ANN_Fashion_MNIST_BatchNormalization_Dropout.ipynb

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      "display_name": "Python 3",
      "language": "python",
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    "colab": {
      "name": "ANN_Fashion_MNIST_BatchNormalization_Dropout.ipynb",
      "provenance": [],
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        "bPJdLQa-j3AK"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pmUFPEFdj2_V"
      },
      "source": [
        "# Artificial Neural Network Working for Fashion MNIST Classification Prediction of 10 product Categories"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QOHMDix9j2_Z"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "%matplotlib inline\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "73DO9r5_j2_c"
      },
      "source": [
        "## Building an Image Classifier\n",
        "First let's install and import TensorFlow and Keras"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ciwSZFaj2_e"
      },
      "source": [
        "> conda install tensorflow\n",
        "\n",
        "> conda install pip \n",
        "\n",
        "> pip install --upgrade tensorflow==2.0.0-rc1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K4lx_CxMj2_g"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers.core import Dense,Activation,Dropout\n",
        "from tensorflow.keras.layers import BatchNormalization"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "F8SOklhBj2_i",
        "outputId": "9c7d63ed-db64-46f3-9a43-5511bfe6c924"
      },
      "source": [
        "keras.__version__"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.8.0'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "vbe3HzPEj2_l",
        "outputId": "b04142aa-e5cd-4434-9254-317d193c4f8f"
      },
      "source": [
        "tf.__version__\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.8.0'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p0kwgbVkj2_o"
      },
      "source": [
        "Link  https://keras.io/datasets/#fashion-mnist-database-of-fashion-articles\n",
        "\n",
        "Dataset of 60,000 28x28 grayscale images of 10 fashion categories, along with a test set of 10,000 images. The class labels are:\n",
        "\n",
        "| Label |\tDescription |\n",
        "--- | --- |\n",
        "| 0\t| T-shirt/top\n",
        "| 1\t| Trouser\n",
        "| 2\t| Pullover\n",
        "| 3\t| Dress\n",
        "| 4\t| Coat\n",
        "| 5\t| Sandal\n",
        "| 6\t| Shirt\n",
        "| 7\t| Sneaker\n",
        "| 8\t| Bag\n",
        "| 9\t| Ankle boot\n",
        "\n",
        "![image.png](attachment:image.png)\n",
        "\n",
        "### Usage:\n",
        "from keras.datasets import fashion_mnist\n",
        "\n",
        "> (x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()\n",
        "\n",
        "Returns:\n",
        "2 tuples:\n",
        "1. x_train, x_test: uint8 array of grayscale image data with shape (num_samples, 28, 28).\n",
        "2. y_train, y_test: uint8 array of labels (integers in range 0-9) with shape (num_samples,)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QGTWBdWCj2_r"
      },
      "source": [
        "fashion_mnist = keras.datasets.fashion_mnist\n",
        "(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4lVEpwG0mDgk",
        "outputId": "6b791372-69ec-4ebc-efb5-614ede1fefd9"
      },
      "source": [
        "# selecting the first sample\n",
        "x = X_train[10]\n",
        "print(\"The dimension of x is 2D matrix as \", x.shape)\n",
        "# Resizing the image\n",
        "x = cv2.resize(x, (18,18))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The dimension of x is 2D matrix as  (28, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b-BUXfrhmQR-",
        "outputId": "2352ecf9-6276-446e-fb26-cfc8e8834759"
      },
      "source": [
        "print(\"The range of pixel varies between 0 to 255\")\n",
        "print(\"The pixel having black is more close to 0 and pixel which is white is more close to 255\")\n",
        "print(x)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The range of pixel varies between 0 to 255\n",
            "The pixel having black is more close to 0 and pixel which is white is more close to 255\n",
            "[[  0   0   0   0  55 165 164  59  44  39  56  86 187  97   9   0   0   0]\n",
            " [  0   0   0  52 177 186 192 212 196 157 204 189 192 184 143   0   0   0]\n",
            " [  0   0   0 137 155 173 175 183 182 183 185 183 180 167 169  32   0   0]\n",
            " [  0   0   1 164 146 168 180 184 182 178 185 191 189 175 171  60   0   0]\n",
            " [  0   0  18 166 147 137 171 175 178 175 173 182 176 157 162  74   0   0]\n",
            " [  0   0  31 142 190 126 144 160 160 156 155 169 142 195 155 103   0   0]\n",
            " [  0   0  15  78 153 144 157 155 158 159 160 179 169 128 169  53   0   0]\n",
            " [  0   0   0   0   0  80 169 154 158 162 164 166 192   0   0   0   0   0]\n",
            " [  0   0   0   1   1  36 162 159 160 161 164 163 200   0   1   0   0   0]\n",
            " [  0   0   0   0   2  45 157 163 162 162 164 167 162   4   2   0   0   0]\n",
            " [  0   0   0   0   2 108 161 165 165 161 169 163 169  23   4   0   0   0]\n",
            " [  0   0   0   1   1 158 164 170 170 163 171 166 168  52   3   0   0   0]\n",
            " [  0   0   0   3   8 154 165 174 173 165 173 168 169  81   0   0   0   0]\n",
            " [  0   0   0   4  34 172 170 177 180 174 170 178 166  87   0   1   0   0]\n",
            " [  0   0   0   4  53 185 180 172 182 181 160 195 169 108   0   1   0   0]\n",
            " [  0   0   0   5  68 176 187 158 188 198 157 206 169 115   0   2   0   0]\n",
            " [  0   0   0   5  68 174 178 161 197 202 163 205 168 111   0   2   0   0]\n",
            " [  0   0   0   3  65 148 132 115 134 148 120 152 129  94   0   1   0   0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "ivgbymIoj2_t",
        "outputId": "3f738c70-8b1e-4ebc-ab1d-bc649c071274"
      },
      "source": [
        "plt.imshow(X_train[10])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f149e99cfd0>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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6zadrLjv/f5823j6X4UM5h8k02Ju7hmjbUxNLaXy6xP9806nkawyKG3id3UJ19kVIZ3aRSCjZRSKhZBeJhJJdJBJKdpFIKNlFIqFkF4mE6uwXBJZsZiY28lrzGBlvDoRX/12XP0/jL564NDEWmoY6ZEPXWRo/PsLH4heLyVMub8rxOvtrOT5F93iBj2dny0UXlvG2wTmVavh7aRWd2UUioWQXiYSSXSQSSnaRSCjZRSKhZBeJhJJdJBKqs9cDH/KNsQKv2nbm+fLAw6XkMeEAr2WHxpv35Ydp/MrO4zT+H+XNNJ7J8HnrmXQHP7DFGX6uyqeTnzspwS9IcMnm2nbfEDqzi0RCyS4SCSW7SCSU7CKRULKLRELJLhIJJbtIJBayPvt6AN8DsBaz5cMd7v4tM3sYwJ8AuDAo+UF339WojrazjgL/n1ksB+rBgVr4q+fW0biT/U8VktdHB4DuFK/xTzkf9z083Enj2XzyePpj03zu9tCc9eXAcaX7nuTHPMRnqr9+oFUWclFNCcDX3P0VM+sB8LKZ7a7EHnX3v21c90SkXhayPvspAKcqt0fN7DCAixvdMRGprw/1OsjMLgVwFYC9lU33mtkBM3vczFYktNluZvvMbF8R/CWjiDTOgpPdzLoBPAngfncfAfBtAJsBbMPsmf/r87Vz9x3u3u/u/ZnwzF4i0iALSnYzy2A20b/v7j8GAHc/7e4z7l4G8B0A1zSumyJSq2Cym5kBeAzAYXf/xpztc6f+/AKAg/XvnojUy0I+jf8sgC8DeNXM9le2PQjgDjPbhtly3FEAdzekh4vA8s18uuX1PXwq6IkSL29d1v0uj/ecSYwtTU/Stv1dR2h8SyZ53wCwa+OVNH7V8uQhsg+tPkTb3lvoofFV3eM03sEGmk4vvtJZrRbyafwLAOabJDvKmrrIYqUr6EQioWQXiYSSXSQSSnaRSCjZRSKhZBeJhKaSvqCGIYtj+1fS+Esrl9N4boj/Gt6e3kTj+XeT68kWeFr/1ncdjU9dxHfQu5+fL47lkqea/sf1v0nbhhZFTk0E7nHlaGLosmODtGlwAOwiHOKqM7tIJJTsIpFQsotEQskuEgklu0gklOwikVCyi0TC3Ju3uKyZDQE4NmfTKgB8sHbrtGvf2rVfgPpWrXr2baO7r54v0NRk/8CDm+1z9/6WdYBo1761a78A9a1azeqbXsaLRELJLhKJVif7jhY/PtOufWvXfgHqW7Wa0reWvmcXkeZp9ZldRJpEyS4SiZYku5ndbGb/a2ZvmtkDrehDEjM7amavmtl+M9vX4r48bmaDZnZwzrZeM9ttZm9Uvs+7xl6L+vawmZ2sHLv9ZnZLi/q23syeM7NDZvaamd1X2d7SY0f61ZTj1vT37GaWAvA6gN8GcALASwDucHe+YkCTmNlRAP3u3vILMMzsNwCMAfieu19R2fY3AM66+yOVf5Qr3P3P2qRvDwMYa/Uy3pXVivrmLjMO4DYAf4wWHjvSr9vRhOPWijP7NQDedPcj7l4A8EMAt7agH23P3Z8H8P7lZm4FsLNyeydm/1iaLqFvbcHdT7n7K5XbowAuLDPe0mNH+tUUrUj2iwHMXRPoBNprvXcH8KyZvWxm21vdmXmsdfdTldsDANa2sjPzCC7j3UzvW2a8bY5dNcuf10of0H3Q9e5+NYDPAbin8nK1Lfnse7B2qp0uaBnvZplnmfFfauWxq3b581q1ItlPAlg/5+dLKtvagrufrHwfBPAU2m8p6tMXVtCtfOczJzZROy3jPd8y42iDY9fK5c9bkewvAdhiZpvMLAvgSwCeaUE/PsDMuiofnMDMugDchPZbivoZAHdWbt8J4OkW9uU92mUZ76RlxtHiY9fy5c/dvelfAG7B7CfybwH481b0IaFflwH4n8rXa63uG4AnMPuyrojZzzbuArASwB4AbwD4GYDeNurbPwB4FcABzCZWX4v6dj1mX6IfALC/8nVLq48d6VdTjpsulxWJhD6gE4mEkl0kEkp2kUgo2UUioWQXiYSSXSQSSnaRSPw/q4AZ2qtQjDEAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jeJadF2Jj2_u",
        "outputId": "569b61f7-5ddd-4e12-af13-d93b4fed3ada"
      },
      "source": [
        "y_train[10]"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "SaRfz050j2_w",
        "outputId": "04878e17-746d-4ed9-943f-6ddefb483cb6"
      },
      "source": [
        "plt.imshow(X_train[0])"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f149e90e710>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VM_s5lU7j2_x",
        "outputId": "54ee44f1-d148-4107-ed70-fcef64edb459"
      },
      "source": [
        "y_train[0]"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "QJlgP-Qnj2_y",
        "outputId": "dd24dad5-048e-4da6-d28d-cc881714740b"
      },
      "source": [
        "plt.imshow(X_train[1])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f149e874e10>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8fIB772Sj2_0",
        "outputId": "a2a67d94-ce59-4092-8756-fc081c019b31"
      },
      "source": [
        "y_train[1]"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "tTMM9ghFj2_1",
        "outputId": "cbca1506-8647-41da-a862-87fd93bcb3f1"
      },
      "source": [
        "plt.imshow(X_train[5])"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f149e8684d0>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MNjxeXs0j2_2",
        "outputId": "001df8d2-c8cc-4001-8a05-ac0daa5afd05"
      },
      "source": [
        "y_train[5]"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gJqRfDlrj2_4"
      },
      "source": [
        "class_names = [\"T-shirt/top\", \"Trouser\", \"Pullover\", \"Dress\", \"Coat\",\n",
        "               \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle boot\"]"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "LvDFYR9Gj2_5",
        "outputId": "6371d328-dd3f-4bb6-e6e4-0d4744538a03"
      },
      "source": [
        "class_names[y_train[10]]"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'T-shirt/top'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "3tmWONG8j2_6",
        "outputId": "9153fde2-49e8-4391-83ef-b6b6aa3efbe5"
      },
      "source": [
        "plt.imshow(X_train[10])"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f149e7d6090>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZgnM6p_Tj2_7"
      },
      "source": [
        "#### X_train_full[10] is giving information abour the 28*28 pixel of image present at index 10 in x_train_full data. where in result we will find the pixel value in first row (28 pixel), 2nd row (28 pixel) upto 28 row (28 pixel). pixel value 0 indicate pixel is black and 255 indicate pixel is white"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0j4wsep2j2_8",
        "outputId": "eac5f737-8b6f-4451-aecc-2541b23471fe"
      },
      "source": [
        "X_train[10]"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  0,   0,   0,   0,   0,   0,   0,  11, 142, 200, 106,   0,   0,\n",
              "          0,   0,   0,   0,   0,  85, 185, 112,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0, 152, 214, 217, 194, 236, 216, 187,\n",
              "        149, 135, 153, 211, 217, 231, 205, 217, 188,  34,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,  66, 185, 166, 180, 181, 190, 211, 221,\n",
              "        197, 146, 198, 206, 191, 168, 190, 172, 188, 175,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0, 135, 153, 160, 175, 180, 170, 186, 187,\n",
              "        190, 188, 190, 187, 174, 195, 185, 174, 161, 175,  59,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0, 161, 147, 160, 170, 178, 177, 180, 168,\n",
              "        173, 174, 171, 185, 184, 185, 172, 171, 164, 174, 120,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   2, 175, 146, 145, 168, 178, 181, 185, 180,\n",
              "        184, 178, 179, 187, 191, 193, 190, 181, 171, 172, 158,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,  35, 177, 155, 140, 151, 172, 191, 187, 186,\n",
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              "          0,   0],\n",
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              "          0,   0],\n",
              "       [  0,   0,   0,   0,  79, 145, 160, 214, 123, 128, 153, 160, 164,\n",
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              "          0,   0],\n",
              "       [  0,   0,   0,   0, 100, 157, 225, 245, 175, 113, 174, 158, 158,\n",
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              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,  31, 174,  28, 126, 153, 166, 152, 158,\n",
              "        158, 160, 161, 157, 168, 191, 188,  18, 132, 159,   7,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,  82, 187, 159, 153, 157,\n",
              "        158, 162, 164, 164, 154, 187, 190,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   1,   3,   5,   0,  37, 175, 158, 155, 162,\n",
              "        158, 160, 162, 165, 153, 177, 205,   0,   0,   3,   3,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   1,   0,  25, 175, 152, 160, 158,\n",
              "        161, 160, 164, 164, 161, 166, 200,   0,   0,   1,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   4,   0,  30, 171, 147, 164, 155,\n",
              "        165, 161, 165, 162, 170, 164, 162,   0,   0,   2,   0,   0,   0,\n",
              "          0,   0],\n",
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              "        161, 161, 164, 167, 165, 165, 162,  28,   0,   3,   0,   0,   0,\n",
              "          0,   0],\n",
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              "        168, 161, 161, 172, 162, 165, 171,  50,   0,   5,   0,   0,   0,\n",
              "          0,   0],\n",
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              "        172, 164, 161, 172, 170, 160, 171,  89,   0,   4,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   2,   0,   4, 171, 164, 166, 173, 159,\n",
              "        179, 166, 160, 174, 167, 162, 166, 128,   0,   2,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   3,   0,  18, 152, 173, 160, 179, 154,\n",
              "        181, 166, 164, 175, 170, 166, 170, 164,   0,   0,   1,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   4,   0,  47, 165, 172, 167, 185, 153,\n",
              "        187, 173, 165, 174, 179, 166, 166, 158,   5,   0,   3,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   4,   0,  87, 180, 162, 179, 179, 157,\n",
              "        191, 182, 165, 168, 190, 173, 165, 166,  20,   0,   4,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   4,   0, 105, 187, 157, 194, 175, 161,\n",
              "        190, 184, 170, 158, 205, 177, 168, 171,  44,   0,   4,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   5,   0, 138, 181, 158, 205, 160, 167,\n",
              "        190, 198, 167, 152, 218, 186, 170, 172,  57,   0,   5,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   5,   0, 135, 174, 167, 199, 155, 166,\n",
              "        201, 219, 165, 158, 218, 188, 167, 175,  56,   0,   7,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   5,   0, 129, 171, 172, 177, 153, 159,\n",
              "        206, 216, 148, 157, 206, 190, 165, 175,  48,   0,   5,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   5,   0, 167, 187, 182, 198, 194, 200,\n",
              "        226, 240, 184, 206, 255, 197, 178, 179,  42,   0,   5,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   3,   0, 115, 135, 113, 106,  85,  82,\n",
              "        108, 133,  83,  90, 121, 120, 110, 158,  18,   0,   3,   0,   0,\n",
              "          0,   0]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P7ZeJhWjj2_9"
      },
      "source": [
        "### Data normalization\n",
        "We then normalize the data dimensions so that they are of approximately the same scale."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H0lcyyLJj2_-"
      },
      "source": [
        "#### Here we have normalize the data by dividing 255. ( float value bcoz to make compatible with python version) bcoz here we know pixel value is in between 0 & 255. so use this method of division by 255. But when we dont he lmit of data range hen we will standardization method for data normalization where we wiil find and standard deviation for the process"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "beQO3eXvj2__"
      },
      "source": [
        "X_train_n = X_train/ 255.\n",
        "X_test_n = X_test / 255."
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YuRz3jAWj3AA"
      },
      "source": [
        "### Split the data into train/validation/test datasets\n",
        "In the earlier step of importing the date, we had 60,000 datasets for training and 10,000 test datasets. Now we further split the training data into train/validation. Here is how each type of dateset is used in deep learning:\n",
        "\n",
        "* __Training data__ — used for training the model\n",
        "* __Validation data__ — used for tuning the hyperparameters and evaluate the models\n",
        "* __Test data__ — used to test the model after the model has gone through initial vetting by the validation set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wroEx5azj3AC"
      },
      "source": [
        "X_valid, X_train = X_train_n[:5000], X_train_n[5000:]\n",
        "y_valid, y_train = y_train_full[:5000], y_train_full[5000:]\n",
        "X_test = X_test_n"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YIVjJb_zj3AD",
        "outputId": "92da80c8-7343-4dc2-fc48-c91c63f42170"
      },
      "source": [
        "X_valid[0]"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
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              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
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              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_pTRw31Rj3AE"
      },
      "source": [
        "### Create the model architecture\n",
        "There are two APIs for defining a model in Keras:\n",
        "* Sequential model API\n",
        "* Functional API"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_zw9F6g5j3AF"
      },
      "source": [
        "![](1.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CwjmlkKYj3AF"
      },
      "source": [
        "![](Functional.JPG \"Title\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o2YX1iLQj3AG"
      },
      "source": [
        "# np.random.seed(42)\n",
        "tf.random.set_seed(42)\n",
        " by using random.seed everytime we run the code we will get same set of training and test distribution so that we will get same result display everytime we run the code"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kd0M1wRgj3AH"
      },
      "source": [
        "np.random.seed(42)\n",
        "#tf.random.set_random_seed(42)\n",
        "tf.random.set_seed(42)\n",
        "#tf.random.set_seed(42)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fe2w3M59j3AI"
      },
      "source": [
        "![](Structure.jpg \"Title\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pgk0JXh2j3AJ"
      },
      "source": [
        "# MODEL DESIGNING"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hCchiNO4j3AJ"
      },
      "source": [
        "### Keras is high end library used for designing model such as number of layer , number of neuron per layer, loss function, optimizer , activation function , evaluation metrics etc"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bPJdLQa-j3AK"
      },
      "source": [
        "### Tensorflow, Theano and CNTK is lower level libray which is completely responible for high level  mathematical operations.Tensorflow, Theano and CNTK work under keras library very efficiently for smooth oprrations "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JJ_qT3DYj3AL"
      },
      "source": [
        "# 1. Create Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tYv3cOPuj3AM"
      },
      "source": [
        "model = keras.models.Sequential()\n",
        "model.add(keras.layers.Flatten(input_shape=[28, 28]))\n",
        "model.add(keras.layers.Dense(300, activation=\"relu\"))\n",
        "model.add(keras.layers.Dense(100, activation=\"relu\"))\n",
        "\n",
        "model.add(keras.layers.Dense(10, activation=\"softmax\"))"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "otE3byQYj3AN"
      },
      "source": [
        "# model summary will give infoarmation about Trainable and non trainable parameters per layers.\n",
        "\n",
        "Trainnable parameters are Weight and Biases.\n",
        "\n",
        "Ex: Flatten layer is having 784 input.\n",
        "    Next layer is Dense layer having 300 neuron. So in this layer total number of Trainable parameters are\n",
        "    \n",
        "    784 *300 +300=235500 (784 are input features and 300 are nueron so weight is associated with each input feauture 784*300) \n",
        "    \n",
        "    Hence total number of weights = 784*300\n",
        "    Total number of biases =300 ( one bias per nueron)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0_YHXI3Mj3AO",
        "outputId": "3efeaa5c-2a06-4265-a955-a74230e1b845"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten (Flatten)           (None, 784)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 300)               235500    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 100)               30100     \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 10)                1010      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 266,610\n",
            "Trainable params: 266,610\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "61blmvt9j3AP"
      },
      "source": [
        ""
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "hdZQ8sQ3j3AQ",
        "outputId": "56f0c082-d34f-4caa-9d7c-5abe4c65b49c"
      },
      "source": [
        "import pydot\n",
        "keras.utils.plot_model(model)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3RlGjYzVj3AR"
      },
      "source": [
        "We can access the parameters using get_weight() method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WiwyVWAej3AR"
      },
      "source": [
        "weights, biases = model.layers[1].get_weights()"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zkxd5521j3AS",
        "outputId": "667fe7b8-4664-4a38-9337-fd4f3fc4ecda"
      },
      "source": [
        "weights"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.02448617, -0.00877795, -0.02189048, ..., -0.02766046,\n",
              "         0.03859074, -0.06889391],\n",
              "       [ 0.00476504, -0.03105379, -0.0586676 , ...,  0.00602964,\n",
              "        -0.02763776, -0.04165364],\n",
              "       [-0.06189284, -0.06901957,  0.07102345, ..., -0.04238207,\n",
              "         0.07121518, -0.07331658],\n",
              "       ...,\n",
              "       [-0.03048757,  0.02155137, -0.05400612, ..., -0.00113463,\n",
              "         0.00228987,  0.05581069],\n",
              "       [ 0.07061854, -0.06960931,  0.07038955, ..., -0.00384101,\n",
              "         0.00034875,  0.02878492],\n",
              "       [-0.06022581,  0.01577859, -0.02585464, ..., -0.00527829,\n",
              "         0.00272203, -0.06793761]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FIPkmSW-j3AT",
        "outputId": "6a9357dc-a085-46af-86eb-ecf2b85e5a55"
      },
      "source": [
        "weights.shape"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(784, 300)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L8bt58g0j3AU",
        "outputId": "b63e4b08-c46c-4024-e5d6-5a6852c561f2"
      },
      "source": [
        "biases"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sYLXAPsGj3AV",
        "outputId": "efb6fdf6-d9cf-4130-b15a-d60ed38a27cd"
      },
      "source": [
        "biases.shape"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(300,)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TgJezKjOj3AW"
      },
      "source": [
        "Documentation - https://keras.io/models/sequential/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lov2DE4mj3AX"
      },
      "source": [
        "# 2. Compile Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xIbKff8jj3AY"
      },
      "source": [
        "# Model Compile is used to set up Learning Process such as Loss function, optimizer and metrics\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ny3FinE-j3AY"
      },
      "source": [
        "Here loss is used as Sparse-Catgorical_crossentropy bcoz output y in form of labels. If output y is in form probabilities then used only categorical_crossentropy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ntr3opwRj3AZ"
      },
      "source": [
        "If output y is in binary 0 0r 1 or yes yes or no then use loss as binary_crossentropy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ux3Udh36j3Aa"
      },
      "source": [
        "optimizer tells keras that perform back propogation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ke3WyjJDj3Ab"
      },
      "source": [
        "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
        "              optimizer=\"sgd\",\n",
        "              metrics=[\"accuracy\"])"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TUEEyhGkj3Ac"
      },
      "source": [
        "# 3. Fit Model/Training Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "czC9y2cAj3Ac",
        "outputId": "e9380924-f627-4165-8f3b-532ceefe836b"
      },
      "source": [
        "model_history = model.fit(X_train, y_train, epochs=30,\n",
        "                    validation_data=(X_valid, y_valid))"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.7237 - accuracy: 0.7643 - val_loss: 0.5213 - val_accuracy: 0.8226\n",
            "Epoch 2/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.4842 - accuracy: 0.8318 - val_loss: 0.4353 - val_accuracy: 0.8526\n",
            "Epoch 3/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.4391 - accuracy: 0.8457 - val_loss: 0.5333 - val_accuracy: 0.7988\n",
            "Epoch 4/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.4123 - accuracy: 0.8564 - val_loss: 0.3916 - val_accuracy: 0.8650\n",
            "Epoch 5/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3939 - accuracy: 0.8616 - val_loss: 0.3741 - val_accuracy: 0.8698\n",
            "Epoch 6/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3751 - accuracy: 0.8678 - val_loss: 0.3706 - val_accuracy: 0.8726\n",
            "Epoch 7/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3630 - accuracy: 0.8715 - val_loss: 0.3621 - val_accuracy: 0.8726\n",
            "Epoch 8/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3515 - accuracy: 0.8752 - val_loss: 0.3855 - val_accuracy: 0.8626\n",
            "Epoch 9/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3413 - accuracy: 0.8792 - val_loss: 0.3575 - val_accuracy: 0.8718\n",
            "Epoch 10/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3318 - accuracy: 0.8822 - val_loss: 0.3423 - val_accuracy: 0.8774\n",
            "Epoch 11/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3238 - accuracy: 0.8838 - val_loss: 0.3449 - val_accuracy: 0.8776\n",
            "Epoch 12/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3147 - accuracy: 0.8867 - val_loss: 0.3306 - val_accuracy: 0.8808\n",
            "Epoch 13/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3078 - accuracy: 0.8894 - val_loss: 0.3265 - val_accuracy: 0.8880\n",
            "Epoch 14/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3019 - accuracy: 0.8915 - val_loss: 0.3422 - val_accuracy: 0.8774\n",
            "Epoch 15/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2944 - accuracy: 0.8938 - val_loss: 0.3224 - val_accuracy: 0.8858\n",
            "Epoch 16/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2888 - accuracy: 0.8970 - val_loss: 0.3093 - val_accuracy: 0.8902\n",
            "Epoch 17/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2835 - accuracy: 0.8981 - val_loss: 0.3549 - val_accuracy: 0.8734\n",
            "Epoch 18/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2775 - accuracy: 0.9001 - val_loss: 0.3128 - val_accuracy: 0.8900\n",
            "Epoch 19/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2726 - accuracy: 0.9025 - val_loss: 0.3123 - val_accuracy: 0.8912\n",
            "Epoch 20/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2672 - accuracy: 0.9037 - val_loss: 0.3289 - val_accuracy: 0.8808\n",
            "Epoch 21/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2621 - accuracy: 0.9059 - val_loss: 0.3074 - val_accuracy: 0.8920\n",
            "Epoch 22/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2575 - accuracy: 0.9075 - val_loss: 0.2955 - val_accuracy: 0.8972\n",
            "Epoch 23/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2533 - accuracy: 0.9084 - val_loss: 0.2995 - val_accuracy: 0.8938\n",
            "Epoch 24/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.2482 - accuracy: 0.9100 - val_loss: 0.3089 - val_accuracy: 0.8878\n",
            "Epoch 25/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.2441 - accuracy: 0.9124 - val_loss: 0.2975 - val_accuracy: 0.8950\n",
            "Epoch 26/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2403 - accuracy: 0.9137 - val_loss: 0.3066 - val_accuracy: 0.8910\n",
            "Epoch 27/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2361 - accuracy: 0.9155 - val_loss: 0.3025 - val_accuracy: 0.8958\n",
            "Epoch 28/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2326 - accuracy: 0.9166 - val_loss: 0.2996 - val_accuracy: 0.8946\n",
            "Epoch 29/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2282 - accuracy: 0.9189 - val_loss: 0.3055 - val_accuracy: 0.8906\n",
            "Epoch 30/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2247 - accuracy: 0.9199 - val_loss: 0.3026 - val_accuracy: 0.8928\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xgy8Tc0Fj3Ae",
        "outputId": "20d9740e-3b54-4433-f164-2d885226ba77"
      },
      "source": [
        "model_history.params"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'epochs': 30, 'steps': 1719, 'verbose': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_UNO-blGj3Af",
        "outputId": "0d53ef71-89c8-447f-f44d-6101464b549e"
      },
      "source": [
        "model_history.history"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': [0.7643091082572937,\n",
              "  0.831781804561615,\n",
              "  0.8456545472145081,\n",
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              "  0.8677818179130554,\n",
              "  0.8715272545814514,\n",
              "  0.8752181529998779,\n",
              "  0.8791999816894531,\n",
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              "  0.8838000297546387,\n",
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              "  0.8938363790512085,\n",
              "  0.8969636559486389,\n",
              "  0.8981454372406006,\n",
              "  0.9000727534294128,\n",
              "  0.902472734451294,\n",
              "  0.9037272930145264,\n",
              "  0.9059090614318848,\n",
              "  0.9075272679328918,\n",
              "  0.9083636403083801,\n",
              "  0.9100000262260437,\n",
              "  0.9124181866645813,\n",
              "  0.9137272834777832,\n",
              "  0.9154909253120422,\n",
              "  0.9166363477706909,\n",
              "  0.9188545346260071,\n",
              "  0.9198727011680603],\n",
              " 'loss': [0.7237029075622559,\n",
              "  0.484192818403244,\n",
              "  0.43905338644981384,\n",
              "  0.41231462359428406,\n",
              "  0.3939222991466522,\n",
              "  0.3750678300857544,\n",
              "  0.3630312979221344,\n",
              "  0.35152509808540344,\n",
              "  0.3412765860557556,\n",
              "  0.3317754566669464,\n",
              "  0.3238329291343689,\n",
              "  0.3146835267543793,\n",
              "  0.30778029561042786,\n",
              "  0.30192798376083374,\n",
              "  0.2943917214870453,\n",
              "  0.28883519768714905,\n",
              "  0.2835114002227783,\n",
              "  0.27753812074661255,\n",
              "  0.2725583016872406,\n",
              "  0.26720917224884033,\n",
              "  0.26212015748023987,\n",
              "  0.2575129568576813,\n",
              "  0.2533215880393982,\n",
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              "  0.22820375859737396,\n",
              "  0.22473183274269104],\n",
              " 'val_accuracy': [0.8226000070571899,\n",
              "  0.8525999784469604,\n",
              "  0.798799991607666,\n",
              "  0.8650000095367432,\n",
              "  0.8697999715805054,\n",
              "  0.8726000189781189,\n",
              "  0.8726000189781189,\n",
              "  0.8626000285148621,\n",
              "  0.8718000054359436,\n",
              "  0.8773999810218811,\n",
              "  0.8776000142097473,\n",
              "  0.8808000087738037,\n",
              "  0.8880000114440918,\n",
              "  0.8773999810218811,\n",
              "  0.8858000040054321,\n",
              "  0.8902000188827515,\n",
              "  0.8733999729156494,\n",
              "  0.8899999856948853,\n",
              "  0.8912000060081482,\n",
              "  0.8808000087738037,\n",
              "  0.8920000195503235,\n",
              "  0.8971999883651733,\n",
              "  0.8938000202178955,\n",
              "  0.8877999782562256,\n",
              "  0.8949999809265137,\n",
              "  0.890999972820282,\n",
              "  0.895799994468689,\n",
              "  0.894599974155426,\n",
              "  0.8906000256538391,\n",
              "  0.892799973487854],\n",
              " 'val_loss': [0.5213177800178528,\n",
              "  0.4353218674659729,\n",
              "  0.5333406329154968,\n",
              "  0.3916124105453491,\n",
              "  0.3740810751914978,\n",
              "  0.3706005811691284,\n",
              "  0.3620573878288269,\n",
              "  0.38551998138427734,\n",
              "  0.35746830701828003,\n",
              "  0.34233367443084717,\n",
              "  0.34490248560905457,\n",
              "  0.3305920362472534,\n",
              "  0.32650965452194214,\n",
              "  0.3421776294708252,\n",
              "  0.3224159777164459,\n",
              "  0.3092650771141052,\n",
              "  0.3549402952194214,\n",
              "  0.31275853514671326,\n",
              "  0.3123112618923187,\n",
              "  0.3288777768611908,\n",
              "  0.3074253797531128,\n",
              "  0.29546183347702026,\n",
              "  0.2995152473449707,\n",
              "  0.30886930227279663,\n",
              "  0.2975446879863739,\n",
              "  0.3065853416919708,\n",
              "  0.3024672269821167,\n",
              "  0.2996077835559845,\n",
              "  0.3055015504360199,\n",
              "  0.3026241064071655]}"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "id": "LkY3WyRaj3Ah",
        "outputId": "a87f8f24-5866-496a-beac-04830c955fe0"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "pd.DataFrame(model_history.history).plot(figsize=(8, 5))\n",
        "plt.grid(True)\n",
        "plt.gca().set_ylim(0, 1)\n",
        "plt.show()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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j/jCBkhL8hw4RKDlM4NAhAsf2E21t7VuGxERMmZmYMrOwzVuIKSMdc2YmpoxMzJkZGJOT9ZqWUr0nVGym+kwK+Mebb1L8kY+c5pk5M8wZGZgzMnBdcgkQuwYWCIz4rSany2C34169Gvfq1USDQdrfegvvKxvxvvYa0WeexTZrFmnf+AauNauxFhaOdnH7MFitGCSIxRglYXyWRVpaCNXV6QHb2NjjVoBY6MbWIy0t+jXUfiirFUNCAganE0OCE6PTiTnFjSHTjcEUwWgMYKATQ9RLqKMeqyEcGzzhFOPOmmzdAx1YnEQ1G/76MP6qdhpL2iCiNx8bnHZs04qwzZqFbe4CbHPmYpk8ud+aohYKETx+HP8HJQQO7SJw6BD+khLC1dXxfQyJidimTcN9+WX6rRuZGfGgNaWn93vtdEScAwMzKKVQYySIT2awWHCtWoVr1Sq9Y09bG6aUs9fzVIjxRsL4LGp67DFq/+NnfUJWWa3xjhHmgnzsixd13waQkoLJbcdoCWGItGAM1aPaq6D1BLRUQOsH4K3ue63VmQaJedQHc/HkT9drrY5Y7dWe3HvZ7tEHTRhANBAgUHIY/8EDeg/WAwdp/vMzaL9/Si+/3Y5t+nRss2ZiysgkePQo/pISgqWl3fcwmkxYCwtxLFmCddpUbNOnY50+HVN6+qj1vhQjQ5nNEsRCfEgSxmdJ+7vbqP3Zf+JceRGeq6/GmJKCKTEBky2MIdqK8tXqoeqtAW85eLdBXTUcqdHvQ+3JYIbEHEjMg8kXQ2IuePL09cQ8/TWzXqPcv2ULxcXFH6rsBqs1fv2tixYOEzh6NBbOBwgcOEjrM88SbW/HlJ6Odfp0ElYsxzp9OtZp07BOnjxgxyYhhJjoJIzPNE0jVPIelbd8FUuqg5wlVRiP/BB21ei37JzMZAd3FriyIXsRuDLBlaXPPfl62Cakj/ojwpTJhG3aNGzTpsHVVwP6rTfRjg6MCWfvGaBCCDEeTNgw1jQN32uv4XvzTdK/+U194IWR4K2Byh1QtQMq30er2Enli0ai7SYKVrZh1BIgbToUFvcOWne2Pre64RxttlUGgwSxEEKchgkZxoHSUmp/8lPat27V1w9+QP5v/3dY470C+i0+VTtjwRubvFX6a8oI6bOoPTKNzoYKcu7+BtbrviDP+RRCCNHHhArjSFsb9b/+Nc2//wMGp5OM734XU1oqld++nYqvfo283zx06ttIOprg2OtwdDMcfwsaD3e/llwEk1boTcs5iyBzHq2vbKb5vm+T/LkbcH/mK2f+CwohhDgnTYgw1iIRWv78NPX33kukpQXPpz5F2q23YEpO1l8Ph6m64zucuPVW8n71q+6ORuEAVLwLRzbrAVy1C9D0puSCFTB/vR682Qv1nsk9+EtKqP7BD7AvXkz6t799lr+xEEKIc8m4D+OO99+n5sc/JnDgIPbFi8n83nexzZrVa5/EdeuIdnZSc/cPqbz5K+R8diGq7B9QtlXvyayMkLsUiu+EwlWQs/iUj4CL+HxU3nIrBqeTnP/+L5RZmqaFEEIMbNyGcaimhrqf30PbCy9gyswk+xf34L788r73tLZVw9EtJJk2o50fpfYf71B1/DWyP5aKWvhZPXwnXag/73UINE2j+q7vEqyooODRRzCnp5+BbyeEEGI8GXdhHA0EaPrd72h4+H8gEiH1qzeRcuON/Q8Ev/138PxtgAaOFJKvKCaab6H+qX9gaLiCzJt/NOwBKZp+9zu8GzeSfscdOJYuHZkvJYQQYlwbP2GsabRt3Ejdf/yMUGUlrjVrSL/jDiy5AzxK68T78OId+i1Gq/8FMuaCwUDqJyCadC+Nv/kNBruN9DvvHHIgt7+7jbpf/BeuSy8l+Z8+P1LfTAghxDg3LsI4UFqK5777qPzgENapU8l/9JFTP2Ozown+9Hn9Ht9P/E4fGrKHtG/cSrSzg6bHHkfZ7aR/4xuDliFUW0vlbbdhmTSJrB//WIZ4FEIIMWTjI4xLSjCXV5Dx/e+TtP66Uz+wPBqFv/6zPvTkF17uE8SgD9CfcdddaJ1+Gh/6DQa7g9SvfHnAQ2rBIJXf+CbRzk4KHn8MY8Iw71cWQggxoY2LMHZddhkNmsbsj31s8J3f+m84/DJcfg/kLh5wN6UUmT/6IVG/n/r//m8MdhvJN9zQ7761//lzOnfuJOe//wtrUdHpfg0hhBAT1LgIY6UU2lBGzzr2Brz27zDnWlh64+DHNRrJ/ulP0Px+an/yU5TdTtInP9lrn9bnX6B5wwZ9YI/LLjvdryCEEGICG9JDXZVSa5VSh5RSpUqpO/t5PV8ptVkptVMptUcpdfnIF/VD8tbAn7+gj5S17r4hj/+sTCZyfnEPzpUXUXP3D2l97rn4azKwhxBCiJEwaBgrpYzA/cBlwCzg00qpWSft9n3gKU3TFgLrgQdGuqAfSiQMf/4iBLzwqcfB6hrW25XFQu4vf4lj2TKq7ryLto0bZWAPIYQQI2YozdTLgFJN044CKKWeBK4CDvTYRwO6RsVIBKpGspAf2uYfQ9mbcPVDkHHy74ihMdhs5D1wP+VfvJHK276FfdYsGdhDCCHEiFCapp16B6U+AazVNO3G2Pr1wHmapn29xz5ZwCtAEuAEPqpp2vv9HOvLwJcBMjIyFj/55JMj9T3w+Xwk9PP4vuTG7czb+29UZa2mZPrX+3nn8KiODpLuvRdzeQXea6+lY/VHP/Qxz6SBzstEJ+elf3Je+ifnpX9yXvo30HlZtWrV+5qmLen3TZqmnXICPgH8b4/164Ffn7TPbcC3YssXoNeaDac67uLFi7WRtHnz5r4bm8s07af5mvbgCk0LdozYZ4VbWrS2TZu0aDQ6Ysc8U/o9L0LOywDkvPRPzkv/5Lz0b6DzAmzXBsjEoXTgqgTyeqznxrb19EXgqVi4vw3YgNQhHPvMCQfgqc+BFtWvE5vtI3ZoY2IirksukYE9hBBCjIihhPF7wFSl1GSllAW9g9azJ+1TDlwCoJSaiR7G9SNZ0GF75ftQtQOuuh+SC0e1KEIIIcSpDBrGmqaFga8DLwMH0XtN71dK/atS6srYbt8CvqSU2g38Efh8rEo+OvY9Ddsehgu+DrOuHHx/IYQQYhQNadAPTdNeBF48advdPZYPACtGtminqeEwPHsL5J0HH/3RaJdGCCGEGNSQBv04ZwQ74KkbwGSFTzwCRrn3VwghxNg3LobDBEDT4IVvQd1B+OzTkDjAoxOFEEKIMWbchHFmzSY49Ae4+E6YcsloF0cIIYQYsvHRTF29h2klv4HCYrj4jtEujRBCCDEs4yOMW0/gt6XDx/8XDMbRLo0QQggxLOOjmXrG5bxXZeXihLTRLokQQggxbOOjZgxoUiMWQghxjho3YSyEEEKcqySMhRBCiFEmYSyEEEKMMgljIYQQYpRJGAshhBCjTMJYCCGEGGUSxkIIIcQoGxdhvLW0gft2+PGHIqNdFCGEEGLYxkUYt/nD7KyLcKC6bbSLIoQQQgzbuAjjhfkeAHaVt4xySYQQQojhGxdhnOG2kWxT7KqQMBZCCHHuGRdhDFCYaJAwFkIIcU4aP2HsMVDe1EGjLzDaRRFCCCGGZdyEcVGi/tSm3SekdiyEEOLcMm7CeJLbgNGgpBOXEEKIc864CWOrSTEtw8VOuW4shBDiHDNuwhhgQZ6H3RUtRKPaaBdFCCGEGLJxFcYL8zy0+cMca2wf7aIIIYQQQzauwniBDP4hhBDiHDSuwrgoLYEEq0nuNxZCCHFOGVdhbDQo5uUmShgLIYQ4p4yrMAa9E9fB6jZ5gpMQQohzxrgM43BUY39V62gXRQghhBiS8RfGsU5cO6UTlxBCiHPEuAvjdJeNHI9dBv8QQghxzhh3YQx6U7Xc3iSEEOJcMW7DuLKlk3qvPMFJCCHE2Dc+w7hr8A9pqhZCCHEOGJdhPCc7UX+CU0XzaBdFCCGEGNS4DGO7xciMTJfUjIUQQpwTxmUYg37deE9FqzzBSQghxJg3rsPYGwhzpN432kURQgghTmnchvHCrsE/pKlaCCHEGDduw7gwNQGXTZ7gJIQQYuwbt2FsMCjm58rgH0IIIca+cRvGoF83PlTrpTMoT3ASQggxdo37MI5ENfZWyhOchBBCjF3jO4zjI3HJ4B9CCCHGriGFsVJqrVLqkFKqVCl15wD7fEopdUAptV8p9YeRLebpSU2wkptkl05cQgghxjTTYDsopYzA/cBq4ATwnlLqWU3TDvTYZypwF7BC07RmpVT6mSrwcC3I87CjTGrGQgghxq6h1IyXAaWaph3VNC0IPAlcddI+XwLu1zStGUDTtLqRLebpW5DnoarVT12bf7SLIoQQQvRrKGGcA1T0WD8R29bTNGCaUuotpdQ7Sqm1I1XAD0sG/xBCCDHWDdpMPYzjTAWKgVzgdaXUXE3TeiWgUurLwJcBMjIy2LJlywh9PPh8vn6PF4xoGBU8++YerPUfjNjnnSsGOi8TnZyX/sl56Z+cl/7Jeenf6ZyXoYRxJZDXYz03tq2nE8C7mqaFgGNKqRL0cH6v506apj0MPAywZMkSrbi4eFiFPZUtW7Yw0PFmHXiTJmWiuPj8Efu8c8WpzstEJuelf3Je+ifnpX9yXvp3OudlKM3U7wFTlVKTlVIWYD3w7En7/A29VoxSKhW92frosEpyBi3I87DnRAsReYKTEEKIMWjQMNY0LQx8HXgZOAg8pWnafqXUvyqlrozt9jLQqJQ6AGwGbtc0rfFMFXq4FuR5aA9GKK2TJzgJIYQYe4Z0zVjTtBeBF0/adnePZQ24LTaNOT0H/5ie6Rrl0gghhBC9jesRuLpMTnHilic4CSGEGKMmRBgbDIr5eR52yhOchBBCjEETIowBFuYnUVLrpT0QHu2iCCGEEL1MnDDO8xDVkCc4CSGEGHMmTBjPz+vqxCVN1UIIIcaWCRPGyU4LBSkOdsl1YyGEEGPMhAlj0O83lpqxEEKIsWbChXFNm5+aVnmCkxBCiLFjwoUx6IN/CCGEEGPFhArjWdluLEaDPE5RCCHEmDKhwthqMjIz2y2duIQQQowpEyqMQb/feG9lqzzBSQghxJgx4cJ4QZ6HjmCEklrvaR9ja+VW1j+/Hm/w9I8hhBBCdJmQYQynP/hHJBrhZ+/9jP2N+3nl+CsjWTQhhBAT1IQL44IUB0kO82lfN37+6PMcbT2KzWjjuaPPjXDphBBCTETjIox31u3k4bqHCUaCg+6rlP4Ep9OpGQcjQR7Y9QCzUmbxxblf5P3a96n0VZ5OkYUQQoi4cRHGHaEO9nbu5bH9jw1p/wV5HkrqvPiG+QSnP5f8mar2Km5ZeAvritYB8MLRF4ZdXiGEEKKncRHGK3JWMM8+j4f3PEy1r3rQ/RfkedA02HNi6LXjjlAHD+95mCUZS1ievZychBwWpS/iuSPPoWnSM1sIIcTpGxdhDHBt8rUA/Hz7zwfd93Q6cf3hgz/Q6G/k1kW3opQCYF3ROo63HWd/4/7TKLEQQgihGzdhnGxK5kvzvsTGso1srdx6yn09DguTU53sHGInrtZAK7/b9zsuzr2YBekL4tvXTFqDxWDhuSPSkUsIIcTpGzdhDPD52Z8n35XPT7f9dNDOXF1PcBpKE/Oj+x/FG/Ry88Kbe213W9xcnHcxLx1/iVA09KHKLoQQYuIaV2FsMVq467y7ON52nMcPPH7KfRfkeaj3Bqga5AlODZ0N/P7g77ls8mVMT57e5/V1heto8jcNWhsXQgghBjKuwhjgwpwL+UjeR3h4z8PUtNcMuF/8uvEgTdUP79Fvmfragq8N+Hkeq0fuORZCCHHaxl0YA9yx7A6iWpT/fO8/B9xnZpYbi8lwyscpVvoq+VPJn7hm6jUUuAv63cdsNLN20lo2l2+W4TGFEEKclnEZxjkJOdw490Y2lm3k7aq3+93HYjIwO9t9yh7VD+x6AAMGvjLvK6f8vHVF6whGg2ws2/ihyi2EEGJiGpdhDPBPc/6JPFceP3n3J4Qi/XeuWhB7gpM/FOnz2pGWIzx/9Hk+PePTZDozT/lZc1PnUuAukF7VQgghTsu4DWOr0cqdy+7keNtxnjj4RL/7fHRmBoFwlP/3v+/S1N679/X9u+7HbrLzxblfHML62iAAACAASURBVPSzlFJcUXgF22u3U+WrGpHyCyGEmDjGbRgDrMxdSXFeMQ/tfqjfzlwrpqTy608vYm9lK9c+uJWyxnYA9jfsZ2PZRj4363Mk2ZKG9FlXFF4ByPCYQgghhm9chzHAd5Z+h6gW5Z7t9/T7+sfmZfH7G8+juSPIxx/Yyq6KFn6585d4rB6un3X9kD8n15WrD495VIbHFEIIMTzjPoxzXbl8ce4Xefn4y7xT/U6/+yydlMzTNy3HYTXy6Sc2sLVqKzfOvZEES8KwPuuKois41nqMA40HRqLoQgghJohxH8YAX5jzBXITcvnpuz8dsDNXUVoCT//zcpyZG4mG3IRbzh/256wpWIPZYJZ7joUQQgzLhAjjrs5cR1uPsuHghgH3O9D6DgHjUYrM1/Bvz5XykxcPEo0Ovck50ZpIcV4xfz/2dxkeUwghxJBNiDAGuDjvYopzi3lw94PUttf2eT2qRfnlzl+S78rnqf93CzdcUMDDrx/l5id39nvr00CuKLyCJn/TgPc3CyGEECebMGEM+shckWiEX2z/RZ/X/n7s7xxuPszXFnwNm8nCv1w5m+9ePoMX9lRz/W/fpaXj1A+e6HJRzkX68Jhyz7EQQoghmlBhnOfK44tzv8jfj/+dbdXb4ttD0RD377qfaUnTWDt5LaDfO/zllUX86tML2V3Ryscf3EpFU8egn2E2mrl00qVsrpDhMYUQQgzNhApj0Dtz5STk6CNzxa7r/q30b1R4K7hl4S0YVO9Tsm5+NhtuPI9GX5BrHniLPScGfwbyuqJ1BCIBNpVtOiPfQQghxPgy4cLYZrJx57I7OdJ6hD8c/AP+sJ+Hdj3EgrQFrMxd2e97lk1O5umbLsBmNnLdb97h1YN9rzn3NC91nj48pvSqFkIIMQQTLowBivOKWZm7kgd2PcCvdv6Kus46bll0C0qpAd8zJd3FX766nCnpCXzp8e08/vbxAQf3UErxscKP8V7Ne1T7qs/QtxBCCDFeTMgwBrhz6Z2Eo2EeP/A4K7JXsDRz6aDvSXfZePLL57Nqejp3P7Ofqx/YyhuH6/sN5fjwmMdkeEwhhBCnNmHDOM+dx41zb8SkTNy86OYhv89pNfHwDUv4j4/Ppb7Nz/W/3cb6h99h+/Gm3sd35bEwfSHPHZHhMYUQQpzahA1jgH+e/89s/ORGZqfMHtb7jAbF+mX5bL69mB+um8WR+nY+8dDbfP6RbeyrbI3vd0XhFRxtPcqBJhkeUwghxMAmdBgrpUi1p572+60mI/+0YjKv31HMd9bOYGd5C1f86k1u2vA+h2u9XDrpUswGM88feX4ESy2EEGK8mdBhPFIcFhM3FRfxxndWccslU3m9pJ41977Ov/ztGEvTV/DisRcJR8OjXUwhhBBjlITxCHLbzNy2ehpvfOcjfOmiQl7YW83m9/Np8jfxwuEto108IYQQY5SE8RmQ7LTw3ctn8vodq/jkzNVoEQd3bXyUf3v+AA2+wGgXTwghxBgzpDBWSq1VSh1SSpUqpe48xX7XKqU0pdSSkSviuSvDbePH1yzgisLLMLsO8MjbB1nxH6/x9T/s4NWDtYQi0dEuohBCiDHANNgOSikjcD+wGjgBvKeUelbTtAMn7ecCbgXePRMFPZetn3U1L5Q9zXeuDVFRnssLe6p5fk81SQ4zV8zL5uqF2SzKTzrloCNCCCHGr0HDGFgGlGqadhRAKfUkcBVw8v06/wb8DLh9REs4DsxPm0++K59t9Rv57dXrufuK2bxxuJ6/7qzkqe0VPPFOGfnJDq5akM1VC3KYkp4w2kUWQghxFg0ljHOAih7rJ4Dzeu6glFoE5Gma9oJSSsL4JEoprii8ggd3P0hNew2ZzkwumZnBJTMz8AXCvLyvhr/tquT+zaX86rVS5uYkcvXCHNbNzyLdZRvt4gshhDjD1GCjQymlPgGs1TTtxtj69cB5mqZ9PbZuAF4DPq9p2nGl1Bbg25qmbe/nWF8GvgyQkZGx+MknnxyxL+Lz+UhIGLs1yvpQPf9a9a+s86xjTeKafvdp8Ud5tybC21VhjrdFUcDsFCMXZBtZlGHCbhp+M/ZYPy+jRc5L/+S89E/OS//kvPRvoPOyatWq9zVN67dP1VDC+ALgR5qmXRpbvwtA07SfxtYTgSOAL/aWTKAJuLK/QO6yZMkSbfv2AV8eti1btlBcXDxixzsTrn/xelqDrTxx2RMkWhNPuW9pnY9ndlXy152VnGjuxGY2cOnsTD6+KJcLp6RiNAwtmM+F8zIa5Lz0T85L/+S89E/OS/8GOi9KqQHDeCjN1O8BU5VSk4FKYD3wma4XNU1rBeLDWJ2qZjzRXTfjOu564y6KnypmefZy1k5ay6q8VSRY+v6CmpKewLfWTOe21dPYUd7MX3ZU8vyeap7ZVUW6y8pVC7L5+KJcZma5R+GbCCGEGEmDhrGmaWGl1NeBlwEj8DtN0/Yrpf4V2K5p2rNnupDjxRWFVzDZPZm/H/s7L5e9zOsnXsdisHBR7kWsnbSWlbkrcZgdvd6jlGJxQTKLC5K5e90sNn9Qx9M7KnnkreP8zxvHmJnl5uMLc7hqQTbp7tG7vtweaucvh//CB00f8JkZn2F26vDG+xZCiIlsKDVjNE17EXjxpG13D7Bv8Ycv1vg1O3U2s1Nnc9uS29hTv4eXjr/EK8df4dXyV7Gb7KzMXcnaSWu5MOdCbKbe4Wo1GVk7J4u1c7Joag/y/J4qnt5RyY9fPMhP/36Qi6am8fFFOayZlYndYjwr36e+o57fH/w9T5U8hTfoxW6y8+yRZ7ls0mXcvOhm8lx5Z6UcQghxLhtSGIuRZ1AGFqQvYEH6Am5fcjs76nbw0rGX2Fi2kZePv4zT7GRV3irWTlrL8uzlmI3mXu9Pdlq44YJJ3HDBJErrfPx15wn+uqOSW5/cRYLVxGVz9OvL0TP0+MYjLUd4dP+jvHD0BSJahEvyL+Hzsz9PYWIhj+x/hCcOPMHG8o1cN/06vjLvKyTZks5IOYQQYjyQMB4DjAYjSzOXsjRzKXeddxfbarbx8vGX2Vi2keePPo/L4uLC7AtJc6ThtrhxW90kWhJxW924LW4SbYnceHEGt15SxPtlbfxlxwle3FvNn94/QZJVsbJ2J0smJbN0UhLT0l0Yhtj562SaprG9djuP7n+U10+8js1o49qp13LDrBvIc3fXgG9eeDPrp6/ngd0P8McP/sgzpc/whTlf4LOzPovdZB+p0yaEEOOGhPEYYzKYWJ69nOXZy/n+ed/n7eq3eenYS7xX+x6tgVY6w52nfH+COQG3xc2MxS7CYTv1TfB6XQYvHEkhGsjAachgSUFaLJyTmZebiM186ibtcDTMpvJNPLbvMfY17iPZlsxXF3yV9dPXD1jjTXOk8cMLfsj1M6/n3h338sudv+TJD57kawu/xlVFV2E0nJ1mdCGEOBdIGI9hZqOZlbkrWZm7Mr4tGAnSFmyjLdCmz4NttAZaB97mr8Bn2Y09UW+uVhjZFU7j7X1pRN9PxxDKpMhTxAX5MzhvcjpLCpJIcloA6Ah18LfSv/H4gcep9FVS4C7gB+f/gCuLruxzPXsghZ5CfvmRX7Kjdge/eP8X/HDrD3niwBN8Y9E3WJm7UoYAFUIIJIzPORajhVR7Kqn21MF3Rr/f7fwLz+dY6zGOtB7haMtRSltKOdx8hErffjSilANl9Qb+UJlK9NV0PKZcMhPNVEW30BnxsiBtAbcvvZ3i3OLTrtEuyljEhss2sKl8E/ftuI+vv/Z1Fmcs5luLv8XctLmndUwhhBgvJIwnAJvJxsyUmcxMmdlruz/sp6ytjNKWUkqaStlZ8wFHW4/SGtrPkSCEvLMINq1kz/EpPFHr4kD+URYVJDE/z0OCdfj/6SilWF2wmuK8Yp4ueZoHdz/IZ178DGsK1nDLolsocBeM1FcWQohzioTxBGYz2ZiePJ3pydP5WGH39kAkgC/QTrPPzI6yFnaUN7OjvJl/lNSjaWBQMC3DxaKCJBbnJ7GoIIlJKY4hNzmbDWbWz1jPuqJ1PLr/UR7b/xivlL3CrJRZerN8zkpmp87GoORx2+eC+o569jfu5+Lci+WygxCnScJY9GE1WrE6rKQ4YEq6i08t1XtKt3aG2F3Rwvtlejg/t6uKP7xbDui3Wi3M87CoIIk5OYnMzUkkOXbteSBOs5OvLfgan5r2Kf5W+jdeP/E6D+95mId2P0SKLYULcy5kZe5Klmcv73eUMjH6SppLuGnTTdR11LGucB0/XP5DrEbraBdLiHOOhLEYskS7mZXT0lg5LQ2AaFSjtN7HjrLmeEC/+kFdfP8cj505OW7m5iTGAzoloe8f6jRHGl+a9yW+NO9LNPubebPyTd448QavVbzGM0eewWQwsTh9MRflXsTFuRczKXHS2frK4hS2VW/j1s234jA5+OzMz7Lh4AaOtx3n3lX3ku5IH+3iCXFOkTAWp81gUEzLcDEtw8X6ZfmAXnveX9XKvspW9la2sa+ylZf318bfk51oiwfznFx9ntojoJNsSawrWse6onWEo2F21+/mHyf+wRsn3uCe7fdwz/Z7yHflx3uZL0xfSCgaoj3UTkeoQ5+H9Xl8W7h7ueu1xvpGGkoaWJG9gqyErLN+7s51fz/2d7735vfId+Xz4EcfJCshiyUZS7jrzbtY//x67lt1n3TME2IYJIzFiEq0m1lelMryou7e3m3+EPtjwby3Ug/qVw50B3SmWw/oWVkuZmW7mZnlJi/JodeIMxazOGMxty2+jUpfJW+ceIN/nPgHfyr5ExsObhhW2RwmB06zE6fZSVOgie1v688ymZw4mRXZK1iRs4LFGYtHfWCSsrYytlRsYUvFFqrbq1ldsJprplxDoadw8DefYZqm8fiBx7ln+z0szljMfavuiz+B7JKCS9jg3sAtr93C51/6PD9a/iPWFa0b5RILcW6QMBZnnNtm5oKiFC4oSolv8/pD7K/qHdCvfVBLNDZ6p9NiZEaWm1lZejjPzHIxIzOT9TPWs37GejrDnWyr3sYHTR9gM9lwmp3xsHWYHb3WnWYnNpOtV4ewzZs3k7cgj7eq3mJr1VaeOvQUGw5uwGKwsDhjMStyVrA8ezlTPFPOeKekSDTCnoY9bK7YzJaKLRxrPQbA1KSpFCYWsuHABh7d/yjz0+ZzzZRrWDt5LU6z84yWaaBy3rP9HjYc3MCagjX85KKf9Lk+PC1pGn/82B+5bcttfPfN73K4+TC3LrpVBnkRYhASxmJUuGxmzi9M4fzC7oDuDEYoqfVysLqNg9VtHKhu4287K3ninTIAlILJKU5mZneF9AzWFSwjK9E27MBUSjElaQpTkqbwudmfozPcyfu17/NWpR7O92y/B4B0RzorslewPGc5F2RdMOhzqIeqI9TB21Vvs7liM6+feJ3mQDMmZWJJ5hKum34dxXnF5CTkANDQ2cALR1/gL4f/wo/e/hE/e+9nrClYwzVTr2FR+qKz0oM5EAlw1xt3sbFsI5+d+VluX3r7gL3dk2xJPLzmYX627Wc8sv8RSltK+dnKn+GyuM54OYU4V0kYizHDbjEyP8/D/DxPfJumaZxo7uRAdRsHqvSQ3nOihRf2VMf3cViMFKY5KUpLoDA1gaJ0J4WpCUxOdQ756VV2k50Lcy7kwpwLAaj2VbO1aitvVb3FpvJN/LX0rxiUgameqaQ6UkmyJpFkSyLJmoTH5iHZmozH5olvd1vcfWqDte21/OPEP9hSsYV3q98lGA3isri4KOciVuWtYkXOin4DK9Weyudmf44bZt3AnoY9/PXwX3np+Es8c+QZCtwFXD3laq4suvKMdZpqDbRyy2u3sKNuB99e8m0+N/tzg77HbDDz/fO/z7Skafz03Z/ymRc+w68+8qtR7XxX3lZObUcti9IXSU1djDkSxmJMU0qRl+wgL9nBpbMz49vb/CE+qPZyqKaNI/XtHG1oZ/vxZp7dXUXXg6qUguxEO0XpCRSmOilKT6AoNtcGeZpVVkIW1067lmunXUs4GmZfwz62Vm1lb8NeWvwtHG89TrO/mY5wR//lRpFoTYwHdme4k4NNBwHITcjluhnXUZxbzMKMhZgN5n6P0d+5mJ82n/lp87lj6R1sKt/EXw7/hft23Mevdv6KC3Mu5Jop13Bx7sV9nvJ1uqp8Vdy06SYqvBX8fOXPWTt57bDe/6npn2Jy4mRu23Ibn3nxM9yz8h6W5ywfkbINRtM0DrccZlPZJjaVb+Jw82EAchJy+OS0T/LxqR+Xp4mJMUPCWJyT3DYzyyYns2xycq/tncEIxxraOdrg40hdbF7vY/vxJjqCkfh+dhPM+mArMzJd+pTlZlqGi0R73xAzGUzxx12eLBAJ0OJvoTnQTLO/mZZAC03+JloCLTT7u7clWBK4ddGtrMpbRWFi4YduWnaYHVxZdCVXFl1JWVsZz5Q+wzOlz/DNE98k2ZbMZZMv44KsC1icsfi079E+1HSImzbdhD/s5zerf8PSzKWndZylmUv548f+yC2bb+GmV2/iW4u/xfWzrj8jzeuaprGvYR+byjexqWwT5d5yFIqF6Qu5fcntpNpT+fPhP3Pvjnt5YNcDXDrpUtbPWM/c1LkyYIkYVRLGYlyxW4zMynYzK9vda7umadS0+Tla386Reh9bdh7CpxTP7a7i9++G4/vleOxMz3QxvSukM90UpjkxG/u/Pmo1WslwZpDhzDij3+tUCtwF3LLoFr664KtsrdrK30r/xp8O/YnfH/w9BmVgdspslmYu5bzM81iQvgCH2THoMd+ueptvbvkmCeYEHrvsMaYmTf1QZcx15bLhsg18983v8vPtP6ekuYS7L7gbi/HUA8MMRSQaYUfdDl4tf5VNZZuo7ajFpEwszVzK52Z/jo/kf6TXWO6XF15OaXMp/3fo/3j2yLM8d/Q5ZqXMYv309Vw2+bIhPwRFiJEkYSwmBKUUWYl2shLtrJiSSn7gOMXFF6BpGtWtfg7VeDlY08ahGi8fVHt5vaSecKxrt9moKEpLYGaWm6kZCUxKcZIfazrvryY9WkwGU/z+60AkwO663Wyr2ca2mm08vv9xfrfvd5gMJualzmNp5lKWZS5jfvr8Pj2inzvyHHe/dTeTPZN54JIHyHRmDvCJw+MwO/iv4v/iod0P8eDuBznWdox7i+8lzZE27GOFIiG21WxjY9lGNldspsnfhMVgYXnOcm5eeDPFecWn7Gw3JWkK3zv/e3xj8Td47shz/N+h/+PurXdzz/Z7uGbKNVw3/bpez+gW4kyTMBYTmlKKbI+dbI+dVTO6O0AFw1GO1Pt6hfTbRxr5687KXu/3OMzkJzt6Tyn6PCvRjtEwOk2fVqOVZVnLWJa1DNB7b++s28m2mm28V/Me/7P3f/jNnt9gMVhYkL6AZZn6vq+0vsJzZc+xLHMZ9666d8R7QBuUga8u+CpTk6byvTe/x5o/r8FitGBQBpRSGJQBozKi0Jd7TgqF0aC/1tjZiDfkxWFysDJ3JZcUXMLKnJVDqvX35DQ7WT9jPddNv47ttdt58oMn+f3B3/PYgcdYkbOCT0//dLxTnxBnkoSxEP2wmAyx+5vdXE1OfHubP0RFUwcVTR2UNXZQ3qRPeytbeWlfTbw2DXqNOjdJr0HnJdljoW8jK9FOdqKdjEQrVtPZ6dXrMDtYkaMPbALgDXrZUbsjXnO+f9f9/HrXrwG4bNJl/PuF/z4iTcgDWV2wmgJ3Ac8ffZ5INEJUi8YnDa3Xete2iKbvp2kaTrOT4rxiLsi+YETGwlZKsTRzKUszl1LXUcfTJU/z55I/8/XXvk5OQg4LjAtw1bqYkzpnzIy93RZs43DzYQ41HaKkuYSS5hK8QS+zU2czP20+89LmMS1p2pA7CA5HKBrieOtxDnUeIqspK95RcaQ6Dk5EarBepWfKkiVLtO3bt4/Y8bZs2UJxcfGIHW+8kPPSvzNxXsKRKNWtfj2om7qDuryxg4rmDlo6Qn3ek5pgjQV0LKQ9vefpLiumAa5Xj6QWfwvba7eza98ubrv8NnliFnrgbC7fzJOHnuS9mvcA/ZatWSmzWJS+iAXpC1iYvvCM98iORCNUeCs41BwL3SY9eKvaq+L7JFoTmZ40HafZyb6GfdR31gNgM9qYlTIrHs7z0+YP67KApmnUtNdwuOUwJc0lHG4+zOGWwxxrPUY4Gu6zf4I5AY/VQ7Kt961+8dsArR6SbEkkWhNxWVz6gDzG4Y8T0FM4Gqahs4Ga9hqq26upaa/ptVzbUYsv6CPBkoDT7CTBHJtbEuLLXWVJMCf02u62uJmePH3YZRro74tS6n1N05b09x6pGQsxQkxGQ/w2rP5u3ukIhqlu9VPd4qeqtZPqFj/VrZ1Utfo5Ut/Om4cbaO/R4xvAZFDkJzuYnOpkUqqTybFpUqqTLLcNwwg1g3tsHj5a8FFMx0wSxDFmg5k1k9awZtIann/1eZxTneys38nO2p1sOLiBR/Y/AsAk9yQWZSxiQdoCFmUsIt+VP6xwiWpRvEFvvOd9s7+Z6vbqeG23tKWUznAnAEZlZJJ7EvPT5/PJpE8yPWk605Kmke5Ij39mV4DubtjN7rrd7GnYw4aDGwjt138MZjmz4sE8L20eM5NnYjFa4jXt+NRymNLmUrwhb7ysmc5MpnqmclHORUxNmkrloUoKZxXG7xzoeVdBfUc9Jc0lNPubCUQCA35/kzLhtPQIyVggxpd7rAPxsO0K3IbOBiJa7/9vEswJZDozyXRmMjt1Ni6Li45QB76QD1/Qhy/ko6a9Bm/QS3uoHV/QR1jr++PCZXax9TNbh/xv+WFIGAtxljgsJorSEihK6/9WI03TaPOHqY4FdVVrJyeaOzne0M6xhnbeOtKAPxSN7281GZiU0h3OhT0COzXBIrfqjKAEYwLF+cWsyl8F6Le07W/Yz866neys28mr5a/yl8N/ASDZlszC9IUsTF/I5MTJtAZa4yEbv+Ut0By/Ja410NonTAA8Vg/Tk6Zz7dRrmZ6sh26Rp2jQZnKlFFkJWWQlZLF2kn5feDAS5GDTQfbU72FP/R521+/m5eMvA/qPjiRrEnWd3U9cc5ldTE2ayuWFlzPVM5WpSVOZkjQFt6X3XQpbyrdQXFB8yvJomkZnuLPXd24JtNAebMcX8tEeau8OxZAelPUd9RwPHY+HZzAajB/PYrCQ4cwgy5nFeVnnxUM305FJljOLTGfmsG/n0zRNf457rDxdnxuK9m3NOlMkjIUYI5RSJNrNJNrNzMh093k9GtWo9fo5Vt/OscZ2jtW3c7yxnZI6L69+UEso0n3JyWExkpmoN39nuu1kJlrJTLST5baRmahPyQ7LiNWsJxqr0cqijEUsylgE6LXb463H2VG3o1dA92RURn0gmFjTbaGnEI/VE2+67Zon2ZJIs6eRZk8bsR9UFqMlPmBMl7qOOvbW72V3w24aOxspTCxkatJUpiVNI8ORMWKfrZTCYXbgMDviQ7wOVzASpD3UTlSLkmxLHvEfmkopbCYbNpOt121wZ5OEsRDnCIOh+/as5VN6/8EIR6JUtfg52uDjeEM7ZU0d1Lb5qW718/aRBmq9ASLR3v1DzEZFhlsP7K65rz6Eb08VGW4bmW4baS4rNrMMHTkYgzJQ6Cmk0FPIJ6Z9AtDHFK/0VcYD12VxjalLAOmOdC4puIRLCi4Z7aIMymK0nNEOhWOBhLEQ44DJaNBvqUpxQD/9TSJRjQZfgJpWPaC7grqmtZOaNj/7KlvZeKCWQDjKHz/Y2eu9SQ4zGW5bbLKS6baRHgvrDLeNjEQrKU7rqN3GNVal2lNHrZYlzj0SxkJMAEaDigfq/AHGstA0jRc2bmHqvCXUtOmBXdvqjy0HqG3zc7C6jQZfgJMq2ZgMisxEG9mxnuBd9273XHbb5LYXIQYiYSyEAPTrZgkWFR8OdCDhSJQGX7A7sGO17OoWvWf49rJmavZU97rnGsBlNZHVM6hjt3Olu62ku/Qm8SSHWTqeiQlJwlgIMSwmoyHeCWwgXc3ilS2dVLXovcPjy61+9p5opbE92Od9ZqMiLcFKmttGWoI1FtRW0lx6YKe79G2pCdYBxwsX4lwkYSyEGHE9m8UX5fc/KIY/FKGm1U+dN0C9N0CdV1+ua9OXTzR3sLO8ud/QVgpSnJZ4R7Pua9hWMhJtZLj0HwtS0xbnCgljIcSosJmNTIrdG30qoUiUBp8e0npo69ev67z6tezqVj+7Klr6DW2L0UB6rNNZ14+Drtp2hjtW03bZcNtNEtpiVEkYCyHGNLPREL+l61SC4Wg8oGvb/NS0+qn16p3QatsCHKxuY/Ohul7Pte5iNRni1667glpvGteXK7xR6r0Bkp0W6TUuzggJYyHEuGAxGchNcpCbdOonN/kCYeraYk3i3kD3cmxeUuvlzdIGvP7ewyP+4K1NGBQkOy2kJlhjU2zZ1Xs9zWUl2WmR69piyCSMhRATSoLVREJaAoUDDEvapTMYiV/H3vzODjILptDgDVDvC9LgC9DgC1BW3k6DN0hnqG9tG/RHbCY7LaQ4LST3mqz9bLPIACsT2JgK41AoxIkTJ/D7/cN+b2JiIgcPHjwDpTq3fZjzYrPZyM3NxWyW+0PFxGO3GClIcVKQ4qT9uIniCyYNuG97IBwP6Hpvd1g3+oI0tQdpbA9wvKGD98taaO4I9hkNrYvTYiQ5QQ/rjFjP8QyXfq07Lbac7rbKUKbj0JgK4xMnTuByuZg0adKwO1N4vV5crpF9EPp4cLrnRdM0GhsbOXHiBJMnTz4DJRNi/HBaTTitJgpSTt0ZDfQxxtv8IRrbY0EdC+zmjq7lAA2+IMcb29l2vKnfR2+aDEq/pu3uusbdfa92Vy07yaHPE+1muc59DhhTYez3+08riMXIU0qRkpJCfX39aBdFiHHFYFB4HBY8DgtFQ3i0sD8Uid/6VdumX9uu7XELWHljB+8NENoABgUeh4Wk+WHGLQAAFWlJREFUWJN5V0gnxZrPkxwWkpxmPA4LyQ593WUzSc37LBtTYQxIEI8h8m8hxOizmY3x52Sfij8UobE9SHN7d027qWs9ttzUHqSssYOdFS00twf7jJLWpSvAPQ6zHtYOczzQPbHATnKYKWuKkFPrJdmp/7iQGvjpG3NhPNoSEhLw+XyjXQwhhBgWm9lIjsdOjufUt4B10TQNbyBMc3uQxvYg/7+9+w+rurwbOP7+CEdQWAjaUNTKtgxTRFIr3fw9s5qpNZE5bYhlj9WkdMucWTlHzUk/t8vLJKdJ08dI88mnH/PJS4i4shId5s+oOU3MHwiInikCh/v54xyOgAc4Kvo9cD6v6/Li+/vc5+N98eG+v9/vfZ88U07JfyooOVPOyTO1fx4+Wcbu709Rcqa81pzaAAu/zAacA7G0a1P9wFpQrdZ3REhr2oee7z5vH+rsPm9jC9A/+l00GSullB8SEa4JtnFNsM2re93Vyioc7lb3J1ty6fLjHhTbzzlb3mfO3wf/V6GdkoPO9Xoa4LQOaMU1bWy0a+ucx7tdGxth7uXWhLUJpF1bZ+IOc7XS24e25gdBLW+QFk3G9TDGMHv2bD766CNEhHnz5pGQkMCRI0dISEjg1KlTVFZWsmTJEgYOHMiDDz5Ibm4uIsLUqVOZOXOm1V9BKaWaXLAtwD0IS2H7AIbGRjV4fFWVofTs+QfWqrvQS89WcPJMBaVnzy8fPVXGvqOnKT1bgf1cZb3XtAUI7UOCaB/amvahQXRwt76d2zqEnm+ddwgNok1r339lzGeT8R/+dzd7vj/l9fEOh4OAgIYDfkvUNTx3b0+vrvfuu++Sl5fHjh07OHHiBP3792fw4MGsXr2aUaNG8fTTT+NwODhz5gx5eXkcPnyYXbt2AXDy5Emvy62UUi1Zq1ZCuKvL+mJUOKo4dbbCmahdP6vvh5+wl1NkP0eRq4t9f6GdE/ZzF3ShVwu2tXK1tJ2t7pqt8ep/7do6t7vXXcddrYFbfDYZWy0nJ4eJEycSEBBAZGQkQ4YMYevWrfTv35+pU6dSUVHBuHHj6NOnDzfeeCP79+9nxowZ/PznP+fOO++0uvhKKdWs2QJauVq6QV6fc6a8kiK7M0EXud7zPvGfc5T8p2ZLvIKCkjPs+d6Z5D0Nj1otpHUAuxfc1RRfp1E+m4y9bcFWu1rvGQ8ePJjs7Gw++OADpkyZwqxZs/j1r3/Njh072LhxI6+//joZGRksX778ipdFKaXUeW1bB9I2IrDRJ89rKq+s4lSZM0mXnq2g9Mz55fqeNr8SfDYZW23QoEEsXbqUxMREiouLyc7OJjU1lYMHD9KlSxemTZvGuXPn2L59O/fccw+tW7fmF7/4BTfffDOTJ0+2uvhKKaW80DqwlXuscStpMq7Hfffdx5YtW4iNjUVEWLRoER07dmTlypWkpqZis9kIDQ0lPT2dw4cPk5SURFWV837Fn/70J4tLr5RSqjnxKhmLyF3Aa0AAsMwYs7DO/lnAQ0AlUAhMNcYcbOKyXhXV7xiLCKmpqaSmptban5iYSGJi4gXnbd++/aqUTymlVMvT6GNiIhIALAbuBm4BJorILXUO+yfQzxjTG1gLLGrqgiqllFItlTfPbN8GfGuM2W+MKQfWAGNrHmCMyTTGnHGtfg50adpiKqWUUi2XN93UnYFDNdYLgNsbOP5B4CNPO0TkYeBhgMjISLKysmrtDwsL4/Tp014U6UIOh+OSz23JLjcuZWVlF/w/tQR2u71Ffq/LpXHxTOPimcbFs0uJS5M+wCUik4F+wBBP+40xaUAaQL9+/czQoUNr7d+7d+8lv56kUyh6drlxCQ4OJi4urglL5BuysrKoW/+UxqU+GhfPNC6eXUpcvEnGh4GuNda7uLbVIiI/A54Ghhhjzl1UKZRSSik/5s09463ATSLSTURaA78ENtQ8QETigKXAGGPM8aYvplJKKdVyNZqMjTGVwG+AjcBeIMMYs1tEFojIGNdhqUAo8I6I5InIhnoup5RSSqk6vLpnbIz5EPiwzrZnayz/rInL1eJVVlYSGKhjriillPKum9rvjBs3jr59+9KzZ0/S0tIA+Mc//sGtt95KbGwsI0aMAJxPzCUlJRETE0Pv3r1Zt24dAKGhoe5rrV27lilTpgAwZcoUpk+fzu23387s2bP58ssvGTBgAHFxcQwcOJCvv/4acD4B/bvf/Y5evXrRu3dv/vrXv7J582bGjRvnvu7HH3/MfffddzXCoZRS6grz3abZR3Pg6E6vD2/jqISARr5Oxxi4e2HDxwDLly8nIiKCs2fP0r9/f8aOHcu0adPIzs6mW7duFBcXA/DHP/6RsLAwdu50lrOkpKTRaxcUFPDZZ58REBDAqVOn+PTTTwkMDGTTpk3MnTuXdevWkZaWxoEDB8jLyyMwMJDi4mLCw8N59NFHKSws5Nprr2XFihVMnTq18cAopZTyeb6bjC30l7/8hfXr1wNw6NAh0tLSGDx4MN26dQMgIiICgE2bNrFmzRr3eeHh4Y1eOz4+3j3vcmlpKYmJiXzzzTeICBUVFe7rTp8+3d2NXf15DzzwAH//+99JSkpiy5YtpKenN9E3VkopZSXfTcZetGBrOttE7xlnZWWxadMmtmzZQtu2bRk6dCh9+vRh3759Xl9DRNzLZWVltfaFhIS4l5955hmGDRvG+vXrOXDgQKPvpSUlJXHvvfcSHBxMfHy83nNWSqkWQu8Z11FaWkp4eDht27Zl3759fP7555SVlZGdnc2///1vAHc39ciRI1m8eLH73Opu6sjISPbu3UtVVZW7hV3fZ3Xu3BmAN99807195MiRLF26lMrKylqfFxUVRVRUFCkpKSQlJTXdl1ZKKWUpTcZ13HXXXVRWVtKjRw/mzJnDHXfcwbXXXktaWhr3338/sbGxJCQkADBv3jxKSkro1asXsbGxZGZmArBw4UJGjx7NwIED6dSpU72fNXv2bH7/+98TFxfnTrwADz30ENdddx29e/cmNjaW1atXu/dNmjSJrl270qNHjysUAaWUUleb9nPWERQUxEcfeRxam7vvvrvWemhoKCtXrrzguPHjxzN+/PgLttds/QIMGDCA/Px893pKSgoAgYGBvPzyy7z88ssXXCMnJ4dp06Y1+j2UUko1H5qMm5G+ffsSEhLCSy+9ZHVRlFJKNSFNxs3Itm3brC6CUkqpK0DvGSullFIW02SslFJKWUyTsVJKKWUxTcZKKaWUxTQZK6WUUhbTZHwZas7OVNeBAwfo1avXVSyNUkqp5kqTsVJKKWUxn33P+M9f/pl9xd5PzuBwONyzIdUnOiKap257qt79c+bMoWvXrjz22GMAzJ8/n8DAQDIzMykpKaGiooKUlBTGjh3rdbnAOVnEI488Qm5urnt0rWHDhrF7926SkpIoLy+nqqqKdevWERUVxYQJEygoKMDhcPDMM8+4h99USinVMvlsMrZCQkICTzzxhDsZZ2RksHHjRpKTk7nmmms4ceIEd9xxB2PGjKk1M1NjFi9ejIiwc+dO9u3bx5133kl+fj6vv/46jz/+OJMmTaK8vByHw8GHH35IVFQUH3zwAeCcTEIppVTL5rPJuKEWrCenm2AKxbi4OI4fP873339PYWEh4eHhdOzYkZkzZ5KdnU2rVq04fPgwx44do2PHjl5fNycnhxkzZgAQHR3N9ddfT35+PgMGDOD555+noKCA+++/n5tuuomYmBh++9vf8tRTTzF69GgGDRp0Wd9JKaWU79N7xnXEx8ezdu1a3n77bRISEli1ahWFhYVs27aNvLw8IiMjL5ij+FL96le/YsOGDbRp04Z77rmHzZs30717d7Zv305MTAzz5s1jwYIFTfJZSimlfJfPtoytkpCQwLRp0zhx4gSffPIJGRkZ/PCHP8Rms5GZmcnBgwcv+pqDBg1i1apVDB8+nPz8fL777jtuvvlm9u/fz4033khycjLfffcdX331FdHR0URERDB58mTatWvHsmXLrsC3VEop5Us0GdfRs2dPTp8+TefOnenUqROTJk3i3nvvJSYmhn79+hEdHX3R13z00Ud55JFHiImJITAwkDfffJOgoCAyMjJ46623sNlsdOzYkblz57J161aefPJJWrVqhc1mY8mSJVfgWyqllPIlmow92Llzp3u5Q4cObNmyxeNxdru93mvccMMN7Nq1C4Dg4GBWrFhxwTFz5sxhzpw5tbaNGjWKUaNGXUqxlVJKNVN6z1gppZSymLaML9POnTt54IEHam0LCgriiy++sKhESimlmhtNxpcpJiaGvLw8q4uhlFKqGdNuaqWUUspimoyVUkopi2kyVkoppSymyVgppZSymCbjy9DQfMZKKaWUtzQZtwCVlZVWF0EppdRl8NlXm46+8ALn9no/n3Glw0FxI/MZB/WIpuPcufXub8r5jO12O2PHjvV4Xnp6Oi+++CIiQu/evXnrrbc4duwY06dPZ//+/QAsWbKEqKgoRo8e7R7J68UXX8RutzN//nyGDh1Knz59yMnJYeLEiXTv3p2UlBTKy8tp3749q1atIjIyErvdTnJyMrm5uYgIzz33HKWlpXz11Ve8+uqrALzxxhvs2bOHV155pfFAK6WUanI+m4yt0JTzGQcHB7N+/foLztuzZw8pKSl89tlndOjQgeLiYgCSk5MZMmQI69evx+FwYLfbKSkpafAzysvLyc3NBaCkpITPP/8cEWHZsmUsWrSIl156iUWLFhEWFuYe4rOkpASbzcbzzz9PamoqNpuNFStWsHTp0ssNn1JKqUvks8m4oRasJ742n7Exhrlz515w3ubNm4mPj6dDhw4AREREALB582bS09MBCAgIICwsrNFknJCQ4F4uKCggISGBI0eOUF5eTrdu3QDIysoiIyPDfVx4eDgAw4cP5/3336dHjx5UVFQQExNzkdFSSinVVHw2GVulej7jo0ePXjCfsc1m44YbbvBqPuNLPa+mwMBAqqqq3Ot1zw8JCXEvz5gxg1mzZjFmzBiysrKYP39+g9d+6KGHeOGFF4iOjiYpKemiyqWUUqpp6QNcdSQkJLBmzRrWrl1LfHw8paWllzSfcX3nDR8+nHfeeYeioiIAdzf1iBEj3NMlOhwOSktLiYyM5Pjx4xQVFXHu3Dnef//9Bj+vc+fOAKxcudK9fdiwYSxevNi9Xt3avv322zl06BCrV69m4sSJ3oZHKaXUFaDJuA5P8xnn5uYSExNDenq61/MZ13dez549efrppxkyZAixsbHMmjULgNdee43MzExiYmLo27cve/bswWaz8eyzz3LbbbcxcuTIBj97/vz5xMfH07dvX3cXOMCTTz5JSUkJvXr1IjY2lszMTPe+CRMm8JOf/MTdda2UUsoa2k3tQVPMZ9zQeYmJiSQmJtbaFhkZyXvvvXfBscnJySQnJ1+wPSsrq9b62LFjPT7lHRoaWqulXFNOTg4zZ86s7ysopZS6SrRl7IdOnjxJ9+7dadOmDSNGjLC6OEop5fe0ZXyZmuN8xu3atSM/P9/qYiillHLRZHyZdD5jpZRSl8vnuqmNMVYXQbno/4VSSl0dPpWMg4ODKSoq0iTgA4wxFBUVERwcbHVRlFKqxfOpbuouXbpQUFBAYWHhRZ9bVlamicODy4lLcHAwXbp0aeISKaWUqsurZCwidwGvAQHAMmPMwjr7g4B0oC9QBCQYYw5cbGFsNpt7GMeLlZWVRVxc3CWd25JpXJRSyvc12k0tIgHAYuBu4BZgoojcUuewB4ESY8yPgVeAPzd1QZVSSqmWypt7xrcB3xpj9htjyoE1QN3RJcYC1SNLrAVGSGPTGimllFIK8C4ZdwYO1VgvcG3zeIwxphIoBdo3RQGVUkqplu6qPsAlIg8DD7tW7SLydRNevgNwogmv11JoXDzTuHimcfFM4+KZxsWz+uJyfX0neJOMDwNda6x3cW3zdEyBiAQCYTgf5KrFGJMGpHnxmRdNRHKNMf2uxLWbM42LZxoXzzQunmlcPNO4eHYpcfGmm3orcJOIdBOR1sAvgQ11jtkAVM98MB7YbPRlYaWUUsorjbaMjTGVIvIbYCPOV5uWG2N2i8gCINcYswH4G/CWiHwLFONM2EoppZTyglf3jI0xHwIf1tn2bI3lMiC+aYt20a5I93cLoHHxTOPimcbFM42LZxoXzy46LqK9yUoppZS1fGpsaqWUUsoftYhkLCJ3icjXIvKtiMyxujy+QkQOiMhOEckTkVyry2MVEVkuIsdFZFeNbREi8rGIfOP6GW5lGa1QT1zmi8hhV53JE5F7rCyjFUSkq4hkisgeEdktIo+7tvt1nWkgLn5dZ0QkWES+FJEdrrj8wbW9m4h84cpLb7segK7/Os29m9o1XGc+MBLngCRbgYnGmD2WFswHiMgBoJ8xxq/fAxSRwYAdSDfG9HJtWwQUG2MWuv6ACzfGPGVlOa+2euIyH7AbY160smxWEpFOQCdjzHYR+QGwDRgHTMGP60wDcZmAH9cZ12iTIcYYu4jYgBzgcWAW8K4xZo2IvA7sMMYsqe86LaFl7M1wncqPGWOycT7lX1PNIVxX4vyl4lfqiYvfM8YcMcZsdy2fBvbiHGXQr+tMA3Hxa8bJ7lq1uf4ZYDjO4aHBi/rSEpKxN8N1+isD/J+IbHONfqbOizTGHHEtHwUirSyMj/mNiHzl6sb2q67YukTkBiAO+AKtM2514gJ+XmdEJEBE8oDjwMfAv4CTruGhwYu81BKSsarfT40xt+KccesxV7ekqsM1QE3zvl/TdJYAPwL6AEeAl6wtjnVEJBRYBzxhjDlVc58/1xkPcfH7OmOMcRhj+uAcofI2IPpir9ESkrE3w3X6JWPMYdfP48B6nJVEOR1z3QOrvhd23OLy+ARjzDHXL5Yq4A38tM647v2tA1YZY951bfb7OuMpLlpnzjPGnAQygQFAO9fw0OBFXmoJydib4Tr9joiEuB6yQERCgDuBXQ2f5VdqDuGaCLxnYVl8RnWycbkPP6wzrgdy/gbsNca8XGOXX9eZ+uLi73VGRK4VkXau5TY4HybeizMpj3cd1mh9afZPUwO4HqV/lfPDdT5vcZEsJyI34mwNg3OktdX+GhcR+W9gKM6ZVI4BzwH/A2QA1wEHgQnGGL96mKmeuAzF2d1ogAPAf9W4T+oXROSnwKfATqDKtXkuzvujfltnGojLRPy4zohIb5wPaAXgbOBmGGMWuH4HrwEigH8Ck40x5+q9TktIxkoppVRz1hK6qZVSSqlmTZOxUkopZTFNxkoppZTFNBkrpZRSFtNkrJRSSllMk7FSSillMU3GSimllMU0GSullFIW+390ViyB4cH6WAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eSW1rzKCj3Ai"
      },
      "source": [
        "# 4. Evaluate Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tHcc9G_gj3Aj",
        "outputId": "bd4f7708-6e71-40fc-81fb-f14927826e43"
      },
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3366 - accuracy: 0.8820\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3366333842277527, 0.8820000290870667]"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eex77rpPj3Al"
      },
      "source": [
        "# 5. Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f0xXuX-fj3Am"
      },
      "source": [
        "X_new = X_test[:3]"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yuHv1MlWj3An",
        "outputId": "af26e7db-6d21-402f-893a-b56189e92511"
      },
      "source": [
        "y_proba = model.predict(X_new)\n",
        "y_proba.round(2)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.  , 0.  , 0.  , 0.  , 0.  , 0.01, 0.  , 0.03, 0.  , 0.96],\n",
              "       [0.  , 0.  , 0.99, 0.  , 0.01, 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 1.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_new)\n",
        "y_pred = np.round(y_pred).astype(int)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QrVfZr083kNb",
        "outputId": "2c6c4ab2-ae84-4d1f-f01d-0698a5a39a32"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
              "       [0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 1, 0, 0, 0, 0, 0, 0, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d3Go_jlcj3Aq",
        "outputId": "f17c3e88-26a6-46cf-9230-759def0516d8"
      },
      "source": [
        "np.array(class_names)[y_pred]"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([['T-shirt/top', 'T-shirt/top', 'T-shirt/top', 'T-shirt/top',\n",
              "        'T-shirt/top', 'T-shirt/top', 'T-shirt/top', 'T-shirt/top',\n",
              "        'T-shirt/top', 'Trouser'],\n",
              "       ['T-shirt/top', 'T-shirt/top', 'Trouser', 'T-shirt/top',\n",
              "        'T-shirt/top', 'T-shirt/top', 'T-shirt/top', 'T-shirt/top',\n",
              "        'T-shirt/top', 'T-shirt/top'],\n",
              "       ['T-shirt/top', 'Trouser', 'T-shirt/top', 'T-shirt/top',\n",
              "        'T-shirt/top', 'T-shirt/top', 'T-shirt/top', 'T-shirt/top',\n",
              "        'T-shirt/top', 'T-shirt/top']], dtype='<U11')"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "VSGg-0Yij3Ar",
        "outputId": "8de811b5-6dcf-4e37-ae37-1722535b89ee"
      },
      "source": [
        "print(plt.imshow(X_test[0]))"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AxesImage(54,36;334.8x217.44)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "-vMXbmSnj3At",
        "outputId": "336fcde2-6ab7-4163-ee60-cad57be19b7d"
      },
      "source": [
        "print(plt.imshow(X_test[1]))"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AxesImage(54,36;334.8x217.44)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "jxXPA9Tjj3Au",
        "outputId": "3bd5b8f9-814b-40a3-eff6-ab3192d3a686"
      },
      "source": [
        "print(plt.imshow(X_test[2]))"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AxesImage(54,36;334.8x217.44)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Batch Normalisation and Dropout"
      ],
      "metadata": {
        "id": "DaK7bMpw24au"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iBknWkOYj3Ax"
      },
      "source": [
        "model = keras.models.Sequential()\n",
        "model.add(keras.layers.Flatten(input_shape=[28, 28]))\n",
        "model.add(keras.layers.Dense(300, activation=\"relu\"))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.5))\n",
        "model.add(keras.layers.Dense(100, activation=\"relu\"))\n",
        "model.add(keras.layers.Dense(10, activation=\"softmax\"))"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
        "              optimizer=\"sgd\",\n",
        "              metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "Ja7ZQOWn4tOh"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_history = model.fit(X_train, y_train, epochs=30,\n",
        "                    validation_data=(X_valid, y_valid))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ysqrWvs5Z4n",
        "outputId": "415e5463-5501-4704-905f-705a7b84c864"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "1719/1719 [==============================] - 9s 5ms/step - loss: 0.6597 - accuracy: 0.7657 - val_loss: 0.4139 - val_accuracy: 0.8544\n",
            "Epoch 2/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.4961 - accuracy: 0.8207 - val_loss: 0.3773 - val_accuracy: 0.8656\n",
            "Epoch 3/30\n",
            "1719/1719 [==============================] - 11s 6ms/step - loss: 0.4550 - accuracy: 0.8355 - val_loss: 0.3897 - val_accuracy: 0.8538\n",
            "Epoch 4/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.4282 - accuracy: 0.8447 - val_loss: 0.3512 - val_accuracy: 0.8754\n",
            "Epoch 5/30\n",
            "1719/1719 [==============================] - 9s 5ms/step - loss: 0.4098 - accuracy: 0.8506 - val_loss: 0.3489 - val_accuracy: 0.8724\n",
            "Epoch 6/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.3965 - accuracy: 0.8545 - val_loss: 0.3330 - val_accuracy: 0.8804\n",
            "Epoch 7/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.3839 - accuracy: 0.8609 - val_loss: 0.3290 - val_accuracy: 0.8798\n",
            "Epoch 8/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.3728 - accuracy: 0.8644 - val_loss: 0.3232 - val_accuracy: 0.8832\n",
            "Epoch 9/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.3695 - accuracy: 0.8652 - val_loss: 0.3166 - val_accuracy: 0.8838\n",
            "Epoch 10/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.3595 - accuracy: 0.8709 - val_loss: 0.3185 - val_accuracy: 0.8836\n",
            "Epoch 11/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.3565 - accuracy: 0.8690 - val_loss: 0.3140 - val_accuracy: 0.8852\n",
            "Epoch 12/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.3473 - accuracy: 0.8728 - val_loss: 0.3225 - val_accuracy: 0.8792\n",
            "Epoch 13/30\n",
            "1719/1719 [==============================] - 9s 5ms/step - loss: 0.3452 - accuracy: 0.8727 - val_loss: 0.3060 - val_accuracy: 0.8906\n",
            "Epoch 14/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.3382 - accuracy: 0.8748 - val_loss: 0.3071 - val_accuracy: 0.8916\n",
            "Epoch 15/30\n",
            "1719/1719 [==============================] - 9s 5ms/step - loss: 0.3351 - accuracy: 0.8765 - val_loss: 0.3047 - val_accuracy: 0.8872\n",
            "Epoch 16/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.3302 - accuracy: 0.8782 - val_loss: 0.2935 - val_accuracy: 0.8920\n",
            "Epoch 17/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.3225 - accuracy: 0.8797 - val_loss: 0.3069 - val_accuracy: 0.8892\n",
            "Epoch 18/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.3220 - accuracy: 0.8811 - val_loss: 0.3077 - val_accuracy: 0.8922\n",
            "Epoch 19/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.3194 - accuracy: 0.8829 - val_loss: 0.3026 - val_accuracy: 0.8890\n",
            "Epoch 20/30\n",
            "1719/1719 [==============================] - 9s 5ms/step - loss: 0.3118 - accuracy: 0.8854 - val_loss: 0.2997 - val_accuracy: 0.8894\n",
            "Epoch 21/30\n",
            "1719/1719 [==============================] - 9s 5ms/step - loss: 0.3111 - accuracy: 0.8849 - val_loss: 0.2982 - val_accuracy: 0.8940\n",
            "Epoch 22/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.3085 - accuracy: 0.8852 - val_loss: 0.2944 - val_accuracy: 0.8936\n",
            "Epoch 23/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.3107 - accuracy: 0.8853 - val_loss: 0.2910 - val_accuracy: 0.8918\n",
            "Epoch 24/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.3033 - accuracy: 0.8891 - val_loss: 0.2932 - val_accuracy: 0.8878\n",
            "Epoch 25/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.3016 - accuracy: 0.8879 - val_loss: 0.2973 - val_accuracy: 0.8924\n",
            "Epoch 26/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.2954 - accuracy: 0.8908 - val_loss: 0.2886 - val_accuracy: 0.8966\n",
            "Epoch 27/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.2978 - accuracy: 0.8894 - val_loss: 0.2860 - val_accuracy: 0.8962\n",
            "Epoch 28/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.2933 - accuracy: 0.8913 - val_loss: 0.2877 - val_accuracy: 0.8936\n",
            "Epoch 29/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.2889 - accuracy: 0.8923 - val_loss: 0.2906 - val_accuracy: 0.8926\n",
            "Epoch 30/30\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.2894 - accuracy: 0.8928 - val_loss: 0.2919 - val_accuracy: 0.8918\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_history.params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kG-_Ltsb5eMQ",
        "outputId": "2e4d7968-e5a8-4d1b-b69f-aaa793a899e1"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'epochs': 30, 'steps': 1719, 'verbose': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_history.history"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U34VHcCM5jfJ",
        "outputId": "4f7bdb37-c59c-4b1b-c554-55461cc90c68"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': [0.7656909227371216,\n",
              "  0.8207091093063354,\n",
              "  0.8355454802513123,\n",
              "  0.8447090983390808,\n",
              "  0.8505818247795105,\n",
              "  0.8545272946357727,\n",
              "  0.860945463180542,\n",
              "  0.8643817901611328,\n",
              "  0.8651636242866516,\n",
              "  0.8708727359771729,\n",
              "  0.8690363764762878,\n",
              "  0.8727818131446838,\n",
              "  0.8726545572280884,\n",
              "  0.8748000264167786,\n",
              "  0.8765272498130798,\n",
              "  0.8781636357307434,\n",
              "  0.8797272443771362,\n",
              "  0.8810727000236511,\n",
              "  0.8828545212745667,\n",
              "  0.8853636384010315,\n",
              "  0.8849272727966309,\n",
              "  0.8852182030677795,\n",
              "  0.8852909207344055,\n",
              "  0.8891454339027405,\n",
              "  0.8878909349441528,\n",
              "  0.8907636404037476,\n",
              "  0.8893636465072632,\n",
              "  0.8912727236747742,\n",
              "  0.8923272490501404,\n",
              "  0.8927817940711975],\n",
              " 'loss': [0.6596782803535461,\n",
              "  0.4961402714252472,\n",
              "  0.45504090189933777,\n",
              "  0.42819082736968994,\n",
              "  0.4098037779331207,\n",
              "  0.3965199291706085,\n",
              "  0.3839183449745178,\n",
              "  0.3728318512439728,\n",
              "  0.369479238986969,\n",
              "  0.35951516032218933,\n",
              "  0.35647425055503845,\n",
              "  0.3473069369792938,\n",
              "  0.34524253010749817,\n",
              "  0.33815065026283264,\n",
              "  0.3350513279438019,\n",
              "  0.3301607370376587,\n",
              "  0.3225299119949341,\n",
              "  0.321958065032959,\n",
              "  0.31936150789260864,\n",
              "  0.3117794692516327,\n",
              "  0.3110809624195099,\n",
              "  0.3085123896598816,\n",
              "  0.310691237449646,\n",
              "  0.303276926279068,\n",
              "  0.30161598324775696,\n",
              "  0.29541724920272827,\n",
              "  0.29783862829208374,\n",
              "  0.29326510429382324,\n",
              "  0.2889074683189392,\n",
              "  0.28940263390541077],\n",
              " 'val_accuracy': [0.8543999791145325,\n",
              "  0.8655999898910522,\n",
              "  0.8537999987602234,\n",
              "  0.8754000067710876,\n",
              "  0.8723999857902527,\n",
              "  0.8804000020027161,\n",
              "  0.879800021648407,\n",
              "  0.8831999897956848,\n",
              "  0.8838000297546387,\n",
              "  0.8835999965667725,\n",
              "  0.885200023651123,\n",
              "  0.8791999816894531,\n",
              "  0.8906000256538391,\n",
              "  0.8916000127792358,\n",
              "  0.8871999979019165,\n",
              "  0.8920000195503235,\n",
              "  0.88919997215271,\n",
              "  0.8921999931335449,\n",
              "  0.8889999985694885,\n",
              "  0.8894000053405762,\n",
              "  0.8939999938011169,\n",
              "  0.8935999870300293,\n",
              "  0.8917999863624573,\n",
              "  0.8877999782562256,\n",
              "  0.8924000263214111,\n",
              "  0.8966000080108643,\n",
              "  0.8962000012397766,\n",
              "  0.8935999870300293,\n",
              "  0.8925999999046326,\n",
              "  0.8917999863624573],\n",
              " 'val_loss': [0.4138527810573578,\n",
              "  0.37734073400497437,\n",
              "  0.38968780636787415,\n",
              "  0.3512457311153412,\n",
              "  0.3489014506340027,\n",
              "  0.3329565227031708,\n",
              "  0.32898885011672974,\n",
              "  0.3231641352176666,\n",
              "  0.3166171908378601,\n",
              "  0.3184919059276581,\n",
              "  0.3140470087528229,\n",
              "  0.3224833011627197,\n",
              "  0.30604463815689087,\n",
              "  0.3071195185184479,\n",
              "  0.30468714237213135,\n",
              "  0.29353848099708557,\n",
              "  0.30685529112815857,\n",
              "  0.30772826075553894,\n",
              "  0.30255845189094543,\n",
              "  0.2996861934661865,\n",
              "  0.2982167601585388,\n",
              "  0.2943911552429199,\n",
              "  0.2910066545009613,\n",
              "  0.29318711161613464,\n",
              "  0.2973320782184601,\n",
              "  0.2885744273662567,\n",
              "  0.2859973609447479,\n",
              "  0.2876856029033661,\n",
              "  0.29059866070747375,\n",
              "  0.29187771677970886]}"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "pd.DataFrame(model_history.history).plot(figsize=(8, 5))\n",
        "plt.grid(True)\n",
        "plt.gca().set_ylim(0, 1)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "id": "gzLUtWn55ji4",
        "outputId": "302963a2-a391-4b6d-e55b-73a6e5ea18de"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uNlmKErQ5jm1",
        "outputId": "155e4657-7323-4e36-a8a7-16ef2b4bd1a4"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3236 - accuracy: 0.8843\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3235678970813751, 0.8842999935150146]"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_new = X_test[:3]"
      ],
      "metadata": {
        "id": "RHFJC-PV5yH7"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_proba = model.predict(X_new)\n",
        "y_proba.round(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HES3hcYe5yLt",
        "outputId": "ea9c0eab-80c5-4228-ca97-3bb9af1f425c"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.99],\n",
              "       [0.  , 0.  , 0.99, 0.  , 0.01, 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 1.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_new)\n",
        "y_pred = np.round(y_pred).astype(int)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fqGeL10D5yPL",
        "outputId": "d872cc9e-a2b3-4004-c7eb-22ee2bb992ce"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
              "       [0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 1, 0, 0, 0, 0, 0, 0, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.array(class_names)[y_pred]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dc3rC4RM6G6h",
        "outputId": "99499b4c-d6ef-4065-fcac-6605058744b2"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([['T-shirt/top', 'T-shirt/top', 'T-shirt/top', 'T-shirt/top',\n",
              "        'T-shirt/top', 'T-shirt/top', 'T-shirt/top', 'T-shirt/top',\n",
              "        'T-shirt/top', 'Trouser'],\n",
              "       ['T-shirt/top', 'T-shirt/top', 'Trouser', 'T-shirt/top',\n",
              "        'T-shirt/top', 'T-shirt/top', 'T-shirt/top', 'T-shirt/top',\n",
              "        'T-shirt/top', 'T-shirt/top'],\n",
              "       ['T-shirt/top', 'Trouser', 'T-shirt/top', 'T-shirt/top',\n",
              "        'T-shirt/top', 'T-shirt/top', 'T-shirt/top', 'T-shirt/top',\n",
              "        'T-shirt/top', 'T-shirt/top']], dtype='<U11')"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "-Gf5Oek46lhu"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}