tensorflow_2_tutorials/text/text_classification_rnn.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "text_classification_rnn.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU",
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "ac8f29be115c4204b2b76f1b242b01a8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_5b0ff0d6aa9f41a093691bda14d88a20",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_65ead7bbc39c41dc93c13f76c82a518c",
              "IPY_MODEL_56f0c412a9664781865568c5c7863aba"
            ]
          }
        },
        "5b0ff0d6aa9f41a093691bda14d88a20": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "65ead7bbc39c41dc93c13f76c82a518c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_942fe4bd3e334ea7ac9ede5a7f12cd16",
            "_dom_classes": [],
            "description": "Dl Completed...: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 1,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 1,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_4907838c992441bf8a500d07eac13e13"
          }
        },
        "56f0c412a9664781865568c5c7863aba": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_e7855c0817ae45d49836bfb7ea643cc2",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 1/1 [00:09&lt;00:00,  9.06s/ url]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_33aaa8e079264ce598c943adb45222bb"
          }
        },
        "942fe4bd3e334ea7ac9ede5a7f12cd16": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "4907838c992441bf8a500d07eac13e13": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "e7855c0817ae45d49836bfb7ea643cc2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "33aaa8e079264ce598c943adb45222bb": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "6547dbecbddd472bb1220605d431704e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_06836c53f1284d1eb81fe9c28a5f87d3",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_73e0d740833a4423bde111ec044f164c",
              "IPY_MODEL_7282a054777747048bbf068a563a8899"
            ]
          }
        },
        "06836c53f1284d1eb81fe9c28a5f87d3": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "73e0d740833a4423bde111ec044f164c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_6487f3875cda44bf928882ec92c259e0",
            "_dom_classes": [],
            "description": "Dl Size...: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 1,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 1,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_513331f40f154ff7be448f51e73efa80"
          }
        },
        "7282a054777747048bbf068a563a8899": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_7cd08fd43cc443418852b8454baca7f2",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 80/80 [00:09&lt;00:00,  8.86 MiB/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_6540a0fd0e024839906c28ea9430dccf"
          }
        },
        "6487f3875cda44bf928882ec92c259e0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "513331f40f154ff7be448f51e73efa80": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "7cd08fd43cc443418852b8454baca7f2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "6540a0fd0e024839906c28ea9430dccf": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "a1c86e33074548578fdccbd315e44a1f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_42778b94beab492e8ac172998145131c",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_1ef0d24434cd4fd984a5ffec26acdda4",
              "IPY_MODEL_8f1822d100b84c6cbff4602da6208be1"
            ]
          }
        },
        "42778b94beab492e8ac172998145131c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "1ef0d24434cd4fd984a5ffec26acdda4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_90daa9f4c554465193ac43f008a11577",
            "_dom_classes": [],
            "description": "",
            "_model_name": "FloatProgressModel",
            "bar_style": "info",
            "max": 1,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 1,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_da4530839f744b36a902132fe22dc9b1"
          }
        },
        "8f1822d100b84c6cbff4602da6208be1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_f3e5701ca27c40e6b03b8298b80bf480",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 25000/0 [00:38&lt;00:00, 778.95 examples/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_725959a70c72438597c8b6c896195b25"
          }
        },
        "90daa9f4c554465193ac43f008a11577": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "da4530839f744b36a902132fe22dc9b1": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "f3e5701ca27c40e6b03b8298b80bf480": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "725959a70c72438597c8b6c896195b25": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "0552951ec205470bae191bd14e8f8ee3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_d50bf1885b8a4c8e872b36885c7e56e9",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_d6a8c781aed9439da5404211b8791e6a",
              "IPY_MODEL_f4e38014eb3d42a29ee91b143c2fb9b6"
            ]
          }
        },
        "d50bf1885b8a4c8e872b36885c7e56e9": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "d6a8c781aed9439da5404211b8791e6a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_f2fc8ea1841145ea99e4db532240092f",
            "_dom_classes": [],
            "description": " 53%",
            "_model_name": "FloatProgressModel",
            "bar_style": "danger",
            "max": 25000,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 13372,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_6dd5e6a509bc4f0788e2d7cfb30e2d4d"
          }
        },
        "f4e38014eb3d42a29ee91b143c2fb9b6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_81b1508ffe804251904086f2689339a2",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 13372/25000 [00:00&lt;00:00, 133717.26 examples/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_aebacb0fb8444400bf453e7844ae0ab1"
          }
        },
        "f2fc8ea1841145ea99e4db532240092f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "6dd5e6a509bc4f0788e2d7cfb30e2d4d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "81b1508ffe804251904086f2689339a2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "aebacb0fb8444400bf453e7844ae0ab1": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "85f0a0f5d7474e9c877c45171fa5b642": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_782f9f304f794599acacc4b3bb61568d",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_89b5cb2d5c6848949c46d29622c23ba2",
              "IPY_MODEL_27a5f2c77df844b8874440caca9d6b0f"
            ]
          }
        },
        "782f9f304f794599acacc4b3bb61568d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "89b5cb2d5c6848949c46d29622c23ba2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_11901976d0314cc4aacee5339c7a083e",
            "_dom_classes": [],
            "description": "",
            "_model_name": "FloatProgressModel",
            "bar_style": "info",
            "max": 1,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 1,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_1303c3a640ef466e892baa32ef2fd756"
          }
        },
        "27a5f2c77df844b8874440caca9d6b0f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_acba515b6fc243edb373c4528a89e4c3",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 25000/0 [00:36&lt;00:00, 883.03 examples/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_d82370d41b02473eb1dafd1a77556827"
          }
        },
        "11901976d0314cc4aacee5339c7a083e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "1303c3a640ef466e892baa32ef2fd756": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "acba515b6fc243edb373c4528a89e4c3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "d82370d41b02473eb1dafd1a77556827": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "1b6a76aae5d141f88e5c138af46c8485": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_f04f349b942948ca956776e38038eb80",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_1b0819716e1f45148db183f827b7df8b",
              "IPY_MODEL_3ebaead8e74a4aef9452c932309f6cc9"
            ]
          }
        },
        "f04f349b942948ca956776e38038eb80": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "1b0819716e1f45148db183f827b7df8b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_435ea59099f342ceb07cf74f8816f7f8",
            "_dom_classes": [],
            "description": " 49%",
            "_model_name": "FloatProgressModel",
            "bar_style": "danger",
            "max": 25000,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 12220,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_6f253f6a2f644513a7d9db0948968c1a"
          }
        },
        "3ebaead8e74a4aef9452c932309f6cc9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_58f93f68659f4946baa8890678fe8f9a",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 12220/25000 [00:00&lt;00:00, 122199.24 examples/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_1b94fe5c24084cefb5b5346476014387"
          }
        },
        "435ea59099f342ceb07cf74f8816f7f8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "6f253f6a2f644513a7d9db0948968c1a": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "58f93f68659f4946baa8890678fe8f9a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "1b94fe5c24084cefb5b5346476014387": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "e0b3f096f49447b28a5699dbfbd58c0c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_62aef7d882964197a20f2db371754e9d",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_3dcba421294f4b4e9390c1247f25b7bc",
              "IPY_MODEL_15184b7237a9489cb0eb84736ce54070"
            ]
          }
        },
        "62aef7d882964197a20f2db371754e9d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "3dcba421294f4b4e9390c1247f25b7bc": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_0815259ea2154ae69bbc946a609d6eb8",
            "_dom_classes": [],
            "description": "",
            "_model_name": "FloatProgressModel",
            "bar_style": "info",
            "max": 1,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 1,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_943659c041e4402383b0793130ba4ab1"
          }
        },
        "15184b7237a9489cb0eb84736ce54070": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_c6e0a4720ef9427eb1d68fb12ca06845",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 50000/0 [01:06&lt;00:00, 791.15 examples/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_d4017ffb3f9d4607a8d1ed081e030df0"
          }
        },
        "0815259ea2154ae69bbc946a609d6eb8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "943659c041e4402383b0793130ba4ab1": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "c6e0a4720ef9427eb1d68fb12ca06845": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "d4017ffb3f9d4607a8d1ed081e030df0": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "00b60323a8cd4ee08307ee083ebbeb67": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_89a7dbc7262041898785caa88326aa84",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_de3ab85f1ef945ae9bb82009f3a0e4a9",
              "IPY_MODEL_d99a6b6ac1fa448c8b7f4663430f8dc2"
            ]
          }
        },
        "89a7dbc7262041898785caa88326aa84": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "de3ab85f1ef945ae9bb82009f3a0e4a9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_0ce0a366454045528f9166bac148b22e",
            "_dom_classes": [],
            "description": " 63%",
            "_model_name": "FloatProgressModel",
            "bar_style": "danger",
            "max": 50000,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 31378,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_d95ec9930281480e9cf83a63e4b34988"
          }
        },
        "d99a6b6ac1fa448c8b7f4663430f8dc2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_b9f40fdfc7a5433d8cc4ffc3997c2b8a",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 31378/50000 [00:07&lt;00:00, 25408.28 examples/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_880c05141e794cbaba415c0ce5cd3c5f"
          }
        },
        "0ce0a366454045528f9166bac148b22e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "d95ec9930281480e9cf83a63e4b34988": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "b9f40fdfc7a5433d8cc4ffc3997c2b8a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "880c05141e794cbaba415c0ce5cd3c5f": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uONT7BaBKSWG",
        "colab_type": "text"
      },
      "source": [
        "[Text classification with a RNN Tutorial in Tensorflow 2.0](https://www.tensorflow.org/tutorials/text/text_classification_rnn)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wpwQGist7fsz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "import matplotlib.pyplot as plt\n",
        "import time"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pGrlE4RuKo0D",
        "colab_type": "text"
      },
      "source": [
        "# Set up input pipeline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aSRBQasTKssD",
        "colab_type": "text"
      },
      "source": [
        "The IMDB large movie review dataset is a binary classification dataset—all the reviews have either a positive or negative sentiment.\n",
        "We will download the dataset using TensorFlow Datasets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uPZuqhNTKhje",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355,
          "referenced_widgets": [
            "ac8f29be115c4204b2b76f1b242b01a8",
            "5b0ff0d6aa9f41a093691bda14d88a20",
            "65ead7bbc39c41dc93c13f76c82a518c",
            "56f0c412a9664781865568c5c7863aba",
            "942fe4bd3e334ea7ac9ede5a7f12cd16",
            "4907838c992441bf8a500d07eac13e13",
            "e7855c0817ae45d49836bfb7ea643cc2",
            "33aaa8e079264ce598c943adb45222bb",
            "6547dbecbddd472bb1220605d431704e",
            "06836c53f1284d1eb81fe9c28a5f87d3",
            "73e0d740833a4423bde111ec044f164c",
            "7282a054777747048bbf068a563a8899",
            "6487f3875cda44bf928882ec92c259e0",
            "513331f40f154ff7be448f51e73efa80",
            "7cd08fd43cc443418852b8454baca7f2",
            "6540a0fd0e024839906c28ea9430dccf",
            "a1c86e33074548578fdccbd315e44a1f",
            "42778b94beab492e8ac172998145131c",
            "1ef0d24434cd4fd984a5ffec26acdda4",
            "8f1822d100b84c6cbff4602da6208be1",
            "90daa9f4c554465193ac43f008a11577",
            "da4530839f744b36a902132fe22dc9b1",
            "f3e5701ca27c40e6b03b8298b80bf480",
            "725959a70c72438597c8b6c896195b25",
            "0552951ec205470bae191bd14e8f8ee3",
            "d50bf1885b8a4c8e872b36885c7e56e9",
            "d6a8c781aed9439da5404211b8791e6a",
            "f4e38014eb3d42a29ee91b143c2fb9b6",
            "f2fc8ea1841145ea99e4db532240092f",
            "6dd5e6a509bc4f0788e2d7cfb30e2d4d",
            "81b1508ffe804251904086f2689339a2",
            "aebacb0fb8444400bf453e7844ae0ab1",
            "85f0a0f5d7474e9c877c45171fa5b642",
            "782f9f304f794599acacc4b3bb61568d",
            "89b5cb2d5c6848949c46d29622c23ba2",
            "27a5f2c77df844b8874440caca9d6b0f",
            "11901976d0314cc4aacee5339c7a083e",
            "1303c3a640ef466e892baa32ef2fd756",
            "acba515b6fc243edb373c4528a89e4c3",
            "d82370d41b02473eb1dafd1a77556827",
            "1b6a76aae5d141f88e5c138af46c8485",
            "f04f349b942948ca956776e38038eb80",
            "1b0819716e1f45148db183f827b7df8b",
            "3ebaead8e74a4aef9452c932309f6cc9",
            "435ea59099f342ceb07cf74f8816f7f8",
            "6f253f6a2f644513a7d9db0948968c1a",
            "58f93f68659f4946baa8890678fe8f9a",
            "1b94fe5c24084cefb5b5346476014387",
            "e0b3f096f49447b28a5699dbfbd58c0c",
            "62aef7d882964197a20f2db371754e9d",
            "3dcba421294f4b4e9390c1247f25b7bc",
            "15184b7237a9489cb0eb84736ce54070",
            "0815259ea2154ae69bbc946a609d6eb8",
            "943659c041e4402383b0793130ba4ab1",
            "c6e0a4720ef9427eb1d68fb12ca06845",
            "d4017ffb3f9d4607a8d1ed081e030df0",
            "00b60323a8cd4ee08307ee083ebbeb67",
            "89a7dbc7262041898785caa88326aa84",
            "de3ab85f1ef945ae9bb82009f3a0e4a9",
            "d99a6b6ac1fa448c8b7f4663430f8dc2",
            "0ce0a366454045528f9166bac148b22e",
            "d95ec9930281480e9cf83a63e4b34988",
            "b9f40fdfc7a5433d8cc4ffc3997c2b8a",
            "880c05141e794cbaba415c0ce5cd3c5f"
          ]
        },
        "outputId": "23595c09-13f4-4de5-f624-a13f7bd14025"
      },
      "source": [
        "dataset, info = tfds.load(\"imdb_reviews/subwords8k\", with_info=True, as_supervised=True)\n",
        "train_dataset, test_dataset = dataset[\"train\"], dataset[\"test\"]"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDownloading and preparing dataset imdb_reviews/subwords8k/1.0.0 (download: 80.23 MiB, generated: Unknown size, total: 80.23 MiB) to /root/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0...\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ac8f29be115c4204b2b76f1b242b01a8",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Completed...', max=1.0, style=Progre…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6547dbecbddd472bb1220605d431704e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Size...', max=1.0, style=ProgressSty…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a1c86e33074548578fdccbd315e44a1f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rShuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0.incomplete168RMN/imdb_reviews-train.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0552951ec205470bae191bd14e8f8ee3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=25000.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "85f0a0f5d7474e9c877c45171fa5b642",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rShuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0.incomplete168RMN/imdb_reviews-test.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1b6a76aae5d141f88e5c138af46c8485",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=25000.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e0b3f096f49447b28a5699dbfbd58c0c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rShuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0.incomplete168RMN/imdb_reviews-unsupervised.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "00b60323a8cd4ee08307ee083ebbeb67",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=50000.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDataset imdb_reviews downloaded and prepared to /root/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f6UxrekKK8Jh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "encoder = info.features[\"text\"].encoder"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-wG04nf3L-2j",
        "colab_type": "code",
        "outputId": "c008dd40-c28f-4b97-f8a6-2c300cccabb7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(f\"Vocabulary size: {encoder.vocab_size}\")"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Vocabulary size: 8185\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BjlB-2uaMHep",
        "colab_type": "text"
      },
      "source": [
        "This text encoder will reversibly encode any string, falling back to byte-encoding if necessary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qOvMIFaKMCem",
        "colab_type": "code",
        "outputId": "a9e04e74-2085-4b1a-b71c-38e29c797539",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "encoded_string = encoder.encode(\"Hello Tensorflow, let's see how you encode this sentence.\")\n",
        "print(encoded_string)\n",
        "decoded_string = encoder.decode(encoded_string)\n",
        "print(decoded_string)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[4025, 222, 6307, 2327, 2934, 2, 920, 7968, 8, 86, 113, 37, 6213, 510, 14, 7745, 7975]\n",
            "Hello Tensorflow, let's see how you encode this sentence.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JZo1JO8JMXFE",
        "colab_type": "code",
        "outputId": "193c5a6d-17bb-4cdc-dd7a-21efb88ddd2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        }
      },
      "source": [
        "for index in encoded_string:\n",
        "  print(f\"{index} --> {encoder.decode([index])}\")"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4025 --> Hell\n",
            "222 --> o \n",
            "6307 --> Ten\n",
            "2327 --> sor\n",
            "2934 --> flow\n",
            "2 --> , \n",
            "920 --> let\n",
            "7968 --> '\n",
            "8 --> s \n",
            "86 --> see \n",
            "113 --> how \n",
            "37 --> you \n",
            "6213 --> enco\n",
            "510 --> de \n",
            "14 --> this \n",
            "7745 --> sentence\n",
            "7975 --> .\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K_r7k3CLMxLP",
        "colab_type": "text"
      },
      "source": [
        "# Prepare data for training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UUX1jDF1M02I",
        "colab_type": "text"
      },
      "source": [
        "We will use the padded_batch introduce in the Word embeddings tutorial to create batches of these encoded strings."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qfJDqTByMpcI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "BUFFER_SIZE = 10000\n",
        "BATCH_SIZE = 64"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PQa-VmIpM8UB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_dataset = train_dataset.shuffle(BUFFER_SIZE).padded_batch(BATCH_SIZE)\n",
        "test_dataset = test_dataset.padded_batch(BATCH_SIZE)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VGkcUJ2pNFlz",
        "colab_type": "text"
      },
      "source": [
        "# Create the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rVdrcdbtNlyJ",
        "colab_type": "text"
      },
      "source": [
        "We will use a Sequential model that starts with an Embedding layer and then goes straight to a bi-directional LSTM. Finally, there is a dense layer with 64 units that is connected to the final dense layer with a single neuron, used for the classification task."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "re46E4TXNEk2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Embedding(input_dim=encoder.vocab_size, output_dim=64, mask_zero=True),\n",
        "  tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(units=64)),\n",
        "  tf.keras.layers.Dense(units=64, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(units=1)                                    \n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qnSceKi5OSbb",
        "colab_type": "code",
        "outputId": "73ee4c46-f5c0-4165-bd81-0700ddcfc2b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, None, 64)          523840    \n",
            "_________________________________________________________________\n",
            "bidirectional (Bidirectional (None, 128)               66048     \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 64)                8256      \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 598,209\n",
            "Trainable params: 598,209\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jg120CKyOhKA",
        "colab_type": "text"
      },
      "source": [
        "**NOTE**: If we wanted to use the stateful RNN layer, we should have built the model with Keras functional API or model subclassing so that the RNN layer states can be retrieved and used. Please check [Keras RNN guide](https://www.tensorflow.org/guide/keras/rnn#rnn_state_reuse) for more details."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bhTfuFphOTeT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(\n",
        "    loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
        "    optimizer=tf.keras.optimizers.Adam(1e-4),\n",
        "    metrics=['accuracy'],\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i0s_I_gVO-z5",
        "colab_type": "text"
      },
      "source": [
        "# Train the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S5rpGAgpO-GP",
        "colab_type": "code",
        "outputId": "ff8b2072-90e7-4830-f730-1de4cd61f24a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        }
      },
      "source": [
        "history = model.fit(\n",
        "    train_dataset, \n",
        "    epochs=10,\n",
        "    validation_data=test_dataset,\n",
        "    validation_steps=30,\n",
        ")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "391/391 [==============================] - 133s 340ms/step - loss: 0.6315 - accuracy: 0.5906 - val_loss: 0.4689 - val_accuracy: 0.8078\n",
            "Epoch 2/10\n",
            "391/391 [==============================] - 129s 330ms/step - loss: 0.3655 - accuracy: 0.8475 - val_loss: 0.3550 - val_accuracy: 0.8344\n",
            "Epoch 3/10\n",
            "391/391 [==============================] - 129s 331ms/step - loss: 0.2640 - accuracy: 0.8974 - val_loss: 0.3334 - val_accuracy: 0.8609\n",
            "Epoch 4/10\n",
            "391/391 [==============================] - 129s 331ms/step - loss: 0.2099 - accuracy: 0.9232 - val_loss: 0.3255 - val_accuracy: 0.8542\n",
            "Epoch 5/10\n",
            "391/391 [==============================] - 129s 330ms/step - loss: 0.1774 - accuracy: 0.9379 - val_loss: 0.3439 - val_accuracy: 0.8687\n",
            "Epoch 6/10\n",
            "391/391 [==============================] - 129s 330ms/step - loss: 0.1545 - accuracy: 0.9461 - val_loss: 0.3526 - val_accuracy: 0.8635\n",
            "Epoch 7/10\n",
            "391/391 [==============================] - 129s 329ms/step - loss: 0.1314 - accuracy: 0.9570 - val_loss: 0.3807 - val_accuracy: 0.8651\n",
            "Epoch 8/10\n",
            "391/391 [==============================] - 128s 329ms/step - loss: 0.1181 - accuracy: 0.9623 - val_loss: 0.4032 - val_accuracy: 0.8604\n",
            "Epoch 9/10\n",
            "391/391 [==============================] - 129s 331ms/step - loss: 0.1041 - accuracy: 0.9674 - val_loss: 0.4448 - val_accuracy: 0.8589\n",
            "Epoch 10/10\n",
            "391/391 [==============================] - 129s 329ms/step - loss: 0.0941 - accuracy: 0.9708 - val_loss: 0.4590 - val_accuracy: 0.8531\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TAvIVuniPIAp",
        "colab_type": "code",
        "outputId": "3c799e47-5fdd-4bc8-fc8d-de7097320777",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_loss, test_acc = model.evaluate(test_dataset)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "391/391 [==============================] - 61s 155ms/step - loss: 0.4453 - accuracy: 0.8578\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FhLBY-LxVnt5",
        "colab_type": "text"
      },
      "source": [
        "We will now create the inference function for a given sample"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W5O8nCp-SQB-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def encode_sample(sample_pred_text):\n",
        "  encoded_sample = encoder.encode(sample_pred_text)\n",
        "  encoded_sample_tensor = tf.constant(encoded_sample, dtype=tf.float32)\n",
        "  return encoded_sample_tensor\n",
        "\n",
        "@tf.function\n",
        "def model_predict(encoded_sample):\n",
        "  logits = model(tf.expand_dims(encoded_sample, axis=0))\n",
        "  predicted_value = tf.sigmoid(logits)\n",
        "  return predicted_value"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hh2ZWnScWDxg",
        "colab_type": "code",
        "outputId": "6ca7d7d9-e3f2-4c29-d1c6-1a42276d2da8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "source": [
        "# Predict on a positive sample\n",
        "sample_pred_text = \"The movie was cool. The animation and the graphics\" \\\n",
        "                   \" were out of this world and, because of that, \" \\\n",
        "                   \"I would definitely recommend this movie!\"\n",
        "print(f\"Text to predict: \\n{sample_pred_text}\")\n",
        "encoded_sample = encode_sample(sample_pred_text)\n",
        "prediction = model_predict(encoded_sample)\n",
        "start = time.time()\n",
        "print(f\"Prediction: {prediction[0][0]} [took {time.time() - start} s]\")"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Text to predict: \n",
            "The movie was cool. The animation and the graphics were out of this world and, because of that, I would definitely recommend this movie!\n",
            "Prediction: 0.9197081923484802 [took 0.0009548664093017578 s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q_w03Mc2WfjJ",
        "colab_type": "code",
        "outputId": "209b8646-0818-4999-ee86-626bef924462",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "source": [
        "# Predict on a negative sample\n",
        "sample_pred_text = \"What an awful movie! I expected much more from this director.\" \\\n",
        "                   \" The plot was ok, but the actors and graphics were terrible...\"\n",
        "print(f\"Text to predict: \\n{sample_pred_text}\")\n",
        "encoded_sample = encode_sample(sample_pred_text)\n",
        "prediction = model_predict(encoded_sample)\n",
        "start = time.time()\n",
        "print(f\"Prediction: {prediction[0][0]} [took {time.time() - start} s]\")"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Text to predict: \n",
            "What an awful movie! I expected much more from this director. The plot was ok, but the actors and graphics were terrible...\n",
            "Prediction: 0.009310523048043251 [took 0.0006847381591796875 s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hg5Dd7vrXY31",
        "colab_type": "code",
        "outputId": "d98d45a6-86c0-4dc7-9875-09ac6481c441",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "# Prediction on a neutral sample\n",
        "sample_pred_text = \"I would say this is an ok movie\"\n",
        "print(f\"Text to predict: \\n{sample_pred_text}\")\n",
        "encoded_sample = encode_sample(sample_pred_text)\n",
        "prediction = model_predict(encoded_sample)\n",
        "start = time.time()\n",
        "print(f\"Prediction: {prediction[0][0]} [took {time.time() - start} s]\")"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Text to predict: \n",
            "I would say this is an ok movie\n",
            "Prediction: 0.4155127704143524 [took 0.0007112026214599609 s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ea-jWIkDYJdn",
        "colab_type": "text"
      },
      "source": [
        "Let's now plot the training and evaluation graphs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "168KXafQXqs2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_graphs(history, metric):\n",
        "  plt.plot(history.history[metric])\n",
        "  plt.plot(history.history['val_'+metric], '')\n",
        "  plt.xlabel(\"Epochs\")\n",
        "  plt.ylabel(metric)\n",
        "  plt.legend([metric, 'val_'+metric])\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E-qtIRSAYUI4",
        "colab_type": "code",
        "outputId": "f68cea7b-51e5-457e-afaf-7f30781b2cc2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "plot_graphs(history, \"loss\")"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEGCAYAAABo25JHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3deXxU5b3H8c9vsu9A9kkI+04IYABBWZSqiASsVnGrxbW1Ku51a3u91t622mp7LVfrvlRFii2CUnDDBesSlrAvYtiSEEgChJCQ/bl/nAmZQIAAMzmZmd/79ZpXMmfOnPwykPM953nOeR4xxqCUUipwOewuQCmllL00CJRSKsBpECilVIDTIFBKqQCnQaCUUgEu2O4CTlZCQoLp3r273WUopZRPWb58eakxJrG113wuCLp3786yZcvsLkMppXyKiGw/1mvaNKSUUgFOg0AppQKcBoFSSgU4n+sjUEoFprq6OgoKCqiurra7lA4tPDyc9PR0QkJC2vweDQKllE8oKCggJiaG7t27IyJ2l9MhGWMoKyujoKCAHj16tPl92jSklPIJ1dXVxMfHawgch4gQHx9/0mdNGgRKKZ+hIXBip/IZBUwQrNyxjz8s2mh3GUop1eEETBCsLSznmU+/Z2PxAbtLUUr5qOjoaLtL8IqACYILM1MJcggLVhXZXYpSSnUoARMECdFhjOkVz4JVu9BZ2ZRSp8MYw3333cfgwYPJzMzk7bffBmDXrl2MGzeOoUOHMnjwYL744gsaGhqYMWPG4XWfeuopm6s/WkBdPpqT5eQXc1ezqqCcoV072V2OUuoU/feCdawv8mwz70BnLP+VM6hN6/7zn/8kLy+PVatWUVpayogRIxg3bhxvvvkmF1xwAQ8//DANDQ1UVVWRl5dHYWEha9euBWD//v0erdsTAuaMAOCCQSmEBjmYn6fNQ0qpU7d06VKuvPJKgoKCSE5OZvz48eTm5jJixAhefvllHnnkEdasWUNMTAw9e/YkPz+f22+/nUWLFhEbG2t3+UcJqDOCuIgQxvdL5L3VRTx80QCCHHopmlK+qK1H7u1t3LhxfP7557z//vvMmDGDu+++m2uvvZZVq1axePFinn32WebMmcNLL71kd6ktBNQZAcDULCd7Kmr4duteu0tRSvmosWPH8vbbb9PQ0EBJSQmff/45I0eOZPv27SQnJ3PTTTdx4403smLFCkpLS2lsbOTSSy/lscceY8WKFXaXf5SAOiMAmDggiYiQIBasLmJ0r3i7y1FK+aAf/vCHfPXVV2RlZSEiPP7446SkpPDqq6/yxBNPEBISQnR0NK+99hqFhYVcd911NDY2AvC73/3O5uqPJr52BU12drY53YlpZr61ki++K+Hbh39ASFDAnRQp5ZM2bNjAgAED7C7DJ7T2WYnIcmNMdmvre3UvKCKTRGSTiGwRkQeOsc7lIrJeRNaJyJverKdJTpaTfVV1LN1S2h4/TimlOjSvBYGIBAGzgAuBgcCVIjLwiHX6AA8CZxljBgF3eqsed+P6JhAbHswCvXpIKaW8ekYwEthijMk3xtQCs4FpR6xzEzDLGLMPwBizx4v1HBYWHMSkwSl8sH431XUN7fEjlVKqw/JmEKQBO92eF7iWuesL9BWRL0XkaxGZ1NqGRORmEVkmIstKSko8UtzUrDQO1tSzZGO7ZI9SSnVYdveUBgN9gAnAlcDzInLULb/GmOeMMdnGmOzExESP/OAze3YhITqUBau1eUgpFdi8GQSFQFe35+muZe4KgPnGmDpjzFZgM1YweF1wkIOLMlP5eMMeDtbUt8ePVEqpDsmbQZAL9BGRHiISClwBzD9inXlYZwOISAJWU1G+F2tqISfLSU19Ix+uL26vH6mUUh2O14LAGFMP3AYsBjYAc4wx60TkURGZ6lptMVAmIuuBJcB9xpgyb9V0pOEZnXHGhbNg1a72+pFKqQBxvLkLtm3bxuDBg9uxmuPz6p3FxpiFwMIjlv3a7XsD3O16tDuHQ8jJcvLi0q3sq6ylc1SoHWUopZStAm6IiSPlZDn52+f5LFpXzJUjM+wuRynVFv9+AIrXeHabKZlw4e+P+fIDDzxA165dufXWWwF45JFHCA4OZsmSJezbt4+6ujoee+wxpk078ir546uuruaWW25h2bJlBAcH8+STT3LOOeewbt06rrvuOmpra2lsbOSdd97B6XRy+eWXU1BQQENDA7/61a+YPn36af3aoEHAIGcsPROimJ9XpEGglDqm6dOnc+eddx4Ogjlz5rB48WJmzpxJbGwspaWlnHnmmUydOvWkJpCfNWsWIsKaNWvYuHEj559/Pps3b+bZZ5/ljjvu4Oqrr6a2tpaGhgYWLlyI0+nk/fffB6C8vNwjv1vAB4GIMCXLydOffMeeA9UkxYbbXZJS6kSOc+TuLcOGDWPPnj0UFRVRUlJC586dSUlJ4a677uLzzz/H4XBQWFjI7t27SUlJafN2ly5dyu233w5A//796datG5s3b2b06NH89re/paCggEsuuYQ+ffqQmZnJPffcw/3338+UKVMYO3asR343u+8j6BCmZqViDLy3WjuNlVLHdtlllzF37lzefvttpk+fzhtvvEFJSQnLly8nLy+P5ORkqqurPfKzrrrqKubPn09ERASTJ0/mk08+oW/fvqxYsYLMzEx++ctf8uijj3rkZ2kQAL2TYhiQGqs3lymljmv69OnMnj2buXPnctlll1FeXk5SUhIhISEsWbKE7du3n/Q2x44dyxtvvAHA5s2b2bFjB/369SM/P5+ePXsyc+ZMpk2bxurVqykqKiIyMpJrrrmG++67z2NzGwR801CTqVlO/rBoIzv3VtG1S6Td5SilOqBBgwZRUVFBWloaqampXH311eTk5JCZmUl2djb9+/c/6W3+/Oc/55ZbbiEzM5Pg4GBeeeUVwsLCmDNnDq+//johISGkpKTw0EMPkZuby3333YfD4SAkJIRnnnnGI79XQM5H0Jqde6sY+/gSfjGpHz+f0Nvj21dKnR6dj6DtOtR8BL6ka5dIhmd00ontlVIBR5uG3ORkOfnvBev5bncFfZJj7C5HKeXj1qxZw49//OMWy8LCwvjmm29sqqh1GgRuLhqSym/eW8+CVUXcfX4/u8tRSh3BGHNS1+jbLTMzk7y8vHb9mafS3K9NQ26SYsI5s2c8C1bvOqUPUynlPeHh4ZSVlenf5nEYYygrKyM8/OTuh9IzgiNMzXLywD/XsK7oAIPT4uwuRynlkp6eTkFBAZ6anMpfhYeHk56eflLv0SA4wqTBKfzq3bXMX1WkQaBUBxISEkKPHj3sLsMvadPQETpFhjKuTyLvrSqisVFPQZVS/k+DoBU5WU6KyqtZvmOf3aUopZTXaRC04ryByYSHOFiwSu8pUEr5Pw2CVkSFBTOxfzIL1+yivqHR7nKUUsqrNAiOIScrldKDtXyV324zZyqllC00CI5hQr8kosOCdcgJpZTf0yA4hvCQIM4flMyidcXU1DfYXY5SSnmNBsFxTM1yUlFdz2eb9AYWpZT/0iA4jrN6J9A5MoQFOnOZUsqPaRAcR0iQg8mZqXy0fjdVtfV2l6OUUl6hQXACOVlODtU18NGGPXaXopRSXqFBcAIju3chOTZMrx5SSvktDYITcDiEKUOcfLZ5D+VVdXaXo5RSHqdB0AZTs5zUNRgWryu2uxSllPI4rwaBiEwSkU0iskVEHmjl9RkiUiIiea7Hjd6s51QNSY+jW3wkC1Zr85BSyv94LQhEJAiYBVwIDASuFJGBraz6tjFmqOvxgrfqOR0iQs4QJ19uKaX0YI3d5SillEd584xgJLDFGJNvjKkFZgPTvPjzvCony0mjgYVr9J4CpZR/8WYQpAE73Z4XuJYd6VIRWS0ic0WkqxfrOS39UmLolxyjQ1MrpfyO3Z3FC4DuxpghwIfAq62tJCI3i8gyEVlm53ylOVmp5G7bR+H+Q7bVoJRSnubNICgE3I/w013LDjPGlBljmhrdXwDOaG1DxpjnjDHZxpjsxMRErxTbFjlZTgDe105jpZQf8WYQ5AJ9RKSHiIQCVwDz3VcQkVS3p1OBDV6s57R1i48iKz2O+do8pJTyI14LAmNMPXAbsBhrBz/HGLNORB4Vkamu1WaKyDoRWQXMBGZ4qx5PyclysrbwAPklB+0uRSmlPMKrfQTGmIXGmL7GmF7GmN+6lv3aGDPf9f2DxphBxpgsY8w5xpiN3qzHE6YMcSICC1bp1UNKKf9gd2exz0mJC2dk9y7MX1WIMcbucpRS6rRpEJyCnCwn35dUsmFXhd2lKKXUadMgOAUXDk4hyCE65IRSyi9oEJyC+Ogwzu6dwIJVRdo8pJTyeYEVBB7caedkOSnYd4iVO/d7bJtKKWWHwAmC7z6Cv18CddUe2dz5g5IJDXbohDVKKZ8XOEFQfwi+/wTev9sjZwax4SGc0y+R99fsoqFRm4eUUr4rcIJgQA6Mvx/y3oBvn/PIJqdmpVFSUcM3+WUe2Z5SStkhcIIAYPwD0G8yLHoQtn5+2ps7t38SUaFBevWQUsr7aiuthxcEVhA4HPDDv0F8b5jzE9i3/bQ2FxEaxHkDk/n32mJq6xs9VKRSKmAZA+WF8P0S+PZ5WHgfvDYNnhwE/+OEte945ccGe2WrHVl4LFzxJjx/Lsy+Gm74AEIjT3lzOVlO5uUVsXRLCef2T/ZgoUopv1V3CMq2QOl3rsdmKPsOSrdAndtRf1isdeDa/WxI6A3O4V4pJ/CCAKwP9EcvwhuXwbu3wo9eApFT2tTYPonERYSwYNUuDQKlVDNjoKLYtYPfbO3kSzdbO/7ynUDTRSYCnbpCQl/IGAMJfVyPvhCdfMr7ppMRmEEA0Oc8mPhr+Pi/ITULzr7zlDYTGuzgwsEpLFhVxKHaBiJCgzxcqFKqQ6urhr35bkf1bjv+WrdhaEKirIPQjFGQ8GPrSD+hL8T3gpAI++onkIMA4Oy7oHgNfPQIJA+GPj84pc1MzXIyO3cnSzbtYXJm6onfoJTyLcZAZUnzEX3pd81H+vt3gHHrI4xNt47oh17VfHQf3wdine1ydH8qAjsIRGDaX61/1LnXw81LrHQ+SaN6xpMYE8b8vCINAqX8RWMjbPkIvnkWCpdBdXnza8ER1tF92hkw5Aq3HX5vCI2yr+ZTFNhBANY/2hVvwHMT4K0r4aaPISzmpDYR5BAuykzlzW93UFFdR0x4iHdqVUp5X101rJkD//krlG6C2DQY/CNI7NfcnBObZl2F6Cc0CAA6d4PLXoHXfwj//ClM//tJ/yPnZDl55T/b+GDdbi49I907dSqlvKdqL+S+aN1wWrkHUjLhkudh0A8hyL8P7vwn0k5Xz/FwwW9h0/vw2R9O+u3DMzqR1ilCby5TytfszYf374UnB8KSx6yLR66dDz/9AoZc7vchAHpG0NKon8Gu1fDZ762jgQFT2vxWESEny8kLX+Szt7KWLlGhXixUKXXadn4L/3kaNiywdvZDLofRt0HSALsra3d6RuBOBKY8Zd208a+fwp4NJ/X2qVlO6hsN/16r8xkr1SE1NsD6+fDi+fDiedZQM2PvhjvXwLRZARkCoEFwtJBwq48gJBJmXwWH9rX5rQNSY+iVGKVDUyvV0dRWWUM2PH0GzPmxdaPXhY/DXeus+4liUuyu0FYaBK2JS7PCYP9OmHuDdRTRBiLC1Kw0vt22l+Jyz8x7oJQ6DQf3wCe/hacGwcJ7IbILXPYqzFwJo34KYdF2V9ghaBAcS8YouOiP8P3H1t3HbTQlKxVj4D3tNFbKPiWbYP7t8NRg+PwJyBgN1y2CGz+GQReDQ0cAcKedxcdzxgzYtQq+/AukDIHMH53wLb0SoxnkjGXB6l3cOLan92tUSlmMgW1LrQ7g7xZDcDgMuxrOvNW6+UsdkwbBiUz6g9Vp/O5t1p2DqVknfMvULCe/+/dGdpRVkRF/6iObKqXaoKEe1s+zAmBXHkQmwIQHYcSNEJVgd3U+QZuGTiQ4FC5/zWpbnH01VJae8C1TspwAek+BUt5UUwFfzYL/HQrv3AC1B2HKn+GutTDhAQ2Bk6BB0BbRSVbncWUJ/GMGNNQdd/W0ThFkd+vMglUaBEp53IEi+PDX1mQtix+CuK5wxVtway5kX2f7SJ6+SIOgrdKGQ85fYNsXsPjhE66ek+VkY3EFm3dXnHBdpVQbFK+xhoD5c6bVDNT7XLjxE7j+39B/sl+N/dPevPrJicgkEdkkIltE5IHjrHepiBgRyfZmPact6wqr4+nbv8HKvx931cmZqTgEPStQ6nQYY40A+trF8OzZ1l3AI26yLv+87BVIP8PuCv2C1zqLRSQImAWcBxQAuSIy3xiz/oj1YoA7gG+8VYtHnfco7FkH790Fif0hvfXsSowJY0yvBOavKuLu8/oiHXQccqU6nMZG629s21JY8br1fXQKTPwvq+knorPdFfodb141NBLYYozJBxCR2cA0YP0R6/0G+ANwnxdr8ZygYPjRy9aw1W9fAzd/esy7EqdmOfnFO6tZU1jOkPRO7VmlUr6joR6KV8P2L2H7f6xH9X7rtaRBMO3/rEu3g8PsrdOPeTMI0oCdbs8LgFHuK4jIcKCrMeZ9ETlmEIjIzcDNABkZGV4o9SRFdoEr3rTGKnn7Gpjxfqv/SS8YlMLD89YwP69Ig0CpJg11ULTS2vFv+xJ2fN08pWOXnjAgx5qsvdsY6NQB/t4DgG33EYiIA3gSmHGidY0xzwHPAWRnZ5sTrN4+UgbDxc/AP34C798DU58+ahq6uMgQxvdN4r3Vu3ho8gAcDm0eUgGovgYKl1s7/e1LrVE/66qs1xL6wZDLoNtZ1o4/1mlvrQHKm0FQCHR1e57uWtYkBhgMfOpqP08B5ovIVGPMMi/W5TmDLobie+GLP1o3mo286ahVcrJS+WjDbnK37WVUz3gbilSqndVWQUFu8xF/QS401FivJQ+GYde4dvxnQXSivbUqwLtBkAv0EZEeWAFwBXBV04vGmHLg8B0fIvIpcK/PhECTcx62Lmtb9AAkDYTuZ7V4+byByUSEBLFgdZEGgfJPNQdh59euI/4voXAFNNaBOKyhWUbcaP1dZIy2mlVVh+O1IDDG1IvIbcBiIAh4yRizTkQeBZYZY+Z762e3K4cDLn0enp8Ic661Oo87NZ8IRYYGM3FAEgvXFPNfOYMICdJrnZWPO7Tfatff7trxF+WBaQAJAucwGP1z6Ha2NXBjeJzd1ao2EGM6RpN7W2VnZ5tlyzrgSUPJZnhhInTpYY1yGNo8xtAH64q5+fXlvHr9SMb31VNh5WOq9rqu5vnSuqSzeA1gICgU0s6wmni6nwXpI3VY5w5MRJYbY1q93l0HnfOUxL7WRNdvXQEL7oBLnjvceTy+XyIx4cHMzyvSIFAdX9Ve2PpZc1PPHtcV38HhkD7CGsen21nWPTQ6nINf0CDwpH6T4NyH4ZPHIHUIjLkdgLDgICYNSmHR2mKq6wYTHqJjoasOpux72PRv67HjK6upJyTKat4ZfKl1OadzmF7L76faFAQicgfwMlABvAAMAx4wxnzgxdp809h7Yddqa1CspIHQeyJgjT30j+UFfLqphEmDA3taPNUBNDZAwTLYtNDa+ZduspYnD7bm8O1zATiHWpO6K7/X1jOC640xfxGRC4DOwI+B1wENgiOJWPcXvPg9zL0ebl4CXXoyplc88VGhLFhdpEGg7FFbCd8vsXb8mxdBVSk4gq2j/RE3QN9J0Lmb3VUqG7Q1CJruhJoMvO66+kfvjjqWsGi44g1rGIrZV8MNHxIcFs3kzFT+sXwnlTX1RIVpq5xqBwd2WTv9Tf+G/E+t6/nD46DP+dDvQuj9A72yR7U5CJaLyAdAD+BB10Bxjd4ryw906WGNjvj3S2Dez+Cy18jJcvL619v5aMNupg1Ns7tC5Y+Mgd3rXO39C6FohbW8UzfrqL/fhdb1/Nrko9y0NQhuAIYC+caYKhHpAlznvbL8RK9z4LzfwAcPwxd/InvsvaTGhTN3eQFTs5w6IqnyjPpa6+qeps7e8h2AWFf1TPw19JtsjZSr/9/UMbQ1CEYDecaYShG5BhgO/MV7ZfmR0bdaIysueQxHymCuHd2XPyzayF8/2cLtE/vYXZ3yVYf2wXcfWUf9Wz6CmgMQHGEdfIy/z+rsjUm2u0rlI9oaBM8AWSKSBdyDdeXQa8B4bxXmN0Ssmc1KNsE7N/GzGz9iy550/vThZhJiwrhypI6uqNpo79bmJp/t/7Eu8YxKssa86jcZeoxvcSOjUm3V1iCoN8YYEZkG/NUY86KI3ODNwvxKSITVefy38cjsq/j9DR+zt7KGh/+1hi5RoVwwSK8iUq1obLRG7Wy6xLNkg7U8aSCcfae183cO1yka1WlraxBUiMiDWJeNjnUNIa29TScjLh2mvw6v5hDy2hT+1vciHiuP4/63aul0/TgdkE5Zaqusq3s2LYTNi6FyjzWGT/ez4IyfWJd4dulhd5XKz7RprCERScEaOTTXGPOFiGQAE4wxr3m7wCN12LGG2mrtO7D0KSheCxgacLCeHqQNOYcu/cdDxpkQnWR3lcrTGhut6/YP7rYeFbubvz+4Gw7ugYpiKC+wLvEMi4U+51lH/b0n6vSM6rQdb6yhNg86JyLJwAjX02+NMXs8VN9J8fkgaFJdDgW5HNj8BVtyP2Sg+Y5waq3X4ntbgZAx2np06alXfHRUtVVwsNjakbfYwR+xrLLEatM/UlisFfzRKdbXuHRrx58xBoJD2//3UX7rtINARC4HngA+xbq5bCxwnzFmrgfrbBO/CQI3m4oruPLZzxkTsZPHR1YRWZxrjfdyaJ+1QlSSFQzdxlhfkzOtuZOVdzQ2QlWZa2fuvnPf07yDr3B9bZpi0Z0EuXbubjv46GRrbusjl2nnrmonngiCVcB5TWcBIpIIfGSMyfJopW3gj0EAkLttL9e88A39U2N588ZRRIU4oHSzFQhNj/07rJVDo61RIDNGW8GQng2hUfb+Ar7GGDhQaI2lvyvPaqqrKGrj0Xty8yOm6fumHXyyNfmKQwcWVB2LJ4JgjTEm0+25A1jlvqy9+GsQAHy4fjc/fX0ZZ/dJ5MWfZB89iU15oTUT1PavrIlBdlv9DDiCrakym4IhYzREJbT6MwKS+06/aKW14y/Ks9rswTqCT+hrTSh0eAefcsROP0nDVvk0TwTBE8AQ4C3XounAamPM/R6rso38OQgA3s7dwf3vrOGHw9L402VZx5/wvrrcmgh8hysYCpY1zw0b36dlc1LnHoHRz2CM1eHatLNvbaefNABSh1qja6YOhZTBOq6+8nue6iy+FGiakPcLY8y/PFTfSfH3IACYtWQLTyzexE1je/DwRQPb/sb6Gmund7g56Wuo3m+9Fp3i1gF9pjXcsK/3Mxy50y9aCbtW6U5fqVZ4ZIYyY8w7wDseq0od088n9KKkoobnv9hKYkwYN4/r1bY3BodZE4lkjALutDo9SzdZd6Hu+Np6rJ9nrRsaAwm9ISrR9UiwvkYmtHweldAxJiMxBsp3tjzK35VndepC806/3yTXjn8YJA/Snb5SbXDcIBCRCqC1UwYBjDEm1itVBTgR4ddTBlJysIb/WbiRhOgwLhmefvIbcjisnWPSAGvkSbCOoHd8bZ0x7NtuXfmye73VQdrUrHSksFi3YHCFw1GB4fo+osvpn2m0aac/0BpJU3f6Sp224/7FGmNi2qsQ1ZLDITx5eRb7q2r5xdzVdI4K5Zx+HrjRLC4dMn9kPdwZAzUVViBUlVlfDz9Km7/u3Wr1S1SVgmltJHKxrpppCofI+KPPOg5/nwDhnVw7/ZUtd/yH9ro+iGBIHOC20x/u2umHn/5noZQCTqKPoKMIhD4CdxXVdVz5/Nd8v6eSN28axbCMDnKHaWOjdZ9DVWkrgdH0vdvzpr6KI4mjOVCadvrOpjb9YbrTV8pDPNJZ3FEEWhAAlFTU8KNn/8OBQ3X842dj6J0UbXdJJ6+h7ogzjdLms49Yp+70lfIyDQI/sL2skkuf+YqwYAfv3DKGlDjdYSql2u54QaDj1/qIbvFRvHLdCMoP1fGTl76lvKrO7pKUUn5Cg8CHDE6L47kfn8HW0kpufC2X6rpWhkFQSqmTpEHgY8b0TuDJ6Vks276P295cSX1Da1fuKKVU22kQ+KApQ5w8kjOIjzbs5pfz1uJr/TxKqY7Fq0EgIpNEZJOIbBGRB1p5/WciskZE8kRkqYicxHgKge0nY7pz+7m9mZ27kyc/3Gx3OUopH+a1wWZEJAiYBZwHFAC5IjLfGLPebbU3jTHPutafCjwJTPJWTf7m7vP6UlJRw9OfbCEhOoyfjOlud0lKKR/kzVHHRgJbjDH5ACIyG5gGHA4CY8wBt/WjaH04C3UMIsJjFw+mrLKWRxasIz46lClDnHaXpZTyMd5sGkoDdro9L3Ata0FEbhWR74HHgZmtbUhEbhaRZSKyrKSkxCvF+qrgIAdPXzmM7G6duevtPL7cUmp3SUopH2N7Z7ExZpYxphdwP/DLY6zznDEm2xiTnZiY2L4F+oDwkCBeuHYEPROi+enry1lbWG53SUopH+LNICgEuro9T3ctO5bZwMVerMevxUWG8Or1I4mLCGHGy7lsL6u0uySllI/wZhDkAn1EpIeIhAJXAPPdVxCRPm5PLwK+82I9fi8lLpxXrx9JQ2Mj1770LSUVxxhWWiml3HgtCIwx9cBtwGJgAzDHGLNORB51XSEEcJuIrBORPOBu4CfeqidQ9E6K5qUZI9hzoIYZL39LRbUORaGUOj4ddM5PLdm0h5teXcaonl14acYIwoKD7C5JKWUjHXQuAJ3TL4k/XDqEL7eUcfecVTQ2+lbgK6Xaj4/PXq6O59Iz0imrdE13GRXKI1MHISJ2l6WU6mA0CPzczeN6UVJRw/NfbCUpNpxbz+ltd0lKqQ5GgyAAPHjhAEoP1vLE4k3ER4VyxcgMu0tSSnUgGgQBwOEQHv/REPZW1vLQv9YQHx3GeQOT7S5LKdVBaGdxgAgJcvB/Vw8nM70Tt725gtxte+0uSSnVQWgQBJCosGBenjGCtM4R3PBKLuuLDpz4TUopv6dBEGC6RIXy2vUjiQgN4iI84PMAABB9SURBVOJZX/LE4o1U1tTbXZZSykYaBAEovXMkC247mylDUpm15Hsm/ukz3s0r1JnOlApQGgQBKik2nCenD+WdW0aTEBPKHbPzmP63r7W5SKkApEEQ4M7o1oV3bz2b//lhJt/tqWDK01/wq3lr2V9Va3dpSql2okGgCHIIV43K4NN7z+Ha0d1545vtTPjjp/z96+006NAUSvk9DQJ1WFxkCI9MHcTCO8bSLzmGX85bS87TS/VSU6X8nAaBOkr/lFhm33wmf71qGPuqarns2a+4c/ZKisur7S5NKeUFGgSqVSLClCFOPr5nPLef25uFa4s590+f8n+fbqGmvsHu8pRSHqRBoI4rMjSYe87vx0d3jees3gk8vmgTFzz1OZ9s3G13aUopD9EgUG2SER/J89dm88p1I3A4hOtfWcb1r+SytVTnRlbK12kQqJMyoV8Si+4Yx0OT+/NNfhkXPPU5f1ikdycr5cs0CNRJCw12cPO4Xiy5dwJTslJ55tPvOfdPn+rdyUr5KA0CdcqSYsN58vKhvHPLGJJiwrljdh6X/+0r1hWV212aUuokaBCo03ZGt87Mu/Usfn9JJt+XVJLz9FJ+OW8N+yr17mSlfIEGgfKIIIdwxcgMltwzgWtHd+etb3cy4Y+f8vpX2/TuZKU6OA0C5VFNdye/P/NsBqTG8Kt31zHl6aV8k19md2lKqWPQIFBe0T8llrduOpNZVw2nvKqW6c99zcy3VrKr/JDdpSmljqBBoLxGRLhoSCof3zOBmef2ZtG6Yib+6TNmLdG7k5XqSDQIlNdFhAZxt+vu5LN7J/DE4k2c/9TnfLxB705WqiPQIFDtJiM+kueuzea160cS5BBueHUZ1738Lcu379X7D5SykfjaH2B2drZZtmyZ3WWo01Rb38hrX23jzx99x8Gaerp2iWBaVhrThjrpkxxjd3lK+R0RWW6MyW71NW8GgYhMAv4CBAEvGGN+f8TrdwM3AvVACXC9MWb78bapQeBfKqrrWLxuN+/mFfLlllIaDQxMjeXiYU5yspykxkXYXaJSfsGWIBCRIGAzcB5QAOQCVxpj1rutcw7wjTGmSkRuASYYY6Yfb7saBP5rT0U1763axburili1cz8iMKpHF6YNTWPy4FTiIkPsLlEpn2VXEIwGHjHGXOB6/iCAMeZ3x1h/GPBXY8xZx9uuBkFg2Fpayfy8It7NKyS/tJLQIAcT+iUybWgaEwckER4SZHeJSvmU4wVBsBd/bhqw0+15ATDqOOvfAPy7tRdE5GbgZoCMjAxP1ac6sB4JUdzxgz7MnNibtYUHmJdXyIJVRXywfjfRYcFcMCiFi4c5Gd0znuAgveZBqdPhzSBoMxG5BsgGxrf2ujHmOeA5sM4I2rE0ZTMRITM9jsz0OB6aPICv88uYt7KQRWuLeWdFAQnRYeRkpXLx0DSGpMchInaXrJTP8WYQFAJd3Z6nu5a1ICI/AB4GxhtjarxYj/JxQQ7hrN4JnNU7gd9cPJglG/fwbl4Rb3y9g5e/3EaPhCimZjmZNtRJz8Rou8tVymd4s48gGKuzeCJWAOQCVxlj1rmtMwyYC0wyxnzXlu1qH4E6UvmhOhavLWZeXiFf5ZdhDAxJj2Pa0DRyhqSSFBtud4lK2c7Oy0cnA3/Gunz0JWPMb0XkUWCZMWa+iHwEZAK7XG/ZYYyZerxtahCo4ykur+a91UXMyytkbeEBHAJjeiUwdaiTSYNTiA3XK49UYLItCLxBg0C11ZY9B5mfV8i7q4rYXlZFaLCDif2TmDY0jXP6JxIWrFceqcChQaACmjGGvJ37eTeviPdWF1F6sJaY8GAmD05l2jAno3rEE+TQTmbl3zQIlHKpb2jky+/LeDevkMVri6msbSAlNpwpQ1KZ0C+J4d06ERnaIS6mU8qjNAiUasWh2gY+3ribeSuL+GzzHuoaDMEOIatrJ87s2YUze8ZzRrfOGgzKL2gQKHUCB2vqWb59H1/nl/F1fhmrC8ppaNRgUP5Dg0Cpk6TBoPyNBoFSp6mypp5lGgzKh2kQKOVhxwuGIelxnNkz/nAwRIVpMCj7aRAo5WWVrTQl1WswqA5Eg0CpdqbBoDoaDQKlbHaiYBjlCoZsDQblJRoESnUwbT1jyO6unc/KMzQIlOrgjhcMelWS8gQNAqV8zLGuSgoJErLSO7XoY4gI1cHz1IlpECjl4w7W1LNs216+zt/L1/llrClsDoahXVsGg87nrFqjQaCUnzlYU0/utr2uM4a9rCnYT6OB0CCHKxispqThGgzKRYNAKT9XUV3Hsm3NTUlrCsubgyGj6YyhC8MzNBgClQaBUgHmQHVdi6aktU3BEOxgmFtT0rCMThoMAUKDQKkAd6C6jtytzU1Ja4vKMa5gGJ7RMhh05jb/pEGglGqh/JBbMGwtY13RAYyBsGAHwzM6c0a3zvRMjKJ7QhQ94qPoHBVqd8nqNB0vCPSCZKUCUFxECD8YmMwPBiYDUF5Vx7eHO5/L+L9Pt9BoWq5vhUKk9dX16J4QRWx4iE2/hfIUPSNQSh2lpr6BnXsPsa20km1llWxt+lpSSVF5dYt146NC6Z4QRff4KHokRLp9H6XDZXQgekaglDopYcFB9E6KpndS9FGvVdc1sL2s6nA4bCu1gmLplhLeWVHTYt3EmDDr7CHe1czkFhTaSd1xaBAopU5KeEgQ/VJi6JcSc9RrVbX1bCutOnwWsbXUCoqPN+6m9GBti3VT48Lp7h4QrrOIjPhI7bBuZxoESimPiQwNZqAzloHO2KNeq6iuY1tpFVtdZxHbSivZWlbJorW72FdVd3g9h4CzU8ThfogeCVH0TIymZ0IUaZ0icDikPX+lgKBBoJRqFzHhIWSmx5GZHnfUa+VVdWwtq2Rr6UG2llYd7pv414pCKmrqD68XFuxwBUMUPROi6ZnYHBRxEdppfao0CJRStouLDGFoZCeGdu3UYrkxhpKDNeSXWM1M+SUHyS+pZMOuChav202D26VNCdGhh8PBPSi6dokkJMjR3r+ST9EgUEp1WCJCUkw4STHhnNkzvsVrtfWN7Nhb1SIg8ksP8uH63ZRVNvdHBDuEjC6RroCwmph6JlohER8Viog2NWkQKKV8Umiww+3KpuQWr5VX1ZFf2hwO+SWV5JdU8vl3pdTWNx5eLzY8mB6J0fRqam5yBUSgXdXk1SAQkUnAX4Ag4AVjzO+PeH0c8GdgCHCFMWauN+tRSgWGuMgQhmV0ZlhG5xbLGxoNRfsPkX/EWcRX+WX8c2Xh4fVEIM3VYd0rMZr0zhGkxkWQEhdOalw4iTFhftXc5LUgEJEgYBZwHlAA5IrIfGPMerfVdgAzgHu9VYdSSjUJcghdu0TStUsk4/smtnitqrbe1cxU2eJMYu7yAg66dViDFRSJ0WGkxoWTEhdOSmw4KXERh5+nxoWTHBvuM2cV3jwjGAlsMcbkA4jIbGAacDgIjDHbXK81trYBpZRqL5GhwQxyxjHI2fKqJmMMBw7Vs+vAIYrLqykur2ZX09cD1WwrreKr78s4UF1/1DY7R4a0CAgrMKygaAqLmA4wRIc3gyAN2On2vAAYdSobEpGbgZsBMjIyTr8ypZRqIxEhLjKEuMgQ+qccfX9Ek8qaeooPNAfF7gPV7Co/dPj5qp37W3RiN4kOCz4cDk1B0fzcCpFOkSFe7dT2ic5iY8xzwHNgjTVkczlKKXWUqLBgeiVG0yvx6GE5mtTUN7DnQA27yluGhBUa1Xy3u5Q9FdUtBvwD6/6JlLhw7j6vL9OGpnm8dm8GQSHQ1e15umuZUkoFpLDgoMN9FMdS39BIyUErLHY3NUO5giIhOswrdXkzCHKBPiLSAysArgCu8uLPU0opnxcc5CA1zrpKqb147fonY0w9cBuwGNgAzDHGrBORR0VkKoCIjBCRAuAy4G8iss5b9SillGqdV/sIjDELgYVHLPu12/e5WE1GSimlbOI/d0QopZQ6JRoESikV4DQIlFIqwGkQKKVUgNMgUEqpAKdBoJRSAU6M8a0RG0SkBNh+im9PAEo9WI6v08+jJf08muln0ZI/fB7djDGJrb3gc0FwOkRkmTEm2+46Ogr9PFrSz6OZfhYt+fvnoU1DSikV4DQIlFIqwAVaEDxndwEdjH4eLenn0Uw/i5b8+vMIqD4CpZRSRwu0MwKllFJH0CBQSqkAFzBBICKTRGSTiGwRkQfsrscuItJVRJaIyHoRWScid9hdU0cgIkEislJE3rO7FruJSCcRmSsiG0Vkg4iMtrsmu4jIXa6/k7Ui8paIhNtdkzcERBCISBAwC7gQGAhcKSID7a3KNvXAPcaYgcCZwK0B/Fm4uwNrAiUFfwEWGWP6A1kE6OciImnATCDbGDMYCMKaadHvBEQQACOBLcaYfGNMLTAbmGZzTbYwxuwyxqxwfV+B9Ufu+dmwfYiIpAMXAS/YXYvdRCQOGAe8CGCMqTXG7Le3KlsFAxEiEgxEAkU21+MVgRIEacBOt+cFBPjOD0BEugPDgG/srcR2fwZ+ATTaXUgH0AMoAV52NZW9ICJRdhdlB2NMIfBHYAewCyg3xnxgb1XeEShBoI4gItHAO8CdxpgDdtdjFxGZAuwxxiy3u5YOIhgYDjxjjBkGVAIB2acmIp2xWg56AE4gSkSusbcq7wiUICgEuro9T3ctC0giEoIVAm8YY/5pdz02OwuYKiLbsJoMzxWRv9tbkq0KgAJjTNNZ4lysYAhEPwC2GmNKjDF1wD+BMTbX5BWBEgS5QB8R6SEioVgdPvNtrskWIiJY7b8bjDFP2l2P3YwxDxpj0o0x3bH+X3xijPHLo762MMYUAztFpJ9r0URgvY0l2WkHcKaIRLr+bibipx3nwXYX0B6MMfUichuwGKvn/yVjzDqby7LLWcCPgTUikuda9pAxZqGNNamO5XbgDddBUz5wnc312MIY842IzAVWYF1ttxI/HWpCh5hQSqkAFyhNQ0oppY5Bg0AppQKcBoFSSgU4DQKllApwGgRKKRXgNAiUchGRBhHJc3t47I5aEekuIms9tT2lPCkg7iNQqo0OGWOG2l2EUu1NzwiUOgER2SYij4vIGhH5VkR6u5Z3F5FPRGS1iHwsIhmu5cki8i8RWeV6NA1LECQiz7vGt/9ARCJc6890zQ+xWkRm2/RrqgCmQaBUs4gjmoamu71WbozJBP6KNVopwNPAq8aYIcAbwP+6lv8v8JkxJgtrnJ6mu9j7ALOMMYOA/cClruUPAMNc2/mZt345pY5F7yxWykVEDhpjoltZvg041xiT7xqwr9gYEy8ipUCqMabOtXyXMSZBREqAdGNMjds2ugMfGmP6uJ7fD4QYYx4TkUXAQWAeMM8Yc9DLv6pSLegZgVJtY47x/cmocfu+geY+uouwZtAbDuS6JkFRqt1oECjVNtPdvn7l+v4/NE9deDXwhev7j4Fb4PBcyHHH2qiIOICuxpglwP1AHHDUWYlS3qRHHko1i3AbkRWseXubLiHtLCKrsY7qr3Qtux1rJq/7sGb1ahql8w7gORG5AevI/xasGa5aEwT83RUWAvxvgE8NqWygfQRKnYCrjyDbGFNqdy1KeYM2DSmlVIDTMwKllApwekaglFIBToNAKaUCnAaBUkoFOA0CpZQKcBoESikV4P4fteUe9nIeDSkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nR1LBfKpYVaj",
        "colab_type": "code",
        "outputId": "808bfc4a-a520-46b9-979f-533a6f9ba693",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "plot_graphs(history, \"accuracy\")"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EOq_2PayYavA",
        "colab_type": "text"
      },
      "source": [
        "# Stack two or more LSTM layers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7tM5QlGWA38H",
        "colab_type": "text"
      },
      "source": [
        "We can set up the LSTM layers so that they return the full sequences using the `return_sequences` options."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OZnIvAVTYX8-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "outputId": "0b1acd12-7d3c-4b8b-f26b-f91ab33dca67"
      },
      "source": [
        "model_v2 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Embedding(encoder.vocab_size, 64, mask_zero=True),\n",
        "  tf.keras.layers.LSTM(units=64, return_sequences=True),\n",
        "  tf.keras.layers.LSTM(units=32),\n",
        "  tf.keras.layers.Dense(64, activation=\"relu\"),\n",
        "  tf.keras.layers.Dropout(0.5),\n",
        "  tf.keras.layers.Dense(1, activation=\"sigmoid\"),\n",
        "])\n",
        "model_v2.summary()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_2 (Embedding)      (None, None, 64)          523840    \n",
            "_________________________________________________________________\n",
            "lstm_3 (LSTM)                (None, None, 64)          33024     \n",
            "_________________________________________________________________\n",
            "lstm_4 (LSTM)                (None, 32)                12416     \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 64)                2112      \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 571,457\n",
            "Trainable params: 571,457\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2B7NIUL_B7O8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_v2.compile(\n",
        "    loss=tf.keras.losses.BinaryCrossentropy(from_logits=False),\n",
        "    optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),\n",
        "    metrics=[\n",
        "      tf.keras.metrics.Precision(),\n",
        "      tf.keras.metrics.Recall()\n",
        "    ],\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oCDYtEMVD31X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "outputId": "7c52079d-5ce1-49dc-c097-9fd52c22c911"
      },
      "source": [
        "history = model_v2.fit(\n",
        "    train_dataset,\n",
        "    epochs=10,\n",
        "    validation_data=test_dataset,\n",
        "    validation_steps=30\n",
        ")"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "391/391 [==============================] - 122s 311ms/step - loss: 0.5434 - precision_1: 0.7509 - recall_1: 0.6871 - val_loss: 0.4203 - val_precision_1: 0.9049 - val_recall_1: 0.7121\n",
            "Epoch 2/10\n",
            "391/391 [==============================] - 117s 299ms/step - loss: 0.3067 - precision_1: 0.8832 - recall_1: 0.8877 - val_loss: 0.3318 - val_precision_1: 0.8848 - val_recall_1: 0.8576\n",
            "Epoch 3/10\n",
            "391/391 [==============================] - 116s 297ms/step - loss: 0.2376 - precision_1: 0.9168 - recall_1: 0.9184 - val_loss: 0.3349 - val_precision_1: 0.8927 - val_recall_1: 0.8350\n",
            "Epoch 4/10\n",
            "391/391 [==============================] - 119s 305ms/step - loss: 0.1950 - precision_1: 0.9352 - recall_1: 0.9370 - val_loss: 0.3557 - val_precision_1: 0.8805 - val_recall_1: 0.8535\n",
            "Epoch 5/10\n",
            "391/391 [==============================] - 118s 302ms/step - loss: 0.1733 - precision_1: 0.9450 - recall_1: 0.9460 - val_loss: 0.3794 - val_precision_1: 0.8678 - val_recall_1: 0.8607\n",
            "Epoch 6/10\n",
            "391/391 [==============================] - 117s 300ms/step - loss: 0.1558 - precision_1: 0.9503 - recall_1: 0.9525 - val_loss: 0.4085 - val_precision_1: 0.8794 - val_recall_1: 0.8371\n",
            "Epoch 7/10\n",
            "391/391 [==============================] - 120s 307ms/step - loss: 0.1460 - precision_1: 0.9543 - recall_1: 0.9565 - val_loss: 0.4320 - val_precision_1: 0.8639 - val_recall_1: 0.8453\n",
            "Epoch 8/10\n",
            "391/391 [==============================] - 118s 301ms/step - loss: 0.1277 - precision_1: 0.9625 - recall_1: 0.9630 - val_loss: 0.4445 - val_precision_1: 0.8641 - val_recall_1: 0.8402\n",
            "Epoch 9/10\n",
            "391/391 [==============================] - 117s 300ms/step - loss: 0.1183 - precision_1: 0.9677 - recall_1: 0.9644 - val_loss: 0.4612 - val_precision_1: 0.8841 - val_recall_1: 0.8053\n",
            "Epoch 10/10\n",
            "391/391 [==============================] - 120s 307ms/step - loss: 0.1090 - precision_1: 0.9703 - recall_1: 0.9698 - val_loss: 0.4926 - val_precision_1: 0.8763 - val_recall_1: 0.8053\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RIuXo5gAEEm0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "79e7c02f-e77a-401d-b6fd-ac4c4193ba02"
      },
      "source": [
        "test_loss, test_precision, test_recall = model_v2.evaluate(test_dataset)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "391/391 [==============================] - 61s 155ms/step - loss: 0.4928 - precision_1: 0.8772 - recall_1: 0.8017\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FN8IusypKgkK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "2339f121-3100-4524-9b96-880438a1cea0"
      },
      "source": [
        "print(f\"Test Loss: {test_loss}\")\n",
        "print(f\"Test Precision: {test_precision}\")\n",
        "print(f\"Test Recall: {test_recall}\")"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test Loss: 0.49279069900512695\n",
            "Test Precision: 0.8771883845329285\n",
            "Test Recall: 0.8016800284385681\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-u50iPrgE2nv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "@tf.function\n",
        "def model_v2_predict(encoded_sample):\n",
        "  return model_v2(tf.expand_dims(encoded_sample, axis=0))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VG10dt0SKsB_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "92b2e599-5e04-49ee-9327-be9c2f9b00c8"
      },
      "source": [
        "# Predict on a positive sample\n",
        "sample_pred_text = \"bla bla bla bla bla\"\n",
        "print(f\"Text to predict: \\n{sample_pred_text}\")\n",
        "encoded_sample = encode_sample(sample_pred_text)\n",
        "prediction_v2 = model_v2_predict(encoded_sample)\n",
        "start = time.time()\n",
        "print(f\"Prediction: {prediction[0][0]} [took {time.time() - start} s]\")"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Text to predict: \n",
            "bla bla bla bla bla\n",
            "Prediction: 0.4155127704143524 [took 0.0008237361907958984 s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kKEFNGIbOkKY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "725e9ad5-9b73-49d1-a345-d4a68101b029"
      },
      "source": [
        "plot_graphs(history, \"loss\")"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G5M6k4UxO_FK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "26d173cc-1164-4169-95c6-01add191baf2"
      },
      "source": [
        "plot_graphs(history, \"precision_1\")"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CBQvCO00PBAa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "d2a6c9f0-1622-452d-c376-697025a79ea0"
      },
      "source": [
        "plot_graphs(history, \"recall_1\")"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bSgRGO-ePM1o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}