diff --git a/TD2 Deep Learning.ipynb b/TD2 Deep Learning.ipynb
index baa86cca135327e8c7a9b26d75edca140fc932a4..7af32c7532d051752882414642b150bc3b85faf0 100644
--- a/TD2 Deep Learning.ipynb	
+++ b/TD2 Deep Learning.ipynb	
@@ -796,11 +796,9 @@
     "        self.fc1 = nn.Linear(64, 512)\n",
     "        self.relu4 = nn.ReLU()\n",
     "        self.dropout1 = nn.Dropout(dropout_rate)\n",
-    "\n",
     "        self.fc2 = nn.Linear(512, 64)\n",
     "        self.relu5 = nn.ReLU()\n",
     "        self.dropout2 = nn.Dropout(dropout_rate)\n",
-    "\n",
     "        self.fc3 = nn.Linear(64, num_classes)\n",
     "\n",
     "    def forward(self, x):\n",
@@ -822,10 +820,7 @@
     "\n",
     "        return x\n",
     "\n",
-    "# Instantiate the model\n",
     "model2 = Net2()\n",
-    "\n",
-    "# Print the model architecture\n",
     "print(model2)\n"
    ]
   },
@@ -1048,10 +1043,7 @@
     "    # Add an extra batch dimension\n",
     "    input_image = input_image.unsqueeze(0)\n",
     "    \n",
-    "    # Move the input tensor to the GPU if available\n",
-    "    if torch.cuda.is_available():\n",
-    "        input_image = input_image.cuda()\n",
-    "\n",
+    "   \n",
     "    # Get the model prediction\n",
     "    with torch.no_grad():\n",
     "        output = model2(input_image)\n",
@@ -1061,7 +1053,7 @@
     "\n",
     "    return predicted_class.item()\n",
     "\n",
-    "# Example usage\n",
+    "# Example \n",
     "image_path = r\"C:\\Users\\ZINEB\\Documents\\GitHub\\mod_4_6-td2\\dog.png\"\n",
     "predicted_class = predict_class(image_path)\n",
     "print(\"Predicted Class:\", classes[predicted_class])\n"
@@ -1320,7 +1312,7 @@
    "source": [
     "# function to evaluate the accuracy of the model\n",
     "\n",
-    "def evaluate_model(model, dataloader):\n",
+    "def eval_model(model, dataloader):\n",
     "    model.eval()\n",
     "    correct = {cls: 0 for cls in classes}\n",
     "    total = {cls: 0 for cls in classes}\n",
@@ -1390,7 +1382,7 @@
    "source": [
     "\n",
     "# Evaluate the accuracy of  the initial model \n",
-    "accuracy_before_quantization = evaluate_model(model2, test_loader)\n",
+    "accuracy_before_quantization = eval_model(model2, test_loader)\n",
     "overall_accuracy_before_quantization = sum(accuracy_before_quantization.values()) / len(classes)\n",
     "\n",
     "print(\"Accuracy before quantization:\")\n",
@@ -1402,7 +1394,7 @@
     "quantized_model = torch.quantization.quantize_dynamic(model2, dtype=torch.qint8)\n",
     "\n",
     "# Evaluate the accuracy of the quantized model on the test set\n",
-    "accuracy_after_quantization = evaluate_model(quantized_model, test_loader)\n",
+    "accuracy_after_quantization = eval_model(quantized_model, test_loader)\n",
     "overall_accuracy_after_quantization = sum(accuracy_after_quantization.values()) / len(classes)\n",
     "\n",
     "print(\"Accuracy after quantization:\")\n",