{
"cells": [
{
"cell_type": "markdown",
"id": "56fc210f-fd9c-4114-8e1f-4f6a62fbc846",
"metadata": {},
"source": [
"# Linear Regression with TensorFlow\n",
"\n",
"In a regression problem, the aim is to predict the output of a continuous value, like a price or a probability. Contrast this with a classification problem, where the aim is to select a class from a list of classes (for example, where a picture contains an apple or an orange, recognizing which fruit is in the picture).\n",
"\n",
"This tutorial uses the classic [Auto (miles per galon) MPG](https://archive.ics.uci.edu/ml/datasets/auto+mpg) dataset and demonstrates how to build models to predict the fuel efficiency of the late-1970s and early 1980s automobiles. To do this, you will provide the models with a description of many automobiles from that time period. This description includes attributes like cylinders, displacement, horsepower, and weight.\n",
"\n",
"This example uses the Keras API. (Visit the Keras tutorials and guides to learn more.)\n",
"\n",
"```{contents}\n",
":local:\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "f465c6c0-fba3-4f79-9c6f-f83b7c952b73",
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from tensorflow.keras import layers\n",
"\n",
"# Make NumPy printouts easier to read.\n",
"np.set_printoptions(precision=3, suppress=True)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "99de2795-e77d-4d2b-8d7c-0c40ccfe38de",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.17.0\n"
]
}
],
"source": [
"print(tf.__version__)"
]
},
{
"cell_type": "markdown",
"id": "881f91d1-5711-4b5e-a4d9-357560707e28",
"metadata": {},
"source": [
"## Dataset\n",
"\n",
"The dataset is available from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/)."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "410b353c-5401-4c23-ba56-bb8fbfe17ce1",
"metadata": {},
"outputs": [],
"source": [
"url = \"http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\"\n",
"column_names = [\n",
" \"MPG\",\n",
" \"Cylinders\",\n",
" \"Displacement\",\n",
" \"Horsepower\",\n",
" \"Weight\",\n",
" \"Acceleration\",\n",
" \"Model Year\",\n",
" \"Origin\",\n",
"]\n",
"\n",
"raw_dataset = pd.read_csv(\n",
" url, names=column_names, na_values=\"?\", comment=\"\\t\", sep=\" \", skipinitialspace=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "359f9aea-a524-47c5-8a8d-6395b85d6d31",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
"
],
"text/plain": [
" count mean std min 25% 50% \\\n",
"MPG 314.0 23.310510 7.728652 10.0 17.00 22.0 \n",
"Cylinders 314.0 5.477707 1.699788 3.0 4.00 4.0 \n",
"Displacement 314.0 195.318471 104.331589 68.0 105.50 151.0 \n",
"Horsepower 314.0 104.869427 38.096214 46.0 76.25 94.5 \n",
"Weight 314.0 2990.251592 843.898596 1649.0 2256.50 2822.5 \n",
"Acceleration 314.0 15.559236 2.789230 8.0 13.80 15.5 \n",
"Model Year 314.0 75.898089 3.675642 70.0 73.00 76.0 \n",
"\n",
" 75% max \n",
"MPG 28.95 46.6 \n",
"Cylinders 8.00 8.0 \n",
"Displacement 265.75 455.0 \n",
"Horsepower 128.00 225.0 \n",
"Weight 3608.00 5140.0 \n",
"Acceleration 17.20 24.8 \n",
"Model Year 79.00 82.0 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Let's also check the overall statistics. Note how each feature covers a very different range\n",
"train_dataset.describe().transpose()"
]
},
{
"cell_type": "markdown",
"id": "a3c43735-939b-4b22-9d47-620663545ee3",
"metadata": {},
"source": [
"### Split features from labels"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "4a52e26a-ce32-4dd0-a36f-3a904b96ecfa",
"metadata": {},
"outputs": [],
"source": [
"train_features = train_dataset.copy()\n",
"test_features = test_dataset.copy()\n",
"\n",
"train_labels = train_features.pop(\"MPG\")\n",
"test_labels = test_features.pop(\"MPG\")"
]
},
{
"cell_type": "markdown",
"id": "aeaed497-1db5-41ed-a174-e2b893b72e06",
"metadata": {},
"source": [
"## Normalization\n",
"\n",
"In the table of statistics it's easy to see how different the ranges of each feature are.\n",
"\n",
"It is good practice to normalize features that use different scales and ranges.\n",
"\n",
"One reason this is important is because the features are multiplied by the model weights. So, the scale of the outputs and the scale of the gradients are affected by the scale of the inputs.\n",
"\n",
"Although a model might converge without feature normalization, normalization makes training much more stable."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "1ce6e29c-70e0-4c5f-8c78-a9309dbdddea",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
mean
\n",
"
std
\n",
"
\n",
" \n",
" \n",
"
\n",
"
MPG
\n",
"
23.310510
\n",
"
7.728652
\n",
"
\n",
"
\n",
"
Cylinders
\n",
"
5.477707
\n",
"
1.699788
\n",
"
\n",
"
\n",
"
Displacement
\n",
"
195.318471
\n",
"
104.331589
\n",
"
\n",
"
\n",
"
Horsepower
\n",
"
104.869427
\n",
"
38.096214
\n",
"
\n",
"
\n",
"
Weight
\n",
"
2990.251592
\n",
"
843.898596
\n",
"
\n",
"
\n",
"
Acceleration
\n",
"
15.559236
\n",
"
2.789230
\n",
"
\n",
"
\n",
"
Model Year
\n",
"
75.898089
\n",
"
3.675642
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" mean std\n",
"MPG 23.310510 7.728652\n",
"Cylinders 5.477707 1.699788\n",
"Displacement 195.318471 104.331589\n",
"Horsepower 104.869427 38.096214\n",
"Weight 2990.251592 843.898596\n",
"Acceleration 15.559236 2.789230\n",
"Model Year 75.898089 3.675642"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_dataset.describe().transpose()[[\"mean\", \"std\"]]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "9c4ecc15-3cc0-46e6-a386-4b5fc5445a1a",
"metadata": {},
"outputs": [],
"source": [
"normalizer = tf.keras.layers.Normalization(axis=-1)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "3ec2673b-9ce7-4b3f-90b8-283b794c1acb",
"metadata": {},
"outputs": [],
"source": [
"normalizer.adapt(np.array(train_features))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "154bb1c6-dd79-487e-a8dc-fee62b71439c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 5.478 195.318 104.869 2990.252 15.559 75.898 0.178 0.197\n",
" 0.624]]\n"
]
}
],
"source": [
"print(normalizer.mean.numpy())"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "ae601ac4-15c8-4fab-8a39-f37f1f299c3e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"First example: [[4 90.0 75.0 2125.0 14.5 74 False False True]]\n",
"\n",
"Normalized: [[-0.87 -1.01 -0.79 -1.03 -0.38 -0.52 -0.47 -0.5 0.78]]\n"
]
}
],
"source": [
"first = np.array(train_features[:1])\n",
"\n",
"with np.printoptions(precision=2, suppress=True):\n",
" print(\"First example:\", first)\n",
" print()\n",
" print(\"Normalized:\", normalizer(np.asarray(first).astype(np.float32)).numpy())"
]
},
{
"cell_type": "markdown",
"id": "324c1445-7b2f-4d14-bf91-7e1a226ab2ab",
"metadata": {},
"source": [
"## Linear regression"
]
},
{
"cell_type": "markdown",
"id": "67100c25-6e04-4497-b34c-9d470d4204b5",
"metadata": {},
"source": [
"### Linear regression with one variable\n",
"\n",
"Begin with a single-variable linear regression to predict 'MPG' from 'Horsepower'.\n",
"\n",
"Training a model with tf.keras typically starts by defining the model architecture. Use a tf.keras.Sequential model, which represents a sequence of steps.\n",
"\n",
"There are two steps in your single-variable linear regression model:\n",
"\n",
"* Normalize the 'Horsepower' input features using the tf.keras.layers.Normalization preprocessing layer.\n",
"* Apply a linear transformation (y = mx + b) to produce 1 output using a linear layer (tf.keras.layers.Dense).\n",
"\n",
"The number of inputs can either be set by the input_shape argument, or automatically when the model is run for the first time."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "64bb1e9e-2193-4c5f-a737-99e8e5b155a3",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/ariefrahmansyah/Library/Caches/pypoetry/virtualenvs/applied-python-training-MLD32oJZ-py3.12/lib/python3.12/site-packages/keras/src/layers/preprocessing/tf_data_layer.py:19: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
" super().__init__(**kwargs)\n"
]
}
],
"source": [
"horsepower = np.array(train_features[\"Horsepower\"])\n",
"\n",
"horsepower_normalizer = layers.Normalization(\n",
" input_shape=[\n",
" 1,\n",
" ],\n",
" axis=None,\n",
")\n",
"horsepower_normalizer.adapt(horsepower)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "6eccbc14-c7a3-476f-9029-b101bdd5f0c8",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_loss(history)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "07bcca1e-4d81-4061-a8ba-48d32d6bc8eb",
"metadata": {},
"outputs": [],
"source": [
"# Collect the results on the test set for later\n",
"test_results = {}\n",
"\n",
"test_results[\"horsepower_model\"] = horsepower_model.evaluate(\n",
" test_features[\"Horsepower\"], test_labels, verbose=0\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "c97337c9-8700-4fd8-91cb-20f80e66c138",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n"
]
}
],
"source": [
"# Since this is a single variable regression, it's easy to view the model's predictions as a function of the input\n",
"x = tf.linspace(0.0, 250, 251)\n",
"y = horsepower_model.predict(x)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "bdebfc46-aa2f-41e3-a0b8-37b72b8011e3",
"metadata": {},
"outputs": [],
"source": [
"def plot_horsepower(x, y):\n",
" plt.scatter(train_features[\"Horsepower\"], train_labels, label=\"Data\")\n",
" plt.plot(x, y, color=\"k\", label=\"Predictions\")\n",
" plt.xlabel(\"Horsepower\")\n",
" plt.ylabel(\"MPG\")\n",
" plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "b27f0c29-4cf5-4f6f-8b63-5b0e4f9cf64d",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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MNzlX7AKYYrcjKi0kEgl8fX0RHR2NBw8eWDocMhNPT08oFIpiHYOJDJV6qsm5BZ/UqibnGrJEgtgFMJv5exU3bKISx9HRETVr1uTjpVLCwcGhWCMxKkxkqFTTNzlXgpeTczsHKkQ9ZuICmETFI5VK2dmXDMKHkFSqmWJyLhfAJCIyH47IUKlmqsm5XACTiMg8mMhQqWbKybl2UgmCq3sbvB8REYlnNY+W5s2bB4lEgokTJ6pfy8jIQGhoKLy9veHm5oZ+/fohLi7OckFSiaOanKttnESCl9VLnJxLRGSdrCKRuXDhAlatWoX69evne33SpEnYu3cvfvnlF5w4cQKPHz9G3759LRRlfoIg4NmzZ5YOg4pJNTkXQKFkhpNziYisn8UTmRcvXmDw4MFYvXo1ypQpo349KSkJa9euxaJFi9CxY0c0adIE69atw59//omzZ89aMOKXfv75Z9SoUQM//vgjW2rbOE7OJSKyXRafIxMaGopXX30VISEh+Pzzz9WvX7p0CdnZ2QgJCVG/VqdOHVSuXBnh4eFo0aKFxuNlZmYiMzNT/efk5GSTxL1u3TokJiZi6NCh2LJlC7777jtUrVrVJJ9Fpid2cq4xuv8SEZHxWDSR+emnn3D58mVcuHCh0HuxsbFwdHSEp6dnvtd9fHwQGxur9Zhz587FrFmzjB1qIb/99hsWLFiAWbNm4eDBgwgKCsIXX3yBcePGGaXBD5mfvsm5xur+S0RExmOxR0uPHj3ChAkTsHnzZqM2PwoLC0NSUpL659GjR0Y7dl4ODg4ICwvDtWvX0KZNG6SmpmLixIlo3bo1bt68aZLPJMtRdf8t2HNG1f33QESMhSIjIirdLJbIXLp0CfHx8WjcuDHs7e1hb2+PEydOYOnSpbC3t4ePjw+ysrLw/PnzfPvFxcXpXJdBJpNBLpfn+zGl2rVr4/jx41i5ciXc3d1x9uxZNGrUCLNmzWKbbRuRqxQQfi8Bu6/+i/B7CchVCoXe19X9F3jZ/bfgfkREZHoWS2Q6deqEGzdu4OrVq+qfpk2bYvDgwer/dnBwwJEjR9T73L59Gw8fPkRwcLClwtZIKpVi9OjRiIyMRK9evZCdnY2ZM2eicePGVjExmbQ7EBGD1l8dxaDVZzHhp6sYtPosWn91NN8Iiym6/xIRkXFYLJFxd3dHUFBQvh9XV1d4e3sjKCgIHh4eGDFiBCZPnoxjx47h0qVLGD58OIKDg7VO9LW0ihUrYvfu3fjpp59Qrlw53Lx5Ey1btsTEiRPx4sULS4dHBYh9XGSq7r9ERFR8Fi+/1mXx4sXo2bMn+vXrh7Zt20KhUGDHjh2WDksniUSCAQMGICoqCkOGDIEgCPjmm28QFBSEP/74w9Lh0X8MeVxkyu6/RERUPBKhhDdBSU5OhoeHB5KSkkw+X0aTgwcP4v3338eDBw8AAEOGDMGiRYvg7c3W9ZYUfi8Bg1brf+y3dVQLNPP3QuuvjiI2KUNj4iPBy54zp6d1ZCk2EZGRiL1/W/WITEnQtWtXREREYMKECZBIJPjxxx8RGBiIn3/+mY30LMiQx0Xs/ktEZL2YyJiBm5sblixZgjNnziAwMBDx8fEYOHAgevfujX/++cfS4ZUKBSuTyrrJRO2nelzE7r9ERNaJj5bMLDMzE3PnzsWXX36J7OxsyOVyzJ8/H6NGjYJUyrzSFDQ1slPIZcjIUeJ5WrbW/dyd7NCnYUVU9XbBO8FV4WgvNWtnX3YRJqLSTOz9m4mMhdy8eRMjRozAuXPnAABt27bF6tWrUatWLQtHVrKoKpOK+0sulQCj2vgjrEegUeLSh12Eiai04xwZK1e3bl2cOXMG33zzDVxdXXHy5EnUr18f8+bNQ3a29lECEk9XZZKhlAKw6mQ05u6LNMLRdGMXYSIi8ZjIWJCdnR3Gjx+PiIgIdO3aFZmZmQgLC0OzZs1w+fJlS4dn8/Q1siuK1aeikZWjNOox82IXYSIiwzCRsQJVq1bF/v378eOPP8LLywtXr15Fs2bNMG3aNKSlpVk6PJtligZ1SgHYGH7f6MdVYRdhIiLDMJGxEhKJBO+88w6ioqIwYMAA5ObmYv78+ahfvz6OHTtm6fBskqka1J2LLrwek7GwizARkWGYyFiZ8uXL46effsKePXtQoUIF3Lt3Dx07dsR7771XaAFN0q2Zvxd8PZwK9X4prj8i4wutx2Qs7CJMRGQYJjJWqlevXrh58yZGjx4NAFi9ejUCAwOxa9cuywZmQ/I2sjM2U0281Zd8SfCyeqmZv1eh9/St4k1EVBKx/NoGnDx5EqNGjcJff/0FAHjjjTewbNkyKBQKC0dmGw5ExODjnTeQmGrcajBTLU2gqloCkG/Sr+oTNDXgY7k2EZU0LL8uQdq2bYtr164hLCwMdnZ2+PXXXxEYGIh169ZxmQMRugX54mxYCLxcHY16XFNNvDW0izDLtYmoNOOIjI25evUqRowYoS7PDgkJwapVq1CtWjULR2b9tI10FNc3Axuid8MKRjziS2I6++YqBbT+6qjWSicuaElEtoojMiVUw4YNce7cOcyfPx9OTk44fPgw6tWrh0WLFiE3N9fS4Vk1bSMdxWWqibd2UgmCq3ujd8MKCK7urTERYbk2EZV2TGRskL29PT788EPcuHED7du3R1paGqZMmYLg4GDcuHHD0uFZtW5Bvjg9rSO2jmqBbwY2RGj76sU6nraJt+bCcm0iKu2YyNiwGjVq4OjRo/j+++/h4eGBCxcuoHHjxvjss8+QmZlp6fCsSt6KnvPRiWjm74XeDSugdc1yxTrujF6BFn1kw3JtIirt7C0dABWPRCLBqFGj8OqrryI0NBS7du3C559/ju3bt2PNmjVo2bKlpUO0iLzzS+4/TcXW8w8Rm/z/yZ2qoqdzoAK+Hk4GL2UgATChU010DixcOVbcVat17V/wvSZVysDXwwmxSRka5/2o5shYctSIiMiUONm3BBEEAdu3b8e4ceMQFxcHiUSC0NBQfPnll3B3d7d0eGajqRS5oLylzACKvEJ2wRLn4pZB69ofgMb3Xmvgi+9PRgMQX65NRGTtxN6/mciUQImJiZg6dSrWrVsHAKhUqRJWrVqF7t27Wzgy01NVJon5pc5b0XMoMhYfbb+B5+mG9ZoRkxBpSig0jbociozVur+281Ed+722/thzLYZ9ZIioxGAi85/SmMioHD58GO+99x6io1/+a33w4MFYsmQJypYta+HITENfKbI2W0e1QHB1b5y58xSD154z+HNVCZEgCPkeX2naRpU0FRxZUchlyMhR4nma4U37VMc+8WEHXHrwrMiPtIiIrAnLrwkhISG4ceMGJk+eDKlUis2bNyMgIABbtmwpkY309JUia6Oq6GlR3btIazOpSpy1JTF5t/n26B3NzeuSM4uUxOQ99qUHz/SWaxMRlTRMZEo4V1dXLFy4EOHh4ahXrx6ePn2KwYMHo2fPnnj06JGlwzOqopYYqyp68q7NZKoUYN2Z+0ZtxpcXS6yJqDRiIlNKNGvWDBcvXsScOXPg6OiIffv2ITAwEMuXL4dSqbR0eEZhaImxpgUYTdU0T8XQOTiGYIk1EZVGnCNTCkVFRWHkyJH4888/AQCtWrXCmjVrUKdOHQtHVjyqOTLaSpE1+U5LRU/eybhl3WSYsu0q4pIzdZY4C4KgcxsPF4ciPz7ShcsQEFFJxDkypFVAQABOnTqFZcuWwc3NDWfOnEGDBg3w+eefIysry9LhFZkxHw3lXR6gVY2ymPlaXY3HVf15Rq9AvdsMb+lfpFj0nYsAyzfmIyKyFCYypZRUKsW4ceNw8+ZNdO/eHVlZWfjss8/QtGlTXLhwwdLhFZmhj4Y+3nkDO6/8i/B7CchVah/HEbMitb5txnWsoXMysQSAp4sDFPLC+7/ftmhJEBFRScdHSwRBELB161ZMmDABT58+hVQqxcSJEzF79my4urpaOrwiyVUKWH8mGnN+jxK9j5i+K1k5SmwMv48HiWmo4uWCd4KrwtE+/78HdHXm1bYCd95eM50DFYW697b7+hhXuCaiUoV9ZP7DREa8J0+eYNKkSdi8eTMAwN/fH6tXr0anTp0sHFnR7L76Lyb8dFX09vo64Rana2/+JRPS/lsyQdxxwu8lYNDqs3rjV/XDISIqCcTev7nWEqmVK1cOmzZtwltvvYXRo0cjOjoaISEhGD58OBYuXIgyZcpYOkSDGFrFI+BlMjNrbyQ6ByryjW5o6xgcm5SBMZsu61wGQFMCpJDLMCmkJqqWddXbvI4rXBMRacc5MlRIjx49cPPmTYSGhkIikWDdunUICAjA9u3bLR2aQZr5exnc4E7VXO58dKL6tVylgFl7IzVWIwn//czaG6lxjo0qASr4WCguORNLDt+BzF6qt3kdV7gmItKOiQxp5O7ujm+//RanTp1CnTp1EBcXhzfeeAN9+/bF48ePLR2eKMWpYso7uiGmY3DB5AfQnwAB2hOgvPQlZJr64RARlRZMZEinVq1a4cqVK/j0009hb2+PnTt3IjAwEGvWrLGJZQ6K2uCurJsM4fcSsPvqvzh954moffLOeQH0J0CaRn800ZWQ5S3/5kRfIiqNONmXRLt+/TpGjhypLs/u0KEDvv/+e9SoUcPCkemnmmwbm5SOOb9H4VlqltbGdZ4uDpDZS3WunaTJZ68GYESbaurP2h8Rgx/DH+jd75uBDdG7YQW92xVnsjERka1h1dJ/mMgYV25uLr755ht8+umnSE9Ph5OTE2bPno1JkybB3t425o7rKoEuzv8Mi/s3gLOjXaFkQx9VtZGusm2Vgts0qVKGK14TUYnEROY/TGRM4++//8Z7772HI0eOAACaNGmCNWvWoGHDhpYNTCRtoxvp2blFXkZgUkhNLDl8R3QylLf/y6HIWINHWzhCQ0QlGROZ/zCRMR1BELB+/XpMnjwZz58/h52dHf73v/9h+vTpcHKy/gqagqMbSqWAwWvPFelYZQx8HJW3Zw0AjaXduvraaCsH19cLh4jIVnCtJTI5iUSC4cOHIyoqCm+88QZyc3Mxd+5cNGjQAKdOnbJ0eHrlXU8puLo3nqYaNicmr+xcwaA5NaplCzoHKgyubDJWNRQRUUnARIaKTaFQ4JdffsGOHTvg6+uLv/76C23btsWYMWOQnJxs9M/LylFi7am/MX13BNae+htZOUqjHLc4fVheZOaI2m5IcBVsHdUCp6d1RLcg3yJVNhmrGiqvXKWgrtLSt+4UEZE1sY3ZmWQTXn/9dXTo0AEffvgh1qxZg++++w6//fYbVq5ciZ49exrlM+bui8TqU9HIe5/9Yl8URrXxR1iPwGIdW9WvJTYpo1iTfnXpHuSbbxmBonTtNXanX861ISJbxhEZMipPT0+sXr0aR48eRfXq1fHPP/+gV69eGDRoEOLj44t17Ln7IrHqZP4kBgCUArDqZDTm7oss1vGL00APALxcHQxuWleUrr3G7PSrrfOwaumFAxExoj7LVnDkiajkYSJDJtGhQwdcv34dH374IaRSKX766ScEBARg48aNRWqkl5WjxOpT0Tq3WX0qutiPmbQ10NNV0axKUj7vHaT+c8H3Ac1N64rStddYnX5L21ybAxExaP3VUQxafRYTfrqKQavPovVXR0tcskZU2jCRIZNxcXHB/Pnzcf78eTRo0ACJiYkYMmQIunfvjgcP9DeKy2tj+P1CIzEFKYWX2xVXtyBfnPiwAz57NQBDgqvgs1cDsHRAI0igO0npUd8PK99uDB+5LN82PnKZ1iqionTtNVanX1PMtbFWpW3kiag0YSJDJtekSRNcuHABX375JWQyGQ4ePIi6deti6dKlyM3NFXWMB4lpRt1OlwMRMWj39THM+T0KP4Y/wJzfo/DF/ii819a/0EiNqvoof5KiLb3QTNsokOZjF32fgkrLqtqlbeSJqLThZF8yCwcHB4SFhaFv374YNWoUTp06hQkTJmDr1q1Ys2YN6tatq3P/Kl4uoj5H13b6OufmKgV8e/QOFh++U2jf2KQMfH8yGssGNkJcSgYeJKahipcL3gmuCkf7l/8eOBARg9H/dQzOt29yBkZvuozvdCQY3YJ80TlQ8XIZheQMJL7IhJerIzycHZGrFDSOruTdR0xn34LnX9ZVpnG7gmx9VW1DRp7yTsQmItvARIbMqnbt2jh+/Di+//57/O9//8PZs2fRqFEjfPLJJwgLC4Ojo6PG/d4Jroov9kXpfLwklbzcThN9lTkHImIwc09koYUfVVQfO/7nK/liWHM6GjN6BaJzoAIf7bih69QRtuMGOgcqtCYadlIJktKzMP/ALdEVRKpeOPpoOn+F3AmeLg5ISsvWuu6UogSsql1aRp6ISis+WiKzk0qlGD16NCIjI9GrVy9kZ2dj5syZaNy4Mc6ePatxH0d7KUa18dd53FFt/NWjI3npmx8xd18kxmy6rDWJyatgIqU6xrIjd/QubfAsLRtn/07Q+r6p5nFoO25ccgae/5fElORVtY1Z5UVE1oeJDFlMxYoVsXv3bvz0008oV64cbt68iZYtW2LixIl48eJFoe3DegTi/bb+hSqIpBLg/baa+8iImR+x+lR0kfvGqPZbc1p3RZVK+D3NiYyp5nHoO64EL5dXKDhB2ZC5NtbOWFVeRGSd+GiJLEoikWDAgAEICQnB5MmT8eOPP+Kbb77Brl278P3336NLly75tg/rEYgpXepgY/h9jfNUChIzP6K4q40JEN/ZV9v62qaaxyHmuM/SsrF5ZHNIJZISuYq2qsprzKbLhVY4L0kjT0SlFUdkyCp4e3tjw4YNOHDgAKpUqYIHDx6ga9euGDp0KBIS8o9iONpLMaJNNczuHYQRbappTWIA65v3EFytrMbXTTWPQ+z2T19k5lt3qqTd1I1R5UVE1okjMmRVunbtioiICHz66adYunQpfvzxRxw4cABLly5F//79IZEYdoO1pnkPni4OaKFlNMVU8zg4P+T/GVrlRUS2gSMyZHXc3NywZMkSnDlzBoGBgYiPj8fAgQPx2mu9sfv0DYPay+ubH2FOA5pW1HjTzFUKUCoFeDo76NxfIZcZPI+D80PyK7jiOZMYItvHRIasVnBwMC5fvowZM2bA3t4Bv/22F693aoF3P5qLgd//Kaq9vJguuOay51pMoeRL1TZ/8NpzeJ6uu+opI0eJQ5GxBn2msboAExFZKyYyZNVkMhlavPE+yg9ZAkff2hCy0pB4cDnitn6MR9H3RJUl65ofseKtxvB00T0SYiwF2/1rK4vWJiktu0hl2JwfQkQlmUQoygp+NiQ5ORkeHh5ISkqCXC63dDhkoFylgNZfHUVMUgYEZS5SLv+G5yd/hJCdCdg5wLP1W6gVMgh/ftxF76iCps6+ANDk80N6e8AYy7utqmJ6r7r5zssQqiZ1p6d1NHgURV9nYyIiayL2/s3JvmTV8pYPS6R2kDftDZeaLZBw4Ftk3L+C5yc24ErUKWystxrDenfUeayCXXBzlQLWn4k2WxIDAD+cuQ8PZwc0reJlcBIDFK+dvtguwEREtoSJDFk1TeXD9h4+KN9/NlJvHsWzI2uQHf83RvTrgqgpUzBz5kw4OzvrPa6mlv3msvjwHXjomdirj7WVlRMRWQrnyJBV01YWLJFI4BbUCX4jV8ClThsoc3Mxf/581K9fH8ePH9d5TEPnpojh6+GkdwmFvJL0TOzVpzSUSxMRicERGbJqqvLh2KQMjT1x7V3LoP6QGZgW8ALjxoXi7t276NChA0aNGoX58+fD09Mz3/a6WvYbwtPZAaEdaqCsmyMUHs7q+SZKpYC1Z+4X8+jalZSFHImIjIUjMmTVxJYP9+nTGzdv3sTo0aMBAKtXr0ZgYCB27dqVbx99LfvFep6ejS/2RWH+wdtISs9ST5oNCVQU+9jasFyaiKgwiyYyK1euRP369SGXyyGXyxEcHIz9+/er38/IyEBoaCi8vb3h5uaGfv36IS4uzoIRkyWILR/28PDAypUrcfz4cdSsWRMxMTF4/fXX8eabbyI29mX/FWPPLSm4MnUzf69il3P7ejjh/bb+8BVRLp2rFBB+L8GgJoFERCWJRcuv9+7dCzs7O9SsWROCIGDDhg34+uuvceXKFdStWxdjxozB77//jvXr18PDwwPjxo2DVCrFmTNnRH8Gy69LDkPKh9PT0zF79mx8/fXXyM3NRZkyZbBo0SI8rxCMJUfuGjWuvCXRQPHKub1dHREe1gmO9lK956tpwrKvhxNm9Apkbxgisnli799W10fGy8sLX3/9Nd544w2UK1cOW7ZswRtvvAEAuHXrFgICAhAeHo4WLVpo3D8zMxOZmZnqPycnJ6NSpUpMZEqpK1euYOTIkbh8+TIAwKlKQ3h1GwcHT+M/Ato66uXv5KDVZ4t9HH1l0qoJywX/51WlOWx0R0S2TmwiYzVzZHJzc/HTTz8hNTUVwcHBuHTpErKzsxESEqLepk6dOqhcuTLCw8O1Hmfu3Lnw8PBQ/1SqVMkc4ZOVatSoEc6dO4e58+ZBYu+IjAdXEfNDKJLP74SgzDXqZx2KjDXKoyt9x9A1YVn12qy9kXzMRESlgsUTmRs3bsDNzQ0ymQyjR4/Gzp07ERgYiNjYWDg6OhaqOvHx8VHPd9AkLCwMSUlJ6p9Hjx6Z+AzI2tnb26PdGyPhO3wZZJXrQcjOxLNjaxG7aSqyntw32uf8cOY+7j9NK/Zx9JVW65uwnLdpHhFRSWfx8uvatWvj6tWrSEpKwq+//oqhQ4fixIkTRT6eTCaDTCYzYoRkCcZupx+fkgEHrwrwGfgFXlz7A8+O/YCsmDuIWT8BHs3fgEfLgZDYF2+SrgTATxceQiGXIS45s0gl3l6uDoh5no61p/6Gl2v+0u685yIGm+YRUWlg8UTG0dERNWrUAAA0adIEFy5cwDfffIMBAwYgKysLz58/zzcqExcXB4XCdCWuZHmmmMSqGuWQSKRwb9gNztVfQeKhlUi/cxZJ4T8j7a8/4dVtPJwqBhQ5btVIyKSQmlhy+A4kgMHJTGJqNib/ci3fawXPXWwzPDbNI6LSwOKPlgpSKpXIzMxEkyZN4ODggCNHjqjfu337Nh4+fIjg4GALRkimpK3rbsEyZ0OpGuupxjXs3b1R7vVPULb3R5C6eiI74RHiNv8PiYe+gzKzeI+HqpZ11VguXlQxGkq8855LQRK8TH6stWkeS8aJyJgsOiITFhaG7t27o3LlykhJScGWLVtw/PhxHDx4EB4eHhgxYgQmT54MLy8vyOVyfPDBBwgODtZasUS2Td8kVgleTmLtHKgw+DGTqrHemE2X1SMlEokErnVaw7lKAyQeW4vUG4eRcvk3pN05B++uofCp2wKNKnuiUhln7LkWg+SMHFGfVd7dCcHVvdGxjg9azD2MxFTtpdgeTvaws5MiMTVL5zEF5D/3gueiYu1N81gyTkTGZtERmfj4eAwZMgS1a9dGp06dcOHCBRw8eBCdO3cGACxevBg9e/ZEv3790LZtWygUCuzYscOSIZMJmXoSq7bGehUU5fDrlh9x4OAf8KtUBbkpTxD/60zUu7MRC1+rjlfrVxCdxOQdCbn04JnOJAYAkjJy9CYxKnnPXXUuPnL9TfOshalG24iodLPoiMzatWt1vu/k5ITly5dj+fLlZoqILMkck1i7Bfmic6BC80TiIF/8FXUT06dPx5IlS7Bly2b88cdBvDN5BgShKiQS/SMceUdCTDHZtvAx849fWVlbKDVTjrYRUelmdXNkqPQy1yRWO6kEwdW90bthBQRX985343R1dcXChQsRHh6OevXq4enTp1j88Qd48uss5CQ/0XncSSG18o2EmGKyreqYqtGN2OTMfO/HJWda5egGS8aJyFSYyJDVsKZJrM2aNcPFixcxZ84cODo6Iv3vi3i8dixSLv8GQVAW2l4hl2Fcx5fVd6rJrLFJ6fByddR5Pgq5DAq59nPOy9PZAUpBQFaOUufohgDg4503kJVTOE5LYck4EZkKExmyGmJXujbXowdHR0d8+umnuHr1KgIbNoWQlY7EQ98hbvNHyE54pI5LAmDma3VhJ5XgQEQMWn91FINWn8WkbdeQmJqlMeFQncHM1+pi5muBouJ5np6NwWvOocXcI3pX8E5MzUaLuUesZmSGJeNEZCpMZMiqiF3p2pwCAgJw49I5jAn7HFJHZ2T+G4nH6z5A0p8/o7ybvToubZNZNcl7PqpzLrjatTZiJwcnpmZZzWMmaxptI6KSxeoWjTQ2rn5tm4zd2ddYou8/wOBhIxF+4jAAoH79+li7di0aNW6C1l8d1ZnEeLk64LOedaGQaz4f1TnHJmfgaUoGvj12D0npRVtFWyXvytyWvn6qRA/QXDJurdVWRGQZNrdoJFFeuibkWpJ/1So4c+wPbNq0Cd7e3rh+/TqaN2+Od94bh3+fPte5b2JqNhRyJ/XK1gWbwqnO+fVGFRBUwbPYSQxgXZNorXG0jYhsn8WXKCCyNRKJBIMHD0aXLl0wceJEbNmyBVvXroC95w54dR0H56oNte4bn5IhqimcsSe9WsskWp3l70RERcARGaIiyFUKuJssRf8P5+Pr1VtQ3rcCcp7HIv7nT/F03zfIzXihcb/7T9P0NoXLVQp4mpKpcf+iUk2itYblAax1tI2IbBPnyFCpYMw5N5pGVMrJlIg+sBoJ5/cCAOxcy6BM59Fwrd0KwMt5ID5yGQAJYpO1j464yuxgL5Ua5bGS6nNVc2QORcZyeQAishli799MZKjEM+b6PqoJqwX/p1GteZTxTyQS9y9FduI/AADnWsHwDhkNe3dvTAypicWH7xTvZAyQdxItAK1xq7ZhMkNE1oSTfYlg3PV9xLTZ963VAA3Hr4I8eAAgtUP6X+GI+WEsejtHoYq3S7HOxVCqSbSdAxU64wZeLg9grsdM1vB4i4hKDk72pRLL2Ov7iGmz/ywtG5tHNof07eY4f2kEvvv8f7h14yq+mfkhTh3Yjez6Q+BQxq+opySaBMDRKe3h7GiH8HsJopcHUFVUmQpXvyYiY+OIDJVYxl7fR2zlz9MXmQiu7o0J/TvjxuULWLBgAZydnXH57GnE/DAOyee2Q1DmijpWUQkAtpx7AMB6lgfg6tdEZApMZKjEMvYNvCht9u3t7TFlyhTcuHEDHTt2hJCThWfH1yF24xRkxf0t6nhF9SAxrVA8uphyeQB9o2OAeR9vEVHJwUSGSixj38BVbfb1eZZauHS6evXqOHz4MNauXQs3dw9kxd5FzIaJeHZiA4QccUsOGKqK18s5OdawPABXvyYiU2EiQxaVlaPE2lN/Y/ruCKw99bdRV2wuzg284ITUrBwlzkcnokugj97Pnf1bJFafzH9OuUoBZ/9OhHfjrthy8Axe79sXEJRIPvsL4taPR+ajiOKdrIZz83B2QPi9BACw6GKcuUoBZ+4+FbWttTTuIyLbwfJrspi5+yKx+lQ08j5NkEqAUW38EdZD3IrQ+hRlfR9NE1KlEqCoTz0kEsDZwQ5pWf8/L8bXwwkhLg+w9qtPERPzcm6Ie8Pu8Gw/HFKZcaubVJNpAZh9oq2ma6nL1lEtTD7hmIhsA/vI/IeJjHWauy8Sq05Ga33//bbGTWbE3sC19YkxNlUi9fVr1XFw3UKsWbMGAGDnXhZeXcbCpUYzeLo4oHWNsjj11xMkZeQU+/NUpdjmWh7AkGtpTYtbEpF1YCLzHyYy1icrR4k6n+3XOcIhlQC35nSHo71xnn6K6eybqxT0rmBtTHlv3idPHMeoUaNw7949AEBIz9fx4+qV8FX44Mydpxi89pzRPssciYIh15JN+YhIEzbEI6u1Mfy+3sc0SuHldsYiZn0ffRNSjS3vBNcOHTrg+vXr+PDDDyGVSnH4t50IqhuIjRs3onk13XN9DPms9WeizdKIzpBr6eHigIkhtdA5UGGyeIio5GIiQ2anKgs21nbGYqmJpqrPdXFxwfz583H+/Hk0aNAAiYmJGDJkCHq+2gOjm7z810hxx1Lm/B6FCT9dxaDVZ9H6q6Mm691iyLV8npaNxYf/Mmk8RFRyMZEhs1OVBRtrO2MxZR8VQz63SZMmuHDhAr788kvIZDIcPHgQoX07oJvkMuROxvtf1pSN6IpyLdkYj4iKgokMmd07wVWhb5qGVPJyO3PSV66ti6ezvd5z0kQhl0EpCIUe9zg4OCAsLAzXrl1DmzZtkJqaiu/mTcet1ZOQ9fRhESIszBSN6FRl67FJ6fBydTToWrIxHhEVBRMZMjtHeylGtfHXuc2oNv5Gm+grlp1UorXfil4SCToFlDf4MzNylBi85pzWxz21a9fGR99uRYWeH0Di6Iysx7cRs248np/eAiE32+DPK8iYjegORMSg9VdHMWj1WUzadg2JqVkGV3+xMR4RGYqJDFlEWI9AvN/Wv9AohlRi3NJrQ3UL8sXKtxtDUaCDr77RlqS0bByKjDf4856n5U9GCj5eORARg9AtV2Fftyv8RqyEc/VXAGUOks5sQcz6Cch8fNvgz9SkuPODtK2jZKl4iKj0YPk1WVRWjhIbw+/jQWIaqni54J3gqmYfidGkYLl2kyplcCE6EWO3XEZSevFHQnRRlUqf+LAD2n19LF9yIAgC0qJOIvHI91CmJQGQwL3pa/Bs8w6kjkWf41OcRnRiSq3LuNhDKUhEXzs2xiMisfdvezPGRFSIo70UI9pUM/nniOkjk5eqXDsvqVT8jbg4VI9XNobfL5QcSCQSuAa2g5N/Izw7shqpN48h5eJupN05C++uoXD2b2zw5xV3nSUxpdbP0sQ39DP1uk9EVLIwkaESz5DOvrqY+3GHrvJzO2c5yvacAtfA9kg4+C1yk+IQv206XIM6oUzHEbBzFj/6WNx1lox9XUy57hMRlTyWH8MnMiFtczeKUuprjPJsL1cHTAqpJWpbMeXnztWawG/ECrg36QVAgtSII3i8ZixSo05CzFPjSSE1i91N15hl66p4Ci7aySomItKGIzJUYuUqBczaG6mxckbAy7kos/ZGonOgQtQIgKo8OzYpo0hrMXm7OiI8rBPspBL8dOGh1uOo5si8E1wVa05H6/08qaMzvELeh2tAWyTsX4bshId4umc+nCOPw6vzWNjLy2rcr4yLA8a0r1GEM8lP33WRAPCRywBIEJes/Vx8PZwwrmNNo42gEVHpwBEZKrH0zd0wtNRXV3m2mAchX7weBEd7qfo42m7oAv7/8crAVyqJTppkFQLgO+wbeLQaBEjtkX73PB6vHYuUq/shCMpC2z9Ly0azLw4VuwGdmOsy87W6mPma9m0keHnOhyJjjTaCRkSlAxMZKrHEzt0wZI6HtvJshYcT3m+ruzeOIa48fIbWXx3F4sN3DNpPYu8Az9aD4TtsCRx9a0PISkPiweWI2/oxshP/LbT98/QcjN50GfuuPy7Woxxd10W1GKS+bToHKnSOoAFslkdEhbH8mkqs8HsJGLT6rN7tilLqq6k8u2CpdF55V58GYJZVtgVlLlIu/Ybnp36EkJ0J2DnAs/VbkL/yOiR2up8qF/VRjthVxjVtY8rvi4hsD8uvqdQTM3dDUcRS34Ll2eH3Egx6jGWOVbYlUjvIX+kN55rNkXhwOTLuX8HzExuQGnUK3t3HQ6bQPj9G9ShHNZoilqaydbHbmGIEjYhKPoMeLd27dw/vvvuu+s+VK1eGl5eX+qdcuXK4fds4nUaJikvM3A1jlfoachM2943YwVOB8v1nw7vHJEid3JAd/zdif5yMZ8fXQZmdqXEfSzzKEVv9ZKnFPYnIOhmUyCxbtgw+Pj7qPz979gxhYWFYvHgxFi9ejFdeeQWLFy82epBERSVm7oYxGHITtsSNWCKRwK1eJ/iNXAmXOm0AQYnkc9sRs24cMh5e17hPwVEkU5dE61u0UwI2yyOiwgx6tHTkyBGsXbs232v9+vVDtWovO7NWrVoVI0eONF50RDqI7dbbLcgXnQMVBnX2NZSY0mwvVwfEJmegvLsMCrkMccmZRSrjLg471zIo13sa0gLbI/GP5ch5FoO4rR/DrUFXlGk/HFInt0L7xKdkmKUkWjWCNmbTZUiAfNdG0wiaod2aiahkMmiyr7u7O6KiolCxYkUAwKRJk/Dpp5/C2/vl8+4HDx6gTp06SE9PN020RcDJviWTNfYaUTXfA6A3QfF0ccDztOxCN2xzUmam4tnx9XhxdT8AwM7NC16dx8ClVnC+7SaF1MSSw3cKxalKGYw5sgWI+26t8fsnIuMSe/82KJHx8PDAoUOH0KxZM43vnz9/HiEhIUhOTjY8YhNhIlPyqBIGc91YDaHpBquJKoFRJTSm4CqzQ2pmrt7tMh7eQMKBZch59hgA4FK7FbxCRsPerQwUHk4QBAGxyZrn0uStxjLmaIiu0RZr/v6JyHjE3r8NmiNTt25dHD58WOv7Bw8eRFBQkCGHJDKIvm69gGV7jXQL8sXpaR2xdVQLLO7fAF6ujhq3U3UWdrKXat2mKMq4OGBEq6rY+G4zpIlIYgDAqXI9+A5fBnmLNwCJFGm3z+Dx2jFIuXEYA5pW0prEAIY3FRRLVdnUu2EFBFf3zvc4yZq/fyIyP4MSmeHDh+OLL77A77//Xui9vXv3Yt68eRg+fLjRgiMqyNjdek1BdRNWeDgjMTVL63YCgNjkTJ3bGOKzVwNw8dPO+KxXXfwVl2LQIyupgwxl2g2D79DFcPSpDmXGCyTsW4L100ch+3ms3v3NVYllC98/EZmXQZN9R40ahaNHj6JXr16oU6cOateuDQC4ffs2bt++jX79+mHUqFEmCZQIsK1eI+aOoay7TD1yoWvlbG3GdaiOmj4N4fXRAJzZtR6zZs7EtbMnIbl0Dp5t3oF7k16QSO007muuSixb+v6JyDwMXqJg69at2LJlC2rWrKlOYGrWrInNmzdj27ZtpoiRSM2Weo2YO4a8nydm5eyCWtUoh94NK6BNbR98NG0arl+/jnbt20PIzsSzo2sQu+lDZD25n28fc5dE29L3T0TmUaTOvgMHDsTAgQONHQuRXqbs1msofeW/TaqUgZero9ZHR2JXhRajjItDvnN+J7gqPv89StQxVXEoBQG7r/6rPpeaNWvi6JEjmDR7EZbNnYGsmL8Qs34CPFq8CY/gAZDaOwDQ3lTQFOXR1vT9E5F1MCiRUSqV+Prrr7Fnzx5kZWWhU6dOmDFjBpydnU0VH1E+hvYaMRV95b+q93UlMcDLVaEBaDwfQxTc7+itOLg42iE1S/+EXwFARo4Sg9ecU7+W91y+mTkVLtWbYumcMKTdOYukP39C2u0zKNt9PD4Y1ENjhZCpyqOt5fsnIuthUPn1nDlzMHPmTISEhMDZ2RkHDx7EoEGD8MMPP5gyxmJh+XXJZMk+IvrKf99r64/vT0brTErE9EUxtDRbtZiitvgMkbeUGXiZaCkFAWm3zyDx0HdQpj0HIIF741exfsUi9G1eU72vOcqj2UeGqOQzSR+ZmjVrYurUqXj//fcBAIcPH8arr76K9PR0SKUGT7cxCyYyJZclOrvmKgWdK1dLAEgkgK7qXy9XB5wNC4Gjff7/Z/KeT1lXGab8cg2xyeInrX4zsCF61vcz2sraqsc0BfvI5Kan4NnRtUiNeNmKwdGzPLZvWoeer/ZAelYumn15GCkZOVqP6SOXYUTranj0LA1VvFzwTnDVQtdCDHb2JSrZTJLIyGQy3L17F5UqVVK/5uTkhLt376q7/VobJjJkTOH3EjBo9dliH0c1emLMz9k6qgUAGCU+MdKjryDh4LfITYoDADRs3xMJ9QZB6uJh0HGkEmBUG3+E9Qg0RZhEZKNM0hAvJycHTk75qwEcHByQnW2azqRE1sZYZb36jmPI5+StHDJn2bGzfyP4vbsc7k17QyKR4urx3/DvmjFIjTwOA/59BKUArDoZjbn7Ik0YLRGVVAZN9hUEAcOGDYNMJlO/lpGRgdGjR8PV1VX92o4dO4wXIZEVMVZZr77jGPo5qgmu5i47ljo6wavTKLgFtsXTfUuR/fQBnu5dAOebx+HVNRT28nKij7X6VDSmdKlTpMdMRFR6GfQ3xpAhQ1C+fHl4eHiof95++234+fnle42opFKV/2qbiSHBy0cl2ojtu9LM3wsKuf6kxMfdERNDaiIzR4nwewloUqWMzvgMoYpVIZfpPJ6zgxSOvrXhO2wJPFoPBuzskf73RTxeOxYpl3+DIChFfZ5SADaG3zdC5ERUmhg0IrN+/XoThUFkG1Tlv6P/W+W6IAHAe21eVi2p/qxiSHnwochYZOToLp1uUsUT/z7LwOLDd9Sv+Xo44bUGvurPL6q8sQK6y8PTs18mKhI7B3i2GgSX2q2QeGAZMv+NQuKh75AaeRLe3T+Ag3clDXvnV5SOxERUuhmUyLz77rt6t5FIJFi7dm2RAyKydY0ql8HKt8sUKg9WiCwPFls+fenB80KvxSZl4PuT0RjZxh9rT0frrJ4CAIVcht4N/bDnWozOWFe+3VjUqt4A4Fi2MnwGf4WUy7/j+ckfkflvJB6v+wCeLQdB3rwfJHba/9opSkdiIirdDKpakkqlqFKlCho1aqRzMt/OnTuNEpwxsGqJjElM+bXCwwmnp3UEAIPLg/UdXwwJAC9XRySIWIxy88jmaFWjLLJylNgYfh8PErWXRL/IyMErXxxSj8CIkZMcj4SDy5Hx9yUAgEO5qvDuPgEy35qFtpVKgFtzunOODBEBEH//NmhEZsyYMdi6dSuio6MxfPhwvP322/DyYitwKj0MWX05uLq3zhLrohxfDAEQlcQAwNMXmRqby605HZ1vRGbuvkh8fyoaBhQjAQDs5eVR/o2ZSI08jtTja5Dx5D5iN06BvGlveLQZDKnD/88DGtXGn0kMERnMoL81li9fjpiYGPzvf//D3r17UalSJfTv3x8HDx40qNySyFaZevVlc6/afP9pKsZsulwoeYpNysDoTZfxzeG/MGL9eaw6aXgSoyKRSOBWtwNW7T6J+u16AIISyRd2IuaHcUi/fxVSCfB+W/aRIaKiMfifPzKZDIMGDcKhQ4cQGRmJunXrYuzYsahatSpevHhhihiJrIapV182Zvm0l6uDzuoqXw8nbD3/UONcHNVriw/fwZFbT4wST/VKfrh2/Hfs3L0HXuV9kfM8FvE/f4p2sdsxOtg6lxXIVQoIv5eA3Vf/Rfi9BOTqm3RERGZXrHFcqVQKiUQCQRCQm6t/cToiWyem/FpMebW+4xeXQi7D572D1DHlpfrzwFcq51t6wOT+++A+r/XC/bu3ERoaCgBYv34dAgMDsX37do27WSqZOBARg9ZfHcWg1Wcx4aerGLT6LFp/dRQHImLM8vlEJI7BiUxmZia2bt2Kzp07o1atWrhx4wa+/fZbPHz4EG5ubgYda+7cuXjllVfg7u6O8uXLo0+fPrh9+3a+bTIyMhAaGgpvb2+4ubmhX79+iIuLMzRsIqNQlV8D2hOE4qy+bCeV4LUGxR+dSMnIgVQqwXtt/SEpEIpE8nJhy6plzVsh9PTF/ydN7u7u+Pbbb3H69GnUqVMHsbGxeOONN9C3b1/ExPx/omCpZEJVOabpkduYTZeZzBBZEYMSmbFjx8LX1xfz5s1Dz5498ejRI/zyyy/o0aNHkRaNPHHiBEJDQ3H27FkcOnQI2dnZ6NKlC1JTU9XbTJo0CXv37sUvv/yCEydO4PHjx+jbt6/Bn0VkLN2CfLHy7cZQFBg5UXg4FXtl51ylgD3Xin+TTM3KxehNl7HqZOESbKUAfH8yGvefpmre2UQ0PTZr1aoVrly5gk8++QT29vbYuXMnAgICsGbNGuy/8dgiyUSuUsCsvZE6H7nN2hvJx0xEVsLg8uvKlSujUaNGkBT8Z14eRV2i4MmTJyhfvjxOnDiBtm3bIikpCeXKlcOWLVvwxhtvAABu3bqFgIAAhIeHo0WLFnqPyfJrMhVTrL5srEUp9ZEAKO/uiCcvsvT2mtF7LAkgl9kjScuK1wBQxsUBFz/trPP6XL9+HSNGjMDFixcBAPJqDeEWMhYOZfw0xq8qczf2itdivwN9C38SUfGYpPx6yJAhOhOY4kpKSgIAdUn3pUuXkJ2djZCQEPU2derUQeXKlbUmMpmZmcjM/P8h7OTkZJPFS6WbnVRi9BtZbFK6UY+njQAgLkVcibY+I1v745dL/+j9PH3q16+P8PBwfPPNN/jk08+Q/PdVpPzwATxaD4b8ld6QSO3yHS9vmbsxmboyjYiMy2qWKFAqlZg4cSJatWqFoKCXkxRjY2Ph6OgIT0/PfNv6+PggNjZW43Hmzp2LWbNmmSxOIlNKFNn/xRpIJS97v7Sv7YPVp3QvifA8LVtU0mFvb48pU6bAtVYLTPpgLDIeXMfz4z8g7dZJeHcfD8fy1fJtf+buE6OOiAHiK8fuP82/nIIpRuiISD+DEhlTCg0NRUREBE6fPl2s44SFhWHy5MnqPycnJ6NSJf1rvBBZAy83mf6NrEQ5NxkaVS5jkhGMBoF1UH7AF0i9cQjPjq5FVuxdxGyYBHnzfvBsORASe0cAwLfH7qn38RW5BIQ+qsqx2KQMnSNJSw7/hdoKN3QL8tXYVNBY8RCRblbRRnPcuHH47bffcOzYMVSsWFH9ukKhQFZWFp4/f55v+7i4OCgUCo3HkslkkMvl+X6IbIWYFa+NQYKXJdoKedFXyo5PycToTZdx6q+norY3pEdOM38v+Hk6w71+F/iOXAmXWi0BZS6Sw7fh8brxyHgUUWgfY00CVlWmiXkcNmtvJPZdt8ykZCJ6yaKJjCAIGDduHHbu3ImjR4/C398/3/tNmjSBg4MDjhw5on7t9u3bePjwIYKDg80dLpHJGauPjD4CgJmv1cXM18TdsLUdAwB+vax7fkxReuvkLXN3cPNCudc/RrnXP4adaxnkJP6DuC0fIeHgcigz///xjjEriroF+WJSSOH1oPJSzdP5dHcEK5yILMiiiUxoaCg2bdqELVu2wN3dHbGxsYiNjUV6+ssJjx4eHhgxYgQmT56MY8eO4dKlSxg+fDiCg4NFVSwR2Rpj9ZEBgI51yundZrueJKS4itNbp2CZu0utlvAbuRJu9bsAAF5c3Y/Ha8ci7e559T55JwEXV9WyrqK2S0zN1vqeMeMhIs0sOkdm5cqVAID27dvne33dunUYNmwYAGDx4sWQSqXo168fMjMz0bVrV6xYscLMkRKZh7H6yADA1UdJOt//6NdreJ5h2o7cPnIZZr5Wt8jzRLoF+aJzoEI9ifZO3At86zQeLoHtkHhgGXKex+LJ9tlwCWgLr07vwc7VE4BxKoqMuVwEK5yITMfij5Y0/aiSGABwcnLC8uXLkZiYiNTUVOzYsUPr/BgiW2eM1a8leLnOkr4KKFMnMcDLeTRXHj4r1jFUZe69G1ZAqxplAQDOVRrA991vIW/WF5BIkRZ1Eo/XjMGLiKMQBMEoSYiY5Si8XR1FHcuYSRER5WcVk32J6KXi/stdddN9vWGF4gfzH0c7SZEnBCsFYNXJaMzdF2mUWPImF1IHJ5Tp8C4U7yyEQ7mqUGakIOH3RUjaOQs+0hTRx9S2lpOY5Sjm9A4y6dpbRKQfExkiK1Lcf7mrlkkICTTeqGWLai97vxSnI8rqU9HIylGK3t6Q5ELmWxO+Q5fAs+0QwM4BSXcuon69ICxdulTvYrb61nLStxxFj/q+Jl17i4j0M2iJAlvEJQrIGohtlparFND6q6N6e5gUJAHwUfc6GN7KH4720iIfR5Oo2d1w4q94zNh9E3EpRV8t+5MeARjVtpre7cT0ZNG2zYggR2yY/7G6H1XzFi0wfsZCuCqqFLruqoUhC14f1beSd90sfd+f2D4ybJpHJJ7Y+zcTGSITM7RZmuoGC4hr7Z9X3uMeiIjB6P+OU1SdA8tj9ZBXMHdfJFafKrwApSFcHOywaEADnRN/jZFcKJVKrFq1ClM+/B/SU18AUnt4BPeHR/Cb8PNyx4xegegcqEDrr45qnY9UlLWcjJXsENFLTGT+w0SGLMmQG3PB/Qre9Fwd7ZCWnQtd/8fmPS4AoyQy1cq6YtVJ3UsQiCWB9nNWjSIZI7k4EBGDUSsOIuGPFUi/dwEA4FC2Msp2nwCZX21MDKmFxYf/0huvsRaGLOrvQXFw9IdsHROZ/zCRIUsp7o05K0eJjeH38SAxDVW8XPBOcFUAwIY/72Pxob+Qlq15/ofquIIgIDa56I+CTEHXORtr1elcpYBW844iNjkDgiAgLeokEo98D2VaEgAJ5E1fQ+Wu7yIlx07rMVS+GdgQvRtWEJUUaNvGmAmaWBz9oZLAJKtfE5F4+kqpda3grOlGtOZ0NGb0CkRQBQ+tSUze41ojXedsrDWbvj16B7HJL7eRSCRwDWwHp6oN8ezoGqTePIbki7sRdecsvLuGwtm/sc5jlXd3KtacnRm9AuHh7Fjk34Oi0Db6o1oywRSjP0SWxKolIhMp6o1ZdSPStnbPoUjNK7/bEk3XRmzFlq7tDkTEYPHhO4Vet3PxQNmeU1D+zVmwk5dDblIc4rdNx9PfFyM3vXCptqps+llqlt51lPR9X4dFfl/GaJqXqxQwa28kl0ygUoWJDJGJFOXGLOZGtPvq4+IHZ2Garo2YBnS6erKorp0uztWawG/ECrg36QWJRILUiCN4vGYMUqNOQfWUXfX5n70agDm/608KZu65qXObnVf/1RmTijGa5hkyCkhUUjCRITKRotyYxdyIElKz4OXqoPO4XiI7zhqDxIBpHbqSETEN6HT1ZBHbFVnq6Iy6/Sbg5KnTqFy9FpRpz/F0z1d4suNz5KQ8VfeIKeMqE5UU6JqHJODlWkxero6ifw+09dARw1iP54hsCRMZIhMpyo1Z7A1G1blX23H7NPQzLFgtOgeWx/tt/XVu07G2/sUp89KVjOhrQKdrbochN+cZvQLRulVL/HXzOj6bPh32Dg5Iv3sOz3/8AEM876BLoI9Rb/Z9GvppLaUX8P/XRF+DPn2M8XiOyNYwkSEyIUNvzGJvMCGBCp3H7Syys6+bTPd8/36NKyKsRyDeb+uPgrmHVAK839YfI9tUF/VZ3q6OoiaadgvyxelpHbF1VAt8M7Ahto5qgdPTOurdryg3Z5lMhtmzZuHK5cto3rw5Ul+kYOzYMejYsSMynhpvZXAPZwe92+ibayMmmSnu4zkiW8TyayIzMFZn34KluvpKfnV19i3j4gBHO6nWbr0FP0tTObijvRTpWbkImH5A7zWImNkVbk7iCiWL0gNFX5mztvNS75+bi2XLluGTTz5BWloaZDIZXFoMhPsrr0NipzluqQQo5+aI+JQsnd+XrlJ4Q7YR20NHU0NF9qwhW8M+Mv9hIkO2xlg3In3HmRhSU2OFT0H6+rasPfU35vwepfc4n70agBFtjLNEga59xTYB1HZe0dHReP/993Ho0CEAgKNPdXh1+wAyRQ2Nx5kUUhNL/ruOmq+zuOZ7xYm5IHP2kWHPGjIVsfdvPloisjLFmSdiyHGqlnUVdRx9c0UeJKaJOo6Y7Yr7eKVbkC9GtKoqKp79ETEaJ9P6+/vj4MGDGD9rMaRObsiKu4fYHyfj2fF1UGYXHjGpWtZVz3V2ERWPGGLn7RT18ZyhjPE4jKi42BCPyAp1C/JF50BFsYfrdR0n/F6CqGPom3tSxUvcjVrfdvpKzyV4We7cOVCh8zqEBCqw9sx9vfH8GP4AP4Y/0Dh6IJFIMHDwO9jxpCwSD3+PtFunkHxuO9L++hPe3T6AU+X66m3LuzshuLp3sa+zGIbMA7KTSozSYE8bY31fRMXFRIbIShnrRqTtOKqJofrm46gmhmqbI/NOcFV8sS9K54KSUgnUSyxoU5xOyIacV0HaOt428/dCRT9f2PeehtTA9kj8YzlynsUgbuvHcGvQFWXaD0cFn7Lq66PrOivkMlFzZOKSM0V9F8ZSnLktxvq+iIqLj5aISilVebiYsuC5+yJR57P9mPN7FH4Mf4A5v0ehzmf7MXdfJBztpRjVRneJ9qg2/nC01/3XjbF6oOgqe9dEW8fbvNfHpWZz+I1cCbeG3QAAL64dxOO1Y9HZ5YHeG/+hyFhk5Ch1fv6MXoGY+VpdjTGL6aFTFMUt9WbPGrIWTGSISKe5+yKx6mR0oREXpQCsOhmNufsi0ahyGZ3H0Pc+YNweKNrmB2kjpuOtVOYK767j4DNoLuzL+CH3RSLmTByBN998E7GxmpchUM0heZ6WrfPzt1/+x2hzo8QwxtwW9qwha8GqJaJSSuyqzPoe0ahKkONSsnQeR1/psKGl52KoHp3sj4jBj+EP9G6vWu06bzyaro8yOxPJf25F0vkdgFKJMmXKYMGChajTtheevMhEeXcnNKlSBu2+PiZ6Ac+o2d3g7Ghn8lJmY63GnZWjRJ3P9ut9pHhrTne9o3F5Y2MZN6lw9Wsi0knsHAd9lAK0JjF5j6NvroTqUY628um8j7rEyjtvRUwik3f0QNf1kTrI4NluGFzqtIH7+TX4K/IGRox4F05VG8GraygcPBXwcnVEYqr261LQl/siMadPPZNP0jXW3JZLD57pTGKAl78blx48s7qScSpZ+GiJqJQy99wFS86VKErHWzHxOvpUx8AvNqJMu2GQ2Dsi4/4VxPwQiuQLu5CQkm5QjPcTxJWxF5ex5rYYc44My7ipOJjIEJVS5p67oO/z9K1erSrnNWQRRZWirHsl9vrsvREPeYs34Dt8GWSVgiBkZ+LZ0TWI3fQhsp7cFx1jVW/j9ZvRpaybzCjbGWuOjJgV34v6vVPpwESGqIiycpRYe+pvTN8dgbWn/kaWjsoUayR2lELfgxypBPBxF7+6szaGPPIQK+9K0h7Ojlj+ViPRk2nFXB8vVwf14yMHrwrwGfQlvLqOg8TRBVkxfyFm/QQ8P7UJQo7uyb4A8HGPQNHnJYbWVbTF5gN6tjPWuk7G+t6Ls2o42TbOkSEqgrn7IrH6VP5Kni/2RWFUG3+EGfmGZCpi56RcefgMq05Gaz3OqDb+aFS5TLHnthi7nFfbnIvPXg1EGVdHvRNKVddnzKbLkEDz8gOvN6yQr/meRCKFe8NucK7eFImHvkP6nbNI+vMnpN0+A69u4+FUMUBjrJ0Dy8PZ0U7UeYmha75JpsiE+2mq5r43KmKuj7m+d86vKd04IkNkIDHlyCWJvtWvjZW4GbOcV9eci9Atl5GUnoXeDSsguLq3zhutvpLoEC2rjNu7l0W51z9B2d4fQeriieyER4jb/D8kHvoOyszCc2Hiko03f0jffJP7T1NFHac4Ze6GlIwX93vn/Bpi+TWRAUxRcmophpbhauvsa6xyXmOVXxsrnoLH1FQWLGbl79z0FDw7uhapEYcBAHbycvDuEgrn6k3zbffD0FfQrna5YpUbizn38u6OePIiy2rKpovzvZviuybrwUUjiUxgY/h9USWnG8PvmyWe4jB0boKjvRQj2lTD7N5BGNGmmvomZ6w5DkWZkKuJKebaqEqiC47ibDmnv6TbztkdZV+diPL958DOwwe5yU8Q/+tMPP1tIXLTktTbvbvhgkGddTURc+5xKbqTGOD/y6bF0nZ9xO5b1O/dFN812R4mMkQGMOZKz5ZmjWW4xnhUYc7W+YZ8z87+jeD37nK4N+0NSKRIvXkMj9eMQWrkCagGxov7OMSYJe7mLJcv6vfOZRII4GRfIoMYa6Vna2CsOSnGblVf3JW/zdk639DvWeroBK9Oo+Aa0BYJ+5ci++kDPN37NZwjj8Ory1jYy8sVa9VoY5bUm7s8vyjfO5dJIIAjMkQGeSe4aqFJrwWJWenZGhirfNZYx8mrOI8qTBGPttJeMb8Pmsj8asN32BJ4tB4M2Nkj/d4FPF47FimXf4dSUOp9HKItHjHnrpDLoJCLvz7mLGs29Hs3xXdtKDHXR8w2tt7OwZI4IkNkANVKz/rKka19oi9gvPJZYx3HWIwdj77SXn2/D9pI7Bzg2WoQXGq3QuKBZcj8NwqJh1YiNeoEvLt9gPiUhkWKR19JvWqVbTHXx9rLmi39uyfm+ojZpiS0c7AkVi0RFYGmv3ikEtjkXzzGullZ203PGPGoSnsL/iWpui2q5m9o+n0whCAokXL5dzw/+SOErHTAzh493hmHXd/Ph4ODg0HxANCayADAd//FrO/6iD13a2CJ3z2x34W+bfT1aTJmiwNbI/b+zUSGqIi0lSPbImOtOmxtqxcboyzY0PL06IRU7L76GCkZOQbHm5Mcj4SDy5Hx9yUAQP369bF27Vo0bdpUdDyCICA2WXMzu4Ixa7s+tljWbM7fPWN9F6YohS9JmMj8h4kMERVF+L0EDFp9Vu92W0e1yLe6s9j9tBEEAamRx/HsyGoo05MhlUoxadIkvDpsAoZvul7k4+ZVMOaCinrupUVxv2NDffZqAEa0qWa2z7MW7CNDRFQMRS3tLW6pr0QigVvdDvAbuRJtuvWGUqnEwoULMah7G6Tfv1qsY4uNkWXNupn7vG2hnYMlMZEhItKgqKW9xir1tXPxwFffrsVvv/2GihUrIu6fB4j/+VM83fcNcjNeFOvY5i6pL2nMfd620M7BkpjIEFGJoyp33Xn5H6w99Td2XjG8dNjQ0l7VZ8YmZ8DL1UHLXuJ5OjtAKQjo1r0Hbt68iTFjxgAAUm8cQsyaMUi9fUZjPAq5rNil1ZYqa7aVFazFXh9934WPu2OJaedgSSy/JqISRVMFi4ohlSxiVwfXVqZcXM/TszF4zTl1zCtWrEC1Fl0QNukD5CT+g6e75iK1VjC8Oo+Bg9vLhELV6r+oMRtSxm3ssmZrq3rTRezvBqC7zH1W7yBRq8uXxom+huDVIaISQ9tKyCoxJlgRWd9nFlfeZQumDumD7X+cRIX2bwFSO6T/FY7Ha8ZA+tdRrBjcyOCycl0rRl95qHutJX3vG6KkrmAtZukFc60uX5KxaomISgR9JbEqxlxF20cuAyBBbLL2bcq4OCIxLUtv/B7ODkhKzxYVc65SwOZ9JzFn2gTcjbwGAOjQoQNWfrcKw7Y/tKmyYFst9TYkZjGl4SWpnYOxsGqJiEoVfSshq4hdEVnMysqxyZlakxjVNmKSGABakxjVcfLGbCeVYEjPdoi6dhELFiyAs7Mzjh07hgYNGuDWH1sgKHN1HkdbEqPaRuwK2cZY5d0WV7A2NGYxSy9oW12e9OOVIqISwdCSWGOVIJtTwZjs7e0xZcoU3LhxAx07dkRmRjqeH/8BsRunICv+b5PHY4yyYFss9bbFmEsyJjJEVCIYWhJrrBJkc9IWU/Xq1XH48GGEfbkEEpkrsmLvImbDJDw7+SOEHHEjQkVhjLJgWyz1tsWYSzImMkRUIqhKYvUx5qreYlaSVsjF3cx8ilk2LZFIMGfaeDSa9ANcarUElLlIDt+Gx+vGI+NRhPpYns4OKOPiYNSy4OKUTRur1NucpdvmXGGd9GP5NRGVCHlXQtZ3CzDWqt5iVpKe+VogVhy/i+v/JGv9rPoV5RjbvoZRVqT+YnAbjBFckXr7TyQeWomcxH8Qt+UjuDXqgTLthuE5tI+iFKUsuLhl08ZYwdrcpdvmXmGddGPVEhGVKMbqI6PreAWPI2ab1749pTGZqV9Rjj3j2og6jtgVqVXH+SfuKZ4f+wEvrv8BALBzLwuvLmPhUqOZxnMtGLO+Vd6NuUJ2UW/mllyl25wrrJdGXDTyP0xkiEofVblrbFI6ElOz4OX28hGQKVf1FrPNi4wcTPr5Ch4+S0flMs5YPKAR3JzsRR2nKCW/Z/9OQOjmy4i5dRGJB5Yh53ksAMAloC28Or0HO1dPeLk64LOedbVeH21lwaYomzZ0BWtrKN025wrrpY3Y+zcfLRFRiaMqdzXn8cRs4+Zkj9VDXynScQwp+VWV+EolEjxPz4ZzlQbwffdbJJ3eguQLu5AWdRIZ96+iTMeREOp2gELupDV2VVlwceMRw9DvzRQxGKo4v2vWEH9JwMm+REQ2oCglv3n/W+rghDId3oXinYVwKFcVyvRkJPy+CPG/zETE7btmicfYrCGG4rD1+K0FR2SIiMykOI8hilLyq2kfmW9N+A5dguTzO/D8zFZkRF/ChDc7IX3ulwgNDYWdnZ3J4jE2U8SQ9zsq6yoDJMDTF5kGf1/GjItl3LoxkSEiMoPiTgxVlfzGJmVorMpSzafIW/KrbR+JnT08gvvDtVZLJB/6FikPIjBhwgRs3boVa9euRWCg/vV9VMfW9WjEFCtka4rBkGuii77FP41dSWTs+EsrPloiIjIxYyyKqCr5BVCof4m2kl99+zh4V8RPew5gxYoVcHd3x9mzZ9GwYUPMmjULWVm6G+nZSSV4rYHuG/prDXxNOkm1KNdEGzGLfxp7EUtjxl+aMZEhIjKhXKWAWXsjNf6LW/XarL2RohqgiVlN2dB9etSvgDFjxuDmzZvo2bMnsrOzMXPmTDRu3Bjnzp3TeV57rum+oe+5FmPyxm5FuSYF6fqO8jL0+xLDGPGXdiy/JiIyofB7CRi0+qze7baOaiG6MqUoc23E7CMIAn7++WeMHz8eT548gUQiwYQJE/D555/D1dXV5OdVHMWZfyT2XPIy9nkVJ/6SiuXXRERWwBSVKUUp+RWzj0QiwcCBAxESEoLJkydj48aNWLJkCXbt2oVVq1ahS5cuBsdrroqb4pRBFyVGY5+XsVsGlCZ8tEREZEK2WJlStmxZ/Pjjj9i/fz8qV66M+/fvo2vXrhg2bBgSExMB2OZ5aVOUGG3hvEoLJjJERCZkigUGzaVbt26IiIjABx98AIlEgg0bNiAgIADbtm3DK1XLWO15GboAo77vKC9r/r5KKyYyREQmZOuVKe7u7li6dCnOnDmDgIAAxMfHY8CAAejX93WENisDwLrO60BEDFp/dRSDVp/FhJ+uYtDqs2j91VGdlUa6vqO8bOH7Ko0smsicPHkSvXr1gp+fHyQSCXbt2pXvfUEQMH36dPj6+sLZ2RkhISG4c+eOZYIlIiqiklCZEhwcjCtXrmD69OlwcHDAnj17ENq3A3o53oSPu2O+bS11XsUpc9f2HeVlS99XaWLRqqX9+/fjzJkzaNKkCfr27YudO3eiT58+6ve/+uorzJ07Fxs2bIC/vz8+++wz3LhxA5GRkXByEvd8klVLRGQtSkplSkREBEaMGIHz588DANq2a4exn34Fp7IVLXZexlqA0ZydfUk3m1v9WiKR5EtkBEGAn58fpkyZgqlTpwIAkpKS4OPjg/Xr12PgwIEaj5OZmYnMzEz1n5OTk1GpUiUmMkRERpSbm4tly5bhk08+QVpaGmQyGWbOnIkpU6bAwcHB7PFYWzk4FZ/YRMZq58hER0cjNjYWISEh6tc8PDzQvHlzhIeHa91v7ty58PDwUP9UqlTJHOESEZUqdnZ2mDhxIiIiItC5c2dkZmYiLCwMzZs3x+XLl80ej7WVg5P5WG0iExsbCwDw8fHJ97qPj4/6PU3CwsKQlJSk/nn06JFJ4yQiKs38/f1x8OBBrF+/HmXKlMGVK1fQrFkzfPTRR0hPTzdbHCWpHJwMY7WJTFHJZDLI5fJ8P0REZDoSiQRDhw5FVFQU3nzzTeTm5uKrr75CgwYNcOLECbPEYMtl7lQ8VpvIKBQKAEBcXFy+1+Pi4tTvERGR9fDx8cG2bduwa9cu+Pn54c6dO2jfvj3ef/99JCUlmfSzbb3MnYrOahMZf39/KBQKHDlyRP1acnIyzp07h+DgYAtGRkREuvTu3RuRkZF47733AADff/89AgMDsXv3bpN+bkkocyfDWXStpRcvXuDu3bvqP0dHR+Pq1avw8vJC5cqVMXHiRHz++eeoWbOmuvzaz88vX4k2ERFZHw8PD6xatQpvvfUWRo4cibt376JPnz548803sWzZskLzH42lW5AvOgcqSkSZO4lj0fLr48ePo0OHDoVeHzp0KNavXw9BEDBjxgx8//33eP78OVq3bo0VK1agVq1aoj+DfWSIiCwrPT0ds2bNwoIFC5Cbm4syZcpg0aJFGDp0KCQSJhikmc31kTEVJjJERNbhypUrGDFiBK5cuQIA6Ny5M1atWgV/f38LR0bWyOb7yBARUcnSqFEjnD9/HvPmzYOTkxMOHTqEoKAgLF68GLm5uZYOj2wUExkiIjIbe3t7TJs2DdevX0e7du2QlpaGyZMno2XLlrhx44alwyMbxESGiIjMrmbNmjh69ChWrVoFuVyO8+fPo3Hjxpg+fXq+ZWaI9GEiQ0REFiGVSvHee+8hMjISvXv3Rk5ODubMmYNGjRrhzz//tHR4ZCOYyBARkUVVqFABO3fuxLZt21C+fHlERUWhdevW+OCDD5CSkmLp8MjKMZEhIiKLk0gkePPNNxEVFYVhw4ZBEAR8++23CAoKwv79+y0dHlkxJjJERGQ1vLy8sG7dOvzxxx+oWrUqHj58iB49euCdd97B06dPLR0eWSEmMkREZHU6d+6MiIgITJo0CRKJBJs2bUJAQAC2bt2KEt7+jAzERIaIiKySq6srFi1ahPDwcAQFBeHp06d466230KtXLzx69MjS4ZGVYCJDRERWrXnz5rh06RJmzZoFBwcH/P7776hbty5WrFgBpVJp6fDIwpjIEBGR1XN0dMT06dNx9epVBAcHIyUlBaGhoWjXrh1u3bpl6fDIgpjIEBGRzQgMDMSpU6ewdOlSuLq64vTp02jQoAG++OILZGdnWzo8sgAmMkREZFPs7OzwwQcf4ObNm+jWrRuysrLw6aefomnTprh48aKlwyMzYyJDREQ2qUqVKti3bx82btwIb29vXL9+Hc2bN8fUqVORlpZm6fDITJjIEBGRzZJIJHj77bcRGRmJQYMGQalUYuHChahXrx6OHj1q6fDIDJjIEBGRzStfvjy2bNmCvXv3omLFivj777/RqVMnjBw5Es+ePbN0eGRCTGSIiKjE6NmzJ27evIkxY8YAANauXYvAwEDs2LHDwpGRqTCRISKiEkUul2PFihU4efIkatWqhdjYWPTr1w/9+vVDTEyMpcMjI2MiQ0REJVKbNm1w7do1fPLJJ7C3t8eOHTsQEBCANWvWcJmDEoSJDBERlVhOTk74/PPPcfHiRTRt2hRJSUkYNWoUOnXqhLt371o6PDICJjJERFTiNWjQAOHh4ViwYAGcnZ1x7Ngx1K9fHwsWLEBOTo6lw6NiYCJDRESlgr29PaZMmYIbN26gY8eOSE9Px4cffogWLVrg2rVrlg6PioiJDBERlSrVq1fH4cOHsWbNGnh4eODSpUto2rQpPvnkE2RkZFg6PDIQExkiIip1JBIJRowYgaioKPTt2xc5OTn48ssv0bBhQ5w6dcrS4ZEBmMgQEVGp5evri+3bt2P79u1QKBS4ffs22rZti7FjxyI5OdnS4ZEITGSIiKjU69u3LyIjIzFixAgAwMqVK1G3bl389ttvFo6M9GEiQ0REBKBMmTJYs2YNjhw5gmrVquGff/5Br169MGjQIMTHx1s6PNKCiQwREVEeHTt2xI0bNzB16lRIpVL89NNPCAwMxMaNG9lIzwoxkSEiIirAxcUFX3/9Nc6dO4f69esjISEBQ4YMQY8ePfDgwQNLh0d5MJEhIiLSomnTprh48SK++OILODo64sCBA6hbty6WLVuG3NxcS4dHYCJDRESkk4ODAz7++GNcu3YNrVu3RmpqKsaPH482bdogMjLS0uGVekxkiIiIRKhTpw5OnDiB5cuXw83NDeHh4WjUqBFmz56NrKwsS4dXajGRISIiEkkqlWLs2LGIjIzEq6++iqysLMyYMQNNmjTBuXPnLB1eqcREhoiIyECVKlXC3r17sWXLFpQtWxYREREIDg7GpEmTkJqaaunwShUmMkREREUgkUgwaNAgREVF4e2334YgCFiyZAmCgoJw6NAhS4dXajCRISIiKoayZcti48aN2LdvHypXroz79++jS5cuGDZsGBITEy0dXonHRIaIiMgIunfvjoiICHzwwQeQSCTYsGEDAgICsG3bNjbSMyEmMkREREbi7u6OpUuX4vTp0wgICEB8fDwGDBiAPn364N9//7V0eCUSExkiIiIja9myJa5cuYLPPvsM9vb22LNnDwIDA7Fq1SoolUpLh1eiMJEhIiIyAZlMhtmzZ+Py5cto1qwZkpOTMXr0aHTs2BF//fWXpcMrMZjIEBERmVC9evXw559/YvHixXBxccGJEydQv359zJs3D9nZ2ZYOz+YxkSEiIjIxOzs7TJw4EREREejcuTMyMzMRFhaG5s2b4/Lly5YOz6YxkSEiIjITf39/HDx4EOvXr0eZMmVw5coVNGvWDB999BHS09MtHZ5NYiJDRERkRhKJBEOHDkVUVBTefPNN5Obm4quvvkKDBg1w4sQJS4dnc5jIEBERWYCPjw+2bduGXbt2wc/PD3fu3EH79u3x/vvvIykpydLh2QwmMkRERBbUu3dvREZG4r333gMAfP/99wgMDMTu3bstHJltYCJDRERkYR4eHli1ahWOHz+OGjVq4PHjx+jTpw/69++PuLg4S4dn1ZjIEBERWYl27drh+vXrmDZtGuzs7PDLL78gICAA69ev5zIHWjCRISIisiLOzs6YN28ezp8/j0aNGuHZs2cYPnw4unbtiujoaEuHZ3WYyBAREVmhxo0b49y5c5g3bx6cnJxw6NAhBAUFYfHixcjNzbV0eFaDiQwREZGVcnBwwLRp03D9+nW0a9cOaWlpmDx5Mlq1aoWIiAhLh2cVmMgQERFZuZo1a+Lo0aNYtWoV5HI5zp07h8aNG2PGjBnIzMy0dHgWxUSGiIjIBkilUrz33nuIjIzEa6+9huzsbMyePRuNGjVCeHi4pcOzGCYyRERENqRChQrYtWsXfv75Z5QvXx5RUVFo1aoVxo8fj5SUFEuHZ3ZMZIiIiGyMRCJB//79ERkZiaFDh0IQBCxbtgxBQUHYv3+/pcMzKyYyRERENsrb2xvr16/HwYMHUbVqVTx8+BA9evTAO++8g6dPn1o6PLNgIkNERGTjunTpghs3bmDixImQSCTYtGkTAgICsHXr1hLfSI+JDBERUQng5uaGxYsXIzw8HHXr1sXTp0/x1ltvoVevXnj06JGlwzMZm0hkli9fjqpVq8LJyQnNmzfH+fPnLR0SERGRVWrevDkuX76MWbNmwcHBAb///jvq1q2LFStWQKlUWjo8o7P6RObnn3/G5MmTMWPGDFy+fBkNGjRA165dER8fb+nQiIiIrJKjoyOmT5+Oq1evIjg4GCkpKQgNDUW7du1w69YtS4dnVFafyCxatAijRo3C8OHDERgYiO+++w4uLi744YcfLB0aERGRVQsMDMSpU6ewdOlSuLq64vTp02jQoAG++OILZGdnWzo8o7DqRCYrKwuXLl1CSEiI+jWpVIqQkBCtzX8yMzORnJyc74eIiKi0srOzwwcffICbN2+iW7duyMrKwqeffoqmTZvi4sWLlg6v2Kw6kXn69Clyc3Ph4+OT73UfHx/ExsZq3Gfu3Lnw8PBQ/1SqVMkcoRIREVm1KlWqYN++fdi4cSO8vb1x/fp1NG/eHFOnTkVaWpqlwysyq05kiiIsLAxJSUnqn5I8U5uIiMgQEokEb7/9NiIjIzFo0CAolUosXLgQ9erVw9GjRy0dXpFYdSJTtmxZ2NnZIS4uLt/rcXFxUCgUGveRyWSQy+X5foiIiOj/lS9fHlu2bMHevXtRsWJF/P333+jUqRNGjhyJZ8+eWTo8g1h1IuPo6IgmTZrgyJEj6teUSiWOHDmC4OBgC0ZGRERk+3r27ImbN29izJgxAIC1a9ciMDAQO3bssHBk4ll1IgMAkydPxurVq7FhwwZERUVhzJgxSE1NxfDhwy0dGhERkc2Ty+VYsWIFTp48iVq1aiE2Nhb9+vVDv379EBMTY+nw9LL6RGbAgAFYsGABpk+fjoYNG+Lq1as4cOBAoQnAREREVHRt2rTBtWvX8PHHH8Pe3h47duxAYGAg1q5da9XLHEgEa47OCJKTk+Hh4YGkpCTOlyEiIhLh2rVrGDFiBC5dugQA6NixI77//ntUr17dbDGIvX9b/YgMERERmVeDBg1w9uxZfP3113B2dsbRo0dRr149LFiwADk5OZYOLx8mMkRERFSIvb09pk6dihs3bqBDhw5IT0/Hhx9+iODgYFy7ds3S4akxkSEiIiKtqlevjiNHjmDNmjXw8PDAxYsX0bRpU3zyySfIyMiwdHhMZIiIiEg3iUSCESNGICoqCn379kVOTg6+/PJLNGzYEKdOnbJobExkiIiISBRfX19s374dv/76KxQKBW7fvo22bdti3rx5FouJiQwREREZpF+/foiMjMS7774LiUSCNm3aWCwWll8TERFRkf3111+oVauW0Y/L8msiIiIyOVMkMYZgIkNEREQ2i4kMERER2SwmMkRERGSzmMgQERGRzWIiQ0RERDaLiQwRERHZLCYyREREZLOYyBAREZHNYiJDRERENouJDBEREdksJjJERERks5jIEBERkc1iIkNEREQ2y97SAZiaIAgAXi4HTkRERLZBdd9W3ce1KfGJTEpKCgCgUqVKFo6EiIiIDJWSkgIPDw+t70sEfamOjVMqlXj8+DHc3d0hkUiMdtzk5GRUqlQJjx49glwuN9pxqTBea/PgdTYPXmfz4HU2D1NeZ0EQkJKSAj8/P0il2mfClPgRGalUiooVK5rs+HK5nP+TmAmvtXnwOpsHr7N58Dqbh6mus66RGBVO9iUiIiKbxUSGiIiIbBYTmSKSyWSYMWMGZDKZpUMp8XitzYPX2Tx4nc2D19k8rOE6l/jJvkRERFRycUSGiIiIbBYTGSIiIrJZTGSIiIjIZjGRISIiIpvFRKaIli9fjqpVq8LJyQnNmzfH+fPnLR2STZs5cyYkEkm+nzp16qjfz8jIQGhoKLy9veHm5oZ+/fohLi7OghHbhpMnT6JXr17w8/ODRCLBrl278r0vCAKmT58OX19fODs7IyQkBHfu3Mm3TWJiIgYPHgy5XA5PT0+MGDECL168MONZWD9913nYsGGFfr+7deuWbxteZ/3mzp2LV155Be7u7ihfvjz69OmD27dv59tGzN8VDx8+xKuvvgoXFxeUL18eH374IXJycsx5KlZNzHVu3759od/p0aNH59vGXNeZiUwR/Pzzz5g8eTJmzJiBy5cvo0GDBujatSvi4+MtHZpNq1u3LmJiYtQ/p0+fVr83adIk7N27F7/88gtOnDiBx48fo2/fvhaM1jakpqaiQYMGWL58ucb358+fj6VLl+K7777DuXPn4Orqiq5duyIjI0O9zeDBg3Hz5k0cOnQIv/32G06ePIn33nvPXKdgE/RdZwDo1q1bvt/vrVu35nuf11m/EydOIDQ0FGfPnsWhQ4eQnZ2NLl26IDU1Vb2Nvr8rcnNz8eqrryIrKwt//vknNmzYgPXr12P69OmWOCWrJOY6A8CoUaPy/U7Pnz9f/Z5Zr7NABmvWrJkQGhqq/nNubq7g5+cnzJ0714JR2bYZM2YIDRo00Pje8+fPBQcHB+GXX35RvxYVFSUAEMLDw80Uoe0DIOzcuVP9Z6VSKSgUCuHrr79Wv/b8+XNBJpMJW7duFQRBECIjIwUAwoULF9Tb7N+/X5BIJMK///5rtthtScHrLAiCMHToUKF3795a9+F1Lpr4+HgBgHDixAlBEMT9XbFv3z5BKpUKsbGx6m1WrlwpyOVyITMz07wnYCMKXmdBEIR27doJEyZM0LqPOa8zR2QMlJWVhUuXLiEkJET9mlQqRUhICMLDwy0Yme27c+cO/Pz8UK1aNQwePBgPHz4EAFy6dAnZ2dn5rnmdOnVQuXJlXvNiiI6ORmxsbL7r6uHhgebNm6uva3h4ODw9PdG0aVP1NiEhIZBKpTh37pzZY7Zlx48fR/ny5VG7dm2MGTMGCQkJ6vd4nYsmKSkJAODl5QVA3N8V4eHhqFevHnx8fNTbdO3aFcnJybh586YZo7cdBa+zyubNm1G2bFkEBQUhLCwMaWlp6vfMeZ1L/KKRxvb06VPk5ubm+3IAwMfHB7du3bJQVLavefPmWL9+PWrXro2YmBjMmjULbdq0QUREBGJjY+Ho6AhPT898+/j4+CA2NtYyAZcAqmun6XdZ9V5sbCzKly+f7317e3t4eXnx2hugW7du6Nu3L/z9/XHv3j18/PHH6N69O8LDw2FnZ8frXARKpRITJ05Eq1atEBQUBACi/q6IjY3V+Duveo/y03SdAeCtt95ClSpV4Ofnh+vXr2PatGm4ffs2duzYAcC815mJDFmF7t27q/+7fv36aN68OapUqYJt27bB2dnZgpERFd/AgQPV/12vXj3Ur18f1atXx/Hjx9GpUycLRma7QkNDERERkW8uHRmftuucd/5WvXr14Ovri06dOuHevXuoXr26WWPkoyUDlS1bFnZ2doVmwcfFxUGhUFgoqpLH09MTtWrVwt27d6FQKJCVlYXnz5/n24bXvHhU107X77JCoSg0iT0nJweJiYm89sVQrVo1lC1bFnfv3gXA62yocePG4bfffsOxY8dQsWJF9eti/q5QKBQaf+dV79H/03adNWnevDkA5PudNtd1ZiJjIEdHRzRp0gRHjhxRv6ZUKnHkyBEEBwdbMLKS5cWLF7h37x58fX3RpEkTODg45Lvmt2/fxsOHD3nNi8Hf3x8KhSLfdU1OTsa5c+fU1zU4OBjPnz/HpUuX1NscPXoUSqVS/RcXGe6ff/5BQkICfH19AfA6iyUIAsaNG4edO3fi6NGj8Pf3z/e+mL8rgoODcePGjXyJ46FDhyCXyxEYGGieE7Fy+q6zJlevXgWAfL/TZrvORp06XEr89NNPgkwmE9avXy9ERkYK7733nuDp6ZlvdjYZZsqUKcLx48eF6Oho4cyZM0JISIhQtmxZIT4+XhAEQRg9erRQuXJl4ejRo8LFixeF4OBgITg42MJRW7+UlBThypUrwpUrVwQAwqJFi4QrV64IDx48EARBEObNmyd4enoKu3fvFq5fvy707t1b8Pf3F9LT09XH6Natm9CoUSPh3LlzwunTp4WaNWsKgwYNstQpWSVd1zklJUWYOnWqEB4eLkRHRwuHDx8WGjduLNSsWVPIyMhQH4PXWb8xY8YIHh4ewvHjx4WYmBj1T1pamnobfX9X5OTkCEFBQUKXLl2Eq1evCgcOHBDKlSsnhIWFWeKUrJK+63z37l1h9uzZwsWLF4Xo6Ghh9+7dQrVq1YS2bduqj2HO68xEpoiWLVsmVK5cWXB0dBSaNWsmnD171tIh2bQBAwYIvr6+gqOjo1ChQgVhwIABwt27d9Xvp6enC2PHjhXKlCkjuLi4CK+//roQExNjwYhtw7FjxwQAhX6GDh0qCMLLEuzPPvtM8PHxEWQymdCpUyfh9u3b+Y6RkJAgDBo0SHBzcxPkcrkwfPhwISUlxQJnY710Xee0tDShS5cuQrly5QQHBwehSpUqwqhRowr9w4fXWT9N1xiAsG7dOvU2Yv6uuH//vtC9e3fB2dlZKFu2rDBlyhQhOzvbzGdjvfRd54cPHwpt27YVvLy8BJlMJtSoUUP48MMPhaSkpHzHMdd1lvwXNBEREZHN4RwZIiIisllMZIiIiMhmMZEhIiIim8VEhoiIiGwWExkiIiKyWUxkiIiIyGYxkSEiIiKbxUSGiIiIbBYTGSIiIrJZTGSISKdhw4ahT58+hV4/fvw4JBJJoZWGiYjMiYkMEVlEVlaWpUMwC0EQkJOTY+kwiEosJjJEZBTbt29H3bp1IZPJULVqVSxcuDDf+1WrVsWcOXMwZMgQyOVyvPfee8jKysK4cePg6+sLJycnVKlSBXPnzlXv8/z5c4wcORLlypWDXC5Hx44dce3aNfX7M2fORMOGDbFq1SpUqlQJLi4u6N+/P5KSktTbKJVKzJ49GxUrVoRMJkPDhg1x4MAB9ftvvPEGxo0bp/7zxIkTIZFIcOvWLQAvEy5XV1ccPnxYfby5c+fC398fzs7OaNCgAX799Vf1/qqRqv3796NJkyaQyWQ4ffq0ka4yERXERIaIiu3SpUvo378/Bg4ciBs3bmDmzJn47LPPsH79+nzbLViwAA0aNMCVK1fw2WefYenSpdizZw+2bduG27dvY/Pmzahatap6+zfffBPx8fHYv38/Ll26hMaNG6NTp05ITExUb3P37l1s27YNe/fuxYEDB3DlyhWMHTtW/f4333yDhQsXYsGCBbh+/Tq6du2K1157DXfu3AEAtGvXDsePH1dvf+LECZQtW1b92oULF5CdnY2WLVsCAObOnYsff/wR3333HW7evIlJkybh7bffxokTJ/Kd60cffYR58+YhKioK9evXN8JVJiKNjL6eNhGVKEOHDhXs7OwEV1fXfD9OTk4CAOHZs2fCW2+9JXTu3Dnffh9++KEQGBio/nOVKlWEPn365Nvmgw8+EDp27CgolcpCn3vq1ClBLpcLGRkZ+V6vXr26sGrVKkEQBGHGjBmCnZ2d8M8//6jf379/vyCVSoWYmBhBEATBz89P+OKLL/Id45VXXhHGjh0rCIIgXL9+XZBIJEJ8fLyQmJgoODo6CnPmzBEGDBggCIIgfP7550LLli0FQRCEjIwMwcXFRfjzzz/zHW/EiBHCoEGDBEEQhGPHjgkAhF27dum6rERkJPaWTqSIyPp16NABK1euzPfauXPn8PbbbwMAoqKi0Lt373zvt2rVCkuWLEFubi7s7OwAAE2bNs23zbBhw9C5c2fUrl0b3bp1Q8+ePdGlSxcAwLVr1/DixQt4e3vn2yc9PR337t1T/7ly5cqoUKGC+s/BwcFQKpW4ffs2XFxc8PjxY7Rq1apQbKpHVEFBQfDy8sKJEyfg6OiIRo0aoWfPnli+fDmAlyM07du3B/By9CctLQ2dO3fOd7ysrCw0atQo32sFz5WITIOJDBHp5erqiho1auR77Z9//inScfJq3LgxoqOjsX//fhw+fBj9+/dHSEgIfv31V7x48QK+vr75HvuoeHp6GvzZ2kgkErRt2xbHjx+HTCZD+/btUb9+fWRmZiIiIgJ//vknpk6dCgB48eIFAOD333/PlzwBgEwm03muRGQaTGSIqNgCAgJw5syZfK+dOXMGtWrVUo/GaCOXyzFgwAAMGDAAb7zxBrp164bExEQ0btwYsbGxsLe3zzdvpqCHDx/i8ePH8PPzAwCcPXsWUqkUtWvXhlwuh5+fH86cOYN27drli61Zs2bqP7dr1w6rV6+GTCbDF198AalUirZt2+Lrr79GZmamekQnMDAQMpkMDx8+zHc8IrIcJjJEVGxTpkzBK6+8gjlz5mDAgAEIDw/Ht99+ixUrVujcb9GiRfD19UWjRo0glUrxyy+/QKFQwNPTEyEhIQgODkafPn0wf/581KpVC48fP8bvv/+O119/Xf3oxsnJCUOHDsWCBQuQnJyM8ePHo3///lAoFACADz/8EDNmzED16tXRsGFDrFu3DlevXsXmzZvVcbRv3x6TJk2Co6MjWrdurX5t6tSpeOWVV9SjK+7u7pg6dSomTZoEpVKJ1q1bIykpCWfOnIFcLsfQoUNNcXmJSAcmMkRUbI0bN8a2bdswffp0zJkzB76+vpg9ezaGDRumcz93d3fMnz8fd+7cgZ2dHV555RXs27cPUunLgsp9+/bhk08+wfDhw/HkyRMoFAq0bdsWPj4+6mPUqFEDffv2RY8ePZCYmIiePXvmS6DGjx+PpKQkTJkyBfHx8QgMDMSePXtQs2ZN9Tb16tWDp6cnatWqBTc3NwAvE5nc3Fz1/BiVOXPmoFy5cpg7dy7+/vtveHp6onHjxvj444+LeRWJqCgkgiAIlg6CiKgoZs6ciV27duHq1auWDoWILIR9ZIiIiMhmMZEhIiIim8VHS0RERGSzOCJDRERENouJDBEREdksJjJERERks5jIEBERkc1iIkNEREQ2i4kMERER2SwmMkRERGSzmMgQERGRzfo/Fav0eBtnO7sAAAAASUVORK5CYII=",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_horsepower(x, y)"
]
},
{
"cell_type": "markdown",
"id": "bed58710-c023-40ce-a45f-4f2d94e96e1c",
"metadata": {},
"source": [
"### Linear regression with multiple inputs\n",
"\n",
"You can use an almost identical setup to make predictions based on multiple inputs. This model still does the same y = mx + b, except that m is a matrix and b is a vector.\n",
"\n",
"Create a two-step Keras Sequential model again with the first layer being normalizer (tf.keras.layers.Normalization(axis=-1)) you defined earlier and adapted to the whole dataset:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "6705af4d-1f9b-48e3-bbf3-f7882c000ed3",
"metadata": {},
"outputs": [],
"source": [
"linear_model = tf.keras.Sequential([normalizer, layers.Dense(units=1)])"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "6daeaee8-eeea-4ede-8733-f86ebccba99f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n"
]
},
{
"data": {
"text/plain": [
"array([[ 0.129],\n",
" [ 0.144],\n",
" [ 0.7 ],\n",
" [-1.14 ],\n",
" [ 0.208],\n",
" [ 0.223],\n",
" [ 0.356],\n",
" [-0.496],\n",
" [ 0.84 ],\n",
" [ 1.644]], dtype=float32)"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"linear_model.predict(train_features[:10])"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "c9a32632-c70f-4810-ae23-a0e01c4a7e39",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"linear_model.layers[1].kernel"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "42764e50-28de-43a5-910f-0686cf000ed2",
"metadata": {},
"outputs": [],
"source": [
"linear_model.compile(\n",
" optimizer=tf.keras.optimizers.Adam(learning_rate=0.1), loss=\"mean_absolute_error\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "c95aefa9-6220-4f9f-86a7-877db3b45679",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 2.01 s, sys: 245 ms, total: 2.25 s\n",
"Wall time: 2.1 s\n"
]
}
],
"source": [
"%%time\n",
"history = linear_model.fit(\n",
" train_features,\n",
" train_labels,\n",
" epochs=100,\n",
" # Suppress logging.\n",
" verbose=0,\n",
" # Calculate validation results on 20% of the training data.\n",
" validation_split=0.2,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "35fce4fe-1374-4083-b445-2e6fcb7cb0f0",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_loss(history)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "6154fe66-5cbd-47e4-82a0-cd028224cdb7",
"metadata": {},
"outputs": [],
"source": [
"test_results[\"linear_model\"] = linear_model.evaluate(\n",
" test_features, test_labels, verbose=0\n",
")"
]
},
{
"cell_type": "markdown",
"id": "aa59face-b7d6-4c7d-8e0a-98d2e78a96de",
"metadata": {},
"source": [
"## Regression with a deep neural network\n",
"\n",
"In the previous section, you implemented two linear models for single and multiple inputs.\n",
"\n",
"Here, you will implement single-input and multiple-input DNN models.\n",
"\n",
"The code is basically the same except the model is expanded to include some \"hidden\" non-linear layers. The name \"hidden\" here just means not directly connected to the inputs or outputs.\n",
"\n",
"These models will contain a few more layers than the linear model:\n",
"\n",
"* The normalization layer, as before (with horsepower_normalizer for a single-input model and normalizer for a multiple-input model).\n",
"* Two hidden, non-linear, Dense layers with the ReLU (relu) activation function nonlinearity.\n",
"* A linear Dense single-output layer."
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "9710f0ea-b08d-46ab-87ac-25aaefb8e01b",
"metadata": {},
"outputs": [],
"source": [
"def build_and_compile_model(norm):\n",
" model = keras.Sequential(\n",
" [\n",
" norm,\n",
" layers.Dense(64, activation=\"relu\"),\n",
" layers.Dense(64, activation=\"relu\"),\n",
" layers.Dense(1),\n",
" ]\n",
" )\n",
"\n",
" model.compile(loss=\"mean_absolute_error\", optimizer=tf.keras.optimizers.Adam(0.001))\n",
" return model"
]
},
{
"cell_type": "markdown",
"id": "d38d3df7-20b5-4e13-aed3-0cc73e660568",
"metadata": {},
"source": [
"### Regression using a DNN and a single input"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "0a94d334-97a4-48d7-b59e-5980ce72c139",
"metadata": {},
"outputs": [],
"source": [
"dnn_horsepower_model = build_and_compile_model(horsepower_normalizer)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "3bacf1ab-3828-4979-b60e-388c08d45229",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_loss(history)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "57c2e67c-1db5-4834-a2d4-43fdc3a3cd6a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:5 out of the last 11 calls to .one_step_on_data_distributed at 0x302b19ee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n",
"\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 32ms/stepWARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x302b19ee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n",
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n"
]
}
],
"source": [
"x = tf.linspace(0.0, 250, 251)\n",
"y = dnn_horsepower_model.predict(x)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "6778aabd-08cd-4c7f-9461-b5e46b0430ef",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
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