Updating pytorch notebook

Signed-off-by: Shriti Priya <[email protected]>

Author: monshri

notebooks/poisoning_attack_sleeper_agent_pytorch.ipynb

@@ -93,64 +93,18 @@
    "id": "69e3ffe8",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# num_classes = 10\n",
-    "# loss_fn = nn.CrossEntropyLoss()\n",
-    "# model = torchvision.models.ResNet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], num_classes=num_classes)\n",
-    "# optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4, nesterov=True)\n",
-    "# model_art = PyTorchClassifier(model,input_shape=x_train.shape[1:], loss=loss_fn, optimizer=optimizer, nb_classes=10, clip_values=(min_, max_), preprocessing=(mean,std))\n",
-    "# model_art.fit(x_train, y_train, batch_size=128, nb_epochs=80,verbose=0)\n",
-    "# predictions = model_art.predict(x_test)\n",
-    "# accuracy = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n",
-    "# print(\"Accuracy on benign test examples: {}%\".format(accuracy * 100))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "3b807d14",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy on benign test examples: 73.95%\n"
-     ]
-    }
-   ],
    "source": [
     "num_classes = 10\n",
     "loss_fn = nn.CrossEntropyLoss()\n",
-    "model = torch.load('model.pt')\n",
+    "model = torchvision.models.ResNet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], num_classes=num_classes)\n",
     "optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4, nesterov=True)\n",
     "model_art = PyTorchClassifier(model,input_shape=x_train.shape[1:], loss=loss_fn, optimizer=optimizer, nb_classes=10, clip_values=(min_, max_), preprocessing=(mean,std))\n",
+    "model_art.fit(x_train, y_train, batch_size=128, nb_epochs=80,verbose=0)\n",
     "predictions = model_art.predict(x_test)\n",
     "accuracy = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n",
     "print(\"Accuracy on benign test examples: {}%\".format(accuracy * 100))"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "d973a78c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_art.model.training"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "9b1ca858",
@@ -601,7 +555,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.9"
   },
   "vscode": {
    "interpreter": {