Lab10,11/NN_Image_Classifications.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0631e6a6",
   "metadata": {},
   "source": [
    "# Apply Convolutional Neural Network Algorithm for an Image Classification Task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "52b3ad39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tensorflow_datasets\n",
      "  Using cached tensorflow_datasets-4.9.2-py3-none-any.whl (5.4 MB)\n",
      "Collecting array-record\n",
      "  Using cached array_record-0.4.0-py38-none-any.whl (3.0 MB)\n",
      "Collecting promise\n",
      "  Using cached promise-2.3-py3-none-any.whl\n",
      "Collecting importlib-resources\n",
      "  Using cached importlib_resources-6.4.0-py3-none-any.whl (38 kB)\n",
      "Requirement already satisfied: click in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow_datasets) (7.1.2)\n",
      "Requirement already satisfied: wrapt in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow_datasets) (1.12.1)\n",
      "Requirement already satisfied: protobuf>=3.20 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow_datasets) (3.20.3)\n",
      "Requirement already satisfied: requests>=2.19.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow_datasets) (2.25.1)\n",
      "Requirement already satisfied: toml in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow_datasets) (0.10.2)\n",
      "Requirement already satisfied: termcolor in c:\\users\\admin\\appdata\\roaming\\python\\python38\\site-packages (from tensorflow_datasets) (2.4.0)\n",
      "Collecting tensorflow-metadata\n",
      "  Using cached tensorflow_metadata-1.14.0-py3-none-any.whl (28 kB)\n",
      "Requirement already satisfied: tqdm in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow_datasets) (4.59.0)\n",
      "Collecting dm-tree\n",
      "  Using cached dm_tree-0.1.8-cp38-cp38-win_amd64.whl (101 kB)\n",
      "Requirement already satisfied: absl-py in c:\\users\\admin\\appdata\\roaming\\python\\python38\\site-packages (from tensorflow_datasets) (2.1.0)\n",
      "Requirement already satisfied: psutil in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow_datasets) (5.8.0)\n",
      "Requirement already satisfied: etils[enp,epath]>=0.9.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow_datasets) (1.3.0)\n",
      "Requirement already satisfied: numpy in c:\\users\\admin\\appdata\\roaming\\python\\python38\\site-packages (from tensorflow_datasets) (1.23.5)\n",
      "Requirement already satisfied: zipp in c:\\programdata\\anaconda3\\lib\\site-packages (from etils[enp,epath]>=0.9.0->tensorflow_datasets) (3.4.1)\n",
      "Requirement already satisfied: typing_extensions in c:\\programdata\\anaconda3\\lib\\site-packages (from etils[enp,epath]>=0.9.0->tensorflow_datasets) (3.7.4.3)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests>=2.19.0->tensorflow_datasets) (1.26.4)\n",
      "Requirement already satisfied: idna<3,>=2.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests>=2.19.0->tensorflow_datasets) (2.10)\n",
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      "Requirement already satisfied: chardet<5,>=3.0.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests>=2.19.0->tensorflow_datasets) (4.0.0)\n",
      "Requirement already satisfied: six in c:\\programdata\\anaconda3\\lib\\site-packages (from promise->tensorflow_datasets) (1.15.0)\n",
      "Collecting googleapis-common-protos<2,>=1.52.0\n",
      "  Using cached googleapis_common_protos-1.63.0-py2.py3-none-any.whl (229 kB)\n",
      "Collecting absl-py\n",
      "  Using cached absl_py-1.4.0-py3-none-any.whl (126 kB)\n",
      "Installing collected packages: importlib-resources, googleapis-common-protos, absl-py, tensorflow-metadata, promise, dm-tree, array-record, tensorflow-datasets\n",
      "  Attempting uninstall: absl-py\n",
      "    Found existing installation: absl-py 2.1.0\n",
      "    Uninstalling absl-py-2.1.0:\n",
      "      Successfully uninstalled absl-py-2.1.0\n",
      "Successfully installed absl-py-1.4.0 array-record-0.4.0 dm-tree-0.1.8 googleapis-common-protos-1.63.0 importlib-resources-6.4.0 promise-2.3 tensorflow-datasets-4.9.2 tensorflow-metadata-1.14.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "-ensorflow-intel 2.13.0 requires keras<2.14,>=2.13.1, but you have keras 2.12.0 which is incompatible.\n",
      "-ensorflow-intel 2.13.0 requires tensorboard<2.14,>=2.13, but you have tensorboard 2.12.3 which is incompatible.\n",
      "-ensorflow-intel 2.13.0 requires tensorflow-estimator<2.14,>=2.13.0, but you have tensorflow-estimator 2.12.0 which is incompatible.\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "58155a42",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models, datasets\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8655e58a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load and preprocess the CIFAR-10 dataset\n",
    "(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()\n",
    "train_images, test_images = train_images / 255.0, test_images / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c436a2ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the CNN model\n",
    "model = models.Sequential([\n",
    "    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),\n",
    "    layers.MaxPooling2D((2, 2)),\n",
    "    layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "    layers.MaxPooling2D((2, 2)),\n",
    "    layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "    layers.Flatten(),\n",
    "    layers.Dense(64, activation='relu'),\n",
    "    layers.Dense(10)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "51bed042",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0a899617",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1563/1563 [==============================] - 25s 16ms/step - loss: 1.5071 - accuracy: 0.4493 - val_loss: 1.2205 - val_accuracy: 0.5584\n",
      "Epoch 2/10\n",
      "1563/1563 [==============================] - 24s 16ms/step - loss: 1.1575 - accuracy: 0.5938 - val_loss: 1.1478 - val_accuracy: 0.5939\n",
      "Epoch 3/10\n",
      "1563/1563 [==============================] - 24s 16ms/step - loss: 0.9991 - accuracy: 0.6459 - val_loss: 0.9499 - val_accuracy: 0.6660\n",
      "Epoch 4/10\n",
      "1563/1563 [==============================] - 25s 16ms/step - loss: 0.9047 - accuracy: 0.6823 - val_loss: 1.0443 - val_accuracy: 0.6340\n",
      "Epoch 5/10\n",
      "1563/1563 [==============================] - 25s 16ms/step - loss: 0.8322 - accuracy: 0.7072 - val_loss: 0.9263 - val_accuracy: 0.6779\n",
      "Epoch 6/10\n",
      "1563/1563 [==============================] - 25s 16ms/step - loss: 0.7803 - accuracy: 0.7281 - val_loss: 0.8730 - val_accuracy: 0.6962\n",
      "Epoch 7/10\n",
      "1563/1563 [==============================] - 25s 16ms/step - loss: 0.7281 - accuracy: 0.7439 - val_loss: 0.8662 - val_accuracy: 0.6988\n",
      "Epoch 8/10\n",
      "1563/1563 [==============================] - 25s 16ms/step - loss: 0.6840 - accuracy: 0.7590 - val_loss: 0.9171 - val_accuracy: 0.6926\n",
      "Epoch 9/10\n",
      "1563/1563 [==============================] - 25s 16ms/step - loss: 0.6479 - accuracy: 0.7740 - val_loss: 0.8780 - val_accuracy: 0.7079\n",
      "Epoch 10/10\n",
      "1563/1563 [==============================] - 25s 16ms/step - loss: 0.6102 - accuracy: 0.7843 - val_loss: 0.8824 - val_accuracy: 0.7053\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "history = model.fit(train_images, train_labels, epochs=10,\n",
    "                    validation_data=(test_images, test_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "28ba0590",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 4ms/step - loss: 0.8824 - accuracy: 0.7053\n",
      "Test accuracy: 0.705299973487854\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
    "print(f'Test accuracy: {test_acc}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "42a7886a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training history\n",
    "plt.plot(history.history['accuracy'], label='accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='val_accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.ylim([0, 1])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a3cc2573",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e9674b26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 4ms/step\n",
      "Confusion Matrix:\n",
      "[[733  14  62  13  28  13  15  10  88  24]\n",
      " [ 28 806   8   6   9   5  18   3  30  87]\n",
      " [ 45   5 525  60 153 106  71  19  12   4]\n",
      " [ 21   3  64 463  95 236  73  19  18   8]\n",
      " [ 13   1  44  41 766  53  37  34   9   2]\n",
      " [ 14   1  30 137  66 678  37  28   6   3]\n",
      " [  9   3  47  43  49  34 808   4   3   0]\n",
      " [ 18   3  29  32 100 107   8 694   3   6]\n",
      " [ 66  32  16  14  19  13   7   1 815  17]\n",
      " [ 37  70  15  16  21  15  17   9  35 765]]\n"
     ]
    }
   ],
   "source": [
    "test_predictions = np.argmax(model.predict(test_images), axis=1)\n",
    "cm = confusion_matrix(test_labels, test_predictions)\n",
    "print(\"Confusion Matrix:\")\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e719c004",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.heatmap(cm,annot=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}