diff --git a/classification/classifier/hyp-metrics.csv b/classification/classifier/hyp-metrics.csv
new file mode 100644
index 0000000..60c41c8
--- /dev/null
+++ b/classification/classifier/hyp-metrics.csv
@@ -0,0 +1,139 @@
+,summary,config,name
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diff --git a/classification/classifier/hyp-metrics.ipynb b/classification/classifier/hyp-metrics.ipynb
new file mode 100644
index 0000000..45f1c0a
--- /dev/null
+++ b/classification/classifier/hyp-metrics.ipynb
@@ -0,0 +1,916 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "747ddcf2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/zenon/.local/share/miniconda3/lib/python3.7/site-packages/requests/__init__.py:104: RequestsDependencyWarning: urllib3 (1.26.13) or chardet (5.1.0)/charset_normalizer (2.0.4) doesn't match a supported version!\n",
+      "  RequestsDependencyWarning)\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33me1527193\u001b[0m (\u001b[33mflower-classification\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import os\n",
+    "import time\n",
+    "import random\n",
+    "import wandb\n",
+    "import torch\n",
+    "wandb.login()\n",
+    "\n",
+    "from evaluation.helpers import set_size\n",
+    "\n",
+    "torch.manual_seed(42)\n",
+    "np.random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "76cc2ca7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "api = wandb.Api()\n",
+    "\n",
+    "# Project is specified by <entity/project-name>\n",
+    "runs = api.runs(\"flower-classification/pytorch-sweeps-demo\")\n",
+    "\n",
+    "summary_list, config_list, name_list = [], [], []\n",
+    "for run in runs: \n",
+    "    # .summary contains the output keys/values for metrics like accuracy.\n",
+    "    #  We call ._json_dict to omit large files \n",
+    "    summary_list.append(run.summary._json_dict)\n",
+    "\n",
+    "    # .config contains the hyperparameters.\n",
+    "    #  We remove special values that start with _.\n",
+    "    config_list.append(\n",
+    "        {k: v for k,v in run.config.items()\n",
+    "          if not k.startswith('_')})\n",
+    "\n",
+    "    # .name is the human-readable name of the run.\n",
+    "    name_list.append(run.name)\n",
+    "\n",
+    "runs_df = pd.DataFrame({\n",
+    "    \"summary\": summary_list,\n",
+    "    \"config\": config_list,\n",
+    "    \"name\": name_list\n",
+    "    })\n",
+    "\n",
+    "runs_df.to_csv(\"hyp-metrics.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "353f9082",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>name</th>\n",
+       "      <th>test/epoch_loss</th>\n",
+       "      <th>train/epoch_acc</th>\n",
+       "      <th>train/batch_loss</th>\n",
+       "      <th>epoch</th>\n",
+       "      <th>_timestamp</th>\n",
+       "      <th>test/recall</th>\n",
+       "      <th>test/precision</th>\n",
+       "      <th>_step</th>\n",
+       "      <th>...</th>\n",
+       "      <th>test/batch_loss</th>\n",
+       "      <th>eps</th>\n",
+       "      <th>gamma</th>\n",
+       "      <th>epochs</th>\n",
+       "      <th>beta_one</th>\n",
+       "      <th>beta_two</th>\n",
+       "      <th>optimizer</th>\n",
+       "      <th>step_size</th>\n",
+       "      <th>batch_size</th>\n",
+       "      <th>learning_rate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>fiery-sweep-26</td>\n",
+       "      <td>0.566462</td>\n",
+       "      <td>0.823096</td>\n",
+       "      <td>0.335779</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.680693e+09</td>\n",
+       "      <td>0.617021</td>\n",
+       "      <td>0.828571</td>\n",
+       "      <td>2059</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.000000e-01</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0.900</td>\n",
+       "      <td>adam</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>radiant-sweep-25</td>\n",
+       "      <td>0.645458</td>\n",
+       "      <td>0.712531</td>\n",
+       "      <td>0.70145</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.680693e+09</td>\n",
+       "      <td>0.822222</td>\n",
+       "      <td>0.685185</td>\n",
+       "      <td>1039</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0.900</td>\n",
+       "      <td>adam</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.0003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>blooming-sweep-24</td>\n",
+       "      <td>0.348129</td>\n",
+       "      <td>0.998771</td>\n",
+       "      <td>0.019566</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.680692e+09</td>\n",
+       "      <td>0.783784</td>\n",
+       "      <td>0.935484</td>\n",
+       "      <td>1039</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.000000e-08</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>0.999</td>\n",
+       "      <td>sgd</td>\n",
+       "      <td>5</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.0030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>visionary-sweep-23</td>\n",
+       "      <td>0.555318</td>\n",
+       "      <td>0.835381</td>\n",
+       "      <td>0.522233</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.680692e+09</td>\n",
+       "      <td>0.833333</td>\n",
+       "      <td>0.760870</td>\n",
+       "      <td>529</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>0.900</td>\n",
+       "      <td>sgd</td>\n",
+       "      <td>2</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.0003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>ancient-sweep-22</td>\n",
+       "      <td>1.560271</td>\n",
+       "      <td>0.557740</td>\n",
+       "      <td>0.508366</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.680692e+09</td>\n",
+       "      <td>0.884615</td>\n",
+       "      <td>0.589744</td>\n",
+       "      <td>410</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.000000e-08</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>0.990</td>\n",
+       "      <td>adam</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>133</th>\n",
+       "      <td>133</td>\n",
+       "      <td>different-sweep-5</td>\n",
+       "      <td>0.493642</td>\n",
+       "      <td>0.821867</td>\n",
+       "      <td>0.443422</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.678732e+09</td>\n",
+       "      <td>0.714286</td>\n",
+       "      <td>0.945946</td>\n",
+       "      <td>1159</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.506896</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sgd</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.0001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>134</th>\n",
+       "      <td>134</td>\n",
+       "      <td>wise-sweep-4</td>\n",
+       "      <td>0.548264</td>\n",
+       "      <td>0.812039</td>\n",
+       "      <td>0.565593</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.678731e+09</td>\n",
+       "      <td>0.846154</td>\n",
+       "      <td>0.825000</td>\n",
+       "      <td>1159</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.515937</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sgd</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.0001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>135</th>\n",
+       "      <td>135</td>\n",
+       "      <td>misty-sweep-3</td>\n",
+       "      <td>0.241948</td>\n",
+       "      <td>0.996314</td>\n",
+       "      <td>0.004703</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.678731e+09</td>\n",
+       "      <td>0.775000</td>\n",
+       "      <td>0.939394</td>\n",
+       "      <td>2289</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.758836</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sgd</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>136</th>\n",
+       "      <td>136</td>\n",
+       "      <td>unique-sweep-2</td>\n",
+       "      <td>0.479234</td>\n",
+       "      <td>0.832924</td>\n",
+       "      <td>0.534751</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.678730e+09</td>\n",
+       "      <td>0.684211</td>\n",
+       "      <td>0.838710</td>\n",
+       "      <td>1159</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.455120</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sgd</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.0003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>137</td>\n",
+       "      <td>polar-sweep-1</td>\n",
+       "      <td>0.544247</td>\n",
+       "      <td>0.990172</td>\n",
+       "      <td>0.00574</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.678730e+09</td>\n",
+       "      <td>0.863636</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>2289</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.532007</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sgd</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0030</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>138 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Unnamed: 0                name  test/epoch_loss  train/epoch_acc  \\\n",
+       "0             0      fiery-sweep-26         0.566462         0.823096   \n",
+       "1             1    radiant-sweep-25         0.645458         0.712531   \n",
+       "2             2   blooming-sweep-24         0.348129         0.998771   \n",
+       "3             3  visionary-sweep-23         0.555318         0.835381   \n",
+       "4             4    ancient-sweep-22         1.560271         0.557740   \n",
+       "..          ...                 ...              ...              ...   \n",
+       "133         133   different-sweep-5         0.493642         0.821867   \n",
+       "134         134        wise-sweep-4         0.548264         0.812039   \n",
+       "135         135       misty-sweep-3         0.241948         0.996314   \n",
+       "136         136      unique-sweep-2         0.479234         0.832924   \n",
+       "137         137       polar-sweep-1         0.544247         0.990172   \n",
+       "\n",
+       "    train/batch_loss  epoch    _timestamp  test/recall  test/precision  _step  \\\n",
+       "0           0.335779      9  1.680693e+09     0.617021        0.828571   2059   \n",
+       "1            0.70145      9  1.680693e+09     0.822222        0.685185   1039   \n",
+       "2           0.019566      9  1.680692e+09     0.783784        0.935484   1039   \n",
+       "3           0.522233      9  1.680692e+09     0.833333        0.760870    529   \n",
+       "4           0.508366      1  1.680692e+09     0.884615        0.589744    410   \n",
+       "..               ...    ...           ...          ...             ...    ...   \n",
+       "133         0.443422      9  1.678732e+09     0.714286        0.945946   1159   \n",
+       "134         0.565593      9  1.678731e+09     0.846154        0.825000   1159   \n",
+       "135         0.004703      9  1.678731e+09     0.775000        0.939394   2289   \n",
+       "136         0.534751      9  1.678730e+09     0.684211        0.838710   1159   \n",
+       "137          0.00574      9  1.678730e+09     0.863636        0.904762   2289   \n",
+       "\n",
+       "     ...  test/batch_loss           eps  gamma epochs  beta_one  beta_two  \\\n",
+       "0    ...              NaN  1.000000e-01    0.1     10      0.99     0.900   \n",
+       "1    ...              NaN  1.000000e+00    0.5     10      0.99     0.900   \n",
+       "2    ...              NaN  1.000000e-08    0.5     10      0.90     0.999   \n",
+       "3    ...              NaN  1.000000e+00    0.1     10      0.90     0.900   \n",
+       "4    ...              NaN  1.000000e-08    0.5     10      0.90     0.990   \n",
+       "..   ...              ...           ...    ...    ...       ...       ...   \n",
+       "133  ...         0.506896           NaN    0.5     10       NaN       NaN   \n",
+       "134  ...         0.515937           NaN    0.5     10       NaN       NaN   \n",
+       "135  ...         1.758836           NaN    0.5     10       NaN       NaN   \n",
+       "136  ...         0.455120           NaN    0.1     10       NaN       NaN   \n",
+       "137  ...         2.532007           NaN    0.5     10       NaN       NaN   \n",
+       "\n",
+       "     optimizer  step_size  batch_size  learning_rate  \n",
+       "0         adam          3           4         0.0003  \n",
+       "1         adam          2           8         0.0003  \n",
+       "2          sgd          5           8         0.0030  \n",
+       "3          sgd          2          16         0.0003  \n",
+       "4         adam          7           4         0.0100  \n",
+       "..         ...        ...         ...            ...  \n",
+       "133        sgd          3           8         0.0001  \n",
+       "134        sgd          2           8         0.0001  \n",
+       "135        sgd          3           4         0.0030  \n",
+       "136        sgd          3           8         0.0003  \n",
+       "137        sgd          7           4         0.0030  \n",
+       "\n",
+       "[138 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('hyp-metrics.csv',\n",
+    "                 delimiter=',')\n",
+    "df['summary'] = df['summary'].map(eval)\n",
+    "df['config'] = df['config'].map(eval)\n",
+    "df = df.join(pd.json_normalize(df['summary'])).drop('summary', axis='columns')\n",
+    "df = df.join(pd.json_normalize(df['config'])).drop('config', axis='columns')\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4679b2f8",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/zenon/.local/share/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: In a future version of pandas all arguments of Series.sort_values will be keyword-only\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.0100    21\n",
+       "0.1000    21\n",
+       "0.0003    23\n",
+       "0.0010    23\n",
+       "0.0001    23\n",
+       "0.0030    27\n",
+       "Name: learning_rate, dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['learning_rate'].value_counts().sort_values(0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1b1a54fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Style the plots (with grid this time)\n",
+    "width = 418\n",
+    "sns.set_theme(style='whitegrid',\n",
+    "              rc={'text.usetex': True, 'font.family': 'serif', 'axes.labelsize': 10,\n",
+    "                  'font.size': 10, 'legend.fontsize': 8,\n",
+    "                  'xtick.labelsize': 8, 'ytick.labelsize': 8})\n",
+    "\n",
+    "fig_save_dir = '../../thesis/graphics/'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "00efa25b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 578.387x357.463 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_prepared = df.rename(columns={'learning_rate': 'learning rate', 'batch_size': 'batch size'})\n",
+    "fig, ax = plt.subplots(1, 1, figsize=set_size(width, subplots=(1,1)))\n",
+    "sns.scatterplot(x=\"learning rate\", y=\"test/f1-score\",\n",
+    "                style=\"optimizer\", hue=\"batch size\",\n",
+    "                palette=sns.cubehelix_palette(5, light=0.8, gamma=1.2),\n",
+    "                sizes=(5, 30), linewidth=0, s=15,\n",
+    "                data=df_prepared, ax=ax)\n",
+    "ax.set_xscale('log')\n",
+    "ax.set_xticks([0.0001, 0.0003, 0.001, 0.003, 0.01, 0.1])\n",
+    "ax.set_xticklabels(labels = ['0.0001', '0.0003', '0.001', '0.003', '0.01', '0.1'])\n",
+    "fig.tight_layout()\n",
+    "fig.savefig(fig_save_dir + 'classifier-hyp-metrics.pdf', format='pdf', bbox_inches='tight')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "44e275ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameters_dict = {\n",
+    "    'optimizer': {\n",
+    "        'values': ['adam', 'sgd']\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "parameters_dict.update({\n",
+    "    'batch_size': {\n",
+    "        'values': [4, 8, 16, 32, 64]},\n",
+    "    'learning_rate': {\n",
+    "        'values': [0.0001, 0.0003, 0.001, 0.003, 0.01, 0.1]},\n",
+    "    'step_size': {\n",
+    "        'values': [2, 3, 5, 7]},\n",
+    "    'gamma': {\n",
+    "        'values': [0.1, 0.5]},\n",
+    "    'beta_one': {\n",
+    "        'values': [0.9, 0.99]},\n",
+    "    'beta_two': {\n",
+    "        'values': [0.5, 0.9, 0.99, 0.999]},\n",
+    "    'eps': {\n",
+    "        'values': [1e-08, 0.1, 1]}\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7d3c2860",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = pd.DataFrame.from_dict(parameters_dict)\n",
+    "params = params.transpose()\n",
+    "params['values_string'] = [', '.join(map(str, l)) for l in params['values']]\n",
+    "params['values'] = params['values_string']\n",
+    "params = params.drop(['values_string'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "acc3a77e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>optimizer</th>\n",
+       "      <td>adam, sgd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>batch_size</th>\n",
+       "      <td>4, 8, 16, 32, 64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>learning_rate</th>\n",
+       "      <td>0.0001, 0.0003, 0.001, 0.003, 0.01, 0.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>step_size</th>\n",
+       "      <td>2, 3, 5, 7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gamma</th>\n",
+       "      <td>0.1, 0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta_one</th>\n",
+       "      <td>0.9, 0.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta_two</th>\n",
+       "      <td>0.5, 0.9, 0.99, 0.999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>eps</th>\n",
+       "      <td>1e-08, 0.1, 1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                values\n",
+       "optimizer                                    adam, sgd\n",
+       "batch_size                            4, 8, 16, 32, 64\n",
+       "learning_rate  0.0001, 0.0003, 0.001, 0.003, 0.01, 0.1\n",
+       "step_size                                   2, 3, 5, 7\n",
+       "gamma                                         0.1, 0.5\n",
+       "beta_one                                     0.9, 0.99\n",
+       "beta_two                         0.5, 0.9, 0.99, 0.999\n",
+       "eps                                      1e-08, 0.1, 1"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "73a26951",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>optimizer</th>\n",
+       "      <th>batch_size</th>\n",
+       "      <th>learning_rate</th>\n",
+       "      <th>step_size</th>\n",
+       "      <th>gamma</th>\n",
+       "      <th>beta_one</th>\n",
+       "      <th>beta_two</th>\n",
+       "      <th>eps</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>values</th>\n",
+       "      <td>adam</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0001</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>values</th>\n",
+       "      <td>adam</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0001</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>values</th>\n",
+       "      <td>adam</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0001</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>values</th>\n",
+       "      <td>adam</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0001</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>values</th>\n",
+       "      <td>adam</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0001</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>values</th>\n",
+       "      <td>sgd</td>\n",
+       "      <td>64</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>values</th>\n",
+       "      <td>sgd</td>\n",
+       "      <td>64</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>values</th>\n",
+       "      <td>sgd</td>\n",
+       "      <td>64</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0.999</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>values</th>\n",
+       "      <td>sgd</td>\n",
+       "      <td>64</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0.999</td>\n",
+       "      <td>0.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>values</th>\n",
+       "      <td>sgd</td>\n",
+       "      <td>64</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0.999</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>11520 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       optimizer batch_size learning_rate step_size gamma beta_one beta_two  \\\n",
+       "values      adam          4        0.0001         2   0.1      0.9      0.5   \n",
+       "values      adam          4        0.0001         2   0.1      0.9      0.5   \n",
+       "values      adam          4        0.0001         2   0.1      0.9      0.5   \n",
+       "values      adam          4        0.0001         2   0.1      0.9      0.9   \n",
+       "values      adam          4        0.0001         2   0.1      0.9      0.9   \n",
+       "...          ...        ...           ...       ...   ...      ...      ...   \n",
+       "values       sgd         64           0.1         7   0.5     0.99     0.99   \n",
+       "values       sgd         64           0.1         7   0.5     0.99     0.99   \n",
+       "values       sgd         64           0.1         7   0.5     0.99    0.999   \n",
+       "values       sgd         64           0.1         7   0.5     0.99    0.999   \n",
+       "values       sgd         64           0.1         7   0.5     0.99    0.999   \n",
+       "\n",
+       "        eps  \n",
+       "values  0.0  \n",
+       "values  0.1  \n",
+       "values    1  \n",
+       "values  0.0  \n",
+       "values  0.1  \n",
+       "...     ...  \n",
+       "values  0.1  \n",
+       "values    1  \n",
+       "values  0.0  \n",
+       "values  0.1  \n",
+       "values    1  \n",
+       "\n",
+       "[11520 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame.from_dict(parameters_dict).explode('optimizer').explode('batch_size').explode('learning_rate').explode('step_size').explode('gamma').explode('beta_one').explode('beta_two').explode('eps')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f163a6c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/classification/classifier/hyp.ipynb b/classification/classifier/hyp.ipynb
new file mode 100644
index 0000000..246b7d2
--- /dev/null
+++ b/classification/classifier/hyp.ipynb
@@ -0,0 +1,4644 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "747ddcf2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33me1527193\u001b[0m (\u001b[33mflower-classification\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import wandb\n",
+    "\n",
+    "wandb.login()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c37343d6",
+   "metadata": {},
+   "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\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "17b25dc7",
+   "metadata": {},
+   "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 = '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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5eff68bf",
+   "metadata": {},
+   "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})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "732a83df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sweep_config = {\n",
+    "    'method': 'random'\n",
+    "}\n",
+    "\n",
+    "metric = {\n",
+    "    'name': 'test/epoch_acc',\n",
+    "    'goal': 'maximize'   \n",
+    "}\n",
+    "\n",
+    "sweep_config['metric'] = metric\n",
+    "\n",
+    "parameters_dict = {\n",
+    "    'optimizer': {\n",
+    "        'values': ['adam', 'sgd']\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "sweep_config['parameters'] = parameters_dict\n",
+    "\n",
+    "parameters_dict.update({\n",
+    "    'epochs': {\n",
+    "        'value': 10},\n",
+    "    'batch_size': {\n",
+    "        'values': [4, 8]},\n",
+    "    'learning_rate': {\n",
+    "        'values': [0.1, 0.01, 0.003, 0.001, 0.0003, 0.0001]},\n",
+    "    'step_size': {\n",
+    "        'values': [2, 3, 5, 7]},\n",
+    "    'gamma': {\n",
+    "        'values': [0.1, 0.5]},\n",
+    "    'beta_one': {\n",
+    "        'values': [0.9, 0.99]},\n",
+    "    'beta_two': {\n",
+    "        'values': [0.5, 0.9, 0.99, 0.999]},\n",
+    "    'eps': {\n",
+    "        'values': [1e-08, 0.1, 1]}\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9a01fef6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Create sweep with ID: eqwnoagh\n",
+      "Sweep URL: https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh\n"
+     ]
+    }
+   ],
+   "source": [
+    "sweep_id = wandb.sweep(sweep_config, project=\"pytorch-sweeps-demo\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e80d1730",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: znahtehx with config:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 4\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: 1\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.1\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.001\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tstep_size: 2\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "Tracking run with wandb version 0.13.11"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Run data is saved locally in <code>/home/zenon/Documents/master-thesis/classification/classifier/wandb/run-20230313_210021-znahtehx</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Syncing run <strong><a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/znahtehx' target=\"_blank\">sparkling-sweep-1</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/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View project at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View sweep at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/znahtehx' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/znahtehx</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb4b99390e384bd5912f1133277e4a65",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(Label(value='0.003 MB of 0.027 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=0.127552…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
+       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
+       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
+       "    </style>\n",
+       "<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>0.85556</td></tr><tr><td>test/epoch_loss</td><td>0.6166</td></tr><tr><td>test/f1-score</td><td>0.86022</td></tr><tr><td>test/precision</td><td>0.81633</td></tr><tr><td>test/recall</td><td>0.90909</td></tr><tr><td>train/batch_loss</td><td>0.66533</td></tr><tr><td>train/epoch_acc</td><td>0.75676</td></tr><tr><td>train/epoch_loss</td><td>0.61072</td></tr></table><br/></div></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run <strong style=\"color:#cdcd00\">sparkling-sweep-1</strong> at: <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/znahtehx' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/znahtehx</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Find logs at: <code>./wandb/run-20230313_210021-znahtehx/logs</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: qutqx8ux with config:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 4\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.9\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_two: 0.9\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 1e-08\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.1\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.1\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: sgd\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tstep_size: 2\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "Tracking run with wandb version 0.13.11"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Run data is saved locally in <code>/home/zenon/Documents/master-thesis/classification/classifier/wandb/run-20230313_210951-qutqx8ux</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Syncing run <strong><a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/qutqx8ux' target=\"_blank\">stoic-sweep-2</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/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View project at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View sweep at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/qutqx8ux' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/qutqx8ux</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "acea58027ff945afa7cd0132f556317c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(Label(value='0.004 MB of 0.027 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=0.129798…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
+       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
+       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
+       "    </style>\n",
+       "<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>0.7</td></tr><tr><td>test/epoch_loss</td><td>0.55412</td></tr><tr><td>test/f1-score</td><td>0.71579</td></tr><tr><td>test/precision</td><td>0.59649</td></tr><tr><td>test/recall</td><td>0.89474</td></tr><tr><td>train/batch_loss</td><td>0.78966</td></tr><tr><td>train/epoch_acc</td><td>0.66585</td></tr><tr><td>train/epoch_loss</td><td>0.61866</td></tr></table><br/></div></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run <strong style=\"color:#cdcd00\">stoic-sweep-2</strong> at: <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/qutqx8ux' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/qutqx8ux</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Find logs at: <code>./wandb/run-20230313_210951-qutqx8ux/logs</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 9j8etw77 with config:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.99\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: 0.1\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.1\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.0001\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tstep_size: 2\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "Tracking run with wandb version 0.13.11"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Run data is saved locally in <code>/home/zenon/Documents/master-thesis/classification/classifier/wandb/run-20230313_211850-9j8etw77</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "Syncing run <strong><a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/9j8etw77' target=\"_blank\">hopeful-sweep-3</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/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
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+       " View project at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo</a>"
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+       " View sweep at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
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+       " View run at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/9j8etw77' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/9j8etw77</a>"
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+       "VBox(children=(Label(value='0.003 MB of 0.027 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=0.127348…"
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+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "    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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+       "    </style>\n",
+       "<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>0.6144</td></tr><tr><td>test/f1-score</td><td>0.76087</td></tr><tr><td>test/precision</td><td>0.67308</td></tr><tr><td>test/recall</td><td>0.875</td></tr><tr><td>train/batch_loss</td><td>0.65108</td></tr><tr><td>train/epoch_acc</td><td>0.7543</td></tr><tr><td>train/epoch_loss</td><td>0.62678</td></tr></table><br/></div></div>"
+      ],
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+       "<IPython.core.display.HTML object>"
+      ]
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run <strong style=\"color:#cdcd00\">hopeful-sweep-3</strong> at: <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/9j8etw77' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/9j8etw77</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
+      ],
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Find logs at: <code>./wandb/run-20230313_211850-9j8etw77/logs</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: k23a02gb with config:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.9\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_two: 0.99\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 1e-08\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.1\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.0003\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: sgd\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tstep_size: 5\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "Tracking run with wandb version 0.13.11"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Run data is saved locally in <code>/home/zenon/Documents/master-thesis/classification/classifier/wandb/run-20230313_212648-k23a02gb</code>"
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+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Syncing run <strong><a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/k23a02gb' target=\"_blank\">dulcet-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/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View project at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View sweep at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/k23a02gb' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/k23a02gb</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
+       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
+       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
+       "    </style>\n",
+       "<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>0.88889</td></tr><tr><td>test/epoch_loss</td><td>0.30289</td></tr><tr><td>test/f1-score</td><td>0.86486</td></tr><tr><td>test/precision</td><td>0.91429</td></tr><tr><td>test/recall</td><td>0.82051</td></tr><tr><td>train/batch_loss</td><td>0.27111</td></tr><tr><td>train/epoch_acc</td><td>0.89681</td></tr><tr><td>train/epoch_loss</td><td>0.28549</td></tr></table><br/></div></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run <strong style=\"color:#cdcd00\">dulcet-sweep-4</strong> at: <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/k23a02gb' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/k23a02gb</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
+      ],
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Find logs at: <code>./wandb/run-20230313_212648-k23a02gb/logs</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 265qnj0c with config:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.99\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_two: 0.99\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 1e-08\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.5\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.001\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: sgd\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tstep_size: 3\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "Tracking run with wandb version 0.13.11"
+      ],
+      "text/plain": [
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Run data is saved locally in <code>/home/zenon/Documents/master-thesis/classification/classifier/wandb/run-20230313_213431-265qnj0c</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Syncing run <strong><a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/265qnj0c' target=\"_blank\">hearty-sweep-5</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/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View project at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View sweep at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/265qnj0c' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/265qnj0c</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
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+      "text/plain": [
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+      "text/plain": [
+       "VBox(children=(Label(value='0.010 MB of 0.027 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=0.369539…"
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+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
+       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
+       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
+       "    </style>\n",
+       "<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>0.88889</td></tr><tr><td>test/epoch_loss</td><td>0.26007</td></tr><tr><td>test/f1-score</td><td>0.8913</td></tr><tr><td>test/precision</td><td>0.95349</td></tr><tr><td>test/recall</td><td>0.83673</td></tr><tr><td>train/batch_loss</td><td>0.01167</td></tr><tr><td>train/epoch_acc</td><td>0.98034</td></tr><tr><td>train/epoch_loss</td><td>0.08153</td></tr></table><br/></div></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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+       "Find logs at: <code>./wandb/run-20230313_213431-265qnj0c/logs</code>"
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+       "<IPython.core.display.HTML object>"
+      ]
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+     "output_type": "display_data"
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+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: eg199ue9 with config:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 4\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.9\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_two: 0.9\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 1\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.5\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.01\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tstep_size: 3\n"
+     ]
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+    {
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+       "Tracking run with wandb version 0.13.11"
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+    {
+     "data": {
+      "text/html": [
+       "Run data is saved locally in <code>/home/zenon/Documents/master-thesis/classification/classifier/wandb/run-20230313_214215-eg199ue9</code>"
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+    {
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+      "text/html": [
+       "Syncing run <strong><a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/eg199ue9' target=\"_blank\">smart-sweep-6</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/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
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+       "<IPython.core.display.HTML object>"
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+     "data": {
+      "text/html": [
+       " View project at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo</a>"
+      ],
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View sweep at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
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+     "data": {
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+       " View run at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/eg199ue9' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/eg199ue9</a>"
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+       "VBox(children=(Label(value='0.003 MB of 0.027 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=0.127718…"
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+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\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>0.22746</td></tr><tr><td>test/f1-score</td><td>0.89655</td></tr><tr><td>test/precision</td><td>0.92857</td></tr><tr><td>test/recall</td><td>0.86667</td></tr><tr><td>train/batch_loss</td><td>0.13858</td></tr><tr><td>train/epoch_acc</td><td>0.98403</td></tr><tr><td>train/epoch_loss</td><td>0.07075</td></tr></table><br/></div></div>"
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+    },
+    {
+     "data": {
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+       " View run <strong style=\"color:#cdcd00\">smart-sweep-6</strong> at: <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/eg199ue9' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/eg199ue9</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "Find logs at: <code>./wandb/run-20230313_214215-eg199ue9/logs</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: vdaaitvt with config:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.99\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: 1\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.1\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.0001\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: sgd\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tstep_size: 7\n"
+     ]
+    },
+    {
+     "data": {
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+    {
+     "data": {
+      "text/html": [
+       "Run data is saved locally in <code>/home/zenon/Documents/master-thesis/classification/classifier/wandb/run-20230313_215145-vdaaitvt</code>"
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+       "<IPython.core.display.HTML object>"
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Syncing run <strong><a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/vdaaitvt' target=\"_blank\">glorious-sweep-7</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/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View project at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo</a>"
+      ],
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+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View sweep at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
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+      ]
+     },
+     "metadata": {},
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+    },
+    {
+     "data": {
+      "text/html": [
+       " View run at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/vdaaitvt' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/vdaaitvt</a>"
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+       "VBox(children=(Label(value='0.003 MB of 0.003 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, max…"
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
+       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
+       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
+       "    </style>\n",
+       "<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>0.77778</td></tr><tr><td>test/epoch_loss</td><td>0.47685</td></tr><tr><td>test/f1-score</td><td>0.72222</td></tr><tr><td>test/precision</td><td>0.83871</td></tr><tr><td>test/recall</td><td>0.63415</td></tr><tr><td>train/batch_loss</td><td>0.37919</td></tr><tr><td>train/epoch_acc</td><td>0.82924</td></tr><tr><td>train/epoch_loss</td><td>0.45283</td></tr></table><br/></div></div>"
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+    },
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+     "output_type": "display_data"
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+     "data": {
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+      ]
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+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 16v61zix with config:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.9\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_two: 0.99\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 1\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.1\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.003\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: sgd\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tstep_size: 5\n"
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+    },
+    {
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+    {
+     "data": {
+      "text/html": [
+       "Run data is saved locally in <code>/home/zenon/Documents/master-thesis/classification/classifier/wandb/run-20230313_215930-16v61zix</code>"
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+       "Syncing run <strong><a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/16v61zix' target=\"_blank\">elated-sweep-8</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/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
+      ],
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+     "metadata": {},
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+       " View sweep at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
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+     },
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+    },
+    {
+     "data": {
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+       " View run at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/16v61zix' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/16v61zix</a>"
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+       "VBox(children=(Label(value='0.003 MB of 0.027 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=0.127347…"
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+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
+       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
+       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
+       "    </style>\n",
+       "<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>0.92222</td></tr><tr><td>test/epoch_loss</td><td>0.16872</td></tr><tr><td>test/f1-score</td><td>0.92135</td></tr><tr><td>test/precision</td><td>0.91111</td></tr><tr><td>test/recall</td><td>0.93182</td></tr><tr><td>train/batch_loss</td><td>0.00228</td></tr><tr><td>train/epoch_acc</td><td>0.99877</td></tr><tr><td>train/epoch_loss</td><td>0.02303</td></tr></table><br/></div></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run <strong style=\"color:#cdcd00\">elated-sweep-8</strong> at: <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/16v61zix' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/16v61zix</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Find logs at: <code>./wandb/run-20230313_215930-16v61zix/logs</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: gy76rrgz with config:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.9\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_two: 0.999\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 1\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.5\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.003\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tstep_size: 3\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "Tracking run with wandb version 0.13.11"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Run data is saved locally in <code>/home/zenon/Documents/master-thesis/classification/classifier/wandb/run-20230313_220712-gy76rrgz</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Syncing run <strong><a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/gy76rrgz' target=\"_blank\">major-sweep-9</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/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View project at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View sweep at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/gy76rrgz' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/gy76rrgz</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      "text/html": [
+       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
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+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(Label(value='0.003 MB of 0.027 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=0.127052…"
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
+       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
+       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
+       "    </style>\n",
+       "<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>0.88889</td></tr><tr><td>test/epoch_loss</td><td>0.26282</td></tr><tr><td>test/f1-score</td><td>0.87179</td></tr><tr><td>test/precision</td><td>0.97143</td></tr><tr><td>test/recall</td><td>0.7907</td></tr><tr><td>train/batch_loss</td><td>0.1486</td></tr><tr><td>train/epoch_acc</td><td>0.88698</td></tr><tr><td>train/epoch_loss</td><td>0.31064</td></tr></table><br/></div></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run <strong style=\"color:#cdcd00\">major-sweep-9</strong> at: <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/gy76rrgz' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/gy76rrgz</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Find logs at: <code>./wandb/run-20230313_220712-gy76rrgz/logs</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 4dx2f0j8 with config:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 4\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.9\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_two: 0.99\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 1e-08\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.1\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.001\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: sgd\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tstep_size: 5\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "Tracking run with wandb version 0.13.11"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Run data is saved locally in <code>/home/zenon/Documents/master-thesis/classification/classifier/wandb/run-20230313_221511-4dx2f0j8</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Syncing run <strong><a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/4dx2f0j8' target=\"_blank\">fallen-sweep-10</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/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View project at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View sweep at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/4dx2f0j8' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/4dx2f0j8</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "model_id": "93aeee346a4849d88f08125cde60a1ef",
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+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(Label(value='0.003 MB of 0.027 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=0.129230…"
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
+       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
+       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
+       "    </style>\n",
+       "<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>0.37958</td></tr><tr><td>test/f1-score</td><td>0.875</td></tr><tr><td>test/precision</td><td>0.95455</td></tr><tr><td>test/recall</td><td>0.80769</td></tr><tr><td>train/batch_loss</td><td>0.077</td></tr><tr><td>train/epoch_acc</td><td>0.9656</td></tr><tr><td>train/epoch_loss</td><td>0.09797</td></tr></table><br/></div></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run <strong style=\"color:#cdcd00\">fallen-sweep-10</strong> at: <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/4dx2f0j8' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/4dx2f0j8</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Find logs at: <code>./wandb/run-20230313_221511-4dx2f0j8/logs</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Sweep Agent: Waiting for job.\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Job received.\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: j93p9uxm with config:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\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",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.1\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.001\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tstep_size: 7\n"
+     ]
+    },
+    {
+     "data": {
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+     "data": {
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+       "<style>\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>2</td></tr><tr><td>test/epoch_acc</td><td>0.56667</td></tr><tr><td>test/epoch_loss</td><td>92431672.3021</td></tr><tr><td>test/f1-score</td><td>0.62136</td></tr><tr><td>test/precision</td><td>0.47761</td></tr><tr><td>test/recall</td><td>0.88889</td></tr><tr><td>train/batch_loss</td><td>7666.14648</td></tr><tr><td>train/epoch_acc</td><td>0.46929</td></tr><tr><td>train/epoch_loss</td><td>4618.08651</td></tr></table><br/></div></div>"
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+       " View run <strong style=\"color:#cdcd00\">revived-sweep-11</strong> at: <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/j93p9uxm' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/j93p9uxm</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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+      "text/plain": [
+       "<IPython.core.display.HTML object>"
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+     "text": [
+      "Run j93p9uxm errored: ZeroDivisionError('division by zero')\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run j93p9uxm errored: ZeroDivisionError('division by zero')\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: pb5m44k2 with config:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 4\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.9\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_two: 0.999\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 1e-08\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.1\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.001\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tstep_size: 5\n"
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+    },
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+       "Run data is saved locally in <code>/home/zenon/Documents/master-thesis/classification/classifier/wandb/run-20230313_222656-pb5m44k2</code>"
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+       "Syncing run <strong><a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/pb5m44k2' target=\"_blank\">faithful-sweep-12</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/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
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+       " View sweep at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
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+       " View run at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/pb5m44k2' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/pb5m44k2</a>"
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+       "VBox(children=(Label(value='0.003 MB of 0.003 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, max…"
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+      "text/html": [
+       "<style>\n",
+       "    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>0.78889</td></tr><tr><td>test/epoch_loss</td><td>0.51027</td></tr><tr><td>test/f1-score</td><td>0.78161</td></tr><tr><td>test/precision</td><td>0.7234</td></tr><tr><td>test/recall</td><td>0.85</td></tr><tr><td>train/batch_loss</td><td>0.42048</td></tr><tr><td>train/epoch_acc</td><td>0.82555</td></tr><tr><td>train/epoch_loss</td><td>0.40512</td></tr></table><br/></div></div>"
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+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run <strong style=\"color:#cdcd00\">faithful-sweep-12</strong> at: <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/pb5m44k2' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/pb5m44k2</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Find logs at: <code>./wandb/run-20230313_222656-pb5m44k2/logs</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
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+     },
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+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: q8m1yt6d with config:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\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: 0.1\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.1\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.0003\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tstep_size: 7\n"
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+    },
+    {
+     "data": {
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+       "Tracking run with wandb version 0.13.11"
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+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Run data is saved locally in <code>/home/zenon/Documents/master-thesis/classification/classifier/wandb/run-20230313_223624-q8m1yt6d</code>"
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+       "Syncing run <strong><a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/q8m1yt6d' target=\"_blank\">fine-sweep-13</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/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
+      ],
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+     "data": {
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+       " View project at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo</a>"
+      ],
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View sweep at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
+      ],
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+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/q8m1yt6d' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/q8m1yt6d</a>"
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Waiting for W&B process to finish... <strong style=\"color:red\">(failed 1).</strong> Press Control-C to abort syncing."
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
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+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
+       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
+       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
+       "    </style>\n",
+       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>train/batch_loss</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>train/batch_loss</td><td>0.67379</td></tr></table><br/></div></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run <strong style=\"color:#cdcd00\">fine-sweep-13</strong> at: <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/q8m1yt6d' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/q8m1yt6d</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
+      ],
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Find logs at: <code>./wandb/run-20230313_223624-q8m1yt6d/logs</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
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+     "output_type": "display_data"
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+      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run q8m1yt6d errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 1.95 GiB total capacity; 1.30 GiB already allocated; 11.31 MiB free; 1.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Sweep Agent: Waiting for job.\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Job received.\n",
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+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\n",
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+      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: sgd\n",
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+      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run f3kiw40d errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 3.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: i0xsie8j with config:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.9\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_two: 0.99\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 0.1\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.5\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.001\n",
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+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tstep_size: 7\n"
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+      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run i0xsie8j errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Sweep Agent: Waiting for job.\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Job received.\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: bi477kch with config:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\n",
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+      "\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 1e-08\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.5\n",
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+      "Run bi477kch errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run bi477kch errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 7jmkpkmh with config:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.99\n",
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+      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n",
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+      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run 7jmkpkmh errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: pc0kaw45 with config:\n",
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+      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n",
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+      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: o04kggii with config:\n",
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+      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run o04kggii errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
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+      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run 292ds63r errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
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+      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: sgd\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tstep_size: 7\n"
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+      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run fdlwffsj errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 3s4wltdw with config:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.99\n",
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+      "Run 3s4wltdw errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run 3s4wltdw errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: kv0nxhmk with config:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.9\n",
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+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
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+      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run kv0nxhmk errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: ixbulpc8 with config:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.9\n",
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+      "\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 1e-08\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.1\n",
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+      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: sgd\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tstep_size: 5\n"
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+      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run ixbulpc8 errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: lfi2onyo with config:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\n",
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+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.5\n",
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+      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run lfi2onyo errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 4uvn2tnq with config:\n",
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+      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n",
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+      "\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 1\n",
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+      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run y4niwbym errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: hxampiva with config:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\n",
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+      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run hxampiva errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Sweep Agent: Waiting for job.\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Job received.\n",
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+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.99\n",
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+       "Find logs at: <code>./wandb/run-20230313_224241-q1v8qruc/logs</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Run q1v8qruc errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run q1v8qruc errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n"
+     ]
+    }
+   ],
+   "source": [
+    "wandb.agent(sweep_id, train, count=30)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/thesis/graphics/classifier-hyp-metrics.pdf b/thesis/graphics/classifier-hyp-metrics.pdf
new file mode 100644
index 0000000..f93f28c
Binary files /dev/null and b/thesis/graphics/classifier-hyp-metrics.pdf differ
diff --git a/thesis/references.bib b/thesis/references.bib
index fdc56ee..2956556 100644
--- a/thesis/references.bib
+++ b/thesis/references.bib
@@ -99,6 +99,19 @@
   keywords = {artificial intelligence,crop management,livestock management,machine learning,precision agriculture,precision livestock farming,soil management,water management}
 }
 
+@article{bergstra2012,
+  title = {Random Search for Hyper-Parameter Optimization},
+  author = {Bergstra, James and Bengio, Yoshua},
+  date = {2012-02-01},
+  journaltitle = {The Journal of Machine Learning Research},
+  shortjournal = {J. Mach. Learn. Res.},
+  volume = {13},
+  pages = {281--305},
+  issn = {1532-4435},
+  issue = {null},
+  keywords = {deep learning,global optimization,model selection,neural networks,response surface modeling}
+}
+
 @online{bochkovskiy2020,
   title = {{{YOLOv4}}: {{Optimal Speed}} and {{Accuracy}} of {{Object Detection}}},
   shorttitle = {{{YOLOv4}}},
@@ -164,6 +177,19 @@
   keywords = {Complexity theory,Degradation,Image recognition,Image segmentation,Neural networks,Training,Visualization}
 }
 
+@online{kingma2017,
+  title = {Adam: {{A Method}} for {{Stochastic Optimization}}},
+  shorttitle = {Adam},
+  author = {Kingma, Diederik P. and Ba, Jimmy},
+  date = {2017-01-29},
+  number = {arXiv:1412.6980},
+  eprint = {arXiv:1412.6980},
+  eprinttype = {arxiv},
+  doi = {10.48550/arXiv.1412.6980},
+  pubstate = {preprint},
+  keywords = {Computer Science - Machine Learning}
+}
+
 @article{kuznetsova2020,
   title = {The {{Open Images Dataset V4}}: {{Unified}} Image Classification, Object Detection, and Visual Relationship Detection at Scale},
   shorttitle = {The {{Open Images Dataset V4}}},
diff --git a/thesis/thesis.pdf b/thesis/thesis.pdf
index 1d301d5..34d8e4c 100644
Binary files a/thesis/thesis.pdf and b/thesis/thesis.pdf differ
diff --git a/thesis/thesis.tex b/thesis/thesis.tex
index 84b6b4b..0bdb94b 100644
--- a/thesis/thesis.tex
+++ b/thesis/thesis.tex
@@ -78,6 +78,7 @@
 \newacronym{map}{mAP}{mean average precision}
 \newacronym{resnet}{ResNet}{Residual Neural Network}
 \newacronym{cnn}{CNN}{Convolutional Neural Network}
+\newacronym{sgd}{SGD}{Stochastic Gradient Descent}
 
 \begin{document}
 
@@ -348,6 +349,78 @@ feature extraction capabilities.
   \label{fig:classifier-training-metrics}
 \end{figure}
 
+\subsection{Hyper-parameter Optimization}
+\label{ssec:resnet-hyp-opt}
+
+In order to improve the aforementioned accuracy values, we perform
+hyper-parameter optimization across a wide range of
+parameters. Table~\ref{tab:resnet-hyps} lists the hyper-parameters and
+their possible values. Since the number of all combinations of values
+is 11520 and each combination is trained for 10 epochs with a training
+time of approximately six minutes per combination, exhausting the
+search space would take 48 days. Due to time limitations, we have
+chosen to not search exhaustively but to pick random combinations
+instead. Random search works surprisingly well---especially compared to
+grid search---in a number of domains, one of which is hyper-parameter
+optimization~\cite{bergstra2012}.
+
+\begin{table}[h]
+  \centering
+  \begin{tabular}{lr}
+    \toprule
+    Parameter &  Values \\
+    \midrule
+    optimizer &	adam, sgd \\
+    batch size & 4, 8, 16, 32, 64 \\
+    learning rate & 0.0001, 0.0003, 0.001, 0.003, 0.01, 0.1 \\
+    step size & 2, 3, 5, 7 \\
+    gamma & 0.1, 0.5 \\
+    beta one & 0.9, 0.99 \\
+    beta two & 0.5, 0.9, 0.99, 0.999 \\
+    eps & 0.00000001, 0.1, 1 \\
+    \bottomrule
+  \end{tabular}
+  \caption{Hyper-parameters and their possible values during
+    optimization.}
+  \label{tab:resnet-hyps}
+\end{table}
+
+The random search was run for 138 iterations which equates to a 75\%
+probability that the best solution lies within 1\% of the theoretical
+maximum~\eqref{eq:opt-prob}. Figure~\ref{fig:resnet-hyp-results} shows
+three of the eight parameters and their impact on a high
+F1-score. \gls{sgd} has less variation in its results than
+Adam~\cite{kingma2017} and manages to provide eight out of the ten
+best results. The number of epochs to train for was chosen based on
+the observation that almost all configurations converge well before
+reaching the tenth epoch. The assumption that a training run with ten
+epochs provides a good proxy for final performance is supported by the
+quick convergence of validation accuracy and loss in
+figure~\ref{fig:classifier-training-metrics}.
+
+\begin{equation}\label{eq:opt-prob}
+  1 - (1 - 0.01)^{138} \approx 0.75
+\end{equation}
+
+\begin{figure}
+  \centering
+  \includegraphics{graphics/classifier-hyp-metrics.pdf}
+  \caption[Classifier hyper-parameter optimization results.]{This
+    figure shows three of the eight hyper-parameters and their
+    performance measured by the F1-score during 138
+    trials. Differently colored markers show the batch size with
+    darker colors representing a larger batch size. The type of marker
+    (circle or cross) shows which optimizer was used. The x-axis shows
+    the learning rate on a logarithmic scale. In general, a learning
+    rate between 0.003 and 0.01 results in more robust and better
+    F1-scores. Larger batch sizes more often lead to better
+    performance as well. As for the type of optimizer, \gls{sgd}
+    produced the best iteration with an F1-score of 0.9783. Adam tends
+    to require more customization of its parameters than \gls{sgd} to
+    achieve good results.}
+  \label{fig:resnet-hyp-results}
+\end{figure}
+
 \subsection{Class Activation Maps}
 \label{ssec:resnet-cam}