Task5_the_dragon_level.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b5ed33db-b60e-413d-8c53-b2d981ff11d0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import pennylane as qml\n",
    "from pennylane import numpy as pnp\n",
    "from pennylane.optimize import AdamOptimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a87dde13-b884-4698-9b31-bdbc52039c5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num GPUs Available:  0\n"
     ]
    }
   ],
   "source": [
    "# Ensure GPU is available if needed\n",
    "print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cfa29a0c-0307-4435-ba9f-05ef1ba3ed88",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Local dataset path (update this path based on your local setup)\n",
    "dataset_path = '/Users/hannasav/downloads/qars/al5083'\n",
    "train_image_dir = os.path.join(dataset_path, 'train')\n",
    "test_image_dir = os.path.join(dataset_path, 'test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ec978e1d-89c7-4c51-b31c-9be8e7ddd5e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters for quicker testing\n",
    "NUM_TRAIN_IMAGES = 100  # Limit number of training images\n",
    "NUM_TEST_IMAGES = 50    # Limit number of testing images\n",
    "IMAGE_SIZE = (64, 64)   # Reduced image size for faster processing\n",
    "BATCH_SIZE = 32         # Define batch size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dd799604-4fd0-4135-ae14-82e526f33aac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to load a limited number of image files from a directory\n",
    "def get_limited_image_paths_and_labels(directory, label, limit=None):\n",
    "    image_paths = []\n",
    "    labels = []\n",
    "    count = 0\n",
    "    for root, _, files in os.walk(directory):\n",
    "        for file in files:\n",
    "            if file.endswith(('.png', '.jpg', '.jpeg')):\n",
    "                image_paths.append(os.path.join(root, file))\n",
    "                labels.append(label)\n",
    "                count += 1\n",
    "                if limit and count >= limit:\n",
    "                    return image_paths, labels\n",
    "    return image_paths, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8f7805e4-4eb1-4cd9-a848-f80e3c2df07d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a simplified image processing function\n",
    "def preprocess_image(image_path, label):\n",
    "    image = tf.io.read_file(image_path)\n",
    "    image = tf.image.decode_image(image, channels=3, expand_animations=False)\n",
    "    image = tf.image.resize(image, IMAGE_SIZE)  # Smaller size for faster processing\n",
    "    image = image / 255.0  # Normalize to [0, 1]\n",
    "    return image, label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6344b5d8-a185-4f89-99d5-29ea12ac6e1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create TensorFlow Dataset with larger batch size if memory allows\n",
    "def create_tf_dataset(image_paths, labels, batch_size=BATCH_SIZE):\n",
    "    dataset = tf.data.Dataset.from_tensor_slices((image_paths, labels))\n",
    "    dataset = dataset.map(lambda x, y: preprocess_image(x, y), num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
    "    dataset = dataset.shuffle(buffer_size=1000)\n",
    "    dataset = dataset.batch(batch_size)\n",
    "    dataset = dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3a7aa42b-04ad-44c7-9d1a-226e120a3021",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get a limited number of images\n",
    "train_image_paths, train_labels = get_limited_image_paths_and_labels(train_image_dir, label=1, limit=NUM_TRAIN_IMAGES)\n",
    "test_image_paths, test_labels = get_limited_image_paths_and_labels(test_image_dir, label=0, limit=NUM_TEST_IMAGES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4e2fc4e5-1799-46c7-98ef-bc02ea7388cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_classical_model():\n",
    "    model = tf.keras.Sequential([\n",
    "        tf.keras.layers.InputLayer(input_shape=(IMAGE_SIZE[0], IMAGE_SIZE[1], 3)),  # Using InputLayer for the first layer\n",
    "        tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),\n",
    "        tf.keras.layers.MaxPooling2D(2, 2),\n",
    "        tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "        tf.keras.layers.MaxPooling2D(2, 2),\n",
    "        tf.keras.layers.Flatten(),\n",
    "        tf.keras.layers.Dense(64, activation='relu'),\n",
    "        tf.keras.layers.Dense(1, activation='sigmoid')  # Assuming binary classification\n",
    "    ])\n",
    "    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8a1a7679-8d22-477c-a1e2-ed62a5b716ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.11/site-packages/keras/src/layers/core/input_layer.py:26: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'train_ds' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[20], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m classical_model \u001b[38;5;241m=\u001b[39m create_classical_model()\n\u001b[0;32m----> 2\u001b[0m history_classical \u001b[38;5;241m=\u001b[39m classical_model\u001b[38;5;241m.\u001b[39mfit(train_ds, epochs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, validation_data\u001b[38;5;241m=\u001b[39mtest_ds)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'train_ds' is not defined"
     ]
    }
   ],
   "source": [
    "classical_model = create_classical_model()\n",
    "history_classical = classical_model.fit(train_ds, epochs=1, validation_data=test_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c0134b98-7e4b-4fd3-be2c-bdd73b5e5083",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_quantum_model():\n",
    "    dev = qml.device(\"default.qubit\", wires=4)\n",
    "    \n",
    "    @qml.qnode(dev)\n",
    "    def quantum_circuit(params, x):\n",
    "        qml.AngleEmbedding(x, wires=range(4))\n",
    "        qml.BasicEntanglerLayers(params, wires=range(4))\n",
    "        return qml.expval(qml.PauliZ(0))\n",
    "    \n",
    "    def qnn(params, x):\n",
    "        return quantum_circuit(params, x)\n",
    "    \n",
    "    class QuantumModel(tf.keras.Model):\n",
    "        def __init__(self, n_qubits=4, n_layers=3):\n",
    "            super(QuantumModel, self).__init__()\n",
    "            self.params = tf.Variable(pnp.random.randn(n_layers, n_qubits, 3))\n",
    "            self.optimizer = AdamOptimizer(0.1)\n",
    "        \n",
    "        def call(self, x):\n",
    "            return tf.map_fn(lambda xi: qnn(self.params, xi), x, dtype=tf.float32)\n",
    "        \n",
    "        def train_step(self, data):\n",
    "            x, y = data\n",
    "            with tf.GradientTape() as tape:\n",
    "                predictions = self(x)\n",
    "                loss = tf.keras.losses.binary_crossentropy(y, predictions)\n",
    "            gradients = tape.gradient(loss, [self.params])\n",
    "            self.optimizer.apply_gradients(zip(gradients, [self.params]))\n",
    "            return {'loss': loss}\n",
    "\n",
    "    return QuantumModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b5859748-3e90-4c4d-be2a-0e63a0b550d8",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train_ds' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[32], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m quantum_model \u001b[38;5;241m=\u001b[39m create_quantum_model()\n\u001b[0;32m----> 2\u001b[0m history_quantum \u001b[38;5;241m=\u001b[39m quantum_model\u001b[38;5;241m.\u001b[39mfit(train_ds, epochs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m, validation_data\u001b[38;5;241m=\u001b[39mtest_ds)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'train_ds' is not defined"
     ]
    }
   ],
   "source": [
    "quantum_model = create_quantum_model()\n",
    "history_quantum = quantum_model.fit(train_ds, epochs=3, validation_data=test_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9fe128fa-d512-4a86-83a9-79fd2ccd263f",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'test_ds' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[34], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m test_loss_classical, test_acc_classical \u001b[38;5;241m=\u001b[39m classical_model\u001b[38;5;241m.\u001b[39mevaluate(test_ds)\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mClassical model test accuracy: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtest_acc_classical\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      4\u001b[0m test_loss_quantum, test_acc_quantum \u001b[38;5;241m=\u001b[39m quantum_model\u001b[38;5;241m.\u001b[39mevaluate(test_ds)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'test_ds' is not defined"
     ]
    }
   ],
   "source": [
    "test_loss_classical, test_acc_classical = classical_model.evaluate(test_ds)\n",
    "print(f'Classical model test accuracy: {test_acc_classical}')\n",
    "\n",
    "test_loss_quantum, test_acc_quantum = quantum_model.evaluate(test_ds)\n",
    "print(f'Quantum model test accuracy: {test_acc_quantum}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7faa9cd3-bca6-45b2-b356-a243562ae13a",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'history_classical' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[36], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m plt\u001b[38;5;241m.\u001b[39mstyle\u001b[38;5;241m.\u001b[39muse(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclassic\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      2\u001b[0m fig, (ax1, ax2) \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m1\u001b[39m, figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m6\u001b[39m, \u001b[38;5;241m9\u001b[39m))\n\u001b[0;32m----> 3\u001b[0m ax1\u001b[38;5;241m.\u001b[39mplot(history_classical\u001b[38;5;241m.\u001b[39mhistory[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval_accuracy\u001b[39m\u001b[38;5;124m'\u001b[39m], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m-og\u001b[39m\u001b[38;5;124m\"\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mClassical model\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      4\u001b[0m ax1\u001b[38;5;241m.\u001b[39mplot(history_quantum\u001b[38;5;241m.\u001b[39mhistory[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval_accuracy\u001b[39m\u001b[38;5;124m'\u001b[39m], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m-ob\u001b[39m\u001b[38;5;124m\"\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mQuantum model\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      5\u001b[0m ax1\u001b[38;5;241m.\u001b[39mset_ylabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mValidation Accuracy\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'history_classical' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use(\"classic\")\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(6, 9))\n",
    "ax1.plot(history_classical.history['val_accuracy'], \"-og\", label=\"Classical model\")\n",
    "ax1.plot(history_quantum.history['val_accuracy'], \"-ob\", label=\"Quantum model\")\n",
    "ax1.set_ylabel(\"Validation Accuracy\")\n",
    "ax1.legend()\n",
    "\n",
    "ax2.plot(history_classical.history['val_loss'], \"-og\", label=\"Classical model\")\n",
    "ax2.plot(history_quantum.history['val_loss'], \"-ob\", label=\"Quantum model\")\n",
    "ax2.set_ylabel(\"Validation Loss\")\n",
    "ax2.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47b3eadf-aa6e-4ddf-b943-4db76c61f447",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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