fraktal/Computer_Vision
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
5c00e9ffe7
Computer_Vision/Chapter03/Varying_loss_optimizer.ipynb

1657 lines
119 KiB
Plaintext
Raw Normal View History

source version
2024-02-13 03:34:51 +01:00
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"accelerator": "GPU",
"colab": {
"name": "Varying_loss_optimizer.ipynb",
"provenance": [],
"include_colab_link": true
},
"hide_input": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"555f8075b30344ffad1a09752ac88ea1": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HBoxModel",
"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_e11f28d88a22465b97f31a8611583a76",
"IPY_MODEL_8cef7279ec104ee5a45602603537ba92"
],
"layout": "IPY_MODEL_40a4dc115817415fb0f956ab8343921c"
}
},
"40a4dc115817415fb0f956ab8343921c": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"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
}
},
"e11f28d88a22465b97f31a8611583a76": {
"model_module": "@jupyter-widgets/controls",
"model_name": "FloatProgressModel",
"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": "info",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_ec6aa5ffe76947d99d74fdc48f3bda87",
"max": 1,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_a8251570eabf4c87a5f54837ccb2a0a0",
"value": 1
}
},
"8cef7279ec104ee5a45602603537ba92": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"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_4ebe7feaf19b463ea64c7ceb939392ae",
"placeholder": "​",
"style": "IPY_MODEL_db11ed571583409992509c9177265bab",
"value": " 26427392/? [00:20<00:00, 83478868.91it/s]"
}
},
"a8251570eabf4c87a5f54837ccb2a0a0": {
"model_module": "@jupyter-widgets/controls",
"model_name": "ProgressStyleModel",
"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": "initial"
}
},
"ec6aa5ffe76947d99d74fdc48f3bda87": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"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
}
},
"db11ed571583409992509c9177265bab": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"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": ""
}
},
"4ebe7feaf19b463ea64c7ceb939392ae": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"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
}
},
"0afd0f24f0b84d01a1ac805461780038": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HBoxModel",
"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_8bfb0bf136954e508e49691710baea94",
"IPY_MODEL_57ff7b0b5c5747498c7c251acfadba63"
],
"layout": "IPY_MODEL_9149b38ce9e24652a226bbb78c57158e"
}
},
"9149b38ce9e24652a226bbb78c57158e": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"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
}
},
"8bfb0bf136954e508e49691710baea94": {
"model_module": "@jupyter-widgets/controls",
"model_name": "FloatProgressModel",
"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_d2d7908b02104b53819195b4b38dc35b",
"max": 1,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_4996fd9c0f29441285827db538683121",
"value": 1
}
},
"57ff7b0b5c5747498c7c251acfadba63": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"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_a0273ce565bb403eb1f180bb1c65782c",
"placeholder": "​",
"style": "IPY_MODEL_7a36dd0db81146289a0014156aadc02f",
"value": " 32768/? [00:00<00:00, 89204.95it/s]"
}
},
"4996fd9c0f29441285827db538683121": {
"model_module": "@jupyter-widgets/controls",
"model_name": "ProgressStyleModel",
"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": "initial"
}
},
"d2d7908b02104b53819195b4b38dc35b": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"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
}
},
"7a36dd0db81146289a0014156aadc02f": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"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": ""
}
},
"a0273ce565bb403eb1f180bb1c65782c": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"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
}
},
"708bd6ef4ee14f8a81c3e0eea17dbb3b": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HBoxModel",
"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_10c602e87fa844deb169ce5400ac9992",
"IPY_MODEL_09d4af85a8474de2a21402b585fcd0ea"
],
"layout": "IPY_MODEL_ebbc2338d4f44948adc9636383cab302"
}
},
"ebbc2338d4f44948adc9636383cab302": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"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
}
},
"10c602e87fa844deb169ce5400ac9992": {
"model_module": "@jupyter-widgets/controls",
"model_name": "FloatProgressModel",
"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_53beddf25c0d4373ba8335491af85fa5",
"max": 1,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_fc3f7edb493b49db9eda12f9c45ff30a",
"value": 1
}
},
"09d4af85a8474de2a21402b585fcd0ea": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"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_ed4d32cfa18849f3b421fe4b9c433a96",
"placeholder": "​",
"style": "IPY_MODEL_f23dccb7ae8d4e6bbde1b96394a044da",
"value": " 4423680/? [00:00<00:00, 17006263.58it/s]"
}
},
"fc3f7edb493b49db9eda12f9c45ff30a": {
"model_module": "@jupyter-widgets/controls",
"model_name": "ProgressStyleModel",
"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": "initial"
}
},
"53beddf25c0d4373ba8335491af85fa5": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"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
}
},
"f23dccb7ae8d4e6bbde1b96394a044da": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"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": ""
}
},
"ed4d32cfa18849f3b421fe4b9c433a96": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"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
}
},
"e7e20d5f7ded4155b5de7a59a5ddf43e": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HBoxModel",
"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_736b7a750d094ff487db2249615fa82a",
"IPY_MODEL_378f2fb19c37421195f2b0c19ea1cf7f"
],
"layout": "IPY_MODEL_7aeec9531faa44d7bd95ff72fd63bb8b"
}
},
"7aeec9531faa44d7bd95ff72fd63bb8b": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"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
}
},
"736b7a750d094ff487db2249615fa82a": {
"model_module": "@jupyter-widgets/controls",
"model_name": "FloatProgressModel",
"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_ac086a6bcf274e91b8d506ea39490592",
"max": 1,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_c6cf46d85249463caaf07823604e0ce3",
"value": 1
}
},
"378f2fb19c37421195f2b0c19ea1cf7f": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"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_abe78c8e154f4fe48ae2753e0f5fc9e7",
"placeholder": "​",
"style": "IPY_MODEL_4e6caf9db2f345e9aa3a83d47d9d635c",
"value": " 8192/? [00:00<00:00, 25459.14it/s]"
}
},
"c6cf46d85249463caaf07823604e0ce3": {
"model_module": "@jupyter-widgets/controls",
"model_name": "ProgressStyleModel",
"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": "initial"
}
},
"ac086a6bcf274e91b8d506ea39490592": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"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
}
},
"4e6caf9db2f345e9aa3a83d47d9d635c": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"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": ""
}
},
"abe78c8e154f4fe48ae2753e0f5fc9e7": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"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
}
}
}
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/PacktPublishing/Hands-On-Computer-Vision-with-PyTorch/blob/master/Chapter03/Varying_loss_optimizer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "-CiYe82L4gom",
"outputId": "7238ae25-5422-41bf-9101-8c42777ebb3e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 437,
"referenced_widgets": [
"555f8075b30344ffad1a09752ac88ea1",
"40a4dc115817415fb0f956ab8343921c",
"e11f28d88a22465b97f31a8611583a76",
"8cef7279ec104ee5a45602603537ba92",
"a8251570eabf4c87a5f54837ccb2a0a0",
"ec6aa5ffe76947d99d74fdc48f3bda87",
"db11ed571583409992509c9177265bab",
"4ebe7feaf19b463ea64c7ceb939392ae",
"0afd0f24f0b84d01a1ac805461780038",
"9149b38ce9e24652a226bbb78c57158e",
"8bfb0bf136954e508e49691710baea94",
"57ff7b0b5c5747498c7c251acfadba63",
"4996fd9c0f29441285827db538683121",
"d2d7908b02104b53819195b4b38dc35b",
"7a36dd0db81146289a0014156aadc02f",
"a0273ce565bb403eb1f180bb1c65782c",
"708bd6ef4ee14f8a81c3e0eea17dbb3b",
"ebbc2338d4f44948adc9636383cab302",
"10c602e87fa844deb169ce5400ac9992",
"09d4af85a8474de2a21402b585fcd0ea",
"fc3f7edb493b49db9eda12f9c45ff30a",
"53beddf25c0d4373ba8335491af85fa5",
"f23dccb7ae8d4e6bbde1b96394a044da",
"ed4d32cfa18849f3b421fe4b9c433a96",
"e7e20d5f7ded4155b5de7a59a5ddf43e",
"7aeec9531faa44d7bd95ff72fd63bb8b",
"736b7a750d094ff487db2249615fa82a",
"378f2fb19c37421195f2b0c19ea1cf7f",
"c6cf46d85249463caaf07823604e0ce3",
"ac086a6bcf274e91b8d506ea39490592",
"4e6caf9db2f345e9aa3a83d47d9d635c",
"abe78c8e154f4fe48ae2753e0f5fc9e7"
]
}
},
"source": [
"from torchvision import datasets\n",
"import torch\n",
"data_folder = '~/data/FMNIST' # This can be any directory you want to \n",
"# download FMNIST to\n",
"fmnist = datasets.FashionMNIST(data_folder, download=True, train=True)\n",
"tr_images = fmnist.data\n",
"tr_targets = fmnist.targets"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "555f8075b30344ffad1a09752ac88ea1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Extracting /root/data/FMNIST/FashionMNIST/raw/train-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0afd0f24f0b84d01a1ac805461780038",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Extracting /root/data/FMNIST/FashionMNIST/raw/train-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "708bd6ef4ee14f8a81c3e0eea17dbb3b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Extracting /root/data/FMNIST/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n",
"\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e7e20d5f7ded4155b5de7a59a5ddf43e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Extracting /root/data/FMNIST/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
"Processing...\n",
"Done!\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:469: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n",
" return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "53UbSwV44pKN"
},
"source": [
"val_fmnist = datasets.FashionMNIST(data_folder, download=True, train=False)\n",
"val_images = val_fmnist.data\n",
"val_targets = val_fmnist.targets"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "hLjiLWl24qjU"
},
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import numpy as np\n",
"from torch.utils.data import Dataset, DataLoader\n",
"import torch\n",
"import torch.nn as nn\n",
"device = 'cuda' if torch.cuda.is_available() else 'cpu'"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "r9CEfaWfxCAZ"
},
"source": [
"### SGD optimizer"
]
},
{
"cell_type": "code",
"metadata": {
"id": "5brrbtrf4rsb"
},
"source": [
"class FMNISTDataset(Dataset):\n",
" def __init__(self, x, y):\n",
" x = x.float()/255\n",
" x = x.view(-1,28*28)\n",
" self.x, self.y = x, y \n",
" def __getitem__(self, ix):\n",
" x, y = self.x[ix], self.y[ix] \n",
" return x.to(device), y.to(device)\n",
" def __len__(self): \n",
" return len(self.x)\n",
"\n",
"from torch.optim import SGD, Adam\n",
"def get_model():\n",
" model = nn.Sequential(\n",
" nn.Linear(28 * 28, 1000),\n",
" nn.ReLU(),\n",
" nn.Linear(1000, 10)\n",
" ).to(device)\n",
"\n",
" loss_fn = nn.CrossEntropyLoss()\n",
" optimizer = SGD(model.parameters(), lr=1e-2)\n",
" return model, loss_fn, optimizer\n",
"\n",
"def train_batch(x, y, model, opt, loss_fn):\n",
" model.train()\n",
" prediction = model(x)\n",
" batch_loss = loss_fn(prediction, y)\n",
" batch_loss.backward()\n",
" optimizer.step()\n",
" optimizer.zero_grad()\n",
" return batch_loss.item()\n",
"\n",
"def accuracy(x, y, model):\n",
" model.eval()\n",
" # this is the same as @torch.no_grad \n",
" # at the top of function, only difference\n",
" # being, grad is not computed in the with scope\n",
" with torch.no_grad():\n",
" prediction = model(x)\n",
" max_values, argmaxes = prediction.max(-1)\n",
" is_correct = argmaxes == y\n",
" return is_correct.cpu().numpy().tolist()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Ipn9JZfJ4yVl"
},
"source": [
"def get_data(): \n",
" train = FMNISTDataset(tr_images, tr_targets) \n",
" trn_dl = DataLoader(train, batch_size=32, shuffle=True)\n",
" val = FMNISTDataset(val_images, val_targets) \n",
" val_dl = DataLoader(val, batch_size=len(val_images), shuffle=False)\n",
" return trn_dl, val_dl"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "q44UDG_q41Up"
},
"source": [
"@torch.no_grad()\n",
"def val_loss(x, y, model):\n",
" prediction = model(x)\n",
" val_loss = loss_fn(prediction, y)\n",
" return val_loss.item()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "mK19mnET42it"
},
"source": [
"trn_dl, val_dl = get_data()\n",
"model, loss_fn, optimizer = get_model()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "TVtikJsV43mD",
"outputId": "f2c6ca32-5347-4062-a57f-ba50f7b74662",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 199
}
},
"source": [
"train_losses, train_accuracies = [], []\n",
"val_losses, val_accuracies = [], []\n",
"for epoch in range(10):\n",
" print(epoch)\n",
" train_epoch_losses, train_epoch_accuracies = [], []\n",
" for ix, batch in enumerate(iter(trn_dl)):\n",
" x, y = batch\n",
" batch_loss = train_batch(x, y, model, optimizer, loss_fn)\n",
" train_epoch_losses.append(batch_loss) \n",
" train_epoch_loss = np.array(train_epoch_losses).mean()\n",
"\n",
" for ix, batch in enumerate(iter(trn_dl)):\n",
" x, y = batch\n",
" is_correct = accuracy(x, y, model)\n",
" train_epoch_accuracies.extend(is_correct)\n",
" train_epoch_accuracy = np.mean(train_epoch_accuracies)\n",
" for ix, batch in enumerate(iter(val_dl)):\n",
" x, y = batch\n",
" val_is_correct = accuracy(x, y, model)\n",
" validation_loss = val_loss(x, y, model)\n",
" val_epoch_accuracy = np.mean(val_is_correct)\n",
" train_losses.append(train_epoch_loss)\n",
" train_accuracies.append(train_epoch_accuracy)\n",
" val_losses.append(validation_loss)\n",
" val_accuracies.append(val_epoch_accuracy)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"0\n",
"1\n",
"2\n",
"3\n",
"4\n",
"5\n",
"6\n",
"7\n",
"8\n",
"9\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "X8qZ13F3452Y",
"outputId": "17ff2d4a-2679-49a8-f03f-0f6b0d02e702",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 337
}
},
"source": [
"epochs = np.arange(10)+1\n",
"import matplotlib.ticker as mtick\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.ticker as mticker\n",
"%matplotlib inline\n",
"plt.subplot(211)\n",
"plt.plot(epochs, train_losses, 'bo', label='Training loss')\n",
"plt.plot(epochs, val_losses, 'r', label='Validation loss')\n",
"plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
"plt.title('Training and validation loss with SGD optimizer')\n",
"plt.xlabel('Epochs')\n",
"plt.ylabel('Loss')\n",
"plt.legend()\n",
"plt.grid('off')\n",
"plt.show()\n",
"plt.subplot(212)\n",
"plt.plot(epochs, train_accuracies, 'bo', label='Training accuracy')\n",
"plt.plot(epochs, val_accuracies, 'r', label='Validation accuracy')\n",
"plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
"plt.title('Training and validation accuracy with SGD optimizer')\n",
"plt.xlabel('Epochs')\n",
"plt.ylabel('Accuracy')\n",
"plt.gca().set_yticklabels(['{:.0f}%'.format(x*100) for x in plt.gca().get_yticks()]) \n",
"plt.legend()\n",
"plt.grid('off')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "display_data",
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Bsv-ldC65IYQ"
},
"source": [
""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "-kDtEW1q5cAn"
},
"source": [
""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "sfeSNp305eB4"
},
"source": [
"### Adam optimizer"
]
},
{
"cell_type": "code",
"metadata": {
"id": "S1iUHZf15cDA"
},
"source": [
"from torch.optim import SGD, Adam\n",
"def get_model():\n",
" model = nn.Sequential(\n",
" nn.Linear(28 * 28, 1000),\n",
" nn.ReLU(),\n",
" nn.Linear(1000, 10)\n",
" ).to(device)\n",
"\n",
" loss_fn = nn.CrossEntropyLoss()\n",
" optimizer = Adam(model.parameters(), lr=1e-2)\n",
" return model, loss_fn, optimizer"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "vy7zpicT5cGw"
},
"source": [
"trn_dl, val_dl = get_data()\n",
"model, loss_fn, optimizer = get_model()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "s86zqzoO5k5s",
"outputId": "22c0e4a3-43a8-4ed4-bc5d-8c9e166e4a30",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 199
}
},
"source": [
"train_losses, train_accuracies = [], []\n",
"val_losses, val_accuracies = [], []\n",
"for epoch in range(10):\n",
" print(epoch)\n",
" train_epoch_losses, train_epoch_accuracies = [], []\n",
" for ix, batch in enumerate(iter(trn_dl)):\n",
" x, y = batch\n",
" batch_loss = train_batch(x, y, model, optimizer, loss_fn)\n",
" train_epoch_losses.append(batch_loss) \n",
" train_epoch_loss = np.array(train_epoch_losses).mean()\n",
"\n",
" for ix, batch in enumerate(iter(trn_dl)):\n",
" x, y = batch\n",
" is_correct = accuracy(x, y, model)\n",
" train_epoch_accuracies.extend(is_correct)\n",
" train_epoch_accuracy = np.mean(train_epoch_accuracies)\n",
" for ix, batch in enumerate(iter(val_dl)):\n",
" x, y = batch\n",
" val_is_correct = accuracy(x, y, model)\n",
" validation_loss = val_loss(x, y, model)\n",
" val_epoch_accuracy = np.mean(val_is_correct)\n",
" train_losses.append(train_epoch_loss)\n",
" train_accuracies.append(train_epoch_accuracy)\n",
" val_losses.append(validation_loss)\n",
" val_accuracies.append(val_epoch_accuracy)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"0\n",
"1\n",
"2\n",
"3\n",
"4\n",
"5\n",
"6\n",
"7\n",
"8\n",
"9\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "7MBiwBMq5mop",
"outputId": "91134f0f-dbcc-4212-de17-c6198f0b3ab9",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 337
}
},
"source": [
"epochs = np.arange(10)+1\n",
"import matplotlib.ticker as mtick\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.ticker as mticker\n",
"%matplotlib inline\n",
"plt.subplot(211)\n",
"plt.plot(epochs, train_losses, 'bo', label='Training loss')\n",
"plt.plot(epochs, val_losses, 'r', label='Validation loss')\n",
"plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
"plt.title('Training and validation loss with Adam optimizer')\n",
"plt.xlabel('Epochs')\n",
"plt.ylabel('Loss')\n",
"plt.legend()\n",
"plt.grid('off')\n",
"plt.show()\n",
"plt.subplot(212)\n",
"plt.plot(epochs, train_accuracies, 'bo', label='Training accuracy')\n",
"plt.plot(epochs, val_accuracies, 'r', label='Validation accuracy')\n",
"plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
"plt.title('Training and validation accuracy with Adam optimizer')\n",
"plt.xlabel('Epochs')\n",
"plt.ylabel('Accuracy')\n",
"plt.gca().set_yticklabels(['{:.0f}%'.format(x*100) for x in plt.gca().get_yticks()]) \n",
"plt.legend()\n",
"plt.grid('off')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "display_data",
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "PeY_NGfF6Kzf"
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
Reference in New Issue Copy Permalink