3370 lines
158 KiB
Plaintext
3370 lines
158 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
|
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"colab": {
|
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"base_uri": "https://localhost:8080/"
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},
|
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"id": "CkZsS-w4atkF",
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"outputId": "f3a78987-dbd2-4771-92ca-69cdf97d0571"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Mounted at /content/drive\n"
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]
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}
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],
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"source": [
|
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"from google.colab import drive\n",
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"drive.mount('/content/drive')"
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],
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"id": "CkZsS-w4atkF"
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "zzWoPgRpd1xn",
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"outputId": "daa8edca-5ddf-4ac8-cb91-b4e77f9cc858"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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"Collecting wandb\n",
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" Downloading wandb-0.14.0-py3-none-any.whl (2.0 MB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m27.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hCollecting sentry-sdk>=1.0.0\n",
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" Downloading sentry_sdk-1.19.0-py2.py3-none-any.whl (199 kB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.2/199.2 KB\u001b[0m \u001b[31m25.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hRequirement already satisfied: setuptools in /usr/local/lib/python3.9/dist-packages (from wandb) (67.6.1)\n",
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"Requirement already satisfied: protobuf!=4.21.0,<5,>=3.15.0 in /usr/local/lib/python3.9/dist-packages (from wandb) (3.20.3)\n",
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"Requirement already satisfied: psutil>=5.0.0 in /usr/local/lib/python3.9/dist-packages (from wandb) (5.9.4)\n",
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"Requirement already satisfied: requests<3,>=2.0.0 in /usr/local/lib/python3.9/dist-packages (from wandb) (2.27.1)\n",
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"Collecting GitPython!=3.1.29,>=1.0.0\n",
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" Downloading GitPython-3.1.31-py3-none-any.whl (184 kB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m184.3/184.3 KB\u001b[0m \u001b[31m22.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hRequirement already satisfied: appdirs>=1.4.3 in /usr/local/lib/python3.9/dist-packages (from wandb) (1.4.4)\n",
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"Collecting docker-pycreds>=0.4.0\n",
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" Downloading docker_pycreds-0.4.0-py2.py3-none-any.whl (9.0 kB)\n",
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"Collecting setproctitle\n",
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" Downloading setproctitle-1.3.2-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (30 kB)\n",
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"Requirement already satisfied: Click!=8.0.0,>=7.0 in /usr/local/lib/python3.9/dist-packages (from wandb) (8.1.3)\n",
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"Requirement already satisfied: PyYAML in /usr/local/lib/python3.9/dist-packages (from wandb) (6.0)\n",
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"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.9/dist-packages (from wandb) (4.5.0)\n",
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"Collecting pathtools\n",
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" Downloading pathtools-0.1.2.tar.gz (11 kB)\n",
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" Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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"Requirement already satisfied: six>=1.4.0 in /usr/local/lib/python3.9/dist-packages (from docker-pycreds>=0.4.0->wandb) (1.16.0)\n",
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"Collecting gitdb<5,>=4.0.1\n",
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" Downloading gitdb-4.0.10-py3-none-any.whl (62 kB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 KB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hRequirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests<3,>=2.0.0->wandb) (3.4)\n",
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"Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests<3,>=2.0.0->wandb) (2.0.12)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests<3,>=2.0.0->wandb) (2022.12.7)\n",
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"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests<3,>=2.0.0->wandb) (1.26.15)\n",
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"Collecting smmap<6,>=3.0.1\n",
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" Downloading smmap-5.0.0-py3-none-any.whl (24 kB)\n",
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"Building wheels for collected packages: pathtools\n",
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" Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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" Created wheel for pathtools: filename=pathtools-0.1.2-py3-none-any.whl size=8807 sha256=e0132d67db355152a3925f1b0367a996c977716084c67122838eac002d321662\n",
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" Stored in directory: /root/.cache/pip/wheels/b7/0a/67/ada2a22079218c75a88361c0782855cc72aebc4d18d0289d05\n",
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"Successfully built pathtools\n",
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"Installing collected packages: pathtools, smmap, setproctitle, sentry-sdk, docker-pycreds, gitdb, GitPython, wandb\n",
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"Successfully installed GitPython-3.1.31 docker-pycreds-0.4.0 gitdb-4.0.10 pathtools-0.1.2 sentry-sdk-1.19.0 setproctitle-1.3.2 smmap-5.0.0 wandb-0.14.0\n"
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]
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}
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||
],
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"source": [
|
||
"!pip install wandb"
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||
],
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"id": "zzWoPgRpd1xn"
|
||
},
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||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
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||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 121
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},
|
||
"id": "747ddcf2",
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||
"outputId": "ebf3b723-ac7b-41ba-a9a6-e2e82e907879"
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},
|
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"outputs": [
|
||
{
|
||
"output_type": "display_data",
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"data": {
|
||
"text/plain": [
|
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"<IPython.core.display.Javascript object>"
|
||
],
|
||
"application/javascript": [
|
||
"\n",
|
||
" window._wandbApiKey = new Promise((resolve, reject) => {\n",
|
||
" function loadScript(url) {\n",
|
||
" return new Promise(function(resolve, reject) {\n",
|
||
" let newScript = document.createElement(\"script\");\n",
|
||
" newScript.onerror = reject;\n",
|
||
" newScript.onload = resolve;\n",
|
||
" document.body.appendChild(newScript);\n",
|
||
" newScript.src = url;\n",
|
||
" });\n",
|
||
" }\n",
|
||
" loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
|
||
" const iframe = document.createElement('iframe')\n",
|
||
" iframe.style.cssText = \"width:0;height:0;border:none\"\n",
|
||
" document.body.appendChild(iframe)\n",
|
||
" const handshake = new Postmate({\n",
|
||
" container: iframe,\n",
|
||
" url: 'https://wandb.ai/authorize'\n",
|
||
" });\n",
|
||
" const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
|
||
" handshake.then(function(child) {\n",
|
||
" child.on('authorize', data => {\n",
|
||
" clearTimeout(timeout)\n",
|
||
" resolve(data)\n",
|
||
" });\n",
|
||
" });\n",
|
||
" })\n",
|
||
" });\n",
|
||
" "
|
||
]
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
|
||
"\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
|
||
"wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
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||
"text": [
|
||
" ··········\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "stream",
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||
"name": "stderr",
|
||
"text": [
|
||
"\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"True"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 3
|
||
}
|
||
],
|
||
"source": [
|
||
"import wandb\n",
|
||
"\n",
|
||
"wandb.login()"
|
||
],
|
||
"id": "747ddcf2"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"metadata": {
|
||
"id": "c37343d6"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import torch\n",
|
||
"import torch.optim as optim\n",
|
||
"import torch.nn.functional as F\n",
|
||
"import torch.nn as nn\n",
|
||
"from torchvision import datasets, transforms\n",
|
||
"from torchvision.models import resnet50, ResNet50_Weights\n",
|
||
"from torch.utils.data import Dataset, DataLoader, random_split, SubsetRandomSampler\n",
|
||
"import numpy as np\n",
|
||
"import os\n",
|
||
"import time\n",
|
||
"import copy\n",
|
||
"import random\n",
|
||
"from sklearn import metrics\n",
|
||
"\n",
|
||
"torch.manual_seed(42)\n",
|
||
"np.random.seed(42)\n",
|
||
"\n",
|
||
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
|
||
],
|
||
"id": "c37343d6"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {
|
||
"id": "17b25dc7"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def build_dataset(batch_size): \n",
|
||
" data_transforms = {\n",
|
||
" 'train': transforms.Compose([\n",
|
||
" transforms.RandomResizedCrop(224),\n",
|
||
" transforms.RandomHorizontalFlip(),\n",
|
||
" transforms.ToTensor(),\n",
|
||
" transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
|
||
" ]),\n",
|
||
" 'test': transforms.Compose([\n",
|
||
" transforms.Resize(256),\n",
|
||
" transforms.CenterCrop(224),\n",
|
||
" transforms.ToTensor(),\n",
|
||
" transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
|
||
" ]),\n",
|
||
" }\n",
|
||
"\n",
|
||
" data_dir = '/content/drive/MyDrive/plantsdata'\n",
|
||
" dataset = datasets.ImageFolder(os.path.join(data_dir))\n",
|
||
"\n",
|
||
" # 90/10 split\n",
|
||
" train_dataset, test_dataset = random_split(dataset, [0.9, 0.1])\n",
|
||
"\n",
|
||
" train_dataset.dataset.transform = data_transforms['train']\n",
|
||
" test_dataset.dataset.transform = data_transforms['test']\n",
|
||
"\n",
|
||
" train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size,\n",
|
||
" shuffle=True, num_workers=4)\n",
|
||
" test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size,\n",
|
||
" shuffle=True, num_workers=4)\n",
|
||
"\n",
|
||
" dataloaders = {'train': train_loader, 'test': test_loader}\n",
|
||
" dataset_size = len(dataset)\n",
|
||
" dataset_sizes = {'train': len(train_dataset), 'test': len(test_dataset)}\n",
|
||
" class_names = dataset.classes\n",
|
||
"\n",
|
||
" return (dataloaders, dataset_sizes)\n",
|
||
"\n",
|
||
"def build_network():\n",
|
||
" network = resnet50(weights=ResNet50_Weights.DEFAULT)\n",
|
||
" num_ftrs = network.fc.in_features\n",
|
||
"\n",
|
||
" # Add linear layer with number of classes\n",
|
||
" network.fc = nn.Linear(num_ftrs, 2)\n",
|
||
"\n",
|
||
" return network.to(device)\n",
|
||
"\n",
|
||
"def build_optimizer(network, optimizer, learning_rate, beta_one, beta_two, eps):\n",
|
||
" if optimizer == \"sgd\":\n",
|
||
" optimizer = optim.SGD(network.parameters(),\n",
|
||
" lr=learning_rate, momentum=0.9)\n",
|
||
" elif optimizer == \"adam\":\n",
|
||
" optimizer = optim.Adam(network.parameters(),\n",
|
||
" lr=learning_rate,\n",
|
||
" betas=(beta_one, beta_two),\n",
|
||
" eps=eps)\n",
|
||
" return optimizer\n",
|
||
"\n",
|
||
"def train_epoch(network, loader, optimizer, criterion, scheduler, dataset_sizes):\n",
|
||
" network.train()\n",
|
||
" running_loss = 0.0\n",
|
||
" running_corrects = 0\n",
|
||
" for _, (data, target) in enumerate(loader):\n",
|
||
" data, target = data.to(device), target.to(device)\n",
|
||
" optimizer.zero_grad()\n",
|
||
"\n",
|
||
" # ➡ Forward pass\n",
|
||
" #loss = F.nll_loss(network(data), target)\n",
|
||
" with torch.set_grad_enabled(True):\n",
|
||
" outputs = network(data)\n",
|
||
" _, preds = torch.max(outputs, 1)\n",
|
||
" loss = criterion(outputs, target)\n",
|
||
" \n",
|
||
" #cumu_loss += loss.item()\n",
|
||
" \n",
|
||
" running_loss += loss.item() * data.size(0)\n",
|
||
" running_corrects += torch.sum(preds == target.data)\n",
|
||
"\n",
|
||
" # ⬅ Backward pass + weight update\n",
|
||
" loss.backward()\n",
|
||
" optimizer.step()\n",
|
||
"\n",
|
||
" wandb.log({'train/batch_loss': loss.item()})\n",
|
||
"\n",
|
||
" scheduler.step()\n",
|
||
"\n",
|
||
" epoch_loss = running_loss / dataset_sizes['train']\n",
|
||
" epoch_acc = running_corrects.double() / dataset_sizes['train']\n",
|
||
" \n",
|
||
" return (epoch_loss, epoch_acc)\n",
|
||
"\n",
|
||
"def test(network, loader, optimizer, criterion, dataset_sizes):\n",
|
||
" network.eval()\n",
|
||
" confusion = torch.empty([0, 1])\n",
|
||
" confusion = confusion.to(device)\n",
|
||
" running_loss = 0.0\n",
|
||
" test_corrects = 0\n",
|
||
" for _, (data, target) in enumerate(loader):\n",
|
||
" data, target = data.to(device), target.to(device)\n",
|
||
" optimizer.zero_grad()\n",
|
||
"\n",
|
||
" # ➡ Forward pass\n",
|
||
" with torch.set_grad_enabled(False):\n",
|
||
" outputs = network(data)\n",
|
||
" _, preds = torch.max(outputs, 1)\n",
|
||
" loss = criterion(outputs, target)\n",
|
||
"\n",
|
||
" running_loss += loss.item() * data.size(0)\n",
|
||
" test_corrects += torch.sum(preds == target.data)\n",
|
||
" \n",
|
||
" confusion = torch.cat((confusion, preds[:, None] / target.data[:, None]))\n",
|
||
"\n",
|
||
" tp = torch.sum(confusion == 1).item()\n",
|
||
" fp = torch.sum(confusion == float('inf')).item()\n",
|
||
" tn = torch.sum(torch.isnan(confusion)).item()\n",
|
||
" fn = torch.sum(confusion == 0).item()\n",
|
||
" \n",
|
||
" precision = tp / (tp + fp)\n",
|
||
" recall = tp / (tp + fn)\n",
|
||
" f = 2 * ((precision * recall) / (precision + recall))\n",
|
||
" \n",
|
||
" epoch_loss = running_loss / dataset_sizes['test']\n",
|
||
" epoch_acc = test_corrects.double() / dataset_sizes['test']\n",
|
||
" \n",
|
||
" return (epoch_loss, epoch_acc, precision, recall, f)"
|
||
],
|
||
"id": "17b25dc7"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {
|
||
"id": "5eff68bf"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def train(config=None):\n",
|
||
" # Initialize a new wandb run\n",
|
||
" with wandb.init(config=config):\n",
|
||
" # If called by wandb.agent, as below,\n",
|
||
" # this config will be set by Sweep Controller\n",
|
||
" config = wandb.config\n",
|
||
"\n",
|
||
" (dataloaders, dataset_sizes) = build_dataset(config.batch_size)\n",
|
||
" network = build_network()\n",
|
||
" optimizer = build_optimizer(network, config.optimizer, config.learning_rate, config.beta_one,\n",
|
||
" config.beta_two, config.eps)\n",
|
||
" criterion = nn.CrossEntropyLoss()\n",
|
||
" # Decay LR by a factor of 0.1 every 7 epochs\n",
|
||
" exp_lr_scheduler = optim.lr_scheduler.StepLR(optimizer, config.step_size, config.gamma)\n",
|
||
"\n",
|
||
" for epoch in range(config.epochs): \n",
|
||
" (epoch_loss, epoch_acc) = train_epoch(network, dataloaders['train'], optimizer,\n",
|
||
" criterion, exp_lr_scheduler,\n",
|
||
" dataset_sizes)\n",
|
||
" wandb.log({\"epoch\": epoch, 'train/epoch_loss': epoch_loss, 'train/epoch_acc': epoch_acc})\n",
|
||
" \n",
|
||
" (test_loss, test_acc, test_precision, test_recall, test_f) = test(network, dataloaders['test'],\n",
|
||
" optimizer, criterion,\n",
|
||
" dataset_sizes)\n",
|
||
" wandb.log({'test/epoch_loss': test_loss, 'test/epoch_acc': test_acc,\n",
|
||
" 'test/precision': test_precision, 'test/recall': test_recall,\n",
|
||
" 'test/f1-score': test_f})"
|
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],
|
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"id": "5eff68bf"
|
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{
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"metadata": {
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"id": "732a83df"
|
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},
|
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"outputs": [],
|
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"source": [
|
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"sweep_config = {\n",
|
||
" 'method': 'random'\n",
|
||
"}\n",
|
||
"\n",
|
||
"metric = {\n",
|
||
" 'name': 'test/epoch_acc',\n",
|
||
" 'goal': 'maximize' \n",
|
||
"}\n",
|
||
"\n",
|
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"sweep_config['metric'] = metric\n",
|
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"\n",
|
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"parameters_dict = {\n",
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" 'optimizer': {\n",
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" 'values': ['adam', 'sgd']\n",
|
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" },\n",
|
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"}\n",
|
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"\n",
|
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|
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"\n",
|
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|
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" 'epochs': {\n",
|
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" 'value': 10},\n",
|
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" 'batch_size': {\n",
|
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" 'values': [4, 8, 16, 32, 64]},\n",
|
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|
||
" 'values': [0.1, 0.01, 0.003, 0.001, 0.0003, 0.0001]},\n",
|
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" 'step_size': {\n",
|
||
" 'values': [2, 3, 5, 7]},\n",
|
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" 'gamma': {\n",
|
||
" 'values': [0.1, 0.5]},\n",
|
||
" 'beta_one': {\n",
|
||
" 'values': [0.9, 0.99]},\n",
|
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" 'beta_two': {\n",
|
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" 'values': [0.5, 0.9, 0.99, 0.999]},\n",
|
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" 'eps': {\n",
|
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" 'values': [1e-08, 0.1, 1]}\n",
|
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"})"
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"id": "732a83df"
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},
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{
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"outputs": [
|
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{
|
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"output_type": "stream",
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"name": "stdout",
|
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"text": [
|
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"Create sweep with ID: 9681wnh0\n",
|
||
"Sweep URL: https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/9681wnh0\n"
|
||
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|
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|
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|
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"source": [
|
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|
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|
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"id": "9a01fef6"
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|
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|
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|
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tstep_size: 3\n",
|
||
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|
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"/usr/local/lib/python3.9/dist-packages/torch/utils/data/dataloader.py:561: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
|
||
" warnings.warn(_create_warning_msg(\n",
|
||
"Downloading: \"https://download.pytorch.org/models/resnet50-11ad3fa6.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-11ad3fa6.pth\n",
|
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|
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"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>test/epoch_acc</td><td>▁▅▆▆██▇▇▇█</td></tr><tr><td>test/epoch_loss</td><td>█▅▂▂▂▂▁▁▂▂</td></tr><tr><td>test/f1-score</td><td>▁▆▇▇███▇▇█</td></tr><tr><td>test/precision</td><td>▁▄▅▅█▇▆▇▅▇</td></tr><tr><td>test/recall</td><td>▁▇▇▇▆▇█▆█▇</td></tr><tr><td>train/batch_loss</td><td>▇▆█▅▅▆▆▆▅▃▄▅▄▄▄▅▃█▄█▃▄▂▁▄▃▁▆▅▁▄▆▂▄▂▂▃▄▆▄</td></tr><tr><td>train/epoch_acc</td><td>▁▆▆▇█▇██▇█</td></tr><tr><td>train/epoch_loss</td><td>█▅▃▂▁▂▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>9</td></tr><tr><td>test/epoch_acc</td><td>0.81111</td></tr><tr><td>test/epoch_loss</td><td>0.60187</td></tr><tr><td>test/f1-score</td><td>0.8172</td></tr><tr><td>test/precision</td><td>0.77551</td></tr><tr><td>test/recall</td><td>0.86364</td></tr><tr><td>train/batch_loss</td><td>0.5635</td></tr><tr><td>train/epoch_acc</td><td>0.77273</td></tr><tr><td>train/epoch_loss</td><td>0.59496</td></tr></table><br/></div></div>"
|
||
]
|
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"\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: ea718wsd with config:\n",
|
||
"\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n",
|
||
"\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.99\n",
|
||
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|
||
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|
||
"\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 1e-08\n",
|
||
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|
||
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|
||
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|
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"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▃▆█</td></tr><tr><td>test/epoch_acc</td><td>▁▁▁</td></tr><tr><td>test/epoch_loss</td><td>█▁▁</td></tr><tr><td>test/f1-score</td><td>▁▁▁</td></tr><tr><td>test/precision</td><td>▁▁▁</td></tr><tr><td>test/recall</td><td>▁▁▁</td></tr><tr><td>train/batch_loss</td><td>▁▁█▁▁▂▁▁▁▁▂▂▁▁▃▁▁▂▁▁▁▁▂▂▁▁▁▂▁▁▂▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train/epoch_acc</td><td>▃▁▇█</td></tr><tr><td>train/epoch_loss</td><td>█▆▃▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>3</td></tr><tr><td>test/epoch_acc</td><td>0.42222</td></tr><tr><td>test/epoch_loss</td><td>109.2288</td></tr><tr><td>test/f1-score</td><td>0.59375</td></tr><tr><td>test/precision</td><td>0.42222</td></tr><tr><td>test/recall</td><td>1.0</td></tr><tr><td>train/batch_loss</td><td>1.26954</td></tr><tr><td>train/epoch_acc</td><td>0.51474</td></tr><tr><td>train/epoch_loss</td><td>3.22592</td></tr></table><br/></div></div>"
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"Run ea718wsd errored: ZeroDivisionError('float division by zero')\n",
|
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"\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run ea718wsd errored: ZeroDivisionError('float division by zero')\n",
|
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"\u001b[34m\u001b[1mwandb\u001b[0m: Sweep Agent: Waiting for job.\n",
|
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"\u001b[34m\u001b[1mwandb\u001b[0m: Job received.\n",
|
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"\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 2igypsdg with config:\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 64\n",
|
||
"\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.9\n",
|
||
"\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_two: 0.5\n",
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|
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"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>test/epoch_acc</td><td>█▅▁▁▁▁▃▁▃▃</td></tr><tr><td>test/epoch_loss</td><td>▁▆█▄▄▄▆▅▃▅</td></tr><tr><td>test/f1-score</td><td>▁█▅▅▅▅▆▅▆▆</td></tr><tr><td>test/precision</td><td>▂█▁▁▁▁▃▁▃▃</td></tr><tr><td>test/recall</td><td>▁█▇▇▇▇▇▇▇▇</td></tr><tr><td>train/batch_loss</td><td>█▇██▄█▆▆▁▂▂▁▃▅▆▄▅▂▇▃▄▁▆▆▁▆▆▄▄▃▃▆▇▂▇█▅▅▁▄</td></tr><tr><td>train/epoch_acc</td><td>▁▄▇█▇█▇▇▆▇</td></tr><tr><td>train/epoch_loss</td><td>█▅▂▂▂▁▁▂▂▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>9</td></tr><tr><td>test/epoch_acc</td><td>0.34444</td></tr><tr><td>test/epoch_loss</td><td>0.72334</td></tr><tr><td>test/f1-score</td><td>0.47788</td></tr><tr><td>test/precision</td><td>0.35065</td></tr><tr><td>test/recall</td><td>0.75</td></tr><tr><td>train/batch_loss</td><td>0.67509</td></tr><tr><td>train/epoch_acc</td><td>0.56265</td></tr><tr><td>train/epoch_loss</td><td>0.67967</td></tr></table><br/></div></div>"
|
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]
|
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"<IPython.core.display.HTML object>"
|
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],
|
||
"text/html": [
|
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" View run <strong style=\"color:#cdcd00\">visionary-sweep-3</strong> at: <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/2igypsdg' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/2igypsdg</a><br/>Synced 4 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
|
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|
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"<IPython.core.display.HTML object>"
|
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|
||
"text/html": [
|
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"Find logs at: <code>./wandb/run-20230404_140346-2igypsdg/logs</code>"
|
||
]
|
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},
|
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"metadata": {}
|
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|
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|
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"\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 37tqne1y with config:\n",
|
||
"\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 64\n",
|
||
"\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.9\n",
|
||
"\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_two: 0.5\n",
|
||
"\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
|
||
"\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 1e-08\n",
|
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.1\n",
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|
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"\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: sgd\n",
|
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tstep_size: 5\n"
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"Syncing run <strong><a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/37tqne1y' target=\"_blank\">proud-sweep-4</a></strong> to <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/9681wnh0' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/9681wnh0</a>"
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|
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|
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|
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"text/html": [
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|
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"<IPython.core.display.HTML object>"
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|
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"text/html": [
|
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" View sweep at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/9681wnh0' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/9681wnh0</a>"
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"text/html": [
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" View run at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/37tqne1y' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/37tqne1y</a>"
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"text/html": [
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|
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"text/html": [
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"<style>\n",
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" table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
|
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" .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
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|
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|
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"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>test/epoch_acc</td><td>▆▅▁▅▃▇▇███</td></tr><tr><td>test/epoch_loss</td><td>▂▁█▁▅▁▁▁▁▁</td></tr><tr><td>test/f1-score</td><td>▅▆▄▄▁▇▇███</td></tr><tr><td>test/precision</td><td>█▅▁█▆▅▅▆▇▇</td></tr><tr><td>test/recall</td><td>▃▆▇▃▁▇█▇▇▇</td></tr><tr><td>train/batch_loss</td><td>█▆▅▄▃▂▁▃▇▃▄▄▂▂▅▃▂▄▂▄▂▄▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train/epoch_acc</td><td>▁▆▅▆▆▆████</td></tr><tr><td>train/epoch_loss</td><td>█▄▆▄▄▃▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>9</td></tr><tr><td>test/epoch_acc</td><td>0.84444</td></tr><tr><td>test/epoch_loss</td><td>0.62389</td></tr><tr><td>test/f1-score</td><td>0.84091</td></tr><tr><td>test/precision</td><td>0.84091</td></tr><tr><td>test/recall</td><td>0.84091</td></tr><tr><td>train/batch_loss</td><td>0.00493</td></tr><tr><td>train/epoch_acc</td><td>1.0</td></tr><tr><td>train/epoch_loss</td><td>0.00446</td></tr></table><br/></div></div>"
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"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>test/epoch_acc</td><td>▄█▁▃▃▅▆▇▇█</td></tr><tr><td>test/epoch_loss</td><td>▃▁▆▄█▃▄▂▃▃</td></tr><tr><td>test/f1-score</td><td>▂▇▁▂▃▆▅▆▆█</td></tr><tr><td>test/precision</td><td>▅█▁▃▂▄▆▆▆▇</td></tr><tr><td>test/recall</td><td>▁▂█▄██▂▃▄▄</td></tr><tr><td>train/batch_loss</td><td>▄▂▃▄▂▂▃█▂▁▃▁▂▂▁▁▃▁▁▂▁▁▁▂▁▁▁▃▁▁▁▁▁▁▅▁▁▁▁▁</td></tr><tr><td>train/epoch_acc</td><td>▁▄▅▆▆▆▇███</td></tr><tr><td>train/epoch_loss</td><td>█▆▄▄▃▃▂▂▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>9</td></tr><tr><td>test/epoch_acc</td><td>0.87778</td></tr><tr><td>test/epoch_loss</td><td>0.53854</td></tr><tr><td>test/f1-score</td><td>0.86747</td></tr><tr><td>test/precision</td><td>0.85714</td></tr><tr><td>test/recall</td><td>0.87805</td></tr><tr><td>train/batch_loss</td><td>0.00185</td></tr><tr><td>train/epoch_acc</td><td>0.99631</td></tr><tr><td>train/epoch_loss</td><td>0.01069</td></tr></table><br/></div></div>"
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\n",
|
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_two: 0.999\n",
|
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"text/html": [
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"<IPython.core.display.HTML object>"
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"text/html": [
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"<style>\n",
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" table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
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"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>test/epoch_acc</td><td>▁▄▃▇▆▇███▇</td></tr><tr><td>test/epoch_loss</td><td>█▄▃▅▃▁▁▁▂▂</td></tr><tr><td>test/f1-score</td><td>▁▄▃▆▆▇███▆</td></tr><tr><td>test/precision</td><td>▁▂▂▆▅▅▆▇█▆</td></tr><tr><td>test/recall</td><td>▁█▃▅▃▆▆▅▃▅</td></tr><tr><td>train/batch_loss</td><td>█▃▆▅▇▆▄▃▄▁▄▃▁▁▂▂▁▁▁▁▃▁▁▃▁▁▁▁▁▁▂▁▁▁▂▂▁▃▁▁</td></tr><tr><td>train/epoch_acc</td><td>▁▃▆▆▇▇████</td></tr><tr><td>train/epoch_loss</td><td>█▆▄▄▂▂▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>9</td></tr><tr><td>test/epoch_acc</td><td>0.87778</td></tr><tr><td>test/epoch_loss</td><td>0.24035</td></tr><tr><td>test/f1-score</td><td>0.86076</td></tr><tr><td>test/precision</td><td>0.85</td></tr><tr><td>test/recall</td><td>0.87179</td></tr><tr><td>train/batch_loss</td><td>0.03008</td></tr><tr><td>train/epoch_acc</td><td>0.99386</td></tr><tr><td>train/epoch_loss</td><td>0.02099</td></tr></table><br/></div></div>"
|
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]
|
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"<IPython.core.display.HTML object>"
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|
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"text/html": [
|
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" View run <strong style=\"color:#cdcd00\">charmed-sweep-6</strong> at: <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/ppthue5q' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/ppthue5q</a><br/>Synced 4 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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|
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"<IPython.core.display.HTML object>"
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|
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"text/html": [
|
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"Find logs at: <code>./wandb/run-20230404_142101-ppthue5q/logs</code>"
|
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|
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"\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: eakg0nsy with config:\n",
|
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 4\n",
|
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.9\n",
|
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_two: 0.99\n",
|
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
|
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"\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 0.1\n",
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|
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"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>test/epoch_acc</td><td>▁▆▅▆▇█████</td></tr><tr><td>test/epoch_loss</td><td>█▂▁▁▁▁▁▁▁▁</td></tr><tr><td>test/f1-score</td><td>▁▂▄▅▆▇▇███</td></tr><tr><td>test/precision</td><td>▁▇▅▆▆█████</td></tr><tr><td>test/recall</td><td>█▁▄▅▅▅▅▅▅▅</td></tr><tr><td>train/batch_loss</td><td>▆▅▄▇▄▄▅█▆▄▃▅▄▃▅▂▄▄▄▃▃▃▄▂▄▃▄▅▁▃▃▂▂▂▂▃▃▂▁▃</td></tr><tr><td>train/epoch_acc</td><td>▁▄▅▅▇▇▇▇██</td></tr><tr><td>train/epoch_loss</td><td>█▅▄▃▂▂▂▂▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>9</td></tr><tr><td>test/epoch_acc</td><td>0.78889</td></tr><tr><td>test/epoch_loss</td><td>0.46169</td></tr><tr><td>test/f1-score</td><td>0.7957</td></tr><tr><td>test/precision</td><td>0.84091</td></tr><tr><td>test/recall</td><td>0.7551</td></tr><tr><td>train/batch_loss</td><td>0.63008</td></tr><tr><td>train/epoch_acc</td><td>0.77396</td></tr><tr><td>train/epoch_loss</td><td>0.4697</td></tr></table><br/></div></div>"
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"text/html": [
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|
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"<IPython.core.display.HTML object>"
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"text/html": [
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" View sweep at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/9681wnh0' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/9681wnh0</a>"
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n",
|
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"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>test/epoch_acc</td><td>▁▂▇███▇▇▄▇</td></tr><tr><td>test/epoch_loss</td><td>█▆▅▃▂▂▁▁▁▁</td></tr><tr><td>test/f1-score</td><td>▁▅▆███▆▆▁▆</td></tr><tr><td>test/precision</td><td>▂▁██████▇█</td></tr><tr><td>test/recall</td><td>▅█▃▅▅▅▃▃▁▃</td></tr><tr><td>train/batch_loss</td><td>███▇▇▆▆▇▆▆▆▆▅▅▅▅▃▅▃▅▃▃▂▄▃▂▄▂▂▁▂▃▃▃▄▄▃▅▃▄</td></tr><tr><td>train/epoch_acc</td><td>▁▅▇▇▇█▇███</td></tr><tr><td>train/epoch_loss</td><td>█▆▅▄▂▂▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>9</td></tr><tr><td>test/epoch_acc</td><td>0.75556</td></tr><tr><td>test/epoch_loss</td><td>0.53376</td></tr><tr><td>test/f1-score</td><td>0.76596</td></tr><tr><td>test/precision</td><td>0.76596</td></tr><tr><td>test/recall</td><td>0.76596</td></tr><tr><td>train/batch_loss</td><td>0.52814</td></tr><tr><td>train/epoch_acc</td><td>0.86118</td></tr><tr><td>train/epoch_loss</td><td>0.46213</td></tr></table><br/></div></div>"
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|
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 64\n",
|
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.99\n",
|
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_two: 0.99\n",
|
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|
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"<IPython.core.display.HTML object>"
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"text/html": [
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"text/html": [
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" table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
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" .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
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|
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"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>test/epoch_acc</td><td>▁▂▅▆▆▆▇███</td></tr><tr><td>test/epoch_loss</td><td>█▇▅▄▄▃▂▂▂▁</td></tr><tr><td>test/f1-score</td><td>▁▂▅▆▇▇▇███</td></tr><tr><td>test/precision</td><td>▁▂▄▅▆▆▇███</td></tr><tr><td>test/recall</td><td>▁▄▇███████</td></tr><tr><td>train/batch_loss</td><td>▇█▆▇▇▆▆▆▆▅▅▆▅▅▃▅▅▄▅▅▃▄▄▂▃▃▃▃▃▃▃▂▁▃▃▃▂▃▁▁</td></tr><tr><td>train/epoch_acc</td><td>▁▄▅▆▇▇▇███</td></tr><tr><td>train/epoch_loss</td><td>█▇▆▄▄▃▂▂▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>9</td></tr><tr><td>test/epoch_acc</td><td>0.86667</td></tr><tr><td>test/epoch_loss</td><td>0.53756</td></tr><tr><td>test/f1-score</td><td>0.86364</td></tr><tr><td>test/precision</td><td>0.80851</td></tr><tr><td>test/recall</td><td>0.92683</td></tr><tr><td>train/batch_loss</td><td>0.52993</td></tr><tr><td>train/epoch_acc</td><td>0.80098</td></tr><tr><td>train/epoch_loss</td><td>0.55775</td></tr></table><br/></div></div>"
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"text/html": [
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" View run <strong style=\"color:#cdcd00\">glowing-sweep-11</strong> at: <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/gxvcwlwu' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/gxvcwlwu</a><br/>Synced 4 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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|
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"text/html": [
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" table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
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"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>test/epoch_acc</td><td>▂▁▄▄▇▆▆▇█▇</td></tr><tr><td>test/epoch_loss</td><td>█▆▅▆▃▂▂▂▂▁</td></tr><tr><td>test/f1-score</td><td>▁▁▅▄▇▇▇███</td></tr><tr><td>test/precision</td><td>▂▁▄▃▆▆▆▆█▇</td></tr><tr><td>test/recall</td><td>▁▂▅▄▆▆▇█▆█</td></tr><tr><td>train/batch_loss</td><td>█▆▆▆▆▆▆▃▇█▄▄▇█▆▅▄▇▇▃▄▄▅▂▃▄▆▆▁▆▂▄▄▅▅▅▄▆▄▄</td></tr><tr><td>train/epoch_acc</td><td>▁▃▄▄▆▆▇▇▇█</td></tr><tr><td>train/epoch_loss</td><td>█▇▆▅▃▃▂▂▂▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>9</td></tr><tr><td>test/epoch_acc</td><td>0.68889</td></tr><tr><td>test/epoch_loss</td><td>0.66123</td></tr><tr><td>test/f1-score</td><td>0.65854</td></tr><tr><td>test/precision</td><td>0.64286</td></tr><tr><td>test/recall</td><td>0.675</td></tr><tr><td>train/batch_loss</td><td>0.60239</td></tr><tr><td>train/epoch_acc</td><td>0.65233</td></tr><tr><td>train/epoch_loss</td><td>0.66732</td></tr></table><br/></div></div>"
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" View run <strong style=\"color:#cdcd00\">glorious-sweep-13</strong> at: <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/w0els6yx' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/w0els6yx</a><br/>Synced 4 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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"text/html": [
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 4\n",
|
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.9\n",
|
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_two: 0.99\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
|
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"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>test/epoch_acc</td><td>▁█▃▅█▇▄▇██</td></tr><tr><td>test/epoch_loss</td><td>█▁▆▃▂▄█▄▃▃</td></tr><tr><td>test/f1-score</td><td>▃█▁▃██▃▇██</td></tr><tr><td>test/precision</td><td>▁▅██▆▄▅▆▆▆</td></tr><tr><td>test/recall</td><td>▅█▁▂▇█▃▆▆▇</td></tr><tr><td>train/batch_loss</td><td>▄▅▃▅▂▇▂▄▂▃█▄▂▃▁▃▄▁▂▁▁▁▃▁▂▁▁▂▁▂▁▁▁▁▁▁▁▅▁▂</td></tr><tr><td>train/epoch_acc</td><td>▁▃▄▆▆▇▇▇██</td></tr><tr><td>train/epoch_loss</td><td>█▆▅▄▃▂▂▂▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>9</td></tr><tr><td>test/epoch_acc</td><td>0.86667</td></tr><tr><td>test/epoch_loss</td><td>0.52816</td></tr><tr><td>test/f1-score</td><td>0.85</td></tr><tr><td>test/precision</td><td>0.85</td></tr><tr><td>test/recall</td><td>0.85</td></tr><tr><td>train/batch_loss</td><td>0.0016</td></tr><tr><td>train/epoch_acc</td><td>0.99509</td></tr><tr><td>train/epoch_loss</td><td>0.02902</td></tr></table><br/></div></div>"
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"text/html": [
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],
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"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>test/epoch_acc</td><td>▁▃▅▆▇▇▇███</td></tr><tr><td>test/epoch_loss</td><td>█▇▅▄▄▃▃▂▂▁</td></tr><tr><td>test/f1-score</td><td>▁▃▆▆▇▇▇███</td></tr><tr><td>test/precision</td><td>▁▃▆▅▆▇▇███</td></tr><tr><td>test/recall</td><td>▁▃▆▇▇▇▇███</td></tr><tr><td>train/batch_loss</td><td>▇█▆▆▆▆▅▇▅▇▅▅▅▅▅▄▄▅▅▄▃▃▄▃▂▃▄▃▂▄▂▃▁▁▃▄▃▂▂▃</td></tr><tr><td>train/epoch_acc</td><td>▁▃▄▆▇▇▇█▇█</td></tr><tr><td>train/epoch_loss</td><td>█▇▆▅▄▃▃▂▂▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>9</td></tr><tr><td>test/epoch_acc</td><td>0.83333</td></tr><tr><td>test/epoch_loss</td><td>0.5844</td></tr><tr><td>test/f1-score</td><td>0.85437</td></tr><tr><td>test/precision</td><td>0.88</td></tr><tr><td>test/recall</td><td>0.83019</td></tr><tr><td>train/batch_loss</td><td>0.60478</td></tr><tr><td>train/epoch_acc</td><td>0.82801</td></tr><tr><td>train/epoch_loss</td><td>0.58084</td></tr></table><br/></div></div>"
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\n",
|
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|
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|
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|
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"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>test/epoch_acc</td><td>▃▁█▆▆▆█▅▅█</td></tr><tr><td>test/epoch_loss</td><td>▅█▂▂▁▁▁▁▁▁</td></tr><tr><td>test/f1-score</td><td>▂▁█▇▇▇█▅▅█</td></tr><tr><td>test/precision</td><td>▇▃█▄▁▁█▁▁█</td></tr><tr><td>test/recall</td><td>▁▁▆▆██▆▆▆▆</td></tr><tr><td>train/batch_loss</td><td>▆█▄▅▃▃▇▁▁▁▁▁▁▁▂▁▂▂▁▁▁▁▁▂▁▄▃▁▁▂▂▁▁▃▁▁▁▁▁▁</td></tr><tr><td>train/epoch_acc</td><td>▁▅██████▇█</td></tr><tr><td>train/epoch_loss</td><td>█▄▁▁▁▁▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>9</td></tr><tr><td>test/epoch_acc</td><td>0.91111</td></tr><tr><td>test/epoch_loss</td><td>0.2015</td></tr><tr><td>test/f1-score</td><td>0.89744</td></tr><tr><td>test/precision</td><td>0.94595</td></tr><tr><td>test/recall</td><td>0.85366</td></tr><tr><td>train/batch_loss</td><td>0.00723</td></tr><tr><td>train/epoch_acc</td><td>0.98157</td></tr><tr><td>train/epoch_loss</td><td>0.07856</td></tr></table><br/></div></div>"
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"text/html": [
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"Find logs at: <code>./wandb/run-20230404_152419-k0hwgfjk/logs</code>"
|
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|
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|
||
"\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.99\n",
|
||
"\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_two: 0.9\n",
|
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|
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"\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 1\n",
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|
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]
|
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|
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"metadata": {}
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{
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"text/plain": [
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"<IPython.core.display.HTML object>"
|
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],
|
||
"text/html": [
|
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" View sweep at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/9681wnh0' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/9681wnh0</a>"
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"<IPython.core.display.HTML object>"
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],
|
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"text/html": [
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"<style>\n",
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" table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
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" .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
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"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>test/epoch_acc</td><td>▁▅▆▇▆▇▆▆▆█</td></tr><tr><td>test/epoch_loss</td><td>█▅▅▃▃▁▃▄▂▁</td></tr><tr><td>test/f1-score</td><td>▁▄▆▇▆▇▆▆▆█</td></tr><tr><td>test/precision</td><td>▁█▆▆▂▄▆█▆▄</td></tr><tr><td>test/recall</td><td>▁▂▅▆▇▇▅▄▅█</td></tr><tr><td>train/batch_loss</td><td>▅▅▄▆▅▅▂▂▂▃▂▅▃▂▂▁▂▃▂▁█▁▁▂▁▁▁▁▁▁▁▁▁▂▂▁▁▁▁▁</td></tr><tr><td>train/epoch_acc</td><td>▁▄▅▇▇▇▇▇██</td></tr><tr><td>train/epoch_loss</td><td>█▆▄▃▂▂▂▂▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>9</td></tr><tr><td>test/epoch_acc</td><td>0.9</td></tr><tr><td>test/epoch_loss</td><td>0.24883</td></tr><tr><td>test/f1-score</td><td>0.89888</td></tr><tr><td>test/precision</td><td>0.93023</td></tr><tr><td>test/recall</td><td>0.86957</td></tr><tr><td>train/batch_loss</td><td>0.01547</td></tr><tr><td>train/epoch_acc</td><td>0.98771</td></tr><tr><td>train/epoch_loss</td><td>0.04667</td></tr></table><br/></div></div>"
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"text/html": [
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