fashion_detection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyMPRwrVPgf2XGfyuqIf4gno"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "nyPleAqpktTQ"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "import PIL\n",
        "from tensorflow.keras.datasets import fashion_mnist\n",
        "from tensorflow.keras.layers import Dense , Conv2D, MaxPooling2D ,Flatten\n",
        "from tensorflow.keras.models import Sequential\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()"
      ],
      "metadata": {
        "id": "Zt4NxXJTlKWB"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(x_train[1] , cmap = 'grey')\n",
        "print(y_train[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "7TEal9v8lfC8",
        "outputId": "7be1a22e-4488-40b3-cf48-2203b14716da"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\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": [
        "0\tT-shirt/top\n",
        "\n",
        "1\tTrouser\n",
        "\n",
        "2\tPullover\n",
        "\n",
        "3\tDress\n",
        "\n",
        "4\tCoat\n",
        "\n",
        "5\tSandal\n",
        "\n",
        "6\tShirt\n",
        "\n",
        "7\tSneaker\n",
        "\n",
        "8\tBag\n",
        "\n",
        "9\tAnkle boot"
      ],
      "metadata": {
        "id": "lprvUwaqnGZf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# checking image size\n",
        "print(x_train.shape)\n",
        "print(x_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EVCButuolpTF",
        "outputId": "08f9334b-767f-4935-fac7-e150310a2b79"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(60000, 28, 28)\n",
            "(10000, 28, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_train[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eNO49oS_oev6",
        "outputId": "10dffe9a-02d3-4e0f-931b-8b4d06952a3a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[  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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_train[0].max())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MjvcBSvfoA9-",
        "outputId": "03e8638c-1eb6-4039-b61d-8aed571dd076"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "255\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# normalise pixel size\n",
        "x_train = x_train /255.0\n",
        "x_test = x_test /255.0"
      ],
      "metadata": {
        "id": "pwSJitU5oMy8"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_test[0])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UTorfclgp5zq",
        "outputId": "c94690f5-0f61-4b78-be71-d1b1781aa7ed"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.01176471 0.00392157 0.         0.         0.02745098\n",
            "  0.         0.14509804 0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.00392157 0.00784314 0.         0.10588235 0.32941176\n",
            "  0.04313725 0.         0.         0.         0.         0.\n",
            "  0.         0.46666667 0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.00392157 0.         0.         0.34509804 0.56078431\n",
            "  0.43137255 0.         0.         0.         0.         0.08627451\n",
            "  0.36470588 0.41568627 0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.01568627 0.         0.20784314 0.50588235 0.47058824\n",
            "  0.57647059 0.68627451 0.61568627 0.65098039 0.52941176 0.60392157\n",
            "  0.65882353 0.54901961 0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.00784314 0.         0.04313725 0.5372549  0.50980392 0.50196078\n",
            "  0.62745098 0.69019608 0.62352941 0.65490196 0.69803922 0.58431373\n",
            "  0.59215686 0.56470588 0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.00392157 0.         0.00784314 0.00392157 0.         0.01176471\n",
            "  0.         0.         0.45098039 0.44705882 0.41568627 0.5372549\n",
            "  0.65882353 0.6        0.61176471 0.64705882 0.65490196 0.56078431\n",
            "  0.61568627 0.61960784 0.04313725 0.        ]\n",
            " [0.         0.         0.         0.         0.00392157 0.\n",
            "  0.         0.         0.         0.         0.01176471 0.\n",
            "  0.         0.34901961 0.54509804 0.35294118 0.36862745 0.6\n",
            "  0.58431373 0.51372549 0.59215686 0.6627451  0.6745098  0.56078431\n",
            "  0.62352941 0.6627451  0.18823529 0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.00784314 0.01568627 0.00392157 0.         0.         0.\n",
            "  0.38431373 0.53333333 0.43137255 0.42745098 0.43137255 0.63529412\n",
            "  0.52941176 0.56470588 0.58431373 0.62352941 0.65490196 0.56470588\n",
            "  0.61960784 0.6627451  0.46666667 0.        ]\n",
            " [0.         0.         0.00784314 0.00784314 0.00392157 0.00784314\n",
            "  0.         0.         0.         0.         0.10196078 0.42352941\n",
            "  0.45882353 0.38823529 0.43529412 0.45882353 0.53333333 0.61176471\n",
            "  0.5254902  0.60392157 0.60392157 0.61176471 0.62745098 0.55294118\n",
            "  0.57647059 0.61176471 0.69803922 0.        ]\n",
            " [0.01176471 0.         0.         0.         0.         0.\n",
            "  0.         0.08235294 0.20784314 0.36078431 0.45882353 0.43529412\n",
            "  0.40392157 0.45098039 0.50588235 0.5254902  0.56078431 0.60392157\n",
            "  0.64705882 0.66666667 0.60392157 0.59215686 0.60392157 0.56078431\n",
            "  0.54117647 0.58823529 0.64705882 0.16862745]\n",
            " [0.         0.         0.09019608 0.21176471 0.25490196 0.29803922\n",
            "  0.33333333 0.4627451  0.50196078 0.48235294 0.43529412 0.44313725\n",
            "  0.4627451  0.49803922 0.49019608 0.54509804 0.52156863 0.53333333\n",
            "  0.62745098 0.54901961 0.60784314 0.63137255 0.56470588 0.60784314\n",
            "  0.6745098  0.63137255 0.74117647 0.24313725]\n",
            " [0.         0.26666667 0.36862745 0.35294118 0.43529412 0.44705882\n",
            "  0.43529412 0.44705882 0.45098039 0.49803922 0.52941176 0.53333333\n",
            "  0.56078431 0.49411765 0.49803922 0.59215686 0.60392157 0.56078431\n",
            "  0.58039216 0.49019608 0.63529412 0.63529412 0.56470588 0.54117647\n",
            "  0.6        0.63529412 0.76862745 0.22745098]\n",
            " [0.2745098  0.6627451  0.50588235 0.40784314 0.38431373 0.39215686\n",
            "  0.36862745 0.38039216 0.38431373 0.4        0.42352941 0.41568627\n",
            "  0.46666667 0.47058824 0.50588235 0.58431373 0.61176471 0.65490196\n",
            "  0.74509804 0.74509804 0.76862745 0.77647059 0.77647059 0.73333333\n",
            "  0.77254902 0.74117647 0.72156863 0.14117647]\n",
            " [0.0627451  0.49411765 0.67058824 0.7372549  0.7372549  0.72156863\n",
            "  0.67058824 0.6        0.52941176 0.47058824 0.49411765 0.49803922\n",
            "  0.57254902 0.7254902  0.76470588 0.81960784 0.81568627 1.\n",
            "  0.81960784 0.69411765 0.96078431 0.98823529 0.98431373 0.98431373\n",
            "  0.96862745 0.8627451  0.80784314 0.19215686]\n",
            " [0.         0.         0.         0.04705882 0.2627451  0.41568627\n",
            "  0.64313725 0.7254902  0.78039216 0.82352941 0.82745098 0.82352941\n",
            "  0.81568627 0.74509804 0.58823529 0.32156863 0.03137255 0.\n",
            "  0.         0.         0.69803922 0.81568627 0.7372549  0.68627451\n",
            "  0.63529412 0.61960784 0.59215686 0.04313725]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# reshape images into single channel\n",
        "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\n",
        "x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)"
      ],
      "metadata": {
        "id": "ZVPqwZAzp9Gn"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# creating val set\n",
        "x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.16, random_state=42)\n"
      ],
      "metadata": {
        "id": "efutxZuz3eja"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_val.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-WQnVw7xsQcm",
        "outputId": "1b4bf547-6032-4f6e-c985-aaaaae1b4dd2"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9600, 28, 28, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_val.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qd1Es5oq3102",
        "outputId": "6d5583c7-01f1-4fd2-bf46-a2da5f064369"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# now train the model based on architecture\n",
        "model = Sequential()\n",
        "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "\n",
        "model.add(Conv2D(64 , (3,3) ,strides=(1, 1), activation = 'relu' ))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128 , activation = 'relu'))\n",
        "model.add(Dense(64 , activation = 'relu'))\n",
        "model.add(Dense(10))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2cBZnb3csfmX",
        "outputId": "ff11f94c-75e0-4f9c-bfd5-65a88efb075e"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "73S9uJkkGA3K",
        "outputId": "9a6f4090-eebd-40f5-8dd1-c62b74e8c28e"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m320\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m18,496\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m64\u001b[0m)            │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1600\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │         \u001b[38;5;34m204,928\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │           \u001b[38;5;34m8,256\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                  │             \u001b[38;5;34m650\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)            │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1600</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │         <span style=\"color: #00af00; text-decoration-color: #00af00\">204,928</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)                  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">650</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m697,952\u001b[0m (2.66 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">697,952</span> (2.66 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m232,650\u001b[0m (908.79 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">232,650</span> (908.79 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m465,302\u001b[0m (1.77 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">465,302</span> (1.77 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer = 'adam' , loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = True) , metrics = ['accuracy'])"
      ],
      "metadata": {
        "id": "DWrYx76u0Blf"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "log_dir = os.mkdir('logs')"
      ],
      "metadata": {
        "id": "mCgi_vuexaje"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir)"
      ],
      "metadata": {
        "id": "pbev0vRS1UFu"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "early_stop = EarlyStopping(monitor = 'val_loss' , patience = 3 , restore_best_weights=True)"
      ],
      "metadata": {
        "id": "7zwh5qkWBFya"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hist = model.fit(x_train , y_train , epochs = 20 , validation_data=(x_val , y_val) , callbacks=[tensorboard_callback , early_stop])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uglrn6F60nvm",
        "outputId": "d0b0c7ca-953a-446e-d5a6-11668bf246bb"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1575/1575\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 4ms/step - accuracy: 0.7503 - loss: 0.6732 - val_accuracy: 0.8687 - val_loss: 0.3569\n",
            "Epoch 2/20\n",
            "\u001b[1m1575/1575\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.8791 - loss: 0.3298 - val_accuracy: 0.8779 - val_loss: 0.3233\n",
            "Epoch 3/20\n",
            "\u001b[1m1575/1575\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.8960 - loss: 0.2810 - val_accuracy: 0.9031 - val_loss: 0.2693\n",
            "Epoch 4/20\n",
            "\u001b[1m1575/1575\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.9121 - loss: 0.2355 - val_accuracy: 0.9076 - val_loss: 0.2598\n",
            "Epoch 5/20\n",
            "\u001b[1m1575/1575\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 4ms/step - accuracy: 0.9221 - loss: 0.2093 - val_accuracy: 0.9110 - val_loss: 0.2479\n",
            "Epoch 6/20\n",
            "\u001b[1m1575/1575\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.9293 - loss: 0.1878 - val_accuracy: 0.8983 - val_loss: 0.2827\n",
            "Epoch 7/20\n",
            "\u001b[1m1575/1575\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.9363 - loss: 0.1679 - val_accuracy: 0.9081 - val_loss: 0.2554\n",
            "Epoch 8/20\n",
            "\u001b[1m1575/1575\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 4ms/step - accuracy: 0.9456 - loss: 0.1448 - val_accuracy: 0.9133 - val_loss: 0.2631\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure()\n",
        "plt.plot(hist.history['accuracy'] , color = 'teal' , label = 'accuracy')\n",
        "plt.plot(hist.history['val_accuracy'] , color = 'orange' , label = 'val_accuracy')\n",
        "fig.suptitle('Accuracy' , fontsize = 20)\n",
        "plt.legend(loc = 'upper left')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "d32QKgvr4_Xt",
        "outputId": "67309ef3-0d0a-4f1c-bc40-d7ff9c78ce99"
      },
      "execution_count": 19,
      "outputs": [
        {
          "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": [
        "fig = plt.figure()\n",
        "plt.plot(hist.history['loss'] , color = 'teal' , label = 'loss')\n",
        "plt.plot(hist.history['val_loss'] , color = 'orange' , label = 'val_loss')\n",
        "fig.suptitle('Loss' , fontsize = 20)\n",
        "plt.legend(loc = 'upper left')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "2E1B4I9r-ejW",
        "outputId": "63f77263-148f-4604-da37-c922814b789c"
      },
      "execution_count": 20,
      "outputs": [
        {
          "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": [
        "# evaluate model\n",
        "test_loss , test_accuracy = model.evaluate(x_test , y_test , verbose = 2)\n",
        "print(\"\\n Test Accuracy\" , test_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ORTvGzey-2Bd",
        "outputId": "644accfb-73c2-474c-cd2d-b6001c8ecbde"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 - 1s - 5ms/step - accuracy: 0.9031 - loss: 0.2681\n",
            "\n",
            " Test Accuracy 0.9031000137329102\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('trained_fashion_detection.h5')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d5c99gYEEkUC",
        "outputId": "87ac4672-80de-4e7b-8547-713ec7790ca4"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "84-hnvUJE12w"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}