train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libs\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import transforms\n",
    "from torch.utils.data import Dataset, DataLoader, random_split\n",
    "\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, f1_score, accuracy_score, classification_report\n",
    "\n",
    "import glob, os\n",
    "import numpy as np\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "device: cuda\n"
     ]
    }
   ],
   "source": [
    "#device\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print('device:',device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataloader\n",
    "\n",
    "class IMDDataset(Dataset):\n",
    "    def __init__(self, root_dir, sub_dir, transform=None):\n",
    "        self.root_dir = root_dir\n",
    "        self.sub_dir = sub_dir\n",
    "        self.transform = transform\n",
    "\n",
    "        self.img_names = []\n",
    "        for sdir in self.sub_dir:\n",
    "            self.img_names.extend(sorted(glob.glob(os.path.join(root_dir, sdir, '*'))))\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.img_names)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        self.img_path = self.img_names[idx]\n",
    "        \n",
    "        self.img = Image.open(self.img_path)\n",
    "\n",
    "        self.y = 1 if self.img_path.split('/')[-1].split('_')[0] == 'Au' else 0\n",
    "\n",
    "        if self.transform:\n",
    "            (self.img, self.y) = self.transform((self.img, self.y))\n",
    "        \n",
    "        return (self.img, self.y)\n",
    "\n",
    "\n",
    "class ToTensor:\n",
    "    def __call__(self, sample):\n",
    "        X, y = sample\n",
    "        return (torch.from_numpy(X),torch.tensor(y))\n",
    "\n",
    "\n",
    "class Resize:\n",
    "    def __init__(self, width, height):\n",
    "        self.w = width\n",
    "        self.h = height\n",
    "\n",
    "    def __call__(self, sample):\n",
    "        X, y = sample\n",
    "        X = X.resize((self.w,self.h))\n",
    "        return (X, y)\n",
    "\n",
    "\n",
    "class Normalize:\n",
    "    def __call__(self, sample):\n",
    "        X, y = sample\n",
    "        X = np.array(X, dtype=np.float32).transpose(2,0,1)/255.0\n",
    "        return (X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# auth -> 1 and tp -> 0\n",
    "class IMDModel(nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(IMDModel,self).__init__()\n",
    "\n",
    "        self.maxpool = nn.MaxPool2d(kernel_size=2)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.down_conv1 = nn.Sequential(\n",
    "                        nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3),\n",
    "                        nn.BatchNorm2d(64),\n",
    "                        self.maxpool,\n",
    "                        self.relu\n",
    "        )\n",
    "        self.down_conv2 = nn.Sequential(\n",
    "                        nn.Conv2d(in_channels=64, out_channels=16, kernel_size=3),\n",
    "                        nn.BatchNorm2d(16),\n",
    "                        self.maxpool,\n",
    "                        self.relu\n",
    "        )\n",
    "        self.linear = nn.Sequential(\n",
    "                        nn.Linear(in_features=16*30*30, out_features=1024),\n",
    "                        nn.BatchNorm1d(1024),\n",
    "                        self.relu,\n",
    "                        nn.Linear(in_features=1024, out_features=64),\n",
    "                        nn.BatchNorm1d(64),\n",
    "                        self.relu,\n",
    "                        nn.Linear(in_features=64, out_features=2),\n",
    "                        nn.Softmax()\n",
    "        )\n",
    "\n",
    "\n",
    "    def forward(self, img):\n",
    "\n",
    "        d1 = self.down_conv1(img)\n",
    "        d2 = self.down_conv2(d1)\n",
    "\n",
    "        d2 = d2.view(-1, d2.shape[1]*d2.shape[2]*d2.shape[3])\n",
    "        out = self.linear(d2)\n",
    "        \n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#hyperparameters\n",
    "\n",
    "model = IMDModel().to(device)\n",
    "\n",
    "epochs = 100\n",
    "batch_size = 128\n",
    "opt = optim.Adam(model.parameters(), lr=1e-4)\n",
    "loss_fn1 = nn.CrossEntropyLoss(reduction='mean')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "root_dir = '../data/'\n",
    "sub_dir = ['auth_model', 'sp_model']\n",
    "\n",
    "composed = transforms.Compose([Resize(128, 128), Normalize(), ToTensor()])\n",
    "dataset = IMDDataset(root_dir=root_dir, sub_dir=sub_dir, transform=composed)\n",
    "\n",
    "train_dataset, validation_dataset, test_dataset = random_split(dataset, [1000, 500, 221])\n",
    "train_data = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)\n",
    "validation_data = DataLoader(dataset=validation_dataset, batch_size=batch_size, shuffle=True)\n",
    "test_data = DataLoader(dataset=test_dataset, batch_size=221, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/100 \n",
      "|--- Train Loss: 0.6446183994412422\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 2/100 \n",
      "|--- Train Loss: 0.590332143008709\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 3/100 \n",
      "|--- Train Loss: 0.5496955290436745\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 4/100 \n",
      "|--- Train Loss: 0.5062691532075405\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 5/100 \n",
      "|--- Train Loss: 0.4890725761651993\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 6/100 \n",
      "|--- Train Loss: 0.460037499666214\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 7/100 \n",
      "|--- Train Loss: 0.44341691583395004\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 8/100 \n",
      "|--- Train Loss: 0.4307126961648464\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 9/100 \n",
      "|--- Train Loss: 0.4108401797711849\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 10/100 \n",
      "|--- Train Loss: 0.4014793038368225\n",
      "|--- Test Loss: 0.4573683440685272 --- Test Accuracy: 0.7939789870689655\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 11/100 \n",
      "|--- Train Loss: 0.38067278265953064\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 12/100 \n",
      "|--- Train Loss: 0.3741195499897003\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 13/100 \n",
      "|--- Train Loss: 0.34950749948620796\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 14/100 \n",
      "|--- Train Loss: 0.32744117826223373\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 15/100 \n",
      "|--- Train Loss: 0.3099687956273556\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 16/100 \n",
      "|--- Train Loss: 0.29957739263772964\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 17/100 \n",
      "|--- Train Loss: 0.26762630231678486\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 18/100 \n",
      "|--- Train Loss: 0.2589342165738344\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 19/100 \n",
      "|--- Train Loss: 0.23074183613061905\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 20/100 \n",
      "|--- Train Loss: 0.22505049034953117\n",
      "|--- Test Loss: 0.41166552156209946 --- Test Accuracy: 0.7982893318965517\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 21/100 \n",
      "|--- Train Loss: 0.19999409280717373\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 22/100 \n",
      "|--- Train Loss: 0.17396287433803082\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 23/100 \n",
      "|--- Train Loss: 0.15791925601661205\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 24/100 \n",
      "|--- Train Loss: 0.1371132107451558\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 25/100 \n",
      "|--- Train Loss: 0.12079901248216629\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 26/100 \n",
      "|--- Train Loss: 0.11452199704945087\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 27/100 \n",
      "|--- Train Loss: 0.09170470293611288\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 28/100 \n",
      "|--- Train Loss: 0.10259854979813099\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 29/100 \n",
      "|--- Train Loss: 0.07674253825098276\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 30/100 \n",
      "|--- Train Loss: 0.06414557946845889\n",
      "|--- Test Loss: 0.4302889257669449 --- Test Accuracy: 0.8185614224137931\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 31/100 \n",
      "|--- Train Loss: 0.053637574426829815\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 32/100 \n",
      "|--- Train Loss: 0.049351201858371496\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 33/100 \n",
      "|--- Train Loss: 0.05184872727841139\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 34/100 \n",
      "|--- Train Loss: 0.04159785574302077\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 35/100 \n",
      "|--- Train Loss: 0.03854098101146519\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 36/100 \n",
      "|--- Train Loss: 0.03564114635810256\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 37/100 \n",
      "|--- Train Loss: 0.039241056656464934\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 38/100 \n",
      "|--- Train Loss: 0.026516597950831056\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 39/100 \n",
      "|--- Train Loss: 0.027174014830961823\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 40/100 \n",
      "|--- Train Loss: 0.02651100722141564\n",
      "|--- Test Loss: 0.5305453613400459 --- Test Accuracy: 0.8178205818965517\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 41/100 \n",
      "|--- Train Loss: 0.021441097953356802\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 42/100 \n",
      "|--- Train Loss: 0.01651025435421616\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 43/100 \n",
      "|--- Train Loss: 0.015675744507461786\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 44/100 \n",
      "|--- Train Loss: 0.012436435790732503\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 45/100 \n",
      "|--- Train Loss: 0.012716614874079823\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 46/100 \n",
      "|--- Train Loss: 0.012545170146040618\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 47/100 \n",
      "|--- Train Loss: 0.011043275299016386\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 48/100 \n",
      "|--- Train Loss: 0.010089052841067314\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 49/100 \n",
      "|--- Train Loss: 0.010818761540576816\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 50/100 \n",
      "|--- Train Loss: 0.008981100050732493\n",
      "|--- Test Loss: 0.5424914956092834 --- Test Accuracy: 0.8307516163793103\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 51/100 \n",
      "|--- Train Loss: 0.009971734369173646\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 52/100 \n",
      "|--- Train Loss: 0.008301076362840831\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 53/100 \n",
      "|--- Train Loss: 0.007528852904215455\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 54/100 \n",
      "|--- Train Loss: 0.007812880910933018\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 55/100 \n",
      "|--- Train Loss: 0.016494723153300583\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 56/100 \n",
      "|--- Train Loss: 0.007326851657126099\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 57/100 \n",
      "|--- Train Loss: 0.008236641064286232\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 58/100 \n",
      "|--- Train Loss: 0.0056334835244342685\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 59/100 \n",
      "|--- Train Loss: 0.005145480710780248\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 60/100 \n",
      "|--- Train Loss: 0.0043128582765348256\n",
      "|--- Test Loss: 0.5711807087063789 --- Test Accuracy: 0.8316945043103449\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 61/100 \n",
      "|--- Train Loss: 0.004272048972779885\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 62/100 \n",
      "|--- Train Loss: 0.008571941580157727\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 63/100 \n",
      "|--- Train Loss: 0.006272476719459519\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 64/100 \n",
      "|--- Train Loss: 0.008922021312173456\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 65/100 \n",
      "|--- Train Loss: 0.008754344889894128\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 66/100 \n",
      "|--- Train Loss: 0.005788104492239654\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 67/100 \n",
      "|--- Train Loss: 0.008216730086132884\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 68/100 \n",
      "|--- Train Loss: 0.008213872439227998\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 69/100 \n",
      "|--- Train Loss: 0.00888856771052815\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 70/100 \n",
      "|--- Train Loss: 0.004440270364284515\n",
      "|--- Test Loss: 0.6039740890264511 --- Test Accuracy: 0.824286099137931\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 71/100 \n",
      "|--- Train Loss: 0.003662666422314942\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 72/100 \n",
      "|--- Train Loss: 0.0028082248754799366\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 73/100 \n",
      "|--- Train Loss: 0.0035942431713920087\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 74/100 \n",
      "|--- Train Loss: 0.0021165237412787974\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 75/100 \n",
      "|--- Train Loss: 0.0026195224199909717\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 76/100 \n",
      "|--- Train Loss: 0.001888902042992413\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 77/100 \n",
      "|--- Train Loss: 0.0020150313794147223\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 78/100 \n",
      "|--- Train Loss: 0.0024341855314560235\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 79/100 \n",
      "|--- Train Loss: 0.0027620871842373163\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 80/100 \n",
      "|--- Train Loss: 0.0019776895351242274\n",
      "|--- Test Loss: 0.6650110334157944 --- Test Accuracy: 0.8230738146551724\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 81/100 \n",
      "|--- Train Loss: 0.0018086265627061948\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 82/100 \n",
      "|--- Train Loss: 0.0017311038391198963\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 83/100 \n",
      "|--- Train Loss: 0.001518294113338925\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 84/100 \n",
      "|--- Train Loss: 0.0017533794743940234\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 85/100 \n",
      "|--- Train Loss: 0.00143166133784689\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 86/100 \n",
      "|--- Train Loss: 0.0012930796219734475\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 87/100 \n",
      "|--- Train Loss: 0.0019265080627519637\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 88/100 \n",
      "|--- Train Loss: 0.0019878262755810283\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 89/100 \n",
      "|--- Train Loss: 0.0012917843559989706\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 90/100 \n",
      "|--- Train Loss: 0.0017757475143298507\n",
      "|--- Test Loss: 0.6998696178197861 --- Test Accuracy: 0.8275862068965517\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 91/100 \n",
      "|--- Train Loss: 0.0012804779835278168\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 92/100 \n",
      "|--- Train Loss: 0.0012872788647655398\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 93/100 \n",
      "|--- Train Loss: 0.0013642328849527985\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 94/100 \n",
      "|--- Train Loss: 0.0012096872233087197\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 95/100 \n",
      "|--- Train Loss: 0.001251705369213596\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 96/100 \n",
      "|--- Train Loss: 0.0010824558412423357\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 97/100 \n",
      "|--- Train Loss: 0.000936491327593103\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 98/100 \n",
      "|--- Train Loss: 0.0009595875526429154\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 99/100 \n",
      "|--- Train Loss: 0.0009255550903617404\n",
      "--------------------------------------------------------------------------------\n",
      "Epoch: 100/100 \n",
      "|--- Train Loss: 0.0008730098343221471\n",
      "|--- Test Loss: 0.7085030451416969 --- Test Accuracy: 0.8273841594827587\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# training\n",
    "\n",
    "for epoch in range(1, epochs+1):\n",
    "    \n",
    "    print('Epoch: {}/{} '.format(epoch,epochs))\n",
    "\n",
    "    model.train()\n",
    "    train_loss = []\n",
    "\n",
    "    for idx, (X, y) in enumerate(train_data):\n",
    "        X, y = X.to(device), y.to(device)\n",
    "\n",
    "        opt.zero_grad()\n",
    "        with torch.set_grad_enabled(True):\n",
    "\n",
    "            output = model(X)\n",
    "\n",
    "            loss = loss_fn1(output, y)\n",
    "\n",
    "            # loss.type(torch.FloatTensor)\n",
    "            train_loss.append(loss.item())\n",
    "\n",
    "            loss.backward()\n",
    "            opt.step()\n",
    "\n",
    "    print('|--- Train Loss: {}'.format(np.mean(train_loss)))\n",
    "\n",
    "    if(epoch % 10 == 0):\n",
    "        val_loss = []\n",
    "        val_acc = []\n",
    "        with torch.no_grad():\n",
    "                model.eval()\n",
    "                for id, (X_val, y_val) in enumerate(validation_data):\n",
    "                    X_val, y_val = X_val.to(device), y_val.to(device)\n",
    "\n",
    "                    output = model(X_val)\n",
    "                    loss1 = loss_fn1(output, y_val)\n",
    "                    val_loss.append(loss1.item())\n",
    "                    # epoch_val_score.append(f1_score(y_true > 0 , y_pred > 0))\n",
    "                    _, y_pred = torch.max(output, dim=1)\n",
    "                    val_acc.append(accuracy_score(y_val.cpu().numpy(), y_pred.cpu().numpy()))\n",
    "\n",
    "                print('|--- Val Loss: {} --- Val Accuracy: {}'.format(np.mean(val_loss), np.mean(val_acc)))\n",
    "            # print('F1 score for validation: %f'%(np.mean(epoch_val_score)))\n",
    "    \n",
    "    print('-'*80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- Test Loss: 0.18185213208198547 --- Test Accuracy: 0.9366515837104072 --- F1 Score: 0.9339622641509434\n",
      "Confusion matrix:\n",
      " [[108   8]\n",
      " [  6  99]]\n",
      "Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.93      0.94       116\n",
      "           1       0.93      0.94      0.93       105\n",
      "\n",
      "    accuracy                           0.94       221\n",
      "   macro avg       0.94      0.94      0.94       221\n",
      "weighted avg       0.94      0.94      0.94       221\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# testing\n",
    "\n",
    "model = torch.load('model/model_c1_e100_v2.pth')\n",
    "ttloss = []\n",
    "ttacc = []\n",
    "ttf1 = []\n",
    "with torch.no_grad():\n",
    "    model.eval()\n",
    "    for id, (X_val, y_val) in enumerate(test_data):\n",
    "        X_val, y_val = X_val.to(device), y_val.to(device)\n",
    "\n",
    "        output = model(X_val)\n",
    "        loss1 = loss_fn1(output, y_val)\n",
    "        ttloss.append(loss1.item())\n",
    "                    # epoch_val_score.append(f1_score(y_true > 0 , y_pred > 0))\n",
    "        _, y_pred = torch.max(output, dim=1)\n",
    "        y_val, y_pred = y_val.cpu().numpy(), y_pred.cpu().numpy()\n",
    "        ttacc.append(accuracy_score(y_val, y_pred))\n",
    "        ttf1.append(f1_score(y_val, y_pred))\n",
    "        res = confusion_matrix(y_val, y_pred)\n",
    "        report = classification_report(y_val, y_pred)\n",
    "\n",
    "    print('|--- Test Loss: {} --- Test Accuracy: {} --- F1 Score: {}'.format(np.mean(ttloss), np.mean(ttacc), np.mean(ttf1)))\n",
    "    print('Confusion matrix:\\n',res)\n",
    "    print('Report:\\n',report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving model\n",
    "\n",
    "torch.save(model,'model/model_c1.pth')"
   ]
  }
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