part2/additions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bcacc844",
   "metadata": {},
   "source": [
    "## NOTE: this notebook is for demonstration only and not really part of the workshop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "59f68767",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install --quiet --upgrade torch torchvision pytorch-lightning wandb matplotlib pillow testresources"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41d997c2",
   "metadata": {},
   "source": [
    "## 3 - forest-vs-houses - additions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfb7c81c",
   "metadata": {},
   "source": [
    "### 3.1 - feature engineering to improve model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6d3d3e69",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b371a54",
   "metadata": {},
   "source": [
    "on cocalc the data is in ``/data``\n",
    "\n",
    "data sources (to use at home):\n",
    "- train/test: https://paste.madflex.de/YtmeRXrY#houses-vs-forest.zip (500mb)\n",
    "- test2: https://paste.madflex.de/pyHrvXyG#test2_f-vs-h.zip (100mb)\n",
    "- test3: https://paste.madflex.de/La6ZMHvf#test3_f-vs-h.zip (90mb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "81aeae5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR = \"/data/houses-vs-forest\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ccd4ba80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = Image.open(f\"{DATA_DIR}/test3/houses/G0109966.JPG\")\n",
    "plt.imshow(img.resize((133, 100)))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6d68a34",
   "metadata": {},
   "source": [
    "hypothesis: cut the bag on the bottom from the images to improve recognition for test3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6483dd10",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img.resize((133, 100)).crop((0, 0, 133, 75)))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "afefb102",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c7a82a85",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 42\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pytorch_lightning as lightning\n",
    "lightning.seed_everything(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d6bd7619",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomCropTransform:\n",
    "    def __call__(self, img):\n",
    "        return torchvision.transforms.functional.crop(img, top=0, left=0, height=75, width=133)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dfac18c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "transforms = torchvision.transforms.Compose([\n",
    "    torchvision.transforms.Resize(100),\n",
    "    CustomCropTransform(),\n",
    "    torchvision.transforms.ToTensor()\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "499ca735",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_train = torchvision.datasets.ImageFolder(f\"{DATA_DIR}/train\", transform=transforms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a591b1b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_count = 107"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d63020dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_ds, validation_ds = torch.utils.data.random_split(image_train,[(image_count-15)*2, 15*2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "84c6bd7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# images from the same tour/day\n",
    "test_ds = torchvision.datasets.ImageFolder(f\"{DATA_DIR}/test\", transform=transforms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "db016236",
   "metadata": {},
   "outputs": [],
   "source": [
    "# images from *another* tour/day\n",
    "test2_ds = torchvision.datasets.ImageFolder(f\"{DATA_DIR}/test2\", transform=transforms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a2be2cac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# images from another tour/day and another bike\n",
    "test3_ds = torchvision.datasets.ImageFolder(f\"{DATA_DIR}/test3\", transform=transforms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d966a475",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_test_ds = test_ds + test2_ds + test3_ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1e7ec4c",
   "metadata": {},
   "source": [
    "bag at the bottom is removed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6147765d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "number = 107\n",
    "plt.imshow(all_test_ds[number][0].permute(1, 2, 0))\n",
    "plt.title('%i' % all_test_ds[number][1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77a18cfc",
   "metadata": {},
   "source": [
    "now train a new model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3163b134",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmfa\u001b[0m (use `wandb login --relogin` to force relogin)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import wandb\n",
    "wandb.login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1667c336",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchmetrics import Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6bfa1848",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ForestHousesCategoricalModel(lightning.LightningModule):\n",
    "    def __init__(self, learning_rate=1e-3):\n",
    "        super().__init__()\n",
    "        self.save_hyperparameters()\n",
    "\n",
    "        self.learning_rate = learning_rate\n",
    "        self.batch_size = 16\n",
    "\n",
    "        channels, width, height = train_ds[0][0].size()\n",
    "\n",
    "        self.model = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(in_channels=3, out_channels=20, kernel_size=3, stride=1, padding=0),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.MaxPool2d(2),\n",
    "            torch.nn.Dropout(0.5),\n",
    "            torch.nn.Flatten(),\n",
    "            #  75x + channels=20, maxpool=4 -> 11520\n",
    "            #  75x + channels=20, maxpool=2 -> 46800\n",
    "            torch.nn.Linear(46800, 2),\n",
    "        )\n",
    "        self.loss = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "        acc = Accuracy()\n",
    "        self.train_acc = acc.clone()\n",
    "        self.valid_acc = acc.clone()\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "\n",
    "    def step(self, batch, batch_idx, name):\n",
    "        x, y = batch\n",
    "        y_hat = self.forward(x)\n",
    "        loss = self.loss(y_hat, y)\n",
    "        self.log(f\"{name}/loss\", loss)\n",
    "\n",
    "        logits = self(x)\n",
    "        preds = torch.argmax(logits, dim=1)\n",
    "        \n",
    "        if name == \"train\":\n",
    "            self.train_acc(preds, y)\n",
    "            self.log(f\"{name}/acc\", self.train_acc)\n",
    "        else:\n",
    "            self.valid_acc(preds, y)\n",
    "            self.log(f\"{name}/acc\", self.valid_acc)\n",
    "\n",
    "        return {\"loss\": loss}\n",
    "    \n",
    "    def training_step(self, batch, batch_nb):\n",
    "        return self.step(batch, batch_nb, name=\"train\")\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        return self.step(batch, batch_idx, name=\"val\")\n",
    "    \n",
    "    def test_step(self, batch, batch_idx):\n",
    "        return self.step(batch, batch_idx, name=\"test\")\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        return torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n",
    "\n",
    "    def train_dataloader(self):\n",
    "        return torch.utils.data.DataLoader(train_ds, batch_size=self.batch_size)\n",
    "\n",
    "    def val_dataloader(self):\n",
    "        return torch.utils.data.DataLoader(validation_ds, batch_size=self.batch_size)\n",
    "\n",
    "    def test_dataloader(self):\n",
    "        return torch.utils.data.DataLoader(all_test_ds, batch_size=self.batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8d99ee18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ForestHousesCategoricalModel(\n",
       "  (model): Sequential(\n",
       "    (0): Conv2d(3, 20, kernel_size=(3, 3), stride=(1, 1))\n",
       "    (1): ReLU()\n",
       "    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (3): Dropout(p=0.5, inplace=False)\n",
       "    (4): Flatten(start_dim=1, end_dim=-1)\n",
       "    (5): Linear(in_features=46800, out_features=2, bias=True)\n",
       "  )\n",
       "  (loss): CrossEntropyLoss()\n",
       "  (train_acc): Accuracy()\n",
       "  (valid_acc): Accuracy()\n",
       ")"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ForestHousesCategoricalModel()\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "91a30168",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/mfa/workshop-p2--FvsH--crop/runs/31d0w88r\" target=\"_blank\">sleek-monkey-4</a></strong> to <a href=\"https://wandb.ai/mfa/workshop-p2--FvsH--crop\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "\n",
       "                "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: logging graph, to disable use `wandb.watch(log_graph=False)`\n",
      "GPU available: True, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "/home/mfa/.virtualenvs/machine-learning-workshop/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1584: UserWarning: GPU available but not used. Set the gpus flag in your trainer `Trainer(gpus=1)` or script `--gpus=1`.\n",
      "  rank_zero_warn(\n",
      "\n",
      "  | Name      | Type             | Params\n",
      "-----------------------------------------------\n",
      "0 | model     | Sequential       | 94.2 K\n",
      "1 | loss      | CrossEntropyLoss | 0     \n",
      "2 | train_acc | Accuracy         | 0     \n",
      "3 | valid_acc | Accuracy         | 0     \n",
      "-----------------------------------------------\n",
      "94.2 K    Trainable params\n",
      "0         Non-trainable params\n",
      "94.2 K    Total params\n",
      "0.377     Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation sanity check: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mfa/.virtualenvs/machine-learning-workshop/lib/python3.9/site-packages/pytorch_lightning/trainer/data_loading.py:132: UserWarning: 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 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n",
      "Global seed set to 42\n",
      "/home/mfa/.virtualenvs/machine-learning-workshop/lib/python3.9/site-packages/pytorch_lightning/trainer/data_loading.py:132: UserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n",
      "/home/mfa/.virtualenvs/machine-learning-workshop/lib/python3.9/site-packages/pytorch_lightning/trainer/data_loading.py:432: UserWarning: The number of training samples (12) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e19a9fb08c48482b88ca4cb2163105c2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wandb_logger = lightning.loggers.WandbLogger(project=\"workshop-p2--FvsH--crop\")\n",
    "wandb_logger.watch(model, log=\"all\")\n",
    "trainer_categorical = lightning.Trainer(gpus=0, max_epochs=10, logger=wandb_logger)\n",
    "trainer_categorical.fit(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4991858b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Restoring states from the checkpoint path at /home/mfa/machine-learning-workshop/part2/workshop-p2--FvsH--crop/31d0w88r/checkpoints/epoch=9-step=119.ckpt\n",
      "Loaded model weights from checkpoint at /home/mfa/machine-learning-workshop/part2/workshop-p2--FvsH--crop/31d0w88r/checkpoints/epoch=9-step=119.ckpt\n",
      "/home/mfa/.virtualenvs/machine-learning-workshop/lib/python3.9/site-packages/pytorch_lightning/trainer/data_loading.py:132: UserWarning: 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 8 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": "a667333a02d649a59c1885cf209bc7b5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "DATALOADER:0 TEST RESULTS\n",
      "{'test/acc': 0.7129629850387573, 'test/loss': 1.272516131401062}\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'test/loss': 1.272516131401062, 'test/acc': 0.7129629850387573}]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer_categorical.test(ckpt_path='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a4e8be4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<br/>Waiting for W&B process to finish, PID 625545... <strong style=\"color:green\">(success).</strong>"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "data": {
      "text/html": [
       "<style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; width: 100% }\n",
       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▂▂▃▄▄▅▅▆▇▇▇█</td></tr><tr><td>test/acc</td><td>▁</td></tr><tr><td>test/loss</td><td>▁</td></tr><tr><td>train/acc</td><td>▁▁</td></tr><tr><td>train/loss</td><td>█▁</td></tr><tr><td>trainer/global_step</td><td>▁▂▃▃▃▄▅▆▆▇▇██</td></tr><tr><td>val/acc</td><td>▁▆▇▇▇█████</td></tr><tr><td>val/loss</td><td>█▄▃▂▂▁▂▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>10</td></tr><tr><td>test/acc</td><td>0.71296</td></tr><tr><td>test/loss</td><td>1.27252</td></tr><tr><td>train/acc</td><td>1.0</td></tr><tr><td>train/loss</td><td>0.02653</td></tr><tr><td>trainer/global_step</td><td>120</td></tr><tr><td>val/acc</td><td>1.0</td></tr><tr><td>val/loss</td><td>0.03317</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 1 media file(s), 0 artifact file(s) and 0 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">sleek-monkey-4</strong>: <a href=\"https://wandb.ai/mfa/workshop-p2--FvsH--crop/runs/31d0w88r\" target=\"_blank\">https://wandb.ai/mfa/workshop-p2--FvsH--crop/runs/31d0w88r</a><br/>\n",
       "Find logs at: <code>./wandb/run-20220227_202912-31d0w88r/logs</code><br/>\n"
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    "wandb.finish()"
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  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ec641a79",
   "metadata": {},
   "outputs": [],
   "source": [
    "import termcolor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "282c7c7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_result(idx, image, model):\n",
    "    img = image[0][None, :]\n",
    "    r = model(img)\n",
    "    is_correct = bool(image[1] == torch.argmax(r))\n",
    "    is_correct = termcolor.colored(is_correct, \"green\" if is_correct else \"red\")\n",
    "    return \" \".join([str(i) for i in [idx, image[1], test_ds.classes[image[1]], is_correct, torch.argmax(r), r]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a36c509c",
   "metadata": {
    "scrolled": false
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     "output_type": "stream",
     "text": [
      "0 0 forest \u001b[32mTrue\u001b[0m tensor(0) tensor([[ 0.2106, -0.1054]], grad_fn=<AddmmBackward0>)\n",
      "1 0 forest \u001b[32mTrue\u001b[0m tensor(0) tensor([[ 2.0530, -1.9594]], grad_fn=<AddmmBackward0>)\n",
      "2 0 forest \u001b[32mTrue\u001b[0m tensor(0) tensor([[ 2.0963, -2.0449]], grad_fn=<AddmmBackward0>)\n",
      "3 0 forest \u001b[32mTrue\u001b[0m tensor(0) tensor([[ 1.7820, -1.7834]], grad_fn=<AddmmBackward0>)\n",
      "4 0 forest \u001b[32mTrue\u001b[0m tensor(0) tensor([[ 2.9644, -2.8847]], grad_fn=<AddmmBackward0>)\n",
      "5 0 forest \u001b[32mTrue\u001b[0m tensor(0) tensor([[ 1.9102, -1.6825]], grad_fn=<AddmmBackward0>)\n",
      "6 0 forest \u001b[32mTrue\u001b[0m tensor(0) tensor([[ 4.0752, -3.7904]], grad_fn=<AddmmBackward0>)\n",
      "7 0 forest \u001b[32mTrue\u001b[0m tensor(0) tensor([[ 2.0483, -2.0108]], grad_fn=<AddmmBackward0>)\n",
      "8 0 forest \u001b[32mTrue\u001b[0m tensor(0) tensor([[ 3.2519, -3.1852]], grad_fn=<AddmmBackward0>)\n",
      "9 0 forest \u001b[32mTrue\u001b[0m tensor(0) tensor([[ 3.2641, -3.1894]], grad_fn=<AddmmBackward0>)\n",
      "10 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-1.5606,  1.6486]], grad_fn=<AddmmBackward0>)\n",
      "11 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-2.3627,  2.3633]], grad_fn=<AddmmBackward0>)\n",
      "12 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-4.5078,  4.6395]], grad_fn=<AddmmBackward0>)\n",
      "13 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-3.1718,  3.4742]], grad_fn=<AddmmBackward0>)\n",
      "14 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-1.4689,  1.6801]], grad_fn=<AddmmBackward0>)\n",
      "15 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-3.3702,  3.5238]], grad_fn=<AddmmBackward0>)\n",
      "16 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-3.1315,  3.1191]], grad_fn=<AddmmBackward0>)\n",
      "17 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-5.2322,  5.2307]], grad_fn=<AddmmBackward0>)\n",
      "18 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-3.4591,  3.6309]], grad_fn=<AddmmBackward0>)\n",
      "19 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-3.4170,  3.8214]], grad_fn=<AddmmBackward0>)\n",
      "20 0 forest \u001b[32mTrue\u001b[0m tensor(0) tensor([[ 1.4326, -1.3241]], grad_fn=<AddmmBackward0>)\n",
      "21 0 forest \u001b[32mTrue\u001b[0m tensor(0) tensor([[ 1.8364, -1.6696]], grad_fn=<AddmmBackward0>)\n",
      "22 0 forest \u001b[31mFalse\u001b[0m tensor(1) tensor([[-2.5863,  2.8456]], grad_fn=<AddmmBackward0>)\n",
      "23 0 forest \u001b[31mFalse\u001b[0m tensor(1) tensor([[-0.9766,  1.1116]], grad_fn=<AddmmBackward0>)\n",
      "24 0 forest \u001b[32mTrue\u001b[0m tensor(0) tensor([[ 2.0977, -2.0534]], grad_fn=<AddmmBackward0>)\n",
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      "30 0 forest \u001b[32mTrue\u001b[0m tensor(0) tensor([[0.1684, 0.0361]], grad_fn=<AddmmBackward0>)\n",
      "31 0 forest \u001b[32mTrue\u001b[0m tensor(0) tensor([[ 1.8734, -1.6024]], grad_fn=<AddmmBackward0>)\n",
      "32 0 forest \u001b[32mTrue\u001b[0m tensor(0) tensor([[ 0.9974, -0.8133]], grad_fn=<AddmmBackward0>)\n",
      "33 0 forest \u001b[31mFalse\u001b[0m tensor(1) tensor([[-0.1316,  0.3332]], grad_fn=<AddmmBackward0>)\n",
      "34 0 forest \u001b[31mFalse\u001b[0m tensor(1) tensor([[-0.0287,  0.3227]], grad_fn=<AddmmBackward0>)\n",
      "35 0 forest \u001b[31mFalse\u001b[0m tensor(1) tensor([[-0.3695,  0.4361]], grad_fn=<AddmmBackward0>)\n",
      "36 0 forest \u001b[31mFalse\u001b[0m tensor(1) tensor([[0.0817, 0.1445]], grad_fn=<AddmmBackward0>)\n",
      "37 0 forest \u001b[32mTrue\u001b[0m tensor(0) tensor([[ 0.3483, -0.2192]], grad_fn=<AddmmBackward0>)\n",
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      "39 0 forest \u001b[31mFalse\u001b[0m tensor(1) tensor([[-2.4071,  2.5828]], grad_fn=<AddmmBackward0>)\n",
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      "41 0 forest \u001b[32mTrue\u001b[0m tensor(0) tensor([[ 0.2394, -0.0745]], grad_fn=<AddmmBackward0>)\n",
      "42 0 forest \u001b[32mTrue\u001b[0m tensor(0) tensor([[ 1.2476, -1.1391]], grad_fn=<AddmmBackward0>)\n",
      "43 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-3.4721,  3.6972]], grad_fn=<AddmmBackward0>)\n",
      "44 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-3.1213,  3.1995]], grad_fn=<AddmmBackward0>)\n",
      "45 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-1.8601,  2.1234]], grad_fn=<AddmmBackward0>)\n",
      "46 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-3.0761,  3.2268]], grad_fn=<AddmmBackward0>)\n",
      "47 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-2.2367,  2.1300]], grad_fn=<AddmmBackward0>)\n",
      "48 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-2.0195,  2.1699]], grad_fn=<AddmmBackward0>)\n",
      "49 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-1.6903,  1.8569]], grad_fn=<AddmmBackward0>)\n",
      "50 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-5.3335,  5.5407]], grad_fn=<AddmmBackward0>)\n",
      "51 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-4.1773,  4.2732]], grad_fn=<AddmmBackward0>)\n",
      "52 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-4.4901,  4.5672]], grad_fn=<AddmmBackward0>)\n",
      "53 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-4.3805,  4.7644]], grad_fn=<AddmmBackward0>)\n",
      "54 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-4.2707,  4.1244]], grad_fn=<AddmmBackward0>)\n",
      "55 1 houses \u001b[31mFalse\u001b[0m tensor(0) tensor([[ 0.9428, -0.7332]], grad_fn=<AddmmBackward0>)\n",
      "56 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-2.9210,  3.0242]], grad_fn=<AddmmBackward0>)\n",
      "57 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-3.8784,  3.9443]], grad_fn=<AddmmBackward0>)\n",
      "58 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-2.7063,  2.7199]], grad_fn=<AddmmBackward0>)\n",
      "59 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-3.2408,  3.4190]], grad_fn=<AddmmBackward0>)\n",
      "60 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-4.4794,  4.5636]], grad_fn=<AddmmBackward0>)\n",
      "61 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-0.5051,  0.5935]], grad_fn=<AddmmBackward0>)\n",
      "62 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-1.8500,  2.0420]], grad_fn=<AddmmBackward0>)\n",
      "63 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-2.5447,  2.7853]], grad_fn=<AddmmBackward0>)\n",
      "64 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-3.1368,  3.1375]], grad_fn=<AddmmBackward0>)\n",
      "65 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-3.7154,  3.8626]], grad_fn=<AddmmBackward0>)\n",
      "66 0 forest \u001b[31mFalse\u001b[0m tensor(1) tensor([[-3.7142,  3.6964]], grad_fn=<AddmmBackward0>)\n",
      "67 0 forest \u001b[31mFalse\u001b[0m tensor(1) tensor([[-3.0703,  3.2448]], grad_fn=<AddmmBackward0>)\n",
      "68 0 forest \u001b[31mFalse\u001b[0m tensor(1) tensor([[-4.7287,  4.7514]], grad_fn=<AddmmBackward0>)\n",
      "69 0 forest \u001b[31mFalse\u001b[0m tensor(1) tensor([[-3.1704,  3.2722]], grad_fn=<AddmmBackward0>)\n",
      "70 0 forest \u001b[31mFalse\u001b[0m tensor(1) tensor([[-1.5597,  1.6326]], grad_fn=<AddmmBackward0>)\n",
      "71 0 forest \u001b[31mFalse\u001b[0m tensor(1) tensor([[-2.6794,  2.7286]], grad_fn=<AddmmBackward0>)\n",
      "72 0 forest \u001b[31mFalse\u001b[0m tensor(1) tensor([[-2.0709,  2.1418]], grad_fn=<AddmmBackward0>)\n",
      "73 0 forest \u001b[31mFalse\u001b[0m tensor(1) tensor([[-2.4486,  2.5471]], grad_fn=<AddmmBackward0>)\n",
      "74 0 forest \u001b[31mFalse\u001b[0m tensor(1) tensor([[-4.2037,  4.4341]], grad_fn=<AddmmBackward0>)\n",
      "75 0 forest \u001b[31mFalse\u001b[0m tensor(1) tensor([[-1.3194,  1.4729]], grad_fn=<AddmmBackward0>)\n",
      "76 0 forest \u001b[31mFalse\u001b[0m tensor(1) tensor([[-3.5315,  3.6867]], grad_fn=<AddmmBackward0>)\n",
      "77 0 forest \u001b[31mFalse\u001b[0m tensor(1) tensor([[-3.1586,  3.3263]], grad_fn=<AddmmBackward0>)\n",
      "78 0 forest \u001b[31mFalse\u001b[0m tensor(1) tensor([[-3.7760,  3.8819]], grad_fn=<AddmmBackward0>)\n",
      "79 0 forest \u001b[31mFalse\u001b[0m tensor(1) tensor([[-3.1378,  3.2859]], grad_fn=<AddmmBackward0>)\n",
      "80 0 forest \u001b[31mFalse\u001b[0m tensor(1) tensor([[-1.6562,  1.7684]], grad_fn=<AddmmBackward0>)\n",
      "81 0 forest \u001b[31mFalse\u001b[0m tensor(1) tensor([[-0.2212,  0.0782]], grad_fn=<AddmmBackward0>)\n",
      "82 0 forest \u001b[32mTrue\u001b[0m tensor(0) tensor([[ 0.4124, -0.2290]], grad_fn=<AddmmBackward0>)\n",
      "83 0 forest \u001b[31mFalse\u001b[0m tensor(1) tensor([[-2.8189,  2.8987]], grad_fn=<AddmmBackward0>)\n",
      "84 0 forest \u001b[31mFalse\u001b[0m tensor(1) tensor([[-2.7439,  2.9186]], grad_fn=<AddmmBackward0>)\n",
      "85 0 forest \u001b[31mFalse\u001b[0m tensor(1) tensor([[-2.7906,  2.7071]], grad_fn=<AddmmBackward0>)\n",
      "86 0 forest \u001b[31mFalse\u001b[0m tensor(1) tensor([[-0.8396,  0.7867]], grad_fn=<AddmmBackward0>)\n",
      "87 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-3.5434,  3.5193]], grad_fn=<AddmmBackward0>)\n",
      "88 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-4.0068,  4.2535]], grad_fn=<AddmmBackward0>)\n",
      "89 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-4.3735,  4.3113]], grad_fn=<AddmmBackward0>)\n",
      "90 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-2.4578,  2.6742]], grad_fn=<AddmmBackward0>)\n",
      "91 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-3.2071,  3.3380]], grad_fn=<AddmmBackward0>)\n",
      "92 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-3.3913,  3.4651]], grad_fn=<AddmmBackward0>)\n",
      "93 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-4.6198,  4.9979]], grad_fn=<AddmmBackward0>)\n",
      "94 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-3.0054,  3.1543]], grad_fn=<AddmmBackward0>)\n",
      "95 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-2.7215,  2.8795]], grad_fn=<AddmmBackward0>)\n",
      "96 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-2.4240,  2.5837]], grad_fn=<AddmmBackward0>)\n",
      "97 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-3.2315,  3.1361]], grad_fn=<AddmmBackward0>)\n",
      "98 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-3.2115,  3.3748]], grad_fn=<AddmmBackward0>)\n",
      "99 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-4.9521,  5.1914]], grad_fn=<AddmmBackward0>)\n",
      "100 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-2.8321,  2.8478]], grad_fn=<AddmmBackward0>)\n",
      "101 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-2.1758,  2.3434]], grad_fn=<AddmmBackward0>)\n",
      "102 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-5.7188,  5.7858]], grad_fn=<AddmmBackward0>)\n",
      "103 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-4.7544,  4.7342]], grad_fn=<AddmmBackward0>)\n",
      "104 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-3.7869,  3.6842]], grad_fn=<AddmmBackward0>)\n",
      "105 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-4.2587,  4.4162]], grad_fn=<AddmmBackward0>)\n",
      "106 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-3.3143,  3.4910]], grad_fn=<AddmmBackward0>)\n",
      "107 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-5.0631,  5.0997]], grad_fn=<AddmmBackward0>)\n"
     ]
    }
   ],
   "source": [
    "for idx, item in enumerate(all_test_ds):\n",
    "    print(test_result(idx, item, trainer_categorical.model))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db1c8f71",
   "metadata": {},
   "source": [
    "conclusion: worse results than before. maybe different hyperparameters needed?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa8bcfaa",
   "metadata": {},
   "source": [
    "### 3.2 retrain with more images\n",
    "(use 2x5 images from each of the two new tours)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79a58054",
   "metadata": {},
   "source": [
    "link to data for use at home -- data is already there on cocalc!\n",
    "\n",
    "https://paste.madflex.de/kRYJAwYw#forest-vs-houses--train--additional.zip (the zip contains only the new images!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9c479cd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR=\"/data/houses-vs-forest--plus-additional\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8df427d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "transforms = torchvision.transforms.Compose([\n",
    "    torchvision.transforms.Resize(100),\n",
    "    torchvision.transforms.ToTensor()\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d334c471",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_train_add = torchvision.datasets.ImageFolder(f\"{DATA_DIR}/train\", transform=transforms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9aa20d41",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_count = 107 + 5 + 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1c81b79c",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_ds, validation_ds = torch.utils.data.random_split(image_train_add,[(image_count-15)*2, 15*2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ab9bb69a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(len(train_ds) / 2) == image_count - 15"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edea4fdd",
   "metadata": {},
   "source": [
    "the transformation changed, so reload the test data too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4baa68fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# images from the same tour/day\n",
    "test_ds = torchvision.datasets.ImageFolder(f\"{DATA_DIR}/test\", transform=transforms)\n",
    "# images from *another* tour/day\n",
    "test2_ds = torchvision.datasets.ImageFolder(f\"{DATA_DIR}/test2\", transform=transforms)\n",
    "# images from another tour/day and another bike\n",
    "test3_ds = torchvision.datasets.ImageFolder(f\"{DATA_DIR}/test3\", transform=transforms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "039c03eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_test_ds = test_ds + test2_ds + test3_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "acf90bd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ForestHousesCategoricalModel(lightning.LightningModule):\n",
    "    def __init__(self, learning_rate=1e-3):\n",
    "        super().__init__()\n",
    "        self.save_hyperparameters()\n",
    "\n",
    "        self.learning_rate = learning_rate\n",
    "        self.batch_size = 16\n",
    "\n",
    "        channels, width, height = train_ds[0][0].size()\n",
    "\n",
    "        self.model = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(in_channels=3, out_channels=20, kernel_size=3, stride=1, padding=0),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.MaxPool2d(2),\n",
    "            torch.nn.Dropout(0.5),\n",
    "            torch.nn.Flatten(),\n",
    "            #  100x + channels=20, maxpool=2 -> 63700\n",
    "            #  100x + channels=50, maxpool=2 -> 159250\n",
    "            #  100x + channels=50, maxpool=4 -> 38400\n",
    "            #  100x + channels=20, maxpool=4 -> 15360\n",
    "            #  channels=50, maxpool=2, kernel=5 -> 153600\n",
    "            torch.nn.Linear(63700, 2),\n",
    "        )\n",
    "        self.loss = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "        acc = Accuracy()\n",
    "        self.train_acc = acc.clone()\n",
    "        self.valid_acc = acc.clone()\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "\n",
    "    def step(self, batch, batch_idx, name):\n",
    "        x, y = batch\n",
    "        y_hat = self.forward(x)\n",
    "        loss = self.loss(y_hat, y)\n",
    "        self.log(f\"{name}/loss\", loss)\n",
    "\n",
    "        logits = self(x)\n",
    "        preds = torch.argmax(logits, dim=1)\n",
    "        \n",
    "        if name == \"train\":\n",
    "            self.train_acc(preds, y)\n",
    "            self.log(f\"{name}/acc\", self.train_acc)\n",
    "        else:\n",
    "            self.valid_acc(preds, y)\n",
    "            self.log(f\"{name}/acc\", self.valid_acc)\n",
    "\n",
    "        return {\"loss\": loss}\n",
    "    \n",
    "    def training_step(self, batch, batch_nb):\n",
    "        return self.step(batch, batch_nb, name=\"train\")\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        return self.step(batch, batch_idx, name=\"val\")\n",
    "    \n",
    "    def test_step(self, batch, batch_idx):\n",
    "        return self.step(batch, batch_idx, name=\"test\")\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        return torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n",
    "\n",
    "    def train_dataloader(self):\n",
    "        return torch.utils.data.DataLoader(train_ds, batch_size=self.batch_size)\n",
    "\n",
    "    def val_dataloader(self):\n",
    "        return torch.utils.data.DataLoader(validation_ds, batch_size=self.batch_size)\n",
    "\n",
    "    def test_dataloader(self):\n",
    "        return torch.utils.data.DataLoader(all_test_ds, batch_size=self.batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3888dbc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ForestHousesCategoricalModel(\n",
       "  (model): Sequential(\n",
       "    (0): Conv2d(3, 20, kernel_size=(3, 3), stride=(1, 1))\n",
       "    (1): ReLU()\n",
       "    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (3): Dropout(p=0.5, inplace=False)\n",
       "    (4): Flatten(start_dim=1, end_dim=-1)\n",
       "    (5): Linear(in_features=63700, out_features=2, bias=True)\n",
       "  )\n",
       "  (loss): CrossEntropyLoss()\n",
       "  (train_acc): Accuracy()\n",
       "  (valid_acc): Accuracy()\n",
       ")"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_categorical = ForestHousesCategoricalModel()\n",
    "model_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "24b234cf",
   "metadata": {},
   "outputs": [
    {
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       "                    Syncing run <strong><a href=\"https://wandb.ai/mfa/workshop-p2--FvsH--add/runs/3odoz8fk\" target=\"_blank\">dry-butterfly-6</a></strong> to <a href=\"https://wandb.ai/mfa/workshop-p2--FvsH--add\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: logging graph, to disable use `wandb.watch(log_graph=False)`\n",
      "GPU available: True, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "/home/mfa/.virtualenvs/machine-learning-workshop/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1584: UserWarning: GPU available but not used. Set the gpus flag in your trainer `Trainer(gpus=1)` or script `--gpus=1`.\n",
      "  rank_zero_warn(\n",
      "\n",
      "  | Name      | Type             | Params\n",
      "-----------------------------------------------\n",
      "0 | model     | Sequential       | 127 K \n",
      "1 | loss      | CrossEntropyLoss | 0     \n",
      "2 | train_acc | Accuracy         | 0     \n",
      "3 | valid_acc | Accuracy         | 0     \n",
      "-----------------------------------------------\n",
      "127 K     Trainable params\n",
      "0         Non-trainable params\n",
      "127 K     Total params\n",
      "0.512     Total estimated model params size (MB)\n"
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       "Validation sanity check: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mfa/.virtualenvs/machine-learning-workshop/lib/python3.9/site-packages/pytorch_lightning/trainer/data_loading.py:132: UserWarning: 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 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n",
      "Global seed set to 42\n",
      "/home/mfa/.virtualenvs/machine-learning-workshop/lib/python3.9/site-packages/pytorch_lightning/trainer/data_loading.py:132: UserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n",
      "/home/mfa/.virtualenvs/machine-learning-workshop/lib/python3.9/site-packages/pytorch_lightning/trainer/data_loading.py:432: UserWarning: The number of training samples (13) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n",
      "  rank_zero_warn(\n"
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     "output_type": "display_data"
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      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wandb_logger = lightning.loggers.WandbLogger(project=\"workshop-p2--FvsH--add\")\n",
    "wandb_logger.watch(model_categorical, log=\"all\")\n",
    "trainer = lightning.Trainer(gpus=0, max_epochs=10, logger=wandb_logger)\n",
    "trainer.fit(model_categorical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d25a8cdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Restoring states from the checkpoint path at /home/mfa/machine-learning-workshop/part2/workshop-p2--FvsH--add/3odoz8fk/checkpoints/epoch=9-step=129.ckpt\n",
      "Loaded model weights from checkpoint at /home/mfa/machine-learning-workshop/part2/workshop-p2--FvsH--add/3odoz8fk/checkpoints/epoch=9-step=129.ckpt\n",
      "/home/mfa/.virtualenvs/machine-learning-workshop/lib/python3.9/site-packages/pytorch_lightning/trainer/data_loading.py:132: UserWarning: 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 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
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       "model_id": "e4d4a39c67324a29aad19e7eda1c6839",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "DATALOADER:0 TEST RESULTS\n",
      "{'test/acc': 0.8148148059844971, 'test/loss': 0.4241182804107666}\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'test/loss': 0.4241182804107666, 'test/acc': 0.8148148059844971}]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.test(ckpt_path='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "993e89cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<br/>Waiting for W&B process to finish, PID 625847... <strong style=\"color:green\">(success).</strong>"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
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      "text/plain": [
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     "data": {
      "text/html": [
       "<style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; width: 100% }\n",
       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▂▂▃▃▄▅▅▆▆▇▇█</td></tr><tr><td>test/acc</td><td>▁</td></tr><tr><td>test/loss</td><td>▁</td></tr><tr><td>train/acc</td><td>▁█</td></tr><tr><td>train/loss</td><td>█▁</td></tr><tr><td>trainer/global_step</td><td>▁▂▃▃▃▄▅▆▆▆▇██</td></tr><tr><td>val/acc</td><td>▁▃▆▆█▆█▆██</td></tr><tr><td>val/loss</td><td>█▅▃▃▃▂▂▅▂▁</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>10</td></tr><tr><td>test/acc</td><td>0.81481</td></tr><tr><td>test/loss</td><td>0.42412</td></tr><tr><td>train/acc</td><td>1.0</td></tr><tr><td>train/loss</td><td>0.02099</td></tr><tr><td>trainer/global_step</td><td>130</td></tr><tr><td>val/acc</td><td>0.96667</td></tr><tr><td>val/loss</td><td>0.09583</td></tr></table>\n",
       "</div></div>\n",
       "Synced 5 W&B file(s), 1 media file(s), 0 artifact file(s) and 0 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">dry-butterfly-6</strong>: <a href=\"https://wandb.ai/mfa/workshop-p2--FvsH--add/runs/3odoz8fk\" target=\"_blank\">https://wandb.ai/mfa/workshop-p2--FvsH--add/runs/3odoz8fk</a><br/>\n",
       "Find logs at: <code>./wandb/run-20220227_203507-3odoz8fk/logs</code><br/>\n"
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       "<IPython.core.display.HTML object>"
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   "source": [
    "wandb.finish()"
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  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4d37cfeb",
   "metadata": {
    "scrolled": false
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   "outputs": [
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     "output_type": "stream",
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      "106 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-0.5742,  0.6657]], grad_fn=<AddmmBackward0>)\n",
      "107 1 houses \u001b[32mTrue\u001b[0m tensor(1) tensor([[-0.1616,  0.2583]], grad_fn=<AddmmBackward0>)\n"
     ]
    }
   ],
   "source": [
    "for idx, item in enumerate(all_test_ds):\n",
    "    print(test_result(idx, item, trainer.model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56697765",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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