9_cifar100_classification_gpu.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "adfa641e",
      "metadata": {
        "id": "adfa641e"
      },
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eac00420",
        "outputId": "a70e361c-6889-4016-a49c-0a3eed2cef92"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz\n",
            "169001437/169001437 [==============================] - 2s 0us/step\n"
          ]
        }
      ],
      "source": [
        "(X_train, y_train), (X_test, y_test) = cifar100.load_data()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "efc926c4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "efc926c4",
        "outputId": "3c9cdebf-6646-4d7e-cd96-44ee2b68c48e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 32, 32, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "X_train.shape "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "43c91edd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "43c91edd",
        "outputId": "f546d449-ab3e-4d46-b861-825d96f97adb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "y_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "45799112",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "45799112",
        "outputId": "0278608c-9f22-44a7-a831-038daf7c96d2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 32, 32, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "X_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "3f82a3f9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3f82a3f9",
        "outputId": "e451867d-130e-427d-977d-45a9913ffc15"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "y_test.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "30bca2e5",
      "metadata": {
        "id": "30bca2e5"
      },
      "source": [
        "### Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "8e4525b5",
      "metadata": {
        "id": "8e4525b5"
      },
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "973f0c4f",
        "outputId": "e5da39c3-bf76-4b0b-c40e-34ae580ff216"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 3072)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "X_train_final.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e98ef903",
      "metadata": {
        "id": "e98ef903"
      },
      "source": [
        "### Modelling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "e6d8cae5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e6d8cae5",
        "outputId": "4c9a9889-4400-452c-bff2-7693b2f546f0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_4 (Dense)             (None, 512)               1573376   \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 128)               65664     \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 100)               12900     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,651,940\n",
            "Trainable params: 1,651,940\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(512, input_dim=3072, activation='relu'))\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dense(100, activation='softmax'))\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "4351066c",
      "metadata": {
        "id": "4351066c"
      },
      "outputs": [],
      "source": [
        "model.compile(loss=\"SparseCategoricalCrossentropy\", optimizer=\"Adam\", metrics=[\"accuracy\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "f4621e03",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f4621e03",
        "outputId": "ab418247-cbbe-425c-e79e-bf9bc8345284"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 4.4162 - accuracy: 0.0301 - val_loss: 4.1457 - val_accuracy: 0.0582\n",
            "Epoch 2/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 3.9597 - accuracy: 0.0862 - val_loss: 3.8753 - val_accuracy: 0.1019\n",
            "Epoch 3/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 3.7707 - accuracy: 0.1184 - val_loss: 3.7343 - val_accuracy: 0.1237\n",
            "Epoch 4/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 3.6668 - accuracy: 0.1359 - val_loss: 3.6386 - val_accuracy: 0.1460\n",
            "Epoch 5/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 3.5907 - accuracy: 0.1504 - val_loss: 3.6333 - val_accuracy: 0.1461\n",
            "Epoch 6/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 3.5314 - accuracy: 0.1602 - val_loss: 3.6467 - val_accuracy: 0.1438\n",
            "Epoch 7/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 3.4821 - accuracy: 0.1712 - val_loss: 3.5249 - val_accuracy: 0.1694\n",
            "Epoch 8/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 3.4329 - accuracy: 0.1800 - val_loss: 3.5441 - val_accuracy: 0.1656\n",
            "Epoch 9/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 3.3908 - accuracy: 0.1871 - val_loss: 3.4929 - val_accuracy: 0.1742\n",
            "Epoch 10/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 3.3583 - accuracy: 0.1954 - val_loss: 3.4598 - val_accuracy: 0.1817\n",
            "Epoch 11/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 3.3292 - accuracy: 0.2002 - val_loss: 3.4161 - val_accuracy: 0.1921\n",
            "Epoch 12/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 3.3069 - accuracy: 0.2007 - val_loss: 3.4222 - val_accuracy: 0.1906\n",
            "Epoch 13/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 3.2862 - accuracy: 0.2063 - val_loss: 3.4182 - val_accuracy: 0.1932\n",
            "Epoch 14/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 3.2595 - accuracy: 0.2111 - val_loss: 3.4045 - val_accuracy: 0.1954\n",
            "Epoch 15/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 3.2417 - accuracy: 0.2127 - val_loss: 3.4780 - val_accuracy: 0.1847\n",
            "Epoch 16/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 3.2161 - accuracy: 0.2202 - val_loss: 3.4395 - val_accuracy: 0.1897\n",
            "Epoch 17/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 3.1984 - accuracy: 0.2215 - val_loss: 3.4183 - val_accuracy: 0.1930\n",
            "Epoch 18/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 3.1834 - accuracy: 0.2240 - val_loss: 3.4041 - val_accuracy: 0.2022\n",
            "Epoch 19/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 3.1734 - accuracy: 0.2285 - val_loss: 3.4190 - val_accuracy: 0.1961\n",
            "Epoch 20/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 3.1546 - accuracy: 0.2307 - val_loss: 3.4736 - val_accuracy: 0.1916\n",
            "Epoch 21/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 3.1424 - accuracy: 0.2319 - val_loss: 3.4336 - val_accuracy: 0.1972\n",
            "Epoch 22/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 3.1337 - accuracy: 0.2333 - val_loss: 3.4422 - val_accuracy: 0.1994\n",
            "Epoch 23/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 3.1137 - accuracy: 0.2370 - val_loss: 3.4074 - val_accuracy: 0.2032\n",
            "Epoch 24/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 3.1038 - accuracy: 0.2409 - val_loss: 3.4459 - val_accuracy: 0.2029\n",
            "Epoch 25/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 3.0924 - accuracy: 0.2415 - val_loss: 3.4712 - val_accuracy: 0.2006\n",
            "Epoch 26/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 3.0822 - accuracy: 0.2438 - val_loss: 3.4469 - val_accuracy: 0.2038\n",
            "Epoch 27/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 3.0702 - accuracy: 0.2450 - val_loss: 3.4448 - val_accuracy: 0.2046\n",
            "Epoch 28/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 3.0607 - accuracy: 0.2461 - val_loss: 3.4645 - val_accuracy: 0.2027\n",
            "Epoch 29/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 3.0509 - accuracy: 0.2511 - val_loss: 3.5238 - val_accuracy: 0.1993\n",
            "Epoch 30/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 3.0433 - accuracy: 0.2493 - val_loss: 3.5447 - val_accuracy: 0.1981\n",
            "Epoch 31/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 3.0243 - accuracy: 0.2531 - val_loss: 3.5067 - val_accuracy: 0.2062\n",
            "Epoch 32/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 3.0229 - accuracy: 0.2538 - val_loss: 3.6082 - val_accuracy: 0.1890\n",
            "Epoch 33/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 3.0111 - accuracy: 0.2566 - val_loss: 3.5956 - val_accuracy: 0.1940\n",
            "Epoch 34/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.9993 - accuracy: 0.2603 - val_loss: 3.5185 - val_accuracy: 0.1977\n",
            "Epoch 35/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 2.9878 - accuracy: 0.2608 - val_loss: 3.5334 - val_accuracy: 0.1978\n",
            "Epoch 36/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.9858 - accuracy: 0.2609 - val_loss: 3.4936 - val_accuracy: 0.1999\n",
            "Epoch 37/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 2.9789 - accuracy: 0.2625 - val_loss: 3.5705 - val_accuracy: 0.1989\n",
            "Epoch 38/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.9677 - accuracy: 0.2656 - val_loss: 3.5501 - val_accuracy: 0.2044\n",
            "Epoch 39/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 2.9575 - accuracy: 0.2667 - val_loss: 3.5863 - val_accuracy: 0.1992\n",
            "Epoch 40/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.9519 - accuracy: 0.2671 - val_loss: 3.6045 - val_accuracy: 0.1990\n",
            "Epoch 41/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 2.9494 - accuracy: 0.2670 - val_loss: 3.5656 - val_accuracy: 0.2053\n",
            "Epoch 42/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.9394 - accuracy: 0.2690 - val_loss: 3.6807 - val_accuracy: 0.1941\n",
            "Epoch 43/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.9369 - accuracy: 0.2691 - val_loss: 3.6918 - val_accuracy: 0.1924\n",
            "Epoch 44/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.9315 - accuracy: 0.2706 - val_loss: 3.6734 - val_accuracy: 0.1987\n",
            "Epoch 45/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.9207 - accuracy: 0.2731 - val_loss: 3.6191 - val_accuracy: 0.2026\n",
            "Epoch 46/100\n",
            "1563/1563 [==============================] - 5s 4ms/step - loss: 2.9170 - accuracy: 0.2739 - val_loss: 3.7455 - val_accuracy: 0.2036\n",
            "Epoch 47/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 2.9113 - accuracy: 0.2773 - val_loss: 3.6310 - val_accuracy: 0.2012\n",
            "Epoch 48/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 2.9059 - accuracy: 0.2768 - val_loss: 3.7542 - val_accuracy: 0.2033\n",
            "Epoch 49/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.8992 - accuracy: 0.2766 - val_loss: 3.6799 - val_accuracy: 0.2053\n",
            "Epoch 50/100\n",
            "1563/1563 [==============================] - 5s 4ms/step - loss: 2.8977 - accuracy: 0.2762 - val_loss: 3.6968 - val_accuracy: 0.2046\n",
            "Epoch 51/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.8869 - accuracy: 0.2796 - val_loss: 3.6648 - val_accuracy: 0.2042\n",
            "Epoch 52/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 2.8787 - accuracy: 0.2812 - val_loss: 3.7611 - val_accuracy: 0.2057\n",
            "Epoch 53/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.8793 - accuracy: 0.2823 - val_loss: 3.8816 - val_accuracy: 0.1996\n",
            "Epoch 54/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 2.8741 - accuracy: 0.2822 - val_loss: 3.6918 - val_accuracy: 0.1987\n",
            "Epoch 55/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.8747 - accuracy: 0.2827 - val_loss: 3.7500 - val_accuracy: 0.2048\n",
            "Epoch 56/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 2.8696 - accuracy: 0.2834 - val_loss: 3.6951 - val_accuracy: 0.2067\n",
            "Epoch 57/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.8606 - accuracy: 0.2862 - val_loss: 3.9009 - val_accuracy: 0.2008\n",
            "Epoch 58/100\n",
            "1563/1563 [==============================] - 5s 4ms/step - loss: 2.8651 - accuracy: 0.2824 - val_loss: 3.8082 - val_accuracy: 0.1993\n",
            "Epoch 59/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.8502 - accuracy: 0.2876 - val_loss: 3.8043 - val_accuracy: 0.2028\n",
            "Epoch 60/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.8499 - accuracy: 0.2843 - val_loss: 3.9447 - val_accuracy: 0.1941\n",
            "Epoch 61/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.8492 - accuracy: 0.2845 - val_loss: 3.8948 - val_accuracy: 0.1991\n",
            "Epoch 62/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.8412 - accuracy: 0.2895 - val_loss: 3.8666 - val_accuracy: 0.1952\n",
            "Epoch 63/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 2.8334 - accuracy: 0.2898 - val_loss: 3.7440 - val_accuracy: 0.2040\n",
            "Epoch 64/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.8291 - accuracy: 0.2922 - val_loss: 3.8565 - val_accuracy: 0.2077\n",
            "Epoch 65/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 2.8270 - accuracy: 0.2902 - val_loss: 3.8622 - val_accuracy: 0.1979\n",
            "Epoch 66/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.8235 - accuracy: 0.2919 - val_loss: 3.8936 - val_accuracy: 0.2013\n",
            "Epoch 67/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 2.8262 - accuracy: 0.2908 - val_loss: 4.0197 - val_accuracy: 0.1987\n",
            "Epoch 68/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.8158 - accuracy: 0.2936 - val_loss: 3.9660 - val_accuracy: 0.2008\n",
            "Epoch 69/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 2.8150 - accuracy: 0.2938 - val_loss: 3.9636 - val_accuracy: 0.2004\n",
            "Epoch 70/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.8096 - accuracy: 0.2963 - val_loss: 4.2125 - val_accuracy: 0.1998\n",
            "Epoch 71/100\n",
            "1563/1563 [==============================] - 5s 4ms/step - loss: 2.8052 - accuracy: 0.2941 - val_loss: 4.0782 - val_accuracy: 0.2054\n",
            "Epoch 72/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.8023 - accuracy: 0.2972 - val_loss: 4.1139 - val_accuracy: 0.1956\n",
            "Epoch 73/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 2.7991 - accuracy: 0.2973 - val_loss: 4.0495 - val_accuracy: 0.2020\n",
            "Epoch 74/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.7935 - accuracy: 0.2982 - val_loss: 3.9485 - val_accuracy: 0.2067\n",
            "Epoch 75/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.7962 - accuracy: 0.2984 - val_loss: 4.0295 - val_accuracy: 0.2019\n",
            "Epoch 76/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.7898 - accuracy: 0.2991 - val_loss: 4.0594 - val_accuracy: 0.2063\n",
            "Epoch 77/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.7902 - accuracy: 0.2986 - val_loss: 4.0117 - val_accuracy: 0.2061\n",
            "Epoch 78/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 2.7951 - accuracy: 0.2992 - val_loss: 4.1096 - val_accuracy: 0.2021\n",
            "Epoch 79/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.7797 - accuracy: 0.2989 - val_loss: 4.0734 - val_accuracy: 0.1929\n",
            "Epoch 80/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 2.7830 - accuracy: 0.2995 - val_loss: 4.1957 - val_accuracy: 0.1982\n",
            "Epoch 81/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.7768 - accuracy: 0.2993 - val_loss: 4.2450 - val_accuracy: 0.1999\n",
            "Epoch 82/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 2.7873 - accuracy: 0.3019 - val_loss: 4.1649 - val_accuracy: 0.2010\n",
            "Epoch 83/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.7703 - accuracy: 0.3024 - val_loss: 4.1891 - val_accuracy: 0.2030\n",
            "Epoch 84/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 2.7719 - accuracy: 0.3032 - val_loss: 4.1117 - val_accuracy: 0.1991\n",
            "Epoch 85/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.7637 - accuracy: 0.3046 - val_loss: 4.3371 - val_accuracy: 0.1941\n",
            "Epoch 86/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 2.7664 - accuracy: 0.3057 - val_loss: 4.3388 - val_accuracy: 0.1980\n",
            "Epoch 87/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.7568 - accuracy: 0.3060 - val_loss: 4.3232 - val_accuracy: 0.1994\n",
            "Epoch 88/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 2.7666 - accuracy: 0.3039 - val_loss: 4.3240 - val_accuracy: 0.1920\n",
            "Epoch 89/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.7545 - accuracy: 0.3069 - val_loss: 4.1718 - val_accuracy: 0.1938\n",
            "Epoch 90/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 2.7544 - accuracy: 0.3080 - val_loss: 4.2765 - val_accuracy: 0.2005\n",
            "Epoch 91/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 2.7526 - accuracy: 0.3069 - val_loss: 4.2930 - val_accuracy: 0.1966\n",
            "Epoch 92/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 2.7472 - accuracy: 0.3074 - val_loss: 4.5108 - val_accuracy: 0.1967\n",
            "Epoch 93/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.7491 - accuracy: 0.3073 - val_loss: 4.2259 - val_accuracy: 0.1915\n",
            "Epoch 94/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 2.7534 - accuracy: 0.3077 - val_loss: 4.3321 - val_accuracy: 0.2039\n",
            "Epoch 95/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.7551 - accuracy: 0.3056 - val_loss: 4.2322 - val_accuracy: 0.1980\n",
            "Epoch 96/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 2.7385 - accuracy: 0.3084 - val_loss: 4.4319 - val_accuracy: 0.1905\n",
            "Epoch 97/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.7318 - accuracy: 0.3117 - val_loss: 4.2949 - val_accuracy: 0.1964\n",
            "Epoch 98/100\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 2.7402 - accuracy: 0.3096 - val_loss: 4.2319 - val_accuracy: 0.1943\n",
            "Epoch 99/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.7379 - accuracy: 0.3118 - val_loss: 4.1479 - val_accuracy: 0.1993\n",
            "Epoch 100/100\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 2.7336 - accuracy: 0.3117 - val_loss: 4.4994 - val_accuracy: 0.1979\n"
          ]
        }
      ],
      "source": [
        "result = model.fit(X_train_final, y_train, verbose=1, epochs=100, validation_data=(X_test_final, y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "cde7f5da",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "cde7f5da",
        "outputId": "f220657e-8715-4450-d7da-057a67da31b9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "ad47f0b5",
        "outputId": "0231ac38-692a-4c7f-bd15-a35d570cf08e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "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": {
        "id": "5a50fe77"
      },
      "outputs": [],
      "source": [
        "model.save(\"models/cifar100_classification_model_gpu.h5\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "85b6f61d",
      "metadata": {
        "id": "85b6f61d"
      },
      "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"
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}