mirror of
https://github.com/sthalles/SimCLR.git
synced 2025-06-03 15:03:00 +08:00
Created using Colaboratory
This commit is contained in:
Thalles Silva
parent
6fcd825902
commit
d1d59400fe
@ -20,261 +20,26 @@
|
|||||||
"version": "3.6.6"
|
"version": "3.6.6"
|
||||||
},
|
},
|
||||||
"colab": {
|
"colab": {
|
||||||
"name": "Mini-batch-logistic-regression-evaluator.ipynb",
|
"name": "Copy of mini-batch-logistic-regression-evaluator.ipynb",
|
||||||
"provenance": []
|
"provenance": [],
|
||||||
|
"include_colab_link": true
|
||||||
},
|
},
|
||||||
"accelerator": "GPU",
|
"accelerator": "GPU",
|
||||||
"widgets": {
|
"widgets": {
|
||||||
"application/vnd.jupyter.widget-state+json": {
|
"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
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
}
|
||||||
},
|
},
|
||||||
"cells": [
|
"cells": [
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {
|
||||||
|
"id": "view-in-github",
|
||||||
|
"colab_type": "text"
|
||||||
|
},
|
||||||
|
"source": [
|
||||||
|
"<a href=\"https://colab.research.google.com/github/sthalles/SimCLR/blob/simclr-refactor/feature_eval/mini_batch_logistic_regression_evaluator.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
|
||||||
|
]
|
||||||
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"metadata": {
|
"metadata": {
|
||||||
@ -285,13 +50,8 @@
|
|||||||
"import sys\n",
|
"import sys\n",
|
||||||
"import numpy as np\n",
|
"import numpy as np\n",
|
||||||
"import os\n",
|
"import os\n",
|
||||||
"from sklearn.neighbors import KNeighborsClassifier\n",
|
|
||||||
"import yaml\n",
|
"import yaml\n",
|
||||||
"import matplotlib.pyplot as plt\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"
|
"import torchvision"
|
||||||
],
|
],
|
||||||
"execution_count": null,
|
"execution_count": null,
|
||||||
@ -522,7 +282,7 @@
|
|||||||
"elif config.arch == 'resnet50':\n",
|
"elif config.arch == 'resnet50':\n",
|
||||||
" model = torchvision.models.resnet50(pretrained=False, num_classes=10).to(device)"
|
" model = torchvision.models.resnet50(pretrained=False, num_classes=10).to(device)"
|
||||||
],
|
],
|
||||||
"execution_count": null,
|
"execution_count": 11,
|
||||||
"outputs": []
|
"outputs": []
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
@ -542,7 +302,7 @@
|
|||||||
" state_dict[k[len(\"backbone.\"):]] = state_dict[k]\n",
|
" state_dict[k[len(\"backbone.\"):]] = state_dict[k]\n",
|
||||||
" del state_dict[k]"
|
" del state_dict[k]"
|
||||||
],
|
],
|
||||||
"execution_count": null,
|
"execution_count": 12,
|
||||||
"outputs": []
|
"outputs": []
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
@ -554,7 +314,7 @@
|
|||||||
"log = model.load_state_dict(state_dict, strict=False)\n",
|
"log = model.load_state_dict(state_dict, strict=False)\n",
|
||||||
"assert log.missing_keys == ['fc.weight', 'fc.bias']"
|
"assert log.missing_keys == ['fc.weight', 'fc.bias']"
|
||||||
],
|
],
|
||||||
"execution_count": null,
|
"execution_count": 13,
|
||||||
"outputs": []
|
"outputs": []
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
@ -563,19 +323,12 @@
|
|||||||
"id": "_GC0a14uWRr6",
|
"id": "_GC0a14uWRr6",
|
||||||
"colab": {
|
"colab": {
|
||||||
"base_uri": "https://localhost:8080/",
|
"base_uri": "https://localhost:8080/",
|
||||||
"height": 117,
|
"height": 102,
|
||||||
"referenced_widgets": [
|
"referenced_widgets": [
|
||||||
"bcf2585d31644e0f86569e604b2e635b",
|
"48ebf2f69d1f4f5a9208cd2923eb5eac"
|
||||||
"2612abdc916d47418dda7287807a00ce",
|
|
||||||
"027c3ca8839846fcae9d6bb23fb10399",
|
|
||||||
"1d09572d2433498caa268567c838e640",
|
|
||||||
"08cddf6f231a4e89ab8e1e026cf11796",
|
|
||||||
"75267826defa4565be4bed232272434e",
|
|
||||||
"8c189a0cd687479dba885a9c2d47fb64",
|
|
||||||
"b6528931de654b3c85b94bec14f4891b"
|
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"outputId": "56db3fac-10cc-4985-932d-878375ccd18f"
|
"outputId": "6c3b86ad-b568-4c68-c1fb-1f7b2abbb6aa"
|
||||||
},
|
},
|
||||||
"source": [
|
"source": [
|
||||||
"if config.dataset_name == 'cifar10':\n",
|
"if config.dataset_name == 'cifar10':\n",
|
||||||
@ -584,7 +337,7 @@
|
|||||||
" train_loader, test_loader = get_stl10_data_loaders(download=True)\n",
|
" train_loader, test_loader = get_stl10_data_loaders(download=True)\n",
|
||||||
"print(\"Dataset:\", config.dataset_name)"
|
"print(\"Dataset:\", config.dataset_name)"
|
||||||
],
|
],
|
||||||
"execution_count": null,
|
"execution_count": 14,
|
||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
@ -597,9 +350,9 @@
|
|||||||
"output_type": "display_data",
|
"output_type": "display_data",
|
||||||
"data": {
|
"data": {
|
||||||
"application/vnd.jupyter.widget-view+json": {
|
"application/vnd.jupyter.widget-view+json": {
|
||||||
"model_id": "bcf2585d31644e0f86569e604b2e635b",
|
"model_id": "48ebf2f69d1f4f5a9208cd2923eb5eac",
|
||||||
"version_minor": 0,
|
"version_major": 2,
|
||||||
"version_major": 2
|
"version_minor": 0
|
||||||
},
|
},
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
|
"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
|
||||||
@ -634,7 +387,7 @@
|
|||||||
"parameters = list(filter(lambda p: p.requires_grad, model.parameters()))\n",
|
"parameters = list(filter(lambda p: p.requires_grad, model.parameters()))\n",
|
||||||
"assert len(parameters) == 2 # fc.weight, fc.bias"
|
"assert len(parameters) == 2 # fc.weight, fc.bias"
|
||||||
],
|
],
|
||||||
"execution_count": null,
|
"execution_count": 15,
|
||||||
"outputs": []
|
"outputs": []
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
@ -646,7 +399,7 @@
|
|||||||
"optimizer = torch.optim.Adam(model.parameters(), lr=3e-4, weight_decay=0.0008)\n",
|
"optimizer = torch.optim.Adam(model.parameters(), lr=3e-4, weight_decay=0.0008)\n",
|
||||||
"criterion = torch.nn.CrossEntropyLoss().to(device)"
|
"criterion = torch.nn.CrossEntropyLoss().to(device)"
|
||||||
],
|
],
|
||||||
"execution_count": null,
|
"execution_count": 16,
|
||||||
"outputs": []
|
"outputs": []
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
@ -671,7 +424,7 @@
|
|||||||
" res.append(correct_k.mul_(100.0 / batch_size))\n",
|
" res.append(correct_k.mul_(100.0 / batch_size))\n",
|
||||||
" return res"
|
" return res"
|
||||||
],
|
],
|
||||||
"execution_count": null,
|
"execution_count": 17,
|
||||||
"outputs": []
|
"outputs": []
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
@ -681,7 +434,7 @@
|
|||||||
"colab": {
|
"colab": {
|
||||||
"base_uri": "https://localhost:8080/"
|
"base_uri": "https://localhost:8080/"
|
||||||
},
|
},
|
||||||
"outputId": "95b285c8-2b26-4d2c-ccc3-bb9111871c8d"
|
"outputId": "d6127d8e-836f-4e69-a344-fee7e836d63a"
|
||||||
},
|
},
|
||||||
"source": [
|
"source": [
|
||||||
"epochs = 100\n",
|
"epochs = 100\n",
|
||||||
@ -717,111 +470,111 @@
|
|||||||
" top5_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()}\")"
|
" 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,
|
"execution_count": 18,
|
||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
"Top1 Train accuracy 29.47265625\tTop1 Test accuracy: 42.4560546875\tTop5 test acc: 92.41943359375\n",
|
"Epoch 0\tTop1 Train accuracy 27.890625\tTop1 Test accuracy: 42.05322265625\tTop5 test acc: 93.29833984375\n",
|
||||||
"Top1 Train accuracy 49.47265625\tTop1 Test accuracy: 53.662109375\tTop5 test acc: 96.15478515625\n",
|
"Epoch 1\tTop1 Train accuracy 49.921875\tTop1 Test accuracy: 54.45556640625\tTop5 test acc: 96.1181640625\n",
|
||||||
"Top1 Train accuracy 56.85546875\tTop1 Test accuracy: 57.92236328125\tTop5 test acc: 96.74072265625\n",
|
"Epoch 2\tTop1 Train accuracy 57.3828125\tTop1 Test accuracy: 58.9599609375\tTop5 test acc: 96.9482421875\n",
|
||||||
"Top1 Train accuracy 59.3359375\tTop1 Test accuracy: 59.9365234375\tTop5 test acc: 97.021484375\n",
|
"Epoch 3\tTop1 Train accuracy 60.01953125\tTop1 Test accuracy: 60.38818359375\tTop5 test acc: 97.03369140625\n",
|
||||||
"Top1 Train accuracy 60.8984375\tTop1 Test accuracy: 61.1572265625\tTop5 test acc: 97.15576171875\n",
|
"Epoch 4\tTop1 Train accuracy 61.7578125\tTop1 Test accuracy: 61.572265625\tTop5 test acc: 97.1923828125\n",
|
||||||
"Top1 Train accuracy 61.89453125\tTop1 Test accuracy: 61.8408203125\tTop5 test acc: 97.2900390625\n",
|
"Epoch 5\tTop1 Train accuracy 62.91015625\tTop1 Test accuracy: 62.21923828125\tTop5 test acc: 97.30224609375\n",
|
||||||
"Top1 Train accuracy 62.48046875\tTop1 Test accuracy: 62.5244140625\tTop5 test acc: 97.3388671875\n",
|
"Epoch 6\tTop1 Train accuracy 63.57421875\tTop1 Test accuracy: 62.6220703125\tTop5 test acc: 97.4365234375\n",
|
||||||
"Top1 Train accuracy 63.125\tTop1 Test accuracy: 63.037109375\tTop5 test acc: 97.44873046875\n",
|
"Epoch 7\tTop1 Train accuracy 64.12109375\tTop1 Test accuracy: 63.18359375\tTop5 test acc: 97.55859375\n",
|
||||||
"Top1 Train accuracy 64.4140625\tTop1 Test accuracy: 63.39111328125\tTop5 test acc: 97.54638671875\n",
|
"Epoch 8\tTop1 Train accuracy 64.82421875\tTop1 Test accuracy: 63.51318359375\tTop5 test acc: 97.57080078125\n",
|
||||||
"Top1 Train accuracy 64.86328125\tTop1 Test accuracy: 63.85498046875\tTop5 test acc: 97.5830078125\n",
|
"Epoch 9\tTop1 Train accuracy 65.17578125\tTop1 Test accuracy: 63.80615234375\tTop5 test acc: 97.59521484375\n",
|
||||||
"Top1 Train accuracy 65.15625\tTop1 Test accuracy: 64.0869140625\tTop5 test acc: 97.65625\n",
|
"Epoch 10\tTop1 Train accuracy 65.5859375\tTop1 Test accuracy: 64.14794921875\tTop5 test acc: 97.6318359375\n",
|
||||||
"Top1 Train accuracy 65.56640625\tTop1 Test accuracy: 64.34326171875\tTop5 test acc: 97.69287109375\n",
|
"Epoch 11\tTop1 Train accuracy 65.80078125\tTop1 Test accuracy: 64.51416015625\tTop5 test acc: 97.61962890625\n",
|
||||||
"Top1 Train accuracy 65.859375\tTop1 Test accuracy: 64.48974609375\tTop5 test acc: 97.7294921875\n",
|
"Epoch 12\tTop1 Train accuracy 66.03515625\tTop1 Test accuracy: 64.70947265625\tTop5 test acc: 97.69287109375\n",
|
||||||
"Top1 Train accuracy 66.03515625\tTop1 Test accuracy: 64.83154296875\tTop5 test acc: 97.75390625\n",
|
"Epoch 13\tTop1 Train accuracy 66.42578125\tTop1 Test accuracy: 64.88037109375\tTop5 test acc: 97.705078125\n",
|
||||||
"Top1 Train accuracy 66.171875\tTop1 Test accuracy: 65.02685546875\tTop5 test acc: 97.79052734375\n",
|
"Epoch 14\tTop1 Train accuracy 66.9140625\tTop1 Test accuracy: 65.07568359375\tTop5 test acc: 97.76611328125\n",
|
||||||
"Top1 Train accuracy 66.484375\tTop1 Test accuracy: 65.46630859375\tTop5 test acc: 97.7783203125\n",
|
"Epoch 15\tTop1 Train accuracy 67.265625\tTop1 Test accuracy: 65.24658203125\tTop5 test acc: 97.81494140625\n",
|
||||||
"Top1 Train accuracy 66.953125\tTop1 Test accuracy: 65.66162109375\tTop5 test acc: 97.8515625\n",
|
"Epoch 16\tTop1 Train accuracy 67.48046875\tTop1 Test accuracy: 65.46630859375\tTop5 test acc: 97.8515625\n",
|
||||||
"Top1 Train accuracy 67.2265625\tTop1 Test accuracy: 65.91796875\tTop5 test acc: 97.93701171875\n",
|
"Epoch 17\tTop1 Train accuracy 67.6171875\tTop1 Test accuracy: 65.71044921875\tTop5 test acc: 97.86376953125\n",
|
||||||
"Top1 Train accuracy 67.48046875\tTop1 Test accuracy: 65.97900390625\tTop5 test acc: 97.91259765625\n",
|
"Epoch 18\tTop1 Train accuracy 67.83203125\tTop1 Test accuracy: 65.966796875\tTop5 test acc: 97.8759765625\n",
|
||||||
"Top1 Train accuracy 67.8125\tTop1 Test accuracy: 66.11328125\tTop5 test acc: 97.93701171875\n",
|
"Epoch 19\tTop1 Train accuracy 68.0078125\tTop1 Test accuracy: 66.05224609375\tTop5 test acc: 97.88818359375\n",
|
||||||
"Top1 Train accuracy 68.046875\tTop1 Test accuracy: 66.3330078125\tTop5 test acc: 97.9736328125\n",
|
"Epoch 20\tTop1 Train accuracy 68.1640625\tTop1 Test accuracy: 66.17431640625\tTop5 test acc: 97.88818359375\n",
|
||||||
"Top1 Train accuracy 68.45703125\tTop1 Test accuracy: 66.5283203125\tTop5 test acc: 97.94921875\n",
|
"Epoch 21\tTop1 Train accuracy 68.37890625\tTop1 Test accuracy: 66.30859375\tTop5 test acc: 97.900390625\n",
|
||||||
"Top1 Train accuracy 68.59375\tTop1 Test accuracy: 66.63818359375\tTop5 test acc: 97.94921875\n",
|
"Epoch 22\tTop1 Train accuracy 68.49609375\tTop1 Test accuracy: 66.50390625\tTop5 test acc: 97.88818359375\n",
|
||||||
"Top1 Train accuracy 68.7890625\tTop1 Test accuracy: 66.748046875\tTop5 test acc: 97.93701171875\n",
|
"Epoch 23\tTop1 Train accuracy 68.75\tTop1 Test accuracy: 66.6259765625\tTop5 test acc: 97.91259765625\n",
|
||||||
"Top1 Train accuracy 69.00390625\tTop1 Test accuracy: 66.90673828125\tTop5 test acc: 97.9248046875\n",
|
"Epoch 24\tTop1 Train accuracy 68.90625\tTop1 Test accuracy: 66.68701171875\tTop5 test acc: 97.96142578125\n",
|
||||||
"Top1 Train accuracy 69.21875\tTop1 Test accuracy: 67.0654296875\tTop5 test acc: 97.9736328125\n",
|
"Epoch 25\tTop1 Train accuracy 68.984375\tTop1 Test accuracy: 66.8212890625\tTop5 test acc: 97.998046875\n",
|
||||||
"Top1 Train accuracy 69.35546875\tTop1 Test accuracy: 67.0654296875\tTop5 test acc: 97.9736328125\n",
|
"Epoch 26\tTop1 Train accuracy 69.39453125\tTop1 Test accuracy: 66.9677734375\tTop5 test acc: 98.0224609375\n",
|
||||||
"Top1 Train accuracy 69.66796875\tTop1 Test accuracy: 67.2119140625\tTop5 test acc: 97.93701171875\n",
|
"Epoch 27\tTop1 Train accuracy 69.4921875\tTop1 Test accuracy: 67.1142578125\tTop5 test acc: 98.01025390625\n",
|
||||||
"Top1 Train accuracy 69.765625\tTop1 Test accuracy: 67.24853515625\tTop5 test acc: 97.9736328125\n",
|
"Epoch 28\tTop1 Train accuracy 69.6484375\tTop1 Test accuracy: 67.1630859375\tTop5 test acc: 98.0224609375\n",
|
||||||
"Top1 Train accuracy 69.82421875\tTop1 Test accuracy: 67.4072265625\tTop5 test acc: 97.98583984375\n",
|
"Epoch 29\tTop1 Train accuracy 69.7265625\tTop1 Test accuracy: 67.19970703125\tTop5 test acc: 98.03466796875\n",
|
||||||
"Top1 Train accuracy 69.9609375\tTop1 Test accuracy: 67.431640625\tTop5 test acc: 97.98583984375\n",
|
"Epoch 30\tTop1 Train accuracy 69.74609375\tTop1 Test accuracy: 67.24853515625\tTop5 test acc: 98.05908203125\n",
|
||||||
"Top1 Train accuracy 70.09765625\tTop1 Test accuracy: 67.4560546875\tTop5 test acc: 97.998046875\n",
|
"Epoch 31\tTop1 Train accuracy 69.921875\tTop1 Test accuracy: 67.37060546875\tTop5 test acc: 98.03466796875\n",
|
||||||
"Top1 Train accuracy 70.15625\tTop1 Test accuracy: 67.44384765625\tTop5 test acc: 98.01025390625\n",
|
"Epoch 32\tTop1 Train accuracy 70.078125\tTop1 Test accuracy: 67.46826171875\tTop5 test acc: 98.03466796875\n",
|
||||||
"Top1 Train accuracy 70.29296875\tTop1 Test accuracy: 67.54150390625\tTop5 test acc: 98.0224609375\n",
|
"Epoch 33\tTop1 Train accuracy 70.25390625\tTop1 Test accuracy: 67.5048828125\tTop5 test acc: 98.0712890625\n",
|
||||||
"Top1 Train accuracy 70.41015625\tTop1 Test accuracy: 67.61474609375\tTop5 test acc: 98.05908203125\n",
|
"Epoch 34\tTop1 Train accuracy 70.33203125\tTop1 Test accuracy: 67.59033203125\tTop5 test acc: 98.095703125\n",
|
||||||
"Top1 Train accuracy 70.5078125\tTop1 Test accuracy: 67.67578125\tTop5 test acc: 98.0712890625\n",
|
"Epoch 35\tTop1 Train accuracy 70.48828125\tTop1 Test accuracy: 67.73681640625\tTop5 test acc: 98.13232421875\n",
|
||||||
"Top1 Train accuracy 70.64453125\tTop1 Test accuracy: 67.73681640625\tTop5 test acc: 98.08349609375\n",
|
"Epoch 36\tTop1 Train accuracy 70.5859375\tTop1 Test accuracy: 67.83447265625\tTop5 test acc: 98.1201171875\n",
|
||||||
"Top1 Train accuracy 70.859375\tTop1 Test accuracy: 67.76123046875\tTop5 test acc: 98.0712890625\n",
|
"Epoch 37\tTop1 Train accuracy 70.625\tTop1 Test accuracy: 67.85888671875\tTop5 test acc: 98.13232421875\n",
|
||||||
"Top1 Train accuracy 70.8984375\tTop1 Test accuracy: 67.88330078125\tTop5 test acc: 98.08349609375\n",
|
"Epoch 38\tTop1 Train accuracy 70.78125\tTop1 Test accuracy: 67.88330078125\tTop5 test acc: 98.13232421875\n",
|
||||||
"Top1 Train accuracy 71.07421875\tTop1 Test accuracy: 67.95654296875\tTop5 test acc: 98.095703125\n",
|
"Epoch 39\tTop1 Train accuracy 70.91796875\tTop1 Test accuracy: 67.919921875\tTop5 test acc: 98.10791015625\n",
|
||||||
"Top1 Train accuracy 71.11328125\tTop1 Test accuracy: 67.93212890625\tTop5 test acc: 98.1201171875\n",
|
"Epoch 40\tTop1 Train accuracy 70.95703125\tTop1 Test accuracy: 67.95654296875\tTop5 test acc: 98.10791015625\n",
|
||||||
"Top1 Train accuracy 71.2890625\tTop1 Test accuracy: 68.0419921875\tTop5 test acc: 98.10791015625\n",
|
"Epoch 41\tTop1 Train accuracy 71.03515625\tTop1 Test accuracy: 68.00537109375\tTop5 test acc: 98.1201171875\n",
|
||||||
"Top1 Train accuracy 71.3671875\tTop1 Test accuracy: 68.10302734375\tTop5 test acc: 98.13232421875\n",
|
"Epoch 42\tTop1 Train accuracy 71.07421875\tTop1 Test accuracy: 68.06640625\tTop5 test acc: 98.15673828125\n",
|
||||||
"Top1 Train accuracy 71.42578125\tTop1 Test accuracy: 68.1396484375\tTop5 test acc: 98.13232421875\n",
|
"Epoch 43\tTop1 Train accuracy 71.15234375\tTop1 Test accuracy: 68.12744140625\tTop5 test acc: 98.15673828125\n",
|
||||||
"Top1 Train accuracy 71.4453125\tTop1 Test accuracy: 68.1396484375\tTop5 test acc: 98.13232421875\n",
|
"Epoch 44\tTop1 Train accuracy 71.2109375\tTop1 Test accuracy: 68.1396484375\tTop5 test acc: 98.1689453125\n",
|
||||||
"Top1 Train accuracy 71.50390625\tTop1 Test accuracy: 68.1640625\tTop5 test acc: 98.1201171875\n",
|
"Epoch 45\tTop1 Train accuracy 71.25\tTop1 Test accuracy: 68.1396484375\tTop5 test acc: 98.1689453125\n",
|
||||||
"Top1 Train accuracy 71.484375\tTop1 Test accuracy: 68.2373046875\tTop5 test acc: 98.14453125\n",
|
"Epoch 46\tTop1 Train accuracy 71.46484375\tTop1 Test accuracy: 68.15185546875\tTop5 test acc: 98.193359375\n",
|
||||||
"Top1 Train accuracy 71.6015625\tTop1 Test accuracy: 68.34716796875\tTop5 test acc: 98.15673828125\n",
|
"Epoch 47\tTop1 Train accuracy 71.58203125\tTop1 Test accuracy: 68.22509765625\tTop5 test acc: 98.2177734375\n",
|
||||||
"Top1 Train accuracy 71.7578125\tTop1 Test accuracy: 68.39599609375\tTop5 test acc: 98.15673828125\n",
|
"Epoch 48\tTop1 Train accuracy 71.6796875\tTop1 Test accuracy: 68.27392578125\tTop5 test acc: 98.22998046875\n",
|
||||||
"Top1 Train accuracy 71.89453125\tTop1 Test accuracy: 68.37158203125\tTop5 test acc: 98.20556640625\n",
|
"Epoch 49\tTop1 Train accuracy 71.8359375\tTop1 Test accuracy: 68.3349609375\tTop5 test acc: 98.22998046875\n",
|
||||||
"Top1 Train accuracy 72.01171875\tTop1 Test accuracy: 68.4326171875\tTop5 test acc: 98.20556640625\n",
|
"Epoch 50\tTop1 Train accuracy 71.93359375\tTop1 Test accuracy: 68.44482421875\tTop5 test acc: 98.2421875\n",
|
||||||
"Top1 Train accuracy 72.1484375\tTop1 Test accuracy: 68.44482421875\tTop5 test acc: 98.2177734375\n",
|
"Epoch 51\tTop1 Train accuracy 72.01171875\tTop1 Test accuracy: 68.4814453125\tTop5 test acc: 98.2177734375\n",
|
||||||
"Top1 Train accuracy 72.1875\tTop1 Test accuracy: 68.51806640625\tTop5 test acc: 98.25439453125\n",
|
"Epoch 52\tTop1 Train accuracy 72.0703125\tTop1 Test accuracy: 68.505859375\tTop5 test acc: 98.2177734375\n",
|
||||||
"Top1 Train accuracy 72.28515625\tTop1 Test accuracy: 68.603515625\tTop5 test acc: 98.2421875\n",
|
"Epoch 53\tTop1 Train accuracy 72.2265625\tTop1 Test accuracy: 68.54248046875\tTop5 test acc: 98.22998046875\n",
|
||||||
"Top1 Train accuracy 72.36328125\tTop1 Test accuracy: 68.5791015625\tTop5 test acc: 98.2666015625\n",
|
"Epoch 54\tTop1 Train accuracy 72.24609375\tTop1 Test accuracy: 68.5791015625\tTop5 test acc: 98.22998046875\n",
|
||||||
"Top1 Train accuracy 72.5390625\tTop1 Test accuracy: 68.61572265625\tTop5 test acc: 98.2666015625\n",
|
"Epoch 55\tTop1 Train accuracy 72.34375\tTop1 Test accuracy: 68.65234375\tTop5 test acc: 98.25439453125\n",
|
||||||
"Top1 Train accuracy 72.59765625\tTop1 Test accuracy: 68.64013671875\tTop5 test acc: 98.2666015625\n",
|
"Epoch 56\tTop1 Train accuracy 72.421875\tTop1 Test accuracy: 68.71337890625\tTop5 test acc: 98.3154296875\n",
|
||||||
"Top1 Train accuracy 73.02734375\tTop1 Test accuracy: 68.7255859375\tTop5 test acc: 98.25439453125\n",
|
"Epoch 57\tTop1 Train accuracy 72.51953125\tTop1 Test accuracy: 68.71337890625\tTop5 test acc: 98.3154296875\n",
|
||||||
"Top1 Train accuracy 73.18359375\tTop1 Test accuracy: 68.76220703125\tTop5 test acc: 98.2666015625\n",
|
"Epoch 58\tTop1 Train accuracy 72.94921875\tTop1 Test accuracy: 68.76220703125\tTop5 test acc: 98.3154296875\n",
|
||||||
"Top1 Train accuracy 73.26171875\tTop1 Test accuracy: 68.8232421875\tTop5 test acc: 98.291015625\n",
|
"Epoch 59\tTop1 Train accuracy 72.98828125\tTop1 Test accuracy: 68.83544921875\tTop5 test acc: 98.3154296875\n",
|
||||||
"Top1 Train accuracy 73.359375\tTop1 Test accuracy: 68.85986328125\tTop5 test acc: 98.27880859375\n",
|
"Epoch 60\tTop1 Train accuracy 73.0859375\tTop1 Test accuracy: 68.88427734375\tTop5 test acc: 98.30322265625\n",
|
||||||
"Top1 Train accuracy 73.45703125\tTop1 Test accuracy: 68.8720703125\tTop5 test acc: 98.32763671875\n",
|
"Epoch 61\tTop1 Train accuracy 73.18359375\tTop1 Test accuracy: 68.896484375\tTop5 test acc: 98.32763671875\n",
|
||||||
"Top1 Train accuracy 73.49609375\tTop1 Test accuracy: 68.9208984375\tTop5 test acc: 98.33984375\n",
|
"Epoch 62\tTop1 Train accuracy 73.3984375\tTop1 Test accuracy: 68.88427734375\tTop5 test acc: 98.33984375\n",
|
||||||
"Top1 Train accuracy 73.53515625\tTop1 Test accuracy: 68.8720703125\tTop5 test acc: 98.33984375\n",
|
"Epoch 63\tTop1 Train accuracy 73.4375\tTop1 Test accuracy: 68.95751953125\tTop5 test acc: 98.33984375\n",
|
||||||
"Top1 Train accuracy 73.53515625\tTop1 Test accuracy: 68.9208984375\tTop5 test acc: 98.3642578125\n",
|
"Epoch 64\tTop1 Train accuracy 73.515625\tTop1 Test accuracy: 68.994140625\tTop5 test acc: 98.32763671875\n",
|
||||||
"Top1 Train accuracy 73.65234375\tTop1 Test accuracy: 69.00634765625\tTop5 test acc: 98.33984375\n",
|
"Epoch 65\tTop1 Train accuracy 73.57421875\tTop1 Test accuracy: 68.9697265625\tTop5 test acc: 98.3154296875\n",
|
||||||
"Top1 Train accuracy 73.76953125\tTop1 Test accuracy: 69.0185546875\tTop5 test acc: 98.33984375\n",
|
"Epoch 66\tTop1 Train accuracy 73.61328125\tTop1 Test accuracy: 69.03076171875\tTop5 test acc: 98.32763671875\n",
|
||||||
"Top1 Train accuracy 73.9453125\tTop1 Test accuracy: 69.0673828125\tTop5 test acc: 98.35205078125\n",
|
"Epoch 67\tTop1 Train accuracy 73.671875\tTop1 Test accuracy: 69.07958984375\tTop5 test acc: 98.3154296875\n",
|
||||||
"Top1 Train accuracy 74.00390625\tTop1 Test accuracy: 69.1162109375\tTop5 test acc: 98.35205078125\n",
|
"Epoch 68\tTop1 Train accuracy 73.7109375\tTop1 Test accuracy: 69.12841796875\tTop5 test acc: 98.3154296875\n",
|
||||||
"Top1 Train accuracy 74.0625\tTop1 Test accuracy: 69.140625\tTop5 test acc: 98.3642578125\n",
|
"Epoch 69\tTop1 Train accuracy 73.8671875\tTop1 Test accuracy: 69.20166015625\tTop5 test acc: 98.3154296875\n",
|
||||||
"Top1 Train accuracy 74.12109375\tTop1 Test accuracy: 69.17724609375\tTop5 test acc: 98.3642578125\n",
|
"Epoch 70\tTop1 Train accuracy 73.984375\tTop1 Test accuracy: 69.25048828125\tTop5 test acc: 98.33984375\n",
|
||||||
"Top1 Train accuracy 74.21875\tTop1 Test accuracy: 69.20166015625\tTop5 test acc: 98.35205078125\n",
|
"Epoch 71\tTop1 Train accuracy 74.00390625\tTop1 Test accuracy: 69.2626953125\tTop5 test acc: 98.35205078125\n",
|
||||||
"Top1 Train accuracy 74.21875\tTop1 Test accuracy: 69.2626953125\tTop5 test acc: 98.33984375\n",
|
"Epoch 72\tTop1 Train accuracy 74.00390625\tTop1 Test accuracy: 69.3115234375\tTop5 test acc: 98.33984375\n",
|
||||||
"Top1 Train accuracy 74.23828125\tTop1 Test accuracy: 69.3359375\tTop5 test acc: 98.33984375\n",
|
"Epoch 73\tTop1 Train accuracy 74.0234375\tTop1 Test accuracy: 69.34814453125\tTop5 test acc: 98.35205078125\n",
|
||||||
"Top1 Train accuracy 74.23828125\tTop1 Test accuracy: 69.37255859375\tTop5 test acc: 98.3154296875\n",
|
"Epoch 74\tTop1 Train accuracy 74.140625\tTop1 Test accuracy: 69.37255859375\tTop5 test acc: 98.33984375\n",
|
||||||
"Top1 Train accuracy 74.2578125\tTop1 Test accuracy: 69.42138671875\tTop5 test acc: 98.3154296875\n",
|
"Epoch 75\tTop1 Train accuracy 74.23828125\tTop1 Test accuracy: 69.4091796875\tTop5 test acc: 98.35205078125\n",
|
||||||
"Top1 Train accuracy 74.27734375\tTop1 Test accuracy: 69.482421875\tTop5 test acc: 98.3154296875\n",
|
"Epoch 76\tTop1 Train accuracy 74.31640625\tTop1 Test accuracy: 69.4091796875\tTop5 test acc: 98.37646484375\n",
|
||||||
"Top1 Train accuracy 74.375\tTop1 Test accuracy: 69.51904296875\tTop5 test acc: 98.30322265625\n",
|
"Epoch 77\tTop1 Train accuracy 74.43359375\tTop1 Test accuracy: 69.4091796875\tTop5 test acc: 98.3642578125\n",
|
||||||
"Top1 Train accuracy 74.39453125\tTop1 Test accuracy: 69.6044921875\tTop5 test acc: 98.30322265625\n",
|
"Epoch 78\tTop1 Train accuracy 74.55078125\tTop1 Test accuracy: 69.3603515625\tTop5 test acc: 98.3642578125\n",
|
||||||
"Top1 Train accuracy 74.43359375\tTop1 Test accuracy: 69.6044921875\tTop5 test acc: 98.30322265625\n",
|
"Epoch 79\tTop1 Train accuracy 74.58984375\tTop1 Test accuracy: 69.37255859375\tTop5 test acc: 98.3642578125\n",
|
||||||
"Top1 Train accuracy 74.43359375\tTop1 Test accuracy: 69.6044921875\tTop5 test acc: 98.30322265625\n",
|
"Epoch 80\tTop1 Train accuracy 74.609375\tTop1 Test accuracy: 69.42138671875\tTop5 test acc: 98.3642578125\n",
|
||||||
"Top1 Train accuracy 74.4921875\tTop1 Test accuracy: 69.64111328125\tTop5 test acc: 98.3154296875\n",
|
"Epoch 81\tTop1 Train accuracy 74.6484375\tTop1 Test accuracy: 69.49462890625\tTop5 test acc: 98.3642578125\n",
|
||||||
"Top1 Train accuracy 74.5703125\tTop1 Test accuracy: 69.7021484375\tTop5 test acc: 98.3154296875\n",
|
"Epoch 82\tTop1 Train accuracy 74.6875\tTop1 Test accuracy: 69.47021484375\tTop5 test acc: 98.35205078125\n",
|
||||||
"Top1 Train accuracy 74.66796875\tTop1 Test accuracy: 69.775390625\tTop5 test acc: 98.3154296875\n",
|
"Epoch 83\tTop1 Train accuracy 74.7265625\tTop1 Test accuracy: 69.5556640625\tTop5 test acc: 98.35205078125\n",
|
||||||
"Top1 Train accuracy 74.6875\tTop1 Test accuracy: 69.775390625\tTop5 test acc: 98.3154296875\n",
|
"Epoch 84\tTop1 Train accuracy 74.78515625\tTop1 Test accuracy: 69.59228515625\tTop5 test acc: 98.35205078125\n",
|
||||||
"Top1 Train accuracy 74.74609375\tTop1 Test accuracy: 69.76318359375\tTop5 test acc: 98.30322265625\n",
|
"Epoch 85\tTop1 Train accuracy 74.8828125\tTop1 Test accuracy: 69.6533203125\tTop5 test acc: 98.35205078125\n",
|
||||||
"Top1 Train accuracy 74.74609375\tTop1 Test accuracy: 69.78759765625\tTop5 test acc: 98.30322265625\n",
|
"Epoch 86\tTop1 Train accuracy 74.94140625\tTop1 Test accuracy: 69.677734375\tTop5 test acc: 98.3642578125\n",
|
||||||
"Top1 Train accuracy 74.84375\tTop1 Test accuracy: 69.81201171875\tTop5 test acc: 98.3154296875\n",
|
"Epoch 87\tTop1 Train accuracy 75.0390625\tTop1 Test accuracy: 69.7509765625\tTop5 test acc: 98.35205078125\n",
|
||||||
"Top1 Train accuracy 74.94140625\tTop1 Test accuracy: 69.88525390625\tTop5 test acc: 98.32763671875\n",
|
"Epoch 88\tTop1 Train accuracy 75.0390625\tTop1 Test accuracy: 69.71435546875\tTop5 test acc: 98.35205078125\n",
|
||||||
"Top1 Train accuracy 75.0390625\tTop1 Test accuracy: 69.8974609375\tTop5 test acc: 98.32763671875\n",
|
"Epoch 89\tTop1 Train accuracy 75.1171875\tTop1 Test accuracy: 69.775390625\tTop5 test acc: 98.33984375\n",
|
||||||
"Top1 Train accuracy 75.05859375\tTop1 Test accuracy: 69.921875\tTop5 test acc: 98.3154296875\n",
|
"Epoch 90\tTop1 Train accuracy 75.21484375\tTop1 Test accuracy: 69.7509765625\tTop5 test acc: 98.33984375\n",
|
||||||
"Top1 Train accuracy 75.078125\tTop1 Test accuracy: 69.95849609375\tTop5 test acc: 98.30322265625\n",
|
"Epoch 91\tTop1 Train accuracy 75.25390625\tTop1 Test accuracy: 69.82421875\tTop5 test acc: 98.32763671875\n",
|
||||||
"Top1 Train accuracy 75.15625\tTop1 Test accuracy: 69.921875\tTop5 test acc: 98.30322265625\n",
|
"Epoch 92\tTop1 Train accuracy 75.29296875\tTop1 Test accuracy: 69.86083984375\tTop5 test acc: 98.33984375\n",
|
||||||
"Top1 Train accuracy 75.21484375\tTop1 Test accuracy: 69.9462890625\tTop5 test acc: 98.30322265625\n",
|
"Epoch 93\tTop1 Train accuracy 75.33203125\tTop1 Test accuracy: 69.88525390625\tTop5 test acc: 98.35205078125\n",
|
||||||
"Top1 Train accuracy 75.17578125\tTop1 Test accuracy: 69.93408203125\tTop5 test acc: 98.30322265625\n",
|
"Epoch 94\tTop1 Train accuracy 75.37109375\tTop1 Test accuracy: 69.81201171875\tTop5 test acc: 98.3642578125\n",
|
||||||
"Top1 Train accuracy 75.17578125\tTop1 Test accuracy: 69.98291015625\tTop5 test acc: 98.291015625\n",
|
"Epoch 95\tTop1 Train accuracy 75.37109375\tTop1 Test accuracy: 69.83642578125\tTop5 test acc: 98.37646484375\n",
|
||||||
"Top1 Train accuracy 75.234375\tTop1 Test accuracy: 69.95849609375\tTop5 test acc: 98.3154296875\n",
|
"Epoch 96\tTop1 Train accuracy 75.37109375\tTop1 Test accuracy: 69.83642578125\tTop5 test acc: 98.37646484375\n",
|
||||||
"Top1 Train accuracy 75.234375\tTop1 Test accuracy: 69.98291015625\tTop5 test acc: 98.3154296875\n",
|
"Epoch 97\tTop1 Train accuracy 75.41015625\tTop1 Test accuracy: 69.86083984375\tTop5 test acc: 98.37646484375\n",
|
||||||
"Top1 Train accuracy 75.2734375\tTop1 Test accuracy: 70.00732421875\tTop5 test acc: 98.3154296875\n",
|
"Epoch 98\tTop1 Train accuracy 75.41015625\tTop1 Test accuracy: 69.90966796875\tTop5 test acc: 98.37646484375\n",
|
||||||
"Top1 Train accuracy 75.37109375\tTop1 Test accuracy: 70.01953125\tTop5 test acc: 98.3154296875\n"
|
"Epoch 99\tTop1 Train accuracy 75.46875\tTop1 Test accuracy: 69.921875\tTop5 test acc: 98.37646484375\n"
|
||||||
],
|
],
|
||||||
"name": "stdout"
|
"name": "stdout"
|
||||||
}
|
}
|
||||||
@ -835,8 +588,8 @@
|
|||||||
"source": [
|
"source": [
|
||||||
""
|
""
|
||||||
],
|
],
|
||||||
"execution_count": null,
|
"execution_count": 18,
|
||||||
"outputs": []
|
"outputs": []
|
||||||
}
|
}
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
Loading…
x
Reference in New Issue
Block a user