07_RNN_PlutonAI.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "colab": {
      "name": "07_RNN_PlutonAI.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "OvHhG7KfgjWe"
      ],
      "include_colab_link": true
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "93460e7cda2643329f7663fdd737ae6b": {
          "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_2d4f9f0c4c9a41c2a1c9c13a153129ac",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_39b51daf9234404587848f6bc3684141",
              "IPY_MODEL_e300e99256164c9ebb29f6cdac99bd65"
            ]
          }
        },
        "2d4f9f0c4c9a41c2a1c9c13a153129ac": {
          "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
          }
        },
        "39b51daf9234404587848f6bc3684141": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_cc82c477fe164829b8ced69f4ea94cd9",
            "_dom_classes": [],
            "description": "Training: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 40000,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 40000,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_d5b318fb2f794597a623fb6aff31b09a"
          }
        },
        "e300e99256164c9ebb29f6cdac99bd65": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_61e1643ee5514bb39266d35c4328a423",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 40000/40000 [03:02&lt;00:00, 218.76it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_e4e95e8c125d4ffaae286fcc6e0781fd"
          }
        },
        "cc82c477fe164829b8ced69f4ea94cd9": {
          "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"
          }
        },
        "d5b318fb2f794597a623fb6aff31b09a": {
          "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
          }
        },
        "61e1643ee5514bb39266d35c4328a423": {
          "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"
          }
        },
        "e4e95e8c125d4ffaae286fcc6e0781fd": {
          "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
          }
        },
        "b5a22500676142f896d2431a68941821": {
          "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_f7cb96335e354464b534e90e989662fe",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_47bc77d5bf7946b792cce35c4c794240",
              "IPY_MODEL_7be9e18a8d0a4c1183a1658133d415f2"
            ]
          }
        },
        "f7cb96335e354464b534e90e989662fe": {
          "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
          }
        },
        "47bc77d5bf7946b792cce35c4c794240": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_20e874a413194463be339f14242675e9",
            "_dom_classes": [],
            "description": "Testing: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 1000,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 1000,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_2e5eec716e294a86ad610745e97931f3"
          }
        },
        "7be9e18a8d0a4c1183a1658133d415f2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_5cb6e878aaad4fc588b1d8c92ccfea88",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 1000/1000 [00:47&lt;00:00, 20.93it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_c353fd37d84a41e7a8d28faf259d2346"
          }
        },
        "20e874a413194463be339f14242675e9": {
          "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"
          }
        },
        "2e5eec716e294a86ad610745e97931f3": {
          "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
          }
        },
        "5cb6e878aaad4fc588b1d8c92ccfea88": {
          "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"
          }
        },
        "c353fd37d84a41e7a8d28faf259d2346": {
          "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
          }
        },
        "e08ea0f519ef4fb39b30ead3a788b52d": {
          "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_a88bacf847fe4837a7849158de1906f5",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_cf97128cb05847f1b63567a1300fe69a",
              "IPY_MODEL_3369a124e221407a85faa0317ac0027a"
            ]
          }
        },
        "a88bacf847fe4837a7849158de1906f5": {
          "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
          }
        },
        "cf97128cb05847f1b63567a1300fe69a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_315d680e702c4599a3faea0692d0e200",
            "_dom_classes": [],
            "description": "Testing: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 1000,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 1000,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_54764296b82643eb8d11c2a9ece404c6"
          }
        },
        "3369a124e221407a85faa0317ac0027a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_12df4b1c70b84df1a493563d333f7da6",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 1000/1000 [00:44&lt;00:00, 22.44it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_0e8977a3fa264ac9b7481dcb107ab3b0"
          }
        },
        "315d680e702c4599a3faea0692d0e200": {
          "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"
          }
        },
        "54764296b82643eb8d11c2a9ece404c6": {
          "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
          }
        },
        "12df4b1c70b84df1a493563d333f7da6": {
          "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"
          }
        },
        "0e8977a3fa264ac9b7481dcb107ab3b0": {
          "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
          }
        },
        "4896476637b84c1eb05cef704504749e": {
          "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_839f7f79e3024f298fe17156f309e6bb",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_92537d3d9c0a40aaa9bc806f82a49291",
              "IPY_MODEL_39bf4106ee09453498f95cfdaf9a369a"
            ]
          }
        },
        "839f7f79e3024f298fe17156f309e6bb": {
          "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
          }
        },
        "92537d3d9c0a40aaa9bc806f82a49291": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_3020aaff0d244356ae311b27b3d521d5",
            "_dom_classes": [],
            "description": "Testing: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 1000,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 1000,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_32a05112ffff42e7836eefe002694387"
          }
        },
        "39bf4106ee09453498f95cfdaf9a369a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_18e58d931b804b6fb2cb13f4a56dd5af",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 1000/1000 [00:41&lt;00:00, 23.85it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_f010d4abe922498d947519265691558d"
          }
        },
        "3020aaff0d244356ae311b27b3d521d5": {
          "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"
          }
        },
        "32a05112ffff42e7836eefe002694387": {
          "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
          }
        },
        "18e58d931b804b6fb2cb13f4a56dd5af": {
          "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"
          }
        },
        "f010d4abe922498d947519265691558d": {
          "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
          }
        },
        "441577e681634603a64bdaa94f852388": {
          "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_9e34a37b7ea54acfb9e9edb5291a27c9",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_f1713e470bc34b2495663799568fe7a5",
              "IPY_MODEL_9cddda4e6d1840fcbb04a9414858e00b"
            ]
          }
        },
        "9e34a37b7ea54acfb9e9edb5291a27c9": {
          "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
          }
        },
        "f1713e470bc34b2495663799568fe7a5": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_3005e8a4f9cf4d4d954ecc17d6439266",
            "_dom_classes": [],
            "description": "Testing: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 1000,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 1000,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_ef0776321c28441faacb4f196d6f37ff"
          }
        },
        "9cddda4e6d1840fcbb04a9414858e00b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_a7aec94d80114414ba79df60694f8813",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 1000/1000 [00:35&lt;00:00, 27.80it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_a633a5e9b1cf48a7851d891871de635e"
          }
        },
        "3005e8a4f9cf4d4d954ecc17d6439266": {
          "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"
          }
        },
        "ef0776321c28441faacb4f196d6f37ff": {
          "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
          }
        },
        "a7aec94d80114414ba79df60694f8813": {
          "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"
          }
        },
        "a633a5e9b1cf48a7851d891871de635e": {
          "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
          }
        },
        "00fdd080cc6440d9b2f74abde3a8a57e": {
          "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_0313b68a885743959fd1b32918c34028",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_d96d745ae9d44e3ca60788c4fa608893",
              "IPY_MODEL_c4a51ff88cba40608b1bc222b274d177"
            ]
          }
        },
        "0313b68a885743959fd1b32918c34028": {
          "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
          }
        },
        "d96d745ae9d44e3ca60788c4fa608893": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_c86b44e242aa4bbf8bcc4ebb3dbac699",
            "_dom_classes": [],
            "description": "Training: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 40000,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 40000,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_629b2330a9b14a5db74b3162e1c8853c"
          }
        },
        "c4a51ff88cba40608b1bc222b274d177": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_7fa2dd05596d4a22aaf6306f65b6a985",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 40000/40000 [06:57&lt;00:00, 95.91it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_48a08f666661432db9a76ad904e7ff96"
          }
        },
        "c86b44e242aa4bbf8bcc4ebb3dbac699": {
          "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"
          }
        },
        "629b2330a9b14a5db74b3162e1c8853c": {
          "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
          }
        },
        "7fa2dd05596d4a22aaf6306f65b6a985": {
          "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"
          }
        },
        "48a08f666661432db9a76ad904e7ff96": {
          "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
          }
        },
        "b9586b1f18b04228939184bac0efb64e": {
          "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_66364f72d2bb421fa703ee68865fbcee",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_0fde52c5b8854f0cade25338def8f293",
              "IPY_MODEL_1f94a1e2b7ab4c46a5027bd6e4e4bcf6"
            ]
          }
        },
        "66364f72d2bb421fa703ee68865fbcee": {
          "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
          }
        },
        "0fde52c5b8854f0cade25338def8f293": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_d5814b4bf7b945d6ba10f4953202e591",
            "_dom_classes": [],
            "description": "Testing: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 1000,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 1000,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_41df41ec8a814133a3b6b283ecc8a36b"
          }
        },
        "1f94a1e2b7ab4c46a5027bd6e4e4bcf6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_7d613f4fa20948aaa674229d0b57303e",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 1000/1000 [00:48&lt;00:00, 20.53it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_24411ad13929472f85a287ff1d7e581c"
          }
        },
        "d5814b4bf7b945d6ba10f4953202e591": {
          "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"
          }
        },
        "41df41ec8a814133a3b6b283ecc8a36b": {
          "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
          }
        },
        "7d613f4fa20948aaa674229d0b57303e": {
          "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"
          }
        },
        "24411ad13929472f85a287ff1d7e581c": {
          "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
          }
        },
        "5e324237453445e2a5a8f353b66c2bde": {
          "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_3a1381a53c0c4acd82f738758a3d292f",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_7dadcb9d66574fbebfb3648389a0dd75",
              "IPY_MODEL_29fd2b6c33054ad2b6acef444109b70c"
            ]
          }
        },
        "3a1381a53c0c4acd82f738758a3d292f": {
          "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
          }
        },
        "7dadcb9d66574fbebfb3648389a0dd75": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_c3c48455b004458b9f92dfe8354f3cec",
            "_dom_classes": [],
            "description": "Testing: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 1000,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 1000,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_652b3db7df3741a886d426787d0668b4"
          }
        },
        "29fd2b6c33054ad2b6acef444109b70c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_1a470f200fec4b29b82f30410be60a61",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 1000/1000 [00:41&lt;00:00, 24.29it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_566e5c28a30b4f0dbdd250a6f603eba9"
          }
        },
        "c3c48455b004458b9f92dfe8354f3cec": {
          "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"
          }
        },
        "652b3db7df3741a886d426787d0668b4": {
          "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
          }
        },
        "1a470f200fec4b29b82f30410be60a61": {
          "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"
          }
        },
        "566e5c28a30b4f0dbdd250a6f603eba9": {
          "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
          }
        },
        "cd2142f397f2480fa64f1cfee56fb72f": {
          "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_bfa7f5da8e324bacb2419e4397dcd4b6",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_2425d944f9d547eeabd569f31ac906f4",
              "IPY_MODEL_2e0bb9cad3c44973967e6cb5f3cf1035"
            ]
          }
        },
        "bfa7f5da8e324bacb2419e4397dcd4b6": {
          "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
          }
        },
        "2425d944f9d547eeabd569f31ac906f4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_4ac18c78c7b64f29ad1ccc6ad1cd118e",
            "_dom_classes": [],
            "description": "Testing: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 1000,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 1000,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_2a94c97266a64236a9cf8f31f93a9492"
          }
        },
        "2e0bb9cad3c44973967e6cb5f3cf1035": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_d6785c14d14d4a178a4e518a7693e31f",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 1000/1000 [00:35&lt;00:00, 28.52it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_e07d699bc2634e4588ed181a9e15c1e7"
          }
        },
        "4ac18c78c7b64f29ad1ccc6ad1cd118e": {
          "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"
          }
        },
        "2a94c97266a64236a9cf8f31f93a9492": {
          "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
          }
        },
        "d6785c14d14d4a178a4e518a7693e31f": {
          "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"
          }
        },
        "e07d699bc2634e4588ed181a9e15c1e7": {
          "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
          }
        },
        "5cbd0fa62f7f4210aced0b81ceaed950": {
          "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_d5f0f03f88a346e881d92d9a86302d31",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_2cb5ef8a4c8843c7a545d6b46807837a",
              "IPY_MODEL_3a5bd7447fb64edc8e4fbcadd6fbcad7"
            ]
          }
        },
        "d5f0f03f88a346e881d92d9a86302d31": {
          "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
          }
        },
        "2cb5ef8a4c8843c7a545d6b46807837a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_c1481000032b40789f848177900f087c",
            "_dom_classes": [],
            "description": "Training: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 40000,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 40000,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_4ecf1d5234a04192afa61501f2ee08ed"
          }
        },
        "3a5bd7447fb64edc8e4fbcadd6fbcad7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_8fffa786468948fa8b134533cdd9bb99",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 40000/40000 [07:48&lt;00:00, 85.30it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_2443cd332f7d436b8070fa436bd2c945"
          }
        },
        "c1481000032b40789f848177900f087c": {
          "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"
          }
        },
        "4ecf1d5234a04192afa61501f2ee08ed": {
          "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
          }
        },
        "8fffa786468948fa8b134533cdd9bb99": {
          "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"
          }
        },
        "2443cd332f7d436b8070fa436bd2c945": {
          "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
          }
        },
        "cb10ac92793f42caad22ec3782bdbb2b": {
          "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_a7492769f9e24c769cbf5553e517d530",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_c8b5b35bdc9a4bd59440d9b99237f768",
              "IPY_MODEL_8f03c5d6bfae4c3d9280329f12f6753b"
            ]
          }
        },
        "a7492769f9e24c769cbf5553e517d530": {
          "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
          }
        },
        "c8b5b35bdc9a4bd59440d9b99237f768": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_35f6ae48a0524e889285636dbccd109f",
            "_dom_classes": [],
            "description": "Testing: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 1000,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 1000,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_7cc087a2510e431aba484dacedebe953"
          }
        },
        "8f03c5d6bfae4c3d9280329f12f6753b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_a0042942f7f64ede963130ef9b8ebf67",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 1000/1000 [00:36&lt;00:00, 27.39it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_f9545d12fdb243c58f2f8b7262a681f0"
          }
        },
        "35f6ae48a0524e889285636dbccd109f": {
          "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"
          }
        },
        "7cc087a2510e431aba484dacedebe953": {
          "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
          }
        },
        "a0042942f7f64ede963130ef9b8ebf67": {
          "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"
          }
        },
        "f9545d12fdb243c58f2f8b7262a681f0": {
          "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
          }
        },
        "76e61f0987354a2ea7e58a93e313dd46": {
          "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_7a41c2123d4e4731b48b79f09ee1b0ee",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_1b4a47c1db2b48e384c6f1d806e001b4",
              "IPY_MODEL_bca6849513ed43c7927bbfc9a93906a2"
            ]
          }
        },
        "7a41c2123d4e4731b48b79f09ee1b0ee": {
          "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
          }
        },
        "1b4a47c1db2b48e384c6f1d806e001b4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_61d2d0fe06974354803fb2ccf3892b61",
            "_dom_classes": [],
            "description": "Testing: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 1000,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 1000,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_aadc20a59d1d435aba943a4549942cc1"
          }
        },
        "bca6849513ed43c7927bbfc9a93906a2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_f45716b0f401429b8c64b784cb9510e1",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 1000/1000 [00:34&lt;00:00, 28.96it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_5cb0245ef5764d409848e2200071c50a"
          }
        },
        "61d2d0fe06974354803fb2ccf3892b61": {
          "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"
          }
        },
        "aadc20a59d1d435aba943a4549942cc1": {
          "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
          }
        },
        "f45716b0f401429b8c64b784cb9510e1": {
          "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"
          }
        },
        "5cb0245ef5764d409848e2200071c50a": {
          "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
          }
        },
        "96ae46bbf6e848639176a40cc8bd0adb": {
          "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_0b0094f43be7450a9e4447f3e7c3d1b6",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_79ef29a6308b41c498e6d53bdccbfa1c",
              "IPY_MODEL_ef31d9a269594215981462ca27f245d8"
            ]
          }
        },
        "0b0094f43be7450a9e4447f3e7c3d1b6": {
          "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
          }
        },
        "79ef29a6308b41c498e6d53bdccbfa1c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_39fe6405028f43fead92231df9bcb6a4",
            "_dom_classes": [],
            "description": "Testing: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 1000,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 1000,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_87bab76d3b534cfea4e89894d05183ea"
          }
        },
        "ef31d9a269594215981462ca27f245d8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_719da0e46c9947f4b231a0301626c698",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 1000/1000 [00:32&lt;00:00, 30.53it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_dad9113a85fb44379414e75f6d99e3a0"
          }
        },
        "39fe6405028f43fead92231df9bcb6a4": {
          "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"
          }
        },
        "87bab76d3b534cfea4e89894d05183ea": {
          "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
          }
        },
        "719da0e46c9947f4b231a0301626c698": {
          "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"
          }
        },
        "dad9113a85fb44379414e75f6d99e3a0": {
          "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
          }
        },
        "f958c02d14a242d29e4101d0c009e04a": {
          "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_4e6e29cffba94eb0b1391de271df8755",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_d89f155295d9415196825cba83e09b1d",
              "IPY_MODEL_496282689bcc4e7a9794d01c5e2f94b7"
            ]
          }
        },
        "4e6e29cffba94eb0b1391de271df8755": {
          "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
          }
        },
        "d89f155295d9415196825cba83e09b1d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_e7b8ff6d037a41f0bda93c251129e16d",
            "_dom_classes": [],
            "description": "Training: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 40000,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 40000,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_7b7e4482239446629099f8dde6b7ddc2"
          }
        },
        "496282689bcc4e7a9794d01c5e2f94b7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_1f78729a6faa4e03bad54eb331375312",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 40000/40000 [03:45&lt;00:00, 177.50it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_1126b52f5c7148b195c15b537c490711"
          }
        },
        "e7b8ff6d037a41f0bda93c251129e16d": {
          "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"
          }
        },
        "7b7e4482239446629099f8dde6b7ddc2": {
          "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
          }
        },
        "1f78729a6faa4e03bad54eb331375312": {
          "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"
          }
        },
        "1126b52f5c7148b195c15b537c490711": {
          "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
          }
        },
        "706489896a6d4001bd86f18ba0b6c911": {
          "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_d20e55d563ad4bb3a214dfc55f9b6a81",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_256117379d0f433cb11bd96afd6ecdf6",
              "IPY_MODEL_1f7ba8ccbb6149928cbdfbdf996c408f"
            ]
          }
        },
        "d20e55d563ad4bb3a214dfc55f9b6a81": {
          "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
          }
        },
        "256117379d0f433cb11bd96afd6ecdf6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_29c36742bc5f4b668e7e043110848451",
            "_dom_classes": [],
            "description": "Testing: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 1000,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 1000,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_9d26faf35935486fa9f4a169afc2f91b"
          }
        },
        "1f7ba8ccbb6149928cbdfbdf996c408f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_978d32b676d5403aa172202929dd4fa7",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 1000/1000 [00:01&lt;00:00, 511.02it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_527a51228c2d42949a8ba6bb4c009e45"
          }
        },
        "29c36742bc5f4b668e7e043110848451": {
          "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"
          }
        },
        "9d26faf35935486fa9f4a169afc2f91b": {
          "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
          }
        },
        "978d32b676d5403aa172202929dd4fa7": {
          "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"
          }
        },
        "527a51228c2d42949a8ba6bb4c009e45": {
          "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
          }
        },
        "a989488d227348fdae782f3f0760bf29": {
          "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_d133b27239ea45d6b439a06a35d277e8",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_1bd4091ad05f432bb1351bc6d5ad2661",
              "IPY_MODEL_5a276fe025d148c98550454c32d57784"
            ]
          }
        },
        "d133b27239ea45d6b439a06a35d277e8": {
          "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
          }
        },
        "1bd4091ad05f432bb1351bc6d5ad2661": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_162c7642cfef41cf97d01a2a572d5c17",
            "_dom_classes": [],
            "description": "Testing: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 1000,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 1000,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_42df29b15274400ea976b316573c44b7"
          }
        },
        "5a276fe025d148c98550454c32d57784": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_77ad349065c34e3a8a63bf57b0634136",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 1000/1000 [00:01&lt;00:00, 572.41it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_bb2635d407c9416589902c50aaa65333"
          }
        },
        "162c7642cfef41cf97d01a2a572d5c17": {
          "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"
          }
        },
        "42df29b15274400ea976b316573c44b7": {
          "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
          }
        },
        "77ad349065c34e3a8a63bf57b0634136": {
          "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"
          }
        },
        "bb2635d407c9416589902c50aaa65333": {
          "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
          }
        },
        "e85dab8789724aee827f3fd1cf2a1251": {
          "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_4417659e44b749b2b82b1587b2d939c4",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_8112e806c6bb4ff8bd7604cdb41b0cd9",
              "IPY_MODEL_10827f64c3b24b17a9928439c90ce3c8"
            ]
          }
        },
        "4417659e44b749b2b82b1587b2d939c4": {
          "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
          }
        },
        "8112e806c6bb4ff8bd7604cdb41b0cd9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_95ae2861d5294ba9acf408921480c480",
            "_dom_classes": [],
            "description": "Testing: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 1000,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 1000,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_396b79ec42264ad6917b6b79bac99541"
          }
        },
        "10827f64c3b24b17a9928439c90ce3c8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_4fe111a8ddc34afebf45acc52c0d722b",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 1000/1000 [00:03&lt;00:00, 320.86it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_9858b75f7db8423e983854913f811390"
          }
        },
        "95ae2861d5294ba9acf408921480c480": {
          "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"
          }
        },
        "396b79ec42264ad6917b6b79bac99541": {
          "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
          }
        },
        "4fe111a8ddc34afebf45acc52c0d722b": {
          "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"
          }
        },
        "9858b75f7db8423e983854913f811390": {
          "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": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Chay16/dataflowr-PlutonAI/blob/master/07_RNN_PlutonAI.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "-8ecFD9cgjM9"
      },
      "source": [
        "# RNN practicals\n",
        "\n",
        "This jupyter notebook allows you to reproduce and explore the results presented in the [lecture on RNN](https://mlelarge.github.io/dataflowr-slides/PlutonAI/lesson7.html)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Pc29CvyHgjNE",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "from collections import OrderedDict\n",
        "import scipy.special\n",
        "from scipy.special import binom\n",
        "import matplotlib.pyplot as plt\n",
        "import time\n",
        "from tqdm.notebook import tqdm as tq"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "utb4MovzgjNe",
        "colab": {}
      },
      "source": [
        "def running_mean(x, N):\n",
        "    cumsum = np.cumsum(np.insert(x, 0, 0)) \n",
        "    return (cumsum[N:] - cumsum[:-N]) / float(N)\n",
        "\n",
        "def Catalan(k):\n",
        "    return binom(2*k,k)/(k+1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "JIGrOVLSgjNx",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "use_gpu = torch.cuda.is_available()\n",
        "def gpu(tensor, gpu=use_gpu):\n",
        "    if gpu:\n",
        "        return tensor.cuda()\n",
        "    else:\n",
        "        return tensor"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "1Ytku22GgjOB"
      },
      "source": [
        "## 1. Generation a dataset\n",
        "\n",
        "We have a problem, where we need to generate a dataset made of valid parenthesis strings but also invalid parenthesis string. You can skip to the end of this section to see how parenthesis strings are generated."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "QfpbFNT1gjOF",
        "colab": {}
      },
      "source": [
        "seq_max_len = 20\n",
        "seq_min_len = 4"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "uHAs_VxrgjOS"
      },
      "source": [
        "### generating positive examples"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "DKlEMULfgjOX",
        "colab": {}
      },
      "source": [
        "# convention: +1 opening parenthesis and -1 closing parenthesis\n",
        "\n",
        "def all_parent(n, a, k=-1):\n",
        "    global res\n",
        "    if k==n-1 and sum(a) == 0:\n",
        "        res.append(a.copy())\n",
        "    elif k==n-1:\n",
        "        pass\n",
        "    else:\n",
        "        k += 1\n",
        "        if sum(a) > 0:\n",
        "            a[k] = 1\n",
        "            all_parent(n,a,k)\n",
        "        \n",
        "            a[k] = -1\n",
        "            all_parent(n,a,k)\n",
        "            a[k] = 0\n",
        "        else:\n",
        "            a[k] = 1\n",
        "            all_parent(n,a,k)\n",
        "            a[k] = 0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "dFcYswvXgjOl"
      },
      "source": [
        "### generating negative examples"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ZXm1-wS9gjOp",
        "colab": {}
      },
      "source": [
        "def all_parent_mistake(n, a, k=-1):\n",
        "    global res\n",
        "    if k==n-1 and sum(a) >= -1 and sum(a) <= 1 and min(np.cumsum(a))<0:\n",
        "        res.append(a.copy())\n",
        "    elif sum(a) > n-k:\n",
        "        pass\n",
        "    elif k==n-1:\n",
        "        pass\n",
        "    else:\n",
        "        k += 1\n",
        "        if sum(a) >= -1 and k != 0:\n",
        "            a[k] = 1\n",
        "            all_parent_mistake(n,a,k)\n",
        "        \n",
        "            a[k] = -1\n",
        "            all_parent_mistake(n,a,k)\n",
        "            a[k] = 0\n",
        "        else:\n",
        "            a[k] = 1\n",
        "            all_parent_mistake(n,a,k)\n",
        "            a[k] = 0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ePYlXZaYgjO6",
        "colab": {}
      },
      "source": [
        "# numbering the parentheses\n",
        "# example: seq of len 6\n",
        "# ( ( ( ) ) ) \n",
        "# 0 1 2 4 5 6\n",
        "# we always have ( + ) = seq_len\n",
        "# 'wrong' parentheses are always closing and numbered as:\n",
        "# ) )\n",
        "# 7 8\n",
        "\n",
        "def reading_par(l, n):\n",
        "    res = [0]*len(l)\n",
        "    s = []\n",
        "    n_plus = -1\n",
        "    n_moins = n+1\n",
        "    c = 0\n",
        "    for i in l:\n",
        "        if i == 1:\n",
        "            n_plus += 1\n",
        "            s.append(n_plus)\n",
        "            res[c] = n_plus\n",
        "            c += 1\n",
        "        else:\n",
        "            try:\n",
        "                res[c] = n-s.pop()\n",
        "            except:\n",
        "                res[c] = n_moins\n",
        "                n_moins += 1\n",
        "            c += 1\n",
        "    return res"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "dA0MR3f7gjPN",
        "colab": {}
      },
      "source": [
        "all_par = OrderedDict()\n",
        "for n in range(seq_min_len,seq_max_len+1,2):\n",
        "    a = [0]*n\n",
        "    res = []\n",
        "    all_parent(n=n,a=a,k=-1)\n",
        "    all_par[n] = [reading_par(k,n) for k in res]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "JfMc7FvagjPi",
        "colab": {}
      },
      "source": [
        "all_par_mist = OrderedDict()\n",
        "for n in range(seq_min_len,seq_max_len+1,2):\n",
        "    a = [0]*n\n",
        "    res = []\n",
        "    all_parent_mistake(n=n,a=a,k=-1)\n",
        "    all_par_mist[n] = [reading_par(k,n) for k in res]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7PtA8pQIgjP2",
        "outputId": "0d6aaf11-f2e0-414a-87ef-62e5165bcba9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "all_par[6]"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[0, 1, 2, 4, 5, 6],\n",
              " [0, 1, 5, 2, 4, 6],\n",
              " [0, 1, 5, 6, 2, 4],\n",
              " [0, 6, 1, 2, 4, 5],\n",
              " [0, 6, 1, 5, 2, 4]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "26eGcBytgjQG",
        "outputId": "a130bf2a-741e-45b1-ab3a-0de151bb51bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "all_par_mist[6]"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[0, 1, 5, 6, 7, 2],\n",
              " [0, 6, 1, 5, 7, 2],\n",
              " [0, 6, 7, 1, 2, 4],\n",
              " [0, 6, 7, 1, 5, 2],\n",
              " [0, 6, 7, 8, 1, 2]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "oCo1lHQpgjQT"
      },
      "source": [
        "### number of negative examples by length"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "mdr-aeg8gjQX",
        "colab": {}
      },
      "source": [
        "long_mist = {i:len(l) for (i,l) in zip(all_par_mist.keys(),all_par_mist.values())}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7gWfeHiEgjQf",
        "outputId": "1cc448a3-a962-4e63-92f0-77cdd96e9210",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "long_mist"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{4: 1, 6: 5, 8: 20, 10: 75, 12: 275, 14: 1001, 16: 3640, 18: 13260, 20: 48450}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "zq8iVHxDgjQm"
      },
      "source": [
        "### number of positive examples by length"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "kxzqq0JCgjQo",
        "colab": {}
      },
      "source": [
        "Catalan_num = {i:len(l) for (i,l) in zip(all_par.keys(),all_par.values())}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "WmRoddtQgjQv",
        "outputId": "de1bfa1f-28e2-48fc-cfb7-d66b30119d6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "Catalan_num"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{4: 2, 6: 5, 8: 14, 10: 42, 12: 132, 14: 429, 16: 1430, 18: 4862, 20: 16796}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "nzCECk4EgjQ5"
      },
      "source": [
        "Sanity check, see [Catalan numbers](https://en.wikipedia.org/wiki/Catalan_number)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "8Thfdkm8gjQ7",
        "outputId": "eafa0dcc-c699-4e3b-8e90-6eeaa024aa49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "[(2*i,Catalan(i)) for i  in range(2,int(seq_max_len/2)+1)]"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(4, 2.0),\n",
              " (6, 5.0),\n",
              " (8, 14.0),\n",
              " (10, 42.0),\n",
              " (12, 132.0),\n",
              " (14, 429.0),\n",
              " (16, 1430.0),\n",
              " (18, 4862.0),\n",
              " (20, 16796.0)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "qri2vYemgjRF",
        "outputId": "61e968ee-0254-4573-b8b1-25d84ac3dd3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# nombre de suites correctes de longueur entre 4 et 10, alphabet de taille nb_symbol.\n",
        "nb_symbol = 10\n",
        "np.sum([Catalan(i)*int(nb_symbol/2)**i for i in range(2,int(seq_max_len/2)+1)])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "174113843800.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "LVc13MYfgjRR",
        "colab": {}
      },
      "source": [
        "import random\n",
        "import torch\n",
        "\n",
        "class SequenceGenerator():\n",
        "    def __init__(self, nb_symbol = 10, seq_min_len = 4, seq_max_len = 10):\n",
        "        self.nb_symbol = nb_symbol\n",
        "        self.seq_min_len = seq_min_len\n",
        "        self.seq_max_len = seq_max_len\n",
        "        self.population = [i for i in range(int(nb_symbol/2))]\n",
        "                \n",
        "    def generate_pattern(self):\n",
        "        len_r = random.randint(self.seq_min_len/2,self.seq_max_len/2)\n",
        "        pattern = random.choices(self.population,k=len_r)\n",
        "        return pattern + pattern[::-1]\n",
        "    \n",
        "    def generate_pattern_parenthesis(self, len_r = None):\n",
        "        if len_r == None:\n",
        "            len_r = int(2*random.randint(self.seq_min_len/2,self.seq_max_len/2))\n",
        "        pattern = np.random.choice(self.population,size=int(len_r/2),replace=True)\n",
        "        ind_r = random.randint(0,Catalan_num[len_r]-1)\n",
        "        res = [pattern[i] if i <= len_r/2 else self.nb_symbol-1-pattern[len_r-i] for i in all_par[len_r][ind_r]]\n",
        "        return res\n",
        "    \n",
        "    def generate_parenthesis_false(self):\n",
        "        len_r = int(2*random.randint(self.seq_min_len/2,self.seq_max_len/2))\n",
        "        pattern = np.random.choice(self.population,size=int(len_r/2),replace=True)\n",
        "        ind_r = random.randint(0,long_mist[len_r]-1)\n",
        "        res = [pattern[i] if i <= len_r/2 \n",
        "               else  self.nb_symbol-1-pattern[len_r-i] if i<= len_r \n",
        "               else self.nb_symbol-1-pattern[i-len_r] for i in all_par_mist[len_r][ind_r]]\n",
        "        return res\n",
        "    \n",
        "    def generate_hard_parenthesis(self, len_r = None):\n",
        "        if len_r == None:\n",
        "            len_r = int(2*random.randint(self.seq_min_len/2,self.seq_max_len/2))\n",
        "        pattern = np.random.choice(self.population,size=int(len_r/2),replace=True)\n",
        "        ind_r = random.randint(0,Catalan_num[len_r]-1)\n",
        "        res = [pattern[i] if i <= len_r/2 else self.nb_symbol-1-pattern[len_r-i] for i in all_par[len_r][ind_r]]\n",
        "        \n",
        "        if len_r == None:\n",
        "            len_r = int(2*random.randint(self.seq_min_len/2,self.seq_max_len/2))\n",
        "        pattern = np.random.choice(self.population,size=int(len_r/2),replace=True)\n",
        "        ind_r = random.randint(0,Catalan_num[len_r]-1)\n",
        "        res2 = [pattern[i] if i <= len_r/2 else self.nb_symbol-1-pattern[len_r-i] for i in all_par[len_r][ind_r]]\n",
        "        return res + res2\n",
        "    \n",
        "    def generate_hard_nonparenthesis(self, len_r = None):\n",
        "        if len_r == None:\n",
        "            len_r = int(2*random.randint(self.seq_min_len/2,self.seq_max_len/2))\n",
        "        pattern = np.random.choice(self.population,size=int(len_r/2),replace=True)\n",
        "        ind_r = random.randint(0,long_mist[len_r]-1)\n",
        "        res = [pattern[i] if i <= len_r/2 \n",
        "               else  self.nb_symbol-1-pattern[len_r-i] if i<= len_r \n",
        "               else self.nb_symbol-1-pattern[i-len_r] for i in all_par_mist[len_r][ind_r]]\n",
        "        \n",
        "        if len_r == None:\n",
        "            len_r = int(2*random.randint(self.seq_min_len/2,self.seq_max_len/2))\n",
        "        pattern = np.random.choice(self.population,size=int(len_r/2),replace=True)\n",
        "        ind_r = random.randint(0,Catalan_num[len_r]-1)\n",
        "        res2 = [pattern[i] if i <= len_r/2 else self.nb_symbol-1-pattern[len_r-i] for i in all_par[len_r][ind_r]]\n",
        "        return  res +[self.nb_symbol-1-pattern[0]]+ res2\n",
        "        \n",
        "    def generate_false(self):\n",
        "        popu = [i for i in range(nb_symbol)]\n",
        "        len = random.randint(self.seq_min_len/2,self.seq_max_len/2)\n",
        "        return random.choices(popu,k=len) + random.choices(popu,k=len)\n",
        "    \n",
        "    def generate_label(self, x):\n",
        "        l = int(len(x)/2)\n",
        "        return 1 if x[:l] == x[:l-1:-1] else 0\n",
        "    \n",
        "    def generate_label_parenthesis(self, x):\n",
        "        s = []\n",
        "        label = 1\n",
        "        lenx = len(x)\n",
        "        for i in x:\n",
        "            if s == [] and i < self.nb_symbol/2:\n",
        "                s.append(i)\n",
        "            elif s == [] and i >= self.nb_symbol/2:\n",
        "                label = 0\n",
        "                break\n",
        "            elif i == self.nb_symbol-1-s[-1]:\n",
        "                s.pop()\n",
        "            else:\n",
        "                s.append(i)\n",
        "        if s != []:\n",
        "            label = 0\n",
        "        return label\n",
        "    \n",
        "    def one_hot(self,seq):\n",
        "        one_hot_seq = []\n",
        "        for s in seq:\n",
        "            one_hot = [0 for _ in range(self.nb_symbol)]\n",
        "            one_hot[s] = 1\n",
        "            one_hot_seq.append(one_hot)\n",
        "        return one_hot_seq\n",
        "    \n",
        "    def generate_input(self, len_r = None, true_parent = False, hard_false = True):\n",
        "        if true_parent:\n",
        "            seq = self.generate_pattern_parenthesis(len_r)\n",
        "        elif bool(random.getrandbits(1)):\n",
        "            seq = self.generate_pattern_parenthesis(len_r)\n",
        "        else:\n",
        "            if hard_false:\n",
        "                seq = self.generate_parenthesis_false()\n",
        "            else:\n",
        "                seq = self.generate_false()\n",
        "        return gpu(torch.from_numpy(np.array(self.one_hot(seq))).type(torch.FloatTensor)), gpu(torch.from_numpy(np.array([self.generate_label_parenthesis(seq)])))\n",
        "\n",
        "    def generate_input_hard(self,true_parent = False):\n",
        "        if true_parent:\n",
        "            seq = self.generate_hard_parenthesis(self.seq_max_len)\n",
        "        elif bool(random.getrandbits(1)):\n",
        "            seq = self.generate_hard_parenthesis(self.seq_max_len)\n",
        "        else:\n",
        "            seq = self.generate_hard_nonparenthesis(self.seq_max_len)\n",
        "            \n",
        "        return gpu(torch.from_numpy(np.array(self.one_hot(seq))).type(torch.FloatTensor)), gpu(torch.from_numpy(np.array([self.generate_label_parenthesis(seq)])))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ivBE7UIYgjRc",
        "colab": {}
      },
      "source": [
        "nb_symbol = 10\n",
        "generator = SequenceGenerator(nb_symbol = nb_symbol, seq_min_len = seq_min_len, seq_max_len = seq_max_len)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Dj5YgzvXgjRq",
        "outputId": "24b72ff8-9d34-439d-af91-02797e79f088",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "generator.generate_pattern_parenthesis()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3, 2, 0, 0, 9, 4, 5, 2, 7, 3, 6, 9, 7, 6]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lbKlAmE_gjRz",
        "colab": {}
      },
      "source": [
        "x = generator.generate_parenthesis_false()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "2kdSGWGCgjR7",
        "outputId": "75a98e6f-26e3-4a80-e885-92169f401ea8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "generator.generate_label_parenthesis(x)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4hL2UnY6gjSC",
        "outputId": "08de4211-60be-4467-baa3-5616c27ba0ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "generator.generate_input()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "         [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
              "         [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
              "         [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.]], device='cuda:0'),\n",
              " tensor([0], device='cuda:0'))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "hkOTjH01gjSN"
      },
      "source": [
        "## 2. First RNN: [Elman network](https://mlelarge.github.io/dataflowr-slides/PlutonAI/lesson7.html#16)\n",
        "\n",
        "Initial hidden state: $h_0 =0$\n",
        "\n",
        "Update:\n",
        "\n",
        "$$\n",
        "h_t = \\mathrm{ReLU}(W_{xh} x_t + W_{hh} h_{t-1} + b_h)\n",
        "$$\n",
        "\n",
        "Final prediction:\n",
        "\n",
        "$$\n",
        "y_T = W_{hy} h_T + b_y.\n",
        "$$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "bLIQlP1QgjSP",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "\n",
        "class RecNet(nn.Module):\n",
        "    def __init__(self, dim_input=10, dim_recurrent=50, dim_output=2):\n",
        "        super(RecNet, self).__init__()\n",
        "        self.fc_x2h = nn.Linear(dim_input, dim_recurrent)\n",
        "        self.fc_h2h = nn.Linear(dim_recurrent, dim_recurrent, bias = False)\n",
        "        self.fc_h2y = nn.Linear(dim_recurrent, dim_output)\n",
        "        \n",
        "    def forward(self, x):\n",
        "        h = x.new_zeros(1, self.fc_h2y.weight.size(1))\n",
        "        for t in range(x.size(0)):\n",
        "            h = torch.relu(self.fc_x2h(x[t,:]) + self.fc_h2h(h))    \n",
        "        return self.fc_h2y(h)\n",
        "    \n",
        "RNN = gpu(RecNet(dim_input = nb_symbol))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "AcbhSU5ggjSW",
        "outputId": "7388b159-c625-4472-d95e-b666b39bdb2d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83,
          "referenced_widgets": [
            "93460e7cda2643329f7663fdd737ae6b",
            "2d4f9f0c4c9a41c2a1c9c13a153129ac",
            "39b51daf9234404587848f6bc3684141",
            "e300e99256164c9ebb29f6cdac99bd65",
            "cc82c477fe164829b8ced69f4ea94cd9",
            "d5b318fb2f794597a623fb6aff31b09a",
            "61e1643ee5514bb39266d35c4328a423",
            "e4e95e8c125d4ffaae286fcc6e0781fd"
          ]
        }
      },
      "source": [
        "cross_entropy = nn.CrossEntropyLoss()\n",
        "learning_rate = 1e-3\n",
        "optimizer = torch.optim.Adam(RNN.parameters(),lr=learning_rate)\n",
        "nb_train = 40000\n",
        "loss_t = []\n",
        "corrects =[]\n",
        "labels = []\n",
        "start = time.time()\n",
        "for k in tq(range(nb_train), desc=\"Training\"):\n",
        "    x,l = generator.generate_input(hard_false = False)\n",
        "    y = RNN(x)\n",
        "    loss = cross_entropy(y,l)\n",
        "    _,preds = torch.max(y.data,1)\n",
        "    corrects.append(preds.item() == l.data.item())\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    loss_t.append(loss)\n",
        "    labels.append(l.data)\n",
        "print(\"Total Training Time : {}\".format(time.strftime(\"%M:%S\", time.localtime(time.time() - start))))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "93460e7cda2643329f7663fdd737ae6b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Training', max=40000.0, style=ProgressStyle(description_w…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Total Training Time : 03:02\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "NilV4syzgjSi",
        "outputId": "42014311-e920-4a1f-a0b8-bbb545c4ef36",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        }
      },
      "source": [
        "plt.plot(running_mean(loss_t,int(nb_train/100)));"
      ],
      "execution_count": 26,
      "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": {
        "colab_type": "code",
        "id": "LINKeGNjgjSs",
        "outputId": "a7557694-47e7-4701-e7c1-16358de0bae6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "plt.plot(running_mean(corrects,int(nb_train/100)));"
      ],
      "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": "code",
      "metadata": {
        "colab_type": "code",
        "id": "O2A1jCLJgjS5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "b5a22500676142f896d2431a68941821",
            "f7cb96335e354464b534e90e989662fe",
            "47bc77d5bf7946b792cce35c4c794240",
            "7be9e18a8d0a4c1183a1658133d415f2",
            "20e874a413194463be339f14242675e9",
            "2e5eec716e294a86ad610745e97931f3",
            "5cb6e878aaad4fc588b1d8c92ccfea88",
            "c353fd37d84a41e7a8d28faf259d2346"
          ]
        },
        "outputId": "1cadb18b-3b99-4449-d8d9-39a84d38f61b"
      },
      "source": [
        "nb_test = 1000\n",
        "corrects_test =[]\n",
        "labels_test = []\n",
        "for k in tq(range(nb_test), desc=\"Testing\"):\n",
        "    x,l = generator.generate_input(len_r=seq_max_len,true_parent=True)\n",
        "    y = RNN(x)\n",
        "    _,preds = torch.max(y.data,1)\n",
        "    corrects_test.append(preds.item() == l.data.item())\n",
        "    labels_test.append(l.data)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b5a22500676142f896d2431a68941821",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Testing', max=1000.0, style=ProgressStyle(description_wid…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TdgtK_tGZvJR",
        "colab_type": "text"
      },
      "source": [
        "Accuracy on valid parenthesis strings only:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "z41nP-UhgjS-",
        "outputId": "230f2ab7-0401-4603-9e48-42a1dedf7a9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "np.sum(corrects_test)/nb_test"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lzOpkaLigjTD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "e08ea0f519ef4fb39b30ead3a788b52d",
            "a88bacf847fe4837a7849158de1906f5",
            "cf97128cb05847f1b63567a1300fe69a",
            "3369a124e221407a85faa0317ac0027a",
            "315d680e702c4599a3faea0692d0e200",
            "54764296b82643eb8d11c2a9ece404c6",
            "12df4b1c70b84df1a493563d333f7da6",
            "0e8977a3fa264ac9b7481dcb107ab3b0"
          ]
        },
        "outputId": "5a0a928a-9ccc-4bda-81e8-7137416a33d2"
      },
      "source": [
        "nb_test = 1000\n",
        "corrects_test =[]\n",
        "labels_test = []\n",
        "for k in tq(range(nb_test), desc=\"Testing\"):\n",
        "    x,l = generator.generate_input(len_r=seq_max_len, hard_false = True)\n",
        "    y = RNN(x)\n",
        "    _,preds = torch.max(y.data,1)\n",
        "    corrects_test.append(preds.item() == l.data.item())\n",
        "    labels_test.append(l.data)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e08ea0f519ef4fb39b30ead3a788b52d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Testing', max=1000.0, style=ProgressStyle(description_wid…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FjT6sbUSZvJV",
        "colab_type": "text"
      },
      "source": [
        "Accuracy on a test set (similar to the training set):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Geelj-5XgjTM",
        "outputId": "7aa8db6f-fbb1-43fc-f5f0-fd7eaa51fb3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "np.sum(corrects_test)/nb_test"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.805"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "crSKTEJHgjTU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "4896476637b84c1eb05cef704504749e",
            "839f7f79e3024f298fe17156f309e6bb",
            "92537d3d9c0a40aaa9bc806f82a49291",
            "39bf4106ee09453498f95cfdaf9a369a",
            "3020aaff0d244356ae311b27b3d521d5",
            "32a05112ffff42e7836eefe002694387",
            "18e58d931b804b6fb2cb13f4a56dd5af",
            "f010d4abe922498d947519265691558d"
          ]
        },
        "outputId": "74e60e46-93aa-4ea7-f939-51c90d5c91d1"
      },
      "source": [
        "nb_test = 1000\n",
        "correctsh_test =[]\n",
        "labelsh_test = []\n",
        "for k in tq(range(nb_test), desc=\"Testing\"):\n",
        "    x,l = generator.generate_input_hard()\n",
        "    y = RNN(x)\n",
        "    _,preds = torch.max(y.data,1)\n",
        "    correctsh_test.append(preds.item() == l.data.item())\n",
        "    labelsh_test.append(l.data)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4896476637b84c1eb05cef704504749e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Testing', max=1000.0, style=ProgressStyle(description_wid…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rioM-_UkZvJa",
        "colab_type": "text"
      },
      "source": [
        "Accuracy on a test set of hard instances, i.e. instances longer than those seen during the training :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "br6HRXOhgjTc",
        "outputId": "82791ee4-8a18-444e-cf0a-ca3c095bdc3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "np.sum(correctsh_test)/nb_test"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.511"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "I5q30nkqgjTn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "441577e681634603a64bdaa94f852388",
            "9e34a37b7ea54acfb9e9edb5291a27c9",
            "f1713e470bc34b2495663799568fe7a5",
            "9cddda4e6d1840fcbb04a9414858e00b",
            "3005e8a4f9cf4d4d954ecc17d6439266",
            "ef0776321c28441faacb4f196d6f37ff",
            "a7aec94d80114414ba79df60694f8813",
            "a633a5e9b1cf48a7851d891871de635e"
          ]
        },
        "outputId": "5b618a2e-3430-4818-c16d-cb70ff180123"
      },
      "source": [
        "nb_test = 1000\n",
        "correctsh_test =[]\n",
        "labelsh_test = []\n",
        "for k in tq(range(nb_test), desc=\"Testing\"):\n",
        "    x,l = generator.generate_input_hard(true_parent=True)\n",
        "    y = RNN(x)\n",
        "    _,preds = torch.max(y.data,1)\n",
        "    correctsh_test.append(preds.item() == l.data.item())\n",
        "    labelsh_test.append(l.data)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "441577e681634603a64bdaa94f852388",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Testing', max=1000.0, style=ProgressStyle(description_wid…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bOPC3MomZvJg",
        "colab_type": "text"
      },
      "source": [
        "It looks like our network is always prediciting a valid label for long sequences:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "L6ZM_4c2gjTw",
        "outputId": "7a415a88-f410-493f-d76e-e7869a6b5f32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "np.sum(correctsh_test)/nb_test"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "wvaIEg6igjT3"
      },
      "source": [
        "## 3. [RNN with Gating](https://mlelarge.github.io/dataflowr-slides/PlutonAI/lesson7.html#20)\n",
        "\n",
        "$$\n",
        "\\overline{h}_t = \\mathrm{ReLU}(W_{xh} x_t + W_{hh} h_{t-1} + b_h)\n",
        "$$\n",
        "Forget gate:\n",
        "$$\n",
        "z_t = \\mathrm{sigm}(W_{xz} x_t + W_{hz}h_{t-1}+b_z)\n",
        "$$\n",
        "Hidden state:\n",
        "$$\n",
        "h_t = z_t\\odot h_{t-1} +(1-z_t) \\odot \\overline{h}_t\n",
        "$$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "kOiH5WsLgjT5",
        "colab": {}
      },
      "source": [
        "class RecNetGating(nn.Module):\n",
        "    def __init__(self, dim_input=10, dim_recurrent=50, dim_output=2):\n",
        "        super(RecNetGating, self).__init__()\n",
        "        self.fc_x2h = nn.Linear(dim_input, dim_recurrent)\n",
        "        self.fc_h2h = nn.Linear(dim_recurrent, dim_recurrent, bias = False)\n",
        "        self.fc_x2z = nn.Linear(dim_input, dim_recurrent)\n",
        "        self.fc_h2z = nn.Linear(dim_recurrent,dim_recurrent, bias = False)\n",
        "        self.fc_h2y = nn.Linear(dim_recurrent, dim_output)\n",
        "        \n",
        "    def forward(self, x):\n",
        "        h = x.new_zeros(1, self.fc_h2y.weight.size(1))\n",
        "        for t in range(x.size(0)):\n",
        "            z = torch.sigmoid(self.fc_x2z(x[t,:])+self.fc_h2z(h))\n",
        "            hb = torch.relu(self.fc_x2h(x[t,:]) + self.fc_h2h(h))\n",
        "            h = z * h + (1-z) * hb   \n",
        "        return self.fc_h2y(h)    \n",
        "    \n",
        "RNNG = gpu(RecNetGating(dim_input = nb_symbol))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "NZuKhEg-gjUB",
        "outputId": "156a2304-f5c1-44fe-996f-6e3f023abf29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83,
          "referenced_widgets": [
            "00fdd080cc6440d9b2f74abde3a8a57e",
            "0313b68a885743959fd1b32918c34028",
            "d96d745ae9d44e3ca60788c4fa608893",
            "c4a51ff88cba40608b1bc222b274d177",
            "c86b44e242aa4bbf8bcc4ebb3dbac699",
            "629b2330a9b14a5db74b3162e1c8853c",
            "7fa2dd05596d4a22aaf6306f65b6a985",
            "48a08f666661432db9a76ad904e7ff96"
          ]
        }
      },
      "source": [
        "optimizerG = torch.optim.Adam(RNNG.parameters(),lr=1e-3)\n",
        "loss_tG = []\n",
        "correctsG =[]\n",
        "labelsG = []\n",
        "start = time.time()\n",
        "for k in tq(range(nb_train), desc=\"Training\"):\n",
        "    x,l = generator.generate_input(hard_false = False)\n",
        "    y = RNNG(x)\n",
        "    loss = cross_entropy(y,l)\n",
        "    _,preds = torch.max(y.data,1)\n",
        "    correctsG.append(preds.item() == l.data.item())\n",
        "    optimizerG.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizerG.step()\n",
        "    loss_tG.append(loss)\n",
        "    labelsG.append(l.item())\n",
        "print(\"Total Training Time : {}\".format(time.strftime(\"%M:%S\", time.localtime(time.time() - start))))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "00fdd080cc6440d9b2f74abde3a8a57e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Training', max=40000.0, style=ProgressStyle(description_w…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Total Training Time : 06:55\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "6BbKxWkTgjUI",
        "outputId": "f80f7d3a-d974-41bc-a619-9d216b6051c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "plt.plot(running_mean(loss_tG,int(nb_train/50)))\n",
        "plt.plot(running_mean(loss_t,int(nb_train/50)));"
      ],
      "execution_count": 38,
      "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": {
        "colab_type": "code",
        "id": "Vjp8ybXqgjUO",
        "outputId": "224ec1da-1994-4ab0-8bc3-e4f6bcfdb986",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "plt.plot(running_mean(correctsG,int(nb_train/50)))\n",
        "plt.plot(running_mean(corrects,int(nb_train/50)));"
      ],
      "execution_count": 39,
      "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": {
        "colab_type": "code",
        "id": "opLeQwWogjUU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "b9586b1f18b04228939184bac0efb64e",
            "66364f72d2bb421fa703ee68865fbcee",
            "0fde52c5b8854f0cade25338def8f293",
            "1f94a1e2b7ab4c46a5027bd6e4e4bcf6",
            "d5814b4bf7b945d6ba10f4953202e591",
            "41df41ec8a814133a3b6b283ecc8a36b",
            "7d613f4fa20948aaa674229d0b57303e",
            "24411ad13929472f85a287ff1d7e581c"
          ]
        },
        "outputId": "2b4b4b3c-f87c-448e-c45c-d80d73007dbd"
      },
      "source": [
        "nb_test = 1000\n",
        "correctsG_test =[]\n",
        "labelsG_test = []\n",
        "for k in tq(range(nb_test), desc=\"Testing\"):\n",
        "    x,l = generator.generate_input(len_r=seq_max_len,true_parent=True)\n",
        "    y = RNNG(x)\n",
        "    _,preds = torch.max(y.data,1)\n",
        "    correctsG_test.append(preds.item() == l.data.item())\n",
        "    labelsG_test.append(l.data)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b9586b1f18b04228939184bac0efb64e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Testing', max=1000.0, style=ProgressStyle(description_wid…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wOL4IOo8ZvJx",
        "colab_type": "text"
      },
      "source": [
        "Accuracy on valid parenthesis strings only:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "FKF7r8dSgjUc",
        "outputId": "85c56b88-d331-4289-d4b9-c65f74a8de9a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "np.sum(correctsG_test)/nb_test"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "IwQg9tXRgjUq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "5e324237453445e2a5a8f353b66c2bde",
            "3a1381a53c0c4acd82f738758a3d292f",
            "7dadcb9d66574fbebfb3648389a0dd75",
            "29fd2b6c33054ad2b6acef444109b70c",
            "c3c48455b004458b9f92dfe8354f3cec",
            "652b3db7df3741a886d426787d0668b4",
            "1a470f200fec4b29b82f30410be60a61",
            "566e5c28a30b4f0dbdd250a6f603eba9"
          ]
        },
        "outputId": "c1d056c6-82bd-4e95-ae8a-953eceb34d92"
      },
      "source": [
        "nb_test = 1000\n",
        "correctsG_test =[]\n",
        "labelsG_test = []\n",
        "for k in tq(range(nb_test), desc=\"Testing\"):\n",
        "    x,l = generator.generate_input(len_r=seq_max_len, hard_false = True)\n",
        "    y = RNNG(x)\n",
        "    _,preds = torch.max(y.data,1)\n",
        "    correctsG_test.append(preds.item() == l.data.item())\n",
        "    labelsG_test.append(l.data)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5e324237453445e2a5a8f353b66c2bde",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Testing', max=1000.0, style=ProgressStyle(description_wid…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OydDhUHiZvJ2",
        "colab_type": "text"
      },
      "source": [
        "Accuracy on a test set (similar to the training set):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "QdWoh4UTgjUv",
        "outputId": "0491891b-0c70-458b-e010-d708c18212bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "np.sum(correctsG_test)/nb_test"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.868"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "OkkC1_jMgjU2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "cd2142f397f2480fa64f1cfee56fb72f",
            "bfa7f5da8e324bacb2419e4397dcd4b6",
            "2425d944f9d547eeabd569f31ac906f4",
            "2e0bb9cad3c44973967e6cb5f3cf1035",
            "4ac18c78c7b64f29ad1ccc6ad1cd118e",
            "2a94c97266a64236a9cf8f31f93a9492",
            "d6785c14d14d4a178a4e518a7693e31f",
            "e07d699bc2634e4588ed181a9e15c1e7"
          ]
        },
        "outputId": "7e36ecd1-1528-40bb-9570-52df2e378442"
      },
      "source": [
        "nb_test = 1000\n",
        "correctshG_test =[]\n",
        "labelshG_test = []\n",
        "for k in tq(range(nb_test), desc=\"Testing\"):\n",
        "    x,l = generator.generate_input_hard()\n",
        "    y = RNNG(x)\n",
        "    _,preds = torch.max(y.data,1)\n",
        "    correctshG_test.append(preds.item() == l.data.item())\n",
        "    labelshG_test.append(l.data)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cd2142f397f2480fa64f1cfee56fb72f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Testing', max=1000.0, style=ProgressStyle(description_wid…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EDiAHxCIZvJ6",
        "colab_type": "text"
      },
      "source": [
        "Accuracy on a test set of hard instances, i.e. instances longer than those seen during the training :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "kUMHjsXrgjVB",
        "outputId": "6967ddbb-e295-448b-a4bb-9bae3c1226e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "np.sum(correctshG_test)/nb_test"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.601"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "9GO-LItVgjVN"
      },
      "source": [
        "## 4. [LSTM](https://mlelarge.github.io/dataflowr-slides/PlutonAI/lesson7.html#27)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4gTATkvrgjVR",
        "colab": {}
      },
      "source": [
        "class LSTMNet(nn.Module):\n",
        "    def __init__(self, dim_input=10, dim_recurrent=50, num_layers=4, dim_output=2):\n",
        "        super(LSTMNet, self).__init__()\n",
        "        self.lstm = nn.LSTM(input_size = dim_input, \n",
        "                           hidden_size = dim_recurrent,\n",
        "                           num_layers = num_layers)\n",
        "        self.fc_o2y = nn.Linear(dim_recurrent,dim_output)\n",
        "        \n",
        "    def forward(self, x):\n",
        "        x = x.unsqueeze(1)\n",
        "        output, _ = self.lstm(x)\n",
        "        output = output.squeeze(1)\n",
        "        output = output.narrow(0, output.size(0)-1,1)\n",
        "        return self.fc_o2y(F.relu(output))\n",
        "    \n",
        "lstm = gpu(LSTMNet(dim_input = nb_symbol))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "WkjccxdsgjVX",
        "colab": {}
      },
      "source": [
        "x, l = generator.generate_input()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "sgdPdLupgjVf",
        "outputId": "1986b140-a7f1-47c1-9827-941fa846713d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "lstm(x)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.0807, 0.0475]], device='cuda:0', grad_fn=<AddmmBackward>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Rp2KI_eSgjVk",
        "outputId": "85836aaf-d13e-4fd5-e4e5-3cd36827d81a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83,
          "referenced_widgets": [
            "5cbd0fa62f7f4210aced0b81ceaed950",
            "d5f0f03f88a346e881d92d9a86302d31",
            "2cb5ef8a4c8843c7a545d6b46807837a",
            "3a5bd7447fb64edc8e4fbcadd6fbcad7",
            "c1481000032b40789f848177900f087c",
            "4ecf1d5234a04192afa61501f2ee08ed",
            "8fffa786468948fa8b134533cdd9bb99",
            "2443cd332f7d436b8070fa436bd2c945"
          ]
        }
      },
      "source": [
        "optimizerL = torch.optim.Adam(lstm.parameters(),lr=1e-3)\n",
        "loss_tL = []\n",
        "correctsL =[]\n",
        "labelsL = []\n",
        "start = time.time()\n",
        "for k in tq(range(nb_train), desc=\"Training\"):\n",
        "    x,l = generator.generate_input(hard_false = False)\n",
        "    y = lstm(x)\n",
        "    loss = cross_entropy(y,l)\n",
        "    _,preds = torch.max(y.data,1)\n",
        "    correctsL.append(preds.item() == l.data.item())\n",
        "    optimizerL.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizerL.step()\n",
        "    loss_tL.append(loss)\n",
        "    labelsL.append(l.item())\n",
        "print(\"Total Training Time : {}\".format(time.strftime(\"%M:%S\", time.localtime(time.time() - start))))"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5cbd0fa62f7f4210aced0b81ceaed950",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Training', max=40000.0, style=ProgressStyle(description_w…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Total Training Time : 03:49\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "LwLenhrCgjVo",
        "outputId": "b3ecc1ae-b162-4f6a-b5ea-433b48b270ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "plt.plot(running_mean(loss_tL,int(nb_train/50)))\n",
        "plt.plot(running_mean(loss_tG,int(nb_train/50)))\n",
        "plt.plot(running_mean(loss_t,int(nb_train/50)));"
      ],
      "execution_count": 65,
      "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": {
        "colab_type": "code",
        "id": "Ng_wRELigjVt",
        "outputId": "911154cf-e73e-4751-ec0d-bd82c1678f5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "plt.plot(running_mean(correctsL,int(nb_train/50)))\n",
        "plt.plot(running_mean(correctsG,int(nb_train/50)))\n",
        "plt.plot(running_mean(corrects,int(nb_train/50)));"
      ],
      "execution_count": 66,
      "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": {
        "colab_type": "code",
        "id": "oFfEMA89gjVw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "cb10ac92793f42caad22ec3782bdbb2b",
            "a7492769f9e24c769cbf5553e517d530",
            "c8b5b35bdc9a4bd59440d9b99237f768",
            "8f03c5d6bfae4c3d9280329f12f6753b",
            "35f6ae48a0524e889285636dbccd109f",
            "7cc087a2510e431aba484dacedebe953",
            "a0042942f7f64ede963130ef9b8ebf67",
            "f9545d12fdb243c58f2f8b7262a681f0"
          ]
        },
        "outputId": "77e4e5ec-4af5-42e2-d153-cea9a8b58708"
      },
      "source": [
        "nb_test = 1000\n",
        "correctsL_test =[]\n",
        "labelsL_test = []\n",
        "for k in tq(range(nb_test), desc=\"Testing\"):\n",
        "    x,l = generator.generate_input(len_r=seq_max_len,true_parent=True)\n",
        "    y = lstm(x)\n",
        "    _,preds = torch.max(y.data,1)\n",
        "    correctsL_test.append(preds.item() == l.data.item())\n",
        "    labelsL_test.append(l.data)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cb10ac92793f42caad22ec3782bdbb2b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Testing', max=1000.0, style=ProgressStyle(description_wid…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sSH-Hg2fZvKP",
        "colab_type": "text"
      },
      "source": [
        "Accuracy on valid parenthesis strings only:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "mn4MCPRDgjV2",
        "outputId": "8add5494-6cf6-478b-aea3-92cdbec5099e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "np.sum(correctsL_test)/nb_test"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "CIvIS-gEgjV-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "76e61f0987354a2ea7e58a93e313dd46",
            "7a41c2123d4e4731b48b79f09ee1b0ee",
            "1b4a47c1db2b48e384c6f1d806e001b4",
            "bca6849513ed43c7927bbfc9a93906a2",
            "61d2d0fe06974354803fb2ccf3892b61",
            "aadc20a59d1d435aba943a4549942cc1",
            "f45716b0f401429b8c64b784cb9510e1",
            "5cb0245ef5764d409848e2200071c50a"
          ]
        },
        "outputId": "5ade165a-bcf2-49c6-f0de-c943f09c876b"
      },
      "source": [
        "nb_test = 1000\n",
        "correctsL_test =[]\n",
        "labelsL_test = []\n",
        "for k in tq(range(nb_test), desc=\"Testing\"):\n",
        "    x,l = generator.generate_input(len_r=seq_max_len,true_parent=False,hard_false = True)\n",
        "    y = lstm(x)\n",
        "    _,preds = torch.max(y.data,1)\n",
        "    correctsL_test.append(preds.item() == l.data.item())\n",
        "    labelsL_test.append(l.data)"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "76e61f0987354a2ea7e58a93e313dd46",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Testing', max=1000.0, style=ProgressStyle(description_wid…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h_GZSFHxZvKR",
        "colab_type": "text"
      },
      "source": [
        "Accuracy on a test set (similar to the training set):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "b06Gp7lrgjWC",
        "outputId": "1a903369-110b-40b4-c144-3250dcd57e8d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "np.sum(correctsL_test)/nb_test"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "rniUHL4mgjWE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "96ae46bbf6e848639176a40cc8bd0adb",
            "0b0094f43be7450a9e4447f3e7c3d1b6",
            "79ef29a6308b41c498e6d53bdccbfa1c",
            "ef31d9a269594215981462ca27f245d8",
            "39fe6405028f43fead92231df9bcb6a4",
            "87bab76d3b534cfea4e89894d05183ea",
            "719da0e46c9947f4b231a0301626c698",
            "dad9113a85fb44379414e75f6d99e3a0"
          ]
        },
        "outputId": "129e06a8-9dbb-42cf-c4da-3b2974630bed"
      },
      "source": [
        "nb_test = 1000\n",
        "correctshL_test =[]\n",
        "labelshL_test = []\n",
        "for k in tq(range(nb_test), desc=\"Testing\"):\n",
        "    x,l = generator.generate_input_hard()\n",
        "    y = lstm(x)\n",
        "    _,preds = torch.max(y.data,1)\n",
        "    correctshL_test.append(preds.item() == l.data.item())\n",
        "    labelshL_test.append(l.data)"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "96ae46bbf6e848639176a40cc8bd0adb",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Testing', max=1000.0, style=ProgressStyle(description_wid…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tskqOH5vZvKV",
        "colab_type": "text"
      },
      "source": [
        "Accuracy on a test set of hard instances, i.e. instances longer than those seen during the training :\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Zh43kUcGgjWI",
        "outputId": "7df8e33b-ba7a-49d1-fb83-aeec59094bb9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "np.sum(correctshL_test)/nb_test"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.831"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "mWUSVdiggjWO"
      },
      "source": [
        "## 5. GRU\n",
        "\n",
        "Implement your RNN with a [GRU](https://pytorch.org/docs/stable/nn.html#gru)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "JSH65GakgjWV",
        "colab": {}
      },
      "source": [
        "class GRUNet(nn.Module):\n",
        "    def __init__(self, dim_input=10, dim_recurrent=50, num_layers=4, dim_output=2):\n",
        "        super(GRUNet, self).__init__()\n",
        "        self.gru = nn.GRU(input_size = dim_input, \n",
        "                           hidden_size = dim_recurrent,\n",
        "                           num_layers = num_layers)\n",
        "        self.fc_o2y = nn.Linear(dim_recurrent,dim_output)\n",
        "        \n",
        "    def forward(self, x):\n",
        "        x = x.unsqueeze(1)\n",
        "        output, _ = self.gru(x)\n",
        "        output = output.squeeze(1)\n",
        "        output = output.narrow(0, output.size(0)-1,1)\n",
        "        return self.fc_o2y(F.relu(output))\n",
        "    \n",
        "gru = gpu(GRUNet(dim_input = nb_symbol))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FlPavCETepUz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b460b228-17bc-4b17-d451-147a3a859ead"
      },
      "source": [
        "x, l = generator.generate_input()\n",
        "gru(x)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-0.0081, -0.0820]], device='cuda:0', grad_fn=<AddmmBackward>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RkQfI4BUerfX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83,
          "referenced_widgets": [
            "f958c02d14a242d29e4101d0c009e04a",
            "4e6e29cffba94eb0b1391de271df8755",
            "d89f155295d9415196825cba83e09b1d",
            "496282689bcc4e7a9794d01c5e2f94b7",
            "e7b8ff6d037a41f0bda93c251129e16d",
            "7b7e4482239446629099f8dde6b7ddc2",
            "1f78729a6faa4e03bad54eb331375312",
            "1126b52f5c7148b195c15b537c490711"
          ]
        },
        "outputId": "e99762ac-e328-49d5-a249-748bcaea540b"
      },
      "source": [
        "optimizerGRU = torch.optim.Adam(gru.parameters(),lr=1e-3)\n",
        "loss_tGRU = []\n",
        "correctsGRU =[]\n",
        "labelsGRU = []\n",
        "start = time.time()\n",
        "for k in tq(range(nb_train), desc=\"Training\"):\n",
        "    x,l = generator.generate_input(hard_false = False)\n",
        "    y = gru(x)\n",
        "    loss = cross_entropy(y,l)\n",
        "    _,preds = torch.max(y.data,1)\n",
        "    correctsGRU.append(preds.item() == l.data.item())\n",
        "    optimizerGRU.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizerGRU.step()\n",
        "    loss_tGRU.append(loss)\n",
        "    labelsGRU.append(l.item())\n",
        "print(\"Total Training Time : {}\".format(time.strftime(\"%M:%S\", time.localtime(time.time() - start))))"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f958c02d14a242d29e4101d0c009e04a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Training', max=40000.0, style=ProgressStyle(description_w…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Total Training Time : 03:44\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mf-rfYPVez9w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "0dc7fbdd-4547-413c-8a64-ddaaf0276a54"
      },
      "source": [
        "plt.plot(running_mean(loss_tGRU,int(nb_train/50)), label=\"GRU\")\n",
        "plt.plot(running_mean(loss_tL,int(nb_train/50)), label=\"LSTM\")\n",
        "plt.plot(running_mean(loss_tG,int(nb_train/50)), label=\"RNN with Gating\")\n",
        "plt.plot(running_mean(loss_t,int(nb_train/50)), label=\"Elman Network\")\n",
        "plt.title(\"Loss\")\n",
        "plt.legend();"
      ],
      "execution_count": 76,
      "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": "5SKsN9H_e2ur",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "427fb6b6-e971-42fa-8212-75f8f2ae109c"
      },
      "source": [
        "plt.plot(running_mean(correctsGRU,int(nb_train/50)), label=\"GRU\")\n",
        "plt.plot(running_mean(correctsL,int(nb_train/50)), label=\"LSTM\")\n",
        "plt.plot(running_mean(correctsG,int(nb_train/50)), label=\"RNN with Gating\")\n",
        "plt.plot(running_mean(corrects,int(nb_train/50)), label=\"Elman Network\")\n",
        "plt.title(\"Accuracy\")\n",
        "plt.legend();"
      ],
      "execution_count": 77,
      "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": "hfDQVl6Ye4nR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "706489896a6d4001bd86f18ba0b6c911",
            "d20e55d563ad4bb3a214dfc55f9b6a81",
            "256117379d0f433cb11bd96afd6ecdf6",
            "1f7ba8ccbb6149928cbdfbdf996c408f",
            "29c36742bc5f4b668e7e043110848451",
            "9d26faf35935486fa9f4a169afc2f91b",
            "978d32b676d5403aa172202929dd4fa7",
            "527a51228c2d42949a8ba6bb4c009e45"
          ]
        },
        "outputId": "8d98d566-aa8a-41a3-fbb8-e65534b63674"
      },
      "source": [
        "nb_test = 1000\n",
        "correctsGRU_test =[]\n",
        "labelsGRU_test = []\n",
        "for k in tq(range(nb_test), desc=\"Testing\"):\n",
        "    x,l = generator.generate_input(len_r=seq_max_len,true_parent=True)\n",
        "    y = gru(x)\n",
        "    _,preds = torch.max(y.data,1)\n",
        "    correctsGRU_test.append(preds.item() == l.data.item())\n",
        "    labelsGRU_test.append(l.data)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "706489896a6d4001bd86f18ba0b6c911",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Testing', max=1000.0, style=ProgressStyle(description_wid…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SoMxefSPgcKE",
        "colab_type": "text"
      },
      "source": [
        "Accuracy on valid parenthesis strings only:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "19mjdyM-fzMD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e79ddbd9-6f87-4658-91bc-09cfb283a5e1"
      },
      "source": [
        "np.sum(correctsGRU_test)/nb_test"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pH7GQTHff7yF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "a989488d227348fdae782f3f0760bf29",
            "d133b27239ea45d6b439a06a35d277e8",
            "1bd4091ad05f432bb1351bc6d5ad2661",
            "5a276fe025d148c98550454c32d57784",
            "162c7642cfef41cf97d01a2a572d5c17",
            "42df29b15274400ea976b316573c44b7",
            "77ad349065c34e3a8a63bf57b0634136",
            "bb2635d407c9416589902c50aaa65333"
          ]
        },
        "outputId": "911768bc-ba9e-4f88-fe64-2caf7b30ba80"
      },
      "source": [
        "nb_test = 1000\n",
        "correctsGRU_test =[]\n",
        "labelsGRU_test = []\n",
        "for k in tq(range(nb_test), desc=\"Testing\"):\n",
        "    x,l = generator.generate_input(len_r=seq_max_len,true_parent=False, hard_false=True)\n",
        "    y = gru(x)\n",
        "    _,preds = torch.max(y.data,1)\n",
        "    correctsGRU_test.append(preds.item() == l.data.item())\n",
        "    labelsGRU_test.append(l.data)"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a989488d227348fdae782f3f0760bf29",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Testing', max=1000.0, style=ProgressStyle(description_wid…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tcTWHtrqgmOZ",
        "colab_type": "text"
      },
      "source": [
        "Accuracy on a test set (similar to the training set):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RxcNlPYMgFQ1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "72a5d93c-31fb-417f-9629-38fb9e7d2fd1"
      },
      "source": [
        "np.sum(correctsGRU_test)/nb_test"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.986"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O2t-ySyxgGgY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "e85dab8789724aee827f3fd1cf2a1251",
            "4417659e44b749b2b82b1587b2d939c4",
            "8112e806c6bb4ff8bd7604cdb41b0cd9",
            "10827f64c3b24b17a9928439c90ce3c8",
            "95ae2861d5294ba9acf408921480c480",
            "396b79ec42264ad6917b6b79bac99541",
            "4fe111a8ddc34afebf45acc52c0d722b",
            "9858b75f7db8423e983854913f811390"
          ]
        },
        "outputId": "4d4c6ffd-5a73-4e46-95c5-6dd734ac217b"
      },
      "source": [
        "nb_test = 1000\n",
        "correctsGRU_test =[]\n",
        "labelsGRU_test = []\n",
        "for k in tq(range(nb_test), desc=\"Testing\"):\n",
        "    x,l = generator.generate_input_hard()\n",
        "    y = gru(x)\n",
        "    _,preds = torch.max(y.data,1)\n",
        "    correctsGRU_test.append(preds.item() == l.data.item())\n",
        "    labelsGRU_test.append(l.data)"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e85dab8789724aee827f3fd1cf2a1251",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Testing', max=1000.0, style=ProgressStyle(description_wid…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mNfBrGt5gy1u",
        "colab_type": "text"
      },
      "source": [
        "Accuracy on a test set of hard instances, i.e. instances longer than those seen during the training :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xPOcxP45gIhO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4853454a-fbde-4674-cd1a-8706fd7a530a"
      },
      "source": [
        "np.sum(correctsGRU_test)/nb_test"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "OvHhG7KfgjWe"
      },
      "source": [
        "## 6. Explore!\n",
        "\n",
        "What are good negative examples?\n",
        "\n",
        "How to be sure that your network 'generalizes'?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lCVb66-xgjWj",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}