Fashion_MNIST_Demo.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dbaweja/dbaweja.github.io/blob/main/Fashion_MNIST_Demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sHzrUDkqONmj"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KzWEd0mIvN7o",
        "outputId": "d9ce5713-2514-40e4-abe3-9aa4db4be09e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "# give googe drive permission\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "bJ7ftx6ZvN7o",
        "outputId": "e460c0fe-5c65-4e96-a2cc-0f5d1dc5fb6f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/drive/.shortcut-targets-by-id/1clyOjhjAFaRHSJdsY0PeySdU957jPxQu/NN_Colab'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "os.chdir(\"/content/drive/MyDrive/NN_Colab\")\n",
        "os.getcwd()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2M8xFc6jVfu4",
        "outputId": "ee771f54-2c4a-492c-8721-1a5c2e501aaa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 135 kB 28.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.6 MB 62.4 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "pip install -q -U keras-tuner"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lyh6VPGqbRim"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lqrKER7XbGJx"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "np.random.seed(42)\n",
        "import pandas as pd\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras import layers\n",
        "from keras.optimizers import SGD\n",
        "from keras.callbacks import LearningRateScheduler\n",
        "from keras.wrappers.scikit_learn import KerasClassifier\n",
        "\n",
        "import keras_tuner as kt\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lnZUp98MlUJy"
      },
      "source": [
        "MNIST Fasion data set: Multiclass classification problem.\n",
        "\n",
        "Training data has 60000, samples of images for each class can be one of the following.\n",
        "\n",
        "\n",
        "![Sample-images-from-Fashion-MNIST-dataset.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LwTaHJwem611"
      },
      "source": [
        "**Label definition**\n",
        "\n",
        "0. T-shirt/top\n",
        "1. Trouser\n",
        "2. Pullover\n",
        "3. Dress\n",
        "4. Coat\n",
        "5. Sandal\n",
        "6. Shirt\n",
        "7. Sneaker\n",
        "8. Bag\n",
        "9. Ankle boot\n",
        "\n",
        "**Image data**\n",
        "- Each image is 28 by 28 pixels in size."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-WNdh-rkbUT6"
      },
      "source": [
        "## Read Data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZvQDxMupQ_qX",
        "outputId": "f47783de-da4a-4bd3-8f44-ce4ba7bbbc30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'Fasion MNIST'\t'House pricing'  'Mnist - Gradient Decent '\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tN8xPZzVibNu"
      },
      "outputs": [],
      "source": [
        "# modify this cell based on your dirive and folder location\n",
        "\n",
        "train = pd.read_csv(\"Fasion MNIST/fashion-mnist/train.csv\")\n",
        "test = pd.read_csv(\"Fasion MNIST/fashion-mnist/test.csv\")\n",
        "\n",
        "x_test = test.loc[:, test.columns != \"label\"]\n",
        "y_test = test['label']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4vZPimbekLEU"
      },
      "source": [
        "Visulize a sample from the training data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "BaLcpnEWkG_z",
        "outputId": "124da9d0-19bc-4b4a-8339-73155da73b7c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f7fc2c91990>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "Image_index = 11\n",
        "\n",
        "img = train.loc[Image_index, train.columns != \"label\"].values.reshape((28,28))\n",
        "plt.imshow(img,cmap='gray')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3abF7CfImgS8"
      },
      "source": [
        "Split the data in train and validation set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xdhyV_plmkOo"
      },
      "outputs": [],
      "source": [
        "y_data = train['label'].values\n",
        "x_data = train.drop('label',axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AuUGNKKMmkV3"
      },
      "outputs": [],
      "source": [
        "X_train, X_val, y_train, y_val = train_test_split(x_data, y_data, test_size = 0.2, random_state=169)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2e00vEX-7glr"
      },
      "source": [
        "## Plotting Functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vN2ZdivL7fvR"
      },
      "outputs": [],
      "source": [
        "def plot_accuracy_vs_epochs(i, history):\n",
        "    fig = plt.figure(figsize=(6, 6))\n",
        "    plt.plot(range(1,epochs+1),history.history['val_accuracy'],label='validation')\n",
        "    plt.plot(range(1,epochs+1),history.history['accuracy'],label='training')\n",
        "    plt.legend(loc=0)\n",
        "    plt.xlabel('epochs')\n",
        "    plt.ylabel('accuracy')\n",
        "    plt.xlim([1,epochs])\n",
        "#     plt.ylim([0,1])\n",
        "    plt.grid(True)\n",
        "    plt.title(\"Model Accuracy\")\n",
        "    plt.show()\n",
        "    fig.savefig('Fasion MNIST/img_mnist_fashion/'+str(i)+'-accuracy.jpg')\n",
        "    plt.close(fig)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GIetl2aqOsL7"
      },
      "outputs": [],
      "source": [
        "def plot_learning_rate(i, loss_history):\n",
        "# plot learning rate\n",
        "  fig = plt.figure()\n",
        "  plt.plot(range(1,epochs+1),loss_history.lrate,label='learning rate')\n",
        "  plt.xlabel(\"epoch\")\n",
        "  plt.xlim([1,epochs+1])\n",
        "  plt.ylabel(\"learning rate\")\n",
        "  plt.legend(loc=0)\n",
        "  plt.grid(True)\n",
        "  plt.title(\"Learning rate\")\n",
        "  plt.show()\n",
        "  fig.savefig('Fasion MNIST/img_mnist_fashion/'+str(i)+'-learning_rate.jpg')\n",
        "  plt.close(fig)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-cHuRPqdnhxE"
      },
      "outputs": [],
      "source": [
        "def model_prediction(model, loc_num):\n",
        "  model.predict(x_test).argmax(axis=1)\n",
        "\n",
        "  img = x_test.iloc[loc_num].values.reshape((28,28))\n",
        "  plt.imshow(img,cmap='gray')\n",
        "  print(\"The predicted label is:\" ,model.predict(x_test).argmax(axis=1)[loc_num])\n",
        "  print(\"The actual label is: \", y_test[loc_num])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UUtNGok4mI2Q"
      },
      "source": [
        "## Model Building Fuction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "768pCHK3k6C0"
      },
      "outputs": [],
      "source": [
        "def nn_model_fancy(perceptrons_in_each_layer,\n",
        "                   hidden_activation,\n",
        "                   initializer,\n",
        "                   dropout_rate,\n",
        "                   optimiser_type,\n",
        "                   loss_fuction):\n",
        "    model = keras.Sequential([\n",
        "            # input layer\n",
        "            keras.layers.Dense(perceptrons_in_each_layer, # hyperparameter\n",
        "                               activation= hidden_activation , # hyperparameter\n",
        "                               input_shape=(784,),\n",
        "                               kernel_initializer=initializer # hyperparameter\n",
        "                               ),\n",
        "            # hidden layer 1\n",
        "            keras.layers.Dense(perceptrons_in_each_layer,\n",
        "                               activation= hidden_activation,\n",
        "                               kernel_initializer=initializer\n",
        "                               ),\n",
        "            # hidden layer 2\n",
        "            keras.layers.Dense(perceptrons_in_each_layer,\n",
        "                               activation= hidden_activation,\n",
        "                               kernel_initializer=initializer\n",
        "                               ),\n",
        "\n",
        "            keras.layers.Dropout(dropout_rate), # hyperparameter\n",
        "            # Output layer\n",
        "            keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "        ]\n",
        "    )\n",
        "    model.summary()\n",
        "\n",
        "    model.compile(optimizer = optimiser_type, # hyperparameter\n",
        "                  loss = loss_fuction,  # hyperparameter\n",
        "                  metrics = ['accuracy'])\n",
        "\n",
        "    return model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B7GpQ1yl3dMp"
      },
      "source": [
        "#### Basic Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q-poLKWxxB0z",
        "outputId": "cc824fee-bcdc-4b65-e194-dcf3b73d72c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 128)               100480    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 128)               0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 134,794\n",
            "Trainable params: 134,794\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "basic_model = nn_model_fancy (\n",
        "perceptrons_in_each_layer = 128,\n",
        "hidden_activation = \"relu\",\n",
        "initializer = tf.keras.initializers.RandomNormal(stddev=0.01),\n",
        "dropout_rate = 0 ,\n",
        "optimiser_type = tf.keras.optimizers.SGD(learning_rate=0.001),\n",
        "loss_fuction = \"sparse_categorical_crossentropy\",\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uw62VeHlnNYa",
        "outputId": "f496947d-0993-4198-d195-46b061f72645"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "375/375 [==============================] - 2s 4ms/step - loss: 0.9840 - accuracy: 0.6779 - val_loss: 0.6478 - val_accuracy: 0.7628\n",
            "Epoch 2/15\n",
            "375/375 [==============================] - 1s 4ms/step - loss: 0.5974 - accuracy: 0.7897 - val_loss: 0.5518 - val_accuracy: 0.8029\n",
            "Epoch 3/15\n",
            "375/375 [==============================] - 1s 4ms/step - loss: 0.5240 - accuracy: 0.8194 - val_loss: 0.4946 - val_accuracy: 0.8255\n",
            "Epoch 4/15\n",
            "375/375 [==============================] - 1s 4ms/step - loss: 0.4874 - accuracy: 0.8307 - val_loss: 0.4601 - val_accuracy: 0.8397\n",
            "Epoch 5/15\n",
            "375/375 [==============================] - 1s 4ms/step - loss: 0.4614 - accuracy: 0.8395 - val_loss: 0.4618 - val_accuracy: 0.8363\n",
            "Epoch 6/15\n",
            "375/375 [==============================] - 1s 4ms/step - loss: 0.4442 - accuracy: 0.8451 - val_loss: 0.4359 - val_accuracy: 0.8434\n",
            "Epoch 7/15\n",
            "375/375 [==============================] - 1s 4ms/step - loss: 0.4251 - accuracy: 0.8510 - val_loss: 0.4256 - val_accuracy: 0.8485\n",
            "Epoch 8/15\n",
            "375/375 [==============================] - 1s 4ms/step - loss: 0.4106 - accuracy: 0.8553 - val_loss: 0.4088 - val_accuracy: 0.8546\n",
            "Epoch 9/15\n",
            "375/375 [==============================] - 1s 3ms/step - loss: 0.3996 - accuracy: 0.8600 - val_loss: 0.4049 - val_accuracy: 0.8574\n",
            "Epoch 10/15\n",
            "375/375 [==============================] - 1s 3ms/step - loss: 0.3887 - accuracy: 0.8627 - val_loss: 0.3938 - val_accuracy: 0.8587\n",
            "Epoch 11/15\n",
            "375/375 [==============================] - 1s 4ms/step - loss: 0.3778 - accuracy: 0.8683 - val_loss: 0.4120 - val_accuracy: 0.8543\n",
            "Epoch 12/15\n",
            "375/375 [==============================] - 1s 4ms/step - loss: 0.3704 - accuracy: 0.8680 - val_loss: 0.3816 - val_accuracy: 0.8626\n",
            "Epoch 13/15\n",
            "375/375 [==============================] - 1s 4ms/step - loss: 0.3625 - accuracy: 0.8709 - val_loss: 0.3873 - val_accuracy: 0.8627\n",
            "Epoch 14/15\n",
            "375/375 [==============================] - 1s 3ms/step - loss: 0.3549 - accuracy: 0.8726 - val_loss: 0.3781 - val_accuracy: 0.8630\n",
            "Epoch 15/15\n",
            "375/375 [==============================] - 1s 4ms/step - loss: 0.3463 - accuracy: 0.8767 - val_loss: 0.3618 - val_accuracy: 0.8708\n"
          ]
        }
      ],
      "source": [
        "batch_size = 128\n",
        "epochs = 15\n",
        "\n",
        "basic_model_history = basic_model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_val, y_val))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "G5fTKTyqOB7U",
        "outputId": "90fc9470-1010-409b-bedc-530b8d021208"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAGDCAYAAAA8mveiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXhV5bX48e/KRCAkISSQAAkkzGGSIQIKKJOIU50QQUvFq9Jra9XW3qq9Vq1Xb+39WWu1DnWq1RYp4kRblIoQBQWZBGQmCZCEMSQkZCBkOOv3xz6BQ8hwEnIyrs/z5MnZe7/7Peuk9ize/U6iqhhjjDHe8mvqAIwxxrQsljiMMcbUiSUOY4wxdWKJwxhjTJ1Y4jDGGFMnljiMMcbUiSUOY6ohIvEioiIS4EXZuSKyqjHiMqapWeIwrYKI7BOREhGJqnT+W/eXf3zTRHZWLB1FpEBEPmnqWIw5H5Y4TGuyF5hdcSAiQ4EOTRfOOW4ETgGXiUhMY76xN60mY7xlicO0Ju8AP/A4vg1427OAiISLyNsikiUi+0XkERHxc1/zF5FnROSYiKQBV1Vx7xsickhEDojIkyLiX4f4bgNeAbYA369U93gR+VpEckUkQ0Tmus+3F5HfuWPNE5FV7nMTRSSzUh37RGSq+/XjIrJIRP4qIieAuSIyWkRWu9/jkIj8UUSCPO4fLCKfiUiOiBwRkV+KSIyIFIlIpEe5ke6/X2AdPrtpRSxxmNZkDRAmIonuL/RZwF8rlXkBCAd6A5fiJJrb3dfuAq4GRgBJwIxK974FlAF93WWmAXd6E5iI9AImAn9z//yg0rVP3LF1AYYDm9yXnwFGARcDnYFfAC5v3hO4FlgEdHK/ZznwUyAKuAiYAvzIHUMosAz4FOju/oyfq+phIBmY6VHvHGCBqpZ6GYdpZSxxmNamotVxGbADOFBxwSOZPKyq+aq6D/gdzhchOF+Oz6lqhqrmAL/xuDcauBK4X1ULVfUo8Ht3fd6YA2xR1e3AAmCwiIxwX7sFWKaq76pqqapmq+omd0voP4D7VPWAqpar6teqesrL91ytqh+pqktVT6rqBlVdo6pl7s/+J5zkCU7CPKyqv1PVYvff5xv3tb/gbiG5/4azcf7Opo2y556mtXkH+BJIoNJjKpx/aQcC+z3O7Qd6uF93BzIqXavQy33vIRGpOOdXqXxNfgC8BqCqB0TkC5xHV98CcUBqFfdEAcHVXPPGWbGJSH/gWZzWVAec//9vcF+uLgaAj4FXRCQBGADkqeraesZkWgFrcZhWRVX343SSXwl8UOnyMaAUJwlU6MmZVskhnC9Qz2sVMnA6tqNUtZP7J0xVB9cWk4hcDPQDHhaRwyJyGBgD3OLutM4A+lRx6zGguJprhXh0/LtbAl0qlam89PXLwE6gn6qGAb8EKrJgBs7ju3OoajGwEKfVMQdrbbR5ljhMa3QHMFlVCz1Pqmo5zhfgUyIS6u5b+Bln+kEWAveKSKyIRAAPedx7CPg38DsRCRMRPxHpIyKXUrvbgM+AQTj9F8OBIUB74Aqc/oepIjJTRAJEJFJEhquqC3gTeFZEurs77y8SkXbAbiBYRK5yd1I/ArSrJY5Q4ARQICIDgbs9rv0T6CYi94tIO/ffZ4zH9beBucD3sMTR5lniMK2Oqqaq6vpqLv8E51/racAqYD7OlzM4j5KWApuBjZzbYvkBEARsB47jdDx3qykWEQnG6Tt5QVUPe/zsxfkCvk1V03FaSA8AOTgd4xe4q/g58B2wzn3tt4CfqubhdGy/jtNiKgTOGmVVhZ/j9Kfkuz/r3ysuqGo+Tr/QNcBhYA8wyeP6Vzid8hvdrTrTholt5GSM8YaILAfmq+rrTR2LaVqWOIwxtRKRC3Eet8W5WyemDbNHVcaYGonIX3DmeNxvScOAtTiMMcbUkbU4jDHG1IklDmOMMXXSJmaOR0VFaXx8vM/qLywsJCQkxGf1+1pLjr8lxw4Wf1Oz+Gu2YcOGY6paeWJp20gc8fHxrF9f3bD+85ecnMzEiRN9Vr+vteT4W3LsYPE3NYu/ZiJS5Zwde1RljDGmTixxGGOMqRNLHMYYY+qkTfRxVKW0tJTMzEyKi4vPu67w8HB27NjRAFE1jYaIPzg4mNjYWAIDbVM4Y1q7Nps4MjMzCQ0NJT4+Ho/9FeolPz+f0NDQBoqs8Z1v/KpKdnY2mZmZJCQkNGBkxpjmqM0+qiouLiYyMvK8k4YBESEyMrJBWm/GmOavzSYOwJJGA7K/pTFtR5tOHC1Jx44dATh48CAzZsyosszEiRNrna/y3HPPUVRUdPr4yiuvJDc3t+ECNca0epY4Wpju3buzaNGiet9fOXEsWbKETp06NURoxpg2whJHE3nooYd48cUXTx8//vjjPPnkk0yZMoWRI0cydOhQPv7443Pu27dvH0OGDAHg5MmTzJo1i8TERK6//npOnjx5utzdd99NUlISgwcP5rHHHgPg+eef5+DBg0yaNIlJk5zN3eLj48nOzgbg2WefZciQIQwZMoTnnnvu9PslJiZy1113MXjwYKZNm3bW+xhj2p42O6rK06//sY3tB0/U+/7y8nL8/f3POjeoexiPXTO42ntuvvlm7r//fn784x8DsHDhQpYuXcq9995LWFgYx44dY+zYsXzve9+rtv/g5ZdfpkOHDuzYsYMtW7YwcuTI09eeeuopOnfuTHl5OVOmTGHLli3ce++9PPvss6xYsYKoqKiz6tqwYQN//vOf+eabb1BVxowZw6WXXkpERAR79uzh3Xff5bXXXmPmzJm8//77fP/736/vn8sY08JZi6OJjBgxgqNHj3Lw4EE2b95MREQEMTEx/PKXv2TYsGFMnTqVAwcOcOTIkWrr+PLLL09/gQ8bNoxhw4advrZw4UJGjhzJiBEj2LZtG9u3b68xnlWrVnH99dcTEhJCx44dueGGG1i5ciUACQkJDB8+HIBRo0axb9++8/z0xphm7VQBpCyr9rJPWxwiMh34A+APvK6qT1e63hP4C9DJXeYhVV0iIrcC/+VRdBgwUlU3iUgy0A2oeF4yTVWPnk+cNbUMvFHfeRA33XQTixYt4vDhw9x888387W9/Iysriw0bNhAYGEh8fHy9hrju3buXZ555hnXr1hEREcHcuXPPa6hsu3btTr/29/e3R1XGtDYlhZC+Bvatgn0r4eC34CqrtrjPWhwi4g+8CFwBDAJmi8igSsUeARaq6ghgFvASgKr+TVWHq+pwYA6wV1U3edx3a8X1800aTenmm29mwYIFLFq0iJtuuom8vDy6du1KYGAgK1asYP/+KhemPO2SSy5h/vz5AGzdupUtW7YAcOLECUJCQggPD+fIkSN88sknp+8JDQ0lP//c3T8nTJjARx99RFFREYWFhXz44YdMmDChAT+tMabZKCmCtGT4/H/gjWnwdE/46w3w9fOAwLj7YM6H1d7uyxbHaCBFVdMARGQBcC3g+cxEgTD363DgYBX1zAYW+DDOJjN48GDy8/Pp0aMH3bp149Zbb+Waa65h6NChJCUlMXDgwBrvv/vuu7n99ttJTEwkMTGRUaNGAXDBBRcwYsQIBg4cSFxcHOPGjTt9z7x585g+fTrdu3dnxYoVp8+PHDmSuXPnMnr0aADuvPNORowYYY+ljGkNSk9C5jrYu9JpVRxYD+UlIP7QfQRc/BOIHw9xY6Fdx1qr89me4yIyA5iuqne6j+cAY1T1Ho8y3YB/AxFACDBVVTdUqicVuFZVt7qPk4FIoBx4H3hSq/gQIjIPmAcQHR09asGCs3NPeHg4ffv2bZDPWlXneEvSUPGnpKSQl5fXABF5r6Cg4PQcl5bI4m9arTV+v/ISQvN3E3H8OzrlbiXsxC78tBTFj/zQPuR2GkpupyHkhSdSHtCh2vonTZq0QVWTKp9v6lFVs4G3VPV3InIR8I6IDFFVF4CIjAGKKpKG262qekBEQnESxxzg7coVq+qrwKsASUlJWnmzkx07djTY+lJtfa2qCsHBwYwYMaIBIvKebcTTtCz+pnU6/rJTcGCD05rY+6XTuigrBvGDmGEw6IeQcAnScyxhweGEAT3P4319mTgOAHEex7Huc57uAKYDqOpqEQkGooCKfotZwLueN6jqAffvfBGZj/NI7JzEYYwxrVLZKcg/BLnp9Ny/EP7yLGSshbKTgEDMUEi6AxImQM+LoH3DT/D1ZeJYB/QTkQSchDELuKVSmXRgCvCWiCQCwUAWgIj4ATOB0z20IhIAdFLVYyISCFwNVD9mzBhjWpLSYsg/CCfcP3mZZ16fOOD8FGadLt4bIHoojJp7JlF06OzzMH2WOFS1TETuAZbiDLV9U1W3icgTwHpVXQw8ALwmIj/F6Sif69FfcQmQUdG57tYOWOpOGv44SeM1X30GY4xpMKUnPRKA+3fegbPPFR07977gThDWA8K6Q7cLIDzWeR3WnVVpBYy/7HuN/lF82sehqkuAJZXOPerxejswrvJ97mvJwNhK5wqBUQ0eqDHGNITiE3B0Bxzd5vw+vv9MYjiZc2759hEQ5k4EPUY5CSLcnSTCekBotxpHOZVlJPvkYxzIPckryanVXm/qznFjjGl5ykshOwWObIOj2+HIdud1XvqZMkGh0DneaSHEjT6TDMJ7nEkKQdWPaGoK6dlFvJScwvsbM2ssZ4mjieTm5jJ//nx+9KMf1em+K6+8kvnz59e4ou2jjz7KJZdcwtSpU883TGPaNlWnI/rItrOTxLFdzjwIAL8AiOwHcRfCqNsgeghED4LwOGgh+9SkZhXw4ooUPt50EH8/Yfbonvzw0j7E/m/V5S1xNJHc3FxeeumlcxJHWVkZAQHV/8+yZMmSaq9VeOKJJ847PmPanFP5zuOlI1ud5HDU3Yoo9tivJrS7kxT6Toaug53XUf0hoF319TZjuw7n88cVKfxzy0HaBfgx9+J45l3Sm+iw4Brvs8TRRB566CFSU1MZPnw4gYGBBAcHExERwc6dO9m9ezfXXXcdGRkZFBcXc9999zFv3jzAWQZ9/fr1FBQUcMUVVzB+/Hi+/vprevTowccff0z79u2ZO3cuV199NTNmzCA+Pp7bbruNf/zjH5SWlvLee+8xcOBAsrKyuOWWWzh48CBJSUkkJyezYcOGc1bNNabRnSqAnf+Cre87o4r8/ME/EPwCnX/d+wdUeu0+9g90yp5+7b5W5WunbELaajj4itMnkVvpMVPXRBh8ndOC6DrIOW6EEUuNYeuBPP64PIVPtx0mJMifH17ShzsnJBDV0bsEaIkD4JOH4PB39b69fXmZ8x+wp5ihcMXTVd8APP3002zdupVNmzaRnJzMVVddxdatW0lISADgzTffpHPnzpw8eZILL7yQG2+8kcjIyLPq8Ha586ioKDZu3MhLL73EM888w+uvv86vf/1rJk+ezMMPP8wHH3zA22/bVBjThMpKIHU5fLcQdi5x5iSExzmT17Tc6VNwlYKr3FlnyVXqLMJXXubF61KcQZvn6okfdOkPPZJg5A/crYjB0Klni3nMVBebMnJ54fM9fL7zKKHBAdw7uS+3j0sgIiSoTvVY4mgmRo8efTppgLPp0ocfOouMZWRksGfPnnMSh7fLnd9www2ny3zwwQeAs4x6Rf2XXXYZERERDfp5jKmVywUZa+C792Dbh3DyuDPKaPhsGDoT4saAXwOtw+oqP5NEKhJQeSkr133HJVOmNcx7NGPr9uXw/Od7WLnnGJ06BPKzy/pz28XxhLcPrFd9ljigxpaBN042wJIdISEhp18nJyezbNkyVq9eTYcOHZg4cWKVy6J7u9x5RTl/f3/KyqpfKtmYRnFkG2xZ6H4UlQEB7WHgVTD0JugzGQLq9q9fr/j5Oz+V+iJc/rsa/r2aCVVldWo2zy/fw5q0HCJDgnhw+kDmXNSLju3O76vfEkcTqW55c4C8vDwiIiLo0KEDO3fuZM2aNQ3+/uPGjWPhwoU8+OCDfP755xw/frzB38OY03LT4btFTuvi6HZnVdY+k2HKozDgSq9WZDXeUVW+2J3FC8tT2LD/OF1D2/HIVYncMqYnHYIa5ivfEkcTiYyMZNy4cQwZMoT27dsTHR19+tr06dN55ZVXSExMZMCAAYwdO7aGmurnscceY/bs2bzzzjskJSURExPTohdqNM1QYTZs/xC2vOc8kgLn8dOVz8Dg6yHEBmI0JFVl2Y6j/HH5HjZn5tE9PJgnrh3MzKQ4ggMbdvVuSxxNqGITpsratWt31uZLnir6MaKioti69cyiwT//+c9Pv37rrbfOKQ+cHj0FzrLyS5cuJSAggGXLlrFp06azHn0ZUy8lhU7n9nfvQernTr9Cl4Ew+VcwdAZExDd1hK2Oy6V8uu0wLyxPYcehE8R1bs9vbhjKjSNjCQrwzV59ljjaqPT0dGbOnInL5cLf35/XXrMlv0w9lZdC6gr3iKh/QWmRMzN67I9g2ExnOGsrHKHU1MpdypqDZfzvH75k95ECekeF8MxNF3Dt8O4E+vtsc1fAEkeb1a9fP7799lug5e8nYhqZy+UsrXF0J/12vwVr/wOKsp3F+IbNdDq5e17ccCOifCQ9u4jt2eX478mi3KW4VHG5oFwVl0txqedrPVNG8SivlCtnynje6z5X5nLuLStXyl2uM8dn/Xa5r3ued519XH72+fziMrILS+jXNZA/zBrO1cO64+/XOAnaEocxpmoulzPqKWunM6O64vex3U6rAojxC4LEq51k0Xeqb0ZENbB9xwr5w+d7+GjTAVSBdWt99l5+AgF+fvj7CQF+gr+/+7efnH2+4py/4O/nd/pcUIAf7T2OPX8H+PsRXZ7FAzMvwa+REkaFNp04VBWxJnSD8NUWxKYRqDoJ4uhOyNoBWbvciWIXlBaeKRfaDboMgJG3QdeB0CWRr3fnMGHqFU0Xex1kHi/ihc9TWLQxk0B/Yd4lvel88iCjRo7Az0/wE8FfBD8/nNfuc37Cmdd+7jKCx2vnnjPlK+7F598vycnJjZ40oA0njuDgYLKzs4mMjLTkcZ5UlezsbIKDa17fxjQxVWd574oE4ZkoSgrOlOsY7XRoj5zj/O6a6CSM9udOEi1PS268+OvpyIliXlyRwrtr0xGEH1zUi7sn9qFraDDJyUdIim8dy4g0pjabOGJjY8nMzCQrK6v2wrUoLi5u0V+aDRF/cHAwsbGxDRSROW+ucjiwETK+8UgSu6DEY+5QSFen5TD8Fo8EMbDVrMd0rOAUrySn8s6a/ZS7lJkXxnHPpL5079S+qUNr8dps4ggMDDxriY/zkZyczIgRIxqkrqbQ0uM3biePO+s97f43pHzmdFgDdIhyksIFs04/YmpNC/ZVlltUwqtfpvHW1/soLi3nhpGx3Du5Hz0jm9feFy1Zm00cxrR4qk5fxJ6lTrLI+MZZELB9BPS9DPpfDgmXQMeuDfBWyoniMo4XlpBTVOL8Liwh/UgZI4pKCe9QvzWPGlJ+cSlvrtrH6yvTKCgp4+ph3bl/aj/6dLFZ6Q3NEocxLUlJEez9Evb82/nJy3DOxwyF8fdDv8shNslZl6kaqkpRSTk5hSUcLyrx+F16TmKoOJ9bVEKZq+oBEC9u+jfDYjsxoV8U4/tGMaJnhM8mnlWlqKSMv3y9nz99mUpuUSmXD47mp5f1Z2BMWKPF0NZY4jCmuTu+30kSu5fCvpVQVgyBIdB7Ilzyc+g3zdmWFCcpZOWfYufhHHYdzifjeFGViaGkzFXlW/kJRHQIIiIkiM4dgkiICmFUryAiOgTROcTjd0gQER0C+fSLNRR2jGVlyjFeXJHCC8tTCAnyZ2zvSMb3i2JCvyj6dOnokwEoxaXl/O2bdF5OTuFYQQkTB3ThZ5f1Z1hs9btjmoZhicOY5qa8FNLXOI+g9nzmzJ8A6NwbRt0O/adBr3EUufzZfaSAnTtPsPPwNnYdzmfXkXxyCktOVxUWHEBUx3ZEhATRo1N7hvYIO50UIk4ngsDTCSEsOLBOwzsHdPZn4sQB/GzaAPJOlrI6NZtVKVms3HOMz3ceBaBbeDDj+jpJZFzfKK83C6pOSZmLhesz+OPyFA6fKObiPpH8aU5/RvVqnX02zZElDmOag4Isp0N791Jn+Y5Tec4udb0uxjViDpldJrC1uCs7D+ez86sT7Prga9JziqiYPtM+0J/+MaFclhjNwG6hDIgJZWBMGJ3ruEHP+QhvH8j0ITFMHxIDQEZOESv3HGNVShafbT/Cog2ZAAzqFuY81uoXxYXxnb1egK+s3MUH3x7g+c/3kHn8JKN6RfDszAu4uK8tltjYLHEY0xTKy+DQJnrtWwCv/hoOfgso5SHRZMVO47sOY0kuHcyWrHJ2L8nnVFkmkImfQHxUCIO7h3HDiFgGxISS2C2UuIgOTTIRrCZxnTtwy5ie3DKmJ+UuZeuBPFbucVojb361lz99mUa7AD9GJ3RmfF8nkSTGhJ3zOcpdyj+3HOS5ZXvYe6yQoT3CefK6IVzav4vNwWoiljiMaQyqcGwPpCXD3i9g70o4lUc8QkbIINaEzuGjwiF8ld0Dsp0vw6iOhQyMCWXO2F6nWxD9ojs2+BLZjcHfT7ggrhMXxHXinsn9KDxVxtq9OadbJL/5ZCd8ApEhQYxzJ5HxfaPYkpnLs5/tZveRAgbGhPKnOaOYNijaEkYTs8RhjK/kH4a0L5xkkZYM+QcBKOzQg7UBF/N+QT++Lh/EyfII+kd3ZECvUH4VE0ZijPOoKfI8+wKas5B2AUwa2JVJA52hwofzilmVcoxVe7JYlZLN4s0HT5ft3SWEF2aP4Kqh3Zpdq6qtssRhTEMpPgH7vzqTKNyd2to+gryYi0kOv5U/ZfZkR04kPTq158ZLe3BxyQFuvnJSo61q2lzFhAczY1QsM0bFoqrsPJzPVynH6BLajquGdiPAx8uEm7qxxGFMfZWVQOY659FTWjJkrncm4AUEQ6+LKUycyb+LE3llZ3t27SiiXYAf04fE8N+j4ri4TyR+fkJy8qE2nzQqExESu4WR2M3mYTRXljiM8ZbL5eyXXdGi2P+1s3qs+EH3kTD+fsriLyW5MJ6/b8pixbKjlLmU4XFBPHV9b64e1p3w9k0/w9qY8+XTxCEi04E/AP7A66r6dKXrPYG/AJ3cZR5S1SUiEg/sAHa5i65R1f903zMKeAtoDywB7lNb09v4Sm76mX6KvV9AoXtRzMh+zuKAvSdC/Hh25fnz3voMPnr3AMcKthLVsR13jE9gxqhY+kXbJlmmdfFZ4hARf+BF4DIgE1gnIotVdbtHsUeAhar6sogMwkkE8e5rqao6vIqqXwbuAr5xl58OVL1BtzH1UZQD3y2CzfPdw2RxlhrvMxkSLoXel0J4LHlFpSzefID33tjKlsw8AvyEKYlduWlUHJcO6OLz7TuNaSq+bHGMBlJUNQ1ARBYA1wKeiUOBigeZ4cBBaiAi3YAwVV3jPn4buA5LHOZ8lZU4y3psfteZhOcqddZ/uux/oN9lznLjIpS7lK9SjvHekm9Zuu0wJWUuBsaE8qurB3Hd8O6teiSUMRXEV095RGQGMF1V73QfzwHGqOo9HmW6Af8GIoAQYKqqbnA/qtoG7AZOAI+o6koRSQKeVtWp7vsnAA+q6tVVvP88YB5AdHT0qAULFvjkcwIUFBTQsWPLXYGzJcd/XrGrEpqfQvSRFXQ9upKg0hOUBHbiSPSlHI6ZTGHH+NNFjxS6WHWgjK8OlpFTrIQEwthuAUzoEUCvML96zytoyX97sPibmq/jnzRp0gZVTap8vqk7x2cDb6nq70TkIuAdERkCHAJ6qmq2u0/jIxEZXJeKVfVV4FWApKQknThxYgOHfkZycjK+rN/XWnL89Yr9xCHY8nendZG1E/zbwcAr4YJbCOozmTj/AOKAE8WlLN16mPfWZ7J2Xw5+Apf078JNo+KYOqgr7QLOfyJeS/7bg8Xf1Joqfl8mjgNAnMdxrPucpztw+ihQ1dUiEgxEqepR4JT7/AYRSQX6u+/33GauqjqNOVdJEez8l5Ms0laAuiBuDFz9exh8/eltUbPyT/HZ9oMs3XaYr1OPUVqu9I4K4RfTB3DDiFhiwlvuTo/GNBRfJo51QD8RScD5cp8F3FKpTDowBXhLRBKBYCBLRLoAOapaLiK9gX5AmqrmiMgJERmL0zn+A+AFH34G05KpQvpq2DQftn3kbJsaHgcTHoALZkNkHwDSs4tYui6NpdsOsyH9OKrQK7IDt49LYPqQGEbEdbIlLozx4LPEoaplInIPsBRnqO2bqrpNRJ4A1qvqYuAB4DUR+SlOR/lcVVURuQR4QkRKARfwn6qa4676R5wZjvsJ1jFuKsvZC5sXOK2L3P3O3hWDr3O2Tu01HhVh5+F8li7bzdJtR9hx6ATgrNp6/5T+XD4kmgHRoZYsjKmGT/s4VHUJzpBZz3OPerzeDoyr4r73gferqXM9MKRhIzUtXnEebP8YNr0L6V8D4mybOumXkHgNroAObEw/ztJPdrJ02xHSc4oQgaReETxyVSKXD44hrrPtSW2MN5q6c9yY+isvIyJnIyx6B3b+09kZL7IfTHkUht1MSUh3Vqdls/RfaXy2/QhZ+acI9BfG9Y3i7ol9mJoYTZdQGz5rTF1Z4jAti6qzJtTWRbD1Ay4oPArBnWD4rTD8FgqjLuCLPcdY+slhlu/cRn5xGR2C/Jk0oCuXD4lh0oAuhAbbsh/GnA9LHKZlOLoTvnvPSRjH9zlDaPtfzla/RHpM+wnL9uSy9PMjrNyzjFNlLjqHBHHFkBguHxzDuL5RLXIPC2OaK0scpvnKTYet78N378OR75zFBHtPhEsfpLTfFXyScpKXl25m929XUu5SenRqzy1jenL54BiSekXYUtzG+IglDtO8FGbD9g+dtaLSVzvnYi+EK/4PBl/PiYAIFqxN563nN3Iwr5joDsLdl/bh8sExDOkRZiOhjGkEljhM0zuVDzuXOI+i0laAq8xZG2ryr2DIjdA5gYycIv68Yh9/X7eRwpJyxvbuzBPXDsHvyHYmTxrQ1J/AmDbFEodpGmUlkLLMSRa7PoGyk87kvIvugaE3QfRgEOHb9OO8/slGPtl6CD8Rrh7WjTsn9GZIj3AAko/uaOIPYkzbY4nDNAV3X8kAACAASURBVB5XubO16nfvwfbFUJwLHSJhxK1OsogdDX5+lLuUz7Yd5rWVe9mw/zihwQHcdUlv5l4cT7fw9k39KYxp8yxxtHGl5S58ug+WKhza5PRZbH0f8g9BUEcYeJWTLHpPBH9neGzhqTIWrt/Pm1/tJSPnJHGd2/PYNYOYmRRHSDv7T9WY5sL+39iGvbs2nccWb6ODv3JhxnqGx3VieFwnhsWGn/9cB1e5swLtyt9Bdgr4BUK/aTB0BvSfDkFnZmkfyjvJW1/vY/436eQXlzGqVwS/vCKRaYNjbD9uY5ohSxxtkKry+8928/zyFC7qHQnFuaQcLeCz7UcAEIG+XTpygTuRDI/rxICYUO93tEtZBp89Bke2QrcL4JrnYdD3Tq9AW2HrgTxeX5nGP7ccwqXKFUO6cceEBEb2jKimYmNMc2CJo40pLXfx8AffsWhDJjOTYnnq+qF8tfJLJk6cSG5RCZsz89ickcumjFyW7zzKog2ZALQL8GNoj/CzkklsRPuzh78e2uwkjLQV0KkX3PgGDL4B/M4kHJdLWb7zKK+vSmNNWg4hQf784KJ4bh8Xb2tFGdNCWOJoQwpOlXH3Xzewcs8x7p/aj/um9Dvri79ThyAu7d+FS/t3AZyWSebxk2xyJ5JNGbn8dc1+3li1F4DIkCAuiOvEhC4nuTLrDbru+xhp3wku/w1ceAcEnFkH6mRJOe9vzOTNVXtJO1ZI9/Bg/vvKRG4eHUeYLQFiTItiiaONOHqimLl/XseuI/n89sah3Hxhz1rvERHiOncgrnMHrrmgO+C0WHYdzmdTRi679mUwJO1Frtv7TwBeKb+aJX6z6Jsey3A9xAVxnegS2o4Fa9P565r9HC8qZVhsOM/PHsEVQ2K8f/RljGlWLHG0ASlH87ntzXUcLyrh9duSmDSga73rCvT3Y0h0MEP2/wP2/T8oyaNk6M1s6fdjXDkd6JaRy1cpx/jw2zMbM4rA1MRo7prQmwvjI2x2tzEtnCWOVm7t3hzuens9gf5+/H3eRQyNDa9/ZS6XM6R2+RPOOlJ9psBlvyYoZihJQMWO9qrK4RPFbErPZX9OEZcPjiEhKqQhPo4xphmwxNGK/WvLIX66cBOxEe35y+2jz6/zOe0L+OxXTgd4zDCY8zz0mVRlURGhW3h7ug21yXrGtEaWOFqp11em8dSSHYzsGcHrP0giIiSofhUd2eaMlEr5zFkS5IbXYMiMs0ZKGWPaFkscrYzLpTz5rx28+dVepg+O4blZw+u3F0XeAVjxv7DpbxAcBtOehAvvgsDghg/aGNOiWOJoRYpLy3lg4Wb+9d0h5l4cz6+uHlT3mdfFebDqOVjzEqgLLvoxTHgAOnT2TdDGmBbHEkcrkVtUwl1vr2fdvuM8clUid4xPqNvopbISWP8mfPFbOJkDQ2fC5EcgopfvgjbGtEiWOFqBzONFzP3zOtKzi3hh9ojTcy68okqXo6vgxfucLVkTLoXLnoDuw30WrzGmZbPE0cJtPZDH7W+t41RpOW/fMZqxvSO9v/nIdlj8EwYfWA9dB8P333eG2No8C2NMDSxxtGBf7s7i7r9uILx9IH+7+2L6R4d6d6PLBWv/5IyWCg5j54B7GXjz4+BXj050Y0ybY4mjhXpvfQYPf/Ad/aJDeev2C4kO83K0U/5h+OhuSF3uLG/+vT9yeP02BlrSMMZ4yRJHC6OqvLA8hWc/2834vlG8/P2R3u+dseMfsPheKD0JV/8eRt1uj6WMMXVmiaMFKSt38auPt/Lu2gxuGNGDp28cRlCAFxPxThXApw/Bt+9At+Fw4+sQ1c/3ARtjWiVLHC1E4aky7pm/kRW7srhnUl8emNbfu+G2mevh/TudEVPjfwYTH4aAes4iN8YYwKfrRojIdBHZJSIpIvJQFdd7isgKEflWRLaIyJXu85eJyAYR+c79e7LHPcnuOje5f+q/1GsLkZV/itmvreGL3Vk8df0Qfn75gNqTRnkZJP8W3pjmbON6+xKY+pglDWPMefNZi0NE/IEXgcuATGCdiCxW1e0exR4BFqrqyyIyCFgCxAPHgGtU9aCIDAGWAj087rtVVdf7KvbmJC2rgNv+vJas/FO8OieJqYOia78pJw0++CFkroVhN8OV/w+Cz2NVXGOM8eDLR1WjgRRVTQMQkQXAtYBn4lAgzP06HDgIoKrfepTZBrQXkXaqesqH8TY7mceLmPHKagRYMO8ihsd1qvkGVWdtqU8eBPF3tm4dOqNRYjXGtB2+TBw9gAyP40xgTKUyjwP/FpGfACHA1CrquRHYWClp/FlEyoH3gSdVVRss6mai3KX87O+bKSlz8fE94+jTpWPNNxTlwD/ugx2LIX4CXPcydIprnGCNMW2K+Oo7V0RmANNV9U738RxgjKre41HmZ+4YficiFwFvAENU1eW+PhhYDExT1VT3uR6qekBEQnESx19V9e0q3n8eMA8gOjp61IIFC3zyOQEKCgro2LGWL/Y6+kdqCe/vKeWuoUGM61HzcNuInE0M3PkHAktPsDfhVjLirnVaHF7yRfyNpSXHDhZ/U7P4azZp0qQNqpp0zgVV9ckPcBGw1OP4YeDhSmW2AXEex2lAV/frWGA3MK6G95gL/LG2WEaNGqW+tGLFigat79v049rn4X/pPfM3qsvlqr5gyUnVTx5WfSxM9YUk1QPf1uv9Gjr+xtSSY1e1+JuaxV8zYL1W8Z3qy1FV64B+IpIgIkHALJzWg6d0YAqAiCQCwUCWiHQC/gU8pKpfVRQWkQARiXK/DgSuBrb68DM0usJTZdy/4Fu6hrbjyeuGVD966sg2eG0yrHnR2Sdj3he2MKExplH4rI9DVctE5B6cEVH+wJuquk1EnsDJYouBB4DXROSnOB3lc1VV3ff1BR4VkUfdVU4DCoGl7qThDywDXvPVZ2gK//PP7ezPKeLdu8YS3r6KR1QuF3zzCix73Bkpdct70H9ao8dpjGm7fDoBUFWX4Ayx9Tz3qMfr7cC4Ku57EniymmpHNWSMzcmnWw+xYF0GP5rYp+pVbk8cctaZSlsB/a+A770AHbs0fqDGmDbNZo43E4fzinnog+8YFhvO/VP7n1tg+8fOqKmyU3D1czBqrq0zZYxpEpY4mgGXS3ngvU2cKnXx3M3Dz15/6lQ+fPIQbPordB8BN7wOUX2bLlhjTJtniaMZePOrvXyVks1vbhhKb8/5GidznQ7w43thws9h4kPg7+VKuMYY4yOWOJrYtoN5/N+nu5g2KJpZF1aasPfVc5CTCnM+gj6TmiZAY4ypxKeLHJqanSwp574Fm+jUIZCnbxx29tDbvAOw5mUYOtOShjGmWbEWRxP6zSc7SDlawDt3jKZzSKVVa5N/A+qCyf/dNMEZY0w1rMXRRFbsPMrbq/dz5/gEJvSrNKT26E5nscIL74SI+CaJzxhjqmOJowlk5Z/ivxZtZmBMKP81fcC5BT7/NQR1dDrEjTGmmbHE0chUlV8s2kx+cRnPzx5Bu4BKixHuXw27lsC4+yCkikmAxhjTxCxxNLJ31uxnxa4sfnllIv2jQ8++qAqfPQodY2Ds3U0ToDHG1MISRyPacySfp/61g0kDuvCDi3qdW2DnP51d+yY9DEEhjR+gMcZ4wRJHIzlVVs69CzbRsV0A/zfjgnNXvS0vg2W/hqj+MPz7TROkMcZ4wYbjNpJnlu5ix6ETvHFbEl1C251b4Nt3IHsP3Pw38Lf/WYwxzZe1OBrBqj3HeG3lXuaM7cWUxOhzC5QUQvLTEDcGBl7V+AEaY0wd2D9tfex4YQkPvLeJvl078ssrE6sutOZlKDgMN71lK94aY5o9a3H4kKry0AdbyCks4Q+zhtM+qIp9wAuz4as/wIAroddFjR+kMcbUkSUOH1q4PoOl247wi8sHMrh7eNWFVj4DJQUw5bHGDc4YY+rJEoePpGUV8Pji7YzrG8kd4xOqLnR8H6x9DYbfCl0HNmp8xhhTX5Y4fKC03MX9f99EUIAfv7tpOH5+1fRbLH8K/Pxh0i8bN0BjjDkPljh84Lllu9mSmcfTNwwlJjy46kKHNsN3C50Z4mHdGzdAY4w5D5Y4Gtg3adm8lJzKzUlxXDG0W/UFlz0O7SNg3P2NFpsxxjQESxwNKO9kKT9buJlenTvw6DWDqi+YugJSlzur37bv1HgBGmNMA7B5HA1EVXnko60cPlHM+3dfTEi7av60LhcsewzCe8Louxo3SGOMaQDW4mggH206wD82H+SnU/sxPK6GVsS2D5z+jcn/DQFVLD1ijDHNnLU4GkBWkYsnVmzjwvgI7p7Yt/qCZSXw+RMQPdTZS9wYY1ogSxznqazcxatbTgF+/P7m4fhXN/QWYP2bkLsfbn0f/KyxZ4xpmSxxnKeXklPZk+viD7OGERvRofqCxSfgy/+DhEug75TGC9AYYxqY/bP3PBw9Uczzn+9hbDd/rh3eo+bCXz8PRdkw9de2kKExpkXzaeIQkekisktEUkTkoSqu9xSRFSLyrYhsEZErPa497L5vl4hc7m2djWnh+gzKXMp1fYNqLph/GFa/CINvgB4jGyc4Y4zxEZ8lDhHxB14ErgAGAbNFpPLkhkeAhao6ApgFvOS+d5D7eDAwHXhJRPy9rLNRuFzKu2szuLhPJDEhtfwZk5+G8hKY/EjjBGeMMT7kyxbHaCBFVdNUtQRYAFxbqYwCYe7X4cBB9+trgQWqekpV9wIp7vq8qbNRrEw5xoHck9wypmfNBY/tgY1vQ9J/QGSfxgnOGGN8yJeJoweQ4XGc6T7n6XHg+yKSCSwBflLLvd7U2Sje/SadyJAgpg2Kqbng509AYHu45BeNE5gxxvhYU4+qmg28paq/E5GLgHdEZEhDVCwi84B5ANHR0SQnJzdEtQDkFrv4bPtJpsUH8vWqLykoKKiy/rC8XYzcsZi98bPZv35bg71/Q6su/pagJccOFn9Ts/jrx5eJ4wAQ53Ec6z7n6Q6cPgxUdbWIBANRtdxbW52463sVeBUgKSlJJ06cWK8PUZUXV6RQrrv4xY3jSIgKITk5mXPqV4U//xZCupIw+xkS2nVssPdvaFXG30K05NjB4m9qFn/9+PJR1Tqgn4gkiEgQTmf34kpl0oEpACKSCAQDWe5ys0SknYgkAP2AtV7W6VNOp3g6F/eJJCEqpPqCu5dC+tcw8UFoxknDGGPqymctDlUtE5F7gKWAP/Cmqm4TkSeA9aq6GHgAeE1EforTUT5XVRXYJiILge1AGfBjVS0HqKpOX32GqqxKOUbm8ZM8OL2GHftc5c6y6Z37wMjbGi02Y4xpDD7t41DVJTid3p7nHvV4vR0YV829TwFPeVNnY3p3bTqdQ4KYNji6+kKb34WsHXDTX8A/sPGCM8aYRmAzx+vgaH4xn20/woxRsbQL8K+6UOlJWPG/0GMUDGqSkcLGGONTTT2qqkV5b30mZS5l1oVx1Rf65k9w4gDc8KotLWKMaZWsxeEll0tZsC6di3pH0rtLNZ3dRTmw6lnoNw3ixzdugMYY00gscXjpq9RjZOScZHZNM8VXPeusgjv18cYKyxhjGp0lDi+9uzadiA6BXF5dp3huBnzzKlwwG6IHN25wxhjTiCxxeCEr/xT/3lZLp/iK/3V+T/pl4wVmjDFNwBKHF97b4CyfPmt01Y+pQgr2OUNwx8yDTjV0nBtjTCtgiaMWLpeyYG0GY3t3pk81neK9096G4DAY/7NGjs4YYxqfJY5afJ2aTXpOEbOraW2wdyWRORucpNGhc+MGZ4wxTcCrxCEiH4jIVSLS5hLNmU7xKpZPV4Vlj1PcLhLG/LDxgzPGmCbgbSJ4CbgF2CMiT4vIAB/G1Gxk5Z9i6bbD3DgyluDAKjrFi3LgwHoO9LjK2XPDGGPaAK8Sh6ouU9VbgZHAPmCZiHwtIreLSKtdjGnRBmemeLVzN3JSASjqUMsugMYY04p4/ehJRCKBucCdwLfAH3ASyWc+iayJVcwUH5NQfac4OWkAFHXo1oiRGWNM0/JqrSoR+RAYALwDXKOqh9yX/i4i630VXFNanZbN/uwifnZZ/+oLZaeC+FEcXMNKucYY08p4u8jh86q6oqoLqprUgPE0G/PXptOpuk7xCjmpEB6H+rXap3XGGHMObx9VDRKRThUHIhIhIj/yUUxN7ljBKf5dU6d4hZw0iOzTeIEZY0wz4G3iuEtVcysOVPU4cJdvQmp6izZkUlqu1c/dAGcobnaas8ufMca0Id4mDn+RM5tLiIg/EOSbkJqWM1M8ndEJnenbtYa9wouy4VSetTiMMW2Ot4njU5yO8CkiMgV4132u1VmTls2+7CJuqam1AU7HOEDn3r4PyhhjmhFvO8cfBH4I3O0+/gx43ScRNbH5a9MJbx/I9CE1dIrD6aG4dO4DBzN9H5gxxjQTXiUOVXUBL7t/Wq1jBc5M8Tlj42vuFAdnRJX4Q6eegCUOY0zb4e08jn7Ab4BBQHDFeVVtVc9p3nd3it8yxoul0bNTnSXUA1plV48xxlTL2z6OP+O0NsqAScDbwF99FVRTUFXeXZvO6PjO9O0aWvsNOTaiyhjTNnmbONqr6ueAqOp+VX0cuMp3YTW+1e5O8dnetDZUbQ6HMabN8rZz/JR7SfU9InIPcACoYaxqy/Pu2gzC2wdyxRAv1p0qPAanTtiIKmNMm+Rti+M+oANwLzAK+D5wm6+CamzZBaf4dOshbhjZo/ZOcTh7RJUxxrQxtbY43JP9blbVnwMFwO0+j6qRvb/R3Sle29yNCu7l1O1RlTGmLaq1xaGq5cD4RoilSTid4hlcGB9Bv2gvOsXBvSpuxVBcY4xpW7zt4/hWRBYD7wGFFSdV9QOfRNWI1qTlsPdYIT+Z3Nf7m3LSnKThb6viGmPaHm/7OIKBbGAycI375+rabhKR6SKyS0RSROShKq7/XkQ2uX92i0iu+/wkj/ObRKRYRK5zX3tLRPZ6XBvu7YetSsVM8SuH1mEzppxUe0xljGmzvJ05Xud+DXffyIvAZThTq9eJyGJV3e5R7089yv8EGOE+vwIY7j7fGUgB/u1R/X+p6qK6xlRZdsEplm49zK1je3rXKe4E56yKGzfmfN/eGGNaJG9njv8Z0MrnVfU/arhtNJCiqmnuOhYA1wLbqyk/G3isivMzgE9UtcibWOvig40HKCl31bx8emWFx6Ak30ZUGWPaLG/7OP7p8ToYuB44WMs9PYAMj+NMoMp/potILyABWF7F5VnAs5XOPSUijwKfAw+p6qkq6pwHzAOIjo4mOTn5rOuqyhsrT9Kvkx8Hd2zg4I5aPo1bWN4ORgJbDhSS466zoKDgnPpbkpYcf0uOHSz+pmbx15Oq1vkHp2/k61rKzABe9zieA/yxmrIPAi9Ucb4bkAUEVjonQDvgL8CjtcU7atQorWx16jHt9eA/ddH6jHOu1WjjX1UfC1M9lnL61IoVK+pWRzPTkuNvybGrWvxNzeKvGbBeq/hO9bZzvLJ+QNdayhwAPNfviHWfq8osnD0+KpsJfKiqpRUnVPWQ+zOdwllDa7TXUXuY/006YcEBXDWsDp3iUGlVXGOMaXu8Shwiki8iJyp+gH/gtBJqsg7oJyIJIhKEkxwWV1H3QCACWF1FHbOplFBEpJv7twDXAVu9+QyecgpL+HTrYW6obU/xKm9Og4heNhTXGNNmeTuqysuZcWfdU+Ze12op4A+8qarbROQJnOZPRRKZBSxwN4tOE5F4nBbLF5Wq/puIdMF5XLUJ+M+6xvbBxsy6d4pXyE61jnFjTJvm7aiq64HlqprnPu4ETFTVj2q6T1WXAEsqnXu00vHj1dy7D6eDvfL5yd7EXENMzF+bzqheEQyIqWM+rFgVt+dF5xOCMca0aN72cTxWkTQAVDWXqofONntr9+aQllVYv9ZGYRaUFNjkP2NMm+Zt4qiqnLdDeZuV+WvTCQ0O4Kq6zBSvkO1e3NAeVRlj2jBvE8d6EXlWRPq4f54FNvgyMF84XljCJ98d5saRsbQPqmOnOJxZFbdzQsMGZowxLYi3ieMnQAnwd2ABUAz82FdB+cr77k7xWaO92OWvKjlp4BcAnXo1bGDGGNOCeDuqqhA4Z5HClkTde4qP7NmJgTFh9askO9VJGv4t8imdMcY0CG/ncXzmHklVcRwhIkt9F1bDW7fvOKn17RSvkJNq28UaY9o8bx9VRblHUgGgqsepfeZ4szL/m/2EBgdw9bDu9atAFXL22ogqY0yb523icInI6X+quyfnnbNabnNV7lKWbD3MDSN61K9THKDgqDMU10ZUGWPaOG8f1v83sEpEvsCZsT0B98qzLcHxohIoczF7zHk+pgJ7VGWMafO8anGo6qdAErALZ+2oB4CTPoyrQeUUljDifDrFwRlRBRBpicMY07Z5u+TIncB9OCvcbgLG4ixKeF7LfzSWU2X1XJfKU3aqMxQ33FbFNca0bd72cdwHXAjsV9VJOFu85tZ8S/PhJ8I19e0Ur5BjQ3GNMQa8TxzFqloMICLtVHUnMMB3YTWsiA6B9e8Ur5CdZiOqjDEG7zvHM93zOD4CPhOR48B+34XVsLqGBp9fBRWr4saPb5iAjDGmBfN25vj17pePi8gKIBz41GdRNbAAfzm/CgqOQGmhtTiMMYZ6rHCrqpU3Vmr9sm1xQ2OMqVDfPcfbloqhuDb5zxhjLHF4JScV/AIhvJ6r6hpjTCtiicMb2akQYUNxjTEGLHF4J2evPaYyxhg3Sxy1qRiKayOqjDEGsMRRu/zDzlBcW9zQGGMASxy1Oz2iyhKHMcaAJY7aVSynbo+qjDEGsMRRu2z3UNyw2KaOxBhjmgVLHLXJSYOIeBuKa4wxbpY4amMjqowx5iyWOGpSMRTXOsaNMeY0nyYOEZkuIrtEJEVEHqri+u9FZJP7Z7eI5HpcK/e4ttjjfIKIfOOu8+8iEuSzD5B/CEqLLHEYY4wHnyUOEfEHXgSuAAYBs0VkkGcZVf2pqg5X1eHAC8AHHpdPVlxT1e95nP8t8HtV7QscB+7w1Wc4s8+4PaoyxpgKvmxxjAZSVDVNVUuABcC1NZSfDbxbU4UiIjj7nC9yn/oLcF0DxFq108upW+IwxpgKvhwq1API8DjOBMZUVVBEegEJwHKP08Eish4oA55W1Y+ASCBXVcs86uxRTZ3zgHkA0dHRJCcn1/kD9E5NJlYC+HJTCsjeassVFBTUq/7moiXH35JjB4u/qVn89dNcxpjOAhaparnHuV6qekBEegPLReQ7IM/bClX1VeBVgKSkJJ04cWLdozryOkT2ZuKkKTUWS05Opl71NxMtOf6WHDtY/E3N4q8fXz6qOgB4bmAR6z5XlVlUekylqgfcv9OAZGAEkA10EpGKhFdTnecvO80eUxljTCW+TBzrgH7uUVBBOMlhceVCIjIQiABWe5yLEJF27tdRwDhgu6oqsAKY4S56G/CxT6J3uWworjHGVMFnicPdD3EPsBTYASxU1W0i8oSIeI6SmgUscCeFConAehHZjJMonlbV7e5rDwI/E5EUnD6PN3zyAQoOQ9lJiLTEYYwxnnzax6GqS4Allc49Wun48Sru+xoYWk2daTgjtnzLRlQZY0yVbOZ4dSpWxbVHVcYYcxZLHNXJSQP/IAi3VXGNMcaTJY7qZKdCRAL4+Td1JMYY06xY4qiOjagyxpgqWeKoissFOXttjSpjjKmCJY6q5B9yhuJai8MYY85hiaMqNqLKGGOqZYmjKhVzOOxRlTHGnMMSR1Vy0sC/HYTZUFxjjKnMEkdVctIgIh787M9jjDGV2TdjVbJT7TGVMcZUwxJHZS4XHN9rHePGGFMNSxyV5R+EsmJrcRhjTDUscVSWbUNxjTGmJpY4KstJc37bcurGGFMlSxyV5aS6h+L2aOpIjDGmWbLEUVl2GnROsKG4xhhTDft2rCwnzR5TGWNMDSxxeKoYimv7jBtjTLUscXg6ccAZimsjqowxplqWODzZiCpjjKmVJQ5PObYqrjHG1MYSh6fsVAgIhtDuTR2JMcY0W5Y4POWkQYQNxTXGmJrYN6SnnDR7TGWMMbWwxFHB5YKcvc7kP2OMMdWyxFHhRCaUn7IRVcYYUwtLHBUqhuLaoypjjKmRTxOHiEwXkV0ikiIiD1Vx/fcissn9s1tEct3nh4vIahHZJiJbRORmj3veEpG9HvcNb5BgTy+nbonDGGNqEuCrikXEH3gRuAzIBNaJyGJV3V5RRlV/6lH+J8AI92ER8ANV3SMi3YENIrJUVXPd1/9LVRc1aMA5ae6huN0atFpjjGltfNniGA2kqGqaqpYAC4Brayg/G3gXQFV3q+oe9+uDwFGgiw9jdS9u2NuG4hpjTC18+S3ZA8jwOM50nzuHiPQCEoDlVVwbDQQBqR6nn3I/wvq9iLRrkGizU22NKmOM8YKoqm8qFpkBTFfVO93Hc4AxqnpPFWUfBGJV9SeVzncDkoHbVHWNx7nDOMnkVSBVVZ+oos55wDyA6OjoUQsWLKg+WC3nki9nkhl7DWl95tb5sxYUFNCxY8c639dctOT4W3LsYPE3NYu/ZpMmTdqgqknnXFBVn/wAFwFLPY4fBh6upuy3wMWVzoUBG4EZNbzH/2/v7oOsqu87jr8/4SnhwQiKxIKjmFINOgkGhpjSOqiNJakjpLUGm1DtQ5xMY5uYTFttO+jQTseZZmIf4iRxDKIjlWaIRMbBB6pgpzOaQAiKYI2wiC41xe4ilmYEdvn2j/NbcrvsLnvOvYfD9XxeM3fu3nPPPfu5DLufPef87u/MBx49UZbZs2fHkPbvibj9tIjN9w293iA2bNhQ6HWninbO387ZI5y/as4/NGBzDPA7tcxDVZuAGZKmSxoNLAbW9l9J0oXARODZhmWjgTXAA9HvJHja40CSgEXAi00n9YgqM7NhK21UVUT0SLoZeAIYASyPiO2SlpG1WF+JLAZWpXbrcx1wGXCGpBvTshsjYiuwUtJkQMBW4AtNOOceRQAADJJJREFUh+2bFdfnOMzMTqi04gCIiHXAun7LlvZ7fMcAr3sQeHCQbV7RwoiZ7t0w8n0eimtmNgweewo/H1HlobhmZifk35SQHary5IZmZsPi4jjaC/tf9RxVZmbD5OI40Am9hz2iysxsmFwcHlFlZpaLi6PvMxw+VGVmNiwuDg/FNTPLxcXRnYbiSlUnMTNrCy6Orl1whs9vmJkNV72Lo28orkdUmZkNW72L48DrcPSIT4ybmeVQ7+Lo8lBcM7O86l0c3R3ZvQ9VmZkNm4tj1FiY8IGqk5iZtY16F0eXh+KameVV7+Lo7vD5DTOznOpbHL09nhXXzKyA+hZH31Bc73GYmeVS3+I4Niuu9zjMzPKocXHszu59qMrMLJf6FkfXLhg1DsZPqTqJmVlbqW9xeFZcM7NCalwcHZ4V18ysgHoWR99QXI+oMjPLrZ7FceA1ONrjEVVmZgXUszj6Jjf0iCozs9zqWRxdnhXXzKyoehZH9y4YPR7Gn1V1EjOztlPT4uiASdM9FNfMrIBSi0PSAkkvS9op6dYBnr9L0tZ0+4mktxqeu0HSK+l2Q8Py2ZK2pW3+o1Tgt3/XLh+mMjMrqLTikDQCuBv4JDATuF7SzMZ1IuKWiJgVEbOAfwIeTq+dBNwOfAyYC9wuaWJ62TeBzwMz0m1BrmC9PfDWHg/FNTMrqMw9jrnAzojoiIjDwCpg4RDrXw88lL7+dWB9RHRHxH5gPbBA0tnAaRHxXEQE8ACwKFeqvqG4HlFlZlbIyBK3PRV4veFxJ9kexHEknQtMB54e4rVT061zgOUDbfMm4CaAKVOmsHHjRgAmdW3hw8CP97zNgQMb87yfQR08ePDY9ttRO+dv5+zg/FVz/mLKLI48FgOrI6K3VRuMiHuAewDmzJkT8+fPz574wcuwDS658rdgQmsmONy4cSPHtt+G2jl/O2cH56+a8xdT5qGqvcA5DY+npWUDWczPD1MN9dq96evhbHNg3R0eimtm1oQyi2MTMEPSdEmjycphbf+VJF0ITASebVj8BHCVpInppPhVwBMR8QbwtqRL02iq3wUeyZWqy7Pimpk1o7TiiIge4GayEngJ+G5EbJe0TNI1DasuBlalk919r+0G/pqsfDYBy9IygD8C7gV2AruAx3IF65tO3czMCin1HEdErAPW9Vu2tN/jOwZ57XJg+QDLNwMXFwrUewT274GLPl3o5WZmVrdPjr/1GkSvP/xnZtaEehVH36y4PlRlZlZYvYqja1d27w//mZkVVq/i6O6A0RNg3OSqk5iZta2aFccuz4prZtakehVH1y4fpjIza1J9iqP3SDaqyiOqzMyaUp/i6BuK6z0OM7Om1Kc4+kZUeSiumVlT6lMcxz7D4T0OM7Nm1Kg4dsGY02DcmVUnMTNra/Upji4PxTUza4X6FEd3hw9TmZm1QE2KI7JRVR5RZWbWtHoUR8/hNCuuR1SZmTWrJsVxKLv3oSozs6bVozh6U3H4UJWZWdPqURw9h7KhuGPPqDqJmVnbq09xTDrfQ3HNzFqgHsXRe8iHqczMWqQexdFz2COqzMxapB7FQXhElZlZi9SkOPChKjOzFqlPcXiPw8ysJepRHO8ZAWMnVZ3CzOxdoR7FMWKMh+KambVIPYpj5JiqE5iZvWvUozhGj686gZnZu0apxSFpgaSXJe2UdOsg61wnaYek7ZL+OS27XNLWhts7khal51ZI2t3w3KwTBvFV/8zMWmZkWRuWNAK4G/gE0AlskrQ2InY0rDMDuA2YFxH7JZ0FEBEbgFlpnUnATuDJhs3/aUSsLiu7mZkNrsw9jrnAzojoiIjDwCpgYb91Pg/cHRH7ASJi3wDbuRZ4LCJ+VmJWMzMbJkVEORuWrgUWRMQfpsdLgI9FxM0N63wf+AkwDxgB3BERj/fbztPA1yPi0fR4BfBx4BDwFHBrRBwa4PvfBNwEMGXKlNmrVq1q+Xvsc/DgQcaPb9/zKO2cv52zg/NXzfmHdvnll/8oIuYc90RElHIj21O4t+HxEuAb/dZ5FFgDjAKmA68Dpzc8fzbwJjCq3zIBY4D7gaUnyjJ79uwo04YNG0rdftnaOX87Z49w/qo5/9CAzTHA79QyD1XtBc5peDwtLWvUCayNiCMRsZts72NGw/PXAWsi4kjfgoh4I72nQ8B9ZIfEzMzsJCmzODYBMyRNlzQaWAys7bfO94H5AJLOBH4J6Gh4/nrgocYXSDo73QtYBLxYRngzMxtYaaOqIqJH0s3AE2TnL5ZHxHZJy8h2f9am566StAPoJRst1QUg6TyyPZZn+m16paTJZIertgJfKOs9mJnZ8UorDoCIWAes67dsacPXAXwl3fq/9lVg6gDLr2h5UDMzG7Z6fHLczMxaxsVhZma5uDjMzCwXF4eZmeXi4jAzs1xKm3LkVCLpTWBPid/iTOC/S9x+2do5fztnB+evmvMP7dyImNx/YS2Ko2ySNsdA87m0iXbO387Zwfmr5vzF+FCVmZnl4uIwM7NcXBytcU/VAZrUzvnbOTs4f9WcvwCf4zAzs1y8x2FmZrm4OAqSdI6kDZJ2SNou6UtVZypC0ghJP5b0aNVZ8pJ0uqTVkv5D0kuSPl51pjwk3ZL+77wo6SFJ760601AkLZe0T9KLDcsmSVov6ZV0P7HKjEMZJP/fpf8/L0haI+n0KjMOZaD8Dc99VVKky1OUzsVRXA/w1YiYCVwKfFHSzIozFfEl4KWqQxT0D8DjEXEh8BHa6H1Imgr8CTAnIi4mu/TA4mpTndAKYEG/ZbcCT0XEDNKlnE92qBxWcHz+9cDFEfFhsgvJ3XayQ+WwguPzI+kc4CrgtZMVxMVRULoS4Zb09f+Q/dI6bhr4U5mkacBvAPdWnSUvSe8HLgO+AxARhyPirWpT5TYSeJ+kkcBY4D8rzjOkiPg3oLvf4oVkl3Am3S86qaFyGCh/RDwZET3p4XNkVyo9JQ3y7w9wF/BnwEk7Ye3iaIF00alLgB9UmyS3vyf7D3e06iAFTCe7Hv196VDbvZLGVR1quCJiL/A1sr8S3wAORMST1aYqZEpEvJG+/ikwpcowTfp94LGqQ+QhaSGwNyKeP5nf18XRJEnjge8BX46It6vOM1ySrgb2RcSPqs5S0Ejgo8A3I+IS4H85tQ+T/D/pXMBCsgL8BWCcpM9Vm6o56cJsbTlMU9Jfkh1+Xll1luGSNBb4C2DpidZtNRdHEySNIiuNlRHxcNV5cpoHXCPpVWAVcIWkB6uNlEsn0BkRfXt5q8mKpF38GrA7It6MiCPAw8AvV5ypiP+SdDZAut9XcZ7cJN0IXA18Ntrr8wkfJPvD4/n0czwN2CLpA2V/YxdHQZJEdnz9pYj4etV58oqI2yJiWkScR3ZS9umIaJu/eCPip8Drki5Ii64EdlQYKa/XgEsljU3/l66kjU7uN1gL3JC+vgF4pMIsuUlaQHa49pqI+FnVefKIiG0RcVZEnJd+jjuBj6afjVK5OIqbBywh+0t9a7p9qupQNfPHwEpJLwCzgL+tOM+wpT2l1cAWYBvZz+Ip/SlmSQ8BzwIXSOqU9AfAncAnJL1Cthd1Z5UZhzJI/m8AE4D16Wf4W5WGHMIg+avJ0l57ZmZmVjXvcZiZWS4uDjMzy8XFYWZmubg4zMwsFxeHmZnl4uIwOwVJmt+OMxZbPbg4zMwsFxeHWRMkfU7SD9OHx76drm9yUNJd6VobT0manNadJem5hms/TEzLf1HSv0p6XtIWSR9Mmx/fcL2RlekT5ki6M10H5gVJX6vorVuNuTjMCpL0IeAzwLyImAX0Ap8FxgGbI+Ii4Bng9vSSB4A/T9d+2NawfCVwd0R8hGy+qr7ZZi8BvgzMBM4H5kk6A/g0cFHazt+U+y7NjufiMCvuSmA2sEnS1vT4fLJp6v8lrfMg8Cvp+iGnR8Qzafn9wGWSJgBTI2INQES80zBn0g8jojMijgJbgfOAA8A7wHck/SbQVvMr2buDi8OsOAH3R8SsdLsgIu4YYL2i8/ocavi6FxiZLjo0l2yeq6uBxwtu26wwF4dZcU8B10o6C45df/tcsp+ra9M6vwP8e0QcAPZL+tW0fAnwTLp6ZKekRWkbY9J1FgaUrv/y/ohYB9xCdslcs5NqZNUBzNpVROyQ9FfAk5LeAxwBvkh2Uam56bl9ZOdBIJt2/FupGDqA30vLlwDflrQsbeO3h/i2E4BHJL2XbI/nKy1+W2Yn5NlxzVpM0sGIGF91DrOy+FCVmZnl4j0OMzPLxXscZmaWi4vDzMxycXGYmVkuLg4zM8vFxWFmZrm4OMzMLJf/A5Ro8YZFLhXsAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_accuracy_vs_epochs(1, basic_model_history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F5nVhxo8F8f3"
      },
      "source": [
        "#### Learning Rate scheduling: **Step Decay**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KK1-gxUeQwCz"
      },
      "outputs": [],
      "source": [
        "# Manual setup for modifying leraning rates per epoch\n",
        "# define step decay function\n",
        "class LossHistory(keras.callbacks.Callback):\n",
        "    def on_train_begin(self, logs={}):\n",
        "        self.loss = []\n",
        "        self.lrate = []\n",
        "\n",
        "    def on_epoch_end(self, batch, logs={}):\n",
        "        self.loss.append(logs.get('loss'))\n",
        "        self.lrate.append(step_decay(len(self.loss)))\n",
        "        print('lrate:', step_decay(len(self.loss)))\n",
        "\n",
        "def step_decay(epoch):\n",
        "    initial_lrate = 0.005\n",
        "    drop = 0.5 # new learning rate will be square root of the old leraning rate\n",
        "    drop_after_epochs = 2\n",
        "    lrate = initial_lrate * math.pow(drop, math.floor((epoch)/drop_after_epochs))\n",
        "    return lrate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z4IPMVmW75yo",
        "outputId": "04eaf023-6649-44ef-ed9b-10dc8f379825"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_4 (Dense)             (None, 128)               100480    \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 134,794\n",
            "Trainable params: 134,794\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "step_scheduling_model = nn_model_fancy (\n",
        "perceptrons_in_each_layer = 128,\n",
        "hidden_activation = \"relu\",\n",
        "initializer = tf.keras.initializers.RandomNormal(stddev=0.01),\n",
        "dropout_rate = 0 ,\n",
        "optimiser_type = tf.keras.optimizers.SGD(),\n",
        "loss_fuction = \"sparse_categorical_crossentropy\",\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mvdU_0gMH6zs",
        "outputId": "41a93009-da7c-4541-9ccc-faa9f1b7c99e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "364/375 [============================>.] - ETA: 0s - loss: 0.7223 - accuracy: 0.7391lrate: 0.005\n",
            "375/375 [==============================] - 2s 4ms/step - loss: 0.7145 - accuracy: 0.7422 - val_loss: 0.4917 - val_accuracy: 0.8213 - lr: 0.0050\n",
            "Epoch 2/15\n",
            "369/375 [============================>.] - ETA: 0s - loss: 0.4673 - accuracy: 0.8318lrate: 0.0025\n",
            "375/375 [==============================] - 1s 4ms/step - loss: 0.4660 - accuracy: 0.8321 - val_loss: 0.4543 - val_accuracy: 0.8357 - lr: 0.0050\n",
            "Epoch 3/15\n",
            "365/375 [============================>.] - ETA: 0s - loss: 0.3865 - accuracy: 0.8596lrate: 0.0025\n",
            "375/375 [==============================] - 1s 4ms/step - loss: 0.3855 - accuracy: 0.8600 - val_loss: 0.3826 - val_accuracy: 0.8614 - lr: 0.0025\n",
            "Epoch 4/15\n",
            "368/375 [============================>.] - ETA: 0s - loss: 0.3684 - accuracy: 0.8676lrate: 0.00125\n",
            "375/375 [==============================] - 1s 4ms/step - loss: 0.3684 - accuracy: 0.8676 - val_loss: 0.3781 - val_accuracy: 0.8626 - lr: 0.0025\n",
            "Epoch 5/15\n",
            "372/375 [============================>.] - ETA: 0s - loss: 0.3414 - accuracy: 0.8775lrate: 0.00125\n",
            "375/375 [==============================] - 1s 4ms/step - loss: 0.3414 - accuracy: 0.8775 - val_loss: 0.3587 - val_accuracy: 0.8700 - lr: 0.0012\n",
            "Epoch 6/15\n",
            "369/375 [============================>.] - ETA: 0s - loss: 0.3324 - accuracy: 0.8801lrate: 0.000625\n",
            "375/375 [==============================] - 1s 4ms/step - loss: 0.3324 - accuracy: 0.8802 - val_loss: 0.3592 - val_accuracy: 0.8702 - lr: 0.0012\n",
            "Epoch 7/15\n",
            "366/375 [============================>.] - ETA: 0s - loss: 0.3200 - accuracy: 0.8847lrate: 0.000625\n",
            "375/375 [==============================] - 2s 4ms/step - loss: 0.3210 - accuracy: 0.8841 - val_loss: 0.3449 - val_accuracy: 0.8740 - lr: 6.2500e-04\n",
            "Epoch 8/15\n",
            "365/375 [============================>.] - ETA: 0s - loss: 0.3179 - accuracy: 0.8852lrate: 0.0003125\n",
            "375/375 [==============================] - 1s 4ms/step - loss: 0.3169 - accuracy: 0.8858 - val_loss: 0.3469 - val_accuracy: 0.8743 - lr: 6.2500e-04\n",
            "Epoch 9/15\n",
            "371/375 [============================>.] - ETA: 0s - loss: 0.3112 - accuracy: 0.8871lrate: 0.0003125\n",
            "375/375 [==============================] - 2s 4ms/step - loss: 0.3116 - accuracy: 0.8872 - val_loss: 0.3430 - val_accuracy: 0.8735 - lr: 3.1250e-04\n",
            "Epoch 10/15\n",
            "371/375 [============================>.] - ETA: 0s - loss: 0.3089 - accuracy: 0.8888lrate: 0.00015625\n",
            "375/375 [==============================] - 1s 4ms/step - loss: 0.3092 - accuracy: 0.8887 - val_loss: 0.3409 - val_accuracy: 0.8751 - lr: 3.1250e-04\n",
            "Epoch 11/15\n",
            "367/375 [============================>.] - ETA: 0s - loss: 0.3062 - accuracy: 0.8895lrate: 0.00015625\n",
            "375/375 [==============================] - 1s 4ms/step - loss: 0.3064 - accuracy: 0.8891 - val_loss: 0.3402 - val_accuracy: 0.8758 - lr: 1.5625e-04\n",
            "Epoch 12/15\n",
            "371/375 [============================>.] - ETA: 0s - loss: 0.3051 - accuracy: 0.8897lrate: 7.8125e-05\n",
            "375/375 [==============================] - 1s 4ms/step - loss: 0.3055 - accuracy: 0.8896 - val_loss: 0.3394 - val_accuracy: 0.8756 - lr: 1.5625e-04\n",
            "Epoch 13/15\n",
            "370/375 [============================>.] - ETA: 0s - loss: 0.3037 - accuracy: 0.8902lrate: 7.8125e-05\n",
            "375/375 [==============================] - 1s 4ms/step - loss: 0.3039 - accuracy: 0.8901 - val_loss: 0.3391 - val_accuracy: 0.8765 - lr: 7.8125e-05\n",
            "Epoch 14/15\n",
            "372/375 [============================>.] - ETA: 0s - loss: 0.3034 - accuracy: 0.8907lrate: 3.90625e-05\n",
            "375/375 [==============================] - 1s 4ms/step - loss: 0.3034 - accuracy: 0.8908 - val_loss: 0.3393 - val_accuracy: 0.8765 - lr: 7.8125e-05\n",
            "Epoch 15/15\n",
            "360/375 [===========================>..] - ETA: 0s - loss: 0.3030 - accuracy: 0.8907lrate: 3.90625e-05\n",
            "375/375 [==============================] - 1s 4ms/step - loss: 0.3026 - accuracy: 0.8907 - val_loss: 0.3390 - val_accuracy: 0.8758 - lr: 3.9062e-05\n"
          ]
        }
      ],
      "source": [
        "batch_size = 128\n",
        "epochs = 15\n",
        "\n",
        "# learning schedule callback\n",
        "lrate_step_decay = LearningRateScheduler(step_decay)\n",
        "loss_history_step_decay = LossHistory()\n",
        "callbacks_list = [loss_history_step_decay, lrate_step_decay]\n",
        "\n",
        "step_scheduling_model_history = step_scheduling_model.fit(X_train, y_train,\n",
        "                                                          batch_size=batch_size,\n",
        "                                                          epochs=epochs,\n",
        "                                                          validation_data=(X_val, y_val),\n",
        "                                                          callbacks=callbacks_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "36tSAYt4OUqG",
        "outputId": "bf598d6d-9597-42c4-dce8-a7eb2dc990c8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_accuracy_vs_epochs(2, step_scheduling_model_history)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "h4db4C0PPGzT",
        "outputId": "7a4a8d9b-26ea-4765-9b7c-18de7183944c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_learning_rate(2, loss_history_step_decay)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bd5B53hmJAbn"
      },
      "source": [
        "#### Adaptive Optimiser: **Adam**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DPMT33pBJAMz",
        "outputId": "ddcbadc4-967b-4acb-8829-0b973598fcb9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_8 (Dense)             (None, 128)               100480    \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 128)               16512     \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 134,794\n",
            "Trainable params: 134,794\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "adam_optimiser_model = nn_model_fancy (\n",
        "perceptrons_in_each_layer = 128,\n",
        "hidden_activation = \"relu\",\n",
        "initializer = tf.keras.initializers.RandomNormal(stddev=0.01),\n",
        "dropout_rate = 0 ,\n",
        "optimiser_type = \"adam\",\n",
        "loss_fuction = \"sparse_categorical_crossentropy\",\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dOShH_m4I8Ey",
        "outputId": "1982b9cc-6ea6-4bea-db25-ee2950fdcf9e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "375/375 [==============================] - 3s 7ms/step - loss: 0.5698 - accuracy: 0.7908 - val_loss: 0.4475 - val_accuracy: 0.8353\n",
            "Epoch 2/15\n",
            "375/375 [==============================] - 2s 6ms/step - loss: 0.4053 - accuracy: 0.8519 - val_loss: 0.3754 - val_accuracy: 0.8617\n",
            "Epoch 3/15\n",
            "375/375 [==============================] - 2s 6ms/step - loss: 0.3812 - accuracy: 0.8629 - val_loss: 0.4047 - val_accuracy: 0.8562\n",
            "Epoch 4/15\n",
            "375/375 [==============================] - 2s 6ms/step - loss: 0.3512 - accuracy: 0.8719 - val_loss: 0.3550 - val_accuracy: 0.8716\n",
            "Epoch 5/15\n",
            "375/375 [==============================] - 2s 6ms/step - loss: 0.3355 - accuracy: 0.8766 - val_loss: 0.3716 - val_accuracy: 0.8675\n",
            "Epoch 6/15\n",
            "375/375 [==============================] - 2s 6ms/step - loss: 0.3244 - accuracy: 0.8813 - val_loss: 0.3558 - val_accuracy: 0.8745\n",
            "Epoch 7/15\n",
            "375/375 [==============================] - 2s 6ms/step - loss: 0.3105 - accuracy: 0.8863 - val_loss: 0.3504 - val_accuracy: 0.8724\n",
            "Epoch 8/15\n",
            "375/375 [==============================] - 2s 6ms/step - loss: 0.3028 - accuracy: 0.8874 - val_loss: 0.3536 - val_accuracy: 0.8787\n",
            "Epoch 9/15\n",
            "375/375 [==============================] - 2s 6ms/step - loss: 0.2907 - accuracy: 0.8916 - val_loss: 0.3711 - val_accuracy: 0.8664\n",
            "Epoch 10/15\n",
            "375/375 [==============================] - 2s 6ms/step - loss: 0.2883 - accuracy: 0.8930 - val_loss: 0.3451 - val_accuracy: 0.8776\n",
            "Epoch 11/15\n",
            "375/375 [==============================] - 2s 6ms/step - loss: 0.2755 - accuracy: 0.8970 - val_loss: 0.3498 - val_accuracy: 0.8747\n",
            "Epoch 12/15\n",
            "375/375 [==============================] - 2s 6ms/step - loss: 0.2747 - accuracy: 0.8978 - val_loss: 0.3669 - val_accuracy: 0.8745\n",
            "Epoch 13/15\n",
            "375/375 [==============================] - 2s 6ms/step - loss: 0.2658 - accuracy: 0.9002 - val_loss: 0.3628 - val_accuracy: 0.8774\n",
            "Epoch 14/15\n",
            "375/375 [==============================] - 2s 6ms/step - loss: 0.2639 - accuracy: 0.9008 - val_loss: 0.3922 - val_accuracy: 0.8646\n",
            "Epoch 15/15\n",
            "375/375 [==============================] - 2s 6ms/step - loss: 0.2543 - accuracy: 0.9036 - val_loss: 0.3628 - val_accuracy: 0.8725\n"
          ]
        }
      ],
      "source": [
        "batch_size = 128\n",
        "epochs = 15\n",
        "\n",
        "adam_optimiser_model_history = adam_optimiser_model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_val, y_val))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "7iSsMayeUuoD",
        "outputId": "f699cd2a-ded0-4428-e2ba-8aa60575c551"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_accuracy_vs_epochs(3, adam_optimiser_model_history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I0iOiF5tMk1M"
      },
      "source": [
        "## Hyperparameter Tuning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ujpCDEYaVAZR"
      },
      "source": [
        "### Tuning the architecture with KerasTuner"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4fEtGKcPVADK"
      },
      "outputs": [],
      "source": [
        "hp = kt.HyperParameters()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1Kj6SbU_VAMY"
      },
      "outputs": [],
      "source": [
        "def build_model(hp):\n",
        "    model = keras.Sequential()\n",
        "    # input layer\n",
        "    model.add(\n",
        "        layers.Dense(input_shape=(784,),\n",
        "            # Tune number of units.\n",
        "            units=hp.Int(\"units\", min_value=32, max_value=512, step=32),\n",
        "            # Tune the activation function to use.\n",
        "            activation=hp.Choice(\"activation\", [\"relu\", \"tanh\"]),\n",
        "        )\n",
        "    )\n",
        "    # hidden layer 1\n",
        "    model.add(\n",
        "        layers.Dense(\n",
        "            units=hp.Int(\"units\", min_value=32, max_value=512, step=32),\n",
        "            activation=hp.Choice(\"activation\", [\"relu\", \"tanh\"]),\n",
        "        )\n",
        "    )\n",
        "    # Hidden layer 2\n",
        "    model.add(\n",
        "        layers.Dense(\n",
        "            units=hp.Int(\"units\", min_value=32, max_value=512, step=32),\n",
        "            # Tune the activation function to use.\n",
        "            activation=hp.Choice(\"activation\", [\"relu\", \"tanh\"]),\n",
        "        )\n",
        "    )\n",
        "    # Tune whether to use dropout.\n",
        "    if hp.Boolean(\"dropout\"):\n",
        "        model.add(layers.Dropout(rate=0.25))\n",
        "    model.add(layers.Dense(10, activation=\"softmax\"))\n",
        "    # Define the optimizer learning rate as a hyperparameter.\n",
        "    learning_rate = hp.Float(\"lr\", min_value=1e-4, max_value=1e-2, sampling=\"log\")\n",
        "    model.compile(\n",
        "        optimizer=keras.optimizers.Adam(learning_rate=learning_rate),\n",
        "        loss=\"sparse_categorical_crossentropy\",\n",
        "        metrics=[\"accuracy\"],\n",
        "    )\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VX-Tu82kYgD8"
      },
      "outputs": [],
      "source": [
        "tuner = kt.RandomSearch(\n",
        "    hypermodel=build_model,\n",
        "    objective=\"val_loss\",\n",
        "    max_trials=3,\n",
        "    executions_per_trial=2,\n",
        "    overwrite=True,\n",
        "    directory=\"Arch_tuning_dir\",\n",
        "    project_name=\"Fashion_MNIST\",\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_mUYkd4BYgBI",
        "outputId": "c38c041b-d328-4dcb-bdda-08d4a5f83eaf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Search space summary\n",
            "Default search space size: 4\n",
            "units (Int)\n",
            "{'default': None, 'conditions': [], 'min_value': 32, 'max_value': 512, 'step': 32, 'sampling': None}\n",
            "activation (Choice)\n",
            "{'default': 'relu', 'conditions': [], 'values': ['relu', 'tanh'], 'ordered': False}\n",
            "dropout (Boolean)\n",
            "{'default': False, 'conditions': []}\n",
            "lr (Float)\n",
            "{'default': 0.0001, 'conditions': [], 'min_value': 0.0001, 'max_value': 0.01, 'step': None, 'sampling': 'log'}\n"
          ]
        }
      ],
      "source": [
        "tuner.search_space_summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I8iR8fEnYf-v",
        "outputId": "6f6a4208-c193-45d7-99ed-78b2cb52e488"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trial 3 Complete [00h 01m 09s]\n",
            "val_loss: 0.7691256701946259\n",
            "\n",
            "Best val_loss So Far: 0.5338768064975739\n",
            "Total elapsed time: 00h 02m 49s\n"
          ]
        }
      ],
      "source": [
        "tuner.search(X_train, y_train, epochs=2, validation_data=(X_val, y_val))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MF3pvKz_dZOe",
        "outputId": "f9345c88-f8a1-4cd5-c3d2-0e2496581e39"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Results summary\n",
            "Results in Arch_tuning_dir/Fashion_MNIST\n",
            "Showing 10 best trials\n",
            "<keras_tuner.engine.objective.Objective object at 0x7f00f82fa890>\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "units: 320\n",
            "activation: relu\n",
            "dropout: True\n",
            "lr: 0.00014910815026432205\n",
            "Score: 0.5338768064975739\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "units: 160\n",
            "activation: tanh\n",
            "dropout: True\n",
            "lr: 0.00040536211023129484\n",
            "Score: 0.7608900666236877\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "units: 384\n",
            "activation: tanh\n",
            "dropout: False\n",
            "lr: 0.00039231791838165793\n",
            "Score: 0.7691256701946259\n"
          ]
        }
      ],
      "source": [
        "tuner.results_summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B-hsrfB2ctFm"
      },
      "source": [
        "### Grid search for rest of the parameters\n",
        "\n",
        "Use the optimised archtecture and tune other parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "geZApS0aPcvm",
        "outputId": "7d81127d-a283-4142-a63a-0fae5a953723"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: KerasClassifier is deprecated, use Sci-Keras (https://github.com/adriangb/scikeras) instead. See https://www.adriangb.com/scikeras/stable/migration.html for help migrating.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        }
      ],
      "source": [
        "model=KerasClassifier(build_fn = nn_model_fancy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N4iBRDW0Ipjv",
        "outputId": "2078d32d-f9b4-4373-e65c-1abb18ccef83"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_22\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_88 (Dense)            (None, 160)               125600    \n",
            "                                                                 \n",
            " dense_89 (Dense)            (None, 160)               25760     \n",
            "                                                                 \n",
            " dense_90 (Dense)            (None, 160)               25760     \n",
            "                                                                 \n",
            " dropout_21 (Dropout)        (None, 160)               0         \n",
            "                                                                 \n",
            " dense_91 (Dense)            (None, 10)                1610      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 178,730\n",
            "Trainable params: 178,730\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "938/938 [==============================] - 3s 3ms/step - loss: 2.9450 - accuracy: 0.1280\n",
            "938/938 [==============================] - 2s 2ms/step - loss: 2.4198 - accuracy: 0.1847\n",
            "Model: \"sequential_23\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_92 (Dense)            (None, 160)               125600    \n",
            "                                                                 \n",
            " dense_93 (Dense)            (None, 160)               25760     \n",
            "                                                                 \n",
            " dense_94 (Dense)            (None, 160)               25760     \n",
            "                                                                 \n",
            " dropout_22 (Dropout)        (None, 160)               0         \n",
            "                                                                 \n",
            " dense_95 (Dense)            (None, 10)                1610      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 178,730\n",
            "Trainable params: 178,730\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "938/938 [==============================] - 4s 3ms/step - loss: 2.9555 - accuracy: 0.1260\n",
            "938/938 [==============================] - 2s 2ms/step - loss: 2.5179 - accuracy: 0.1661\n",
            "Model: \"sequential_24\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_96 (Dense)            (None, 160)               125600    \n",
            "                                                                 \n",
            " dense_97 (Dense)            (None, 160)               25760     \n",
            "                                                                 \n",
            " dense_98 (Dense)            (None, 160)               25760     \n",
            "                                                                 \n",
            " dropout_23 (Dropout)        (None, 160)               0         \n",
            "                                                                 \n",
            " dense_99 (Dense)            (None, 10)                1610      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 178,730\n",
            "Trainable params: 178,730\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "938/938 [==============================] - 5s 4ms/step - loss: 1.2905 - accuracy: 0.5390\n",
            "938/938 [==============================] - 4s 4ms/step - loss: 0.8990 - accuracy: 0.6673\n",
            "Model: \"sequential_25\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_100 (Dense)           (None, 160)               125600    \n",
            "                                                                 \n",
            " dense_101 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dense_102 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dropout_24 (Dropout)        (None, 160)               0         \n",
            "                                                                 \n",
            " dense_103 (Dense)           (None, 10)                1610      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 178,730\n",
            "Trainable params: 178,730\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "938/938 [==============================] - 4s 4ms/step - loss: 1.3564 - accuracy: 0.5186\n",
            "938/938 [==============================] - 2s 2ms/step - loss: 0.9311 - accuracy: 0.6545\n",
            "Model: \"sequential_26\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_104 (Dense)           (None, 160)               125600    \n",
            "                                                                 \n",
            " dense_105 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dense_106 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dropout_25 (Dropout)        (None, 160)               0         \n",
            "                                                                 \n",
            " dense_107 (Dense)           (None, 10)                1610      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 178,730\n",
            "Trainable params: 178,730\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "938/938 [==============================] - 4s 4ms/step - loss: 2.6007 - accuracy: 0.1771\n",
            "938/938 [==============================] - 2s 2ms/step - loss: 2.1260 - accuracy: 0.2612\n",
            "Model: \"sequential_27\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_108 (Dense)           (None, 160)               125600    \n",
            "                                                                 \n",
            " dense_109 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dense_110 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dropout_26 (Dropout)        (None, 160)               0         \n",
            "                                                                 \n",
            " dense_111 (Dense)           (None, 10)                1610      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 178,730\n",
            "Trainable params: 178,730\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "938/938 [==============================] - 4s 4ms/step - loss: 2.5364 - accuracy: 0.1909\n",
            "938/938 [==============================] - 2s 2ms/step - loss: 2.0732 - accuracy: 0.2681\n",
            "Model: \"sequential_28\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_112 (Dense)           (None, 160)               125600    \n",
            "                                                                 \n",
            " dense_113 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dense_114 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dropout_27 (Dropout)        (None, 160)               0         \n",
            "                                                                 \n",
            " dense_115 (Dense)           (None, 10)                1610      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 178,730\n",
            "Trainable params: 178,730\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "938/938 [==============================] - 4s 3ms/step - loss: 2.3026 - accuracy: 0.0994\n",
            "938/938 [==============================] - 2s 2ms/step - loss: 2.3026 - accuracy: 0.0967\n",
            "Model: \"sequential_29\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_116 (Dense)           (None, 160)               125600    \n",
            "                                                                 \n",
            " dense_117 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dense_118 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dropout_28 (Dropout)        (None, 160)               0         \n",
            "                                                                 \n",
            " dense_119 (Dense)           (None, 10)                1610      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 178,730\n",
            "Trainable params: 178,730\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "938/938 [==============================] - 4s 3ms/step - loss: 2.3026 - accuracy: 0.1001\n",
            "938/938 [==============================] - 2s 2ms/step - loss: 2.3026 - accuracy: 0.0981\n",
            "Model: \"sequential_30\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_120 (Dense)           (None, 160)               125600    \n",
            "                                                                 \n",
            " dense_121 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dense_122 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dropout_29 (Dropout)        (None, 160)               0         \n",
            "                                                                 \n",
            " dense_123 (Dense)           (None, 10)                1610      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 178,730\n",
            "Trainable params: 178,730\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "938/938 [==============================] - 4s 4ms/step - loss: 2.3032 - accuracy: 0.1018\n",
            "938/938 [==============================] - 2s 2ms/step - loss: 2.3038 - accuracy: 0.0997\n",
            "Model: \"sequential_31\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_124 (Dense)           (None, 160)               125600    \n",
            "                                                                 \n",
            " dense_125 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dense_126 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dropout_30 (Dropout)        (None, 160)               0         \n",
            "                                                                 \n",
            " dense_127 (Dense)           (None, 10)                1610      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 178,730\n",
            "Trainable params: 178,730\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "938/938 [==============================] - 4s 4ms/step - loss: 2.3036 - accuracy: 0.0973\n",
            "938/938 [==============================] - 2s 2ms/step - loss: 2.3032 - accuracy: 0.0996\n",
            "Model: \"sequential_32\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_128 (Dense)           (None, 160)               125600    \n",
            "                                                                 \n",
            " dense_129 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dense_130 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dropout_31 (Dropout)        (None, 160)               0         \n",
            "                                                                 \n",
            " dense_131 (Dense)           (None, 10)                1610      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 178,730\n",
            "Trainable params: 178,730\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "938/938 [==============================] - 4s 4ms/step - loss: 2.3026 - accuracy: 0.1022\n",
            "938/938 [==============================] - 3s 3ms/step - loss: 2.3027 - accuracy: 0.0967\n",
            "Model: \"sequential_33\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_132 (Dense)           (None, 160)               125600    \n",
            "                                                                 \n",
            " dense_133 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dense_134 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dropout_32 (Dropout)        (None, 160)               0         \n",
            "                                                                 \n",
            " dense_135 (Dense)           (None, 10)                1610      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 178,730\n",
            "Trainable params: 178,730\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "938/938 [==============================] - 4s 4ms/step - loss: 2.3026 - accuracy: 0.0983\n",
            "938/938 [==============================] - 2s 2ms/step - loss: 2.3027 - accuracy: 0.0981\n",
            "Model: \"sequential_34\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_136 (Dense)           (None, 160)               125600    \n",
            "                                                                 \n",
            " dense_137 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dense_138 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dropout_33 (Dropout)        (None, 160)               0         \n",
            "                                                                 \n",
            " dense_139 (Dense)           (None, 10)                1610      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 178,730\n",
            "Trainable params: 178,730\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "938/938 [==============================] - 3s 3ms/step - loss: 2.6296 - accuracy: 0.1012\n",
            "938/938 [==============================] - 2s 2ms/step - loss: 2.3031 - accuracy: 0.0997\n",
            "Model: \"sequential_35\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_140 (Dense)           (None, 160)               125600    \n",
            "                                                                 \n",
            " dense_141 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dense_142 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dropout_34 (Dropout)        (None, 160)               0         \n",
            "                                                                 \n",
            " dense_143 (Dense)           (None, 10)                1610      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 178,730\n",
            "Trainable params: 178,730\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "938/938 [==============================] - 4s 3ms/step - loss: 2.6478 - accuracy: 0.1005\n",
            "938/938 [==============================] - 2s 2ms/step - loss: 2.3030 - accuracy: 0.0999\n",
            "Model: \"sequential_36\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_144 (Dense)           (None, 160)               125600    \n",
            "                                                                 \n",
            " dense_145 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dense_146 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dropout_35 (Dropout)        (None, 160)               0         \n",
            "                                                                 \n",
            " dense_147 (Dense)           (None, 10)                1610      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 178,730\n",
            "Trainable params: 178,730\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "938/938 [==============================] - 4s 4ms/step - loss: 2.5092 - accuracy: 0.0985\n",
            "938/938 [==============================] - 2s 2ms/step - loss: 2.3316 - accuracy: 0.1004\n",
            "Model: \"sequential_37\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_148 (Dense)           (None, 160)               125600    \n",
            "                                                                 \n",
            " dense_149 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dense_150 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dropout_36 (Dropout)        (None, 160)               0         \n",
            "                                                                 \n",
            " dense_151 (Dense)           (None, 10)                1610      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 178,730\n",
            "Trainable params: 178,730\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "938/938 [==============================] - 4s 4ms/step - loss: 2.5121 - accuracy: 0.0992\n",
            "938/938 [==============================] - 2s 2ms/step - loss: 2.3191 - accuracy: 0.1006\n",
            "Model: \"sequential_38\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_152 (Dense)           (None, 160)               125600    \n",
            "                                                                 \n",
            " dense_153 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dense_154 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dropout_37 (Dropout)        (None, 160)               0         \n",
            "                                                                 \n",
            " dense_155 (Dense)           (None, 10)                1610      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 178,730\n",
            "Trainable params: 178,730\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "938/938 [==============================] - 4s 4ms/step - loss: 2.5740 - accuracy: 0.0984\n",
            "938/938 [==============================] - 2s 2ms/step - loss: 2.3038 - accuracy: 0.0998\n",
            "Model: \"sequential_39\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_156 (Dense)           (None, 160)               125600    \n",
            "                                                                 \n",
            " dense_157 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dense_158 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dropout_38 (Dropout)        (None, 160)               0         \n",
            "                                                                 \n",
            " dense_159 (Dense)           (None, 10)                1610      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 178,730\n",
            "Trainable params: 178,730\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "938/938 [==============================] - 4s 4ms/step - loss: 2.5842 - accuracy: 0.1021\n",
            "938/938 [==============================] - 4s 4ms/step - loss: 2.3032 - accuracy: 0.0981\n",
            "Model: \"sequential_40\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_160 (Dense)           (None, 160)               125600    \n",
            "                                                                 \n",
            " dense_161 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dense_162 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dropout_39 (Dropout)        (None, 160)               0         \n",
            "                                                                 \n",
            " dense_163 (Dense)           (None, 10)                1610      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 178,730\n",
            "Trainable params: 178,730\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 1.0934 - accuracy: 0.6054\n"
          ]
        }
      ],
      "source": [
        "params={'perceptrons_in_each_layer': [ 160],\n",
        "        'hidden_activation': [\"tanh\" ],\n",
        "        'initializer': [tf.keras.initializers.RandomNormal(mean=0., stddev=1.), tf.keras.initializers.Zeros(), tf.keras.initializers.Ones() ],\n",
        "        'dropout_rate': [0.25],\n",
        "        'optimiser_type': [tf.keras.optimizers.SGD(learning_rate=0.0004) , \"adam\", \"adagrad\"],\n",
        "        'loss_fuction': [\"sparse_categorical_crossentropy\" ],\n",
        "        'nb_epoch':[15]\n",
        "        }\n",
        "\n",
        "gs=GridSearchCV(estimator=model, param_grid=params, cv=2)\n",
        "\n",
        "# now fit the dataset to the GridSearchCV object.\n",
        "gs = gs.fit(train_x, train_y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HmsWoQwYg1iu",
        "outputId": "77714b99-fbcb-4e8f-a4ed-a5adb70bc34b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'dropout_rate': 0.25,\n",
              " 'hidden_activation': 'tanh',\n",
              " 'initializer': <keras.initializers.initializers_v2.RandomNormal at 0x7f00f7c04e10>,\n",
              " 'loss_fuction': 'sparse_categorical_crossentropy',\n",
              " 'nb_epoch': 15,\n",
              " 'optimiser_type': 'adam',\n",
              " 'perceptrons_in_each_layer': 160}"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "gs.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dJyi5slKg7f7",
        "outputId": "064c4096-96da-441e-dda9-3de32a6f0bf5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7889999747276306"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "gs.best_score_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FOXPlDTEfdZw"
      },
      "source": [
        "## Early Stopping"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gUKVLBKlTVes"
      },
      "outputs": [],
      "source": [
        "callback_early_stop = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MHKDialMf9yI",
        "outputId": "8b22add0-23cf-42b3-a772-20acdfc0ada7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_41\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_164 (Dense)           (None, 160)               125600    \n",
            "                                                                 \n",
            " dense_165 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dense_166 (Dense)           (None, 160)               25760     \n",
            "                                                                 \n",
            " dropout_40 (Dropout)        (None, 160)               0         \n",
            "                                                                 \n",
            " dense_167 (Dense)           (None, 10)                1610      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 178,730\n",
            "Trainable params: 178,730\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "Early_stopping_model = nn_model_fancy (\n",
        "perceptrons_in_each_layer = 160,\n",
        "hidden_activation = \"tanh\",\n",
        "initializer = tf.keras.initializers.RandomNormal(mean=0., stddev=1.),\n",
        "dropout_rate = 0.25 ,\n",
        "optimiser_type = \"adam\",\n",
        "loss_fuction = \"sparse_categorical_crossentropy\",\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xKtx93v6hKAJ",
        "outputId": "be71f907-2e74-48a3-a695-155a5bded44c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "375/375 [==============================] - 4s 9ms/step - loss: 1.4886 - accuracy: 0.4842 - val_loss: 0.9400 - val_accuracy: 0.6528\n",
            "Epoch 2/100\n",
            "375/375 [==============================] - 3s 7ms/step - loss: 0.9677 - accuracy: 0.6432 - val_loss: 0.8336 - val_accuracy: 0.6918\n",
            "Epoch 3/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.8697 - accuracy: 0.6805 - val_loss: 0.7818 - val_accuracy: 0.7128\n",
            "Epoch 4/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.8261 - accuracy: 0.6976 - val_loss: 0.7603 - val_accuracy: 0.7170\n",
            "Epoch 5/100\n",
            "375/375 [==============================] - 4s 11ms/step - loss: 0.8131 - accuracy: 0.7039 - val_loss: 0.7660 - val_accuracy: 0.7125\n",
            "Epoch 6/100\n",
            "375/375 [==============================] - 4s 10ms/step - loss: 0.8051 - accuracy: 0.7008 - val_loss: 0.7429 - val_accuracy: 0.7197\n",
            "Epoch 7/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.7782 - accuracy: 0.7084 - val_loss: 0.7416 - val_accuracy: 0.7268\n",
            "Epoch 8/100\n",
            "375/375 [==============================] - 4s 10ms/step - loss: 0.7781 - accuracy: 0.7100 - val_loss: 0.7219 - val_accuracy: 0.7379\n",
            "Epoch 9/100\n",
            "375/375 [==============================] - 4s 11ms/step - loss: 0.7604 - accuracy: 0.7170 - val_loss: 0.7155 - val_accuracy: 0.7322\n",
            "Epoch 10/100\n",
            "375/375 [==============================] - 5s 12ms/step - loss: 0.7436 - accuracy: 0.7225 - val_loss: 0.6903 - val_accuracy: 0.7402\n",
            "Epoch 11/100\n",
            "375/375 [==============================] - 4s 11ms/step - loss: 0.7456 - accuracy: 0.7182 - val_loss: 0.7224 - val_accuracy: 0.7275\n",
            "Epoch 12/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.7590 - accuracy: 0.7157 - val_loss: 0.7170 - val_accuracy: 0.7271\n",
            "Epoch 13/100\n",
            "375/375 [==============================] - 3s 7ms/step - loss: 0.7433 - accuracy: 0.7227 - val_loss: 0.6935 - val_accuracy: 0.7408\n",
            "Epoch 14/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.7397 - accuracy: 0.7247 - val_loss: 0.6966 - val_accuracy: 0.7380\n",
            "Epoch 15/100\n",
            "375/375 [==============================] - 4s 12ms/step - loss: 0.7343 - accuracy: 0.7259 - val_loss: 0.6913 - val_accuracy: 0.7393\n"
          ]
        }
      ],
      "source": [
        "batch_size = 128\n",
        "epochs = 100\n",
        "\n",
        "Early_stopping_model_history_with_early_stopping = Early_stopping_model.fit(X_train, y_train,\n",
        "                                                        batch_size=batch_size,\n",
        "                                                        epochs=epochs,\n",
        "                                                        callbacks=[callback_early_stop],\n",
        "                                                        validation_data=(X_val, y_val))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PxCD_ucLno4P"
      },
      "source": [
        "## Manual Prediction check"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "ZTX_c13bnYXk",
        "outputId": "332ce964-4825-4bb6-872f-c8ff876d2f58"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step\n",
            "313/313 [==============================] - 1s 2ms/step\n",
            "The predicted label is: 0\n",
            "The actual label is:  0\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"
          }
        }
      ],
      "source": [
        "model_prediction(Early_stopping_model, 9)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dcAzZpn_obrY"
      },
      "source": [
        "**Label defination**\n",
        "\n",
        "0. T-shirt/top\n",
        "1. Trouser\n",
        "2. Pullover\n",
        "3. Dress\n",
        "4. Coat\n",
        "5. Sandal\n",
        "6. Shirt\n",
        "7. Sneaker\n",
        "8. Bag\n",
        "9. Ankle boot"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Overall Test Accuracy"
      ],
      "metadata": {
        "id": "vB4bd28dweHU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = Early_stopping_model.predict(x_test).argmax(axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "66bvlFWGwGtZ",
        "outputId": "d128ba4d-6a47-4f02-bb1e-7ed7e7cd04e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LvBVtGd8wJ42",
        "outputId": "c788b123-52c0-4c07-c72d-dd2797743aa7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7403"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}