9_cifar100_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "adfa641e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.losses import BinaryCrossentropy, MeanAbsoluteError\n",
    "from keras.models import Sequential, load_model\n",
    "from keras.layers import Dense \n",
    "\n",
    "from keras.datasets import cifar100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eac00420",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz\n",
      "169001437/169001437 [==============================] - 71s 0us/step\n"
     ]
    }
   ],
   "source": [
    "(X_train, y_train), (X_test, y_test) = cifar100.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "efc926c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 32, 32, 3)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "43c91edd",
   "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": "45799112",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 32, 32, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3f82a3f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 1)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30bca2e5",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8e4525b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize and reeshape images \n",
    "X_train_final = X_train.reshape(-1, 32*32*3) / 255\n",
    "X_test_final = X_test.reshape(-1, 32*32*3) / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "973f0c4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 3072)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_final.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e98ef903",
   "metadata": {},
   "source": [
    "### Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e6d8cae5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_4 (Dense)             (None, 1024)              3146752   \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 128)               131200    \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 64)                8256      \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 100)               6500      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 3,292,708\n",
      "Trainable params: 3,292,708\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(1024, input_dim=3072, activation='relu'))\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dense(64, activation='relu'))\n",
    "model.add(Dense(100, activation='softmax'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4351066c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=\"SparseCategoricalCrossentropy\", optimizer=\"Adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f4621e03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1563/1563 [==============================] - 166s 105ms/step - loss: 4.1926 - accuracy: 0.0545 - val_loss: 3.9901 - val_accuracy: 0.0799\n",
      "Epoch 2/10\n",
      "1563/1563 [==============================] - 171s 110ms/step - loss: 3.8623 - accuracy: 0.0991 - val_loss: 3.8195 - val_accuracy: 0.1081\n",
      "Epoch 3/10\n",
      "1563/1563 [==============================] - 248s 159ms/step - loss: 3.7174 - accuracy: 0.1246 - val_loss: 3.6903 - val_accuracy: 0.1378\n",
      "Epoch 4/10\n",
      "1563/1563 [==============================] - 214s 137ms/step - loss: 3.6088 - accuracy: 0.1440 - val_loss: 3.6054 - val_accuracy: 0.1560\n",
      "Epoch 5/10\n",
      "1563/1563 [==============================] - 246s 157ms/step - loss: 3.5256 - accuracy: 0.1611 - val_loss: 3.5398 - val_accuracy: 0.1600\n",
      "Epoch 6/10\n",
      "1563/1563 [==============================] - 160s 102ms/step - loss: 3.4675 - accuracy: 0.1707 - val_loss: 3.5599 - val_accuracy: 0.1574\n",
      "Epoch 7/10\n",
      "1563/1563 [==============================] - 172s 110ms/step - loss: 3.4209 - accuracy: 0.1797 - val_loss: 3.5404 - val_accuracy: 0.1635\n",
      "Epoch 8/10\n",
      "1563/1563 [==============================] - 199s 127ms/step - loss: 3.3729 - accuracy: 0.1856 - val_loss: 3.4858 - val_accuracy: 0.1703\n",
      "Epoch 9/10\n",
      "1563/1563 [==============================] - 176s 113ms/step - loss: 3.3391 - accuracy: 0.1947 - val_loss: 3.4948 - val_accuracy: 0.1763\n",
      "Epoch 10/10\n",
      "1563/1563 [==============================] - 172s 110ms/step - loss: 3.3053 - accuracy: 0.1996 - val_loss: 3.4262 - val_accuracy: 0.1876\n"
     ]
    }
   ],
   "source": [
    "result = model.fit(X_train_final, y_train, verbose=1, epochs=10, validation_data=(X_test_final, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cde7f5da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(result.history[\"loss\"])\n",
    "plt.plot(result.history[\"val_loss\"])\n",
    "plt.title(\"Model Loss\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.legend([\"Train\", \"Test\"], loc=\"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ad47f0b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(result.history[\"accuracy\"])\n",
    "plt.plot(result.history[\"val_accuracy\"])\n",
    "plt.title(\"Model Accuracy\")\n",
    "plt.ylabel(\"Accuuracy\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.legend([\"Train\", \"Test\"], loc=\"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5a50fe77",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"models/cifar100_classification_model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85b6f61d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}