1_neural_networks_with_keras_and_tensorflow.ipynb

{
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  "metadata": {
    "colab": {
      "name": "1_neural_networks_with_keras_and_tensorflow.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMOaLMRZ6le3+wVFpRIJKgT",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mikeusru/neural-network-training/blob/main/1_neural_networks_with_keras_and_tensorflow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3BVxTz8o6sjj"
      },
      "source": [
        "# Neural Networks\n",
        "In today's world, the meat of why people get into data science and machine learning is Neural Networks. Neural Networks were difficult to wield until recently due to the massive computational power they require, however, the advancement of hardware, especially gaming GPUs, has given breath to what mostly existed as a theoretical concept. Now, neural networks are as plentiful as they are variable, and are being used for everything under the sun.\n",
        "\n",
        "We'll be using Tensorflow with a Keras backend for handling neural networks, and we'll try to do as much as we can in google colab, which offers you a free GPU/TPU to use. THANKS, GOOGLE."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xzl4G2XQ8fH-"
      },
      "source": [
        "The dataset we'll start with is called the fashin MNIST - similar to the MNIST dataset of labeled digits, but a more difficult challenge (it's harder to tell the difference between a coat/shirt/pullover/t-shirt than a 3 and a 7)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MVmuaMmK6nJB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "cd45a231-3dd7-4e1d-8e21-939a12b35b64"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "tf.__version__"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.4.1'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "bUuwrwCt9Fx4",
        "outputId": "08557c81-20b4-48c3-c3ac-af5b58ff0740"
      },
      "source": [
        "keras.__version__"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.4.0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Njr9Vn859Hcg",
        "outputId": "62d06069-1a93-4829-f678-d34b227372de"
      },
      "source": [
        "fashion_mnist = keras.datasets.fashion_mnist\n",
        "\n",
        "(X_train_full, y_train_full), (X_test, y_test) = fashion_mnist.load_data()\n",
        "print(f\"\\nTraining Set Shape: {X_train_full.shape}\")"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Training Set Shape: (60000, 28, 28)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TpNJRFLn9p_7"
      },
      "source": [
        "We loaded the data, and see that it's a long list of 28x28 images (unlike in scikit-learn, where images are represented by 1D arrays).\n",
        "\n",
        "The data is already split into training and test sets for us, but there are no validation sets. Let's make those now, while also converting the images from uint8 (integers between 0 and 255) to floats between 0 and 1."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NbgXAv1H9gSt"
      },
      "source": [
        "X_valid, X_train = X_train_full[:5000] / 255.0, X_train_full[5000:] / 255.0\n",
        "y_valid, y_train = y_train_full[:5000], y_train_full[5000:]\n",
        "X_test = X_test / 255.0"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cEuXiWwt-sNs"
      },
      "source": [
        "We need to keep track of class names, which we can look up online."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s1QYRFsM-cAh"
      },
      "source": [
        "class_names = [\"T-shirt/top\", \"Trouser\", \"Pullover\", \"Dress\", \"Coat\",\n",
        "               \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle boot\"]"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iws2L5I4_OHO"
      },
      "source": [
        "Now let's take a look at some of the data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "MAfaWhps-k6U",
        "outputId": "6b8ecb08-096b-4c00-bc7c-0ccbfa81488f"
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "n_rows = 4\n",
        "n_cols = 10\n",
        "plt.figure(figsize=(n_cols * 1.2, n_rows * 1.2))\n",
        "for row in range(n_rows):\n",
        "    for col in range(n_cols):\n",
        "        index = n_cols * row + col\n",
        "        plt.subplot(n_rows, n_cols, index + 1)\n",
        "        plt.imshow(X_train[index], cmap=\"binary\", interpolation=\"nearest\")\n",
        "        plt.axis('off')\n",
        "        plt.title(class_names[y_train[index]], fontsize=12)\n",
        "plt.subplots_adjust(wspace=0.2, hspace=0.5)\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x345.6 with 40 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4AYCSl4D_W_G"
      },
      "source": [
        "Truth be told, I didn't know what a pullover was before looking at this dataset, and am still curious if it's a british vs american thing.\n",
        "\n",
        "## Building a sequential model\n",
        "We don't need to do any preprocessing on this data, so we're going to get right to building our model. We'll build a sequential model, which is the easiest way to start and visualize what's happening."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "72wNXfLC_GDz"
      },
      "source": [
        "model = keras.models.Sequential()\n",
        "model.add(keras.layers.Flatten(input_shape=[28,28]))\n",
        "model.add(keras.layers.Dense(300, activation='relu'))\n",
        "model.add(keras.layers.Dense(100, activation='relu'))\n",
        "model.add(keras.layers.Dense(10, activation='softmax'))"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mkf43RclAOhs"
      },
      "source": [
        "We defined our model, added a layer to flatten our data from (28,28) to 784, and added three dense layers with 300, 100, and 10 neurons. The last dense layer is the output layer, containing 10 classes, and using a softmax function since the classes are mutually exclusive. *That's literally the whole thing*. This is like the microsoft excel of neural networks you guys - very high level and approachable.\n",
        "\n",
        "## Visualization\n",
        "Let's visualize the thing we just built."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OeZ45I6_AM0Q",
        "outputId": "ba014dae-825c-4375-c675-10d0acceb1e9"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten (Flatten)            (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 300)               235500    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 100)               30100     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                1010      \n",
            "=================================================================\n",
            "Total params: 266,610\n",
            "Trainable params: 266,610\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8rYo1icpBQD_"
      },
      "source": [
        "266,610 total trainable parameters! I hope we don't overfit anything, because that's a lot more than what we saw in our previous scikit-learn exercises. Let's visualize the network as a flowchart as well. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "4RktTVS7BNPj",
        "outputId": "4143f8b0-8dfd-468f-80c1-397dff12d99e"
      },
      "source": [
        "keras.utils.plot_model(model, show_shapes=True)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I67V1GVZB8co"
      },
      "source": [
        "Beautiful! There are plenty of tools which can help us visualize our networks, but since we're still in the simple stages, this one works great. We can also inspect the weights and biases of each layer. Note what the weights are already initialized to random numbers, since training wouldn't work if the weights were all 0."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HJQSH9smB2qm",
        "outputId": "1c140fe5-c87c-49aa-9f35-033dd9012c40"
      },
      "source": [
        "weights, biases = model.layers[1].get_weights()\n",
        "weights"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.01402917,  0.034097  , -0.01354246, ..., -0.0562462 ,\n",
              "         0.06414632, -0.0072024 ],\n",
              "       [-0.01576182, -0.06202665,  0.02105144, ..., -0.01639875,\n",
              "        -0.05632627,  0.00564876],\n",
              "       [-0.04118737,  0.0482738 , -0.03652361, ..., -0.05202379,\n",
              "         0.00177146, -0.0590772 ],\n",
              "       ...,\n",
              "       [ 0.02461005, -0.03393954, -0.03816525, ...,  0.0463922 ,\n",
              "        -0.01021606,  0.05352996],\n",
              "       [-0.07262366,  0.02355223,  0.02847734, ..., -0.03742551,\n",
              "         0.00363456,  0.0005184 ],\n",
              "       [ 0.06181386,  0.02406657,  0.03251253, ...,  0.05663282,\n",
              "         0.06874937, -0.07273462]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OJ1XsQVBCvMP",
        "outputId": "d0dfe9de-345b-49ad-890d-b684cf79710b"
      },
      "source": [
        "biases"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RupyYdWbDO9V"
      },
      "source": [
        "Notice that the network has defined shapes for the input, and defined amounts of weights and biases. This means that if it's trained on a 28x28 image and we try to predict an 8x8 image, it won't work. The input has to match the network architecture.\n",
        "\n",
        "## Compiling and Training\n",
        "We compile the model once it's built, adding a loss function, optimizer, and metrics. We have lots of options, but the loss and optimizer was chosen due to us having a list of mutually exclusive outputs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BgBbYC44DAKR"
      },
      "source": [
        "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
        "                optimizer=\"sgd\",\n",
        "                metrics=[\"accuracy\"])"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "feEViufSD3GL",
        "outputId": "c9ac3868-1370-4998-acb9-0e22124767f1"
      },
      "source": [
        "history = model.fit(X_train, y_train, epochs=30, validation_data=(X_valid, y_valid))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "1719/1719 [==============================] - 8s 4ms/step - loss: 1.0057 - accuracy: 0.6769 - val_loss: 0.5405 - val_accuracy: 0.8118\n",
            "Epoch 2/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.5020 - accuracy: 0.8273 - val_loss: 0.4743 - val_accuracy: 0.8360\n",
            "Epoch 3/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.4540 - accuracy: 0.8416 - val_loss: 0.4152 - val_accuracy: 0.8574\n",
            "Epoch 4/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.4247 - accuracy: 0.8519 - val_loss: 0.4073 - val_accuracy: 0.8616\n",
            "Epoch 5/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.4004 - accuracy: 0.8593 - val_loss: 0.4036 - val_accuracy: 0.8636\n",
            "Epoch 6/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.3841 - accuracy: 0.8647 - val_loss: 0.3781 - val_accuracy: 0.8708\n",
            "Epoch 7/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.3689 - accuracy: 0.8706 - val_loss: 0.3640 - val_accuracy: 0.8748\n",
            "Epoch 8/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.3566 - accuracy: 0.8725 - val_loss: 0.3741 - val_accuracy: 0.8694\n",
            "Epoch 9/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3466 - accuracy: 0.8763 - val_loss: 0.3438 - val_accuracy: 0.8776\n",
            "Epoch 10/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.3343 - accuracy: 0.8787 - val_loss: 0.3394 - val_accuracy: 0.8820\n",
            "Epoch 11/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.3294 - accuracy: 0.8820 - val_loss: 0.3648 - val_accuracy: 0.8688\n",
            "Epoch 12/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3219 - accuracy: 0.8838 - val_loss: 0.3330 - val_accuracy: 0.8830\n",
            "Epoch 13/30\n",
            "1719/1719 [==============================] - 6s 4ms/step - loss: 0.3121 - accuracy: 0.8884 - val_loss: 0.3324 - val_accuracy: 0.8846\n",
            "Epoch 14/30\n",
            "1719/1719 [==============================] - 6s 4ms/step - loss: 0.3068 - accuracy: 0.8890 - val_loss: 0.3390 - val_accuracy: 0.8826\n",
            "Epoch 15/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.3016 - accuracy: 0.8910 - val_loss: 0.3375 - val_accuracy: 0.8740\n",
            "Epoch 16/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.2941 - accuracy: 0.8945 - val_loss: 0.3151 - val_accuracy: 0.8900\n",
            "Epoch 17/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.2902 - accuracy: 0.8953 - val_loss: 0.3165 - val_accuracy: 0.8870\n",
            "Epoch 18/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.2774 - accuracy: 0.9010 - val_loss: 0.3204 - val_accuracy: 0.8856\n",
            "Epoch 19/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.2802 - accuracy: 0.8996 - val_loss: 0.3106 - val_accuracy: 0.8892\n",
            "Epoch 20/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.2690 - accuracy: 0.9035 - val_loss: 0.3055 - val_accuracy: 0.8906\n",
            "Epoch 21/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.2673 - accuracy: 0.9051 - val_loss: 0.3040 - val_accuracy: 0.8902\n",
            "Epoch 22/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.2695 - accuracy: 0.9015 - val_loss: 0.3077 - val_accuracy: 0.8914\n",
            "Epoch 23/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.2598 - accuracy: 0.9060 - val_loss: 0.3136 - val_accuracy: 0.8896\n",
            "Epoch 24/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.2544 - accuracy: 0.9084 - val_loss: 0.3189 - val_accuracy: 0.8884\n",
            "Epoch 25/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.2477 - accuracy: 0.9092 - val_loss: 0.3000 - val_accuracy: 0.8902\n",
            "Epoch 26/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.2452 - accuracy: 0.9125 - val_loss: 0.3102 - val_accuracy: 0.8880\n",
            "Epoch 27/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.2388 - accuracy: 0.9140 - val_loss: 0.2929 - val_accuracy: 0.8950\n",
            "Epoch 28/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.2384 - accuracy: 0.9134 - val_loss: 0.3232 - val_accuracy: 0.8872\n",
            "Epoch 29/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.2347 - accuracy: 0.9169 - val_loss: 0.3067 - val_accuracy: 0.8910\n",
            "Epoch 30/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.2265 - accuracy: 0.9187 - val_loss: 0.2932 - val_accuracy: 0.8942\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aLybuFy9JNN6"
      },
      "source": [
        "There are plenty of tools to visualize our training session. Let's try pandas first."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "1n8j2em_Fh2p",
        "outputId": "bf137b31-6250-4c3f-fca7-90d0cc68604a"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "pd.DataFrame(history.history).plot(figsize=(8,5))\n",
        "plt.grid(True)\n",
        "plt.gca().set_ylim(0,1)\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WaQQMpvWMdvr"
      },
      "source": [
        "The metric we really care about here is val_accuracy and val_loss... as long as val_loss hasn't plateaued, we can keep on training. Either way, let's check out accuracy at this point."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t79ptSyuGXtJ",
        "outputId": "65e5948b-8474-45da-b71b-e9b75171b475"
      },
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3248 - accuracy: 0.8857\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3247720003128052, 0.885699987411499]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7cOiT4M2NI9T"
      },
      "source": [
        "88.6% - Not bad! Let's use this trained model to make a prediction."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FNc31mlcNGkV",
        "outputId": "fa4042da-310b-4e80-9489-e4db9264b2a4"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "X_new_cases = X_test[:3]\n",
        "y_proba = model.predict(X_new_cases)\n",
        "y_pred = np.argmax(y_proba, axis=-1)\n",
        "y_pred"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 2, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "id": "ZmtthPq7NZl5",
        "outputId": "67f41210-bb89-4f9b-f43b-b32073a50a4c"
      },
      "source": [
        "plt.figure(figsize=(7.2, 2.4))\n",
        "for index, image in enumerate(X_new_cases):\n",
        "    plt.subplot(1, 3, index + 1)\n",
        "    plt.imshow(image, cmap=\"binary\", interpolation=\"nearest\")\n",
        "    plt.axis('off')\n",
        "    plt.title(class_names[y_pred[index]], fontsize=12)\n",
        "plt.subplots_adjust(wspace=0.2, hspace=0.5)\n"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 518.4x172.8 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WhAry4ZjPKzH"
      },
      "source": [
        "## Non-sequential models\n",
        "\n",
        "In the previous model we built, every output flowed into the next layer's input. Not all models work this way. As a matter of fact, virtually all of the newer networks have more complicated structures with non-seqential parts. We'll train what's called a *wide and deep* network, where the input layer will flow both into the first hidden layer, as wel as a concatenate layer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a0UzL9W-OmRH"
      },
      "source": [
        "input = keras.layers.Input(shape=X_train.shape[1:])\n",
        "flatten = keras.layers.Flatten()(input)\n",
        "hidden1 = keras.layers.Dense(300, activation=\"relu\")(flatten)\n",
        "hidden2 = keras.layers.Dense(100, activation=\"relu\")(hidden1)\n",
        "concat = keras.layers.Concatenate()([flatten, hidden2])\n",
        "output = keras.layers.Dense(10, activation='softmax')(concat)\n",
        "model = keras.Model(inputs=[input], outputs=[output])"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RHxAhxluQ3LC"
      },
      "source": [
        "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
        "                optimizer=\"sgd\",\n",
        "                metrics=[\"accuracy\"])"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "id": "YwvRQMbnWvaC",
        "outputId": "901dc317-6397-4b6e-e2cb-a6aa5d8868db"
      },
      "source": [
        "keras.utils.plot_model(model, show_shapes=True)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2CmBEwwlZoBs"
      },
      "source": [
        "To direct inputs and outputs between layers, we gave each layer a name and added its input as a function call following its instantiation. What fun we're having! Now, what if i told you that a neural network can have multiple input layers, and even multiple output layers!? THAT WOULD BE A NEW THING I TOLD YOU! Let's see how we're gonna get it done. We're gonna load up the california housing dataset here, with a fun goal of predicting how much a house's price is going to be based on some features."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XNh70dixYf4m"
      },
      "source": [
        "from sklearn.datasets import fetch_california_housing\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "housing = fetch_california_housing()\n",
        "X_train_full, X_test, y_train_full, y_test = train_test_split(housing.data, housing.target)\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_train_full, y_train_full)\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_val = scaler.fit_transform(X_val)\n",
        "X_test = scaler.fit_transform(X_test)"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "--yk1WVCbeSd"
      },
      "source": [
        "input_A = keras.layers.Input(shape=[5], name=\"wide_input\")\n",
        "input_B = keras.layers.Input(shape=[6], name=\"deep_input\")\n",
        "hidden1 = keras.layers.Dense(30, activation=\"relu\")(input_B)\n",
        "hidden2 = keras.layers.Dense(30, activation=\"relu\")(hidden1)\n",
        "concat = keras.layers.concatenate([input_A, hidden2])\n",
        "output = keras.layers.Dense(1, name=\"main_output\")(concat)\n",
        "aux_output = keras.layers.Dense(1, name=\"aux_output\")(hidden2)\n",
        "model = keras.Model(inputs=[input_A, input_B], outputs=[output, aux_output])"
      ],
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "49Axc5rLb_Lj"
      },
      "source": [
        "model.compile(loss=[\"mse\", \"mse\"], loss_weights=[0.9, 0.1], optimizer=\"sgd\", metrics=\"accuracy\")"
      ],
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "C59HlZWAb_qM",
        "outputId": "41489cd2-2dd5-4023-d7a6-0b1a6e1e8f1d"
      },
      "source": [
        "keras.utils.plot_model(model, show_shapes=True)"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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9sWDBAvHDDz90qcaFCxeKhQsXdqktPdadf19Id3rxeuXyziAREdEA0NTUhLS0NLz00kuIjY2Fra0tZDIZ7O3tVdo9fPgQWVlZmD9/PoKDg2Fra4v169fD1NQU2dnZT13fzsTEBCNHjoS5uTlGjRqFrKws1NfXq7TRhsDAQFhbW8PR0RFhYWFoaGjAlStXVNr0di2zZ89GXV0dNmzY0KP96MOxaGrhwoXYtGkT7OzsYG9vjzlz5qC2tha3bt0CAKxYsQKtra0q9dXV1eHrr7/GrFmzADz9O/fP3nnnHbz++us4ePAgRowY0XcHSmSgOJooERHRAFBeXo7GxkZMnTq103aXLl1CY2MjfH19lctkMhmcnZ1x8eLFp65/kvHjx0Mul3fapqfMzMwAAAqFotN2fVFLT/XXY2mfN619gvV/+7d/g7e3N/74xz9i3bp1kEgk2L9/P8LCwmBsbAzg6d85bThw4ADfj+0GnjNiGCQiIhoAqqurAQCOjo6dtmtoaAAArF+/HuvXr1dZ5+Li8tT1nTE3N1feMdI1faqlp3R5LIWFhXj33XdRWlqKuro6tfAqkUgQGRmJ2NhY/OUvf8FLL72EP/3pT9i7d6+yTU++U13l7++PmJgYrezLEJw+fRrp6ekq7wqT/mq/Xr2BYZCIiGgAkEqlAIBHjx512q49LKalpSE6OlptfVlZWafrn0ShUODevXtwc3Pr8ja9RZ9q6am+PpaTJ0/i22+/RUxMDK5cuYL58+djwYIF+OMf/4hf/OIXeO+99/Af//EfKttERERg3bp12LVrFwYPHgxra2sMGTJEuf5p3zltcHNzw8svv9wr+x6o0tPTec76kd4Kg3xnkIiIaADw9fWFkZERvvzyy07bDR48GFKpFOfOnevW+icpKiqCEAL+/v4abdcbOqrFxMTkqY9k6qO+PpZvv/0WFhYWAIALFy5AoVBg5cqV8PDwgFQq7fCxQjs7O4SGhiI/Px9/+MMfsGzZMpX13f1OEVHvYxgkIiIaABwdHREcHIwDBw5g9+7dqKurw/nz57Fz506VdlKpFEuWLMG+ffuQlZWFuro6tLa2orq6Gjdu3Hjq+nZtbW24e/cuWlpacP78eURHR8Pd3R0RERF9fORdq8XLywt37txBfn4+FAoFbt26haqqKrV92dvb4/r166isrER9fT0UCgWOHj3a7akl9O1YnkShUODmzZsoKipShkF3d3cAwIkTJ/Dw4UOUlZU9cfqQFStW4NGjR/j0008RFBSksq6r3yki0oHeGKOUiIiIeqY7Q4nX19eL1157TTg4OAhLS0vx4osvio0bNwoAws3NTXz33XdCCCEePXok1qxZI9zd3YWJiYlwdHQUwcHBorS0tEvrly9fLkxNTYWrq6swMTER1tbWYt68eaKiokLj43zvvfeEs7OzACDkcrmYM2eOyMzMFHK5XAAQw4cPFxUVFWLnzp3C2tpaABBDhgwRP/74o0a11NbWiilTpgipVCqGDRsm3njjDfH73/9eABBeXl7KqRuKi4vFkCFDhEwmEy+++KL4+eefxZEjR4SVlZVITk5+4nGcOXNGjB49WhgZGQkAwtnZWWzZskWvjuX9998Xnp6eAkCnn0OHDin7WrNmjbC3txe2trYiJCREbN++XQAQnp6eatOIPPfccyI+Pr7D89PZd2rr1q1CJpMJAGLw4MHif//3f7vy1VHi1BKa49QS/UtvTi0hEUKIvgyfRERE9HS5ubkIDQ2FPv43HRkZiby8PNTW1uq6FL2qpaf6+7HMnj0b27dvx7Bhw/q035CQEABAXl5en/bbn+nzvy+krhevVx4fEyUiIiKNtU8toA/0qZae6k/H8s+PnZ4/fx5SqbTPgyAR9QzDIBEREWnVxYsXIZFInvoJCwvTdanUA2vWrEFZWRl+/PFHLFmyBElJSbouiXpRZGSkyt/f8PBwtTYnTpxAfHw8Dh48CA8PD2XbV155Ra3t9OnTYWVlBWNjY4wePRrFxcV9cRg9olAokJKSAi8vL5iZmcHW1ha+vr6orKwEABw+fBhbt25V+6VOfn6+yrl75plndFB9xxgGiYiIqMvWrVuH7Oxs3L9/H8OGDcOBAwfU2owYMQJCiKd+9u/f3+u19Bf98VjkcjlGjBiBl156CQkJCRg1apSuS6JeZm9vj6NHj+LSpUvYvXu3yrpNmzYhIyMD69atQ3BwMC5fvgxPT084ODhgz549KCwsVGl//Phx5OXlISgoCKWlpRg3blxfHkq3hIaGKufRbGxsxA8//ABPT088ePAAADBnzhxIpVJMnToV9+7dU243d+5cVFdX4+TJk5g1a5auyu8QwyARERF1WUpKCh49egQhBH766ScsXLiQtWhBfzyW5ORktLa24sqVK2ojiBqapqYmBAYG9vs+nkYmk2HGjBnw9vaGubm5cvk777yD/fv3Izc3F1ZWVirbZGRkwMjICMuXL8f9+/f7umSt2b9/P/Lz85GXl4df/epXMDExgYuLCwoKCuDr66tst3r1ajz77LOYNWsWWlpaAAASiQSurq6YOHEihg8frqtD6BDDIBERERFRD+zevRs1NTX9vo/uKC8vx4YNG7B582ZIpVK19YGBgYiOjsa1a9fw5ptv6qBC7Xj//fcxbtw4jBkz5qltExIScO7cuV6bKF6bGAaJiIiIyKAIIZCamoqRI0fC3NwcdnZ2mDdvHi5evKhsExUVBTMzMzg7OyuXrVq1ChYWFpBIJLh9+zYAIDo6GnFxcaioqIBEIoGXlxcyMjIglUrh5OSEyMhIuLi4QCqVIjAwUGWuxp70AQDHjh3rszkwnyQjIwNCCMyZM+eJbZKTk+Ht7Y1du3bhxIkTne6vK9cmKysLFhYWkMvlKCgowMyZM2FtbQ03Nzfs27dPZX+tra3YuHEj3N3dIZPJ4Ofnh5ycHI2Osbm5GWfOnMHYsWO71N7Ozg6TJk1Cenq63o/YyjBIRERERAYlISEB8fHxeOutt1BTU4OTJ0/i6tWrmDhxIm7evAngcch5+eWXVbbLzMzE5s2bVZalp6cjKCgInp6eEEKgvLwcUVFRiIiIQGNjI1avXo3KykoUFxejpaUF06ZNw9WrV3vcB/CP0Wfb2tq0d3I0VFhYCB8fH8jl8ie2kclk+PDDD2FkZIRly5ahoaHhiW27cm1WrlyJmJgYNDU1wcrKCjk5OaioqICHhweWLVumMtLt2rVrsW3bNqSlpeHGjRsICgrC4sWL8c0333T5GK9fv47m5mZ8++23mDJlijLcjxw5EpmZmR0Gvueeew7Xrl3Dd9991+V+dIFhkIiIiIgMRlNTE1JTU7FgwQKEh4fDxsYGY8aMwY4dO3D79m3s3LlTa32ZmJgo73CNGjUKWVlZqK+vR3Z2tlb2P3v2bNTV1WHDhg1a2Z+mGhoa8NNPP8HT0/OpbQMCAhATE4PKykqsXbu2wzbduTaBgYGwtraGo6MjwsLC0NDQgCtXrgAAHj58iKysLMyfPx/BwcGwtbXF+vXrYWpqqtE1aB8gxtHREVu2bEFpaSlu3ryJefPm4fXXX8dHH32ktk37u4EXLlzocj+6wDBIRERERAajtLQUDx48wPjx41WWT5gwAWZmZiqPcWrb+PHjIZfLVR557M9qamoghOj0ruA/S05Oho+PDzIzM3Hq1Cm19T29NmZmZgD+MQfmpUuX0NjYqDLAi0wmg7Ozs0bXoH2wnNGjRyMwMBD29vawsbHB5s2bYWNj02FIbT8n7Xcz9RXDIBEREREZjPYh/y0tLdXW2draor6+vlf7Nzc3x61bt3q1j77y8OFDAFAZWbQzUqkU2dnZkEgkWLp0KZqamlTWa/vatD+Oun79epV5/qqqqtDY2Njl/bi4uACA8h3OdmZmZhgyZAgqKirUtpHJZAD+cY70FcMgERERERkMW1tbAOgwWNy7dw9ubm691rdCoej1PvpSe+D510nWOxMQEIDY2FiUlZUhKSlJZZ22r42joyMAIC0tTW2e09OnT3d5P5aWlhg+fDi+//57tXUtLS2wsbFRW97c3AzgH+dIXzEMEhEREZHB8PX1haWlpdoAImfPnkVzczOef/555TITExOVwUh6qqioCEII+Pv791offcnJyQkSiUTj+QOTkpIwYsQIlJSUqCzX5Np0xeDBgyGVSnHu3DmNtutIaGgoSkpKcPnyZeWyxsZGVFVVdTjdRPs5GTRoUI/77k0Mg0RERERkMKRSKeLi4nDo0CHs2bMHdXV1uHDhAlasWAEXFxcsX75c2dbLywt37txBfn4+FAoFbt26haqqKrV92tvb4/r166isrER9fb0y3LW1teHu3btoaWnB+fPnER0dDXd3d0RERGilj6NHj+p0agm5XA4PDw9UV1drtF3746LGxsZqy7t6bbraz5IlS7Bv3z5kZWWhrq4Ora2tqK6uxo0bNwAAYWFhGDRoEIqLizvdV2xsLIYMGYKIiAhcuXIFtbW1WLNmDZqamjocEKf9nHRlXkJdYhgkIiIiIoOyadMmpKSkIDExEc888wwmTZqEoUOHoqioCBYWFsp2K1euxJQpU7Bo0SL4+PggKSlJ+dhfQECAcoqIFStWwMnJCaNGjcKsWbNw584dAI/fFxszZgxkMhkmTpwIb29vfPHFFyrv2PW0D12bPXs2SktLVd7/+/jjj+Hl5YWKigpMmDABb7zxhtp2/v7+iI2NVVvelWuTlZWFtLQ0AICfnx8uX76MDz74AHFxcQCAGTNmoKysDMDjaTliYmKwdetWODg4wMXFBdHR0bh79y6Ax49z1tTUoKCgoNPjtLOzw1dffQU3NzeMHTsWrq6u+Nvf/obCwsIO5x/8+uuv4erqCj8/v66cRp2RCH2fCZGIiMgA5ebmIjQ0VO8nLCbStZCQEABAXl6ejitRFRkZiby8PNTW1uq6FDXd+fclMjISn376qdpdwPLycowcORLZ2dkIDw/Xdqm9rq2tDZMnT0ZERASWLl2qlX3W1tbCzc0NycnJyoDaLjo6Gnv27FEbjKYzvfj/QR7vDBIRERER9QJNBlbpD5qamvDZZ5+hrKxMOUCKl5cXEhMTkZiYqJyPr79obW1Ffn4+6uvrERYWprX9JiQkYOzYsYiKigIACCFw/fp1nDp1CuXl5VrrRxsYBomIiIiI6Knu3LmDGTNmwNvbW+UuWnx8PEJCQhAWFqbxYDK6VFRUhIMHD+Lo0aNdnivxaVJTU3Hu3DkcOXIEpqamAICCggK4urpi4sSJKCws1Eo/2sIwSERERESkRevWrUN2djbu37+PYcOG4cCBA7ouqcd27NihMjXDnj17VNZv2bIFUVFRePvtt3VUoeamTp2KvXv3wtnZWSv7KygowKNHj1BUVAQ7Ozvl8nnz5qmcO00eEe1tJrougIiIiIhoIElJSUFKSoquy+hz06dPx/Tp03Vdhs7MnTsXc+fO1XUZGuGdQSIiIiIiIgPEMEhERERERGSAGAaJiIiIiIgMEMMgERERERGRAeIAMkRERHosNzdX1yUQ6bX2SdD5d6XrTp8+DYDnrL9ov169QSJ6YSp7IiIi6pnc3FyEhobqugwiItITvRDb8hgGiYiIiEgvtf9ShD+uEvWKPL4zSEREREREZIAYBomIiIiIiAwQwyAREREREZEBYhgkIiIiIiIyQAyDREREREREBohhkIiIiIiIyAAxDBIRERERERkghkEiIiIiIiIDxDBIRERERERkgBgGiYiIiIiIDBDDIBERERERkQFiGCQiIiIiIjJADINEREREREQGiGGQiIiIiIjIADEMEhERERERGSCGQSIiIiIiIgPEMEhERERERGSAGAaJiIiIiIgMEMMgERERERGRAWIYJCIiIiIiMkAMg0RERERERAaIYZCIiIiIiMgAMQwSEREREREZIIZBIiIiIiIiA8QwSEREREREZIAYBomIiIiIiAwQwyAREREREZEBYhgkIiIiIiIyQAyDREREREREBohhkIiIiIiIyAAxDBIRERERES2DrxUAACAASURBVBkghkEiIiIiIiIDxDBIRERERERkgEx0XQARERERUXV1NV599VW0trYql929exdWVlaYPHmySlsfHx/893//dx9XSDTwMAwSERERkc65ubmhqqoKFRUVauu+/PJLlT//+te/7quyiAY0PiZKRERERHrhd7/7HUxNTZ/aLiwsrA+qIRr4GAaJiIiISC/89re/RUtLS6dtRo8ejVGjRvVRRUQDG8MgEREREekFT09P+Pn5QSKRdLje1NQUr776ah9XRTRwMQwSERERkd743e9+B2Nj4w7XtbS0ICQkpI8rIhq4GAaJiIiISG8sWrQIbW1tasuNjIzg7++PoUOH9n1RRAMUwyARERER6Q0XFxe88MILMDJS/THVyMgIv/vd73RUFdHAxDBIRERERHrllVdeUVsmhMCCBQt0UA3RwMUwSERERER6ZeHChSrvDRobG+Oll16Ck5OTDqsiGngYBomIiIhIr9jZ2WHatGnKQCiEQHh4uI6rIhp4GAaJiIiISO+Eh4crB5IxNTXFvHnzdFwR0cDDMEhEREREemfOnDkwNzcHAAQFBcHS0lLHFRENPAyDRERERKR3LCwslHcD+YgoUe+QCCGErosgIiIi3cjNzUVoaKiuyyAi6rf6cZzKM9F1BURERKR7OTk5ui6BBqDTp08jPT2929+v1tZW5OTkYPHixVquTL+FhoYiOjoaAQEBui6FOtH+/e7PGAaJiIgIL7/8sq5LoAEqPT29R9+v+fPnQyqVarEi/RcaGoqAgAD+vewH+nsY5DuDRERERKS3DC0IEvUlhkEiIiIiIiIDxDBIRERERERkgBgGiYiIiIiIDBDDIBERERERkQFiGCQiIiIivXbkyBHY2Njgk08+0XUp/daJEycQHx+PgwcPwsPDAxKJBBKJBK+88opa2+nTp8PKygrGxsYYPXo0iouLdVCxZhQKBVJSUuDl5QUzMzPY2trC19cXlZWVAIDDhw9j69ataG1t1W2heoZhkIiIiIj0Wj+e1FsvbNq0CRkZGVi3bh2Cg4Nx+fJleHp6wsHBAXv27EFhYaFK++PHjyMvLw9BQUEoLS3FuHHjdFR514WGhuJPf/oT9u7di8bGRvzwww/w9PTEgwcPAABz5syBVCrF1KlTce/ePR1Xqz8YBomIiIhIr82ePRv3799HUFCQrktBU1MTAgMDdV1Gl73zzjvYv38/cnNzYWVlpbIuIyMDRkZGWL58Oe7fv6+jCntu//79yM/PR15eHn71q1/BxMQELi4uKCgogK+vr7Ld6tWr8eyzz2LWrFloaWnRYcX6g2GQiIiIiKiLdu/ejZqaGl2X0SXl5eXYsGEDNm/e3OF8jYGBgYiOjsa1a9fw5ptv6qBC7Xj//fcxbtw4jBkz5qltExIScO7cuX4/Wby2MAwSERERkd46deoU3N3dIZFIsH37dgBAVlYWLCwsIJfLUVBQgJkzZ8La2hpubm7Yt2+fctuMjAxIpVI4OTkhMjISLi4ukEqlCAwMxNmzZ5XtoqKiYGZmBmdnZ+WyVatWwcLCAhKJBLdv3wYAREdHIy4uDhUVFZBIJPDy8gIAHDt2DNbW1tiyZUtfnJIuy8jIgBACc+bMeWKb5ORkeHt7Y9euXThx4kSn+xNCIDU1FSNHjoS5uTns7Owwb948XLx4Udmmq9cGAFpbW7Fx40a4u7tDJpPBz88POTk5Gh1jc3Mzzpw5g7Fjx3apvZ2dHSZNmoT09HQ+fgyGQSIiIiLSYy+++CL++te/qixbuXIlYmJi0NTUBCsrK+Tk5KCiogIeHh5YtmwZFAoFgMchLyIiAo2NjVi9ejUqKytRXFyMlpYWTJs2DVevXgXwODS9/PLLKn1kZmZi8+bNKsvS09MRFBQET09PCCFQXl4OAMpBSdra2nrlHHRXYWEhfHx8IJfLn9hGJpPhww8/hJGREZYtW4aGhoYntk1ISEB8fDzeeust1NTU4OTJk7h69SomTpyImzdvAuj6tQGAtWvXYtu2bUhLS8ONGzcQFBSExYsX45tvvunyMV6/fh3Nzc349ttvMWXKFGXgHzlyJDIzMzsMfM899xyuXbuG7777rsv9DFQMg0RERETUbwUGBsLa2hqOjo4ICwtDQ0MDrly5otLGxMREeTdr1KhRyMrKQn19PbKzs7VSw+zZs1FXV4cNGzZoZX/a0NDQgJ9++gmenp5PbRsQEICYmBhUVlZi7dq1HbZpampCamoqFixYgPDwcNjY2GDMmDHYsWMHbt++jZ07d6pt09m1efjwIbKysjB//nwEBwfD1tYW69evh6mpqUbXpX2AGEdHR2zZsgWlpaW4efMm5s2bh9dffx0fffSR2jbDhw8HAFy4cKHL/QxUDINERERENCCYmZkBgMrdp46MHz8ecrlc5fHGgaampgZCiE7vCv6z5ORk+Pj4IDMzE6dOnVJbX1paigcPHmD8+PEqyydMmAAzMzOVx2478q/X5tKlS2hsbFQZ4EUmk8HZ2Vmj62Jubg4AGD16NAIDA2Fvbw8bGxts3rwZNjY2HYbU9nPSfjfTkDEMEhEREZHBMTc3x61bt3RdRq95+PAhgH+EpaeRSqXIzs6GRCLB0qVL0dTUpLK+fToGS0tLtW1tbW1RX1+vUX3tj6OuX79eOeehRCJBVVUVGhsbu7wfFxcXAFC+19nOzMwMQ4YMQUVFhdo2MpkMwD/OkSFjGCQiIiIig6JQKHDv3j24ubnpupRe0x54NJlkPSAgALGxsSgrK0NSUpLKOltbWwDoMPR151w6OjoCANLS0iCEUPmcPn26y/uxtLTE8OHD8f3336uta2lpgY2Njdry5uZmAP84R4aMYZCIiIiIDEpRURGEEPD391cuMzExeerjpf2Jk5MTJBKJxvMHJiUlYcSIESgpKVFZ7uvrC0tLS7XBXc6ePYvm5mY8//zzGvUzePBgSKVSnDt3TqPtOhIaGoqSkhJcvnxZuayxsRFVVVUdTjfRfk4GDRrU4777O4ZBIiIiIhrQ2tracPfuXbS0tOD8+fOIjo6Gu7s7IiIilG28vLxw584d5OfnQ6FQ4NatW6iqqlLbl729Pa5fv47KykrU19dDoVDg6NGjeje1hFwuh4eHB6qrqzXarv1xUWNjY7XlcXFxOHToEPbs2YO6ujpcuHABK1asgIuLC5YvX65xP0uWLMG+ffuQlZWFuro6tLa2orq6Gjdu3AAAhIWFYdCgQSguLu50X7GxsRgyZAgiIiJw5coV1NbWYs2aNWhqaupwQJz2c9KVeQkHOoZBIiIiItJb27dvx4QJEwAAa9aswdy5c5GVlYW0tDQAgJ+fHy5fvowPPvgAcXFxAIAZM2agrKxMuY+HDx9izJgxkMlkmDhxIry9vfHFF1+ovE+3cuVKTJkyBYsWLYKPjw+SkpKUjxEGBAQop6FYsWIFnJycMGrUKMyaNQt37tzpk/PQHbNnz0ZpaanK+38ff/wxvLy8UFFRgQkTJuCNN95Q287f3x+xsbFqyzdt2oSUlBQkJibimWeewaRJkzB06FAUFRXBwsICADS6Nunp6YiJicHWrVvh4OAAFxcXREdH4+7duwAeP85ZU1ODgoKCTo/Tzs4OX331Fdzc3DB27Fi4urrib3/7GwoLCzucf/Drr7+Gq6sr/Pz8unIaBzSJ4GyLREREBis3NxehoaGcfJl6hT58vyIjI5GXl4fa2lqd1aApiUSCnJwctbkPNVVeXo6RI0ciOzsb4eHhWqqu77S1tWHy5MmIiIjA0qVLtbLP2tpauLm5ITk5WRlQu0sfvt89lMc7g0REREQ0oGkyiMpA4uXlhcTERCQmJirn4+svWltbkZ+fj/r6eoSFhWltvwkJCRg7diyioqK0ts/+jGGQiIiIeuS1116DlZUVJBKJVgaD0IWtW7dixIgRkMlksLCwwIgRI7BhwwbU1dV1q11XHDx4EB4eHirD6kskEpiZmcHJyQmTJ0/Gu+++q3xkjqg74uPjERISgrCwMI0Hk9GloqIiHDx4EEePHu3yXIlPk5qainPnzuHIkSMwNTXVyj77O4ZBIiIi6pFdu3bhgw8+0HUZPfLVV19h2bJluHLlCm7evImkpCRs3boVCxcu7Fa7rggODsbly5fh6ekJGxsbCCHQ1taGmpoa5ObmYtiwYVizZg1Gjx6tNoIjdc26deuQnZ2N+/fvY9iwYThw4ICuS9KJLVu2ICoqCm+//bauS+myqVOnYu/evXB2dtbK/goKCvDo0SMUFRXBzs5OK/scCBgGiYiIyOCZmZlh1apVcHR0hKWlJUJCQjBv3jz8+c9/Vo5sqEm77pJIJLC1tcXkyZORnZ2N3Nxc3Lx5E7Nnz+5Xd3X0RUpKCh49egQhBH766aduhfaBYvr06XjnnXd0XYbOzJ07F/Hx8WqjpBo6hkEiIiLqMYlEousSeuTQoUOQSqUqy1xdXQFA5V2rrrbTloULFyIiIgI1NTXYsWOH1vdPRIaNYZCIiIg0IoTAu+++Cx8fH5ibm8PGxga///3v1dq1trZi48aNcHd3h0wmg5+fH3JycgA8Hn7ewsICcrkcBQUFmDlzJqytreHm5oZ9+/ap7OfLL7/EL3/5S8jlclhbW2PMmDHKd/Q666OnysrKYGtriyFDhmjc7tixY1qbd659LryjR48ql/X3c0tE+oFhkIiIiDSyYcMGrFmzBsuXL8fNmzfx888/dzix89q1a7Ft2zakpaXhxo0bCAoKwuLFi/HNN99g5cqViImJQVNTE6ysrJCTk4OKigp4eHhg2bJlUCgUAICGhgbMmTMHCxcuxJ07d1BWVgZvb280Nzc/tY/uUCgUuHbtGrZv344TJ07gvffeg5mZmcbt2kevbGtr61Yd/6x9nrTLly8rl/XHc0tEekgQERGRwcrJyRGa/DjQ2Ngo5HK5mDZtmsryffv2CQCipKRECCFEU1OTkMvlIiwsTGVbc3NzsXLlSiGEEG+99ZYAIJqampRtMjMzBQBRXl4uhBDi73//uwAgPv30U7VautKHpgYNGiQACAcHB/Ff//Vform5uUftusLT01PY2Nh02kYikQhbW1shRP86t5p+v+gxACInJ0fXZdBTDIDvd66JThIoERER9Uvl5eVobGzE1KlTO2136dIlNDY2wtfXV7lMJpPB2dkZFy9efOJ27XfX2u9eeXh4wMnJCeHh4Vi9ejUiIiIwdOjQHvXRmatXr+LevXsoKSlBfHw8du7cic8//xxOTk7daqcNDQ0NEELA2toaQP88t7m5uRpvY+hOnz6t6xLoKQbENdJ1HCUiIiLd0fQ320eOHBEAxO7du1WW/+udwf/7v/8TADr8+Pv7CyE6vnv1wQcfCADihx9+UC77+9//Ln7zm98IExMTIZFIRGhoqGhsbOxSHz3x448/CgBi9erVWmn3JE+7M1hcXCwAiOnTpwsh+te5bf9+8cPPQP70Y7l8Z5CIiIi6rH0kzUePHnXaztHREQCQlpYGIYTKR9Pfpo8ePRqffPIJrl+/jjVr1iAnJwd/+MMftNpHR7y8vGBsbIzS0lKttOuuY8eOAQBmzpwJoH+e23/dBz+dfwAgJydH53Xw0/lnIAyoxDBIREREXebr6wsjIyN8+eWXnbYbPHgwpFIpzp0716P+rl+/ju+//x7A4xD09ttvY9y4cfj++++11kdtbS0WL16strysrAytra0YPHiwRu206eeff0ZaWhrc3NywdOlSAP3r3BKRfmMYJCIioi5zdHREcHAwDhw4gN27d6Ourg7nz5/Hzp07VdpJpVIsWbIE+/btQ1ZWFurq6tDa2orq6mqNJme/fv06IiMjcfHiRTQ3N6OkpARVVVXw9/fXWh8WFhY4fvw4Pv/8c9TV1UGhUKCkpASvvvoqLCwsEBsbq1E74PE0EJpMLSGEwIMHD9DW1gYhBG7duoWcnBy88MILMDY2Rn5+vvKdwf50bolIzwkiIiIyWN0ZDa++vl689tprwsHBQVhaWooXX3xRbNy4UQAQbm5u4rvvvhNCCPHo0SOxZs0a4e7uLkxMTISjo6MIDg4WpaWlIjMzU8jlcgFADB8+XFRUVIidO3cKa2trAUAMGTJE/Pjjj6KyslIEBgYKOzs7YWxsLH7xi1+It956S7S0tDy1D03MmTNHDBs2TFhaWgpzc3Ph6ekpwsLCxIULF7rV7siRI8LKykokJyc/sc/Dhw8LPz8/IZfLhZmZmTAyMhIAlCOH/vKXvxSJiYmitrZWbdv+cm4HwGiLOgFwNNH+YAB8v3MlQvz/B5OJiIjI4OTm5iI0NBT8cYB6A79f3SORSJCTk4OXX35Z16VQJwbA9zuPj4kSEREREREZIIZBIiIiGnAuXrwIiUTy1E9YWJiuSyUi0hmGQSIiIhpwRowY0aWh4ffv36/rUom06sSJE4iPj8fBgwfh4eGh/MXHK6+8otZ2+vTpsLKygrGxMUaPHo3i4mIdVNx1W7duxYgRIyCTyWBhYYERI0Zgw4YNqKurU2t76tQpvPDCC5DL5XBxccGaNWtUpsQ5fPgwtm7ditbW1r48BL3DMEhERERENABs2rQJGRkZWLduHYKDg3H58mV4enrCwcEBe/bsQWFhoUr748ePIy8vD0FBQSgtLcW4ceN0VHnXfPXVV1i2bBmuXLmCmzdvIikpCVu3bsXChQtV2pWWlmL69OmYOnUqbt26hUOHDuGPf/wjVqxYoWwzZ84cSKVSTJ06Fffu3evrQ9EbDINERERENGA1NTUhMDCw3/fxNO+88w7279+P3NxcWFlZqazLyMiAkZERli9fjvv37+uowp4zMzPDqlWr4OjoCEtLS4SEhGDevHn485//rDLlSVJSEpydnbF582ZYWFggICAAa9aswYcffoiLFy8q261evRrPPvssZs2ahZaWFl0cks4xDBIRERHRgLV7927U1NT0+z46U15ejg0bNmDz5s2QSqVq6wMDAxEdHY1r167hzTff1EGF2nHo0CG143N1dQUAPHjwAADQ0tKCwsJCTJo0CRKJRNlu5syZEEKgoKBAZfuEhAScO3cO6enpvVy9fmIYJCIiIiK9IYRAamoqRo4cCXNzc9jZ2WHevHkqd3SioqJgZmYGZ2dn5bJVq1bBwsICEokEt2/fBgBER0cjLi4OFRUVkEgk8PLyQkZGBqRSKZycnBAZGQkXFxdIpVIEBgbi7NmzWukDAI4dOwZra2ts2bKlV88X8PjOnxACc+bMeWKb5ORkeHt7Y9euXThx4kSn++vKNcjKyoKFhQXkcjkKCgowc+ZMWFtbw83NDfv27VPZX2trKzZu3Ah3d3fIZDL4+fkhJyenZwf9/5WVlcHW1hZDhgwBAFy+fBkPHjyAu7u7SjtPT08AwPnz51WW29nZYdKkSUhPT+/PU0R0G8MgEREREemNhIQExMfH46233kJNTQ1OnjyJq1evYuLEibh58yaAx+HnX+fgy8zMxObNm1WWpaenIygoCJ6enhBCoLy8HFFRUYiIiEBjYyNWr16NyspKFBcXo6WlBdOmTcPVq1d73AcA5cAkbW1t2js5T1BYWAgfHx/I5fIntpHJZPjwww9hZGSEZcuWoaGh4Yltu3INVq5ciZiYGDQ1NcHKygo5OTmoqKiAh4cHli1bBoVCodzf2rVrsW3bNqSlpeHGjRsICgrC4sWL8c0333TreBUKBa5du4bt27fjxIkTeO+992BmZgYA+PnnnwFA7VFZqVQKmUymrP+fPffcc7h27Rq+++67btXTnzEMEhEREZFeaGpqQmpqKhYsWIDw8HDY2NhgzJgx2LFjB27fvo2dO3dqrS8TExPlna9Ro0YhKysL9fX1yM7O1sr+Z8+ejbq6OmzYsEEr+3uShoYG/PTTT8o7X50JCAhATEwMKisrsXbt2g7bdOcaBAYGwtraGo6OjggLC0NDQwOuXLkCAHj48CGysrIwf/58BAcHw9bWFuvXr4epqWm3z/XgwYPh5uaGhIQEbNu2DaGhocp17SOGGhsbq21namqKpqYmteXDhw8HAFy4cKFb9fRnDINEREREpBdKS0vx4MEDjB8/XmX5hAkTYGZmpvIYp7aNHz8ecrlc5VHI/qCmpgZCiE7vCv6z5ORk+Pj4IDMzE6dOnVJb39Nr0H6Hrv3O4KVLl9DY2AhfX19lG5lMBmdn526f66tXr6KmpgYfffQR/ud//gfPPfec8p3N9ncKOxoQprm5GTKZTG15+7nr6K7hQMcwSERERER6oX2If0tLS7V1tra2qK+v79X+zc3NcevWrV7tQ9sePnwI4HHtXSGVSpGdnQ2JRIKlS5eq3SnT9jVofxx1/fr1yjkPJRIJqqqq0NjYqNG+2pmamsLR0RHTp0/H/v37UVpaipSUFABQvuP5r3MPNjY24uHDh3BxcVHbX3tAbD+XhoRhkIiIiIj0gq2tLQB0GDju3bsHNze3XutboVD0eh+9oT3IaDJ5ekBAAGJjY1FWVoakpCSVddq+Bo6OjgCAtLQ0CCFUPqdPn9ZoXx3x8vKCsbExSktLAQDDhg2DlZUVqqqqVNq1v8vp5+ento/m5mYA6PCu4UDHMEhEREREesHX1xeWlpZqA4ucPXsWzc3NeP7555XLTExMVAYp6amioiIIIeDv799rffQGJycnSCQSjecPTEpKwogRI1BSUqKyXJNr0BWDBw+GVCrFuXPnNNruX9XW1mLx4sVqy8vKytDa2orBgwcDeHzNZs2ahZMnT6oM3nP06FFIJJIOR1xtP3eDBg3qUY39EcMgEREREekFqVSKuLg4HDp0CHv27EFdXR0uXLiAFStWwMXFBcuXL1e29fLywp07d5Cfnw+FQoFbt26p3Q0CAHt7e1y/fh2VlZWor69Xhru2tjbcvXsXLS0tOH/+PKKjo+Hu7o6IiAit9HH06NE+mVpCLpfDw8MD1dXVGm3X/rjovw60osk16Go/S5Yswb59+5CVlYW6ujq0traiurpaOVF8WFgYBg0ahOLi4ifux8LCAsePH8fnn3+Ouro6KBQKlJSU4NVXX4WFhQViY2OVbTds2ICbN29i06ZNaGhowOnTp/Huu+8iIiICPj4+avtuP3djxozR6NgGAoZBIiIiItIbmzZtQkpKChITE/HMM89g0qRJGDp0KIqKimBhYaFst3LlSkyZMgWLFi2Cj48PkpKSlI/5BQQEKKeIWLFiBZycnDBq1CjMmjULd+7cAfD4/bAxY8ZAJpNh4sSJ8Pb2xhdffKHy7l1P++grs2fPRmlpqcr7fx9//DG8vLxQUVGBCRMm4I033lDbzt/fXyVEtevKNcjKykJaWhqAx49eXr58GR988AHi4uIAADNmzEBZWRmAx9NvxMTEYOvWrXBwcICLiwuio6Nx9+5dAI8f06ypqVGbEP6fSaVSvPDCC3jttdfg6uoKKysrhISEYOjQoThz5ozKADWjR4/GZ599huPHj8PBwQHBwcFYunQp3n///Q73/fXXX8PV1bXDR0gHOokwxNkViYiICACQm5uL0NBQg5xsmXqfvn6/IiMjkZeXh9raWl2X0iGJRIKcnBy1eQ6fpLy8HCNHjkR2djbCw8N7uTrta2trw+TJkxEREYGlS5f2ad+1tbVwc3NDcnKyMsh2lb5+vzWQxzuDRERERGRwNBlwRd95eXkhMTERiYmJePDgga7L0Uhrayvy8/NRX1+PsLCwPu8/ISEBY8eORVRUVJ/3rQ8YBomIiIiI+rn4+HiEhIQgLCxM48FkdKmoqAgHDx7E0aNHuzxXorakpqbi3LlzOHLkCExNTfu0b33BMEhEREREBmPdunXIzs7G/fv3MWzYMBw4cEDXJWnNli1bEBUVhbffflvXpXTZ1KlTsXfvXuX8gH2loKAAjx49QlFREezs7Pq0b31iousCiIiIiIj6SkpKinKC8oFo+vTpmD59uq7L0Htz587F3LlzdV2GzvHOIBERERERkQFiGCQiIiIiIjJADINEREREREQGiGGQiIiIiIjIAHEAGSIiIkJISIiuS6ABqLq6GgC/X92RlpaGvLw8XZdBnWj/fvdnEiGE0HURREREpBunT59Gamqqrssg6tDPP/+MkpISzJw5U9elED1RPw7teQyDRERERKSXcnNzERoaCv64StQr8vjOIBERERERkQFiGCQiIiIiIjJADINEREREREQGiGGQiIiIiIjIADEMEhERERERGSCGQSIiIiIiIgPEMEhERERERGSAGAaJiIiIiIgMEMMgERERERGRAWIYJCIiIiIiMkAMg0RERERERAaIYZCIiIiIiMgAMQwSEREREREZIIZBIiIiIiIiA8QwSEREREREZIAYBomIiIiIiAwQwyAREREREZEBYhgkIiIiIiIyQAyDREREREREBohhkIiIiIiIyAAxDBIRERERERkghkEiIiIiIiIDxDBIRERERERkgBgGiYiIiIiIDBDDIBERERERkQFiGCQiIiIiIjJADINEREREREQGiGGQiIiIiIjIADEMEhERERERGSCGQSIiIiIiIgPEMEhERERERGSAGAaJiIiIiIgMkImuCyAiIiIiUigUePDggcqyhoYGAMDdu3dVlkskEtja2vZZbUQDFcMgEREREencnTt34OrqitbWVrV19vb2Kn+eMmUKPv/8874qjWjA4mOiRERERKRzgwYNwq9//WsYGXX+46lEIsGiRYv6qCqigY1hkIiIiIj0wiuvvPLUNsbGxliwYEEfVEM08DEMEhEREZFeCA4OhonJk99iMjY2xowZM+Dg4NCHVRENXAyDRERERKQXrK2tMXPmzCcGQiEEwsPD+7gqooGLYZCIiIiI9EZ4eHiHg8gAgJmZGX7zm9/0cUVEAxfDIBERERHpjd/85jeQy+Vqy01NTTF//nxYWFjooCqigYlhkIiIiIj0hlQqxYIFC2BqaqqyXKFQ4Le//a2OqiIamBgGiYiIiEivLF68GAqFQmWZjm8YjAAAIABJREFUtbU1pk2bpqOKiAYmhkEiIiIi0isvvfSSykTzpqamWLRoEczMzHRYFdHAwzBIRERERHrFxMQEixYtUj4qqvh/7N15WBRX9jfwb7P2wq4IhC0sInGLGk0EIepAcIuAohGNGZeJg46JaxzivoBJCBk1GBnHiSGZOMGVARPFGHRciMYxUaPBqIgLIMqi7GsD5/3Dl/rZdos0NnRDn8/z+DzJrVtVp6q763C6q+6VyzFlyhQtR8VY58PFIGOMMcYY0zmTJ08WbhW1s7ODn5+fliNirPPhYpAxxhhjjOkcX19fODo6AgD++Mc/wsCA/2xlTNNUz+iph3Jzc3Hq1Clth8EYY0yH+Pr6wsnJSdthtCnOf0yXDRo0CHfu3EGXLl2we/dubYfDmEpvvPGGtkNoNRERkbaD0AW7d+/GpEmTtB0GY4wxHbJr164OneRbgvMfY4w9mw5cTu3hXwYf04FfTMbaTdMfj/x5UY9IJNKL4qKzEIlE2g6hXfHnmbUFTeSLvXv3YsKECRqMSvdxvugYOsOXaXzzNWOMMcYY01n6Vggy1p64GGSMMcYYY4wxPcTFIGOMMcYYY4zpIS4GGWOMMcYYY0wPcTHIGGOMMcYYY3qIi0HGGGOMMcYY00NcDDLGtObgwYOwtLTEt99+q+1QdNLs2bMhEomEf1OnTlXqk5aWhqVLl2Lfvn1wd3cX+r711ltKfYOCgmBubg5DQ0P06tUL586da4/DaLXo6GiF42/617t3b6HP/v37ERMTg4aGBoV1k5OTFdbp2rVre4fPGNMgzhct15nzQkxMDLy9vSGRSCCTyeDt7Y2VK1eirKxMqW96ejqGDBkCqVQKBwcHREZGora2Vlj+pPyhb7gYZIxpDc9r9nQ2NjZITU3F1atXsX37doVlq1evRlxcHJYtW4awsDDcuHEDHh4e6NKlC3bs2IEDBw4o9D98+DD27NmDsWPHIiMjAwMGDGjPQ2kTwcHBEIvFCAgIQElJidAeEhKC3NxcnDhxAqNHj9ZihIwxTeB80TKdPS+cPHkSs2bNQnZ2NvLz8xEVFYWYmBil6UcyMjIQFBSEgIAAFBYWIikpCV988QXmzJkj9HlS/tA3XAwyxrRmzJgxKC0txdixY7UdCqqrq+Hr66vtMJRIJBKMHDkSXl5eMDU1Fdo/+ugj7Ny5E7t374a5ubnCOnFxcTAwMEBERARKS0vbO2SN+vrrr0FECv9+++03hT7z58/Hiy++iNGjR6O+vh7AwwmbHR0d4e/vj+7du2sjdMaYBnG+eDp9yAsmJiaYO3cubG1tYWZmhokTJyI0NBQ//PAD7t69K/SLioqCvb091q5dC5lMBh8fH0RGRuLLL7/ElStXhH6q8oe+4WKQMcYAbN++HQUFBdoOo0WuX7+OlStXYu3atRCLxUrLfX19sWDBAty5cwfvvfeeFiJsf2vWrMGFCxewadMmbYfCGOvkdDFf6EteSEpKUjo+R0dHAEBFRQUAoL6+HgcOHMDQoUMhEomEfqNGjQIRISUlRWF9fc8fXAwyxrQiPT0dLi4uEIlE+OyzzwAA8fHxkMlkkEqlSElJwahRo2BhYQEnJyckJiYK68bFxUEsFqNbt26YPXs2HBwcIBaL4evrizNnzgj95s2bBxMTE9jb2wttc+fOhUwmg0gkQlFREQBgwYIFWLx4MbKysiASieDp6QkAOHToECwsLLB+/fr2OCUtFhcXByJCcHDwE/tER0fDy8sLn3/+OdLS0prdHhFhw4YNeOGFF2Bqagpra2uEhoYqfHva0tcGABoaGrBq1Sq4uLhAIpGgb9++2LVr17Md9FNYW1tj6NCh2LRpE99Oxlgnw/ni6fQ5L2RmZsLKygqurq4AgBs3bqCiogIuLi4K/Tw8PAAAFy9eVGjX9/zBxSBjTCv8/Pxw6tQphba//OUvWLhwIaqrq2Fubo5du3YhKysL7u7umDVrFuRyOYCHSXv69OmoqqrC/PnzcevWLZw7dw719fV47bXXkJOTA+BhcnzjjTcU9rFlyxasXbtWoW3Tpk0YO3YsPDw8QES4fv06AAgPlTc2NrbJOWitAwcOoEePHpBKpU/sI5FI8OWXX8LAwACzZs1CZWXlE/uuWbMGS5cuxfLly1FQUIATJ04gJycH/v7+yM/PB9Dy1wYA3n//fXz88cfYuHEj7t69i7Fjx2LKlCn4+eef1T7WpUuXwtraGiYmJnBzc0NoaCjOnj2rsm///v1x584d/Prrr2rvhzGmuzhfPJ0+5QUAkMvluHPnDj777DOkpaVh8+bNMDExAQDcu3cPAJRulRWLxZBIJEL8j9Ln/MHFIGNMJ/n6+sLCwgK2trYIDw9HZWUlsrOzFfoYGRkJ31r27NkT8fHxKC8vR0JCgkZiGDNmDMrKyrBy5UqNbE8TKisrcfPmTeEbzub4+Phg4cKFuHXrFt5//32Vfaqrq7FhwwaMHz8eU6dOhaWlJfr06YOtW7eiqKgI27ZtU1qnudempqYG8fHxGDduHMLCwmBlZYUVK1bA2NhY7ddl2rRp2L9/P3JyclBRUYHExERkZ2dj6NChyMjIUOrf9GzgpUuX1NoPY6xj0/d8oU95oYmzszOcnJywZs0afPzxx5g0aZKwrGnEUENDQ6X1jI2NUV1drdSuz/mDi0HGmM5r+rbv0W8ZVRk4cCCkUqnCbSydTUFBAYio2W9/HxUdHY0ePXpgy5YtSE9PV1qekZGBiooKDBw4UKF90KBBMDExUbiNSpXHX5urV6+iqqpKYfoHiUQCe3t7tV8XZ2dn9O/fH2ZmZjAxMcHgwYORkJCA6upqbNmyRal/0zlR9a0vY0w/6GO+0Ke80CQnJwcFBQX45ptv8NVXX6F///7Cc5xNzxSqGhCmrq4OEolEqV2f8wcXg4yxTsXU1BSFhYXaDqPN1NTUAIDCyKLNEYvFSEhIgEgkwsyZM5W+EW0aTtvMzExpXSsrK5SXl6sVX9NtRytWrFCY5+/27duoqqpSa1uq9OnTB4aGhrh27ZrSsqYE33SOGGOsOZ0lX+hjXjA2NoatrS2CgoKwc+dOZGRk4IMPPgAA4bnPx+cerKqqQk1NDRwcHJS2p8/5g4tBxlinIZfLUVJSAicnJ22H0maaEpY6k+T6+Phg0aJFyMzMRFRUlMIyKysrAFCZ3FtzLm1tbQEAGzduVJoS4vTp02ptS5XGxkY0Njaq/KOnrq4OAFR+68sYY4/qTPlC3/OCp6cnDA0NhccH3NzcYG5ujtu3byv0a3q+s2/fvkrb0Of8wcUgY6zTOHbsGIgIgwcPFtqMjIyeertQR9KtWzeIRCK154mKioqCt7c3zp8/r9Deu3dvmJmZKT3Ef+bMGdTV1eGll15Saz/Ozs4Qi8W4cOGCWuupMmLECKW2s2fPgojg4+OjtKzpnNjZ2T3zvhljnVtnyhf6khfu37+PKVOmKLVnZmaioaEBzs7OAB6+jqNHj8aJEycUBvRJTU2FSCRSOeKqPucPLgYZYx1WY2MjiouLUV9fj4sXL2LBggVwcXHB9OnThT6enp548OABkpOTIZfLUVhYqPRtIQDY2NggLy8Pt27dQnl5OeRyOVJTU3VuagmpVAp3d3fk5uaqtV7TbUGPP1AvFouxePFiJCUlYceOHSgrK8OlS5cwZ84cODg4ICIiQu39zJgxA4mJiYiPj0dZWRkaGhqQm5srTAgcHh4OOzs7nDt3rtlt3blzBzt37kRJSQnkcjlOnz6Nt99+Gy4uLpgzZ45S/6Zz0qdPH7ViZox1fp05X+hLXpDJZDh8+DCOHj2KsrIyyOVynD9/HtOmTYNMJsOiRYuEvitXrkR+fj5Wr16NyspKnD59GrGxsZg+fTp69OihtG29zh/EiIho165dxKeDsZbRxOdl8+bNZG9vTwBIKpVScHAwbdmyhaRSKQGg7t27U1ZWFm3bto0sLCwIALm6utK1a9eIiCgiIoKMjY3J0dGRjIyMyMLCgkJDQykrK0thP/fv36fhw4eTWCwmNzc3evfdd2nJkiUEgDw9PSk7O5uIiM6dO0eurq4kkUjIz8+P7t27RwcPHiRzc3OKjo5+pmNtAoB27drV4v4RERHk6Oio1D5v3jwyNjamqqoqoS0pKYk8PDwIAHXt2pXeeecdldtcsmQJhYSEKLQ1NjZSbGwsde/enYyNjcna2prGjRtHV69eFfqo89rU1tZSZGQkubi4kJGREdna2lJYWBhlZGQQEdG4ceMIAK1atarZ41+8eDF5eHiQTCYjIyMjcnJyolmzZlFeXp7K/mPGjCFHR0dqbGxUaJ8/fz516dKl2X2pou7r1VFx/mNtifNF66h7/dGXvBAcHExubm5kZmZGpqam5OHhQeHh4XTp0iWlvsePH6eXX36ZTE1NycHBgZYsWUI1NTUqt/uk/PE0neD6ubtDR69JneDFZKzd6MLnJSIigmxsbLQag7o0VQxmZmaSkZERff3115oMr900NDSQv78/bd++XWPbLCoqIrFYTJ988onSMi4Gm6cLn2fWeenC+0sf8gXnhdZrLn88jS68v5/Rbr5NlDHWYanzsHxHVV1dje+//x6ZmZnCA+6enp5Yt24d1q1bh4qKCi1HqJ6GhgYkJyejvLwc4eHhGtvumjVr0K9fP8ybNw8AQETIy8tDenq6MGgAY0x/dfZ8wXmh9R7PH/qGi0EtOHjwICwtLfHtt98+sc/bb78Nc3NziEQijQzE0JoYdN1PP/2EF154AQYGBhCJRLCzs0N0dLS2w1Kwb98+uLu7C8Mo29vbY+rUqdoOi3UgDx48wMiRI+Hl5YWZM2cK7UuXLsXEiRMRHh6u9qAB2nTs2DHs27cPqampLZ4T62k2bNiACxcu4ODBgzA2NgYApKSkwNHREf7+/jhw4IBG9sOerK1zVnuIiYmBt7c3JBIJZDIZvL29sXLlSqXh6R9XU1MDb29vrFixQu19Pp4jmv6ZmJigW7duGDZsGGJjY1FcXNzaw2J6hPOC+lTlD33DxaAWENFT+3z++ef45z//qdUYdN3gwYPx+++/IygoCMDDSU1bk4zbUlhYGG7cuAEPDw9YWlri3r172LFjh7bD6vCWLVuGhIQElJaWws3NDXv37tV2SG1i69atCkNwP/7eWb9+PebNm4cPP/xQSxGqLyAgAP/+97+FeaCeVUpKCmpra3Hs2DFYW1sL7aGhoQrnrqioSCP7Y6q1dc5qDydPnsSsWbOQnZ2N/Px8REVFISYmBhMmTGh2veXLl+Pq1aut2ufjOYKI0NjYiIKCAuzevRtubm6IjIxEr169lEZ2ZC2jL/miCeeFlntS/tA3RtoOQB+NGTNG69/Y6EIMTaqrqxEQEIBTp05pO5Rn1pmORZd98MEHwuSy+i4oKEj4QkQfhYSEICQkRNthsE7AxMQEc+fOhVgsBgBMnDgRe/bswZ49e3D37l2VE1WfOnUKv/32m0bjEIlEsLKywrBhwzBs2DCMGTMGkyZNwpgxY3Dt2jVYWlpqdH+dnT7mC33PCy3F+eMh/mVQh4lEIm2H0C62b9+OgoICbYehEZ3pWBhjTB0dPWclJSUJhWATR0dHAFD5DFZ1dTWWLFmCTZs2tWlcEyZMwPTp01FQUICtW7e26b4YY/qHi8FWGjhwoHBvf9++fZGTk6Oy35o1a2BjYwOxWIzo6Gikp6fDxcUFIpEIn332mdCPiBAbG4sePXrA1NQUlpaWWLJkidL2GhoasGrVKri4uEAikaBv377YtWuXWrGriiE+Ph4ymQxSqRQpKSkYNWoULCws4OTkhMTERGHduLg4iMVidOvWDbNnz4aDgwPEYjF8fX1x5swZod+8efNgYmKi8JP/3LlzIZPJIBKJhFu2FixYgMWLFyMrKwsikQienp4AgEOHDrV6vh5dOxZ1nTx5Ej179oSlpSXEYjH69OmD77//HsDD53Ka3nceHh7CRLEzZsyAVCqFpaUl9u/fD6D598rHH38MqVQKc3NzFBQUYPHixXB0dGz1rU6MMf2iiZzV0ms1ABw/fhwvv/wypFIpLCws0KdPH+FZPk3kxSfJzMyElZUVXF1dlZYtX74cc+fOha2trcp1nyWPPa5pLrzU1FShraOfW8aYjtDOKKa6pzVDww4ZMoScnZ0V5iT59ttvycvLS6FfXFwcrV+/Xvj/nJwcAkCbN28W2pYvX04ikYj+9re/UXFxMVVVVdGWLVsIAJ0/f17o995775GpqSnt3buXiouLadmyZWRgYEBnz55VK/YnxQCAjhw5QqWlpVRQUED+/v4kk8morq5O6BcREUEymYwuX75MNTU1lJGRQYMGDSJzc3NhDh4iojfffJPs7OwU9hsbG0sAqLCwUGgLCwsjDw8PhX7fffcdmZub07p16556LCNGjCAAVFxcrJPHQkTk4eFBlpaWTz0WIqI9e/bQmjVr6MGDB3T//n0aPHiwwrD4YWFhZGhoSHfu3FFYb8qUKbR//37h/5/2Xmk6R/Pnz6fNmzfT+PHj6ffff29RjJ1gKGWtgJ5MVdBZ6Mvr1ZrPs6ZyVkuu1RUVFWRhYUExMTFUXV1N9+7do/HjxwvXXk3lxSZ1dXWUm5tLmzdvJlNTU5VD9aenp1NwcDARERUWFhIAWr58uUIfdfLY03JEWVkZASBnZ2ehraOcW84XraMv15+OrhO8v3mewSateTH/+c9/EgA6evSo0DZhwgQCQKdOnRLahgwZQrdv3xb+//FCrKqqiqRSKb322msK209MTFRIrNXV1SSVSik8PFzoU1VVRaampvSXv/xFrdibKwarq6uFtqbkfv36daEtIiJCKWmdPXuWANDatWuFtmctoFqquWJQV45FnWLwcR988AEBoIKCAiIiSktLIwAKE9uWlpZS9+7dqb6+noha9l5RdY5aqhNc/LSCk3vHoi+vl7qfZ03mrJZcq3/77TcCQN99951SLJrMi03s7OwIAHXp0oU+/fRThS8Qm7Y/cOBAys3NJaInF4PqaEmOEIlEZGVlRUQd69xyvmgdfbn+dHSd4P29mweQeQaTJk3C/Pnz8a9//QvDhw9HcXExsrKyYGpqin/961/w8fHBrVu3YGJiAhcXlydu5/r166iqqkJAQECz+7t69SqqqqrQu3dvoU0ikcDe3h5XrlzR2HE9ysTEBAAgl8ub7Tdw4EBIpdI2i0MTOuqxNA113DRH0h/+8Ad4eXnhiy++wLJlyyASibBz506Eh4fD0NAQQPu9VyZOnKixbemLjRs3Ys+ePdoOg7FWa+uc9fi12t3dHd26dcPUqVMxf/58TJ8+Hc8///wz7aM5OTk5KCkpwfnz57F06VJs27YNR48eRbdu3QA8HJ3yz3/+s/A8YXuorKwEEcHCwgJAxzy3nC/Ux/lC9+Xm5mo7hGfGzww+A3Nzc4wfPx779u1DVVUVEhMT8ac//Qljx47Frl27UFtbi8TExKfOK9f0RnrScwdNKisrAQArVqxQmI/o9u3bqKqq0sxBPQNTU1MUFhZqOwyN0OaxHDhwAMOGDYOtrS1MTU3x17/+VWG5SCTC7NmzcePGDRw5cgQA8K9//Qt/+tOfhD66/l5hjHVc7Z2zJBIJjh49Cj8/P6xfvx7u7u4IDw9HdXV1m1zrjI2NYWtri6CgIOzcuRMZGRnCaJTp6em4dOkS3n777VZtu7WuXbsGAPD29gbQcc8tY0z38C+Dz2jGjBnYsWMH/vOf/yAxMRHJycnCPDbfffcdkpOT8cMPPzS7jabRy2pra5vt15R4N27ciAULFmjmADRELpejpKQETk5O2g7lmbX3sZw4cQK//PILFi5ciOzsbIwbNw7jx4/HF198geeeew6bN29WKginT5+OZcuW4fPPP4ezszMsLCwUBjhor/cKf2OpHpFIhIULF+KNN97QdiisBTr66JhtRRs5q1evXvj2229RWFiIDRs24KOPPkKvXr0QHh6usX2o4unpCUNDQ2RkZAB4OGL0kSNHYGCg/F36+vXrsX79epw9exYDBw7UaByHDh0CAIwaNQpAxzy3nC/Uw/miY9i9ezcmTZqk7TCeCf8y+IyGDx8OV1dXREdHo1u3bujSpQtGjBgBBwcHrF69Gm5ubsJtHU/Su3dvGBgY4Pjx4832c3Z2hlgsxoULFzR5CBpx7NgxEBEGDx4stBkZGT31lkxd1N7H8ssvv0AmkwEALl26BLlcjr/85S9wd3eHWCxW+QeptbU1Jk2ahOTkZHzyySeYNWuWwnJdfq8wxjq29s5ZeXl5uHz5MoCHRdCHH36IAQMG4PLlyxrbx/379zFlyhSl9szMTDQ0NMDZ2RkAkJCQACJS+Nd0F8ny5ctBRBovBO/du4eNGzfCyckJM2fOBNCxzi1jTLdxMfiMRCIRpk2bhitXrmDatGkAAENDQ7z11lvIyMjAW2+99dRt2NraIiwsDHv37sX27dtRVlaGixcvYtu2bQr9xGIxZsyYgcTERMTHx6OsrAwNDQ3Izc3F3bt32+T4nqSxsRHFxcWor6/HxYsXsWDBAri4uAjDXwMPv1F98OABkpOTIZfLUVhYiNu3bytty8bGBnl5ebh16xbKy8shl8uRmpqqsSG5tX0sTyKXy5Gfn49jx44JxWDTs6VpaWmoqalBZmamwjQXj5ozZw5qa2vx3XffYezYsQrLdOm9whjrXNo7Z+Xl5WH27Nm4cuUK6urqcP78edy+fRuDBw/W2D5kMhkOHz6Mo0ePoqysDHK5HOfPn8e0adMgk8mwaNGiFm+ribp5jIhQUVGBxsZGocjctWsXhgwZAkNDQyQnJwtfLnekc8sY03HaGrpG1zzLaEA3btygbt26KYw49vvvv1O3bt1ILpcr9N28eTPZ29sTAJJKpcLQ1OXl5fT2229Tly5dyMzMjPz8/GjVqlUEgJycnOjXX38lIqLa2lqKjIwkFxcXMjIyIltbWwoLC6OMjIwWx6sqhi1btpBUKiUA1L17d8rKyqJt27aRhYUFASBXV1e6du0aET0cgdPY2JgcHR3JyMiILCwsKDQ0lLKyshT2c//+fRo+fDiJxWJyc3Ojd999l5YsWUIAyNPTU5i64dy5c+Tq6koSiYT8/Pzo3r17dPDgQTI3N1cYMfNxP/30E/Xq1YsMDAwIANnb29P69et16lj+/ve/k4eHBwFo9l9SUpKwr8jISLKxsSErKyuaOHEiffbZZwSAPDw8FKa7ICLq378/LV26VOX5ae69EhMTQxKJRBiqXNXQ6c3pBKNnaQV4dLgORV9er9Z8njWRs1p6rb516xb5+vqStbU1GRoa0nPPPUfLly8XRk/WRF4kIgoODiY3NzcyMzMjU1NT8vDwoPDwcLp06VKz6z1pNNGW5LH9+/dT3759SSqVkomJiZDPmkYOffnll2ndunV0//59pXU7yrnlfNE6+nL96eg6wft7t4iIqI3rzQ6h6Z5fPh1PN3v2bOzZswf379/XdijPrKMfy5gxY/DZZ5/Bzc2tXffLn5fWEYlE2LVrFz8D0kHoy+vFn2fWlvj91Tr6cv3p6DrB+3sP3ybKWqVpmoPOoCMdy6O3nV68eBFisbjdC0HGGGOMMdY5cDHYSVy5ckVh6Ocn/WsaHYx1TJGRkcjMzMS1a9cwY8YMREVFaTsk1oZmz56t8PlVNU1NWloali5din379sHd3V3oq+p55aCgIJibm8PQ0BC9evXCuXPn2uMwWi06OlrldezRec/279+PmJgYpS91kpOTFdbp2rVre4fPtIzzItNXnTkvxMTEwNvbGxKJBDKZDN7e3li5ciXKysqU+qanp2PIkCGQSqVwcHBAZGSkwijIT8of+oaLwU7C29tbaYQzVf927tz5TPtZtmwZEhISUFpaKkyh0VF1xGORSqXw9vZGYGAg1qxZg549e2o7JNbGbGxskJqaiqtXr2L79u0Ky1avXo24uDgsW7YMYWFhuHHjBjw8PNClSxfs2LEDBw4cUOh/+PBh7NmzB2PHjkVGRgYGDBjQnofSJoKDgyEWixEQEICSkhKhPSQkBLm5uThx4gRGjx6txQiZtrRXXmRMl3T2vHDy5EnMmjUL2dnZyM/PR1RUFGJiYjBhwgSFfhkZGQgKCkJAQAAKCwuRlJSEL774AnPmzBH6PCl/6BsuBplaPvjgA9TW1oKIcPPmTaUPX0fSEY8lOjoaDQ0NyM7OVhpBVN9UV1fD19e3w+/jaSQSCUaOHAkvLy+YmpoK7R999BF27tyJ3bt3w9zcXGGduLg4GBgYICIiAqWlpe0dskZ9/fXXSn+8//bbbwp95s+fjxdffBGjR49GfX09gIfP2zg6OsLf3x/du3fXRuiMMR2hL/lCH/KCiYkJ5s6dC1tbW5iZmWHixIkIDQ3FDz/8oDDKbVRUFOzt7bF27VrIZDL4+PggMjISX375Ja5cuSL0U5U/9A0Xg4yxDmn79u0oKCjo8PtojevXr2PlypVYu3atMAH4o3x9fbFgwQLcuXMH7733nhYibH9r1qzBhQsXsGnTJm2HwhjTMfqQL/QlLyQlJSkdn6OjIwCgoqICAFBfX48DBw5g6NChCnM1jxo1CkSElJQUhfX1PX9wMcgYaxdEhA0bNuCFF16AqakprK2tERoaqvAN3bx582BiYgJ7e3uhbe7cuZDJZBCJRCgqKgIALFiwAIsXL0ZWVhZEIhE8PT0RFxcHsViMbt26Yfbs2XBwcIBYLIavr6/CXI3Psg8AOHToULvNgfkkcXFxICIEBwc/sU90dDS8vLzw+eefIy0trdntteS1iY+Ph0wmg1QqRUpKCkaNGgULCws4OTkhMTFRYXsNDQ1YtWoVXFxcIJFI0LdvX+zatevZDvoprK2tMXToUGzatKkjj+rGGAPni9bQ57yQmZkJKysruLq6AgBu3LiBiooKYe7mJh4eHgAeDsD3KL3PH203bUXH0gnmCWGs3bTm87Jq1SoyMTGhr7/+mkpKSujBvhpFAAAgAElEQVTixYs0YMAA6tq1K927d0/o9+abb5KdnZ3CurGxsQSACgsLhbawsDDy8PBQ6BcREUEymYwuX75MNTU1lJGRQYMGDSJzc3OFORqfZR/fffcdmZub07p169Q6fiL1542KiIggR0dHpXZ3d3fq2bOnynU8PDzo5s2bRER06tQpMjAwoOeff54qKiqIiCg1NZVCQkIU1mnpa7N8+XICQEeOHKHS0lIqKCggf39/kslkCvOsvvfee2Rqakp79+6l4uJiWrZsGRkYGNDZs2dbfOxERFFRUeTk5ERWVlZkbGxMzz//PIWEhND//vc/lf2XLl1KAOj8+fMK7fPnz6cuXbqotW8i/Znni/Mfa0ucL9onX+hLXmhSV1dHubm5tHnzZjI1NVWYL/n48eMEgGJjY5XWk0gkFBAQoNT+pPzxNJ3g+rmbfxlkjLW56upqbNiwAePHj8fUqVNhaWmJPn36YOvWrSgqKsK2bds0ti8jIyPhm8yePXsiPj4e5eXlSEhI0Mj2x4wZg7KyMqxcuVIj21NXZWUlbt68KXzD2RwfHx8sXLgQt27dwvvvv6+yT2teG19fX1hYWMDW1hbh4eGorKxEdnY2AKCmpgbx8fEYN24cwsLCYGVlhRUrVsDY2Fjt12DatGnYv38/cnJyUFFRgcTERGRnZ2Po0KHIyMhQ6t/0bOClS5fU2g9jTHdwvlCfPuWFJs7OznBycsKaNWvw8ccfY9KkScKyphFDDQ0NldYzNjZGdXW1Urs+5w8uBhljbS4jIwMVFRUYOHCgQvugQYNgYmKicFuOpg0cOBBSqVTh1paOrKCgAEQEqVTaov7R0dHo0aMHtmzZgvT0dKXlz/ramJiYAPi/OTCvXr2KqqoqhekfJBIJ7O3t1X4NnJ2d0b9/f5iZmcHExASDBw9GQkICqqursWXLFqX+TeckPz9frf0wxnQH5wv16VNeaJKTk4OCggJ88803+Oqrr9C/f3/hmc2mZwpVDQhTV1cHiUSi1K7P+YOLQcZYm2sastnMzExpmZWVFcrLy9t0/6ampigsLGzTfbSXmpoaAFAYWbQ5YrEYCQkJEIlEmDlzptI3opp+bSorKwEAK1asUJjL7fbt26iqqlJrW6r06dMHhoaGuHbtmtKypgTfdI4YYx0P5wv16WNeMDY2hq2tLYKCgrBz505kZGTggw8+AADhGc/H5x6sqqpCTU0NHBwclLanz/mDi0HGWJuzsrICAJUJpKSkBE5OTm22b7lc3ub7aE9NCUudSXJ9fHywaNEiZGZmIioqSmGZpl8bW1tbAMDGjRuVpoQ4ffq0WttSpbGxEY2NjSr/6KmrqwMAld/6MsY6Bs4X6tP3vODp6QlDQ0Ph8QE3NzeYm5vj9u3bCv2uX78OAOjbt6/SNvQ5f3AxyBhrc71794aZmRl+/vlnhfYzZ86grq4OL730ktBmZGQk3FqiCceOHQMRYfDgwW22j/bUrVs3iEQiteeJioqKgre3N86fP6/Qrs5r0xLOzs4Qi8W4cOGCWuupMmLECKW2s2fPgojg4+OjtKzpnNjZ2T3zvhlj2sH5Qn36khfu37+PKVOmKLVnZmaioaEBzs7OAB6+ZqNHj8aJEyfQ2Ngo9EtNTYVIJFI54qo+5w8uBhljbU4sFmPx4sVISkrCjh07UFZWhkuXLmHOnDlwcHBARESE0NfT0xMPHjxAcnIy5HI5CgsLlb7dAwAbGxvk5eXh1q1bKC8vF5J1Y2MjiouLUV9fj4sXL2LBggVwcXHB9OnTNbKP1NRUrU4tIZVK4e7ujtzcXLXWa7ot6PEH6tV5bVq6nxkzZiAxMRHx8fEoKytDQ0MDcnNzhQmBw8PDYWdnh3PnzjW7rTt37mDnzp0oKSmBXC7H6dOn8fbbb8PFxQVz5sxR6t90Tvr06aNWzIwx3cH5Qn36khdkMhkOHz6Mo0ePoqysDHK5HOfPn8e0adMgk8mwaNEioe/KlSuRn5+P1atXo7KyEqdPn0ZsbCymT5+OHj16KG1br/OHVgYx1UGdYGhYxtpNaz4vjY2NFBsbS927dydjY2OytramcePG0dWrVxX63b9/n4YPH05isZjc3Nzo3XffpSVLlhAA8vT0FIb8PnfuHLm6upJEIiE/Pz+6d+8eRUREkLGxMTk6OpKRkRFZWFhQaGgoZWVlaWwfBw8eJHNzc4qOjlb7vEFDU0vMmzePjI2NqaqqSmhLSkoiDw8PAkBdu3ald955R+U2lyxZojSEeEtemy1btpBUKiUA1L17d8rKyqJt27aRhYUFASBXV1e6du0aERHV1tZSZGQkubi4kJGREdna2lJYWBhlZGQQEdG4ceMIAK1atarZ41+8eDF5eHiQTCYjIyMjcnJyolmzZlFeXp7K/mPGjCFHR0dqbGxUaOepJZrH+Y+1Jc4X7ZMv9CUvBAcHk5ubG5mZmZGpqSl5eHhQeHg4Xbp0Sanv8ePH6eWXXyZTU1NycHCgJUuWUE1NjcrtPil/PE0nuH7u7tDRa1IneDEZaze6+nmJiIggGxsbbYfxRJoqBjMzM8nIyEhhXqWOpKGhgfz9/Wn79u0a22ZRURGJxWL65JNPlJZxMdg8Xf08s85BV99fnS1fcF5ovebyx9Po6vtbDTzPIGOsc1HnAfqOoLq6Gt9//z0yMzOFB9w9PT2xbt06rFu3DhUVFVqOUD0NDQ1ITk5GeXk5wsPDNbbdNWvWoF+/fpg3bx4AgIiQl5eH9PR0YdAAxhh7VGfKF5wXWu/x/KFvuBhkjDEd9uDBA4wcORJeXl6YOXOm0L506VJMnDgR4eHhag8aoE3Hjh3Dvn37kJqa2uI5sZ5mw4YNuHDhAg4ePAhjY2MAQEpKChwdHeHv748DBw5oZD+MMabLOC+oT1X+0DdcDDLGOoVly5YhISEBpaWlcHNzw969e7Ud0jPbunWrwhDcO3bsUFi+fv16zJs3Dx9++KGWIlRfQEAA/v3vfwvzQD2rlJQU1NbW4tixY7C2thbaQ0NDFc5dUVGRRvbHGOv4OmO+aMJ5oeWelD/0jZG2A2CMMU344IMPhAln9UlQUBCCgoK0HYbWhISEICQkRNthMMY6kM6eL/Q9L7QU54+H+JdBxhhjjDHGGNNDXAwyxhhjjDHGmB7iYpAxxhhjjDHG9BAXg4wxxhhjjDGmh7gYZIwxxhhjjDE9xKOJPkYkEmk7BMY6DP68qG/SpEmYNGmStsNgTAl/nllb4veX+jhfsPbAxeD/5+vri127dmk7DMa0buPGjQCAhQsXajkSxrTP19dX2yG0uY6Y/3Jzc/Hbb7/h0qVL+P3331FZWQlra2v07t0br776Kvr27avtEFk727NnD44cOYK///3vXHgypgYREZG2g2CM6Y433ngDALB7924tR8IYYw/du3cPJ0+eRFpaGg4dOoTs7GyYmZlh8ODBCAwMRGBgIAYMGMBFgB67cOEC+vfvjzNnzuDll1/WdjiMdRR7+JdBxhhjjOmU8vJynDlzBmlpaUhLS8O5c+dgaGiIF198EZMnT0ZgYCCGDh0KY2NjbYfKdES/fv3g5uaGlJQULgYZUwMXg4wxxhjTqurqavz4449IS0tDeno6zpw5AyJCv379EBgYiNWrV2PYsGEwNzfXdqhMh4WEhOA///kP1q9fr+1QGOswuBhkjDHGWLtqaGjAhQsXhF/+0tPTUVNTA3d3dwQGBmL+/PkICAiAjY2NtkNlHUhoaCg2bdqEq1evokePHtoOh7EOgYtBxhhjjLW5GzduCMVfWloaiouLYWdnh1dffRWffvopRo4cCRcXF22HyTowPz8/2NraIjk5GZGRkdoOh7EOgYtBxhhjjGnc3bt3kZ6ejrS0NKSmpiInJ0cY9CUyMpIHfWEaZ2hoiNdffx0pKSlcDDLWQlwMMsYYY+yZFRUV4fTp08Kzf7/88gskEgmGDBmCmTNnws/Pjwd9YW0uJCQEX331Fe7evQsHBwdth8OYzuNikDHGGGNqq6qqwqlTp4TbPs+fPw+RSCQM+vLRRx/Bz88PYrFY26EyPRIUFASJRIL9+/cjIiJC2+EwpvO4GGSMMcbYUz0+6MvJkydRW1srDPrSdOuntbW1tkNlekwikWDEiBFITk7mYpCxFuBikDHGGGMqPTroyw8//ICSkhLY29vD398fcXFxGDVqFJydnbUdJmMKQkJC8Pbbb6OkpARWVlbaDocxncbFIGOMMcYAAHl5ecIzfwcOHMCdO3dgbm6OV155Be+//z4CAwPx0ksvaTtMxpoVHBwMADh06BDCw8O1HA1juo2LQcYYY0xPFRYW4tixY8Kvfzdu3IBUKoWvry/effddBAYGon///jAwMNB2qIy1mJWVFfz9/ZGSksLFIGNPwcUgY4wxpieaG/Rl4sSJCAwMhL+/P0xNTbUdKmPPJDQ0FMuXL0dNTQ0PYsRYM7gYZIwxxjqp+vp6/Prrr80O+vLaa6/xc1Ws0xk/fjzmz5+P//73vxg1apS2w2FMZ3ExyBhjjHUijw76cvjwYZSWlsLBwQF+fn6Ii4vD6NGj4eTkpO0wGWtTjo6OGDBgAFJSUrgYZKwZXAwyxhhjHVhT8Zeeno4jR44gLy8PXbt2xfDhw/Hxxx9jyJAh6NWrl7bDZKzdhYSEYMuWLYiPj+fnXhl7Ai4GGWOMsQ6koKAAx48fF6Z7uHnzpjDoy7x583jQF8b+v3HjxmHVqlU4c+YMfHx8tB0OYzqJi0HGGGNMh1VWVuL06dPCrZ/nzp2DgYEB+vXrhzfeeIMHfWHsCXr37g0vLy8kJydzMcjYE3AxyBhjjOmQxwd9OXHiBOrq6hQGfQkKCoKlpaW2Q2VM5wUHByMpKQkxMTHaDoUxncTFIGOMMaZFjY2N+P3334XJ3r///nuUlZXB3d0dQ4YMwebNmzFmzBg4OjpqO1TGOpyQkBB88sknuHz5Mnr27KntcBjTOVwMMsYYY+3s0RE/jx49ivv378PW1hbDhg1DbGwsXnvtNbi5uWk7TMY6PF9fX9jb2yM5OZmLQcZU4GKQMcYYa2P5+fk4ceKE8Mvf7du3IZPJ4OPjgyVLliAwMBADBgyASCTSdqiMdSoGBgZ4/fXXkZycjGXLlmk7HMZ0DheDjDHGmIZVVFTgp59+Uhj0xdDQEC+++CLCw8MRGBiIV199FSYmJtoOlbFOLzQ0FNu3b0dOTg6cnZ21HQ5jOoWLQcYYY+wZPT7oy/HjxyGXyxUGfRkxYgQsLCy0HSpjeicwMBBmZmbYv38/5s6dq+1wGNMpXAwyxhhjampoaMCFCxeE4u/HH39EdXW1UPz9+c9/xh/+8Ad06dJF26EypvdMTU0xcuRIpKSkcDHI2GO4GGSMMcZa4NFBX44cOYIHDx6gW7duGDp0KDZt2oSgoCA8//zz2g6TMaZCSEgIZsyYgeLiYlhbW2s7HMZ0BheDjDHGmAqPDvpy6NAhZGdnC4O+/PWvf+VBXxjrQF5//XWIRCIcPHgQb775prbDYUxncDHIGGOMoflBXyZPnsyDvjDWgVlaWmLYsGFITk7mYpCxR3AxyBhjTC9VV1fjxx9/RHp6On788UccP34cDQ0N6N+/PwIDA7F69WoMGzYM5ubm2g6VMaYBISEhiIyMRE1NDcRisbbDYUwncDHIGGNMLzw+6Et6ejpqamoUBn0JCAiAjY2NtkNljLWB0NBQvPPOO0hLS8Prr7+u7XAY0wlcDDLGGOu0Hh30JS0tDcXFxbCzs8Orr76KTz/9FCNGjICrq6u2w2SMtYPnnnsOgwYNQkpKCheDjP1/XAwyxhjrNO7du4eTJ08iLS0NqampyMnJgZmZGQYPHozIyEge9IUxPRcaGopNmzZh69atMDQ01HY4jGkdF4OMMcY6rPLycpw5c0b45e+XX36BRCLBgAEDMGXKFAQGBmLo0KEwNjbWdqiMMR0QGhqKZcuW4dSpU/D39xfab9++jdraWnh5eWkxOsbaHxeDjOmxM2fO4Ndff1Vou3HjBgBg27ZtCu0vvvgiXnnllXaLjTFVqqqqcOrUKaH4O3/+PEQiEfr164fAwEB89NFH8PPz48EhGGMqvfDCC+jRowdSUlJgaWmJ5ORk7N27F5cuXUJ8fDwXg0zvcDHImB4rLCxEREQEDA0NYWBgAAAgIgDAO++8AwBobGxEQ0MDvv32W63FyfTX44O+nDx5ErW1tcKgL023fvIk0oyxp2loaMCpU6fg4uKCL7/8En/7299gYmKCuro6iEQiSCQSbYfIWLvjYpAxPTZixAhYWFigrKwMDQ0NT+xnYWGBoKCgdoyM6bNHB3354YcfUFJSAnt7e/j7+yMuLg6jRo2Cs7OztsNkjHUAdXV1+P7775GcnIz//Oc/KC4uFgrApuVN+I4Cpo+4GGRMjxkbG2Py5MlISEhQSIiq+vBE26yt3L17F+np6UhLS8PBgweRm5sLc3NzvPLKK3j//fcRGBiIl156SdthMsY6oMbGRqxcuVLhkQhV+Y6I+JdBppe4GGRMz02ePBn/+Mc/nrhcLpdjypQp7RgR6+yKiorw3//+V5jr7/Lly5BIJBgyZAjeeecdDBkyBIMHD4aREacoxtizEYvF2Lt3L1588UVUV1cLj0I8qS9j+kZEzX0qGGOdXmNjI5577jnk5+erXG5ra4t79+4JzxQypq6nDfoSGBjIg74wxtrU7t27MWnSpGb7HD9+HK+++mo7RcSYTtjDf90xpucMDAzw1ltvqbwN1MTEBNOnT+dCUI+UlZUhIiICBw8ebPU26uvr8csvvyAmJgavvfYabGxs8Nprr2HPnj146aWXsHPnThQVFeHnn3/GRx99hMDAQC4EGWNt6o033sCf/vSnZu844OsQ00d8Dw5jDJMnT8Ynn3yi1F5XV4fJkydrISKmDWlpaZg2bRry8vIgk8kwevToFq/76KAvhw8fRmlpKRwcHODn54e4uDiMHj0aTk5ObRg9Y4w177PPPsPp06dx7do11NfXKy3nZwaZPuLbRBljAIDu3bvj+vXrCm3u7u7IysrSUkSsvVRUVGDJkiX4xz/+AQMDAzQ0NOCFF17A5cuXn7jOjRs3kJ6ejh9//BEHDhzAnTt30LVrVwwfPhxDhgyBn58fD/rCGNM5165dQ79+/VBdXa1yWffu3bUQFWNas4d/GWSMAQCmTp2K9evXQy6XA3h4i+i0adO0HBVra6dOncKbb76J3NxcEJEwxciVK1eQn58POzs7AA/npDx27Jgw3cPNmzchlUrh6+uLd999F4GBgejfvz/fUswY02leXl6Ij4/HjBkzlJbxL4NMH/Evg4wxAMD169eVvhG9evUqvLy8tBQRa0vV1dVYu3YtPv74Y+HXwEcZGBggMjIS1dXVOHLkCH777TcYGRnhlVdeQUBAAAICAjB48GAYGxtr6QgYY6z1pk2bhm+++UbhdtHCwkJ07dpVi1Ex1u72cDHIGBP069cPFy9eBAD07dsXFy5c0HJErC389NNPePPNN5Gdna3yuRng4fySXbt2hUQiEUb8DAoKgqWlZTtHyxhjmldZWYn+/fvj5s2bwnWwoqICMplMy5Ex1q74NlHG2P/54x//iMjISOG/WedSU1ODNWvWIDY2FiKRSOnXwEfJ5XIYGBjwM6OMsU5JJpNh3759GDhwoNDGo4kyfcS/DDLGBHl5eXB2dgYRIScnB46OjtoOiWnIr7/+ismTJyMzM/OJvwaqcuPGDbi5ubVhZIwxpj3//Oc/8ec//xmGhoZqXRsZ6ySUfxk8ffo0NmzYoI1gGGM6wMbGBgCwYMECLUfCNKGxsRG///47rly5AnW/+xOJRJg4cSIXg3po0aJF8PHxaZNtT5w4sU22y1hrOTs74+7du/zeZJ3enj17lNqUhn3LycnB3r172yUgxljr7N27F7m5uW2ybRcXF7i6urbJtrXpp59+wk8//aTtMNpdfn4+Ghoa4OLiAnt7e1hZWUEikcDQ0FCpr0gkgoGBgfCPiJCfn6+FqJk27d27Fzk5OW26/ba6fjHWGi+99BKkUin//dsK/HnuGHJzc5/4/n7iM4OqKkfGmG4QiURYuHAh3njjDY1v+8GDBwD+7xfCzqLpG1++tv2fmpoaFBUVoaioCAUFBSgsLMT9+/eFtnv37sHAwIDPmZ4RiURtvo+2un4x1lrx8fGYO3cuX+/U1JZ/jzDN2b17NyZNmqRyGQ8gwxhT0NmKQPZkYrEYTk5OcHJy0nYojDGmVTylBNNXPDswY4wxxhhjjOkhLgYZY4wxxhhjTA9xMcgYY4wxxhhjeoiLQcYYY4wxxhjTQ1wMMqbHDh48CEtLS3z77bfaDkUnzZ49GyKRSPg3depUpT5paWlYunQp9u3bB3d3d6HvW2+9pdQ3KCgI5ubmMDQ0RK9evXDu3Ln2OIxWi46OVjj+pn+9e/cW+uzfvx8xMTFoaGjQyD478/mMiYmBt7c3JBIJZDIZvL29sXLlSpSVlSn1TU9Px5AhQyCVSuHg4IDIyEjU1tYKy5903pOTkxVeKx4Ug7H2xXm15fh6/1Brr/eawsUgY3pM3UnI9ZGNjQ1SU1Nx9epVbN++XWHZ6tWrERcXh2XLliEsLAw3btyAh4cHunTpgh07duDAgQMK/Q8fPow9e/Zg7NixyMjIwIABA9rzUNpEcHAwxGIxAgICUFJS8kzb6uzn8+TJk5g1axays7ORn5+PqKgoxMTEYMKECQr9MjIyEBQUhICAABQWFiIpKQlffPEF5syZI/R50nkPCQlBbm4uTpw4gdGjR7fbsTHGHuK82jJ8vX/oWa73msLFIGN6bMyYMSgtLcXYsWO1HQqqq6vh6+ur7TCUSCQSjBw5El5eXjA1NRXaP/roI+zcuRO7d++Gubm5wjpxcXEwMDBAREQESktL2ztkjfr6669BRAr/fvvtN4U+8+fPx4svvojRo0ejvr6+VfvRh/NpYmKCuXPnwtbWFmZmZpg4cSJCQ0Pxww8/4O7du0K/qKgo2NvbY+3atZDJZPDx8UFkZCS+/PJLXLlyRein6ryLRCI4OjrC398f3bt3b/djZEzfcV59Or7ea+Z6rylcDDLGdML27dtRUFCg7TBa5Pr161i5ciXWrl0LsVistNzX1xcLFizAnTt38N5772khwva3Zs0aXLhwAZs2bVJ7XX05n0lJSUrH5+joCACoqKgAANTX1+PAgQMYOnSowuTvo0aNAhEhJSVFYf1nOe+Msc5NF/MqX+9173rPxSBjeio9PR0uLi4QiUT47LPPAADx8fGQyWSQSqVISUnBqFGjYGFhAScnJyQmJgrrxsXFQSwWo1u3bpg9ezYcHBwgFovh6+uLM2fOCP3mzZsHExMT2NvbC21z586FTCaDSCRCUVERAGDBggVYvHgxsrKyIBKJ4OnpCQA4dOgQLCwssH79+vY4JS0WFxcHIkJwcPAT+0RHR8PLywuff/450tLSmt0eEWHDhg144YUXYGpqCmtra4SGhip8K9jS1wYAGhoasGrVKri4uEAikaBv377YtWvXsx30U1hbW2Po0KHYtGmT2rdJ6fP5zMzMhJWVFVxdXQEAN27cQEVFBVxcXBT6eXh4AAAuXryo0P4s550xplmcV5+Or/c6eL2nx+zatYtUNDPGdAgA2rVr1zNvJycnhwDQ5s2bhbbly5cTADpy5AiVlpZSQUEB+fv7k0wmo7q6OqFfREQEyWQyunz5MtXU1FBGRgYNGjSIzM3NKTs7W+j35ptvkp2dncJ+Y2NjCQAVFhYKbWFhYeTh4aHQ77vvviNzc3Nat27dMx/rhAkTaMKECWqtExERQY6Ojkrt7u7u1LNnT5XreHh40M2bN4mI6NSpU2RgYEDPP/88VVRUEBFRamoqhYSEKKyzatUqMjExoa+//ppKSkro4sWLNGDAAOratSvdu3dP6NfS1+a9994jU1NT2rt3LxUXF9OyZcvIwMCAzp49q9bxR0VFkZOTE1lZWZGxsTE9//zzFBISQv/73/9U9l+6dCkBoPPnz6u1H305n03q6uooNzeXNm/eTKampvT1118Ly44fP04AKDY2Vmk9iURCAQEBSu1POu/z58+nLl26qB2fpq4v2to+Y62hqb9/9SmvEqn/eebrfdtc75+mmff3bv5lkDGmkq+vLywsLGBra4vw8HBUVlYiOztboY+RkZHwbVzPnj0RHx+P8vJyJCQkaCSGMWPGoKysDCtXrtTI9jShsrISN2/eFL65a46Pjw8WLlyIW7du4f3331fZp7q6Ghs2bMD48eMxdepUWFpaok+fPti6dSuKioqwbds2pXWae21qamoQHx+PcePGISwsDFZWVlixYgWMjY3Vfl2mTZuG/fv3IycnBxUVFUhMTER2djaGDh2KjIwMpf5Nz6hdunSpxfvQp/PZxNnZGU5OTlizZg0+/vhjTJo0SVjWNIKcoaGh0nrGxsaorq5Wam/NeWeMtT99z6t8vdfN6z0Xg4yxpzIxMQEAyOXyZvsNHDgQUqlU4faMzqagoABEBKlU2qL+0dHR6NGjB7Zs2YL09HSl5RkZGaioqMDAgQMV2gcNGgQTExOF24NUefy1uXr1KqqqqhSmf5BIJLC3t1f7dXF2dkb//v1hZmYGExMTDB48GAkJCaiursaWLVuU+jedk/z8/BbvQ5/OZ5OcnBwUFBTgm2++wVdffYX+/fsLz/U0PWOiaoCAuro6SCQSpfbWnHfGmHbpY17l671uXu+5GGSMaZSpqSkKCwu1HUabqampAQCFkUWbIxaLkZCQAJFIhJkzZyp909c0TLSZmZnSulZWVigvL1crvsrKSgDAihUrFOabu337NqqqqtTalip9+vSBoaEhrl27prSsKXE1naOW0MfzaWxsDFtbWwQFBWHnzp3IyMjABx98AADCc0CPz0VVVVWFmpoaODg4KG2vNeedMdZxdJa8ytd73bzeczHIGNMYuVyOkpISODk5aZJukp8AACAASURBVDuUNtN0IVZn8lcfHx8sWrQImZmZiIqKUlhmZWUFACqTVmvOpa2tLQBg48aNSlNCnD59Wq1tqdLY2IjGxkaVybyurg4AVH6b+ST6fj49PT1haGgo3Hbr5uYGc3Nz3L59W6Hf9evXAQB9+/ZV2kZrzjtjrGPoTHmVr/e6eb3nYpAxpjHHjh0DEWHw4MFCm5GR0VNvg+lIunXrBpFIpPb8R1FRUfD29sb58+cV2nv37g0zMzP8/PPPCu1nzpxBXV0dXnrpJbX24+zsDLFYjAsXLqi1niojRoxQajt79iyICD4+PkrLms6JnZ1di/ehL+fz/v37mDJlilJ7ZmYmGhoa4OzsDODh52X06NE4ceIEGhsbhX6pqakQiUQqR+BrzXlnjHUMnSmv8vVeN6/3XAwyxlqtsbERxcXFqK+vx8WLF7FgwQK4uLhg+vTpQh9PT088ePAAycnJkMvlKCwsVPoWDABsbGyQl5eHW7duoby8HHK5HKmpqTo3tYRUKoW7uztyc3PVWq/pdpfHHxQXi8VYvHgxkpKSsGPHDpSVleHSpUuYM2cOHBwcEBERofZ+ZsyYgcTERMTHx6OsrAwNDQ3Izc0VJroNDw+HnZ0dzp071+y27ty5g507d6KkpARyuRynT5/G22+/DRcXF8yZM0epf9M56dOnT4v3oy/nUyaT4fDhwzh69CjKysogl8tx/vx5TJs2DTKZDIsWLRL6rly5Evn5+Vi9ejUqKytx+vRpxMbGYvr06ejRo4fSth8/74yxjqsz51W+3uvo9V6NoUcZYzoCGhiaffPmzWRvb08ASCqVUnBwMG3ZsoWkUikBoO7du1NWVhZt27aNLCwsCAC5urrStWvXiOjhENjGxsbk6OhIRkZGZGFhQaGhoZSVlaWwn/v379Pw4cNJLBaTm5sbvfvuu7RkyRICQJ6ensJw2efOnSNXV1eSSCTk5+dH9+7do4MHD5K5uTlFR0c/07ESaXZqiXnz5pGxsTFVVVUJbUlJSeTh4UEAqGvXrvTOO++o3OaSJUuUhsZubGyk2NhY6t69OxkbG5O1tTWNGzeOrl69KvRR57Wpra2lyMhIcnFxISMjI7K1taWwsDDKyMggIqJx48YRAFq1alWzx7948WLy8PAgmUxGRkZG5OTkRLNmzaK8vDyV/ceMGUOOjo7U2Nio1n705XwGBweTm5sbmZmZkampKXl4eFB4eDhdunRJqe/x48fp5ZdfJlNTU3JwcKAlS5ZQTU2Nyu0+ft6b8NQSjLWcJv7+1be8SqT+55mv921zvX+a5qaW4GKQsQ5IF/6YioiIIBsbG63GoA5NFoOZmZlkZGSkMF9QR9LQ0ED+/v60fft2jW2zqKiIxGIxffLJJ2rvh89n66k67024GGSs5XTh79+OlleJ1P888/W+9Zq73j8NzzPIGGsT6jwE3lFVV1fj+++/R2ZmpvDgtqenJ9atW4d169ahoqJCyxGqp6GhAcnJySgvL0d4eLjGtrtmzRr069cP8+bNU3s/fD5b7/HzTkTIy8tDenq6MAgBY6zj6Ox5la/3rff49V5TuBhkjLFmPHjwACNHjoSXlxdmzpwptC9duhQTJ05EeHi42g/Da9OxY8ewb98+pKamtniup6fZsGEDLly4gIMHD8LY2LhV++HzqT5V5z0lJQWOjo7w9/fHgQMH2jUexhhrCb7eq0/V9V5TuBjsRORyOVatWgV3d3eYmJjA0dER7733ntK8LOq6evUq3n33XfTq1Qvm5uYwMjKCpaUlvLy8MGbMGI0Mt8s6lmXLliEhIQGlpaVwc3PD3r17tR1Sm9i6davC0NI7duxQWL5+/XrMmzcPH374oZYiVF9AwP9r786jojjT/YF/m6VpulkalS0CKmBc0Wg0AxhHPc4YjdcVFSaaxDjjxeUGUUzclyBqjHOAQ5TjmHiZOWoQUEdNXCZjEibJxDh61GjwqogirgFRdpDt+f3hj046oHZDQwP9/ZzDP1Vvvc9TVW29/dhV9Y7E7t27dfMbNdXBgwfx6NEjpKenw8XFpUlxeDwN96TjPnHiRL3P7P3791s0L2qf9u3bB19fX7251xQKBZRKJdzc3DB8+HBs3rwZDx8+NHeqbZaljKt1eL033JOu9yZjxD2l1MrNmzdPVCqVJCcnS1FRkXz11Vfi5OQkr732WqP7/Pjjj8XW1lZ++9vfyrFjx+Thw4dSUVEhWVlZsmfPHgkODpa//OUvJtwLMgT4zI3RGvPMIJElau7rC69fbZefn584OzuLyOMXdzx8+FC++uormTlzpigUCvH09JRTp06ZOcvG4fffxuG/57aBzwz+Snl5OYKDg9tV7GvXrmHbtm144403EBYWBkdHRwwfPhwRERH45JNP8H//939G9/n9998jPDwcQ4cOxRdffIFXXnkFWq0WdnZ28PX1RWhoKFavXq17jqo1ao/nmoiI2p+WGDNMGUOhUECr1WL48OFISkpCamoqfvrpJ4wdO7ZN3fpHZOksshjcsWMHcnNz21XsU6dOoba2Fr/5zW/0lo8ePRoA8I9//MPoPmNiYlBTU4ONGzfCxsamwTavvPIK/ud//sf4hFtIezzXRETU/rTEmNGcMaZMmYKZM2ciNzcX27Zta5YYRGR6JisGd+7ciUGDBkGlUkGj0aBr165Yt24dgMdvN4uNjUWvXr1gZ2cHFxcXTJw4EZcuXdJtn5iYCI1GA7VajYMHD2LMmDFwcnKCl5cXkpOTjYr3zTffoHfv3nB2doZKpUJAQICuGIqMjERUVBSysrKgUCjg7+8P4PEbglavXg0fHx/Y29ujX79+SElJMTo3U8c2lJXV41Npb2+vt7x79+4AoPfL4LFjx5454WhlZSW++OILdOzYES+99JLBefBcN/+5JiKyJE+71kZERECpVOo9wzN//nxoNBooFArdM5N//etf4eDgAIVCARcXFxw4cACnT59Gly5dYG1tjddee83ovAwZ7wzNr6ExIyEhASqVCm5ubpgzZw48PT2hUqkQHByMkydPmiQGYNh3AkPVTYx+9OhR3TJTjbn/+te/8NJLL0GtVsPJyQkBAQEoKip6ZgwiegYj7il9ori4OAEgGzdulPz8fHnw4IH85S9/kenTp4uIyOrVq0WpVMrOnTuloKBAzp8/LwMHDpROnTrJvXv3dP2sWLFCAMgXX3whhYWFkpubK0OHDhWNRiOVlZUGx0tLS5O1a9fKgwcPJD8/XwIDA/XmWgoJCRE/Pz+9fVi8eLHY2dnJ3r175eHDh7J8+XKxsrLS3ftuaG7NEdsQ58+fFwCyatUqveXV1dUCQCZNmqRb9tlnn4mjo6NER0c/sb8rV64IAAkMDDQ4BxGe65Y41yK8R78x+MwgkWGa+/pibP/PutZOnz5d3N3d9bbZvHmzAJC8vDzdsosXL4parZY333xTt2zZsmXy8ccfN2o/DB3vDM2voTEjPDxcNBqNXLx4USoqKiQjI0MGDx4sjo6OuonFmxrDkO8EdX75zGBDioqKBIB4e3vrlplizC0pKREnJyfZtGmTlJeXy71792Ty5Mm6fTPF2MpnBhuH30fahmaddL6yslK0Wq2MGDFCb3l1dbXEx8dLWVmZODg4SFhYmN76//znPwJA7+JTd0EoLy/XLdu6dasAkKtXrxoUryEbNmwQAJKbmysi9S+G5eXlolar9XIsKysTOzs7mTdvnsG5NVdsQ40ePVo6dOggX3zxhZSXl8vdu3clNTVVFAqF/Nd//ZdRfZ0+fVoAyO9+9zuDt+G5brlzzYuv8VgMEhmmtRWDv/bra62hhZCIyF/+8hcBILt27ZJPPvlEFi1a1KgcjBnvmloM/rr4OnXqlACQ9957zyQxjPGsYlBERKFQiFarFRHTjbk//vijAJDPPvusXjxTja0sBhuH30fahqcVgw0/CGaE8+fPo6CgAK+88orecmtrayxYsACnT59GSUkJBg0apLd+8ODBUCqVerc6NESpVAJ4PG2CIfEaUjcfx5Mm8rx8+TLKysrQt29f3TJ7e3t4eHjo3e7xrNxaMnZD9uzZgyVLluCNN97AgwcP4Onpid/85jcQEXTs2NGovhwcHAAAZWVlBm+TkZHBc90MsZ8kNDQUoaGhRm9n6RQKhblTIKImeNa19mn++7//G//85z8xZ84c/O53v2v06/ubOt41xaBBg6BWqxs1bjS30tJSiAicnJwAmG7M9fX1hZubG2bMmIEFCxZg5syZ6Nq1a5NiPAnHCOPx+0jb1uRisO5+ba1W2+D6goICAD8XF7+k1WpRXFxs0ngAcPjwYWzevBkZGRkoKip66hd44PHFCwBWrlyJlStX6q3z9PQ0Kj9zxnZ2dq730Pbdu3eRnJyM5557zqi+unbtCpVKhStXrhi8Dc91y8UGHj//ERQUZPR2liouLg4AsHDhQjNnQtS6tbYvdcZea59l/fr12Lt3b5NepGLq8c5YdnZ2yMvLa9YYjVH3naFnz54ATDfu2dvb48svv8TSpUuxfv16REdHY9q0aUhKSjL52MpnDY0TGhrK7yNtwIkTJxAfH9/guiYXg3VFxpMmtq37It/QhbGgoABeXl4mjZeTk4NJkyZh8uTJ+N///V8899xz+PDDD/Huu+8+sU9XV1cAj78sRkZGGpVPa4n9JKdOnQIAjBgxwqjt7Ozs8Morr+DgwYP497//jSFDhjTY7sGDB3j33Xfx8ccf81y38LkOCgrCtGnTmtyPpUhLSwMAHjOiZ2hNxWBjrrVPU1VVhQULFiA2NhZRUVGIiYnBmjVrjO7H1OOdMaqqqpo9RmMdO3YMADBmzBgAph33+vTpg08//RR5eXmIjY3F+++/jz59+iAsLMxkMQCOEcYKDQ3l95E24knFYJPfJtq1a1d06NABn3/+eYPr+/btCwcHB5w+fVpv+cmTJ1FZWYkXX3zRpPEuXLiAqqoqzJs3D76+vlCpVM/8yd/b2xsqlQrnzp0zKpfWFPtJPvroI3Tr1g3Dhg0zetu1a9fCzs4OixYtQnl5eYNtfvzxR920EzzX5j3XRETtjSHXWhsbG4N/LXz77bcxe/ZsLFy4EIsWLcK6detw4sQJo/MyZrwzJj9DpKenQ0QQGBjYbDEa4969e4iLi4OXlxdmzZoFwHTj3p07d3Dx4kUAjwvMjRs3YuDAgbh48SLHVqImanIxaGdnh+XLl+Prr79GREQEbt++jdraWhQXF+PixYtQqVSIiorC/v37sWvXLhQVFeHChQuYO3cuPD09ER4ebtJ4Pj4+AIDjx4+joqICmZmZ9e7d79ChA+7cuYPs7GwUFxfD2toab731FpKTk5GYmIiioiLU1NTg1q1buHv3rsG5mTM2ALz00ku4ceMGqqurkZ2djcWLF+P48ePYsWOH7v574PErnw15jfQLL7yA3bt348cff8TQoUNx5MgRFBYWoqqqCtevX8dHH32EP/7xj7rnN3iuW+5cExFZAkOutf7+/njw4AEOHDiAqqoq5OXl4caNG/X62rp1Kzp37ozJkycDADZs2IDevXtj+vTpuscSDGXMeGdofr8eM+qKu9raWjx8+BDV1dU4f/48IiMj4ePjo5vGoakxDP1OUEdEUFJSgtraWogI8vLykJKSgiFDhsDa2hoHDhzQPTOoUqlMMu7duXMHc+bMwaVLl1BZWYmzZ8/ixo0bCAwMNFkMIotlxNtmnmrLli0SEBAgKpVKVCqVDBgwQLZu3SoiIrW1tbJ582bp3r272NraiouLi0yaNEkuX76s237r1q2iVqsFgHTv3l2ysrJk+/bt4uTkJACkS5cucuXKFYPiLVmyRDp06CBarVamTp0qW7ZsEQDi5+cnOTk5cubMGenSpYvY29vLyy+/LPfu3ZNHjx7JkiVLxMfHR2xsbMTV1VVCQkIkIyPDqNxMHdsYv//970Wr1YqNjY24uLjI2LFjG3yt8pEjR8TR0VFiYmIM6jcnJ0cWL14sAQEB4uDgINbW1qLVamXAgAHyxz/+Uf7973/r2vJct8y5Bt/eZTS+TZTIMM19fTG2/2dda/Pz82XEiBGiUqmkW7du8vbbb8s777wjAMTf319ycnJk3LhxolAopEOHDvLdd9+JiMjChQvFyspKAIizs7OcPn3aqP0wZLwTEYPyE5EGx4zw8HCxtbWVzp07i42NjTg5OcnEiRMlKyvLZDEM+U5w6NAh6devn6jValEqlbrjVvfm0Jdeekmio6MlPz+/3ramGHOzs7MlODhYXFxcxNraWp577jlZsWKFVFdXPzOGofg20cbh95G24WlvE1WIiPyyOExNTUVoaCh+tZiIWhGFQoGUlBTeo2+EqVOnAvj52UEialhzX194/TLcnDlzkJaWhvz8fHOn0u7x+2/j8N9z2/CUz3dak28TJSIiIqLm0ZgpNIiIDMVisBW7dOkSFArFM//q3qRFRPQkx48fx7Jly7Bv3z74+vrqrh+vv/56vbajRo2Co6MjrK2t0adPH5w5c8YMGRtu06ZN6NmzJ+zt7aHRaNCzZ0+sWrWq3nNgMTExDV5Dfzk/2aFDh7Bp0yZ+AbdAHHOJWof2PF7Vqa2tRVxcHIKDg+uta+lxiMVgK9azZ0+IyDP/9uzZY+5UiagVW7NmDRISErB8+XKEhITg2rVr8PPzQ8eOHbFr1y4cPnxYr/3nn3+OtLQ0jBs3DhkZGRg4cKCZMjfMN998g9mzZyMnJwc//fQT1q1bh02bNmHKlClG9zV+/HioVCqMHDlSN5ccWYbWNuYuX74cSUlJKCwsRLdu3bB3794WiUtkTu19vAKAzMxM/Pa3v8WiRYtQVlZWb31Lj0MsBonIaOXl5Q3+b1Zbi2EJ3n//fezZswepqalwdHTUW5eQkAArKyuEh4ejsLDQTBk2nVKpxPz58+Hq6goHBwdMnToVEydOxD//+c96bxPcuXNnvS/3P/74o16bBQsWoH///nj11VdRXV3dkrtCpLNhwwY8evQIIoLr16836j83qG2x9LHVEsarH374AUuXLsXcuXPxwgsvPLFdS45DLAaJyGg7duxAbm5um4/R3l29ehWrVq3Ce++9B5VKVW99cHAwIiMjcfv2bSxevNgMGZrG/v376+1f586dAQAlJSWN6nPt2rU4d+7cEyfpJSIyNUseWy1lvOrfvz/27duH6dOnw87O7qltW2ocYjFIZAFEBLGxsejVqxfs7Ozg4uKCiRMn4tKlS7o2ERERUCqV8PDw0C2bP38+NBoNFAoF7t+/DwCIjIxEVFQUsrKyoFAo4O/vj4SEBKhUKri5uWHOnDnw9PSESqVCcHCw3pxgTYkBAMeOHTNqPixLl5CQABHB+PHjn9gmJiYGzz//PD7++GMcP378qf0Z8jlKTEyERqOBWq3GwYMHMWbMGDg5OcHLywvJycl6/dXU1GD16tXw8fGBvb09+vXrh5SUlKbt9P+XmZkJrVaLLl26NGp7FxcXDBs2DPHx8Xy7IBE1iGOr6VjyePUkLTYOGTEPBRG1EjByXp/Vq1eLUqmUnTt3SkFBgZw/f14GDhwonTp1knv37unaTZ8+Xdzd3fW23bx5swCQvLw83bKQkBDx8/PTaxceHi4ajUYuXrwoFRUVkpGRIYMHDxZHR0fdHFdNjfHZZ5+Jo6OjREdHG7zvdSxxnkFfX1/p3bt3g+v8/Pzk+vXrIiLy3XffiZWVlXTt2lVKSkpEROTo0aMyYcIEvW0M/RytWLFCAMgXX3whhYWFkpubK0OHDhWNRiOVlZW6dosXLxY7OzvZu3evPHz4UJYvXy5WVlYNzo9qiMrKSrl165Z8+OGHYmdnJzt37tRbv27dOvHy8hKtViu2trbStWtXmTBhgvznP/9psL9ly5YJADl79myj8mmrjL2+tLb+iRqjMd9/Obaa7t+zpY1XIiK/+c1vpH///k9tY6px6GnzDPKXQaJ2rry8HLGxsZg8eTJmzJgBZ2dnBAQEYNu2bbh//z62b99uslg2Nja6/4Xr3bs3EhMTUVxcjKSkJJP0P3bsWBQVFWHVqlUm6a89Ky0txfXr1+Hn5/fMtkFBQVi4cCGys7OxdOnSBts05nMUHBwMJycnuLq6IiwsDKWlpcjJyQEAVFRUIDExEZMmTUJISAi0Wi1WrlwJW1vbRn9evL294eXlhbVr1+KDDz5AaGio3vo333wThw4dws2bN1FSUoLk5GTk5ORg2LBhyMjIqNdf9+7dAQAXLlxoVD5E1H5xbDUdSxyvDNUS4xCLQaJ2LiMjAyUlJRg0aJDe8sGDB0OpVOrdamJqgwYNglqt1rstg1pGbm4uRARqtdqg9jExMejRowe2bt2Kb7/9tt76pn6OlEolAKCqqgoAcPnyZZSVlelN62Bvbw8PD49Gf15u3ryJ3NxcfPLJJ/jb3/6GAQMG6D0b4+3tjQEDBsDBwQFKpRKBgYFISkpCeXk5tm7dWq+/umP3008/NSofImq/OLaajiWOV4ZqiXGIxSBRO1f3WmIHB4d667RaLYqLi5s1vp2dHfLy8po1BtVXUVEBAM98QL2OSqVCUlISFAoFZs2ahfLycr31pv4clZaWAgBWrlypN4fbjRs3GnzVtiFsbW3h6uqKUaNGYc+ePcjIyMCGDRueuk1AQACsra1x5cqVeuvs7e0B/HwsiYjqcGw1HUscrwzVEuMQi0Gidk6r1QJAgxe/goICeHl5NVvsqqqqZo9BDasbQIyZtDYoKAiLFi1CZmYm1q1bp7fO1J8jV1dXAEBcXFy9qR5OnDhhVF8N8ff3h7W1dYO3f/5SbW0tamtrG/wSUllZCeDnY0lEVIdjq+lY+nj1NC0xDrEYJGrn+vbtCwcHB5w+fVpv+cmTJ1FZWYkXX3xRt8zGxkZ3W4QppKenQ0QQGBjYbDGoYW5ublAoFEbPx7Ru3Tr07NkTZ8+e1VtuzOfIEN7e3lCpVDh37pxR2/1afn4+XnvttXrLMzMzUVNTA29vb92yV155pV67U6dOQUQQFBRUb13dsXN3d29SjkTU/nBsNR1LGa8aoyXGIRaDRO2cSqVCVFQU9u/fj127dqGoqAgXLlzA3Llz4enpifDwcF1bf39/PHjwAAcOHEBVVRXy8vJw48aNen126NABd+7cQXZ2NoqLi3UDUG1tLR4+fIjq6mqcP38ekZGR8PHxwcyZM00S4+jRo2Z//XVboVar4evri1u3bhm1Xd3tN9bW1vWWG/o5MjTOW2+9heTkZCQmJqKoqAg1NTW4deuWbqL4sLAwuLu748yZM0/sR6PR4PPPP8eXX36JoqIiVFVV4ezZs3jzzTeh0WiwaNEiXdvbt29jz549KCgoQFVVFU6cOIE//elP8PHxwdy5c+v1XXfsAgICjNo3Imr/OLaajqWMV43RIuOQEa8eJaJWAka+yrm2tlY2b94s3bt3F1tbW3FxcZFJkybJ5cuX9drl5+fLiBEjRKVSSbdu3eTtt9+Wd955RwCIv7+/7jXWZ86ckS5duoi9vb28/PLLcu/ePQkPDxdbW1vp3Lmz2NjYiJOTk0ycOFGysrJMFuPIkSPi6OgoMTExRh8zS5xaIiIiQmxtbaWsrEy3bP/+/eLn5ycApFOnTvI///M/DW77zjvv1HtVtyGfo61bt4parRYA0r17d8nKypLt27eLk5OTAJAuXbrIlStXRETk0aNHsmTJEvHx8REbGxtxdXWVkJAQycjIEBGRSZMmCQBZvXr1U/dz/Pjx0q1bN3FwcBA7Ozvx8/OTsLAwuXDhgl67qKgo8fPzE41GIzY2NuLl5SWzZ8+WO3fuNNjv2LFjpXPnzlJbW/vU+O2NsdeX1tY/UWM05vsvx1bT/Xu2lPHqxIkTMmTIEPH09BQAAkA8PDwkODhY/vWvf9Vrb6px6GlTS7AYJGqDWuOXqfDwcOnQoYO503giSywGMzMzxcbGpt58e21FTU2NDB06VHbs2NHise/fvy8qlUr+/Oc/t3hsc2MxSJaotX7/be1jq6n+PXO8qs+U4xDnGSSiFmHMw9/U/Pz9/REdHY3o6GiUlJSYOx2j1NTU4MCBAyguLkZYWFiLx1+7di1eeOEFREREtHhsIqJfsoSxleNVfS01DrEYJCJqx5YtW4apU6ciLCzM6IfzzSk9PR379u3D0aNHDZ57ylRiY2Nx7tw5HDlyBLa2ti0am4jIUnG8+llLjkMsBomoyZYvX46kpCQUFhaiW7du2Lt3r7lTol9Yv349IiIisHHjRnOnYrCRI0di9+7d8PDwaNG4Bw8exKNHj5Ceng4XF5cWjU1E9EuWOLZyvGr5ccim2SMQUbu3YcOGZ07uTeY1atQojBo1ytxptHoTJkzAhAkTzJ0GEZHFjq2WPl619DjEXwaJiIiIiIgsEItBIiIiIiIiC8RikIiIiIiIyAKxGCQiIiIiIrJAT3yBTGpqakvmQURGOnHihLlTaFNu3boFgNc2otaA1y9qbeo+kxwjjMd/z63f086RQkTklwtSU1MRGhra7EkRERFR65SSkoJp06Y1S98KhaJZ+iUioqf7VdkHAGn1ikEismx1XwD5v6NERERE7VoanxkkIiIiIiKyQCwGiYiIiIiILBCLQSIiIiIiIgvEYpCIiIiIiMgCsRgkIiIiIiKyQCwGiYiIiIiILBCLQSIiIiIiIgvEYpCIiIiIiMgCsRgkIiIiIiKyQCwGiYiIiIiILBCLQSIiIiIiIgvEYpCIiIiIiMgCsRgkIiIiIiKyQCwGiYiIiIiILBCLQSIiIiIiIgvEYpCIiIiIiMgCsRgkIiIiIiKyQCwGiYiIiIiILBCLQSIiIiIiIgvEYpCIiIiIiMgCsRgkIiIiIiKyQCwGiYiIiIiILBCLQSIiIiIiIgvEYpCIiIiIiMgCsRgkIiIiIiKyQCwGiYiIiIiILBCLQSIiIiIiIgvEYpCIiIiIiMgCsRgkIiIiIiKyQCwGiYiIiIiILBCLQSIiIiIiIgvECsrpBwAAHxlJREFUYpCIiIiIiMgCsRgkIiIiIiKyQAoREXMnQUTmsXv3buzYsQO1tbW6ZdevXwcAdOvWTbfMysoKf/zjHzF9+vQWz5GIiIiImkUai0EiC3b+/Hn079/foLY//PAD+vXr18wZEREREVELSeNtokQWrF+/fujRo8cz2/n7+7MQJCIiImpnWAwSWbjXX38dtra2T1xva2uLt956qwUzIiIiIqKWwNtEiSzctWvX4O/vj6ddCjIzM+Hv79+CWRERERFRM+NtokSWztfXFwMHDoRCoai3TqFQYNCgQSwEiYiIiNohFoNEhDfeeAPW1tb1lltbW+ONN94wQ0ZERERE1Nx4mygRITc3F56ennpTTACPp5S4c+cO3N3dzZQZERERETUT3iZKRICbmxuGDRum9+ugtbU1hg8fzkKQiIiIqJ1iMUhEAB6/VfTXNwq8/vrrZsqGiIiIiJobbxMlIgBAUVERXF1dUVlZCeDxlBK5ubnQarVmzoyIiIiImgFvEyWix5ycnDB69GjY2NjAxsYGr776KgtBIiIionaMxSAR6cyYMQM1NTWoqanB9OnTzZ0OERERETUj3iZKRDoVFRXo1KkTRAT379+Hvb29uVMiIiIiouaRZmPuDNq61NRUhIaGmjsNIpNTq9XmToHIZFJSUjBt2jRzp0FERNSqsBg0kZSUFHOnQNRooaGhiIyMRFBQEM6dOweFQoH+/fubO61WLS4uDgCwcOFCM2dCz8L/sCMiImoYi0ET4f84U1sWGhqKoKAgTJs2DZMnTwYA2Njw8vA0aWlpAPhvvy1gMUhERNQwftsjIj0sAomIiIgsA98mSkREREREZIFYDBIREREREVkgFoNEREREREQWiMUgERERERGRBWIxSEQmc+TIETg7O+PTTz81dyqt3vHjx7Fs2TLs27cPvr6+UCgUUCgUeP311+u1HTVqFBwdHWFtbY0+ffrgzJkzZsjYcJs2bULPnj1hb28PjUaDnj17YtWqVSgqKtJrFxMTo9vvX/717dtX1+bQoUPYtGkTampqWno3iIiI2j0Wg0RkMiJi7hTahDVr1iAhIQHLly9HSEgIrl27Bj8/P3Ts2BG7du3C4cOH9dp//vnnSEtLw7hx45CRkYGBAweaKXPDfPPNN5g9ezZycnLw008/Yd26ddi0aROmTJlidF/jx4+HSqXCyJEjUVBQ0AzZEhERWS4Wg0RkMmPHjkVhYSHGjRtn7lRQXl6O4OBgc6dRz/vvv489e/YgNTUVjo6OeusSEhJgZWWF8PBwFBYWminDplMqlZg/fz5cXV3h4OCAqVOnYuLEifjnP/+Ju3fv6rXduXMnRETv78cff9Rrs2DBAvTv3x+vvvoqqqurW3JXiIiI2jUWg0TULu3YsQO5ubnmTkPP1atXsWrVKrz33ntQqVT11gcHByMyMhK3b9/G4sWLzZChaezfv7/e/nXu3BkAUFJS0qg+165di3PnziE+Pr7J+REREdFjLAaJyCS+/fZb+Pj4QKFQYMuWLQCAxMREaDQaqNVqHDx4EGPGjIGTkxO8vLyQnJys2zYhIQEqlQpubm6YM2cOPD09oVKpEBwcjJMnT+raRUREQKlUwsPDQ7ds/vz50Gg0UCgUuH//PgAgMjISUVFRyMrKgkKhgL+/PwDg2LFjcHJywvr161vikNSTkJAAEcH48eOf2CYmJgbPP/88Pv74Yxw/fvyp/YkIYmNj0atXL9jZ2cHFxQUTJ07EpUuXdG0MPQcAUFNTg9WrV8PHxwf29vbo168fUlJSmrbT/19mZia0Wi26dOnSqO1dXFwwbNgwxMfH83ZkIiIiE2ExSEQm8fLLL+O7777TWzZv3jwsXLgQ5eXlcHR0REpKCrKysuDr64vZs2ejqqoKwOMib+bMmSgrK8OCBQuQnZ2NM2fOoLq6Gr///e9x8+ZNAI+LqWnTpunF2Lp1K9577z29ZfHx8Rg3bhz8/PwgIrh69SoA6F5CUltb2yzH4FkOHz6MHj16QK1WP7GNvb09/vrXv8LKygqzZ89GaWnpE9uuXbsWy5Ytw4oVK5Cbm4uvv/4aN2/exNChQ/HTTz8BMPwcAMDSpUvxwQcfIC4uDnfv3sW4cePw2muv4fTp043a36qqKty+fRtbtmzB8ePH8eGHH0KpVOq1WbZsGVxcXKBUKtGtWzdMnDgRp06darC/AQMG4Pbt2/jhhx8alQ8RERHpYzFIRC0iODgYTk5OcHV1RVhYGEpLS5GTk6PXxsbGRvcrV+/evZGYmIji4mIkJSWZJIexY8eiqKgIq1atMkl/xigtLcX169fh5+f3zLZBQUFYuHAhsrOzsXTp0gbblJeXIzY2FpMnT8aMGTPg7OyMgIAAbNu2Dffv38f27dvrbfO0c1BRUYHExERMmjQJISEh0Gq1WLlyJWxtbRt9/L29veHl5YW1a9figw8+QGhoqN76N998E4cOHcLNmzdRUlKC5ORk5OTkYNiwYcjIyKjXX/fu3QEAFy5caFQ+REREpI/FIBG1uLpfh375q1RDBg0aBLVarXfbY1uVm5sLEXnqr4K/FBMTgx49emDr1q349ttv663PyMhASUkJBg0apLd88ODBUCqVerfXNuTX5+Dy5csoKyvTm9bB3t4eHh4ejT7+N2/eRG5uLj755BP87W9/w4ABA/Se4/T29saAAQPg4OAApVKJwMBAJCUloby8HFu3bq3XX92xq/vVk4iIiJqGxSARtWp2dnbIy8szdxpNVlFRAeDx/hhCpVIhKSkJCoUCs2bNQnl5ud76umkWHBwc6m2r1WpRXFxsVH51t6OuXLlSb86/GzduoKyszKi+6tja2sLV1RWjRo3Cnj17kJGRgQ0bNjx1m4CAAFhbW+PKlSv11tnb2wP4+VgSERFR07AYJKJWq6qqCgUFBfDy8jJ3Kk1WV8gYM3l6UFAQFi1ahMzMTKxbt05vnVarBYAGi77GHDNXV1cAQFxcXL2pHk6cOGFUXw3x9/eHtbV1g7d//lJtbS1qa2sbLJorKysB/HwsiYiIqGlYDBJRq5Weng4RQWBgoG6ZjY3NM28vbY3c3NygUCiMnj9w3bp16NmzJ86ePau3vG/fvnBwcKj3cpeTJ0+isrISL774olFxvL29oVKpcO7cOaO2+7X8/Hy89tpr9ZZnZmaipqYG3t7eumWvvPJKvXanTp2CiCAoKKjeurpj5+7u3qQciYiI6DEWg0TUatTW1uLhw4eorq7G+fPnERkZCR8fH8ycOVPXxt/fHw8ePMCBAwdQVVWFvLw83Lhxo15fHTp0wJ07d5CdnY3i4mJUVVXh6NGjZptaQq1Ww9fXF7du3TJqu7rbRa2trestj4qKwv79+7Fr1y4UFRXhwoULmDt3Ljw9PREeHm50nLfeegvJyclITExEUVERampqcOvWLd1E8WFhYXB3d8eZM2ee2I9Go8Hnn3+OL7/8EkVFRaiqqsLZs2fx5ptvQqPRYNGiRbq2t2/fxp49e1BQUICqqiqcOHECf/rTn+Dj44O5c+fW67vu2AUEBBi1b0RERNQwFoNEZBJbtmzB4MGDAQBLlizBhAkTkJiYiLi4OABAv379cO3aNXz00UeIiooCAIwePRqZmZm6PioqKhAQEAB7e3sMHToUzz//PL766iu9WwbnzZuHESNG4A9/+AN69OiBdevW6W4bDAoK0k1DMXfuXLi5uaF379549dVX8eDBgxY5Dk8zduxYZGRk6D3/9/e//x3+/v7IysrC4MGD8fbbb9fbLjAwUK+IqrNmzRps2LAB0dHR6NSpE4YNG4auXbsiPT0dGo0GAIw6B/Hx8Vi4cCE2bdqEjh07wtPTE5GRkXj48CGAx7dp5ubm4uDBg0/cR5VKhSFDhuBPf/oTOnfuDEdHR0ydOhVdu3bF999/r/eCmtGjR2PlypXw8vKCWq3GtGnTMGTIEHz//ffo2LFjvb5PnTqFzp07o1+/fs881kRERPRsCuHsvU2SmpqK0NBQToJMbZpCoUBKSkq9Ofxa0pw5c5CWlob8/Hyz5WCMqVOnAgDS0tIM3ubq1avo1asXkpKSMGPGjOZKrdnU1tZi+PDhmDlzJmbNmtWisfPz8+Hl5YWYmBhdIWuo1vD5JiIiaoXS+MsgEbUaxrxcpS3y9/dHdHQ0oqOjUVJSYu50jFJTU4MDBw6guLgYYWFhLR5/7dq1eOGFFxAREdHisYmIiNorFoPtzJEjR+Ds7IxPP/3U3Km0C/v27YOvr6/eq/YVCgWUSiXc3NwwfPhwbN68WXcbHdGzLFu2DFOnTkVYWJjRL5Mxp/T0dOzbtw9Hjx41eK5EU4mNjcW5c+dw5MgR2NratmhsIiKi9ozFYDvD21VNKyQkBNeuXYOfnx+cnZ0hIqitrUVubi5SU1PRrVs3LFmyBH369Kn3Vkcy3PLly5GUlITCwkJ069YNe/fuNXdKzWr9+vWIiIjAxo0bzZ2KwUaOHIndu3fDw8OjReMePHgQjx49Qnp6OlxcXFo0NhERUXvHZwabiM8MNl15eTlGjhyJ7777rtXG8Pf3x/3793UTff/S3r17ERoaik6dOuHKlStwdnZuarotjs9UGa8xzwySefDzTURE1CA+M0jmt2PHDuTm5rbZGFOmTMHMmTORm5uLbdu2NUsMIiIiIiJTYzHYwuLj46HRaGBlZYUXX3wR7u7usLW1hUajwcCBAzF06FDd5M9arRbvvvuu3vbffPMNevfuDWdnZ6hUKgQEBOAf//gHAODbb7+Fj48PFAoFtmzZAuDxa+U1Gg3UajUOHjyIMWPGwMnJCV5eXkhOTm7UPogIYmNj0atXL9jZ2cHFxQUTJ07EpUuXdG0iIiKgVCr1bimbP38+NBoNFAoF7t+/DwCIjIxEVFQUsrKyoFAo4O/vj4SEBKhUKri5uWHOnDnw9PSESqVCcHAwTp48aZIYAHDs2DGTzTlXNw/e0aNHdctqamqwevVq+Pj4wN7eHv369UNKSgoA487Lv/71L7z00ktQq9VwcnJCQEAAioqKnhmDiIiIiOiphJokJSVFjD2Ma9asEQBy8uRJKS0tlfv378vo0aMFgBw+fFjy8vKktLRUIiIiBICcO3dOt21aWpqsXbtWHjx4IPn5+RIYGCgdO3bUrb9586YAkA8//FC3bMWKFQJAvvjiCyksLJTc3FwZOnSoaDQaqaysNHqfV69eLUqlUnbu3CkFBQVy/vx5GThwoHTq1Enu3bunazd9+nRxd3fX23bz5s0CQPLy8nTLQkJCxM/PT69deHi4aDQauXjxolRUVEhGRoYMHjxYHB0dJScnxyQxPvvsM3F0dJTo6Ohn7rOfn584Ozs/cX1RUZEAEG9vb92yxYsXi52dnezdu1cePnwoy5cvFysrKzl16pSIGHZeSkpKxMnJSTZt2iTl5eVy7949mTx5sm7fnhXDUAAkJSXFqG0s3ZQpU2TKlCnmToMMwM83ERFRg1L5y6AZ9e7dG2q1Gh07dsQf/vAHAICPjw86deoEtVqtm4fsl7+4TZkyBWvWrIGLiws6dOiA8ePHIz8/H3l5ec+MFxwcDCcnJ7i6uiIsLAylpaXIyckxKufy8nLExsZi8uTJmDFjBpydnREQEIBt27bh/v372L59u1H9PY2NjY3u18fevXsjMTERxcXFSEpKMkn/Y8eORVFREVatWtXkvhwdHaFQKFBcXAzg8eTpiYmJmDRpEkJCQqDVarFy5UrY2trWy/9p5yU7OxtFRUXo06cPVCoV3N3dsW/fPnTq1MmoGEREREREv2Zj7gToMaVSCQCorq7WLat7hXpVVdUTt6trY+z8bHXxntZ3QzIyMlBSUoJBgwbpLR88eDCUSqXebZymNmjQIKjVar3iuLUoLS2FiMDJyQkAcPnyZZSVlaFv3766Nvb29vDw8Hhq/r8+L76+vnBzc8OMGTOwYMECzJw5E127dm1SjCc5ceKE0dtYslu3bgF4/BIpIiIioraIxWAbc/jwYWzevBkZGRkoKioyuphrqrq3aTo4ONRbp9Vqdb+MNRc7OzuDfgVtaVeuXAEA9OzZE8Dj4hAAVq5ciZUrV+q19fT0NLhfe3t7fPnll1i6dCnWr1+P6OhoTJs2DUlJSSaLUSc+Ph7x8fFGb2fpQkNDzZ0CERERUaPwNtE2JCcnB5MmTYKHhwdOnjyJwsJCbNq0qUVz0Gq1ANBg0VdQUAAvL69mi11VVdXsMRrr2LFjAIAxY8YAAFxdXQEAcXFxEBG9P2N/gevTpw8+/fRT3LlzB0uWLEFKSgr+/Oc/mzQGAKSkpNTrh39P/psyZQqmTJli9jz49+w/IiIiahiLwTbkwoULqKqqwrx58+Dr6wuVSgWFQtGiOfTt2xcODg71Jlg/efIkKisr8eKLL+qW2djYmPSXy/T0dIgIAgMDmy1GY9y7dw9xcXHw8vLCrFmzAED3Rthz5841qe87d+7g4sWLAB4XmBs3bsTAgQNx8eJFk8UgIiIiIsvEYrAN8fHxAQAcP34cFRUVyMzMbNZn9BqiUqkQFRWF/fv3Y9euXSgqKsKFCxcwd+5ceHp6Ijw8XNfW398fDx48wIEDB1BVVYW8vDzcuHGjXp8dOnTAnTt3kJ2djeLiYl1xV1tbi4cPH6K6uhrnz59HZGQkfHx8dNM4NDXG0aNHjZpaQkRQUlKC2tpaiAjy8vKQkpKCIUOGwNraGgcOHNA9M6hSqfDWW28hOTkZiYmJKCoqQk1NDW7duoW7d+8afLzv3LmDOXPm4NKlS6isrMTZs2dx48YNBAYGmiwGEREREVkooSYxdmqJ+Ph4UavVAkC6du0q33zzjbz//vvi7OwsAMTd3V12794te/bsEXd3dwEgLi4ukpycLCIiS5YskQ4dOohWq5WpU6fKli1bBID4+fnJu+++Kx4eHgJA1Gq1jB8/XrZu3aqL1717d8nKypLt27eLk5OTAJAuXbrIlStXjNrn2tpa2bx5s3Tv3l1sbW3FxcVFJk2aJJcvX9Zrl5+fLyNGjBCVSiXdunWTt99+W9555x0BIP7+/ropIs6cOSNdunQRe3t7efnll+XevXsSHh4utra20rlzZ7GxsREnJyeZOHGiZGVlmSzGkSNHxNHRUWJiYp64r4cOHZJ+/fqJWq0WpVIpVlZWAkAUCoVotVp56aWXJDo6WvLz8+tt++jRI1myZIn4+PiIjY2NuLq6SkhIiGRkZBh8XrKzsyU4OFhcXFzE2tpannvuOVmxYoVUV1c/M4YxwFfvG41TS7Qd/HwTERE1KFUhwgcqmiI1NRWhoaF8LsXE5syZg7S0NOTn55s7FYugUCiQkpKCadOmmTuVNmPq1KkAgLS0NDNnQs/CzzcREVGD0nibKLVaxk6XQUREREREhmMxSLh06RIUCsUz/8LCwsydKhERERERmQiLQULPnj0Nej37nj17WiSf5cuXIykpCYWFhejWrRv27t3bInGJzO348eNYtmwZ9u3bB19fX91/xLz++uv12o4aNQqOjo6wtrZGnz59cObMGTNkbLza2lrExcUhODi43rpDhw5h06ZNvCuAiIiohbAYpFZnw4YNePToEUQE169fx5QpU8ydElGzW7NmDRISErB8+XKEhITg2rVr8PPzQ8eOHbFr1y4cPnxYr/3nn3+OtLQ0jBs3DhkZGRg4cKCZMjdcZmYmfvvb32LRokUoKyurt378+PFQqVQYOXIkCgoKzJAhERGRZWExSERmV15e3uAvRW0tRmO9//772LNnD1JTU+Ho6Ki3LiEhAVZWVggPD0dhYaGZMmy6H374AUuXLsXcuXPxwgsvPLHdggUL0L9/f7z66quorq5uwQyJiIgsD4tBIjK7HTt2IDc3t83HaIyrV69i1apVeO+996BSqeqtDw4ORmRkJG7fvo3FixebIUPT6N+/P/bt24fp06fDzs7uqW3Xrl2Lc+fOIT4+voWyIyIiskwsBonIaCKC2NhY9OrVC3Z2dnBxccHEiRNx6dIlXZuIiAgolUp4eHjols2fPx8ajQYKhQL3798HAERGRiIqKgpZWVlQKBTw9/dHQkICVCoV3NzcMGfOHHh6ekKlUiE4OBgnT540SQwAOHbsGJycnLB+/fpmPV5Pk5CQABHB+PHjn9gmJiYGzz//PD7++GMcP378qf0Zcm4SExOh0WigVqtx8OBBjBkzBk5OTvDy8kJycrJefzU1NVi9ejV8fHxgb2+Pfv36ISUlpWk7/QwuLi4YNmwY4uPjOW0PERFRM2IxSERGW7t2LZYtW4YVK1YgNzcXX3/9NW7evImhQ4fip59+AvC4yPn1vG5bt27Fe++9p7csPj4e48aNg5+fH0QEV69eRUREBGbOnImysjIsWLAA2dnZOHPmDKqrq/H73/8eN2/ebHIM4OfpS2pra013cIx0+PBh9OjRA2q1+olt7O3t8de//hVWVlaYPXs2SktLn9jWkHMzb948LFy4EOXl5XB0dERKSgqysrLg6+uL2bNno6qqStff0qVL8cEHHyAuLg53797FuHHj8Nprr+H06dOmOwgNGDBgAG7fvo0ffvihWeMQERFZMhaDRGSU8vJyxMbGYvLkyZgxYwacnZ0REBCAbdu24f79+9i+fbvJYtnY2Oh+4erduzcSExNRXFyMpKQkk/Q/duxYFBUVYdWqVSbpz1ilpaW4fv06/Pz8ntk2KCgICxcuRHZ2NpYuXdpgm8acm+DgYDg5OcHV1RVhYWEoLS1FTk4OAKCiogKJiYmYNGkSQkJCoNVqsXLlStja2prsHDxJ9+7dAQAXLlxo1jhERESWjMUgERklIyMDJSUlGDRokN7ywYMHQ6lU6t3GaWqDBg2CWq3Wu+WxLcvNzYWIPPVXwV+KiYlBjx49sHXrVnz77bf11jf13CiVSgDQ/TJ4+fJllJWVoW/fvro29vb28PDwaPZzUHdM6n7NJCIiItNjMUhERql75b+Dg0O9dVqtFsXFxc0a387ODnl5ec0ao6VUVFQAwDNfqFJHpVIhKSkJCoUCs2bNQnl5ud56U5+buttRV65cqZvzUKFQ4MaNGw1ODWFK9vb2AH4+RkRERGR6LAaJyCharRYAGiwsCgoK4OXl1Wyxq6qqmj1GS6oreIyZZD0oKAiLFi1CZmYm1q1bp7fO1OfG1dUVABAXFwcR0fs7ceKEUX0Zq7KyEsDPx4iIiIhMj8UgERmlb9++cHBwqPcCkZMnT6KyshIvvviibpmNjY3ey0iaKj09HSKCwMDAZovRktzc3KBQKIyeP3DdunXo2bMnzp49q7fcmHNjCG9vb6hUKpw7d86o7Uyh7pi4u7u3eGwiIiJLwWKQiIyiUqkQFRWF/fv3Y9euXSgqKsKFCxcwd+5ceHp6Ijw8XNfW398fDx48wIEDB1BVVYW8vDzcuHGjXp8dOnTAnTt3kJ2djeLiYl1xV1tbi4cPH6K6uhrnz59HZGQkfHx8MHPmTJPEOHr0qFmnllCr1fD19cWtW7eM2q7udlFra+t6yw09N4bGeeutt5CcnIzExEQUFRWhpqYGt27dwt27dwEAYWFhcHd3x5kzZ4zq+1nqjklAQIBJ+yUiIqKfsRgkIqOtWbMGGzZsQHR0NDp16oRhw4aha9euSE9Ph0aj0bWbN28eRowYgT/84Q/o0aMH1q1bp7vtLygoSDdFxNy5c+Hm5obevXvj1VdfxYMHDwA8fl4sICAA9vb2GDp0KJ5//nl89dVXes/YNTWGuY0dOxYZGRl6z//9/e9/h7+/P7KysjB48GC8/fbb9bYLDAzEokWL6i035NwkJiYiLi4OANCvXz9cu3YNH330EaKiogAAo0ePRmZmJoDH03IsXLgQmzZtQseOHeHp6YnIyEg8fPgQwOPbOXNzc3Hw4MGn7uf333+Pl19+Gc899xxOnjyJH374AZ6enhgyZAi+/vrreu1PnTqFzp07o1+/foYcRiIiImoEhXBG3yZJTU1FaGgoJ0amNk2hUCAlJaXenH3mNGfOHKSlpSE/P9/cqTRo6tSpAIC0tLQm9XP16lX06tULSUlJmDFjhilSa1G1tbUYPnw4Zs6ciVmzZpmkz/z8fHh5eSEmJkZXoDZFa/x8ExERtQJp/GWQiFotY16s0lb5+/sjOjoa0dHRKCkpMXc6RqmpqcGBAwdQXFyMsLAwk/W7du1avPDCC4iIiDBZn0RERFQfi0EiIjNbtmwZpk6dirCwMKNfJmNO6enp2LdvH44ePWrwXInPEhsbi3PnzuHIkSOwtbU1SZ9ERETUMBaDRNTqLF++HElJSSgsLES3bt2wd+9ec6fU7NavX4+IiAhs3LjR3KkYbOTIkdi9ezc8PDxM0t/Bgwfx6NEjpKenw8XFxSR9EhER0ZPZmDsBIqJf27BhAzZs2GDuNFrcqFGjMGrUKHOnYTYTJkzAhAkTzJ0GERGRxeAvg0RERERERBaIxSAREREREZEFYjFIRERERERkgVgMEhERERERWSC+QMZE6iagJmqr4uLimjyBuiX5/vvvAfDfPhEREbVdLAabyNvbG1OmTDF3GkRNws+w8QIDA82dAhloypQp8Pb2NncaRERErY5CRMTcSRAREREREVGLSuMzg0RERERERBaIxSAREREREZEFYjFIRERERERkgVgMEhERERERWaD/B4ZyFjtjnFL2AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DA4Bmr_0djrY"
      },
      "source": [
        "We could have trained two separate neural networks for this, but instead, we have two input layers and two output layers. You might also have noticed that information can flow from the deep_nput layer to both the main_ and aux_output layers, however, the wide_input layer does not affect the result in the aux_output layer. Thus, we can control the flow of information in our neural nets.\n",
        "\n",
        "You may also have noticed that we have multiple values for loss and loss_weights. This indicates a loss for each output, and the loss_weights of `[0.9, 0.1]` mean that the network takes into account the first loss much more than the second one. So, during training, we're saying it's more important for the first output to be accurate than the second one.\n",
        "\n",
        "When training a model like this, we also need to provide two separate X training and y label sets, one for each input and output. In this particular case, the labels are the same for both outputs (stupid)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ToLEH3reda2P",
        "outputId": "57fba4eb-88e2-4863-fcda-830ec5035198"
      },
      "source": [
        "X_train_A, X_train_B = X_train[:, :5], X_train[:, 2:]\n",
        "X_val_A, X_val_B = X_val[:, :5], X_val[:, 2:]\n",
        "X_test_A, X_test_B = X_test[:, :5], X_test[:, 2:]\n",
        "\n",
        "X_new_A, X_new_B = X_test_A[:3], X_test_B[:3]\n",
        "\n",
        "history = model.fit(\n",
        "    [X_train_A, X_train_B], [y_train, y_train], epochs=20,\n",
        "    validation_data=([X_val_A, X_val_B], [y_val, y_val]))"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.3557 - main_output_loss: 0.3429 - aux_output_loss: 0.4710 - main_output_accuracy: 0.0035 - aux_output_accuracy: 0.0035 - val_loss: 1.9372 - val_main_output_loss: 1.8798 - val_aux_output_loss: 2.4542 - val_main_output_accuracy: 0.0028 - val_aux_output_accuracy: 0.0026\n",
            "Epoch 2/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.3700 - main_output_loss: 0.3580 - aux_output_loss: 0.4785 - main_output_accuracy: 0.0023 - aux_output_accuracy: 0.0023 - val_loss: 2.1890 - val_main_output_loss: 2.1368 - val_aux_output_loss: 2.6583 - val_main_output_accuracy: 0.0028 - val_aux_output_accuracy: 0.0026\n",
            "Epoch 3/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.3481 - main_output_loss: 0.3357 - aux_output_loss: 0.4594 - main_output_accuracy: 0.0025 - aux_output_accuracy: 0.0025 - val_loss: 2.1937 - val_main_output_loss: 2.1368 - val_aux_output_loss: 2.7056 - val_main_output_accuracy: 0.0028 - val_aux_output_accuracy: 0.0028\n",
            "Epoch 4/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.3458 - main_output_loss: 0.3326 - aux_output_loss: 0.4653 - main_output_accuracy: 0.0027 - aux_output_accuracy: 0.0027 - val_loss: 2.1783 - val_main_output_loss: 2.1183 - val_aux_output_loss: 2.7182 - val_main_output_accuracy: 0.0028 - val_aux_output_accuracy: 0.0028\n",
            "Epoch 5/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.3491 - main_output_loss: 0.3371 - aux_output_loss: 0.4574 - main_output_accuracy: 0.0039 - aux_output_accuracy: 0.0039 - val_loss: 2.4189 - val_main_output_loss: 2.3673 - val_aux_output_loss: 2.8834 - val_main_output_accuracy: 0.0028 - val_aux_output_accuracy: 0.0028\n",
            "Epoch 6/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.3434 - main_output_loss: 0.3311 - aux_output_loss: 0.4536 - main_output_accuracy: 0.0027 - aux_output_accuracy: 0.0027 - val_loss: 2.4799 - val_main_output_loss: 2.4273 - val_aux_output_loss: 2.9531 - val_main_output_accuracy: 0.0028 - val_aux_output_accuracy: 0.0026\n",
            "Epoch 7/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.3512 - main_output_loss: 0.3383 - aux_output_loss: 0.4679 - main_output_accuracy: 0.0031 - aux_output_accuracy: 0.0031 - val_loss: 2.5990 - val_main_output_loss: 2.5452 - val_aux_output_loss: 3.0829 - val_main_output_accuracy: 0.0031 - val_aux_output_accuracy: 0.0026\n",
            "Epoch 8/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.3264 - main_output_loss: 0.3150 - aux_output_loss: 0.4287 - main_output_accuracy: 0.0040 - aux_output_accuracy: 0.0040 - val_loss: 2.9011 - val_main_output_loss: 2.8599 - val_aux_output_loss: 3.2727 - val_main_output_accuracy: 0.0031 - val_aux_output_accuracy: 0.0028\n",
            "Epoch 9/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.3439 - main_output_loss: 0.3320 - aux_output_loss: 0.4505 - main_output_accuracy: 0.0034 - aux_output_accuracy: 0.0034 - val_loss: 2.8455 - val_main_output_loss: 2.7949 - val_aux_output_loss: 3.3005 - val_main_output_accuracy: 0.0031 - val_aux_output_accuracy: 0.0028\n",
            "Epoch 10/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.3456 - main_output_loss: 0.3348 - aux_output_loss: 0.4429 - main_output_accuracy: 0.0040 - aux_output_accuracy: 0.0040 - val_loss: 3.3203 - val_main_output_loss: 3.2852 - val_aux_output_loss: 3.6359 - val_main_output_accuracy: 0.0031 - val_aux_output_accuracy: 0.0026\n",
            "Epoch 11/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.3392 - main_output_loss: 0.3283 - aux_output_loss: 0.4376 - main_output_accuracy: 0.0030 - aux_output_accuracy: 0.0030 - val_loss: 3.3700 - val_main_output_loss: 3.3301 - val_aux_output_loss: 3.7297 - val_main_output_accuracy: 0.0031 - val_aux_output_accuracy: 0.0028\n",
            "Epoch 12/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.3416 - main_output_loss: 0.3297 - aux_output_loss: 0.4493 - main_output_accuracy: 0.0027 - aux_output_accuracy: 0.0027 - val_loss: 3.4491 - val_main_output_loss: 3.4094 - val_aux_output_loss: 3.8064 - val_main_output_accuracy: 0.0031 - val_aux_output_accuracy: 0.0028\n",
            "Epoch 13/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.3386 - main_output_loss: 0.3275 - aux_output_loss: 0.4379 - main_output_accuracy: 0.0039 - aux_output_accuracy: 0.0038 - val_loss: 3.9407 - val_main_output_loss: 3.9086 - val_aux_output_loss: 4.2305 - val_main_output_accuracy: 0.0031 - val_aux_output_accuracy: 0.0028\n",
            "Epoch 14/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.3495 - main_output_loss: 0.3389 - aux_output_loss: 0.4453 - main_output_accuracy: 0.0029 - aux_output_accuracy: 0.0029 - val_loss: 3.5496 - val_main_output_loss: 3.5074 - val_aux_output_loss: 3.9293 - val_main_output_accuracy: 0.0031 - val_aux_output_accuracy: 0.0026\n",
            "Epoch 15/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.3559 - main_output_loss: 0.3441 - aux_output_loss: 0.4622 - main_output_accuracy: 0.0043 - aux_output_accuracy: 0.0043 - val_loss: 4.0277 - val_main_output_loss: 3.9992 - val_aux_output_loss: 4.2834 - val_main_output_accuracy: 0.0031 - val_aux_output_accuracy: 0.0028\n",
            "Epoch 16/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.3458 - main_output_loss: 0.3359 - aux_output_loss: 0.4349 - main_output_accuracy: 0.0027 - aux_output_accuracy: 0.0027 - val_loss: 3.7350 - val_main_output_loss: 3.6956 - val_aux_output_loss: 4.0897 - val_main_output_accuracy: 0.0031 - val_aux_output_accuracy: 0.0026\n",
            "Epoch 17/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.3330 - main_output_loss: 0.3225 - aux_output_loss: 0.4269 - main_output_accuracy: 0.0027 - aux_output_accuracy: 0.0027 - val_loss: 3.9821 - val_main_output_loss: 3.9470 - val_aux_output_loss: 4.2981 - val_main_output_accuracy: 0.0031 - val_aux_output_accuracy: 0.0026\n",
            "Epoch 18/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.3397 - main_output_loss: 0.3297 - aux_output_loss: 0.4296 - main_output_accuracy: 0.0043 - aux_output_accuracy: 0.0043 - val_loss: 4.2615 - val_main_output_loss: 4.2376 - val_aux_output_loss: 4.4767 - val_main_output_accuracy: 0.0031 - val_aux_output_accuracy: 0.0026\n",
            "Epoch 19/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.3347 - main_output_loss: 0.3248 - aux_output_loss: 0.4233 - main_output_accuracy: 0.0039 - aux_output_accuracy: 0.0039 - val_loss: 4.1938 - val_main_output_loss: 4.1631 - val_aux_output_loss: 4.4697 - val_main_output_accuracy: 0.0031 - val_aux_output_accuracy: 0.0026\n",
            "Epoch 20/20\n",
            "363/363 [==============================] - 1s 2ms/step - loss: 0.3441 - main_output_loss: 0.3345 - aux_output_loss: 0.4299 - main_output_accuracy: 0.0030 - aux_output_accuracy: 0.0030 - val_loss: 4.6562 - val_main_output_loss: 4.6287 - val_aux_output_loss: 4.9039 - val_main_output_accuracy: 0.0031 - val_aux_output_accuracy: 0.0026\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k6lsCNDOiYTD",
        "outputId": "ac244cea-5ec6-4649-e97a-d72607a94d11"
      },
      "source": [
        "model.evaluate([X_test_A, X_test_B],[y_test, y_test])"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "162/162 [==============================] - 0s 1ms/step - loss: 0.3979 - main_output_loss: 0.3878 - aux_output_loss: 0.4891 - main_output_accuracy: 0.0027 - aux_output_accuracy: 0.0025\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3978932499885559,\n",
              " 0.3877545893192291,\n",
              " 0.4891403913497925,\n",
              " 0.0027131782844662666,\n",
              " 0.002519379835575819]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RofR5GzhomH5"
      },
      "source": [
        "this model, it is how you say, a-not-so-good. Don't use it. \n",
        "\n",
        "## Saving the model\n",
        "But let's say it's not terrible and we actually understood what it's doing, so we'd want to save it to use later. Easy! the h5 file contains the architecture and weights of the model. Note that if you're using the subclass API to build the model, you'll need to rebuild it in code, and can't just load it up from the file like this. You can still save its weights though."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ubAi-Qbjm_F2"
      },
      "source": [
        "model.save('terrible_housing_model.h5')"
      ],
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uu-s7ek7q_Mj"
      },
      "source": [
        "model = keras.models.load_model('terrible_housing_model.h5')"
      ],
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d10hzEaTrZCD"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}