add answers for may-june 2025 + november-december 2025 pyqs for unit 6 (Reinforcement Learning)

Codes/Code-3b.md

@@ -38,34 +38,14 @@ from sklearn.metrics import confusion_matrix, classification_report
 # Fashion MNIST is built into Keras, downloads automatically on first run
 (X_train, y_train), (X_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()
 
-'''
-import numpy as np
-import gzip
-import os
-
-def load_fashion_mnist(path):
-    """Load Fashion MNIST from local .gz files (Kaggle Zalando format)."""
-    files = {
-        'X_train': 'train-images-idx3-ubyte.gz',
-        'y_train': 'train-labels-idx1-ubyte.gz',
-        'X_test':  't10k-images-idx3-ubyte.gz',
-        'y_test':  't10k-labels-idx1-ubyte.gz',
-    }
-
-    with gzip.open(os.path.join(path, files['X_train'])) as f:
-        X_train = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1, 28, 28)
-    with gzip.open(os.path.join(path, files['y_train'])) as f:
-        y_train = np.frombuffer(f.read(), np.uint8, offset=8)
-    with gzip.open(os.path.join(path, files['X_test'])) as f:
-        X_test = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1, 28, 28)
-    with gzip.open(os.path.join(path, files['y_test'])) as f:
-        y_test = np.frombuffer(f.read(), np.uint8, offset=8)
-
-    return (X_train, y_train), (X_test, y_test)
-
-# Replace the Keras load line with:
-(X_train, y_train), (X_test, y_test) = load_fashion_mnist('./fashion-mnist/')
-'''
+# --- Offline alternative (comment out tf.keras line above and use this instead) ---
+# import pandas as pd
+# train_df = pd.read_csv('fashion-mnist_train.csv')
+# test_df  = pd.read_csv('fashion-mnist_test.csv')
+# y_train = train_df['label'].values
+# y_test  = test_df['label'].values
+# X_train = train_df.drop('label', axis=1).values.reshape(-1, 28, 28)  # unflatten pixels to 28x28
+# X_test  = test_df.drop('label', axis=1).values.reshape(-1, 28, 28)
 
 print("Training set shape:", X_train.shape)   # (60000, 28, 28)
 print("Test set shape:    ", X_test.shape)    # (10000, 28, 28)

Notebooks/Notebook-3b.ipynb

@@ -51,58 +51,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "859cbc0f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training set shape: (60000, 28, 28)\n",
-      "Test set shape:     (10000, 28, 28)\n",
-      "Classes: [0 1 2 3 4 5 6 7 8 9]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 2. Load Dataset\n",
-    "# Fashion MNIST is built into Keras, downloads automatically on first run\n",
-    "(X_train, y_train), (X_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()\n",
-    "\n",
-    "'''\n",
-    "import numpy as np\n",
-    "import gzip\n",
-    "import os\n",
-    "\n",
-    "def load_fashion_mnist(path):\n",
-    "    \"\"\"Load Fashion MNIST from local .gz files (Kaggle Zalando format).\"\"\"\n",
-    "    files = {\n",
-    "        'X_train': 'train-images-idx3-ubyte.gz',\n",
-    "        'y_train': 'train-labels-idx1-ubyte.gz',\n",
-    "        'X_test':  't10k-images-idx3-ubyte.gz',\n",
-    "        'y_test':  't10k-labels-idx1-ubyte.gz',\n",
-    "    }\n",
-    "\n",
-    "    with gzip.open(os.path.join(path, files['X_train'])) as f:\n",
-    "        X_train = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1, 28, 28)\n",
-    "    with gzip.open(os.path.join(path, files['y_train'])) as f:\n",
-    "        y_train = np.frombuffer(f.read(), np.uint8, offset=8)\n",
-    "    with gzip.open(os.path.join(path, files['X_test'])) as f:\n",
-    "        X_test = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1, 28, 28)\n",
-    "    with gzip.open(os.path.join(path, files['y_test'])) as f:\n",
-    "        y_test = np.frombuffer(f.read(), np.uint8, offset=8)\n",
-    "\n",
-    "    return (X_train, y_train), (X_test, y_test)\n",
-    "\n",
-    "# Replace the Keras load line with:\n",
-    "(X_train, y_train), (X_test, y_test) = load_fashion_mnist('./fashion-mnist/')\n",
-    "'''\n",
-    "\n",
-    "print(\"Training set shape:\", X_train.shape)   # (60000, 28, 28)\n",
-    "print(\"Test set shape:    \", X_test.shape)    # (10000, 28, 28)\n",
-    "print(\"Classes:\", np.unique(y_train))"
-   ]
+   "outputs": [],
+   "source": "# 2. Load Dataset\n# Fashion MNIST is built into Keras — downloads automatically on first run\n(X_train, y_train), (X_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()\n\n# --- Offline alternative (comment out tf.keras line above and use this instead) ---\n# import pandas as pd\n# train_df = pd.read_csv('fashion-mnist_train.csv')\n# test_df  = pd.read_csv('fashion-mnist_test.csv')\n# y_train = train_df['label'].values\n# y_test  = test_df['label'].values\n# X_train = train_df.drop('label', axis=1).values.reshape(-1, 28, 28)  # unflatten pixels to 28x28\n# X_test  = test_df.drop('label', axis=1).values.reshape(-1, 28, 28)\n\nprint(\"Training set shape:\", X_train.shape)   # (60000, 28, 28)\nprint(\"Test set shape:    \", X_test.shape)    # (10000, 28, 28)\nprint(\"Classes:\", np.unique(y_train))"
   },
   {
    "cell_type": "code",
@@ -597,4 +550,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

README.md

@@ -59,6 +59,8 @@ This repository gathers comprehensive material for the SPPU Computer Engineering
 
 ### [IN-SEM PYQ Answers](Notes/IN-SEM%20PYQ%20Answers/)
 
+### [END-SEM PYQ Answers](Notes/END-SEM%20PYQ%20Answers/)
+
 ### [Question Bank](DL%20-%20Question%20Bank.pdf)
 
 ---