Image_classifcation_clothing.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOJkqJQhoSf2asT7RgvPBvJ",
      "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/kpr-03/DeepLearning_TensorFlow/blob/main/Image_classifcation_clothing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A highly valued Fashion brand company wanted to develop a web application to classify images of different items of clothing.\n",
        "\n",
        "Our team got the task to build a neural network to Classify images of different items of clothing.\n",
        "\n",
        "The sample data was provided by the company(here tensorflow data is being used just for demonstartion purpose).\n"
      ],
      "metadata": {
        "id": "tOcDth6Rn1hM"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S9E0FdtptCUU",
        "outputId": "309b183c-2676-4ffb-bf19-065b611c4228"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "29515/29515 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26421880/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "5148/5148 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4422102/4422102 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.datasets import fashion_mnist\n",
        "\n",
        "# The data has already been sorted into training and test sets for us\n",
        "(train_data, train_labels), (test_data, test_labels) = fashion_mnist.load_data()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# show the first training example\n",
        "print(f\"training sample:\\n{train_data[0]}\\n\")\n",
        "print(f\"training label:\\n{train_labels[0]}\\n\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nC8Mw5fstDd5",
        "outputId": "4be2d680-ef46-4907-b171-04fd2a797840"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "training sample:\n",
            "[[  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]\n",
            " [  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]\n",
            " [  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]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   1   0   0  13  73   0\n",
            "    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\n",
            "   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\n",
            "  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\n",
            "  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\n",
            "  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\n",
            "  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\n",
            "  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\n",
            "  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\n",
            "  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\n",
            "  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\n",
            "  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\n",
            "  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\n",
            "  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\n",
            "  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\n",
            "  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\n",
            "  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\n",
            "  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\n",
            "  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\n",
            "  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\n",
            "  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\n",
            "  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\n",
            "  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\n",
            "    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\n",
            "    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\n",
            "    0   0   0   0   0   0   0   0   0   0]]\n",
            "\n",
            "training label:\n",
            "9\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check the shape of single example\n",
        "train_data[0].shape,train_labels[0].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qaje1vltt6S_",
        "outputId": "933fb09c-80d2-4b4c-9297-af96224d6eb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((28, 28), ())"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot a single sample\n",
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(train_data[7]);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "7QWPTRZ2uTZq",
        "outputId": "94c5ccb7-859a-4317-84c1-b1e0a17f1c10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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fxe23M9Zh6GJWyH684+ZTk60zkpQ/4i/WmZDshxrGFLDOoP8FA/YHeVPc/lvQpeHPrDOjgvaDUiW3Yy/iMMh1VKDLOvPvp8ZaZyQlbbCoC86AAABeUEAAAC+sC2jHjh264YYblJ+fr0AgoC1btvS6fdmyZQoEAr0uCxcuTNR6AQBDhHUBtbW1qaioSOvWrTvvfRYuXKhjx471XF544YWLWiQAYOixfgWwrKxMZWVlX3ifSCSi3Nxc50UBAIa+pLwGVFVVpezsbF155ZW6++67dfLkyfPet6OjQy0tLb0uAIChL+EFtHDhQj333HPavn27fvazn6m6ulplZWWKxfp+a2JlZaUyMjJ6LgUFBYleEgBgAEr47wHdcsstPf+ePn26ZsyYoSlTpqiqqkrz588/5/4VFRVavXp1z8ctLS2UEAAMA0l/G/bkyZM1btw4HThwoM/bI5GI0tPTe10AAENf0gvoyJEjOnnypPLy8pK9KQDAIGL9X3CnTp3qdTZTX1+vPXv2KCsrS1lZWXr00Ue1ZMkS5ebm6uDBg7r//vt12WWXqbS0NKELBwAMbtYFtHv3bl1//fU9H3/++s3SpUu1fv167d27V7/+9a/V1NSk/Px8LViwQP/4j/+oSCSSuFUDAAY96wKaN2+ezBcMs3vjjTcuakH9KXS52xDOvBF7rDO1HZ3WmfzQaeuMy3DHTuP2XpQUh6GLUWP/v75u2wlZZ7pz9vtidMD+sXX5mly0G7chnJ0O+8/la/osNso6c2XY/lc1WuNu+/tEV7Z15vJws3WmLW7/vPi70ef/9ZYv8q9y+76XDMyCAwB4QQEBALyggAAAXlBAAAAvKCAAgBcUEADACwoIAOAFBQQA8IICAgB4QQEBALyggAAAXlBAAAAvKCAAgBcJ/5Pcg0lDSU6/bavdYcpyZrDLOtPcFbDOfBpNs85I0tWpn1hnWoz9n+WIOUzQdplqLUkx2e+/gbydga4pbj8N+2is3TrjOh19cspx68yogP1je8LheI0E3CadDyScAQEAvKCAAABeUEAAAC8oIACAFxQQAMALCggA4AUFBADwggICAHhBAQEAvKCAAABeUEAAAC8oIACAF8N6GGkw2n/bOhkbY50Jp3RaZ4IBY5352sg/W2ckKUVx60yrw1DIcMB+KGun4/DJlEDMOhN2yLTFR/bLdly57Ie47J9QTTH7YaQnYvbDc122I0lFEfvnRmrA/thrMynWmaGAMyAAgBcUEADACwoIAOAFBQQA8IICAgB4QQEBALyggAAAXlBAAAAvKCAAgBcUEADACwoIAOAFBQQA8GJYDyPN2XbILfiYfSTu0PVRYz/ss92ErTMuwz4lqc1hWy5DWVMD9kMuXfa3JI0KnrbOpDoM7nR5nEKyHzTbn0NZXR4nFy7H66hgh9O20oL2z8HTxv5xihuX49V+bQMNZ0AAAC8oIACAFxQQAMALCggA4AUFBADwggICAHhBAQEAvKCAAABeUEAAAC8oIACAFxQQAMALCggA4MWwHkZ65O8n9du2mmKj7DNx+2GDsyP2Qy5/224/gFOSmuL2X5PLkMvRDoMkY07DHaX2uP3+a3IYChmW/X6IKWCdSQ26DQhN6af1pYXOWGdOdKVbZ1z3Q2rA/mtqdxhG6jY0lmGkAAA4oYAAAF5YFVBlZaVmzZqltLQ0ZWdna/Hixaqrq+t1n/b2dpWXl2vs2LEaM2aMlixZosbGxoQuGgAw+FkVUHV1tcrLy7Vz5069+eabikajWrBggdra2nruc++99+q1117TK6+8ourqah09elQ33XRTwhcOABjcrN6EsG3btl4fb9y4UdnZ2aqtrdXcuXPV3NysX/7yl9q0aZO++93vSpI2bNigr371q9q5c6e+9a1vJW7lAIBB7aJeA2pubpYkZWVlSZJqa2sVjUZVUlLSc5+pU6dq4sSJqqmp6fNzdHR0qKWlpdcFADD0ORdQPB7XqlWrdM0112jatGmSpIaGBqWkpCgzM7PXfXNyctTQ0NDn56msrFRGRkbPpaCgwHVJAIBBxLmAysvLtW/fPr344osXtYCKigo1Nzf3XA4fPnxRnw8AMDg4/SLqypUr9frrr2vHjh2aMGFCz/W5ubnq7OxUU1NTr7OgxsZG5ebm9vm5IpGIIpGIyzIAAIOY1RmQMUYrV67U5s2b9fbbb6uwsLDX7TNnzlQ4HNb27dt7rqurq9OhQ4c0Z86cxKwYADAkWJ0BlZeXa9OmTdq6davS0tJ6XtfJyMjQyJEjlZGRoTvvvFOrV69WVlaW0tPTdc8992jOnDm8Aw4A0ItVAa1fv16SNG/evF7Xb9iwQcuWLZMk/fznP1cwGNSSJUvU0dGh0tJS/eIXv0jIYgEAQ4dVAZkvMWQvNTVV69at07p165wX1V9GzP+037bVGhtpnfksnmKdKbzwXc6x6vFyh5T0H488YZ3JCNp/TfVd9oMxo47DSJvi9o9Tu7EfYOoylNVl2GfccT902m9KY4P2g0XHOwwjvWLUaOvMHYeus85I0uKJ/8s681Gn23Df/jLi0onWma4/HUrCSpgFBwDwhAICAHhBAQEAvKCAAABeUEAAAC8oIACAFxQQAMALCggA4AUFBADwggICAHhBAQEAvKCAAABeUEAAAC+c/iLqUDEy3OWUq4+ess4UpJy0zkRNyDrjIutXNU65b89abZ3573/znHVm8ojPrDNXO/6V3e1n7MdAjw32z/TjToefF12nYbfEU60zhSmd1pmOLzFh/2w/OvYN68y+f51mnZEkPW4/DTvq8Di5TFSXog4Z6dB/mXDhO50lfy3TsAEAQwgFBADwggICAHhBAQEAvKCAAABeUEAAAC8oIACAFxQQAMALCggA4AUFBADwggICAHhBAQEAvBjWw0jtxyB2KwyPsc58GHUbfDqQXXH3e9aZpzU1CStJnODo0faZrEscNmQ/9FRxhyPWYdinJJn2duvMP39qP3DXTdw6kSW3gbt63D4ScvjOMjrYYZ05HmuzzkhSbulh+9Bap01dEGdAAAAvKCAAgBcUEADACwoIAOAFBQQA8IICAgB4QQEBALyggAAAXlBAAAAvKCAAgBcUEADACwoIAODFsB5GmrH0lFvwA/vIV0LN1plwwH7oYocZ1g/pRYu32Q94dMlgcHj5VIZ15tup9t9X/k+n/YDjscGodUaSPnlvgnWmUA4DTL8EzoAAAF5QQAAALyggAIAXFBAAwAsKCADgBQUEAPCCAgIAeEEBAQC8oIAAAF5QQAAALyggAIAXFBAAwIthPbky1njcKfe9+d+3zqx6bYt15vLwX6wzs37/D9aZPH1knRnwgiGnWCBknwuE7H+OM8ZYZ5zE+2k7kkwsZh+KO2QCAfuM4/7+ye9vss7s/c7/sM5MCZ+wzvxt3Y3WGUkqrKhxyiUDZ0AAAC8oIACAF1YFVFlZqVmzZiktLU3Z2dlavHix6urqet1n3rx5CgQCvS533XVXQhcNABj8rAqourpa5eXl2rlzp958801Fo1EtWLBAbWf9Qa7ly5fr2LFjPZe1a9cmdNEAgMHP6k0I27Zt6/Xxxo0blZ2drdraWs2dO7fn+lGjRik3NzcxKwQADEkX9RpQc3P3n5nOysrqdf3zzz+vcePGadq0aaqoqNDp06fP+zk6OjrU0tLS6wIAGPqc34Ydj8e1atUqXXPNNZo2bVrP9bfddpsmTZqk/Px87d27Vw888IDq6ur06quv9vl5Kisr9eijj7ouAwAwSDkXUHl5ufbt26d333231/UrVqzo+ff06dOVl5en+fPn6+DBg5oyZco5n6eiokKrV6/u+bilpUUFBQWuywIADBJOBbRy5Uq9/vrr2rFjhyZMmPCF9y0uLpYkHThwoM8CikQiikQiLssAAAxiVgVkjNE999yjzZs3q6qqSoWFhRfM7NmzR5KUl5fntEAAwNBkVUDl5eXatGmTtm7dqrS0NDU0NEiSMjIyNHLkSB08eFCbNm3S9773PY0dO1Z79+7Vvffeq7lz52rGjBlJ+QIAAIOTVQGtX79eUvcvm/7/NmzYoGXLliklJUVvvfWWnnrqKbW1tamgoEBLlizRgw8+mLAFAwCGBuv/gvsiBQUFqq6uvqgFAQCGh2E9DdtV7KP91pnM0Pl/F+p8CsNjrDNX5/zZOtNonegWysywzsSamh23ZsllyrIk45AzUadNwVFgRNg6Y6KdTttK/cNI68ypufYHxCSH78TN/+b2buF02X+PSBaGkQIAvKCAAABeUEAAAC8oIACAFxQQAMALCggA4AUFBADwggICAHhBAQEAvKCAAABeUEAAAC8oIACAFwwjdREIWEeW/8sPrTOpn33x9PG+jPmz/dDFEaq1zkhSvO2MUw64KCbeb5tKPWH/HGyIhawzTfFU60yg/3ZD0nAGBADwggICAHhBAQEAvKCAAABeUEAAAC8oIACAFxQQAMALCggA4AUFBADwggICAHhBAQEAvBhws+CM6Z691KWoZD+GqZ/Yz4KLdbTbZzrtd0BXl/0sOJmofUZSwNjvB+O4LeBzAWP/vDCmy2lbsU775+2pVvshbW1x+0xX1H5tktTVD8/BLnVvw1zgsQqYC92jnx05ckQFBQW+lwEAuEiHDx/WhAkTznv7gCugeDyuo0ePKi0tTYGzpk63tLSooKBAhw8fVnp6uqcV+sd+6MZ+6MZ+6MZ+6DYQ9oMxRq2trcrPz1cweP5Xegbcf8EFg8EvbExJSk9PH9YH2OfYD93YD93YD93YD91874eMjIwL3oc3IQAAvKCAAABeDKoCikQiWrNmjSKRiO+leMV+6MZ+6MZ+6MZ+6DaY9sOAexMCAGB4GFRnQACAoYMCAgB4QQEBALyggAAAXgyaAlq3bp0uvfRSpaamqri4WO+9957vJfW7Rx55RIFAoNdl6tSpvpeVdDt27NANN9yg/Px8BQIBbdmypdftxhg9/PDDysvL08iRI1VSUqL9+/f7WWwSXWg/LFu27JzjY+HChX4WmySVlZWaNWuW0tLSlJ2drcWLF6uurq7Xfdrb21VeXq6xY8dqzJgxWrJkiRobGz2tODm+zH6YN2/eOcfDXXfd5WnFfRsUBfTSSy9p9erVWrNmjd5//30VFRWptLRUx48f9720fnfVVVfp2LFjPZd3333X95KSrq2tTUVFRVq3bl2ft69du1ZPP/20nn32We3atUujR49WaWmp2tvdhjUOVBfaD5K0cOHCXsfHCy+80I8rTL7q6mqVl5dr586devPNNxWNRrVgwQK1tbX13Ofee+/Va6+9pldeeUXV1dU6evSobrrpJo+rTrwvsx8kafny5b2Oh7Vr13pa8XmYQWD27NmmvLy85+NYLGby8/NNZWWlx1X1vzVr1piioiLfy/BKktm8eXPPx/F43OTm5ponnnii57qmpiYTiUTMCy+84GGF/ePs/WCMMUuXLjWLFi3ysh5fjh8/biSZ6upqY0z3Yx8Oh80rr7zSc5+PPvrISDI1NTW+lpl0Z+8HY4z5zne+Y374wx/6W9SXMODPgDo7O1VbW6uSkpKe64LBoEpKSlRTU+NxZX7s379f+fn5mjx5sm6//XYdOnTI95K8qq+vV0NDQ6/jIyMjQ8XFxcPy+KiqqlJ2drauvPJK3X333Tp58qTvJSVVc3OzJCkrK0uSVFtbq2g02ut4mDp1qiZOnDikj4ez98Pnnn/+eY0bN07Tpk1TRUWFTp8+7WN55zXghpGe7dNPP1UsFlNOTk6v63NycvTxxx97WpUfxcXF2rhxo6688kodO3ZMjz76qK677jrt27dPaWlpvpfnRUNDgyT1eXx8fttwsXDhQt10000qLCzUwYMH9ZOf/ERlZWWqqalRKBTyvbyEi8fjWrVqla655hpNmzZNUvfxkJKSoszMzF73HcrHQ1/7QZJuu+02TZo0Sfn5+dq7d68eeOAB1dXV6dVXX/W42t4GfAHhr8rKynr+PWPGDBUXF2vSpEl6+eWXdeedd3pcGQaCW265peff06dP14wZMzRlyhRVVVVp/vz5HleWHOXl5dq3b9+weB30i5xvP6xYsaLn39OnT1deXp7mz5+vgwcPasqUKf29zD4N+P+CGzdunEKh0DnvYmlsbFRubq6nVQ0MmZmZuuKKK3TgwAHfS/Hm82OA4+NckydP1rhx44bk8bFy5Uq9/vrreuedd3r9+Zbc3Fx1dnaqqamp1/2H6vFwvv3Ql+LiYkkaUMfDgC+glJQUzZw5U9u3b++5Lh6Pa/v27ZozZ47Hlfl36tQpHTx4UHl5eb6X4k1hYaFyc3N7HR8tLS3atWvXsD8+jhw5opMnTw6p48MYo5UrV2rz5s16++23VVhY2Ov2mTNnKhwO9zoe6urqdOjQoSF1PFxoP/Rlz549kjSwjgff74L4Ml588UUTiUTMxo0bzYcffmhWrFhhMjMzTUNDg++l9asf/ehHpqqqytTX15vf/va3pqSkxIwbN84cP37c99KSqrW11XzwwQfmgw8+MJLMk08+aT744APzySefGGOM+elPf2oyMzPN1q1bzd69e82iRYtMYWGhOXPmjOeVJ9YX7YfW1lZz3333mZqaGlNfX2/eeust841vfMNcfvnlpr293ffSE+buu+82GRkZpqqqyhw7dqzncvr06Z773HXXXWbixInm7bffNrt37zZz5swxc+bM8bjqxLvQfjhw4IB57LHHzO7du019fb3ZunWrmTx5spk7d67nlfc2KArIGGOeeeYZM3HiRJOSkmJmz55tdu7c6XtJ/e7mm282eXl5JiUlxXzlK18xN998szlw4IDvZSXdO++8YySdc1m6dKkxpvut2A899JDJyckxkUjEzJ8/39TV1flddBJ80X44ffq0WbBggRk/frwJh8Nm0qRJZvny5UPuh7S+vn5JZsOGDT33OXPmjPnBD35gLrnkEjNq1Chz4403mmPHjvlbdBJcaD8cOnTIzJ0712RlZZlIJGIuu+wy8+Mf/9g0Nzf7XfhZ+HMMAAAvBvxrQACAoYkCAgB4QQEBALyggAAAXlBAAAAvKCAAgBcUEADACwoIAOAFBQQA8IICAgB4QQEBALyggAAAXvxf5qrceS98qJUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
        "\n",
        "# How many classes are there (this'll be our output shape)?\n",
        "len(class_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vF1MZW4dufHR",
        "outputId": "8ad6d2ff-2ae0-45ed-e756-9618c8d5dcba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot an example image and its label\n",
        "index_of_choice= int(input(\"Enter your desired label:\"))\n",
        "plt.imshow(train_data[17],cmap=plt.cm.binary)\n",
        "plt.title(class_names[train_labels[index_of_choice]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "-xz16THJveOZ",
        "outputId": "6bc52078-60b7-435c-867e-1cf003424e8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter your desired label:2000\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Coat')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot multiple random images of fashion mnist\n",
        "import random\n",
        "plt.figure(figsize=(7,7))\n",
        "for i in range(4):\n",
        "  ax=plt.subplot(2,2,i+1)\n",
        "  rand_index=random.choice(range(len(train_data)))\n",
        "  plt.imshow(train_data[rand_index],cmap=plt.cm.binary)\n",
        "  plt.title(class_names[train_labels[rand_index]])\n",
        "  plt.axis(False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "iUdxbBuVwEke",
        "outputId": "869d81a5-6bf8-4ea0-b3c8-601e302f0eed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Building a multiclass classification model\n",
        "\n",
        "For our multi-class classification model,we can use a similar architecture to oue binary classifiers,however,we're gong to have top tweak a few things:\n",
        "* Input shape = 28 x 28 (the shape of one image)\n",
        "* output shape = 10 (one per class of clothing)\n",
        "* Loss function = tf.keras.losses.Categoricalcrossentropy()\n",
        "  * More specifically, since our labels are in integer form, we'll use [`tf.keras.losses.SparseCategoricalCrossentropy()`](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/losses/SparseCategoricalCrossentropy), if our labels were one-hot encoded (e.g. they looked something like `[0, 0, 1, 0, 0...]`), we'd use [`tf.keras.losses.CategoricalCrossentropy()`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/CategoricalCrossentropy).\n",
        "* output layer activation = Softmax(not sigmoid)"
      ],
      "metadata": {
        "id": "qFBh8f6HxG5f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create the model\n",
        "model_11 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Flatten(input_shape=(28, 28)), # input layer (we had to reshape 28x28 to 784, the Flatten layer does this for us)\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(10, activation=\"softmax\") # output shape is 10, activation is softmax\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model_11.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(), # different loss function for multiclass classifcation\n",
        "                 optimizer=tf.keras.optimizers.Adam(),\n",
        "                 metrics=[\"accuracy\"])\n",
        "\n",
        "# Fit the model\n",
        "non_norm_history = model_11.fit(train_data,\n",
        "                                train_labels,\n",
        "                                epochs=10,\n",
        "                                validation_data=(test_data, test_labels)) # see how the model performs on the test set during training"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KvOm29j8BA1e",
        "outputId": "0eda1b69-8d02-4448-e53a-ede9c79884e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 13s 6ms/step - loss: 2.3663 - accuracy: 0.0982 - val_loss: 2.3028 - val_accuracy: 0.1000\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 2.3025 - accuracy: 0.0988 - val_loss: 2.3027 - val_accuracy: 0.1000\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 2.3043 - accuracy: 0.1005 - val_loss: 2.3027 - val_accuracy: 0.1000\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0976 - val_loss: 2.3026 - val_accuracy: 0.1000\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3030 - accuracy: 0.0979 - val_loss: 2.3024 - val_accuracy: 0.1001\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.2915 - accuracy: 0.1055 - val_loss: 2.2398 - val_accuracy: 0.1460\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.1849 - accuracy: 0.1542 - val_loss: 2.1291 - val_accuracy: 0.1704\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.9180 - accuracy: 0.2099 - val_loss: 1.7760 - val_accuracy: 0.2408\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.7295 - accuracy: 0.2585 - val_loss: 1.6821 - val_accuracy: 0.2637\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.6618 - accuracy: 0.2813 - val_loss: 1.6649 - val_accuracy: 0.2628\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "flatten_model = tf.keras.Sequential([tf.keras.layers.Flatten(input_shape=(28,28))])\n",
        "flatten_model.output_shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mKQjnO5UBOvd",
        "outputId": "98486363-bd4d-45d9-ecc5-7260be1ce46a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None, 784)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "#Create the model\n",
        "model_11 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape=(28,28)),\n",
        "    tf.keras.layers.Dense(4,activation=\"relu\"),\n",
        "    tf.keras.layers.Dense(4,activation=\"relu\"),\n",
        "    tf.keras.layers.Dense(10,activation=tf.keras.activations.softmax\n",
        ")])\n",
        "\n",
        "# Compile the model\n",
        "model_11.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "                 optimizer=tf.keras.optimizers.Adam(),\n",
        "                 metrics=[\"accuracy\"])\n",
        "\n",
        "# Fit the model\n",
        "non_norm_history = model_11.fit(train_data,\n",
        "                                train_labels,\n",
        "                                epochs=10,\n",
        "                                validation_data=(test_data,test_labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jnqTIN6c8RGE",
        "outputId": "b4b0c4ac-9b23-4f55-9942-cac1e3639fa4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.5000 - accuracy: 0.0994 - val_loss: 2.3073 - val_accuracy: 0.1000\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3029 - accuracy: 0.0992 - val_loss: 2.3069 - val_accuracy: 0.1000\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3027 - accuracy: 0.1003 - val_loss: 2.3064 - val_accuracy: 0.1000\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.0975 - val_loss: 2.3063 - val_accuracy: 0.1000\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.3027 - accuracy: 0.0972 - val_loss: 2.3063 - val_accuracy: 0.1000\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0992 - val_loss: 2.3063 - val_accuracy: 0.1000\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.0987 - val_loss: 2.3063 - val_accuracy: 0.1000\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3028 - accuracy: 0.0982 - val_loss: 2.3064 - val_accuracy: 0.1000\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3028 - accuracy: 0.0989 - val_loss: 2.3064 - val_accuracy: 0.1000\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3028 - accuracy: 0.0986 - val_loss: 2.3064 - val_accuracy: 0.1000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check the model summary\n",
        "model_11.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "So4bz3Ej_26Z",
        "outputId": "73283314-d5e4-431c-8217-f71ecaaaad0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten_2 (Flatten)         (None, 784)               0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 4)                 3140      \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 4)                 20        \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 10)                50        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3,210\n",
            "Trainable params: 3,210\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check the min and max values of the training data\n",
        "train_data.min(),train_data.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DmxRlxIaD0Dz",
        "outputId": "8fa07539-b691-44af-a10e-26a55bcf9f2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 255)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Neural networks prefer data to be scaled(or normalized),this means they like to have the numbers in the tensors then try to find patterns between 0 & 1."
      ],
      "metadata": {
        "id": "IGiTS3FqEpWX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# we can get our training and testing data between 0 & 1 by dividing by maximum\n",
        "train_data_norm = train_data/255.0\n",
        "test_data_norm = test_data/255.0\n",
        "\n",
        "# check the min and max values of the scaled training data\n",
        "train_data_norm.min(),train_data_norm.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wTxUm8XnEm9g",
        "outputId": "0a1843a8-c2f9-4c5d-be42-445d7b2e6628"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Now our data is normalized,let's build a model to find patterns in it\n",
        "\n",
        "# set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create a model(same as model_11)\n",
        "model_12 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape=(28,28)),\n",
        "    tf.keras.layers.Dense(4,activation=\"relu\"),\n",
        "    tf.keras.layers.Dense(4,activation=\"relu\"),\n",
        "    tf.keras.layers.Dense(10,activation=\"softmax\")\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model_12.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "                 optimizer=tf.keras.optimizers.Adam(),\n",
        "                 metrics=[\"accuracy\"])\n",
        "\n",
        "# Fit the model\n",
        "norm_history = model_12.fit(train_data_norm,\n",
        "                                train_labels,\n",
        "                                epochs=10,\n",
        "                                validation_data=(test_data_norm,test_labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8wy9RWm0Gu5y",
        "outputId": "7b4455cf-d2c9-4ee8-9781-abf0ff9fa2b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 1.5696 - accuracy: 0.4256 - val_loss: 1.2937 - val_accuracy: 0.5211\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 1.1947 - accuracy: 0.5624 - val_loss: 1.1396 - val_accuracy: 0.5958\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.0838 - accuracy: 0.6147 - val_loss: 1.0500 - val_accuracy: 0.6343\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.9670 - accuracy: 0.6671 - val_loss: 0.9125 - val_accuracy: 0.6783\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.8566 - accuracy: 0.6995 - val_loss: 0.8558 - val_accuracy: 0.7098\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.8115 - accuracy: 0.7093 - val_loss: 0.8277 - val_accuracy: 0.7071\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.7829 - accuracy: 0.7193 - val_loss: 0.8144 - val_accuracy: 0.6965\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.7597 - accuracy: 0.7265 - val_loss: 0.7756 - val_accuracy: 0.7237\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.7411 - accuracy: 0.7341 - val_loss: 0.7601 - val_accuracy: 0.7345\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 0.7243 - accuracy: 0.7390 - val_loss: 0.7439 - val_accuracy: 0.7410\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "🔑**Note:** Neural Networks tend to prefer data in numerical form as well as scaled/normalized(numbers between 0 &1)"
      ],
      "metadata": {
        "id": "NTNUc37UID53"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "# plot non-normalized data loss curves\n",
        "pd.DataFrame(non_norm_history.history).plot(title=\"Non-normalized data\")\n",
        "# plot normalized data loss curves\n",
        "pd.DataFrame(norm_history.history).plot(title=\"Normalized data\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        },
        "id": "hBRPm14xHvR9",
        "outputId": "7ad639cd-5d60-4583-9951-da5a0d941fae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> 🔑**Note:** The same model with even *slightly* different data can produce *dramatically* different results. so when you're comparing models,it's important to make sure you're comparing them on the same criteria(e.g.same architecture but different data or same data but different architecture)."
      ],
      "metadata": {
        "id": "PclzD5LoJepG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Finding the ideal learning rate"
      ],
      "metadata": {
        "id": "1lcvDo_tJKA4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create model\n",
        "model_13 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape=(28,28)),\n",
        "    tf.keras.layers.Dense(4,activation=\"relu\"),\n",
        "    tf.keras.layers.Dense(4,activation=\"relu\"),\n",
        "    tf.keras.layers.Dense(10,activation=\"softmax\")\n",
        "\n",
        "])\n",
        "\n",
        "# compile model\n",
        "\n",
        "model_13.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "                 optimizer=tf.keras.optimizers.Adam(),\n",
        "                 metrics=[\"accuracy\"])\n",
        "\n",
        "# create the learning rate callback\n",
        "lr_scheduler = tf.keras.callbacks.LearningRateScheduler(lambda epoch : 1e-3* 10**(epoch/20))\n",
        "\n",
        "# Fit the model\n",
        "find_lr_history = model_13.fit(train_data_norm,\n",
        "                               train_labels,\n",
        "                               epochs=40,\n",
        "                               validation_data=(test_data_norm,test_labels),\n",
        "                               callbacks=[lr_scheduler])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cNv8N2rOPHxC",
        "outputId": "22543b26-4007-445f-d46e-e728c7aebf19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 1.0149 - accuracy: 0.6561 - val_loss: 0.7276 - val_accuracy: 0.7408 - lr: 0.0010\n",
            "Epoch 2/40\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.6646 - accuracy: 0.7570 - val_loss: 0.6755 - val_accuracy: 0.7599 - lr: 0.0011\n",
            "Epoch 3/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6238 - accuracy: 0.7690 - val_loss: 0.6645 - val_accuracy: 0.7443 - lr: 0.0013\n",
            "Epoch 4/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6038 - accuracy: 0.7745 - val_loss: 0.6352 - val_accuracy: 0.7604 - lr: 0.0014\n",
            "Epoch 5/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5907 - accuracy: 0.7789 - val_loss: 0.6275 - val_accuracy: 0.7766 - lr: 0.0016\n",
            "Epoch 6/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5768 - accuracy: 0.7870 - val_loss: 0.6688 - val_accuracy: 0.7690 - lr: 0.0018\n",
            "Epoch 7/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5645 - accuracy: 0.7972 - val_loss: 0.6019 - val_accuracy: 0.7940 - lr: 0.0020\n",
            "Epoch 8/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5519 - accuracy: 0.8059 - val_loss: 0.5988 - val_accuracy: 0.7958 - lr: 0.0022\n",
            "Epoch 9/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5460 - accuracy: 0.8080 - val_loss: 0.5808 - val_accuracy: 0.8021 - lr: 0.0025\n",
            "Epoch 10/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5409 - accuracy: 0.8109 - val_loss: 0.5961 - val_accuracy: 0.7944 - lr: 0.0028\n",
            "Epoch 11/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5392 - accuracy: 0.8105 - val_loss: 0.5825 - val_accuracy: 0.8008 - lr: 0.0032\n",
            "Epoch 12/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5365 - accuracy: 0.8100 - val_loss: 0.5784 - val_accuracy: 0.8005 - lr: 0.0035\n",
            "Epoch 13/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5392 - accuracy: 0.8092 - val_loss: 0.5795 - val_accuracy: 0.7989 - lr: 0.0040\n",
            "Epoch 14/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5370 - accuracy: 0.8108 - val_loss: 0.6193 - val_accuracy: 0.7933 - lr: 0.0045\n",
            "Epoch 15/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5385 - accuracy: 0.8084 - val_loss: 0.5803 - val_accuracy: 0.8011 - lr: 0.0050\n",
            "Epoch 16/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5464 - accuracy: 0.8061 - val_loss: 0.5855 - val_accuracy: 0.7997 - lr: 0.0056\n",
            "Epoch 17/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5397 - accuracy: 0.8092 - val_loss: 0.5712 - val_accuracy: 0.8036 - lr: 0.0063\n",
            "Epoch 18/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5424 - accuracy: 0.8081 - val_loss: 0.5639 - val_accuracy: 0.8049 - lr: 0.0071\n",
            "Epoch 19/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5387 - accuracy: 0.8120 - val_loss: 0.5847 - val_accuracy: 0.8012 - lr: 0.0079\n",
            "Epoch 20/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5398 - accuracy: 0.8123 - val_loss: 0.5710 - val_accuracy: 0.8070 - lr: 0.0089\n",
            "Epoch 21/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5423 - accuracy: 0.8117 - val_loss: 0.5770 - val_accuracy: 0.8022 - lr: 0.0100\n",
            "Epoch 22/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5442 - accuracy: 0.8115 - val_loss: 0.5685 - val_accuracy: 0.8000 - lr: 0.0112\n",
            "Epoch 23/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5529 - accuracy: 0.8109 - val_loss: 0.5776 - val_accuracy: 0.8034 - lr: 0.0126\n",
            "Epoch 24/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5584 - accuracy: 0.8086 - val_loss: 0.5995 - val_accuracy: 0.7987 - lr: 0.0141\n",
            "Epoch 25/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5615 - accuracy: 0.8078 - val_loss: 0.6124 - val_accuracy: 0.7907 - lr: 0.0158\n",
            "Epoch 26/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5700 - accuracy: 0.8061 - val_loss: 0.7374 - val_accuracy: 0.7589 - lr: 0.0178\n",
            "Epoch 27/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5868 - accuracy: 0.7987 - val_loss: 0.6558 - val_accuracy: 0.7920 - lr: 0.0200\n",
            "Epoch 28/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5900 - accuracy: 0.7987 - val_loss: 0.6919 - val_accuracy: 0.7745 - lr: 0.0224\n",
            "Epoch 29/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.6161 - accuracy: 0.7924 - val_loss: 0.6120 - val_accuracy: 0.7906 - lr: 0.0251\n",
            "Epoch 30/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.6338 - accuracy: 0.7882 - val_loss: 0.6611 - val_accuracy: 0.7801 - lr: 0.0282\n",
            "Epoch 31/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6534 - accuracy: 0.7812 - val_loss: 0.6374 - val_accuracy: 0.7970 - lr: 0.0316\n",
            "Epoch 32/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.6880 - accuracy: 0.7735 - val_loss: 0.6950 - val_accuracy: 0.7848 - lr: 0.0355\n",
            "Epoch 33/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.7251 - accuracy: 0.7668 - val_loss: 0.8274 - val_accuracy: 0.7228 - lr: 0.0398\n",
            "Epoch 34/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.8067 - accuracy: 0.7374 - val_loss: 0.7186 - val_accuracy: 0.7674 - lr: 0.0447\n",
            "Epoch 35/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.8489 - accuracy: 0.7084 - val_loss: 0.9521 - val_accuracy: 0.6583 - lr: 0.0501\n",
            "Epoch 36/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.9421 - accuracy: 0.6745 - val_loss: 0.8471 - val_accuracy: 0.6874 - lr: 0.0562\n",
            "Epoch 37/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.0155 - accuracy: 0.6070 - val_loss: 1.1979 - val_accuracy: 0.5188 - lr: 0.0631\n",
            "Epoch 38/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.2161 - accuracy: 0.4908 - val_loss: 1.0921 - val_accuracy: 0.5100 - lr: 0.0708\n",
            "Epoch 39/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.3646 - accuracy: 0.4175 - val_loss: 1.5035 - val_accuracy: 0.4092 - lr: 0.0794\n",
            "Epoch 40/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.3143 - accuracy: 0.4275 - val_loss: 1.3600 - val_accuracy: 0.4472 - lr: 0.0891\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plot the learning rate decay curve\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "lrs=1e-3*(10**(tf.range(40)/20))\n",
        "plt.semilogx(lrs, find_lr_history.history[\"loss\"])\n",
        "plt.xlabel(\"Learning rate\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.title(\"Finding the ideal learning rate\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "id": "5-rC0LRCSE1p",
        "outputId": "c0959692-04e8-409f-86aa-8d3c873d5e29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Finding the ideal learning rate')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# let's refit the model with ideal learning rate\n",
        "# set random seed\n",
        "tf.random.set_seed(42)\n",
        "# create model\n",
        "model_14 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape=(28,28)),\n",
        "    tf.keras.layers.Dense(4,activation= \"relu\"),\n",
        "    tf.keras.layers.Dense(4,activation=\"relu\"),\n",
        "    tf.keras.layers.Dense(10,activation=\"softmax\")\n",
        "])\n",
        "\n",
        "# compile the model\n",
        "model_14.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "                 optimizer = tf.keras.optimizers.Adam(lr=0.001),\n",
        "                 metrics=[\"accuracy\"])\n",
        "\n",
        "# Fit the model\n",
        "history_14 = model_14.fit(train_data_norm,\n",
        "                          train_labels,\n",
        "                          epochs=20,\n",
        "                          validation_data= (test_data_norm,test_labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3KUnkv6hTeO3",
        "outputId": "8a7bce15-59ee-4015-969e-17004df50a65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.0617 - accuracy: 0.6039 - val_loss: 0.7483 - val_accuracy: 0.7315\n",
            "Epoch 2/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.6942 - accuracy: 0.7484 - val_loss: 0.6821 - val_accuracy: 0.7533\n",
            "Epoch 3/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.6307 - accuracy: 0.7700 - val_loss: 0.6339 - val_accuracy: 0.7674\n",
            "Epoch 4/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5980 - accuracy: 0.7819 - val_loss: 0.6105 - val_accuracy: 0.7731\n",
            "Epoch 5/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5780 - accuracy: 0.7902 - val_loss: 0.6062 - val_accuracy: 0.7778\n",
            "Epoch 6/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5612 - accuracy: 0.7975 - val_loss: 0.5970 - val_accuracy: 0.7844\n",
            "Epoch 7/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5524 - accuracy: 0.8019 - val_loss: 0.5985 - val_accuracy: 0.7838\n",
            "Epoch 8/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5415 - accuracy: 0.8051 - val_loss: 0.5606 - val_accuracy: 0.7975\n",
            "Epoch 9/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5341 - accuracy: 0.8081 - val_loss: 0.5569 - val_accuracy: 0.7995\n",
            "Epoch 10/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5296 - accuracy: 0.8107 - val_loss: 0.5498 - val_accuracy: 0.8037\n",
            "Epoch 11/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5263 - accuracy: 0.8122 - val_loss: 0.5495 - val_accuracy: 0.8029\n",
            "Epoch 12/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5211 - accuracy: 0.8127 - val_loss: 0.5552 - val_accuracy: 0.8031\n",
            "Epoch 13/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5197 - accuracy: 0.8155 - val_loss: 0.5463 - val_accuracy: 0.8021\n",
            "Epoch 14/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5159 - accuracy: 0.8179 - val_loss: 0.5465 - val_accuracy: 0.8046\n",
            "Epoch 15/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5130 - accuracy: 0.8178 - val_loss: 0.5466 - val_accuracy: 0.8081\n",
            "Epoch 16/20\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.5099 - accuracy: 0.8198 - val_loss: 0.5546 - val_accuracy: 0.8025\n",
            "Epoch 17/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5074 - accuracy: 0.8205 - val_loss: 0.5472 - val_accuracy: 0.8052\n",
            "Epoch 18/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5072 - accuracy: 0.8205 - val_loss: 0.5384 - val_accuracy: 0.8076\n",
            "Epoch 19/20\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.5051 - accuracy: 0.8203 - val_loss: 0.5432 - val_accuracy: 0.8068\n",
            "Epoch 20/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5025 - accuracy: 0.8219 - val_loss: 0.5560 - val_accuracy: 0.8034\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluating our multi-class classification model\n",
        "\n",
        "To evaluate our multi-class classification model we could:\n",
        "* Evaluate its performance using other classification metrics (such as a confusion matrix)\n",
        "* Improve its results(by training  it for longer or changing the architecture)\n",
        "* save and export it for use in an application\n",
        "\n",
        "Let's go through top 2..."
      ],
      "metadata": {
        "id": "zc5wyZENVZOA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create a confusion matrix\n",
        "import itertools\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "\n",
        "def make_confusion_matrix(y_true,y_pred,classes=None,figsize =(10,10),text_size=15):\n",
        "  # create the confusion matrix\n",
        "  cm = confusion_matrix(y_true,tf.round(y_pred))\n",
        "  cm_norm = cm.astype(\"float\")/cm.sum(axis=1)[:,np.newaxis] # normalize our confusion matrix\n",
        "  n_classes =cm.shape[0]\n",
        "\n",
        "  # Let's prettify it\n",
        "  fig,ax = plt.subplots(figsize=figsize)\n",
        "\n",
        "  # create a matrix plot\n",
        "  cax= ax.matshow(cm,cmap=plt.cm.Blues)\n",
        "  fig.colorbar(cax)\n",
        "\n",
        "  # set labels to be classes\n",
        "  if classes:\n",
        "    labels = classes\n",
        "  else:\n",
        "    labels = np.arange(cm.shape[0])\n",
        "\n",
        "  # label the axes\n",
        "  ax.set(title = \"confusion matrix\",\n",
        "        xlabel=\"Predicted Label\",\n",
        "        ylabel= \"True label\",\n",
        "        xticks= np.arange(n_classes),\n",
        "        yticks=np.arange(n_classes),\n",
        "        xticklabels=labels,\n",
        "        yticklabels=labels)\n",
        "\n",
        "  # Set x-axis labels to bottom\n",
        "  ax.xaxis.set_label_position(\"bottom\")\n",
        "  ax.xaxis.tick_bottom()\n",
        "\n",
        "  # Adjust label size\n",
        "  ax.yaxis.label.set_size(text_size)\n",
        "  ax.xaxis.label.set_size(text_size)\n",
        "  ax.title.set_size(text_size)\n",
        "\n",
        "  # Set threshold for different colors\n",
        "  threshold = (cm.max()+cm.min())/2.\n",
        "\n",
        "  # Plot the text on each cell\n",
        "  for i,j in itertools.product(range(cm.shape[0]),range(cm.shape[1])):\n",
        "    plt.text(j,i,f\"{cm[i,j]} ({cm_norm[i,j]*100:.1f}%)\",\n",
        "            horizontalalignment=\"center\",\n",
        "            color=\"white\" if cm[i,j]>threshold else \"black\",\n",
        "            size=text_size)"
      ],
      "metadata": {
        "id": "BQ3QiopWT27q"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "aX2yS76VV5j9",
        "outputId": "3c310788-6dd2-43bd-a044-3bf88fc26c3d"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['T-shirt/top',\n",
              " 'Trouser',\n",
              " 'Pullover',\n",
              " 'Dress',\n",
              " 'Coat',\n",
              " 'Sandal',\n",
              " 'Shirt',\n",
              " 'Sneaker',\n",
              " 'Bag',\n",
              " 'Ankle boot']"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make some predictions with our model\n",
        "y_probs  = model_14.predict(test_data_norm) # probs is short for \"prediction probabilities\"\n",
        "\n",
        "# View the first 5 predictions\n",
        "y_probs[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "PbMAgM84WAmd",
        "outputId": "e82dfe9a-6b8b-42f7-ae3a-df3607ee1e71"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 3ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[9.51813883e-04, 2.85129891e-14, 1.56375118e-05, 5.65621758e-06,\n",
              "        7.14136042e-11, 1.44056842e-01, 1.79809085e-05, 7.03330785e-02,\n",
              "        1.55387431e-01, 6.29231513e-01],\n",
              "       [1.18089607e-03, 6.34611775e-13, 8.40259433e-01, 1.10388982e-07,\n",
              "        2.73956750e-02, 1.04153110e-16, 1.30496815e-01, 1.62995402e-25,\n",
              "        6.67074637e-04, 6.89093490e-16],\n",
              "       [9.13368808e-07, 9.97288525e-01, 1.23621351e-06, 7.37748196e-05,\n",
              "        2.62256199e-03, 1.86241471e-13, 1.29327864e-05, 3.52485191e-16,\n",
              "        9.55710858e-11, 6.49651109e-17],\n",
              "       [1.31037705e-05, 9.94270027e-01, 1.21253888e-05, 7.37752125e-04,\n",
              "        4.86615160e-03, 1.84287828e-11, 1.00803569e-04, 2.57467658e-13,\n",
              "        9.22262977e-09, 2.71994917e-14],\n",
              "       [1.04518861e-01, 2.27513766e-07, 2.10562825e-01, 4.94345557e-03,\n",
              "        4.16558310e-02, 2.29366482e-12, 5.71058393e-01, 5.39816342e-15,\n",
              "        6.72603324e-02, 7.21559687e-11]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "🔑**Note:**Remember to make predictions on the same kind of data your model was trained on(e.g.If your model was trained on normalized data,you'will want to make predictions on normalized data"
      ],
      "metadata": {
        "id": "3HVtk9eFXCVp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_probs[0],tf.argmax(y_probs[0]),class_names[tf.argmax(y_probs[0])]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "aLCSQAs1V68s",
        "outputId": "7c063af5-0f8c-4751-8f99-133ea2b62cd7"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([9.5181388e-04, 2.8512989e-14, 1.5637512e-05, 5.6562176e-06,\n",
              "        7.1413604e-11, 1.4405684e-01, 1.7980909e-05, 7.0333079e-02,\n",
              "        1.5538743e-01, 6.2923151e-01], dtype=float32),\n",
              " <tf.Tensor: shape=(), dtype=int64, numpy=9>,\n",
              " 'Ankle boot')"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert all of the prediction probabilities into integers\n",
        "y_preds =y_probs.argmax(axis=1)\n",
        "\n",
        "#view the first 10 prediction labels\n",
        "y_preds[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "IBDDJp8uV_sm",
        "outputId": "47bbcb0f-a624-43be-a0dc-5537f9acb2d7"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 2, 1, 1, 6, 1, 4, 4, 5, 7])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "confusion_matrix(y_true=test_labels,\n",
        "                 y_pred=y_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "pRG3_o5PYapS",
        "outputId": "58f71ff6-c6f1-41b5-ca5e-1ce5738dc557"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[647,   2,  20,  77,   7,   5, 227,   0,  15,   0],\n",
              "       [  0, 946,   9,  32,  10,   1,   2,   0,   0,   0],\n",
              "       [  5,   4, 686,   8, 207,   0,  86,   0,   4,   0],\n",
              "       [ 26,  12,  11, 815,  58,   2,  72,   0,   4,   0],\n",
              "       [  0,   4, 116,  18, 822,   0,  39,   0,   1,   0],\n",
              "       [  1,   0,   0,   1,   0, 905,   0,  45,   9,  39],\n",
              "       [109,   6, 173,  41, 204,   2, 458,   0,   7,   0],\n",
              "       [  0,   0,   0,   0,   0,  35,   0, 903,   3,  59],\n",
              "       [  6,   1,  27,   7,   3,   4,  30,   2, 919,   1],\n",
              "       [  0,   0,   0,   0,   0,  22,   0,  35,  10, 933]])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make a prettier confusion matrix\n",
        "make_confusion_matrix(y_true=test_labels,\n",
        "                      y_pred=y_preds,\n",
        "                      classes=class_names,\n",
        "                      figsize=(15,15),\n",
        "                      text_size=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1200
        },
        "id": "Y_Gp41rzY6eJ",
        "outputId": "d34e36f8-ea85-413a-b774-853a8bbddbae"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 2 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> 🔑 **Note:** Often when working with images and other forms of visual data, it's a good idea to visualize as much as possible to develop a further understanding of the data and the outputs of your model.\n",
        "\n",
        "How about we create a fun little function for:\n",
        "* Plot a random image\n",
        "* Make a prediction on said image\n",
        "* Label the plot with the truth label & the predicted label\n"
      ],
      "metadata": {
        "id": "hH4Ro2gdaLZs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "def plot_random_image(model,images,true_labels,classes):\n",
        "  \"\"\"\n",
        "  Picks a random image,plots it and labels it with a prediction and truth label.\n",
        "\n",
        "  \"\"\"\n",
        "  # set mup random integer\n",
        "  i=random.randint(0,len(images))\n",
        "\n",
        "  # create predictions and targets\n",
        "  target_image =images[i]\n",
        "  pred_probs = model.predict(target_image.reshape(1,28,28))\n",
        "  pred_label = classes[pred_probs.argmax()]\n",
        "  true_label=classes[true_labels[i]]\n",
        "\n",
        "  # Plot the image\n",
        "  plt.imshow(target_image,cmap=plt.cm.binary)\n",
        "\n",
        "  # change the color of tyhe titles depending on if the prediction is right or wrong\n",
        "  if pred_label ==  true_label:\n",
        "    color = \"green\"\n",
        "  else:\n",
        "    color = \"red\"\n",
        "  # add xlabel information (prediction/true label)\n",
        "  plt.xlabel(\"Pred: {}{:2.0f}% (True:{})\".format(pred_label,\n",
        "                                                  100*tf.reduce_max(pred_probs),\n",
        "                                                  true_label),\n",
        "              color= color) # Set the color to green or red based on if prediction is right or wrong\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "ulxWSWeCZT6z"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check out a random image as well as its prediction\n",
        "plot_random_image(model=model_14,\n",
        "                  images=test_data_norm,\n",
        "                  true_labels=test_labels,\n",
        "                  classes=class_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "o2ybNrlzczJI",
        "outputId": "90e61bd0-8b50-49c6-9c61-9a901d63176c"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 22ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## What patterns ismour model learning?"
      ],
      "metadata": {
        "id": "5pdjuyHqdMuf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Find the layers of our most reent model\n",
        "model_14.layers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "JtHHX95DedX0",
        "outputId": "c6353768-3923-4a35-a49a-5ff30d70640c"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<keras.layers.reshaping.flatten.Flatten at 0x7f6305d07f40>,\n",
              " <keras.layers.core.dense.Dense at 0x7f6305d05ea0>,\n",
              " <keras.layers.core.dense.Dense at 0x7f6305d053c0>,\n",
              " <keras.layers.core.dense.Dense at 0x7f6305d04eb0>]"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extarct particular layer\n",
        "model_14.layers[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "760u8s4bfS06",
        "outputId": "ddb953b9-f9d8-418c-e7dd-5955efe86c2d"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.layers.core.dense.Dense at 0x7f6305d05ea0>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# get the patterns of a layer in our network\n",
        "weights,biases = model_14.layers[1].get_weights()\n",
        "\n",
        "#shapes\n",
        "weights,weights.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "VOrdKwSIfcJa",
        "outputId": "1e65022a-7b68-42c1-cc2f-aa22e113fa87"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[-0.2995977 ,  0.06911346,  0.03714171, -0.66080546],\n",
              "        [-0.74309653,  0.37279215,  0.53108615, -0.20997778],\n",
              "        [-0.02265791, -0.61378014,  0.5725993 , -1.4535936 ],\n",
              "        ...,\n",
              "        [ 0.04380158, -1.2072797 ,  0.0054824 , -0.13766158],\n",
              "        [ 0.28523684, -0.48737118, -0.7514186 ,  0.17604522],\n",
              "        [ 0.14291966, -0.520983  ,  0.21626757, -0.15089561]],\n",
              "       dtype=float32),\n",
              " (784, 4))"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_14.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "dkwXIKk3fw7a",
        "outputId": "81d35acf-f95c-481f-f6eb-2877d2b3f3f1"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten_5 (Flatten)         (None, 784)               0         \n",
            "                                                                 \n",
            " dense_12 (Dense)            (None, 4)                 3140      \n",
            "                                                                 \n",
            " dense_13 (Dense)            (None, 4)                 20        \n",
            "                                                                 \n",
            " dense_14 (Dense)            (None, 10)                50        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3,210\n",
            "Trainable params: 3,210\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's check out bias veactor..."
      ],
      "metadata": {
        "id": "LsbcGBq3f5ra"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Biases and biases shapes\n",
        "biases,biases.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "u4y3BT8ihBNq",
        "outputId": "a157d3e1-dd68-4a13-ede4-ad168415a56a"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([-0.41906786,  1.0162745 ,  1.3436246 ,  0.6383397 ], dtype=float32),\n",
              " (4,))"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Every neuron has bias vector each of these is paired with weights matrix.\n",
        "\n",
        "The bias vector gets initialized as zeros(at least in the case of TensorFlow Sense Layer).\n",
        "\n",
        "The bias vector dictates how much the patterns within the corresponding weights matrix should influence the next layer."
      ],
      "metadata": {
        "id": "RBDmpHf2hOJx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Can now calculate the number of paramters in our model\n",
        "model_14.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "7bhoLCi-hI3C",
        "outputId": "23730d06-4812-4a88-fe2d-8207f2e983f0"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten_5 (Flatten)         (None, 784)               0         \n",
            "                                                                 \n",
            " dense_12 (Dense)            (None, 4)                 3140      \n",
            "                                                                 \n",
            " dense_13 (Dense)            (None, 4)                 20        \n",
            "                                                                 \n",
            " dense_14 (Dense)            (None, 10)                50        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3,210\n",
            "Trainable params: 3,210\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's check out another way of viewing our deep learning models\n",
        "from tensorflow.keras.utils import plot_model\n",
        "\n",
        "# See the inputs and outputs of each layer\n",
        "plot_model(model_14, show_shapes=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "IXAtikFoi9Th",
        "outputId": "f41990e4-ebe8-4593-a6df-171179678193"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Conclusion:** The Neural network model was able to identify the images of different clothing items successfully.\n",
        "\n",
        "However,a little improvement in dataset is required(like tshirts and shirts can be clubbed together..) for more better results\n",
        "or,we can gather more data to train our model."
      ],
      "metadata": {
        "id": "QeznsJ_qpY8h"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TRVazcUTqM1I"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}