mirror of
https://github.com/sthalles/SimCLR.git
synced 2025-06-03 15:03:00 +08:00
Merge remote-tracking branch 'origin/simclr-refactor' into simclr-refactor
This commit is contained in:
Thalles Silva
commit
ad30ba02b5
@ -21,12 +21,13 @@
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@ -275,6 +276,16 @@
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}
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},
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"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",
|
||||
"metadata": {
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||||
@ -285,16 +296,11 @@
|
||||
"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,
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||||
"execution_count": 10,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
@ -304,12 +310,12 @@
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"outputId": "e44ac358-6480-4a5f-a358-6eb6ace26c8b"
|
||||
"outputId": "a6477424-66e6-4a59-bef2-42e5cbada7cf"
|
||||
},
|
||||
"source": [
|
||||
"!pip install gdown"
|
||||
],
|
||||
"execution_count": null,
|
||||
"execution_count": 11,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
@ -320,8 +326,8 @@
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||||
"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"
|
||||
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->gdown) (2.10)\n",
|
||||
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->gdown) (2020.12.5)\n"
|
||||
],
|
||||
"name": "stdout"
|
||||
}
|
||||
@ -338,7 +344,7 @@
|
||||
" 'resnet18_100-epochs_cifar10': '1lc2aoVtrAetGn0PnTkOyFzPCIucOJq7C'}\n",
|
||||
" return file_id.get(folder_name, \"Model not found.\")"
|
||||
],
|
||||
"execution_count": null,
|
||||
"execution_count": 12,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
@ -348,19 +354,19 @@
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"outputId": "36932a7d-c7e5-492a-f37d-8be6b18f787a"
|
||||
"outputId": "da3bc13b-f989-4a19-dc02-5172e5e370c0"
|
||||
},
|
||||
"source": [
|
||||
"folder_name = 'resnet18_100-epochs_stl10'\n",
|
||||
"folder_name = 'resnet18_100-epochs_cifar10'\n",
|
||||
"file_id = get_file_id_by_model(folder_name)\n",
|
||||
"print(folder_name, file_id)"
|
||||
],
|
||||
"execution_count": null,
|
||||
"execution_count": 13,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"resnet18_100-epochs_stl10 14_nH2FkyKbt61cieQDiSbBVNP8-gtwgF\n"
|
||||
"resnet18_100-epochs_cifar10 1lc2aoVtrAetGn0PnTkOyFzPCIucOJq7C\n"
|
||||
],
|
||||
"name": "stdout"
|
||||
}
|
||||
@ -373,7 +379,7 @@
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"outputId": "8d52756d-707b-4a3f-9e8c-0d191408deab"
|
||||
"outputId": "63d1d89d-ad11-48ba-8bb3-4da15b930073"
|
||||
},
|
||||
"source": [
|
||||
"# download and extract model files\n",
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||||
@ -381,45 +387,18 @@
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||||
"os.system('unzip {}'.format(folder_name))\n",
|
||||
"!ls"
|
||||
],
|
||||
"execution_count": null,
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||||
"execution_count": 14,
|
||||
"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"
|
||||
"events.out.tfevents.1610901418.4cb2c837708d.2683796.0\n",
|
||||
"resnet18_100-epochs_cifar10.zip\n",
|
||||
"resnet18_100-epochs-cifar10.zip\n",
|
||||
"run.log\n",
|
||||
"sample_data\n"
|
||||
],
|
||||
"name": "stdout"
|
||||
}
|
||||
@ -435,7 +414,7 @@
|
||||
"import torchvision.transforms as transforms\n",
|
||||
"from torchvision import datasets"
|
||||
],
|
||||
"execution_count": null,
|
||||
"execution_count": 15,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
@ -445,13 +424,13 @@
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"outputId": "5f58bd9b-4428-4b8c-e271-b47ca6694f34"
|
||||
"outputId": "028ac120-c51d-4eb2-cf00-da69aed6e310"
|
||||
},
|
||||
"source": [
|
||||
"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
|
||||
"print(\"Using device:\", device)"
|
||||
],
|
||||
"execution_count": null,
|
||||
"execution_count": 16,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
@ -468,7 +447,7 @@
|
||||
"id": "BfIPl0G6_RrT"
|
||||
},
|
||||
"source": [
|
||||
"def get_stl10_data_loaders(download, shuffle=False, batch_size=128):\n",
|
||||
"def get_stl10_data_loaders(download, shuffle=False, batch_size=256):\n",
|
||||
" train_dataset = datasets.STL10('./data', split='train', download=download,\n",
|
||||
" transform=transforms.ToTensor())\n",
|
||||
"\n",
|
||||
@ -482,7 +461,7 @@
|
||||
" 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",
|
||||
"def get_cifar10_data_loaders(download, shuffle=False, batch_size=256):\n",
|
||||
" train_dataset = datasets.CIFAR10('./data', train=True, download=download,\n",
|
||||
" transform=transforms.ToTensor())\n",
|
||||
"\n",
|
||||
@ -496,7 +475,7 @@
|
||||
" num_workers=10, drop_last=False, shuffle=shuffle)\n",
|
||||
" return train_loader, test_loader"
|
||||
],
|
||||
"execution_count": null,
|
||||
"execution_count": 17,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
@ -508,7 +487,7 @@
|
||||
"with open(os.path.join('./config.yml')) as file:\n",
|
||||
" config = yaml.load(file)"
|
||||
],
|
||||
"execution_count": null,
|
||||
"execution_count": 18,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
@ -522,7 +501,7 @@
|
||||
"elif config.arch == 'resnet50':\n",
|
||||
" model = torchvision.models.resnet50(pretrained=False, num_classes=10).to(device)"
|
||||
],
|
||||
"execution_count": null,
|
||||
"execution_count": 19,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
@ -542,7 +521,7 @@
|
||||
" state_dict[k[len(\"backbone.\"):]] = state_dict[k]\n",
|
||||
" del state_dict[k]"
|
||||
],
|
||||
"execution_count": null,
|
||||
"execution_count": 20,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
@ -554,7 +533,7 @@
|
||||
"log = model.load_state_dict(state_dict, strict=False)\n",
|
||||
"assert log.missing_keys == ['fc.weight', 'fc.bias']"
|
||||
],
|
||||
"execution_count": null,
|
||||
"execution_count": 21,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
@ -565,17 +544,17 @@
|
||||
"base_uri": "https://localhost:8080/",
|
||||
"height": 117,
|
||||
"referenced_widgets": [
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"bcf2585d31644e0f86569e604b2e635b",
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|
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"1d09572d2433498caa268567c838e640",
|
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"08cddf6f231a4e89ab8e1e026cf11796",
|
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"75267826defa4565be4bed232272434e",
|
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"8c189a0cd687479dba885a9c2d47fb64",
|
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|
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|
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|
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|
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|
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|
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|
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|
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},
|
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"outputId": "56db3fac-10cc-4985-932d-878375ccd18f"
|
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"outputId": "4382995f-e0fa-48fc-d341-71400a06b6d9"
|
||||
},
|
||||
"source": [
|
||||
"if config.dataset_name == 'cifar10':\n",
|
||||
@ -584,12 +563,12 @@
|
||||
" train_loader, test_loader = get_stl10_data_loaders(download=True)\n",
|
||||
"print(\"Dataset:\", config.dataset_name)"
|
||||
],
|
||||
"execution_count": null,
|
||||
"execution_count": 22,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Downloading http://ai.stanford.edu/~acoates/stl10/stl10_binary.tar.gz to ./data/stl10_binary.tar.gz\n"
|
||||
"Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
|
||||
],
|
||||
"name": "stdout"
|
||||
},
|
||||
@ -597,7 +576,7 @@
|
||||
"output_type": "display_data",
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "bcf2585d31644e0f86569e604b2e635b",
|
||||
"model_id": "1b97f76ec8314fe3985e9183af3fdd9b",
|
||||
"version_minor": 0,
|
||||
"version_major": 2
|
||||
},
|
||||
@ -612,9 +591,9 @@
|
||||
{
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Extracting ./data/stl10_binary.tar.gz to ./data\n",
|
||||
"Extracting ./data/cifar-10-python.tar.gz to ./data\n",
|
||||
"Files already downloaded and verified\n",
|
||||
"Dataset: stl10\n"
|
||||
"Dataset: cifar10\n"
|
||||
],
|
||||
"name": "stdout"
|
||||
}
|
||||
@ -634,7 +613,7 @@
|
||||
"parameters = list(filter(lambda p: p.requires_grad, model.parameters()))\n",
|
||||
"assert len(parameters) == 2 # fc.weight, fc.bias"
|
||||
],
|
||||
"execution_count": null,
|
||||
"execution_count": 23,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
@ -646,7 +625,7 @@
|
||||
"optimizer = torch.optim.Adam(model.parameters(), lr=3e-4, weight_decay=0.0008)\n",
|
||||
"criterion = torch.nn.CrossEntropyLoss().to(device)"
|
||||
],
|
||||
"execution_count": null,
|
||||
"execution_count": 24,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
@ -671,7 +650,7 @@
|
||||
" res.append(correct_k.mul_(100.0 / batch_size))\n",
|
||||
" return res"
|
||||
],
|
||||
"execution_count": null,
|
||||
"execution_count": 25,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
@ -681,7 +660,7 @@
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"outputId": "95b285c8-2b26-4d2c-ccc3-bb9111871c8d"
|
||||
"outputId": "48816318-655c-4c2d-b4fa-4549316a8477"
|
||||
},
|
||||
"source": [
|
||||
"epochs = 100\n",
|
||||
@ -717,111 +696,111 @@
|
||||
" 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,
|
||||
"execution_count": 26,
|
||||
"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"
|
||||
"Epoch 0\tTop1 Train accuracy 49.823020935058594\tTop1 Test accuracy: 57.63786697387695\tTop5 test acc: 94.96036529541016\n",
|
||||
"Epoch 1\tTop1 Train accuracy 59.0130729675293\tTop1 Test accuracy: 59.57088851928711\tTop5 test acc: 95.76114654541016\n",
|
||||
"Epoch 2\tTop1 Train accuracy 60.604671478271484\tTop1 Test accuracy: 60.32686233520508\tTop5 test acc: 96.07250213623047\n",
|
||||
"Epoch 3\tTop1 Train accuracy 61.547752380371094\tTop1 Test accuracy: 61.19715118408203\tTop5 test acc: 96.14946746826172\n",
|
||||
"Epoch 4\tTop1 Train accuracy 62.19586944580078\tTop1 Test accuracy: 61.48035430908203\tTop5 test acc: 96.37407684326172\n",
|
||||
"Epoch 5\tTop1 Train accuracy 62.677772521972656\tTop1 Test accuracy: 61.784236907958984\tTop5 test acc: 96.40337371826172\n",
|
||||
"Epoch 6\tTop1 Train accuracy 63.06640625\tTop1 Test accuracy: 62.2346076965332\tTop5 test acc: 96.50102996826172\n",
|
||||
"Epoch 7\tTop1 Train accuracy 63.40122604370117\tTop1 Test accuracy: 62.52527618408203\tTop5 test acc: 96.46196746826172\n",
|
||||
"Epoch 8\tTop1 Train accuracy 63.698577880859375\tTop1 Test accuracy: 62.83777618408203\tTop5 test acc: 96.54009246826172\n",
|
||||
"Epoch 9\tTop1 Train accuracy 63.90983581542969\tTop1 Test accuracy: 63.118682861328125\tTop5 test acc: 96.58892059326172\n",
|
||||
"Epoch 10\tTop1 Train accuracy 64.14102172851562\tTop1 Test accuracy: 63.20772171020508\tTop5 test acc: 96.68657684326172\n",
|
||||
"Epoch 11\tTop1 Train accuracy 64.33633422851562\tTop1 Test accuracy: 63.469093322753906\tTop5 test acc: 96.75609588623047\n",
|
||||
"Epoch 12\tTop1 Train accuracy 64.5057373046875\tTop1 Test accuracy: 63.556983947753906\tTop5 test acc: 96.71703338623047\n",
|
||||
"Epoch 13\tTop1 Train accuracy 64.6436538696289\tTop1 Test accuracy: 63.66325759887695\tTop5 test acc: 96.69750213623047\n",
|
||||
"Epoch 14\tTop1 Train accuracy 64.75326538085938\tTop1 Test accuracy: 63.62419509887695\tTop5 test acc: 96.68773651123047\n",
|
||||
"Epoch 15\tTop1 Train accuracy 64.87284851074219\tTop1 Test accuracy: 63.84650802612305\tTop5 test acc: 96.66820526123047\n",
|
||||
"Epoch 16\tTop1 Train accuracy 64.97688293457031\tTop1 Test accuracy: 64.00276184082031\tTop5 test acc: 96.72563934326172\n",
|
||||
"Epoch 17\tTop1 Train accuracy 65.05500793457031\tTop1 Test accuracy: 63.95392990112305\tTop5 test acc: 96.71587371826172\n",
|
||||
"Epoch 18\tTop1 Train accuracy 65.11439514160156\tTop1 Test accuracy: 64.01252746582031\tTop5 test acc: 96.72563934326172\n",
|
||||
"Epoch 19\tTop1 Train accuracy 65.21205139160156\tTop1 Test accuracy: 64.07112121582031\tTop5 test acc: 96.71587371826172\n",
|
||||
"Epoch 20\tTop1 Train accuracy 65.31169891357422\tTop1 Test accuracy: 64.06135559082031\tTop5 test acc: 96.73540496826172\n",
|
||||
"Epoch 21\tTop1 Train accuracy 65.40338134765625\tTop1 Test accuracy: 64.18830871582031\tTop5 test acc: 96.74517059326172\n",
|
||||
"Epoch 22\tTop1 Train accuracy 65.45320129394531\tTop1 Test accuracy: 64.1969223022461\tTop5 test acc: 96.74517059326172\n",
|
||||
"Epoch 23\tTop1 Train accuracy 65.53292083740234\tTop1 Test accuracy: 64.23828125\tTop5 test acc: 96.71587371826172\n",
|
||||
"Epoch 24\tTop1 Train accuracy 65.61064910888672\tTop1 Test accuracy: 64.30549621582031\tTop5 test acc: 96.71587371826172\n",
|
||||
"Epoch 25\tTop1 Train accuracy 65.68638610839844\tTop1 Test accuracy: 64.31526184082031\tTop5 test acc: 96.69634246826172\n",
|
||||
"Epoch 26\tTop1 Train accuracy 65.75055694580078\tTop1 Test accuracy: 64.39338684082031\tTop5 test acc: 96.66704559326172\n",
|
||||
"Epoch 27\tTop1 Train accuracy 65.80635833740234\tTop1 Test accuracy: 64.40315246582031\tTop5 test acc: 96.67681121826172\n",
|
||||
"Epoch 28\tTop1 Train accuracy 65.8581771850586\tTop1 Test accuracy: 64.39338684082031\tTop5 test acc: 96.67681121826172\n",
|
||||
"Epoch 29\tTop1 Train accuracy 65.91397857666016\tTop1 Test accuracy: 64.42268371582031\tTop5 test acc: 96.65727996826172\n",
|
||||
"Epoch 30\tTop1 Train accuracy 65.96340942382812\tTop1 Test accuracy: 64.42268371582031\tTop5 test acc: 96.63774871826172\n",
|
||||
"Epoch 31\tTop1 Train accuracy 66.00127410888672\tTop1 Test accuracy: 64.39338684082031\tTop5 test acc: 96.62798309326172\n",
|
||||
"Epoch 32\tTop1 Train accuracy 66.05707550048828\tTop1 Test accuracy: 64.39338684082031\tTop5 test acc: 96.65727996826172\n",
|
||||
"Epoch 33\tTop1 Train accuracy 66.10092163085938\tTop1 Test accuracy: 64.43244934082031\tTop5 test acc: 96.66704559326172\n",
|
||||
"Epoch 34\tTop1 Train accuracy 66.13480377197266\tTop1 Test accuracy: 64.44221496582031\tTop5 test acc: 96.64751434326172\n",
|
||||
"Epoch 35\tTop1 Train accuracy 66.16669464111328\tTop1 Test accuracy: 64.4801254272461\tTop5 test acc: 96.63774871826172\n",
|
||||
"Epoch 36\tTop1 Train accuracy 66.21452331542969\tTop1 Test accuracy: 64.4801254272461\tTop5 test acc: 96.63774871826172\n",
|
||||
"Epoch 37\tTop1 Train accuracy 66.2547836303711\tTop1 Test accuracy: 64.5191879272461\tTop5 test acc: 96.61821746826172\n",
|
||||
"Epoch 38\tTop1 Train accuracy 66.28069305419922\tTop1 Test accuracy: 64.5582504272461\tTop5 test acc: 96.62798309326172\n",
|
||||
"Epoch 39\tTop1 Train accuracy 66.32653045654297\tTop1 Test accuracy: 64.57662963867188\tTop5 test acc: 96.63774871826172\n",
|
||||
"Epoch 40\tTop1 Train accuracy 66.35881805419922\tTop1 Test accuracy: 64.62431335449219\tTop5 test acc: 96.61821746826172\n",
|
||||
"Epoch 41\tTop1 Train accuracy 66.37077331542969\tTop1 Test accuracy: 64.68290710449219\tTop5 test acc: 96.61821746826172\n",
|
||||
"Epoch 42\tTop1 Train accuracy 66.39269256591797\tTop1 Test accuracy: 64.66337585449219\tTop5 test acc: 96.61821746826172\n",
|
||||
"Epoch 43\tTop1 Train accuracy 66.41262817382812\tTop1 Test accuracy: 64.66337585449219\tTop5 test acc: 96.63774871826172\n",
|
||||
"Epoch 44\tTop1 Train accuracy 66.45248413085938\tTop1 Test accuracy: 64.62431335449219\tTop5 test acc: 96.65727996826172\n",
|
||||
"Epoch 45\tTop1 Train accuracy 66.48238372802734\tTop1 Test accuracy: 64.65361022949219\tTop5 test acc: 96.66704559326172\n",
|
||||
"Epoch 46\tTop1 Train accuracy 66.51825714111328\tTop1 Test accuracy: 64.65361022949219\tTop5 test acc: 96.67681121826172\n",
|
||||
"Epoch 47\tTop1 Train accuracy 66.56608581542969\tTop1 Test accuracy: 64.64384460449219\tTop5 test acc: 96.65727996826172\n",
|
||||
"Epoch 48\tTop1 Train accuracy 66.59996795654297\tTop1 Test accuracy: 64.61454772949219\tTop5 test acc: 96.67681121826172\n",
|
||||
"Epoch 49\tTop1 Train accuracy 66.64381408691406\tTop1 Test accuracy: 64.67314147949219\tTop5 test acc: 96.67681121826172\n",
|
||||
"Epoch 50\tTop1 Train accuracy 66.65178680419922\tTop1 Test accuracy: 64.70243835449219\tTop5 test acc: 96.69519805908203\n",
|
||||
"Epoch 51\tTop1 Train accuracy 66.65178680419922\tTop1 Test accuracy: 64.72196960449219\tTop5 test acc: 96.69519805908203\n",
|
||||
"Epoch 52\tTop1 Train accuracy 66.69363403320312\tTop1 Test accuracy: 64.70358276367188\tTop5 test acc: 96.72449493408203\n",
|
||||
"Epoch 53\tTop1 Train accuracy 66.70957946777344\tTop1 Test accuracy: 64.75241088867188\tTop5 test acc: 96.71472930908203\n",
|
||||
"Epoch 54\tTop1 Train accuracy 66.72552490234375\tTop1 Test accuracy: 64.81100463867188\tTop5 test acc: 96.71472930908203\n",
|
||||
"Epoch 55\tTop1 Train accuracy 66.73548889160156\tTop1 Test accuracy: 64.84892272949219\tTop5 test acc: 96.69519805908203\n",
|
||||
"Epoch 56\tTop1 Train accuracy 66.77734375\tTop1 Test accuracy: 64.82077026367188\tTop5 test acc: 96.71472930908203\n",
|
||||
"Epoch 57\tTop1 Train accuracy 66.78730773925781\tTop1 Test accuracy: 64.81100463867188\tTop5 test acc: 96.73426055908203\n",
|
||||
"Epoch 58\tTop1 Train accuracy 66.8092269897461\tTop1 Test accuracy: 64.82077026367188\tTop5 test acc: 96.73426055908203\n",
|
||||
"Epoch 59\tTop1 Train accuracy 66.82716369628906\tTop1 Test accuracy: 64.81962585449219\tTop5 test acc: 96.74402618408203\n",
|
||||
"Epoch 60\tTop1 Train accuracy 66.84510040283203\tTop1 Test accuracy: 64.83800506591797\tTop5 test acc: 96.74402618408203\n",
|
||||
"Epoch 61\tTop1 Train accuracy 66.875\tTop1 Test accuracy: 64.80009460449219\tTop5 test acc: 96.75379180908203\n",
|
||||
"Epoch 62\tTop1 Train accuracy 66.88894653320312\tTop1 Test accuracy: 64.79032897949219\tTop5 test acc: 96.76355743408203\n",
|
||||
"Epoch 63\tTop1 Train accuracy 66.91127014160156\tTop1 Test accuracy: 64.78056335449219\tTop5 test acc: 96.76355743408203\n",
|
||||
"Epoch 64\tTop1 Train accuracy 66.93319702148438\tTop1 Test accuracy: 64.76103210449219\tTop5 test acc: 96.77332305908203\n",
|
||||
"Epoch 65\tTop1 Train accuracy 66.96907043457031\tTop1 Test accuracy: 64.78056335449219\tTop5 test acc: 96.77332305908203\n",
|
||||
"Epoch 66\tTop1 Train accuracy 66.97704315185547\tTop1 Test accuracy: 64.79032897949219\tTop5 test acc: 96.77332305908203\n",
|
||||
"Epoch 67\tTop1 Train accuracy 67.00494384765625\tTop1 Test accuracy: 64.76103210449219\tTop5 test acc: 96.77332305908203\n",
|
||||
"Epoch 68\tTop1 Train accuracy 67.02487182617188\tTop1 Test accuracy: 64.74150085449219\tTop5 test acc: 96.77332305908203\n",
|
||||
"Epoch 69\tTop1 Train accuracy 67.04280853271484\tTop1 Test accuracy: 64.73173522949219\tTop5 test acc: 96.78308868408203\n",
|
||||
"Epoch 70\tTop1 Train accuracy 67.04280853271484\tTop1 Test accuracy: 64.77079772949219\tTop5 test acc: 96.77332305908203\n",
|
||||
"Epoch 71\tTop1 Train accuracy 67.0447998046875\tTop1 Test accuracy: 64.79032897949219\tTop5 test acc: 96.77332305908203\n",
|
||||
"Epoch 72\tTop1 Train accuracy 67.05078125\tTop1 Test accuracy: 64.75241088867188\tTop5 test acc: 96.77332305908203\n",
|
||||
"Epoch 73\tTop1 Train accuracy 67.06074523925781\tTop1 Test accuracy: 64.76217651367188\tTop5 test acc: 96.77332305908203\n",
|
||||
"Epoch 74\tTop1 Train accuracy 67.07270050048828\tTop1 Test accuracy: 64.74264526367188\tTop5 test acc: 96.77332305908203\n",
|
||||
"Epoch 75\tTop1 Train accuracy 67.0826644897461\tTop1 Test accuracy: 64.7340316772461\tTop5 test acc: 96.77332305908203\n",
|
||||
"Epoch 76\tTop1 Train accuracy 67.09263610839844\tTop1 Test accuracy: 64.7242660522461\tTop5 test acc: 96.78308868408203\n",
|
||||
"Epoch 77\tTop1 Train accuracy 67.1045913696289\tTop1 Test accuracy: 64.6949691772461\tTop5 test acc: 96.76470184326172\n",
|
||||
"Epoch 78\tTop1 Train accuracy 67.1105728149414\tTop1 Test accuracy: 64.6949691772461\tTop5 test acc: 96.75493621826172\n",
|
||||
"Epoch 79\tTop1 Train accuracy 67.13288879394531\tTop1 Test accuracy: 64.6949691772461\tTop5 test acc: 96.75493621826172\n",
|
||||
"Epoch 80\tTop1 Train accuracy 67.13887023925781\tTop1 Test accuracy: 64.7242660522461\tTop5 test acc: 96.76470184326172\n",
|
||||
"Epoch 81\tTop1 Train accuracy 67.14684295654297\tTop1 Test accuracy: 64.7145004272461\tTop5 test acc: 96.76470184326172\n",
|
||||
"Epoch 82\tTop1 Train accuracy 67.17076110839844\tTop1 Test accuracy: 64.7242660522461\tTop5 test acc: 96.75493621826172\n",
|
||||
"Epoch 83\tTop1 Train accuracy 67.20065307617188\tTop1 Test accuracy: 64.71565246582031\tTop5 test acc: 96.75493621826172\n",
|
||||
"Epoch 84\tTop1 Train accuracy 67.21659851074219\tTop1 Test accuracy: 64.72541809082031\tTop5 test acc: 96.74517059326172\n",
|
||||
"Epoch 85\tTop1 Train accuracy 67.21061706542969\tTop1 Test accuracy: 64.7437973022461\tTop5 test acc: 96.74517059326172\n",
|
||||
"Epoch 86\tTop1 Train accuracy 67.23851776123047\tTop1 Test accuracy: 64.7535629272461\tTop5 test acc: 96.74517059326172\n",
|
||||
"Epoch 87\tTop1 Train accuracy 67.25247192382812\tTop1 Test accuracy: 64.72541809082031\tTop5 test acc: 96.74517059326172\n",
|
||||
"Epoch 88\tTop1 Train accuracy 67.2584457397461\tTop1 Test accuracy: 64.71565246582031\tTop5 test acc: 96.73540496826172\n",
|
||||
"Epoch 89\tTop1 Train accuracy 67.26641845703125\tTop1 Test accuracy: 64.79377746582031\tTop5 test acc: 96.73540496826172\n",
|
||||
"Epoch 90\tTop1 Train accuracy 67.2704086303711\tTop1 Test accuracy: 64.79377746582031\tTop5 test acc: 96.72563934326172\n",
|
||||
"Epoch 91\tTop1 Train accuracy 67.2803726196289\tTop1 Test accuracy: 64.77424621582031\tTop5 test acc: 96.73540496826172\n",
|
||||
"Epoch 92\tTop1 Train accuracy 67.29033660888672\tTop1 Test accuracy: 64.78401184082031\tTop5 test acc: 96.74517059326172\n",
|
||||
"Epoch 93\tTop1 Train accuracy 67.29830932617188\tTop1 Test accuracy: 64.78401184082031\tTop5 test acc: 96.74517059326172\n",
|
||||
"Epoch 94\tTop1 Train accuracy 67.30429077148438\tTop1 Test accuracy: 64.78401184082031\tTop5 test acc: 96.74517059326172\n",
|
||||
"Epoch 95\tTop1 Train accuracy 67.30030059814453\tTop1 Test accuracy: 64.79377746582031\tTop5 test acc: 96.74517059326172\n",
|
||||
"Epoch 96\tTop1 Train accuracy 67.30827331542969\tTop1 Test accuracy: 64.77424621582031\tTop5 test acc: 96.72563934326172\n",
|
||||
"Epoch 97\tTop1 Train accuracy 67.31624603271484\tTop1 Test accuracy: 64.7926254272461\tTop5 test acc: 96.71587371826172\n",
|
||||
"Epoch 98\tTop1 Train accuracy 67.32222747802734\tTop1 Test accuracy: 64.8219223022461\tTop5 test acc: 96.71587371826172\n",
|
||||
"Epoch 99\tTop1 Train accuracy 67.32820129394531\tTop1 Test accuracy: 64.8121566772461\tTop5 test acc: 96.71587371826172\n"
|
||||
],
|
||||
"name": "stdout"
|
||||
}
|
||||
@ -835,7 +814,7 @@
|
||||
"source": [
|
||||
""
|
||||
],
|
||||
"execution_count": null,
|
||||
"execution_count": 26,
|
||||
"outputs": []
|
||||
}
|
||||
]
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user