finished CNN, good accruacy overall but not good for minority and harder classes

notebooks/tutorial-cnn.ipynb

@@ -433,6 +433,85 @@
     "PATH = '../models/tutorial-cnn.pth'\n",
     "torch.save(model.state_dict(), PATH)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a24dd4f0",
+   "metadata": {},
+   "source": [
+    "Test on test set using Pytorch tutorial method for both total and per class (maybe spot overfitting as well)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "bc158602",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Accuracy: 74.53%\n"
+     ]
+    }
+   ],
+   "source": [
+    "correct = 0\n",
+    "total = 0\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "    for data in test_loader:\n",
+    "        images, labels = data\n",
+    "        images, labels = images.to(device), labels.to(device)\n",
+    "        outputs = model(images)\n",
+    "        _, predicted = torch.max(outputs, 1)\n",
+    "        total += labels.size(0)\n",
+    "        correct += (predicted == labels).sum().item()\n",
+    "\n",
+    "print(f'Test Accuracy: {100 * correct / total:.2f}%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "8cc7ed40",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy for class: Bicycle    is 43.5%\n",
+      "Accuracy for class: Bus        is 53.8%\n",
+      "Accuracy for class: Car        is 87.6%\n",
+      "Accuracy for class: Motorcycle is 75.2%\n",
+      "Accuracy for class: NonVehicles is 99.0%\n",
+      "Accuracy for class: Taxi       is 20.0%\n",
+      "Accuracy for class: Truck      is 13.9%\n",
+      "Accuracy for class: Van        is 24.7%\n"
+     ]
+    }
+   ],
+   "source": [
+    "correct_pred = {classname: 0 for classname in dataset.classes}\n",
+    "total_pred = {classname: 0 for classname in dataset.classes}\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "    for data in test_loader:\n",
+    "        images, labels = data\n",
+    "        images, labels = images.to(device), labels.to(device)\n",
+    "        outputs = model(images)\n",
+    "        _, predictions = torch.max(outputs, 1)\n",
+    "        for label, prediction in zip(labels, predictions):\n",
+    "            if label == prediction:\n",
+    "                correct_pred[dataset.classes[label]] += 1\n",
+    "            total_pred[dataset.classes[label]] += 1\n",
+    "\n",
+    "for classname, correct_count in correct_pred.items():\n",
+    "    accuracy = 100 * float(correct_count) / total_pred[classname]\n",
+    "    print(f'Accuracy for class: {classname:10s} is {accuracy:.1f}%')"
+   ]
   }
  ],
  "metadata": {