Lab10,11/Img-Classification-CNN-CIFAR10.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0ee9e7c3",
   "metadata": {},
   "source": [
    "# Apply Convolutional Neural Network Algorithm for an Image Classification Task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d6b93195",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import datasets, layers, models\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b0090333",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 32, 32, 3)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(X_train, y_train), (X_test,y_test) = datasets.cifar10.load_data()\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a7e92fe3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 32, 32, 3)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d1c6e3da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 1)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3fd85b38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6],\n",
       "       [9],\n",
       "       [9],\n",
       "       [4],\n",
       "       [1]], dtype=uint8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "958729b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 9, 9, 4, 1], dtype=uint8)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train = y_train.reshape(-1,)\n",
    "y_train[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c5aa3d5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = y_test.reshape(-1,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "635292af",
   "metadata": {},
   "outputs": [],
   "source": [
    "classes = [\"airplane\",\"automobile\",\"bird\",\"cat\",\"deer\",\"dog\",\"frog\",\"horse\",\"ship\",\"truck\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3050136b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_sample(X, y, index):\n",
    "    plt.figure(figsize = (15,2))\n",
    "    plt.imshow(X[index])\n",
    "    plt.xlabel(classes[y[index]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "566495a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sample(X_train, y_train, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "686fb18c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sample(X_train, y_train, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9b1d8f0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train / 255.0\n",
    "X_test = X_test / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4ee27556",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn = models.Sequential([\n",
    "    layers.Conv2D(filters=32, kernel_size=(3, 3), activation='relu', input_shape=(32, 32, 3)),\n",
    "    layers.MaxPooling2D((2, 2)),\n",
    "    \n",
    "    layers.Conv2D(filters=64, kernel_size=(3, 3), activation='relu'),\n",
    "    layers.MaxPooling2D((2, 2)),\n",
    "    \n",
    "    layers.Flatten(),\n",
    "    layers.Dense(64, activation='relu'),\n",
    "    layers.Dense(10, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4afd61b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 30, 30, 32)        896       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 15, 15, 32)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 13, 13, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 6, 6, 64)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 2304)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 64)                147520    \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                650       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 167,562\n",
      "Trainable params: 167,562\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cnn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "45c4439d",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "efc1eaf4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1563/1563 [==============================] - 32s 20ms/step - loss: 1.4789 - accuracy: 0.4735\n",
      "Epoch 2/10\n",
      "1563/1563 [==============================] - 30s 19ms/step - loss: 1.1447 - accuracy: 0.5970\n",
      "Epoch 3/10\n",
      "1563/1563 [==============================] - 31s 20ms/step - loss: 1.0130 - accuracy: 0.6466\n",
      "Epoch 4/10\n",
      "1563/1563 [==============================] - 33s 21ms/step - loss: 0.9209 - accuracy: 0.6818\n",
      "Epoch 5/10\n",
      "1563/1563 [==============================] - 34s 22ms/step - loss: 0.8607 - accuracy: 0.6995\n",
      "Epoch 6/10\n",
      "1563/1563 [==============================] - 41s 26ms/step - loss: 0.8088 - accuracy: 0.7178\n",
      "Epoch 7/10\n",
      "1563/1563 [==============================] - 39s 25ms/step - loss: 0.7652 - accuracy: 0.7354\n",
      "Epoch 8/10\n",
      "1563/1563 [==============================] - 35s 23ms/step - loss: 0.7266 - accuracy: 0.7482\n",
      "Epoch 9/10\n",
      "1563/1563 [==============================] - 35s 23ms/step - loss: 0.6887 - accuracy: 0.7594\n",
      "Epoch 10/10\n",
      "1563/1563 [==============================] - 39s 25ms/step - loss: 0.6551 - accuracy: 0.7714\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x27715669f50>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnn.fit(X_train, y_train, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2f588a30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 3s 8ms/step - loss: 0.9363 - accuracy: 0.6934\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.9362601637840271, 0.6934000253677368]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnn.evaluate(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f184f95a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 3s 8ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[8.4038498e-04, 2.2897107e-04, 6.0475781e-04, 3.2618016e-01,\n",
       "        4.0375596e-04, 6.1674064e-01, 6.1846846e-03, 3.0443154e-05,\n",
       "        4.8758525e-02, 2.7692835e-05],\n",
       "       [1.0166233e-03, 2.1018440e-02, 1.3960339e-06, 2.6144588e-07,\n",
       "        7.2558265e-08, 5.8015197e-09, 1.9510167e-09, 7.3137868e-10,\n",
       "        9.7686404e-01, 1.0992251e-03],\n",
       "       [3.4124576e-02, 2.2035692e-02, 1.6435728e-04, 6.4909039e-04,\n",
       "        1.1150514e-04, 2.9559817e-05, 5.6759633e-05, 6.7641436e-06,\n",
       "        9.3397337e-01, 8.8483328e-03],\n",
       "       [1.6205765e-01, 1.1567493e-03, 4.2221372e-04, 2.6603951e-04,\n",
       "        2.0974728e-04, 4.0990963e-06, 1.2799371e-04, 6.9718867e-06,\n",
       "        8.3565807e-01, 9.0470334e-05],\n",
       "       [1.4441246e-05, 1.3341712e-05, 3.2107618e-02, 2.2657486e-02,\n",
       "        4.8633581e-01, 2.3079084e-03, 4.5651633e-01, 1.3730059e-05,\n",
       "        2.9313902e-05, 4.0484697e-06]], dtype=float32)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = cnn.predict(X_test)\n",
    "y_pred[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b130a87e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 8, 8, 8, 4]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_classes = [np.argmax(element) for element in y_pred]\n",
    "y_classes[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cf9c5f2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 8, 8, 0, 6], dtype=uint8)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3f940557",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sample(X_test, y_test,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "19aad698",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ship'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes[y_classes[3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "326e4042",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'automobile'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes[y_classes[9]]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}