02_neural_network_classification_in_tensorflow.ipynb

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        "# 02. Neural Network Classification with TensorFlow\n",
        "\n",
        "Okay, we've seen how to deal with a regression problem in TensorFlow, let's look at how we can approach a classification problem.\n",
        "\n",
        "A [classification problem](https://en.wikipedia.org/wiki/Statistical_classification) involves predicting whether something is one thing or another.\n",
        "\n",
        "For example, you might want to:\n",
        "* Predict whether or not someone has heart disease based on their health parameters. This is called **binary classification** since there are only two options.\n",
        "* Decide whether a photo of is of food, a person or a dog. This is called **multi-class classification** since there are more than two options.\n",
        "* Predict what categories should be assigned to a Wikipedia article. This is called **multi-label classification** since a single article could have more than one category assigned.\n",
        "\n",
        "In this notebook, we're going to work through a number of different classification problems with TensorFlow. In other words, taking a set of inputs and predicting what class those set of inputs belong to.\n",
        "\n",
        "## What we're going to cover\n",
        "\n",
        "Specifically, we're going to go through doing the following with TensorFlow:\n",
        "- Architecture of a classification model\n",
        "- Input shapes and output shapes\n",
        "  - `X`: features/data (inputs)\n",
        "  - `y`: labels (outputs)\n",
        "    - \"What class do the inputs belong to?\"\n",
        "- Creating custom data to view and fit\n",
        "- Steps in modelling for binary and mutliclass classification\n",
        "  - Creating a model\n",
        "  - Compiling a model\n",
        "    - Defining a loss function\n",
        "    - Setting up an optimizer\n",
        "      - Finding the best learning rate\n",
        "    - Creating evaluation metrics\n",
        "  - Fitting a model (getting it to find patterns in our data)\n",
        "  - Improving a model\n",
        "- The power of non-linearity\n",
        "- Evaluating classification models\n",
        "  - Visualizng the model (\"visualize, visualize, visualize\")\n",
        "  - Looking at training curves\n",
        "  - Compare predictions to ground truth (using our evaluation metrics)\n",
        "\n",
        "## How you can use this notebook\n",
        "\n",
        "You can read through the descriptions and the code (it should all run, except for the cells which error on purpose), but there's a better option.\n",
        "\n",
        "Write all of the code yourself.\n",
        "\n",
        "Yes. I'm serious. Create a new notebook, and rewrite each line by yourself. Investigate it, see if you can break it, why does it break?\n",
        "\n",
        "You don't have to write the text descriptions but writing the code yourself is a great way to get hands-on experience.\n",
        "\n",
        "Don't worry if you make mistakes, we all do. The way to get better and make less mistakes is to **write more code**."
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        "## Typical architecture of a classification neural network\n",
        "\n",
        "The word *typical* is on purpose.\n",
        "\n",
        "Because the architecture of a classification neural network can widely vary depending on the problem you're working on.\n",
        "\n",
        "However, there are some fundamentals all deep neural networks contain:\n",
        "* An input layer.\n",
        "* Some hidden layers.\n",
        "* An output layer.\n",
        "\n",
        "Much of the rest is up to the data analyst creating the model.\n",
        "\n",
        "The following are some standard values you'll often use in your classification neural networks.\n",
        "\n",
        "| **Hyperparameter** | **Binary Classification** | **Multiclass classification** |\n",
        "| --- | --- | --- |\n",
        "| Input layer shape | Same as number of features (e.g. 5 for age, sex, height, weight, smoking status in heart disease prediction) | Same as binary classification |\n",
        "| Hidden layer(s) | Problem specific, minimum = 1, maximum = unlimited | Same as binary classification |\n",
        "| Neurons per hidden layer | Problem specific, generally 10 to 100 | Same as binary classification |\n",
        "| Output layer shape | 1 (one class or the other) | 1 per class (e.g. 3 for food, person or dog photo) |\n",
        "| Hidden activation | Usually [ReLU](https://www.kaggle.com/dansbecker/rectified-linear-units-relu-in-deep-learning) (rectified linear unit) | Same as binary classification |\n",
        "| Output activation | [Sigmoid](https://en.wikipedia.org/wiki/Sigmoid_function) | [Softmax](https://en.wikipedia.org/wiki/Softmax_function) |\n",
        "| Loss function | [Cross entropy](https://en.wikipedia.org/wiki/Cross_entropy#Cross-entropy_loss_function_and_logistic_regression) ([`tf.keras.losses.BinaryCrossentropy`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/BinaryCrossentropy) in TensorFlow) | Cross entropy ([`tf.keras.losses.CategoricalCrossentropy`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/CategoricalCrossentropy) in TensorFlow) |\n",
        "| Optimizer | [SGD](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/SGD) (stochastic gradient descent), [Adam](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam) | Same as binary classification |\n",
        "\n",
        "Table 1: Typical architecture of a classification network. Source: Adapted from page 295 of [Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow Book by Aurélien Géron](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/)\n",
        "\n",
        "Don't worry if not much of the above makes sense right now, we'll get plenty of experience as we go through this notebook.\n",
        "\n",
        "Let's start by importing TensorFlow as the common alias `tf`. For this notebook, make sure you're using version 2.x+."
      ]
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        "outputId": "84031be1-2658-44c7-d393-8a5a87241996"
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)\n",
        "\n",
        "import datetime\n",
        "print(f\"Notebook last run (end-to-end): {datetime.datetime.now()}\")"
      ],
      "execution_count": 1,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "2.13.0\n",
            "Notebook last run (end-to-end): 2023-10-12 04:07:12.774646\n"
          ]
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        "## Creating data to view and fit\n",
        "\n",
        "We could start by importing a classification dataset but let's practice making some of our own classification data.\n",
        "\n",
        "> 🔑 **Note:** It's a common practice to get you and model you build working on a toy (or simple) dataset before moving to your actual problem. Treat it as a rehersal experiment before the actual experiment(s).\n",
        "\n",
        "Since classification is predicting whether something is one thing or another, let's make some data to reflect that.\n",
        "\n",
        "To do so, we'll use Scikit-Learn's [`make_circles()`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_circles.html#sklearn.datasets.make_circles) function.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7dT80sWqLYPf"
      },
      "source": [
        "from sklearn.datasets import make_circles\n",
        "\n",
        "# Make 1000 examples\n",
        "n_samples = 1000\n",
        "\n",
        "# Create circles\n",
        "X, y = make_circles(n_samples,\n",
        "                    noise=0.03,\n",
        "                    random_state=42)"
      ],
      "execution_count": 2,
      "outputs": []
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      "source": [
        "Wonderful, now we've created some data, let's look at the features (`X`) and labels (`y`)."
      ]
    },
    {
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        "outputId": "0ba91256-e6ae-4549-af0f-4fed85830cac"
      },
      "source": [
        "# Check out the features\n",
        "X"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.75424625,  0.23148074],\n",
              "       [-0.75615888,  0.15325888],\n",
              "       [-0.81539193,  0.17328203],\n",
              "       ...,\n",
              "       [-0.13690036, -0.81001183],\n",
              "       [ 0.67036156, -0.76750154],\n",
              "       [ 0.28105665,  0.96382443]])"
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          },
          "metadata": {},
          "execution_count": 3
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    {
      "cell_type": "code",
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        "outputId": "610ef4b6-9c30-47a1-fdb7-e94a4116095d"
      },
      "source": [
        "# See the first 10 labels\n",
        "y[:10]"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
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        "Okay, we've seen some of our data and labels, how about we move towards visualizing?\n",
        "\n",
        "> 🔑 **Note:** One important step of starting any kind of machine learning project is to [become one with the data](https://karpathy.github.io/2019/04/25/recipe/). And one of the best ways to do this is to visualize the data you're working with as much as possible. The data explorer's motto is \"visualize, visualize, visualize\".\n",
        "\n",
        "We'll start with a DataFrame."
      ]
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      "source": [
        "# Make dataframe of features and labels\n",
        "import pandas as pd\n",
        "circles = pd.DataFrame({\"X0\":X[:, 0], \"X1\":X[:, 1], \"label\":y})\n",
        "circles.head()"
      ],
      "execution_count": 5,
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c2234cc3-7a95-4485-a986-c0cc033d2762 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ngNwBOxevcv"
      },
      "source": [
        "What kind of labels are we dealing with?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cfgZGtMGe0XB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08bbab03-16b9-4591-92f5-ae7bbf4e7e64"
      },
      "source": [
        "# Check out the different labels\n",
        "circles.label.value_counts()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    500\n",
              "0    500\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ajz7TdQPesKt"
      },
      "source": [
        "Alright, looks like we're dealing with a **binary classification** problem. It's binary because there are only two labels (0 or 1).\n",
        "\n",
        "If there were more label options (e.g. 0, 1, 2, 3 or 4), it would be called **multiclass classification**.\n",
        "\n",
        "Let's take our visualization a step further and plot our data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KIPTzrETMemP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "0f0fd41b-9548-4e02-c88d-e717eb17a70f"
      },
      "source": [
        "# Visualize with a plot\n",
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.RdYlBu);"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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aRqEVgSsPu6/QKsS30nn1b5Tv3tbqGNVo5PBb33Lq2z8wpWc6ZKMzqf/2OBq+82y+Y4a0dJaF9SbzapzlysgS+DWpR+LBExYFk6RTqNCvCx3m3wgMTjx0kvXdHiE7Icl8QNPMolCDltPeo/pjw5zxsQT/ERx5ft9RAbsCwe0kJymFox/8wMLKnfjbpR7zy7fl0OtfkRkTb9f1xz/7hc0DniF+56ESttQ2kt52OJtmMqG4Fb7FkG8unY5Oy6dZFC6appn7HBVWml9V0VSN/RM+oufef/GqXvBtXOflQedVv1oULlnx17jw12KHhYtn1Qr02vsvIb075Eu59qxagVYzPqHyiN4255D0Ovyb1LU5TtbpaPTRRAZHb6PTip+pO+kpm/2N7MLBDKej707h6IdT8x27OGspGVeirbd00DAXurPyPdaMJuK23kjHNqZnsL7Ho+QkpuQ1qwTyfg52PfEmsVv3OmS/QGAvd1SROoHgdpEVm8DqtiNIO3c57wGcFR3P8U+mEfHbP/TYNhuvajcevJqmkXLyHIbkVDxDK5KTmMzB3GZ6tyGGJXcLRNLp8KpeidQztuNLFDcXqj7cn3O//5sX31AYbf76jKCOLSyeT9x/jOSbUn0LoGmkX4wi/cIV+p1ZQ/zOg1ycvQxTRhble7Wn0uAeSFbquVzbe7RIDSAbffwi3jWq0HHxj2RExZB2NhKdtyd+4XWQZJn13cfYnKPKiD4WY3AKQ+/lSUivDoT06kBWdDzn/1xo9XtrE1nGu3olUs9csLuw3uE3viawZXhe9d7If1dhV4CPHdzcc+nCrKVkxyVYnFZSZE5+/hvl2jUr9roCwa0I8SIQAHuefof0C1cKCA/NpJIdn8j2kS/RY9tswPwwOPzG16ScPGceJMt4hVa4bXEsjb94FUmS0Pt4EtS1NUtq9rAtoCSJ1jM+wb9ZfS7OWY4pPaOA7ZKi4Ne0HpWG9rQ6VdqFK3bZmX4xCkmSKNu6MWVbN7brmlxb7eJ6LIzex4smX79GleE3PCseIUF4hNwIKk6/dNWuTJ7gYmTY1Jn4COf/WlS8ar5GE/XfGYfOw529494j80qM7WsUmZNfz8gTL8b0TKfF4nhWq5j396srt1j9bJrRRNTKLU5ZVyC4FSFeBP95Mq7EcGnhWosPfM1oIn77ARL2HeXM1L859+s/+QeoKmkRjlV2lhQFt6AAMqPji+6pkWV8alWlzvOP5Hku0iOjbL/pSxKVBnWj8rD7AOi2fgZbhj5H+oUr5joqmoZmUgnq2oq2f3+JbKPSq2tAGbvMtXfcrQS2DEd2dUHNzrE6Luylx/ELr0PFgd0sZtzkkmFHF2ZJUciKtW/LsDDK1K9Fx8VT2Tp0PMaMTHOAs2T+edL5eOEXXoes2ARST18oXADIMq4BvlQe2hPFxYWchER2Pf6G7YVNKrGb9+R96Rdeh4Rdh4rnAbpO4r7jJB46iV94HVSj0WbgtGY0oWmaVc+aQFAUhHgR/OdJPHjCLgGxttNITE5qFuhbtzretUO5vGBt0Zz5sowkyzT/8d18DwYXf9+81FZLSLKEX5N6eV/7N61P/4i1XF29lWt7jyK76Anp3ZEy9WvZZUrZdk1xK1+WLCu1XFz8fAttQmgPLmV8qP7oUM78NLvQfydJUQjp24nGH79o95yugba3gjRVxcWBLaPCCOnVgUFRWzj/1yKu7TmKpNcR0qs9Ffp1RtbrMWZksr7bI8TvOpRPCEg6BVmno/0/36G4mPs8VXmgLwde+tQcY2KDm38majw1nDM/zCrW58hFzclhfdfR9D21Ev+m9biyZINlr44s49e0nhAughJBBOwK/vPILnrbg8BpwiXs1Sfouedfgjq1RCui1yWgeX26bphRIBZF7+VJpaG9rFai1VSN0JED8h2TZJmQXh2o/8Yz1H35CbuFC5iDVRt/Yl04hH84oVjNFht/8QpBnc2fNS/u4npAbJnw2rT67SOH5vOuFUqZ8DpWA2JlvY5Kg7sXzeCb0Pt4UeuZh2j1+2RaTnufSoN75HXR1nm402XdDBp/9gqe1SqBJKHzdCd01CB6HViQr1ChzsOdDoum2gzGlnQKwd1vCEW/hnWoOe6hYn8OADTITkzm8JvfcPr7mda3o1SVOuNHAWDMyOTSgjWc/XkuV9dsQ7UWPCwQ2IFIlRb85zGkpTM/qA2mjKxSWa/G2BGU69iCcm2bsLRub7tjEmQXPe3/+RbvWlXxqV3N4riUU+dY2XwopozMQmNwwl58lMafvVKsz1AYZ3+Zx/4XPsaYkpYX/6PzdCf8o4nUfm5UsedXjUauLF7P2V/mkX7xCu7BZak2ZjCVh91XJGEUtXIzG3s/af6ikO9//TefoeF744trtkPYs8WSsPcoq1sPtxrE3H3rLMq2bZr39ZqODxG32YmZP5Jk/s+K+K7+xP00//FdTn/7B4ff/hZjSnreOY+KwTT/8V0q9OnkPJsEdz2izosQLwI7yIiK4cz3s0g+EUHKiYgbAbilhOLmSqWhPbk4d4U5pdhGTEL4h89T77Wxds2dePgkux59nWv7juYd03l7UveVJ6j32tgSc+UbM7O4sng9GVdicA8OpOKArnnl9e9ELs1fze4n3yQ7IelGxpaLntTO97E3tA1u7i507hhKrx41cXe3z0NXGkSv28GmfmNRc3LyBKqkU9BMKs2mvEmtZ254WlLOXGBpLetB186m4UcTqffqk5z8ajoHCitOeF38dFn1a5G3EwX3HkK8CPEisMG+5z/i1NczrI7JLdNuT7AoYI5D0evQcgwOZXdUe2woiqsLl/5ZhSEtA9VgyFdyXvFwo8Fb/yPs5SccFh2Jh06SfCICvZcHQV1a5SsFLzBjyskhatkm0s5fZt/pFH7Yq2JwccNk0vKSacqV9eSnHwZQpXKZ221uHhlXYjg7bQ5Ryzai5hgo274ZNZ9+oMCW35Xlm9jU50mb8ynenphS022Os4WkU6g17mEavvcc84PaYsq04NGUJfwa1uG+AwuLvabg3kCIFyFe/nOoJhNoGrLOcjyAMTOLC38t5si735F5JdbmnN41q9Ds+7c59e0f5iZ1NjwjkiLT5KvXOPDSp2hGo91p05IiMyByY14qr2o0cnX1NtIvXMY1wI+QPh3Re3naNZeg6GzddpHnJhbeBFZRJMqV9WLhvw+id2Zn61Igbts+1rR70Oa4Oi88ysmvphe7TpGkKFR9uD/B3VqzY+TLNsf3Ob4c37DqxVpTcG/gyPNbZBsJ7mqiVm3hxOe/ErN+J6gaZcLrUHPscHzCaiBJEn6Nw9B7e5GdkMi6zqNIOnLa7rlTz1zEo2IwtZ55kKilG62O1ft40frPT6nYvytl2zTm0OtfcXXVVrvW0TS4OHs5YRPNRdNknY4KvTvabafAOUz/cz+yLKEWkv5rMmlcjU5l46bzdO9a4zZYV3QCWobbzAZT3Fyp/+YzlO/Rlv0vfkKyA78nBdHwqlaRrNhrdtU+yrgcTeKhk1xduQXVYCCgRUOqjRqIi59vMWwQ3OsI8SIodQwpaZz7YyEXZy/DkJyKb72a1HxqOOU6tXRoW+TUt3+wb/yHSIqSl2aadOgke55+N2+M4u5GjSfvJ+3cJZKPRzhkp6RTOP/HQsI/mkilYb3MjfcKwb9lQ7pt+gudqysA3rWq4la+nM2U5bx1FJns2ASHbBM4l5wcE/sPXLU6RlEktu+IvOvEi6zT0fC98ex+wnKNmLCXHsPF15vyPdrR+1Bbkk9EcH76fC4tWGtXN/Cb0TSN0EcGc+63f+zyPu585FUyo2LN27RoXPx7GYde+5J2c76mQt/ODq0t+O8gxIugVEmNiGRtx4fJjLq+bXO9zH7knOXUeHI4zae+U6BbcWEkn4xg3wRzeqy1ni2mzCxOfftnkSqMakYT6ReukJOYzFVL1VglSDp4ksxL0XjXqIIpO4f13cZwbd9Ru7eNNKMJj0rlHbZP4DxMdm6VGI23uRN4Eanx+DAMKWkcmvQFqsGYF9wLEDbxERrc1MxRkiTK1K1B409fpvGnL2NITeP8HwvZO+59u9Zq+N54jn/0E2d/mm19oCwh63RkxZiF+82/x6bMbDYPGkfLXz4w1x/adwydhxuVhvSkxhP341YuwC5bYrfu5fSUmcRvP4Cs11GhX2dqjXsY7xpV7LpecOciYl4EpYamqiyr14fUsxcteiSaTXmLWv+zXZNi3/MfcXrKX06pGmoN9wpBGNMzMCSlWhwj6RRqPvMgzb55g4jf/mHXY687tIbsomfQ1a24+pcpprWCoqJpGoPun8WlyGSLRQMlCV56oT0jhhXeQPJuIPtaEhf/XkbGpau4lgugyoje+domWCJ2y17WdrD9exn+0UR03p7se9aG0JEkc40dawL/eruHfB5MSULv60XXtdPxb1rf6hJHP/iBw29+k+96SacgyTLtF3wvtmbvQETArhAvdyRXV29lQ8/HrI7xrBJC/3PrbHpf1nR4kLgt+6yOKU3cQ8ox6MoWVrUeTsLuww4FPTb+/BXCXnjUqfZkZRlJTc3G29sVN7fSc7BGRaWwYPEJIiKu4eamo3OnUDp1DL0rglzn/XuUyZ9uLvScJIGrq45Vy0bj7eVaypbdfjRNY2ntnqRGXLJQ5VgmsE1jum38i0WhXciItL4FlytMiors5kqLn9+nTN0a+DWuW2C7OWrVFjb2erzwiyUJ2dWFARfW4x4UWGQbBM5HBOwK7khiNuxC0umsFtdKvxjFhdnLuLxwLUmHT6H38aLK8N5Uf3RIvgA+xc2teA3vnIwpMxuAjEtRdgsXt/JlafjeeGo8PsxpdkRGJvHTr3tZs+YsRpOKTifTs3sNnny8OZUqlmwA5Oy5h/nsy61IkjnoVZYlVq4+Q5XKZfhxSn+CgryKPLemaZw4GcfJU/G4uMi0almZwADn1o8ZPLAue/dfYc3aiHyBu4oiIcsSn03uiYe7ns1bL3DseCw6RaZN60rUq2vbc3G3I0kSraZ/zLquo9EMpnxbPJKioPN0p/nUd0m/eMW2cIFiCRcANSubndczmXzqVKPpt29QvnvbvPOnvpqeV+qgAJqGmmMg4pd51H/96WLZIbh9CM+LoNQ48MpnnPxqer4aJpbI5yqWJVwD/em28c+8lMrT389k77Pv3xHiRVIUyrZvSrcNf7Ki6SASD5ywbJck4VuvBs2nvkNgq0ZWU7sd5fSZeB57agFZWUZMphvrK4qEu7ue6T8Pplo1/2KvYzCaiIxMRlU1qlQug4uLwuatF5jwwvJCxyuKRNUqfsyZORzZSjl+S5y/kMjrb63h5KkbTRJlWaJfn9qMH9eabTsiiYlJw8/Pna6dq+HjY70pozVUVWP5ytPM/ecIZ84m4Oqio0vnajz0QDjZ2UZefGUl0TFp6BQZDQ2TSaNReDCfTe5FgJPF1J3ItQPHOfzWN0Qt2wSahqTIVBrai4bvPUfG5WgOv/kN8dsPlK5R1wvedVr6IyH3mbeC5niG26yYHdyjLV1W/VYaFgrsRGwbCfFyR3J58To2D3imSNdKioJH5fL0O7MaWVEwpKSxqFpXchKS7J7DvWIQmZdjiu2yLox2/3xL5SE9OTXlL/Y994FVUdVq+sdUGz3IqesDPDR6LqdOJxSa6qvIEvXqlmP6r0OKPL/JpPLnzIP8NesQ1xIzAfD2duH+oQ3Yvfsyx07EFrp2Lt993Ze2rSs7tGZ0TCoPPDyXtPScfIIM8leoVxSzp0Snk3nisWY89khTp1YRjrqayvCHZpOZaSzwGRVFIjTUj5kzht0V22POIPtaEtkJSbiVC8DF15tzMxawc8wkJFmyO1DdqUgSXqEV6Xd2DZIkMcerEab0TKuXlO/Zjs4rfy0lAwX24MjzWzRmFJQaIX064VG5vDm12UE0k4n085eJWrYx75jiZmfsgSzhXasqvQ8uouVvH1G2TRM8q1WkXMfm+DWpVyR7bib0kUFUGtwDgGqPDMK7VtVC55QUhTLhdagyvHex1iuMk6fiOHEy3qJ4MKkah4/GMHTE37z4ykq2bL1gVWjciqZpvPP+er79fmeecAFITc3ht+n7OHIsxup8OkVm6zbHUm5VVePTz7eQnJJdQLiYbbqxQ2cyaWgaGAwqP/y4mxl/Ovftf9bsQ2RlFRQuuWufPXuNjZvOO3XNOxlX/zL41KyKi683WbEJ7HriDdC02yNcADSNtHOXiN++39wN3FaNGFkiqEur0rFNUCII8SIoNWRFoeOSH9H7et3oDAz5/m4NSa8jZsMuACJ+/5fMKzF2Xae4utBlze/EbNxN1PJNyC56yvdoR5MvJ9F1/QwCWoVft8MsOKx1ZL4ZnY8XLX7+gFa/fpT3lq/38qT75pn5uvqaJ5UI6duJrutn2C+6HODc+US7x23afJ7xLyxn/AvLyMmxna2laRp/zznMshWFFy6z13drz1q5GIwmXn5tFRs3X7D7mpv5+de9ZGQYLJ6Pjknl9bfW0HvAH/ToPZ1nnlvM1m0XLQqwFStPFyqgcpFlidVrzpKZaWDBouO88/563v9oA6tWn8FguLc7KJ/7/d/bJ1pu4fLi9Zz/YyGZl6OtjlPcXKn2aNG9kILbjwjYFZQqfg3r0OfYMs78OJuLs5ZiSE3DN6w6lYf3Zs/Yt21PcP1JGfHrP3avKel1bOr/NEmHTuZV/IzdvIezP86m1nOj6LrxT6JXbeXCzCVkx13Dq1olQh8dwtF3pxC9emvBG7Mio7jo6blzbqFlzd3KBdB5xS+knLlA3NZ9SJJEuY7N8QqtZLfNjuLubv+vsun6A3r7jki+/X4HLz7fzuLY5OQsXnh5BfsP2hGEaQWjSSWsTuGZHfEJGcyafYjFS06SlJyFj48rvj6uXIxMLvJ6mVlGtm6/SI9uBQvK/ThtN9N+zd9hOT4hg527LtO+XRU+/7gXen1+AZuWblkIgdlLdCUqhV59Z5CaloOimMXsgkUnKFfWkynf9KVGdftqk9xtJB05bY6dv92GYC5c6V2jis2t4dCRA3ALLH78l+D2IWJeBHcEmqqyuFpX0i9GWR2XG1syz6+Z1doreUgSioc7ala2xWJ2Tb97k9rjHi5w3JiewbYHX+DK4vV5c6FpeFQqT9s5X1G2dWPb65cS6ek5dO89naws28HQN+PqorBmxRi8vFwKnNM0jceeWsCRozFWvQ62kCRwc9OxetkjeHrmX+fylWTGPLGAxMRMh7ax7GHo4Lo89XiLfIG08xce54PJG61eN+rhRkx49obnLC0th4dGz+PSZctiSpalvOS3wj6HXi/z8Qc96NypmuMf5A5n15Nvmr0vJVxzyS7syUCUJCoP7Um7ud+Ujk0CuxExL4K7DkmWqTNxjPnmU9h5RcGjYjAVB3TN+9ouNA1TeobVKrwnPv3Z3NjxFi4tXEvUis0FbZLArWzpvrUZDCaOn4jl6LGYQrdDPD1dGPlQI4fnzc4xceRo4S72AwevcvBQdLGEiznN2Pzgvlm4ZGQY+HfBMUaN+Zdr1zKcLlwA/pl/nF79ZvDmu2tJS8vBZFL54cddNq+bO+8IGRkGzp2/xsuTVtGp+69WhQuYBYvJpFn8HAaDyguvrGTqT7sBszDcuOk8Y8ctolP3X+neezqTP93E+Qv2bf/dSVQc0NWqcJF05mD73L+XJPZU55ZkCcmJWX6C24P4FxTcMdQa9zAJe45w4a/F+Ws0yDJ6Hy86Lv0pL7VY7+1pV6aRR8VgMq/GWRUvGZeiST19Id8WUNz2/eaOuDe/xV3/e2ZULOu7PULfU6tQXAt6LJyJyaTy+4z9zPz7EMkp5loybm46WreqjK+PKzk5JqpWLUO7NlU4eMi618oSlh646zacQ1GkIosXFxeF9m2rULmSL0eOxnA1Oo0e3Wtw6VIyzz6/lJTrn6ckMZk0Vqw8w4ULSTz/XJt8wcaWyMo2MX/hMaZO201Ojsmpwurn3/bSsEEQm7Zc4J/5x/LVk5m/8DgLF5/gy89625WVFXkpmchLSZw/n8jBw1eJiUmjbFlP+vWpQ4d2VdHpSufdtHyv9vg2qEXKiYiCIkaS0FSNNjO/QM3J4cyPs0k+dga9rzdVR/TBtVwgZ6bOIm7zHqfZo/fxwpCabtEDo5lUyvdq77T1BLcHsW0kKFUMKWmYsnNwDShT6FtSakQkB176lJj1OzFmZKH39aLa6EGEvfRYXjVMeyr1grkIXLUxgznx6S82Xdr3HVqEX8M6eV9vHvQ/rizdYPW6NrO+oOoDfW3aUVQ0TeONt9eycvUZa2Vjro8tWs0+RZFYtXQ0/v4Fa5S8/9EGFi89WSTx4u/vzohhDZj2615MJhVFkTGZVGRZQlFkjEa1RLwt1hg9shEz/jxYqmsWRvlgL65GpxV6TpLA1UXHiiWj8PUtvF7NmbMJfPL55kIbSeaKocaNyvPdV33x8NA71XZLZF6NZUOvx0k6fOq6V8OceSS76Gk94xObGXa7n36HiF/mlvjWk6QouJXzp/+5dSUSOC8oHqLCruCO48rSDRyb/FNeASu34EBqjXuYsBcfy/NeXJi9jB0jXwLtRpM2Q2Iyp7+fSXDXVrhfL0B15N0pdq3ZYuo7SHo9x40/WR2n8/LI16hN0zSuLNto3RWuyFxZsqFY4uXosRgWLDrOxcgkfH3d6Nm9Jp073Silv2ffFVasOmN1jkIcQ3YjyxI9u9fMJ1wyswzIkoSrq47QUP8iCwzVpPHD9S0SuNHQ0GTSMFnxgpUUsiSxd9+VUl+3MCwJFzD/G2ZlG1my/BQPPxCed1xVNaJj0rgYmciLr6wkO7vw72Huv9fBQ1d5edJKvvu6r1Pr3VjCvXw57juwkKtrtnFl0TpMmdmUCa9N6KiBdvXsCnvxUS78tQhTZlaBAHlJUUCWzJW5i6N3JXAN9KPzmt+FcLkHEOJFUOKcmvKXuVHbTZ6WrOh4Dr/1DTHrdtBpxS+knj7PjodfLHDj0kwqmprD5kHj6Hd6FbKri10VPMu2b0bFAd1QTSY8q4SQcTm60HROSZGp/sT96Dzcb6ypqjarAGuqhpqdY9OOwlBVjU8+38y8f4/lbcvIssSGjeepWSOAH6f0x8/PnQULjxdr28YSuc+y6tX8eeXF9qiqxsLFJ/jjrwNEXjLHdoQ3CGbo4HrodDIGg2NpsLIskZ5RtO9NSaFqGomJWbRuWYkduy7dbnNssvy6eNE0jbn/HOXPmQeJumpHgPp1NA2277zE2HGL+fqL3ri7lbwHRpJlQnq2J6Sn41sy3tUr02XdDLYMHkfmlRiz90bT0EwmyrZvSr3Xx7JlyHOY0jOtbgFbwiWgDOEfTKDqQ/3Qexe9TYXgzkGIF0GJkh4Zxb7xH5q/uLXnj6oRs3E3Z6bOIvl4hMVgXTQNzWjk7E9zkPS2f2QlRSagRUPAXFum/fwprOsyCmN6Zr7utAD+zRsQ/v74fNfLioJvvRpmm6zs1/g1DrNpS2H8Pecw8/49BpAnTHLfmM+dv8ZLk1bxy48Dibyc7DThUq6cJ+5ueq4lZhIc5MXggXXp368Obq46xo5bxJ69+eNlDh+N5tCRaLp3rc6adREOreXmqiMj03pqcWkjSVC2rCevvNSBkWPmkZp6Z4mrWzl5Op5ly09x8PBV/l1wvMjz7Nl7hWfHLyU01A+DQSUkxJsK5X3w9HShSePyxWql4GwCWzRkwIX1RC3fxLW9R5Fd9IT07oh/k3oA9Dm8mFPf/cXFWUvISU5F5+WJa0AZNJOJ1LMXraZG5yQkEbNhJ64BZfCuFUqZhrXt9khpmkbctn2c+30+GZejcQ8uS9WH+xPctbVdAcKCkkHEvAhKlMNvf8uxD6daLmIlSXhVq4iaYyDjkvXCUmXCa5N66gKmLBuBnhK0+Ol9ajxxf96h9ItXOPn1DC7MXIwhJR2v0ArUGPsANZ8aUagL+ey0Oex+6i3LS+gUBkZuxL18Oeu23ILRqNK7/x/EJ2RYHTfrj2FM+WEXO3dfKnZsiKJIdO4YyqeTexU49/5HG1iw6ITV6z09XUhPt+9hHxjozvChDfnejqweR9DrzXEyxblbvfV6Zwb2D+NKVArffb+DtevPlXrcjSP4+7tz7ZrtAGN7uDkwOBe9XmbwwHo8/1wbXFzu3rYGe5/7gDM//m1Xz7RcvGuH0uTzV6jQt7PVcarBwPZRLxM5e3lev7XcP8v3bEf7Bd+jc79zBODdjkiVFtwxJB89g2btAaFppEVcQrUjUC87PgmTHVs1spsbVUbkDxD0rFKBpl+9xpDYnYzIOkLfEyupM360xb3vao8NpdKQnuYvbnpDk3QKSBKtfvvIYeEC5uaJtoSLLEvs2HmJ+3rVdMrD1WTSGNCvoJcoNjbNpnAB7BYuAM8/25aWLSo6ZJ81fH1ceWliO3ZueYoVi0cVeR53dx3lg71QVY0KIT58/GFPNq97HG/vks0WKw7XrmXirBf7wn6ODAaVuf8c4ZXXVnE3v8N6Va/k8FZS6ukLbOr/NJH/rrI67tAbXxM5ZwVAntc298+ra7azd9x7RbBY4AyEeBGUGKrJhOzmarP8v6zXU7ZDc6s1ICSdgnuFcnbViWjw5jPF3teWFYW2c76ixU/v4Vu3ep4NIb070m3zX4SOHOjwnHPmHeGJpxfaHCdJ5hTp7l1rUL2af1611qIgAW1bV6Z1q4Kpt79O31fkeS2xftM56tUtR7VQP4sdpHO14M3nc/8+8sFwZv0xjE8+6sH33/Rl9fJHeGB4QyRJokwZd1xdi7bTnZlp5Olnl9Bv0F95PYg8PPQMHVSvSJ2uS4tbd1qdjabBpi0X2LuvaGn2dwJVH+rneH8yTQNNY88z76IaCt/iNKSmcXrKX5a3jlXV3IogOs5BiwXOQIgXgdOJ23GATQOeZo5rAy7OWmI9/VGSKNuxObWffdjqOE1VCe7e1q43rNCRA4pidgFkRaHGk8Ppc3QZI4zHGZFzjI6LplKuXTOH5/p7zmE++XwLmZm2Xdsmk0a9uuVwcVH46fv+NG4UUhTzAXNyxgvPt833gD52PIYHRs7Ni7txJunpOUiSxJuvd0ankwsIA1mWcHFRmPBsa8IbBucJmbphZfn4wx5MeK4NdWqXpXvXGrRuVTlfmX4XF4WO7atQHKKjU3nhlRV5AubhBxsRGOhRqECUJChX1rNY690NKIrE4qW2PXB3IqrBwNF3vy9SEC9AdmwCV1dtLfRc3PYDmDKyrF6vGU1Er9tRpLUFxUOIF4FTMKSlc2bqLJbU6cWaNiO4sni9fTcUTSNm7XaOT55G+EcTgfxVOHO3aVr+8iG1nx1pNUBOkmXKdmiGR8XgYn+eW5EVxa4AP1XVOHoshm07IvOqpWZmGpgydad968gSFSr40LKFuQ+Sv78HP33fn8CAgnVY7J3v5oaKp07H8/jYhZw+HV+k+ayhKBKhVc2Vh8MbBPPbtEG0aH5jC0mSoFWLikz/ZQijHm7Mrz8NYve2sezZPpY/fhtKj241rH6P4+LS2bHrcrFs1K7/77Mvt6KqGn5+7kz/ZTAtmuXf6nJ11THq4cY8OKJhkdcqToayIktUqVymWHPYi8mkERObXvILlQC7nnyT09/PtBqsaxVJstiSRM2xL+jc3nEC5yKyjQTFJj0yirWdRpJ+vugPlqgVm9H7etFty0xOffsnsRt3gyRRvmc76owfhX/T+gCEvfQ4xycXUrdFlkCWaXRdAN0Oli4/xRdfbc2rhAtQvrw3/XrXtsvjksv7b3fN57H44afdNuNkLKGqGmfO3BAq332/g5wcU4k00TOZNAb2vxFbUzesHD9824/4hAyuJWTgH+BRQIQpdnYUB5jzzxGH4m8soQFXo1M5eOgqTRqHEBzkzfff9uPylWROnY7HRa/QpHEInp4unI1I4Ovv7H+zvjm1PbdwoISEqmkFCgrm/r2wOdzd9Xz4XjcmvrScuPiMYgUq22OzNQ+Tqmqs2xDBL7/vIzo6FRcXHRVDvElOySYhIYMyfu4M6FuHoYPr4ePjRmJiJouWnGDP9bo6TRqHMLBfWL4eU84g+WQE56cvKN4kmoZroF/el+kXr5ARFYt7UCD+jevaVfkxoFn94tkgKBJCvAiKjCkrm6yERDYNeIaMS8XrOoyqcvHvZYR/+DztrTRMC/9gAoqrC8c/nmbOOpIADTxCgmj520eUbdu0eHYUkbn/HOHjz7YUOH71airTft1rd/VbTdNYvPQkjcLNvWAuX0nmt2LEpsiylBcnkpCQwfadJVvj5Icfd/Hpxz3zCu0BBBYiWorC8pWnrQYwS4CikzAa7XvSx8Xn9zZUrOBLxQq++Y7d2l3aGgEB7sydNZyHR/9DTGwaqno9tOK6VJQkCQ8PPaMeasS584lIMgQHeXHwUDQHrnftlmWJjh1CefaZVkjXP5UjwiVXPDlSH8hk0ujXt06h5wwGE4+PXciRozE3Hc0h4SYxnZqWww8/7ubfBcd59pmWvPfRRrKzjXl279x1mV9+28vnn9xnV9sDe7k4a2le5k9R0Xm6U6FvJxL2HuHAC58Qe1ObgoCWDSnbrinx2w8U6kWWdAoBLcMp06B2kdcXFB0hXgQOkx4ZxZF3v+fCzMVFLtRmiahlm6j1v4csnpdkmQZvj6P2hNFELduIITkVr+qVCeraGtnRoD0nkZVl5LMvC983z8XeB5CmwbIVp5jwbGt8fNxYsvQUslz0QnWqqtGxQyhAvgdOcQjwdyfBQgrv5q0X+OnnPYx7upVT1rqZ9DTrP2saUKtmIKMfbswrr6+2OV9goO14luAgL9zd9WTaUbfmo/e7s2nzBYsVdFVVIyPDQGaWkY/e757vXFxcOknJWZQt60kZXzdyckwMGjaLhGv2/5tJEvzwbX/C6pTFzU3HvH+P8tmXWwtNk775mho1Ajh+IpbY2DQ6dQzF2+tGBt7kTzffIlwKR9U0YmJTef3ttcCtlZ81cnJMTHxpBf/OGVFAIBaV7PjE4u3NAQ3eG0/SkdOs7TTSXMH3JhL2HAUJ3IMCyYqOR7spelpSZFwD/Wjz56fFWl9QdETMi8AhUiMiWdl0MOf/WOB04SLJMqZM6wFyubj4elP1wX7UfPpByvdod9uEC8CSZUXr/2MJg0Hl9JkEAKKuphR5HkWRCCnvTbcu5mwpP393G1fYZviw+haFC5gfWnPnHSUry/5tMnupXLmM1dRhc8yNH106V6NsoHVPT1CQF42ve7es4eqqY9CAMJvPyCGD69K8aUVWrTlrdayqaqxYdbrA8bJlPalZI4Ay1/sZrdsQwdXoVKs/V5Jk/sySZK7Z8t7bXWnerAJeXi7odDIPDG/IN1/0Jryh5RgwSYIzZxL4fupO3n5vPT16T2fGnwfQNLPQWrLspPUPnu+z5SXxFEDTQDWpzPvnqN3z2cKzSghYqh9liev/OIqHO40/f4U6zz/CnqffQTUYC9aiUlVzLI0sUf+dcXhUCkbSKbgFB1L3lSe57+AivEIrOenTCBxFeF4EeagmE1HLN3F5/hpyEpMxpGeSEXkVQ3IKnlUrUuOp4VyYtYScxJQiR/dbQzOZKNPwznHBZmcbWb7yNOvWR5BwLZPywV506Vyd7l2r50vZtefN1FFyY0EsNeezfq3ZU1M+2JsfvuuXV4CsbKAntWsFcqoIwbqtWlZk5IONiDifaPVNHiAtPYczZxNoUD/I4XWsMXRwPd79INbieZNJY/DAeiiKzIsT2/HKa5a9Ly9OaGt3ivRTTzRn997LRERcK/TBPLB/HV572dx3KyU126aXLT3dthdny9aLNr/PAPf1rEXNGgH061snT/jcTPt2VdHpFZ5/cRk5OQUf9LnOhNw/s7NNfDNlB3q9TM0agU4V5SZVY9PWCzw/vq1T5qs6cgCHXv/K8gBZxqdWVfwahZlrwSARu3EXhuRUfGqH4lW9Mtf2HyPxoJVMK00j83IMZRrUZmDkJqfYLXAOQrwIAMiMjmNDz8dIOnwKFLnAG01W3DUSdh0qOQNkGc9KwQR3a1NyazjA1m0XeeX1VfkCbU+djmfj5gt89e02vv+mH3VqlwXgmgOufXvw9NQTVsc89309azJr9mG7r5VliT731aZ92yp07BCKTpffVfHaKx0Y/dh8u+dSFJlpP/QnvKHZSxFx7ppd15ZE0bPe99VixarT7Nl7pVCBMHhgXRqFm70M3bvWQPsQvvhqK3HxN/59ypX15MXn29H1ujfKHry9XPl92mD++Osg8+YfJSkpC0mCRuHlGT+uNQ0b3PBsVKvqx+nT8RYf+rIkUbVyGZtr5uSYbH4PNQ3efqOz1aDnY8djGDd+icMBvz9O28MDwxs4dpEdGB3sk2UNj5AgGrz7HIff+LrAOUlRcAkoQ+fVv+FRMZh9z33A6Sl/5cXIpJyI4NK/q/Gqbl8Mzv7nP6LSwG5Os11QfER7AAGaprGq+VASD50o8Zb0lpBc9HRaNo3yd4B4OXwkmjFPzLd6w/fxcWXBvAeJjU3ngZFznba2JMGohxtTsYIP6zeeIyPDQFx8Olevptp8AMmSRJ06gfw1fZjVcXPmHeaTz63H6AA0a1qBCc+2pm7YjUrCx47HMHLMv1avc3fXsXbFGNzdnd8MMCfHxLRf9zDv36N5/YnKlfVk5EONeGB4wwLeFJNJZe/+KOLj0ylb1pOmjUMcynC6FU3TSE834Oqm5AtKzuXgoas8+qT1DJh33+pCvz6FB8jm8stve/nx5z1WY1Wqhfox7+8HrM7zv+eW3DGNKBVFoke3Gnz43o14n0uXk1m46DgXIpPw9HCha5fqtGtT2e5/I03TiPh5LkfenUJm1HWvnCQR0qcjzb57E6+qFTn59XT2Pz+58Alk2e5KgIHtmlL90SG4lQvAr3EYHiHO9SwKHHt+C/EiIHr9DtZ3fcS5k8oSqBqKpzumdPv7s5RtZ+4gG9Krg3PtsRNV1biv/x/ExVmveyFJ8OwzrThyLIZNm887rRJq29aVOXM2nti4jLwMJQfur7z3dhf69rb+YASzCPn8y20cOmLuJ6XTSfTuVZvBA+uiKBKBAZ4EBRVepXjkmH84eSquUO+CLEk8+EBDJjppa8ASOTkmLl1ORlEkKlX0LZYgcSaapvHJ51uYW0hshyRB61aV+frz3gU8YrcSF59On/5/YrQS0/H6qx0ZMqiexfNpaTl06PqL/cYXE3sy6n7/ZTDh1z1Vv8/Yz5SpO/MC0nO3O2vXCuT7b/ri729/hppqMnFt3zGMaen41ArNq/WkGo0srNSRrGgn1zWSZSoN6UHz79/Grax/3mFjZhapp84jKTI+daoh60u+m/e9hBAvQrw4xL7nP+L0lJkFou2LiltQAN61Q6k59gHKdWrBwood7X/6Xt+yajEtf2PF0mLOvCN88nnBlOfCcNHL5DjRDe7l6YKPjwsxsekWtx0a1A/iyNGYfILmerY43btVZ/L7PRwqd5+amk1Kajb+fu52e0qirqby+FMLiIlNy3tY5cZnNG9WgW+/7FPkMv73ApqmMfefo8z48wDRMebMI19fN0bc34BHRzexO/162YpTvPXuunzZZrkCoWuXanz8QQ+roi06JpXe/f8s/geyE39/d4YMqsfPv+7Nl6qd+/dxT7fk0UfMpQxWrT7DpDfXFDqPokjUq1uO338ebHfnZ0skHj7JinAbFbdzf4EcRZZwKeNDvTeepuqIPpz65g9OT52FMcX84uNaLoCwF8YQ9uJjovu0nQjxIsSLQ+x55h3O/jLPoa6shSJJ1J4wmqZfTsp3eO9zH3D6O8duopJOYeClTbgHly2eTQ4ycOhMIi8ll+qa9iJLErVrB/DMUy2Z/ucB9u03VwatXNmXh0aEX/ealM5NMjU1m4WLT7B0+SmSkjKpWNGXwQPr0qN7jUK3U/6LqKrGlagUVFUjJMS7SN+Xg4eu8sdfB9iy7SImk0b1an6MuL8hA/uH2fy3zs420qHrLxicKLCtIUnQskUlRj3ciL/nHM6LTWrWJIQHRzTM66+laRr3PziHc+cLD4DO5WYvTVFJ2HuEVc2HWrdbUYqfgGCp6qAEoaMG0er3ycUWYv8FHHl+/3dfjwR5+DUKc4rXJXTUABp/+lKB402+eAVDWjrnf59vDpgDsBFbo6ka536fT71JTxXbLnvJyjLeduFiLcNE1TROnIynYcNgfp46kJwcEyZVxc1VV+o3Rm9vV0Y+1IiRDzUq1XXvJmTZvKVVHBqFl6dReHk0TUNVNYfEqaurjv596/DvguPFssFeNA127rrE8KH1+eaLPhbHxcWl2wz8VhSJLVsvFFu8+NQORXF3s1qCQTOZKNe5JbEbdhV9IUsqTIPzMxZQbfRAgjo7v/bRfxnhyxJQ+f5eKO7FrAEiy7T8+QNkXUE9LOv1tP5tMn1OLKfepKeoNnqgzekkCZKPny2eTQ5yO7sLS5I5y8geDZLrjndxUXB304s3uv8AkiQVyav25OPN8SvjeLp9UZFliQWLC6YeHz8Ry6/T9/Hzr3vZtcd2GxFJkjDkFD95QO/tRfXHhlrsbC8pCl7VK9Nx0Q/o/UrGUy/pFM7+7LygfoEZ4Xn5D2PMzOLEZ79w+vtZmDLsD6q9FUmnUHFQd5vBab51qtPwvfGoBgPnfl9gPQ5GktF5FL+omiO4uChUC/Xj3PnEUl0XzC9unTpUY9mKUxbHSBKUD/bG18fV4hiB4GbKBnoy64/7+fDjjWzdHlni66mqxtWrqXlfJyRk8NKklRw8FI0smwvqmUyazeBeo1GlTh3nbBmHf/Q8cTsOkLj/ugfq+sKSoqDz9qD9v9+h9/ai46KprO000v74PDvRjCZSTl1w6pwCIV7+s5iystnQ41Hith/I/8t6U/Ca5KJHs9UxVZJAkqj36pNWh6WcOsepb//k0vzVmLJzcPHzIScxxeKNQjMaqTi4e6HnSpI0GyXonU1uMOPDD4Yz9snmbN5ynvQMQ6FbR5oGD45oKDwtAocICvLi26/6kpiYybkLiUhA/XrlWLHqDO9+sMGpa8mylNfHKifHxFP/W8TFyCQAmwX38uaQJLy9Xeja2f5aPNbQe3vRffNMzvw4mzNT/ybj4hV0Pl6EjhxAnQmj8axSAYBy7ZvRcfFUto98GUOiE7ePZRnXAOe0RBDcQIiX/yinvplB3Pb9BVvJ39yTxEoAr6TXoRmMuPj50vbvL/BvYjll8+rqrWzq/zSayXSjjsz1VOpC59YplGlQm/LdSzbd9lZOn44n1kaKtD24uCgYDCaLb5aSBH5l3KlUyZfQqn4MGVSXenXNNSO+/Ow+xk1YhtFoytseyo2D6dqlGsOHOb9wmOC/gZ+fO039bngzB/QL4+df9xJ1k6ekuKiqRv/rTR7Xro+w6MW09LuhKBKyLPPp5F55laGdgc7DnbCJYwibOMbquAp9OjH46lZOfjODI29+g2owFC0T6WZUFZ2XB0lHT+Nbr6Z4+XASQrzc42RcjubSwrUYU9PxrlWVCv06I+v1nJoy06J4yMOKXze4WxtCRw6g0uAeKK4uFsflJKeyZfCz5pvAzevdsrak14Fm9rj4NQqj07JppZ5e+O/CY06ZZ2D/MOb9a7mHi6bBm691ymuYeDNNm1Rgzl/38/fcI6xZe5asbCPVq/kzfFgDenavccfUMxHcGzhTIMiyRJ3agQCMf2EZ+w/Y12ne19eV5ORsXFwUenavwciHGlGjeoDT7HIUxdWFei8/QaVB3Tn20Y9cmLmk2JmYlxes5fKCtXiGViT8o4lUHWE5oFlgHyJV+h5FNRjY+9wHnJ02F9CQZBnNaMI1oAzNp73H1iHPFWt+t+BABl/dZnPcqe/+ZN/4Dy0LIQncypejYr/OKO5uVBzQlXIdW1h9O0lJyWL5qjOcP5+Ih4ee7l2r56sCW1TGPDGfQ4ejizXHgyMa8r+nW/LIY/M5d+4apltEmixLNG9WgSlf9xVCRHDb+fKbbfw9+3CBn1NHkSTo2L4qV6NTOXU6weFr/5o+lIuRSSxeeorY2DTKlfNiQN86dOlS7ban3ptycjCmphO9fifb7p/glDmbfDmJOs8/4pS57iVEnRchXtj52Guc+31+4aLBypaNI/Q/vw6vqhWtjtn20AtcnLPcZvfX4VlHrHpwclm6/CQfTN6EwWBCUWQ0TcNk0mjVshKfftQTLy/bc1jif+OXsGNn0Uqpu7vpePGFtgzqb94+S0nJ4uPPtrB67dm8vX69XmbQgLpMeLYNbm7C6Sm4/Vy6nMyQ4X9jMqoO7454eupp0jiEju2r0qplZV58ZQUnTxWvkm3uFmnun+ENgpnyTV88PYv+e+0sUk6fZ2ntXk6ZS1JkBkRuFC0GbkHUefmPkxoRybnfrPSfUTVzvRVVK1ZkfXZ8ok3xIikKEpLNG6NkR5ryjp2RvPXu+ryvjcYbtu/ec5lXXl/F99/0szlPLiaTej0Dwrx2ty7V7RYvkgSdO1WjWdMKBJXzpG3rKvlc8D4+bnz0fncmTmjL8ROxyJJEwwZB+PiUXtqqQGCLShV9+ezjXrw8aSUmk2Z3UO1z41rx0APhZGUZWbDwOKMf/YeEa0XPWMwld/3cP48ci+Hjzzbz/ju3vymiT61QPCqXJyPSvu0wa2ganJ++gHqvjXWCZf9NhHi5B4mcsxxJkdGseDuc0YAxt3+INcp3b8OFPxdZPC8pMmXbNrWrB8i0X/daLOKmqho7dl7ixMm4vI7MhZGTY+KnX/aweMkJEq5lotfLdO1cndEjG9OrR02m/bqX+HjL5flvZuJzbQgJsf52EBjgQYd2VW3OJRDcLjq2r8rifx9m/sLj7NpziWPHY63+/MuyRJ9etUm8lsljTy0gyo6moUVFVTVWrj7D+Gfb5GUx3U7KtmnMRSeIF0mWSD170aFrTDk5SJIk+iVdR2y634PkJCbbHewq27FVUxjle3e0q3R/5WH34RZcFkkpfN9aM6mEvfSYzXkSkzI5dDja6puhLMM/84/m88jczMmTcXTr9Tu/z9if95ZoMKisWnOGkWP+Yf+BKKb9MIAKVgRJbiG758e3tSlcBIK7haAgL55+qgXTfxnCc/9rbXGcLEn061ObsmU9efPddUTHpJWYcMnFZNI4eLD4gsEZlAkPM2+7OwG9r7fNMZqmcX7mYlY0GcQc1wbMdqnP6rYjuLSg8L5Q/yWE5+UexKtaJVQ7ouMlnULVh/pZ32IqBMXLgyafvWzfWDdXuqz+lXVdHyE7/nrapHZ928pootEnL1Khb2eb82Rm2v48qgoLFp1g3YZzVKlchgoVvAkJ9iEtPYcVq06Tmlp4DRdNM29BvfL6atYsf4R/5zzAth2RbNt+kWPHY7lwMYmMDHO9mzq1A3lsTFM6d6xm1+cXCO42Hn4wnPj4dP6cdQhFkdA08orLdWhfhVdf6sC589fYs/dKqdmkFTtf2TlUHtaLQ5O+KPY8mtFEleG9rY/RNPaN/9DcF+6ml9H4nYfYMngcDd55lgZvjyu2LXcrImD3HiQnKYV/g9ugZVsvMCfpdQR3b8vV5Zvsntunbg3azv4Svwa1HbMpOZXzMxZwaeFaTBlZ+DetR82nH6BM/Vr2XZ9jokvP3/JEREnx1uudGNi/br5jJpPKtWuZ6F0UyviKmBXBf4OLkUksWnKCq1dT8fNzp3evWtSvZw4wXbj4OO99uLFU7JAkWLZoJMFBtj0VpcGORydxfsaCIic9SIpMcLe2dFrxs9WsyqiVm9l43xNW5+q5ex4BzRsWyY47ERGw+x/HpYwPTb54lX3j3rc6TjMa8apawf6JZZmU42e58MdC/D57xTGbfL2p/dwoaj83yqHr8q53URjYP4zZc4/YHVToKLIsceJkPAP75z+elJTFnH+OsGjJSZKTsggM9GDwoLoMH9oAb29Rql9wb1KlchmLW0hyKdVgUhSJjh1CCwiX3BcKnV7Gr0zpthFp8eO7yDqFiF/+sd7j4GauVyJHVanQrwut//zUZrG609/PzPNQFzqlTuH0D3/T+vd7R7w4ghAv9yi1//cw0au3cWXx+sIHSBKKmwsZV2Ptn/R6ZtKJz3+jfM/2BHdr4wRL7eepx5uzY+clLkYmlYiA0TStQNGuK1EpjHliPteuZeatGR2Txo/T9rBk6Sl+mzaIgDsgkFAgKE2aNa1wcycRp5P7XA+t6scbkzrlHTcYTPw58yB/zz1CQkIGALVrBTJmdBN6dKtRQtbkR3FxoeW0D6j/xjNsuO9xUk6csypi9P6+1P7fQyge7lQc0BXfMPvaHiTuP241sUIzmri294jD9t8riIDde5g2f31GmfA6+fZLwZy+jASmzGyiljje20TSKZye8pezzLQbb29Xpv8ymIdGNMTT0/kR95pGgcyg199aS2JiZgGxpKoaUVdTmPzZZqfbIRDc6YSU96ZL52oodgav3tyx/WaHgyybvSsAiixRPtibgAAPatcK5NWXOjDjtyF5W7UGo4nnnl/G9z/uyhMuAKdOx/Pq66v5dfo+J3wy+/GsHEL915+2KlwkRabW0w/Q8L3x1Hv1SVzL+nHii9/YPvIldj3+OpcXrUU1Fh7Pp7jZ9uoq7v/dbWzhebmH0Xt70X3LTI5/+gtnpv5NTkISSBKBbRoTt2UvULSUac1oImGv5fL3JU2lSr507VyNlavPkpNT/JTvXKqF+tGsaUje12fOJnD4iOWKuyaTxsZN5/MqggoE/yXeer0z0TFpHDsem1fCILfRaMUKPvToXoNmTSpgMJr486+D7N0fBUDVKn48OKIh1ar5s2HjOTIyDIRW9aNv79r4FhJTlp1tZPbcI/w+Yz8pqdkW7fl+6i46dwylWqh/iX3mW6k0pCe+k38i5eS5AvdSSVHQl/Gm1v8eAuDinOXsGPUyqtGYt2UU8es/uJUvS8clPxLQtD5g9gDHbNiJ7GYjE1SWqTS4h/M/1F2CEC/3IFnx14j4eR4XZi8jO+4abkEB1HhyONWfHIZ7UFn2T5xM/I4Dxar1Yk81XGcz79+jfP7VVgwG57asB3NMzdQp/fPtQx87bntLTVU1Tp6OF+JF8J/D29uV334exIaN51my9CTxCRmUD/Zi4IC6tG1dOV/7i/Ztq2IwmFBVDVfXG4+dxuHlra6RnW3kf+OXcODgVbvCS56buIxqVf0ICvIirHZZKlbypUmjEHS6ktlkUFxd6LpuBlvvH0/spj1IigxIaCYTXjUq02H+FNzLlyNu+362PfhC3tb7zdlTWVfjWNVsCNWfGEbjLyaxc/QrXF6wxmJ5CTB7dHQ+XlR/fGi+47n5N/+F5o9CvNxjJB09zbrOo8hOSMpzZ2ZdjSPp4ElOfPoL9d98mpiNu4slXCRFodLg7k6y2D5WrjrD5E9Lbotm6nf9KBvome+YXm/fDU9fQjdGgeBOR69T6NGthl3xJnq94z2K/px50G7hAhAVlUpUVP4u2d7eLgwf1oCnHm9eIv3E3MoF0G3jX1w7cJzoNdvQjCYCWoUT1LlVnog4/ok5s8jax4j45R9iN+3JK16nmSzfo138fem84hfcAv1RTSbO/fYvp777k+SjZ5Bd9VTs35WwFx+9pzKRbkWkSt9DqEYji6t3J+NytNWy/y6BfuTk1lyxhiwXnEeWUFxd6HNsGV6hlYppsX1omkb/ITO5ciWlxNb46L3u9OpZM9+xuLh07uv/h9XgYDc3HWtXjMHDQ1S9FAiKS3JyFgnXMvAr446vrxs9+kznmhPaDgB4ebkw4dk2DB5Y1/ZgJ6KpKrNd6lsVI3YjSTT+4hVqPjUCnYc7qtHI1mHjubxo3fXFrntedApoGm1nf0Xloc7px1QaiFTp/yhXlm4kIzLK5ricBDuEC6DzcMOYngHS9bcVTUPn4U6HRT+UmnABOHMmoUSFCxReBKtsWU9696rF8pWnCxUwkgQj7m8ghItAUEzOX0hkytSdbNp8AVXVkCRo2iTEacIFIC0thw8mbyQ1NZvRIxs7bV5bqEajc4QLgKbhHhSIzsOcHn522hwuL1pbIO1LM5pAktj+8EsEdW6Ja4Cfc9a/gxDi5R4idvMe0Clga0tIkmzWJ5B0ClUf7o9fozBi1u8ETaNs+2aEjhqIix1lrZ1JanrhlXHtxdbHlYDwhoXvvU96pQPx8Rns3H0pLxgx98/uXWvw9FMtimWbQPBf52xEAo88Pp/sbGPeS4Kmwf4DJdMS4Pupu+jftw5+fqVTH0ZxccGrRmXSIi7ZXxfGCqen/k1gm8Z4Va3IqW/+AEtJ65qGajBwbvoCwl54tNjr3mkI8fIfRJIVNNVWuX0JzaRS86kR1HxqRKnYZYlKFXzs0VsWqVe3HMdPxBXqPVEUiXZtqxBSvnBB5u6mZ8o3fdm95zJLl58i4VoGwUFeDOgXRnjD4P9EYJxAUJJ89MkmsrKMhZYjKAlMqsqKVad5cER4icxfGLWfHcm+CR85Za74HQdY2XQIXTf9SerpCzZGS1zbd8wp695pCPFyDxHUsTmnvppuc5ymqiheHpjSMiyPMZko09CxFgAlRblyXrRrU4Ut2xzrwprLgyPC+XPmQU6cjMsTQbl/Vq3ix9tvdLF6vSxLtGpZiVYtS2+rTCD4L3AxMomDhyyXIygJZFkiOiatVNes+fQDRK3YzNWVW4o/mUnFkJzKvmc/sOlWlmQJ2eXe3NYWaRJ3CarJxJXlm9g3cTJ7x3/AxTnLMeXk304J6dMJzyohFma4gSRL1Bw7okDxuhsDzNV3Q0cOcIbpTuHFie2KdJ2bm44unarx27RBvPV6J+rXC6JcOU/qhpXjtVc68sfvQ0S/IoHgNnExMqnU11RVrdRbCsh6PR0XT6XBO89eT6cuHprJROzGXZRt19RqSrVmNFGhb6dir3cnUiri5fvvv6dq1aq4ubnRsmVLdu/ebXHs9OnTkSQp339ubv/th0vq2Yssq9ubTX2e5PSUvzgz9W+2jXieRZU7E7/7cN44Waej47Jp6MtYj9Ku/8YzNHxvPIGtwi1U35Vo/cenpR7bYo1KFX1p0dyBPkzXefaZVri4KLi66hjYvy4zfh3CyiWj+fP3oQwdXA93t3vzrUQguBvw8rSvXpSlbd2ioGnQpLH1+jIlgazX0+DtcfQ7t47yvTs6ZU41O8diMLCkKHiGVqTigK5OWetOo8TFy5w5c5g4cSJvv/02+/fvJzw8nJ49exIba7kAmI+PD1evXs377+LFom0X3AsY0tJZ12W0OdgL0AxGNIM5XiU7/hrruo4mZvNu4ncdJHbLXlzK+NDvzCpqjR+F4pFf9CnubvjWq0Hi4VNc/HspHRZPJfzD53GvYO4UiywR0rsD3bfMvCPT6x57pJlD44cOrscDw+/dOgcCwd1OwwbB+NsInHV1Vfhz+lDee7sLjRuVx83N8XoxtzJ23GJ2771c7HmKglflEDovm8bAS5to8fMHxZrr2v5j5nhduNF34foLqUfFILqs+R1Zf2++oJV4nZeWLVvSvHlzpkyZAoCqqlSqVIlnn32WV199tcD46dOnM2HCBJKSkoq03r1W5+XMj3+z55l37O+AJkHIfR1p9OlLpF+4QvzOg1w7cJyrK7YAmrmN+/X6LW5BgXRZ+zu+9WpiTM9AdtGjuJR85dyk5CwWLDzO6rVnycg0ULNGAMOG1KdFswpIksTRYzEsXHyCK1Ep+Pu5c1/PWuh0MlN+2Mnxk3EFP/It2751agcycUJbmjVx3FMjEAhKl3/mH+OjTzZZPP/k480Y+8SNrL6HRs/jRCH3AUeQJPOW8rJFo27rtrEhJY1/AloUq2jozfg3q49XtUpUHNCVSkN63pZK6MXhjqnzkpOTw759+5g0aVLeMVmW6datGzt27LB4XVpaGlWqVEFVVZo0acJHH31EvXr1Ch2bnZ1NdvaNfhcpKSVbD6S0iZy7AoupcIWhQdTKzUSt2Gw5kOt64bns+ETWdxtD/4g16L08Cx/rZM5GJPDkM4tITs7KMy8qKoX1G84xZFBdjEaVRUtO5qUjy7LEilVngPwN3W6ma+dqPPZoU/z9PPBw1+NppytaIBDcfoYOrkdaeg4//LgLk0lFUeS8TKOHHwznycea5xvv4V78x5amQVaWkcVLTjDq4dKr+XIreh8vyjSsQ+J+J2QESRLGtAzazv7qP5EFWaLiJT4+HpPJRFBQUL7jQUFBnDx5stBrateuzW+//UbDhg1JTk7m888/p02bNhw7doyKFSsWGD958mTefffdErH/TsCQku54jrCdKYaayURWTDwX/l5GjceHFcE662RnG1m24jQLFh0nOiYNfz93omNSSUvLyfeRTCbzF/8uOF7g2M3pkoV9G2RZIjY2ndo1yzrdfoFAUDo8MrIxA/uHsXL1GWJi0gjwd6dn95qULVvwpapmzUD2O9AywBKaBnv3Xbmt4gWgfM92zhEvmkbKyXOknrmAT63Q4s93h3PHpUq3bt2a1q1b533dpk0bwsLC+Omnn3j//fcLjJ80aRITJ07M+zolJYVKle6dlFbfBrVIPHTCaW7FAkgSUcs2Ol28pKZl8/T/FnP8pvTkm9vYOwtV1Th8NIYLFxOpWuXeqyIpEPxXKOPrxohhDWyOGzSgLrPnHnHKmpoGKSlZbN9xicwsA9VC/WjYoHTrN1Xo15njk39y2nxGKyUw7iVKVLwEBgaiKAoxMTH5jsfExBAcHGzXHHq9nsaNG3P27NlCz7u6uuLq6lpsW+9Uaj41nPPT55fcApqGKctym/mi8vmXWzl5Oj53iRJn774oIV4Egv8ANWsE8MRjzfj5173Fniszy0D33tPzdaoPrerH++90pW5YuWLPbw+BrRrh17guiQeO2x5sA0mvwyu04A7FvUiJZhu5uLjQtGlT1q1bl3dMVVXWrVuXz7tiDZPJxJEjRyhfvvRT2+4EJJ1S4kWGvKpXdup8165lWOwHVFIsX3mq1NYSCAS3l6efbMH773Qtcl8xSTJvOR84eDWfcAFz7ZnHxy7k3LlrzjDVDlsk2s//znJQn73z6BSqDO+Ni5+vkyy7synxVOmJEyfy888/M2PGDE6cOMHTTz9Neno6Y8aMAWDUqFH5Anrfe+89Vq9ezblz59i/fz8PP/wwFy9e5PHHHy9pU+84VKORzQOeQTUYSnQdZ4uj4yfi8mJWSouDh6K5du2/4S4VCATQ577aPDjC8VIIEqDTmYOCC/MKq6qGwWBimhM8O/biVbUiZds2KbKAkXQK7uXL0fjTl5xs2Z1LiYuX4cOH8/nnn/PWW2/RqFEjDh48yMqVK/OCeCMjI7l69UYDrsTERJ544gnCwsLo3bs3KSkpbN++nbp1S7eN+Z3AlSUbyIyKtT9NuigoMqbMLKdOebsC3ePjhXgRCP5LdO1czeFrNOC+nrVQFMs3KpNJY+36CDIzS/bF8WZqTxhdpD12xcONGk8Op+eef3AvX3CrKyvuGknHzpAVVzqepNKiVAJ2x40bx7hx4wo9t3Hjxnxff/XVV3z11VelYNWdT/zOg0h6XV5ROoeQJbuyjiTAo0KQzXGO0KB+EHq9XMAdW9KUVpdYgUBwZ1CUvkiyLHH+fOL1oFzL90hV1UhJycbdvXSKvFUa3IPQ0QM5P2Oh3dfoPN0JHTOEui89hntQYL5ziYdPcui1r4havimvoVvIfR0I/2gifuF1nGx96SN6G93BSIrikNdF0utoP38K3bfOwr+Z7ah9AE3VnN7DyMfHjQH9wpDl0nHByLJEs6YhhaZVCgSCe5f4hAx0OsceY7IsgQQmk/WXK0kC31IsYCdJEq1+m0xw9zZ2X2NMz+Tsj3+zvNEAEg/fKD+SsPcIq1sNNzeCzPXmaBpXV21ldev787WVuVsR4uUOpnzPdmhG+70uiqsLlQZ1p2zbpvTaNY8+x5fT6rePULw9LTZhDHvpMTyrOL8S7cTxbWnSqOSDrGVJQpYlxj3dqsTXEggEdxYBAR42RcitGI0q1av52dyh0TRITnbulrotJFkm5eR5h67RjCaMKWlsHTYeTdPQNI1dj72OmlOw75FmMqFmG9j1+OuUcHH9EkeIlzuYch2a49coDElnu5eHpFMI6twy3zHfsOpUGzOEXrvmUbZN/kJMel9vGn3yIo0+ftGpNoO5ON2KVae5EpXs9LlvJTjYi++/6UfDBval3gsEgnuHnt1qIFt4MbPG0uWn7Rq3dXvp99Uzpjseu6eZVFJPXyB67XYSDxwn6fApNAuiTlNVko+c5tq+o8U19bZyxxWp+69hSE3j3O/zOTdjAdlxiXhVq0iNJ4dTeVgvZL2eDounsq7LKNLORlqdRzOaqPP8I4We8w2rTvcts0g+EUHKiQh0Xh6U69Acxc359XHS0nIYO24Rx08Ur/fIrUgSBAd5UaN6ADVrBlA+2JvKlcrQtElIqW1PCQSCOws/P3eeerwZP/y026HrjEbb3hpZksjOLkK8YTHxrV+T+G37LYoPa2we+AwV+nWxa2zqmYsE2BlecCcixMttJONKDGs7PkzaOXPHaDSNzCsxxG7aQ8Qv8+i4bBqelcrT58hSLs5Zzunv/+La3mP5I9IVGUwqjT97maDO1rdOfMOq4xtWvVg2a5rGseOxHD0ei06RaNmiEpUq+uade2nSymI3TSt8XbganYavrxuDBtSlU8d7v/y1QCCwzWNjmuLmpmPqT7vJzHKe2FA1jZo1Am0PdDK1nnmIuM1FS9M2ZWQROWe5XWP1Pl5FWuNOocS7Spc2d1NX6TUdHiR+x0GLpf9d/HypPWEUNcc+gFu5AFLPXmTDfU+Qdja/K9OjSghdVv9W4v0sIiOTePWN1Zw8FZ+vk3PnjqG88HxbXn51VaFdn52JLEmomsaklzswbEj9El1LIBDcPWRnG1my7CTr1p/j1Ok4kpKLVzlcliV2bnkSnR3b9s5EU1W2PTCRyHkrSqxMht7Xi8HR20vE+14cHHl+C/Fym0g8dJIVjezI8pFlXMr40HHxD2y9fwJZMQkFgrAknYJroB99ji3D1b9MidibkJDB8IfmkJySVaAAnSxL6HQyOTkl1H+pEHSKzMqlo/D39yi1NQUCwd1Dy3Y/YTAU7540f+4Dt6XtiGoycfq7Pzn19QzSL0YB4BYUSFZMvFPmb/z5K4S98KhT5nImjjy/RcBuKZJ8/Cxnps7izNRZXJyz3L5qbqqKITmFTf3Gknk1roBwAXO8S1ZsAhG//lMCVpv5e+5hkpILCheziVqpChcAk6qxZJloCSAQCAqnWZMQlGLGw6mlXCk8F1lRqDPhEfqfW8eg6G0MjtvBwCubqfnMg0WbUJFBkpB0Ohq88yx1Jo5xrsG3ARHzUgpkRsex/aEXiVm/01wVDsmhSoqaSSUnMcX6IFXjwswl1H2pZNooLFl2qlR7FdlCls09SAQCgaAwHnownB27LhX5ep1OZvOWCwQHe+froWQwmNDp5FLpPC3Jcr7ic82/f5vqjw1lTfsHMWU4kMZtUinXsQVt53xVoJjd3YoQLyWMMT3jlqDcvP85nZwkGwKnGKSklG69A3vw9HS53SYIBII7DE3TOHU6npxsE0MG1eXfBcdRFMnhfmtGo8q3P+zku6k76dqlGoYcld17L5OZaUSvl+nVoyajRzamWqh/CX2SwvFvUo/az47kxOe/OpSRFLtpN9f2HKFC384AJB48QdSKzag5Bvyb1ad8r/bISunG9xQHIV5KmPN/LCT1zIWS7U+EuRqvb53iZRJZI6icF5GXnFO3xcVFsWubSbLioDKZNLp1LbnPKxAI7j72HYjik882czbiRh+fAH93Klcuw9mIBFJTcxyeU9Ng7bpz+Y4ZDCpLl59i1ZqzTP2uP41LoSBnLsbMLHPbGAfDVSVF4dS3fxLYuhFb759AzPqdSNe3kzSjCY/K5Wn/73d3Tfq0iHkpYc7NWMj1vaISRTOZqDF2RInNP2RQvWI1XGzZvAJzZw5n/epHWb1stNWmaLlIUuFhQbIs0bxZBRrWd25PJoFAcPey/0AUT/9vMRHn8jcgTLiWyYGDV5k4vi1t21QGsOv+YwtNg5wcE6+8vsquujHOwJiZxfruYzj20Y929a67Gc1kImbDTpbU6knMxt3Xj6l52a4Zl6NZ23nUjV2COxwhXkqYrNiEInUKdQhJotKQnlTsb19xoqIwZFA9qlcPcDgATpYlatcK5PNP7qNGjQDK+Lrh4+NGn/tqW7yByLJEhQo+fPbxfXl7zTqdnDe+ZYuKfP5Jr1LZcxYIBHcHn3+1FZOqWrzdfv3tdj79qCdffnofrVpWIqicc3qhxcdnsHVb6VTiPT75J+J3HHRYuOSiGU3kXEsGtRCxpWqY0jI49MbXxbKxtBDbRiWMV2hFc6pbYT8sTsC1XAB1xo8i7OXHkYpQJtseLkYmsXzFaerWKYtOkTh79hpGO/ZaPTz0jH64MQ89EJ4v4A3g+efacPhoNJGRyfkCgRVFwtVVxycf9qBuWDlWL3+EtesiOBtxDTc3HV06VaN2rXsj4EwgEDiHiHPXOHnKehpxcko2O3dfonPHanTqGMpzE5cRn5DhcCzMrciSxIlTcSVeOFM1Gjn9w6wSe5bkcnH2Mpp//xYufr4luk5xEeKlhKk0pKc5y6gEUDzdGXRlM7KuZP4ZjUaVyZ9uZsEic8CbJEmoqrnxV7cu1di157LFPeQKId58/kkvatUMLNRD4uvrxoxfhzBr9mH+mX+MhIQM3Nx09LmvFiMfakzlSuZfHHc3Pf363P3t2wUCQckRG5tm17i5/xxlwcITuLnp2LvvSrGFC4CGhqtLyQe6ZsUkkJOQVOLroGlE/D6fsDs8nVqIlxIiauVmDr/1Ldf2HCmR+SWdQoW+nUtMuAB8+/0OFi4+DnD9l/zGL/ra9ecsXGXmSlQqD4ycR40a/vxvbCs6tq9aYIy3lytPPd6cpx5vjtGo5gkkgUAgcIQAO4tV7t5zGU0zb007q/SDpkGHQu5vzkZ21dse5CQi/1l5x4sXEfNSApz/axEbez/JtX3HSmwNzaRabMToDJKSs5gz70ixw3UiIq7x/IvLWbbCekG50qqbIBAI7j1q1gwgtKqfzaSC3PuZM2tWVazgTY3qAU6bzxJugf74N29gX3FTQCrGi60xzfHO1qWNEC9OxpCaxu6n3jL/lpTU3qQs0Wr6xwS2DC+Z+YFt2y9iMBTf/tybxeRPNpOZaSj2fAKBQHArkiQxcULb638v3bUvX0nlSlTJ1di6mXqvjbU7AaTh++NRPNwcXkPSKfiF3/lb9UK8OJmLs5djyixeQzCryDLVxwyh2qiBJbcGkJnp3FbwGZkG1m2wvtUkEAgERaVt68p8+VlvygY6J4vIEV5/ay3RMaklvk6lgd1o+s3rNhWaW3BZwl56jOqPDTPXcnEAzWii5tMPFMfMUkGIFyeTevo8cgl2IZUUGcXDvcTmz6VaqHObkekUudTeTgQCwX+Tju2rsmzRSKZ+14+3Xu/E5Pe7lcq6h49EM3TEbI4eiynxtWo/N4oBF9bjHWa5SGdWdBwnPvmZ8A8mUCa8jrmfys0Upn2ul8EIe+kxyrZp4kSLSwYhXpyM3tcbrQR7AGkGI4GtG5XY/Lk0blSeypV9kYvZ2CwXk6ri431ntV8XCAT3Hooi07JFJQb2r0uP7jUJCfEula2krCwjE15YXuxO1vYgyTKpJ617sg+98TWLa3bHM7QiNZ68H4/K5irAOk93qj9+Pw3eeRbvmlXyxvvWq0nrPz+l0ScvlajtzkLSHK0xfIfjSEvtkiD5ZATLwnpbHiDL+NarQfKR00WaX/F0Z2jCbhTXkuvrYzKpXIxM4tSpeN79cAMmk1r8WgiyxIrFoyhbtvRdugKB4L/LrNmH+OLrbSVeKzSX7l2r079fGK1bVnLay9+tnP1lHrufeMOusZJOQTOaaPLVJGo/NypfPTBN08hJTEaSZVzKlP7z8lYceX6LVGkn41unOpWH9yZy3sqCAbvXm/U0/vQldox8mez4RIfnV3MMmDKzSkS8qKrGzL8P8efMg8QnmKPNPT30VAjx5vyFpGLN3bZNZSZ/tpno6FQCAzzo26cOnTuFoi/BLTaBQCC4f2gD9uy9wqYtF6z2S3MWa9dHsGZdBCEh3nz+cS/q1C7r9DXU7Bzrzd9uIrf8//7nJxPYunG+RA9JknD1L4OmqlxauJaz0+aQdvYiLoF+hD7cn9BRA9F73ZkvnMLzUgIYM7PY9ehrXJy9DElRkGQZ1WBA5+VBy18+pNLg7ux68k3OT1/g+OQStPz5A6o/NsypNmuaxnsfbmDRkpPFmsfLU09auiGvjoIkQXCQF1ej01BkCZOq5Z1rUD+I77/ph5eX6A4tEAhKDqNRZfHSk8yee5hz5xNxcVGoUMGHiIhrJSZoZFnC3V3HnL+GExLi3GdR3I4DrGnjWC87SadQZXhv2vz1eb7jppwctg59jitLNiApCprJlBcQ7BVakW6bZ+JRoXT6yDny/BbipSRtOXWOS/+uxpCShnetqlQZ3pusuGus7z6GtLORBZWzHb9Fkk6h4XvjqTfpKafaum//FZ54elGx5vD00FOtmj9HjtoXtCbLEl27VOOTD3sWa12BQCBwFE3TWLbiNNP/2M+582YvuKLkVhF3zhqKInH/0Aa8NLGdcya8TnZSCssb9CXzSqxDysutfFkGR20FwJiewekfZnHsox8xJBWeKSXpFAKaN6TH9tlOsdsWQrzcIeLlVlSDgaV1e5N+4UqeK68otP7jE0JHDnSeYcBrb65hzbqzxYptkWWzG9KROSQJli4cSflg7yKvKxAIBEVF0zQSrmViMqqkpefwxNMLSUnJdlohO18fVzaseSzfMZNJ5fCRGJKTs6hQwYeaNewvcnfyq+kceu1LTFlFK8kR3K0NYa8+ycEXPyHx8Em7mjz22vsv/k3rF2k9RxAxL3colxevN3tcioHsoqfS4B5OsugGFy4mFjso1xzi42Cbds3s9enb+84viiQQCO49JEkiMMDcXiAImDNzOLPnHGbJ8lMkJWVhNBavWGd6ev7inEuXn2LKDzuJjUvPO1andiCvvtSBhg2Crc51Zuos9k+cXCx7YjbsJHrdDvOboz0CTZaJ2bi7VMSLI4hU6VLAmJFJ5tVYLi9ci1TMAFWdtyc6T/v6eDiC921MY3ZGczSBQCBwBmUDPXn2f61ZvewRtm18ggb1g4qcai1JUD7khld5/sLjvPXuunzCBeD0mQSeeHoRx45b3nI35eRw6I2vi2bITWgm1aEK8BKUfJRzERDipQRJOnqarcMnMM+nCQtC2nNx1lJzMFQxyElMdpJ1ZiIvJTPt1z1ERFxz6ryOYOttQyAQCG4Her3C99/0K/I9StNg2OB6AGRmGvjy622FjlNVDZNJ5atvt1ucK2bDLnKu2XH/d3IGp6aqlO3Q3KlzOgOxbVRCxO86xLrOo1ANBrPSxfxDUFz0Ps6JDTEYTHz48SYWLz1pd7S9p6e+gAu0OCiKRNMmIYRWdW41X4FAIHAWXl4ujH2yBU+PW+zwtTpFYkD/MAA2bDpPhpX+bqqqsf/AVa5EpVChkOwku4QL4N+4Ltf2HHHY1sKQdAp+jcIIaN7AKfM5E+F5KQE0TWP7wy9iys4pVmDurUg6hdCRA5wy1yefb2HJMnNatL0eQWcKF0mC4GBv3n+7dMp3CwQCgT1omobhlvt2syYh6PWOPy6NJo2zZ81e7di4dBTF9v5TbGx6oce9qlWya01nCRcA95BytP/nW6TS7nZpB8LzUgLEbt5T7MDcW5EUBZ2nO3UmPlLsuaJjUlmw6HipbmOGVimDopOJjUvH38+dAf3CGDyoLt5eomWAQCC4/Zw6Hc/0P/ezfsM5DAaV8uW9GT60PsOHNcDVVUeTRiHs2nPZ4XnT03MACPB3tyu+L8C/8N51AS0a4lOnGimnL9gdr1JUdN6eNHz3Oao9OgQX3zszE1SIFztIOnqaM1P/Jm7bfmS9jpA+ncy9IkIKL9yTfOyM3dUPbXJ9Hs+qFWj/z7d4Va1Y7Ck3bDxffLssUK6sJ6mp2WRmmbtSu7vrGTq4Hv8b2xIXF1FNVyAQ3Hls2xHJ8y8uR9O0PIFx9Woq30zZwYZN55n6XX/GPtWiSOKlUiVfAGpU90dRLJeSkGWoU7sslSuXKfS8JEm0mPY+67uORoViCxjf8NokHzqV1z4gt0BdQKtwOi//GRc/32LNX9II8WKDU1P+Yt9zHyApct4WUOL+45z47Fc6LfuJoE4tC1yj8/RwWnR2jbEjqDSoO8FdW+frSVEc0jNykGXH6rHYgyxL1K1bjg/e6caZs/EA1KoZiLu73qnrCAQCgbPIzDIw6Y3VmExqgdu2psGRozH8/sd+xj7RnNatKrFj5yW75pVliYYNggkO8uL1t9awYtUZi1lL5uMSE55tY3XOcu2b0W3zTA68/BlxW/baZUdhVBzUnXZzvybp8CkifplHakQkrgF+VH2wL+Xv64Cs3PkvmqJInRVit+xlbYeHCj8pS+jc3eh/YT1ugf75TmXFJrCgQgc0o9G+hQrz0khQ7ZHBtPz1I6fvN65Zd5ZXXlvt1Dlz+fjDHvToVqNE5hYIBAJns2jJCd79YIPVMb6+bqxZ/ggAY8ctYv+BqzbndXPT8cdvQ/h9xn5WrTlrtehdcJAXr0/qRNvWle22O+3CZZbV64spI9O+CyRAkmj8xavUGT+a+O37STpyGsXDnZBe7XErZ3+hvJJCFKlzEie//D3PpVYAVcOYmcW53+dT96XH8w5rqoprWX9qjh3B6e9n2vbAKDLe1auQfuEyao45INbFz5c6L4yh7qtPlkigVKcOofj6upGcnOW0ORVFIjTUn84dQ502p0AgEJQ0p07Ho9PJVovRJSdnkZCQQVCQF+OebsWjT9ruS/fA8Abo9QorVp2xOEaSoGpVP+bNGuFwB2qvqhVxCwog/byVrSxJwi04EP+m9Qho3oDqjw8jKzqeZXV7k3Ly3I1hOh01nhpOky9fRXHJ32vOlJNDws5DGNMz8Amr7pTQBWcgso2sEL1uh/VsIVUjeu12VJOJiF/nsaxhP/7W1WWOa33SI6Mo36u97UVMKkgw6OpWOq/5nbqvj8W/eX2ilm1iz9PvcG3f0WJ/jtTUbDZsOsfqtWe5dDkZvV7hnTe7FHteyOvfRXiDYH78rh96/Z3vbhQIBIJc9HrFrl3+3Jg9e94nJQkCAzxZuz7CqijRNDh/PpGEhAx7zc1HtTGDrYcTaBo5Sal4Va1ItdGDMKams7bjw6SeuZB/mNHImR9msXPMazddqnHyq+ksCGnP2o4Ps7H3kyyu1o0N9z1O2nn7ts5KEuF5sYI9dVk0o4ntD0wkct7KvO0f1WAkavkmNJOKS0AZchKSrM4hyTLG1HR2P/mmWUXL5rLNCXsOE/HzXMJefpxGH7/osBfGYDDxzZQdzPv3KAbDjc/SqkUl3nqjE88/15qvvt3h0JwFbJck3n2zC3371C7WPAKBQHA76NC+Kn/OPGjxvCxJ1K4diJ+fOQuoVq1APDz0ZGRYLh2hadC8WQWWrTiNLEs2+ySlpedQtqynw7bXeuZBIqbNJTM6zuKLtpqZxZkf/+b8zMWUa9cUU1Z2Xu2xW42+OGsJdV95HJ861dj16GtcmLmkwJjoNdtZ1Wo49+2bj0fF21dgVHherFCufTMka4FLsozO28MsXCDfFpFmNJlVb2Ky1TkkRaF8z3Zs7PMUGZeu76Ne/0HP/WE88ekvnJs+3yHbNU1j0ptr+HvO4XzCBWDPvsuMeXw+fe6rTedOxd/mWbP+bLHnEAgEgttBk0blqVe3nMUaLKqm8diYpnlfR0Ym06xJiNU5fX3diIvP4MqVZJu9kfR6mXJFEC4ArgF+dNsyk4DmDa2O04wmDMlpXFmywepugqRT2DfhI+YHtSkoXHLnMpnIuZbE0Q9+KJLNzkKIFyvUnjDacjl/SULWKaScumDdj6iBpqmFj5EkkCX8GoWRfOyM5R8qSeL4Jz/jSGz1wUPRrN9wrlB3qMmkERefwd9zj/Dxhz14ZFTjfBlBOp1MqxYVcXW1vQWkqhpbt13Mq2UgEAgEdxOSJPH1572pXs2ceKEokvnWLJv/nDihLV06VeP0mXgeGj2XB0bOZfPWi1bnTE7O4n/PLWHt+nNWxymKRO9etfD0dLE6zhpeVSvSY/tsuqyfYfbaW8KenQRVJXbDLgxJqdbHGU2c/2Mhppzbd98X20ZWCOnZngbvPMuRd77LF7hrbq4o0Xb2V2wZPM7mPH5N65O4/3i+ZliSoiDJEm3nfE38jgNIOp3l7CRNI/XUeTKvxlqsLXMrS5eftFpTQFU1Zs0+TFxcOnHx6dSs4U92tonQUD/uH9qA9RsiOHQk2q61NA0yMgzF+gUUCASC20VAgAczZwxj245I1q2PICPDQGhVPwb2DyMkxIeLkUk89tQCMjPtzCC1A0Uxd7N+ZmzBchtFQQL7ukRbw4HrTZnZ5CSm4B4UWLw1i4gQLzZo8PY4ynVqwekpf+UVqavQrwu1xj2ET+1qSHodmsHaD7RG8qGTYDKBLKH388U9pCwV+3amxtgReFWtSOyWvdd/8qzjSKuB2Lh0m3VcMjMNLF56Mt+xM2cTWHm9HoG9jh53dx1lyrjZbZtAIBDcaSiKTId2VenQrmqBc9N+2UNWltFm7Iq9SECd2oF06lCNnBzntJCRXUq3npak193W6rtCvNhBUMcWBHVsUei5Cn06cWWplX1EjbwUaFQNY2oa6RFZhPTplJdyFtgqnFNfTbdqg1twIO4V7PO6gLmtuzXPiyVyfzntFS6KIjGgX5jIMhIIBPckmVkG1qyNcGpRTw04fiKO4yfi+OHHXXTrWp233+iCh4djAkTTNGI27OTc7/NJO3fJjpdp5yApMpWH9kJxu33tXUTMSzEJe+mxwiO3LaAZTZhyDGwdPgH1+jaRd+1QZFcrWy6yRO1nRzpU9bBv79pOr6B7K4oiUa6sF4/fFMwmEAgE9xKpKdkYHbjH24umXf8PWLfhHC++stKhuEbVYGDrsPGs7/oIF2cvI377gSI3AlbcHRMhkk5H/TefKdJazkKIl2JStk0TWv/xCZJeZ25OcT0I1yqqStbVOCJ++Ycj705hdcv7Ua2o5ZD7OhD20mMO2dW4UXk6dQy1qyZBUfHw0PP2G53x9/couUUEAoHgNpGRYeDU6Ti7ukEXB1XV2Ln7EvsP2q7cm8vhN7/h0nxzpfQ80XKz+HHAZH0ZH4fGV32wH75h1e2/oAQQ7QGcRObVWM7+Mo/E/ccxpmcSvWab9QvsDCrxrBJCh0VTidmwE82kEtgqnMA2Teyq+ZKTY+Krb7czf+GxAunSzkCWJdzcdMycMYwqFpqJCQQCwd2GwWjihx93M2feEbKySn4bBm5swb8xqZPNsYa0dOYHt8WUbr01gF/TeiTuO+YkC29QrnNLuq3/w+nzivYAtwH38uVo8Ob/AEg+EcGyur2tX2CnZky/GMWKRgNAlpEwp7L5NqhF+3++xaeW9RotLi4Kr7zYnrFPNGfn7ku8+8EGp/4iqqpGdraRX37by/vvdHPavAKBQHC70DSN199cwzoLpSZKClXV7G7ZkrD7sE3hAlBz7AiOTZ5G+oUrhaZKS4pC2U7NiV230yFbSyOuxhZi26gE8Kkdit7Z7cRVNa/ib8qJCNa0f4jMmHi7LvX1dUOnk0vkDcJk0li15qzTIuYFAoHgdrJvfxRr19snXJy5LS/LEiHl7cvesTu2RdVo89dnKK76AsVSJUVGdtWj8/RA52X/1r+kKAS2bmT3+JJCiJcSQDUa0UqweI9mNJGTkMiZH2bZfU1kZHKJ7dsajSppadklMrdAIBCUJouWnrB5rwzwd+e5/7VyqmfGZNIY0D/MrrF+jcKu1xuzTkCrcMq2bkzPXfOoOLg7kmJ+5EuKjGZSMWXncHXZJoz2dqbG7Jmq8dQIu8eXFEK8lADX9hzBaIdLrzhoJpVz0213Ns3Fy8vFaTUKbsXFRcHb+/alzAkEAoGzuHo11WamZlp6DpqGw52gc6v3FsbIB8OpFupv1zxu5QKoPLy3xdYzkk4hsHVj/BrWAaBMg9q0n/sNw5L3Uf2xYTcyZE2quYr8zc8GCwZKOgUkiVa/foh39cp22VmSCPHiRJJPRBC9fgcppy+Uyno515LsHtupQ6jDjR3tQVEk+vauLeq8CASCe4IAfw8UG6KkjK+7Q8JFkmDe7OF8/UXvvAaPuSiKRP165cjKNjJ77mFSUuyLe2n27Rv41KlWQGxIioxbuQDazPq8wDWmrGzO/7nQuq2yhEtAGVzLBeBZtQKyuxt6Hy8qDe5Bjx1zqPbIYLvsK2lEwK4TiNm0m/0TPyZxv5OiunMzkaxlJEkSXtUq2T1l2bKeDB1cj3n/HrU4Zd06ZYk4d40cg8kud6iiSHh7ueZrWiYQCAR3M31612bNugiL52VZon+/OrRqWYlvpuywa05Ng+EPzEWj4I3VZNI4eiyWEyfjUFWNr7/dwRuvdaJv79pW53T1L0OPnXM4O20uZ3+aTcblaFwD/an+6BBqPvMgbmULenGurt52o2iqJVtNKu1mf0VwtzZ2fbbbhRAvxSR63Q429HoMzRlbMpKEe/my1Hr2YbxqVGHbsPFWh9d4arhD07/wfFuMRpUFC48jyRKyLGEyqej1ChOebcOI+xuwZesFXrheLCnXdSpJEpqmFajY27hRCG9M6kT54NtXIlogEAicSdvWlWnSuDwHD0UX2GpXFAl/P3dGDGuAn587TRqV59CRaLsKgqo23ghz58gxmHjr3XUE+LvTupX17Rm9lydhE8cQNnGMzfUBTJn2eXWMdo67nYg6L8VA0zSW1u5JakRksRtiSXod1R4ZTNOvJqHz9EDTNHaMetnclvyWfyJJkfFvVp9uG/+yWp75wsVE5v5zlK3bLmJSNRqHl2fE/Q3w93Nn9dqzJKdkUyHEh549auDtdWOeM2cTmPn3IdZtOEdOjpEa1QMYPqwB3bpU48jRGLKyjISG+lO5kpMzqgQCgeAOICPDwPsfbWD12rP5br8NGwTx0XvdCQkxP1sSEjIYO24xEeeuOXV9WZZo2CCY36YNcuq81/YdZWWzITbH9T+3Fs8qFYhavomzP88lLSIS17L+hI4cQJUH+qJzL5ledo48v4V4KQZx2/ezpu0DxZrDJ6w6jT9/hcCWDXEN8Mt3TjWZOD75J05+OZ2cxGQAFHc3qj06hEYfv4Dey9PivBs2neOVSavRuOFByfWcTBzfhocfbISqaqxZd5a/Zh0i4tw1XFwUenavwYMjwq0WncvMMhAfn4GXlwt+ZdwtjhMIBIK7meiYVHbvuYLJpFKvbjlq1SzYQTknx8TzLy5nx65LTl9/3aoxTr/HrmgyiKTDp8yBurcgKQpBXVrRcemPbB02niuL1yMpinmsLIGq4Vu3Bl3WzyiRbtJCvJSSeLnw91K2P/hC0SeQZRq8/T8avDXO6jBTdg5Jh0+iGk2UqVcTvY+X1fHRMan0HzwTk0m1GLsy7YcB/DnzIFu2XSxwTqeT+e7rvrRsXjHf8bj4dH6ctodlK07l1XVp1rQCY59oTpPGIVZtEggEgnuRzEwDXXv9XiJ1tJYuHGl37RdrmLJzuLxwLalnLmBMz+T0DzMxZWTlqxcj6RRcA/zMcTQ/zeH4pz8XuqMgKQrlOjan67oZxbbrVkSF3VLCNdDP9iBLSBKKqws1nrjf5lDF1YWA5g3tnnr+guOoqmZRuCiKxORPN3H+QlKh541GlQkTl7Fu1aN5XU7j4tMZNeYf4hMy8u3v7j8QxZPPLOLzj3vRqaP1ir8CgUBwr3H+QmKJCBcPDz2BAcXvG3dpwRp2Pfa62Xsvy3mVdnU+XpiystByjOi8PKj26BDqvvIELmV8OP39TIuhEJrJRMz6nSQdPU2Z+rWKbV9REanSxSCoUwtcC4notoWkyChuLnRc8iPu5cs53a49+65YreliMmkWhUsu2TkmFi05kff1N9/tKCBcgOsiSePt99eTnX37S0YLBAJBaaJTnP8YlWWJwQPr4uJSvBIU0et3sGXoc3lhBze3CDCmpKHlGKn++DCGJu2l2Tdv4BESROLBExhT061PLEnErHespYCzEeKlGMh6PY0+dmDbSJbxb1aPem88Q7+zawju2triUNVkIjshEVO245V6nVXOZd16c7pgamo2q9eetRhRr2nmMRs2nnfOwgKBQHCXEFrND38/x+JSJMl6gbsyvm4MHlC3uKZx+I2vbfbRi/hlHmd/mn3jgF19EcwJK7cTIV6KSfVHh9J86jvovC0Hz+ahqriVC6ThO8/iERJU6JDshEQOvPQJ/wa04N/AVsz1DGfLsOdIPHii0PGF0aJ5Rau/GPaKm8zrrtCrV1MxGq13pdbpZM5fTLTbRoFAILgX0OsUHn6okd3jJcDb2xV/f3eqVvGlevX84QeSBEnJWQx9YDa/z9hfZLsyLkcTv+OgXWLkxGe/5vXOK9OwNoqtbCJVo1y721vfS4gXJ1Bz7AMMjt6GewXbW0BXV22x2EciK+4aq1rez8mvZmBITgPMBYMuL1zLqlb3E7PBPjfdkIH10OlkiyLFXsFcu1YAAB6eeptjVVXD08P2OIFAILjXGPVQIwYNMPclsvVyqAEpKdnEx2dw6XIKERH5X/o0zXw/VVWN737YycLFx4tkU95WkR2kX7hCemQUAHpvL6o/McwcH1MIkk4hoGU4/k3rF8kuZyHEi5PQebhjsLVPiFmMpFpoH3Dg5c9Iv3ClQAqbZjShGoxse+AFVIP16ohgrqb7+ce90OmUfB6Y3L4ar7zY3q5AsLFPtACgQogPNar7W/2l1FSNLp2r2ZxTIBAI7jVkWeKNSZ34/edBBAbaH2RrT3G7qT/tLlJfOo+KwRZ7HxXGikYDmevdmHVdRhHYpgll2zYxn7hFxGiqStr5y+x++m2ST1iuRFzSCPHiRHQe9u17yi4FPRQ5SSlcnLWk0Nx7AFSVrJh4rizbZNca7dpWYcHcBxj5UCNqVPenapUyDOwfxt9/3o/BqBKfkGFzjq3bIwFzhd2nn2ph0WMjyxK976tFxQqiaJ1AIPhvIkkS4Q3L07F9qM2u1I4QF5/BqdPxDl/n4udLmYb2ZwMZklMxpmUQu3kP20c8j2/d6rT8fTKBLcNRPK5vI0nmWi/ZsQlE/DKP5eH9ubJ0g8O2OQMhXpyEISXNZs8IABc/H7xrF0wpTouItHm9pNORfPS03TaFhPgwflxr5s4awfy5D/L6q51ITMzky6+32XX9h5M3cujwVQA6d6zGm691wkWvIEnmGJfc5mU9utXgjUmd7LZLIBAI7lUG9g+zy6PiCJGXkop0naRzvBpKbsfpsz/NQefuRthLj2HKuN4u4KY3WM1oQjOa2DJsPFmxCUWyrziIOi9O4sxPs8m5ZnuPsd5rY5ELceUpdnhtNFW1a5w1/vjrIIosYbLDDSkrEn/OOkR4w/IADBpQl66dq7F85WmOHovFZFJp17YKvXrURCmBdEGBQCC4U0hLy2Hj5vMkJWdRPsiL9u2qFprKXDesHEMG1eXfBUWLVSmM9HTHs04By558O5AUmVPfzDALIEUGUyFJG5qGmmMg4rd/qffqk0VeqygI8eIkIn6ZZ9c4jyqFV6L1qVMNr+qVSYuItHyxqlKxf5eimAeYU9v27Ltil3AB837szltKXp+/kMjCxSc4fcastFetOct33+/kuXGt6d3r9hUsEggEgpJA0zSm/3GAab/uITvbhCxLqKqGj7crr7zUnvt6FrzvTXq5IxVCfJjx10GSk4vf5NDPwVTsvOsa1TW3AjA6LmI0k0rCniPmraLChEsuqkrctn1Fsq84iNdlJ5EZFWvXuP0TPiKpkK0fSZKo/9b/rF5bvld7vGtUKZJ9uTiam2+66Yf2yNEYnnxmEWcj8jchi41L54231xY5Kl4gEAjuRC5fSebt99bz3Q87yc42C4Dc4NmU1Gxef2st6zeeK3CdLEs8MqoJq5eN5o/fhvDLjwMJrVqmSLEwOp1E0yK2X6n1zANFEi65mLeGbBQflSSHAoOdhRAvTsLeSruZUbEsb9CPk19NL3DOxc96L4fkY2fsyjayhCRJNKgfbLUGzM3kdjbN5Yuvt2EyaRYj37/4ehuZWUW3TyAQCO4ErkSl8PSzi+k/eCZLl5+yOE4Cvp2yw+JLoV6vUL9eEE0ah/DlZ73x9XGz+/6by5CB9fDxKVoXZ/+m9W+8FFtIfbaJHe+7wd3aFG3uYiDEi5Oo/qjtNuM3s3/iZK6u3prv2PFPfjbvLVog41I0lxets2v+zCwDS5ad5POvtvLd9zs4dCQaTdN46IGGdqfdqaoGmsa+A1FEXkri8JFoq9empxvYvPmCXXMLBALBnUhcXDqPPPYve/ddsTlWAyIvJXPylO1soCqVyzB31nAef7Qp7u72RWz4lXFj7YYIOnb9hZFj/mHRkhMYHPSkNHz3OdrN/RqfOoWUspBlc7fooiLL6H29qDZqYNHnKOrSpbHI999/T9WqVXFzc6Nly5bs3r3b6vh58+ZRp04d3NzcaNCgAcuXLy8NM4tFzWcetOk5yYcsc+LzX/O+NGZmEb9tv9W9RUmncHXlFptTb9sRSc8+M3j7vfXM/ecof8w8yJjH5/PokwtoHF6eWjUD7DZz/8GrPDF2IR9+bDtFW5YlYmLT7J5bIBAI7jR+/2M/SclZDmUM2RvX4u/vwdgnWjCwX5hdjpDEpCwSEjJJTcvh2PFY3v1gA889v4ycHMcEjFe1SqSfv4R068uxpllswGgTWULv5UHnFb+g9/Eq2hzFoMTFy5w5c5g4cSJvv/02+/fvJzw8nJ49exIbW3iMyPbt23nggQd47LHHOHDgAAMHDmTgwIEcPXq0pE0tFq7+Zei+cy6yq4t9F6gqMet35bkb7YoK10C1obpPnorj+ReX50WnG41q3i/h0WMxPD52YV6wrT3kXrtnr+23EFXV8PcvfhdUgUAguB2YTCqLlpxwONW5fHlvh8bf16vWzT0SHWL3niv88tteh67Z+9wHqDmGvDToPDQNJNB5eeBnT8VcCdwrBBHYpjHhH06k39k1BLZq5JAtzqLExcuXX37JE088wZgxY6hbty4//vgjHh4e/Pbbb4WO/+abb+jVqxcvvfQSYWFhvP/++zRp0oQpU6aUtKnFxrdWaOGuOQtompqXN6/z9MC7ZhWrtaU1VSWgRQOrc07/4wCaphVaUM5k0rhwMclu+xzFzU1H544Fa9gIBALB3UBmppHMTBsBqjchyxIN6wdRpXIZh9apV7ccHdtXdTj+BcxJF3P/OWq39yXlzAXitx8oKFzyJgRjWgahD/a1YzaJOs8/Qo9ts6n36pO42RnrWRKUqHjJyclh3759dOvW7caCsky3bt3YsWNHodfs2LEj33iAnj17WhyfnZ1NSkpKvv9uF6kRkSQdOmn3eL9GYUjXfYeSJFF7wmjLg2UJnYc7oQ8PsDhEVTXWbzzn9AJJ9vLMUy3w9LTT8yQQCAR3GO7uukJrtxSGLEvodDIvv9je4XUkSWLyBz24r2dNJMn+Zrm5pKRmc+myfb2LMi5G2R4kS2iYq/JaRdOIXrMNQ8rtDw8oUfESHx+PyWQiKCh/B+WgoCCio6MLvSY6Otqh8ZMnT8bX1zfvv0qVKjnH+CKQevaiQ+Nv9dLUeGoElYffZ/7ipg1RSacg63S0+/dbq3uLBoPJZvdnZ3DrL5qXpwsvTWzHQw+El/jaAoFAUFIoikyf+2rZldIc3jCY36YNom6Y9Ya8mqaxfWckE19ewYChM3lo9Dz+nHmQnBwj77/TjaULR/LqSx3wd7CWi71p1y4BZWwPUjXcgwOpPWGUTSUVvXY7G+57HNVWCnUJc9cXqZs0aRITJ07M+zolJeW2CRgXX8f2PXVe+eNDZEWh7cwvqNC3M6e/+4uko6dR3FypPKQntcePwrduDevruyiUK+tJbJztBpHFQdPg7Tc6o2kavr5utG5ZGTe3u/5HSSAQCHj0kaasWRdBRoah0OzKls0r8tqrHalU0XYvN1XVePeD9SxZdgpFkfK84idPxfHHzINMfr8b0THpuLrqqFzZl6TkLLuyQcuV9bRrfTB7+L1rViH1bCSWGtRJeh3B3VpT+f77SD4RQeRsy0kymkklfvsBrixeT6XBPeyyoSQo0SdOYGAgiqIQExOT73hMTAzBwcGFXhMcHOzQeFdXV1xdXZ1jcDHxb94At6BAsmLsaKIlSbgW4qKTZJnQh/oT+lB/h9eXJIn7h9bnhx93ozpYjM5RmjYJEY0YBQLBPUeFEB9+/3kwb76zNl8KtF4vM2xIfcY/2xq9zr6tpdlzD7NkmblOzM3b+ZoGCQkZPPnM4iLZOPKhRna3ZJEkiUafvsyWQZaLoGoGI5v6P03XdTNoO+tL4rbuI/NyjMXxSBJH3v2egJbheFQIsjyuBCnRbSMXFxeaNm3KunU3apOoqsq6deto3bp1ode0bt0633iANWvWWBx/JyErCjX/96B9gzWNKiP6ON2GB0Y0JCysrMN7qI5QsYIPFUIcSAsXCASCu4jq1fyZ9cf9/DV9KO+82YXJH3Rn9bJHePH5dnYLF1XV+HPWIafbNmhAXR4Y3tChayoN7Ebrvz6zWtPl2t6jHHr9KyRJwpiWYX1CTSPp8EkWVu7Ezsdew5SV7ZA9zqDEff0TJ05k9OjRNGvWjBYtWvD111+Tnp7OmDFjABg1ahQVKlRg8uTJAIwfP56OHTvyxRdf0KdPH2bPns3evXuZNm1aSZvqFOo8/whH3/8BzWB9PzCgZTh+jcKcvr67m55pPwzg/Y82snL1GafPD9CubRWkklRHAoFAcAdQN6yczZgWS0THpBETU7TA1tz+Sbn4+LjSsnlFhg9rQONG5Yt0/3UPCrRa00UzqZyZ+je+DWrjVs4fQ1Kq7UlVlXPTF5BzLZn286eU6nOhxMXL8OHDiYuL46233iI6OppGjRqxcuXKvKDcyMhI5JuCU9u0acOsWbN44403eO2116hZsyYLFy6kfn07ctDvAPRentR4cjhnf/zbYmqa4u5G5zWFp4o7A3d3PePHtS4x8fLP/KOMGNaAyg6mBwoEAsF/hbNn7QgfsEDFCj48P74t/L+98w6L4urC+Dszu+zSe28KCPbee8feuzFq7IkmJsbEFDWa4hdTNVFjTIyJvffeu1gRQURAqvTeYXf2fn8srCJsZZei9/c8JDJ7596zbJl3zj2FEDRp4gg726rXz0q/HQRGwKnsdSQrkeD2zC+0m1gmQ/yhc0i/8wh27bXzCFUFhmjbqa+Wk5OTA0tLS2RnZ8PComa2Nkqyc3Gux1vIevQU5SoRsQw4kRF6n/8X9p1aGdyODz46jhu3YvWeOs1xDMaNaYbFH3XV67wUCoVSl8nJKUJwSAp+33BLo5YByrCwEOHS2Rl6tAx4vHoTHn7+s/J6L1WAEXBoMG8S2q79skrzaHP9pr2NDICRpTn6XduB5ivfh7GbPNCYMxbDe/oYDAw8XC3CBQA++bgbLCzEOvfjUgbPE1y9Fq3fSSkUCqWOkp5egKVfnUPfgVswf+GxKgkXBoCjg37L7RNCUJiUZhDhUroAitMyDTO3Emh+q4EQmpmi6Rfz0PSLeZDxPFiOQ054NCL+2IW0m4FgRUZwHdwDXtNHQWRrbRAbXF0ssG3LGMyccxCJSfotKiQ11IeAQqFQ6hCZmYWYOmM/klPy9OPlZoCRIxpXfZ6XCP5mPcJ+2aLXOcvBMDD1dDHc/JVAxUs1wHIcIjbtwe25y8AwjEL9ply+jeBvNqDXqb8M1h/C2ckcObklep2T4xi0aF556jqFQqG8Sfy95Z5WwkXAMXBxtkBsJRVyOZaBl5cNhg9tqDf7itIyELxyvd7mqwwi5eE1fZRB13gVum1UDaRev4fbc5YCMlLebUcIJNm5uDhgJkqyDNPWQCLlIdWyhbo6eJ5gwrjqC8yiUCiU2ohEyuPQEe0aOfIygrW/DMb4MU3LtSLgOAb9+vpg04bhMBYL9WZj7O6TmjX+rQKNPn4HFr7V29eOel6qgeBvNgAq3tuS7Fw8+/cgGn6goreRDjxPyMG7C46iuFg/b9yyCpHvv9cRLZpRzwuFQnmzyc0pRkGBRKtzCAFGjd+J4UMbYfuWMUhKzgMvI2jSyAG2esgqepXCpFR5lpGa8h26ILKzRuPP5qDhh9P0Prc6qHipAjKeR/qtQJRk58Hcx0Op8kw6d0PtXEFL1yD46/UQmJrAc/xA+M5/C6Yeuu8hSiQ85s4/gqQkDXL1VWBkxEImkwd8OTmaYcb0NhgxTL/7sRQKhVIXMTExqlCTRRNkMoIjx0Jx6UoU/v17lEGrlZu4OKhMj9YKhkHjT2bCqX8XcGIRbNs1AyvUn5dIG+i2kY5Ebt6Hwx49cbbrJFwePBvH/AbgbLdJyAp+WmGsJm8caW4+StKzUBCbgCc/b8HxJoORditQZ/suXHqG589zqhxAVlIiAyEEMp4gKTkPK7+9hF/W3sBrlmFPoVAoankanoZde4Kwc3cQwp6mQSwWoHu3eho3SXwZnifIySnCd/+7YgBLX+AxfhBYoQo/BcuCM9awxQ4hyAoOh1PvTrDv3LrGhAtAPS868eTXLbj/4aoKx9NuBuJM5wnof3M3RHbW4IyEKNYhloXwPKSFRbg8bC5GxF0BJzLSeo4rV6N1uiOojDIBVPb/rdsD4exkpoh7yc4uQmZWIaytjGFpKa7yehQKhVKbSE3Lx2dfnsX9BwmK1iuEyDtLz5zeBteuR+s0L88T3Lodh+cJOQZruSKysULzrz9A4Kc/VniM4ViwRkLUmzICkZv3ARrcaKdcuYNdoqbgjMXwGDcQDT+aBsuG3oYwXSVUvGhJSWZ2pW8CoFR05BXgVJtRkBVXMcOHl6E4NROx+07p1KSxqFiqs3dEE9Gzect9tGrpjD/+vIMr16JBiLyTevdu9fDe3A7w8bbVaW0KhUKpTRQWSjBr7iE8T5DfiL78tRockoz//XgVnTp64Oq1GJ3XiIrKNGi/uEaLZ0JgaoKg5WtRkp6lOG7Z1BcdNn2NtJuBKlsHvIw0Nx+AvBrvs3/2I2rrIfQ6sQmOvToawHLlUPGiJTG7T0CmKvCJkKoLl1IYoQBp1+9rLF4IIbgfmIiDhx4jJCRFWfdz5esxQD1PKzT0s8epM+Eqz09LL8DUGQfAS2WKcYQA167HIOB2PP76Y4TOPUEoFAqltnDsRBhi4yqmNQNyz8nz5zlISKhatqhYbNhLMcMw8H1vMrxnjUXyhQBIsnNg4eel6K9n4uaE+x+tUpVXUilEyoPICK6OXoARz69CYFx9nnca86IlBXFJYDTsKqoPGE6ztXhehuUrL2DW3EM4fTYcKan5Wq9FCBAdk4WTp1ULlzIkEhn4V9Q6zxOUlPD4+rtLWq9PoVAotY1jJ8Kgrt9gVUIATU0EyMwsRODDRL1s8yvj+fFLuOg/A5cGz8L1CR8hYOaXiN5xFIQQGDs7yDOGdOmrKJOhJDMHsXtO6ttklVDxoiUie2uD58yXQSRSOPXtpNHY/7YF4tiJMACoUpCuNh9CZdtSMhlB2NM0hD5J1dkOCoVCqQ1kZhVWSZyoI79Aik+/OIN3Zh/E0JHbcP5ipN7XePz9n7g8ZA5Sr95VbA9lPHiMG5M/xt0FX4MQgpbfL0bjT2eDNSoNwi0VMoxAvVeIEQqQfjtI73argooXLfEcP6h62n4zDARmJsgJj0FRWobKoRIpj207Ag1vUymaPv3Y2CyD2kGhUCiGxt3VEixbDd/5ABKTcrF4yWmcPhuutzkzHz5B4JKfAKB8kdTSpsHh67Yj4cRlMCyLlqsWYWTCVXTY/B1a//QZuh9ejxFxlzRaRyF6qgkqXrTE2NkBjRbPNPxChIAvLEbgpz/gkGt3PNtyQOnQyMgMZGYVqZyOZRkIBGyVP4QMA9jaGms01tRU+ywpCoVCqU2MHN7YoNs5lfHDT9cg0bE2CyEECaev4tLQOTjg1Blnu05UecfJcByCv16PK6PnY69NOxz19UfC8UuwadcUbsP6wMjKAiZuqouSEokULoN66GSvrtCAXR1o8e2H4MQiPP7fn+CLig22Ttn2lKxEglvvfA5jFwc49+9aYZwmHyxCCHieVNn9SQiQllaodpyZqRHatXWt2mIUCoVSw/TqWR+dO7rjZkCcQbePXiYjsxC3b8ejS2dPrc4jhOD+ov8h7Jct8qq6GgggwvNID3hYbnz84fOI238GLVcvRvzBsyiIT1J6PiPgYOHnBac+moU46AvqedEBhmXRbPl8jEq+gc47f0a7P1ag/aav5W4zA7kXGZaRtxmohPr1rGGsJlqdkKoFlWnLzHfaQCSi2phCodRtOI7Fzz8MwpTJLWFs/GJrRCwW6LWB4qvoknQRt++Uonu0tlV1Xx5f9u/AT35AWoDqWBYTTxf0PPEnGLZ65QS9ulQBoYUZ6k0YrPjdxN0Z18Z+IM+DF3AAL9ObYiC8DKlX76IkMxtG1uVLSRsbCzFyRGPs2vOoUi8MxzIVsoIMBcsymDG9DaZMblkt61EoFIqhMTLisHBBZ8yZ2Q5hT9NACODna4ez5yNw+OgTg6ypS5+jJ79sAcOyIDKZ+sGaomau9htWVKmVja5Qz4secfHvhlGJ1+Azd4K8Ku5LwsWmXVN4TR+tebSrEqSFlce2vDevA5o1dQRQfgmWZWBuIYKLi3lVl9aIb1b0wbzZ7asnqJlCoVCqgYyMAmzZ+gDffX8Zx0+GoaBQArFYgJSUfAg4w1xGO3Vw12o8kcmQFvBQv8JFDQzHIfN+SLWt9zLU86JnHnz6AyL+2FXheMbdEGQ/joTA3Ax8fkHFdGuGUeulEVpZQGxvU+ljxmIhNq4bjiPHnmD/wRDEP8+GubkIQwc1xNgxTXHufAR++Pmazs9LU2jLIwqF8jpx7MQTrPz2EmQ8ARj5F9z+g4/RwMcGgwb4gTeAWLC2EkMo1LKeGMNAnt+swZcwywJEptFQVRBCNEqlNgRUvOiR2P2nEL5uR+UPEgK+oAi2HZqjKDEV+TEJYIQCgABEKoW5jycK4hLBF5dUqgAYjkWDuRNUNsIyMuIwZlQTjBnVpMJjI4c3xumzEXgUnFxua6lMMzVuZI/HoVWvy2LIEtcUCoVSndy+G49lKy68OPDSV3N4RAa273oIlgW0Kf2libyY8lYrbcyUz8swcOzVHimX7qisRWbTpgkKk9JQ+DxZ6zUqIJPBqV/nqs+jA3TbSE8QQnB/YcVmja8MQvqth+h9aSt6HP0DDT+chkYfv4NeZzZjyJOT6PTf9wDDVKiqy3AsrJr5ocnnc3S2TyQSYP1vQ/H2Wy1h9lIKs4uzOb78rCesrTRLf1YGwwCenlaKrSsKhUKp6/z9zz2V2+1paQVab5Ev+7IX/Pv5VDovy8gTMMaMrHgDqgmNFr2jXLiwLITWFpDxvF6EC8NxcOzdEdbNDRe0rHJ9omv3vlpKTk4OLC0tkZ2dDQuL6vMC5IQ9w7GGAzUa2/fKdjh0a1vu3Ogdx1CclgnC88gJi0LK5TsAITCytUKDeRPR+NNZEJqZ6sXW4mIpnifkQCjk4OpiAZZlsHjJKVy8/ExdbFalsCwDlmGw/rehsLQUIzgkGSzLoH07Nzg7mevFZgqFQqlOCosk6NJjk97mY1kGrVu5YN3aIWDAYONfd7BzdxAKCiQAAI5j0K+PDz75uBusLHXvEfR49SYEfvpj+VRploXQ3ASmXu7IehCqj6cDc7/66Hd1h9JQBl3Q5vpNt430RElWrsZjRbZWAACZRILbc5fj2eb9cm8Ly8gzlBig2bL30OC9yTCysQSrYX8jjdcXCeBVv/wbrkf3+jh/8ZlO8zVt7IC3JrfE+o0BCHz4oh4AwwB9+3hj6We9YGZGC9ZRKJS6Q5mo0AcmJkKMGdUE82a3h7C0N957czvgnWmtERKSAqlUBt8GtrCx0T7D6FUafzILzv274un6HUi/HQROLILbiL4w83bH9XELqzw/AIBlYN+ptV6Fi7ZQz4ueKExOw0HnrmojVk3cnTA85hIYhsHd97/B09+3KT2n3R8r0GDOBEOYW4HiYilGj9+J5JQ8lb2ROI6Bs7M51v48GJmZRbCzM4G5uQgT39qNtPSCCueyLIPmTR3x54YREAjoLiWFQqn95OYWY96CI1WKA2QYoH07N7z/XifUq2cFY3H1ls9/lZvTliBq22H5DbIeEJgaY1xeoF7mKkOb6ze9mugJY0c7uA3rrTYVus2aL5F2KxBXRs3H09+2qhQ7wSvXQVZNTSDTMwrRtq2rUnPKnpaDvRnWrx2Kep7WaNXSGe5ulti7LxipaRWFCyCv/hsYlIRr12MMaD2FQqHoj2UrzuNJWFqV5iAEuHP3eWkR0ZoVLgBQnJ6pN+ECANL8QqXNeasDum2kR1r9tATJV+5CkpVTqSjx+3AacsOjcXXUfHmqmhoKE1KQcTcYdh1aGMJcBY9DUzDnvcMoKpJWKHJnZmYEP187WJiL0KtnffTt7QPxK9V8Dx8NVdmigGUZHD3xBD171DeI/RQKhaIvYuOycflqtF7mkskI8vNLKnxn1gRm9dw0bhmgCaZe7jVaz4t6XvSIubcHBtzZB/fR/cu1CRA52qLBu5Ng7GiLwE9/lB/UMDK2IC7REKYq4HkZFn16CoWF0ko9J3l5JXCwN8VPqwdiyKCGlX4Is7JVN4WUyQjS0gr0ZjOFQqEYitt34vQ2l1gsgIWlSOUYQgjuPUjAvgPBOHYiTO33qa54zxijN+EChoHvvIn6mUtHal4OvmaYe3ug2961KExNx+Nv/0DEpj0oTk5H+Hol9V/UcG/hd7Dv0R4JR84j69FTcCbGcBvRF7btmulF9V67EYvk5DyVY06eDsflK9Fwc7PAqBFNMGxIeRHjYG+KmNgspVtOHMfAxZlmHVEolNqPlCea1AxVC8syGDakoSJAtzKCQ5Lx5VfnEBubrTgmELAYP6YpPljQWW2coEwiQdyBs4jcvA8FcYkwdnWE17RR8Bg7AJyRPEkiPy4Rz7YcQH5UPKxaNETWw6q3MzD38USD9yZXeZ6qQMWLgYjavB9ha/6r8jyFz5Nx0EHerZMVCkAIweNVG+HQsz267f8NIhurKs0fEpIMjmNUBukCQEGhBOER6fjfD1dw5FgoNq4bDtPSejGjRjTGL2tvKD2X5wlGDGtUJTspFAqlOmjWxKHKwoVhADtbE8x8p63SMZHPMjBr3mFIJOW9IVKpDDt2ByG/QIJlX/RSer4kLx+XBs5C6rV7AMcCvAy5YdFIPncTT9duRa8zmxG+bjseLv1VfqPLMIoYFdZICFmJ7tlUTZfOg8BY93RufUC3jQxASXYugr76Te/zyiRShdsv9eo9XB42r8oBU5yA1fiDWjbuSVgafl5zXXF81IgmSuu5MAzQs0d9tG/nViU7KRQKpTpo3MgBDf3swHG6e7Z7dK+H/zaPhp2K5oob/7oDqZSvNF6QEODQkVBERWcqPf/e+98g7eYD+S+lgbhlfY0y7gXjQv/pePjFL4CMgPAy+bWjdJxMIoXYya5ceIPGMAwce3XU/jw9Q8WLAYg/dA6yohKDrkF4HmnX7yPlyp0qzdOpg7vKYNvKkMkIjp0IQ06OfG/29p14JCRWXueG4xjMnN6aNmqkUCh1AoZh8L9v/WFlKQary8UdgI+XLRwczJQ+XlgowYWLz9SWpTh56mmljxWlZiBq6xEQJdlDhJch4/Yj5QYSgqKkNEDL736G4+A+qj9M3Jy0Os8QUPFiABLPGL4BIgAwAgFi956q0hzNmjrC3U37ejgSiQxhT9MhkfL4etUlKPuIy2TAL2tvVslGCoVCqU483C2xe/t4zJjeBo6OZjAWC+DubgkTY81SnvfuD4ZERXBsXl6J2ptGhmGQmVVY6WNpN+6DSKUa2aJ8AYAzFmuU+QqGARjAsrE32v+5smrr6gkqXvRMcUYWYvecrKbVCKR5VcviYRgGq/83QKdz//7nLi5djkJmZqHSRmMyGcG9+wmIf56tZASFQqHUPmxsTDBvdnucPPI2rl+ejcP7JmPHf2NhY62+D1x2TjFSU5V/N1tYiNR2jZbJCBwdK/fe6KO8CsNx8Jo+EgJT43IChikNMHYZ3AM2bZpA7GwPm9aN0W79V+h/a0+V4yz1BQ3Y1TNR/x5S2dFTnxAZgWUjryrP8/x5jk7n3b77HI+fpGgUmf88IRdurpY6rUOhUCi1AQ8PKwwb2hBbtwWCV+M5CQpOUpplKRIJMHigL44ef6Jy62jooMqbHtp1aC4XHLo0oyuFSHm4DOqJpkvfQ8SfuxF/6Bz4wmLYtGsG33cnwa5jS53nrg6o50XPZAU/rdAV2nAQCK0twRdXLb6mKj088vIkGt0FWFqornVAoVAodYFuXeqpFS4A8PmXZ7HvQIjSx2fPbAsLC7HSwOCyLavKMHZ2gMe4Abpfa1gWxq6OcB7QDcZO9mi2bD4G3j+EIaEn0fm/1bVeuABUvOgdgYkxoDQCRDmdt/0I9/GD5L9oHNzK4M6cZTjo0g3xh89pvWYZXvWtdT5XHQzk+8d+vnYGW4NCoVCqi5YtnNC4kb1GoSL/++GK0jpaTo7m+PfvUWjf1q3cFcPaSoxPFnXF3FntVM7dfv1XsGzaQP4lq0VCBCPgwBoJ0GXHT3pv+lud0MaMeibp/E1c6DtN6/MGh56AhW99RG7ej7Bf/0V2SDjAMnDs1QEiW2sknr4KSbaSYnKlOfx9zm+BY88OWq9NCMHEKXsQEZmhdeaRJvywyh99envrfV4KhUKpTgLuxOOff+/j9p14jcazLIOZ77TB3FntVY5LSMhBVEwWjMUCNGvmqLKw3ctIC4sQvfUwwjftQVZgqPoKuiwLjzH+8PtwGvKjn6MoOQ0mro5wHdILnLjmvePaXL+peNEzhBCc7jAWmQ8ea1yKmeFYjE4LgJHVC3tlEgnAsgplnPPkGY41Gqh8EpaFfZfW6Hdlu052hz1Nw4zZB1FcUnmbAF0wNRXik0Xd0KuHF2QyGczNRTRlmkKh1EkOHQnFym8vgmUZjW/yGAbo3dMLP+iYFKEp+THPcbheb9W2cCyafPkuRHbWePjZT/JkDwYAAViREZqvWIDGn84GIL/+FKdnQWhuCoGp8lo1+kab6zcN2NUzDMOg5/E/cWnwbGTceaSofKh0PMfBbVS/csIFAFhh+ZS8+MPnwHCs0rx+yGRIvXoXhYkpMHZ20NpuP187bN0yBn/+fQfnzkdWWcCIxRzat3XDT79ex/KVFwAAbq4WeGtSS4wZ1UTn+gkUCoVS3aSm5ePb/10CAK280yzDQCQy/GVWWqBBPySWRdqth0g6ffXFsdKnIisuQeCSn5B64wFM67ni2eb9cnHDMnAd0gvNlr0HmzZNDWO8jtCYFwMgtreBf8Be9D63Bb7zJsGmXeUvOsNxEJgZo8U3C9XOWZKZA0aDTdaSrMqLxWlC/XrWWPV1f1w5PxMnj7yNRg0129etjKIiHhcvRyEnp1hxLP55Dv73wxUsW3HeINtTFAqFYggOHw3VKT2ZlxH06lFf/wa9gqmni7xmiwqIRIrUa3dVjnl+5AKe/rbtRQkOGUHC8cs43Wk8ks4pbwFTE1DxYiAYhoFTn05o+9tSDLi9H+3//Fpejvkl7Lu2Qf+be2Dhq/7NbeblDpmabShGIICxi/Zel1cxNhbC0dEMq1f5w87WtMrzvcqJU09x4eIzvc9LoVAohiAiIkPrcziOgYeHJfLySzDnvcMYM2En3v/oOC5efgZehTdeFwQmxvB6Z7Ty7COWgcDMBHx+5UXvyvGKSiM8D8LzuD75Y3k4Qy2BbhsZGL64BHEHziAnLAr1p46EqYczTOu5wcK3Hsx9PDWaI+PBY0gLi8AIOBBJ5VUVGQEHz/EDYWSpv+7Nri4W2LV9PPYdCMaff92FVKqfDxzDALv3PULfPjSIl0Kh1H4IIVr3kXN1sYCAY7Him4tgGQYyQhAdk4Vr12PQqYM7fv5hoF63lFp8/QGSL9xC7tOocuEFDMeBAHAe0A1x+07rNrmMoDglHc+PXYL7yH76MbiKUPFiQJIv38a1Me+jOC0TjFAAEAIi5WHbvjm6H9lQ6TmZgaF4suZfJJ66BplUCshkKMlQXZ2W4TgY2VihxXcf6f05WFmKMXN6W9jamODr7y7pZU5CgIhI7e9kKBQKpbo5fDQU5y5EarVt1LaNK8QiDjcD4gAAstKTy7bLA27HY+3vN7F4UTe92WlkbYn+N3bh8febEL5xFySZOQDLwmVIT3AioypXfmcEAmSHhNca8UK3jQxEdmgkLg2YieJS4UFe6gidcT8EF/pNl4uTl3i08necbDUCUVsOoigpFSVpmRoIFxZuI/tiwO29MPVwMcyTATB8aCMMHewHAHoJthWLqW6mUCi1C0IInkVlIPBhIpKT83Dv/nOs/OaiVsJFwLGYN7s9rt2IVZr4ICMEBw4/Rl6efhv4GllZoOWqRfC/sx9d9q5B77P/wHVoL720rCEyWbVmHqmDXkEMROiPmxWek1chUh7Zj57i+dGLChUbvmkPHi3/Tas1Wv/6OepNGgqxvY1ebFYFyzL4amlvdO9aD1t3PETY01SAyAPSdNlO6tDezQBWUigUim5cuRaN39ffUniFGQCWVmJFOrEmiMUCbFw3TCPPcnExj5DHyejQ3l13o18hNyIGd95dgaSz1xXHGI7T6jkohRC4De9TxUn0B/W8GIjYPSdU1nlhOE7REVpaUIh787Xv1Bmz81i1CJcyZDKCoEdJCA5JRkkJr7NwAYB+tGgdhUKpJZw+E44PF51A5LMXooMAyMoq0tjr0rWzB65dnIlmTZ00PkefVdbyouNxpuM4JF+8VX4Nnq+6cGFZeE4cDDMv/QmtqkI9LwaAEAJpgeqobsLzkOTKK+bG7j0FWYn2UdzpAUHIjYyFubeHTnZqy7o/ArB1x0PF77oKF5YF7t5PgLW1MRo3qnp2FIVCobxKXHw2duwKwolTYSgokEAkEsDbywazZ7RFp44eiu3v4mIpvv3+MqBBg1lV3LgVh7S0Ajg4mKFlcye14xkGsHfQXzbnoxW/oyQ7V+PiqNrgNrwPOvz1rd7nrQrU82IAGIaRCwoV1WQZAQcLP3lH6KygMF0XQsTGXbqdqyWZWYXY9pJwqQqEANt2BOKtafswd/5h5OYWqz+JQqFQNORBYCLGT96N3XsfITe3BDxPUFAgwaPgZCz48DgWLDyKyGfpuHQ5Cn/+fRd5eSVV9oLIZARfLJP3mPPyskHbNq5Kmy4C8u/B+e8fRWpavuKYtLAIUduP4NGK3/Fkzb8oeJ6s0drS/ALE7DhmEOEicrBF9wO/Q6Cmjkx1Q8WLgWjw7iSVjxMpj+L0LKTdCgQrNtKqsdaLSQjSbgbqZqCWXL4SpbdUaUKgCGS7ey8BCz8+oXUaIoVCoVRGcbEUHy0+geKiystKAMDNgHiMnbgbH31yEv/8e19va997kICEhBwAwLcr+sLJUXXpirT0AqzbEAAAiNl9Agedu+DmW4sR/O0G3P9oFQ559MSd91ZUSO54leK0TJ2895rACmvnBg0VLwaiwbxJsO/aRnlVXJZF9LbDONNpPJLO3tDZX8kaCSscy7gXjLDftyF8ww7kPI3Sad6X4XkZjh3X0TukBpmM4EFgIq7fiDXI/BQK5c3i7PlIZOcUVznMQ1ceh6YCAOztTfH2lJYqx/I8wcnTT/Hs4AVcn/gRJDlyLwyRSAEZAWQyhG/YiXsfqN6yMbK2hNbl0FkGjIBTu0Pg2Ev7Zr/VQe2UVK8BnMgIvU7/jZDv/kD4uh0oyXwl5VkmAyl1ZGTefwwjG0t5af9KspNU4TK4p+LfeVFxuD7hI6TfDnrxhiQEzgO7o/PW1RDZWuv0XNb9EYD7gYk6naspCz8+galvtcS7czuA46implAouhEckqxV80R1MAxgZ2uC9IxCjebkBC/EQHJyHgQCVqXXWiKRIWjZr/KMoMrmJwThf+xCk8/mwMRNHksjzS9A1LYjiN52BMXpWTBv4Anb9s2QcSdYHqCrDpYBZAREpnoskfLwXTBF/Xw1AL1KGBCBsRgtvl6IkUnXYNnMV6nCJTyPkoxsWDVrID+gxRaSxxh/AEBxeibOdpuMjPshpZMShTcn6cx1XOg3HXyJ9jUFsrKL9BbrogqZjGDL1gf49n+XDb4WhUJ5fdHnzQ9TmmL85Wc98fuvg9WOFwhYtGr5ot6WqYmRWsFjUZyDguCwyoXLS8Tuk2enFsQn4USL4bgzbzlSr99HTmgkEo5fRvqth/Ltd008MGrWYgTyNgNtflsKu/bN1c9XA1DxUg2UpGch+9FTlVtDjICD27A+6HPhX/jMGa/x3HyhvJto+B+7UJiYWmnAFuF5ZD4IRdyBs1rbvmPnQ41iXcoi93UJ3SmDEHnb+ZfTFSkUCkUbOnVwr7LXpez7zMRYiO++6YduXeuhYwcP9O+rvMQDwzAYMawRrCxfBLb26eWl0haGARq7G6u1h+FYlGTlghCCq6MXID/muTz9ufSaovC2yGQwsjTT4BkqR+xsj3qThsL/zj74zX+rSnMZEipeqgGpJs2wGAZ8YREce3VE+w0rIHa212huoYX8jfpsy0HVW04si6j/Dmk0ZxnbdgTir3/uaTT2i897YPV3/ujY3h2eHlbwqq/bFhXLAkePP9HpXAqFQunUUf4dpCsM82LLKb9Agu07HypuqJYv7a0osFnm4CjLKOrUwQ2LFnYpN5eHhxUGDvBVWpWcEGDCu73BqPEWEYkU5t7uSL/zCOm3g1RmFckIgdDKQv0TrQSGY9Fg3kR0+vd72LZtptMc1QUVL9WAsasjBKaq1TWRSGHR2Efxu9/76vcZbdo0gbGzvE5KcXqm6sEyGYpTNPdo3HuQgJ/XaN4CvWM7d/Tt4411a4fi4N5JaN7MSWWaoAozcflKFB6Hpmh9LoVCoXAci99+HQwHe91qqLya+fg4NBXTZx1AXHw2jMVCrFszFGt/How+vbzRvJkjevfyxm+/DsHaX4ZU2mhx2ec94d9P/t3OsgwEAhYMA4hFAqxc3gfdBzaD28h+iq2ayhCYm8J9tD9SLt5SK3SkWbmKGmJawzAoSk7X7dxqhgbsVgMCYzG8Z47F09+3Vx5MxcjblXuOH6Q41PiTWYj4Y7fcPaiE5t9+qPi3qacrsrJylW9NMQxMvTQvyb9zVxA4jlHam6MMjmPQrq0bnJ3KpwRKJLzOdRNiYrPx1rR96N/XB19/1QdCofxDTYi8wm9qagFsbU3QormTXvosUSiU1ws3V0sc3DcJy1dcwLkLkVWaSyYjKCyU4K/Nd7FiWR+wLIOuXTzRtYunRueLRAJ8u7IfZs1oi3PnI5GXXwIPdyv49/OBqakRAKDV6sVIuRgg3xp6+RrBMgAB2v+xAtL8QmTcfwyiyZYYr1tZCyLlFUHBtR3qeakmmn21ABZ+9SuoZobjwLAMOm9dDYGJ3DtDCEHWo6do/etnsGzuV2EuViRC5+0/wsX/RUdSn1ljVadbEwJjJzuN7b17/7la4QLI94QXf9S1wvFGDe2rXLvl7PkI/PSrvEfHtesxGDZ6O6bPOohPPj+NGXMOYujIbbhw6VmV1qBQKK8nxmIhXF0tIBBU/TLH8wSnzoSjuFh1vRVV1PO0xsx32mLhgs4YNaKxQrgAgFl9d/jf2Qe3EX3KXSOsWzRCt4O/I+1mIA66dJM3WDRwTaz6bw0z6Pz6gnpeqgkjKwv0u74TIas2IuLP3ZBk5QIMA6f+XdD0y3mw79waAJB49jruf7gK2SHhinPNfevBrktriO1tYOFbDx7jB0FoVt4lauykPkbm+bFLaLPmSzAaRNVq4s+wtBTh379Gw6OS/eUhg/zw+/oAFJdIdf6sEQLsP/gYLVs4KSpXvkxSUi4Wf3oKq1f5ow/tlUShUF7BylKst5RpiUSG3LySSreG9IFZfXd02/cbitIyUBCbCCMrC5h5ueP6pEWI2XXc4KIFAMAy1PNCqYiRlQVafb8Yo1NvYVTyDYzLvY9eJzYphMvTddtx0X9GOeECyDuFRv93CE59O8N7xtgKwgUAUq/fB1TsmQJAflQ8ChM0iyVp385NZcwKyzIYP7ZZpcIFACwsxFj1TT+wLFulrR2el+GHn+Xel1c/u6T0Z/VPV8Hr6CalUCivL/37+eiterdQyMLczEj9wCoitrOBTesmMPNyR8aDx4jZeUwvwkVgZqJ6AMPAtpamRVcGFS81ACsQQOxgC4Gp/M1UlJaBs72m4O78lZW/SWUEREZw592vlH4QCSEaeVQ0/RBMmtBc6bYRw8jrGYwe0UTlHD2618e2LWPQr0/VvCKZmYUqzU5NK8Dd+wlVWoNCobweSKUynL8YiSVfnME3qy7ByEj1TZ0mcByDAf0bGMzroozobUdUBvJqgzSvQPUAQtBw4TS9rFUdUPFSw8gkElzs/w5Sr9xVPZAQ5EXEIu1WYKUPO3RtIy8prQITd2cYu2jWxblFc2csWdwdDINyHhh5tDyHH/83APYaRPP7+dphxbI+6Nhe82BhXUhN1TG6nkKhvDZkZBRg4pQ9WLzkNM6ci8DNW3EoLq5as0KWZWAsFmLmO231ZKXmFKWko7r6HJj71YfHuIHVs5geoDEvNUz8kQvIfBCq8fiC2ESgU6sKx12H9YaJm5O8UF2lGU1Aww+nKe+1VAnjxjRFyxZO2Ls/GPfuJ0AgYNGlsyfGjmoCFxfN6gg8Dk3B+x8eR0amBrVuXjWZAdxcLRAXn6N2rK2NGpcohUJ5rSGEYMGHx3QucskwlTumnZzMsOanwXB3s6yihZpDCEHs3pNIunBLfbl/jtU5u6jcmlJeM+99LYGKlxomZvcJMBwLouGbT2RfvvhbQUIynvy8BXEHz6AwNQNEJiv3KWQ4DoTn4TlhCHw1qB3zKr4N7PDFkp5qxxUWSnDqTDgeBSeD4xh06uiBxo3sMfe9Iygo1K3bKcex+PH7gVi46ASSknKV3oDY2BijXVvDenYoFErtJuhREkKfpOl8vrKt6YSEXEQ+y4C3l43Oc2tnB8H9D1chbM2/akuWMxwH71lj8fzIBY3jGZVRW7tHK6NuWfsaUpKepbFwETvbw6F7OwBAUWoG7i38DjE7j1Z0KzLySolGttawbtEQDd6dBLdhvbXyumjD7bvxWPTJKeTnl4DjGDBgsP/gY5ibGyG/oESnWDOGAcaMaoIGPrZY9GEXLP70lNKxixZ20Us6JIVCqbvs3P1Ip/PKvhZVFSj/ff0t9OvjXS2eicTTV+XCBVDbUkZkb4Nmy95D29+X4VjjQch7Gl3JQAYiWyuUZOYo9eIwHAfXIT2rbnw1Qr/xaxjzBp4aB2S1Wr0YrECA4owsnOk0vnLhAsiPMQxM3Z3R+8xmuI/oazDhEh2Tifc/PI6CAnnTR54nkJaKsdxc3YQLUFo2e6y8PHXvnl5Yvcof9nblt4ZsbIzx7cq+GOjvq/sToFAorwWxcVk6nScSCVQKFwCIf56DsKe6e3W04elvW8Fw6q8JTv26wP/WHhg7O4DlOAwNPYmmy+eXyypiRUbwmT0Ovc9tkdePqUx8MQwYAYcG8ybp82kYHOp5qWF8Zo1DxMbdasdxpsaI+u8wjKwsEPH3XuRFxqocT6Q8Mu4FI/3uI4P2qNi5Owg8L9N7CYL69a2x+d/78G1giyGD/NCntzd69qiPu/cTkJqaB1sbE7Rr60Y9LhQKBQBgaSFWP6gSCgs1KzyXm1ui0/zakn4nWG2ci0UTH/Q6sancMYZl0fyrBWj65TxkBYVBViKBRSNvGFnKq593O7gOV0cvgKxE8sLNxLJgjYTotv83mHm5G+T5GAoqXmoYmzZN4btgCp7+tlXlOD6/EMkXbiHp7HXNJ2cYpAcEGVS8nDsfqVElXk0pC9eJiclEbGwWjh4nWLvuFr75qi/69vFGh3Y0toVCoQAJCTl48DARhACtWjqjT29vBNyJN9h6ri7m6gfpAdZIqHZMmSCp9HyBADatK5axcB3UA8OjziNi0x4kXwwAADj2bA+fWeMUPfLqElS81ALarPkC5j4eeLz6LxQ+T1Y6Tm3UeSXoq0aAMoqqUC4bAH7/dTDMzESIiMzAn3/dQVp6PggpuzGQi6KSEh5LvjyDfxxHoVlTx6obTaFQ6izZ2UX46psLuHIlWrFrzgDo0tkDYrEARUVV+06qDHc3C40zLKu81sh+CP9jp/LO0SwDt+F9dJrb2MkezZa+h2ZL36uChbUD6nOvBTAMA7/338bwmIsYFHwMfh+8LW/IpQec+nbWyzzK8PG21bqCbtm268TxzdCpoweaN3OCjbUxUlLzle49y2QEn35+Gnl51eO6pVAotY/iYinmvHcY167FlAv3IwCu34ytUu8hVQwZ1NAg81aG7/tT5DEvSuJTOGMxPCcOrjZ7aisGFS8ZGRmYPHkyLCwsYGVlhRkzZiAvT3UxsZ49e4JhmHI/c+fONaSZtQaW42DVpAHyYxKqXpiIZeE2rDfMvT30Ypsyxo1pqnXvkPr1rPHV0t74+MOuiuj9i5ejoC6mOCk5D3PeO4Sr16Jx6Egorl6LhkRStQJUFAql7nDydDiehqeDr+Q7hxDDtP/hOAZjxzTV/8RKsGhQD90PrwdnLKooYAgBn1+I402HIvSnzXprfVAXMei20eTJk5GYmIizZ89CIpFg+vTpmD17Nnbs2KHyvFmzZmHlypWK301M3qwCZJpEmquDExvBoWd7yCQSsEL1e6i6MqB/A1y+Go1z5zVrO79uzRB07OBeLuUwOCQZNwNi1Ub8A0DokzR8sOiE4ndLSzEWLuiE4UMbaW07hUKpWxw5Fqq0mJy2sCwDmYxg+NCGOHz0idJxb09uCStL3YKBdcXFvxtGxF3Go5Xr5PGQr4g1aU4eHnz8PfiiYjT9Yl612lZbMJh4CQ0NxalTp3Dnzh20bSsvq/zbb79h0KBB+PHHH+Hi4qL0XBMTEzg51Y3OlobAqW8nxB04XaU5+IIi3P9wFRKOX0aPYxvBiYzAFxUjZvcJJJ65BiLlYdu+Obymj4LIxkrndTiOxaqv+4HnZbh4KUrteGNjYTnhcuNWLD746ARkmiiXSsjOLsKKby4CABUwFMprTlpagd68Ky1bOGHalNbo2sUTLVs4Y/VPV1FYKIVAwILnZWBZBm9Naon35nXUz4IqKEpJR15UPISWZrDw8wIAZAWFIW7faZVKLWjpGjj16QS7ji0NbmNtgyEG8jtt3rwZixYtQmZmpuKYVCqFWCzG3r17MXLkyErP69mzJ0JCQkAIgZOTE4YOHYqlS5cq9b4UFxejuLhY8XtOTg7c3d2RnZ0NC4vqCbDSN5LcPByu3weSrByNC9gphWHQdNl78Bjjjwv930FRYqq86i6R92TmREboumcNXIf0qtIyj4KTMXXGfpVjRCIO505Oh6mpvDNrSQmPAUP+RXZOUZW/kCwtxThzfCqEQsMGKFMolJpj1rxDeBCYqPVW9au0bOGEzX+OKnesoECC8xcjkZCYC0tLMfr29oadrWG9/nlRcbi/6HvEHz6vSF8296sPMAxynzzTaA5OLMKABwdh2bBqDXBrAzk5ObC0tNTo+m2wmJekpCQ4OJRPvxIIBLCxsUFSUpLS8yZNmoRt27bh4sWL+Oyzz7B161a89dZbSsevWrUKlpaWih9397qVq14ZQnMz9Dr1FwQWZuUCdxmBPIjLY+wACC3NNJuMEDz+35840WwoihJT5Yd4Xv5BkRHwRSW4Mmo+soKfVsnmpk0c0NDPrlwTx5dhWQYjhjVWCBcAuHQlClnZVRcugNwDczMgruoTUSiUWsuIYY2qLFwAwMJcVOGYiYkQQwc3xJyZ7TBhbDPDC5foeJxuLy/t//KeeW5YlMbCBQD44hLcX/idIUys1WgtXpYsWVIhoPbVnydPlO8fqmP27Nnw9/dHs2bNMHnyZPz33384ePAgIiMrj6n47LPPkJ2drfiJi3s9LmC2bZthWPgZtFr9CRy6t4Nt++ZoMHciBgcfQ9c9azAy8TrabfhKo7lkxSoydEqj3MJ+/a9K9jIMg9Wr/GFjbVwu+6hsh6hZE0e8/1559+uzqAy9FpnLyFDT8p1CodRp+vf1QeNG9lWex7wS8VLdBC75CSVZykv2awwhSDxzHflxifoxrI6gdczLokWLMG3aNJVjvLy84OTkhJSU8o2ipFIpMjIytIpn6dChAwAgIiIC3t4V3WIikQgiUc2/EQ2ByNYajRa9g0aL3qnwmMBYrLfQeiLlEbXtMBrMmwCbNrpH1bu5WmL3jgnYfzAER489QXZ2EVxcLDBmVBMMHugHI6PyWzrGYqFe7qLKcHDQ0BtFoVDqJEIhh/Vrh6Fnv791noNhUG1NFpVRkpmNuP2nlddy0RZCkB/9HKbuzvqZrw6gtXixt7eHvb165dupUydkZWXh3r17aNOmDQDgwoULkMlkCkGiCYGBgQAAZ+c350XRFGlRsfpBGiIrLsGptqPhu2AK2qz5QucGZFaWYsyY1gYzprVRO7Znj/pY8/tNndZ5FVtbE7SnnaUplDeAqt3wcCyLYUOqr25LZRTEJ+lPuJRiZFU9FYBrCwaLeWnUqBEGDBiAWbNm4fbt27h+/Trmz5+PCRMmKDKNnj9/joYNG+L27dsAgMjISHz99de4d+8eoqOjceTIEbz99tvo3r07mjdvbihT6ywFccpjh3Tl6W9b8fT3bXqftzI8PaxKO7WqHysy4sCqGNilkwftc0ShvOZkZBRg/gfHdDq3bDt7ySfdYWNT/eU3+OISZAU/RXZoJATmpnqd29y3HiybvlkNag1a52X79u2YP38++vTpA5ZlMXr0aKxdu1bxuEQiQVhYGAoK5LEKRkZGOHfuHH799Vfk5+fD3d0do0ePxpdffmlIM+skhBAkHL9kkLlDV/+FBu9OAquHejPq+PLzHrh4OQpSqeqsqulTW+P8hUiER2ZU+viRY/I4q0fByUhKkmcLDB3SEG1aOePs+Ug8CUuDibEQvXt5YfBAP5iZGVU6D4VCqZ3IZARz5x/Bs6hM9YMB9O3tjdt34pGTK/dQN2vqiJnT26BLZ09DmlkBvrgEwV+vw9N1OyDJygEAiJ3sYOLpIr8B1bFMxMu0XLVIZ295XcVgqdI1hTapVnUZaWER9pi0MNj8gx+fgGUjw6feJSblYvBw1U0pteHlAlZl/y4rRsUAAAPY2Jhg47ph8Kqvet+b52UIepSMrOxCODuZw8/X7o37gqBQqpuSEh7nLkTg4KFQpKXnw97eFN261sPRY08QoeTmpTJ8G9jCydEc9vYmGDa4IZo1q/7aYTKpFJcGzUbS+Zt6ESmV4TK4B3oe+9Mgc1c32ly/aWPGOgpbmjZtkHrYAGQSSbnf+eISJF8KgCQ7Fxa+9WHdUj8F4fRdl+XlP0fZv8uCgknpf7IyCzH/g2M4fGAyhEoaV546HY5ff7uBlNR8xTEfbxssWdwdrVspL7BIoVB0IzwiHVu3B+L02XBIJC8u9DGx2bh7L0Hr+Z6Gp+NpeDo4jsH+g48x6522mDu7XbXegDz9fTuSzl5XOYY1FkNWWKTzGu3Wf6XzuXUZKl7qKKxQCMc+HZFy8bbKVDuBuQmkudqlEAvMTGDuI3etEkIQtuZfPFqxTuHyBACRgw3a/PoF6k0cgoz7IYjefhTFaZkw8XCG9/TRMPPSrN6OrY0xfBvYIjwi3VA6rAK8jCApOQ+XLkehXx+fCo8fOfYEX319ocLxZ1EZmDv/CDZtGI4WzWkAOYWiL/7ecg/rNgQYZG6el3+xbNp8F87O5hgxrHoqcccfvYD7H61SOYYRcLBs7I3MeyE6rWHXuRVMPd7Mmyka4ViHafzJLKXCheE4mNZ3Q7s/Vlb6uFJYBt6zxkFgYozMoCe40G867n+4qpxwAYDilAzcmLQIB1y64lSbUQhb+x+idxzF41V/4ohPPwR+/rNGTcMYhsGM6W2qTbiUwXEMAm7HVzheXCzFj79cq/QcmUzuxfnpV9V3UhQKRXP+2/7AYMLlVTZvuWfwZoYp1+7i0tA5uDJsnlrPOJHyKEnP1mkdTixCh7+/1enc1wEqXuowzv26oN0fKwCWfdHMsbQ1s7GrI7rtXYOA6Z9pN6mMQGhphosDZuBki+FIPq86lVlRtVfKy394Xl7Vd9VGhK3VrPBdvz4++GB+JzAMyhW4MzR8Ja0XrlyLRl6e8qJ+MhlBcEgKomM0CxqkUCjK+XfrA/y6Vj/lEjQh/nkO4uJz1A9UQl5UHAI/+wkXB87E1dEL8Ozfg5CWbvkQQhD42U84120yEo5f1nhOU08XzSuml34/mnl7oO+Vba9FSwBdodtGdZwGcybA2b8rIjftRebDJ+CMRXAb1hseYwfiwac/QFYiUT/JKwR/9TsYruq6NuS7jfB9d5JGXa2nTmmF3r28cOjwYwSHJOOODnvc2sDzBM0rCeBLSclXBPiqIv55Nup5WhvKPArltefmrVi91XnSBolEt/oq4Rt24M78lWAYVn6TxrKIO3AGDz//Gd0PrUNe1HM8/l9p4KwW3p3Mh6Gw69IaiSeuqB3r2Ksjmnw+B469Or7xyQM02+g15oBzFxQlpdWoDf1u7IJ9p1ZanSOTEQwduQ2JSbkGsYlh5IHC5uZGyM4uAsOwsLYSY/iwhrC1McH/friqdo5WrZzx14YRb/wXCIXyMmnpBdi7PxgnTz1Fbl4J6nlaYezopujf16dCHaZ33z+K23fi9VplWx3GxkKcPzUdYrF29+2JZ6/jYv+Klc5fhhWLINOxcCjDsTCysUJJZrbq4nUMg6HhZ2Du7aHTOrWdWtGYkVLzaBuoawh4HaLoWZbB7JltDWDNiwStkhIe6emFkEoJJBIeKan52PT3Pfy85jpERuozoB48SERwSIracRTKm0J4RDrGTtiJv/+5h/jnOcjOLkLQoyR8ufwcOnbbiIlTduPQkceQlF6c7957Xq3ChWUZjBrRWGvhAgCPv//zxda8EnQVLgBAeBmK07PUFg9mWBZR/x7UeZ3XCSpeXmNM67vWrAEsC4uGXjqdOnxoI7w/vxNYlgHLMuA4RtGxul9fb9jb6VYh09hY9RZWSYlMoy9UjmNw8nTVOnFTKK8LPC/DR4tPIDevpNzn5+VyBWFP07Hy20tYsPAYSkr4qlb5V8mrsXMMAzT0tcO82e21nksmkSD5QkDVGyiqXUimfg0GyI99sxowKoPGvLzG+L47CXfeXVEjazMCDq5DesHExVHnOaZNaYXBA3xx7EQYnifkwNJChAH+vmjgY4vxk3chNU07z1L/fj44czZC7TiJmmq/gPxLOTunoldJJiO4/yCh1F4xOnV0h0hEP2aU15tbAXF4nqDZNu+duwn4+597aNHCCQ8CEzW6WRAZcRg5ojEcHUyx4c/bKClR/hkVCBgM6N8A5y8+Q2GhFM7O5hg3uinGjW0KY7H6+LtXkUl5g9XT0gWxPY21A6h4ea2pP20UIv7ai8wHjw1zl8MygIyA4ViQlzJ3GI6DsZM92v62tMpL2NubYvrU1hWOZ2Zpvh1lLBZg+rTWKCnmwbKaFbrkOEZRH6IyGAZwdS6/J3srIA7f/O8SEl76EjczNcLc2e0wcXxzGh9DeW0JDEqCQMCqbfMByLNy/vrnLjp38lApXBgG+PD9zmjcyAGNGzkotnsIGKxVEujLMMDE8c3x4ftdsHK5/GaiqhmMnFgEc996yA2PqRYR8+r36csQKY96bw03uA11Abpt9BojMBaj78Wt8Jo+GoySSrIqefViy5T+QB6c5j1jLHqd2QzPSUPBCuVfLAIzE/gueAv+d/fDxM1w5bg18WaIRBxWf+ePsyenY+b0tuAErMbfPSYmQpVfejIZwfCXil3de5CABR8eQ2Ji+bvPvPwS/PjLdWzdHqjZwhRKHSE8Ih2nz4bj6rVo8LxMq/ophMizjVR9xggBNv19F/XrW5eLU5n6Vku8/VZLRWmFl7eUhw9thAXvdlKMrYpwKUrNkHd/5nn4ffC2zvNog9DSDJyxuPJsT5aB54TBsG5Rsx2xaws02+gNoTg9E5kPQlGYlIabUxYrH8gwsGzkjYaLZyDk2w3Ii4gFAHAmYnjPGIumX80Hy3EQmJmUa9wok0ggzSuAwMKsWho6frb0DM6ei1R559a9az38+tMgxe8PgxIxfZZmwW4MA9jamiAzs7BSD8ysGW3L7Z9PeGs3noanK51PJOJw9sR02hCSUueJiEzHim8uIuTxi4B1oZAtV9Jfn3jVt8a+XRMrHH+ekIOjx58gJSUfNjbGGDzQD/XrVX1LJe7gWQR/swGZ9+VVb41srdBg7kRkhYTj+aFzuk/MMLBs2gA5jyOUelZafPcRnPt3wfXJHyM3LEp+s0jk2/A+s8ah9a+fgzN6fb9DtLl+U/HyBnLjrY8RvfO40v2TrnvXwH1Uf2TcD0FOaCTEzvaw79QKAtPqbyOvjAeBiZgxR7UQ2fDbUHRo/6JNASEEk6fuxZMwzdLHOY5Bh/buCLgdryhoZ2UlhquzBUokPExNhejf1wdNmzji7Xf2q51vxbLeGDqY3jVR6i6xsVmYPG0vCgul1ZopdOTAZLi5Whp8nSe/bsH9D1fh1f1lhmVh27EF6r09HHfnrdBq+4jhOBCeh8f4gWj3xwpcG/M+ks/fAiPgQKS84v8+s8ej3YavwLAsCCFIvXYP2cFPwRmL4TKoB8QOtoZ4yrUK2piRopL2m74BX1yCuH2n5dtJDAPC82A5Dq1//Rx8YRGOePVBfkxpoTiGgbN/V7RZ+yUsGtSrUdvLaNXSGdPfboV//ntQrqgcwzBykTKhOdq3cyt3DsMw+OWHQRg8YqtGX7w8TwBCcPbkNETHZGL3nkc4fTYCubnF4Hl5l+qHQUkaBQEyDJCeXvOp6xRKVdj4910UFVWvcAGAkyfDMctA5RPKyI95jvuL/if/5ZUbOyKTIe1WINxG9IXYwQZFycq9rC/DioRwHtAdPrPHw2VgdzAMg95ntyD5wi1EbzuCorQMmHq6wnvGGNi0aqw4j2EYOHRrC4duhn3OdRkqXt5ABMZidNu7FlmPwhCz5yQkWbkw83ZHvbeGIWbHMdx8+9PyJxCCpLM3cKbjOPjf3ldrCiTNf7cj/Pzs8d+2B3gcKm9T4Odri7cmtcRA/waVBsg6OprBxtoYaRoKiZsBcQCAyMgMnC7NVCrbRirrUl1QqL6KMSHAjVuxGD6sEaytjDVam0KpTRQWSXD2bITKQHZDISOG2ZJ6mci/98lvfpRlN8gInq7bDkag+WVTVixBl50/Q2AsVhxjGAZOfTrBqU8nFWdS1EHFyxuMVTM/WDXzU/xekpmNB4tXVzqW8Dwk2Xm4PWcZRLZWSL1xH6xAANchveA7fzIs/LSv50JkMuSERYEvKISZjyeMLM21Op9hGPTv64P+fX1QXCwFoFkgr42N5uKFECArqxD/bQss237WmfsPEjB1xn78t3kMrCzF6k+gUGoROdnFkCqJ1TA0vXrqVi9KG7JDI0HUeJQKYhLg0LM9CpNSAQ3/FvpotUKpCBUvFAXRO49BJpEqfZzwvLxRI8cqPrjhf+xE+MZd6LZvLdyG9UFRagYSjl+CJK8Alo284dirAxi24of32b8HEbxyHfKeyT0brJEQ9SYPQ8vVH0NsZ6O17drUUunRvb7K4NqX4Tj5NlRcvG6dX19GJgMSEnKx6e+7WPxR1yrPR6HoE0IIgh4l4cKlKBQVSeHjbYOB/r6KIHMLCxEEHFvtAsbZ2Ry+DewMvo7QzFSepqyqPD8A5wHdkXLptvoJORb2nVq91gG2NQkN2KUAAAqeJ+PiwJnIfqRD1ViGASsUoN5bwxH13yEQqVRRh9+0nis6b/sB9l3aKIaH/O9PPPzsp4rTCDiY1nODf8AeiGysqvBsVJOSkofBI7aqdX8zDODfrwHem9cBQ0du09v6JsZCXDz7DoRCw2dlUd5sHoem4PDRJ0hJyYONjTEGDfRD65bOFbZUs7OLsOiTk7gfmAiOY8AwDHheBpGRACuW90a/Pj4AgC+WncWZc5pvHTGMPI25rP6LpnWWyhAKWOzfM7FagnWfH7uIy0Pnqh0nsDKHbesmSL4UAKjx1HQ/sgFuQ3vry8TXHpptRMWLVuTHJeJ0+zEoSklX+2FUirI9FZYFaySA/629sG7REPlxiThcr5fSdRiOQ8OPpqHV6k90s0NDTpx6ii+Xq057NDMzwvZ/x2LLf/dx8HCoXtc/fWwq7O1N9TonhVIGz8vw9XeXcOTYE0XBxbL/d+3iidXf+b8o+kYIZsw5iEfByRVECcPIBcif64ejdSsXxMRmYfLUvVoF7VpYiDBhXHN06uCOw0dDEfI4BSIjDj2610NsXDaOHg9T9Bx7GXc3C2z+cyRsbavncyLjeZxqMwpZj56qVFgMx6LhxzPACjiErd0KaW5++QGlnumW/1uExp/ONrDVrxdUvFDxohVXx76P+EPn1LpLdYXhOLgO7YXuB9fh0crfEbxyndI6BwAgtDLHmPTblW436ZOr16Kx5IszKCyquFXWvJkjVizrA0IIRo3bqdd1GQa4fG5mhZov4RHpCHmcAqGQRYf27rCzlaemE0IQFZWJwiIJXF0tabzMG0JOThFOnHqKmNhsmJgI0a+PNxr62Wt07oaNt/HXP3crzehlWQZDBvli6ee9wHEs7j1IwKy5h5TOxbEMOrR3w+9rhgIAnoanYcU3FxH6JFUjW1iWgUjEYduWsRXqsBBCcOhIKLZuD0R0TBYAoJ6nFaZMbokRwxpVe1XqwuQ0HPX1hzQnT+U4U09XDI++AL6oGGm3g5Bw4jLSAx6CLyqBTevGaDB3Qrl4QopmUPFCxYvGFKWk46BLV5ViQi+wLMZm3cHd+V8jesdRtUJpTOYdGFkZ/vUrKeFx4eIzPHiYgKysInjVt8GgAQ3g7m4FQH4R2PzvPb1lWLAsg47t3fH7miGKY/HPs/Hl8nMIepRcbtyQQX5o2cIZ//x7D3HxOQAAjmPRv683Fi7oXMFzU1LCI+hREgoLJahf37paXO0U/UIIwd17Cdi7/xEuXooCLyMQCOR1P3ieoHtXT6z6pr/KBqOFRRL0G7gFBQXqs+Aa+tnBylKM23fj1W7nXL88q1xZgLCnabh2PQbHT4YphIcyOI5Bvz4++O7rfpU+TghBTo68K7OFhahGW2mcaDMSWfcfqxxjZGOJMekaxL1QtILWeaFoTN6zOMMLFwCQyVCSlQsja/WChBFw4Eyqx7tgZMRhgH8DDPBvUOnj6RkFpV+k+hEvMhnB5InNX8yfXoDpsw4iK6uwwrgjx57gyLEn5Y7zvAxnzkXgfmAitv0zBra2JiCEYNuOh9i85R6ySy8AANC+nRu+WNID7m7VK2JKSnicOPUUBw8/RnJKHuxsTTBsSCMMHeyntqv3m0zkswx8vOQUYl4RAi/3C7p2IxZfLD+Hn1cPVDpPUFCSRsIFgMYFGwFgyPCtGDTQDxPGNYOriwX8fO3g52uHGdPbYNXqK9h/METpVhLPE5w7H4llX/QqV+q/DIZhYGlgj2JO2DM8Xb9DHmxbWruqwdwJMKvvXm6cVWMfZAeFKb/BYlmY+9U3qK0U9dAcrjccgZkequZqcJPEiowgsrOG54TBKr0ujICD+2j/WhOh72BvCpmexd2zqEzFv3fseqi0BYEyeJ4gLS0ff/59FwDw+4YA/LL2RjnhAgD37j/H1Bn7kZikWbdffZCXV4IZcw5i5bcXEfI4GSkp+Qh9korvf7yCt6btQ0YGLdSXm1uMQ0ce4+8t93D0+BPk55cgJSUPM+YcRFyc6qw2mYzg0uUoRD7LUDrGUGX6M7OKsH3nQwwduQ179gWXe4znZWr7CEl5GXLzilWOMRTP/juEY40HI3z9DmQFhSHr4RM8+ekfHPMbgPjD5WPffOaMV+0ZlsngPqIvHnz6A66OeR8Bs75EzO4TSL/7CAXPk5WfR9ErVLy84Vg2aQBTL3f1A1/i5SaPDMeCE4nkHaZVjPecOBSZgaEoTs+CTYcWlQsehgHDcWjy2Ryt7DEkQwb56RzDrIxrN2IU/z587IlO1Up5Xu6ZiY7Jwpb/7isdk5tbjL//uaezrdryw89XFbEQZdsQRF6oGLFxWfjq6wvVZkttgxCCLVsfoO/ALVj57SX88edtLF95AX0HbsGylReQl1ei0XuB4xicvxCp9PEGPrYVeqrqm//9cAU3b8Uqfnd0MFNru1DIwsJcZFjDKiHz4RPcmrYEkMnKiRLC85BJeVwb+wHyouMVx+27tIHP3Iq9lMowsrdG4Kc/IvSHvxG3/zQi/9qL6xM+xOl2Y3DIrTvO934b6XcfGfQ5Uah4eeNhGAYeo/trNphl4dS/C7xnjIFl0wawbtUIjT+bg6HhZ9B85QeVz89x4IzFSDx9BWc7T8DlIXOQEfCw8l0YQtDww2m1qmuqk5M5TEz0u9VRUvLiCzQrq0jneYqLpTh4KERlfADPExw7EVZuTUORmVmIE6fCVW4dXLsRq5eaOXWRHbuCsPb3m5BI5K9FmbetuFiK23fiNRaxDMOorOpsZSU2uEhgWQb//PdA8fvgQX5qu0oLOBbhEZrVV9InYWu3Ki8URwiIjCDij12KQwzDoM1vX8L3g7dhZGelOC60NAcjFKAkNVNxbmWkXLmDs10nIfVG5TcVFP1AY14oyA6JkHtO1H15ymTwGO0Pn9njKzzU5PO5ENlZ49GK31GUWJqFwDCwaOiF7JDwiumESnjy679ovGS21tV2DUX88xyN4wc0gWWBZk0dFb+LRQKN2gtUBsMAWdlFYBkGMhUxOSUlPHJyixXZS4YiOCRZ0cBSFYEPE6s9DqemKSqSYuOmO3qZSyqVKe2eHP88Gyu+uVhhC1HfyGQEd+89h0TCQyjk4OJsjmlTWpUTNK9SVCzFnPcOY+d/4+DhYWVQ+9LvPkLcgbOQ5hcg7sAZldtAhOcRveMYLBp6wdm/K5Iv3caDxatR+NIWkGl9VxSnZoKoKOL5Yj4ZCJHi9uxlGPToaI0GH7/OUPFCQca9YI3qu4jsrFFv8tByxyS5eUg8cx3S3HxYt2iI4TEXkXkvBNK8AohdHXG67WitbJEVlyB6+xH4vjtZ6Zicp1GI/GsvcsKiIDQ3hfuYAXAd0hOsFj1HNLZHm4paGsFgzKgmit+EQhYoVDFcCSzLoEsnD9jbm6rt+yLgWJiZGiEruwiXr0QhN68EHu6W6NzRAwKB/pyvmn5Jv4nf5QG345CXX1LleRgAxibybuavcuDQY3z3/eVqbZoolcoUxRbnv9sR2TnFOHCo8kwdQuRC+r/tgfjys546rUdkMiScuIxnWw6gID4ZJm6O8Jo2Cs4Du4PlOJRk5+La2A+QdPb6i6azGgiOgrhE3Jr+GSotOAMgPzpBq07SkMmQHRKOjLuPYNuuufrxFK2h4oUCVqQ+OJYRcOh1+m8ITEtrj8hkuPfRKoSv217ursbY1RFd962FU9/OeLblAPhC7a/MgUt+wtPftsF9VH80mDcRJm5OiseCv1mPoKVrXrST5zhEbz8Kq+Z+6HVmM4wd9VtG3NXVApaWYmRn67698zID/Rvg4OFQGBsL0Kt7fZ1zmBhGvj2wZ1+wyhRXlgX69vHCpr/vYNvOh5BIZIou3La2Jlj+RS907eKpoxXladrUEQIBWy47pjK7W7d00ct61UFuXjHOnotEckoebKyN0b+vD6ytVTfWfPgoCXv2BSMoKAlCIYse3evrrRknATBlUgtF1tbTp2l4/CQF8c9zsHlL9W5TuLtZlsscKqvKq6qKLs8TnDj1FF8s6aG1R0JaUIjLw+ch+dxNMBwHwvPIuMshbv8ZOPbthO6HN+Dq6AVIuRQAALrVrVImUHSsKJIbGUfFi4GgdV4ouPfhd3j62zYQXsmHnQGaLn0PzVe8rzh0fdIixOw8Vvl4lsXAe/sRf/Qigleul7cL0AWWAWtkhJbffQSv6aPw/Pgl3HxrceUmchxs2jVD/xu79O6m3fjXHfz5151Kv78YBjA3E6F1axdcuRqlUelzgYCFTEZ0vkNmGMDO1gTpGYUazdG1szuu3YirdB6GYbBx/XC0aeUCiZRHcnIehAIODg6mOv0dv1l1CYeOhFZqF8sCTRo7okM7N3ACFh07uKN5U8da61bftScIv/4mj1HhOLb0wszinamtMXd2u0rt/mvzXazfeFtRzRaQe8k4jtFLFlCZY2DyxOYICkrGo5Cay275ZFFXTBhX/sK86NOTuHQpSq0ov319rtZev4DZSxH5977KlRHLwnVoLzw/fF6rOQ1Nz5Ob4DKge02bUWegReqoeNGK3MhYHG86GLISSYXtI4ZjITA3xdDwM4qGiTlPo3HMz1/lnJbNfOH77iTceXeFznct5ewwEkJoaoKSrByV8/W7vhP2nVtXeb2XkUh4fLj4JG7cjJXHl5AXFyUzMyP8uW448gskmDHnoF7XrQyGATw9rBAXn62Xwnksy6BpEwe0a+uGvfuCkZMrj5Wo52mFd6a1wZBB2lUJLSyUYP7CY3gQmKjw8JT9zcq8Mhwnv+jzPEHTJg74afVA2NvVrlYJh4+GYsU3F5U+/u7c9pg5vW25Y9dvxmLBQiWCHi+2y5SJYIbRru+Ptn2C9M3Zk9Nga1M+juqXNdexY3eQyvemvZ0JTh+fptVaRakZOOjSTfWNkC5/RANiZGOJkQnXwGng2abI0eb6TbONKDD39kDPY39CYGIs//CzrPwHgJGVJXqf/adcp+egpb+qnTP70VM49O1ULq26KpASCUoys1UKF0bAIeH4Zb2s9zJCIYdffxyElcv7oEkTB1hYiODsZI7pU1tj744J8PW1Q8sWTmjfzlVtrYuqQgj0JlwAeeBl0KNkbN5yTyFcACAmNgvLVpzHxr9eBJmmpuYj8lkGcnOVB4MaGwvxx7phWPVNP7Rr6woPd0s0aeIAkYhTeGN4nijsD32SijnvHq6WbChNkUpl+G3dLZVjNm+5j/xXYli27QgEp+L1J0Tu6arsPUKI9tfcGr9GV/IWHD6skcr3JssyGDOqqdqpZVIp4o+cx8MvfkHQ8rWI+HO3eg+uhn9EsZPhO1QDQLMV71PhYkBozAsFAODUpxNGxF9B1H+HkHr9PhiWhWPvjqg3cbAizqWMnCfPNJpTkpGNpl/Mw6OvflM+iOMAZdtVWsIwDPjiqgdFVoZAwGLIID+lngiGYfDT9wPxxbKzuHItBiwrv0ipiv/QhrJtiLffaon/tgXqZc6XeVUTlv2+cdMdODuZ4eDhUDwMSlLY0rePNxa82wkuzhWzwoQCDv79GsC/n7xq8S9rb+Dx45RKt5J4niA6JgvnLkRi0ABf/T4pHXnwMBEZmapjtYqKpLh6LRqtWrmgpEQKWxtT3H+QAF7NNp61tRhmZqIKVXTrGo6OZpXG/njVt8G0t1thSyVZRxzHoJ6nNSaOVx0DknEvGFdGvIeC+CQwQoE8nVlPfdeEluZo/+fXuDLyPUDb4pNlhbZLGy9WgGUBQsAaCdF85fvwfU950gGl6lDxQlFgZGkOvwVT4LdgispxAnPNXPxiB1s0XfYeWKEAwd9uAF/wIujV2NURHuMGgmEYPPn5nyrZXYZMIoVNmybqBxoIU1Mj/PrTYEQ+y8DlK1EoLubh4GCCb/93Raf5hEIWhMi/9Du0d8eUSS1gbiYyiHhRBssCX319sZy3oKzUe8DteGz9ZwxcXVS7d0+eeqryos4wwOkz4bVGvLxcfE0Vny97UZm1bItMHenphSgurj1eJl1gGGDiuGZKvYwL3u0IJ0dzbP73HlJS5CUSjIw4DBnkhw/md6rQkPRl8mOe43zvqZDmyysxa5IppLHdHAunfp1xZcR72rutSrekmi57F8kXbiH1qry6tXXrxvCZMwEgBEVJaTB2tofH2AEwsn6zSgHUBFS8ULTGe8ZopF1XndkgtDSDqacrGIZBk8/nwnfBW0g4cQUlWTkw83KHY++OYDn5llLKtXvIuBtcdT+4gINVcz8UJqfpPetIG7y9bODtJd9mk0h5/LL2pta1YjiOwcTxzbFwQedyx3leBgcHU8VFwdCUvSSvXph5Xt5Ib+Gi42jfzh329qYY5N8ADg5mFeZQlyJMiDyrJzu7EA8eJiEvtxj29qZo3cpFkYb7KoEPE7F910ME3I4HkRG0aO6Ejh3cIRIJIBIJ0KWTB2x1rGsT9ChJ63O0Cb7OyzOMd1AXOI6Bk5M5fLxscPV6jMrnUeZ46NGtHiZNaKF8HMNg3JimGD2yMZ5FZUIi4eHhbqVStJQRtnYrpPmF+u+3xrIw862PjPuPNYrBY42E8hjAUkzcHNFuwwq4Du4JfLUAMqlU7mUR0l5dNQUN2KVojUwqxX6HTpBk5igd03L1YjRePFOj+WL3nsS1cQv1ZJ0cqxYN0fTLefAYM0Cv8+rCT79ex67dQWq3FMpgWQZisQB7d06As1PFbZl9B4Lx3fe6eXMMAccxiuvBO1NbY96c9opMnJjYLIyduEvl9hnLMrC0ECHzlWrD5uZGmD+vI8aOLh8jse9AMFZ9fwXsSxk9ldk0cnhjfPxhVxgZaRd3NentPVo1LKyNvOwJKstQEgpYSKSycoHDzZs54vtv/ZGeUYCpMw5AJpMpDShu1NAeE8Y1w0B/X3DKKtZWkQPOXVCUZJi/vbGrAwqfp6gdV++t4aj/9nA8+/cg+PxC2HdtA7+FUxU3WxTDQbONqHgxONlhz3Cm/VhIcvIqPOY1Yww6bPpG4xTY2ANncG30Av0aWFoxuNUPn6DRxzP0O7eWZGcX4e139iMhMUfpxbYsbVQqlcHWxhi//DgITZs4VjqWEIING2/j7y33wLLKL+Cv4upijucJhm/S+OH7nTFlckvk5RVj2OjtVWqBAABTJrfEB/M7gWUZPHuWgbGTdmmUwMYwQJ/e3lj9nerMuDIIIdi99xF+WXvDYM0Nq0rZVo2VpRjZOUUVXnuGAfr388F3K/shNTUfV67FoLBQAq/61ujYwR0RkRm4d/85CAFat3JBo4b2inOvXovGZ0vPoqBAAoGABSHywOpmTR3x64+D1Na3UQaRyZD16CkkuXkw9/aAsbNDhTHZjyMQ8ddePF37n+G63GtSRRyAibszCuISFckGRMrDopE3uh9aBwtf2k3akFDxQsVLtSDJzcOzLQcR+fc+SPMKYNXCD02WzNa6KNPJViOQGfREoy8WrWEYDIs8W6HtfXWTmVWIDRtv4+jxJ4qYhwY+tpg1oy2MxQLcuh0PGS9D8+ZO6N3TS+l2ycvExWfj8NFQ3L+fgMAg1VsdXbt44Ksve6PvwC36eDoqMTc3wqF9kzFj9kFE6ykw1cHeFDPfaYuIyHTsPxiiVbbV9n/HlrtIK2PN7zfx71bl5e1rGhtrYwwe6IvRo5rC1ESIVT9cwaXLUQoPi7GxEBPGNcO82e11rpxcWCjB6bMReBqeBpFIgB7d6qFFcyeda/FEbT+CoKVrkB9V2viQYeA6pCfarPkCZvXdQQjBwy9+weNVGxWFJ2ucSnLQGY6DyN4ag4OPQWRbeWsGStWh4oWKlzpDwfNkHHIzXBEnhmPR+NPZaPHthwZbQxsKCiRISsqF2FgIZyczvRRok0h5jJ2wC88TKvfsMAywacMIODmZY8iIrVVeTxN8vK0REZmp93ltbIyRkaF51WaOYzB+bDN8/GFXxTGel+HqtRgcOPwYz5/nwNpajE4dPLDujwC926tPvv+uP/r1Kd8WICUlD2FP0yAUcmjR3ElRebc2EPb7Ntxb8HWF4wzHwcjaAgPu7kfimeu4PXtpDVinAyyLFt9+iCZLZte0Ja8t2ly/acAupUaRFujQ2EcLiIwgOzTSoGtog4mJEF5eNuoHaoFQwGH92qGYt+Ao4uKz5TEoMgCMfJth2Re90LqVCyQSHmamRnrpsaMOQwgXAFoJF0Ae15GZ9eIciYTHok9P4dr1GEVcSHQMcP9Bor5N1Rssy8Da2hg9e1TcsnBwMKs0SLqmKU7PxINF/6v0McLzKMnKwcMvfpV3XlbST8iglEUfa4NMhuith6l4qSVQ8UKpUUzdnSEwM4E0r8Ag8zMsC4GZYbsp1wZcXCywb/cEXLkSjctXo1FcIoVfAzsMH9pIkXUjELAYNbIxtm4PrPZrhb5QVaVW2XgnxxdBz+v+CMD1GzEAXmQI1ba/xcvBthzLQGwswK8/DoRQTwUfq4PoncchU5HmTKQ8Ynaf0L11SBVgBAKIHWxQmPAieFfkYAuGY1GUmKry3JIs5UkKlOqFihdKjcKJRfCeORZha7capGQo4Xl4jFEfsCmTSsGwLBi27hadFgo49OntjT69vRXHMjML8fuGWzh0OBQZmYWwtBTDykqMzEz9NJqsbggpC1olGokOnicYPMAXl69GIyYmEzt3B+ksVqrDQdC4kT04jkV0dCZMTITw798A48c2qzTrrDaT9ywODMsq75cG1Ixw4Th4TR2JdhtXIOXSbRQ8T4bY0RZOvTvi6tgPkHD8kvK4G46FhR8N2K0tUPFCqXGafbUASeduIPtxhF6DdhmOg2UTH7gM7lnp40QmQ+Tm/Qhb8y+yg8MBloWzf1c0XjwDjr066s2OmiIpORfTZx5AalqB4k4+O7sILMuo7f5cW5EX7HPD/QeJKC6WqhUTRkYcps48gPz8kiqLD44z7N+MYYCB/r6YPFF5DZW6QPbjCDz7Z79K4VIGZ2oMPr+KW8cabgGVxdo0XfYuWI6DU59O5R5vMGe86saOvExekI5SK6i7t5mU1wYjS3P0v74LTT6bA85Ut3TMMhihAIxArslt2jRBrzObK63PQGQy3Jj8MW7P+hLZIRHygzIZks5cx/k+0xCxaU+V7KgNfP3tJaSlF1QoPCbvaC2DUKj6429qUnuCP8vgeYK3JrbE8UNTMHN6W6iLdy4p4RU9iKrqNTG02BOJBBg2pKFB1zA0xRlZONdzCiQ5qosoMhwHp76d4TN7PBhda8aUeklZI83ep/bd26L/rT0w9XCp9HHnAd3hOWEwKn1TMQxchvSEuwZeXEr1QD0vlFqB0MIMLb75EM1XfoDCxFREbT+Ch5/+qPkELAuPMf1h7OwAVmQE1yG9YN+1DRiGASEEabcCEbv7BEoyc2Dm7Q6BuRlidh2Xn/vSVa3sbvH23OVw7t8Fpp6uAOSBxYlnrqMwMRXFaZkwdraDuY8nHLq3q5VbTXHx2bgZEKf0cZkMkMlkaOhnV2lBNhtrMdavHYabAXFY8/tNQ5oKkYjDgP4NcOpMuMrS+SzLoHlTR7Rv5waWZdC+vRs2bb5rUNt0gWUZMAy0SudmWQa//DgI5uYiA1pmeCL/3ofi9Ez1HlSGQbOV78OqmS9Sr91Dxt1HmgfQsixEdlbwnDAYLgO74/EPfyPlgupGmgDgNqw3zL09VJjEoNO2H2DVoiGe/LIFxSnpAOTdoX0XTEHTL+bSQnW1CCpeKLUKhmVh4uqIJp/MQub9EMTtO61h0SoCu86t0fCDqeWOSvLycXXkAiSdu/7Cvaz4v/J9BIYBIjbtQfOvFyLs138R9NVaSCu5mzSt54oOm76BU9/OlcxSc4RpWCF2yuSWaOhrj517ghATkwUbG2N07uSB/n19IBIJ4OtrB08PS3z6xRlF4TZ1tb7kbWA06/VjZMRhw2/D0LKFMz7/tAeiY7LwNDwdv627gZTUAkX1XpmMoFFDO8x/t6PixlgoMGwHb11gWQY9u9fH/Hc7YO26W7h6LVqtiDEy4vDbL4PRrq1bNVlpOJ5t3q9WuDAch57HN8K+UysAQN9LW3HUt7/66relbzy7ji3Q/fB6Raf7/NgE9eKFZZEVFKbWfpbj0GTJbDRaNB05T6MBQmDuWw+cEe0OXdugdV4otRZJXj6ujlqApLPXNRo/JOxUhQqYF/zfQdIZzc5/FecB3eDYsz0Cl/ykfBAj/zLuc+E/OHRrq9M6huDylSh8uPik2nE/rPIvF+CrjOzsIhw9EYagoCSwLAM/Pzts2xGI7OziciKlrALsN1/1wakz4bhyLUblvFv+GoXmzZwqHOd5GW7cjMXFy1G4cTMWKakvhKOnpxU+fL8zrlyNwoFDoWptV0eZGBII2CpV1nV2Nsf2f8fCylIMAEhNy8e0GQeQnJKnVMh16+qJRQu7wsO9Zhr5EUKQeu0e8iJjIbSygHP/LhCY6LZ1G7R8LYJXrlM7TuzqgFHxVxW/8yUl2C1qpvokhoFFQy90+Osb2HVqVa4+UnF2DvZbtVN9uoCDz+zxaLduuVr7KDUHLVJHxctrAyEEqVfv4sGnPyL9VmClYxiOg8ugHuhxZAMAoCglHZF/70PCqatIvXJH98U1zctlWdh1aI7+N3brvpaeyc0rRr+BW1BSonwbRihkceb4NFiWXmy1JTEpF+s2BODMuQhFPEib1i6YO6sd2rR2BSEE3/94FXv2BYNlGMhK/45lqcDvzmmPme8oF3wPAhMx573DpTE6L14DbdOlVWFmZgRnJ3MMHeyHtLQCbNv5UKsmiy/btGhhlwoNCzOzCrFteyAOHHqM7JxiGBsL0L1rPfTt443mzZxgb6dZh3Z9kfXoKR4u/RU5T56ByGQoychGSXqW4nGBuSmaLXsPDRe9o1UBxdCfN+PBou/VD2QYmNZzRc8Tf8KyoVw0ayJeGI6DfdfWEFqao/B5Ckw8nOA1fTRcBvUAy3E41/MtpFy5o3LrqdeZzXDu10Xj50Spfqh4oeLltYMQgnsffIunv21VlBEv+79dl9boefxPGFmaI/7oBVwb+4G8xoQBUq9VMSzyHMy8arYNwcv8vOY6tu98qLTR3rjRTfHp4qpXN87LK0FqWj7MzUWwe6WTMyEEZ89HYtuOQASHpIBhgFYtnTFlciv06FZP5bwTp+xBeES6TmJCE0xNhTh7YjrEYvnueXJyHoaN3gaptPLmhMpgWQaNGtpj04YRirlehRACiUQeJK2PqsraIuN5XBv3AeIPnNVofPOvP0DTL9/VaGzeszgc8emnuZosLb/vt3AqWv+0BAzL4kzn8Ui/HaR2i5jhWBBeBobjQHgeTv26oPvh9Ui7+QAX+k6v1AZGwMGqqS8G3DtQK+PTKC+g4oWKl9eWzIdPEPnXXuQ9i4ORrRXqTRoC5/5dwbAsskMjcaLFMHmdhhp4W7f83yLYdWoFu86twApqPpxMIuWxdPl5nDkXAa60A3PZ/3v1rI9VX/fXuuNyVeB5GRiGUWwtqeJpeBomvGXYjK+vlvaukN1z5Vo0Fi85BZ5/4e3hOLmn6PNPe6CkhMeW/+4jNU1eVNFYLMCI4Y3x3twOMKmF2VllBMxeikgtMugYoQCjEq9p1Mfnwac/IPSHv3X6zLX8/mM0/mQW4g6cwVVlzVlVpUKzLHxmj0f7DV/h2b8HcXvWl5CVvs/AMCBSKaxaNESvU3/B2El9fytKzULFCxUvbyS35y1H5F97tW/upmG3WU0RO9ujxbcfwnv6aL3NqSuEEAQFJ+PosSdITc2HnZ0phgzyQ8sWujfbqw6uXIvGwkUnDDa/hYUIl85W3m08ISEHew+E4PrNWMh4Gdq0dsHY0U3h420LQC7ComOyIJXK4OFuWav6CVVGUVoGDjh21u49zjBot345fOZMQGFCCgjPw9jVsdJsm3O9piDl0m2dbDOytsTIxGvgREaKmJlyDRo1+GyyRkKMTLwGkY0VilIz8GzLAWQ9egqBiRhuI/vBuV8X6nGpI1DxQsXLG8kBl65qy3u/isDMFPXfGYXwtfpvWOgzZzw4sRgFcYkQOdnCuV9XuAzsDk5EMxfU8Sg4GVNn7DfY/I4Opjh5dKr6ga8Bkf/sR8A7n2t3koCDy8AeyH0SidxwedC12Nkefu+/jUaLpoPwMhCeB2dijEsDZyLx9DWd7etzaSsce7QHAKTdDkL4uu3IuBcCzlgEM28PxO5WL2K7H9kAt6G9dbaBUjugjRkpbySyEonW5zT5ci7Sbz00gDVAxMbyAbwR63dCYGGG1j98Ap/Z4w2y5utCk8YOcHExR0JCrtIxpTsDWoc2cRyDjh1qT2ySoZFkK/8bKkXKI+HohXIF24oSU/Hw858R8u0fkObJs7+E1hawbd+8Sr0T+KJixb/t2jeHXfvmit+jth3WSLxoVk6B8jpBfWmU1wbb9s3BaFFEymVwT/gumIJEHVOpdUGak4fbc5Yh/I+dWp2XGxmLx6s34eHSXxG19RCkhXWzN5GmsCyDDxeorp0ze2Y7eNWX1/rgOHnLAwCwszMBxzFKq+/KZAQTxjWv/MHXEPNXygdoxauChBCFcAEASWYOkk5fk4sXXbZmGAZWTRoofdiuY0v1c7AsbNupSbWmvHbQbSPKa0PCycu4NEhNu3qWgVVTX/gumAKv6aOQcS8EZzqM1W6h0nTP/Kh4nW0VWpphZOJ1CIxVpynzxSUImPUlorcdAcPKLxBEIoXQ0gwd/v4OHqNf73Llp06H4/ufriI7u0hxc29iIsT8eR0wYVxzEEJw914CAu7EQSYjaNHMCV27eOLSlSh89sVZEBBFkbiywNtlX/TC8KGNaviZVR8ynscBx87lUqINASsygqy4ROPxjKC0xMHhDSrHXRwwE0nnb1Qay8ZwHNxG9kW3vWu1tpdS+6AxL1S8vJEQQvDg4+/x5Od/AI4FSl3JZQGAbdZ+Cb8FU8qdk3rjPs52maj1WsbuziiMS6ySvWZebhBaWsCucyv4vjsJlo19Koy5PvljxOw8VvEOuHTPpPfZzXDq3anCea8TEgmP6zdikZSSBxtrY3Tr6gljsfog2di4bOzdH4ybt2JBCNC2jSvGjWkKby+barDa8BBCIM0vACcWqc1uSzx7HRf7v2NYgwTy6rQh36gWIwAAhoGJqyP639wNE7eKRQpfpjApFWe7TkJeVNyL4N1St5pFQy/0vbJNUW2XUreh4oWKlzcWQgjiD53Dk1+2IO1WIBiWhVPfzmi0aHqlnaJLsnNxwKkzZEUa3jGyLGzaNEHGnUd6s5kRcCC8DG1/XwrfdycrnkfIdxsQ9OUalbbYd26Ffld3AACkhUUoSkqF0MJMoxRXSt1EkpOH0J82I3zDThSnZoARcHAfMwBNPpsN6+aVN3YM/2Mn7sz7qtLH9NLZuZRuh9bh6oj31I6z69wK3Q78DmNHO43mLcnOReSmPYj8ay8Kk9Ng4uII71lj4T1zLIRm1Vvoj2I4qHih4oWiBbfnLUfkpj3qC2QJBXDs0wk5IREoqKLXRRl9r2yHQ7e2eLTydzxa/ptG5wwMOoLwddsR9e8hRfCjY5+OaPbVAjh0bQsZzyPx1FUknrkGWYkExs4OcOjZHjatG9Mv/jpGSVYOznabhJzQyHLvV0bAyXsGnfizgieuMDkNh9y6Ky8hoKqOipb0vb4Tt97+FHnP4lQG8A64dwA2rZvoZ1HKawPNNqJQtKDV6sXIuBuMjHsh8gOlX7oMx0JoaYFmK+bD1MMFBc+Tcfe9lQazgxFwePLTZph6OOPRV79rfN5F/xkoTslQdMQGgJRLt3G+5xS0Xf8Vnvz4tzzdtbSyaRmsyAgN5k1Ei28/VPSzKc7IgjSvAGJHO5rSrQJCCJLO3UDExl3IDn0GI2sL1Js0BPWnDIfQ3Mxg6wYtXYOc0GcVhDaR8iAyguvjP8SI51fKNRKM2nIARFWtFH3dvrIsrBp5o8nncxAw44tKh5SV+afChVJVqHihvPEIzc3Q98p2RP61F+F/7ER+9HMY2VjCa+pI+M5/C8ZO9pDk5eOgUxeDVu4lUh6J524g8p8DYFgGRE03YgAAy1QQLkBp6ijD4M7c5S/SXV/JKZYVlyBszX/IuBuMJl/Ow+NVfyLlsrzYmMDcFN4zxqDp0nchsrHSx9N7bZDxPG5N/wzRWw+/KKjGMEi78QChq/9Cn0tbYVZPvx2iJbl5eLpuB56u2678PSiToTgtE/GHzsFz3CDF4ZywKPn7ycDZxG7D+8DI2hJe00cjLyoeId9seNHKo7Scv1WLhui6jwbXUqoO3TaiUDQgcvM+BMz8QuO7VLcRfZF+JwiFz1O0WocTi+A+dgBidhyrIEgqUFZmX0/VgRmWBXlJ4DAcBzNvd/S/ufuNEzBEJkPi2et4fvg8pAVFsGrmi/pTR0BsZ4PQH//Gg09WV/peYAQcLBv7YGDgYb1VMC5Ky8C57m8hJzRS7VhGKEDjT2aixTcfKo7d/eAbhK/fob7y9EtB7tpi4u6MQQ8Pw8j6RXfs7NBIRP61F7nh0RBamMFj/CBFI0UKpTLothGFomdyI2LBCAQgEqnqgQwjv3isWgQAuDhgBpLO3dSskhrLwKKRN4ysLeRxCGowsrVCSWqmBtZrBnnFRsLzyIuMw6MVv6Ptmi/1tk5tpzApFZcGzkJmYCgYAQcQgBAZHn7+M9pv+hqhP/+jVMQSKY+soDAkX7wFaV4B8mMTIbKzhuuQnhCamSLtdhCe/LIFiSevgPA87Dq1gt/CqXAd1EOpPbfnLEPu02jNjJcRcGJRuUOmHi4qhQvDcXDs1QGFCSnIfhwh95bIiMbV/9xG90enzasgtCi/XWbZyButf1qimd0UipZQzwuFogGPV2/Cw89/VhvU2z9gb7kKoUnnbuBCv+larSV2skNRUprKMSb1XNFl+486pXlri8DUGKPTAipcFHVBJpEg/sgFpN8OAisQwKl/Fzh0b1dr+iwRmQyn2oxCVvBT7XtklcKwLFiREfjCIkVvHoGpMVyG9ELs7hPleveUbac0/mwOWn73UYW58uMScdizl1bblQMfHlZkHT1Z8y/uL/xOhbEMGI5Fv2s7YdO2KRJPXkH84fNIPHMNBfFJar16PnPGo92GFbXm9aPUbajnhULRMx5jBiBwyU/KB7AsbNs3KydcAPnFR1uKktLknhemkqZ0LAvWSIjuB9ch5fJtMEINvEFVRJpfiMKEFJh5Va2kftrtIFwd8S4KE1PBCAUAAUK++wPWrRqjx5ENaut9qIIvKUHc/jOI2XUcJRnZsPCrD+9Z42DXoYVW8ySevY7MwFDlAzQog09kMrlwARSvnzS/UFHm/mVRVLY1+HjVRjh0bwuXAd3LzZVx55HGwoXhODj166wQLjlhz3D/w1Uqz+HERuiy6xfF38l1SC+4DumFEy2HoyBW9XtXYGZChQulxqDihULRADMvd3hNH4VnWw5UFBSlF7TmKz8od1jG8wj68led1xQ72KIoOR1gSuNRpDxMXBzQadsPCPzkBySdu6H6wqbHbtkCM5MqnZ8XHY8LfaeBL5DXE3lZcGU9CsP5PlMxKOioThlOhclpuNBnGrJDwhUZVWm3AhH59z40eHcS2v6+rMIFlhCChOOX8HTddmQGhoIzFsNjjD8KElLk24NSJYLQQI5qhuMQtnZrBfHCcJqX3Lfr0gpddv2i+D38j11gOFZFijQD61aN4DasT4WHRHZWat8/xi4OVLhQagwqXigUDWm34SuAZfHs730KdzuRSCE0N0X7TV/DuV+XcuPTAx6iMEG7gF0FRO6B8b+7H2nX74MvLIJlMz+AENyc+ikKYhLUTuExZgBM67si9Pu/dLMBAFgWdh1bQOxgq/scAMLW/Ae+oKjSbTci5ZH7NBqxe0+i/lvDtZ772pj3kfPkmfyX0jiNsgt2+PodMPeth4YfvOggTQjBnbnLEfHnbsW2DQA8+XkLwMgfr24IzyPt5oMKx+26tFbvXWMYdN23Bu4j+5cTExn3QlRvfRGCrKCnlT5Ub/IwJJ+/pfxclkX9Kdq/VhSKvqDihULREM7ICB03fYOmX8xF3P4zkOTkwbyBJ9xH+1fao0inbr6vIDQzgd/7bwMAoncdx41Ji9Tf/TMMvGeORYc/v0ZReiZCf9isPvjylRowCghB02UVK6am332EtJuBYDh5BWOL0uZ/+bEJiNl1HMVpmTDxcEG9iYMhsrVWnz3FsojdIxcvhBCk3biP58cvQ1YigU2bJjBr4ImUiwEgUh627ZvDsXdHMAyD9DtBSL12T+VTC139Fxy6t0VeZByElubIexaPiD/lHb9ftonwfJW6I1eVyrJwRLbW8Jw4BNFbDyu1S+xgC1lRCYhUCkb4om0CJxapfT7K4pg8JwxG6Oq/kBsRU0EAMQIOIjtr+MydoMnTolAMAhUvFIqWmNVzQ6NF6vvEmPl4Vm0hloHYyR4AIC0oxO3ZSzU7jxAUJqQg48Fj3H1vhUZZIxZ+9ZETGinPrmEYECkPVmSE9htXwsW/m2Jc3rM4XBu/EBl3g1/UjyEEzoN6wMTdCZF/7gFT6pWS8TwefLQKLb9fDEl+gWoDZDKUZOWiMDkNV4a/i/SAhy9sKfM6MIx8+4znYd7AE90O/I6kszfKeU8qozAhBadaj3pxQFX34xoSLoyAg1P/Lni25QAKE1IgsreGJDsP4Rt3IS8i9qWBFcVIUWoGbkz+GIGf/YwG705EvUlDYeruDLfhfeRbiyrWdBvRt9LHBMZi9Ln4H66N/QCp1+6Vbl8xIDwPi0be6Lb/N9pPiFKj0GwjCsWAnO0+GWk3Hqiv2VIJVs39MOjhEQDAs/8O4dbUTzU7kWXh0L0t0m4GQiaRaBT30m79cth2bIm4fachycmDRSMv1Js8DEaW5ooxRWkZONF8GIpTM7TOxDF2d0bhcxXZKwwDE08X8AWFKEnPVvv3zgsAIAAAFkhJREFUYjgOQkszeE0fjbA1/+qcGVQpZV4oHbwwnLkJ+PwijdOMFTAMWCMhZCUl8vUry2rT1B6GQYN3J6H5yvdxrNEglKRnVtyuYxgwAg4DHxyCVZMGKqfLuB+C5Au3QGQy2HVuBfsubWisC8Ug1Ipso2+//RbHjx9HYGAgjIyMkJWVpfYcQgiWL1+OTZs2ISsrC126dMGGDRvQoIHqDxeFUltpt345znSeUBrvod0FtuX/Plb8Oy8iRvPMIpkM2cHhkEmkGgfsOvXvCnNvD9i0aqx0TPj6HfIAYm0vzACkefmqbSEEBdHPNZ6P8Dwk2bnIj3muX+ECgGEZOPl3R370c40Kw5WzS8qDFcoDfssJhlLhwRmLwBeXKP4WDMfKY2xkBLLi0uagytLxNRVShCB8/Q6wAgH6nN+CC/3eQVFSqtxDRYjcDrERuu5Zo1a4AIBN6ya0nD+l1qF5KLuWlJSUYOzYsZg3b57G56xevRpr167FH3/8gYCAAJiamsLf3x9FRUWGMpNCMShWTX3hH7AXrkN7lduuMPerr/I8hx7t4DLwReaJ0MpCbY0ZQO6REDvaojgtU2ORYWRrBXNvD8Xv0vwCRPy1F5dHvosTLYfjbPfJePDpD4j4c49OwgUAJJk5sGnX7EVVYD1AeBlSrt6DmZc7GD1WbSVSHvUmD0W/q9vlKd1aICssRreD6+TP9SWMbCzRYfN3GBF3Ga2+Xwynvp3h0KM9Gn08o1xVWr1BCJ6u2waRnTWGR51Hp/++h8f4gfAY44/WPy3BiPgrcB3SS//rUijVhMG3jbZs2YKFCxeq9bwQQuDi4oJFixbh44/ld5zZ2dlwdHTEli1bMGGCZsFhdNuIUlspSstAUWIqRHbWEDvZI2jZmhf9X3hZaT8jGey7tkGPYxvLbdnkxzzH4fp91N59m3l7wLZDc8TsOKaxXTZtm2LAnf0AgPQ7QbgwYCYkGdm6PUkV9Di2Ec/+PYi4vaf0NidnLEKjxTMR/PV6vcSrMBwHkb0NhkdfACcyQsiqjXj4+c9azSGytwYjFKAoIVWR6kyI3PnS+tcv4Df/LQAAX1SMuMPncWPCh6qm0x2GQbt1y9Bg3iTDzE+h6JlasW2kLVFRUUhKSkLfvi8CyCwtLdGhQwfcvHlTY/FCodRWxHY25YIcW3y9EPXfHoHIv/YiLzIORlbm8Jw4RJFJ8zKmnq7wnjUWkZv2Kr1IN/lyHuy7tMalQbM1tokRcLDr1AoAUJSSjgv93oEkJ0+HZ6cey8beKIhLrNBDqSqwQiGCV67Ty1xgAKGVOXqd+ktRb6bxktngjEUIWrpWvvWlAcUvtWx42VtGANxb8DUEJmKk33mEqH8Pgi8s1o/tlcBwLEqyDfNaUig1Ta0RL0lJSQAAR0fHcscdHR0Vj1VGcXExiotffAHk5OQYxkAKxQBYNKiHVt8v1mhsu9+XAQSI/GtvuTozAnNTtN+4Ep4TBuNky+Hy6ryahkfwMjQoTXmN2LRHLlwM4Iy1adcUxi6OSL/1UK/zSjQUFBpBAJ85E2DdoqHiEMMwaLhwGupNHoaDLl2rHl/DMrg9dzlAiN5jdV6FSHmY+3ioH0ih1EG0inlZsmSJPA1Sxc+TJ08MZWulrFq1CpaWloofd/eqlTCnUGorrFCIDn9+jeFR59Fq9WI0WTIbHf/9HqOSrqPexCHICY1EVlCYZkG6pZ6dNr9+DsvGPgCA+INnDZMqzDDouHmVYebWUwXhMh5//yeK0jIqHBfb28BtZL+qLyAjIBJplYSLRvE9DAMjWyu4Duut8zoUSm1GK8/LokWLMG3aNJVjvLy8dDLEyUne1yQ5ORnOzs6K48nJyWjZsqXS8z777DN89NGLhmY5OTlUwFBea0w9XSutM1OUkq7ZBAzg3L8LGn08A059OysOSws1D4wv11ywNLtGmben8ZLZsGrqCwCwbOYrL+NfVdHBMDCysURJelbV5nkVXobYPSfh++7kcocfr96EuH36i9XRFaGFGfpc/A8Zd4MR/scuZD58UjGIujQwvOPf34Iz0r7dAoVSF9BKvNjb28Pe3t4ghtSvXx9OTk44f/68Qqzk5OQgICBAZcaSSCSCSFT1brcUSl3H2NVR/SAAXXb+As/xgyoct23bDDlhUcpTdV+i2/7fILQ0ByvgYNGkAe7MW47YXaUdkwmRCxmZDI0/nYUW374ISG300XTcmv6Z2vlN67mCSHl5Z+NXYeSPF6dnVnxMD+SERZX7PXrHUQR++qNB1tIGRijAgHsHYO7jCZvWTeAzezzynsXhwZIfEX/gjCK+xq5jC7T4ZiEce3WsYYspFMNhsJiX2NhYZGRkIDY2FjzPIzAwEADg4+MDMzMzAEDDhg2xatUqjBw5EgzDYOHChfjmm2/QoEED1K9fH0uXLoWLiwtGjBhhKDMplNcGiwb1YNepJdJvBylNqxZamsFteMVGfADQ4L3JiPrvkOpFGMDEzRnOA7uDKy1FL5NI4DFmACRZuch5GgWBsRgO3duh0cczKnSirj91JNJuBSJi4+5KK+OKHGzBGYuQr6R3k4mnC/zmvwXzhl64MnSualt1xMjqRZYDIUSeyVSNbQPk9VhkL7xTLAtOJET3Q+th/krVZjMvd3TbswbFGVkoiE+CyMaqSt25KZS6gsHEy7Jly/Dvv/8qfm/VSp7RcPHiRfTs2RMAEBYWhuzsFymZn3zyCfLz8zF79mxkZWWha9euOHXqFMTiin1jKBRKRVr/8jnOdZ8sv85WktHT5tcvlPazsWvfHE2Xz0fwit+VL0CAgrhEHPbshYYfvA3PSUNwaeBsZIeEK8QIw3HIDomAiZsTmnxeXmAwDIN2G1bAdWhveUfn+yHgjMVwH90fPnMn4tlfexH6w9+VCwWWhayoBH4fvI34Q+e0+bNohfeMMYp/50fHv2j6WEUYjgXDcZCVSFQMYtD/5m4knr6K5IsBAMPAqXdHeM8cq7I5psjGCiIbK73YSaHUBWh7AArlNSP15gPcfXcFMgNDFcdM3J3R8vuPUW/iELXnxx06h+BvNyDzbrDqgQwDoYWZPONHiaen886fUW/CYABAVkg4Mu+HgDUyglPfThDZWpcbyxeX4IBjZ7UNLbvuXQMTNyec6TRe7XPRFtv2zeEfsFfxe/bjCBxvMrjK8xrZWsH33Ukw8XDB7VlfVj6IZVB/ygh02vK/Kq9HodRF6mSdFwqFoh/sO7XCwAeHkBn0BPnRzyGys4Zdx5ZgVDUkfAn3EX3hPqIvZFIpUi7dxoV+0ysfSIhqocEyeLxqI2zaNkXA9M/KdX9mhAL4zBqH1j9/pqipkh8dr1a4MEIBMu4Gw320P8wbeCI3Ilbj7RyG48CJjSAtKKw0uFhkZ4XuR/8od8y0nis4EzH4AtXBzJbN/JAd/FRhi7GzA5p9NR/1pgyHrLgEQgszxd9fml+AB4tXywsTcqwibdpz3CC037hSo+dCobzpUPFCobymWDdvCOvmDdUPVAIrECBq6+FymUVaISPICgrD2U4TUJJZvmIvkUgR/scuFCamotv+38CUNiZUCyFgRUZgGAZt1y3HpYEzQWQoL2BK08C9Z41D6tU7yI96DqGlGepPGQ6/D6Yi71kcHq1ch+TzN+XPUyyC9zuj0WzFggqdkgUmxvCeMRbh63dU3puKZSGyscSAu/vACgSQSaVghcLyRQaNy297N/xgKupNHIKorYeR9ywORjaW8JwwWKM+QxQKRQ7dNqJQKEo52XYUMu+FVG2Ssi7NSuh3YxfsO7UCIQTH/PzVelP6B+yFXfvmAICkczdwb+F38vTrUiwaeqH1z0vgMrCHSrNKsnIgyc2H2MFW4f2pdFx2Ls52m4ScxxHlAqEZAQeG49DzxJ9w6t1J5VoUCkU9dNuIQqHoBSNL86pl2jCMSuHCCDhE/XsQ9p1agWEYNPl8rtJUakbAwbZDC9i+1PTQqW9nDHp0FFkPn6DgeTKMne1h3apxhfYKlWFkZVEus0jpOEtz9L+2E6E//4Pw9TtQnJoBRsDBfcwANFkyu1xFXgqFUj1Q8UKhUJTiMW4gki/e0u1kNR4XQF7CvjAxVfF7/akjkfcsDsFfr1dsV5VlMVk2aYDuB36vIEwYhoF1y0awbtlINzs1QGhhhuZfLUCz5fPBFxSCFRmBFdCvTwqlpqCfPgqFopR6k4ci5Ns/UJiQUiHmg+FYsGIRZBIpIJO9iIspFRf2XVsjOyRCZRVcRsDB5KXiegzDoPnKD+A5cQgiNu1B7tMoCC3M4TFuIFyH9KxxwcAwDASmJjVqA4VCoTEvFApFDXnP4nBpyBzkhEaCEQgARh5wa+xsjx5H/wBnLEboj5sRu+cEpAVFMG/gCd/3JsNnzgQ8+uo3hP7wl9KieQDgf3svbNs1r8ZnRKFQaiPaXL+peKFQKGohMhmSzt1A0rkbILwMdp1bwW1Yb7DC8hlChJBy2zpFqRk41XokCpNSK2YsMQw8Jw1Bl201X3qfQqHUPFS8UPFCodQaCuKTEDDrSySevqYI/OVMxPBbMAXNv1lY41tBFAqldkCzjSgUSq3BxM0JvU7+hbyoOGQGPgErMoJDtzYQmpvVtGkUCqWOQsULhUKpFszqu8Osvrv6gRQKhaIGzeqFUygUCoVCodQSqHihUCgUCoVSp6DihUKhUCgUSp2CihcKhUKhUCh1CipeKBQKhUKh1CmoeKFQKBQKhVKnoOKFQqFQKBRKnYKKFwqFQqFQKHUKKl4oFAqFQqHUKV67CrtlrZpycnJq2BIKhUKhUCiaUnbd1qTl4msnXnJzcwEA7u60DDmFQqFQKHWN3NxcWFpaqhzz2nWVlslkSEhIgLm5ORiGqWlzkJOTA3d3d8TFxdEu17Uc+lrVLejrVbegr1fdoiZeL0IIcnNz4eLiApZVHdXy2nleWJaFm5tbTZtRAQsLC/qBrSPQ16puQV+vugV9veoW1f16qfO4lEEDdikUCoVCodQpqHihUCgUCoVSp6DixcCIRCIsX74cIpGopk2hqIG+VnUL+nrVLejrVbeo7a/XaxewS6FQKBQK5fWGel4oFAqFQqHUKah4oVAoFAqFUqeg4oVCoVAoFEqdgooXCoVCoVAodQoqXvTMt99+i86dO8PExARWVlYanUMIwbJly+Ds7AxjY2P07dsX4eHhhjWUAgDIyMjA5MmTYWFhASsrK8yYMQN5eXkqz+nZsycYhin3M3fu3Gqy+M1i3bp1qFevHsRiMTp06IDbt2+rHL937140bNgQYrEYzZo1w4kTJ6rJUgqg3eu1ZcuWCp8jsVhcjda+uVy5cgVDhw6Fi4sLGIbBoUOH1J5z6dIltG7dGiKRCD4+PtiyZYvB7VQFFS96pqSkBGPHjsW8efM0Pmf16tVYu3Yt/vjjDwQEBMDU1BT+/v4oKioyoKUUAJg8eTJCQkJw9uxZHDt2DFeuXMHs2bPVnjdr1iwkJiYqflavXl0N1r5Z7N69Gx999BGWL1+O+/fvo0WLFvD390dKSkql42/cuIGJEydixowZePDgAUaMGIERI0YgODi4mi1/M9H29QLk1Vtf/hzFxMRUo8VvLvn5+WjRogXWrVun0fioqCgMHjwYvXr1QmBgIBYuXIiZM2fi9OnTBrZUBYRiEP755x9iaWmpdpxMJiNOTk7khx9+UBzLysoiIpGI7Ny504AWUh4/fkwAkDt37iiOnTx5kjAMQ54/f670vB49epAPPvigGix8s2nfvj157733FL/zPE9cXFzIqlWrKh0/btw4Mnjw4HLHOnToQObMmWNQOylytH29NP2OpBgWAOTgwYMqx3zyySekSZMm5Y6NHz+e+Pv7G9Ay1VDPSw0TFRWFpKQk9O3bV3HM0tISHTp0wM2bN2vQstefmzdvwsrKCm3btlUc69u3L1iWRUBAgMpzt2/fDjs7OzRt2hSfffYZCgoKDG3uG0VJSQnu3btX7nPBsiz69u2r9HNx8+bNcuMBwN/fn36OqgFdXi8AyMvLg6enJ9zd3TF8+HCEhIRUh7kULamNn63XrjFjXSMpKQkA4OjoWO64o6Oj4jGKYUhKSoKDg0O5YwKBADY2Nir/9pMmTYKnpydcXFwQFBSETz/9FGFhYThw4IChTX5jSEtLA8/zlX4unjx5Uuk5SUlJ9HNUQ+jyevn5+WHz5s1o3rw5srOz8eOPP6Jz584ICQmplc1132SUfbZycnJQWFgIY2PjareJel40YMmSJRUCy179UfYBpVQ/hn69Zs+eDX9/fzRr1gyTJ0/Gf//9h4MHDyIyMlKPz4JCeb3p1KkT3n77bbRs2RI9evTAgQMHYG9vj40bN9a0aZQ6APW8aMCiRYswbdo0lWO8vLx0mtvJyQkAkJycDGdnZ8Xx5ORktGzZUqc533Q0fb2cnJwqBBNKpVJkZGQoXhdN6NChAwAgIiIC3t7eWttLqYidnR04jkNycnK548nJyUpfGycnJ63GU/SHLq/XqwiFQrRq1QoRERGGMJFSBZR9tiwsLGrE6wJQ8aIR9vb2sLe3N8jc9evXh5OTE86fP68QKzk5OQgICNAqY4nyAk1fr06dOiErKwv37t1DmzZtAAAXLlyATCZTCBJNCAwMBIBy4pNSNYyMjNCmTRucP38eI0aMAADIZDKcP38e8+fPr/ScTp064fz581i4cKHi2NmzZ9GpU6dqsPjNRpfX61V4nsejR48waNAgA1pK0YVOnTpVKDtQ45+tGgsVfk2JiYkhDx48ICtWrCBmZmbkwYMH5MGDByQ3N1cxxs/Pjxw4cEDx+//+9z9iZWVFDh8+TIKCgsjw4cNJ/fr1SWFhYU08hTeKAQMGkFatWpGAgABy7do10qBBAzJx4kTF4/Hx8cTPz48EBAQQQgiJiIggK1euJHfv3iVRUVHk8OHDxMvLi3Tv3r2mnsJry65du4hIJCJbtmwhjx8/JrNnzyZWVlYkKSmJEELIlClTyJIlSxTjr1+/TgQCAfnxxx9JaGgoWb58OREKheTRo0c19RTeKLR9vVasWEFOnz5NIiMjyb1798iECROIWCwmISEhNfUU3hhyc3MV1yYA5OeffyYPHjwgMTExhBBClixZQqZMmaIY/+zZM2JiYkIWL15MQkNDybp16wjHceTUqVM19RQIFS96ZurUqQRAhZ+LFy8qxgAg//zzj+J3mUxGli5dShwdHYlIJCJ9+vQhYWFh1W/8G0h6ejqZOHEiMTMzIxYWFmT69OnlhGZUVFS51y82NpZ0796d2NjYEJFIRHx8fMjixYtJdnZ2DT2D15vffvuNeHh4ECMjI9K+fXty69YtxWM9evQgU6dOLTd+z549xNfXlxgZGZEmTZqQ48ePV7PFbzbavF4LFy5UjHV0dCSDBg0i9+/frwGr3zwuXrxY6XWq7PWZOnUq6dGjR4VzWrZsSYyMjIiXl1e5a1hNwBBCSI24fCgUCoVCoVB0gGYbUSgUCoVCqVNQ8UKhUCgUCqVOQcULhUKhUCiUOgUVLxQKhUKhUOoUVLxQKBQKhUKpU1DxQqFQKBQKpU5BxQuFQqFQKJQ6BRUvFAqFQqFQ6hRUvFAoFAqFQqlTUPFCoVAoFAqlTkHFC4VCoVAolDoFFS8UCoVCoVDqFP8HGHy3EG5f1LYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Jty66H6cbJp"
      },
      "source": [
        "Nice! From the plot, can you guess what kind of model we might want to build?\n",
        "\n",
        "How about we try and build one to classify blue or red dots? As in, a model which is able to distinguish blue from red dots.\n",
        "\n",
        "> 🛠 **Practice:** Before pushing forward, you might want to spend 10 minutes playing around with the [TensorFlow Playground](https://playground.tensorflow.org/#activation=relu&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=2,2&seed=0.93799&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularization_hide=true&regularizationRate_hide=true&batchSize_hide=true). Try adjusting the different hyperparameters you see and click play to see a neural network train. I think you'll find the data very similar to what we've just created."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cv4fEHihOAq4"
      },
      "source": [
        "## Input and output shapes\n",
        "\n",
        "One of the most common issues you'll run into when building neural networks is shape mismatches.\n",
        "\n",
        "More specifically, the shape of the input data and the shape of the output data.\n",
        "\n",
        "In our case, we want to input `X` and get our model to predict `y`.\n",
        "\n",
        "So let's check out the shapes of `X` and `y`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lZxjjmsHNt_K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e4cb8f41-d1e8-4c11-929f-5d6705b2269a"
      },
      "source": [
        "# Check the shapes of our features and labels\n",
        "X.shape, y.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1000, 2), (1000,))"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5BSq58RogJMa"
      },
      "source": [
        "Hmm, where do these numbers come from?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SPr81SsKgHvZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "74e433d6-d7e3-4c36-9637-8855dcb7d99b"
      },
      "source": [
        "# Check how many samples we have\n",
        "len(X), len(y)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 1000)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XCBbrNaOgPWC"
      },
      "source": [
        "So we've got as many `X` values as we do `y` values, that makes sense.\n",
        "\n",
        "Let's check out one example of each."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hwrSEVubgZNb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a16c8162-6af2-4936-cc91-76c8262c15e5"
      },
      "source": [
        "# View the first example of features and labels\n",
        "X[0], y[0]"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0.75424625, 0.23148074]), 1)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OMeqY8JSgg4L"
      },
      "source": [
        "Alright, so we've got two `X` features which lead to one `y` value.\n",
        "\n",
        "This means our neural network input shape will has to accept a tensor with at least one dimension being two and output a tensor with at least one value.\n",
        "\n",
        "> 🤔 **Note:** `y` having a shape of (1000,) can seem confusing. However, this is because all `y` values are actually scalars (single values) and therefore don't have a dimension. For now, think of your output shape as being at least the same value as one example of `y` (in our case, the output from our neural network has to be at least one value)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vSjUbiCBN9fc"
      },
      "source": [
        "## Steps in modelling\n",
        "\n",
        "Now we know what data we have as well as the input and output shapes, let's see how we'd build a neural network to model it.\n",
        "\n",
        "In TensorFlow, there are typically 3 fundamental steps to creating and training a model.\n",
        "\n",
        "1. **Creating a model** - piece together the layers of a neural network yourself (using the [functional](https://www.tensorflow.org/guide/keras/functional) or [sequential API](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential)) or import a previously built model (known as transfer learning).\n",
        "2. **Compiling a model** - defining how a model's performance should be measured (loss/metrics) as well as defining how it should improve (optimizer).\n",
        "3. **Fitting a model** - letting the model try to find patterns in the data (how does `X` get to `y`).\n",
        "\n",
        "Let's see these in action using the Sequential API to build a model for our regression data. And then we'll step through each."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lt4pbcZ7OEif",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cb103763-1cab-4177-fa84-e0154c9fd420"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model using the Sequential API\n",
        "model_1 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model_1.compile(loss=tf.keras.losses.BinaryCrossentropy(), # binary since we are working with 2 clases (0 & 1)\n",
        "                optimizer=tf.keras.optimizers.SGD(),\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "# 3. Fit the model\n",
        "model_1.fit(X, y, epochs=5)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "32/32 [==============================] - 5s 3ms/step - loss: 5.9177 - accuracy: 0.4800\n",
            "Epoch 2/5\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 5.1146 - accuracy: 0.4620\n",
            "Epoch 3/5\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 4.6022 - accuracy: 0.4720\n",
            "Epoch 4/5\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 5/5\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7a25080c58d0>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oa8Tcv9ePRq6"
      },
      "source": [
        "Looking at the accuracy metric, our model performs poorly (50% accuracy on a binary classification problem is the equivalent of guessing), but what if we trained it for longer?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GqVVD_IqPHm5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77df3599-6992-4e9a-f251-661aceac9875"
      },
      "source": [
        "# Train our model for longer (more chances to look at the data)\n",
        "model_1.fit(X, y, epochs=200, verbose=0) # set verbose=0 to remove training updates\n",
        "model_1.evaluate(X, y)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32/32 [==============================] - 0s 2ms/step - loss: 7.7125 - accuracy: 0.5000\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[7.712474346160889, 0.5]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H-qaMmKrPegL"
      },
      "source": [
        "Even after 200 passes of the data, it's still performing as if it's guessing.\n",
        "\n",
        "What if we added an extra layer and trained for a little longer?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TED0ZkOuPklW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48b523dd-7f52-49d2-dbb7-7bae550fdc50"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model (same as model_1 but with an extra layer)\n",
        "model_2 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(1), # add an extra layer\n",
        "  tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model_2.compile(loss=tf.keras.losses.BinaryCrossentropy(),\n",
        "                optimizer=tf.keras.optimizers.SGD(),\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "# 3. Fit the model\n",
        "model_2.fit(X, y, epochs=100, verbose=0) # set verbose=0 to make the output print less"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7a24b4b323e0>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qFWWlhByirLa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "05ca8301-e6b8-47ab-8b21-325d708a15c2"
      },
      "source": [
        "# Evaluate the model\n",
        "model_2.evaluate(X, y)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32/32 [==============================] - 0s 2ms/step - loss: 0.6934 - accuracy: 0.5000\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6933949589729309, 0.5]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HC7vhAHKQQRY"
      },
      "source": [
        "Still not even as good as guessing (~50% accuracy)... hmm...?\n",
        "\n",
        "Let's remind ourselves of a couple more ways we can use to improve our models."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wyTs6QgijF6_"
      },
      "source": [
        "## Improving a model\n",
        "\n",
        "To improve our model, we can alter almost every part of the 3 steps we went through before.\n",
        "\n",
        "1. **Creating a model** - here you might want to add more layers, increase the number of hidden units (also called neurons) within each layer, change the activation functions of each layer.\n",
        "2. **Compiling a model** - you might want to choose a different optimization function (such as the [Adam](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam) optimizer, which is usually pretty good for many problems) or perhaps change the learning rate of the optimization function.\n",
        "3. **Fitting a model** - perhaps you could fit a model for more epochs (leave it training for longer).\n",
        "\n",
        "![various options you can use to improve a neural network model](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/02-improving-a-model-from-model-perspective.png)\n",
        "*There are many different ways to potentially improve a neural network. Some of the most common include: increasing the number of layers (making the network deeper), increasing the number of hidden units (making the network wider) and changing the learning rate. Because these values are all human-changeable, they're referred to as [hyperparameters](https://en.wikipedia.org/wiki/Hyperparameter_(machine_learning)) and the practice of trying to find the best hyperparameters is referred to as [hyperparameter tuning](https://en.wikipedia.org/wiki/Hyperparameter_optimization).*\n",
        "\n",
        "How about we try adding more neurons, an extra layer and our friend the Adam optimizer?\n",
        "\n",
        "Surely doing this will result in predictions better than guessing...\n",
        "\n",
        "> **Note:** The following message (below this one) can be ignored if you're running TensorFlow 2.8.0+, the error seems to have been fixed.\n",
        "\n",
        "> **Note:** If you're using TensorFlow 2.7.0+ (but not 2.8.0+) the original code from the following cells may have caused some errors. They've since been updated to fix those errors. You can see explanations on what happened at the following resources:\n",
        "* [Example Colab Notebook](https://colab.research.google.com/drive/1_dlrB_DJOBS9c9foYJs49I0YwN7LTakl?usp=sharing)\n",
        "* [TensorFlow for Deep Learning GitHub Discussion on TensorFlow 2.7.0 breaking changes](https://github.com/mrdbourke/tensorflow-deep-learning/discussions/278)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "46pebMB4Qeth",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46dd2baa-3b8b-4c4f-943a-048378588956"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model (this time 3 layers)\n",
        "model_3 = tf.keras.Sequential([\n",
        "  # Before TensorFlow 2.7.0\n",
        "  # tf.keras.layers.Dense(100), # add 100 dense neurons\n",
        "\n",
        "  # With TensorFlow 2.7.0\n",
        "  # tf.keras.layers.Dense(100, input_shape=(None, 1)), # add 100 dense neurons\n",
        "\n",
        "  ## After TensorFlow 2.8.0 ##\n",
        "  tf.keras.layers.Dense(100), # add 100 dense neurons\n",
        "  tf.keras.layers.Dense(10), # add another layer with 10 neurons\n",
        "  tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model_3.compile(loss=tf.keras.losses.BinaryCrossentropy(),\n",
        "                optimizer=tf.keras.optimizers.Adam(), # use Adam instead of SGD\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "# 3. Fit the model\n",
        "model_3.fit(X, y, epochs=100, verbose=1) # fit for 100 passes of the data"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "32/32 [==============================] - 2s 3ms/step - loss: 3.5433 - accuracy: 0.4520\n",
            "Epoch 2/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.0533 - accuracy: 0.4910\n",
            "Epoch 3/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7210 - accuracy: 0.5000\n",
            "Epoch 4/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6998 - accuracy: 0.5000\n",
            "Epoch 5/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6952 - accuracy: 0.4830\n",
            "Epoch 6/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6941 - accuracy: 0.4910\n",
            "Epoch 7/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6938 - accuracy: 0.4940\n",
            "Epoch 8/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6945 - accuracy: 0.4990\n",
            "Epoch 9/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6943 - accuracy: 0.4880\n",
            "Epoch 10/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6945 - accuracy: 0.4550\n",
            "Epoch 11/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6954 - accuracy: 0.4490\n",
            "Epoch 12/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6946 - accuracy: 0.4860\n",
            "Epoch 13/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6958 - accuracy: 0.4920\n",
            "Epoch 14/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6949 - accuracy: 0.5150\n",
            "Epoch 15/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6961 - accuracy: 0.4720\n",
            "Epoch 16/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6943 - accuracy: 0.4880\n",
            "Epoch 17/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6983 - accuracy: 0.4930\n",
            "Epoch 18/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6945 - accuracy: 0.4730\n",
            "Epoch 19/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6954 - accuracy: 0.5030\n",
            "Epoch 20/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6957 - accuracy: 0.4600\n",
            "Epoch 21/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6956 - accuracy: 0.4790\n",
            "Epoch 22/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6948 - accuracy: 0.4440\n",
            "Epoch 23/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6943 - accuracy: 0.4850\n",
            "Epoch 24/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6962 - accuracy: 0.4690\n",
            "Epoch 25/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6973 - accuracy: 0.5070\n",
            "Epoch 26/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6978 - accuracy: 0.4870\n",
            "Epoch 27/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6965 - accuracy: 0.5010\n",
            "Epoch 28/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6947 - accuracy: 0.4690\n",
            "Epoch 29/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6974 - accuracy: 0.4860\n",
            "Epoch 30/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6997 - accuracy: 0.4870\n",
            "Epoch 31/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6947 - accuracy: 0.5060\n",
            "Epoch 32/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6965 - accuracy: 0.4710\n",
            "Epoch 33/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6955 - accuracy: 0.4590\n",
            "Epoch 34/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6972 - accuracy: 0.4770\n",
            "Epoch 35/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6961 - accuracy: 0.5020\n",
            "Epoch 36/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6946 - accuracy: 0.4680\n",
            "Epoch 37/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6952 - accuracy: 0.4980\n",
            "Epoch 38/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6976 - accuracy: 0.4930\n",
            "Epoch 39/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6950 - accuracy: 0.4750\n",
            "Epoch 40/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6966 - accuracy: 0.4970\n",
            "Epoch 41/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6978 - accuracy: 0.4840\n",
            "Epoch 42/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6994 - accuracy: 0.4770\n",
            "Epoch 43/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6954 - accuracy: 0.5060\n",
            "Epoch 44/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6982 - accuracy: 0.4900\n",
            "Epoch 45/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6960 - accuracy: 0.5040\n",
            "Epoch 46/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6948 - accuracy: 0.4810\n",
            "Epoch 47/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6974 - accuracy: 0.5120\n",
            "Epoch 48/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6967 - accuracy: 0.4930\n",
            "Epoch 49/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6975 - accuracy: 0.4820\n",
            "Epoch 50/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6970 - accuracy: 0.4640\n",
            "Epoch 51/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6977 - accuracy: 0.4810\n",
            "Epoch 52/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6963 - accuracy: 0.5080\n",
            "Epoch 53/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6969 - accuracy: 0.5070\n",
            "Epoch 54/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6997 - accuracy: 0.5120\n",
            "Epoch 55/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6952 - accuracy: 0.5180\n",
            "Epoch 56/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6965 - accuracy: 0.4890\n",
            "Epoch 57/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6980 - accuracy: 0.4730\n",
            "Epoch 58/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6965 - accuracy: 0.5080\n",
            "Epoch 59/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7003 - accuracy: 0.4970\n",
            "Epoch 60/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7009 - accuracy: 0.4930\n",
            "Epoch 61/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6997 - accuracy: 0.4710\n",
            "Epoch 62/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6965 - accuracy: 0.4950\n",
            "Epoch 63/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6948 - accuracy: 0.4840\n",
            "Epoch 64/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6961 - accuracy: 0.4920\n",
            "Epoch 65/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6993 - accuracy: 0.4830\n",
            "Epoch 66/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6966 - accuracy: 0.4980\n",
            "Epoch 67/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6977 - accuracy: 0.4490\n",
            "Epoch 68/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6958 - accuracy: 0.5060\n",
            "Epoch 69/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6949 - accuracy: 0.5280\n",
            "Epoch 70/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6987 - accuracy: 0.4720\n",
            "Epoch 71/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6976 - accuracy: 0.4720\n",
            "Epoch 72/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6965 - accuracy: 0.5010\n",
            "Epoch 73/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6960 - accuracy: 0.4890\n",
            "Epoch 74/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6958 - accuracy: 0.5050\n",
            "Epoch 75/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6960 - accuracy: 0.5070\n",
            "Epoch 76/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6980 - accuracy: 0.4810\n",
            "Epoch 77/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6972 - accuracy: 0.4990\n",
            "Epoch 78/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6972 - accuracy: 0.4710\n",
            "Epoch 79/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7007 - accuracy: 0.5130\n",
            "Epoch 80/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6978 - accuracy: 0.5000\n",
            "Epoch 81/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6975 - accuracy: 0.5020\n",
            "Epoch 82/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6966 - accuracy: 0.4880\n",
            "Epoch 83/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7003 - accuracy: 0.4510\n",
            "Epoch 84/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6964 - accuracy: 0.5010\n",
            "Epoch 85/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6968 - accuracy: 0.4660\n",
            "Epoch 86/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7005 - accuracy: 0.4940\n",
            "Epoch 87/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6976 - accuracy: 0.4540\n",
            "Epoch 88/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6981 - accuracy: 0.4570\n",
            "Epoch 89/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6982 - accuracy: 0.4740\n",
            "Epoch 90/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6978 - accuracy: 0.4560\n",
            "Epoch 91/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6980 - accuracy: 0.4880\n",
            "Epoch 92/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6984 - accuracy: 0.4730\n",
            "Epoch 93/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6982 - accuracy: 0.4710\n",
            "Epoch 94/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7001 - accuracy: 0.4790\n",
            "Epoch 95/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6972 - accuracy: 0.4560\n",
            "Epoch 96/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6979 - accuracy: 0.4860\n",
            "Epoch 97/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6972 - accuracy: 0.4580\n",
            "Epoch 98/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6987 - accuracy: 0.4800\n",
            "Epoch 99/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6975 - accuracy: 0.5080\n",
            "Epoch 100/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6967 - accuracy: 0.4810\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7a249bd86620>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TJJL0YT7RVzv"
      },
      "source": [
        "Still!\n",
        "\n",
        "We've pulled out a few tricks but our model isn't even doing better than guessing.\n",
        "\n",
        "Let's make some visualizations to see what's happening.\n",
        "\n",
        "> 🔑 **Note:** Whenever your model is performing strangely or there's something going on with your data you're not quite sure of, remember these three words: **visualize, visualize, visualize**. Inspect your data, inspect your model, inpsect your model's predictions.\n",
        "\n",
        "To visualize our model's predictions we're going to create a function `plot_decision_boundary()` which:\n",
        "* Takes in a trained model, features (`X`) and labels (`y`).\n",
        "* Creates a [meshgrid](https://numpy.org/doc/stable/reference/generated/numpy.meshgrid.html) of the different `X` values.\n",
        "* Makes predictions across the meshgrid.\n",
        "* Plots the predictions as well as a line between the different zones (where each unique class falls).\n",
        "\n",
        "If this sounds confusing, let's see it in code and then see the output.\n",
        "\n",
        "> 🔑 **Note:** If you're ever unsure of what a function does, try unraveling it and writing it line by line for yourself to see what it does. Break it into small parts and see what each part outputs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MuwGjU_XR0aG"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "def plot_decision_boundary(model, X, y):\n",
        "  \"\"\"\n",
        "  Plots the decision boundary created by a model predicting on X.\n",
        "  This function has been adapted from two phenomenal resources:\n",
        "   1. CS231n - https://cs231n.github.io/neural-networks-case-study/\n",
        "   2. Made with ML basics - https://github.com/GokuMohandas/MadeWithML/blob/main/notebooks/08_Neural_Networks.ipynb\n",
        "  \"\"\"\n",
        "  # Define the axis boundaries of the plot and create a meshgrid\n",
        "  x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1\n",
        "  y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1\n",
        "  xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),\n",
        "                       np.linspace(y_min, y_max, 100))\n",
        "\n",
        "  # Create X values (we're going to predict on all of these)\n",
        "  x_in = np.c_[xx.ravel(), yy.ravel()] # stack 2D arrays together: https://numpy.org/devdocs/reference/generated/numpy.c_.html\n",
        "\n",
        "  # Make predictions using the trained model\n",
        "  y_pred = model.predict(x_in)\n",
        "\n",
        "  # Check for multi-class\n",
        "  if model.output_shape[-1] > 1: # checks the final dimension of the model's output shape, if this is > (greater than) 1, it's multi-class\n",
        "    print(\"doing multiclass classification...\")\n",
        "    # We have to reshape our predictions to get them ready for plotting\n",
        "    y_pred = np.argmax(y_pred, axis=1).reshape(xx.shape)\n",
        "  else:\n",
        "    print(\"doing binary classifcation...\")\n",
        "    y_pred = np.round(np.max(y_pred, axis=1)).reshape(xx.shape)\n",
        "\n",
        "  # Plot decision boundary\n",
        "  plt.contourf(xx, yy, y_pred, cmap=plt.cm.RdYlBu, alpha=0.7)\n",
        "  plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.RdYlBu)\n",
        "  plt.xlim(xx.min(), xx.max())\n",
        "  plt.ylim(yy.min(), yy.max())"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OgekXFM2clQW"
      },
      "source": [
        "Now we've got a function to plot our model's decision boundary (the cut off point its making between red and blue dots), let's try it out."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XIyCVjolTmy4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "5b7f3e29-e124-4756-8b6c-421ec1001b9a"
      },
      "source": [
        "# Check out the predictions our model is making\n",
        "plot_decision_boundary(model_3, X, y)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 0s 1ms/step\n",
            "doing binary classifcation...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XIzcQrknUJPK"
      },
      "source": [
        "Looks like our model is trying to draw a straight line through the data.\n",
        "\n",
        "What's wrong with doing this?\n",
        "\n",
        "The main issue is our data isn't separable by a straight line.\n",
        "\n",
        "In a regression problem, our model might work. In fact, let's try it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7d9O9subUshC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 841
        },
        "outputId": "eba7a4a5-7d79-49fe-b8e2-ca7092ccbc3e"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create some regression data\n",
        "X_regression = np.arange(0, 1000, 5)\n",
        "y_regression = np.arange(100, 1100, 5)\n",
        "\n",
        "# Split it into training and test sets\n",
        "X_reg_train = X_regression[:150]\n",
        "X_reg_test = X_regression[150:]\n",
        "y_reg_train = y_regression[:150]\n",
        "y_reg_test = y_regression[150:]\n",
        "\n",
        "# Fit our model to the data\n",
        "# Note: Before TensorFlow 2.7.0, this line would work\n",
        "# model_3.fit(X_reg_train, y_reg_train, epochs=100)\n",
        "\n",
        "# After TensorFlow 2.7.0, see here for more: https://github.com/mrdbourke/tensorflow-deep-learning/discussions/278\n",
        "model_3.fit(tf.expand_dims(X_reg_train, axis=-1),\n",
        "            y_reg_train,\n",
        "            epochs=100)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-18-2f683d96fa34>\u001b[0m in \u001b[0;36m<cell line: 19>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;31m# After TensorFlow 2.7.0, see here for more: https://github.com/mrdbourke/tensorflow-deep-learning/discussions/278\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m model_3.fit(tf.expand_dims(X_reg_train, axis=-1),\n\u001b[0m\u001b[1;32m     20\u001b[0m             \u001b[0my_reg_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m             epochs=100)\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\u001b[0m in \u001b[0;36mtf__train_function\u001b[0;34m(iterator)\u001b[0m\n\u001b[1;32m     13\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m                     \u001b[0mdo_return\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m                     \u001b[0mretval_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverted_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep_function\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfscope\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m                 \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m                     \u001b[0mdo_return\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: in user code:\n\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 1338, in train_function  *\n        return step_function(self, iterator)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 1322, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 1303, in run_step  **\n        outputs = model.train_step(data)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 1080, in train_step\n        y_pred = self(x, training=True)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/input_spec.py\", line 280, in assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Exception encountered when calling layer 'sequential_2' (type Sequential).\n    \n    Input 0 of layer \"dense_3\" is incompatible with the layer: expected axis -1 of input shape to have value 2, but received input with shape (None, 1)\n    \n    Call arguments received by layer 'sequential_2' (type Sequential):\n      • inputs=tf.Tensor(shape=(None, 1), dtype=int64)\n      • training=True\n      • mask=None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lFGjyziVn_Zh",
        "outputId": "7e606d8d-578a-468b-be6e-c8a1d32bba0a"
      },
      "source": [
        "model_3.summary()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_3 (Dense)             (None, 100)               300       \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 10)                1010      \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 1)                 11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1321 (5.16 KB)\n",
            "Trainable params: 1321 (5.16 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SMu37fayVEZI"
      },
      "source": [
        "Oh wait... we compiled our model for a binary classification problem.\n",
        "\n",
        "No trouble, we can recreate it for a regression problem."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OUJtmMPZVOZ9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8cd07774-2800-4e1d-a487-7800a69c2d18"
      },
      "source": [
        "# Setup random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Recreate the model\n",
        "model_3 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(100),\n",
        "  tf.keras.layers.Dense(10),\n",
        "  tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# Change the loss and metrics of our compiled model\n",
        "model_3.compile(loss=tf.keras.losses.mae, # change the loss function to be regression-specific\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=['mae']) # change the metric to be regression-specific\n",
        "\n",
        "# Fit the recompiled model\n",
        "model_3.fit(tf.expand_dims(X_reg_train, axis=-1),\n",
        "            y_reg_train,\n",
        "            epochs=100)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "5/5 [==============================] - 1s 5ms/step - loss: 447.6102 - mae: 447.6102\n",
            "Epoch 2/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 313.3108 - mae: 313.3108\n",
            "Epoch 3/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 182.3118 - mae: 182.3118\n",
            "Epoch 4/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 58.6317 - mae: 58.6317\n",
            "Epoch 5/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 83.0025 - mae: 83.0025\n",
            "Epoch 6/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 86.4671 - mae: 86.4671\n",
            "Epoch 7/100\n",
            "5/5 [==============================] - 0s 3ms/step - loss: 49.6151 - mae: 49.6151\n",
            "Epoch 8/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 57.7703 - mae: 57.7703\n",
            "Epoch 9/100\n",
            "5/5 [==============================] - 0s 3ms/step - loss: 50.1641 - mae: 50.1641\n",
            "Epoch 10/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 47.7698 - mae: 47.7698\n",
            "Epoch 11/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 48.6194 - mae: 48.6194\n",
            "Epoch 12/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 43.2044 - mae: 43.2044\n",
            "Epoch 13/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 42.6293 - mae: 42.6293\n",
            "Epoch 14/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 42.4557 - mae: 42.4557\n",
            "Epoch 15/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 41.9446 - mae: 41.9446\n",
            "Epoch 16/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 41.7290 - mae: 41.7290\n",
            "Epoch 17/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 41.5669 - mae: 41.5669\n",
            "Epoch 18/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 41.2955 - mae: 41.2955\n",
            "Epoch 19/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 41.1274 - mae: 41.1274\n",
            "Epoch 20/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 41.1121 - mae: 41.1121\n",
            "Epoch 21/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 41.1519 - mae: 41.1519\n",
            "Epoch 22/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 41.0565 - mae: 41.0565\n",
            "Epoch 23/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 41.1133 - mae: 41.1133\n",
            "Epoch 24/100\n",
            "5/5 [==============================] - 0s 60ms/step - loss: 41.0074 - mae: 41.0074\n",
            "Epoch 25/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 40.9870 - mae: 40.9870\n",
            "Epoch 26/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 41.0249 - mae: 41.0249\n",
            "Epoch 27/100\n",
            "5/5 [==============================] - 0s 3ms/step - loss: 40.8403 - mae: 40.8403\n",
            "Epoch 28/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 40.9965 - mae: 40.9965\n",
            "Epoch 29/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 41.0225 - mae: 41.0225\n",
            "Epoch 30/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 40.8058 - mae: 40.8058\n",
            "Epoch 31/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 41.3589 - mae: 41.3589\n",
            "Epoch 32/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 41.0084 - mae: 41.0084\n",
            "Epoch 33/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 41.0701 - mae: 41.0701\n",
            "Epoch 34/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 41.2035 - mae: 41.2035\n",
            "Epoch 35/100\n",
            "5/5 [==============================] - 0s 3ms/step - loss: 40.5885 - mae: 40.5885\n",
            "Epoch 36/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 41.0615 - mae: 41.0615\n",
            "Epoch 37/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 40.6438 - mae: 40.6438\n",
            "Epoch 38/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 40.3412 - mae: 40.3412\n",
            "Epoch 39/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 40.6498 - mae: 40.6498\n",
            "Epoch 40/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 40.4421 - mae: 40.4421\n",
            "Epoch 41/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 40.3558 - mae: 40.3558\n",
            "Epoch 42/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 40.3041 - mae: 40.3041\n",
            "Epoch 43/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 40.5277 - mae: 40.5277\n",
            "Epoch 44/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 40.1808 - mae: 40.1808\n",
            "Epoch 45/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 40.6292 - mae: 40.6292\n",
            "Epoch 46/100\n",
            "5/5 [==============================] - 0s 3ms/step - loss: 40.4382 - mae: 40.4382\n",
            "Epoch 47/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 40.1801 - mae: 40.1801\n",
            "Epoch 48/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 40.2386 - mae: 40.2386\n",
            "Epoch 49/100\n",
            "5/5 [==============================] - 0s 3ms/step - loss: 40.7914 - mae: 40.7914\n",
            "Epoch 50/100\n",
            "5/5 [==============================] - 0s 3ms/step - loss: 40.1259 - mae: 40.1259\n",
            "Epoch 51/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 40.4617 - mae: 40.4617\n",
            "Epoch 52/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 40.8686 - mae: 40.8686\n",
            "Epoch 53/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 41.0441 - mae: 41.0441\n",
            "Epoch 54/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 41.1022 - mae: 41.1022\n",
            "Epoch 55/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 42.1498 - mae: 42.1498\n",
            "Epoch 56/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 42.3732 - mae: 42.3732\n",
            "Epoch 57/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 40.9868 - mae: 40.9868\n",
            "Epoch 58/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 40.4022 - mae: 40.4022\n",
            "Epoch 59/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 41.1762 - mae: 41.1762\n",
            "Epoch 60/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 40.0280 - mae: 40.0280\n",
            "Epoch 61/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 39.4377 - mae: 39.4377\n",
            "Epoch 62/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 40.2227 - mae: 40.2227\n",
            "Epoch 63/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 39.7379 - mae: 39.7379\n",
            "Epoch 64/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 39.4851 - mae: 39.4851\n",
            "Epoch 65/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 39.8735 - mae: 39.8735\n",
            "Epoch 66/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 39.5498 - mae: 39.5498\n",
            "Epoch 67/100\n",
            "5/5 [==============================] - 0s 3ms/step - loss: 39.5944 - mae: 39.5944\n",
            "Epoch 68/100\n",
            "5/5 [==============================] - 0s 3ms/step - loss: 39.5087 - mae: 39.5087\n",
            "Epoch 69/100\n",
            "5/5 [==============================] - 0s 3ms/step - loss: 39.3758 - mae: 39.3758\n",
            "Epoch 70/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 39.8543 - mae: 39.8543\n",
            "Epoch 71/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 41.2924 - mae: 41.2924\n",
            "Epoch 72/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 39.0309 - mae: 39.0309\n",
            "Epoch 73/100\n",
            "5/5 [==============================] - 0s 3ms/step - loss: 39.7582 - mae: 39.7582\n",
            "Epoch 74/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 39.1621 - mae: 39.1621\n",
            "Epoch 75/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 39.9158 - mae: 39.9158\n",
            "Epoch 76/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 40.2419 - mae: 40.2419\n",
            "Epoch 77/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 38.9030 - mae: 38.9030\n",
            "Epoch 78/100\n",
            "5/5 [==============================] - 0s 3ms/step - loss: 39.5400 - mae: 39.5400\n",
            "Epoch 79/100\n",
            "5/5 [==============================] - 0s 3ms/step - loss: 39.2044 - mae: 39.2044\n",
            "Epoch 80/100\n",
            "5/5 [==============================] - 0s 3ms/step - loss: 38.7893 - mae: 38.7893\n",
            "Epoch 81/100\n",
            "5/5 [==============================] - 0s 3ms/step - loss: 38.8879 - mae: 38.8879\n",
            "Epoch 82/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 38.9441 - mae: 38.9441\n",
            "Epoch 83/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 38.6721 - mae: 38.6721\n",
            "Epoch 84/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 38.7601 - mae: 38.7601\n",
            "Epoch 85/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 39.0045 - mae: 39.0045\n",
            "Epoch 86/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 38.9378 - mae: 38.9378\n",
            "Epoch 87/100\n",
            "5/5 [==============================] - 0s 3ms/step - loss: 38.3988 - mae: 38.3988\n",
            "Epoch 88/100\n",
            "5/5 [==============================] - 0s 3ms/step - loss: 38.5840 - mae: 38.5840\n",
            "Epoch 89/100\n",
            "5/5 [==============================] - 0s 3ms/step - loss: 38.4868 - mae: 38.4868\n",
            "Epoch 90/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 38.3730 - mae: 38.3730\n",
            "Epoch 91/100\n",
            "5/5 [==============================] - 0s 3ms/step - loss: 38.2209 - mae: 38.2209\n",
            "Epoch 92/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 38.3540 - mae: 38.3540\n",
            "Epoch 93/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 38.6931 - mae: 38.6931\n",
            "Epoch 94/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 37.9931 - mae: 37.9931\n",
            "Epoch 95/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 38.0585 - mae: 38.0585\n",
            "Epoch 96/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 38.4031 - mae: 38.4031\n",
            "Epoch 97/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 38.0610 - mae: 38.0610\n",
            "Epoch 98/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 38.3810 - mae: 38.3810\n",
            "Epoch 99/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 38.4900 - mae: 38.4900\n",
            "Epoch 100/100\n",
            "5/5 [==============================] - 0s 4ms/step - loss: 37.9673 - mae: 37.9673\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7a24c5b179a0>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FYHvt7fRWBUL"
      },
      "source": [
        "Okay, it seems like our model is learning something (the `mae` value trends down with each epoch), let's plot its predictions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qUf5DL_fWMZA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "129785a2-7de7-4a0b-e488-ba7ff32d3cbb"
      },
      "source": [
        "# Make predictions with our trained model\n",
        "y_reg_preds = model_3.predict(X_reg_test)\n",
        "\n",
        "# Plot the model's predictions against our regression data\n",
        "plt.figure(figsize=(10, 7))\n",
        "plt.scatter(X_reg_train, y_reg_train, c='b', label='Training data')\n",
        "plt.scatter(X_reg_test, y_reg_test, c='g', label='Testing data')\n",
        "plt.scatter(X_reg_test, y_reg_preds.squeeze(), c='r', label='Predictions')\n",
        "plt.legend();"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 4ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V-LGK_jQYUcA"
      },
      "source": [
        "Okay, the predictions aren't perfect (if the predictions were perfect, the red would line up with the green), but they look better than complete guessing.\n",
        "\n",
        "So this means our model must be learning something...\n",
        "\n",
        "There must be something we're missing out on for our classification problem."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RZqShArhUh6d"
      },
      "source": [
        "## The missing piece: Non-linearity\n",
        "\n",
        "Okay, so we saw our neural network can model straight lines (with ability a little bit better than guessing).\n",
        "\n",
        "What about non-straight (non-linear) lines?\n",
        "\n",
        "If we're going to model our classification data (the red and blue circles), we're going to need some non-linear lines.\n",
        "\n",
        "> 🔨 **Practice:** Before we get to the next steps, I'd encourage you to play around with the [TensorFlow Playground](https://playground.tensorflow.org/#activation=linear&batchSize=1&dataset=circle&regDataset=reg-plane&learningRate=0.01&regularizationRate=0&noise=0&networkShape=1&seed=0.09561&showTestData=false&discretize=false&percTrainData=70&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularizationRate_hide=true&discretize_hide=true&regularization_hide=true&dataset_hide=true&noise_hide=true&batchSize_hide=true) (check out what the data has in common with our own classification data) for 10-minutes. In particular the tab which says \"activation\". Once you're done, come back.\n",
        "\n",
        "Did you try out the activation options? If so, what did you find?\n",
        "\n",
        "If you didn't, don't worry, let's see it in code.\n",
        "\n",
        "We're going to replicate the neural network you can see at this link: [TensorFlow Playground](https://playground.tensorflow.org/#activation=linear&batchSize=1&dataset=circle&regDataset=reg-plane&learningRate=0.01&regularizationRate=0&noise=0&networkShape=1&seed=0.09561&showTestData=false&discretize=false&percTrainData=70&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularizationRate_hide=true&discretize_hide=true&regularization_hide=true&dataset_hide=true&noise_hide=true&batchSize_hide=true).\n",
        "\n",
        "![simple neural net created with TensorFlow playground](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/02-tensorflow-playground-simple-net-linear-activation.png)\n",
        "*The neural network we're going to recreate with TensorFlow code. See it live at [TensorFlow Playground](https://playground.tensorflow.org/#activation=linear&batchSize=1&dataset=circle&regDataset=reg-plane&learningRate=0.01&regularizationRate=0&noise=0&networkShape=1&seed=0.09561&showTestData=false&discretize=false&percTrainData=70&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularizationRate_hide=true&discretize_hide=true&regularization_hide=true&dataset_hide=true&noise_hide=true&batchSize_hide=true).*\n",
        "\n",
        "The main change we'll add to models we've built before is the use of the `activation` keyword.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p54Sk5WeYue9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "925f9b9b-7a1e-4309-97c6-ea3bec2433ac"
      },
      "source": [
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create the model\n",
        "model_4 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(1, activation=tf.keras.activations.linear), # 1 hidden layer with linear activation\n",
        "  tf.keras.layers.Dense(1) # output layer\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model_4.compile(loss=tf.keras.losses.binary_crossentropy,\n",
        "                optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), # note: \"lr\" used to be what was used, now \"learning_rate\" is favoured\n",
        "                metrics=[\"accuracy\"])\n",
        "\n",
        "# Fit the model\n",
        "history = model_4.fit(X, y, epochs=100)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "32/32 [==============================] - 1s 3ms/step - loss: 4.2584 - accuracy: 0.5000\n",
            "Epoch 2/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 4.0332 - accuracy: 0.5000\n",
            "Epoch 3/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 3.8635 - accuracy: 0.4970\n",
            "Epoch 4/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 3.6277 - accuracy: 0.4670\n",
            "Epoch 5/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 3.3772 - accuracy: 0.4490\n",
            "Epoch 6/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 3.0635 - accuracy: 0.4460\n",
            "Epoch 7/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 2.7472 - accuracy: 0.4450\n",
            "Epoch 8/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 2.1925 - accuracy: 0.4470\n",
            "Epoch 9/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.1191 - accuracy: 0.4790\n",
            "Epoch 10/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.9511 - accuracy: 0.4900\n",
            "Epoch 11/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.9207 - accuracy: 0.4880\n",
            "Epoch 12/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8991 - accuracy: 0.4850\n",
            "Epoch 13/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8814 - accuracy: 0.4770\n",
            "Epoch 14/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8662 - accuracy: 0.4720\n",
            "Epoch 15/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8530 - accuracy: 0.4620\n",
            "Epoch 16/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8421 - accuracy: 0.4510\n",
            "Epoch 17/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8320 - accuracy: 0.4470\n",
            "Epoch 18/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8227 - accuracy: 0.4420\n",
            "Epoch 19/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8146 - accuracy: 0.4330\n",
            "Epoch 20/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8070 - accuracy: 0.4290\n",
            "Epoch 21/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8001 - accuracy: 0.4210\n",
            "Epoch 22/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7937 - accuracy: 0.4140\n",
            "Epoch 23/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7877 - accuracy: 0.4090\n",
            "Epoch 24/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7823 - accuracy: 0.4090\n",
            "Epoch 25/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7771 - accuracy: 0.4100\n",
            "Epoch 26/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7722 - accuracy: 0.4140\n",
            "Epoch 27/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7677 - accuracy: 0.4220\n",
            "Epoch 28/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7633 - accuracy: 0.4320\n",
            "Epoch 29/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7594 - accuracy: 0.4350\n",
            "Epoch 30/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7557 - accuracy: 0.4460\n",
            "Epoch 31/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7522 - accuracy: 0.4480\n",
            "Epoch 32/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7489 - accuracy: 0.4470\n",
            "Epoch 33/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7460 - accuracy: 0.4540\n",
            "Epoch 34/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7432 - accuracy: 0.4520\n",
            "Epoch 35/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7406 - accuracy: 0.4590\n",
            "Epoch 36/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7381 - accuracy: 0.4620\n",
            "Epoch 37/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7357 - accuracy: 0.4640\n",
            "Epoch 38/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7335 - accuracy: 0.4640\n",
            "Epoch 39/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7314 - accuracy: 0.4660\n",
            "Epoch 40/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7295 - accuracy: 0.4670\n",
            "Epoch 41/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7276 - accuracy: 0.4720\n",
            "Epoch 42/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7259 - accuracy: 0.4740\n",
            "Epoch 43/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7241 - accuracy: 0.4780\n",
            "Epoch 44/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7227 - accuracy: 0.4770\n",
            "Epoch 45/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7212 - accuracy: 0.4770\n",
            "Epoch 46/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7198 - accuracy: 0.4760\n",
            "Epoch 47/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7186 - accuracy: 0.4760\n",
            "Epoch 48/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7173 - accuracy: 0.4780\n",
            "Epoch 49/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7161 - accuracy: 0.4810\n",
            "Epoch 50/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7150 - accuracy: 0.4780\n",
            "Epoch 51/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7139 - accuracy: 0.4800\n",
            "Epoch 52/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7129 - accuracy: 0.4820\n",
            "Epoch 53/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7119 - accuracy: 0.4850\n",
            "Epoch 54/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7110 - accuracy: 0.4850\n",
            "Epoch 55/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7101 - accuracy: 0.4880\n",
            "Epoch 56/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7092 - accuracy: 0.4870\n",
            "Epoch 57/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7085 - accuracy: 0.4860\n",
            "Epoch 58/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7078 - accuracy: 0.4910\n",
            "Epoch 59/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7072 - accuracy: 0.4870\n",
            "Epoch 60/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7064 - accuracy: 0.4900\n",
            "Epoch 61/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7058 - accuracy: 0.4890\n",
            "Epoch 62/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7053 - accuracy: 0.4900\n",
            "Epoch 63/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7046 - accuracy: 0.4900\n",
            "Epoch 64/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7041 - accuracy: 0.4900\n",
            "Epoch 65/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7036 - accuracy: 0.4910\n",
            "Epoch 66/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7031 - accuracy: 0.4870\n",
            "Epoch 67/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7026 - accuracy: 0.4880\n",
            "Epoch 68/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7022 - accuracy: 0.4880\n",
            "Epoch 69/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7017 - accuracy: 0.4860\n",
            "Epoch 70/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7013 - accuracy: 0.4860\n",
            "Epoch 71/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7009 - accuracy: 0.4870\n",
            "Epoch 72/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7005 - accuracy: 0.4900\n",
            "Epoch 73/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7002 - accuracy: 0.4890\n",
            "Epoch 74/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6999 - accuracy: 0.4890\n",
            "Epoch 75/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6996 - accuracy: 0.4900\n",
            "Epoch 76/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6993 - accuracy: 0.4900\n",
            "Epoch 77/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6990 - accuracy: 0.4890\n",
            "Epoch 78/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6987 - accuracy: 0.4890\n",
            "Epoch 79/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6984 - accuracy: 0.4890\n",
            "Epoch 80/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6981 - accuracy: 0.4870\n",
            "Epoch 81/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6978 - accuracy: 0.4850\n",
            "Epoch 82/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6976 - accuracy: 0.4850\n",
            "Epoch 83/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6974 - accuracy: 0.4860\n",
            "Epoch 84/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6971 - accuracy: 0.4870\n",
            "Epoch 85/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6969 - accuracy: 0.4880\n",
            "Epoch 86/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6968 - accuracy: 0.4880\n",
            "Epoch 87/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6965 - accuracy: 0.4900\n",
            "Epoch 88/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6964 - accuracy: 0.4860\n",
            "Epoch 89/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6963 - accuracy: 0.4840\n",
            "Epoch 90/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6962 - accuracy: 0.4900\n",
            "Epoch 91/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6959 - accuracy: 0.4890\n",
            "Epoch 92/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6958 - accuracy: 0.4880\n",
            "Epoch 93/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6957 - accuracy: 0.4910\n",
            "Epoch 94/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6956 - accuracy: 0.4860\n",
            "Epoch 95/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6954 - accuracy: 0.4880\n",
            "Epoch 96/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6953 - accuracy: 0.4850\n",
            "Epoch 97/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6952 - accuracy: 0.4880\n",
            "Epoch 98/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6951 - accuracy: 0.4880\n",
            "Epoch 99/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6950 - accuracy: 0.4850\n",
            "Epoch 100/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6948 - accuracy: 0.4900\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TTNzKBvwf2uJ"
      },
      "source": [
        "Okay, our model performs a little worse than guessing.\n",
        "\n",
        "Let's remind ourselves what our data looks like."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RcwI7-s1f2a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "3c5b4ade-4df5-4d1e-8c59-41381f7265a1"
      },
      "source": [
        "# Check out our data\n",
        "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.RdYlBu);"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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aRqEVgSsPu6/QKsS30nn1b5Tv3tbqGNVo5PBb33Lq2z8wpWc6ZKMzqf/2OBq+82y+Y4a0dJaF9SbzapzlysgS+DWpR+LBExYFk6RTqNCvCx3m3wgMTjx0kvXdHiE7Icl8QNPMolCDltPeo/pjw5zxsQT/ERx5ft9RAbsCwe0kJymFox/8wMLKnfjbpR7zy7fl0OtfkRkTb9f1xz/7hc0DniF+56ESttQ2kt52OJtmMqG4Fb7FkG8unY5Oy6dZFC6appn7HBVWml9V0VSN/RM+oufef/GqXvBtXOflQedVv1oULlnx17jw12KHhYtn1Qr02vsvIb075Eu59qxagVYzPqHyiN4255D0Ovyb1LU5TtbpaPTRRAZHb6PTip+pO+kpm/2N7MLBDKej707h6IdT8x27OGspGVeirbd00DAXurPyPdaMJuK23kjHNqZnsL7Ho+QkpuQ1qwTyfg52PfEmsVv3OmS/QGAvd1SROoHgdpEVm8DqtiNIO3c57wGcFR3P8U+mEfHbP/TYNhuvajcevJqmkXLyHIbkVDxDK5KTmMzB3GZ6tyGGJXcLRNLp8KpeidQztuNLFDcXqj7cn3O//5sX31AYbf76jKCOLSyeT9x/jOSbUn0LoGmkX4wi/cIV+p1ZQ/zOg1ycvQxTRhble7Wn0uAeSFbquVzbe7RIDSAbffwi3jWq0HHxj2RExZB2NhKdtyd+4XWQZJn13cfYnKPKiD4WY3AKQ+/lSUivDoT06kBWdDzn/1xo9XtrE1nGu3olUs9csLuw3uE3viawZXhe9d7If1dhV4CPHdzcc+nCrKVkxyVYnFZSZE5+/hvl2jUr9roCwa0I8SIQAHuefof0C1cKCA/NpJIdn8j2kS/RY9tswPwwOPzG16ScPGceJMt4hVa4bXEsjb94FUmS0Pt4EtS1NUtq9rAtoCSJ1jM+wb9ZfS7OWY4pPaOA7ZKi4Ne0HpWG9rQ6VdqFK3bZmX4xCkmSKNu6MWVbN7brmlxb7eJ6LIzex4smX79GleE3PCseIUF4hNwIKk6/dNWuTJ7gYmTY1Jn4COf/WlS8ar5GE/XfGYfOw529494j80qM7WsUmZNfz8gTL8b0TKfF4nhWq5j396srt1j9bJrRRNTKLU5ZVyC4FSFeBP95Mq7EcGnhWosPfM1oIn77ARL2HeXM1L859+s/+QeoKmkRjlV2lhQFt6AAMqPji+6pkWV8alWlzvOP5Hku0iOjbL/pSxKVBnWj8rD7AOi2fgZbhj5H+oUr5joqmoZmUgnq2oq2f3+JbKPSq2tAGbvMtXfcrQS2DEd2dUHNzrE6Luylx/ELr0PFgd0sZtzkkmFHF2ZJUciKtW/LsDDK1K9Fx8VT2Tp0PMaMTHOAs2T+edL5eOEXXoes2ARST18oXADIMq4BvlQe2hPFxYWchER2Pf6G7YVNKrGb9+R96Rdeh4Rdh4rnAbpO4r7jJB46iV94HVSj0WbgtGY0oWmaVc+aQFAUhHgR/OdJPHjCLgGxttNITE5qFuhbtzretUO5vGBt0Zz5sowkyzT/8d18DwYXf9+81FZLSLKEX5N6eV/7N61P/4i1XF29lWt7jyK76Anp3ZEy9WvZZUrZdk1xK1+WLCu1XFz8fAttQmgPLmV8qP7oUM78NLvQfydJUQjp24nGH79o95yugba3gjRVxcWBLaPCCOnVgUFRWzj/1yKu7TmKpNcR0qs9Ffp1RtbrMWZksr7bI8TvOpRPCEg6BVmno/0/36G4mPs8VXmgLwde+tQcY2KDm38majw1nDM/zCrW58hFzclhfdfR9D21Ev+m9biyZINlr44s49e0nhAughJBBOwK/vPILnrbg8BpwiXs1Sfouedfgjq1RCui1yWgeX26bphRIBZF7+VJpaG9rFai1VSN0JED8h2TZJmQXh2o/8Yz1H35CbuFC5iDVRt/Yl04hH84oVjNFht/8QpBnc2fNS/u4npAbJnw2rT67SOH5vOuFUqZ8DpWA2JlvY5Kg7sXzeCb0Pt4UeuZh2j1+2RaTnufSoN75HXR1nm402XdDBp/9gqe1SqBJKHzdCd01CB6HViQr1ChzsOdDoum2gzGlnQKwd1vCEW/hnWoOe6hYn8OADTITkzm8JvfcPr7mda3o1SVOuNHAWDMyOTSgjWc/XkuV9dsQ7UWPCwQ2IFIlRb85zGkpTM/qA2mjKxSWa/G2BGU69iCcm2bsLRub7tjEmQXPe3/+RbvWlXxqV3N4riUU+dY2XwopozMQmNwwl58lMafvVKsz1AYZ3+Zx/4XPsaYkpYX/6PzdCf8o4nUfm5UsedXjUauLF7P2V/mkX7xCu7BZak2ZjCVh91XJGEUtXIzG3s/af6ikO9//TefoeF744trtkPYs8WSsPcoq1sPtxrE3H3rLMq2bZr39ZqODxG32YmZP5Jk/s+K+K7+xP00//FdTn/7B4ff/hZjSnreOY+KwTT/8V0q9OnkPJsEdz2izosQLwI7yIiK4cz3s0g+EUHKiYgbAbilhOLmSqWhPbk4d4U5pdhGTEL4h89T77Wxds2dePgkux59nWv7juYd03l7UveVJ6j32tgSc+UbM7O4sng9GVdicA8OpOKArnnl9e9ELs1fze4n3yQ7IelGxpaLntTO97E3tA1u7i507hhKrx41cXe3z0NXGkSv28GmfmNRc3LyBKqkU9BMKs2mvEmtZ254WlLOXGBpLetB186m4UcTqffqk5z8ajoHCitOeF38dFn1a5G3EwX3HkK8CPEisMG+5z/i1NczrI7JLdNuT7AoYI5D0evQcgwOZXdUe2woiqsLl/5ZhSEtA9VgyFdyXvFwo8Fb/yPs5SccFh2Jh06SfCICvZcHQV1a5SsFLzBjyskhatkm0s5fZt/pFH7Yq2JwccNk0vKSacqV9eSnHwZQpXKZ221uHhlXYjg7bQ5Ryzai5hgo274ZNZ9+oMCW35Xlm9jU50mb8ynenphS022Os4WkU6g17mEavvcc84PaYsq04NGUJfwa1uG+AwuLvabg3kCIFyFe/nOoJhNoGrLOcjyAMTOLC38t5si735F5JdbmnN41q9Ds+7c59e0f5iZ1NjwjkiLT5KvXOPDSp2hGo91p05IiMyByY14qr2o0cnX1NtIvXMY1wI+QPh3Re3naNZeg6GzddpHnJhbeBFZRJMqV9WLhvw+id2Zn61Igbts+1rR70Oa4Oi88ysmvphe7TpGkKFR9uD/B3VqzY+TLNsf3Ob4c37DqxVpTcG/gyPNbZBsJ7mqiVm3hxOe/ErN+J6gaZcLrUHPscHzCaiBJEn6Nw9B7e5GdkMi6zqNIOnLa7rlTz1zEo2IwtZ55kKilG62O1ft40frPT6nYvytl2zTm0OtfcXXVVrvW0TS4OHs5YRPNRdNknY4KvTvabafAOUz/cz+yLKEWkv5rMmlcjU5l46bzdO9a4zZYV3QCWobbzAZT3Fyp/+YzlO/Rlv0vfkKyA78nBdHwqlaRrNhrdtU+yrgcTeKhk1xduQXVYCCgRUOqjRqIi59vMWwQ3OsI8SIodQwpaZz7YyEXZy/DkJyKb72a1HxqOOU6tXRoW+TUt3+wb/yHSIqSl2aadOgke55+N2+M4u5GjSfvJ+3cJZKPRzhkp6RTOP/HQsI/mkilYb3MjfcKwb9lQ7pt+gudqysA3rWq4la+nM2U5bx1FJns2ASHbBM4l5wcE/sPXLU6RlEktu+IvOvEi6zT0fC98ex+wnKNmLCXHsPF15vyPdrR+1Bbkk9EcH76fC4tWGtXN/Cb0TSN0EcGc+63f+zyPu585FUyo2LN27RoXPx7GYde+5J2c76mQt/ODq0t+O8gxIugVEmNiGRtx4fJjLq+bXO9zH7knOXUeHI4zae+U6BbcWEkn4xg3wRzeqy1ni2mzCxOfftnkSqMakYT6ReukJOYzFVL1VglSDp4ksxL0XjXqIIpO4f13cZwbd9Ru7eNNKMJj0rlHbZP4DxMdm6VGI23uRN4Eanx+DAMKWkcmvQFqsGYF9wLEDbxERrc1MxRkiTK1K1B409fpvGnL2NITeP8HwvZO+59u9Zq+N54jn/0E2d/mm19oCwh63RkxZiF+82/x6bMbDYPGkfLXz4w1x/adwydhxuVhvSkxhP341YuwC5bYrfu5fSUmcRvP4Cs11GhX2dqjXsY7xpV7LpecOciYl4EpYamqiyr14fUsxcteiSaTXmLWv+zXZNi3/MfcXrKX06pGmoN9wpBGNMzMCSlWhwj6RRqPvMgzb55g4jf/mHXY687tIbsomfQ1a24+pcpprWCoqJpGoPun8WlyGSLRQMlCV56oT0jhhXeQPJuIPtaEhf/XkbGpau4lgugyoje+domWCJ2y17WdrD9exn+0UR03p7se9aG0JEkc40dawL/eruHfB5MSULv60XXtdPxb1rf6hJHP/iBw29+k+96SacgyTLtF3wvtmbvQETArhAvdyRXV29lQ8/HrI7xrBJC/3PrbHpf1nR4kLgt+6yOKU3cQ8ox6MoWVrUeTsLuww4FPTb+/BXCXnjUqfZkZRlJTc3G29sVN7fSc7BGRaWwYPEJIiKu4eamo3OnUDp1DL0rglzn/XuUyZ9uLvScJIGrq45Vy0bj7eVaypbdfjRNY2ntnqRGXLJQ5VgmsE1jum38i0WhXciItL4FlytMiors5kqLn9+nTN0a+DWuW2C7OWrVFjb2erzwiyUJ2dWFARfW4x4UWGQbBM5HBOwK7khiNuxC0umsFtdKvxjFhdnLuLxwLUmHT6H38aLK8N5Uf3RIvgA+xc2teA3vnIwpMxuAjEtRdgsXt/JlafjeeGo8PsxpdkRGJvHTr3tZs+YsRpOKTifTs3sNnny8OZUqlmwA5Oy5h/nsy61IkjnoVZYlVq4+Q5XKZfhxSn+CgryKPLemaZw4GcfJU/G4uMi0almZwADn1o8ZPLAue/dfYc3aiHyBu4oiIcsSn03uiYe7ns1bL3DseCw6RaZN60rUq2vbc3G3I0kSraZ/zLquo9EMpnxbPJKioPN0p/nUd0m/eMW2cIFiCRcANSubndczmXzqVKPpt29QvnvbvPOnvpqeV+qgAJqGmmMg4pd51H/96WLZIbh9CM+LoNQ48MpnnPxqer4aJpbI5yqWJVwD/em28c+8lMrT389k77Pv3xHiRVIUyrZvSrcNf7Ki6SASD5ywbJck4VuvBs2nvkNgq0ZWU7sd5fSZeB57agFZWUZMphvrK4qEu7ue6T8Pplo1/2KvYzCaiIxMRlU1qlQug4uLwuatF5jwwvJCxyuKRNUqfsyZORzZSjl+S5y/kMjrb63h5KkbTRJlWaJfn9qMH9eabTsiiYlJw8/Pna6dq+HjY70pozVUVWP5ytPM/ecIZ84m4Oqio0vnajz0QDjZ2UZefGUl0TFp6BQZDQ2TSaNReDCfTe5FgJPF1J3ItQPHOfzWN0Qt2wSahqTIVBrai4bvPUfG5WgOv/kN8dsPlK5R1wvedVr6IyH3mbeC5niG26yYHdyjLV1W/VYaFgrsRGwbCfFyR3J58To2D3imSNdKioJH5fL0O7MaWVEwpKSxqFpXchKS7J7DvWIQmZdjiu2yLox2/3xL5SE9OTXlL/Y994FVUdVq+sdUGz3IqesDPDR6LqdOJxSa6qvIEvXqlmP6r0OKPL/JpPLnzIP8NesQ1xIzAfD2duH+oQ3Yvfsyx07EFrp2Lt993Ze2rSs7tGZ0TCoPPDyXtPScfIIM8leoVxSzp0Snk3nisWY89khTp1YRjrqayvCHZpOZaSzwGRVFIjTUj5kzht0V22POIPtaEtkJSbiVC8DF15tzMxawc8wkJFmyO1DdqUgSXqEV6Xd2DZIkMcerEab0TKuXlO/Zjs4rfy0lAwX24MjzWzRmFJQaIX064VG5vDm12UE0k4n085eJWrYx75jiZmfsgSzhXasqvQ8uouVvH1G2TRM8q1WkXMfm+DWpVyR7bib0kUFUGtwDgGqPDMK7VtVC55QUhTLhdagyvHex1iuMk6fiOHEy3qJ4MKkah4/GMHTE37z4ykq2bL1gVWjciqZpvPP+er79fmeecAFITc3ht+n7OHIsxup8OkVm6zbHUm5VVePTz7eQnJJdQLiYbbqxQ2cyaWgaGAwqP/y4mxl/Ovftf9bsQ2RlFRQuuWufPXuNjZvOO3XNOxlX/zL41KyKi683WbEJ7HriDdC02yNcADSNtHOXiN++39wN3FaNGFkiqEur0rFNUCII8SIoNWRFoeOSH9H7et3oDAz5/m4NSa8jZsMuACJ+/5fMKzF2Xae4utBlze/EbNxN1PJNyC56yvdoR5MvJ9F1/QwCWoVft8MsOKx1ZL4ZnY8XLX7+gFa/fpT3lq/38qT75pn5uvqaJ5UI6duJrutn2C+6HODc+US7x23afJ7xLyxn/AvLyMmxna2laRp/zznMshWFFy6z13drz1q5GIwmXn5tFRs3X7D7mpv5+de9ZGQYLJ6Pjknl9bfW0HvAH/ToPZ1nnlvM1m0XLQqwFStPFyqgcpFlidVrzpKZaWDBouO88/563v9oA6tWn8FguLc7KJ/7/d/bJ1pu4fLi9Zz/YyGZl6OtjlPcXKn2aNG9kILbjwjYFZQqfg3r0OfYMs78OJuLs5ZiSE3DN6w6lYf3Zs/Yt21PcP1JGfHrP3avKel1bOr/NEmHTuZV/IzdvIezP86m1nOj6LrxT6JXbeXCzCVkx13Dq1olQh8dwtF3pxC9emvBG7Mio7jo6blzbqFlzd3KBdB5xS+knLlA3NZ9SJJEuY7N8QqtZLfNjuLubv+vsun6A3r7jki+/X4HLz7fzuLY5OQsXnh5BfsP2hGEaQWjSSWsTuGZHfEJGcyafYjFS06SlJyFj48rvj6uXIxMLvJ6mVlGtm6/SI9uBQvK/ThtN9N+zd9hOT4hg527LtO+XRU+/7gXen1+AZuWblkIgdlLdCUqhV59Z5CaloOimMXsgkUnKFfWkynf9KVGdftqk9xtJB05bY6dv92GYC5c6V2jis2t4dCRA3ALLH78l+D2IWJeBHcEmqqyuFpX0i9GWR2XG1syz6+Z1doreUgSioc7ala2xWJ2Tb97k9rjHi5w3JiewbYHX+DK4vV5c6FpeFQqT9s5X1G2dWPb65cS6ek5dO89naws28HQN+PqorBmxRi8vFwKnNM0jceeWsCRozFWvQ62kCRwc9OxetkjeHrmX+fylWTGPLGAxMRMh7ax7GHo4Lo89XiLfIG08xce54PJG61eN+rhRkx49obnLC0th4dGz+PSZctiSpalvOS3wj6HXi/z8Qc96NypmuMf5A5n15Nvmr0vJVxzyS7syUCUJCoP7Um7ud+Ujk0CuxExL4K7DkmWqTNxjPnmU9h5RcGjYjAVB3TN+9ouNA1TeobVKrwnPv3Z3NjxFi4tXEvUis0FbZLArWzpvrUZDCaOn4jl6LGYQrdDPD1dGPlQI4fnzc4xceRo4S72AwevcvBQdLGEiznN2Pzgvlm4ZGQY+HfBMUaN+Zdr1zKcLlwA/pl/nF79ZvDmu2tJS8vBZFL54cddNq+bO+8IGRkGzp2/xsuTVtGp+69WhQuYBYvJpFn8HAaDyguvrGTqT7sBszDcuOk8Y8ctolP3X+neezqTP93E+Qv2bf/dSVQc0NWqcJF05mD73L+XJPZU55ZkCcmJWX6C24P4FxTcMdQa9zAJe45w4a/F+Ws0yDJ6Hy86Lv0pL7VY7+1pV6aRR8VgMq/GWRUvGZeiST19Id8WUNz2/eaOuDe/xV3/e2ZULOu7PULfU6tQXAt6LJyJyaTy+4z9zPz7EMkp5loybm46WreqjK+PKzk5JqpWLUO7NlU4eMi618oSlh646zacQ1GkIosXFxeF9m2rULmSL0eOxnA1Oo0e3Wtw6VIyzz6/lJTrn6ckMZk0Vqw8w4ULSTz/XJt8wcaWyMo2MX/hMaZO201Ojsmpwurn3/bSsEEQm7Zc4J/5x/LVk5m/8DgLF5/gy89625WVFXkpmchLSZw/n8jBw1eJiUmjbFlP+vWpQ4d2VdHpSufdtHyv9vg2qEXKiYiCIkaS0FSNNjO/QM3J4cyPs0k+dga9rzdVR/TBtVwgZ6bOIm7zHqfZo/fxwpCabtEDo5lUyvdq77T1BLcHsW0kKFUMKWmYsnNwDShT6FtSakQkB176lJj1OzFmZKH39aLa6EGEvfRYXjVMeyr1grkIXLUxgznx6S82Xdr3HVqEX8M6eV9vHvQ/rizdYPW6NrO+oOoDfW3aUVQ0TeONt9eycvUZa2Vjro8tWs0+RZFYtXQ0/v4Fa5S8/9EGFi89WSTx4u/vzohhDZj2615MJhVFkTGZVGRZQlFkjEa1RLwt1hg9shEz/jxYqmsWRvlgL65GpxV6TpLA1UXHiiWj8PUtvF7NmbMJfPL55kIbSeaKocaNyvPdV33x8NA71XZLZF6NZUOvx0k6fOq6V8OceSS76Gk94xObGXa7n36HiF/mlvjWk6QouJXzp/+5dSUSOC8oHqLCruCO48rSDRyb/FNeASu34EBqjXuYsBcfy/NeXJi9jB0jXwLtRpM2Q2Iyp7+fSXDXVrhfL0B15N0pdq3ZYuo7SHo9x40/WR2n8/LI16hN0zSuLNto3RWuyFxZsqFY4uXosRgWLDrOxcgkfH3d6Nm9Jp073Silv2ffFVasOmN1jkIcQ3YjyxI9u9fMJ1wyswzIkoSrq47QUP8iCwzVpPHD9S0SuNHQ0GTSMFnxgpUUsiSxd9+VUl+3MCwJFzD/G2ZlG1my/BQPPxCed1xVNaJj0rgYmciLr6wkO7vw72Huv9fBQ1d5edJKvvu6r1Pr3VjCvXw57juwkKtrtnFl0TpMmdmUCa9N6KiBdvXsCnvxUS78tQhTZlaBAHlJUUCWzJW5i6N3JXAN9KPzmt+FcLkHEOJFUOKcmvKXuVHbTZ6WrOh4Dr/1DTHrdtBpxS+knj7PjodfLHDj0kwqmprD5kHj6Hd6FbKri10VPMu2b0bFAd1QTSY8q4SQcTm60HROSZGp/sT96Dzcb6ypqjarAGuqhpqdY9OOwlBVjU8+38y8f4/lbcvIssSGjeepWSOAH6f0x8/PnQULjxdr28YSuc+y6tX8eeXF9qiqxsLFJ/jjrwNEXjLHdoQ3CGbo4HrodDIGg2NpsLIskZ5RtO9NSaFqGomJWbRuWYkduy7dbnNssvy6eNE0jbn/HOXPmQeJumpHgPp1NA2277zE2HGL+fqL3ri7lbwHRpJlQnq2J6Sn41sy3tUr02XdDLYMHkfmlRiz90bT0EwmyrZvSr3Xx7JlyHOY0jOtbgFbwiWgDOEfTKDqQ/3Qexe9TYXgzkGIF0GJkh4Zxb7xH5q/uLXnj6oRs3E3Z6bOIvl4hMVgXTQNzWjk7E9zkPS2f2QlRSagRUPAXFum/fwprOsyCmN6Zr7utAD+zRsQ/v74fNfLioJvvRpmm6zs1/g1DrNpS2H8Pecw8/49BpAnTHLfmM+dv8ZLk1bxy48Dibyc7DThUq6cJ+5ueq4lZhIc5MXggXXp368Obq46xo5bxJ69+eNlDh+N5tCRaLp3rc6adREOreXmqiMj03pqcWkjSVC2rCevvNSBkWPmkZp6Z4mrWzl5Op5ly09x8PBV/l1wvMjz7Nl7hWfHLyU01A+DQSUkxJsK5X3w9HShSePyxWql4GwCWzRkwIX1RC3fxLW9R5Fd9IT07oh/k3oA9Dm8mFPf/cXFWUvISU5F5+WJa0AZNJOJ1LMXraZG5yQkEbNhJ64BZfCuFUqZhrXt9khpmkbctn2c+30+GZejcQ8uS9WH+xPctbVdAcKCkkHEvAhKlMNvf8uxD6daLmIlSXhVq4iaYyDjkvXCUmXCa5N66gKmLBuBnhK0+Ol9ajxxf96h9ItXOPn1DC7MXIwhJR2v0ArUGPsANZ8aUagL+ey0Oex+6i3LS+gUBkZuxL18Oeu23ILRqNK7/x/EJ2RYHTfrj2FM+WEXO3dfKnZsiKJIdO4YyqeTexU49/5HG1iw6ITV6z09XUhPt+9hHxjozvChDfnejqweR9DrzXEyxblbvfV6Zwb2D+NKVArffb+DtevPlXrcjSP4+7tz7ZrtAGN7uDkwOBe9XmbwwHo8/1wbXFzu3rYGe5/7gDM//m1Xz7RcvGuH0uTzV6jQt7PVcarBwPZRLxM5e3lev7XcP8v3bEf7Bd+jc79zBODdjkiVFtwxJB89g2btAaFppEVcQrUjUC87PgmTHVs1spsbVUbkDxD0rFKBpl+9xpDYnYzIOkLfEyupM360xb3vao8NpdKQnuYvbnpDk3QKSBKtfvvIYeEC5uaJtoSLLEvs2HmJ+3rVdMrD1WTSGNCvoJcoNjbNpnAB7BYuAM8/25aWLSo6ZJ81fH1ceWliO3ZueYoVi0cVeR53dx3lg71QVY0KIT58/GFPNq97HG/vks0WKw7XrmXirBf7wn6ODAaVuf8c4ZXXVnE3v8N6Va/k8FZS6ukLbOr/NJH/rrI67tAbXxM5ZwVAntc298+ra7azd9x7RbBY4AyEeBGUGKrJhOzmarP8v6zXU7ZDc6s1ICSdgnuFcnbViWjw5jPF3teWFYW2c76ixU/v4Vu3ep4NIb070m3zX4SOHOjwnHPmHeGJpxfaHCdJ5hTp7l1rUL2af1611qIgAW1bV6Z1q4Kpt79O31fkeS2xftM56tUtR7VQP4sdpHO14M3nc/8+8sFwZv0xjE8+6sH33/Rl9fJHeGB4QyRJokwZd1xdi7bTnZlp5Olnl9Bv0F95PYg8PPQMHVSvSJ2uS4tbd1qdjabBpi0X2LuvaGn2dwJVH+rneH8yTQNNY88z76IaCt/iNKSmcXrKX5a3jlXV3IogOs5BiwXOQIgXgdOJ23GATQOeZo5rAy7OWmI9/VGSKNuxObWffdjqOE1VCe7e1q43rNCRA4pidgFkRaHGk8Ppc3QZI4zHGZFzjI6LplKuXTOH5/p7zmE++XwLmZm2Xdsmk0a9uuVwcVH46fv+NG4UUhTzAXNyxgvPt833gD52PIYHRs7Ni7txJunpOUiSxJuvd0ankwsIA1mWcHFRmPBsa8IbBucJmbphZfn4wx5MeK4NdWqXpXvXGrRuVTlfmX4XF4WO7atQHKKjU3nhlRV5AubhBxsRGOhRqECUJChX1rNY690NKIrE4qW2PXB3IqrBwNF3vy9SEC9AdmwCV1dtLfRc3PYDmDKyrF6vGU1Er9tRpLUFxUOIF4FTMKSlc2bqLJbU6cWaNiO4sni9fTcUTSNm7XaOT55G+EcTgfxVOHO3aVr+8iG1nx1pNUBOkmXKdmiGR8XgYn+eW5EVxa4AP1XVOHoshm07IvOqpWZmGpgydad968gSFSr40LKFuQ+Sv78HP33fn8CAgnVY7J3v5oaKp07H8/jYhZw+HV+k+ayhKBKhVc2Vh8MbBPPbtEG0aH5jC0mSoFWLikz/ZQijHm7Mrz8NYve2sezZPpY/fhtKj241rH6P4+LS2bHrcrFs1K7/77Mvt6KqGn5+7kz/ZTAtmuXf6nJ11THq4cY8OKJhkdcqToayIktUqVymWHPYi8mkERObXvILlQC7nnyT09/PtBqsaxVJstiSRM2xL+jc3nEC5yKyjQTFJj0yirWdRpJ+vugPlqgVm9H7etFty0xOffsnsRt3gyRRvmc76owfhX/T+gCEvfQ4xycXUrdFlkCWaXRdAN0Oli4/xRdfbc2rhAtQvrw3/XrXtsvjksv7b3fN57H44afdNuNkLKGqGmfO3BAq332/g5wcU4k00TOZNAb2vxFbUzesHD9824/4hAyuJWTgH+BRQIQpdnYUB5jzzxGH4m8soQFXo1M5eOgqTRqHEBzkzfff9uPylWROnY7HRa/QpHEInp4unI1I4Ovv7H+zvjm1PbdwoISEqmkFCgrm/r2wOdzd9Xz4XjcmvrScuPiMYgUq22OzNQ+Tqmqs2xDBL7/vIzo6FRcXHRVDvElOySYhIYMyfu4M6FuHoYPr4ePjRmJiJouWnGDP9bo6TRqHMLBfWL4eU84g+WQE56cvKN4kmoZroF/el+kXr5ARFYt7UCD+jevaVfkxoFn94tkgKBJCvAiKjCkrm6yERDYNeIaMS8XrOoyqcvHvZYR/+DztrTRMC/9gAoqrC8c/nmbOOpIADTxCgmj520eUbdu0eHYUkbn/HOHjz7YUOH71airTft1rd/VbTdNYvPQkjcLNvWAuX0nmt2LEpsiylBcnkpCQwfadJVvj5Icfd/Hpxz3zCu0BBBYiWorC8pWnrQYwS4CikzAa7XvSx8Xn9zZUrOBLxQq++Y7d2l3aGgEB7sydNZyHR/9DTGwaqno9tOK6VJQkCQ8PPaMeasS584lIMgQHeXHwUDQHrnftlmWJjh1CefaZVkjXP5UjwiVXPDlSH8hk0ujXt06h5wwGE4+PXciRozE3Hc0h4SYxnZqWww8/7ubfBcd59pmWvPfRRrKzjXl279x1mV9+28vnn9xnV9sDe7k4a2le5k9R0Xm6U6FvJxL2HuHAC58Qe1ObgoCWDSnbrinx2w8U6kWWdAoBLcMp06B2kdcXFB0hXgQOkx4ZxZF3v+fCzMVFLtRmiahlm6j1v4csnpdkmQZvj6P2hNFELduIITkVr+qVCeraGtnRoD0nkZVl5LMvC983z8XeB5CmwbIVp5jwbGt8fNxYsvQUslz0QnWqqtGxQyhAvgdOcQjwdyfBQgrv5q0X+OnnPYx7upVT1rqZ9DTrP2saUKtmIKMfbswrr6+2OV9goO14luAgL9zd9WTaUbfmo/e7s2nzBYsVdFVVIyPDQGaWkY/e757vXFxcOknJWZQt60kZXzdyckwMGjaLhGv2/5tJEvzwbX/C6pTFzU3HvH+P8tmXWwtNk775mho1Ajh+IpbY2DQ6dQzF2+tGBt7kTzffIlwKR9U0YmJTef3ttcCtlZ81cnJMTHxpBf/OGVFAIBaV7PjE4u3NAQ3eG0/SkdOs7TTSXMH3JhL2HAUJ3IMCyYqOR7spelpSZFwD/Wjz56fFWl9QdETMi8AhUiMiWdl0MOf/WOB04SLJMqZM6wFyubj4elP1wX7UfPpByvdod9uEC8CSZUXr/2MJg0Hl9JkEAKKuphR5HkWRCCnvTbcu5mwpP393G1fYZviw+haFC5gfWnPnHSUry/5tMnupXLmM1dRhc8yNH106V6NsoHVPT1CQF42ve7es4eqqY9CAMJvPyCGD69K8aUVWrTlrdayqaqxYdbrA8bJlPalZI4Ay1/sZrdsQwdXoVKs/V5Jk/sySZK7Z8t7bXWnerAJeXi7odDIPDG/IN1/0Jryh5RgwSYIzZxL4fupO3n5vPT16T2fGnwfQNLPQWrLspPUPnu+z5SXxFEDTQDWpzPvnqN3z2cKzSghYqh9liev/OIqHO40/f4U6zz/CnqffQTUYC9aiUlVzLI0sUf+dcXhUCkbSKbgFB1L3lSe57+AivEIrOenTCBxFeF4EeagmE1HLN3F5/hpyEpMxpGeSEXkVQ3IKnlUrUuOp4VyYtYScxJQiR/dbQzOZKNPwznHBZmcbWb7yNOvWR5BwLZPywV506Vyd7l2r50vZtefN1FFyY0EsNeezfq3ZU1M+2JsfvuuXV4CsbKAntWsFcqoIwbqtWlZk5IONiDifaPVNHiAtPYczZxNoUD/I4XWsMXRwPd79INbieZNJY/DAeiiKzIsT2/HKa5a9Ly9OaGt3ivRTTzRn997LRERcK/TBPLB/HV572dx3KyU126aXLT3dthdny9aLNr/PAPf1rEXNGgH061snT/jcTPt2VdHpFZ5/cRk5OQUf9LnOhNw/s7NNfDNlB3q9TM0agU4V5SZVY9PWCzw/vq1T5qs6cgCHXv/K8gBZxqdWVfwahZlrwSARu3EXhuRUfGqH4lW9Mtf2HyPxoJVMK00j83IMZRrUZmDkJqfYLXAOQrwIAMiMjmNDz8dIOnwKFLnAG01W3DUSdh0qOQNkGc9KwQR3a1NyazjA1m0XeeX1VfkCbU+djmfj5gt89e02vv+mH3VqlwXgmgOufXvw9NQTVsc89309azJr9mG7r5VliT731aZ92yp07BCKTpffVfHaKx0Y/dh8u+dSFJlpP/QnvKHZSxFx7ppd15ZE0bPe99VixarT7Nl7pVCBMHhgXRqFm70M3bvWQPsQvvhqK3HxN/59ypX15MXn29H1ujfKHry9XPl92mD++Osg8+YfJSkpC0mCRuHlGT+uNQ0b3PBsVKvqx+nT8RYf+rIkUbVyGZtr5uSYbH4PNQ3efqOz1aDnY8djGDd+icMBvz9O28MDwxs4dpEdGB3sk2UNj5AgGrz7HIff+LrAOUlRcAkoQ+fVv+FRMZh9z33A6Sl/5cXIpJyI4NK/q/Gqbl8Mzv7nP6LSwG5Os11QfER7AAGaprGq+VASD50o8Zb0lpBc9HRaNo3yd4B4OXwkmjFPzLd6w/fxcWXBvAeJjU3ngZFznba2JMGohxtTsYIP6zeeIyPDQFx8Olevptp8AMmSRJ06gfw1fZjVcXPmHeaTz63H6AA0a1qBCc+2pm7YjUrCx47HMHLMv1avc3fXsXbFGNzdnd8MMCfHxLRf9zDv36N5/YnKlfVk5EONeGB4wwLeFJNJZe/+KOLj0ylb1pOmjUMcynC6FU3TSE834Oqm5AtKzuXgoas8+qT1DJh33+pCvz6FB8jm8stve/nx5z1WY1Wqhfox7+8HrM7zv+eW3DGNKBVFoke3Gnz43o14n0uXk1m46DgXIpPw9HCha5fqtGtT2e5/I03TiPh5LkfenUJm1HWvnCQR0qcjzb57E6+qFTn59XT2Pz+58Alk2e5KgIHtmlL90SG4lQvAr3EYHiHO9SwKHHt+C/EiIHr9DtZ3fcS5k8oSqBqKpzumdPv7s5RtZ+4gG9Krg3PtsRNV1biv/x/ExVmveyFJ8OwzrThyLIZNm887rRJq29aVOXM2nti4jLwMJQfur7z3dhf69rb+YASzCPn8y20cOmLuJ6XTSfTuVZvBA+uiKBKBAZ4EBRVepXjkmH84eSquUO+CLEk8+EBDJjppa8ASOTkmLl1ORlEkKlX0LZYgcSaapvHJ51uYW0hshyRB61aV+frz3gU8YrcSF59On/5/YrQS0/H6qx0ZMqiexfNpaTl06PqL/cYXE3sy6n7/ZTDh1z1Vv8/Yz5SpO/MC0nO3O2vXCuT7b/ri729/hppqMnFt3zGMaen41ArNq/WkGo0srNSRrGgn1zWSZSoN6UHz79/Grax/3mFjZhapp84jKTI+daoh60u+m/e9hBAvQrw4xL7nP+L0lJkFou2LiltQAN61Q6k59gHKdWrBwood7X/6Xt+yajEtf2PF0mLOvCN88nnBlOfCcNHL5DjRDe7l6YKPjwsxsekWtx0a1A/iyNGYfILmerY43btVZ/L7PRwqd5+amk1Kajb+fu52e0qirqby+FMLiIlNy3tY5cZnNG9WgW+/7FPkMv73ApqmMfefo8z48wDRMebMI19fN0bc34BHRzexO/162YpTvPXuunzZZrkCoWuXanz8QQ+roi06JpXe/f8s/geyE39/d4YMqsfPv+7Nl6qd+/dxT7fk0UfMpQxWrT7DpDfXFDqPokjUq1uO338ebHfnZ0skHj7JinAbFbdzf4EcRZZwKeNDvTeepuqIPpz65g9OT52FMcX84uNaLoCwF8YQ9uJjovu0nQjxIsSLQ+x55h3O/jLPoa6shSJJ1J4wmqZfTsp3eO9zH3D6O8duopJOYeClTbgHly2eTQ4ycOhMIi8ll+qa9iJLErVrB/DMUy2Z/ucB9u03VwatXNmXh0aEX/ealM5NMjU1m4WLT7B0+SmSkjKpWNGXwQPr0qN7jUK3U/6LqKrGlagUVFUjJMS7SN+Xg4eu8sdfB9iy7SImk0b1an6MuL8hA/uH2fy3zs420qHrLxicKLCtIUnQskUlRj3ciL/nHM6LTWrWJIQHRzTM66+laRr3PziHc+cLD4DO5WYvTVFJ2HuEVc2HWrdbUYqfgGCp6qAEoaMG0er3ycUWYv8FHHl+/3dfjwR5+DUKc4rXJXTUABp/+lKB402+eAVDWjrnf59vDpgDsBFbo6ka536fT71JTxXbLnvJyjLeduFiLcNE1TROnIynYcNgfp46kJwcEyZVxc1VV+o3Rm9vV0Y+1IiRDzUq1XXvJmTZvKVVHBqFl6dReHk0TUNVNYfEqaurjv596/DvguPFssFeNA127rrE8KH1+eaLPhbHxcWl2wz8VhSJLVsvFFu8+NQORXF3s1qCQTOZKNe5JbEbdhV9IUsqTIPzMxZQbfRAgjo7v/bRfxnhyxJQ+f5eKO7FrAEiy7T8+QNkXUE9LOv1tP5tMn1OLKfepKeoNnqgzekkCZKPny2eTQ5yO7sLS5I5y8geDZLrjndxUXB304s3uv8AkiQVyav25OPN8SvjeLp9UZFliQWLC6YeHz8Ry6/T9/Hzr3vZtcd2GxFJkjDkFD95QO/tRfXHhlrsbC8pCl7VK9Nx0Q/o/UrGUy/pFM7+7LygfoEZ4Xn5D2PMzOLEZ79w+vtZmDLsD6q9FUmnUHFQd5vBab51qtPwvfGoBgPnfl9gPQ5GktF5FL+omiO4uChUC/Xj3PnEUl0XzC9unTpUY9mKUxbHSBKUD/bG18fV4hiB4GbKBnoy64/7+fDjjWzdHlni66mqxtWrqXlfJyRk8NKklRw8FI0smwvqmUyazeBeo1GlTh3nbBmHf/Q8cTsOkLj/ugfq+sKSoqDz9qD9v9+h9/ai46KprO000v74PDvRjCZSTl1w6pwCIV7+s5iystnQ41Hith/I/8t6U/Ca5KJHs9UxVZJAkqj36pNWh6WcOsepb//k0vzVmLJzcPHzIScxxeKNQjMaqTi4e6HnSpI0GyXonU1uMOPDD4Yz9snmbN5ynvQMQ6FbR5oGD45oKDwtAocICvLi26/6kpiYybkLiUhA/XrlWLHqDO9+sMGpa8mylNfHKifHxFP/W8TFyCQAmwX38uaQJLy9Xeja2f5aPNbQe3vRffNMzvw4mzNT/ybj4hV0Pl6EjhxAnQmj8axSAYBy7ZvRcfFUto98GUOiE7ePZRnXAOe0RBDcQIiX/yinvplB3Pb9BVvJ39yTxEoAr6TXoRmMuPj50vbvL/BvYjll8+rqrWzq/zSayXSjjsz1VOpC59YplGlQm/LdSzbd9lZOn44n1kaKtD24uCgYDCaLb5aSBH5l3KlUyZfQqn4MGVSXenXNNSO+/Ow+xk1YhtFoytseyo2D6dqlGsOHOb9wmOC/gZ+fO039bngzB/QL4+df9xJ1k6ekuKiqRv/rTR7Xro+w6MW09LuhKBKyLPPp5F55laGdgc7DnbCJYwibOMbquAp9OjH46lZOfjODI29+g2owFC0T6WZUFZ2XB0lHT+Nbr6Z4+XASQrzc42RcjubSwrUYU9PxrlWVCv06I+v1nJoy06J4yMOKXze4WxtCRw6g0uAeKK4uFsflJKeyZfCz5pvAzevdsrak14Fm9rj4NQqj07JppZ5e+O/CY06ZZ2D/MOb9a7mHi6bBm691ymuYeDNNm1Rgzl/38/fcI6xZe5asbCPVq/kzfFgDenavccfUMxHcGzhTIMiyRJ3agQCMf2EZ+w/Y12ne19eV5ORsXFwUenavwciHGlGjeoDT7HIUxdWFei8/QaVB3Tn20Y9cmLmk2JmYlxes5fKCtXiGViT8o4lUHWE5oFlgHyJV+h5FNRjY+9wHnJ02F9CQZBnNaMI1oAzNp73H1iHPFWt+t+BABl/dZnPcqe/+ZN/4Dy0LIQncypejYr/OKO5uVBzQlXIdW1h9O0lJyWL5qjOcP5+Ih4ee7l2r56sCW1TGPDGfQ4ejizXHgyMa8r+nW/LIY/M5d+4apltEmixLNG9WgSlf9xVCRHDb+fKbbfw9+3CBn1NHkSTo2L4qV6NTOXU6weFr/5o+lIuRSSxeeorY2DTKlfNiQN86dOlS7ban3ptycjCmphO9fifb7p/glDmbfDmJOs8/4pS57iVEnRchXtj52Guc+31+4aLBypaNI/Q/vw6vqhWtjtn20AtcnLPcZvfX4VlHrHpwclm6/CQfTN6EwWBCUWQ0TcNk0mjVshKfftQTLy/bc1jif+OXsGNn0Uqpu7vpePGFtgzqb94+S0nJ4uPPtrB67dm8vX69XmbQgLpMeLYNbm7C6Sm4/Vy6nMyQ4X9jMqoO7454eupp0jiEju2r0qplZV58ZQUnTxWvkm3uFmnun+ENgpnyTV88PYv+e+0sUk6fZ2ntXk6ZS1JkBkRuFC0GbkHUefmPkxoRybnfrPSfUTVzvRVVK1ZkfXZ8ok3xIikKEpLNG6NkR5ryjp2RvPXu+ryvjcYbtu/ec5lXXl/F99/0szlPLiaTej0Dwrx2ty7V7RYvkgSdO1WjWdMKBJXzpG3rKvlc8D4+bnz0fncmTmjL8ROxyJJEwwZB+PiUXtqqQGCLShV9+ezjXrw8aSUmk2Z3UO1z41rx0APhZGUZWbDwOKMf/YeEa0XPWMwld/3cP48ci+Hjzzbz/ju3vymiT61QPCqXJyPSvu0wa2ganJ++gHqvjXWCZf9NhHi5B4mcsxxJkdGseDuc0YAxt3+INcp3b8OFPxdZPC8pMmXbNrWrB8i0X/daLOKmqho7dl7ixMm4vI7MhZGTY+KnX/aweMkJEq5lotfLdO1cndEjG9OrR02m/bqX+HjL5flvZuJzbQgJsf52EBjgQYd2VW3OJRDcLjq2r8rifx9m/sLj7NpziWPHY63+/MuyRJ9etUm8lsljTy0gyo6moUVFVTVWrj7D+Gfb5GUx3U7KtmnMRSeIF0mWSD170aFrTDk5SJIk+iVdR2y634PkJCbbHewq27FVUxjle3e0q3R/5WH34RZcFkkpfN9aM6mEvfSYzXkSkzI5dDja6puhLMM/84/m88jczMmTcXTr9Tu/z9if95ZoMKisWnOGkWP+Yf+BKKb9MIAKVgRJbiG758e3tSlcBIK7haAgL55+qgXTfxnCc/9rbXGcLEn061ObsmU9efPddUTHpJWYcMnFZNI4eLD4gsEZlAkPM2+7OwG9r7fNMZqmcX7mYlY0GcQc1wbMdqnP6rYjuLSg8L5Q/yWE5+UexKtaJVQ7ouMlnULVh/pZ32IqBMXLgyafvWzfWDdXuqz+lXVdHyE7/nrapHZ928pootEnL1Khb2eb82Rm2v48qgoLFp1g3YZzVKlchgoVvAkJ9iEtPYcVq06Tmlp4DRdNM29BvfL6atYsf4R/5zzAth2RbNt+kWPHY7lwMYmMDHO9mzq1A3lsTFM6d6xm1+cXCO42Hn4wnPj4dP6cdQhFkdA08orLdWhfhVdf6sC589fYs/dKqdmkFTtf2TlUHtaLQ5O+KPY8mtFEleG9rY/RNPaN/9DcF+6ml9H4nYfYMngcDd55lgZvjyu2LXcrImD3HiQnKYV/g9ugZVsvMCfpdQR3b8vV5Zvsntunbg3azv4Svwa1HbMpOZXzMxZwaeFaTBlZ+DetR82nH6BM/Vr2XZ9jokvP3/JEREnx1uudGNi/br5jJpPKtWuZ6F0UyviKmBXBf4OLkUksWnKCq1dT8fNzp3evWtSvZw4wXbj4OO99uLFU7JAkWLZoJMFBtj0VpcGORydxfsaCIic9SIpMcLe2dFrxs9WsyqiVm9l43xNW5+q5ex4BzRsWyY47ERGw+x/HpYwPTb54lX3j3rc6TjMa8apawf6JZZmU42e58MdC/D57xTGbfL2p/dwoaj83yqHr8q53URjYP4zZc4/YHVToKLIsceJkPAP75z+elJTFnH+OsGjJSZKTsggM9GDwoLoMH9oAb29Rql9wb1KlchmLW0hyKdVgUhSJjh1CCwiX3BcKnV7Gr0zpthFp8eO7yDqFiF/+sd7j4GauVyJHVanQrwut//zUZrG609/PzPNQFzqlTuH0D3/T+vd7R7w4ghAv9yi1//cw0au3cWXx+sIHSBKKmwsZV2Ptn/R6ZtKJz3+jfM/2BHdr4wRL7eepx5uzY+clLkYmlYiA0TStQNGuK1EpjHliPteuZeatGR2Txo/T9rBk6Sl+mzaIgDsgkFAgKE2aNa1wcycRp5P7XA+t6scbkzrlHTcYTPw58yB/zz1CQkIGALVrBTJmdBN6dKtRQtbkR3FxoeW0D6j/xjNsuO9xUk6csypi9P6+1P7fQyge7lQc0BXfMPvaHiTuP241sUIzmri294jD9t8riIDde5g2f31GmfA6+fZLwZy+jASmzGyiljje20TSKZye8pezzLQbb29Xpv8ymIdGNMTT0/kR95pGgcyg199aS2JiZgGxpKoaUVdTmPzZZqfbIRDc6YSU96ZL52oodgav3tyx/WaHgyybvSsAiixRPtibgAAPatcK5NWXOjDjtyF5W7UGo4nnnl/G9z/uyhMuAKdOx/Pq66v5dfo+J3wy+/GsHEL915+2KlwkRabW0w/Q8L3x1Hv1SVzL+nHii9/YPvIldj3+OpcXrUU1Fh7Pp7jZ9uoq7v/dbWzhebmH0Xt70X3LTI5/+gtnpv5NTkISSBKBbRoTt2UvULSUac1oImGv5fL3JU2lSr507VyNlavPkpNT/JTvXKqF+tGsaUje12fOJnD4iOWKuyaTxsZN5/MqggoE/yXeer0z0TFpHDsem1fCILfRaMUKPvToXoNmTSpgMJr486+D7N0fBUDVKn48OKIh1ar5s2HjOTIyDIRW9aNv79r4FhJTlp1tZPbcI/w+Yz8pqdkW7fl+6i46dwylWqh/iX3mW6k0pCe+k38i5eS5AvdSSVHQl/Gm1v8eAuDinOXsGPUyqtGYt2UU8es/uJUvS8clPxLQtD5g9gDHbNiJ7GYjE1SWqTS4h/M/1F2CEC/3IFnx14j4eR4XZi8jO+4abkEB1HhyONWfHIZ7UFn2T5xM/I4Dxar1Yk81XGcz79+jfP7VVgwG57asB3NMzdQp/fPtQx87bntLTVU1Tp6OF+JF8J/D29uV334exIaN51my9CTxCRmUD/Zi4IC6tG1dOV/7i/Ztq2IwmFBVDVfXG4+dxuHlra6RnW3kf+OXcODgVbvCS56buIxqVf0ICvIirHZZKlbypUmjEHS6ktlkUFxd6LpuBlvvH0/spj1IigxIaCYTXjUq02H+FNzLlyNu+362PfhC3tb7zdlTWVfjWNVsCNWfGEbjLyaxc/QrXF6wxmJ5CTB7dHQ+XlR/fGi+47n5N/+F5o9CvNxjJB09zbrOo8hOSMpzZ2ZdjSPp4ElOfPoL9d98mpiNu4slXCRFodLg7k6y2D5WrjrD5E9Lbotm6nf9KBvome+YXm/fDU9fQjdGgeBOR69T6NGthl3xJnq94z2K/px50G7hAhAVlUpUVP4u2d7eLgwf1oCnHm9eIv3E3MoF0G3jX1w7cJzoNdvQjCYCWoUT1LlVnog4/ok5s8jax4j45R9iN+3JK16nmSzfo138fem84hfcAv1RTSbO/fYvp777k+SjZ5Bd9VTs35WwFx+9pzKRbkWkSt9DqEYji6t3J+NytNWy/y6BfuTk1lyxhiwXnEeWUFxd6HNsGV6hlYppsX1omkb/ITO5ciWlxNb46L3u9OpZM9+xuLh07uv/h9XgYDc3HWtXjMHDQ1S9FAiKS3JyFgnXMvAr446vrxs9+kznmhPaDgB4ebkw4dk2DB5Y1/ZgJ6KpKrNd6lsVI3YjSTT+4hVqPjUCnYc7qtHI1mHjubxo3fXFrntedApoGm1nf0Xloc7px1QaiFTp/yhXlm4kIzLK5ricBDuEC6DzcMOYngHS9bcVTUPn4U6HRT+UmnABOHMmoUSFCxReBKtsWU9696rF8pWnCxUwkgQj7m8ghItAUEzOX0hkytSdbNp8AVXVkCRo2iTEacIFIC0thw8mbyQ1NZvRIxs7bV5bqEajc4QLgKbhHhSIzsOcHn522hwuL1pbIO1LM5pAktj+8EsEdW6Ja4Cfc9a/gxDi5R4idvMe0Clga0tIkmzWJ5B0ClUf7o9fozBi1u8ETaNs+2aEjhqIix1lrZ1JanrhlXHtxdbHlYDwhoXvvU96pQPx8Rns3H0pLxgx98/uXWvw9FMtimWbQPBf52xEAo88Pp/sbGPeS4Kmwf4DJdMS4Pupu+jftw5+fqVTH0ZxccGrRmXSIi7ZXxfGCqen/k1gm8Z4Va3IqW/+AEtJ65qGajBwbvoCwl54tNjr3mkI8fIfRJIVNNVWuX0JzaRS86kR1HxqRKnYZYlKFXzs0VsWqVe3HMdPxBXqPVEUiXZtqxBSvnBB5u6mZ8o3fdm95zJLl58i4VoGwUFeDOgXRnjD4P9EYJxAUJJ89MkmsrKMhZYjKAlMqsqKVad5cER4icxfGLWfHcm+CR85Za74HQdY2XQIXTf9SerpCzZGS1zbd8wp695pCPFyDxHUsTmnvppuc5ymqiheHpjSMiyPMZko09CxFgAlRblyXrRrU4Ut2xzrwprLgyPC+XPmQU6cjMsTQbl/Vq3ix9tvdLF6vSxLtGpZiVYtS2+rTCD4L3AxMomDhyyXIygJZFkiOiatVNes+fQDRK3YzNWVW4o/mUnFkJzKvmc/sOlWlmQJ2eXe3NYWaRJ3CarJxJXlm9g3cTJ7x3/AxTnLMeXk304J6dMJzyohFma4gSRL1Bw7okDxuhsDzNV3Q0cOcIbpTuHFie2KdJ2bm44unarx27RBvPV6J+rXC6JcOU/qhpXjtVc68sfvQ0S/IoHgNnExMqnU11RVrdRbCsh6PR0XT6XBO89eT6cuHprJROzGXZRt19RqSrVmNFGhb6dir3cnUiri5fvvv6dq1aq4ubnRsmVLdu/ebXHs9OnTkSQp339ubv/th0vq2Yssq9ubTX2e5PSUvzgz9W+2jXieRZU7E7/7cN44Waej47Jp6MtYj9Ku/8YzNHxvPIGtwi1U35Vo/cenpR7bYo1KFX1p0dyBPkzXefaZVri4KLi66hjYvy4zfh3CyiWj+fP3oQwdXA93t3vzrUQguBvw8rSvXpSlbd2ioGnQpLH1+jIlgazX0+DtcfQ7t47yvTs6ZU41O8diMLCkKHiGVqTigK5OWetOo8TFy5w5c5g4cSJvv/02+/fvJzw8nJ49exIba7kAmI+PD1evXs377+LFom0X3AsY0tJZ12W0OdgL0AxGNIM5XiU7/hrruo4mZvNu4ncdJHbLXlzK+NDvzCpqjR+F4pFf9CnubvjWq0Hi4VNc/HspHRZPJfzD53GvYO4UiywR0rsD3bfMvCPT6x57pJlD44cOrscDw+/dOgcCwd1OwwbB+NsInHV1Vfhz+lDee7sLjRuVx83N8XoxtzJ23GJ2771c7HmKglflEDovm8bAS5to8fMHxZrr2v5j5nhduNF34foLqUfFILqs+R1Zf2++oJV4nZeWLVvSvHlzpkyZAoCqqlSqVIlnn32WV199tcD46dOnM2HCBJKSkoq03r1W5+XMj3+z55l37O+AJkHIfR1p9OlLpF+4QvzOg1w7cJyrK7YAmrmN+/X6LW5BgXRZ+zu+9WpiTM9AdtGjuJR85dyk5CwWLDzO6rVnycg0ULNGAMOG1KdFswpIksTRYzEsXHyCK1Ep+Pu5c1/PWuh0MlN+2Mnxk3EFP/It2751agcycUJbmjVx3FMjEAhKl3/mH+OjTzZZPP/k480Y+8SNrL6HRs/jRCH3AUeQJPOW8rJFo27rtrEhJY1/AloUq2jozfg3q49XtUpUHNCVSkN63pZK6MXhjqnzkpOTw759+5g0aVLeMVmW6datGzt27LB4XVpaGlWqVEFVVZo0acJHH31EvXr1Ch2bnZ1NdvaNfhcpKSVbD6S0iZy7AoupcIWhQdTKzUSt2Gw5kOt64bns+ETWdxtD/4g16L08Cx/rZM5GJPDkM4tITs7KMy8qKoX1G84xZFBdjEaVRUtO5qUjy7LEilVngPwN3W6ma+dqPPZoU/z9PPBw1+NppytaIBDcfoYOrkdaeg4//LgLk0lFUeS8TKOHHwznycea5xvv4V78x5amQVaWkcVLTjDq4dKr+XIreh8vyjSsQ+J+J2QESRLGtAzazv7qP5EFWaLiJT4+HpPJRFBQUL7jQUFBnDx5stBrateuzW+//UbDhg1JTk7m888/p02bNhw7doyKFSsWGD958mTefffdErH/TsCQku54jrCdKYaayURWTDwX/l5GjceHFcE662RnG1m24jQLFh0nOiYNfz93omNSSUvLyfeRTCbzF/8uOF7g2M3pkoV9G2RZIjY2ndo1yzrdfoFAUDo8MrIxA/uHsXL1GWJi0gjwd6dn95qULVvwpapmzUD2O9AywBKaBnv3Xbmt4gWgfM92zhEvmkbKyXOknrmAT63Q4s93h3PHpUq3bt2a1q1b533dpk0bwsLC+Omnn3j//fcLjJ80aRITJ07M+zolJYVKle6dlFbfBrVIPHTCaW7FAkgSUcs2Ol28pKZl8/T/FnP8pvTkm9vYOwtV1Th8NIYLFxOpWuXeqyIpEPxXKOPrxohhDWyOGzSgLrPnHnHKmpoGKSlZbN9xicwsA9VC/WjYoHTrN1Xo15njk39y2nxGKyUw7iVKVLwEBgaiKAoxMTH5jsfExBAcHGzXHHq9nsaNG3P27NlCz7u6uuLq6lpsW+9Uaj41nPPT55fcApqGKctym/mi8vmXWzl5Oj53iRJn774oIV4Egv8ANWsE8MRjzfj5173Fniszy0D33tPzdaoPrerH++90pW5YuWLPbw+BrRrh17guiQeO2x5sA0mvwyu04A7FvUiJZhu5uLjQtGlT1q1bl3dMVVXWrVuXz7tiDZPJxJEjRyhfvvRT2+4EJJ1S4kWGvKpXdup8165lWOwHVFIsX3mq1NYSCAS3l6efbMH773Qtcl8xSTJvOR84eDWfcAFz7ZnHxy7k3LlrzjDVDlsk2s//znJQn73z6BSqDO+Ni5+vkyy7synxVOmJEyfy888/M2PGDE6cOMHTTz9Neno6Y8aMAWDUqFH5Anrfe+89Vq9ezblz59i/fz8PP/wwFy9e5PHHHy9pU+84VKORzQOeQTUYSnQdZ4uj4yfi8mJWSouDh6K5du2/4S4VCATQ577aPDjC8VIIEqDTmYOCC/MKq6qGwWBimhM8O/biVbUiZds2KbKAkXQK7uXL0fjTl5xs2Z1LiYuX4cOH8/nnn/PWW2/RqFEjDh48yMqVK/OCeCMjI7l69UYDrsTERJ544gnCwsLo3bs3KSkpbN++nbp1S7eN+Z3AlSUbyIyKtT9NuigoMqbMLKdOebsC3ePjhXgRCP5LdO1czeFrNOC+nrVQFMs3KpNJY+36CDIzS/bF8WZqTxhdpD12xcONGk8Op+eef3AvX3CrKyvuGknHzpAVVzqepNKiVAJ2x40bx7hx4wo9t3Hjxnxff/XVV3z11VelYNWdT/zOg0h6XV5ROoeQJbuyjiTAo0KQzXGO0KB+EHq9XMAdW9KUVpdYgUBwZ1CUvkiyLHH+fOL1oFzL90hV1UhJycbdvXSKvFUa3IPQ0QM5P2Oh3dfoPN0JHTOEui89hntQYL5ziYdPcui1r4havimvoVvIfR0I/2gifuF1nGx96SN6G93BSIrikNdF0utoP38K3bfOwr+Z7ah9AE3VnN7DyMfHjQH9wpDl0nHByLJEs6YhhaZVCgSCe5f4hAx0OsceY7IsgQQmk/WXK0kC31IsYCdJEq1+m0xw9zZ2X2NMz+Tsj3+zvNEAEg/fKD+SsPcIq1sNNzeCzPXmaBpXV21ldev787WVuVsR4uUOpnzPdmhG+70uiqsLlQZ1p2zbpvTaNY8+x5fT6rePULw9LTZhDHvpMTyrOL8S7cTxbWnSqOSDrGVJQpYlxj3dqsTXEggEdxYBAR42RcitGI0q1av52dyh0TRITnbulrotJFkm5eR5h67RjCaMKWlsHTYeTdPQNI1dj72OmlOw75FmMqFmG9j1+OuUcHH9EkeIlzuYch2a49coDElnu5eHpFMI6twy3zHfsOpUGzOEXrvmUbZN/kJMel9vGn3yIo0+ftGpNoO5ON2KVae5EpXs9LlvJTjYi++/6UfDBval3gsEgnuHnt1qIFt4MbPG0uWn7Rq3dXvp99Uzpjseu6eZVFJPXyB67XYSDxwn6fApNAuiTlNVko+c5tq+o8U19bZyxxWp+69hSE3j3O/zOTdjAdlxiXhVq0iNJ4dTeVgvZL2eDounsq7LKNLORlqdRzOaqPP8I4We8w2rTvcts0g+EUHKiQh0Xh6U69Acxc359XHS0nIYO24Rx08Ur/fIrUgSBAd5UaN6ADVrBlA+2JvKlcrQtElIqW1PCQSCOws/P3eeerwZP/y026HrjEbb3hpZksjOLkK8YTHxrV+T+G37LYoPa2we+AwV+nWxa2zqmYsE2BlecCcixMttJONKDGs7PkzaOXPHaDSNzCsxxG7aQ8Qv8+i4bBqelcrT58hSLs5Zzunv/+La3mP5I9IVGUwqjT97maDO1rdOfMOq4xtWvVg2a5rGseOxHD0ei06RaNmiEpUq+uade2nSymI3TSt8XbganYavrxuDBtSlU8d7v/y1QCCwzWNjmuLmpmPqT7vJzHKe2FA1jZo1Am0PdDK1nnmIuM1FS9M2ZWQROWe5XWP1Pl5FWuNOocS7Spc2d1NX6TUdHiR+x0GLpf9d/HypPWEUNcc+gFu5AFLPXmTDfU+Qdja/K9OjSghdVv9W4v0sIiOTePWN1Zw8FZ+vk3PnjqG88HxbXn51VaFdn52JLEmomsaklzswbEj9El1LIBDcPWRnG1my7CTr1p/j1Ok4kpKLVzlcliV2bnkSnR3b9s5EU1W2PTCRyHkrSqxMht7Xi8HR20vE+14cHHl+C/Fym0g8dJIVjezI8pFlXMr40HHxD2y9fwJZMQkFgrAknYJroB99ji3D1b9MidibkJDB8IfmkJySVaAAnSxL6HQyOTkl1H+pEHSKzMqlo/D39yi1NQUCwd1Dy3Y/YTAU7540f+4Dt6XtiGoycfq7Pzn19QzSL0YB4BYUSFZMvFPmb/z5K4S98KhT5nImjjy/RcBuKZJ8/Cxnps7izNRZXJyz3L5qbqqKITmFTf3Gknk1roBwAXO8S1ZsAhG//lMCVpv5e+5hkpILCheziVqpChcAk6qxZJloCSAQCAqnWZMQlGLGw6mlXCk8F1lRqDPhEfqfW8eg6G0MjtvBwCubqfnMg0WbUJFBkpB0Ohq88yx1Jo5xrsG3ARHzUgpkRsex/aEXiVm/01wVDsmhSoqaSSUnMcX6IFXjwswl1H2pZNooLFl2qlR7FdlCls09SAQCgaAwHnownB27LhX5ep1OZvOWCwQHe+froWQwmNDp5FLpPC3Jcr7ic82/f5vqjw1lTfsHMWU4kMZtUinXsQVt53xVoJjd3YoQLyWMMT3jlqDcvP85nZwkGwKnGKSklG69A3vw9HS53SYIBII7DE3TOHU6npxsE0MG1eXfBcdRFMnhfmtGo8q3P+zku6k76dqlGoYcld17L5OZaUSvl+nVoyajRzamWqh/CX2SwvFvUo/az47kxOe/OpSRFLtpN9f2HKFC384AJB48QdSKzag5Bvyb1ad8r/bISunG9xQHIV5KmPN/LCT1zIWS7U+EuRqvb53iZRJZI6icF5GXnFO3xcVFsWubSbLioDKZNLp1LbnPKxAI7j72HYjik882czbiRh+fAH93Klcuw9mIBFJTcxyeU9Ng7bpz+Y4ZDCpLl59i1ZqzTP2uP41LoSBnLsbMLHPbGAfDVSVF4dS3fxLYuhFb759AzPqdSNe3kzSjCY/K5Wn/73d3Tfq0iHkpYc7NWMj1vaISRTOZqDF2RInNP2RQvWI1XGzZvAJzZw5n/epHWb1stNWmaLlIUuFhQbIs0bxZBRrWd25PJoFAcPey/0AUT/9vMRHn8jcgTLiWyYGDV5k4vi1t21QGsOv+YwtNg5wcE6+8vsquujHOwJiZxfruYzj20Y929a67Gc1kImbDTpbU6knMxt3Xj6l52a4Zl6NZ23nUjV2COxwhXkqYrNiEInUKdQhJotKQnlTsb19xoqIwZFA9qlcPcDgATpYlatcK5PNP7qNGjQDK+Lrh4+NGn/tqW7yByLJEhQo+fPbxfXl7zTqdnDe+ZYuKfP5Jr1LZcxYIBHcHn3+1FZOqWrzdfv3tdj79qCdffnofrVpWIqicc3qhxcdnsHVb6VTiPT75J+J3HHRYuOSiGU3kXEsGtRCxpWqY0jI49MbXxbKxtBDbRiWMV2hFc6pbYT8sTsC1XAB1xo8i7OXHkYpQJtseLkYmsXzFaerWKYtOkTh79hpGO/ZaPTz0jH64MQ89EJ4v4A3g+efacPhoNJGRyfkCgRVFwtVVxycf9qBuWDlWL3+EtesiOBtxDTc3HV06VaN2rXsj4EwgEDiHiHPXOHnKehpxcko2O3dfonPHanTqGMpzE5cRn5DhcCzMrciSxIlTcSVeOFM1Gjn9w6wSe5bkcnH2Mpp//xYufr4luk5xEeKlhKk0pKc5y6gEUDzdGXRlM7KuZP4ZjUaVyZ9uZsEic8CbJEmoqrnxV7cu1di157LFPeQKId58/kkvatUMLNRD4uvrxoxfhzBr9mH+mX+MhIQM3Nx09LmvFiMfakzlSuZfHHc3Pf363P3t2wUCQckRG5tm17i5/xxlwcITuLnp2LvvSrGFC4CGhqtLyQe6ZsUkkJOQVOLroGlE/D6fsDs8nVqIlxIiauVmDr/1Ldf2HCmR+SWdQoW+nUtMuAB8+/0OFi4+DnD9l/zGL/ra9ecsXGXmSlQqD4ycR40a/vxvbCs6tq9aYIy3lytPPd6cpx5vjtGo5gkkgUAgcIQAO4tV7t5zGU0zb007q/SDpkGHQu5vzkZ21dse5CQi/1l5x4sXEfNSApz/axEbez/JtX3HSmwNzaRabMToDJKSs5gz70ixw3UiIq7x/IvLWbbCekG50qqbIBAI7j1q1gwgtKqfzaSC3PuZM2tWVazgTY3qAU6bzxJugf74N29gX3FTQCrGi60xzfHO1qWNEC9OxpCaxu6n3jL/lpTU3qQs0Wr6xwS2DC+Z+YFt2y9iMBTf/tybxeRPNpOZaSj2fAKBQHArkiQxcULb638v3bUvX0nlSlTJ1di6mXqvjbU7AaTh++NRPNwcXkPSKfiF3/lb9UK8OJmLs5djyixeQzCryDLVxwyh2qiBJbcGkJnp3FbwGZkG1m2wvtUkEAgERaVt68p8+VlvygY6J4vIEV5/ay3RMaklvk6lgd1o+s3rNhWaW3BZwl56jOqPDTPXcnEAzWii5tMPFMfMUkGIFyeTevo8cgl2IZUUGcXDvcTmz6VaqHObkekUudTeTgQCwX+Tju2rsmzRSKZ+14+3Xu/E5Pe7lcq6h49EM3TEbI4eiynxtWo/N4oBF9bjHWa5SGdWdBwnPvmZ8A8mUCa8jrmfys0Upn2ul8EIe+kxyrZp4kSLSwYhXpyM3tcbrQR7AGkGI4GtG5XY/Lk0blSeypV9kYvZ2CwXk6ri431ntV8XCAT3Hooi07JFJQb2r0uP7jUJCfEula2krCwjE15YXuxO1vYgyTKpJ617sg+98TWLa3bHM7QiNZ68H4/K5irAOk93qj9+Pw3eeRbvmlXyxvvWq0nrPz+l0ScvlajtzkLSHK0xfIfjSEvtkiD5ZATLwnpbHiDL+NarQfKR00WaX/F0Z2jCbhTXkuvrYzKpXIxM4tSpeN79cAMmk1r8WgiyxIrFoyhbtvRdugKB4L/LrNmH+OLrbSVeKzSX7l2r079fGK1bVnLay9+tnP1lHrufeMOusZJOQTOaaPLVJGo/NypfPTBN08hJTEaSZVzKlP7z8lYceX6LVGkn41unOpWH9yZy3sqCAbvXm/U0/vQldox8mez4RIfnV3MMmDKzSkS8qKrGzL8P8efMg8QnmKPNPT30VAjx5vyFpGLN3bZNZSZ/tpno6FQCAzzo26cOnTuFoi/BLTaBQCC4f2gD9uy9wqYtF6z2S3MWa9dHsGZdBCEh3nz+cS/q1C7r9DXU7Bzrzd9uIrf8//7nJxPYunG+RA9JknD1L4OmqlxauJaz0+aQdvYiLoF+hD7cn9BRA9F73ZkvnMLzUgIYM7PY9ehrXJy9DElRkGQZ1WBA5+VBy18+pNLg7ux68k3OT1/g+OQStPz5A6o/NsypNmuaxnsfbmDRkpPFmsfLU09auiGvjoIkQXCQF1ej01BkCZOq5Z1rUD+I77/ph5eX6A4tEAhKDqNRZfHSk8yee5hz5xNxcVGoUMGHiIhrJSZoZFnC3V3HnL+GExLi3GdR3I4DrGnjWC87SadQZXhv2vz1eb7jppwctg59jitLNiApCprJlBcQ7BVakW6bZ+JRoXT6yDny/BbipSRtOXWOS/+uxpCShnetqlQZ3pusuGus7z6GtLORBZWzHb9Fkk6h4XvjqTfpKafaum//FZ54elGx5vD00FOtmj9HjtoXtCbLEl27VOOTD3sWa12BQCBwFE3TWLbiNNP/2M+582YvuKLkVhF3zhqKInH/0Aa8NLGdcya8TnZSCssb9CXzSqxDysutfFkGR20FwJiewekfZnHsox8xJBWeKSXpFAKaN6TH9tlOsdsWQrzcIeLlVlSDgaV1e5N+4UqeK68otP7jE0JHDnSeYcBrb65hzbqzxYptkWWzG9KROSQJli4cSflg7yKvKxAIBEVF0zQSrmViMqqkpefwxNMLSUnJdlohO18fVzaseSzfMZNJ5fCRGJKTs6hQwYeaNewvcnfyq+kceu1LTFlFK8kR3K0NYa8+ycEXPyHx8Em7mjz22vsv/k3rF2k9RxAxL3colxevN3tcioHsoqfS4B5OsugGFy4mFjso1xzi42Cbds3s9enb+84viiQQCO49JEkiMMDcXiAImDNzOLPnHGbJ8lMkJWVhNBavWGd6ev7inEuXn2LKDzuJjUvPO1andiCvvtSBhg2Crc51Zuos9k+cXCx7YjbsJHrdDvOboz0CTZaJ2bi7VMSLI4hU6VLAmJFJ5tVYLi9ci1TMAFWdtyc6T/v6eDiC921MY3ZGczSBQCBwBmUDPXn2f61ZvewRtm18ggb1g4qcai1JUD7khld5/sLjvPXuunzCBeD0mQSeeHoRx45b3nI35eRw6I2vi2bITWgm1aEK8BKUfJRzERDipQRJOnqarcMnMM+nCQtC2nNx1lJzMFQxyElMdpJ1ZiIvJTPt1z1ERFxz6ryOYOttQyAQCG4Her3C99/0K/I9StNg2OB6AGRmGvjy622FjlNVDZNJ5atvt1ucK2bDLnKu2XH/d3IGp6aqlO3Q3KlzOgOxbVRCxO86xLrOo1ANBrPSxfxDUFz0Ps6JDTEYTHz48SYWLz1pd7S9p6e+gAu0OCiKRNMmIYRWdW41X4FAIHAWXl4ujH2yBU+PW+zwtTpFYkD/MAA2bDpPhpX+bqqqsf/AVa5EpVChkOwku4QL4N+4Ltf2HHHY1sKQdAp+jcIIaN7AKfM5E+F5KQE0TWP7wy9iys4pVmDurUg6hdCRA5wy1yefb2HJMnNatL0eQWcKF0mC4GBv3n+7dMp3CwQCgT1omobhlvt2syYh6PWOPy6NJo2zZ81e7di4dBTF9v5TbGx6oce9qlWya01nCRcA95BytP/nW6TS7nZpB8LzUgLEbt5T7MDcW5EUBZ2nO3UmPlLsuaJjUlmw6HipbmOGVimDopOJjUvH38+dAf3CGDyoLt5eomWAQCC4/Zw6Hc/0P/ezfsM5DAaV8uW9GT60PsOHNcDVVUeTRiHs2nPZ4XnT03MACPB3tyu+L8C/8N51AS0a4lOnGimnL9gdr1JUdN6eNHz3Oao9OgQX3zszE1SIFztIOnqaM1P/Jm7bfmS9jpA+ncy9IkIKL9yTfOyM3dUPbXJ9Hs+qFWj/z7d4Va1Y7Ck3bDxffLssUK6sJ6mp2WRmmbtSu7vrGTq4Hv8b2xIXF1FNVyAQ3Hls2xHJ8y8uR9O0PIFx9Woq30zZwYZN55n6XX/GPtWiSOKlUiVfAGpU90dRLJeSkGWoU7sslSuXKfS8JEm0mPY+67uORoViCxjf8NokHzqV1z4gt0BdQKtwOi//GRc/32LNX9II8WKDU1P+Yt9zHyApct4WUOL+45z47Fc6LfuJoE4tC1yj8/RwWnR2jbEjqDSoO8FdW+frSVEc0jNykGXH6rHYgyxL1K1bjg/e6caZs/EA1KoZiLu73qnrCAQCgbPIzDIw6Y3VmExqgdu2psGRozH8/sd+xj7RnNatKrFj5yW75pVliYYNggkO8uL1t9awYtUZi1lL5uMSE55tY3XOcu2b0W3zTA68/BlxW/baZUdhVBzUnXZzvybp8CkifplHakQkrgF+VH2wL+Xv64Cs3PkvmqJInRVit+xlbYeHCj8pS+jc3eh/YT1ugf75TmXFJrCgQgc0o9G+hQrz0khQ7ZHBtPz1I6fvN65Zd5ZXXlvt1Dlz+fjDHvToVqNE5hYIBAJns2jJCd79YIPVMb6+bqxZ/ggAY8ctYv+BqzbndXPT8cdvQ/h9xn5WrTlrtehdcJAXr0/qRNvWle22O+3CZZbV64spI9O+CyRAkmj8xavUGT+a+O37STpyGsXDnZBe7XErZ3+hvJJCFKlzEie//D3PpVYAVcOYmcW53+dT96XH8w5rqoprWX9qjh3B6e9n2vbAKDLe1auQfuEyao45INbFz5c6L4yh7qtPlkigVKcOofj6upGcnOW0ORVFIjTUn84dQ502p0AgEJQ0p07Ho9PJVovRJSdnkZCQQVCQF+OebsWjT9ruS/fA8Abo9QorVp2xOEaSoGpVP+bNGuFwB2qvqhVxCwog/byVrSxJwi04EP+m9Qho3oDqjw8jKzqeZXV7k3Ly3I1hOh01nhpOky9fRXHJ32vOlJNDws5DGNMz8Amr7pTQBWcgso2sEL1uh/VsIVUjeu12VJOJiF/nsaxhP/7W1WWOa33SI6Mo36u97UVMKkgw6OpWOq/5nbqvj8W/eX2ilm1iz9PvcG3f0WJ/jtTUbDZsOsfqtWe5dDkZvV7hnTe7FHteyOvfRXiDYH78rh96/Z3vbhQIBIJc9HrFrl3+3Jg9e94nJQkCAzxZuz7CqijRNDh/PpGEhAx7zc1HtTGDrYcTaBo5Sal4Va1ItdGDMKams7bjw6SeuZB/mNHImR9msXPMazddqnHyq+ksCGnP2o4Ps7H3kyyu1o0N9z1O2nn7ts5KEuF5sYI9dVk0o4ntD0wkct7KvO0f1WAkavkmNJOKS0AZchKSrM4hyTLG1HR2P/mmWUXL5rLNCXsOE/HzXMJefpxGH7/osBfGYDDxzZQdzPv3KAbDjc/SqkUl3nqjE88/15qvvt3h0JwFbJck3n2zC3371C7WPAKBQHA76NC+Kn/OPGjxvCxJ1K4diJ+fOQuoVq1APDz0ZGRYLh2hadC8WQWWrTiNLEs2+ySlpedQtqynw7bXeuZBIqbNJTM6zuKLtpqZxZkf/+b8zMWUa9cUU1Z2Xu2xW42+OGsJdV95HJ861dj16GtcmLmkwJjoNdtZ1Wo49+2bj0fF21dgVHherFCufTMka4FLsozO28MsXCDfFpFmNJlVb2Ky1TkkRaF8z3Zs7PMUGZeu76Ne/0HP/WE88ekvnJs+3yHbNU1j0ptr+HvO4XzCBWDPvsuMeXw+fe6rTedOxd/mWbP+bLHnEAgEgttBk0blqVe3nMUaLKqm8diYpnlfR0Ym06xJiNU5fX3diIvP4MqVZJu9kfR6mXJFEC4ArgF+dNsyk4DmDa2O04wmDMlpXFmywepugqRT2DfhI+YHtSkoXHLnMpnIuZbE0Q9+KJLNzkKIFyvUnjDacjl/SULWKaScumDdj6iBpqmFj5EkkCX8GoWRfOyM5R8qSeL4Jz/jSGz1wUPRrN9wrlB3qMmkERefwd9zj/Dxhz14ZFTjfBlBOp1MqxYVcXW1vQWkqhpbt13Mq2UgEAgEdxOSJPH1572pXs2ceKEokvnWLJv/nDihLV06VeP0mXgeGj2XB0bOZfPWi1bnTE7O4n/PLWHt+nNWxymKRO9etfD0dLE6zhpeVSvSY/tsuqyfYfbaW8KenQRVJXbDLgxJqdbHGU2c/2Mhppzbd98X20ZWCOnZngbvPMuRd77LF7hrbq4o0Xb2V2wZPM7mPH5N65O4/3i+ZliSoiDJEm3nfE38jgNIOp3l7CRNI/XUeTKvxlqsLXMrS5eftFpTQFU1Zs0+TFxcOnHx6dSs4U92tonQUD/uH9qA9RsiOHQk2q61NA0yMgzF+gUUCASC20VAgAczZwxj245I1q2PICPDQGhVPwb2DyMkxIeLkUk89tQCMjPtzCC1A0Uxd7N+ZmzBchtFQQL7ukRbw4HrTZnZ5CSm4B4UWLw1i4gQLzZo8PY4ynVqwekpf+UVqavQrwu1xj2ET+1qSHodmsHaD7RG8qGTYDKBLKH388U9pCwV+3amxtgReFWtSOyWvdd/8qzjSKuB2Lh0m3VcMjMNLF56Mt+xM2cTWHm9HoG9jh53dx1lyrjZbZtAIBDcaSiKTId2VenQrmqBc9N+2UNWltFm7Iq9SECd2oF06lCNnBzntJCRXUq3npak193W6rtCvNhBUMcWBHVsUei5Cn06cWWplX1EjbwUaFQNY2oa6RFZhPTplJdyFtgqnFNfTbdqg1twIO4V7PO6gLmtuzXPiyVyfzntFS6KIjGgX5jIMhIIBPckmVkG1qyNcGpRTw04fiKO4yfi+OHHXXTrWp233+iCh4djAkTTNGI27OTc7/NJO3fJjpdp5yApMpWH9kJxu33tXUTMSzEJe+mxwiO3LaAZTZhyDGwdPgH1+jaRd+1QZFcrWy6yRO1nRzpU9bBv79pOr6B7K4oiUa6sF4/fFMwmEAgE9xKpKdkYHbjH24umXf8PWLfhHC++stKhuEbVYGDrsPGs7/oIF2cvI377gSI3AlbcHRMhkk5H/TefKdJazkKIl2JStk0TWv/xCZJeZ25OcT0I1yqqStbVOCJ++Ycj705hdcv7Ua2o5ZD7OhD20mMO2dW4UXk6dQy1qyZBUfHw0PP2G53x9/couUUEAoHgNpGRYeDU6Ti7ukEXB1XV2Ln7EvsP2q7cm8vhN7/h0nxzpfQ80XKz+HHAZH0ZH4fGV32wH75h1e2/oAQQ7QGcRObVWM7+Mo/E/ccxpmcSvWab9QvsDCrxrBJCh0VTidmwE82kEtgqnMA2Teyq+ZKTY+Krb7czf+GxAunSzkCWJdzcdMycMYwqFpqJCQQCwd2GwWjihx93M2feEbKySn4bBm5swb8xqZPNsYa0dOYHt8WUbr01gF/TeiTuO+YkC29QrnNLuq3/w+nzivYAtwH38uVo8Ob/AEg+EcGyur2tX2CnZky/GMWKRgNAlpEwp7L5NqhF+3++xaeW9RotLi4Kr7zYnrFPNGfn7ku8+8EGp/4iqqpGdraRX37by/vvdHPavAKBQHC70DSN199cwzoLpSZKClXV7G7ZkrD7sE3hAlBz7AiOTZ5G+oUrhaZKS4pC2U7NiV230yFbSyOuxhZi26gE8Kkdit7Z7cRVNa/ib8qJCNa0f4jMmHi7LvX1dUOnk0vkDcJk0li15qzTIuYFAoHgdrJvfxRr19snXJy5LS/LEiHl7cvesTu2RdVo89dnKK76AsVSJUVGdtWj8/RA52X/1r+kKAS2bmT3+JJCiJcSQDUa0UqweI9mNJGTkMiZH2bZfU1kZHKJ7dsajSppadklMrdAIBCUJouWnrB5rwzwd+e5/7VyqmfGZNIY0D/MrrF+jcKu1xuzTkCrcMq2bkzPXfOoOLg7kmJ+5EuKjGZSMWXncHXZJoz2dqbG7Jmq8dQIu8eXFEK8lADX9hzBaIdLrzhoJpVz0213Ns3Fy8vFaTUKbsXFRcHb+/alzAkEAoGzuHo11WamZlp6DpqGw52gc6v3FsbIB8OpFupv1zxu5QKoPLy3xdYzkk4hsHVj/BrWAaBMg9q0n/sNw5L3Uf2xYTcyZE2quYr8zc8GCwZKOgUkiVa/foh39cp22VmSCPHiRJJPRBC9fgcppy+Uyno515LsHtupQ6jDjR3tQVEk+vauLeq8CASCe4IAfw8UG6KkjK+7Q8JFkmDe7OF8/UXvvAaPuSiKRP165cjKNjJ77mFSUuyLe2n27Rv41KlWQGxIioxbuQDazPq8wDWmrGzO/7nQuq2yhEtAGVzLBeBZtQKyuxt6Hy8qDe5Bjx1zqPbIYLvsK2lEwK4TiNm0m/0TPyZxv5OiunMzkaxlJEkSXtUq2T1l2bKeDB1cj3n/HrU4Zd06ZYk4d40cg8kud6iiSHh7ueZrWiYQCAR3M31612bNugiL52VZon+/OrRqWYlvpuywa05Ng+EPzEWj4I3VZNI4eiyWEyfjUFWNr7/dwRuvdaJv79pW53T1L0OPnXM4O20uZ3+aTcblaFwD/an+6BBqPvMgbmULenGurt52o2iqJVtNKu1mf0VwtzZ2fbbbhRAvxSR63Q429HoMzRlbMpKEe/my1Hr2YbxqVGHbsPFWh9d4arhD07/wfFuMRpUFC48jyRKyLGEyqej1ChOebcOI+xuwZesFXrheLCnXdSpJEpqmFajY27hRCG9M6kT54NtXIlogEAicSdvWlWnSuDwHD0UX2GpXFAl/P3dGDGuAn587TRqV59CRaLsKgqo23ghz58gxmHjr3XUE+LvTupX17Rm9lydhE8cQNnGMzfUBTJn2eXWMdo67nYg6L8VA0zSW1u5JakRksRtiSXod1R4ZTNOvJqHz9EDTNHaMetnclvyWfyJJkfFvVp9uG/+yWp75wsVE5v5zlK3bLmJSNRqHl2fE/Q3w93Nn9dqzJKdkUyHEh549auDtdWOeM2cTmPn3IdZtOEdOjpEa1QMYPqwB3bpU48jRGLKyjISG+lO5kpMzqgQCgeAOICPDwPsfbWD12rP5br8NGwTx0XvdCQkxP1sSEjIYO24xEeeuOXV9WZZo2CCY36YNcuq81/YdZWWzITbH9T+3Fs8qFYhavomzP88lLSIS17L+hI4cQJUH+qJzL5ledo48v4V4KQZx2/ezpu0DxZrDJ6w6jT9/hcCWDXEN8Mt3TjWZOD75J05+OZ2cxGQAFHc3qj06hEYfv4Dey9PivBs2neOVSavRuOFByfWcTBzfhocfbISqaqxZd5a/Zh0i4tw1XFwUenavwYMjwq0WncvMMhAfn4GXlwt+ZdwtjhMIBIK7meiYVHbvuYLJpFKvbjlq1SzYQTknx8TzLy5nx65LTl9/3aoxTr/HrmgyiKTDp8yBurcgKQpBXVrRcemPbB02niuL1yMpinmsLIGq4Vu3Bl3WzyiRbtJCvJSSeLnw91K2P/hC0SeQZRq8/T8avDXO6jBTdg5Jh0+iGk2UqVcTvY+X1fHRMan0HzwTk0m1GLsy7YcB/DnzIFu2XSxwTqeT+e7rvrRsXjHf8bj4dH6ctodlK07l1XVp1rQCY59oTpPGIVZtEggEgnuRzEwDXXv9XiJ1tJYuHGl37RdrmLJzuLxwLalnLmBMz+T0DzMxZWTlqxcj6RRcA/zMcTQ/zeH4pz8XuqMgKQrlOjan67oZxbbrVkSF3VLCNdDP9iBLSBKKqws1nrjf5lDF1YWA5g3tnnr+guOoqmZRuCiKxORPN3H+QlKh541GlQkTl7Fu1aN5XU7j4tMZNeYf4hMy8u3v7j8QxZPPLOLzj3vRqaP1ir8CgUBwr3H+QmKJCBcPDz2BAcXvG3dpwRp2Pfa62Xsvy3mVdnU+XpiystByjOi8PKj26BDqvvIELmV8OP39TIuhEJrJRMz6nSQdPU2Z+rWKbV9REanSxSCoUwtcC4notoWkyChuLnRc8iPu5cs53a49+65YreliMmkWhUsu2TkmFi05kff1N9/tKCBcgOsiSePt99eTnX37S0YLBAJBaaJTnP8YlWWJwQPr4uJSvBIU0et3sGXoc3lhBze3CDCmpKHlGKn++DCGJu2l2Tdv4BESROLBExhT061PLEnErHespYCzEeKlGMh6PY0+dmDbSJbxb1aPem88Q7+zawju2triUNVkIjshEVO245V6nVXOZd16c7pgamo2q9eetRhRr2nmMRs2nnfOwgKBQHCXEFrND38/x+JSJMl6gbsyvm4MHlC3uKZx+I2vbfbRi/hlHmd/mn3jgF19EcwJK7cTIV6KSfVHh9J86jvovC0Hz+ahqriVC6ThO8/iERJU6JDshEQOvPQJ/wa04N/AVsz1DGfLsOdIPHii0PGF0aJ5Rau/GPaKm8zrrtCrV1MxGq13pdbpZM5fTLTbRoFAILgX0OsUHn6okd3jJcDb2xV/f3eqVvGlevX84QeSBEnJWQx9YDa/z9hfZLsyLkcTv+OgXWLkxGe/5vXOK9OwNoqtbCJVo1y721vfS4gXJ1Bz7AMMjt6GewXbW0BXV22x2EciK+4aq1rez8mvZmBITgPMBYMuL1zLqlb3E7PBPjfdkIH10OlkiyLFXsFcu1YAAB6eeptjVVXD08P2OIFAILjXGPVQIwYNMPclsvVyqAEpKdnEx2dw6XIKERH5X/o0zXw/VVWN737YycLFx4tkU95WkR2kX7hCemQUAHpvL6o/McwcH1MIkk4hoGU4/k3rF8kuZyHEi5PQebhjsLVPiFmMpFpoH3Dg5c9Iv3ClQAqbZjShGoxse+AFVIP16ohgrqb7+ce90OmUfB6Y3L4ar7zY3q5AsLFPtACgQogPNar7W/2l1FSNLp2r2ZxTIBAI7jVkWeKNSZ34/edBBAbaH2RrT3G7qT/tLlJfOo+KwRZ7HxXGikYDmevdmHVdRhHYpgll2zYxn7hFxGiqStr5y+x++m2ST1iuRFzSCPHiRHQe9u17yi4FPRQ5SSlcnLWk0Nx7AFSVrJh4rizbZNca7dpWYcHcBxj5UCNqVPenapUyDOwfxt9/3o/BqBKfkGFzjq3bIwFzhd2nn2ph0WMjyxK976tFxQqiaJ1AIPhvIkkS4Q3L07F9qM2u1I4QF5/BqdPxDl/n4udLmYb2ZwMZklMxpmUQu3kP20c8j2/d6rT8fTKBLcNRPK5vI0nmWi/ZsQlE/DKP5eH9ubJ0g8O2OQMhXpyEISXNZs8IABc/H7xrF0wpTouItHm9pNORfPS03TaFhPgwflxr5s4awfy5D/L6q51ITMzky6+32XX9h5M3cujwVQA6d6zGm691wkWvIEnmGJfc5mU9utXgjUmd7LZLIBAI7lUG9g+zy6PiCJGXkop0naRzvBpKbsfpsz/NQefuRthLj2HKuN4u4KY3WM1oQjOa2DJsPFmxCUWyrziIOi9O4sxPs8m5ZnuPsd5rY5ELceUpdnhtNFW1a5w1/vjrIIosYbLDDSkrEn/OOkR4w/IADBpQl66dq7F85WmOHovFZFJp17YKvXrURCmBdEGBQCC4U0hLy2Hj5vMkJWdRPsiL9u2qFprKXDesHEMG1eXfBUWLVSmM9HTHs04By558O5AUmVPfzDALIEUGUyFJG5qGmmMg4rd/qffqk0VeqygI8eIkIn6ZZ9c4jyqFV6L1qVMNr+qVSYuItHyxqlKxf5eimAeYU9v27Ltil3AB837szltKXp+/kMjCxSc4fcastFetOct33+/kuXGt6d3r9hUsEggEgpJA0zSm/3GAab/uITvbhCxLqKqGj7crr7zUnvt6FrzvTXq5IxVCfJjx10GSk4vf5NDPwVTsvOsa1TW3AjA6LmI0k0rCniPmraLChEsuqkrctn1Fsq84iNdlJ5EZFWvXuP0TPiKpkK0fSZKo/9b/rF5bvld7vGtUKZJ9uTiam2+66Yf2yNEYnnxmEWcj8jchi41L54231xY5Kl4gEAjuRC5fSebt99bz3Q87yc42C4Dc4NmU1Gxef2st6zeeK3CdLEs8MqoJq5eN5o/fhvDLjwMJrVqmSLEwOp1E0yK2X6n1zANFEi65mLeGbBQflSSHAoOdhRAvTsLeSruZUbEsb9CPk19NL3DOxc96L4fkY2fsyjayhCRJNKgfbLUGzM3kdjbN5Yuvt2EyaRYj37/4ehuZWUW3TyAQCO4ErkSl8PSzi+k/eCZLl5+yOE4Cvp2yw+JLoV6vUL9eEE0ah/DlZ73x9XGz+/6by5CB9fDxKVoXZ/+m9W+8FFtIfbaJHe+7wd3aFG3uYiDEi5Oo/qjtNuM3s3/iZK6u3prv2PFPfjbvLVog41I0lxets2v+zCwDS5ad5POvtvLd9zs4dCQaTdN46IGGdqfdqaoGmsa+A1FEXkri8JFoq9empxvYvPmCXXMLBALBnUhcXDqPPPYve/ddsTlWAyIvJXPylO1soCqVyzB31nAef7Qp7u72RWz4lXFj7YYIOnb9hZFj/mHRkhMYHPSkNHz3OdrN/RqfOoWUspBlc7fooiLL6H29qDZqYNHnKOrSpbHI999/T9WqVXFzc6Nly5bs3r3b6vh58+ZRp04d3NzcaNCgAcuXLy8NM4tFzWcetOk5yYcsc+LzX/O+NGZmEb9tv9W9RUmncHXlFptTb9sRSc8+M3j7vfXM/ecof8w8yJjH5/PokwtoHF6eWjUD7DZz/8GrPDF2IR9+bDtFW5YlYmLT7J5bIBAI7jR+/2M/SclZDmUM2RvX4u/vwdgnWjCwX5hdjpDEpCwSEjJJTcvh2PFY3v1gA889v4ycHMcEjFe1SqSfv4R068uxpllswGgTWULv5UHnFb+g9/Eq2hzFoMTFy5w5c5g4cSJvv/02+/fvJzw8nJ49exIbW3iMyPbt23nggQd47LHHOHDgAAMHDmTgwIEcPXq0pE0tFq7+Zei+cy6yq4t9F6gqMet35bkb7YoK10C1obpPnorj+ReX50WnG41q3i/h0WMxPD52YV6wrT3kXrtnr+23EFXV8PcvfhdUgUAguB2YTCqLlpxwONW5fHlvh8bf16vWzT0SHWL3niv88tteh67Z+9wHqDmGvDToPDQNJNB5eeBnT8VcCdwrBBHYpjHhH06k39k1BLZq5JAtzqLExcuXX37JE088wZgxY6hbty4//vgjHh4e/Pbbb4WO/+abb+jVqxcvvfQSYWFhvP/++zRp0oQpU6aUtKnFxrdWaOGuOQtompqXN6/z9MC7ZhWrtaU1VSWgRQOrc07/4wCaphVaUM5k0rhwMclu+xzFzU1H544Fa9gIBALB3UBmppHMTBsBqjchyxIN6wdRpXIZh9apV7ccHdtXdTj+BcxJF3P/OWq39yXlzAXitx8oKFzyJgRjWgahD/a1YzaJOs8/Qo9ts6n36pO42RnrWRKUqHjJyclh3759dOvW7caCsky3bt3YsWNHodfs2LEj33iAnj17WhyfnZ1NSkpKvv9uF6kRkSQdOmn3eL9GYUjXfYeSJFF7wmjLg2UJnYc7oQ8PsDhEVTXWbzzn9AJJ9vLMUy3w9LTT8yQQCAR3GO7uukJrtxSGLEvodDIvv9je4XUkSWLyBz24r2dNJMn+Zrm5pKRmc+myfb2LMi5G2R4kS2iYq/JaRdOIXrMNQ8rtDw8oUfESHx+PyWQiKCh/B+WgoCCio6MLvSY6Otqh8ZMnT8bX1zfvv0qVKjnH+CKQevaiQ+Nv9dLUeGoElYffZ/7ipg1RSacg63S0+/dbq3uLBoPJZvdnZ3DrL5qXpwsvTWzHQw+El/jaAoFAUFIoikyf+2rZldIc3jCY36YNom6Y9Ya8mqaxfWckE19ewYChM3lo9Dz+nHmQnBwj77/TjaULR/LqSx3wd7CWi71p1y4BZWwPUjXcgwOpPWGUTSUVvXY7G+57HNVWCnUJc9cXqZs0aRITJ07M+zolJeW2CRgXX8f2PXVe+eNDZEWh7cwvqNC3M6e/+4uko6dR3FypPKQntcePwrduDevruyiUK+tJbJztBpHFQdPg7Tc6o2kavr5utG5ZGTe3u/5HSSAQCHj0kaasWRdBRoah0OzKls0r8tqrHalU0XYvN1XVePeD9SxZdgpFkfK84idPxfHHzINMfr8b0THpuLrqqFzZl6TkLLuyQcuV9bRrfTB7+L1rViH1bCSWGtRJeh3B3VpT+f77SD4RQeRsy0kymkklfvsBrixeT6XBPeyyoSQo0SdOYGAgiqIQExOT73hMTAzBwcGFXhMcHOzQeFdXV1xdXZ1jcDHxb94At6BAsmLsaKIlSbgW4qKTZJnQh/oT+lB/h9eXJIn7h9bnhx93ozpYjM5RmjYJEY0YBQLBPUeFEB9+/3kwb76zNl8KtF4vM2xIfcY/2xq9zr6tpdlzD7NkmblOzM3b+ZoGCQkZPPnM4iLZOPKhRna3ZJEkiUafvsyWQZaLoGoGI5v6P03XdTNoO+tL4rbuI/NyjMXxSBJH3v2egJbheFQIsjyuBCnRbSMXFxeaNm3KunU3apOoqsq6deto3bp1ode0bt0633iANWvWWBx/JyErCjX/96B9gzWNKiP6ON2GB0Y0JCysrMN7qI5QsYIPFUIcSAsXCASCu4jq1fyZ9cf9/DV9KO+82YXJH3Rn9bJHePH5dnYLF1XV+HPWIafbNmhAXR4Y3tChayoN7Ebrvz6zWtPl2t6jHHr9KyRJwpiWYX1CTSPp8EkWVu7Ezsdew5SV7ZA9zqDEff0TJ05k9OjRNGvWjBYtWvD111+Tnp7OmDFjABg1ahQVKlRg8uTJAIwfP56OHTvyxRdf0KdPH2bPns3evXuZNm1aSZvqFOo8/whH3/8BzWB9PzCgZTh+jcKcvr67m55pPwzg/Y82snL1GafPD9CubRWkklRHAoFAcAdQN6yczZgWS0THpBETU7TA1tz+Sbn4+LjSsnlFhg9rQONG5Yt0/3UPCrRa00UzqZyZ+je+DWrjVs4fQ1Kq7UlVlXPTF5BzLZn286eU6nOhxMXL8OHDiYuL46233iI6OppGjRqxcuXKvKDcyMhI5JuCU9u0acOsWbN44403eO2116hZsyYLFy6kfn07ctDvAPRentR4cjhnf/zbYmqa4u5G5zWFp4o7A3d3PePHtS4x8fLP/KOMGNaAyg6mBwoEAsF/hbNn7QgfsEDFCj48P74t/L+98w6L4urC+Dszu+zSe28KCPbee8feuzFq7IkmJsbEFDWa4hdTNVFjTIyJvffeu1gRQURAqvTeYXf2fn8srCJsZZei9/c8JDJ7596zbJl3zj2FEDRp4gg726rXz0q/HQRGwKnsdSQrkeD2zC+0m1gmQ/yhc0i/8wh27bXzCFUFhmjbqa+Wk5OTA0tLS2RnZ8PComa2Nkqyc3Gux1vIevQU5SoRsQw4kRF6n/8X9p1aGdyODz46jhu3YvWeOs1xDMaNaYbFH3XV67wUCoVSl8nJKUJwSAp+33BLo5YByrCwEOHS2Rl6tAx4vHoTHn7+s/J6L1WAEXBoMG8S2q79skrzaHP9pr2NDICRpTn6XduB5ivfh7GbPNCYMxbDe/oYDAw8XC3CBQA++bgbLCzEOvfjUgbPE1y9Fq3fSSkUCqWOkp5egKVfnUPfgVswf+GxKgkXBoCjg37L7RNCUJiUZhDhUroAitMyDTO3Emh+q4EQmpmi6Rfz0PSLeZDxPFiOQ054NCL+2IW0m4FgRUZwHdwDXtNHQWRrbRAbXF0ssG3LGMyccxCJSfotKiQ11IeAQqFQ6hCZmYWYOmM/klPy9OPlZoCRIxpXfZ6XCP5mPcJ+2aLXOcvBMDD1dDHc/JVAxUs1wHIcIjbtwe25y8AwjEL9ply+jeBvNqDXqb8M1h/C2ckcObklep2T4xi0aF556jqFQqG8Sfy95Z5WwkXAMXBxtkBsJRVyOZaBl5cNhg9tqDf7itIyELxyvd7mqwwi5eE1fZRB13gVum1UDaRev4fbc5YCMlLebUcIJNm5uDhgJkqyDNPWQCLlIdWyhbo6eJ5gwrjqC8yiUCiU2ohEyuPQEe0aOfIygrW/DMb4MU3LtSLgOAb9+vpg04bhMBYL9WZj7O6TmjX+rQKNPn4HFr7V29eOel6qgeBvNgAq3tuS7Fw8+/cgGn6goreRDjxPyMG7C46iuFg/b9yyCpHvv9cRLZpRzwuFQnmzyc0pRkGBRKtzCAFGjd+J4UMbYfuWMUhKzgMvI2jSyAG2esgqepXCpFR5lpGa8h26ILKzRuPP5qDhh9P0Prc6qHipAjKeR/qtQJRk58Hcx0Op8kw6d0PtXEFL1yD46/UQmJrAc/xA+M5/C6Yeuu8hSiQ85s4/gqQkDXL1VWBkxEImkwd8OTmaYcb0NhgxTL/7sRQKhVIXMTExqlCTRRNkMoIjx0Jx6UoU/v17lEGrlZu4OKhMj9YKhkHjT2bCqX8XcGIRbNs1AyvUn5dIG+i2kY5Ebt6Hwx49cbbrJFwePBvH/AbgbLdJyAp+WmGsJm8caW4+StKzUBCbgCc/b8HxJoORditQZ/suXHqG589zqhxAVlIiAyEEMp4gKTkPK7+9hF/W3sBrlmFPoVAoankanoZde4Kwc3cQwp6mQSwWoHu3eho3SXwZnifIySnCd/+7YgBLX+AxfhBYoQo/BcuCM9awxQ4hyAoOh1PvTrDv3LrGhAtAPS868eTXLbj/4aoKx9NuBuJM5wnof3M3RHbW4IyEKNYhloXwPKSFRbg8bC5GxF0BJzLSeo4rV6N1uiOojDIBVPb/rdsD4exkpoh7yc4uQmZWIaytjGFpKa7yehQKhVKbSE3Lx2dfnsX9BwmK1iuEyDtLz5zeBteuR+s0L88T3Lodh+cJOQZruSKysULzrz9A4Kc/VniM4ViwRkLUmzICkZv3ARrcaKdcuYNdoqbgjMXwGDcQDT+aBsuG3oYwXSVUvGhJSWZ2pW8CoFR05BXgVJtRkBVXMcOHl6E4NROx+07p1KSxqFiqs3dEE9Gzect9tGrpjD/+vIMr16JBiLyTevdu9fDe3A7w8bbVaW0KhUKpTRQWSjBr7iE8T5DfiL78tRockoz//XgVnTp64Oq1GJ3XiIrKNGi/uEaLZ0JgaoKg5WtRkp6lOG7Z1BcdNn2NtJuBKlsHvIw0Nx+AvBrvs3/2I2rrIfQ6sQmOvToawHLlUPGiJTG7T0CmKvCJkKoLl1IYoQBp1+9rLF4IIbgfmIiDhx4jJCRFWfdz5esxQD1PKzT0s8epM+Eqz09LL8DUGQfAS2WKcYQA167HIOB2PP76Y4TOPUEoFAqltnDsRBhi4yqmNQNyz8nz5zlISKhatqhYbNhLMcMw8H1vMrxnjUXyhQBIsnNg4eel6K9n4uaE+x+tUpVXUilEyoPICK6OXoARz69CYFx9nnca86IlBXFJYDTsKqoPGE6ztXhehuUrL2DW3EM4fTYcKan5Wq9FCBAdk4WTp1ULlzIkEhn4V9Q6zxOUlPD4+rtLWq9PoVAotY1jJ8Kgrt9gVUIATU0EyMwsRODDRL1s8yvj+fFLuOg/A5cGz8L1CR8hYOaXiN5xFIQQGDs7yDOGdOmrKJOhJDMHsXtO6ttklVDxoiUie2uD58yXQSRSOPXtpNHY/7YF4tiJMACoUpCuNh9CZdtSMhlB2NM0hD5J1dkOCoVCqQ1kZhVWSZyoI79Aik+/OIN3Zh/E0JHbcP5ipN7XePz9n7g8ZA5Sr95VbA9lPHiMG5M/xt0FX4MQgpbfL0bjT2eDNSoNwi0VMoxAvVeIEQqQfjtI73argooXLfEcP6h62n4zDARmJsgJj0FRWobKoRIpj207Ag1vUymaPv3Y2CyD2kGhUCiGxt3VEixbDd/5ABKTcrF4yWmcPhuutzkzHz5B4JKfAKB8kdTSpsHh67Yj4cRlMCyLlqsWYWTCVXTY/B1a//QZuh9ejxFxlzRaRyF6qgkqXrTE2NkBjRbPNPxChIAvLEbgpz/gkGt3PNtyQOnQyMgMZGYVqZyOZRkIBGyVP4QMA9jaGms01tRU+ywpCoVCqU2MHN7YoNs5lfHDT9cg0bE2CyEECaev4tLQOTjg1Blnu05UecfJcByCv16PK6PnY69NOxz19UfC8UuwadcUbsP6wMjKAiZuqouSEokULoN66GSvrtCAXR1o8e2H4MQiPP7fn+CLig22Ttn2lKxEglvvfA5jFwc49+9aYZwmHyxCCHieVNn9SQiQllaodpyZqRHatXWt2mIUCoVSw/TqWR+dO7rjZkCcQbePXiYjsxC3b8ejS2dPrc4jhOD+ov8h7Jct8qq6GgggwvNID3hYbnz84fOI238GLVcvRvzBsyiIT1J6PiPgYOHnBac+moU46AvqedEBhmXRbPl8jEq+gc47f0a7P1ag/aav5W4zA7kXGZaRtxmohPr1rGGsJlqdkKoFlWnLzHfaQCSi2phCodRtOI7Fzz8MwpTJLWFs/GJrRCwW6LWB4qvoknQRt++Uonu0tlV1Xx5f9u/AT35AWoDqWBYTTxf0PPEnGLZ65QS9ulQBoYUZ6k0YrPjdxN0Z18Z+IM+DF3AAL9ObYiC8DKlX76IkMxtG1uVLSRsbCzFyRGPs2vOoUi8MxzIVsoIMBcsymDG9DaZMblkt61EoFIqhMTLisHBBZ8yZ2Q5hT9NACODna4ez5yNw+OgTg6ypS5+jJ79sAcOyIDKZ+sGaomau9htWVKmVja5Qz4secfHvhlGJ1+Azd4K8Ku5LwsWmXVN4TR+tebSrEqSFlce2vDevA5o1dQRQfgmWZWBuIYKLi3lVl9aIb1b0wbzZ7asnqJlCoVCqgYyMAmzZ+gDffX8Zx0+GoaBQArFYgJSUfAg4w1xGO3Vw12o8kcmQFvBQv8JFDQzHIfN+SLWt9zLU86JnHnz6AyL+2FXheMbdEGQ/joTA3Ax8fkHFdGuGUeulEVpZQGxvU+ljxmIhNq4bjiPHnmD/wRDEP8+GubkIQwc1xNgxTXHufAR++Pmazs9LU2jLIwqF8jpx7MQTrPz2EmQ8ARj5F9z+g4/RwMcGgwb4gTeAWLC2EkMo1LKeGMNAnt+swZcwywJEptFQVRBCNEqlNgRUvOiR2P2nEL5uR+UPEgK+oAi2HZqjKDEV+TEJYIQCgABEKoW5jycK4hLBF5dUqgAYjkWDuRNUNsIyMuIwZlQTjBnVpMJjI4c3xumzEXgUnFxua6lMMzVuZI/HoVWvy2LIEtcUCoVSndy+G49lKy68OPDSV3N4RAa273oIlgW0Kf2libyY8lYrbcyUz8swcOzVHimX7qisRWbTpgkKk9JQ+DxZ6zUqIJPBqV/nqs+jA3TbSE8QQnB/YcVmja8MQvqth+h9aSt6HP0DDT+chkYfv4NeZzZjyJOT6PTf9wDDVKiqy3AsrJr5ocnnc3S2TyQSYP1vQ/H2Wy1h9lIKs4uzOb78rCesrTRLf1YGwwCenlaKrSsKhUKp6/z9zz2V2+1paQVab5Ev+7IX/Pv5VDovy8gTMMaMrHgDqgmNFr2jXLiwLITWFpDxvF6EC8NxcOzdEdbNDRe0rHJ9omv3vlpKTk4OLC0tkZ2dDQuL6vMC5IQ9w7GGAzUa2/fKdjh0a1vu3Ogdx1CclgnC88gJi0LK5TsAITCytUKDeRPR+NNZEJqZ6sXW4mIpnifkQCjk4OpiAZZlsHjJKVy8/ExdbFalsCwDlmGw/rehsLQUIzgkGSzLoH07Nzg7mevFZgqFQqlOCosk6NJjk97mY1kGrVu5YN3aIWDAYONfd7BzdxAKCiQAAI5j0K+PDz75uBusLHXvEfR49SYEfvpj+VRploXQ3ASmXu7IehCqj6cDc7/66Hd1h9JQBl3Q5vpNt430RElWrsZjRbZWAACZRILbc5fj2eb9cm8Ly8gzlBig2bL30OC9yTCysQSrYX8jjdcXCeBVv/wbrkf3+jh/8ZlO8zVt7IC3JrfE+o0BCHz4oh4AwwB9+3hj6We9YGZGC9ZRKJS6Q5mo0AcmJkKMGdUE82a3h7C0N957czvgnWmtERKSAqlUBt8GtrCx0T7D6FUafzILzv274un6HUi/HQROLILbiL4w83bH9XELqzw/AIBlYN+ptV6Fi7ZQz4ueKExOw0HnrmojVk3cnTA85hIYhsHd97/B09+3KT2n3R8r0GDOBEOYW4HiYilGj9+J5JQ8lb2ROI6Bs7M51v48GJmZRbCzM4G5uQgT39qNtPSCCueyLIPmTR3x54YREAjoLiWFQqn95OYWY96CI1WKA2QYoH07N7z/XifUq2cFY3H1ls9/lZvTliBq22H5DbIeEJgaY1xeoF7mKkOb6ze9mugJY0c7uA3rrTYVus2aL5F2KxBXRs3H09+2qhQ7wSvXQVZNTSDTMwrRtq2rUnPKnpaDvRnWrx2Kep7WaNXSGe5ulti7LxipaRWFCyCv/hsYlIRr12MMaD2FQqHoj2UrzuNJWFqV5iAEuHP3eWkR0ZoVLgBQnJ6pN+ECANL8QqXNeasDum2kR1r9tATJV+5CkpVTqSjx+3AacsOjcXXUfHmqmhoKE1KQcTcYdh1aGMJcBY9DUzDnvcMoKpJWKHJnZmYEP187WJiL0KtnffTt7QPxK9V8Dx8NVdmigGUZHD3xBD171DeI/RQKhaIvYuOycflqtF7mkskI8vNLKnxn1gRm9dw0bhmgCaZe7jVaz4t6XvSIubcHBtzZB/fR/cu1CRA52qLBu5Ng7GiLwE9/lB/UMDK2IC7REKYq4HkZFn16CoWF0ko9J3l5JXCwN8VPqwdiyKCGlX4Is7JVN4WUyQjS0gr0ZjOFQqEYitt34vQ2l1gsgIWlSOUYQgjuPUjAvgPBOHYiTO33qa54zxijN+EChoHvvIn6mUtHal4OvmaYe3ug2961KExNx+Nv/0DEpj0oTk5H+Hol9V/UcG/hd7Dv0R4JR84j69FTcCbGcBvRF7btmulF9V67EYvk5DyVY06eDsflK9Fwc7PAqBFNMGxIeRHjYG+KmNgspVtOHMfAxZlmHVEolNqPlCea1AxVC8syGDakoSJAtzKCQ5Lx5VfnEBubrTgmELAYP6YpPljQWW2coEwiQdyBs4jcvA8FcYkwdnWE17RR8Bg7AJyRPEkiPy4Rz7YcQH5UPKxaNETWw6q3MzD38USD9yZXeZ6qQMWLgYjavB9ha/6r8jyFz5Nx0EHerZMVCkAIweNVG+HQsz267f8NIhurKs0fEpIMjmNUBukCQEGhBOER6fjfD1dw5FgoNq4bDtPSejGjRjTGL2tvKD2X5wlGDGtUJTspFAqlOmjWxKHKwoVhADtbE8x8p63SMZHPMjBr3mFIJOW9IVKpDDt2ByG/QIJlX/RSer4kLx+XBs5C6rV7AMcCvAy5YdFIPncTT9duRa8zmxG+bjseLv1VfqPLMIoYFdZICFmJ7tlUTZfOg8BY93RufUC3jQxASXYugr76Te/zyiRShdsv9eo9XB42r8oBU5yA1fiDWjbuSVgafl5zXXF81IgmSuu5MAzQs0d9tG/nViU7KRQKpTpo3MgBDf3swHG6e7Z7dK+H/zaPhp2K5oob/7oDqZSvNF6QEODQkVBERWcqPf/e+98g7eYD+S+lgbhlfY0y7gXjQv/pePjFL4CMgPAy+bWjdJxMIoXYya5ceIPGMAwce3XU/jw9Q8WLAYg/dA6yohKDrkF4HmnX7yPlyp0qzdOpg7vKYNvKkMkIjp0IQ06OfG/29p14JCRWXueG4xjMnN6aNmqkUCh1AoZh8L9v/WFlKQary8UdgI+XLRwczJQ+XlgowYWLz9SWpTh56mmljxWlZiBq6xEQJdlDhJch4/Yj5QYSgqKkNEDL736G4+A+qj9M3Jy0Os8QUPFiABLPGL4BIgAwAgFi956q0hzNmjrC3U37ejgSiQxhT9MhkfL4etUlKPuIy2TAL2tvVslGCoVCqU483C2xe/t4zJjeBo6OZjAWC+DubgkTY81SnvfuD4ZERXBsXl6J2ptGhmGQmVVY6WNpN+6DSKUa2aJ8AYAzFmuU+QqGARjAsrE32v+5smrr6gkqXvRMcUYWYvecrKbVCKR5VcviYRgGq/83QKdz//7nLi5djkJmZqHSRmMyGcG9+wmIf56tZASFQqHUPmxsTDBvdnucPPI2rl+ejcP7JmPHf2NhY62+D1x2TjFSU5V/N1tYiNR2jZbJCBwdK/fe6KO8CsNx8Jo+EgJT43IChikNMHYZ3AM2bZpA7GwPm9aN0W79V+h/a0+V4yz1BQ3Y1TNR/x5S2dFTnxAZgWUjryrP8/x5jk7n3b77HI+fpGgUmf88IRdurpY6rUOhUCi1AQ8PKwwb2hBbtwWCV+M5CQpOUpplKRIJMHigL44ef6Jy62jooMqbHtp1aC4XHLo0oyuFSHm4DOqJpkvfQ8SfuxF/6Bz4wmLYtGsG33cnwa5jS53nrg6o50XPZAU/rdAV2nAQCK0twRdXLb6mKj088vIkGt0FWFqornVAoVAodYFuXeqpFS4A8PmXZ7HvQIjSx2fPbAsLC7HSwOCyLavKMHZ2gMe4Abpfa1gWxq6OcB7QDcZO9mi2bD4G3j+EIaEn0fm/1bVeuABUvOgdgYkxoDQCRDmdt/0I9/GD5L9oHNzK4M6cZTjo0g3xh89pvWYZXvWtdT5XHQzk+8d+vnYGW4NCoVCqi5YtnNC4kb1GoSL/++GK0jpaTo7m+PfvUWjf1q3cFcPaSoxPFnXF3FntVM7dfv1XsGzaQP4lq0VCBCPgwBoJ0GXHT3pv+lud0MaMeibp/E1c6DtN6/MGh56AhW99RG7ej7Bf/0V2SDjAMnDs1QEiW2sknr4KSbaSYnKlOfx9zm+BY88OWq9NCMHEKXsQEZmhdeaRJvywyh99envrfV4KhUKpTgLuxOOff+/j9p14jcazLIOZ77TB3FntVY5LSMhBVEwWjMUCNGvmqLKw3ctIC4sQvfUwwjftQVZgqPoKuiwLjzH+8PtwGvKjn6MoOQ0mro5wHdILnLjmvePaXL+peNEzhBCc7jAWmQ8ea1yKmeFYjE4LgJHVC3tlEgnAsgplnPPkGY41Gqh8EpaFfZfW6Hdlu052hz1Nw4zZB1FcUnmbAF0wNRXik0Xd0KuHF2QyGczNRTRlmkKh1EkOHQnFym8vgmUZjW/yGAbo3dMLP+iYFKEp+THPcbheb9W2cCyafPkuRHbWePjZT/JkDwYAAViREZqvWIDGn84GIL/+FKdnQWhuCoGp8lo1+kab6zcN2NUzDMOg5/E/cWnwbGTceaSofKh0PMfBbVS/csIFAFhh+ZS8+MPnwHCs0rx+yGRIvXoXhYkpMHZ20NpuP187bN0yBn/+fQfnzkdWWcCIxRzat3XDT79ex/KVFwAAbq4WeGtSS4wZ1UTn+gkUCoVS3aSm5ePb/10CAK280yzDQCQy/GVWWqBBPySWRdqth0g6ffXFsdKnIisuQeCSn5B64wFM67ni2eb9cnHDMnAd0gvNlr0HmzZNDWO8jtCYFwMgtreBf8Be9D63Bb7zJsGmXeUvOsNxEJgZo8U3C9XOWZKZA0aDTdaSrMqLxWlC/XrWWPV1f1w5PxMnj7yNRg0129etjKIiHhcvRyEnp1hxLP55Dv73wxUsW3HeINtTFAqFYggOHw3VKT2ZlxH06lFf/wa9gqmni7xmiwqIRIrUa3dVjnl+5AKe/rbtRQkOGUHC8cs43Wk8ks4pbwFTE1DxYiAYhoFTn05o+9tSDLi9H+3//Fpejvkl7Lu2Qf+be2Dhq/7NbeblDpmabShGIICxi/Zel1cxNhbC0dEMq1f5w87WtMrzvcqJU09x4eIzvc9LoVAohiAiIkPrcziOgYeHJfLySzDnvcMYM2En3v/oOC5efgZehTdeFwQmxvB6Z7Ty7COWgcDMBHx+5UXvyvGKSiM8D8LzuD75Y3k4Qy2BbhsZGL64BHEHziAnLAr1p46EqYczTOu5wcK3Hsx9PDWaI+PBY0gLi8AIOBBJ5VUVGQEHz/EDYWSpv+7Nri4W2LV9PPYdCMaff92FVKqfDxzDALv3PULfPjSIl0Kh1H4IIVr3kXN1sYCAY7Him4tgGQYyQhAdk4Vr12PQqYM7fv5hoF63lFp8/QGSL9xC7tOocuEFDMeBAHAe0A1x+07rNrmMoDglHc+PXYL7yH76MbiKUPFiQJIv38a1Me+jOC0TjFAAEAIi5WHbvjm6H9lQ6TmZgaF4suZfJJ66BplUCshkKMlQXZ2W4TgY2VihxXcf6f05WFmKMXN6W9jamODr7y7pZU5CgIhI7e9kKBQKpbo5fDQU5y5EarVt1LaNK8QiDjcD4gAAstKTy7bLA27HY+3vN7F4UTe92WlkbYn+N3bh8febEL5xFySZOQDLwmVIT3AioypXfmcEAmSHhNca8UK3jQxEdmgkLg2YieJS4UFe6gidcT8EF/pNl4uTl3i08necbDUCUVsOoigpFSVpmRoIFxZuI/tiwO29MPVwMcyTATB8aCMMHewHAHoJthWLqW6mUCi1C0IInkVlIPBhIpKT83Dv/nOs/OaiVsJFwLGYN7s9rt2IVZr4ICMEBw4/Rl6efhv4GllZoOWqRfC/sx9d9q5B77P/wHVoL720rCEyWbVmHqmDXkEMROiPmxWek1chUh7Zj57i+dGLChUbvmkPHi3/Tas1Wv/6OepNGgqxvY1ebFYFyzL4amlvdO9aD1t3PETY01SAyAPSdNlO6tDezQBWUigUim5cuRaN39ffUniFGQCWVmJFOrEmiMUCbFw3TCPPcnExj5DHyejQ3l13o18hNyIGd95dgaSz1xXHGI7T6jkohRC4De9TxUn0B/W8GIjYPSdU1nlhOE7REVpaUIh787Xv1Bmz81i1CJcyZDKCoEdJCA5JRkkJr7NwAYB+tGgdhUKpJZw+E44PF51A5LMXooMAyMoq0tjr0rWzB65dnIlmTZ00PkefVdbyouNxpuM4JF+8VX4Nnq+6cGFZeE4cDDMv/QmtqkI9LwaAEAJpgeqobsLzkOTKK+bG7j0FWYn2UdzpAUHIjYyFubeHTnZqy7o/ArB1x0PF77oKF5YF7t5PgLW1MRo3qnp2FIVCobxKXHw2duwKwolTYSgokEAkEsDbywazZ7RFp44eiu3v4mIpvv3+MqBBg1lV3LgVh7S0Ajg4mKFlcye14xkGsHfQXzbnoxW/oyQ7V+PiqNrgNrwPOvz1rd7nrQrU82IAGIaRCwoV1WQZAQcLP3lH6KygMF0XQsTGXbqdqyWZWYXY9pJwqQqEANt2BOKtafswd/5h5OYWqz+JQqFQNORBYCLGT96N3XsfITe3BDxPUFAgwaPgZCz48DgWLDyKyGfpuHQ5Cn/+fRd5eSVV9oLIZARfLJP3mPPyskHbNq5Kmy4C8u/B+e8fRWpavuKYtLAIUduP4NGK3/Fkzb8oeJ6s0drS/ALE7DhmEOEicrBF9wO/Q6Cmjkx1Q8WLgWjw7iSVjxMpj+L0LKTdCgQrNtKqsdaLSQjSbgbqZqCWXL4SpbdUaUKgCGS7ey8BCz8+oXUaIoVCoVRGcbEUHy0+geKiystKAMDNgHiMnbgbH31yEv/8e19va997kICEhBwAwLcr+sLJUXXpirT0AqzbEAAAiNl9Agedu+DmW4sR/O0G3P9oFQ559MSd91ZUSO54leK0TJ2895rACmvnBg0VLwaiwbxJsO/aRnlVXJZF9LbDONNpPJLO3tDZX8kaCSscy7gXjLDftyF8ww7kPI3Sad6X4XkZjh3X0TukBpmM4EFgIq7fiDXI/BQK5c3i7PlIZOcUVznMQ1ceh6YCAOztTfH2lJYqx/I8wcnTT/Hs4AVcn/gRJDlyLwyRSAEZAWQyhG/YiXsfqN6yMbK2hNbl0FkGjIBTu0Pg2Ev7Zr/VQe2UVK8BnMgIvU7/jZDv/kD4uh0oyXwl5VkmAyl1ZGTefwwjG0t5af9KspNU4TK4p+LfeVFxuD7hI6TfDnrxhiQEzgO7o/PW1RDZWuv0XNb9EYD7gYk6naspCz8+galvtcS7czuA46implAouhEckqxV80R1MAxgZ2uC9IxCjebkBC/EQHJyHgQCVqXXWiKRIWjZr/KMoMrmJwThf+xCk8/mwMRNHksjzS9A1LYjiN52BMXpWTBv4Anb9s2QcSdYHqCrDpYBZAREpnoskfLwXTBF/Xw1AL1KGBCBsRgtvl6IkUnXYNnMV6nCJTyPkoxsWDVrID+gxRaSxxh/AEBxeibOdpuMjPshpZMShTcn6cx1XOg3HXyJ9jUFsrKL9BbrogqZjGDL1gf49n+XDb4WhUJ5fdHnzQ9TmmL85Wc98fuvg9WOFwhYtGr5ot6WqYmRWsFjUZyDguCwyoXLS8Tuk2enFsQn4USL4bgzbzlSr99HTmgkEo5fRvqth/Ltd008MGrWYgTyNgNtflsKu/bN1c9XA1DxUg2UpGch+9FTlVtDjICD27A+6HPhX/jMGa/x3HyhvJto+B+7UJiYWmnAFuF5ZD4IRdyBs1rbvmPnQ41iXcoi93UJ3SmDEHnb+ZfTFSkUCkUbOnVwr7LXpez7zMRYiO++6YduXeuhYwcP9O+rvMQDwzAYMawRrCxfBLb26eWl0haGARq7G6u1h+FYlGTlghCCq6MXID/muTz9ufSaovC2yGQwsjTT4BkqR+xsj3qThsL/zj74zX+rSnMZEipeqgGpJs2wGAZ8YREce3VE+w0rIHa212huoYX8jfpsy0HVW04si6j/Dmk0ZxnbdgTir3/uaTT2i897YPV3/ujY3h2eHlbwqq/bFhXLAkePP9HpXAqFQunUUf4dpCsM82LLKb9Agu07HypuqJYv7a0osFnm4CjLKOrUwQ2LFnYpN5eHhxUGDvBVWpWcEGDCu73BqPEWEYkU5t7uSL/zCOm3g1RmFckIgdDKQv0TrQSGY9Fg3kR0+vd72LZtptMc1QUVL9WAsasjBKaq1TWRSGHR2Efxu9/76vcZbdo0gbGzvE5KcXqm6sEyGYpTNPdo3HuQgJ/XaN4CvWM7d/Tt4411a4fi4N5JaN7MSWWaoAozcflKFB6Hpmh9LoVCoXAci99+HQwHe91qqLya+fg4NBXTZx1AXHw2jMVCrFszFGt/How+vbzRvJkjevfyxm+/DsHaX4ZU2mhx2ec94d9P/t3OsgwEAhYMA4hFAqxc3gfdBzaD28h+iq2ayhCYm8J9tD9SLt5SK3SkWbmKGmJawzAoSk7X7dxqhgbsVgMCYzG8Z47F09+3Vx5MxcjblXuOH6Q41PiTWYj4Y7fcPaiE5t9+qPi3qacrsrJylW9NMQxMvTQvyb9zVxA4jlHam6MMjmPQrq0bnJ3KpwRKJLzOdRNiYrPx1rR96N/XB19/1QdCofxDTYi8wm9qagFsbU3QormTXvosUSiU1ws3V0sc3DcJy1dcwLkLkVWaSyYjKCyU4K/Nd7FiWR+wLIOuXTzRtYunRueLRAJ8u7IfZs1oi3PnI5GXXwIPdyv49/OBqakRAKDV6sVIuRgg3xp6+RrBMgAB2v+xAtL8QmTcfwyiyZYYr1tZCyLlFUHBtR3qeakmmn21ABZ+9SuoZobjwLAMOm9dDYGJ3DtDCEHWo6do/etnsGzuV2EuViRC5+0/wsX/RUdSn1ljVadbEwJjJzuN7b17/7la4QLI94QXf9S1wvFGDe2rXLvl7PkI/PSrvEfHtesxGDZ6O6bPOohPPj+NGXMOYujIbbhw6VmV1qBQKK8nxmIhXF0tIBBU/TLH8wSnzoSjuFh1vRVV1PO0xsx32mLhgs4YNaKxQrgAgFl9d/jf2Qe3EX3KXSOsWzRCt4O/I+1mIA66dJM3WDRwTaz6bw0z6Pz6gnpeqgkjKwv0u74TIas2IuLP3ZBk5QIMA6f+XdD0y3mw79waAJB49jruf7gK2SHhinPNfevBrktriO1tYOFbDx7jB0FoVt4lauykPkbm+bFLaLPmSzAaRNVq4s+wtBTh379Gw6OS/eUhg/zw+/oAFJdIdf6sEQLsP/gYLVs4KSpXvkxSUi4Wf3oKq1f5ow/tlUShUF7BylKst5RpiUSG3LySSreG9IFZfXd02/cbitIyUBCbCCMrC5h5ueP6pEWI2XXc4KIFAMAy1PNCqYiRlQVafb8Yo1NvYVTyDYzLvY9eJzYphMvTddtx0X9GOeECyDuFRv93CE59O8N7xtgKwgUAUq/fB1TsmQJAflQ8ChM0iyVp385NZcwKyzIYP7ZZpcIFACwsxFj1TT+wLFulrR2el+GHn+Xel1c/u6T0Z/VPV8Hr6CalUCivL/37+eiterdQyMLczEj9wCoitrOBTesmMPNyR8aDx4jZeUwvwkVgZqJ6AMPAtpamRVcGFS81ACsQQOxgC4Gp/M1UlJaBs72m4O78lZW/SWUEREZw592vlH4QCSEaeVQ0/RBMmtBc6bYRw8jrGYwe0UTlHD2618e2LWPQr0/VvCKZmYUqzU5NK8Dd+wlVWoNCobweSKUynL8YiSVfnME3qy7ByEj1TZ0mcByDAf0bGMzroozobUdUBvJqgzSvQPUAQtBw4TS9rFUdUPFSw8gkElzs/w5Sr9xVPZAQ5EXEIu1WYKUPO3RtIy8prQITd2cYu2jWxblFc2csWdwdDINyHhh5tDyHH/83APYaRPP7+dphxbI+6Nhe82BhXUhN1TG6nkKhvDZkZBRg4pQ9WLzkNM6ci8DNW3EoLq5as0KWZWAsFmLmO231ZKXmFKWko7r6HJj71YfHuIHVs5geoDEvNUz8kQvIfBCq8fiC2ESgU6sKx12H9YaJm5O8UF2lGU1Aww+nKe+1VAnjxjRFyxZO2Ls/GPfuJ0AgYNGlsyfGjmoCFxfN6gg8Dk3B+x8eR0amBrVuXjWZAdxcLRAXn6N2rK2NGpcohUJ5rSGEYMGHx3QucskwlTumnZzMsOanwXB3s6yihZpDCEHs3pNIunBLfbl/jtU5u6jcmlJeM+99LYGKlxomZvcJMBwLouGbT2RfvvhbQUIynvy8BXEHz6AwNQNEJiv3KWQ4DoTn4TlhCHw1qB3zKr4N7PDFkp5qxxUWSnDqTDgeBSeD4xh06uiBxo3sMfe9Iygo1K3bKcex+PH7gVi46ASSknKV3oDY2BijXVvDenYoFErtJuhREkKfpOl8vrKt6YSEXEQ+y4C3l43Oc2tnB8H9D1chbM2/akuWMxwH71lj8fzIBY3jGZVRW7tHK6NuWfsaUpKepbFwETvbw6F7OwBAUWoG7i38DjE7j1Z0KzLySolGttawbtEQDd6dBLdhvbXyumjD7bvxWPTJKeTnl4DjGDBgsP/gY5ibGyG/oESnWDOGAcaMaoIGPrZY9GEXLP70lNKxixZ20Us6JIVCqbvs3P1Ip/PKvhZVFSj/ff0t9OvjXS2eicTTV+XCBVDbUkZkb4Nmy95D29+X4VjjQch7Gl3JQAYiWyuUZOYo9eIwHAfXIT2rbnw1Qr/xaxjzBp4aB2S1Wr0YrECA4owsnOk0vnLhAsiPMQxM3Z3R+8xmuI/oazDhEh2Tifc/PI6CAnnTR54nkJaKsdxc3YQLUFo2e6y8PHXvnl5Yvcof9nblt4ZsbIzx7cq+GOjvq/sToFAorwWxcVk6nScSCVQKFwCIf56DsKe6e3W04elvW8Fw6q8JTv26wP/WHhg7O4DlOAwNPYmmy+eXyypiRUbwmT0Ovc9tkdePqUx8MQwYAYcG8ybp82kYHOp5qWF8Zo1DxMbdasdxpsaI+u8wjKwsEPH3XuRFxqocT6Q8Mu4FI/3uI4P2qNi5Owg8L9N7CYL69a2x+d/78G1giyGD/NCntzd69qiPu/cTkJqaB1sbE7Rr60Y9LhQKBQBgaSFWP6gSCgs1KzyXm1ui0/zakn4nWG2ci0UTH/Q6sancMYZl0fyrBWj65TxkBYVBViKBRSNvGFnKq593O7gOV0cvgKxE8sLNxLJgjYTotv83mHm5G+T5GAoqXmoYmzZN4btgCp7+tlXlOD6/EMkXbiHp7HXNJ2cYpAcEGVS8nDsfqVElXk0pC9eJiclEbGwWjh4nWLvuFr75qi/69vFGh3Y0toVCoQAJCTl48DARhACtWjqjT29vBNyJN9h6ri7m6gfpAdZIqHZMmSCp9HyBADatK5axcB3UA8OjziNi0x4kXwwAADj2bA+fWeMUPfLqElS81ALarPkC5j4eeLz6LxQ+T1Y6Tm3UeSXoq0aAMoqqUC4bAH7/dTDMzESIiMzAn3/dQVp6PggpuzGQi6KSEh5LvjyDfxxHoVlTx6obTaFQ6izZ2UX46psLuHIlWrFrzgDo0tkDYrEARUVV+06qDHc3C40zLKu81sh+CP9jp/LO0SwDt+F9dJrb2MkezZa+h2ZL36uChbUD6nOvBTAMA7/338bwmIsYFHwMfh+8LW/IpQec+nbWyzzK8PG21bqCbtm268TxzdCpoweaN3OCjbUxUlLzle49y2QEn35+Gnl51eO6pVAotY/iYinmvHcY167FlAv3IwCu34ytUu8hVQwZ1NAg81aG7/tT5DEvSuJTOGMxPCcOrjZ7aisGFS8ZGRmYPHkyLCwsYGVlhRkzZiAvT3UxsZ49e4JhmHI/c+fONaSZtQaW42DVpAHyYxKqXpiIZeE2rDfMvT30Ypsyxo1pqnXvkPr1rPHV0t74+MOuiuj9i5ejoC6mOCk5D3PeO4Sr16Jx6Egorl6LhkRStQJUFAql7nDydDiehqeDr+Q7hxDDtP/hOAZjxzTV/8RKsGhQD90PrwdnLKooYAgBn1+I402HIvSnzXprfVAXMei20eTJk5GYmIizZ89CIpFg+vTpmD17Nnbs2KHyvFmzZmHlypWK301M3qwCZJpEmquDExvBoWd7yCQSsEL1e6i6MqB/A1y+Go1z5zVrO79uzRB07OBeLuUwOCQZNwNi1Ub8A0DokzR8sOiE4ndLSzEWLuiE4UMbaW07hUKpWxw5Fqq0mJy2sCwDmYxg+NCGOHz0idJxb09uCStL3YKBdcXFvxtGxF3Go5Xr5PGQr4g1aU4eHnz8PfiiYjT9Yl612lZbMJh4CQ0NxalTp3Dnzh20bSsvq/zbb79h0KBB+PHHH+Hi4qL0XBMTEzg51Y3OlobAqW8nxB04XaU5+IIi3P9wFRKOX0aPYxvBiYzAFxUjZvcJJJ65BiLlYdu+Obymj4LIxkrndTiOxaqv+4HnZbh4KUrteGNjYTnhcuNWLD746ARkmiiXSsjOLsKKby4CABUwFMprTlpagd68Ky1bOGHalNbo2sUTLVs4Y/VPV1FYKIVAwILnZWBZBm9Naon35nXUz4IqKEpJR15UPISWZrDw8wIAZAWFIW7faZVKLWjpGjj16QS7ji0NbmNtgyEG8jtt3rwZixYtQmZmpuKYVCqFWCzG3r17MXLkyErP69mzJ0JCQkAIgZOTE4YOHYqlS5cq9b4UFxejuLhY8XtOTg7c3d2RnZ0NC4vqCbDSN5LcPByu3weSrByNC9gphWHQdNl78Bjjjwv930FRYqq86i6R92TmREboumcNXIf0qtIyj4KTMXXGfpVjRCIO505Oh6mpvDNrSQmPAUP+RXZOUZW/kCwtxThzfCqEQsMGKFMolJpj1rxDeBCYqPVW9au0bOGEzX+OKnesoECC8xcjkZCYC0tLMfr29oadrWG9/nlRcbi/6HvEHz6vSF8296sPMAxynzzTaA5OLMKABwdh2bBqDXBrAzk5ObC0tNTo+m2wmJekpCQ4OJRPvxIIBLCxsUFSUpLS8yZNmoRt27bh4sWL+Oyzz7B161a89dZbSsevWrUKlpaWih9397qVq14ZQnMz9Dr1FwQWZuUCdxmBPIjLY+wACC3NNJuMEDz+35840WwoihJT5Yd4Xv5BkRHwRSW4Mmo+soKfVsnmpk0c0NDPrlwTx5dhWQYjhjVWCBcAuHQlClnZVRcugNwDczMgruoTUSiUWsuIYY2qLFwAwMJcVOGYiYkQQwc3xJyZ7TBhbDPDC5foeJxuLy/t//KeeW5YlMbCBQD44hLcX/idIUys1WgtXpYsWVIhoPbVnydPlO8fqmP27Nnw9/dHs2bNMHnyZPz33384ePAgIiMrj6n47LPPkJ2drfiJi3s9LmC2bZthWPgZtFr9CRy6t4Nt++ZoMHciBgcfQ9c9azAy8TrabfhKo7lkxSoydEqj3MJ+/a9K9jIMg9Wr/GFjbVwu+6hsh6hZE0e8/1559+uzqAy9FpnLyFDT8p1CodRp+vf1QeNG9lWex7wS8VLdBC75CSVZykv2awwhSDxzHflxifoxrI6gdczLokWLMG3aNJVjvLy84OTkhJSU8o2ipFIpMjIytIpn6dChAwAgIiIC3t4V3WIikQgiUc2/EQ2ByNYajRa9g0aL3qnwmMBYrLfQeiLlEbXtMBrMmwCbNrpH1bu5WmL3jgnYfzAER489QXZ2EVxcLDBmVBMMHugHI6PyWzrGYqFe7qLKcHDQ0BtFoVDqJEIhh/Vrh6Fnv791noNhUG1NFpVRkpmNuP2nlddy0RZCkB/9HKbuzvqZrw6gtXixt7eHvb165dupUydkZWXh3r17aNOmDQDgwoULkMlkCkGiCYGBgQAAZ+c350XRFGlRsfpBGiIrLsGptqPhu2AK2qz5QucGZFaWYsyY1gYzprVRO7Znj/pY8/tNndZ5FVtbE7SnnaUplDeAqt3wcCyLYUOqr25LZRTEJ+lPuJRiZFU9FYBrCwaLeWnUqBEGDBiAWbNm4fbt27h+/Trmz5+PCRMmKDKNnj9/joYNG+L27dsAgMjISHz99de4d+8eoqOjceTIEbz99tvo3r07mjdvbihT6ywFccpjh3Tl6W9b8fT3bXqftzI8PaxKO7WqHysy4sCqGNilkwftc0ShvOZkZBRg/gfHdDq3bDt7ySfdYWNT/eU3+OISZAU/RXZoJATmpnqd29y3HiybvlkNag1a52X79u2YP38++vTpA5ZlMXr0aKxdu1bxuEQiQVhYGAoK5LEKRkZGOHfuHH799Vfk5+fD3d0do0ePxpdffmlIM+skhBAkHL9kkLlDV/+FBu9OAquHejPq+PLzHrh4OQpSqeqsqulTW+P8hUiER2ZU+viRY/I4q0fByUhKkmcLDB3SEG1aOePs+Ug8CUuDibEQvXt5YfBAP5iZGVU6D4VCqZ3IZARz5x/Bs6hM9YMB9O3tjdt34pGTK/dQN2vqiJnT26BLZ09DmlkBvrgEwV+vw9N1OyDJygEAiJ3sYOLpIr8B1bFMxMu0XLVIZ295XcVgqdI1hTapVnUZaWER9pi0MNj8gx+fgGUjw6feJSblYvBw1U0pteHlAlZl/y4rRsUAAAPY2Jhg47ph8Kqvet+b52UIepSMrOxCODuZw8/X7o37gqBQqpuSEh7nLkTg4KFQpKXnw97eFN261sPRY08QoeTmpTJ8G9jCydEc9vYmGDa4IZo1q/7aYTKpFJcGzUbS+Zt6ESmV4TK4B3oe+9Mgc1c32ly/aWPGOgpbmjZtkHrYAGQSSbnf+eISJF8KgCQ7Fxa+9WHdUj8F4fRdl+XlP0fZv8uCgknpf7IyCzH/g2M4fGAyhEoaV546HY5ff7uBlNR8xTEfbxssWdwdrVspL7BIoVB0IzwiHVu3B+L02XBIJC8u9DGx2bh7L0Hr+Z6Gp+NpeDo4jsH+g48x6522mDu7XbXegDz9fTuSzl5XOYY1FkNWWKTzGu3Wf6XzuXUZKl7qKKxQCMc+HZFy8bbKVDuBuQmkudqlEAvMTGDuI3etEkIQtuZfPFqxTuHyBACRgw3a/PoF6k0cgoz7IYjefhTFaZkw8XCG9/TRMPPSrN6OrY0xfBvYIjwi3VA6rAK8jCApOQ+XLkehXx+fCo8fOfYEX319ocLxZ1EZmDv/CDZtGI4WzWkAOYWiL/7ecg/rNgQYZG6el3+xbNp8F87O5hgxrHoqcccfvYD7H61SOYYRcLBs7I3MeyE6rWHXuRVMPd7Mmyka4ViHafzJLKXCheE4mNZ3Q7s/Vlb6uFJYBt6zxkFgYozMoCe40G867n+4qpxwAYDilAzcmLQIB1y64lSbUQhb+x+idxzF41V/4ohPPwR+/rNGTcMYhsGM6W2qTbiUwXEMAm7HVzheXCzFj79cq/QcmUzuxfnpV9V3UhQKRXP+2/7AYMLlVTZvuWfwZoYp1+7i0tA5uDJsnlrPOJHyKEnP1mkdTixCh7+/1enc1wEqXuowzv26oN0fKwCWfdHMsbQ1s7GrI7rtXYOA6Z9pN6mMQGhphosDZuBki+FIPq86lVlRtVfKy394Xl7Vd9VGhK3VrPBdvz4++GB+JzAMyhW4MzR8Ja0XrlyLRl6e8qJ+MhlBcEgKomM0CxqkUCjK+XfrA/y6Vj/lEjQh/nkO4uJz1A9UQl5UHAI/+wkXB87E1dEL8Ozfg5CWbvkQQhD42U84120yEo5f1nhOU08XzSuml34/mnl7oO+Vba9FSwBdodtGdZwGcybA2b8rIjftRebDJ+CMRXAb1hseYwfiwac/QFYiUT/JKwR/9TsYruq6NuS7jfB9d5JGXa2nTmmF3r28cOjwYwSHJOOODnvc2sDzBM0rCeBLSclXBPiqIv55Nup5WhvKPArltefmrVi91XnSBolEt/oq4Rt24M78lWAYVn6TxrKIO3AGDz//Gd0PrUNe1HM8/l9p4KwW3p3Mh6Gw69IaiSeuqB3r2Ksjmnw+B469Or7xyQM02+g15oBzFxQlpdWoDf1u7IJ9p1ZanSOTEQwduQ2JSbkGsYlh5IHC5uZGyM4uAsOwsLYSY/iwhrC1McH/friqdo5WrZzx14YRb/wXCIXyMmnpBdi7PxgnTz1Fbl4J6nlaYezopujf16dCHaZ33z+K23fi9VplWx3GxkKcPzUdYrF29+2JZ6/jYv+Klc5fhhWLINOxcCjDsTCysUJJZrbq4nUMg6HhZ2Du7aHTOrWdWtGYkVLzaBuoawh4HaLoWZbB7JltDWDNiwStkhIe6emFkEoJJBIeKan52PT3Pfy85jpERuozoB48SERwSIracRTKm0J4RDrGTtiJv/+5h/jnOcjOLkLQoyR8ufwcOnbbiIlTduPQkceQlF6c7957Xq3ChWUZjBrRWGvhAgCPv//zxda8EnQVLgBAeBmK07PUFg9mWBZR/x7UeZ3XCSpeXmNM67vWrAEsC4uGXjqdOnxoI7w/vxNYlgHLMuA4RtGxul9fb9jb6VYh09hY9RZWSYlMoy9UjmNw8nTVOnFTKK8LPC/DR4tPIDevpNzn5+VyBWFP07Hy20tYsPAYSkr4qlb5V8mrsXMMAzT0tcO82e21nksmkSD5QkDVGyiqXUimfg0GyI99sxowKoPGvLzG+L47CXfeXVEjazMCDq5DesHExVHnOaZNaYXBA3xx7EQYnifkwNJChAH+vmjgY4vxk3chNU07z1L/fj44czZC7TiJmmq/gPxLOTunoldJJiO4/yCh1F4xOnV0h0hEP2aU15tbAXF4nqDZNu+duwn4+597aNHCCQ8CEzW6WRAZcRg5ojEcHUyx4c/bKClR/hkVCBgM6N8A5y8+Q2GhFM7O5hg3uinGjW0KY7H6+LtXkUl5g9XT0gWxPY21A6h4ea2pP20UIv7ai8wHjw1zl8MygIyA4ViQlzJ3GI6DsZM92v62tMpL2NubYvrU1hWOZ2Zpvh1lLBZg+rTWKCnmwbKaFbrkOEZRH6IyGAZwdS6/J3srIA7f/O8SEl76EjczNcLc2e0wcXxzGh9DeW0JDEqCQMCqbfMByLNy/vrnLjp38lApXBgG+PD9zmjcyAGNGzkotnsIGKxVEujLMMDE8c3x4ftdsHK5/GaiqhmMnFgEc996yA2PqRYR8+r36csQKY96bw03uA11Abpt9BojMBaj78Wt8Jo+GoySSrIqefViy5T+QB6c5j1jLHqd2QzPSUPBCuVfLAIzE/gueAv+d/fDxM1w5bg18WaIRBxWf+ePsyenY+b0tuAErMbfPSYmQpVfejIZwfCXil3de5CABR8eQ2Ji+bvPvPwS/PjLdWzdHqjZwhRKHSE8Ih2nz4bj6rVo8LxMq/ophMizjVR9xggBNv19F/XrW5eLU5n6Vku8/VZLRWmFl7eUhw9thAXvdlKMrYpwKUrNkHd/5nn4ffC2zvNog9DSDJyxuPJsT5aB54TBsG5Rsx2xaws02+gNoTg9E5kPQlGYlIabUxYrH8gwsGzkjYaLZyDk2w3Ii4gFAHAmYnjPGIumX80Hy3EQmJmUa9wok0ggzSuAwMKsWho6frb0DM6ei1R559a9az38+tMgxe8PgxIxfZZmwW4MA9jamiAzs7BSD8ysGW3L7Z9PeGs3noanK51PJOJw9sR02hCSUueJiEzHim8uIuTxi4B1oZAtV9Jfn3jVt8a+XRMrHH+ekIOjx58gJSUfNjbGGDzQD/XrVX1LJe7gWQR/swGZ9+VVb41srdBg7kRkhYTj+aFzuk/MMLBs2gA5jyOUelZafPcRnPt3wfXJHyM3LEp+s0jk2/A+s8ah9a+fgzN6fb9DtLl+U/HyBnLjrY8RvfO40v2TrnvXwH1Uf2TcD0FOaCTEzvaw79QKAtPqbyOvjAeBiZgxR7UQ2fDbUHRo/6JNASEEk6fuxZMwzdLHOY5Bh/buCLgdryhoZ2UlhquzBUokPExNhejf1wdNmzji7Xf2q51vxbLeGDqY3jVR6i6xsVmYPG0vCgul1ZopdOTAZLi5Whp8nSe/bsH9D1fh1f1lhmVh27EF6r09HHfnrdBq+4jhOBCeh8f4gWj3xwpcG/M+ks/fAiPgQKS84v8+s8ej3YavwLAsCCFIvXYP2cFPwRmL4TKoB8QOtoZ4yrUK2piRopL2m74BX1yCuH2n5dtJDAPC82A5Dq1//Rx8YRGOePVBfkxpoTiGgbN/V7RZ+yUsGtSrUdvLaNXSGdPfboV//ntQrqgcwzBykTKhOdq3cyt3DsMw+OWHQRg8YqtGX7w8TwBCcPbkNETHZGL3nkc4fTYCubnF4Hl5l+qHQUkaBQEyDJCeXvOp6xRKVdj4910UFVWvcAGAkyfDMctA5RPKyI95jvuL/if/5ZUbOyKTIe1WINxG9IXYwQZFycq9rC/DioRwHtAdPrPHw2VgdzAMg95ntyD5wi1EbzuCorQMmHq6wnvGGNi0aqw4j2EYOHRrC4duhn3OdRkqXt5ABMZidNu7FlmPwhCz5yQkWbkw83ZHvbeGIWbHMdx8+9PyJxCCpLM3cKbjOPjf3ldrCiTNf7cj/Pzs8d+2B3gcKm9T4Odri7cmtcRA/waVBsg6OprBxtoYaRoKiZsBcQCAyMgMnC7NVCrbRirrUl1QqL6KMSHAjVuxGD6sEaytjDVam0KpTRQWSXD2bITKQHZDISOG2ZJ6mci/98lvfpRlN8gInq7bDkag+WVTVixBl50/Q2AsVhxjGAZOfTrBqU8nFWdS1EHFyxuMVTM/WDXzU/xekpmNB4tXVzqW8Dwk2Xm4PWcZRLZWSL1xH6xAANchveA7fzIs/LSv50JkMuSERYEvKISZjyeMLM21Op9hGPTv64P+fX1QXCwFoFkgr42N5uKFECArqxD/bQss237WmfsPEjB1xn78t3kMrCzF6k+gUGoROdnFkCqJ1TA0vXrqVi9KG7JDI0HUeJQKYhLg0LM9CpNSAQ3/FvpotUKpCBUvFAXRO49BJpEqfZzwvLxRI8cqPrjhf+xE+MZd6LZvLdyG9UFRagYSjl+CJK8Alo284dirAxi24of32b8HEbxyHfKeyT0brJEQ9SYPQ8vVH0NsZ6O17drUUunRvb7K4NqX4Tj5NlRcvG6dX19GJgMSEnKx6e+7WPxR1yrPR6HoE0IIgh4l4cKlKBQVSeHjbYOB/r6KIHMLCxEEHFvtAsbZ2Ry+DewMvo7QzFSepqyqPD8A5wHdkXLptvoJORb2nVq91gG2NQkN2KUAAAqeJ+PiwJnIfqRD1ViGASsUoN5bwxH13yEQqVRRh9+0nis6b/sB9l3aKIaH/O9PPPzsp4rTCDiY1nODf8AeiGysqvBsVJOSkofBI7aqdX8zDODfrwHem9cBQ0du09v6JsZCXDz7DoRCw2dlUd5sHoem4PDRJ0hJyYONjTEGDfRD65bOFbZUs7OLsOiTk7gfmAiOY8AwDHheBpGRACuW90a/Pj4AgC+WncWZc5pvHTGMPI25rP6LpnWWyhAKWOzfM7FagnWfH7uIy0Pnqh0nsDKHbesmSL4UAKjx1HQ/sgFuQ3vry8TXHpptRMWLVuTHJeJ0+zEoSklX+2FUirI9FZYFaySA/629sG7REPlxiThcr5fSdRiOQ8OPpqHV6k90s0NDTpx6ii+Xq057NDMzwvZ/x2LLf/dx8HCoXtc/fWwq7O1N9TonhVIGz8vw9XeXcOTYE0XBxbL/d+3iidXf+b8o+kYIZsw5iEfByRVECcPIBcif64ejdSsXxMRmYfLUvVoF7VpYiDBhXHN06uCOw0dDEfI4BSIjDj2610NsXDaOHg9T9Bx7GXc3C2z+cyRsbavncyLjeZxqMwpZj56qVFgMx6LhxzPACjiErd0KaW5++QGlnumW/1uExp/ONrDVrxdUvFDxohVXx76P+EPn1LpLdYXhOLgO7YXuB9fh0crfEbxyndI6BwAgtDLHmPTblW436ZOr16Kx5IszKCyquFXWvJkjVizrA0IIRo3bqdd1GQa4fG5mhZov4RHpCHmcAqGQRYf27rCzlaemE0IQFZWJwiIJXF0tabzMG0JOThFOnHqKmNhsmJgI0a+PNxr62Wt07oaNt/HXP3crzehlWQZDBvli6ee9wHEs7j1IwKy5h5TOxbEMOrR3w+9rhgIAnoanYcU3FxH6JFUjW1iWgUjEYduWsRXqsBBCcOhIKLZuD0R0TBYAoJ6nFaZMbokRwxpVe1XqwuQ0HPX1hzQnT+U4U09XDI++AL6oGGm3g5Bw4jLSAx6CLyqBTevGaDB3Qrl4QopmUPFCxYvGFKWk46BLV5ViQi+wLMZm3cHd+V8jesdRtUJpTOYdGFkZ/vUrKeFx4eIzPHiYgKysInjVt8GgAQ3g7m4FQH4R2PzvPb1lWLAsg47t3fH7miGKY/HPs/Hl8nMIepRcbtyQQX5o2cIZ//x7D3HxOQAAjmPRv683Fi7oXMFzU1LCI+hREgoLJahf37paXO0U/UIIwd17Cdi7/xEuXooCLyMQCOR1P3ieoHtXT6z6pr/KBqOFRRL0G7gFBQXqs+Aa+tnBylKM23fj1W7nXL88q1xZgLCnabh2PQbHT4YphIcyOI5Bvz4++O7rfpU+TghBTo68K7OFhahGW2mcaDMSWfcfqxxjZGOJMekaxL1QtILWeaFoTN6zOMMLFwCQyVCSlQsja/WChBFw4Eyqx7tgZMRhgH8DDPBvUOnj6RkFpV+k+hEvMhnB5InNX8yfXoDpsw4iK6uwwrgjx57gyLEn5Y7zvAxnzkXgfmAitv0zBra2JiCEYNuOh9i85R6ySy8AANC+nRu+WNID7m7VK2JKSnicOPUUBw8/RnJKHuxsTTBsSCMMHeyntqv3m0zkswx8vOQUYl4RAi/3C7p2IxZfLD+Hn1cPVDpPUFCSRsIFgMYFGwFgyPCtGDTQDxPGNYOriwX8fO3g52uHGdPbYNXqK9h/METpVhLPE5w7H4llX/QqV+q/DIZhYGlgj2JO2DM8Xb9DHmxbWruqwdwJMKvvXm6cVWMfZAeFKb/BYlmY+9U3qK0U9dAcrjccgZkequZqcJPEiowgsrOG54TBKr0ujICD+2j/WhOh72BvCpmexd2zqEzFv3fseqi0BYEyeJ4gLS0ff/59FwDw+4YA/LL2RjnhAgD37j/H1Bn7kZikWbdffZCXV4IZcw5i5bcXEfI4GSkp+Qh9korvf7yCt6btQ0YGLdSXm1uMQ0ce4+8t93D0+BPk55cgJSUPM+YcRFyc6qw2mYzg0uUoRD7LUDrGUGX6M7OKsH3nQwwduQ179gWXe4znZWr7CEl5GXLzilWOMRTP/juEY40HI3z9DmQFhSHr4RM8+ekfHPMbgPjD5WPffOaMV+0ZlsngPqIvHnz6A66OeR8Bs75EzO4TSL/7CAXPk5WfR9ErVLy84Vg2aQBTL3f1A1/i5SaPDMeCE4nkHaZVjPecOBSZgaEoTs+CTYcWlQsehgHDcWjy2Ryt7DEkQwb56RzDrIxrN2IU/z587IlO1Up5Xu6ZiY7Jwpb/7isdk5tbjL//uaezrdryw89XFbEQZdsQRF6oGLFxWfjq6wvVZkttgxCCLVsfoO/ALVj57SX88edtLF95AX0HbsGylReQl1ei0XuB4xicvxCp9PEGPrYVeqrqm//9cAU3b8Uqfnd0MFNru1DIwsJcZFjDKiHz4RPcmrYEkMnKiRLC85BJeVwb+wHyouMVx+27tIHP3Iq9lMowsrdG4Kc/IvSHvxG3/zQi/9qL6xM+xOl2Y3DIrTvO934b6XcfGfQ5Uah4eeNhGAYeo/trNphl4dS/C7xnjIFl0wawbtUIjT+bg6HhZ9B85QeVz89x4IzFSDx9BWc7T8DlIXOQEfCw8l0YQtDww2m1qmuqk5M5TEz0u9VRUvLiCzQrq0jneYqLpTh4KERlfADPExw7EVZuTUORmVmIE6fCVW4dXLsRq5eaOXWRHbuCsPb3m5BI5K9FmbetuFiK23fiNRaxDMOorOpsZSU2uEhgWQb//PdA8fvgQX5qu0oLOBbhEZrVV9InYWu3Ki8URwiIjCDij12KQwzDoM1vX8L3g7dhZGelOC60NAcjFKAkNVNxbmWkXLmDs10nIfVG5TcVFP1AY14oyA6JkHtO1H15ymTwGO0Pn9njKzzU5PO5ENlZ49GK31GUWJqFwDCwaOiF7JDwiumESnjy679ovGS21tV2DUX88xyN4wc0gWWBZk0dFb+LRQKN2gtUBsMAWdlFYBkGMhUxOSUlPHJyixXZS4YiOCRZ0cBSFYEPE6s9DqemKSqSYuOmO3qZSyqVKe2eHP88Gyu+uVhhC1HfyGQEd+89h0TCQyjk4OJsjmlTWpUTNK9SVCzFnPcOY+d/4+DhYWVQ+9LvPkLcgbOQ5hcg7sAZldtAhOcRveMYLBp6wdm/K5Iv3caDxatR+NIWkGl9VxSnZoKoKOL5Yj4ZCJHi9uxlGPToaI0GH7/OUPFCQca9YI3qu4jsrFFv8tByxyS5eUg8cx3S3HxYt2iI4TEXkXkvBNK8AohdHXG67WitbJEVlyB6+xH4vjtZ6Zicp1GI/GsvcsKiIDQ3hfuYAXAd0hOsFj1HNLZHm4paGsFgzKgmit+EQhYoVDFcCSzLoEsnD9jbm6rt+yLgWJiZGiEruwiXr0QhN68EHu6W6NzRAwKB/pyvmn5Jv4nf5QG345CXX1LleRgAxibybuavcuDQY3z3/eVqbZoolcoUxRbnv9sR2TnFOHCo8kwdQuRC+r/tgfjys546rUdkMiScuIxnWw6gID4ZJm6O8Jo2Cs4Du4PlOJRk5+La2A+QdPb6i6azGgiOgrhE3Jr+GSotOAMgPzpBq07SkMmQHRKOjLuPYNuuufrxFK2h4oUCVqQ+OJYRcOh1+m8ITEtrj8hkuPfRKoSv217ursbY1RFd962FU9/OeLblAPhC7a/MgUt+wtPftsF9VH80mDcRJm5OiseCv1mPoKVrXrST5zhEbz8Kq+Z+6HVmM4wd9VtG3NXVApaWYmRn67698zID/Rvg4OFQGBsL0Kt7fZ1zmBhGvj2wZ1+wyhRXlgX69vHCpr/vYNvOh5BIZIou3La2Jlj+RS907eKpoxXladrUEQIBWy47pjK7W7d00ct61UFuXjHOnotEckoebKyN0b+vD6ytVTfWfPgoCXv2BSMoKAlCIYse3evrrRknATBlUgtF1tbTp2l4/CQF8c9zsHlL9W5TuLtZlsscKqvKq6qKLs8TnDj1FF8s6aG1R0JaUIjLw+ch+dxNMBwHwvPIuMshbv8ZOPbthO6HN+Dq6AVIuRQAALrVrVImUHSsKJIbGUfFi4GgdV4ouPfhd3j62zYQXsmHnQGaLn0PzVe8rzh0fdIixOw8Vvl4lsXAe/sRf/Qigleul7cL0AWWAWtkhJbffQSv6aPw/Pgl3HxrceUmchxs2jVD/xu79O6m3fjXHfz5151Kv78YBjA3E6F1axdcuRqlUelzgYCFTEZ0vkNmGMDO1gTpGYUazdG1szuu3YirdB6GYbBx/XC0aeUCiZRHcnIehAIODg6mOv0dv1l1CYeOhFZqF8sCTRo7okM7N3ACFh07uKN5U8da61bftScIv/4mj1HhOLb0wszinamtMXd2u0rt/mvzXazfeFtRzRaQe8k4jtFLFlCZY2DyxOYICkrGo5Cay275ZFFXTBhX/sK86NOTuHQpSq0ov319rtZev4DZSxH5977KlRHLwnVoLzw/fF6rOQ1Nz5Ob4DKge02bUWegReqoeNGK3MhYHG86GLISSYXtI4ZjITA3xdDwM4qGiTlPo3HMz1/lnJbNfOH77iTceXeFznct5ewwEkJoaoKSrByV8/W7vhP2nVtXeb2XkUh4fLj4JG7cjJXHl5AXFyUzMyP8uW448gskmDHnoF7XrQyGATw9rBAXn62Xwnksy6BpEwe0a+uGvfuCkZMrj5Wo52mFd6a1wZBB2lUJLSyUYP7CY3gQmKjw8JT9zcq8Mhwnv+jzPEHTJg74afVA2NvVrlYJh4+GYsU3F5U+/u7c9pg5vW25Y9dvxmLBQiWCHi+2y5SJYIbRru+Ptn2C9M3Zk9Nga1M+juqXNdexY3eQyvemvZ0JTh+fptVaRakZOOjSTfWNkC5/RANiZGOJkQnXwGng2abI0eb6TbONKDD39kDPY39CYGIs//CzrPwHgJGVJXqf/adcp+egpb+qnTP70VM49O1ULq26KpASCUoys1UKF0bAIeH4Zb2s9zJCIYdffxyElcv7oEkTB1hYiODsZI7pU1tj744J8PW1Q8sWTmjfzlVtrYuqQgj0JlwAeeBl0KNkbN5yTyFcACAmNgvLVpzHxr9eBJmmpuYj8lkGcnOVB4MaGwvxx7phWPVNP7Rr6woPd0s0aeIAkYhTeGN4nijsD32SijnvHq6WbChNkUpl+G3dLZVjNm+5j/xXYli27QgEp+L1J0Tu6arsPUKI9tfcGr9GV/IWHD6skcr3JssyGDOqqdqpZVIp4o+cx8MvfkHQ8rWI+HO3eg+uhn9EsZPhO1QDQLMV71PhYkBozAsFAODUpxNGxF9B1H+HkHr9PhiWhWPvjqg3cbAizqWMnCfPNJpTkpGNpl/Mw6OvflM+iOMAZdtVWsIwDPjiqgdFVoZAwGLIID+lngiGYfDT9wPxxbKzuHItBiwrv0ipiv/QhrJtiLffaon/tgXqZc6XeVUTlv2+cdMdODuZ4eDhUDwMSlLY0rePNxa82wkuzhWzwoQCDv79GsC/n7xq8S9rb+Dx45RKt5J4niA6JgvnLkRi0ABf/T4pHXnwMBEZmapjtYqKpLh6LRqtWrmgpEQKWxtT3H+QAF7NNp61tRhmZqIKVXTrGo6OZpXG/njVt8G0t1thSyVZRxzHoJ6nNSaOVx0DknEvGFdGvIeC+CQwQoE8nVlPfdeEluZo/+fXuDLyPUDb4pNlhbZLGy9WgGUBQsAaCdF85fvwfU950gGl6lDxQlFgZGkOvwVT4LdgispxAnPNXPxiB1s0XfYeWKEAwd9uAF/wIujV2NURHuMGgmEYPPn5nyrZXYZMIoVNmybqBxoIU1Mj/PrTYEQ+y8DlK1EoLubh4GCCb/93Raf5hEIWhMi/9Du0d8eUSS1gbiYyiHhRBssCX319sZy3oKzUe8DteGz9ZwxcXVS7d0+eeqryos4wwOkz4bVGvLxcfE0Vny97UZm1bItMHenphSgurj1eJl1gGGDiuGZKvYwL3u0IJ0dzbP73HlJS5CUSjIw4DBnkhw/md6rQkPRl8mOe43zvqZDmyysxa5IppLHdHAunfp1xZcR72rutSrekmi57F8kXbiH1qry6tXXrxvCZMwEgBEVJaTB2tofH2AEwsn6zSgHUBFS8ULTGe8ZopF1XndkgtDSDqacrGIZBk8/nwnfBW0g4cQUlWTkw83KHY++OYDn5llLKtXvIuBtcdT+4gINVcz8UJqfpPetIG7y9bODtJd9mk0h5/LL2pta1YjiOwcTxzbFwQedyx3leBgcHU8VFwdCUvSSvXph5Xt5Ib+Gi42jfzh329qYY5N8ADg5mFeZQlyJMiDyrJzu7EA8eJiEvtxj29qZo3cpFkYb7KoEPE7F910ME3I4HkRG0aO6Ejh3cIRIJIBIJ0KWTB2x1rGsT9ChJ63O0Cb7OyzOMd1AXOI6Bk5M5fLxscPV6jMrnUeZ46NGtHiZNaKF8HMNg3JimGD2yMZ5FZUIi4eHhbqVStJQRtnYrpPmF+u+3xrIw862PjPuPNYrBY42E8hjAUkzcHNFuwwq4Du4JfLUAMqlU7mUR0l5dNQUN2KVojUwqxX6HTpBk5igd03L1YjRePFOj+WL3nsS1cQv1ZJ0cqxYN0fTLefAYM0Cv8+rCT79ex67dQWq3FMpgWQZisQB7d06As1PFbZl9B4Lx3fe6eXMMAccxiuvBO1NbY96c9opMnJjYLIyduEvl9hnLMrC0ECHzlWrD5uZGmD+vI8aOLh8jse9AMFZ9fwXsSxk9ldk0cnhjfPxhVxgZaRd3NentPVo1LKyNvOwJKstQEgpYSKSycoHDzZs54vtv/ZGeUYCpMw5AJpMpDShu1NAeE8Y1w0B/X3DKKtZWkQPOXVCUZJi/vbGrAwqfp6gdV++t4aj/9nA8+/cg+PxC2HdtA7+FUxU3WxTDQbONqHgxONlhz3Cm/VhIcvIqPOY1Yww6bPpG4xTY2ANncG30Av0aWFoxuNUPn6DRxzP0O7eWZGcX4e139iMhMUfpxbYsbVQqlcHWxhi//DgITZs4VjqWEIING2/j7y33wLLKL+Cv4upijucJhm/S+OH7nTFlckvk5RVj2OjtVWqBAABTJrfEB/M7gWUZPHuWgbGTdmmUwMYwQJ/e3lj9nerMuDIIIdi99xF+WXvDYM0Nq0rZVo2VpRjZOUUVXnuGAfr388F3K/shNTUfV67FoLBQAq/61ujYwR0RkRm4d/85CAFat3JBo4b2inOvXovGZ0vPoqBAAoGABSHywOpmTR3x64+D1Na3UQaRyZD16CkkuXkw9/aAsbNDhTHZjyMQ8ddePF37n+G63GtSRRyAibszCuISFckGRMrDopE3uh9aBwtf2k3akFDxQsVLtSDJzcOzLQcR+fc+SPMKYNXCD02WzNa6KNPJViOQGfREoy8WrWEYDIs8W6HtfXWTmVWIDRtv4+jxJ4qYhwY+tpg1oy2MxQLcuh0PGS9D8+ZO6N3TS+l2ycvExWfj8NFQ3L+fgMAg1VsdXbt44Ksve6PvwC36eDoqMTc3wqF9kzFj9kFE6ykw1cHeFDPfaYuIyHTsPxiiVbbV9n/HlrtIK2PN7zfx71bl5e1rGhtrYwwe6IvRo5rC1ESIVT9cwaXLUQoPi7GxEBPGNcO82e11rpxcWCjB6bMReBqeBpFIgB7d6qFFcyeda/FEbT+CoKVrkB9V2viQYeA6pCfarPkCZvXdQQjBwy9+weNVGxWFJ2ucSnLQGY6DyN4ag4OPQWRbeWsGStWh4oWKlzpDwfNkHHIzXBEnhmPR+NPZaPHthwZbQxsKCiRISsqF2FgIZyczvRRok0h5jJ2wC88TKvfsMAywacMIODmZY8iIrVVeTxN8vK0REZmp93ltbIyRkaF51WaOYzB+bDN8/GFXxTGel+HqtRgcOPwYz5/nwNpajE4dPLDujwC926tPvv+uP/r1Kd8WICUlD2FP0yAUcmjR3ElRebc2EPb7Ntxb8HWF4wzHwcjaAgPu7kfimeu4PXtpDVinAyyLFt9+iCZLZte0Ja8t2ly/acAupUaRFujQ2EcLiIwgOzTSoGtog4mJEF5eNuoHaoFQwGH92qGYt+Ao4uKz5TEoMgCMfJth2Re90LqVCyQSHmamRnrpsaMOQwgXAFoJF0Ae15GZ9eIciYTHok9P4dr1GEVcSHQMcP9Bor5N1Rssy8Da2hg9e1TcsnBwMKs0SLqmKU7PxINF/6v0McLzKMnKwcMvfpV3XlbST8iglEUfa4NMhuith6l4qSVQ8UKpUUzdnSEwM4E0r8Ag8zMsC4GZYbsp1wZcXCywb/cEXLkSjctXo1FcIoVfAzsMH9pIkXUjELAYNbIxtm4PrPZrhb5QVaVW2XgnxxdBz+v+CMD1GzEAXmQI1ba/xcvBthzLQGwswK8/DoRQTwUfq4PoncchU5HmTKQ8Ynaf0L11SBVgBAKIHWxQmPAieFfkYAuGY1GUmKry3JIs5UkKlOqFihdKjcKJRfCeORZha7capGQo4Xl4jFEfsCmTSsGwLBi27hadFgo49OntjT69vRXHMjML8fuGWzh0OBQZmYWwtBTDykqMzEz9NJqsbggpC1olGokOnicYPMAXl69GIyYmEzt3B+ksVqrDQdC4kT04jkV0dCZMTITw798A48c2qzTrrDaT9ywODMsq75cG1Ixw4Th4TR2JdhtXIOXSbRQ8T4bY0RZOvTvi6tgPkHD8kvK4G46FhR8N2K0tUPFCqXGafbUASeduIPtxhF6DdhmOg2UTH7gM7lnp40QmQ+Tm/Qhb8y+yg8MBloWzf1c0XjwDjr066s2OmiIpORfTZx5AalqB4k4+O7sILMuo7f5cW5EX7HPD/QeJKC6WqhUTRkYcps48gPz8kiqLD44z7N+MYYCB/r6YPFF5DZW6QPbjCDz7Z79K4VIGZ2oMPr+KW8cabgGVxdo0XfYuWI6DU59O5R5vMGe86saOvExekI5SK6i7t5mU1wYjS3P0v74LTT6bA85Ut3TMMhihAIxArslt2jRBrzObK63PQGQy3Jj8MW7P+hLZIRHygzIZks5cx/k+0xCxaU+V7KgNfP3tJaSlF1QoPCbvaC2DUKj6429qUnuCP8vgeYK3JrbE8UNTMHN6W6iLdy4p4RU9iKrqNTG02BOJBBg2pKFB1zA0xRlZONdzCiQ5qosoMhwHp76d4TN7PBhda8aUeklZI83ep/bd26L/rT0w9XCp9HHnAd3hOWEwKn1TMQxchvSEuwZeXEr1QD0vlFqB0MIMLb75EM1XfoDCxFREbT+Ch5/+qPkELAuPMf1h7OwAVmQE1yG9YN+1DRiGASEEabcCEbv7BEoyc2Dm7Q6BuRlidh2Xn/vSVa3sbvH23OVw7t8Fpp6uAOSBxYlnrqMwMRXFaZkwdraDuY8nHLq3q5VbTXHx2bgZEKf0cZkMkMlkaOhnV2lBNhtrMdavHYabAXFY8/tNQ5oKkYjDgP4NcOpMuMrS+SzLoHlTR7Rv5waWZdC+vRs2bb5rUNt0gWUZMAy0SudmWQa//DgI5uYiA1pmeCL/3ofi9Ez1HlSGQbOV78OqmS9Sr91Dxt1HmgfQsixEdlbwnDAYLgO74/EPfyPlgupGmgDgNqw3zL09VJjEoNO2H2DVoiGe/LIFxSnpAOTdoX0XTEHTL+bSQnW1CCpeKLUKhmVh4uqIJp/MQub9EMTtO61h0SoCu86t0fCDqeWOSvLycXXkAiSdu/7Cvaz4v/J9BIYBIjbtQfOvFyLs138R9NVaSCu5mzSt54oOm76BU9/OlcxSc4RpWCF2yuSWaOhrj517ghATkwUbG2N07uSB/n19IBIJ4OtrB08PS3z6xRlF4TZ1tb7kbWA06/VjZMRhw2/D0LKFMz7/tAeiY7LwNDwdv627gZTUAkX1XpmMoFFDO8x/t6PixlgoMGwHb11gWQY9u9fH/Hc7YO26W7h6LVqtiDEy4vDbL4PRrq1bNVlpOJ5t3q9WuDAch57HN8K+UysAQN9LW3HUt7/66relbzy7ji3Q/fB6Raf7/NgE9eKFZZEVFKbWfpbj0GTJbDRaNB05T6MBQmDuWw+cEe0OXdugdV4otRZJXj6ujlqApLPXNRo/JOxUhQqYF/zfQdIZzc5/FecB3eDYsz0Cl/ykfBAj/zLuc+E/OHRrq9M6huDylSh8uPik2nE/rPIvF+CrjOzsIhw9EYagoCSwLAM/Pzts2xGI7OziciKlrALsN1/1wakz4bhyLUblvFv+GoXmzZwqHOd5GW7cjMXFy1G4cTMWKakvhKOnpxU+fL8zrlyNwoFDoWptV0eZGBII2CpV1nV2Nsf2f8fCylIMAEhNy8e0GQeQnJKnVMh16+qJRQu7wsO9Zhr5EUKQeu0e8iJjIbSygHP/LhCY6LZ1G7R8LYJXrlM7TuzqgFHxVxW/8yUl2C1qpvokhoFFQy90+Osb2HVqVa4+UnF2DvZbtVN9uoCDz+zxaLduuVr7KDUHLVJHxctrAyEEqVfv4sGnPyL9VmClYxiOg8ugHuhxZAMAoCglHZF/70PCqatIvXJH98U1zctlWdh1aI7+N3brvpaeyc0rRr+BW1BSonwbRihkceb4NFiWXmy1JTEpF+s2BODMuQhFPEib1i6YO6sd2rR2BSEE3/94FXv2BYNlGMhK/45lqcDvzmmPme8oF3wPAhMx573DpTE6L14DbdOlVWFmZgRnJ3MMHeyHtLQCbNv5UKsmiy/btGhhlwoNCzOzCrFteyAOHHqM7JxiGBsL0L1rPfTt443mzZxgb6dZh3Z9kfXoKR4u/RU5T56ByGQoychGSXqW4nGBuSmaLXsPDRe9o1UBxdCfN+PBou/VD2QYmNZzRc8Tf8KyoVw0ayJeGI6DfdfWEFqao/B5Ckw8nOA1fTRcBvUAy3E41/MtpFy5o3LrqdeZzXDu10Xj50Spfqh4oeLltYMQgnsffIunv21VlBEv+79dl9boefxPGFmaI/7oBVwb+4G8xoQBUq9VMSzyHMy8arYNwcv8vOY6tu98qLTR3rjRTfHp4qpXN87LK0FqWj7MzUWwe6WTMyEEZ89HYtuOQASHpIBhgFYtnTFlciv06FZP5bwTp+xBeES6TmJCE0xNhTh7YjrEYvnueXJyHoaN3gaptPLmhMpgWQaNGtpj04YRirlehRACiUQeJK2PqsraIuN5XBv3AeIPnNVofPOvP0DTL9/VaGzeszgc8emnuZosLb/vt3AqWv+0BAzL4kzn8Ui/HaR2i5jhWBBeBobjQHgeTv26oPvh9Ui7+QAX+k6v1AZGwMGqqS8G3DtQK+PTKC+g4oWKl9eWzIdPEPnXXuQ9i4ORrRXqTRoC5/5dwbAsskMjcaLFMHmdhhp4W7f83yLYdWoFu86twApqPpxMIuWxdPl5nDkXAa60A3PZ/3v1rI9VX/fXuuNyVeB5GRiGUWwtqeJpeBomvGXYjK+vlvaukN1z5Vo0Fi85BZ5/4e3hOLmn6PNPe6CkhMeW/+4jNU1eVNFYLMCI4Y3x3twOMKmF2VllBMxeikgtMugYoQCjEq9p1Mfnwac/IPSHv3X6zLX8/mM0/mQW4g6cwVVlzVlVpUKzLHxmj0f7DV/h2b8HcXvWl5CVvs/AMCBSKaxaNESvU3/B2El9fytKzULFCxUvbyS35y1H5F97tW/upmG3WU0RO9ujxbcfwnv6aL3NqSuEEAQFJ+PosSdITc2HnZ0phgzyQ8sWujfbqw6uXIvGwkUnDDa/hYUIl85W3m08ISEHew+E4PrNWMh4Gdq0dsHY0U3h420LQC7ComOyIJXK4OFuWav6CVVGUVoGDjh21u49zjBot345fOZMQGFCCgjPw9jVsdJsm3O9piDl0m2dbDOytsTIxGvgREaKmJlyDRo1+GyyRkKMTLwGkY0VilIz8GzLAWQ9egqBiRhuI/vBuV8X6nGpI1DxQsXLG8kBl65qy3u/isDMFPXfGYXwtfpvWOgzZzw4sRgFcYkQOdnCuV9XuAzsDk5EMxfU8Sg4GVNn7DfY/I4Opjh5dKr6ga8Bkf/sR8A7n2t3koCDy8AeyH0SidxwedC12Nkefu+/jUaLpoPwMhCeB2dijEsDZyLx9DWd7etzaSsce7QHAKTdDkL4uu3IuBcCzlgEM28PxO5WL2K7H9kAt6G9dbaBUjugjRkpbySyEonW5zT5ci7Sbz00gDVAxMbyAbwR63dCYGGG1j98Ap/Z4w2y5utCk8YOcHExR0JCrtIxpTsDWoc2cRyDjh1qT2ySoZFkK/8bKkXKI+HohXIF24oSU/Hw858R8u0fkObJs7+E1hawbd+8Sr0T+KJixb/t2jeHXfvmit+jth3WSLxoVk6B8jpBfWmU1wbb9s3BaFFEymVwT/gumIJEHVOpdUGak4fbc5Yh/I+dWp2XGxmLx6s34eHSXxG19RCkhXWzN5GmsCyDDxeorp0ze2Y7eNWX1/rgOHnLAwCwszMBxzFKq+/KZAQTxjWv/MHXEPNXygdoxauChBCFcAEASWYOkk5fk4sXXbZmGAZWTRoofdiuY0v1c7AsbNupSbWmvHbQbSPKa0PCycu4NEhNu3qWgVVTX/gumAKv6aOQcS8EZzqM1W6h0nTP/Kh4nW0VWpphZOJ1CIxVpynzxSUImPUlorcdAcPKLxBEIoXQ0gwd/v4OHqNf73Llp06H4/ufriI7u0hxc29iIsT8eR0wYVxzEEJw914CAu7EQSYjaNHMCV27eOLSlSh89sVZEBBFkbiywNtlX/TC8KGNaviZVR8ynscBx87lUqINASsygqy4ROPxjKC0xMHhDSrHXRwwE0nnb1Qay8ZwHNxG9kW3vWu1tpdS+6AxL1S8vJEQQvDg4+/x5Od/AI4FSl3JZQGAbdZ+Cb8FU8qdk3rjPs52maj1WsbuziiMS6ySvWZebhBaWsCucyv4vjsJlo19Koy5PvljxOw8VvEOuHTPpPfZzXDq3anCea8TEgmP6zdikZSSBxtrY3Tr6gljsfog2di4bOzdH4ybt2JBCNC2jSvGjWkKby+barDa8BBCIM0vACcWqc1uSzx7HRf7v2NYgwTy6rQh36gWIwAAhoGJqyP639wNE7eKRQpfpjApFWe7TkJeVNyL4N1St5pFQy/0vbJNUW2XUreh4oWKlzcWQgjiD53Dk1+2IO1WIBiWhVPfzmi0aHqlnaJLsnNxwKkzZEUa3jGyLGzaNEHGnUd6s5kRcCC8DG1/XwrfdycrnkfIdxsQ9OUalbbYd26Ffld3AACkhUUoSkqF0MJMoxRXSt1EkpOH0J82I3zDThSnZoARcHAfMwBNPpsN6+aVN3YM/2Mn7sz7qtLH9NLZuZRuh9bh6oj31I6z69wK3Q78DmNHO43mLcnOReSmPYj8ay8Kk9Ng4uII71lj4T1zLIRm1Vvoj2I4qHih4oWiBbfnLUfkpj3qC2QJBXDs0wk5IREoqKLXRRl9r2yHQ7e2eLTydzxa/ptG5wwMOoLwddsR9e8hRfCjY5+OaPbVAjh0bQsZzyPx1FUknrkGWYkExs4OcOjZHjatG9Mv/jpGSVYOznabhJzQyHLvV0bAyXsGnfizgieuMDkNh9y6Ky8hoKqOipb0vb4Tt97+FHnP4lQG8A64dwA2rZvoZ1HKawPNNqJQtKDV6sXIuBuMjHsh8gOlX7oMx0JoaYFmK+bD1MMFBc+Tcfe9lQazgxFwePLTZph6OOPRV79rfN5F/xkoTslQdMQGgJRLt3G+5xS0Xf8Vnvz4tzzdtbSyaRmsyAgN5k1Ei28/VPSzKc7IgjSvAGJHO5rSrQJCCJLO3UDExl3IDn0GI2sL1Js0BPWnDIfQ3Mxg6wYtXYOc0GcVhDaR8iAyguvjP8SI51fKNRKM2nIARFWtFH3dvrIsrBp5o8nncxAw44tKh5SV+afChVJVqHihvPEIzc3Q98p2RP61F+F/7ER+9HMY2VjCa+pI+M5/C8ZO9pDk5eOgUxeDVu4lUh6J524g8p8DYFgGRE03YgAAy1QQLkBp6ijD4M7c5S/SXV/JKZYVlyBszX/IuBuMJl/Ow+NVfyLlsrzYmMDcFN4zxqDp0nchsrHSx9N7bZDxPG5N/wzRWw+/KKjGMEi78QChq/9Cn0tbYVZPvx2iJbl5eLpuB56u2678PSiToTgtE/GHzsFz3CDF4ZywKPn7ycDZxG7D+8DI2hJe00cjLyoeId9seNHKo7Scv1WLhui6jwbXUqoO3TaiUDQgcvM+BMz8QuO7VLcRfZF+JwiFz1O0WocTi+A+dgBidhyrIEgqUFZmX0/VgRmWBXlJ4DAcBzNvd/S/ufuNEzBEJkPi2et4fvg8pAVFsGrmi/pTR0BsZ4PQH//Gg09WV/peYAQcLBv7YGDgYb1VMC5Ky8C57m8hJzRS7VhGKEDjT2aixTcfKo7d/eAbhK/fob7y9EtB7tpi4u6MQQ8Pw8j6RXfs7NBIRP61F7nh0RBamMFj/CBFI0UKpTLothGFomdyI2LBCAQgEqnqgQwjv3isWgQAuDhgBpLO3dSskhrLwKKRN4ysLeRxCGowsrVCSWqmBtZrBnnFRsLzyIuMw6MVv6Ptmi/1tk5tpzApFZcGzkJmYCgYAQcQgBAZHn7+M9pv+hqhP/+jVMQSKY+soDAkX7wFaV4B8mMTIbKzhuuQnhCamSLtdhCe/LIFiSevgPA87Dq1gt/CqXAd1EOpPbfnLEPu02jNjJcRcGJRuUOmHi4qhQvDcXDs1QGFCSnIfhwh95bIiMbV/9xG90enzasgtCi/XWbZyButf1qimd0UipZQzwuFogGPV2/Cw89/VhvU2z9gb7kKoUnnbuBCv+larSV2skNRUprKMSb1XNFl+486pXlri8DUGKPTAipcFHVBJpEg/sgFpN8OAisQwKl/Fzh0b1dr+iwRmQyn2oxCVvBT7XtklcKwLFiREfjCIkVvHoGpMVyG9ELs7hPleveUbac0/mwOWn73UYW58uMScdizl1bblQMfHlZkHT1Z8y/uL/xOhbEMGI5Fv2s7YdO2KRJPXkH84fNIPHMNBfFJar16PnPGo92GFbXm9aPUbajnhULRMx5jBiBwyU/KB7AsbNs3KydcAPnFR1uKktLknhemkqZ0LAvWSIjuB9ch5fJtMEINvEFVRJpfiMKEFJh5Va2kftrtIFwd8S4KE1PBCAUAAUK++wPWrRqjx5ENaut9qIIvKUHc/jOI2XUcJRnZsPCrD+9Z42DXoYVW8ySevY7MwFDlAzQog09kMrlwARSvnzS/UFHm/mVRVLY1+HjVRjh0bwuXAd3LzZVx55HGwoXhODj166wQLjlhz3D/w1Uqz+HERuiy6xfF38l1SC+4DumFEy2HoyBW9XtXYGZChQulxqDihULRADMvd3hNH4VnWw5UFBSlF7TmKz8od1jG8wj68led1xQ72KIoOR1gSuNRpDxMXBzQadsPCPzkBySdu6H6wqbHbtkCM5MqnZ8XHY8LfaeBL5DXE3lZcGU9CsP5PlMxKOioThlOhclpuNBnGrJDwhUZVWm3AhH59z40eHcS2v6+rMIFlhCChOOX8HTddmQGhoIzFsNjjD8KElLk24NSJYLQQI5qhuMQtnZrBfHCcJqX3Lfr0gpddv2i+D38j11gOFZFijQD61aN4DasT4WHRHZWat8/xi4OVLhQagwqXigUDWm34SuAZfHs730KdzuRSCE0N0X7TV/DuV+XcuPTAx6iMEG7gF0FRO6B8b+7H2nX74MvLIJlMz+AENyc+ikKYhLUTuExZgBM67si9Pu/dLMBAFgWdh1bQOxgq/scAMLW/Ae+oKjSbTci5ZH7NBqxe0+i/lvDtZ772pj3kfPkmfyX0jiNsgt2+PodMPeth4YfvOggTQjBnbnLEfHnbsW2DQA8+XkLwMgfr24IzyPt5oMKx+26tFbvXWMYdN23Bu4j+5cTExn3QlRvfRGCrKCnlT5Ub/IwJJ+/pfxclkX9Kdq/VhSKvqDihULREM7ICB03fYOmX8xF3P4zkOTkwbyBJ9xH+1fao0inbr6vIDQzgd/7bwMAoncdx41Ji9Tf/TMMvGeORYc/v0ZReiZCf9isPvjylRowCghB02UVK6am332EtJuBYDh5BWOL0uZ/+bEJiNl1HMVpmTDxcEG9iYMhsrVWnz3FsojdIxcvhBCk3biP58cvQ1YigU2bJjBr4ImUiwEgUh627ZvDsXdHMAyD9DtBSL12T+VTC139Fxy6t0VeZByElubIexaPiD/lHb9ftonwfJW6I1eVyrJwRLbW8Jw4BNFbDyu1S+xgC1lRCYhUCkb4om0CJxapfT7K4pg8JwxG6Oq/kBsRU0EAMQIOIjtr+MydoMnTolAMAhUvFIqWmNVzQ6NF6vvEmPl4Vm0hloHYyR4AIC0oxO3ZSzU7jxAUJqQg48Fj3H1vhUZZIxZ+9ZETGinPrmEYECkPVmSE9htXwsW/m2Jc3rM4XBu/EBl3g1/UjyEEzoN6wMTdCZF/7gFT6pWS8TwefLQKLb9fDEl+gWoDZDKUZOWiMDkNV4a/i/SAhy9sKfM6MIx8+4znYd7AE90O/I6kszfKeU8qozAhBadaj3pxQFX34xoSLoyAg1P/Lni25QAKE1IgsreGJDsP4Rt3IS8i9qWBFcVIUWoGbkz+GIGf/YwG705EvUlDYeruDLfhfeRbiyrWdBvRt9LHBMZi9Ln4H66N/QCp1+6Vbl8xIDwPi0be6Lb/N9pPiFKj0GwjCsWAnO0+GWk3Hqiv2VIJVs39MOjhEQDAs/8O4dbUTzU7kWXh0L0t0m4GQiaRaBT30m79cth2bIm4fachycmDRSMv1Js8DEaW5ooxRWkZONF8GIpTM7TOxDF2d0bhcxXZKwwDE08X8AWFKEnPVvv3zgsAIAAAFkhJREFUYjgOQkszeE0fjbA1/+qcGVQpZV4oHbwwnLkJ+PwijdOMFTAMWCMhZCUl8vUry2rT1B6GQYN3J6H5yvdxrNEglKRnVtyuYxgwAg4DHxyCVZMGKqfLuB+C5Au3QGQy2HVuBfsubWisC8Ug1Ipso2+//RbHjx9HYGAgjIyMkJWVpfYcQgiWL1+OTZs2ISsrC126dMGGDRvQoIHqDxeFUltpt345znSeUBrvod0FtuX/Plb8Oy8iRvPMIpkM2cHhkEmkGgfsOvXvCnNvD9i0aqx0TPj6HfIAYm0vzACkefmqbSEEBdHPNZ6P8Dwk2bnIj3muX+ECgGEZOPl3R370c40Kw5WzS8qDFcoDfssJhlLhwRmLwBeXKP4WDMfKY2xkBLLi0uagytLxNRVShCB8/Q6wAgH6nN+CC/3eQVFSqtxDRYjcDrERuu5Zo1a4AIBN6ya0nD+l1qF5KLuWlJSUYOzYsZg3b57G56xevRpr167FH3/8gYCAAJiamsLf3x9FRUWGMpNCMShWTX3hH7AXrkN7lduuMPerr/I8hx7t4DLwReaJ0MpCbY0ZQO6REDvaojgtU2ORYWRrBXNvD8Xv0vwCRPy1F5dHvosTLYfjbPfJePDpD4j4c49OwgUAJJk5sGnX7EVVYD1AeBlSrt6DmZc7GD1WbSVSHvUmD0W/q9vlKd1aICssRreD6+TP9SWMbCzRYfN3GBF3Ga2+Xwynvp3h0KM9Gn08o1xVWr1BCJ6u2waRnTWGR51Hp/++h8f4gfAY44/WPy3BiPgrcB3SS//rUijVhMG3jbZs2YKFCxeq9bwQQuDi4oJFixbh44/ld5zZ2dlwdHTEli1bMGGCZsFhdNuIUlspSstAUWIqRHbWEDvZI2jZmhf9X3hZaT8jGey7tkGPYxvLbdnkxzzH4fp91N59m3l7wLZDc8TsOKaxXTZtm2LAnf0AgPQ7QbgwYCYkGdm6PUkV9Di2Ec/+PYi4vaf0NidnLEKjxTMR/PV6vcSrMBwHkb0NhkdfACcyQsiqjXj4+c9azSGytwYjFKAoIVWR6kyI3PnS+tcv4Df/LQAAX1SMuMPncWPCh6qm0x2GQbt1y9Bg3iTDzE+h6JlasW2kLVFRUUhKSkLfvi8CyCwtLdGhQwfcvHlTY/FCodRWxHY25YIcW3y9EPXfHoHIv/YiLzIORlbm8Jw4RJFJ8zKmnq7wnjUWkZv2Kr1IN/lyHuy7tMalQbM1tokRcLDr1AoAUJSSjgv93oEkJ0+HZ6cey8beKIhLrNBDqSqwQiGCV67Ty1xgAKGVOXqd+ktRb6bxktngjEUIWrpWvvWlAcUvtWx42VtGANxb8DUEJmKk33mEqH8Pgi8s1o/tlcBwLEqyDfNaUig1Ta0RL0lJSQAAR0fHcscdHR0Vj1VGcXExiotffAHk5OQYxkAKxQBYNKiHVt8v1mhsu9+XAQSI/GtvuTozAnNTtN+4Ep4TBuNky+Hy6ryahkfwMjQoTXmN2LRHLlwM4Iy1adcUxi6OSL/1UK/zSjQUFBpBAJ85E2DdoqHiEMMwaLhwGupNHoaDLl2rHl/DMrg9dzlAiN5jdV6FSHmY+3ioH0ih1EG0inlZsmSJPA1Sxc+TJ08MZWulrFq1CpaWloofd/eqlTCnUGorrFCIDn9+jeFR59Fq9WI0WTIbHf/9HqOSrqPexCHICY1EVlCYZkG6pZ6dNr9+DsvGPgCA+INnDZMqzDDouHmVYebWUwXhMh5//yeK0jIqHBfb28BtZL+qLyAjIBJplYSLRvE9DAMjWyu4Duut8zoUSm1GK8/LokWLMG3aNJVjvLy8dDLEyUne1yQ5ORnOzs6K48nJyWjZsqXS8z777DN89NGLhmY5OTlUwFBea0w9XSutM1OUkq7ZBAzg3L8LGn08A059OysOSws1D4wv11ywNLtGmben8ZLZsGrqCwCwbOYrL+NfVdHBMDCysURJelbV5nkVXobYPSfh++7kcocfr96EuH36i9XRFaGFGfpc/A8Zd4MR/scuZD58UjGIujQwvOPf34Iz0r7dAoVSF9BKvNjb28Pe3t4ghtSvXx9OTk44f/68Qqzk5OQgICBAZcaSSCSCSFT1brcUSl3H2NVR/SAAXXb+As/xgyoct23bDDlhUcpTdV+i2/7fILQ0ByvgYNGkAe7MW47YXaUdkwmRCxmZDI0/nYUW374ISG300XTcmv6Z2vlN67mCSHl5Z+NXYeSPF6dnVnxMD+SERZX7PXrHUQR++qNB1tIGRijAgHsHYO7jCZvWTeAzezzynsXhwZIfEX/gjCK+xq5jC7T4ZiEce3WsYYspFMNhsJiX2NhYZGRkIDY2FjzPIzAwEADg4+MDMzMzAEDDhg2xatUqjBw5EgzDYOHChfjmm2/QoEED1K9fH0uXLoWLiwtGjBhhKDMplNcGiwb1YNepJdJvBylNqxZamsFteMVGfADQ4L3JiPrvkOpFGMDEzRnOA7uDKy1FL5NI4DFmACRZuch5GgWBsRgO3duh0cczKnSirj91JNJuBSJi4+5KK+OKHGzBGYuQr6R3k4mnC/zmvwXzhl64MnSualt1xMjqRZYDIUSeyVSNbQPk9VhkL7xTLAtOJET3Q+th/krVZjMvd3TbswbFGVkoiE+CyMaqSt25KZS6gsHEy7Jly/Dvv/8qfm/VSp7RcPHiRfTs2RMAEBYWhuzsFymZn3zyCfLz8zF79mxkZWWha9euOHXqFMTiin1jKBRKRVr/8jnOdZ8sv85WktHT5tcvlPazsWvfHE2Xz0fwit+VL0CAgrhEHPbshYYfvA3PSUNwaeBsZIeEK8QIw3HIDomAiZsTmnxeXmAwDIN2G1bAdWhveUfn+yHgjMVwH90fPnMn4tlfexH6w9+VCwWWhayoBH4fvI34Q+e0+bNohfeMMYp/50fHv2j6WEUYjgXDcZCVSFQMYtD/5m4knr6K5IsBAMPAqXdHeM8cq7I5psjGCiIbK73YSaHUBWh7AArlNSP15gPcfXcFMgNDFcdM3J3R8vuPUW/iELXnxx06h+BvNyDzbrDqgQwDoYWZPONHiaen886fUW/CYABAVkg4Mu+HgDUyglPfThDZWpcbyxeX4IBjZ7UNLbvuXQMTNyec6TRe7XPRFtv2zeEfsFfxe/bjCBxvMrjK8xrZWsH33Ukw8XDB7VlfVj6IZVB/ygh02vK/Kq9HodRF6mSdFwqFoh/sO7XCwAeHkBn0BPnRzyGys4Zdx5ZgVDUkfAn3EX3hPqIvZFIpUi7dxoV+0ysfSIhqocEyeLxqI2zaNkXA9M/KdX9mhAL4zBqH1j9/pqipkh8dr1a4MEIBMu4Gw320P8wbeCI3Ilbj7RyG48CJjSAtKKw0uFhkZ4XuR/8od8y0nis4EzH4AtXBzJbN/JAd/FRhi7GzA5p9NR/1pgyHrLgEQgszxd9fml+AB4tXywsTcqwibdpz3CC037hSo+dCobzpUPFCobymWDdvCOvmDdUPVAIrECBq6+FymUVaISPICgrD2U4TUJJZvmIvkUgR/scuFCamotv+38CUNiZUCyFgRUZgGAZt1y3HpYEzQWQoL2BK08C9Z41D6tU7yI96DqGlGepPGQ6/D6Yi71kcHq1ch+TzN+XPUyyC9zuj0WzFggqdkgUmxvCeMRbh63dU3puKZSGyscSAu/vACgSQSaVghcLyRQaNy297N/xgKupNHIKorYeR9ywORjaW8JwwWKM+QxQKRQ7dNqJQKEo52XYUMu+FVG2Ssi7NSuh3YxfsO7UCIQTH/PzVelP6B+yFXfvmAICkczdwb+F38vTrUiwaeqH1z0vgMrCHSrNKsnIgyc2H2MFW4f2pdFx2Ls52m4ScxxHlAqEZAQeG49DzxJ9w6t1J5VoUCkU9dNuIQqHoBSNL86pl2jCMSuHCCDhE/XsQ9p1agWEYNPl8rtJUakbAwbZDC9i+1PTQqW9nDHp0FFkPn6DgeTKMne1h3apxhfYKlWFkZVEus0jpOEtz9L+2E6E//4Pw9TtQnJoBRsDBfcwANFkyu1xFXgqFUj1Q8UKhUJTiMW4gki/e0u1kNR4XQF7CvjAxVfF7/akjkfcsDsFfr1dsV5VlMVk2aYDuB36vIEwYhoF1y0awbtlINzs1QGhhhuZfLUCz5fPBFxSCFRmBFdCvTwqlpqCfPgqFopR6k4ci5Ns/UJiQUiHmg+FYsGIRZBIpIJO9iIspFRf2XVsjOyRCZRVcRsDB5KXiegzDoPnKD+A5cQgiNu1B7tMoCC3M4TFuIFyH9KxxwcAwDASmJjVqA4VCoTEvFApFDXnP4nBpyBzkhEaCEQgARh5wa+xsjx5H/wBnLEboj5sRu+cEpAVFMG/gCd/3JsNnzgQ8+uo3hP7wl9KieQDgf3svbNs1r8ZnRKFQaiPaXL+peKFQKGohMhmSzt1A0rkbILwMdp1bwW1Yb7DC8hlChJBy2zpFqRk41XokCpNSK2YsMQw8Jw1Bl201X3qfQqHUPFS8UPFCodQaCuKTEDDrSySevqYI/OVMxPBbMAXNv1lY41tBFAqldkCzjSgUSq3BxM0JvU7+hbyoOGQGPgErMoJDtzYQmpvVtGkUCqWOQsULhUKpFszqu8Osvrv6gRQKhaIGzeqFUygUCoVCodQSqHihUCgUCoVSp6DihUKhUCgUSp2CihcKhUKhUCh1CipeKBQKhUKh1CmoeKFQKBQKhVKnoOKFQqFQKBRKnYKKFwqFQqFQKHUKKl4oFAqFQqHUKV67CrtlrZpycnJq2BIKhUKhUCiaUnbd1qTl4msnXnJzcwEA7u60DDmFQqFQKHWN3NxcWFpaqhzz2nWVlslkSEhIgLm5ORiGqWlzkJOTA3d3d8TFxdEu17Uc+lrVLejrVbegr1fdoiZeL0IIcnNz4eLiApZVHdXy2nleWJaFm5tbTZtRAQsLC/qBrSPQ16puQV+vugV9veoW1f16qfO4lEEDdikUCoVCodQpqHihUCgUCoVSp6DixcCIRCIsX74cIpGopk2hqIG+VnUL+nrVLejrVbeo7a/XaxewS6FQKBQK5fWGel4oFAqFQqHUKah4oVAoFAqFUqeg4oVCoVAoFEqdgooXCoVCoVAodQoqXvTMt99+i86dO8PExARWVlYanUMIwbJly+Ds7AxjY2P07dsX4eHhhjWUAgDIyMjA5MmTYWFhASsrK8yYMQN5eXkqz+nZsycYhin3M3fu3Gqy+M1i3bp1qFevHsRiMTp06IDbt2+rHL937140bNgQYrEYzZo1w4kTJ6rJUgqg3eu1ZcuWCp8jsVhcjda+uVy5cgVDhw6Fi4sLGIbBoUOH1J5z6dIltG7dGiKRCD4+PtiyZYvB7VQFFS96pqSkBGPHjsW8efM0Pmf16tVYu3Yt/vjjDwQEBMDU1BT+/v4oKioyoKUUAJg8eTJCQkJw9uxZHDt2DFeuXMHs2bPVnjdr1iwkJiYqflavXl0N1r5Z7N69Gx999BGWL1+O+/fvo0WLFvD390dKSkql42/cuIGJEydixowZePDgAUaMGIERI0YgODi4mi1/M9H29QLk1Vtf/hzFxMRUo8VvLvn5+WjRogXWrVun0fioqCgMHjwYvXr1QmBgIBYuXIiZM2fi9OnTBrZUBYRiEP755x9iaWmpdpxMJiNOTk7khx9+UBzLysoiIpGI7Ny504AWUh4/fkwAkDt37iiOnTx5kjAMQ54/f670vB49epAPPvigGix8s2nfvj157733FL/zPE9cXFzIqlWrKh0/btw4Mnjw4HLHOnToQObMmWNQOylytH29NP2OpBgWAOTgwYMqx3zyySekSZMm5Y6NHz+e+Pv7G9Ay1VDPSw0TFRWFpKQk9O3bV3HM0tISHTp0wM2bN2vQstefmzdvwsrKCm3btlUc69u3L1iWRUBAgMpzt2/fDjs7OzRt2hSfffYZCgoKDG3uG0VJSQnu3btX7nPBsiz69u2r9HNx8+bNcuMBwN/fn36OqgFdXi8AyMvLg6enJ9zd3TF8+HCEhIRUh7kULamNn63XrjFjXSMpKQkA4OjoWO64o6Oj4jGKYUhKSoKDg0O5YwKBADY2Nir/9pMmTYKnpydcXFwQFBSETz/9FGFhYThw4IChTX5jSEtLA8/zlX4unjx5Uuk5SUlJ9HNUQ+jyevn5+WHz5s1o3rw5srOz8eOPP6Jz584ICQmplc1132SUfbZycnJQWFgIY2PjareJel40YMmSJRUCy179UfYBpVQ/hn69Zs+eDX9/fzRr1gyTJ0/Gf//9h4MHDyIyMlKPz4JCeb3p1KkT3n77bbRs2RI9evTAgQMHYG9vj40bN9a0aZQ6APW8aMCiRYswbdo0lWO8vLx0mtvJyQkAkJycDGdnZ8Xx5ORktGzZUqc533Q0fb2cnJwqBBNKpVJkZGQoXhdN6NChAwAgIiIC3t7eWttLqYidnR04jkNycnK548nJyUpfGycnJ63GU/SHLq/XqwiFQrRq1QoRERGGMJFSBZR9tiwsLGrE6wJQ8aIR9vb2sLe3N8jc9evXh5OTE86fP68QKzk5OQgICNAqY4nyAk1fr06dOiErKwv37t1DmzZtAAAXLlyATCZTCBJNCAwMBIBy4pNSNYyMjNCmTRucP38eI0aMAADIZDKcP38e8+fPr/ScTp064fz581i4cKHi2NmzZ9GpU6dqsPjNRpfX61V4nsejR48waNAgA1pK0YVOnTpVKDtQ45+tGgsVfk2JiYkhDx48ICtWrCBmZmbkwYMH5MGDByQ3N1cxxs/Pjxw4cEDx+//+9z9iZWVFDh8+TIKCgsjw4cNJ/fr1SWFhYU08hTeKAQMGkFatWpGAgABy7do10qBBAzJx4kTF4/Hx8cTPz48EBAQQQgiJiIggK1euJHfv3iVRUVHk8OHDxMvLi3Tv3r2mnsJry65du4hIJCJbtmwhjx8/JrNnzyZWVlYkKSmJEELIlClTyJIlSxTjr1+/TgQCAfnxxx9JaGgoWb58OREKheTRo0c19RTeKLR9vVasWEFOnz5NIiMjyb1798iECROIWCwmISEhNfUU3hhyc3MV1yYA5OeffyYPHjwgMTExhBBClixZQqZMmaIY/+zZM2JiYkIWL15MQkNDybp16wjHceTUqVM19RQIFS96ZurUqQRAhZ+LFy8qxgAg//zzj+J3mUxGli5dShwdHYlIJCJ9+vQhYWFh1W/8G0h6ejqZOHEiMTMzIxYWFmT69OnlhGZUVFS51y82NpZ0796d2NjYEJFIRHx8fMjixYtJdnZ2DT2D15vffvuNeHh4ECMjI9K+fXty69YtxWM9evQgU6dOLTd+z549xNfXlxgZGZEmTZqQ48ePV7PFbzbavF4LFy5UjHV0dCSDBg0i9+/frwGr3zwuXrxY6XWq7PWZOnUq6dGjR4VzWrZsSYyMjIiXl1e5a1hNwBBCSI24fCgUCoVCoVB0gGYbUSgUCoVCqVNQ8UKhUCgUCqVOQcULhUKhUCiUOgUVLxQKhUKhUOoUVLxQKBQKhUKpU1DxQqFQKBQKpU5BxQuFQqFQKJQ6BRUvFAqFQqFQ6hRUvFAoFAqFQqlTUPFCoVAoFAqlTkHFC4VCoVAolDoFFS8UCoVCoVDqFP8HGHy3EG5f1LYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EmuUVtenj-tC"
      },
      "source": [
        "And let's see how our model is making predictions on it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_vFgOI88itVD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "ab033701-1f06-4103-fee3-6547d95fd2d4"
      },
      "source": [
        "# Check the deicison boundary (blue is blue class, yellow is the crossover, red is red class)\n",
        "plot_decision_boundary(model_4, X, y)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 0s 1ms/step\n",
            "doing binary classifcation...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XgNyVx3XkG4K"
      },
      "source": [
        "Well, it looks like we're getting a straight (linear) line prediction again.\n",
        "\n",
        "But our data is non-linear (not a straight line)...\n",
        "\n",
        "What we're going to have to do is add some non-linearity to our model.\n",
        "\n",
        "To do so, we'll use the `activation` parameter in on of our layers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "utJIUKmHkbyV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eff53b99-231b-4015-fcbf-98ad65469f0c"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create a model with a non-linear activation\n",
        "model_5 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(1, activation=tf.keras.activations.relu), # can also do activation='relu'\n",
        "  tf.keras.layers.Dense(1) # output layer\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model_5.compile(loss=tf.keras.losses.binary_crossentropy,\n",
        "              optimizer=tf.keras.optimizers.Adam(),\n",
        "              metrics=[\"accuracy\"])\n",
        "\n",
        "# Fit the model\n",
        "history = model_5.fit(X, y, epochs=100)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "32/32 [==============================] - 1s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 2/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 3/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 4/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 5/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 6/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 7/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 8/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 9/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 10/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 11/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 12/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 13/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 14/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 15/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 16/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 17/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 18/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 19/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 20/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 21/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 22/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 23/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 24/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 25/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 26/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 27/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 28/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 29/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 30/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 31/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 32/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 33/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 34/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 35/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 36/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 37/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 38/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 39/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 40/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 41/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 42/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 43/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 44/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 45/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 46/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 47/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 48/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 49/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 50/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 51/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 52/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 53/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 54/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 55/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 56/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 57/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 58/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 59/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 60/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 61/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 62/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 63/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 64/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 65/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 66/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 67/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 68/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 69/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 70/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 71/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 72/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 73/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 74/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 75/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 76/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 77/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 78/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 79/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 80/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 81/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 82/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 83/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 84/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 85/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 86/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 87/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 88/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 89/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 90/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 91/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 92/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 93/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 94/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 95/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 96/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 97/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 98/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 99/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
            "Epoch 100/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eW3l-KicgLW8"
      },
      "source": [
        "Hmm... still not learning...\n",
        "\n",
        "What we if increased the number of neurons and layers?\n",
        "\n",
        "Say, 2 hidden layers, with [ReLU](https://www.tensorflow.org/api_docs/python/tf/keras/activations/relu), pronounced \"rel-u\", (short for [rectified linear unit](https://machinelearningmastery.com/rectified-linear-activation-function-for-deep-learning-neural-networks/)), activation on the first one, and 4 neurons each?\n",
        "\n",
        "To see this network in action, check out the [TensorFlow Playground demo](https://playground.tensorflow.org/#activation=relu&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.001&regularizationRate=0&noise=0&networkShape=4,4&seed=0.93799&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularization_hide=true&regularizationRate_hide=true&batchSize_hide=true&dataset_hide=true).\n",
        "\n",
        "![multi-layer neural net created with TensorFlow playground](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/02-tensorflow-playground-two-layer-net-relu-activation.png)\n",
        "*The neural network we're going to recreate with TensorFlow code. See it live at [TensorFlow Playground](https://playground.tensorflow.org/#activation=relu&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.001&regularizationRate=0&noise=0&networkShape=4,4&seed=0.93799&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularization_hide=true&regularizationRate_hide=true&batchSize_hide=true&dataset_hide=true).*\n",
        "\n",
        "Let's try.\n",
        "\n",
        "**Note:** in the course, Daniel used `lr` instead of `learning_rate`. But for the update, we had changed to `learning_rate` instead of `lr`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yXxtQFHwlc9w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b23cc830-95af-4a13-dd9d-03006df9b701"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create a model\n",
        "model_6 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(4, activation=tf.keras.activations.relu), # hidden layer 1, 4 neurons, ReLU activation\n",
        "  tf.keras.layers.Dense(4, activation=tf.keras.activations.relu), # hidden layer 2, 4 neurons, ReLU activation\n",
        "  tf.keras.layers.Dense(1) # ouput layer\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model_6.compile(loss=tf.keras.losses.binary_crossentropy,\n",
        "                optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), # Adam's default learning rate is 0.001\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "# Fit the model\n",
        "history = model_6.fit(X, y, epochs=100)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "32/32 [==============================] - 2s 3ms/step - loss: 4.3069 - accuracy: 0.5000\n",
            "Epoch 2/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 4.0916 - accuracy: 0.5000\n",
            "Epoch 3/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 3.9820 - accuracy: 0.4520\n",
            "Epoch 4/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 3.8302 - accuracy: 0.4150\n",
            "Epoch 5/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 3.7048 - accuracy: 0.4500\n",
            "Epoch 6/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 3.5944 - accuracy: 0.4650\n",
            "Epoch 7/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 3.1921 - accuracy: 0.4650\n",
            "Epoch 8/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 2.6196 - accuracy: 0.4680\n",
            "Epoch 9/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 1.3332 - accuracy: 0.4740\n",
            "Epoch 10/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.9951 - accuracy: 0.4750\n",
            "Epoch 11/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.9489 - accuracy: 0.4750\n",
            "Epoch 12/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.9082 - accuracy: 0.4730\n",
            "Epoch 13/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.8345 - accuracy: 0.4720\n",
            "Epoch 14/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7435 - accuracy: 0.4690\n",
            "Epoch 15/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7288 - accuracy: 0.4550\n",
            "Epoch 16/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7245 - accuracy: 0.4620\n",
            "Epoch 17/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7213 - accuracy: 0.4630\n",
            "Epoch 18/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7186 - accuracy: 0.4670\n",
            "Epoch 19/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7166 - accuracy: 0.4600\n",
            "Epoch 20/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7143 - accuracy: 0.4560\n",
            "Epoch 21/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7127 - accuracy: 0.4600\n",
            "Epoch 22/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7107 - accuracy: 0.4580\n",
            "Epoch 23/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7090 - accuracy: 0.4590\n",
            "Epoch 24/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7075 - accuracy: 0.4640\n",
            "Epoch 25/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7061 - accuracy: 0.4690\n",
            "Epoch 26/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7046 - accuracy: 0.4640\n",
            "Epoch 27/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7033 - accuracy: 0.4720\n",
            "Epoch 28/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7012 - accuracy: 0.4650\n",
            "Epoch 29/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.7002 - accuracy: 0.4660\n",
            "Epoch 30/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6987 - accuracy: 0.4650\n",
            "Epoch 31/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6973 - accuracy: 0.4660\n",
            "Epoch 32/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6961 - accuracy: 0.4710\n",
            "Epoch 33/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6944 - accuracy: 0.4760\n",
            "Epoch 34/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6935 - accuracy: 0.4690\n",
            "Epoch 35/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6922 - accuracy: 0.4850\n",
            "Epoch 36/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6907 - accuracy: 0.4740\n",
            "Epoch 37/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6895 - accuracy: 0.4940\n",
            "Epoch 38/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6887 - accuracy: 0.4770\n",
            "Epoch 39/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6875 - accuracy: 0.4680\n",
            "Epoch 40/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6867 - accuracy: 0.4800\n",
            "Epoch 41/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6862 - accuracy: 0.4850\n",
            "Epoch 42/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6847 - accuracy: 0.4640\n",
            "Epoch 43/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6837 - accuracy: 0.4730\n",
            "Epoch 44/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6841 - accuracy: 0.4590\n",
            "Epoch 45/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6820 - accuracy: 0.4520\n",
            "Epoch 46/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6812 - accuracy: 0.4500\n",
            "Epoch 47/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6813 - accuracy: 0.4730\n",
            "Epoch 48/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6798 - accuracy: 0.4570\n",
            "Epoch 49/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6791 - accuracy: 0.5190\n",
            "Epoch 50/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6784 - accuracy: 0.5360\n",
            "Epoch 51/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6779 - accuracy: 0.5270\n",
            "Epoch 52/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6773 - accuracy: 0.5030\n",
            "Epoch 53/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6766 - accuracy: 0.5350\n",
            "Epoch 54/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6757 - accuracy: 0.5400\n",
            "Epoch 55/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6756 - accuracy: 0.5410\n",
            "Epoch 56/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6749 - accuracy: 0.5430\n",
            "Epoch 57/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6744 - accuracy: 0.5420\n",
            "Epoch 58/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6741 - accuracy: 0.5430\n",
            "Epoch 59/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6737 - accuracy: 0.5420\n",
            "Epoch 60/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6729 - accuracy: 0.5400\n",
            "Epoch 61/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6727 - accuracy: 0.5410\n",
            "Epoch 62/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6722 - accuracy: 0.5420\n",
            "Epoch 63/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6715 - accuracy: 0.5390\n",
            "Epoch 64/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6712 - accuracy: 0.5410\n",
            "Epoch 65/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6709 - accuracy: 0.5400\n",
            "Epoch 66/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6704 - accuracy: 0.5390\n",
            "Epoch 67/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6701 - accuracy: 0.5400\n",
            "Epoch 68/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6698 - accuracy: 0.5400\n",
            "Epoch 69/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6691 - accuracy: 0.5420\n",
            "Epoch 70/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6691 - accuracy: 0.5420\n",
            "Epoch 71/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6684 - accuracy: 0.5410\n",
            "Epoch 72/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6682 - accuracy: 0.5440\n",
            "Epoch 73/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6680 - accuracy: 0.5480\n",
            "Epoch 74/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6673 - accuracy: 0.5400\n",
            "Epoch 75/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6675 - accuracy: 0.5500\n",
            "Epoch 76/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6671 - accuracy: 0.5420\n",
            "Epoch 77/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6666 - accuracy: 0.5530\n",
            "Epoch 78/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6661 - accuracy: 0.5440\n",
            "Epoch 79/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6657 - accuracy: 0.5490\n",
            "Epoch 80/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6648 - accuracy: 0.5570\n",
            "Epoch 81/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6647 - accuracy: 0.5580\n",
            "Epoch 82/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6643 - accuracy: 0.5510\n",
            "Epoch 83/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6637 - accuracy: 0.5460\n",
            "Epoch 84/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6628 - accuracy: 0.5590\n",
            "Epoch 85/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6625 - accuracy: 0.5540\n",
            "Epoch 86/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6624 - accuracy: 0.5600\n",
            "Epoch 87/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6615 - accuracy: 0.5560\n",
            "Epoch 88/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6606 - accuracy: 0.5590\n",
            "Epoch 89/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6605 - accuracy: 0.5650\n",
            "Epoch 90/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6604 - accuracy: 0.5480\n",
            "Epoch 91/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6588 - accuracy: 0.5580\n",
            "Epoch 92/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6587 - accuracy: 0.5580\n",
            "Epoch 93/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6586 - accuracy: 0.5490\n",
            "Epoch 94/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6574 - accuracy: 0.5610\n",
            "Epoch 95/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6567 - accuracy: 0.5560\n",
            "Epoch 96/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6559 - accuracy: 0.5560\n",
            "Epoch 97/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6551 - accuracy: 0.5590\n",
            "Epoch 98/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6535 - accuracy: 0.5580\n",
            "Epoch 99/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6538 - accuracy: 0.5760\n",
            "Epoch 100/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6523 - accuracy: 0.5640\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bJqimQ0UsiO5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "23826f30-23de-43e9-864e-af5d646a1ed7"
      },
      "source": [
        "# Evaluate the model\n",
        "model_6.evaluate(X, y)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32/32 [==============================] - 0s 2ms/step - loss: 0.6508 - accuracy: 0.5640\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6507635116577148, 0.5640000104904175]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dh6l8egoc-Wr"
      },
      "source": [
        "We're still hitting 50% accuracy, our model is still practically as good as guessing.\n",
        "\n",
        "How do the predictions look?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OtmmuzJkcclw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "d22d69bd-f54e-485f-971f-aa9cf03a02f1"
      },
      "source": [
        "# Check out the predictions using 2 hidden layers\n",
        "plot_decision_boundary(model_6, X, y)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 0s 1ms/step\n",
            "doing binary classifcation...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q-hj-lAodLYU"
      },
      "source": [
        "What gives?\n",
        "\n",
        "It seems like our model is the same as the one in the [TensorFlow Playground](https://playground.tensorflow.org/#activation=relu&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=4,4&seed=0.93799&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularization_hide=true&regularizationRate_hide=true&batchSize_hide=true) but model it's still drawing straight lines...\n",
        "\n",
        "Ideally, the yellow lines go on the inside of the red circle and the blue circle.\n",
        "\n",
        "Okay, okay, let's model this circle once and for all.\n",
        "\n",
        "One more model (I promise... actually, I'm going to have to break that promise... we'll be building plenty more models).\n",
        "\n",
        "This time we'll change the activation function on our output layer too. Remember the architecture of a classification model? For binary classification, the output layer activation is usually the [Sigmoid activation function](https://www.tensorflow.org/api_docs/python/tf/math/sigmoid)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zgyJ67E3corL"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create a model\n",
        "model_7 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(4, activation=tf.keras.activations.relu), # hidden layer 1, ReLU activation\n",
        "  tf.keras.layers.Dense(4, activation=tf.keras.activations.relu), # hidden layer 2, ReLU activation\n",
        "  tf.keras.layers.Dense(1, activation=tf.keras.activations.sigmoid) # ouput layer, sigmoid activation\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model_7.compile(loss=tf.keras.losses.binary_crossentropy,\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "# Fit the model\n",
        "history = model_7.fit(X, y, epochs=100, verbose=0)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e5wpRgBVtSRK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6aabaeae-f6a2-4989-893c-5f22916f4935"
      },
      "source": [
        "# Evaluate our model\n",
        "model_7.evaluate(X, y)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32/32 [==============================] - 0s 2ms/step - loss: 0.2082 - accuracy: 0.9950\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.20816797018051147, 0.9950000047683716]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XsB0aZ42eIS2"
      },
      "source": [
        "Woah! It looks like our model is getting some incredible results, let's check them out."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zSoLyVMmczP4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "17ff2287-ecc6-4d54-d10a-5ca88815dae2"
      },
      "source": [
        "# View the predictions of the model with relu and sigmoid activations\n",
        "plot_decision_boundary(model_7, X, y)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 0s 1ms/step\n",
            "doing binary classifcation...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zKR2JO4PeTie"
      },
      "source": [
        "Nice! It looks like our model is almost perfectly (apart from a few examples) separating the two circles.\n",
        "\n",
        "> 🤔 **Question:** What's wrong with the predictions we've made? Are we really evaluating our model correctly here? Hint: what data did the model learn on and what did we predict on?\n",
        "\n",
        "Before we answer that, it's important to recognize what we've just covered.\n",
        "\n",
        "> 🔑 **Note:** The combination of **linear (straight lines) and non-linear (non-straight lines) functions** is one of the key fundamentals of neural networks.\n",
        "\n",
        "Think of it like this:\n",
        "\n",
        "If I gave you an unlimited amount of straight lines and non-straight lines, what kind of patterns could you draw?\n",
        "\n",
        "That's essentially what neural networks do to find patterns in data.\n",
        "\n",
        "Now you might be thinking, \"but I haven't seen a linear function or a non-linear function before...\"\n",
        "\n",
        "Oh but you have.\n",
        "\n",
        "We've been using them the whole time.\n",
        "\n",
        "They're what power the layers in the models we just built.\n",
        "\n",
        "To get some intuition about the activation functions we've just used, let's create them and then try them on some toy data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qm-b9aoemQ8A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b23c3ef1-cce8-49d8-a217-827a77c5db09"
      },
      "source": [
        "# Create a toy tensor (similar to the data we pass into our model)\n",
        "A = tf.cast(tf.range(-10, 10), tf.float32)\n",
        "A"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(20,), dtype=float32, numpy=\n",
              "array([-10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,\n",
              "         1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.],\n",
              "      dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ti67IeWI9vnP"
      },
      "source": [
        "How does this look?\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mYWO_14e-Pf8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "80eadd59-501b-442a-a5cd-755773185333"
      },
      "source": [
        "# Visualize our toy tensor\n",
        "plt.plot(A);"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5f9qOgTb-O7L"
      },
      "source": [
        "A straight (linear) line!\n",
        "\n",
        "Nice, now let's recreate the [sigmoid function](https://en.wikipedia.org/wiki/Sigmoid_function) and see what it does to our data. You can also find a pre-built sigmoid function at [`tf.keras.activations.sigmoid`](https://www.tensorflow.org/api_docs/python/tf/keras/activations/sigmoid)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "corG975yiwAP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4cee1ee7-8db1-4316-f7b1-a55efc6f8d79"
      },
      "source": [
        "# Sigmoid - https://www.tensorflow.org/api_docs/python/tf/keras/activations/sigmoid\n",
        "def sigmoid(x):\n",
        "  return 1 / (1 + tf.exp(-x))\n",
        "\n",
        "# Use the sigmoid function on our tensor\n",
        "sigmoid(A)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(20,), dtype=float32, numpy=\n",
              "array([4.5397868e-05, 1.2339458e-04, 3.3535014e-04, 9.1105117e-04,\n",
              "       2.4726230e-03, 6.6928510e-03, 1.7986210e-02, 4.7425874e-02,\n",
              "       1.1920292e-01, 2.6894143e-01, 5.0000000e-01, 7.3105854e-01,\n",
              "       8.8079703e-01, 9.5257413e-01, 9.8201376e-01, 9.9330717e-01,\n",
              "       9.9752742e-01, 9.9908900e-01, 9.9966466e-01, 9.9987662e-01],\n",
              "      dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3EpZb0F6-cn7"
      },
      "source": [
        "And how does it look?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vz4Pr2Mk-eko",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "bd15792e-3e53-42a3-b64d-bf140b6104e5"
      },
      "source": [
        "# Plot sigmoid modified tensor\n",
        "plt.plot(sigmoid(A));"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EGT2qtUV-k5f"
      },
      "source": [
        "A non-straight (non-linear) line!\n",
        "\n",
        "Okay, how about the [ReLU function](https://machinelearningmastery.com/rectified-linear-activation-function-for-deep-learning-neural-networks/#:~:text=The%20rectified%20linear%20activation%20function,otherwise%2C%20it%20will%20output%20zero.) (ReLU turns all negatives to 0 and positive numbers stay the same)?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_PUMn--Hiwyz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6ff6f7fc-436e-4e50-e213-3be14f0b21e6"
      },
      "source": [
        "# ReLU - https://www.tensorflow.org/api_docs/python/tf/keras/activations/relu\n",
        "def relu(x):\n",
        "  return tf.maximum(0, x)\n",
        "\n",
        "# Pass toy tensor through ReLU function\n",
        "relu(A)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(20,), dtype=float32, numpy=\n",
              "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 2., 3., 4., 5., 6.,\n",
              "       7., 8., 9.], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xAkmlNeZ-90e"
      },
      "source": [
        "How does the ReLU-modified tensor look?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iXWCO2R4mlk7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "e48282e6-86c9-423b-f0db-b3792dd43767"
      },
      "source": [
        "# Plot ReLU-modified tensor\n",
        "plt.plot(relu(A));"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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cmWx7gfq+vEK5BSV6dnBHPXxDK6tHCih2u11RUVFV/vx26RWLpk2bqlOnTuUe69ixow4dOlThPjabTZGRkeVuAAC46rnPtym3oESdm0fpwT68v89buRQs+vTpo4yMjHKP7dq1Sy1btnTrUAAAXGrx9uP6Ij1LIcFBmj6iM7XdXsylYPH73/9ea9eu1bRp07Rnzx69//77euONNzRq1ChPzQcACHC5BcUaP7e0tvu3N7RSpzhe+fZmLgWL7t27a86cOfrggw+UnJysKVOm6JVXXtHIkSM9NR8AIMC9tChDWfYCtWxUR6P7Udvt7VzqsZCk22+/XbfffrsnZgEAoJwNB8/o32tLa7unDqO22xfwWSEAAK9UVOJU6uwtMka687rm+llbart9AcECAOCV3li5V7uOn1PDumF6ltpun0GwAAB4nX0nzulvF2u7b++kBnWp7fYVBAsAgFdxOo1SZ6erqMSpG9s10dBr4qweCS4gWAAAvMonGzL13f7Tql0rRFOp7fY5BAsAgNfIzi3Q1AU7JElP92+n+IZ1LJ4IriJYAAC8xqTPt8teUKKUZlF6oHeC1eOgGggWAACvsHTHcS3YckwhwUFKG5Gi0BB+RPkiVg0AYLlzhSVltd0P/yxRyc2iLJ4I1UWwAABY7qVFGTqaU6D4hrU1ul87q8fBVSBYAAAstenQGc1ac0CSNG14imqHUdvtywgWAADLFDucSp2dLmOkEdc20w1tm1g9Eq4SwQIAYJk3Vu7TzqxcNahTS8/e3snqceAGBAsAgCX2n8zTX5fuliSNv72TGlLb7RcIFgCAGmeM0TMXartvaNtYw69tZvVIcBOCBQCgxn2y4bDW7Dul8FrBmjoshdpuP0KwAADUqBO5hWW13b/v104tGlHb7U8IFgCAGjV5/nblnC9WUlykHvpZotXjwM0IFgCAGrNsZ7Y+33xUwUHS9BGdqe32Q6woAKBG5BWW6NkLtd0P/SxRKc2p7fZHBAsAQI34y1e7dOTseTVvUFu//zm13f6KYAEA8LjNmWc1c/V+SdLU4SmqExZq8UTwFIIFAMCjih1OjZ2dLqeRhl0Tp5vaUdvtzwgWAACP+tc3+7XjmF31qe0OCAQLAIDHHDiZp1eW7JIkPTu4kxrXs1k8ETyNYAEA8AhjjMbNTVdhiVM/a9NYd15HbXcgIFgAADzi041HtGrPKdlCgzV1eDK13QGCYAEAcLuT5wr1/ILtkqTR/dqpZaO6Fk+EmkKwAAC43fPzt+tsfrE6No3UwzdQ2x1ICBYAALdaseuE5v5wsbY7RbWo7Q4orDYAwG3yi0o0bk66JOmB3onqEl/f2oFQ4wgWAAC3efmrXTp85rya1a+tp/tT2x2ICBYAALdIP5yjt1eV1nY/PzxZdW3UdgciggUA4KqVOJwaO3uLnEa6o0ucbmkfbfVIsAjBAgBw1d76dr+2HbUrqnYtjae2O6ARLAAAV+XQqXz9z4Xa7nGDO6pJBLXdgYxgAQCotou13QXFTvVu3Uh3dW1u9UiwGMECAFBtczYd0Te7T8oWGqxpw1Oo7QbBAgBQPafOFWrK/NLa7if6tlVCY2q7QbAAAFTT8wt26Ex+sTrERuiRG1tZPQ68BMECAOCylbtOaM6mIwoKkqbf2ZnabpThOwEA4JL8ohKNm3uxtjtB11DbjUsQLAAALnllyW5lnj6vuKhwPd2/vdXjwMsQLAAAV2zrkRz965t9kkpru+tR240fIVgAAK5IicOpMZ+W1nbf3rmpbu0QY/VI8EIECwDAFXln1QFtO2pXZHioJg5JsnoceCmCBQCgSpmn8/XyYmq7UTWCBQCgUsYYPTMnXeeLHbq+VUPd3S3e6pHgxQgWAIBKzf2htLY7LDRYaSM6U9uNShEsAAAVOp1XpCnzd0iSnuzbVonUdqMKBAsAQIWeX7Bdp/OK1D4mQr+9gdpuVI1gAQC4rG92n9DsjRdru1MUFsqPDFSN7xIAwE+cL3Jo3JytkqT7eyXo2hYNLJ4IvoJgAQD4iVeW7tKh0/lqGhWuPwygthtXjmABACintLZ7vyRpylBqu+EaggUAoEyJw6nU2elyOI0GpzRVv07UdsM1BAsAQJmZqw8o/UhOaW33HZ2sHgc+iGABAJBUWtv9l69Ka7ufua2joiPCLZ4IvohgAQCQMUbPzt2q88UO9UxsqHu6U9uN6iFYAAD02eajWrHrhMJCgzVtRAq13ag2ggUABLgzeUWa/Pl2SdJ/39JGrZvUs3gi+DKCBQAEuKlf7NCpC7Xdv7uptdXjwMcRLAAggK3ac1L/t+GwgoKkaSOo7cbV4zsIAAJUQbFDz8xJlyT96vqW6tqS2m5cvasKFtOnT1dQUJBGjx7tpnEAADXllSW7dfBUvmIjw/VHarvhJtUOFuvXr9frr7+uzp07u3MeAEAN2H7Urje/2SdJmjIsWRHhtSyeCP6iWsHi3LlzGjlypN588001aMBLZwDgSxxOo9TZW+RwGt2WEqufU9sNN6pWsBg1apQGDx6sfv36VbltYWGh7HZ7uRsAwDozVx/Q5sM5iggP1XNDkqweB37G5Y+s+/DDD7Vx40atX7/+irZPS0vTpEmTXB4MAOB+h8/k6y9fZUiSUgd1VHQktd1wL5descjMzNSTTz6p9957T+HhV/bNmJqaqpycnLJbZmZmtQYFAFydi7Xd+UUO9UhoqHup7YYHuPSKxYYNG5Sdna3rrruu7DGHw6GVK1fq73//uwoLCxUSElJuH5vNJpvN5p5pAQDV9vmWY1qecUJhIaW13cHB1HbD/VwKFn379lV6enq5xx588EF16NBBY8aM+UmoAAB4h7P5RZr8+TZJ0qhb2qhNNLXd8AyXgkVERISSk5PLPVa3bl01atToJ48DALzH1AU7dPJckdpG19OjN1PbDc+heRMA/NzqPSf1yYbDkqTpd1LbDc9y+V+F/Njy5cvdMAYAwBMure3+5fUt1LVlQ4sngr8jtgKAH/vb0t06cCpfMZE2/WlgB6vHQQAgWACAn9pxzK43VpbWdk8emqxIartRAwgWAOCHHE6jsbPTVeI0GpgUqwFJsVaPhABBsAAAP/TvNQe0OfOsImyhmjSU2m7UHIIFAPiZI2fP68VFpbXdYwZ1UAy13ahBBAsA8CPGGI2/UNvdPaGBftGjhdUjIcAQLADAjyxIP6avd2arVkiQ0qjthgUIFgDgJ3Lyi/XcZ9slSY/d3EZtoiMsngiBiGABAH5i2hc7dPJcodpE19Njt1DbDWsQLADAD6zZe0offZ8pSUobkSJbKB8KCWsQLADAxxUUOzTuQm33L3q2UPcEarthHYIFAPi4v3+9R/tO5ik6wqaxg6jthrUIFgDgwzKycvXPFXslSZOHJlHbDcsRLADAR5XWdm9RidOof6cYDUxuavVIAMECAHzVu2sPatOhs6pnC9XkoclWjwNIIlgAgE86eva8/rxwpyRpzMD2io2ithvegWABAD7GGKMJ87Yqr8ihri0baGTPllaPBJQhWACAj/lya5aW7KC2G96JYAEAPiQnv1gTP9smSXr0ptZqF0NtN7wLwQIAfMj0hTt0IrdQrZrU1WO3tLF6HOAnCBYA4CO+23dKH6y7UNs9PEXhtajthvchWACADygodij1Qm33fT3i1bNVI4snAi6PYAEAPuAfy/Zo34k8NYmwaeygjlaPA1SIYAEAXm7X8VzNuFDbPemOJEXVprYb3otgAQBezOk0Sp2drmKHUb+OMRqUHGv1SEClCBYA4MXe++6gNhw8o7phIZo8NElBQXRWwLsRLADASx3LOa8XFmZIkv40sIPi6te2eCKgagQLAPBSE+dt07nCEl3bor5+eT213fANBAsA8EILtx7TV9uPKzQ4SNNHdFYItd3wEQQLAPAy9oJiTZhXWtv9Xze1VvtYarvhOwgWAOBlpn+5U9m5hWrVuK4ev5XabvgWggUAeJH1B07r/e8OSZKmjaC2G76HYAEAXqKwxKGxn26RJN3bPV7XU9sNH0SwAAAv8Y9le7X3RJ4a17Mpldpu+CiCBQB4gd3Hc/WP5XskSc/d0UlRdajthm8iWACAxS6t7e7bIVqDU5paPRJQbQQLALDY++sO6fuLtd3Dkqnthk8jWACAhbJyCvTClzslSX8Y0F7NqO2GjyNYAICFnvtsm3ILS3RNfH39uleC1eMAV41gAQAWWbQtSwu3ZSk0OEhpI1Ko7YZfIFgAgAVKa7u3SpIeubGVOjaNtHgiwD0IFgBggT8v3Knj9kIlNKqjJ/q2tXocwG0IFgBQw74/cFrvrqW2G/6JYAEANaiwxKHU2emSpLu7NVfv1o0tnghwL4IFANSgfy7fp93Z59S4XpieuY3abvgfggUA1JA92bl6bVlpbfeEIUmqXyfM4okA9yNYAEANcDqNnpm9VUUOp25p30RDOlPbDf9EsACAGvDh+kytO3BadcJCNIXabvgxggUAeFi2vUBpX+6QJD3dv72aN6hj8USA5xAsAMDDJn62TbkFJerSPEoP9E6wehzAowgWAOBBX23L0pdbsxQSHKS0EZ2p7YbfI1gAgIfkFhRrwrxtkkpruzvFUdsN/0ewAAAPeXFRhrLsBWrZqI6epLYbAYJgAQAesOHgGf3v2oOSpGnDqe1G4CBYAICbFZU4lTp7i4yR/l/X5urThtpuBA6CBQC42esr9mrX8XNqVDdM46jtRoAhWACAG+09cU6vfn2xtruTGtSlthuBhWABAG7idBqlzk5XkcOpm9o10R1d4qweCahxBAsAcJOPv8/Uuv2nVbtWiJ6nthsBimABAG6QnVugaV9crO1up/iG1HYjMBEsAMANJn22XfaCEqU0o7YbgY1gAQBXacn241qQfkwhwUGafmeKQkP4qxWBi+9+ALgK5wpLNH7eVknSwzckKikuyuKJAGsRLADgKry0KEPHcgrUomEdje7bzupxAMu5FCzS0tLUvXt3RUREKDo6WsOGDVNGRoanZgMAr7bx0BnNWnNAkjR1eLJqh1HbDbgULFasWKFRo0Zp7dq1Wrx4sYqLi9W/f3/l5eV5aj4A8ErFDqdSP02XMdKI65rphrZNrB4J8Aqhrmy8cOHCcvdnzpyp6OhobdiwQTfeeKNbBwMAb/bGyn3KOJ6rhnXD9OzgTlaPA3gNl4LFj+Xk5EiSGjZsWOE2hYWFKiwsLLtvt9uv5ikBwHL7TpzTX5fuliSNv72jGlLbDZSp9ps3nU6nRo8erT59+ig5ObnC7dLS0hQVFVV2i4+Pr+5TAoDljDF6Zk66ikqcuqFtYw27ppnVIwFepdrBYtSoUdq6das+/PDDSrdLTU1VTk5O2S0zM7O6TwkAlvvk+8Nau6+0tnva8BRqu4EfqdavQh5//HHNnz9fK1euVPPmzSvd1mazyWazVWs4APAmJ3ILNfVCbfdTP6e2G7gcl4KFMUb//d//rTlz5mj58uVKTEz01FwA4HUmfb5NOeeLldwsUg/2SbB6HMAruRQsRo0apffff1/z5s1TRESEsrKyJElRUVGqXbu2RwYEAG/w9c7jmr/lQm33iM7UdgMVcOnKmDFjhnJycnTzzTeradOmZbePPvrIU/MBgOXyCkv07JzS2u6Hfpao5GbUdgMVcflXIQAQaF76KkNHcwrUvEFtje7X1upxAK/Ga3kAUIkfMs9q5uoDkqRpw1NUJ+yq6n8Av0ewAIAKFDucGvvpFhkjDb+2mW5sR203UBWCBQBU4M1v9mlnVq4a1KmlZwd3tHocwCcQLADgMg6czNNfl5TWdj87uJMa1aOPB7gSBAsA+JGLtd2FF2q7R1xHbTdwpQgWAPAj/7fhsFbvPaXwWsGaOozabsAVBAsAuMTJc/+p7f59v3Zq0YjabsAVBAsAuMTkz7frbH6xOjWN1EM/42MLAFcRLADggmUZ2fps81EFB0kv3EltN1AdXDUAoPK13b/pk6iU5tR2A9VBsAAASS8v3qUjZ8+rWf3aeqp/O6vHAXwWwQJAwNuceVbvrNovSZo6PJnabuAqECwABLRih1NjZ6fLaaSh18Tp5vbRVo8E+DSCBYCA9ta3+7XjmF3169TS+Ns7WT0O4PMIFgAC1sFTefqfxbskSeNu66jG1HYDV41gASAgXVrb3adNI/2/rs2tHgnwCwQLAAFp9sYjWrXnlGyh1HYD7kSwABBwTp0r1PMLtkuSRvdrp4TGdS2eCPAfBAsAAWfK/O06k1+sjk0j9fAN1HYD7kSwABBQVuw6obk/lNZ2Tx+RolrUdgNuxRUFIGDkF5Vo3Jx0SdIDvRPVJb6+tQMBfohgASBg/M/iXTp8prS2+2lquwGPIFgACAjph3P01reltd3PD0tWXRu13YAnECwA+L0Sh1NjZ2+R00hDusTplg7UdgOeQrAA4PfeXrVf247aFVW7liZQ2w14FMECgF87dCpfL19S290kgtpuwJMIFgD8ljFG4+amq6DYqV6tGumubtR2A55GsADgt+b+cETf7D6psNBgTRtBbTdQEwgWAPzS6bwiTZm/Q5L0ZN+2SqS2G6gRBAsAfun5+dt1Oq9IHWIj9MiNraweBwgYBAsAfmflrhOavemIgoKkNGq7gRrF1QbAr5wvcmjc3NLa7vt7JejaFg0snggILAQLAH7llSW7lHn6vOKiwvWHAe2tHgcIOAQLAH5j65Ec/etCbfeUYcmqR203UOMIFgD8wsXabofTaHDnpurbMcbqkYCARLAA4Bdmrj6grUfsigwP1cQh1HYDViFYAPB5mafz9ZevSmu7n7mto6Ijwi2eCAhcBAsAPq20tnurzhc71DOxoe7pHm/1SEBAI1gA8GmfbT6qlbtOKCw0WGnUdgOWI1gA8Fln8oo0+fPtkqQnbm2jVk3qWTwRAIIFAJ/1/IIdOpVXpPYxEXrkxtZWjwNABAsAPurb3Sf16cbDpbXdd6YoLJS/zgBvwJUIwOecL3LomTmltd2/vr6lrqO2G/AaBAsAPuevS3fr0Ol8NY0K1x8HdrB6HACXIFgA8CnbjubozW/2SZImD6W2G/A2BAsAPsPhNEqdnS6H0+i2lFj9vBO13YC3IVgA8BkzVx/QlsM5iggP1XNDkqweB8BlECwA+ITDZ/L1l68yJEmpgzoqOpLabsAbESwAeD1jjJ6du1X5RQ71SGioe6ntBrwWwQKA1/ts81EtzzihsJBgTRuRouBgarsBb0WwAODVzub/p7b78VvbqE00td2ANyNYAPBqUy/UdreNrqf/uonabsDbESwAeK3Ve07qkw2ltd3Tqe0GfAJXKQCvVFD8n9ruX/Zsqa4tG1o8EYArQbAA4JX+tnS3DpzKV2xkuP40sL3V4wC4QgQLAF5nxzG73lhZWts9aWiSIsJrWTwRgCtFsADgVRxOo7GfblGJ02hgUqwGJMVaPRIAFxAsAHiVf685oM2HcxRhC9WkodR2A76GYAHAaxw5e14vLiqt7R4zqINiqO0GfA7BAoBXMMZo/IXa7u4JDfSLHi2sHglANRAsAHiF+VuO6eud2QoLCVYatd2AzyJYALBcTn6xJn2+TZL02C2t1SY6wuKJAFQXwQKA5aZ9sUMnzxWpTXQ9PXoztd2AL6tWsHjttdeUkJCg8PBw9ezZU+vWrXP3XAACxJq9p/TR95mSpLQRKbKFhlg8EYCr4XKw+Oijj/TUU09p4sSJ2rhxo7p06aIBAwYoOzvbE/MB8GOX1naP7NlC3ROo7QZ8ncvB4uWXX9Zvf/tbPfjgg+rUqZP++c9/qk6dOnr77bc9MR8AP/b3r/do/8k8RUfYNGZQB6vHAeAGoa5sXFRUpA0bNig1NbXsseDgYPXr109r1qy57D6FhYUqLCwsu2+326s5auVe/ipDuYUlHjk2APdzOo3e++6QJGny0CRFUtsN+AWXgsXJkyflcDgUExNT7vGYmBjt3LnzsvukpaVp0qRJ1Z/wCn24PlPZuYVVbwjAq/TvFKOByU2tHgOAm7gULKojNTVVTz31VNl9u92u+Ph4tz/PA30SlMcrFoBPCQ8N0a96tbR6DABu5FKwaNy4sUJCQnT8+PFyjx8/flyxsZf/oCCbzSabzVb9Ca/QYze38fhzAACAyrn05s2wsDB17dpVS5cuLXvM6XRq6dKl6tWrl9uHAwAAvsXlX4U89dRTuv/++9WtWzf16NFDr7zyivLy8vTggw96Yj4AAOBDXA4W99xzj06cOKEJEyYoKytL11xzjRYuXPiTN3QCAIDAE2SMMTX5hHa7XVFRUcrJyVFkZGRNPjUAAKimK/35zWeFAAAAtyFYAAAAtyFYAAAAtyFYAAAAtyFYAAAAtyFYAAAAtyFYAAAAtyFYAAAAtyFYAAAAt/H4x6b/2MWiT7vdXtNPDQAAquniz+2qCrtrPFjk5uZKkuLj42v6qQEAwFXKzc1VVFRUhV+v8c8KcTqdOnr0qCIiIhQUFOS249rtdsXHxyszM9PvP4MkkM5VCqzz5Vz9VyCdL+fqn4wxys3NVVxcnIKDK34nRY2/YhEcHKzmzZt77PiRkZF+v7gXBdK5SoF1vpyr/wqk8+Vc/U9lr1RcxJs3AQCA2xAsAACA2/hNsLDZbJo4caJsNpvVo3hcIJ2rFFjny7n6r0A6X841sNX4mzcBAID/8ptXLAAAgPUIFgAAwG0IFgAAwG0IFgAAwG18Kli89tprSkhIUHh4uHr27Kl169ZVuv0nn3yiDh06KDw8XCkpKfriiy9qaNKrk5aWpu7duysiIkLR0dEaNmyYMjIyKt1n5syZCgoKKncLDw+voYmr77nnnvvJ3B06dKh0H19d14SEhJ+ca1BQkEaNGnXZ7X1tTVeuXKkhQ4YoLi5OQUFBmjt3brmvG2M0YcIENW3aVLVr11a/fv20e/fuKo/r6nVfEyo71+LiYo0ZM0YpKSmqW7eu4uLi9Otf/1pHjx6t9JjVuRZqQlXr+sADD/xk7oEDB1Z5XG9cV6nq873cNRwUFKQXX3yxwmN669p6is8Ei48++khPPfWUJk6cqI0bN6pLly4aMGCAsrOzL7v96tWrdd999+mhhx7Spk2bNGzYMA0bNkxbt26t4cldt2LFCo0aNUpr167V4sWLVVxcrP79+ysvL6/S/SIjI3Xs2LGy28GDB2to4quTlJRUbu5vv/22wm19eV3Xr19f7jwXL14sSbrrrrsq3MeX1jQvL09dunTRa6+9dtmv//nPf9bf/vY3/fOf/9R3332nunXrasCAASooKKjwmK5e9zWlsnPNz8/Xxo0bNX78eG3cuFGzZ89WRkaG7rjjjiqP68q1UFOqWldJGjhwYLm5P/jgg0qP6a3rKlV9vpee57Fjx/T2228rKChId955Z6XH9ca19RjjI3r06GFGjRpVdt/hcJi4uDiTlpZ22e3vvvtuM3jw4HKP9ezZ0/zud7/z6JyekJ2dbSSZFStWVLjNO++8Y6KiompuKDeZOHGi6dKlyxVv70/r+uSTT5rWrVsbp9N52a/76poaY4wkM2fOnLL7TqfTxMbGmhdffLHssbNnzxqbzWY++OCDCo/j6nVvhR+f6+WsW7fOSDIHDx6scBtXrwUrXO5c77//fjN06FCXjuML62rMla3t0KFDza233lrpNr6wtu7kE69YFBUVacOGDerXr1/ZY8HBwerXr5/WrFlz2X3WrFlTbntJGjBgQIXbe7OcnBxJUsOGDSvd7ty5c2rZsqXi4+M1dOhQbdu2rSbGu2q7d+9WXFycWrVqpZEjR+rQoUMVbusv61pUVKR3331Xv/nNbyr9MD5fXdMf279/v7KyssqtXVRUlHr27Fnh2lXnuvdWOTk5CgoKUv369SvdzpVrwZssX75c0dHRat++vR599FGdOnWqwm39aV2PHz+uBQsW6KGHHqpyW19d2+rwiWBx8uRJORwOxcTElHs8JiZGWVlZl90nKyvLpe29ldPp1OjRo9WnTx8lJydXuF379u319ttva968eXr33XfldDrVu3dvHT58uAandV3Pnj01c+ZMLVy4UDNmzND+/ft1ww03KDc397Lb+8u6zp07V2fPntUDDzxQ4Ta+uqaXc3F9XFm76lz33qigoEBjxozRfffdV+mHVLl6LXiLgQMH6t///reWLl2qF154QStWrNCgQYPkcDguu72/rKskzZo1SxERERoxYkSl2/nq2lZXjX+6KVwzatQobd26tcrfx/Xq1Uu9evUqu9+7d2917NhRr7/+uqZMmeLpMatt0KBBZf/duXNn9ezZUy1bttTHH398Rf8X4KveeustDRo0SHFxcRVu46triv8oLi7W3XffLWOMZsyYUem2vnot3HvvvWX/nZKSos6dO6t169Zavny5+vbta+Fknvf2229r5MiRVb6p2lfXtrp84hWLxo0bKyQkRMePHy/3+PHjxxUbG3vZfWJjY13a3hs9/vjjmj9/vpYtW+byR83XqlVL1157rfbs2eOh6Tyjfv36ateuXYVz+8O6Hjx4UEuWLNHDDz/s0n6+uqaSytbHlbWrznXvTS6GioMHD2rx4sUuf6R2VdeCt2rVqpUaN25c4dy+vq4XffPNN8rIyHD5OpZ8d22vlE8Ei7CwMHXt2lVLly4te8zpdGrp0qXl/o/uUr169Sq3vSQtXry4wu29iTFGjz/+uObMmaOvv/5aiYmJLh/D4XAoPT1dTZs29cCEnnPu3Dnt3bu3wrl9eV0veueddxQdHa3Bgwe7tJ+vrqkkJSYmKjY2ttza2e12fffddxWuXXWue29xMVTs3r1bS5YsUaNGjVw+RlXXgrc6fPiwTp06VeHcvryul3rrrbfUtWtXdenSxeV9fXVtr5jV7x69Uh9++KGx2Wxm5syZZvv27eaRRx4x9evXN1lZWcYYY371q1+ZsWPHlm2/atUqExoaal566SWzY8cOM3HiRFOrVi2Tnp5u1SlcsUcffdRERUWZ5cuXm2PHjpXd8vPzy7b58flOmjTJLFq0yOzdu9ds2LDB3HvvvSY8PNxs27bNilO4Yk8//bRZvny52b9/v1m1apXp16+fady4scnOzjbG+Ne6GlP67vcWLVqYMWPG/ORrvr6mubm5ZtOmTWbTpk1Gknn55ZfNpk2byv4lxPTp0039+vXNvHnzzJYtW8zQoUNNYmKiOX/+fNkxbr31VvPqq6+W3a/qurdKZedaVFRk7rjjDtO8eXPzww8/lLuGCwsLy47x43Ot6lqwSmXnmpuba/7whz+YNWvWmP3795slS5aY6667zrRt29YUFBSUHcNX1tWYqr+PjTEmJyfH1KlTx8yYMeOyx/CVtfUUnwkWxhjz6quvmhYtWpiwsDDTo0cPs3bt2rKv3XTTTeb+++8vt/3HH39s2rVrZ8LCwkxSUpJZsGBBDU9cPZIue3vnnXfKtvnx+Y4ePbrszyYmJsbcdtttZuPGjTU/vIvuuece07RpUxMWFmaaNWtm7rnnHrNnz56yr/vTuhpjzKJFi4wkk5GR8ZOv+fqaLlu27LLftxfPyel0mvHjx5uYmBhjs9lM3759f/Ln0LJlSzNx4sRyj1V23VulsnPdv39/hdfwsmXLyo7x43Ot6lqwSmXnmp+fb/r372+aNGliatWqZVq2bGl++9vf/iQg+Mq6GlP197Exxrz++uumdu3a5uzZs5c9hq+srafwsekAAMBtfOI9FgAAwDcQLAAAgNsQLAAAgNsQLAAAgNsQLAAAgNsQLAAAgNsQLAAAgNsQLAAAgNsQLAAAgNsQLAAAgNsQLAAAgNsQLAAAgNv8fz10H9TsM2NxAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-NLdoxJr_Grk"
      },
      "source": [
        "Another non-straight line!\n",
        "\n",
        "Well, how about TensorFlow's [linear activation function](https://www.tensorflow.org/api_docs/python/tf/keras/activations/linear)?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cg_kBQLVoXRB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f93a41ec-4524-40ef-f61d-9d69c18018b2"
      },
      "source": [
        "# Linear - https://www.tensorflow.org/api_docs/python/tf/keras/activations/linear (returns input non-modified...)\n",
        "tf.keras.activations.linear(A)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(20,), dtype=float32, numpy=\n",
              "array([-10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,\n",
              "         1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.],\n",
              "      dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d0NnpC_N_uWT"
      },
      "source": [
        "Hmm, it looks like our inputs are unmodified..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iwlgKpco_7V2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e65c03e2-d366-42fd-8d9d-a4cb40da6cc0"
      },
      "source": [
        "# Does the linear activation change anything?\n",
        "A == tf.keras.activations.linear(A)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(20,), dtype=bool, numpy=\n",
              "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True])>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KcCT_ipvADpR"
      },
      "source": [
        "Okay, so it makes sense now the model doesn't really learn anything when using only linear activation functions, because the linear activation function doesn't change our input data in anyway.\n",
        "\n",
        "Where as, with our non-linear functions, our data gets manipulated. A neural network uses these kind of transformations at a large scale to figure draw patterns between its inputs and outputs.\n",
        "\n",
        "Now rather than dive into the guts of neural networks, we're going to keep coding applying what we've learned to different problems but if you want a more in-depth look at what's going on behind the scenes, check out the Extra Curriculum section below.\n",
        "\n",
        "> 📖  **Resource:** For more on activation functions, check out the [machine learning cheatsheet page](https://ml-cheatsheet.readthedocs.io/en/latest/activation_functions.html#) on them."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BPCpA90uQBjk"
      },
      "source": [
        "## Evaluating and improving our classification model\n",
        "\n",
        "If you answered the question above, you might've picked up what we've been doing wrong.\n",
        "\n",
        "We've been evaluating our model on the same data it was trained on.\n",
        "\n",
        "A better approach would be to split our data into training, validation (optional) and test sets.\n",
        "\n",
        "Once we've done that, we'll train our model on the training set (let it find patterns in the data) and then see how well it learned the patterns by using it to predict values on the test set.\n",
        "\n",
        "Let's do it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NjOviFscgl4S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7f29ac3-2d14-4364-9baf-49df0ecb0671"
      },
      "source": [
        "# How many examples are in the whole dataset?\n",
        "len(X)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1000"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qNvKa8QrkSWR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b8a6ff14-64ef-4db9-d1fa-43471aa0ebed"
      },
      "source": [
        "# Split data into train and test sets\n",
        "X_train, y_train = X[:800], y[:800] # 80% of the data for the training set\n",
        "X_test, y_test = X[800:], y[800:] # 20% of the data for the test set\n",
        "\n",
        "# Check the shapes of the data\n",
        "X_train.shape, X_test.shape # 800 examples in the training set, 200 examples in the test set"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((800, 2), (200, 2))"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YtNge18ahgqa"
      },
      "source": [
        "Great, now we've got training and test sets, let's model the training data and evaluate what our model has learned on the test set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0RUOBhA1g8Kx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ff3ffd8-be45-43c1-a37d-c55de5769728"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create the model (same as model_7)\n",
        "model_8 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"), # hidden layer 1, using \"relu\" for activation (same as tf.keras.activations.relu)\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(1, activation=\"sigmoid\") # output layer, using 'sigmoid' for the output\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model_8.compile(loss=tf.keras.losses.binary_crossentropy,\n",
        "                optimizer=tf.keras.optimizers.Adam(learning_rate=0.01), # increase learning rate from 0.001 to 0.01 for faster learning\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "# Fit the model\n",
        "history = model_8.fit(X_train, y_train, epochs=25)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "25/25 [==============================] - 1s 3ms/step - loss: 0.6902 - accuracy: 0.5075\n",
            "Epoch 2/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6878 - accuracy: 0.5325\n",
            "Epoch 3/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6862 - accuracy: 0.5200\n",
            "Epoch 4/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6830 - accuracy: 0.5462\n",
            "Epoch 5/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6770 - accuracy: 0.5700\n",
            "Epoch 6/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6676 - accuracy: 0.5450\n",
            "Epoch 7/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6511 - accuracy: 0.6650\n",
            "Epoch 8/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6341 - accuracy: 0.7075\n",
            "Epoch 9/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6085 - accuracy: 0.7588\n",
            "Epoch 10/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.5821 - accuracy: 0.7538\n",
            "Epoch 11/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.5565 - accuracy: 0.7763\n",
            "Epoch 12/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.5284 - accuracy: 0.7962\n",
            "Epoch 13/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.5035 - accuracy: 0.8037\n",
            "Epoch 14/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.4585 - accuracy: 0.8450\n",
            "Epoch 15/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.4156 - accuracy: 0.8838\n",
            "Epoch 16/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3831 - accuracy: 0.8975\n",
            "Epoch 17/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3542 - accuracy: 0.9125\n",
            "Epoch 18/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3288 - accuracy: 0.9312\n",
            "Epoch 19/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2976 - accuracy: 0.9588\n",
            "Epoch 20/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2850 - accuracy: 0.9450\n",
            "Epoch 21/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2571 - accuracy: 0.9563\n",
            "Epoch 22/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2353 - accuracy: 0.9613\n",
            "Epoch 23/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2209 - accuracy: 0.9725\n",
            "Epoch 24/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2108 - accuracy: 0.9588\n",
            "Epoch 25/25\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2010 - accuracy: 0.9700\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aAnKqoDxloAA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "85adbcd4-508f-4025-8fe8-7b19a5018bca"
      },
      "source": [
        "# Evaluate our model on the test set\n",
        "loss, accuracy = model_8.evaluate(X_test, y_test)\n",
        "print(f\"Model loss on the test set: {loss}\")\n",
        "print(f\"Model accuracy on the test set: {100*accuracy:.2f}%\")"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 0s 3ms/step - loss: 0.2052 - accuracy: 0.9700\n",
            "Model loss on the test set: 0.20516908168792725\n",
            "Model accuracy on the test set: 97.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c1lWpsNwjD5C"
      },
      "source": [
        "100% accuracy? Nice!\n",
        "\n",
        "Now, when we started to create `model_8` we said it was going to be the same as `model_7` but you might've found that to be a little lie.\n",
        "\n",
        "That's because we changed a few things:\n",
        "* **The `activation` parameter** - We used strings (`\"relu\"` & `\"sigmoid\"`) instead of using library paths (`tf.keras.activations.relu`), in TensorFlow, they both offer the same functionality.\n",
        "* **The `learning_rate` (also `lr`) parameter** - We increased the **learning rate** parameter in the [Adam optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam) to `0.01` instead of `0.001` (an increase of 10x).\n",
        "  * You can think of the learning rate as how quickly a model learns. The higher the learning rate, the faster the model's capacity to learn, however, there's such a thing as a *too high* learning rate, where a model tries to learn too fast and doesn't learn anything. We'll see a trick to find the ideal learning rate soon.\n",
        "* **The number of epochs** - We lowered the number of epochs (using the `epochs` parameter) from 100 to 25 but our model still got an incredible result on both the training and test sets.\n",
        "  * One of the reasons our model performed well in even less epochs (remember a single epoch is the model trying to learn patterns in the data by looking at it once, so 25 epochs means the model gets 25 chances) than before is because we increased the learning rate.\n",
        "\n",
        "We know our model is performing well based on the evaluation metrics but let's see how it performs visually.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LRvAIBfAmtdc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "outputId": "52f5f62b-28b2-4ccd-9d7d-f34985e1a088"
      },
      "source": [
        "# Plot the decision boundaries for the training and test sets\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Train\")\n",
        "plot_decision_boundary(model_8, X=X_train, y=y_train)\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Test\")\n",
        "plot_decision_boundary(model_8, X=X_test, y=y_test)\n",
        "plt.show()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 0s 1ms/step\n",
            "doing binary classifcation...\n",
            "313/313 [==============================] - 0s 1ms/step\n",
            "doing binary classifcation...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "42KDFaHonmGN"
      },
      "source": [
        "Check that out! How cool. With a few tweaks, our model is now predicting the blue and red circles almost perfectly."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EqaFRxFaiklC"
      },
      "source": [
        "### Plot the loss curves\n",
        "\n",
        "Looking at the plots above, we can see the outputs of our model are very good.\n",
        "\n",
        "But how did our model go whilst it was learning?\n",
        "\n",
        "As in, how did the performance change everytime the model had a chance to look at the data (once every epoch)?\n",
        "\n",
        "To figure this out, we can check the **loss curves** (also referred to as the **learning curves**).\n",
        "\n",
        "You might've seen we've been using the variable `history` when calling the `fit()` function on a model ([`fit()` returns a `History` object](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit)).\n",
        "\n",
        "This is where we'll get the information for how our model is performing as it learns.\n",
        "\n",
        "Let's see how we might use it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sBHuMm9mpoOz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "outputId": "9af3a96f-8189-4649-cfe2-57e7a320c6d6"
      },
      "source": [
        "# You can access the information in the history variable using the .history attribute\n",
        "pd.DataFrame(history.history)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        loss  accuracy\n",
              "0   0.690183   0.50750\n",
              "1   0.687798   0.53250\n",
              "2   0.686171   0.52000\n",
              "3   0.683011   0.54625\n",
              "4   0.677036   0.57000\n",
              "5   0.667617   0.54500\n",
              "6   0.651129   0.66500\n",
              "7   0.634132   0.70750\n",
              "8   0.608484   0.75875\n",
              "9   0.582073   0.75375\n",
              "10  0.556544   0.77625\n",
              "11  0.528435   0.79625\n",
              "12  0.503492   0.80375\n",
              "13  0.458543   0.84500\n",
              "14  0.415571   0.88375\n",
              "15  0.383102   0.89750\n",
              "16  0.354211   0.91250\n",
              "17  0.328809   0.93125\n",
              "18  0.297566   0.95875\n",
              "19  0.285039   0.94500\n",
              "20  0.257121   0.95625\n",
              "21  0.235265   0.96125\n",
              "22  0.220926   0.97250\n",
              "23  0.210754   0.95875\n",
              "24  0.201047   0.97000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b81eb2ec-b336-4728-bb05-d9c18d782770\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.690183</td>\n",
              "      <td>0.50750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.687798</td>\n",
              "      <td>0.53250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.686171</td>\n",
              "      <td>0.52000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.683011</td>\n",
              "      <td>0.54625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.677036</td>\n",
              "      <td>0.57000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.667617</td>\n",
              "      <td>0.54500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.651129</td>\n",
              "      <td>0.66500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.634132</td>\n",
              "      <td>0.70750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.608484</td>\n",
              "      <td>0.75875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.582073</td>\n",
              "      <td>0.75375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.556544</td>\n",
              "      <td>0.77625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.528435</td>\n",
              "      <td>0.79625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.503492</td>\n",
              "      <td>0.80375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.458543</td>\n",
              "      <td>0.84500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.415571</td>\n",
              "      <td>0.88375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.383102</td>\n",
              "      <td>0.89750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.354211</td>\n",
              "      <td>0.91250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.328809</td>\n",
              "      <td>0.93125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.297566</td>\n",
              "      <td>0.95875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.285039</td>\n",
              "      <td>0.94500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.257121</td>\n",
              "      <td>0.95625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.235265</td>\n",
              "      <td>0.96125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.220926</td>\n",
              "      <td>0.97250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.210754</td>\n",
              "      <td>0.95875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.201047</td>\n",
              "      <td>0.97000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b81eb2ec-b336-4728-bb05-d9c18d782770')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b81eb2ec-b336-4728-bb05-d9c18d782770 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b81eb2ec-b336-4728-bb05-d9c18d782770');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-29d75e69-f626-4318-8847-b2c4ed124e8c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-29d75e69-f626-4318-8847-b2c4ed124e8c')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-29d75e69-f626-4318-8847-b2c4ed124e8c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mblFGnDap48J"
      },
      "source": [
        "Inspecting the outputs, we can see the loss values going down and the accuracy going up.\n",
        "\n",
        "How's it look (visualize, visualize, visualize)?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mqlwnZGJpYNF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "outputId": "3da15e5b-ff69-41b7-b4ac-ce75d3618ecb"
      },
      "source": [
        "# Plot the loss curves\n",
        "pd.DataFrame(history.history).plot()\n",
        "plt.title(\"Model_8 training curves\")"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Model_8 training curves')"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mofXV4znqmNh"
      },
      "source": [
        "Beautiful. This is the ideal plot we'd be looking for when dealing with a classification problem, loss going down, accuracy going up.\n",
        "\n",
        "> 🔑 **Note:** For many problems, the loss function going down means the model is improving (the predictions it's making are getting closer to the ground truth labels)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QyZA0CYLir5-"
      },
      "source": [
        "### Finding the best learning rate\n",
        "\n",
        "Aside from the architecture itself (the layers, number of neurons, activations, etc), the most important hyperparameter you can tune for your neural network models is the **learning rate**.\n",
        "\n",
        "In `model_8` you saw we lowered the Adam optimizer's learning rate from the default of `0.001` (default) to `0.01`.\n",
        "\n",
        "And you might be wondering why we did this.\n",
        "\n",
        "Put it this way, it was a lucky guess.\n",
        "\n",
        "I just decided to try a lower learning rate and see how the model went.\n",
        "\n",
        "Now you might be thinking, \"Seriously? You can do that?\"\n",
        "\n",
        "And the answer is yes. You can change any of the hyperparamaters of your neural networks.\n",
        "\n",
        "With practice, you'll start to see what kind of hyperparameters work and what don't.\n",
        "\n",
        "That's an important thing to understand about machine learning and deep learning in general. It's very experimental. You build a model and evaluate it, build a model and evaluate it.\n",
        "\n",
        "That being said, I want to introduce you a trick which will help you find the optimal learning rate (at least to begin training with) for your models going forward.\n",
        "\n",
        "To do so, we're going to use the following:\n",
        "* A [learning rate **callback**](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/LearningRateScheduler).\n",
        "   * You can think of a callback as an extra piece of functionality you can add to your model *while* its training.\n",
        "* Another model (we could use the same ones as above, we we're practicing building models here).\n",
        "* A modified loss curves plot.\n",
        "\n",
        "We'll go through each with code, then explain what's going on.\n",
        "\n",
        "> 🔑 **Note:** The default hyperparameters of many neural network building blocks in TensorFlow are setup in a way which usually work right out of the box (e.g. the [Adam optimizer's](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam) default settings can usually get good results on many datasets). So it's a good idea to try the defaults first, then adjust as needed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3g05waDawhsZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2808640d-93ee-4350-8fec-3c6ec34d4782"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create a model (same as model_8)\n",
        "model_9 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(1, activation=\"sigmoid\")\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model_9.compile(loss=\"binary_crossentropy\", # we can use strings here too\n",
        "              optimizer=\"Adam\", # same as tf.keras.optimizers.Adam() with default settings\n",
        "              metrics=[\"accuracy\"])\n",
        "\n",
        "# Create a learning rate scheduler callback\n",
        "lr_scheduler = tf.keras.callbacks.LearningRateScheduler(lambda epoch: 1e-4 * 10**(epoch/20)) # traverse a set of learning rate values starting from 1e-4, increasing by 10**(epoch/20) every epoch\n",
        "\n",
        "# Fit the model (passing the lr_scheduler callback)\n",
        "history = model_9.fit(X_train,\n",
        "                      y_train,\n",
        "                      epochs=100,\n",
        "                      callbacks=[lr_scheduler])"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "25/25 [==============================] - 1s 3ms/step - loss: 0.6918 - accuracy: 0.5088 - lr: 1.0000e-04\n",
            "Epoch 2/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6918 - accuracy: 0.5038 - lr: 1.1220e-04\n",
            "Epoch 3/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6917 - accuracy: 0.5038 - lr: 1.2589e-04\n",
            "Epoch 4/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6917 - accuracy: 0.5025 - lr: 1.4125e-04\n",
            "Epoch 5/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6917 - accuracy: 0.5063 - lr: 1.5849e-04\n",
            "Epoch 6/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6916 - accuracy: 0.5050 - lr: 1.7783e-04\n",
            "Epoch 7/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6915 - accuracy: 0.5088 - lr: 1.9953e-04\n",
            "Epoch 8/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6915 - accuracy: 0.5075 - lr: 2.2387e-04\n",
            "Epoch 9/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6914 - accuracy: 0.5088 - lr: 2.5119e-04\n",
            "Epoch 10/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6913 - accuracy: 0.5088 - lr: 2.8184e-04\n",
            "Epoch 11/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6912 - accuracy: 0.5075 - lr: 3.1623e-04\n",
            "Epoch 12/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6912 - accuracy: 0.5100 - lr: 3.5481e-04\n",
            "Epoch 13/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6910 - accuracy: 0.5113 - lr: 3.9811e-04\n",
            "Epoch 14/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6909 - accuracy: 0.5138 - lr: 4.4668e-04\n",
            "Epoch 15/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6907 - accuracy: 0.5150 - lr: 5.0119e-04\n",
            "Epoch 16/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6905 - accuracy: 0.5188 - lr: 5.6234e-04\n",
            "Epoch 17/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6903 - accuracy: 0.5225 - lr: 6.3096e-04\n",
            "Epoch 18/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6901 - accuracy: 0.5238 - lr: 7.0795e-04\n",
            "Epoch 19/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6899 - accuracy: 0.5250 - lr: 7.9433e-04\n",
            "Epoch 20/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6895 - accuracy: 0.5288 - lr: 8.9125e-04\n",
            "Epoch 21/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6891 - accuracy: 0.5300 - lr: 0.0010\n",
            "Epoch 22/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6885 - accuracy: 0.5375 - lr: 0.0011\n",
            "Epoch 23/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6880 - accuracy: 0.5350 - lr: 0.0013\n",
            "Epoch 24/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6868 - accuracy: 0.5412 - lr: 0.0014\n",
            "Epoch 25/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6856 - accuracy: 0.5350 - lr: 0.0016\n",
            "Epoch 26/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6844 - accuracy: 0.5387 - lr: 0.0018\n",
            "Epoch 27/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6830 - accuracy: 0.5425 - lr: 0.0020\n",
            "Epoch 28/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6811 - accuracy: 0.5437 - lr: 0.0022\n",
            "Epoch 29/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6794 - accuracy: 0.5475 - lr: 0.0025\n",
            "Epoch 30/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6773 - accuracy: 0.5587 - lr: 0.0028\n",
            "Epoch 31/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6743 - accuracy: 0.5600 - lr: 0.0032\n",
            "Epoch 32/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6712 - accuracy: 0.5788 - lr: 0.0035\n",
            "Epoch 33/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6668 - accuracy: 0.5938 - lr: 0.0040\n",
            "Epoch 34/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6611 - accuracy: 0.6300 - lr: 0.0045\n",
            "Epoch 35/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6541 - accuracy: 0.6513 - lr: 0.0050\n",
            "Epoch 36/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6456 - accuracy: 0.6400 - lr: 0.0056\n",
            "Epoch 37/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6316 - accuracy: 0.6862 - lr: 0.0063\n",
            "Epoch 38/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6141 - accuracy: 0.6850 - lr: 0.0071\n",
            "Epoch 39/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.5986 - accuracy: 0.7287 - lr: 0.0079\n",
            "Epoch 40/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.5662 - accuracy: 0.7588 - lr: 0.0089\n",
            "Epoch 41/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.5288 - accuracy: 0.7663 - lr: 0.0100\n",
            "Epoch 42/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.5150 - accuracy: 0.7588 - lr: 0.0112\n",
            "Epoch 43/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.5029 - accuracy: 0.7837 - lr: 0.0126\n",
            "Epoch 44/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.4472 - accuracy: 0.8213 - lr: 0.0141\n",
            "Epoch 45/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3744 - accuracy: 0.8662 - lr: 0.0158\n",
            "Epoch 46/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2755 - accuracy: 0.9312 - lr: 0.0178\n",
            "Epoch 47/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1886 - accuracy: 0.9600 - lr: 0.0200\n",
            "Epoch 48/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1305 - accuracy: 0.9812 - lr: 0.0224\n",
            "Epoch 49/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1161 - accuracy: 0.9762 - lr: 0.0251\n",
            "Epoch 50/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0870 - accuracy: 0.9900 - lr: 0.0282\n",
            "Epoch 51/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1532 - accuracy: 0.9325 - lr: 0.0316\n",
            "Epoch 52/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0809 - accuracy: 0.9775 - lr: 0.0355\n",
            "Epoch 53/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1048 - accuracy: 0.9600 - lr: 0.0398\n",
            "Epoch 54/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0923 - accuracy: 0.9600 - lr: 0.0447\n",
            "Epoch 55/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1136 - accuracy: 0.9538 - lr: 0.0501\n",
            "Epoch 56/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2233 - accuracy: 0.9200 - lr: 0.0562\n",
            "Epoch 57/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1119 - accuracy: 0.9563 - lr: 0.0631\n",
            "Epoch 58/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2204 - accuracy: 0.9125 - lr: 0.0708\n",
            "Epoch 59/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0781 - accuracy: 0.9725 - lr: 0.0794\n",
            "Epoch 60/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0548 - accuracy: 0.9787 - lr: 0.0891\n",
            "Epoch 61/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0916 - accuracy: 0.9688 - lr: 0.1000\n",
            "Epoch 62/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.4185 - accuracy: 0.8838 - lr: 0.1122\n",
            "Epoch 63/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1851 - accuracy: 0.9350 - lr: 0.1259\n",
            "Epoch 64/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0893 - accuracy: 0.9688 - lr: 0.1413\n",
            "Epoch 65/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0730 - accuracy: 0.9775 - lr: 0.1585\n",
            "Epoch 66/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3190 - accuracy: 0.9050 - lr: 0.1778\n",
            "Epoch 67/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6285 - accuracy: 0.6950 - lr: 0.1995\n",
            "Epoch 68/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.4084 - accuracy: 0.7812 - lr: 0.2239\n",
            "Epoch 69/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1803 - accuracy: 0.9350 - lr: 0.2512\n",
            "Epoch 70/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1364 - accuracy: 0.9500 - lr: 0.2818\n",
            "Epoch 71/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3171 - accuracy: 0.9013 - lr: 0.3162\n",
            "Epoch 72/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2477 - accuracy: 0.9150 - lr: 0.3548\n",
            "Epoch 73/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1354 - accuracy: 0.9525 - lr: 0.3981\n",
            "Epoch 74/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3741 - accuracy: 0.8712 - lr: 0.4467\n",
            "Epoch 75/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3701 - accuracy: 0.8550 - lr: 0.5012\n",
            "Epoch 76/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2392 - accuracy: 0.9125 - lr: 0.5623\n",
            "Epoch 77/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1684 - accuracy: 0.9312 - lr: 0.6310\n",
            "Epoch 78/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1817 - accuracy: 0.9312 - lr: 0.7079\n",
            "Epoch 79/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.4512 - accuracy: 0.8250 - lr: 0.7943\n",
            "Epoch 80/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6677 - accuracy: 0.5938 - lr: 0.8913\n",
            "Epoch 81/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6764 - accuracy: 0.5400 - lr: 1.0000\n",
            "Epoch 82/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6678 - accuracy: 0.5775 - lr: 1.1220\n",
            "Epoch 83/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.7416 - accuracy: 0.4900 - lr: 1.2589\n",
            "Epoch 84/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.7138 - accuracy: 0.5063 - lr: 1.4125\n",
            "Epoch 85/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.7237 - accuracy: 0.5038 - lr: 1.5849\n",
            "Epoch 86/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.7751 - accuracy: 0.5063 - lr: 1.7783\n",
            "Epoch 87/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.7665 - accuracy: 0.5063 - lr: 1.9953\n",
            "Epoch 88/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.7566 - accuracy: 0.5163 - lr: 2.2387\n",
            "Epoch 89/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.7552 - accuracy: 0.4938 - lr: 2.5119\n",
            "Epoch 90/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.7816 - accuracy: 0.5238 - lr: 2.8184\n",
            "Epoch 91/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.8113 - accuracy: 0.5213 - lr: 3.1623\n",
            "Epoch 92/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.7351 - accuracy: 0.4888 - lr: 3.5481\n",
            "Epoch 93/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.7429 - accuracy: 0.5063 - lr: 3.9811\n",
            "Epoch 94/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.7601 - accuracy: 0.5063 - lr: 4.4668\n",
            "Epoch 95/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.8247 - accuracy: 0.4863 - lr: 5.0119\n",
            "Epoch 96/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.7877 - accuracy: 0.4737 - lr: 5.6234\n",
            "Epoch 97/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.8081 - accuracy: 0.5013 - lr: 6.3096\n",
            "Epoch 98/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.9653 - accuracy: 0.4963 - lr: 7.0795\n",
            "Epoch 99/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.9762 - accuracy: 0.4913 - lr: 7.9433\n",
            "Epoch 100/100\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.8582 - accuracy: 0.4613 - lr: 8.9125\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KKln6dnbNPVR"
      },
      "source": [
        "Now our model has finished training, let's have a look at the training history."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wCd12upnyO4y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "outputId": "e9f1168a-39f1-41cd-d095-1f32de75f39b"
      },
      "source": [
        "# Checkout the history\n",
        "pd.DataFrame(history.history).plot(figsize=(10,7), xlabel=\"epochs\");"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9OkUFgZSx6hP"
      },
      "source": [
        "As you you see the learning rate exponentially increases as the number of epochs increases.\n",
        "\n",
        "And you can see the model's accuracy goes up (and loss goes down) at a specific point when the learning rate slowly increases.\n",
        "\n",
        "To figure out where this infliction point is, we can plot the loss versus the log-scale learning rate."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8fnEklbYyGBG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "outputId": "862f4c1b-fc7a-4100-908a-85366b9072da"
      },
      "source": [
        "# Plot the learning rate versus the loss\n",
        "lrs = 1e-4 * (10 ** (np.arange(100)/20))\n",
        "plt.figure(figsize=(10, 7))\n",
        "plt.semilogx(lrs, history.history[\"loss\"]) # we want the x-axis (learning rate) to be log scale\n",
        "plt.xlabel(\"Learning Rate\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.title(\"Learning rate vs. loss\");"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w0kMFKLvOF8s"
      },
      "source": [
        "To figure out the ideal value of the learning rate (at least the ideal value to *begin* training our model), the rule of thumb is to take the learning rate value where the loss is still decreasing but not quite flattened out (usually about 10x smaller than the bottom of the curve).\n",
        "\n",
        "In this case, our ideal learning rate ends up between `0.01` ($10^{-2}$) and `0.02`.\n",
        "\n",
        "![finding the ideal learning rate by plotting learning rate vs. loss](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/02-learning-rate-vs-loss.png)\n",
        "\n",
        "*The ideal learning rate at the start of model training is somewhere just before the loss curve bottoms out (a value where the loss is still decreasing).*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vJo-nDw4zFfx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1007235c-2d9d-40f8-9528-1c7b1d3ca339"
      },
      "source": [
        "# Example of other typical learning rate values\n",
        "10**0, 10**-1, 10**-2, 10**-3, 1e-4"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 0.1, 0.01, 0.001, 0.0001)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iALar-WvPlEc"
      },
      "source": [
        "Now we've estimated the ideal learning rate (we'll use `0.02`) for our model, let's refit it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EJ9wbXblzPPL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b1c370e4-54b5-4ab3-a352-8e90d29dde21"
      },
      "source": [
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create the model\n",
        "model_10 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(1, activation=\"sigmoid\")\n",
        "])\n",
        "\n",
        "# Compile the model with the ideal learning rate\n",
        "model_10.compile(loss=\"binary_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(learning_rate=0.02), # to adjust the learning rate, you need to use tf.keras.optimizers.Adam (not \"adam\")\n",
        "                metrics=[\"accuracy\"])\n",
        "\n",
        "# Fit the model for 20 epochs (5 less than before)\n",
        "history = model_10.fit(X_train, y_train, epochs=20)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "25/25 [==============================] - 2s 3ms/step - loss: 0.6844 - accuracy: 0.5650\n",
            "Epoch 2/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6692 - accuracy: 0.6612\n",
            "Epoch 3/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6408 - accuracy: 0.7250\n",
            "Epoch 4/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.5839 - accuracy: 0.7812\n",
            "Epoch 5/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.5135 - accuracy: 0.8250\n",
            "Epoch 6/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.4106 - accuracy: 0.9187\n",
            "Epoch 7/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3194 - accuracy: 0.9513\n",
            "Epoch 8/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2379 - accuracy: 0.9762\n",
            "Epoch 9/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1835 - accuracy: 0.9850\n",
            "Epoch 10/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1439 - accuracy: 0.9925\n",
            "Epoch 11/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1122 - accuracy: 0.9950\n",
            "Epoch 12/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0928 - accuracy: 0.9937\n",
            "Epoch 13/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0849 - accuracy: 0.9937\n",
            "Epoch 14/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0818 - accuracy: 0.9875\n",
            "Epoch 15/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0714 - accuracy: 0.9925\n",
            "Epoch 16/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0624 - accuracy: 0.9950\n",
            "Epoch 17/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0535 - accuracy: 0.9912\n",
            "Epoch 18/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0501 - accuracy: 0.9975\n",
            "Epoch 19/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0588 - accuracy: 0.9875\n",
            "Epoch 20/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0470 - accuracy: 0.9887\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qb9xwISc0pnF"
      },
      "source": [
        "Nice! With a little higher learning rate (`0.02` instead of `0.01`) we reach a higher accuracy than `model_8` in less epochs (`20` instead of `25`).\n",
        "\n",
        "> 🛠 **Practice:** Now you've seen an example of what can happen when you change the learning rate, try changing the learning rate value in the [TensorFlow Playground](https://playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=4,2&seed=0.03154&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularization_hide=true&regularizationRate_hide=true&problem_hide=true) and see what happens. What happens if you increase it? What happens if you decrease it?\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ZCYAqitKUk2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d4af111-b8b4-492a-b1cd-3eefa369897d"
      },
      "source": [
        "# Evaluate model on the test dataset\n",
        "model_10.evaluate(X_test, y_test)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 0s 3ms/step - loss: 0.0425 - accuracy: 1.0000\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.042508091777563095, 1.0]"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YWqQShzZKT7R"
      },
      "source": [
        "\n",
        "\n",
        "Let's see how the predictions look."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rJbzQ5kQ2HSh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "outputId": "435b323c-8a6d-48d5-8a92-fec7ffbc4b03"
      },
      "source": [
        "# Plot the decision boundaries for the training and test sets\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Train\")\n",
        "plot_decision_boundary(model_10, X=X_train, y=y_train)\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Test\")\n",
        "plot_decision_boundary(model_10, X=X_test, y=y_test)\n",
        "plt.show()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 0s 1ms/step\n",
            "doing binary classifcation...\n",
            "313/313 [==============================] - 0s 1ms/step\n",
            "doing binary classifcation...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ppVLzAao2Hkl"
      },
      "source": [
        "And as we can see, almost perfect again.\n",
        "\n",
        "These are the kind of experiments you'll be running often when building your own models.\n",
        "\n",
        "Start with default settings and see how they perform on your data.\n",
        "\n",
        "And if they don't perform as well as you'd like, improve them.\n",
        "\n",
        "Let's look at a few more ways to evaluate our classification models."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0bNdy1EbftYp"
      },
      "source": [
        "### More classification evaluation methods\n",
        "\n",
        "Alongside the visualizations we've been making, there are a number of different evaluation metrics we can use to evaluate our classification models.\n",
        "\n",
        "| **Metric name/Evaluation method** | **Defintion** | **Code** |\n",
        "| --- | --- | --- |\n",
        "| Accuracy | Out of 100 predictions, how many does your model get correct? E.g. 95% accuracy means it gets 95/100 predictions correct. | [`sklearn.metrics.accuracy_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html) or [`tf.keras.metrics.Accuracy()`](tensorflow.org/api_docs/python/tf/keras/metrics/Accuracy) |\n",
        "| Precision | Proportion of true positives over total number of samples. Higher precision leads to less false positives (model predicts 1 when it should've been 0). | [`sklearn.metrics.precision_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html) or [`tf.keras.metrics.Precision()`](tensorflow.org/api_docs/python/tf/keras/metrics/Precision) |\n",
        "| Recall | Proportion of true positives over total number of true positives and false negatives (model predicts 0 when it should've been 1). Higher recall leads to less false negatives. | [`sklearn.metrics.recall_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html) or [`tf.keras.metrics.Recall()`](tensorflow.org/api_docs/python/tf/keras/metrics/Recall) |\n",
        "| F1-score | Combines precision and recall into one metric. 1 is best, 0 is worst. | [`sklearn.metrics.f1_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) |\n",
        "| [Confusion matrix](https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/)  | Compares the predicted values with the true values in a tabular way, if 100% correct, all values in the matrix will be top left to bottom right (diagnol line). | Custom function or [`sklearn.metrics.plot_confusion_matrix()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_confusion_matrix.html) |\n",
        "| Classification report | Collection of some of the main classification metrics such as precision, recall and f1-score. | [`sklearn.metrics.classification_report()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html) |\n",
        "\n",
        "> 🔑 **Note:** Every classification problem will require different kinds of evaluation methods. But you should be familiar with at least the ones above.\n",
        "\n",
        "Let's start with accuracy.\n",
        "\n",
        "Because we passed `[\"accuracy\"]` to the `metrics` parameter when we compiled our model, calling `evaluate()` on it will return the loss as well as accuracy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LUvEwzqp4zVW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "013829dc-0828-45cd-e2c4-a851b852af31"
      },
      "source": [
        "# Check the accuracy of our model\n",
        "loss, accuracy = model_10.evaluate(X_test, y_test)\n",
        "print(f\"Model loss on test set: {loss}\")\n",
        "print(f\"Model accuracy on test set: {(accuracy*100):.2f}%\")"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 0s 3ms/step - loss: 0.0425 - accuracy: 1.0000\n",
            "Model loss on test set: 0.042508091777563095\n",
            "Model accuracy on test set: 100.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a5fXPTGi5aRX"
      },
      "source": [
        "How about a confusion matrix?\n",
        "\n",
        "![anatomy of a confusion matrix](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/02-anatomy-of-a-confusion-matrix.png)\n",
        "*Anatomy of a confusion matrix (what we're going to be creating). Correct predictions appear down the diagonal (from top left to bottom right).*\n",
        "\n",
        "We can make a confusion matrix using [Scikit-Learn's `confusion_matrix`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html) method."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g_Zee4lI5vi2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "outputId": "fbea1b1e-9c0d-42f7-90e8-ee61000f206b"
      },
      "source": [
        "# Create a confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "# Make predictions\n",
        "y_preds = model_10.predict(X_test)\n",
        "\n",
        "# Create confusion matrix\n",
        "confusion_matrix(y_test, y_preds)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 0s 2ms/step\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-58-f9843efd97f5>\u001b[0m in \u001b[0;36m<cell line: 8>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# Create confusion matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_preds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36mconfusion_matrix\u001b[0;34m(y_true, y_pred, labels, sample_weight, normalize)\u001b[0m\n\u001b[1;32m    315\u001b[0m     \u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    316\u001b[0m     \"\"\"\n\u001b[0;32m--> 317\u001b[0;31m     \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    318\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"binary\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"multiclass\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    319\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s is not supported\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m     96\u001b[0m             \"Classification metrics can't handle a mix of {0} and {1} targets\".format(\n\u001b[1;32m     97\u001b[0m                 \u001b[0mtype_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype_pred\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Classification metrics can't handle a mix of binary and continuous targets"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4GLOPxx9fx__"
      },
      "source": [
        "Ahh, it seems our predictions aren't in the format they need to be.\n",
        "\n",
        "Let's check them out."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aLBs249Gfu-W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0dd52e56-c3d9-4ea9-8739-0a22cea59e92"
      },
      "source": [
        "# View the first 10 predictions\n",
        "y_preds[:10]"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.9740965 ],\n",
              "       [0.9740965 ],\n",
              "       [0.9740965 ],\n",
              "       [0.9740965 ],\n",
              "       [0.47072026],\n",
              "       [0.00771922],\n",
              "       [0.9740965 ],\n",
              "       [0.00127994],\n",
              "       [0.9740965 ],\n",
              "       [0.00113649]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zacw9BNegVkY"
      },
      "source": [
        "What about our test labels?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3BH8XHAegYMd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7e5153e3-a131-49fb-ce35-830fa39f918f"
      },
      "source": [
        "# View the first 10 test labels\n",
        "y_test[:10]"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 0, 0, 1, 0, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4vd8EA95ggks"
      },
      "source": [
        "It looks like we need to get our predictions into the binary format (0 or 1).\n",
        "\n",
        "But you might be wondering, what format are they currently in?\n",
        "\n",
        "In their current format (`9.8526537e-01`), they're in a form called **prediction probabilities**.\n",
        "\n",
        "You'll see this often with the outputs of neural networks. Often they won't be exact values but more a probability of how *likely* they are to be one value or another.\n",
        "\n",
        "So one of the steps you'll often see after making predicitons with a neural network is converting the prediction probabilities into labels.\n",
        "\n",
        "In our case, since our ground truth labels (`y_test`) are binary (0 or 1), we can convert the prediction probabilities using to their binary form using [`tf.round()`](https://www.tensorflow.org/api_docs/python/tf/math/round)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YaS_yvGA6KV3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "074293ed-11fa-4323-c84e-a1d0c7ff189d"
      },
      "source": [
        "# Convert prediction probabilities to binary format and view the first 10\n",
        "tf.round(y_preds)[:10]"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10, 1), dtype=float32, numpy=\n",
              "array([[1.],\n",
              "       [1.],\n",
              "       [1.],\n",
              "       [1.],\n",
              "       [0.],\n",
              "       [0.],\n",
              "       [1.],\n",
              "       [0.],\n",
              "       [1.],\n",
              "       [0.]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mLvWYgFIh_A9"
      },
      "source": [
        "Wonderful! Now we can use the `confusion_matrix` function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GEZx1WHCiC8n",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2169f2e9-6837-4d30-eb56-37c93e4d1fa6"
      },
      "source": [
        "# Create a confusion matrix\n",
        "confusion_matrix(y_test, tf.round(y_preds))"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[101,   0],\n",
              "       [  0,  99]])"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VY1LmNeTiKHl"
      },
      "source": [
        "Alright, we can see the highest numbers are down the diagonal (from top left to bottom right) so this a good sign, but the rest of the matrix doesn't really tell us much.\n",
        "\n",
        "How about we make a function to make our confusion matrix a little more visual?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IIPSs9ERi78w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 815
        },
        "outputId": "2b652eb8-8e9c-4a37-9b53-c2ff65ec1653"
      },
      "source": [
        "# Note: The following confusion matrix code is a remix of Scikit-Learn's\n",
        "# plot_confusion_matrix function - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_confusion_matrix.html\n",
        "# and Made with ML's introductory notebook - https://github.com/GokuMohandas/MadeWithML/blob/main/notebooks/08_Neural_Networks.ipynb\n",
        "import itertools\n",
        "\n",
        "figsize = (10, 10)\n",
        "\n",
        "# Create the confusion matrix\n",
        "cm = confusion_matrix(y_test, tf.round(y_preds))\n",
        "cm_norm = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis] # normalize it\n",
        "n_classes = cm.shape[0]\n",
        "\n",
        "# Let's prettify it\n",
        "fig, ax = plt.subplots(figsize=figsize)\n",
        "# Create a matrix plot\n",
        "cax = ax.matshow(cm, cmap=plt.cm.Blues) # https://matplotlib.org/3.2.0/api/_as_gen/matplotlib.axes.Axes.matshow.html\n",
        "fig.colorbar(cax)\n",
        "\n",
        "# Create classes\n",
        "classes = False\n",
        "\n",
        "if classes:\n",
        "  labels = classes\n",
        "else:\n",
        "  labels = np.arange(cm.shape[0])\n",
        "\n",
        "# Label the axes\n",
        "ax.set(title=\"Confusion Matrix\",\n",
        "       xlabel=\"Predicted label\",\n",
        "       ylabel=\"True label\",\n",
        "       xticks=np.arange(n_classes),\n",
        "       yticks=np.arange(n_classes),\n",
        "       xticklabels=labels,\n",
        "       yticklabels=labels)\n",
        "\n",
        "# Set x-axis labels to bottom\n",
        "ax.xaxis.set_label_position(\"bottom\")\n",
        "ax.xaxis.tick_bottom()\n",
        "\n",
        "# Adjust label size\n",
        "ax.xaxis.label.set_size(20)\n",
        "ax.yaxis.label.set_size(20)\n",
        "ax.title.set_size(20)\n",
        "\n",
        "# Set threshold for different colors\n",
        "threshold = (cm.max() + cm.min()) / 2.\n",
        "\n",
        "# Plot the text on each cell\n",
        "for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "  plt.text(j, i, f\"{cm[i, j]} ({cm_norm[i, j]*100:.1f}%)\",\n",
        "           horizontalalignment=\"center\",\n",
        "           color=\"white\" if cm[i, j] > threshold else \"black\",\n",
        "           size=15)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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++ijDZ3TcumjBn3/+aVecWUkr4Vq/fr2OHj1q2f/dd9/pxo0b8vf3V+fOnW1qq0SJEukSklv5+/vr7bfflpQ6p+nW3+/s+uCDD6wSEkc0a9ZMw4YNkyT98ssvmjFjhq5fv66ePXsqMTFR/v7+mjdvntzc+KcZQN7D33xAHnb16lXL9z4+Ptlub+XKlZKkqlWrZvkp8q1zV9KuycgTTzyR6UPubh3ZufXN7t1WuHBheXh4SEotR0pKSnJa22n3JiAgQA8//HCm5z3//PPprsnII488Ik9PzwyPVaxY0TK65Mz72ahRI5UrV06SNH/+fMv+tNKtRx99VF5eXg61HRsbq+PHj2vv3r3as2eP9uzZYzVKdOtIYHZ4eHjo0UcfdUpbb731luV3d/DgwXriiSd04MABSdK0adNUqlQpp/QDADkNSQmQh906hyQ2NjZbbcXHx+vQoUOSbl/WUrt2bcubxz179mR6XlYrgd066nBrcnW3eXp6qkePHpJSP/0vV66chg4dqqVLlyoqKipbbafdmzp16mS5rHFwcLBl9S5H76ckBQYGSnL+/UwbLUlLSk6dOmVZWc3W0q00Fy9e1GuvvaaKFSvK19dXYWFhqlatmqpXr67q1atbPcwzbb5UdpUvX97hxOm/0iaxe3t768aNG5YStscff1w9e/Z0Sh8A7iKjV9pi9S0AuUHhwoUt30dGRmarrStXrli+v10pWP78+S19ZzXPIqtSnVtLXJKTk20N846YOnWqpQTpxIkTGj9+vDp16qTChQurfv36Gj9+vKKjo+1uN+3e2FJaV6xYMatrMpLV/ZT+vafOvp9pS0cfOnRIGzdu1Pz582U2m1W6dGndd999NrezdetWVapUSWPGjNHBgwdv+wR1Zy2pm5asOUvFihUtc4Wk1AUM7sSKYQCQk5CUAHnYrStfbdu2zWntZlZylVv5+fnpp59+0qZNmzR48GDVrVtX7u7uSklJ0ZYtWzR06FBVqFDB4bkvOf1+lilTRk2aNJGUWraVVrrVq1cvm19bQkKCunfvrkuXLil//vwKDw/XmjVrdO7cOcXFxclsNstsNuvIkSOWa26XtNjK3d3dKe2kiYmJsSzFLaWO6Djz/z8AyIlISoA8rGrVqipSpIik1MnNMTExDrd166fJtxt1SUpKskxCtnfyt7OlvSlOm6CfGVvK2xo0aKAJEyZoy5YtunLlin766SfLXJDz58+rW7dudn16n3ZvbBnFSntei9H3MzNpZVqfffaZ5Vkotj4wUZJ+//13y1yX6dOna+LEiWrWrJnlAZBp7F3hzAj9+/e3PHTR19dXZrNZTz/9dLbL/QAYIG31LVfacqicGzmAbDOZTOrdu7ek1Dfdn3zyicNteXp6qnz58pKU5TK/krR9+3bLilV3aqleW6XNq7m1/Oy/zGazDh8+bHe7nTt31vfff6+XX35ZUupDFtetW2dzG2n3Ztu2bVlOoD9//rxOnDhhdY2r6d69uzw9PRUXFycpdd5RhQoVbL5+7969lu/T5vBkZMuWLVm2Y/So07fffmsZKXr++ee1YMECSanzbPr27WtkaABgKJISII8bNGiQZa7B6NGj9c8//9h0XUpKir788kurfW3atJGU+gZy8+bNmV57a/KTdo1RwsLCJGX9ZvbXX3/N1qfYrVu3tnxvz+TrtHsTFRWlH374IdPzPv30U0upktH3MzMBAQHq2rWrPD095enpaUmGbXVrUpbZqFVKSopmz56dZTu3TliPj4+3K4bsOnPmjF588UVJqZPnJ0+erAceeMDy/Javv/463f9TAJBXkJQAeVyJEiU0depUSalv9po3b255snRm9u3bp/vvv1/jx4+32t+3b1/LZOk+ffpkWA62YsUKffrpp5JSy53q16/vjJfhsObNm0tKHd3566+/0h2PiIjQgAEDMr3+6NGjt71fK1assHyflgTZ4plnnrEkjIMHD9aZM2fSnbNz5069//77klL/LLt27Wpz+3fb119/rbi4OMXFxdk9KpA2CidJc+fOzfCcESNG3HZuRvHixS3f3zr/5E4zm83q3bu3rly5onz58mn+/PmWZbgnTpyoihUrSpL69eunkydP3rW4AGST0Stt5aLVt3iiOwA988wzOn36tEaPHq3z589bnrzepUsXVa5cWQEBAbp8+bIOHjyoJUuWaNmyZUpOTraaKC9J1atX1+DBgzV+/Hjt3LlTderU0bBhw1S7dm3Fxsbq559/1pQpU5ScnCwPDw/NmjXLoFf8rz59+mj69OlKSkpS586dNXr0aN13331KSEjQX3/9pUmTJikxMVHly5e3LHl8q5MnT6ply5aqUqWKHnroIdWrV08lSpSQlFqS880332jhwoWSpFq1atn1FPCgoCCNHz9e/fr10+nTp1W3bl0NHz5cjRs3VlJSklauXKnx48fr2rVrMplM+vjjj7NcOjgna9++vYoWLarz589r5MiROn78uB566CEVKVJEhw8f1uzZs7Vq1So1adIkw+QyTePGjS3fDxo0SK+//rqKFy9uKesqXbq08uVz/j+NH374oVatWiVJGjVqlBo0aGA55u3trfnz56tx48aKjo7WU089pd9//52HKALIU0hKAEhKfaNUtWpVDR48WMePH9eKFSusPuH/r6pVq2rcuHHp9n/wwQeKjY3V9OnTdeTIEfXp0yfdOf7+/lq4cKFq1arlzJfgkLTXER4eritXrmjQoEFWxwsVKqRFixZp1KhRGSYlafbt26d9+/ZlerxSpUr64Ycf7J7T8NJLLykqKkqjRo1SZGRkuvik1Pk8H3/8sTp27GhX2zmJj4+P5s2bp65duyouLk6zZs1Kl9S2aNFCU6dOzXJeTbly5dS9e3ctXLgww9/xY8eOWZ754iy7d+/Wa6+9Jin1YZKvv/56unPq1aunN954QyNHjtSaNWs0YcIEDR061KlxAIAr42MYABYPP/ywDhw4oC+//FK9evVSxYoVFRgYqHz58qlQoUKqU6eOXnrpJf3+++/avXu32rVrl64NNzc3TZs2TWvXrlXPnj1VsmRJeXp6ys/PT7Vq1dJrr72mQ4cOZXitUQYNGqRly5apffv2CgwMlKenp8LCwtSvXz9t375dTZs2zfTapk2bavXq1RoxYoRatmypcuXKydfXV/nz51dwcLDatWunmTNnaseOHXaVbt3qtdde0/bt2/XCCy+obNmyKlCggHx8fFS5cmW98sor+ueff+x+CGFO1L59e23ZskW9evVSSEiI8ufPr6CgIDVv3lwff/yxVq1aZSmJysr8+fM1btw4NWjQQP7+/nd0RCI+Pl49e/ZUfHy8ChYsqC+++CLTJYaHDx9ueW7LqFGjtGPHjjsWFwAnMXqlrVy0+pbJ7KyF3AEAAIA8ICYmRv7+/vJs/Z5M+bxuf8FdYk6KU/yq1xUdHS0/Pz+jw7FLzk2nAAAAAOQKzCkBAAAAHOFqK165Uix2YqQEAAAAgKFISgAAAAAYivItAAAAwCGutuKVK8Vin5wbOQAAAIBcIU+OlKSkpOjs2bPy9fW1+0FmAAAAuPPMZrOuXr2qkJCQO/o8IbiGPJmUnD17VqGhoUaHAQAAgNs4deqU7rnnHqPDyBirbzlNnkxKfH19JUkeVXrL5O5hcDQA4BwnV08wOgQAcJqrMTEqFxZqed+G3C1PJiVpJVsmdw+SEgC5Rk57ei8A2IJS+7whTyYlAAAAQLaZTK61+lYOTuBc6C4CAAAAyItISgAAAAAYivItAAAAwBEmF3t4oivFYqecGzkAAACAXIGkBAAAAIChKN8CAAAAHMHDE52GkRIAAAAAhiIpAQAAAGAoyrcAAAAAR7D6ltPk3MgBAAAA5AokJQAAAAAMRfkWAAAA4AhW33IaRkoAAAAAGIqkBAAAAIChKN8CAAAAHMHqW06TcyMHAAAAkCuQlAAAAAAwFOVbAAAAgCNYfctpGCkBAAAAYCiSEgAAAACGonwLAAAAcIDJZJLJlUqmXCkWOzFSAgAAAMBQJCUAAAAADEX5FgAAAOAAyrech5ESAAAAAIYiKQEAAABgKMq3AAAAAEeYbm6uwpVisRMjJQAAAAAMRVICAAAAwFCUbwEAAAAOYPUt52GkBAAAAIChSEoAAAAAGIryLQAAAMABlG85DyMlAAAAAAxFUgIAAADAUJRvAQAAAA6gfMt5GCkBAAAAYCiSEgAAAACGonwLAAAAcADlW87DSAkAAAAAQ5GUAAAAADAU5VsAAACAI0w3N1fhSrHYiZESAAAAAIYiKQEAAABgKMq3AAAAAAew+pbzMFICAAAAwFAkJQAAAAAMRfkWAAAA4ACTSS5WvmV0AI5jpAQAAACAoUhKAAAAABiK8i0AAADAASa52OpbObh+i5ESAAAAAIYiKQEAAABgKMq3AAAAAAfw8ETnYaQEAAAAgKFISgAAAAAYivItAAAAwBEmudaCV64Ui50YKQEAAABgKJISAAAAAIaifAsAAABwhIutvmV2oVjsxUgJAAAAAEORlAAAAAAwFOVbAAAAgANc7eGJrhSLvRgpAQAAAGAokhIAAAAAhqJ8CwAAAHAA5VvOw0gJAAAAAEORlAAAAAAwFOVbAAAAgCNMNzdX4Uqx2ImREgAAAACGIikBAAAAYCjKtwAAAAAHsPqW8zBSAgAAAMBQJCUAAAAADEX5FgAAAOAAyrech5ESAAAAAIYiKQEAAABgKJISAAAAwAFp5VuutNkjOTlZo0aNUlhYmAoUKKCyZcvqnXfekdlstpxjNps1evRoFS9eXAUKFFCbNm106NAhZ99KkhIAAAAgLxo7dqxmzJihqVOnav/+/Ro7dqzGjRunjz76yHLOuHHjNGXKFM2cOVObNm2Sj4+P2rdvr7i4OKfGwkR3AAAAIA9av369unTpok6dOkmSSpcura+++kqbN2+WlDpKMnnyZI0cOVJdunSRJM2bN0/BwcFatGiRHnvsMafFwkgJAAAA4ACjS7UyK9+KiYmx2uLj4zOMv3Hjxlq1apUOHjwoSdq5c6fWrVunDh06SJKOHTumiIgItWnTxnKNv7+/GjZsqA0bNjj1XjJSAgAAAOQioaGhVj+/8cYbevPNN9OdN3z4cMXExKhSpUpyd3dXcnKy3nvvPfXs2VOSFBERIUkKDg62ui44ONhyzFlISgAAAIBc5NSpU/Lz87P87OnpmeF5Cxcu1JdffqkFCxaoatWq2rFjhwYOHKiQkBD17t37boUriaQEAAAAcIzp5uYqbsbi5+dnlZRk5tVXX9Xw4cMtc0OqV6+uEydOaMyYMerdu7eKFSsmSYqMjFTx4sUt10VGRqpWrVpODZ05JQAAAEAedP36dbm5WacD7u7uSklJkSSFhYWpWLFiWrVqleV4TEyMNm3apEaNGjk1FkZKAAAAgDyoc+fOeu+991SyZElVrVpV27dv16RJk/Tss89KSp3IP3DgQL377rsqX768wsLCNGrUKIWEhKhr165OjYWkBAAAAHCAIw8svJPsjeWjjz7SqFGj9NJLL+n8+fMKCQnRiy++qNGjR1vOGTp0qGJjY9WnTx9FRUXpvvvu07Jly+Tl5eXc2M23PrIxj4iJiZG/v788q78gk7uH0eEAgFNc+Xuq0SEAgNPExMQouLC/oqOjbZofcTelvZcs9ux8uXl4Gx2ORUrCdUV81ssl79ntMKcEAAAAgKEo3wIAAAAckNPLt1wJIyUAAAAADEVSAgAAAMBQlG8BAAAADqB8y3kYKQEAAABgKJISAAAAAIaifAsAAABwhOnm5ipcKRY7MVICAAAAwFAkJQAAAAAMRfkWAAAA4ABW33IeRkoAAAAAGIqkBAAAAIChKN8CAAAAHED5lvMwUgIAAADAUCQlAAAAAAxF+RYAAADgAJNcrHwrBz89kZESAAAAAIYiKQEAAABgKMq3AAAAAAew+pbzMFICAAAAwFAkJQAAAAAMRfkWAAAA4AjTzc1VuFIsdmKkBAAAAIChSEoAAAAAGIryLQAAAMABrL7lPIyUAAAAADAUSQkAAAAAQ1G+BQAAADiA8i3nYaQEAAAAgKFISgAAAAAYivItAAAAwAEmU+rmKlwpFnsxUgIAAADAUCQlAAAAAAxF+RYAAADggNTyLdepmXKhUOzGSAkAAAAAQ5GUAAAAADAU5VsAAACAI1xs9S25Uix2YqQEAAAAgKFISgAAAAAYivItAAAAwAEmk8nFVt9ynVjsxUgJAAAAAEORlAAAAAAwFOVbAAAAgANMLrb6livFYi9GSgAAAAAYiqQEAAAAgKEo3wIAAAAc4OZmkpub69RMmV0oFnsxUgKnql05VEOeaauvJzyvw8ve0Y3tU3Vj+1Sbru3VuaH+/GKILvw1UWdWj9WPH/XVvTXDMjzX28tDj3eqr0nDHtXaeUMUtelD3dg+Va+/2DFb8ZcJLaLozZP1/sCuTuuvRNEAzXqzl46ueE9XNn6oXYtGa+T/dZSnR+afCXh55teovp20a9FoXdn4oY6ueE8z3+ipkCB/h15XgG8BTXi1mw4sfVtRmz7UgaVva/yQbvIvWCDD86tXKKHfPh2oKxs/1JHl7+r1Fztmuszgo+3r6sb2qWrTqHKGx2tWvEc3tk9VeO82DsUO5DU3btzQ22+OVvUqFRRQ0EthJUP04vPP6syZMw63+eLzz6qwv4/Onz9/x/u7cuWKBg96RRXKlpK/j6cqlC2lIeEDFRUVleH5u3buVJuWzRRQ0EtlSpXQu2+/qZSUlAzPXfjN1yqQ36TfVizP8PiO7dtVIL9JEyeMcyh2AMZhpARONeKF+9W5ZU27rxs/pJv692yp6zcStGrjfnl65FfrhpXU5t5KeuLVT/Xz6l1W55crGaTP3u3trLAt3hnwoBISkzXp85VO6a9MaBGt/nywggJ9tefQWf217bDqVCmp11/sqJYNKqrDix8pITHJ6hpPj3xa9vHLalgjTOcuROuX1btUKqSwendtpA7Nqqn5UxN0/Mwlm2MoHOCj1Z8PVrmSRXX01AX9/McuVS5bXP17tlS7JlXUovdEXYm5bjnf18dLS2b2l6+3l1Zu/EflSwZp5P91VHxCoibM+c2qbZ8CHhozqKt++n2nVm7Yn2H/Ow+c1s+rd2nIs+0058f1Vn0BsBYXF6f727bS5k0bVax4cT3wYBedOH5c8z6fo1+X/qI16zYqrEwZu9rcs3u35n/xuV4ZNFhFixa9o/1dvHhRLZo20pHDhxVWpow6d+mq/fv2atpH/9OK5b9q9Z8bVKhQIcv5MTEx6nR/G129elVt2rbToUMH9d47b8nTy0uvDh1u1fa1a9c0YtgQde7SVW3btc+w/1q1a+uBzg9qwtgxeubZ5636AuDaGCmBU23adUzvf/yrur0yU6XbjFBcfOJtr2nZsKL692ypi1euqcFjY9Q9fLa69J+uts9PVnJKima91SvdJ/pXr8drzo/r1f/dr9To8Q/01vRfsh17rUr36OG2dfT5ovW6eOWaU/qb/daTCgr01bQFf6h+9/f15PA5qvHQO1q8aoca1y6rV59tl+6a4c/fr4Y1wrRx51FV7/KWnhw+R82emqBhE39Q0UK+mvVmL7te1/gh3VSuZFEtWrVDNR56R08On6N6j76v6V+tVoXSwRo7+GGr85/v1kRBgb56YfQXenTgLDV87APtOXRW4U+3Vb581n9ljHihgwL9fPTqhO+zjGHCZysU6OetwU+3tSt2IK/54P13tXnTRjW8t5F27zuo+Qu+0Z/rN+mDcRN14cIFvfjCs3a3+dYbI+Xu7q6B4UPueH+vhg/UkcOH1eWhh7Vr7wHNX/CNtu7Yo779BujQwYMaNiTc6vxPZs/SxYsXNfuzz/Xdjz9p05Ydqlq1miaNH6vEROt/P8a8946uXL6s8RM+zDKGIUNHKCoqShPHj7UrdsARaatvudKWU5GUwKkmzl2pd2Ys0dK1exR56apN17zcq5Ukaewny3Tk5AXL/k27jumT79Yp0M9bTz/UyOqaY6cv6qW3F+jT7//Sjn9OKykpOduxv/BoU0nSl79sTnfMkf7qVS2lxrXLKvJSjF6bvNiyPzk5RS+//40SEpP00uPN5e7+7/+G+fO56/8eayZJGjhmoWJvJFiOTZn/u3YdPK1m9cqrduVQm15TsSJ+6n5/PcUnJOqV979RcvK/JREjPlyk85ev6vGO9RUUWNCyv0bFe3T9RoK+W7FNkhSfkKRvlm1RoJ+3Shb/91PHciWLqn/PFpowd4VOnrucZRybdx/X4ZPn9VTXe5U/n7tNsQN5TUJCgmZOTy13nTxlmgoW/Pf/y1cGhat69Rr6c+0abdu61eY2T506paVLflHbdu3TjZI4u79z585p4TdfycPDQ//7aLry5fu3GGPM2PEKCgrSVwvmW5WQ7dqxQwUKFNAjj3aXJHl5eanH408oKipKJ0+csJx36OBBTZ0yWUOGDlep0qWzjKPhvfeqbLlymjf3MyUkJGR5LgDXQVICQ3l55leL+hUkST+u3JHueNq+js2q39E4fAp46NH2dXXoxHlt33/KKW3e37SqJGnp2j3pSrTOX76qv7YdUSF/HzWuVdayv1GtMgrw9daRkxe088DpdG3aez/aNq4id3c3/bX9iM5ftk4SExKTtHTtbuXL567291W17A/w81b0tRtW50bdLLkK9PW27Js07FGdiYzSpLnWpW6ZWbhsq4ICfdWllf3lfUBesGH9X4qOjlaZsmVVq3btdMcf6vaIJGnpkp9tbnPe3M+UkpKiR3s8fsf7+235MqWkpKjJfU0VHBxsdczT01MdO3VWcnKylv+61LI/KuqK/P39reasBQQESkqdm5ImfOAAlbjnHoUPGWpTLN17PK6LFy9q8aIfbTofgPFISmCoCqWKysszv85fvqoz56PSHU9LEKqVD7mjcTStW16+Pl5au+WQ09qsUaGEJGlHJknOjn9S91evEJL+mn8yueZmW9VtvB+3jWH/6ZsxlLDsOxVxRUGBBa1K5iqUKmo5JkldWtVU28aV9eqE7xWfYJ1wZSbt3qYlawCs7dq5U5JUq3adDI+n7d+9e1eGxzOydElqqWmz5i3ueH+7dtnfXmhoSV24cMFqEvzBgwdSj5UsKUla9OMPWvnbCo2fOFleXl42xZL2epctXWLT+YCjTCaTy205FUkJDBV6sxzoTGRUhsevxyXoSsx1FfL3UUFvzzsWR5M6qaMVW/eeuM2Ztrun2M3XlkGyJf37mm8tibLcj8yuubk/tLhtkzdDiwXepr0r6WJYvm6v8uVz15jwhxTgW0BNapdVrwfv1dZ9J3X+8lV5eebX2MEPa+naPVq6do9NcUjStn0nlZycoqZ1y9t8DZCXnDp1UpJUosQ9GR5P23/qhG1/T127dk07d2xX8ZAQhYSk/yDD2f3dtr17UvefPPlve+07dFRycrJGDB2iK1euaN26PzV/3lzVqVNXwcHBunHjhoa9Gq4OHTup0wOdbYpDkurUrSc3Nzf9+ecam68BYCxW34KhChZITTRuxGVe93v9RrwC/bzl6+Ola9fj70gc1cqnjhQcPBHptDbTkqjrmby22Lj4m+f9+8mfz837cf1GJtfcSL3G18e2Twt9bhfDzX5uTfiWrNmt39bv1zMPNdYzDzWWJMXFJ2rYzcnsrz7bTsGF/dThxY+s2vL0yJflqMnV2DhFXIxWyeKFFOBbQFFXb2R6LpAXxV5LXWDD29s7w+M+Pj6SpKvXbJuvt3/fPiUnJ6tChYp3pT9b27t29d/2Huj8oNq0bae5cz7V3DmfSkot9Ro7MXUy+/ixYxQZEaFfl6+yXGM2mxUfH5/lqImfn5+KFS+uUydP6sqVKwoMDLTpNQAwDkkJICmokK8kKSqGN8qS9PArM9Src0PVqVJKUTHX9eUvm3TgWKRKlyisQU+11ofzVurY6Yvy8syv8UO66fFO9eVTwFP7jpzToA8WZloGdzn6ukoEB6pIoC9JCXCHXbiQOqE8wMXfkP+w+BfNn/e5tm3dooDAQPXs9ZQqVa6sY0eP6sOJ4zVo8KsqU7asbty4oVfDB+qrBfN1/fp1Va5SRR/+b6qat2iZYbuFAgvp7JkzunjhAkkJ7hhXW/HKlWKxF0kJDHXt5if/Bbw8Mj3H++bowdXYuDsWh39BL6f3kTaq453Ja/Px8rx53r99po2EeBfI5Bo770Xs7WK42c9/R6CSklI098cNmvvjBqv9E159RBeuXNO4T1dIkt57pYue7tpI7338q/YdPqtXn22n7//3f6re5S1FXIxJ119a3AG+GT+0EcjLfG6ufnX9esbP8omNjZUk+Rb0tam96OjoLM93dn+2tlfQ17q9/Pnz65nnntczzz1vtX9I+CsKKlpUQ4e/Jkl6fcQwzZ3zqV4f9YaqVK2m8WPHqFvXztq9/5CKFy+erj9fPz9JyvShjQBcC3NKYKhTN5eSLREckOFxby8PBfp563J07B0r3ZKk6Gupb5ZtLYuyxemIm6+taECGx9Ne863L6VruR2bX3Nx/6jZL8FrauzkxPfP2AtPFkJn291VRp+bVNXTC94qLT5S3l4eef+Q+LVjytz6YvUw//bFLvYbNUUFvT73YvVmGbfjdnDzPKAmQXmho6sTuM2fSr7x36/7QUqVsas/f319S5uVXzu7vtu2dTt1fsuTt21v261ItXfKLxk34UAUKFFBsbKw++Ximnuj5pEa8Pkpduj6k+Qu+UWxsrGbNmJZhGzE3k7KAgACb4gdgLJISGOrgifOKi09U0UK+CgnyT3c87Xkcew6dvaNxXLi5XG6gf8a10I7YdfCMJKlWJs8UqVUpdf/ug2fTX1Mpk2tutrXbxvtx2xgq33MzhjNZtuORP58mvPqIVm7Yr8W/p66wUya0iDzy57NaHODE2Us6f/mqKpcplmE7gX6pScnFK7bVqAN5SY2aqctl79i+LcPjafurV69hU3tBQamr5l25nPGHDs7ur0YN57QXHx+vIeGvqHWbtur6UOrDXY8eOaLExETVrVffcl7psDAFBQVp//59GbZzJSr1Q5kiQUE2xQ84wuiVtlh9y0VMmzZNpUuXlpeXlxo2bKjNm9M/9A6uLS4+Uav/PihJerhtBuvkt6klSVq6dvcdjSPtTXmFUsG3OdN2y/7cK0nq2KyaPPJbV0oWLeSrJnXK6nJ0rDbsPGLZv2HHUUVdva6yJYMsy/neyt778dv6fUpOTlGT2mWtHpAopSYaHZtVV1JSspav25tlOwOfaq2SxQspfOy36Y79tzTM28tDKWZzuvN8fbxUPMhfJ89dZqQEyECjxk3k7++vo0eOaOeOHemO//j9d5Kkjp1sW4WqStWqypcvn2WJ3TvdX9v298vNzU1/rfvT6gGJUmqisXTJz3J3d1f7Dh2zbGfypAk6eeKEJk3+KN2x6zesS8OuX78uN7f0b2ViYmJ07uxZhZYsyXwSIIfIsUnJN998o/DwcL3xxhvatm2batasqfbt26f7ixCub8r83yVJw56/X2VL/vuJVsMaYXqu2326EnM93dwGZ/tr+2FJUt2qJZ3W5pa9J7R++xEFF/bTe690sex3d3fT/17rIY/8+TT9qzVKSvr3KeuJScma+fVaSdLkEd2t3vC/3KuValS4R2u3HEr3gMf/69FMO34YqbcHPGi1P+JijBYu2yJPj/z632s9rJ4e//7ALipayFdfLf1bF65cy/R1hBYL1NDn2umj+X/o0Il///86cuqC4uIT1a1dbeXLl9pu5xY1VNA7dcL7f9WtWjJ1ic6tznsWDJCbeHh46P9e6i9JGvhyP8scDEn634eTtHv3LjVt1lx16ta1qT0fHx/VrFVbEefO6cyZ9KOhjvY3Y9pU1axWSaNeH2G1v3jx4ure43ElJCTolQEvKSnp39X4Xhs+VBcuXNDjT/RK92T5W508eVLjPnhfA14ZpAoV/101rGy5cvL09NT33y5UYmKiJOmnxYsUGxurKlXSP/to65a/ZTab1bRp86xuEQAXkmMnuk+aNEkvvPCCnnnmGUnSzJkztWTJEn322WcaPny4wdHlXfffV1UjXrjf8rNHfndJ0prPB1v2jZm9TMtu+WT+j00HNPXLP9S/Z0tt+nq4ft/4j/Lnz6fWDSvJZJJefGN+uieMS9I3E19QsSKpExmL3yz9euahRmrXuLKk1DfkPQbPtinudVsP62psnJrVy/wZGo701+fN+Vo9d7D692yp5vUr6J9jEapbpaTKhAZpw44jGv/ZinT9fPDJMrVqWFGNapXVnp/e0F/bDqtk8UJqUCNM5y9f1Ytvzk93TeGAgqoYVkzFdh9Pd+zVCd+rQfUwPdSmtnb+cI+27T+pymWKq1r5EB06cV7DJv6Q5b0ZO/hhRcVc15jZv1rtvxGXqBlfr9Gg3m206esROnLyvNo0qqzoqzc065u16dpJu7dpI0gA0hv+2kj9vmqlNm5Yr2qVy6vJfU118sQJ/b15k4KCgjRr9md2tdehYydt3fK31q5Zrcef6OmU/i5duqiDBw4o4lz6Dx/GT5qszZs3atEP36tmtUqqU7ee9u/dq71796hc+fIaO2FSlvEOezVcAYGBGvH6KKv93t7e6ttvgCZPmqCG9WqpbLnyWrliufz8/PRi337p2lm7ZrUk6f6OnbLsD8guVyuZcqVY7JUjR0oSEhK0detWtWnTxrLPzc1Nbdq00YYN6T9Rj4+PV0xMjNWGO6NIYEE1qBFm2dKG1W/dV+Q/ZURS6hvnF0Z/oX+ORarVvZXUsEaYft/8j9o+P1k/r874acI1K91jaTPtYYIlggMt+2pWyvgBXhmJvZGghcu2qlzJoqpbJePREkf6O3Lygho9/oHmLd6oIoEF9WDLGkoxm/X+x7+qw4sfKSEx/XM94hOS1L7PFL3/8a+6fiNBnVvWUGjxQpq3eKMaPz5Wx89csvl1SdKlqFg1fXK8pn+1Wh753fVgyxryL+ilaQv+UNNe43UlJuOVciSpZcOKeqhNbY34cJHlmSa3emPqz5o8b5UK+fuoXZMq2nngtB7sNy3DkZfu7evpwpWrljkpANLz8vLS8pV/aMTro+Tt7a2fFy/SqZMn9ORTT2v95m0KK1PGrvaeevpZubu7a+HXC+5Kf0WKFNGf6zerb78BSkhI0E+LflR0TLRe6v+y/ly/WYUKZf7g199XrdSiH77XmLETVLBg+n8n3nrnPb0yaLAuX7qkFct+Vc1atfXTkuUZjrws/OYrFSlSRF26PmRX/ACMYzKbMyj+dnFnz55ViRIltH79ejVq1Miyf+jQoVqzZo02bdpkdf6bb76pt956K107ntVfkMk986VokbfUqFBCm74ZoRlfr8lw7gQc17BGmFZ/PlgT5/ymkVMWGx1OrnXl76lGhwAX1P2Rh/Trkl906NgpFSuW8SIUucnGDRvUslljhQ8ZqvfGjDU6HGRDTEyMggv7Kzo6Wn43l3h2FTExMfL391fVYYvl7uljdDgWyfGx2ju2i0ves9vJkSMl9hoxYoSio6Mt26lTp25/EfKcXQfP6PsV2/RUl3vTTQpH9gx+pq2uxFzXxLm/GR0KkOe88eY7SklJ0eRJE4wO5a6YOP4DBQQEaPCrw4wOBXlA2sMTXWnLqXJkUlKkSBG5u7srMjLSan9kZGSGnwJ5enrKz8/PagMyMuqjn5Q/n7vCn25rdCi5Rs2K96hzixqa8NmKLEvFANwZVatVU68ne2v2rBm5fjGYHdu365eff9KQYSOyLBUD4Hpy5ER3Dw8P1a1bV6tWrVLXrl0lSSkpKVq1apX69+9vbHDI0Y6dvij/BgONDiNX2XngtArU5v9LwEizPvlMsz6xb5J8TlSrdm3dSMxxVekAlEOTEkkKDw9X7969Va9ePTVo0ECTJ09WbGysZTUuAAAA4E4yycVW35LrxGKvHJuU9OjRQxcuXNDo0aMVERGhWrVqadmyZQoOdt7D7wAAAADceTk2KZGk/v37U64FAAAA5HA5OikBAAAAjOJqK165Uiz2ypGrbwEAAADIPUhKAAAAABiK8i0AAADAASaTi62+5UKx2IuREgAAAACGIikBAAAAYCjKtwAAAAAHsPqW8zBSAgAAAMBQJCUAAAAADEX5FgAAAOAAVt9yHkZKAAAAABiKpAQAAACAoSjfAgAAABzA6lvOw0gJAAAAAEORlAAAAAAwFOVbAAAAgANYfct5GCkBAAAAYCiSEgAAAACGonwLAAAAcISLrb4lV4rFToyUAAAAADAUSQkAAAAAQ1G+BQAAADiA1bech5ESAAAAAIYiKQEAAABgKMq3AAAAAAeYXGz1LVeKxV6MlAAAAAAwFEkJAAAAAENRvgUAAAA4gNW3nIeREgAAAACGIikBAAAAYCjKtwAAAAAHsPqW8zBSAgAAAMBQJCUAAAAADEX5FgAAAOAAVt9yHkZKAAAAABiKpAQAAACAoSjfAgAAABxA+ZbzMFICAAAAwFAkJQAAAAAMRfkWAAAA4AAenug8jJQAAAAAMBRJCQAAAABDUb4FAAAAOIDVt5yHkRIAAAAAhiIpAQAAAGAoyrcAAAAAB7D6lvMwUgIAAADAUCQlAAAAAAxF+RYAAADgAFbfch5GSgAAAAAYiqQEAAAAgKEo3wIAAAAcYJJrrXjlQqHYjZESAAAAAIYiKQEAAABgKMq3AAAAAAe4mUxyc6H6LVeKxV6MlAAAAAAwFEkJAAAAAENRvgUAAAA4wGRysdW3XCgWezFSAgAAAMBQJCUAAAAADEX5FgAAAOAAk8kkkwvVTLlSLPZipAQAAACAoUhKAAAAABiK8i0AAADAAW6m1M1VuFIs9mKkBAAAAIChSEoAAAAAGIryLQAAAMARJhdb8cqFQrEXIyUAAAAADEVSAgAAAMBQlG8BAAAADjCZUjdX4Uqx2IuREgAAAACGIikBAAAA8qgzZ86oV69eKly4sAoUKKDq1atry5YtluNms1mjR49W8eLFVaBAAbVp00aHDh1yehwkJQAAAIADTC74ZY8rV66oSZMmyp8/v3799Vft27dPEydOVGBgoOWccePGacqUKZo5c6Y2bdokHx8ftW/fXnFxcU69l8wpAQAAAPKgsWPHKjQ0VHPmzLHsCwsLs3xvNps1efJkjRw5Ul26dJEkzZs3T8HBwVq0aJEee+wxp8XCSAkAAACQi8TExFht8fHxGZ73008/qV69enr00UdVtGhR1a5dW7Nnz7YcP3bsmCIiItSmTRvLPn9/fzVs2FAbNmxwaswkJQAAAIAD3Eyut0lSaGio/P39LduYMWMyjP/o0aOaMWOGypcvr+XLl6tv3756+eWX9fnnn0uSIiIiJEnBwcFW1wUHB1uOOQvlWwAAAEAucurUKfn5+Vl+9vT0zPC8lJQU1atXT++//74kqXbt2tqzZ49mzpyp3r1735VY0zBSAgAAAOQifn5+VltmSUnx4sVVpUoVq32VK1fWyZMnJUnFihWTJEVGRlqdExkZaTnmLCQlAAAAgANMJpPLbfZo0qSJDhw4YLXv4MGDKlWqlKTUSe/FihXTqlWrLMdjYmK0adMmNWrUKPs38BaUbwEAAAB50KBBg9S4cWO9//776t69uzZv3qyPP/5YH3/8saTUpGvgwIF69913Vb58eYWFhWnUqFEKCQlR165dnRoLSQkAAACQB9WvX18//vijRowYobffflthYWGaPHmyevbsaTln6NChio2NVZ8+fRQVFaX77rtPy5Ytk5eXl1NjISkBAAAAHGAypW6uwpFYHnjgAT3wwANZtGnS22+/rbfffjsbkd0ec0oAAAAAGMqmkZK1a9fekc6bNWt2R9oFAAAAkHPYlJS0aNHC7tn8t2MymZSUlOTUNgEAAIC7xc1kkpsL1W+5Uiz2snlOidlsvpNxAAAAAMijbEpK/vjjjzsdBwAAAIA8yqakpHnz5nc6DgAAACBHyQ2rb7kKVt8CAAAAYCiSEgAAAACGctrDE1NSUvTHH39ow4YNioiI0PXr1/Xee++pePHilnMSEhKUlJQkd3d3eXp6OqtrAAAA4K4zmUxOX6E2O1wpFns5JSn55Zdf9PLLL+vEiRNW+4cMGWKVlHzyyScaMGCAChYsqLNnz8rHx8cZ3QMAAADIwbJdvjV79mx16dJFx48fl9lsVuHChTNdPvj555+Xv7+/rl27ph9//DG7XQMAAADIBbKVlBw6dEj9+vWTJLVq1Ur79u3T+fPnMz3fw8ND3bp1k9ls1ooVK7LTNQAAAGCotNW3XGnLqbKVlHz44YdKSkpS1apVtXTpUlWqVOm21zRt2lSStH379ux0DQAAACCXyFZS8vvvv8tkMmngwIHy8PCw6Zpy5cpJkk6dOpWdrgEAAADkEtma6H769GlJUs2aNW2+Jm1y+/Xr17PTNQAAAGAoN5NJbi5UM+VKsdgrWyMlacuO2ZNgXLp0SZLk7++fna4BAAAA5BLZSkpKlCghSTp69KjN16xbt06SVKZMmex0DQAAACCXyFZS0qJFC5nNZn3++ec2nR8dHa2ZM2fKZDKpVatW2ekaAAAAMJTJBbecKltJyYsvviiTyaQ1a9Zo7ty5WZ576dIlde3aVREREcqXL5/+7//+LztdAwAAAMglspWU1K5dW6+88orMZrOee+459ejRQwsXLrQcX79+vRYsWKB+/fqpXLlyWrt2rUwmk0aNGqVSpUplO3gAAAAAOV+2Vt+SpIkTJyo+Pl4zZszQd999p++++84yAf7FF1+0nJf2lPeBAwdq5MiR2e0WAAAAMJTJZLK873UFrhSLvbI1UiKlvvhp06Zp+fLlatGihUwmk8xms9UmSY0aNdKSJUs0adKkbAcNAAAAIPfI9khJmrZt26pt27a6evWqtm/frvPnzys5OVmFCxdWrVq1VKRIEWd1BQAAACAXcVpSksbX11fNmjVzdrMAAACAS3EzpW6uwpVisVe2y7cAAAAAIDucOlJy/vx5rV69Wrt379bly5clSYUKFVK1atXUokULBQcHO7M7AAAAALmAU5KSM2fOaPDgwfrxxx+VlJSU4Tnu7u566KGHNH78eJUsWdIZ3QIAAACGYfUt58l2+da6detUrVo1ffvtt0pMTEy38lbalpSUpO+++041atTQunXrnBE7AAAAgFwgW0nJ2bNn1blzZ0VHR8tsNqtDhw769ttvdeLECcXFxSkuLk4nTpzQd999p44dO8psNismJkadO3fW2bNnnfUaAAAAAORg2UpKxowZo+joaLm7u2vevHlasmSJunXrptDQUHl4eMjDw0OhoaF6+OGH9csvv2j+/Plyc3NTTEyMPvjgA2e9BgAAAMAQJpPrbDlZtpKSpUuXymQy6YUXXlCvXr1ue/4TTzyhPn36yGw2a8mSJdnpGgAAAEAuke3yLUl69NFHbb4m7VzKtwAAAABI2Vx9KzAwUJGRkfL397f5mrRzAwMDs9M1AAAAYChW33KebI2U1KtXT5K0e/dum69JOzftWgAAAAB5W7aSkpdffllms1njxo3T9evXb3v+9evXNXbsWJlMJg0YMCA7XQMAAADIJbKVlLRp00ZvvPGG9u/frxYtWmjHjh2Znrtz5061bNlSBw4c0BtvvKG2bdtmp2sAAADAUG4m19tyKpvmlLz99tuZHjOZTKpXr562bNmiunXrqnr16qpfv76KFi0qk8mkyMhI/f333+nKtt5++22NHj3aCS8BAAAAQE5mU1Ly5ptv3nbijMlkktls1u7duzOcY2I2m2UymbRlyxZt2bJFkkhKAAAAANi++pbZbM72eba2AQAAALg6Vt9yHpuSkpSUlDsdBwAAAIA8KlsT3QEAAAAgu7L18EQAAAAgrzLd3FyFK8ViL0ZKAAAAABiKpAQAAACAoZxSvpWQkKAvv/xSixYt0s6dO3Xx4kXduHEjy2tMJpOSkpKc0T0AAABw17mZTHJzoRWvXCkWe2U7KTl48KC6du2qAwcOsOQvAAAAALtlKymJjY1Vhw4ddOzYMbm5ualLly4KCgrS7NmzZTKZNHLkSF2+fFlbtmzRpk2bZDKZ1KhRI7Vt29ZZ8QMAAADI4bKVlMycOVPHjh2Tu7u7li9frlatWmnv3r2aPXu2JOmtt96ynLt9+3Y9+eST2rhxox577DH1798/e5EDAAAABjKZUjdX4Uqx2CtbE91//vlnmUwmde/eXa1atcry3Nq1a+uPP/5Q0aJFFR4erq1bt2anawAAAAC5RLaSkn379kmSHnrooQyP//dJ8EFBQQoPD1dSUpKmTp2ana4BAAAA5BLZKt+KioqSJJUqVcqyz9PT0/J9bGysfH19ra5p0qSJJGnNmjXZ6RoAAAAwlMlkksmFaqZcKRZ7ZWukxNvbW5L1DQgICLB8f/LkyUyvjYiIyE7XAAAAAHKJbCUlYWFhkqSzZ89a9hUpUkSFChWSJP3111/prkmbS+Lh4ZGdrgEAAADkEtlKSurVqydJ2rJli9X+1q1by2w2a/z48bp8+bJl/9GjR/XBBx/IZDKpVq1a2ekaAAAAMFTa6luutOVU2UpK2rZtK7PZrJ9++slq/8svvywpNQmpUKGCHn30UXXs2FG1atWyjKr06dMnO10DAAAAyCWylZQ88MADatasmXx9fXXkyBHL/iZNmmj06NEym826fPmyfvjhBy1fvlzXrl2TJD3zzDN64oknshc5AAAAgFwhW6tveXt7a/Xq1Rkee/PNN9W0aVN98skn2rt3r5KSklS+fHk99dRT6tatW3a6BQAAAAznZjLJzYVqplwpFntlKym5ndatW6t169Z3sgsAAAAAOVy2yrcAAAAAILvu6EgJAAAAkFu52opXrhSLvRgpAQAAAGAom0ZKypQp4/SOTSaT1YpdAAAAAPImm5KS48ePO71jU04eXwIAAECeZzKZXOo9rSvFYi+bkpLevXvf6TgAAAAA5FE2JSVz5sy503EY4uTqCfLz8zM6DABwisDGg40OAQCcxpwcb3QIuIuY6A4AAADAUCwJDAAAADjATa71Cb8rxWKvnBw7AAAAgFyApAQAAACAoSjfAgAAABzAksDOw0gJAAAAAEORlAAAAAAwFOVbAAAAgANMJsnNhSqmcnD1FiMlAAAAAIxFUgIAAADAUJRvAQAAAA5wc7HyLVeKxV5OTUpu3LihrVu3KiIiQtevX1fXrl3l5+fnzC4AAAAA5DJOSUpOnTql1157Td9++60SExMt++vVq6cqVapYfv700081a9Ys+fv7a8WKFTl6LWUAAAAAzpHtpGTTpk3q1KmTrly5IrPZbNmfUcLRuXNn9evXT4mJiVqxYoXat2+f3e4BAAAAQ/DwROfJ1kT3qKgodenSRZcvX1axYsU0ffp07d69O9PzixYtqg4dOkiSlixZkp2uAQAAAOQS2RopmTJlis6fP68iRYpow4YNKlmy5G2vadOmjRYvXqzNmzdnp2sAAAAAuUS2kpKff/5ZJpNJ4eHhNiUkklS1alVJ0pEjR7LTNQAAAGAoVt9ynmyVbx0+fFiS1KxZM5uvCQwMlCTFxMRkp2sAAAAAuUS2kpK4uDhJUv78+W2+JjY2VpJUoECB7HQNAAAAIJfIVlJStGhRSdKxY8dsvmbHjh2SpJCQkOx0DQAAABjKZHK9LafKVlLSsGFDSdKvv/5q0/lms1mzZ8+WyWRS06ZNs9M1AAAAgFwiW0lJz549ZTab9eWXX1pGQLIyePBg7dy5U5LUu3fv7HQNAAAAIJfIVlLSpUsXtWzZUklJSWrdurVmzJih8+fPW44nJSXp7Nmz+vbbb9W0aVP973//k8lk0sMPP6zGjRtnO3gAAADAKG4mk8ttOVW2n+j+/fffq3Xr1tq+fbv69++v/v37W54mWbt2batzzWaz7r33Xs2dOze73QIAAADIJbI1UiJJAQEB2rBhg0aMGCE/Pz+ZzeYMtwIFCmjo0KFavXq1fHx8nBE7AAAAgFwg2yMlkuTh4aH33ntPr732mtasWaMtW7bo/PnzSk5OVuHChVW7dm21adNG/v7+zugOAAAAMJybnPAJvxO5Uiz2ckpSksbHx0cdO3ZUx44dndksAAAAgFwsJydUAAAAAHIBp46UAAAAAHmFqz2w0JVisVe2kpJ58+Zlq/OnnnoqW9cDAAAAyPmylZQ8/fTTluV/7WUymUhKAAAAAGS/fMtsNjsjDgAAACBHcZNrPbDQTa4Ti72ylZQcO3bstufExsbq4MGDWrBggb777js1adJEH3/8sby9vbPTNQAAAIBcIltJSalSpWw6r0qVKuratasWLlyoJ554QgMGDNBvv/2Wna4BAAAA5BJ3dUng7t27q3fv3vrjjz80a9asu9k1AAAA4FRpq2+50pZT3fXnlHTv3l1ms1lz5869210DAAAAcEF3PSkJDg6WJB04cOBudw0AAADABd31hyeePHlSkpSYmHi3uwYAAACcxs2UurkKV4rFXnd1pCQxMVHjxo2TJJUrV+5udg0AAADARWVrpCRt1CMrKSkpunLlirZs2aKpU6dqz549MplMeuyxx7LTNQAAAIBcIltJSVhYmN3XmM1mNWrUSIMGDcpO1wAAAIChTCa51MMTXSgUu2WrfMtsNtu1BQYGasSIEVq5cqU8PT2d9RoAAAAA5GDZGimZM2fObc9xc3OTr6+vwsLCVK1aNbm7u2enSwAAAAC5TLaSkt69ezsrDgAAACBHcbUHFrpSLPZyykT3ggULqlChQk4JCAAAAEDekq05JaVLl1ZYWJi+/vprZ8UDAAAAII/J1khJgQIFFBcXp/r16zsrHgAAACBH4OGJzpOtkZISJUpIkpKTk50SDAAAAIC8J1tJSbt27SRJ69atc0owAAAAAPKebCUlr7zyigoUKKAJEybozJkzzooJAAAAcHkmF/zKqbKVlJQvX14LFizQ9evXde+992rBggVKSEhwVmwAAAAA8gCbJ7q7ubnJzc1Nu3btUpUqVSRJrVq1kiQFBQXp2LFjevLJJ/Xcc8+pfPnyCgwMzPJBiSaTSatWrcpm+AAAAAByOrtW3zKbzVY/r169WqZbntJiNpsVHx+vPXv2ZNqGyWSS2Wy2ug4AAADIaVh9y3mytSRws2bNSC4AAAAAZEu2kpLVq1c7KQwAAAAAeVW2khIAAAAgr6J8y3mytfoWAAAAAGQXSQkAAAAAQ9ldvvXMM8/Ix8cn2x2zJDAAAAByMpPJ5FKLPrlSLPayOynZsmVLtjtlSWAAAAAAaexOSv77rBIAAAAAyA67k5I9e/ZYnugOAAAA5FWsvuU8THQHAAAAYCiSEgAAAACG4uGJAAAAgANMptTNVbhSLPZipAQAAACAoUhKAAAAABiK8i0AAADAAW4mk9xcqGbKlWKxl81JybFjxyRJJUqUuGPBAAAAAMh7bE5KSpUqdSfjAAAAAJBHUb4FAAAAOICHJzoPE90BAAAAGIqkBAAAAIChKN8CAAAAHOFiD0+UK8ViJ0ZKAAAAABiKpAQAAACAoSjfAgAAABzgJpPcXKhmypVisRcjJQAAAAD0wQcfyGQyaeDAgZZ9cXFx6tevnwoXLqyCBQuqW7duioyMdHrfJCUAAABAHvf3339r1qxZqlGjhtX+QYMG6eeff9a3336rNWvW6OzZs3r44Yed3j9JCQAAAOAAk8n1Nkdcu3ZNPXv21OzZsxUYGGjZHx0drU8//VSTJk1Sq1atVLduXc2ZM0fr16/Xxo0bnXQXU5GUAAAAALlITEyM1RYfH5/l+f369VOnTp3Upk0bq/1bt25VYmKi1f5KlSqpZMmS2rBhg1NjJikBAAAAcpHQ0FD5+/tbtjFjxmR67tdff61t27ZleE5ERIQ8PDwUEBBgtT84OFgRERFOjZnVtwAAAAAHuJlSN1eRFsupU6fk5+dn2e/p6Znh+adOndIrr7yi3377TV5eXncjxEwxUgIAAADkIn5+flZbZknJ1q1bdf78edWpU0f58uVTvnz5tGbNGk2ZMkX58uVTcHCwEhISFBUVZXVdZGSkihUr5tSYGSkBAAAA8qDWrVtr9+7dVvueeeYZVapUScOGDVNoaKjy58+vVatWqVu3bpKkAwcO6OTJk2rUqJFTYyEpAQAAABzgZjLJzdElr+4Ae2Px9fVVtWrVrPb5+PiocOHClv3PPfecwsPDVahQIfn5+WnAgAFq1KiR7r33XqfFLZGUAAAAAMjEhx9+KDc3N3Xr1k3x8fFq3769pk+f7vR+SEoAAAAASJJWr15t9bOXl5emTZumadOm3dF+SUoAAAAAB2TngYV3givFYi9W3wIAAABgKJISAAAAAIaifAsAAABwgJtcbPUtuU4s9mKkBAAAAIChSEoAAAAAGIryLQAAAMABrL7lPIyUAAAAADAUSQkAAAAAQ1G+BQAAADjATa71Cb8rxWKvnBw7AAAAgFyApAQAAACAoSjfAgAAABxgMplkcqElr1wpFnsxUgIAAADAUCQlAAAAAAxF+RYAAADgANPNzVW4Uiz2YqQEAAAAgKFISgAAAAAYivItAAAAwAFuJpPcXGjFK1eKxV6MlAAAAAAwFEkJAAAAAENRvgUAAAA4KOcWTLkWRkoAAAAAGIqkBAAAAIChKN8CAAAAHGAypW6uwpVisRcjJQAAAAAMRVICAAAAwFCUbwEAAAAOMJlMMrlQzZQrxWIvRkoAAAAAGIqkBAAAAIChKN8CAAAAHOAm1/qE35VisVdOjh0AAABALkBSAgAAAMBQlG8BAAAADmD1LedhpAQAAACAoUhKAAAAABiK8i0AAADAAaabm6twpVjsxUgJAAAAAEORlAAAAAAwFOVbAAAAgANYfct5GCkBAAAAYCiSEgAAAACGonwLAAAAcICbXOsTfleKxV45OXYAAAAAuQBJCQAAAABDUb4FAAAAOIDVt5yHkRIAAAAAhiIpAQAAAGAoyrcAAAAAB5hubq7ClWKxFyMlAAAAAAxFUgIAAADAUJRvAQAAAA4wmVI3V+FKsdiLkRLkaDdu3NDbb45W9SoVFFDQS2ElQ/Ti88/qzJkzDrf54vPPqrC/j86fP3/H+7ty5YoGD3pFFcqWkr+PpyqULaUh4QMVFRWV4fm7du5Um5bNFFDQS2VKldC7b7+plJSUDM9d+M3XKpDfpN9WLM/w+I7t21Ugv0kTJ4xzKHYAqSqWLqo5b/fU0aVvKGrdWP2z6HV9OOQhFfb3yfSaAp75NezZNtr69au6vPYDnf7tbS2a/Lya1inrcBxl7ims6L/G6v0BD1jt9/by0OMd6mrSkIe09rOXFbVurG5snqjXX2h32zZLFPXXrFE9dHTJaF358wPt+m64Rr7QXp4emX+m6eWZT6P6tNeu74bryp8f6OiS0Zo5sodCgvwcel0BvgU0IbyLDix+XVHrxurA4tc1flAX+Rf0yvD86uWL67dZL+nKnx/oyC+j9foL7TJdJvXRtrV0Y/NEtbm3YobHa1YooRubJyr8yZYOxQ7Adiaz2Ww2Ooi7LSYmRv7+/oq8FC0/P8f+koTx4uLi1L5NS23etFHFihdXk/ua6sTx49ry92YFBQVpzbqNCitTxq429+zerYb1aumVQYP1/gfWb9ad3d/FixfVomkjHTl8WGFlyqhO3Xrav2+v9u3dq/IVKmj1nxtUqFAhy/kxMTGqWrGsrl69qjZt2+nQoYM6eOCA3n5vjF4dOtyq7WvXrqlmtUqqW6++Fn73Y6YxPPpwF637c632Hjhi1RdypsDGg40OIc9pXq+cvp/4rHwKeOqfY5H651ikqpQtpgqliup0ZJRaPDdFZ85HW13jU8BDy2f0Vd0qJXUpOlYbdx2Xf0EvNahWSvnc3dT3vW817+fNdsfy5Zin1K5RJVXu+p4uRsVa9tcoH6JNX6b/3Xh39nK9N3tFpu2VuaewVn/6soICC2rP4XP651ik6lS+R2XuKaL1O46pQ78ZSkhMtrrG0yOfls/oq4bVS+vchWj9teOYShUPVP1qpXT+8lU1f3aKjp+9bPNrKuzvo9WfDlC5kkE6evqitu0/rcplglW1bHEdPHFeLZ6boisxNyzn+/p4au8Pr8nX21MrNx1U+ZJBqli6qEZNXaIJ8363atungId2LhymrftPqcfQuZnGsHD8M7qvdhlVffh9q75w55mT4xW/bZqio13v/Vrae8kF6w/Ku6Cv0eFYXL92VU80ruCS9+x2GClBjvXB++9q86aNanhvI+3ed1DzF3yjP9dv0gfjJurChQt68YVn7W7zrTdGyt3dXQPDh9zx/l4NH6gjhw+ry0MPa9feA5q/4Btt3bFHffsN0KGDBzVsSLjV+Z/MnqWLFy9q9mef67sff9KmLTtUtWo1TRo/VomJiVbnjnnvHV25fFnjJ3yYZQxDho5QVFSUJo4fa1fsAFJHO+a+01M+BTz13uwVqt1jnB4f/rlqPjpWH37xh+4JDtCMkT3SXfdOv06qW6Wktu4/pVqPjtUjgz9T2xenq/3/zVDsjQRNGdZNJYsF2hVLrYol9HDrmvr8p01WCYkkXb0erzmLN6n/mG/V6MlJemvmrza1OXv04woKLKhpX69V/Scm6MnXv1CNR8dq8R+71LhWmF59unW6a4Y/20YNq5fWxl3HVf2RD/Tk61+o2bNTNGzyYhUt5KtZo9Lfj6yMD++iciWDtOj3Xarx6Fg9+foXqvf4BE3/5k9VKFVUYwd2sTr/+YcaKSiwoF5462s9OuQzNew1UXsOn1P4Uy2Vz936Lc+I59oq0N9br364OMsYJny+SoF+3hr8VCu7Ykfe4CaTy205FUkJcqSEhATNnD5VkjR5yjQVLFjQcuyVQeGqXr2G/ly7Rtu2brW5zVOnTmnpkl/Utl17FS1a9I72d+7cOS385it5eHjofx9NV758/5ZCjBk7XkFBQfpqwXyrErJdO3aoQIECeuTR7pIkLy8v9Xj8CUVFRenkiROW8w4dPKipUyZryNDhKlW6dJZxNLz3XpUtV07z5n6mhIQEm2IHkKpLy+oqVthPB46f13ufWI84jJ6+VMfPXlLbeyuqevnilv3587nrqc71JUlDJiyySiA27j6u6QvXydMjn/o/3tSuWF7o1liS9OXS9H8HHTtzSS+9t1Cf/rhROw6cUVJSxiWft6pXJVSNa4Up8tJVvfbRL5b9yckpenns90pITNJL3ZvK/ZY3+vnzuev/Hr1PkjRw3A+KvfHv3ylTFqzVrkNn1axuOdWudI9Nr6lYYV91b1db8QlJemXc90pO/jfuEVN+1vnLV/X4/XUUFPjv38c1KpTQ9bgEfbdyhyQpPiFJ3yzfpkA/b5Us/m+iV65kEfV/rJkmfP67Tp67kmUcm/ec1OGTF/RU5wbKn8/dptgB2I+kBDnShvV/KTo6WmXKllWt2rXTHX+o2yOSpKVLfra5zXlzP1NKSooe7fH4He/vt+XLlJKSoib3NVVwcLDVMU9PT3Xs1FnJycla/utSy/6oqCvy9/e3qo0OCEj9R/bKlX//UQ0fOEAl7rlH4UOG2hRL9x6P6+LFi1q8KPMyLwDp1amc+uZ63fYj+m8ldFJyijbsPC5JeqBZNcv+SmFF5VPAU3Hxidq4+3i6NtdsOZzumtvxKeChR9vW0qGTF7T9n9N2voqM3d+kiiRp6bp96Uq0zl++pr92HFMhf281rhlm2d+oZmkF+BbQkVMXtfNg+nl2P67aKUnq2LSKTTG0bVRJ7u5u+mvHUZ2/fM3qWEJispau26d8+dzVvnFly/4A3wKKvhZndW7U1dSSq0Bfb8u+SUMe1pnzUZr0xR82xbJwxXYFBRZUlxbVbTofgP1ISpAj7dqZ+o9brdp1Mjyetn/37l02t7l0Seqngc2at7jj/e3aZX97oaEldeHCBatJ8AcPHkg9VrKkJGnRjz9o5W8rNH7iZHl5ZTwJ9L/SXu+ypUtsOh9AKm8vD0n/vun9r8vRqaMgNcqHWPb5eHlKUro3zv9ec12SFFaisHx9PG2Ko2mdsvL18dLarYdtC9wGNSqkxrwjkyQnbX/1cv+OAqW9zh0HMrnmwJl012QZg6W9jBcS2fHPzfZuGYk6FXlFQQE+VpPgK5QKshyTpC4tqqvtvRX16qTFik9IsimWtduOSJLuv6/ybc5EXpO2+pYrbTkVSQlypFOnTkqSSpTIuAwgbf+pW8qasnLt2jXt3LFdxUNCFBISku64s/u7bXv3pO4/efLf9tp36Kjk5GSNGDpEV65c0bp1f2r+vLmqU6eugoODdePGDQ17NVwdOnZSpwc62xSHJNWpW09ubm768881Nl8DQJbSq8zmf5QKKSxJCr2lbOhCVOon/kGBPvLyTL+CVemQfxecCA22bV5Jk1qpC2xs3XfKpvNtcU9wgCSlm6SfJm3/rSVRoTfvQ+bXRFmddzuhxW7GEBmVZXu33v/lf/2jfPncNeaVBxXgW0BNaoWpV6f62rr/lM5fviYvz3waO/BBLV23T0vX7bMpDknatv+UkpNT1LS2fYunALAdSQlypNhrqf+we3t7Z3jcxyd1Kc6r167a1N7+ffuUnJysChUyXhbS2f3Z2t61q/+290DnB9WmbTvNnfOpQooWUtuWzXTjxg2NnZg6mX382DGKjIjQhEn/s1xjNpsVF5fxJ7Jp/Pz8VKx4cZ06edKqDAxA1tZtPypJur9J5XTL/4YE+al1gwqSJF/vf0c8jpy6qHMXouXm5qZeneqna/OpBxtYvrd1pKTazZGHgycv2PcCslDwZszX4zKeaxZ7c3/BW16bTwGPrK+5OcfE18e2UVyfAjdjiE/M8Hhae7fGsOTPvfpt4wE906Whzq16Vys/7p+6/PLNyeyv9m6t4MK+GjJxkVVbWS1xLElXY+MVcTFGJYsXUoBvAZviB2AfkhJA0oULqRPKAwLtW/Hmbvth8S+aPnO2nn/hRQ0ZOlwb/96u++5rqmNHj+rDieM1aPCrKlO2rG7cuKH+fV9UkYCCCvQtoDo1q2rN6sxrpwsFpn46e/GC897UALndyo0HtG3/Kfn6eGnx/55XvSqh8ingoYbVS2nR5BeUL1/qP7EpKdbzTSZ8nro07fsDHtCTD9RXoF8BlSweqAnhXdTxvipKTErO8LrMBBVKnegdFXPdWS8tR3t40Cfq+95Czf5hvcbPXaV7n5ykv3YcU+mQQhrUq6U+nL9ax85ckpdnPn00/BFdXPO+otaN1davX1Wzupk/J+byzftbJDDz588g7zG54FdOxRPdkSP53Fz96vr1jP8Rjo1NLavwtXHt8Ojo6CzPd3Z/trZX0Ne6vfz58+uZ557XM889b7V/SPgrCipaVEOHvyZJen3EMM2d86leH/WGqlStpvFjx6hb187avf+QihdPX8/te3Mt88we2gggY48P+1w/fPic6lYpqT/nDrTsj7gUo3dnL9dbfTumm3MyfeE6lQ0topd6NNXHox+z7E9JSdEbM5aqX4+mKlrIV1eu2pZk+N8cebh6PT77L+imazfbSps3818+N/dfu6XPtJGLTK+5OZJyNTbr0dt/27sZg2f+LNu79p/XnZScormLN2nu4k1W+ycM7qoLV65q3NyVkqT3+j+gpx9soPc+WaF9RyP0au/W+n7ic6rebYwiLqUf9b4am9pPQEFGSoA7gaQEOVJoaOrE7jNnMp5QmbY/tFQpm9rz9/eXlHn5lbP7u217p1P3lyx5+/aW/bpUS5f8oq8Wfq8CBQooNjZWn3w8U0/0fFIjXh8lSapZs5YqVyijWTOm6c23303XRszNpCwgIMCm+AGkOhlxRQ17TVKXFtV0b43S8vLMr/1HI/X1sq3q0rKGJGnf0Yh01w2euEif/7RZDzSvppAgP124ck2Lft+t/cciNOqF9roel6BjZ2x7yGD0zTf5t5aJZdfpyCjVrnSPShT1z/B42v5bl9M9FXHF6lj6awKszrudUxFRqdfdnN+SWXsnbWivfeNK6tS0qh4bOldx8Uny9vLQ8w830oJft+qDz1KTlJ0Hz+qfRa/rxUeb6K2Zy9K14Xdz8nzUNR6gCNwJJCXIkWrUrClJ2rF9W4bH0/ZXr17DpvaCglKfS3LlcsZvApzdX40azmkvPj5eQ8JfUes2bdX1oYclSUePHFFiYqLq1vu3Xr10WJiCgoK0f3/GEzuvRKX+o14kKMim+AH8Kzk5RT+s2qUfVlmvvndv9dKSpD+3Hsnwul2HzmrXobNW+5rULqN8+dy1ZusRq+dyZOXCzeVyA/0ynqPmiF0Hz6pz82qqlckzRdL27z587t9rbr6WWhUzuaZiiXTXZBmDpb0SmcRws71DWbfnkd9dE8K7auXGA1q8erek1KfVe+TPZ7U4wImzl3X+8lVVDgvOsJ3Am3NJLl6JzfA48iZXW/HKlWKxV46cU7J27Vp17txZISEhMplMWrRokdEh4S5r1LiJ/P39dfTIEe3csSPd8R+//06S1LGTbatQValaVfny5bMssXun+2vb/n65ubnpr3V/Wj0gUUpNNJYu+Vnu7u5q36Fjlu1MnjRBJ0+c0KTJH6U7dv2GdenH9evX5eaW/n/5mJgYnTt7VqElSyrQxefUADlFcGFfPdSqhi5GxWrRatuXJn+pe+rDBz9btMHma3bffPNeoVTR25xpu2V/pX6A0fG+KvLIb/3AwKKFCqpJrTBdjr6uDTuPWfZv2HlcUVdvqGxoEatlkNM81Dr1w5ilf9q26tVvG/5RcnKKmtQqY/WARCk10eh4XxUlJSVr+fr9WbYzsFcLlSweqPAJ6Z/F9N9SM28vjwzn8vj6eKp4kJ9Onruc6RLQALInRyYlsbGxqlmzpqZNm2Z0KDCIh4eH/u+l/pKkgS/3s8zBkKT/fThJu3fvUtNmzVWnbl2b2vPx8VHNWrUVce6czpxJvya+o/3NmDZVNatV0qjXR1jtL168uLr3eFwJCQl6ZcBLSkr6d63814YP1YULF/T4E73SPVn+VidPntS4D97XgFcGqULFf1cNK1uunDw9PfX9twuVmJi6as1PixcpNjZWVapUTdfO1i1/y2w2q2nT5lndIgAZqFKmWLqVm0oU9de345+RX0EvDf/fT4qLt34WRlBgQYX+pyTJ3d1NI19or4db19TqLYfSjbpk5a8dqauA1a0S6tiLyMCWfae0fscxBRf21Xv9H7CK839Du8kjfz5NX/inkm4ZzUlMStbMb9dJkiYPfdjqDf/LTzRTjfIhWrv1cLoHPP7fo020Y+Ewvf2S9YcwEZeuauGK7fL0yKf/DX3Y6unx7w94QEUL+eqrZdt04Yr1gxVvFRocoKFPt9ZHX63VoVtWJzty6qLi4hPVrU1N5bvZbufm1VTQ21P7jkama6dulZKpS6ffXHENgPPlyPKtDh06qEOHDkaHAYMNf22kfl+1Uhs3rFe1yuXV5L6mOnnihP7evElBQUGaNfszu9rr0LGTtm75W2vXrNbjT/R0Sn+XLl3UwQMHFHEufXnB+EmTtXnzRi364XvVrFZJderW0/69e7V37x6VK19eYydMyjLeYa+GKyAw0DJvJI23t7f69hugyZMmqGG9WipbrrxWrlguPz8/vdi3X7p21q5ZLUm6v2OnLPsDkN7AXi30YItq2vHPGUVcilFQYEE1rhkmL8/8ev+TFfpyyZZ011QKC9ay6f+nHQfO6MTZyzKZTGpYvZSKB/lr+z+n9cTwz+2KYd32o7oaG6dmdTJfOeqbcU+rWOHUBS2KB6X+95kHG6rdvZUkpU7M7zF0rtU1fd75Wqs/fVn9H2+m5vXK6Z9jkapbJVRl7imiDTuPafzcVen6+eCzlWpVv4Ia1QzTnu+H668dx1SyWKAaVC+l85ev6sV3vkl3TeEAH1UsXVTFivilO/bqpMVqUK2UHmpdUzsrlNC2/adUuUwxVStXXIdOXtCwyYuzvDdjBz6oqJgbGvPpb1b7b8QnasbCdRr0ZEtt+nKwjpy6qDYNKyr62g3N+u6vdO2k3dtl67IelUHeY5JJbi604lVOXn0rR46U2Cs+Pl4xMTFWG3I+Ly8vLV/5h0a8Pkre3t76efEinTp5Qk8+9bTWb96msDL2PeTqqaeflbu7uxZ+veCu9FekSBH9uX6z+vYboISEBP206EdFx0Trpf4v68/1m1WoUKFMr/191Uot+uF7jRk7QQULFkx3/K133tMrgwbr8qVLWrHsV9WsVVs/LVme4cjLwm++UpEiRdSl60N2xQ9A+nnNHm3afUIVShfVQ61qqGrZ4vpt4wG1+7/peufj5Rlec+z0Jc1fskW+Pl5q17iSWjesoLMXYzRs8mI1f3aKrsTYVx4UeyNBC1dsV7mSQapbOePRkpoVSqhB9VJqUL2U5eGFJYIDLPtqVkg/b+PIqYtq1GuS5v28WUUCC+rBFtWVkmLW+5+sUId+M5WQmJzumviEJLV/abre/2SFrsclqnPzagotHqh5P29W46c+1PGztk3eT3MpOlZNn5ms6d/8KY/87nqwRXX5F/TStK/XqunTk7O8Vy3rl9dDrWtqxJSfLSuD3eqNGb9q8vzVKuTvrXaNKmnnwTN68OWPMxx56d6uti5cuWaZkwLA+Uxms9m2hdBdlMlk0o8//qiuXbtmes6bb76pt956K93+yEvR8vNL/8kM8q7ujzykX5f8okPHTqlYsWJGh3PHbdywQS2bNVb4kKF6b8xYo8NBNgU2Hmx0CDBIjfIh2vTlYM1YuC7DuRNwXMPqpbT605c1cd7vGjl1idHh5Cnm5HjFb5um6GjXe78WExMjf39/fbfxiHxsfBzA3RB77aoeubesS96z28kTIyUjRoxQdHS0ZTt16tTtL0Ke9Mab7yglJUWTJ00wOpS7YuL4DxQQEKDBrw4zOhQA2bDr0Fl9v3KHnupcP92kcGTP4Kda6UrMdU2c97vRocAFpa2+5UpbTpUnkhJPT0/5+flZbUBGqlarpl5P9tbsWTPSrYqV2+zYvl2//PyThgwbkWWpGICcYdS0pcqfz13hT7Y0OpRco2aFEurcvJomfP673WV1AOyTIye6A3fSrE8+06xP7JsknxPVql1bNxJzdPUmgFscO3NJ/k0Y9XSmnQfPqEADyiKBuyFHJiXXrl3T4cOHLT8fO3ZMO3bsUKFChVSyZEkDIwMAAEBe4WolU64Ui71yZFKyZcsWtWz57/B0eHi4JKl3796aO3euQVEBAAAAcESOTEpatGihHL5oGAAAAICbcmRSAgAAABjNdPPLVbhSLPbKE6tvAQAAAHBdJCUAAAAADEX5FgAAAOAAN1Pq5ipcKRZ7MVICAAAAwFAkJQAAAAAMRfkWAAAA4ABW33IeRkoAAAAAGIqkBAAAAIChKN8CAAAAHGAypW6uwpVisRcjJQAAAAAMRVICAAAAwFCUbwEAAAAOMMm1VrxynUjsx0gJAAAAAEORlAAAAAAwFOVbAAAAgAPcTKmbq3ClWOzFSAkAAAAAQ5GUAAAAADAU5VsAAACAA0w3v1yFK8ViL0ZKAAAAABiKpAQAAACAoSjfAgAAABxgMqVursKVYrEXIyUAAAAADEVSAgAAAMBQlG8BAAAADjDd3FyFK8ViL0ZKAAAAABiKpAQAAACAoSjfAgAAABzgJpPcXGjJK7ccXMDFSAkAAAAAQ5GUAAAAADAU5VsAAACAA1h9y3kYKQEAAABgKJISAAAAAIaifAsAAABwBPVbTsNICQAAAABDkZQAAAAAMBTlWwAAAIADTDe/XIUrxWIvRkoAAAAAGIqkBAAAAIChKN8CAAAAHGGSTK5UMeVKsdiJkRIAAAAAhiIpAQAAAGAoyrcAAAAAB/DsROdhpAQAAACAoUhKAAAAABiK8i0AAADAEdRvOQ0jJQAAAAAMRVICAAAAwFCUbwEAAAAOMN38chWuFIu9GCkBAAAAYCiSEgAAAACGonwLAAAAcIDJlLq5CleKxV6MlAAAAAAwFEkJAAAAAENRvgUAAAA4gGcnOg8jJQAAAAAMRVICAAAAwFCUbwEAAACOoH7LaRgpAQAAAGAokhIAAAAAhqJ8CwAAAHCA6eaXq3ClWOzFSAkAAAAAQ5GUAAAAADAU5VsAAACAA0ym1M1VuFIs9mKkBAAAAIChSEoAAAAAGIqkBAAAAHCAyQU3e4wZM0b169eXr6+vihYtqq5du+rAgQNW58TFxalfv34qXLiwChYsqG7duikyMtLOnm6PpAQAAADIg9asWaN+/fpp48aN+u2335SYmKh27dopNjbWcs6gQYP0888/69tvv9WaNWt09uxZPfzww06PhYnuAAAAQB60bNkyq5/nzp2rokWLauvWrWrWrJmio6P16aefasGCBWrVqpUkac6cOapcubI2btyoe++912mxMFICAAAAOMLoWq1M6rdiYmKstvj4eJteTnR0tCSpUKFCkqStW7cqMTFRbdq0sZxTqVIllSxZUhs2bLD9PtmApAQAAADIRUJDQ+Xv72/ZxowZc9trUlJSNHDgQDVp0kTVqlWTJEVERMjDw0MBAQFW5wYHBysiIsKpMVO+BQAAAOQip06dkp+fn+VnT0/P217Tr18/7dmzR+vWrbuToWWKpAQAAABwgOnml6tIi8XPz88qKbmd/v3765dfftHatWt1zz33WPYXK1ZMCQkJioqKshotiYyMVLFixZwWt0T5FgAAAJAnmc1m9e/fXz/++KN+//13hYWFWR2vW7eu8ufPr1WrVln2HThwQCdPnlSjRo2cGgsjJQAAAEAe1K9fPy1YsECLFy+Wr6+vZZ6Iv7+/ChQoIH9/fz333HMKDw9XoUKF5OfnpwEDBqhRo0ZOXXlLIikBAAAAHGIypW6uwt5YZsyYIUlq0aKF1f45c+bo6aefliR9+OGHcnNzU7du3RQfH6/27dtr+vTpTojWGkkJAAAAkAeZzebbnuPl5aVp06Zp2rRpdzQW5pQAAAAAMBQjJQAAAIADbnleoUtwpVjsxUgJAAAAAEORlAAAAAAwFOVbAAAAgCOo33IaRkoAAAAAGIqkBAAAAIChKN8CAAAAHGC6+eUqXCkWezFSAgAAAMBQJCUAAAAADEX5FgAAAOAAkyl1cxWuFIu9GCkBAAAAYCiSEgAAAACGonwLAAAAcADPTnQeRkoAAAAAGIqkBAAAAIChKN8CAAAAHEH9ltMwUgIAAADAUCQlAAAAAAxF+RYAAADgANPNL1fhSrHYi5ESAAAAAIYiKQEAAABgKMq3AAAAAAeYTKmbq3ClWOzFSAkAAAAAQ5GUAAAAADAU5VsAAACAA3h2ovMwUgIAAADAUCQlAAAAAAxF+RYAAADgCOq3nIaREgAAAACGIikBAAAAYCjKtwAAAAAHmG5+uQpXisVejJQAAAAAMBRJCQAAAABDUb4FAAAAOMIkmVypYsqVYrETIyUAAAAADEVSAgAAAMBQlG8BAAAADuDZic7DSAkAAAAAQ5GUAAAAADAU5VsAAACAI6jfchpGSgAAAAAYiqQEAAAAgKEo3wIAAAAcYLr55SpcKRZ7MVICAAAAwFAkJQAAAAAMRfkWAAAA4ACTKXVzFa4Ui70YKQEAAABgKJISAAAAAIaifAsAAABwAM9OdB5GSgAAAAAYiqQEAAAAgKEo3wIAAAAcQf2W0zBSAgAAAMBQJCUAAAAADEX5FgAAAOAA080vV+FKsdiLkRIAAAAAhiIpAQAAAGAoyrcAAAAAB5gkmVyoYsqFQrEbIyUAAAAADEVSAgAAAMBQlG8BAAAADuDZic7DSAkAAAAAQ5GUAAAAADAU5VsAAACAA0wmF1t9y4VisRcjJQAAAAAMRVICAAAAwFCUbwEAAAAOYf0tZ2GkBAAAAIChSEoAAAAAGCpPlm+ZzWZJUsL1GCXkyTsAIDeKXPmG0SEAgNPExMQoNHSa5X2bK2L1LefJk2/Jr169KkkKDQ01OBIAAABk5erVq/L39zc6DNxheTIpCQkJ0alTp+Tr6ytTTk4p4fJSP+UJ1alTp+Tn52d0OACQbfy9hrvFbDbr6tWrCgkJMToU3AV5Milxc3PTPffcY3QYyEP8/Pz4xxtArsLfa7gbXH2EhLW3nIeJ7gAAAAAMRVICAAAAwFB5snwLuFs8PT31xhtvyNPT0+hQAMAp+HsN+BerbzmPyezK66wBAAAALiYmJkb+/v46cPKCfF1obtXVmBhVLBmk6OjoHDfni/ItAAAAAIaifAsAAABwgOnml6twpVjsxUgJAAAAAEORlAAAAAAwFEkJcAdNmzZNpUuXlpeXlxo2bKjNmzcbHRIAOGTt2rXq3LmzQkJCZDKZtGjRIqNDAoxncsEthyIpAe6Qb775RuHh4XrjjTe0bds21axZU+3bt9f58+eNDg0A7BYbG6uaNWtq2rRpRocCIBdiSWDgDmnYsKHq16+vqVOnSpJSUlIUGhqqAQMGaPjw4QZHBwCOM5lM+vHHH9W1a1ejQwEMkbYk8MFTF11uSeAKoUVYEhhAqoSEBG3dulVt2rSx7HNzc1ObNm20YcMGAyMDAADOYnSlVi6q3iIpAe6EixcvKjk5WcHBwVb7g4ODFRERYVBUAAAAromkBAAAAICheHgicAcUKVJE7u7uioyMtNofGRmpYsWKGRQVAABwJpMpdXMVrhSLvRgpAe4ADw8P1a1bV6tWrbLsS0lJ0apVq9SoUSMDIwMAAHA9jJQAd0h4eLh69+6tevXqqUGDBpo8ebJiY2P1zDPPGB0aANjt2rVrOnz4sOXnY8eOaceOHSpUqJBKlixpYGQAcgOSEuAO6dGjhy5cuKDRo0crIiJCtWrV0rJly9JNfgeAnGDLli1q2bKl5efw8HBJUu/evTV37lyDogKMZbr55SpcKRZ78ZwSAAAAwA5pzyk5cvqSyz2npOw9hXlOCQAAAADYi/ItAAAAwBGu9sRCV4rFToyUAAAAADAUSQkAAAAAQ1G+BQAAADiA6i3nYaQEAAAAgKFISgAAAAAYiqQEAG6xevVqmUwmmUwmrV69Ot3xp59+WiaTSaVLl77rsRmlRYsWMplMatGihUPX3+6eOoMr/bmULl1aJpNJTz/9tNGhALjDTCbX23IqkhIADrn1jeZ/N29vb5UqVUpdu3bVggULlJSUZHS4AADAhZGUAHC6Gzdu6OTJk1q8eLF69uypxo0bKyIiwuiwXJ4rfdoPAMDdRFICINv69u2r3bt3W7YNGzboo48+sry5/vvvv9WlSxeZzWZjA3WCuXPnymw26/jx40aHAgAwnMmlvnLy+lssCQwg24oWLapq1apZ7bv33nvVs2dPNWjQQIcPH9bmzZv1yy+/qHPnzgZFCQAAXBUjJQDumMDAQI0YMcLy87JlywyMBgAAuCqSEgB3VIMGDSzfnzhxwvL9f1dkSklJ0WeffaaWLVsqODhYbm5uGa5etG3bNv3f//2fKlasqIIFC8rHx0cVK1ZU3759dfDgwdvGc+PGDb3//vuqWbOmfHx8VLhwYTVp0kSzZ89WSkrKba+3dd7H1atXNXHiRLVq1UrFihWTh4eH/Pz8VLt2bQ0YMEB//fWX5dw333xTJpNJn3/+ueU+ZbSAQEbi4uI0depUtW7d2tJP0aJF1aZNG3366ac2LTKwceNGPfrooypWrJi8vLwUFhamPn366MCBA7e91lk2btyokSNHqkWLFlb3q0qVKurbt6/27dtnV3tnzpxReHi4KlSoIG9vbwUFBalTp042J8bR0dEaM2aMmjRpoqCgIHl4eKh48eLq3Lmzvvvuu1xRiggg+4xeaSs3rb4lMwA44I8//jBLMksyv/HGG5me988//1jOu//++zO8/tdffzW3adPG8nPa1rt3b8v5ycnJ5kGDBplNJlO689K2fPnymWfNmpVpLOfOnTNXrlw50+vbt29vXr58ueXnP/74I10bvXv3NksylypVKtN+fvvtN3ORIkUy7SdtS/PGG2/c9tyM/rresWOHuVSpUlleU79+fXNERESmsU6aNMns5uaW4bU+Pj7mJUuWmJs3b26WZG7evHmm7WTl1j/rjO7pnDlzbvva3d3dzdOmTcu0j1v/XP7++29z0aJFM20rPDw8y3hXrlxpLly4cJbxdOzY0Xz16tUMr0/7M7n19xdA7hIdHW2WZD5+7rL5cmySy2zHz102SzJHR0cbfYvsxpwSAHfU7t27Ld+HhIRkeM6wYcO0a9cuPfjgg3r66adVqlQpRUZGKiYmxnLOgAEDNH36dElSs2bN9PTTT6tMmTLy9vbWzp07NXnyZO3du1cvvviiihUrpgcffNCqj6SkJD3wwAPav3+/JKldu3bq27evQkNDdfLkSU2fPl3Lly/X5cuXs/V6//jjD3Xo0EFJSUlyd3fXk08+qS5duqhkyZKKi4vTvn379Ouvv+rnn3+2XPPSSy/pkUce0ciRI7V48WKFhIRo+fLlWfZz+PBhNW/eXNHR0fLz81O/fv3UoEEDhYaG6tKlS/rpp580a9YsyyIDf/75p/Lnz2/Vxo8//qjw8HBJkr+/v4YNG2Z5Fsnvv/+ucePGqWfPngoKCsrWPbmdpKQkBQYGqkuXLmrWrJnKly8vHx8fnT17Vtu2bdOUKVN08eJF9e/fX5UqVVKrVq0ybev69et69NFHFR0dreHDh6tjx47y9PTUpk2bNGbMGJ07d06TJk1SyZIl9corr6S7/q+//lKHDh2UmJio4OBgDRgwQDVr1lRISIjOnj2rb775RvPnz9fSpUvVu3dvff/993fy1gBA3mF0VgQgZ7JlpCQxMdF87733Ws6bN29ehtdLMo8cOTLTvlasWGE575NPPsnwnBs3bphbtWpl+bQ8MTHR6vjUqVMtbfTp0yfDNp599lmrmOwdKblx44Y5JCTELMns7e2d4fVpTp48aVfb/9W4cWOzJHPt2rXNFy5cyPCcX3/91TIK8vHHH1sdi4+Pt8Tq7+9v3rdvX7rrd+/ebfbz87Pcjzs1UnL69GlzbGxsptdHRUWZa9SoYZZkvu+++zI8J+3eSTLnz5/fvGbNmnTnnDlzxnzPPfdYRoHOnz9vdTwhIcFcunRpy6heZjF9/PHHlr5WrFiR7jgjJUDux0iJ8zGnBIDTxcbGas2aNWrbtq02btwoSSpVqpS6d++e4fkVKlTQm2++mWl7H3zwgSSpW7dueu655zI8x8vLS1OnTpWUOifjjz/+sDqeNsoSHBysDz/8MMM2/ve//2VrVGDevHk6e/asJOn999/P8gnooaGhDvfz559/av369ZKkzz//XEWKFMnwvPvvv1+PPPKIpNSljG+1ePFiS6yjRo1S5cqV011frVo1vf766w7HaasSJUrI29s70+P+/v56++23JUnr1q3TpUuXsmzvxRdfVLNmzdLtDwkJ0cSJEyWl/o6mzeFJ8/XXX+v48ePy8vLSvHnzMo3phRdesMyV+u99BQA4hqQEQLa99dZbVhOyCxYsqBYtWmj16tWSUpcMXrRokTw9PTO8vkePHnJ3d8/wWExMjKWdtDfYmalcubLlDfqGDRss+8+dO2eZKN29e/dM32wWLFgw08TJFr/88oskycfHRy+88ILD7dzOTz/9JEmqWLGiqlevnuW5aW/O//77b6tJ7ytXrpQkmUwm9e7dO9Prn3nmmUwn2d8psbGxOn78uPbu3as9e/Zoz549VqVnO3fuzPL6Z555JtNjDz30kAICAiT9ew/SpN3X5s2b3zY5Tbuvt/6eAQAcx5wSAHdMWFiYHnnkEQ0ZMkRFixbN9LwaNWpkemz79u2WVbEef/xxPf744zb1fesT5G+d11K/fv0sr2vQoIGmTZtmUx//tX37dklS3bp1s/zkP7u2bNkiSTpw4IDNCUNiYqIuX75s+XNIuydhYWGZjrRIUlBQkEqXLq1jx45lM+qsXbx4UZMmTdL333+vQ4cOZbm61cWLFzM95uHhoZo1a2Z6PH/+/Kpdu7b++OMPq98L6d/7unz5cpvv662/ZwDyHldb8cqVYrEXSQmAbOvbt69eeuklSamfvHt5ealIkSLy9/e36frAwMBMj50/f96hmK5fv275/tbJ61klR1JqeZej0t4sFy9e3OE2bOHMe3K7+yGl3pM7mZRs3bpV7du3v21ZVpobN25keqxQoUKZjrqlSfsz/u+iBo7c16xiAQDYjqQEQLZl9ER3e2T1JjI5Odny/axZs9S4cWOb2sws0bnbpUh3Qto9qVmzpubPn2/zdSVKlEi3z+j7kZCQoO7du+vSpUvKnz+/BgwYoC5duqhChQoKDAy0lPwdPXpUZcuWlaQsR1Gy83rS7muHDh00btw4h9sBANiPpASASytcuLDle29vb4eSn1sTlMjIyCzPvd3xrBQpUkSnT5/WuXPnHG7DFmn35Nq1aw4ng2n3xJbXm517cju///67jh49Kil1MYLnn38+w/NsXar50qVLSk5OzjLRTXs9hQoVstpfuHBhnT17VgkJCdlKsgHkHaabX67ClWKxFxPdAbi0WrVqWT79vvUp6Pa4dTL433//neW5tzuelTp16khKnZtwa6mUrWz9lL927dqSUkcPHJ3TkHZPjh07lmXZ1IULF3T8+HGH+rDF3r17Ld/36NEj0/PS5nvcTkJCQpYT4ZOSkrRjxw5JSpd4pN3XLVu2KCEhwab+AADOQVICwKUFBQXp3nvvlSQtWLBAFy5csLuNkJAQy5K33377babzAGJjY7Vw4UKHY+3cubOk1LkbH3/8sd3Xe3l5SZLi4+OzPC/twZBms1n/+9//7O5Hktq0aWNpY968eZmeN3fu3CzLpbLr1hXBYmNjMzwnJSVFs2fPtrnN/y71e6sff/xRV65ckfTvPUiTdl+jo6M1Z84cm/sDAGQfSQkAlzdy5EhJqcsDP/LII4qKisr03Pj4eE37//buLSSq7o3j+G+yZkYzKzyVZBZlkQhGidkBlDANu7EDGGSJQaAXWR4oItHECiKIQlIpSKII6qKgwiyQygTDPARGaEkGFaVFoqX/CnT/L3obXp08Vu7p9fsZNoyLvdZ+Zu0bH9bp9Gl9+fKlX3laWpqk77slZWVl/bRuRkbGmBeRS1JSUpJj3cbBgwd1//79Qe99/fq1U9mPBfLt7e369OnToHVjY2Md52QcP3582ESqsbGx3wnykpSQkOB4XkFBgZqbm53qPX36VEeOHBmy7V8VHBzs+D7YmR8HDhxQfX39iNssLi5WVVWVU/m7d++UnZ0t6ftUwIFbIScnJzvOj8nOzlZlZeWQz6mqqhryHQP47/ux+5YrXX8r1pQAcHnx8fHas2ePTp06pcrKSi1ZskSpqalas2aNvL291d3drZaWFj148EBXr15VR0eH0z+caWlpKi0tVUNDg4qLi9Xa2qrU1FQFBgbq1atXKioq0p07dxQeHj7iqUID2e12XbhwQbGxserp6VFMTIy2b9+uhIQEzZkzR1+/flVTU5PKysp0/fp1pxGRH4v4+/r6lJqaqt27d/fbrnfhwoWO75cuXVJERIQ+fvyoxMREXbx4UYmJiQoODpabm5va29vV0NCgGzdu6OHDh8rKynKM5Ejft84tLCzUli1b1NHRocjISO3fv1/R0dEyDEP37t3TsWPHHM9taWkZU58MJy4uTn5+fmpvb1dOTo5evnypjRs3ysfHRy0tLTp79qwqKiq0evXqEU3f8/X1lYeHh9atW6eMjAzFx8fLZrOppqZGR48edRwYWVBQ4LTzmM1m05UrVxQdHa3Pnz9r7dq12rp1qxISEjR//nz19fXp7du3qqur07Vr19TY2KjCwkJFRUX9kb4BgAnFzOPkAfy97t69a0gyJBl5eXm/VP/u3bvD3t/X12fk5+cbkydPdtQb7Jo6darR09Pj1MabN2+MxYsXD1ovNjbWuH379pBxJScnG5KMoKCgQWMtLy83Zs6cOWycA/X29hqRkZEjvr+5udkIDQ0d9jmSjPz8/J/Gevz4ccNisfy0joeHh3Hz5k0jKirKkGRERUUN+puHMty7Li8vN+x2+6CxR0dHG0+ePHH8XVpa6tTGv9/Lo0ePDB8fn0HbS09PHzLe6upqIzAwcET9ev78eaf6QUFBhiQjOTl5TP0FwPV1dnYakoxXbR1G5/96XeZ61dZhSDI6OzvN7qJRY/oWgL+CxWJRbm6unj17pn379ik8PNxxJsW0adMUEhKibdu26fz583r79q3c3d2d2ggICFBDQ4MOHz6s0NBQubu7a8aMGYqMjFRRUZFu3bolq9X6y7HGxcXpxYsXOnr0qFatWiVvb2+5ubnJy8tLy5Yt0969e1VTU+NUb9KkSbpz545ycnIUFhYmT0/PIRe/L1q0SI8fP9alS5e0efNmzZ07V+7u7rJarZo9e7aio6OVk5Ojuro65ebm/rSN7OxsVVVVadOmTfLz85PNZlNQUJB27typ2tpabdiw4Zf7YzhxcXGqra1VUlKSAgICNGXKFPn6+ioqKkpnzpxRRUWFpk6dOuL2wsPDVV9fr/T0dC1YsEB2u13e3t5av369ysrKhl2HExkZqefPn6ukpEQbNmxQQECArFar7Ha7AgMDFRsbqyNHjqipqUk7duz41Z8P4C9mccHrb2UxjD+4ghEAAAD4j+nq6tL06dP1uq1DXl5eZofj0NXVpTn+M9XZ2elScY0EIyUAAAAATMVCdwAAAGAsXG3OlCvFMkqMlAAAAAAwFUkJAAAAAFMxfQsAAAAYA8s/H1fhSrGMFiMlAAAAAExFUgIAAADAVEzfAgAAAMbAYvl+uQpXimW0GCkBAAAAYCqSEgAAAACmYvoWAAAAMAacnfj7MFICAAAAwFQkJQAAAABMxfQtAAAAYCyYv/XbMFICAAAAwFQkJQAAAABMxfQtAAAAYAws/3xchSvFMlqMlAAAAAAwFUkJAAAAAFORlAAAAABjYLG43jUWp0+f1rx582S327VixQrV1NT83o4aAZISAAAAYIK6fPmyMjMzlZeXp/r6eoWFhSkuLk7t7e3jGgdJCQAAADBBnThxQrt27VJKSopCQkJUUlIiDw8PnTt3blzjYPctAAAAYAy6urrMDqGfH/EMjMtms8lmsznd/+3bN9XV1enAgQOOskmTJikmJkbV1dV/NtgBSEoAAACAUbBarZo1a5aC5weaHYoTT09PBQb2jysvL0+HDh1yuvfDhw/q7e2Vv79/v3J/f381NTX9yTCdkJQAAAAAo2C329Xa2qpv376ZHYoTwzBkGbDi/WejJK6GpAQAAAAYJbvdLrvdbnYYv8THx0dubm5qa2vrV97W1qZZs2aNaywsdAcAAAAmIKvVquXLl6uiosJR1tfXp4qKCq1cuXJcY2GkBAAAAJigMjMzlZycrPDwcEVEROjkyZPq7u5WSkrKuMZBUgIAAABMUImJiXr//r1yc3P17t07LV26VOXl5U6L3/80i2EYxrg+EQAAAAD+hTUlAAAAAExFUgIAAADAVCQlAAAAAExFUgIAAADAVCQlAAAAAExFUgIAAADAVCQlAAAAAExFUgIAAADAVCQlAAAAAExFUgIAAADAVCQlAAAAAEz1fw8xngoZRTFyAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0EaKOeeNnWbq"
      },
      "source": [
        "That looks much better. It seems our model has made almost perfect predictions on the test set except for two false positives (top right corner)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZvNQjM-rk7uE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1fb9e92a-3882-4403-a5f3-b01f079cc551"
      },
      "source": [
        "# What does itertools.product do? Combines two things into each combination\n",
        "import itertools\n",
        "for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "  print(i, j)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 0\n",
            "0 1\n",
            "1 0\n",
            "1 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2WLKnUi6fI6B"
      },
      "source": [
        "## Working with a larger example (multiclass classification)\n",
        "\n",
        "We've seen a binary classification example (predicting if a data point is part of a red circle or blue circle) but what if you had multiple different classes of things?\n",
        "\n",
        "For example, say you were a fashion company and you wanted to build a neural network to predict whether a piece of clothing was a shoe, a shirt or a jacket (3 different options).\n",
        "\n",
        "When you have more than two classes as an option, this is known as **multiclass classification**.\n",
        "\n",
        "The good news is, the things we've learned so far (with a few tweaks) can be applied to multiclass classification problems as well.\n",
        "\n",
        "Let's see it in action.\n",
        "\n",
        "To start, we'll need some data. The good thing for us is TensorFlow has a multiclass classication dataset known as [Fashion MNIST built-in](https://github.com/zalandoresearch/fashion-mnist). Meaning we can get started straight away.\n",
        "\n",
        "We can import it using the [`tf.keras.datasets`](https://www.tensorflow.org/api_docs/python/tf/keras/datasets) module.\n",
        "\n",
        "> 📖 **Resource:** The following multiclass classification problem has been adapted from the [TensorFlow classification guide](https://www.tensorflow.org/tutorials/keras/classification). A good exercise would be to once you've gone through the following example, replicate the TensorFlow guide."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zl50sxPTqpw4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a84d56e1-3caa-4fe3-da3d-daa957de8bb8"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.datasets import fashion_mnist\n",
        "\n",
        "# The data has already been sorted into training and test sets for us\n",
        "(train_data, train_labels), (test_data, test_labels) = fashion_mnist.load_data()"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "29515/29515 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26421880/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "5148/5148 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4422102/4422102 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D6qJforMrCZy"
      },
      "source": [
        "Now let's check out an example."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8PWdrQsyrBcy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a30aada7-d124-466f-9264-5e6a25dfaaf6"
      },
      "source": [
        "# Show the first training example\n",
        "print(f\"Training sample:\\n{train_data[0]}\\n\")\n",
        "print(f\"Training label: {train_labels[0]}\")"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training sample:\n",
            "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   1   0   0  13  73   0\n",
            "    0   1   4   0   0   0   0   1   1   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   3   0  36 136 127  62\n",
            "   54   0   0   0   1   3   4   0   0   3]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   6   0 102 204 176 134\n",
            "  144 123  23   0   0   0   0  12  10   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0 155 236 207 178\n",
            "  107 156 161 109  64  23  77 130  72  15]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   1   0  69 207 223 218 216\n",
            "  216 163 127 121 122 146 141  88 172  66]\n",
            " [  0   0   0   0   0   0   0   0   0   1   1   1   0 200 232 232 233 229\n",
            "  223 223 215 213 164 127 123 196 229   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0 183 225 216 223 228\n",
            "  235 227 224 222 224 221 223 245 173   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0 193 228 218 213 198\n",
            "  180 212 210 211 213 223 220 243 202   0]\n",
            " [  0   0   0   0   0   0   0   0   0   1   3   0  12 219 220 212 218 192\n",
            "  169 227 208 218 224 212 226 197 209  52]\n",
            " [  0   0   0   0   0   0   0   0   0   0   6   0  99 244 222 220 218 203\n",
            "  198 221 215 213 222 220 245 119 167  56]\n",
            " [  0   0   0   0   0   0   0   0   0   4   0   0  55 236 228 230 228 240\n",
            "  232 213 218 223 234 217 217 209  92   0]\n",
            " [  0   0   1   4   6   7   2   0   0   0   0   0 237 226 217 223 222 219\n",
            "  222 221 216 223 229 215 218 255  77   0]\n",
            " [  0   3   0   0   0   0   0   0   0  62 145 204 228 207 213 221 218 208\n",
            "  211 218 224 223 219 215 224 244 159   0]\n",
            " [  0   0   0   0  18  44  82 107 189 228 220 222 217 226 200 205 211 230\n",
            "  224 234 176 188 250 248 233 238 215   0]\n",
            " [  0  57 187 208 224 221 224 208 204 214 208 209 200 159 245 193 206 223\n",
            "  255 255 221 234 221 211 220 232 246   0]\n",
            " [  3 202 228 224 221 211 211 214 205 205 205 220 240  80 150 255 229 221\n",
            "  188 154 191 210 204 209 222 228 225   0]\n",
            " [ 98 233 198 210 222 229 229 234 249 220 194 215 217 241  65  73 106 117\n",
            "  168 219 221 215 217 223 223 224 229  29]\n",
            " [ 75 204 212 204 193 205 211 225 216 185 197 206 198 213 240 195 227 245\n",
            "  239 223 218 212 209 222 220 221 230  67]\n",
            " [ 48 203 183 194 213 197 185 190 194 192 202 214 219 221 220 236 225 216\n",
            "  199 206 186 181 177 172 181 205 206 115]\n",
            " [  0 122 219 193 179 171 183 196 204 210 213 207 211 210 200 196 194 191\n",
            "  195 191 198 192 176 156 167 177 210  92]\n",
            " [  0   0  74 189 212 191 175 172 175 181 185 188 189 188 193 198 204 209\n",
            "  210 210 211 188 188 194 192 216 170   0]\n",
            " [  2   0   0   0  66 200 222 237 239 242 246 243 244 221 220 193 191 179\n",
            "  182 182 181 176 166 168  99  58   0   0]\n",
            " [  0   0   0   0   0   0   0  40  61  44  72  41  35   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]]\n",
            "\n",
            "Training label: 9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MMqbRp8jrdtv"
      },
      "source": [
        "Woah, we get a large list of numbers, followed (the data) by a single number (the class label).\n",
        "\n",
        "What about the shapes?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gN5-jr6arj19",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "875e43f8-6f45-4de7-a690-3e5f22dc9346"
      },
      "source": [
        "# Check the shape of our data\n",
        "train_data.shape, train_labels.shape, test_data.shape, test_labels.shape"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60000, 28, 28), (60000,), (10000, 28, 28), (10000,))"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wNfIOUUEsJRt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb29775a-9247-4b6f-f635-aa14a4152fa1"
      },
      "source": [
        "# Check shape of a single example\n",
        "train_data[0].shape, train_labels[0].shape"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((28, 28), ())"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r2wW0cEfsAve"
      },
      "source": [
        "Okay, 60,000 training examples each with shape (28, 28) and a label each as well as 10,000 test examples of shape (28, 28).\n",
        "\n",
        "But these are just numbers, let's visualize."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RmC2VsWOscKP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "90805579-22f4-410a-e140-8051a20665e8"
      },
      "source": [
        "# Plot a single example\n",
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(train_data[7]);"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sOqirdtfstdQ"
      },
      "source": [
        "Hmm, but what about its label?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hzTDEpaYsxga",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e436486f-eab2-4327-e8d0-13e6a6e78f99"
      },
      "source": [
        "# Check our samples label\n",
        "train_labels[7]"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZHdVBrCUs10A"
      },
      "source": [
        "It looks like our labels are in numerical form. And while this is fine for a neural network, you might want to have them in human readable form.\n",
        "\n",
        "Let's create a small list of the class names (we can find them on [the dataset's GitHub page](https://github.com/zalandoresearch/fashion-mnist#labels)).\n",
        "\n",
        "> 🔑 **Note:** Whilst this dataset has been prepared for us and ready to go, it's important to remember many datasets won't be ready to go like this one. Often you'll have to do a few preprocessing steps to have it ready to use with a neural network (we'll see more of this when we work with our own data later)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uGOi32T8s1ai",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c585fda2-b5c2-4c34-b0dd-85fd5d24ecae"
      },
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
        "\n",
        "# How many classes are there (this'll be our output shape)?\n",
        "len(class_names)"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zOiwINQQtzys"
      },
      "source": [
        "Now we have these, let's plot another example.\n",
        "\n",
        "> 🤔 **Question:** Pay particular attention to what the data we're working with *looks* like. Is it only straight lines? Or does it have non-straight lines as well? Do you think if we wanted to find patterns in the photos of clothes (which are actually collections of pixels), will our model need non-linearities (non-straight lines) or not?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_qD40id2tytn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "outputId": "aa25c6ac-c202-4b3e-e35e-69c9437b9076"
      },
      "source": [
        "# Plot an example image and its label\n",
        "plt.imshow(train_data[17], cmap=plt.cm.binary) # change the colours to black & white\n",
        "plt.title(class_names[train_labels[17]]);"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rtz9__w3s4JF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "outputId": "eaf46307-c934-417a-e4b3-208cf3f37bc0"
      },
      "source": [
        "# Plot multiple random images of fashion MNIST\n",
        "import random\n",
        "plt.figure(figsize=(7, 7))\n",
        "for i in range(4):\n",
        "  ax = plt.subplot(2, 2, i + 1)\n",
        "  rand_index = random.choice(range(len(train_data)))\n",
        "  plt.imshow(train_data[rand_index], cmap=plt.cm.binary)\n",
        "  plt.title(class_names[train_labels[rand_index]])\n",
        "  plt.axis(False)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TLqZif3Rv0Aq"
      },
      "source": [
        "Alright, let's build a model to figure out the relationship between the pixel values and their labels.\n",
        "\n",
        "Since this is a multiclass classification problem, we'll need to make a few changes to our architecture (inline with Table 1 above):\n",
        "\n",
        "* The **input shape** will have to deal with 28x28 tensors (the height and width of our images).\n",
        "  * We're actually going to squash the input into a tensor (vector) of shape `(784)`.\n",
        "* The **output shape** will have to be 10 because we need our model to predict for 10 different classes.\n",
        "  * We'll also change the `activation` parameter of our output layer to be [`\"softmax\"`](https://www.tensorflow.org/api_docs/python/tf/keras/activations/softmax) instead of `'sigmoid'`. As we'll see the `\"softmax\"` activation function outputs a series of values between 0 & 1 (the same shape as **output shape**, which together add up to ~1. The index with the highest value is predicted by the model to be the most *likely* class.\n",
        "* We'll need to change our loss function from a binary loss function to a multiclass loss function.\n",
        "  * More specifically, since our labels are in integer form, we'll use [`tf.keras.losses.SparseCategoricalCrossentropy()`](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/losses/SparseCategoricalCrossentropy), if our labels were one-hot encoded (e.g. they looked something like `[0, 0, 1, 0, 0...]`), we'd use [`tf.keras.losses.CategoricalCrossentropy()`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/CategoricalCrossentropy).\n",
        "* We'll also use the `validation_data` parameter when calling the `fit()` function. This will give us an idea of how the model performs on the test set during training.\n",
        "\n",
        "You ready? Let's go."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qUFHuzIpv30K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1cf10905-3f65-433d-cf34-fe0cc3f26158"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create the model\n",
        "model_11 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Flatten(input_shape=(28, 28)), # input layer (we had to reshape 28x28 to 784, the Flatten layer does this for us)\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(10, activation=\"softmax\") # output shape is 10, activation is softmax\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model_11.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(), # different loss function for multiclass classifcation\n",
        "                 optimizer=tf.keras.optimizers.Adam(),\n",
        "                 metrics=[\"accuracy\"])\n",
        "\n",
        "# Fit the model\n",
        "non_norm_history = model_11.fit(train_data,\n",
        "                                train_labels,\n",
        "                                epochs=10,\n",
        "                                validation_data=(test_data, test_labels)) # see how the model performs on the test set during training"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 8s 3ms/step - loss: 2.1829 - accuracy: 0.1931 - val_loss: 2.1994 - val_accuracy: 0.2037\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.8995 - accuracy: 0.2442 - val_loss: 1.8438 - val_accuracy: 0.2579\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.7651 - accuracy: 0.2615 - val_loss: 1.7387 - val_accuracy: 0.2779\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.6032 - accuracy: 0.2936 - val_loss: 1.5459 - val_accuracy: 0.3104\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.5402 - accuracy: 0.3038 - val_loss: 1.5040 - val_accuracy: 0.3089\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.4902 - accuracy: 0.3207 - val_loss: 1.4725 - val_accuracy: 0.3131\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.4667 - accuracy: 0.3323 - val_loss: 1.4497 - val_accuracy: 0.3634\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.4550 - accuracy: 0.3469 - val_loss: 1.4640 - val_accuracy: 0.3559\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.4280 - accuracy: 0.3549 - val_loss: 1.4428 - val_accuracy: 0.3422\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.4292 - accuracy: 0.3542 - val_loss: 1.5142 - val_accuracy: 0.3405\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3hzYWEgoVJ_p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6c30b8af-5f19-4c4c-c65e-68436f8d87d7"
      },
      "source": [
        "# Check the shapes of our model\n",
        "# Note: the \"None\" in (None, 784) is for batch_size, we'll cover this in a later module\n",
        "model_11.summary()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_11\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten (Flatten)           (None, 784)               0         \n",
            "                                                                 \n",
            " dense_28 (Dense)            (None, 4)                 3140      \n",
            "                                                                 \n",
            " dense_29 (Dense)            (None, 4)                 20        \n",
            "                                                                 \n",
            " dense_30 (Dense)            (None, 10)                50        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3210 (12.54 KB)\n",
            "Trainable params: 3210 (12.54 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XRfkre59zSto"
      },
      "source": [
        "Alright, our model gets to about ~35% accuracy after 10 epochs using a similar style model to what we used on our binary classification problem.\n",
        "\n",
        "Which is better than guessing (guessing with 10 classes would result in about 10% accuracy) but we can do better.\n",
        "\n",
        "Do you remember when we talked about neural networks preferring numbers between 0 and 1? (if not, treat this as a reminder)\n",
        "\n",
        "Well, right now, the data we have isn't between 0 and 1, in other words, it's not normalized (hence why we used the `non_norm_history` variable when calling `fit()`). It's pixel values are between 0 and 255.\n",
        "\n",
        "Let's see."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tGiweanwz82_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "334ad24e-1c94-4973-8b4c-3e3c1eef7bc7"
      },
      "source": [
        "# Check the min and max values of the training data\n",
        "train_data.min(), train_data.max()"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 255)"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "syB7LOk30H0_"
      },
      "source": [
        "We can get these values between 0 and 1 by dividing the entire array by the maximum: `255.0` (dividing by a float also converts to a float).\n",
        "\n",
        "\n",
        "Doing so will result in all of our data being between 0 and 1 (known as **scaling** or **normalization**)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ABRKp5U8voV_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5443c261-26ad-49f8-cf36-37d259224037"
      },
      "source": [
        "# Divide train and test images by the maximum value (normalize it)\n",
        "train_data = train_data / 255.0\n",
        "test_data = test_data / 255.0\n",
        "\n",
        "# Check the min and max values of the training data\n",
        "train_data.min(), train_data.max()"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LeYQEXOf06oo"
      },
      "source": [
        "Beautiful! Now our data is between 0 and 1. Let's see what happens when we model it.\n",
        "\n",
        "We'll use the same model as before (`model_11`) except this time the data will be normalized."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z1QRy7y_K_87",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "340beb7f-f094-4225-b8a4-0422aab83c6c"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create the model\n",
        "model_12 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Flatten(input_shape=(28, 28)), # input layer (we had to reshape 28x28 to 784)\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(10, activation=\"softmax\") # output shape is 10, activation is softmax\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model_12.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "                 optimizer=tf.keras.optimizers.Adam(),\n",
        "                 metrics=[\"accuracy\"])\n",
        "\n",
        "# Fit the model (to the normalized data)\n",
        "norm_history = model_12.fit(train_data,\n",
        "                            train_labels,\n",
        "                            epochs=10,\n",
        "                            validation_data=(test_data, test_labels))"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 1.2368 - accuracy: 0.5151 - val_loss: 0.9158 - val_accuracy: 0.6197\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.8054 - accuracy: 0.6939 - val_loss: 0.7300 - val_accuracy: 0.7337\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6805 - accuracy: 0.7524 - val_loss: 0.6827 - val_accuracy: 0.7570\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6427 - accuracy: 0.7661 - val_loss: 0.6599 - val_accuracy: 0.7663\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6258 - accuracy: 0.7735 - val_loss: 0.6568 - val_accuracy: 0.7681\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6138 - accuracy: 0.7784 - val_loss: 0.6378 - val_accuracy: 0.7772\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6056 - accuracy: 0.7817 - val_loss: 0.6611 - val_accuracy: 0.7562\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5993 - accuracy: 0.7842 - val_loss: 0.6351 - val_accuracy: 0.7798\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5905 - accuracy: 0.7881 - val_loss: 0.6232 - val_accuracy: 0.7782\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5867 - accuracy: 0.7911 - val_loss: 0.6203 - val_accuracy: 0.7818\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C_I2KNJiMWZ8"
      },
      "source": [
        "Woah, we used the exact same model as before but we with normalized data we're now seeing a much higher accuracy value!\n",
        "\n",
        "Let's plot each model's history (their loss curves)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zmRcYU7xN1wQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        },
        "outputId": "16b69ea0-3b10-4280-eedc-af4c1bd7493a"
      },
      "source": [
        "import pandas as pd\n",
        "# Plot non-normalized data loss curves\n",
        "pd.DataFrame(non_norm_history.history).plot(title=\"Non-normalized Data\")\n",
        "# Plot normalized data loss curves\n",
        "pd.DataFrame(norm_history.history).plot(title=\"Normalized data\");"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VKm8MsACOm4j"
      },
      "source": [
        "Wow. From these two plots, we can see how much quicker our model with the normalized data (`model_12`) improved than the model with the non-normalized data (`model_11`).\n",
        "\n",
        "> 🔑 **Note:** The same model with even *slightly* different data can produce *dramatically* different results. So when you're comparing models, it's important to make sure you're comparing them on the same criteria (e.g. same architecture but different data or same data but different architecture).\n",
        "\n",
        "How about we find the ideal learning rate and see what happens?\n",
        "\n",
        "We'll use the same architecture we've been using."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LcR_wb4nPSb2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "53b1f56a-ac02-4039-dd6a-d12165886022"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create the model\n",
        "model_13 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Flatten(input_shape=(28, 28)), # input layer (we had to reshape 28x28 to 784)\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(10, activation=\"softmax\") # output shape is 10, activation is softmax\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model_13.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "                 optimizer=tf.keras.optimizers.Adam(),\n",
        "                 metrics=[\"accuracy\"])\n",
        "\n",
        "# Create the learning rate callback\n",
        "lr_scheduler = tf.keras.callbacks.LearningRateScheduler(lambda epoch: 1e-3 * 10**(epoch/20))\n",
        "\n",
        "# Fit the model\n",
        "find_lr_history = model_13.fit(train_data,\n",
        "                               train_labels,\n",
        "                               epochs=40, # model already doing pretty good with current LR, probably don't need 100 epochs\n",
        "                               validation_data=(test_data, test_labels),\n",
        "                               callbacks=[lr_scheduler])"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 1.3489 - accuracy: 0.5091 - val_loss: 1.0140 - val_accuracy: 0.6485 - lr: 0.0010\n",
            "Epoch 2/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.8974 - accuracy: 0.6739 - val_loss: 0.8554 - val_accuracy: 0.6812 - lr: 0.0011\n",
            "Epoch 3/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.7930 - accuracy: 0.7102 - val_loss: 0.7868 - val_accuracy: 0.6940 - lr: 0.0013\n",
            "Epoch 4/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.7509 - accuracy: 0.7236 - val_loss: 0.7557 - val_accuracy: 0.7129 - lr: 0.0014\n",
            "Epoch 5/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.7246 - accuracy: 0.7306 - val_loss: 0.7407 - val_accuracy: 0.7340 - lr: 0.0016\n",
            "Epoch 6/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.7055 - accuracy: 0.7378 - val_loss: 0.7294 - val_accuracy: 0.7424 - lr: 0.0018\n",
            "Epoch 7/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6916 - accuracy: 0.7442 - val_loss: 0.7072 - val_accuracy: 0.7379 - lr: 0.0020\n",
            "Epoch 8/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6764 - accuracy: 0.7509 - val_loss: 0.7037 - val_accuracy: 0.7459 - lr: 0.0022\n",
            "Epoch 9/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6649 - accuracy: 0.7562 - val_loss: 0.6898 - val_accuracy: 0.7578 - lr: 0.0025\n",
            "Epoch 10/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6596 - accuracy: 0.7585 - val_loss: 0.7213 - val_accuracy: 0.7497 - lr: 0.0028\n",
            "Epoch 11/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6526 - accuracy: 0.7628 - val_loss: 0.7046 - val_accuracy: 0.7515 - lr: 0.0032\n",
            "Epoch 12/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6484 - accuracy: 0.7644 - val_loss: 0.6996 - val_accuracy: 0.7602 - lr: 0.0035\n",
            "Epoch 13/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6383 - accuracy: 0.7674 - val_loss: 0.6949 - val_accuracy: 0.7657 - lr: 0.0040\n",
            "Epoch 14/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6361 - accuracy: 0.7695 - val_loss: 0.6696 - val_accuracy: 0.7616 - lr: 0.0045\n",
            "Epoch 15/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6343 - accuracy: 0.7717 - val_loss: 0.6948 - val_accuracy: 0.7563 - lr: 0.0050\n",
            "Epoch 16/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6297 - accuracy: 0.7751 - val_loss: 0.6591 - val_accuracy: 0.7772 - lr: 0.0056\n",
            "Epoch 17/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6207 - accuracy: 0.7827 - val_loss: 0.6500 - val_accuracy: 0.7791 - lr: 0.0063\n",
            "Epoch 18/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6184 - accuracy: 0.7862 - val_loss: 0.6448 - val_accuracy: 0.7823 - lr: 0.0071\n",
            "Epoch 19/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6161 - accuracy: 0.7865 - val_loss: 0.6276 - val_accuracy: 0.7931 - lr: 0.0079\n",
            "Epoch 20/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6152 - accuracy: 0.7868 - val_loss: 0.6336 - val_accuracy: 0.7889 - lr: 0.0089\n",
            "Epoch 21/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6146 - accuracy: 0.7876 - val_loss: 0.6349 - val_accuracy: 0.7811 - lr: 0.0100\n",
            "Epoch 22/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6176 - accuracy: 0.7863 - val_loss: 0.6349 - val_accuracy: 0.7908 - lr: 0.0112\n",
            "Epoch 23/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6217 - accuracy: 0.7857 - val_loss: 0.7139 - val_accuracy: 0.7555 - lr: 0.0126\n",
            "Epoch 24/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6278 - accuracy: 0.7825 - val_loss: 0.7166 - val_accuracy: 0.7612 - lr: 0.0141\n",
            "Epoch 25/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6356 - accuracy: 0.7805 - val_loss: 0.7001 - val_accuracy: 0.7508 - lr: 0.0158\n",
            "Epoch 26/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6377 - accuracy: 0.7787 - val_loss: 0.7146 - val_accuracy: 0.7597 - lr: 0.0178\n",
            "Epoch 27/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6791 - accuracy: 0.7617 - val_loss: 0.6618 - val_accuracy: 0.7748 - lr: 0.0200\n",
            "Epoch 28/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6538 - accuracy: 0.7726 - val_loss: 0.6899 - val_accuracy: 0.7610 - lr: 0.0224\n",
            "Epoch 29/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6791 - accuracy: 0.7612 - val_loss: 0.6711 - val_accuracy: 0.7719 - lr: 0.0251\n",
            "Epoch 30/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6992 - accuracy: 0.7521 - val_loss: 0.7585 - val_accuracy: 0.7172 - lr: 0.0282\n",
            "Epoch 31/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.7305 - accuracy: 0.7406 - val_loss: 0.7314 - val_accuracy: 0.7392 - lr: 0.0316\n",
            "Epoch 32/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.7689 - accuracy: 0.7179 - val_loss: 0.8037 - val_accuracy: 0.6799 - lr: 0.0355\n",
            "Epoch 33/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.0027 - accuracy: 0.6074 - val_loss: 1.0677 - val_accuracy: 0.5509 - lr: 0.0398\n",
            "Epoch 34/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.9718 - accuracy: 0.6132 - val_loss: 1.0379 - val_accuracy: 0.5977 - lr: 0.0447\n",
            "Epoch 35/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.0091 - accuracy: 0.6153 - val_loss: 0.9773 - val_accuracy: 0.6447 - lr: 0.0501\n",
            "Epoch 36/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.9577 - accuracy: 0.6454 - val_loss: 0.8809 - val_accuracy: 0.6806 - lr: 0.0562\n",
            "Epoch 37/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.0135 - accuracy: 0.6252 - val_loss: 1.0258 - val_accuracy: 0.6005 - lr: 0.0631\n",
            "Epoch 38/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.0135 - accuracy: 0.6163 - val_loss: 0.9384 - val_accuracy: 0.6446 - lr: 0.0708\n",
            "Epoch 39/40\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.1192 - accuracy: 0.5747 - val_loss: 1.0548 - val_accuracy: 0.5785 - lr: 0.0794\n",
            "Epoch 40/40\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.2500 - accuracy: 0.5285 - val_loss: 1.5707 - val_accuracy: 0.4179 - lr: 0.0891\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hi2YODTwQ1ie",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "outputId": "a43aa562-143f-450c-8371-c09a822d8a8a"
      },
      "source": [
        "# Plot the learning rate decay curve\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "lrs = 1e-3 * (10**(np.arange(40)/20))\n",
        "plt.semilogx(lrs, find_lr_history.history[\"loss\"]) # want the x-axis to be log-scale\n",
        "plt.xlabel(\"Learning rate\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.title(\"Finding the ideal learning rate\");"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GqtOjggqWcfS"
      },
      "source": [
        "In this case, it looks like somewhere close to the default learning rate of the [Adam optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam) (`0.001`) is the ideal learning rate.\n",
        "\n",
        "Let's refit a model using the ideal learning rate."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i9KFhAwKXjWd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e846742a-83b8-4702-a0a4-7413391509a3"
      },
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create the model\n",
        "model_14 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Flatten(input_shape=(28, 28)), # input layer (we had to reshape 28x28 to 784)\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(10, activation=\"softmax\") # output shape is 10, activation is softmax\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model_14.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "                 optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), # ideal learning rate (same as default)\n",
        "                 metrics=[\"accuracy\"])\n",
        "\n",
        "# Fit the model\n",
        "history = model_14.fit(train_data,\n",
        "                       train_labels,\n",
        "                       epochs=20,\n",
        "                       validation_data=(test_data, test_labels))"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 1.1588 - accuracy: 0.6050 - val_loss: 0.7818 - val_accuracy: 0.7258\n",
            "Epoch 2/20\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.7097 - accuracy: 0.7484 - val_loss: 0.7062 - val_accuracy: 0.7526\n",
            "Epoch 3/20\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6528 - accuracy: 0.7655 - val_loss: 0.6678 - val_accuracy: 0.7645\n",
            "Epoch 4/20\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6249 - accuracy: 0.7756 - val_loss: 0.6516 - val_accuracy: 0.7684\n",
            "Epoch 5/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6082 - accuracy: 0.7798 - val_loss: 0.6405 - val_accuracy: 0.7733\n",
            "Epoch 6/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5946 - accuracy: 0.7838 - val_loss: 0.6344 - val_accuracy: 0.7743\n",
            "Epoch 7/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5855 - accuracy: 0.7868 - val_loss: 0.6231 - val_accuracy: 0.7768\n",
            "Epoch 8/20\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5773 - accuracy: 0.7880 - val_loss: 0.6256 - val_accuracy: 0.7737\n",
            "Epoch 9/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5716 - accuracy: 0.7909 - val_loss: 0.6134 - val_accuracy: 0.7812\n",
            "Epoch 10/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5653 - accuracy: 0.7930 - val_loss: 0.6030 - val_accuracy: 0.7843\n",
            "Epoch 11/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5604 - accuracy: 0.7944 - val_loss: 0.5985 - val_accuracy: 0.7849\n",
            "Epoch 12/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5544 - accuracy: 0.7972 - val_loss: 0.5951 - val_accuracy: 0.7868\n",
            "Epoch 13/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5513 - accuracy: 0.7966 - val_loss: 0.5940 - val_accuracy: 0.7893\n",
            "Epoch 14/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5462 - accuracy: 0.7994 - val_loss: 0.5953 - val_accuracy: 0.7864\n",
            "Epoch 15/20\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5420 - accuracy: 0.8008 - val_loss: 0.5860 - val_accuracy: 0.7911\n",
            "Epoch 16/20\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5388 - accuracy: 0.8016 - val_loss: 0.5901 - val_accuracy: 0.7927\n",
            "Epoch 17/20\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5359 - accuracy: 0.8025 - val_loss: 0.5889 - val_accuracy: 0.7898\n",
            "Epoch 18/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5341 - accuracy: 0.8028 - val_loss: 0.5746 - val_accuracy: 0.7959\n",
            "Epoch 19/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5305 - accuracy: 0.8036 - val_loss: 0.5861 - val_accuracy: 0.7897\n",
            "Epoch 20/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5271 - accuracy: 0.8071 - val_loss: 0.5726 - val_accuracy: 0.7970\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ODWXAlnWqri"
      },
      "source": [
        "Now we've got a model trained with a close-to-ideal learning rate and performing pretty well, we've got a couple of options.\n",
        "\n",
        "We could:\n",
        "* Evaluate its performance using other classification metrics (such as a [confusion matrix](https://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html#sphx-glr-auto-examples-model-selection-plot-confusion-matrix-py) or [classification report](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html)).\n",
        "* Assess some of its predictions (through visualizations).\n",
        "* Improve its accuracy (by training it for longer or changing the architecture).\n",
        "* Save and export it for use in an application.\n",
        "\n",
        "Let's go through the first two options.\n",
        "\n",
        "First we'll create a classification matrix to visualize its predictions across the different classes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jK4zA47sYVp5"
      },
      "source": [
        "# Note: The following confusion matrix code is a remix of Scikit-Learn's\n",
        "# plot_confusion_matrix function - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_confusion_matrix.html\n",
        "# and Made with ML's introductory notebook - https://github.com/GokuMohandas/MadeWithML/blob/main/notebooks/08_Neural_Networks.ipynb\n",
        "import itertools\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "# Our function needs a different name to sklearn's plot_confusion_matrix\n",
        "def make_confusion_matrix(y_true, y_pred, classes=None, figsize=(10, 10), text_size=15):\n",
        "  \"\"\"Makes a labelled confusion matrix comparing predictions and ground truth labels.\n",
        "\n",
        "  If classes is passed, confusion matrix will be labelled, if not, integer class values\n",
        "  will be used.\n",
        "\n",
        "  Args:\n",
        "    y_true: Array of truth labels (must be same shape as y_pred).\n",
        "    y_pred: Array of predicted labels (must be same shape as y_true).\n",
        "    classes: Array of class labels (e.g. string form). If `None`, integer labels are used.\n",
        "    figsize: Size of output figure (default=(10, 10)).\n",
        "    text_size: Size of output figure text (default=15).\n",
        "\n",
        "  Returns:\n",
        "    A labelled confusion matrix plot comparing y_true and y_pred.\n",
        "\n",
        "  Example usage:\n",
        "    make_confusion_matrix(y_true=test_labels, # ground truth test labels\n",
        "                          y_pred=y_preds, # predicted labels\n",
        "                          classes=class_names, # array of class label names\n",
        "                          figsize=(15, 15),\n",
        "                          text_size=10)\n",
        "  \"\"\"\n",
        "  # Create the confustion matrix\n",
        "  cm = confusion_matrix(y_true, y_pred)\n",
        "  cm_norm = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis] # normalize it\n",
        "  n_classes = cm.shape[0] # find the number of classes we're dealing with\n",
        "\n",
        "  # Plot the figure and make it pretty\n",
        "  fig, ax = plt.subplots(figsize=figsize)\n",
        "  cax = ax.matshow(cm, cmap=plt.cm.Blues) # colors will represent how 'correct' a class is, darker == better\n",
        "  fig.colorbar(cax)\n",
        "\n",
        "  # Are there a list of classes?\n",
        "  if classes:\n",
        "    labels = classes\n",
        "  else:\n",
        "    labels = np.arange(cm.shape[0])\n",
        "\n",
        "  # Label the axes\n",
        "  ax.set(title=\"Confusion Matrix\",\n",
        "         xlabel=\"Predicted label\",\n",
        "         ylabel=\"True label\",\n",
        "         xticks=np.arange(n_classes), # create enough axis slots for each class\n",
        "         yticks=np.arange(n_classes),\n",
        "         xticklabels=labels, # axes will labeled with class names (if they exist) or ints\n",
        "         yticklabels=labels)\n",
        "\n",
        "  # Make x-axis labels appear on bottom\n",
        "  ax.xaxis.set_label_position(\"bottom\")\n",
        "  ax.xaxis.tick_bottom()\n",
        "\n",
        "  # Set the threshold for different colors\n",
        "  threshold = (cm.max() + cm.min()) / 2.\n",
        "\n",
        "  # Plot the text on each cell\n",
        "  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "    plt.text(j, i, f\"{cm[i, j]} ({cm_norm[i, j]*100:.1f}%)\",\n",
        "             horizontalalignment=\"center\",\n",
        "             color=\"white\" if cm[i, j] > threshold else \"black\",\n",
        "             size=text_size)"
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mi5kNRyZdyXA"
      },
      "source": [
        "Since a confusion matrix compares the truth labels (`test_labels`) to the predicted labels, we have to make some predictions with our model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HhxrXhrjbjja",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a3caac74-fdaf-4d21-8d50-01a1ca050d52"
      },
      "source": [
        "# Make predictions with the most recent model\n",
        "y_probs = model_14.predict(test_data) # \"probs\" is short for probabilities\n",
        "\n",
        "# View the first 5 predictions\n",
        "y_probs[:5]"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 1ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[7.1295396e-08, 0.0000000e+00, 3.3289107e-12, 5.1906879e-20,\n",
              "        2.2590930e-15, 2.9898265e-01, 1.3648585e-07, 7.8765094e-02,\n",
              "        8.4200781e-03, 6.1383194e-01],\n",
              "       [2.3802532e-02, 4.3833437e-03, 7.4165797e-01, 9.3923630e-03,\n",
              "        4.8104558e-02, 1.6303338e-03, 1.6198176e-01, 2.8633869e-07,\n",
              "        9.0436097e-03, 3.2003202e-06],\n",
              "       [1.6573335e-06, 9.9551845e-01, 9.1274740e-07, 4.4626528e-03,\n",
              "        1.4068096e-05, 4.6464342e-16, 2.2328247e-06, 6.8920781e-22,\n",
              "        2.8975861e-10, 3.0581186e-15],\n",
              "       [2.8563186e-08, 9.9863142e-01, 4.3472301e-09, 1.3678216e-03,\n",
              "        6.5454623e-07, 6.7064722e-21, 3.1206884e-08, 3.7914244e-28,\n",
              "        6.2471518e-13, 5.6674063e-19],\n",
              "       [2.8196907e-01, 5.4220832e-06, 4.3520968e-02, 3.0695686e-02,\n",
              "        2.1239575e-02, 2.4910512e-05, 5.9459400e-01, 1.5045735e-09,\n",
              "        2.7928762e-02, 2.1589773e-05]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eP5zslXbf5ZY"
      },
      "source": [
        "Our model outputs a list of **prediction probabilities**, meaning, it outputs a number for how likely it thinks a particular class is to be the label.\n",
        "\n",
        "The higher the number in the prediction probabilities list, the more likely the model believes that is the right class.\n",
        "\n",
        "To find the highest value we can use the [`argmax()`](https://numpy.org/doc/stable/reference/generated/numpy.argmax.html) method."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hQvrJbbWf4Es",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c79be721-21f7-49d4-accc-33d280fd03a6"
      },
      "source": [
        "# See the predicted class number and label for the first example\n",
        "y_probs[0].argmax(), class_names[y_probs[0].argmax()]"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9, 'Ankle boot')"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BFPgnBCghrTz"
      },
      "source": [
        "Now let's do the same for all of the predictions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ozUpeZU6g2An",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a9e1bca8-50fe-4973-dc4b-df5f67c874e7"
      },
      "source": [
        "# Convert all of the predictions from probabilities to labels\n",
        "y_preds = y_probs.argmax(axis=1)\n",
        "\n",
        "# View the first 10 prediction labels\n",
        "y_preds[:10]"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 2, 1, 1, 6, 1, 4, 4, 5, 7])"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "47g1wNk6iYTd"
      },
      "source": [
        "Wonderful, now we've got our model's predictions in label form, let's create a confusion matrix to view them against the truth labels."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FBMSVSRqcU_m",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8bdcc733-796d-4bdb-a5c8-62086c85eb01"
      },
      "source": [
        "# Check out the non-prettified confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "confusion_matrix(y_true=test_labels,\n",
        "                 y_pred=y_preds)"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[759,   0,  30,  71,   6,   1, 124,   0,   8,   1],\n",
              "       [  0, 939,  11,  40,   5,   0,   5,   0,   0,   0],\n",
              "       [ 21,   2, 730,   9, 176,   0,  59,   0,   3,   0],\n",
              "       [ 38,  11,  17, 839,  41,   0,  50,   0,   4,   0],\n",
              "       [  0,   0, 125,  28, 805,   0,  38,   0,   4,   0],\n",
              "       [  1,   0,   0,   0,   0, 901,   1,  55,  11,  31],\n",
              "       [177,   2, 185,  42, 321,   0, 264,   0,   9,   0],\n",
              "       [  0,   0,   0,   0,   0,  50,   0, 917,   0,  33],\n",
              "       [  5,   0,   8,   3,  16,  17,  49,   4, 897,   1],\n",
              "       [  0,   0,   0,   0,   0,  21,   0,  49,  11, 919]])"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ql-TQxuYiqPl"
      },
      "source": [
        "That confusion matrix is hard to comprehend, let's make it prettier using the function we created before."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DLr6daZAbzRi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c545686e-2ad3-4cb7-e257-a30b2d5762da"
      },
      "source": [
        "# Make a prettier confusion matrix\n",
        "make_confusion_matrix(y_true=test_labels,\n",
        "                      y_pred=y_preds,\n",
        "                      classes=class_names,\n",
        "                      figsize=(15, 15),\n",
        "                      text_size=10)"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 2 Axes>"
            ],
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79PyN7j99bdxKZHbRNc9n2nfhgUm6Vz7+Juk+DO5UyplUZ297a+y6s7p2/7n2X3ygwb+fVJMiKRX7o5lNH6qcM4l2nfXUG7/3vwI8e+VrUk4W13iyixVdS/aa/qLo7x9gks7ruU+Y7ayRJ5kmbjivbafv6cbDF5q384q2nb6n1iXTSpKSJYyjZ698tebwLZ24/lh7z99XGuegD4JVciaRr3+ANhwL+cPnlhN3VTln0jDL/68mTRinJk2bq2HjJkqfIYMmT52umLFiaf68OaGec+zoUV29ckVlypYz7jt/7py2bN6kqTNmKWeuXMqXP7/GTZisFcuW6u7dkH/F9vb21ry5szXql3EqXKSosmbLppmz5urA/n06eOCAJOnC+XOqUbO2UqdJo6bNWuj8+XOSgn4xb9+mlSb9Ot34q/aHMmTMKGcXF61dveq/dM9/VqZcBZUsXVYpU6VWqtRp1G/QUMWOE0eHDx2UFNQHC+fP0bBRY1SocFFlyZpNv86YrYMH9uvwoQOh5rt65Qplcsssl0SJjPtck6dQ/YaN5eaeWUmTJlPZ8hVUo1Yd7d+3J9R8tv+1RefPndXM2QvknjmLSpQqoz79BmnWzGny8Qm69i9eOKdGTZopVeo0ql6jti6ePy9Jun7tqhbOn6t+A4eEmHfpcuV1/NgRXbt65bP77b+wt08oB0cn47Z1859yTZ5SefMXNKZp0bq92nXurqw5coWRk6ltWzYqenRrZfvgnJgxY5qUZREtmvbs3qG6DRqHmVf33gPUsk0Hpc+QKcTjly6cV+WqNZUyVRrVb9xUly4G9bmvr6+6d2qj0eN/DfG6T5c+oxydXLRx/Zpwt+tbGffLKCVOnEQzZ81Rjhw55Zo8uYqXKKkUKUMP1D948EA7d2xX2XLvf9319vbW/LlzNGr02KBxIms2zfhtjg7s36dDB0P/PzKwf1+VKl1Ww0aOVhYPD6VImVLlK1SUg0NQsPHC+XMqWLCwsmXLrpq168jGxkbX384w6dOru5q3bKUkSYOPv9GiRVOp0mX0+/JlX9o130yp0mU0cPBQVapcJdznvOvzch/1+afG5pDUrd9Avfv2V9FixUM8fv78ORUoVFjZsmdXrXd9fu1tn/fsruYtflbSUPu8rFYsWxrudn2JwsVKqUuvgSpVrlKIx+Pa2GrB7+tVrlI1pUiVRh7Zc2rgiHH65+Rx3b1t+oPdzm2btWfnNvUaOCLEvD62fvUK5S9U1Bh0laTESZOp/7AxqlqrnuLa2IZ43vjpc1X/p5bK4JZZKVOn1Yjx0xQYEKB9u3d+ssy+3dqrQrWa8shu+qPErRvXFDeujcpXri53j+zKna+gLl+8IEla+8dyWVlZqdQHwbcPFS1ZVutXrwhXm78Un1si3oH9+1WuQkWVLltOyVxdVaVqdRUrXlJHjoQ+K/DBgwfatTOE8XzeHI18O557ZM2m6TM/PZ4fOLBf9Ro0VMFChZXM1VU/NWshN/fMOnIkaIbe+bfjedZs2VWzVh3FtbHRjbfjed9e3dWsRdjj+YoVjOcfM/fxHIhIBLV+EFbRLFQ9dzIt3nM92LFquZPq3IRK2jW4lPpUdVPM6O/f6K0tLfTG13Rtidc+/ooZ3VKZk4X+a3/uNAl18vrjMOtUN38K7T7nqduPXprsT+4YV6fGVtDhkWU1rXkuJYofK8x8olta6HUIdcyZ2l6SdNXzmWJGt1SmpHayix1dHsnj6+ztJ7KNZaWelTOp16JjoeZ97JqXEsWPpSQJwq7Dl/Lx8dHxY0dN3rAsLCxUtGhxHTqwP9Tz9u75W6nTpDHe9iZJBw/sl52dnbJlz27cV7RYcVlYWBgDOB87fuyofH19TcpPmy6dkiRNqoNvy3dzz6ydO7fLz89PW7dsVia3oFl648aMNr6ZhiZ7jpzau+fvT/RCxPH399fKFcv08sUL5cyVW5J04nhQHxQqUsyYLk3adEqcJGmYH/D27d0jj6zZQj0uSVevXNa2rVuU74NgzscOHTygDBnd5ODoaNxXtERJPX361DibLJObu3Zs/0t+fn7atXO7MroFzRjo1L61Bg0daXIdfChJkqRycHDUvr2hB9W+NR8fH61ctlh16jf6z/fpH9y/V+5ZsoaZZsWS/ylmrFgqXynsX50/JWMmN+3ZvUN+fn7auW2rMrydpfHrhDHKm7+QsoTx2ntky64D+yOvz9/ZsH6dsmbLpnq1aypZIkflzpFVc2aHPYNj3949ihUrltKlT2/c926cKBLGOPGxgIAAbdq4QalSp1bFcqWVLJGjCubLbXL7hpt7Zh07dkSPHz/WsWNH9erVK6VMmUr79u7RiePH1bpt8Jlw72TPkVN7934/Y8t/EVafhzU2fwl398w6dvRtnx992+epUmnvnj06fvyY2rQzvz5/9vSpDAaD4tq+Dzo9vO+pPp3baMyvsxQzZvjevw8f2Ce3zGGPL+Hx6tVL+fr5yi5e2DMif1+yQLduXFP7rn2CHXNNkUqvX73SmdMn9OSxl04fP6p0GTLJ+8ljjR85WANGhDyTTZIyZ82ue3fv6PbNG/+5LSHhc0vkyJ0nj3bu2K5LFy9Kkk6dOql9+/aoZKnQl+jYv+/t2JIuhPG86OeNLblz59GG9et0984dBQYGatfOHbp86aKKFS8p6e14/nZsOX7sqF6/eqUU78bzE58Yz7Pn1L7vcGz5EoznQOQgqPWDKOPhIttYVlq6z3RW1B8Hb6rNbwdV9ZedmrThnGrkSaapzd7Pgthx5p5ypEqgKjmTyMJgkJNdTHWpmFGS5GgbQ6FJnCCW7j15FepxR7sYKubmpP/tvmqy/+jVR2o/55Bqj9+t7guPKql9bK3tWSTMWWE7/rmnViXTKLlDHBkMUqEMjiqbNZGxft4vfdVu9iFNaZpTm/sU0/J9N7TjjKcG1sys2dsvK2nC2No2oIR2DS6l8tkSm+T9rg2JE3ybdbUePnwof39/OTg4mux3cHTUvXv3Qj3v5s0bcnZ2Mdnn6XlPCR1Mb7eytLRU/Pjx5RlKXvfu3VP06NFlZ2dnWr6Dozw9g87p2r2nLC0tlSFtSq1ds0rTZ87W5UuX9L+F89WrTz+1a91K6dOkUL06NeXt7W2Sj7Ozi25+ow/Wn+PMP6eVKKGtHOxiqVP71vrf0t+VLn0GSdJ9T89Q+sBB9z1Dfw1u3bwpJ+eQb2spWSS/HOPFVla3dMqTN7969x8Uaj73PT3l4Gj6ur27Ht6VP2nqTK1ZtVJZMqZR9OjR1alrTy1d/D/FjBlLWbNlV9WKZeSRKa2GDuwXLH8nZxfdisTXYOP6NfL2fqJa9Rr+57xu37ohp0/cSrR44VxVqV5bMWPG/E9ltevUXZaWlsqVJZ3+XL9G46bM0NUrl7R8yUJ16t5b3Tu2UU73tGreqI6efnTdOzm76Patm6HkHHGuXbuq32ZMV8pUqbRm/SY1b9lKXTt10P8WzA/1nFs3b8jB0dHktgnPsMaJUMaW+/fv6/nz5xr7yyiVKFlKazdsVsVKlVWnZjX9vXuXJKlEyVKqXaeeCuTNqZZNm2jm7HmKHTu2OrRtrUm/TtPMGdOUOWM6FS2UX2fPnDHJ39nZRbdv3YoSa4LcvBG8z8MzNn+JEiVLqU7d+sqfJ4daNG2s3+bMf9vnP2vyr9M1c/o0uWdMqyIF8wXvc5fvr8/fvH6tUUP6qkKVmsZb8QIDA9WtfQvVadRM7lnC/uHhQ3du3wz3rYphGT24rxwdnZWvYNFQ01y7elmjh/TXuKlzZGkZ/PONrV08jZ48U13bNleVUgVVpWZdFSxaQiMG9lLDpq10++Z1VSiaW6ULZtfGdaazit614c7tbzMG8bklcnTp1lPVa9SSh3t62caOrrw5s6pNuw6qXadeqOfcvHFDDg4fjeeeXza2jJ0wWenSZ1DqFElkF8dalSuU0biJU5S/QNCPdiVKllLtuvVUMG9OtWj2wXjerrUmTZmm32ZMU5ZM6VSscH6dPct4LjGem6vIXsSdheJDFnqkwIwNHDhQq1ev1okQFtR7p3DhwsqSJYsmTJgQYfWKTPUKpNC20/fk+eS1yf6FHwSVzt3xlqf3a/3RrbBcE8bW9QcvtPOMpwatOKVfGmTTr81y6Y1fgMatO6s8aRIqIIy1KWNYRQs2e+pDtfK6yvulrzYeN51evv2f94P72dveOnrVS8dGl1Ol7Em0eE/IC5/2XXJC4xpn175hpRUYKF1/8FxL915XnfyuxjR/Hr+jP4+/v8UwT5qEypDYTr0XH9fBEWXVcsYB3fd+rc19i+nAxQd6+OyNJBnb8OHste/B61evFCNG6EHFr8nW1lbzF5ouNl66RFENH/mLli5epGvXrurUmQtq3bK5hg8drFG/vF98NWbMmHr58uXHWUa41GnS6u8DR/XU21trVq/Uzy1+0obN242BrS/x+nXor8GchUv0/Nkz/XP6lPr37qHJE8aqQ+fgCwyHV/oMGfXnlh3Gv70ePdKIoYP055Yd6t6lg3LmyqOFS35X0QK5lS1HTpX5YNp7zJgx9OpV5L0GSxbOU9ESpeT00ZeZL/H61WtZO4d+3R85dECXLpzXlBnz/nNZNra2mjZ7ocm+auVLqv+Qkfpj+RLduH5Ne4/+oy7tW2ns6GEaNGy0MV2MGDH06ju47gMCApQ1W3YNHjpckpTFw0Nnz/yjWb/NUP2GjUI859WrV4ph/d/HlncflMtXqKR2HTpJkjJnyaID+/dr1swZKlCwkCSpb/+B6tt/oPG8YUMGqUixYrKytNKoEcN0+NgpbfxzvZr91Ej7Dr5f4D5mzJgKCAjQmzdv/nMAM7KFNZZ8CyH3eXFZWVlp1IihOnz8tDZuWK9mTRpq36GjxnTfW5/7+vqqXfP6UmCgBv8y0bh//qxpevH8uX7u8Hlj7pvXr0xuPfwS0yeN0frVv2vxqk2h5uXv769OrRqrY/c+Sp4ydah5lSpXyeQ2zIP7/tb5s/9owPBxKporkybMmK+EDo6qUqqgcuTOJ/u3awjGiBH02kTmuB8SPrf8Nyt/X65lSxdr7oJFSp8ho06dPKEeXTvJ2dlF9RuEMp5/xbFl2q+TdfjgAa1YuUZJkiXT3r93q3OHtnJ2djHOQOrTb6D69BtoPGf40EEqUrSYLK2sNGrkMB06GjSeN/+pkfYeeD+ex/jOxpb/gvEciBzfxUytT0ULBw4c+NXL/OOPPzRkSMjr0Lxz/fp1GQyGUINjgwYNUv369SUFtWH16tVfuZZfR+IEsVQwg4MW/X31k2mPXX0kSUruEMe4b/qWi0rVbrU8uq9X+g5rtOlEUHDoxoPQn07j9fyN7GJFD/V43fzJtWL/Dfn6h/0LwdNXvrri+dykPh979PyNGk3ZK9ef/1DW7huUt88mvXjjpxsPXoSYPrqlhUbVz6quC44ouUMcRbMwaP/FB7ri+UxXPJ8ra4r4xrTxYge14dHbINfXZm9vr2jRoun+fdMnoNz39JSTk1MoZ0kJ7O31+LHp7Z2Ojk568NEirX5+fvLy8pJjKHk5OTnJx8dHTz568tz9+55ydAz5nAXz5srWzk4VKlbS7t07VaFiZVlZWalq9Rr6e9dOk7SPvbyUMGHwJ8tFtOjRoytFylTKkjWbBgwerkxu7pr+dnFVB0fHUPrgvhxC6QNJSpAggZ48DvkW28SJkyhd+gyqXrO2BgwZrpHDBof6iHgHR0fd9zR93d5dD6GV37tnV/3ctr0SJU6sPX/vUuWq1RU7dmyVLF1Ge/7eZZL28ePHsrePnNfg1s0b2r1zm+o1/Omr5Bc/QQJ5Pwn9tuZF8+cok1tmZfb477cQfWzJ/+bL1tZWpctV1L6/d6l0uYqysrJShcrVtD+EPk8QSX3+ISdnZ5NbICQpbbr0uhXGLLIE9vZ6/FEfO4Y1ToQyttjb28vS0jJY+enSpQu1/Avnz2vpkkXqP3CI/t69U/nzF1TChAlVrXpNnTh+TM+ePTOmfezlpdixY0eJD+MJEgQfz79kbP4SF86f15LF/9OAQUO0e9dO5Svwts9r1NTxj/rc6zvqc19fX7VrVl93bt3S/BXrTRZM3//3Th0/clDpE9spjXNcFc0VtG5e5ZL51bVt81BylOLFD3t8+ZTffp2g6ZPGat7ytUoXyqLykvTi+TOdPnFMA3t1VhrnuErjHFeTx47QuTOnlcY5rvb9vTPYOW/evNGAHh01dMxk3bh2RX7+/sqVt4BSpEqj5ClTmTxt0ftx0CLn8RPYf3FbwsLnlsjRp1d3denaQzVq1lamTG6qW6+B2rbvqLGjR4Z6jn2CEMZzx8/vv1evXmlg/z4aOXqsypavIDc3d7Vq3VbVatTUxI+eIvnOhfPntXTx2/F81yfG88ffz9jyXzGeA5Hjuwhq/fvvv8ZtwoQJsrGxMdnXtWvXr15m/PjxQ12HRpJxgeawrFmzRhUrBn887femTr7kevj0jbae+veTad89MdDT+3WwY55PXuu1r7+q5kyq249e6NSNJ6Hmc/rmE6VxsQnxWN60CZXCMa4W7/l0kC22taVcHWKHWJ+PvfEL0L0nr2QZzaDyWRNp04mQFxntVD6DdvxzT6dvPpGFhUGW0d5Ps7SKZlA0i/d/p0tkKx8/f124+zSkrP6z6NGjyyNrNu3Yvs24LyAgQDt2bFPO3HlCPS9zFg9dvHDe5FHOuXLn0ZMnT3Ts6PtfYnbu2K6AgADlyBnyYtweWbPJysrKpPyLFy7o1s2byhVC+Q8ePNDwYYM1bkJQQCjA31++vr6Sgr5kfBy4OXPmH2XO4hFWF0SKgIAAvfEJClRm8Qjqg1073z9l89LFC7p966Zx3a2QuGf2MC4++6myfH19Q53inTNXbp09c9rkg/3ObX/JxsYmxJlku3Zs08Xz59SiVZug/E1eAz/5fxAofv36ta5dvSL3zFk+Wc9vYemi+bJP6KDipcp+lfwyuWfRxQsh9/mL58+1dvXvqvPBkxG/locPH2jcqGEa9ssESZJ/gL/8/IL63M/XV/4Bptf9hXNn5Oae+avX43PlyZPPuP7KO5cvXVTSpMlCPSdzFg953rtn8qH83TixM5zjhBQ0tmXLniNY+ZcuXQqx/MDAQLVr00ojR49VnDhx5P/R2CLJZHz5XseWLxFWn4d3bP4SgYGBatu6pUb9Mi5cfX72O+nzdwGt69euaMHv6xUvfgKT4wOGj9X6HQe1bvsBrdt+QLMXB92eN2nmQnXpPSCkLCVJGdwy6/LbB0J8rhmTx2nKuJGau3TNJ295jBPXRn/uOmys37rtB1S3UTOlSJVG67YfUJasOYKd8+v4kSpYpIQyuXvIP8Bf/n7vH+jj99F774XzZ2VlZaU0ab98JnJY+NwSOV69fGlyS5skWUSLFubtY2GO5zvCP7b4+vrK19dXho/Kj2YRcvmBgYFq3zb84/n3MrZ8DYznQOT4LoJaTk5Oxs3W1lYGg8FkX5w4wWfp7Ny5Uzlz5lTs2LFlZ2enfPny6cYN03vgFy5cKFdXV9na2qp27domEerChQurY8eOxr9dXV01ZMgQNWzYUDY2NmrRooWSJ08uSfLw8JDBYFDhwoWN6W/duqUzZ86odOnScnV1lSRVqVJFBoPB+LckTZs2TSlTplT06NGVNm1aLVxoejuLwWDQtGnTVKZMGcWMGVMpUqTQ77///oU9GZzBINXO76pl+67L/6P7BV0Txlbn8hnkniyekiSIpVKZXTSlaS7tu3BfZ2+/X2OgTam0Sp/IVmldbNS5fAa1K5tOfZYcV0Bg6Pcf7vjnnnKlDvlXwnoFkuvIlUc6fyd4oGhgzczKkyahkiSIpRwpE2hem7zyDwjUqoPvf9mf0jSn+lR9/yto1uTxVS5rIiWzj61cqe21tFNBWVgYNGVj8A+naZxtVDlHEo1a/Y8k6fK/zxQQEDRzrLi7s1I52+j4tfdvRLlTJ9SBSw/DvJXyv2rfsbPmzv5N/1swX+fPnVP7Nj/r5YsXatgo9C/nhQoX0fPnz03ukU+XPr1KliqtNq2a6/ChQ9q3d686dWirGrVqy8Ul6NavO3fuKHOmdDp8KOhpNba2tmrcpKl6dOusXTt36NjRo2rRrIly5c6jXLmDB3S6de6oDh27KNHbJ/7lzptPSxYt1Plz5zRn1kzlyZvPmPbly5c6fuyoipUo+VX66UsN6t9be/fs1o0b13Xmn9Ma1L+39uzepZq16kgK6oMGjX5Snx5dtXvXDp04dlRtWjZVzly5lSNn6EGtosVL6PChAyYfEpYvXaxVK1fowvlzun7tqlatXKHB/fuoavWasrKykiStW7NaObJk/CCfkkqXPoNaNmuk06dOatvWzRo6uL+atfhZ1tbWJmW+fv1a3Tp30IQp040fbnPlzqtZM6bp9KmTWrf6D+XOk9eY/vChA7K2tlaOXF/nQ9PnCAgI0NJFC1SzTv0Q14y573lP/5w6oetvn8x47uw/+ufUCT0O41HqRYqV0IVzZ0OcIbfmjxXy9/NT9Zp1gx07dvSw8mfPpH/vvr8F+fatm/rn1AnduX1T/v7++ufUCf1z6oRePA8+A7V/zy5q1a6jnF2CrvscufJqxdJFunjhnBbOm60cud73+cuXL3XqxDEVKloijN6JGG07dNShgwc0euRwXbl8WcuWLNacWb+pZavWoZ6TJYuH7O3ttX/fXuM+W1tbNWryk3p07xI0Thw7qpbNf1Ku3HlMAr9ZMqXXmg+eGtaxc1f9vmKZ5sz+TVcuX9a0qVP054Z1atHq52Dlzp0zS/b2CVWufNCts7nz5tOundt16OABTZ44XunTZzBZj2Tf3j0qVjzy+/hjz58/18kTJ3Ty7Qzv69eu6eSJE7p5M/TZcVk8Qu7z8IzNmTOlM+lzLy8vnTxxQufOnZUkXbx4QSdPnAhxraO5s2fJPuH7Ps+TN5927diugwfe9nkG0z7fu+dvFf/G4/mL58919vRJnT19UpJ0++YNnT190vhkQ19fX7VtWlenTx7T+KlzFODvrwee9/TA857xx0iXxEmUNn1G4/buFr+krsnl7JI45IIlFShSQkcO7gu2/119Xrx4Lq+HD3X29Eld+iC4PmPSWE0YNVijJkxX4iRJjfX5cCz5ZWh/dWnTTFLQouof1i9t+oxKYJ9Q1tbWSps+o2LFNl3D89KFc9qweqU69ghaMzFlqrSysLDQ8kXztGPrRl25fFHuHu8DaUcO7FX23PkU4xvOwOBzS8QrU66CRo8ark1/btCN69e1ds0qTZk4XhUqVQ71nMxZPJTA3l4HPh7PG/+knm/H8+PHjqpVi+DjuYdbeq1dEzS22NjYqEDBQurTq7t279qp69euaeGCeVq8aKEqhlD+vLfjedkPx5a34/mUSYznP8p4HmUZfoDNDJnlmlp+fn6qXLmymjdvriVLlsjHx0eHDh0yWdjsypUrWr16tdavX6/Hjx+rZs2aGjlypIYNGxZqvmPGjFH//v01YEDQL3lt2rRRzpw59ddffyljxoyKHv397XRr165V4cKFZWNjo8OHD8vBwUFz585V6dKljY8JXrVqlTp06KAJEyaoePHiWr9+vZo0aaLEiROrSJEixrz69eunkSNHauLEiVq4cKFq166t06dPK/1Ht2288+bNG7158/52uKdPQ59FVCiDo5IkiB3ielQ+fgEqmMFBLUqkVixrS931eqn1R29r3PqzJumKujmpY/n0im5poTO3vNVw8l6Tta9CsvLgTfWv4a6UjnF1xfN9MDFuTCuVy5pYfZeeCPE853gxNaNlbsWLHV2Pnr3RwcsPVXbYNj16/r69ieLHMgmoWVtFU88qmZQsYRy9eO2nbaf/VZtZB/X0lW+w/Mc2yqb+y07opU9QIOK1r7/azzmkkfWzytrSQr0WHTNZ4L5yziT6Ze2ZYPl8TTVq1tLDBw80eFB/ed67J/fMWbRm/SY5OjqGek6CBAlUsXIVLV2ySEOGvX9c+dwFi9SpQ1uVLVVMFhYWqlylmsZOmGQ87ufrq4sXLpistTF67Hh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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tH3FhuGEjTu9"
      },
      "source": [
        "That looks much better! (one of my favourites sights in the world is a confusion matrix with dark squares down the diagonal)\n",
        "\n",
        "Except the results aren't as good as they could be...\n",
        "\n",
        "It looks like our model is getting confused between the `Shirt` and `T-shirt/top` classes (e.g. predicting `Shirt` when it's actually a `T-shirt/top`).\n",
        "\n",
        "> 🤔 **Question:** Does it make sense that our model is getting confused between the `Shirt` and `T-shirt/top` classes? Why do you think this might be? What's one way you could investigate?\n",
        "\n",
        "We've seen how our models predictions line up to the truth labels using a confusion matrix, but how about we visualize some?\n",
        "\n",
        "Let's create a function to plot a random image along with its prediction.\n",
        "\n",
        "> 🔑 **Note:** Often when working with images and other forms of visual data, it's a good idea to visualize as much as possible to develop a further understanding of the data and the outputs of your model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2XIuAjgJri9e"
      },
      "source": [
        "import random\n",
        "\n",
        "# Create a function for plotting a random image along with its prediction\n",
        "def plot_random_image(model, images, true_labels, classes):\n",
        "  \"\"\"Picks a random image, plots it and labels it with a predicted and truth label.\n",
        "\n",
        "  Args:\n",
        "    model: a trained model (trained on data similar to what's in images).\n",
        "    images: a set of random images (in tensor form).\n",
        "    true_labels: array of ground truth labels for images.\n",
        "    classes: array of class names for images.\n",
        "\n",
        "  Returns:\n",
        "    A plot of a random image from `images` with a predicted class label from `model`\n",
        "    as well as the truth class label from `true_labels`.\n",
        "  \"\"\"\n",
        "  # Setup random integer\n",
        "  i = random.randint(0, len(images))\n",
        "\n",
        "  # Create predictions and targets\n",
        "  target_image = images[i]\n",
        "  pred_probs = model.predict(target_image.reshape(1, 28, 28)) # have to reshape to get into right size for model\n",
        "  pred_label = classes[pred_probs.argmax()]\n",
        "  true_label = classes[true_labels[i]]\n",
        "\n",
        "  # Plot the target image\n",
        "  plt.imshow(target_image, cmap=plt.cm.binary)\n",
        "\n",
        "  # Change the color of the titles depending on if the prediction is right or wrong\n",
        "  if pred_label == true_label:\n",
        "    color = \"green\"\n",
        "  else:\n",
        "    color = \"red\"\n",
        "\n",
        "  # Add xlabel information (prediction/true label)\n",
        "  plt.xlabel(\"Pred: {} {:2.0f}% (True: {})\".format(pred_label,\n",
        "                                                   100*tf.reduce_max(pred_probs),\n",
        "                                                   true_label),\n",
        "             color=color) # set the color to green or red"
      ],
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RAAIrpcEumyE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "outputId": "42b7d0e2-176b-4539-968e-bcf7e10d33b7"
      },
      "source": [
        "# Check out a random image as well as its prediction\n",
        "plot_random_image(model=model_14,\n",
        "                  images=test_data,\n",
        "                  true_labels=test_labels,\n",
        "                  classes=class_names)"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 22ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FNebpPbCw52S"
      },
      "source": [
        "After running the cell above a few times you'll start to get a visual understanding of the relationship between the model's predictions and the true labels.\n",
        "\n",
        "Did you figure out which predictions the model gets confused on?\n",
        "\n",
        "It seems to mix up classes which are similar, for example, `Sneaker` with `Ankle boot`.\n",
        "\n",
        "Looking at the images, you can see how this might be the case.\n",
        "\n",
        "The overall shape of a `Sneaker` and an `Ankle Boot` are similar.\n",
        "\n",
        "The overall shape might be one of the patterns the model has learned and so therefore when two images have a similar shape, their predictions get mixed up."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pOwM1rqhx6p3"
      },
      "source": [
        "### What patterns is our model learning?\n",
        "\n",
        "We've been talking a lot about how a neural network finds patterns in numbers, but what exactly do these patterns look like?\n",
        "\n",
        "Let's crack open one of our models and find out.\n",
        "\n",
        "First, we'll get a list of layers in our most recent model (`model_14`) using the `layers` attribute."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kcwMsgFuySTi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "846f6504-0cd9-4c1b-d4ff-ebcb336b819a"
      },
      "source": [
        "# Find the layers of our most recent model\n",
        "model_14.layers"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<keras.src.layers.reshaping.flatten.Flatten at 0x7a2407e038b0>,\n",
              " <keras.src.layers.core.dense.Dense at 0x7a2407e02260>,\n",
              " <keras.src.layers.core.dense.Dense at 0x7a2407e02f50>,\n",
              " <keras.src.layers.core.dense.Dense at 0x7a2407e03070>]"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w9si0o-h4oO1"
      },
      "source": [
        "We can access a target layer using indexing."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DXuQmsNX1mGR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d2567dc-0553-4baa-d066-2d199203fb2b"
      },
      "source": [
        "# Extract a particular layer\n",
        "model_14.layers[1]"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.layers.core.dense.Dense at 0x7a2407e02260>"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W6Cftaib4uG-"
      },
      "source": [
        "And we can find the patterns learned by a particular layer using the `get_weights()` method.\n",
        "\n",
        "The `get_weights()` method returns the **weights** (also known as a weights matrix) and biases (also known as a bias vector) of a particular layer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WdmZy5xi1srE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46f2e539-df59-4c48-aa81-318ca44f2e63"
      },
      "source": [
        "# Get the patterns of a layer in our network\n",
        "weights, biases = model_14.layers[1].get_weights()\n",
        "\n",
        "# Shape = 1 weight matrix the size of our input data (28x28) per neuron (4)\n",
        "weights, weights.shape"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[ 0.41736275,  0.16016883,  0.32807097,  0.51932573],\n",
              "        [ 0.38906115, -0.11863507, -0.98573697,  0.45491368],\n",
              "        [ 0.18559982, -1.1637362 , -0.4268363 , -0.01036255],\n",
              "        ...,\n",
              "        [-0.11902154, -0.19244409, -0.49631563, -0.086859  ],\n",
              "        [-0.18131663,  0.0480358 ,  0.13416374, -0.32421213],\n",
              "        [ 0.37639815, -0.5836524 ,  0.3026454 ,  0.4631417 ]],\n",
              "       dtype=float32),\n",
              " (784, 4))"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yCjuNSD73oG0"
      },
      "source": [
        "The weights matrix is the same shape as the input data, which in our case is 784 (28x28 pixels). And there's a copy of the weights matrix for each neuron the in the selected layer (our selected layer has 4 neurons).\n",
        "\n",
        "Each value in the weights matrix corresponds to how a particular value in the input data influences the network's decisions.\n",
        "\n",
        "These values start out as random numbers (they're set by the [`kernel_initializer` parameter](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense) when creating a layer, the default is [`\"glorot_uniform\"`](https://www.tensorflow.org/api_docs/python/tf/keras/initializers/GlorotUniform)) and are then updated to better representative values of the data (non-random) by the neural network during training.\n",
        "\n",
        "![neural network supervised learning weight updates](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/02-fashion-mnist-learning.png)\n",
        "*Example workflow of how a supervised neural network starts with random weights and updates them to better represent the data by looking at examples of ideal outputs.*\n",
        "\n",
        "Now let's check out the bias vector."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ndG-h2yz1z2_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "39194e99-0f2d-408c-d2cc-109879326b0c"
      },
      "source": [
        "# Shape = 1 bias per neuron (we use 4 neurons in the first layer)\n",
        "biases, biases.shape"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1.2710664 , 1.8170385 , 0.24927875, 1.4434327 ], dtype=float32), (4,))"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3JRQFh3U374U"
      },
      "source": [
        "Every neuron has a bias vector. Each of these is paired with a weight matrix.\n",
        "\n",
        "The bias values get initialized as zeroes by default (using the [`bias_initializer` parameter](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense)).\n",
        "\n",
        "The bias vector dictates how much the patterns within the corresponding weights matrix should influence the next layer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_QCUb7GeSGYF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2ee22d59-fa12-4c8d-eca7-4c2b2c0eec21"
      },
      "source": [
        "# Can now calculate the number of paramters in our model\n",
        "model_14.summary()"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_14\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten_3 (Flatten)         (None, 784)               0         \n",
            "                                                                 \n",
            " dense_37 (Dense)            (None, 4)                 3140      \n",
            "                                                                 \n",
            " dense_38 (Dense)            (None, 4)                 20        \n",
            "                                                                 \n",
            " dense_39 (Dense)            (None, 10)                50        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3210 (12.54 KB)\n",
            "Trainable params: 3210 (12.54 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "trRRZLIRyLXe"
      },
      "source": [
        "Now we've built a few deep learning models, it's a good time to point out the whole concept of inputs and outputs not only relates to a model as a whole but to *every* layer within a model.\n",
        "\n",
        "You might've already guessed this, but starting from the input layer, each subsequent layer's input is the output of the previous layer.\n",
        "\n",
        "We can see this clearly using the utility [`plot_model()`](https://www.tensorflow.org/api_docs/python/tf/keras/utils/plot_model)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YJD0GqGl3NY0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "outputId": "9077f7d2-5e2f-4025-fd93-fb28d179398b"
      },
      "source": [
        "from tensorflow.keras.utils import plot_model\n",
        "\n",
        "# See the inputs and outputs of each layer\n",
        "plot_model(model_14, show_shapes=True)"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OY5HO72ATJR4"
      },
      "source": [
        "## How a model learns (in brief)\n",
        "\n",
        "Alright, we've trained a bunch of models, but we've never really discussed what's going on under the hood. So how exactly does a model learn?\n",
        "\n",
        "A model learns by updating and improving its weight matrices and biases values every epoch (in our case, when we call the `fit()` fucntion).\n",
        "\n",
        "It does so by comparing the patterns its learned between the data and labels to the actual labels.\n",
        "\n",
        "If the current patterns (weight matrices and bias values) don't result in a desirable decrease in the loss function (higher loss means worse predictions), the optimizer tries to steer the model to update its patterns in the right way (using the real labels as a reference).\n",
        "\n",
        "This process of using the real labels as a reference to improve the model's predictions is called [**backpropagation**](https://en.wikipedia.org/wiki/Backpropagation).\n",
        "\n",
        "In other words, data and labels pass through a model (**forward pass**) and it attempts to learn the relationship between the data and labels.\n",
        "\n",
        "And if this learned relationship isn't close to the actual relationship or it could be improved, the model does so by going back through itself (**backward pass**) and tweaking its weights matrices and bias values to better represent the data.\n",
        "\n",
        "If all of this sounds confusing (and it's fine if it does, the above is a very succinct description), check out the resources in the extra-curriculum section for more."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LXmMG6fcpoIp"
      },
      "source": [
        "## Exercises 🛠\n",
        "\n",
        "1. Play with neural networks in the [TensorFlow Playground](https://playground.tensorflow.org/) for 10-minutes. Especially try different values of the learning, what happens when you decrease it? What happens when you increase it?\n",
        "2. Replicate the model pictured in the [TensorFlow Playground diagram](https://playground.tensorflow.org/#activation=relu&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.001&regularizationRate=0&noise=0&networkShape=6,6,6,6,6&seed=0.51287&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularization_hide=true&discretize_hide=true&regularizationRate_hide=true&percTrainData_hide=true&dataset_hide=true&problem_hide=true&noise_hide=true&batchSize_hide=true) below using TensorFlow code. Compile it using the Adam optimizer, binary crossentropy loss and accuracy metric. Once it's compiled check a summary of the model.\n",
        "![tensorflow playground example neural network](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/02-tensorflow-playground-replication-exercise.png)\n",
        "*Try this network out for yourself on the [TensorFlow Playground website](https://playground.tensorflow.org/#activation=relu&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.001&regularizationRate=0&noise=0&networkShape=6,6,6,6,6&seed=0.51287&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularization_hide=true&discretize_hide=true&regularizationRate_hide=true&percTrainData_hide=true&dataset_hide=true&problem_hide=true&noise_hide=true&batchSize_hide=true). Hint: there are 5 hidden layers but the output layer isn't pictured, you'll have to decide what the output layer should be based on the input data.*\n",
        "3. Create a classification dataset using Scikit-Learn's [`make_moons()`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html) function, visualize it and then build a model to fit it at over 85% accuracy.\n",
        "4. Create a function (or write code) to visualize multiple image predictions for the fashion MNIST at the same time. Plot at least three different images and their prediciton labels at the same time. Hint: see the [classifcation tutorial in the TensorFlow documentation](https://www.tensorflow.org/tutorials/keras/classification) for ideas.\n",
        "5. Recreate [TensorFlow's](https://www.tensorflow.org/api_docs/python/tf/keras/activations/softmax) [softmax activation function](https://en.wikipedia.org/wiki/Softmax_function) in your own code. Make sure it can accept a tensor and return that tensor after having the softmax function applied to it.\n",
        "6. Train a model to get 88%+ accuracy on the fashion MNIST test set. Plot a confusion matrix to see the results after.\n",
        "7. Make a function to show an image of a certain class of the fashion MNIST dataset and make a prediction on it. For example, plot 3 images of the `T-shirt` class with their predictions.\n",
        "\n"
      ]
    },
    {
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      "metadata": {
        "id": "oksgPs-meGHj"
      },
      "source": [
        "## Extra curriculum 📖\n",
        "* Watch 3Blue1Brown's neural networks video 2: [*Gradient descent, how neural networks learn*](https://www.youtube.com/watch?v=IHZwWFHWa-w). After you're done, write 100 words about what you've learned.\n",
        "  * If you haven't already, watch video 1: [*But what is a Neural Network?*](https://youtu.be/aircAruvnKk). Note the activation function they talk about at the end.\n",
        "* Watch [MIT's introduction to deep learning lecture 1](https://youtu.be/njKP3FqW3Sk) (if you haven't already) to get an idea of the concepts behind using linear and non-linear functions.\n",
        "* Spend 1-hour reading [Michael Nielsen's Neural Networks and Deep Learning book](http://neuralnetworksanddeeplearning.com/index.html).\n",
        "* Read the [ML-Glossary documentation on activation functions](https://ml-cheatsheet.readthedocs.io/en/latest/activation_functions.html). Which one is your favourite?\n",
        "  * After you've read the ML-Glossary, see which activation functions are available in TensorFlow by searching \"tensorflow activation functions\"."
      ]
    }
  ]
}