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RBM with hands.ipynb
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/XinyueZ/4cff8fef9aeaa23e6c3f3b8326ee316c/rbm-with-hands.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "P2c5KbIki1sV" | |
| }, | |
| "source": [ | |
| "Goal ⚽️ 🥅 : Generative and unsupervised learning with Restricted Boltzmann Machine (RBM)\n", | |
| "===\n", | |
| "\n", | |
| "An RBM are a basic form of [Autoencoder](https://www.cs.toronto.edu/~hinton/science.pdf?). It can automatically extract <b>meaningful</b> features from a given input." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Train RBM with [Laurence Moroney](https://laurencemoroney.com/datasets.html) Rock Paper Scissors Dataset.\n", | |
| "\n", | |
| "Think of RBM as a model (kind of [Autoencoder](https://www.cs.toronto.edu/~hinton/science.pdf?)) that has been trained based on images of a dataset of many hand movements. Also, imagine that the RBM network has only two hidden nodes, where one node encodes the weight and, and the other encodes the size. \n", | |
| "\n", | |
| "In a sense, the different configurations represent different movement of hand: Rock Paper Scissors.\n", | |
| "\n", | |
| "In a training process, through many forward and backward passes, the RBM adjusts its weights to send a stronger signal to either the Rock node (0, 0, 1) or the Paper node (0, 1, 0) or Scissors(1, 0, 0) in the hidden layer, given the pixels of images. \n", | |
| "\n", | |
| "Now, given a random hand movements in hidden layer, which distribution of pixels should we expect? RBM can give you 2 things. First, it encodes your images in hidden layer. Second, it gives you the probability of observing a case, given some hidden values.\n", | |
| "\n", | |
| "After training the model is briefly enable to generate photo of hand, ie:\n", | |
| "\n", | |
| "> Here give source with random image and do some noises on it.\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "" | |
| ], | |
| "metadata": { | |
| "id": "E1x6cEn5jaa-" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# How does it work? \n", | |
| "RBM is a 2 layer neural network. Simply, RBM takes the inputs and translates those into a set of binary values that represents them in the hidden layer. Then, these numbers can be translated back to reconstruct the inputs. Through several forward and backward passes, the RBM will be trained, and a trained RBM can reveal which features are the most important ones when detecting patterns. \n", | |
| "\n", | |
| "<h4>What are the applications of an RBM?</h4>\n", | |
| "RBM is useful for <a href='http://www.cs.utoronto.ca/~hinton/absps/netflixICML.pdf?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDL0120ENSkillsNetwork20629446-2021-01-01'> Collaborative Filtering</a>, dimensionality reduction, classification, regression, feature learning, topic modeling." | |
| ], | |
| "metadata": { | |
| "id": "bxhpqCAZkcx3" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Unsupervised learning\n", | |
| "\n", | |
| "In an unsupervised task, we try to form a model for $P(x)$, where $P$ is the probability given $x$ as an input vector. \n", | |
| "\n", | |
| "Generative learning is one kind of unsupervised learning algorithms. " | |
| ], | |
| "metadata": { | |
| "id": "hwBQbbQZk2RY" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Generative learning\n", | |
| "\n", | |
| "Give an example:\n", | |
| "\n", | |
| "Looking at fruits, we can build a model of what Apple looks like. Then, looking at Kiwi, we can build a separate model of what Kiwi looks like. \n", | |
| "\n", | |
| "**Our goal**: We build a model of what a hand looks like" | |
| ], | |
| "metadata": { | |
| "id": "pDZ0DdPklHL1" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "id": "Vqy2BRp3d01r" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from PIL import Image\n", | |
| "from PIL import ImageOps \n", | |
| "from PIL import ImageDraw \n", | |
| "import cv2\n", | |
| "import matplotlib.pyplot as plt \n", | |
| "\n", | |
| "import numpy as np\n", | |
| "\n", | |
| "import tensorflow as tf \n", | |
| "from tensorflow.keras.utils import *\n", | |
| "\n", | |
| "import os\n", | |
| "import sys\n", | |
| "from IPython.display import clear_output" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "coBSeRUp_i2j" | |
| }, | |
| "source": [ | |
| "# Help methods" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "id": "7J8cHEQxi1sb" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def scale_to_unit_interval(ndar, eps=1e-8):\n", | |
| " \"\"\" Scales all values in the ndarray ndar to be between 0 and 1 \"\"\"\n", | |
| " ndar = ndar.copy()\n", | |
| " ndar -= ndar.min()\n", | |
| " ndar *= 1.0 / (ndar.max() + eps)\n", | |
| " return ndar\n", | |
| "\n", | |
| "\n", | |
| "def tile_raster_images(X, img_shape, tile_shape, tile_spacing=(0, 0),\n", | |
| " scale_rows_to_unit_interval=True,\n", | |
| " output_pixel_vals=True):\n", | |
| " \"\"\"\n", | |
| " Transform an array with one flattened image per row, into an array in\n", | |
| " which images are reshaped and layed out like tiles on a floor.\n", | |
| "\n", | |
| " This function is useful for visualizing datasets whose rows are images,\n", | |
| " and also columns of matrices for transforming those rows\n", | |
| " (such as the first layer of a neural net).\n", | |
| "\n", | |
| " :type X: a 2-D ndarray or a tuple of 4 channels, elements of which can\n", | |
| " be 2-D ndarrays or None;\n", | |
| " :param X: a 2-D array in which every row is a flattened image.\n", | |
| "\n", | |
| " :type img_shape: tuple; (height, width)\n", | |
| " :param img_shape: the original shape of each image\n", | |
| "\n", | |
| " :type tile_shape: tuple; (rows, cols)\n", | |
| " :param tile_shape: the number of images to tile (rows, cols)\n", | |
| "\n", | |
| " :param output_pixel_vals: if output should be pixel values (i.e. int8\n", | |
| " values) or floats\n", | |
| "\n", | |
| " :param scale_rows_to_unit_interval: if the values need to be scaled before\n", | |
| " being plotted to [0,1] or not\n", | |
| "\n", | |
| "\n", | |
| " :returns: array suitable for viewing as an image.\n", | |
| " (See:`Image.fromarray`.)\n", | |
| " :rtype: a 2-d array with same dtype as X.\n", | |
| "\n", | |
| " \"\"\"\n", | |
| "\n", | |
| " assert len(img_shape) == 2\n", | |
| " assert len(tile_shape) == 2\n", | |
| " assert len(tile_spacing) == 2\n", | |
| "\n", | |
| " # The expression below can be re-written in a more C style as\n", | |
| " # follows :\n", | |
| " #\n", | |
| " # out_shape = [0,0]\n", | |
| " # out_shape[0] = (img_shape[0]+tile_spacing[0])*tile_shape[0] -\n", | |
| " # tile_spacing[0]\n", | |
| " # out_shape[1] = (img_shape[1]+tile_spacing[1])*tile_shape[1] -\n", | |
| " # tile_spacing[1]\n", | |
| " out_shape = [\n", | |
| " (ishp + tsp) * tshp - tsp\n", | |
| " for ishp, tshp, tsp in zip(img_shape, tile_shape, tile_spacing)\n", | |
| " ]\n", | |
| "\n", | |
| " if isinstance(X, tuple):\n", | |
| " assert len(X) == 4\n", | |
| " # Create an output numpy ndarray to store the image\n", | |
| " if output_pixel_vals:\n", | |
| " out_array = np.zeros((out_shape[0], out_shape[1], 4),\n", | |
| " dtype='uint8')\n", | |
| " else:\n", | |
| " out_array = np.zeros((out_shape[0], out_shape[1], 4),\n", | |
| " dtype=X.dtype)\n", | |
| "\n", | |
| " #colors default to 0, alpha defaults to 1 (opaque)\n", | |
| " if output_pixel_vals:\n", | |
| " channel_defaults = [0, 0, 0, 255]\n", | |
| " else:\n", | |
| " channel_defaults = [0., 0., 0., 1.]\n", | |
| "\n", | |
| " for i in range(4):\n", | |
| " if X[i] is None:\n", | |
| " # if channel is None, fill it with zeros of the correct\n", | |
| " # dtype\n", | |
| " dt = out_array.dtype\n", | |
| " if output_pixel_vals:\n", | |
| " dt = 'uint8'\n", | |
| " out_array[:, :, i] = np.zeros(\n", | |
| " out_shape,\n", | |
| " dtype=dt\n", | |
| " ) + channel_defaults[i]\n", | |
| " else:\n", | |
| " # use a recurrent call to compute the channel and store it\n", | |
| " # in the output\n", | |
| " out_array[:, :, i] = tile_raster_images(\n", | |
| " X[i], img_shape, tile_shape, tile_spacing,\n", | |
| " scale_rows_to_unit_interval, output_pixel_vals)\n", | |
| " return out_array\n", | |
| "\n", | |
| " else:\n", | |
| " # if we are dealing with only one channel\n", | |
| " H, W = img_shape\n", | |
| " Hs, Ws = tile_spacing\n", | |
| "\n", | |
| " # generate a matrix to store the output\n", | |
| " dt = X.dtype\n", | |
| " if output_pixel_vals:\n", | |
| " dt = 'uint8'\n", | |
| " out_array = np.zeros(out_shape, dtype=dt)\n", | |
| "\n", | |
| " for tile_row in range(tile_shape[0]):\n", | |
| " for tile_col in range(tile_shape[1]):\n", | |
| " if tile_row * tile_shape[1] + tile_col < X.shape[0]:\n", | |
| " this_x = X[tile_row * tile_shape[1] + tile_col]\n", | |
| " if scale_rows_to_unit_interval:\n", | |
| " # if we should scale values to be between 0 and 1\n", | |
| " # do this by calling the `scale_to_unit_interval`\n", | |
| " # function\n", | |
| " this_img = scale_to_unit_interval(\n", | |
| " this_x.reshape(img_shape))\n", | |
| " else:\n", | |
| " this_img = this_x.reshape(img_shape)\n", | |
| " # add the slice to the corresponding position in the\n", | |
| " # output array\n", | |
| " c = 1\n", | |
| " if output_pixel_vals:\n", | |
| " c = 255\n", | |
| " out_array[\n", | |
| " tile_row * (H + Hs): tile_row * (H + Hs) + H,\n", | |
| " tile_col * (W + Ws): tile_col * (W + Ws) + W\n", | |
| " ] = this_img * c\n", | |
| " return out_array\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "id": "e8Kt_CCbd8lK" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def process_in_duration(message, proc):\n", | |
| " import datetime\n", | |
| " \n", | |
| " tm_start = datetime.datetime.now()\n", | |
| "\n", | |
| " result = proc()\n", | |
| "\n", | |
| " tm_end = datetime.datetime.now() \n", | |
| " delta = tm_end - tm_start\n", | |
| " \n", | |
| " print(f\"\\033[92m{message} used {delta}\")\n", | |
| " return result" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "id": "nVjVq03BbKpJ" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def plot_history(history):\n", | |
| " loss = history[\"loss\"]\n", | |
| "\n", | |
| " plt.plot(loss) \n", | |
| "\n", | |
| " if history.get(\"best_loss\") is not None:\n", | |
| " plt.scatter(history[\"best_history\"], history[\"best_loss\"], c='r')\n", | |
| "\n", | |
| " plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": { | |
| "id": "33RnkKLyvliM" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def plot_image(img1, img2=None, img3=None):\n", | |
| " plt.figure(figsize=(5, 5))\n", | |
| "\n", | |
| " ax = plt.subplot(131)\n", | |
| " ax.get_xaxis().set_visible(False)\n", | |
| " ax.get_yaxis().set_visible(False)\n", | |
| "\n", | |
| " plt.imshow(img1, cmap='gray')\n", | |
| "\n", | |
| " if img2 is not None:\n", | |
| " ax = plt.subplot(132)\n", | |
| " plt.imshow(img2, cmap='gray')\n", | |
| " ax.get_xaxis().set_visible(False)\n", | |
| " ax.get_yaxis().set_visible(False)\n", | |
| "\n", | |
| " if img3 is not None:\n", | |
| " ax = plt.subplot(133)\n", | |
| " plt.imshow(img3, cmap='gray')\n", | |
| " ax.get_xaxis().set_visible(False)\n", | |
| " ax.get_yaxis().set_visible(False)\n", | |
| "\n", | |
| " plt.show()\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": { | |
| "id": "6WgQvWkfi1se" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def plot_image_3D_scatter(img1_array, label1, color1, img2_array=None, label2=None, color2=None):\n", | |
| "\n", | |
| " xx, yy = np.mgrid[0:img1_array.shape[0], 0:img1_array.shape[1]]\n", | |
| "\n", | |
| " plt.figure(figsize=(5, 5))\n", | |
| " ax = plt.axes(projection='3d')\n", | |
| "\n", | |
| " ax.scatter3D(xx, yy, img1_array, c=color1, label=label1)\n", | |
| " if img2_array is not None:\n", | |
| " ax.scatter3D(xx, yy, img2_array, c=color2, label=label2)\n", | |
| " ax.legend()\n", | |
| "\n", | |
| " plt.show()\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": { | |
| "id": "WtcUCb3ei1sf" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def plot_image_hist(title, img1, label1, img2, label2):\n", | |
| " time_line = [i for i in range(256)]\n", | |
| "\n", | |
| " histr = cv2.calcHist([np.array(img1)], [0], None, [256], [0, 256])\n", | |
| " plt.plot(time_line, histr, color=\"blue\", label=label1)\n", | |
| "\n", | |
| " histr = cv2.calcHist([np.array(img2)], [0], None, [256], [0, 256])\n", | |
| " plt.plot(time_line, histr, color=\"red\", label=label2)\n", | |
| " plt.xlim([0, 256])\n", | |
| "\n", | |
| " plt.legend()\n", | |
| " plt.title(title)\n", | |
| " plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": { | |
| "id": "lP7K7Dqii1sg" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def noisy_image(origin_img,\n", | |
| " left=10,\n", | |
| " upper=10,\n", | |
| " right=13,\n", | |
| " lower=13):\n", | |
| "\n", | |
| " shape = [left, upper, right, lower]\n", | |
| "\n", | |
| " noisied_image = origin_img.copy()\n", | |
| " image_fn = ImageDraw.Draw(im=noisied_image)\n", | |
| "\n", | |
| " image_fn.rectangle(xy=shape, fill=\"white\")\n", | |
| " return noisied_image\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def generate_random_image(low, high, size):\n", | |
| " random_mn = np.random.randint(low=low, high=high, size=size)\n", | |
| " random_mn = np.float32(random_mn)\n", | |
| " random_img = Image.fromarray(random_mn)\n", | |
| " random_noisied_img = noisy_image(random_img)\n", | |
| " return random_img, random_noisied_img" | |
| ], | |
| "metadata": { | |
| "id": "K4nEYVhGzj8V" | |
| }, | |
| "execution_count": 9, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "ycPAGuKw_fDR" | |
| }, | |
| "source": [ | |
| "# Data\n", | |
| "\n", | |
| "[Laurence Moroney](https://laurencemoroney.com/datasets.html) Rock Paper Scissors Dataset.\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Convenient download\n", | |
| "\n", | |
| "https://dl.dropbox.com/s/w19k944w35af101/train.zip\n", | |
| "\n", | |
| "\n", | |
| "Using Keras [get_file()](https://www.tensorflow.org/api_docs/python/tf/keras/utils/get_file) is quite easy to download a zip and extract it.\n", | |
| "\n", | |
| "In this zip there are 3 different subfolders for train, cross validation and test. \n", | |
| "\n", | |
| "We use all of them for our training purpose." | |
| ], | |
| "metadata": { | |
| "id": "n2_WL7OHowt-" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 458 | |
| }, | |
| "id": "NNmbscYci1sg", | |
| "outputId": "cbeb8c75-284d-4509-d917-5fc5d1f3b24a" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "/content/my_datasets/train\n", | |
| "Downloading data from https://dl.dropbox.com/s/w19k944w35af101/train.zip\n", | |
| "201834496/201833815 [==============================] - 18s 0us/step\n", | |
| "201842688/201833815 [==============================] - 18s 0us/step\n", | |
| "/content/train.zip\n", | |
| "['.DS_Store', 'rock', 'paper', 'scissors']\n", | |
| "/content/my_datasets/valid\n", | |
| "Downloading data from https://dl.dropbox.com/s/tyha48tjw4gkiy8/cv.zip\n", | |
| "29687808/29683601 [==============================] - 4s 0us/step\n", | |
| "29696000/29683601 [==============================] - 4s 0us/step\n", | |
| "/content/valid.zip\n", | |
| "['.DS_Store', 'rock', 'paper', 'scissors']\n", | |
| "image_shape (64, 64)\n", | |
| "dim_visible 4096\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 360x360 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 360x360 with 2 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ], | |
| "source": [ | |
| "def download_dataset(src, name):\n", | |
| " download_dir = os.getcwd()\n", | |
| "\n", | |
| " dataset_root = os.getcwd()\n", | |
| " dataset_subdir = \"my_datasets\"\n", | |
| "\n", | |
| " # Location of downloaded file will be \"$dataset_root/$name.zip\".\n", | |
| " # Location of all extracted data will be in \"$dataset_root/$dataset_subdir/$name\".\n", | |
| "\n", | |
| " download_file = os.path.join(download_dir, f\"{name}.zip\")\n", | |
| " dataset_dir = os.path.join(dataset_root, dataset_subdir, name)\n", | |
| " print(dataset_dir)\n", | |
| "\n", | |
| " if os.path.exists(dataset_dir):\n", | |
| " return None, dataset_dir\n", | |
| "\n", | |
| " get_file(\n", | |
| " fname=download_file,\n", | |
| " cache_dir=dataset_root,\n", | |
| " cache_subdir=dataset_subdir,\n", | |
| " origin=src,\n", | |
| " extract=True)\n", | |
| "\n", | |
| " assert os.path.exists(download_file)\n", | |
| " assert os.path.exists(dataset_dir)\n", | |
| "\n", | |
| " return download_file, dataset_dir\n", | |
| "\n", | |
| "\n", | |
| "# Download dataset\n", | |
| "train_download_file, train_dir = download_dataset(\n", | |
| " \"https://dl.dropbox.com/s/w19k944w35af101/train.zip\", \"train\")\n", | |
| "print(train_download_file)\n", | |
| "print(os.listdir(train_dir))\n", | |
| "\n", | |
| "cv_download_file, cv_dir = download_dataset(\"https://dl.dropbox.com/s/tyha48tjw4gkiy8/cv.zip\", \"valid\")\n", | |
| "print(cv_download_file)\n", | |
| "print(os.listdir(cv_dir))\n", | |
| "\n", | |
| "'''\n", | |
| "test_download_file, test_dir = download_dataset(\"https://dl.dropbox.com/s/1x0tt0j53tymn1b/test.zip\", \"test\")\n", | |
| "print(test_download_file)\n", | |
| "print(os.listdir(test_dir))\n", | |
| "'''\n", | |
| "\n", | |
| "# Load dataset\n", | |
| "def load_images(path, resize=(64, 64)):\n", | |
| " X = []\n", | |
| " labels = ['scissors', 'paper', 'rock']\n", | |
| "\n", | |
| " for _, label in enumerate(labels):\n", | |
| " path_label_dir = os.path.join(path, label)\n", | |
| " for image_path in os.listdir(path_label_dir):\n", | |
| " image_path = os.path.join(path_label_dir, image_path)\n", | |
| " image_array = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n", | |
| " image_array = cv2.resize(image_array, resize)\n", | |
| " image_array = (image_array / 255.0).astype(np.float32)\n", | |
| " X.append(image_array)\n", | |
| "\n", | |
| " return X\n", | |
| "\n", | |
| "\n", | |
| "x_train = load_images(train_dir, resize=(64, 64)) + \\\n", | |
| " load_images(cv_dir, resize=(64, 64)) \n", | |
| " \n", | |
| "x_train = np.array(x_train)\n", | |
| "\n", | |
| "image_shape = (next(iter(x_train))).shape\n", | |
| "dim_visible = image_shape[0] * image_shape[1]\n", | |
| "\n", | |
| "print(\"image_shape\", image_shape)\n", | |
| "print(\"dim_visible\", dim_visible)\n", | |
| "\n", | |
| "test_image = Image.fromarray((next(iter(x_train*255.0))))\n", | |
| "plot_image(test_image)\n", | |
| "\n", | |
| "noisied_image = noisy_image(Image.fromarray(next(iter(x_train*255.0))),\n", | |
| " left=33,\n", | |
| " upper=40,\n", | |
| " right=43,\n", | |
| " lower=50)\n", | |
| "\n", | |
| "plot_image(test_image, noisied_image)\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 875 | |
| }, | |
| "id": "CiP5I9tGi1sh", | |
| "outputId": "92e1d2d6-cef9-4395-c99b-a0e3eadfb78b" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 360x360 with 1 Axes>" | |
| ], | |
| "image/png": 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jxqjCMKCuTlBbaychIYuPPrLw3HMWCgtNsrMlU6dKVq0yyMyUdHRARYWG3w8FBZK8PDloXGokAXC5wGKBowuRIfqKVEFBAQ6Hg/379yOE6GdJBWNSkYhUJO5bpMQ6IShiojRGIsnMHqzIVkpJY2Mjhw4dYt68eaSmpgJQXw+PPTaVDz6w4HarBMfRL/WfGLxe9QXx+Sz09Gg0NEji4zUmTZJMmGDyu99ZSEuDri5BTo6JpsGRIxqFhQYTJ6paPKsVjhwRWK2S8vJpzJljIzNTkpQEZWUamgaLFhl0dgr27dNobxcUFZksX26ydKkBqCx1v18JZWamRNNUZnZBQQEFBQVIKXG73XR0dHDo0CEcDkfYIjXeQfPhCLcTQt/A+elQvydGuDAnrxXhKYBpmhw5coSkpKSwEiG7urqoq6ujuLgYUF0hy8vL0TSNOXPmYLFYkBJefFHw85/r7N9v4PVaotY6Cofgd8RmUyKRmCiJj4eUFImuQ2qqpKND0NMj8PmU5ZOVJenpAdN0YrEkEQhInE6NzEyTlBSorRXEx0uKi006OjR6eiA9XWK1SlJTlVt56JBg8mSTnh7BrFkesrNbOP/8XAxDYLfD5MlmyDrrK1KdnZ3DilR7eztHjhxhxowZY742hmGwY8cOlixZMqrtg/V7we9wZ2cncXFxpKamngoiNeSXJSZKo6BvMPsf//gHZ511Vljb9fT0cPDgQRYsWEBnZydlZWVMnTqVvLw8ADo64J57dP78Zw2nE5xOVct2uqDrSqR0XZW+WCxgtUpUqZkgI8Okq0tD1+VRIVaibLEoK9FiUcKmaWp7TVMi5/UKdF25c3FxEqtVkJ5u0tysER+v9h8X5ycpyUJKirKeXC5BcbHBkiUmCxaYzJxphtzAgSLldDqJi4sjPT0dIQQej4fp06eP+Xr4/X52797N4sWLx7wvgP3794eENEgUFxkPeRAx9y1CxjJRRAgR6grZ1tbGokWLSDj6c71lC9xxh4WqKhXM9npByshuHouFUAcAq1XtQ4hgysDJxzDUH79fCYrfr8phgpfw8GENw1DnrR4TSNnrtgbr+AxDnZfPp1xGTVPnbppqfz090N6uLASfT60W+nw6XV0q0bS2ViMhwaSqysLrr8PcuSbTppl85jMGKSnKeps1K4GCgoR+7l5nZyfNzc04nU5cLldodS9oKUeKlLJf8uVYMU0zZCEF938qdkKIiVIEBK2j0bYZ8fl8tLe3k5iYSElJCZqmEQjAb3+r8eCDOh0d4HZHHsy2WGDCBC9ut5XMTEF6OnR3g9MpCARkqFNANDFYvP/YY9TRNCVCus5Ri+pYgmIX/HdfgtfS41Ei1dqqPrOODg2LBbxeyaZNOtu3a/z5zxbi4tRnkJwsKSkxWLnSZP58kzlzEkhISMBiseBwOMjNzaWzs5Pa2tqQJRW0UsIVqeAq7XhhGEY/dy2STgi1tbUUFBSEEnRPJjFRCoOxthkBaG5upqqqioSEhFA8oqUFbr1V5733tJB1NBo0DWw2yaRJAe65R2PCBPjoI9i1S6OiQiCEitv0JlqeOgSjC2M/7v5ffimVYPn96nG3W2C1qseTkyWdnRqtrRrbt5tMnCgpLTX40pcC2O2SQMCCYSSQn59Afn4+Uko8Hg8dHR3U1dXhcDjCEqlgn6fxwjTNYS2v4Toh/PjHP+bWW28ddXxrPImJ0giMtc1IIBCgoqICv9/PokWLqKioAODttwV33KFTWyvweoeyjoLvNXxoz+eDmho7hmHy9NOCBx4wUDmXBh0d8Oqrgv/+b439+zUMQ+JwCEZY+Dmp6HpQhEyk1EKxqECAY4476K6OFdPs/VHo6hKh/zscGq2tkm3bNP7wByuJifnoumTqVCtJSXDuuQHOPNOksDCe+Pj4iERqvNMLIt1fX5Fyu92hUMLJJiZKQ9A3mD2SdTTUMnF3dzd79uyhsLCQiRMnEggEcDol3/uexp/+pNPdrVwFxXBiJ+gVpsGESolMc7PGm28KfvYzybe/bTJhAqSnw9VXS9LSDH7/e4Nt2yzouqSnR4xgffR9zxNL73Gpa26aSnwslmPF+3iIa98YFsDhw+qad3WBlHEkJxs0Nqo+54cO2fjgA5NLL/XT0qJhs0FxsUlBQTz5+UOLlN1uD02ZGa80g7GIXLDHVTQQE6VBiCSYHUyIHFgtfujQIZqamliwYEHowy4v17jzznnU1Og4HCreE37N2uBN2vr+3+dTPbk3bNCprdW4806DBQskQsCyZYeZPLkNw8hiz540fvnLOOrrNRwOtXWvEIhh3qMvgx378RGxvsHuwRgva2kkgsfQ2ak+a7td0NoKn3yi8fLLFmbPNnC5BD6fYP58ky99yc8Xv6jiRvHxvZYUgNvtprGxEZfLxdatW7Hb7f0C56MRF8MwRh04j1lKUUyk47EHipLX62XPnj0kJiaybNmyo8/DH/+o8dOfWmlutuDzCUwzEkEKF+V2uFzw1ltQVqZzzz0B8vKqsVjamTBhAl1dzRQWVrJixRRqatLZuTMJt1vgdEbq0g127EOJ1PhZXYMJ0PEVpMHOSZ3PwBjgxx/r2O2QmAj798N//IeVTZt08vMlbW2QlARTp0ouuihAYmI86enpSCmZPn16aHWvvr4+ZEn1dffCEamYpXSaEQxmf/LJJ8ybNy9UjzQSfUtHDh8+TGVlJTNnziQ7OxtQhbN33KHzyiva0bq14H6P3zKsaUJ3txKau+7yUVycx0MPTSYrK0Bubi4AM2b4ePppD1IGqK/X6eqy0dZmxzC0iFMRhmYkqytyNQkKUDBnaShBisx66ntsI/1YiAF/977eMNQPgtstaW/XkRL27tWJi4P4eJWxnpoKTz5pYcYMk/j4ZCZOlBQWErKkgjlrQZFqaGigp6cnLJEKWvajwe/395uCczKJiRL969aCf4eLWtYPhFpoLF26FLvdDsD27YJvfEOnslIctY6O1xkMjmFATU0ira2werXkxhvhsssMUlMhM9PGunU2rr0W1q+3cfCgwaFDDhobE44W/OoYRvCYg03NQAiJpnE0K9ukpUUL5Q1Fnug5egtqpBSH4T/CYwXl2OfCZbBVtd54nWmC3y/p6VFdFSwWFbQvK9PJzk5A1+28/rqNSZNMsrNhyRKDVauMUYnUWHKOoilf6VOd0T3YvLWPP/6YefPmERcXF9Y+tm3bhtfrpaCggKKiIoRQOUEPPKDxyCM6PT1qdexkZ2ZrGiQlSfLyTG6/3ceZZ6ql7k2bdJ56ykphoY+2tiMIMYG6OoHfb7Btm05PjwVQ9WPJyQZWq8BuFyxYYOBwCKZMMQkEBB9+qNPSEpycEkniZyQ/AL1iMzaXLSiGJ+szkaGsdl03MU0VIM/JkSQlqQGhwSGhbrdgwgTJeecFuPBCI1S2Ewycd3Z20tPTg81mw+FwMH/+fJKTkyNy46SUrFy5ku3bt59IYYpldA9kqGB2uD2PpJTU19fT1dXF3LlzQ79oNTXwrW/pbN6s4fWqYPbJFiTodem6uzXWrYtj9myTG27wk5ws0TT1BZBSFb4WF0v+/d8D3Hmnzr59Jk4nBAImnZ2SrCwXN920j7S0BHJzk1i4UHWI3LdP4+OPNTZuhK1bNYSwhIK+UioLQUp1HL3WV2TKYpoqH8vvV1nco7M8B7pfJ4O+lpSKRQYCUFsLVqtGZaUkORl0XXVPmDBB8u67dp580mTVqgBLlpjMmBFHXl5e6L7zeDxs376dxsbGkEgFA+fhilTMUjpJjDRRZNeuXUydOnXYoJ/f76esrAyLxYJpmhQVFZGSksorrwjuustCc7MqbwgE5DjGZ8aftDTJihUGCQmSzEyDnp42YAJr1gRYtMjk+9+3kZYm2bZNx+lU+VT33uvlC1/w09PTQ0dHBx0dHQQCAVJSUkITct99t5W//nUef/+7jmFIUlKUkPzzPwdYssTg0UdVALi9fXyvTfBjlLL338qtDL2CY1MrohVlyVksEtNU7jJAcjJkZJjoumDhQoPSUoOiIvNoW5ZyvvzlWcTFKZHq7Oyko6MjJFJBd2+gSJmmycqVK9mxY8eJPMFYQS6M3IQNYM+ePUyaNImUIdomtre3s3fvXqZNm0Zubi7l5eXk5eXxzjuZ3HWXzuHDyuQ+FRBCuXQTJ5rMmBFAiCNccUU6F15oYBhwzz12enpU1f7hwyrj+d57vaEvSBDDMOju7qajo4MjR47gdrvJy8sjLi6D+voM2tosdHcLUlMlZ5xhUlgoaWmBX//ayoYNtlBawvE+1/7z706m+zZ61CKvRNeVYGmaOrfERElhYQfLlyfz/e/7jvmMvF5vyN3r7u4OiVRycjIWi4U1a9awefPmYd+7rq6Oa665hpaWFoQQ3Hjjjdx8882sX7+eDRs2hBZ3fvazn3HRRRcBcO+99/L444+j6zq/+tWvuOCCC4K7i4lSuOOxgyKTPmBcR7CQtqOjg/nz5xN/9FMvL99LR0c+t92WRVUVOByn1o2uaaqHUWKixGLxU1qq861v+VixwqS5WfDYY1bq6jRyc01uvNHPpEnD3xJut5uqqioKCgqO9ity8vvfT8bvTyA+3orNZuP22/1Mm6b2090N991n5de/to16ISAYa+q9lQe6hqeShTQ6LBaJ1WqQmKixfLnJ+vU+Zs4c+oIGRWrnzp1873vfw+l0sm7dOlauXMmKFSsGdfeamppoampi8eLF9PT0sGTJEl566SWee+45kpKSuO222/q9vry8nCuvvJItW7bQ2NjI5z//eSorK4PpM5/emFKkdWuDxZRcLhe7d+8mKyuLkpKSoyUCsHGj4Cc/mUFNTdyYrCMhVFW/zSbxeAjt3zCO/6+5aSq3LBAQgJUtW+Dmm+3ce6+PpUsNfvADX6ggNlyEEGRmZpKZmUl5uU5cnM7EiS48nh6am002bPDw9a+rhvnJycn87Gd+8vMl996rLLM+e+rz7+HaCA/cZuB16y9IJyrZ8kQSCAgCAbVium0b/OhHNn7+cy95eYOfqN1uJzc3l9zcXObOncu3vvUtpkyZwjPPPMOKFSsG3aZvDCs5OZk5c+bQ0NAw5DG9/PLLXHHFFdjtdqZMmcL06dPZsmXLkPsPclqLkmmatLW1hYYZhhPIGzgGqampiQMHDjB37tyQ9eR0wre/rfoeORw6gcDoEtaC7lNiIsyfrxLs0tMlmZlw8KCgvj6A06njdmuYphhgDYwvKvCq0doKhw/DNdfEkZ8vWbLE4OKLA1x8ce/KTyR4vQKLRZCQoKrsNQ3S0wMkJKhf3X379mG321m9Op1p03K488506uq0QUpghq8D7P1oZZ9r1Fec+lbLD7aHU9Od64842mdK0NIiqKjQyMsbuZLZ5XKRmZnJlVdeyZVXXhnWO9XU1PDJJ5+wbNkyNm3axCOPPMKTTz7J0qVLeeCBB0hPT6ehoYHly5eHtpk4ceKwIhYkatvSjYWgdeT1eikrK4soByNoKQUCAXbv3k1rayulpaUhQZISvvc9nVdf1XC5ghbGaI9TJQFee61BSooSp6ws1RPo6qtNXnihll/+so21a02mTzdJTzdJTfWRlHR8+5BIqVrKVlVp/OUvOg88YONPfxrd79eSJQaGIWhvVyOhOjo0Vq6ECRMmMHv2bEpLS5k5cyYWi4VJk6q5776/c/HFreTl+YHh/DnBQKE5VrRHXmnrvS1OdUFSBC1a04Sj6XIj4nK5IioxcTgcrFmzhv/8z/8kJSWFm266ierqanbs2EFeXh7f+c53RnHkvZx2ltLAYHakaJpGT08P1dXVFBUVkZ+fH9pPVxesW6fz5z/r+P1yXAZAdnQIHnrIghCqZYbXC7m5kjff1FixQvDZzzr56lfTqalp5le/8rJzZxHV1RZSUkw6O7XQmCVl3I3vr71KI9DYu1dy2212/u//1Iy3z3wm/D4iU6dKvv1tH6++quP3w2WX+Vi2rL/Y9K0LmztXcsYZbrZubeLFF/289VYRTmffLPihkh2HO/fBnxPidLCOegk29HO7VeO6RYvC+5yCgxTCwe/3s2bNGq6++mouu+wyQP3ABLnhhhu45JJLACgoKKCuri70XH19PQUFBSO+x2klSoMFswcrmB0KKSUdHR04HA6WLFlCYmqX1YcAACAASURBVGIiAA0NsGGDxm9/q9PRIY52QBy/mznYKcDlErS1qdau8fHw0ENZ3HbbYbq7y/F6vdx/fzE+Hxw44GP3bo22NhPDUG1QduwINpbXQ6OPNE0txev62ATU4xHY7ZKGBo2HHrKRkeFlwYLwo9Jz5pjMmRPe64UQRzs+JlBQsJVvfjOPO+6QlJdb8Pv7plgMnYltsUgCAYGm9c0R6xUgJUZqm9MltqTrEl1X/c9vuMHPrbf6j1mBGwqXyxW614dDSsl1113HnDlzuPXWW0OPNzU1hWJNGzduDPWgX716NVdddRW33norjY2NVFVVUVpaOuL7nBaiNFwwW02WHbl62uPxsHv3boQQFBUVhT6k+nq49loLu3ap5vbH+yYOdlGUEnbvtvKzn+lcfnkGEybksmMHHDqkUVYmyM2VfPWrJunpcMst8OGHrfzHf6TyySdJZGZKzjhDJSyWlUnsdujpkTQ0CDye4cpdBrccpFSugHI3JTt2aBGJ0lhYvlzy+ON+brlFp74eWlqCgfmBH0TvcQdd6l5B6rv6dmzu2OkQ+BZCLZR84xsGd93lJxInIdwOAZs2beKpp55i/vz5LFy4EFDL/08//TQ7duxACMHkyZN57LHHAJg3bx6XX345c+fOxWKx8F//9V9hGQenfErASE3Ytm/fzty5c4ctG2ltbaWqqorZs2fj9Xrx+XxMmDCZX/1K4+GHVZLfyRv+qBrhW629q0zTp0tAYrEIbrjB4HOfM4EmfD43Tzwxgz17BImJKiD/uc+ZrF1r8sILgjfegAMHNJqatKPTQtS1UpYlCGGSkKCFxFc91luismyZid8PX/uan0svHTyu5Xa72b9/P/Pnzx/zmW/dupWSkhIAmpsF//3fViorBZWV6hhNU3WIPLZj57GV/Up4xGkhQANRCyYm//qvzfzoRykRCRLAn/70J7q6urjjjjuOzwEOzumXEjBSZnYQTdMwhuhmZhgG+/btw+PxUFJSgs1mo7m5ma4uk5/8ROeNNzQ6Ok72NFpxtG1r7yN79qgVLSHg9tstpKdLZszI4Z//+TDZ2ZLEREFamuQrXzG54AJlMV13nclXv+qjvV1j/37Bb35j5R//0CksVPVWO3dq2O1eJkywU1+vejMlJak+3z09gpwcNWFk0iST88478Q2/c3MlP/iBD7cb4uJg716Nhx+28t57gu5uJaIul7pevfRafkHr6HQTJLWaKfnJT1o588wjCBH5rPRIA93Hm1NSlCJpwjZwiT9IT08Pe/bsIT8/nzlz5iCEqkf605+SeeSRXJqa9Ki+gYNV8n5/cGx2HB98UEhqqrKkdu7UcLkE8fGq0VtGhnp9ZqYkM1OyYIGXn/7Uxt69asDjxRcbFBdXsGjRbGbONPngA5133rEQHy9ZtSpAIKCstcWLDcIIPxwXhCA0q23ePJNHH/WyaZPG3Xfb2LVLx2IRIdc3aBn15/QKbFssarrwH/7gJi/Pgc83ugZvLpcrrAD0ieKUE6VIJ4oMtJSklNTV1dHQ0EBxcXFoekNLC3z96zp//Wtanxa1pw7BQHZnJ3z8sSApSfLyy4LXX7cyaZJk5UqD88/X+PBDK4cPC+bMMZg922TKFDX3bMECg127uli6VAn4JZcYXHJJdE8ZEALOPtvkzTc93HKLjTfesOB2i6Ojl2DktiSjEanoEDarVTJjhsmzz3ooKpI0No6+wVs0dZ2EU0iUpJQ4HA46OzvJzs4O+wMIBrpBTW3Ys2cPcXFxlJaWhoJub70lWLfOQlOTwOeLjptutARzdbq7e8/hwAHYv9/Chg0WLBYVX/F4rCQkSIqKTOLj4YorAjQ3T+CFF2wEAmoU9lVXBcjKimJz8ShWK/z4xz7sdkF9vcqJOnBAUFhocviwoKVFWZaaNnDU1GDpBL2dJcPrrnniiY+X5OdLnnxSCRKMvetkOKtvJ4pTQpSCwWyXy0VLSws5OTlhbxt0344cOUJFRQUzZswgJycHKWHXLsGDD2q88oqG292/odnpgspj6j0nny+4VK4SJMvLdYSQ7NxpB2Zhs2lHi3TVHLQpU0ySkyXnnWcwb57J5MnRKVLp6bB+vZe9e9UXs6xM4733LMyZY/LSSxqGoVqoWK0qRqcmpihre7AAeG86gRKnaAmQ67qK9f3yl15mzuw9IMMwwu6WOpCYpRQBA4PZVqt1yKD1UAghqKurwzAMlixZQlxcHFLCvffqbNig0do60lSP04++/Z2CQqyugY7Pp4qKm5vV8598oiEE/OEPVqZMUf2lb7rJT3Z2FHxDB5CcDKWlyv1cuNBE0+Dvf7dQWtpNW1sCBw/aAJUiYberlbu+n33fOJRpavS1mnrjUyfPktZ1KCgwefZZN/Pm9b/+4ebiDUbMUgqTwYLZfV2xcHC5XDQ0NJCamsqiRYtC8adHHtF46CH96LL48TqD0wOfr/cLWF4uqKiw8bvfWZk71+T88wOcd16AKVMkQ3R6OWnYbLB2bYC1awNUVh4kKSmHhx/O4cABVR60ebNOXJzss2CgklYDgb49mEYuUzlRWCySuXNNnn/ezdE8xX4MnI4bCU6nMyZK4TDY6lokotTQ0MChQ4fIzc0lISEBIQROJ/zbv+ls3KiPehrtpx3ThPZ21f5261adhx+2kZwsWbzY4AtfMLj44pN9hMcipUqTuPtuHxs3WvjgA53p01WWeWWlyttqb1epE6bZaznrOkez9/vu7cQKlKapoPY55xg89ZRnyJXPsVhKbrc7JkrhMJjqhyNKgUAgVIRbWlpKS0sLfr+f7dvhK1+xUlNzMhMhTy/8fhVQ93jg7bctHDigUVmZwNGSqDERyfCGcPYlhCAhAa6+OsDZZxt873t2UlJg2TKTsjKoqBCYpoq3BeOLg99qJ859C2Zp/9M/Bfj1r70MFzI6nQLdUdslYLClfhWUHPpm7ezsZPPmzWRnZ7NgwQIsFgtC6Pz2t6mcf76NAwdigjTeGIZKZtR1FWx+/30bTufofrGPF30n0EqpxPTccwM0NgqamwV5eSaLF6tODUVFJunpEqu1/312ottXa5pqvDd1quT73/cNK0gwNvct2kQpai2lwRgqJ0lKyYEDB2hra2PRokUkJCQgJezbB9/8Zg5bt9qPGfccY+wEx2kHm8DZbONv3YTD/v2CDz9UnRbOPVfFuAbbl5TwxBMW3n3Xgq6rer4vfSnAxo0WnE4RspDi4iQOh4bNJkPnF+xndeLcNzU04NZbfSN2+4SxuW9erzc0FiwaiFpRCveGDBbSpqWlUVJSgqZpNDbC179u4a9/1fD5jvOBnkbouklmpqC9XfV5SkqCnh6VB5Sfb+J0qmBwc7N29PWqC0FPj6C42ODwYcFnPuMlMfHELWdWVQkefNAWKhjessXG7bf7+qUuBEWpslLjnXcsFBaq3tbd3bBhg5W0NElxsUlCgqS8XMfphOJig/R0SXm5RiAgcDjUWCSfzyQ+Xg0WVf8XxMerEhyrVVlhKjal3ts0VYFwUMBH9kpVe+IlSwwee8x7jMAOxVjcNxg8XHKyiFpRCoeWlhb279/PnDlzyMjICOUeXXuthYMHiVlHI6BpyroJBCApKUBiopfUVJM5c2DaNA2fz8aKFSZz55q0tqrExIMHVf7PtGkmgYAq+q2u1li2zGDxYpOLL3Zx6NDYjy1cS+mDDyzExRFKUWhuVkH4yZN7sySD++rqUkIS/P4lJ4PDAWlpSjCmTZOkpwfwegUOhyAzU7lze/boGAakpEBCgg9Ni8NqVWkFhw8L4uNh+XKDujqNjg7BpEkmWVkStxsyM6GoyKSoyOTZZ61UV2t0dytxUj+Yvedot0uklEyd6uKuu7bidMbT0JBOeno68fHxw16P0bpv4xm7Gy9OSVEKBALs27cPn88XKqSVEv7f/9P53e90OjtP9hFGP7oO2dkm+fmSrCy46y4/99/fjd+fxdKl3Vx4YQ0dHU4yM60kJ2fy3/89ie3bbXg8cPiwQEqN9HSJwyHJyYFvfEMNA3C7oadHp7JSIyfHJC3t+J7HYNbHwO9uUJQKC5U75nKpflVNTUpMmppUvpquK5G67TYf9fUab76p43AIZs0yiY+XVFRoeL068fFK2GbONLnvPi9/+5uF8nKNefPUoM/i4sEDl5dearB/v+Cdd3RqagTV1T1UV2eE+h55vWCxCJ54QjBnzlKcTicdHR3s378ft9tNcnIy6enpoVFWfRmL+6au2clPewgStaI01EUyTZMtW7ZQWFjIxIkTQ697/33BK68M1sYixmAUFRl84xsBbDbVZ2n16gR8Pjvp6YLNmzN56KEsUlIkYJKT4+XAATXjTkqBx6NRXi5CX/4JE0yuuSae22/30dkZxy9+MZ/kZDs2G/zwh15WrjT42990Dh4UFBRIPvMZY8TAbbice26AbdtstLSogwkE4Kyz+ruPQWsgL0/yrW/5+M1vbLS1wYwZJuvW+ThyRPDqqxZ8PjjvPIOlS01KS03+6Z8CrFmjepULAfHxJuXlAdasga9/3UdenhL31auNsIYrZGVJsrIky5erCoU9e8rZv7+E3/zGGhLXdet8zJ2rVviSkpJISkqisLAQKWVo1l5FRQU+ny80ay89PT2UPnM6ELX9lIKlJaEDkZJDhw5RVVVFSUkJaQN+gp99VuOHP9SPjpw+0Ud7eqDrJoYRmQsQbCkb7G6p+i+ZSKlhtarcn5kzDerqdDRNuXs2G0ybZnLppQGWLTMoKFDCmJsrQ7VcgUCAXbt2sXjx4hGPobpaBboBVq4MHFMKs2vXLmbNmhUK5pqmcp3Cmcz+ta/FAWpysGFIKiqcPPignTPOGNsyrtfrpaKigjPOOIPqakFzs0Z+vhlRDCk4a6+jo4Pu7m7y8/NDU3GtVmtY+/H5fKxatYqtW7eO5XRGw6ndT8nr9bJnzx4SEhIGNV0BZsyQxMX1BhtjRE6kggS9ZRnBzGg1mls7uj/13Pbt6jYLWgNCwPbtOjt26GiaErOEBBXgLSoyWb7cYNUq8PmsPPOMhbIyjaIik/PPN8jNlXzyiU5lpaC1VZCcDIWFJldf7R/U+goEVJ/xQKC3kb6mhSdIAN/6lo9777XT3a1Gly9e3MX8+VkRX6eB9A1MT5smmTYtssUBTdNIS0sjLS2NKVOmsGXLFrKysujo6KC2thYpZWgiblpa2pCuncvlCs0wjBaiXpTa2trYt28fM2fOJDs7mx07dgyaQLl4seSWWwzuv1/564ET34fsU87IrkPQKJeyt+1vcLtgV4MjR3R27dJ54gkrhqE6OdjtJk6nxvr1yvIyDBV/8fkEcXGS9HRVz5aeroL2CQnKNbv8cj8vvWSltnYqmZmJrFtnMH9+ZMb/4sUmDz3k4cABjbg4H7regKaNryiNB0IIMjIyyDjaOCsQCNDZ2Ul7ezsHDhxA1/WQSKWmpobeO5wSk6Em47a3t/PlL3+ZmpoaJk+ezHPPPUd6ejpSSm6++WZee+01EhISeOKJJ8KyeINErShJKamoqMDhcLB06dKQ6T1cVvdXv2qyZo1JVRU8+aTOM8/oeDwSpxOioX4pRngEAsE+2+rX3eVSX6C+dXhBXC6By3Vsz+2KCo1nn7UycaJBe3sapmnllVdg5UqDz33O4Ior/BxtpTUi+fmS/HwDj8dPZeX43EfjLUoDsVgsZGVlkZWlBNTn89HZ2Rlq/Wy1WjFNkwMHDoxoKVksFh544IF+k3HPP/98nnjiCT73uc/x3e9+l/vuu4/77ruP+++/n9dff52qqiqqqqrYvHkzN91004gjwfsSPckJA/D5fMTHx7NkyZJ+iV0jlZokJMAZZ8ADDxi89pqfq64yycvzMHGiDK2axDi9kVKNrvJ6obpap6PDRleXwOkUvPaahbvvtrFyZQItLZHuN/yEzpEYT1EKZ1nfZrORk5PDrFmzKC0tZe7cuXi9Xp555hk++OADVq9ezaOPPjrotnl5eSFLp+9k3Jdffpm1a9cCsHbtWl566SVATca95pprEEKwfPlyOjs7aWpqCvt8otZSiouLo6io6JjHIynKPeMMycMPG/zLv2xhwoQzMQyoq4Mf/MBCTw/U1io3LwpTNWIcR1wuwf79goULE3n6aTczZ0oyMuSIcabxFJLxFqVIxdJut3PmmWeiaRrPP/88d955J2VlZSNu13cybktLS2i0Um5uLi1HVb6hoYHCwsLQNsHJuHmDtTcYhKgVpaGItH0JqLYPs2Yp5Zk7F5Yv91NVpbJsf/ADnUOHVAvVxsZYbdynCadTpUIE41ErVwb47GcN/uVfAgy2eDXeltJ47WusbUuSkpKYNm0a06ZNG/a1Ayfj9iWSKdQjEbXOzFAnOBpRGkhqKixdKiktlfzxjwHOP99k4kRYtcpkzRqD3FzvoDdljNMTr1dlgj//vJV77rFx4YXxvP66fswP1HiKkpRyXK2usTR4C6fr5FCTcYNuWVNTU6gj7Ggn4waJWlGCwYVpPESpLxkZ8NBDBu+/7+fFFwP84Q8GP//5AW65pYHi4i4SE9VSdSwWdfpjmtDaqrF1q866dXFccUUcmzdrIfc+Wt234922ZKjJuKtXr+b3v/89AL///e+59NJLQ48/+eSTSCn56KOPSE1NDdt1g1PUffOPIhEp3F85v99HTk49l12Wyvr1CzAMP++/L3j7bY2339Zob1e9rd3uYE7OaM4iRjQjpSql+b//s/D22xaSkyVLlhhccIGPiRMTmDVr7O8xnqJ0vNuWDDUZ97vf/S6XX345jz/+OEVFRTz33HMAXHTRRbz22mtMnz6dhIQE/ud//ieiYzolRcmjuuFHtE04jdU7OjooLy8nOTmZwsJCNE3NRDv/fMn55xuAstBME+67T+eppzSOHFG1VKox/WjPKka0Egiolbx33rHw17+mk5ubSEMD3Hjj2DJ0x9tSGov7lp2dPexrzj777CFX+N55551jHhNC8F//9V+jOh6IcvdtMEbjvmmaNuhAyiDBfkyVlZUsXryY5OTkYV+vaXDXXQZlZX6am/288cbHrFnjJTFR1T/F4lGnH6o5nKClxcrdd9v4/vdt9PSMfn+nkqV0oolqURqvmNJw2/h8PrZv347f76ekpIT4+PgRRSyIpqk/ycnw6KMONm/2cfbZaiaX3T5ygWaMUw+fT43jeuwxKytWJPDb31pH1bNrPFffTkSg+0RySrpvo7GUBtumvb2dvXv3hkpYRnr9cPs3TZPp0+HVV/3U16s6qz17BHfcYaGrS63uaBqx8pfTBK9XUFsr+NnPbGzfrvHII96IFkOiJdAdbUMD4BQUpXCtmL4EB1IGCbprR44cCc2CG8t79H29xQKTJ6vH8/IkH3zg58ABgc0mcbkEzz8Pr7/uoaEhGZcrFig/1enqEvz976o7RbDDQTiMZ0rAWPOUYpbSGBmr++b1etm9ezcpKSksXbp0yKkpoxWlgSQnq8xyhWTRIpNLL91OYeEKPvkENmxwsnNnAt3ddny+oSZoxIhW1Iy4yAvAoyXQHbOUImS8YkpB0QiO7h7org32+kAEd1kkllVwIktWloecnJ089FAOkydPZtcuPw0NqhfUvfdqVFaqoYkxohspYe5cI+Jx5tHivkVjoDuqRWkwRitK9fX1eL3eQd21wV4/mphSOAghCAQCfPzxx6He4qCsqaBFdcklJu++K/jgA40nnvDT1qaON5ZyEF1omuTiizv46U/dCJFMJOtG4y1KI6W7DEXMUhoHIhUlr9dLc3MzycnJQ7prAxlLTGk4grEsr9fLWWedS3e3ndZWyM7u31da13tzoy6+eAvV1cu5+24bra29CZsxgTq5zJpl8tRTPWRkdNLR0cG2bfuw2+2kp6eTkZFBYmLisKtr450SMFr3LdpGdkOUi9JQAynDJeiuZWZmkpaWFvZNMJ4xpSA+n4/du3eTlJSEpiXx61/HU12tShhKSkzWrjUH7ZwohODLXw5wxRVQWwvPPafz2muCfftUN0W3OxaHOlFommTBAg8PPihZvNhECB3ICdV8ud1uOjo6qKmpCRW6ZmRkDNnoPxraoHg8nljnyROBlJL9+/fT2dnJ0qVLaW1tPW7uWDiv7+rqYs+ePcyYMYOcnBxeeKGK+nqNSZMkUsJHH2nMmiU5++xjzZ/gvm02mDIF7rzT4M47ob1d9btOSIC9ewXPPquxcWOAnh57bHjCcSAtTXLnnfWsWeMjN3fCoK+Jj48nPj6e/Px8pJQ4HI5+jf6DnR+D3RmjIaYkpRzTFJTjwWknSh6Ph127dpGRkcHSpUsRQqBpWkT1cqOJKQ32eikl9fX11NfXhyb3AjQ3x5GaqgRICEhIkNTXCwab0zDUqPKMDPUH4OyzJWefbXDFFf8gN/dMPvpI47nnNDZtUtNdYj2jRo+mwbXX+vjFL3zU1XWi60lhbSeEIDk5meTkZCZNmoRhGHR3d9Pe3s6hQ4dwuVzYbDays7P7tacdDaN136Jx5hucZqJ0+PBhKisrmT17NpmZmaHHI62XG42lNFD0DMOgrKwMIQSlpaX9bprcXA91dYLUVGUpud1QWDj4DTKUKA3+Wpg2TU0Kufpqk64uF88/f5DKysm8914aVVXiaKvZsE/tU8+tt3q55x712Y6ldYmu6yErCWD79u0kJSWF2tNGEo8ayFjjU9E2mimqRWm4i9X3BjFNk/3799Pd3d2vn3eQSEVmrDElp9PJrl27QrPpBrJixRESEkwqK1VM6cwzTZYvH1x4RpMsCr0C/S//UkxqaiLgp73dz3PP6fzkJ3YcDpWVHKU/licdIWD6dJO33rJy993+o5NYxq+fEkBOTg75+fnA4PGooEiNtFo8luTJaCSqRWko+lb9B921zMxMlixZMi65TWOJKbW0tFBdXU1xcfEx3fmCxMWZ3HyzyZEjJrre64YNRiSWEiiBPnjwIO3t7aHpwS4XtLaqqbBXXOGnsFDywQc6W7fq7Nyph/KhYgKl0DQ44wyD5GRoaxMYhsrUP565RSPFo1JTU8nIyBh0pttokyejVcxOaVHq6OigsrKyX77PUK+PVGQiETEhBIZhUFFRgcvloqSkZMRhgEJIsrNH/tWNVJR27NhBQkICS5YsQdM0qqvh/vstuFzgdms4HCoDGWDKFJM//tFNU5PG009b2LjRoK0tDo8nusz5E0VWlsQ0YcIEic2meiqdd17vNN/x7jw51L4GxqNM06Srq4v29nZqa2sBSEtLIyMjg9TU1FGLZTQW40KUi9JQH5qmaVRVVeH1ekPWwHAcz2RIUDO2mpqaKCoqYtasWSPeuEGhCecGD1eU1C+rmylTJjNtWj6aBj098P3vq3HUEydKdu9WqQRnnmkQHw81NRobN1q4/voAP/2pj3/+5x1kZs5j5844nnnGyqZNOn6/ONovKsyLcYoye7bJ5s0uqqsFDzxgo6VF4/zz/axb139K84kQpYFomtYvHuX3+/uNS3K73TQ0NJCZmUlSUlLY+42J0jjhdrvp7OwMjX0J5wM4nu5be3s7VVVVpKamMnXq1Ij2P16JnK2trbz3Xj3btk3jwIF8Nm/W+MxnTN58U2P/fkFGhmTfPkFPj8RikXg8gvh4NXKqqan3GDRNjc6eNMngi1808PuhrEzlQzkccPPN8TQ2qljU6URGhuTFF900NQn+8hcLmZmSlSv9fOlLAfqGJ8fTfRuLuFmtVrKzs0OlUps3b8Zms1FbW4vD4Qg7HhUTpXEg+MuQlpZGXl5eRL80kbpvI1knUkpqampobW1l9uzZtLa2juv+gwxnKQXzsRobHbS0LCIt7SB5eWqa7JNPamRmQm6uxOtVWeJCqEZlNpuJYag8pwULjGP2GcRqhYULe6/b3//u5MMPdf7v/3RefNFKZ6c4uk3Ypx51TJli8te/utA0WL9eFUUnJ0veftuCwwHXX9+7VDnege7xQtM08vPzQ/Eop9NJe3s7+/btw+v1DhmPilZRir4o1yCYpklFRQV1dXWhRmyRWD7jPWzA7/ezY8cOPB5P6HgiET0hxLCvb2yEDRs0fvlLjS1bkgkEjv3W+/1+tm/fjpSS6dMXHh1vrZ5LTVWumxDwxS+aWCyC7m4V6L70Uj9dXYKWFsGllwY47zyDujr1/5GmCCcmwgUXGDz4oI+aGie33OIjMZFTcrCCxQLXXONn2zYXaWnKlXU4VDwpIQEKC022bLH0S58Yzyzs44UQgqSkJCZNmsQZZ5zB0qVLycnJoaenh127drFt2zaqq6tpaGigo6NjxBKTf/3XfyUnJ4fi4uLQY+vXr6egoICFCxeycOFCXnvttdBz9957L9OnT2fWrFm88cYbozqHqLaUhBC4XC52794dmu4phMBisRzXJf7h6O7uZs+ePUydOpXc3FxgfDPA29vhkUf0o0mVsGNHOomJcPnlva9panKyadM+5s0rJDExh5deErz8soauZ/KZz0BcHLS2ChoaBBMnmlx6qUFrK1x4oY/58w28XoEQagDCU09ZcbmUtZOSks7MmeHnRK1f7+Oee3yYprLEDh0S3HyzlQ8/tKJpIiozy4WA0lKDl19209dIsNlAymCsD/x+sNlkv+6h45mFfaISF4eKR7355pv8/Oc/R0rJL37xCy644ALmz59/zPbXXnst69at45prrun3+C233MJtt93W77Hy8nKeeeYZysrKaGxs5POf/zyVlZURrwxGtSiZpsnu3buZNWsWaWlpocfHK+M6Uurr66mrq2PBggUkJfVm9kYqSsO5ZAcPCjweCA4Hzs31s2VLUkiUPvywjV//2iQtbSFvv23FbgePR5VB7N0bx6uv6iQnC1avllitkq1bBbW1krVrJYWFkj17NDo7BTk5km3bdKRUQXApYevWJGpqBHPmhH9Ngi2BASZPljz1VA+XXaZTWZmG3S4JBMDnU8vqJ9vNs9sl993n5Zprjh02OX26ydy5Bnv26Fgs6rjXrg30K5SOO47wxQAAIABJREFUVvctEoLxqKuvvpq0tDQ+/PBDsrKyeOuttwYVpXPPPZeampqw9v3yyy9zxRVXYLfbmTJlCtOnT2fLli2sWLEiomOMalHSNI1ly5Yd83ik7thYbyTDMNi7dy+maVJSUnJMm4jxtJRUPkzv/wMBDbvdxDQlL7xQx8MPZ5GXl0RenqCzEz76SDBtmmTqVLBYHPj9dnQdcnOVWFx4oaSxEaZNk/zlLzqVlYL4ePj4Y6irE6GYkRCg6xK3e2zXym6HX/yiHE1biNcL8+ebbNum853v2Nm/X8M0T06Xg4ICk7/9zU1W1uBvbLHAv/2bn48/NunshKlTJbNm9f+MxivQPd6reKPF5XKRm5vLtddeG/G2jzzyCE8++SRLly7lgQceID09nYaGBpYvXx56TXBcd6REtSjB4FbFeMeIhsPlcrFr1y7y8/MpLCwc9GYaT0tp1izJxImSgwcFNhu0t1u4+moHjz9eyzvvFODzpdDWBps2QXGxREqBzydD+01MNPF6LQTr6Nxu5QZ2dkJ1tcbEiQaapsaTV1ZqNDQIJk9WwXAp1UrdWBFCsmBB7/U4+2yD115z8/jjFmpqNPx+dVw5OZI33rDS0HB8x6XPm9fDe+8JRkiMxmaDFSuGvq/GS0zGMzY1lmMabS+lm266ibvvvhshBHfffTff+c53+N3vfjeqYxiMqBelwdB1He8JCFgEV/uKi4tJTU0d8nXjaSnFxcG//ZvJ9u0ChwOEaMZma2DfvmUUFyfS1aVyhtxuFczOzpbYbILqaonTaWXRIsnUqSaHDimrRQi48EIT01RL/sH7N1hGkZtrUl+vYbHA2We3k5NjH/S4xkpmpuTaawN8/LFKMZg3z2TqVAn48HrhxRctvPeezsaN1nGLRVmtcOedPVx4YTlxcfPGvL/xEpNo6RDgdDqHva+HYsKE3i4JN9xwA5dccgkw9nHdQaJelE6GpSSlxOfzcejQobCTM8ez1Ul8PJx1lqSpqYmKijomTZpMUlIimgbLlkk2bxb09KhA8k9/aqDrkspKjfr6NqZPz6O+3orHA0lJkosukuTmKiHLzTVpbBSkpEBnp2DuXJMLLjDw+w10HcrL3WGfw2jIyZFceOGxn5vdDldeGeDKKwM89piXxx6zcs89NgxDpTAMT9/ne++ThQsDvP22B5/PSW1tZEIiJfzv/1p44gkrgQCsWRPguuv84yYm0dLgze12j0o0mpqaQmO4N27cGFqZW716NVdddRW33norjY2NVFVVUVpaGvH+o16UBuN4ipLX62XXrl0IITjjjDNGFCSIPGY1UGirq1VPpMREKCmRR4t1K3G73RQWFmK32/nCF0z+9CeNlBSYPVu1zr3jDiNUNzdnjsmHH7r4+GONggKYMEGNddqxA1atUqtIl1xisGWL5MgRndmzDRYtMpESnnrKwh//aMXhKGbVKrjlFji6WHPCEQK+8Q0/F1zQysGDTqZOnch55yXS0SHCziq/6KIObr99N7t2mSQkJODz+SISgv/P3pdHx3XWWd631CqVqqRSabMka3Es27IWrzEhG4QOBGhCIJBJGJIeyNA03XMYGujJmZxh6J7pnmT6TJ/QdBpCE0gCISELZG8gQGxIguPYsXbZkrVZe+379pZv/vj0PVWVqqSqUimxTe45PkmcWl5VvXffb7m/+zt6VMB99+lhs9Fxk4ce0qGigqCtrXTp24UQKeWjU7r11ltx9OhRuN1uNDY24m//9m9x9OhR9PX1geM4tLS04IEHHgAAdHZ24tOf/jT27NkDURRx//33F0WYfzSkxLRB6/2AbG33zp07cf78+ZLJCDKRGin19XH4/vcF6PUEksThd7+T8L73nca2bTZ0dHRgZmYGqqriiisoWQ0NcbBYCK6+mqwhjnCY1pJY7aSmBhgb49DURAu5tbXAe9+rguNWCfGf/1nEt75lgE5HIMs6PP20CI5T8M1vJvFONposFgUtLUm0tAC//W0UX/qSEWNjNPVLJIBolDkcEKRGS3//9wn8l/+iA7Afsixjfn4ewWAQJ0+ehMFgQFVVFaqqqmA2m3MSzGuvCdDpoH2PZWUEx44JaGsrjc3HhURKG9WUHnvssTV/9/nPfz7n4++++27cfffdRR0Pwx8NKa032kEIwczMDJaXl7F//36YTCbMzc1tKSmxSOmFF3g4HATl5UA8HsPp035ccUU72tttax7b20vQ25u7EM1xwMwMnfqvqqLOAH19QH8/j5ERDqrK45prJHz84yoaGgjCYeCxx/TQ6wksFiCRUJFIAH19glYgXw+hEJ2iNxjojrtCrldJoloqjqNp3Xq+9y0tBH/3dwn8v/+nh8/HobtbQWUlwW9/K2BkREA4TGtWDz8cx1VXrZ4XoiiisrIS8XgcHR0diMVi8Hq9mJycRCwWQ0VFhWZXm6p0rqwkaYLVZLI0DQCGd1d2r48LnpRKvbo7s50vyzKGhoag1+tx6NAh7QcupeAyE6mK7mSS1lQCgQD8fj8cjm2oqODB6iP5DuTG48DAgAWqqmJ4mMfUFODz0fqI18utXGgcnnxSj+eeo2mJLNMUjxDaMdPpoAkhDRvUuxcXOTz1lAhJ4qAotNt2/fX5/SbRKC1sO530vZuaCG68MX3OLLWrND/P4ZvfNMBkIqipIfjFL3QACMxmAouFoLaWoLVVRUvL2t8r9XVMJhO2bduGbdu2QVVVzQmSFWfZvNinPmXFr38trqjcaW3uzjsluFx5fbwN8e7K7vVxwZNSNhRLSpkkEwqFMDg4iJaWFs1si6FYc7V8kBr9XHmlgoceCqGiIg6LpQlGI48dO1Y/G7NF2QgLC0AoJOLyy2XMzqpYXqbdO0WhJOt00ouAGWRyHKDTcdDpCBIJDm43B71eB4sFuPPOBPx+DhUVZI3IkOGXvxRgNFKSIAQYGOCxe7eK2uz21Wk4eZKH282hsZF+B7OzHAYHeRw8mP37Hh/noShAeTntOqoqgdvNwWajyz5lGQgEgEceEXH33ekOoLkIned52Gw2TZQrSRJ8Ph8WFxcRDJ7F179uweTkNphM5bjqKhE1NaRkpHShdN9isViaCPhCwR8NKWWquhcWFjA9PY2uri5YLJYNH19KsEgpkUigoqIff/qnzVhYaIDFAnz4wwpSnHzXDO+GQnScIxymupqKCiqMpNEOPeFpxEFTjsVFSkSSRKMmqqzmIIoEkkTb5mVlBDwPtLaG8YlPEIyMmHH2LFWJf+ITMrJ1jQMBDg7Hqs+4ICBvHyavl0dZ2epnojqq3M+tqCCa4NLj4eDzcUgmqd9RMEg/s6JwGBwUAEiQJGjaJ5OJRkqLixwWFjiYzcDOnSoygwudToeaGrqZhBCCaDSKpiYvvN5pTE8n4PNZIcsyZFkuescaQynN1Ta7XundSKkIlDp9Y8O9yWQShw8fznmCbXWkFA6HMTU1hY6ODhw5Ug0g+3ulpm/Ly8B99wmYnQX6+nhwHC12795N8PWvKyvzZzz0esDvJ6io4NDQoGJ+nl/jy00IreckkxyMRirYbG1N4Pe/r8BHPkIjpMVFDr/+tYhPfnKtqXd7O7XzbWggiMcpYdjt+dVdmptVjI+LsFiofioUArZto8+NRIBXXhEwPl6O2lqgsZE6FVx1lYJjxwRNU2Uy0TQwEqH6q6UlSlR///c6VFXRYwcAs7kcO3dG8eabOo2Uu7tV3HabDEGARsyZ33lZWRnKysrQ1NSkmay5XC709fWB53mtYG6xWApOxd6NlNbHBU9K2VCI9Ufqc2KxGM6cOYPa2lrs3r173ZOpmJpSvupav98Pv9+PQ4cObXinYqSkKMCDD/JYXATeeouHz0c7UMEgVWt/5SsiduzQIRIRsLTE4fBh6qE0Ps5BEGiHKnW3gaLQPzodgcHAoalJgdGoYGyMx+nT1NhtcZGmTe3tapqFCQBcd50CWaYqcaMR+PjHZdjtJC/hY0+PikBAwenTPDgOuPxyBSdP8vinf9JheppHba0KQozweIyYm9Pha1+T8N//exJXXcXjwQd1CIc5vPUWvzJASz8HLZgreP55EW1tKt7/fnq8ExMcfvzjmpXuJSXPwUEer7zC49VXRYTDHNraVHzmM1LWiBBYHWo1GAw4ePAgkskkvF4v5ubmEAqFUFZWppHURn7awIVV6H43UnoHkUgkcPbsWXR3d2sT0+uhWEHkeqG0qqoYHR1FNBpFc3NzXieE1ytidlbEI4/wePFFHqEQjZgMBnqHTyRoB8zrJSDEgIMHZcgyUF9P587uu48SC7UyoWMpAL2I9XqadrW0qBgd5TA/XwNJ4jE2Rv//nj0E9fUqvvtdHb74RSmNmEwm4OMfV1YuChTUeRME4H3vU9Dbq8Dv5/DDH+rwwgsidDoCj4fH9DSPlhYBOp2MJ54QIcsEt9yi4PBhFQ8+CExPU/GoqtL35ThKTCMjAgSBNg+uvVYFzwNGI0E4LGrtfeYp9dOf6tDaqqK5mWBmhsOjj+rwpS/lt4ZLr9ejrq4OdXV1af5FZ86cgSRJmlWtzWbLej6UWhJQbPq2mXXfW4kL74hKDEIIJiYmEAwGsWPHjrwICSjeQjfXCRKPx9Hf34/a2lrYbDYkk0kANP14/XUOfj+Hyy6jokh2gf/2txyeecaC48etWFwUoNPRAq+q0rSD56HZjggCh1OnyhAMKti5ExgYAGZneSwtUY2SJKkaIdHJfhWiqCCREDE4uDoomxpRnTgBOBy0NvX97+vwH/+jhCNH0ok68+OOjAh4+WUH5uYEXHWVgszdCbJMnRCOHhXx7LMCZmc5nD9PUzKLhUZoqgp4PALicQHJJIejRwXwPI8DB2RYrURzJhDF9J12ySQlnakpHo8/LuDTn1YQCPDo7Ixhbs64YoBHH2cy0cI5QFPHyUk+ayq3EZh/EfMwUhQFfr9fkx6IoqhFUcyqttSkdCESy2ZwwX+azbROk8kkBgYGUFFRgcbGxoJOhFKOjjBRJttHt7i4CEJoqvPAAzyWl+nk/vHjHAIBFddcQzA/D7z8Mr1YIxEeOh0QDtOh0khkNf1iUFUgEhFw9qyAuTlqWRuJ0FEUuvKOS3ssITwkiV9JDZkAce137XJxCAQ41NYSHD0qoqMjmVPt/Yc/8PjXfzUhHrdhdFSHo0dF/Of/nITVSong6afpfJvTSYlIUeixqSpVa6+mfgSTkzoIAhWAzs0JeOMNBb/5jR7T0xxUlUN5OUEyySESSXdVYAQ1OCigtpbgox+NYd8+D0ZGLBgZEWCzEdx+u4Sf/lQHRaGkGonQYn8qIZ09y2NwkEN5OXDFFQoslvzKBYIgwG63a3sHE4mEZvjPrGpZzaoUKDZ9u1AXUQIXASmth/VqOJmrsqenp9/21d2EEMzOzmJhYUETZaY+dmqKFmiZd5LNRono6qsVRCIceJ6Si9FI7/CEUFFkPE4jApa+8Dz9f6q6epHTVI2sdNmyCUZpkTsf0k8mgddfF7C0BBw6xEGn4yAIwL59qiawXFzk8K1v6QEosFplVFerOHZMwOKiHhwHnDvHw2IhmJvjsbwM5F5YTAmSRW7z8/Tzzc3lf6qqKo2ggkEO114bgSQR3HyzAmD1919akvG734la1HXnnUnt/506xePf/k0Ho5F+9j/8QcBXvxov6gZpMBhQX1+P+vp6bXXS5OQkFhcX4XQ6NW2U1WotKg3bTPoGXHiLKIGLmJRybQRJJYLUVdmFFq4FQdBSrHyQSUqKomBkZAQAcOjQobQThx07IfQuPTdH0zC9HtoONrudpnFGI6DXqygvJwiFOLhcNO0QBGrmD9ALkNVWRJHe9UMhFYpCV5azNCf750RK6pYbsgyMjQm4+WYzqqrosZlMBB/7mIx9+1T86Ec6jI7yUFUehNhhNIrwejl4vZT8ZBkwmTg4nak2Jey3Sx0Vyfwn0VZCpYMg/fkp/2el+H38uID77rPizjvXVt9vvJHO/kUiHGpq1DQZxvPPi5rKHqASjOFhDgbD5lIutjqpsrIStbW1qK6uht/vh9vtxrlz56DX62G32zccg0lFsanghWzte8GTUq4vjrX4U38QWZYxMjICnufXEEGhdifF7H5jIXEsFkN/f39ODyZGYPE4cPYsB6eT3tUB2ul6+mkON99McNttKv7xHwV4PBxkmdZ2kkkODQ0ETieH5maCxcXVi7yiQoXdLsPn41beQ8jq+CiKVBKgKFSImP2izw5FAVwuSqDxOE3Jvvc9XrOQpUVn/Ur9iiAS4SEI9HmBQDEXQa7ncOs8hn5gSQL+7d/s+OUvy/G1rwEHDqjw+TicOsWjooLg8GEFx4/zWFoScdllKm64QYbFkl0mQDt8pVNh05uFiOrqalRXVwNA2hhMNBrVxmCqqqpy7hEslpTi8bgWuV9ouOBJKRcYKbEfKxwOY3BwMOeq7FLbi+R6vNfrxejoKPbs2ZOzqM4e+8tf8rjyShXPP89rmhlJ4nDffSLa22U0NNA1SFdeGYDX68DMDBAMUlsTUSQYGOCgqgQHD6oIBnnMzCThdhPIsgE2G69FYuxaYuREvZXIStGVpon0o6YPt+YG1QQlkzR6o6+bGbWsMiHl9vVet9R37NQoC5ieNuCv/5rAaqUK8N5eBQMDPGZmqEunxUK9pYaHedx9dxLXXqvgqad0sNtVxOMczGaC9nZZGzvZLHIRSeYYTCgU0qQHhJC0VI89v1jxZCQSeZeUSo1UAeXS0hImJyfXXZW91au7OY7TJtIPHDiwrl6FkgFBNEo3j5SV0doFz9P/jkYJXnqJw8c/TolkYsIIo5EKCYeGCH73O34l9aBDuk1NHCyWEFTVj+pqG5JJ08oYCb0ol5Y4rc7Cohaa7lGJgKpmEkm+Fx+vdfRSPh3yJ7etxio5JZMcXC6a/i4s0MhTlikZBwK0sG2zUXnABz6gwGgE3nyTRlQf/rCMykoFLtfbJ3jkeR5WqxVWqxWtra2a4f/y8jLGx8dhNBpRVVWFZDJZVAR3oQ7jAhc5KUmShNHRUcRisQ1XZReajhVSg2IrxM1mc9pQ73rHQoiKri6Ct97iYDAQRKO0eDw7S8cu4nEOFRUqYjEOsRgPk4mmerQbB/zhD9Qu99w5DmNjCczPG1BeXot4nAfHcWhtJbBaCXQ6Dj4fvfgMBurDHYlwWqqVXo/JVqPhkFmzSUc2AroQCCkV6ceTOg7DOnCJBKeNunAccNVVSprjQCxWOl/tYlKu1AWUhBAt1WPzmzabDXa7HZWVlXlJBC5U4SRwEZBSrhOBEILh4WE0NDRg165dG54whRa68yWxaDSK/v5+mEwmbN++Pe+tt4QQbSdbPM5jaorezcvLKYFMTQEnTtARkLk5A86fp2lWfT3TKtGNJAsLEoJBPWSZh9dL0zJBoLNkbW0Ek5N0OwrTNZnNHOx2ok3or4/UonOuB19oBFQ4kkl6PjkctGP4zDMcOjup68Grrwro6+NhtSrYu7f4LlcqNqtT4jgOZrMZZrMZLpcLe/bsQSwWg8fjwczMDDiOSxuDyfZesVjsXVIqJTweD9xuN1pbW9Ha2prXc7YiffN4PDhz5gw6OzvhcrnyJj0moDMagZtuIrjpJgWiKGBhgSAUooRy8iSPsTEOBgMHnU6C16uDTkc7QYrC2uQKrFYOisKvFK3pa0sS7dqdP8/DbCaw22m0RLt0dBCX1bBWjijjCLMRUGpadqGkZ6WDLHMIhzk8/bQOMzMc4nEODocKg4FFrmbU1bXhoYew4QKCjVBq6xJRFNc4Hni9XiwsLCAYDMJsNmskxepI+Qzjfu5zn8MLL7yAmpoaDA0NAaBr6m+55RZMT0+jpaUFTzzxBCorK0EIwZe//GW89NJLMJvNeOihh7B///6iPtNFsdeU/YCEEExOTmJiYgINDQ0FMX0pC92EEExNTWFiYgIHDx6EzWYr6PWzze7Z7cCRI3TC32Yj4DjqILm8zPyOoBGP0aiC45KQZQHJ5KoIMPU8Zw6NiQSH6mqCzk6qhI7FOEgSB4uFtvSzfLqVf2a7aDLb9ZcOCAHeeEPA6Ci1VfH5OIyOCpicpHvyQiEOIyNleOut0qxY2sqBXJ1Op813Hj58GG1tbVBVarF84sQJvPTSS3j11Vc3tHr+sz/7M/ziF79I+7t77rkH1113HcbHx3HdddfhnnvuAQD8+7//O8bHxzE+Po7vfe97+Iu/+IuiP9NFQUoAZf/Tp08jmUzi4MGDMBgMW7q6O1e6J8sy+vv7EYvFtOMACiO9bGu7r7tOxcQEh8lJHlNTdOTBZAIiEQ7JpACzGSl6Ixl1dSJsNhq1GI2ru9QYcSWTNBKKx4GhIQ6zs7SNX19PHx+PY0XHRFO+1fM6P0HlpYhIhBrjSdJqahsMchgZ4TE9LWBx0YCXX958ClfKMRNgfakCU483NTWhp6cHBw4cQHV1NYaHh/Hkk0/i/e9/Px555JGsz7366qtRxUzgV/Dss8/ijjvuAADccccdeOaZZ7S/v/3228FxHI4cOQK/34/FxcWiPs9Fkb4xdXZ7e7u23mWru2nZakqsftTc3LxmC0ShkVLmY/fvJ3j+eYJ4nEZLssxheJggGOTAcTwMBkAUFeh0KpqadPjIR1TMz6uYm+MQjRKMjnKIRKi7JAMhNFoSReo+SU3UVLzxhqilgJSEyMr8HFk5Pg48T83f8kO2YvnFiFXXgUzQrSocvvc9Pd73PmqlQoecqZi1EB4vNSkVAkEQcPjwYdxwww24+uqr8dnPfrYg8lheXtY2mdTV1WF5eRkAMD8/j6amJu1xbBEle2whuChIaX5+Hj09PWktTNZ9yxebJTGXy4WxsbGcO+AKjZQy07fz52lhu7YWmJ6md+xkklsxbCMwGCTY7RIIMWHnTkoYf/InKvr66PhGMsljeprA51u9y1PTN5raCQIwPs6tDLmuKrjZuApA/0mFlezfKcnx/Hqq6mx/t5ECu1R4+2tb4TCHO+80oKtLXblhAPv3K/j615OaKpwRfi6ieidJiYFJAmpra9P2uBUCjtuaqPqiIKXOzs41F7wgCIjTSdO8UGxNidWxvF7vujvgeJ7PmySzHcupUxyiUbqTLRqlg7Q8T1Xa4bAKSRJhsYj4m79RcOAA9dh2OIBAgODcOerBPTOTLpRk5CRJ9EKRZU5TeJeVsb+nD7JYVMRiZCVKAgA6ZhEMUr+lQIBoIsu1Xbts4yK5HlNKcOA45n7w9pATIcDSEo+lJSp4NRrpNuNz53h84xsJPPKIDmNjAqqrCT73OQlXXrn2RnihkJLD4Sj4ebW1tdret8XFRdTU1AAo3SJK4CKqKWWi0MinUEZnxNHX1wdJknDgwIF1C4P5GvynvnYmqqvpyiVmRyLLgMcDAHTuymYjuPJKgm3bKCEBwH/4Dyp6egi6uoCuriSMRiXtDs1m4iwWegFVVBCUlxMIAtFm5srKaIfJYFi1BZFlHuEwB0niEQrRF2SHLAiMBPIlA4JsSu/sj9voMRnPIDxYR1AQ1IKeu1nQTcVU3/TqqwKuv96Mxx7TYWKCw8QEh/vu0+GllwRMTaXLLy6EubNiJQEf+9jH8PDDDwMAHn74Ydx4443a3z/yyCMghOD48eOwWq1FpW7ARRIpZcNWb8kNh8OIRCJobW1ds1QgGzabvnV0MPsMKoqkpEQn/EVRRVUV0N5OU7DLL199bnk5cOutKm69FXA6PRgfD+GrX+3AmTOc1voXRcBsptYcsRjVPjH/aoOBiiojESAe52Ew0GiHpmz0PejgM1ODrw5C546YUkGjutXicfbHVFYSbNumroyFCIjH85/JEwRaE2NdTY4jWepCW0MCqoo1tbdYjHqJnz1La3mNjQTXXSfj9ttlzUe9VJt2iyW3SCSyoaI72yLKu+66C5/+9Kfx4IMPYvv27XjiiScAAB/+8Ifx0ksvYceOHTCbzfjhD39Y1HEBFwkplcqnO184nU6Mj4/DZDLlRUhA4aSUia4ugg98gODkSYJkUl7ZQkIHag0GFe9/vwKDgaVWuV/X4VBw1VUEzc108240SuflwmEOLS0Ee/dSt0dRVHHmDN1IEo0CySRVmdvtKpaXeSSTdNNJWRmB0UgL7tRQjWhWKfQ9U9O5tekbz9Pa2NrHUuh0BHv3qrjxRhnNzQSRCNDXp+CVVyiZejw6+Hz8ui4G9fUEfj+H+noFfj+PQAArW2DoMXFcYUPHmwUbYQGoXfCuXTJ+/WsRl1+uYteu0vm+vxOLKAHgN7/5zZq/4zgO999/f1HHkomLgpSyYSv2sjGXSuafffLkybyfW4pFAzfemMBrr3lx7JgDOp1uhZCAysoYpqcrcPgwQUfHessoaRRTXQ2UlamwWmlkpdMBN9wg48gRKgd44AEBx47poCg04tHrCWRZhcHAYWlJB1EkUFUW4VBCE0UaKdF5Mdaxy6UKJ9DrqSWIx8MjmYRWy0o/XipE/MxnJESjlDTKyugihB07XDCZdFBV24oZnh5uNxU4ZmJujocgELhcPGprCWIxqmKn3URWrMfKjN/bmzb5fBwefVRERQVBMKjDl74ko6KCx+wsXWvlcBBt1VSh2OzSgHcV3SVGqVcgSZKEwcFBlJWV4cCBA0XXoIpFOBzGyMgA7rprBxoaRLz5JkFrK4HDQfDGG3Hs2mXBF7+41l42FYyUPvUpBQ88wK1MxBPs309w663US/t//A8Rg4N0LbXJRIdR9XoZBoMOFguBy0VTITZGSB0FVlMUJh+gdS4mG0gXXJpMSezeLaK7W8Xzz3NagT39+wIaG1VcdpmKjg4V587xmJvjtK7VkSMRNDfrUFtbDlWlaePzz4sYGeEQCvEpzgYUisLB76efB1glUTrnRkn07esKZrwDoe6dR49yOHVKxGWX7UVLi2FlYJrDZz4j4X3vK/xc3sx6pXcHcjeJbAQhimLBpJTLHzkcDmNgYABtbW2oq6sr6hg3Q0pMbtDd3Q2LxYK/+At1pd0ehY+6AAAgAElEQVRKazm1tUl84QsKVqYIcoJ9vqYmCX/918DSkgCjEWhrWxVHjozQnWvURpb5UYkwmWiEodcDFgutPwUCdH5uenq1a8dcAQjJdI/ktKJ6WRmNUkZG6CaV1dRpdesvM5czGqkzwjXXKPB6KSm1thKEQjI4Tr/y3QJ/9VcSrrlGwaOPinjiCR3MZoLlZW5FP7SaGhJCyZJa7XIAVPA8nRUURbIyhrPWVG51qcLWkRQhHEIhoK+vCokEwQc/qEKSCB57TMSBA+vfcLJhs+nbhbheCbhISCkbiomUshnDMduTXEsp812bVAwpEUIwPT0Nt9udJjeorQX+/M8VnDpFyWDv3nm0tKxPlqx46vF4UF5evmIetvaE3b6dYHycQ1lZEn6/ACYY3L2bWnNYrTTdWV5eXVDACCsz/Volm9XvRxQJ9HoVNpsAj4cW1mldB9oGFULYlhEOLS106JXn6fvceKOMqiq6fSX9+6WrmVpakpiY4PHmm4ImFGWvTYvdq26aOh1dUslsWhSFpqqUpFKJaJVstz5qot/b1BSHwUG67pz6XtGNxIVgs6T0bvpWYhS7JZcRByEE4+PjCIVCOW1P8lmblO218wEhBIODgxBFEQcOHFhzctXVAR/5CD1JX399fcdMQghUVUVZWRm6urrg8XgwODgIVVVRVVUFh8OBiooKcByHr3wljr4+CQsLZbDZeDQ0qLj77iRqazk89piA8nJa/5md5TAzQ7fZ6vWsW7je4C63QgoqwmERCwtYSZmgER0VGxJUV6uw2Wg0ptPRhZk8D3i9wLFjIm66Sc55M7BagYcfjuNXvxLwz/+sx8gIj3h8lTCpnIFKHCIRGrHp9WQloiJIdzh+59ryqkq1TQ4Hgc2W/yLPVGwmfbtQF1ECFzEpFdMKZUTGtpxYrVbs379/XcvdrSCleDy+sha6Cc3NzQV9hkwwQmLtYYvFAovFgpaWFm1afG5uDsFgECaTCeFwGN/9biuCQRN4PonubgJqv0Ngtyt48kkB589z8Hrpd9LTo+J3v+PBcXxGpLT6H4KAlaFhKmFIJnksLpIVGQBZWX7A1oljJSqi67Orq1dTy4oKpstaH+XlwCc+oeA974njBz8Q8eSTOgQCVHQqy9AGj6kWi0MsRiM0FlFJUi4R6NsDQlZlA3o98F//axIbzMZmxWYiJUmSNhzIfadwUZBSqYRmgiAgFAphYmIibY4uF1iKuJ55XOpj8yElv9+P4eFhGAyGkhCSoihaVJH5PbFp8draWvj9fgwNDaGyshJ+/zx4fhGVlXbEYtVIJsvx4osiPB4OO3eqiEZ5eDz0In/9dR6RCFWMe72cprNhYGMqoqigpkaCx2MAx1EhqMsFRKM8LBZqAWK1qmhoUHHkiIy2NgVHjqh46SU9kklKGi4X3VbLPttGqK8nuPtuCf/tv0n4/OcNePVVGumJIh3VoeMetKbF9FU6HSXRsjKi7Zh7O+UCFKymRtDRoWgrywvFhaAM3wpcFKRUKsTjcYyNjWHfvn15ha6bHbLNxPz8PM6fP499+/ahr68v73pVNhBCIK+IYTY6MZeWljAzM4MDBw5ofjrJZBJutxvDwzP41rfqEY+bYbXq8fLLRpSXA7t3A04nPTbmUqnTrW5FYZtTrFaCQIBAFDkkkwaIIrVeEUX6eJ6ntZzdu1Vs305w991JVFcTBAIKTpzgQYiCU6d4NDQA27eruOqq1e8w3+/mV78SMDRECUmW6bonQaBEp9Mp0Ot5+P38ipsCtxJdUpV7fb2K8XFhpVNHP1M2+UJpQWAycaivpxtXPvc5uSiPpmLTtwt55xvwR0JKzEsmFothz549eefSpSIlQgjGxsYQjUZx6NAhiKKote+LISVFUbR0bb3ns7k95hueapOq1+vR0NCAhYVGCIKAtrY4YrEYdLowhoYq0Nsbwo4dViwu6iFJtKNWX0+wuAgt3eM4IBJRYLXyqKwkqKxUUVFBzeWWl2mRvqwM2LVLwYc/rOK22xRYLFS39PLLeoRClBTo6nAJH/wgZTyaXql5Xzxvvilg2zYVk5M0qmN6qooKAlVVkEgImqLcZKKEaTRSlfv+/SrKy+lWXVocp+TE89A6e6XBqkEezxM0NqowGjno9dyaLcP5YjOR0mZuiFuNi56UNvpyk8kk+vv7UVVVhdra2oJ+iEIEmrlISZIkrX7V29urvT97fL4nFbtAU+tH630WtndOr9envW82cBwHo9G48gdwu4GFBR0UxY3OThUWiw1utxmqyqO1FWhrU3HunIqFBQAQUVtLh3avu06BLHPweAg8Hg5mM90u+4EPqHC5OJw9S/2iAgGC2VkOu3cTlJfzKCsDFhYMAHjodASJRAJ+v18zxhcEARzH5fyuqqro8HBHh4qTJwUoCoemJnosi4s6TeYgCNRnymhkNTDg2DEB+/YpSCZpTUsQoC0ISCSItrNOEFQoCg+9nqykguk6KVZMT+XR1OFoKpWg1jGyDITDCVRXJ3HNNQlwnBHFXIrFktK7kVIJsNHut1xG6cyHaefOnXA4HDh37lzB9iX5Pj7bPFskEkF/f39W/VMhA7yppnD5EFIikcDAwADq6+uzrptiiEYBh4MWm2dnOZhMdJzkk59UYLGUo7y8DAcPypicDOHYsSCAEAYHqzExYYIgKKiqsmjultdco2B5mcdHPyqju1vFyy/z2LePrLgeAMvLdPOsqtKNs1NTPJxOFd3dKurrWTrIQ5LiGBoaQktLC+x2uxYVqqqq/RY8z6eR1M03yxgcFKCqQFeXgpkZHq2tBMkkgc9H0NVFsG0bh4EBHhMTAsxmgl276PEuL3N45RURdjvBnj0q9u1T8OKLIs6eXVWiA1gZ+6EjM4JAUFlJbYuZBKG+HgiFCOrqVFx7rYynntJrQ8+xGNVIfexjCrxeGTwfwPXXV6CpSUJjoxN9fV7wPI+qqirY7XZttfdGUBSlqGJ1IpFYd9vOO42LgpRyYT1SYvWb3t5eTblazPKAYufZmCCyq6sr69qnQl9blmXtYlzvhA2FQhgeHsZll12m7bPPht//nscPfyiCEKC8nODIEQWyDMzM8OjvF1bcKGk0c911VpSX83j9dQdcriQ8Hh2MRh7hsIzduxPgOD3GxwUkkxxOnBDQ26vimmtUzMzwCAZpRGE203RoYIBDczMQDhOEw8CJEzy6uwne/34F8Tjd3bd7927Nb5oRDyMm9odFyDzPo7aWw733xjEyQmtDNhud69PpCH7+cx/C4WpUVNBllE4nj8OHFYyN8UgkoA0mB4Mcjh8X8NvfijAaaUHcYKDHqapcWlSkKHTzLyvy6/UEgQB9na98RcKnPiXj4x9XcP/9esgyrbu95z0KJIlDbW0UTU3n0d29B4AZQBuANiSTSXi9Xpw/fx7hcBgWi0XbllvqRZQXskYJuERIKRWqquLs2bNIJBJa/YahUMHlZgSRLpdrQ/+lfCIlQgjMZjP6+/vhcDhQXV2d84RyuVyYmJhAV1fXuiME8/McHnxQRE0NbdM7ncD8PI/PfEbGvfcK2L6d1ovcboKvf12HD3xAxd69MuLxBbz3vWU4dcoMl0sHUeTgdgOEKIhEoqivV1Bbq2B8vAI33MCht1dBLEYjpW9+UwdZBtxuDnv3EtTXE2zfTonpuusUNDV5MDREVe3Zjp3n+TSCApAWRZWXKzh8WNIKv7t20e/WZJrFM8/YsbBAJQ0f+ICMhQU6Q8dxVI5gsdDieFWVimiUpnc+Hwe7XUU4zGsuBKJIiZVJC3Q6ogk1q6tVfPObSXzkI/T8OnxYRU9PHOEwB6uVyS6AYDCBhYW1RKLX61FXV4e6ujoQQhAKheDxeDA3NwcAWhRlsVi0m1KmEDhf5LM04J3ERUFKG6VvDIlEQrt4s61dKpVPdy4wQaQgCDh48OC6J0w2n+5MsItuz549iMfjcLvdGuFWVVWhuroaNpsNHMdhdnYWLpcL+/fv3zCkX16mFxbTDTkcwPnzVNwoCJSQkkmgr4+a5s/NyXj11RBsNgfq6sxaFGEyEQQCIgwGEQ0Nelx5ZQSEBOB0zuOttwJ4z3uMqK+vxje+YUdTE13rRMcsONxwg4qmJjoOYrUu4Ny5Wezbt0/zPF8P7HvNFkWx35ddsJWVSdxzTxR+vx4mE0FZGfD44yL+6Z/ooslUNwHqfsDBYFBXolOanikKLVCndh4Jod8bxwHd3QrKygg++tH0c4vawqTfePIhEo7jUFFRgYqKCm0RJdObhUIhlJeXo6qqCpIkFdV9i8ViF+zcG3CRkFIupJIM0/90dHRou9kzUYg7JHt8viRWqCByo25dZkHbZDKhqakJTU1NUBQFXq8Xy8vLOHv2LBRFgcFgQFdXV141hqoqSgaSRLtNfj9QU0PQ2qpCFAX4/TS68Xo5tLRISCSW0N5ehaEhM+bnaSrW2kqwtES378oyMDHB4ac/LcfnP29EfT2Hzs44jh2LYnExjIEBER0dEpqazKipMWFsTEBZGRVb7tkzA5/Pjf379+e1RDHXd5lKUOz7i8fjiMfj4DgZDgc9X3iex+23y9i2jeCBB3QAVMzOUg/0eJzDtm3U5raiQoUg0DGYeJzDwgI1vmMmeAC1hKmsXLUbzgfFeCml6s0IIQiHw9qasUAggOrqatjtdlRUVOT12u9GSiVCtsIwI425uTnMztI77Xpf9lYtG2AF9UIEkbkK3dkIKROCIMDhcMBms2FgYADl5eXQ6XQYHBwEANjtdjgcjpwF05YWgk99SsbTT4sQBGpn8pd/KaO2FvjqV2X84Aci5uc5WK0S6uuXYLU6MDhoRChECW1qisOOHQShEIdYjMO2bQQWCx1NefppAXfeKePHPzYhHDZBr7cjFOKxuCihvDyC+fkQCOHxvve54XB4wXEc9uzpKZkIkL1OMpnUblJGoxGKomh/AODaaxXs2CFjeVlY2WLC46WXBASDHGw2guuvV9DdrWLnTvr7/6//tYznntsOt5sW1CMR+tl37KD+VP/pP+V3syvFIkqm2o9Go6ivr4csy1haWsLY2Ji2481ut+eMOt8lpS2EIAiYmpqCTqfD4cOHNwxlS7n7jWFhYQEzMzOaIHIzr72RQjsV0WhUczZgPsltbbRg6vF4MDU1hUgkAqvVCofDgaqqqrTv50//VMXllycxPs6D51drHh0dBPfeK2FqahH/5/+ISCTqcO6cAJcL2L1bRTJJ/ZXefJPXBJWUW2n05XJxeOQRHbxe4EMfonYp+/YpOHlSRCxWCVEEtm+X8NhjOtx2WxhGIzA2Nobq6mpUVlYWPcuVilAohKGhIXR2dmpNBlEU06IoRVHQ0KCioUHWCuaf+ERusmhtjcJupwrs/n5eqz3ZbARf+1oSV16Z33lVShW2qqowGAyorKzU1nlHo1F4PB6MjIxAlmVUVlbCbrfDarVq71uMl1JLSwssFgsEQYAoijh58mTOxZSbxUVLSvF4HIuLi6isrERXV1deLdRiakrJ9AlODUwQGYlE0grqhbgKpEZKhSi0fT6ftpk3s7On1+tRX1+P+vp6qKqKQCAAt9uNiYkJ6PV6rVhuMpkwMMDj8cdFbVr+xhtldHWpcLmm0NfH4dChBvh8HJaWVHg8AsbGeMzPcysdKwCg7gE8D0xP0xZ6Z6cKnY6mc0NDHFwuWqtyOjkcOaJi924ZLtcyAoEqWK116O2V4fP54Ha7ce7cORgMhhWHg2pNfV4IvF6vZgOTWTdh36sgCNDpdBo5sX/mkhwAQGdnADffnMAPfmCG38+huZmgp0fF8jKH2VkewDtDSqmvxXa8lZWVobm5GbIsw+/3a06qRqMRiUQCs7OzRdWUXnnllbTSCFtMedddd+Gee+7BPffcg3vvvXfTn+uiIaXUdMfn82FkZCSt0JsPiknfsj2eLaSsqKjAvn37tPcvRKWdWujOV6EN0Mhsbm4O+/bt21BrQgu9laisrMRll12GWCwGl8uF0dFR+Hwqvv/9TjQ16WAwGHHuHHDXXTrs3OnF0tJ21Ncb0dhIEIlwsNup/1I4vLoRJRql68AJod08Zi2yYweB3w+EwzyOHRO07pbVSjA5Ceh0bmzfboMkmSDLtOhrt9s1+UI0GoXb7cbo6CgkSdJSEbaFeD0sLy9rUWu+BfNstajUYvkqORHccYcEl0vC8eMiGhpWHRAGB3nceuuGb6e9T6mU1BuNmYiiqBE8IQSxWAy//OUv8dBDD8HtdiMUCuGWW27B5ZdfXtT7P/vsszh69CgAupjy2muv/eMiJYBGE7Ozs1hYWMD+/fvhdru3fCFl5uOZILK1tXXNtoZCVNrssYWMjJw7dw7RaBQHDhwoKs0xmUxobm5Gc3MzZmdVGAwELlcUp08TOJ1mKIqCpiYDJMmEpSWgu5uguppgYIBDVRVBILCa6rG14A4HXQN12WVk5TiB6WkqxBQEbmWJJlBXJ2NyUoWi2BEMiigvB3bsWPtbmM1m7RgzC/plZWVaUTeTdGZnZ+F0OosumOeKogghkCQJiUQCsiyjuVnBa68Jmko7EqHjN/liKyOl9cBxHMxmM2666SZ4PB4QQrBnzx548rFlWHn+9ddfD47j8Od//uf4whe+kHMx5WZx0ZCSoigYGhoCABw6dAiCIEAQBCQS63sNpWKzhW7Wki+VIDJfQmKfvaysDN3d3SW509bU8HA4dPjd78wwGBQA6kp9RwdFkaDTcTh2jEMoJKw4PFJ1syjSC5Ft36VroKjVye7dBMPDNHLq6SEIBmmHSpKoirm93Yb6eg6XXUbwyU8qyNgIvQasoM/qJZFIBG63W/OKYhGW2+1GNBrFvn37SlowZ5Hy0NAQGhsbodfr8ZGPJPDmmwImJmiKV1dH8JnP5N/RJYSUpG4GbE48uW3bNnzoQx/K+zmvvvoqtm3bBqfTiT/5kz/Brl270v5/KRdTXjSkNDIyAqvViqamJu3Db/XqbqZTIoRgZmYGy8vLOHjwYM7UIN/XZ4LI8fFx2O121NTUwGq1Zv1R4/E4BgYG0NjYmPdmlXxgMABf/KKMV14REI8nYDCYUFbGweejosJAgEM8LkGWZSiKsCIcpAVeBlnmMDdHW+RmMzAzA3R2Euzbp2BpiUd7u4pTp2T4/QT19ZX47GdVfOpTMoo5dzmOQ3l5OcrLyzWvKOpyMAxJklBdXQ2n0wm73Z6X1Uw+kCQJfX19aGxs1CICux34x3+UMTJCnQba2yUYjdQ8bqMZPaD0diPFEEExhW62WLKmpgY33XQTTpw4kXMx5WZx0ZDS3r1717TQiylcF0pisixjeHgYAI3Q1juh8iElVtCura1FTU0NPB4PFhYWMDo6CovFoqUnOp0OwWAQw8PD2LVrV0m6GpkoK1tEb6+IsjIHABU//7kIjqNzXIEAIEkijEa6RVdR1JU/4spnJSubTZiVLMHSEodgEIhGRagqcPy4grIyDk1NIu64Q8EnP6kWRUjZwPM8nE4n6uvr0dLSglAoBLfbjfPnz2t1KofDgbKysqIu3Hg8rs0tZm6SNRp57N9P/51ZobCoF0ivRWWeLxeCB1KhSwMikQhUVYXFYkEkEsGvfvUrfOMb39AWU951111piyk3i4uGlLIVnbealGRZhsvlQnt7O5qbmzc8uTdSaWdL12pqalBTUwNCCILBoHZhKYoCSZLQ2dlZckJiozA+nw/f+EYPvvMdER4P1SAdPEiV1r/9rbCyXAAIBHgA1I+Iak/ZRhP275y2RTcUoj5GiqJCkgQYjTze8x4Fjz8ugOOAG29UUaRGUoMkSejv70ddXZ02cGy1WmG1WtHe3o5EIgGPx4PJyUlEIhHYbDZUV1evkUXkApNbdHR0bPjdp9aiAKTVolhHj6VsuRZXvN0odPZteXkZN910EwB6Tdx222340Ic+hEOHDmVdTLlZXDSklA3FrO7OdzI/EAhgZGQEZWVl2L59e17PyTXPlo8gkuM4WK1WTZXrdrvR0NCA8+fPY3x8XPPaTtWbFANVVXHmzBkAQG9vL3iex//+3xL8fuDJJwX09fGQJFq8nZ6mjpOBAMDzHBIJOlqSTNIOHB23oFESs8NVFG7FLI3umwsGOfziF3RQ9jvf4XD+vIq/+isZxWZYLIJpbW3NmS4YDAY0NDSgoaEBqqrC7/fD4/FosgjWkcp2YWbTOBWCbB291CgqmUxq58I7RU6FRkptbW3o7+9f8/d2uz3rYsrN4o+OlPLB4uIipqam0NnZienp6bxfP5cgkt09mf4lF1RVxcjISNoyge3bt2tdqMXFRZw5cwbl5eVwOBwF10/Ybruqqips375dOxZRpPa1n/ucgpdeIjhzhseHPqTg8GEqlvR6aSF7cZHD6dMczp3jMDbGp205IYTNiLF/h/bakQhQUwM0NtK1S2+9xePyywtfRxWJRDA4OIhdu3ZpLgIbgVmCVFVVabKI1BnCyspKTbgZDAZx5syZnEPBhSIzilpeXtZWwW+ki9pKvOsSUCK8Hau7UzecHD58OE2zkg8ySSlVob0RIbFlBjU1NWtGVTK7UKFQCC6XS6ufMEHkehdSLBbDwMAAWlpacnqTGwzATTdRI7J0pIo8qTPjW2/xuP9+AQMDdE12LEY9pwlhFxabpleRTEoQBBm1tSqCQROCwZyHmRMscu3q6trUFo7MGUKfz6dptyRJQmtra8m6Y6lYXFzE/Pw89u/fr0kOMqMoZk+TrRaVic0YtV3IO9+Ai4iUsqGUpCTLsjZHxjacyLJctK6pEIV2OBzG0NAQduzYkXOYmCF1gry9vV1zDxgfH0c8Hk9zD2DvywrmqT5FxYLjqJr7ve9V0dOj4qc/5fGjHwGDgwJEUUBlJZ2JkyQ65NvQAFgsIvbsSSIcjmJ5OYJEYhnz83Q3XT4iR6ZI7+3tLUrlnQuCIKC6uhqyLCMYDGLv3r0IBAIYHh6GLMuw2+2orq7O2RnNF3Nzc3A6ndi3b59GeLlqUfmoy4HNr1d6N1LaIpSKlKLRKPr7+7F9+/a0tnuxYstCFNoejwfj4+PYu3dvUXcvo9GIxsZGNDY2ZhUbGgwGeL1e9PT0lPxELC8HPvrRRXi9YcRiuyCKRLM/4Xngf/5PCR/8oIoXXxRw/LgZBoMZf/M3Mrq6dHC5XBgaGoKiKNrFz3bTpWJhYQHz8/PYt2/flqwEmpubw/Lysia6tNls2L59O2RZhtfrxfz8PEZHR7UFn3a7vaDjmJmZgc/nQ09Pz7okslEtKjX9L9RKORORSOTdSKkUyHZx52uUlonUURCPx4MzZ85g7969sFqtm3r91OgqH0KanZ3VLohSXHCZad74+DicTid0Op02luNwOGA2m0sidJuZmYHH48Ff/mU3JieBsTECg4ES0gc/qOCmm1SYTMDttyv47GeVFDkAnc9ieiOPx4PZ2VmEQiFUVFRonbL5+Xn4fD7s37+/5CkV60AGg8GsoktRFNM6o0xywAq+GzkxsKUNkUgE3d3dBRFIahSVzdBOURQkEomiySmZTOYVob5TuGhIqVRIJZrz589jaWkJBw4c2LRnMSEEer0e09PTSCaTcDgcOX94tl1FlmXs37+/5AVOQgjOnj0LWZZxxRVXgOd5JBIJbeg1Fotpk+X5zJRle/2xsTFIkqR18P7v/5Xws58JmJ/n0NKi4vrrVaRmWrk4UKfTpTkuBgIBuFwurUO4fft2JBKJkkZ5jLAlSUJXV1dBpmupTgzT09MIh8OwWq0akYqiuOb1N3MDyGZopygKFhcXYTabIcurLgeFFMvfaVnCevijJCVJkjA+Pg5CyIaCyHzAQu2GhgbYbDa43W4MDAwAgBadMBGfJEkYGhqC1WpFR0dHyaT5DIqiYHBwEBaLJe31DQYDtm3bhm3btmkFXqfTmTZTVl1dvWHEljrysnPnTu31LRbgjjs2l0qzi392dhYNDQ1obGyEx+NJ65QVS6QMqqpidHQUOp0Oe/bsKer7T3ViYETqdrsxNTUFURShKArMZnPRr78eOI7D5OQkAGDHjh3aZ8p03mS6qMz3J4S8u83kQgPHcTh9+jTq6urS2uLFIrPDljoKwRY+TkxMIBqNoqKiAn6/H+3t7Wu2m5QCzA64qalpzbBwKliBl02Ph8PhtNQkk0gZsokWSwnWbKiurtY6kKn1smKJlIERakVFBVpaWkpCGBzHwWazwWazoa2tDYODg9okwIkTJzThZim8ogghOHPmDHieT7N7Ti2WZxbKgezF8gt15xtwEZHSRhP0+XzJwWAQgUAAu3btKslFlVrQznbnZgsfGxoa4PV6MTw8jIqKCkxNTcHpdGqt/FLMarEO3s6dO1G10aRrClKdDFtbW9cQKYtOjEYjBgcH0d7evmbsohRIJpPo6+tDc3NzVsLOJNJIJAKXy4WBgQEQQrRieaqxfiqY3Uxtbe2WEKqqqhgcHNR8tdnflcorihCiRXg7duzIWWPleX6NoV0qWeXjDf9O46IhpVzI18NoaWkJk5OTqKqqWlPQLhT5KLRTsbi4iNnZWRw6dAhGo1GLTlwuF06fPq1dcKwIXShYB2+jLSb5IJVI2UU1Pz8Pl8sFq9UKSZKQTCZL2gmLxWLo7+/fcC0UQ+pwLjPWd7vdmJmZSavx2O12zaiPRZBbEaEqiqItrGhqatL+Ph+vKCY5WC8dJYRgeHgYRqMR7e3teZsIAmslB0888QRCodBmPu6W46InJSYLyPWjMh+iYDCIQ4cOYWxsrKi1SexEKEShTQjBxMQEwuFwms9PanTS1ta2ZlMJ6+zko4+Zm5vD4uJiyTp4qWBNgWg0iiNHjoAQApfLlZbmVVdX5708MRs2O9YB0GJ5NrfNqakpCIKAWCyWdSFoKcAisPr6+g1dHLJ5RTGVfq50VFVVDA8Po6ysDG1tbUUfJ8/zePLJJ/HjH/9Yq3deqOA2KHpdUBWxbN5Jp06dQmdnZ9bumSzLGBwchNls1oqyo6OjqK2tzTvFOQyw7RsAACAASURBVH78uObfVIiHtqIo2t3tsssuy/uiVRQFHo8HLpcLwWAQFRUVmsd2qnkZI9tYLIbOzs4tUSHPz89jYWEBPT09awiPpXlutxuRSEQb16iqqsq7CJ1qXbsVYj5myFddXY1IJKIRfj7RST5g1ibNzc05VfL5ILWu5/F40ryipqenYbPZ0NLSsqljffzxx/Hwww/jhRdegMVi2dRrlQg5L4hLJlLKRC5BZLG735hLQT6ExNZmNzQ0aD40hbxfqj6GtcjZggRGUBMTEzCZTJtuOWcD09iEQqGcGqFsaR6rnZhMJu2un0sWUah1baFgKvauri7tIswWnbAZwkKjzEQigb6+vqzWJoUis67H0lFWL9PpdFheXl53W+56eOyxx/CjH/3oQiKkdXFRkVK2Kf9sJOP1ejE6OorOzs41YxXFqLTZrrh8dCAsHeno6Cio4JwNqZ0dgBLt0tISTpw4AVEUUVZWhlAolLO4WwxYy1wQBPT09ORdv2B3dpbuuVwuzSGS1ctYmrdZ69qN4PP5cPbs2TUq9kxxabauYz7paDweR19fX8FNhXzB8zyWlpbQ2tqKxsZGzdJmZmYGPM9rx5mPV9RPfvITPProo3jhhRcuaBV3Ki6q9C2ZTK4hpZGREdTX12u+N+fPn8fi4iJ6enqypnRTU1OatcVGIITg9OnTcDgcqK2t3TBFcjqdmJycLEnBORvYlPyOHTtgtVq1NC8cDsNms8HhcGyq9czS3crKypLIJQBod32Xy4VIJAKO4yAIAnp7e0vmEJkKl8uFyclJ9Pb2FhSBZaajuTyYmNdSIU4FhYAVzWtqarJ2CZlX1EbHCQCPPvoofvKTn+D555+/EAkp58l10ZPS2bNnYbfbUVVVhdHRUSiKsm6NhU3Wb9QWZgXtYDCIhYUF+Hw+LdzPbOMzu1yPx4Pu7u4tudjYWqW9e/euCcGZZ5DL5YLX64XZbNaOM9+0JF+NU7FgEZgkSTCZTPD5fDAajVoUVYoUbnFxEXNzc5smPPZ9ut1ueL1erZVvNpsxNjaW9TcoBQopmmcep8/ng06ng8FgAM/zOHXqFB5//HE899xzFyIhAZcyKbEaxvz8PBwOx4aiuLm5OSiKsq5xW7aCdmob3+12a6mA3W7HzMwMAGDXrl1bIt9nkoLu7u4Nx2FSNTxutxtAbjEkA4vAtiodYSpzq9Wa9vukHicr7jocjqLS0fPnz8Ptdm84+FoMotEo5ubmMDc3B5PJpBH+Zt0DUiHLMvr6+rBt27aibwqxWAwnTpzA3XffjampKXz2s5/FzTffjKuvvrokx1hiXBqF7mw1JVmWMT4+js7OzrwKjustmARyb6nN1sZfWlrCm2++CZ7n0dDQgHA4XNL6DiEEU1NTCAQCeddfMjU8mWJI5mDJRjX8fj9GR0c37VOUC0wFXl9fv6bozxYnpg7mpmqNsm32zUTq4Cubwys1kskkvF4vjhw5Ar1en+YekOmrXgxK1cUzGo2YmZlBZWUlfvWrX+HNN9/E8PDwhUpKOXFRRUqSJKUVqZeXlzE6OorGxkZtDmgjLC8vIxQKZX18IZYjLLpob29HZWUlPB4PnE5nWn2nkPZ4JlILzqWakUs1NfP7/RBFEYlEAr29vVtCSPlY12YD0xqxdNRgMGjRSWqkyAaPVVXF7t27t2R0gglTe3t710SpqYZ7Ho+n4CI0QM/p06dPo6WlZVPbQAgh+NGPfoSnnnoKzz777JbUNEuMSyN9Y6TERIl+vx81NTVQVTVvHQfTgnR0dGh/V6hCm+lrOjs7N6zv5KpDbfQ5BwYG4HA41rhQlgozMzNYWlpCVVUVfD5fWmeqFEZqxVjX5gLr5rHlo6zTNzs7C5PJlLfKuVAwKUZvb29etTnmxMD20KVa7WaL9thoTWtr66ZkBYQQPPzww/jZz36G55577oI2cEvBpUFKsiwjkUhgaGgIRqMRO3fuhNPpRCQSQXt7e16v4fP5sLS0hN27dwMonJCYgrq7u3vD4myuOtR6Fz6zrS00usgXzFYjkUigs7NTi+Ti8ThcLhdcLheSyWRBqvJMbGVKKEkSXC4Xzp07B0KIRvhspKRUWFpawuzsbNFF81TtVmpRn0V7TOe0Y8eOvEZrcoER0s9//nM8++yzFwshAZcKKYVCIZw6dQpNTU1a98zlcsHn82Hnzp15vUYgEMDs7Ky2Ry5fQSS7mOPxeNEK6tQLX5Ik7cJnjovMh3rPnj2bns/LBlVVMTQ0BJPJlHOoE4DmupipKs/nwmct+VySjM2CRZF1dXVoaGjQuk8ej0frkrEB4mIxPz+PpaUl9PT0lExHxbb7ut1ubQ14W1sbGhsbi47yCCF46KGH8Oyzz+KZZ565mAgJuFRI6dy5cygvL0/bxcXsX1nksxHC4TAmJibQ3d2dNyHJsoyhoSGtyF2KVEGWZU1nFAqFYDAYEIvF0NvbuyX1AHYx19TUpA2NboRUVTm78LPVd4BV69qt0iCxdGf79u1ZC8Js4NXlcmke26mknw/Onz+vSTu2YnQnHo/j9OnTqKmpQTwe10ifRXv5kiAhBD/84Q/x3HPPXYyEBFwqpERXSKert1Mjn3wQjUZx5swZbTxjo5M1FothcHBwy/Q7zJbV6XTCYrEgEAgUVYdaD6VMCTPrOywl8Xg88Pv9W3YxF+okkEn6uWYIUzE1NYVQKIS9e/duSRePfYbUOhtbQspqkKkWLbluToQQPPjgg3jxxRfxzDPPlHSZwtuIS4OUFEXRNoQwhMNhTE5Ooru7e8PnE0KQTCbxxhtvaD5B67WcWTpVii0g2aCqalr3iE3lF1qHWg9s7GUrUkKm1p6cnEQymURdXR1qampQWVlZ0os6HA5jcHCw6M+QGu15vV5thpD5GrHh5kQigT179mwJITEl+O7du9f9DMwxwu12Ix6PpxXL2fnx4IMP4qWXXsLPf/7zi5WQgEuZlKLRKM6ePYt9+/at+9zUgjZACcfpdObskC0tLWFmZgbd3d1b8sOzkQ42AZ4rYtuoDrUeWDt7q6bwma2G0WhEW1tb2oVfjKo8G0q17y0VbCEl+04JIdpw81YQUiQSwcDAQMH2LEzCwYrl3/3ud2E2mzE7O4sXX3zxYiYk4FImJbbE8eDBgzmft15BO1tkwhwBent7t2RgNB6PY2BgIKfLYi5kpiTr6aFYfSeb7UgpkM26liFTVc5xXJqqPF8w6UVPT8+WXICMVBVFgSiK2nAzK+qX4rdnUd5mR1NUVcU999yDX/ziF9rCgJ/85CebtjR5B3FpkJKqqtrEPoMsyzh16hQuv/zyrM8p1ANpcHBQ2xDBaiY1NTV5i+E2AkunNpsS5tJD2e12zM3NIRgMoqura0vqOxtZ12aC6XdcLpe2MJPJDXJFJk6nE9PT0+jp6dkSaxPWiWR2IUB6fcfj8UCn02lkupnUebNRHiEE3/ve9/Dyyy/jZz/7GYxGI3w+H8rLy7ekofA24dIlJUII/vCHP+CKK65Y8/hCFNrMMrWurk7rTrGaidPpRCwWQ1VVFWpqaoqeeXK5XJiYmCi5iwCL9pxOJ+bm5sBxnDayUOoIg9VG8i04Z4J5GrlcLgQCgayRycLCgmYutxUXnaIoGBgYgN1uX1ecuhntFvNz6u7u3tRvTQjBAw88gN/85jd4+umnt0Rm8Q7h0iUlAHj99dfTSKlQQSQz3V/vQmMXk9PpRDAYzHs2i4Etnuzu7t6SdIpdaDabDXV1dWk1k2Ja49lQCuvaVLDIhBV2RVGEIAiQZTltxXUpwSbx6+rqCjLgY46gbrc7jUyzGa8FAgGMjo5uupZHCMF3v/tdvPLKK3jqqacuJUIC/phIqVBCYm6JhUQvrJvDCuUmkwk1NTVZW/ipCuo9e/ZsWTrV39+PxsbGNbKFQupQ62GrrWvZHFsgEIAoikXrjNYDG3zd7AKBVDL1eDxah7S6uhrJZBJnzpzZdB2MEILvfOc7OHbsGJ588smSEZLf78edd96JoaEhcByHH/zgB+jo6MAtt9yC6elptLS04IknnkjTAm4RLg1SYi39TDBSKlShzRwQNxO9sKKu0+lMa+HX1NRAp9Npixu3aj6LzZjlk04VO5fHrGu3qr7D9plxHKcNH2eSaaGRaSZYHWyzg6/ZwNr4CwsLCIVCaGhoQF1dXdE+4IQQ/Ou//it+//vf48knnyzpd37HHXfgqquuwp133olkMoloNIp/+Id/QFVVFe666y7cc8898Pl8uPfee0v2njlw6ZPSe97znrwJKZs+qFRgdYilpSWEQiFUVVVhx44dJSuUp4LNmBXT2cnWdWRF/dQ7PCPuUo5cpIIVnNm2jmzfESEkjUyZqjxfczjmVrDZObP14PV6tTVXrPMYCARQXl6u1czyqY8RQnD//ffj1VdfLTkhBQIB9Pb2YnJyMu177ujowNGjR1FfX4/FxUVce+21OHv2bMneNwcubVJ67bXXcPDgQW1V8XqQJAmDg4OoqqoqmeVrJliNqrW1FaqqwuVyaVPjNTU1sNlsm35fp9OJqampks2YZdNDJRIJyLK8ZfqdfAvOmcg0h8v0AE9FNhV1qeHxeHDu3Lk1FryZ1iYbqbUJIfiXf/kXvP7663jiiSdKHpX29fXhC1/4Avbs2YP+/n4cOHAA3/rWt7Bt2zb4/X7tGCorK7X/3kJcmqTE6kfT09NYXFzUio/V1dVZQ/xoNIrBwcEtm8AHVmsve/fuTWsDZ3adKioqUFNTU1Q6kpp2btWM2eDgIOLxOHieL4k/VCaY+RvbiLKZY2VpHlv1xLzK2XhNqQrz2VCIvUmmWpsto2Q3qW9/+9s4fvw4nnjiiS1phpw8eRJHjhzBa6+9hssvvxxf/vKXUVFRgW9/+9tpJFRZWQmfz1fy98/ApUFKwOrut8yCNkDbsE6nEx6PB0ajETU1NXA4HNDpdJrH9VaeoEywuJGtSbZCOUtH1iMZNg7BnAq2KnpJta7NTJ3MZnPOon6+YH7gm/URygSzC2GRSTKZREtLy/9v79yjorquP/4dXooFRRgGDISHAsrLESlqjcafAhp5DDUSS7KySmJtrEajTeO7NaCu+LbamsTV2Ko1law0PFQ0+AyNUZRgRYwBRYQAMjIwiMDgAHPv+f1hz81AYJjXdXjcz1qu5YyLO2fwzp5z9v7u74anpycvwZtqqcLCwgy+Pv2Sqq+vR0pKChQKBRiGwalTp0xyn9TFw4cPMWXKFFRUVAAALl26hG3btuHevXvC8c0U2tvbuQm1uvJHNF9CO8YZhsH48eN5sQShpnMqlQohISEG7Xy01c91dXU99rppt3Tosh0xBV3WtdprpUl9KysrLqmvb6WJHqf48gMHfsy1+fn5obm5mVurMarynqitrUVlZaXJjgiEEPz5z3/GlStXMHnyZJw7dw6rV6+GTCYzeY3dMX36dBw8eBBjx45FSkoKVCoVAMDFxYVLdDc0NGDHjh28vL4WAyco0TyHvhW2e/fucQlnpVIJhmG4D5I5bk4aLOzs7LgpvKbQNbdDx+eUlZVBIpHw5kRJk8GGDFekxxGFQqGXHoq2XPC5W6XH5672tW1tbdzvta2trZNXuaH/Z3K5nLNoMSX5TwPSjRs3kJaWxh3Z6L3NB4WFhVzlbfTo0Th06BBYlsWCBQtQWVkJb29vfP7557x9YWgxMILSnTt3sGvXLshkMkyfPl3nuZthmG7L8dRIX6FQQK1Wc9+exmhh6M7Czc3NII8iQ64vl8tRVlYGGxsbbnKuOUZOa0MT86Ykg3vTQzU2NnKWMXz5R+s7800fVXlP1NTUcHMFTQ1Ie/bswc2bN3Hs2DFeckh9nIERlDQaDS5evIiMjAx88803kEqlkMlkiIyM7CToow2vnp6eOpOoDMNwAaqlpaVTday3Dz1ttxgzZoxZ8yLaaPfJDR8+nFOU00S5vm6QuuDDurarHsrGxgZqtRoTJkzgbWy0scep7vrd6PG5a1Wzurqak0eY8jsnhGD37t0oKipCWlpaf+5fM4WBEZS0YRgGV69eRUZGBs6fP4/Ro0cjPj4ezs7OuHr1KpYvX26QKpUmSRUKBRobG3V+6OkH+VkcQ7rbWXR1g6RJfUNtQvi2rgWeJlfLy8shFou5AQXd6aFMgfbKmcPV4cmTJ51ykbSE//jxY7M4UhJCsHPnTty+fRvHjh0za0Dy8fGBo6MjrK2tYWNjg4KCAjQ0NFhCra0PAy8oacOyLG7duoUtW7bgq6++QkREBGJjYxEbGwuJRGLwsUy7OqZUKjtVnJRKJSorK/UaDGksdPikvgpqmnyuq6uDlZUVV3XU9aHn27oWeLqzqK2t7XTUMTQP1RtVVVWoq6vjZQglPZKWl5ejtbUV7u7uJqnKCSHYsWMHiouL8a9//cvsv3cfHx8UFBRALBZzz61evdoSam19GNhBCQDy8/OxceNGpKWlob6+HhkZGTh58iSsrKwQExODhIQEeHl5GRWgaAf+gwcPwLIsfH194e7ubnZxG7XGbWxsRGhoqFHf+t0lyiUSCScs1H4NvqxrAXBDNHXZp3TXSkK1W/rkzMrLyzmLFj7kEdqvERwczAkhGxoaMHToUE4Tp899QAjB9u3bcefOHXz66ae8fBF0F5QspNbWh4EflKhuSfsDQAhBTU0NMjMzkZWVhaamJsydOxfx8fEGDS9kWRYlJSUAAG9vb64Dn474kUgkJjep0v4vQojZxn/TqbN0DNXIkSPR1tYGa2tr3mxfjbWW7ZqHos6V3Wm3qARDrVbz9j4AcJN3u9OEaavKCSE6R6MTQrBt2zaUlpbi6NGjvO1MfX19MXLkSIhEIixevBhvvfUWnJycLKHW1oeBH5T0QalU4uTJk8jMzMQPP/yAqKgoyGQyTJw4sccbW1dbSnt7O3dsam9v/8muRF+oYHH48OHw9fXlpRxMZ9VTOYWjoyMkEolZ56URQripvqbII7pqt6geih5J7969C4ZheJuKqx30goOD9fLiol9UXUeji0QibN26FWVlZTh69Cgv/YOUBw8ewMPDAwqFAtHR0fjrX/8KmUxmCbW2PghBqSstLS348ssvkZmZiaKiIkyfPh0JCQmYOnUqd+PQNgUfH59eVbYajYbLlahUKs4QrjcdDLUd8fDwMKndore1aVvX0oqTtvqdfuiNLU3TvB51cjRnsNDOQzU1NcHe3h5jx4412mxPF3Sn19HRYVTQY1mWU2qvWbMGjY2NsLW1xYkTJzodq/gmJSUFDg4O+OSTT4TjW3+kra2Nkxrk5eUhPDwcQUFB+Prrr/HJJ58YrN1hWZY7NlFDuO5yJaa6OOqDPta12rsSkUjEBSh9j6Q06Lm6uvKi1wI6D9J0dHREfX19pzxUT6OxDYEQgrt374JlWYwbN86kgEcIwebNm1FSUoKwsDCcOXMGf/zjHxETE2PSGntCpVKBZVk4OjpCpVIhOjoaGzduxIULFyyh1tYHISjpi0ajwc6dO7Fv3z54eXnBy8sLMpkMs2fPNqr8T3vHtCenSCQS2NnZ4c6dO7zKCowJelT5TKtjNFfi6OjY7YdUl8GcuaDHWzr5hWJIHqo3aE7PysrKZGU+IQRbtmxBVVUVDh8+zO28+VRq379/H/PmzQPw9B5+7bXXsGHDBiiVSkuotfVBCEr60tLSgt/85jc4cOAARowYgRs3biAjIwM5OTmQSCSQyWSIiYmBWCw2qpLX3NyMiooK1NXVYcSIERg1apRJx6aeMId1La2O9SQuNaY1xVAYhsHNmzchkUi4Ue3doSsP1duOj+bCbG1tTe4rZFkWW7ZswYMHD3Do0CFec0j9HCEomQq1a83IyEB2djbs7OwQFxcHmUwGDw8PvW/k6upqbk59R0cHlyinxyZziAr5sK7tKi61t7dHc3MzAgMDecuV0DYeDw8Pg3dhNA/V1fS/qx6KEMI1OpvqDsqyLDZv3oyamhohIPWOEJTMCbXSpVKDJ0+eICYmBvHx8T1u/WlFp7W1FcHBwT/Jf7S1tXEBiiqJjRntxLd1LfDUIqaoqAgjR45ES0sL7OzsOMGmuXZ85rSv7cla18nJCSUlJZzrpSmwLItNmzbh4cOHOHTokNn1XwzD4Oc//zk8PDyQnZ2N8vJyJCUlQalUIjw8HEePHu1v/XNCUOILQgjq6+tx/PhxZGZmQi6XIzo6GjKZDFKpFFZWVtBoNCgpKYGtra1e+Yquo530He1D1c3jx4/n7VuaNtZq78K0j00ATNZutbW1obCwEGPGjDH7LozmoRQKBeRyOYYMGQIfHx+TJvmyLIvU1FQoFAr84x//4EWQumfPHhQUFKCpqQnZ2dlYsGABXn75ZSQlJeF3v/sdpFIplixZYvbX5REhKD0rmpqacOrUKWRmZqKkpARTpkxBQUEBUlJSEBUVZfD16GgfhULBdd/TahOt5GnvwkJCQngTE9bX16OsrExnr5y2RQg9Nkkkkh4T5V2hfktjx47lrUeLZVluHJVYLDYqD6V9rZSUFNTX1+Pvf/87LwGpuroaycnJ2LBhA/bs2YOTJ0/C1dUVDx8+hI2NDfLy8pCSkoIzZ86Y/bV5RAhKlqCsrAwxMTHw9fVFdXU1Jk2aBJlMhhkzZhh1tNL+ln/06BEcHR0hFou5mWl0EggfyOVyVFdXG9Qr112inFrVdhc46WSWwMBAXsz4gB99wcVi8U/kC9p5qLa2tl5tbViWxfvvv4+GhgYcPHiQt5adxMRErFu3Ds3Nzdi1axcOHz6MKVOm4N69ewCe7pDnzp2L7777jpfX54keb1QhE8cjZ8+excGDBzF9+nRoNBpcunQJ6enp+NOf/oSgoCDIZDJER0frbRliZWUFZ2dnODs7c1KD27dvg2VZDB8+HHK53KhyeG/QY2FYWJhBx0IbGxu4ubnBzc2tk1Xt3bt34eDgwCnKbWxsuGqhMZNZ9KW3St7QoUPh6ekJT09PLqBWVVV1O+KJZVls3LgRjY2NvAak7OxsSCQShIeHIzc3l5fX6GsIOyULwLIsCgoKkJ6ejrNnz8LT0xPx8fGIiYnRW0PS1bpW2ylAe/acKU4GhBCUl5ejubnZrE2vVBpBFeUikQhqtRqhoaG8HdkYhkFhYSFGjRplsHJeWw8ll8uxZ88eODo6YtiwYfjnP//JW0ACgHXr1nHtKWq1Gk1NTZg3bx7OnDkjHN8E+IGWpDMyMnD69GkMGzYM8fHxkMlkcHd37/bY0Js+SK1WcwHKWPtfqm7WaDQICgri7Vj46NEjFBcXw9XVFY8fP+aanM3lpQ382PdnjLSgu2u99957uH37NgDAzs4On3/+OW86LW1yc3Oxa9cuZGdn45VXXsH8+fO5RPf48eOxdOlS3tdgRoSg1B+gO5PMzEycOHECGo2GkxpQDQ2tfulrXWuM/S/Lsvj+++8xZMgQ3oYUAD8mzrXta9vb2zlFuT55nd6go7q9vLxMnhLCsizWr18PtVqNjz/+GNbW1pDL5XBzc+OtuKCNdlC6f/8+kpKS0NDQgLCwMHz66ae8SUB4YmAFpZycHKxYsQIMw2DRokVYu3atpZdkdgghUCgUyMrKQmZmJurr6xEWFoZr164hJyfHKC9tfex/tUcs+fr6mvttcdDxRLrmpXXn+9218qiLjo4O3LhxwyxaJ5ZlsW7dOrS3t+Pjjz9+JkFogDNwghLDMAgICMC5c+fg6emJiIgIpKWlISgoyNJL45XPPvsM69evh1Qqxf379zFz5kzIZDJEREQYldPozv7XxcUF1dXVcHd319nSYSp0GohUKtU7Kd+18kjHYYvF4m6T71R8aY7ZcizLYu3atdBoNPjoo4+EgGQeBk71LT8/H35+fpwCNykpCcePHx/QQYkQguvXr6OgoADOzs5obW3F2bNncejQIbzzzjv4xS9+odeEF22srKzg4uICFxcXTgD6/fffw8rKqtOIaXNX8qj5flhYmEHBtGvlkbpA/vDDD7C1teUU5UOGDDGr+JJlWaxZswYMw5g1IKnVarz44ovcyLDExESkpqYOBKW2yfS7ndIXX3yBnJwcHDx4EABw9OhRXLt2Dfv377fwyixDR0cHcnNzkZ6ezk14iY+PR1RUlN4CQJo49/Pzg7Ozc6eBk3S0E/3Am4K21a85K1atra2cAJJhGKjVavj7+5vsT8WyLFavXg0A2L9/v1l3SLSB2MHBAR0dHZg2bRr27duHPXv29Heltr70uFMS9qH9HFtbW0RHR+PAgQO4efMmli5dim+//RaRkZF47bXXkJaWptP+VKVSobCwEGPHjoWLiwtEIhEcHBwwevRoTJo0CYGBgVye6dtvv0VFRQVaW1sNWiNVnDc3N/PiCz5s2DB4e3sjJCQEDMPgueeeg0KhwNWrV1FaWspV9QyBZVmsWrUKIpHI7AEJAPd7Bp5+sXR0dEAkEuHixYtITEwEACQnJyMrK8usr9sf6HfHNw8PD1RVVXGPq6urux0xPRixtrbGCy+8gBdeeIFzgkxPT0dCQgJGjhyJ+Ph4xMXFcRNeHj16xA2I7EnAaW9vD29vb3h7e3P2v3fu3NHb/pcQgtLSUmg0GoSEhPBWyaPtKUFBQVwRgLboUAFk1wGZPcGyLN577z3Y2NjgL3/5C285JIZhEB4ejnv37uHtt9/GmDFj4OTkxOXIPD098eDBA15euy/T745vGo0GAQEBuHDhAjw8PBAREYFjx44hODi4159duHAhp5Clkvw+PBfLbFCLVyo1EIlEkEqluH79Ok6ePGlU46w+9r/mNE7TBTWz09We0tUQjprtubi4dMqbsSyLP/zhD7Czs8O+ffueSVK7sbER8+bNw+bNm/HGG2/09/YRfRk41TcAOH36NFauXAmGYbBw4UJs2LBBr5/7+uuv4eDggF//+tfcf3QfnovFC4QQHDlyBKmpqfD390djY6NRE160ofa/dAz2iBEjIBaLUVtbC3t7e5N9inShUqlQVFRkkJmdORToAQAACqlJREFU9tis+vp62NraQqVSwdPTE/v27cPQoUOxd+/eZ1pl27RpE+zt7bF9+/b+rtTWl4EVlEyhoqICcXFxXFDqw3OxeIFhGCxbtgxbt26Fk5NTpwkvlZWViIyMRHx8PMLDw436UBJC0NDQgOLiYjAMw2mheirdm0JLSwtu3bplcr/ckydP8O9//xt79+5FU1MTli9fjvnz5yMgIMCMq+1MXV0dbG1t4eTkhCdPnmD27NlYs2YNjhw50t+V2voiBCVK16DUh+diPXO0J7zcunUL06ZNg0wmw9SpU/WWBtCmV1dXV3h6enbakZjTDI428OrKh+kLwzD4/e9/DwcHB2zYsAFffvkl2tvbsWjRIpOuq4uioiIkJyeDYRiwLIsFCxZg48aNA0GprS9CUKLoCkpAn5qLZVHohJfMzExcuXIFEydOhEwmw6xZs3ps8tVoNFyTcHfl+NbWVrPY/zY1NeH27dsYP368yf1xDMNg5cqVGD58OHbv3i0II58dQlCiDPbjmzFoNBpcvnwZmZmZuHjxIvz9/SGTyTBnzhwuj2Noj5mx9r+PHz9GcXGxWfzHGYbBihUrMHLkSOzcuVMISM8WQafUEzKZDEeOHAEAHDlyBAkJCb3+TFVVFWbOnImgoCAEBwdj3759AJ5W8qKjo+Hv74/o6OgBs+OysbHBjBkzsHfvXhQWFmL9+vW4e/cuYmNjMX/+fHz44YeYM2cORo0apXfT65AhQ/D8889j4sSJCAsLg729PcrKynDt2jWUlpaisbHxJ9qixsZGFBcXQyqVmiUgvfPOO3B2djZrQBps9wYfDKqd0quvvorc3FzU19fDzc0Nqamp+OUvf2nwXCy5XA65XI6JEyeiubkZ4eHhyMrKwuHDhwddJS83Nxevv/46Ro8eDWtra6MmvGhDtUV1dXVoamrimnABoLS0FBMmTDDJI4q+xvLly+Hq6ort27ebdYck3Bt6Ixzf+CQhIQHLli3DsmXLBt1RcMmSJUhOTsbkyZO5CS/Hjx9Ha2sr5s6dC5lMZrRGiWqLKisroVQqIRaL4e7uDrFYbLQqnFYf3dzcsG3bNt6PbIP53ugFISjxRUVFBV588UV899138PLyEip5/6Ourg7Hjx9HVlYWampqEBUVhYSEBG7Ci74olUrcu3cPUqmUy0MplUoMHTqUq+QZUhl8++23MWrUKGzdupX3gCTcGzoRghIftLS0YMaMGdiwYQNefvlloZLXA01NTTh9+jQyMjJQXFyMGTNmQCaTYcqUKTq1S3V1dSgvL+/Wc8lQ+1+GYbB06VJ4eHjggw8+4D0gCfdGrwhBydx0dHQgLi4Oc+bMwbvvvgtAqOTpg1qtxvnz55Geno78/HxMnjy52wkv1AQuLCys151Qb/a/Go0GS5cuhZeXF7Zs2cJ7QBLuDb0QgpI5IYQgOTkZzs7O2Lt3L/f8qlWr4OLiwiUzGxoasGPHDp3XGsy+OtoTXv7zn/9wE14UCgU0Gg3eeustg/2ctO1/79y5g8uXL6Ourg7BwcHPJCCZ894Y4PScZCSE6Poj0A2XLl0iAEhoaCiRSqVEKpWSU6dOkfr6ejJr1izi5+dHIiMjiVKp7PVaLMuS5uZmQggh7e3tZNKkSSQvL4+88sorJC0tjRBCyOLFi8lHH33E63uyNAzDkGvXrpHY2Fji7u5O4uLiyIEDB0hVVRVRqVRG/amuriYymYyMHz+ehIaGkhUrVhCGYXh9H+a8NwY4Pcadfmdd0heYNm1aj/48Fy5cMOhaunx1jh07BuCpr05KSspANfsCAG68uVqtxt27d1FZWYmMjAwkJibqNeGlK3TqSGhoKLKystDR0YHr16/zvlMy570xWBGOb32Arr46q1atGggTUA2GZVm0t7d3SlYTQlBRUcFJDTo6OhAbG9tpwktX6NHP398fmzZt4s2hQMAkBEV3X8ba2hqFhYWorq5Gfn4+SkpKLL0ki2BlZfWT6plIJIKvry/effdd5ObmIjMzE2KxGKtXr8aMGTOwZcsWFBUVgWVZAE8D0m9/+1sEBAQIAamfIgSlPoSTkxNmzpyJvLw8NDY2QqPRABDcNSkikQhubm5YvHgxcnJycP78eQQFBWHnzp2YOnUq1qxZg/nz5yMwMBCpqalmDUgLFy6ERCJBSEgI95zQOsIPQlCyMHV1dZx+5cmTJzh37hwCAwMxc+ZMfPHFFwD078kDnh4Fw8LCEBcXBwAoLy/H5MmT4efnh1/96ldob2/n541YACcnJ7z++utIT0/H1atXMWnSJDz33HN4//33zb5DeuONN5CTk9PpuW3btiEyMhKlpaWIjIzEtm3bzPqagxZdWfBnno8fhNy8eZNMmDCBhIaGkuDgYJKamkoIIaSsrIxERESQMWPGkMTERKJWq/W63u7du8mrr75KYmNjCSFk0FXx+KS8vJwEBwdzjwMCAkhNTQ0hhJCamhoSEBBgqaX1R3qMO0JQGkBUVVWRWbNmkQsXLpDY2FjCsixxcXEhHR0dhBBCrly5QmbPnm3hVfZfugalESNGcH9nWbbTY4Fe6THuCMe3AcTKlSuxY8cOruytVCqF6RjPCJFIJCTVzYQQlAYIdEpLeHi4pZcyaHBzc4NcLgfw1LKEWqwImIYQlAYIly9fxokTJ+Dj44OkpCRcvHgRK1asEKp4PGKMQaCAHug621nkpClgMl999RWX6E5MTOyU6P7www/1vo63tzcJCQkhUqmUhIeHE0IIUSqVJCoqivj5+ZGoqCjS0NBg/jfQB0lKSiLu7u7ExsaGeHh4kIMHDwqtI6bRY9wRFN0DkNzcXOzatQvZ2dkmTcfw8fFBQUEBxGIx99xgm5MnwBuCS4CA4XQXlAQLDgEzIbSZCBiOSCTC7NmzER4ejr/97W8AgNraWowaNQoA4O7ujtraWksu0azk5ORg7Nix8PPzE4SQFkRwCRDokW+++QYeHh5QKBSIjo7GuHHjOv37QCqDU6vcc+fOwdPTExEREZDJZAgKCrL00gYdwk5JoEdopU4ikWDevHnIz88fsGXw/Px8+Pn5YfTo0bCzs0NSUhKOHz9u6WUNSoSgJNAtKpUKzc3N3N/Pnj2LkJAQo8vgjY2NSExMxLhx4xAYGIi8vLw+1dD64MEDPP/889xjQWhqOYSgJNAttbW1mDZtGqRSKSZNmoTY2Fi89NJLWLt2Lc6dOwd/f3+cP38ea9eu1et6K1aswEsvvYSSkhLcvHkTgYGBQkOrQLcI1TcB3nn8+DEmTJiA+/fvd8pB9aVKXl5eHlJSUnDmzBkAwNatWwEA69ats8h6BgFC9U3AcpSXl8PV1RVvvvkmwsLCsGjRIqhUqj5VyYuIiEBpaSnKy8vR3t6Ozz77DDKZzGLrGcwIQUmAdzQaDf773/9iyZIluHHjBn72s5/95Khm6UqejY0N9u/fjzlz5iAwMBALFixAcHCwxdYzmOnt+CYgYDIikcgdwFVCiM//Hk8HsBaAH4D/I4TIRSLRKAC5hJCxllupQF9A2CkJ8A4h5CGAKpFIRANOJIDvAZwAkPy/55IBCDV4AWGnJPBsEIlEEwAcBGAH4D6AN/H0S/FzAF4AfgCwgBDSYLFFCvQJhKAkICDQpxCObwICAn0KISgJCAj0KYSgJCAg0Kf4f+xREMqSkCP+AAAAAElFTkSuQmCC\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 360x360 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 360x360 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ], | |
| "source": [ | |
| "plot_image_3D_scatter(np.array(test_image), \"origin image\", \"blue\")\n", | |
| "plot_image_3D_scatter(np.array(noisied_image), \"nosied image\", \"green\")\n", | |
| "plot_image_3D_scatter(np.array(test_image),\n", | |
| " \"origin image\",\n", | |
| " \"blue\",\n", | |
| " np.array(noisied_image),\n", | |
| " \"nosied image\",\n", | |
| " \"green\")\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "3yee_V7Li1si" | |
| }, | |
| "source": [ | |
| "# Data processing" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Flatten all images\n", | |
| "\n", | |
| "Convert images from high-dimensional to 1-D." | |
| ], | |
| "metadata": { | |
| "id": "mECjkNvfp-cS" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "xV30pF1hi1si", | |
| "outputId": "b7a3fea8-f9ec-48d9-e24c-3bf5a9b6ec0b" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "(2892, 4096)" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 12 | |
| } | |
| ], | |
| "source": [ | |
| "x_train = x_train.reshape(x_train.shape[0], -1)\n", | |
| "x_train.shape" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "metadata": { | |
| "id": "RxBTYusci1si" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "m = x_train.shape[0] # Sample count\n", | |
| "assert dim_visible == x_train.shape[1] # The dimension of input layout" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "OWzy7_8wBm8a" | |
| }, | |
| "source": [ | |
| "# Train RBM" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "metadata": { | |
| "id": "4OE-Nl88i1sj" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def gen_predict(img1, img2, proc):\n", | |
| " rebuilt_image = proc()\n", | |
| " plot_image(img1,\n", | |
| " img2,\n", | |
| " rebuilt_image)\n", | |
| " plot_image_3D_scatter(np.array(img1),\n", | |
| " \"origin image\",\n", | |
| " \"blue\",\n", | |
| " np.array(rebuilt_image),\n", | |
| " \"rebuilt image\",\n", | |
| " \"red\")\n", | |
| " plot_image_hist(\"Color(grayscaled) intensity\", img1,\n", | |
| " \"origin\", rebuilt_image, \"rebuilt\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "QGXOL5muK3JW" | |
| }, | |
| "source": [ | |
| "## Architecture\n", | |
| "\n", | |
| "For training RBM we rely on 2 steps:\n", | |
| "\n", | |
| "<ol>\n", | |
| " <li>Gibbs Sampling</li>\n", | |
| " <li>Contrastive Divergence</li>\n", | |
| "</ol> \n", | |
| "\n", | |
| "We cannot be easily train the model with typical <b>gradient descent (ie. SGD)</b>, this is because that the step of updating of weights and bias comes up with the stochastic, random probabilities of visible and hidden layers." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "> I crop some important sections from origin paper regarding of those topic, hopefully it gives good hint and cheat sheet." | |
| ], | |
| "metadata": { | |
| "id": "fI0lYUDpuqNL" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Gibbs Sampling\n", | |
| "\n", | |
| "<h4>Step 1</h4> \n", | |
| "Given an input vector $\\mathbf{v}$, we are using $p(\\mathbf{h}|\\mathbf{v})$ to predict the hidden values $\\mathbf{h}$. \n", | |
| " $$p({h_j}|\\mathbf v)= \\sigma\\left(\\sum_{i=1}^V W_{ij} v_i + h_{bias} \\right)$$\n", | |
| "The samples are generated from this distribution by generating the uniform random variate vector $\\mathbf{\\xi} \\sim U[0,1]$ of length $H$ and comparing to the computed probabilities as\n", | |
| "\n", | |
| "<center>If $\\xi_j>p(h_{j}|\\mathbf{v})$, then $h_j=1$, else $h_j=0$.</center>\n", | |
| "\n", | |
| "<h4>Gibbs Sampling Step 2</h4> \n", | |
| "Then, knowing the hidden values, we use $p(\\mathbf v| \\mathbf h)$ for reconstructing of new input values v. \n", | |
| "\n", | |
| "$$p({v_i}|\\mathbf h)= \\sigma\\left(\\sum\\_{j=1}^H W^{T}*{ij} h_j + v*{bias} \\right)$$\n", | |
| "\n", | |
| "The samples are generated from this distribution by generating a uniform random variate vector $\\mathbf{\\xi} \\sim U\\[0,1]$ of length $V$ and comparing to the computed probabilities as\n", | |
| "\n", | |
| "<center>If $\\xi_i>p(v_{i}|\\mathbf{h})$, then $v_i=1$, else $v_i=0$.</center>\n", | |
| "\n", | |
| "Let vectors $\\mathbf v_k$ and $\\mathbf h_k$ be for the $k$th iteration. In general, the $kth$ state is generrated as:\n", | |
| "\n", | |
| "<b>Iteration</b> $k$:\n", | |
| "\n", | |
| "$$\\mathbf v\\_{k-1} \\Rightarrow p(\\mathbf h\\_{k-1}|\\mathbf v\\_{k-1})\\Rightarrow \\mathbf h\\_{k-1}\\Rightarrow p(\\mathbf v\\_{k}|\\mathbf h\\_{k-1})\\Rightarrow \\mathbf v_k$$" | |
| ], | |
| "metadata": { | |
| "id": "rB104YjnuFmY" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Objective: Contrastive Divergence (CD-K)\n", | |
| "\n", | |
| "The update of the weight matrix is done during the Contrastive Divergence step. \n", | |
| "\n", | |
| "Vectors v0 and vk are used to calculate the activation probabilities for hidden values h0 and hk. The difference between the outer products of those probabilities with input vectors v0 and vk results in the update matrix:\n", | |
| "\n", | |
| "$$\\Delta \\mathbf W_k =\\mathbf v_k \\otimes \\mathbf h_k - \\mathbf v\\_{k-1} \\otimes \\mathbf h\\_{k-1}$$\n", | |
| "\n", | |
| "Contrastive Divergence is actually matrix of values that is computed and used to adjust values of the $\\mathbf W$ matrix. Changing $\\mathbf W$ incrementally leads to training of the $\\mathbf W$ values. Then, on each step (epoch), $\\mathbf W$ is updated using the following:\n", | |
| "\n", | |
| "$$\\mathbf W_k = \\mathbf W\\_{k-1} + \\alpha \\* \\Delta \\mathbf W_k$$" | |
| ], | |
| "metadata": { | |
| "id": "ImXyzt5juUVi" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Reconstruction steps (rebuiding)\n", | |
| "\n", | |
| "<ul>\n", | |
| " <li> Get one data point from data set, like <i>x</i>, and pass it through the following steps:</li>\n", | |
| "\n", | |
| "<b>Iteration</b> $k=1$:\n", | |
| "\n", | |
| "Sampling (starting with input image)\n", | |
| "$$\\mathbf x = \\mathbf v\\_0 \\Rightarrow p(\\mathbf h\\_0|\\mathbf v\\_0)\\Rightarrow \\mathbf h\\_0 \\Rightarrow p(\\mathbf v\\_1|\\mathbf h\\_0)\\Rightarrow \\mathbf v\\_1$$\\\n", | |
| "followed by the CD-k step\n", | |
| "$$\\Delta \\mathbf W\\_1 =\\mathbf v\\_1 \\otimes \\mathbf h\\_1 - \\mathbf v\\_{0} \\otimes \\mathbf h\\_{0}$$\\\n", | |
| "$$\\mathbf W\\_1 = \\mathbf W\\_{0} + \\alpha \\* \\Delta \\mathbf W\\_1$$\n", | |
| "\n", | |
| "<li> $\\mathbf v_1$ is the reconstruction of $\\mathbf x$ sent to the next iteration).</li>\n", | |
| "\n", | |
| "<b>Iteration</b> $k=2$:\n", | |
| "\n", | |
| "Sampling (starting with $\\mathbf v\\_1$)\n", | |
| "\n", | |
| "$$\\mathbf v\\_1 \\Rightarrow p(\\mathbf h\\_1|\\mathbf v\\_1)\\Rightarrow \\mathbf h\\_1\\Rightarrow p(\\mathbf v\\_2|\\mathbf h\\_1)\\Rightarrow \\mathbf v\\_2$$\n", | |
| "\n", | |
| "followed by the CD-k step\n", | |
| "$$\\Delta \\mathbf W\\_2 =\\mathbf v\\_2 \\otimes \\mathbf h\\_2 - \\mathbf v\\_{1} \\otimes \\mathbf h\\_{1}$$\\\n", | |
| "$$\\mathbf W\\_2 = \\mathbf W\\_{1} + \\alpha \\* \\Delta \\mathbf W\\_2$$\n", | |
| "\n", | |
| "<li> $\\mathbf v_2$ is the reconstruction of $\\mathbf v_1$ sent to the next iteration).</li> \n", | |
| "\n", | |
| "<b>Iteration</b> $k=K$:\n", | |
| "\n", | |
| "Sampling (starting with $\\mathbf v\\_{K-1}$)\n", | |
| "\n", | |
| "$$\\mathbf v\\_{K-1} \\Rightarrow p(\\mathbf h\\_{K-1}|\\mathbf v\\_{K-1})\\Rightarrow \\mathbf h\\_{K-1}\\Rightarrow p(\\mathbf v_K|\\mathbf h\\_{K-1})\\Rightarrow \\mathbf v_K$$\n", | |
| "\n", | |
| "followed by the CD-k step\n", | |
| "$$\\Delta \\mathbf W_K =\\mathbf v_K \\otimes \\mathbf h_K - \\mathbf v\\_{K-1} \\otimes \\mathbf h\\_{K-1}$$\\\n", | |
| "$$\\mathbf W_K = \\mathbf W\\_{K-1} + \\alpha \\* \\Delta \\mathbf W_K$$" | |
| ], | |
| "metadata": { | |
| "id": "MYiyKXlXvFYV" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "" | |
| ], | |
| "metadata": { | |
| "id": "ocCSZsApvwpP" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Cost or loss\n", | |
| "\n", | |
| "Although the loss or cost is not the objective of updating parameter, it turns out that a minimum loss for a stochastic training sample can end the CD-K for that training sample early, which can be useful for low-configuration training machines to \"finish the job first\".\n", | |
| "\n", | |
| "For a full training job, it is not recommended to switch to a training sample early because of a minimum loss. The reason is that this will prevent the net from continuing to learn more samples.\n", | |
| "\n", | |
| "Check `early_stopping_patience` in `class RBM` .\n" | |
| ], | |
| "metadata": { | |
| "id": "3PxaK3O2v4n3" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 44, | |
| "metadata": { | |
| "id": "PrzhYONfOjVy", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "6a21409c-1e46-4875-ae8f-3a54b4b82bdd" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "dim_hidden 3413\n", | |
| "dim_visible 4096\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "BATCH_SZ = 200\n", | |
| "train_ds = tf.data.Dataset.from_tensor_slices(x_train).shuffle(1024).batch(BATCH_SZ)\n", | |
| "\n", | |
| "class RBM:\n", | |
| " def __init__(self, dim_visible, dim_hidden, early_stopping_patience):\n", | |
| " \"\"\"_summary_\n", | |
| "\n", | |
| " Args:\n", | |
| " dim_visible (int): Visible layer dimension, input feature\n", | |
| " dim_hidden (int): Hidden layer dimension, independent events that are inferred from feature units.\n", | |
| " early_stopping_patience (int): When set, if the current loss cannot beat the [best_loss] [early_stopping_patience] times, \n", | |
| " the CD-K won't continue train on the current stochastic training sample.\n", | |
| " \"\"\"\n", | |
| " self.history = {}\n", | |
| " self.history[\"loss\"] = []\n", | |
| " self.history[\"best_loss\"] = None\n", | |
| "\n", | |
| " self.early_stopping_patience = early_stopping_patience\n", | |
| " self.best_loss_count = 0\n", | |
| "\n", | |
| " # Interceptors (bias)\n", | |
| " self.bias_visible = tf.Variable(tf.zeros([dim_visible]), tf.float32)\n", | |
| " self.bias_hidden = tf.Variable(tf.zeros([dim_hidden]), tf.float32)\n", | |
| "\n", | |
| " # Coofficients (weights)\n", | |
| " ## We define the parameters in the network, the first dimention is connection counts, the 2nd is hidden nodes count.\n", | |
| " self.theta = tf.Variable(tf.zeros([dim_visible, dim_hidden]), tf.float32)\n", | |
| "\n", | |
| " def forwards(self, visible_state, theta, bias_hidden, train=True):\n", | |
| " z = tf.matmul([visible_state], theta) + bias_hidden\n", | |
| "\n", | |
| " proba_hidden = tf.nn.sigmoid(z)\n", | |
| "\n", | |
| " hidden_state = tf.nn.relu(\n", | |
| " tf.sign(proba_hidden - tf.random.uniform(tf.shape(proba_hidden))))\n", | |
| "\n", | |
| " return hidden_state if train == True else proba_hidden\n", | |
| "\n", | |
| " def backwards(self, hidden_state, theta, bias_visible, train=True):\n", | |
| " z = tf.matmul(hidden_state, tf.transpose(theta)) + bias_visible\n", | |
| "\n", | |
| " proba_visible = tf.nn.sigmoid(z)\n", | |
| "\n", | |
| " visible_state = tf.nn.relu(\n", | |
| " tf.sign(proba_visible - tf.random.uniform(tf.shape(proba_visible))))\n", | |
| "\n", | |
| " return visible_state[0] if train == True else proba_visible\n", | |
| "\n", | |
| " def loss_object(self, x0, x1):\n", | |
| " return tf.reduce_mean(tf.square(x0 - x1))\n", | |
| "\n", | |
| " def fit(self, epochs, K, train_ds, learning_rate):\n", | |
| " for epoch in range(epochs):\n", | |
| " for batch_num, Xs in enumerate(train_ds):\n", | |
| " for idx, x0 in enumerate(Xs):\n", | |
| " for k in range(K):\n", | |
| " x1, delta_W, delta_V, delta_H = self.train_step(x0)\n", | |
| " self.update(delta_W, delta_V, delta_H, learning_rate)\n", | |
| "\n", | |
| " still_train = self.measure(\n", | |
| " epoch, batch_num, k, K, x0, x1, idx == len(Xs)-1)\n", | |
| " \n", | |
| " if still_train == False:\n", | |
| " break\n", | |
| "\n", | |
| " def train_step(self, x):\n", | |
| " ##Gibbs sampling\n", | |
| " # Forward\n", | |
| " visible_state = x\n", | |
| " hidden_state = self.forwards(visible_state, self.theta, self.bias_hidden)\n", | |
| " visible_state_T = tf.transpose([visible_state])\n", | |
| "\n", | |
| " ## Backward\n", | |
| " rebuilt_visible_state = self.backwards(\n", | |
| " hidden_state, self.theta, self.bias_visible)\n", | |
| " rebuilt_hidden_state = self.forwards(\n", | |
| " rebuilt_visible_state, self.theta, self.bias_hidden)\n", | |
| " rebuilt_visible_state_T = tf.transpose([rebuilt_visible_state])\n", | |
| "\n", | |
| " # contrastive divergence，CD\n", | |
| " delta_W = tf.matmul(visible_state_T, hidden_state) - \\\n", | |
| " tf.matmul(rebuilt_visible_state_T, rebuilt_hidden_state)\n", | |
| " delta_V = visible_state - rebuilt_visible_state\n", | |
| " delta_H = hidden_state - rebuilt_hidden_state\n", | |
| "\n", | |
| " return rebuilt_visible_state, delta_W, delta_V, delta_H\n", | |
| "\n", | |
| " def update(self, delta_W, delta_V, delta_H, learning_rate):\n", | |
| " self.theta = self.theta + learning_rate * delta_W\n", | |
| " self.bias_visible = self.bias_visible + \\\n", | |
| " learning_rate * tf.reduce_mean(delta_V, 0)\n", | |
| " self.bias_hidden = self.bias_hidden + \\\n", | |
| " learning_rate * tf.reduce_mean(delta_H, 0)\n", | |
| "\n", | |
| " def measure(self, epoch, batch_num, k, K, x0, x1, print):\n", | |
| " def clean():\n", | |
| " sys.stdout.flush()\n", | |
| " clear_output(wait=True)\n", | |
| "\n", | |
| " loss = self.loss_object(x0, x1)\n", | |
| " self.history[\"loss\"].append(loss)\n", | |
| "\n", | |
| " if self.early_stopping_patience != None:\n", | |
| " # Remember the best loss and its parameters\n", | |
| " if self.history.get(\"best_loss\") is None or loss < self.history[\"best_loss\"]:\n", | |
| " self.history[\"best_history\"] = len(self.history[\"loss\"])-1\n", | |
| " self.history[\"best_loss\"] = loss\n", | |
| "\n", | |
| " self.history[\"best_theta\"] = tf.Variable(self.theta, tf.float32)\n", | |
| " self.history[\"best_bias_visible\"] = tf.Variable(\n", | |
| " self.bias_visible, tf.float32)\n", | |
| " self.history[\"best_bias_hidden\"] = tf.Variable(\n", | |
| " self.bias_hidden, tf.float32)\n", | |
| "\n", | |
| " self.best_loss_count = 1\n", | |
| " else:\n", | |
| " if self.best_loss_count >= self.early_stopping_patience:\n", | |
| " sys.stdout.write(\n", | |
| " f\"loss: {loss:.3f}, best_loss: {self.history['best_loss']}, best_loss_count: {self.best_loss_count}, early_stopping_patience: {self.early_stopping_patience}\\n\")\n", | |
| " self.best_loss_count = 1\n", | |
| " clean()\n", | |
| " return False\n", | |
| " else:\n", | |
| " self.best_loss_count = self.best_loss_count + 1\n", | |
| "\n", | |
| " if print == True:\n", | |
| " sys.stdout.write(\n", | |
| " f\"epoch: {epoch}, batch_num: {batch_num}, k: {k}/{K}, loss: {loss:.3f}, best_loss: {'--' if self.history.get('best_loss') is None else self.history['best_loss']}\\n\")\n", | |
| "\n", | |
| " clean()\n", | |
| " return True\n", | |
| "\n", | |
| " def rebuild(self, image, image_shape):\n", | |
| " image = np.array(image.convert('I').resize(\n", | |
| " image_shape)).ravel().reshape((1, -1))/255.0\n", | |
| " image = tf.cast(image, dtype=tf.float32)\n", | |
| "\n", | |
| " theta, bias_hidden, bias_visible = (self.theta, self.bias_hidden, self.bias_visible) if self.early_stopping_patience is None else (\n", | |
| " self.history[\"best_theta\"], self.history[\"best_bias_hidden\"], self.history[\"best_bias_visible\"])\n", | |
| "\n", | |
| " hidden_state = self.forwards(image, theta, bias_hidden, train=False)\n", | |
| " hidden_state = tf.round(hidden_state)\n", | |
| " rebuilt_visible_state = self.backwards(\n", | |
| " hidden_state, theta, bias_visible, train=False)\n", | |
| "\n", | |
| " rebuilt_visible_state = Image.fromarray(tile_raster_images(\n", | |
| " X=rebuilt_visible_state.numpy(),\n", | |
| " img_shape=image_shape,\n", | |
| " tile_shape=(1, 1),\n", | |
| " tile_spacing=(1, 1)))\n", | |
| "\n", | |
| " return rebuilt_visible_state\n", | |
| "\n", | |
| "\n", | |
| "dim_hidden = int(dim_visible*5.0/6.0)\n", | |
| "print(\"dim_hidden\", dim_hidden)\n", | |
| "print(\"dim_visible\", dim_visible)\n", | |
| "\n", | |
| "restricted_boltzmann = RBM(dim_visible=dim_visible, dim_hidden=dim_hidden, early_stopping_patience=None)\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## 🏃♂️ Execute training" | |
| ], | |
| "metadata": { | |
| "id": "thIKPkPE1Chw" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 45, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "9RM31qMxvQKh", | |
| "outputId": "95624075-a99d-43aa-98f4-6a3d5a7bed40" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\u001b[92mTraining done. used 0:25:03.359212\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "process_in_duration(\"Training done.\", lambda: restricted_boltzmann.fit(epochs=50, K=2, train_ds=train_ds, learning_rate=1e-8))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## 📉 Post-training\n", | |
| "\n", | |
| "- Plot loss\n", | |
| "- Do one prediction to generate objective" | |
| ], | |
| "metadata": { | |
| "id": "_Whc8yMY1Xet" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 46, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 266 | |
| }, | |
| "id": "NDaN_7RxYhAt", | |
| "outputId": "6ff522a8-19aa-4223-dee3-fa4d2d8655f2" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ], | |
| "source": [ | |
| "plot_history(restricted_boltzmann.history)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 47, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 666 | |
| }, | |
| "id": "hBsCa2IdzMCl", | |
| "outputId": "d09cd6ec-a8a8-430d-c7cb-bb032b5badc8" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 360x360 with 3 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 360x360 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ], | |
| "source": [ | |
| "gen_predict(test_image, noisied_image, lambda: restricted_boltzmann.rebuild(noisied_image, image_shape))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## 🏁 Generate image from random noised image source" | |
| ], | |
| "metadata": { | |
| "id": "cQQJ-6SB0aYt" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "random_img, random_noisied_img = generate_random_image(low=0, high=256, size=(image_shape[0],image_shape[1]))\n", | |
| "gen_predict(random_img, random_noisied_img, lambda: restricted_boltzmann.rebuild(random_noisied_img, image_shape))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 666 | |
| }, | |
| "id": "H7_hEz0Q0WaW", | |
| "outputId": "213fb4e9-ea26-4696-c927-a5c97904a1ed" | |
| }, | |
| "execution_count": 48, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 360x360 with 3 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 360x360 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "_3-RbP9vi1sl" | |
| }, | |
| "source": [ | |
| " 😜 Bouns: use classical [Autoencoder](https://www.cs.toronto.edu/~hinton/science.pdf?)\n", | |
| "=====\n", | |
| "" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "We can use classical [Autoencoder](https://www.cs.toronto.edu/~hinton/science.pdf?) to build a model for the same purpose, it is not part of this notebook, later there will be a medium post to talk about it.\n", | |
| "\n", | |
| "Here just leave the architecture." | |
| ], | |
| "metadata": { | |
| "id": "xmhIMKHl0kmz" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 61, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "dSlT4MKAi1sl", | |
| "outputId": "b997f604-75ef-41f0-9f15-117fe4a47568" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "hidden_layers [4096, 2048, 1365, 1024, 819, 682, 585, 512, 455, 409, 372, 341, 315, 292, 273, 256, 240, 227, 215, 204, 195, 186, 178, 170, 163, 157, 151, 146, 141, 136, 132, 128, 124, 120, 117, 113, 110, 107, 105, 102, 99, 97, 95, 93, 91, 89, 87, 85, 83, 81, 80, 78, 77, 75, 74, 73, 71, 70, 69, 68, 67, 66, 65, 64, 63, 62, 61, 60, 59, 58, 57, 56, 56, 55, 54, 53, 53, 52, 51, 51, 50, 49, 49, 48, 48, 47, 47, 46, 46, 45, 45, 44, 44, 43, 43, 42, 42, 41, 41, 40, 40, 40, 39, 39, 39, 38, 38, 37, 37, 37, 36, 36, 36, 35, 35, 35, 35, 34, 34]\n", | |
| "Model: \"model_9\"\n", | |
| "_________________________________________________________________\n", | |
| " Layer (type) Output Shape Param # \n", | |
| "=================================================================\n", | |
| " input_10 (InputLayer) [(None, 4096)] 0 \n", | |
| " \n", | |
| " dense_1131 (Dense) (None, 4096) 16781312 \n", | |
| " \n", | |
| " dense_1132 (Dense) (None, 2048) 8390656 \n", | |
| " \n", | |
| " dense_1133 (Dense) (None, 1365) 2796885 \n", | |
| " \n", | |
| " dense_1134 (Dense) (None, 1024) 1398784 \n", | |
| " \n", | |
| " dense_1135 (Dense) (None, 819) 839475 \n", | |
| " \n", | |
| " dense_1136 (Dense) (None, 682) 559240 \n", | |
| " \n", | |
| " dense_1137 (Dense) (None, 585) 399555 \n", | |
| " \n", | |
| " dense_1138 (Dense) (None, 512) 300032 \n", | |
| " \n", | |
| " dense_1139 (Dense) (None, 455) 233415 \n", | |
| " \n", | |
| " dense_1140 (Dense) (None, 409) 186504 \n", | |
| " \n", | |
| " dense_1141 (Dense) (None, 372) 152520 \n", | |
| " \n", | |
| " dense_1142 (Dense) (None, 341) 127193 \n", | |
| " \n", | |
| " dense_1143 (Dense) (None, 315) 107730 \n", | |
| " \n", | |
| " dense_1144 (Dense) (None, 292) 92272 \n", | |
| " \n", | |
| " dense_1145 (Dense) (None, 273) 79989 \n", | |
| " \n", | |
| " dense_1146 (Dense) (None, 256) 70144 \n", | |
| " \n", | |
| " dense_1147 (Dense) (None, 240) 61680 \n", | |
| " \n", | |
| " dense_1148 (Dense) (None, 227) 54707 \n", | |
| " \n", | |
| " dense_1149 (Dense) (None, 215) 49020 \n", | |
| " \n", | |
| " dense_1150 (Dense) (None, 204) 44064 \n", | |
| " \n", | |
| " dense_1151 (Dense) (None, 195) 39975 \n", | |
| " \n", | |
| " dense_1152 (Dense) (None, 186) 36456 \n", | |
| " \n", | |
| " dense_1153 (Dense) (None, 178) 33286 \n", | |
| " \n", | |
| " dense_1154 (Dense) (None, 170) 30430 \n", | |
| " \n", | |
| " dense_1155 (Dense) (None, 163) 27873 \n", | |
| " \n", | |
| " dense_1156 (Dense) (None, 157) 25748 \n", | |
| " \n", | |
| " dense_1157 (Dense) (None, 151) 23858 \n", | |
| " \n", | |
| " dense_1158 (Dense) (None, 146) 22192 \n", | |
| " \n", | |
| " dense_1159 (Dense) (None, 141) 20727 \n", | |
| " \n", | |
| " dense_1160 (Dense) (None, 136) 19312 \n", | |
| " \n", | |
| " dense_1161 (Dense) (None, 132) 18084 \n", | |
| " \n", | |
| " dense_1162 (Dense) (None, 128) 17024 \n", | |
| " \n", | |
| " dense_1163 (Dense) (None, 124) 15996 \n", | |
| " \n", | |
| " dense_1164 (Dense) (None, 120) 15000 \n", | |
| " \n", | |
| " dense_1165 (Dense) (None, 117) 14157 \n", | |
| " \n", | |
| " dense_1166 (Dense) (None, 113) 13334 \n", | |
| " \n", | |
| " dense_1167 (Dense) (None, 110) 12540 \n", | |
| " \n", | |
| " dense_1168 (Dense) (None, 107) 11877 \n", | |
| " \n", | |
| " dense_1169 (Dense) (None, 105) 11340 \n", | |
| " \n", | |
| " dense_1170 (Dense) (None, 102) 10812 \n", | |
| " \n", | |
| " dense_1171 (Dense) (None, 99) 10197 \n", | |
| " \n", | |
| " dense_1172 (Dense) (None, 97) 9700 \n", | |
| " \n", | |
| " dense_1173 (Dense) (None, 95) 9310 \n", | |
| " \n", | |
| " dense_1174 (Dense) (None, 93) 8928 \n", | |
| " \n", | |
| " dense_1175 (Dense) (None, 91) 8554 \n", | |
| " \n", | |
| " dense_1176 (Dense) (None, 89) 8188 \n", | |
| " \n", | |
| " dense_1177 (Dense) (None, 87) 7830 \n", | |
| " \n", | |
| " dense_1178 (Dense) (None, 85) 7480 \n", | |
| " \n", | |
| " dense_1179 (Dense) (None, 83) 7138 \n", | |
| " \n", | |
| " dense_1180 (Dense) (None, 81) 6804 \n", | |
| " \n", | |
| " dense_1181 (Dense) (None, 80) 6560 \n", | |
| " \n", | |
| " dense_1182 (Dense) (None, 78) 6318 \n", | |
| " \n", | |
| " dense_1183 (Dense) (None, 77) 6083 \n", | |
| " \n", | |
| " dense_1184 (Dense) (None, 75) 5850 \n", | |
| " \n", | |
| " dense_1185 (Dense) (None, 74) 5624 \n", | |
| " \n", | |
| " dense_1186 (Dense) (None, 73) 5475 \n", | |
| " \n", | |
| " dense_1187 (Dense) (None, 71) 5254 \n", | |
| " \n", | |
| " dense_1188 (Dense) (None, 70) 5040 \n", | |
| " \n", | |
| " dense_1189 (Dense) (None, 69) 4899 \n", | |
| " \n", | |
| " dense_1190 (Dense) (None, 68) 4760 \n", | |
| " \n", | |
| " dense_1191 (Dense) (None, 67) 4623 \n", | |
| " \n", | |
| " dense_1192 (Dense) (None, 66) 4488 \n", | |
| " \n", | |
| " dense_1193 (Dense) (None, 65) 4355 \n", | |
| " \n", | |
| " dense_1194 (Dense) (None, 64) 4224 \n", | |
| " \n", | |
| " dense_1195 (Dense) (None, 63) 4095 \n", | |
| " \n", | |
| " dense_1196 (Dense) (None, 62) 3968 \n", | |
| " \n", | |
| " dense_1197 (Dense) (None, 61) 3843 \n", | |
| " \n", | |
| " dense_1198 (Dense) (None, 60) 3720 \n", | |
| " \n", | |
| " dense_1199 (Dense) (None, 59) 3599 \n", | |
| " \n", | |
| " dense_1200 (Dense) (None, 58) 3480 \n", | |
| " \n", | |
| " dense_1201 (Dense) (None, 57) 3363 \n", | |
| " \n", | |
| " dense_1202 (Dense) (None, 56) 3248 \n", | |
| " \n", | |
| " dense_1203 (Dense) (None, 56) 3192 \n", | |
| " \n", | |
| " dense_1204 (Dense) (None, 55) 3135 \n", | |
| " \n", | |
| " dense_1205 (Dense) (None, 54) 3024 \n", | |
| " \n", | |
| " dense_1206 (Dense) (None, 53) 2915 \n", | |
| " \n", | |
| " dense_1207 (Dense) (None, 53) 2862 \n", | |
| " \n", | |
| " dense_1208 (Dense) (None, 52) 2808 \n", | |
| " \n", | |
| " dense_1209 (Dense) (None, 51) 2703 \n", | |
| " \n", | |
| " dense_1210 (Dense) (None, 51) 2652 \n", | |
| " \n", | |
| " dense_1211 (Dense) (None, 50) 2600 \n", | |
| " \n", | |
| " dense_1212 (Dense) (None, 49) 2499 \n", | |
| " \n", | |
| " dense_1213 (Dense) (None, 49) 2450 \n", | |
| " \n", | |
| " dense_1214 (Dense) (None, 48) 2400 \n", | |
| " \n", | |
| " dense_1215 (Dense) (None, 48) 2352 \n", | |
| " \n", | |
| " dense_1216 (Dense) (None, 47) 2303 \n", | |
| " \n", | |
| " dense_1217 (Dense) (None, 47) 2256 \n", | |
| " \n", | |
| " dense_1218 (Dense) (None, 46) 2208 \n", | |
| " \n", | |
| " dense_1219 (Dense) (None, 46) 2162 \n", | |
| " \n", | |
| " dense_1220 (Dense) (None, 45) 2115 \n", | |
| " \n", | |
| " dense_1221 (Dense) (None, 45) 2070 \n", | |
| " \n", | |
| " dense_1222 (Dense) (None, 44) 2024 \n", | |
| " \n", | |
| " dense_1223 (Dense) (None, 44) 1980 \n", | |
| " \n", | |
| " dense_1224 (Dense) (None, 43) 1935 \n", | |
| " \n", | |
| " dense_1225 (Dense) (None, 43) 1892 \n", | |
| " \n", | |
| " dense_1226 (Dense) (None, 42) 1848 \n", | |
| " \n", | |
| " dense_1227 (Dense) (None, 42) 1806 \n", | |
| " \n", | |
| " dense_1228 (Dense) (None, 41) 1763 \n", | |
| " \n", | |
| " dense_1229 (Dense) (None, 41) 1722 \n", | |
| " \n", | |
| " dense_1230 (Dense) (None, 40) 1680 \n", | |
| " \n", | |
| " dense_1231 (Dense) (None, 40) 1640 \n", | |
| " \n", | |
| " dense_1232 (Dense) (None, 40) 1640 \n", | |
| " \n", | |
| " dense_1233 (Dense) (None, 39) 1599 \n", | |
| " \n", | |
| " dense_1234 (Dense) (None, 39) 1560 \n", | |
| " \n", | |
| " dense_1235 (Dense) (None, 39) 1560 \n", | |
| " \n", | |
| " dense_1236 (Dense) (None, 38) 1520 \n", | |
| " \n", | |
| " dense_1237 (Dense) (None, 38) 1482 \n", | |
| " \n", | |
| " dense_1238 (Dense) (None, 37) 1443 \n", | |
| " \n", | |
| " dense_1239 (Dense) (None, 37) 1406 \n", | |
| " \n", | |
| " dense_1240 (Dense) (None, 37) 1406 \n", | |
| " \n", | |
| " dense_1241 (Dense) (None, 36) 1368 \n", | |
| " \n", | |
| " dense_1242 (Dense) (None, 36) 1332 \n", | |
| " \n", | |
| " dense_1243 (Dense) (None, 36) 1332 \n", | |
| " \n", | |
| " dense_1244 (Dense) (None, 35) 1295 \n", | |
| " \n", | |
| " dense_1245 (Dense) (None, 35) 1260 \n", | |
| " \n", | |
| " dense_1246 (Dense) (None, 35) 1260 \n", | |
| " \n", | |
| " dense_1247 (Dense) (None, 35) 1260 \n", | |
| " \n", | |
| " dense_1248 (Dense) (None, 34) 1224 \n", | |
| " \n", | |
| " dense_1249 (Dense) (None, 34) 1190 \n", | |
| " \n", | |
| " dense_1250 (Dense) (None, 20) 700 \n", | |
| " \n", | |
| " dense_1251 (Dense) (None, 34) 714 \n", | |
| " \n", | |
| " dense_1252 (Dense) (None, 34) 1190 \n", | |
| " \n", | |
| " dense_1253 (Dense) (None, 35) 1225 \n", | |
| " \n", | |
| " dense_1254 (Dense) (None, 35) 1260 \n", | |
| " \n", | |
| " dense_1255 (Dense) (None, 35) 1260 \n", | |
| " \n", | |
| " dense_1256 (Dense) (None, 35) 1260 \n", | |
| " \n", | |
| " dense_1257 (Dense) (None, 36) 1296 \n", | |
| " \n", | |
| " dense_1258 (Dense) (None, 36) 1332 \n", | |
| " \n", | |
| " dense_1259 (Dense) (None, 36) 1332 \n", | |
| " \n", | |
| " dense_1260 (Dense) (None, 37) 1369 \n", | |
| " \n", | |
| " dense_1261 (Dense) (None, 37) 1406 \n", | |
| " \n", | |
| " dense_1262 (Dense) (None, 37) 1406 \n", | |
| " \n", | |
| " dense_1263 (Dense) (None, 38) 1444 \n", | |
| " \n", | |
| " dense_1264 (Dense) (None, 38) 1482 \n", | |
| " \n", | |
| " dense_1265 (Dense) (None, 39) 1521 \n", | |
| " \n", | |
| " dense_1266 (Dense) (None, 39) 1560 \n", | |
| " \n", | |
| " dense_1267 (Dense) (None, 39) 1560 \n", | |
| " \n", | |
| " dense_1268 (Dense) (None, 40) 1600 \n", | |
| " \n", | |
| " dense_1269 (Dense) (None, 40) 1640 \n", | |
| " \n", | |
| " dense_1270 (Dense) (None, 40) 1640 \n", | |
| " \n", | |
| " dense_1271 (Dense) (None, 41) 1681 \n", | |
| " \n", | |
| " dense_1272 (Dense) (None, 41) 1722 \n", | |
| " \n", | |
| " dense_1273 (Dense) (None, 42) 1764 \n", | |
| " \n", | |
| " dense_1274 (Dense) (None, 42) 1806 \n", | |
| " \n", | |
| " dense_1275 (Dense) (None, 43) 1849 \n", | |
| " \n", | |
| " dense_1276 (Dense) (None, 43) 1892 \n", | |
| " \n", | |
| " dense_1277 (Dense) (None, 44) 1936 \n", | |
| " \n", | |
| " dense_1278 (Dense) (None, 44) 1980 \n", | |
| " \n", | |
| " dense_1279 (Dense) (None, 45) 2025 \n", | |
| " \n", | |
| " dense_1280 (Dense) (None, 45) 2070 \n", | |
| " \n", | |
| " dense_1281 (Dense) (None, 46) 2116 \n", | |
| " \n", | |
| " dense_1282 (Dense) (None, 46) 2162 \n", | |
| " \n", | |
| " dense_1283 (Dense) (None, 47) 2209 \n", | |
| " \n", | |
| " dense_1284 (Dense) (None, 47) 2256 \n", | |
| " \n", | |
| " dense_1285 (Dense) (None, 48) 2304 \n", | |
| " \n", | |
| " dense_1286 (Dense) (None, 48) 2352 \n", | |
| " \n", | |
| " dense_1287 (Dense) (None, 49) 2401 \n", | |
| " \n", | |
| " dense_1288 (Dense) (None, 49) 2450 \n", | |
| " \n", | |
| " dense_1289 (Dense) (None, 50) 2500 \n", | |
| " \n", | |
| " dense_1290 (Dense) (None, 51) 2601 \n", | |
| " \n", | |
| " dense_1291 (Dense) (None, 51) 2652 \n", | |
| " \n", | |
| " dense_1292 (Dense) (None, 52) 2704 \n", | |
| " \n", | |
| " dense_1293 (Dense) (None, 53) 2809 \n", | |
| " \n", | |
| " dense_1294 (Dense) (None, 53) 2862 \n", | |
| " \n", | |
| " dense_1295 (Dense) (None, 54) 2916 \n", | |
| " \n", | |
| " dense_1296 (Dense) (None, 55) 3025 \n", | |
| " \n", | |
| " dense_1297 (Dense) (None, 56) 3136 \n", | |
| " \n", | |
| " dense_1298 (Dense) (None, 56) 3192 \n", | |
| " \n", | |
| " dense_1299 (Dense) (None, 57) 3249 \n", | |
| " \n", | |
| " dense_1300 (Dense) (None, 58) 3364 \n", | |
| " \n", | |
| " dense_1301 (Dense) (None, 59) 3481 \n", | |
| " \n", | |
| " dense_1302 (Dense) (None, 60) 3600 \n", | |
| " \n", | |
| " dense_1303 (Dense) (None, 61) 3721 \n", | |
| " \n", | |
| " dense_1304 (Dense) (None, 62) 3844 \n", | |
| " \n", | |
| " dense_1305 (Dense) (None, 63) 3969 \n", | |
| " \n", | |
| " dense_1306 (Dense) (None, 64) 4096 \n", | |
| " \n", | |
| " dense_1307 (Dense) (None, 65) 4225 \n", | |
| " \n", | |
| " dense_1308 (Dense) (None, 66) 4356 \n", | |
| " \n", | |
| " dense_1309 (Dense) (None, 67) 4489 \n", | |
| " \n", | |
| " dense_1310 (Dense) (None, 68) 4624 \n", | |
| " \n", | |
| " dense_1311 (Dense) (None, 69) 4761 \n", | |
| " \n", | |
| " dense_1312 (Dense) (None, 70) 4900 \n", | |
| " \n", | |
| " dense_1313 (Dense) (None, 71) 5041 \n", | |
| " \n", | |
| " dense_1314 (Dense) (None, 73) 5256 \n", | |
| " \n", | |
| " dense_1315 (Dense) (None, 74) 5476 \n", | |
| " \n", | |
| " dense_1316 (Dense) (None, 75) 5625 \n", | |
| " \n", | |
| " dense_1317 (Dense) (None, 77) 5852 \n", | |
| " \n", | |
| " dense_1318 (Dense) (None, 78) 6084 \n", | |
| " \n", | |
| " dense_1319 (Dense) (None, 80) 6320 \n", | |
| " \n", | |
| " dense_1320 (Dense) (None, 81) 6561 \n", | |
| " \n", | |
| " dense_1321 (Dense) (None, 83) 6806 \n", | |
| " \n", | |
| " dense_1322 (Dense) (None, 85) 7140 \n", | |
| " \n", | |
| " dense_1323 (Dense) (None, 87) 7482 \n", | |
| " \n", | |
| " dense_1324 (Dense) (None, 89) 7832 \n", | |
| " \n", | |
| " dense_1325 (Dense) (None, 91) 8190 \n", | |
| " \n", | |
| " dense_1326 (Dense) (None, 93) 8556 \n", | |
| " \n", | |
| " dense_1327 (Dense) (None, 95) 8930 \n", | |
| " \n", | |
| " dense_1328 (Dense) (None, 97) 9312 \n", | |
| " \n", | |
| " dense_1329 (Dense) (None, 99) 9702 \n", | |
| " \n", | |
| " dense_1330 (Dense) (None, 102) 10200 \n", | |
| " \n", | |
| " dense_1331 (Dense) (None, 105) 10815 \n", | |
| " \n", | |
| " dense_1332 (Dense) (None, 107) 11342 \n", | |
| " \n", | |
| " dense_1333 (Dense) (None, 110) 11880 \n", | |
| " \n", | |
| " dense_1334 (Dense) (None, 113) 12543 \n", | |
| " \n", | |
| " dense_1335 (Dense) (None, 117) 13338 \n", | |
| " \n", | |
| " dense_1336 (Dense) (None, 120) 14160 \n", | |
| " \n", | |
| " dense_1337 (Dense) (None, 124) 15004 \n", | |
| " \n", | |
| " dense_1338 (Dense) (None, 128) 16000 \n", | |
| " \n", | |
| " dense_1339 (Dense) (None, 132) 17028 \n", | |
| " \n", | |
| " dense_1340 (Dense) (None, 136) 18088 \n", | |
| " \n", | |
| " dense_1341 (Dense) (None, 141) 19317 \n", | |
| " \n", | |
| " dense_1342 (Dense) (None, 146) 20732 \n", | |
| " \n", | |
| " dense_1343 (Dense) (None, 151) 22197 \n", | |
| " \n", | |
| " dense_1344 (Dense) (None, 157) 23864 \n", | |
| " \n", | |
| " dense_1345 (Dense) (None, 163) 25754 \n", | |
| " \n", | |
| " dense_1346 (Dense) (None, 170) 27880 \n", | |
| " \n", | |
| " dense_1347 (Dense) (None, 178) 30438 \n", | |
| " \n", | |
| " dense_1348 (Dense) (None, 186) 33294 \n", | |
| " \n", | |
| " dense_1349 (Dense) (None, 195) 36465 \n", | |
| " \n", | |
| " dense_1350 (Dense) (None, 204) 39984 \n", | |
| " \n", | |
| " dense_1351 (Dense) (None, 215) 44075 \n", | |
| " \n", | |
| " dense_1352 (Dense) (None, 227) 49032 \n", | |
| " \n", | |
| " dense_1353 (Dense) (None, 240) 54720 \n", | |
| " \n", | |
| " dense_1354 (Dense) (None, 256) 61696 \n", | |
| " \n", | |
| " dense_1355 (Dense) (None, 273) 70161 \n", | |
| " \n", | |
| " dense_1356 (Dense) (None, 292) 80008 \n", | |
| " \n", | |
| " dense_1357 (Dense) (None, 315) 92295 \n", | |
| " \n", | |
| " dense_1358 (Dense) (None, 341) 107756 \n", | |
| " \n", | |
| " dense_1359 (Dense) (None, 372) 127224 \n", | |
| " \n", | |
| " dense_1360 (Dense) (None, 409) 152557 \n", | |
| " \n", | |
| " dense_1361 (Dense) (None, 455) 186550 \n", | |
| " \n", | |
| " dense_1362 (Dense) (None, 512) 233472 \n", | |
| " \n", | |
| " dense_1363 (Dense) (None, 585) 300105 \n", | |
| " \n", | |
| " dense_1364 (Dense) (None, 682) 399652 \n", | |
| " \n", | |
| " dense_1365 (Dense) (None, 819) 559377 \n", | |
| " \n", | |
| " dense_1366 (Dense) (None, 1024) 839680 \n", | |
| " \n", | |
| " dense_1367 (Dense) (None, 1365) 1399125 \n", | |
| " \n", | |
| " dense_1368 (Dense) (None, 2048) 2797568 \n", | |
| " \n", | |
| " dense_1369 (Dense) (None, 4096) 8392704 \n", | |
| " \n", | |
| "=================================================================\n", | |
| "Total params: 50,072,822\n", | |
| "Trainable params: 50,072,822\n", | |
| "Non-trainable params: 0\n", | |
| "_________________________________________________________________\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "BATCH_SZ = 200\n", | |
| "train_ds = tf.data.Dataset.from_tensor_slices((x_train, x_train)).shuffle(1024).batch(BATCH_SZ)\n", | |
| "\n", | |
| "class Autoencoder:\n", | |
| " def __init__(self, input_shape, hidden_layers, latent_units):\n", | |
| " self.history = None\n", | |
| "\n", | |
| " input = tf.keras.Input(shape=input_shape)\n", | |
| " x = input\n", | |
| "\n", | |
| " # Encoding\n", | |
| " for hidden_layer in hidden_layers:\n", | |
| " x = tf.keras.layers.Dense(\n", | |
| " hidden_layer, activation=tf.keras.activations.relu)(x)\n", | |
| "\n", | |
| " # Latent\n", | |
| " x = tf.keras.layers.Dense(latent_units)(x)\n", | |
| "\n", | |
| " # Decoding\n", | |
| " for i, hidden_layer in enumerate(reversed(hidden_layers)):\n", | |
| " activation = tf.keras.activations.relu if i != len(\n", | |
| " hidden_layers) - 1 else tf.keras.activations.sigmoid\n", | |
| " x = tf.keras.layers.Dense(\n", | |
| " hidden_layer, activation=activation)(x)\n", | |
| "\n", | |
| " self.model = tf.keras.Model(inputs=[input], outputs=[x])\n", | |
| " self.model.summary()\n", | |
| "\n", | |
| " def fit(self, epochs, train_ds, learning_rate):\n", | |
| " optimizer = tf.keras.optimizers.SGD(\n", | |
| " learning_rate=learning_rate, momentum=.9)\n", | |
| " loss = tf.keras.losses.BinaryCrossentropy()\n", | |
| " self.model.compile(loss=loss, optimizer=optimizer)\n", | |
| " self.history = self.model.fit(\n", | |
| " train_ds, epochs=epochs, verbose=1)\n", | |
| " self.history = self.history.history\n", | |
| "\n", | |
| " def rebuild(self, image, image_shape):\n", | |
| " image = np.array(image.convert('I').resize(\n", | |
| " image_shape)).ravel().reshape((1, -1))/255.0\n", | |
| " image = tf.cast(image, dtype=tf.float32)\n", | |
| "\n", | |
| " preds = auto_encoder.model.predict([image])\n", | |
| "\n", | |
| " image = Image.fromarray(tile_raster_images(\n", | |
| " X=np.asarray(preds),\n", | |
| " img_shape=image_shape,\n", | |
| " tile_shape=(1, 1),\n", | |
| " tile_spacing=(1, 1)))\n", | |
| "\n", | |
| " return image\n", | |
| "\n", | |
| "\n", | |
| "hidden_layers = [int(dim_visible/i) for i in range(2, 120)]\n", | |
| "hidden_layers = [dim_visible] + hidden_layers\n", | |
| "print(\"hidden_layers\", hidden_layers)\n", | |
| "autoencoder = Autoencoder((dim_visible,), hidden_layers, 20)\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## 🏃♂️ Execute training" | |
| ], | |
| "metadata": { | |
| "id": "CbQQuoxD1HPZ" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 62, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "e8-j0mjFi1sl", | |
| "outputId": "56380653-e757-490f-a715-d87fe0b48d8e" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Epoch 1/200\n", | |
| "15/15 [==============================] - 8s 40ms/step - loss: 0.6931\n", | |
| "Epoch 2/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6927\n", | |
| "Epoch 3/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6922\n", | |
| "Epoch 4/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6916\n", | |
| "Epoch 5/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6911\n", | |
| "Epoch 6/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6906\n", | |
| "Epoch 7/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6900\n", | |
| "Epoch 8/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6895\n", | |
| "Epoch 9/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6890\n", | |
| "Epoch 10/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6884\n", | |
| "Epoch 11/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6879\n", | |
| "Epoch 12/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6874\n", | |
| "Epoch 13/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6869\n", | |
| "Epoch 14/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6863\n", | |
| "Epoch 15/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6858\n", | |
| "Epoch 16/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6853\n", | |
| "Epoch 17/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6848\n", | |
| "Epoch 18/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6843\n", | |
| "Epoch 19/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6837\n", | |
| "Epoch 20/200\n", | |
| "15/15 [==============================] - 1s 36ms/step - loss: 0.6832\n", | |
| "Epoch 21/200\n", | |
| "15/15 [==============================] - 1s 36ms/step - loss: 0.6827\n", | |
| "Epoch 22/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6822\n", | |
| "Epoch 23/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6817\n", | |
| "Epoch 24/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6812\n", | |
| "Epoch 25/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6807\n", | |
| "Epoch 26/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6801\n", | |
| "Epoch 27/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6796\n", | |
| "Epoch 28/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6791\n", | |
| "Epoch 29/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6786\n", | |
| "Epoch 30/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6781\n", | |
| "Epoch 31/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6776\n", | |
| "Epoch 32/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6771\n", | |
| "Epoch 33/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6766\n", | |
| "Epoch 34/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6761\n", | |
| "Epoch 35/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6756\n", | |
| "Epoch 36/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6751\n", | |
| "Epoch 37/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6746\n", | |
| "Epoch 38/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6741\n", | |
| "Epoch 39/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6736\n", | |
| "Epoch 40/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6731\n", | |
| "Epoch 41/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6726\n", | |
| "Epoch 42/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6721\n", | |
| "Epoch 43/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6716\n", | |
| "Epoch 44/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6711\n", | |
| "Epoch 45/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6706\n", | |
| "Epoch 46/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6701\n", | |
| "Epoch 47/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6696\n", | |
| "Epoch 48/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6691\n", | |
| "Epoch 49/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6686\n", | |
| "Epoch 50/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6681\n", | |
| "Epoch 51/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6676\n", | |
| "Epoch 52/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6672\n", | |
| "Epoch 53/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6667\n", | |
| "Epoch 54/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6662\n", | |
| "Epoch 55/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6657\n", | |
| "Epoch 56/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6652\n", | |
| "Epoch 57/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6647\n", | |
| "Epoch 58/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6642\n", | |
| "Epoch 59/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6638\n", | |
| "Epoch 60/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6633\n", | |
| "Epoch 61/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6628\n", | |
| "Epoch 62/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6623\n", | |
| "Epoch 63/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6618\n", | |
| "Epoch 64/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6614\n", | |
| "Epoch 65/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6609\n", | |
| "Epoch 66/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6604\n", | |
| "Epoch 67/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6599\n", | |
| "Epoch 68/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6595\n", | |
| "Epoch 69/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6590\n", | |
| "Epoch 70/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6585\n", | |
| "Epoch 71/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6580\n", | |
| "Epoch 72/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6576\n", | |
| "Epoch 73/200\n", | |
| "15/15 [==============================] - 1s 43ms/step - loss: 0.6571\n", | |
| "Epoch 74/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6566\n", | |
| "Epoch 75/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6562\n", | |
| "Epoch 76/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6557\n", | |
| "Epoch 77/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6552\n", | |
| "Epoch 78/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6548\n", | |
| "Epoch 79/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6543\n", | |
| "Epoch 80/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6538\n", | |
| "Epoch 81/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6534\n", | |
| "Epoch 82/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6529\n", | |
| "Epoch 83/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6525\n", | |
| "Epoch 84/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6520\n", | |
| "Epoch 85/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6515\n", | |
| "Epoch 86/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6511\n", | |
| "Epoch 87/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6506\n", | |
| "Epoch 88/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6502\n", | |
| "Epoch 89/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6497\n", | |
| "Epoch 90/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6492\n", | |
| "Epoch 91/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6488\n", | |
| "Epoch 92/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6483\n", | |
| "Epoch 93/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6479\n", | |
| "Epoch 94/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6474\n", | |
| "Epoch 95/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6470\n", | |
| "Epoch 96/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6465\n", | |
| "Epoch 97/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6461\n", | |
| "Epoch 98/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6456\n", | |
| "Epoch 99/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6452\n", | |
| "Epoch 100/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6447\n", | |
| "Epoch 101/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6443\n", | |
| "Epoch 102/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6438\n", | |
| "Epoch 103/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6434\n", | |
| "Epoch 104/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6430\n", | |
| "Epoch 105/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6425\n", | |
| "Epoch 106/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6421\n", | |
| "Epoch 107/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6416\n", | |
| "Epoch 108/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6412\n", | |
| "Epoch 109/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6408\n", | |
| "Epoch 110/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6403\n", | |
| "Epoch 111/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6399\n", | |
| "Epoch 112/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6394\n", | |
| "Epoch 113/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6390\n", | |
| "Epoch 114/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6386\n", | |
| "Epoch 115/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6381\n", | |
| "Epoch 116/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6377\n", | |
| "Epoch 117/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6373\n", | |
| "Epoch 118/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6368\n", | |
| "Epoch 119/200\n", | |
| "15/15 [==============================] - 1s 34ms/step - loss: 0.6364\n", | |
| "Epoch 120/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6360\n", | |
| "Epoch 121/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6355\n", | |
| "Epoch 122/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6351\n", | |
| "Epoch 123/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6347\n", | |
| "Epoch 124/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6342\n", | |
| "Epoch 125/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6338\n", | |
| "Epoch 126/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6334\n", | |
| "Epoch 127/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6330\n", | |
| "Epoch 128/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6325\n", | |
| "Epoch 129/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6321\n", | |
| "Epoch 130/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6317\n", | |
| "Epoch 131/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6313\n", | |
| "Epoch 132/200\n", | |
| "15/15 [==============================] - 1s 35ms/step - loss: 0.6308\n", | |
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