fashion_pynb.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOIfjevmYYpKmTePJkfB2yS",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/xenon0906/Fashion-MNIST-Classification-with-TensorFlow/blob/main/fashion_pynb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "print(tf.__version__)"
      ],
      "metadata": {
        "id": "_ZEjE4SpPaoH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00d4e2bf-47e2-4edc-b549-1972b8551f4b"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.17.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fmnist = tf.keras.datasets.fashion_mnist"
      ],
      "metadata": {
        "id": "eEs7hbnrSMpg"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(training_images, training_labels), (test_images, test_labels) = fmnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MwP8KVV7SyjP",
        "outputId": "12623550-1324-41e1-accf-28f7c8840b10"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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[1m0s\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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "index = 0\n",
        "\n",
        "np.set_printoptions(linewidth=320)\n",
        "\n",
        "print(f'LABEL: {training_labels[index]}')\n",
        "print(f'\\nIMAGE PIXEL ARRAY:\\n {training_images[index]}')\n",
        "\n",
        "plt.imshow(training_images[index], cmap='Greys')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 985
        },
        "id": "31wl29APTF1_",
        "outputId": "bc4e895c-23e1-4240-fe42-67423133dcb6"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LABEL: 9\n",
            "\n",
            "IMAGE PIXEL ARRAY:\n",
            " [[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   1   0   0  13  73   0   0   1   4   0   0   0   0   1   1   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   3   0  36 136 127  62  54   0   0   0   1   3   4   0   0   3]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   6   0 102 204 176 134 144 123  23   0   0   0   0  12  10   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0 155 236 207 178 107 156 161 109  64  23  77 130  72  15]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   1   0  69 207 223 218 216 216 163 127 121 122 146 141  88 172  66]\n",
            " [  0   0   0   0   0   0   0   0   0   1   1   1   0 200 232 232 233 229 223 223 215 213 164 127 123 196 229   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0 183 225 216 223 228 235 227 224 222 224 221 223 245 173   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0 193 228 218 213 198 180 212 210 211 213 223 220 243 202   0]\n",
            " [  0   0   0   0   0   0   0   0   0   1   3   0  12 219 220 212 218 192 169 227 208 218 224 212 226 197 209  52]\n",
            " [  0   0   0   0   0   0   0   0   0   0   6   0  99 244 222 220 218 203 198 221 215 213 222 220 245 119 167  56]\n",
            " [  0   0   0   0   0   0   0   0   0   4   0   0  55 236 228 230 228 240 232 213 218 223 234 217 217 209  92   0]\n",
            " [  0   0   1   4   6   7   2   0   0   0   0   0 237 226 217 223 222 219 222 221 216 223 229 215 218 255  77   0]\n",
            " [  0   3   0   0   0   0   0   0   0  62 145 204 228 207 213 221 218 208 211 218 224 223 219 215 224 244 159   0]\n",
            " [  0   0   0   0  18  44  82 107 189 228 220 222 217 226 200 205 211 230 224 234 176 188 250 248 233 238 215   0]\n",
            " [  0  57 187 208 224 221 224 208 204 214 208 209 200 159 245 193 206 223 255 255 221 234 221 211 220 232 246   0]\n",
            " [  3 202 228 224 221 211 211 214 205 205 205 220 240  80 150 255 229 221 188 154 191 210 204 209 222 228 225   0]\n",
            " [ 98 233 198 210 222 229 229 234 249 220 194 215 217 241  65  73 106 117 168 219 221 215 217 223 223 224 229  29]\n",
            " [ 75 204 212 204 193 205 211 225 216 185 197 206 198 213 240 195 227 245 239 223 218 212 209 222 220 221 230  67]\n",
            " [ 48 203 183 194 213 197 185 190 194 192 202 214 219 221 220 236 225 216 199 206 186 181 177 172 181 205 206 115]\n",
            " [  0 122 219 193 179 171 183 196 204 210 213 207 211 210 200 196 194 191 195 191 198 192 176 156 167 177 210  92]\n",
            " [  0   0  74 189 212 191 175 172 175 181 185 188 189 188 193 198 204 209 210 210 211 188 188 194 192 216 170   0]\n",
            " [  2   0   0   0  66 200 222 237 239 242 246 243 244 221 220 193 191 179 182 182 181 176 166 168  99  58   0   0]\n",
            " [  0   0   0   0   0   0   0  40  61  44  72  41  35   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f2b796025f0>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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MmKEnn3xSwWCw61YNAOj1Ap7nedaL+LpIJKJQKKRwONynXg/yM2wwHA47Zw4cOOCcOXbsmHNGkl566SXnzAcffOCcSUhIcM5c6F2XF+PnH0p+hmOOGjXKOdPc3Oyc8TP0VPJ3TsTHxztnGhoanDMX+xzi+fj5O5J0zqSXSxEbG+ucGTx4sHOmpaXFOSPJ15u8duzY4bT9pT6PMwsOAGCCAgIAmKCAAAAmKCAAgAkKCABgggICAJiggAAAJiggAIAJCggAYIICAgCYoIAAACYoIACACQoIAGCiy38kt5VPP/3UOVNSUuJrX4cPH3bO1NbWOmfi4uKcM62trc6Z9PR054zkb/pxSkqKc2bAgAHOmfb2dueMJCUlJTlnJkyY4JxZsWKFc6awsNA588UXXzhnJKl///7Omb179/ral6uKigrnTF1dna99jRw50jnjZ4p/fX29c8bPtHxJ+u9//+srFw1cAQEATFBAAAATFBAAwAQFBAAwQQEBAExQQAAAExQQAMAEBQQAMEEBAQBMUEAAABMUEADABAUEADDRY4eRtre3Ow2UnD9/vvM+9u/f75yRpH793A+bn8GifoYa+nH69GlfOT/Hwc+wTz9OnDjhK1dVVeWceeqpp5wzCQkJzpknn3zSOTN8+HDnjORvfbfddptzxs+wTz/DNI8cOeKckfwNwm1qanLOtLW1OWf8PKdIUkZGhq9cNHAFBAAwQQEBAExQQAAAExQQAMAEBQQAMEEBAQBMUEAAABMUEADABAUEADBBAQEATFBAAAATFBAAwESPHUZaVlamxMTES95+z549zvvIy8tzzkjSl19+2S2Zmpoa54wfLS0tvnIfffSRc8bP8MnRo0c7ZyKRiHNGkoYNG+acmTFjhnOmoqLCOTNnzhznzGeffeackfwdv8rKSufM3//+d+eMy5Dis/r37++ckaTGxkbnjJ9hpH74GQYsSa2trc4Z1/PhUrfnCggAYIICAgCYoIAAACYoIACACQoIAGCCAgIAmKCAAAAmKCAAgAkKCABgggICAJiggAAAJiggAICJHjuMNDU1VQMHDrzk7ceOHeu8j88//9w5I8lpXWdlZGQ4Z/wMMPUzCNHvcUhPT3fOXH311c6ZcDjsnElKSnLOSHIagHtWfHy8c+ZHP/qRc2bKlCnOmd27dztnJOnEiRPOmWAw6JwZMmRIt+zH7+BOP0NMm5ubnTOxsbHOGc/znDOSv+HDR44ccdr+1KlTl7QdV0AAABMUEADAhFMBlZaWatKkSUpKSlJaWppmz56tqqqqTts0NTWpuLhYQ4YM0cCBAzVnzhzV1tZ26aIBAL2fUwGVl5eruLhYlZWVevvtt9Xa2qoZM2aooaGhY5sHHnhAb7zxhl577TWVl5fr6NGjuvXWW7t84QCA3s3plbmNGzd2+nrVqlVKS0vT9u3bNXXqVIXDYf3lL3/R6tWr9ZOf/ESStHLlSl199dWqrKzUD3/4w65bOQCgV7us14DOvjspJSVFkrR9+3a1traqsLCwY5tx48Zp+PDhF/wRxM3NzYpEIp1uAIC+z3cBtbe3a/HixZoyZYrGjx8vSaqpqVF8fLwGDRrUadv09HTV1NSc9/cpLS1VKBTquGVnZ/tdEgCgF/FdQMXFxdq9e7fWrFlzWQsoKSlROBzuuB06dOiyfj8AQO/g69NZixYt0oYNG7RlyxYNGzas4/6MjAy1tLSorq6u01VQbW3tBT+IGQwGfX2wDADQuzldAXmep0WLFmnt2rXavHmzcnJyOj0+ceJExcXFadOmTR33VVVV6eDBgyooKOiaFQMA+gSnK6Di4mKtXr1a69evV1JSUsfrOqFQSAMGDFAoFNK9996rJUuWKCUlRcnJybr//vtVUFDAO+AAAJ04FdDy5cslSdOmTet0/8qVKzVv3jxJ0h//+EfFxMRozpw5am5u1syZM/XnP/+5SxYLAOg7Ap7fiXZREolEFAqFtGfPHqeBkj//+c+d95WZmemckS590N7XuQ7zk6S0tDTnTCgUcs60trY6Z/zmYmLc3/fi5635fga5Sv4GSba3tztnAoGAc+bkyZPOGT+DcyV/w1z9DNxtbGx0zmRlZTln4uLinDOSvyGmfvZ1+vRp58zBgwedM5K/IaYLFixw2r6xsVHz589XOBxWcnLyBbdjFhwAwAQFBAAwQQEBAExQQAAAExQQAMAEBQQAMEEBAQBMUEAAABMUEADABAUEADBBAQEATFBAAAATFBAAwISvn4jaHbKysr51iuo33X333c77ePbZZ50zkjR69GjnzDXXXOOc6d+/v3PGz6TupqYm54wkNTQ0OGf8TP396quvnDMJCQnOGcnfJGM/k61dzu2zcnNznTOxsbHOGcnfFOiWlhbnzNChQ50z4XDYOePne0mSBg8e3C2Z+Ph454yf80GS9uzZ45y56qqrnLa/1OcGroAAACYoIACACQoIAGCCAgIAmKCAAAAmKCAAgAkKCABgggICAJiggAAAJiggAIAJCggAYIICAgCYCHie51kv4usikYhCoZDC4bCvgY0udu7c6Sv31FNPOWc+++wz58zw4cOdM4MGDXLO+B1Y2dbW5pzxM7DSzzBSP2uTJD/fDn6Gkfo5Ds3Nzc4Zv4Nm/eS666nEz35GjBgRhZWcn5+/p5gY92uB6upq54wkFRQUOGeWL1/utP2lPo9zBQQAMEEBAQBMUEAAABMUEADABAUEADBBAQEATFBAAAATFBAAwAQFBAAwQQEBAExQQAAAExQQAMBEjx1GWldX5zSM1M9AyO70ySefOGd++ctfOmcOHDjgnPniiy+cM5LU3t7unPEzJLS1tdU543fAqp9vh2HDhjln/JyvY8aMcc74PQ4DBw50zvgdAOvKz7GLi4vzta/ExETnjJ/vi5/97GfOmdGjRztnJCk3N9dXzgXDSAEAPRoFBAAwQQEBAExQQAAAExQQAMAEBQQAMEEBAQBMUEAAABMUEADABAUEADBBAQEATFBAAAAT/awXcCGBQKDHDxh1MW7cOOfMW2+9FYWVnOvEiRO+cnV1dc6ZpKQk58zx48edMxkZGc4ZSerXz/1bIiUlxde+gCsdV0AAABMUEADAhFMBlZaWatKkSUpKSlJaWppmz56tqqqqTttMmzat47/Pzt4WLFjQpYsGAPR+TgVUXl6u4uJiVVZW6u2331Zra6tmzJihhoaGTtvNnz9fx44d67g988wzXbpoAEDv5/SK68aNGzt9vWrVKqWlpWn79u2aOnVqx/0JCQm+XwQGAFwZLus1oHA4LOncdwG99NJLSk1N1fjx41VSUqLGxsYL/h7Nzc2KRCKdbgCAvs/327Db29u1ePFiTZkyRePHj++4/6677tKIESOUlZWlXbt26eGHH1ZVVZVef/318/4+paWleuKJJ/wuAwDQSwU8z/P8BBcuXKg333xT7733noYNG3bB7TZv3qzp06dr3759Gjly5DmPNzc3q7m5uePrSCSi7OxshcNhJScn+1kaHPE5oP/hc0DA5YtEIgqFQhd9Hvd1BbRo0SJt2LBBW7Zs+dbykaT8/HxJumABBYNBBYNBP8sAAPRiTgXkeZ7uv/9+rV27VmVlZcrJybloZufOnZKkzMxMXwsEAPRNTgVUXFys1atXa/369UpKSlJNTY0kKRQKacCAAdq/f79Wr16tn/70pxoyZIh27dqlBx54QFOnTtWECROi8gcAAPROTgW0fPlySWc+bPp1K1eu1Lx58xQfH6933nlHzz33nBoaGpSdna05c+bokUce6bIFAwD6Buf/gvs22dnZKi8vv6wFAQCuDD12Gja6z9ChQ7s154oPNQN9E8NIAQAmKCAAgAkKCABgggICAJiggAAAJiggAIAJCggAYIICAgCYoIAAACYoIACACQoIAGCCAgIAmKCAAAAmKCAAgAkKCABgggICAJiggAAAJiggAIAJCggAYIICAgCYoIAAACYoIACACQoIAGCCAgIAmOhnvYBv8jxPkhSJRIxXAgDw4+zz99nn8wvpcQVUX18vScrOzjZeCQDgctTX1ysUCl3w8YB3sYrqZu3t7Tp69KiSkpIUCAQ6PRaJRJSdna1Dhw4pOTnZaIX2OA5ncBzO4DicwXE4oyccB8/zVF9fr6ysLMXEXPiVnh53BRQTE6Nhw4Z96zbJyclX9Al2FsfhDI7DGRyHMzgOZ1gfh2+78jmLNyEAAExQQAAAE72qgILBoJYuXapgMGi9FFMchzM4DmdwHM7gOJzRm45Dj3sTAgDgytCrroAAAH0HBQQAMEEBAQBMUEAAABO9poCWLVum73znO+rfv7/y8/P173//23pJ3e7xxx9XIBDodBs3bpz1sqJuy5Ytuummm5SVlaVAIKB169Z1etzzPD322GPKzMzUgAEDVFhYqL1799osNooudhzmzZt3zvkxa9Ysm8VGSWlpqSZNmqSkpCSlpaVp9uzZqqqq6rRNU1OTiouLNWTIEA0cOFBz5sxRbW2t0Yqj41KOw7Rp0845HxYsWGC04vPrFQX0yiuvaMmSJVq6dKk+/PBD5eXlaebMmTp+/Lj10rrdNddco2PHjnXc3nvvPeslRV1DQ4Py8vK0bNmy8z7+zDPP6Pnnn9eKFSu0detWJSYmaubMmWpqaurmlUbXxY6DJM2aNavT+fHyyy934wqjr7y8XMXFxaqsrNTbb7+t1tZWzZgxQw0NDR3bPPDAA3rjjTf02muvqby8XEePHtWtt95quOqudynHQZLmz5/f6Xx45plnjFZ8AV4vMHnyZK+4uLjj67a2Ni8rK8srLS01XFX3W7p0qZeXl2e9DFOSvLVr13Z83d7e7mVkZHh/+MMfOu6rq6vzgsGg9/LLLxussHt88zh4nufNnTvXu/nmm03WY+X48eOeJK+8vNzzvDN/93Fxcd5rr73Wsc2ePXs8SV5FRYXVMqPum8fB8zzvxz/+sferX/3KblGXoMdfAbW0tGj79u0qLCzsuC8mJkaFhYWqqKgwXJmNvXv3KisrS7m5ubr77rt18OBB6yWZqq6uVk1NTafzIxQKKT8//4o8P8rKypSWlqaxY8dq4cKFOnnypPWSoiocDkuSUlJSJEnbt29Xa2trp/Nh3LhxGj58eJ8+H755HM566aWXlJqaqvHjx6ukpESNjY0Wy7ugHjeM9Js+//xztbW1KT09vdP96enp+uSTT4xWZSM/P1+rVq3S2LFjdezYMT3xxBO64YYbtHv3biUlJVkvz0RNTY0knff8OPvYlWLWrFm69dZblZOTo/379+u3v/2tioqKVFFRodjYWOvldbn29nYtXrxYU6ZM0fjx4yWdOR/i4+M1aNCgTtv25fPhfMdBku666y6NGDFCWVlZ2rVrlx5++GFVVVXp9ddfN1xtZz2+gPA/RUVFHb+eMGGC8vPzNWLECL366qu69957DVeGnuCOO+7o+PW1116rCRMmaOTIkSorK9P06dMNVxYdxcXF2r179xXxOui3udBxuO+++zp+fe211yozM1PTp0/X/v37NXLkyO5e5nn1+P+CS01NVWxs7DnvYqmtrVVGRobRqnqGQYMGacyYMdq3b5/1UsycPQc4P86Vm5ur1NTUPnl+LFq0SBs2bNC7777b6ce3ZGRkqKWlRXV1dZ2276vnw4WOw/nk5+dLUo86H3p8AcXHx2vixInatGlTx33t7e3atGmTCgoKDFdm79SpU9q/f78yMzOtl2ImJydHGRkZnc6PSCSirVu3XvHnx+HDh3Xy5Mk+dX54nqdFixZp7dq12rx5s3Jycjo9PnHiRMXFxXU6H6qqqnTw4ME+dT5c7Dicz86dOyWpZ50P1u+CuBRr1qzxgsGgt2rVKu/jjz/27rvvPm/QoEFeTU2N9dK61a9//WuvrKzMq66u9v71r395hYWFXmpqqnf8+HHrpUVVfX29t2PHDm/Hjh2eJO/ZZ5/1duzY4R04cMDzPM97+umnvUGDBnnr16/3du3a5d18881eTk6Od/r0aeOVd61vOw719fXegw8+6FVUVHjV1dXeO++841133XXe6NGjvaamJuuld5mFCxd6oVDIKysr844dO9Zxa2xs7NhmwYIF3vDhw73Nmzd727Zt8woKCryCggLDVXe9ix2Hffv2eb/73e+8bdu2edXV1d769eu93Nxcb+rUqcYr76xXFJDned4LL7zgDR8+3IuPj/cmT57sVVZWWi+p291+++1eZmamFx8f71111VXe7bff7u3bt896WVH37rvvepLOuc2dO9fzvDNvxX700Ue99PR0LxgMetOnT/eqqqpsFx0F33YcGhsbvRkzZnhDhw714uLivBEjRnjz58/vc/9IO9+fX5K3cuXKjm1Onz7t/eIXv/AGDx7sJSQkeLfccot37Ngxu0VHwcWOw8GDB72pU6d6KSkpXjAY9EaNGuX95je/8cLhsO3Cv4EfxwAAMNHjXwMCAPRNFBAAwAQFBAAwQQEBAExQQAAAExQQAMAEBQQAMEEBAQBMUEAAABMUEADABAUEADBBAQEATPw/ujflcCL66CwAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_images = training_images / 255.0\n",
        "test_images = test_images / 255.0"
      ],
      "metadata": {
        "id": "hehbWBTlT_2X"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(128, activation=tf.nn.relu),\n",
        "    tf.keras.layers.Dense(10, activation=tf.nn.softmax)])"
      ],
      "metadata": {
        "id": "EB1UvuRLUbkf"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = np.array([[1.0, 3.0, 4.0, 2.0]])\n",
        "inputs = tf.convert_to_tensor(inputs)\n",
        "print(f'input to softmax function: {inputs.numpy()}')\n",
        "\n",
        "outputs = tf.keras.activations.softmax(inputs)\n",
        "print(f'output of softmax function: {outputs.numpy()}')\n",
        "\n",
        "sum = tf.reduce_sum(outputs)\n",
        "print(f'sum of outputs: {sum}')\n",
        "\n",
        "prediction = np.argmax(outputs)\n",
        "print(f'class with highest probability: {prediction}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DVV1W6C0Uvae",
        "outputId": "88f81ff5-7b99-471e-f73f-a8856378f46f"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input to softmax function: [[1. 3. 4. 2.]]\n",
            "output of softmax function: [[0.0320586  0.23688282 0.64391426 0.08714432]]\n",
            "sum of outputs: 1.0\n",
            "class with highest probability: 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer = tf.optimizers.Adam(),\n",
        "              loss = 'sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.fit(training_images, training_labels, epochs=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H3vEmlymV3z_",
        "outputId": "96153542-107b-4402-e1cd-63084afc589d"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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: 0.7744 - loss: 0.6513\n",
            "Epoch 2/5\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - accuracy: 0.8632 - loss: 0.3817\n",
            "Epoch 3/5\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.8769 - loss: 0.3385\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: 0.8824 - loss: 0.3189\n",
            "Epoch 5/5\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 4ms/step - accuracy: 0.8922 - loss: 0.2930\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7f2b7e8de170>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(test_images, test_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "grpZVS8oWl-n",
        "outputId": "5d7102cf-5f43-490a-924b-3cac510b7f84"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8725 - loss: 0.3443\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.34537532925605774, 0.8747000098228455]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    }
  ]
}