mnist_torch.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "mnist_torch.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU",
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "9754f014369a4c3bb3f2a0d52e863d1b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_0b820a9d76874cbf99a2d92126e6c0f2",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_241e179380834c288635ab1145e2a1f2",
              "IPY_MODEL_36aec247c2f944f98aa79accc9dc33a5"
            ]
          }
        },
        "0b820a9d76874cbf99a2d92126e6c0f2": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "241e179380834c288635ab1145e2a1f2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_7cb52ec1bf68435b8c98d413f01e6cae",
            "_dom_classes": [],
            "description": "",
            "_model_name": "IntProgressModel",
            "bar_style": "info",
            "max": 1,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 1,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_0d67a6158e4741908093e765f11a7206"
          }
        },
        "36aec247c2f944f98aa79accc9dc33a5": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_406f803415024c64b547494500dd7a1c",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 9920512/? [02:50&lt;00:00, 58736.52it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_3c45e5e6969643dfb614bb0f1224e8a8"
          }
        },
        "7cb52ec1bf68435b8c98d413f01e6cae": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "0d67a6158e4741908093e765f11a7206": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "406f803415024c64b547494500dd7a1c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "3c45e5e6969643dfb614bb0f1224e8a8": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "c1469316d36b4d6ba8cfc15ac3b8a12a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_2de58413836140e79e898700fa82dd26",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_da4c1909c136400aa892db07d3817941",
              "IPY_MODEL_d9e3380689764f7d9eb8efe5bd9eee27"
            ]
          }
        },
        "2de58413836140e79e898700fa82dd26": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "da4c1909c136400aa892db07d3817941": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_0526cc446ebd43a2aa9f668ba8ee63a2",
            "_dom_classes": [],
            "description": "",
            "_model_name": "IntProgressModel",
            "bar_style": "info",
            "max": 1,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 1,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_ded855ee155f4b90b12853d1bc347cb8"
          }
        },
        "d9e3380689764f7d9eb8efe5bd9eee27": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_35de48897260416c86954bdd8827f06d",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 32768/? [00:19&lt;00:00, 226294.70it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_365341fd4a2d46749e067c3bbee01587"
          }
        },
        "0526cc446ebd43a2aa9f668ba8ee63a2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "ded855ee155f4b90b12853d1bc347cb8": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "35de48897260416c86954bdd8827f06d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "365341fd4a2d46749e067c3bbee01587": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "7184e76a37bb42d0add2f2da02155cee": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_f9f2b324425b447eb7140fccfd6ed28a",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_71a13134455e489bbc2f80acc6a40f13",
              "IPY_MODEL_24c3cdcd8ec34c18a46cb328dd10b68a"
            ]
          }
        },
        "f9f2b324425b447eb7140fccfd6ed28a": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "71a13134455e489bbc2f80acc6a40f13": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_57c9725545ec4201b330764c07f6dd2e",
            "_dom_classes": [],
            "description": "",
            "_model_name": "IntProgressModel",
            "bar_style": "info",
            "max": 1,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 1,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_b81d32b93e0f468582de1706da46cefd"
          }
        },
        "24c3cdcd8ec34c18a46cb328dd10b68a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_ae35bc35b10241c9b52770beb4b64d6f",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 1654784/? [00:39&lt;00:00, 77038.68it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_48efc82f9e6945cebe1aa9e37c00fef9"
          }
        },
        "57c9725545ec4201b330764c07f6dd2e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "b81d32b93e0f468582de1706da46cefd": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "ae35bc35b10241c9b52770beb4b64d6f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "48efc82f9e6945cebe1aa9e37c00fef9": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "0af69c50762848f2a081d44e69c5235c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_62215b2631fb4ce5870e258069b99fc7",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_e0d2b282c69143639f1448509a9bf322",
              "IPY_MODEL_303c71c7a4d2490fa52b4786fd3a46e8"
            ]
          }
        },
        "62215b2631fb4ce5870e258069b99fc7": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "e0d2b282c69143639f1448509a9bf322": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_9378eab674d54d358885c1d6f07d6053",
            "_dom_classes": [],
            "description": "  0%",
            "_model_name": "IntProgressModel",
            "bar_style": "info",
            "max": 1,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 0,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_a8fdf5e369254f82b700859b7ff66064"
          }
        },
        "303c71c7a4d2490fa52b4786fd3a46e8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_92b5beb7b4c540998c886719a2e6ec2e",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 0/4542 [00:00&lt;?, ?it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_65abb682fd7b430ab00fdb9b21e81d53"
          }
        },
        "9378eab674d54d358885c1d6f07d6053": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "a8fdf5e369254f82b700859b7ff66064": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "92b5beb7b4c540998c886719a2e6ec2e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "65abb682fd7b430ab00fdb9b21e81d53": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "9tXGO0z71-_x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "import torchvision.transforms as transforms # Used for common image transformations\n",
        "import torchvision.datasets as dsets\n",
        "import torch.utils.data as loader\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WGRMTcjJ2lrm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315,
          "referenced_widgets": [
            "9754f014369a4c3bb3f2a0d52e863d1b",
            "0b820a9d76874cbf99a2d92126e6c0f2",
            "241e179380834c288635ab1145e2a1f2",
            "36aec247c2f944f98aa79accc9dc33a5",
            "7cb52ec1bf68435b8c98d413f01e6cae",
            "0d67a6158e4741908093e765f11a7206",
            "406f803415024c64b547494500dd7a1c",
            "3c45e5e6969643dfb614bb0f1224e8a8",
            "c1469316d36b4d6ba8cfc15ac3b8a12a",
            "2de58413836140e79e898700fa82dd26",
            "da4c1909c136400aa892db07d3817941",
            "d9e3380689764f7d9eb8efe5bd9eee27",
            "0526cc446ebd43a2aa9f668ba8ee63a2",
            "ded855ee155f4b90b12853d1bc347cb8",
            "35de48897260416c86954bdd8827f06d",
            "365341fd4a2d46749e067c3bbee01587",
            "7184e76a37bb42d0add2f2da02155cee",
            "f9f2b324425b447eb7140fccfd6ed28a",
            "71a13134455e489bbc2f80acc6a40f13",
            "24c3cdcd8ec34c18a46cb328dd10b68a",
            "57c9725545ec4201b330764c07f6dd2e",
            "b81d32b93e0f468582de1706da46cefd",
            "ae35bc35b10241c9b52770beb4b64d6f",
            "48efc82f9e6945cebe1aa9e37c00fef9",
            "0af69c50762848f2a081d44e69c5235c",
            "62215b2631fb4ce5870e258069b99fc7",
            "e0d2b282c69143639f1448509a9bf322",
            "303c71c7a4d2490fa52b4786fd3a46e8",
            "9378eab674d54d358885c1d6f07d6053",
            "a8fdf5e369254f82b700859b7ff66064",
            "92b5beb7b4c540998c886719a2e6ec2e",
            "65abb682fd7b430ab00fdb9b21e81d53"
          ]
        },
        "outputId": "2074f8b2-3411-4d03-e6e1-4c80fe895cf9"
      },
      "source": [
        "# Let us import the dataset\n",
        "# transforms.Compose class lets us combine all the transformations\n",
        "transform=transforms.Compose([transforms.ToTensor(), # ToTensor converts a PIL image to a Tensor\n",
        "                              transforms.Normalize((0.5,),(0.5,))]) # Normalize normalizes the image to bring all the values in a particular range. (mean,std-dev)\n",
        "\n",
        "trainset=dsets.MNIST(root='./data', train=True, transform=transform, download=True)\n",
        "# We dont need to do any transform on targets, so target_transform=False by default.\n",
        "testset=dsets.MNIST(root='./data', train=False, transform=transform, download=True)\n",
        "# now create trainloader and testloader\n",
        "# torch.utils.data.DataLoader is at the heart of pytorch data loading utility\n",
        "trainloader=loader.DataLoader(dataset=trainset, batch_size=5, shuffle=True, num_workers=0) # batch_size=5 will give 12000 iterations\n",
        "testloader=loader.DataLoader(dataset=testset, batch_size=1, shuffle=False, num_workers=0) # testing every image in a single batch\n",
        "classes = ('0 - zero', '1 - one', '2 - two', '3 - three', '4 - four', '5 - five', '6 - six', '7 - seven', '8 - eight', '9 - nine')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9754f014369a4c3bb3f2a0d52e863d1b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c1469316d36b4d6ba8cfc15ac3b8a12a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7184e76a37bb42d0add2f2da02155cee",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0af69c50762848f2a081d44e69c5235c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nG3YO3HAfHU6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "0fbcd940-556d-4fee-d949-a020437f0d13"
      },
      "source": [
        "print(trainloader)\n",
        "print(testloader)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<torch.utils.data.dataloader.DataLoader object at 0x7fc797bdee80>\n",
            "<torch.utils.data.dataloader.DataLoader object at 0x7fc797bdee48>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uD_nrx9qIsqh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Let us define the neural network\n",
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.conv1=nn.Conv2d(in_channels=1,out_channels=10,kernel_size=(3,3),stride=1)\n",
        "        self.conv2=nn.Conv2d(in_channels=10, out_channels=25, kernel_size=(3,3), stride=1)\n",
        "        self.fc1=nn.Linear(in_features=25*(5*5),out_features=125)\n",
        "        self.fc2=nn.Linear(in_features=125, out_features=75)\n",
        "        self.fc3=nn.Linear(in_features=75, out_features=10)\n",
        "        self.pool=nn.MaxPool2d(kernel_size=(2,2), stride=2)\n",
        "    \n",
        "    def forward(self, x):\n",
        "        x=self.pool(F.relu(self.conv1(x)))\n",
        "        x=self.pool(F.relu(self.conv2(x)))\n",
        "        x=x.view(-1,25*(5*5))\n",
        "        x=F.relu(self.fc1(x))\n",
        "        x=F.relu(self.fc2(x))\n",
        "        x=self.fc3(x)\n",
        "        return x\n",
        "\n",
        "device= torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "net=Net().to(device)\n",
        "criterion=nn.CrossEntropyLoss() #  taking loss as cross-entropy loss\n",
        "optimizer=optim.Adam(net.parameters()) # using ADAM as optimizer"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ov8funvmR6UE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "0c5b67a6-31b0-4f14-efdd-8181a461916a"
      },
      "source": [
        "# training the network here\n",
        "lossarr=[]\n",
        "for epoch in range(12):\n",
        "    running_loss=0.0\n",
        "    for i,data in enumerate(trainloader,0): # enumerate(iterable,start)\n",
        "        inputs,labels=data[0].to(device), data[1].to(device)\n",
        "        optimizer.zero_grad()\n",
        "        outputs=net(inputs)\n",
        "        loss=criterion(outputs,labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        running_loss+=loss.item()\n",
        "        if i%2000==1999:\n",
        "            if i%6000==5999 : print(\"Epoch:\",epoch+1,\" ,iteration:\",i+1,\" ,loss:\",running_loss/2000)\n",
        "            lossarr.append(running_loss)\n",
        "            running_loss=0\n",
        "print(\"Model trained on 60000 images and 12 epochs.\")"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 1  ,iteration: 6000  ,loss: 0.019517751323365046\n",
            "Epoch: 1  ,iteration: 12000  ,loss: 0.018480358452196154\n",
            "Epoch: 2  ,iteration: 6000  ,loss: 0.010589195018041658\n",
            "Epoch: 2  ,iteration: 12000  ,loss: 0.018704339684250573\n",
            "Epoch: 3  ,iteration: 6000  ,loss: 0.0225825772932897\n",
            "Epoch: 3  ,iteration: 12000  ,loss: 0.02047960427413589\n",
            "Epoch: 4  ,iteration: 6000  ,loss: 0.01720186210537782\n",
            "Epoch: 4  ,iteration: 12000  ,loss: 0.01708396250797699\n",
            "Epoch: 5  ,iteration: 6000  ,loss: 0.017342766017490355\n",
            "Epoch: 5  ,iteration: 12000  ,loss: 0.013920141425007214\n",
            "Epoch: 6  ,iteration: 6000  ,loss: 0.01878439554236197\n",
            "Epoch: 6  ,iteration: 12000  ,loss: 0.016720844633892857\n",
            "Epoch: 7  ,iteration: 6000  ,loss: 0.018180308186680958\n",
            "Epoch: 7  ,iteration: 12000  ,loss: 0.01686895746904109\n",
            "Epoch: 8  ,iteration: 6000  ,loss: 0.01790232768518206\n",
            "Epoch: 8  ,iteration: 12000  ,loss: 0.0133398774732645\n",
            "Epoch: 9  ,iteration: 6000  ,loss: 0.014354839571483488\n",
            "Epoch: 9  ,iteration: 12000  ,loss: 0.012264139978198904\n",
            "Epoch: 10  ,iteration: 6000  ,loss: 0.010241472130268882\n",
            "Epoch: 10  ,iteration: 12000  ,loss: 0.020827467268593105\n",
            "Epoch: 11  ,iteration: 6000  ,loss: 0.01441786017299161\n",
            "Epoch: 11  ,iteration: 12000  ,loss: 0.023345920939419777\n",
            "Epoch: 12  ,iteration: 6000  ,loss: 0.012956993172510808\n",
            "Epoch: 12  ,iteration: 12000  ,loss: 0.016044597381361776\n",
            "Model trained on 60000 images and 12 epochs.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uZnFjX1XBtFR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "47420e0b-c5ad-4fbd-e70f-960843419f9f"
      },
      "source": [
        "points=np.arange(1,73,1)\n",
        "plt.plot(points,lossarr)\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njVIBFxFplAq4iEijVMBFRBqlAi4i0qgfVR+jtl75FdYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "frVFrmckX485",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "75621e6a-25b4-4715-a197-0bcacaa9005b"
      },
      "source": [
        "correct=0\n",
        "total=0\n",
        "with torch.no_grad():\n",
        "    for data in testloader:\n",
        "        images, labels = data[0].to(device), data[1].to(device)\n",
        "        outputs=net(images)\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "print(\"10000 images tested in total.\")\n",
        "print(\"Accuracy of network=\",correct*100/total)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000 images tested in total.\n",
            "Accuracy of network= 99.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IW1C0Vl_gRL7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "PATH='./mnist_net.pth'\n",
        "torch.save(net.state_dict(),PATH)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4RNu-lvQF94d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}