unit06-dl-tips/6.2-learning-rates/6.2-part5-2-scheduler-plateau.ipynb

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    "# Unit 6. Essential Deep Learning Tips & Tricks\n",
    "\n",
    "# 6.2 Learning Rates and Learning Rate Schedulers\n",
    "\n",
    "## Part 5. Using a Reduce-on-Plateau Scheduler"
   ]
  },
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   "execution_count": null,
   "id": "eec68953",
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     "text": [
      "torch    : 1.13.1\n",
      "lightning: 2.0.0rc1\n",
      "\n",
      "conda environment: dl-fundamentals\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -p torch,lightning --conda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e80fef60",
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   "outputs": [],
   "source": [
    "import lightning as L\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import torchmetrics\n",
    "from lightning.pytorch.loggers import CSVLogger\n",
    "\n",
    "from shared_utilities import CustomDataModule, PyTorchMLP"
   ]
  },
  {
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    "class LightningModel(L.LightningModule):\n",
    "    def __init__(self, model, learning_rate):\n",
    "        super().__init__()\n",
    "\n",
    "        self.learning_rate = learning_rate\n",
    "        self.model = model\n",
    "\n",
    "        self.save_hyperparameters(ignore=[\"model\"])\n",
    "\n",
    "        self.train_acc = torchmetrics.Accuracy(task=\"multiclass\", num_classes=2)\n",
    "        self.val_acc = torchmetrics.Accuracy(task=\"multiclass\", num_classes=2)\n",
    "        self.test_acc = torchmetrics.Accuracy(task=\"multiclass\", num_classes=2)\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "\n",
    "    def _shared_step(self, batch):\n",
    "        features, true_labels = batch\n",
    "        logits = self(features)\n",
    "\n",
    "        loss = F.cross_entropy(logits, true_labels)\n",
    "        predicted_labels = torch.argmax(logits, dim=1)\n",
    "        return loss, true_labels, predicted_labels\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "\n",
    "        self.log(\"train_loss\", loss)\n",
    "        self.train_acc(predicted_labels, true_labels)\n",
    "        self.log(\n",
    "            \"train_acc\", self.train_acc, prog_bar=True, on_epoch=True, on_step=False\n",
    "        )\n",
    "        return loss\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "\n",
    "        self.log(\"val_loss\", loss, prog_bar=True)\n",
    "        self.val_acc(predicted_labels, true_labels)\n",
    "        self.log(\"val_acc\", self.val_acc, prog_bar=True)\n",
    "\n",
    "    def test_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "        self.test_acc(predicted_labels, true_labels)\n",
    "        self.log(\"test_acc\", self.test_acc)\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        opt = torch.optim.SGD(self.parameters(), lr=self.learning_rate)\n",
    "        sch = torch.optim.lr_scheduler.ReduceLROnPlateau(opt, mode='min', factor=0.1, patience=5)\n",
    "\n",
    "\n",
    "        return {\n",
    "            \"optimizer\": opt,\n",
    "            \"lr_scheduler\": {\n",
    "                \"scheduler\": sch,\n",
    "                \"monitor\": \"train_loss\",\n",
    "                \"interval\": \"epoch\", # default\n",
    "                \"frequency\": 1, # default\n",
    "            },\n",
    "        }"
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     "text": [
      "Global seed set to 123\n",
      "GPU available: True (mps), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
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   "source": [
    "L.seed_everything(123)\n",
    "dm = CustomDataModule()\n",
    "\n",
    "pytorch_model = PyTorchMLP(num_features=100, num_classes=2)\n",
    "lightning_model = LightningModel(model=pytorch_model, learning_rate=0.1)\n",
    "\n",
    "trainer = L.Trainer(\n",
    "    max_epochs=100,\n",
    "    accelerator=\"cpu\",\n",
    "    devices=\"auto\",\n",
    "    logger=CSVLogger(save_dir=\"logs/\", name=\"my-model\"),\n",
    "    deterministic=True,\n",
    ")"
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      "/Users/sebastianraschka/Desktop/lightning-master/src/lightning/pytorch/callbacks/model_checkpoint.py:612: UserWarning: Checkpoint directory logs/my-model/version_0/checkpoints exists and is not empty.\n",
      "  rank_zero_warn(f\"Checkpoint directory {dirpath} exists and is not empty.\")\n",
      "\n",
      "  | Name      | Type               | Params\n",
      "-------------------------------------------------\n",
      "0 | model     | PyTorchMLP         | 15.3 K\n",
      "1 | train_acc | MulticlassAccuracy | 0     \n",
      "2 | val_acc   | MulticlassAccuracy | 0     \n",
      "3 | test_acc  | MulticlassAccuracy | 0     \n",
      "-------------------------------------------------\n",
      "15.3 K    Trainable params\n",
      "0         Non-trainable params\n",
      "15.3 K    Total params\n",
      "0.061     Total estimated model params size (MB)\n",
      "/Users/sebastianraschka/Desktop/lightning-master/src/lightning/fabric/loggers/csv_logs.py:188: UserWarning: Experiment logs directory logs/my-model/version_0 exists and is not empty. Previous log files in this directory will be deleted when the new ones are saved!\n",
      "  rank_zero_warn(\n"
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      "/Users/sebastianraschka/Desktop/lightning-master/src/lightning/pytorch/trainer/connectors/data_connector.py:208: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 10 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n",
      "/Users/sebastianraschka/Desktop/lightning-master/src/lightning/pytorch/trainer/connectors/data_connector.py:208: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 10 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
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     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f4a5217d9caa41aaa2bfbc3635517263",
       "version_major": 2,
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      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "04ffe6e5f58d4b7f8a0b9651ede93af2",
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      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "43f6fee3f1f248b7860166b35f50199b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9130648f73db4f49b99eabd9264351ac",
       "version_major": 2,
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      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b28904cb743f4a3985e839f7d030340e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "35b58ed2687042bcb8fa1584fb6b18c5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainer.fit(model=lightning_model, datamodule=dm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d4b7eb3-9f3c-4095-ad85-b37acf4557dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Plot\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "metrics = pd.read_csv(f\"{trainer.logger.log_dir}/metrics.csv\")\n",
    "\n",
    "aggreg_metrics = []\n",
    "agg_col = \"epoch\"\n",
    "for i, dfg in metrics.groupby(agg_col):\n",
    "    agg = dict(dfg.mean())\n",
    "    agg[agg_col] = i\n",
    "    aggreg_metrics.append(agg)\n",
    "\n",
    "df_metrics = pd.DataFrame(aggreg_metrics)\n",
    "df_metrics[[\"train_loss\", \"val_loss\"]].plot(\n",
    "    grid=True, legend=True, xlabel=\"Epoch\", ylabel=\"Loss\"\n",
    ")\n",
    "\n",
    "plt.savefig(\"suggest_loss.pdf\")\n",
    "\n",
    "df_metrics[[\"train_acc\", \"val_acc\"]].plot(\n",
    "    grid=True, legend=True, xlabel=\"Epoch\", ylabel=\"ACC\"\n",
    ")\n",
    "\n",
    "plt.savefig(\"suggest_acc.pdf\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27a7e4b8-a243-4a2b-99a9-35b32dbf0398",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sebastianraschka/Desktop/lightning-master/src/lightning/pytorch/trainer/connectors/data_connector.py:208: PossibleUserWarning: The dataloader, test_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 10 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bd2b0a3c13384d699f80a1b6473f1b3c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\">        Test metric        </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">         test_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8917499780654907     </span>│\n",
       "└───────────────────────────┴───────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│\u001b[36m \u001b[0m\u001b[36m        test_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8917499780654907    \u001b[0m\u001b[35m \u001b[0m│\n",
       "└───────────────────────────┴───────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[{'test_acc': 0.8917499780654907}]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.test(model=lightning_model, datamodule=dm)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
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