2020-03-15 10:45:25 -03:00
|
|
|
|
{
|
2021-01-17 20:08:58 -03:00
|
|
|
|
"nbformat": 4,
|
|
|
|
|
|
"nbformat_minor": 0,
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"kernelspec": {
|
|
|
|
|
|
"display_name": "pytorch",
|
|
|
|
|
|
"language": "python",
|
|
|
|
|
|
"name": "pytorch"
|
|
|
|
|
|
},
|
|
|
|
|
|
"language_info": {
|
|
|
|
|
|
"codemirror_mode": {
|
|
|
|
|
|
"name": "ipython",
|
|
|
|
|
|
"version": 3
|
|
|
|
|
|
},
|
|
|
|
|
|
"file_extension": ".py",
|
|
|
|
|
|
"mimetype": "text/x-python",
|
|
|
|
|
|
"name": "python",
|
|
|
|
|
|
"nbconvert_exporter": "python",
|
|
|
|
|
|
"pygments_lexer": "ipython3",
|
|
|
|
|
|
"version": "3.6.6"
|
|
|
|
|
|
},
|
|
|
|
|
|
"colab": {
|
2021-01-17 20:12:27 -03:00
|
|
|
|
"name": "Mini-batch-logistic-regression-evaluator.ipynb",
|
2021-01-17 20:08:58 -03:00
|
|
|
|
"provenance": []
|
|
|
|
|
|
},
|
|
|
|
|
|
"accelerator": "GPU",
|
|
|
|
|
|
"widgets": {
|
|
|
|
|
|
"application/vnd.jupyter.widget-state+json": {
|
|
|
|
|
|
"bcf2585d31644e0f86569e604b2e635b": {
|
|
|
|
|
|
"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_2612abdc916d47418dda7287807a00ce",
|
|
|
|
|
|
"_model_module": "@jupyter-widgets/controls",
|
|
|
|
|
|
"children": [
|
|
|
|
|
|
"IPY_MODEL_027c3ca8839846fcae9d6bb23fb10399",
|
|
|
|
|
|
"IPY_MODEL_1d09572d2433498caa268567c838e640"
|
|
|
|
|
|
]
|
|
|
|
|
|
}
|
|
|
|
|
|
},
|
|
|
|
|
|
"2612abdc916d47418dda7287807a00ce": {
|
|
|
|
|
|
"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
|
|
|
|
|
|
}
|
|
|
|
|
|
},
|
|
|
|
|
|
"027c3ca8839846fcae9d6bb23fb10399": {
|
|
|
|
|
|
"model_module": "@jupyter-widgets/controls",
|
|
|
|
|
|
"model_name": "FloatProgressModel",
|
|
|
|
|
|
"state": {
|
|
|
|
|
|
"_view_name": "ProgressView",
|
|
|
|
|
|
"style": "IPY_MODEL_08cddf6f231a4e89ab8e1e026cf11796",
|
|
|
|
|
|
"_dom_classes": [],
|
|
|
|
|
|
"description": "",
|
|
|
|
|
|
"_model_name": "FloatProgressModel",
|
|
|
|
|
|
"bar_style": "info",
|
|
|
|
|
|
"max": 1,
|
|
|
|
|
|
"_view_module": "@jupyter-widgets/controls",
|
|
|
|
|
|
"_model_module_version": "1.5.0",
|
|
|
|
|
|
"value": 1,
|
|
|
|
|
|
"_view_count": null,
|
|
|
|
|
|
"_view_module_version": "1.5.0",
|
|
|
|
|
|
"orientation": "horizontal",
|
|
|
|
|
|
"min": 0,
|
|
|
|
|
|
"description_tooltip": null,
|
|
|
|
|
|
"_model_module": "@jupyter-widgets/controls",
|
|
|
|
|
|
"layout": "IPY_MODEL_75267826defa4565be4bed232272434e"
|
|
|
|
|
|
}
|
|
|
|
|
|
},
|
|
|
|
|
|
"1d09572d2433498caa268567c838e640": {
|
|
|
|
|
|
"model_module": "@jupyter-widgets/controls",
|
|
|
|
|
|
"model_name": "HTMLModel",
|
|
|
|
|
|
"state": {
|
|
|
|
|
|
"_view_name": "HTMLView",
|
|
|
|
|
|
"style": "IPY_MODEL_8c189a0cd687479dba885a9c2d47fb64",
|
|
|
|
|
|
"_dom_classes": [],
|
|
|
|
|
|
"description": "",
|
|
|
|
|
|
"_model_name": "HTMLModel",
|
|
|
|
|
|
"placeholder": "",
|
|
|
|
|
|
"_view_module": "@jupyter-widgets/controls",
|
|
|
|
|
|
"_model_module_version": "1.5.0",
|
|
|
|
|
|
"value": " 2640404480/? [01:10<00:00, 98486594.59it/s]",
|
|
|
|
|
|
"_view_count": null,
|
|
|
|
|
|
"_view_module_version": "1.5.0",
|
|
|
|
|
|
"description_tooltip": null,
|
|
|
|
|
|
"_model_module": "@jupyter-widgets/controls",
|
|
|
|
|
|
"layout": "IPY_MODEL_b6528931de654b3c85b94bec14f4891b"
|
|
|
|
|
|
}
|
|
|
|
|
|
},
|
|
|
|
|
|
"08cddf6f231a4e89ab8e1e026cf11796": {
|
|
|
|
|
|
"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"
|
|
|
|
|
|
}
|
|
|
|
|
|
},
|
|
|
|
|
|
"75267826defa4565be4bed232272434e": {
|
|
|
|
|
|
"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
|
|
|
|
|
|
}
|
|
|
|
|
|
},
|
|
|
|
|
|
"8c189a0cd687479dba885a9c2d47fb64": {
|
|
|
|
|
|
"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"
|
|
|
|
|
|
}
|
|
|
|
|
|
},
|
|
|
|
|
|
"b6528931de654b3c85b94bec14f4891b": {
|
|
|
|
|
|
"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
|
|
|
|
|
|
}
|
|
|
|
|
|
}
|
|
|
|
|
|
}
|
|
|
|
|
|
}
|
2020-03-15 10:45:25 -03:00
|
|
|
|
},
|
2021-01-17 20:08:58 -03:00
|
|
|
|
"cells": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"id": "YUemQib7ZE4D"
|
|
|
|
|
|
},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"import torch\n",
|
|
|
|
|
|
"import sys\n",
|
|
|
|
|
|
"import numpy as np\n",
|
|
|
|
|
|
"import os\n",
|
|
|
|
|
|
"from sklearn.neighbors import KNeighborsClassifier\n",
|
|
|
|
|
|
"import yaml\n",
|
|
|
|
|
|
"import matplotlib.pyplot as plt\n",
|
|
|
|
|
|
"from sklearn.decomposition import PCA\n",
|
|
|
|
|
|
"from sklearn.linear_model import LogisticRegression\n",
|
|
|
|
|
|
"from sklearn import preprocessing\n",
|
|
|
|
|
|
"import importlib.util\n",
|
|
|
|
|
|
"import torchvision"
|
|
|
|
|
|
],
|
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
|
"outputs": []
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"id": "WSgRE1CcLqdS",
|
|
|
|
|
|
"colab": {
|
|
|
|
|
|
"base_uri": "https://localhost:8080/"
|
|
|
|
|
|
},
|
|
|
|
|
|
"outputId": "e44ac358-6480-4a5f-a358-6eb6ace26c8b"
|
|
|
|
|
|
},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"!pip install gdown"
|
|
|
|
|
|
],
|
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
|
"text": [
|
|
|
|
|
|
"Requirement already satisfied: gdown in /usr/local/lib/python3.6/dist-packages (3.6.4)\n",
|
|
|
|
|
|
"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from gdown) (1.15.0)\n",
|
|
|
|
|
|
"Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from gdown) (2.23.0)\n",
|
|
|
|
|
|
"Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from gdown) (4.41.1)\n",
|
|
|
|
|
|
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->gdown) (1.24.3)\n",
|
|
|
|
|
|
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->gdown) (3.0.4)\n",
|
|
|
|
|
|
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->gdown) (2020.12.5)\n",
|
|
|
|
|
|
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->gdown) (2.10)\n"
|
|
|
|
|
|
],
|
|
|
|
|
|
"name": "stdout"
|
|
|
|
|
|
}
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"id": "NOIJEui1ZziV"
|
|
|
|
|
|
},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"def get_file_id_by_model(folder_name):\n",
|
|
|
|
|
|
" file_id = {'resnet18_100-epochs_stl10': '14_nH2FkyKbt61cieQDiSbBVNP8-gtwgF',\n",
|
|
|
|
|
|
" 'resnet18_100-epochs_cifar10': '1lc2aoVtrAetGn0PnTkOyFzPCIucOJq7C'}\n",
|
|
|
|
|
|
" return file_id.get(folder_name, \"Model not found.\")"
|
|
|
|
|
|
],
|
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
|
"outputs": []
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"id": "G7YMxsvEZMrX",
|
|
|
|
|
|
"colab": {
|
|
|
|
|
|
"base_uri": "https://localhost:8080/"
|
|
|
|
|
|
},
|
|
|
|
|
|
"outputId": "36932a7d-c7e5-492a-f37d-8be6b18f787a"
|
|
|
|
|
|
},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"folder_name = 'resnet18_100-epochs_stl10'\n",
|
|
|
|
|
|
"file_id = get_file_id_by_model(folder_name)\n",
|
|
|
|
|
|
"print(folder_name, file_id)"
|
|
|
|
|
|
],
|
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
|
"text": [
|
|
|
|
|
|
"resnet18_100-epochs_stl10 14_nH2FkyKbt61cieQDiSbBVNP8-gtwgF\n"
|
|
|
|
|
|
],
|
|
|
|
|
|
"name": "stdout"
|
|
|
|
|
|
}
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"id": "PWZ8fet_YoJm",
|
|
|
|
|
|
"colab": {
|
|
|
|
|
|
"base_uri": "https://localhost:8080/"
|
|
|
|
|
|
},
|
|
|
|
|
|
"outputId": "8d52756d-707b-4a3f-9e8c-0d191408deab"
|
|
|
|
|
|
},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"# download and extract model files\n",
|
|
|
|
|
|
"os.system('gdown https://drive.google.com/uc?id={}'.format(file_id))\n",
|
|
|
|
|
|
"os.system('unzip {}'.format(folder_name))\n",
|
|
|
|
|
|
"!ls"
|
|
|
|
|
|
],
|
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
|
"text": [
|
|
|
|
|
|
"checkpoint_0100.pth.tar\n",
|
|
|
|
|
|
"config.yml\n",
|
|
|
|
|
|
"events.out.tfevents.1610901470.4cb2c837708d.2683858.0\n",
|
|
|
|
|
|
"resnet18_100-epochs_stl10.zip\n",
|
|
|
|
|
|
"sample_data\n",
|
|
|
|
|
|
"training.log\n"
|
|
|
|
|
|
],
|
|
|
|
|
|
"name": "stdout"
|
|
|
|
|
|
}
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"id": "ooyhd8piZ1w1",
|
|
|
|
|
|
"colab": {
|
|
|
|
|
|
"base_uri": "https://localhost:8080/"
|
|
|
|
|
|
},
|
|
|
|
|
|
"outputId": "6ffb73aa-35c5-4df2-bd1f-6de6a235a9e5"
|
|
|
|
|
|
},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"!unzip resnet18_100-epochs_stl10"
|
|
|
|
|
|
],
|
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
|
"text": [
|
|
|
|
|
|
"Archive: resnet18_100-epochs_stl10.zip\n",
|
|
|
|
|
|
"replace checkpoint_0100.pth.tar? [y]es, [n]o, [A]ll, [N]one, [r]ename: A\n",
|
|
|
|
|
|
" inflating: checkpoint_0100.pth.tar \n",
|
|
|
|
|
|
" inflating: config.yml \n",
|
|
|
|
|
|
" inflating: events.out.tfevents.1610901470.4cb2c837708d.2683858.0 \n",
|
|
|
|
|
|
" inflating: training.log \n"
|
|
|
|
|
|
],
|
|
|
|
|
|
"name": "stdout"
|
|
|
|
|
|
}
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"id": "3_nypQVEv-hn"
|
|
|
|
|
|
},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"from torch.utils.data import DataLoader\n",
|
|
|
|
|
|
"import torchvision.transforms as transforms\n",
|
|
|
|
|
|
"from torchvision import datasets"
|
|
|
|
|
|
],
|
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
|
"outputs": []
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"id": "lDfbL3w_Z0Od",
|
|
|
|
|
|
"colab": {
|
|
|
|
|
|
"base_uri": "https://localhost:8080/"
|
|
|
|
|
|
},
|
|
|
|
|
|
"outputId": "5f58bd9b-4428-4b8c-e271-b47ca6694f34"
|
|
|
|
|
|
},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
|
|
|
|
|
|
"print(\"Using device:\", device)"
|
|
|
|
|
|
],
|
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
|
"text": [
|
|
|
|
|
|
"Using device: cuda\n"
|
|
|
|
|
|
],
|
|
|
|
|
|
"name": "stdout"
|
|
|
|
|
|
}
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"id": "BfIPl0G6_RrT"
|
|
|
|
|
|
},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"def get_stl10_data_loaders(download, shuffle=False, batch_size=128):\n",
|
|
|
|
|
|
" train_dataset = datasets.STL10('./data', split='train', download=download,\n",
|
|
|
|
|
|
" transform=transforms.ToTensor())\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" train_loader = DataLoader(train_dataset, batch_size=batch_size,\n",
|
|
|
|
|
|
" num_workers=0, drop_last=False, shuffle=shuffle)\n",
|
|
|
|
|
|
" \n",
|
|
|
|
|
|
" test_dataset = datasets.STL10('./data', split='test', download=download,\n",
|
|
|
|
|
|
" transform=transforms.ToTensor())\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" test_loader = DataLoader(test_dataset, batch_size=2*batch_size,\n",
|
|
|
|
|
|
" num_workers=10, drop_last=False, shuffle=shuffle)\n",
|
|
|
|
|
|
" return train_loader, test_loader\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"def get_cifar10_data_loaders(download, shuffle=False, batch_size=128):\n",
|
|
|
|
|
|
" train_dataset = datasets.CIFAR10('./data', train=True, download=download,\n",
|
|
|
|
|
|
" transform=transforms.ToTensor())\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" train_loader = DataLoader(train_dataset, batch_size=batch_size,\n",
|
|
|
|
|
|
" num_workers=0, drop_last=False, shuffle=shuffle)\n",
|
|
|
|
|
|
" \n",
|
|
|
|
|
|
" test_dataset = datasets.CIFAR10('./data', train=False, download=download,\n",
|
|
|
|
|
|
" transform=transforms.ToTensor())\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" test_loader = DataLoader(test_dataset, batch_size=2*batch_size,\n",
|
|
|
|
|
|
" num_workers=10, drop_last=False, shuffle=shuffle)\n",
|
|
|
|
|
|
" return train_loader, test_loader"
|
|
|
|
|
|
],
|
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
|
"outputs": []
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"id": "6N8lYkbmDTaK"
|
|
|
|
|
|
},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"with open(os.path.join('./config.yml')) as file:\n",
|
|
|
|
|
|
" config = yaml.load(file)"
|
|
|
|
|
|
],
|
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
|
"outputs": []
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"id": "a18lPD-tIle6"
|
|
|
|
|
|
},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"if config.arch == 'resnet18':\n",
|
|
|
|
|
|
" model = torchvision.models.resnet18(pretrained=False, num_classes=10).to(device)\n",
|
|
|
|
|
|
"elif config.arch == 'resnet50':\n",
|
|
|
|
|
|
" model = torchvision.models.resnet50(pretrained=False, num_classes=10).to(device)"
|
|
|
|
|
|
],
|
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
|
"outputs": []
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"id": "4AIfgq41GuTT"
|
|
|
|
|
|
},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"checkpoint = torch.load('checkpoint_0100.pth.tar', map_location=device)\n",
|
|
|
|
|
|
"state_dict = checkpoint['state_dict']\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"for k in list(state_dict.keys()):\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" if k.startswith('backbone.'):\n",
|
|
|
|
|
|
" if k.startswith('backbone') and not k.startswith('backbone.fc'):\n",
|
|
|
|
|
|
" # remove prefix\n",
|
|
|
|
|
|
" state_dict[k[len(\"backbone.\"):]] = state_dict[k]\n",
|
|
|
|
|
|
" del state_dict[k]"
|
|
|
|
|
|
],
|
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
|
"outputs": []
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"id": "VVjA83PPJYWl"
|
|
|
|
|
|
},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"log = model.load_state_dict(state_dict, strict=False)\n",
|
|
|
|
|
|
"assert log.missing_keys == ['fc.weight', 'fc.bias']"
|
|
|
|
|
|
],
|
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
|
"outputs": []
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"id": "_GC0a14uWRr6",
|
|
|
|
|
|
"colab": {
|
|
|
|
|
|
"base_uri": "https://localhost:8080/",
|
|
|
|
|
|
"height": 117,
|
|
|
|
|
|
"referenced_widgets": [
|
|
|
|
|
|
"bcf2585d31644e0f86569e604b2e635b",
|
|
|
|
|
|
"2612abdc916d47418dda7287807a00ce",
|
|
|
|
|
|
"027c3ca8839846fcae9d6bb23fb10399",
|
|
|
|
|
|
"1d09572d2433498caa268567c838e640",
|
|
|
|
|
|
"08cddf6f231a4e89ab8e1e026cf11796",
|
|
|
|
|
|
"75267826defa4565be4bed232272434e",
|
|
|
|
|
|
"8c189a0cd687479dba885a9c2d47fb64",
|
|
|
|
|
|
"b6528931de654b3c85b94bec14f4891b"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"outputId": "56db3fac-10cc-4985-932d-878375ccd18f"
|
|
|
|
|
|
},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"if config.dataset_name == 'cifar10':\n",
|
|
|
|
|
|
" train_loader, test_loader = get_cifar10_data_loaders(download=True)\n",
|
|
|
|
|
|
"elif config.dataset_name == 'stl10':\n",
|
|
|
|
|
|
" train_loader, test_loader = get_stl10_data_loaders(download=True)\n",
|
|
|
|
|
|
"print(\"Dataset:\", config.dataset_name)"
|
|
|
|
|
|
],
|
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
|
"text": [
|
|
|
|
|
|
"Downloading http://ai.stanford.edu/~acoates/stl10/stl10_binary.tar.gz to ./data/stl10_binary.tar.gz\n"
|
|
|
|
|
|
],
|
|
|
|
|
|
"name": "stdout"
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"output_type": "display_data",
|
|
|
|
|
|
"data": {
|
|
|
|
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
|
|
|
|
"model_id": "bcf2585d31644e0f86569e604b2e635b",
|
|
|
|
|
|
"version_minor": 0,
|
|
|
|
|
|
"version_major": 2
|
|
|
|
|
|
},
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"tags": []
|
|
|
|
|
|
}
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
|
"text": [
|
|
|
|
|
|
"Extracting ./data/stl10_binary.tar.gz to ./data\n",
|
|
|
|
|
|
"Files already downloaded and verified\n",
|
|
|
|
|
|
"Dataset: stl10\n"
|
|
|
|
|
|
],
|
|
|
|
|
|
"name": "stdout"
|
|
|
|
|
|
}
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"id": "pYT_KsM0Mnnr"
|
|
|
|
|
|
},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"# freeze all layers but the last fc\n",
|
|
|
|
|
|
"for name, param in model.named_parameters():\n",
|
|
|
|
|
|
" if name not in ['fc.weight', 'fc.bias']:\n",
|
|
|
|
|
|
" param.requires_grad = False\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"parameters = list(filter(lambda p: p.requires_grad, model.parameters()))\n",
|
|
|
|
|
|
"assert len(parameters) == 2 # fc.weight, fc.bias"
|
|
|
|
|
|
],
|
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
|
"outputs": []
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"id": "aPVh1S_eMRDU"
|
|
|
|
|
|
},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"optimizer = torch.optim.Adam(model.parameters(), lr=3e-4, weight_decay=0.0008)\n",
|
|
|
|
|
|
"criterion = torch.nn.CrossEntropyLoss().to(device)"
|
|
|
|
|
|
],
|
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
|
"outputs": []
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"id": "edr6RhP2PdVq"
|
|
|
|
|
|
},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"def accuracy(output, target, topk=(1,)):\n",
|
|
|
|
|
|
" \"\"\"Computes the accuracy over the k top predictions for the specified values of k\"\"\"\n",
|
|
|
|
|
|
" with torch.no_grad():\n",
|
|
|
|
|
|
" maxk = max(topk)\n",
|
|
|
|
|
|
" batch_size = target.size(0)\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" _, pred = output.topk(maxk, 1, True, True)\n",
|
|
|
|
|
|
" pred = pred.t()\n",
|
|
|
|
|
|
" correct = pred.eq(target.view(1, -1).expand_as(pred))\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" res = []\n",
|
|
|
|
|
|
" for k in topk:\n",
|
|
|
|
|
|
" correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)\n",
|
|
|
|
|
|
" res.append(correct_k.mul_(100.0 / batch_size))\n",
|
|
|
|
|
|
" return res"
|
|
|
|
|
|
],
|
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
|
"outputs": []
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"id": "qOder0dAMI7X",
|
|
|
|
|
|
"colab": {
|
|
|
|
|
|
"base_uri": "https://localhost:8080/"
|
|
|
|
|
|
},
|
|
|
|
|
|
"outputId": "95b285c8-2b26-4d2c-ccc3-bb9111871c8d"
|
|
|
|
|
|
},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"epochs = 100\n",
|
|
|
|
|
|
"for epoch in range(epochs):\n",
|
|
|
|
|
|
" top1_train_accuracy = 0\n",
|
|
|
|
|
|
" for counter, (x_batch, y_batch) in enumerate(train_loader):\n",
|
|
|
|
|
|
" x_batch = x_batch.to(device)\n",
|
|
|
|
|
|
" y_batch = y_batch.to(device)\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" logits = model(x_batch)\n",
|
|
|
|
|
|
" loss = criterion(logits, y_batch)\n",
|
|
|
|
|
|
" top1 = accuracy(logits, y_batch, topk=(1,))\n",
|
|
|
|
|
|
" top1_train_accuracy += top1[0]\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" optimizer.zero_grad()\n",
|
|
|
|
|
|
" loss.backward()\n",
|
|
|
|
|
|
" optimizer.step()\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" top1_train_accuracy /= (counter + 1)\n",
|
|
|
|
|
|
" top1_accuracy = 0\n",
|
|
|
|
|
|
" top5_accuracy = 0\n",
|
|
|
|
|
|
" for counter, (x_batch, y_batch) in enumerate(test_loader):\n",
|
|
|
|
|
|
" x_batch = x_batch.to(device)\n",
|
|
|
|
|
|
" y_batch = y_batch.to(device)\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" logits = model(x_batch)\n",
|
|
|
|
|
|
" \n",
|
|
|
|
|
|
" top1, top5 = accuracy(logits, y_batch, topk=(1,5))\n",
|
|
|
|
|
|
" top1_accuracy += top1[0]\n",
|
|
|
|
|
|
" top5_accuracy += top5[0]\n",
|
|
|
|
|
|
" \n",
|
|
|
|
|
|
" top1_accuracy /= (counter + 1)\n",
|
|
|
|
|
|
" top5_accuracy /= (counter + 1)\n",
|
|
|
|
|
|
" print(f\"Epoch {epoch}\\tTop1 Train accuracy {top1_train_accuracy.item()}\\tTop1 Test accuracy: {top1_accuracy.item()}\\tTop5 test acc: {top5_accuracy.item()}\")"
|
|
|
|
|
|
],
|
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
|
"text": [
|
|
|
|
|
|
"Top1 Train accuracy 29.47265625\tTop1 Test accuracy: 42.4560546875\tTop5 test acc: 92.41943359375\n",
|
|
|
|
|
|
"Top1 Train accuracy 49.47265625\tTop1 Test accuracy: 53.662109375\tTop5 test acc: 96.15478515625\n",
|
|
|
|
|
|
"Top1 Train accuracy 56.85546875\tTop1 Test accuracy: 57.92236328125\tTop5 test acc: 96.74072265625\n",
|
|
|
|
|
|
"Top1 Train accuracy 59.3359375\tTop1 Test accuracy: 59.9365234375\tTop5 test acc: 97.021484375\n",
|
|
|
|
|
|
"Top1 Train accuracy 60.8984375\tTop1 Test accuracy: 61.1572265625\tTop5 test acc: 97.15576171875\n",
|
|
|
|
|
|
"Top1 Train accuracy 61.89453125\tTop1 Test accuracy: 61.8408203125\tTop5 test acc: 97.2900390625\n",
|
|
|
|
|
|
"Top1 Train accuracy 62.48046875\tTop1 Test accuracy: 62.5244140625\tTop5 test acc: 97.3388671875\n",
|
|
|
|
|
|
"Top1 Train accuracy 63.125\tTop1 Test accuracy: 63.037109375\tTop5 test acc: 97.44873046875\n",
|
|
|
|
|
|
"Top1 Train accuracy 64.4140625\tTop1 Test accuracy: 63.39111328125\tTop5 test acc: 97.54638671875\n",
|
|
|
|
|
|
"Top1 Train accuracy 64.86328125\tTop1 Test accuracy: 63.85498046875\tTop5 test acc: 97.5830078125\n",
|
|
|
|
|
|
"Top1 Train accuracy 65.15625\tTop1 Test accuracy: 64.0869140625\tTop5 test acc: 97.65625\n",
|
|
|
|
|
|
"Top1 Train accuracy 65.56640625\tTop1 Test accuracy: 64.34326171875\tTop5 test acc: 97.69287109375\n",
|
|
|
|
|
|
"Top1 Train accuracy 65.859375\tTop1 Test accuracy: 64.48974609375\tTop5 test acc: 97.7294921875\n",
|
|
|
|
|
|
"Top1 Train accuracy 66.03515625\tTop1 Test accuracy: 64.83154296875\tTop5 test acc: 97.75390625\n",
|
|
|
|
|
|
"Top1 Train accuracy 66.171875\tTop1 Test accuracy: 65.02685546875\tTop5 test acc: 97.79052734375\n",
|
|
|
|
|
|
"Top1 Train accuracy 66.484375\tTop1 Test accuracy: 65.46630859375\tTop5 test acc: 97.7783203125\n",
|
|
|
|
|
|
"Top1 Train accuracy 66.953125\tTop1 Test accuracy: 65.66162109375\tTop5 test acc: 97.8515625\n",
|
|
|
|
|
|
"Top1 Train accuracy 67.2265625\tTop1 Test accuracy: 65.91796875\tTop5 test acc: 97.93701171875\n",
|
|
|
|
|
|
"Top1 Train accuracy 67.48046875\tTop1 Test accuracy: 65.97900390625\tTop5 test acc: 97.91259765625\n",
|
|
|
|
|
|
"Top1 Train accuracy 67.8125\tTop1 Test accuracy: 66.11328125\tTop5 test acc: 97.93701171875\n",
|
|
|
|
|
|
"Top1 Train accuracy 68.046875\tTop1 Test accuracy: 66.3330078125\tTop5 test acc: 97.9736328125\n",
|
|
|
|
|
|
"Top1 Train accuracy 68.45703125\tTop1 Test accuracy: 66.5283203125\tTop5 test acc: 97.94921875\n",
|
|
|
|
|
|
"Top1 Train accuracy 68.59375\tTop1 Test accuracy: 66.63818359375\tTop5 test acc: 97.94921875\n",
|
|
|
|
|
|
"Top1 Train accuracy 68.7890625\tTop1 Test accuracy: 66.748046875\tTop5 test acc: 97.93701171875\n",
|
|
|
|
|
|
"Top1 Train accuracy 69.00390625\tTop1 Test accuracy: 66.90673828125\tTop5 test acc: 97.9248046875\n",
|
|
|
|
|
|
"Top1 Train accuracy 69.21875\tTop1 Test accuracy: 67.0654296875\tTop5 test acc: 97.9736328125\n",
|
|
|
|
|
|
"Top1 Train accuracy 69.35546875\tTop1 Test accuracy: 67.0654296875\tTop5 test acc: 97.9736328125\n",
|
|
|
|
|
|
"Top1 Train accuracy 69.66796875\tTop1 Test accuracy: 67.2119140625\tTop5 test acc: 97.93701171875\n",
|
|
|
|
|
|
"Top1 Train accuracy 69.765625\tTop1 Test accuracy: 67.24853515625\tTop5 test acc: 97.9736328125\n",
|
|
|
|
|
|
"Top1 Train accuracy 69.82421875\tTop1 Test accuracy: 67.4072265625\tTop5 test acc: 97.98583984375\n",
|
|
|
|
|
|
"Top1 Train accuracy 69.9609375\tTop1 Test accuracy: 67.431640625\tTop5 test acc: 97.98583984375\n",
|
|
|
|
|
|
"Top1 Train accuracy 70.09765625\tTop1 Test accuracy: 67.4560546875\tTop5 test acc: 97.998046875\n",
|
|
|
|
|
|
"Top1 Train accuracy 70.15625\tTop1 Test accuracy: 67.44384765625\tTop5 test acc: 98.01025390625\n",
|
|
|
|
|
|
"Top1 Train accuracy 70.29296875\tTop1 Test accuracy: 67.54150390625\tTop5 test acc: 98.0224609375\n",
|
|
|
|
|
|
"Top1 Train accuracy 70.41015625\tTop1 Test accuracy: 67.61474609375\tTop5 test acc: 98.05908203125\n",
|
|
|
|
|
|
"Top1 Train accuracy 70.5078125\tTop1 Test accuracy: 67.67578125\tTop5 test acc: 98.0712890625\n",
|
|
|
|
|
|
"Top1 Train accuracy 70.64453125\tTop1 Test accuracy: 67.73681640625\tTop5 test acc: 98.08349609375\n",
|
|
|
|
|
|
"Top1 Train accuracy 70.859375\tTop1 Test accuracy: 67.76123046875\tTop5 test acc: 98.0712890625\n",
|
|
|
|
|
|
"Top1 Train accuracy 70.8984375\tTop1 Test accuracy: 67.88330078125\tTop5 test acc: 98.08349609375\n",
|
|
|
|
|
|
"Top1 Train accuracy 71.07421875\tTop1 Test accuracy: 67.95654296875\tTop5 test acc: 98.095703125\n",
|
|
|
|
|
|
"Top1 Train accuracy 71.11328125\tTop1 Test accuracy: 67.93212890625\tTop5 test acc: 98.1201171875\n",
|
|
|
|
|
|
"Top1 Train accuracy 71.2890625\tTop1 Test accuracy: 68.0419921875\tTop5 test acc: 98.10791015625\n",
|
|
|
|
|
|
"Top1 Train accuracy 71.3671875\tTop1 Test accuracy: 68.10302734375\tTop5 test acc: 98.13232421875\n",
|
|
|
|
|
|
"Top1 Train accuracy 71.42578125\tTop1 Test accuracy: 68.1396484375\tTop5 test acc: 98.13232421875\n",
|
|
|
|
|
|
"Top1 Train accuracy 71.4453125\tTop1 Test accuracy: 68.1396484375\tTop5 test acc: 98.13232421875\n",
|
|
|
|
|
|
"Top1 Train accuracy 71.50390625\tTop1 Test accuracy: 68.1640625\tTop5 test acc: 98.1201171875\n",
|
|
|
|
|
|
"Top1 Train accuracy 71.484375\tTop1 Test accuracy: 68.2373046875\tTop5 test acc: 98.14453125\n",
|
|
|
|
|
|
"Top1 Train accuracy 71.6015625\tTop1 Test accuracy: 68.34716796875\tTop5 test acc: 98.15673828125\n",
|
|
|
|
|
|
"Top1 Train accuracy 71.7578125\tTop1 Test accuracy: 68.39599609375\tTop5 test acc: 98.15673828125\n",
|
|
|
|
|
|
"Top1 Train accuracy 71.89453125\tTop1 Test accuracy: 68.37158203125\tTop5 test acc: 98.20556640625\n",
|
|
|
|
|
|
"Top1 Train accuracy 72.01171875\tTop1 Test accuracy: 68.4326171875\tTop5 test acc: 98.20556640625\n",
|
|
|
|
|
|
"Top1 Train accuracy 72.1484375\tTop1 Test accuracy: 68.44482421875\tTop5 test acc: 98.2177734375\n",
|
|
|
|
|
|
"Top1 Train accuracy 72.1875\tTop1 Test accuracy: 68.51806640625\tTop5 test acc: 98.25439453125\n",
|
|
|
|
|
|
"Top1 Train accuracy 72.28515625\tTop1 Test accuracy: 68.603515625\tTop5 test acc: 98.2421875\n",
|
|
|
|
|
|
"Top1 Train accuracy 72.36328125\tTop1 Test accuracy: 68.5791015625\tTop5 test acc: 98.2666015625\n",
|
|
|
|
|
|
"Top1 Train accuracy 72.5390625\tTop1 Test accuracy: 68.61572265625\tTop5 test acc: 98.2666015625\n",
|
|
|
|
|
|
"Top1 Train accuracy 72.59765625\tTop1 Test accuracy: 68.64013671875\tTop5 test acc: 98.2666015625\n",
|
|
|
|
|
|
"Top1 Train accuracy 73.02734375\tTop1 Test accuracy: 68.7255859375\tTop5 test acc: 98.25439453125\n",
|
|
|
|
|
|
"Top1 Train accuracy 73.18359375\tTop1 Test accuracy: 68.76220703125\tTop5 test acc: 98.2666015625\n",
|
|
|
|
|
|
"Top1 Train accuracy 73.26171875\tTop1 Test accuracy: 68.8232421875\tTop5 test acc: 98.291015625\n",
|
|
|
|
|
|
"Top1 Train accuracy 73.359375\tTop1 Test accuracy: 68.85986328125\tTop5 test acc: 98.27880859375\n",
|
|
|
|
|
|
"Top1 Train accuracy 73.45703125\tTop1 Test accuracy: 68.8720703125\tTop5 test acc: 98.32763671875\n",
|
|
|
|
|
|
"Top1 Train accuracy 73.49609375\tTop1 Test accuracy: 68.9208984375\tTop5 test acc: 98.33984375\n",
|
|
|
|
|
|
"Top1 Train accuracy 73.53515625\tTop1 Test accuracy: 68.8720703125\tTop5 test acc: 98.33984375\n",
|
|
|
|
|
|
"Top1 Train accuracy 73.53515625\tTop1 Test accuracy: 68.9208984375\tTop5 test acc: 98.3642578125\n",
|
|
|
|
|
|
"Top1 Train accuracy 73.65234375\tTop1 Test accuracy: 69.00634765625\tTop5 test acc: 98.33984375\n",
|
|
|
|
|
|
"Top1 Train accuracy 73.76953125\tTop1 Test accuracy: 69.0185546875\tTop5 test acc: 98.33984375\n",
|
|
|
|
|
|
"Top1 Train accuracy 73.9453125\tTop1 Test accuracy: 69.0673828125\tTop5 test acc: 98.35205078125\n",
|
|
|
|
|
|
"Top1 Train accuracy 74.00390625\tTop1 Test accuracy: 69.1162109375\tTop5 test acc: 98.35205078125\n",
|
|
|
|
|
|
"Top1 Train accuracy 74.0625\tTop1 Test accuracy: 69.140625\tTop5 test acc: 98.3642578125\n",
|
|
|
|
|
|
"Top1 Train accuracy 74.12109375\tTop1 Test accuracy: 69.17724609375\tTop5 test acc: 98.3642578125\n",
|
|
|
|
|
|
"Top1 Train accuracy 74.21875\tTop1 Test accuracy: 69.20166015625\tTop5 test acc: 98.35205078125\n",
|
|
|
|
|
|
"Top1 Train accuracy 74.21875\tTop1 Test accuracy: 69.2626953125\tTop5 test acc: 98.33984375\n",
|
|
|
|
|
|
"Top1 Train accuracy 74.23828125\tTop1 Test accuracy: 69.3359375\tTop5 test acc: 98.33984375\n",
|
|
|
|
|
|
"Top1 Train accuracy 74.23828125\tTop1 Test accuracy: 69.37255859375\tTop5 test acc: 98.3154296875\n",
|
|
|
|
|
|
"Top1 Train accuracy 74.2578125\tTop1 Test accuracy: 69.42138671875\tTop5 test acc: 98.3154296875\n",
|
|
|
|
|
|
"Top1 Train accuracy 74.27734375\tTop1 Test accuracy: 69.482421875\tTop5 test acc: 98.3154296875\n",
|
|
|
|
|
|
"Top1 Train accuracy 74.375\tTop1 Test accuracy: 69.51904296875\tTop5 test acc: 98.30322265625\n",
|
|
|
|
|
|
"Top1 Train accuracy 74.39453125\tTop1 Test accuracy: 69.6044921875\tTop5 test acc: 98.30322265625\n",
|
|
|
|
|
|
"Top1 Train accuracy 74.43359375\tTop1 Test accuracy: 69.6044921875\tTop5 test acc: 98.30322265625\n",
|
|
|
|
|
|
"Top1 Train accuracy 74.43359375\tTop1 Test accuracy: 69.6044921875\tTop5 test acc: 98.30322265625\n",
|
|
|
|
|
|
"Top1 Train accuracy 74.4921875\tTop1 Test accuracy: 69.64111328125\tTop5 test acc: 98.3154296875\n",
|
|
|
|
|
|
"Top1 Train accuracy 74.5703125\tTop1 Test accuracy: 69.7021484375\tTop5 test acc: 98.3154296875\n",
|
|
|
|
|
|
"Top1 Train accuracy 74.66796875\tTop1 Test accuracy: 69.775390625\tTop5 test acc: 98.3154296875\n",
|
|
|
|
|
|
"Top1 Train accuracy 74.6875\tTop1 Test accuracy: 69.775390625\tTop5 test acc: 98.3154296875\n",
|
|
|
|
|
|
"Top1 Train accuracy 74.74609375\tTop1 Test accuracy: 69.76318359375\tTop5 test acc: 98.30322265625\n",
|
|
|
|
|
|
"Top1 Train accuracy 74.74609375\tTop1 Test accuracy: 69.78759765625\tTop5 test acc: 98.30322265625\n",
|
|
|
|
|
|
"Top1 Train accuracy 74.84375\tTop1 Test accuracy: 69.81201171875\tTop5 test acc: 98.3154296875\n",
|
|
|
|
|
|
"Top1 Train accuracy 74.94140625\tTop1 Test accuracy: 69.88525390625\tTop5 test acc: 98.32763671875\n",
|
|
|
|
|
|
"Top1 Train accuracy 75.0390625\tTop1 Test accuracy: 69.8974609375\tTop5 test acc: 98.32763671875\n",
|
|
|
|
|
|
"Top1 Train accuracy 75.05859375\tTop1 Test accuracy: 69.921875\tTop5 test acc: 98.3154296875\n",
|
|
|
|
|
|
"Top1 Train accuracy 75.078125\tTop1 Test accuracy: 69.95849609375\tTop5 test acc: 98.30322265625\n",
|
|
|
|
|
|
"Top1 Train accuracy 75.15625\tTop1 Test accuracy: 69.921875\tTop5 test acc: 98.30322265625\n",
|
|
|
|
|
|
"Top1 Train accuracy 75.21484375\tTop1 Test accuracy: 69.9462890625\tTop5 test acc: 98.30322265625\n",
|
|
|
|
|
|
"Top1 Train accuracy 75.17578125\tTop1 Test accuracy: 69.93408203125\tTop5 test acc: 98.30322265625\n",
|
|
|
|
|
|
"Top1 Train accuracy 75.17578125\tTop1 Test accuracy: 69.98291015625\tTop5 test acc: 98.291015625\n",
|
|
|
|
|
|
"Top1 Train accuracy 75.234375\tTop1 Test accuracy: 69.95849609375\tTop5 test acc: 98.3154296875\n",
|
|
|
|
|
|
"Top1 Train accuracy 75.234375\tTop1 Test accuracy: 69.98291015625\tTop5 test acc: 98.3154296875\n",
|
|
|
|
|
|
"Top1 Train accuracy 75.2734375\tTop1 Test accuracy: 70.00732421875\tTop5 test acc: 98.3154296875\n",
|
|
|
|
|
|
"Top1 Train accuracy 75.37109375\tTop1 Test accuracy: 70.01953125\tTop5 test acc: 98.3154296875\n"
|
|
|
|
|
|
],
|
|
|
|
|
|
"name": "stdout"
|
|
|
|
|
|
}
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"id": "dtYqHZirMNZk"
|
|
|
|
|
|
},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
""
|
|
|
|
|
|
],
|
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
|
"outputs": []
|
|
|
|
|
|
}
|
|
|
|
|
|
]
|
2021-01-17 20:12:27 -03:00
|
|
|
|
}
|
