unit06-dl-tips/6.2-learning-rates/6.2-part5-3-scheduler-cosine.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7d6d3cf5-82f0-4281-b10c-ea14c2e0eeb6",
   "metadata": {},
   "source": [
    "# Unit 6. Essential Deep Learning Tips & Tricks\n",
    "\n",
    "# 6.2 Learning Rates and Learning Rate Schedulers\n",
    "\n",
    "## Part 5. Using a Cosine Scheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eec68953",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch    : 1.13.1\n",
      "lightning: 2.0.0rc1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -p torch,lightning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e80fef60",
   "metadata": {},
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "729e8e09-5aa1-4809-9497-46e0b8757608",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_epochs = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c76f84e-efa8-4899-83e1-7358b7d7607d",
   "metadata": {},
   "source": [
    "## Cosine annealing with restarts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2891608-1e41-498b-becc-d994e33046bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "model = torch.nn.Linear(1, 1)\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.1)\n",
    "scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_epochs/10)\n",
    "lrs = []\n",
    "\n",
    "\n",
    "for i in range(num_epochs):\n",
    "    optimizer.step()\n",
    "    lrs.append(optimizer.param_groups[0][\"lr\"])\n",
    "    scheduler.step()\n",
    "\n",
    "plt.ylabel(\"Learning rate\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.plot(lrs)\n",
    "#plt.savefig(\"cosine-restart.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63157376-f28c-439f-bd8d-fe92dfb22e3e",
   "metadata": {},
   "source": [
    "## Cosine annealing with 1-cycle schedule, decay each epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a24da07-6204-443e-a89d-9227c7f95d74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = torch.nn.Linear(1, 1)\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.1)\n",
    "scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_epochs)\n",
    "lrs = []\n",
    "\n",
    "for i in range(num_epochs):\n",
    "    optimizer.step()\n",
    "    lrs.append(optimizer.param_groups[0][\"lr\"])\n",
    "    scheduler.step()\n",
    "\n",
    "plt.ylabel(\"Learning rate\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.plot(lrs)\n",
    "#plt.savefig(\"cosine-1cycle-epoch.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a85f6cef-c6d5-4401-93c1-5c9103a08aee",
   "metadata": {},
   "source": [
    "## Cosine annealing with 1-cycle schedule, decay each batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "238636d3-b536-4e08-8902-c5dd4b7e7090",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = torch.nn.Linear(1, 1)\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.1)\n",
    "\n",
    "\n",
    "dm = CustomDataModule()\n",
    "dm.setup(\"training\")\n",
    "num_steps = num_epochs * len(dm.train_dataloader())\n",
    "scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_steps)\n",
    "lrs = []\n",
    "\n",
    "\n",
    "for i in range(num_steps):\n",
    "    optimizer.step()\n",
    "    lrs.append(optimizer.param_groups[0][\"lr\"])\n",
    "    scheduler.step()\n",
    "\n",
    "\n",
    "ax1 = plt.subplot(1, 1, 1)\n",
    "ax1.set_ylabel(\"Learning rate\")\n",
    "ax1.set_xlabel(\"Iterations\")\n",
    "ax1.plot(lrs)\n",
    "\n",
    "###################\n",
    "# Set second x-axis\n",
    "ax2 = ax1.twiny()\n",
    "newlabel = list(range(num_epochs+1))\n",
    "\n",
    "newpos = [e * (num_steps // num_epochs) for e in newlabel]\n",
    "\n",
    "ax2.set_xticks(newpos[::20])\n",
    "ax2.set_xticklabels(newlabel[::20])\n",
    "\n",
    "ax2.xaxis.set_ticks_position('bottom')\n",
    "ax2.xaxis.set_label_position('bottom')\n",
    "ax2.spines['bottom'].set_position(('outward', 45))\n",
    "ax2.set_xlabel('Epochs')\n",
    "ax2.set_xlim(ax1.get_xlim());\n",
    "\n",
    "#plt.tight_layout()\n",
    "#plt.savefig(\"cosine-1cycle-batch.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26845f11-4a0d-48f3-818a-22e4093b79ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "class LightningModel(L.LightningModule):\n",
    "    def __init__(self, model, learning_rate, cosine_t_max):  # New!\n",
    "        super().__init__()\n",
    "\n",
    "        self.learning_rate = learning_rate\n",
    "        self.cosine_t_max = cosine_t_max  # New!\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.CosineAnnealingLR(opt, T_max=self.cosine_t_max) # New!\n",
    "\n",
    "        return {\n",
    "            \"optimizer\": opt,\n",
    "            \"lr_scheduler\": {\n",
    "                \"scheduler\": sch,\n",
    "                \"monitor\": \"train_loss\",\n",
    "                \"interval\": \"step\", # step means \"batch\" here, default: epoch   # New!\n",
    "                \"frequency\": 1, # default\n",
    "            },\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa7bcf14-8046-4213-accf-544b67e74883",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "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",
    "                                 cosine_t_max=num_steps) # New!\n",
    "\n",
    "trainer = L.Trainer(\n",
    "    max_epochs=num_epochs,\n",
    "    accelerator=\"cpu\",\n",
    "    devices=\"auto\",\n",
    "    logger=CSVLogger(save_dir=\"logs/\", name=\"my-model\"),\n",
    "    deterministic=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14d23b49-4c47-4bb1-bf6c-b87aabf0b22d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/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",
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      "-------------------------------------------------\n",
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       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "afbf5df444814bacaa7bbd33360fa8c1",
       "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": "135b967963ba43139de3382bc930e836",
       "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": "dadca999514f4e9aa71132db796fcd01",
       "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": "c26d0a9487e94793a4c321ba62fb07d5",
       "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": "4f0db013c1ba495a93f4380d110bf564",
       "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": "0e85539862a74edb8cf0cb9065b82566",
       "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": "aa2cefdf1e7e43d5ba7e45a930b6df84",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=100` reached.\n"
     ]
    }
   ],
   "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": "6399ebaea0234aba924ff0628739085a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
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     "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\">    0.8857499957084656     </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   0.8857499957084656    \u001b[0m\u001b[35m \u001b[0m│\n",
       "└───────────────────────────┴───────────────────────────┘\n"
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    {
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      "text/plain": [
       "[{'test_acc': 0.8857499957084656}]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.test(model=lightning_model, datamodule=dm)"
   ]
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