Working training loop and testing of trained CharacterModel.

97 files changed, 2836 insertions, 218 deletions

diff --git a/src/notebooks/Untitled.ipynb b/src/notebooks/Untitled.ipynb
index 1cb7acb..97c523d 100644
--- a/src/notebooks/Untitled.ipynb
+++ b/src/notebooks/Untitled.ipynb
@@ -24,6 +24,24 @@
    "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\")()"
    ]
   },
@@ -43,6 +61,33 @@
    "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()"
    ]
@@ -99,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -108,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -117,17 +162,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-07-05 21:16:55.100 | DEBUG    | training.gpu_manager:_get_free_gpu:55 - pid 29777 picking gpu 0\n",
-      "2020-07-05 21:16:55.704 | DEBUG    | training.gpu_manager:_get_free_gpu:59 - pid 29777 could not get lock.\n",
-      "2020-07-05 21:16:55.705 | DEBUG    | training.gpu_manager:get_free_gpu:37 - pid 29777 sleeping\n",
-      "2020-07-05 21:17:00.722 | DEBUG    | training.gpu_manager:_get_free_gpu:55 - pid 29777 picking gpu 0\n"
+      "2020-07-21 14:10:13.170 | DEBUG    | training.gpu_manager:_get_free_gpu:57 - pid 11721 picking gpu 0\n"
      ]
     },
     {
@@ -136,7 +178,7 @@
        "0"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -150,6 +192,701 @@
    "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": []
   }
  ],
diff --git a/src/text_recognizer/character_predictor.py b/src/text_recognizer/character_predictor.py
index 69ef896..a773f36 100644
--- a/src/text_recognizer/character_predictor.py
+++ b/src/text_recognizer/character_predictor.py
@@ -1,8 +1,8 @@
 """CharacterPredictor class."""
-
-from typing import Tuple, Union
+from typing import Dict, Tuple, Type, Union
 
 import numpy as np
+from torch import nn
 
 from text_recognizer.models import CharacterModel
 from text_recognizer.util import read_image
@@ -11,9 +11,9 @@ from text_recognizer.util import read_image
 class CharacterPredictor:
     """Recognizes the character in handwritten character images."""
 
-    def __init__(self) -> None:
+    def __init__(self, network_fn: Type[nn.Module], network_args: Dict) -> None:
         """Intializes the CharacterModel and load the pretrained weights."""
-        self.model = CharacterModel()
+        self.model = CharacterModel(network_fn=network_fn, network_args=network_args)
         self.model.load_weights()
         self.model.eval()
 
diff --git a/src/text_recognizer/datasets/__init__.py b/src/text_recognizer/datasets/__init__.py
index aec5bf9..795be90 100644
--- a/src/text_recognizer/datasets/__init__.py
+++ b/src/text_recognizer/datasets/__init__.py
@@ -1,2 +1,4 @@
 """Dataset modules."""
 from .emnist_dataset import EmnistDataLoader
+
+__all__ = ["EmnistDataLoader"]
diff --git a/src/text_recognizer/datasets/emnist_dataset.py b/src/text_recognizer/datasets/emnist_dataset.py
index a17d7a9..b92b57d 100644
--- a/src/text_recognizer/datasets/emnist_dataset.py
+++ b/src/text_recognizer/datasets/emnist_dataset.py
@@ -2,7 +2,7 @@
 
 import json
 from pathlib import Path
-from typing import Callable, Dict, List, Optional
+from typing import Callable, Dict, List, Optional, Type
 
 from loguru import logger
 import numpy as np
@@ -102,21 +102,22 @@ class EmnistDataLoader:
         self.shuffle = shuffle
         self.num_workers = num_workers
         self.cuda = cuda
+        self.seed = seed
         self._data_loaders = self._fetch_emnist_data_loaders()
 
     @property
     def __name__(self) -> str:
         """Returns the name of the dataset."""
-        return "EMNIST"
+        return "Emnist"
 
-    def __call__(self, split: str) -> Optional[DataLoader]:
+    def __call__(self, split: str) -> DataLoader:
         """Returns the `split` DataLoader.
 
         Args:
             split (str): The dataset split, i.e. train or val.
 
         Returns:
-            type: A PyTorch DataLoader.
+            DataLoader: A PyTorch DataLoader.
 
         Raises:
             ValueError: If the split does not exist.
diff --git a/src/text_recognizer/models/__init__.py b/src/text_recognizer/models/__init__.py
index d265dcf..ff10a07 100644
--- a/src/text_recognizer/models/__init__.py
+++ b/src/text_recognizer/models/__init__.py
@@ -1,2 +1,6 @@
 """Model modules."""
+from .base import Model
 from .character_model import CharacterModel
+from .metrics import accuracy
+
+__all__ = ["Model", "CharacterModel", "accuracy"]
diff --git a/src/text_recognizer/models/base.py b/src/text_recognizer/models/base.py
index 0cc531a..b78eacb 100644
--- a/src/text_recognizer/models/base.py
+++ b/src/text_recognizer/models/base.py
@@ -1,9 +1,11 @@
 """Abstract Model class for PyTorch neural networks."""
 
 from abc import ABC, abstractmethod
+from glob import glob
 from pathlib import Path
+import re
 import shutil
-from typing import Callable, Dict, Optional, Tuple
+from typing import Callable, Dict, Optional, Tuple, Type
 
 from loguru import logger
 import torch
@@ -19,7 +21,7 @@ class Model(ABC):
 
     def __init__(
         self,
-        network_fn: Callable,
+        network_fn: Type[nn.Module],
         network_args: Dict,
         data_loader: Optional[Callable] = None,
         data_loader_args: Optional[Dict] = None,
@@ -35,7 +37,7 @@ class Model(ABC):
         """Base class, to be inherited by model for specific type of data.
 
         Args:
-            network_fn (Callable): The PyTorch network.
+            network_fn (Type[nn.Module]): The PyTorch network.
             network_args (Dict): Arguments for the network.
             data_loader (Optional[Callable]): A function that fetches train and val DataLoader.
             data_loader_args (Optional[Dict]):  Arguments for the DataLoader.
@@ -57,27 +59,29 @@ class Model(ABC):
             self._data_loaders = data_loader(**data_loader_args)
             dataset_name = self._data_loaders.__name__
         else:
-            dataset_name = ""
+            dataset_name = "*"
             self._data_loaders = None
 
-        self.name = f"{self.__class__.__name__}_{dataset_name}_{network_fn.__name__}"
+        self._name = f"{self.__class__.__name__}_{dataset_name}_{network_fn.__name__}"
 
         # Extract the input shape for the torchsummary.
-        self._input_shape = network_args.pop("input_shape")
+        if isinstance(network_args["input_size"], int):
+            self._input_shape = (1,) + tuple([network_args["input_size"]])
+        else:
+            self._input_shape = (1,) + tuple(network_args["input_size"])
 
         if metrics is not None:
             self._metrics = metrics
 
         # Set the device.
-        if self.device is None:
-            self._device = torch.device(
-                "cuda:0" if torch.cuda.is_available() else "cpu"
-            )
+        if device is None:
+            self._device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         else:
             self._device = device
 
         # Load network.
-        self._network = network_fn(**network_args)
+        self.network_args = network_args
+        self._network = network_fn(**self.network_args)
 
         # To device.
         self._network.to(self._device)
@@ -95,13 +99,29 @@ class Model(ABC):
         # Set learning rate scheduler.
         self._lr_scheduler = None
         if lr_scheduler is not None:
+            # OneCycleLR needs the number of steps in an epoch as an input argument.
+            if "OneCycleLR" in str(lr_scheduler):
+                lr_scheduler_args["steps_per_epoch"] = len(self._data_loaders("train"))
             self._lr_scheduler = lr_scheduler(self._optimizer, **lr_scheduler_args)
 
+        # Class mapping.
+        self._mapping = None
+
+    @property
+    def __name__(self) -> str:
+        """Returns the name of the model."""
+        return self._name
+
     @property
     def input_shape(self) -> Tuple[int, ...]:
         """The input shape."""
         return self._input_shape
 
+    @property
+    def mapping(self) -> Dict:
+        """Returns the class mapping."""
+        return self._mapping
+
     def eval(self) -> None:
         """Sets the network to evaluation mode."""
         self._network.eval()
@@ -149,13 +169,14 @@ class Model(ABC):
     def weights_filename(self) -> str:
         """Filepath to the network weights."""
         WEIGHT_DIRNAME.mkdir(parents=True, exist_ok=True)
-        return str(WEIGHT_DIRNAME / f"{self.name}_weights.pt")
+        return str(WEIGHT_DIRNAME / f"{self._name}_weights.pt")
 
     def summary(self) -> None:
         """Prints a summary of the network architecture."""
-        summary(self._network, self._input_shape, device=self.device)
+        device = re.sub("[^A-Za-z]+", "", self.device)
+        summary(self._network, self._input_shape, device=device)
 
-    def _get_state(self) -> Dict:
+    def _get_state_dict(self) -> Dict:
         """Get the state dict of the model."""
         state = {"model_state": self._network.state_dict()}
         if self._optimizer is not None:
@@ -172,6 +193,7 @@ class Model(ABC):
             epoch (int): The last epoch when the checkpoint was created.
 
         """
+        logger.debug("Loading checkpoint...")
         if not path.exists():
             logger.debug("File does not exist {str(path)}")
 
@@ -200,6 +222,7 @@ class Model(ABC):
         state = self._get_state_dict()
         state["is_best"] = is_best
         state["epoch"] = epoch
+        state["network_args"] = self.network_args
 
         path.mkdir(parents=True, exist_ok=True)
 
@@ -216,15 +239,18 @@ class Model(ABC):
     def load_weights(self) -> None:
         """Load the network weights."""
         logger.debug("Loading network weights.")
-        weights = torch.load(self.weights_filename)["model_state"]
+        filename = glob(self.weights_filename)[0]
+        weights = torch.load(filename, map_location=torch.device(self._device))[
+            "model_state"
+        ]
         self._network.load_state_dict(weights)
 
-    def save_weights(self) -> None:
+    def save_weights(self, path: Path) -> None:
         """Save the network weights."""
-        logger.debug("Saving network weights.")
-        torch.save({"model_state": self._network.state_dict()}, self.weights_filename)
+        logger.debug("Saving the best network weights.")
+        shutil.copyfile(str(path / "best.pt"), self.weights_filename)
 
     @abstractmethod
-    def mapping(self) -> Dict:
-        """Mapping from network output to class."""
+    def load_mapping(self) -> None:
+        """Loads class mapping from network output to character."""
         ...
diff --git a/src/text_recognizer/models/character_model.py b/src/text_recognizer/models/character_model.py
index fd69bf2..527fc7d 100644
--- a/src/text_recognizer/models/character_model.py
+++ b/src/text_recognizer/models/character_model.py
@@ -1,5 +1,5 @@
 """Defines the CharacterModel class."""
-from typing import Callable, Dict, Optional, Tuple
+from typing import Callable, Dict, Optional, Tuple, Type
 
 import numpy as np
 import torch
@@ -8,7 +8,6 @@ from torchvision.transforms import ToTensor
 
 from text_recognizer.datasets.emnist_dataset import load_emnist_mapping
 from text_recognizer.models.base import Model
-from text_recognizer.networks.mlp import mlp
 
 
 class CharacterModel(Model):
@@ -16,8 +15,9 @@ class CharacterModel(Model):
 
     def __init__(
         self,
-        network_fn: Callable,
+        network_fn: Type[nn.Module],
         network_args: Dict,
+        data_loader: Optional[Callable] = None,
         data_loader_args: Optional[Dict] = None,
         metrics: Optional[Dict] = None,
         criterion: Optional[Callable] = None,
@@ -33,6 +33,7 @@ class CharacterModel(Model):
         super().__init__(
             network_fn,
             network_args,
+            data_loader,
             data_loader_args,
             metrics,
             criterion,
@@ -43,13 +44,13 @@ class CharacterModel(Model):
             lr_scheduler_args,
             device,
         )
-        self.emnist_mapping = self.mapping()
-        self.eval()
+        self.load_mapping()
+        self.tensor_transform = ToTensor()
+        self.softmax = nn.Softmax(dim=0)
 
-    def mapping(self) -> Dict[int, str]:
+    def load_mapping(self) -> None:
         """Mapping between integers and classes."""
-        mapping = load_emnist_mapping()
-        return mapping
+        self._mapping = load_emnist_mapping()
 
     def predict_on_image(self, image: np.ndarray) -> Tuple[str, float]:
         """Character prediction on an image.
@@ -61,15 +62,20 @@ class CharacterModel(Model):
             Tuple[str, float]: The predicted character and the confidence in the prediction.
 
         """
+
         if image.dtype == np.uint8:
             image = (image / 255).astype(np.float32)
 
         # Conver to Pytorch Tensor.
-        image = ToTensor(image)
+        image = self.tensor_transform(image)
+
+        with torch.no_grad():
+            logits = self.network(image)
+
+        prediction = self.softmax(logits.data.squeeze())
 
-        prediction = self.network(image)
-        index = torch.argmax(prediction, dim=1)
+        index = int(torch.argmax(prediction, dim=0))
         confidence_of_prediction = prediction[index]
-        predicted_character = self.emnist_mapping[index]
+        predicted_character = self._mapping[index]
 
         return predicted_character, confidence_of_prediction
diff --git a/src/text_recognizer/models/metrics.py b/src/text_recognizer/models/metrics.py
index e2a30a9..ac8d68e 100644
--- a/src/text_recognizer/models/metrics.py
+++ b/src/text_recognizer/models/metrics.py
@@ -3,7 +3,7 @@
 import torch
 
 
-def accuracy(outputs: torch.Tensor, labels: torch.Tensro) -> float:
+def accuracy(outputs: torch.Tensor, labels: torch.Tensor) -> float:
     """Computes the accuracy.
 
     Args:
diff --git a/src/text_recognizer/networks/__init__.py b/src/text_recognizer/networks/__init__.py
index 4ea5bb3..e6b6946 100644
--- a/src/text_recognizer/networks/__init__.py
+++ b/src/text_recognizer/networks/__init__.py
@@ -1 +1,5 @@
 """Network modules."""
+from .lenet import LeNet
+from .mlp import MLP
+
+__all__ = ["MLP", "LeNet"]
diff --git a/src/text_recognizer/networks/lenet.py b/src/text_recognizer/networks/lenet.py
index 71d247f..2839a0c 100644
--- a/src/text_recognizer/networks/lenet.py
+++ b/src/text_recognizer/networks/lenet.py
@@ -1,5 +1,5 @@
 """Defines the LeNet network."""
-from typing import Callable, Optional, Tuple
+from typing import Callable, Dict, Optional, Tuple
 
 import torch
 from torch import nn
@@ -18,28 +18,37 @@ class LeNet(nn.Module):
 
     def __init__(
         self,
-        channels: Tuple[int, ...],
-        kernel_sizes: Tuple[int, ...],
-        hidden_size: Tuple[int, ...],
-        dropout_rate: float,
-        output_size: int,
+        input_size: Tuple[int, ...] = (1, 28, 28),
+        channels: Tuple[int, ...] = (1, 32, 64),
+        kernel_sizes: Tuple[int, ...] = (3, 3, 2),
+        hidden_size: Tuple[int, ...] = (9216, 128),
+        dropout_rate: float = 0.2,
+        output_size: int = 10,
         activation_fn: Optional[Callable] = None,
+        activation_fn_args: Optional[Dict] = None,
     ) -> None:
         """The LeNet network.
 
         Args:
-            channels (Tuple[int, ...]): Channels in the convolutional layers.
-            kernel_sizes (Tuple[int, ...]): Kernel sizes in the convolutional layers.
+            input_size (Tuple[int, ...]): The input shape of the network. Defaults to (1, 28, 28).
+            channels (Tuple[int, ...]): Channels in the convolutional layers. Defaults to (1, 32, 64).
+            kernel_sizes (Tuple[int, ...]): Kernel sizes in the convolutional layers. Defaults to (3, 3, 2).
             hidden_size (Tuple[int, ...]): Size of the flattend output form the convolutional layers.
-            dropout_rate (float): The dropout rate.
-            output_size (int): Number of classes.
+                Defaults to (9216, 128).
+            dropout_rate (float): The dropout rate. Defaults to 0.2.
+            output_size (int): Number of classes. Defaults to 10.
             activation_fn (Optional[Callable]): The non-linear activation function. Defaults to
                 nn.ReLU(inplace).
+            activation_fn_args (Optional[Dict]): The arguments for the activation function. Defaults to None.
 
         """
         super().__init__()
 
-        if activation_fn is None:
+        self._input_size = input_size
+
+        if activation_fn is not None:
+            activation_fn = getattr(nn, activation_fn)(activation_fn_args)
+        else:
             activation_fn = nn.ReLU(inplace=True)
 
         self.layers = [
@@ -68,26 +77,6 @@ class LeNet(nn.Module):
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         """The feedforward."""
+        if len(x.shape) == 3:
+            x = x.unsqueeze(0)
         return self.layers(x)
-
-
-# def test():
-#     x = torch.randn([1, 1, 28, 28])
-#     channels = [1, 32, 64]
-#     kernel_sizes = [3, 3, 2]
-#     hidden_size = [9216, 128]
-#     output_size = 10
-#     dropout_rate = 0.2
-#     activation_fn = nn.ReLU()
-#     net = LeNet(
-#         channels=channels,
-#         kernel_sizes=kernel_sizes,
-#         dropout_rate=dropout_rate,
-#         hidden_size=hidden_size,
-#         output_size=output_size,
-#         activation_fn=activation_fn,
-#     )
-#     from torchsummary import summary
-#
-#     summary(net, (1, 28, 28), device="cpu")
-#     out = net(x)
diff --git a/src/text_recognizer/networks/mlp.py b/src/text_recognizer/networks/mlp.py
index 2a41790..d704d99 100644
--- a/src/text_recognizer/networks/mlp.py
+++ b/src/text_recognizer/networks/mlp.py
@@ -1,5 +1,5 @@
 """Defines the MLP network."""
-from typing import Callable, Optional
+from typing import Callable, Dict, List, Optional, Union
 
 import torch
 from torch import nn
@@ -10,45 +10,54 @@ class MLP(nn.Module):
 
     def __init__(
         self,
-        input_size: int,
-        output_size: int,
-        hidden_size: int,
-        num_layers: int,
-        dropout_rate: float,
+        input_size: int = 784,
+        output_size: int = 10,
+        hidden_size: Union[int, List] = 128,
+        num_layers: int = 3,
+        dropout_rate: float = 0.2,
         activation_fn: Optional[Callable] = None,
+        activation_fn_args: Optional[Dict] = None,
     ) -> None:
         """Initialization of the MLP network.
 
         Args:
-            input_size (int): The input shape of the network.
-            output_size (int): Number of classes in the dataset.
-            hidden_size (int): The number of `neurons` in each hidden layer.
-            num_layers (int): The number of hidden layers.
-            dropout_rate (float): The dropout rate at each layer.
-            activation_fn (Optional[Callable]): The activation function in the hidden layers, (default:
-                nn.ReLU()).
+            input_size (int): The input shape of the network. Defaults to 784.
+            output_size (int): Number of classes in the dataset. Defaults to 10.
+            hidden_size (Union[int, List]): The number of `neurons` in each hidden layer. Defaults to 128.
+            num_layers (int): The number of hidden layers. Defaults to 3.
+            dropout_rate (float): The dropout rate at each layer. Defaults to 0.2.
+            activation_fn (Optional[Callable]): The activation function in the hidden layers. Defaults to
+                None.
+            activation_fn_args (Optional[Dict]): The arguments for the activation function. Defaults to None.
 
         """
         super().__init__()
 
-        if activation_fn is None:
+        if activation_fn is not None:
+            activation_fn = getattr(nn, activation_fn)(activation_fn_args)
+        else:
             activation_fn = nn.ReLU(inplace=True)
 
+        if isinstance(hidden_size, int):
+            hidden_size = [hidden_size] * num_layers
+
         self.layers = [
-            nn.Linear(in_features=input_size, out_features=hidden_size),
+            nn.Linear(in_features=input_size, out_features=hidden_size[0]),
             activation_fn,
         ]
 
-        for _ in range(num_layers):
+        for i in range(num_layers - 1):
             self.layers += [
-                nn.Linear(in_features=hidden_size, out_features=hidden_size),
+                nn.Linear(in_features=hidden_size[i], out_features=hidden_size[i + 1]),
                 activation_fn,
             ]
 
             if dropout_rate:
                 self.layers.append(nn.Dropout(p=dropout_rate))
 
-        self.layers.append(nn.Linear(in_features=hidden_size, out_features=output_size))
+        self.layers.append(
+            nn.Linear(in_features=hidden_size[-1], out_features=output_size)
+        )
 
         self.layers = nn.Sequential(*self.layers)
 
@@ -57,25 +66,7 @@ class MLP(nn.Module):
         x = torch.flatten(x, start_dim=1)
         return self.layers(x)
 
-
-# def test():
-#     x = torch.randn([1, 28, 28])
-#     input_size = torch.flatten(x).shape[0]
-#     output_size = 10
-#     hidden_size = 128
-#     num_layers = 5
-#     dropout_rate = 0.25
-#     activation_fn = nn.GELU()
-#     net = MLP(
-#         input_size=input_size,
-#         output_size=output_size,
-#         hidden_size=hidden_size,
-#         num_layers=num_layers,
-#         dropout_rate=dropout_rate,
-#         activation_fn=activation_fn,
-#     )
-#     from torchsummary import summary
-#
-#     summary(net, (1, 28, 28), device="cpu")
-#
-#     out = net(x)
+    @property
+    def __name__(self) -> str:
+        """Returns the name of the network."""
+        return "mlp"
diff --git a/src/text_recognizer/tests/test_character_predictor.py b/src/text_recognizer/tests/test_character_predictor.py
index 7c094ef..c603a3a 100644
--- a/src/text_recognizer/tests/test_character_predictor.py
+++ b/src/text_recognizer/tests/test_character_predictor.py
@@ -1,9 +1,14 @@
 """Test for CharacterPredictor class."""
+import importlib
 import os
 from pathlib import Path
 import unittest
 
+import click
+from loguru import logger
+
 from text_recognizer.character_predictor import CharacterPredictor
+from text_recognizer.networks import MLP
 
 SUPPORT_DIRNAME = Path(__file__).parents[0].resolve() / "support" / "emnist"
 
@@ -13,13 +18,23 @@ os.environ["CUDA_VISIBLE_DEVICES"] = ""
 class TestCharacterPredictor(unittest.TestCase):
     """Tests for the CharacterPredictor class."""
 
+    # @click.command()
+    # @click.option(
+    #     "--network", type=str, help="Network to load, e.g. MLP or LeNet.", default="MLP"
+    # )
     def test_filename(self) -> None:
         """Test that CharacterPredictor correctly predicts on a single image, for serveral test images."""
-        predictor = CharacterPredictor()
+        network_module = importlib.import_module("text_recognizer.networks")
+        network_fn_ = getattr(network_module, "MLP")
+        # network_args = {"input_size": [28, 28], "output_size": 62, "dropout_rate": 0}
+        network_args = {"input_size": 784, "output_size": 62, "dropout_rate": 0.2}
+        predictor = CharacterPredictor(
+            network_fn=network_fn_, network_args=network_args
+        )
 
         for filename in SUPPORT_DIRNAME.glob("*.png"):
             pred, conf = predictor.predict(str(filename))
-            print(
+            logger.info(
                 f"Prediction: {pred} at confidence: {conf} for image with character {filename.stem}"
             )
             self.assertEqual(pred, filename.stem)
diff --git a/src/text_recognizer/util.py b/src/text_recognizer/util.py
index 52fa1e4..6c07c60 100644
--- a/src/text_recognizer/util.py
+++ b/src/text_recognizer/util.py
@@ -25,7 +25,7 @@ def read_image(image_uri: Union[Path, str], grayscale: bool = False) -> np.ndarr
             ) from None
 
     imread_flag = cv2.IMREAD_GRAYSCALE if grayscale else cv2.IMREAD_COLOR
-    local_file = os.path.exsits(image_uri)
+    local_file = os.path.exists(image_uri)
     try:
         image = None
         if local_file:
diff --git a/src/text_recognizer/weights/CharacterModel_Emnist_LeNet_weights.pt b/src/text_recognizer/weights/CharacterModel_Emnist_LeNet_weights.pt
new file mode 100644
index 0000000..43a3891
--- /dev/null
+++ b/src/text_recognizer/weights/CharacterModel_Emnist_LeNet_weights.pt
diff --git a/src/text_recognizer/weights/CharacterModel_Emnist_MLP_weights.pt b/src/text_recognizer/weights/CharacterModel_Emnist_MLP_weights.pt
new file mode 100644
index 0000000..0dde787
--- /dev/null
+++ b/src/text_recognizer/weights/CharacterModel_Emnist_MLP_weights.pt
diff --git a/src/training/callbacks/__init__.py b/src/training/callbacks/__init__.py
new file mode 100644
index 0000000..868d739
--- /dev/null
+++ b/src/training/callbacks/__init__.py
@@ -0,0 +1 @@
+"""TBC."""
diff --git a/src/training/callbacks/base.py b/src/training/callbacks/base.py
new file mode 100644
index 0000000..d80a1e5
--- /dev/null
+++ b/src/training/callbacks/base.py
@@ -0,0 +1,101 @@
+"""Metaclass for callback functions."""
+
+from abc import ABC
+from typing import Callable, List, Type
+
+
+class Callback(ABC):
+    """Metaclass for callbacks used in training."""
+
+    def on_fit_begin(self) -> None:
+        """Called when fit begins."""
+        pass
+
+    def on_fit_end(self) -> None:
+        """Called when fit ends."""
+        pass
+
+    def on_train_epoch_begin(self) -> None:
+        """Called at the beginning of an epoch."""
+        pass
+
+    def on_train_epoch_end(self) -> None:
+        """Called at the end of an epoch."""
+        pass
+
+    def on_val_epoch_begin(self) -> None:
+        """Called at the beginning of an epoch."""
+        pass
+
+    def on_val_epoch_end(self) -> None:
+        """Called at the end of an epoch."""
+        pass
+
+    def on_train_batch_begin(self) -> None:
+        """Called at the beginning of an epoch."""
+        pass
+
+    def on_train_batch_end(self) -> None:
+        """Called at the end of an epoch."""
+        pass
+
+    def on_val_batch_begin(self) -> None:
+        """Called at the beginning of an epoch."""
+        pass
+
+    def on_val_batch_end(self) -> None:
+        """Called at the end of an epoch."""
+        pass
+
+
+class CallbackList:
+    """Container for abstracting away callback calls."""
+
+    def __init__(self, callbacks: List[Callable] = None) -> None:
+        """TBC."""
+        self._callbacks = callbacks if callbacks is not None else []
+
+    def append(self, callback: Type[Callback]) -> None:
+        """Append new callback to callback list."""
+        self.callbacks.append(callback)
+
+    def on_fit_begin(self) -> None:
+        """Called when fit begins."""
+        for _ in self._callbacks:
+            pass
+
+    def on_fit_end(self) -> None:
+        """Called when fit ends."""
+        pass
+
+    def on_train_epoch_begin(self) -> None:
+        """Called at the beginning of an epoch."""
+        pass
+
+    def on_train_epoch_end(self) -> None:
+        """Called at the end of an epoch."""
+        pass
+
+    def on_val_epoch_begin(self) -> None:
+        """Called at the beginning of an epoch."""
+        pass
+
+    def on_val_epoch_end(self) -> None:
+        """Called at the end of an epoch."""
+        pass
+
+    def on_train_batch_begin(self) -> None:
+        """Called at the beginning of an epoch."""
+        pass
+
+    def on_train_batch_end(self) -> None:
+        """Called at the end of an epoch."""
+        pass
+
+    def on_val_batch_begin(self) -> None:
+        """Called at the beginning of an epoch."""
+        pass
+
+    def on_val_batch_end(self) -> None:
+        """Called at the end of an epoch."""
+        pass
diff --git a/src/training/callbacks/early_stopping.py b/src/training/callbacks/early_stopping.py
new file mode 100644
index 0000000..4da0e85
--- /dev/null
+++ b/src/training/callbacks/early_stopping.py
@@ -0,0 +1 @@
+"""Implements Early stopping for PyTorch model."""
diff --git a/src/training/experiments/CharacterModel_Emnist_LeNet/0721_231455/config.yml b/src/training/experiments/CharacterModel_Emnist_LeNet/0721_231455/config.yml
new file mode 100644
index 0000000..2595325
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_LeNet/0721_231455/config.yml
@@ -0,0 +1,48 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 8
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.001
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: LeNet
+network_args:
+  input_size:
+  - 28
+  - 28
+  output_size: 62
+optimizer: AdamW
+optimizer_args:
+  amsgrad: false
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.01
+  weight_decay: 0
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_LeNet/0721_231455/model/best.pt b/src/training/experiments/CharacterModel_Emnist_LeNet/0721_231455/model/best.pt
new file mode 100644
index 0000000..6d78bad
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_LeNet/0721_231455/model/best.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_LeNet/0721_231455/model/last.pt b/src/training/experiments/CharacterModel_Emnist_LeNet/0721_231455/model/last.pt
new file mode 100644
index 0000000..6d78bad
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_LeNet/0721_231455/model/last.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_LeNet/0722_190746/config.yml b/src/training/experiments/CharacterModel_Emnist_LeNet/0722_190746/config.yml
new file mode 100644
index 0000000..2595325
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_LeNet/0722_190746/config.yml
@@ -0,0 +1,48 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 8
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.001
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: LeNet
+network_args:
+  input_size:
+  - 28
+  - 28
+  output_size: 62
+optimizer: AdamW
+optimizer_args:
+  amsgrad: false
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.01
+  weight_decay: 0
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_LeNet/0722_190746/model/best.pt b/src/training/experiments/CharacterModel_Emnist_LeNet/0722_190746/model/best.pt
new file mode 100644
index 0000000..43a3891
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_LeNet/0722_190746/model/best.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_LeNet/0722_190746/model/last.pt b/src/training/experiments/CharacterModel_Emnist_LeNet/0722_190746/model/last.pt
new file mode 100644
index 0000000..61c03f0
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_LeNet/0722_190746/model/last.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_124928/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0721_124928/config.yml
new file mode 100644
index 0000000..2aa52cd
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_124928/config.yml
@@ -0,0 +1,43 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 0
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: null
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: AdamW
+optimizer_args:
+  amsgrad: false
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.001
+  weight_decay: 0
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_141139/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0721_141139/config.yml
new file mode 100644
index 0000000..829297d
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_141139/config.yml
@@ -0,0 +1,47 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 0
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.0003
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: AdamW
+optimizer_args:
+  amsgrad: false
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.0006
+  weight_decay: 5.0e-05
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_141213/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0721_141213/config.yml
new file mode 100644
index 0000000..829297d
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_141213/config.yml
@@ -0,0 +1,47 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 0
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.0003
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: AdamW
+optimizer_args:
+  amsgrad: false
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.0006
+  weight_decay: 5.0e-05
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_141213/model/best.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_141213/model/best.pt
new file mode 100644
index 0000000..d0db78b
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_141213/model/best.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_141213/model/last.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_141213/model/last.pt
new file mode 100644
index 0000000..d0db78b
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_141213/model/last.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_141433/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0721_141433/config.yml
new file mode 100644
index 0000000..3df32bb
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_141433/config.yml
@@ -0,0 +1,47 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 0
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.01
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: AdamW
+optimizer_args:
+  amsgrad: false
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.1
+  weight_decay: 5.0e-05
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_141433/model/best.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_141433/model/best.pt
new file mode 100644
index 0000000..5914c8f
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_141433/model/best.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_141433/model/last.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_141433/model/last.pt
new file mode 100644
index 0000000..5ba44bb
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_141433/model/last.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_141702/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0721_141702/config.yml
new file mode 100644
index 0000000..fb75736
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_141702/config.yml
@@ -0,0 +1,47 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 0
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.001
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: AdamW
+optimizer_args:
+  amsgrad: false
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.01
+  weight_decay: 5.0e-05
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_141702/model/best.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_141702/model/best.pt
new file mode 100644
index 0000000..96c21c1
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_141702/model/best.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_141702/model/last.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_141702/model/last.pt
new file mode 100644
index 0000000..f024c0d
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_141702/model/last.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_145028/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0721_145028/config.yml
new file mode 100644
index 0000000..fb75736
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_145028/config.yml
@@ -0,0 +1,47 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 0
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.001
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: AdamW
+optimizer_args:
+  amsgrad: false
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.01
+  weight_decay: 5.0e-05
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_150212/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0721_150212/config.yml
new file mode 100644
index 0000000..fb75736
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_150212/config.yml
@@ -0,0 +1,47 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 0
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.001
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: AdamW
+optimizer_args:
+  amsgrad: false
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.01
+  weight_decay: 5.0e-05
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_150301/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0721_150301/config.yml
new file mode 100644
index 0000000..fb75736
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_150301/config.yml
@@ -0,0 +1,47 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 0
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.001
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: AdamW
+optimizer_args:
+  amsgrad: false
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.01
+  weight_decay: 5.0e-05
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_150317/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0721_150317/config.yml
new file mode 100644
index 0000000..fb75736
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_150317/config.yml
@@ -0,0 +1,47 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 0
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.001
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: AdamW
+optimizer_args:
+  amsgrad: false
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.01
+  weight_decay: 5.0e-05
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_151135/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0721_151135/config.yml
new file mode 100644
index 0000000..fb75736
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_151135/config.yml
@@ -0,0 +1,47 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 0
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.001
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: AdamW
+optimizer_args:
+  amsgrad: false
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.01
+  weight_decay: 5.0e-05
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_151135/model/best.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_151135/model/best.pt
new file mode 100644
index 0000000..f833a89
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_151135/model/best.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_151135/model/last.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_151135/model/last.pt
new file mode 100644
index 0000000..f833a89
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_151135/model/last.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_151408/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0721_151408/config.yml
new file mode 100644
index 0000000..fb75736
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_151408/config.yml
@@ -0,0 +1,47 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 0
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.001
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: AdamW
+optimizer_args:
+  amsgrad: false
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.01
+  weight_decay: 5.0e-05
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_153144/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0721_153144/config.yml
new file mode 100644
index 0000000..829297d
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_153144/config.yml
@@ -0,0 +1,47 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 0
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.0003
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: AdamW
+optimizer_args:
+  amsgrad: false
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.0006
+  weight_decay: 5.0e-05
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_153207/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0721_153207/config.yml
new file mode 100644
index 0000000..fb75736
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_153207/config.yml
@@ -0,0 +1,47 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 0
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.001
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: AdamW
+optimizer_args:
+  amsgrad: false
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.01
+  weight_decay: 5.0e-05
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_153310/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0721_153310/config.yml
new file mode 100644
index 0000000..fb75736
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_153310/config.yml
@@ -0,0 +1,47 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 0
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.001
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: AdamW
+optimizer_args:
+  amsgrad: false
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.01
+  weight_decay: 5.0e-05
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_153310/model/best.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_153310/model/best.pt
new file mode 100644
index 0000000..cbbc5e1
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_153310/model/best.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_153310/model/last.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_153310/model/last.pt
new file mode 100644
index 0000000..cbbc5e1
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_153310/model/last.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_175150/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0721_175150/config.yml
new file mode 100644
index 0000000..fb75736
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_175150/config.yml
@@ -0,0 +1,47 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 0
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.001
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: AdamW
+optimizer_args:
+  amsgrad: false
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.01
+  weight_decay: 5.0e-05
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_175150/model/best.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_175150/model/best.pt
new file mode 100644
index 0000000..c93e3c6
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_175150/model/best.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_175150/model/last.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_175150/model/last.pt
new file mode 100644
index 0000000..c93e3c6
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_175150/model/last.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_180741/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0721_180741/config.yml
new file mode 100644
index 0000000..1be5113
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_180741/config.yml
@@ -0,0 +1,47 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 0
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.001
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: Adam
+optimizer_args:
+  amsgrad: false
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.01
+  weight_decay: 5.0e-05
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_180741/model/best.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_180741/model/best.pt
new file mode 100644
index 0000000..580bad2
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_180741/model/best.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_180741/model/last.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_180741/model/last.pt
new file mode 100644
index 0000000..97e245c
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_180741/model/last.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_181933/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0721_181933/config.yml
new file mode 100644
index 0000000..d2f98a2
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_181933/config.yml
@@ -0,0 +1,46 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 0
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.001
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: Adamax
+optimizer_args:
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.01
+  weight_decay: 0
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_181933/model/best.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_181933/model/best.pt
new file mode 100644
index 0000000..5a3df56
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_181933/model/best.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_181933/model/last.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_181933/model/last.pt
new file mode 100644
index 0000000..7f28dc3
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_181933/model/last.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_183347/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0721_183347/config.yml
new file mode 100644
index 0000000..d2f98a2
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_183347/config.yml
@@ -0,0 +1,46 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 0
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.001
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: Adamax
+optimizer_args:
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.01
+  weight_decay: 0
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_183347/model/best.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_183347/model/best.pt
new file mode 100644
index 0000000..6f09780
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_183347/model/best.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_183347/model/last.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_183347/model/last.pt
new file mode 100644
index 0000000..3bb103e
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_183347/model/last.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_190044/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0721_190044/config.yml
new file mode 100644
index 0000000..a7c66c5
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_190044/config.yml
@@ -0,0 +1,46 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 0
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.001
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: AdamW
+optimizer_args:
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.01
+  weight_decay: 0
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_190044/model/best.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_190044/model/best.pt
new file mode 100644
index 0000000..c3e3618
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_190044/model/best.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_190044/model/last.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_190044/model/last.pt
new file mode 100644
index 0000000..c3e3618
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_190044/model/last.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_190633/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0721_190633/config.yml
new file mode 100644
index 0000000..a7c66c5
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_190633/config.yml
@@ -0,0 +1,46 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 0
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.001
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: AdamW
+optimizer_args:
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.01
+  weight_decay: 0
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_190633/model/best.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_190633/model/best.pt
new file mode 100644
index 0000000..44d9b9b
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_190633/model/best.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_190633/model/last.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_190633/model/last.pt
new file mode 100644
index 0000000..44d9b9b
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_190633/model/last.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_190738/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0721_190738/config.yml
new file mode 100644
index 0000000..a7c66c5
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_190738/config.yml
@@ -0,0 +1,46 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 0
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.001
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: AdamW
+optimizer_args:
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.01
+  weight_decay: 0
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_190738/model/best.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_190738/model/best.pt
new file mode 100644
index 0000000..4a0333c
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_190738/model/best.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_190738/model/last.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_190738/model/last.pt
new file mode 100644
index 0000000..4a0333c
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_190738/model/last.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_191111/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0721_191111/config.yml
new file mode 100644
index 0000000..a7c66c5
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_191111/config.yml
@@ -0,0 +1,46 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 0
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.001
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: AdamW
+optimizer_args:
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.01
+  weight_decay: 0
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_191310/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0721_191310/config.yml
new file mode 100644
index 0000000..08c344c
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_191310/config.yml
@@ -0,0 +1,46 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 1
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.001
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: AdamW
+optimizer_args:
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.01
+  weight_decay: 0
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_191310/model/best.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_191310/model/best.pt
new file mode 100644
index 0000000..076aae1
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_191310/model/best.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_191310/model/last.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_191310/model/last.pt
new file mode 100644
index 0000000..076aae1
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_191310/model/last.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_191412/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0721_191412/config.yml
new file mode 100644
index 0000000..0b9b10e
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_191412/config.yml
@@ -0,0 +1,42 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 1
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: null
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: RMSprop
+optimizer_args:
+  alpha: 0.9
+  centered: false
+  eps: 1.0e-07
+  lr: 0.001
+  momentum: 0
+  weight_decay: 0
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_191412/model/best.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_191412/model/best.pt
new file mode 100644
index 0000000..2fb0195
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_191412/model/best.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_191412/model/last.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_191412/model/last.pt
new file mode 100644
index 0000000..2fb0195
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_191412/model/last.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_191504/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0721_191504/config.yml
new file mode 100644
index 0000000..93c2854
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_191504/config.yml
@@ -0,0 +1,42 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 4
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: null
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: RMSprop
+optimizer_args:
+  alpha: 0.9
+  centered: false
+  eps: 1.0e-07
+  lr: 0.001
+  momentum: 0
+  weight_decay: 0
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_191504/model/best.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_191504/model/best.pt
new file mode 100644
index 0000000..9acc5b1
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_191504/model/best.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_191504/model/last.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_191504/model/last.pt
new file mode 100644
index 0000000..b8cc01c
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_191504/model/last.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_191826/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0721_191826/config.yml
new file mode 100644
index 0000000..7340941
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_191826/config.yml
@@ -0,0 +1,47 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 8
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.001
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: AdamW
+optimizer_args:
+  amsgrad: false
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.01
+  weight_decay: 0
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_191826/model/best.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_191826/model/best.pt
new file mode 100644
index 0000000..26bfb07
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_191826/model/best.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0721_191826/model/last.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0721_191826/model/last.pt
new file mode 100644
index 0000000..26bfb07
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0721_191826/model/last.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0722_191559/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0722_191559/config.yml
new file mode 100644
index 0000000..90f0e13
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0722_191559/config.yml
@@ -0,0 +1,49 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 8
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 33
+  max_lr: 0.001
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: AdamW
+optimizer_args:
+  amsgrad: false
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.01
+  weight_decay: 0
+resume_experiment: last
+train_args:
+  batch_size: 256
+  epochs: 33
+  val_metric: accuracy
+verbosity: 1
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0722_191559/model/best.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0722_191559/model/best.pt
new file mode 100644
index 0000000..f0f297b
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0722_191559/model/best.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0722_191559/model/last.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0722_191559/model/last.pt
new file mode 100644
index 0000000..c1adda5
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0722_191559/model/last.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0722_213125/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0722_213125/config.yml
new file mode 100644
index 0000000..8d77de5
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0722_213125/config.yml
@@ -0,0 +1,49 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 8
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.001
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: AdamW
+optimizer_args:
+  amsgrad: false
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.01
+  weight_decay: 0
+resume_experiment: null
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
+verbosity: 2
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0722_213413/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0722_213413/config.yml
new file mode 100644
index 0000000..8d77de5
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0722_213413/config.yml
@@ -0,0 +1,49 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 8
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.001
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: AdamW
+optimizer_args:
+  amsgrad: false
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.01
+  weight_decay: 0
+resume_experiment: null
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
+verbosity: 2
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0722_213413/model/best.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0722_213413/model/best.pt
new file mode 100644
index 0000000..e985997
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0722_213413/model/best.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0722_213413/model/last.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0722_213413/model/last.pt
new file mode 100644
index 0000000..e985997
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0722_213413/model/last.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0722_213549/config.yml b/src/training/experiments/CharacterModel_Emnist_MLP/0722_213549/config.yml
new file mode 100644
index 0000000..8d77de5
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0722_213549/config.yml
@@ -0,0 +1,49 @@
+criterion: CrossEntropyLoss
+criterion_args:
+  ignore_index: -100
+  reduction: mean
+  weight: null
+data_loader_args:
+  batch_size: 256
+  cuda: true
+  num_workers: 8
+  sample_to_balance: true
+  seed: 4711
+  shuffle: true
+  splits:
+  - train
+  - val
+  subsample_fraction: null
+  target_transform: null
+  transform: null
+dataloader: EmnistDataLoader
+device: cuda:0
+experiment_group: Sample Experiments
+lr_scheduler: OneCycleLR
+lr_scheduler_args:
+  epochs: 16
+  max_lr: 0.001
+  steps_per_epoch: 1314
+metrics:
+- accuracy
+model: CharacterModel
+network: MLP
+network_args:
+  input_size: 784
+  num_layers: 3
+  output_size: 62
+optimizer: AdamW
+optimizer_args:
+  amsgrad: false
+  betas:
+  - 0.9
+  - 0.999
+  eps: 1.0e-08
+  lr: 0.01
+  weight_decay: 0
+resume_experiment: null
+train_args:
+  batch_size: 256
+  epochs: 16
+  val_metric: accuracy
+verbosity: 2
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0722_213549/model/best.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0722_213549/model/best.pt
new file mode 100644
index 0000000..0dde787
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0722_213549/model/best.pt
diff --git a/src/training/experiments/CharacterModel_Emnist_MLP/0722_213549/model/last.pt b/src/training/experiments/CharacterModel_Emnist_MLP/0722_213549/model/last.pt
new file mode 100644
index 0000000..e02738b
--- /dev/null
+++ b/src/training/experiments/CharacterModel_Emnist_MLP/0722_213549/model/last.pt
diff --git a/src/training/experiments/sample.yml b/src/training/experiments/sample.yml
new file mode 100644
index 0000000..0ed560d
--- /dev/null
+++ b/src/training/experiments/sample.yml
@@ -0,0 +1,43 @@
+experiment_group: Sample Experiments
+experiments:
+    - dataloader: EmnistDataLoader
+      model: CharacterModel
+      metrics: [accuracy]
+      network: MLP
+      network_args:
+        input_shape: 784
+        num_layers: 2
+      train_args:
+        batch_size: 256
+        epochs: 16
+      criterion: CrossEntropyLoss
+      criterion_args:
+        weight: null
+        ignore_index: -100
+        reduction: mean
+      optimizer: AdamW
+      optimizer_args:
+        lr: 3.e-4
+        betas: [0.9, 0.999]
+        eps: 1.e-08
+        weight_decay: 0
+        amsgrad: false
+      lr_scheduler: OneCycleLR
+      lr_scheduler_args:
+        max_lr: 3.e-5
+        epochs: 16
+    # - dataloader: EmnistDataLoader
+    #   model: CharacterModel
+    #   network: MLP
+    #   network_args:
+    #     input_shape: 784
+    #     num_layers: 4
+    #   train_args:
+    #     batch_size: 256
+    # - dataloader: EmnistDataLoader
+    #   model: CharacterModel
+    #   network: LeNet
+    #   network_args:
+    #     input_shape: [28, 28]
+    #   train_args:
+    #     batch_size: 256
diff --git a/src/training/experiments/sample_experiment.yml b/src/training/experiments/sample_experiment.yml
new file mode 100644
index 0000000..e8d5023
--- /dev/null
+++ b/src/training/experiments/sample_experiment.yml
@@ -0,0 +1,56 @@
+experiment_group: Sample Experiments
+experiments:
+    - dataloader: EmnistDataLoader
+      data_loader_args:
+        splits: [train, val]
+        sample_to_balance: true
+        subsample_fraction: null
+        transform: null
+        target_transform: null
+        batch_size: 256
+        shuffle: true
+        num_workers: 8
+        cuda: true
+        seed: 4711
+      model: CharacterModel
+      metrics: [accuracy]
+      network: MLP
+      network_args:
+        input_size: 784
+        output_size: 62
+        num_layers: 3
+      # network: LeNet
+      # network_args:
+      #   input_size: [28, 28]
+      #   output_size: 62
+      train_args:
+        batch_size: 256
+        epochs: 16
+        val_metric: accuracy
+      criterion: CrossEntropyLoss
+      criterion_args:
+        weight: null
+        ignore_index: -100
+        reduction: mean
+      # optimizer: RMSprop
+      # optimizer_args:
+      #   lr: 1.e-3
+      #   alpha: 0.9
+      #   eps: 1.e-7
+      #   momentum: 0
+      #   weight_decay: 0
+      #   centered: false
+      optimizer: AdamW
+      optimizer_args:
+        lr: 1.e-2
+        betas: [0.9, 0.999]
+        eps: 1.e-08
+        weight_decay: 0
+        amsgrad: false
+      # lr_scheduler: null
+      lr_scheduler: OneCycleLR
+      lr_scheduler_args:
+        max_lr: 1.e-3
+        epochs: 16
+      verbosity: 2 # 0, 1, 2
+      resume_experiment: null
diff --git a/src/training/prepare_experiments.py b/src/training/prepare_experiments.py
index 1ab8f00..eb872d7 100644
--- a/src/training/prepare_experiments.py
+++ b/src/training/prepare_experiments.py
@@ -1,22 +1,24 @@
 """Run a experiment from a config file."""
 import json
+from subprocess import check_call
 
 import click
 from loguru import logger
 import yaml
 
 
-def run_experiment(experiment_filename: str) -> None:
+def run_experiments(experiments_filename: str) -> None:
     """Run experiment from file."""
-    with open(experiment_filename) as f:
+    with open(experiments_filename) as f:
         experiments_config = yaml.safe_load(f)
     num_experiments = len(experiments_config["experiments"])
     for index in range(num_experiments):
         experiment_config = experiments_config["experiments"][index]
         experiment_config["experiment_group"] = experiments_config["experiment_group"]
-        print(
-            f"python training/run_experiment.py --gpu=-1 '{json.dumps(experiment_config)}'"
-        )
+        # cmd = f"python training/run_experiment.py --gpu=-1 '{json.dumps(experiment_config)}'"
+        cmd = f"poetry run run-experiment --gpu=-1 --save --experiment_config '{json.dumps(experiment_config)}'"
+        print(cmd)
+        check_call(cmd, shell=True)
 
 
 @click.command()
@@ -26,9 +28,9 @@ def run_experiment(experiment_filename: str) -> None:
     type=str,
     help="Filename of Yaml file of experiments to run.",
 )
-def main(experiment_filename: str) -> None:
+def main(experiments_filename: str) -> None:
     """Parse command-line arguments and run experiments from provided file."""
-    run_experiment(experiment_filename)
+    run_experiments(experiments_filename)
 
 
 if __name__ == "__main__":
diff --git a/src/training/run_experiment.py b/src/training/run_experiment.py
index 8296e59..0b29ce9 100644
--- a/src/training/run_experiment.py
+++ b/src/training/run_experiment.py
@@ -1,17 +1,64 @@
 """Script to run experiments."""
+from datetime import datetime
+from glob import glob
 import importlib
+import json
 import os
-from typing import Dict
+from pathlib import Path
+import re
+from typing import Callable, Dict, Tuple
 
 import click
+from loguru import logger
 import torch
+from tqdm import tqdm
+from training.gpu_manager import GPUManager
 from training.train import Trainer
+import yaml
 
 
-def run_experiment(
-    experiment_config: Dict, save_weights: bool, gpu_index: int, use_wandb: bool = False
-) -> None:
-    """Short summary."""
+EXPERIMENTS_DIRNAME = Path(__file__).parents[0].resolve() / "experiments"
+
+
+DEFAULT_TRAIN_ARGS = {"batch_size": 64, "epochs": 16}
+
+
+def get_level(experiment_config: Dict) -> int:
+    """Sets the logger level."""
+    if experiment_config["verbosity"] == 0:
+        return 40
+    elif experiment_config["verbosity"] == 1:
+        return 20
+    else:
+        return 10
+
+
+def create_experiment_dir(model: Callable, experiment_config: Dict) -> Path:
+    """Create new experiment."""
+    EXPERIMENTS_DIRNAME.mkdir(parents=True, exist_ok=True)
+    experiment_dir = EXPERIMENTS_DIRNAME / model.__name__
+    if experiment_config["resume_experiment"] is None:
+        experiment = datetime.now().strftime("%m%d_%H%M%S")
+        logger.debug(f"Creating a new experiment called {experiment}")
+    else:
+        available_experiments = glob(str(experiment_dir) + "/*")
+        available_experiments.sort()
+        if experiment_config["resume_experiment"] == "last":
+            experiment = available_experiments[-1]
+            logger.debug(f"Resuming the latest experiment {experiment}")
+        else:
+            experiment = experiment_config["resume_experiment"]
+            assert (
+                str(experiment_dir / experiment) in available_experiments
+            ), "Experiment does not exist."
+            logger.debug(f"Resuming the experiment {experiment}")
+
+    experiment_dir = experiment_dir / experiment
+    return experiment_dir
+
+
+def load_modules_and_arguments(experiment_config: Dict) -> Tuple[Callable, Dict]:
+    """Loads all modules and arguments."""
     # Import the data loader module and arguments.
     datasets_module = importlib.import_module("text_recognizer.datasets")
     data_loader_ = getattr(datasets_module, experiment_config["dataloader"])
@@ -21,8 +68,11 @@ def run_experiment(
     models_module = importlib.import_module("text_recognizer.models")
     model_class_ = getattr(models_module, experiment_config["model"])
 
-    # Import metric.
-    metric_fn_ = getattr(models_module, experiment_config["metric"])
+    # Import metrics.
+    metric_fns_ = {
+        metric: getattr(models_module, metric)
+        for metric in experiment_config["metrics"]
+    }
 
     # Import network module and arguments.
     network_module = importlib.import_module("text_recognizer.networks")
@@ -38,38 +88,145 @@ def run_experiment(
     optimizer_args = experiment_config.get("optimizer_args", {})
 
     # Learning rate scheduler
-    lr_scheduler_ = None
-    lr_scheduler_args = None
     if experiment_config["lr_scheduler"] is not None:
         lr_scheduler_ = getattr(
             torch.optim.lr_scheduler, experiment_config["lr_scheduler"]
         )
         lr_scheduler_args = experiment_config.get("lr_scheduler_args", {})
+    else:
+        lr_scheduler_ = None
+        lr_scheduler_args = None
+
+    model_args = {
+        "data_loader": data_loader_,
+        "data_loader_args": data_loader_args,
+        "metrics": metric_fns_,
+        "network_fn": network_fn_,
+        "network_args": network_args,
+        "criterion": criterion_,
+        "criterion_args": criterion_args,
+        "optimizer": optimizer_,
+        "optimizer_args": optimizer_args,
+        "lr_scheduler": lr_scheduler_,
+        "lr_scheduler_args": lr_scheduler_args,
+    }
+
+    return model_class_, model_args
+
+
+def run_experiment(
+    experiment_config: Dict, save_weights: bool, device: str, use_wandb: bool = False
+) -> None:
+    """Runs an experiment."""
+
+    # Load the modules and model arguments.
+    model_class_, model_args = load_modules_and_arguments(experiment_config)
+
+    # Initializes the model with experiment config.
+    model = model_class_(**model_args, device=device)
+
+    # Create new experiment.
+    experiment_dir = create_experiment_dir(model, experiment_config)
+
+    # Create log and model directories.
+    log_dir = experiment_dir / "log"
+    model_dir = experiment_dir / "model"
+
+    # Get checkpoint path.
+    checkpoint_path = model_dir / "last.pt"
+    if not checkpoint_path.exists():
+        checkpoint_path = None
 
-    # Device
-    # TODO fix gpu manager
-    device = None
-
-    model = model_class_(
-        network_fn=network_fn_,
-        network_args=network_args,
-        data_loader=data_loader_,
-        data_loader_args=data_loader_args,
-        metrics=metric_fn_,
-        criterion=criterion_,
-        criterion_args=criterion_args,
-        optimizer=optimizer_,
-        optimizer_args=optimizer_args,
-        lr_scheduler=lr_scheduler_,
-        lr_scheduler_args=lr_scheduler_args,
-        device=device,
+    # Make sure the log directory exists.
+    log_dir.mkdir(parents=True, exist_ok=True)
+
+    # Have to remove default logger to get tqdm to work properly.
+    logger.remove()
+
+    # Fetch verbosity level.
+    level = get_level(experiment_config)
+
+    logger.add(lambda msg: tqdm.write(msg, end=""), colorize=True, level=level)
+    logger.add(
+        str(log_dir / "train.log"),
+        format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}",
     )
 
-    # TODO: Fix checkpoint path and wandb
+    if "cuda" in device:
+        gpu_index = re.sub("[^0-9]+", "", device)
+        logger.info(
+            f"Running experiment with config {experiment_config} on GPU {gpu_index}"
+        )
+    else:
+        logger.info(f"Running experiment with config {experiment_config} on CPU")
+
+    logger.info(f"The class mapping is {model.mapping}")
+
+    # Pŕints a summary of the network in terminal.
+    model.summary()
+
+    experiment_config["train_args"] = {
+        **DEFAULT_TRAIN_ARGS,
+        **experiment_config.get("train_args", {}),
+    }
+
+    experiment_config["experiment_group"] = experiment_config.get(
+        "experiment_group", None
+    )
+
+    experiment_config["device"] = device
+
+    # Save the config used in the experiment folder.
+    config_path = experiment_dir / "config.yml"
+    with open(str(config_path), "w") as f:
+        yaml.dump(experiment_config, f)
+
+    # TODO: wandb
     trainer = Trainer(
         model=model,
-        epochs=experiment_config["epochs"],
-        val_metric=experiment_config["metric"],
+        model_dir=model_dir,
+        epochs=experiment_config["train_args"]["epochs"],
+        val_metric=experiment_config["train_args"]["val_metric"],
+        checkpoint_path=checkpoint_path,
     )
 
     trainer.fit()
+
+    score = trainer.validate()
+
+    logger.info(f"Validation set evaluation: {score}")
+
+    if save_weights:
+        model.save_weights(model_dir)
+
+
+@click.command()
+@click.option(
+    "--experiment_config",
+    type=str,
+    help='Experiment JSON, e.g. \'{"dataloader": "EmnistDataLoader", "model": "CharacterModel", "network": "mlp"}\'',
+)
+@click.option("--gpu", type=int, default=0, help="Provide the index of the GPU to use.")
+@click.option(
+    "--save",
+    is_flag=True,
+    help="If set, the final weights will be saved to a canonical, version-controlled location.",
+)
+@click.option(
+    "--nowandb", is_flag=False, help="If true, do not use wandb for this run."
+)
+def main(experiment_config: str, gpu: int, save: bool, nowandb: bool) -> None:
+    """Run experiment."""
+    if gpu < 0:
+        gpu_manager = GPUManager(True)
+        gpu = gpu_manager.get_free_gpu()
+
+    device = "cuda:" + str(gpu)
+
+    experiment_config = json.loads(experiment_config)
+    os.environ["CUDA_VISIBLE_DEVICES"] = f"{gpu}"
+    run_experiment(experiment_config, save, device, nowandb)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/training/train.py b/src/training/train.py
index 4a452b6..8cd5110 100644
--- a/src/training/train.py
+++ b/src/training/train.py
@@ -1,8 +1,8 @@
 """Training script for PyTorch models."""
 
-from datetime import datetime
 from pathlib import Path
-from typing import Callable, Dict, Optional
+import time
+from typing import Dict, Optional, Type
 
 from loguru import logger
 import numpy as np
@@ -11,6 +11,7 @@ from tqdm import tqdm, trange
 from training.util import RunningAverage
 import wandb
 
+from text_recognizer.models import Model
 
 torch.backends.cudnn.benchmark = True
 np.random.seed(4711)
@@ -18,17 +19,16 @@ torch.manual_seed(4711)
 torch.cuda.manual_seed(4711)
 
 
-EXPERIMENTS_DIRNAME = Path(__file__).parents[0].resolve() / "experiments"
-
-
 class Trainer:
     """Trainer for training PyTorch models."""
 
     # TODO implement wandb.
+    # TODO implement Bayesian parameter search.
 
     def __init__(
         self,
-        model: Callable,
+        model: Type[Model],
+        model_dir: Path,
         epochs: int,
         val_metric: str = "accuracy",
         checkpoint_path: Optional[Path] = None,
@@ -37,7 +37,8 @@ class Trainer:
         """Initialization of the Trainer.
 
         Args:
-            model (Callable): A model object.
+            model (Type[Model]): A model object.
+            model_dir (Path): Path to the model directory.
             epochs (int): Number of epochs to train.
             val_metric (str): The validation metric to evaluate the model on. Defaults to "accuracy".
             checkpoint_path (Optional[Path]): The path to a previously trained model. Defaults to None.
@@ -45,6 +46,7 @@ class Trainer:
 
         """
         self.model = model
+        self.model_dir = model_dir
         self.epochs = epochs
         self.checkpoint_path = checkpoint_path
         self.start_epoch = 0
@@ -58,7 +60,10 @@ class Trainer:
 
         self.val_metric = val_metric
         self.best_val_metric = 0.0
-        logger.add(self.model.name + "_{time}.log")
+
+        # Parse the name of the experiment.
+        experiment_dir = str(self.model_dir.parents[1]).split("/")
+        self.experiment_name = experiment_dir[-2] + "/" + experiment_dir[-1]
 
     def train(self) -> None:
         """Training loop."""
@@ -68,13 +73,13 @@ class Trainer:
         # Running average for the loss.
         loss_avg = RunningAverage()
 
-        data_loader = self.model.data_loaders["train"]
+        data_loader = self.model.data_loaders("train")
 
         with tqdm(
             total=len(data_loader),
             leave=False,
             unit="step",
-            bar_format="{n_fmt}/{total_fmt} {bar} {remaining} {rate_inv_fmt}{postfix}",
+            bar_format="{n_fmt}/{total_fmt} |{bar:20}| {remaining} {rate_inv_fmt}{postfix}",
         ) as t:
             for data, targets in data_loader:
 
@@ -85,7 +90,7 @@ class Trainer:
 
                 # Forward pass.
                 # Get the network prediction.
-                output = self.model.predict(data)
+                output = self.model.network(data)
 
                 # Compute the loss.
                 loss = self.model.criterion(output, targets)
@@ -105,16 +110,20 @@ class Trainer:
                 output = output.data.cpu()
                 targets = targets.data.cpu()
                 metrics = {
-                    metric: round(self.model.metrics[metric](output, targets), 4)
+                    metric: self.model.metrics[metric](output, targets)
                     for metric in self.model.metrics
                 }
-                metrics["loss"] = round(loss_avg(), 4)
+                metrics["loss"] = loss_avg()
 
                 # Update Tqdm progress bar.
                 t.set_postfix(**metrics)
                 t.update()
 
-    def evaluate(self) -> Dict:
+                # If the model has a learning rate scheduler, compute a step.
+                if self.model.lr_scheduler is not None:
+                    self.model.lr_scheduler.step()
+
+    def validate(self) -> Dict:
         """Evaluation loop.
 
         Returns:
@@ -125,7 +134,7 @@ class Trainer:
         self.model.eval()
 
         # Running average for the loss.
-        data_loader = self.model.data_loaders["val"]
+        data_loader = self.model.data_loaders("val")
 
         # Running average for the loss.
         loss_avg = RunningAverage()
@@ -137,7 +146,7 @@ class Trainer:
             total=len(data_loader),
             leave=False,
             unit="step",
-            bar_format="{n_fmt}/{total_fmt} {bar} {remaining} {rate_inv_fmt}{postfix}",
+            bar_format="{n_fmt}/{total_fmt} |{bar:20}| {remaining} {rate_inv_fmt}{postfix}",
         ) as t:
             for data, targets in data_loader:
                 data, targets = (
@@ -145,22 +154,23 @@ class Trainer:
                     targets.to(self.model.device),
                 )
 
-                # Forward pass.
-                # Get the network prediction.
-                output = self.model.predict(data)
+                with torch.no_grad():
+                    # Forward pass.
+                    # Get the network prediction.
+                    output = self.model.network(data)
 
-                # Compute the loss.
-                loss = self.model.criterion(output, targets)
+                    # Compute the loss.
+                    loss = self.model.criterion(output, targets)
 
                 # Compute metrics.
                 loss_avg.update(loss.item())
                 output = output.data.cpu()
                 targets = targets.data.cpu()
                 metrics = {
-                    metric: round(self.model.metrics[metric](output, targets), 4)
+                    metric: self.model.metrics[metric](output, targets)
                     for metric in self.model.metrics
                 }
-                metrics["loss"] = round(loss.item(), 4)
+                metrics["loss"] = loss.item()
 
                 summary.append(metrics)
 
@@ -170,7 +180,7 @@ class Trainer:
 
         # Compute mean of all metrics.
         metrics_mean = {
-            metric: np.mean(x[metric] for x in summary) for metric in summary[0]
+            metric: np.mean([x[metric] for x in summary]) for metric in summary[0]
         }
         metrics_str = " - ".join(f"{k}: {v}" for k, v in metrics_mean.items())
         logger.debug(metrics_str)
@@ -179,55 +189,34 @@ class Trainer:
 
     def fit(self) -> None:
         """Runs the training and evaluation loop."""
-        # Create new experiment.
-        EXPERIMENTS_DIRNAME.mkdir(parents=True, exist_ok=True)
-        experiment = datetime.now().strftime("%m%d_%H%M%S")
-        experiment_dir = EXPERIMENTS_DIRNAME / self.model.network.__name__ / experiment
-
-        # Create log and model directories.
-        log_dir = experiment_dir / "log"
-        model_dir = experiment_dir / "model"
-
-        # Make sure the log directory exists.
-        log_dir.mkdir(parents=True, exist_ok=True)
-
-        logger.add(
-            str(log_dir / "train.log"),
-            format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}",
-        )
-
-        logger.debug(
-            f"Running an experiment called {self.model.network.__name__}/{experiment}."
-        )
-
-        # Pŕints a summary of the network in terminal.
-        self.model.summary()
 
+        logger.debug(f"Running an experiment called {self.experiment_name}.")
+        t_start = time.time()
         # Run the training loop.
         for epoch in trange(
-            total=self.epochs,
+            self.epochs,
             initial=self.start_epoch,
-            leave=True,
-            bar_format="{desc}: {n_fmt}/{total_fmt} {bar} {remaining}{postfix}",
+            leave=False,
+            bar_format="{desc}: {n_fmt}/{total_fmt} |{bar:10}| {remaining}{postfix}",
             desc="Epoch",
         ):
             # Perform one training pass over the training set.
             self.train()
 
             # Evaluate the model on the validation set.
-            val_metrics = self.evaluate()
-
-            # If the model has a learning rate scheduler, compute a step.
-            if self.model.lr_scheduler is not None:
-                self.model.lr_scheduler.step()
+            val_metrics = self.validate()
 
             # The validation metric to evaluate the model on, e.g. accuracy.
             val_metric = val_metrics[self.val_metric]
             is_best = val_metric >= self.best_val_metric
-
+            self.best_val_metric = val_metric if is_best else self.best_val_metric
             # Save checkpoint.
-            self.model.save_checkpoint(model_dir, is_best, epoch, self.val_metric)
+            self.model.save_checkpoint(self.model_dir, is_best, epoch, self.val_metric)
 
             if self.start_epoch > 0 and epoch + self.start_epoch == self.epochs:
                 logger.debug(f"Trained the model for {self.epochs} number of epochs.")
                 break
+
+        t_end = time.time()
+        t_training = t_end - t_start
+        logger.info(f"Training took {t_training:.2f} s.")