Data partitioning and normalization

Author: Sayed-Hossein-Hosseini

Audio_Classification_Using_CNNs.ipynb

@@ -298,17 +298,22 @@
         "\n",
         "from tensorflow.keras.utils import to_categorical\n",
         "from sklearn.model_selection import train_test_split\n",
+        "import numpy as np\n",
         "\n",
-        "##############################################\n",
-        "############# YOUR CODES GO HERE #############\n",
-        "x_train, x_test, y_train, y_test = ...\n",
+        "# Convert lists to numpy arrays\n",
+        "x = np.array(x)\n",
+        "y = np.array(y)\n",
         "\n",
-        "x_train_norm = ...\n",
-        "x_test_norm = ...\n",
+        "# Split into training and test sets (e.g., 80% train, 20% test)\n",
+        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42, stratify=y)\n",
         "\n",
-        "y_train_encoded = ...\n",
-        "y_test_encoded = ....\n",
-        "##############################################"
+        "# Normalize pixel values\n",
+        "x_train_norm = x_train / 255.0\n",
+        "x_test_norm = x_test / 255.0\n",
+        "\n",
+        "# One-hot encode the labels\n",
+        "y_train_encoded = to_categorical(y_train, num_classes=4)\n",
+        "y_test_encoded = to_categorical(y_test, num_classes=4)"
       ],
       "metadata": {
         "id": "Jw3uR_FBOy67"