cats_230.jpg\n",
            "Downloaded cats_231.jpg\n",
            "Downloaded cats_232.jpg\n",
            "Downloaded cats_233.jpg\n",
            "Downloaded cats_234.jpg\n",
            "Downloaded cats_235.jpg\n",
            "Downloaded cats_236.jpg\n",
            "Downloaded cats_237.jpg\n",
            "Downloaded cats_238.jpg\n",
            "Downloaded cats_239.jpg\n",
            "Downloaded cats_240.jpg\n",
            "Downloaded cats_241.jpg\n",
            "Downloaded cats_242.jpg\n",
            "Downloaded cats_243.jpg\n",
            "Downloaded cats_244.jpg\n",
            "Downloaded cats_245.jpg\n",
            "Downloaded cats_246.jpg\n",
            "Downloaded cats_247.jpg\n",
            "Downloaded cats_248.jpg\n",
            "Downloaded cats_249.jpg\n",
            "\n",
            "Found 250 images of dogs\n",
            "Downloaded dogs_0.jpg\n",
            "Downloaded dogs_1.jpg\n",
            "Downloaded dogs_2.jpg\n",
            "Downloaded dogs_3.jpg\n",
            "Downloaded dogs_4.jpg\n",
            "Downloaded dogs_5.jpg\n",
            "Downloaded dogs_6.jpg\n",
            "Downloaded dogs_7.jpg\n",
            "Downloaded dogs_8.jpg\n",
            "Downloaded dogs_9.jpg\n",
            "Downloaded dogs_10.jpg\n",
            "Downloaded dogs_11.jpg\n",
            "Downloaded dogs_12.jpg\n",
            "Downloaded dogs_13.jpg\n",
            "Downloaded dogs_14.jpg\n",
            "Downloaded dogs_15.jpg\n",
            "Downloaded dogs_16.jpg\n",
            "Downloaded dogs_17.jpg\n",
            "Downloaded dogs_18.jpg\n",
            "Downloaded dogs_19.jpg\n",
            "Downloaded dogs_20.jpg\n",
            "Downloaded dogs_21.jpg\n",
            "Downloaded dogs_22.jpg\n",
            "Downloaded dogs_23.jpg\n",
            "Downloaded dogs_24.jpg\n",
            "Downloaded dogs_25.jpg\n",
            "Downloaded dogs_26.jpg\n",
            "Downloaded dogs_27.jpg\n",
            "Downloaded dogs_28.jpg\n",
            "Downloaded dogs_29.jpg\n",
            "Downloaded dogs_30.jpg\n",
            "Downloaded dogs_31.jpg\n",
            "Downloaded dogs_32.jpg\n",
            "Downloaded dogs_33.jpg\n",
            "Downloaded dogs_34.jpg\n",
            "Downloaded dogs_35.jpg\n",
            "Downloaded dogs_36.jpg\n",
            "Downloaded dogs_37.jpg\n",
            "Downloaded dogs_38.jpg\n",
            "Downloaded dogs_39.jpg\n",
            "Downloaded dogs_40.jpg\n",
            "Downloaded dogs_41.jpg\n",
            "Downloaded dogs_42.jpg\n",
            "Downloaded dogs_43.jpg\n",
            "Downloaded dogs_44.jpg\n",
            "Downloaded dogs_45.jpg\n",
            "Downloaded dogs_46.jpg\n",
            "Downloaded dogs_47.jpg\n",
            "Downloaded dogs_48.jpg\n",
            "Downloaded dogs_49.jpg\n",
            "Downloaded dogs_50.jpg\n",
            "Downloaded dogs_51.jpg\n",
            "Downloaded dogs_52.jpg\n",
            "Downloaded dogs_53.jpg\n",
            "Downloaded dogs_54.jpg\n",
            "Downloaded dogs_55.jpg\n",
            "Downloaded dogs_56.jpg\n",
            "Downloaded dogs_57.jpg\n",
            "Downloaded dogs_58.jpg\n",
            "Downloaded dogs_59.jpg\n",
            "Downloaded dogs_60.jpg\n",
            "Downloaded dogs_61.jpg\n",
            "Downloaded dogs_62.jpg\n",
            "Downloaded dogs_63.jpg\n",
            "Downloaded dogs_64.jpg\n",
            "Downloaded dogs_65.jpg\n",
            "Downloaded dogs_66.jpg\n",
            "Downloaded dogs_67.jpg\n",
            "Downloaded dogs_68.jpg\n",
            "Downloaded dogs_69.jpg\n",
            "Downloaded dogs_70.jpg\n",
            "Downloaded dogs_71.jpg\n",
            "Downloaded dogs_72.jpg\n",
            "Downloaded dogs_73.jpg\n",
            "Downloaded dogs_74.jpg\n",
            "Downloaded dogs_75.jpg\n",
            "Downloaded dogs_76.jpg\n",
            "Downloaded dogs_77.jpg\n",
            "Downloaded dogs_78.jpg\n",
            "Downloaded dogs_79.jpg\n",
            "Downloaded dogs_80.jpg\n",
            "Downloaded dogs_81.jpg\n",
            "Downloaded dogs_82.jpg\n",
            "Downloaded dogs_83.jpg\n",
            "Downloaded dogs_84.jpg\n",
            "Downloaded dogs_85.jpg\n",
            "Downloaded dogs_86.jpg\n",
            "Downloaded dogs_87.jpg\n",
            "Downloaded dogs_88.jpg\n",
            "Downloaded dogs_89.jpg\n",
            "Downloaded dogs_90.jpg\n",
            "Downloaded dogs_91.jpg\n",
            "Downloaded dogs_92.jpg\n",
            "Downloaded dogs_93.jpg\n",
            "Downloaded dogs_94.jpg\n",
            "Downloaded dogs_95.jpg\n",
            "Downloaded dogs_96.jpg\n",
            "Downloaded dogs_97.jpg\n",
            "Downloaded dogs_98.jpg\n",
            "Downloaded dogs_99.jpg\n",
            "Downloaded dogs_100.jpg\n",
            "Downloaded dogs_101.jpg\n",
            "Downloaded dogs_102.jpg\n",
            "Downloaded dogs_103.jpg\n",
            "Downloaded dogs_104.jpg\n",
            "Downloaded dogs_105.jpg\n",
            "Downloaded dogs_106.jpg\n",
            "Downloaded dogs_107.jpg\n",
            "Downloaded dogs_108.jpg\n",
            "Downloaded dogs_109.jpg\n",
            "Downloaded dogs_110.jpg\n",
            "Downloaded dogs_111.jpg\n",
            "Downloaded dogs_112.jpg\n",
            "Downloaded dogs_113.jpg\n",
            "Downloaded dogs_114.jpg\n",
            "Downloaded dogs_115.jpg\n",
            "Downloaded dogs_116.jpg\n",
            "Downloaded dogs_117.jpg\n",
            "Downloaded dogs_118.jpg\n",
            "Downloaded dogs_119.jpg\n",
            "Downloaded dogs_120.jpg\n",
            "Downloaded dogs_121.jpg\n",
            "Downloaded dogs_122.jpg\n",
            "Downloaded dogs_123.jpg\n",
            "Downloaded dogs_124.jpg\n",
            "Downloaded dogs_125.jpg\n",
            "Downloaded dogs_126.jpg\n",
            "Downloaded dogs_127.jpg\n",
            "Downloaded dogs_128.jpg\n",
            "Downloaded dogs_129.jpg\n",
            "Downloaded dogs_130.jpg\n",
            "Downloaded dogs_131.jpg\n",
            "Downloaded dogs_132.jpg\n",
            "Downloaded dogs_133.jpg\n",
            "Downloaded dogs_134.jpg\n",
            "Downloaded dogs_135.jpg\n",
            "Downloaded dogs_136.jpg\n",
            "Downloaded dogs_137.jpg\n",
            "Downloaded dogs_138.jpg\n",
            "Downloaded dogs_139.jpg\n",
            "Downloaded dogs_140.jpg\n",
            "Downloaded dogs_141.jpg\n",
            "Downloaded dogs_142.jpg\n",
            "Downloaded dogs_143.jpg\n",
            "Downloaded dogs_144.jpg\n",
            "Downloaded dogs_145.jpg\n",
            "Downloaded dogs_146.jpg\n",
            "Downloaded dogs_147.jpg\n",
            "Downloaded dogs_148.jpg\n",
            "Downloaded dogs_149.jpg\n",
            "Downloaded dogs_150.jpg\n",
            "Downloaded dogs_151.jpg\n",
            "Downloaded dogs_152.jpg\n",
            "Downloaded dogs_153.jpg\n",
            "Downloaded dogs_154.jpg\n",
            "Downloaded dogs_155.jpg\n",
            "Downloaded dogs_156.jpg\n",
            "Downloaded dogs_157.jpg\n",
            "Downloaded dogs_158.jpg\n",
            "Downloaded dogs_159.jpg\n",
            "Downloaded dogs_160.jpg\n",
            "Downloaded dogs_161.jpg\n",
            "Downloaded dogs_162.jpg\n",
            "Downloaded dogs_163.jpg\n",
            "Downloaded dogs_164.jpg\n",
            "Downloaded dogs_165.jpg\n",
            "Downloaded dogs_166.jpg\n",
            "Downloaded dogs_167.jpg\n",
            "Downloaded dogs_168.jpg\n",
            "Downloaded dogs_169.jpg\n",
            "Downloaded dogs_170.jpg\n",
            "Downloaded dogs_171.jpg\n",
            "Downloaded dogs_172.jpg\n",
            "Downloaded dogs_173.jpg\n",
            "Downloaded dogs_174.jpg\n",
            "Downloaded dogs_175.jpg\n",
            "Downloaded dogs_176.jpg\n",
            "Downloaded dogs_177.jpg\n",
            "Downloaded dogs_178.jpg\n",
            "Downloaded dogs_179.jpg\n",
            "Downloaded dogs_180.jpg\n",
            "Downloaded dogs_181.jpg\n",
            "Downloaded dogs_182.jpg\n",
            "Downloaded dogs_183.jpg\n",
            "Downloaded dogs_184.jpg\n",
            "Downloaded dogs_185.jpg\n",
            "Downloaded dogs_186.jpg\n",
            "Downloaded dogs_187.jpg\n",
            "Downloaded dogs_188.jpg\n",
            "Downloaded dogs_189.jpg\n",
            "Downloaded dogs_190.jpg\n",
            "Downloaded dogs_191.jpg\n",
            "Downloaded dogs_192.jpg\n",
            "Downloaded dogs_193.jpg\n",
            "Downloaded dogs_194.jpg\n",
            "Downloaded dogs_195.jpg\n",
            "Downloaded dogs_196.jpg\n",
            "Downloaded dogs_197.jpg\n",
            "Downloaded dogs_198.jpg\n",
            "Downloaded dogs_199.jpg\n",
            "Downloaded dogs_200.jpg\n",
            "Downloaded dogs_201.jpg\n",
            "Downloaded dogs_202.jpg\n",
            "Downloaded dogs_203.jpg\n",
            "Downloaded dogs_204.jpg\n",
            "Downloaded dogs_205.jpg\n",
            "Downloaded dogs_206.jpg\n",
            "Downloaded dogs_207.jpg\n",
            "Downloaded dogs_208.jpg\n",
            "Downloaded dogs_209.jpg\n",
            "Downloaded dogs_210.jpg\n",
            "Downloaded dogs_211.jpg\n",
            "Downloaded dogs_212.jpg\n",
            "Downloaded dogs_213.jpg\n",
            "Downloaded dogs_214.jpg\n",
            "Downloaded dogs_215.jpg\n",
            "Downloaded dogs_216.jpg\n",
            "Downloaded dogs_217.jpg\n",
            "Downloaded dogs_218.jpg\n",
            "Downloaded dogs_219.jpg\n",
            "Downloaded dogs_220.jpg\n",
            "Downloaded dogs_221.jpg\n",
            "Downloaded dogs_222.jpg\n",
            "Downloaded dogs_223.jpg\n",
            "Downloaded dogs_224.jpg\n",
            "Downloaded dogs_225.jpg\n",
            "Downloaded dogs_226.jpg\n",
            "Downloaded dogs_227.jpg\n",
            "Downloaded dogs_228.jpg\n",
            "Downloaded dogs_229.jpg\n",
            "Downloaded dogs_230.jpg\n",
            "Downloaded dogs_231.jpg\n",
            "Downloaded dogs_232.jpg\n",
            "Downloaded dogs_233.jpg\n",
            "Downloaded dogs_234.jpg\n",
            "Downloaded dogs_235.jpg\n",
            "Downloaded dogs_236.jpg\n",
            "Downloaded dogs_237.jpg\n",
            "Downloaded dogs_238.jpg\n",
            "Downloaded dogs_239.jpg\n",
            "Downloaded dogs_240.jpg\n",
            "Downloaded dogs_241.jpg\n",
            "Downloaded dogs_242.jpg\n",
            "Downloaded dogs_243.jpg\n",
            "Downloaded dogs_244.jpg\n",
            "Downloaded dogs_245.jpg\n",
            "Downloaded dogs_246.jpg\n",
            "Downloaded dogs_247.jpg\n",
            "Downloaded dogs_248.jpg\n",
            "Downloaded dogs_249.jpg\n",
            "\n",
            "Found 250 images of birds\n",
            "Downloaded birds_0.jpg\n",
            "Downloaded birds_1.jpg\n",
            "Downloaded birds_2.jpg\n",
            "Downloaded birds_3.jpg\n",
            "Downloaded birds_4.jpg\n",
            "Downloaded birds_5.jpg\n",
            "Downloaded birds_6.jpg\n",
            "Downloaded birds_7.jpg\n",
            "Downloaded birds_8.jpg\n",
            "Downloaded birds_9.jpg\n",
            "Downloaded birds_10.jpg\n",
            "Downloaded birds_11.jpg\n",
            "Downloaded birds_12.jpg\n",
            "Downloaded birds_13.jpg\n",
            "Downloaded birds_14.jpg\n",
            "Downloaded birds_15.jpg\n",
            "Downloaded birds_16.jpg\n",
            "Downloaded birds_17.jpg\n",
            "Downloaded birds_18.jpg\n",
            "Downloaded birds_19.jpg\n",
            "Downloaded birds_20.jpg\n",
            "Downloaded birds_21.jpg\n",
            "Downloaded birds_22.jpg\n",
            "Downloaded birds_23.jpg\n",
            "Downloaded birds_24.jpg\n",
            "Downloaded birds_25.jpg\n",
            "Downloaded birds_26.jpg\n",
            "Downloaded birds_27.jpg\n",
            "Downloaded birds_28.jpg\n",
            "Downloaded birds_29.jpg\n",
            "Downloaded birds_30.jpg\n",
            "Downloaded birds_31.jpg\n",
            "Downloaded birds_32.jpg\n",
            "Downloaded birds_33.jpg\n",
            "Downloaded birds_34.jpg\n",
            "Downloaded birds_35.jpg\n",
            "Downloaded birds_36.jpg\n",
            "Downloaded birds_37.jpg\n",
            "Downloaded birds_38.jpg\n",
            "Downloaded birds_39.jpg\n",
            "Downloaded birds_40.jpg\n",
            "Downloaded birds_41.jpg\n",
            "Downloaded birds_42.jpg\n",
            "Downloaded birds_43.jpg\n",
            "Downloaded birds_44.jpg\n",
            "Downloaded birds_45.jpg\n",
            "Downloaded birds_46.jpg\n",
            "Downloaded birds_47.jpg\n",
            "Downloaded birds_48.jpg\n",
            "Downloaded birds_49.jpg\n",
            "Downloaded birds_50.jpg\n",
            "Downloaded birds_51.jpg\n",
            "Downloaded birds_52.jpg\n",
            "Downloaded birds_53.jpg\n",
            "Downloaded birds_54.jpg\n",
            "Downloaded birds_55.jpg\n",
            "Downloaded birds_56.jpg\n",
            "Downloaded birds_57.jpg\n",
            "Downloaded birds_58.jpg\n",
            "Downloaded birds_59.jpg\n",
            "Downloaded birds_60.jpg\n",
            "Downloaded birds_61.jpg\n",
            "Downloaded birds_62.jpg\n",
            "Downloaded birds_63.jpg\n",
            "Downloaded birds_64.jpg\n",
            "Downloaded birds_65.jpg\n",
            "Downloaded birds_66.jpg\n",
            "Downloaded birds_67.jpg\n",
            "Downloaded birds_68.jpg\n",
            "Downloaded birds_69.jpg\n",
            "Downloaded birds_70.jpg\n",
            "Downloaded birds_71.jpg\n",
            "Downloaded birds_72.jpg\n",
            "Downloaded birds_73.jpg\n",
            "Downloaded birds_74.jpg\n",
            "Downloaded birds_75.jpg\n",
            "Downloaded birds_76.jpg\n",
            "Downloaded birds_77.jpg\n",
            "Downloaded birds_78.jpg\n",
            "Downloaded birds_79.jpg\n",
            "Downloaded birds_80.jpg\n",
            "Downloaded birds_81.jpg\n",
            "Downloaded birds_82.jpg\n",
            "Downloaded birds_83.jpg\n",
            "Downloaded birds_84.jpg\n",
            "Downloaded birds_85.jpg\n",
            "Downloaded birds_86.jpg\n",
            "Downloaded birds_87.jpg\n",
            "Downloaded birds_88.jpg\n",
            "Downloaded birds_89.jpg\n",
            "Downloaded birds_90.jpg\n",
            "Downloaded birds_91.jpg\n",
            "Downloaded birds_92.jpg\n",
            "Downloaded birds_93.jpg\n",
            "Downloaded birds_94.jpg\n",
            "Downloaded birds_95.jpg\n",
            "Downloaded birds_96.jpg\n",
            "Downloaded birds_97.jpg\n",
            "Downloaded birds_98.jpg\n",
            "Downloaded birds_99.jpg\n",
            "Downloaded birds_100.jpg\n",
            "Downloaded birds_101.jpg\n",
            "Downloaded birds_102.jpg\n",
            "Downloaded birds_103.jpg\n",
            "Downloaded birds_104.jpg\n",
            "Downloaded birds_105.jpg\n",
            "Downloaded birds_106.jpg\n",
            "Downloaded birds_107.jpg\n",
            "Downloaded birds_108.jpg\n",
            "Downloaded birds_109.jpg\n",
            "Downloaded birds_110.jpg\n",
            "Downloaded birds_111.jpg\n",
            "Downloaded birds_112.jpg\n",
            "Downloaded birds_113.jpg\n",
            "Downloaded birds_114.jpg\n",
            "Downloaded birds_115.jpg\n",
            "Downloaded birds_116.jpg\n",
            "Downloaded birds_117.jpg\n",
            "Downloaded birds_118.jpg\n",
            "Downloaded birds_119.jpg\n",
            "Downloaded birds_120.jpg\n",
            "Downloaded birds_121.jpg\n",
            "Downloaded birds_122.jpg\n",
            "Downloaded birds_123.jpg\n",
            "Downloaded birds_124.jpg\n",
            "Downloaded birds_125.jpg\n",
            "Downloaded birds_126.jpg\n",
            "Downloaded birds_127.jpg\n",
            "Downloaded birds_128.jpg\n",
            "Downloaded birds_129.jpg\n",
            "Downloaded birds_130.jpg\n",
            "Downloaded birds_131.jpg\n",
            "Downloaded birds_132.jpg\n",
            "Downloaded birds_133.jpg\n",
            "Downloaded birds_134.jpg\n",
            "Downloaded birds_135.jpg\n",
            "Downloaded birds_136.jpg\n",
            "Downloaded birds_137.jpg\n",
            "Downloaded birds_138.jpg\n",
            "Downloaded birds_139.jpg\n",
            "Downloaded birds_140.jpg\n",
            "Downloaded birds_141.jpg\n",
            "Downloaded birds_142.jpg\n",
            "Downloaded birds_143.jpg\n",
            "Downloaded birds_144.jpg\n",
            "Downloaded birds_145.jpg\n",
            "Downloaded birds_146.jpg\n",
            "Downloaded birds_147.jpg\n",
            "Downloaded birds_148.jpg\n",
            "Downloaded birds_149.jpg\n",
            "Downloaded birds_150.jpg\n",
            "Downloaded birds_151.jpg\n",
            "Downloaded birds_152.jpg\n",
            "Downloaded birds_153.jpg\n",
            "Downloaded birds_154.jpg\n",
            "Downloaded birds_155.jpg\n",
            "Downloaded birds_156.jpg\n",
            "Downloaded birds_157.jpg\n",
            "Downloaded birds_158.jpg\n",
            "Downloaded birds_159.jpg\n",
            "Downloaded birds_160.jpg\n",
            "Downloaded birds_161.jpg\n",
            "Downloaded birds_162.jpg\n",
            "Downloaded birds_163.jpg\n",
            "Downloaded birds_164.jpg\n",
            "Downloaded birds_165.jpg\n",
            "Downloaded birds_166.jpg\n",
            "Downloaded birds_167.jpg\n",
            "Downloaded birds_168.jpg\n",
            "Downloaded birds_169.jpg\n",
            "Downloaded birds_170.jpg\n",
            "Downloaded birds_171.jpg\n",
            "Downloaded birds_172.jpg\n",
            "Downloaded birds_173.jpg\n",
            "Downloaded birds_174.jpg\n",
            "Downloaded birds_175.jpg\n",
            "Downloaded birds_176.jpg\n",
            "Downloaded birds_177.jpg\n",
            "Downloaded birds_178.jpg\n",
            "Downloaded birds_179.jpg\n",
            "Downloaded birds_180.jpg\n",
            "Downloaded birds_181.jpg\n",
            "Downloaded birds_182.jpg\n",
            "Downloaded birds_183.jpg\n",
            "Downloaded birds_184.jpg\n",
            "Downloaded birds_185.jpg\n",
            "Downloaded birds_186.jpg\n",
            "Downloaded birds_187.jpg\n",
            "Downloaded birds_188.jpg\n",
            "Downloaded birds_189.jpg\n",
            "Downloaded birds_190.jpg\n",
            "Downloaded birds_191.jpg\n",
            "Downloaded birds_192.jpg\n",
            "Downloaded birds_193.jpg\n",
            "Downloaded birds_194.jpg\n",
            "Downloaded birds_195.jpg\n",
            "Downloaded birds_196.jpg\n",
            "Downloaded birds_197.jpg\n",
            "Downloaded birds_198.jpg\n",
            "Downloaded birds_199.jpg\n",
            "Downloaded birds_200.jpg\n",
            "Downloaded birds_201.jpg\n",
            "Downloaded birds_202.jpg\n",
            "Downloaded birds_203.jpg\n",
            "Downloaded birds_204.jpg\n",
            "Downloaded birds_205.jpg\n",
            "Downloaded birds_206.jpg\n",
            "Downloaded birds_207.jpg\n",
            "Downloaded birds_208.jpg\n",
            "Downloaded birds_209.jpg\n",
            "Downloaded birds_210.jpg\n",
            "Downloaded birds_211.jpg\n",
            "Downloaded birds_212.jpg\n",
            "Downloaded birds_213.jpg\n",
            "Downloaded birds_214.jpg\n",
            "Downloaded birds_215.jpg\n",
            "Downloaded birds_216.jpg\n",
            "Downloaded birds_217.jpg\n",
            "Downloaded birds_218.jpg\n",
            "Downloaded birds_219.jpg\n",
            "Downloaded birds_220.jpg\n",
            "Downloaded birds_221.jpg\n",
            "Downloaded birds_222.jpg\n",
            "Downloaded birds_223.jpg\n",
            "Downloaded birds_224.jpg\n",
            "Downloaded birds_225.jpg\n",
            "Downloaded birds_226.jpg\n",
            "Downloaded birds_227.jpg\n",
            "Downloaded birds_228.jpg\n",
            "Downloaded birds_229.jpg\n",
            "Downloaded birds_230.jpg\n",
            "Downloaded birds_231.jpg\n",
            "Downloaded birds_232.jpg\n",
            "Downloaded birds_233.jpg\n",
            "Downloaded birds_234.jpg\n",
            "Downloaded birds_235.jpg\n",
            "Downloaded birds_236.jpg\n",
            "Downloaded birds_237.jpg\n",
            "Downloaded birds_238.jpg\n",
            "Downloaded birds_239.jpg\n",
            "Downloaded birds_240.jpg\n",
            "Downloaded birds_241.jpg\n",
            "Downloaded birds_242.jpg\n",
            "Downloaded birds_243.jpg\n",
            "Downloaded birds_244.jpg\n",
            "Downloaded birds_245.jpg\n",
            "Downloaded birds_246.jpg\n",
            "Downloaded birds_247.jpg\n",
            "Downloaded birds_248.jpg\n",
            "Downloaded birds_249.jpg\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set path of animals folder\n",
        "data_dir = Path(\"data/\")\n",
        "image_dir = data_dir / \"animals\"\n",
        "\n",
        "# Print path, # of sub-folders\n",
        "image_dir, len(list(image_dir.iterdir()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "puqaWP5VfASF",
        "outputId": "79049cd7-f57d-4fc1-db25-c71a2f81018b"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(PosixPath('data/animals'), 3)"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "from torch.utils.data import DataLoader, random_split\n",
        "from torchvision import datasets, transforms\n",
        "\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "\n",
        "# Ensure reproducibility\n",
        "torch.manual_seed(42)\n",
        "\n",
        "# Augment data (assists CNN in learning minute details)\n",
        "data_transforms = transforms.Compose([\n",
        "    # Resize images to 64x64 for TinyVGG compatibility (basis of CNN)\n",
        "    transforms.Resize(size=(64, 64)),\n",
        "\n",
        "    # Flip the images randomly on the horizontal\n",
        "    transforms.RandomHorizontalFlip(),\n",
        "\n",
        "    transforms.RandomResizedCrop(64, scale=(.08, 1)),\n",
        "\n",
        "    # Turn the image into a torch.Tensor\n",
        "    transforms.ToTensor(),\n",
        "\n",
        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
        "])\n",
        "\n",
        "# Load the images from subfolders\n",
        "imageDataset = datasets.ImageFolder(root=image_dir, transform=data_transforms)\n",
        "\n",
        "# 80/20 split between train & validation\n",
        "train_size = int(0.8 * len(imageDataset))\n",
        "validation_size = len(imageDataset) - train_size\n",
        "\n",
        "# Randomly split the data\n",
        "train_dataset, validation_dataset = random_split(imageDataset, [train_size, validation_size])\n",
        "\n",
        "# Create DataLoaders\n",
        "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
        "validation_loader = DataLoader(validation_dataset, batch_size=32, shuffle=False)"
      ],
      "metadata": {
        "id": "wZmUjP7GeGcQ"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "iJIDLXRioFbz",
        "outputId": "4d7e1eb0-7d08-48d2-ccef-64588933d34a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cuda'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "imageDataset[0][0].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EtbSrlWRcjQC",
        "outputId": "69b6d289-9100-4d25-d605-585ac7f39273"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([3, 64, 64])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verify lengths of dataset\n",
        "len(train_dataset), len(validation_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BfJLSBLarZK6",
        "outputId": "306071a4-ce68-4ba0-df08-afb432370f88"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(225, 57)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verify classes of folders in list & dict form\n",
        "imageDataset.classes, imageDataset.class_to_idx"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T2PeVFOQsRSW",
        "outputId": "35cd9935-6657-43a2-b3cc-0e7244110482"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(['birds', 'cats', 'dogs'], {'birds': 0, 'cats': 1, 'dogs': 2})"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Building a CNN\n",
        "class animalIdentifier(nn.Module):\n",
        "  \"\"\"\n",
        "  Model architecture to determine animal\n",
        "  Structure is as follows: For the two conv blocks: conv -> ReLU -> conv -> ReLU -> MaxPool\n",
        "  MaxPool is especially important as it decreases the spatial size of an image,\n",
        "  reducing the parameters & computation of the network. Essentially it allows for higher\n",
        "  higher level of pattern recognition in images (i.e. from edges to parts to objects & onward)\n",
        "  \"\"\"\n",
        "\n",
        "  def __init__(self, input_shape: int, hidden_units: int, output_shape: int):\n",
        "    # Instantiate NN\n",
        "    super().__init__()\n",
        "\n",
        "    # First conv block\n",
        "    self.conv_block_1 = nn.Sequential(\n",
        "        nn.Conv2d(in_channels=input_shape,\n",
        "                  out_channels=hidden_units,\n",
        "                  kernel_size=3,\n",
        "                  stride=1,\n",
        "                  padding=1),\n",
        "        nn.ReLU(),\n",
        "        nn.Conv2d(in_channels=hidden_units,\n",
        "                  out_channels=hidden_units,\n",
        "                  kernel_size=3,\n",
        "                  stride=1,\n",
        "                  padding=1),\n",
        "        nn.ReLU(),\n",
        "        nn.MaxPool2d(kernel_size=2)\n",
        "    )\n",
        "\n",
        "    # Second conv block\n",
        "    self.conv_block_2 = nn.Sequential(\n",
        "        nn.Conv2d(in_channels=hidden_units,\n",
        "                  out_channels=hidden_units,\n",
        "                  kernel_size=3,\n",
        "                  stride=1,\n",
        "                  padding=1),\n",
        "        nn.ReLU(),\n",
        "        nn.Conv2d(in_channels=hidden_units,\n",
        "                  out_channels=hidden_units,\n",
        "                  kernel_size=3,\n",
        "                  stride=1,\n",
        "                  padding=1),\n",
        "        nn.ReLU(),\n",
        "        nn.MaxPool2d(kernel_size=2)\n",
        "    )\n",
        "\n",
        "    # Third conv block\n",
        "    self.conv_block_3 = nn.Sequential(\n",
        "        nn.Conv2d(in_channels=hidden_units,\n",
        "                  out_channels=hidden_units,\n",
        "                  kernel_size=3,\n",
        "                  stride=1,\n",
        "                  padding=1),\n",
        "        nn.ReLU(),\n",
        "        nn.Conv2d(in_channels=hidden_units,\n",
        "                  out_channels=hidden_units,\n",
        "                  kernel_size=3,\n",
        "                  stride=1,\n",
        "                  padding=1),\n",
        "        nn.ReLU(),\n",
        "        nn.MaxPool2d(kernel_size=2)\n",
        "    )\n",
        "\n",
        "    self.classifier = nn.Sequential(\n",
        "        nn.Flatten(),\n",
        "        nn.Dropout(p=0.5),  # 50% dropout\n",
        "        nn.Linear(in_features=hidden_units * 16 * 16, # Exact spatial dimension calculated\n",
        "                  out_features=output_shape)\n",
        "    )\n",
        "\n",
        "  def forward(self, x):\n",
        "    return self.classifier(self.conv_block_2(self.conv_block_1(x)))"
      ],
      "metadata": {
        "id": "1DJ8vAHoBio9"
      },
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Instantiate the model with, in order: # of color channels, hidden_units, # of classes\n",
        "animalModel = animalIdentifier(input_shape=3,\n",
        "                               hidden_units=64,\n",
        "                               output_shape=len(imageDataset.classes)).to(device)\n",
        "\n",
        "animalModel"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OEtDi4XsdOTt",
        "outputId": "4c5fcae1-b73c-4ba5-92a9-49db07c2fb76"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "animalIdentifier(\n",
              "  (conv_block_1): Sequential(\n",
              "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): ReLU()\n",
              "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (3): ReLU()\n",
              "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (conv_block_2): Sequential(\n",
              "    (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): ReLU()\n",
              "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (3): ReLU()\n",
              "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (conv_block_3): Sequential(\n",
              "    (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): ReLU()\n",
              "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (3): ReLU()\n",
              "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (classifier): Sequential(\n",
              "    (0): Flatten(start_dim=1, end_dim=-1)\n",
              "    (1): Dropout(p=0.5, inplace=False)\n",
              "    (2): Linear(in_features=16384, out_features=3, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Retrieve a single batch of data (batch of images & corresponding labels)\n",
        "image_batch, label_batch = next(iter(train_loader))\n",
        "image_batch.shape, label_batch.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0GtFaXZrugsJ",
        "outputId": "3208aee5-9c22-42a1-82a9-c094f8339684"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([32, 3, 64, 64]), torch.Size([32]))"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Try a forward pass\n",
        "animalModel(image_batch)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WgBhL_YHwIRP",
        "outputId": "dc34a100-bed6-4b72-84bb-36b91ee20412"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 0.0252,  0.0194, -0.0065],\n",
              "        [ 0.0253,  0.0195, -0.0068],\n",
              "        [ 0.0252,  0.0196, -0.0070],\n",
              "        [ 0.0258,  0.0202, -0.0075],\n",
              "        [ 0.0257,  0.0196, -0.0068],\n",
              "        [ 0.0251,  0.0197, -0.0066],\n",
              "        [ 0.0256,  0.0193, -0.0070],\n",
              "        [ 0.0251,  0.0197, -0.0074],\n",
              "        [ 0.0261,  0.0195, -0.0069],\n",
              "        [ 0.0257,  0.0198, -0.0070],\n",
              "        [ 0.0257,  0.0200, -0.0068],\n",
              "        [ 0.0254,  0.0201, -0.0076],\n",
              "        [ 0.0253,  0.0193, -0.0069],\n",
              "        [ 0.0254,  0.0193, -0.0070],\n",
              "        [ 0.0254,  0.0193, -0.0067],\n",
              "        [ 0.0258,  0.0201, -0.0070],\n",
              "        [ 0.0252,  0.0197, -0.0068],\n",
              "        [ 0.0252,  0.0196, -0.0068],\n",
              "        [ 0.0259,  0.0193, -0.0071],\n",
              "        [ 0.0252,  0.0195, -0.0066],\n",
              "        [ 0.0257,  0.0196, -0.0067],\n",
              "        [ 0.0252,  0.0196, -0.0068],\n",
              "        [ 0.0254,  0.0196, -0.0065],\n",
              "        [ 0.0253,  0.0199, -0.0071],\n",
              "        [ 0.0255,  0.0198, -0.0068],\n",
              "        [ 0.0253,  0.0197, -0.0071],\n",
              "        [ 0.0255,  0.0196, -0.0066],\n",
              "        [ 0.0258,  0.0195, -0.0070],\n",
              "        [ 0.0253,  0.0193, -0.0068],\n",
              "        [ 0.0251,  0.0195, -0.0066],\n",
              "        [ 0.0254,  0.0197, -0.0069],\n",
              "        [ 0.0255,  0.0193, -0.0067]], grad_fn=<AddmmBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train step\n",
        "def train_step(model: torch.nn.Module,\n",
        "               dataloader: torch.utils.data.DataLoader,\n",
        "               loss_fn: torch.nn.Module,\n",
        "               optimizer: torch.optim.Optimizer,\n",
        "               device=device):\n",
        "\n",
        "  # Put the model in train mode\n",
        "  model.train()\n",
        "\n",
        "  train_loss, train_acc = 0, 0\n",
        "\n",
        "  # Loop through batches\n",
        "  for batch in dataloader:\n",
        "    # Extract values from batch\n",
        "    images, labels = batch\n",
        "\n",
        "    # Send to device\n",
        "    images, labels = images.to(device), labels.to(device)\n",
        "\n",
        "    # Forward pass\n",
        "    predictions = model(images)\n",
        "\n",
        "    # Calculate loss\n",
        "    loss = loss_fn(predictions, labels) # Logits\n",
        "    train_loss += loss.item() # Cumulative loss\n",
        "\n",
        "    # Calculate accuracy\n",
        "    prediction_classes = torch.argmax(predictions, dim=1) # Predicted class (logits to classes)\n",
        "    train_acc += (prediction_classes == labels).sum().item() # Sum the number of correct predictions\n",
        "\n",
        "    # Set gradients to zero\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    # Backpropagation\n",
        "    loss.backward()\n",
        "\n",
        "    # Step (learning rate)\n",
        "    optimizer.step()\n",
        "\n",
        "  # Average loss & accuracy per batch\n",
        "  train_loss = train_loss / len(dataloader)\n",
        "  train_acc = train_acc / len(dataloader.dataset) # Total correct / total samples\n",
        "\n",
        "  return train_loss, train_acc"
      ],
      "metadata": {
        "id": "LfiiBstBxF93"
      },
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Validation step\n",
        "def validation_step(model: torch.nn.Module,\n",
        "              dataloader: torch.utils.data.DataLoader,\n",
        "              loss_fn: torch.nn.Module,\n",
        "              device=device):\n",
        "\n",
        "  # Put the model in eval mode\n",
        "  model.eval()\n",
        "\n",
        "  validation_loss, validation_acc = 0, 0\n",
        "\n",
        "  # Disables gradient computation, reduces memory usage & increases speed\n",
        "  with torch.inference_mode():\n",
        "\n",
        "    # Loop through batches\n",
        "    for batch in dataloader:\n",
        "      # Extract values from batch\n",
        "      images, labels = batch\n",
        "\n",
        "      # Send to device\n",
        "      images, labels = images.to(device), labels.to(device)\n",
        "\n",
        "      # Forward pass\n",
        "      predictions = model(images)\n",
        "\n",
        "      # Calculate the loss\n",
        "      loss = loss_fn(predictions, labels)\n",
        "      validation_loss += loss.item()\n",
        "\n",
        "      # Calculate accuracy\n",
        "      prediction_classes = torch.argmax(predictions, dim=1) # Predicted class (logits to classes)\n",
        "      validation_acc += (prediction_classes == labels).sum().item() # Sum the number of correct predictions\n",
        "\n",
        "  # Average loss & accuracy per batch\n",
        "  validation_loss = validation_loss / len(dataloader)\n",
        "  validation_acc = validation_acc / len(dataloader.dataset)\n",
        "\n",
        "  return validation_loss, validation_acc"
      ],
      "metadata": {
        "id": "N8khLkIS5gr_"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Combine both train_step() & validation_step() into one function\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "# Takes in train & validation dataloaders, as well as everything needed to compute\n",
        "def train(model: torch.nn.Module,\n",
        "          train_dataloader: torch.utils.data.DataLoader,\n",
        "          validation_dataloader: torch.utils.data.DataLoader,\n",
        "          optimizer: torch.optim.Optimizer,\n",
        "          loss_fn: torch.nn.Module,\n",
        "          epochs: int = 5,\n",
        "          device=device):\n",
        "\n",
        "  # Create dictionary to store results\n",
        "  results = {\"train_loss\": [],\n",
        "             \"train_acc\": [],\n",
        "             \"validation_loss\": [],\n",
        "             \"validation_acc\": []}\n",
        "\n",
        "  # Loop through train & validation steps for # of epochs\n",
        "  for epoch in tqdm(range(epochs)):\n",
        "    train_loss, train_acc = train_step(model=model,\n",
        "                                       dataloader=train_dataloader,\n",
        "                                       loss_fn=loss_fn,\n",
        "                                       optimizer=optimizer,\n",
        "                                       device=device)\n",
        "\n",
        "    validation_loss, validation_acc = validation_step(model=model,\n",
        "                                    dataloader=validation_dataloader,\n",
        "                                    loss_fn=loss_fn,\n",
        "                                    device=device)\n",
        "\n",
        "    # Print data per epoch\n",
        "    print(f\"Epoch: {epoch} | Train loss: {train_loss:.4f} | Train acc: {train_acc:.4f} | Validation loss: {validation_loss:.4f} | Validation acc: {validation_acc:.4f}\")\n",
        "\n",
        "    # Update results dictionary\n",
        "    results[\"train_loss\"].append(train_loss)\n",
        "    results[\"train_acc\"].append(train_acc)\n",
        "    results[\"validation_loss\"].append(validation_loss)\n",
        "    results[\"validation_acc\"].append(validation_acc)\n",
        "\n",
        "  # Return filled results\n",
        "  return results"
      ],
      "metadata": {
        "id": "Eeql0YIZ7Z1T"
      },
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Time to train\n",
        "\n",
        "NUM_EPOCHS = 125\n",
        "\n",
        "# Recreate model from above\n",
        "animalModelV2 = animalIdentifier(input_shape=3,\n",
        "                               hidden_units=64,\n",
        "                               output_shape=len(imageDataset.classes)).to(device)\n",
        "\n",
        "# Configure loss function & optimizer\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(params=animalModelV2.parameters(),\n",
        "                            lr=0.001)\n",
        "\n",
        "# Timer\n",
        "from timeit import default_timer as timer\n",
        "start_time = timer()\n",
        "\n",
        "# Train\n",
        "results = train(model=animalModelV2,\n",
        "                train_dataloader=train_loader,\n",
        "                validation_dataloader=validation_loader,\n",
        "                optimizer=optimizer,\n",
        "                loss_fn=loss_fn,\n",
        "                epochs=NUM_EPOCHS,\n",
        "                device=device)\n",
        "\n",
        "# End the timer\n",
        "end_time = timer()\n",
        "print(f\"Total training time: {end_time - start_time:.3f} seconds\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "4e835eceb0e74dbca2433126991960d6",
            "06b3df2af4a9401c95a47ea82f4a61d7",
            "d7f43ad422c84452bd4c86ba24965a26",
            "93f3e754dfe649f38e094aa016a9d355",
            "231ef276e2074cfe9facbf25653d67d1",
            "116d9b293f4c46089ad2c6824d4109cf",
            "09319c10b21e402c9c8b41ba89e8ca05",
            "5b1da2a605774cc08ba455457b4a0368",
            "52e8008befac41dd804581844762a747",
            "b8f9fe9f7a30433bbe1a805bc547272c",
            "d2aec0e782d44c9ab3203c6ad0f6ac47"
          ]
        },
        "id": "bqZq4szb9G4I",
        "outputId": "0c47bdd7-b31d-4932-8661-71ae162cf137"
      },
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/125 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4e835eceb0e74dbca2433126991960d6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0 | Train loss: 1.1014 | Train acc: 0.3917 | Validation loss: 1.0889 | Validation acc: 0.4583\n",
            "Epoch: 1 | Train loss: 0.9796 | Train acc: 0.4833 | Validation loss: 0.8601 | Validation acc: 0.5833\n",
            "Epoch: 2 | Train loss: 0.8870 | Train acc: 0.5792 | Validation loss: 0.8581 | Validation acc: 0.5917\n",
            "Epoch: 3 | Train loss: 0.8435 | Train acc: 0.6146 | Validation loss: 0.8356 | Validation acc: 0.5917\n",
            "Epoch: 4 | Train loss: 0.8177 | Train acc: 0.6229 | Validation loss: 0.8722 | Validation acc: 0.5583\n",
            "Epoch: 5 | Train loss: 0.8146 | Train acc: 0.6104 | Validation loss: 0.7911 | Validation acc: 0.6167\n",
            "Epoch: 6 | Train loss: 0.7388 | Train acc: 0.6750 | Validation loss: 0.8026 | Validation acc: 0.6500\n",
            "Epoch: 7 | Train loss: 0.7319 | Train acc: 0.7042 | Validation loss: 0.7412 | Validation acc: 0.6833\n",
            "Epoch: 8 | Train loss: 0.7246 | Train acc: 0.6521 | Validation loss: 0.7565 | Validation acc: 0.6667\n",
            "Epoch: 9 | Train loss: 0.6949 | Train acc: 0.6750 | Validation loss: 0.8198 | Validation acc: 0.6250\n",
            "Epoch: 10 | Train loss: 0.7300 | Train acc: 0.6771 | Validation loss: 0.7644 | Validation acc: 0.6583\n",
            "Epoch: 11 | Train loss: 0.7352 | Train acc: 0.6937 | Validation loss: 0.7354 | Validation acc: 0.6750\n",
            "Epoch: 12 | Train loss: 0.6558 | Train acc: 0.7312 | Validation loss: 0.7390 | Validation acc: 0.6917\n",
            "Epoch: 13 | Train loss: 0.7255 | Train acc: 0.6521 | Validation loss: 0.7303 | Validation acc: 0.7000\n",
            "Epoch: 14 | Train loss: 0.6599 | Train acc: 0.7146 | Validation loss: 0.8106 | Validation acc: 0.6500\n",
            "Epoch: 15 | Train loss: 0.6971 | Train acc: 0.7021 | Validation loss: 0.8051 | Validation acc: 0.6333\n",
            "Epoch: 16 | Train loss: 0.6340 | Train acc: 0.7354 | Validation loss: 0.7156 | Validation acc: 0.6667\n",
            "Epoch: 17 | Train loss: 0.6844 | Train acc: 0.7083 | Validation loss: 0.7484 | Validation acc: 0.6500\n",
            "Epoch: 18 | Train loss: 0.5987 | Train acc: 0.7250 | Validation loss: 0.8284 | Validation acc: 0.7417\n",
            "Epoch: 19 | Train loss: 0.6576 | Train acc: 0.7188 | Validation loss: 0.6468 | Validation acc: 0.7417\n",
            "Epoch: 20 | Train loss: 0.6740 | Train acc: 0.7125 | Validation loss: 0.6770 | Validation acc: 0.7417\n",
            "Epoch: 21 | Train loss: 0.6335 | Train acc: 0.7250 | Validation loss: 0.6570 | Validation acc: 0.7000\n",
            "Epoch: 22 | Train loss: 0.5847 | Train acc: 0.7396 | Validation loss: 0.6822 | Validation acc: 0.7000\n",
            "Epoch: 23 | Train loss: 0.6071 | Train acc: 0.7146 | Validation loss: 0.6626 | Validation acc: 0.7000\n",
            "Epoch: 24 | Train loss: 0.6540 | Train acc: 0.6896 | Validation loss: 0.7400 | Validation acc: 0.6833\n",
            "Epoch: 25 | Train loss: 0.5990 | Train acc: 0.7375 | Validation loss: 0.7322 | Validation acc: 0.6417\n",
            "Epoch: 26 | Train loss: 0.6189 | Train acc: 0.7396 | Validation loss: 0.6355 | Validation acc: 0.6667\n",
            "Epoch: 27 | Train loss: 0.5806 | Train acc: 0.7333 | Validation loss: 0.6144 | Validation acc: 0.7750\n",
            "Epoch: 28 | Train loss: 0.5404 | Train acc: 0.7542 | Validation loss: 0.7689 | Validation acc: 0.7083\n",
            "Epoch: 29 | Train loss: 0.5627 | Train acc: 0.7417 | Validation loss: 0.6125 | Validation acc: 0.7417\n",
            "Epoch: 30 | Train loss: 0.4969 | Train acc: 0.8021 | Validation loss: 0.6172 | Validation acc: 0.7667\n",
            "Epoch: 31 | Train loss: 0.5180 | Train acc: 0.7771 | Validation loss: 0.6859 | Validation acc: 0.7333\n",
            "Epoch: 32 | Train loss: 0.5220 | Train acc: 0.7917 | Validation loss: 0.6270 | Validation acc: 0.7417\n",
            "Epoch: 33 | Train loss: 0.5710 | Train acc: 0.7625 | Validation loss: 0.7265 | Validation acc: 0.6583\n",
            "Epoch: 34 | Train loss: 0.5482 | Train acc: 0.7646 | Validation loss: 0.5895 | Validation acc: 0.7583\n",
            "Epoch: 35 | Train loss: 0.5159 | Train acc: 0.7604 | Validation loss: 0.6280 | Validation acc: 0.7583\n",
            "Epoch: 36 | Train loss: 0.4745 | Train acc: 0.7896 | Validation loss: 0.5746 | Validation acc: 0.7333\n",
            "Epoch: 37 | Train loss: 0.5344 | Train acc: 0.7646 | Validation loss: 0.6865 | Validation acc: 0.6750\n",
            "Epoch: 38 | Train loss: 0.5163 | Train acc: 0.7792 | Validation loss: 0.5879 | Validation acc: 0.8000\n",
            "Epoch: 39 | Train loss: 0.5296 | Train acc: 0.7688 | Validation loss: 0.5578 | Validation acc: 0.7083\n",
            "Epoch: 40 | Train loss: 0.4685 | Train acc: 0.8000 | Validation loss: 0.6508 | Validation acc: 0.7667\n",
            "Epoch: 41 | Train loss: 0.5001 | Train acc: 0.8000 | Validation loss: 0.5907 | Validation acc: 0.7333\n",
            "Epoch: 42 | Train loss: 0.4461 | Train acc: 0.8083 | Validation loss: 0.6272 | Validation acc: 0.7167\n",
            "Epoch: 43 | Train loss: 0.4556 | Train acc: 0.7917 | Validation loss: 0.6970 | Validation acc: 0.7000\n",
            "Epoch: 44 | Train loss: 0.5312 | Train acc: 0.8021 | Validation loss: 0.6681 | Validation acc: 0.7167\n",
            "Epoch: 45 | Train loss: 0.4168 | Train acc: 0.8375 | Validation loss: 0.6301 | Validation acc: 0.7417\n",
            "Epoch: 46 | Train loss: 0.4545 | Train acc: 0.8208 | Validation loss: 0.5572 | Validation acc: 0.7667\n",
            "Epoch: 47 | Train loss: 0.5307 | Train acc: 0.7812 | Validation loss: 0.5479 | Validation acc: 0.7667\n",
            "Epoch: 48 | Train loss: 0.4474 | Train acc: 0.8313 | Validation loss: 0.5221 | Validation acc: 0.8083\n",
            "Epoch: 49 | Train loss: 0.4075 | Train acc: 0.8250 | Validation loss: 0.5310 | Validation acc: 0.7750\n",
            "Epoch: 50 | Train loss: 0.3849 | Train acc: 0.8271 | Validation loss: 0.6135 | Validation acc: 0.7833\n",
            "Epoch: 51 | Train loss: 0.4511 | Train acc: 0.8187 | Validation loss: 0.6324 | Validation acc: 0.7583\n",
            "Epoch: 52 | Train loss: 0.4513 | Train acc: 0.8146 | Validation loss: 0.5290 | Validation acc: 0.8167\n",
            "Epoch: 53 | Train loss: 0.4114 | Train acc: 0.8333 | Validation loss: 0.6201 | Validation acc: 0.7667\n",
            "Epoch: 54 | Train loss: 0.4255 | Train acc: 0.8229 | Validation loss: 0.5494 | Validation acc: 0.8000\n",
            "Epoch: 55 | Train loss: 0.4318 | Train acc: 0.8313 | Validation loss: 0.6178 | Validation acc: 0.7417\n",
            "Epoch: 56 | Train loss: 0.4629 | Train acc: 0.7979 | Validation loss: 0.5686 | Validation acc: 0.8333\n",
            "Epoch: 57 | Train loss: 0.4293 | Train acc: 0.8354 | Validation loss: 0.4316 | Validation acc: 0.8250\n",
            "Epoch: 58 | Train loss: 0.3932 | Train acc: 0.8562 | Validation loss: 0.7187 | Validation acc: 0.7417\n",
            "Epoch: 59 | Train loss: 0.4236 | Train acc: 0.8104 | Validation loss: 0.4557 | Validation acc: 0.8250\n",
            "Epoch: 60 | Train loss: 0.4065 | Train acc: 0.8271 | Validation loss: 0.5929 | Validation acc: 0.7583\n",
            "Epoch: 61 | Train loss: 0.3792 | Train acc: 0.8313 | Validation loss: 0.4662 | Validation acc: 0.8417\n",
            "Epoch: 62 | Train loss: 0.3546 | Train acc: 0.8542 | Validation loss: 0.5727 | Validation acc: 0.8417\n",
            "Epoch: 63 | Train loss: 0.3492 | Train acc: 0.8521 | Validation loss: 0.5717 | Validation acc: 0.7917\n",
            "Epoch: 64 | Train loss: 0.3318 | Train acc: 0.8625 | Validation loss: 0.5191 | Validation acc: 0.8500\n",
            "Epoch: 65 | Train loss: 0.3044 | Train acc: 0.8938 | Validation loss: 0.6894 | Validation acc: 0.7417\n",
            "Epoch: 66 | Train loss: 0.3363 | Train acc: 0.8708 | Validation loss: 0.5633 | Validation acc: 0.7667\n",
            "Epoch: 67 | Train loss: 0.3196 | Train acc: 0.8625 | Validation loss: 0.5062 | Validation acc: 0.8250\n",
            "Epoch: 68 | Train loss: 0.3535 | Train acc: 0.8708 | Validation loss: 0.5840 | Validation acc: 0.7750\n",
            "Epoch: 69 | Train loss: 0.3381 | Train acc: 0.8667 | Validation loss: 0.5718 | Validation acc: 0.7250\n",
            "Epoch: 70 | Train loss: 0.3726 | Train acc: 0.8292 | Validation loss: 0.5013 | Validation acc: 0.8167\n",
            "Epoch: 71 | Train loss: 0.3337 | Train acc: 0.8729 | Validation loss: 0.5478 | Validation acc: 0.7917\n",
            "Epoch: 72 | Train loss: 0.2963 | Train acc: 0.8833 | Validation loss: 0.4699 | Validation acc: 0.8333\n",
            "Epoch: 73 | Train loss: 0.3185 | Train acc: 0.8521 | Validation loss: 0.5662 | Validation acc: 0.7917\n",
            "Epoch: 74 | Train loss: 0.2922 | Train acc: 0.8729 | Validation loss: 0.4225 | Validation acc: 0.8333\n",
            "Epoch: 75 | Train loss: 0.3255 | Train acc: 0.8854 | Validation loss: 0.4640 | Validation acc: 0.8250\n",
            "Epoch: 76 | Train loss: 0.3415 | Train acc: 0.8750 | Validation loss: 0.5419 | Validation acc: 0.8000\n",
            "Epoch: 77 | Train loss: 0.3235 | Train acc: 0.8729 | Validation loss: 0.4119 | Validation acc: 0.8250\n",
            "Epoch: 78 | Train loss: 0.3789 | Train acc: 0.8583 | Validation loss: 0.6150 | Validation acc: 0.8083\n",
            "Epoch: 79 | Train loss: 0.3464 | Train acc: 0.8500 | Validation loss: 0.4461 | Validation acc: 0.8333\n",
            "Epoch: 80 | Train loss: 0.3088 | Train acc: 0.8812 | Validation loss: 0.4641 | Validation acc: 0.8000\n",
            "Epoch: 81 | Train loss: 0.3257 | Train acc: 0.8771 | Validation loss: 0.4515 | Validation acc: 0.8500\n",
            "Epoch: 82 | Train loss: 0.2985 | Train acc: 0.9021 | Validation loss: 0.5349 | Validation acc: 0.8000\n",
            "Epoch: 83 | Train loss: 0.3623 | Train acc: 0.8792 | Validation loss: 0.6158 | Validation acc: 0.7500\n",
            "Epoch: 84 | Train loss: 0.3402 | Train acc: 0.8708 | Validation loss: 0.4236 | Validation acc: 0.8750\n",
            "Epoch: 85 | Train loss: 0.3700 | Train acc: 0.8583 | Validation loss: 0.4185 | Validation acc: 0.8333\n",
            "Epoch: 86 | Train loss: 0.3014 | Train acc: 0.8646 | Validation loss: 0.3789 | Validation acc: 0.8667\n",
            "Epoch: 87 | Train loss: 0.3094 | Train acc: 0.8854 | Validation loss: 0.4023 | Validation acc: 0.8417\n",
            "Epoch: 88 | Train loss: 0.3290 | Train acc: 0.8854 | Validation loss: 0.4878 | Validation acc: 0.8167\n",
            "Epoch: 89 | Train loss: 0.2752 | Train acc: 0.8938 | Validation loss: 0.5758 | Validation acc: 0.8500\n",
            "Epoch: 90 | Train loss: 0.2739 | Train acc: 0.9104 | Validation loss: 0.4417 | Validation acc: 0.8333\n",
            "Epoch: 91 | Train loss: 0.3204 | Train acc: 0.8708 | Validation loss: 0.5647 | Validation acc: 0.7917\n",
            "Epoch: 92 | Train loss: 0.3028 | Train acc: 0.8938 | Validation loss: 0.3802 | Validation acc: 0.8500\n",
            "Epoch: 93 | Train loss: 0.2684 | Train acc: 0.9042 | Validation loss: 0.4100 | Validation acc: 0.8917\n",
            "Epoch: 94 | Train loss: 0.2545 | Train acc: 0.8958 | Validation loss: 0.5231 | Validation acc: 0.7917\n",
            "Epoch: 95 | Train loss: 0.2954 | Train acc: 0.8812 | Validation loss: 0.3472 | Validation acc: 0.8583\n",
            "Epoch: 96 | Train loss: 0.2080 | Train acc: 0.9187 | Validation loss: 0.5340 | Validation acc: 0.7750\n",
            "Epoch: 97 | Train loss: 0.2540 | Train acc: 0.8979 | Validation loss: 0.4283 | Validation acc: 0.8417\n",
            "Epoch: 98 | Train loss: 0.2752 | Train acc: 0.8833 | Validation loss: 0.4108 | Validation acc: 0.8417\n",
            "Epoch: 99 | Train loss: 0.3376 | Train acc: 0.8792 | Validation loss: 0.6952 | Validation acc: 0.7583\n",
            "Epoch: 100 | Train loss: 0.3460 | Train acc: 0.8542 | Validation loss: 0.5184 | Validation acc: 0.7917\n",
            "Epoch: 101 | Train loss: 0.2818 | Train acc: 0.8833 | Validation loss: 0.5273 | Validation acc: 0.8167\n",
            "Epoch: 102 | Train loss: 0.3543 | Train acc: 0.8625 | Validation loss: 0.7769 | Validation acc: 0.7667\n",
            "Epoch: 103 | Train loss: 0.3242 | Train acc: 0.8833 | Validation loss: 0.4466 | Validation acc: 0.8417\n",
            "Epoch: 104 | Train loss: 0.3056 | Train acc: 0.8812 | Validation loss: 0.4509 | Validation acc: 0.8417\n",
            "Epoch: 105 | Train loss: 0.2766 | Train acc: 0.8875 | Validation loss: 0.3192 | Validation acc: 0.8833\n",
            "Epoch: 106 | Train loss: 0.2447 | Train acc: 0.8979 | Validation loss: 0.4284 | Validation acc: 0.8583\n",
            "Epoch: 107 | Train loss: 0.2805 | Train acc: 0.9042 | Validation loss: 0.3971 | Validation acc: 0.8583\n",
            "Epoch: 108 | Train loss: 0.2755 | Train acc: 0.8917 | Validation loss: 0.3467 | Validation acc: 0.8750\n",
            "Epoch: 109 | Train loss: 0.2111 | Train acc: 0.9292 | Validation loss: 0.4828 | Validation acc: 0.9000\n",
            "Epoch: 110 | Train loss: 0.2381 | Train acc: 0.9146 | Validation loss: 0.4988 | Validation acc: 0.8250\n",
            "Epoch: 111 | Train loss: 0.2422 | Train acc: 0.9083 | Validation loss: 0.4583 | Validation acc: 0.8333\n",
            "Epoch: 112 | Train loss: 0.2580 | Train acc: 0.9000 | Validation loss: 0.6748 | Validation acc: 0.8083\n",
            "Epoch: 113 | Train loss: 0.2911 | Train acc: 0.9021 | Validation loss: 0.3777 | Validation acc: 0.8750\n",
            "Epoch: 114 | Train loss: 0.2366 | Train acc: 0.9042 | Validation loss: 0.4114 | Validation acc: 0.8833\n",
            "Epoch: 115 | Train loss: 0.2573 | Train acc: 0.9021 | Validation loss: 0.3947 | Validation acc: 0.8833\n",
            "Epoch: 116 | Train loss: 0.2792 | Train acc: 0.8812 | Validation loss: 0.2645 | Validation acc: 0.9250\n",
            "Epoch: 117 | Train loss: 0.2169 | Train acc: 0.9146 | Validation loss: 0.4409 | Validation acc: 0.8417\n",
            "Epoch: 118 | Train loss: 0.2703 | Train acc: 0.8958 | Validation loss: 0.3606 | Validation acc: 0.8417\n",
            "Epoch: 119 | Train loss: 0.2976 | Train acc: 0.8812 | Validation loss: 0.3004 | Validation acc: 0.8833\n",
            "Epoch: 120 | Train loss: 0.1905 | Train acc: 0.9313 | Validation loss: 0.4986 | Validation acc: 0.8583\n",
            "Epoch: 121 | Train loss: 0.2447 | Train acc: 0.9187 | Validation loss: 0.3718 | Validation acc: 0.8833\n",
            "Epoch: 122 | Train loss: 0.2467 | Train acc: 0.9083 | Validation loss: 0.4231 | Validation acc: 0.8917\n",
            "Epoch: 123 | Train loss: 0.2948 | Train acc: 0.8979 | Validation loss: 0.4373 | Validation acc: 0.8667\n",
            "Epoch: 124 | Train loss: 0.2354 | Train acc: 0.9042 | Validation loss: 0.4182 | Validation acc: 0.8583\n",
            "Total training time: 230.964 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create model directory\n",
        "MODEL_PATH = Path(\"models\")\n",
        "MODEL_PATH.mkdir(parents=True,\n",
        "                 exist_ok=True)\n",
        "\n",
        "# Create model name & save path\n",
        "MODEL_NAME = \"bcd_ImageClassifier.pth\"\n",
        "MODEL_SAVE_PATH = MODEL_PATH / MODEL_NAME\n",
        "\n",
        "# Save model's weights\n",
        "print(f\"Saving model to: {MODEL_SAVE_PATH}\")\n",
        "torch.save(obj=animalModelV2.state_dict(),\n",
        "           f=MODEL_SAVE_PATH)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Edm8cFA--bgF",
        "outputId": "87899cb5-3fd0-4dd3-a0b1-c5a6549df03a"
      },
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving model to: models/bcd_ImageClassifier.pth\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ziaYzPJBPqtb"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}