A3_COMP551.ipynb

{
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Giuliano-1/COMP551/blob/main/A3_COMP551.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AliDHZn20ABS"
      },
      "source": [
        "# Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WnzJsuiV-K52"
      },
      "outputs": [],
      "source": [
        "#Useful Packages\n",
        "!pip3 install datasets > /dev/null 2>&1\n",
        "%pip install -qq tensorflow\n",
        "import datasets\n",
        "import cv2 as cv\n",
        "from google.colab.patches import cv2_imshow\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import scipy as sc\n",
        "import pandas as pd\n",
        "import math\n",
        "import nltk\n",
        "import sklearn\n",
        "from sklearn import preprocessing\n",
        "from abc import ABC, abstractmethod\n",
        "from numpy.core.memmap import dtype\n",
        "from zmq import NULL\n",
        "from google.colab import data_table\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.datasets import fashion_mnist\n",
        "from typing import List\n",
        "from tqdm import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K4SZd7Hl-PzK"
      },
      "outputs": [],
      "source": [
        "#from google.colab import drive\n",
        "#drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZBJIj4HkpYWy"
      },
      "source": [
        "# Task 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xkK5DxvNmqw0"
      },
      "outputs": [],
      "source": [
        "def normalization (data):\n",
        "  data_mean = data.mean(axis=0, dtype=np.float64)\n",
        "  #print(\"data_mean_shape \", data_mean.shape)\n",
        "  data_dot = data - data_mean\n",
        "  data_std = np.std(data, axis=0, dtype=np.float64)\n",
        "  data_tildy = np.divide(data_dot, data_std)\n",
        "  return data_tildy.astype(np.float128)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XbrFhf1xm4T_"
      },
      "outputs": [],
      "source": [
        "# Image conversions \n",
        "\n",
        "def to_vector(image):\n",
        "  return np.array([pixel for row in np.array(image) for pixel in row])\n",
        "\n",
        "# Constructing a matrix where each row is 1 image from the dataset\n",
        "def to_matrix(images):\n",
        "  return np.array([to_vector(image) for image in images], dtype=np.float64)"
      ]
    },
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          "text": [
            "Downloading and preparing dataset fashion_mnist/fashion_mnist to /root/.cache/huggingface/datasets/fashion_mnist/fashion_mnist/1.0.0/8d6c32399aa01613d96e2cbc9b13638f359ef62bb33612b077b4c247f6ef99c1...\n"
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            "    "
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          "text": [
            "Dataset fashion_mnist downloaded and prepared to /root/.cache/huggingface/datasets/fashion_mnist/fashion_mnist/1.0.0/8d6c32399aa01613d96e2cbc9b13638f359ef62bb33612b077b4c247f6ef99c1. Subsequent calls will reuse this data.\n"
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          "text": [
            "(60000, 784)\n",
            "(10000, 784)\n"
          ]
        }
      ],
      "source": [
        "# Fashion-MNIST with duplicates removed\n",
        "dataset = datasets.load_dataset(\"fashion_mnist\")\n",
        "train = dataset.get(\"train\")\n",
        "test = dataset.get(\"test\")\n",
        "X_train = to_matrix([train[i].get(\"image\") for i in range(len(train))])\n",
        "X_test = to_matrix([test[i].get(\"image\") for i in range(len(test))])\n",
        "y_train = np.array([train[i].get(\"label\") for i in range(len(train))])\n",
        "y_test = np.array([test[i].get(\"label\") for i in range(len(test))])\n",
        "\n",
        "# Saving non-normalized date\n",
        "X_train_unnorm = X_train\n",
        "X_test_unnorm  = X_test\n",
        "\n",
        "\n",
        "# Normalizing data\n",
        "X_train = normalization(X_train)\n",
        "X_test = normalization(X_test)\n",
        "#y_train = normalization(y_train)\n",
        "#y_test = normalization(y_test)\n",
        "\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z8T02VZrqjf0",
        "outputId": "17e270d6-7d23-449b-9127-0bf0a4d6206c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9\n",
            "(60000, 784)\n"
          ]
        }
      ],
      "source": [
        "print(np.amax(y_train)) # 10 (0 to 9) articles of clothing\n",
        "print(X_train.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C8lctp91gTHI"
      },
      "outputs": [],
      "source": [
        "# Mini Batching\n",
        "def minibatch (data, batch_size):\n",
        "  num_data_points = data.shape[0]\n",
        "  indices = np.random.choice(num_data_points, batch_size)\n",
        "  batch = data[indices,:]\n",
        "  return batch, indices"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gPRiBS2VU83i"
      },
      "outputs": [],
      "source": [
        "class_labels = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 678
        },
        "id": "ilr1BY0PgP2f",
        "outputId": "b8e504d3-dd03-4db0-e65a-09a9003d257a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-d3cfb8de8020>:13: UserWarning: Casting input data from 'float128' to 'float64' for imshow\n",
            "  plt.savefig(\"data\", bbox_inches=\"tight\", dpi=300)\n",
            "/usr/local/lib/python3.8/dist-packages/IPython/core/pylabtools.py:128: UserWarning: Casting input data from 'float128' to 'float64' for imshow\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "\n",
        "rows = 5\n",
        "columns = 5\n",
        "for i in range(rows*columns):\n",
        "\n",
        "  plt.subplot(rows, columns, i+1)\n",
        "  plt.imshow(X_train[i].reshape(28, 28), cmap=plt.get_cmap('gray'))\n",
        "  plt.xlabel(class_labels[y_train[i]])\n",
        "  plt.yticks([])\n",
        "  plt.xticks([])\n",
        "\n",
        "plt.savefig(\"data\", bbox_inches=\"tight\", dpi=300)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "WTEQ6zdmyYKK",
        "outputId": "786bba4b-d179-459c-cd8c-eb66446dfc6d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "\n",
        "_, counts = np.unique(y_train, return_counts=True)\n",
        "plt.bar(class_labels, counts, align='center')\n",
        "plt.title(\"Class disitrubtion in the training dataset\")\n",
        "plt.savefig(\"trainin-data\", bbox_inches=\"tight\", dpi=300)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "rp4iD4_07rRp",
        "outputId": "467899b7-ebbf-45a2-c7cb-23aae734140a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "\n",
        "_, counts = np.unique(y_test, return_counts=True)\n",
        "plt.bar(class_labels, counts, align='center')\n",
        "plt.title(\"Class disitrubtion in the testing dataset\")\n",
        "plt.savefig(\"testing-data\", bbox_inches=\"tight\", dpi=300)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RXnC4dappiO_"
      },
      "source": [
        "# Task 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lvi7-hXQUYuC"
      },
      "source": [
        "Neural Net Layer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9KlNdTOjUepm"
      },
      "outputs": [],
      "source": [
        "class NeuralNetLayer:\n",
        "    def __init__(self, input_size, output_size):\n",
        "        self.gradient = None\n",
        "        self.parameters = None\n",
        "        self.input_size = input_size\n",
        "        self.output_size = output_size\n",
        "        self.input = None\n",
        "        self.output = None\n",
        "        \n",
        "    def forward_impl(self, x): #Wrapping the forward function\n",
        "        raise NotImplementedError\n",
        "\n",
        "    def backward_impl(self, gradient): #Wrapping the backward function\n",
        "        raise NotImplementedError\n",
        "    \n",
        "    def forward(self, x):\n",
        "        self.input = x\n",
        "        result = self.forward_impl(self.input)\n",
        "        self.output = result\n",
        "        return result\n",
        "    \n",
        "    def backward(self, gradient):\n",
        "        result = self.backward_impl(gradient)\n",
        "        return result\n",
        "    \n",
        "    def is_linear_layer(self):\n",
        "        raise NotImplementedError"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tumjo0-lUkV7"
      },
      "source": [
        "Linear Layer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PoT5fGiHUjz6"
      },
      "outputs": [],
      "source": [
        "class LinearLayer(NeuralNetLayer):\n",
        "    def __init__(self, input_size, output_size):\n",
        "        super().__init__(input_size, output_size)\n",
        "        self.w = np.random.randn(output_size, input_size)/10\n",
        "        self.b = np.random.randn(output_size)/10\n",
        "        self.parameters = [self.w, self.b]\n",
        "\n",
        "    def forward_impl(self, x):\n",
        "        return (self.w[None, :, :] @ x[:, :, None]).squeeze() + self.b\n",
        "        #return (self.w[None, :, :] @ x[:, :, None]) + self.b ISSUE WITH SQUEEZE FOR CHECK GRAD!!! BATCH SIZE OF 1 DOESN'T WORK\n",
        "    \n",
        "    def backward_impl(self, gradient): \n",
        "        dw = gradient[:, :, None] @ self.input[:, None, :]\n",
        "        db = gradient\n",
        "        self.gradient = [dw, db]\n",
        "        return gradient.dot(self.w)\n",
        "\n",
        "    def return_w(self):\n",
        "      return self.w\n",
        "\n",
        "    def is_linear_layer(self):\n",
        "      return True"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S_4ivLKqUn7q"
      },
      "source": [
        "ReLU Layer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7xz5PrgjUq4e"
      },
      "outputs": [],
      "source": [
        "class ReLULayer(NeuralNetLayer):\n",
        "    def __init__(self, input_size, output_size):\n",
        "        super().__init__(input_size, output_size)\n",
        "        \n",
        "    def forward_impl(self, x):\n",
        "      return np.maximum(0,x)\n",
        "\n",
        "    def backward_impl(self, gradient):\n",
        "        return gradient * (np.where(self.input > 0, 1.0, 0.0))\n",
        "\n",
        "    def is_linear_layer(self):\n",
        "      return False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UOMwO4SzxaGI"
      },
      "source": [
        "Tanh Layer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nSe0TRlPxZjX"
      },
      "outputs": [],
      "source": [
        "class TanhLayer(NeuralNetLayer):\n",
        "    def __init__(self, input_size, output_size):\n",
        "        super().__init__(input_size, output_size)\n",
        "        \n",
        "    def forward_impl(self, x):\n",
        "        result = np.tanh(x)\n",
        "        return result\n",
        "\n",
        "    def backward_impl(self, gradient):\n",
        "        return gradient * (1.0-np.square(self.output))\n",
        "\n",
        "    def is_linear_layer(self):\n",
        "      return False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8_LWSLK_zKd6"
      },
      "source": [
        "Leaky ReLU Layer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vn4y5JF5xd9Z"
      },
      "outputs": [],
      "source": [
        "class LeakyReLULayer(NeuralNetLayer):\n",
        "    def __init__(self, alpha, input_size, output_size):\n",
        "        super().__init__(input_size, output_size)\n",
        "        self.alpha = alpha\n",
        "        \n",
        "    def forward_impl(self, x):\n",
        "        return np.maximum(self.alpha*x, x)\n",
        "\n",
        "    def backward_impl(self, gradient):\n",
        "        return gradient * (np.where(self.input*(self.alpha-1.0)>0.0, self.alpha*1.0, 1.0))\n",
        "\n",
        "    def is_linear_layer(self):\n",
        "      return False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pc5DJegxVC63"
      },
      "source": [
        "Softmax Output Layer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LnOPFfuTVFQe"
      },
      "outputs": [],
      "source": [
        "class SoftmaxOutputLayer(NeuralNetLayer):\n",
        "    def __init__(self, input_size, output_size):\n",
        "        super().__init__(input_size, output_size)\n",
        "\n",
        "    def forward_impl(self, x):\n",
        "        exps = np.exp(x)\n",
        "        probs = exps / np.sum(exps, axis=-1)[:, None]\n",
        "        return probs\n",
        "\n",
        "    def backward_impl(self, target):\n",
        "        return self.output - target\n",
        "        \n",
        "    def is_linear_layer(self):\n",
        "      return False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6YGV9SILi8QP"
      },
      "outputs": [],
      "source": [
        "class SoftmaxOutputLayer_minusMAX(NeuralNetLayer):\n",
        "    def __init__(self, input_size, output_size):\n",
        "        super().__init__(input_size, output_size)\n",
        "\n",
        "    def forward_impl(self, x):\n",
        "        exps = np.exp(x-np.max(x))\n",
        "        probs = exps / np.sum(exps, axis=-1)[:, None]\n",
        "        return probs\n",
        "\n",
        "    def backward_impl(self, target):\n",
        "        return self.output - target\n",
        "\n",
        "    def is_linear_layer(self):\n",
        "      return False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oeLqoInJXduV"
      },
      "source": [
        "MLP CLASS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nXOxVYPJR4He"
      },
      "outputs": [],
      "source": [
        "class MLP:\n",
        "    class Optimizer:\n",
        "      def __init__(self, layers):\n",
        "          self.layers = layers\n",
        "\n",
        "      def step(self):\n",
        "          for layer in self.layers[::-1]:\n",
        "              if layer.parameters is not None:\n",
        "                  self.update(layer.parameters, layer.gradient)\n",
        "\n",
        "      def update(self, params, gradient):\n",
        "          raise NotImplementedError\n",
        "\n",
        "    class GradientDescentOptimizer(Optimizer):\n",
        "      def __init__(self, layers, lr: float):\n",
        "          super().__init__(layers)\n",
        "          self.lr = lr\n",
        "          self.updated_params = []\n",
        "\n",
        "      def update(self, params, gradient):\n",
        "          for (p, g) in zip(params, gradient):\n",
        "              p -= self.lr * g.mean(axis=0)\n",
        "\n",
        "          self.updated_params.append(p)\n",
        "          return self.updated_params\n",
        "  \n",
        "  # Methods for MLP\n",
        "\n",
        "    def __init__(self, *args: List[NeuralNetLayer]):\n",
        "        self.layers = args\n",
        "\n",
        "    def forward(self, x):\n",
        "        for layer in self.layers:\n",
        "            x = layer.forward(x)\n",
        "        return x\n",
        "\n",
        "    def backward(self, target):\n",
        "        for layer in self.layers[::-1]:\n",
        "            target = layer.backward(target)\n",
        "    \n",
        "    def predict(self, x):\n",
        "        return np.argmax(self.forward(x),axis=1)\n",
        "\n",
        "    def fit(self, lr, data_x, data_y, grad_steps, batch_size):\n",
        "      optimizer = self.GradientDescentOptimizer(self.layers, lr) #CHANGE GRAD ALGO ONCE HERE\n",
        "      losses = []\n",
        "      train_acc = []\n",
        "      test_acc = []\n",
        "      labels = np.eye(10)[np.array(data_y)]\n",
        "      for _ in tqdm(range(grad_steps)):\n",
        "          batch_data_x, indices = minibatch(data_x, batch_size)\n",
        "          batch_data_y = np.eye(10)[data_y[indices]]\n",
        "          predictions = self.forward(batch_data_x)\n",
        "          loss = -(batch_data_y * np.log(predictions)).sum(axis=-1).mean()\n",
        "          losses.append(loss)\n",
        "          test_batch_x, test_indices = minibatch(X_test, batch_size)          \n",
        "          train_acc.append(self.evaluate_acc(data_y[indices],self.predict(batch_data_x)))         \n",
        "          self.backward(batch_data_y)\n",
        "          test_acc.append(self.evaluate_acc(y_test[test_indices], self.predict(test_batch_x)))         \n",
        "          optimizer.step()\n",
        "      \n",
        "      plt.plot(losses)\n",
        "      plt.xlabel(\"Epoch\")\n",
        "      plt.ylabel(\"Cross entropy loss\")\n",
        "      plt.show()\n",
        "\n",
        "      plt.plot(train_acc, label=\"Train\")\n",
        "      plt.plot(test_acc, label=\"Test\")\n",
        "      plt.xlabel(\"Epoch\")\n",
        "      plt.ylabel(\"Accuracy\")\n",
        "      plt.legend(loc='best')\n",
        "      plt.show()\n",
        "\n",
        "    def l2_reg(self, alpha_l2=0.5):\n",
        "      s = 0\n",
        "      for layer in self.layers:\n",
        "        if(layer.is_linear_layer()):\n",
        "          s += np.linalg.norm(layer.return_w())**2\n",
        "        else:\n",
        "          pass\n",
        "      return alpha_l2*s\n",
        "\n",
        "    def fit_withl2_reg(self, lr, data_x, data_y, grad_steps, batch_size):\n",
        "      optimizer = self.GradientDescentOptimizer(self.layers, lr) #CHANGE GRAD ALGO ONCE HERE\n",
        "      losses = []\n",
        "      labels = np.eye(10)[np.array(data_y)]\n",
        "      for _ in tqdm(range(grad_steps)):\n",
        "          batch_data_x, indices = minibatch(data_x, batch_size)\n",
        "          batch_data_y = np.eye(10)[data_y[indices]]\n",
        "          predictions = self.forward(batch_data_x)\n",
        "          loss = -(batch_data_y * np.log(predictions)).sum(axis=-1).mean()\n",
        "          l2_penalty = self.l2_reg()\n",
        "          loss = loss + l2_penalty\n",
        "          losses.append(loss)\n",
        "          self.backward(batch_data_y)\n",
        "          optimizer.step()\n",
        "      plt.plot(losses)\n",
        "      plt.xlabel(\"Epoch\")\n",
        "      plt.ylabel(\"Cross entropy loss\")\n",
        "    \n",
        "    def grad_check(self, x):\n",
        "      cumul_forward = [x]\n",
        "\n",
        "      for layer in self.layers:\n",
        "        back = cumul_forward[-1]\n",
        "        cumul_forward.append(layer.forward(back))\n",
        "      \n",
        "      cumul_backward = [cumul_forward[-1]]\n",
        "\n",
        "      for layer in self.layers[::-1]:\n",
        "        cumul_backward.insert(0, layer.backward(cumul_backward[0]))\n",
        "      \n",
        "      eps = 1e-6\n",
        "      f_r = cumul_forward[-1] #Final output\n",
        "      for layer_index in range(len(self.layers)):\n",
        "        layer = self.layers[layer_index]\n",
        "        J_analytical = cumul_backward[layer_index]\n",
        "        r = cumul_forward[layer_index]\n",
        "        size = layer.input_size\n",
        "        if (size is None):\n",
        "          size = r.shape[-1]\n",
        "        I = np.eye(size)*(eps)\n",
        "        r_perturbed = r + I\n",
        "        f_r_perturbed = r_perturbed\n",
        "        for lyr in self.layers[layer_index:]:\n",
        "          f_r_perturbed = lyr.forward(f_r_perturbed)\n",
        "        J_approx = (f_r_perturbed - f_r)/eps\n",
        "\n",
        "        if (np.max(np.abs(J_analytical-J_approx)) > 1e-4):\n",
        "          print(f'Layer at index {layer_index} has incorrect gradient')\n",
        "\n",
        "    def evaluate_acc(self, y_true, y_target):\n",
        "      len_true = y_true.shape[0]\n",
        "      len_target = y_target.shape[0]\n",
        "\n",
        "      if len_true != len_target:\n",
        "        raise Exception(\"True and predicted label inputs to evaluate() are not of equal length. True lable size: \" + str(len_true) + \"; Target lable size: \" + str(len_target))\n",
        "\n",
        "      return np.sum(y_target == y_true)/len_true"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.array([[1,2,3], [3,4,5]])\n",
        "print(a)\n",
        "cum = [a]\n",
        "print(cum)\n",
        "back = cum[-1]\n",
        "print(back)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tHtr2zFKqEXJ",
        "outputId": "6c372115-085e-4a19-c73d-1b2374f79da7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1 2 3]\n",
            " [3 4 5]]\n",
            "[array([[1, 2, 3],\n",
            "       [3, 4, 5]])]\n",
            "[[1 2 3]\n",
            " [3 4 5]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yJG833tmxeyW"
      },
      "source": [
        "Hyperparameter"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QNIi22TFxhYU"
      },
      "outputs": [],
      "source": [
        "def learning_rate_hyper(mlp, X_train, y_train, X_test, y_test, grad_steps): #For fixed gradient steps\n",
        "  \n",
        "  train_accuracies = []\n",
        "  test_accuracies = []\n",
        "  lrs = [0.1, 1, 10, 100, 1000]\n",
        "  c = 0\n",
        "\n",
        "  for lr in lrs:\n",
        "    if (lr == 0):\n",
        "      lr = 1\n",
        "    mlp.fit(lr/1000, X_train, y_train, grad_steps, 128)\n",
        "\n",
        "    y_hat = mlp.predict(X_train)\n",
        "    accuracy = mlp.evaluate_acc(y_train, y_hat)\n",
        "    train_accuracies.append(accuracy)\n",
        "\n",
        "    y_hat = mlp.predict(X_test)\n",
        "    accuracy = mlp.evaluate_acc(y_test, y_hat)\n",
        "    test_accuracies.append(accuracy)\n",
        "\n",
        "    if (test_accuracies[c] > test_accuracies[c-1]):\n",
        "      best_lr = lr/1000\n",
        "    c=c+1\n",
        "  print(c)\n",
        "  return best_lr"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_grad_steps = 1000\n",
        "mlp_opti = MLP(LinearLayer(X_train.shape[-1], 10), SoftmaxOutputLayer(None, None))\n",
        "best_lr = learning_rate_hyper(mlp_opti, X_train, y_train, X_test, y_test, num_grad_steps)\n",
        "plt.legend(['lr=1e-4', 'lr=1e-3', 'lr=0.01', 'lr=0.1', 'lr=1'])\n",
        "plt.savefig(\"lr-HYPERPARAM\", bbox_inches=\"tight\", dpi=300)\n",
        "plt.show"
      ],
      "metadata": {
        "id": "OHGboO4qEMH3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "afe14b03-5b42-4d2a-985a-8a3b9953f3cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1000/1000 [00:46<00:00, 21.51it/s]\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"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1000/1000 [00:32<00:00, 30.75it/s]\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"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1000/1000 [00:36<00:00, 27.38it/s]\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"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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qpa48nG6u5t/4cRDxbRCJgsUqEUkdboRQTySEQ/kYRFMyN94gI7B0MAhNaQ2we/j2+FJtFOnFFHQuKXHF0Pi9mDoGKsoghBCHCyFmCyHmCiHGKo5vIYSYJISYJoT4WAhxpLm/XgixXgjxofnv9krOsytDdpCVyIaG5CvAMksQRbmmqlGckTr8+p0qHC8DWpOQQXihu1RM1gIpmMAr7RNl+mz8xmfHnlxTcMMOgooxCCFEBrgVOAIYDpwihBjuaXYp8JiUcldgFOAs/jpPSjnC/DemUvPsDBj/5ucce4s/FUcsWCtJ68ete8L795dnYl0IJ93xNre/Nq9s47lSO8+aAH/dAXL+FM5njp/iaOjsqcYbNeciHeqak25/m9te9c/bjiZOOnETR/z9dR569wsAdF1nUvV5/DQzAcb1Zthtgxgsvlb2s67i6zXNnj243uF73yzYE5xV9wawgiMm7MHYm++Dvw437l0AZj5/Gy9UX4iU8OtHpnHFM9Ndp7zllbm88/kKQM0Eamjh3ZpfcrBWsPntfvXLNLXmiw6UO/xvk3n4vS9c57ux6lb7t5XETyIh12zvl8BB2ge8UXMu1bQaD65xOe/X/JxdxNzCCQIi7Y/RKlOauJISxB7AXCnlfCllC/AIcJynjQTqzN+9gS8rOJ9OB0uVMO65GXzcUFyRdun9tWQWPHduyXPranjv8+Vc9/ys4jorAgNsN1ckTPw9rG6Add/4ur4yy9wXU8U0SCxFtqwrzHvBcq5/wT/vUjWKMxev5uKnPjHH0hmifc2lVQ/ax7+nvanslzfjHWZ9tcY3kaB32MkgDs58QC8aOfKbe2D1Inj+wsA5/rnqDrbTFqLJHM98+CX3v/0/45Tm8Wc/+pKl63LmNPw3ZJBYwiZiJZdlH7D3LVnTzMLljYpUG9EMQkrJrK/WcNH/FVL1SwTfzxTulcUgdAnorY6+cE3VPQwSS+nHakPCWvge/cQafp39v8JJJl3tPKP964aqOyPnVwwqySAGAgsd2w3mPifGAacKIRqAicA5jmNDTNXTa0IIpYleCHGWEGKqEGJqV4x1KEvp2lTF1IbwP7D48WEJVEwx5IJkdgPP+J7GuuIiskJ9YdbKOW8PEX3iFkf954xpK8jHIE3NsgqA2vxa137n/HXcKibn5yACpCyp2BfnBsapNW3ZQqSUoLttEN0xJIocmjHPml4A9BQFDy/nPJxSSoauWZP6FGC8lHIQcCTwgBBCAxYDW5iqp98CDwkh6rydpZR3SilHSilHDhgwoE0n3hYoRwS4TUwq7IWRAtc9lk5VQqy+8VRMcce0R7MLF8WbBkBr3sNcdL9NIIgguVxMjR2R53MyiKw5boFBBN+PNXQDoMbLIBy/bQahyK5qreaF8DPTYgLlmnPONurrthiEIUE4GISE7hg2iSrr3lYbbrA9cdgqXEzBz1jLjUoyiEXAYMf2IHOfEz8BHgOQUr6NEU7SX0rZLKVcZu5/H5gHDKvgXDskymO3Eo7/U1QUTgbh1zpF9E0gQcSg9t7hkqRkaM27iY2e9xOfKIJUeN+SzTVjM4jo1NrrpBF95pcgHL8JNlJrAXOTyKJUTM2tfqbpPUNBxSRdrr+SglSWEW4bSA/UEoRz/hlRHmrhRSUZxBRgGyHEECFENYYR+llPmy+A7wIIIbbHYBBLhBADTCM3QoihwDbA/ArOtUOiHJ6p0U6GbYv1LXnqx07g2Y/KaG569ly484DAwyvWtVA/dgKTZvttAD588E8Y1wfyrerjr/8FrvdG5xt3d8JHXzLiyv849qjv+7/e+R8vVl/A5dl/OoYwiENOwlYXT+SNOUupHzuBb9Y0+fo7JcsdxXwW1I7mW+IzFtSOZm/tU47U3qH39f2hea1t45BSMnXBcurHTmDUDY/DuN6Bt8DJINZNupHt7tve18ZYCRtkbLvLnrf3vzV3ieM4iV9ia/XcGoNBrMZYYe+8/EXXfqeEZUkQr84yjOq1uVXGtY/rzQs1PsdKe8r+BZUx5kF/ftV4lx75mzHOhN8Zfx843pYgnNLHr7JukmcR9bzuVjFd8cyn9u/Xa85j0uwlHHuzYZB2qZhcY1VeK1AxBiGlzAFnAy8CMzG8lT4VQlwphDjWbHY+8DMhxEfAw8Dp0nj79wM+FkJ8CDwBjJFSbnAFDcqZiCtpqoFK4ctVxst+40ufRbRMgA/uhy+nBR7+9EsjmvWuyTHWGC9eCkhoWas+/t8rYb36VXzi/S9Y2WgwFrukpms5a/y+9OnpbKs1cGb2BUdv41gur5PXpe1RNe2Llb7zSEdU8kmZ1wA4P/s4AKdl/mMbNeXK/9ntdAkPvGNsD14VnvrdqfLp8do4hCIKOmupSnRJU6uDUJnXqBW5NCnYIEwGESL6fqIPNeaiu73DnAKWLs04CHN13nddtKeaVBkhTAY+f6nhILDjzL8Y+6fcZfyd91/b2J7VtMBpC+v6PAzi5Zlur7CZi1fb9zBIWquUWsmJiqbakFJOxDA+O/dd7vg9A9hX0e9J4MlKzm1DgXTqYDtAsJyVntin460gXAVkYiP5BJ0rOqUKPkxNYR6zn1fI6XWFobLgPlkYI5/PK/tEwWuDUMFWBQWMW6wE4bdBBCOHlanVc1+dDAJvydF4Nh5/qg33OVTqKUuCyGYEQTbjTACDUME2aLvmolYxVQrtbaROEYLy0HPHy9UBGIRFUDJtyCHsqOIKW2JUH6xrT1hNAkvi8MxR+cgUaSssQ6uOKDAIx1JaV6pN1GjNRa9MXYROgWKJV1ZYjCfaSG0RUM1DvFUqJicDjYIjx597p+vcKgZhzD2jiUCJ3VYxySgGIR0MQo1OrWJKUTrKoWIqrJsk8T6PyiJvSxBtyCDMvxU5pSxIZi6iYf50PcNQn9cAg6kqNbSCKNteOmg2g2jNOb1k/FJHELxGahUsCcLr2mmNnRGlSRC5GDYI2wvJw3il/zEUno2irrT3fsTxYlIRZ0uCcGZpDZqzrktyuQA7l9lOueBwGfRTBrFBoxzkPMhnvn7sBJpa8/DhQ4aRbb1f110JWC+4JgSvzv6G+rETaFjRGLv/9S/Mon7sBHYa96L/4J0HBpwzyZ002z75U/jT1q4j93qyiQIc8ffJrGoyPvSCK6Vkj4mH88+qa/n9yquMgC8IVDHVj51AU0ur8+y8Pmdp8Ayl5JH3vqB+bCHKWDhWyNYYa9YXCFCSxUZLLAYhWbq2mZNufxuAzVnKgtrRfCdjRDPvpc0EYNk6t5G9D2tYUDuaYz2BdgtqR3NR9kGb6J2SnQTAF8sbyT95FozrzUvVvwegmlYW1I5mdPYVwK1iqh87wcW0JIVcTLdU3cTx086IvLYzxr+nzOZ666RCRLOKydbNfpJDtKl8kD+B31U9rhzb6nfC7W9zyu2Fe3BLlTujroaOpvBMmvP1akebVMW0QaOccRDCGNB1bG1zDt420wCsWkhbwJIgMprg0SnGOZNEiVtpJZT5er5Up0kvSBAJRIi5L8E6d/DlTWaBeycEkmVrjQAnzaF26bF6HvtlPmGfFkcKhBAV08pGfxoO59zdO/PcaRrcC8FelopJs9UqX60qeL84hZeouxDHBmGpgmYsNgjWbpra6eDrVW4GMVQsBuD0rJPBG+f7eXaCLUEUjggynzwKwDaawWi70exqE5ZnyboXGXSOzrwT2M6JpWtbFNKm5C//KdQfVxHnzWbfzw8z/w0d27nqt+4hwNGZd13tNJeKqTCZxpaco00qQWzQKGcktUrF5FLztJF9wrJBaJqwV3oVVzfZNojSoCtVOwW40igoBwizQagMkurHInV/qJwzubY1Rs6RLVSXBd/+cqiYvMQpSN0RdC7nVXZ3EHwvg1DBO6LXBuGExSDCxlXNMSrdd7HqHed9CxtDOFRMQUbqVMW0oaMMNNvO3a/wYmpLTyILFpHNiII0ka3wRIrzYlKMo17O25+vKwhK2TS5DUIFXSGJ2BG6Dgkil3MEYjmGj2QQMYzUXoLrXA074dPvK9h0HYbraIvMkCUX2d67x+fF5IBOnHTfKiT3YpIxyKkzajuMaWnmkwxDKkFs4ChPHWlnJLXXoNj2HMJaXWc0UVA3ZSrMIKIkiHwrNbS4GyugIvxOAmgRqsAhQlRMhXodHglCaagMlmQMgmoaqR2SQJJ3KY4Noga3gbUUYtXLDARbSzc7viIM3nMFMYjuNNkMJny1rjiHwotJCGGnw1CdM06OLCdjCbtnQUZq9xxTBrFBoxwqJtdL6ysd6VxWJiDSn/3HMGx/M1N5uH7sBPufM1Pn63OWcNIdhlFTE8ImuN95fIRhYG5eY4xrlak0ce7D06gfO4FFKws69YnVFykimtUYPPMeFtSOppt0RKR+8Q6M683yWW8w9Q/7MKXml8EDjOsNj/xQ+Tz+XXMpQxo/Nq7JvJ/Vd31bOYwVGevEvJofAnDSHYatwnsKJbORkh+0PMuC2tHUCcPAXzBSF9xccw5CH/tdWjqHAx7ehgW1o/lj9t7AZodm3rd/n5SZxA1VdynbxakO1wvjGvqKtZyUfc11rF7zpxX3vqndc6tYUDuaH2cKdo3B4mtm1J5pByPGZWDDxEIW1I7m6o++zSXZf6FL82y37c1HVWcwo/ZMFtSOplohMcWpvTK55jwGsgSQ3Fv958B2G4m1jK++wRjXsX+EZkTOX5Z9IDVSb+goi5E6xAbhDuJKcK6Zzxh/G6ZENl3vyE/zn08LH7tTgsjm1hkGZssoPPUe1xhWWo7ZXxU8OIZr/wuMaPZi0LyHAOidX1HYOfdlABpnvcxI7TOb0AZi1r8jPYHsyNclM5THVatYK4dOkNpHrdXSObbVSG+xsVjpaqlLUVAxuRhEYakQSsYWFqKsf5R9ObCZTTiBH2VeCmwXJ4K/KmEmUi9h7NPyFQCnZF6x923CClebKH2/hW9pBUeEn2Unus7UU/jTnjgRR8UEsJ32ReR92VJ85ZqhFz/JPu+6JrnNobHOnRQpg+jAKIubq61e8dsgdGVOgSIGd+0KnrVTYtGEiJUeuRyQpu+7KgOpV6ETOk7EdKNEfg098P6oDZIBcRCykAfJG0mtU2AQzjiI2Lc6LJjPAd0xz2qCpYQgG4RwvQtJVCXSN6bKi8mbjjwT8xzO61onaxLMy+gbhyHGUR/1IJwZGeMUrklsf2xIy+KRMogOjHLmYlJBOv4v25g+HYm6neaQIPyd1Uyr2NshNSOjjPuj9Ke00BRSlhNRzyOKOGjIQP1+klxZUtd9jMTJYOxAubzTSO20lYScK8zTKgBee4QTfoNyuB0nzrl8DMIklM5zeQ3AcT1+dAdJbKQ2ll3BghTxyKmmYHJedPe48gaNYyPmuZMiZRAdGUUQxLwu7ZB/Y4iYKiYvWgoql+Zc3qWusKBydtGlpNbxcjtz9XjPt77F4/NuZVC1qrFJaQTzhc03KOuqiabWvP3RZ8izpqnVZRdxEs7uNJmrczXiqJhqQz7sjNBZt3atz8CZIU+taSQ3jOWe87S41V+qtNJOG4Q1VoujXVNrnpwuyZKjyrvil8Y7k9cluoNB5GQ88lAjwhiE+n5WkTP9rXRfXEPouWj12ROqdX+2Uy+DCFNjOYm1U3W2TiZjEHkpYrWvodX1DPLS38eZwdV2oPCgD46EkhVyFU8ZRAdGMQvm0+97j20vdWYJDU7WJ4NUTAvfg2s2gzmGbnnbS1/g5Dv9QUaXPP2Jbx+zJjCr9gx2FEYgV1CunsmfLbGDrCw03muIyZbq6bbX5rHdZYVrUSaGu2lX5fgWtrvsBRauMj6w9+YvYadx/2HYpc/bH9RjUwoBgtNrf4poDbZFuC5FMZfdtVnMqg2O1M2Qp+/ftuDK7HjX/nm1P2JCzSUA1Igcv8g8Zx/rvfxj41k48KsHp9KSt1RL7tXzMZm32Vn7HHCrmK6ZOItnP/qSyTW/YVzVP13j8d8r2fbSFzjrn1N5/uMGe3dQ1TgvgggY4PNKsgjoDtr/jEjz7GPcU/2XWOexzuV9Y7vl/Zl3vQxiTPY5XxsV8h4JIgnmfLMuuhFwU/UtvFBdSDWuK8iw5foLUBeQ7vuxmj86tlIGscHBXzYymmV4UzS4lTZefXAAFppRnfMm2bve/5/T6Bf8Mop5hjpmqugAACAASURBVGFzhGZEPDuvIcoM26PJMGJb7pnPTHPXjFAGcIVEgFv3y1oJq/zOvdXE4sPfbw8xW9GuAItYhhl/AY5yRPz2W+5PYy4c1pyC4dkYu5eDmOTy/uvdXCgM+1PuBuC/s77hgwXLQudmwblSDiNNPVETN4BvZz7lpMyrsc5noZdYH+iRVGwJTncYmiNqmZpEi7Qk0saWWqE2iYpBdBfxpSogVTFtiPDyg2J08DIkWtq96dRnmgnNAg2WIRPxvKhRhujQCFfP95aLkQJCde68rWIqo994EQ/DGwQWhFZHFn6hyPjpjr1wG6ld48QIeAMM92IT5Q6+6i3cq+pSLV51NAZqU5z3JalnlAUXg5A1yWwQRa7iVanNu4VIZUqkKqYNDzHtveFjOL2YPCME6tQ1k0EUYbB0JkcDdXoKJ9QGTvXLHicFhBNWdk01gyj1g1J4F0X0iBMEBtDiLNOiYNICtTeQF85UG+FweBQVSViD0Jt4ape4qBPrAu0azieaRIJwwkms11FLpVQ3TuiKc9QmZhCpBNHhsK4550qeVTTWfA3r/KL9qkY38UwcF7F2CaE2COdG8xpoNV3rrJctUIIwxvSqD9Y259DXGSouV2lFe/7+kaoDPGCWrnWL2HWsJdca46NpXQ+Ny8m1tthZYq3qZCpXx8jPf11wVlUvekT4ycfJMwTGKhkkG7Eacn5Vg4buYBCYf/031ygYJOlHnGSIwZJIcGvDJmAFuqmgykiqGicu6miMRbKrAlJ/qLARBQmqnyjYxXREqAHei/4iftJJJ1TxFVsIf4BgeyBlECVghyteZKdx/yltkLXfwF+GwZ+G+jxyjrzJH3kbG9/MhD9vTf9Z/wICbBBOij3+KLjnYON3TAnisqp/ubZPH/d3qmc9bQxhMYgIpqaSINa35hl51cvM+qrw4X5cexb7TPu9OXbISvzmkXDDED665Ycc/rfXzTkobBBxRfI/baXer7iufmKNomEBcVVM22tfcGbmBabVjmHHObf5jmsuG0QwYc/l8/w0M5H3a38RSXDOyJgRx6H5ovx4vPoPsY3ZKiRdn/cSjbHUYEnUiU5m8seq8fbvozLhpVm92EH7HwdkPkrUJwhW2vTYSCWIjokgL53YWOOImIwoQZjoTMuM3PWaNMZUrTB9TkxfmV5Jtg0i2Ye/g/Y/+7edftrt+uPrE1a60YstvzFSKYd+/KsNL5zdVhUYt6U2UBPo8hmpoxBXxQRwfCZ4ceB+llKxz0A+n+dA7UMABotvfMedOEAzCFtSO43lMdVWMNxc1ffebYMog2TfmZDaIDZMOOMPSoubU7m5BjTVimMQToiYEoRaBSE8W9HVvMJgMQi34TJe6utAFGOkTqD26CP8rpsWjDm7VUyqfIf5XM7n7RQ+ZvyI42JRrCHXgipQToW2SIXdsZAyiA0SzsI45cnuWkDgeKJ4I7UFZ/GcMISlarbg/diTEnWrfKVqVVk8IUn+LJKsageE2A1caSrsOAj/fPR8LpYx24m4zLdUQl8swhiEc39cdV6XQapi6rhY25xjyZqEfssxsbqpoKP3Llqbc3mmfbEilppLAF+tKhgT+7EK2RygM9cKbq7frGky9dfmOVrXw+ov1f0cqDd13nldsnxdC599vcY3/x6sZ2P8pU575leygyioLrxEPC5R3xzDwGz5mQ8WSzxJ0OIbjsEol2mjCAkiCTMKM46O0ObZBNpStwTZIJwMokdITEIxc0yKTVjOlh5bSH+xOqC1GjWihSGeZ+iEhs4IMZdtRUNgmy6JzqhiEkIcLoSYLYSYK4QYqzi+hRBikhBimhDiYyHEkY5jF5n9ZgshDqvkPEvFoX99jd2vDg9+igUF0VGW1jRx+6vz+f4/3uKVWeH6ZTAIxJF/L+i136/9BYP/uXdAY/O10PNccvN4Jtecx6kZ8/oeOw3mhZdVBBidfYWBLCGvS069+10OvXGyL/Xzf2t+x5M1f/D17SXXMqHmEg7XDCOhl4jH9bR5q/ZcqsiRM1/zi6oe5rWa37KbmG1/UEnsAh/W/tz+va4luXRVrlXtVVX3GdlscTAIhWpId7i5CuDFmgsDxywYuyvHIN6tPZubq28paYyTMq9xV/VfA48fqk3l6ZrLfWnDuzw6mwQhhMgAtwJHAMOBU4QQwz3NLgUek1LuCowC/mH2HW5u7wAcDvzDHK9D4stV0ZkXgxFuxA3L/7NgmeFjvnp9tCueUCQIyzQFpMt2SBA91hqEaKRmRgnPeVHdR4GeYj26lHZKjaZWN/HZVKxQdbOxvfaFMU+TQRRWwwlW4rT4IlUHiUKt6WKJdksuOYOoxOrcIugqSSif110SxCAR7LJr2yA6uO5+QIgrqUCG2m46O8bnwlJ6dz4JYg9grpRyvpSyBXgEOM7TRgJ15u/egKW7OA54RErZLKX8HJhrjte1oWAGTu2R93CSV6JQZyxO4xAbRLZb7HOupwZnbFvSOI5WaczDWuUXykfGH6eanG2DsNBEDdbdSCJBOFGMcTtTZhuSMabu+uuErucctoIoW5B7vGi0jw1iQ0aDHBB8sLNJEMBAwJkop8Hc58Q44FQhRAMwETgnQV+EEGcJIaYKIaYuWbLEe7gTIlyCCDIqxyU7sYmaHSin21GeNjmo7hHzbMb5nPaRKI8mLyzCbkkQul3XIT5Rr6bVJ0Gsp9q+l0lsEC4UYYOohPombOWv5/OJ3434RuqOibaostZecKZg8aEz2iBi4BRgvJRyEHAk8IAQ8VmhlPJOKeVIKeXIAQNCuGsF0ZNG6sXi8gzmIDqGkVYipZHZcR9tOlL3fLzCUJdUt/gNvV4EMoev/BlZ17caapfm1laGaYaxL0MeGqZCo1tNMX3RKnRdsuzzj3zpATLoNoPbjGX0yIWrlLzY1EwsZ63yq2QLNbQkIgI1opWcdEsQm4nlyJXG+iOJ66kblTVSx4VmSxD+6+jdWlg0Rbu5Guq24Y5YljDUilYj0rsDYaj2FQdq/uSGXQWhDKITqpgWAYMd24PMfU78BHgMQEr5NlAL9I/Zt0Pg4eqreLXm/OIHcNf9BOAg7QMm1FzCSZlXkVJyZdV9PFR9DWLxh77ub9T8miNePiRiXHUkdRD+9vJnACyYO5Ozs0Z50aMz78Ld3/W1PfrmN7jnlen0u38/Lqp62HNOaSfMe7v2HG5ddFKs81s4PWsEu2UcRPz6qjsTF5jJez6e66vuQptmpLwuNmdPUQyiaGYUMmYIg9g/81FsN1chJJdlH2APLTwjrRPTasckmGnb4JDMB+09hbLhtfzOru1WQsywnVDFNAXYRggxRAhRjWF0ftbT5gvguwBCiO0xGMQSs90oIUSNEGIIsA2QLO69jbCTtqB8g5mBaVsJwxSztfgSXcJwYa7qAmoVVOX9+73qqHilTAx8sdQw9PWN6YI4s0Ft/NSQkcn64sCpBtpHm5FIVVOjUDE5UfSqvojLqogEIcJVZZaKMI7Utac2q3wT6+Q4vnlcu5x31cZ72r+/3PePIS09qJBJKExmKQlSypwQ4mzgRSAD3Cul/FQIcSUwVUr5LHA+cJcQ4jyMT+50aVgyPxVCPAbMAHLAr6SMWSy3M0O69ck6Al3KAmHxSQXBb4WU3ncmusxhYVwrfUM8BHl8auilpyLBTVgz5JPFE9ASyhqLjqTuYComFYOoo5FVxLMZCWTx9pguiNV0b5fz6tlCkaLqGnfBotB3tUISRMUYBICUciKG8dm573LH7xnAvgF9rwauruT8Oio0h9eOLguEPUkktcqGGj+aNji/jwqtARHXGXTyUpZsnHUSriryiYr81Ijg3D3WHItCEXylEkZq69pU11En1oHp+RLnWaYMooD2ihR3PScP0Q+XAjufDaJLIZfXeXd+vGpbACycAi0Jc+FLd1TsDmIBWvOaQkF6WUiA9/a88Ll4ycXu2uxYqYvfmrs0MYPQA+o0CCSLVqynZ0g66DhwuqLWiUY7sVwcROXu2TIiiV0wknMIW1VYRoTFkvRivW10jrRBIEsw2Hc9tBeDcK3sNLfNIXQx0wltEF0KN70yl5PvfIf3Pg8ILnNi3VIjdfZTP49uqwiUs16E/TMfs91rPy+sPM2m97zxOafc9Q6vfRZG3NwE4cDMR4zL3h85m9F3v2v3jestlA8oTKMheffzZYE1dePCa4C9quq+2H2jGERcrx0/kjOIxCmcS0QvsZ6BwlhIxEnWt8FlQA1BKQwiL4vv6woCFQUFzyLZj2n61iEdUwmiXTFviWG4/Xp1jKjpFjOa88uEueEtCcKhQum5fEZh2zRizzeNyEvXBhfQUamYdohJDJNKEKrax8Y4OtmMFlrUPg5KUX1k0CviGy9KS63b5oij3uqMKqaTmi+ryLg6glv7/L6ovs1Ul2cODqngiOZrmSHrgxunEkT7wuLPYakv7EjhQp3PhPAHK+WqehZsEAmIUik2iLAiNCrkA2pFZ9DJ52VAWdH4KMW4qxmWnJLOr0KSdB8dAXHuYWdkEN4o+XJBRyAyVUX1bSnBtOu2QRSubT21itbunpVAyiBiQoshwhU8dqy/CR+aVDMIm1AnYRBK4p7MSB1X5ZAPKHRkxEHopTOIEmoUZNArIn2LEmpltAei4j0EHT8PkwqVshVIBEIrjjyGB7SFw/VeOaQCVd1qd8eUQbQrNPP+h0kQdiqJ5Z8bfyMeWi6v89QHhfi/RSsbmb5olStnTy7b08EwQj7gBW8YZUZNNKzw6/3jvEJ7aTPY19SVx11RLliiTpD2bW06a7+ax7e0z+x9PVjPyZlJ1BE/qVrxwWzw7cwnnJCZXHT/IPTSi6s/3F6IIv4jtc8C64N3ZFSMQUhBgqQOLjRTnORhwPGcHEbqfBSpbi83VyHEMcAEKTvZkqnMECax92a7cMI+9q/jrV6hY946aR4vv/k5368xtr9/65t8w0ZcnC2cJJ/pVvBi0i1Vk2Kw8UeZPx4C4E8vzuYOjyo0jorpkeqr7N9xaw0HqaLOq3oS1j+J83s5JvM211XdzWYinkeYoDQJ5AeZN4ru2xbQpQioqldexJHCqjuhF9MyelVkXIlAK1KCaJHZojU+azfdi15fmTHBrgTWcZKllB9x7sDJwBwhxA1CiO0qMotOAEsYCPuUfcnoIiSIRSvdBdhVCdN0LVtUHIQKlUpkloTAWVLJxsTLy5RFp1eJbrJeLJL9yjpeKYhcGZYJ5Xr2s/TBPJbbvyxjxcW5LWcr9x/fPI51Mn524STQEQgtnn1jx6a7XdtDN64LaGnioEsDDzX1LZBYGXL+L2uGune0l5FaSnkqsCswDxgvhHjbzKJaGdbdQWHZIEJVTL6o4XAGITyx0KrMnBLN4eZa2kdefNRw1LjxhUvrWrqJeJ5NGjp1orwMoiNl/Kxqo1V7Oe0LlXqPghCmf4/UzRd9Ti22BOFj8lGla7LBBmcXPQgh+tW6x5uyPW0QUsrVwBMYNR02A74PfCCEOCe0YxeCnVU/hEj78g5FPDQh8EgQBlwShMg6AuVK+8grQxhl7HFbZMY2fHeL6fqaJV92CaKSVdM6Ksp5zRWiRYEIYgISUTEGISG2kdqX6ytS8ghJ/SKcv4PbdRgGIYQ4VgjxFPAqhjZ5DynlEcAuGLmUuizWNud47iMjcZ4lQXy4MMg4KflqdROvzHLW3HU8tIapMGsizJrIwtfuZ/qiVb5n+oPMZDZiNUdkptj7dJGxP+4V61rI5XUembKQHqznaO1t3yyO1yYHeh9VYuWXQWcbES/RbrXI24yhO/Gq8H1Lm1N2CWJDLHUzJvtce0+haAQlWxTI0ESMpUCixfJcBMX8otQ9oePGO2e19DqhtJ8E8QPgRinlTlLKP0kpvwGQUjZipOvusrjkqU845+FpTF+0Cmsx8fB7XyjbCiQn3v42Z46f6tjpeGh3fxceOQUeOYXBk87lilvuQQjhWtn9rupxptWOYWNRqO+gC81eoV89YQYzF68B4Nqqu7ml+mbfPP5afTu/zDwTOMdyQ0Nyd/VfYrc/N/t/AHQTzbHaP1B9HXUkTFkSgOWyJ9A2EsSDOX9qdBU+0311sCqCsHKjSdHWKqagWIdTdh8UyiByMoK8ba1Ik2/CkCDi2SD8Kqb4TOsDT3S08Py+JXcczdLvS/Ri/zM852w/BjEOR6ptIUQ3IUQ9gJQyunp9J8aXKw0u3diSDxX3wHiYa5u9K/fgPj1EE5qI5vtSZFyRza2mq9TmIV5A/QPq9laGQSQjtpanTA/iMQigbBLEt5rv5KnsEUp9/LWb3kiLLF/Q1R35o0OPL+02hPqmh5gnNy/bOYuBDNGHW3hP39a1PaiPv8/lraeVbU5erJbqzKon7jaIGVceEdjvF62/CR/45Ad45nszqG96kH/n93Id0hN4Mc2/1vOstSjn0MJXPz53GO8OcyhiHHRGCPhz7mS2bTZql0y99GD72FsbHUN900MFBtmOkdSP43bAz5v7NihoEZRcSXxDmIo0TdRRRFt3GKkF0raBFEPqK7HGKJbpJLEr1JXRBlFTlVHGVcgyqypaFKs+Fdpd3ZWPtgV5Yw1Ur3Ulk9utDkxZLkMJY3SUtfD8dY4c34vJd0Pi9sOs0xLQ3jtsxrHDfl9F8DWUA3G+iqyU0n6LzN/lSTbSiRBWe8E4riKU4d4XQkS7iOqi4OYqKDgyFfNBVkK1Uqx3TK8EUkGdKI+KCaC2uko553ITuLjRtG2trvGdvxOEN60JcmWVeigxjhNcFqQZMCSIIt+JKC8mp5SA21vJSWe8NMc5n0LSBiutT/tJEEuEEMdaG0KI44DyKTQ7KmY/z6BcIbldtASh2hnGIAwj2AgxN3TcuUsbHV5C0vErXDpRoTZGuu+kOD9bnDCZRIIIS2mdFDVVWXWEeJFBUUGIYhDWM+pILrdB8EkQbZyocE1Q8R5ZogQRKuFraAkkARcS9nMxCJcXk7tdxkGEpPe629EGMQa4WAjxhRBiIXAhECePdefGw6O4calRc1dKGcMGkUyCsD66sVWPhI67ZE2zQ8WkcKUNnE/b4MzsC759C/UBkf0yCYLr+lO+tBbVWTWDKNYb5tn83sr9UQnb6mqt8PL2ZRAth14f2UZ60ldvNSC82lqD7F/0fFoVdqA1dGNddiPFxHSDQfTaDI7+m+9wPsqmFMJcJJCJIvSD9oDegxONawxeeOaGismZcwn+njueGfqWvm5uFZO1wDAlwExllDpxAuXmSSn3AoYD20sp95FShi97uyCKYtBhKxQpYo2pIW1iKmLGUpeb5CRJqTxdr+f01gtKPucz+X3s33FTfsSBpgklcyrGn76+6SFeye8KwNOO+UL06rWmKsMxu2zeriqmYblHYI+zIts5Z1iT1ejb3SBGH+pbKduf1/LLoue0TfMDivNr3LTb87DNYf6ZCQHnz4KRZ/j65SLJm1OJ434OOhrdayNyKv30JThPUeMjoQThlFRyecmNuRM4suVa36LUKeT6FjQ1EdHbRSKWolQIcRSwA1BrTVpKeWVFZtRBEeUTXYwNIo6ftVMFYRipjd/FqJiKRRLiKSl+Ne5Eo6wpeQwVgtQGxRqpLXtG3sMQou+BcPzfTpButUWijrjfMxlIassI7/cSYT/xPpPI8ZxDA92qi8zKGmWDcDb1SBC5gOqMAFlHO983Xts7/vwSIE6g3O0Y+ZjOwXifTwT88k8XR1EfckgnPdLsbQ3hZBDJakKUC0kIvkSUJb/QeirDIIKiY/NFkuqM6bab9/jcx2Wq7SlBSGSkbc1o5zScSltFEjTzing0SePs7n3hDCJSghDBUryORveaUrKyhsH9TTufQc5ZDsLTy/WsvBOvinZXLgZxvuR9pJQ/BlZIKf8A7A0Mq8hsOgjmK9JXez0alq9zuwcWY4NY//ZdkXM5KDPNdQ4rIaBXL+w/c/kIT9IPvhzpD9ZFFkgpDlqAfthL4OPCqpftJ0bR90DK+GlKKgEpw9M52O0C9gQtHCrm8uplCBG3LlKCINw7sUfFGITj/ML9DjglCO+jifOsyo04X4WVE6FRCLE50IqRj6nL4uib/Smivc9m7JMfu48njIPQ0Lm66t7IufQTa1zneHOuESAX9m1UQsX0Wn7n2O2jmFccrC+ziumBnBFktKlU1/H+3rcGFTWuFVMRRozmqKKlD78WaF8Jwpt9eJ6+GfN0/6f97/4/YdnOPwPgtm4Fm8WbNd92tXurel++2v1C6roVSVi3UwcXblJXw+g9t4A5L7oPDN7dtXlJ65mubZcdaHvbEZPJ+Z34UPdkQ/XAkCDKaPjt5jCy73C865Dza9lq417sP2wAVxwznG9v3Z+BfdQuvtajm7b972D7Y8o3Tw/iMIjnhBB9gD8BHwALsIoOdFE0tvi9XLwrjXUtOc9xC1K514tiVo4CyJqSTCVWaUFpsHU0bsp9P9YYRgI1/2s1X980UaRyuSWIy3OnAyCy6o9+cN+eRY2bsSWI4Gs7pOUG945fvgND9zfSOcR4D5oyRqDYxa3lzWzj1Vae1noh323xpE353m1c9+uf8M3el1Pf9BAfVe1id/z1cd+GkQWifH3dxWx61MWMP2PP4iY06kHl7ncvPpgt+7mD5eQVK3169wfzB7u2XZLsyQ/ALqcA8Ky+D99rucrVdrPe7vdNR9BTYaTOF7H4mfftv8CFCwo76jaDXUYDxvN3xif1qM5w/5l7cMa+Q+jTvZo3xx4UOvacoafDyf9KPKe4CGUQwnDQ/a+UcqWU8kkM28N2UsrL4wwuhDhcCDFbCDFXCDFWcfxGIcSH5r/PhCgkIRJC5B3Hnk14XWWHV1frNTBbH3qV04UyTIIoqkiMpFt1NJEtlnkEqVl0w5QWawyJOkBJIMklKMXYWGYbhDX/IDG9WCN11pYgwvp7X56sddJYaNF6JGleNJSSn4cIG7xBHZxlz69CQVtOFKVuMRmbHoPIS4RSxVRMDewo7YKIuagMGLyiCP1ipZS6EOJWjHoQSCmbIV4SHSFEBrgVOARoAKYIIZ6VUs5wjH+eo/051nlMrJdSjoh7IZWGlyF4vT8KDMIhWYR8KMVENVdaXx308idlOKr2GpLWBB9Xoyy3DcLyGlLf92IdaS0G4b13ocZf05NKImNJEM3ZntD6TUXTWUCATcFkEK7X3xY93PMJ2N1xYNowXEb3QCO1oJtCxWSoEiNqtfsWj8pG9q9SKgpW+lbHYfX/FUL8QCRn2XsAc6WU8830HI8Ax4W0PwV4OOE5yorl61q47vlZymPdcysZm33Y1jl7GcaJmdcAyLpeHgHrV8ATbt0owOHaFN++KAgkubxkdOa/jNCCQ1E2Fiv5XfaxxOMH6dF1tETEKchIXRuzBgSUWtc3GEGpJYr9RDXbzdX9KYV+Lo5kbnGYfnPGUH9V2l6hlIKU7pPqeRSidCpEtrRS3wm/e27hkL/YVybjvx/RsRVGX/dW8PJDIN3vZEIyW2nDdZyr/TlGcr5mIcRqIcQaIcTqGP0GAgsd2w3mPh+EEFsCQ4BXHLtrhRBThRDvCCG+F9DvLLPN1CVLlsSYUjiuePZTbn9tnvLYgQv+ypjscxysfQD4GcQVVUaAj0vFhISXroDpT/rG+3H2pcTzs7yYrqm6hx4h6bKPzrzDVtrixOMHvfw6SQqzCFbSk6WyzrNXUiMiVl4mXsmPYLmsTMHCIAaxaV1N7Pz/UEglnXUwiHnbGDaCB3IHc9/puwf2dTKIMKL/dH4f2PoQJm59OVP1YUzMF6nbjwmLcE7exbSZbLkv9DWMuUP792Tfrftx/QkOZwUhXIS1IEFE38f7ct6gNwWGH+cuzxmZJdW8Z3ufzWv5nVkcYFM7aPtN+MOxOwCw37ABjNxyI/ofV7BJLB2wJ6N2H4yK0UW6fNcNghGGrWMx/Xhf34bVWyqudf/fM6d6OC/kd3e/A0PilXO1ehSbLiou4kRS95JSalLKaillnbld7rC9UcATUkondd1SSjkSGA38TQjhC9uUUt4ppRwppRw5YEB0eocotOZCglR0Y/VrrRgViwujnZNBSAl6PKIYB4LwQJpSEZQ/SEdw0Pab+PZflv+pb58RB5Hh1JaLXfuTvMc/az2/KF2vBYv4vJHfwXcsiCBXacmSs1nSViEOIsPsnX9PfdNDXJY7k/2GhbyPJqGTMvy+XN96Cpz6BKu7bcEJLeNYhduQ/o6+feQ8X9b2iWxjwVoELB50BIxbBWdMhGrD/lGd1Xjwp3sxYnAfpS5JIhy8Ivo+3pM/MnpCB1wE+/2+sB2DQfym9Ww47GpOax3rf5/NCR67y0BO26cegJ41WZ74xT5sOWwXu1n/s57huh/srFQRR76XP30JuvUFoIFN+UHLH9Br+/jbbVTPlZv8jdX0LEiRe58NNcmcJSrt+Rp5x4UQ+6n2SyknR3RdBDgTlQwy96kwCviVZ/xF5t/5QohXKdTFrhjCbraXsAStNqtcq2SZOOw+DEJIRd3r8iHMBqESy8PUTn6VS/x559Fi+bAHoclMNqyaXbmyl1rX57RBxOYvjkjbsPsStUoMqpPgHiO5ajAbN3Ghx9AqFfsD5xXndfBGJGeSRTb7pF7r2UfNLxPsIBEdBCooxIkY5wl6flZ9e/udTGDctwJm44XbFo84d9zBwqnFsC28D4T7X8EUYBshxBAMxjAKQxpwQQixHbAR8LZj30ZAo5SyWQjRH9gXuMHbt9yIw40L3jDq4y4Vk9QThd1HQSDJVZBBBEsQmtLDJQmDSIbSorEt+4VKWghjEEk+NWt+GfMceYJTR/tgGalluIopyvMquE6Cc4z499G6pqps/GRz7t3xbRCxGJd3cZXQBuFXi8b8diwG2eJPMx+dIVbzGcODiLhZ+6sgQRTh/dXuEoSU0hWFIYQYDPhTJ/r75YQQZwMvAhngXinlp0KIK4GpUkrLdXUU8Ih055DYHrhDCKFjqMGuc3o/VQrHLb2bwRnJHXlV4Ik7vUDfP0/HxQAAIABJREFU3Ddck73b1aKGFm6ouqOwY/FHxr8y4UeZl/g//fSyjedFLiBOQQ8gY1LxdlrtfBJEQgNrPGOgGs3SkCDU3iHllSAyLgkiLoOIZ4OwM3YK9aIksE6CYow4sJhJdSaqj1rFZCNWdHYRDCIhAfWdw66dEPOeNK/x7cpLLZz/CWEzCCuNd6CnlE3ykksQbYViZtSAQcAjIaWcKKUcJqXcSkp5tbnvcgdzQEo5Tko51tPvLbMG9i7m33uKmGdiHLbiIS6qUjtSZT1y4qlf38Do7Cuufcdm3mJ37bOKzW9n7XNaK2iDCFod6Qh//nlgzP7b+PZZH6VXRVSlwd82vtrY+M753m6Kcxb/sTSFSRDmvlxVQdf7HjvARkMSfaAWg/hH7lj+nd+LR/IHIoC7fjyS8w8xMtG07ncxP2v5rb+zZYOIcHP1qpi8DMhptF6xtSM697h/sLa/4SHuyz31o6fsn/ecNtJ1KLaK6YgbYKcTYeuDca7Kk9ggYi0YYtgcgvD4mL35/q6DmLTRD1j/o+fNvQm9rIZ8B3Ye5dqVI8PZLeeweM9LAjoJqN8Pdj6ZpsP+woHbDqC+v1rSs59vhARx+6nf4oLDzbKvox+H/S+0Vc2x1YFFIo4N4mac1wIjMCKqNyyYd6BHjXHLNIWqoi3SJhRrg7g59z3OyT4d2iYoTkEGuLlu0S/YoOYNutu4Vy2/+eXZwNkw52XgL8p+NkTxxKEppOChVewme8I98PDJAJzP+byuaQgtC/l4tbItBraCOs5uPRcwNBMHbbcJhww3DPr57/yOl/7jr5fhliDCYEoQmlqX3eiINt/o1Ptg3P8ZG7v+kCXLGun5xodkMhm32/5WBc3wdz2OBxaDiFQx9RkMP7jbt9u2QniXzPuPhdeuc+3S4qRwL0E9u3t9X3av7ws40tkklSCyNXD8HfBxoWaLLjL8W9+bC/c8UN1HaJCthuPvZH9g/73UzYzpWHaEcAZx+I6O9CfDDoVhh9Iy3nCTr456ViUizlc41fE7BzwspXyzQvPpwHD7UCsNoG0wi2JtEHF6BUU66zKA9SleaOv++FRETg1ijFWPSmKJi2YZZoMwbUSO8RuFaexN4FCgkrZ8+fuDCFFiG4R6/DA7TS5vXGcmwQrTYnpVkSomNQIlCMV9iFWq1idBlLoAKz1Ow3Jv7lUbQDoTGAWsT1krwkjdYmoSOgKDeAJoslxQhRAZIUR3KWX5Ksl3CnhernaKFi1WgohjrAySIAJjICJKqnpnUOgXTYj1Ery/WmKomJxMKm/NJ8E5VcTZyxACvZoc7cIC5QoqJqN9xjN+WGxKU4tRXjabjX9Num2DSE50XMoyXxpS/3jxGIRn7qWmuk8qQShgPfeeNcVLuBZKsUE0my75xTyrJIgVSQ04rWHdgJcrM512RMjLJx3HR8sJLFnTzNI1/qjgtlAxHTn9vOhGCsTJPxPkxRSUgE8tQRjwrbCd9zfGhyBLUC9Y16G8YsVqzbYvJdB5q/JWReXr8k2FuEZqY3t9qzuJZJgE0Zo3xs1k43v+2CqmJERHWGlDnAzMe93++xDPBuF9B0r8vqzxSpBOrcVHNugeJRjbeu9syTDBAqUl13EkiFoppV0gQUq5VggR7YDd2eBiEMEv4u58yrhJc4kRB1oRHJIpzvwTJxI6yIspT0a96gohgD1rvXYAp4rJfZ57c4dzaGYqg8RSx9gxXs3dzoD37/PtbrYZRIgEITTkD5/g5bfe46HDTUVxAqbkZYCn71PP3kPdkbtRC1Vn4rvZ+iC21RrcxxV9Lm09g6uqjGs+/7Dt4VX12MMP+wnvLnyf7U65Hm55LnwiJnQEdbVZdhqYoDrZQZciEWzEj7hzb9NpwV/IwNft2IMOAIWi+s8n7gLWdIOex3fVuUKvPG4HhvYPCTQ78s/QcxMYdrj6+En/hNYm//5D/giDdoeZzyEGjuKSFSHnSCCd3HjyCO57cwGbfPc7kF0Le46J3ddiEDUdgEGsE0J8S0r5AYAQYjdgfUVn1R5wBHEHOHXavzJae2bxLw5BboXN2Z7U5Az+H+TF1Gp7+3vgWC3N0zdjK20x/XvWwnL49aHbw3+cEwhWMX2q17Oi17acv/7vheZxVmKHXaNkENYqT6W+cdogxDaHcMg2hxQOJpAgvBLVuGMVUdsxiIXV4pLWM3mi5krXMeuZOW/dv/KH2AziyJ02D2QQtd16sOc590ee330+jUuPHp4oopzufRFH/wV3FfJwFZOuVfHrQ4YpGcQJuw0qMAjv87BuxFC1gfjHe9eHz7XnxnDkn4KPDw9IFbev4YTAlnszAsNLJxjx792gjbpz2dHDjY2jb4zdDzqWDeI3wONCiC8xrn5TjBKkXQu6k0Eo4PhKvS6vhX4dl20ESRDOUIEgG0SODChVVGE2iBD1gEKU9hFToUVrFAJE8lYZLEEUVEyKvgkMuqXEaTgmY89RpcKzXYaD1J9ljNIvKyIkiNjBe0Eqpg4YL2Cjjaq+WRJEInVgEYgTKDfFjHY2HXGZLaVsreis2gMOt9UoQl9coff2RfBHWbjuIC+mVrJqCcTxodrHzQ9E937EUTYID3EWWgb8dZsCz+9EQRIKiaRWzqF4CaJYaKEMwvwb9Dp2WEIZLkHE9lALUjF12OumzebWVjaIyNGFEL8Cekgpp0sppwM9hRC/rOis2gMJVEz3vPG5coj9tY+V+zsCAiUIx3UFtWklo/6oFQzC4p3ZTIhx1LMylPi9MWIRkYA21nWorkZWmf4WqvklYBClJBO05+Jwc1Xfe0vFFMAhypjGpayI8GLKZ6NThADBEl071GaOj7aZW02VcW86gg3iZ1LKW60NKeUKIcTPgH9UblrtAIcEoXQ9dHykzTkdVbmCwzPJazy0FQJlIsd1bSJWBPTV1CO4GISBgRt15xc7bsX3dxsEEwNmILwMQnDojpu5dNL5TDfY4xx46+agmRvn/95tRs2CR4w0X58OPJGdWvrAEhjavzscOwFWL7aL5K467GZqP3sYBu7mHy+AQfy19QR2HNSHQ78uBIcVLUEc5/5sLHKSRzOinJtWw+OnudpYns1D+/fgplN25ZJ/nMmH+tZMKFbFdOL9UONIpz7mTf7v6cdhQaXIW2HUq1tHs++hP+YAgB8/C41LDfVu3ebRw8hUxWTh/jP24IVPv6J/z/JWXvQiDoPICCGElSvJrBRXxmreHQR6sAQRlZZZie79jZe/DbFW1tJClr5ire9YHIIWVuVOrWLy5+LJahoXHr6dYoBwFVNtlZvjZjOakeo5lEEIGGHmf+y3DSybww7f+z1XtayDu6BXTQbqv20cf2iCMY2em8KBFwWM5ye4zVW9uanpeE7ZZDCHrnkKGpcB8ZMRHrDtAF6d7ahVsusP3ad0BmBaUc6PU9hHwV/+6F02Z8eBvQu1l4sllDt4yqtsuiNv9s3DgobyWNG8WQYc78ld+aMZ0bPe2Bi6f9KBzfE6IoMQgGyzudX378GY/X0VEMqOOAziBeBRIYSVhe7nwPMh7TsnQrJ8SmTyD6edxOAgb6Wg/U5mWEOwaSm2DSJwAMf9jWGkzmYEZJOUHXUEMlrzUjzTOKVA1eO6UVq22sLI4UZqA3bErS+8oJyp5Ms2FOQ9MUK+2tVFsqGOLEHYxZM6svorOeIwiAuBswDLSfdjDE+mroUwI7VK4xT5IrT9ixJUtwFC4iAcK/vuIeXGoxmEvTNilqgJm4dCVWc0yCQQVJ1RstZYKq1YzFKgUQiKGQmaVhAKEoSir8cG4Qu8q3CitqKR87xHZSfoHZEIu500ugoin5yUUgfeBRZg1II4CJhZ2Wm1Axwqphuq7nQdGn33u6xZH7+eMtDhXpQ4EkRYkrtuqtQCjmu0Yg/opqieBdC9b+G3irCpJIhE99AhQVgFXxRzCZUgevT3T8v8602tEFeCqOsWbKzvVZt1q5g88KqY/BJE+QhvqVksQlHljqstPj1EB5YgepRe0bIjInDJJIQYBpxi/lsKPAogpVRHqXR2OLyYjs287Tu8Yl0zyRxXyscgpujDYqcRD/rOgyWeguR0Re40vpuZ5msx9ojt+PH2GfBW5HB8qFuOOBA2/UnBJuDEEX+CbR3Rq0oJwv3Re9MYT9fr2VFbEHANuCWIAdvC4dfDjsf7moVKEN+7DZ47F2YWIo+rMoZN5fR96uHTQtOdt+jL/fvvwWn3vhc8HnDV93Zkp4F17mrrJq48bkf0L6qhMYhBGNDtSzPaPPOrfflqdZP/Po56CHoqhPtTHoGHR/n3K1CWt3bzXeHQq2GH78PHj8IupzBtqSBTtzljm7fkYEX5WhfGvAlLZoVMsmMtvgA48wVY8LraO64TI0ymngW8DhwtpZwLIIQoLhFQZ0CZSlHaCHmJJ+T34KhMOGFx4sHcwexe7WcQq2R3eotCzsSwRWCcQLnlAaXGx+y/FaxqUBwpjNmney3sc7b65Hv8zH0/YtggvBlFP9K3YhOxnAFitfocLi8pAXup0xaEShDd+xp1gR0MQgC/OMBvDOzToxv7h9WdNtG7WxVn7beVkkH07lYF3augUf18/BKEsb3L4D7sAtDqSWiw3VHqSQw9IHKeZYUQhXfhO0Y9jF0PPRWAneP033RH458XtpDYASWIvkOMf10MYXf6eGAxMEkIcZcQ4rt0TOVfeaBHRWUlRViUcbIXPE4MQ9R5gyWIwhjhapOIRx9a0NvrF59cghBR2T9jZuqMTn8R8xUvl4E41DnCnWqjeBVTF/tsO6IE0UUR+IZJKZ+WUo4CtgMmYaTc2FgIcZsQ4tC2mmCbIUKCSJxGI1Eq7HAE2w/it41jgwidl4oYOe9Zko/WFygn/BJE1ssgCv+HI7xN2YLgy5XmQgbbICzoeoCROi6TilUCtOOmiSmgA9sguijiGKnXSSkfMmtTDwKmYXg2dRl8vbqJg/6s0AEAA1jJgtrR7KkV7PILakezk6aOprawaJUiK6SJpC6SQe17CW/OxGBCsFqqE/AKJA2yf/S8lETGSVQSUF5lVLa7/6Z17gCgVYRk0PTNJRiRdaN9x4PyIJVeD8A5vopBVJm1HPQg4WhDkyA6sptrF0WiOy2lXCGlvFNK+d1KTag98Na8pYFBYrtrhrGsWrhVUHUivF6S6oOfoW8J+Ffqf8v5jalOJJE4gsjkD3/yG77ca5yRAfW3s/hosxPsY9+c8Ay/ajnXOM8xNwWMoCAyUfmVTn4Qjr9LMZS/rbDUKQiu+f5OXHGMkR11VMulPJw7kP3P+quhsw+CrZ+OUjGFHiY2MTUliFfO399X2zkRbAnCj4nn7gf4bRCFOcTNaRR9TaJTMJGUQbQ1yrUM6tRozcnAyl5hFb+S4t/5PRmu/c/nUviuvn1ov+iYC6tdcNu9thoAWxV8DGZvfBS7LH4CgG/ttCPHP/g/48DgPdWDq4hMlIpp+6PVY6nUMyaxE5lqRu+5hb37HX047+jDWbDFxhDqHulwcw1BcgkiqJ1xDUMH9GTogCjpJgzBgXJb9DNyFskgN9cNFumNaCukrBgjt3pQCcRyMggLXmKgK6qTudtX4IMIIoSBq7MIBpFIxaTSnZv9syXmlilZgoiJcqmYzHuofsssLybzlEVziC5CUFMVU5sjvdMYqXODVEyR3jMJYH2mec8H6932IgmDiC1tBKZSTsA4EpYRtRHm5pokejpoLmGnLpcXU7mimKX1RxWp7nZzjVOASIkuY6Q2kTKINkNF77QQ4nAhxGwhxFwhxFjF8RuFEB+a/z4TQqx0HDtNCDHH/Heat2850ZrX20TFFJR3J8rGENeo/bncNPZs+/QIyHPk/fj6bW3uj1IxJa9jbA/j7F9soJHlgx6Rv6kkE0RfRzxEudxczXk3y+Dr3riXcU19uxebIzM+YymaCbUJUgmirVExG4SZ9fVW4BCgAZgihHhWSmnH40opz3O0PwfY1fzdF7gCGInxVrxv9lXnoy4RLbnyq5jCMqPGZhBaFvRc7ApcZ7RcwISai2O1PWSHzeANxQEngR79uBEVG4QyurnKalOPr+ei+5/xAj6lzEn/hC/eMcpKhp26FAli9KPw5E9g3ivlUzGddD/z3n+Jh4cdGTiXXx64FfX9u3PkTkWmQOvQRL8IdNbrOXsqrK8ICasYKsmK9wDmSinnSylbgEeAgKKvgJHS42Hz92HAS1LK5SZTeAkIqDReOgwJIoBBiOJUTKpXOKg4TKAKqc8W4cc9WEFdbBWTlgkgcM4V+LBDoacZLRwlQZTo5iqrzShub6I3FfoOgS33ce/r3he2UxFZz6mT0hYnH+reF7Y2U22Xi0F024itvn0SW2+sMHSbk63KaBw3YmAHX923AWIGQ3ZY9N8GBu/R3rNIhEoyiIHAQsd2g7nPByHElsAQCgkJYvUVQpwlhJgqhJi6ZMkS7+HYaMnLYAZRpAShCqyzXmu/BBH0wouI4yUgSEwPNBJHxEEk+WgVbWWtWcAmFxw/Ug7CEElko45bEfdtUQ+6XIQwwTiB1es6BFIVU1ujo9zpUcATUspE+S7MmIyRUsqRAwYUn02xNa+TCbRBFCtBqBiEsc9rU4hSISWLgygxVUSQDt82IjsYSLE2CAVkjZl51VtLoM0Rcf9kGzKIFG6kXkxtjkre6UXAYMf2IHOfCqMoqJeS9i0ZrXmdR2v+qDxW7Bpuk4K93QcvEY8yQscm+kkQ5IUT6EVkzmELR5xEd0d67DqlcBgbupUSehNFkjYLmxjBc8V4Om01IGYd5KjVtpUttc8W4e3aEpnSy05u2de4PwN6VbaEZXnQSVVMnRCVDJSbAmwjhBiCQdxHAb5c0EKI7YCNAGeO7ReBa4QQG5nbhwIBdSJLR04PFquLlSC8OKr5Gg7W3gf8KqOrT9gNnlN0MonVNcfvDP92H5I1vfjokEcZ8W+13n3qiKsY+eGlwRMKWoUFEcjaOjjtOdhsBFxn8u6h+8Pox4xV/3YBQXExIdHgzP8UvKZUOPF+WPyRu7ZETDw+Zh/mL/GXYk2MXUZBt41gm4TpyMa86a4DXS6c9Sr0jEifHQO/OnArdhpUxwHbhhv52xepBNHWqBiDkFLmhBBnYxD7DHCvlPJTIcSVwFQp5bNm01HAI9Kh/JRSLhdC/BGDyQBcKaVcXrG56sFMoFwM4jM5iEPEVMCvMtptqyDvFINYD+1fMGA2yypqRCv6bj9lxMh9/YxDChCQq+4dPqFi3DSH7OffN+yw5OMEYYuAKG4LtXUw5DtFDd23RzV9e8RhLDFsFNsW4S+hSl9dDoR5mSVANqNx0HalM5o2Qcog2gwVTbUhpZwITPTsu9yzPS6g773AvRWbnANa3pv0znGsTHEQzmq1PpVSVO1lxwfRSsasHa2el7036iPqYDr0DmMa7aweMhsCOrsXUydEyor1PJs3Bldry1CeOhFOO4LPKykqvYTjg8iZZe0i049HMYgOtQoTHch7JiU+HR/pM2ordCQq0T5oXM6vFpwTeLi6TAzCYAoBqZ0dRsaG7JaF/TZjKLQPM2iPGNynMHYkg0gliBSdDFvsZfztUIubro30TkeoWnpGpPW28HI+XBcsEYVAOW9yPocE8cdN/uo4YBH7eBLEP3+yB5v1NtRVQtM4sPkvwRMK+8jO+QDOnx18vCvDuteaFVGesq4Og1EPwc8nQ1CQZ4qyI73TEauROuIxiC9l/4gWhYz7PpLjYACNWi/F/sLxVvORqRhEXW0VZC2Gp/G53Cx4OmHJ5vr5azBXGh1Gw2Td69o6aFzWvlNJ4UZtHWy2S3vPYoNCKkFESBBRhYEsVJS+OZhYTprzDaKoJlMRUdlGO5iKqcOs1HOmw0JNXfvOI0WKDoCUQUTk1OkVU4KoSDCbBcfQscuVRqaU6DiPXtKBJIim1cbf2pRBpEjRcahEeyFiJd0rtgQheDUfLv6KICO1iSXSEbuQqabAGfwqJpuibrqT5yTGIw2s92BBITkliqLtOzR+2xioCysn2pbo3s/4ayXlG7p/+80lRYp2RmqDiFAxVSXwYhrT+htmZc4IPB7GICYfNYmzn5zDyIwGv51pxEaMP8rsKIxSoAvftY3UtkrmjOfh2kGFgaqtlBLJVExvXHggvWpjEulffwzd+sRrG4TzPoVnzob5k7jkqO3ZpE4dC/Luxf/f3r0HyVWcZxz+vburGyIsK0sgIYEuYcX9IrzIYKAAEwkFU8blOBjhFAqmIOCAcULhQLkwhKRix0l8IVHZgINNUmBSsR2ioogJwU6KGHORygIjYUAWxEgFRmABMlhoL1/+OL2ro9GZ3ZnVzI525n2oQXP69Mx0b0vzbXef030WHWO51+asY+GKR2DGEXDchdC5Z0uImI1nDhAjDLVUcx/Edir7DbxoNOXtybN4i5eZ2N4G+x1UclbQOScFiFTewR5E6fINaexcA73AMGsWldR7Ttc+FZUdgK65I+cZSeecoSAzs3NK2WzlAkddDa75NH2YZT/MWoCHmEYYq++ocKmNSuYghsuxoz/7nIkd+SbJ3dOQlpnuG4rpZQbtU8Do6Ht7+MLsZXdSm9nexwFiBHP1y4ryVRYgYuhZqR19WYCY0F7QJNLQTmv9pT2IUkMBYoSF6bxcgZmNwAFiBPuogh3OqOwizZ33QRQEiKEeRO5c/ks87aD2H/1pR6rZ7y3+kAVnAPDO1DnF5/cmh6Rd4Wo84T3uzRlfu45Z8/IcRI2M1IPIf9dffvoCeGTX84M9iIlFPYgIOOmTvDV/GRdPmMmvez/NvjPL3Mx2/IUw9xTe2DwJWFNFDRpg8aXZarC1mNNoJitWwbvbGl0KMweIWqnmPojOgks6e4ebgwCQ2G/mb5NNQY9wp3PXXNj8SsXlaRjJwaHIhCnZw6zBPMRUIyMNMSmfq2D8v7c/O1c4B7G33GVsZi3FAWKMSBp2kvrdvoIehOeRzayBHCBqJD/EtHZg90nXjyyazeMDh2cHBRPMiw7J7gk4cV7BrmcjrUMx/bDKC1pqwZmjf62ZNTXPQZT47ol386G1f8SE3somCW/p+zCf6rgXEE9+binbel/g9z//MM9PvmiXfJ//yDG8fe6REFcW7ql85mEH8JMbltA1NX9zW9n1X3d1+cPQ31tReXdx3Usj72ZnZi3LAaJE29QZKCrfh3p7ZHdPB9C5zwTe2bHfzvWScjra2+ic0gaU3xd51+BA5fcqdEwaeVe6Il6QzsyG4SGmEmoTimp2kdt1faU234BmZk2i5QPE9t7SYNBWVYAoHQSqS3zYa9bCNrNW0vIBYtv2vl2O29RW1RfyowNHAPC/A8ek12cR4o2YyuvxW2VfV5kK5yCGsWHS0XtYBjNrVXUNEJKWSXpW0gZJ15XJc76k9ZLWSbo7l94vaW16rKpXGaeVjPtHm1CFC/Rxw+usicNYuP1OfjyQrQDangLEe9/9Ok+e/1gF7/Fa+XOD3ZE96ED8zUFfghu8daaZVa9uk9SS2oGVwBJgE/CEpFURsT6Xpxu4HjglIrZKOiD3Fr+JiOPrVb5B7SV7DUjtu01SvxOTitdkSpun72DnndGD3+n9tDNh4jDLbQ+9R303ygm1e5N3MxuVevYgFgMbImJjROwA7gHOK8lzKbAyIrYCRMSrdSxPRVQwibCNypc9KHq9mdl4VM8AMRt4KXe8KaXlLQQWSvqRpEclLcudmyxpdUr/cNEHSLos5Vm9ZcuWmhRaBbuXvROjuIS0JvZ8DsLMbLQaPUndAXQDZwDLgdslDe5lOTcieoALga9I2m2Fuoi4LSJ6IqJnxowZoy9F1/yhp1Ibvzjisl1Of6s/xa2Zx47+M0pN2AfmnTZ8np60fWnXvNp9rplZheoZIDYDB+eO56S0vE3AqojojYgXgOfIAgYRsTn9uRH4b2BR3Up69dqdz9XGi4s+s8vpO/vPhpvehA/+XUVvd+qh00fO9NmX4Q/vGz7PCRdlnzu1gvczM6uxegaIJ4BuSfMlTQQuAEqvRrqXrPeApOlkQ04bJXVJmpRLPwVYz1hoa/Nma2Zm1PEqpojok3Ql8ADQDtwREesk3QysjohV6dxSSeuBfuDaiHhd0vuBWyUNkAWxL+Svfqonpf8Kjasb1iL3fzOz6tX1+seIuB+4vyTtc7nnAfxpeuTzPAIcU8+ylaO29mHO+uvWzFpHoyep9zqSyg8xTV9Y/Rsu+gOYugcT6KN0/MFdAFz4vkPG/LPNrDn4DqoSais7wJQt033Tm3BTZ+VveN7KWhSrajM7J/PiFz7YkM82s+bgHsRu2ryTm5kZDhC7CbWVn6Q2M2shDhAlBmKYOQgzsxbiAFGidyBG7j8ccBScdk3Z05efnt30ffRBVcxVmJntZTxJXaJ/IEZecO+Tjwx7+tTu6Z4gNrNxzz2IEv0DvtfBzAwcIHaT9SAaXQozs8ZzgCjRV8kchJlZC3CAKDHYg7i975xGF8Wsdg4/F04v3PXXrCxPUpcYnIP4Rt85XNpx/wi5zcaJC+5qdAlsHHIPokTfwADv7OhnwANNZtbiHCBK9A0E27b34fU2zKzVOUCUGBgItm3vdQ/CzFqeA0Tyxjlf5+GJp3H+iQdz9lEzOWrO/kPnrlkyimW+zczGOU9SJ/svXs5pi5cPHf/TJ06CL2bPrzqru0GlMjNrHPcgyvHdcmbW4hwgynKAMLPW5gBRjvyjMbPW5m/BciocYrpmyUJm7jeZb158Yp0LZGY2tjxJXU6FPYirzur2JLaZNSX3IMrxEJOZtbi6fgtKWibpWUkbJBWuFCbpfEnrJa2TdHcufYWk59NjRT3LWcyT1GbW2uo2xCSpHVgJLAE2AU9IWhUR63N5uoHrgVMiYqukA1L6NOBGoAcIYE167dZ6lXf3CrgHYWatrZ7fgouBDRGxMSJ2APcA55XkuRRYOfjFHxGvpvSzgQcj4lfp3IPAsjqWdXe+D8LMWlw9A8Rs4KXc8aaUlrcQWCjpR5IelbRX+MxNAAAGwElEQVSsitci6TJJqyWt3rJlSw2LjnsQZtbyGv0t2AF0A2cAy4HbJe0/7CtyIuK2iOiJiJ4ZM2bUuGjuQZhZa6tngNgMHJw7npPS8jYBqyKiNyJeAJ4jCxiVvLa+PMRkZi2ungHiCaBb0nxJE4ELgFUlee4l6z0gaTrZkNNG4AFgqaQuSV3A0pQ2dgYDRNuEMf1YM7O9Rd2uYoqIPklXkn2xtwN3RMQ6STcDqyNiFTsDwXqgH7g2Il4HkPQXZEEG4OaI+FW9ylrW2X8FC84c8481M9sbKCIaXYaa6OnpidWrVze6GGZm44qkNRHRU3Su0ZPUZma2l3KAMDOzQg4QZmZWyAHCzMwKOUCYmVkhBwgzMyvkAGFmZoUcIMzMrFDT3CgnaQvwf3vwFtOB12pUnPHCdW5+rVZfcJ2rNTciClc7bZoAsackrS53N2Gzcp2bX6vVF1znWvIQk5mZFXKAMDOzQg4QO93W6AI0gOvc/FqtvuA614znIMzMrJB7EGZmVsgBwszMCrV8gJC0TNKzkjZIuq7R5akVSQdL+qGk9ZLWSbo6pU+T9KCk59OfXSldkm5JP4enJJ3Q2BqMnqR2ST+RdF86ni/psVS3f0lb4CJpUjrekM7Pa2S5R0vS/pK+I+lnkp6RdHKzt7OkP0l/r5+W9G1Jk5utnSXdIelVSU/n0qpuV0krUv7nJa2opgwtHSAktQMrgd8FjgSWSzqysaWqmT7gmog4EjgJ+ONUt+uAhyKiG3goHUP2M+hOj8uAr419kWvmauCZ3PFfA1+OiEOBrcAlKf0SYGtK/3LKNx59Ffh+RBwOHEdW96ZtZ0mzgU8BPRFxNNmWxhfQfO38LWBZSVpV7SppGnAj8D5gMXDjYFCpSES07AM4GXggd3w9cH2jy1Wnuv47sAR4FpiV0mYBz6bntwLLc/mH8o2nBzAn/cP5AHAfILI7TDtK25xsT/ST0/OOlE+NrkOV9e0EXigtdzO3MzAbeAmYltrtPuDsZmxnYB7w9GjbFVgO3JpL3yXfSI+W7kGw8y/aoE0pramkLvUi4DHgwIh4OZ16BTgwPW+Wn8VXgM8AA+n4PcAbEdGXjvP1GqpzOv9myj+ezAe2AN9Mw2rfkDSVJm7niNgM/C3wC+BlsnZbQ3O386Bq23WP2rvVA0TTk7Qv8F3g0xHxVv5cZL9SNM11zpLOBV6NiDWNLssY6gBOAL4WEYuAt9k57AA0ZTt3AeeRBceDgKnsPhTT9MaiXVs9QGwGDs4dz0lpTUHSBLLgcFdEfC8l/1LSrHR+FvBqSm+Gn8UpwIckvQjcQzbM9FVgf0kdKU++XkN1Tuc7gdfHssA1sAnYFBGPpePvkAWMZm7n3wFeiIgtEdELfI+s7Zu5nQdV26571N6tHiCeALrT1Q8TySa6VjW4TDUhScA/As9ExJdyp1YBg1cyrCCbmxhMvyhdDXES8GauKzsuRMT1ETEnIuaRteUPIuLjwA+Bj6ZspXUe/Fl8NOUfV79pR8QrwEuSDktJZwHraeJ2JhtaOknSPunv+WCdm7adc6pt1weApZK6Us9raUqrTKMnYRr9AM4BngN+Dny20eWpYb1OJet+PgWsTY9zyMZeHwKeB/4LmJbyi+yKrp8DPyW7QqTh9diD+p8B3JeeLwAeBzYA/wpMSumT0/GGdH5Bo8s9yroeD6xObX0v0NXs7Qz8OfAz4Gngn4FJzdbOwLfJ5lh6yXqKl4ymXYFPpLpvAC6upgxeasPMzAq1+hCTmZmV4QBhZmaFHCDMzKyQA4SZmRVygDAzs0IOEGZVkNQvaW3uUbMVgCXNy6/cadZoHSNnMbOc30TE8Y0uhNlYcA/CrAYkvSjpi5J+KulxSYem9HmSfpDW6H9I0iEp/UBJ/ybpyfR4f3qrdkm3p70O/lPSlIZVylqeA4RZdaaUDDF9LHfuzYg4BvgHslVlAf4euDMijgXuAm5J6bcA/xMRx5GtnbQupXcDKyPiKOAN4PfqXB+zsnwntVkVJP06IvYtSH8R+EBEbEyLJL4SEe+R9BrZ+v29Kf3liJguaQswJyLezb3HPODByDaDQdKfARMi4i/rXzOz3bkHYVY7UeZ5Nd7NPe/H84TWQA4QZrXzsdyfP07PHyFbWRbg48DD6flDwBUwtId251gV0qxS/u3ErDpTJK3NHX8/IgYvde2S9BRZL2B5SruKbLe3a8l2frs4pV8N3CbpErKewhVkK3ea7TU8B2FWA2kOoiciXmt0WcxqxUNMZmZWyD0IMzMr5B6EmZkVcoAwM7NCDhBmZlbIAcLMzAo5QJiZWaH/B1VTrHHlPN3UAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1000/1000 [00:33<00:00, 29.85it/s]\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"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1000/1000 [00:32<00:00, 30.37it/s]\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"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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sweJNzmd6xurtar6AHHY9eHTrT8bnECkA5xg/b6k05nnwy7cryixlNvwKE9n0gHJNQwHmhghBkImnh4CeHoKR42fnPmuQbn9k/ux+bpEKb822RRf98CLw/tXA4vcc6z78kVYEK5R24p79zPcY/sx3/IOOOx546QSc9/xM3PD6j05BkKoFxp+CXePPx+XjZ+OFb1bj4dQ/8V70Qe7udBLaA60/4uc+9z3XNMQL09MdZlZBoK53uLTC+D45+mcc/u1IXD3BOQLHPw7BC3tGA3C/rgDw26em45jHvkKNj/jtkETAvwaqWbLjT+FGf6hJSxzNBwoun3ceXoo8iS/oBsfvz3y1Esc/Mc3ojJOaJpBREDw9BOfNcM70etTYr3Dfh4tx6zt8B7iZKKmjcInTbul/9wGvnQVs/gnJRC3ej47B+MjjOO/5GY4KmUZ39sLR+NtGZ06H3t+ZTX5tpo/BoztvdawL6CNqviA48YlvcObTzmf64pd+wKNTlmFvTcL2S3r7E2aOND6nwiXcdRZtqsAxj33NbZcbl4+fbQmG4IW9yjxha3oveIIgxYncyxdCEGTBuvIcHVVGLKA/QZBwcZxurrB1OHHNZMJxNoehqdYmjUAPseRmO1akhcyiTRXOzk1re3G56qtYtW0PerPMIYGGdkPmZc7j80ZN+svgWrsGQDuomk/J7pVYsZkT2mnSwjI5ZxMp5qtMtMOfwbmv1YkU1znL6wB4SFq744pPQZCBn7dU+k4j4wkC2q5pflXlUFLqszWIVF+CvTCb+X6ltQtTkp7WEi/BbEZR+G0CgEoPv8+W3TVI2c2FtmdpK1OTO5VQLI8jbut+eBo+7zkwvwO8YneJvcHN2SUEQRaYJfYbP6zHGE6J2I8X/oJRb/5oXUhOjeD7lfy5ly95eRbmb+DfcHsGblwuAgBM/M5ZTTJCuiAIA2+OAKbei3Wxi3GR/CV63T3FMx6+ojqBV6fbTD5GJIn6IsWTCmrhDBm954NF+K9WbO3hj5fi39+ttWzXjzZgXexivBZ+FBIUhJHEm+G/ILLFWcY3bi6vAHWU/cp3Vvv8fqT6Tqpj+yJsmuX0xnGfObIzXYuXffUXXCNPBqAKi33grBC6/rGj0B7qKC8kWzuTZMq5X3fTEF8QfL5UrSl0T0j1FWzdpQr5H9arz4KbINhSUYNznv3e+D5SnooXw0/ieMmqAZSkduPC0DT1y+TRnsXfZChG59UKe/BB5H5UbFY7/dqkglZTrrecy+i3rMfiTSZzqpQ+nt4X8/IIi1GF9yP3Y39K55kojBlC1Wy2evyzn3G29B0eD73o2M/x0gJcsOJ2MJsgmLPeakbS96eupx4jXJZ2Rt8sp7XtypoEznt+BtaUeY/MZShGiDAATF2yBWNCE3CJ/IVpLbvQYZg67h58FPkzXgj/A7WJJK6XJ2GU/IGxRqq6DiHDGRCCIAvMEvveDxZjwox1jnVGvTkfH5vS4wGYBEF60Q1v8F/E71e5O4XtHdCGnapzqWav8wGJQlOJpRCw4lNjSr5Hw/8GANzytrep4H8LXSZA15pQm1QQJ6cgePOHDbhDK7b27+/W4kVtqkH9Rb4nrHZ0v5YXoR0qcCBtxFHyUuz3nXO6P10j0C/7wtIKrDc5fCUoKCZVc0mEWiJkEgS91/8XK7aYX1iP0f63f8M94bcAqD6N3uQs4dCjegl+ryU02TWCmoRzVFqTpUagT4ASppRxbkA6kSxlEwT6s/jK92stiUwPhl/FqfJcTIhYo7iuamsbnCybBDcIiuGs/K08G4OlVWifUK/J6m17ULRaLfUgkdoGs5PcjRcj6YgeXRDwTOfHSQsxRFqF20LvGMsY0gql+R4/N201/hV5Dr8POcuNjA//DSfJ85FMWPNLvlhm1Z6N+8H4NaX+GE4LgmnLyzBv/U78/XPvTPMQUhZT57TlZbgi9DkeCacnYLRrOBIYRqVew6HSGpwmz4ESr8ad4Ym4PfxfY51kPH9Jk3YKRhBs3FGF6SvLci4itS92gBJW01AXlAFJ64NWhBrsC+toO8F001D6ISafZiIz9sgaRdNQisnm5KypwOGS6nirdTlMq5jqRNu0yzoTF2kvRgy2BK3dvxhr9KLNiCdTDo2g3Dy/6871afOUiU6maxNB0nixFbI69RhjqNDmHKiKJ7GlogayRGhPaaEXRdx4kRmA/aV0B96JdqA4FjKtm7BWm3SBMYbO5B2hFbYJ5AUbrA5TCQrCFeu4giBM/kJYu5DqZNU1gaTtVd1Tk0Rp6QaE4t6jRN2/4jTDuBuKJDDs1CZPt9vkwz4sVEXwnh9iP2UbkEpy/UK6HqV3lN1pK0hJGteyh6QKnV92VKIr8bJtmel/IBm3xurb9bSI/oyyFPCLTVja9qkLMAaG1tiDthzNEfBn/rMLAvu7wuv0kwm7vyN/FIwg+GTRZlz279k5F3P6ITYKN6y90fjeEtX4PnYzMPlmy3pvRP6KH2KjLMu+X611fqbOX2LZT69or9uvaGpvC9RY/QqP98aZshrN8tYcfthZj3YtAABHj/0KRz6azjXQH2JDo9ApVUPzQsm9+Dp6G47bPQlxWDvvw/6iqr6tsBf410A8FHrFeVwp/fLGyNSR2/IU3p6T9lmMGDcLRz76JUISYVQoPcNUzCQIWlZtwhuRR43fOlCFpaMpQi3XNGSPCFEY8FTkWcd6QLpTtAvkUa9bwxZvDr2HC2eehR7kHCn/MfQud992ekubLcesZdZrvXJbJbq+PAB3LDjVcz+6Wa06aet0PfwuMhQc+zjfQcrL97DzduRhz9+/Co8GvniAG6GlaF2SBIaetBnfRv+I3+2ZaHTffwuPQwy1mPrkVfgueotj++s1E59+3ZK2MOjOZB2k6QOewVvfN/J47JTA2Sn/FLsG82PXcde3awQ8dG1Kxy4IiBOAkEwKQVBn9He3Lv6grjXpOOci7QFieq6AxhBpFewY5dRNgoB8hpKacWgEmh03hJTVAauYEmVcnGwHdCjmvoiym0Zg49Dq2VzTEAC0IrVzPVb2ToIrQq1h11dsgmCuKSxRd/Lazz+KhGFCCSnWkV8YSYtTvAhxrtPOPruZn7LB9lXs1/hISbWR78fRLPblZKB6HVMfCVcjalm+1lyzxgP9FtfaBYGHRuCVgSy7b2YwUFqbeaU107imIbNG0EMb8fdNLDU0VQAII4VfS/y5Ho7Vluv7Sdk0AnsGsBo2CsgeA7OINigyyn5keEQkKBktD2TTGoptwqaInO9fKpn/ubl1CkYQ6DfRTVL/tHEXnp+mZVQm48CUP6Fsq49p4vx0HJyooXMwzXObElRhTGgCoqYOOWQbjSW06A0ZimtKutmmauagrZOQWu4s66uvH+M8iGbapModpqHfSdPxangsfiWpTnTG0r0Gr3MpQhyyZhNnkjVzljdpt10QPBx+hWt+Us8jaeloiqjWco0Wlu7CC9+stiy7Tp6E9Ytcwms9sAsCZjNveLG+fC8enOyeaChBwWG0HFeFrPeqsiZ93g9yNC8AwLSxRoeUjUagC9dHP13mOIPoVqtv6YHQq2iDSrwzxxrabL6uF8n87HZektudoYlq86AYg5GyaussyAzu5pcWVKuto74rG8rsgjf7kaD+TnTZOBmnSvwJZcw8FJ5g8U2cb0p+fCD0KkJIOsRwCdkEATjlJwLUCAqm6FzavsfnbC3y4vrjewNLPgBmv4ifl65HB5f1me2vF7q6y1jaQnmf/KrnNteGJuOK0OfYyDrg3ym12JTdNJTUzFxhJF2T0NxemNNX/wVYDQBv2tZX9+kwDdmIsRqkbI/PPyLPAwCOk9VRWab6OEVkKv1L1n0lOCMq+yjqZPlHrGWdufsOkTYqIwlgiqYRpPepz4Uw/oqhxrK7whOB7yZ6thkAOre2Vk+zJ2DpAjBjbR8Ad723CDPXlOMBy+mnt5OhcHM1zGaukaH/OX4HAEx7FDG8ghpEUZuFj0DWjv/iN2vwe5tPoOO8v1u+Xxmaiiji+NN7Jfi96bJ8ungz9KwGPUDBDk8jPUDz88hQENM6wxpELNeSwE96A9IdqL728k3lOMZ0bUM+w3fN6A78QXPuwIsR4Do23HP9s+SZ2L7rWwAHAgCeCKejmq4MTcUM5WBH+0tgNVHyNPKE8BHUHWOiD95zkKyF4WRizHDqeplvjHrxPjQCvfuviftR7RgiSBhRIsUmx1tSYWCJakMLSWqqYhHVIh7nPyQhys4EFUUCUcQdD6adlI/xrgTFECw8SlBtZLPaTUP2MhQRJECchBo3E1YIalkEFlJ7p9a0hysssynmp7fIXj9HF2gEBREkjPvtx2kYkxXD9GAsM51TxEXjcStk5myzZitX7MvdkSi7zpLXufrJEfCyo0eRMEb31SxqEwSKw8au05LU50k/b7vGWCxn35kWodYitCSWudJtKLEXYSRdBZb92dDNqTotyKkRJBL5q7Brp2AEgeEjsL8CtZXAXzrilpAaJuZ38gu3G2ynOp4yNIKaeOYbeZ08GStiI43OwTxqfvrD6aBHOgFzXsbE2RuMXIDDpRXYZ8rV3P25mYbcmBO7EctjV1jC5ngoPh6d7lIZPorcB4BvQXs28hSej/xL3Z9NIzDnbBwpLcWK2Ei0WOcc+V7uMhoOI4mbJ87Hzrjazjcjf4Vc4yyH4LdDNWMfyX4TvRXDaBkeCY3HithI4wnzYxp6ZePpWBEbibaUnlj+59iVxueXI3/nbZY2Y2ZA70DtkbM/uuSqmLcBnFFDPHjrZIzOY4zrI9A5Sl5qaBK1CFuupQSWUSNQXARBTMk+BPN/0T/hwcnpnKHn1pyWcZvLy/6GlbHL8UrYWYzxpciTGG6rQ9XKNvDimYbiQhDUHf1RdTx8WkXQ82W15rd51Oj1ChjRLhk0gupEyniEFRfzjZkRshqtoY8Ki1CLU/rvCwA4WFqnrrT8U7w7r9TycrRcw5/Gzz7azBcKJF8zUR2itTmTmcSuEZjrER0pqTb0lhud8eJuhJDC6rK9Fj+GFHeG+2VTvlrv8Hi27WPlRbg4pBYS000rfgcLAN+xnA/0Z8Q+//uqre5hp36zn3V4V9DPdfUbyp2CZOnQZSiubdSDD/R7FSHr898iQ2irG18uya3om24mzUTMpgEU8UxDviwKuRGoj4CITgPwLwAygJcZY2Ntv3cH8CqANto6dzHG6jbPoHtbAGTuuM2C4Oi9LrZXWF/yvrQB3WkbAOfEETNnTsdJkhqf7DbJhRn9wa1ASwBAS6rFVcp/UUad0xEPJZ2wpzzpsJkDcOQ18Gz9vSid8NYGldwqmZng583yuUz+HLKLKm/sj0JYVFqBZVt2I5liFtNCgmmPacp/bfuDpfXogF2oYWFDotcozsc9XONvxjEzvFIVZoGr5wr0k/gzz/EYJnkX77PjFsNu5+bQe9jG2uIM2z2OSO73ozf9gtbSXnys/MrXMezPgsIIHcpmZt4u5d+8Zc6/UDUCfvv191Jvk920lqsgsEel5Zt2qLR8/53snNI2kQxOIwhMEBCRDOBZACcDKAUwh4gmMcbMIRJ/BvAOY+x5IuoPYAqAnkG0J20a8oZX44O/v/R6U6N6VuwDjvXO+O5cU6mhzA++3nHvZaptux9twCHrvsUzkXaYkNRixmNtUBV3sdLPeMrylacRfB1Nx0vfF/4PzpOzj5RRMkxeYubh8AQf+5MtBfH6dCw2PhtO5yxfhOci/7RoBMmU81ocs5A/5wAPfRQ67ts1uMc225ZZ4AalhZkxOyC9uDb0CXd5xOP2jQm/BgD4uMafIGA2w0IVojh57jWZt1P8XScCs2gEBOaqYYbsGoFNEOg+hGwJK7lt55c7w9YghRPknxzrNNWEsmEAVjHG1jDG4gAmArCXR2SAMVtbawA+4jVzhFzCR23f/foIzBmtfvGjEegdij6i0Z22Xag83fFLEqriSb4gqLGq/JEMmawtOLZIPyhM8mU/9kvK5iMw12PXp2nMRiMAgH1QiRpT0luKk0wYjXtMFmTDLVQVsAqCTBFXbuxp29/3uu3In0bghpdGkC3WXF5gK2ubeSMiKD7DIcNIWjp0CYqr2c2oHeTiI8hdIwhWEPihqQqCLgDMunGptszMGACXElEpVG3gJvT2PfMAACAASURBVN6OiOgaIppLRHPLyvjF2jJhRF56PP8Ph8ajyz87AR9e777Sro3AmNaYpo+qs3iftuzMnARUrI1Y9EiMYlN8sR6BU5tSnZyOl+G/VzgalGl0upu1zNgmHtmYhvywfOtePBp6CV9G1OtqNr8kNcV1xSZnbXsvGAg1pkSsVCqFu95biFO1OWCPoGXosOdnt80deEVgXRRKZ+J6CQwvEiVdfa9bVyEc9iEI1sUuxn7I7LtgIMugpNwxE6+T3TUJMI6GxiOCpKWg4KzYTa6moQil0Ap70IbUd+03sjXvoSUnGsdXGwIUBFUsmnklAMcv/TNQlb0p0w8N7Sy+CMAExlhXAL8F8B8ip82BMTaOMTaUMTa0Qwe3yH5v0gll7utcFvrC/UedDXbbpynCgjHsZkWumy7d5P8m6p2+OXw0qvkPdlfHkUxx1OMlHzg0nGiGTikbh6YZheS8ioINeyRcFPraKK1grlWvm4ayNbmkICHO0ppGKpXExDkbsXyrao+9xM/9NuHVwe81vcyZtDA3WLS173W5/qEs8OsQPkF2VoW1owqC7NqzY28cis9M2TAlHd2+V/v3py1ZtcVXGwL0EVRzqvi64lE1ti4E6SzeBKCb6XtXbZmZqwCcBgCMsZlEFAPQHgCvmlSdcA0f1dBLDGcNY5aPXmaBbKIxjtKyc6PkNDmQkkSI1eJEKfNLepDkPWfABaFvfbfJzIDUUgzIaUs++vnqtEuWYWjr3dhduduYr9ctpt6NvlIpdilpX0M8kYL5kfcKrf2l04nYb4s1IzaCJPrTOtRwXlyraSg3px5F3AcRdnJJjDIT9xHKDKg1mzKhgHCMlC4n4idkuWdyLRb59BEcLi13aAD2TNygibLgBEE4UgQkKjOvCACVwVjPgxQEcwD0IaJeUAXACAD26Yo2ADgRwAQiOghADEButp8MeJW+BYAY1d3+piSqjVmeeGQT03+M7JzrwOhsUrW4XX4b3STOpQpwOrsgMQssGSl8Fb0NRbVxIAK8kFQzOXNxwh4hpU0/P6wpgzoeUfEa4TP7FIfa+lOi93DXD5mCBzJpYW5QOBtBkFvxRJ2dlXvgZ66bdj6ik9rQHkyI/M347rdtFPfX+XWl7EyCQRBmwZmGoqksZh7bvTnzOjkQmGmIMZYEMArAVADLoEYHLSGih4joLG212wBcTUQ/AXgLwBXMT6puDriHj2Z7OKtBxLw1q/F+afyUHPDC0AhSCaNMsQOfo6zGTDGqLUW39BG2XSNY2P/2rPZrN1+EvTos2TnqP6WvDycoco8akrLQCHL1Q+j41Vr8DJDsHb9fbUWu9u+ob2haZKi9VRcWdDrf8j0VLnZZE8CR/IqndSXQPAItJ2CKbdn9ps9LARwdZBt00iUhbD/UVe6Ytt+zazvaAtjBirEPOaW8XEdBoCcssVQcVXBxMNUEN4tRfVFiS7e/MjQVANBTspZ1Doeco3Yv7Ka5kJdGIDlfjX1K/fkUctUu5XAs80p5wp7ZWhfsWeZeZUXMUECCwFzkLV/wMn3zRcJWxZeFWwIJFy2B81zmg4Z2FtcbaY3A9kPWE8S4axTzV6sukCrwX+hwnuLLWTKOapNz8rueNxlzrzomnW+C+A2/7NvZv3MVcGpkYY8oIOJoBOHaHDqu3z7hWDQtegLKWx7gWC5noRGsZZ2yb0tA2K+rX9NQck8gVmBcGuJXO60LLQPSCK6P3+yYhlQJt3DfgOo2d7UbBSMIXJ3FWWoEW3bb6tebPus1ze2TiOiElPw8TCwVt9Snb/Gb2/BE8vfql53r8nKMhmQA+ahnD0CSsxsd9ZfWoy+pvogWqEEvj+gSxhEEOXHYFY7wwM1yZ/zU/XLHqnLUvyA4WnL6kMzckcic0JUvDiJrQEImQfBZ6nAAwNbNmR2fC5T9jc9b2gzOoXX5gacRfJIaVqd97mVRfKocgTizdu4s7BHSLQlBUCdcncVZagSPT7G9gKb9yUwdySZcLG5SKk+j9WTcEnLWKhZCimm3crMzI7Gp8c/Ic/5WzCK7GVBnt5oavQvHSwswIfKYY5ISy645zuKcIMmoJKnXU2KQuG0PZaER8CYuMaOw+nu1D5CsHXqmuSx2M3XEu2K9d0QbAPygHGR8Lm7pMVIOmAgnasieUZ0tek5DiyKrBYFFPASB0AjqhuTmLM5SEDiTiky14w1BwL9ZUpaZsW4wJWV5CEti4Yy1/3n8PXF+5pUaMy6joxVXLvbcrDf9krm2T740AlOHvyemzp2ggLgvNIX8C4JM5Dvhzy/V4TboyAs5PTZd1uSIvmpeaaYs33Wnv46flN7G9+Ki3ARBgnl3nnbTDA+e4GUA3ksdm1ObdEaf2AclLayCQIl5BCVkOfjxS8EIgv02fYrXw4+AaXOY1lTvxeynLsXu8uzCsewREeZSCLIWsWOey1cxzdK1bWd+HLn7bvsON4feN76XxELG/AXZUIY2eWlPg+E2OopZz8tPyWzHrrN0RANADXF8Q6YSrXs1QcBAgMRpU8hfhqkfGkoQ7Cnqwo+OM9m9W8RUgec6oY5GbYtO1gxqjnD2k2Ht5QsCgISU2UnPMw0pkAztJlfatggjaZv7Wynax30D3nOTBwpGELSo3qLG5mud9aJPX8awHZOxa5L/omOAMyLCrGHwTEPmCTTykavAoygs56QRuPky0PFgvJU8wfg6t9XJuTYtWDgawfaB18I2kZujxLWfkGGeszgTSZc5nHXikTba0fkaAbLII8hEPutAZcPeFvYqMhomgRjy6QtJSVFrKG6Ic3391ELPgNvc22b2IWfOQz7i3C8Y2s3xfCoxD0EQEAUjCPROgyn67GOK5a9fvBxhuiAwlzUwk2vGaSaICMcc2DHjeiul/S3fa+EiCI65BXcn0xPdDB35WJ3a5+YzqTOczrSiz7mGGVDHPumNL1zMNNtZ5jo6bughqYqLjyBfGsGKjqc3mCCoaeFWL8kkCHz6QpIUtmTWQ+I9r+nr+K/k73zt106NWyg2ALTrg81tD0cPTmBBLpqmneJoyPF+xMO5P2O5UjiCQO80UpogyFGe22PRza8bpXTTEL/jiQVYnpi4L4ltHds5uwoCeydSR8epvbJo3uBoBJJEHEGQvdmMuXTKbvfWF4YgIL5/I5SfPAJGUl46qVyoLc6sEYSj/s5TkcLYzNqlF3CuGTMJ1LCc2zlXMQ+NQEmiqmhfDJLWOI+d09GcJG3vR6jII6EsIApOECjanAC5CgKnRmDaT0od8SddBUFw2Ym8l6Siz7mW7/Zzvnv4IMv371IHayvaBEHxvnVqWjScpwgcOxyzAEmyD0HgY/pFl0457mZO0/b6VWqQ6+9M10pJ4puGOMfM5OjktoOo/nwENhNai2K3TswsCPyahsKYrgzEL7IuXDjnZLrXFx7ew9d+8es7LF/3MM69jpSof5UkFFM450Lqhx8VNQeEQTLeqUqPYpOZsPv32rhew+AoGEGgv4RQrBpBlcuk725cbZvoo5Wp+BUzBAH/5T2aUz8ob/BGS627Wb7bBcEB+1krue5ECX/fdTRZUJbx/nU8mMOfxmyd7n3h1zPvJxeNgIBS5lEdV9PaFObiLOb4CHLp0NUZfetJEHS0zqHQtmXmDtFvBnWK1Ou1MtzPfSWTRlAU8fmc2cIzK8Fx+HYZojUiAcjpZ2GZfKBhyjEHIC5lPoUQB4fpNE+aYTYUjCAwOkot6Ut3KCaS2fkI2nGcRgbaRBvJBrisxEk9l2RrB+joGjK8kH9PnI/JqSPVLyeNybltSAY7zZ+Zzm1a5MU05PYyFrmFMHbsj0k97sGrqVPc96lPjgQC8drEMcHdnsihtgwR/Gg9ecF2rUtKXLK9zev5LJPQv1sHdCyJon8Xr+i29H7tz7tfuBrBSWOAfXoDZz5pcSYTSUaOhtn8NlPpj52UXaa7jiPQI4/RY34pHEGgJ/PU0UfgiaYR5BLKmZFuR3j+TJwRpl04OM452sr2u/5B/fR06lzclBitLjvmj1gq9/XdXAuJqszr5ISzs4uGQ05BkMP9YDL/ZezWQeuUSMY3v/kg/cMNM7Gu/fFYzVxs5DDlsrj5CGzCYcthd2CyclR2DYd6VfxoBB+mst83lzbdjY+Rdm4jY1N7PCJ9ZqTSGkZxLIzZ956EDiUeWoZJI5B9h1Zaj1+lcMx9XYYAo38E+p2B3QnTfSEyBnrmt2knK8GFrf7j3M8BJ2VsTSRuCysXgiBA9E5Rmy5S7xTzKhC00FS/Mw5lRaZEEs4oyy4cIva+xzEC1a9FnkeTLTNHNOUNkjimoezPh8Iu91AXECRBsnXm0ZD3PdJboZDkIgjs7cxxzgHyZxrK2102PXvU2kUQFpuegRbt+OsA2IsYELONrD0ammzd0/gs2+OGfVLl4fcBADJ3zCTjWFlNWOwRqczcf7gMKMy0iNmc1UIQBIiuEWiddSAagTa5ervDzsXijsNdV3skYZ+WwQcZzBs805B92b6tbA8Y2U1H6Wvy3Z0nwI7+O0/j2TzoZlwdv9Wx/M2DngOu/sq94QMvdP/Nhjk5zx3m0Ahy6fLcnMWG8JRCjrj2aDjDPTLqXRHfPGLLes+5IjuRr6ihE/tlMdvfKI+ZsTTfxwvJ4dYO38zAC4HfvQic+zLQ97euuypjrYHrZwIjJ6cX8gZBf1wKXPg6dp2UngfBtxyw7a/GK2oIwNADOqe/mEYZw4q34eT+aiAFg0vZMh8Rd0ec+QeMjo8ybSMEQXAYPgK7RpA/ynerpWMHHNAVVYff6Lreu6lfZ7/zDGqvH9OQo5vyKGDVtS3HFq496Lxw0PJ+l+B/ylDH8pUtBgFtujmWGwy4wP03G26lOywwpyDIKaZedhEE+mhNjoBswiKjRmCugMsVBHWbdcx8HD8ipNihInrQvo/7b9q5TE0577+pUcChI4CBF3iahrawdkDrLkAv0zuiD1i0iD90O0Jd56DhkGIuAQ5e2I7Pm3HOTCiSvs9k2laOlWC/1hmc4z4SEyVZRvn+w7FQ6aUdUDiLA8MI3UvpGgG0v/nTDNZuVcsUSySDedSNqfZKYLGR1IuHZTAN8YqkpfY/0bogYZvez6ER6B/4L2rHEvWhtkfhAEC3ffjnW5PI0LllUTvFEo01+DJ+OxmDTGTRHnIR9vZO3kB/seWQ1WQA4LgDvUfY+ohVYXAmR+1/PNCyvfpZKzHQpii3sFtGks+CaJxnv2VHVEfbZ3dA7d3qvW9uzlIAYFG1Q5+icCp66oOF7lrggkmISl6acguX8+hh9Y0c2jMdHr1C6QL0sE2RYhqhW6ZUt0V5Wa7m8ffoG7i3z4REhAnJU9Uv7fYH+p0JtOoCHPNHX9vXlYIRBGRkFtszivMnCPRZoyRZ9oyMsCdy9ax5M/3l8kmW3wwzTCZBwAsf3fcg677t851KbqYh/sPbrqXabt6kLa1bxLBu7BmO5VXxDDNpeajOrFUX4yU8uObfhiDYOHIOcPYzbltBlu3iPYeRtlu5B8M0FIZkW6fPviXca2BsqkmClKJYpsLc1f0U4PKP1LDGMRXA4X8AoJYO8dqfF77CR3m2jOtnYOqRr3lu9sDg763mPu15eOL83GexZl2HoWfNm1jFOJnJPY9Wr8s++1uOBwCy7HGeV33OX75Pb3V/vX8DADj6wPTcDqfE/wZcOcW6vsVHYHoPvUx3x9+pHsOnlicR4X3l1/j6opVAUVtgxBvArUvV6KUxwU82VTCCALo5QwsfzUOJEgeGIJAk7w7O67LbVEkjFDVTCKSjcybI9pO0q6k24VKpF9DKoJpyo3BcBFVGB55XKGEqYSSzKSAj3tpzHgKSEJKsNnJ9nois4NW10fav7jTs7lB226UmeJMKAzNpBEl7yWjjvmUYpLgk+jH4TCiLchKXJDljElvCXsvdCMTIffpMvbWtYh73VssBsmgEXiGjEZfELP0e6sUJ3dbTMQkCqwmWIeM98i0ItNUbaM7xwhEE+g3UH6Y82WPNRLQqh6pG4C4Inr9kiPtObB2QEWPMkVyl7Y/F9nPfAQDI5hfi7OeAG39wOk2v/go43lRkz6YRJE9+BDjxAaCPSyy89pCmuJ03v+O5/8z+3OXpNni8+NU7gCs+Bs74O6oRMzSCLq1dOuD9jwfa9YYsWTtCySQI9nZMX/vf1T6Y3vYQW0lu11miyGi34qOsh5mQNnpNpZjlvNs5wiONXkH9e/4r6Z9OfCD9+f8+s26mJRBKkg9n8YkPAKf+1bmcCJmsecmUku7/GEsPenIQBHqeCgEYe+4AfDTqGPeV9f2bNQIvTdlNq9O3OfNJ4KQHgQNO5K+nY7rPRBL+ltAmgTL1IQzE78Sz0AgAI6ix3ikgQaCPWnRBkH9nsaERkOQpCE4f0Nn1N/t2hmmI80B1Pey3aD9QtStaTEODLwE69HX6l/c9GBh8afq7Tcu4+LgBwLG3ejim1WvGLeLmomK1aZHBWcYRBHqCDylJoG0Pw1Ri+AgUl2zwYdcCAGQii4NYUtJlQVpemzYXzGcmB6gt2oUyVQKVwxk7TMcm2rAvqSgWfwfP0a+idSyHmEqFHHlD+rNuKtE56iYAQMhPYtWxtzrDNAGAZNQq3m9FMmXXCGzO3Cz4RE9YBMOIYd3Rq73HpCyGIEifn+x1rm5auS4IitoCx9yS2aFrEjYkyfhRf278dPI+BUF64iyhEQSLET6qdgrMFj2UDyw+gkxlFQ51CSG1jdKNCKMM5hqes1gfZWxhpokudEHjFstuomtbW2eoPaR801COIpUjCAwfyoGnGctkifB+ShstGh2Y7ZjaPbWPiPdGzIXL/GW1GmUx3MxpUhipLIdvIcNHwKyOb/uolnct22lzHHu1X7s/IVn2nqHM7dkDAEnGwV35ZZBrWRh7WRTxlGKKtiCgnxYq3caWTNY7w0gbWZbQaH+g+tf0XEhepke3a2V/7rMQBCBKzwZoD/flbWsXBFG+Q12PRnLMoFhP1GMRmAbGZsfU72FdBMHOs15F20kjje9pH4FsqWdfe8YziH4yyrrx2c9g4A/HO+uMKNaidhfeOwGgWuCT2+BFlFNnRfcR/Lr2n1jxsBaRoAsMOZrR7/DlbcfZnnVdI/DvI3Bw0FlqPPlfNa2I87IWR0PYe9N6tGyRHh1+fNMx+O2/Ung/dh6mFbnM4MTS187cwewNt0P70V+rxzJ1sqNOOACYaZyAZVeSJAF3lwJLJwEfmUbh+vZyCAn7yDgDhkaQUqz33e0+mC/+9TPVZ9dLeGudTkiWnR3snevSgwAvbYdkDOvFj3769MwfcOd7C3GC/byHXQ0ceqFTw7j4beBh7wgkQyD6GQl3OBC4a4MlI142Xw/7LtwEgf1ZzSgITBFoJJvarFjbzTsF+3ndvkJd8ZFOlsWyEVrcMJKggASB3UfgPWuRL2wvVMTkLCbTSCXagu+U2w3Ocpt63aooCvgIN41GnA+z/vzGEXa+/OFYRo0gGuL/zq/d43NkF4oCEZP9naPJEICWJdb6MrGwDAYJe8zXwj5yNo2+zB0hQVHNADY6tXbXsogAREs410j3EYShZDl8C0np0gRJs0PWfgyjozLtPxQBMsS769cjHOIIAjfh6diHu6ZY1KIlahHRzBfpAGwQ8c1MPpKp0pqRz2tpO47klV/jpqU6BEGGdprXl0wlvm0+Au4rYNcIXOp7Gd1TA2kEgZqGiOg0IlpORKuI6C7O7/8gogXavxVEHrOJ17Ut+uiA6aYhnz6Ck8a479P2oJlNQxaN1Ry/f9Hbxse7T++H/p1tk1B0PhQYehXvYJ7NjIR5goCzTVFb4FejgJEfq/s89nbP/VrQHtKcNAJzgpAZU6cT76wmJIU5AsgRAQUAB54ODBkJXPMNMORyS8aq2Uewn0uHH5GtKr+lWWTq6ACg57Hqsfpos7XJYfzmoI6Y3Pkm7DrnDf656Zz3b+DsZ41DXHtcbyTMfha3TGg/o8NT/wp0OQy48jP1Ggy+FG1P/zOGdM9xGlLJPfT5hL4dcdGwbnjw7IOBzoOAw68Gzv+39/6Ouwu4yn1KyjrPm2Ax2/COfydw1Rfu2wCZNQLTM0okpSP5WDpq6Ff774OXLuck1OmC4Oib07kFAHDyw8Bl6VpVadMQ/55/2OevuCd5dWAaQ2AaAanlFZ8FcDKAUgBziGgSY2ypvg5j7I+m9W8CMDio9tgdWmlBkOHCduUkuGjYO9p9SU0oU9VV02+VpnmR+6btm9ce1xs7quJYunm3tZ1nPgnMtb1g3AfANA1mxGcECxFw6iPp7yfeB0x/wt+22vG44a+ZfASDLwPWfgvH22rqdCLnjwOeHsJ9n9O7t42Sz3pK/XzW05b1zR1MyKVpEY9MYMcmJZ3UY/08xWh3NCRj+LV/cd2HwQAtImnTjwCAbm2LkLIUMnPTzHy89L+6Uf2nc/azkAFcfXR34H3XrdxxmysB6vV69NyB6QVn+HhuTvA5FWwdyml4H/8e5zJHWLV/jYCIrxGcMaAz0IGj4evrdD/K8u7j6NHWQ2h/3QTBL11OxZuLluO+hIKibDLCfRKkRjAMwCrG2BrGWBzARABne6x/EYC3gmqMEVWjO/gUv1VIPX4vsUb/9JY2a8eSrBpBK/eKlKQ9AltLMoRZZiAWrYf6JNpDynijf33ZPr2992F/Cc1RUh6mqmwLin2nHGL6xr+HXjNaGUK+bU/1b+dDtR/SeQQ5w5g1d8DhLPaxj/belWClnAvWudRBCog6j2+zyEx3JYOJ1HwMRkBEn2iJKch4s3RBkKGdg7qpGpwjQEOjJKYes7ImmFkOgxQEXQBsNH0v1ZY5IKIeAHoB8KhOVjcY6ZNJaDZ4PcLE/MKYzDbpDT0e1fZ9gau+wPTjrPIrGg5ZtQWPwmo1CVUgTT3sJWD0Ao8z4JE+Rrgus4Ddthy4fZWPFXWNgDtmV/9c83V2xzZ3Oh4vi1HC2WfPcXviOjzOifc2I0vAkJoXMKzmWYeAMkZm3Y8ArvteNaepO9Ma5H69B9e8kLF9Fn+rIyIsg2no5p+AP3zB/03fg98kuttX4fCaZ63LfJdzzie5igTOszjkcvWZzhem57IqzvDa1VqJCqbAd0JZBkFw1TG98OnNx+KwHvyIrZaaFlAVz4Nvk0NjcRaPAPAuY3wPLhFdA+AaAOjevTtvlYzoGgHZEspCZkHgVViLu08A3Q7H3m0rHT9YBrAe6qtegiHSohWwT7bnln4IHZNbZENJp8zrZMLI1syy3ox5NKbvg3O9su2bahHBKn1uANdYbsIO8CcKt8TKdzJpF1qtKi+NYKfLPs1YQk/tDsRMmcW6luKB5DcYorgDyuDTkRwAORUENMPrYFt2yM8zzTlGZVxBqyLtfpmfK7d33BAE3udJRDjI7i80UaRVtq1OBCMIghT9mwCYy0521ZbxGAEPsxBjbBxjbChjbGiHDlmUzjVhJO1oN0bPI5DJdGG5sfru5hDDoWjvFEgyTD6Z2KtJ+Mx2P7Udn6UO5/7aq2PmzqfOMA+NIJOt1m10K4eBo28Beh0HFHdSnZBnP+dYLT2pi3+SLsl4G/v+H94vOhfti01OwqFXWco+tCt2cSDqyWwe5pNbTz4wY9tS5ibZn7uBI9SY/MOuyLgfN8gt6a6RkDjzKXyZGowFygFqfsCJY3Lbkd/8lUMv8v79oOG4K/EHl2Ok3/3TDulsGpUw4Mgb1Xt10Fn8bX1qBJmIRZquIJgDoA8R9SKiCNTOfpJ9JSLqB6AtTBHdQaB3zIbXXbtBYfNk9LwJIYyEA2dHbYz67b+R5Pv5rNYEQQuf861+6iIIYpzw0fzjJQgyPUouBe2kEHDyg8DISWoS3rXfAAc6S1zkMm5ULNEdabpd9A+ce+crVmfxPr2w4+p5xldX/0FKM7l4RJqMPtFNs0y3Q2FMrd8POJ+71l2AWxb6Gvm7IQVQQiWf0ODLcFXiDlQhBoyaA3Q9LMcd+XwyfpfBXHfh65iY+o3LMdLPQvcOrdPvO2NA+wPUe+U2D4PRf9Stq9U1gpqATEOBCQKmGuNHAZgKYBmAdxhjS4joISIyi88RACaygDMpdI3gkFm34fPPJuG7lWUAgJBZEJhf7oi1zjmv8zM0AvvoMIubvl8bdTTomDTGjpa16TrBvMcxvaJjskLL7IzLvDo8GV5IvSRwG5v5K0vHZDaPSdpcxt/GMW+Bn05FyWwa8kOHkiiiUCcyCqL+PMvWRMejiG+vzgfOyYNyJZ9FYtwOYc4jCKfNmX6E7T691L9FOYbzagRtGgrUR8AYmwJgim3Z/bbvY4Jsg475cdn240dGtFARTBOrR02d7KjZwK6NQFLLAeA8uPoikgh/TlyJv4S14mAkQ2EMv639K7rs2x4vAcANs7haxZ/P6I/f9OuIgV0zPCjH3wV0GYJvJ7h0hC4v1nvXH2UImzpzznPAkJHYOZVTpth8/CumABNss1AdcCIw4k1nQTu/JR9y6Dgi0Dptl6kRcxIEqcymIXfS+7/gsK4oXdIaWItABIHS8zhcHb8Vq1gXfHrdYGR9hP+bqg4+nuyX/cFHzQOSNZ6r5E8O5G8s+9Vtx6E2yencLYIglP7uRxCc/rg6t4AedZYjLZqwaahRYc70VRRguKxaomJksqWan85W+6kRIx6qnd6REICvUqYUCCIwBixlPbFF1hyWHQ9SU+RtxMIyftOPX07YghwG+p2BbEdAh/Voi86ZZlHyS7QE6OMyGbf52vU8mv97vzM4/pTszicbtbGTltdhD/PVccxtrL0OSa/Xwoez2A9EhG4l2nECEAQSEf6nDMVa1hlSlxzSc7ofCbTyKI7oRfsDrA52DrkIdpcd5Wc/APbvUMx32FoEgZydIAgXAQeeWue2xcLBRg0VjCAwd6C9lPXoQuXWn7uoWYGqA8tW1dG2vY4uCCQilJsjRUhKW8RzfU6PuN5SU4VLA9UlyQsn3JvV6sVRdQTuxxGrM0s5SP0w9Eru7/ZsYfsHfgAAGf1JREFUZbWcB7wzTbXJTDBwhO92uDJIK/7W06P0co6Y+66QjxyMV5KnpuvzNyXyqBH4OgZJSIf31p8fRg8mqW2KpqHGhHn014JVOVfQ4rKvStwBAFhn/OClEah/QzKh1lwHhiTokbA5j3xOH6v+a4TUOeQPAI77k/rPJ5GQlPVsXatZF2y/fRvaF/P9L/Z7E9N8KSGvCWfaH5C/GaP2Py6w2afMZi/PCp0aDyZH4sq73gmkLYFSH4LAHuKcjUaQJ9q1jGDlI6d7JkHWhYx7JaLhRPVxtYOFTKd6GFti+S1BEY84YC1ShpN9qHckIbuNgSSjeFQ9uLLqnaAexiDg1ijSf7N3kPqL3YozXWITI8tE7AbDj7biTT07i0lKmwXd5kQOoglEgb53fjSCCwH8k4jeAzCeMfZzYK0JEPJ44BJSFJktvll0KCRB1ySayguZDQd3aQNsbehW+MNrNOz4qWV7YPhT7jO0NSGyicp59f+GeU8RGRBPXTQYh+xXx/yX+jYNSbIaAXTWM2kzYTMg491njF1KRK2g1gKaQEQMwCsA3mKMVQbdwHzh9VokJa/QTX1o776HsH0CbZLSPuYgJkduYOo+iqs/vGoUcTvLw0Y6l+WLenwWsjnUcQfmlqRZV846dL+676Q+rqn5GLpQGHJZ8MetR3yJU8bYbgDvQi0c1xnA7wD8qFUMbRp4jBw8BUE7NTmous9w11X0zuZVRQuZlEJ5nPeskWOf67eRcM2vVYe/Q0ib8GM7zysHaBFXXXJMnsqC/MXpN3Ky0Qi6H1X3Y2Q6XhdOKeomQEaNQEv+uhLAAQBeAzCMMbaNiFoAWArgaa/tGwteI3NPQdC2B3DvFlStXYTiRZz4eaR9BGPZpRh57yuAHDI0giY0ePaP+VqeO07NL2hk3HVaP9x68oGuk+sA3v6DQOh7OnDvFu8ZwvJEwQiCbHwEV3zsmAHQ3yHMgiBDKZj/m1qvTuR84ccweB6AfzDGvjUvZIxVERFnBpXGiVftn6SUIY47XOQxwbgaNQRAnSfWVkDM4Uhubkhy5jK+DYAkEWIZ2tUgt6YehADQPIMUuGQj8HJ9Vs2df6bjZZqrvJHip9VjABgzqxBREYB9GWPrGGNfBtWw+sTbR6DiJUh0m7nZHHRYj7b4v6N74Q/H9qpr8xoh2rXoNMB9lfPHo7F3R8151Jy3Uzv7Of/TXDYE9eIjyMI01ETxIwj+C8BsXEtpy/jVzxorHjYaX4LATxgisy67f3jdJptptOjXYrCHw+yQ8+qnLXWg3k1D9UjeghQGX5Kf/TRl7FFDzRA/4i2kzTAGANA+10epy7ziaRqSM6f4e5mG9PheVu8u4gZ2STflzGY0b42g4Nj/+OD2LTQCAEAZEZ3FGJsEAER0NoDtwTarfkmZNIIF95+MpOLs4PxoBPXRLxIBk1NHYrg8K/iDubeiAY+dP3TZHog8uGN1s+00Gh23LFZzQGY+E8z+hSAAAFwH4A0iegZqD7ARwOWBtioAvBLKUiaNoE0LvrLjlVzN8xEEyVam22wbukNuHhpBIFexZf1lnRY8bbplXqcuWPIImqdpyE9C2WoARxJRsfZ9T+CtCgDysIKlfPgIJM+oIc00VA8qwfiRh6P2k/eASsDRER81us51z33xqxuBDTOAARcEf6wA0TW55pj0J8gjZr9A54EN144A8RXrRERnADgYQEx/aRhjDwXYrnol5cdH4KPERH2Mj0/o1xFY35k/n9spD9dDC6DmVlz3Xf0cK0CM+SQathmCxo5hDaB6C/+tb/wUnXsBar2hm6C+MxcA6BFwu/KPx6gvJfuIGvLIUA3Vo49AkD9009CArnmYzUvQfDEEQfN9wf1oBEcxxgYS0ULG2INE9HcAnwbdsLzjIQjMk5a7b545oUzQtAjLEt697lfo09Fl+k+BAGi2DmIzfgSBPudcFRHtB6Acar2hZoMfG7F3Qlnzf1CaK0N7Bjcvr6CZUACCwM8ZTiaiNgD+BuBHqHO2vBlko4LAq7P3Iwi8nMUSqcXNHqivBLKBv1f/Hnh6/RxPIChkCkAQeGoE2oQ0XzLGdgF4j4g+BhBjjAUzrVKAeM6t4+NGeyWUERFWPvJb19/zTudDA5vZSiAQ2GimIaNmPHtAxpgC4FnT99qmKAQy4dXJG+sUwKhAIGjS6HMux/Ls/NctBiV5mD+hkeLHR/AlEZ0H4H1WH4HyAeFl/WE+LGQi1FwgaOQcdqUaujf0yvzulwg4/xWga9Mqr5YNfgTBtQBuBZAkohqoIaSMMVbHOebqF08/gI9O3stHIBAIGgFyCDjimmD2fci5wey3kZCxd2OMlTDGJMZYhDHWSvvuSwgQ0WlEtJyIVhHRXS7r/J6IlhLREiJqECf0ljaZZxVyFCjbb0hArREIBIL6xc8MZb/mLbdPVMPZTobqXzgZQCmAOUQ0iTG21LROHwB3AziaMbaTiDpm0/iscLHx9675D+5vfXDmze3bj5yUj1YJBAJBg+PHNHSH6XMMwDAA8wD8JsN2wwCsYoytAQAimgjgbKjTW+pcDeBZxthOAGCMbfPZ7qxxMw2l4DMiwC4IhPNYIBA0E/wUnbPM2k5E3QD808e+u0CtVKpTCuAI2zoHavv8HoAMYAxj7DP7jojoGgDXAED37t19HDoAHIJEeI8FjZvHzhuA7vu0bOhmCJoAuUywWQrgoDwevw+A4wF0BfAtEQ3Q8hYMGGPjAIwDgKFDh+YUueSVGewvGMq2vQgjEjRyLjy8gQZNgiaHHx/B00hXW5IADIKaYZyJTQDMhcK7asvMlAL4gTGWALCWiFZAFQxzfOw/OzzmI/CF0AgEAkEzxY9GMNf0OQngLcbY9z62mwOgDxH1gioARgC42LbOhwAuAvAKEbWHaipa42PfDYDo+AUCQfPEjyB4F0ANYywFqNFARNSCMVbltRFjLElEowBMhWr/H88YW0JEDwGYq019ORXAKUS0FEAKwB2MsfK6nJAbXhPT+LI1OTSCJptbJxAIBBZ8ZRYDOAmAPjNZEYDPARyVaUPG2BQAU2zL7jd9ZlCT1W712d6c8Zqq0ucO0p/7nw2EMk9mIxAIBE0BPzGQMfP0lNrnFsE1qf7JunDG718TzmKBQNBs8CMI9hKRkUZLRIcBqA6uScHgFTXkbwei4xcIBM0TP6ahWwD8l4h+geox7QR16sqmRZ1rBQlBIBAImid+EsrmEFE/AH21Rcu1cM/CQmgEAoGgmeJn8vobAbRkjC1mjC0GUExENwTftPxi78ans0PxYzs1adqfi0AIAoFA0DzxYy+52pzpq9UFujq4JgWDvdbQNco9mNzz7ix2IGoLCQSC5omf3k0mUy+qVRWNBNekYBDOYoFAIODjx1n8GYC3iehF7fu1AD4NrkkB4dGR51RrSCAQCJoJfgTBnVArf16nfV8INXKoacERBFlpCUIjEAgEzRQ/M5QpAH4AsA7qHAO/AbAs2GblH3s//odje+Gk/uo8OEfu387PHvLfKIFAIGgEuGoERHQg1IJwFwHYDuBtAGCMnVA/TcszNklw2ylqNOy6sWfktL1AIBA0F7xMQz8DmA7gTMbYKgAgoj/WS6sCoO7OYhE1JBAImidevdu5ADYD+JqIXiKiE9GE7SNuU1VmsYe8tEMgEAgaG66CgDH2IWNsBIB+AL6GWmqiIxE9T0Sn1FcD80ZdBYEwDQkEgmaKH2fxXsbYm9rcxV0BzIcaSdSkyJtp6JDz694YgUAgaERkNWexllVszB/clKj7fAQE3LEGiLXKT4MEAoGgkZDL5PWFS0s/YaYCgUDQtCiYUBivqSoFAoGgkCmY3rHOpiGBQCBophSMIBAIBAIBnwISBEIjEAgEAh4FIwiEaUggEAj4FIwgsHDqow3dAoFAIGg0FIwgsCSU/arJzbQpEAgEgRGoICCi04hoORGtIqK7OL9fQURlRLRA+/eH4NoS1J4FAoGgaRNYQpk2peWzAE4GUApgDhFNYowtta36NmNsVFDtEAgEAoE3QWoEwwCsYoytYYzFAUwEcHaAx/NEKAQCgUDAJ0hB0AXARtP3Um2ZnfOIaCERvUtE3Xg7IqJriGguEc0tKyvLqTF1L0MtEAgEzZOGdhZPBtCTMTYQwP8AvMpbiTE2jjE2lDE2tEOHDvXaQIFAIGjuBCkINgEwj/C7assMGGPljLFa7evLAA4LqjFCHxAIBAI+QQqCOQD6EFEvIooAGAFgknkFIups+noWgGVBNUZYhgQCgYBPYFFDjLEkEY0CMBWADGA8Y2wJET0EYC5jbBKA0UR0FoAkgB0ArgiqPQKBQCDgE+h8BIyxKQCm2Jbdb/p8N4C7g2yDjnAWCwQCAZ+GdhYLBAKBoIERgkAgEAgKHCEIBAKBoMARgkAgEAgKHCEIBAKBoMARgkAgEAgKHCEIBAKBoMARgkAgEAgKHCEIBAKBoMARgkAgEAgKHCEIBAKBoMARgkAgEAgKHCEIBAKBoMARgkAgEAgKHCEIBAKBoMARgkAgEAgKHCEIBAKBoMARgkAgEAgKHCEIBAKBoMARgkAgEAgKHCEIBAKBoMARgkAgEAgKHCEIBAKBoMARgkAgEAgKnEAFARGdRkTLiWgVEd3lsd55RMSIaGiQ7REIBAKBk8AEARHJAJ4FcDqA/gAuIqL+nPVKANwM4Ieg2iIQCAQCd4LUCIYBWMUYW8MYiwOYCOBsznoPA3gMQE2AbREIBAKBC0EKgi4ANpq+l2rLDIhoCIBujLFPvHZERNcQ0VwimltWVpb/lgoEAkEBE2qoAxORBOBJAFdkWpcxNg7AOAAYOnQoC7ZlAoGguZFIJFBaWoqamuZveIjFYujatSvC4bDvbYIUBJsAdDN976ot0ykBcAiAaUQEAJ0ATCKisxhjcwNsl0AgKDBKS0tRUlKCnj17QutvmiWMMZSXl6O0tBS9evXyvV2QpqE5APoQUS8iigAYAWCS/iNjrIIx1p4x1pMx1hPALABCCAgEgrxTU1ODdu3aNWshAABEhHbt2mWt+QQmCBhjSQCjAEwFsAzAO4yxJUT0EBGdFdRxBQKBgEdzFwI6uZxnoD4CxtgUAFNsy+53Wff4INsiEAgEAj4is1ggEAgCpry8HIMGDcKgQYPQqVMndOnSxfgej8c9t507dy5Gjx4daPsaLGpIIBAICoV27dphwYIFAIAxY8aguLgYt99+u/F7MplEKMTvjocOHYqhQ4MtuiAEgUAgKCgenLwES3/Zndd99t+vFR4YfnBW21xxxRWIxWKYP38+jj76aIwYMQI333wzampqUFRUhFdeeQV9+/bFtGnT8MQTT+Djjz/GmDFjsGHDBqxZswYbNmzALbfckhdtQQgCgUAgaCBKS0sxY8YMyLKM3bt3Y/r06QiFQvjiiy9wzz334L333nNs8/PPP+Prr79GZWUl+vbti+uvvz6rnAEeQhAIBIKCItuRe5BccMEFkGUZAFBRUYGRI0di5cqVICIkEgnuNmeccQai0Sii0Sg6duyIrVu3omvXrnVqh3AWCwQCQQPRsmVL4/N9992HE044AYsXL8bkyZNdcwGi0ajxWZZlJJPJOrdDCAKBQCBoBFRUVKBLF7Uc24QJE+r12EIQCAQCQSPgT3/6E+6++24MHjw4L6P8bCDGmlYNt6FDh7K5c3OsQjGmtfa3In8NEggEjZ5ly5bhoIMOauhm1Bu88yWieYwxbhyq0AgEAoGgwBGCQCAQCAocIQgEAoGgwBGCQCAQCAqcgkooO7n2cVSyIsxq6IYIBAJBI6KgBMFKVrfsO4FAIGiOFJQgEAgEgoagvLwcJ554IgBgy5YtkGUZHTp0AADMnj0bkUjEc/tp06YhEongqKOOCqR9QhAIBAJBwGQqQ52JadOmobi4WAgCgUAgyAuf3gVsWZTffXYaAJw+NqtN5s2bh1tvvRV79uxB+/btMWHCBHTu3BlPPfUUXnjhBYRCIfTv3x9jx47FCy+8AFmW8frrr+Ppp5/Gsccem9fmC0EgEAgE9QxjDDfddBM++ugjdOjQAW+//TbuvfdejB8/HmPHjsXatWsRjUaxa9cutGnTBtddd13WWkQ2CEEgEAgKiyxH7kFQW1uLxYsX4+STTwYApFIpdO7cGQAwcOBAXHLJJTjnnHNwzjnn1Et7hCAQCASCeoYxhoMPPhgzZ850/PbJJ5/g22+/xeTJk/HII49g0aI8m7E4iIQygUAgqGei0SjKysoMQZBIJLBkyRIoioKNGzfihBNOwGOPPYaKigrs2bMHJSUlqKysDKw9QhAIBAJBPSNJEt59913ceeedOPTQQzFo0CDMmDEDqVQKl156KQYMGIDBgwdj9OjRaNOmDYYPH44PPvgAgwYNwvTp0/PenoIqQ/3+j6Xo1DqGo3q3z3OrBAJBY0aUoW7AMtREdBoRLSeiVUR0F+f364hoEREtIKLviKh/kO05d0hXIQQEAoHARmCCgIhkAM8COB1AfwAXcTr6NxljAxhjgwA8DuDJoNojEAgEAj5BagTDAKxijK1hjMUBTARwtnkFxthu09eWAJqWnUogEDQZmpoZPFdyOc8gBUEXABtN30u1ZRaI6EYiWg1VIxjN2xERXUNEc4lobllZWSCNFQgEzZdYLIby8vJmLwwYYygvL0csFstquwbPI2CMPQvgWSK6GMCfAYzkrDMOwDhAdRbXbwsFAkFTp2vXrigtLUUhDCRjsRi6ds2u0nKQgmATgG6m7121ZW5MBPB8gO0RCAQFSjgcRq9evRq6GY2WIE1DcwD0IaJeRBQBMALAJPMKRNTH9PUMACsDbI9AIBAIOASmETDGkkQ0CsBUADKA8YyxJUT0EIC5jLFJAEYR0UkAEgB2gmMWEggEAkGwBOojYIxNATDFtux+0+ebgzy+QCAQCDLT5DKLiagMwPocN28PYHsem9MUEOdcGIhzLgzqcs49GGMdeD80OUFQF4horluKdXNFnHNhIM65MAjqnEXROYFAIChwhCAQCASCAqfQBMG4hm5AAyDOuTAQ51wYBHLOBeUjEAgEAoGTQtMIBAKBQGBDCAKBQCAocApGEGSaJKepQkTdiOhrIlr6/+2dXYhVVRTHf38cUUvQGYNhSmIUpejLD4S0eggrC4l6SDAREhsIJMIi+pAeJOiliKypCPuOEInMPpgHzcaIoFCSTMfUHFPM0NRIoQhRWz3sdcfjTXPOOM71nrN+cLh7r7257HX+F9bZe5+7tqQtkha6vUnSGkk7/LPR7ZLU7vdhk6TJtfWgb0gaJOl7SR1eHyNpnfv1gac1QdIQr3d7e2stx91XJI2UtELSNklbJU0rgcaP+G+6S9JySUOLqLOktyUdkNSVseXWVtI8779DUq4sDaUIBL08JKdeOQ48amZXAVOBB923J4FOMxsPdHod0j0Y79cD1G+iv4XA1kz9WWCJmY0jpStpc3sb8Ifbl3i/euQlYJWZXQlMIPleWI0lXUZKSz/FzK4hpam5l2Lq/C5wR5Utl7aSmoDFwPWks2AWV4JHrzCzwl/ANGB1pr4IWFTrcZ0nXz8FbgO2Ay1uawG2e3kpMCfTv6dfvVykTLadwHSgAxDp35YN1XqTcl1N83KD91Otfcjp7whgV/W4C65x5TyTJtetA7i9qDoDrUBXX7UF5gBLM/ZT+p3tKsWMgF4eklPv+HR4ErAOaDazfd60H2j2chHuxYvA48A/Xh8FHDaz417P+tTjr7cf8f71xBjgIPCOL4e9KeliCqyxmf0KPA/sAfaRdNtAsXXOklfbc9K8LIGg8EgaDnwEPGynHgGKpUeEQrwnLOlO4ICZbaj1WAaQBmAy8JqZTQL+4uRSAVAsjQF8WeNuUhC8lHSUbfXySSkYCG3LEgjyHpJTV0gaTAoCy8xspZt/k9Ti7S3AAbfX+724EbhL0m7SYUbTSevnIyVVsulmferx19tHAL8P5ID7gb3AXjNb5/UVpMBQVI0BbgV2mdlBMzsGrCRpX2Sds+TV9pw0L0sgOOshOfWKJAFvAVvN7IVM02ecPN9hHmnvoGK/z98+mAocyUxBL3jMbJGZjTazVpKOa81sLvAlMMu7VftbuQ+zvH9dPTmb2X7gF0lXuOkW4EcKqrGzB5gq6SL/jVd8LqzOVeTVdjUwQ1Kjz6ZmuK131HqTZAA3Y2YCPwE7gadqPZ5+9Osm0rRxE7DRr5mk9dFO0qlvXwBN3l+kN6h2AptJb2XU3I8++n4z0OHlscB6oBv4EBji9qFe7/b2sbUedx99nQh85zp/AjQWXWPgaWAb0AW8Dwwpos7ActI+yDHS7K+tL9oC97v/3cD8PGOIFBNBEAQlpyxLQ0EQBMEZiEAQBEFQciIQBEEQlJwIBEEQBCUnAkEQBEHJiUAQBFVIOiFpY+bqt2y1klqzWSaD4EKg4exdgqB0/G1mE2s9iCAYKGJGEAS9RNJuSc9J2ixpvaRxbm+VtNbzw3dKutztzZI+lvSDXzf4Vw2S9Ibn2v9c0rCaORUERCAIgtMxrGppaHam7YiZXQu8QsqCCvAy8J6ZXQcsA9rd3g58ZWYTSLmBtrh9PPCqmV0NHAbuOc/+BMH/Ev8sDoIqJP1pZsNPY98NTDeznz3R334zGyXpECl3/DG37zOzSyQdBEab2dHMd7QCaywdOIKkJ4DBZvbM+fcsCE5PzAiCIB92hnIejmbKJ4i9uqDGRCAIgnzMznx+6+VvSJlQAeYCX3u5E1gAPWcsjxioQQZBHuJJJAj+yzBJGzP1VWZWeYW0UdIm0lP9HLc9RDo97DHSSWLz3b4QeF1SG+nJfwEpy2QQXFDEHkEQ9BLfI5hiZodqPZYg6E9iaSgIgqDkxIwgCIKg5MSMIAiCoOREIAiCICg5EQiCIAhKTgSCIAiCkhOBIAiCoOT8CzcnAQIj+JfJAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show(*args, **kw)>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(best_lr)"
      ],
      "metadata": {
        "id": "S-vJflRpKiGh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "568871ee-ff01-4711-f35f-c14b3d146d96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bmf2dMewpkjd"
      },
      "source": [
        "# Task 3"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2m9_gphFzo-i"
      },
      "source": [
        "# Experiment 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "id": "pS62Ce9Yxzia",
        "outputId": "7a890871-e6ad-4c5e-aeeb-3233ec39c47a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1000/1000 [02:05<00:00,  7.95it/s]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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lxZMj+AAoNrMewBBCYwP9K6GpSpCM1BR2FClHICISKZ5AUOLuRcAFwFPufhuhXMIeJz0thR3FCgQiIpHiCQQ7zewy4EpgaLAtPXFJSpxGqSnsVCAQEYkSTyC4BhgAPOjui82sG/BGYpOVGOkqGhIRqaDaQBDMMfxHQqOIHgYsd/eHE56yBMhIS2GnBp0TEYlSbauhoKXQa8ASQqOJdjazq9x9fGKTVveUIxARqSie5qN/B05393kQmsweeBs4KpEJS4QMVRaLiFQQTx1BemkQAHD3+eyhlcUZqcaOohJc4w2JiITFEwimmNmLZjYweP0TyK7uTWb2spmtNbOZlewfaGabzGxa8Lq3pomvqfTU0OWqU5mISJl4ioZuJDSL2G+D9QnAs3G871XgaaruhTzB3c+J41x1IiMtFAh2FJWEg4KISLKrMhCYWSow3d0PBiqbgD4mdx9vZlm1T1rdK/3yV18CEZEyVT4Wu3sxMM/MuiTo8weY2XQz+9TMeiXoM8IicwQiIhIST9FQa2CWmU0GwhPYu/t5u/jZU4Gu7l5gZoOA/wA9Yx1oZjcANwB06VL7mJQR5AjUckhEpEw8geCeRHywu+dHLA83s2fNrJ2758U4dgihAe/o169frWt6lSMQEamo0kAQjDa6n7t/UW77CcCqXf1gM2sPrHF3N7P+hIqp1u3qeatSVkegVkMiIqWqqiP4B5AfY/umYF+VzOxtYCJwkJktN7PrzOxGM7sxOORCYKaZTQeeBC71BDfwV45ARKSiqoqG9nP378tvdPfv42kN5O6XVbP/aULNS+tNeqoBqiMQEYlUVY6gVRX7mtR1QuqDcgQiIhVVFQiyzex/ym80s+uBKYlLUuJkqB+BiEgFVRUN3Qp8ZGaXU/bF3w/IAH6a6IQlgnIEIiIVVRoI3H0NcJyZnQQcFmwe5u5j6iVlCaCexSIiFVXbj8DdxwJj6yEtCRfOESgQiIiEJdXIa6V1BIUqGhIRCUuqQJDZvBGpKcay9VsbOikiIruNpAoEjdNT6blvM75fsamhkyIisttIqkAAcHjHloybl8uWwqKGToqIyG4h6QJBt8x9ADj3qS8bOCUiIruHpAsErZpkALAob0s1R4qIJIekCwRmZctfLqgw4rWISNJJukDQZp+M8PLazdsbMCUiIruHpAsEpx+6X3i5ReP0BkyJiMjuIekCgUWWDYmISPIFAoBnL+8LaKgJERFI0kBwcPvmAPzqrakNnBIRkYaXsEBgZi+b2Vozm1nJfjOzJ80sx8xmmFnfRKWlvNLB5wAKi4p5fNR8tu8srq+PFxHZrSQyR/AqcGYV+88CegavG4DnEpiWKJGB4I2JS3li9AL+OX5RfX28iMhuJWGBwN3HA+urOOR84HUP+QZoZWYdEpWeSI3SUsPLW3eEcgLblCMQkSTVkHUEHYFlEevLg20VmNkNZpZtZtm5ubm7/MGNInIEBRpzSESS3B5RWezuQ9y9n7v3y8zM3OXzlc5LAJC/bWfoM3b5rCIie6aGDAQrgM4R652CbQmXklLWl2D9lh0AuCKBiCSphgwEnwBXBq2HjgU2ufuq+vrwt//nWABGzl5TXx8pIrJbqnbO4toys7eBgUA7M1sODAbSAdz9eWA4MAjIAbYC1yQqLbG0aho9vITjrNi4jfYtGpOaot7HIpI8EhYI3P2yavY7cHOiPr86zRpFX/rKjds5/qEx/ObkHvzh9IMaKFUiIvVvj6gsToR9WzSKWl+5cRsA4+aVtUrKWbuZkhJVHojI3i1pA0GjtFTO7l3WbaE0EBQHX/yzV+Zz6mPjeXZcToOkT0SkviRtIABo0zQ0N0FairFqU2hugpKg+dDH00INmKYt00T3IrJ3S1gdwZ7g1yf3oE/nVoyZt5ZhM0INluau3szVr0wOFxGlpRjj5+eydUcxZx7WviGTKyKSEEkdCPZr0ZifHdWJxeXmL46sJ0hNNa58eTIASx46u17TJyJSH5K6aKjUb07pUem+dDUlFZG9nAIB0YPQlRfZC3lnDSeyeWZsDoM/jjkKt4jIbkOBoBofTi0b9aLnnz9l2fqtzFmVH9723LiFzF+zOeZ7/2/EPF6buDThaRQR2RVJXUcQ6aELDueVr5Ywr5Iv9VIXPv81a/ILaZoRykVs3VHM45/PZ/4DZ9VHMkVE6pxyBIFL+3fh1lN7VnvcmvxCIBQASucy2FFUVmQ0YtZq+j0wisIizW8gInsGBYIIjTMqryuI1/3/nU1ewQ5yNxeGt7k7ExeuwysZ4rS4xCvdN3tlPmPmamA8EUkcBYII23bU/im+/JzHkbmE96cs57J/fsN72ct4cvQCpi3bGHVs97uGc8xfR0dtm79mM/+a9AODnpzAta9mV/nZhUXFURXZOWsLWB10kBMRqY7qCCJ0a7dP+Gf5vgXV+c3b3zF+fi5t9wn1Vl4RDFkBsCg41+0ffA/AY6Pms+Shs1mxcRvj54f6LKzdXMjUHzbQt0tr1hUUcvrj46POv31nMY3TY+dYDh88ku77NuPTW34EwKmPfQGo34OIxEc5ggiHdGjB9MGnc/kxXaK2R45JNPnPp8R876jZaygsKmFl8CR+xUuTw/uyl1Scunn+ms2c+fh47vzw+/C20lzFoyPnVzh+5cZtzFq5icMGj+CHdVsBeGr0Ar5emMeO4hLmrMrnvexlPDZyXvg9U5Zu4KucPKYsrWrqaBFJdsoRlNOySTqnH9qeKUs3cFyPdrzy1WLuO68Xz/y8b63P+e2SDRW2lX/ih1Bxkrvz9uQfKux7f8py1m4upKCwiJGzV3NZ/y78fVR0wPjT+zOi1n/23Nfh5Yl3nsyqTdvp26V1hXP3uGs4vzzxAG474+C4rylRcjcXsnHrDnru1zzm/oc/m8uZvdpzROdW9ZyymnN3zNQhUXZ/yhHE0KVtU577xVFccWxXxvxhIO2aNar+TXXg6le+pdudw2Pue3bcwnA9wKyV+cxdXXUz1/IG/G0MFzz7Nd8vjx5Ez90pKnGeGbswanteQSGfzVzNZzNXhYfiziso5IbXs/l42opKK7dXbdpGUQ073kU67fEvOC1GkAQoKCziuXEL+cVLk2p9/vryVU4e3e4czsLcgoZOiki1FAjqUI99m4WXP/n18XV+/o+nrQTgo+9W8Md/T6/VOfIKCqPWC4uiv7SLS5ztO4u58qXJ3PjmFG58cyr/nrIMd+fxUfMZOXsNt7wzjY+nreTvI+fxdU4em7btZPqyjeQVFDLgb2N4dOR8FuYWMOiJCeE5oct/RtYdw3j+i+jgs6OohI1bdwJUCDSfzVzFkqCupVFaKiUlHlUhD7CuoDA8nHhNrdi4jaw7hvHf6Str/N68gkLyt++M2jZ+Qaju56OpNZ+Gu6SKVmQ1taWwKFzftThvCy9OWFQn562NbTuK6+y6pG4lNBCY2ZlmNs/Mcszsjhj7rzazXDObFryuT2R66sqEP53E+NtOCq+XTm35wE8OA0Kzn3Vu3TShaahpZXapJ0YvCD/hn/mP8Rx8z2fhfYM/nkn3u4Zz8D2fMTui9/TtH3zPkPGLeGtSWZHVpMXreGpMDj9/cRKXvDCR85/5ioVrQ0+/b0xcwil//4LZq/L5bOZq8rfv5JHP5pK/fSeHDR7BoCcmAPDYyPksztsSDhb/mlTWC3tbRCusH9Zt5cY3p3LOU18CoWlGf//eNA68+1Oy7hjGKX8fh7tz1AOfc9xDYypcc3GJM2buGtydi1+YyF+Gzq5wzNzgej+cujzu3+Ub3yxlzqp8+j3wOT96eGzUvk6tmgBUCExXvjyZx0ZVrAMqlXXHMA64a3jMY6Yv28jazdt545ulMQNsLP/zejYnPRr6/Vz58iQeGDaHTdt2Vv/GwOTF6xkxa3Xcx1dm9abtHHLvZ7w5qWKxpzS8hAUCM0sFngHOAg4FLjOzQ2Mc+q679wleLyYqPXWpc5umdGnblJZNQvMe/+2nh9O8cRoHBuXah3RoTvPGZdUv/7n5eCb8qSxwjP3jQF65+uj6TXRg2rKNDHx0HIM/nlmheKmq4TD+9uncqPW3Jy8LL5eeJycoBtkS0Qx3w9Yd3PzWVJ4dt5AHhs6moLAo3Ht7R3EJJz06jl++EWoeuzq/LLdSmjMA2Lgt+kuvZZN0/jOt7Ml9Ye4W7vtvxS93dyd3cyG3vjuNa1/N5tlxC5m8eD0vfbk4fMykRevIumMYS4MK+Hi4O//OXsY9/5nJoCdDQW3Ttp3cHtTRTFq0Llzhv2LjNu788Hs2BF/c4+fn8uToBUDoCfmHiM+NnA3v6bE5zFu9Oao/yvnPfEX/B0dzz39mcscHZfVBVT1lf71wHQAL1hawcUvod7quXK6wKhe/MJFfvjElvP5VTh4nPzquQnPpLYVFVRYJTv0hVE82bu7auD87lmEzVvHA0Nk8FfwOAWau2MSy9fHfv/KKikuYvTKfx0bOqzbH8vnsNfR/8PPw9c9fs5knPl+wx+d0EllZ3B/IcfdFAGb2DnA+UPE/dg818nc/Jq+gkF77t+TiozsD8MIVR3F0VhvSUlN46/pjOKh98wp1DN3a7ROeCa3Up7f8iLOCJ+WXr+7Hre9MI397UYXPnHrPafT9y6hdSvcP67cmZAykJTFyKf83oqwV06yV+RX2A2QvDX1JrMkv6/vwXvYyumc24zdvf8e5R+wfdXyTGM1oR0Y8ta7atI2mGWm8++0P/HV4WQCLTEvWHcN4/dr+PD0mNAPdzJWhupMthcUUFhWHByIc9MQELujbkYEHZbKuYAfHHNCWsfPWclvwpR/5//9u9jIevrA3lwz5Jrxt0uL1TFq8nu9+2ED3zLKiw3cm/8AdQYuxnAfPoqjEo/qXuMMZ/xhPm30ymHrPabz7bfST9MjZa8K/r2P+Opp/XNKHnxzZscLvJS3FKCrxqMYJeQU76NYuuiL7s5mrGNC9XfjhBoj5xX7ff2exKG8LC3ML6LV/y/D2XoNHcGav9jx/xVEV3gOwNkhrh1aNKSou4frXs7nxxO4ce0BbNmzZwc6SEvZt3jh8/ObtO0lPTYlqMr1x6w5u/tfU8PqNA7uTnpoSzinWtLn0xIXrWLelkF//67vwtut/fABN0lNJT439jPzX4XNYu7mQGcs30b9bGy5+YSIbt+6kXfMMjj2gLd0zm/HtkvVc9PxExvzhRA6IuOe76q6PvueEHu0YdHiH6g+uoUQGgo7Asoj15cAxMY77mZn9GJgP/M7dl5U/wMxuAG4A6NKlS/ndDWa/Fo3Zr0XjqG1n9CqbvOb4Hu2i9g08KJOi4tA3R5ugv8HF/Trx8M96R/1TnnTQvoz43Y8Z8LdQMcd395zGkcGXf5t9MmjdNJ0NEU/M0+49jT73xw4Opx6yL5/P2bWnsHj9c8LiKvdXFgjc4fWJS/jou7Ly9H98XvbEV77c/sucvArnWBnRgW7A38bQKC2lQv1HeaXzTEDZ4IKTl6znoLs/Y/HfBrFx605mr8pn9rB8Hhg2Bwh9ac9YXvmsdfMqqcSfu3pzVA7sjohmwz//5yTMQkGjvNIioNI+KJGO+eto3r9xAAC3vjuNW9+dxvUndOOuQYeQkmLMW72Zohhzbl/76rc0Tk/lL+f3ovU+GXRq3YQb35xKj32bcfNJ3clZW0DrphkVfs+RxVEFEQ8ppU/Hn81azZxV+XTPbEZGWgqFRcVMXrye9i0aszEojnrzmx84snNrxs3LZeaKTWTffRo/emQsBYVFfPir42i3TyO6tG3KpUO+YdWm7Uy95zQAJizIrfCgsXl7UVTOe03+dobNWMXVx2VhFvq7SqlkGPnzn/6S6THuY+//HQmUBZUleVto3TSDR0bM5Zc/7h7uE3TxCxM5p3eHcM71zx/NJD3VWPDgoPDf8Vc5eVUGgpkrNnHOU1/y4a+Oi2rNt31nMYtyt3Do/i3Kft+FRfxr0g90KPd9U1cauvnof4G33b3QzH4JvAacXP4gdx8CDAHo16/fHpsHe/Wa/uHlNvtkMOXuU2ndNCMcBP5yfi+aNU7DzOjQsglj/ziQWSs30ToIGqUGn9uLW9+dxivXHM22HcW0aprBDT8+gHbNMqKegLu0acpTl/XlkHvL6gE6tmpCSgosW191peptZxwU9QRdXhDfDvoAABDmSURBVOk/WrxuGtid58YtrHT/vR/Piv9kcaguCFTnf17PjhlAe/z50yrf98Y3S2r8WZNj9DOJVNrpMJYd5Z7aX/xyMS9+uZijurZmytKKzZYh9KVSUFjETW+Fnq7bNQv9feWsLeB378ZuhLB8w1ZOiKgHySvYwb0fz+SMXu2j6sPOemICnds0oUubpnyVsy7muUrrP/IKdnDbv6dTUBgKKhc8G2ruPP3e08MPDUPGL+SGH3eP6pdTavP2nYyJKGq6f+hshs1YxeGdWvLIZ3P5dskGHvzpYRzcvgVmcHD75izJ28qh+7eIGQQiTVm6gXHz1vLUmLI5y98qV78xNJjVsNTO4CHvX8Fxm7btZNATE3jskiMYOWsNi3IL+OWJ3VmxYRs/OrBdOCczfMYq+nZpzbAZq8hq15QXJyzmo+9WcN95vbj8mC6kpaZw05uh4rkubRNT95jIQLAC6Byx3inYFubukX8pLwKPJDA9u5225YqMrhiQFbXerd0+4d7OkX5yZMcKxQB3DToEgFZNMshIS+HWd6fxxKV9aBIxflJpBXfnNk0qbaYKcFjHFpzbe/8qA8H8B87ifz+ZVeGfozIFMYq5Yjn90P1o3TSDd7NDGcMWjdNiFpElWm1zUW9+U/eVoZE5l/KmxOijAlQaBGLJK6i+4vnf2dGV6DOWb+T1iUt5feJS/nDagVH7lq3fVuWDRmSv+39PqVg5f8T9I8PLfx0+lyYZsb+mhs5YFfU3Wjrd7O/fmxb+/D9/VDYfyGEdWzBzRexcaXmRfXBq4ouIoF1aT/ToiPl8Pic0Xlhp3daPepaVFoyeu5a7Bh0SLvbKbB76Xhj8ySxaNU3n/D4dw7mxAd3b1ipd1Ulkq6FvgZ5m1s3MMoBLgU8iDzCzyMKu84A5CUzPHu3z35/Il7efVO1xFx/dmZ8c2ZElD53NkeU6j3VpG6rkNjNuO+MgANqXy2r269qav1/Uh/1algWp43u05YlL+0Q1iU1PTeHec0N1/1cN6MrtZx7Ms5dHd7o7qmvZ5x97QPQf8BXHdo2Z/jN6tQ8Hrz+cdiD9u9XuD/+iozpVub9rHT9ZnXrIvnV6vnj7rpTvVFid9NTadXB7IqJyFuCF8WXNUGuahpq65z+xJ3eq7EGlsiAUbxDYFWNjVIaXBoFIExaUFbstztsSrqMCohoIDJuxilMf+4JZK/Np36JxVD1KXUpYIHD3IuDXwAhCX/DvufssM7vfzM4LDvutmc0ys+nAb4GrE5WePV2PfZvRqZZNUt+54Vie/vmRUdtuPqkHSx46m2/uOoVFfx0U/oJ4/6bjOKh986hZ2966/ljO79OR3p1a8ciFvfnTmaEg0igtlVn3ncHgc3tx08DuDDq8A1/fUVay97cLDg8vDzq8PXP/cma4GKJ9y8YMPvdQJvzpJE46KJOM1BS+vP0kLujbMfxEdFD75uFg0iIoC/5zkPMBOKZbm/Byz4g+HFltm/LIhb259OjO/HnQIRWC3ZKHzubRi44Irz90weHhawJ47vL4epGf36esEvuifp3pGDQZ7Z/VpsKxH98cCqL9urbmnN4d6NulFU9edmTMiu+Pbz6e7LtPjRrO5NgDKp6zNlJ3YerVIzqFKoebxhil1wye/0Xte98DHNqhRfUHxTDo8PYcuF/NK2VPPDCTE8rV4+2qpetq16z7vKe/ilo/JPhdjJy9hpygWfbq/MQNJJnQOgJ3Hw4ML7ft3ojlO4E7E5kGqfg0Xl5KijHt3tMpX+T/whVH0aFl9Jfoxf06R63v0yj6T2j/Vk24+aTudM9sxoH7NeerO04mPdUwMxqnp/L6tccw6MkJnHhgJod1DH2xvBJRdwJwzfFZHN6xJT/q2Y5THU45ZF86tmrCmvztHJDZjAeHhzKOjYIv0Y6tmvDy1UezatN2Ln5hYlAmbDz0s94AXHlcVwqLSvjv9JXhVjGHdGhBu2YZ/OOSIzkhyKY/8lnoCXPgQfvSvFEamwuL+OqOkzn+oTFcFsxX8fKXi8NPw49f3IdPv1/NjuISTujRjq/uOJmCwiLSU403Ji5lwoK8cFFB704t+eqOk9mveSPSIlqknNgzk4V5BeHycYCsoDgw8unvnRsG8Pio+TwxegEXHtWJ96cs58YTu7Mwt4BRs0NPnK9f2z9cjHT5MV14a9IP3HxS93Cv8dIAu31n9c1Hrz4ui1e/XhK17YObjiMtNYVHR8zj6bE5Ufvc4czDQhn8tBRjxv+ezooN2yrtJV7eNcdn8ZuTe1bbIq5xegrbd5bVi1x9XBb/e14vikucFycs4uhubfj9u9NYEqM58BGdWnJZ/y7hivpXrzkaM+PQez9j645i3r9xAKNmrwnf330yUqOaQkea+5czuXTIN0xbtpH7z+9Fyybp3PLONMbOy+W47m1JTbGop34IPawsWBtfT/OBB2VGzYSYaA1dWSy7ifJf6BDdAqomIscsKn1KLnXo/i2qbebXNCONHx+YCYSeNEv7Z5RvgZHVtinjgUcvOoLObZrSuU1T3riuf4UisUZpqTRKS+XyY8qKo5o1SiP77tNifn7j9BTe/eUARsxazf4tG0el985Bh7B1RzFNG6WSkmLMfzB6Zrpmwe/x+h8dwE+O7MinM1dzZq/2mFmF3wVAy6bp9O3Smpn3ncFhg0cA0DzGvQD43WkH8rvTDsTd6d+tDef32b/S+bYf/OnhPPjTUI7sNyf3ZNiMVfz0yI58s2gdX+bk8WxQcT998OlcNuQbZq/K5/lf9OXGN6dyQo92nHhQZjgQXHt8Ny7t3zkcwE46eF+eHpvDrwZ254K+ncKj3QJMuftU0lJTaJqRRs/9mjN98Onkb9tJy6bplJQ4hUUl4SHXmzVKC1cU33P2oVEPIq9cczT7Nm/EjqISfhoRJK8/4QB+f9qBLMrbwk1vTuG6E7oBoZzOL0/sDoT66dz339n8O3tZ1Bf5x78+IdzfINSyKJQ7mnTXKZSUhO7F5u1FvDB+EZ///kTGzl0bfuj44+kH8uuTe5J1xzAAGqen0rFVE6Yt20jvTq3o1rasLu+MXu256rgsvs7Jo13zRtz14fdkL93AIR1a8PglfcKVxACX9e8SHlvsnnMO5fkvFtK7Y0suPbozXds0jWpd1j2zYn1hnXH3Pep11FFHuSS3yYvX+Xc/bPBtO4r8o6nLvaSkpE7OO3T6Sv/Vm1Pq5Fy18cGUZX7jG9lR28bMWeNvT1oa9zke+nSOPzs2p9rjvpi31kfPWe3u7iUlJb6+oNDd3Reu3eybtu3wkpISHzFzlb/wRY4XFVf8/a4Ljnd3f3rMAh86fWXcaex6+1DvevtQLykp8bFz1/iSvIKofQ9/Oie8vqOo2K9/7Vv/KifX87ftiJmWymzevtNHz1kd/rxSS/IKfEvhzkrfV/oZJSUlUWl1d3/v2x/8ncmh+7GuoNBf/WpxeN/cVfn+4LDZvrOoOOp8pWkYOn2lby0sCp+z6+1DffmGrd7zruHe9fahvnl7xTR9uSA3fOy2HUVxX3ssQLZX8r1qvof1iOvXr59nZ1c9UYuI7L6+ysljTf52LuhbdYV+XXlmbA79urbmmGqKSGMpzQHU5dweXy7I4xcvTaJ103S+u/f0Ko+dsyqfs56YwCkH78tLuzgagZlNcfd+MfcpEIiIxDZ23lryt+3k/D4Ve23vitJOeJVNNlXK3Xlz0g+cfXiHcCfU2qoqEKiOQESkEicdVLfNgktVFwBKmVmlTa3rkoahFhFJcgoEIiJJToFARCTJKRCIiCQ5BQIRkSSnQCAikuQUCEREkpwCgYhIktvjehabWS5Q2wl32wEV5zncu+mak4OuOTnsyjV3dffMWDv2uECwK8wsu7Iu1nsrXXNy0DUnh0Rds4qGRESSnAKBiEiSS7ZAMKShE9AAdM3JQdecHBJyzUlVRyAiIhUlW45ARETKUSAQEUlySRMIzOxMM5tnZjlmdkdDp6eumFlnMxtrZrPNbJaZ3RJsb2Nmo8xsQfCzdbDdzOzJ4Pcww8z6NuwV1I6ZpZrZd2Y2NFjvZmaTgut618wygu2NgvWcYH9WQ6Z7V5hZKzN738zmmtkcMxuwN99nM/td8Dc908zeNrPGe+N9NrOXzWytmc2M2Fbj+2pmVwXHLzCzq2qShqQIBGaWCjwDnAUcClxmZoc2bKrqTBHwB3c/FDgWuDm4tjuA0e7eExgdrEPod9AzeN0APFf/Sa4TtwBzItYfBh539x7ABuC6YPt1wIZg++PBcXuqJ4DP3P1g4AhC179X3mcz6wj8Fujn7ocBqcCl7J33+VXgzHLbanRfzawNMBg4BugPDC4NHnGpbFb7vekFDABGRKzfCdzZ0OlK0LV+DJwGzAM6BNs6APOC5ReAyyKODx+3p7yATsE/x8nAUMAI9bZMK3+/gRHAgGA5LTjOGvoaanHNLYHF5dO+t95noCOwDGgT3LehwBl7630GsoCZtb2vwGXACxHbo46r7pUUOQLK/qhKLQ+27VWC7PCRwCRgP3dfFexaDewXLO8Nv4t/AH8CSoL1tsBGdy8K1iOvKXy9wf5NwfF7mm5ALvBKUCT2opntw156n919BfAo8AOwitB9m8Lef59L1fS+7tL9TpZAsNczs2bAB8Ct7p4fuc9Djwh7RTthMzsHWOvuUxo6LfUsDegLPOfuRwJbKCsuAPa6+9waOJ9QANwf2IeKxSdJoT7ua7IEghVA54j1TsG2vYKZpRMKAm+5+4fB5jVm1iHY3wFYG2zf038XxwPnmdkS4B1CxUNPAK3MLC04JvKawtcb7G8JrKvPBNeR5cByd58UrL9PKDDsrff5VGCxu+e6+07gQ0L3fm+/z6Vqel936X4nSyD4FugZtDjIIFTp9EkDp6lOmJkBLwFz3P2xiF2fAKUtB64iVHdQuv3KoPXBscCmiCzobs/d73T3Tu6eReg+jnH3y4GxwIXBYeWvt/T3cGFw/B731Ozuq4FlZnZQsOkUYDZ76X0mVCR0rJk1Df7GS693r77PEWp6X0cAp5tZ6yA3dXqwLT4NXUlSj5Uxg4D5wELgzw2dnjq8rhMIZRtnANOC1yBC5aOjgQXA50Cb4Hgj1IJqIfA9oVYZDX4dtbz2gcDQYPkAYDKQA/wbaBRsbxys5wT7D2jodO/C9fYBsoN7/R+g9d58n4H7gLnATOANoNHeeJ+BtwnVg+wklPO7rjb3Fbg2uP4c4JqapEFDTIiIJLlkKRoSEZFKKBCIiCQ5BQIRkSSnQCAikuQUCEREkpwCgUg5ZlZsZtMiXnU2Wq2ZZUWOMimyO0ir/hCRpLPN3fs0dCJE6otyBCJxMrMlZvaImX1vZpPNrEewPcvMxgTjw482sy7B9v3M7CMzmx68jgtOlWpm/wzG2h9pZk0a7KJEUCAQiaVJuaKhSyL2bXL3w4GnCY2CCvAU8Jq79wbeAp4Mtj8JfOHuRxAaF2hWsL0n8Iy79wI2Aj9L8PWIVEk9i0XKMbMCd28WY/sS4GR3XxQM9Lfa3duaWR6hseN3BttXuXs7M8sFOrl7YcQ5soBRHppwBDO7HUh39wcSf2UisSlHIFIzXslyTRRGLBejujppYAoEIjVzScTPicHy14RGQgW4HJgQLI8GboLwHMst6yuRIjWhJxGRipqY2bSI9c/cvbQJaWszm0Hoqf6yYNtvCM0cdhuhWcSuCbbfAgwxs+sIPfnfRGiUSZHdiuoIROIU1BH0c/e8hk6LSF1S0ZCISJJTjkBEJMkpRyAikuQUCEREkpwCgYhIklMgEBFJcgoEIiJJ7v8BoAOvOAbpfH0AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy: 0.8523666666666667\n",
            "Testing Accuracy: 0.8312\n"
          ]
        }
      ],
      "source": [
        "num_grad_steps = 1000 #EPOCHS\n",
        "\n",
        "mlp1 = MLP(LinearLayer(X_train.shape[-1], 10), SoftmaxOutputLayer(None, None))\n",
        "\n",
        "mlp1.fit(best_lr, X_train, y_train, num_grad_steps, 512)\n",
        "\n",
        "y_hat = mlp1.predict(X_train)\n",
        "accuracy = mlp1.evaluate_acc(y_train, y_hat)\n",
        "print(\"Training Accuracy:\", accuracy)\n",
        "\n",
        "y_hat = mlp1.predict(X_test)\n",
        "accuracy = mlp1.evaluate_acc(y_test, y_hat)\n",
        "print(\"Testing Accuracy:\", accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dv_nz9l2ebc0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "outputId": "c2752ac7-0ff8-4c4c-b7c4-124b642f51fe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1000/1000 [26:23<00:00,  1.58s/it]\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"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy: 0.8817166666666667\n",
            "Testing Accuracy: 0.8817166666666667\n"
          ]
        }
      ],
      "source": [
        "hidden_units = 128\n",
        "num_grad_steps = 1000\n",
        "\n",
        "mlp2 = MLP(\n",
        "    LinearLayer(X_train.shape[-1], hidden_units), \n",
        "    ReLULayer(None, None), \n",
        "    LinearLayer(hidden_units, 10), \n",
        "    SoftmaxOutputLayer(None, None)\n",
        "    )\n",
        "mlp2.fit(0.1, X_train, y_train, num_grad_steps, 512)\n",
        "\n",
        "y_hat = mlp2.predict(X_train)\n",
        "accuracy = mlp2.evaluate_acc(y_train, y_hat)\n",
        "print(\"Training Accuracy:\", accuracy)\n",
        "\n",
        "y_hat = mlp2.predict(X_test)\n",
        "acc = mlp2.evaluate_acc(y_test, y_hat)\n",
        "print(\"Testing Accuracy:\", accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iMIOq0K1ecUO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc887fdf-6174-4231-c696-49b31c6febda"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 78%|███████▊  | 784/1000 [24:30<06:52,  1.91s/it]"
          ]
        }
      ],
      "source": [
        "hidden_units = 128\n",
        "num_grad_steps = 1000\n",
        "\n",
        "mlp3 = MLP(\n",
        "    LinearLayer(X_train.shape[-1], hidden_units),\n",
        "    ReLULayer(None, None),\n",
        "    LinearLayer(hidden_units, hidden_units),\n",
        "    ReLULayer(None, None),\n",
        "    LinearLayer(hidden_units, 10),\n",
        "    SoftmaxOutputLayer(None, None)\n",
        ")\n",
        "mlp3.fit(best_lr, X_train, y_train, num_grad_steps, 512)\n",
        "\n",
        "y_hat = mlp3.predict(X_train)\n",
        "accuracy = mlp3.evaluate_acc(y_train, y_hat)\n",
        "print(\"Training Accuracy:\", accuracy)\n",
        "\n",
        "y_hat = mlp3.predict(X_test)\n",
        "accuracy = mlp3.evaluate_acc(y_test, y_hat)\n",
        "print(\"Testing Accuracy:\", accuracy)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yp6-oQpcz3_E"
      },
      "source": [
        "# Experiment 2"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " #lr Hyperparameter\n",
        "num_grad_steps = 1000\n",
        "mlp_opti2 = MLP(\n",
        "    LinearLayer(X_train.shape[-1], hidden_units),\n",
        "    TanhLayer(None, None),\n",
        "    LinearLayer(hidden_units, hidden_units),\n",
        "    TanhLayer(None, None),\n",
        "    LinearLayer(hidden_units, 10),\n",
        "    SoftmaxOutputLayer(None, None)\n",
        ")\n",
        "best_lr2 = learning_rate_hyper(mlp_opti2, X_train, y_train, X_test, y_test, num_grad_steps)\n",
        "plt.legend(['lr=1e-4', 'lr=1e-3', 'lr=0.01', 'lr=0.1', 'lr=1'])\n",
        "plt.savefig(\"lr-HYPERPARAM\", bbox_inches=\"tight\", dpi=300)\n",
        "plt.show"
      ],
      "metadata": {
        "id": "u7fcIuip1Gya"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(best_lr2)"
      ],
      "metadata": {
        "id": "M3kjMit38d7I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fWbnBBrj0EHo"
      },
      "outputs": [],
      "source": [
        "hidden_units  = 128\n",
        "num_grad_steps = 1000\n",
        "\n",
        "mlp4 = MLP(\n",
        "    LinearLayer(X_train.shape[-1], hidden_units),\n",
        "    TanhLayer(None, None),\n",
        "    LinearLayer(hidden_units, hidden_units),\n",
        "    TanhLayer(None, None),\n",
        "    LinearLayer(hidden_units, 10),\n",
        "    SoftmaxOutputLayer(None, None)\n",
        ")\n",
        "mlp4.fit(0.1, X_train, y_train, num_grad_steps, 512)\n",
        "\n",
        "y_hat = mlp4.predict(X_train)\n",
        "accuracy = mlp4.evaluate_acc(y_train, y_hat)\n",
        "print(\"Training Accuracy:\", accuracy)\n",
        "\n",
        "y_hat = mlp4.predict(X_test)\n",
        "acc = mlp4.evaluate_acc(y_test, y_hat)\n",
        "print(\"Testing Accuracy:\", accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#lr Hyperparameter\n",
        "num_grad_steps = 1000\n",
        "mlp_opti3 = MLP(\n",
        "    LinearLayer(X_train.shape[-1], hidden_units),\n",
        "    LeakyReLULayer(0.1, None, None),\n",
        "    LinearLayer(hidden_units, hidden_units),\n",
        "    LeakyReLULayer(0.1, None, None),\n",
        "    LinearLayer(hidden_units, 10),\n",
        "    SoftmaxOutputLayer(None, None)\n",
        ")\n",
        "best_lr3 = learning_rate_hyper(mlp_opti2, X_train, y_train, X_test, y_test, num_grad_steps)\n",
        "plt.legend(['lr=1e-4', 'lr=1e-3', 'lr=0.01', 'lr=0.1', 'lr=1'])\n",
        "plt.savefig(\"lr-HYPERPARAM\", bbox_inches=\"tight\", dpi=300)\n",
        "plt.show"
      ],
      "metadata": {
        "id": "xGVTok1gPf1z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(best_lr3)"
      ],
      "metadata": {
        "id": "EXwFn11gPgFs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K69TUmNpRvAW"
      },
      "outputs": [],
      "source": [
        "hidden_units = 128\n",
        "num_grad_steps = 1000\n",
        "\n",
        "mlp5 = MLP(\n",
        "    LinearLayer(X_train.shape[-1], hidden_units),\n",
        "    LeakyReLULayer(0.1, None, None),\n",
        "    LinearLayer(hidden_units, hidden_units),\n",
        "    LeakyReLULayer(0.1, None, None),\n",
        "    LinearLayer(hidden_units, 10),\n",
        "    SoftmaxOutputLayer(None, None)\n",
        ")\n",
        "mlp5.fit(0.1, X_train, y_train, num_grad_steps, 512)\n",
        "\n",
        "y_hat = mlp5.predict(X_train)\n",
        "accuracy = mlp5.evaluate_acc(y_train, y_hat)\n",
        "print(\"Training Accuracy:\", accuracy)\n",
        "\n",
        "y_hat = mlp5.predict(X_test)\n",
        "accuracy = mlp5.evaluate_acc(y_test, y_hat)\n",
        "print(\"Testing Accuracy:\", accuracy)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F3J9gZNr0DeO"
      },
      "source": [
        "# Experiment 3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ibLIgus30Gsv"
      },
      "outputs": [],
      "source": [
        "hidden_units = 128\n",
        "num_grad_steps = 500\n",
        "\n",
        "mlpexp3 = MLP(\n",
        "    LinearLayer(X_train.shape[-1], hidden_units),\n",
        "    ReLULayer(None, None),\n",
        "    LinearLayer(hidden_units, hidden_units),\n",
        "    ReLULayer(None, None),\n",
        "    LinearLayer(hidden_units, 10),\n",
        "    SoftmaxOutputLayer(None, None)\n",
        ")\n",
        "mlpexp3.fit_withl2_reg(1e-2, X_train, y_train, num_grad_steps, 512)\n",
        "\n",
        "y_hat = mlpexp3.predict(X_train)\n",
        "accuracy = mlpexp3.evaluate_acc(y_train, y_hat)\n",
        "print(\"Training Accuracy:\", accuracy)\n",
        "\n",
        "y_hat = mlpexp3.predict(X_test)\n",
        "accuracy = mlpexp3.evaluate_acc(y_test, y_hat)\n",
        "print(\"Testing Accuracy:\", accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-dUnVIrt-OlW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xEMf0FCN0HHl"
      },
      "source": [
        "# Experiment 4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d--5TAen0KOH"
      },
      "outputs": [],
      "source": [
        "hidden_units = 128\n",
        "num_grad_steps = 1000\n",
        "\n",
        "mlpexp4 = MLP(\n",
        "    LinearLayer(X_train_unnorm.shape[-1], hidden_units),\n",
        "    ReLULayer(),\n",
        "    LinearLayer(hidden_units, hidden_units),\n",
        "    ReLULayer(),\n",
        "    LinearLayer(hidden_units, 10),\n",
        "    SoftmaxOutputLayer()\n",
        ")\n",
        "mlpexp4.fit(0.1, X_train_unnorm, y_train, num_grad_steps, 50)\n",
        "\n",
        "y_hat = mlpexp4.predict(X_train_unnorm)\n",
        "accuracy = mlpexp4.evaluate_acc(y_train, y_hat)\n",
        "print(\"Training Accuracy:\", accuracy)\n",
        "\n",
        "y_hat = mlpexp4.predict(X_test_unnorm)\n",
        "accuracy = mlpexp4.evaluate_acc(y_test, y_hat)\n",
        "print(\"Testing Accuracy:\", accuracy)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q1zVwkN90KqX"
      },
      "source": [
        "# Experiment 5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dHkynfMqUlYN"
      },
      "outputs": [],
      "source": [
        "#Imports for tensor flow\n",
        "import keras\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.layers import BatchNormalization\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.models import model_from_json\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import Conv2D\n",
        "from tensorflow.keras.layers import MaxPool2D, Dropout, MaxPooling2D, Activation, ZeroPadding2D\n",
        "from tensorflow.keras.layers import Flatten\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "from matplotlib import pyplot"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load Fashion MNIST dataset\n",
        "from keras.datasets import fashion_mnist\n",
        "(x_train, conv_y_train), (x_test, conv_y_test) = fashion_mnist.load_data()\n",
        "\n",
        "assert x_train.shape == (60000, 28, 28)\n",
        "assert x_test.shape == (10000, 28, 28)\n",
        "assert conv_y_train.shape == (60000,)\n",
        "assert conv_y_test.shape == (10000,)\n",
        "CLASS_DESC = ['T-shirt', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
        "num_classes = 10"
      ],
      "metadata": {
        "id": "rbkhkmfB_ghP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels = to_categorical(conv_y_train, num_classes)\n",
        "test_labels  = to_categorical(conv_y_test, num_classes)\n",
        "\n",
        "train_images = (x_train / 255)\n",
        "test_images = (x_test / 255)\n",
        "\n",
        "# Reshape the images.\n",
        "train_images = np.expand_dims(train_images, axis=3)\n",
        "test_images = np.expand_dims(test_images, axis=3)\n",
        "\n",
        "assert train_images.shape == (60000, 28, 28, 1)\n",
        "assert test_images.shape == (10000, 28, 28, 1)\n",
        "assert train_labels.shape == (60000, 10)\n",
        "assert test_labels.shape == (10000, 10)"
      ],
      "metadata": {
        "id": "C624lxXt_h73"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def conv_model(input_shape, num_classes):\n",
        "  alexnet_conv_model = Sequential()\n",
        "\n",
        "  #Conv Layer 1\n",
        "  alexnet_conv_model.add(Conv2D(32, (3,3), input_shape = input_shape, kernel_regularizer = 'l2'))\n",
        "  alexnet_conv_model.add(BatchNormalization())\n",
        "  alexnet_conv_model.add(Activation('relu'))\n",
        "\n",
        "  #Conv Layer 2\n",
        "  alexnet_conv_model.add(Conv2D(32, (3,3), input_shape = input_shape))\n",
        "  alexnet_conv_model.add(BatchNormalization())\n",
        "  alexnet_conv_model.add(Activation('relu'))\n",
        "\n",
        "  #Flatten\n",
        "  alexnet_conv_model.add(Flatten())\n",
        "\n",
        "  #Linear Layer 1\n",
        "  alexnet_conv_model.add(Dense(128))\n",
        "  alexnet_conv_model.add(Activation('relu'))\n",
        "\n",
        "  #Linear Layer 2\n",
        "  alexnet_conv_model.add(Dense(num_classes))\n",
        "  alexnet_conv_model.add(Activation('softmax'))\n",
        "\n",
        "  return alexnet_conv_model"
      ],
      "metadata": {
        "id": "R4xDI0t1_jVE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "conv_model = conv_model((28,28,1), 10)\n",
        "conv_model.summary()\n",
        "\n",
        "conv_model.compile(loss = tf.losses.categorical_crossentropy , optimizer='sgd', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "8mt4op4i_k9X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Data augmentation (optional: for performance improvement)\n",
        "datagen = ImageDataGenerator(\n",
        "  featurewise_center=False,\t# set input mean to 0 over dataset\n",
        "  samplewise_center=False,\t# set each sample mean to 0\n",
        "  featurewise_std_normalization=False,\t# divide inputs by std of dataset\n",
        "  samplewise_std_normalization=False,\t#divide each input by its std\n",
        "  zca_whitening=False,\t# apply ZCA whitening\n",
        "  rotation_range=0,\t# randomly roate images in the range (degrees, 0 to 180)\n",
        "  width_shift_range=0.1,\t# randomly shift image horizontally (fraction of width)\n",
        "  height_shift_range=0.1,\t# randomly shift image vertically (fraction of height)\n",
        "  horizontal_flip=True,\t# randomly flip images horizontally\n",
        "  vertical_flip=False)\t# randomly flip images vertically\n",
        "\n",
        "datagen.fit(train_images)"
      ],
      "metadata": {
        "id": "61TLEjXO_mYC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = 3\n",
        "\n",
        "history = conv_model.fit(datagen.flow(train_images, train_labels, batch_size=128), epochs=epochs, verbose = 1, validation_data=(test_images, test_labels))"
      ],
      "metadata": {
        "id": "PGB1Bbnf_nuD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.clf()\n",
        "plt.title('Model Accuracies')\n",
        "plt.plot(history.history['val_accuracy'], label='test', color='red')\n",
        "plt.plot(history.history['accuracy'], label='train', color='blue')\n",
        "plt.xlabel(\"epoch\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.legend(loc='best')\n",
        "plt.savefig(\"CNN_Accuracies.png\")\n",
        "plt.show()\n",
        "\n",
        "plt.clf()\n",
        "plt.title('Model Cross-entropy Loss')\n",
        "plt.plot(history.history['val_loss'], label='test', color='red')\n",
        "plt.plot(history.history['loss'], label='train', color='blue')\n",
        "plt.xlabel(\"epoch\")\n",
        "plt.ylabel(\"CE loss\")\n",
        "plt.legend(loc='best')\n",
        "plt.savefig(\"CNN_CE_loss.png\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Im_MTfzm_pZW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define confusion matrix\n",
        "def calc_error(predictions, labels):\n",
        "    \"\"\"Return the error rate and confusions.\"\"\"\n",
        "    correct = np.sum(predictions == labels)\n",
        "    total = predictions.shape[0]\n",
        "\n",
        "    error = 100.0 - (100 * float(correct) / float(total))\n",
        "\n",
        "    confusions = np.zeros([10, 10], np.float32)\n",
        "    bundled = zip(predictions, labels)\n",
        "    for predicted, actual in bundled:\n",
        "        confusions[predicted, actual] += 1\n",
        "    \n",
        "    return error, confusions"
      ],
      "metadata": {
        "id": "M0zlnvww_qkx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions for all test images\n",
        "test_predictions          = np.argmax(conv_model.predict(test_images),1)\n",
        "test_error, test_conf_mat = calc_error(test_predictions, np.argmax(test_labels,1))\n",
        "print('Test error: %.1f%%' % test_error)\n",
        "\n",
        "plt.figure(figsize=(6, 6))\n",
        "plt.xlabel('Actual')\n",
        "plt.ylabel('Predicted')\n",
        "plt.grid(False)\n",
        "plt.xticks(np.arange(num_classes))\n",
        "plt.yticks(np.arange(num_classes))\n",
        "plt.imshow(test_conf_mat, cmap=plt.cm.jet, interpolation='nearest');\n",
        "\n",
        "for i, cas in enumerate(test_conf_mat):\n",
        "    for j, count in enumerate(cas):\n",
        "        if count > 0:\n",
        "            xoff = .07 * len(str(count))\n",
        "            plt.text(j-xoff, i+.2, int(count), fontsize=9, color='white')\n",
        "\n",
        "plt.savefig(\"Confusion_Matrix_CNN.png\")"
      ],
      "metadata": {
        "id": "FyCqbm8k_rn_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SJRCG1Xe0NsE"
      },
      "source": [
        "# Experiment 6"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "efawHcoe1Gmf"
      },
      "outputs": [],
      "source": [
        "hidden_units  = 128\n",
        "num_grad_steps = 2000\n",
        "\n",
        "mlp8 = MLP(\n",
        "    LinearLayer(X_train.shape[-1], hidden_units),\n",
        "    TanhLayer(None, None),\n",
        "    LinearLayer(hidden_units, hidden_units),\n",
        "    TanhLayer(None, None),\n",
        "    LinearLayer(hidden_units, 10),\n",
        "    SoftmaxOutputLayer(None, None)\n",
        ")\n",
        "mlp8.fit(0.1, X_train, y_train, num_grad_steps, 512)\n",
        "\n",
        "y_hat = mlp8.predict(X_train)\n",
        "accuracy = mlp8.evaluate_acc(y_train, y_hat)\n",
        "print(\"Training Accuracy:\", accuracy)\n",
        "\n",
        "y_hat = mlp8.predict(X_test)\n",
        "acc = mlp8.evaluate_acc(y_test, y_hat)\n",
        "print(\"Testing Accuracy:\", accuracy)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Beyond minimum requirement\n"
      ],
      "metadata": {
        "id": "VLCO4pH19UVo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hidden_units = 128\n",
        "num_grad_steps = 100\n",
        "\n",
        "grad_step = np.linspace(0, num_grad_steps, 10)\n",
        "alpha_l2_reg = np.array([0.0005, 0.001, 0.005, 0.01, 0.05, 0.1])\n",
        "accuracy = np.full((len(alpha_l2_reg), 2), 0.0)\n",
        "for ia in range(len(alpha_l2_reg)):\n",
        "  mlpexpb = MLP(\n",
        "    LinearLayer(X_train.shape[-1], hidden_units),\n",
        "    ReLULayer(),\n",
        "    LinearLayer(hidden_units, hidden_units),\n",
        "    ReLULayer(),\n",
        "    LinearLayer(hidden_units, 10),\n",
        "    SoftmaxOutputLayer()\n",
        "  )\n",
        "\n",
        "  mlpexpb.fit_withl2_reg(0.1, X_train, y_train, 100, 50, alpha_l2_reg[ia])\n",
        "\n",
        "  y_hat = mlpexpb.predict(X_train)\n",
        "  accuracy[ia, 0] = mlpexp3.evaluate_acc(y_train, y_hat)\n",
        "  print(\"Training Accuracy:\", accuracy[ia, 0])\n",
        "\n",
        "  y_hat = mlpexpb.predict(X_test)\n",
        "  accuracy[ia, 1] = mlpexp3.evaluate_acc(y_test, y_hat)\n",
        "  print(\"Testing Accuracy:\", accuracy[ia, 1])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "bUO29hst8n_E",
        "outputId": "bcb98d5b-9d6e-4cd5-a317-c6f925b0ae8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 100/100 [00:19<00:00,  5.12it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy: 0.7654333333333333\n",
            "Testing Accuracy: 0.759\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 100/100 [00:19<00:00,  5.06it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy: 0.7938\n",
            "Testing Accuracy: 0.7809\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 100/100 [00:18<00:00,  5.30it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy: 0.7963166666666667\n",
            "Testing Accuracy: 0.7865\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 100/100 [00:17<00:00,  5.64it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy: 0.7230833333333333\n",
            "Testing Accuracy: 0.7139\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 100/100 [00:17<00:00,  5.65it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy: 0.7984166666666667\n",
            "Testing Accuracy: 0.7866\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 100/100 [00:17<00:00,  5.62it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy: 0.7963\n",
            "Testing Accuracy: 0.7874\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "fig = plt.figure()\n",
        "plt.plot(alpha_l2_reg, accuracy[:, 0], label='training accuracy')\n",
        "plt.plot(alpha_l2_reg, accuracy[:, 1], label='testing accuracy')\n",
        "plt.xlabel('L2 weight')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "qq4cVUSkIK1N",
        "outputId": "1e1cea9e-f032-491d-ef31-3f520e648125"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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