002_training.ipynb

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 "cells": [
  {
   "source": [
    "# Training and Evaluation"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# system imports\n",
    "import os\n",
    "from datetime import datetime\n",
    "\n",
    "# additional imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import torch\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "\n",
    "# internal imports\n",
    "from utils import plot_confusion_matrix\n",
    "\n",
    "# device config\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "source": [
    "## Hyperparameters"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "hparams = {    \n",
    "    'dataset': 'data/prepared_data_balanced.csv',\n",
    "    'epochs': 15,\n",
    "    'batch_size': 16,\n",
    "    'lr': 1e-3,\n",
    "    'features': [\n",
    "        'chroma_stft', 'rmse', 'spectral_centroid', 'spectral_bandwidth', 'rolloff', 'zero_crossing_rate',\n",
    "        'mfcc1', 'mfcc2', 'mfcc3', 'mfcc4', 'mfcc5', 'mfcc6', 'mfcc7', 'mfcc8', 'mfcc9', 'mfcc10', \n",
    "        'mfcc11', 'mfcc12', 'mfcc13', 'mfcc14', 'mfcc15', 'mfcc16', 'mfcc17', 'mfcc18', 'mfcc19', 'mfcc20'\n",
    "    ]\n",
    "}"
   ]
  },
  {
   "source": [
    "## Prepare Data"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "classes: ['covid' 'not_covid']\nX_train.shape: (107, 26)\ny_train.shape: (107,)\n"
     ]
    }
   ],
   "source": [
    "df_features = pd.read_csv(hparams['dataset'])\n",
    "\n",
    "X = np.array(df_features[hparams['features']], dtype=np.float32)\n",
    "\n",
    "encoder = LabelEncoder()\n",
    "y = encoder.fit_transform(df_features['label'])\n",
    "print('classes:', encoder.classes_)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# scale data\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "print('X_train.shape:', X_train.shape)\n",
    "print('y_train.shape:', y_train.shape)\n",
    "\n",
    "# create pytorch dataloader\n",
    "torch.manual_seed(42)\n",
    "train_dataset = torch.utils.data.TensorDataset(torch.Tensor(X_train), torch.Tensor(y_train).long())\n",
    "test_dataset = torch.utils.data.TensorDataset(torch.Tensor(X_test), torch.Tensor(y_test).long())\n",
    "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=hparams['batch_size'], shuffle=True)\n",
    "test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=hparams['batch_size'], shuffle=False)"
   ]
  },
  {
   "source": [
    "## Setup Model"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Design model (input, output size, forward pass)\n",
    "class CoughNet(torch.nn.Module):\n",
    "    def __init__(self, input_size):\n",
    "        super(CoughNet, self).__init__()\n",
    "        self.l1 = torch.nn.Linear(input_size, 512)\n",
    "        self.l2 = torch.nn.Linear(512, 256)\n",
    "        self.l3 = torch.nn.Linear(256, 128)\n",
    "        self.l4 = torch.nn.Linear(128, 64)\n",
    "        self.l5 = torch.nn.Linear(64, 10)\n",
    "        self.l6 = torch.nn.Linear(10, 2)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = torch.relu(self.l1(x))\n",
    "        x = torch.relu(self.l2(x))\n",
    "        x = torch.relu(self.l3(x))\n",
    "        x = torch.relu(self.l4(x))\n",
    "        x = torch.relu(self.l5(x))\n",
    "        x = self.l6(x)\n",
    "        return x\n",
    "\n",
    "model = CoughNet(len(hparams['features'])).to(device)"
   ]
  },
  {
   "source": [
    "## Training"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "[Training Epoch 1]: 100%|██████████| 7/7 [00:00<00:00, 17.00it/s, loss=0.0449, train_accuracy=0.533]\n",
      "[Evaluating Epoch 1]: 100%|██████████| 2/2 [00:00<00:00, 124.99it/s, loss=0.0515, eval_accuracy=0.481]\n",
      "[Training Epoch 2]: 100%|██████████| 7/7 [00:00<00:00, 17.68it/s, loss=0.0423, train_accuracy=0.785]\n",
      "[Evaluating Epoch 2]: 100%|██████████| 2/2 [00:00<00:00, 43.48it/s, loss=0.0462, eval_accuracy=0.667]\n",
      "[Training Epoch 3]: 100%|██████████| 7/7 [00:00<00:00, 21.21it/s, loss=0.0358, train_accuracy=0.85]\n",
      "[Evaluating Epoch 3]: 100%|██████████| 2/2 [00:00<00:00, 39.22it/s, loss=0.0367, eval_accuracy=0.741]\n",
      "[Training Epoch 4]: 100%|██████████| 7/7 [00:00<00:00, 18.59it/s, loss=0.0274, train_accuracy=0.888]\n",
      "[Evaluating Epoch 4]: 100%|██████████| 2/2 [00:00<00:00, 90.35it/s, loss=0.0326, eval_accuracy=0.778]\n",
      "[Training Epoch 5]: 100%|██████████| 7/7 [00:00<00:00, 24.81it/s, loss=0.0205, train_accuracy=0.879]\n",
      "[Evaluating Epoch 5]: 100%|██████████| 2/2 [00:00<00:00, 28.93it/s, loss=0.0355, eval_accuracy=0.815]\n",
      "[Training Epoch 6]: 100%|██████████| 7/7 [00:00<00:00, 21.41it/s, loss=0.0132, train_accuracy=0.925]\n",
      "[Evaluating Epoch 6]: 100%|██████████| 2/2 [00:00<00:00, 54.06it/s, loss=0.0432, eval_accuracy=0.815]\n",
      "[Training Epoch 7]: 100%|██████████| 7/7 [00:00<00:00, 25.00it/s, loss=0.00872, train_accuracy=0.944]\n",
      "[Evaluating Epoch 7]: 100%|██████████| 2/2 [00:00<00:00, 43.38it/s, loss=0.0477, eval_accuracy=0.815]\n",
      "[Training Epoch 8]: 100%|██████████| 7/7 [00:00<00:00, 21.14it/s, loss=0.0069, train_accuracy=0.972]\n",
      "[Evaluating Epoch 8]: 100%|██████████| 2/2 [00:00<00:00, 55.55it/s, loss=0.0474, eval_accuracy=0.852]\n",
      "[Training Epoch 9]: 100%|██████████| 7/7 [00:00<00:00, 15.98it/s, loss=0.00384, train_accuracy=0.981]\n",
      "[Evaluating Epoch 9]: 100%|██████████| 2/2 [00:00<00:00, 40.82it/s, loss=0.0647, eval_accuracy=0.815]\n",
      "[Training Epoch 10]: 100%|██████████| 7/7 [00:00<00:00, 18.87it/s, loss=0.00342, train_accuracy=0.981]\n",
      "[Evaluating Epoch 10]: 100%|██████████| 2/2 [00:00<00:00, 52.64it/s, loss=0.0509, eval_accuracy=0.852]\n",
      "[Training Epoch 11]: 100%|██████████| 7/7 [00:00<00:00, 16.18it/s, loss=0.00245, train_accuracy=0.991]\n",
      "[Evaluating Epoch 11]: 100%|██████████| 2/2 [00:00<00:00, 48.78it/s, loss=0.0645, eval_accuracy=0.815]\n",
      "[Training Epoch 12]: 100%|██████████| 7/7 [00:00<00:00, 20.29it/s, loss=0.00162, train_accuracy=0.981]\n",
      "[Evaluating Epoch 12]: 100%|██████████| 2/2 [00:00<00:00, 58.83it/s, loss=0.063, eval_accuracy=0.889]\n",
      "[Training Epoch 13]: 100%|██████████| 7/7 [00:00<00:00, 19.77it/s, loss=0.000906, train_accuracy=1]\n",
      "[Evaluating Epoch 13]: 100%|██████████| 2/2 [00:00<00:00, 95.23it/s, loss=0.0818, eval_accuracy=0.852]\n",
      "[Training Epoch 14]: 100%|██████████| 7/7 [00:00<00:00, 18.37it/s, loss=0.000647, train_accuracy=1]\n",
      "[Evaluating Epoch 14]: 100%|██████████| 2/2 [00:00<00:00, 74.09it/s, loss=0.0801, eval_accuracy=0.889]\n",
      "[Training Epoch 15]: 100%|██████████| 7/7 [00:00<00:00, 19.77it/s, loss=0.000418, train_accuracy=1]\n",
      "[Evaluating Epoch 15]: 100%|██████████| 2/2 [00:00<00:00, 46.50it/s, loss=0.0869, eval_accuracy=0.889]\n"
     ]
    }
   ],
   "source": [
    "# Construct loss and optimizer\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=hparams['lr'])\n",
    "criterion = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "def train(loader_train, model, optimizer, epoch):\n",
    "    model.train()\n",
    "    running_loss = 0.0\n",
    "    running_correct = 0.0\n",
    "    total = 0\n",
    "    pbar = tqdm(enumerate(loader_train), total=len(loader_train))\n",
    "    for batch_ndx, sample in pbar: \n",
    "        features, labels = sample[0].to(device), sample[1].to(device) \n",
    "\n",
    "        # forward pass and loss calculation\n",
    "        outputs = model(features)\n",
    "        loss = criterion(outputs, labels)  \n",
    "        \n",
    "        # backward pass    \n",
    "        loss.backward()\n",
    "        \n",
    "        # update weights\n",
    "        optimizer.step()\n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        # calculate metrics\n",
    "        running_loss += loss.item()\n",
    "        predictions = torch.argmax(outputs.data, 1)\n",
    "        running_correct += (predictions == labels).sum().item()\n",
    "\n",
    "        # print informations\n",
    "        pbar.set_description(f'[Training Epoch {epoch+1}]') \n",
    "        total += labels.shape[0]\n",
    "        pbar.set_postfix({'loss': running_loss / total, 'train_accuracy': running_correct / total})\n",
    "        \n",
    "    # write informations to tensorboard\n",
    "    writer.add_scalar('Loss/Train', running_loss / total, epoch+1)\n",
    "    writer.add_scalar('Accuracy/Train', running_correct / total, epoch+1)\n",
    "\n",
    "def evaluate(loader_test, model, epoch):\n",
    "    model.eval()\n",
    "    running_loss = 0.0\n",
    "    running_correct = 0.0\n",
    "    total = 0\n",
    "    with torch.no_grad():\n",
    "        pbar = tqdm(enumerate(loader_test), total=len(loader_test))\n",
    "        for batch_ndx, sample in pbar:\n",
    "            features, labels = sample[0].to(device), sample[1].to(device) \n",
    "\n",
    "            # forward pass and loss calculation\n",
    "            outputs = model(features)\n",
    "            loss = criterion(outputs, labels)  \n",
    "\n",
    "            # calculate metrics\n",
    "            running_loss += loss.item()\n",
    "            predictions = torch.argmax(outputs.data, 1)\n",
    "            running_correct += (predictions == labels).sum().item()\n",
    "\n",
    "            # print informations\n",
    "            pbar.set_description(f'[Evaluating Epoch {epoch+1}]')\n",
    "            total += labels.shape[0]\n",
    "            pbar.set_postfix({'loss': running_loss / total, 'eval_accuracy': running_correct / total})\n",
    "        \n",
    "    # write informations to tensorboard\n",
    "    writer.add_scalar('Loss/Eval', running_loss / total, epoch+1)\n",
    "    writer.add_scalar('Accuracy/Eval', running_correct / total, epoch+1)\n",
    "\n",
    "# initialize tensorboard summary writer\n",
    "time_stamp = datetime.now().strftime('%Y%m%d-%H%M%S')\n",
    "writer = SummaryWriter(f'logs/{time_stamp}/')\n",
    "\n",
    "# add graph to tensorboard\n",
    "features = iter(test_loader).next()[0]\n",
    "writer.add_graph(model, features)\n",
    "\n",
    "# training loop\n",
    "for epoch in range(hparams['epochs']):\n",
    "    train(train_loader, model, optimizer, epoch)\n",
    "    evaluate(test_loader, model, epoch)\n",
    "\n",
    "# close tensorboard\n",
    "writer.close()\n",
    "\n",
    "# open tensorboard\n",
    "# tensorboard --logdir logs"
   ]
  },
  {
   "source": [
    "## Plot confusion matrix"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    outputs = model(torch.tensor(X_test))\n",
    "    predictions = torch.argmax(outputs.data, 1)\n",
    "\n",
    "plot_confusion_matrix(y_test, predictions, encoder.classes_)"
   ]
  },
  {
   "source": [
    "## Save checkpoint after training"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint = {\n",
    "    'hparams': hparams,\n",
    "    'model_state': model.state_dict(),\n",
    "    'scaler': scaler,\n",
    "    'encoder': encoder\n",
    "}\n",
    "torch.save(checkpoint, 'checkpoints/checkpoint.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}