gpu_working.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyMXki4hMFLsIR1hIGCXjQCn",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/M-TALH007/M-TALH007/blob/main/gpu_working.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "EZdg85qgwj3n"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow import keras\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "f54QdhDcwmAR"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.config.experimental.list_physical_devices()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JCQJMkYFw5cO",
        "outputId": "8ba18ffb-d31b-4cbb-a924-ec296ccaa402"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'),\n",
              " PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.test.is_built_with_cuda()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d1ppxAldxElp",
        "outputId": "c33c230d-4222-4f67-ee5b-cf3d3b0fe410"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(x_train,y_train),(x_test,y_test)= tf.keras.datasets.cifar10.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6P7AfalKxQwW",
        "outputId": "9048f026-4be6-4035-d455-42f435700b96"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170498071/170498071 [==============================] - 14s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_sample(index):\n",
        "    plt.figure(figsize = (10,1))\n",
        "    plt.imshow(x_train[index])\n"
      ],
      "metadata": {
        "id": "ZRHw6eEyDkAu"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_sample(0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "lrIyqLHtDlAK",
        "outputId": "3701a4f7-58c0-4248-8aa3-e0a2dec2081d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x100 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAHwAAAB9CAYAAABgQgcbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAzDklEQVR4nO29W6ylaVX3+xvP4X3naa1VVV3d1ScaUPeOEty6g9ASEyBKJPHKyIWJN2oMRNMQtROjeCHRGy410dYrbS62bIyJhEQTblrBrYEQ2xADhN4fbj9poA8UVbUO8/C+z2Hsi/HMuaq+FqzGLnpZVSOZtapmzTXnO9/xPOMZh//4D1FV5a7cMeJe7Qu4K99duavwO0zuKvwOk7sKv8PkrsLvMLmr8DtM7ir8DpO7Cr/D5K7C7zC5q/A7TG6Zwp944gle97rXMZlMePTRR/nsZz97qz7qrrwMuSUK/4u/+Asef/xxPvjBD/LP//zP/NAP/RDvete7ePHFF2/Fx92VlyFyK4onjz76KG9+85v5oz/6IwBqrbzmNa/h/e9/P7/1W7/1bX+31srXv/519vb2EJFX+tJuS1FVjo+PefDBB3Hu2+/h8Ep/+DiOPP3003zgAx/YPeec453vfCef/vSnX/L6YRgYhmH376997Wu84Q1veKUv646QZ599locffvjbvuYVV/jly5cppXDp0qUbnr906RJf+tKXXvL6D33oQ/zu7/7uS57/P9/0I+SSGYYNDiU4xQvMvNI5WPSBg3lH9I7FvCNGz7TvmPYBJw4vDhGH+IhzHhUB56ilslqvSLlQq1Ir+BCYTSc4cYgWRCu1VnLJ9jMVqlZC8IQQUJRKRVFSGik5E7rIdNI3q1QBpZRKKQVBcO1WOxFEhOADXd/hnafrI955nAs4H6hVyblQUUBQBERAHKpQ2nWpChVhPYx88I/+b/b29v5T/bziCn+58oEPfIDHH3989++joyNe85rXcOXqFcZhYNhsCA4mXglO8BOHBKHUCL7DdYFO53R4Yi247JB2kxRBXKCKoyqUqtRaGYbBlFGVWhXnHToEnAheFIe9rpSKqqIKqKIhUENAtZJKQlXJJVG14GsHfmoKF0UALQXNBSdCkIAgVFVUlSLCODjECbXr8d7jxCPOo0BVALGF2n6vVDPfJRdUK+BAHDklgJs6Al9xhV+8eBHvPS+88MINz7/wwgvcf//9L3l93/f0ff+S569evcqwXjNsNkQH8wjRQzePSOcZNZJ9jy8RZoK4SFUhVQFVqKDbryiOnAtj29VjSjtlVzXlrJ0gAtEp3tF2v72DE4+IkL3Hh0CthTENVK3UWlAq2vcEEs45pB2jtWRqMYWLiwhiO7dWtFkREaFrCkccIh4QcL4tHnvkUkmpUFWppaCqiHic9wyp3LR+XnEvves63vSmN/HUU0/tnqu18tRTT/HWt771pt/HC3gH0UHnYRod0+iZdYFpH5h2kb7r6LqIDwHnbXfYDfNUFWoVSq2kXCjVdqoC4hzOOZx39jvOgW0kfPCEGIgxtJ+R2NkjxIj3Hu8DIURC6PAh4n1AxKMqZg0QRFwz0RHnIz5EXIg4H0yZziPtmlWkHQKgaNO3w3kPIs3K6M46bL3sqpWcMinlm76vt8SkP/744/z8z/88P/IjP8Jb3vIW/uAP/oDlcskv/uIv3vyFOUG8EIIp+/w80AfHhb2eeR9ZTHv2FjNi9PR9Twx2c0RAtVJqpqqSS7UbRdvxIrgQcCoUVbOdqqAZcRD7yKT31Aq1KILgfcSJt5cpVFVcCM2kZ0opeO+o1Y4RH4J5y85usDgh+GhmXhI1J0yvFQDFUaGd9bYgY4yIc2aNcqVqpdS2s9tvlVxIKb+sHX5LFP6zP/uzfOMb3+B3fud3eP755/nhH/5hPvGJT7zEkft24kTN/jiIXuiDow+ePni64Aje4Z3gnNiNBETVHJmtud6Z7bozjfbetqNr3T6lzT0C7x3ee0QUUXvWNXOv222IKWf3Xk4RkXYN2/+nLUDZOWp2HNvuVxRUrn/xdd9eTx963eN02baFrZRqR8PNyi2Jw/8rcnR0xMHBAQ/fdw5XBiQP7E0jD16Y0QfP/sR2ehcDkz7inBCjmWUFUKEUJY123iGKCoToiV00BfgAIoxjYkyZ7R5zTji/P2U+7ezMH7Pd6yrNO67kYjd/e4u99zjnUK2oFjuT+7B73jm3UzxAzmYRaq3UYjvTOWcRAu1kESGEsDu7azUHM5dTk66qrDaJ5WpkSIX/6//5/zg8PGR/f//b3t9X3Uv/VuJRggcvjknnmU8iffBMO0fnBe/AaUGqULIZRFXbZabwiiqItwdqvyNO8N6UkN2pP++2PkNwdjyoUtouyrWiVam5Uktlt0VEcN7jnUOrUqqZW1GzDlvlmQlStqeva9bAPG0QHLI1HbZqqSXt/q1VEQUnNA/e3qtWJeVKyje/w8+swjvv6MXjtDKNnj4G+uCZNIU7Z6ay3VVozrniAKWGZgDFdrm468wr7RyshVqy/d0p4BiGhEPa+Wg7MaXSdlghlYKIw7uA4MhV0Wq+gKpeZ5ntM523s76oLRSHOWpOHeLrLuSrzZfYGtztEZFKIRdbTGPW5pfY69dDYT1kxttB4dMgTJwnUJl1gUlnO3zWOybRNa/VvqgdrZZYsdjbzmsFspppFyc4522hYLuQWtCcUGlm21U265GSiu3oYs7SZkyUUkhayLXgXKCPgnMKxVIwO0uhLVUi5nxJCGit1GxRQpRAEE8l4SgtyZKp1c7jUraOnB0bmyEx5kLKyrotvGGsbRFAKkK+HRQevSN6JeAJYeug0XYqIILH7czr1sqqCFSoTq/3cXamcvvTflR2b9AWSNGKq6feeG3Jmt3f7VCnVjOrSiXXglBxWAzvsxK0Uqim1FIZh2yWIHhwDvBAh5OK+ZC1OXyV2j5bVSlFybmSSmVsCt8MmZQrpQq5OvLLcNrOrMIX08gseqKzuLuLjhgcLT+Bd87OTmgxtjZDLRSxPV8rFNQir1qpuaBCswxKzQmt2Q5HAIRUEgVBK1QVtCpDO58t22WftakFQRlrotSKiOKl4hxMB/P2VSrVJWoppLWFYvvTwKzzdF1gOp0iXsFvUDLDMKDFFtg4FkqtrIfEMGaGXFhuMmOqXDvasNlkyyDiLLy8STmzCo/B0UUhOiEGj/On4dH2sasMad2ZdWV3pFtoxS6nstvNqhW0Nqfp+jjKdrhWqFWo2pyxlvSwc5ad9w/KmCupWBhpD0GoeG9HTRU7GvKQERVGD504vHcIwY4BZwtRXN5dSNWW7WvndymWUyjFFsMwFltQ4lrO/ebkzCp8MYsseuh8pQueSR/wIjhX2dYSTOzGC5ZyLLVYHrpWBKELnk48MXj6LqIoecxUreaN01OB3G7wMCRSyWYuSzsySoVqRZvO2UJyLpuPsIF10hsW28o7nAPvLQvYh457Du4h+ogvE1wJSBY0gfMQe8uqaXCU4LZVACwHYJas7xzOd5SqdN2EMVXGqgxZyVX59+OrN3Vfz6zC57PAohc6Vwne00WPE3BsEyJwwwGtSq2ZkvMuiSI4fOhwPhJjoOssD16TVbu8c0gXbAflgtbKepNZDwOpCGMWC+e2OaDomAaxI8VnqkIeM5uhUlRo/j4eOyX6rqPvO+Ks4/z+eSZxwnCkjOuKFCDbtUcn+ODILaGk1RI5gkUV3jmC8/R9RIHZXKgKq7FwMmRSLsB/c4VH73clUSfawjDw4s3LRgDX8syCojjnCe0b6XU7xJIe2pIjarsGTy1CKZgHvM7kUllvCuuxkotjKKboCRY7z+cdl/ameC/0nbdj5NoGvxrZZOVkKJzu9RYOWvxHyYkijlKqxfNems+ooAW02jdysju+7O8Or9rOKfsZQmil34wKjPnmSyJnVuGzPtK7hK8VL47oHM4LsSU6aqmUXFuMW1AVYoiE0N/guVd1VDWFl5bM8DHiCYyrwpAq603lm1cHxpQ52WQ2qZJVGYsniOKjI3i4f3HA//Ga+5j0gXP7HRX4/Nev8JUrS66cbHj2ypJcC1VbBk3Ai4V/42YJKTGuM3mwWL5MrDxqroTiXSVGD0CIDhUliiKufZdacd6ztzeh63tWm8R0Pbz6ufRXQoL3eCnbRd0cMdmVDBXZVkFvKIxIy5O/JF67TmRXYy4W8uRKSpUxKakoudqxXWqLoJqP1nnHohVXFs28zruOeZdYxULwttNy3SZUaKdNJeWMVEjZLIkvjlI9snUOW8QoIi1nYDvdq9ulUsGygd4LwQsxCH30p9/1Zu7rd6SN74LMp1M6FepYrIzY6sQpK1AoZZtSPHVwsoKWbbrS7kOulaJKkLDLTxe1kG05rLl8bckwFk7WhVwqWZ1tTdmaVeiDMgkw95mF29Crp1sXFDiQyL2TfTRHjmbCmAurYUUqCadCTplVVp4fLuPEUceK5sqsBkrf0xWHTmb0LlhJVxzioe8jofpd7F+reeki4MmQlUkQ+kVkSLeBSe+7Hp8zpdW4d7uyVouvq3mnoHgsXCvbvLPILgwrTeEeRbyhSaiWch1TZbkeGVNlSNWccSdN4a1cKeC9Er3SuUIvmU4rPpnvMJXIIkRWEeZdxksi5bGFfUrNSiEzbjKoItsUaoj0qVIkMCk9rvr2zc1ChehxVXabt9SKy9ujwjJD0QdC8LjbISzrJz1RhRq9gQmCmdCUEqUWyIVibm7z2nUXcFuatZUvPYhaDbzSUWrhZLVmTJnlZmgQJTPdIgLevPDmihMFpqHSeWXIlctHa4I4gnoqwrUyZVMjlUrfecQpuURGZ6a8Uhqkycx8zS3BJ6flW63t/NhVS5sDd12NANeK69vULdxQRb1ZObMKn84WTHyFPG+1ZI+qst6sSTmBS2QZ267ZpkhbRcq2pXm0voERCFQCY0lcObzKarPmeLlmyCOoa6gVCM1DDiEQukgUWLhKL8pqzHztmyegQi0OxZF6KNESNrOJpysC2jNmT63ZQkWtjDlZdUst5lesoFJqge0DmmVRrMBnK1jFQr3gfbMa2YAQzaLdFgo3GJKAKw3r5Uy5W4dmW2tWO8O3odAWeNC2LM4HnPPUaoiUWiDnQk4ZUSV6qznWlp0L3uLeLlrcGxxMqERRnPcUaencVqIs2C5GFO9MQV0XG7atQ0QpNbMeV5RSWEsGiq1HFBHFzpjCdoubA7q9Eadgi+3TKttt//LlzCocMYgQfnt+A2qYM6RSBUp72iHNm2/IFKCIWhkz9ngXGYbMOGSGoTIuN6TViolT5rNoiNZsygox4ENgbz7lwvk9vEAo2cq03lO9p5TCIBvbZX40xAuePgQQz2x2DvAs9mbs7c8Z04prh19nGDdcuXrC8fEaH4QYMsFVhAErmji02nWI881xdIa5U1q9wCL9yvb7OtjBMf5zObMK192BJjc8b1GZmAXwHrQpHNn9n8EharthAfEBRJuJbajPXPC9YxodtW7NrKFnfAjM+sjetMOLQHZItcwc0kCH3rJd3ikipWXEQMQRYodzHfv7e1y4sM8wRmq9ymZU1usNw+BwznLvZpkqW5itqrvuLLc/BTnd8dt6OTfel5uVM6vwnCuJilSDIG0zZWMaKCVbPbgoqKBiHu4W31W0MOaMOGE2F7oOSh7ROuBILHqlV9ifCYuZKTwlAXHM9s/RTxdMOs9iGkEbXKooMXaE2OFzRoMBH2azGV3XkSuMFbwPLA7O0XUT5ntTFntTxgQuXGAYZoxDIQ+ZosUSQcoO3SrOI+pPqwMN63YKkqjtCDP5TlR+ZhVeciVLaQq3HaC1krYKr1CKpVdrA4KPKbfERmY9bgzhEiLiPaUmtI44EvNeUSec33OcXzhKFcYE4iIH9x4w278HRyFoptbC0iVKUUIfCZMpJWVqC/T39vaZT6eMObMeR0KMXLy0z3Q2p59E+mkkJUeMFxiGDcfXlqyOVqSUWGWroIkEnIsoAVFL2ZZWydtmGbSVeHULaNyeci/zLD+zClfZ5r5bLu26TJRzjtAcOUXIxXbBmAqbYaTUQi4GKBzHhPcb8jCShg2UkegU1wn7i44L53tyUVabChKYzTsms44ghpatpUDw5JKJkwmxn1JLZdJ1pvDZnEnfE1ICDy54QlCcz1SUlDMpjwxjYhxzCwE9zltRyHmrDNR6fd0PtsHXNgOnu2ftD/nOfLazq3DzfathibkuteiDVblcQFwkF+VoaQiQ49XA4dFJw7AZCtWHY3LeUFYb0smS6Ar3TCvTLvDwA3u8/pFzbMbMN4/WFHVMzi3oZnv0Xc9sOqfWyvHJMSmN9JOOftIhuvUYpRV5PJthzcnJETilm2ZcWJNqYb3OjEPh2tGaccikpHjfAQ6ZNOxaFVLCoFYtf7BNESsN56bsnNft+X26OG5ezqzCRa7Lm5/CRAHzTHf/j7bGv0LOxXLWoriGVM0p4wVKTha/+try0I7JJDKbT5CQ6IdEVkfsvEGaJz393BQ+1oJLkUkf6HvrERO1nelbCbNooOsDKtpQORVKQdUaERULp5z3hBgbmGNb2291d9lBNRoEq/Wo7Wp/so3LrkN1vLyT/MwqfDLt6J1HU8ODZwMWmjer5FQsPEqFa9eWbIbEyWrFMAyWlApCFVjmkY1AVKWv0MXAYjFlfx7ZO7jAdP8CdTPgN0otINGa2Lq9PQ7ufw2qSjg6JKWRThLRJbYOJKh1hdRKFxyum+8SPyrgakVqxfWgQclZif2CvZOBnAbGzZJaC8PQsOriqdsO0Vra7tWW2hdLDjXwpvWWCXiHL/7b3cob5MwqPMZAdIJSESnmkYtlSLRCzQYfHofEarUy7NdmIOe8S5MqWDOfKuIdk+Dx4pn0U6bTnn66oJstGPH4bknJigQP3hEmU2bnLth7OE8eB0JdEXSFHTjJooaxoLnivSPE3hbjFuyI+VeuCho8tYCTjskkkYY1w9KTc0J1yTDCttWmctpsYM6hpVmdc6bwbbKhhafiboM4PKWR4ApkA/Zt2+3EHHNLprVccymZnBNOlC76U0gQoGNCS6GLkem0YzrrmZ27yGx/RvYLri47lsvE4ZFVow4mgV46qJ5xtPpzN90jTOYEneF1g2qhVPvpWjdozYU6tpRna1aI3jPxnqJCzFYNm8wqZYQ8rhmWR6RxoPAcnCwZU+tFh5ZhczuzryJsq95lm3hRkFYculk5swof10tCqLiSUCy5YufWti4slNZXVtJIGjeEEJn2RhIw7SKCklUompj2HfO9GXv7Cw4uPcL+uQNGKs8fKsuTkRe/aWHf4iDSuxlSI5tNJXSB2cE9hBgREkJCa6LkE2stoiKqpM3IsFyhpbUQVSVMJoTphKKOVQ1UHI4pQqSkDWl1xGZ1wpAMuVJO1pTN2jav961w1horZJsc2gapYt0tzX+5WTmzCrfSUkW3Z9m28e4GyEPLZzf4sFRFquXFrX9Q2tnn6LqO2WLOdL6gmy6IkwXr9Yr1esWwKWiL6aWFuFoLaRwtxYvHha71iXeoJvBqCq8GT3LF4UJBXQUxfBzeYmucx7seJ57gZng/IY8BJ9Zb7mPAeQEHSjWfTNx1DqstcmtX113D6/Y+nTYQ/+dyZhVu3ZoZTcb/og2mtDXtWu1vhcy6KqsCroLL0DlFcyY4YRY8ceK5596LPPK/vZ75Yo97H/peJrMF1/7H/8vX/v0bSB2J1VtfmSouJ9JyyaCX6WcL9u65hA8zQt/h+w6lQF2jmsmbNTUNqBupzNFaKWk0aHKtbAZFfEecnseFjunsHF0/p+QlaZiyOp7Q7fXIMTBUakiWbNnCfNQWvS33bbBe7B6ooMXxMjb42VU4YDv8Jd+mtujT6DBU7QzLzadz7WduWDAXDd8+mU7ZP3eO2WKPyXyfbjKnFMfyeE10hUnniA0giRZqSaTNBuc7VFtOPkxw3RQooL69TqjVIcHjouXppTpzNMfEmBMeoXMd3vXEbmqfnUFkTUprXHRW//TtC8Du6NLmwimK7HD02yzctuXqdtjhLYWouzpxAzRsmwuqQlKDNLX7EKOni4HewSJCFxwX71mwt+i59PB9XHz4QWI/Rb1jzCNaBnwd6IPjYDGh7wMxehQlhECczeimExxCzRXGgkoBTmk78gAlOWrxFI2UKhyvl4zDwNHxIYdHh0ymMy4pTCYzumlHP+2pdcM4rhjTGlzBR+hmnmmOALv6gBbL2au2n7WSh9GcxBKoNZJuizMcdl8QYNtjbSetmnXLCrna2a3WrTLpIxOvzLvKpPNcvHfB+Qt73PfQvVx8+CEQz+o4MQ4jWkZTuOvYn/dMJh1hixoNnslsSpxMEDWFq7T8n1oHSFWlJqEWQYun0pEVjleJ1WrN5ctXePHF51gs5kynHaUuWOR9YM8UnpaktEal4CJ0U8dMDNmjroWVuVqjY5Vdu3LWkaq5pZDr7dFbtq3/lmp1bXFtxe/KpYpIxYm1Ek+LMomeLhhbxGQamXSB+f4+i3PnrAo23zMw4NUVm80aKJY9m3T08zndpCfM9vDTGWE6x3d9g1Zpg1WNzZcsDOOKWgt5WFHSiNZMzYmcE3kc0JIRKj6AuMKYloRBGYcVOa3JabAybS2WPq7akC9WHdTmoFG3mPVqXTdaCVG2BWG02mlws3JmFV5LbRBiPeVIcULdshuVipNCFzznFzNmXSJ2gRCE2aTjnvNzprMJD7z2ddz3wCXOPfgI5+5/mPVyxfLL/5PDy98AHTh/fsZif4+DBx6gm84Ii3twkwUu9oR+jvhI0UpNG/IwtMrawMnRITmNrE8OSRtz4KyJX5vHrQSfWSw83meOl88xjBYpdJ2FZWVYU8bBcgUpwTgg4wrVgpLZdqeCRRzBG9YpOKjVkwbHsBZkvPn7emYVDrQUowEFioJToeqWI4WWhbP+MdVqLcUCITi6yYTJdMZkvmCy2KefLgjdBDckSsmkcURQO/e7SJxMidMpfjLD9TNwoTmFSsoJqQ2zXpRxHFgvj5vCjxk3TUklIU7ophHf+su66HHOUrG15kblZWVXVHe+ilS13HvOoAXVtAvRQBvTlGeLzxQH1YOPgq83n08/swr34hhLZb0ZwVfIpugYA955a9iXSvUwnQRiFDZjYrMZme8dcP7S97J3cMD5B3+Acw88wGTvANwcdKSOlTqMBITYT+jn+8wv3k8326N2e9QwZbVccnT5SusNN994TCPjmEjDwPG1K9Y+NG7QPDYYneKDZ1EXdH1kPg2cP7jHlOWNZXE6OYeXqVXyfKG6kVAUlxK6WpOuHVr/m9qCqNJSEC1vLiL4PiDeGZvjfk8cboMzXESskT4XtEAtxjrYqeDDaRswTomdI1QYxmScZRKZnbvE3oV7WJx/iPn5Bwhdh8gE1WDOT8oIgo8dcTKl3ztPP99jcDNUOtJyzeHJCTml3Xk7DBvGcSANG46vXqHmhGjBaW3kQo4QA9OJUF1Pv7fPuf09EE9xPeICXZwjEs3/cCPRBcv1l4IOI3m5MqBDsQpbbWlzFajOId7RqeK7gI9CP42ou0XEfB/60Id485vfzN7eHvfddx8//dM/zTPPPHPDazabDY899hj33HMPi8WCd7/73S9hZbwZqS3Z4N1pW00MYg2GKJ6Kp+CoRA8xOs7fc46HX/swDz3yEJcefoCLD1xitr9H7CcgMK5OSOslWq2EWrU0cMKGYXnE5uSQ5bVvcnz1Ra6+8HVeePZ/8uJX/50rz32Nay9+ncNvPMfh5edYXrsMeYnTDVEyMVSEkc1myXp9wsnyhJPlkvUmkRJUjUym55jNLhC7Gc5FUE9OlTRWcsaaGov1m49FSVWsgzXBMMJmFFYDLDfK8apydFJYbYz6I98qk/6pT32Kxx57jDe/+c3knPnt3/5tfvInf5IvfvGLzOdzAH7913+dv/mbv+Ev//IvOTg44H3vex8/8zM/wz/+4z++LIWXZAwLXXAGOY6ukekYxda2qV8EpLNK0rlL97N/8QHuuf8hXv+G/53Z3OBK3WTO5uSQ1bUrrI+uoWVEpJCLoWDC6oSTq5eJ6yVHg7LO8PWvfIV//cIXqDm1ggykPJLLSAyevUVPCO38j4HVesPx8THiPMUp3XpKnM6ZH8As9Cz27qfrpggdaCDryLApbDaFcVTGLGwSrNO2L631qSVjpcoKA0ZBxlDAVy74QreAl0HE+PIU/olPfOKGf3/4wx/mvvvu4+mnn+Ztb3sbh4eH/Omf/ikf+chH+PEf/3EAnnzySX7gB36Az3zmM/zoj/7ozX+Y7qh6OAVtaktAaSPhs4zbFvbUTyYs9hbM53O6rifE0HBwI2kYGIcNadwYP6oqtZZdpS2NGxBIg7UPp2FDHjfUko0Y1wmUjNSCaxRgXgwdq7VQS2mOmOHenTdKzO0jp4JzFa0WwqVhMPxdLi136Kk4slr2zLWmaG3VQcCQNjvKL3Ngt2XUm5X/0hl+eHgIwIULFwB4+umnSSnxzne+c/ea7//+7+eRRx7h05/+9MtSeMXiTyt+O4tTxfhZToE9Vuf23uNj5Pw99/Ca176W2f4FJn2HEzi8coXNmBmWh6yufZNhdcRmvTIlDwNptcJ5x9XLLxC7nnXxjOrI62N8Sbha6MQRjNLB/IgusDeb4pxjM6zZrNfG9JQL4pQ0DKCwPDrhan+F9TIBc2I3IY2mZNGMlIFhWDFqRwl7DHLMsk4BJYRoNfDOE8TjxdGJZQHHkihacK6FjPpdcNpqrfzar/0aP/ZjP8Yb3/hGAJ5//nm6ruPcuXM3vPbSpUs8//zz/+H7/K8E+UdHR9tPOK0CbUMXbbll3TbaWwFBxDDqfd8zXyyYNO5zqrLZrFkuNwyrE9arE9J6teMbL3m7u0eG9YqaM4lIIVDzaNzpVLyo0Wo7a+WNwShEnHOs17VxphultVZzNkuxattmvQENrE6WhJgZh4GUR2twcJDGRFGHuo4ikaS+4esD6lwj5DV2QXGNq31s1kasdPpdqZY99thjfP7zn+cf/uEfvtO3AL41Qb6l2lqlyFlnCUKrEeqOjVC8Af9j7Ll29ZB/+x9fppsu2PvGMc5HcjFEaBpWpPWaPNri8s4TQkRjTwwRL+4UDYtj0kXmsym1ZgRFS0bF4XBoKWzWBoNOY24020LnAyoOSqGMI6ujQ2qqxK5nOF7jfWDMI6VkC+O8IXJOTq4xpAH1lbgXQRTXGS5OQjE0bFt4AKEIWgMHF6ZcuLjPZrjFufT3ve99/PVf/zV///d/f8PIhfvvv59xHLl27doNu/xbcaXDtybIR63ebMWT1jvW0q2ocbRUKk49oeuIXce1q1e5eu2E2M+Y73+TEDsWe+foJ1NKHinjhpoHBOs5C6FDYm/m0zkjsvceJ5aTn8+nlJRIw4paMs55Y2MoyrBuExFy3rEuRx9QxCxIKSzHQ1ZHx3gXOOkuI+LINVG3VGHOWbjVzmr1StyLVlPv7XiQUBCvOBWCGlFRJOBxXLhnxr33Htw6hasq73//+/nYxz7GJz/5SV7/+tff8P9vetObiDHy1FNP8e53vxuAZ555hq985Svfkiv9WxHkb1sEt1AmZzjdXe/YFr3qG84LVUpK5NZO5F0kxI4+BLxUas7UbA6TF4cLgTCZgKs2gsJHRLwBJlUp2wrULuKR3YIz1q7Tlh/X8PHbPP+2V6D9go3UKMkyhDW3hWyuiWHmWnLFJXyvqKvULoNT1AvFASpIMQvTOYcXIXSOrovkeosIAR577DE+8pGP8PGPf5y9vb3duXxwcMB0OuXg4IBf+qVf4vHHH+fChQvs7+/z/ve/n7e+9a0vz0Ongf9ECI3bxejQr+u4EI+KP0Vy5sKwXrIeRpzzbI6uEkLEpxWy2LMb296z7zq86+j25nRbGjBnvdrDyZLVMLLZbNiR34qzuB3QWq3f3PDFeBcI3sAY7bRB6rb4QYswCpSB7RqQxkszlIRKJfuMukKdJCbTRHGZ1G2orpC1Wgq5CGEUgnjm/QW6MGW6F1nsLXAx3fR9fVkK/5M/+RMA3vGOd9zw/JNPPskv/MIvAPD7v//7OOd497vfzTAMvOtd7+KP//iPX87HNNnu8O0uPw3RLKFsmTfXOiu3tfOaE7hCVkVKpoxrSooNtG+dmILHOU+Mjj5avry03PUWVmQKO+0CYccERYsW7Hq2LFFuFzjqjvV4+2+AWo1ObEfFXYuREUiliileJOOigSByqOBa/r2xRpuFU/BWoLGpCQ7vbhFM+WbivclkwhNPPMETTzzxct76JSIEvA8Q4q7hADF+ci+CCx0hTqAVUiiZ6GHW+92wGu8LkteUtaDizCo4TwqKamDSz/GTqSm4GCXHfpwx08q1b36T1WpjyiFRtVqXqtCIdy3b0UXBB29ZwdYtItSm1NpGcFRO1gOlKGPLrGXNJB3AK/05xfdK74XpxBksuikx5cwwVlwRfPLgBO97usnMOm+SktLtgHgRhxNr9T0dYKHNhAZC7Oj6qcWlje80CPjOI2qTiZyrUAZqMrYGFQ8hUJK39xPBx952a4M3zWbWfDhskoEQc8Wq0Q1xg+3anC15433Aa+vl3tbq3RZ6ZJskl8LJemBMhfW6Mo7G+5IZcBH25kLXCdEFfGfEQw7XyHwgZ0WKAT58kEY0OEEknHbR3qScWYW7tqtLawrEN6yLeFRsRkipZceH7gTrrW670IvDid8xMYrzuMbiVGtGUyXnRozrPHE6B8QcNiD0U/bP30MeB4a+p+SRnAZyHhqLhHXAlFzIYqS/rg3L2Tb65ZRYbzZshszqZMOYC2MScgG84qPHRzG2ia4ld2oLQbNRevlNR9xAFE/vOia+ZxbmTOMUsrA8Glitb9EZ/t0U58QQoDm3yUOdecMSUPEUQGuyJIWzRIz3bZSEeLzvLK7uZlYRi5FJHym1crw8sSF48wVjLnSTjvn+ecR5jpYrxmGkm5/j0sORkhPDyTXKOHJ49TKH165QymDk+aXaEDyFGDGGRKSBFirDsOHo2iHrIXP1ypoxGXQZF4gTR99FYu+YzSZMZ4EQCpIzFEGWHVIq3crhh45JHzmYz5jEnguT8+xP9zk5SVy+fMxqffMIiDOr8OBBnbUZ0ThNrh/nJLXuGurcNteup/NFZMsLA7t26h2dV5tikFOjrBZPnzLilTSOjMOA5tTQoFuIVaPeYJvcbsDhLZ/6dcNmLFdfb2jgD95ZVjBY83+INrclRuNR9WIsEySxztTskAy+ekSFjo5pnDCJPV3oiD6CZvKYSbc68fLdkMUcio6wWVIJFp5UT0pQSmDXMdB2togQO0OYQJtroo6SMlqtjBqcM7RpFjQ7rl2+ytWr15jMZlxYHuG85/DwmNVqg6EkTYGe0sx3xbmA95UYe6qzmH8cEjllxnFkewxpmynWtdlns+keiMN3PT5E8CARxGvrsFHqupJXUIvAJiBFmbgO74Rz0wUP3Xsvfd9zfv8808mM4ysjq6M1q9VtsMP7DlKoeN9mfNXSAITZWsZF0UZeq+pbh0lH9baTW/tGgxMr1ftW0VLTY4XNuGaTR8Y00k9s2N3J4THr9ZqtL+AahMo5aTn8bZnWKC9ryc0bp7X8GJfM1hoF74z0N07tZz8hdNF41KUhYWVsFTcxnt0iyCBQIXQ2tmsSJuxNF/STnmk3oQvWp56GRBpvgzN8b89CqInzDEk4XtuNHVNjJEZOOzSiFTZyzogbLHVJ2c00EzGOl2Ez2nCYdSLnylgGxjJQy0DfKd57aioEtX6tMZni1i1jMo6JlBKi2vL30TJnte7ialRxwbDl2xloThwhmA+Ct1Pemv8tpMx6anlKcrgKUu3aF/2E2azjYG/GYj4lxGAl13HFajWwXmWGzW1g0g/2PX4SGPrIat3oK5MwjpCzp6inVN8SH94qlzmDVIoTlMF2aEuJ1NKaBQpsBms/znXTQBABz4YQPH0/JfqOkgaGkxWlVoYh2fSiljLtYmBvMbOBdo36ehxH1hvb1c6bdZj0xpfunBBajF62YVTjklMEslBqU/bom0WzMG9vMuXgYMbB/oL9vRnihKvXbATGybGZ8/XmVR5F+UqID4IvRintvRKiFRlCZ2GTFEWzUXsopQ2FMWCAmX5DtGrjIa9GyNBIanOzEgXEzupainVo5gwqNkpat86YNozZ9r0tDStSraNbtpMLrF695Umvqm1CsRjgVGU3FhNg2whoIyhbhOHcjnPOO0fsOyaT3qyJGrHgajVwcjJwstywWic2t4PCJ1OPUCnrkdjDdCHE4qCHPsOwqayX2jJaNrJCXG/VLDWqTrYdKhWqlqb0wiatyaUQgjOstxRyStRajKFZvLFIbXu/SxtQh4A6qlSGTcJ7R995XPD4AJNpW1A52byzMRupkHN0fWwjMFtyZtvpT5te7D1hy+0u5tV77zk4v8/F+87jo6cUYRgSX332Ci9+44jnXjzh2eePbg++dN/A/OKqFU8i4JWoinrbrT7U1rRhTprumuykhWKnefFtOFe1mvI1Ax4RuwXb3q0tH8vW2Tt9tAtTc/q0tME6ailQg1m1RdpCw1KMdlO9xxffosaWE2/vteV12bEqOtdID4w834dA7CI42U0iXK5Gjo5WnCwH1ptk8f1NyplVuIsO6T1h4umqMI1CUUFGpSuZ0IGPSikwboxH1TvFtTHOMcwM6lxpYyJzK1Hae/s2rtk5j3cB5zp7Pdv5ZdJmhF6v7O1PtfFXCJpBxW64VdAsrlYRSk6MQ0JcJteKa1ODfbBZqa6915Zms4gj4dFSyWPCO+Fkk1kNp9OFT042/PuzV/j3r3yDw2XmynEi1dsgtSre4YMjdJbJ6jtTuMaKL+C8Ik6t7dY1rtSWcvfO48MEo8xQ1ClaRwQbPeGCg2q1dFsgW072linX7VGx7dxk2w1gF6etS6T1Z293rnOWAwjOt5lnkFMGEXLV3cTE7VhMtBHzNFNfcGRcG0ZXEIH1YGM6cikM48jh8YbnXzziq1+/xirBSYJ6Oyi8FIeoBwmIKEFahyjgihqCU8UUjlCKUHPrz5aMMqC4RscJuaQd7swTLFSSQHAWNmlVc+Bkm89p+Xlhx8oE5ng5L/i4HVMB2zkdO/I8Z9FBCI4YvZH3akVLCx0FfGuStHVk/sEwZk7WI2lMHB8uUYV7L5ywt5i2vENpJEB2dHnv6HE28fgm5cwqfBgcQgTpca7QRWuu80afQt+JDXYvsJk2IP9QjEA/K+Og1CqM2eiytkNxDIrU21D2NjAHVbS0iUQNqOhbmw/uevK7Bon2Qte51i7kjBVET2eh+mDncJwEJtqRciGvB8vKUanJYFmuoWBzNajy0XLgG1dXrFcDLzx3jZoLs77Di9JPPPP9wJisVRhRm9QYbZYZLG/qvp5ZhecEWRylbM1sK082tKp3oN642Do1jjXTzJbmqxUhnJn+UzjKaQatuUtWcWvOWXt74EZ00xaQsc3Vb+ei3HC8t6T9Dimjxocuch0NV1UKBWlTBhHIKFWFccwMQ2IYEptNombLAaQxW9sxseHubOS18bp5y8HfpJxZhR9ehQ2eMgRcJ8SZgfqcr0Y97SD2AMJsZjd+HGwEdMrCZjWSi7BeOVIS0qAMG0Ad3jgdoOG8TREGSpSyxaVdx1q8Y58wXjScDXlli8sojVGpYeZrSohWchqpybJ7VYTi/O5YCW3EpCKsx0wqypVra755ZcWwySyPN1CVcVPIY8VPA4vJHp1L3HtwjuFCIVfHWByp3AYK32xsAHwZvZ13wZmytxgDv0M5WT+2CN5XQqqEBFDIGWoNOxhUbvNGpKE/d5WvXRdLQ/UIuyrXVuvCdryUnbtmUGTHTS+q+KZ0agI1uJWWbM4d3n5Pt0kcw5VXhc3GBu2slmvWy7Ut3DGfMk8UEHX0ocOpZz6ZsDebMmbwCcLtoHB1UyrnKQ4z0YyoVlwdQLLlptoQ2jix4kZo87dzgbiBkqGbBsbBMwzQLY06Q3M0sIQ2fvLr0KiGhjbv3JCrjQGRRsrvDHyRDDXdqDet381pq8qpx1EQGVFJFho2z118wdVikUSMOIWOHgmFeY4clI5SlL19m6B43wOXOH/xIufOLdg/d5GcCxfvO6JoYDMUluvKkG+DTFv1MyqR6mcUVyiam8JPQEZLZ0bw0TGZB3y0URAq1WLzsVKKsF5FxtEzboT10lGLkEbX2Jc8Wi2U2rrh1YZ3W+dIah61D40M1xy1oiCpTRseM5pah4pmnCi9JCupykiVwcLJYlm0IAa/Cs4TfLAF5jKhVrKM1DAiInRdT4yBhx55gIv3X+Rgf8H5ey5SSuGBB9fEbsrJMnF0PLL571wt25rW1SaRSJQx43MlY8140RerRxtHAD4p1QkhVgurgFyVMVlSZrOxgsu4ETZjNYUPlqbcjoA0adzkrXxaig3CAQihoVOddasWhU2Lf/OYrE6uxa6RikrBU9hkI//dVBiqJ2FHgcMwDurtM1M+HbyXiiVofK24NiR+PSTiMLJcj5YaHjNDyow5N5/lRorxbyeiL5dS/xbLV7/6Ves8uSsvW5599tkbOoH+IzlzCq+18swzz/CGN7yBZ599lv39/Vf7kr4rsm2x+k6+s6pyfHzMgw8+2Ga0fWs5cybdOcdDDz0EwP7+/h2j8K18p9/54ODgpl73sig/7sp/f7mr8DtMzqTC+77ngx/84H/YVXq7ynfrO585p+2u3Fo5kzv8rtw6uavwO0zuKvwOk7sKv8PkTCr8iSee4HWvex2TyYRHH32Uz372s6/2Jb0icjPUpe94xzvYDufZPn75l3/5lbsIPWPy0Y9+VLuu0z/7sz/TL3zhC/qe97xHz507py+88MKrfWn/ZXnXu96lTz75pH7+85/Xz33uc/pTP/VT+sgjj+jJycnuNW9/+9v1Pe95jz733HO7x+Hh4St2DWdO4W95y1v0scce2/27lKIPPvigfuhDH3oVr+rWyIsvvqiAfupTn9o99/a3v11/9Vd/9ZZ95pky6eM48vTTT99A3emc453vfCef/vSnX8UruzXyv1KXbuXP//zPuXjxIm984xv5wAc+wGq1esU+80wVTy5fvkwphUuXLt3w/KVLl/jSl770Kl3VrZH/iLoU4Od+7ud47Wtfy4MPPsi//Mu/8Ju/+Zs888wz/NVf/dUr8rlnSuF3knwr6tL3vve9u7//4A/+IA888AA/8RM/wb/+67/yvd/7vf/lzz1TJv3ixYt4719CqP/tqDv/O8qWuvTv/u7v/lPAwqOPPgrAl7/85Vfks8+Uwruu401vehNPPfXU7rlaK0899dS3pO787ySqyvve9z4+9rGP8bd/+7cvoS79j+Rzn/scAA888MArdhFnSj760Y9q3/f64Q9/WL/4xS/qe9/7Xj137pw+//zzr/al/ZflV37lV/Tg4EA/+clP3hB2rVYrVVX98pe/rL/3e7+n//RP/6T/9m//ph//+Mf1e77ne/Rtb3vbK3YNZ07hqqp/+Id/qI888oh2Xadvectb9DOf+cyrfUmviLBrd7zx8eSTT6qq6le+8hV929vephcuXNC+7/X7vu/79Dd+4zde0Tj8bnn0DpMzdYbflVsvdxV+h8ldhd9hclfhd5jcVfgdJncVfofJXYXfYXJX4XeY3FX4HSZ3FX6HyV2F32FyV+F3mPz/QK9lzgUp7qIAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classes = [\"airplane\",\"automobile\",\"bird\",\"cat\",\"deer\",\"dog\",\"frog\",\"horse\",\"ship\",\"truck\"]"
      ],
      "metadata": {
        "id": "V3KI7uNxDwT4"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_scaled = x_train / 255\n",
        "X_test_scaled = x_test / 255"
      ],
      "metadata": {
        "id": "nrRsgwDEDz0o"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_categorical = keras.utils.to_categorical(\n",
        "    y_train, num_classes=10, dtype='float32'\n",
        ")\n",
        "y_test_categorical = keras.utils.to_categorical(\n",
        "    y_test, num_classes=10, dtype='float32'\n",
        ")"
      ],
      "metadata": {
        "id": "gPFaY3PnD4N7"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_categorical[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PR36dtcLD9qG",
        "outputId": "7e62ba55-9c2a-41c4-992d-d06eb188d5fb"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
              "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential([\n",
        "        keras.layers.Flatten(input_shape=(32,32,3)),\n",
        "        keras.layers.Dense(3000, activation='relu'),\n",
        "        keras.layers.Dense(1000, activation='relu'),\n",
        "        keras.layers.Dense(10, activation='sigmoid')\n",
        "    ])\n",
        "\n",
        "model.compile(optimizer='SGD',\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.fit(X_train_scaled, y_train_categorical, epochs=50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jHwTMoctEA-x",
        "outputId": "4fdcc9a5-e723-41a2-e4e0-88d571930bc7"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "1563/1563 [==============================] - 11s 4ms/step - loss: 1.8178 - accuracy: 0.3530\n",
            "Epoch 2/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.6261 - accuracy: 0.4238\n",
            "Epoch 3/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 1.5439 - accuracy: 0.4564\n",
            "Epoch 4/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.4840 - accuracy: 0.4779\n",
            "Epoch 5/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 1.4327 - accuracy: 0.4943\n",
            "Epoch 6/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 1.3897 - accuracy: 0.5101\n",
            "Epoch 7/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.3540 - accuracy: 0.5238\n",
            "Epoch 8/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 1.3166 - accuracy: 0.5375\n",
            "Epoch 9/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.2861 - accuracy: 0.5477\n",
            "Epoch 10/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 1.2531 - accuracy: 0.5614\n",
            "Epoch 11/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.2251 - accuracy: 0.5698\n",
            "Epoch 12/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 1.1943 - accuracy: 0.5825\n",
            "Epoch 13/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.1686 - accuracy: 0.5918\n",
            "Epoch 14/50\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 1.1421 - accuracy: 0.6005\n",
            "Epoch 15/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 1.1151 - accuracy: 0.6112\n",
            "Epoch 16/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.0863 - accuracy: 0.6216\n",
            "Epoch 17/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 1.0608 - accuracy: 0.6301\n",
            "Epoch 18/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.0355 - accuracy: 0.6406\n",
            "Epoch 19/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 1.0129 - accuracy: 0.6468\n",
            "Epoch 20/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.9844 - accuracy: 0.6587\n",
            "Epoch 21/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.9579 - accuracy: 0.6707\n",
            "Epoch 22/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.9355 - accuracy: 0.6754\n",
            "Epoch 23/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.9091 - accuracy: 0.6873\n",
            "Epoch 24/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.8826 - accuracy: 0.6971\n",
            "Epoch 25/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.8572 - accuracy: 0.7059\n",
            "Epoch 26/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.8308 - accuracy: 0.7152\n",
            "Epoch 27/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.8071 - accuracy: 0.7227\n",
            "Epoch 28/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.7801 - accuracy: 0.7336\n",
            "Epoch 29/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.7584 - accuracy: 0.7393\n",
            "Epoch 30/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.7339 - accuracy: 0.7497\n",
            "Epoch 31/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.7051 - accuracy: 0.7604\n",
            "Epoch 32/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.6838 - accuracy: 0.7679\n",
            "Epoch 33/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.6573 - accuracy: 0.7774\n",
            "Epoch 34/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.6317 - accuracy: 0.7872\n",
            "Epoch 35/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.6100 - accuracy: 0.7929\n",
            "Epoch 36/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.5842 - accuracy: 0.8050\n",
            "Epoch 37/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.5612 - accuracy: 0.8122\n",
            "Epoch 38/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.5386 - accuracy: 0.8221\n",
            "Epoch 39/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.5153 - accuracy: 0.8309\n",
            "Epoch 40/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.4955 - accuracy: 0.8363\n",
            "Epoch 41/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.4734 - accuracy: 0.8461\n",
            "Epoch 42/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.4480 - accuracy: 0.8564\n",
            "Epoch 43/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.4309 - accuracy: 0.8617\n",
            "Epoch 44/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.4112 - accuracy: 0.8685\n",
            "Epoch 45/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.3871 - accuracy: 0.8781\n",
            "Epoch 46/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.3693 - accuracy: 0.8844\n",
            "Epoch 47/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.3497 - accuracy: 0.8908\n",
            "Epoch 48/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.3295 - accuracy: 0.8997\n",
            "Epoch 49/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.3161 - accuracy: 0.9037\n",
            "Epoch 50/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.2980 - accuracy: 0.9105\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x79e249b17a60>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.argmax(model.predict(X_test_scaled)[0])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tNmt8yTtEFe0",
        "outputId": "afb95d77-5fb4-4574-fde1-c9019f5af4aa"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "y_test[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SHphri8FFk--",
        "outputId": "21499b68-c282-4690-ab66-a0c7b2fb8c1f"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_model():\n",
        "    model = keras.Sequential([\n",
        "            keras.layers.Flatten(input_shape=(32,32,3)),\n",
        "            keras.layers.Dense(3000, activation='relu'),\n",
        "            keras.layers.Dense(1000, activation='relu'),\n",
        "            keras.layers.Dense(10, activation='sigmoid')\n",
        "        ])\n",
        "\n",
        "    model.compile(optimizer='SGD',\n",
        "                  loss='categorical_crossentropy',\n",
        "                  metrics=['accuracy'])\n",
        "    return model"
      ],
      "metadata": {
        "id": "rSAT7XFUFmnx"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%timeit -n1 -r1\n",
        "with tf.device('/CPU:0'):\n",
        "    cpu_model = get_model()\n",
        "    cpu_model.fit(X_train_scaled, y_train_categorical, epochs=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M7vwJx5iFsXi",
        "outputId": "82433dbb-5856-4f5b-d490-5fdec67484e2"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1563/1563 [==============================] - 123s 79ms/step - loss: 1.8174 - accuracy: 0.3522\n",
            "2min 24s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "%%timeit -n1 -r1\n",
        "with tf.device('/GPU:0'):\n",
        "    cpu_model = get_model()\n",
        "    cpu_model.fit(X_train_scaled, y_train_categorical, epochs=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4D6eVPtgF2Ge",
        "outputId": "9f45b962-84bf-443c-e5fe-34b831bfadd7"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1563/1563 [==============================] - 8s 4ms/step - loss: 1.8127 - accuracy: 0.3526\n",
            "9.75 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "W3ltwzjPF3zK"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}