fashion-mnist.ipynb

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   "source": [
    "##### Copyright 2018 The TensorFlow Authors."
   ]
  },
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   "source": [
    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
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   "source": [
    "#@title MIT License\n",
    "#\n",
    "# Copyright (c) 2017 François Chollet\n",
    "#\n",
    "# Permission is hereby granted, free of charge, to any person obtaining a\n",
    "# copy of this software and associated documentation files (the \"Software\"),\n",
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    "# and/or sell copies of the Software, and to permit persons to whom the\n",
    "# Software is furnished to do so, subject to the following conditions:\n",
    "#\n",
    "# The above copyright notice and this permission notice shall be included in\n",
    "# all copies or substantial portions of the Software.\n",
    "#\n",
    "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
    "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
    "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
    "# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
    "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
    "# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
    "# DEALINGS IN THE SOFTWARE."
   ]
  },
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   "source": [
    "# Basic classification: Classify images of clothing"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/keras/classification\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/keras/classification.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/keras/classification.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://storage.googleapis.com/tensorflow_docs/docs/site/en/tutorials/keras/classification.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FbVhjPpzn6BM"
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   "source": [
    "This guide trains a neural network model to classify images of clothing, like sneakers and shirts. It's okay if you don't understand all the details; this is a fast-paced overview of a complete TensorFlow program with the details explained as you go.\n",
    "\n",
    "This guide uses [tf.keras](https://www.tensorflow.org/guide/keras), a high-level API to build and train models in TensorFlow."
   ]
  },
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   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2.0\n"
     ]
    }
   ],
   "source": [
    "# TensorFlow and tf.keras\n",
    "import tensorflow as tf\n",
    "\n",
    "# Helper libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
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   "source": [
    "## Import the Fashion MNIST dataset"
   ]
  },
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   "source": [
    "This guide uses the [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset which contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 by 28 pixels), as seen here:\n",
    "\n",
    "<table>\n",
    "  <tr><td>\n",
    "    <img src=\"https://tensorflow.org/images/fashion-mnist-sprite.png\"\n",
    "         alt=\"Fashion MNIST sprite\"  width=\"600\">\n",
    "  </td></tr>\n",
    "  <tr><td align=\"center\">\n",
    "    <b>Figure 1.</b> <a href=\"https://github.com/zalandoresearch/fashion-mnist\">Fashion-MNIST samples</a> (by Zalando, MIT License).<br/>&nbsp;\n",
    "  </td></tr>\n",
    "</table>\n",
    "\n",
    "Fashion MNIST is intended as a drop-in replacement for the classic [MNIST](http://yann.lecun.com/exdb/mnist/) dataset—often used as the \"Hello, World\" of machine learning programs for computer vision. The MNIST dataset contains images of handwritten digits (0, 1, 2, etc.) in a format identical to that of the articles of clothing you'll use here.\n",
    "\n",
    "This guide uses Fashion MNIST for variety, and because it's a slightly more challenging problem than regular MNIST. Both datasets are relatively small and are used to verify that an algorithm works as expected. They're good starting points to test and debug code.\n",
    "\n",
    "Here, 60,000 images are used to train the network and 10,000 images to evaluate how accurately the network learned to classify images. You can access the Fashion MNIST directly from TensorFlow. Import and [load the Fashion MNIST data](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/fashion_mnist/load_data) directly from TensorFlow:"
   ]
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   "source": [
    "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
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   "source": [
    "Loading the dataset returns four NumPy arrays:\n",
    "\n",
    "* The `train_images` and `train_labels` arrays are the *training set*—the data the model uses to learn.\n",
    "* The model is tested against the *test set*, the `test_images`, and `test_labels` arrays.\n",
    "\n",
    "The images are 28x28 NumPy arrays, with pixel values ranging from 0 to 255. The *labels* are an array of integers, ranging from 0 to 9. These correspond to the *class* of clothing the image represents:\n",
    "\n",
    "<table>\n",
    "  <tr>\n",
    "    <th>Label</th>\n",
    "    <th>Class</th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>0</td>\n",
    "    <td>T-shirt/top</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>1</td>\n",
    "    <td>Trouser</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>2</td>\n",
    "    <td>Pullover</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>3</td>\n",
    "    <td>Dress</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>4</td>\n",
    "    <td>Coat</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>5</td>\n",
    "    <td>Sandal</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>6</td>\n",
    "    <td>Shirt</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>7</td>\n",
    "    <td>Sneaker</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>8</td>\n",
    "    <td>Bag</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>9</td>\n",
    "    <td>Ankle boot</td>\n",
    "  </tr>\n",
    "</table>\n",
    "\n",
    "Each image is mapped to a single label. Since the *class names* are not included with the dataset, store them here to use later when plotting the images:"
   ]
  },
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   "execution_count": 3,
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   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Brm0b_KACaWX"
   },
   "source": [
    "## Explore the data\n",
    "\n",
    "Let's explore the format of the dataset before training the model. The following shows there are 60,000 images in the training set, with each image represented as 28 x 28 pixels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cIAcvQqMCaWf"
   },
   "source": [
    "Likewise, there are 60,000 labels in the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "TRFYHB2mCaWb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YSlYxFuRCaWk"
   },
   "source": [
    "Each label is an integer between 0 and 9:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "XKnCTHz4CaWg"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TMPI88iZpO2T"
   },
   "source": [
    "There are 10,000 images in the test set. Again, each image is represented as 28 x 28 pixels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "2KFnYlcwCaWl"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rd0A0Iu0CaWq"
   },
   "source": [
    "And the test set contains 10,000 images labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "iJmPr5-ACaWn"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ES6uQoLKCaWr"
   },
   "source": [
    "## Preprocess the data\n",
    "\n",
    "The data must be preprocessed before training the network. If you inspect the first image in the training set, you will see that the pixel values fall in the range of 0 to 255:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "m4VEw8Ud9Quh"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(train_images[0])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Wz7l27Lz9S1P"
   },
   "source": [
    "Scale these values to a range of 0 to 1 before feeding them to the neural network model. To do so, divide the values by 255. It's important that the *training set* and the *testing set* be preprocessed in the same way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "bW5WzIPlCaWv"
   },
   "outputs": [],
   "source": [
    "train_images = train_images / 255.0\n",
    "\n",
    "test_images = test_images / 255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ee638AlnCaWz"
   },
   "source": [
    "To verify that the data is in the correct format and that you're ready to build and train the network, let's display the first 25 images from the *training set* and display the class name below each image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "oZTImqg_CaW1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "59veuiEZCaW4"
   },
   "source": [
    "## Build the model\n",
    "\n",
    "Building the neural network requires configuring the layers of the model, then compiling the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gxg1XGm0eOBy"
   },
   "source": [
    "### Set up the layers\n",
    "\n",
    "The basic building block of a neural network is the [*layer*](https://www.tensorflow.org/api_docs/python/tf/keras/layers). Layers extract representations from the data fed into them. Hopefully, these representations are meaningful for the problem at hand.\n",
    "\n",
    "Most of deep learning consists of chaining together simple layers. Most layers, such as `tf.keras.layers.Dense`, have parameters that are learned during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "9ODch-OFCaW4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-12-03 21:07:12.865266: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
      "2021-12-03 21:07:12.865307: E tensorflow/stream_executor/cuda/cuda_driver.cc:313] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2021-12-03 21:07:12.865327: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (dicoding-20tbs1em00): /proc/driver/nvidia/version does not exist\n",
      "2021-12-03 21:07:12.865547: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 AVX512F FMA\n",
      "2021-12-03 21:07:12.892224: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2804000000 Hz\n",
      "2021-12-03 21:07:12.893145: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f143c000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
      "2021-12-03 21:07:12.893180: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    tf.keras.layers.Dense(128, activation='relu'),\n",
    "    tf.keras.layers.Dense(10)\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gut8A_7rCaW6"
   },
   "source": [
    "The first layer in this network, `tf.keras.layers.Flatten`, transforms the format of the images from a two-dimensional array (of 28 by 28 pixels) to a one-dimensional array (of 28 * 28 = 784 pixels). Think of this layer as unstacking rows of pixels in the image and lining them up. This layer has no parameters to learn; it only reformats the data.\n",
    "\n",
    "After the pixels are flattened, the network consists of a sequence of two `tf.keras.layers.Dense` layers. These are densely connected, or fully connected, neural layers. The first `Dense` layer has 128 nodes (or neurons). The second (and last) layer returns a logits array with length of 10. Each node contains a score that indicates the current image belongs to one of the 10 classes.\n",
    "\n",
    "### Compile the model\n",
    "\n",
    "Before the model is ready for training, it needs a few more settings. These are added during the model's [*compile*](https://www.tensorflow.org/api_docs/python/tf/keras/Model#compile) step:\n",
    "\n",
    "* [*Loss function*](https://www.tensorflow.org/api_docs/python/tf/keras/losses) —This measures how accurate the model is during training. You want to minimize this function to \"steer\" the model in the right direction.\n",
    "* [*Optimizer*](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers) —This is how the model is updated based on the data it sees and its loss function.\n",
    "* [*Metrics*](https://www.tensorflow.org/api_docs/python/tf/keras/metrics) —Used to monitor the training and testing steps. The following example uses *accuracy*, the fraction of the images that are correctly classified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "Lhan11blCaW7"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qKF6uW-BCaW-"
   },
   "source": [
    "## Train the model\n",
    "\n",
    "Training the neural network model requires the following steps:\n",
    "\n",
    "1. Feed the training data to the model. In this example, the training data is in the `train_images` and `train_labels` arrays.\n",
    "2. The model learns to associate images and labels.\n",
    "3. You ask the model to make predictions about a test set—in this example, the `test_images` array.\n",
    "4. Verify that the predictions match the labels from the `test_labels` array.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z4P4zIV7E28Z"
   },
   "source": [
    "### Feed the model\n",
    "\n",
    "To start training,  call the [`model.fit`](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit) method—so called because it \"fits\" the model to the training data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "xvwvpA64CaW_"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 2s 984us/step - loss: 0.4996 - accuracy: 0.8245\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 2s 937us/step - loss: 0.3762 - accuracy: 0.8633\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 2s 958us/step - loss: 0.3371 - accuracy: 0.8751\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 2s 867us/step - loss: 0.3135 - accuracy: 0.8835\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 2s 931us/step - loss: 0.2948 - accuracy: 0.8915\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.2819 - accuracy: 0.8952\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.2682 - accuracy: 0.8993\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.2564 - accuracy: 0.9045\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.2485 - accuracy: 0.9071\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 2s 986us/step - loss: 0.2389 - accuracy: 0.9108\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f1491c3d550>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels, epochs=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W3ZVOhugCaXA"
   },
   "source": [
    "As the model trains, the loss and accuracy metrics are displayed. This model reaches an accuracy of about 0.91 (or 91%) on the training data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wCpr6DGyE28h"
   },
   "source": [
    "### Evaluate accuracy\n",
    "\n",
    "Next, compare how the model performs on the test dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "VflXLEeECaXC"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 - 0s - loss: 0.3290 - accuracy: 0.8850\n",
      "\n",
      "Test accuracy: 0.8849999904632568\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
    "\n",
    "print('\\nTest accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yWfgsmVXCaXG"
   },
   "source": [
    "It turns out that the accuracy on the test dataset is a little less than the accuracy on the training dataset. This gap between training accuracy and test accuracy represents *overfitting*. Overfitting happens when a machine learning model performs worse on new, previously unseen inputs than it does on the training data. An overfitted model \"memorizes\" the noise and details in the training dataset to a point where it negatively impacts the performance of the model on the new data. For more information, see the following:\n",
    "*   [Demonstrate overfitting](https://www.tensorflow.org/tutorials/keras/overfit_and_underfit#demonstrate_overfitting)\n",
    "*   [Strategies to prevent overfitting](https://www.tensorflow.org/tutorials/keras/overfit_and_underfit#strategies_to_prevent_overfitting)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v-PyD1SYE28q"
   },
   "source": [
    "### Make predictions\n",
    "\n",
    "With the model trained, you can use it to make predictions about some images.\n",
    "The model's linear outputs, [logits](https://developers.google.com/machine-learning/glossary#logits). Attach a softmax layer to convert the logits to probabilities, which are easier to interpret. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "DnfNA0CrQLSD"
   },
   "outputs": [],
   "source": [
    "probability_model = tf.keras.Sequential([model, \n",
    "                                         tf.keras.layers.Softmax()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "Gl91RPhdCaXI"
   },
   "outputs": [],
   "source": [
    "predictions = probability_model.predict(test_images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x9Kk1voUCaXJ"
   },
   "source": [
    "Here, the model has predicted the label for each image in the testing set. Let's take a look at the first prediction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "3DmJEUinCaXK"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.50037188e-08, 6.49650200e-09, 1.01872736e-07, 3.02583807e-08,\n",
       "       1.46260334e-07, 1.86579349e-03, 6.87689237e-07, 1.49772596e-02,\n",
       "       1.45760026e-07, 9.83155847e-01], dtype=float32)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-hw1hgeSCaXN"
   },
   "source": [
    "A prediction is an array of 10 numbers. They represent the model's \"confidence\" that the image corresponds to each of the 10 different articles of clothing. You can see which label has the highest confidence value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "qsqenuPnCaXO"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E51yS7iCCaXO"
   },
   "source": [
    "So, the model is most confident that this image is an ankle boot, or `class_names[9]`. Examining the test label shows that this classification is correct:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "Sd7Pgsu6CaXP"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ygh2yYC972ne"
   },
   "source": [
    "Graph this to look at the full set of 10 class predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "DvYmmrpIy6Y1"
   },
   "outputs": [],
   "source": [
    "def plot_image(i, predictions_array, true_label, img):\n",
    "  true_label, img = true_label[i], img[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "\n",
    "  plt.imshow(img, cmap=plt.cm.binary)\n",
    "\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "  if predicted_label == true_label:\n",
    "    color = 'blue'\n",
    "  else:\n",
    "    color = 'red'\n",
    "\n",
    "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
    "                                100*np.max(predictions_array),\n",
    "                                class_names[true_label]),\n",
    "                                color=color)\n",
    "\n",
    "def plot_value_array(i, predictions_array, true_label):\n",
    "  true_label = true_label[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks(range(10))\n",
    "  plt.yticks([])\n",
    "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
    "  plt.ylim([0, 1])\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "\n",
    "  thisplot[predicted_label].set_color('red')\n",
    "  thisplot[true_label].set_color('blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zh9yABaME29S"
   },
   "source": [
    "### Verify predictions\n",
    "\n",
    "With the model trained, you can use it to make predictions about some images."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d4Ov9OFDMmOD"
   },
   "source": [
    "Let's look at the 0th image, predictions, and prediction array. Correct prediction labels are blue and incorrect prediction labels are red. The number gives the percentage (out of 100) for the predicted label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "HV5jw-5HwSmO"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 0\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions[i], test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions[i],  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "Ko-uzOufSCSe"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 12\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions[i], test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions[i],  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kgdvGD52CaXR"
   },
   "source": [
    "Let's plot several images with their predictions. Note that the model can be wrong even when very confident."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "hQlnbqaw2Qu_"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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J7Oen8vVYUnF9g58vwtdP+VoGv05fy+TrJVasWFF2fb42SiqeI8f3ydeR+fnKXn311bJ9rDRvlq8Rk4rnN+vYsWPZ1xTO9dGuXbuy2wNaCj+nnlS5/snnjClTpiSx33/ymPfOr9PnIB9XqlObNm1aEg8cODCJfQ4rlSOYiwdoeEcccUQS//nPf05if8w0duzYrd6mz4GV6larqUsFWjL2AAAAAADIEYMuAAAAAMgRgy4AAAAAyFEuNV1dunRJ5ox4/vnnk+cnTJiQxM8880zFdfraAV+j1bt377Jxjx49ktjXS/marUWLFiXx1KlTk7hU3cLy5cuT2NdTjB8/Pon32muvJB4yZEgSP/zww0ns58Co5vpoX8Ox7bbbJnH37t2TuLCWLY8aFKApKlXrVOn338/ttXjx4iTu3LlzEpea268ufD6phq9Lq1Rzcffddyexz0ljxoxJ4lI5aMmSJXXoIYD6cMghhySxr9/2uaCwfntL+eMHfxzlbW0OBJo7znQBAAAAQI4YdAEAAABAjhh0AQAAAECOcqnpatOmjXr27Lk5vvzyy8u2X7lyZRK/8MILRW18TdWzzz6bxDNmzEjiV155JYn9fFX+2mNfL+FrFXyN2J577lnUx+OOOy6JTz755CT211hXctpppyXxW2+9lcR9+vRJYn99tVRc++ZrVzp06JDEI0aM2Py4rv0FmqtStUnvvvtu2df4ebl8zaXft3wNmK+xqFQPUer5SnnMq1RT4fOorzu9/fbbK27Pv08A+dtxxx2T2B8P+Pzk89v06dOTeKeddqq4TV9rX2nfp04crR1nugAAAAAgRwy6AAAAACBHDLoAAAAAIEe51HTVVdeuXZP42GOPLWrjl1144YW59qkpuOeeexp1+9XMAwa0BL7+Sqpc/+Tno/I1En6dvobL8/ubj0vVT/llPvY1Xz728xc+99xzSVxY41lKqT6tWbOm7GsA5M/XcPk5+vxcpVtS0zVo0KAk9jWhvXr1SmJqutDacVQNAAAAADli0AUAAAAAOWLQBQAAAAA5ahI1XQDQmPx8M5LUuXPnJPbzCX7ta19L4kceeSSJfW1TXWskK9VrSZXn9vJ1ab4Py5YtS+KjjjoqiU855ZQk/v73v5/EperUfC0JgPpXaY6+M844I4lvuummJPa54ZlnnkliP+9oKT5HVuqjr/ECWhvOdAEAAABAjhh0AQAAAECOGHQBAAAAQI4YdAEAAABAjriRBoBWb9WqVUXL/E0i/M021q9fn8T9+vVL4tdffz2J/WSjlSZf3hKViuv9e/ATPPfv3z+J+/btW3Z7pW4OMnPmzIr9BLB1Ku3rp59+ehLfeOONSdy+ffskvuOOO5L4e9/7XsU++MmOK938p9Qk9EBrwpkuAAAAAMgRgy4AAAAAyBGDLgAAAADIETVdAFq9Qw89tGjZc889l8QdO3ZM4hEjRiTxa6+9Vv8da2TTp09P4m7duiVxqYmQDzjggFz7BKDyxOcnnXRSEvuJif2+W9fJ2yVpjz32SOIJEyYksc+Zc+bMqfM2gJaEM10AAAAAkCMGXQAAAACQIwZdAAAAAJAjaroAtHql6pDWrFmTxH5emy2pgWhu/Fxkvg5k3bp1Ra/p0qVLrn0CUDyPYCU77rhjEj///PNJvHr16iR+9tlni9ZxyCGHJLGfp+vdd99NYp8fFi5cWF1ngRaq5R81AAAAAEAjYtAFAAAAADli0AUAAAAAOaKmC0CrN3jw4KJlo0aNSmI/50yl2qUNGzYksa/BCCHUpYu58H3wfRw2bFgSf+ADH0jipUuXFq3z4IMPrp/OAaiVmdWp/Wc/+9kkHjlyZBKfffbZSezrt0o555xzknjZsmVJ3LVr1yQ+/PDDK64TaMk40wUAAAAAOWLQBQAAAAA54vJCoAW67LLLKra58sorG6AnAAAAsDzqCsxsgaSZ9b5iNLQdQwj96nOFdfzd6CupLhN70L7+2ue57sb+vULT1Zi/G01p/6N9/bUn36A2jf270VT2kS1p35T60tTa1/p7lcugC6gPZvZyCGF/2jd8+7z7AjQ1TWn/o339tweamqa2jzSlY4Tm3r421HQBAAAAQI4YdAEAAABAjhh0oSm7jvaN1j7vvgBNTVPa/2hf/+2Bpqap7SNN6RihubcviZouAAAAAMhRVWe6zPRBMwUzjazcWjLTDDP1LbF8ZV06V9f2ZdZzvpm2reW5vc30nJkmmOnfZuoel7cz041x+WQz/U9c3s9Mz5hpopk+WLCeu2vbRnz+YjOdWxC3NdMCM1V1324zfc9M3yix/C9mOrOadWxJ+zLr2cdMJxfEp5jpB1u7XqBaZtpopnFxX7zNTJ0rtH/CTPvHxyVzVF7M9GUzTYt5tG/BcjPT7+Nzr5hp34LnzjPT6/HnvLisg5keiO/5woK21xW+tsT2P2imy+Pj75npnYLP7rQKfT/KTPfGx+eb6aot/yQqI5egqTNTn7j/jDPT3IL9aZyZ2jeB/n3ETJPMtKkm5xU89z8x30w10/sLlp8Yl00z02UFy/8Rc9NPCpZ9p/D4p8T2R5npejNdUPC5rIvHU+OqPe7JQzyGe6Cxto/WrdrLCz8m6Zn4b3N0vlTrgOhPki4LQXtKukvSN+Pyj0jqEJfvJ+nzZhqi7DO4VtIBki6WJDOdKmlsCJpdagNmaivpU5JuKlh8vKTXJH3ETLalb6wR7SO9N+iS9B9Jp1Y68AXq0ZoQtE8I2kPSOklfaOwOSZsHUj63/lfScSq+lfBJkobHn89Juiauo7ekKyQdqCzXXGGmXpLerywX7yXpnNh2b0ltQtCYMt36f5KuLoh/E4L2UZbnbijR30YRcyW5BE1aCFoUc88+yo4HflMTh6B18fe4wZipjVs0UdKHJD3l2u0m6WxJu0s6UdLVZmoTX/+/yvLRbpI+ZqbdzLSXsjy7l6T3mamHmQZJOjAE/atMl74l6fch6M8Fn9NsSUfHuHBQ5/ueGzO1DUELJM0x06ENtV2gRsU/tGbqKukwSZ9WtrPWLD8qfnN8u5mmxG9DzL22k5nuN9NnS6z3m2Z6KX6D8v0y2/9N/MbmUTP1i8v2MdPz8bV3xYORksstO6uzv6R/xG9YOrlNjNB7ielhSR+Oj4OkLjF5dlJ2ULdc0npJnSV1kLQxPn+xpJ+X+RiPkTQmBG0oWPYxSb+T9Jakgwve7wwzfd9MY+K3QkVnF8302fi5dnLL9zPTk2YabaYHY3Is5TgzvWym18x0SnxtRzP9OW5zrJmOrm15/CbvB5LOip/pWSEoSHpCyta3NczsRDObambTzKziLL9mdoOZzTeziVW03d7MHjezV81skpldVKF9RzN70czGx/a1/q6617Uxs7Fmdm8VbWeY2QQzG2dmL1fRvqeZ3W5mU8xsspkdXKbtLnG9NT/LzeziCuu/JL7XiWZ2s5l1rND+oth2UqV15+hpScNiXtr8mZvpKjOdX+6FZvqaZWd8Jppt/iLlSjN9qaDN5jPNpXKXmYbEb4n/quyAZ/vCbYSgsSFoRonNny7pryEohKDnJfWM++37JT0cghaHoCXKctOJei//tJM259sfSvpumfc3QtLaEIrnGAlBkyVtkNTX0jOBfc1K9rdwvUPM9Fj8HB410w7xoGxmzSDOTF3M9LZlVw7sbNlZutFmeromt1l29v1aM70g6ef1mUuqVZecU5d8E9vnnnPqkm9i+yaTc5ppvinif4/LHKeU3M/MtLuZXox/U18x0/C4/JMFy//P4iDFTCvN9CszjVfBMYSU7dchaGqJbp4u6ZYQtDYEvSlpmrIvdQ6QNC0ETQ9B6yTdEtuul9Qp7s/tJG1U9rf/ijKfQzdJe4Wg8WXaJH2vJQcPMdPEgtd8w0zfi4+/aqZX4+d0S1zWxUw3xM9qrJlOj8vPN9M9ZnpM0qNxdf+S9Ina+pc3jnEqtm+5xzgh/rWv7UcKn5DC9fHxs1LYLz4+SgrLpLCdFLaRwnNSOCw+N0MKQ6TwiBTOLVjXyvjvCVK4TgoWX3uvFI4ose0ghU/Ex5dL4ar4+BUpHBkf/0AKv62w/Akp7F/L+3tWCh+Mj78mhRXxcTsp3CKFBVJYJYXPxeU9pPAfKbwshWOl8FUpnF/hM/y+FL5SEHeUwmwpdJLC56Twh4LnZtS0lcKFUvhTfPw9KXxDCl+Wwt1S6BCX/0UKZ8b+PiuFfnH5WVK4oURf/iKFB+LnPlwKs2J/vl7TXgojpfBWheXn1/x/uN+VP5T7LCr/vqmNpDck7SSpvaTxknar8JojJO0raWIV6x8kad/4uJuys421rl/ZgW3X+LidpBckHVTFdr6m7MzmvVW0nSGpbx0+oxslfSY+bi+pZx0+27nKJu6rrc1gSW9K6hTjWyXV+vstaQ9lg4zOktpKekTSsK35Haj+c9icT9rGfeKLMS/dW9Dmqpr9szAPxP2srxT2k8IEKXSRQlcpTJLCqPjzZMF6XpXC9rXlrpjvNkmh7O9GzXYL4ntr8maMH5XC/nFf/07B8u/GZW2lcJMUxkrh41I4TQrfq7DNC6Twq4L4e1L4Rnx8YMxF5j6fvlKYER9v/kwL93sp/FsK58XHn5LCv+Lju6VwdHx8VkEOe1QKwwu2+1h8/Jf4ObQp6ONW55I67E91yjl1yTexfe45py75JrZvEjmnOeWb2vu0+W9z8nusKo5H3H72B713vNNe2fHBrnE/axeXX614TCWFIIWPVujb5m3F+CopfLIgvl7Z8cOZNftpXH5OwX7+WymMi8cC+ygeD5bZ5tFSuKPE8s25r7DvZXLwEClMLHj9N2pyXcxZNcdAPeO/P6l5b1LoKYXX4jrPV3ac07tgXYOlMKFxfl84xqmifYs9xqnmkpKPKfvWQ/HfwksMXwxBs0LQJknjJA0peO5uSX8OQX8tsc4T4s9YSWMkjVR2eY23SdI/4+O/SzrMTD2y/wA9GZffKOmI2pZX8f4+JelCM41W9gu6Li4/QNm3OttKGirp62baKQQtC0EfCEH7x76fKul2M/3RsrN+pUbkgyQtKIhPkfR4CFoj6Q5JH7T0FPud8d/RSj/Tc5Wd/j8zBK1129hF2S/Hw2YaJ+k7krar5T3fGoI2haDXJU1X9vkfpuwzVgiaouwyqBFllpcyX7Vfxlmt+I1bmB5CKPzGrVYhhKckLa5m5SGEOSGEMfHxCkmTle2EtbUPIYSa2sJ28SeU24aZbSfpA8ouXa1XZtZD2e/19bF/60IIS6t8+bGS3ggh+EvcvLaSOplZW2WJpuRls9Gukl4IIawOIWyQ9KSyy1oaQqf4u/6ysjPG12/BOg6TdFcIWhWCVirb9w4PQWMl9TfTtpZdvrckBL2t8rlrZsjOVuUmBG0IQR8PQaMk3absLPuvzPTrmH9K1Wf5/CNJl8TP7pfS5jPVdXWw3rtk+m/KPkspy9lnxcdnS/qnZVdMHCLptrjd/4v9qnFbCNpYENdHLqlWnXJOXfJNbJ9rzskz38T1551zmku+qcZtIWjjFh6PPCfpW2a6VNKO8fjgWGXlDS/F/eZYZQfrUnZ8ckd9vwEvBF0csksCf6V4Vt1M3zbTrVbiKiaVzjdeYd9L5uAKr39F2dVLn5Q2X0F0gqTL4uf0hKSOknaIzz0cQrLPNmR+8TjGKb/uFn2MU3bQZVldwTGS/hRPgX9T0kftvcsICw/8N0rJdcz/lXSila5XMkk/De9dAz0shKoOmLbkwKD8CoOmhKATQtB+km5W9g2EJH1c0gMhaH0Imq/s/fjZqL8r6cd6r+btPCk7/e2sUZYAanxM2SV+M5QNrPoo+5xr1Hyu/jOdoGwQVmowZZImFXyme4agE2p72xXiLdVR2XvdGoMlvV0Qz1KZhLE1zGyIpFHKvtkp166NmY1TlqgfDiGUbS/pt8pqaDZV2ZUg6SEzG21mn6vQdqiyP2h/jqf2/2RmXarcztnKfsdr70gI7yg7EH9L0hxJy0IID5V5yURJh5tZHzPrrKzOb/sy7evTmoLf96+E7LKYDUrzWtnLBiq4TdKZygYQNV/+lMtdq7ZgG+8o/by2i8tqW17oQkl/lXSQpGWxn18vsQ2ff6T3alAOD0FPx2WFn93WfG73KMv9vZUdMD4W17u04HPbJwTtWvAa/9nVRy6pVnPPOb9V3fKN1ERyTjPLN9WoJgeU3M9C0E2STlP2e3+fmY5Rlm9uLNhndglh8zHGu+6Limpscb6Jl+uNltRV0s4h6KOSzrTi2stS+carpu/lcvkHlNWg7atsQNpW2Wf14YLPaoeQXT4tNW5+8Zp7vpE4xtninFPpTNeZkv4WgnYMQUNC0PbKTstV+hZCki6XtETZjuE9KOlT8dtPmWmwmfrX0r+aO+19XNIzIWiZpCVmm/twjqQna1seH69QdharSM124zXL31FWFCtl/yHHxOe6KDuwmVLwuuGStgtBTygbKW9S9ovla8ak7JuGYfF13ZV9fjvEz3SIpC+pupuUjJX0eUn3WPGdEqdK6ldzpi3WUOxey3o+YqZtzLSzsm/NpiqriflEfO0IZd8QlVte6jMdIb13DXZTZmZdlX3TdnEIYXm5tiGEjSGEfZT9ITrAzPYos95TJM0PIYyuQ3cOCyHsq+ws5pfMrNw3om2V/aG5JoQwStkfk2quCW+v7A/6bRXa9VL2rdtQZd8EdjGzT9bWPoQwWdLPJD0k6QFlZ7zreiBQn2ZK2s2yu/z1VPbNVzlPKzvT3Dnu52fEZVI20DpbWQ6q+dyqzV3VukfSuZbdfOMgSctC0Jy4nRMsq0vtpexb3AdrXhSXnaJs0FV1/qlghrJBkqSq7nD6rN6r8/2E4ucWv61+SVnN6r0haGMIWi7pTTN9JPbf4hnE2jSbXFKtPHLOFuYbqYnknBaQb0qqcDwyQyX2MzPtJGl6CPq9siuF9lJWg3RmwXFKbzPtuBVdu0fS2TE/DlV2lv5FZfvrcDMNtaxm++zYtqZv7fRe7XonvfdFbRup6G6N1eabGrXl4HnKrjboY6YO0ub6820kbR+CHpd0qaQeygaCD0r6Ss0X/WYaVWabLS6/eBzj1NquUXNOpUHXx5Td0a/QHar+LoYXKbsEKLnJRAh6SNllKc+ZaYKk21V6ULRK0gGxmPIYafNthM+T9AszvaLsLnqVlv9F0rVW+kYaHzPTa8oGVLMl/Tku/19JXc00SVlC+nMIeqXgdT+W9O34+GZJX9R7Bxre/Xrv0oIzJD3mLg+8W9ndujqUeG0iBD0j6RuS/mMFt56O3/KfKelnsTh1nLLLeUp5S1mivV/SF0LQu8rubLZN/P/4p6TzYx9rW/64soPbcWabLyU6Wtmdx7ZGNd/wbxUza6fs9/gfIYQ7K7WvEU9xP67shga1OVTSaWY2Q9llA8eY2d8rrPed+O98ZfvbAWWaz5I0q+CbqNul2m8VXuAkSWNCCPMqtDtO0pshhAUhhPXKLvWo7feopv/XhxD2CyEcoeyLlteq6E8u4iWAtyr7g3qrsi8qyrUfoyw/vKjs28A/xUsLFYImKctL78SBUF1yVyIWfs9S9vv8itnmyzLuU3aJ7zRJf1R29krxUpgfKsspL0n6gbs85nJJP46Xdj+o7IucCcou8/OekjSqlqsOCv1S0hfNNFaq6nb6X5F0Qcy35yjL9zX+KemTeu8MoZQNzD4d89Mklb+kpj5ySbWac86pc76J620qOadZ55sKajseqW0/+6ikifHyuD2U3WDnVWVfBj8U1/OwVOsNsjYz0xkx3xys7FjhQWlzTrtV0qvKDiC/FL8U2SDpy8pyyWRlJQiTClb5JWVn3FYru7Svc8x/o0PQ0sJtxzKEHmaV82JsXzIHh6D1yj6zF+P7rvnSu42kv8ftj1V2l8SlyvJlO2X5dVKMa9OQ+cVrzvlG4hhn63KOL/LiJ58fKdylWETeEn+kMEAKj279etRW2UHoUL1XZLp7Fa8bouqKTE3Z2YHfVtmffopFnMq+4Xta0ilVvvYoVSgyldRFUreCx89KOrHCa56WtEt8/D1Jv6iiL7dIuqCKdgcqOyDuHD+rGyV9pcJr+sd/d1D2h7FnY/8+8uP/j8LvpHBcY/ejyr7WSy6pfnt1zznV5pvYtkFyTjX5JrZrMjmHfNMyf6RwiRQ+09j9KNO/p6TQq3G2zTFOaz7GadC5JFq5y5R9Q/V6Y3ckJzuodD1JnYQQNphZzTdubSTdEEKYVO41Znazsp2/r5nNknRFCKG2GsFDlX0rPyFewyxJ3woh3FdL+0GSbjSzNsrODN8aQqjqtsxVGiDpLjOTsmR8Uwih0sSNX5H0j3g6fbqkC8o1jtdDH6/s0tSyQggvmNntym4SsUHZN4nXVXjZHWbWR9nthb8Uqi96RcP5ibI/Ns1BveSSatU159Qx30jknFqRb1qsa5TNAdjkWDb10K9DNhVHg+MYp3Uf41gcvQEAAAAAclDNLeMBAAAAAFuIQRcAAAAA5IhBFwAAAADkiEEXAAAAAOSIQRcAAAAA5IhBFwAAAADkiEEXAAAAAOSIQRcAAAAA5IhBFwAAAADkiEEXAAAAAOSIQRcAAAAA5KhtHivt27dvGDJkSB6r3mIbNmxI4gULFiRxmzZtknibbcqPR337aoQQkrht2/Tj79atWxKbWZ23UZ9mzJihhQsX1msn6ut3Y/x4yf2XltS2rbT33lu9OdSj0aNHLwwh9KvPdTbFnFPJqlWrknjTpk1l42r417Rr1y6Ju3btWud1NqSmnHPQPLXWfDN16tQk9scTPvbHJ+3bty9ap2+zfv36JK503ORfP3z48LLt89aU8001xzgc3zRN5XJOLoOuIUOG6OWXX85j1VvMD7L+7//+L4l79uyZxJ06dSq7vh49ehQt80ls48aNSbxu3bok7t+/fxIfddRRSVwq6TWk/fffv97XWV+/G9WORzdskJrYr2KrZ2Yz63uddf298oOTUgcL/gDB29ovRZ577rkkXr16dRL7fOHzSSlr165N4n790rx/xBFH1KWLDa4p5xw0T00h3zQGfzzhvyju0KFDEr/77rtJXGrg4NvMmzcvif0Xxz5n+fi+++4r2kZDasr5ppo/LxzfNE3lcg6XFwIAAABAjnI509UU3XbbbUn8ox/9KIl79eqVxIMGDUriN998M4kHDx5ctI0RI0Yk8eTJk5O4Y8eOSXzcccclsf/W6JxzzinaBoCtV+nSmmpe461YsSKJH3vssSQeM2ZMEt9///1JvMsuu5Td3sqVK4u2uWjRoiTu06dPEvtvpn/84x8n8amnnprEp512WhLvsMMORdsE0PQsX748iSdNmpTE/qy3t2bNmiR+4403itr4Yxh/hUDnzp2T2J+tr9QHoKXjTBcAAAAA5IhBFwAAAADkiEEXAAAAAOSo1dR0+bsX+jvzVLrV6cCBA5O41J3EfH3FsmXLkrh79+5J/M477yTxyJEjy/YBQP2opqarUg3Xddddl8T+Fs3+Dol+/z7rrLOSeNy4cUns7y7mp72QiuvA/N3DunTpksQ+D86cmd5k6ZJLLin7+iuvvDKJt91226I+AWh4vn6z0t2U/d2Rfezr3Eutw9eR+eMof5xV6a7QQEvHmS4AAAAAyBGDLgAAAADIEYMuAAAAAMhRq6np8vVWfr4IPydF7969k9jPweNrJyRp6dKlSezrRCpd/7znnnsWrRNA/fP7ZqX6LUm6+uqrk3jx4sVJPHTo0CRu165dEvt6iP79+yfxkUcemcR33nlnEvu6Uqm4DqNSTvFzgw0fPjyJe/TokcS+5us73/lOEt9www1FfQLQ8O64444k9sc82223XRL7fORrUH1Naak2fm4vX3fq69pnz56dxKNHj07i/fbbr2ibQEvCmS4AAAAAyBGDLgAAAADIEYMuAACw2cCBklnlnxJXvAIAatFqarp23HHHJB4/fnwSt2nTpmzs56vxtRRS8TXSvgZjyZIlSVxpHh8A+aimpuvtt98uG++0005JvHLlyrLb9Dlk3rx5SbzzzjuXjV9//fWidfra0wMPPDCJn3rqqST282r5uX1Wr16dxH5enblz5ybx3/72t6I+nXPOOUm8JfVzaFzuV3Or2yF/f/rTn5J40KBBSexrSH3+ads2PRz0+U6SOnfunMT+OKljx45l1zl//vwkfvHFF5OYmi60dJzpAgAAAIAcMegCAAAAgBwx6AIAAACAHDHoAgAAAIActZobafjibT9pqC9y98XffvJkf1MMqfjGGCNGjCjbJ18o74tOAeTDTyJcyrRp05LYF437iUC7du2axGvXrk1if6Md395Prn7SSScl8TPPPFPUR3+jC98nH/ub+6xatSqJ/STw69atS2I/YerYsWOL+uRvpMGNM4D8TZ06NYn333//JPYTGa9fvz6J/fGLz09ScT7w+cVPru5jn3f9ZMlAS8eZLgAAAADIEYMuAAAAAMgRgy4AAAAAyFGrKSLy1xJvv/32Sbzbbrslsa9DuO2225J48eLFRduYNGlSEh9xxBFJ7Cf+Gzx4cBL766X9RIQAGo7fn/3En75my9eB+v3X10z4GrHly5cnsZ/c9IQTTijqY6VJ3YcNG1a2j36yY1+j4SdP9vzkpgAaxpw5c5LY14z6yZD9xMT+mKh9+/ZJXGpyZJ8Dfd2XrxPz+cS/3teIAi0dZ7oAAAAAIEcMugAAAAAgRwy6AAAAACBHraama9ddd03iRx99tOzz/lrj3XffPYkPOOCAom187nOfS+Iddtghibfbbrsk7tWrVxL7OXcANJ5Zs2Ylcffu3ZPY13R5AwYMSOLVq1cnsa93aNeuXRL7mjI/t6BUPF/gtttum8R+Hhw/F9i8efOS2M/j5fswdOjQJO7Tp09Rn3xtqq8VAbD1fD1mpRpwX8/pjzcWLlyYxH6eL0maOHFiEq9cuTKJfY2XrzPzNae+xgto6TjTBQAAAAA5YtAFAAAAADli0AUAAAAAOWo1NV2+nqJLly5J7K+P9vVWnq/HkIprPPy8PP765bZt04/fz4nDHBZAw/C1TaX4+gVfH7XXXnslsa/R8vUNnq938Pu/355UXD/l6zb8vDl+bh+/Db++Utss5HOcJL3yyitJXKo2BMDWee2115LY5xt/jOP5uUh9bnjjjTeKXjNq1Kgknjp1ahLvuOOOSezrOf0xD8c4aG040wUAAAAAOWLQBQAAAAA5YtAFAAAAADlqNTVd/vpmX+O1zTbp+NPPb+NruPbZZ5+ibfhrpNesWZPEvl7C13j4a7IBNIzp06cXLfNzzviay1WrViWx3/8XL16cxL6+yq/P8/VSvuar1Dbnz59f9nm/Td8nnxf9Z+DrVn2NhiS9+eabSUxNF1D/pkyZksR+ni6fn3z+8PWa/fr1q7jNgw46KInHjRuXxD7f+Hzhn2cOP7Q2nOkCAAAAgBwx6AIAAACAHDHoAgAAAIActZqark6dOiWxr+HytQuef97PV1GKr4/wffBzVFDTBTSOt99+u2iZn1ev1JxUhWbOnJnEQ4YMSWJfv+BrOn3daLdu3ZK4VH7w2/R99DVX/j35Pvj5Cn0trO9DqT75uXsA1L9p06YlcY8ePZLY15D7fdXXrZ9//vkVt/mpT30qia+99tokrpQjfV1ZqTpVoCXjTBcAAAAA5IhBFwAAAADkiEEXAAAAAOSo1dR0+WuH/fXNfv4IH1eq+ZKKa7b8HDi+PoLrm4Gmwdc3SMV1n927d09iPwfNihUryr7e12z5/d0/71/vtycV11D4OrAlS5Yksa/p8nMJ+ve4YMGCJPZ1I6VqOMaPH1+0DED9Wr58eRL74w9/DOOPR3x88cUXV9zm+973vrLbqDS3oK9j55gHrQ1nugAAAAAgRwy6AAAAACBHDLoAAAAAIEetpqarb9++SVzpWmQ/x4WvhSjF11OEEMquY/DgwUnsazgANIyVK1cWLfPzavXq1SuJ/RxZp59+etl1+pzj60p9zZaPfQ2GVDwPl2/z7rvvlu2Dz0kjR45M4rvvvjuJfY4qNU+XrxMDUP/8vu/rzv2+7vfLgQMHJvFOO+1U5z744yp/HNW7d+8kXrRoUdk+AS0dR/kAAAAAkCMGXQAAAACQIwZdAAAAAJCjVlPTNWjQoCT2NVu+/mr16tVJXKqewvPz7Ph5ufwcOKXmuAHQ8Hztk1Q8742vofB22223JH766aeTuNJcf75eaunSpUnsa8pKvcbXWPk++zznjRgxIol9zYV/vZ93R5KWLVtWdhsAtl6fPn2S2B9/eL7G9MQTT9zqPvi6MD/vlq/5Wrx4cRJzDITWhjNdAAAAAJAjBl0AAAAAkCMGXQAAAACQo1ZT09W5c+eysa+38Nca+2uRS/E1XH6eHV//4K/JBtAwfP1DqZrNjRs3JrGvZ/L1U9tuu23Z9p6vG/X1V6tWrUriUvnCz8XjYz/XmOff47Bhw8r20bcv9bn52hEfV6ptA1CZ34+WLFmSxD7HTZs2LYl/9atflV1/qXorX0M6dOjQJJ41a1YS9+vXL4l9/vDtgZaOM10AAAAAkCMGXQAAAACQIwZdAAAAAJAjBl0AAAAAkKNWcyMNP2mfv+mFLxr1Rai+ILSU4cOHJ7GfWNQXnZeakBVA/hYuXJjEpW564W9K4YvA/Y00fM7wsb9Rhp+g3RfG+5v9lLpphc8x/fv3T2Kf9/z79M/7m4H4wnnPTyAtFX9uc+fOTWJ/sw4AdedvzOWPJ/wNbPy+7ydz93y+k4rzwe67757Eb775ZhJ369YtiRcsWJDEpSZ8B1oyznQBAAAAQI4YdAEAAABAjhh0AQAAAECOWk1Nl+frLfzkx/75aq499tdIv/3220m8fPnyJC5VDwEgf0uXLk1iv79LUseOHcu+ZocddkhiX7/gJzceMGBA2W36ulJfb1WqBtTXdPnX+DoyXxe2YsWKJPZ1IL7Pfv2l6j587cj8+fOTmJouYOvtueeeSfzCCy8ksc8XvuZ84MCBZddfqZ5Tkk4++eQk/v3vf5/EfnJ1X9/Zu3fvitsAWhLOdAEAAABAjhh0AQAAAECOGHQBAAAAQI5abU3XokWLkthf73z//fcn8ec///mK69x3332T+MUXX0ziwYMHJ7Gv4QDQMPxcUn6OLKl4HpypU6cm8ciRI8uuw9dTeb4eytdb+T76/kjFdaG+jsNvw6/T17L6+Qt93YivAStV6+q34evEAGy9s846K4n//Oc/J7HPP76m/LHHHkviE044IYlLzV3o+Ry4/fbbJ7GvC/Pr9PkEaOk40wUAAAAAOWLQBQAAAAA5YtAFAAAAADlqtTVdTz75ZBJPmzYtiX1N19/+9reK69xjjz2S2NdLXHXVVUm89957J/F+++1XcRsAtp6v6SxVf+XnwFq2bFkS+/13wYIFSexrKHytk6/hWrt2bRJ37ty5Yh99zYTvs68bbdeuXRL7ebfeeuutJN55552T+Nlnny27Pam4zsN/DgC2nt93/b7tayl9e39M42u6KtWkSlLfvn2T2M/DNXPmzLJ98nMhAi0dZ7oAAAAAIEcMugAAAAAgRwy6AAAAACBHraamy88P4eev8TVdft6uaq499tdA+xoQP2/Xhg0bKq4TQP0bM2ZMEpeqTfLL5s2bl8R+jqqXX345iX1Nlq+/8rHPSe3bt0/iUvnCv8bHfm4vH/ucNX78+CTu3r17Evt5wUp9bqtXr05i/7mceeaZRa8BsHV8vZTfN/0xjD8eqQ9+nsDRo0cnsa9jLZU/gJaMM10AAAAAkCMGXQAAAACQIwZdAAAAAJCjVlPT5efIWbduXRL7a4t97UM1/Dr99cu+xss/D6BhdOnSJYl9LYIkvfPOO0m8YsWKJPbzdPl6qJ49eyaxr3XyfN2pn7fL12tJxXPvdO3aNYl9XZhv7/PijBkzkvi0005L4k9/+tNJ/NGPfrSoT76WbdCgQUVtANSvQw89NIlvuummJO7du3cS+9xQH4YMGZLES5YsSeJqchrQknGmCwAAAAByxKALAAAAAHLEoAsAAAAActRqaro8fz3z8uXLk9jXfFSjXbt2SeznwPE1XAMHDqzzNgBsvQsuuKBiGz/vzfTp05N45513TuI777wzif08Xn59mzZtSmJfA7Zw4cIk9jWjUnEtmp/Ly8d+brD+/fsn8fPPP5/En//855N4wYIFSexryKTq5jQEUL++/OUvJ/Htt9+exH7fX7p0aRL7/LbTTjvVuQ/dunVLYl8H63Oez5FAS8eZLgAAAADIEYMuAAAAAMgRgy4AAAAAyFGrrenq1KlTEvvaiC2pS/B1Yn7eHX89cx7zZACoH75eaa+99kpiX6+waNGiJPbz4lSq6fTzePn1+XwiFecQX7fh58WplHN8H8aNG5fEJ598ctnXA2gcgwcPTmJfI+prSn2N6IsvvpjEW1LT5fOLryn1+ahUnSrQknGmCwAAAAByxKALAAAAAHLEoAsAAAAActRqa7rmzp2bxBs3bkxiX39VDV8D4usr/DZ8XRmAxlGqXsrngDZt2iTxM888k8R+Xj6vc+fOZdc/bdq0JK6mpsLnMb9OX6vq5x/0OcjXhTz11FNJ7Gu6Sn1uZlamxwDqg9/3/H53/PHHJ/Edd9yRxL7+6u67707is88+u8598sdAs2fPTuJKde5AS8eZLgAAAADIEYMuAAAAAMgRgy4AAAAAyBGDLgAAAADIUau9kcaAAQOSeP78+Unsi+ar0atXrySuNFFp//7967wNAPWv1M0fKuWAqVOnJrGfjNTv7/5GG/71Q4cOTWJ/04t33nmnqA9+G74wfc2aNUnsC9l9Mb2P/Y06vFKfW6UCfwBbr9KNfvxNb26//fYk9jfRmTVr1lb3qUePHknsJz/2x0iLFy/e6m0CzQlnugAAAAAgRwy6AAAAACBHDLoAAAAAIEettqbrpJNOSuKXX345ibekpqtbt25J7K9v9hOV7rjjjnXeBoCG4Scz9zlh5syZSezrq0aMGFH29SNHjkzi3r17J/Grr76axKVqo9avX5/Evm6sUk7yNRf+Paxevbrs8x06dCjqEzVdQP58zbh32GGHJbGf+Hzp0qVJ7Os3x48fX7TOvffeu+w2u3fvnsQ+f7Rr1y6JfR0s0NJxpgsAAAAAcsSgCwAAAABy1GovLwQAtE6XXXZZVe2uvPLKnHsCAGgtWu2gq2PHjkns6622pKbL83Pk+Oubt9tuu63eBoB8VKpF+slPfpLEv/jFL5L4/vvvT2JfQ+Hn5fL1WD5/lJrXb8mSJUm8fPnyss/7ug1fU9G3b98k/vKXv5zEpWq4vEq1JgC2Xl1rJXfYYYckHjduXBL7equHH364aB2VarpWrFiRxD6HefPmzSv7PNDS8NcRAAAAAHLEoAsAAAAAcsSgCwAAAABy1Gprus4999wkfuaZZ5LYz+O1JU477bSyz++5555bvQ0A+ahUm9SpU6ckvvzyy8u2f+utt5LYz8Pl6xt8fdamTZvKrl8qrsvwsa/rOPTQQ5O4a9euFbeByrhRB5qab3/720k8cODAJPa54sgjj6zzNs4666wkHjBgQBL7GtJjjz22ztsAmjPOdAEAAABAjhh0AQAAAECOGHQBAAAAQI4shFD/KzVbIGlmva8YDW3HEEK/+lxhHX83+kpaWIfV077+2ue57sb+vULT1Zi/G01p/6N9/bUn36A2jf270VT2kS1p35T60tTa1/p7lcugC6gPZvZyCGF/2jd8+7z7AjQ1TWn/o339tweamqa2jzSlY4Tm3r42XF4IAAAAADli0AUAAAAAOWLQhabsOto3Wvu8+wI0NU1p/6N9/bcHmpqmto80pWOE5t6+JGq6AAAAACBHuZzpMlMfM42LP3PN9E5B3D6Pbdaxfx8x0yQzbTLT/u65/zHTNDNNNdP7C5afGJdNM9NlBcv/YaZXzPSTgmXfMdMHy2x/lJmuN9MFBZ/LOjNNiI+vrOe3XDUz9TPTA421faAxmOnbMSe8EvfBA+PyGWbqW6L9aYV5wD13lJkOqeW5Hmb6t5nGx+1dUPDcz+OyyWb6vZnMTB3M9ICZJprpwoK215lp3zLv54Nmujw+3sVMT8T3Ndks+8bOTOeb6apaXn+fmXrW8ty3Ch63N9NTZmpbW1+AloZjHI5xgC2Ryx/KELRI0j6SZKbvSVoZgn5Z87yZ2oagDXlsuxQztQlBGwsWTZT0IUn/59rtJulsSbtL2lbSI2YaEZ/+X0nHS5ol6SUz3aPs81sTgvYy08Nm6iGps6QDQ9CPynTpW5J+FILGS/pz3PYMSUeHkN6SskTfcxP/XxaYaY6ZDg1B/22I7QKNyUwHSzpF0r4haG0cZJU9cApB90i6p8S62ko6StJKSc+WeOmXJL0agk41Uz9JU830D0n7SzpU0l6x3TOSjpTUPT7+iaT/SrraTHtLahOCxpTp4v+TdFp8/HtJvwlBd8c+7lnuvcX3d3KJ92aSTFn++klst85Mj0o6S9I/Kq0XaAk4xuEYB9gSDVbTZaa/mOlaM70g6edm2sdMz8dvUO4yU6/Y7omab2bM1DfuqDLT7mZ6MX5L8oqZhsflnyxY/n9mahOXrzTTr8w0XtLBhX0JQZND0NQS3Txd0i0haG0IelPSNEkHxJ9pIWh6CFon6ZbYdr2kTmbaRlI7SRsl/UDSFWU+h26S9orJqLY2Sd/N9LX4TfdEM10c2wwx08SC13wjJn+Z6atmejV+TrfEZV3MdEP8rMaa6fS4/Hwz3WOmxyQ9Glf3L0mfqK1/eTOzE81sqplNM7OSZxNc+xvMbL6ZTayi7fZm9riZvWpmk8zsogrtO5rZi2Y2Prb/fpXvoY2ZjTWze6toO8PMJpjZODN7uYr2Pc3sdjObYmaTzezgMm13ieut+VluZhdXWP8l8b1ONLObzaxjhfYXxbaTKq27iRokaWEIWitJIWhhCJpd8PxXzDQmfks7UkrPErncdqukL0i6JOakw922gqRucQDTVdJiSRvi8o7KBnsdlOWTecpyTOcYW1zHDyV9t7Y3Ew+i1hYc3AxSdiCl+P4mFDTf1rIzaa+b6ecF65gR8++Q+O33X5UdyF2vLOeNi4NFqZHzRX2oS86pS76J7XPPOXXJN7F9k8k5LSXfcIyz+XPgGKcCjnEqtm+5xzghhFx/pPA9KXxDCn+Rwr1SaBOXvyKFI+PjH0jht/HxE1LYPz7uK4UZ8fEfpPCJ+Li9FDpJYVcp/FsK7eLyq6VwbnwcpPDRCn3bvK0YXyWFTxbE10vhzPjzp4Ll50jhqvj4t1IYJ4WvS2EfKVxfYZtHS+GOEstnSKGv77sU9pPCBCl0kUJXKUySwigpDJHCxILXf0MK34uPZ0uhQ3zcM/77k5r3JoWeUngtrvN8KcySQu+CdQ2WwoS8fzdKfz5qI+kNSTspOwAdL2m3Cq85QtK+kiZWsf5BkvaNj7tJeq3c+pUd6HaNj9tJekHSQVVs52uSbpJ0bxVtZ0jqW4fP6EZJn4mP20vqWYfPdq6yiftqazNY0puSOsX4Vknnl2m/h7KD8c7KvhV9RNKwxvjd2fLfudA17sOvxRxyZMFzM6Twlfj4wpo8EPebmhzgc9v3pPCNWrbVTQqPS2GOFFZK4QMFz/1SCkulsEwKP47L2krhJimMlcLHpXBazX5e5v1cIIVfuXiZFO6XwiUFOeF8KUyXQg8pdJTCTClsX/C++8Y8s0kKBxWsb6XbXhspLGjs/8ct//+vW86pS76J7XPPOXXJN7F9k8g5LSHfcIxTtE2OcSrvExzjlG/fYo9xGvruhbeFoI3xFHXPEPRkXH6jsl+qcp6T9C0zXSppxxC0RtKxkvZTdip8XIx3iu03Srqjvt+AF4IuDkH7hKBfKX4DbVl9yK1m+myJlwyStKDCagv7fpiku0LQqhC0UtKdUtG3594rkv5hpk9Kmy9xOEHSZfFzekLZt+o7xOceDkGLC14/X9mlB40hfuMWpocQCr9xq1UI4Skp6X+5tnNCCGPi4xWSJivbCWtrH0IIK2PYLv6Ectsws+0kfUDSn6rpU12YWQ9l+8r1sX/rQghLq3z5sZLeCCHMrNCuraROZtZWWaKZXabtrpJeCCGsDiFskPSksstamo24X+0n6XPK9s1/mun8giZ3xn9HSxpSy2puC9VdIvN+SeOU7V/7SLrKTN3NNEzZZ7mdst/HY8x0eAjaEII+HoJGSbpN0sWSfmWmX5vpdrPNlxAWSnJMCPpzXPdtyi59fN5MHeLTj4agZSHoXUmvStqxxPpmhqDna3tD8X2vi99wN0d1yjl1yTexfa45J898E9efd85pSfmGYxyOcSrhGKf8ulv0MU5DD7pWVdFmg97r1+ZTfiHoJmU1Cmsk3WemY5SN0G+MCWGfELRLCNnpZ0nvVnkQVOgdSdsXxNvFZbUt3yyeyh6t7JKhnUPQRyWdaabObhtrCt9XLarpe+HnJLfODyi7PntfZcm6rbLP6sMFn9UOIWhybO//XzrGfjaGwZLeLohnqUzC2BpmNkTSKGXf7JRr18bMxilL1A+HEMq2l/RbZTU1m6rsSpD0kJmNNrPPVWg7VNkftD/HU/t/MrMuVW7nbEk3l+1ICO9I+qWktyTNkbQshPBQmZdMlHS4mfUxs86STla6rzQLIWhjCHoiBF0h6cuSPlzw9Nr470bVXgdbTW6TpAsk3Rm/9Jqm7Bu3kZLOkPR8CFoZDzzul7tkSNKFkv4q6SBJy5TVUX29xDaKckwImh2CbghBpyvLHXu491bu/VXz3jpIereKdk1Rc885v1Xd8o3URHJOC8w3HONwjFNJc883Esc4W5xzGmWerhC0TNKSgnqHc6TN3wjNUPbNjiSdWfMaM+0kaXoI+r2ku5UVnD+qbKfvH9v0Niv5TW217pF0tmV3DBsqabikFyW9JGm4mYZadmeis1VQRG+mdsq+gf65pE5671uCNiouyJ8saVgd+vS0pA+aqbOZuig7OHtaWb1Hf8vuotRB2Y0AFK+93j4EPS7pUkk9lCXJB5XVplhsN6rMNkdI711L3RKZWVdl37RdHEJYXq5tCGFjCGEfZX+IDjCzPWpra2anSJofQhhdh+4cFkLYV9JJkr5kZuW+EW2r7A/NNSGEUcr+mFRzTXh7ZX/Qb6vQrpeyb92GKvsmsIuZfbK29iGEyZJ+JukhSQ8oO4vTIEXR9cWyu/sNL1i0j6RK35SVs0Kq9azPW8q+jZOZBkjaRdL0uPxIM7WN+eRIafMBg2I9yCnKBl2dlf2xC8ryjZfkGMvuStYuPh4oqY/cAVUdra9ZX1xnH2U1ceu3Yp0tXh45ZwvzjdREck5LzTcc43CM09g4xqm1XaPmnMacHPk8Sb8w0yvKDnJ+EJf/UtIXzTRWSm7V/FFJE+Op4z0k/TUEvSrpO5Ieiut5WNmp7bLMdIaZZin7Jvk/ZnpQkkLQJGXXd76q7MP9UvwGfIOyb78fVJZQbo1ta3xJ2bdRq5Wd9u5spgmSRoegpYXbDkFTJPWo9lKckN2h7C/KEuMLkv4UgsbGA5wfxOUPS5oSX9JG0t/j9sdK+n3sww+VnTZ+xUyTYlyboyX9p5r+5aDiN25by8zaKUtG/wgh3FmpfY14ivtxSSeWaXaopNPMbIayywaOMbO/V1jvO/Hf+ZLuUnb5QW1mSZpV8E3U7VLttw4vcJKkMSGEeRXaHSfpzRDCghDCemWXepS8/XlB/68PIewXQjhC0hJl15A3J10l3VhTmC1pN2nzt8lb4t+SzrDSN9L4oaRD4v75qKRLQ3bDi9uVXec/Qdk1/uND0L8LXne5pB+HoE3K8tDhse3fSmz/KUmjag4+lF12MzEWrT8o6ZshaO5WvL/rlOWRmhtpNGa+qA/NOefUOd/E9TaVnNOS8w3HOFXgGKdZ5RuJY5ytyznlCr74yedHWTH7Zxq7H2X695QUejXOttVW2Tf/Q/VekenuVbxuiKorMjVlZwt+W2V/+ikWcSr7hu9pSadU+dqjVKHIVFIXSd0KHj8r6cQKr3la0i7x8fck/aKKvtwi6YIq2h0oaZKysymmrBbhKxVe0z/+u4OyP4w9G+N3h5/C/5PwOykc10DbulMKIxr7PW95/+uec6rNN7Ftg+ScavJNbNdkcg75pmX+cIxTbtsc47TmYxwmtGwc10j6SGN3ohTL5g76dQha0hjbDyFsMLOab9zaSLohhDCp3GvM7GZlO39fM5sl6YoQwvW1ND9U2aUeE+I1zJL0rRDCfbW0HyTpRjNro+zM8K0hhKpuy1ylAZLuMjMpS8Y3hRAqTdz4FUn/iKfTp0vvTbBbSrwe+nhJn6/UmRDCC2Z2u6Qxyq6pH6vszEY5d5hZH2W3F/5SqL7oFfn5ibI/LrmKlyL9K4Qme7ahorrmnDrmG4mcUyvyTYvFMU4tOMZp3cc4FkdvAAAAAIAcNGZNFwAAAAC0eAy6AAAAACBHDLoAAAAAIEcMugAAAAAgRwy6AAAAACBHDLoAAAAAIEcMugAAAAAgRwy6AAAAACBHDLoAAAAAIEcMugAAAAAgRwy6AAAAACBHbfNYad++fcOQIUPyWHVuNm7cmMRt2rRJ4rVr1ybxhg0bitZhZmXjTp06bU0XG9yMGTO0cOFCq9yyes3xd6Ma48dLJX4lEm3bSnvv3TD9acpGjx69MITQrz7X2RJ+rxYtWpTEq1atSuIQQtFrfJ7q2LFjEvft27eeetcwyDmob+Qb1IZ8k5/WfExULufkMugaMmSIXn755TxWXTV/gOIHQN6SJUuSuFevXkn8xhtvJPHChQuL1uEPgDp06JDEe+65Z9k+NDX7779/va+zKfxu5KHCr5ekLAG1wLdeZ2Y2s77X2RR+rzZt2pTEPgf5/OD99a9/TeLnnnsuiUt90ePz1MiRI5P4U5/6VNlt1jVPVnr9lqyjEDmnaRg4UJo3r3K7AQOkuXPz78/WaKn5BluPfJOf1nxMVC7ncHkhAADYrJoBV13aAQByOtPVGCpdHui/kfVnodavX5/E/lLANWvWJHHPnj2L+uDX0a5duyT+7Gc/m8Q///nPi9YBoHnaZpu6fYf1yiuvJPF5552XxAcffHDF9fsc85vf/KbsOn1e9Gel6nrma2vOagEA0JpwpgsAAAAAcsSgCwAAAAByxKALAAAAAHLUYmq6Kt0Z7J///GcSX3755Uns6ytuu+22JP7mN7+ZxGPHji3axiOPPJLExx13XBJfeOGFSezvRta2bfrfsbV3FgPQeKZMmZLE89xdB/r375/EL7zwQhJfccUVSbxs2bKibfja0z/96U9J/NRTTyXxM888k8SXXnppErdv375oGwAAYOtxpgsAAAAAcsSgCwAAAAByxKALAAAAAHLUYmq6KvH1Uttuu20Sf+c730nik08+OYkfeOCBJH7zzTcrbvPqq69O4iFDhlR8TSFquICma/To0Un8r3/9K4lnz56dxIceemgSL126NIl79+6dxLvssksSz58/v6gPvqZr7733TuJ169Ylcffu3ZPYzxV45JFHJvGuu+6axH379i3qAwAAqIwzXQAAAACQIwZdAAAAAJAjBl0AAAAAkKMmWdPl56eSiuubfK3CmDFjktjXS7z77rtJPG3atCSeOHFiEt93331J3LNnzyQeNGhQUR9fe+21omWFpk6dmsRr165NYl9ntn79+iQeMGBAEm+zDWNmoKH4Oa2OPfbYJPb1Tr4ma4899kjiGTNmJPHf/va3JN5vv/2SeMSIEUV98jnknnvuSeL3v//9SexrtJ5//vkk9nMN+uc/+MEPJvHw4cOL+gQAAIpx1A4AAAAAOWLQBQAAAAA5YtAFAAAAADli0AUAAAAAOWqSN9KoZlLgV199NYlfeumlJPZF7L4IfZ999knid955J4lXrlyZxH7i01GjRhX1aeHChUm8Zs2aJO7SpUsSL1q0KIlff/31JG7fvn0St2vXLomZqBTIz4QJE5LY36TiZz/7WRL7yc/9hOw77bRT2fZLlixJ4gsuuCCJp0+fXtTH1atXJ/G4ceOS+MADDyzb3t+8Z/DgwWXX9+tf/zqJr7nmmqI+AQCAYpzpAgAAAIAcMegCAAAAgBwx6AIAAACAHDXJmq5q+PqHYcOGJbGvyerXr18SL1++PIn79OmTxL5e6uWXX07iF198sahPfvLTBQsWJPGKFSuSuFevXmX74Cc/9jViAPIzevToJH7ggQeS+IYbbkjiu+++O4n9/uwnJp4yZUoS//vf/05in6P8ZMqSNG/evCT2daF+QnU/QbuvE+vdu3cS77bbbkn8gQ98oKgPAACgMs50AQAAAECOGHQBAAAAQI4YdAEAAABAjppNTZev0fL1UYMGDUpiP6fOnnvumcTvvvtu2e117do1idetW5fEpeqr/DxaGzduTGI//1jnzp3Lxn5OHR8DyM9jjz2WxEOHDk1iP9dfjx49ktjnEF/zOXPmzCT2OeyYY45J4jfeeKOoj+vXr09iP7eYr031NWC+5suvz5s1a1YS+7kJS20TAABwpgsAAAAAcsWgCwAAAAByxKALAAAAAHLUbGq6li5dmsRr165N4oEDByaxr13wc2Z16dIlidu0aZPEHTt2TOLu3bsnsa/fkqQQQhL7eXp8zcemTZvKxr6OzNdb+M+gQ4cORX0CsGX8PFlvv/12Eu+///5J7GuyfN1oz549k9jPNehzyvDhw5N42bJlRX30daB+Hi5f++r74PPckUcemcR33HFHEvt5wBYtWlTUJ2q6AAAoxpkuAAAAAMgRgy4AAAAAyBGDLgAAAADIUbOt6Wrfvn0S+1qFXr16JbGvf/LP+3qsbbZJx6O+PqNTp05FffT1Ef41fm4vX4/h69A2bNiQxP49+pqTfv36FfUJwJapVIN13333JbHf//z+7utOZ8yYUad4ypQpRX3s3bt3Ek+fPj2JP/OZzyTx7Nmzk3jcuHFJ/OSTTybxs88+m8Q+Z/m8CgAASuNMFwAAAADkiEEXAAAAAOSIQRcAAAAA5KjZ1HT5+ghf0+Xn2fLtFy5cmMS+/sLXcJlZ2f60bVv80W3cuDGJ/bxbfh4tvw5fE1ZqG+XaA6g/++23XxKfd955SezrnXw91eLFi5N4zpw5SexrxFauXJnEvo7Vz7klFeccP2/WrFmzktjPs7V69eok9nnSz0Xm69x8TRkAACiNM10AAAAAkCMGXQAAAACQIwZdAAAAAJCjZlPT5ee88jVcvr7Jz3nl6yd8bYKvhVi3bl0S+/oqv32puM7M14n5Gq/u3bsnsZ8jZ9SoUUns68z83GIAtsyECROKlt18881J/LGPfSyJ/f7s59Xr0aNHEnft2rXs8z7n+Hj9+vVFffT69OlTdhs+j/kc5XPYiSeemMRz585N4scff7yoD+ecc07FfgKoG3+M4mtIfX3mW2+9lcR77LFHEl933XVJ7PfbbbfdtqgPPp/4+U49nyN9vqnEH+NUqrUHmjrOdAEAAABAjhh0AQAAAECOGHQBAAAAQI6aTU2Xr2/o3LlzEvtrf5cvX57EAwcOTGI/B46/Vthfe+xrIfy1yqXW0a5duyT2NR/e7bffnsQjRoxIYn+Nta9zA7BlVq1aVbTM1y/95S9/SeL77rsvia+44ook9vvvgAEDktjXaL3zzjtJfPDBBydxqXqI/v37J7GfN2v48OFl2/ta1zPOOCOJJ0+enMTjx49P4n333beoT9R0oTUoV1NdqfbIz68nFdeJP/bYY0n8hz/8IYnfeOONJPY5zNdn7rzzzkns696PPPLIJL7qqquK+vjII48k8T333JPEBx10UBJXquHyx3W+z9RwoaXhTBcAAAAA5IhBFwAAAADkiEEXAAAAAOSo2dR0rV27Non9/BD++uopU6YksZ/Hq0OHDkm8Zs2aJC51zXWl5yvNy+Xn6fHuuuuuJP7617+exP5655UrV5ZdH4Dq7LbbbkXLfvrTnybxCSeckMT9+vVL4jvuuCOJ/Zw22223XRL7fHHTTTcl8U477ZTEvoZDkubMmZPETz/9dBL7PPn2228n8YoVK4rWWejkk09O4qOPPjqJS31uQGtT1/moSs3zOWbMmCT+7W9/m8S77LJLEp911llJvN9++yWxn4vU16A+99xzSfzHP/4xibt161bUR1+X6mtAhw4dmsSXXXZZEp922mlJ7I9pgJaOM10AAAAAkCMGXQAAAACQIwZdAAAAAJCjZlPT5edr6N69exL7mq8ZM2Yksb8+2bf3c175Obb8NdqlrtkudZ12oUpzi/m5xPy8PXvttVcSl5orDEDdvf7660XLXnvttST2+/f8+fOT2M/D5+s+fd2oX5+vt5o0aVIS+zpVqTiP+Zzi5wJ76623knjx4sVJvPvuuyexr+Hwn9Mrr7xS1Cefp4CWqPCYpNLf/mr4mqxFixYlsZ+Dr67OO++8srH35ptvFi370Y9+lMTjxo1LYl9n7uti/ToHDRqUxD4f+XxWqpbeHwdVyoHHHHPM5serV68uWh+QJ850AQAAAECOGHQBAAAAQI4YdAEAAABAjhh0AQAAAECOmuSNNPxNLaTiAko/ufHy5cvLrtMXTHbp0iWJ27ZNPwp/Iw1fjFmKL6T3N//wEwH6G2XMnj07iWfNmlV2e9xIA6gfpW6k4SdU9zng1ltvTeIrr7wyif1NKfxkpX7/9Tfa+fjHP57EY8eOrdhHX6h+0kknJfHBBx+cxL5w/ZJLLim7TZ9HfZ6UpKVLlyaxf99Ac7du3brk77O/6Y7flzt16pTEpW7EdfHFFyexv0nOs88+m8R+P/PHTT5f+WOoF198MYnnzp2bxP5mZZI0cuTIJD7++OOTePjw4UnsJ4T/17/+lcR+Mnd/wzOfX0od8/jjLt/Gf07ve9/7Nj8udWMOIE+c6QIAAACAHDHoAgAAAIAcMegCAAAAgBw1yZquaq6z9dcb+2uqPX9Nta8J89v0k/z5yQ9LXVvsr9P2bfy1x4MHD07iShORer5mrNTnVh+TNgIt3ejRo4uW+clI/WSlU6dOTWJfF/rYY48l8S677JLEPsc8+eSTSTxq1KgkLpXjfL2C7+MRRxyRxM8991wS+zrTHXbYIYl9TZfPWQsXLizq04IFC5KYmi60NG3atFHXrl03x76eyk9C7uuzS9V07bnnnkl8/fXXl+2Dr/ny+7KvW+/fv38Sf/SjH03ioUOHJrGfuLg+fP7zn09iX4vvc6g/ZirFT4bsY68wH/kaMiBvnOkCAAAAgBwx6AIAAACAHDHoAgAAAIAcNcmarlL8NdB+HowxY8aUfb2v6VqzZk0S+9onPz9ENbVR/npk3+dK1xoXXiMuFdeMeJVqxiRquoBq+PmrJOmggw5K4okTJybxYYcdlsS9evVK4gkTJiTxunXrktjvvz4/+BpNn/Ok4vop/xqfE/zcPb4OxOcgX3Ph605WrFhR1CdfOwK0NG3atElqg04++eTG60wzVmouMKAl40wXAAAAAOSIQRcAAAAA5IhBFwAAAADkqEnWdPm6A6m4/sHPUbV48eKy6/TzMaxatSqJfb2Fr43wtQ/VzB/h66l8HZmvG+vTp08Sl/ocCtW1ZgxAaePGjStaNmzYsLJt/JxVc+bMSeJ33nknif28N74eqtLcPm+++WZRH32b1atXJ/G8efPKbsPnnBEjRiSxz4vbbbddEs+cObOoT0uWLEniHj16FLUBAKC14UwXAAAAAOSIQRcAAAAA5IhBFwAAAADkqEnWdPk6Aqm4psvPieXrpTxfVzB37twk9vVRK1euTOK1a9eWbS8V15n5miz/HvwcFb6Pvv7C85+BXz+A6tx7771Fy3yN5O9+97skfv/735/E++23XxL7HLHvvvsm8dtvv53EBxxwQBLvvvvuSVxq//Y5w9ea7r333knsa1/93GJ+3q+vfe1rSeznDvR1a5L0rW99K4mHDBlS1AYAgNaGM10AAAAAkCMGXQAAAACQIwZdAAAAAJCjJlnTVWq+KV8v5fk5coYPH1729X4OLV8fVSn283hJlefJKlUHVmjXXXdN4ilTppRtT00XUD9++ctfFi07+OCDk9jXee68885JvHTp0iT2NZ0dO3ZM4p49eybxwIEDk9jPA1Zq/549e3YSL1++PIl93tt+++2T+N13301iX0/7mc98JokPO+ywin3ybQAAAGe6AAAAACBXDLoAAAAAIEcMugAAAAAgRy2mpsvPabXddtuVXaefd8vXaPl5v3ztQqn+VGpTaS6xrl27lu2Tj31dmp+jB0B1pk+fXrTM12D5/W+XXXZJ4kcffTSJ77zzziQeM2ZMEvt6rL/85S9JvGTJkiT283pJ0uTJk5PY12j5bYwbNy6JFy1alMQnnHBCEvt5u+bNm5fEpeZU9LVt/fr1K2oDAEBrw5kuAAAAAMgRgy4AAAAAyBGDLgAAAADIUZOs6SrFz3nj+XqpYcOGJbGvf+rQoUMSV5rHyz9fqpbB8+uopEuXLkns39Pq1auT2M/TVU2fABRbtWpV0TJfz+Tj/fffP4n33XffJPZzBfr5q8aPH5/Evobs7LPPTuJJkyYV9dFv09edffzjHy/b58WLFyfxiSeeWHabfq6yUp9bpdpVAABaI850AQAAAECOGHQBAAAAQI4YdAEAAABAjppkTZefa0aqXB81Y8aMJD7kkEOS+M0330ziOXPmJHGnTp2SuFevXknsa8p87YRUPE+Wf02lujTfh2XLlpXdpq/pArBlVqxYUbTMz4s1bdq0JO7cuXMSP/jgg0ns91efH+bOnZvEu+22W9k++u1J0p577pnEfr6xnj17JnH//v2T2M+75fOinzvQz4dY6nMrlb8BAGjtONMFAAAAADli0AUAAAAAOWLQBQAAAAA5YtAFAAAAADlqkndi8AXnUvHEob5I3Rdv+0lAQwhJ3L59+7Lr85OG+oLyTZs2FfXRTxTqi8y32SYd4/o++4lOBw4cmMS+sH+XXXZJ4ko36gBQmr8hhSQddNBBSfzaa68lcbt27ZJ4+fLlSexzjL8xznPPPZfEffv2TeJHHnkkif3ExJK00047JfELL7yQxMcff3wS+xzib0A0YsSIJD7yyCOT+NVXX03i7t27F/Vp5513LloGAEBrx5kuAAAAAMgRgy4AAAAAyBGDLgAAAADIUZOs6TKzistmz56dxOvWrUviM888s/47Vkd9+vSpU3tfh+ZrOB577LEk9nUovqYMQHV22GGHomWPPvpoEvuJgX2N5iuvvJLE2267bRKvXr06iX09Ve/evcv2sVSt65o1a8rGvq7U98HXePna1w4dOiSxn0x58ODBRX3yE8sDAADOdAEAAABArhh0AQAAAECOGHQBAAAAQI6aZE3XzJkzi5b5OW6WLl2axN/97nfz7FKjuOiii5J46NChSTx37twkLjV3GPUVQGWl5un6wx/+kMQvvvhi2XWce+65Sfz8888ncZs2bZLY12z6GtA33ngjif28YFJxjZaPfd2Zr331+WHkyJFJ7OvUfDxkyJCiPpWqyQUAoLXjTBcAAAAA5IhBFwAAAADkiEEXAAAAAOSoSdZ0de3atWjZ+vXrk7h79+5JfNRRR9VpG34+mqZYh/DhD384idu3b5/EGzdubMjuAC1W27bFqfBDH/pQEg8cOLDsOvbYY4+ysfepT30qiffbb78k9jnPz/slFddUDRo0KIl32223su1PPfXUsn30ffI1Yttvv33Ra5piLgUAoLFxpgsAAAAAcsSgCwAAAABy1CQvLwQAAKVddtllVbW78sorc+4JAKBa5mub6mWlZgskFU+2heZmxxBCv/pcYR1/N/pKWliH1dO+/trnue7G/r1C09WYvxtNaf+jff21J9+gNo39u9FU9pEtad+U+tLU2tf6e5XLoAuoD2b2cghhf9o3fPu8+wI0NU1p/6N9/bcHmpqmto80pWOE5t6+NtR0AQAAAECOGHQBAAAAQI4YdKEpu472jdY+774ATU1T2v9oX//tgaamqe0jTekYobm3Ly2E0GR+pDBQCrdI4Q0pjJbCfVIYsQXr6SmFC8s8f4MU5ktholveWwoPS+H1+G+vuNyk8HspTJPCK1LYNy7fJfbzFSkcHJe1lcIjUuhcZvu/lcIR8XE7KVwZtzlGCs9J4aQt/PwuLtxu7Eevxv5/5Yefpv4jhW9LYVLcl8dJ4cC4fIYU+pZof5oULqtlXUdJ4ZBanuslhbvidl6Uwh4Fz10khYmxHxcXLP9ZbP/XgmWfLGxTYjuDpHBvfNxZCv+QwoS4/mek0FUKQ3wOLHj9D6RwXC3PnS+FbQviW6QwvLH/D/nhpzF+assd9bDeJ6Sw/5a0kcKX4/FKKMxftR3LxOfOi8chr0vhvLisgxQeiHnjwoK21xW+tsT2PyiFy+PjXWI/x0lhshSuq6fP56iaHFdNGymcIoUfNPbvCz+t+6fJnOkyk0m6S9ITIWjnELSfpP+RNGALVtdT0oVlnv+LpBNLLL9M0qMhaLikR2MsSSdJGh5/Pifpmrj885IuknSypG/EZV+U9PcQtLrUhs3UR9JBIeipuOiHkgZJ2iME7Svpg5K6lX13tbtYUueC+G8q/zkArZ6ZDpZ0iqR9Q9Beko6T9Ha514Sge0JQ0f24zdRW0lGSDqnlpd+SNC5u51xJv4uv20PSZyUdIGlvSaeYaZiZehT0a52Z9jRTJ0kXSPrfMl38mqQ/xscXSZoXgvYMQXtI+rSk9RXe3+Uh6JES76+NpPMlbVuw+BpJ/6/c+oCWaEtyRwP5r7K++LvolTyWMVNvSVdIOlBZDrrCTL0kvV/SM5L2knRObLu3pDYhaEyZ7f8/SVfHx7+X9JsQtE8I2lXSH7b63W2Z/0g61Sw5RgIaVJMZdEk6WtL6EHRtzYIQND4EPW0mM9MvzDTRTBPMdJYkmamrmR4105i4/PT40isl7WymcWb6hd9QHPAsLtGH0yXdGB/fqGwAVLP8r3Gg+ryknmYapOzApXP8WW+mnpJOlfTXMu/zw5IeiP3vrOxA6yshaG3s27wQdGt8/mPxfU00089qVmCma8z0spkmmen7cdlXlR0IPW6mx2PTeyR9rExfAGRfeiws2AcXhqDZBc9/pSDHjJQkM51vpqvi47+Y6VozvSDpVklfkHRJzD+Hu23tJumxuJ0pkoaYaYCkXSW9EIJWh6ANkp6U9CFJmyS1i19KdVaWc74h6Q8hlB04bc4z8f29U/NECJpa814ltTHTH2MueSgO6Gre05nx8Qwz/cxMY5Tlk/0l/SO+v06SnpZ0XBxwAq1JrbnDTJeb6aX49/u6uA/LTE/E/elFM71WkyPM1MlMt5hpspnukrJ9MT5X9De/nBA0NgTNKPFUbccy75f0cAhaHIKWSHpY2RfTNcc47aSs/8q+KP5ubds20whJa0PYfHvtQZJmFfRtQmw3xExPx9w6xiz7ospMR8XP6HYzTTHTPwo+uxPjsjHK8mPNNg8w03NmGmumZ820S4nPJEh6QtkgGWgUTWnQtYek0bU89yFJ+yj7Bvg4Sb+IieJdSWfEM0RHS/pV3Dkvk/RG/Gblm3Xow4AQNCc+nqv3zrINVvrt1ay47H+VfXN9o6SfKEtEPwlBm8ps49CC9zlM0lshaLlvZKZtJf1M0jHxvb/PbPMg8NshaH9l3z4daaa9QtDvJc2WdHQIOlqSYvLsEM+uNRtmdqKZTTWzaWZWcRZQM7vBzOab2cQq2m5vZo+b2atmNsnMLqrQvqOZvWhm42P7in/w4uvamNlYM7u3irYzzGyCmY0zs5eraN/TzG43sylmNtnMDi7Tdpe43pqf5WZ2cYX1XxLf60Qzu9nMOlZof1FsO6nSupuohyRtHw+ArjbTke75hTHHXKP3zmh720k6JAR9SNK1eu+b3addu/GKBwtmOkDSjvG1EyUdbqY+8cuYkyVtH4JWSLpP0lhJcyQtk3RgCPpXbW/GTEMlLSkYWN0g6dJ4UPIjMw0vaD5c0v+GoN0lLVU2WCtlUQjaNwT9XdLLkj4R39+amO+mKcvPzVJdck5d8k1sn3vOqUu+ie2bTM5p5vmmXO64KgS9L55d7qT0YL9tCDpA2dUpV8RlX5S0Op4NukLSfgXti/7mb2F/azuWqW35w5KGSHpe0u/NdJqkMe5LKe9QKTkL9htJj5npfjNdEr+clqT5ko6PufUsZWfEaoxS9tnsJmknSYeaqaOys/enKvtsBha0nyLp8BA0StLlyo7HSnlZKvoirMFxjFOxfYs9xmlKg65yDpN0cwjaGILmKfsW+H3Kvnn5iZlekfSIsiSxJZcjFonfipSdxCwEvRWCjgpBB0tarezgabKZ/mamf8ZvfLxBkhZU0YX3KbvUckH85vsfko6Iz300ftMzVtLuyhJTbeYrvRSoSTOzNsoGsycpe18fM7Ny70+q/XLRUjZI+noIYTdJB0n6UoX1r5V0TAhhb2WD3xPN7KAqtnORpMlV9kmSjg4h7BOqmwfid5IeCCGMVHagW+t2QghT43r3UfaHarWyy3hLMrPBkr4qaf8Qwh6S2kg6u0z7EpfF2bAq3kOTEYJWKvtsPqds3/ynmc4vaHJn/He0sgOQUm4LQRur2NyVyr5dHifpK8r24Y0haLKyL1keUnaGapyUrS8E/TwOcL6u7Fvmy830GTPdaqbvlNhGkmNC0DhlBy6/kNRb0ktm2jU+/WZ8vtL7+2eF99Ws8kyhLcg5f1H1+UZqmJxT13wjNYGc09zzTYXccbSZXjDTBGVfnu5e8NJSOeUISX+P631F0isF7evyN7/ehKANIejjcTBzm7KB0K/M9Ot4Juq0Ei/z+efPys7k36bs0uvnzdRB2dmzP8bP5zal7+nFEDQrfqEzTtlnNFJZvno9Hp/9vaB9D0m3mWmiskFe4WddqNHzFMc4rfsYpykNuiYp/WanGp+Q1E/SfiFoH0nzJJUdsVYwL55BU/x3flz+jqTtC9ptp4LLdaIfS/qOsv/MPym7pvkKFVtT0MdpknYwU/dqOxi/xf6GpGPjNeT/Ufn33DFus7k4QNK0EML0EMI6SbdImy8bLSmEUNvloqXazgkhjImPVyjbmQeXaR9CCCtj2C7+lB2Mm9l2kj6g7PegXplZD2V/nK+P/VsXQlha5cuPlfRGCMFf5++1ldTJzNoqu7Sk3Lea8bK4sDqEUHhZXLMSv9B5IgRdIenLSs/41Jwx2ijVegndqiq3szwEXRDz1bnK8tf0+Nz1IWi/EHSEpCWSXit8rZlGKfuiaaqkj4Sgjyq7jLrwzJWU5pia7a4MQXeGoAuVHayc7N7b1r6/5pZnCtUp59Ql38T2ueacPPNNXH/eOadZ55tSuSOelbla0pkhaE9lZ2gK98lqcoqkLfqbX05txzLVHONcqKx04iBlZ9zPkvT1EtsolX9mh6AbQtDpygYFe0i6RNkx297KLlluX/CSavNSjR9KejyeVTzVb79AU8hTHOOUX3eLPsZpSoOux5RdCve5mgVm2ite7/y0pLPM1MZM/ZT9h7yo7NuN+SFovZmOVnapjiSt0JbdjOIeSefFx+dJurtg+bmW1ZYdJGlZwWWIipcUzA5Bryv7D9wUf0oVbE5WdlmhQnazjesl/c4sSzhm6memj8T3d6SZ+lpWvP4xZf/Z3ZUdAC2LtSAnFaw7ed/xUsuBUslru5uq2i5zqHdmNkTZZQwvVGjXxszGKRuEPxxCKNte0m+VDbrLXWZaKEh6yMxGm9nnKrQdquxbxD/HU/t/MrMuVW7nbEk3l+1ICO9I+qWktxQvZwshPFTmJfGyOOtjZpsvi6uyP02CmXZxA5d9VFyAXhe15h8z9azZ1yV9RtJTNZcXm6l//HcHZUn9JvfymlqKdsq+nZNK55nXVHDGykyHWlYUr7jt3VT/72+Est+F5qi555zfqm75RmoiOae555syuaPmoH+hmbpKWX1kBU9J+nhc7x7S5ksIy/3Nr6vajmUelHSCmXrFXHFCXKbYn17KLo/8q947xgkqqDsrsPkYJ772RDO1i48HSuqjbEDXQ9KceDbrHL2X02pTUwO7c4wL69V76L1B4vll1tEU8lRzzzcSxzhbnHOazKArni4+Q1lB9htmmiTpp8pqq+5Sdqp9vLLB2f8LQXOVXXK3fzw9fa6ynVIhaJGk/1pWwFp0Iw0z3SzpOUm7mGmWmT4dn7pS0vFmel1Z7VjN3cnuU/Zt9DRl31hdWLAuU3aG64dx0XXKTo3+R9l/rPcfZafYa3xH2S/Yq/HU+L2SlsdEeJmkx+P7Hh2C7g5B45VdYjBF2UHZfwvWdZ2kB+y9G2nsJ+n5eHkiCphZV0l3SLo4hFBUU1cohLAxnrreTtIB8XRzbes9RdL8EEJt9YmlHBZC2FfZH9MvmdkRZdq2lbL6ohDCKGV/jKu5Jry9pNOUXcZRrl0vZd+6DVV2GUYXM/tkbe1DCLVeFteMdJV0o5lejZcq7ybpe1uxvn9LOsNK30hjV0kTzTRV2f934fX2d5jp1fj6L4WgpTVPxHrOl+M3xksljYt5r2PMCZuFoFWS3jDbfOCzs6QnY/uxyuoa7tiK9/cXSdfG99cpHgiuiTkZtcgj52xhvpGaSM5pAfmmZO6I++gflR2wPSjppSrWdY2krmaaLOkHirXfFf7ml2Smr5pplrLfn1fMNp+RKHksE4IWKzuGeSn+/CAuq3G5pB/HAdKDyuqiJii7Q7L3lKRR8dhIygZwE800Pr72mzFXXC3pvLh8pCqcTQ9B7yq7jPM/8VLL+QVP/1zST800VuXPih2t7BisxeMYp9Z2jZtztvRe8/xs+U+cJ6dnA2znd1I4trHfb936rIMlPVgQ/4+k/6nidUMklZxzqETbdsqS/9e2oH+XS/pGmed/quybqxnKvjBYLenvdVj/9yqsf6CkGQXx4ZL+U8V6T5f0UBXtPiLp+oL4XElX16H/P5FU6xx5/DTMjxTOkMKPGmhbl0jh0439nre8/3XPOXXJN7F9Ljlna/NNXEej5RzyTcv8icceJef5a8Q+DZDCo43fD45xWvMxTpM509XKfF3SDg2wnYkh6NEG2E59eknScDMbGr+5OFvZJRH1wsxM2SWdk0MIv66ifT8z6xkfd5J0vOIZ1VJCCP8TQtguhDBEWd8fCyHU+i2KmXUxs241jxW/FSyz/rmS3jazmlviHivp1UrvQ9mlGGVPu0dvSTrIzDrHz+pYVSiWNbN4WZzVdlkcGlgIuksNd1nxUr031UZz1GxzTl3zTVxnU8o55JuW6ScqXV7RmHZQ6Rq0htZs843EMY62Mucwr0ojCKH89bX1uJ0/Vm7VtIQQNpjZl5V9S9NG0g0hhEnlXmNmNyu7ZLOvmc2SdEUI4fpamh+q7PrxCfEaZkn6VgjhvlraD5J0o2V3HNpG0q0hhKpuy1ylAZLuyvZ9tZV0UwjhgfIv0Vck/SMm7OnKJsqtVUx0xyubzLusEMILZna7slv+blB2Wct1FV52h5n1UTany5dC9UWvyFEI+dxYocR2/twQ28lLXXNOHfONRM6pFfmmZQrZXabrbSBRH0Ko6hLP3HGM07qPcSyeLgMAAAAA5IDLCwEAAAAgRwy6AAAAACBHDLoAAAAAIEcMugAAAAAgRwy6AAAAACBHDLoAAAAAIEcMugAAAAAgRwy6AAAAACBHDLoAAAAAIEcMugAAAAAgRwy6AAAAACBHbfNYad++fcOQIUPyWDUa0IwZM7Rw4UKrz3Xyu9Ewxo+XNmwo36ZtW2nvvRumP4VGjx69MITQrz7X2Rx/r95+++0kXrNmTRL37t07iTdt2lS0DrN091yyZEkSDxgwIIl79OhR5342JHIO6hv5BrUh3+SnKR+D5K1czsll0DVkyBC9/PLLeawaDWj//fev93Xyu9EwrIo/Ixs2SI3xX2FmM+t7nc3x9+qiiy5K4gkTJiTxOeeck8QrV64sWkfbtmkKv/POO8tu45RTTqlTH/1Ab5tt8r04gpyD+ka+QW3IN/lpyscgeSuXc7i8EAAAAABylMuZLgBo7Z544okkvvrqq5O4Q4cOSbx48eIk/upXv5rEbdq0KdpG586dk/iggw5K4ltvvTWJ77nnniS+8sork9hf0pj3mS0AAFoL/qICAAAAQI4YdAEAAABAjhh0AQAAAECOqOkCgC0wderUJP7Zz36WxK+99loS77XXXkk8efLkJO7UqVMS9+3bN4kXLlxY1Ic99tgjif0t4/3dDX0d2cUXX5zEw4YNS+IvfOELSdy/f/+iPgAAgMo40wUAAAAAOWLQBQAAAAA5YtAFAAAAADmipgtAq7dx48aiZX5erGuuuSaJn3/++STu0qVLEh9wwAFJ3LVr1yR+9913k3jKlClJ7Gu8StVT+X6/9NJLSfzpT386iXv16pXEy5cvT+I5c+Yk8ec///kkvvbaa5N4wIABRX3atGlTEjPXFwAAnOkCAAAAgFwx6AIAAACAHDHoAgAAAIAcUdMFoNXz9VulTJgwIYkHDhxYdh1+jiw/h9Zpp52WxK+++moS+/qqX/3qV0V9+sEPfpDEJ5xwQtk++Tqyzp07J3H37t2T2Ndn3XTTTUl8ySWXFPWJGi4AAIrx1xEAAAAAcsSgCwAAAAByxKALAAAAAHLEoAsAAAAAcsSNNACgBH/jC38Tin79+pVtv2HDhiTu1q1bEi9YsCCJjzrqqCSeN29eEt96661FfRw6dGgSjxw5MolXrVqVxOvWrUvi9evXJ7GfkNnfLGTWrFlJXM2k0gAAgDNdAAAAAJArBl0AAAAAkCMGXQAAAACQI2q6AKCEN998s+zzvsZr7dq1Sexrm7p27ZrEb731VhIvX748iQcNGpTEvn5LkubOnZvEM2bMSGJfRzZgwIAkNrMk9jVaK1asSGL/npctW1bUp969exctAwCgteNMFwAAAADkiEEXAAAAAOSIQRcAAAAA5IiaLgAo4Z133kliX8/k66n8nFa+Rmvy5MlJvHTp0iSeM2dOEvs5s3x7SRo7dmwS9+3bN4n9vF1vv/12EvsarpUrVyaxf0/elClTipYdcsghZV8DAEBrxJkuAAAAAMgRgy4AAAAAyBGDLgAAAADIUaut6QohlI232ab+x6NPPfVUEh9xxBH1vo26WrVqVRJ36dKlkXoCNC2+pqtDhw5J7PedDRs2JHGfPn2SeObMmUm8ZMmSJO7YsWPZ7fXv37+oj7vuumsSt2vXruw6fV3aiBEjkviRRx5JYj+3mK8ZmzRpUlGfqOkCWiZ/nOTrULfddtsk9jny17/+dRJ/+ctfTmJ//NG+ffuKffJ1qX5+RKAp4UwXAAAAAOSIQRcAAM3IwIGSWeWfCjefBAA0IAZdAAA0I/Pm1W87AED+Wm1Nl5mVjSv56le/WrTsrbfeSuLDDz88iR999NEkHjp0aBJvv/32deqDryFp27byf+cvfvGLJL7tttuS+LHHHtv8eNOmTXXqD9CS+PonP4fVtGnTknjNmjVJPGTIkCT2NV6+/mrRokVJ7Gu+Vq9eXdTHFStWJPFOO+1Udhu+3mHZsmVJ/NxzzyXxHnvskcQnnHBCEvvPAEDz4Ouz/DHQ9OnTi15z8cUXJ/EXvvCFJB4zZkwSX3TRRUn8z3/+M4n/85//JPFNN92UxKecckoS+xoySercuXMSf+5zn0tin3f9+wYaEme6AAAAACBHDLoAAAAAIEcMugAAAAAgRy2mpsvXH21tzZa/nvl973tfEn/84x8ves2+++6bxL5+wl9b/JWvfCWJ//Wvf9Wpj9XUcP3tb39L4ltuuSWJfZ3KlClTNj/2NS1Aa7J8+fIk9nPO+H3H12j653feeeck9vNwvfjii0m8YMGCJN5tt92K+ui3sX79+iT2dWa+/sH3+frrr0/ib3/720ns68r8ZwKgeah0TOTrQyXpnnvuKfuaO++8M4mPP/74JPbz+q1duzaJfV37k08+mcR+3sFSqjkuAhoLZ7oAAAAAIEcMugAAAAAgRwy6AAAAACBHTeLi10rzRVTTZpttyo8f161bl8Rz585N4lGjRiWxn4/i0ksvTeK99tqraBszZsxIYl/vsOuuuybxI488ksS9evVK4m9961tJ/MEPfjCJ/Rw8zzzzTFGfrr766rKv2XvvvZN48ODBtbYFWhO/P/saLF+z+YlPfCKJr7zyyiT2+5PPWb6GzM/bNX/+/KI+jh8/Pol9Xmrfvn0S+7n9/Dxffm4xXwPma8iY8wZomQrn7KzxxhtvJPEOO+yQxH/5y1+S2B/z+Lr1Ll26JLE/rvPzch122GEV+/Tvf/87iT/5yU8m8caNGzc/Jn+hoXGmCwAAAAByxKALAAAAAHLEoAsAAAAActQkarqqmUOrUpunn3667PNXXHFFEhfWLknF89P4eb9mzZqVxH5OnVL8HDn++uEPfOADSdyjR48kvuaaa5L4hhtuSOJu3bol8cKFC4v64K+5Pvjgg5P4hRdeSOLCupLCa5+B1sbXE/Tt2zeJly5dmsR+fx8+fHgS+3qqwjnxpOK6U58PfI2ZJM2ePTuJDz300LLrmDlzZhL7HOLnJ/Q1X36eHF/jJRXP5eXrwoCWoFw9kD9e8W398YVUXCNaic8nfo6+Suvz9Z4//elPk9jnAql4/x84cGAS/9///V8S+/lNfS445phjkrh3795J7OvU/dyFUnHd2B133JHEvqarcB6vus7fCmwtznQBAAAAQI4YdAEAAABAjhh0AQAAAECOmkRNVzWmTZuWxL6e4uabb05iXy/x3e9+N4n9HFp+3i7/vL9+2l/bLBXXQPnrtt99990kXrt2bRJ/5CMfSeLTTjstiadOnZrEfn6K7bffvqhPxx13XBL7Go9//vOfSVx4nTfXO6O18PVUpZb5ebV8fUKlOa18ztpxxx3LPu/n5SpVP+XnF/Q5xr/Gb9PPDda1a9ck9jUWvm7U13RIxbl0p512KmoDNHd1+ftYTdtKc0b5dRTWJpWKK/Fzavma0T333LPoNf64qE+fPkk8aNCgJPa18BdeeGESz5s3L4lHjhyZxP74pXv37kV9+tSnPpXEPm/+/e9/T2Jf4wU0JM50AQAAAECOGHQBAAAAQI4YdAEAAABAjnKp6Vq7dq1ef/31zfEtt9ySPN+/f/8k9nUIfm4YqXgOCl9rcPTRRyexnx/Cz6vl6zP8tcJ+jgtfr7V48eKiPvpaBv8+/Dw+vqbLP+/rK3bZZZckPuyww5K4V69eRX3y/fzXv/6VxP6a7EmTJm1+7P9fgJbK14xKUocOHZLY56Bly5Ylsa9n8DUWvi60U6dOZde3aNGiJPY5TpJee+21JC41V18hX3fm85zvo5/Hy8e+j1Lp/A20NJVqsMqp65xcpfh99dprr03isWPHJrGfZ/D8889PYj9n1k033VS0zVdffTWJfU485JBDau+wpP/93/9N4ksuuSSJfZ/9cZmfh1AqnovUxy+//HLZPgENiTNdAAAAAJAjBl0AAAAAkCMGXQAAAACQIwZdAAAAAJCjXG6kMX/+fF1zzTWb4/HjxyfP+wL1ok6VmOTPT+q7YMGCJPZF6P5mHV26dEniN998M4knTpyYxH5SPz9xqb/phVR84wl/8w3Pfw6+sH7//fdP4pdeeimJr7rqqiT2NweRpN133z2J/QSL/jXDhg2rtX9AS+X3b6nyjTT22muvJPYTBfsc4m+MU2nyZL+vlpqIuPCGRaX66Iv9/WTIvhi/X79+SezzQ6Wb/UjFuRhoieoyObJX6tjA31zD3/TK5xN/0wmfT84777wkfvLJJ5N41113TeLp06cnsT/GkoqPcfxxViX+M/MTGfvPZfXq1UnsJ3SWpBNOOCGJfU7yN9Z46623Nj9et25d+Q4D9YwzXQAAAACQIwZdAAAAAJAjBl0AAAAAkKNcarp69eqlM888c3PsJzJ+++23k3jJkiVJXGpyzdmzZyexr/GaMWNG2ed9DdeqVauS2NeR+VoGvz4/yagk7bnnnknsJzP1E4neeeedSfzQQw8VrbMc/zn5659L8bVt7du3T+LC68JL1dYBLZGvLZAqT2bsa6x8PZWvfxgwYEAS+8nR/f7m2z/22GNFffSTle60005J7CdM933y78nXlfj84Gsy/HuWSte7Ai1ZpYmSN23alMTVTI48bty4JPb7brt27ZL4m9/8ZhKPGjUqif3f/smTJyexr+f0NWNS8fv8+9//nsRf+MIXil5Tjs8fM2fOTOIRI0Yksa+jlaS77roric8555wk3meffZJ4woQJmx/7zxTIG2e6AAAAACBHDLoAAAAAIEcMugAAAAAgR7kU7XTq1CmZH2rHHXdMnh80aFDZ15eaw8Jfe+vnlPA1Gffff38Sn3/++Unsrw3u06dPEvtahjyceuqpSfzAAw8k8d57753Evs7MXxdeas4cfw22r2WbM2dOEhfWhZWq1wBaooULFxYt69atWxL7/WHo0KFJ7Gsk/Lw5vobL14T5WldfG+VrY6Ximixft+Gf93Vqlebi8+/Zty9Vy0KdBFqDwt/9SnNy+npNP1+eJL3xxhtJ7OubfM24r+e89NJLk/jWW28tu83tt98+if0x0OOPP17Ux/e9731J7I+7fN3pMcccU7SOQv4YZ968eUl81llnJbE/ZpKkk046KYk//vGPJ7GvfS/MT77WDsgbZ7oAAAAAIEcMugAAAAAgRwy6AAAAACBHudR0tWnTJpnXyl9L/OijjyaxrzPw809IUs+ePZN4jz32SGI/R9WXv/zlJPbz16xbty6JfU1HqbnCCpWai8Yv8/UO/rruwYMHJ7Gvl3j66aeT2F//7OstSs394a8195+Trzvx13UDrUGp/bljx45l2/Tt2zeJfX2Dn9vP11wuXbo0iX1+8PWXvgZMkhYvXpzEvp5q7ty5SezzaKU853Ozj0vN5edzK9ASFc5ZV9c5LUvVQt59991JPHXq1CT2+7afx2vixIlJ7OcFXbBgQRLfc889SXzxxRcn8RNPPFHUx+9///tJ7PPLD3/4wyT2NV3Lli1L4v79+xdto9z6S/H99vx8Y4X1/D7HA3njTBcAAAAA5IhBFwAAAADkiEEXAAAAAOQol5ouz88H4WNv2rRpRct8PcXrr7+exL4+ws9p5a+P9nPmdO/ePYl9XVnh9dtS6Tmx/Dw6vubKX8ftr1fu169f2T74OSX8+pYsWVLUJ8/PPeT7vPPOO29+7Of4AVoTv/9Wqm+aNGlSEvsc5GOfs3yO6dWrV9n+SMU5wu+zvk7U1336+iufk3wtrFeqlsXXjQItzapVq/Tcc89tjq+99trkeV8r7fcTnwtKtfF/q33NqK/P9HNuPv/880ns5y71x0BeqRpSX5Pl+TqyAw88MIn9cdzxxx+fxD7n3XLLLUl80UUXFW1z+PDhSbzvvvsmsZ/v7He/+93mx6Vq64A8caYLAAAAAHLEoAsAAAAAcsSgCwAAAABy1CA1XXU1bNiwOr9mzz33zKEnAFoDX08lFddL+TrOyZMnJ/EhhxySxCNHjkxiXy/l66v8PDq+xsPPuVdqma/78jUYvoahffv2SezrRivNBVhqnhtf6wa0NJ06dUrme/rMZz6TPO/3ZV9vXc08n35eLv+83xe/853vJLHfl33dup+T089n5WvGJOnrX/96Evs6dF8H5vPPj3/84ySeNWtWEg8aNCiJfb7yz0vFdaxdunRJYp+HC/MTNV1oaE1y0AUAQHN12WWXVdXuyiuvzLknAICmgssLAQAAACBHDLoAAAAAIEdcXgig1St1bb+vqfJ1X36Ouy9+8YtJPH369CQeM2ZMEvt6iAkTJiTxq6++WnZ7UnFNl6+p8HVps2fPTuJzzz03iQ866KAk9jUZvo+llJqDCGhJttlmm6R26PDDD2/E3jQffq6wxta5c+fG7gJaGf46AgAAAECOGHQBAAAAQI4YdAEAAABAjqjpAtDqlZqny/P1U4cddljZ9jvttFPZ2DvyyCPLPu/n3ZGktWvXJrGfs2Zr+bqzaj6nUv0EAKC140wXAAAAAOSIQRcAAAAA5IhBFwAAAADkiJouAK1ehw4dipZVql/yc2B5vgasTZs2SeznBqu0vVLzX21tDVelPnTr1i2J/XsoVb+1bt26reoTAAAtEWe6AAAAACBHDLoAAAAAIEcMugAAAAAgRwy6AAAAACBH3EgDQKu3cOHComXr169PYn8TibZtty59+ptW1PXGGvXB3wjDv0d/Iw0/GbN/Xqp8gxEAAFojznQBAAAAQI4YdAEAAABAjhh0AQAAAECOqOkC0Or5iYyl4tqkDRs2JPGgQYPqtQ9bUsNVqQ6s0vOVarr85Mu+zs1/JlLpOi8AAFo7znQBAAAAQI4YdAEAAABAjhh0AQAAAECOqOkC0Opts03x908rVqxI4qVLlyZxqTqwQpXqpepDpTqwrZ3ry89F5t9zqTm5unTpslXbBACgJeJMFwAAAADkiEEXAAAAAOSIQRcAAAAA5IiaLgCt3gUXXFC0bPTo0Unsa7r222+/suv09VBNUalatkJ+LjIfl3qPPXv23Op+AQDQ0nCmCwAAAABy1PS/igUAAA3msssuq6rdlVdemXNPAKDl4EwXAAAAAOTIQgj1v1KzBZJm1vuK0dB2DCH0q88V1vF3o6+khXVYPe3rr32e627s3ys0XY35u9GU9j/a11978g1q09i/G01lH9mS9k2pL02tfa2/V7kMuoD6YGYvhxD2p33Dt8+7L0BT05T2P9rXf3ugqWlq+0hTOkZo7u1rw+WFAAAAAJAjBl0AAAAAkCMGXWjKrqN9o7XPuy9AU9OU9j/a1397oKlpavtIUzpGaO7tSwsh1MuPFL4thUlSeEUK46RwYH2tO67/KCncW0/rGimF56SwVgrfcM+dKIWpUpgmhcsKlg+Vwgtx+T+l0D4u/4oUJkrhvoJlh0nhN2W230kKT0qhjRS2kcLv4zomSOElKQyt58/ufClctRWv/6UUjqnPPvHDT1P7kcJAKdwihTekMDru0yO2YD09pXBhLc/tEvNjzc9yKVwcn+sthYel8Hr8t1dc/uGYW5+WQp+4bGcp/LNMH0wKj0mhe57vTQr9pPBAY//f8cNPY/3kdewjhSeksP+WtJHCl+OxSpBC34LlFo83psX+7lvw3Hkx97wuhfPisg5SeCAenxTu99cVvrbE9j8ohcvj411iP8dJYbIUrqunz6fiMWFhGymcIoUfNPbvCz+t+6deznSZ6WBJp0jaNwTtJek4SW/Xx7rrg1nRfGSLJX1V0i9duzaS/lfSSZJ2k/QxM+0Wn/6ZpN+EoGGSlkj6dFz+CUl7SXpW0vvNZJK+K+mHZbr0KUl3hqCNks6StK2kvULQnpLOkLR0C95mLuJn8gdJ1U3cAjRDcb+9S9ITIWjnELSfpP+RNGALVtdT0oWlnghBU0PQPiFoH0n7SVodtytl+9ijIWi4pEf13j73FUnvk/R/kj4el/1I0nfK9OFkSeND0PI831sIWiBpjpkO3YJ1Ac1aEz72+a+yvvi76J0kaXj8+ZykayTJTL0lXSHpQEkHSLrCTL0kvV/SM8qOcc6JbfeW1CYEjSmz/f8n6er4+PfKjp32CUG7KjueaAz/kXSqmTo30vaBeru8cJCkhSForSSFoIUhaLYkmWmGmb5vpjFmmmCmkXF5FzPdYKYXzTTWTKfH5UPM9HRsP8ZMh/iNmel98TU7m2k/Mz1pptFmetBMg2KbJ8z0WzO9LOmiwteHoPkh6CVJ692qD5A0LQRND0HrJN0i6fR40HKMpNtjuxslfbCmO5LaSeoc1/dJSfeHoMVlPq9PSLq74LObE4I2xb7NCkFL4ntYaaYfm2m8mZ43yw6SzNTPTHeY6aX4c2hcfoCZnoufzbNm2qXEZ/eB2KavmU6Ij8eY6TYzdS34P/uZmcZI+kgImimpj5kGlnlPQHN2tKT1IejamgUhaHwIetpMZqZfmGlizGFnSZKZuprp0YLcdnp86ZWSdjbTODP9osw2j5X0Rty/JOl0ZblFSnPMJkkdFHOMmQ6XNDcEvV5m3YU5Ju/39q+4PaC1KXfsc3n8+zzRTNfF44iaY5OfxWOf1+L+LDN1MtMtZppsprskdarZiJmuMdPLZppkpu9X6lQIGhuCZpR46nRJf41fuj8vqWc8Znq/pIdD0OJ4/PGwpBOVHdN0VnaMY3EdP1T2xXJJZhohaW0Im2+vPUjSrIK+TYjtSh7rmemo+BndbqYpZvpHwWd3Ylw2RtKHCrZZ8dgnBAVJTygbJAONoz5Ol0mhazx1/JoUrpbCkQXPzZDCV+LjC6Xwp/j4J1L4ZHzcM762ixQ6S6FjXD5cCi/Hx0dJ4V4pHBIvj9lBCu2k8KwU+sU2Z0nhhvj4CSlcXaHf31PB5YVSOLOmfzE+RwpXSaGvFKYVLN9eChML2oyVwt+l0C1e0tOuzDbbS2FuQbxd/IzGSeFXUhhV8FyQwqnx8c+l8J34+CYpHBYf7yCFyfFxdym0jY+Pk8Id8fH58X2coewSpV7xPT0lhS6xzaUFlwPMkML/c/3+oxQ+3BCnX5Ul+6mSpkm6rIr2N0iaL2liFW23l/S4pFclTZJ0UYX2HSW9KGl8bP/9Kt9DG0ljJVW8JFbSDEkTJI2T9HIV7Xsq+wJgiqTJkg4u03aXuN6an+WSLq6w/kvie50o6WZJHSu0vyi2nVRp3U31RwpfVS2XBCu7vO9hZZcDD5DCW1IYJIW2eu/yvb7xkh2TwpCa/FBhmzdI4csF8dKCx1YTS+H4mPP+LYUeUnhICr0rrHumFLo1xHuTwmApTGjs/8Ot+/+vPufUJd/E9rnnnLrkm9i+yeSc5pxvKhz79C54/LeCv+VPSOFX8fHJUngkPv5awfHLXlLYoHjpYM264n76hBT2KlhXrZcgxr/lhZcX3ltz7BDjR6WwvxS+UXN8EZd/Ny5rG483xkrh41I4TQrfq/CZXFDz/griZVK4XwqXSKFnXF7uWG9ZPDbaRlkpyGFS6CiFt2Nbk8Kteu/SwdqOfY5SwSWIUviEFP7QmL8z8XeYY5zy7XPLN/E1jZZz6vGXKLSJv+Dfl8JcKZwfl8+QwuD4+MCCBPOysuuEx8Wft6Swazyo+Juy+qZxUlgd2x+lrB5hohS2jcv2UFYTUbOOCVJ4KD73RGECrKXP39NWDrrc+i5Xdi3zaVK4XQq/kcI2rs22UpjilnWQwklS+IUUFkvh2Lh8rRTiXGrhLL03YJ2vtC7kHWXJf3sp3KX36sOmxPbnS+FVKTyv9w6kTpHCwoJ1vCqF6wv+z3Z0ffyx4uA5z5+4I78haSdJ7WMi2K3Ca46QtG+VCWmQpH3j426SXiu3fmXf7nWNj9tJekHSQVVs52uSbqpDQupbqV1B+xslfSY+bi+pZx0+27nKJu6rrc1gSW9K6hTjWyWdX6b9HjEZdZbUVtIjkobl/XtS/793ZQcmv5HCpwriv8V9vF3MDzW1HGuU1U4NKZUf3Drbx/1vQMGypa7NkhKvO1cKF0vhoJhj/iiFziXarWio9xbbLmrs/8Mt/7+vW86pS76J7XPPOXXJN7F9k8g5LSHflDn2+bCyOvAJ8W/0ZXH5E1I4ND4eUHNsIYV/qaB2Wgpj9N6g6wsxfkUKC6RwdsG6cht0uXW1U/alcjcp/Drmn9NKbPNbKqiHj8u2lcKnpHC3FKbEY55yx3oPF7z2Gil8Ugr7SOGpguWn6b1BV23HPkcpHXQdrzgga7zfF45xqmjfYo9x6u3uhSFoYwh6IgRdIenLkj5c8PTa+O/G2Gkp+4/+cIj1DSFohxA0WdkIdJ6kvSXtHz/wGnMkvStpVME6JhWsY88QdEJB+1V1fBvvKPuWoMZ2cdkiZafh27rlm5lpW0kHhKB/Sfq6slqtpcouISq0Rtk3C5uFoLUh6P4Q9E1JP9F7lxWtD0EhPi787LaRdFDB+x4cglYqO+3/eAjaQ9KpbjtvKNsBR9R0WdnlBDXr2C2EzXVqUvFn1zH2PW/xEs8wPYSw+RLPci8IITwllb2cs7DtnBDCmPh4hbJvUQaXaR9CCCtj2C7+hNraS5KZbSfpA5L+VE2f6sLMeihLwNfH/q0LISyt8uXxcrbgr/P32krqZGZtlSWa2WXa7irphRDC6hDCBklPquCyj2ZkkrIaq7r4hKR+kvYLWY3WPLl9u4yTJI0JQfMKls0ruDx6kLJvNjeLtQjnK6s7/b6k85TVW5S6tG+D2eb8nvd7a6jckJc65Zy65JvYPteck2e+ievPO+c063xT6tjHTB2V1TSdGbJa7T8q3X9KHROVZKahkr4h6diQ1Y39R9XnGa+2Y5zalhe6UNJfJR0kaZmyY5yvl9hGqWOc2SHohhB0uqQNyg5kyx3rrS14XPEzUvljn0JNIVdxjFN+3S36GKe+bqSxi5mGFyzaR8UFnN6Dkr5ScK1uzUCqh96rcTpH2ci1xlJl/9E/NdNRyk7P9rOsmFVmamem3bfirbwkabiZhpqpvaSzJd0TBz6PSzoztjtP79VL1PihpMvj407Kfmk3SWnRZsiul24Tk7LMtG8csCkeJO2lyp/dQ8qK6xVft0982EPvJcrz3WtmKhsI/zV+Rs9LOtRMw+I6usRrsWszQtloP2+DlRYiz1KZhLE1zGyIsgH8CxXatTGzccoOgh8OIZRtL+m3ygqJN1XZlSDpITMbbWafq9B2qKQFkv5sZmPN7E9m1qXK7Zyt7FR67R0J4R1lN5h5S9mXHMtCCA+VeclESYebWR8z66zsBg7bl2nfVD0mqYOZNn/+Ztor1ls8LeksM7UxUz9lfxBeVLa/zQ9B6810tKQd40tXKPuCo5yPqfj/4h5luUUqnWO+Ken3IWi9yuSYaKqyb1Ib4r01VG7IS3PPOb9V3fKN1ERyTnPPN2WOfWoO+hfGWukz/WtLeErxRjlm2kPZsYAkdVf2Jegyy+q6T9qKLt8j6dxYy3mQpGUhaI6y47ETzNTLshtonBCXKfanl7JaqL8qyzeblP0OdfIbUHaQP6zgtSeaqV18PFBSH2XHKeWO9UqZImmImXaO8ccKnit37FOoKeSq5p5vJI5xtjjn1NeZrq6SbjTTq2Z6Rdmd/75X4TU/VDaifsVMk/Te3f6ulnSemcZLGil3xiV+M3yKsm97RylLZj+L7cdJxTfe8Mw00EyzlJ0e/Y6ZZpmpewjaoOybqgeVJY5bQ9Ck+LJLJX3NTNOUJY3rC9Y3Kvat5m4+Nym7fvVQSQ+U6MJDkg6Lj/tL+reZJkp6Rdm3QFdVeAtflbS/mV4x06uSvhCX/1zZgHSsSnwzFIKmKPsG+zZlifx8STfH/7PnlH3eRWLCHCbp5Qr9ajbMrKukO5Rdn7u8XNsQwsYQwj7Kvv07wMz2KLPeUyTNDyGMrkN3Dgsh7Kvsj+mXzOyIMm3bKrvM4JoQwihl+0fFO0uaWXtJpyn7vy/Xrpeyb92GKrurZhcz+2Rt7UMIk5Xd2fMhZb/r45R9M9msxC9WzpB0nJneiDnpp8ouVbhL2b45XtkA5v+FoLmS/qFsP5wg6VxlBwUKQYsk/TcW0BfdSMNMXSQdL+lO99SVko430+vK7jx2ZcFrCs+kS9kdwF5Stu/fVOIt/UfSUQ303o6O20MZeeScLcw3UhPJOS0g35Q89glBS5Wd3Zqo7HjipSrWdY2krmaaLOkHkkZLUggar6x2Zoqyff2/lVZkpq/GY5ztlB1j1ZyRuE/SdGW1RH9UvBNpyG789cPYz5ck/SCkNwO7XNKP4wDpQUmHKzvG+VuJzT8laVTNF+rKBnAT4zHag5K+GXNM2WM9LwS9q+yOi/+JN9IovBKg7LFPgVaTqzjGqbVd4+acLb0ukZ8t/5HCvlL4W2P3ow79PUMKP2yYbelgSQ8WxP8j6X+qeN0QVV9j0U5Z8v/aFvTvcknfKPP8T5V9czVD2UHtakl/r8P6v1dh/QMlzSiID5f0nyrWe7qkh6po9xFJ1xfE50oqe0Ma9/qfSCo5RxU/Dfej7GYYDzfQtp5SnFOsOf5sSc6pS76J7XPJOVubb+I6Gi3nkG9a5o8UfieF4xq7H65PA6TwaOP3g2Oc1nyMU281XaheyM6IPW5W8XR6U9FW0q8aaFvxEk8bGr+5OFvZJRH1wsxM2VnKySGEX1fRvp+Z9YyPOyk7QzGltvYhhP8JIWwXQhiirO+PhRBq/RbFzLqYWbeax4rfCpZZ/1xJb5tZzS1xj1V2l6JKSl3OVspbkg4ys87xszpW2VnfWplZ//jvDsqudS515gUNKGSXDP3RTN3z3E68JPHXIU5z0Uw125xT13wT19mUcg75pmX6iUpf9tyYdlDpGrSG1mzzjcQxjrYy51QqTkROQtANjd2HaoVQ/nRt/W4rbDCzmks820i6IYQwqdxrzOxmZZdS9TWzWZKuCCFcX0vzQ5VdPz4hXsMsSd8KIdxXS/tBkm40szbKLse9NYRwb13eUwUDJN2V7ftqK+mmEEKpS1ILfUXSP2LCni7pgnKNY6I7XtLnK3UmhPDC/2/v3sOkqs58j/8WDTQNNDY3AUFAUbwBYmI0TkYNcRw9yUyI0ZnEaDyak8nJjMaY2wwZk3iZqGRMnERzzEUTk5M4UU8SjUaMl0QiarygclVAUDQCXsAgckd8zx97NdR6q7p2NfTuLuD7eZ56qLdq1dprV1ct9qq93r1CCL+U9KSyqa5PSfphzst+FUIYqGxNl3Ot9qRXFMhMt3TCNl6Ttk153CW1t89pZ38j0ee0if5m92RZGkiHDSQ6gllNUzwLxzHOnn2MEy9HDgAAAAAoAtMLAQAAAKBADLoAAAAAoEAMugAAAACgQAy6AAAAAKBADLoAAAAAoEAMugAAAACgQAy6AAAAAKBADLoAAAAAoEAMugAAAACgQAy6AAAAAKBADLoAAAAAoEDdi6h00KBBNnr06CKqRidaunSpVq5cGTqyTj4beOKJJ1aa2eCOrHNX/Fxt2rQpiRsbGzt8Gxs2bEjipqamDt9GR9pT+5zZs6W33sov1727dPjhxbdnd0J/U9nKlSuT+K0aPoDduqW/0/fs2TOJW1padrpdnWlP7W9QrGp9TiGDrtGjR2vmzJlFVI1OdOSRR3Z4nXw2EEJ4oaPr3BU+V1u3bk3ipUuXJvGYMWN2us6GhoYknjt3bhKPGzcuiUPo0OONnban9jm1/hneekuq812pO3tqf5PnuuuuS+LVq1cncaVBWN++fZN4xIgRSXzKKad0TOM6yZ7a36BY1focphcCAAAAQIEKOdMFAEht2bIlif/85z8ncd6ZLjMre8yf2fKWL1+exOPHj69aHkDxKn2X8846+9f4M1E9evRIYn8WvHv39HDPT2eu5ay3L+OnL5988slJfNddd1Wtz++DbyOwu+FMFwAAAAAUiEEXAAAAABSIQRcAAAAAFIgJtADQCXr16pXE119/fRL7yy1PnDgxiWvJufjNb36TxN/5zneS+KSTTsqtA0Cxasnpevvtt5PYX67d53B55513XhL7HK5hw4Ylsb/8+8aNG8vq3Lx5cxI3Nzcn8axZs6q2yfM5XHlXYwV2dZzpAgAAAIACMegCAAAAgAIx6AIAAACAApHTBQCdwK/TNWPGjCR+/PHHk3jChAlJfM4555TVeemllyaxz8MYN25cu9sJoFg+P0sq7x/ycramTZuWxN/85jeTeMmSJUk8YMCAJPY5ZMOHD09iv8afVJ5z5evwuWo+b+xLX/pSEl9wwQVJTA4Xdnec6QIAAACAAjHoAgAAAIACMegCAAAAgAKR0wUAncDnaAwdOjSJ33rrrSResGBBEp977rlldfq1v/r375/EgwcPbnc7ARTLr8El5edwnX766Ul8yy23JHHfvn2TuHfv3kns86/Wrl2bxCtWrKi6fUnasGFDEjc1NSWxz/natGlTEl944YVJfOWVVybxNddck8SnnXZaEvs+Uipf6wuoZ5zpAgAAAIACMegCAAAAgAIx6AIAAACAAjHoAgAAAIACkYEIAF3AJ6EvW7YsiZubm5O4paWlrI7GxsYk9osj9+nTZydaCKCr3H///Ul82223JfGoUaOS2C+uXOmiE6U2b96cxEuXLk3iQw89tOw1/sIYq1evTmJ/YR8f+/7It/kTn/hEEk+cODGJDzjggLI2+QWZ/QVDgHrCmS4AAAAAKBCDLgAAAAAoEIMuAAAAACgQOV0A0AV8zsSSJUuSOG+x1EplfE7X8OHDq76efAig83Xrlv979w9+8IMkbmhoSGKfs+UXJvbfbb8gs+87fLx8+fKyNvkc0rz+wz/v2+y36d+Xz33uc0l8xx13lLWJPgu7Es50AQAAAECBGHQBAAAAQIEYdAEAAABAgcjpAoAC5OU7+DVrundPu+Na8q2GDBmSxKtWrapaB4D65L+rDz74YBL37t07if0aV3n5VL68z8/yOWM+B0yS1q1bl8R+rUG/zbz+x+d49evXL4kfeOCBJJ47d25ZHePHj6+6DaCecKYLAAAAAArEoAsAAAAACsSgCwAAAAAKRE4XABQgb/2YxYsXJ3He2j2bNm0qe+zNN99M4oEDBybxCy+8ULVO1rgB6sPNN9+cxK+//noS+3wnn3Plv8t77bVXEq9fvz6JfY6XX+fL55xW2qbvk3r16lW1TXk5Xnk5Yd/61rfKXvOTn/ykap1APeFMFwAAAAAUiEEXAAAAABSIQRcAAAAAFIicrujaa69N4nnz5lV9vha1rLMDYM90//33J/HIkSOTuEePHklcad0cz/cxCxYs2MHWAehMDz/8cBL7dbN8DpbXs2fPJN6wYUPV1/v+xa+Z1dLSUnV7Uvkxjs8L83mqecdEvg3+PZgxY0Zum4B6xpkuAAAAACgQgy4AAAAAKBCDLgAAAAAoUKfkdPm5xU1NTe0qL5XPV87j5wJ7v/3tb5N4+fLlSbz33nsn8VlnnZXEl112WVmd++67bxLn5XD5+c9e3j4A2HU8++yzSTx48OAkbmxsrPp6v+6OVN7H+HjFihXtaSKALvLkk08mcV4+lD8m8t/9jRs3JrFfQ8vnT9XSd/gyecdlmzdvrlret8Hvo+8Te/fuXXV7QL3jTBcAAAAAFIhBFwAAAAAUiEEX0BWGDpVCqH4bOrSrWwkAAIAO0Ck5XT4f6rzzzkvi448/Ponzcr46gl9366ijjkpiP/d4xIgRSXzzzTeX1enzwE455ZQkbm5uTmKfs+VzvPz85h3B2mB16pVXOqYMdhk+Z8PnO/jvat66OlJ53obPA3nppZfa3U4AnW/JkiVJ7I8P/PGAX7fPf/e7d08P7/Lyp3x5X59U3if5Oj1fR155fwzk27R27dqqrwfqHWe6AAAAAKBADLoAAAAAoEAMugAAAACgQIXkdL399ttat27dttjnFdx+++1JvH79+iQeN25cWZ0DBgxIYr9eg5/f/OKLLybxDTfckMRD3UUKBg0alMR33HFHEk+ePDmJV69eXdbGadOmJfGCBQuSeP/990/iE088MYlHjRpVVmd7VFr3K2/eN2uBAZ3j0UcfTWL/XczL6ayUY+HL+LywYcOGJfHixYuT+IADDqjSYgCd5RWXw+uPSXY2nypvTT9fnz92qPQavw3/Gp+H6vehvTnnS5cuLXtszZo1SdyvX7921Ql0Js50AQAAAECBGHQBAAAAQIEYdAEAAABAgQrJ6dqwYYPmzZvX5vOl+V6SdOONNybxhAkTyl7j183ysc9VmDt3bhL7NXGOPfbYJPZr6Jx00klJ7HPI/PYl6eSTT07iV199NYkXLVqUxH/605+S+JBDDkniww47LImPPPLIJB48eHASV8rPImcLqA/z589PYp8P4fsUvyZNpfwHn4eRt9bXqlWrkpicLqA++PxM/3933rp+Pic0L4fL8/lYPqdMKs+/97Fvc6W8sFK+zXnlK1m4cGESv+td72p3HUBn4UwXAAAAABSIQRcAAACAioYOlUKofnMXBUcFDLoAAAAAVORWNNjhMnu6QnK6tm7dmqxj9frrr6cb7Z5u9o033kjiW2+9tazO/v37J7HPVWhubk7iY445JonHjh2bxD6fwq8NtnLlyiT2c5f9umFS+X76PLCRI0dWjf16EzNmzEjixx9/vGr9LS0tZW3ya3/tvffeSXzwwQcncWNjY1kdAHaeX2PG53D5/Cwf+35TKs/z8Hwdzz77bBIfffTRVV8PoOMtW7Yst4zPwfI5Xx3N118pv8r3J/44rNJagtX41/s+sZZ9fv7555OYnC7UM850AQAAAECBGHQBAAAAQIEYdAEAAABAgQrJ6QIAAABQvClTpuSWmTp1aie0BNUUMujq1q2b+vTpsy32iwKfc845STx69Ogk9hekkKSNGzcmsb9oRK9evaqWnzNnTtU29+3bN4n9RSp8wvrLL79cVodPAu3Xr1/VOvyFM/xihJUu1lHK76NfjFmSli9fnsR+v77+9a8n8ZlnnrntfunFUADsnBdffDGJDzrooCT2SeVepcVN/cU1fPK7T2z3i8YD6Hx+Qd9a5H23d5Zf2NgvpF6pjD/u8m3Ma7O/MIc/hqplseQVK1bklgHqBdMLAQAAAKBADLoAAAAAoEAMugAAAACgQIXkdK1evVq33377tnjYsGHJ8z7PyOc27b///mV1+oWE/VxgX+emTZuSeOvWrbltLuUXbO7Ro0cS+0WGpfycLs/nVw0ZMiSJfZt9TpifT+1jqfy99e+TzxO56qqrtt1/heXFgR3mv78+B9PnN+QtdFwpv8F/f32/53MwKuWiAuhczz33XLtf4/M3/cLBvi/w/Uteea+xsbHsMd+n+T6pvW3ysS9fS07Xa6+9llsGqBec6QIAAACAAjHoAgAAAIACMegCAAAAgAIVktO1adMmLV68eFs8ZsyY5Plx48Yl8bx585L4pZdeKqszb72pvLm//nmfP+FjP7fYz2+uNI/Yl2lqakpinxfmrVy5Mol9m998880k9nlo/nmpfP0xn1fy7LPPtllnXo4JgLa98MILVZ/3fdi6deuS2PcXlfo4n0PhY5/n6dcKA9D5Kq2pmccfk/j8Kp+/2V6+76ilv/FtymujP0byOV1+rcK8Yyap8rquQL3iTBcAAAAAFIhBFwAAAAAUiEEXAAAAABSokJyubt26JfkKjzzySPJ83npTlXKJ1q9fn8R+DaxBgwYl8dq1a5M4b50uPx/ar4nhYz8XWSpfp8vz85N9vpWf5+332a+55fOz/Hxoqbzdfn0zX8cll1yy7f5FF11UVh+A2ixYsKDq83n5DL4/qdTn+H7N51z47/+yZcuqtglA8ZYsWZJbxn/f/THKhg0bkriW/KdqfA7XPvvsU1Zm1apVSeyPH3xOl+9//LFd//79q9bv98nXJ7FOF3YtnOkCAAAAgAIx6AIAAACAAjHoAgAAAIACFZLTNXLkSF1zzTVJXGrAgAFJ7NenqpTT5fMbfL6TX6uhubk5iX0uk58v7ecK+/nNfv60n7tcqY1+P/K2mfe8f99aWlqS2OfGVXrNQQcdlMQnnnhi2WtaXX311W0+B6C69uZP+T7Kq2XdHJ8X5vupSmv5Aehc/phHKv//3n+X/fffl/d9gZe3pp8//lixYkVZHXnroeYdw7zxxhtJPGnSpCS+8847k9j3iZXy1nweGFDPONMFAAAAAAVi0AUAAAAABWLQBQAAAAAFKiSnq6GhIVl/4fLLLy9iMwBQt3z+VHtzNnw+Q6V8ikq5paV8jkVe3hiA4lXKrfT5Sj5vfdSoUUns89YfffTRJB4+fHgSb9q0KYnz+o685yvxfZTvf/z6qV7eul2V1unKW4MVqCec6QIAAACAAjHoAgAAAIACMegCAAAAgAIVktMFoGsNHSq98kr1MkOGSC+/3Dnt2RP5dbp8zobPf/C5CbXkVPicCR/7bfi8Dp9XVmkdHAAdq1JOV1NTUxL7tUcnTpyYxD6/6ZFHHklivw5XXn/iy9eS/+nrzIv9NnwO19ixY5P4vvvuS+JBgwaVtSFv7TCgnnCmC9gN5Q24ai0DAACAncegCwAAAAAKxKALAAAAAApEThcAFGDNmjVJ3NjYmMQ+v8FraGjILe9zJvJyvDyfNzJkyJCq5QHsvEprS+XlU06aNCmJ58+fX7V83nff832JXwdMKl87bGfX/Rs4cGAS+5wtn9NVaZ/y+lGgnnCmCwAAAAAKxKALAAAAAArEoAsAAAAACkROFwAUYO3atUnc3jWwfK5CpdwFn/eVtw2/Ltfq1auTmJwuoHg+v1OqnOdVavLkyUk8a9asquX9d92vZ5W3pl+l/mbz5s1V6/Cv8esCej179kzi4447LomvuOKKJK601li/fv2qbgOoJ5zpAgAAAIACMegCAAAAgAIx6AIAAACAAjHoAgAAAIACcSENACjAxo0bk7hPnz5J7BPnfewT2ystROoT8v2FNXzi+3777Ve1jQCK5y8gUUnfvn2T2C8cvG7duiT2F5nw/YeP87z55ptlj/kLZ/g+y7eh0oUvSvmLYPj+zPd5lfYh7wIkQD1h0AVAU6ZMyS0zderUTmgJAADA7ofphQAAAABQIAZdAAAAAFAgphcCQAEeeuihJG5ubq5avqmpqWpcKQ/EL4bscyj8YqU+h2vhwoVJfPjhh1dtI4Cd5/M7pfLF1PPyLf133+dD+fwrH/v8z7ycsEplfOy30b17eojZq1evJF6zZk3V2Ku0qPTAgQOrvgaoJ5zpAgAAAIACMegCAAAAgAIx6AIAAACAApHTBQAF+PSnP53EV1xxRRL7NbT8ujgrVqxI4gEDBpRtY8uWLUns8758Htn69euTuH///mV1AijWtGnTyh5buXJlEm/YsKFqHYsXL27XNvPWBfT5nz4fSyrP4fJ5YX5dLV+nN2fOnCT+6le/2q7XA7saznQBAAAAQIEYdAEAAABAgRh0AQAAAECByOkCgAJceumlSTx+/Pgkfvrpp5PY53CMHTs2iSdOnFi2DZ+j1bt37yT263CdfvrpbTcYQJcZNGhQu8r7/E2/BpZfx8vHPh/U50/5+qT8PLC88j7H9OCDD676emB3w5kuAAAAACgQgy4AAAAAKBDTCwEAAAB0iSlTpuSWmTp1aie0pFihiHUQQgivSXqhwytGZxtlZoM7ssJ2fjYGSVqZW4ryRZQvsu6u/lyhfnXlZ6Oevn+U77jy9DdoS1d/NurlO7Ij5eupLfVWvs3PVSGDLqAjhBBmmtmRlO/88kW3Bag39fT9o3zHlwfqTb19R+rpGGFXL98WcroAAAAAoEAMugAAAACgQAy6UM9+SPkuK190W4B6U0/fP8p3fHmg3tTbd6SejhF29fKVmVmxN+lCk+abNMekWSYd3UH1TjfpyB0qIwWTLjNpkUnPmHR+fPyM2M65Jj1s0uHx8cEmPWjSPJM+VFLPb0zap8r2LzDprHj/3SY9Gt+DZ0y6uID3eqlJg3bwtYNN+l3hnwdu3LrgJtlWyWZJNl+y2ZJ9QbJunbTtf4jbfVuyI91zX5ZssWQLJTup5PGT42OLJZtS8viNks2R7PKSx74i2YeqbP8IyX4U758t2WuSPSXZs5LdLdlfFbjvgyWjX+HGzd0kGyrZTZItkewJyaZJNnYH6mmR7F+qPL9Usrmx/5tZ8viVki2I/cmtkrXEx98TH5sp2YEl27inWp8p2S8l2z/e7yvZD0r2bbpkO3TsJ9m/l9zvKdkDknXv6r8fN247civ2TFcIx0j6O0nvkNkESX8j6c+FbrM2Z0vaV9LBMjtE0k3x8eclHS+z8ZL+Q9tHtqdL+r6koyRdIEkK4e8lPSWz5RW3EEJ3SZ+Q9N/xkZ9K+pTMJkoaJ+mWDtyfnRNCd5m9JmmFQnhPVzcHKMAGM00002GSTpT0PyRd5AuFUMgyGvMkfVjSA25bh0r6qKTDJJ0s6doQ1BCCGiT9n9jGQyWdHoIODUET4n5MkPSuELRXCBom6Wgz3VZl+/8u6eqS+GYzHWGmAyVNlfTrEHSIf1FHvBdmek3SihBEvwJEIShIulXSdDONMdM7JX1Z0pAdqK5F0r/klJkU+7/SCwHcK2lc7E8Wxe1L0hckvV/Zsc6n42NfkXS5md6uVHkIOkxSg5meiw9dL+l1SQfGfTtH2dXfdsS/t94x02ZJv5f0kR2sC+hSRU8vHCZppcw2SZLMVm4bpITwNYXwuEKYpxB+qBBCfHy6QviGQnhMISxSCMfGx5sUwk0K4RmFcKukpm1bCeF7CmGmQpivEC6poV3/LOlSmb0d2/Vq/Pdhmf0llnlE0oh4f4uk3pIaJW2NA6oLJP1nlW28T9KTMnsrxntLWhG3s1VmT8e2X6wQfhz3+zmFcH7Jfp0Z34dZCuEHCqGhpv3N3qu7FMI/KYQ+sf7HFMJTCmFyLHO2QrhdIfxBWScmSbdJOiPvzQN2ZWZ6VdKnJJ0XgkIIOjsE3R6C/iDp9yGoTwj6cQh6LAQ9FYImS9mBRXxsVgiaE4IOjGXvDEGzQ9C8EMoPBsz0jJkWVmjKZEk3mWmTmZ6XtFjZDztHSVpspufiQcZNsewWSU0hqJukHpK2SrpUFQaPrUJQs6QJZprdxntxv7Iflz4Vy08PQd8OQTMlfTYEvTME/TEEPRGC7o6DPIWg80PQ0/F9uCk+dnx8b2bF9605buY20a8ApSZJ2mKm77c+YKbZZpoR+6QrY38yt7VPCUF9Q9DvQ9CT8fHJ8aVTJY2J37sra22Ame4xU+vxSaXjnd6StoSgMZL2NdP0KtWdIek3sZ1jJB0t6SutgzQzPW+mO+Pzn4/7Ni+E+CN29vhtsZ+ZH8K2/miqsj5vVgi6MRa9TfQn2FUVeipN6hun0y0y6VqTji95bkDJ/Z+Z9Pfx/nSTvhXvv9+k++L9z5v043h/gklvbZs62FqX1BBfP6GkrkrTC1fFaY8zTbrLpAMrlPmiSdfH+3uZdGcsf4JJ55t0ds6+X2LSZ0rir5n0F5NuNel/m9QrPn5xnMrYaNKg2LYeJh1i0h0m9Yjlri2ZqtjW/i41abRJ95WUvdykM+P9lvi36GPS2Sa95P4Ow02a29WnX5X96r9Q2UHolBrK/1jSq5Lm1VB2X0n3S3pa0nxJn80p30vSY5Jmx/KX1LgPDZKekvTbGsoulTRX0ixJM2so3yLpl5IWSHpG0jFVyh4U6229rZF0QU79n4v7Ok/SL9T6WW27/Gdj2fl5dXfdZ8rWVnhstWRD4pS7lyQbEB+/XLIz4/0WyRZJ1keyayQ7Iz7eU7ImyU6V7LqSOveq0obppdMLJftu63Zi/CPJTou360se/7hk3433vx2nCX1Bsomt0warbHOSZL8qic9uravksQ9JdldJG6+N93tI9rBkg2P8Ecl+HO8vl6yx9T2K/94h2Xvi/b6tU4AkGy5Zl/crOZ/hmvuc9vQ3sXzhfU57+ptYvm76nN2xv8l/P+18yf6rjedOlexeyRpi//SiZMMk6y5Zv1hmkLKpx0Gy0ZK1+VmU7HnJnozT/D7VRpk7Svq8iZI9Itn9ko2IUyDLj5HS1/9RsvHx/gclu7WNcu+MUx37xD5ivmRHxOda+98myeZJNjDGa10dDZK91tV/w537+3OMk1O+sP4mvqbL+pziP2DZwOC9cRDy8rbBinSqZTlOc01aZq0fvGwQ8Z54f4hJi+P920x6X0m9T9r2QdenYzzHpNdM+mhJXZUGXWtN+kK8/2GTZrjnJ1mWdzWwwmv7m3SvZQPK60z6pVX6QEg/3NaO7Y+NMemfTfqjSdPjYxebdGFJmWdMGmHSeSYtt2zQOsukhdaaB9b2/i41abZJZ5TUN9OyXLTWel60bEB3tkk3uPb1MGlV4Z+J6h/uBklLJO0vqWfsCA7Nec1xkt5RY4c0TNI74v1mZdMq2qxfUpDUN97vIelRSe+uYTufVza1tNYOqeZcPGVTVT8Z7/eU1NKO9/ZlZQv3tVVmuLJptk0xvkVVfmBQNlV2nrJfRbtLuk/SAV35GarcztxB1w0lj8+M/+nPircXJTtEso/Fg4R/0/Zch7HKcia+IdmxOW2YvrODLlffHZLtI9mFkt0i2T9VKPMxyb5fEp9dYdB1iht0HR/vj5NsTcn7MFeye+Jzv1OWw3GmZPH7YVMkezQeUI4oqb+HZF3ar1T/u7Svz2lPfxPLF97ntKe/ieXros/ZXfub/Pel6qDrvyT7REn8sziQ6RH7jDnx+7hBWV7Y6JxB1/D4797K8lmPc89fqCynK1R47XGxPWMlu1myn0s2pEK5RZINjferDbo+K9mlJfF/SHZ+vH9xbN9syd6Q7N3x8Up99zLJmrv677hjf3uOcWoov9se4xR/9cJsKt10mV0k6TxJpyqEXpKulXSasvyp65SNtlttiv9ujTvZthD2k/RFSScoyxu709VVyUuSfh3v3yppQkl9E5TNR54ss1UVXvtVSZcpy/N6UNL/lHRxhXIbytphtkRm35N0gqTDFcLA+MymklKt+xwk/VRmE+PtIJldXMP+PiTp5G3TNbN6Ti2pZ6TMnonPrXNt7hXb3ZXi1Cp7zsxKp1a1ycweUDZ/PJeZrTCzJ+P9N5X9ijK8Snkzs7Ux7BFvVm0bIYQRkj6g7HPUoUIIeynrgH8U27fZzFbX+PITJC0xsxdyynWX1BSyabS9JVXOW8wcIulRM1tv2VTaPyrLX6prIWh/Zd+1V+NDpd+FIOlUy3IgJppppGVTBP9b0geVfUemhaD3mWmRsv8M50r6egj6WjuasUzZr5KtRsTH2nq8tP2TJT0hqa+kMWb6R0mnhaDebhvl/VC5I5R9D1q1vhdB0vyS92G8mf42PvcBZXln75D0eAjqbqapkj6pbOr3QyHo4Fi2HvqVatrV57Snv4nlC+1ziuxvYv1F9zm7fX9TwXxJ72zna86QNFjSO800UdIryv9uyyzrOyybVn2rss+7JCkEna0s7/4Ms/QzFvPOvqIsv/0iSf+q7FjtfJUr7WfmSzo85qbWJAS9V1nO/zFmOlzZGZRq+9YoaWOt9dcZjnGq171bH+MUfSGNgxTCgSWPTJT0grZ/mVYqhL6STquhtgckfSzWO07bB0r9lB0kvKEQhihLPs9zm7I51ZJ0vLJfAqQQRiobjH1cZosq7M+BkkbIbLqyP9Tbyj6cTWVlsw/6ASWv/UDJQOhAZQd8q6u08feSTlMIe8fXD1AIo5S/v1+T9BdlB0SSdLekz5TkzB1RZZtjlY3ou9JwpRdbeUlVOoydEUIYreyA89Gccg0hhFnKDtDvNbOq5SV9W9l/UBWTjiswSfeEEJ4IIXwqp+x+kl6TdEMI4akQwvUhhD41buejyk6lt90Qs2WSvinpRWU5iG+Y2T1VXjJP0rEhhIEhhN7KErD3rVK+y4WgwcoujPNdf6AR3S3pM/GgQyHoiPjv/pKeM9PVyvIXJoSgfSStN9PPJV2pbBBSq9slfTQENYag/ZT1C49JelzSgSFovxDUU9nf7faS9vfQ9pzSJm3/D7JB2a+CpdJ+yAlBxyvL57quwtMLJQ0OQce0bjfmtXVTluNxv6R/k7SXpL4haIyZ5prpG3EfWgdd9dCvVLOr9znfVvv6G6lO+pw9ob9pwx8kNbbmLklSCJoQgo6VNEPSR+JFdQYrOwB9TNn37FUzbQlBkySNii99U9qWP5mIOafNrfcl/a3idzEEnazsc/NBM62v8PKzJE0z0+vafrzzdrzvbetnzLRE0kxJl5T0oaND0Afivn0oBPWO7TklPraXpL+YaX38sebdJXVviX1e6z4NlLTSTFsq7fMuYFfvbySOcXa4zyn6TFdfST9VCE8rhDnKrsR1sbJR63XKduZuZf9B5/mepL4K4RllyeNPSJLMZiv7VWSBslOdD9VQ11RlZ9zmSrpC2a+zUjZgGSjp2njxipnudZdJujDe/4WyC3I8Luk7FbZxl7LOstXHJS1U9sH+maQzZLa1zRZmF9r4iqR74nt3r6RhNe7vZyU1KYT/VPYrVQ9JcxTC/Bi3ZZKyM2e7vZAN9n+lbH7ummplzWyrZVedHCHpqJAN+tuq9+8kvWpmT7SjOX9tZu9QNoA+N4RwXJWy3ZUd2H/PzI5QNgCfkreBEEJPZWdp/l9Ouf7KfnXbT9I+kvqEEM5sq7xlZ02/IekeSb9TNqe67c9112lNxp6vbHrAPZLauujOtu9MLN/6nflHSfNC0CxlUw7+r6TxUnZxDWW/Bn/dVxaCTglBL0k6RtKdIehuSTLTfGVTG55W9t6da6atliW3n6esb3xG0i2xbKtzJf00HijNkdQ7BM2V9IRZ+kOOmRZI2qvkohZSdkA3KwQtUnZlsFPNkjNdra/drOwHsW+EoNnK/rZ/pWxw9/O4zackXR23e0FMjp+jLBn/rljVHtOvVFNEn7OD/Y1UJ33ObtzfVBV/7DlF0t+EoCWxn7lC2dSoW5V9r2crG5z9q5lelnSjpCPj9+4sZccAMtMqZWeW51W4kMYQSQ/G7+9jku400+/ic99VNli7N/YH2y7qEc+Yn63tP95eJWmasoPtbeVK3CnpvSXxJ+O2F4egeZJ+omzA+GS8/5iygcD1ZnpK2d+yewh6Rtnx2SMldf1QWV/ceiEN+pMacIzTZrmu7XN2dF4itxpu2UUzqiag1tVNesCk/l3ZBmUHpneXxF+W9OUaXjdatedY9FB2QPv5HWjf1yR9scrzVyj75Wqpsv9A10v6eTvqvzin/qGSlpbEx0q6s4Z6J0u6p4Zy/yDpRyXxWZKubUf7L5fU5pox3Dr/JtnnJPtkF27/Acm6tF+p3r729znt6W9i+UL6nJ3tb2IdXdbn0N/sHjdlF794RLKGTtjWr7UD65nVy41jnD37GKf4nK492xRlSY31L4TBkq7S9kvmd5U4tSrsF3+5SKZW7ayQTbP8kaRnzOyqGsoPDiG0xPtNytZ4WtBWeTP7spmNMLPRytr+BzNr81eUEEKfEEJz632VTP9oo/6XJf05hHBQfOgEZWdK8pyunNPu0YuS3h1C6B3fqxOk8rMgpUKcAhuy6bkf1va16VAfvqc0b7TTxOlRV5mpq/uVanbZPqe9/U2ss576HPqb3YCZNig701/INLlWcbr1bWYqT//Ydeyy/Y3EMY52ts/Z0dEaN25F3ZTNmV2k7Ao/F9ZQ/hfK5uZuUfYLzP+qUvavlc0vnqPtlxh9f5XyE5RNoZqjrKP4Wjv2473KubKPsisYzdb2y7XWsr8Tlc2Zn6MsP7HqWQRJfSStkrRXje2+RFmnO0/ZVNjGnPIzlHWKsyWd0NWfH27c2ntrT5/Tnv4mlu+UPqeW/iaWq6s+h/6G25524xgnt97d9hgnxAoBAAAAAAVgeiEAAAAAFIhBFwAAAAAUiEEXAAAAABSIQRcAAAAAFIhBFwAAAAAUiEEXAAAAABSIQRcAAAAAFOj/A3007Iet67F2AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 864x720 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the first X test images, their predicted labels, and the true labels.\n",
    "# Color correct predictions in blue and incorrect predictions in red.\n",
    "num_rows = 5\n",
    "num_cols = 3\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "for i in range(num_images):\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "  plot_image(i, predictions[i], test_labels, test_images)\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "  plot_value_array(i, predictions[i], test_labels)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R32zteKHCaXT"
   },
   "source": [
    "## Use the trained model\n",
    "\n",
    "Finally, use the trained model to make a prediction about a single image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "yRJ7JU7JCaXT"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Grab an image from the test dataset.\n",
    "img = test_images[1]\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vz3bVp21CaXV"
   },
   "source": [
    "`tf.keras` models are optimized to make predictions on a *batch*, or collection, of examples at once. Accordingly, even though you're using a single image, you need to add it to a list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "lDFh5yF_CaXW"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Add the image to a batch where it's the only member.\n",
    "img = (np.expand_dims(img,0))\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EQ5wLTkcCaXY"
   },
   "source": [
    "Now predict the correct label for this image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "o_rzNSdrCaXY"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.2694876e-05 2.8829100e-16 9.9962330e-01 2.3878533e-11 2.7362458e-04\n",
      "  9.3431998e-11 9.0290516e-05 7.9445922e-15 3.2490030e-10 8.7814930e-15]]\n"
     ]
    }
   ],
   "source": [
    "predictions_single = probability_model.predict(img)\n",
    "\n",
    "print(predictions_single)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "6Ai-cpLjO-3A"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_value_array(1, predictions_single[0], test_labels)\n",
    "_ = plt.xticks(range(10), class_names, rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cU1Y2OAMCaXb"
   },
   "source": [
    "`tf.keras.Model.predict` returns a list of lists—one list for each image in the batch of data. Grab the predictions for our (only) image in the batch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "2tRmdq_8CaXb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions_single[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YFc2HbEVCaXd"
   },
   "source": [
    "And the model predicts a label as expected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/dicoding/.local/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:1817: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-12-03 21:08:02.780033: W tensorflow/python/util/util.cc:329] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: fashion-mnist/1/assets\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "model_version = \"1\"\n",
    "model_save = \"fashion-mnist\"\n",
    "model_path = os.path.join(model_save, model_version)\n",
    "tf.saved_model.save(probability_model, model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[01;34mfashion-mnist\u001b[00m\n",
      "└── \u001b[01;34m1\u001b[00m\n",
      "    ├── \u001b[01;34massets\u001b[00m\n",
      "    ├── saved_model.pb\n",
      "    └── \u001b[01;34mvariables\u001b[00m\n",
      "        ├── variables.data-00000-of-00001\n",
      "        └── variables.index\n",
      "\n",
      "3 directories, 3 files\n"
     ]
    }
   ],
   "source": [
    "!tree fashion-mnist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Upload Model to GCS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Copy model to bucket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Copying file://fashion-mnist/1/saved_model.pb [Content-Type=application/octet-stream]...\n",
      "Copying file://fashion-mnist/1/variables/variables.index [Content-Type=application/octet-stream]...\n",
      "Copying file://fashion-mnist/1/variables/variables.data-00000-of-00001 [Content-Type=application/octet-stream]...\n",
      "\n",
      "Operation completed over 3 objects/1.3 MiB.                                      \n"
     ]
    }
   ],
   "source": [
    "!gsutil cp -r fashion-mnist gs://tf-deploy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check isi Bucket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gs://tensorflow-deployment/fashion-mnist/\n"
     ]
    }
   ],
   "source": [
    "!gsutil ls gs://tensorflow-deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n",
      "(1, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Grab an image from the test dataset.\n",
    "img = test_images[1]\n",
    "\n",
    "print(img.shape)\n",
    "\n",
    "# Add the image to a batch where it's the only member.\n",
    "img = (np.expand_dims(img,0))\n",
    "\n",
    "print(img.shape)\n",
    "# print(img.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Consume AI Platform API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "HttpError",
     "evalue": "<HttpError 403 when requesting https://ml.googleapis.com/v1/projects/tensorflow-deployment/models/fashion_mnist:predict?alt=json returned \"Access to project denied. This might be a transient error and a retry may succeed. If the error persists, please check the IAM permissions on your project.\". Details: \"Access to project denied. This might be a transient error and a retry may succeed. If the error persists, please check the IAM permissions on your project.\">",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mHttpError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_2040/1416869501.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m \u001b[0mpredict_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproject\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/tmp/ipykernel_2040/1416869501.py\u001b[0m in \u001b[0;36mpredict_json\u001b[0;34m(project, model, instances, version)\u001b[0m\n\u001b[1;32m     33\u001b[0m     response = service.projects().predict(\n\u001b[1;32m     34\u001b[0m         \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m         \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'instances'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0minstances\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m     ).execute()\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/googleapiclient/_helpers.py\u001b[0m in \u001b[0;36mpositional_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    132\u001b[0m                 \u001b[0;32melif\u001b[0m \u001b[0mpositional_parameters_enforcement\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mPOSITIONAL_WARNING\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m                     \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 134\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mpositional_wrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/googleapiclient/http.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, http, num_retries)\u001b[0m\n\u001b[1;32m    913\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 915\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mHttpError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muri\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muri\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    916\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpostproc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    917\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mHttpError\u001b[0m: <HttpError 403 when requesting https://ml.googleapis.com/v1/projects/tensorflow-deployment/models/fashion_mnist:predict?alt=json returned \"Access to project denied. This might be a transient error and a retry may succeed. If the error persists, please check the IAM permissions on your project.\". Details: \"Access to project denied. This might be a transient error and a retry may succeed. If the error persists, please check the IAM permissions on your project.\">"
     ]
    }
   ],
   "source": [
    "import googleapiclient.discovery\n",
    "\n",
    "# Setup\n",
    "project = 'tensorflow-deployment'\n",
    "model = 'fashion_mnist'\n",
    "image = img.tolist()\n",
    "\n",
    "\n",
    "def predict_json(project, model, instances, version=None):\n",
    "    \"\"\"Send json data to a deployed model for prediction.\n",
    "\n",
    "    Args:\n",
    "        project (str): project where the Cloud ML Engine Model is deployed.\n",
    "        model (str): model name.\n",
    "        instances ([Mapping[str: Any]]): Keys should be the names of Tensors\n",
    "            your deployed model expects as inputs. Values should be datatypes\n",
    "            convertible to Tensors, or (potentially nested) lists of datatypes\n",
    "            convertible to tensors.\n",
    "        version: str, version of the model to target.\n",
    "    Returns:\n",
    "        Mapping[str: any]: dictionary of prediction results defined by the\n",
    "            model.\n",
    "    \"\"\"\n",
    "    # Create the ML Engine service object.\n",
    "    # To authenticate set the environment variable\n",
    "    \n",
    "    service = googleapiclient.discovery.build('ml', 'v1')\n",
    "    name = 'projects/{}/models/{}'.format(project, model)\n",
    "\n",
    "    if version is not None:\n",
    "        name += '/versions/{}'.format(version)\n",
    "\n",
    "    response = service.projects().predict(\n",
    "        name=name,\n",
    "        body={'instances': instances}\n",
    "    ).execute()\n",
    "\n",
    "    if 'error' in response:\n",
    "        raise RuntimeError(response['error'])\n",
    "\n",
    "    return response['predictions']\n",
    "\n",
    "\n",
    "predict_json(project, model, image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Container Based Deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Consume Flask Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n",
      "(1, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Grab an image from the test dataset.\n",
    "img = test_images[1]\n",
    "\n",
    "print(img.shape)\n",
    "\n",
    "# Add the image to a batch where it's the only member.\n",
    "img = (np.expand_dims(img,0))\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'predictions': [[2.999614139298501e-07, 1.363733073949519e-13, 0.9914463758468628, 1.4539765231917374e-15, 0.007513206452131271, 2.572075219475778e-09, 0.0010400890605524182, 1.0165382689830747e-16, 4.4440182089822144e-10, 8.144133696423583e-15]]}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import requests\n",
    "\n",
    "\n",
    "input_json = json.dumps(\n",
    "    {\n",
    "        \"data\": img.tolist()\n",
    "    }\n",
    ")\n",
    "\n",
    "# print(input_json)\n",
    "\n",
    "server_url = \"http://127.0.0.1:5000/predict\"\n",
    "\n",
    "headers = {\n",
    "    'content-type': 'application/json'\n",
    "}\n",
    "\n",
    "response = requests.post(\n",
    "    server_url, \n",
    "    data=input_json,\n",
    "    headers=headers\n",
    ")\n",
    "print(response.json())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"predictions\": [[2.999614139298501e-07, 1.363733073949519e-13, 0.9914463758468628, 1.4539765231917374e-15, 0.007513206452131271, 2.572075219475778e-09, 0.0010400890605524182, 1.0165382689830747e-16, 4.4440182089822144e-10, 8.144133696423583e-15]]}\n",
      "[[2.999614139298501e-07, 1.363733073949519e-13, 0.9914463758468628, 1.4539765231917374e-15, 0.007513206452131271, 2.572075219475778e-09, 0.0010400890605524182, 1.0165382689830747e-16, 4.4440182089822144e-10, 8.144133696423583e-15]]\n"
     ]
    }
   ],
   "source": [
    "output = json.dumps(response.json())\n",
    "print(output)\n",
    "result = json.loads(output)\n",
    "print(result['predictions'])\n",
    "predictions = result['predictions']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_value_array(1, predictions[0], test_labels)\n",
    "_ = plt.xticks(range(10), class_names, rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TensorFlow Serving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'predictions': [[0.000203685908, 1.65716728e-14, 0.98714149, 2.17279617e-09, 0.0124731818, 2.2860252e-14, 0.000181691095, 5.09977838e-16, 1.35720532e-10, 2.44194093e-15]]}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import requests\n",
    "\n",
    "\n",
    "input_json = json.dumps(\n",
    "    {\n",
    "        \"signature_name\": \"serving_default\",\n",
    "        \"instances\": img.tolist()\n",
    "    }\n",
    ")\n",
    "\n",
    "# print(input_json)\n",
    "\n",
    "server_url = \"http://127.0.0.1:8080/v1/models/fashion-mnist:predict\"\n",
    "response = requests.post(\n",
    "    server_url, \n",
    "    data=input_json,\n",
    ")\n",
    "print(response.json())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_value_array(1, response.json()['predictions'][0], test_labels)\n",
    "_ = plt.xticks(range(10), class_names, rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TensorFlow Serving - Heroku"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Locally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'predictions': [[0.000203685908, 1.65716728e-14, 0.98714149, 2.17279617e-09, 0.0124731818, 2.2860252e-14, 0.000181691095, 5.09977838e-16, 1.35720532e-10, 2.44194093e-15]]}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import requests\n",
    "\n",
    "\n",
    "input_json = json.dumps(\n",
    "    {\n",
    "        \"signature_name\": \"serving_default\",\n",
    "        \"instances\": img.tolist()\n",
    "    }\n",
    ")\n",
    "\n",
    "# print(input_json)\n",
    "\n",
    "server_url = \"http://127.0.0.1:8080/v1/models/fashion-mnist:predict\"\n",
    "response = requests.post(\n",
    "    server_url, \n",
    "    data=input_json,\n",
    ")\n",
    "print(response.json())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Heroku"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'predictions': [[0.000203685908, 1.65716728e-14, 0.98714149, 2.17279617e-09, 0.0124731818, 2.2860252e-14, 0.000181691095, 5.09977838e-16, 1.35720532e-10, 2.44194093e-15]]}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import requests\n",
    "\n",
    "\n",
    "input_json = json.dumps(\n",
    "    {\n",
    "        \"signature_name\": \"serving_default\",\n",
    "        \"instances\": img.tolist()\n",
    "    }\n",
    ")\n",
    "\n",
    "# print(input_json)\n",
    "\n",
    "server_url = \"https://fashion-mnist-demo.herokuapp.com/v1/models/fashion-mnist:predict\"\n",
    "response = requests.post(\n",
    "    server_url, \n",
    "    data=input_json,\n",
    ")\n",
    "print(response.json())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_value_array(1, response.json()['predictions'][0], test_labels)\n",
    "_ = plt.xticks(range(10), class_names, rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TF Serving - Cloud Run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'predictions': [[0.000203685908, 1.65716728e-14, 0.98714149, 2.17279617e-09, 0.0124731818, 2.2860252e-14, 0.000181691095, 5.09977838e-16, 1.35720532e-10, 2.44194093e-15]]}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import requests\n",
    "\n",
    "\n",
    "input_json = json.dumps(\n",
    "    {\n",
    "        \"signature_name\": \"serving_default\",\n",
    "        \"instances\": img.tolist()\n",
    "    }\n",
    ")\n",
    "\n",
    "# print(input_json)\n",
    "\n",
    "server_url = \"https://tf-serving-fashion-mnist-6isfvux7qa-et.a.run.app/v1/models/fashion-mnist:predict\"\n",
    "response = requests.post(\n",
    "    server_url, \n",
    "    data=input_json,\n",
    ")\n",
    "print(response.json())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_value_array(1, response.json()['predictions'][0], test_labels)\n",
    "_ = plt.xticks(range(10), class_names, rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Copy of classification.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}