Working Emnist lines dataset.

1 files changed, 0 insertions, 914 deletions

diff --git a/src/notebooks/Untitled.ipynb b/src/notebooks/Untitled.ipynb
deleted file mode 100644
index 97c523d..0000000
--- a/src/notebooks/Untitled.ipynb
+++ /dev/null
@@ -1,914 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "getattr(torch.optim.lr_scheduler, \"StepLR\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = getattr(torch.nn, \"ReLU\")()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loss = getattr(torch.nn, \"L1Loss\")()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "input = torch.randn(3, 5, requires_grad=True)\n",
-    "target = torch.randn(3, 5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b = torch.randn(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a(b)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output = loss(input, target)\n",
-    "output.backward()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s = 1.\n",
-    "if s is not None:\n",
-    "    assert 0.0 < s < 1.0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class A:\n",
-    "    @property\n",
-    "    def __name__(self):\n",
-    "        return \"adafa\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = A()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a.__name__"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from training.gpu_manager import GPUManager"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gpu_manager = GPUManager(True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2020-07-21 14:10:13.170 | DEBUG    | training.gpu_manager:_get_free_gpu:57 - pid 11721 picking gpu 0\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gpu_manager.get_free_gpu()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pathlib import Path"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p = Path(\"/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "str(p).split(\"/\")[0] + \"/\" + str(p).split(\"/\")[1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p.parents[0].resolve()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p.exists()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d = 'Experiment JSON, e.g. \\'{\"dataset\": \"EmnistDataset\", \"model\": \"CharacterModel\", \"network\": \"mlp\"}\\''"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(d)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import yaml"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "path = \"/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/sample_experiment.yml\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(path) as f:\n",
-    "    d = yaml.safe_load(f)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "experiment_config = d[\"experiments\"][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'dataloader': 'EmnistDataLoader',\n",
-       " 'data_loader_args': {'splits': ['train', 'val'],\n",
-       "  'sample_to_balance': True,\n",
-       "  'subsample_fraction': None,\n",
-       "  'transform': None,\n",
-       "  'target_transform': None,\n",
-       "  'batch_size': 256,\n",
-       "  'shuffle': True,\n",
-       "  'num_workers': 0,\n",
-       "  'cuda': True,\n",
-       "  'seed': 4711},\n",
-       " 'model': 'CharacterModel',\n",
-       " 'metrics': ['accuracy'],\n",
-       " 'network': 'MLP',\n",
-       " 'network_args': {'input_size': 784, 'num_layers': 2},\n",
-       " 'train_args': {'batch_size': 256, 'epochs': 16},\n",
-       " 'criterion': 'CrossEntropyLoss',\n",
-       " 'criterion_args': {'weight': None, 'ignore_index': -100, 'reduction': 'mean'},\n",
-       " 'optimizer': 'AdamW',\n",
-       " 'optimizer_args': {'lr': 0.0003,\n",
-       "  'betas': [0.9, 0.999],\n",
-       "  'eps': 1e-08,\n",
-       "  'weight_decay': 0,\n",
-       "  'amsgrad': False},\n",
-       " 'lr_scheduler': 'OneCycleLR',\n",
-       " 'lr_scheduler_args': {'max_lr': 3e-05, 'epochs': 16}}"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "experiment_config"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import importlib"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "network_module = importlib.import_module(\"text_recognizer.networks\")\n",
-    "network_fn_ = getattr(network_module, experiment_config[\"network\"])\n",
-    "network_args = experiment_config.get(\"network_args\", {})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 784)"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(1,) + (network_args[\"input_size\"],)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "optimizer_ = getattr(torch.optim, experiment_config[\"optimizer\"])\n",
-    "optimizer_args = experiment_config.get(\"optimizer_args\", {})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "optimizer_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "optimizer_args"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "network_args"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "network_fn_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "net = network_fn_(**network_args)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "optimizer_(net.parameters() , **optimizer_args)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "criterion_ = getattr(torch.nn, experiment_config[\"criterion\"])\n",
-    "criterion_args = experiment_config.get(\"criterion_args\", {})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "criterion_(**criterion_args)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "models_module = importlib.import_module(\"text_recognizer.models\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metrics = {metric: getattr(models_module, metric) for metric in experiment_config[\"metrics\"]}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "torch.randn(3, 10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "torch.randn(3, 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metrics['accuracy'](torch.randn(3, 10), torch.randn(3, 1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metric_fn_ = getattr(models_module, experiment_config[\"metric\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metric_fn_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "2.e-3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lr_scheduler_ = getattr(\n",
-    "    torch.optim.lr_scheduler, experiment_config[\"lr_scheduler\"]\n",
-    ")\n",
-    "lr_scheduler_args = experiment_config.get(\"lr_scheduler_args\", {})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"OneCycleLR\" in str(lr_scheduler_)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "datasets_module = importlib.import_module(\"text_recognizer.datasets\")\n",
-    "data_loader_ = getattr(datasets_module, experiment_config[\"dataloader\"])\n",
-    "data_loader_args = experiment_config.get(\"data_loader_args\", {})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_loader_(**data_loader_args)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cuda = \"cuda:0\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import re\n",
-    "cleanString = re.sub('[^A-Za-z]+','', cuda )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cleanString = re.sub('[^0-9]+','', cuda )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'0'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cleanString"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "([28, 28], 1)"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "([28, 28], ) + (1,)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(range(3-1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1,)"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tuple([1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from glob import glob"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/text_recognizer/weights/CharacterModel_Emnist_MLP_weights.pt']"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "glob(\"/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/text_recognizer/weights/CharacterModel_*MLP_weights.pt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def test(a, b, c, d):\n",
-    "    print(a,b,c,d)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "f = {\"a\": 2, \"b\": 3, \"c\": 4}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "dict_items([('a', 2), ('b', 3), ('c', 4)])\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f.items())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2 3 4 1\n"
-     ]
-    }
-   ],
-   "source": [
-    "test(**f, d=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "path = \"/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/*\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "l = glob(path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "l.sort()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "'/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_124928' in l"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_124928',\n",
-       " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_141139',\n",
-       " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_141213',\n",
-       " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_141433',\n",
-       " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_141702',\n",
-       " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_145028',\n",
-       " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_150212',\n",
-       " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_150301',\n",
-       " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_150317',\n",
-       " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_151135',\n",
-       " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_151408',\n",
-       " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_153144',\n",
-       " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_153207',\n",
-       " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_153310',\n",
-       " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_175150',\n",
-       " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_180741',\n",
-       " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_181933',\n",
-       " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_183347',\n",
-       " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_190044',\n",
-       " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_190633',\n",
-       " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_190738',\n",
-       " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_191111',\n",
-       " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_191310',\n",
-       " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_191412',\n",
-       " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_191504',\n",
-       " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_191826',\n",
-       " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0722_191559']"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "l"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from loguru import logger"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'Logger' object has no attribute 'DEBUG'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-18-e1360ed6a5af>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDEBUG\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m: 'Logger' object has no attribute 'DEBUG'"
-     ]
-    }
-   ],
-   "source": [
-    "logger.DEBUG"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.8.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}