{"cells":[{"cell_type":"markdown","metadata":{"id":"3JhHIQqNe4Qs"},"source":["# Lecture 15 - Convolutional Neural Networks with Keras-TensorFlow"]},{"cell_type":"markdown","metadata":{"id":"kPG5INqg-_qn"},"source":["[![View notebook on Github](https://img.shields.io/static/v1.svg?logo=github&label=Repo&message=View%20On%20Github&color=lightgrey)](https://github.com/avakanski/Fall-2025-Applied-Data-Science-with-Python/blob/main/docs/Lectures/Theme_3-Model_Engineering/Lecture_15-ConvNets/Lecture_15-ConvNets.ipynb)\n","[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/avakanski/Fall-2025-Applied-Data-Science-with-Python/blob/main/docs/Lectures/Theme_3-Model_Engineering/Lecture_15-ConvNets/Lecture_15-ConvNets.ipynb)"]},{"cell_type":"markdown","metadata":{"id":"mU2KzrFhAmHe"},"source":[""]},{"cell_type":"markdown","metadata":{"id":"iEkmemKte4Qv"},"source":["- [15.1 Introduction to Convolutional Neural Networks](#15.1-introduction-to-convolutional-neural-networks)\n","- [15.2 Loading the Dataset](#15.2-loading-the-dataset)\n","- [15.3 Creating, Training, and Evaluating a CNN Model](#15.3-creating,-training,-and-evaluating-a-cnn-model)\n","- [15.4 Introduce a Validation Dataset](#15.4-introduce-a-validation-dataset)\n","- [15.5 Dropout Layers](#15.5-dropout-layers)\n","- [15.6 Batch Normalization](#15.6-batch-normalization)\n","- [15.7 Data Augmentation](#15.7-data-augmentation)\n","- [15.8 Transfer Learning](#15.8-transfer-learning)\n","- [References](#references)"]},{"cell_type":"markdown","metadata":{"id":"gTFgm_bJe4Qw"},"source":["## 15.1 Introduction to Convolutional Neural Networks "]},{"cell_type":"markdown","metadata":{"id":"vntv_OZeLW9X"},"source":["**Convolutional Neural Networks (CNNs)**, also known as **ConvNets** have accelerated various computer vision tasks, such as image recognition and classification, image segmentation, and object detection. CNNs have been used in related applications, including autonomous vehicles, medical image diagnosis, intelligent robots, and others.\n","\n","A typical architecture of CNN is shown below and consists of 3 main types of layers: convolutional layers, pooling layers, and fully-connected (dense) layers. CNNs typically have multiple blocks of convolutional and pooling layers, followed by fully-connected layers. The optimal number of layers is task-dependent, and it is a hyperparameter that needs to be tuned by the user during the training and model selection."]},{"cell_type":"markdown","metadata":{"id":"PJCris5Cb1_g"},"source":["\n","\n","*Figure: Architecture of a CNN.*\n","\n","Each layer in CNNs transforms the input images into a higher-level representation. The initial layers in CNNs learn low-level features (such as edges and lines), the middle layers learn mid-level features (e.g., textures and parts), and the last layers learn high-level features (such as objects). The fully-connected layers use the high-level features to classify the input image."]},{"cell_type":"markdown","metadata":{"id":"ElfqfEcdoMGI"},"source":["\n","\n","*Figure: Extracted features in a CNN.*"]},{"cell_type":"markdown","metadata":{"id":"kMvSZgqZmHcD"},"source":["### Convolutional Layers\n","\n","The process of convolution is to pass a convolutional filter to each pixel in an image, multiply the corresponding pixels, and calculate the sum. This process is repeated until the filter is slid over all image pixels.\n","\n","\n","\n","*Figure: Convolution process.*"]},{"cell_type":"markdown","metadata":{"id":"0nPXLn9g5zY7"},"source":["Modern deep learning libraries, such as TensorFlow and PyTorch, allow to create convolutional layers in one line of code, as follows.\n","\n","\n"," tensorflow.keras.layers.Conv2D(.....)\n","\n","\n","Beside 2D convolutional filters that are used for processing images, 3D convolutional layers are used for analyzing videos, and 1D convolutional layers are used for analyzing time-series data.\n","\n","The output of a convolutional layer is high-dimensional feature maps, and its dimension depends on the number of convolutional filters used in the layer. For example, if the layer has 32 filters, then we will have 32 feature maps at the output.\n","\n","### Pooling layers\n","\n","Pooling layers are used to reduce the size of the feature maps output by convolutional layers, which reduces the number of network parameters and the computational cost for training the model. Pooling layers are also helpful for selecting more imporant information from the feature maps in the previous layer, they reduce the sensitivity of the model to the exact location of objects in inputs images, and they reduce the impact of noise in input data.\n","\n","There are different pooling options, although the most common is the Maxpooling layer, which reduces the image sizes by keeping the pixels with the maximum intensity.\n","\n","Implementing a pooling layer in most frameworks is very simple. The following figure depicts Maxpooling with a pool_size=2, which means that the output is the maximum value for each square of 2x2 pixels.\n","\n","\n"," tensorflow.keras.layers.MaxPooling2D(...)\n","\n","\n","\n","\n","*Figure: Pooling layer with a pool_size=2.*\n","\n","The portion of the network that consists of convolutional and pooling layers is often called an **encoder**, since it is used for encoding the information in input data into a representation that is more useful for the task at hand. Similarly, the portion of the network that consists of fully-connected layers is referred to as *classifier* (*head*, *top*) of the network."]},{"cell_type":"markdown","metadata":{"id":"IfklXOJdmwvS"},"source":["\n","### Fully-connected Layers (Densely-connected Layers)\n","\n","The last layers in ConvNets are fully-connected layers, that match the produced feature maps from the previous layers to the labels of the original image.\n","\n","\n","```\n","tensorflow.keras.layers.Dense(....)\n","```"]},{"cell_type":"markdown","metadata":{"id":"vTEkWfuxAg1d"},"source":["## 15.2 Loading the Dataset "]},{"cell_type":"markdown","metadata":{"id":"C56dWwwDkeJ1"},"source":["In this notebook, we are going to use one of the most well-known datasets for image classification called `CIFAR-10`.\n","\n","CIFAR-10 consists of 60,000 color images in 10 categories. The 10 classes are: `airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck`.\n","\n","Each class contains 6,000 images. In the dataset, 50,000 images are allocated for training, and 10,000 for testing. The top accuracy on the test dataset is about 90%.\n","\n","You can learn more about the dataset [here](https://www.cs.toronto.edu/%7Ekriz/cifar.html).\n","\n","Note that there is a larger version with 100 classes called CIFAR-100.\n","\n","CIFAR-10 is available in Keras built-in datasets, so we can simply load the data by using the helper function `keras.datasets.cifar10.load_data()`."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"SZ2qJTZms7fl"},"outputs":[],"source":["# import required libraries\n","import tensorflow as tf\n","\n","import numpy as np\n","import matplotlib.pyplot as plt"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1761238941292,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"x_ley-yjto1E","outputId":"319c9b15-9a34-4715-f64c-abc40679f47e"},"outputs":[{"output_type":"stream","name":"stdout","text":["TensorFlow version:2.19.0\n"]}],"source":["# Print the version of tf\n","print(\"TensorFlow version:{}\".format(tf.__version__))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7630,"status":"ok","timestamp":1761238948915,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"0I77B_touxef","outputId":"4c52e887-8a57-4911-ca58-bfe26dfa9791"},"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n","\u001b[1m170498071/170498071\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 0us/step\n"]}],"source":["# Load the train and test images and labels\n","(train_data, train_label), (test_data, test_label) = tf.keras.datasets.cifar10.load_data()"]},{"cell_type":"markdown","metadata":{"id":"h1D1boUL4FCX"},"source":["As usual, we can inspect the shapes of the training and testing datasets.\n","\n","Note that each image is a 32x32x3 tensor, having a size of 32x32 pixels and 3 channels corresponding to the RGB (red-green-blue) channels."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":19,"status":"ok","timestamp":1761238948933,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"90ULfCPD4I9D","outputId":"d6377c81-7f9f-4c72-f6e2-331cea04e8da"},"outputs":[{"output_type":"stream","name":"stdout","text":["Training images (50000, 32, 32, 3)\n","Training labels (50000, 1)\n","Testing images (10000, 32, 32, 3)\n","Testing labels (10000, 1)\n"]}],"source":["print('Training images', train_data.shape)\n","print('Training labels', train_label.shape)\n","print('Testing images', test_data.shape)\n","print('Testing labels', test_label.shape)"]},{"cell_type":"markdown","metadata":{"id":"a5D5z_0kjLUy"},"source":["### Data Preprocessing\n","\n","The values for the pixel intensities in the images are in the range between 0 and 255. The type is `uint8` which stands for unsigned integer with 8 bits, that is, the possible values are between $2^0=1$ and $2^8=256$. This means that each pixel has an intensity value in that range, where 0 is a black pixel, 255 is a white pixel, and all other colors are in between."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":196,"status":"ok","timestamp":1761238949129,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"euPUiiMZnctz","outputId":"1eb1d6ed-b6b9-4a54-e361-a85d0cffa919"},"outputs":[{"output_type":"stream","name":"stdout","text":["Max pixel value 255\n","Min pixel value 0\n","Average pixel value 120.70756512369792\n","Data type uint8\n"]}],"source":["# Display the range of images\n","print('Max pixel value', np.max(train_data))\n","print('Min pixel value', np.min(train_data))\n","print('Average pixel value', np.mean(train_data))\n","print('Data type', train_data[0].dtype)"]},{"cell_type":"markdown","metadata":{"id":"S4yw3K7Ktadx"},"source":["When processing image data with neural networks, the pixel values are commonly normalized to the [0, 1] range. This allows the networks to train faster and it usually leads to better results. Let's normalize the images by dividing them with the maximum intensity of 255, and check again if the scaled data look correct. Note that the data type after the normalization is `float64`."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NZdRD_HRh7l7"},"outputs":[],"source":["# Normalize the images\n","train_data = train_data / 255\n","test_data = test_data / 255"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":312,"status":"ok","timestamp":1761238950001,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"bgwijQULiPw5","outputId":"39dad137-2cea-42f5-89e5-cf4e4824f481"},"outputs":[{"output_type":"stream","name":"stdout","text":["Max pixel value 1.0\n","Min pixel value 0.0\n","Average pixel value 0.4733630004850874\n","Data type float64\n"]}],"source":["# Display the range of images (to make sure they are in the [0, 1] range)\n","print('Max pixel value', np.max(train_data))\n","print('Min pixel value', np.min(train_data))\n","print('Average pixel value', np.mean(train_data))\n","print('Data type', train_data[0].dtype)"]},{"cell_type":"markdown","metadata":{"id":"5YCLmkNjDqWM"},"source":[" The labels for the 10 classes in CIFAR-10 are shown below.\n"," \n","| Label | Description |\n","| ----- | ----------- |\n","| 0 | airplane |\n","| 1 | automobile |\n","| 2 | bird |\n","| 3 | cat |\n","| 4 | deer |\n","| 5 | dog |\n","| 6 | frog |\n","| 7 | horse |\n","| 8 | ship |\n","| 9 | truck |\n"]},{"cell_type":"markdown","metadata":{"id":"YSy_y4nAt-xI"},"source":["Let's inspect the first 10 values in the labels. We can see that each label corresponds to one of the 10 categories.\n","\n","Also, in the next cell we plotted the first 100 labels, and we can notice that they have values between 0 and 9, so everything looks good."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":22,"status":"ok","timestamp":1761238950026,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"SMZDOLzuihpG","outputId":"54ed174c-6dd2-4ec8-808b-cb36bc356e21"},"outputs":[{"output_type":"stream","name":"stdout","text":["[[6]\n"," [9]\n"," [9]\n"," [4]\n"," [1]\n"," [1]\n"," [2]\n"," [7]\n"," [8]\n"," [3]]\n"]}],"source":["print(train_label[:10])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"executionInfo":{"elapsed":227,"status":"ok","timestamp":1761238950262,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"QNzq_v1Pnnec","outputId":"c879ddf5-ae53-4cda-eb50-d06449c2c314"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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AAAAMwQLAABghmABAADMECwAAIAZggUAADBDsAAAAGYIFgAAwAzBAgAAmCFYAAAAMwQLAABghmABAADMECwAAIAZggUAADBDsAAAAGYIFgAAwAzBAgAAmCFYAAAAMwQLAABghmABAADMECwAAIAZggUAADBDsAAAAGYIFgAAwAzBAgAAmCFYAAAAM3VhTwAox3TBaXBkQuOTU2puSKuzrUm1NamwpxWIpNQaZB1J6ZFfSanfSx1JqbUaECwQGweGRtU7MKzR3FTxsdZsWj3d7erqaA1xZvaSUmuQdSSlR34lpX4vdSSl1mqRcs65Sg6Yz+eVzWaVy+WUyWQqOTRi7MDQqLbtPaLZJ+vM/6/0b1mXmCeYpNQaZB1J6ZFfSanfSx2SElFrEnh9/eYzFoi86YJT78DwGU8skoqP9Q4Ma7pQ0YwciKTUGmQdSemRX0mp30sdux99UbsfjX+t1YZggcgbHJkoeQt0NidpNDelwZGJyk0qIEmpNcg6ktIjv5JSv5c6xvKnNJaPf63VhmCByBufnP+Jxc92UZaUWoOsIyk98isp9VvOL+q1VhuCBSKvuSFtul2UJaXWIOtISo/8Skr9lvOLeq3VhmCByOtsa1JrNq35vliW0ulPiHe2NVVyWoFISq1B1pGUHvmVlPq91NGSqVdLJv61VhuCBSKvtialnu52STrjCWbmzz3d7Yn4TntSag2yjqT0yK+k1O+ljt2bLtXuTfGvtdoQLBALXR2t6t+yTi3Z0rc8W7LpxH3dLCm1BllHUnrkV1Lq91JHUmqtJtzHArFSTXffS0qt3HkzOEmpnztvxoPX12+CBQAAWBQ3yAIAABVHsAAAAGYIFgAAwAzBAgAAmCFYAAAAMwQLAABghmABAADMECwAAIAZggUAADBDsAAAAGYIFgAAwAzBAgAAmCFYAAAAMwQLAABghmABAADMECwAAIAZggUAADBDsAAAAGYIFgAAwAzBAgAAmCFYAAAAMwQLAABghmABAADMECwAAIAZggUAADBTF/YEwjRdcBocmdD45JSaG9LqbGtSbU2qasb3I4pzruScolg/FhbFNYvinKLGqkd+jxP1NYry/JYULO68807t2rVLO3bs0De+8Q2jKVXGgaFR9Q4MazQ3VXysNZtWT3e7ujpaEz++H1GccyXnFMX6sbAorlkU5xQ1Vj3ye5yor1HU55dyzjk/Ox46dEh/9md/pkwmo0984hOeg0U+n1c2m1Uul1Mmk/Ez9JIdGBrVtr1HNLvwmazXv2VdoIsT9vh+RHHOlZxTFOvHwqK4ZlGcU9RY9cjvcaK+RmHOz+vrt6/PWLz99tvavHmz7r//fp1zzjm+JxmG6YJT78DwGYsiqfhY78Cwpgu+8lbkx/cjinOu5JyiWD8WFsU1i+KcosaqR36PE/U1ivr8ZvgKFtu3b9e1116rjRs3LrrtqVOnlM/nS37CNDgyUfL20WxO0mhuSoMjE4kc348ozrmSc4pi/VhYFNcsinOKGqse+T1O1Nco6vObUfZnLPbt26cjR47o0KFDnrbv6+tTb29v2RMLyvjk/IviZ7u4je9HFOdcyTlFsX4sLIprFsU5RY1Vj/weJ+prFPX5zSjrHYtjx45px44deuihh5ROpz3ts2vXLuVyueLPsWPHfE3USnODt3l73S5u4/sRxTlXck5RrB8Li+KaRXFOUWPVI7/HifoaRX1+M8oKFocPH9b4+LjWrVunuro61dXV6cknn9Q999yjuro6TU9Pn7FPfX29MplMyU+YOtua1JpNa74v5aR0+tO1nW1NiRzfjyjOuZJzimL9WFgU1yyKc4oaqx75PU7U1yjq85tRVrC45ppr9JOf/ERHjx4t/qxfv16bN2/W0aNHVVtbG9Q8zdTWpNTT3S5JZyzOzJ97utsD+z5w2OP7EcU5V3JOUawfC4vimkVxTlFj1SO/x4n6GkV9fjPKChYNDQ3q6Ogo+fnQhz6kc889Vx0dHUHN0VxXR6v6t6xTS7b07aKWbLoiXyUKe3w/ojjnSs4pivVjYVFcsyjOKWqseuT3OFFfo6jPT1rCfSxm/N7v/Z4++tGPxuo+FjPCvnNZ2OP7EcU5c+dNLCSKaxbFOUUNd95cWBjz8/r6veRgUa4oBQsAAOBNoDfIAgAAmAvBAgAAmCFYAAAAMwQLAABghmABAADMECwAAIAZggUAADBDsAAAAGYIFgAAwAzBAgAAmCFYAAAAMwQLAABghmABAADMECwAAIAZggUAADBDsAAAAGYIFgAAwAzBAgAAmCFYAAAAMwQLAABghmABAADMECwAAIAZggUAADBDsAAAAGYIFgAAwExd2BOoFtMFp8GRCY1PTqm5Ia3OtibV1qQSMacgawuzb37H9rLfXNtIitw5klRRvB7DVOnngqT0Pyl1WCNYVMCBoVH1DgxrNDdVfKw1m1ZPd7u6OlpjPacgawuzb37H9rLfXNs0rlgmSTpx8r2yxkP5ong9hqnSzwVJ6X9S6ghCyjnnKjlgPp9XNptVLpdTJpOp5NChODA0qm17j2h2k2cybf+WdRU/Ca3mFGRtYfbN79he9pM05zZzCfMcSaooXo9hqvRzQVL6n5Q6yuX19ZvPWARouuDUOzA854vIzGO9A8OaLlQu21nNKcjawuyb37G97Lf70Re1+9G5t5lLWOdIUkXxegxTpZ8L3v1VIRH95zxaHMEiQIMjEyVvk83mJI3mpjQ4MhG7OQVZW5h98zu2l/3G8qc0lp9/m3LGQ/mieD2GqdLPBf/v6Z8nov+cR4sjWARofNLbi4jX7SxYzSnI2sLsm9+xg17DSp4jSRXF6zFMlX4ueH3ipMl4YeM8WhzBIkDNDWnT7SxYzSnI2sLsm9+xg17DSp4jSRXF6zFMlX4uuKBphcl4YeM8WhzBIkCdbU1qzaY135ePUjr9KeKZrxrGaU5B1hZm3/yO7WW/lky9WjLzb1POeChfFK/HMFX6ueBzGy5MRP85jxZHsAhQbU1KPd3tknTGSTjz557u9op+79lqTkHWFmbf/I7tZb/dmy7V7k1zbzOXsM6RpIri9RimSj8XLK+rSUT/OY8WR7AIWFdHq/q3rFNLtvRtsZZsOrSvJFnNKcjawuyb37G97DffNo0rlhXvZeF1PJQvitdjmCr9XJCU/ieljqBwH4sKieId2rjzZjBjc+fN6Ivi9Rgm7rzpT1Lq8Mrr6zfBAgAALIobZAEAgIojWAAAADMECwAAYIZgAQAAzBAsAACAGYIFAAAwQ7AAAABmCBYAAMAMwQIAAJghWAAAADMECwAAYIZgAQAAzBAsAACAGYIFAAAwQ7AAAABmCBYAAMAMwQIAAJghWAAAADMECwAAYIZgAQAAzBAsAACAGYIFAAAwQ7AAAABmCBYAAMAMwQIAAJghWAAAADN1YU8gbqYLToMjExqfnFJzQ1qdbU2qrUklYvxK1hZ2H+Nqdt+uuOAcHX79lxU7HySFeo54GZ/zGEFgrb0rK1j09fXpkUce0U9/+lOdddZZuuqqq3TXXXfpN3/zN4OaX6QcGBpV78CwRnNTxcdas2n1dLerq6M11uNXsraw+xhXc/WtJiUV3PvbBHk+NK5YJkk6cfI98/Gsxuc8RhBY6/KknHNu8c1O6+rq0nXXXaePfexj+tWvfqWvfOUrGhoa0vDwsD70oQ95OkY+n1c2m1Uul1Mmk/E98Uo7MDSqbXuPaHazZvJq/5Z1gZ5gQY5fydrC7mNczde32YI8H4Iaz2p8SZzHMMdav8/r63dZwWK2//7v/1Zzc7OefPJJ/c7v/I7pxKJkuuD08bseL0mrH5SS1JJN66nbfj+Qt8aCHL+StYXdx7harG+zBXk+WI9nNf7KTL2klMbynMeww1qX8vr6vaQPb+ZyOUlSU1PTvNucOnVK+Xy+5CduBkcmFnyic5JGc1MaHJmI3fiVrC3sPsbVYn2bLcjzwXo8q/HH8qfmDRUz23Aeo1ystT++g0WhUNAtt9yiq6++Wh0dHfNu19fXp2w2W/xZvXq13yFDMz7p7YnO63ZRGr+StYXdx7jy248gzwfrfS2PEfTxOY+rB2vtj+9gsX37dg0NDWnfvn0Lbrdr1y7lcrniz7Fjx/wOGZrmhrTpdlEav5K1hd3HuPLbjyDPB+t9LY8R9PE5j6sHa+2Pr2Bx880367HHHtMTTzyh888/f8Ft6+vrlclkSn7iprOtSa3ZtOb7G7SUTn9CeObrcHEav5K1hd3HuFqsb7MFeT5Yj2c1fkumXi0ZzmPYYq39KStYOOd08803a//+/Xr88cfV1tYW1LwipbYmpZ7udkk64wSb+XNPd3tgH94JcvxK1hZ2H+Nqob7NFuT5EMR4VuPv3nSpdm/iPIYt1tqfsoLF9u3btXfvXj388MNqaGjQ2NiYxsbG9M477wQ1v8jo6mhV/5Z1asmWvuXVkk1X5OtGQY5fydrC7mNczde32c9nQZ4PjSuWFe8lYTme1ficxwgCa12+sr5umkrNncoeeOAB3XDDDZ6OEcevm35Q2Hdf486b1Y07b3LnTYSDta7QfSz8iHuwAACgGlXkPhYAAAAfRLAAAABmCBYAAMAMwQIAAJghWAAAADMECwAAYIZgAQAAzBAsAACAGYIFAAAwQ7AAAABmCBYAAMAMwQIAAJghWAAAADMECwAAYIZgAQAAzBAsAACAGYIFAAAwQ7AAAABmCBYAAMAMwQIAAJghWAAAADMECwAAYIZgAQAAzBAsAACAGYIFAAAwUxf2BKJkuuA0ODKh8ckpNTek1dnWJElnPFZbkwp5pgjCXOuflLWeXdsVF5yjw6//knP9/1he+156HYe+Wl0PSb6u/PDSD7/nY1R6TbD4PweGRtU7MKzR3FTxscYVyyRJJ06+V3ysNZtWT3e7ujpaKz5HBGeu9U/KWs9VW01KKrj3t6nmc93y2vfS6zj01ep6SPJ15YeXfvg9H6PU65Rzzi2+mZ18Pq9sNqtcLqdMJlPJoed1YGhU2/YekZdGzGS//i3rqvLCSKL51j8Ja13OuT1bEupfjOW17/VYUe+r1fWQ5OvKDy/9kOTrfJxvP+tee339rvrPWEwXnHoHhj0/8c5s1zswrOlCRTMZArDQ+sd9rcs9t2eLe/2Lsbz2yzlWlPtqdT0k+bryw0s/dj/6onY/Wv75uNB+YfW66oPF4MhEyVtHXjhJo7kpDY5MBDMpVMxi6x/ntfZzbs8W5/oXY3ntl3usqPbV6npI8nXlh5d+jOVPaSxf/vm42H5h9Lrqg8X4pP8n3qXsi2jwuoZxXGvLOcex/sVYXvt+jxW1vlpdD0m+rvyIQp2VnEPVB4vmhnQo+yIavK5hHNfacs5xrH8xlte+32NFra9W10OSrys/olBnJedQ9cGis61Jrdm0yvlCTkqnP2078xUgxNdi6x/ntfZzbs8W5/oXY3ntl3usqPbV6npI8nXlh5d+tGTq1ZIp/3xcbL8wel31waK2JqWe7nZJ8rSgM9v0dLdX9Xexk2Kh9Y/7Wpd7bs8W9/oXY3ntl3OsKPfV6npI8nXlh5d+7N50qXZvKv98XGi/sHpd9cFCkro6WtW/ZZ1asqVvFTWuWFb8/vCMlmy66r4mlXTzrX8S1nq+2mY/x1TruW557XvtddT7anU9JPm68sNLP/yej1HrNfex+ADuvFndonLXuiBw582FcefNM3HnzWDE+c6bXl+/CRYAAGBR3CALAABUHMECAACYIVgAAAAzBAsAAGCGYAEAAMwQLAAAgBmCBQAAMEOwAAAAZggWAADADMECAACYIVgAAAAzBAsAAGCGYAEAAMwQLAAAgBmCBQAAMEOwAAAAZggWAADADMECAACYIVgAAAAzBAsAAGCGYAEAAMwQLAAAgBmCBQAAMEOwAAAAZggWAADATF3YEwCsTRecBkcmND45peaGtDrbmiTpjMdqa1KBjWd17Kir5trB+ntRjT3yFSy+9a1v6etf/7rGxsZ0+eWX695771VnZ6f13ICyHRgaVe/AsEZzU8XHGlcskySdOPle8bHWbFo93e3q6mg1H8/q2FFXzbWD9feiWntU9l+F/NM//ZNuvfVW9fT06MiRI7r88sv1yU9+UuPj40HMD/DswNCotu09UnIRS6cDxQdDhSSN5aa0be8RHRgaNR/P4thRV821g/X3opp7VHawuPvuu3XTTTfpxhtvVHt7u+677z6tWLFC3/nOd4KYH+DJdMGpd2BYzuP2M9v1DgxruuB1L2/jLfXYUVfNtYP196Lae1RWsHj33Xd1+PBhbdy48f0D1NRo48aNevrpp+fc59SpU8rn8yU/gLXBkYkz/s9gMU7SaG5KgyMT5uMt5dhRV821g/X3otp7VFaw+MUvfqHp6WmtXLmy5PGVK1dqbGxszn36+vqUzWaLP6tXr/Y/W2Ae45PlhYql7ut1n6XMK6qquXaw/l5Ue48C/7rprl27lMvlij/Hjh0LekhUoeaGdEX39brPUuYVVdVcO1h/L6q9R2UFi/POO0+1tbU6fvx4yePHjx9XS0vLnPvU19crk8mU/ADWOtua1JpNq5wvcaV0+hPaM19HtRxvKceOumquHay/F9Xeo7KCxfLly3XFFVfo4MGDxccKhYIOHjyoDRs2mE8O8Kq2JqWe7nZJ8hQuZrbp6W739Z3yhcZb6rGjrpprB+vvRbX3qOy/Crn11lt1//3368EHH9RLL72kbdu26X//93914403BjE/wLOujlb1b1mnlmzp24uNK5YV72UxoyWbVv+WdUv6Lvl841kcO+qquXaw/l5Uc49Szrmyv+/yzW9+s3iDrI9+9KO65557dOWVV3raN5/PK5vNKpfL8dciCAR33qycaq4drL8XSeqR19dvX8FiKQgWAADEj9fXb/4RMgAAYIZgAQAAzBAsAACAGYIFAAAwQ7AAAABmCBYAAMAMwQIAAJghWAAAADMECwAAYKau0gPO3Ogzn89XemgAAODTzOv2YjfsrniwmJyclCStXr260kMDAIAlmpycVDabnff3Ff+3QgqFgt566y01NDQolbL7h1jy+bxWr16tY8eO8W+QBIxeVw69rhx6XVn0u3Kseu2c0+TkpFatWqWamvk/SVHxdyxqamp0/vnnB3b8TCbDSVoh9Lpy6HXl0OvKot+VY9Hrhd6pmMGHNwEAgBmCBQAAMJOYYFFfX6+enh7V19eHPZXEo9eVQ68rh15XFv2unEr3uuIf3gQAAMmVmHcsAABA+AgWAADADMECAACYIVgAAAAziQkW3/rWt3ThhRcqnU7ryiuv1ODgYNhTirW+vj597GMfU0NDg5qbm/VHf/RHevnll0u2mZqa0vbt23Xuuefq7LPP1p/8yZ/o+PHjIc04Oe68806lUindcsstxcfota0333xTW7Zs0bnnnquzzjpLl112mZ577rni751z+trXvqbW1ladddZZ2rhxo1599dUQZxxP09PTuv3229XW1qazzjpLF110kf7mb/6m5N+aoNf+/Pu//7u6u7u1atUqpVIpff/73y/5vZe+TkxMaPPmzcpkMmpsbNRf/MVf6O2331765FwC7Nu3zy1fvtx95zvfcS+++KK76aabXGNjozt+/HjYU4utT37yk+6BBx5wQ0ND7ujRo+4P//AP3Zo1a9zbb79d3OYLX/iCW716tTt48KB77rnn3G//9m+7q666KsRZx9/g4KC78MIL3W/91m+5HTt2FB+n13YmJibcBRdc4G644Qb37LPPutdee8398Ic/dD/72c+K29x5550um82673//++6FF15wmzZtcm1tbe6dd94Jcebxs2fPHnfuuee6xx57zI2MjLjvfe977uyzz3Z///d/X9yGXvvzr//6r+6rX/2qe+SRR5wkt3///pLfe+lrV1eXu/zyy90zzzzjfvzjH7sPf/jD7vrrr1/y3BIRLDo7O9327duLf56ennarVq1yfX19Ic4qWcbHx50k9+STTzrnnDtx4oRbtmyZ+973vlfc5qWXXnKS3NNPPx3WNGNtcnLSXXzxxe5HP/qR+93f/d1isKDXtm677Tb38Y9/fN7fFwoF19LS4r7+9a8XHztx4oSrr693//iP/1iJKSbGtdde6/78z/+85LE//uM/dps3b3bO0Wsrs4OFl74ODw87Se7QoUPFbX7wgx+4VCrl3nzzzSXNJ/Z/FfLuu+/q8OHD2rhxY/Gxmpoabdy4UU8//XSIM0uWXC4nSWpqapIkHT58WO+9915J3y+55BKtWbOGvvu0fft2XXvttSU9lei1tUcffVTr16/XZz/7WTU3N2vt2rW6//77i78fGRnR2NhYSb+z2ayuvPJK+l2mq666SgcPHtQrr7wiSXrhhRf01FNP6VOf+pQkeh0UL319+umn1djYqPXr1xe32bhxo2pqavTss88uafyK/yNk1n7xi19oenpaK1euLHl85cqV+ulPfxrSrJKlUCjolltu0dVXX62Ojg5J0tjYmJYvX67GxsaSbVeuXKmxsbEQZhlv+/bt05EjR3To0KEzfkevbb322mvq7+/Xrbfeqq985Ss6dOiQvvjFL2r58uXaunVrsadzPafQ7/Ls3LlT+Xxel1xyiWprazU9Pa09e/Zo8+bNkkSvA+Klr2NjY2pubi75fV1dnZqampbc+9gHCwRv+/btGhoa0lNPPRX2VBLp2LFj2rFjh370ox8pnU6HPZ3EKxQKWr9+ve644w5J0tq1azU0NKT77rtPW7duDXl2yfLd735XDz30kB5++GFdeumlOnr0qG655RatWrWKXidY7P8q5LzzzlNtbe0Zn5A/fvy4WlpaQppVctx888167LHH9MQTT5T8c/ctLS169913deLEiZLt6Xv5Dh8+rPHxca1bt051dXWqq6vTk08+qXvuuUd1dXVauXIlvTbU2tqq9vb2ksc+8pGP6I033pCkYk95Tlm6L3/5y9q5c6euu+46XXbZZfrc5z6nL33pS+rr65NEr4Pipa8tLS0aHx8v+f2vfvUrTUxMLLn3sQ8Wy5cv1xVXXKGDBw8WHysUCjp48KA2bNgQ4szizTmnm2++Wfv379fjjz+utra2kt9fccUVWrZsWUnfX375Zb3xxhv0vUzXXHONfvKTn+jo0aPFn/Xr12vz5s3F/6bXdq6++uozvjr9yiuv6IILLpAktbW1qaWlpaTf+Xxezz77LP0u08mTJ1VTU/oyU1tbq0KhIIleB8VLXzds2KATJ07o8OHDxW0ef/xxFQoFXXnllUubwJI++hkR+/btc/X19e4f/uEf3PDwsPv85z/vGhsb3djYWNhTi61t27a5bDbr/u3f/s2Njo4Wf06ePFnc5gtf+IJbs2aNe/zxx91zzz3nNmzY4DZs2BDirJPjg98KcY5eWxocHHR1dXVuz5497tVXX3UPPfSQW7Fihdu7d29xmzvvvNM1Nja6f/7nf3b/9V//5T796U/zFUgftm7d6n7913+9+HXTRx55xJ133nnur/7qr4rb0Gt/Jicn3fPPP++ef/55J8ndfffd7vnnn3evv/66c85bX7u6utzatWvds88+65566il38cUX83XTD7r33nvdmjVr3PLly11nZ6d75plnwp5SrEma8+eBBx4obvPOO++4v/zLv3TnnHOOW7FihfvMZz7jRkdHw5t0gswOFvTa1sDAgOvo6HD19fXukksucd/+9rdLfl8oFNztt9/uVq5c6err690111zjXn755ZBmG1/5fN7t2LHDrVmzxqXTafcbv/Eb7qtf/ao7depUcRt67c8TTzwx53P01q1bnXPe+vo///M/7vrrr3dnn322y2Qy7sYbb3STk5NLnhv/bDoAADAT+89YAACA6CBYAAAAMwQLAABghmABAADMECwAAIAZggUAADBDsAAAAGYIFgAAwAzBAgAAmCFYAAAAMwQLAABghmABAADM/H9tKWLE0k/5PwAAAABJRU5ErkJggg==\n"},"metadata":{}}],"source":["plt.plot(train_label[:100], 'o')\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"FLxJ51fl2NG6"},"source":["Let's visualize some images to see what they look like. We can notice that the resolution is quite low, since they are 32x32 pixels."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":752},"executionInfo":{"elapsed":626,"status":"ok","timestamp":1761238950905,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"y5xezSpVicHx","outputId":"3808b3df-a770-4a1e-8fe1-68f90d9ba093"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["label_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n","\n","# Plot a few images to check if the labels are correct\n","plt.figure(figsize=(12, 9))\n","for n in range(9):\n"," i = np.random.randint(0, len(train_data), 1)\n"," ax = plt.subplot(3, 3, n+1)\n"," plt.imshow(train_data[i[0]])\n"," plt.title('Label: ' + str(label_names[train_label[i[0]][0]]))\n"," plt.axis('off')"]},{"cell_type":"markdown","metadata":{"id":"giLJg2i4GkCU"},"source":["## 15.3 Creating, Training, and Evaluating a CNN Model \n","\n","### Create the CNN"]},{"cell_type":"markdown","metadata":{"id":"i4EJITkbGzdZ"},"source":["Next, we are going to define the network architecture with the TensorFlow-Keras library. Recall from the previous lecture that the main data structures in Keras are **layers** and **models**.\n","\n","In the first cell below we imported the layers, and afterward we defined the layers.\n","\n","We need to first introduce an **Input** layer to provide the size of the data, and in this case, the shape of the input data is set to (32,32,3). This is because the images are arrays with shape 32x32x3.\n","\n","Next, we will include a convolutional layer **Conv2D**. The arguments of Conv2D layer in Keras are:\n","\n","- `filters`: define the number of filters in the layer.\n","- `kernel_size`: the height and width of each filter, defined as an integer such as `kernel_size=3`, or a tuple such as `kernel_size=(3,3)`.\n","- `padding`: it is not a very important argument, when it is equal to `'same'` it means that the output images are the same size as input images; otherwise, the output images can be slightly smaller.\n","\n","Next, we will include a pooling layer **MaxPooling2D**. We can specify the pooling size with the `pool_size` argument, such as `pool_size=3`. Otherwise, the default pool size is 2.\n","\n","ConvNets typically consist of several blocks of convolutional and pooling layers.\n","\n","And **Fully Connected Layers** are used for matching the compressed feature maps to their labels.\n","\n","And there is one more layer that is used called **Flatten**. This layer is needed because the outputs of the convolutional and maxpooling layers are 3-dimensional tensors, but the dense layers require one-dimensional data (vectors). The Flatten layer just concatenates (flattens) all dimensions of a tensor into an 1D array.\n","\n","For example, the following layer that we defined below,\n","\n","```\n","conv1b = Conv2D(filters=32, kernel_size=3, padding='same')(conv1a)\n","```\n","\n","is a convolutional layer, which takes as input the previous layer named `conv1a`. The layer `conv1b` has 32 convolutional filters of size 3, and padding is applied to preserve the size of the images."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"e_YS-JqfkyiK"},"outputs":[],"source":["# import the layers and the model\n","from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Dense, Flatten\n","from tensorflow.keras.models import Model"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gjqyblPbHmmc"},"outputs":[],"source":["# Define the layers in the model\n","inputs = Input(shape=(32, 32, 3))\n","conv1a = Conv2D(filters=32, kernel_size=3, padding='same')(inputs)\n","conv1b = Conv2D(filters=32, kernel_size=3, padding='same')(conv1a)\n","pool1 = MaxPooling2D()(conv1b)\n","conv2a = Conv2D(filters=64, kernel_size=3, padding='same')(pool1)\n","conv2b = Conv2D(filters=64, kernel_size=3, padding='same')(conv2a)\n","pool2 = MaxPooling2D()(conv2b)\n","conv3a = Conv2D(filters=128, kernel_size=3, padding='same')(pool2)\n","conv3b = Conv2D(filters=128, kernel_size=3, padding='same')(conv3a)\n","pool3 = MaxPooling2D()(conv3b)\n","flat = Flatten()(pool3)\n","dense1 = Dense(128, activation='relu')(flat)\n","dense2 = Dense(64, activation='relu')(dense1)\n","outputs = Dense(10, activation='softmax')(dense2)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bFqy5anBgZiV"},"outputs":[],"source":["# Define the model with inputs and outputs\n","cifar_cnn = Model(inputs, outputs)"]},{"cell_type":"markdown","metadata":{"id":"2nL-Znyr1WKt"},"source":["Also note that in multi-class classification problems, the last Dense layer has\n","**softmax** activation function. Softmax activation outputs a multiclass probability distribution, that is, it computes the probability that an image belongs to one of the 10 classes.\n","\n","After we define the layers, we create the model as an instance of the `Model` class, for which the arguments are the input and output layers.\n","\n","We can inspect the architecture of the CNN with `cifar_cnn.summary()`."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":577},"executionInfo":{"elapsed":41,"status":"ok","timestamp":1761238954649,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"nVvtKIGF1gnc","outputId":"c6f12a19-0f31-4cd4-f1c5-2fd218848c28"},"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"functional\"\u001b[0m\n"],"text/html":["
Model: \"functional\"\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m896\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m9,248\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m36,928\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m147,584\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m262,272\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m8,256\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m650\u001b[0m │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n"],"text/html":["
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ input_layer (InputLayer)        │ (None, 32, 32, 3)      │             0 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d (Conv2D)                 │ (None, 32, 32, 32)     │           896 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_1 (Conv2D)               │ (None, 32, 32, 32)     │         9,248 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d (MaxPooling2D)    │ (None, 16, 16, 32)     │             0 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_2 (Conv2D)               │ (None, 16, 16, 64)     │        18,496 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_3 (Conv2D)               │ (None, 16, 16, 64)     │        36,928 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_1 (MaxPooling2D)  │ (None, 8, 8, 64)       │             0 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_4 (Conv2D)               │ (None, 8, 8, 128)      │        73,856 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_5 (Conv2D)               │ (None, 8, 8, 128)      │       147,584 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_2 (MaxPooling2D)  │ (None, 4, 4, 128)      │             0 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ flatten (Flatten)               │ (None, 2048)           │             0 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense (Dense)                   │ (None, 128)            │       262,272 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_1 (Dense)                 │ (None, 64)             │         8,256 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_2 (Dense)                 │ (None, 10)             │           650 │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m558,186\u001b[0m (2.13 MB)\n"],"text/html":["
 Total params: 558,186 (2.13 MB)\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m558,186\u001b[0m (2.13 MB)\n"],"text/html":["
 Trainable params: 558,186 (2.13 MB)\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"],"text/html":["
 Non-trainable params: 0 (0.00 B)\n","
\n"]},"metadata":{}}],"source":["# Model summary\n","cifar_cnn.summary()"]},{"cell_type":"markdown","metadata":{"id":"XDYdR1SG1zh4"},"source":["### Compile the CNN"]},{"cell_type":"markdown","metadata":{"id":"GrJHF5Qe2wwr"},"source":["Next, we need to compile the model, and provide the loss function, optimizer, and metric.\n","\n","From the previous lecture, we know that the following three crossentropy losses are commonly used in Keras:\n","\n","- `binary_crosssentropy` used for binary classification (i.e., there are only 2 classes).\n","- `categorical_crossentropy` used for multiclass classification (3 and more classes) and the target labels are encoded in one-hot matrix format.\n","- `sparse_categorical_crossentropy` used for multiclass classification (3 and more classes) and the target labels are encoded in ordinal format.\n","\n","Since the target labels are encoded in ordinal format, we will use sparse categorical crossentropy.\n","\n","Let's use the Adam optimizer, and accuracy metric."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OZuZ18XZ2zVV"},"outputs":[],"source":["cifar_cnn.compile(optimizer='adam',\n"," loss='sparse_categorical_crossentropy',\n"," metrics=['accuracy'])"]},{"cell_type":"markdown","metadata":{"id":"f7Bqx-N03K1D"},"source":["### Train the CNN\n","\n","Training CNNs is similar to training Fully-connected NNs. We use the `fit` function and list the train data and labels, number of epochs, and batch size. Here we used a batch size of 128 images, hence, since the training dataset has 50,000 images, the updates of the model parameters will be repeated 50,000/128 = 391 times (notice this number under the Epoch in the cell output below). Since we selected 10 epochs, this means there will be 3,910 training steps in total.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":63041,"status":"ok","timestamp":1761239022052,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"zmkoLSQJ39IZ","outputId":"fb417cd5-1d9e-4c4e-dbee-fc1261c6d766"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 22ms/step - accuracy: 0.3859 - loss: 1.6923\n","Epoch 2/10\n","\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 12ms/step - accuracy: 0.6537 - loss: 0.9874\n","Epoch 3/10\n","\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 13ms/step - accuracy: 0.7307 - loss: 0.7726\n","Epoch 4/10\n","\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 13ms/step - accuracy: 0.7786 - loss: 0.6254\n","Epoch 5/10\n","\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 13ms/step - accuracy: 0.8208 - loss: 0.5158\n","Epoch 6/10\n","\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 13ms/step - accuracy: 0.8491 - loss: 0.4243\n","Epoch 7/10\n","\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 13ms/step - accuracy: 0.8780 - loss: 0.3479\n","Epoch 8/10\n","\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 13ms/step - accuracy: 0.8945 - loss: 0.2940\n","Epoch 9/10\n","\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 12ms/step - accuracy: 0.9120 - loss: 0.2487\n","Epoch 10/10\n","\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 13ms/step - accuracy: 0.9224 - loss: 0.2181\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":16}],"source":["cifar_cnn.fit(train_data, train_label, epochs=10, batch_size=128)"]},{"cell_type":"markdown","metadata":{"id":"dkO2ycHHrbgT"},"source":["### Evaluate on Test Data\n","\n"]},{"cell_type":"markdown","metadata":{"id":"ZT0dJ-vxs3pV"},"source":["We can first use the `predict` function to output the class for each image in the test dataset, and afterward use `accuracy_score` to calculate the accuracy."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3597,"status":"ok","timestamp":1761239025651,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"K79im89prfzv","outputId":"88355664-46fc-449b-d76b-54f5d70ec990"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n","The test accuracy is 73.18 %\n"]}],"source":["from sklearn.metrics import accuracy_score\n","\n","preds = cifar_cnn.predict(test_data)\n","\n","accuracy = accuracy_score(test_label, np.argmax(preds, axis=1))\n","print('The test accuracy is {0:5.2f} %'.format(accuracy*100))"]},{"cell_type":"markdown","metadata":{"id":"mYF9aQTj-GTa"},"source":["We can check that the shape of the predicted outputs is `(10000,10)`, since there are 10,000 test images and 10 classes.\n","\n","Let's also display the predictions for the first 5 test images in the following cell. The model outputs probabilities for each of the 10 classes. The probabilities sum to 1. For instance, for the first test image, the model assigned the highest probability of 0.801 to the class with index 5."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14,"status":"ok","timestamp":1761239025666,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"Ai46Kj1d7p1m","outputId":"f550ea1b-86e1-44fa-a06e-dcb463b5724f"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(10000, 10)"]},"metadata":{},"execution_count":18}],"source":["# check the shape of the predictions\n","preds.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1761239025674,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"Ph2r53Kt77Hg","outputId":"30c3ef6e-953f-47d3-fece-85c21587a354"},"outputs":[{"output_type":"stream","name":"stdout","text":["Predictions for first 5 test images:\n"," [[0. 0. 0. 0.694 0. 0.304 0.003 0. 0. 0. ]\n"," [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. ]\n"," [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. ]\n"," [0.999 0. 0. 0. 0. 0. 0. 0. 0. 0. ]\n"," [0. 0. 0. 0.88 0.005 0. 0.115 0. 0. 0. ]]\n"]}],"source":["# display the predictions for the first 5 test images\n","print('Predictions for first 5 test images:\\n', np.around(preds[:5],3))"]},{"cell_type":"markdown","metadata":{"id":"bOG64FhY-zdH"},"source":["Next, let's output the indices with the highest probability for each image with `np.argmax`, and compare them to the ground-truth labels."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1761239025695,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"C7bmDQ2k8191","outputId":"e6884f37-1081-4436-c1fc-d953741cbb5b"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([3, 8, 8, 0, 3])"]},"metadata":{},"execution_count":20}],"source":["# print the index with highest probability for the first 5 test images\n","np.argmax(preds[:5], axis=1)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":22,"status":"ok","timestamp":1761239025733,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"5r3luKAi9IJp","outputId":"6b0b1d7e-52a5-4c07-fd8b-f4ba11010993"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[3],\n"," [8],\n"," [8],\n"," [0],\n"," [6]], dtype=uint8)"]},"metadata":{},"execution_count":21}],"source":["# print the ground-truth label for the first 5 test images\n","test_label[:5]"]},{"cell_type":"markdown","metadata":{"id":"AzPfBj7dtLWy"},"source":["And, a simpler way to calculate only the accuracy is with `evaluate`."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2556,"status":"ok","timestamp":1761239028287,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"BKW5SWlbqMBb","outputId":"5993da5a-d496-4e30-df83-458181e1483d"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.7316 - loss: 1.1418\n","Classification Accuracy: 0.7318000197410583\n"]}],"source":["# Evaluate on test dataset\n","evals_test = cifar_cnn.evaluate(test_data, test_label)\n","print(\"Classification Accuracy: \", evals_test[1])"]},{"cell_type":"markdown","metadata":{"id":"XYoQL6RTy8TX"},"source":["The important thing to notice is that the accuracy on the training dataset reached above 90%, while the accuracy on the test dataset is about 73%. This means that the model overfits the training data. That is, the model begins to memorize the training data, and it learns to predict on the training dataset very well, but the generalization ability on unseen images is quite low. In the next sections we will learn how to deal with that. "]},{"cell_type":"markdown","metadata":{"id":"5D24LVEj0B6G"},"source":["### Plot the Predictions\n","\n","But first, let's plot a few images and the predicted class labels by the model."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":521},"executionInfo":{"elapsed":435,"status":"ok","timestamp":1761239028733,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"GUzSpQw30BHp","outputId":"e8487380-4e5c-4542-84de-bed9245d0f34"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# Plot a few images to check them\n","plt.figure(figsize=(12, 6))\n","for n in range(9):\n"," i = np.random.randint(0, len(test_data), 1)\n"," ax = plt.subplot(3, 3, n+1)\n"," plt.imshow(test_data[i[0]])\n"," plt.title('Label: ' + str(label_names[test_label[i[0]][0]]) + ' Predicted: ' + str(label_names[np.argmax(preds[i[0]])]))\n"," plt.axis('off')"]},{"cell_type":"markdown","metadata":{"id":"4NBsfPgbt8v0"},"source":["## 15.4 Introduce a Validation Dataset "]},{"cell_type":"markdown","metadata":{"id":"bZkS3-A4z2ia"},"source":["To be able to observe if the model overfits, one more dataset is introduced, referred to as a **validation dataset**. The original training dataset is typically randomly split, and approximately 70-80% is used as a training dataset, and 20-30% is used as a validation dataset.\n","\n","This way, at the end of every epoch, we will calculate the accuracy of the model on the validation dataset. As the model is trained, if the training accuracy increases, but the validation accuracy decreases, it means that the model begins to overfit.\n","\n","In Keras, the `fit` function has a `validation_split` argument, which allows us to use a percent of the training data for validation. In the next cell, we will use 20%, meaning that out of the 50,000 training images, the model will use 40,000 for training, and 10,000 for validation. An alternative way to introduce validation dataset is to first manually split the training dataset into two subsets of data, and in the `fit` function to pass the validation features and labels as a tuple named `validation_data`. However, the first option with using a `validation_split` argument is much simpler, and therefore preferred.\n","\n","Note also that in the cell below we will just continue training the same model. I.e., the accuracy value in the first epoch below is a continuation of the accuracy in the last epoch of the previosly trained model.\n","\n","Now we can notice the overfitting, because the training accuracy continues to increase, but the validation accuracy decreases."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":52563,"status":"ok","timestamp":1761239081298,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"X2fLe3YVx6aR","outputId":"e0be73f5-d50c-481f-b6df-78bbb76e8bfd"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 29ms/step - accuracy: 0.9375 - loss: 0.1786 - val_accuracy: 0.9064 - val_loss: 0.2612\n","Epoch 2/10\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 13ms/step - accuracy: 0.9444 - loss: 0.1644 - val_accuracy: 0.8966 - val_loss: 0.3236\n","Epoch 3/10\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 17ms/step - accuracy: 0.9495 - loss: 0.1504 - val_accuracy: 0.8679 - val_loss: 0.4524\n","Epoch 4/10\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 16ms/step - accuracy: 0.9466 - loss: 0.1512 - val_accuracy: 0.8739 - val_loss: 0.4109\n","Epoch 5/10\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 15ms/step - accuracy: 0.9540 - loss: 0.1319 - val_accuracy: 0.8589 - val_loss: 0.4641\n","Epoch 6/10\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 15ms/step - accuracy: 0.9553 - loss: 0.1344 - val_accuracy: 0.8666 - val_loss: 0.4602\n","Epoch 7/10\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - accuracy: 0.9588 - loss: 0.1195 - val_accuracy: 0.8561 - val_loss: 0.5280\n","Epoch 8/10\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - accuracy: 0.9633 - loss: 0.1061 - val_accuracy: 0.8328 - val_loss: 0.6999\n","Epoch 9/10\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - accuracy: 0.9574 - loss: 0.1238 - val_accuracy: 0.8400 - val_loss: 0.6186\n","Epoch 10/10\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 15ms/step - accuracy: 0.9665 - loss: 0.0972 - val_accuracy: 0.8429 - val_loss: 0.6448\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":24}],"source":["cifar_cnn.fit(train_data, train_label,\n"," epochs=10, batch_size=128,\n"," validation_split=0.2)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1261,"status":"ok","timestamp":1761239082561,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"S9wQ3UyltaZS","outputId":"c16d8d57-bac8-4e98-f9df-c93ee87532a1"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7269 - loss: 1.7199\n","Classification Accuracy: 0.7267000079154968\n"]}],"source":["# Evaluate on test dataset\n","evals_test = cifar_cnn.evaluate(test_data, test_label)\n","print(\"Classification Accuracy: \", evals_test[1])"]},{"cell_type":"markdown","source":["### Training the Model from Scratch\n","\n","Let's also check the outputs when the same model is trained from scratch, or in other words, the model wieghts are re-initilialized to random values. To do that, we need to recreate the model, compile it again, and then fit it to the data."],"metadata":{"id":"tTviNWQDAbzF"}},{"cell_type":"code","source":["# Define the layers in the model\n","inputs = Input(shape=(32, 32, 3))\n","conv1a = Conv2D(filters=32, kernel_size=3, padding='same')(inputs)\n","conv1b = Conv2D(filters=32, kernel_size=3, padding='same')(conv1a)\n","pool1 = MaxPooling2D()(conv1b)\n","conv2a = Conv2D(filters=64, kernel_size=3, padding='same')(pool1)\n","conv2b = Conv2D(filters=64, kernel_size=3, padding='same')(conv2a)\n","pool2 = MaxPooling2D()(conv2b)\n","conv3a = Conv2D(filters=128, kernel_size=3, padding='same')(pool2)\n","conv3b = Conv2D(filters=128, kernel_size=3, padding='same')(conv3a)\n","pool3 = MaxPooling2D()(conv3b)\n","flat = Flatten()(pool3)\n","dense1 = Dense(128, activation='relu')(flat)\n","dense2 = Dense(64, activation='relu')(dense1)\n","outputs = Dense(10, activation='softmax')(dense2)\n","\n","# Define the model with inputs and outputs\n","cifar_cnn = Model(inputs, outputs)\n","\n","cifar_cnn.compile(optimizer='adam',\n"," loss='sparse_categorical_crossentropy',\n"," metrics=['accuracy'])\n","\n","history = cifar_cnn.fit(train_data, train_label,\n"," epochs=20, batch_size=128,\n"," validation_split=0.2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sc7U6vlkBIC-","executionInfo":{"status":"ok","timestamp":1761239181072,"user_tz":360,"elapsed":98503,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"}},"outputId":"f19a8683-7819-4279-cd19-e883aab6093c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 26ms/step - accuracy: 0.3301 - loss: 1.8207 - val_accuracy: 0.5762 - val_loss: 1.1786\n","Epoch 2/20\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 13ms/step - accuracy: 0.6239 - loss: 1.0593 - val_accuracy: 0.6571 - val_loss: 0.9864\n","Epoch 3/20\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - accuracy: 0.7195 - loss: 0.8018 - val_accuracy: 0.7031 - val_loss: 0.8547\n","Epoch 4/20\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 14ms/step - accuracy: 0.7742 - loss: 0.6469 - val_accuracy: 0.7269 - val_loss: 0.8096\n","Epoch 5/20\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 13ms/step - accuracy: 0.8124 - loss: 0.5348 - val_accuracy: 0.7236 - val_loss: 0.8589\n","Epoch 6/20\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - accuracy: 0.8485 - loss: 0.4315 - val_accuracy: 0.7228 - val_loss: 0.8785\n","Epoch 7/20\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - accuracy: 0.8705 - loss: 0.3662 - val_accuracy: 0.7334 - val_loss: 0.9049\n","Epoch 8/20\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 13ms/step - accuracy: 0.8942 - loss: 0.2983 - val_accuracy: 0.7219 - val_loss: 0.9864\n","Epoch 9/20\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 13ms/step - accuracy: 0.9099 - loss: 0.2497 - val_accuracy: 0.7326 - val_loss: 1.0785\n","Epoch 10/20\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 13ms/step - accuracy: 0.9310 - loss: 0.1986 - val_accuracy: 0.7187 - val_loss: 1.1757\n","Epoch 11/20\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 13ms/step - accuracy: 0.9293 - loss: 0.2036 - val_accuracy: 0.7147 - val_loss: 1.3182\n","Epoch 12/20\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - accuracy: 0.9460 - loss: 0.1531 - val_accuracy: 0.7075 - val_loss: 1.3760\n","Epoch 13/20\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - accuracy: 0.9451 - loss: 0.1621 - val_accuracy: 0.7186 - val_loss: 1.4332\n","Epoch 14/20\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 15ms/step - accuracy: 0.9543 - loss: 0.1304 - val_accuracy: 0.7181 - val_loss: 1.4729\n","Epoch 15/20\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 14ms/step - accuracy: 0.9545 - loss: 0.1292 - val_accuracy: 0.7144 - val_loss: 1.5252\n","Epoch 16/20\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - accuracy: 0.9507 - loss: 0.1443 - val_accuracy: 0.7158 - val_loss: 1.4890\n","Epoch 17/20\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - accuracy: 0.9594 - loss: 0.1161 - val_accuracy: 0.7221 - val_loss: 1.5586\n","Epoch 18/20\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 15ms/step - accuracy: 0.9586 - loss: 0.1192 - val_accuracy: 0.7193 - val_loss: 1.7029\n","Epoch 19/20\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 15ms/step - accuracy: 0.9661 - loss: 0.1037 - val_accuracy: 0.7190 - val_loss: 1.7059\n","Epoch 20/20\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 14ms/step - accuracy: 0.9599 - loss: 0.1172 - val_accuracy: 0.7209 - val_loss: 1.7623\n"]}]},{"cell_type":"markdown","source":["The next cell displays the **learning curves** for the model that show the accuracy and loss of the model. The blue lines indicate the model performance on the training dataset and the red lines indicate the performance on the validation dataset.\n","\n","The overfitting is noticeable, as the training loss continues decreasing while the validation loss begins to increase after the first 5 epochs. Also, while the training accuracy increases as the training progresses, the validation accuracy is stalled at about 72%."],"metadata":{"id":"pE-6fPZXBQ2n"}},{"cell_type":"code","source":["# plot the accuracy and loss\n","train_loss = history.history['loss']\n","val_loss = history.history['val_loss']\n","acc = history.history['accuracy']\n","val_acc = history.history['val_accuracy']\n","\n","epochsn = np.arange(1, len(train_loss)+1,1)\n","plt.figure(figsize=(12, 4))\n","\n","plt.subplot(1,2,1)\n","plt.plot(epochsn, acc, 'b', label='Training Accuracy')\n","plt.plot(epochsn, val_acc, 'r', label='Validation Accuracy')\n","plt.grid(color='gray', linestyle='--')\n","plt.legend()\n","plt.title('ACCURACY')\n","plt.xlabel('Epochs')\n","plt.ylabel('Accuracy')\n","\n","plt.subplot(1,2,2)\n","plt.plot(epochsn,train_loss, 'b', label='Training Loss')\n","plt.plot(epochsn,val_loss, 'r', label='Validation Loss')\n","plt.grid(color='gray', linestyle='--')\n","plt.legend()\n","plt.title('LOSS')\n","plt.xlabel('Epochs')\n","plt.ylabel('Loss')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":410},"id":"E7iIH0siBihS","executionInfo":{"status":"ok","timestamp":1761239181512,"user_tz":360,"elapsed":428,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"}},"outputId":"e4eb2f5b-4171-40cf-d88f-e8b317594618"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"1WdoDXEKBiIu"},"source":["## 15.5 Dropout Layers "]},{"cell_type":"markdown","metadata":{"id":"pEGOb-cbBtws"},"source":["One way to deal with overfitting in neural networks is to introduce **Dropout** layers. The idea of dropout is very simple: during the training, at each step of processing a batch of images, a portion of the neurons in a layer are randomly disabled. This introduces randomness in the network, and it helps to improve model performance and reduce overfitting.\n","\n","Dropout can be considered to be similar to the ensemble methods, where during each iteration, a slightly different model is trained, which uses only a portion of all neurons.\n","\n","In the next cell, we add several dropout layers, where for instance, dropout of 0.2 means that 20% of the neurons in that layer are randomly dropped."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kFvKugKnuOAK"},"outputs":[],"source":["from tensorflow.keras.layers import Dropout\n","\n","# Define the layers\n","inputs = Input(shape=(32, 32, 3))\n","conv1a = Conv2D(filters=32, kernel_size=3, padding='same')(inputs)\n","conv1b = Conv2D(filters=32, kernel_size=3, padding='same')(conv1a)\n","pool1 = MaxPooling2D()(conv1b)\n","dropout1 = Dropout(0.2)(pool1)\n","conv2a = Conv2D(filters=64, kernel_size=3, padding='same')(dropout1)\n","conv2b = Conv2D(filters=64, kernel_size=3, padding='same')(conv2a)\n","pool2 = MaxPooling2D()(conv2b)\n","dropout2 = Dropout(0.2)(pool2)\n","conv3a = Conv2D(filters=256, kernel_size=3, padding='same')(dropout2)\n","conv3b = Conv2D(filters=256, kernel_size=3, padding='same')(conv3a)\n","pool3 = MaxPooling2D()(conv3b)\n","flat = Flatten()(pool3)\n","dense1 = Dense(128, activation='relu')(flat)\n","dropout3 = Dropout(0.2)(dense1)\n","dense2 = Dense(64, activation='relu')(dropout3)\n","dropout4 = Dropout(0.2)(dense2)\n","outputs = Dense(10, activation='softmax')(dropout4)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"u-WyWjOehK6E"},"outputs":[],"source":["# Define the model with inputs and outputs\n","cifar_cnn_2 = Model(inputs, outputs)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wisTs_NTurjP"},"outputs":[],"source":["# compile model\n","cifar_cnn_2.compile(optimizer='adam',\n"," loss='sparse_categorical_crossentropy',\n"," metrics=['accuracy'])\n","\n","# fit model\n","history = cifar_cnn_2.fit(train_data, train_label,\n"," epochs=40, batch_size=128,\n"," validation_split=0.2, verbose=0)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1975,"status":"ok","timestamp":1761163364053,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"eQZY8Xaaurng","outputId":"9cd78caa-8ca4-4c58-b0de-96707d8d42f2"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.7634 - loss: 0.9489\n","Classification Accuracy: 0.757099986076355\n"]}],"source":["# Evaluate on test dataset\n","evals_test = cifar_cnn_2.evaluate(test_data, test_label)\n","print(\"Classification Accuracy: \", evals_test[1])"]},{"cell_type":"markdown","metadata":{"id":"1KDHTF0vmKhx"},"source":["The performance of the model with dropout layers increased slightly to above 75%.\n","\n","Let's create a figure with the learning curves to show the accuracy and loss of the model. We can notice that overfitting begins around epoch 15, when the training accuracy increases, but the validation accuracy stays about the same. Similarly, the training loss decreases, but the validation loss increases after epoch 15. The plots of the accuracy and loss are fairly correlated, however as we can see the accuracy can stay constant when the loss is changing."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":410},"executionInfo":{"elapsed":329,"status":"ok","timestamp":1761163364400,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"1SLzE4yuPAEK","outputId":"0dfeae48-983c-4e53-df3a-89400166cd75"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# plot the accuracy and loss\n","train_loss = history.history['loss']\n","val_loss = history.history['val_loss']\n","acc = history.history['accuracy']\n","val_acc = history.history['val_accuracy']\n","\n","epochsn = np.arange(1, len(train_loss)+1,1)\n","plt.figure(figsize=(12, 4))\n","\n","plt.subplot(1,2,1)\n","plt.plot(epochsn, acc, 'b', label='Training Accuracy')\n","plt.plot(epochsn, val_acc, 'r', label='Validation Accuracy')\n","plt.grid(color='gray', linestyle='--')\n","plt.legend()\n","plt.title('ACCURACY')\n","plt.xlabel('Epochs')\n","plt.ylabel('Accuracy')\n","\n","plt.subplot(1,2,2)\n","plt.plot(epochsn,train_loss, 'b', label='Training Loss')\n","plt.plot(epochsn,val_loss, 'r', label='Validation Loss')\n","plt.grid(color='gray', linestyle='--')\n","plt.legend()\n","plt.title('LOSS')\n","plt.xlabel('Epochs')\n","plt.ylabel('Loss')\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"sj7hlnWZby9V"},"source":["## 15.6 Batch Normalization "]},{"cell_type":"markdown","metadata":{"id":"CMwSro-YBWNB"},"source":["Another type of layers that are used to reduce overfitting are **Batch Normalization** layers.\n","\n","Similarly to scaling the input features to range [0,1] or to a Gaussian distribution with 0 mean and 1 standard deviation, Batch Normalization layers scale a batch of data to have 0 mean and 1 standard deviation. This reduces the instability in training the network due to differences among batches of data, and also reduces the overfitting."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QalCw2dJboMS"},"outputs":[],"source":["from tensorflow.keras.layers import BatchNormalization\n","\n","# Define the layers\n","inputs = Input(shape=(32, 32, 3))\n","conv1a = Conv2D(filters=32, kernel_size=3, padding='same')(inputs)\n","conv1b = Conv2D(filters=32, kernel_size=3, padding='same')(conv1a)\n","bn1 = BatchNormalization()(conv1b)\n","pool1 = MaxPooling2D()(bn1)\n","dropout1 = Dropout(0.2)(pool1)\n","conv2a = Conv2D(filters=64, kernel_size=3, padding='same')(dropout1)\n","conv2b = Conv2D(filters=64, kernel_size=3, padding='same')(conv2a)\n","bn2 = BatchNormalization()(conv2b)\n","pool2 = MaxPooling2D()(bn2)\n","dropout2 = Dropout(0.2)(pool2)\n","conv3a = Conv2D(filters=256, kernel_size=3, padding='same')(dropout2)\n","conv3b = Conv2D(filters=256, kernel_size=3, padding='same')(conv3a)\n","bn3 = BatchNormalization()(conv3b)\n","pool3 = MaxPooling2D()(bn3)\n","flat = Flatten()(pool3)\n","dense1 = Dense(128, activation='relu')(flat)\n","dropout3 = Dropout(0.2)(dense1)\n","dense2 = Dense(64, activation='relu')(dropout3)\n","dropout4 = Dropout(0.2)(dense2)\n","outputs = Dense(10, activation='softmax')(dropout4)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yM9NYHKWheCq"},"outputs":[],"source":["# Define the model with inputs and outputs\n","cifar_cnn_3 = Model(inputs, outputs)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vJZRw2DH3wls"},"outputs":[],"source":["# compile model\n","cifar_cnn_3.compile(optimizer='adam',\n"," loss='sparse_categorical_crossentropy',\n"," metrics=['accuracy'])"]},{"cell_type":"markdown","metadata":{"id":"wBNiUaNgxTdw"},"source":["Also, let's use the argument `verbose=0` to not display the loss and accuracy after every epoch. Instead, after the training is complete, we will display the learning curves to observe the performance of the model. We increased the number of epochs to 60 for this model.\n","\n","And, we will use the `datetime` python library to measure and display the training time of the model."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":366677,"status":"ok","timestamp":1761163731168,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"HBeizSe3_pQf","outputId":"c6ded86f-d433-4f00-cdbb-6ae82bc783be"},"outputs":[{"output_type":"stream","name":"stdout","text":["Training time: 0:06:06.667332\n"]}],"source":["import datetime\n","now = datetime.datetime.now\n","\n","t = now()\n","# fit model\n","history = cifar_cnn_3.fit(train_data, train_label,\n"," epochs=60, batch_size=128,\n"," validation_split=0.2, verbose=0)\n","\n","print('Training time: %s' % (now() - t))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":410},"executionInfo":{"elapsed":276,"status":"ok","timestamp":1761163731446,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"_rkzWwx842Tq","outputId":"afcb9130-9580-4efc-93e5-c5b2f6e047d3"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# plot the accuracy and loss\n","train_loss = history.history['loss']\n","val_loss = history.history['val_loss']\n","acc = history.history['accuracy']\n","val_acc = history.history['val_accuracy']\n","\n","epochsn = np.arange(1, len(train_loss)+1,1)\n","plt.figure(figsize=(12, 4))\n","\n","plt.subplot(1,2,1)\n","plt.plot(epochsn, acc, 'b', label='Training Accuracy')\n","plt.plot(epochsn, val_acc, 'r', label='Validation Accuracy')\n","plt.grid(color='gray', linestyle='--')\n","plt.legend()\n","plt.title('ACCURACY')\n","plt.xlabel('Epochs')\n","plt.ylabel('Accuracy')\n","\n","plt.subplot(1,2,2)\n","plt.plot(epochsn,train_loss, 'b', label='Training Loss')\n","plt.plot(epochsn,val_loss, 'r', label='Validation Loss')\n","plt.grid(color='gray', linestyle='--')\n","plt.legend()\n","plt.title('LOSS')\n","plt.xlabel('Epochs')\n","plt.ylabel('Loss')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1903,"status":"ok","timestamp":1761163733347,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"UvcbDLp-9COb","outputId":"b41f8c2e-5226-401a-da39-fe8474de5109"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.7733 - loss: 0.8943\n","Classification Accuracy: 0.774399995803833\n"]}],"source":["# Evaluate on test dataset\n","evals_test = cifar_cnn_3.evaluate(test_data, test_label)\n","print(\"Classification Accuracy: \", evals_test[1])"]},{"cell_type":"markdown","source":["With the Batch Normalization layers in the model, the test accuracy increased to 77%."],"metadata":{"id":"T32-SzjCTDd5"}},{"cell_type":"markdown","metadata":{"id":"Np9mB-EFcvy4"},"source":["## 15.7 Data Augmentation "]},{"cell_type":"markdown","metadata":{"id":"RiFBo4vJ1BkV"},"source":["One of the main reasons for overfitting is the lack of sufficient training samples, or lack of diversity in the training samples, for training a model.\n","\n","To deal with overfitting we can use **data augmentation**, which refers to expanding the existing dataset by applying different image transformation operations. Examples are flipping the image vertically or horizontally, cropping the image, changing the contrast and color of the image, adding noise to the image, rotating the image at a given degree, and similar.\n","\n","![Image Augmentation](https://cdn.hashnode.com/res/hashnode/image/upload/v1623166213173/JTRR1Btgm.png)\n","\n","By doing data augmentation, we are synthesizing new images from existing data, as well as introducing some diversity in the images.\n","\n","Keras provides the function `ImageDataGenerator` for data augmentation. Among the available operations for data augmentation are:\n","\n","- **rotation_range** is a value in degrees (0–180) to randomly rotate images.\n","- **width_shift** and **height_shift** are ranges of fraction of total width or height within which to translate pictures, either vertically or horizontally.\n","- **shear_range** is for applying shearing randomly.\n","- **zoom_range** is for zooming in pictures randomly.\n","- **horizontal_flip** and **vertical_flip** are for flipping images.\n","\n","In the example below we applied width shift, height shift, and horizontal flip."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IQD2s2UAQQO5"},"outputs":[],"source":["from tensorflow.keras.preprocessing.image import ImageDataGenerator\n","\n","# create data generator\n","datagen = ImageDataGenerator(width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=True)"]},{"cell_type":"markdown","metadata":{"id":"cHYVQidO7iNS"},"source":["ImageDataGenerator returns both the augmented images and their labels for each batch of images.\n","\n","Let's plot a few images to see what they look like. They don't look much different, but that is because the applied shift and flip are not very large, and we didn't use other operations."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":367},"executionInfo":{"elapsed":606,"status":"ok","timestamp":1761163905030,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"U2M9gFJ37QMG","outputId":"673b52e1-a9b1-4010-e3d4-f5d5e647a490"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# Get images in batch of 20\n","aug_iterator = datagen.flow(train_data, train_label, batch_size=1)\n","\n","plt.figure(figsize=(12,4))\n","for i in range(6):\n"," ax = plt.subplot(2, 3, i + 1)\n"," plt.imshow(aug_iterator[i][0][0])\n"," plt.title(str(label_names[aug_iterator[i][1][0][0]]))\n"," plt.axis(\"off\")"]},{"cell_type":"markdown","metadata":{"id":"DctH3QgL6XMd"},"source":["When we use data augmentation, the arguments in the `fit` function are slightly different. First, we need to provide the name of our defined generator with `flow` to yield a batch of augmented images. In this case, we will need to use `datagen.flow`, since we assigned the image generator to the name `datagen`.\n","\n","Also, the generator in Keras does not accept a `validation_split` argument, and therefore we need to manually create a validation dataset, and then use it. This has been done in the next cells."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yBTx9rXUEFCI"},"outputs":[],"source":["# Define the model with inputs and outputs\n","cifar_cnn_4 = Model(inputs, outputs)\n","\n","# compile model\n","cifar_cnn_4.compile(optimizer='adam',\n"," loss='sparse_categorical_crossentropy',\n"," metrics=['accuracy'])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":375},"executionInfo":{"elapsed":286,"status":"error","timestamp":1761163908399,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"uFEyAsjcW4bw","outputId":"49348c3e-425f-4300-987d-c301931cc51e"},"outputs":[{"output_type":"error","ename":"ValueError","evalue":"Argument `validation_split` is only supported for tensors or NumPy arrays.Found incompatible type in the input: []","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)","\u001b[0;32m/tmp/ipython-input-387528026.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# fit model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m history = cifar_cnn_4.fit(datagen.flow(train_data, train_label, batch_size=128),\n\u001b[0m\u001b[1;32m 3\u001b[0m epochs=120, validation_split=0.2, verbose=0)\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# note that the generator does not work with validation split\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;31m# `keras.config.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 123\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/keras/src/trainers/data_adapters/array_slicing.py\u001b[0m in \u001b[0;36mtrain_validation_split\u001b[0;34m(arrays, validation_split)\u001b[0m\n\u001b[1;32m 478\u001b[0m \u001b[0munsplitable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mflat_arrays\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcan_slice_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 479\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0munsplitable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 480\u001b[0;31m raise ValueError(\n\u001b[0m\u001b[1;32m 481\u001b[0m \u001b[0;34m\"Argument `validation_split` is only supported \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 482\u001b[0m \u001b[0;34m\"for tensors or NumPy arrays.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: Argument `validation_split` is only supported for tensors or NumPy arrays.Found incompatible type in the input: []"]}],"source":["# fit model\n","history = cifar_cnn_4.fit(datagen.flow(train_data, train_label, batch_size=128),\n"," epochs=120, validation_split=0.2, verbose=0)\n","\n","# note that the generator does not work with validation split"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"U2S2jV_aXIV9"},"outputs":[],"source":["from sklearn.model_selection import train_test_split\n","\n","train_data_1, validation_data_1, train_label_1, validation_label_1 = train_test_split(train_data, train_label, test_size=0.2, random_state=20, stratify=train_label)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1761163911407,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"-BTZ3qaXXnq6","outputId":"b2229fc5-f986-4b6d-a84b-0ba5ab6c6d5b"},"outputs":[{"output_type":"stream","name":"stdout","text":["Training images (40000, 32, 32, 3)\n","Training labels (40000, 1)\n","Validation_images (10000, 32, 32, 3)\n","Validation labels (10000, 1)\n"]}],"source":["print('Training images', train_data_1.shape)\n","print('Training labels', train_label_1.shape)\n","print('Validation_images', validation_data_1.shape)\n","print('Validation labels', validation_label_1.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3JBjOhQcFsIe","executionInfo":{"status":"ok","timestamp":1761166332159,"user_tz":360,"elapsed":2403850,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"}},"outputId":"0f5476fc-1f3c-4b43-ab47-2355d46cf036"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n"," self._warn_if_super_not_called()\n"]},{"output_type":"stream","name":"stdout","text":["Training time: 0:40:03.832752\n"]}],"source":["t = now()\n","# fit model\n","history= cifar_cnn_4.fit(datagen.flow(train_data_1, train_label_1, batch_size=128),\n"," epochs=100, validation_data=(validation_data_1, validation_label_1), verbose=0)\n","\n","print('Training time: %s' % (now() - t))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":410},"id":"YSuGN-gMLjVG","executionInfo":{"status":"ok","timestamp":1761166332569,"user_tz":360,"elapsed":409,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"}},"outputId":"60ec2771-2b6a-4878-e254-bf2a42166cc7"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA+kAAAGJCAYAAAD2VnIMAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzsnXd4FMUbxz93l0YghBY60pEOoQqIgIKICgiiICDYpYmCqGAFREFBQH4oFpoKiKCioIIgCkhRpCNVeu+QEELukrv5/TG5S44UsptyKe/nee65u9nZ2Xe+s3t7787MOxallEIQBEEQBEEQBEEQBJ9j9bUBgiAIgiAIgiAIgiBoxEkXBEEQBEEQBEEQhGyCOOmCIAiCIAiCIAiCkE0QJ10QBEEQBEEQBEEQsgnipAuCIAiCIAiCIAhCNkGcdEEQBEEQBEEQBEHIJoiTLgiCIAiCIAiCIAjZBHHSBUEQBEEQBEEQBCGbIE66IAiCIAiCIAiCIGQTxEkXBEEQBEEQBEEQhGyCOOmCkI35+OOPsVgsNG3aNMU8Z8+eZdiwYVSvXp3g4GDy589Pw4YNGTNmDFeuXEmSf9GiRXTo0IFixYoREBBA6dKlefjhh/n99989eWbPno3FYmHTpk3JHvP++++nQoUKXmkWi8XrVbBgQVq1asXPP/+cou1XrlwhKCgIi8XCnj17UszndDqZNWsWrVu3pkiRIgQGBlKhQgUef/xxNm3axJUrVyhVqhQtWrRAKZVk/7/++gur1cpLL72U4jEEQRAEIbdws/s4gFKKr776ijvuuINChQoRHBxMnTp1GD16NNeuXUuS3+Vy8eWXX9K0aVOKFClCSEgI1apVo0+fPvz1119eeY8cOcLjjz9O5cqVCQoKomTJktxxxx289dZbGV5XQciN+PnaAEEQUmbu3LlUqFCBjRs3cuDAAapUqeK1/Z9//uHee+8lKiqK3r1707BhQwA2bdrEuHHjWLNmDcuXLwf0zfiJJ55g9uzZhIeHM3ToUEqWLMnp06dZtGgRd911F+vWraN58+am7W3Xrh19+vRBKcXRo0eZNm0aHTt2ZOnSpbRv3z5J/oULF2KxWChZsiRz585lzJgxSfJcv36drl27smzZMu644w5effVVihQpwpEjR1iwYAFffPEFx44dY/LkyfTo0YPPP/+cZ555xrN/XFwc/fr1o3z58owaNcp03QRBEAQht+B0OunZsycLFiygZcuWjBw5kuDgYP78809GjRrFwoUL+e233yhRooRnn8GDB/PRRx/RuXNnevXqhZ+fH/v27WPp0qVUqlSJ2267DYADBw7QuHFj8uXLxxNPPEGFChU4ffo0W7Zs4b333pN7sSCkBSUIQrbk0KFDClDff/+9CgsLUyNHjvTafvnyZVWmTBlVokQJtWfPniT7nzlzRr399tue7+PHj1eAeuGFF5TL5UqS/8svv1R///23UkqpWbNmKUD9888/ydp23333qfLly3ulAWrgwIFeabt371aA6tChQ7Ll3HHHHapr165qyJAhqmLFisnmGThwoALUpEmTkmyLi4tT48ePV8ePH1dKKdWhQwdVuHBhdebMGU+eCRMmKED98ssvyZYvCIIgCLmNm93H3333XQWoYcOGJdm2ePFiZbVa1T333ONJO3PmjLJYLOrpp59Okt/lcqmzZ896vg8YMED5+fmpI0eOJMmbOJ8gCCkjw90FIZsyd+5cChcuzH333Ue3bt2YO3eu1/ZPP/2UkydPMnHiRKpXr55k/xIlSvD6668Dujd67NixVK9enQkTJmCxWJLkf/TRR2nSpEmG1qFGjRoUK1aMgwcPJtl27Ngx/vzzT3r06EGPHj04fPgw69ev98pz4sQJPv30U9q1a8cLL7yQpAybzcawYcMoW7YsoKcH2O12hg4dCsDx48cZOXIk3bt3p0OHDhlaN0EQBEHIiVy/fp3x48dTrVo1xo4dm2R7x44d6du3L8uWLfMMYz98+DBKKVq0aJEkv8VioXjx4p7vBw8epGzZspQvXz5J3sT5BEFIGXHSBSGbMnfuXLp27UpAQACPPPII//33H//8849n++LFi8mXLx/dunW7aVlr167l0qVL9OzZE5vNlplmexEREcHly5cpXLhwkm1ff/01+fPn5/7776dJkyZUrlw5yYOIpUuXEhcXx6OPPpqm41WoUIFRo0Yxb948VqxYweDBg/Hz82Py5MkZUR1BEARByPGsXbuWy5cv07NnT/z8kp/52qdPHwB++uknAI/DvXDhQqKjo1Mtv3z58hw/ftwr1o0gCMYQJ10QsiGbN29m79699OjRA4Dbb7+dsmXLejmxe/bsoVq1agQEBNy0PHdQtjp16mSOwfHExMRw4cIFzp8/z+bNm+nRowdOpzPZBwlz586lc+fO5MuXD4Du3buzYMEC4uLi0mX3Cy+8QP369enRowc//PAD7733HiVLlkxnzQRBEAQhd7B7924A6tWrl2Ie9zb3fbhUqVL06dOHn3/+mbJly9K1a1c++OAD9u7dm2TfwYMHExAQwF133UV4eDgvvPACP/74402de0EQEhAnXRCyIXPnzqVEiRK0adMG0EPJunfvzvz583E6nQBERkYSEhKSpvIiIyMB0pzfLDNmzCAsLIzixYvTqFEjVq5cycsvv+wZfu5mx44d7Ny5k0ceecST9sgjj3DhwgV+/fXXdNnt5+fHZ599xqVLl7jtttt4+umn01krQRAEQcg9XL16FUj93ure5r4PA8yaNYupU6dSsWJFFi1axLBhw6hRowZ33XUXJ0+e9OSrVasW27Zto3fv3hw5coQPP/yQBx54gBIlSvD5559nUq0EIXchTrogZDOcTifz58+nTZs2HD58mAMHDnDgwAGaNm3K2bNnWblyJQAFCxb03GhvRsGCBQHSnD8tJDevvXPnzqxYsYKff/6ZkSNHYrFYiI6Oxmr1/qmZM2cO+fPnp1KlSp76BQUFUaFCBa/RAmbtbty4MQANGzZM1k5BEARByKu4HfDU7q3JOfJWq5WBAweyefNmLly4wI8//kiHDh34/fffPSP/3FSrVo2vvvqKCxcusGPHDt599138/Px45pln+O233zKhVoKQu5Al2AQhm/H7779z+vRp5s+fz/z585Nsnzt3LnfffTfVq1dn27ZtOByOmw55dweW27lzJw888MBNbQgKCgJ0cJnkiI6O9uRJTNmyZWnbti0A9957L8WKFWPQoEG0adOGrl27AnopuK+//ppr165Rs2bNJGWcO3eOqKgoChQo4GV3/fr1b2q3IAiCIAipU6NGDUCPakvpP8GOHTsAkr1PAxQtWpROnTrRqVMnWrduzerVqzl69GiSYHE2m406depQp04dmjVrRps2bZg7d67nv4IgCMkjPemCkM2YO3cuxYsXZ+HChUlejzzyCIsWLeL69et07NiR69ev89133920zNtvv53ChQvz9ddfe4bLp4b7Jrtv375kt+/fvz/ZqK038uyzz1K5cmVef/11lFIArF69mhMnTjB69Ogk9fvss8+Ijo7mhx9+AKBDhw7YbDbmzJlz02MJgiAIgnBzbr/9dgoVKsS8efNS/E/w5ZdfAnD//ffftLxGjRoBcPr06QzJJwgCsk66IGQnoqOjVUhIiHriiSeS3b5u3ToFqPnz56tLly6pUqVKqVKlSql9+/YlyXv27FmvddLHjRunAPXiiy8mu076V1995Vkn3W63q+LFi6umTZuqmJgYr3yLFi1SgJo8ebJXOsmsk66UUh9//LEC1KJFi5RSSj355JMqf/786vr168nWsWrVql5rs/br108BasqUKUnyOp1ONWHCBM866WmxRxAEQRByOzdbJ33MmDEKUK+88kqSbT/99JOyWq2qffv2nrTTp0+rXbt2Jclrt9tV/fr1ldVqVefPn1dKKbVmzRrlcDiS5P3mm28UoAYPHmy2WoKQZ5Dh7oKQjVi8eDFXr16lU6dOyW6/7bbbCAsLY+7cuXTv3p1FixZx7733Ur9+fXr37k3Dhg0B2LJlC19//TXNmjXz7PvSSy+xa9cuPvjgA/744w+6detGyZIlOXPmDD/88AMbN270rFMeEBDAhAkT6Nu3L40bN6Z79+4ULVqUrVu3MnPmTOrWrcszzzyTpjo99thjvPnmm7z33nt06NCB7777jnbt2iU7XB6gU6dOfPjhh5w7d47ixYvzwQcfcPDgQQYPHsz333/P/fffT+HChTl27BgLFy70ioIvCIIgCEICM2fOZNmyZUnSn3vuObZu3cp7773Hhg0bePDBB8mXLx9r165lzpw51KhRgy+++MKT/8SJEzRp0oQ777yTu+66i5IlS3Lu3Dm+/vprtm/fzgsvvECxYsUAeO+999i8eTNdu3albt26gP5f8uWXX1KkSBFeeOGFLKm7IORofP2UQBCEBDp27KiCgoLUtWvXUszz2GOPKX9/f3XhwgWllFKnTp1SQ4YMUdWqVVNBQUEqODhYNWzYUL3zzjsqIiIiyf7ffvutuvvuu1WRIkWUn5+fKlWqlOrevbtatWpVkrxLly5Vbdq0UQULFlT+/v6qYsWKaujQoery5ctJ8pJKz/XIkSMVoL777jsFqBkzZqRYv1WrVilAffjhh560uLg4NX36dNWyZUsVGhqq/P39Vfny5dXjjz+utm7dmmw5qdkjCIIgCLkZd096Sq/jx48rp9OpZs2apVq0aKEKFiyogoKCVK1atdSoUaNUVFSUV3mRkZHqww8/VO3bt1dly5ZV/v7+KiQkRDVr1kx9/vnnXiP01q1bpwYOHKhq167tuWffcsst6rHHHlMHDx7MaikEIUdiUSp+oqggCIIgCIIgCIIgCD5FAscJgiAIgiAIgiAIQjZBnHRBEARBEARBEARByCaIky4IgiAIgiAIgiAI2QRx0gVBEARBEARBEAQhmyBOuiAIgiAIgiAIgiBkE8RJFwRBEARBEARBEIRsgp+vDchqXC4Xp06dIiQkBIvF4mtzBEEQBAGlFFevXqV06dJYrfL8PCOQ+70gCIKQnTByr89zTvqpU6coV66cr80QBEEQhCQcP36csmXL+tqMXIHc7wVBEITsSFru9XnOSQ8JCQG0OAULFjS8v91uZ+LEiQwdOpTAwMCMNi9XIpqZQ3Qzh+hmHNHMHBmpW2RkJOXKlfPco4T0k577vVwT5hDdjCOamUN0M45oZg5f3estSimVrqPlMCIjIwkNDSUiIsKUk+5yuTh06BCVKlWSIYlpRDQzh+hmDtHNOKKZOTJSt/Tem4SkpEdTuSbMIboZRzQzh+hmHNHMHL6614uTLgiCIAg+Ru5NGY9oKgiCIGQnjNyX5DGKQex2O2PHjsVut/valByDaGYO0c0coptxRDNziG65F2lbc4huxhHNzCG6GUc0M4evdBMn3QQOh8PXJuQ4RDNziG7mEN2MI5qZQ3TLvUjbmkN0M45oZg7RzTiimTl8oVueCxwnCIIgCIIgCELeRSlFXFwcTqfT16ZkGQ6Hg/z582O328ljs53ThVHd/P39sdls6T6uOOmCIAiCIAiCIOQJHA4Hp0+fJjo62temZClKKVq0aMGJEyewWCy+NifHYFQ3i8VC2bJlKVCgQLqOK4HjDOJyubhw4QLFihWTyIhpRDQzh+hmDtHNOKKZOTJSNwlylvGkN7q7XBPGEd2MI5qZw6xuLpeL//77D5vNRlhYGAEBAXnGYXWPHvDz88szdc4IjOimlOL8+fNER0dTtWrVJD3qRu5L0pNuEIvFQmhoqJzcBhDNzCG6mUN0M45oZg7RLfcibWsO0c04opk5zOrmcDhwuVyUK1eO4ODgTLIue6KUQimFxWKR880ARnULCwvjyJEjxMbGpmvYuzyyM4jD4WDcuHESeMEAopk5RDdziG7GEc3MIbrlXqRtzSG6GUc0M0d6dcuLoxaUUpw5c0bmoxvEqG4Z9QAk752hgiAIgiAIgiAIgpBNkeHugiAIgmCQiAhYs8bCvn1VfW2KkNmcPQsbNkCBAtC2ra+tEQRBEPIA0pMuCIIgCKngcMC//8K8eTB4MISHQ5Ei0KlTAH/80cbX5gmZzebN0KULvPKKry0RBEHIMCpUqMDkyZPTnH/VqlVYLBauXLmSaTYJCUh0d4MopXA4HHkqGmR6Ec3MIbqZQ3Qzjmjmzblz8Ntv+vXPP7BvH8TGJs1XubLi9ttdzJhhxWZLn24S3T3jSY+mXtfEqlVw551Qowbs3p05xuYS5LfEOKKZOczqFhMTw+HDh6lYsSJBQUGZaGHGcbP6vfXWW4wcOfKm5dwYAO38+fPkz58/zQH0HA4Hly5dokSJEpl6rq5atYo2bdpw+fJlChUqlGnHSStGA8eldo5JdPdMRClFREQExYoVkx/TNCKamUN0M4foZpwco1lUFFFtHyDiltpEjpxE0WIWCheGqCg4c0a/Tp+GI0fg0CE4fBiiTkZQoEwoFStCpUpQtqwuKi4OnE6IiYFLlxJeO3fC9u1JD100xMHkgm/iV60itv7P0qIFlCypuHDhIhZLMSAb6yYYxuuayJdPJ16/7lujcgA55rckGyGamSMv6Xb69GnP52+++YY333yTffv2edISr8etlMLpdOLnl7yLl3hbWFiYITsCAgIoWbKkoX1yC6lpmlnIcHeDxMbGMm3aNGKT61YRkkU0M4foZg7RzTg+1WznTpg7F1IZ1BUXBwsXwqj6iyjw90rKLPyQt2t9TYkSEBCgh57XrKk7O3v1gtdegxkzoM7vk/l7XyHK/v4FM2bo9L599evJJ+GZZ/Tw9ZEjYcoUmDMnwUEPD4eXXoIlS+DoEcX5Lk/T++R79Fg9gIcaH6F0aTnXcjNebStOepqRa8I4opk5Mko3peDaNd+80jqWuWTJkp6Xe9k59/e9e/cSEhLC0qVLadiwIYGBgaxdu5aDBw/SuXNnSpQoQYECBWjcuDErVqzg/PnznijlNw53t1gsTJ8+nS5duhAcHEzVqlVZvHixZ/uNw91nz55NoUKF+PXXX6lRowYFChTgnnvu8XqoEBcXx+DBgylUqBBFixbllVdeoW/fvjzwwAOm2+zy5cv06dOHwoULExwcTIcOHfjvv/88248ePUrHjh0pXLgw+fPnp1atWvzyyy+efXv16kVYWBj58uWjatWqzJo1K9Xjudc+z+rB59KTLgiCIPiGyEgdiOvcOVyxTs7c3YdDh3Rv+OXL+nXhgnbQjx6FH/jWs+tHlkH8odpwhlIAFCoEJUtCiRJQoQLUL3yUQR+/Cg4YU/9bKj3Ql8OHddlWK9hs+hUYqJ189+uWW6BNG/DqYHjtdfjyS/3Z5YKpU2HChCyTSfAx4qQLQq4lOlrHhPQFUVGQP3/GlDV8+HAmTJhApUqVKFy4MMePH+fee+/lnXfeITAwkC+//JLOnTuzZs2aVHvDR40axfvvv8/48eP53//+R69evTh69ChFihRJNn90dDQTJkzgq6++wmq10rt3b4YNG8bcuXMBeO+995g7dy6zZs2iRo0afPjhh/zwww+0aWM+nstjjz3Gf//9x+LFiylYsCCvvPIK9957L7t378bf35+BAwficDhYs2YN+fPnZ/fu3Z7RBm+88Qa7d+9m6dKlFCtWjAMHDnA9m/62i5MuCIIgZBr//QcrV0Lt2tCkie75Bt07fvzZsVQ8dw6AE4+/QTUexk7ycwTLF7nKvRG/ghOoUIHCR45w8v5nuTTzRwqEWEgytbDrEHDoG2+5E3/x1psKzAyJ/OQTePdd/fnRR+Grr2D6dN397u9vvDwh5yFOuiAI2ZzRo0fTrl07z/ciRYpQr149z/e3336bRYsWsXz5cho2bJhiOY899hiPPPIIAO+++y5Tpkxh48aN3HPPPcnmj42N5ZNPPqFy5coADBo0iNGjR3u2/+9//2PEiBF06dIFgKlTp3p6tc3gds7XrVtH8+bNAZg7dy7lypXjhx9+4KGHHuLYsWM8+OCD1KlTB4BKlSp59j927Bjh4eE0atQI0KMJsisy3N0EAe5/mUKaEc3MIbqZQ3QDDh6EtWth6VL45htYtEhPwk6BjNZs1y7o2ROqV4f+/aFlS91T3aGDHmretMQRSs2fBEAU+bmFYwyyfEzFijpvp056WPrzz8PMmbB/0s/4O+1QrRosXgz+/lh/WkKxZXOSOui//qrra7NpR/rCBT1J3QhKwYIFMHCg/j5qFMyeDVWr6vXX4nvW5Vy7OWPHjqVx48aEhIRQvHhxHnjgAa/5lCmxcOFCqlevTlBQEHXq1EnXHzszeNrW7aTHxqZ6DQkauSaMI5qZIyN0Cw7WPdq+eKUxXluacDudbqKiohg2bBg1atSgUKFCFChQgD179nDy5MlUy6lbt67nc/78+SlYsCDn4h+mJ0dwcLDHQQcoVaqUJ39ERARnz56lSZMmnu02my3VhwQ3Y8+ePfj5+dG0aVNPWtGiRbn11lvZs2cPAIMHD2bMmDG0aNGCt956ix07dnjy9u/fn/nz51O/fn1efvll1q9fn6bj+iLugfSkGyQwMJARI0b42owchWhmDtHNHKIb8NZbkOhJtocPP9STsBPhdMLcuYEcOTKC11+H0FA9dFwp7du6XzExOs09JeuWW6BhQ2jUSM8Hv3JFL1P277+65/yHHxKO0awZHDgA58/DsmU6bR4jCMLO3tJ34ujWk7pTnmJ84XeYsOUJbcCNdIsf6t6tG9Spo53mV1/V9bnzTihTRm+32+G55/TnwYPhr7/0GtcbNkCiPxIpcuSInpw+Z44O6w7w1FPwxhu6J/6553S5U6YQ2K+fnGtpYPXq1QwcOJDGjRsTFxfHq6++yt13383u3bvJn8JYz/Xr1/PII48wduxY7r//fubNm8cDDzzAli1bqF27dqbb7PU7EheXsOH6dd+Njc0ByO+vcUQzc2SUbhZLxg059yU3/pYOGzaMFStWMGHCBKpUqUK+fPno1q0bAQEBWK0p99H63zBCzGKx4HK5DOX39cJhTz31FO3bt+fnn39m+fLljB07lg8++IDnnnuODh06cPToUX755RdWrFjBXXfdxcCBA5mQyhQ2q9VKqVKlsrAG8ag8RkREhAJURESEqf2dTqf677//lNPpzGDLci+imTlEN3Pked2WLHH70kpVrqxUeLhStWvr7zVrKuVyebKuX69UgwYJ2c2+/PySplksSnXrptTub3cp9eyzyvXHKrVjh1KTJyv1YY/1SoFyWSxKbdumVGystg2UGj48aZ2iopTKl09v37xZp8XGKtW4sU4rVUqpV15Ravdupd59V6eVLKlURIRSQ4bo7wMHJi13xw6lXnxRqe7dlWrZUqlKlbwrkS+fUgMG6GO5iYxUqmBBpUA5f/45w8619N6bchLnzp1TgFq9enWKeR5++GF13333eaU1bdpUPfvss2k+Tno09fodcToTzolz5wyXlZfI87+/JhDNzGFWt+vXr6vdu3er69evZ5JlmcusWbNUaGio5/sff/yhAHX58mWvfLVr11ajR4/2fL969aoKDQ1VAwcOVK74/wHly5dXkyZN8uQB1KJFi7zKCQ0NVbNmzUr2WDfaopRSixYtUondyxIlSqgJEyZ4vsfFxany5curzp07p1jHlOqklFL79+9XgFq3bp0n7cKFCypfvnxq4cKFyZY3fPhwVadOnWS3ffLJJyokJCRFW5RSyuVyqevXr3t0uxmpnWNG7kvSk26Q2NhY5s6dy/DhwwkMDPS1OTkC0cwcops58rRuhw/redPAqW6DOfbih+TLB8GxEVRuWQrr7t3smL6R07c0Zf58PXobIDRUUavWelo0rEetbd/QcudHuCw25rX/kriqNShaNKGnwWLRsdP274dNm2DLFh3/DfQSZ7Vr647uRx6BWrWAJybArFlYPv2UOvfcQ5133oGvh+iynngC3HPmxo6Fzp1h8mQYNCihZxz0kP3r16FiRR12HcDPTw85v+suOHUK3ntPv9w9BOPHQ8GCcNtt+vtffyXVq3dvSDQMzlPBNm20jl276jISExICTzyh7fzwQ+Y2b543z7V0EBERAZBiICKADRs2MHToUK+09u3b80PiIRo3YLfbsdvtnu+R8Sdm4nSr1Yq/vz+xsbFevUM2mw0/Pz8cDgdKKex2O3PnzuWll14iODgYFRCAxeHAfuUKFCyIv78/VqvV63ige5UsFgsOh8MrPSAgAKVUkkjUgYGBuFwur3SLxUJAQABOp5O4RL347vS4uDiciYbdp7VObvz8/LDZbEnSM6JObt1efvllr966nFwnyNx2iouLY+7cuQwdOtRr+HZOrlNWtJP7XBsyZIjn9zctdXIf222vil8DOzFWqzVJ77F7jeyMSHcfNy3pVqvVy8bE5SWuh8vlwuVyefJXqVKF77//nvvuuw+LxcJbb72Fy+UiJiYGl8vlNXw7cZkul8uzJviNZSe2wf1KbPON9lgsFgYNGsTYsWOpVKkS1atXZ+rUqVy+fDlZbdy2u9O3b99OSEiIJx2gXr16dOrUiaeffppPP/2U/PnzM2LECMqUKUPHjh1RSjFkyBDat29PtWrVuHz5Mn/88Qc1atQAdOC4Bg0aUKtWLex2Oz/99BM1atRItZ2UUp714d2/a6m1k1srh8NBUFCQ1/V04zmeGuKkC4IgZAf+/VfPm77zTlT+Auzbp4eQFysGxaMOEfL7j1hKl4IHH/QELFNKj87+5x/Y9redx6Y/TLXIK/xFU+74djyxnmDooXxBN/rwFX89M4NnSZjL9cQTMHrwKQ48/xp3zN+N5fx5z7Y3f2oCs2bpIeYp4Dp3gatvvId/j24Et2maNMOBAwmfly1LGO+ePz+8/XbCto4doUULWLdOB2X7/POEbd99p9+7dfMO/la9un4w8dNP+onDL7/o8fstW+q12CDBSd++XYfxdU8C3L9fO+h+ftq5L1sWSpfWc85LlEixvoAe8v7hh1iXL6do1aqp5xW8cLlcvPDCC7Ro0SLVYetnzpyhxA3tUKJECc6cOZPiPmPHjmXUqFFJ0idOnEhQfOCC8PBwOnXqxNKlS9m6dasnT6tWrWjdujULFizg4MGDnvR///2XJk2a4LDZCAQ+nzKFi8WK0atXL6pUqcLEiRO9HIj+/fsTGhrKuHHjvGwYPnw4ERERTJs2zZMWEBDAiBEjOHTokCcSMui1iwcMGMD27dtZsmSJJ71y5cr07t2btWvXsnr1ak+60Tp17NiRBg0aMH36dM4nut4zqk4AR44cYcGCBbmmTpnZTi1atABg0aJFHD58OFfUKSva6eLFiwBMmjTJUJ3y589PixYtiIiIIDg4mKioKK5everJHxwcTKFChYiMjCQ6OtqTHhISQkhICJcvX/ZytEJDQ8mfPz8XLlzwerBRpEgRgoKCOHv2rJdDFxYWhs1mS/JbVrJkSZxOp5cuFouFUqVKYbfbuXTpEqAfcrrLi46O9qS7j1O0aFGioqIYPnw4Q4cOpUWLFhQtWpQRI0Z45YUERzNxna5cuUJ0dLSnTip+PfozZ854Hn6cO3eOmJgYjy1xcXGeOrmXZztz5gwlS5bkxRdf5NChQ/Tt2xebzUavXr1o3769J48bPz8/ihcv7lWn1q1be2lks9k4duwYY8eOZfTo0dx///04HA6aNm3KrFmzuHjxIiEhITidTgYOHMipU6coUKAArVu3Zvz48YDuyHnllVc4fvw4QUFB3H777cyfPz9N7XT27Nk0tVNcXBwRERH89ddfPPHEE17XU0xMDGkmTf32uYj0DimMiYlRI0eOVDExMRlsWe5FNDOH6GaOVHU7elSpBQu8hnyniMulh1C/917GG3kjf/+tXPHDue22ILU06AHVi6/Us0xTa2nuNQT7mF8F9XaZaapV0+uqWDGdbCVOfUR/pUBdoIi6haOqfHmlKlbUI8ELF1bqnqA/lAJ11RKimtaOUvfdp9SGDUqpqCjlrFIl4Ri33KLr3Lp1QtrQoUo5HEntjohIGC9/553J161sWb19zhylevZMKPPtt5PmXbcuYaz8xIm6DaKjlSpQQKf/9VfqOp45o9S8eUpdvJiQ5nIpVbq03n/NmoR097D49u1v0jgp0KmTUqA2Nm6cIddoXhnu3q9fP1W+fHl1/PjxVPP5+/urefPmeaV99NFHqnjx4inuExMToyIiIjyv48ePK0CdO3dOxcTEqJiYGOWIP48dDocnLSYmRsXGT2mw2+2eckaOHKmuXbumlFLKVbKkvj7//lvFxMR4htgmLsOd7nK5kqS7XC7ldDqTpCulkqTb7XallB4Wmlx6bGysV3pa6+R+xcXFJZueEXVy6xYdHZ1r6pTZ7RQVFaVGjhypIiMjc02dsqKdoqOj1ciRI1VERIShOkVERKhdu3YlXNvx+iR+uctJ/HIPdc6I9OSOmVJ6ajYaTY+Li1MnT55UcXFxPqtTbGysqlatmnrttdcypE5Z0U6JdUtLO127dk3t2rXLcz9PfD25p3vJcPdMwGKxEBYW5pMofzkV0cwcolsacDh06O9779WRzEhFN4cD2rXTPajTpkG/fqmXvX277mEFuPVWPRQ7Eziz7iAF2t1PgevXiSI/BZzXuMf5A/fwgyePEyuraUUtdlEu7givn+zPEydHc4xbKMNJSlnO4Kf0E/zzE+ew6+lbksa2Uq2gamUKHDzIX8O+1aHTAYa8jvXAAaJCQwmcNg3/hx7SvctDh+rAbOPHw8SJOvDa9Ok6Shzo4eedOunx7qB1vRG7HdyRZNu21b3br7yie7Djl3jxonlzT1A2hg6FvXt1m0VFQblyeg231ChRImm5FovuTf/+ez3kvWVLnf799/r9wQdTLzMlnn8eFi+m7s6dWGJi9ILrQqoMGjSIn376iTVr1lC2bNlU85YsWdLT2+PG3YuREoGBgclOO0gu/cZgR27cPcHu3xE/P/03yRI/AiPA6fRq65SmOSSXbrFYkk23Wq3JpttsNmw2W5J0Pz8/j12JuVmd0pqenjq5dbPZbMmWnxPr5Caz2smtWWBgYLL258Q6ucnMdrLZbISFhREUFORV3s3qpOKHcruHLbuHpd9ISsHVMio9pf92yaWnZKOZdD8/P6/6m7HdSJ2OHj3K8uXLadWqFXa7nalTp3L48GF69eqVbDlG65QV7eRyuTy6uW1IzUar1eqZZgHe15OhqXE3deNzGXmlt0IQciQul1KvvabU448r9d13OmBYanzwge4NLVdOqZMn05bXHWgsOjr1/B9/nJC/dGml4gOYxMXpgGsfPLVbbQxpo44EVFHH/SuqU/63qOOBldSYht+rZ55RauRIpWbMUOqff5S6MXbIpUtK/fGHUs/3Oq/2U1UpUJsJVw2qRqoPHt2qDvR8XTnr1FWqYUOlxo9X1w+eVCdOKLXrn2vq0JApKjqsXNJIbf7+SiUKzpIs77yj87Zsqb+vW6d7rUGpZcuS3+e775QKCUk4xltvKXX1qlIdO+q0/PkTbLhR0/37dXpwcNpGLyil833wQYJdgYH6/YUX0rZ/crz/vi6jSxf9/ciR+CEIVqXOnjVXpsul1NixSh06ZN6uROTme5PL5VIDBw5UpUuXVvv370/TPg8//LC6//77vdKaNWuWZYHjklCrlj5nVq5Mf1mCIPiMnB44Lqdx7Ngx1bx5c1WwYEEVEhKimjVrlmrQ0NxARgWOEyfdIHFxcWrz5s2eITvCzRHNzJEndZsxw9vxDAxU6v77tTOZHOHhCXnr11cqMjJ53c6eVSo01NvpGz8+SXGRkdqBVkop1aePly3RvZ9W/fopVby4UpX5T52kVLKhzg9TXvnh8Er281Oqbl2l7r1XqQoVdFoQ0Z6h7KcDb1Er55xKsx+r7HalfvxRqe+/V+rvv5U6ccI7AnlKnDihHVPQkc2rV9dRyvv0Sf1cO3ZMt4O7Qm6nPShIqVWrErTdtct7v19/1ek1a6axYolYvNj7AcDatcbLcLNmjS6jZEntXE+cqL+3amW+TJWx12hudtL79++vQkND1apVq9Tp06c9r+hED3UeffRRNTxRZP9169YpPz8/NWHCBLVnzx711ltvKX9/f7Vz5840Hzc9miZp20aN9Dnz00+Gy8pL5Mn7VjoRzcxhVre87KS7XC4VFRWV5ijlgsaobhnlpKe8UF4W8dFHH1GhQgWCgoJo2rQpGzduTDX/5MmTufXWW8mXLx/lypVjyJAhxibhp5O4uDiWLFniFRxCSB3RzBx5TrdTp/QQZ4B77tGhwu12HRTsnnsSQoi72bMHtm7VQ7PDwmDbNnjoIeKuX0+q2xtvQEQENGgAH32k08aN8yrzhx+gQgUoVQqGDQPnOh0NXL0yHIB8cz5n3ye/E3juGH9Y7qI0p7lStjZbJ61i44cb2DDpL+yhxanAURY88DVPP61HeBcrppdZ3rFDxzU7ckQfb2aB52nBeuJCClFyy1Lu7FWKFEa/JSUgQA8179JFDwEvU0brcDPKlIEOHRI03rsXSpYkdty41M+1cuVg8WJYsEAPKb96FWw2WLgQWrVKWH88cZA40EHdQEdlN0rHjjqIXJUq0LSpXmzdLA0ban3OnIHjx9M/1D2ePHeNmmTatGlERETQunVrSpUq5Xl98803njzHjh3j9OnTnu/Nmzdn3rx5fPbZZ9SrV49vv/2WH374IUvWSIdk2jZfPv1+/XqWHD+nIteEcUQzc4huxlHxQeDUDZHJhdTxlW4+nZP+zTffMHToUD755BOaNm3K5MmTad++Pfv27aN48eJJ8s+bN4/hw4czc+ZMmjdvzv79+3nsscewWCxMnDjRBzUQBCFDUAoGDtSOdMOGsGSJdgJ37dKO1P79Onr34MEJ+3z9tX5v3x7eegtat4Zff8XvueegfPmEfNu2JUQKnzxZO3vjx8O+fTB5MtHD3uTFF+GTTxJ2mfXBRSag51j33v4SLYhgANP4IuBpioTZyH/yGFStSqE1KwhPPEfWPhSGD6fL3rF0+a43WK0oBSdO6OcJJ09CjRpQr7aTwpW1g+I3f27CPO+s4Ikn4Oef9UMRgI8/hlSWwvJgscBDD+knDx99pB3ndu30tipV9Nz0RFF8AR2tHsw56aCXZ9u3Tx87PbEZgoN1WZs3w6JF2vkH/ZBDyHTS8sdm1apVSdIeeughHnrooUywyATipAuCIAhZiE970idOnMjTTz/N448/Ts2aNfnkk08IDg5m5syZyeZfv349LVq0oGfPnlSoUIG7776bRx555Ka974IgZHO++053Zfv5wYwZ+t1i0YtuP/+8zjN1ql6gG7RT73bSe/aExo1h/nywWrHNns1Tn3+ObcoU7Yi+8ILO3707tGxJHH5cfH40ALHjJtA2/KLHQX/pJf184JGKfwOwl1uZt6wIb/iNI6JgWco5DpH/5H/6IcDKlXBjEKv+/SE0VPdQx6/nbLHojuhOnfTm1q2h8Onduhe/QAG4++5MkzVZ7r9fjzwA7XQbdVQLF4bXX09w0CFzetLdWK3pc9DduJdiGzNGnw9Nm+pl1wQhLYiTLgiCIGQhPutJdzgcbN68mREjRnjSrFYrbdu2ZcOGDcnu07x5c+bMmcPGjRtp0qQJhw4d4pdffuHRRx9N8Th2u91rPcPI+OGtidOtViv+/v7ExsZ6LWZvs9nw8/PD4XB4egIcDgeVKlXCYrF4pYOObmm1WpMsVO/v7+/Jn5iAgACUUp51B90EBgbicrm80t1RAp1Op9fQHnd6XFwcTqfTS8u01gl0xE6bzZYpdXI4HFSuXBmllFc5OblOkPntZLFYqFixopc9Ob1OybZTZCS2gQMBiBs2DGf16mC3J9Spe3cCRozA8t9/OH7+Gb/77sOyaROWAwdQwcE42rcHu52A++9HffwxlkGDKHPqFLz8sn4BrsAgpld5l1nNXGzcaEG5urGZ+oRf30bn/e9zuOQ4pk+PpV07rU2H9etgLOwp2JRmtVx89FE+Qk9/irr/fihZEscvv0Dx4tji4rzrFBiIrX9//MaNg3ffxXHffSTuQ3TXKXb1avwBV+PGxDqd+MdHAs2qdrJOmYJt8WKsU6YQGxuLw+HwnGumrqcqVbTO+/cTG38O+vv7Yzl8GAsQW64crvh0n517t92mRwBcuKDPtc6dsTid6bqe3LoBSWw3Wqcb217wLRaLhcqVKydE8BUnPU0k0U24KaKZOUQ3cxiKLi548IVuPnPSL1y4gNPppESJEl7pJUqUYO/evcnu07NnTy5cuMDtt9+OUoq4uDj69evHq6++muJxxo4dy6hRo5KkT5w4kaCgIADCw8Pp1KkTS5cuZevWrZ48rVq1onXr1ixYsICDiYZxduzYkYCAAD7++GOvxex79epFlSpVmDhxotcfrv79+xMaGsq4ceO8bBg+fDgRERFMmzbNkxYQEMCIESM4dOgQc+fO9aSHhYUxYMAAtm/fzpIlSzzplStXpnfv3qxdu5bVq1d70s3UqUGDBkyfPj3T6nTgwIFcV6fMbier1eo1lSOr6vTXqFEUjIjgSuHCWCtXpveIEWzfudNUnW7/809u27CBM127Uv7jj1nw44+edioYEcET//xD6LlzXC5Rgo/8/HDG2+qp02ef0aZmTW776y/2DHid/uXa8+KJeTwIbC5Tg5/+NxWLBYYMGc6e+t2Y08VOnX07aX50A7UjdgEw2v4Ko96p5LHRalVMLjyGLy7ez/OWD4npEsimTTYuX9Z1ilj+K0UA/zsu0b7R2xw/Hk54p078MXUqG48fx75woVc7JT738jmdvBgUhG3zZn4dNowtxYolaae9M2dSB/jT6WTVuHG+Ofd69KBTsWIsXbzYc+5NnDjR3PUU35N+ZfNm/hdfh/79+xMW35M+4/ffObt7d+bXKbXr6YY57R+fOcPt27dnyPUE+p6WnjqVLl06SZmC7wgICKB3794ArFgBFxfnoweIk34TEusmpA3RzByim3GsVitFixb1tRk5Dl/pZlE+ih5w6tQpypQpw/r162mW6M/Tyy+/zOrVq/n777+T7LNq1Sp69OjBmDFjaNq0KQcOHOD555/n6aef5o033kj2OMn1pJcrV45z585RsGBBwFjPX1xcHH///Td33HEHLpcr9/RmZmIPbVxcHP/88w/Nmzf3Kjsn1wkyv504fJj1O3bQ8J57POuSmq1T7C+/YFu8mLg33oCwsNTr9N13WLt180rHZsPVvTuxU6fq+b1prJNatw7/O+/E4ra1eHHi3nwT55134jdxItYvvsASGwtWK7G//47LPST5hjq59h8kqG5tFFCDPayiNaU4Q0cWs7XM/TidcOZM0qfpFThMDfawjHu4rRl06+bivvuc3HKLhXxB/qgmTbBs2kTcmDE4hw3TdbLZUIULY7l6FcfGjai6dQ2fe/4vv4x1yhRcd9xB7PLlSerkqloV64EDOBYvRt19t8/PPYfDwYYNG2jWrBmBgYHGr6fTp6FsWZTNhuPKFfD3xz8mBmuhQgDYz52D+N9bn/3u2Wyo4sWxXLiAq25dYjduTPdvRFxcHBs2bKBVq1bYbLZ01enq1auEhYURERHhuTcJ6SMyMpLQ0FBTmsbFxbF27Vpuv/121qzxY89dAxnIx/Dmm5DMg39Bk1i35NbTFpIimpnDrG4xMTEcPnyYihUrejrr8gpKKaKioihQoICMQDCAUd1SO8cM3ZfSFEs+E7Db7cpms6lFixZ5pffp00d16tQp2X1uv/12NWzYMK+0r776SuXLl085nc40HTe9y9zExMSokSNHqpiYGFP750XyrGYbNyr1xhtKXblifN9jx5QrOFidLlEi/brFxipVpoxePqhZs9TXBz9wQKmCBROWNLv11oQly0Cp225T6ty5tB03OlqpatX0fu3aKVW1arJLlqnWrZX6888Uizl9Wqm2bZX6iXuVAnWwiF4KKcKvsMrvb/cqKl8+papVc6pKlQ6o3r3j1GuvKfXZZ3oFsWRxL/lWpUrCOt47dyas/212SZzjx/Wa4pB0+bhz5xIM9qz35lvSfY06nXo5NtDnkFJKbd+uvxcpknGGppcHHtA2vf12hhSXkb9tuXkJNl+RHk0Tt+2WLUqN50V97rz0UiZYmnvIs/f7dCCamcOsbnl5CbZWrVqpJ5980uMzlS9fXk2aNCnVfYAkvpoZMqocX+B0OtXJkyfT7Gvm+CXYAgICaNiwIStXrvSkuVwuVq5c6dWznpjo6GisVm+TbTYbkLbosYKQZaxbpyOEvf12wrJiRvj5ZyzR0ZQ8exbOnUufLYsX67DiABs2QJ8+CQHYEnP9OnTrpgOaNW8OGzfqAGjR0fD77zpg2F9/6W03BghLjjfegP37cYSVZlStbxjT419WP/gh9gI6kvjZum1ZPXo1Cwf8wcz9tzNqFDz9tF4hrGFDHYusSBG9athvv8Hngc8BUOnSJgAKPvEQZy4F8PvvsGkTnD8P167Bjh2x9Okzh+nT4xgzRpdZrlwKNnbvDiEhuj7u6NJ/6aXXaNJER5g3Q9my4I6V8emn3tvcMTdq1tSa5gas1qTB4zIiaFxG88EHMHYsvPiiry0RchCFC8N1ZE66IAi+oWPHjtxzzz3Jbvvzzz+xWCzs2LHDcLn//PMPzzzzTHrN82LkyJHUr18/Sfrp06fp4F4CNpOYPXs2heJH8OUGfDquZujQofTt25dGjRrRpEkTJk+ezLVr13j88ccB6NOnD2XKlGHs2LGAPkknTpxIeHi4Z7j7G2+8QceOHT3OuiD4nM2b4d57tXMLMGsWDBigPc+0kmiItGX79lS8zDTgXhe8Qwft7X77LQwfDu+/753v+ef1cmXFisE334C/v063WqFNG/3goUMH7YQ1a6YX/W7cOPljrl+PmjgRC/Dghc/4abLbGR1MCI9RitPs33ErpPGeUq8evDvnbuhaFf77Tyf27EmBAto00+TPr6PDf/qpXqatTZsEJzrR0HtTPPkkzJyp1+SeNs0zTYD16/V78+bpKz+7UbmyXjLPPY89OzrplSrpc18QDJDYSY+LjPbtHydBEPIcTz75JA8++CAnTpyg7A2rksyaNYtGjRpRt25dw+WGuVd6yQJK3rgajnBTfLoEW/fu3ZkwYQJvvvkm9evXZ9u2bSxbtswTTO7YsWOcPn3ak//111/nxRdf5PXXX6dmzZo8+eSTtG/fnk9v7KnKRKxWK+Hh4Ul69IWUyVOa/fuvXlIrMhJattQ900ppBzitoz3i4vTyXvHYUno6ev78zcvcs0f3gluteiHwWbN0+vjxukdx+XK9VNjIkdpJtVhg3jyvpamU0n7l35E1cK3/Cxo00BGy77+fmAMnWLgQnnpKT9VctAiO7r1OTM/HsSjFbPryk7qPzp3hmWf0il+3tStIsea30qKFluiOO7Tv//TTMHq09mt//lkfc/duOH1arzFes7YVBg3SRpUpo3dOBsPn29NP6/fvvoOLFxN60lMY0ZNmmjWDChUgKgp++ikhPRs66RlyjcZHePf0pKd3jfQcQJ76bctjJG7bggXBbtFOuiNSetJTQ64J44hm5sgw3ZTSw/B88Urj/8L777+fsLAwZs+e7ZUeFRXFwoULefLJJ7l48SKPPPIIZcqUITg4mDp16vC1e6naRPi7O2CAChUqMHnyZM/3//77jzvuuIOgoCBq1qzJihUrkuz/yiuvUK1aNYKDg6lUqRJvvPGGJ+7K7NmzGTVqFNu3b8disWCxWDw2WywWfohfmhZg586d3HnnneTLl4+iRYvyzDPPEBUV5dn+2GOP8cADDzBhwgRKlSpF0aJFGThwYJLYL0Y4duwYnTt3pkCBAhQsWJCHH36Ys2fPerZv376dNm3aEBISQsGCBWnYsCGbNunRmxcuXKBTp04ULlyY/PnzU6tWLX755RfTtqQJo+Pyczoy70/INA4eVKpkST1vsXFjpSIi9Nzk4GCdNn9+2spZt857znb37knzrFyZMJ/7woWUyxo0SOfr3Dkh7e23k58bDkqNGuW1+5EjSrVvn7C5eHGlnu11VV28pZ5SoDbamqoAYjzb/bGr+TysFKiTlFJVil5S332XtmqniZgYpV5/Xak//si4Ml0uPf8elHrrrYTKnj2b/rJffVWX5Y6z4XAkzN3euzf95WcnPvrIu67336+/T5vmW7tyCHJvyngyUtMX80/TsTDufCADLBMEwVckmS8cFZXyf6LMfkVFpdnul156SVWuXFm53PFzlFIzZ85U+fLlU1euXFEnTpxQ48ePV1u3blUHDx5UU6ZMUTabTf3999+e/K1atVLPP/+853viOelOp1PVrl1b3XXXXWrbtm1q9erVKjw8PMlc8rffflutW7dOHT58WC1evFiVKFFCvffee0oppaKjo9WLL76oatWqpU6fPq1Onz6touPjICUuJyoqSpUqVUp17dpV7dy5U61cuVJVrFhR9e3b13Ocvn37qoIFC6p+/fqpPXv2qCVLlqjg4GD12WefpajRrFmzVGhoaLLbnE6nql+/vrr99tvVpk2b1F9//aUaNmyoWrVq5clTq1Yt1bt3b7Vnzx61f/9+tWDBArVt2zallFL33XefateundqxY4c6ePCgWrJkiVq9enWyx8qoOenipBvE4XCoH3/8UTkcjgy2LPeSZzR79FH9o1unjlIXLyakjx6t08uVU+ratYT0/fuTd9TiHUVXiRL6vWrVpHkGDkz4ka9cWak9e5LmiYxUKiRE51m+PCHd5dLHqF5dO6fNm+vIbKNGeQKlOZ1KTZmiY6eBjh3njicHSlXkoLpIYaVAfZm/nxo6VKlne0ep9SF3KwXKjr8a1XxZhvi5RjF1vrkdTHeQvMqVM8aYXbvin17463Ni48aEYGqJbrS+JkOu0V9/1XWrWVN/r1VLf1+2LGOMzIZk5G+bOOkZT3o0vbFtXyo+WylQl5q0z2gzcxV55n6fgYhm5jCrW0510vfs2aMA9UeiToqWLVuq3r17p7jPfffdp1588UXP91atWql+/folGzju119/VX5+furkyZOe/EuXLk3ipN/I+PHjVcOGDT3f33rrLVWvXr0k+RKX89lnn6nChQurqET1//nnn5XValVnzpxRSmknvXz58iouUQDfhx56SHVPruMqntSc9OXLlyubzaaOJYokvGvXLgWojRs3KqWUCgkJUbNnz06yr9PpVDVr1lRvvfVWisdOTI4PHJdTcblcbN261Wt5HyF18oRmdrsO0AZ6DniRIgnbhg2DW26B48f1mPApU/Rc7mrVoG5dPaY7MfHz0Z0DBujvBw7oIdOJ+fNP/Z4vn54DfNtter55YubMgatX9XHuuish3WLRw9v37NHjyNet0wsBv/km2GycPatj3g0erEdjtWwJO3boEe4rV8KQIVC0USVmtJmLslh49NonfFB2Ep/8dxfNri5HBQcT/c1PvLmuPcWLp0tVU5g633r10lq6l9xK73x0NzVr6gn1sbE6FoB7qHuzZrodsgkZco26A8cdOqQDE2bHOekZTJ74bcuj3Ni2fiF6uLvrmgx3Tw25Jowjmpkjw3QLDtb/sXzxcseqSQPVq1enefPmzJw5E4ADBw7w559/8uSTTwLgdDp5++23qVOnDkWKFKFAgQL8+uuvHDt2zKuclIaL79mzh3LlylG6dGlPWnKBvL/55htatGhByZIlKVCgAK+//nqSY9yMPXv2UK9ePfLnz+9Ja9GiBS6Xi3379nnSatWq5RVzrFSpUpwzGUzZXb9yiWI81axZk0KFCrFnzx5Ax0p76qmnaNu2LePGjeOgO8YO8Pjjj/POO+/QokUL3nrrLVOB+owiTrqQt4mN1Z5oelm5EiIioFQpaNHCe1u+fHoOOOjo0s8/r8ORAzgcMG5cQt4rV+DvvwFw9uzJ1QIF9BrjiX8MrlyBnTv1Z3e09YgIuOcePb/677/1M1p3wLgBA9i81crEiXpqdKIwD0nYtQuaNtXPAEJC4OOPddDzatV0HLk774SJE+Gff+Cl3ztgGT1a7zh0qD5ukSJYVq6k0MN3m1HRd4SGwsMPJ3xP73z0xPTqpd/nzs2W89EzjPLlwc8PYmJg+3YdONFi0emCkMNxO+kqWpx0QchVWCw6iKwvXgYf1j/55JN89913XL16lVmzZlG5cmVatWoFwPjx4/nwww955ZVX+OOPP9i2bRvt27fH4XBkmFQbNmygV69e3Hvvvfz0009s3bqV1157LUOPkZjE8+dBz2vPzIdZI0eOZNeuXdx33338/vvv1KxZk0WLFgHQs2dPDhw4wKOPPsrOnTtp1KgR//vf/zLNFhAnXcjLKKV7tCtVStuSYqnx3Xf6vWtXHaTtRh56SAeUA33MKVNg6VL9fd48OHJEf/79d90LWaMGlCvH6VKldPrWrQllrVunba9SRffEr1wJvXuD0wnTp+te4KpVYdcuVHAwI/b2pXFjvepUx45QurR+ltClC8yYkbDC24oV2nc8elQXvWkT9O+ffHU8vPqqLhR0MLc//8y4Xuisxh1ADjK2Dj166BvxmjUJUftzo5Pu55fgkLuDzZQuDYGBvrNJEDKIgND4JdhixEkXBME3PPzww1itVubNm8eXX37JE088gSXe0V+3bh2dO3emd+/e1KtXj0qVKrF///40l12jRg2OHz/uFbD7L3cg3XjWr19P+fLlee2112jUqBFVq1bl6NGjXnkCAgJwOp03Pdb27du5lqiTbN26dVitVm699dY022wEd/2OHz/uSdu9ezdXrlyhZs2anrRq1aoxZMgQli9fTteuXZnlDrgMlCtXjn79+vH999/z4osv8vnnn2eKrW7ESTeIzWajVatWsuSbAbKtZgcO6B6/c+e0c+1eMs0osbE6QjrAgw8mn8digSVL4MwZvf74c8/pnu927bRz7e5pdztxd9+NzWYjsGlT/X3btoSy3EPd3dHNg4JQX3zJkS9Xc6Xjo7gCgzzLYM239mLcJ4VQSveC16qlne4zZ7TJTz0FJUtqn7RDh4Sg9H/9pXvPb4rVCvPnw+zZuns90Q+drzB9vjVvrh92dOkCyazxaZpy5XQIe9CjIGy2lJeu8xEZdo26I7y7p17k4qHukI1/24R0c2PbBhbSTrpFnPRUkWvCOKKZOfKibgUKFKB79+6MGDGC06dP89hjj3m2Va1alRUrVrB+/Xr27NnDs88+6xW53E1AQIDHsU9M27ZtqVatGn379mX79u38+eefvPbaa155qlatyrFjx5g/fz4HDx5kypQpnp5mNxUqVODw4cNs27aNCxcuYHdPI0xEr169CAoKom/fvvz777/88ccfPPfcczz66KOeFb7M4nQ62bZtm9drz549tG3bljp16tCrVy+2bNnCxo0b6dOnD61ataJRo0Zcv36dQYMGsWrVKo4ePcq6dev4559/qFGjBhaLhTFjxrB8+XIOHz7Mli1b+OOPP6hRo0a6bL0paZoBn4uQ4Dy5DLtdqdWrddRvo8ye7R3Ao3dvc8G8li/X+4eFKRUba2zf33/X+wYFKXXmjFIVKujvP/+sty9cqL8nCsqhmjfXaTNnKqWUOnXKOwJ7KJdVfz5Sn/CMKs4ZdcstSi1dmrB7VJQOID96tFINGiSVwIyUwk347LMEkRs08LU1mceAAQnnM+hgikKakHtTxpORmk59YrNSoC7nL50BlgmC4CtSC+qVE1i/fr0C1L333uuVfvHiRdW5c2dVoEABVbx4cfX666+rPn36qM6JVvdJLbq7Ukrt27dP3X777SogIEBVq1ZNLVu2LEnguJdeekkVLVpUFShQQHXv3l1NmjTJK1hbTEyMevDBB1WhQoUUoGbNmqWUUknK2bFjh2rTpo0KCgpSRYoUUU8//bS6evWqZ3vfvn29bFdKqeeff94rGvuNzJo1SwFJXpXjgwEfPXpUderUSeXPn1+FhISohx56yBOozm63qx49eqhy5cqpgIAAVbp0aTVo0CDPeTJo0CBVuXJlFRgYqMLCwtSjjz6qLqSwupJEdzdJem/adrtdffXVV8put2ewZbmXTNPsyBG91Bko1bKlUoku7jTx7LN631atlLLZ9OePPjJuh7ucZ54xvq/LpVTTpnr/Bx9MiAQeFaXsdrtaNGGCTgsI0Mt3RUfr7aDUf/+pH39UqlixhN2KFEnYbLMpNXjwzWU5elRXe+bMbBVw3DTZ8hq9eDGhYQYN8rU1ScgwzSZO9H7qk8ZIqDmVjDzXxEnPeNKj6Y1tO2PYbqVAXQ0onNFm5iqy5e9vNkc0M4dZ3XK6k54enE6nunDhgie6u5A2jOom0d19hFKKgwcPopTytSk5hkzR7JdfIDxcD7EGPQT83nuTRkFPDXcQr+efh/fe059feEGP9U4rTid8/73+3K1b2vdzY7Hoed2QMK/99tshf36UUmyPjEQVLAgOB4eX7mXrpxshNpbo0JI88U5lOnfWUdfr1dMj4i9e1LHoYmK0FB9+CAUKpG7CLbfAgAHw+OPZKuC4abLlNVqkSEJguvvu860tyZBhmrkjvLvJ5cPds+W5JmQIN7Zt/mJ6uLt/nAx3Tw25JowjmplDdDNHcsPPhZvjC93ESRdyFi4XvP66dnQuX9Zze7/7Tkfn/vNPPbE6LY56RAT8+6/+3KyZjk7erZueX96li16eLC38+SecPw+FC+t1y8xw//16srib+ABz27ZZWLu+Jf/61QPgrc5b+W6Ino/+U0RLZs3WHvWwYTqweuLp4IGBEBRkzhwhk/jsM/0k5Z57fG1J5uGek+4mlzvpQt6hQJh20gNdMXqciCAIgiBkIuKkCzmLzz+Hd97RnwcN0k5y1646mnRoKKxdmzZH3b1MWcWKOnKaxQIzZ+po6WfOQKtWCcucpca33+r3Bx7Qa5SlgsulA7MlwWqF4cM9X7cUu5t27eC22wL47be2/H4pHIDGtq3cE6yd9Is176BPHx0Mfvx4CaCdIwgO1kMecjM3OuXipAu5hJDi+RK+xMT4zhBBEAQhTyBOukH8/Pzo2LEjfn5+vjYlx5BhmiWOgv7OO/C//yV4p40bezvqjzyi86fEhg36PfFSWCEhejmz8HDdO966NWzZknIZLleah7rv2QONGukO9/bt4euv4Xr8qEml4MhtPTgT3oF1RTvS6On6/PYb+PkpWrW6QrWHtWM38LZN3G7VQ/T7z2vJF19AmzapHjZPIteocTJMs3z5oGxZ/dnfXy/BlouRcy33cmPbFiyRyEm/LkPeU0KuCeOIZuYQ3YxjsVgIDQ1NNrq7kDK+0s2i8thkjsjISEJDQ4mIiKBgwYK+Nkcwwrff6vXGixbVi3nnz580z19/aefabodXXoFx45Ivq317vdzZRx/pCdmJuXxZD0neuBEKFYJff4UmTZKWsW6dnj8eGgpnzybbna0UfPKJHk1/Y+dLwYJ6lPuuXd497P7+8MQTunO9QgX0MnH16+vefqX08S5e1Et5CUJ2o00bWLVKD33/7z9fW5NjkHtTxpORmh47BqXL++GHE3X8BJayZTLISkEQspKYmBgOHz5MhQoVyJcv3813EASDXL9+nSNHjlCxYkWCbph7auS+JD3pBnE4HHz88cc4HA5fm5JjyBDNlIL339efBw5M3kEHveD3zJn683vvwVdfJc3jciUEh2vWLOn2woV1r3yLFnpd6y5d4Nq1pPkmTdLvnTsn66CfPatHwQ8YoB30u+/WHfhvvAHly2vHfMMG/e7vr0dCP/+8XuL8k0+gdOl43SpX1hncz9NatBAHPRXkGjVOhmrmDh6XB4a6y7mWe7mxbQsXhuvoP/Qxl6UnPSXkmjCOaGYOs7r5x09NjI6OzgyzsjUul4tz587hcrl8bUqOwqhu7nPSls7/6jJGxCBKKc6fPy/RJA2QIZqtWaMjuQcF6bnoqdGzJ+zerYfEP/WU7tFL7Izv3q094/z5oU6d5MsoWBCWLdNz1A8fhokTtXft5p9/dMA6iwVeeslr16tX4YMP9CsqCgIC9POCwYP19PPbboORI/Wo/JMnoXZtuPVWnS8xHt38/XWmrVv1hpYt06ZZHkWuUeNkqGbhOoZCrp9/j5xruZkb27ZAAThPPkKIIvLsdfKlcOvI68g1YRzRzBxmdbPZbBQqVIhz584BEBwcnGeGf7tcLmJiYoiJicFqlX7atGJEN5fLxfnz5wkODk73VAxx0oWcgbsX/YknICzs5vlHj9bO+KJFujt7yxYoEz880b30WtOmkNoFVKAAvPuunt/+/vvwzDNQooTeFr9smqP7oxwNrM21bRAdrX33d97RU9pBT5X//POk/orVCnfckaaaa8LDxUkXcgZPPaWHirRq5WtLBCHDsFggxhoMLog6f50SvjZIEATTlCxZEsDjqOcVlFJEREQQFRWVZx5MZARGdbNardxyyy3p1licdCH78++/el10q1VP7k4LVit8+aWeM759Ozz9NPz8s/6n5XbSEweNS4nu3XUv+j//6O7vadN0SPXffsNp86fmwlEcnJ90t6pVtX//4IMZtPZ4/fr6PTBQR6AThOxKYKBeVlAQchmxtnzggmsXZLi7IORkLBYLpUqVonjx4sTGxvranCzD4XDwyy+/8MwzzxBw4/BNIUWM6hYQEJAxIxVUHiMiIkIBKiIiwtT+TqdT/ffff8rpdGawZbmXdGvWt69SoNRDDxnfd/dupQID9f6zZum0qlX1919+SVsZq1fr/DabLq9JE6VATWGQAqVCQpQqWVKpypWVatRIqWnTlHI4jJt6I1667d2r69GjR/oLzuXINWoc0cwcGalbeu9N2ZnVq1er+++/X5UqVUoBatGiRTfdZ86cOapu3boqX758qmTJkurxxx9XFy5cMHTc9GiaXNvuCQ5XCtT6N5caLi+vIL8lxhHNzCG6GUc0M4ev7vUS3V3I3hw/DpUqQVycjrbeuLHxMt5/X0d6Dw2F1asTeqUvXdLRgFLhwgUdry3sqc6weLEOt37kCNcIphKHGDiqBG++adwkU1y4oJeJk0XRBSHXkZvvTUuXLmXdunU0bNiQrl27smjRIh544IEU869bt4477riDSZMm0bFjR06ePEm/fv2oVq0a37uXvUwDGa3prsItqHVlPb8P+p47/9cl3eUJgiAIeQuJ7p6J2O12xo4di91u97UpOYZ0afbmm9pBb9PGnIMOeoh8kyYQEQH33qvTatRI1UG32/USaCVKQPHicO/O93BabHDkCACTGMLz72Sug55Et2LFxEFPA3KNGkc0M4foljY6dOjAmDFj6NIlbY7thg0bqFChAoMHD6ZixYrcfvvtPPvss2zcuDGTLU0gubZVgRLd/WbINWEc0cwcoptxRDNz+Eo3mZNuAlkmwzimNNu+Hb74Qn9Oab3ztODnB7Nm6eBrp07ptFTmo2/eDH376vXL3Sw9XJ1PeYYBTOMShQkZOYznXzVvUlqRc80coptxRDNziG4ZT7NmzXj11Vf55Zdf6NChA+fOnePbb7/lXvdD1hSw2+1ef6IiIyOTpFutVvz9/YmNjfVaTsdms+Hn54fD4UAphd1ux+Fw4HQ6Ad3OrkC93u31y1G4XC6sVmuSP23+/v5YLJYk50VAQABKqSTzXwMDA3G5XF7pFouFgIAAnE4ncXFxSdLj4uI8dhmpkxs/Pz9sNluSdH9//3TXya2by+XyKicn1wkyt53i4uJwOBxJbMnJdcqKdnK5XDgcDq99cnqdsqKdEmuWW+qU2e3k/l2z2+3prpMRR1+cdCH78tJLeqx59+66Jzw91KypI74PH66/J+OkX7iglz5//33deV+8OHz6qQ6mvm4dbFo+hp+/s2Pp0oXn3yqUPnsEQRCEZGnRogVz586le/fuxMTEEBcXR8eOHfnoo49S3W/s2LGMGjUqSfrEiRMJCtIOdnh4OJ06dWLp0qVsda+YAbRq1YrWrVuzYMECDh486En/999/adKkCdOnT6d69DUADu36j0OHDlGlShUmTpzo9ce0f//+hIaGMu6GB8vDhw8nIiKCadOmedICAgIYMWIEhw4dYu7cuZ70sLAwBgwYwPbt21myZIknvXLlyvTu3Zu1a9eyevVqT7rROnXs2JEGDRowffp0zruXIgF69eqVIXUCOHLkCAsWLMg1dcrMdmrRogUAixYt4vDhw7miTlnRThcvXgRg0qRJuaZOmd1OK1as8NIsN9QpK9tp0qRJ6a5TTEwMaUXmpBvEbrczbtw4hg8fTqAMPU4TpjT79Ve45x7w94e9e/W89PQSFwd33w2bNsGePZ4l2fbtg8mTdaf99fhRjN26wccfp221t8xCzjVziG7GEc3MkZG65eY56YmxWCw3nZO+e/du2rZty5AhQ2jfvj2nT5/mpZdeonHjxsyYMSPF/ZLrSS9Xrhznzp3zaGqkJ33SpEm89NJLBAcH43A42NfsSepsmcPceuN4ZMtLebJH6WZ1cuv28ssve0U3zsl1gszvSZ8wYQJDhw71ihydk+uUFe0UExPD+++/z5AhQzy/vzm9TpndTlFRUXzwwQcezXJDnbKqJ33SpEkMGTKEkJCQdNUpMjKS4sWLp+leL066QVwuFxcuXKBYsWIZE14/D2BYM6dTD03fuVPPJ//ggwyxw+mED96L49efYrkalw+HA2JitJPupkEDeO016NIlg5ZOSwdyrplDdDOOaGaOjNRNnPQEHn30UWJiYli4cKEnbe3atbRs2ZJTp05RqlSpNB0rPZom17Z7W/ej+upPmVN1FL33Z1XE0JyF/JYYRzQzh+hmHNHMHL6618twd4NYLBZCQ0PTvUB9XsKwZl9+qR30QoW0x5wBXLwIvXrBr7/6ceNpb7FAx47w4ot6aHt2aVo518whuhlHNDOH6JY5REdH4+fn/Ttts9kAyKp+heTa1i9EB45zXZPAcSkh14RxRDNziG7GEc3M4Svd5DGKQRwOB+PGjZNgQQYwpJndDq+/rj+//joUKZLu42/eDA0b6hH0+fLpoe0//aS///EHHD4MP/4Id9yRfRx0kHPNLKKbcUQzc4huaSMqKopt27axbds2AA4fPsy2bds4duwYACNGjKBPnz6e/B07duT7779n2rRpHDp0iHXr1jF48GCaNGlC6dKls8Tm5No2IFQ76eq6OOkpIdeEcUQzc4huxhHNzOEr3aQnXch6oqO1M57cEmibNukI7GFhMGiQoWJjYmDsWJg7F6xWyJ9fvzZt0oerXBm+/x7q1s2gegiCIAg3ZdOmTbRp08bzfejQoQD07duX2bNnc/r0aY/DDvDYY49x9epVpk6dyosvvkihQoW48847ee+997Lc9sS4nXSLOOmCIAhCJiNOupD1dOigh7Pv3atDqCdm+3b93rixoTXB//gD+vWD/fuT396xox5FX6iQOZMFQRAEc7Ru3TrVYeqzZ89Okvbcc8/x3HPPZaJVxgkqrJ10mz0apbLXyCtBEAQhdyFOupB+Tp+G2bN1SPSqVVPPe+0arFmjP69dC127em93O+n16t30sJcv66Hsc+YkLKdeqpReQq18eX2oa9cgNBTuvFP3rguCIAiCGfIV0U56oLpOdLQeqSUIgiAImYFEdzeIUgqHw0FAQEDuCrwQHQ3nz0NIiH75+6d936FD9QLjAQF6HfLhw/Xk73i8NNuyBRo10htefRXeece7rNtug7//hq+/hh49vDY5HPDLL/Dtt/DXX5Bo+UMsFt2TPnasdspzA7n2XMtkRDfjiGbmyEjd8kp096wkPZom17ZqxkwsTz3Jz9xLveM/U7ZsZlids5HfEuOIZuYQ3YwjmpnDV/d66Vs0iFKKiIiILIswmyVcu6Z7wCtUgKJFtbMdHAxpHWro7v12OGD0aKhdG5Yu9Wz20mz37oT9Nm/2Lsfl0sPgwasnfdMmGDBA95J36aLnnLsd9EqVtC+/bp1e1zy3OOiQS8+1LEB0M45oZg7RLfeSXNtagvXD53xc59IlX1mWvZFrwjiimTlEN+OIZubwlW7ipBskNjaWadOmJVnEPkfz2286WFtirl+HqVPhwIGb7+92vN94A8qUgUOH4N579URxbtBsz56E/TZvhsQn/MGDukc/KAiqVsXp1J3tjRvDtGlw6ZJ21F98EZYv18uqHTyoO92bNUunBtmQXHmuZQGim3FEM3OIbrmXZNs2X4KTfvmyjwzL5sg1YRzRzByim3FEM3P4Sjdx0gX4+Wf9PmiQ7g2/eBHat9dpH3+c+r6XLsGZM/rzSy9pJ7xDB/39p5+S5k/ck37hAhw/nvDd3SNfuzYR1/zo1EkPXwfo3l075sePw4QJ0K5dhqzOJgiCIAhpQ5x0QRAEIYsQJz2vo5Se6A1w3316LnqRIjB4sE6bNUv3bqeEu2f8llsS5rM/9JBO27Il5fw2W9I88U76lQr1aNpUmxUUpAPDzZ+vHXP3boIgCIKQpYiTLgiCIGQR4qSbICAgwNcmZBzbt8PJk3oOeuvWCen33KMnfF+5AvPmpby/u2e8Zs2EtAYN9PuWLXqeOfGa2e0Jw+fbtdPvmzcTE6NH3O+er530N7+rx759UK6cDgDfq1f6q5lTyVXnWhYiuhlHNDOH6JZ7SdK24qSnCbkmjCOamUN0M45oZg5f6CbR3fM6774Lr72mFxJfvNh72wcfwLBhOojb1q3JLwo7ZAhMnqwjvH/wgU6LjYUCBfTQ+QMHoHJlnf7vv1Cnjo7u9s47MGgQEc07UPvYL5w4AUcoT3mO0YpV+N/Vinnzki6jLgiCkBuRe1PGk+Gaxt/DzlOMKa+f5+2301+kIAiCkHeQ6O6ZiMvl4sCBA7jie4hzPO756Pfem3Tb44/r8ebbt8P69cnvn1xPur8/1K2rP2/dmqDZv//qtBo1oGFDAGLWb+bECUXVsCuU5xgAc3fU5bffxEHPdedaFiG6GUc0M4folntJtm2lJ/2myDVhHNHMHKKbcUQzc/hKN3HSDRIbG8vcuXNzR2TEixf1guOQvJNepEjCWPOpU5MvIzknHbyGvLs1c+3aBYCqUZORi+oRh40SnKPPnSfZ+sUOnf+WWyhbp3A6KpV7yFXnWhYiuhlHNDOH6JZ7SbZtEznply7mqUGIaUauCeOIZuYQ3YwjmpnDV7qJk56XWbZMzxmvU0cHfkuOgQP1+7ffwunT3tsiI+HECf25Rg3vbYnnpcdj2bsXgBnrazDq/XzsRjv2MwdtJv+B+MjuidZHFwRBEIRsQ7yTbsPF1UvyJ1cQBEHIPMRJz8u4h7rfd1/KecLDoXlziIuDzz/33uaO1F6qFBQqlHQ/0E66UsTG+nF+tXbSv99Xk4AACL5dD3m3bduSsPyaOOmCIAhCdiTeSQeIvnjdh4YIgiAIuZ1s4aR/9NFHVKhQgaCgIJo2bcrGjRtTzNu6dWssFkuS132pOZoZiMViISwsDEtyQdRyEnFxuicdUnfSAQYM0O9z5ninpzTUHXTvvM0G58+z9pvTfD6tP4XP7wegaIsabN8OVR6O723fvDnBSXfPZRdyz7mWxYhuxhHNzCG65V6SbdvAQFT895jL4qQnh1wTxhHNzCG6GUc0M4evdPN5dPdvvvmGPn368Mknn9C0aVMmT57MwoUL2bdvH8WTiRx26dIlHA6H5/vFixepV68e06dP57HHHrvp8SSCbjxr10LLllC4MJw7B35+Kee9ehWKFdPR2vfuhVtv1ekvvQQTJsBzz8GUKUl2U3XrYtm5k878yG5q8B/ViAvIhy06CovNqoPRtWihI8RFRkJMDOzbB9WqZVKlBUEQsidyb8p4MkNTV75grDHXaVTkEJsuVsyQMgVBEIS8QY6K7j5x4kSefvppHn/8cWrWrMknn3xCcHAwM2fOTDZ/kSJFKFmypOe1YsUKgoODeeihh7LEXqfTyZYtW3A6nVlyvEzDPdT9nntSd9ABQkIS1lBPvExbKj3pUVGwOlL3lIezhSdv2wCAX63q2kEHqF8frFb9kCAmRq/V7l6uTcg951oWI7oZRzQzh+iWe0mpbVWQHvLuiLhO3lrANm3INWEc0cwcoptxRDNz+Eq3m3hnmYvD4WDz5s2MGDHCk2a1Wmnbti0bNmxIUxkzZsygR48e5M+fP9ntdrsdu93u+R4ZGZkk3Wq14u/vT2xsrFd4fZvNhp+fHw6HA/eAA7vdzpIlS6hVqxZOp5PEAxH8/f2xWq1ex3OnWywWrxEAAAEBASilkkQLDAwMxOVyeaVbLBYCAgJwOp3ExcUlSY+Li/M6eVKtk82Ga8kSrEDs3Xfjstvx8/PDZrN51TVxnWLvvRf/5ctx/fgjsYMH6zrt3o0FcFSpgrLbiY6Go0cD2LtX8c470OZoA1rzBY/W+ofD+dcC4Lz1VuLi9bH4+RFQowbER3131a5NbFwcFqfTeJ1uaCfgpnXK7u3kdDpZsmQJVapUITAwMFfUKSva6dq1a1665YY6ZXY7Xb9+3aNZcHBwrqhTVrST+35Qs2ZNLBZLuup0o06Cb4mLi/Pc6202myfdkj8YrlzC33mdqCj9DFtIICXdhJQRzcwhuhlHNDOHr3TzqZN+4cIFnE4nJUqU8EovUaIEe+MjgafGxo0b+ffff5kxY0aKecaOHcuoUaOSpE+cOJGgoCAAwsPD6dSpE0uXLmXr1q2ePK1ataJ169YsWLCAgwcPJilj+vTpnD9/3vO9V69eVKlShYkTJ3r94erfvz+hoaGMGzfOa//hw4cTERHBtGnTPGkBAQGMGDGCQ4cOMXfuXE96WFgYAwYMYPv27SxZssSTXrlyZXr37s3atWtZvXq1Jz3VOl29inXXLuJsNibt2cP1cePo2LEjDRo0SLFOn508yUCA9ev58M03eaxfP4ofOaLr978jLHq4AZcvF4nfS8/ZKBlUHWIg7PhaomwVAFh97hx/xusQFhbGgIYNPU76VqeTn8aNM1enZNrpZnXK7u3UrFkzACZNmpRr6pSV7TRp0qRcVyfI3HaaNGlSrqsTZH47ORwOIiMj01Wn0qVLI2R/LMHea6WLky4IgiBkBj6dk37q1CnKlCnD+vXrPQ4JwMsvv8zq1av5+++/U93/2WefZcOGDezYsSPFPMn1pJcrV45z58555gIY7UmfNGkSw4cPx2Kx5LweJZcLv/Bw2LePuGHDcI4ZA6StR8m/SROsO3YQO2MGtjp1sDZpQnSBMApcO4tS2jEvXFhx661Qu7aLEYMuUzG8OBaluBIaSqGICGK/+QZX584Jtn/yCTz/PACxH36I69lns00vmU/bKb4nfdy4cQwZMkR60g3UKSoqikmTJnl0yw11yoqedLdm0pNurCd90qRJvPLKKwQEBKSrTlevXiUsLEzmpGcg6ZmTbrfbGTduHMOHD/f8/gJ6BZIdO2jHciZsaycLktxAiroJKSKamUN0M45oZo6M1M3IfcmnPenFihXDZrNx9uxZr/SzZ89SsmTJVPe9du0a8+fPZ/To0anmc/9JT0u6v79/smUEBAR4PlssFipXruz5o5XSMdOabrFYkk23Wq3JpttstmSHWvj5+eGXzNzyJHWaNk0HZytWDL/XX8fvhmOkWqdOnWDHDvyXLYP4cjdG1URhoV8/GD0aihWzoIMf2oBiOgjcvn0UiojQ9tSrB4mP6V5PHfBv0MBrW5rrdBPbc2Q7AS6Xi8qVKxMUFJSkDjm1TpD57RQUFJREt5xep8xuJ6WURzN3npxep+TI6Dq57wdWqzVF29NapxsfCAi+JfG93ot83j3pgjcp6iakiGhmDtHNOKKZOXylm8+juzdt2pQmTZrwv//9D9COyS233MKgQYMYPnx4ivvNnj2bfv36cfLkSYoWLZrm4+XpCLqRkVClCpw/D1OnwsCBxvb/5x9o0gQVEsLa6k/T8p+JfEx/Toz4mHfegWTP3Z494euv9Wd/f4iO9g5UFxUFpUuDUnDyJOS1NhEEQSCP35syiUzRtE0bWLWK7synx/fd6dIlY4oVBEEQcj85Krr70KFD+fzzz/niiy/Ys2cP/fv359q1azz++OMA9OnTxyuwnJsZM2bwwAMPGHLQM4K4uDhWrVrlNUwxx/Dee9pBv/VWeOYZ4/s3bIizeEksV69S458vAKjWuSbvvpuCgw4QHu75qKpVSxpJvkABWL0aVq0SB/0GcvS55kNEN+OIZuYQ3XIvKbat9KSnilwTxhHNzCG6GUc0M4evdPO5k969e3cmTJjAm2++Sf369dm2bRvLli3zBJM7duwYp0+f9tpn3759rF27lieffDLL7XU6naxevTrnLV9w/DhMnKg/v/++Z7h6WlEKZs628tXljgAU4yIAbQcnXX7Ni0TD2V3u9dVvJDwcGjY0ZE9eIMeeaz5GdDOOaGYO0S33kmLbipOeKnJNGEc0M4foZhzRzBy+0s2nc9LdDBo0iEGDBiW7bdWqVUnSbr31Vnw8Sj/n8cYbei3yVq2gY0dDux4/rjvely2D++nIY3yesDGZNdK9SNyTXr26oeMKgiAIQrYikZN+6ZKPbREEQRByLT7vSReygPPnYd48/fn991MZm+6NUjBjBtSurR30wEC4c8xdqPg/KRQuDDcsn5eEIkVQ5cvr8sRJFwRBEHIy0pMuCIIgZAHipBvEarUSHh6O1ZqDpPvqK4iNhUaNoEmTNO1y7Bjccw889ZSON9e0KWzdCkNeC8bStq3OVLNmmhx+5zvvcOKOO3R0eCHN5MhzLRsguhlHNDOH6JZ7SbFtxUlPFbkmjCOamUN0M45oZg5f6SatZBB/f386deqU4hI/2Q53dzhAGufw//MP1K0Ly5fr3vPx42HdOqhRIz7DE0/o97vvTlN5fr16UXb1avxDQw0an7fJcedaNkF0M45oZg7RLW2sWbOGjh07Urp0aSwWCz/88MNN97Hb7bz22muUL1+ewMBAKlSowMyZMzPf2HhSbNtETvqZM/FpV67o5U0vXMgy+7Irck0YRzQzh+hmHNHMHL7STZx0g8TGxrJ48WJiY2N9bUra2LgRdu+GoCB45JGbZt+1S/egR0RA48awbRsMGwZeSxQ/8ACcOAGvvpomE3KcZtkE0c0coptxRDNziG5p49q1a9SrV4+PPvoozfs8/PDDrFy5khkzZrBv3z6+/vprbk0p+GgmkGLbJnLSjxyJT5s6FQYMSAjOmoeRa8I4opk5RDfjiGbm8JVu4qQbxOVysXXrVlwul69N8UYpkh175+5F79YNbtKTfegQtGsHly7pUfErV0KK08jLlEm6nFoKZFvNsjmimzlEN+OIZuYQ3dJGhw4dGDNmDF3SuKj4smXLWL16Nb/88gtt27alQoUKNGvWjBYtWmSypQmk2LaJnPTjxyEuDti7V2/zdK3nXeSaMI5oZg7RzTiimTl8pVu2iO4uAE4nPP88lC8PL71kbL/vvoN334Xt23VgOPf+167B/Pn6802Gup86BW3bwunTOlDc0qUQEmKyLoIgCIJgksWLF9OoUSPef/99vvrqK/Lnz0+nTp14++23yecOXJoMdrsdu93u+R4ZGZkk3Wq14u/vT2xsrNcfLpvNhp+fHw6HA6WUJ797yR13us3fHz+ggCUapxMOHbJT5dAhrIAzMhKLy4XFYsHhcHjZFhAQgFIqSU9MYGAgLpfLK91isRAQEIDT6fRal9edHhcX57UUUFrr5MbPzw+bzZYk3d/fH6vV6qWhOz2tdXLv63K5vMrJyXWCzG0n97432pKT65QV7eQ+XuJ9cnqdsqKdEmuWW+qU2e3k3ma329NdpxuPkxripGcX/vwT3EMBO3eGatVSz+9y6YBwY8fCvn0J6S+/DMWLQ9++8O23cPUqVK6sl15Lgb//hj594PBhnXX5cihSJAPqJAiCIAgGOXToEGvXriUoKIhFixZx4cIFBgwYwMWLF5k1a1aK+40dO5ZRo0YlSZ84cSJBQUEAhIeH06lTJ5YuXcrWrVs9eVq1akXr1q1ZsGABBw8e9KT/+++/NGnShOnTp3P+/HkabN5MR6BQ0DW4DhMmLGTCv/9SEDi0fTuhFy4QGhrKuHHjvGwYPnw4ERERTJs2zZMWEBDAiBEjOHToEHPnzvWkh4WFMWDAALZv386SJUs86ZUrV6Z3796sXbuW1atXe9KN1qljx440aNDAUyc3vXr1okqVKkycONHrz3b//v0N1QngyJEjLFiwINfUKTPbyT1CZNGiRRw+fDhX1Ckr2unixYsATJo0KdfUKbPbacWKFV6a5YY6ZWU7TZo0Kd11iomJIa1YVB5bcDwyMpLQ0FAiIiIoWLCg4f3j4uJYu3Ytt99+O35pHO6dJl57TfeGAwweDB9+mHr+Tz6B/v3158KF9T4RETB5sp5AvmSJduD//BPeeSfZ+eNRUfD66zBlih4tX7aszl6hQsZVCzJRs1yO6GYO0c04opk5MlK39N6bcgoWi4VFixbxwAMPpJjn7rvv5s8//+TMmTOExk/T+v777+nWrRvXrl1LsTc9uZ70cuXKce7cOY+mae19iYuLY8OGDbRs2ZLAwEBPunXePPyfeILNRdrS6NIKZk6L4rEBBbEohat5c/jzz1zdo3SzOsXFxfHPP//QvHlzr7Jzcp0gc9tJKcVff/1FkyZNsCUKAJST65QV7RQbG8uaNWto1qyZ5/c3p9cps9vp+vXrrFu3zqNZbqhTVrST+37QrFkzgoOD01WnyMhIihcvnqZ7vTjp2YXGjWHTJv05JAROnkx9vPlzz+lgNQ8+CLNm6bwul+5BnzNHz5u7fh2sVr2eWpkyXruvWKGXVzt2TH/v0wc++ACKFcuk+gmCIAgpkm3vTRlMWpz0vn37sm7dOg4cOOBJ27NnDzVr1mT//v1UrVo1TcfKFE2/+w66deO/Ei2odnYtUwYf4Lkp8fbUr6/XKhUEQRCEZDByX5LAcQZxOBzMmTMnydOidHHxImzerD+XLauHqH/5Zer7nDql31u3TnDmrVYdKO7uu7WDDjpU+w0O+rp1cN992kGvWBF+/RW++CLzHPRM0SwPILqZQ3QzjmhmDtEtc2jRogWnTp0iKirKk7Z//36sVitly5bNEhtSbNv4XvwCNn2Pte87krAtkb15FbkmjCOamUN0M45oZg5f6SZOukGUUhw8eJAMHYCwcqUeb16rFrzyik6bOlWnpcTp0/q9VCnv9IAAPRe9USP9fdAgr83Hj0PXrhAbq1dS27kzzcudmyZTNMsDiG7mEN2MI5qZQ3RLG1FRUWzbto1t27YBcPjwYbZt28ax+KFcI0aMoE+fPp78PXv2pGjRojz++OPs3r2bNWvW8NJLL/HEE0+kGjguI0mxbRNFdwewHD2SsO3q1SyxLTsj14RxRDNziG7GEc3M4SvdxEnPDsQHcuDuu/Vw9ZAQvaTLb7+lvI+7J7106aTbQkL05PKdO6FDB09ydLR2zM+dg7p19aj4/PkzrhqCIAiCcCObNm0iPDyc8PBwAIYOHUp4eDhvvvkmAKdPn/Y47AAFChRgxYoVXLlyhUaNGtGrVy86duzIlClTfGK/F/FOepDSTnrQmSMJ26QnXRAEQcggJEKQr1FKh1MH7aSHhMBjj8H//qcjurVrl/w+KfWkuwkK0mupJdrlqadgyxY9rP3HH8VBFwRBEDKf1q1bp9oDMXv27CRp1atX90QizlbEO+n+cdpJLxxxJGHbtWs6NoxV+j8EQRCE9CF3EoP4+fnRsWPHjIuA/N9/enJ4QADccYdOcw9R//lnOHQo6T6XL4N7XkRKTvoNvP8+fP01+Pnp0fAZHcE9NTJcszyC6GYO0c04opk5RLfcS4ptG++kW+3X8fODW9QR7+3XrmWNgdkUuSaMI5qZQ3QzjmhmDl/pJtHdfc3UqTpS+5136rnpbjp0gGXLYOhQHXY9Mf/+C3Xq6MXM49eJTI2FC+Hhh/Xnjz9OWLlNEARByB5ku3tTLiBTND1+HG65Bfz9qVzOwepDZSnLyYTtp06l+eG5IAiCkLeQ6O6ZiMPh4OOPP864CH/u4Xw3Dmvv10+///RT0n3cQ92Tm49+A+vXw6OP6s/PPZdQbFaS4ZrlEUQ3c4huxhHNzCG65V5SbFt34LrYWKqWvU5p4uPDWCz6PY/PS5drwjiimTlEN+OIZubwlW7ipBtEKcX58+czJsJfbCz88Yf+fGOI9Xr19PvRo3qOW2LcQeNu8rT+v/+gUyew2/X7pEkJ/yOykgzVLA8huplDdDOOaGYO0S33kmLbJoou37TQPqwoYv3zQcmSOjGPO+lyTRhHNDOH6GYc0cwcvtJNnHRf8vffesmWYsWgfn3vbWXK6OAzdrsOx56YNPSkX7gA996rR8M3agTz5oHNlrHmC4IgCEKeIpGTXjdgLwDn81fQQV9BlmETBEEQMgRx0n2JO6p727ZJo8H6+0PZsvrzkSPe227Sk+5yQY8ecOCADhD3008SyV0QBEEQ0o3VqgO9AlVi9wBwwlYBChTQ2/N4T7ogCIKQMYiTbhB/f3969eqFv79/+gtLvPRacrhDsN/opN+kJ/1//9Mx6IKDdYD4EiXSbWm6yFDN8hCimzlEN+OIZuYQ3XIvqbZtcDAApSK0k/5fbAVx0uORa8I4opk5RDfjiGbm8JVuEoPfIFarlSpVqqS/oOvX4Z9/9Oe2bZPPU768fj961Ds9lZ70PXtg+HD9ecIEqFkz/aamlwzTLI8huplDdDOOaGYO0S33kmrb5ssHV65Q6LR20nddq4AqcAwL5HknXa4J44hm5hDdjCOamcNXuklPukHsdjtjx47Fbrenr6AjR/S49IIFE4a134jBnvTYWB3JPSYG7rnHN5HckyPDNMtjiG7mEN2MI5qZQ3TLvaTatvHz0v0P7wfggLMCMX7xPel5fE66XBPGEc3MIboZRzQzh690EyfdBBkSgv/QIf1eqVLKIdfdPemJnXSlUuxJHzMGNm+GwoVhxgzfRHJPCVnuwRyimzlEN+OIZuYQ3XIvKbZtvJNuid9+hApEumS4uxu5JowjmplDdDOOaGYOX+gmTrqvSOykp4S7Jz3xcPcrV3TEd/By0jdsgHfe0Z+nTUvTEuqCIAiCIBglUYR3gKOU53JcfHR3cdIFQRCEDECcdF9hxEk/ckT3oENCL3rhwhAUBMC6ddChAzid8Mgj0L17plgsCIIgCEIiJ91hC+IcxbkQIz3pgiAIQsYhTrpB/P396d+/f/oj/Lmd9MqVU85Trpwes379Opw/r9Pc89Hje9GXLYN27SAiAlq2hE8+SZ9ZmUGGaZbHEN3MIboZRzQzh+iWe0m1bRM56RGFKwAWzkXLnHSQa8IMopk5RDfjiGbm8JVu4qQbxGKxEBoaiiW9E77T0pMeEJAwbt095N3dk166NAsXQqdO2ofv0EE77AULps+szCDDNMtjiG7mEN2MI5qZQ3TLvaTatomcdHupCgCcipSedJBrwgyimTlEN+OIZubwlW7ipBvE4XAwbty49AUQUCptTjokjfAe35N+wlWKHj10RPfu3eGHHzxLt2Y7MkSzPIjoZg7RzTiimTlEt9xLqm2byEm3xN+jj12ROekg14QZRDNziG7GEc3M4SvdxEn3BWfPQnQ0WK1wyy2p570xwnu8k750e2lcLujbF+bO1Z3ugiAIgiBkMomc9Hw1KgBw9IIMdxcEQRAyDnHSfYG7F71cuZt71zdGeI8f7v7vxVIUKgQffgg2W6ZYKQiCIAjCjSRy0gvWrYDVCpfjZLi7IAiCkHGIk+4L0jrUHZL0pKtTuif9FKUZNgxCQzPBPkEQBEEQkieRk+5XpQJly0IU4qQLgiAIGYdFKffaXnmDyMhIQkNDiYiIoKCJKGtKKRwOBwEBAeYDCIweDW+9BU8+CdOnp553+XJo3x5q14adO7lavDIh5w9xb8iffHPydkJCzJmQlWSIZnkQ0c0coptxRDNzZKRu6b03CUlJj6aptu0bb8CYMfrzmTPc8VAJLv+5k53UheLF9ZS2PIr8lhhHNDOH6GYc0cwcvrrXS0+6QZRSREREkK5nG0Z60hMFjot1KPwu6J70Tv1K5wgHHTJIszyI6GYO0c04opk5RLfcS6pt6+5JDwqC4sWpUCFRT3oen5Mu14RxRDNziG7GEc3M4SvdxEk3SGxsLNOmTSM2NtZ8IUacdHdguagofph0mHzqOgC9Xypl/vhZTIZolgcR3cwhuhlHNDOH6JZ7SbVt3U56hQpgsVCxYiIn/fp1cDqzzM7shlwTxhHNzCG6GUc0M4evdBMn3RcYcdKDgqBkSQDWTdgAQEy+QhQIy5faXoIgCIIgZAbuYWwVKwJQty5cJdHQtmvXfGCUIAiCkJvw87UBeY6YGDh5Un9Oi5MOOnjcmTNUvbAegIDyOacXXRAEQRByFZ07w/r18MQTAISHg51A4rDhh1MHj5O4AoIgCEI6MNyTXqFCBUaPHs2xY8cyw54cQUB6FiV3r3ceEgJFi6Zpl6hiFQBohu5Jt5Ypbf74PiJdmuVhRDdziG7GEc3MIbrlXlJs27AwmDkTbr8d0B3qoaEWmZcej1wTxhHNzCG6GUc0M4cvdDMc3X3y5MnMnj2bf//9lzZt2vDkk0/SpUsXAgMDM8vGDMXnEXR/+QXuuw/q1YNt226aXSmYX2E4jxx7D6fFhk05oXdv+OqrzLdVEARByBJ8fm/KhWSlpq1bw5zVZSnLSdi0CRo2zNTjCYIgCDmPTI3u/sILL7Bt2zY2btxIjRo1eO655yhVqhSDBg1iy5Ytpo3OKbhcLg4cOIDL5TJXgJH56OiH9WuO6bXSbSo+GE2pnDXcPd2a5VFEN3OIbsYRzcwhuqWNNWvW0LFjR0qXLo3FYuGHH35I877r1q3Dz8+P+vXrZ5p9yWG0bevXTzQvPQ+vlS7XhHFEM3OIbsYRzczhK91MB45r0KABU6ZM4dSpU7z11ltMnz6dxo0bU79+fWbOnJlrw/vHxsYyd+5c8xH+DDjpp07Biy/CESp4byids4a7p1uzPIroZg7RzTiimTlEt7Rx7do16tWrx0cffWRovytXrtCnTx/uuuuuTLIsZYy2bXh4ogjvedhJl2vCOKKZOUQ344hm5vCVbqad9NjYWBYsWECnTp148cUXadSoEdOnT+fBBx/k1VdfpVevXmkq56OPPqJChQoEBQXRtGlTNm7cmGr+K1euMHDgQEqVKkVgYCDVqlXjl19+MVuNrCeNTrpSMHAgRERAwdrlvTfmsJ50QRAEIe/SoUMHxowZQ5cuXQzt169fP3r27EmzZs0yybKMI7GT7orI23PSBUEQhPRjOLr7li1bmDVrFl9//TVWq5U+ffowadIkqlev7snTpUsXGjdufNOyvvnmG4YOHconn3xC06ZNmTx5Mu3bt2ffvn0UL148SX6Hw0G7du0oXrw43377LWXKlOHo0aMUKlTIaDV8x8GD+v0mTvrvv8MPP4CfH7wxvTzclmhjDutJFwRBEAQjzJo1i0OHDjFnzhzGjBmTpn3sdjt2u93zPTIyMkm61WrF39+f2NhYr6GLNpsNPz8/HA4HSilPfmf8mufudDf+/v5YrVZPvkqV4LilACi4cOQqoYnsAB10SCmVpCcmMDAQl8vllW6xWAgICMDpdBIXF5ckPS4uzmOXkTq58fPzw2az3bROidMtFgsOh+OmdXLv63K5vMrJyXWCzG0n97432pKT65QV7eQ+XuJ9cnqdsqKdEmuWW+qU2e3k3ma329NdpxuPkxqGnfTGjRvTrl07pk2bxgMPPIC/v3+SPBUrVqRHjx43LWvixIk8/fTTPP744wB88skn/Pzzz8ycOZPhw4cnyT9z5kwuXbrE+vXrPcetUKFCqsfIyJs26B/RYsWKeRrOUEPb7QQcOoQFcJQti79SKTb05MkKsPDUU06q1vdDhYVhOX9e2160KMQfIzucvIlJ7uR1OByEhYV5/fFJbHt2vCBvVifI/B8Zi8VCsWLFvOzJ6XXKinZyOBwULVrUsz031Cmz2ymxZrmlTlnRTm7dgCS2G63TjTrlZf777z+GDx/On3/+iZ9f2v+mjB07llGjRiVJnzhxIkFBQQCEh4fTqVMnli5dytatWz15WrVqRevWrVmwYAEH3Q/TgX///ZcmTZowffp0zsffgwF69epFlSpVmDhxoqft6gb6QQyc2BvJtHHjvGwYPnw4ERERTJs2zZMWEBDAiBEjOHToEHPnzvWkh4WFMWDAALZv386SJUs86ZUrV6Z3796sXbuW1atXe9KN1qljx440aNAgTXUC6N+/P6GhoYxLY53CwsI4evQo33zzTa6pU2a20+23305YWBiLFi3i8OHDuaJOWdFOly5dAmDSpEm5pk6Z3U6//fabl2a5oU5Z2U6TJk1Kd51iYmJIK4ajux89epTy5cvfPONNcDgcBAcH8+233/LAAw940vv27cuVK1f48ccfk+xz7733UqRIEYKDg/nxxx8JCwujZ8+evPLKK9hstmSPM3LkyGRv2sOHD09y0168eHGyDT1nzpxkG/rjjz9OtqHHjh2bbEP/7/XXGTZhAgp45/XXeen115Nt6IcfHkHVqgqlLAwaNJVixS7Sf+ZMiscve/fuq68SG78UgPvkXbVqVbInb2bXycgFeeDAgWRP3i1btiR7QUqdpE5SJ6lTXqlT6dKleeaZZ3J9dHeLxcKiRYu87vuJcTqd3HbbbTz55JP069cP0PfxH374gW03WREluYfy5cqV49y5cx5NM/NBzoa6g2i9fzq/3TGKlstf8bItNz2ckjpJnaROUiepk7k6RUZGUrx48TTd6w076f/88w8ul4umTZt6pf/999/YbDYaNWqUpnJOnTpFmTJlWL9+vdd8s5dffpnVq1fz999/J9mnevXqHDlyhF69ejFgwAAOHDjAgAEDGDx4MG+99Vayx8nom7bT6WT37t2Eh4fjdDoNNXTsmjUEtG6NKlcOx3//pdjQI0YEMmkS3H23i8WL9Tb/nj2xfv89qmBBHOfOefLmhJPX6XSyd+9e6tSp42VLXrkgzdbJYrGwefNmateu7XkIldPrlBXtFBMTw86dO6lTpw42my1X1Cmz2yk2NtajWUBAQK6oU1a0k9PpZOfOnTRs2BCr1ZquOl29epWwsLA876RfuXKFwoULez14d7lcKKWw2WwsX76cO++8M03HSs8SbE6nk+3bt1OvXr0UOwFuZNudQ6n/xyS+rfQy3Q6+Z+h4uQUzuuV1RDNziG7GEc3MkZG6GbkvGR7uPnDgQF5++eUkTvrJkyd57733knWuMwqXy0Xx4sX57LPPsNlsNGzYkJMnTzJ+/PgUnfTAwMBk13BPLj25ofvgvYC93W7n559/pk6dOimuDZ9SesCJEwBYKlf25LFYLF75o6L0smsAL7xgTdhWsaLOX7p0suX7+fklOywwLXVKS7qRut5YJ7vdzpIlS6hVq1ay+W02W7InfXaukxur1ZppdbLb7SxdupR69eolOUZOrRNkfjvZbDaWLVtG/fr1PXlyep0yu51cLpdHM3eenF6n5MjoOtntdi/d0lMnI/PUcjMFCxZk586dXmkff/wxv//+O99++y0V4++FmU1cXJznvpXWP2VFKugl2KLO5N3o7mZ0y+uIZuYQ3YwjmpnDV7oZdtJ3795NgwYNkqSHh4eze/fuNJdTrFgxbDYbZ8+e9Uo/e/YsJUuWTHafUqVK4e/v7yVQjRo1OHPmDA6HI8U/WtmGNER2/+orHdG9alVo3z7RBvfce4nsLgiCIOQgoqKiOHDggOf74cOH2bZtG0WKFOGWW25hxIgRnDx5ki+//BKr1Urt2rW99i9evDhBQUFJ0rMbJSrp6O6W6CjOnYNk4t8KgiAIQpowvARbYGBgEsca4PTp04YCvAQEBNCwYUNWrlzpSXO5XKxcuTLF5VZatGiRZDH5/fv3U6pUqezvoMNNnXSl4H//05+few6siVuna1do1w4GD85cGwVBEAQhA9m0aRPh4eGEh4cDMHToUMLDw3nzzTcB/f/hWHzMlZxMYFHtpIdwlUQhEQRBEATBMIad9LvvvpsRI0YQERHhSbty5Qqvvvoq7dq1M1TW0KFD+fzzz/niiy/Ys2cP/fv359q1a55o73369GHEiBGe/P379+fSpUs8//zz7N+/n59//pl3332XgQMHGq2GaSwWC5UrV8ZisRjf+SZO+sqVsGcPFCgAffvesLF0aVi+HFKYx5edSZdmeRjRzRyim3FEM3OIbmmjdevWqPjVTBK/Zs+eDcDs2bNZtWpVivuPHDnypkHjMhpTbVtAO+kFiMqzTrpcE8YRzcwhuhlHNDOHr3QzHDju5MmT3HHHHVy8eNHzVHzbtm2UKFGCFStWUK5cOUMGTJ06lfHjx3PmzBnq16/PlClTPPPdW7duTYUKFTw3coANGzYwZMgQtm3bRpkyZXjyySdTje5+I+kJJJNubrkFjh+HDRvgttuSbO7UCZYs0b3oU6ZkrWmCIAiC7/DpvSmXkuWa/vADdOnCepoxpft65s/P/EMKgiAIOQcj9yXDPellypRhx44dvP/++9SsWZOGDRvy4YcfsnPnTsMOOsCgQYM4evQodrudv//+2ysg3apVq7wcdIBmzZrx119/ERMTw8GDB3n11VezdBJ/XFwcq1at8orKm2bcS/wkM+f+xAn46Sf9edCgdBiYDUmXZnkY0c0coptxRDNziG65F1NtKz3pck2YQDQzh+hmHNHMHL7SzbCTDpA/f36eeeYZPvroIyZMmECfPn1SjKab23A6naxevdpr2Z80cf06uBewL1Ikyebly/Wc9Ntug2rVMsDQbIRpzfI4ops5RDfjiGbmEN1yL6batkDCnPT//tOrteQ15JowjmhmDtHNOKKZOXylm+Ho7m52797NsWPHkqyB26lTp3QblSu5dEm/22wQEpJk84oV+t3gtH5BEARBELID8ff2gtYolAu2b4cWLXxskyAIgpAjMeykHzp0iC5durBz504sFgvuKe3uyfTydCYF3E56kSJwQ+ABl0sHjQNo2zaL7RIEQRCEFDh+/DgWi4WyZcsCsHHjRubNm0fNmjV55plnfGxdNiPRcHeATZvESRcEQRDMYXi4+/PPP0/FihU5d+4cwcHB7Nq1izVr1tCoUaNUo7PmFqxWK+Hh4VitBqVL7KTfwI4derp6/vzJxpPL8ZjWLI8juplDdDOOaGaOvKBbz549+eOPPwA4c+YM7dq1Y+PGjbz22muMHj3ax9ZlHqbaNt5JD3TFYCOOZcsyybhsTF64JjIa0cwcoptxRDNz+Eo3w9HdixUrxu+//07dunUJDQ1l48aN3Hrrrfz++++8+OKLbM3m0VJ8FkF30SK91nmzZrB+vdem8ePh5ZfhvvsSgscJgiAIeYfsGt29cOHC/PXXX9x6661MmTKFb775hnXr1rF8+XL69evHIffSotmQLNfUboegIAAKcZnrAYW4eNHjuwuCIAh5nEyN7u50OgmJn3dVrFgxTp06BUD58uXZt2+fCXNzFrGxsSxevJjY2FhjO168qN+LFk2y6bff9HtunY9uWrM8juhmDtHNOKKZOfKCbrGxsQQGBgLw22+/eeLOVK9endOnT/vStEzFVNsGBkJ8EN1at0ThcCTEm8kr5IVrIqMRzcwhuhlHNDOHr3Qz7KTXrl2b7du3A9C0aVPef/991q1bx+jRo6lUqVKGG5jdcLlcbN26FZfLZWzHFIa7x8TAmjX6c26dj25aszyO6GYO0c04opk58oJutWrV4pNPPuHPP/9kxYoV3HPPPQCcOnWKosk8dM4tmG7b+G7z+1rpeelLlmS0ZdmbvHBNZDSimTlEN+OIZubwlW6GnfTXX3/dY+To0aM5fPgwLVu25JdffmHKlCkZbmCuIQUnfd067aiXLg01a/rALkEQBEFIgffee49PP/2U1q1b88gjj1CvXj0AFi9eTJMmTXxsXTYk3klv2/QqAD//rIPDCoIgCIIRDEd3b9++vedzlSpV2Lt3L5cuXaJw4cKeCO9CMqTgpLuHurdtmyTouyAIgiD4lNatW3PhwgUiIyMpXLiwJ/2ZZ54hODjYh5ZlU+Kd9PCqURQsCOfOwT//QNOmPrZLEARByFEY6kmPjY3Fz8+Pf//91yu9SJEiecZBt9lstGrVCpvNZmzHFJz0vLA+umnN8jiimzlEN+OIZubIC7pdv34du93ucdCPHj3K5MmT2bdvH8WLF/exdZmH6baNj9njb4/C3aeRl4a854VrIqMRzcwhuhlHNDOHr3QzHN29UqVKLFq0yDPkLafhswi6d94Jf/wB8+bBI48AOpZcWBgoBadOQalSWWeOIAiCkH3IrtHd7777brp27Uq/fv24cuUK1atXx9/fnwsXLjBx4kT69+/vaxNTxCea3nUX/P47zJvHV3GP0KcP1K0L8aF8BEEQhDxMpkZ3f+2113j11Ve55O4ZzmM4HA7mzJmDw+EwtmMyPem//64d9Nq1c7eDblqzPI7oZg7RzTiimTnygm5btmyhZcuWAHz77beUKFGCo0eP8uWXX+bqODSm29a93trVq9x7L1itsGMHHD2a8TZmR/LCNZHRiGbmEN2MI5qZw1e6GZ6TPnXqVA4cOEDp0qUpX748+fPn99q+ZcuWDDMuO6KU4uDBgxgcgJCsk54XhrpDOjTL44hu5hDdjCOamSMv6BYdHe1ZdnX58uV07doVq9XKbbfdxtFc7Hmabtt4rYiKomhRaN4c1q6Fn36CgQMz3s7sRl64JjIa0cwcoptxRDNz+Eo3w076Aw88kAlm5AFucNKVSnDSc+vSa4IgCELOpkqVKvzwww906dKFX3/9lSFDhgBw7ty5bDUsP9vg7kmP0kuw3X9/3nLSBUEQhIzBsJP+1ltvZYYduRu7Ha5d05/jnfS9e+HIEQgIgDvu8J1pgiAIgpASb775Jj179mTIkCHceeedNGvWDNC96uHh4T62Lhtyg5PesSMMH66nt0VFJWwWBEEQhNQwPCc9r+Pn50fHjh3x8zPwfMPdi261QmgoAD/+qJPuuiv337RNaSaIbiYR3YwjmpkjL+jWrVs3jh07xqZNm/j111896XfddReTJk3yoWWZi+m2TTQnHaBGDahUCRwOWLYsg43MhuSFayKjEc3MIboZRzQzh690Mxzd3Wq1prrcmtPpTLdRmYlPor3u2qWjwxUtChcuANCsGfz1F3zyCTz7bNaYIQiCIGRPsmt098ScOHECgLJly/rYkrThE00/+ACGDYPeveGrrwB4+WUYPx4efBC+/TZrzBAEQRCyH5ka3X3RokV8//33ntc333zD8OHDKVWqFJ999plpo3MKDoeDjz/+2FiEvxvmo585A3//rZM6dsxgA7MhpjQTRDeTiG7GEc3MkRd0c7lcjB49mtDQUMqXL0/58uUpVKgQb7/9Ni6Xy9fmZRqm2/aG4e7gWXWVn36CyMgMMjCbkheuiYxGNDOH6GYc0cwcvtLNcL99586dk6R169aNWrVq8c033/Dkk09miGHZFaUU58+fNxbh7wYnfckSHTiuSRMoXToTjMxmmNJMEN1MIroZRzQzR17Q7bXXXmPGjBmMGzeOFi1aALB27VpGjhxJTEwM77zzjo8tzBxMt20yTnr9+nDrrbBvH/zwA/Tpk2FmZjvywjWR0Yhm5hDdjCOamcNXumXYnPTbbruNlStXZlRxuYsbnHT3fPRknncIgiAIQrbhiy++YPr06fTv35+6detSt25dBgwYwOeff87s2bN9bV7244Y56QAWS0Jv+tdf+8AmQRAEIceRIU769evXmTJlCmXKlMmI4nIfiZz0qCj47Tf9VZx0QRAEITtz6dIlqlevniS9evXqXHLf24QEEq2Tnhi3k75iBZw/n8U2CYIgCDkOw0564cKFKVKkiOdVuHBhQkJCmDlzJuPHj88MG7MV/v7+9OrVC39//7TvlMhJX75cr8hWuTLUrJk5NmY3TGkmiG4mEd2MI5qZIy/oVq9ePaZOnZokferUqdStW9cHFmUNpts2meHuANWqQcOG4HTCwoUZZGQ2JC9cExmNaGYO0c04opk5fKWb4TnpkyZN8orubrVaCQsLo2nTphQuXDhDjcuOWK1WqlSpYmynRE564qHuqQTJz1WY0kwQ3UwiuhlHNDNHXtDt/fff57777uO3337zrJG+YcMGjh8/zi+//OJj6zIP022bzHB3N488Aps36yHvAwak08BsSl64JjIa0cwcoptxRDNz+Eo3wz3pjz32GH379vW8Hn30Ue6555484aAD2O12xo4di91uT/tOFy8C4Awtwk8/6aS8NNTdlGaC6GYS0c04opk58oJurVq1Yv/+/XTp0oUrV65w5coVunbtyq5du/gqfomxm7FmzRo6duxI6dKlsVgs/PDDD6nm//7772nXrh1hYWEULFiQZs2aea3RnhWYbtsUhrsDdO+uH86vXQvHjmWAkdmQvHBNZDSimTlEN+OIZubwlW6GnfRZs2axMJmxWgsXLuSLL77IEKOyO4ZD8Mf3pO+7UJRLl/Ry6c2bZ4Jh2RhZ7sEcops5RDfjiGbmyAu6lS5dmnfeeYfvvvuO7777jjFjxnD58mVmzJiRpv2vXbtGvXr1+Oijj9KUf82aNbRr145ffvmFzZs306ZNGzp27MjWrVvTUw3DmGpbd0+6w6FfiShbFu64Q3+ePz+dxmVj8sI1kdGIZuYQ3YwjmpnDF7oZHu4+duxYPv300yTpxYsX55lnnqFv374ZYliuIt5J/3OXju5+//3gZ1h5QRAEQch5dOjQgQ4dOqQ5/+TJk72+v/vuu/z4448sWbKE8PDwFPez2+1ePR2R8YuSJ063Wq34+/sTGxvrtc67zWbDz88Ph8OBUsqT3+l0AnjS3fj7+2O1WpP0rPjny+fp/bBfugTxowwDAgJQStGtm5PVq/2ZN8/F88/HEhgYiMvlIjY21lOGxWIhICAAp9NJXFxckvS4uDiPXUbq5MbPzw+bzZb2Ovn7Y7FYkvxJddcpse3ufV0ul1c5OblOQKa2k3vfG23JyXXKinZyHy/xPjm9TlnRTok1yy11yux2cm+z2+3prpOR3njDruKxY8eoWLFikvTy5ctzLLeO30ov8U76ii3aSc9LQ90FQRAEIT24XC6uXr1KkfhlTFNi7NixjBo1Kkn6xIkTCQoKAiA8PJxOnTqxdOlSr575Vq1a0bp1axYsWMDBgwc96f/++y9NmjRh+vTpnE8Ulr1Xr15UqVKFiRMnev0x7d+/P2EBAVgcDj5+7z0iCxUCYPjw4URERHD8+Gys1hfZvt3Gyy9/xYcfPsWhQ4eYO3eup4ywsDAGDBjA9u3bWbJkiSe9cuXK9O7dm7Vr17J69WpPutE6dezYkQYNGhiqU2hoKOPGjfPS1V2nadOmedICAgIAOHLkCAsWLMg1dRoxYkSmtVOLFi0AWLRoEYcPH84VdcqKdroYP5V00qRJuaZOmd1OK1as8NIsN9QpK9tp0qRJ6a5TTEwMacWiDK7MfssttzB16lQ6derklf7jjz8ycOBATpw4YaS4LCcyMpLQ0FAiIiIoWLCg4f1dLhcXLlygWLFiWK1pnC1QsCBcvUpV9nOAqly4oIe85xVMaSaIbiYR3YwjmpkjI3VL770pq9m+fTsNGjTw6tlICxaLhUWLFvHAAw+keZ/333+fcePGsXfvXooXL55ivuR60suVK8e5c+c8mqa198XlcnHx4kVKlCiBv7+/sd6X4sWxXLyIY+tWVI0agHePUs+efnz/vY1HHnEyb54tR/UoJSa5HqXED1QS25iT6wSZ2/NnsVi4cuUKBQsW9ArMnJPrlBXt5HQ6OXPmDEWLFvX8/ub0OmV2O9ntds6fP+/RLDfUKSvayX0/KFq0KEFBQemqU2RkJMWLF0/Tvd5wT/ojjzzC4MGDCQkJ4Y74yVWrV6/m+eefp0ePHkaLy3FYLBZCQ0O9fkhTJTbWE+X1EkUoUSJvOehgQjMBEN3MIroZRzQzR27WrWvXrqluv3LlSpbYMW/ePEaNGsWPP/6YqoMO+k9SYGBgmtJTWkrH3ROslCIsLAy/+Llp7vTkyk5CgQJw8SIBDgck2m6xWAgMDOTVV+H772HBAhtjxkClSlbvct58E0qUwDZwIDabLUnxfn5+HruM1Cmt6cnWKYV0d53cKKW8/vzfiM1my3F1cmO1WpNNT2+dlFKEhoYSEBCQ7G9JTqyTm8xsJ5vNRlhYWBLdcnKdMrudAgICktUsJ9cpK9rJfT9w65aeOqV0nOQw/Oj/7bffpmnTptx1113ky5ePfPnycffdd3PnnXfy7rvvGi0ux+FwOBg3blzaAwhcvuz5eIVC1KqVSYZlYwxrJgCim1lEN+OIZubIzbqFhoam+ipfvjx9+vTJVBvmz5/PU089xYIFC2jbtm2mHutG0tW2KayV7qZhQ2jfXq+ZPn78DRuPHoW334YXXoBEvUk5hdx8TWQWopk5RDfjiGbm8JVuhnvSAwIC+OabbxgzZgzbtm0jX7581KlTh/Lly2eGfTmf+Pno0YGFcNltedJJFwRBEHIWs2bN8unxv/76a5544gnmz5/Pfffd51NbDJPKWuluXn0Vfv0VZs6EN96A0qXjN7hj+8TFwZkzOiS8IAiCkOcwHWO86v/bO++4pq73j38ySNggigy3uHAj7lF3XcVt60ZbbV2t1lqrX+tq6+hy1Fb6a91172rrLK466gQnTpwow8USCEnO749DQgIBci+BBHjer1deSc49995zPufenDz3nPM81aujevXqlixL8STDSI+Xcoc3ZKQTBEEQJYmkpCTcvXtX//3+/fsIDw+Hh4cHKlasiOnTpyMqKgrr1q0DwKe4BwcHY+nSpWjWrBmio6MBAA4ODnBzc7NKHQSRS6x0HW3aAK1aAadOAYsWAT/8kLEhKiozU1QUGekEQRAlFMHT3fv164dvv/02W/p3332HAQMGWKRQxYoM75OxajLSCYIgiJLHhQsXEBAQoA+fNnnyZAQEBGDWrFkAgGfPnhlFh/ntt9+gVqsxfvx4+Pj46F8TJ060SvkFk8d0dwCQSPhoOgD8+qv+rwJg6HzX0GAnCIIgShSCvbt7enriyJEjqFevnlH61atX0alTJ8TExFi0gJYmvx50GWNQqVQ5OvjIxtq1wIgROIi30RUHYRA2tcQgWDMCAOkmFtJNOKSZOCypW1Hz7l4UyI+m+Wrb4GBg3TpgwQJg2rRczgEEBACXLwOzZwNz5gD49FNAFyd+6VLgk0+EndvK0G+JcEgzcZBuwiHNxGGtvl7wSHpSUpJJz3h2dnZISEgQergiB2MM8fHxMPvZRsZ09xcoDR+fkmegAyI0IwCQbmIh3YRDmomDdCu+5Ktt/fz4++3buWYzHE3/6aeMJexZp7sXMeieEA5pJg7STTikmTispZtgI71evXrYsmVLtvTNmzejdu3aFimULZOeno6QkJBs8fFyJMNIfwmPEjvVXbBmBADSTSykm3BIM3GQbsWXfLVtrVr8/ebNPLP26wfUqMEDwfz6K4q8kU73hHBIM3GQbsIhzcRhLd0EO46bOXMm+vbti3v37qFDhw4AgNDQUGzcuBHbt2+3eAGLPGSkEwRBEETJwdBIZ4wPmeeATMZnxL//PvDjj8BniieZoydF0EgnCIIgLIPgkfSgoCDs3r0bd+/exbhx4/DZZ58hKioKR44cQbVq1QqijEUbMtIJgiAIouRQvTo3zF+9AuLi8sw+dChQsSIQG6MFi3qauYGMdIIgiBKLYCMdAHr06IFTp04hOTkZkZGRePfddzFlyhQ0aNDA0uWzSUytyc8RMtIBCNSM0EO6iYN0Ew5pJg7Srfgium0dHIDKlflnM6a829kBX3wBeCIOMq06c8OTJ3wkvohB94RwSDNxkG7CIc3EYQ3dBHt313HixAmsXLkSO3bsgK+vL/r27Yt+/fqhSZMmgo/1yy+/4Pvvv0d0dDQaNGiAZcuWoWnTpibzrlmzBiNHjjRKUyqVSE1NNetche1BV92oCeRhF/AO9mL9q3fg7l7gpyQIgiCKGOTd3fJYVdPu3YH9+/lC848+yjN7airQs/wlHHoRiDRHdyjfvOYbSmJIGIIgiGJKgXl3j46OxsKFC1G9enUMGDAArq6uSEtLw+7du7Fw4UJRBvqWLVswefJkzJ49G5cuXUKDBg3QpUsXxMbG5riPq6srnj17pn89fPhQ8HnFotVqcffuXWi1WrPyq2P4SLqsjEeJNdCFakZwSDdxkG7CIc3EQboVX/LdtgKcxwGAvT0wNojHSL+t8QPz8OAbitiUd7onhEOaiYN0Ew5pJg5r6Wa2kR4UFISaNWviypUrWLJkCZ4+fYply5bluwCLFi3C6NGjMXLkSNSuXRu//vorHB0dsWrVqhz3kUgk8Pb21r+8vLzyXQ5zSU9Px4YNG8z28Cd59QIA4FnToyCLZdMI1YzgkG7iIN2EQ5qJg3QrvuS7bf39+buZRjoAdGvADfJ7aeUR71SOJxYxI53uCeGQZuIg3YRDmonDWrqZ7d19//79+OSTTzB27FhUr17dIidXqVS4ePEipk+frk+TSqXo1KkTzpw5k+N+SUlJqFSpErRaLRo1aoT58+ejTg4LvtPS0pCWlqb/rovlbpgulUphZ2eH9PR0o6ckMpkMcrkcKpVKHxvP8FiG6QCPFS+VSjPzqNVQpsQDAHzqlDLaF+DrGxhj2RpdqVRCq9UapUskEigUCmg0GqjV6mzparUaGo1Gny6kTgAgl8shk8nyrpNBukQigUqlyrNOun21Wq3RcYpynYCCb6es+hWHOhVGO+ny696LQ50Kup0MNSsudSqMdtJtZ4xlK7vQOmXViSjiCBxJBwD759wgj0I5XHmlwlu4WuSMdIIgCMIymG2knzx5EitXrkRgYCD8/f0xbNgwDBw4MF8nf/78OTQaTbaRcC8vL9zMoWOrWbMmVq1ahfr16yM+Ph4//PADWrZsievXr6N8+fLZ8i9YsABz587Nlr5o0SLY29sDAAICAtCzZ0/s378fYWFh+jxt27ZFu3btsHXrVty7dy/bMVasWIE4A8+tQ4YMQbVq1bBo0SKoVCo4JCdjasa28vVdsXDhQqP9p02bhvj4eISEhOjTFAoFpk+fjsjISGzYsEGf7unpiXHjxuHy5cvYu3evPt3Pzw9Dhw7FyZMncfz4cX260DoFBQWhUaNGedZJx9ixY+Hm5mZ2nQDgwYMH2Lp1a7GpU0G3U4sWLQAAixcvLjZ1Ksx2Wrx4cbGrE1Cw7bR48eJiVyeg4NtJpVIhISEhX3Xy9fUFUYzQGekPHwJv3gCOjnnvk2GQv3Ioh1tJKrxlkEYQBEGULAQ7jktOTsaWLVuwatUqnDt3DhqNBosWLcL7778PFxcXQSd/+vQpypUrh9OnT+sNEgCYOnUqjh8/jrNnz+Z5jPT0dPj7+2PQoEH4+uuvs203NZJeoUIFxMbG6hfsCxl9UalUWLduHUaPHg0AuY6+SO7cgaJePcTDFTdOv0ajRsV3RCm3OqlUKvzxxx94//33ITGIF1uU6wQUfDtptVr8/vvvGD58uP5BR1GvU2G0U3JyMtauXYvg4GAoFIpiUaeCbqfU1FS9Zg4ODsWiToXRTiqVCmvXrsXo0aP15xVbp8TERHh6epLjOAuSH8dxKpUKK1aswKhRo8R59WUMKFOGO34LDwfMiX7TuTPwzz8Im7gGfy69jzmYi4ddP0Sl/f8n/PxWIt+6lUBIM3GQbsIhzcRhSd2E9EuivbsDwK1bt7By5Ur88ccfeP36NTp37ow9e/aYvb9KpYKjoyO2b9+O3r1769ODg4Px+vVr/Pnnn2YdZ8CAAZDL5di0aVOeeQvT22vCof/g2qUF7qMySsffB/3vIgiCIExB3t0tj9U1bdUKOH0a2LQJMGfmYe3aQEQE8M8/2Dw/EgOPfIhDdj1Q98FfoIkWBEEQRZ8C8+6elZo1a+K7777DkydPzDKQs6JQKBAYGIjQ0FB9mlarRWhoqNHIem5oNBpcvXoVPj4+gs8vBo1Gg0uXLhmN3uTEkyvcs3uinUeJNtCFaEZkQrqJg3QTDmkmDtKt+GKRthW6Ll03tb1cOfT7hDuO80yPwtChQFG5xOieEA5pJg7STTikmTispVu+jHQdMpkMvXv3FjSKrmPy5Mn4/fffsXbtWkRERGDs2LFITk7Wx0IfPny4kWO5r776CocOHUJkZCQuXbqEoUOH4uHDhxg1apQlqpInarUae/fuNZqmmBPRN7iRrnYtuZ7dAWGaEZmQbuIg3YRDmomDdCu+WKRthXh4T0wEMhzbolw52FXmRnp5ROHoUWDePPHFKEzonhAOaSYO0k04pJk4rKWb2Y7jCor33nsPcXFxmDVrFqKjo9GwYUMcOHBA70zu0aNHkEoznyW8evUKo0ePRnR0NEqVKoXAwECcPn0atWvXtlYVcuTVPW6kS0uXbCOdIAiCIEocQkbSdaPorq6AiwuQ4QjXE3FQIA1z5ijRuDHQvXsBlZUgCIKwKaxupAPAhAkTMGHCBJPbjh07ZvR98eLFRt6ubZnkx9xIV/qSkU4QBEEQJQqdkX7rFqDVAtJcJi8aTHUHAHh4AEolkJaGzwY+xYLNVTB4MHDuHFCjRsEWmyAIgrA+FpnuXpKQSCTw8/Mz8lKeIy9fAACcypdsI12QZoQe0k0cpJtwSDNxkG7FF4u0beXKgEIBpKbyUGy5kdVIl0j0n+d+GIWWLYH4eKB378xZ8bYI3RPCIc3EQboJhzQTh7V0y5d396JIYXl7ZQzYYjcEAzUb8XzaDyiz4LMCOxdBEARRtLG6J/JiiE1oWrcucP06sG8f0K1bzvnmzwdmzACCg4E1a3jaW28B//4LbN6M6LbvoXFjbsv37Ans2pX7wDxBEARhexSad/eSiFqtxrFjx0w7D7h8GRg5Ehg1CmnjJyNQcw4A4FqldCGX0rbIVTMiR0g3cZBuwiHNxEG6FV8s1rbmrkvXjaRnrEUHkDmqHhUFb29umCuVwJ49wJw5+StWQUH3hHBIM3GQbsIhzcRhLd3ISBeIRqPB8ePHTbvhX7iQPwFfuRL2IYtRHXcBAIoKXoVbSBsjV82IHCHdxEG6CYc0EwfpVnyxWNsKNdJ1hjmQabBnbGvSBPi//+NJX38NbNmSv6IVBHRPCIc0EwfpJhzSTBzW0s0mHMcVG16/5u99+uC+vDr2b0uE1LMMxnTubNViEQRBEARhBcwNw2bKSNd9fvJEnxQcDFy9Cvz4IzBiBODnBzRubLniEgRBELYBjaRbkjdv+PugQTjW7VuMx3LsCvgKkNOzEIIgCIIocehG0iMics+nM8RNGek6Az6Db78FevTg/uh69QKePrVQWQmCIAibgYx0gUilUgQEBBjFbtejM9IdHfWdpmF/W1LJVTMiR0g3cZBuwiHNxEG6FV8s1rY1a/L3uDjgxQvTedLTgZgY/jmHNemGyGTAxo1A7drcQO/dG0hJyV8xLQXdE8IhzcRBugmHNBOHtXQj7+6WpE4d4MYNIDQU47Z3QEgI8OWXfO0YQRAEQeSETXgiL2bYjKYVKvCR8lOngJYts29//BioWBGws+PD47o/gg8fGodxyxL+JzISaNqU2/7duwPbtwMODgVfHYIgCEIc5N29AElPT8eePXuQnp6efaOJkXRf38Irm62Sq2ZEjpBu4iDdhEOaiYN0K75YtG3r1+fvx46Z3q4bKffxMY6r5uPDDXOVCnj+PNtuVatyj+8ODjzCW48eQGJi/oubH+ieEA5pJg7STTikmTispRsZ6QLRarUICwuDVqvNvtHASDflA6akkqtmRI6QbuIg3YRDmomDdDOPEydOICgoCL6+vpBIJNi9e3ee+xw7dgyNGjWCUqlEtWrVsEYXO7yQsGjb9u3L3zdtMr3d1Hp0gI+gly1rnCcLbdoABw8CLi7A0aNA587Aq1f5L7JY6J4QDmkmDtJNOKSZOKylGxnplkS3KIyMdIIgCIIAACQnJ6NBgwb45ZdfzMp///599OjRA+3bt0d4eDgmTZqEUaNG4eDBgwVc0gKib18+lf3aNf7KiqkY6TpyWJduSJs2wJEjgIcHcPYs0K4dEBub/2ITBEEQ1oPcjlsKxvQj6WqFo94HDE13JwiCIEoy3bp1Q7du3czO/+uvv6JKlSr48ccfAQD+/v44efIkFi9ejC5duuS4X1paGtLS0vTfExISsqVLpVLY2dkhPT3daFREJpNBLpdDpVKBMabPr4uLq0vXYWdnB6lUanQ+XbpEIoFKpcpMdHSEols3SPbsgXr9emjmztVvUiqVYE+eQAJA7e0NTVoaJBIJFAoFP7ePD2QA0h88AFOpoFAooFarjeL1SqVSNG5sh8OH09G9uxxXrkjw9ttahIZqUbq0PFvZ5XI5ZDJZ/uoEQKFQgDFmNAVUt69WqzU6jmGd1Gp1tnRTdTKnnQqjTgBvJ61Wa5RuqTrp9s1alqJcp8JoJ935DPcp6nUqjHYy1Ky41Kmg20m3LS0tLd91ynqe3CAjXSAymQxt27aFTCYz3pCeDmRcPLFJjtBquQdW3Uy1kkyOmhG5QrqJg3QTDmkmDtKtYDhz5gw6depklNalSxdMmjQp1/0WLFiAuQYGsI5FixbB3t4eABAQEICePXti//79CAsL0+dp27Yt2rVrh61bt+LevXv69OvXr6NJkyZYsWIF4uLi9OlDhgxBtWrVsGjRIqM/pmPHjoWbmxsWLlxoVIb/9e8Puz17kPDbb1hmbw9k/HmbPn06km7dgguAIzdv4szChfD09MS4ceNw+fJlaGNi0BjA6W3b8NjdHUOHDsXJkydx/Phx/bF1dXryZD8GDHiE1atH4PJlZ7Rvn4D//nPHzp3GdQoKCkKjRo3yXadp06YhPj4eISEh+jSFQoG2bdvi0aNH2Lx5sz7dsE579+7Vp/v5+eVaJ3PbqaDrNH36dERGRmLDhg0Wr1ObNm3Qtm1b7N69G5GRkcWiToXRTq9fvwYALF68uNjUqaDb6Z9//jHSrDjUqTDbafHixfmuU2pqKsyFvLtbitevgVKlAADnT6ahaWsFypfnTlsJgiAIIjdsxhN5ASORSLBr1y707t07xzw1atTAyJEjMX36dH3avn370KNHD7x58wYOObgwNzWSXqFCBcTGxuo1tdooWXo64OUFyZs3UP37L1iTJgAyRtLbtoXkxAmkr1sH7bvvGo2+sK+/hnzuXGiCg6H57TezRpQuX5agc2c7JCRI0K0bsHWrCnZ2NEpGdaI6UZ2oTtauU0JCAsqWLWtWX08j6QJRqVTYunUr3n33XSgUiswNOqdxMhmiYu0A0FR3HTlqRuQK6SYO0k04pJk4SDfbQqlUQqlUmpVuZ2dn8hi6djRsW5lMlmP7mjqfyXSlEujZE9i8GYodO4DWrfWbJBnrze0qV+b5MpDJZDwEGwDZs2eQZZRBLpdDLs/+901Xp6ZNgb//Bt5+G9i/Hxg9WoH16/nsPlN1FV0n8D+hhukqlQrr16/Hu+++azK/TCYzOfMkrzplJaeyF0SddEil0gKpU16aFcU66SjIdkpPT8e2bduy/f4W5ToVdDsxxkxqVpTrVBjtZNgfSCSSfNUpp/OYghzHCYQxhnv37iHbBARDz+5PeSxTchrHyVEzIldIN3GQbsIhzcRBuhUM3t7eiNE5dskgJiYGrq6uOY6iW5oCadtBg/j7li365XE4cYLHQwdydxwXFsYDo5tJ69bAjh2AXA5s3gwMHMhDrQvm0CHgyhWzs9M9IRzSTBykm3BIM3FYSzcy0i2FifBrNJJOEARBEMJo0aIFQkNDjdIOHz6MFi1aWKlEFqJLF8DdHXj2DPj3Xx47rWtXQK3m7xmj5ka0bAlUqgTExQHNm3P37YYwBjx9CpgIDdStGzfQ7eyA7dv56QWFZ7t+nZcrl6UJBEEQRMFARrql0BnpDg54+pR/pJF0giAIoqSTlJSE8PBwhIeHA+Ah1sLDw/Ho0SMAwPTp0zF8+HB9/jFjxiAyMhJTp07FzZs3sXz5cmzduhWffvqpNYpvOZRKoF8//nnqVD79PSUF6NED2LkTkEiy7+PkBJw+DQQEcEO9XTtg1y7g9m1g9mygenX+Z+Obb0yesl8/4MABwNWVD9q3aSPAV87hw/whwP37mf9xCIIgiEKBjHSByOVyBAUFZV8XYWIknYx0To6aEblCuomDdBMOaSYO0s08Lly4gICAAAQEBAAAJk+ejICAAMyaNQsA8OzZM73BDgBVqlTB33//jcOHD6NBgwb48ccfsWLFilzDr1maAmtb3ZT38+cBlQro358b6LlN4/f15RZ29+58znrfvkDNmsBXXwE678W//po5hT4LHTrw3X18+OB4y5bA3btmlPXYsczPBu2TG3RPCIc0EwfpJhzSTBzW0o28u1uKv/4CgoKAxo1RO/k8IiL4Q+gsUWQIgiAIIhslxbt7YWKTmmo0QMWKfIp6cDCwYgVfOG4OajXw8cfcIJfJuGe4wYN52uvXwNGjfKQ9Bx4+5FPgIyL4zPqTJ3MZTNBqgTJlMufHHzzIz5cbWi1w6xZ/gCClMSCCIIisCOmX6FdUICqVCsuXL8/mkh8pKfzd0ZGmu2chR82IXCHdxEG6CYc0EwfpVnwpsLaVyYC9e4F164BVq8w30AGed/lyPgofFQXs2wcMHcpH1gHukC4XKlXidny1asCDB0DnzsDz5zlkvnrVeAG7zrldboSEALVr41T//nRPCIB+R8RBugmHNBOHtXQjI10gjDHExcXl6N1drXREfDxPIiOdk6NmRK6QbuIg3YRDmomDdCu+FGjbNmoEDBsmbrRZIgEaNwa8vDLTBg7k79u3A1li92bFywv45x/+/yQigo+sJySYyGg41R0wb7r70aMAAPdbt+ieEAD9joiDdBMOaSYOa+lGRrqlyDDSUySOALivFxcXaxaIIAiCIIhiT/v2gKcnHxY/ciTP7JUq8eV4ZcoAFy7wlXqvX2fJpDPSy5Th7+aMpF+9CgDwePnS9PbwcODbb/N8kEAQBEGQkW45Moz0ZMaN9HLlTDtqJQiCIAiCsBhyOTBgAP+8ebNZu/j7c6/vLi7cqVzTpsCNGxkbtVrg+HH+efBg/p7XSPqbN8CdOwAyjHRTI06ffAJMm8an+xMEQRC5Qka6QOzs7DBkyBDY2dkZb8gw0hPVmUY6wclRMyJXSDdxkG7CIc3EQboVX4pc2+qmvO/cCaSlmbVLYCAfMK9QgdvXzZoBu3cjcz26szPQpw/PnNdI+vXresNcqVLBLmtAdsb0I+24dcus8pUUity1ZiOQbsIhzcRhLd3ISBeIVCpFtWrVIM26lizDSI9P50a6r29hl8x2yVEzIldIN3GQbsIhzcRBuhVfilzbtmrFRwcSEvgQuZk0asSnvLdtCyQlcZt872fH+MbWrQE/P/75yZMcQ7wByDTAM5BGRhpvj43NnFOvCxtHACiC15qNQLoJhzQTh7V0o1YSSFpaGhYsWIC0rE+qM4z0l2k0kp6VHDUjcoV0EwfpJhzSTBykW/GlyLWtVAq8+y7/bDjlPTUViI7OddeyZfka9U8+4d/VoccAAMfQDsluvtwjvVoNPHuW80GuXDH6mh4RYbz95s3Mz2SkG1HkrjUbgXQTDmkmDmvpRka6CEy64M8w0l+8ISPdFBTuQRykmzhIN+GQZuIg3YovRa5tdVPe9+zh89aHD+cWuI9Pdm/tWbCzA5YuBbZv1aKd9AQA4IsD7VC1ugxJ7uV5ptzWpWcY6SzDY64kqyFuaKRnHWUnit61ZiOQbsIhzcRhDd3ISLcUGUZ6XLIDAJruThAEQRBEIdKkCVClCv8/0qcP8McfQGIi37Zjh1mH6FfzGkppXyLd3hmvqzRCbCxw8UUlvjGndemM6Y10bbduAEwY6Ybr0B8/NnvdPEEQREmFjHRLkWGkxyTQSDpBEARBEIWMRAKMHcs/e3kBEyYAM2fy7//+a94xMkbc7dq1xrVbdvjkE+ARKgIA7oTmMJIeHQ28eAFIpdD26MGLknW03HAknTHgwQPzykMQBFFCISNdIHZ2dhg7dmyO3t2fxZPjuKzkqBmRK6SbOEg34ZBm4iDdii9Ftm2nTAHu3weiooBlyzKN9itXTARDN4FuWny7drCzAxYvBpxq85H0Y2sfIizMxD669eg1akDWsCEAQHL3rnEYNp2RrnO8RFPe9RTZa83KkG7CIc3EYS3dyEgXiEQigZubGyRZg6CnpAAAEjTcSPfxKeyS2S45akbkCukmDtJNOKSZOEi34kuRbVuJBKhcmTt7A/ifET8/bjCfPp37vobx0du1A8Bt6qDx3Ej3UT9C9+7AuXOA0fJMnZFevz4kGd7gJfHxwMuXPD0lJXPkvHlz/k7O4/QU2WvNypBuwiHNxGEt3chIF4hKpcLChQuzOxDIGEl/A0eULQsoFFYonI2So2ZErpBu4iDdhEOaiYN0K74Uq7Zt04a/5zXl/fRpbli7ufHYbBnY+fHp7jWVDxEdzeOpOzoC/v7AgAHAw78ywq/VqweVXI6EDOdxuHuXv9+5wx8SlCpFRroJitW1VoiQbsIhzcRhLd3ISLcUBkY6TXUnCIIgCMIm0BnpJ0/mnk/nXK5nT+7uXUclPpLup3iEbt0AV1ceMv3mTWD7duDVCT6SfjKxPrRa4KWHB99PZ6TrprrXqpUZd52muxMEQeQKGemWwsBIJ6dxBEEQBEHYBDoj/dw5HjfdFIxlGun9+xtvq1ABACBNTMC+ja/x+jV30H7wIDBjajpq4wYAYOh39dG4sR0eKfnIu95I13l2r1kz00inkXSCIIhcISPdUpCRThAEQRCErVGtGvf2rlIB58+bznP+PLe8nZ2Bt9823ubkBJQpwz8/egSJBChfnmf7ZtgtKJCONIULXrlUwo0bUuy/0xUAwEyNpFetyj9HRho7liMIgiCMkDBWsn4lExIS4Obmhvj4eLi6ugrenzEGlUoFhUJh7EDA2RlITkZV3EPwnKqYPduChS7i5KgZkSukmzhIN+GQZuKwpG757ZuI7ORH02J3TwwYwOemz5sH/O9/2bd/8QXw3XfAwIHApk3ZtwcGApcuAXv2AEFBmembNgGDBwMtW+LVX6cwfjyDatMObMcA3C7dHD73z8ClXca+u3cD3boBDg7cSd3Tp+RlF8XwWiskSDfhkGbisFZfTyPpAmGMIT4+HkbPNhgzGkn39rZS4WwUk5oReUK6iYN0Ew5pJg7SrfhS7No2N+dxjHEDHgD69TO9f8a6dDx8aJxu4Nm9VCngjz8Yuo7nhnepF3fRvKkWmhsGI+kKBVAxYzo8TXkHUAyvtUKCdBMOaSYOa+lGRrpA0tPTERISgvT09MzEtDT9tK03cITOZwrBMakZkSekmzhIN+GQZuIg3Yovxa5tdUb66dPc65shly/z6ecODnyk2xQ6w/rRI+N0AyMdANTqdMS5/g0A8MRzSG9ehyz1DdQSOR7KMqa6G055J4rftVZIkG7CIc3EYS3dbMJI/+WXX1C5cmXY29ujWbNmOHfunFn7bd68GRKJBL179y7YAuZFxig6AKTAAaVKWbEsBEEQBEEQhtSvz92yJyRkGtY6dKPo3brx9eemyGskvV49fZJKqQTz8gIAzA7kBvsdVg0169rhf/8D0iqQ8ziCIIi8sLqRvmXLFkyePBmzZ8/GpUuX0KBBA3Tp0gWxsbG57vfgwQNMmTIFbXRPh61JhpGeDjnUsKORdIIgCIIgbAeZDGjZkn82nPJuzlR3wPRI+qtXwJMn/LOBkQ4ALGO0vL/9XwCAF6VrIi0NWLAAmL+FG+mqm2SkEwRB5ITVjfRFixZh9OjRGDlyJGrXro1ff/0Vjo6OWLVqVY77aDQaDBkyBHPnzkVV3bSpQkShUBgnpKQA4FPdAdBIugmyaUaYBekmDtJNOKSZOEi34kuxa1tT69Jv3OAh0hQK4J13ct7X1Ej61auZ29zc9MkKhQJMF2rtzBkAQKsPamH3bqBuXeBGKv/fFr4jErNmcZ9yWm1+Klb0KXbXWiFBugmHNBOHNXSzqnd3lUoFR0dHbN++3WjKenBwMF6/fo0///zT5H6zZ8/GlStXsGvXLowYMQKvX7/G7t27TeZNS0tDWlqa/ntCQgIqVKiA2NhYvVc9qVQKOzs7pKenQ2vQU8hkMsjlcqhUKiNnAXK5HDKZTJ8uuXIFiqZN8Qze8MUzREenwd09swx2dnaQSCRQqVRGZVMoFGCMZVvjoFQqodVqjdIlEgkUCgU0Gg3UanW2dLVaDY3BOrP81smw7FKp1EhDqhPViepEdaI6WbZOiYmJ8PT0JO/uFoQ85mfh33+Bt97i4diePQMkEuCrr4DZs7mBvndvzvvGxvL9AB5rXakEPvkEWLYM6NkTyPp/7ZtvgJkzM7+vXg2MGAGtFvjnu0t4e3ogYlAW3ogBAHh68pBu/fsDvXrxohEEQRQ3hPRL8kIqk0meP38OjUYDL90PfwZeXl64qYurmYWTJ09i5cqVCA8PN+scCxYswNy5c7OlL1q0CPb29gCAgIAA9OzZE/v370dYWJg+T9u2bdGuXTts3boV9wzWTjVr1gxvv/02VqxYgbi4OJR//BgfgI+kSyTAihWLoFZn/okbO3Ys3NzcsHDhQqMyTJs2DfHx8QgJCdGnKRQKTJ8+HZGRkdiwYYM+3dPTE+PGjcPly5ex16Aj9fPzw9ChQ3Hy5EkcP35cny60TkFBQWjUqJG+TjqGDBmCatWqYdGiRUZ/TIXWacCAAWCMYePGjcWmTgXdTm+99RbWrFmDZ8+eFZs6Fcd2ojpRnaZOnYrExMR81cnX1xeE7aDVahEZGYmqVatCKrX6pEPL0KQJHzGPieEj36mpgO4BUv/+ue/r6QnY2/N9njzhMwiXL+fbJkzQZzPSzXD/WrUAAFIp8PaYqsB0wAuxeK97Iv4+4YK4OGDDBv5q0wZYuhQICLBc1W2ZYnmtFQKkm3BIM3FYTTdmRaKiohgAdvr0aaP0zz//nDVt2jRb/oSEBFa5cmW2b98+fVpwcDDr1atXjudITU1l8fHx+tfjx48ZABYbG8tSU1NZamoqU6lUjDHGVCqVPi01NZWlp6czxhhLS0vTp8XHx7M5c+aw1NRUfXravn2MAewK6jIPD2Z0jNTUVKbRaJhWq82WrtVqmUajyZbOGMuWnpaWxhhjTK1Wm0xPT083ShdSp9TUVKZWq02mazQavY5i66TT7M2bN8WmToXRTqmpqWzOnDksPj6+2NSpMNpJd73pdCsOdSrodjLUrLjUqTDaSadbSkpKvusUFxfHALD4+HhWXPn5559ZpUqVmFKpZE2bNmVnz57NNf/ixYtZjRo1mL29PStfvjybNGkSS0lJMft88fHxojVNTU3V9/XFit69GeMr0TNfFSsy9vp13vvWqMHzh4Yy1qED/9ynj1EWnW5pp04Zn+PlS+NjeXjw9MuXWVoaY0ePMjZ5MmMODjxZImHsww8Zi4mxXNVtlWJ7rRUwpJtwSDNxWFI3If2SVUfSy5QpA5lMhpiYGKP0mJgYeJsINn7v3j08ePAAQUFB+jTdlEa5XI5bt27BT7cOKgOlUgmlUpntWKbS7ezsTJYzp3UI+vSMKYtv4IhSpWDyfLpzZkUikZhMl0qlJtNlMhlkMlm2dLlcDrk8e3OKrlMWqE6FXyfdNF5T12pRrRNQeO1kqFtxqZMhlqyT7ndUqVTq8xT1OpmioOokkUhyLLu5dco6tb64oXMS++uvv6JZs2ZYsmQJunTpglu3bqFs2bLZ8m/cuBHTpk3DqlWr0LJlS9y+fRsjRoyARCLBokWLrFCDYsLWrcDt23xE3cGBv9zduWO5vKhUie+7ZAlw5AgfWc+hLZihvyAvr+zOevz8gJcvgXv3oKhfH+3aAe3aAZMmAV98AWzaBPz2G7BuHTBwIDB+PNC4sbgqEwRBFEWsOtdBoVAgMDAQoaGh+jStVovQ0FC0aNEiW/5atWrh6tWrCA8P17969uyJ9u3bIzw8HBUqVCjM4meS4d1dZ6QTBEEQBJGJUCexp0+fRqtWrTB48GBUrlwZb7/9NgYNGmR2iFYiB+zsgDp1gOrVgfLlgdKlzTPQgUwP77plGlOnApUrm85bqhQ/NgDUrJl9u86IzxKGrUIFYONGvny+SRM+u37NGv65WTNutBfz51kEQRAArLwmHQAmT56M4OBgNG7cGE2bNsWSJUuQnJyMkSNHAgCGDx+OcuXKYcGCBbC3t0fdunWN9nfP8NCWNb2gkEgk8PT0hMTQqwkZ6bliUjMiT0g3cZBuwiHNxEG6mYdKpcLFixcxffp0fZpUKkWnTp1wJsP7d1ZatmyJ9evX49y5c2jatCkiIyOxb98+DBs2LMfzmHIUmzXdXOeCKpUKpUuX1uexBeeC1naYqClXTv+nkVWoAEydCpal7CqVCp6enmCMQevnB+mLF9BUrw6NSmVUJ1mlSpAD0Ny5AxmQrU7Nmklx9qwdTp5UIyREgh07pDh3ToJz54DPPwdGjdJg1Cg1dK4cLOoEUquF9uuvAaUSmsmTC7ydNBoNPD09s10btuTY0trXXk51Kl26tNG24lCngmwntVptpFlxqFNhtJOuP1Bl/I7lp05CZs1Z3Uh/7733EBcXh1mzZiE6OhoNGzbEgQMH9M7kHj16ZFPODRQKBcaNG2ecaGCkU4z07JjUjMgT0k0cpJtwSDNxkG7mIcZJ7ODBg/H8+XO0bt0ajDGo1WqMGTMG//vf/3I8T0E4io2IiCCHiRl12n/lCnpnpG1r3hy95HLEP39usk53795FglqNRgD2R0fj0YoVRnVqePMmegGIOXMGvkCOdXr1ah9q1gzDxImOuHSpEa5caYXYWHvMny/DwoWAp2cc3NziERBQCs2bl0VKykaoVJlh4sS0U/Lp0/DIuI5+jolBipdXgbfTuHHjsH79enLWKaBOCQkJePHihX75S3GoU0G3U2hoqJFm+amTJjkZAzdvxoPKlVF/48YSce0tWrQo33VKTU2FuVg1BJs1yG9IFo1Gg8uXL6NBgwaZ6wm//x6YOhVrMRz/jVkLg3YjkINmRJ6QbuIg3YRDmonDkroV53BhT58+Rbly5XD69GmjpWxTp07F8ePHcfbs2Wz7HDt2DAMHDsQ333yDZs2a4e7du5g4cSJGjx6NmYahvQywZMhVjUaDq1evIiAgAAqFgkaUJBKoIiKgaNgQ2rffhnr7diiUymx10mg0uHnzJurVqwdtdDSkhw5B278/JA4ORnWSHD8ORZcuYFWrQnLvntl1YkyGv/6SY+lSLU6ezD6A4+zMMGOGBhMmaGBnJ66dMHMmJPPmAQDSf/oJ2g8/LNB2AoDr16/D39/faFCKRjNzr5NarcalS5dQr149/e9vUa9TQbdTamoqLl++rNcsP3WS/PsvFJ07g3l6gkVHF+trT9cf1KtXDw4ODvmqU0JCAsqWLWv7IdiKImq1Gnv37kWdOnUy/5RljKSnwIGmu5vApGZEnpBu4iDdhEOaiYN0Mw+hTmIBYObMmRg2bBhGjRoFAKhXrx6Sk5Px4YcfYsaMGSZn2FnSUWxaWhoOHDiAhg0bGqWbOra56UXeYWLt2sDz55A5OenLlbVOaWlp+ntCWaEC8MEHpuvk78/3f/QIUKsF1al/f6B/fynu3eN+7B4+BB48AEJDgQsXJJg+XY516+RYtgzo2DGPOplK37kz8/yHDwMffwyg4NrJSDMTx6drz3S6VqvV36OG24tynQq6nSQSiUnNRNXp+XN+zOfPIdFqAbm82F57hv2BRCLJV51yOo8pbGceeVEmJQUArUknCIIgiKwIdRILAG/evMlmiOv+5JSwCYC2haur+Y7mcqNcOe5hXq3mFrYI/PyAbt2AMWOAhQuBs2eBVat4SPeICKBTJ+4R/uefgRcvzDzojRt8Zx1HjpCnOoIwxbNn/J0xvcFOWBYy0i0BrUknCIIgiByZPHkyfv/9d6xduxYREREYO3ZsNiexho7lgoKCEBISgs2bN+P+/fs4fPgwZs6ciaCgIJq1UByQSoHAQP55/nyLHXLkSD66Pm3Uc3wi/Rk3Lybh448BHx+gXz/g1Kk8DrJ9O3/v0YPvlJzMXc0TBGGMzkgHgCyzpAjLQNPdBSKRSODn50fe3QVgUjMiT0g3cZBuwiHNxEG6mY9QJ7FffvklJBIJvvzyS0RFRcHT0xNBQUGYl7FWuKChthWHIN1++AFo3RpYvRoYOhTo0MEiZXB3Y1hwdwCgPYYhXWIxNu4rXLrEZ7Hv3MlPOX06H4XPVkydkd6/P1C2LC/bvn18WL6AoGtNHKSbcCyq2dOnmZ+jo4EGDfJ/TBvFWtcaOY6zBIMHA5s2YRIWo9eRSWjf3jKHJQiCIEoGxdlxnLUgTYsA48cDy5cD1aoBV64ADg75P+b69YAuVF/z5sCZM7h6FVi2DFi7FtD5tgoIAFasABo1ytjv9m0e010u5yODoaHAu+8CtWoZT4EvaGbPBhISgEWLTDxFIAgboWNHvhwE4DfW8OHWLU8RQUi/RNPdBaJWq3Hs2DEjz4M0kp47JjUj8oR0EwfpJhzSTBykW/GF2lYcgnWbPx/w9QXu3gW++Sb/BXj9Gvjss8zv588DiYmoVw/47TcgMpJvdnICwsKAZs2ABQsAjQbAjh18n44dAQ8PoHNnvv7+5k3R6+bNwUizV6+Ar74CliwB7t8vsHMWB+geFY5FNStB092tda2RkS4QjUaD48ePG4UBYLQmPVdMaUbkDekmDtJNOKSZOEi34gu1rTgE6+bmBvzyC//83XfA1av5K8CXXwKxsXxEvHJlbn0brCkvV47Psr9/H+jbl/ut+9//gHbtgOS1fKr7lRr9sX49cPmhO6BzbLh/f/7KlQtGmt24kbmhMEfviyB0jwrHopqVICPdWtcaGekWQJtEI+kEQRAEQRCC6d0b6NOHW8yjRwNZYoibzcWLfOo8wN918deOHs2W1dOTLz9fvRpwcQGiTkbC6dYlaCBFx2W9MGwYnw5/1LE736EAjXQjrl/P/GxosBOELZGSwmet6IiOtlpRijNkpFsATSI30tOkjnB2tnJhCIIgCIIgihLLlnFr+exZYMsW4ftrNMDYsTwc1ODB3AmdzkGQCSMd4Mu9R4wALl8GplfnU93P2rdDpUBPNGvGD/XpoW4AABYaCqSmiqmZMAwNczLSCVslq1FezEfSrQUZ6QKRSqUICAgw8kKrG0mXuTiSjw8TmNKMyBvSTRykm3BIM3GQbsUXaltxiNatXDngiy/45y+/zPTulheMce/r7drx9eeurnw+O5BppF+6xNd650CVygyjXTYDAFou6o8LF4D//gN+/x2IsGuAp/CB5M0bHJn7LzZtAv74A1izBrh1S1gVc8JIM8ORdJrunis2e4/GxAADBwJnzli7JNmwmGaGnt2BYm+kW+taI+/uFiDVqyLsYx+jX8Xz2PGwsUWOSRAEQZQcyBO55SFNixjJydzLe3Q0H1mfMCHnvIzxEff58zPXsdvZAatW8XBuOmrV4tb07t1Ar16mj/V//weMGQMolcDDh0BGWEAAOHcOuN/+fbz3ZjUW4VN8hkVGu3bpAkycyN8t8v/d1zdzra+LCxAfTx7eixozZ3IniO+8A+zda+3SFAzbtwMDBvBrNDGRhyss5oa6pSDv7gVIeno69uzZg/T0dH2aJIWPpCvcLBA6pBhiSjMib0g3cZBuwiHNxEG6FV+obcWRL92cnHj4MYB7OE9MzDnvb78BgwZxA93Zmbtsv3/f2EAH8pzyjrt3gcmT+eeFC40MdABo2hToupRPee/vsA8dO3KD/K23uO188CDQvTvg78/DuYmptl6z2NhMA10q5fWPihJ+wBKCzd6j//3H321wJoTFNNNdp7rY6M+fZ4RIKJ5Y61ojI10gWq0WYWFh0Bo4NpGmpQAAlKUcrVUsm8aUZkTekG7iIN2EQ5qJg3QrvlDbiiPfun3wAVC9OhAXB/z4o+k80dGZU+M/+QR49IhPcS9XLnveDh34uy6esyEaDY/t/OYNny7/yScmT+fWn4diq5hyC//8FokDB4DjxzPtezc3HmJ99GigRg0zjPWDB4Fx47jzLWRqxnRT3StU4BoANmno2Qo2eY9qNNyvAsAfGhWGHwMBWEwznZFevz5/WqXV8nu2mGKta42M9PzCGOxUfCTd3oOMdIIgCIIgCFHY2fEp7AA30k1NoZ08mU8DDwwEFi1CrmF12rXj71evZjcivv+erxt2deWLzHOar+7uDrRqxT8beHmvWpUX8ckTXgwvLx5OffRobmMPHQrMmsU9yP/zD39OEHogHWmDgoGQEKStWm90GonOUVzt2vwFkPO4osaNG5kzQLRa4M4d65anoNAZ6eXLA2XK8M803d3iyK1dgCKPwVMyJ08y0gkiP2g0mnxPJ1KpVHByckJaWhpKmMsN0ZBm4hCim52dHWQyWSGVjCCKMP36AU2acEdwM2fyNeO6ddmHDwObNnGD+v/+D8jrnvL0BOrWBa5d48Pf/fvz9MuXuQUNAD/9BFSqlPtxuncHTpzgTurGjzfa5OwMfPop8NFHvEjffsuXtj98aKJq+BMdwY2ZnR8fxdI/RqNlSxlevqwMyfMLPFOdOoC9PbBrFxnpRQ3dVHcdERFAvXrWKUtBonMc5+PDn07FxZGRXgCQkS4QmUyGtm3bZv7ZevNGv83Zk9akmyKbZoRZlCTdGGOIjo7Ga8O4m/k4VseOHfHkyRNIyOGOWZBm4hCqm7u7O7y9vUnjIkBJ+v21JBbRTSLhlm6HDtzFemQkt37LlePTxAHuVC4w0LzjdejAjfSjR7mRfvIk8N57fE567958yntedO8OTJvGh8NTUgCH7P/3HB0zjfUDB/gg6v37vPhPn/JqTXnwK5DE87djRzH4LMPZs3IAwZji/QfqAtxIVyp5JpruniP5utaSkvgDkKZNLVuorB7dbaz9LPa7phtJ9/EBvL35/VWMjXRr9QdkpAtELpejnW76FKA30tOggFtpktMU2TQjzKIk6aYz0MuWLQtHR0cyYohiBWMMb968QWxsLADAx8fHyiUi8qIk/f5aEovp1r49sGQJN4xDQ/loeMuWfCG4ry/w9dfCjvXTT/w4338PTJ/O1w77+3MHdOb0N3Xr8qm9T54Ax44B3brlmNXREejb18SGO3eAGqGARAJmZwcfVTR2LbiFXRG1sH494BHNR80jJLXhXzvDSKeR9BzJ17U2bBj3+H/oENC5s+UKpRtJf+stPvPi5k3LHdsCWOz+1Bnpvr6Zzhazxk4vRlirPyCrUiAqlQpbt27Fu+++C4VCoTfS38Ax12VRJZlsmhFmUVJ002g0egO9dOnS+T6eVqvFq1evUKpUKduLn2qjkGbiEKKbQ8bIW2xsLMqWLUsjtDZOSfn9tTQW1W3iRB7G6qOPuIGtc/62dClfR24ubdtyQ/zWLWDqVJ42eDAfnXd2Nu8YEgkfTf/tNz7lPRcjPUd++42/d+8OyZs3wNGj6O16BN1/r4rqZfbCdxE3fFp/WBuDh8uxFBJIX7zAws/i4NvAE++8A3h4CD+tRTl3jhtleS0PKAREX2sPHgB//sk/Hz9uOSP91avMkfMRI7iRbmMj6Ra5P1Uq7s0dyJzuDhTrkXRr9Qf0b0wgjDHcu3cvc/2hgZFu9R9PGyWbZoRZlBTddGvQHR0t59MhLS3NYscqKZBm4hCim+4at7mQQUQ2Ssrvr6WxuG5+fnwd+urV3Ov5iBF8zboQSpUCAgL4Z4UCCAkB1q8330DX0b07f9+3j8dpF0JqKq8DwB866LzOHz0Kxhh8X+8DAMQ5VMBLtSt+XuWIB6gMANi/6AaCg3ko6k6dgOXLgStX+IztQuXsWaB5c/6Kjy/kk2dH9LW2cmVm+125YrkCnTvH36tVA9q04Z9v3bKp0GQWuT91xridHVC6tG0a6bGx/EGcqagOIrBWf0BGen6hkXSCsAg0xZ0o7tA1ThAikEi4cf7oETd0xdxHX30FBAUBp08DY8aIO0bHjtwwiYzkMddy4vRpoHFjPuKvC9m0cyfw4gV/0NC9e2b89mPHAK0WnhlLYcq8VRvbtgGffw6kVuEe3se0uYH69bmtFxrK/dY1aAC4uPDlwK1bAxs3Cq+OYBYs4MZtdLSw5Qa2hFoNrFqV+d2SRrpuPXrz5kCVKvyBUGoqv26LE7qp7t7e/D7y9ubfbclI37KFO5icO9faJckXZKTnFzLSCYIgCIIgbJcePYA9e8x3NmcKZ2c+dR7go+k58fnnwMWLwKRJ/LwxMcCvv/Jto0Zxj/RNmvDF68+fQ3L9OjwzwsNJ6tRB//7Ad98Btfv5AwAGNYzA5ct8Of733/NBWt3MzZgY4NQpYMgQHua9wCbpRERkThEH+AMIG1tvbRb793Mvfu7u/PvDh5abFaAz0lu04G1cowb/bmNT3vONoWd3wDbXpN+9y98vXxY+68WGICNdIHK5HEFBQZDL+XJ+lsyN9BQ4kJGeA1k1I8yDdBOHRCKBm5tbkR21rFy5MpYsWWJ2/mPHjkEikeTLM35R18xakG7FF/r9FUex181wyrspwsP5SLpczsOoHTjAvbX/+y833D74gOdTKPRTouX//ovauv3r1Mk8VpZY6X5+wJQpfKnzixd8CfSFC8D//sezLVvGl1fHxvIB41On+LbOnXmxBwzgExKmTQOiogTW+7vv+Hvv3nxGglrNfQZY0QASda3p/AKMGsUdAQLcM3l+0Wr5cgCAj6QD3DEhYFNGukXuT0PP7oBtTne/d4+/x8ebjoUoEGv9rpGRLhCZTIZGjRrpnf6kvkoBQGvScyOrZoR5kG7ikEgkcHJyKnDDSSKR5PqaM2eOqOOeP38eH374odn5W7ZsiWfPnsHNzU3U+QDhmtWqVQtKpRLRtvTk3AoU1rVGFD70+yuOYq+bzkg/ftz0ovCQEP7ety8fTa9fn1vUANCzJw8jpyNjyrv0+HG46qZE166duT2LkZ4Vd3c+MWDePO6o3MWFF6t+fb5+vXVrPkP9n3/4APL27cDatTy6Xa1awI8/mjny/vgxsGED//zFF8CiRfwhw6FDfHZCQcAYMHQo9z+Qw5puwdfakyeZD1dGjeJCAZaZ8n7zJjcIHRwyj6sz0m1oxoFF7s+cjPTnz21n/b3OSAf4g7N8Yq3fNTLSBaJSqbB8+XKoVCoAwJvnfCQ9VeJoKmwmgeyaEeZBuolDq9UiNjYWWt1awALi2bNn+teSJUvg6upqlDZlyhR9XsYY1Gq1Wcf19PQU5ERPoVDkO/a2EM1OnjyJlJQU9O/fH2vXrhV9TkthTSdshXWtEYUP/f6Ko9jrVqMGULUqt25DQ423xcdnGrPjxnEj++xZYPJkbrDNmmWcP8NIZ//8k2n4GBrptWrx92fPAFMzpeLj+chwz57opd2Fs2d58WJi+Ch7qVLAwIE81PyqVXyk/dtv+WzspCQ+Kh8QwI34XLunxYt5fdu25aPE1arxnQEeGD411SzpBHH7Ntdy584cR7oFX2urV/MR77feAmrWtKyRrgu91qQJn0UBZLafDY2kW+T+NAy/BgCennxtulab6fXdmmi1wP37md8vX873Ia31u0ZGukAYY4iLi9N7+Et5yY10tcJRlB+SkkBWzQjzKMm6MQYkJ4t/JSRoRO9rrtze3t76l27Ks+77zZs34eLigv379yMwMBBKpRInT57EvXv30KtXL3h5ecHZ2RlNmjTBP//8Y3TcrNPdJRIJVqxYgT59+sDR0RHVq1fHHoPRi6zT3desWQN3d3ccPHgQ/v7+cHZ2RteuXfFM17ECUKvV+OSTT+Du7o7SpUtj2rRpGD9+PPr06ZNnvVeuXInBgwdj2LBhWGXogCeDJ0+eYNCgQfDw8ICTkxMaN26Ms7ppgAD27t2LJk2awN7eHmXKlDE6p0Qiwe7du42O5+7ujjVr1gAAHjx4AIlEgi1btqBt27awt7fHhg0b8OLFCwwaNAjlypWDo6Mj6tWrh02bNhkdR6vV4rvvvkO1atWgVCpRsWJFzJs3DwDQoUMHTJgwwSh/XFwcFAoFQrP+Ec+CuQ9fiKJFSf79zQ/FXjddKDYg+yjyH3/wTqR2bW4IAnzK+48/8tHwhg2N8zdqBLi4QJKcDABg5csbh5Zzc8sceTc09E6dAoYP5yOZH30E7N0LDB8Of+fHOHeOL3//918+7X3TJj5oPHIkMGECj0B38iR3cF6mDHD9Op8O7+7O/eLNmgWsW8eP8eOPwPfTX0K9PGOK+LRpmWWYPp2X7f594Icf8iWpSQx/d0+fNplF0LWm0QArVvDPuplqljTSDdej6zCc7m4j94NF7s+sI+lyOb+YANuY8v70KWAYdcUCI+nW+l0jIz2fpGUY6Rql5cJHEURJ580b7qNHzMvVVYrq1X3g6ioVtX+GL0iLMG3aNCxcuBARERGoX78+kpKS0L17d4SGhiIsLAxdu3ZFUFAQHuXh/XXu3Ll49913ceXKFXTv3h1DhgzBy5cvc9HvDX744Qf88ccfOHHiBB49emQ0sv/tt99iw4YNWL16NU6dOoWEhAQcPHgwz/okJiZi27ZtGDp0KDp37oz4+Hj8+++/+u1JSUlo27YtoqKisGfPHly+fBlTp07VjzT//fff6NOnD7p3746wsDCEhoaiadOmeZ43K9OmTcPEiRMRERGBLl26IDU1FYGBgfj7779x7do1fPjhhxg2bBjO6ULiAJg+fToWLlyImTNn4saNG9i4cSO8MqbpjRo1Chs3bjQKp7Z+/XqUK1cOHXShkgiCIAC+Lhvgw9O6h4GM8dhoAB9FN2fURi7PdEQHgOmMOkN0aTdu8HPMns3nsf/xB5CSwh8I1K7Nh8bHj4ebK8NHH/Es+uWzWaYgS6XA++/z6GDjxvFnAcnJPFrV118DwcHA2LF8sDxh4S+QpyXjlkMDhMq7ZB7E2Zl7sQOAhQst7zTMMHSWzgDOD4cPcy/rpUplhvCrV4+/X72afyNaN5KuW48O8GkNEgnw8iWQ4RiwWJDVcRxgW87jDKe6AxYx0q0FGen5RPWa/6NnDmSkEwRhzFdffYXOnTvDz88PHh4eaNCgAT766CPUrVsX1atXx9dffw0/Pz+jkXFTjBgxAoMGDUK1atUwf/58JCUlGRmgWUlPT8evv/6Kxo0bo1GjRpgwYYLRiPCyZcswffp09OnTB7Vq1cKyZcvgajiCkwObN29G9erVUadOHchkMgwcOBArV67Ub9+4cSPi4uKwe/dutG7dGtWqVcO7776LFhmjC/PmzcPAgQMxd+5c+Pv7o0GDBpg+fXqe583KpEmT0LdvX1SpUgU+Pj4oV64cpkyZgoYNG6Jq1ar4+OOP0bVrV2zduhUAf7iwdOlSfPfddwgODoafnx9at26NUaNGAQD69u0LAPjTwHvxmjVrMGLECFpvThCEMR068GFpgI9oHzjAF4NHRABOTsCwYeYfSxeKDYDWcKq7Dl3a5cvcev7qq8zz/vcfnwq+dSsPDbd3L58erkOj4R7m7e35vPcsa9s9PIBffuE25NWrfPR82DA+st67NzC6/ytMVf4EAJiT8gU6dZage3fu2P1//wPePzQQt0o1A5KTkfTZbPPrnBdaLXD0aOb3HEbSBaGLUTd0KNcD4FPe7eyAxMT8OReLj+dTEgBjI93REahUiX+2oXXp+SbrSDpgW87jdEZ6kyb8/cED08tFigBkpAvEzs4OQ4YMgZ2dHQAgPT5j2E3AGtKSRlbNCPMoybo5OvKBATGvxESG589TkZjIRO1vyVu5cePGRt+TkpIwZcoU+Pv7w93dHc7OzoiIiMhzJL2+bloeACcnJ7i6uiI2I66uKRwdHeHn56f/7uPjo88fHx+PmJgYoxFsuVyOwMDAPA3SVatWYejQofrvQ4cOxbZt25CYmAgACA8PR0BAADxy8KIZHh6Ojh075noOc8iqq0ajwddff4169erBw8MDzs7OOHjwoF7XiIgIpKWl5Xhue3t7o+n7ly5dwrVr1zBixIhcyyGRSODh4UGGfDGkJP/+5ocSoZtEwi3VQYP4Yu5+/bhDNYAbgWY88NRjYKRL69bNvl1npP/8Mx89l8n4OvS1a4FmzXhZ6tTJPP/HH3OjMSWFu3NfupSXccsWoG5dXuYsa6SlUr7po4/4VPdDh4Bdu4DfnD6FS9pzqKvXgveEAZDLuQO6SZO4Q7rVayT44BWf6u6wcQXGtbuBv/7io/L54soV/uTA0ZHX7949Pnc/C2Zfa4zxkXQgcxYEP0CmvvmZ8n7qFD9H5cqZMcN12JiH93zfnxpNZlsYGum2FCtdZ6Q3bgxUrMg/53NJg7V+14ppjIyCQyqVolq1avrvmkRupEucyUjPiayaEeZRknWTSPiAhMi94exsb8niiMYpSyWmTJmCw4cP44cffkC1atXg4OCA/v375+mMJGvHIJFIcnVWZip/bmupJBIJZDJZrse8ceMG/vvvP5w7dw5f6P4QghvImzdvxujRo+GQh/fMvLabKqcpx3BZdf3++++xdOlSLFmyBPXq1YOTkxMmTZqk1zWv8wJ8ynvDhg3x5MkTrF69Gh06dEAl3ShILuW1t7eNa42wLCX59zc/lBjdpFJgzRpuTB48COhmNo0dK+w4DRpwx1txcZAGBGTfrjPyGONTzLdvB7p0yZ5vxgxuiN+5ww31e/f4CLRCwcOnnTjBR9k3bwa2beMG/6BBOZfr77/5gwCJBPK1q7C4hRzjJ/IZ7q9f84FTb2/A2bk1TnzdB28934Vux79A0PG9ALht5O/PB6s9PQEfPINf7BmUZTGovuB92Dkrcz63bqp7u3Z8hPv6dT7lvVcvo2xmX2sREXwatr090LKl8bb69fkshStXuPd9MejCuvXokX2bvz9/smEjRnq+78/YWD7TQSrlIQR02OJIup8fv78ePeJT3nV+IkRgrd81GkkXSFpaGhYsWKBfu6hJ4ka6jIz0HMmqGWEepJs4tFotnj17ZpMet0+dOoURI0agT58+qFevHry9vfHgwYNCLYObmxu8vLxw/vx5fVp6ejrOnz+fqyG/cuVKvPXWW7h8+TLCw8P1r8mTJ+unvNevXx/h4eE5rpevX79+ro7YPD09jRzc3blzB2/McBJw6tQp9OrVC0OHDkWDBg1QtWpV3L59W7+9evXqcHBwyPXc9erVQ+PGjfH7779j48aNeP/99/M8ry1fa0T+oN9fcZQo3RQKYMeOzCnOrVpxo0AIUilUmzfj7759kWZqJD0ggDvlKl+ee3wzZaAD3AD9v//jn//4gxvo7u58BHniRF7OsDDu9E6j4VPndaPLWXn9OtO52uTJemdo1arxU2zZAvz0E5/y/sknwFunFoLJ5QjCX+hbik9Tf/SIIfXgMTT5aSiGzKyCD2b6ot2yfqj98zgsLfcdfvwRSEgwPm1CQoZPGJ2R3qFDplFtYsq72deazjlr69aZU9115OQ8bu5cHtM+L2/eDx7wZQYAMH589u06D+82Mt093/enrn8uW5bP6tBhi2vS/fwynTXmc126tX7XyEgXgdGoVzL/A2nnRkZ6bhTbcCwFDOkmDlv1LFy9enXs3LkT4eHhuHz5MgYPHmwVA+/jjz/GggUL8Oeff+LWrVuYNGkS4uPjc5y2nZ6ejj/++AODBg1C3bp1jV6jRo3C2bNncf36dQwaNAje3t7o3bs3Tp06hcjISOzYsQNnMhz/zJ49G5s2bcLs2bMRERGBq1ev4ttvv9Wfp0OHDvj5558RFhaGCxcuYMyYMWZNL6tevToOHz6M06dPIyIiAh999BFiDJ7o29vb44svvsDUqVOxbt063Lt3D//995/RenqAj6YvXLgQjDGzPN0Dtnut2SK//PILKleuDHt7ezRr1ixXvwoA8Pr1a4wfPx4+Pj5QKpWoUaMG9uniHBcC9PsrjhKlm5MTj739zTfckZwIWKtWuGCwpMkIFxcgMhK4ezfvBwDt23M37gAfyj51ynj0sGFDblC+9x4PqaaL5Z6VyZO5c7AaNbgnubyoUQOSMWMAADuqTEH85v14Xa8NjqE9hmIDquABtJAg2r4yAGBIwnJMn6JChQrA4MF8wNzHhzuwK1sqHWmHj/PjduyYq5EOlQoqc8K/6R7OduqUfZtO96tXM9MuXgTmzOHtOXdu7scOCeEjyx07Zs56MMTGprsD+bw/dU7jdOHXdNjiSHrVqplGuoXCsBU2ZKTnlxQy0gmCMI9FixahVKlSaNmyJYKCgtClSxc0atSo0MvxxRdfYNCgQRg+fDhatGgBZ2dnfUgzU+zZswcvXrwwabj6+/vD398fK1euhEKhwKFDh1C2bFl0794d9erVw8KFCyHLeOLerl07bNu2DXv27EHDhg3RoUMHI0Ptxx9/RIUKFdCmTRsMHjwYU6ZMMStm/JdffolGjRqhS5cuaNeunf5BgSEzZ87EZ599hlmzZsHf3x/vvfdetnX9gwYNglwux6BBg2gau4XZsmULJk+ejNmzZ+PSpUto0KABunTpkqNvBZVKhc6dO+PBgwfYvn07bt26hd9//x3ldCGpCMJWKFWKTzevUaNgju/iAihzmR5uSEgIn9J+4YJxzHUdUimfxt6xI3fC0q0bnyLPGB9B37yZxxOXSLiRasZSIQA8dpurK3DpElwHdofb1VO8zGPHAocPQxr/Gt7xt8B8fOCDaEz03Y6EBO4c//jxzAHYuqqLUKqS8Erqgb8e1deP4mvPX8C+3Srs3s0HxsO33ILMtwI+XbQYsrlzc3b8plZnOqEzZaTrPLzfvs3X8QN8wb2OhQszPbdnJSUlM6zbxx+bzqMz0h894noXNq9f8wdIOuM6v5hyGgfYzpr0V6/4C+BGuu7B1rVr/MFUUYOVMOLj4xkAFh8fL2r/1NRUNmfOHJaamsoYY+yiRyfGAHbio/WWLGaxIqtmhHmUFN1SUlLYjRs3WEpKikWOp9FoWFRUFNNoNBY5XkkgPT2dVa1alc2YMcPaRbEq9+/fZ1KplF28eNGs/EKvtdyu9fz2TbZO06ZN2fjx4/XfNRoN8/X1ZQsWLDCZPyQkhFWtWpWpVCrR58yPpiXl99fSkG7CsYpm8fGMBQQwBjDm7MyYoyP/rHt9+qnwY377Ld/XwYHvHxWVPc9XXzEGMG2zZmzfPsa+/pqxP/5g7Nw5xl6/ZuzKwHmMAWwb+jGAMW8vLXsOD8YA1gRn9cVbjWCj8molEqbu0o2xGzeMz3f6NM9TqhRjanX28mi1jJUpw/NcuMD3l0j49/bt+XuNGowlJ2ffd9Uqvr1SJdPH1qE7vpn9ikX59FN+7tatGdNq83+tzZ3LjzdqlHF6WBhP9/LKd5HzxYULxuXQaBhzceFpV66IPqwl71Eh/RI5jhOInZ0dxo4dq5+CKUvjI+n2HjSSnhNZNSPMg3QTh0QigaenJ3nczoWHDx/i0KFDaNu2LdLS0rBs2TI8fvwYgwcPtnbRrEJ6ejpevHiBL7/8Es2bNzd7dgNda+ahUqlw8eJFo3B7UqkUnTp10i+FyMqePXvQokULjB8/Hn/++Sc8PT0xePBgfPHFF/qZGVlJS0szWjOYkLHo1TBdKpXCzs4O6enpRktNZDIZ5HI5VCoVGGPQarX44IMPIJVK9XVgBksb7OzsIJVKs61RtLOzg0QiyTY1UqFQgDGWzRGiUqmEVqs1SpdIJFAoFNBoNFCr1dnS1Wo1NAaxr82tkw65XA6ZTFYgddJqtRg7dixkMpnRcYpynYCCbSeJRIKxY8eCMWZUzgKtk1IJ5f79YC1bQhIZqU9mpUpB0rEjNHPnQm2i/XKt0yefQNKwIZi/P2Q+Pqbb6YMPIPvmG0jOnkVHl5Po8HkTozrVieHrx6Ud20J+nCE6RoLTaIkg/IX+Pqcgq9QYTq+eYMitDQCA6ZiPjghFJxYK2cH9eNTkPha/fxU1/Blq12ZosPcg3AGgQwdoAJN10tarB+nRo0i/eBHSf/+FjDGgVy+k//475AEBkNy+DfXnnwNLl2bWSauF3U8/QQpAO2YMpLm0k7ZmTUifP0f6lSvQ1qlTeNeeWg3F9u2QANyfwY4dQFAQPvjgA2i1WqSlpQm+nxRPn0ICQO3pCY2BlnaenpACYHFxUL15o1+vLqRO0vPnYRceDs1HH0Gd5f4w936S3rwJOwDw89OX3a5ePUhPn4Y2LAzSevVE3U+SX3/F+AoVoE1KAstnOwlZ105GukAkEgnc3Nz0f8rs0rmR7liGjPScyKoZYR6km3hy+hNPcKRSKdasWYMpU6aAMYa6devi0KFD8De1pq4EcOrUKbRv3x41atTA9u3bBe1L11rePH/+HBqNBl66dYsZeHl54WYODpUiIyNx5MgRDBkyBPv27cPdu3cxbtw4pKenY/Zs0zGZFyxYgLkm1pAuWrRIv3whICAAPXv2xP79+xEWFqbP07ZtW7Rr1w5bt27FPd2aRgDvvPMOAgMDsWLFCsTFxenThwwZgmrVqmHRokVGf7bHjh0LNzc3LFy40KgM06ZNQ3x8PEJCQvRpCoUC06dPR2RkJDZs2KBP9/T0xLhx43D58mXs1TmlAuDn54ehQ4fi5MmTOH78uD5daJ2CgoLQqFGjAqvT5MmTcf/+fWzUxaYuBnUqyHZ666230LJlS2zduhWRBgZzYdTp/tatOPbLL0hydkaiiwtKlSvH63Tpkvg6nTmTezsNHAisW4eI8eOxu2/fzDqVLw/Nv/9CCuBprVv4uP5idOo0Ai2PNwW++wvdPVYipWsCOh88CDuokda8OeLqlMWX177B1Gvf4Z/kTqiYfBPRy7ZhCbjn+qM4hnYAZh3viH/aJKFatT3w84s0qlNU6dKoAODOkiWoeesWL+j06dh/5gzi27fHsPXrIV++HOFVq6LhZ59h69atSDt6FB+EhyNdLseNpk3RAMixncLT09EIwL2FC7HlwQOMHTeuUK69W5s346PHjzNPMHUqDkskuHjtmj4p13aqVw83334bsrg47OvRA2+cnPDZvXtwBvDP9es4a1D+saNHw1MigUSrxbLZs5Hs7Gx+nRhDi9On0Sk0FNBq8SA1FesN4vgJufZa//svOvKd9HXqplajKYC4w4fhNXy44Pvp+2++wdRvv0WZ9HT8eu4cRi5dmq92SjXHj4KOfI/bFzEsPd39jrQGYwC7v+6EJYtZrKDpb+IoKbrRdHfrQ5qJg6a7m0dUVBQDwE6fPm2U/vnnn7OmTZua3Kd69eqsQoUKTG0wjfTHH39k3t7eOZ4nNTWVxcfH61+PHz9mAFhsbCxLTU1lqamp+unzKpVKn5aamsrS09MZY4ylpaXpjzNnzhyWnDHNVZeue+na3DBNl67NmFZq+NJqtUyj0WRLZ4xlS09LS2OMMaZWq02mp6enG6WbWyfdS6dpQdRJp9ubN2+KTZ0Kup2SkpLYnDlzWEJCQrGpU67tlDElWWtnx1IfPMis05EjPN3Hh6WmpGTWSZderhxLjY5mWmdnxgCWunMnmzNnDouPj2dv3qSyB6PmMAawZ6VqsU7tVayaTyJLhYIxgFXDbf3s+HffVbMHD3idNBrGrny6wmjqvKZ9B6M6qT/8kJ9foWCsY0eWvmABU3ftyhjA1MHBebZT2qlTTGtnxxjA0r/+Wng7paczzZ07LO34cabato2pli9nqgsX8myn9OnTeX06dWJaHx+u2ddf6zXLtZ1UKsaGDs3UpFo1lnrjBtM2bcoYwFRbt2a/9jw9GQNY2vnzPD0mhmkXLmSae/dyvvbi4pi6Xz9j/T/4QPS1px4xgh9nzhx9nVTLl/P269BB1P2Utn8/YwBLcHZm8a9f5/t+io2NpenuhYFWCyi1fCTdqSyNpBMEQRBEVsqUKQOZTGbkcR8AYmJi4K1zOJQFHx8f2NnZGc1U8Pf3R3R0NFQqFRQKRbZ9lEollCYcbJlKz2kZUdbjygymbZrC1PlySpdIJCbTpVKpyXSZTGZypoZcLodcnv3vm7l1yiud6mS9Opk6flGvUzYCA4GWLSE5fRrKNWsA3cyYjNBrkg4doDR03Nm0KSCTQRIVBeVXX3EHbHXrcqd3V67o7+9KP34K7FgK71c3cXj0dsDDA+iqgtq3AlZtqobtO4Cffwa2bpXh4EEZhgzhjvlLP2iACwbF+/zVDAy5BDRqlFGnRYuAa9cgOX0aCA2F3CCUp2ziRKOp3SY1aNmSn/ijjyCfNQto3Bjo2tW8drp3D+jaFdK7d2F0dBcX4M4dyLy8cmwnXWg46fDhPOzeyJFQfP89HD/8MNtvolHZGeOx9dav53Xz8oL07l0o27UDMqZq21WsmN2ZoZcXEBcHxatXPDzhBx8Af/0FycmTUBqM9uuQPngAZVAQcOMGYGcHDBwI/PEHpIcOQalQcOeFWeqU57WnC2nr55dZpyZ8SYXk8mWAMcH3kyLjurzn5wd/e3tIJJJ83U85nccU5N09HyQkAI7gRrqrFxnpBEEQBJEVhUKBwMBAozj1Wq0WoaGhaJHhvTkrrVq1wt27d43W7t6+fRs+Pj45/skiCKKI8Mkn/P2XX4DRo4G33waWLeNpHToY53VyygyltXw5f//882xGHFxdgc8+45+/+go4dAgAIO/SCW3ekmDpUuDcOW4jx8fzQz14AES51oZWws2hc5KmWBTeHoGBQL9+wJo1wJNXTnxN982bwJIlQNeu3Ot9//48jr0BKSk8/Px33wFnzxps+PBD/mIMGDSIh9TTaoHz54GZM7ked+4Y1yciAmjThudVKoHKlfkDi3LlgMREvl9O3L3LPZrL5cA77wDDhwONGkGSkID2x47lvB/APfX//DPXd+1aXsYGDYDYWC4ckD0EG2Achu3XX4G//uLfDx7M9LhuyJgx3ED38QGOHeP7KJXA48fiQ9YZhl/TUacOf9jw4oU4L/f79wMA7lavLq5M+YCM9Hzw6lWmka4sRUY6QRAEQZhi8uTJ+P3337F27VpERERg7NixSE5OxsiMuM7Dhw83ciw3duxYvHz5EhMnTsTt27fx999/Y/78+Rg/fry1qkAQhKXo25cbenFxPIzZ4cPcAHR05EZwVgwf5lWowA1dU3z8MQ+Jd/MmfwAAGIVeCwzkEdVCQoBhw/hgcWS0I6QNeaiuSv83A4MHc+N/504edr5CBaCWvwTD59XEjNiJCOm5H3s2JePvEduwaRPwf/8HzJvHi+3hwZ83fPEF0Lw50LkzcOJExsl/+onX4/VrHgKvQgVudH/zDX9AUbcuN7zfvOFxvdu25SHP6tblIebu3+eW/5Yt/HgrVgDh4aZ12LWLv7drx/WQSoHFiwEAjS5e5LMCssIYL8s33/DvP/8MDBnC2+nECV5mQD+6ng1d2pEjwOTJ/LNSyUOf7d5tnPfxYx5LD+AGesuWvO3btuVpBw+arldupKUBT57wz35+mekODkDNmvzz6dP8AcCBA9yRXl5h2Z48Aa5dA5NKEWlo+BcWeU6ILwR+/vlnVqlSJaZUKlnTpk3Z2bNnc8y7Y8cOFhgYyNzc3JijoyNr0KABW7dundnnyu+6P906Eq1Wyy6e12Suo4iNFXW8koChZoT5lBTdLL0mXbdeqLjrZklIM3EI1a2krknXsWzZMlaxYkWmUChY06ZN2X///aff1rZtWxYcHGyU//Tp06xZs2ZMqVSyqlWrsnnz5hmtUc+L/GhaUn5/LQ3pJpwSq9mxY4x99BFjc+Ywtno1X5Oe03/pjRsz/28vWsQYy0W3b74xDif37FneZbl9m7F9+/Rfw8IYmzGDsWbNGJNKjQ+X18vXl7GuXRmTyzPTWrdm7LPPGPtlRhRLdvPO3ODszFj//oy9/XZmWuXKPGQcwMPkxcVlL+/AgXx727Y8jFxWWrTg23/+2ShZ27+/3h8A+/FHHqKMMcZiYhjr3j2zDPPmZT9mWhpjs2YxFhJiWsPJk42FePttfcg91qWLcd558zLLb8iPP2bumxsvXjC2fr1xWLubN/m+Tk7ZNRk82HRjjR6d+3lWcH8F2ubNLXaPCumXrG6kb968mSkUCrZq1Sp2/fp1Nnr0aObu7s5iYmJM5j969CjbuXMnu3HjBrt79y5bsmQJk8lk7MCBA2adL79/hDQaDYuJiWEajYYd2ZuU2dBJSaKOVxIw1Iwwn5KiW0EY6SqVquT94ckHpJk4hOpW0o30wiY/mpaU319LQ7oJhzQzgydPGFMqGfP0ZCwhgTGWi27x8Yx58NjqrG7dfJ/61SvGdu1ibMECxsaPZ6xXL8YCAxlr3JixDh0Y692bseHDuX157VqmfXj/Pn8GoVAY24W1cY3Nw3Q2uNQ+9r/JKSwigvGdduxgrEKFzIzNm/OTm+LhQ8bs7Xm+HTuMtz19mhnr/ckTo02aly9ZyjvvZJ6jWzfGtm1jzDvjwYFSydjy5aYN/7z49tvM45Ypw8tx6xb/LpMx9vw5z6fV8tjzAI81b8j165nlyBqbPjGRsT/+YKxHj8wnIM7O/AEDY4z9/TdPq18/e9nWr88sm4sLY3XqZGr0xx851ynjoYZm9myL3aNFykhv2rQpGz9+vP67RqNhvr6+bMGCBWYfIyAggH355Zdm5bWkd/fdK2IzG51+XHPEUDPCfEqKbuTd3fqQZuIg7+62TX40LSm/v5aGdBMOaWYmYWGM3bun/5qrbroR2TlzCq98OfD4MWOLFzM2ZQp3mN6pE7dhDQ33WrX484SG1ZPY4lJz2B8uY1j9KgmsZk3GatdmrGZNxsqVY8zdnTE7Oz7AfrXPTL5zlSqMGWrw66883UTkjNTUVDZn9mymWraMG8JGTw9qM3bliviKrlmTeaw//8xMb9iQp/32G/9+5gz/7uiof+CiR6vNfFCxf39m+uvXjFWtalxe3UOKTz7heX76iX/v08d0+aKijB96zJ6dWY7r17PnT09nzM2Ne6w/ccJi96iQfsmq3t1VKhUuXrxotA5NKpWiU6dOOHPmTJ77M8Zw5MgR3Lp1C99++63JPGlpaUaB4xMSErKlS6VS2NnZIT093chJjUwmg1wuNwp8b3is+KcZx5LaA+npsLOzg1QqzRao3s7ODhKJxCj+HsCd6TDGjALfA9zzn1arNUqXSCRQKBTQaDRQq9XZ0tVqNTQajT5dSJ0A7jVRJpNlS7dEnXT7arVao+MU5ToBBd9OWfUrDnUy1U66fbVarVF+nQdNwzRdOgCjY5hKZ4xBq9VCKpWC8QeSRvnFpJsqS05lNDe9Q4cOaNCgAZYsWQLGGKpWrYqJEydi4sSJOdZVJpNh586d6NWrV77qJJPJsGPHDv1xdHnyWydduqmy55Ru6+1kquyG5zanjFqtVv856/2U9R4lCIIgDNA5jzOHTz/lC8Jr1Sqw4phL+fLApEnGaSoV9yy/ahV/v3lTt8UJ4ZjNPybmfMywMKB52FTcla2E9/37eDl8EmJHz4Dauzwq/bELLgASO/eBNJkvyZYaeiCTSKAdPRp46y3uUT0iAvjoI+7F3jEf/rU6dwZq1AAGDwZ69sxMf+89vnZ+61buJHDNGp7erx/3Um+IRMIX9//+O183rvNPMHs2EBnJ172PGcOP+fQp9zcQEgJMnJjpNM5wPbohWZ3dzZwJnDrF18b3788d5Dk5ZW4/e5b7SShdGiwwUB99oDCxqpH+/PlzaDQaeGVxQODl5YWbmVdsNuLj41GuXDmkpaVBJpNh+fLl6Ny5s8m8CxYswNy5c7OlL1q0CPYZIR4CAgLQs2dP7N+/H2FhYfo8bdu2Rbt27bB161bc0zW+AWePncNwAKkSBZYsXIghQ4agWrVqWLRokdEfrrFjx8LNzQ0LFy402n/atGmIj49HSEiIPk2hUGD69OmIjIzEhg0b9Omenp4YN24cLl++jL0GoQz8/PwwdOhQnDx5EsePH9enC61TUFAQGjVqhBUrViAuLk6fbqk6AcCDBw+wdevWYlOngm4nndfjxRnOPopDnUy10zvvvANHR0e8evVKbwQBgIeHB+zt7RETE2Nk/Hh6ekImkyE6OtqoTt7e3tBoNHpdYmJiIJFI4OPjg7S0NLx8+VKfVy6Xo2zZsnjz5g3idd5KwR9SlC5dGklJSUhMzOwhHR0d4e7ujoSEBLx5w51FBgcHgzGGf/75B69evTJ6QHLt2jV06dIFR44cQU2dw5Ic6qR7aMEYQ3R0NPbu3QtHR0dER0dnqxOQaSimp6cbaZBbnZYtW4adO3fiwIEDRnV69uwZJBKJPjRWTEwMXFxc4OLikq1Obm5ucHJywvPnz42MS3PbKSUlBY0bN4ZMJsODBw/0D0x1dSqodgJQYHXSwRgz2U5Z66RWq5GUlAQA2e4nX1PecgmCIAjhSCRAvXrWLkWOKBRA7978FR3N/cTJ5TwSmS76mEbDX2o199Xm7MxtSLkc2LQJWLrUGZ8/X4g/MBweW3+F+9b/wwm8hRrgTuEaz+uD2/N4/latgF69svjkq1+fG89Pn3LP8fnF1xe4dSt7+rvvAtOncyP30aNMx3fBwaaP06ULN9J1zuOuXMn0/L9uHffMBwD+/vzzoUPc4Nb9pzDXwZtMBmzYwB/+RERw43/dusyoARle3fH22/owe4WOwFF6ixIVFcUAsNOnTxulf/7556ypiWkaOjQaDbtz5w4LCwtjP/zwA3Nzc2NHjx41mTc1NZXFx8frX48fP2YAWGxsrD6wvEqlYowxplKpjILQp6enM8aMA9/Hx8ezefPmsdTUVLZ42DnGAPbKqZw+8L3unIYvnWOhrOk6p0NZ03V1NExLS0tjjDGmVqtNpqenpxulC6lTamqq3hlP1nRL1Ck+Pp7Nnz+fvXnzptjUqTDaKTU1lc2bN4/Fx8cXmzqZKntSUhK7ceMGS05OZhqNRv/SrfM1TNOl68pvKl2tVrOnT58ytVqt18VUfjHpht937NjBpFIpe/z4cba8I0aMYI0bNzZZxqzHadu2Lfvkk09yrVPWdABs586dZpd99uzZrEGDBjnWyVCz3HQXmm5YlrVr17LWrVuzVq1asU2bNlmkPcxpJ1NlVKvVLC0tLd910umm25ZXGZOTk9n169dZSkpKtvspLi6OprtbmPxOd58/f77+d5EwD9JNOKSZOEq6bsnJjP20VMum+q5jp+3aGE0FvympZdJPGsCYp2cc69FDzcaMYezrr/mS7EePCriwjRvzk3fowN8rVMh5mfCrV3wNO8AX9rduzT/365c976VLmRXTOds7eFBY2Y4fzzzftGmZ6/EDA3na2rUWvdaKzJr0tLQ0JpPJ2K5du4zShw8fznr27Gn2cT744AP2dl6eADOw5Lq/Ve//yxjAXpSpnu9jEURJxeQ6Xa2WO2O0xstMhynp6enMy8uLff3110bpiYmJzNnZmYWEhLDnz5+zgQMHMl9fX+bg4MDq1q3LNm7caJS/bdu2bOLEifrvlSpVYosXL9Z/v337NmvTpg1TKpXM39+fHTp0iAEw+t2cOnUqq169OnNwcGBVqlRhX375pf5hyerVqxkAo9fq1asZYyzbca5cucLat2/P7O3tmYeHBxs9ejRLTEzUbw8ODma9evVi33//PfP29mYeHh5s3Lhx+nPlRrt27divv/7KQkJCWOfOnbNtv3btGuvRowdzcXFhzs7OrHXr1uzu3bv67StXrmS1a9dmCoWCeXt7632Z3L9/nwFgYWFh+ryvXr1iAPQPb48ePcoAsH379rFGjRoxOzs7dvToUXb37l3Ws2dPVrZsWebk5MQaN27MDh8+bFSu1NRUNnXqVFa+fHmmUCiYn58fW7FiBdNqtczPz499//33RvnDwsIYAHbnzp1sdaQ16YULaUoQRInh4UPGvvuOu5bfu5dpNPwvze3bjC1Zwlj79pm2qKlXjRqMjR3LfawdPcpYRAS3l1+/5g7x9u9n7Pff+ZL3rVsZO3yYsQsX+HLumzcZu3OH29SxsdwRvBHffWd8shkzcq+LzjDXGfWOjrx+phg0yPjYBv8bzEa3nh1gbNw4HhFA992c6AACKDJr0hUKBQIDAxEaGorevXsD4Gv2QkNDMWHCBLOPk3Wtc0Gi1WoRGRmJqlWrYuR7b4BVgEc5ipGeG4aaSY0WxhC5UaJ1e/OGz+2yBklJxuuSckAul2P48OFYs2YNZsyYoZ+Gvm3bNmg0GgwaNAhJSUkIDAzEF198AVdXV/z9998YNmwY/Pz80LRp0zzPodVq0bdvX3h5eeHs2bOIj4/HpKyL28Cnc69Zswa+vr64evUqRo8eDRcXF0ydOhXvvfcerl27hgMHDuCfjLikbm5uRvszxvDy5Ut06dIFLVq0wPnz5xEbG4tRo0ZhwoQJWKNbQwbg6NGj8PHxwdGjR3H37l289957aNiwIUaPHp1jPe7du4czZ85g586dYIzh008/xcOHD1GpUiUAQFRUFN566y20a9cOR44cgaurK06dOqWfgh4SEoLJkydj4cKF6NatG+Lj43Hq1Kk89cvKtGnT8MMPP6Bq1aooVaoUHj9+jO7du2PevHlQKpVYt24dgoKCcOvWLVSsWBEAj9995swZ/PTTT2jQoAHu37+P58+fQyKRYOTIkVi1ahU+++wzffuvXr0ab731FqpVqya4fITtUKJ/f/MB6SYc0kwcpFsWKlYEPv+cvwBIwf/KVK/Ol2xPnAi8eKHFzp3RUKu98eyZFFFRwPXrfDn27dv8ZbAKMl84OPAQ7V26AOPfeReBmJq5Maep7jq6dAFOnsxcB/7ll7x+pvjmG2D7dh7zXCbLOV9ufPwxX2cwdiywfHnmeQMCAG9v611rFn08IILNmzczpVLJ1qxZw27cuME+/PBD5u7uzqKjoxljjA0bNoxNmzZNn3/+/Pns0KFD7N69e+zGjRvshx9+YHK5nP3+++9mnc+S3t3Zrl38KUuLFqKOVVIw0owwm5Kim8nRxaSknB/3FvRLQDjFiIgIoxFbxhhr06YNGzp0aI779OjRg3322Wf677mNpB88eJDJ5XIWFRWl375///5sI+BZ+f7771lgYKD+u266e1Z0x9FoNOy7775jpUqVYkkG9f/777+ZVCrV/x4HBwezSpUqGcWqHjBgAHvvvfdyLAtjjP3vf/9jvXv31n/v1asXmz17tv779OnTWZUqVXIckff19WUzcnjyLmQkfffu3bmWkzHG6tSpw5YtW8YYY+zWrVsMQLbRdR2PHz9mMpmMnTlzhjHGl3iUKVOGrVmzxmR+GkkvXMi7e+FDugmHNBMH6SacnDR79Yqx3bsZmzCBsbfe4t7kMxyb62eS16/PI7b17MlYmzbcG72vL2OlS3Ov8y4ujDk45PzXKtyhGWMAe1K5JfviC8bef5+HL58/n7EDB7KEgz9/3niIP682/vhjnrdq1fwJtHGjcYD76dNz1U0MRWYkHQDee+89xMXFYdasWYiOjkbDhg1x4MABvTO5R48eGT21SE5Oxrhx4/DkyRM4ODigVq1aWL9+Pd57773CL7zOMVF+vCESBJEdR0c+oi0CrVaLmJgYeHl5iXviKeB+rlWrFlq2bIlVq1ahXbt2uHv3Lv7991989dVXALgH7/nz52Pr1q2IioqCSqVCWloaHM08R0REBCpUqGDkVEznUNCQLVu24KeffsK9e/eQlJQEtVoNV1dXs+sBAHfu3EGDBg3gZDCLoFWrVtBqtbh165b+N7lOnTpG0Qd8fHxw9erVHI+r0Wiwdu1aLF26VJ82dOhQTJkyBbNmzYJUKkV4eDjatGkDOzu7bPvHxsbi6dOn6Nixo6D6mKJx48ZG35OSkjBnzhz8/fffePbsGdRqNVJSUvDo0SMAQHh4OGQyGdq2bWvyeL6+vujYsSNWr16N5s2bY3VBpHcAABkDSURBVO/evUhLS8OAAQPyXVaCIAiCKAzc3bljuSwBY5CSwq1VIWaOWs19uMXHAw8eAGvXcl9xc1K+wAqMwqgHX+KA6YBcqFIF6NYN6NGtEbr5loPkaRR3GqdU6vNotfzYL18CZctmOIifPRt4/BgIChJadWMGDeIH7N8fSEsD3nknf8fLJ1Y30gFgwoQJOU5vP3bsmNH3b775Bt98800hlMoMyEgniIJBIjFryrlJtFowR0e+fyFMS/rggw/w8ccf45dffsHq1avh5+enN+q+//57LF26FEuWLEG9evXg5OSESZMmWTTc1pkzZzBkyBDMnTsXXbp0gZubGzZv3owff/zRYucwJKshbSpUmSEHDx5EVFRUtgepGo0GoaGh6Ny5MxwcHHLcP7dtAPQPYpiBF/asoQV1OGW5pqZMmYLDhw/jhx9+QLVq1eDg4ID+/fvr2yevcwPAoEGDMGnSJCxZsgSrV6/Ge++9Z/ZDGIIgCIKwVczoArMhlwMeHvxVpQrQvj2wZAmwYUMfvH+oDzycgYmegKcnn51++TJw8SJw5w5w/z6fbb58uRQNlP+gRa1onJ3WDqmTgNRUbvy/esUNdYDv36IF8PbbpfH2tF2oWxcQ+c8xk3feAc6d43P/W7bM79HyhU0Y6UUJiUQCT09Pvv6QjHSzMNKMMBvSTTxyeeH9tL377ruYOHEiNm7ciHXr1mHs2LH6Njt16hR69eqFoUOHAuCj/Ldv30bt2rXNOra/vz8eP36MZ8+ewcfHBwDw33//GeU5ffo0KlWqhBkzZujTHj58aJRHF+c+N2rWrInt27cjOTlZb8yeOnUKUqnUKIycUFauXImBAwcalQ8A5s2bh5UrV6Jz586oX78+1q5di/T09GwPAVxcXFC5cmWEhoaiffv22Y7v6ekJAHj27BkCAgIA8BFwczh16hRGjBiBPn36AOAj6w8ePNBvr1evHrRaLY4fP45OnTqZPEaXLl3g5OSEkJAQHDhwACdOnDDr3IRtQ7+/4iDdhEOaiYN0E461NHN3B8aP56+ciI8H/v0X+Ptv4K+/gMtPauHyzZzj3Ds48JH+kyf5a9Ysnu7oyEfYPT35AwPd3HWplD80qFWLR2+rWZMvX8/ioodTvz5/ZWC1ay3fk+uLGBZd97dgAW/7kSPzfyyCKKHktk63qPDBBx+wUqVKMZlMZrR+/NNPP2UVKlRgp06dYjdu3GCjRo1irq6urFevXvo8ua1J12g0rHbt2qxz584sPDycnThxggUGBhqtSf/zzz+ZXC5nmzZtYnfv3mVLly5lHh4ezM3NTX/MDRs2MCcnJxYWFsbi4uL066oMj5OcnMx8fHxYv3792NWrV9mRI0dY1apVWXBwsP44Ou/uhkycOJG1bdvWpC6xsbHMzs6O7d+/P9u2ffv2MaVSyV68eMGeP3/OSpcuzfr27cvOnz/Pbt++zdatW8du3rzJGGNszZo1zN7eni1dupTdvn2bXbx4kf3000/6YzVv3py1adOG3bhxgx07dow1bdrU5Jr0V69eGZWhT58+rGHDhiwsLIyFh4ezoKAg5uLiYtQeI0aMYBUqVGC7du1ikZGR7OjRo2zLli1Gx/nf//7HFAoF8/f3N6mDDlqTXriQpgRBEEUPrZax8HC+Tv7AAe5t/swZxq5eZezp08wl6pGR3Nt83758XbwYN0QuLozVrs3X2q9YwdiLFwVbNyH9ErlDFIhGo8GlS5f4qBSNpJuFkWaE2ZBu4mCMITk52Wj6c0HzwQcf4NWrV+jSpYvR+vEvv/wSjRo1QpcuXdCuXTt4e3vrI1mYg1Qqxa5du5CSkoKmTZti1KhRmDdvnlGenj174tNPP8WECRPQsGFDnD59GjNnzjTK069fP3Tt2hXt27eHp6cnNm3aZLSd8XCcOHDgAF6+fIkmTZqgf//+6NixI37++WfhgmSwbt06ODk5mVxP3rFjRzg4OGD9+vUoXbo0jhw5gqSkJLRt2xaBgYH4/fff9aPqwcHBWLJkCZYvX446dergnXfewZ07d/THWrVqFdRqNQIDAzFp0iSzl0QtWrQIpUqVQsuWLREUFIQuXbqgUaNGRnlCQkLQv39/jBs3DrVq1cLo0aORnJwMIPNae//996FSqTBy5EixUhE2Bv3+ioN0Ew5pJg7STThFRTOJBGjQgK+R79IFaNcOaN4cqFsX8PHJXKJepQrw0UfAjh18jXpiInDvHnDmDLBnD7BrF7B7N/Dnn9wB/MKF3LF8s2bc8zzA97lxg+cfNQrw9uYz3pctA1atAtavBzZv1uLHH+8hMbGQdSvY5wW2h0W9u0+Zwh/DfP65hUtZvCAPnOIoKbpZeiRdo9GwqKgoptFoLHK8kgBpJg6dbseOHWN2dnZ6L/g5QSPphQt5dy98SDfhkGbiIN2EQ5oZk5jI48EfPszYN98wVq9e7qPud++WMO/uRZqKFYFWrQA/P2uXhCAIgihk0tLS8PTpU3z11VcYMGCA3gM+QRAEQRC2jbMzX6NeqxbQqRMwYwYQEcG90V++zEOvq1RAaqoW9+9HwcGhbKGWj4z0/PDxx/xFEARBlDg2bdqE0aNHo2HDhli3bp21i0MQBEEQRD7w9wfmzDFOS0tLx8KFq1C69LRCLQsZ6QKRSCTw8/Mjb5ICIM3EQbqJR2kQU5MwD9JMOCNGjECvXr1QqlQpfSg4onhAv7/iIN2EQ5qJg3QTDmkmDmvpJmGsEL0r2QAJCQlwc3NDfHw8XF1drV0cgijxpKam4v79+6hSpQrs7e2tXRyCKDByu9apb7I8pClBEARhSwjpl+jRv0DUajWOHTsGtVpt7aIUGUgzcZQ03Sz1vJAxhsTExEL17l7UIc3EIVQ30rfoUNJ+fy0F6SYc0kwcpJtwSDNxWEs3MtIFotFocPz4cZsPX2BLkGbiKCm66cJsvdGFNMwnZHAKhzQTh1DddNe47ponbJeS8vtraUg34ZBm4iDdhEOaicNautGadIIgrIpMJoO7uztiY2MBAI6Ojvla96PVaqFWq5GamkrrhM2ENBOHuboxxvDmzRvExsbC3d0dMpmsEEtJEARBEERRg4x0giCsjre3NwDoDfX8wBhDfHw8kpKSyDmKmZBm4hCqm7u7u/5aJwiCIAiCyAky0gUilUoREBBAo00CIM3EUZJ0k0gk8PHxQdmyZZGenp6vY6Wnp+PBgweoW7cuTSs2E9JMHEJ0s7OzoxH0IkRJ+v21JKSbcEgzcZBuwiHNxGEt3ci7O0EQBEFYGeqbLA9pShAEQdgS5N29AElPT8eePXvyPdpXkiDNxEG6iYN0Ew5pJg7SrfhCbSsO0k04pJk4SDfhkGbisJZuZKQLRKvVIiwsDFqt1tpFKTKQZuIg3cRBugmHNBMH6VZ8obYVB+kmHNJMHKSbcEgzcVhLNzLSCYIgCIIgCIIgCMJGKHGO43RL8BMSEkTtn5aWhtTUVCQkJECpVFqyaMUW0kwcpJs4SDfhkGbisKRuuj6phLmJKVDy09/TPSEO0k04pJk4SDfhkGbisFZfX+Icxz158gQVKlSwdjEIgiAIIhuPHz9G+fLlrV2MYgH19wRBEIQtYk5fX+KMdK1Wi6dPn8LFxUVUPOCEhARUqFABjx8/Jm+xZkKaiYN0EwfpJhzSTByW1I0xhsTERPj6+lJ4HAuRn/6e7glxkG7CIc3EQboJhzQTh7X6+hI33V0qlVpklMLV1ZUucIGQZuIg3cRBugmHNBOHpXRzc3OzQGkIHZbo7+meEAfpJhzSTBykm3BIM3EUdl9Pj+sJgiAIgiAIgiAIwkYgI50gCIIgCIIgCIIgbAQy0gWiVCoxe/Zs8oooANJMHKSbOEg34ZBm4iDdii/UtuIg3YRDmomDdBMOaSYOa+lW4hzHEQRBEARBEARBEIStQiPpBEEQBEEQBEEQBGEjkJFOEARBEARBEARBEDYCGekEQRAEQRAEQRAEYSOQkU4QBEEQBEEQBEEQNgIZ6QL45ZdfULlyZdjb26NZs2Y4d+6ctYtkMyxYsABNmjSBi4sLypYti969e+PWrVtGeVJTUzF+/HiULl0azs7O6NevH2JiYqxUYttk4cKFkEgkmDRpkj6NdDNNVFQUhg4ditKlS8PBwQH16tXDhQsX9NsZY5g1axZ8fHzg4OCATp064c6dO1YssXXRaDSYOXMmqlSpAgcHB/j5+eHrr7+Goe9Q0gw4ceIEgoKC4OvrC4lEgt27dxttN0ejly9fYsiQIXB1dYW7uzs++OADJCUlFWItiPxAfX3uUH+ff6ivNx/q64VD/X3eFIm+nhFmsXnzZqZQKNiqVavY9evX2ejRo5m7uzuLiYmxdtFsgi5durDVq1eza9eusfDwcNa9e3dWsWJFlpSUpM8zZswYVqFCBRYaGsouXLjAmjdvzlq2bGnFUtsW586dY5UrV2b169dnEydO1KeTbtl5+fIlq1SpEhsxYgQ7e/Ysi4yMZAcPHmR3797V51m4cCFzc3Nju3fvZpcvX2Y9e/ZkVapUYSkpKVYsufWYN28eK126NPvrr7/Y/fv32bZt25izszNbunSpPg9pxti+ffvYjBkz2M6dOxkAtmvXLqPt5mjUtWtX1qBBA/bff/+xf//9l1WrVo0NGjSokGtCiIH6+ryh/j5/UF9vPtTXi4P6+7wpCn09Gelm0rRpUzZ+/Hj9d41Gw3x9fdmCBQusWCrbJTY2lgFgx48fZ4wx9vr1a2ZnZ8e2bdumzxMREcEAsDNnzlirmDZDYmIiq169Ojt8+DBr27atvuMm3UzzxRdfsNatW+e4XavVMm9vb/b999/r016/fs2USiXbtGlTYRTR5ujRowd7//33jdL69u3LhgwZwhgjzUyRteM2R6MbN24wAOz8+fP6PPv372cSiYRFRUUVWtkJcVBfLxzq782H+nphUF8vDurvhWGrfT1NdzcDlUqFixcvolOnTvo0qVSKTp064cyZM1Ysme0SHx8PAPDw8AAAXLx4Eenp6UYa1qpVCxUrViQNAYwfPx49evQw0gcg3XJiz549aNy4MQYMGICyZcsiICAAv//+u377/fv3ER0dbaSbm5sbmjVrVmJ1a9myJUJDQ3H79m0AwOXLl3Hy5El069YNAGlmDuZodObMGbi7u6Nx48b6PJ06dYJUKsXZs2cLvcyE+VBfLw7q782H+nphUF8vDurv84et9PVyixylmPP8+XNoNBp4eXkZpXt5eeHmzZtWKpXtotVqMWnSJLRq1Qp169YFAERHR0OhUMDd3d0or5eXF6Kjo61QStth8+bNuHTpEs6fP59tG+lmmsjISISEhGDy5Mn43//+h/Pnz+OTTz6BQqFAcHCwXhtT92xJ1W3atGlISEhArVq1IJPJoNFoMG/ePAwZMgQASDMzMEej6OholC1b1mi7XC6Hh4cH6WjjUF8vHOrvzYf6euFQXy8O6u/zh6309WSkExZn/PjxuHbtGk6ePGntotg8jx8/xsSJE3H48GHY29tbuzhFBq1Wi8aNG2P+/PkAgICAAFy7dg2//vorgoODrVw622Tr1q3YsGEDNm7ciDp16iA8PByTJk2Cr68vaUYQhCiovzcP6uvFQX29OKi/Lx7QdHczKFOmDGQyWTYvmzExMfD29rZSqWyTCRMm4K+//sLRo0dRvnx5fbq3tzdUKhVev35tlL+ka3jx4kXExsaiUaNGkMvlkMvlOH78OH766SfI5XJ4eXmRbibw8fFB7dq1jdL8/f3x6NEjANBrQ/dsJp9//jmmTZuGgQMHol69ehg2bBg+/fRTLFiwAABpZg7maOTt7Y3Y2Fij7Wq1Gi9fviQdbRzq64VB/b35UF8vDurrxUH9ff6wlb6ejHQzUCgUCAwMRGhoqD5Nq9UiNDQULVq0sGLJbAfGGCZMmIBdu3bhyJEjqFKlitH2wMBA2NnZGWl469YtPHr0qERr2LFjR1y9ehXh4eH6V+PGjTFkyBD9Z9ItO61atcoW8uf27duoVKkSAKBKlSrw9vY20i0hIQFnz54tsbq9efMGUqnxT75MJoNWqwVAmpmDORq1aNECr1+/xsWLF/V5jhw5Aq1Wi2bNmhV6mQnzob7ePKi/Fw719eKgvl4c1N/nD5vp6y3ifq4EsHnzZqZUKtmaNWvYjRs32Icffsjc3d1ZdHS0tYtmE4wdO5a5ubmxY8eOsWfPnulfb9680ecZM2YMq1ixIjty5Ai7cOECa9GiBWvRooUVS22bGHp8ZYx0M8W5c+eYXC5n8+bNY3fu3GEbNmxgjo6ObP369fo8CxcuZO7u7uzPP/9kV65cYb169SpR4UWyEhwczMqVK6cPybJz505WpkwZNnXqVH0e0ox7Xw4LC2NhYWEMAFu0aBELCwtjDx8+ZIyZp1HXrl1ZQEAAO3v2LDt58iSrXr06hWArIlBfnzfU31sG6uvzhvp6cVB/nzdFoa8nI10Ay5YtYxUrVmQKhYI1bdqU/ffff9Yuks0AwORr9erV+jwpKSls3LhxrFSpUszR0ZH16dOHPXv2zHqFtlGydtykm2n27t3L6taty5RKJatVqxb77bffjLZrtVo2c+ZM5uXlxZRKJevYsSO7deuWlUprfRISEtjEiRNZxYoVmb29PatatSqbMWMGS0tL0+chzRg7evSoyd+y4OBgxph5Gr148YINGjSIOTs7M1dXVzZy5EiWmJhohdoQYqC+Pneov7cM1NebB/X1wqH+Pm+KQl8vYYwxy4zJEwRBEARBEARBEASRH2hNOkEQBEEQBEEQBEHYCGSkEwRBEARBEARBEISNQEY6QRAEQRAEQRAEQdgIZKQTBEEQBEEQBEEQhI1ARjpBEARBEARBEARB2AhkpBMEQRAEQRAEQRCEjUBGOkEQBEEQBEEQBEHYCGSkEwRBEARBEARBEISNQEY6QRAFjkQiwe7du61dDIIgCIIgCgjq6wnCcpCRThDFnBEjRkAikWR7de3a1dpFIwiCIAjCAlBfTxDFC7m1C0AQRMHTtWtXrF692ihNqVRaqTQEQRAEQVga6usJovhAI+kEUQJQKpXw9vY2epUqVQoAn54WEhKCbt26wcHBAVWrVsX27duN9r969So6dOgABwcHlC5dGh9++CGSkpKM8qxatQp16tSBUqmEj48PJkyYYLT9+fPn6NOnDxwdHVG9enXs2bNHv+3Vq1cYMmQIPD094eDggOrVq2f7o0EQBEEQRM5QX08QxQcy0gmCwMyZM9GvXz9cvnwZQ4YMwcCBAxEREQEASE5ORpcuXVCqVCmcP38e27Ztwz///GPUMYeEhGD8+PH48MMPcfXqVezZswfVqlUzOsfcuXPx7rvv4sqVK+jevTuGDBmCly9f6s9/48YN7N+/HxEREQgJCUGZMmUKTwCCIAiCKOZQX08QRQhGEESxJjg4mMlkMubk5GT0mjdvHmOMMQBszJgxRvs0a9aMjR07ljHG2G+//cZKlSrFkpKS9Nv//vtvJpVKWXR0NGOMMV9fXzZjxowcywCAffnll/rvSUlJDADbv38/Y4yxoKAgNnLkSMtUmCAIgiBKGNTXE0TxgtakE0QJoH379ggJCTFK8/Dw0H9u0aKF0bYWLVogPDwcABAREYEGDRrAyclJv71Vq1bQarW4desWJBIJnj59io4dO+Zahvr16+s/Ozk5wdXVFbGxsQCAsWPHol+/frh06RLefvtt9O7dGy1bthRVV4IgCIIoiVBfTxDFBzLSCaIE4OTklG1KmqVwcHAwK5+dnZ3Rd4lEAq1WCwDo1q0bHj58iH379uHw4cPo2LEjxo8fjx9++MHi5SUIgiCI4gj19QRRfKA16QRB4L///sv23d/fHwDg7++Py5cvIzk5Wb/91KlTkEqlqFmzJlxcXFC5cmWEhobmqwyenp4IDg7G+vXrsWTJEvz222/5Oh5BEARBEJlQX08QRQcaSSeIEkBaWhqio6ON0uRyud5hy7Zt29C4cWO0bt0aGzZswLlz57By5UoAwJAhQzB79mwEBwdjzpw5iIuLw8cff4xhw4bBy8sLADBnzhyMGTMGZcuWRbdu3ZCYmIhTp07h448/Nqt8s2bNQmBgIOrUqYO0tDT89ddf+j8OBEEQBEHkDfX1BFF8ICOdIEoABw4cgI+Pj1FazZo1cfPmTQDcG+vmzZsxbtw4+Pj4YNOmTahduzYAwNHREQcPHsTEiRPRpEkTODo6ol+/fli0aJH+WMHBwUhNTcXixYsxZcoUlClTBv379ze7fAqFAtOnT8eDBw/g4OCANm3aYPPmzRaoOUEQBEGUDKivJ4jig4QxxqxdCIIgrIdEIsGuXbvQu3dvaxeFIAiCIIgCgPp6giha0Jp0giAIgiAIgiAIgrARyEgnCIIgCIIgCIIgCBuBprsTBEEQBEEQBEEQhI1AI+kEQRAEQRAEQRAEYSOQkU4QBEEQBEEQBEEQNgIZ6QRBEARBEARBEARhI5CRThAEQRAEQRAEQRA2AhnpBEEQBEEQBEEQBGEjkJFOEARBEARBEARBEDYCGekEQRAEQRAEQRAEYSOQkU4QBEEQBEEQBEEQNsL/Ay/FyQ4h4rVgAAAAAElFTkSuQmCC\n"},"metadata":{}}],"source":["# plot the accuracy and loss\n","train_loss = history.history['loss']\n","val_loss = history.history['val_loss']\n","acc = history.history['accuracy']\n","val_acc = history.history['val_accuracy']\n","\n","epochsn = np.arange(1, len(train_loss)+1,1)\n","plt.figure(figsize=(12, 4))\n","\n","plt.subplot(1,2,1)\n","plt.plot(epochsn, acc, 'b', label='Training Accuracy')\n","plt.plot(epochsn, val_acc, 'r', label='Validation Accuracy')\n","plt.grid(color='gray', linestyle='--')\n","plt.legend()\n","plt.title('ACCURACY')\n","plt.xlabel('Epochs')\n","plt.ylabel('Accuracy')\n","\n","plt.subplot(1,2,2)\n","plt.plot(epochsn,train_loss, 'b', label='Training Loss')\n","plt.plot(epochsn,val_loss, 'r', label='Validation Loss')\n","plt.grid(color='gray', linestyle='--')\n","plt.legend()\n","plt.title('LOSS')\n","plt.xlabel('Epochs')\n","plt.ylabel('Loss')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RhiFueRJISfR","executionInfo":{"status":"ok","timestamp":1761166334295,"user_tz":360,"elapsed":1724,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"}},"outputId":"84bb7225-8749-41de-b6f3-fd16f1e728f2"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8148 - loss: 0.6107\n","Classification Accuracy: 0.8140000104904175\n"]}],"source":["# Evaluate on test dataset\n","evals_test = cifar_cnn_4.evaluate(test_data, test_label)\n","print(\"Classification Accuracy: \", evals_test[1])"]},{"cell_type":"markdown","metadata":{"id":"fzfn60wHE9wC"},"source":["The data augmentation was helpful to further improve the performance of the model to about 81% accuracy. We trained the model for 100 epochs, which took about 40 minutes. From the learning curves we can conclude that the accuracy was still increasing at epoch 100, therefore we could continue training the model, or we could have selected a higher number of epochs initially."]},{"cell_type":"markdown","metadata":{"id":"CPmTsXovhNQq"},"source":["## 15.8 Transfer Learning "]},{"cell_type":"markdown","metadata":{"id":"k29r7L-LAeAG"},"source":["**Transfer learning** uses pretrained models that are trained on very large datasets to improve the performance on smaller datasets.\n","\n","There are many pretrained models available in the `tensorflow.keras.applications` module.\n","\n","Typical steps in transfer learning include:\n","\n","- Initialize the pretrained model (base model) and weights, and remove the top layers (fully-connected layers) of the pretrained model.\n","- Create a new model by adding new trainable fully-connected layers on top of the primary model.\n","- Train the new model, and evaluate the performance.\n","\n","In this case we will use the VGG16 model, which is popular for image classification. The model is imported with the weights pretrained on ImageNet, which is a very large dataset with 1.2 million images in 1,000 classes. Since our task has only 10 classes, we will remove the dense layers which are very specific to ImageNet's 1,000 classes, and add our own custom layers with 10 classes.\n","\n","Transfer learning is also often referred to as **model fine-tuning**, because we start with a pretrained model and we fine-tune its parameters to our custom dataset.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CiKULXs8e4RB"},"outputs":[],"source":["from tensorflow.keras.applications import vgg16\n","from tensorflow.keras.layers import GlobalAveragePooling2D\n","\n","base_model = vgg16.VGG16(weights='imagenet', include_top=False, input_shape=(32,32,3))\n","\n","# Add a global spatial average pooling (is similar to the flatten flayer)\n","x = base_model.output\n","x = GlobalAveragePooling2D()(x)\n","\n","x = Dense(128, activation='relu')(x)\n","x = Dropout(0.2)(x)\n","x = Dense(64, activation='relu')(x)\n","x = Dropout(0.2)(x)\n","predictions = Dense(10, activation='softmax')(x)\n","\n","# The model we will train\n","cifar_cnn_5 = Model(inputs=base_model.input, outputs=predictions)"]},{"cell_type":"markdown","metadata":{"id":"dJ1NMvpmPXKs"},"source":["### Early Stopping"]},{"cell_type":"markdown","metadata":{"id":"Cnd2oWhIOjdO"},"source":["TensorFlow-Keras has implemented several callbacks that allow to monitor the training process and have more control over it. We will study the various callbacks in the next lecture, and in this lecture we we explain how the callback Early Stopping works.\n","\n","**EarlyStopping** monitors a metric (e.g., validation loss) and if the metric doesn't improve for a certain number of epochs (a.k.a. patience), terminates the training. By improve, we mean the decrease if the metric is loss, or increase if the metric is accuracy."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NwEj4sNZ-RKK","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1761166832448,"user_tz":360,"elapsed":487464,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"}},"outputId":"bc51222a-05f5-40f8-da05-5ca08f198ba1"},"outputs":[{"output_type":"stream","name":"stdout","text":["Training time: 0:08:07.423428\n"]}],"source":["from tensorflow.keras.callbacks import EarlyStopping\n","\n","cifar_cnn_5.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics = ['accuracy'])\n","\n","# fit model\n","t = now()\n","history = cifar_cnn_5.fit(train_data, train_label, validation_split=0.2, batch_size=128,\n"," epochs=100, verbose=0, callbacks=[EarlyStopping(monitor='val_loss', patience = 10)])\n","\n","print('Training time: %s' % (now() - t))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"a01FmtI2t1QO","colab":{"base_uri":"https://localhost:8080/","height":410},"executionInfo":{"status":"ok","timestamp":1761166832787,"user_tz":360,"elapsed":338,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"}},"outputId":"a821c10f-d8eb-4771-e398-561863b29171"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# plot the accuracy and loss\n","train_loss = history.history['loss']\n","val_loss = history.history['val_loss']\n","acc = history.history['accuracy']\n","val_acc = history.history['val_accuracy']\n","\n","epochsn = np.arange(1, len(train_loss)+1,1)\n","plt.figure(figsize=(12, 4))\n","\n","plt.subplot(1,2,1)\n","plt.plot(epochsn, acc, 'b', label='Training Accuracy')\n","plt.plot(epochsn, val_acc, 'r', label='Validation Accuracy')\n","plt.grid(color='gray', linestyle='--')\n","plt.legend()\n","plt.title('ACCURACY')\n","plt.xlabel('Epochs')\n","plt.ylabel('Accuracy')\n","\n","plt.subplot(1,2,2)\n","plt.plot(epochsn,train_loss, 'b', label='Training Loss')\n","plt.plot(epochsn,val_loss, 'r', label='Validation Loss')\n","plt.grid(color='gray', linestyle='--')\n","plt.legend()\n","plt.title('LOSS')\n","plt.xlabel('Epochs')\n","plt.ylabel('Loss')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MVq2V_pItfXA","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1761166837225,"user_tz":360,"elapsed":4437,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"}},"outputId":"ec315d7d-0208-4123-ae52-01b0b7c65ef4"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 8ms/step - accuracy: 0.7940 - loss: 0.8292\n","Classification Accuracy: 0.7943999767303467\n"]}],"source":["# Evaluate on test dataset\n","evals_test = cifar_cnn_5.evaluate(test_data, test_label)\n","print(\"Classification Accuracy: \", evals_test[1])"]},{"cell_type":"markdown","metadata":{"id":"PawwVj2rjZsK"},"source":["Although the accuracy is reduced, note that the model was trained in about 15 epochs, in comparison to 100 epochs or more epochs when trained from scratch.\n","\n","The following figure illustrates the early stopping callback. When the validation loss starts increasing, it terminates the training. In our case, the validation loss reached minimum at epoch 6, and since we specified a 10 epochs patience, the model stopped at epoch 16.\n","\n","\n","\n","*Figure: Early stopping.*"]},{"cell_type":"markdown","metadata":{"id":"_uad8ljQEp07"},"source":["Let's try to use a pretrained model and combine it with data augmentation and early stopping."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"c4Wa4EWvRVkQ"},"outputs":[],"source":["# The model we will train\n","cifar_cnn_6 = Model(inputs=base_model.input, outputs=predictions)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TQ0Fs2KtRgCp"},"outputs":[],"source":["# compile model\n","cifar_cnn_6.compile(optimizer='adam',\n"," loss='sparse_categorical_crossentropy',\n"," metrics=['accuracy'])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":879827,"status":"ok","timestamp":1761167798887,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"OhYu6897Axla","outputId":"165502a4-9b0e-4448-edb0-09bd2339789e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Training time: 0:14:39.791182\n"]}],"source":["t = now()\n","# fit model\n","history= cifar_cnn_6.fit(datagen.flow(train_data_1, train_label_1, batch_size=128),\n"," epochs=100, validation_data=(validation_data_1, validation_label_1), verbose=0,\n"," callbacks=[EarlyStopping(monitor='val_loss', patience = 10)])\n","\n","print('Training time: %s' % (now() - t))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":410},"executionInfo":{"elapsed":385,"status":"ok","timestamp":1761167799274,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"OnrPyR1atOWL","outputId":"0b1316a4-8b94-4010-adda-0b95188fde4d"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# plot the accuracy and loss\n","train_loss = history.history['loss']\n","val_loss = history.history['val_loss']\n","acc = history.history['accuracy']\n","val_acc = history.history['val_accuracy']\n","\n","epochsn = np.arange(1, len(train_loss)+1,1)\n","plt.figure(figsize=(12, 4))\n","\n","plt.subplot(1,2,1)\n","plt.plot(epochsn, acc, 'b', label='Training Accuracy')\n","plt.plot(epochsn, val_acc, 'r', label='Validation Accuracy')\n","plt.grid(color='gray', linestyle='--')\n","plt.legend()\n","plt.title('ACCURACY')\n","plt.xlabel('Epochs')\n","plt.ylabel('Accuracy')\n","\n","plt.subplot(1,2,2)\n","plt.plot(epochsn,train_loss, 'b', label='Training Loss')\n","plt.plot(epochsn,val_loss, 'r', label='Validation Loss')\n","plt.grid(color='gray', linestyle='--')\n","plt.legend()\n","plt.title('LOSS')\n","plt.xlabel('Epochs')\n","plt.ylabel('Loss')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2488,"status":"ok","timestamp":1761167801764,"user":{"displayName":"Aleksandar Vakanski","userId":"07675307153279708378"},"user_tz":360},"id":"ThYRPaEWWGQw","outputId":"d62f4041-32b9-4ed9-c973-2f20c00d5b47"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.8419 - loss: 0.5698\n","Classification Accuracy: 0.8348000049591064\n"]}],"source":["# Evaluate on test dataset\n","evals_test = cifar_cnn_6.evaluate(test_data, test_label)\n","print(\"Classification Accuracy: \", evals_test[1])"]},{"cell_type":"markdown","metadata":{"id":"48ygKtOVWS0Z"},"source":["Combining transfer learning with data augmentation increased the accuracy to above 83%."]},{"cell_type":"markdown","metadata":{"id":"vweobvFVe4RB"},"source":["## References \n","\n","1. Complete Machine Learning Package, Jean de Dieu Nyandwi, available at: [https://github.com/Nyandwi/machine_learning_complete](https://github.com/Nyandwi/machine_learning_complete).\n","2. How to Develop a CNN from Scratch for CIFAR-10 Photo Classification, Jason Brownlee, available at: [https://machinelearningmastery.com/how-to-develop-a-cnn-from-scratch-for-cifar-10-photo-classification/](https://machinelearningmastery.com/how-to-develop-a-cnn-from-scratch-for-cifar-10-photo-classification/).\n","3. Python Machine Learning (2nd Ed.) Code Repository, Sebastian Raschka, available at: [https://github.com/rasbt/python-machine-learning-book-2nd-edition](https://github.com/rasbt/python-machine-learning-book-2nd-edition).\n"]},{"cell_type":"markdown","metadata":{"id":"bDbzcXmWe4RB"},"source":["[BACK TO TOP](#top)"]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[{"file_id":"1zniWJ7YmbvOhFjB9LWDHtZG0V7nQOn6B","timestamp":1665618878004}]},"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":"3.9.13"}},"nbformat":4,"nbformat_minor":0}