exercise_fashion_mnist_gpu_benchmarking.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GPU performance for fashion mnist dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.16.1\n"
     ]
    }
   ],
   "source": [
    "# TensorFlow and tf.keras\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "# Helper libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
      "\u001b[1m29515/29515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
      "\u001b[1m26421880/26421880\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
      "\u001b[1m5148/5148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
      "\u001b[1m4422102/4422102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"
     ]
    }
   ],
   "source": [
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d3613e9720>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(train_images[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Ankle boot'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_names[train_labels[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARYAAAEnCAYAAABsa2xHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAhUElEQVR4nO3de1SU17038O8MMMN9ELkM06CiSUxSIzZGEDTWVCohaV5NPDlJmq6Drcs0BkyVtElMjUjaVRrTi02lerqOr8SuGFPXiXFJLG2CAY4W0BAv8cZRSiKGi5cIw3UYmP3+4eukU2FvkE1mMN/PWrOWM79n5tk8A18fHn6zt0EIIUBEpJHR2wMgohsPg4WItGOwEJF2DBYi0o7BQkTaMViISDsGCxFpx2AhIu0YLESknb+3B/CvXC4XGhoaEBYWBoPB4O3hENH/J4RAW1sbbDYbjEbFOYkYIRs2bBDjx48XZrNZJCUliaqqqkE9r76+XgDgjTfefPRWX1+v/DkekTOWt956Czk5Odi0aROSk5Oxfv16pKeno6amBjExMdLnhoWFAQBm4374I2AkhkdE16EXTuzDHvfPqIxBCP0fQkxOTsaMGTOwYcMGAFd+vYmPj8fy5cvxwgsvSJ9rt9thsVgwFwvgb2CwEPmKXuFEKXahtbUV4eHh0m21X7zt6elBdXU10tLSvtiJ0Yi0tDRUVFRcs73D4YDdbve4EdHopj1YLl68iL6+PsTGxno8Hhsbi6ampmu2z8/Ph8Vicd/i4+N1D4mIvmRe/3PzqlWr0Nra6r7V19d7e0hENEzaL95GRUXBz88Pzc3NHo83NzfDarVes73ZbIbZbNY9DCLyIu1nLCaTCdOnT0dJSYn7MZfLhZKSEqSkpOjeHRH5oBH5c3NOTg4yMzNx9913IykpCevXr0dHRwe+//3vj8TuiMjHjEiwPProo7hw4QLWrFmDpqYmTJs2DcXFxddc0CWiG9OI9LEMB/tYiHyTV/tYiIgYLESkHYOFiLRjsBCRdgwWItKOwUJE2jFYiEg7BgsRacdgISLtGCxEpB2DhYi0Y7AQkXYMFiLSjsFCRNr53EqI5GNUq1FqmHXDb2yktH45/VZpPXxb5bDHoPo6Df7yKTyEs2f4YxguHSuHappFhWcsRKQdg4WItGOwEJF2DBYi0o7BQkTaMViISDsGCxFpxz4WkjL4+UnrordXWjdOu0O5j5M/DJW/Rpf8+QEdSdK6f5dLOYaAv30orQ+7T2UQPSaqYw2D/DxARy+NwX/gSDAIAcjfbjeesRCRdgwWItKOwUJE2jFYiEg7BgsRacdgISLtGCxEpJ32Ppa1a9ciLy/P47HJkyfj1KlTundFXwJZXwOg7mOpT49Q7uOJlP+R1vdfmCitf2q2SusiSDkE+KelSOu3/uEzab33k7PyHQxinhPVsVTxGzNGvkFfn/I1+uz2AWtCDH58I9Ig9/Wvfx3vv//+FztRfHMS0Y1lRH7i/f39YbXK/xchohvXiFxjOX36NGw2GyZOnIgnnngCZ88OfJrocDhgt9s9bkQ0umkPluTkZBQWFqK4uBgbN25EXV0d7rnnHrS1tfW7fX5+PiwWi/sWHx+ve0hE9CXTHiwZGRl45JFHMHXqVKSnp2PPnj1oaWnBn//85363X7VqFVpbW923+vp63UMioi/ZiF9VjYiIwK233oozZ870WzebzTCbzSM9DCL6Eo14H0t7eztqa2sRFxc30rsiIh+h/Yzlxz/+MR588EGMHz8eDQ0NyM3NhZ+fHx5//HHdu6Ivgau7e1jP7/lGu3Kbf7PI50IJNDql9TKjfL6Vz/aqr9v1TZWP89PfhEnrrkOp0vrYY+oekvBDjdL6xTlfk9YvTJf3ysQOYvmlMe/XDlgTrh7govo1gBEIlnPnzuHxxx/HpUuXEB0djdmzZ6OyshLR0dG6d0VEPkp7sGzfvl33SxLRKMPPChGRdgwWItKOwUJE2jFYiEg7BgsRacdgISLtOFHKV51qIS3FBEXt/z5TWv+PO0qVQ6h1ynucbjJ9Lq0/YquW7+B7ijqADTXflNY7/mGR1o0h8uPUNFP9f/hnC+THQTjlEy2N+Uj+42zMbFaOwd4z8KRavc5uYJfyJa7sa3CbERENHoOFiLRjsBCRdgwWItKOwUJE2jFYiEg7BgsRacc+ltFM1YPyJZj5/AFp/d7QE8Pex9cg7xHpECZpvaUvRLmP3DveldYv3Cqf6Mkp5D9K/3VaPhEUALQremX8euXv98wfHJLWF0UeVI5h3X/fOWCtV8gn3PpnPGMhIu0YLESkHYOFiLRjsBCRdgwWItKOwUJE2jFYiEg79rGMZoq5Ur4Mp9tjpPVL4aHK12jqjZDWx/rJFxMLM3ZJ6xMC1KtsXeiT96n4BcgXResRftJ63td3K8fQfXuAtB5gkC96lhrYIK0/cuI/lGMIwT+U2wwGz1iISDsGCxFpx2AhIu0YLESkHYOFiLRjsBCRdgwWItKOfSw0LNFmeY9JoEE9h4fJIF8vp8E5Rlo/3TVZWv9fu7zXBgDuiz0urTsVfSp+ijljVD0oAGALuCytdwt5n4vqSM+KVfeoHFZuMThDPmMpLy/Hgw8+CJvNBoPBgHfeecejLoTAmjVrEBcXh6CgIKSlpeH06dOahktEo8GQg6WjowOJiYkoKCjot75u3Tq89tpr2LRpE6qqqhASEoL09HR0d3cPe7BENDoM+VehjIwMZGRk9FsTQmD9+vVYvXo1FixYAADYunUrYmNj8c477+Cxxx675jkOhwMOh8N93263D3VIRORjtF68raurQ1NTE9LS0tyPWSwWJCcno6Kiot/n5Ofnw2KxuG/x8fE6h0REXqA1WJqamgAAsbGxHo/Hxsa6a/9q1apVaG1tdd/q6+t1DomIvMDrfxUym80wm83eHgYRaaT1jMVqtQIAmpubPR5vbm5214joxqc1WBISEmC1WlFSUuJ+zG63o6qqCikpKTp3RUQ+bMi/CrW3t+PMmTPu+3V1dTh8+DAiIyMxbtw4rFixAj//+c9xyy23ICEhAS+99BJsNhsWLlyoc9wEKBcsM/jJm7oAQPTKm9P8xsib074Z8bG0fqEvXDmGlr5gaT3Cr1Nab+sNlNY/75K/PgDcZm6U1j/qnCCtR5vkzW2qrwEAPumJktZvMfd/nfKqdc3zpPX4wM+VY+idN2fgWm83ULpL+RrAdQTLhx9+iHvvvdd9PycnBwCQmZmJwsJCPPfcc+jo6MCTTz6JlpYWzJ49G8XFxQgMlL/5RHTjGHKwzJ07F0IyJaLBYMDLL7+Ml19+eVgDI6LRix9CJCLtGCxEpB2DhYi0Y7AQkXYMFiLSzust/TQMigXLDP7qt1fVx1K/5HZp/VvB8oW4/t79NeUYov3bpHXVJEtx5lZpPSxWPWWHqpcm0l8+oVVbX5C0Hmx0SOuA+jjcZZIvvLby/buk9bApl5RjCA8Y+FzDNYTzEJ6xEJF2DBYi0o7BQkTaMViISDsGCxFpx2AhIu0YLESkHftYRjFDgElad2lYciXq4x5p/WKffBGtCKN6HhKTYjGvHkUfS2pknbR+QdFjAgAfdSVI62F+XdJ6tFHegxIfoO4h+bhbPpH8no6bpfUl33lfWn/zj99WjsFU/PcBa0ahXnzOve2gtyQiGiQGCxFpx2AhIu0YLESkHYOFiLRjsBCRdgwWItLuxuxjUay3AwAGf3n/hcFPkblGed3VrZ5/Ay55/4aKcMp7THT43X9ukNbreyOk9SanvA6o19zpg/z9rOyySOuBRnX/RbS/XVq3u9S9MDJtLvXyN6p5Z1Rfx/NjT0vrb7emKcegC89YiEg7BgsRacdgISLtGCxEpB2DhYi0Y7AQkXYMFiLSjsFCRNoNuUGuvLwcr776Kqqrq9HY2IidO3di4cKF7vrixYvx+uuvezwnPT0dxcXFwx7sVaqFuFSLcAHq5rIhzGnjNV0LkqT1+oXqBrwnvnFAWm/qDZPWD3VOkNYtigmSACBEsZhXt5A3Mzb0jJHWB9Mgp1qQLEbRQNcn5P9Hf+aUj3EwVI2E53oVi6r9H/lkVAAQsXVIQxrQkM9YOjo6kJiYiIKCggG3ue+++9DY2Oi+vfnmm8MaJBGNLkM+Y8nIyEBGRoZ0G7PZDKvVet2DIqLRbUSusZSWliImJgaTJ0/GsmXLcOnSwPN9OhwO2O12jxsRjW7ag+W+++7D1q1bUVJSgldeeQVlZWXIyMhAX1//v+/n5+fDYrG4b/Hx8gmFicj3af9082OPPeb+95133ompU6di0qRJKC0txbx5867ZftWqVcjJyXHft9vtDBeiUW7E/9w8ceJEREVF4cyZM/3WzWYzwsPDPW5ENLqNeLCcO3cOly5dQlxc3Ejvioh8xJB/FWpvb/c4+6irq8Phw4cRGRmJyMhI5OXlYdGiRbBaraitrcVzzz2Hm2++Genp6doGPZg+leHyj5P/VcuZECutf357sHIfnVb5BEbT7j8prS+O3SKtX+hTn/0FGOTHst45Vlr/RvAn0vre1juUY7joHyqtq3phUkPkExy1uNTvhc3/srT+/Jl/k9Zjg+U9Iv81fo9yDE7hktZrnGZpvdUlnyjqmTs+UI5hJ6KV2wzGkIPlww8/xL333uu+f/X6SGZmJjZu3IijR4/i9ddfR0tLC2w2G+bPn4+f/exnMJvlB4WIbhxDDpa5c+dCCDFg/a9//euwBkREox8/K0RE2jFYiEg7BgsRacdgISLtGCxEpN2oXLDMkTFDWo/56T+UrzEt/Jy0fkfQPmm92yWfI2Qwc4Cc6PqatN7pMknrp3vkvTatver+DT+DvHfifI98PpZf18kXwSpJ2qQcw+qG+6R1Y9DAf4UEgEt98j6YRaGD+WCr/P384bhyaX2i6by0XtShbhBtUMzZEhvQKq1PCLggrT8c9r/KMejqY+EZCxFpx2AhIu0YLESkHYOFiLRjsBCRdgwWItKOwUJE2vlsH4vB3x8GQ//DS/7FQelz54UdV75+p5BP46DqU1H1HAyGxV++TozDKX97zjuHP9vereYmaf2h8MPSevmGZGl9dvdy5RhqvyWfV6akSz7PyIVe+XF4rO5byjF8dFY+HerMCXXS+p1hn0nrg+kpCvPrltZVc+d0uOTf05Xd8n4fnXjGQkTaMViISDsGCxFpx2AhIu0YLESkHYOFiLRjsBCRdgwWItLOZxvkGpdNh585sN/aWsvvpc/d9vlM5evHB34urY83XZTWE4M+Ve5DJcwob4iaHC5viCrquElaL225TTmGuIAWaf1/OidJ69vXviqtL175rHIMKXuektbtE+T///WGyCeCCk+8pBzD6m+8K62bDH3SekufvAEu0tyhHEOEn7xhUkXV9BlmlC/8BgB+k28esCb6HIB8bTg3nrEQkXYMFiLSjsFCRNoxWIhIOwYLEWnHYCEi7RgsRKTdkPpY8vPz8fbbb+PUqVMICgpCamoqXnnlFUyePNm9TXd3N5599lls374dDocD6enp+MMf/oDY2NghDSz4vAt+pv4X0yqyT5M+d2KQfOEmALjolC/E9df2O6X1m4IuS+sWP3XPwM2KSZYOd0dI68UXvi6t24LUC3U1Oy3S+iVniLTeqZhcaPNvf6Mcw6+b5YuePRT5kbSeaJL3qbS41P9/nlAs/tbm6r+n6qpuIZ8YrFXR5wIAYYrvGaeQ/7j6CfnicxFGdZ+M/c6xA9Z6nd0j08dSVlaGrKwsVFZW4r333oPT6cT8+fPR0fFF88/KlSuxe/du7NixA2VlZWhoaMDDDz88lN0Q0Sg3pDOW4uJij/uFhYWIiYlBdXU15syZg9bWVmzevBnbtm3Dt751ZTrALVu24Pbbb0dlZSVmzlR3xBLR6DesayytrVfWko2MjAQAVFdXw+l0Ii3ti1Pb2267DePGjUNFRUW/r+FwOGC32z1uRDS6XXewuFwurFixArNmzcKUKVMAAE1NTTCZTIiIiPDYNjY2Fk1N/V9PyM/Ph8Vicd/i4+WTGhOR77vuYMnKysKxY8ewffv2YQ1g1apVaG1tdd/q6+uH9XpE5H3X9enm7OxsFBUVoby8HDfd9MUnbK1WK3p6etDS0uJx1tLc3Ayrtf+r7mazGWaz/C8LRDS6DOmMRQiB7Oxs7Ny5E3v37kVCQoJHffr06QgICEBJSYn7sZqaGpw9exYpKSl6RkxEPm9IZyxZWVnYtm0bdu3ahbCwMPd1E4vFgqCgIFgsFixZsgQ5OTmIjIxEeHg4li9fjpSUlCH/RSj0Mwf8/Q391lyi/8ev2ntRPQ9JbGCbtD4tTP4rWU2nvO/h4y6bcgwf+Y+T1oP8nNK6xSSfzyXE36EcQ1SA/DgkmM9L66p5Sg52y79GAFgWXSqtn+2VLw63u+NWaf1Ep/q9GKNYPO5ju/w1OntN0rqjT/2j1t0r752ymOXv94xI+RxBNYhTjuFC4sDnGq5uI/CO8iUADDFYNm7cCACYO3eux+NbtmzB4sWLAQC//e1vYTQasWjRIo8GOSL66hhSsAghn6kLAAIDA1FQUICCgoLrHhQRjW78rBARacdgISLtGCxEpB2DhYi0Y7AQkXY+u66Qcd9RGA39z3Gx42+zpM99acEO5euXKdbcKWqS9xTYe+TdwtHB6nVkwhU9JJEB8tewKHovAg3ydYkA4HKvfL4Vh1E+z0gf5D1FTQ75fC8AsN91i7TudPlJ6w5FXdUPBACf90RJ67agVmm9rVc+X8snbZHKMVxsDZXWu4PlP677+uRrQN1nPa4cQ9D5gd/PPof8vf5nPGMhIu0YLESkHYOFiLRjsBCRdgwWItKOwUJE2jFYiEg7BgsRaWcQg5kL4Utkt9thsVgwFwvgP0CDnErrE+pJpSY+XSOtJ0XUSesf2eUTGJ0dREOUU7GQVoBRvgBVcECPtB44iMYwk598oiYj5N8eLkWDXIiffIyAekKqcH/5BEdhfvK60SA/joPhpzgOB1onDHsfYYrj0Cvk3y8pllpp/f/WpSrHYLn/jGT/TpRiF1pbWxEeHi59HZ6xEJF2DBYi0o7BQkTaMViISDsGCxFpx2AhIu0YLESkne/2sRgfHriPxSXvvdChY1GytJ784kF5PUzeUwAAt5mapfUAyPsvAhX9GSFG9cQ83Yq3X/U/z76ueGm9bxD/d+29fLu07lT0bzR3ynsqAhS9OoOhWiSvq1fec9XaJZ8ICgD8jPL3ortUPhnV2BPyviXzHvn3rAr7WIjIqxgsRKQdg4WItGOwEJF2DBYi0o7BQkTaMViISLsh9bHk5+fj7bffxqlTpxAUFITU1FS88sormDx5snubuXPnoqyszON5P/zhD7Fp06ZB7UPHfCyjhWGGfFG0LmuQtG6+JJ+/o228/PkAEF4rXxTN6JAveuY6clK5D7oxjFgfS1lZGbKyslBZWYn33nsPTqcT8+fPR0eH5zfn0qVL0djY6L6tW7du6F8FEY1aQ1pitbi42ON+YWEhYmJiUF1djTlz5rgfDw4OhtVq1TNCIhp1hnWNpbX1ynq2kZGe0zC+8cYbiIqKwpQpU7Bq1Sp0dg68xrDD4YDdbve4EdHodt2LwrtcLqxYsQKzZs3ClClT3I9/97vfxfjx42Gz2XD06FE8//zzqKmpwdtvv93v6+Tn5yMvL+96h0FEPui6P4S4bNky/OUvf8G+fftw0003Dbjd3r17MW/ePJw5cwaTJk26pu5wOOBwfHER0m63Iz4+nhdvwYu35FuGcvH2us5YsrOzUVRUhPLycmmoAEBy8pVPCQ8ULGazGWaz+XqGQUQ+akjBIoTA8uXLsXPnTpSWliIhIUH5nMOHDwMA4uLirmuARDT6DClYsrKysG3bNuzatQthYWFoamoCAFgsFgQFBaG2thbbtm3D/fffj7Fjx+Lo0aNYuXIl5syZg6lTp47IFzCaiYMfS+vqGTzkwv8+zBcAFDPCEPVvSNdYDIb+J7vZsmULFi9ejPr6enzve9/DsWPH0NHRgfj4eDz00ENYvXq18neyq75KDXJEo8mIXWNRZVB8fPw1XbdE9NXDzwoRkXYMFiLSjsFCRNoxWIhIOwYLEWnHYCEi7RgsRKQdg4WItGOwEJF2DBYi0o7BQkTaMViISDsGCxFpd91z3o6Uq5+g7oUTuK5JM4loJPTCCUA9ywHgg8HS1tYGANiHPV4eCRH1p62tDRaLRbrNdU+mPVJcLhcaGhoQFhYGg8Hgnly7vr5+0JNFUf94LPX4qh5HIQTa2tpgs9lgNMqvovjcGYvRaOx3gu7w8PCv1Js4kngs9fgqHkfVmcpVvHhLRNoxWIhIO58PFrPZjNzcXK49pAGPpR48jmo+d/GWiEY/nz9jIaLRh8FCRNoxWIhIOwYLEWnn88FSUFCACRMmIDAwEMnJyThw4IC3h+TzysvL8eCDD8Jms8FgMOCdd97xqAshsGbNGsTFxSEoKAhpaWk4ffq0dwbrw/Lz8zFjxgyEhYUhJiYGCxcuRE1Njcc23d3dyMrKwtixYxEaGopFixahubnZSyP2HT4dLG+99RZycnKQm5uLjz76CImJiUhPT8f58+e9PTSf1tHRgcTERBQUFPRbX7duHV577TVs2rQJVVVVCAkJQXp6Orq7u7/kkfq2srIyZGVlobKyEu+99x6cTifmz5+Pjo4O9zYrV67E7t27sWPHDpSVlaGhoQEPP/ywF0ftI4QPS0pKEllZWe77fX19wmazifz8fC+OanQBIHbu3Om+73K5hNVqFa+++qr7sZaWFmE2m8Wbb77phRGOHufPnxcARFlZmRDiynELCAgQO3bscG9z8uRJAUBUVFR4a5g+wWfPWHp6elBdXY20tDT3Y0ajEWlpaaioqPDiyEa3uro6NDU1eRxXi8WC5ORkHleF1tZWAEBkZCQAoLq6Gk6n0+NY3nbbbRg3btxX/lj6bLBcvHgRfX19iI2N9Xg8NjYWTU1NXhrV6Hf12PG4Do3L5cKKFSswa9YsTJkyBcCVY2kymRAREeGxLY+lD366mcgXZWVl4dixY9i3b5+3hzIq+OwZS1RUFPz8/K65wt7c3Ayr1eqlUY1+V48dj+vgZWdno6ioCB988IHHlB5WqxU9PT1oaWnx2J7H0oeDxWQyYfr06SgpKXE/5nK5UFJSgpSUFC+ObHRLSEiA1Wr1OK52ux1VVVU8rv9CCIHs7Gzs3LkTe/fuRUJCgkd9+vTpCAgI8DiWNTU1OHv2LI+lt68ey2zfvl2YzWZRWFgoTpw4IZ588kkREREhmpqavD00n9bW1iYOHTokDh06JACI3/zmN+LQoUPi008/FUII8ctf/lJERESIXbt2iaNHj4oFCxaIhIQE0dXV5eWR+5Zly5YJi8UiSktLRWNjo/vW2dnp3uapp54S48aNE3v37hUffvihSElJESkpKV4ctW/w6WARQojf//73Yty4ccJkMomkpCRRWVnp7SH5vA8++EDgylTkHrfMzEwhxJU/Ob/00ksiNjZWmM1mMW/ePFFTU+PdQfug/o4hALFlyxb3Nl1dXeLpp58WY8aMEcHBweKhhx4SjY2N3hu0j+C0CUSknc9eYyGi0YvBQkTaMViISDsGCxFpx2AhIu0YLESkHYOFiLRjsBCRdgwWGtDatWsxbdq0AeuFhYXXTBkwVIsXL8bChQuH9RrkexgsN7CKigr4+fnhgQce8PZQvG7u3LlYsWKFt4fxlcFguYFt3rwZy5cvR3l5ORoaGrw9HPoKYbDcoNrb2/HWW29h2bJleOCBB1BYWOhRLy0thcFgQElJCe6++24EBwcjNTX1mlno/1ltbS0mTpyI7OxsDPQRs127duGuu+5CYGAgJk6ciLy8PPT29irHm5eXh+joaISHh+Opp55CT0+Pu+ZwOPDMM88gJiYGgYGBmD17Ng4ePOjx/LKyMiQlJcFsNiMuLg4vvPCCe7+LFy9GWVkZfve738FgMMBgMOCTTz5RjomGwcsfgqQRsnnzZnH33XcLIYTYvXu3mDRpknC5XO761U9AJycni9LSUnH8+HFxzz33iNTUVPc2ubm5IjExUQghxJEjR4TVahU//elP3fUtW7YIi8Xivl9eXi7Cw8NFYWGhqK2tFX/729/EhAkTxNq1awccZ2ZmpggNDRWPPvqoOHbsmCgqKhLR0dHixRdfdG/zzDPPCJvNJvbs2SOOHz8uMjMzxZgxY8SlS5eEEEKcO3dOBAcHi6efflqcPHlS7Ny5U0RFRYnc3FwhxJVJr1NSUsTSpUvdUx/09vZe97ElNQbLDSo1NVWsX79eCCGE0+kUUVFR4oMPPnDXrwbL+++/737s3XffFQDc87JcDZb9+/eLMWPGiF/96lce+/jXYJk3b574xS9+4bHNn/70JxEXFzfgODMzM0VkZKTo6OhwP7Zx40YRGhoq+vr6RHt7uwgICBBvvPGGu97T0yNsNptYt26dEEKIF198UUyePNkjOAsKCtyvIYQQ3/zmN8WPfvQj2SEjjfir0A2opqYGBw4cwOOPPw4A8Pf3x6OPPorNmzdfs+3UqVPd/46LiwMAj3Wbzp49i29/+9tYs2YNnn32Wel+jxw5gpdffhmhoaHu29KlS9HY2IjOzs4Bn5eYmIjg4GD3/ZSUFLS3t6O+vh61tbVwOp2YNWuWux4QEICkpCScPHkSAHDy5EmkpKTAYDC4t5k1axba29tx7tw56ZhpZHAy7RvQ5s2b0dvbC5vN5n5MCAGz2YwNGzbAYrG4Hw8ICHD/++oPpsvlcj8WHR0Nm82GN998Ez/4wQ8QHh4+4H7b29uRl5fX74JdgYGBw/qaaHThGcsNpre3F1u3bsWvf/1rHD582H07cuSIOyCGIigoCEVFRQgMDER6ejra2toG3Pauu+5CTU0Nbr755mtuRuPA32pHjhxBV1eX+35lZSVCQ0MRHx+PSZMmwWQyYf/+/e660+nEwYMHcccddwAAbr/9dlRUVHhcUN6/fz/CwsLck1+bTCb09fUN6Wun68dgucEUFRXh8uXLWLJkCaZMmeJxW7RoUb+/DqmEhITg3Xffhb+/PzIyMtDe3t7vdmvWrMHWrVuRl5eH48eP4+TJk9i+fTtWr14tff2enh4sWbIEJ06cwJ49e5Cbm4vs7GwYjUaEhIRg2bJl+MlPfoLi4mKcOHECS5cuRWdnJ5YsWQIAePrpp1FfX4/ly5fj1KlT2LVrF3Jzc5GTk+MOtAkTJqCqqgqffPIJLl686HFWRiPA2xd5SK/vfOc74v777++3VlVVJQCII0eOuC/eXr582V2/Ovl2XV2dEMLzr0JCXJmkOzU1VcyZM0e0t7dfc/FWCCGKi4tFamqqCAoKEuHh4SIpKUn88Y9/HHC8mZmZYsGCBWLNmjVi7NixIjQ0VCxdulR0d3e7t+nq6hLLly8XUVFRwmw2i1mzZokDBw54vE5paamYMWOGMJlMwmq1iueff144nU53vaamRsycOVMEBQV5fI00MjjnLRFpx1+FiEg7BgsRacdgISLtGCxEpB2DhYi0Y7AQkXYMFiLSjsFCRNoxWIhIOwYLEWnHYCEi7f4fbL3q5URZjDkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3,3))\n",
    "for i in range(5):\n",
    "    plt.imshow(train_images[i])\n",
    "    plt.xlabel(class_names[train_labels[i]])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images_scaled = train_images / 255.0\n",
    "test_images_scaled = test_images / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_model(hidden_layers):\n",
    "    # Create Flatten input layers\n",
    "    layers = [\n",
    "        keras.layers.Flatten(input_shape = (28, 28))\n",
    "    ]\n",
    "    for i in range(hidden_layers):\n",
    "        layers.append(keras.layers.Dense(500,activation='relu'))\n",
    "    \n",
    "    layers.append(keras.layers.Dense(10,activation='sigmoid'))\n",
    "    \n",
    "    model = keras.Sequential(layers)\n",
    "    \n",
    "    model.compile(optimizer='adam',\n",
    "                  loss='sparse_categorical_crossentropy',\n",
    "                  metrics=['accuracy'])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.7925 - loss: 0.5891\n",
      "Epoch 2/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.8672 - loss: 0.3646\n",
      "Epoch 3/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8772 - loss: 0.3330\n",
      "Epoch 4/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8907 - loss: 0.2986\n",
      "Epoch 5/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8964 - loss: 0.2833\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x1d36ebc96f0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = get_model(1)\n",
    "model.fit(train_images_scaled, train_labels, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([3.03669963e-02, 9.99999702e-01, 1.19439479e-04, 1.23972766e-01,\n",
       "       2.75427587e-02, 6.22768948e-09, 1.45040092e-03, 4.18559691e-13,\n",
       "       2.15701834e-06, 6.86553814e-10], dtype=float32)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(test_images_scaled)[2]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "test_labels[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.config.experimental.list_physical_devices() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4 style=\"color:purple\">5 Epochs performance comparison for 1 hidden layer</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\udaya_z6clw8x\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\layers\\reshaping\\flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 3ms/step - accuracy: 0.7918 - loss: 0.5895\n",
      "Epoch 2/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8665 - loss: 0.3644\n",
      "Epoch 3/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8813 - loss: 0.3256\n",
      "Epoch 4/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.8879 - loss: 0.3021\n",
      "Epoch 5/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8955 - loss: 0.2773\n",
      "29.5 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n1 -r1\n",
    "with tf.device('/CPU:0'):\n",
    "    model = get_model(1)\n",
    "    model.fit(train_images_scaled, train_labels, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 3ms/step - accuracy: 0.7907 - loss: 0.5920\n",
      "Epoch 2/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8644 - loss: 0.3685\n",
      "Epoch 3/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.8831 - loss: 0.3193\n",
      "Epoch 4/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8887 - loss: 0.2989\n",
      "Epoch 5/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.8947 - loss: 0.2820\n",
      "31.4 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n1 -r1\n",
    "with tf.device('/GPU:0'):\n",
    "    model = get_model(1)\n",
    "    model.fit(train_images_scaled, train_labels, epochs=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4 style=\"color:purple\">5 Epocs performance comparison with 5 hidden layers</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 8ms/step - accuracy: 0.7669 - loss: 0.6375\n",
      "Epoch 2/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 8ms/step - accuracy: 0.8568 - loss: 0.4012\n",
      "Epoch 3/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 7ms/step - accuracy: 0.8711 - loss: 0.3592\n",
      "Epoch 4/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 6ms/step - accuracy: 0.8819 - loss: 0.3238\n",
      "Epoch 5/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 7ms/step - accuracy: 0.8855 - loss: 0.3129\n",
      "1min 9s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n1 -r1\n",
    "with tf.device('/CPU:0'):\n",
    "    model = get_model(5)\n",
    "    model.fit(train_images_scaled, train_labels, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 8ms/step - accuracy: 0.7587 - loss: 0.6545\n",
      "Epoch 2/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 9ms/step - accuracy: 0.8589 - loss: 0.3953\n",
      "Epoch 3/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 7ms/step - accuracy: 0.8701 - loss: 0.3597\n",
      "Epoch 4/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 7ms/step - accuracy: 0.8796 - loss: 0.3272\n",
      "Epoch 5/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 9ms/step - accuracy: 0.8885 - loss: 0.3049\n",
      "1min 16s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n1 -r1\n",
    "with tf.device('/GPU:0'):\n",
    "    model = get_model(5)\n",
    "    model.fit(train_images_scaled, train_labels, epochs=5)"
   ]
  }
 ],
 "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": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}