eeglstm.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "premium",
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "c9500c15e24f42aaba70dc6a38552e61": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_8236ce49be664e26a66b25b3d9d3172a",
              "IPY_MODEL_2e546643233841b2ba8fd43e4e5a09c9",
              "IPY_MODEL_628cb67d65cc43a3a4fc88d884e65c68"
            ],
            "layout": "IPY_MODEL_a173647b9f0340128e69463dceb65795"
          }
        },
        "8236ce49be664e26a66b25b3d9d3172a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_fcfba4966eb3476095c6170bcc42da16",
            "placeholder": "​",
            "style": "IPY_MODEL_1f35e50277254bfbbda90a1c023ac3e7",
            "value": "100%"
          }
        },
        "2e546643233841b2ba8fd43e4e5a09c9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_170f70f4a3b146b4b011c5cac11623f5",
            "max": 50,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_02cf1352df9646f4b19b40fe75da0fc3",
            "value": 50
          }
        },
        "628cb67d65cc43a3a4fc88d884e65c68": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_e3bfcef810204683aa8641e48d588187",
            "placeholder": "​",
            "style": "IPY_MODEL_e51590161f0c4adda30e9e252d6dc4e9",
            "value": " 50/50 [01:12&lt;00:00,  1.24s/it]"
          }
        },
        "a173647b9f0340128e69463dceb65795": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "fcfba4966eb3476095c6170bcc42da16": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "1f35e50277254bfbbda90a1c023ac3e7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "170f70f4a3b146b4b011c5cac11623f5": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "02cf1352df9646f4b19b40fe75da0fc3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "e3bfcef810204683aa8641e48d588187": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e51590161f0c4adda30e9e252d6dc4e9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "bbb550f9000043e6a9b035c3b5c02c1c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_54c9201ab86c40cd832951a5db8de2c1",
              "IPY_MODEL_0944a73ba1174185868a5b6633706ea8",
              "IPY_MODEL_11f2dda3ae6b425cb9b1ae904a9af2f1"
            ],
            "layout": "IPY_MODEL_804f4b66b43e460698596b107beb3cc9"
          }
        },
        "54c9201ab86c40cd832951a5db8de2c1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_a85ece9bb5b2423dac5734b9627b90af",
            "placeholder": "​",
            "style": "IPY_MODEL_2bac769d7faf4d89858768be3e70d259",
            "value": "100%"
          }
        },
        "0944a73ba1174185868a5b6633706ea8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_89404301937543538b36f4d689def74b",
            "max": 50,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_3fc694d77d75440189004551e345fe7c",
            "value": 50
          }
        },
        "11f2dda3ae6b425cb9b1ae904a9af2f1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_b83a9733643c4b8f9b8b8ce4a9fc54b6",
            "placeholder": "​",
            "style": "IPY_MODEL_e5314bde00d04b51b070360e8d10b748",
            "value": " 50/50 [01:04&lt;00:00,  1.18s/it]"
          }
        },
        "804f4b66b43e460698596b107beb3cc9": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a85ece9bb5b2423dac5734b9627b90af": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "2bac769d7faf4d89858768be3e70d259": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "89404301937543538b36f4d689def74b": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "3fc694d77d75440189004551e345fe7c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "b83a9733643c4b8f9b8b8ce4a9fc54b6": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e5314bde00d04b51b070360e8d10b748": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "8d4d9e985dba4c6b8efd471d924404e1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_62598364a0e44585ad68c5bed2c4b240",
              "IPY_MODEL_0aab96fd5e704d3bbc3c2381b1cce0e8",
              "IPY_MODEL_efc0a6e8bd604cdd919be1e607e1329c"
            ],
            "layout": "IPY_MODEL_135ca010e59a46d1bca46795bad0c656"
          }
        },
        "62598364a0e44585ad68c5bed2c4b240": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_4bd6c40d566249b48b2da2bca5486715",
            "placeholder": "​",
            "style": "IPY_MODEL_aaac29dc0ab94267950984e2105dc2ac",
            "value": "100%"
          }
        },
        "0aab96fd5e704d3bbc3c2381b1cce0e8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_942b8590d95e47b4ac85e6950e2ca8c5",
            "max": 50,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_033cd43a12e84f6e86e75d9d001d7066",
            "value": 50
          }
        },
        "efc0a6e8bd604cdd919be1e607e1329c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_cc5e1da4cc874e84b2be6fa1714533fd",
            "placeholder": "​",
            "style": "IPY_MODEL_1d19ef3e628f4f0fb51b899ddc7db2d4",
            "value": " 50/50 [01:01&lt;00:00,  1.17s/it]"
          }
        },
        "135ca010e59a46d1bca46795bad0c656": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "4bd6c40d566249b48b2da2bca5486715": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "aaac29dc0ab94267950984e2105dc2ac": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "942b8590d95e47b4ac85e6950e2ca8c5": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "033cd43a12e84f6e86e75d9d001d7066": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "cc5e1da4cc874e84b2be6fa1714533fd": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "1d19ef3e628f4f0fb51b899ddc7db2d4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "2d38d3cc0eb04fff95cc8a699d6bff21": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_4b15620e87b046e98830b518d0566c2c",
              "IPY_MODEL_435fb42ebaf5409c8de92b1c9ff5904d",
              "IPY_MODEL_4e221fc709894991bcc9f46119ac3a32"
            ],
            "layout": "IPY_MODEL_7818ef8654fa4e6484218a168e1f25b1"
          }
        },
        "4b15620e87b046e98830b518d0566c2c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_9f366d0ed189458d9c7bc8adbf875e1d",
            "placeholder": "​",
            "style": "IPY_MODEL_9e7e7706de28432ea914fb2aa58ace6d",
            "value": "100%"
          }
        },
        "435fb42ebaf5409c8de92b1c9ff5904d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_499aa7fb34b045dc986adeb7139361f0",
            "max": 50,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_f9878b9efd72483b8723e866fd77d3ae",
            "value": 50
          }
        },
        "4e221fc709894991bcc9f46119ac3a32": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_cec6eaa9f1054fee9378b56771725297",
            "placeholder": "​",
            "style": "IPY_MODEL_d910071a320347fc97382e96563b4b0e",
            "value": " 50/50 [01:02&lt;00:00,  1.28s/it]"
          }
        },
        "7818ef8654fa4e6484218a168e1f25b1": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9f366d0ed189458d9c7bc8adbf875e1d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9e7e7706de28432ea914fb2aa58ace6d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "499aa7fb34b045dc986adeb7139361f0": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "f9878b9efd72483b8723e866fd77d3ae": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "cec6eaa9f1054fee9378b56771725297": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "d910071a320347fc97382e96563b4b0e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "53bcd933693e4b82a762f25c4b76f254": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_b4190782517148988e25353417ab6490",
              "IPY_MODEL_7af35cccbc4c4d4a81e18a306141f23e",
              "IPY_MODEL_be239e8e675949dd97367236e4ed3d92"
            ],
            "layout": "IPY_MODEL_2841a995795042bab7e72de93b26b6f0"
          }
        },
        "b4190782517148988e25353417ab6490": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_4f4e09bba03e4162a2df527a7b73e06c",
            "placeholder": "​",
            "style": "IPY_MODEL_e8be1e3e32924036b2b10069e050c910",
            "value": "100%"
          }
        },
        "7af35cccbc4c4d4a81e18a306141f23e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_aea5eb883db1488fbcb136712fec860c",
            "max": 50,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_b6d88ba62d3c4290be3701eaedc4fa48",
            "value": 50
          }
        },
        "be239e8e675949dd97367236e4ed3d92": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_bfc09e8d820844acb0f3564b4d8279c9",
            "placeholder": "​",
            "style": "IPY_MODEL_a48f29d9d07449c99cd73028928f83be",
            "value": " 50/50 [01:01&lt;00:00,  1.15s/it]"
          }
        },
        "2841a995795042bab7e72de93b26b6f0": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "4f4e09bba03e4162a2df527a7b73e06c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e8be1e3e32924036b2b10069e050c910": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "aea5eb883db1488fbcb136712fec860c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "b6d88ba62d3c4290be3701eaedc4fa48": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "bfc09e8d820844acb0f3564b4d8279c9": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a48f29d9d07449c99cd73028928f83be": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Install required packages"
      ],
      "metadata": {
        "id": "03TaUFyGrlS7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install timm"
      ],
      "metadata": {
        "id": "hFyEr5eiAKJx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a7b3c308-07af-493f-ecc6-3c2c0a6c7ede"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: timm in /usr/local/lib/python3.9/dist-packages (0.6.12)\n",
            "Requirement already satisfied: torch>=1.7 in /usr/local/lib/python3.9/dist-packages (from timm) (1.13.1+cu116)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.9/dist-packages (from timm) (0.14.1+cu116)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.9/dist-packages (from timm) (0.13.2)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.9/dist-packages (from timm) (6.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.9/dist-packages (from torch>=1.7->timm) (4.5.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from huggingface-hub->timm) (2.25.1)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.9/dist-packages (from huggingface-hub->timm) (23.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.9/dist-packages (from huggingface-hub->timm) (4.65.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from huggingface-hub->timm) (3.9.0)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.9/dist-packages (from torchvision->timm) (8.4.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (from torchvision->timm) (1.22.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->huggingface-hub->timm) (2022.12.7)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->huggingface-hub->timm) (2.10)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->huggingface-hub->timm) (1.26.14)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.9/dist-packages (from requests->huggingface-hub->timm) (4.0.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Check GPU status"
      ],
      "metadata": {
        "id": "qETdGgm4rnX6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! nvidia-smi"
      ],
      "metadata": {
        "id": "SQvtjTu_M42s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e060476-5b33-4a81-822d-447e7610ad54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tue Mar 14 08:39:32 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   71C    P0    31W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import"
      ],
      "metadata": {
        "id": "VeHNI4WwrpLr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import sys\n",
        "import time\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import cv2\n",
        "import PIL.Image\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "from tqdm.notebook import tqdm\n",
        "from sklearn.metrics import roc_auc_score\n",
        "import torch\n",
        "from torch.utils.data import TensorDataset, DataLoader,Dataset\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "import torch.optim as optim\n",
        "from torch.optim import lr_scheduler\n",
        "from torch.utils.data.sampler import SubsetRandomSampler, RandomSampler, SequentialSampler\n",
        "from torch.optim.lr_scheduler import CosineAnnealingLR\n",
        "import albumentations as A\n",
        "import timm\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "device = torch.device('cuda')"
      ],
      "metadata": {
        "id": "C7yem24vMqw5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Read Data"
      ],
      "metadata": {
        "id": "2HqMSLBerq4u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! ls /content/drive/MyDrive/247_proj_cnn"
      ],
      "metadata": {
        "id": "SGYDc8vjMyqo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9660adb9-fd6d-4626-edef-3f0ffdab1d35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'CNN-LSTM Hybrid with data preprocessing.ipynb'   fold_4_best_acc.ckpt\n",
            "'CNN with data preprocessing.ipynb'\t\t  person_test.npy\n",
            " EEG_loading.ipynb\t\t\t\t  person_train_valid.npy\n",
            " fold_0_best_acc.ckpt\t\t\t\t  X_test.npy\n",
            " fold_1_best_acc.ckpt\t\t\t\t  X_train_valid.npy\n",
            " fold_2_best_acc.ckpt\t\t\t\t  y_test.npy\n",
            " fold_3_best_acc.ckpt\t\t\t\t  y_train_valid.npy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "os.chdir('/content/drive/MyDrive/247_proj_cnn')"
      ],
      "metadata": {
        "id": "F9DcuRkYN7bd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_valid = np.load('X_train_valid.npy')\n",
        "y_train_valid = np.load('y_train_valid.npy')"
      ],
      "metadata": {
        "id": "ARbCBpxBOFfI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_valid.shape"
      ],
      "metadata": {
        "id": "qeC3Hmd0jUX_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e40c5e17-ada8-4e03-bd80-9bf1fae78b4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2115, 22, 1000)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = np.load('X_test.npy')\n",
        "y_test = np.load('y_test.npy')"
      ],
      "metadata": {
        "id": "AC__tJa3XUeV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Define dataset"
      ],
      "metadata": {
        "id": "ObweIGWkp7Ki"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class BCIDataset(Dataset):\n",
        "    def __init__(self, X, y):\n",
        "        self.X = X\n",
        "        self.y = y\n",
        "        self.encode_class = {769:0, 770:1, 771:2, 772:3}\n",
        "\n",
        "    def __len__(self):\n",
        "        return self.X.shape[0]\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        x = torch.tensor(self.X[index,:,:500]).float()\n",
        "        y = self.y[index]\n",
        "        y = self.encode_class[y]\n",
        "        y = torch.tensor(y).long()\n",
        "        # return x, y\n",
        "        return x.permute(1, 0), y\n",
        "        # 1000, 22, 1\n",
        "        # return torch.stack([x,x,x]), y\n"
      ],
      "metadata": {
        "id": "HAz0VsYUkg0o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "next(iter(BCIDataset(X_test, y_test)))[0].shape"
      ],
      "metadata": {
        "id": "HNWyMEFGFipD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "92611fd9-c283-473f-def0-97f8e32192fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([500, 22])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "plt.imshow(next(iter(BCIDataset(X_test, y_test)))[0].transpose(1,0), cmap='gray')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 99
        },
        "id": "uUYfxguyMwQf",
        "outputId": "4e23ca97-b1cb-4efb-e32a-ff0f152ab7b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fea99274f70>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Define model"
      ],
      "metadata": {
        "id": "DXxzlcX5p8Y_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "class ERUConv(nn.Module):\n",
        "    def __init__(self, in_channel, out_channel, kernel_size, mp_kernal_size, padding, dropout):\n",
        "        super().__init__()\n",
        "        self.conv = nn.Conv2d(in_channel, out_channel, \n",
        "                              kernel_size = kernel_size, padding=padding)\n",
        "        nn.init.xavier_uniform_(self.conv.weight)\n",
        "        self.elu = nn.ELU()\n",
        "        self.maxpool = nn.MaxPool2d(kernel_size = mp_kernal_size)\n",
        "        self.BN = nn.BatchNorm2d(out_channel)\n",
        "        self.dropout = nn.Dropout(p=dropout)\n",
        "    \n",
        "    def forward(self, x):\n",
        "        x = self.elu(self.conv(x))\n",
        "        x = self.maxpool(x)\n",
        "        x = self.BN(x)\n",
        "        x = self.dropout(x)\n",
        "        return x\n",
        "\n",
        "class EEGLSTMModel(nn.Module):\n",
        "    def __init__(self, input_size=22, hidden_size=256, num_layers=1, drop_out=0.4):\n",
        "        super().__init__()\n",
        "        self.hidden_size = hidden_size\n",
        "        self.num_layers = num_layers\n",
        "        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)\n",
        "        self.fc = nn.Sequential(\n",
        "            nn.Linear(52736, 64), \n",
        "            nn.BatchNorm1d(64),\n",
        "            nn.ELU(),\n",
        "            nn.Dropout(drop_out),\n",
        "            nn.Linear(64,4)\n",
        "        )\n",
        "\n",
        "        self.conv_temp = nn.Conv2d(1, 1, (1, 5), stride = (1, 3)) # 3\n",
        "        self.elu = nn.ELU()\n",
        "        nn.init.xavier_uniform_(self.conv_temp.weight)\n",
        "\n",
        "        self.conv1 = ERUConv(22, 44, (10, 1), (2, 1), 'same', drop_out)\n",
        "        self.conv2 = ERUConv(44, 88, (10, 1), (2, 1), 'same', drop_out)\n",
        "        self.conv3 = ERUConv(88, 512, (10, 1), (2, 1), 'same', drop_out)\n",
        "\n",
        "    def forward(self, x):\n",
        "        # CNN features\n",
        "        # X [bs, 500, 22]\n",
        "        x_cnn = x.permute(0,2,1).unsqueeze(1) \n",
        "        x_cnn = self.conv_temp(x_cnn)\n",
        "        # print(x_cnn.shape)\n",
        "        # extra rnn feature\n",
        "        # Set initial hidden and cell states\n",
        "        h0 = torch.zeros(self.num_layers, x_cnn.size(0), self.hidden_size).to(x.device)\n",
        "        c0 = torch.zeros(self.num_layers, x_cnn.size(0), self.hidden_size).to(x.device)\n",
        "        x_rnn, _ = self.lstm(x_cnn.squeeze(1).permute(0,2,1), (h0, c0))\n",
        "        \n",
        "        # print(x_rnn.shape)\n",
        "        x_cnn = x_cnn.permute(0,2,3,1)\n",
        "        x_cnn = self.conv1(x_cnn)\n",
        "        # print(x_cnn.shape)\n",
        "        x_cnn = self.conv2(x_cnn)\n",
        "        # print(x_cnn.shape)\n",
        "        x_cnn = self.conv3(x_cnn)\n",
        "        # print(x_cnn.shape)\n",
        "        x_cnn = x_cnn.squeeze(-1).permute(0,2,1)\n",
        "        \n",
        "        bs = x_cnn.shape[0]\n",
        "        x_cnn = x_cnn.reshape(bs, -1)\n",
        "        x_rnn = x_rnn.reshape(bs, -1)\n",
        "        x_feature = torch.concat([x_cnn, x_rnn], dim=1)\n",
        "\n",
        "        out = self.fc(x_feature)\n",
        "        \n",
        "        return out\n",
        "\n",
        "model = EEGLSTMModel()\n",
        "model(torch.randn(2,500,22)).shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WQsynJ9qx-Y3",
        "outputId": "069afe0e-c8dd-449b-e9d0-d137f52987a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/torch/nn/modules/conv.py:459: UserWarning: Using padding='same' with even kernel lengths and odd dilation may require a zero-padded copy of the input be created (Triggered internally at ../aten/src/ATen/native/Convolution.cpp:895.)\n",
            "  return F.conv2d(input, weight, bias, self.stride,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([2, 4])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training and validation"
      ],
      "metadata": {
        "id": "X79MQadLp3KH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## training configurations"
      ],
      "metadata": {
        "id": "H36St9mpuYFw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_epochs = 50\n",
        "lr = 0.0003\n",
        "k = 5 # number of folds\n",
        "batch_size = 64\n",
        "num_worker = 2\n",
        "\n",
        "input_size=22\n",
        "hidden_size=256\n",
        "num_layers=1\n",
        "drop_out=0.5"
      ],
      "metadata": {
        "id": "dj7yBLfemCja"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_valid_fold(X_train, y_train, X_valid, y_valid, fold_id):\n",
        "    print('Building dataset...')\n",
        "    bcidataset_train = BCIDataset(X_train, y_train)\n",
        "    bcidataset_train_dl = torch.utils.data.DataLoader(bcidataset_train, batch_size=batch_size, num_workers=num_worker, shuffle=True)\n",
        "    bcidataset_test = BCIDataset(X_valid, y_valid)\n",
        "    bcidataset_test_dl = torch.utils.data.DataLoader(bcidataset_test, batch_size=batch_size, num_workers=num_worker, shuffle=False)\n",
        "\n",
        "    print('Create new model...')\n",
        "    model = EEGLSTMModel(input_size=input_size,\n",
        "                         hidden_size=hidden_size,\n",
        "                         num_layers=num_layers,\n",
        "                         drop_out=drop_out)\n",
        "    model = model.cuda()\n",
        "\n",
        "    print('Setting up loss function, optimizer and scheduler...')\n",
        "    criterion = nn.CrossEntropyLoss()\n",
        "    optimizer = optim.Adam(model.parameters(), lr=lr)\n",
        "    scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_epochs,)\n",
        "\n",
        "    # Training loop\n",
        "    accuracy_list = []\n",
        "    loss_by_epoch = []\n",
        "    val_loss_by_epoch = []\n",
        "    max_accuracy = 0\n",
        "    best_model = model\n",
        "    for epoch in tqdm(range(num_epochs)):\n",
        "        losses = []\n",
        "        model.train()\n",
        "        for i, (inputs, labels) in enumerate(bcidataset_train_dl):\n",
        "            inputs = inputs.cuda()\n",
        "            labels = labels.cuda()\n",
        "            optimizer.zero_grad()\n",
        "            outputs = model(inputs)\n",
        "            loss = criterion(outputs, labels)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            losses.append(loss.item())\n",
        "        loss = np.mean(losses)\n",
        "        loss_by_epoch.append(loss)\n",
        "        \n",
        "        # validate with the testing dataset\n",
        "        predicted_label = []\n",
        "        actual_label = []\n",
        "        model.eval()\n",
        "        val_losses = []\n",
        "        for i, (inputs, labels) in enumerate(bcidataset_test_dl):\n",
        "            inputs = inputs.cuda()\n",
        "            actual_label += labels.tolist()\n",
        "            labels = labels.cuda()\n",
        "            outputs = model(inputs)\n",
        "            loss = criterion(outputs, labels)\n",
        "            val_losses.append(loss.item())\n",
        "            predicted = outputs.argmax(1)\n",
        "            predicted_label += predicted.cpu().numpy().tolist()\n",
        "        val_loss_by_epoch.append(np.mean(losses))\n",
        "        predicted_label = torch.tensor(predicted_label)\n",
        "        actual_label = torch.tensor(actual_label)\n",
        "        accuracy = (np.array(actual_label) == np.array(predicted_label)).sum() / len(predicted_label)\n",
        "        if accuracy > max_accuracy:\n",
        "            print(f'Better accuracy found ({max_accuracy} -> {accuracy}), best model saved.')\n",
        "            max_accuracy = accuracy\n",
        "            torch.save(model.state_dict(), f'fold_{fold_id}_best_acc.ckpt')\n",
        "        accuracy_list.append(accuracy)\n",
        "        scheduler.step()\n",
        "\n",
        "    axes[0].plot(accuracy_list)\n",
        "    axes[0].set_xlabel('Epoch')\n",
        "    axes[0].set_ylabel('Validation Accuracy')\n",
        "    \n",
        "    axes[1].plot(np.arange(10, num_epochs), loss_by_epoch[10:])\n",
        "    axes[1].set_xlabel('Epoch')\n",
        "    axes[1].set_ylabel('Training Loss')\n",
        "\n",
        "    axes[2].plot(np.arange(10, num_epochs), val_loss_by_epoch[10:])\n",
        "    axes[2].set_xlabel('Epoch')\n",
        "    axes[2].set_ylabel('Validation Loss')"
      ],
      "metadata": {
        "id": "m_2qHFw9lk1o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import KFold\n",
        "\n",
        "\n",
        "# Split the data into k folds\n",
        "kf = KFold(n_splits=k, shuffle=True, random_state=42)\n",
        "\n",
        "fig, axes = plt.subplots(1, 3, figsize=(20, 5))\n",
        "axes = axes.flatten()\n",
        "\n",
        "# Loop over each fold\n",
        "for fold, (train_index, test_index) in enumerate(kf.split(X_train_valid)):\n",
        "\n",
        "    # Get the training and test sets for this fold\n",
        "    X_train, X_valid = X_train_valid[train_index], X_train_valid[test_index]\n",
        "    y_train, y_valid = y_train_valid[train_index], y_train_valid[test_index]\n",
        "    train_valid_fold(X_train, y_train, X_valid, y_valid, fold)"
      ],
      "metadata": {
        "id": "fPmLYFNVo5Vz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "c9500c15e24f42aaba70dc6a38552e61",
            "8236ce49be664e26a66b25b3d9d3172a",
            "2e546643233841b2ba8fd43e4e5a09c9",
            "628cb67d65cc43a3a4fc88d884e65c68",
            "a173647b9f0340128e69463dceb65795",
            "fcfba4966eb3476095c6170bcc42da16",
            "1f35e50277254bfbbda90a1c023ac3e7",
            "170f70f4a3b146b4b011c5cac11623f5",
            "02cf1352df9646f4b19b40fe75da0fc3",
            "e3bfcef810204683aa8641e48d588187",
            "e51590161f0c4adda30e9e252d6dc4e9",
            "bbb550f9000043e6a9b035c3b5c02c1c",
            "54c9201ab86c40cd832951a5db8de2c1",
            "0944a73ba1174185868a5b6633706ea8",
            "11f2dda3ae6b425cb9b1ae904a9af2f1",
            "804f4b66b43e460698596b107beb3cc9",
            "a85ece9bb5b2423dac5734b9627b90af",
            "2bac769d7faf4d89858768be3e70d259",
            "89404301937543538b36f4d689def74b",
            "3fc694d77d75440189004551e345fe7c",
            "b83a9733643c4b8f9b8b8ce4a9fc54b6",
            "e5314bde00d04b51b070360e8d10b748",
            "8d4d9e985dba4c6b8efd471d924404e1",
            "62598364a0e44585ad68c5bed2c4b240",
            "0aab96fd5e704d3bbc3c2381b1cce0e8",
            "efc0a6e8bd604cdd919be1e607e1329c",
            "135ca010e59a46d1bca46795bad0c656",
            "4bd6c40d566249b48b2da2bca5486715",
            "aaac29dc0ab94267950984e2105dc2ac",
            "942b8590d95e47b4ac85e6950e2ca8c5",
            "033cd43a12e84f6e86e75d9d001d7066",
            "cc5e1da4cc874e84b2be6fa1714533fd",
            "1d19ef3e628f4f0fb51b899ddc7db2d4",
            "2d38d3cc0eb04fff95cc8a699d6bff21",
            "4b15620e87b046e98830b518d0566c2c",
            "435fb42ebaf5409c8de92b1c9ff5904d",
            "4e221fc709894991bcc9f46119ac3a32",
            "7818ef8654fa4e6484218a168e1f25b1",
            "9f366d0ed189458d9c7bc8adbf875e1d",
            "9e7e7706de28432ea914fb2aa58ace6d",
            "499aa7fb34b045dc986adeb7139361f0",
            "f9878b9efd72483b8723e866fd77d3ae",
            "cec6eaa9f1054fee9378b56771725297",
            "d910071a320347fc97382e96563b4b0e",
            "53bcd933693e4b82a762f25c4b76f254",
            "b4190782517148988e25353417ab6490",
            "7af35cccbc4c4d4a81e18a306141f23e",
            "be239e8e675949dd97367236e4ed3d92",
            "2841a995795042bab7e72de93b26b6f0",
            "4f4e09bba03e4162a2df527a7b73e06c",
            "e8be1e3e32924036b2b10069e050c910",
            "aea5eb883db1488fbcb136712fec860c",
            "b6d88ba62d3c4290be3701eaedc4fa48",
            "bfc09e8d820844acb0f3564b4d8279c9",
            "a48f29d9d07449c99cd73028928f83be"
          ]
        },
        "outputId": "40ea0bab-48d2-4a94-9388-91503d754d2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Building dataset...\n",
            "Create new model...\n",
            "Setting up loss function, optimizer and scheduler...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c9500c15e24f42aaba70dc6a38552e61"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Better accuracy found (0 -> 0.4231678486997636), best model saved.\n",
            "Better accuracy found (0.4231678486997636 -> 0.5035460992907801), best model saved.\n",
            "Better accuracy found (0.5035460992907801 -> 0.5508274231678487), best model saved.\n",
            "Better accuracy found (0.5508274231678487 -> 0.5602836879432624), best model saved.\n",
            "Better accuracy found (0.5602836879432624 -> 0.5650118203309693), best model saved.\n",
            "Better accuracy found (0.5650118203309693 -> 0.5791962174940898), best model saved.\n",
            "Better accuracy found (0.5791962174940898 -> 0.5862884160756501), best model saved.\n",
            "Building dataset...\n",
            "Create new model...\n",
            "Setting up loss function, optimizer and scheduler...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bbb550f9000043e6a9b035c3b5c02c1c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Better accuracy found (0 -> 0.38534278959810875), best model saved.\n",
            "Better accuracy found (0.38534278959810875 -> 0.45390070921985815), best model saved.\n",
            "Better accuracy found (0.45390070921985815 -> 0.5059101654846335), best model saved.\n",
            "Better accuracy found (0.5059101654846335 -> 0.5602836879432624), best model saved.\n",
            "Better accuracy found (0.5602836879432624 -> 0.5626477541371159), best model saved.\n",
            "Better accuracy found (0.5626477541371159 -> 0.5815602836879432), best model saved.\n",
            "Better accuracy found (0.5815602836879432 -> 0.5839243498817966), best model saved.\n",
            "Better accuracy found (0.5839243498817966 -> 0.6193853427895981), best model saved.\n",
            "Better accuracy found (0.6193853427895981 -> 0.6335697399527187), best model saved.\n",
            "Better accuracy found (0.6335697399527187 -> 0.640661938534279), best model saved.\n",
            "Building dataset...\n",
            "Create new model...\n",
            "Setting up loss function, optimizer and scheduler...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8d4d9e985dba4c6b8efd471d924404e1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Better accuracy found (0 -> 0.38534278959810875), best model saved.\n",
            "Better accuracy found (0.38534278959810875 -> 0.45390070921985815), best model saved.\n",
            "Better accuracy found (0.45390070921985815 -> 0.5130023640661938), best model saved.\n",
            "Better accuracy found (0.5130023640661938 -> 0.5342789598108747), best model saved.\n",
            "Better accuracy found (0.5342789598108747 -> 0.5484633569739953), best model saved.\n",
            "Better accuracy found (0.5484633569739953 -> 0.557919621749409), best model saved.\n",
            "Better accuracy found (0.557919621749409 -> 0.5839243498817966), best model saved.\n",
            "Better accuracy found (0.5839243498817966 -> 0.5862884160756501), best model saved.\n",
            "Better accuracy found (0.5862884160756501 -> 0.5910165484633569), best model saved.\n",
            "Better accuracy found (0.5910165484633569 -> 0.6028368794326241), best model saved.\n",
            "Better accuracy found (0.6028368794326241 -> 0.6146572104018913), best model saved.\n",
            "Better accuracy found (0.6146572104018913 -> 0.6170212765957447), best model saved.\n",
            "Better accuracy found (0.6170212765957447 -> 0.6312056737588653), best model saved.\n",
            "Building dataset...\n",
            "Create new model...\n",
            "Setting up loss function, optimizer and scheduler...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2d38d3cc0eb04fff95cc8a699d6bff21"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Better accuracy found (0 -> 0.44208037825059104), best model saved.\n",
            "Better accuracy found (0.44208037825059104 -> 0.4799054373522459), best model saved.\n",
            "Better accuracy found (0.4799054373522459 -> 0.5342789598108747), best model saved.\n",
            "Better accuracy found (0.5342789598108747 -> 0.5390070921985816), best model saved.\n",
            "Better accuracy found (0.5390070921985816 -> 0.5602836879432624), best model saved.\n",
            "Better accuracy found (0.5602836879432624 -> 0.5721040189125296), best model saved.\n",
            "Better accuracy found (0.5721040189125296 -> 0.5910165484633569), best model saved.\n",
            "Better accuracy found (0.5910165484633569 -> 0.5957446808510638), best model saved.\n",
            "Better accuracy found (0.5957446808510638 -> 0.6004728132387707), best model saved.\n",
            "Better accuracy found (0.6004728132387707 -> 0.6028368794326241), best model saved.\n",
            "Building dataset...\n",
            "Create new model...\n",
            "Setting up loss function, optimizer and scheduler...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "53bcd933693e4b82a762f25c4b76f254"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Better accuracy found (0 -> 0.4940898345153664), best model saved.\n",
            "Better accuracy found (0.4940898345153664 -> 0.5531914893617021), best model saved.\n",
            "Better accuracy found (0.5531914893617021 -> 0.5910165484633569), best model saved.\n",
            "Better accuracy found (0.5910165484633569 -> 0.5933806146572104), best model saved.\n",
            "Better accuracy found (0.5933806146572104 -> 0.6146572104018913), best model saved.\n",
            "Better accuracy found (0.6146572104018913 -> 0.6193853427895981), best model saved.\n",
            "Better accuracy found (0.6193853427895981 -> 0.6335697399527187), best model saved.\n",
            "Better accuracy found (0.6335697399527187 -> 0.6382978723404256), best model saved.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x360 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Predict the test dataset"
      ],
      "metadata": {
        "id": "Xd2TaycZpzUU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bcidataset_test = BCIDataset(X_test, y_test)\n",
        "bcidataset_test_dl = torch.utils.data.DataLoader(bcidataset_test, batch_size=64, num_workers=8, shuffle=False)"
      ],
      "metadata": {
        "id": "8y4AJkRoqS3U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cfc54e68-d8ce-4fb6-b357-95760fa2c89c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_proba_list = []\n",
        "for fold_id in range(k):\n",
        "    model = EEGLSTMModel(input_size=input_size,\n",
        "                         hidden_size=hidden_size,\n",
        "                         num_layers=num_layers,\n",
        "                         drop_out=drop_out)\n",
        "    model.load_state_dict(torch.load(f'fold_{fold_id}_best_acc.ckpt'))\n",
        "    model = model.cuda()\n",
        "    # validate with the testing dataset\n",
        "    predicted_label = []\n",
        "    predicted_proba = []\n",
        "    actual_label = []\n",
        "    model.eval()\n",
        "    for i, (inputs, labels) in enumerate(bcidataset_test_dl):\n",
        "        inputs = inputs.cuda()\n",
        "        actual_label += labels.tolist()\n",
        "        outputs = model(inputs)\n",
        "        predicted_label += outputs.argmax(1).cpu().numpy().tolist()\n",
        "        predicted_proba += outputs.softmax(1).cpu().tolist()\n",
        "    pred_proba_list.append(predicted_proba)"
      ],
      "metadata": {
        "id": "UyYYXHJSK2d9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_accuracy = (np.array(pred_proba_list).mean(0).argmax(1) == np.array(actual_label)).sum() / len(np.array(actual_label))"
      ],
      "metadata": {
        "id": "wVFdJp6rs6j3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Final test accuracy is {test_accuracy: .5f}')"
      ],
      "metadata": {
        "id": "LLhDhdRItVFm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d2d78e0-3118-4bb7-87c0-22d12c06c67f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final test accuracy is  0.62528\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cf_matrix = confusion_matrix(np.array(actual_label), np.array(pred_proba_list).mean(0).argmax(1))\n",
        "\n",
        "sns.heatmap(cf_matrix/np.sum(cf_matrix), annot=True, \n",
        "            fmt='.2%', cmap='Blues')"
      ],
      "metadata": {
        "id": "7A5fQjWrt4eQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "414ca5b8-fdf0-4b78-8619-a8cb99cd264e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LniUSgonzj8g"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}