{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "オリジナルの作成: 2016/05/15" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\tHiroshi TAKEMOTO\n", "\t(take.pwave@gmail.com)\n", "\t\n", "\t

SageでTheanoのDenosingオートエンコーダを試す

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参考サイト

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\t\n", "\t\tここでは、\n", "\t\t 人工知能に関する断創録\n", "\t\t のTheanoに関連する記事をSageのノートブックで実装し、Thenoの修得を試みます。\t\n", "\t

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\n", "\t\t今回は、TheanoのTutorialからDenoisingオートエンコーダの例を以下のページを参考にSageのノートブックで試してみます。\n", "\t\t

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前準備

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処理系をSageからPythonに変更

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\n", "\t\tSageでTheanoのtutorialのCNNを実行すると、TypeError: 'sage.rings.integer.Integer' object is not iterable\n", "\t\tのエラーになるため、今回もPythonを使用します。\t\n", "\t

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\n", "\t\tそこで、ノートブックの処理系をSageからPythonに切り替えます。上部の左から4つめのプルダウンメニューから\n", "\t\t「python」を選択してください。\n", "\t

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必要なライブラリのimport

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\n", "\t\t最初に、theanoを使うのに必要なライブラリをインポートします。\n", "\t

\n", "" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 必要なライブラリのインポート\n", "import six.moves.cPickle as pickle\n", "import gzip\n", "import timeit\n", "import time\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt \n", "%matplotlib inline\n", "\n", "import theano\n", "import theano.tensor as T\n", "from theano.tensor.shared_randomstreams import RandomStreams\n", "\n", "# これまで確認したlogistic_sgd.pyのLogisticRegressionをインポートする\n", "from logistic_sgd import LogisticRegression, load_data\n", "\n", "# 重みの可視化用\n", "from PIL import Image\n", "from utils import tile_raster_images" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "\n", "\t

オートエンコーダ (Autoencoder)

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\n", "\t\tオートエンコーダ(Autoencoder)は、日本語では自己符号化器と呼ばれています。\n", "\t

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\n", "\t\tニューラルネットワークの計算では、重みの初期値が収束に大きく影響を及ぼすことが知られています。\n", "\t\tオートエンコーダでは特徴を抽出しやすいような重みの初期値を、入力・隠れ層・出力から成る3層の\n", "\t\tニューらネットワークを使って入力と同じ出力を生成することで求めます。\n", "\t

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\n", "\t\tDeepLearning 0.1 Documentationの式では、真ん中の隠れ層の値yは、以下の様になります。\n", "$$\n", "\ty = s(W x + b)\n", "$$\t\t\n", "\t\tそして、出力層の値zをyを使って表すと、\n", "$$\n", "\tz = s(W' y + b')\n", "$$\t\t\n", "\t\tのようになります。xをyに変換するプロセスを符号化(encode)、yをzに変換するプロセスを復号化(decode)と呼びます。\n", "\t\tこれらを一つにまとめると以下の様になります。\n", "$$\n", "\tz = s( W'( s(W x + b) ) + b')\n", "$$\t\t\n", "\t

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\n", "\t\tTheano で Deep Learning <4> : Denoising オートエンコーダ\n", "\t\tからオートエンコーダの構成図を引用します。\n", "\t

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オートエンコーダの損失関数

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\n", "\t\tTheanoによる自己符号化器の実装\n", "\t\tのコメントに、入力xが[0,1]で正規化されていることを前提にしてとあるこの部分が大切です。\n", "\t\tオートエンコーダの損失関数は、交差エントロピー誤差関数が使えます。\n", "$$\n", "\tL_H(x, z) = - \\sum_{k=1}^d \\left (x_k log z_k + (1 - x_k) log( 1 - z_k) \\right )\n", "$$\t\t\n", "\t

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オートエンコーダの実装

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\n", "\t\tオートエンコーダとDenoisingオートエンコーダでコードを再利用できるように、\n", "\t\tオートエンコーダに入力をそのまま返すget_corrupted_inputメソッドを追加しました。\n", "\t\t

\n",
    "    def get_corrupted_input(self, input, corruption_level):\n",
    "        return input\t\t\t\n",
    "\t\t
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\n", "\t\t損失関数の値L(結果はミニバッチサイズのベクトル)は、get_hidden_valuesメソッドは隠れ層の出力yを計算し、get_reconstructed_inputメソッドは、\n", "\t\t出力層の出力zを使って以下の様に計算します。\n", "\t\t

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    "        tilde_x = self.get_corrupted_input(self.x, corruption_level)      \n",
    "        y = self.get_hidden_values(tilde_x)\n",
    "        z = self.get_reconstructed_input(y)\n",
    "        L = - T.sum(self.x * T.log(z) + (1 - self.x) * T.log(1 - z), axis=1)\n",
    "\t\t
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\n", "\t\tTheanoによる自己符号化器の実装\n", "\t\tの、損失関数計算の模式図がとても分かりやすいので、引用させて頂きます。\n", "\t

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\n", "" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Autoencoder(object):\n", " def __init__(self, numpy_rng, theano_rng=None,\n", " input=None,\n", " n_visible=784, n_hidden=500,\n", " W=None, bhid=None, bvis=None):\n", " self.n_visible = n_visible\n", " self.n_hidden = n_hidden\n", "\n", " if not theano_rng:\n", " theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))\n", "\n", " if not W:\n", " # 入力層と出力層の間の重み\n", " initial_W = np.asarray(\n", " numpy_rng.uniform(\n", " low=-4 * np.sqrt(6.0 / (n_hidden + n_visible)),\n", " high=4 * np.sqrt(6.0 / (n_hidden + n_visible)),\n", " size=(n_visible, n_hidden)\n", " ),\n", " dtype=theano.config.floatX\n", " )\n", " W = theano.shared(value=initial_W, name='W', borrow=True)\n", "\n", " if not bvis:\n", " # 入力層(visible)のユニットのバイアス\n", " bvis = theano.shared(\n", " value=np.zeros(n_visible, dtype=theano.config.floatX),\n", " borrow=True)\n", "\n", " if not bhid:\n", " # 隠れ層(hidden)のユニットのバイアス\n", " bhid = theano.shared(\n", " value=np.zeros(n_hidden, dtype=theano.config.floatX),\n", " name='b',\n", " borrow=True)\n", "\n", " # パラメータ\n", " self.W = W\n", " self.b = bhid\n", " self.W_prime = self.W.T\n", " self.b_prime = bvis\n", " self.params = [self.W, self.b, self.b_prime]\n", "\n", " self.theano_rng = theano_rng\n", "\n", " if input is None:\n", " self.x = T.dmatrix(name='input')\n", " else:\n", " self.x = input\n", "\n", " # Denoisingに合わせたメソッドで入力をそのまま返す\n", " def get_corrupted_input(self, input, corruption_level):\n", " return input\n", " \n", " def get_hidden_values(self, input):\n", " \"\"\"入力層の値を隠れ層の値に変換\"\"\"\n", " return T.nnet.sigmoid(T.dot(input, self.W) + self.b)\n", "\n", " def get_reconstructed_input(self, hidden):\n", " \"\"\"隠れ層の値を入力層の値に逆変換\"\"\"\n", " return T.nnet.sigmoid(T.dot(hidden, self.W_prime) + self.b_prime)\n", "\n", " def get_cost_updates(self, corruption_level, learning_rate):\n", " \"\"\"コスト関数と更新式のシンボルを返す\"\"\"\n", " # Denoisingされたxを求める\n", " tilde_x = self.get_corrupted_input(self.x, corruption_level)\n", " # 入力を変換\n", " y = self.get_hidden_values(tilde_x)\n", "\n", " # 変換した値を逆変換で入力に戻す\n", " z = self.get_reconstructed_input(y)\n", "\n", " # コスト関数のシンボル\n", " # 元の入力と再構築した入力の交差エントロピー誤差を計算\n", " # 入力xがミニバッチのときLはベクトルになる\n", " L = - T.sum(self.x * T.log(z) + (1 - self.x) * T.log(1 - z), axis=1)\n", "\n", " # Lはミニバッチの各サンプルの交差エントロピー誤差なので全サンプルで平均を取る\n", " cost = T.mean(L)\n", "\n", " # 誤差関数の微分\n", " gparams = T.grad(cost, self.params)\n", "\n", " # 更新式のシンボル\n", " updates = [(param, param - learning_rate * gparam)\n", " for param, gparam in zip(self.params, gparams)]\n", "\n", " return cost, updates\n", "\n", " def feedforward(self):\n", " \"\"\"入力をフィードフォワードさせて出力を計算\"\"\"\n", " y = self.get_hidden_values(self.x)\n", " z = self.get_reconstructed_input(y)\n", " return z\n", "\n", " def __getstate__(self):\n", " \"\"\"パラメータの状態を返す\"\"\"\n", " return (self.W.get_value(), self.b.get_value(), self.b_prime.get_value())\n", "\n", " def __setstate__(self, state):\n", " \"\"\"パラメータの状態をセット\"\"\"\n", " self.W.set_value(state[0])\n", " self.b.set_value(state[1])\n", " self.b_prime.set_value(state[2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\t

ミニMNISTのデータでオートエンコーダを試す

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\n", "\t\tMNISTのデータの1/10のサブセットミニMNISTのデータを使って、オートエンコーダを試してみましょう。\n", "\t\ttraining_epochsは、サイズを1/10にしたので、10倍の200としました。\n", "\t

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\n", "\t\tさくらの1GメモリのVPSで、約15分掛かりました。\n", "\t

\n", "" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "... loading data\n", "Training epoch 0, cost 132.518026\n", "Training epoch 10, cost 80.232354\n", "Training epoch 20, cost 75.058689\n", "Training epoch 30, cost 73.097621\n", "Training epoch 40, cost 72.148517\n", "Training epoch 50, cost 71.565361\n", "Training epoch 60, cost 71.142682\n", "Training epoch 70, cost 70.810096\n", "Training epoch 80, cost 70.536880\n", "Training epoch 90, cost 70.316258\n", "Training epoch 100, cost 70.139160\n", "Training epoch 110, cost 69.992795\n", "Training epoch 120, cost 69.867721\n", "Training epoch 130, cost 69.757125\n", "Training epoch 140, cost 69.657424\n", "Training epoch 150, cost 69.567555\n", "Training epoch 160, cost 69.485009\n", "Training epoch 170, cost 69.409063\n", "Training epoch 180, cost 69.339174\n", "Training epoch 190, cost 69.275876\n", "time: 182s\n" ] } ], "source": [ "# MNISTの1/10のサブセットなので、学習回数のtraining_epochsを10倍の200で実行\n", "learning_rate = 0.1\n", "training_epochs = 200\n", "batch_size = 20\n", "\n", "# 学習データのロード\n", "datasets = load_data('data/mini_mnist.pkl.gz')\n", "# 自己符号化器は教師なし学習なので訓練データのラベルは使わない\n", "train_set_x = datasets[0][0]\n", "\n", "# ミニバッチ数\n", "n_train_batches = train_set_x.get_value(borrow=True).shape[0] // batch_size\n", "\n", "# ミニバッチのインデックスを表すシンボル\n", "index = T.lscalar()\n", "\n", "# ミニバッチの学習データを表すシンボル\n", "x = T.matrix('x')\n", "\n", "# モデル構築\n", "rng = np.random.RandomState(123)\n", "theano_rng = RandomStreams(rng.randint(2 ** 30))\n", "\n", "autoencoder = Autoencoder(numpy_rng=rng,\n", " theano_rng=theano_rng,\n", " input=x,\n", " n_visible=28 * 28,\n", " n_hidden=100)\n", "\n", "# コスト関数と更新式のシンボルを取得\n", "cost, updates = autoencoder.get_cost_updates(corruption_level=0., learning_rate=learning_rate)\n", "\n", "# 訓練用の関数を定義\n", "train_da = theano.function([index],\n", " cost,\n", " updates=updates,\n", " givens={\n", " x: train_set_x[index * batch_size: (index + 1) * batch_size]\n", " })\n", "\n", "# モデル訓練\n", "start_time = time.clock()\n", "for epoch in xrange(training_epochs):\n", " c = []\n", " for batch_index in xrange(n_train_batches):\n", " c.append(train_da(batch_index))\n", " if epoch%10 == 0:\n", " print \"Training epoch %d, cost %f\" % (epoch, np.mean(c))\n", "\n", "end_time = time.clock()\n", "training_time = (end_time - start_time)\n", "\n", "print \"time: %ds\" % (training_time)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\t

\n", "\t\t重みの可視化は、Deep Learning Tutorialのソースutils.pyに含まれているtile_raster_images関数を使用しました。\n", "\t

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\n", "\t\t結果は、ちょっと気持ち悪い形をしていますが、黒色のくぼみの部分に注目してみてください。\n", "\t

\n", "" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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lJYUWd0Ia4yClHbW1WSFrPiXGdM06FhG+e1FjMC8TcBdYB09R2QDofBo+/SF6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\n3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xJJ1d80vlf82rfXYJAGuV3Si5zACVaw3p3/bXc+1b9D//3O5E776Iw4vri+RZ7hkMRL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\nm9PXqpBuvEcXr+85mEZvqqq3BUoor/hfgPhDlsk8V0VsrFCCvu73j7HbI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"text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 重みの可視化(utils.pyのtile_raster_imagesを利用)\n", "Image.fromarray(tile_raster_images(\n", " X=autoencoder.W.get_value(borrow=True).T,\n", " img_shape=(28, 28), tile_shape=(10, 10),\n", " tile_spacing=(1, 1)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\t

\n", "\t\tテストデータを使ってオートエンコーダで復元された画像をオリジナルと比べてみましょう。そこそこ復元できているのが分かります。\n", "\t

\t\n", "" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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5NWYKrBqgh+7DOQAoGlQBmZoz1Pnu9BmN6B6cuKC7cF3ZIceJCUBFXS4SF4QR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\nzvw+gK5XpYIdWKCUIqViRgk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"text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# テスト用データで確認\n", "test_set_x = datasets[2][0]\n", "y = autoencoder.get_hidden_values(test_set_x[:100])\n", "z = autoencoder.get_reconstructed_input(y).eval()\n", "\n", "# テスト画像\n", "Image.fromarray(tile_raster_images(\n", " X=test_set_x.eval(),\n", " img_shape=(28, 28), tile_shape=(10, 10),\n", " tile_spacing=(1, 1)))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Ibf4iw9cUXriKzv8VCAfnZ6LfXaMrxrSzCDwZAMCoRIgrydQrdF0ue4eVKq3Y\n3WarNGgzogw2v3TWk1Vs1z9NK1srJ6kPd6LMeeM3j0p6WZMESLc0KnYZw89TQR2KqgFv5yHn/T3f\n7fHaTYKhmKNmOhAA5t2083R9MZ3sJpCa4SopYUyLMXGyoz7488lCVhvmgMA55xCbuec7Q8Pdz3Q5\nZjxxSqEsZOXAgSau6y6KLjjyOgBwJZZMuMGcBhwwa28YRhOOJ2FIzRziwFdPW/T2317b0ylr/+Qy\n08qOFnPeNKndKauETnc/QXX882/SsmQiDw3v3AGQ1dHjLNBJICh263fpgd1vfXdXO5NxdAWfendG\nTqpLHjTVyfS1ihxqRMnzdrATwYtg64bS/laohcpRaPb+ldJ+OWRo1hXlYHt09Ya5p75KGx6t4zpj\nEPMma99wAOQstAogyThQgOOf7ouKyJlZl4+CU6LyPapyh8UtgM7w73MAEnlsmJXq/RjlhxobHul/\nW1V0cRLa31Z3jcrWAXCR5i4P+r8Zu7K+wRgFhzg/W/7+28v+Nu7W0tArbp3BWtjsb6peNHnY+IKU\nITf0SZ+tBA8mBrJCKTsVAYDK416Z1dlgVk2a8ukhXywWadjS6P/VZHPuEVKeooRSAUlRKPqAxTVR\n/ko9lhiarjkyCgCwIGC3J0l0sYXBhy8auSwKnEfXhWRRP8PUvVy+/opcOwy49rtzJkR2bNa7LnOt\n4nxbYxB3Dce7VusbZcGrJEdeyqGGE08pv0R0oHv0xFBN9deqvfPPLfKp3Qj+ui+xUaUGOABypTh3\n6E96lAZv+v57Ig3sMxoqCmVqiiYSahS2iQogcp7DG5aYGMQhJpNse6R1EAIAcqoCIOHgeN8We6nY\nzCDg+3wjeu1kAB7KPtJLAtrKl0dlJLEYivdO98Pe1aox6iKrBQDM1NQHTc2qaqgWORTvzN0NOr7j\nT6enQdYt9fX9mjzpWUi5tll3QHCNAwDncKJrUqppLwEtvHrT/DC50JVmZBBpfCR7l6YBImCBVm06\nB5EDpLZEwTjJIQ4aADq1iO2WLuHPZsQsXFF4ldbpiTtOcHqz4JtnKGt8+T2xb36nq1NctgXf+g+b\n5TcAgNPar3v3sFUBcAD1n70jmu5Uic0gV9rxzMqKE4fGilzTTqx9XzfF+Emp3pHvN3iTNxdES7mt\nMa1O0oMtEPr07J/15lGGkpZ+gVBmCOkTD7VzeLBO2Dm5ysT8KjXvOlj8TgM3GboBoOnL9n0+npM+\nqu+xv28zHXRNuAom3ChA9Yx96T79+YkAAueZnwyrfeqnNj2A2C9KwFB4YMtVBKTk0I7uyRA7/sk/\ny3S2Uw68Oc7FWHFrlV784Ig8PgSbwDbrJWrk8qBh3fvmP1qX1c15Uw822yjcaQBABEWTzLIJiE6H\nfElV5S3eNkyg2PEzfyha+ny+BVAbUXA82hg51I6iwU0CBdH1xu4mjWnLerWtXcOsbqZbdvJxjUXC\nkaZ6JXTkGistIxIAaF9aNcUUR/Skr0ZuhW+6UUmGBY9XByuPlflCvqiyzeSYAADo/jRQ38Os10NR\ndJ15InLijCTLwKUAAGccrlraydp1noq2Lg/dbPJ8B5BPuXxlRFXVxg8TI2noXowA2GWCCZtlu/lA\nbZWvoeLYvjXTLT8aAgA+UL0iw6o/BACgf2PdKabPIqbPrg2oGmOx0OYCy7Gkz685PMZsc0Uqn1qn\nxbZ0sIoWEifBIsZNa33BYRVBOGnqVl+08tF+QtsabIJABs8ycZd60tO7Xn1LV5fbiFtLOIjJhUue\nbDOoAw49cswUx5Ug8a6MqdFI7dq725gLdMiDT/ewir2Z8VVM9V1s7bJl0bl/jRxZC3c92u4320QA\n0vnbW0zLgRI0haIx9oLcs3DKb7z82rcs4/N5unf0umxtf28xwxhtprm917Y33kUsvMf+PfpdlhIA\nQO+wP8j6eNWCg/5B2LYtgFqgUf4Vaqu9pKx/IejHf4v+hC0DkYzav7/wd5/9d+k/jZP5P0lCx1ju\nwZr/6V78RX/R/wWEJjeuvyiRyL+3/3eJO3L+D9J//+siyB0b6lQGxMLY9nt1cyIhDFP4U3EQfoMo\nmrXmrS/VF/1L2SgAAAgVVausEObIe4bOCKRzbjkn1BaL/t6zhoZd9s55Q3r0Ftgd35sd95pbJ8wy\n9AKxC8E22YqdX+1S9+xC65gNKIjdux/aagUOl729bj01xx2Xt01Ti7a//6NhFuwU7WfPPVjpK97z\nigeRSIKxJkr2zM4iAAAx4h6Q5HhEAABEQmySzbCYKKY/vruuQWEstnfrvNG/I14bfnW9+MNYU2DX\n5jK54Cm/ptXkWJQBAJCOm5sWO8yt42mLGpVozf5tL3WyfKnU/+9rX5WM8FMKtH2lqmlMjTXenikS\narrZYu6F7z2UhAAo2VzJbs9VJ99KwLtp54GHuyTldxx+zoPrGkvvbK+vSTt9VReq/P69N8en5NxU\n/Hm6vlkquigBpF6blVCNuV/WfWfJIRRSv4pqjLGaJLSOHkNOadAC3c1T5LQylWm+0oim7rAE0xL7\n5DVzHYCyrlUE4dJIqP74Lx+sPVI6v5fFJRxPmbfxE6+AAFRw9134coKyeuB3pY3Fm7Zt3XuoOqox\ntbSfrrb09r5Nq1ddnGu3AUCXTSuSEtpMO+28z8rqfY0VlXUVP3W20MeQvJ/DVckWXvyE2GaqLLz9\nRGBNehv3FeE2hWkzHS137eaF487QFHXpAwPfEUl+aqXFjsqUEjmTA2d6oAUXfV99sSmocu+SHgNy\nj0fMFoKqpcOXcA4AGsby+o46o0+Lj9KRu5/vvaYgT6urzu3qQqiQdB5Hz1+x7kmHY0WYAwB4/Et8\nrUXJX2amikkEwcNVJXtoqUV3eaWQXPQrmCLOMCDpSmDU0WEP/mKnGlDzhk3k3gRggKiw5k2zmUO+\nJUflLUFedk9vtlwWYmiKXwpMcWnUGASTgbJqQ1MUAHiu2FDVQ9hkVJ1j6ECwSZSFkAqgBivTMjoV\nN/OB+0JPZuykXrmOp141vpOyZX9LtHEO6MorvrbMASEucg3sT/p/bW3X8+JoqF3MDtZU9vUt9F/e\n62sLBtVsvpAM+9XqGNGeeHGbglUVWxstzw3ksWqO7EmfucQhAEDHquChK7LlxIQI8VIAzCs9NlYy\nzksEKlIAwO+Uph5yVg+jxgodA+b9eG2KKFLBlpU6oyb4TasCCmlOMkUkCELhj1Vf9mlZvwRA6Dy4\niKBIEKmItjOP/JjcUoqnxjRtjp0iIEEg9+0xJTwBANI1xOYQq42IihSAdOjvsEotRBBB/JaxCrvV\nFkZQvLNeCy/tY6HzlAlA8qHQrnyLziAi2laq6rtCPKy2jmxFu+v3d0EAEE65+4adId+RhB1X8DQn\n5yGjKhuv0B+UDkQAQikVnXK3VxZOTQhZM7EstC9uB0MA56HIRaavBgCpVep6awcORCDZffKIxS6F\nBAC6hJj2g9CqT27hBueeJX9PQiHVETSvazcBCFeJOVabP0fxkvKRRJmLwI1hfAEe7Op+6ygHQDlt\nZ9AXUNu1itiohSMMgCCmf50enasXX0KcAzBO7F7J01eb9bPWMljc+sXmc3xEpIgA5IECNFpXOAJA\nNMAcbcl6JPeqJzsTsHAFYwD4kgywWUQLPzEs2q0xptRfbtGww0lQ+CRW08fqAkD6+RlTqz58cowp\nwBDp7mO+dARARCIWHguGY626TwRAAEkm3l2a8pr+fc1PCClduo649qlrXaKQ1NIt4mgvCGlDbYgA\nSWXKKlOYUoKIaRXaca9lxChA1+XFeyzDZgEAOHZqzDfOWh/veOioPxALbB1gVkKiIBHsU3r0DNFq\n8V7vV6tfvaNYUd40bENy1j9V7WePLHkEACCp82p3m4Yji/YvIrFN+tvbyWw2VLQ7Rm/5aoyL0uTE\n7iBxuAEA8NSqDWYYPKIkZhxhDZ7mhEfG4pTXK+ZYmTkACNKMA6p2pA3lpNB5ytjz/1HfuPpyc7NI\nALz/3HSPlUkHi+6bkGbzvKRoNT3082/o04d9FQNESggKFLDb259eYqrs6rtXZdVdLfI3ABKKQKaX\nPJRhJ0QHPm2OzoX51UesIL9I0bmPlTnBMssRZG45PhEBzIGziIC0V4hpP0pwcorr27XLiPY3ygM7\nrRls+67qn5YcEigAoPfjmprnOukG+vq+miO3ZBBA4n1otOT6ZMethhWMaB9Qzxh7WtRr7eN/oyRS\nkvLFd+lUMBhSmt0k5GK22xI2TkA6xo67zGHpAQCEJ+rnCQCA7vuy9DwiBMnoKIucT8DMHgAgdgTA\n00pjm63XqLwquKot5zWkSL0Dn5j3UF5inxYHA1V7nu3a97qZvxxZefEPofqrjNUE50cqY1UdDDOI\nyiISwZF/+QWvF5d0RRT0C79Z40RGqqwsXTDJxogAtipWnQFW2QLplLINMgCCfYNSepXdcHfHCeHI\nVy6S0GRCOY9wAPH8THzZYhvnAGxNPyqY77yEcyCZvCnq21o58zrnSwlwV1GjjtzrMoT+ZV+mjrkj\n17fpS0NdrgldCd949XHjcTTVfgCyzqO9bW65TkJRJsyIPQXA9h8TSLr8/YBR9EPUgJWmyWm1rNVF\nvIU855RMjgJ1QJ/uNGlVxKhZCYfIAUe0tcFEDwQOgK4vT8cd35kZJKgctB9utoof5A4pQHyyxjmP\nZSWPTTyUnrkvO0P46nMhdjDiXjgpNnWd0TmepD3cn68fHwIEPVpgzASSjYyE/NETJXmSd6L3gQaj\nsG4b+WJ70Eq2apIhnCWRFABtR1cQrRAI5Jb0VxpBeGX4O8wnvnnM5Ojvi7kuib3W+j3YSQ4RzgFd\nF97aA4+cbXYF4BoAYCdnQTtzTgSRqsAiEU5AuL5d3ZFEcX1FOFv1bY4ywri/9OMUvsH4sV0zJ0rh\naSFAKnA1QQIhhV4RkbCj321Myrg21rV9qSYa/CEw9coCriy/oZQbLw+oMADME0mSke8AAKlXkhO2\nvu92V7t/ch1fqhf9KOPgA3v+VYmfuWUO2UhG4dmTcnH7KCMCGSAOxee5VLQAbEZklRPg9ryiKefW\nfTpTtxq2pnXcHyaCI0BYE2pfUCMKtP8okX9cDYBcYYlj4TFJiPqi4bvXxlznd5Htfn/3bfq6nDTN\ny8tfeXuTmQcMOIAQA4hYXGnJo+2Vh+zD7LF1b1TuB70STZAVlddVZ0B9GE5e6ZCf5JCQ+skIgQWr\n1l9txaA4qbGYZuIQ14T8XlOcdSm53VxN336sR+QojZsZQdER1VTABp6ghkUOQIZ96sXYRxyaYSmt\npdqLQ5V9e+eu83EIlKwct+fTup2GWziy6MruvT4zXzCbOaYeHqdYKRGFnmpsYGRPxew1TaZi5g4H\nOJR2Vz9hJ5tpZT/xvBJjTPm+i6dtxTUW/nz0UkO0VAC0dbr9aExVQv7Gus8KjLXj11pqbTg+t0nV\nlI0W4W0AyL1fjImDOTH35eutbey2r0vvaluJfF5tw4PmUuKaesHknl3zXdQKUEJlgdpvrlljITAS\nJJdWaLE56fERtfFS0uWqgRYCkZD+enWw+rt7zpkwwowc+Q2GixcdjsW2FRFi9SBtOW+xzegAbzWt\nNEZNbCXHs48lW/zsxpNBrtrqFrE5rMvQ3sX9GxV/k9Abl5P+WGWStdu/8rU2HfB+l4RjwZus4xX/\nTmf+7Bv/j6daIAQtLwX/anV6x7Ez/5e4wP5Ff9Ff9P8oiVYg0P87ifzLpu//FAk5Z8Stv/+BQDr/\nddwikVK69JGxDSb9dzI5Yd6Zp+8c5CEAlrHZ/mBjCX8TMT9YZW3vjz/7+7gEdJvvAXKKQhzOknBz\n6r7Ep1P+1qdh11ONbTdHBIUh4eZcFDji8nZ1L5gCyAEAoLNvEh1euiEWLA//ESQFgCt8cvRtDBWT\nXn2nOcb2H5KNhLigLWQNyXGZQMqUyDZBnvDxQ5JZOHa/WRUOnri6zZgVxP1BXe0vs2c/2jdPNweJ\n7Er+KqJW9KRAqDEuAxJvUZ4sULH31L6WOSrtuZkCAAD1GnSp9MFbpWaTkJBrBTQGbL+uypzeoYUk\nK0w5AKbPe1wGECTPQ2Wbe1qsJoJIhu/b3t1snjrz642+QPW3eQTjxjzjh5HujWhquLF6w4s5hrES\nxzKNncgDaInlmkCCgAhIk264zyKMi/2ag/7DIymAa+BDd+gDIYyoP9SbgDMVAaSzupw00OnaiET9\n5st7M9Gznk6t37Tz4Kqw0ZL03uSCl4HIMSVVEi04j8iB9nDyFFvUsILJ6rXykAEjU0TgAEC51nqT\nRg7I0XsJ9y0vU3dGokYbHuM2RkIaAJht86ACIACP+qW4L1zCkCgZ/kguwH4G4kO3Yf2RsoTaeI7H\ndeFu4ogQDce99FSJalaskIxnfjLh/AEA7FNO7/eJX2NaOLjMdDl1+7RFCGgjdLZSlk5MtxfikGny\nj6GjOaJo4S0hZq1uuJoAWCWrhvRVB0YKlAo2hznH/fuK1nABWl+WkFKCSLOuijtDYOt1W3Cfv6zO\n/6UdUfxMY4HTE7UGdJMWm0iIKAuAL8b2u5uNbYlziDUuiFgF7iC3PhaZ+fWAjnD8mH2WSQ1La+y/\ncOAK6TaVHqtnZsWnovGkIcLsSoamuJPAVaUd38cAkFnsfbL2yXoVwAqkx947NQ1Hr6qNzzui92ZF\nzoGDFszosZEBQEKgd0dOSpU/9J6CXBIBiDexmhZS645yDowB30ZyaTO6Q/dxlLoOHa1McEXaFy//\nNDQrqed1Xy0xz+l0UgQALHelBO9ZnIRUY7yXLfIpB25h6SXto3M3w0nlvJ6kc5L3WU0tAAB+7JNG\n2+RxcpznRL/UOEcEAPH0m+IKkJYlSrIfuGxERhgdKI8djqAQLbH1f8TUungvMSemndasN9DtQ5gz\n9Pgyc2/Y2wsWKeAHgPSBR+uNYEa3C9YDQNKNHoisNlemGgN6jvr+8eY+6uGe6Lqg4ZO2BAz7tIwL\nepQc2RmyCoIfXvOA1O7ynfH4B8ZJFnchz6TGNJNkcIfsVF4rdxDvGZMOsGph4jdDJ3U1N+TND7lu\nqlxvjGECgGeusUxFefI7Or6u+irPKKbeyrQuAFkLg0xbY+FihgCYde+LI+IByw2JmVC+vj5aMthj\nnVz1+mg0GNzxzVQrNycidTjBWN2tpoghcaIEAM4tXWzorNjjOV+07p03j9SHVMaiI/QHy/Vhf6/4\nzua8Yl+09p347GnNZQEAxBkrM01ZiM9/5ADaVw1zFKMlZAiLNWHqunzk0WILIZ8D2HKX1qgaZYjM\nePLIXT2k3bLGrx8yB1EG8rQIsUObC99I+8QYlBgA1JLrvnTZT51rUREQZU1hohyeY5jwWnkfG8Fh\nWRmgIofGalW3MZaC5kUOgBDdUFrAg7IKoIuuA0CzBBFRUK0OfA4A2i+LTNAZDAf22pO/ySfAmcPK\nCkAKb83+rDIiuXwcomg88DFs06T06TP3m99ZlALrzghJA5+90vuaqUucw8+vPSDKFrwDSJ3OtxwJ\nptqfmG+sNqQ3QUeBCzQtKvLiMCeJnt2SqkQBACjxXNMrvOlNRhjo9yHsfFU+Jcx4N2gZjTj2nE9+\nNf7Kn08NPTSsCDkjsmq6BiFA8pe9/XYSVDuIlfXuiXfqWCR5l/1wNO+J0d7zXjRtRjjdP/96BSLr\nL3/I424wlXJgK67xUKv7XsriDo2OAN3J7zUevI4rvSpwERSgQqDu55Du6MDy9f3u2rGv3Du814Bc\nvuG6cm467UnocKrAeBsbJ3WO/lq8dNIG4+cs/uV6mqKFN+d0Rrtpgjm5Z0EfdPU6D3BEZMuXd+X2\n3JUYWSwjsjQMJTOGi1e/6TdUBWFZ+E0FALTKVKuw2AhYuqqz1KHRVCY+7P7l29RpPSZFTW1mq7/S\nfawT2DtlUcG3qJFj4gm6b3b/Cy8CxjlwqsysapYgEjmklbxzitTWXQ8d4jkiOD5/+WNjiPjPI5n7\nvAeDn2ix7YY6VEy7/1InAo9+ciT9p/4/F5+InXUgITMH90UjANxG0BQ6AJBv3RxfQp2GLjSvXg6c\n1dTn8yuqKw0YNBza4eNXo3Tuu1Mc579tODsjr+1RgCbbUi+92M3dAAAJ+Fse/Pnry2TQiLohMkiu\nsWQE3hLtClbXVgQggneDpoVOvG2BXkakjqubAp8UGcoufKlEZSzWtDQTAUWJ0JSOg22JMqlAAMDz\neUw7bPJz9RY0W7Q89epsCxAVAEgvljdsud5o7hDu/nw4Aun9z3rlhzSHXReUXCIAgIJgH/Tc7tID\n02WjsCUmZbs83buKno9OfHASC6brmfxEaYn1vR454Kldosvv8hxkxgJgABqf4ih74oRBaPbf4CDs\nmnmEN3HgCgBp8tcmbq2ocYCka4er2rPGpU3GROOJe8iqJK7fcJEDUq4B8KWTMzpf/72hpiYO9jyN\nV+Zs/bardlpAW6UmyNtKs3wZ2xnzT0eBGKVUpbERYC+Aprl6t99twYf+7Epsw4KHtNPR2vPaMJ4C\nYLdadY6NGBRiqZvq658irVgvYsLoYdqsRn+wdp7JMohDhsfDSbyosEMGIQJFggQBbT03BNRQH6PO\nIPmrnSXHSt5xoJA54Zrnupvy0IiCQKWhbx1aMtbsY9H8g/3X0JK05oq6nTpr4wawCigIACCenVTz\nk+UmjsgACqTojyoY0uHW39hJWc8BgUiKBgDMlH7Evbw7suDM1+qNSiteNy28LwL0wltgy0SDVMMV\nAKDAKbYnSB3GvFMNV7iikFIRA61xndceM80/FIWY0GOgWHfYfNGhQoxzIFl1/i9UOWLaqdG910cs\nrp4AANzdtf5mS+Ej/t5DFZ4IMeYL5lt3csYBWgKZmJouKgRt4/PrLADKoYlnrlqQek3fys9eslKl\nco2CWr/tFKGyyTStIxEAPwAQiJUboVsAoHEie4YV8NSCclOrzIFRDkLHqmW5FmAykE+92dlmdPnk\nj+Zb2cEBAAAJzV27e6YjHnjVUJbwX3PFtEnd7CjYLEIAkPOXH9x14PhT6W2bAZCkXrzgXJf1vQOJ\nYGsrjQOxn1YWU361MmlTkSKSjLtfWN7d4sWC0cUrsdEbfu3ZpvoRiZj1etnzXpp9RhscarNmvPEU\nK81bUs/evbv+nqXBbnEVbKY2PzaA+KXKohdZdohSACCFk4b8UQONMOau3+gsInV/daUFvva/T3/m\njWhfqtW/81tmFmJSC/x+P6Qss0LY8Mj/Jvrf1du/6C/6i/5rJFgH+vm/iyzONAnbyE31HyeOFH4j\nNMD/pXRXycrxbchgkpWp6N8gfLkucnSmHeFPHS2t1rc/ORGJOVTtv0D0cYWpM61C1yGVbdJvCGEt\nD5rYS2R7x+uey7XQqRxRtfAj3e0Op8ctQBsjRSr+ToYzy1oEQHS00d2TINt/7YMnrDJEDo5eQaFK\nF60XOSCnjLoG3Q8Lv6xXgJvcBChROQAglSAlZ0zjka0JOlO0XX7mcczs59/8velWlplJ/J/MakBv\nKKyeNBbo2rWzgttPO/ze2gbNUCjmTU96sYEjRapqpv4AUKIR+eWRL39lrJhI3CJRAwAAiRskT9ZM\nyEZBONCOzvk79x9OdBnnkN33Asjy5GQKSoq69pCmscRQG8gBuMoBgHgePZs4qjtGVl3Y2ina8d1B\n5Qu3h231R017m22mPXTrAj9KBIBwxoHpg4mS7H7TJiSR3B7v/z2kn1+Y/FNh3dsNAIwzpE6TPhBB\nARDPzb5vYRM3fRdEonGgHjVgzTqkjAEAArXTWEjHISHGhUdP+fidKNV9FfH9SRAREEFpFG4es231\nFq7LrAoADAmRx/bNO090NjVsK/+wlcGY9OjI8Jzv/EwzR/SHVyayfetUgVI/BdEWUKBV8YIcABxZ\n54xyo688nJ92XD9OOqoIWTUQqgEnWfPaT9jOdCqQuKMOA2bEDxHO0eXSgtrIWweV3LnDUlfBFQAg\nKQXD+1QXH9lVowkJJRz7T458qxp0GG+dgSxyQtIaKo+c0tuZ9YNmitcrpE8fO9qpNmKlNuPjqE1t\nnb3Yrmtg/sx6BA1bY7me7G1mkJe4iOd4PQNsTu3YklEVAEBjhV6y/57K9FtOn6XnLxsI/PUYt3U6\nEYDkH3ry8Vv1ZjjkACDUpG4zriMGIGQMvULqksKj+d/MfdAaoobUlv9SdyG4zP6rwhLmEOeQ/aY2\nc7cxVcC9o/Ibb93AbN5Ado8YLakxMQiTrn3Qhlyrm/W9Y6cGiVoyAmsOPFIdB5drBqOE1LN2/V6h\n/eFaDsCBaACo5yHNzXOE1h4pY6f03bhXn2fGztgcBiwgB7RYGkTmWWiBUMqGOcwcBBh6PZbhoJFf\ntg4bMOXzrablyYHmPTsuRF1NGxbMVY0T334w9KVdMHk12J7LSxYcLk/+dWU1G840+cCi/YZKVQkv\nKbQLxHDqEKnznVfIAIDuYalG95Urqtc937t7dwqAkiN9ZIpAh+kUUES+waeUjJSkkWvL3tf7oggL\n1IYCACSUgLQs8Hcr6QQHNtUmm0GmOHy7r6xkdargOndn5UXG00xwZVy+N6IFj7167qCTA01oQfyi\nU81tYYwa50j0ec2ZRUOdJl6TVj9no2liypldlD0zFxW3ehy1EFOVwlKbBu6k8e3KIwZD24Wip9Bz\npFYDx0tj9gtNXx7uOrHmmdaPhoSMkNSNOxRSvC+vT5ouIzWTkTcCcA4IttTKNy2UwyjcZt/fBEYb\nL+Z/kjbvHr+iQXhHY864dWW6cky64Bm3EP35nmMqb7GrJuzUvSbxu2qtEEsxx/WnzvdcUkS5UxIE\nZthQcvo3zX2htg0ta6dxxTi8d+1XKfszbSX69bng1C4ps5pUkFb2xrT5NZHp12QG3qprLRcHjxbq\nb/Rz8C8b0/nSZxLr2npBJAQAiAQ6pdW6TAc6Ag6+ULifgZF3nh/y112jAACKOVmu0w7omcvFHrL6\n5TNHWKKM0MohchPUfMdRlMOGj81plztcfR0SBZRu6n5sRgMVwq0NCxeM6PZOLUeKFvG2SF+Gt6Uq\nX30Iaa5L5X/4mxLcBpeL6C6LcBzekdfP+KhR7SOF/K3ZeBDIPbkY9nGA4BZNaK/bT7I8/IQMQrIP\nUJpqFwYXG6zPItHIdKlph6k/eF4Rm6kBEPRee12eINQYYDXBI/94uZJDXDY0cgg943CbRgruHbnh\nH5sMLBqUAkI44KbBg/JEafiMNM+c6pamedpo+/hPWIf3XF+vXhMzGNOkpYcrh/rn13BodC0b8cXx\nu6tbz9+KqC12HMA+0sW+nV0XhU/s50jnJ3DINgahkSDhoHiwWNej9qot/4nN3YvqtghdL8Cgzwi+\nZFzjB3w/mC21wgBNmxQpP6Ym3X9OqhBbZUQ+kBVHfRwAkbCWs7d1DvXK5ik59IKLvN3HDmjSjROj\nYTLvoUYi24A+d8aIIYeet7W2zEI20vspPiqVPnTX4isj+lnL9h2AzSoAgOZfceAqd05CjFIWlAKU\nClPGaU2HKiRV9c9L7tuKFOI0yw4QJZwBqJqqs1nQWI0r4/YwKNEJgk0KHvzRuHUyDuhZ9bIZ3Mc+\njEy8cHrsyPsdBruAV79vSN1CPGndt1eGIaax1l215QpGigJbb21omPWPKLbrr2+Xrn3hqrvKAv66\nsvLj1118TOh8S1ZCaQNTD9jDFxR2/gbG3GzAgapqLKKe7HXNiS11CXMsuDHAr+qWUlIVOHS0U48M\nmSTXFbTGweXcDaCukSgCH56M6xInJ9u/VAOloramuF4VHTVvmKJCcYCUq9OPmTUUfPsj3zREqnd0\nXPPsFs7JQQMPkWWOf/WmCb3BmA0BAADEV4/0ERDQs16p1ptJRJvLIyIAIiKga2aYRRN8StD7/J0F\nHgkACspjW9sbXpl4d5R+WJGZGMl76K6GxrXdpO6f3DbsvnnnuRzXFvuqWx8XRlSoR4Z4ku0ozImV\n6iwaSHKveL2HN7Mwr/v75cGHTQocBMCHo19ZWx7kDgVOm9vuui+i7jFdXQUxraDDZROppTeH82Co\nOwKAZ2uDwapBUmRKRQEBBSoIOWtjrFiXmVqS41ayaX52rMDwRgRoMaLl7CvtnHhS2r/ya8EuIPTq\nPXVH/dxhp83csShxbjrvKxtIkSJIn/tm2AypeBAAiSCI4481nmtlgBL3xe5AMxodW/4j3Xsi+JiZ\nCwBYtPwVCmhv4XvLHxn5wr33h6L2FwtLZunnGItK1NEtdDRMPa4eKSPbq5HH6hN52IxO6Xi/XT1a\nr38d0QBOyiR4ZhHLP5hQGHl0mK2qWszs6e+QKXeYxpd8fyQxnZhiW7ZfAw2wi3fXPE3PBgYAFJlG\nDtnDRy2wRdxeHP6KA1jgk5u3l9TBdR+9ayoCAPG0njNZItyyhUPl6wdNGFW9e0jX6NdG55Vgyog7\nh6q+EJRnFiHXjj78EzdtgmTU7Az2490GwzwHACSMA+HoeZTUbdMVHr3r8vLusUn1/Qsi0Wpx94p6\n/eWqX1ABAPDcRmav0/SiFgJSuxRgJIvWNZpHScB1fHc9ADBDBAWOPI4oIrf3WfJak7km4KDbyhfx\nxGgGLRyKTR5+8/iCQURd9HeTXNNYbxecSYxlSwKqZR8sSri5tadeX8inef42zaYsvLrJiDZB4i3K\n2F1Dk4j0jrRjnH6Kad/8nJU+rqjEt2vhZg2PRAwfPH3klbMU29XXhlfM9hsCnKAILpmEgHSJOFJN\n0QY5Iy4xjjU3brkcgJIYh4ybbDstHYekAVV3NLaF7gB658Haihe9FiWY8lpQjQbKTjQeW6TP8DD9\nm5qYGji6qzbScIalHbfb1sZV3y3btGfDT6MtwOzU1uH6C8/Kz7HQXuKkiBrxR9Xw0jaiJCCi+6fg\nkTstlJfyI1VL09qwERMEEGYwdrmVdx59pmqjpVfQSbLZrAOAAZCMS844bdrE8wsMD7imNDJNqVm+\n5o3x1oZuMujnow2B2kPvZLXRsrOwg2wNWXpOYYwpB5La6i2i+4jqu8I8UuKc2VQzqG0lKybvZuFe\nFv0hg5vY/raRAL9JQnZWktfrtMB2eLpkJQmU2ttEAqDn9D5pTmvXKwAAYgq3e5J6Nfi/vaONDlOK\nCM5ydY4xsxIAANjPyf8N5TbpXKX8aFXNNsPvf5/+OdMAsUn/eZfP323vN7ESFAEcyX9mNOIUf+Qs\nq3krZP2p9gDAyoTxH6Bm6ejPRotA+LMf7b6KC/4X2DL/R+kv/vxFf9H/bvovRLT5c8frnzT4/99H\nmDq0qwtAHvQfCDHRQgQc+YUS+c8nS0HJbo72b3BUbvOtLQ/9kRxepMdTUaa80baw8CeHWPDVktfT\nHWYnakPjxCSnoCiIguRwtwFwASAiAvG2hPKNS8KMxG9xAmccOHDCLAAB8pC0vUKH/A17VOPVXlZ0\n1gF9F+kE4Nqo3jsYWD9z0oaNRmw9IAKRi2x7rQLmZa5O823XBNIWCP6kkrn3mfWf65zTsMtTE0I+\nKpW+ucDCvRGBuAubTqj2UYXvRuM9EgAASHZd3AymEQCUgDNjpA0kYsdv2rHdvLOr8fE5BtNCh+n2\nFe7R04Rdtx8wcUBw2YLrHSx/wdLrlLhL58kx2RQGAIJH8MeAMxDNWmUkINvysskmM0hD+DSL+zdk\npx1sArR0IkCRcQby9fdkKwsTOERtSwcRcCcDzXh1325zb/MKphV2kZY/G8s8VV64VwMAEABh1Ite\nz0W7goxzriEikcHkVE+6PTfOzmvXVU3u773hex7StT3k6kOH95TFkgcMPmDqqeLbdQbYuk2/eqp2\nvQbQGs2FTuo/SbRXVkQquySVrdiQfEI6bHDfQNCIihv7hi0gLn2HwvvP1pMyIMyeUmlUNyOQZK9b\nDda2m+AMbU4sFfr3QK7s2Tqwoz3l/IPGkOyY/mpRLClVOIe9X5l5//3/eDoIAALyXuf3pzh7cyyw\ndIdGsmzHq0MxE1DAs6AdqnsvqiALH5iUfqvn9eLE8urSbXOUHTMLTtljYhCAFmXAGz+2nzth5EoO\nLUZH0uetJImo/kJnngxq4fju0rfvHzYYoQAlOwvsy0CzqdIR/dtLMYAoEaW7bnjzRV2ZlBsKi0Iw\nEoqJGeGlm7Y1tg6EpOLbXQ4u2SUMeai3cMbrhkCqHE9N+26b94oBinv4ioW3Ok776BADAMi/Zk5Q\n2/ft1nA0GGho8odKb0kzZ3saH44ee7wjBaCFv9TXNfyoK/R2tQEAEZNS29h1XSmyfezxxYl530nO\nhkMli4ZKrkv8amjtFe/XBMsvMNUTR45OEjvdaxEu+6z34+Ffzlz8D592XFeUeceXN/RI8xAUaMYz\nx4aJQqtaiiIRqI0SyT3iiKoVtzNcCUm3rcvGC84JN44ptAk5zy17pUt8Nx+Y2nPTJ/0Knz9cebyq\nsrouHFx9jjnbwvDqkmmpLgKAGV9Ho8WbEh/A1N4iQUDR2cbBgnYK0H7fiaREUcrpHtRBBgDHBbd2\ny+y/NFy7+1RTvYFrzkZ8qlxnnIp75z7eGQAARyiRXRUsrFO8zNj3Tb9kFwEA0mHLwVST/IZE6HTj\n10GmVbYH0MEtJq7bf0Mne0pOikgJUs+Nuz+O+8NJVOzoFiWvzZnWvc/QB+buqzlaaPJMG3xw66hs\nOwII0ytD/kl5iUelOGKInSIANUdFAQBAKgoAwpPVO/Q6RiIRAABEQHFscaTkIVOqI2HW0SQQSvyX\nGjlPhizoAIAo79AOdpsaqksMst9lz9bJLrsNAYBc2XDIZpYmhIE1oYiqBV82yGf0QM2zefbsJIGg\nIFFCiw7F5rbUcQsUAYicknnT5zv9vs6mYeYeaVh6uxOBDt8XDu9L1skS0t0PZ8sIYJ2JjeQ+M8lL\n6HkVNaP1BSgRQCohAKStDNbcmG0SUFJD4Xb0BjVcZByk+FbFNQQR7GuWCODYeSCBQzQSWNiOiBIl\nCPL60OsURKOg6ipjTFMqZxidwUYHaweJDg8BEJK8EpLUCtbsbE2QeHIJAABxDXnrrS17m/JM45RW\nR33rhiWl37C1Nrq4vz4xgvTx2gtEANrFabUN5R1Tm2ZPn11ybKgxBQsSmjQqGQDSN0XC5b3N4XE/\nZax8c4wdM7FOXtBwvYwA8qUuAO+OzxJW2Zka2+tABEBChjZu9QBIhqA+OEFhLLzvy8HGD7o4fDhN\nkGwA1Gl32iXiamBK8zNIU3PiuQsEd27OObvrbjGrTE85ESr+obzs+LZvZ2UZFK7CgzU/JKF06+Zx\nVpPo5ojqOxqI+u4zxeRF+7h/lC6f1vmnqKpEK99rl2bYMrCRMSWoxr41NWtbduSKZAHp4x+KAKcf\nStSGvcTY8XguDpK3V70dAY1B6jIPa2r1F79uvsKQjhl/VT5MsckioiQR4kx1ppcz1vJqhyyKlBAq\neyTaLcwa2+kuIQCAXZbUNQQVZWXHdkluI1K76MjOB055sFL70pRWCAAHPXHb3zYe3LfDZb5feO45\nGA4EoowxxReo/fUSm123UJObNOXF7vcd/MDk2+D9ct3tXV3tHm5cSMD5zNrshKLcCAsvcyPQ5N6v\nRkL9jag4AOHLcKRixud+LfhDH936c9Qpd9kIIYJdAkBRsKWUMk06eeuIosNZryHpNfAz3k0GJRFM\n4VYZEwZ94I3KANF7jhGwafoEVFBy7xXjzhBTULNpxCj+0t27FHsvcZpLDppwz+EN5BqvbAfgexvb\nyUWjF/oSoY5EvK1hRRAiU9NS6lT9hcSLudmVpOhG+R0UBrTfVpUQ/bti2Xhh0JZdpX2z7B5eV0+p\nsUMptV+WrI6dLqHt1FteOJZY4NQkhpKsxWIAwDgWJKt7c1ueIAU9KSLJ7C5757Poj4nM9bR/ftfh\niBo5sqJUqe8BFjkw0D124ZoPD/kuMTmbC1n5MoLsytgdfNkiaptsS794RUQNb5+cdtr0azvoYyFR\nyYGA4n3+5V5jZvOsVdWfFsgZP63o4ZlybPc0PffWnyhvqi2vKTtcE9lcKJh8ySWHKMiye6vGtGO6\n09MTVbZ3dNpdIgKQpOyMqXuVsgEJED1vPMsMYvYPoZ90CRNHHI5Eavx1B2f+rYkpk4jl3d926qDM\n8W8ZDDAiQO7lOQTQ7nhJY8XNgBJDl4dWhHavuyadIjHsfYhAEFB+P7LNZImUZ25e+oB7yoL5t++J\nxY4aTKBJKXL77jnts/Ju8NebbWIiASAyIYN8jJXoAhuRQ2r4pSHpaS4EoefiQ3urAoH7BWi1Ssf9\nuTlAONtXcShxFc1JpSAr3D3NKQGWcgCg1BjHKracw695IQ6J6FyF0C4PVlbz5HFXTkB15cljQVfR\nNiu5/OUTB2o5GO/KHACJymPHhRxTlIjYg3edfYYUlTuMSILQdAP2tBGgBAAAFdHWZ5eRQ8hAHKDs\n1uzAQY8T4GsK5DumfBeq2V4xbGBRBj2+9bFdaiuHuNoMZkZvnlfV5ca45QMX8dtcFAnnG/ZxAAKm\nuzYDAG+W8XbO+D2Fs2fbz0oVMPLkN4H4NpTIfMRp3qPP/iCFm1miY0P8WV6rrtTvehyAN77qz/Lk\n9xbdEHt7j1WYBOSA7ShsNhVoRHjiJv8Pa2+TWFR/t+d32c8Xci4VkfoOQapacdeGOMogHjrCEeZU\nQw6A6U8k8926d85/53SRN/WAaty/5n0VACSrsFFAB/dCMEACyO4zUm7WCFVLTq3gFjZZoevVvue+\nUYR4JxL1Qy3MxNTYPywALo1vprefvnJJz+rZtaoFCoFygOQLtMPFphKNZF7s8V5/FfKa42+gDj8a\neJwPD9S1d9Dw4ePrS5bXNis4BAAAzjhXgQOgbdSp2s75+kafecuRTuXShhiPo10t42qBbVjQJLdo\nj56dH9hq15a+X89Rj7CVFA5AzkqZPTcGqLQwxdxwqKHO6vfoiarimhgStEmauUMMkA4Q6pZYpOhl\n/t35SKnWuOfRHfpYVFrJO083+BWxq7e4SowZkn2c3BxI6gdlR2803EBREmzZrt9xLUPbS4uNYjwA\niAICEIkiGjW3FIDcHazLAADSrBe2egOOfN0UTiWh0zQ1O8VS/Y2Z3+/8Yiiaa4Bwdr3GlCN39TaK\n6mi3xSPWkba1zIjJywMlwy3gJva033NXcx8NTv4jVhBE5+k10U1u/B3jiZT0WwYAKamtD2cr7Fhg\nFYQPUO7/6NIZpzrM9wYESRJ+z0R+ri8QvKCozad+o7ZYGujxR2z7KCxi6lsIcYTCb9S0/WZ4CauE\nagCAYs546vqPeJ0aXu/YHd5XLJp06iepLXc/AFDatV1m2RT/YJz/S6IBiCzWlh0AAKBNXT0AtAkW\n41p5OQSsy/4gGWaFQ+MeT5k5peZf9Bf9RX/RX/Sn6D8JnPt96zkV2sLJAgAK3v9KXon/a0iwJdqk\nrDJCyaMWH9v3/eQkC2eRMSeamuqCwWjdx+O9fwI5gyYdzr9IyT9HGp7/DZHnj+AvmulfqmEJOMHk\n27bXR6MRX/mc4fosxbTrRzGmlu3YvqgxEql/yZjCGONocWKzTmMHAMKDx2b8Jsa7rX52XKEw5h+N\nAGBgkyvbTv8cfjR+hSaiaLpj0RbRFJGIptUk5b20rWTPwQ3ldRFN812lkzJtg+dUvtYnySNn3l+p\nVhutJCgjUnno6gpfrGKEpUvzuIi2t40UvBAfJqHEInGt1Pe2BTWBvRNNtysgUkqaS05rYz4TIooA\n6Hx57xXm+YeEIHEOuOTMYb1asogLAK7k0/ZPXbjWpkUFQQ5GORPVxJA3EsuUbOmpWvQfW3KnDcki\nrrdXJKp3ebcftn1/Isqg9u2uZ6tdK/SqMB5FwXvHLR6OJHPJxQvMqhxsRzHEATBR9402whUFAEXC\nVeLJn/jzbqNkTe2lM95Jve4U2ej9w4FmXTveVk7Lly2vsFAdESQKkIveTMZ3Nx7hutg+RNRsiGLq\nBd0F2eZ74GBLxErXM/0zxKThA3Iraf/SJ3dHAwpNVFXEUKEThoquJqVy+4KiRybZxUueTWhWne2k\nfqR2NRJ5Y0efwb4qY37f7GdOtzfeUvxeT5phIaqLzdF2dZoamtOpV/ESteiWjoFFlUXDe+6YbwqD\nQNCnQIRAnZFDKIwbf6VD6NbU5ZTXy6YdNmGSVHRycsuzVOUVOSUqSbAdYFGnjOycbkquW44Vp3Sd\n9cl3J31qHB88t/3gwpl767aEWeTgT3vXzHzhssS5i9KDe2pqSmZP9RAi2aY0RncnWBQFoJRQyZFK\nAJD221M1zajMv+lo4MQUCh39ypfUvJjwFsZWIuiNWuiYvX3jq1uCqhoMBJVY6EML1w0RgdxYsb6j\nXsufQr3v7tu+a/eG+UNH/BhlTT1bfe5OEpU9DzRFNvS+d8vDenxC1pe1ZQ1RTVViaqi2UVUqpzcv\nQyJP7XXPj/deMHTIDSsq69aWRsKx2M+6vbPg10DT8X/0yhVQEIjrh/rjUxIGgxSJ4Iqn/sFR0Wgh\n6Cnzm8b6XRdItq80dthjPi1wmqJeZXJOGPDz7v17q1XVX1cfZMxXBKb4kygI8vSSmhuJvqZArtXq\nr/c4clOo3L+OKReDkZB4zl999EmX9ELdCh2oBHv8GgwEFBYLhCOhyhDTApe2RwAQgMUW8LLF9j0h\n3Pp934ajnkuvysMiV8K6l67tAiUvLAwDR8J4bMlA++T5LZOXA+OAJ2F7Z5CoPuUtIO7rnN7+1Qvz\neiPbHDPfxXllk7IPueFyv+vifmdjfZ9d8w7fcqekvnIITBU5y/5gpHDscwY6M5xKb1be/oCBQll0\n57MPmGKKA3CQuypvfhibeoPjuE45yau+uiqpNje6Rom5GjImAjTsrW5pGYnDhgAoUEQx86ogqypI\nqJr/fV3wOQ8Sh8tupyj/LRL7Rq+aQpkSREQQZkaLDWcLdaVcciCmxBRNq80GM9FzKyqeEE1nM3FP\nTBNcIpKzm6KLLQ8l+26Nsf1mk/68F0WIhwDDvOUfWenQso6Wf/nADo3VGfqDjsnnd+uWk5w/aNp1\nRzQW+z7B1ovYYg4m1DEhHFuQkOsJvVsCJ861ETHZJssUnP9U1F2puqZFBwAVCNLUReE1ho2GIEjX\nlUQ0pkUet5CzsN1GRT3kATC6mNF4gBY8OxiKrxRj5b4hxtjxJGOLco4bAaiNANAzlk+yilc2NaqG\nwoyxx00r3uWWBUT3Fb/Ua4yV5MVfSQAAOD95diEFPAPZvrrWmcs9UtXGnVyQVCIRDZQUZD6dbgol\njvFFcEovfN8cHCD28fQGzpQTn1gcZYK0N8KSANBuAJppcagkaS+RghQqCUlGnFT9vP2+iDz/DYN7\nWbQyjICiyoH0v9+xzSpbjCsUjnLg5e8Z+8PDYUXlPNJpmBch/EajuS4AAJL2xWp0auIX6/3tuocL\nRDEpvcMAJwC+EW6Yq9N9otwswSZvq29KthBlhXPLIuFNj1tm7hK6HVYqPICyWZ0qTTp/0gdNjFWv\n+/7r508xiHcoyHZvZq9d1Ws6G8V4CREBhKE/HX/Xyv+TpHYc//juyjdshp4iYNyk4NijMi36fWdz\nsEgklCCkrI0FVulCLxR8d2hJd8Ge0vW8LgIAvXFT8fXYWge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"text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Autoencoderで復元された画像\n", "Image.fromarray(tile_raster_images(\n", " X=z,\n", " img_shape=(28, 28), tile_shape=(10, 10),\n", " tile_spacing=(1, 1)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\t

Denoisingオートエンコーダ

\n", "\t

\n", "\t\tDenosingとは聞き慣れない言葉ですが、ノイズを画像に加えることを言います。\n", "\t

\n", "\t

\n", "\t\t与えるノイズは二項分布の乱数で生成します。以下に10 x 10の空間に、ノイズ30%(corruption_level=0.3)で\n", "\t\t生成されたノイズの分布を示します。30%と言ってもかなりノイズが埋まってしまうものだと感じました。\n", "\t

\n", "\t

\n", "\t\tこの様子を\t説明した\n", "\t\tTheano で Deep Learning <4> : Denoising オートエンコーダ\n", "\t\tのアニメキャラクタの図を引用します。\n", "\t

\n", "\t

\n", "\t\t\n", "" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1., 1., 1., 1., 1., 0., 1., 0., 1., 1.],\n", " [ 1., 1., 1., 1., 0., 0., 1., 1., 1., 1.],\n", " [ 1., 1., 0., 1., 1., 0., 1., 1., 0., 1.],\n", " [ 1., 0., 0., 0., 1., 1., 1., 0., 1., 1.],\n", " [ 1., 1., 0., 1., 1., 0., 0., 0., 0., 1.],\n", " [ 1., 1., 0., 1., 0., 0., 1., 0., 1., 1.],\n", " [ 1., 1., 0., 1., 1., 1., 1., 1., 1., 1.],\n", " [ 1., 1., 1., 1., 0., 1., 1., 1., 0., 1.],\n", " [ 1., 1., 1., 0., 0., 1., 1., 1., 0., 1.],\n", " [ 1., 0., 1., 0., 1., 0., 1., 1., 1., 1.]])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "corruption_level = 0.3\n", "\n", "rng = np.random.RandomState(123)\n", "theano_rng = RandomStreams(rng.randint(2 ** 30))\n", "binomial = theano_rng.binomial(size=(10, 10), n=1, p=1 - corruption_level, dtype=theano.config.floatX)\n", "binomial.eval()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\t

Denoisingオートエンコーダ

\n", "\t

\n", "\t\tDenoisingオートエンコーダの実装は、とても簡単です。\n", "\t\tget_corrupted_inputメソッドで入力データに二項分布のノイズを掛け合わせた結果を返すだけです。\n", "\t

\n", "" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class DenoisingAutoencoder(Autoencoder):\n", " def __init__(self, numpy_rng, theano_rng=None,\n", " input=None,\n", " n_visible=784, n_hidden=500,\n", " W=None, bhid=None, bvis=None):\n", " Autoencoder.__init__(self, numpy_rng, theano_rng, input, n_visible, n_hidden, W, bhid, bvis)\n", " \n", " def get_corrupted_input(self, input, corruption_level):\n", " return self.theano_rng.binomial(size=input.shape, n=1,\n", " p=1 - corruption_level,\n", " dtype=theano.config.floatX) * input" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\t

ミニMNISTのデータでDenosingオートエンコーダを試す

\n", "\t

\n", "\t\t早速、ミニMNISTのデータでDenosingの効果をみてみましょう。\n", "\t

\n", "\t

\n", "\t\tミニバッチの条件はオートエンコーダと同じなので、まずDenosingAutoencoderを生成し、dAにセットします。\n", "\t

\n", "\t

\n", "\t\tつぎに、コスト関数と更新式を取得し、訓練用の関数を定義します。\n", "\t

\n", "\t

\n", "\t\t計算はさくらのVPSで約17分で、コスト関数の値はオートエンコーダのときより若干高いです。\n", "\t

\n", "" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training epoch 0, cost 138.405057\n", "Training epoch 10, cost 87.162786\n", "Training epoch 20, cost 83.476929\n", "Training epoch 30, cost 81.844325\n", "Training epoch 40, cost 80.727820\n", "Training epoch 50, cost 79.953129\n", "Training epoch 60, cost 79.360116\n", "Training epoch 70, cost 78.839937\n", "Training epoch 80, cost 78.408522\n", "Training epoch 90, cost 77.952378\n", "Training epoch 100, cost 77.572687\n", "Training epoch 110, cost 77.342092\n", "Training epoch 120, cost 77.164052\n", "Training epoch 130, cost 76.968730\n", "Training epoch 140, cost 76.653768\n", "Training epoch 150, cost 76.618222\n", "Training epoch 160, cost 76.326465\n", "Training epoch 170, cost 76.133495\n", "Training epoch 180, cost 76.044698\n", "Training epoch 190, cost 75.996942\n", "time: 216s\n" ] } ], "source": [ "# 以下の項目は、Autoencoderで設定しているので省略\n", "## 学習データのロード\n", "## ミニバッチ数\n", "## ミニバッチのインデックスを表すシンボル\n", "## ミニバッチの学習データを表すシンボル\n", "\n", "# モデル構築\n", "dA = DenoisingAutoencoder(numpy_rng=rng,\n", " theano_rng=theano_rng,\n", " input=x,\n", " n_visible=28 * 28,\n", " n_hidden=100)\n", "\n", "# コスト関数と更新式のシンボルを取得\n", "cost, updates = dA.get_cost_updates(corruption_level=0.3, learning_rate=learning_rate)\n", "\n", "# 訓練用の関数を定義\n", "train_da = theano.function([index],\n", " cost,\n", " updates=updates,\n", " givens={\n", " x: train_set_x[index * batch_size: (index + 1) * batch_size]\n", " })\n", "\n", "# モデル訓練\n", "start_time = time.clock()\n", "for epoch in xrange(training_epochs):\n", " c = []\n", " for batch_index in xrange(n_train_batches):\n", " c.append(train_da(batch_index))\n", " if epoch%10 == 0:\n", " print \"Training epoch %d, cost %f\" % (epoch, np.mean(c))\n", "\n", "end_time = time.clock()\n", "training_time = (end_time - start_time)\n", "\n", "print \"time: %ds\" % (training_time)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\t

結果の可視化

\n", "\t

\n", "\t\tオートエンコーダの場合と同様に重み行列を可視化して比べてみましょう。\n", "\t

\n", "\t

\n", "\t\tDenosingオートエンコーダの方が、形がシャープになっていることが分かります。\n", "\t

\n", "\t

\n", "\t\tあの気持ち悪い画像もいくつか残っています。もしかすると気持ちが悪い画像はランダムなもので、\n", "\t\t隠れ層のサイズを100としましたが、まだ小さくても数値画像の特徴を表現するには十分であることを\n", "\t\t意味してるのではないかと感じました。\n", "\t

\n", "" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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0+FWT1eETkV1MRrxvDl9ce9p+nbjHMBtNot0Zx7GwY204YjjM/IJqFhQYRoNC\nxdnC7g8gkakhLZIBdVPzZzEUgz0026haJVv7+v5A7ajsabS6/jOkl3BK+kM8wlaxQFmOsD6KuPfH\nCzniDYwasvX95ofdZocy1giGGkSuuq/gvYHCdOxuMzv+1P33//rvsV+/XmMyNCzjq9vCZZRm6Fmw\nTh00tfS2wwVh1qbk3T6vOWRbJTh2WGjbeu5xAAG2dwc5yQqQdcYYmgzlm1e//ZtFQjyhVHdGpJC0\nYLx8uW+//76wccyOlckLtPzWi0/jR37yDRAcDTanDZddQLSzljHMh17a/ShgMqb+GyGicdcBCYMe\nyukKA+X6OgsA3SPsLquTk8P13U/24tPnC3Q2C29/9e+tPnTQh9OjKgwFn1IpvO20xZTEF1kQCbR5\npTeHNJQQTfNIGyyszoV7Hz/TBjZPtme/iG/5Jz/4UfRm8/J1hdK09wMh6fGMUj1cMlpblk3g2JXL\nGfea8s6ZcT8Fu0xaNJ/caOoIYxgRhMaR5H5dfHoDNoznlGwwl/S//f7TKVUuYAoSjkhrIBfHPDn/\ndMkaEtoOT9JiGq/wALm6ZYDo0OUXWPTIx6AwjnBLA6FNOB4K6Ba/Pps3qubaU6joPHRtIG100nWY\nwH5sPvyDP9eSmKS5mH/4GFVUh1Jl6Y9n72AWDW4qoziiCIh3gTsagmTl+EH0C1jJaKPTOee4yhMc\nQp6QWh29WuhEw8nbc/FEv1bn1//g8PJmC60Q3HzpVvmaNHTK6gBeh5PEdft3dhHPD77ymj9DCQDB\nPmOPokpzP+LEj1pQDZI4nR5r4AUiZ759FOLvJRdPsnbElqLg9eixA3tqyLPVHB0OIIw4mwnsTaSw\n11t2CY0Yzl1F5eBFYITFZOcNcgFlZLqGNXvuMT6PIHbeCSEUq/bGJbZasznMsP1wfRJ2KVPfzj95\nXP7qNgYpTrd//eoX3/u/o66zWhsU4x6jHjk3SkRNQOgQDdAHO5uVHmgRBHajN+Pgrak1zF0Ezbh9\nkvXF/d3L334VklOYanKwobapOdxTze1goC9mPYLqa/lMUJSMMDInvIAxZxeQDGiMgcUEE6cl04yK\nb8LOA9VEB0UXpPDpSa4teA5+IGTeYg5dPb75juxLPQbLTmbQ3/eIVm3gzf0nYBhjfezH0FFGgFqD\nEeMNMk7dn0K6enDR6eCp8zETObp7PUCazvFJbzns50Ufov2WMpc9/475/GUrvXoqXn7xG/s96GVg\nx0jT3FvU6lwbj0nwpmcPkxSCFkvSQhIdhphb3aa6RShwkGKyg+M2unAXXoVqnSzOE9Mo5zgyOH4y\n1HRvpjl6q7l2Q3N0aWqrzud2kJ5nEfTxDYklcdAGgkzQANYyCm7xJufQ5E3Y1sEXEY6RHTEQ7y1h\nmIJsgNxF3TprLBccJbiq1g94vvIOhgNlkDf93tWJNok2QreWIcwR1Z09SgIX671+rzY8t3oWJdnm\nYU8fPYW2CuJKgdHr9dnh4PxAJH399lc7LNV9bH/6k7PfC3B4/N4DdSwTA7JcsAHHGDyFBY/WApyN\n2qwvGNQ8n+xtIKMuCll+fnx7HYCm31mwkF4eUfHkHO4Mi0expK5Ox6fvaFFOnrwGHIAPZBHPHqHS\nZIgm1zd0BIjKNUdFylWHCOYmUGw1Z9aNIkaAXRxKPUrm8TgGD2ApZljQDb1LQLEwF4ddkSQC242u\nSzQ7Tavcjxy1wNRM3V96bqwBr7tk6nBwkTScLgGqc6w0sTAIztE4vDvOzs+WZfjscNJM4fz4vj00\ntxMecyG/fqmfYY3gtvxGoqoEweJTobWgwps0cUcSmZLJha+wd+BSunQ8Sg5qmbNtKVuUr5bwcHcM\nRQ+T5PvFjaEs8PMnzS/Ve8HiSV62uy3QnO5WdP+NS1g8SpL7edYojgxyEz3uTiHqyCFpBBcdRVo7\nBsFTETmp6dkGRE6daQSyxlnvGHPecQ7Ys91JBbqJP1xtKSMEuqajUEw4OqDIHgwGsIv+8DDFBNGe\nJCKR0ehR5BA2DFUgYdXoXpiYCX/s7H56upItVK4+CRhKxt98f9pBMcnznZp9SFRt/XG0ZicC9GNL\nLApDnkYNotUoiU0Ct+WeqADKZgGQKVuE2vYokSYTfz989mefXhgCaHXq65Xa1E+f1H/7mx/3VZih\n3eevZGhHRpnBa4uIa3GgiPp3WtgePOMnaGPgwOZ3aZ1h5HScGkaghUA1HP3VoQTfx7oFZIn3VqWR\nMUDBWU3GzORwVltV9nZssdnriEAaak38I2kLugZCFvspBj7zOZ+M2prI8NDhZ/g1j0FXF8k7RFPq\nJCWuJFHsWiDmOFaPNdD4PkvvZ7aWvHsIE8QsoWW2+GjpP+5BVvYxvT5OZlNnfDuIBLhj6FCN84kH\nQu86azvtwvZORTzBTNaH4bdvnjZXDdjJ61MKh9KcLt41l3lVj8UAcHppf32c0HhR6ZJlI4K+x3Pr\nNPIDk5yfXAMCMkR83eczjhsRCCHG0AyNWvFkTwDTituZxpZoTGkwhhBQjuhEcQDVGGGn9QAcvIg0\nBOiqPB+Lc/f1BIgukskqLYrSU0JtiBUxyiFKFMeQiOtv/0ZngSkX0TFQVY8QcnXssxWGzbw4HlB0\no8bz+ka4hddROM2HiwmnFFRqUH13O3u8AK56TSNEEPHWz49P34LSMO4Oq1Mu75Whk36Rm6Pfv9uG\ns0MA01RzPb5DLGnoX4nMnc9T3M4ukftq+pQ2Q5piGatOazdhKEvAGBsaS3H9GJLsXlzv5pPl0IUA\nhoyWBD1oRvdawmAmk5EbojUlAenAgQRn2QD+WEC5wG1WJzqlfk6BVIEkCMY9P/2NszAqez4kcWrJ\n6+NezT/ItfU8BKSzloPep5seQe7FgCyiMGiWGz9ZySH/Gi6b4dlIZDTUIczmmexpSp0qaRLuJHiV\nouPfbT95e4rD3nhBwTlA3stzfgpn4fa4H3dZIolXx9tshjzvyvD86aLJwIbPSjVe8+Rz0GfFPosS\nObIi7b5a5teU4KaPUiqJ7nGaKJiGI+4b0ob7uIVuyI/1dvU0LzrkECYhJkdqExnWyQGysrGFM4S4\n4KnFFMCTqB1Tf0g1pHJuTZiZyAOLzIPHhORU+STDVEJU9bfvqWho1j/56e30958E3uhopE0yvYlB\nMlC5w3YYbeNxvBeLFbFjfPHEpgIa+emy3MWTFUZqfmL6gXWearXYzFoGF3vjqjfXeXNbtI4w6r22\nPOX517O4hbKdmgW2JCER7uvZxbkhJzub2s/TSQfozSdr3WGSjGXgrpaxL/VJIBWNF4bOTBVG0oiI\nCc2PHQLKMd/2tN1xCjTa2vhwu1uhDBDgwBAbggAf+3MEFkln+mA8J2AJNQGBQMFle4UomPK96Rux\ndbxrU9l1yILnNGaWoPtzuOch5aPd9W9f/p/xf/c0sa3mqURcKT8B3y46jnl7VIVbMaW0FANrh3P/\nmT+HbDwu3fh6ucB9o74JOXZ7NLGiI4dnAe7D8292Evt2QC3YBOsREVc2Zugkh6LPmtMBnVnXRXp1\nctrcFnmcjEeY/IUETujdyfWDwPOz9bt9RWaNUXP1EGTrTl7RPc16GCkH2du2qQjmFMdRM5DTMELQ\noULd681jGTWEElVnngni4kW1psDAauw5EMeCd856RC2haJAsjiGStjhrX1FFUBhqizpijeS723e/\nmUawVCA6FXkTX/135j/4gLUVCwBePX5gA/R9jy4i5ZrodE7adeX2weNdfFUnYwuGH53pTWPTzWHM\nMYEOp17D7fT1dA9pqG7b08m3X0xC0CHgwQuBNe9q/WaE7Tl6fnN85DUoAzIaPqszbU//6bfmbKhh\n/Wwp3DbGi7wlweRBewK7b/hqf2It9ZrANGoQ9gNXXmIWNBtxCG2qU0h7sF34+kHPGx+cGIMFQvGI\n7vdSA4ia15noA6GjDwgJcB4F1brYpNCUngq2GJGkEmaa+8FK7vv2WMgdWL9zlBk3OTv93ZQk7YjB\nHwyvXS9aMFe38+6yB9pQPKyvOy5o3wASW71mMJLPFlGXKf22CRJx62LA1KNgzNspcDQZf8zhe8v5\nuj/KKcJUOjDb+vp9AjAOj4bRRNmxD2YMWkR5QvKPUj//VT/CavfpXXf60pTjGOuFYG1HSODT5d8g\n5TMqfZBCaKgpHoHHnFiGa3ZZ1enXp7CJR5Et/9YP3380dxoI4xgzQKSERBlweNlBBKQXo0PeIeQM\nplAiEekBMv5FlJE4USgX9EDkaLCLkuxg1zyDkA7ZBBTo5Se93fcdIZ5Sp6gi8wrwN+nDpNNY1Ldv\nu2HkRFJqnKs3k2MGGVB+20dMp1MmU2jTnqGEl8WwxWdAmrHwH05OiXYmJIn3VnNtxq+vv7VeAoxk\nkgbbGizLHotsTlEXTp+cNPVbAXeP9H3Ipne3Ey+mEjHsDES/9/7q9B8t/xMKGS+9IcrJTFgkCFdW\ni0nSLvoLDtGQAiXw7u3N6XIcrGfSQ809YYneQzB0YJYRr50ggRDvJQTFPFUIQQrF1+fMR8JHDNcq\nS4JB8lSpQ8Ep9GKOrvbOdG8mskNEgnS0P2C4I4WHJKrfs7VTYd0EnhQezdPF1Bh3StYCdM/Lnsai\niyJCWZsSQcfBKTx7PNGAzajvH6WdsJ7Ecx5KlIpJd29OLEkgNd1FynuLslEmMs/5sW2Hxgz2o+0W\nUnU3GHEu957FYDnRNa+LZPqtHfo1ozEwGAqNeCqwDYICAjSdeOlO7BGYrE/Z+tE39/dn6QgeWFDd\nEGEaHWMPAV1LrRHNmkBtQMg5hEXo5TiBBm4xa34zYyfMleAGkeWbXQh6/EakuoXkiJNGRVibWhk8\nUgGUMD6D+1nSwXF+NsN405XdXOYAezc+Xg67+83IPYOHaPvHJlykGA8J1TGftR1tACfFA+6hS0Ka\nppgxAM1g1MNslgxrdS6eth6w1Wy6iTAli3kAr3a3dZOz+28uJ0EAX794rd7LhiVujyaKTSjiiafJ\nvB8e7emoutSMGx7GPBA1zlAKnlR7H7coA2h7Op48er1uBxwI0GRUFeNMm57OQbvZEIEjQagegIeR\nSU8rk0nlY0h6Wu/uVsZx4x2bPzdbpQTZ2ETNGqjYZBdhozm3AQwVngrTc5ddTqsYRiutYEmoJgvm\nWn0IycnH8Md/3MycdPBUp3+M391fLpUMSJqo7Kw34jMVPX9YwJi50xhrWnSjNyVXHmz/5fWefyrD\nHvLE/XRJgAXfzKL1Dh5uVPQ0eVeLvzcYQB8fnv/q9sf+1hmFeQLRKUL3y+Or//h+l1KUVFv77qvV\nIpl1FfvgchaOd+82j96zyI7gnaxp8Xwum17w4DGXnkwye0dQI4BxypFGxjHmtKXGBzc8FKshMMsg\nnPvXF6N1bqRxy158e/fVO0B0ubiFhyUk0rwwjQ1UzXjYuYEGI+nDs2VpsQBM9tvTMx4q3KNOziZc\n5nFInoemmyN4cxE/EZGeItfYJtQTrc+l/pqzyd6lMN/7q5ssTidNI7QJSkL17j6cLeyoMaxHf3k3\nkJR3hO3Ww8S9LgiSz95ViAvgX1rOv+BhKJNzoe4P+NLFnf5iO35dlFTN2fRo+fEefbO+fr56fNE3\nLz+X+Z2OowZ4dkih/mH0o4+Hg78/sIygxXBznHWz4wi0xjjqt7O58q4om57PvUjoMZW4RTC0Z99V\nFXqWYCh3i6iY/LEL5bxJRhMj8H1ynXSDZEPPVY1C4nzbP8DLc6pHIJuUvUKvP//JRnbRj388nGW/\n2KBO/CxN8QAI959OH1U/2m+02PbndgeuLC7kqn37/g4s9m8UP3sqS+ii9+WXiajo49wUN/2phVGY\nqqt/lLltdNlic/lo+rPzJxf6EO9MB7e5ObftT378nXdt2vDNqzF9T2fNBv0iQhElXTz6q2zOenb6\nfr+cP3sbnd8yHOXKDsAOOEb9s8mHQRfiuDlrl6f07XE+go8paKDBQaTLuHfJ1Z5E1rnMMaMBAJKx\nT/3vvF99fhq/Pe7qB/SAV/5hN6eLeg0ga/kwPLVhVQMqTJNZT/US5JBjDCd3Klp8dht938b+Lhmj\n/lij6Ng/RcMmwBOLg/MJ+lZe3/3+R29/UvxuukPdXfEq6idA2OQ4j9uHx1T/8iQ5SYtaWxKxJlve\n5PBkaPPdyeThDb2o7m5/f/WR+mXmx1Z2Y5bAEyV3i2L39k/3F5dm9o/Gkz877A7T8/Pzev2Idqux\nOrvm5+70GO+z4U9/2rXl5RVz+1WYw6Go00TS+HW/GIc7kbOmNR4p0CSyECHq1HJYILpwLM57aLPu\nEKdbiUIHKt+zsfr5bij7nh5sUm8flXPRQfFQJMCOC034V8HF4ZzENik3EDvqE4aghrIgB3wqeVcv\nxgvMN6th3ny9UiDUhYF3j4bd50+zl5s9mR/+TL/ifzJpHr0+/9OT4aGAkigYdH64Vky4r/AKQdQa\n3RNyYAC97uiVf0U+LBfN+NF681Rebj/b1fGRxwDx7ZkvZr/3ij6emMj8tQfgn+7hse3HcRX9F/0G\n/ssP/TtYc+pRUo8LVE7H/XkZClxm61wH+6/8j5FfwUv7yLNut5B2/uccPmpuP6AGjrP/+d9X/bRM\nW88mvnVFLwdnMmpTfzc/pv/Mn4eknPiWIYQ4oprW+ZGOLBl1pvRf+F94Sun69Jj0fMiG4Lhb6I61\nKW1m+n/2uUfC66DTViKxWx4NpwGTbtK4tPwL/4FsZRMRzrZFU+yyWPkR9acP+YBQ9Ff/D4krFD0u\nvy4Kb3ZPvnk67k8fzNJJenPxL/3pIe8J3Yez7WwIPtc9d6ooycL7I/+rn/kupEq4keAEucFMwTWE\ns5JHDcEuQzgnTKdPInV2x19MNYnQCZw47jh0Qv4ST1aL86fET8pPvrmD63dyLn/el6sbYIZTBB4x\nSz68XGT9y0ZjVhi9AjkAIiNQwXHZ2WZzn1Npc4pBGxpGDAr6o88M60aXRY9Pvv3i8qCzsIhZrjCM\nqD66DkWHriOkMNFlwaIkP9k7pxcw7BmNVXV4h/QZwvsyOp7Ej/BJRGeRh3yAh/VRHM7LV+7efVFe\n36C3XfLgw37WwJEfhSbL8/Bi+vxE3Prn7ImILpK2pSKCfkizLjHkyZnctyWYzbG21rsZ9Rwoc8hU\nZupTvPW3UWi/CNOSLlpPHMogQlB8taruHp9950+673/W/MH91es/+z35h/bVz06htendhffTR6mi\n0+0bS65nj+freN9FQMB6XNChxb4/9MlZVvZhXDHGX4mIJJBGh0m5bIHdUbiUJ+qmlMyMaLFNMAXH\n4y7q0yaKlpNXJQ/M3byXWE+npSaAlzXO8tvTapNTL0fbvj9NhjT+FXRhgF4OuTn6/MDTO7XvwltC\n48iK7uHcL4DzGI37czTHZ7q729g97Obv2CwymowQGVLSXvjtMZrbTO5h1CBED7y1OY0qWPpiQnfF\nlHaEty9ul5fz3TO0feAaTtR9iR8OP3yjHnX63ae//84x/P2nZ+Gzu6UGyrqzdfoim7HuxsSXFXnO\ngFxtW0yHFGZ91w+2qDk+HZzY4SvrdeA49iJ0oIWrJ7uyGb6+WfwloXFYHGBmqhSZ0ULaO2KnsBoO\nx3G2wF27s3eCjKjNjw4mO3GQKkcLXIaIWN0wSabvTk6MbggoPZENjTJ983Z4ylAbfXttrqRtFiZ0\nUF6svbzE9aIe3EPqpx0afo2v9lqk/QKq1E37iPVWEp90SgiuEqOnDz4JhDJsvH9Ad+TgZjIcgS99\n70n+0zfPBgI3Obz36s9XX4e1nN+iX4ab9XIy+53JH93/zq/m4HVyjGPVrbkeyWE3SrF8eKjdj579\nghMwbXEwC3/VySC8cL5nhEYRtk0UURgh1mp7bA+/XnxrQVwpo2fDgKgsjkMEyAB1kvFQMB6XI45X\nmxAc6nLsJbgZZBZI1k4SgrsceTKiTbMe54dlBldqxCfu/bR5/Lrzp7PpIvqo5iv8c9s8voV8lLHR\n9tEgyd1An0Fm7WlbLd/f6BQg7dOWs+5iG5AaeIwJ4Uco8NMBHWuqRmqicLc79e4QgMRTcHUva2G+\nWE7hcud++fU3BrknX+T7UhlxxaLMXX/2a2sCSKdWh91uIh7NJlrxYjnrRVSMUJIyAb3aGZHwllpk\nSYh6a7gLE3rVJKAghKFd4hOze/Txd8+DDRRGcnE2arWEA4xxen268AYS3RyNi6lYEmtkMseTN4Da\nZaAdq9p6iUjuQcahqSo6R328BQ++RayOynp6UZYhC2oQuBHyyMrHQHhyc7l1Q4E22grtey+BdFBk\nUf8AFPF6PS/eJNw5KuwYIjCoj6ta0JFWyVQePzdPFZvQIWBroqY64cRfbrsbqKPd/fDin18ZBgLf\nqWdPpjfD4dfrw6K82sHen70WKuEnOMyqNDq5DO944lG70zen4HfRMF09jIxYj4MjBLkxtRQl6SYB\nRiSeVK+a04uPnrpKC+paz6eXh50GC320p7xyG9dF1HtkJBUdBk/xyC5AZ/XlBikCUwgoYBZDW7kp\nnZmQN/DuVM4+OKpfjed0T194QK3QVLeLw+f/7CuA9WK+G1ZZp8B6gCB8JHRsfPfo4RTqWYkLXQtJ\n1QCICHDAvR97NiOaLgMViyvIBUnAjiH0KpA2QYjhGYaHDy7f6u+zpDELzKbswwtGXr37Gp0mGi9h\nBrtVGuqx1ZPt0WWzdNt0nA4HTBEFbGZuEuVUIT5IogE7w7AvhwAxAWZ2bX3Xr9LFFRw6yeLGmXrz\nUZp9iTgsNoJsxt1X1cXFhCPKNPW8tTa3QwwwtmfZOBt80gFzNgxG9yGL0tTPiwae9mNfk7KEQxjJ\nNUyl2WMT6Ua4//RTqOU1JxNaGUGQSRkKiCGewqg2JIY4xJCNjdXBeRoowh0wTEyE+oHT4WH2fg9X\nE9v6cWTSHw80gp6Jx5PhCOY/utr/Ad0dy/TI9vbxqgee/OKL7L+5/CY6gftxtZTdtIl832gcjduX\nb7JlwuGEiR0EdpPv5aAltix4QD6adN6LGvo8gPGDkNmj02EqhxZRMhru+vL6w0OvFdTQ5/aLL7/c\n/JAXOWhAXhvug8byoIH3u8xs+cRjAN9jMSiY5c7ciTBaCGT1s3nrLNTpxIwR9CNKE+AIxVkHFOOs\nr5VNECLMKEIiDZRG9RGlJVDBexIzTbxBhgGxKDAxIlZs0IzCIO9VoVWrwIeUB59KjNQwqTccYJJf\nP30U5Kvx2CV7LLbX5ceraK92361xBej0o5s9ixl3rUOg++6uWeVJXw1dcg9Od5NmBx6ctURESPMs\nbjTlrfQ9kBZ/7OZnUNJOBYYbJXKotw/2ZhQBYNI9QFK9/skHaLY8Ih1GHbhsgdORAzoL1oHVFGOw\nrCAVSub09Tf3czLDAOG1mMt6WeVTBplLjji7cCWLzeM9glm/3HE8He0wEmJJ5MdGsJ5ovPESuN81\nDhixmI9o9CQ4yjCysY1RT6NZuP9UHLbJQJ0fkGeehjGKk+a0PoF09iaOOzfdaGdVcXj9j2A1f/ZR\nkwVRxbBQLG7QGAMSApFp7M9hmroGtsf5BSQeJxCko0MggeLN2j46123W5fjrHOQd//6Gisq0jEpf\nNeiEYKw36qvJFiAd4nE+4PCHy8cLvDTbziJGkRlbDxzEyd6D14SnrjORNI7EZPvLnz88T77H4Kjd\nWF0RO6QutkiHEAlf6b43u8cAyNq9u9DF0QvGg7DUplxh1awy/BaOnOzi2GJPER1AR8J7pId2yJk5\nUCV/+d487aqQ6WgcEA2C2RMgIfiza1j/4y/m+NkHw2rF3uKFobnqh/xcmj+n5QL68f7ByJR3XlqU\nyuDgJOqaWyCmjmHAq4pDQNoan3TXP/n1s79ymetByh2WoE/VN2cIt6o+jY+9lyTRjanKu1LKHeyy\nQuo6X373By8Y6reNwzH1HgUbL9fA+m2y0sEKwA0VakDcHL/+6h+n8x9EIwzP2vk9u8ULjR8UxAcQ\n8EDmu2T/3VkLa8yzkejBSx8b38lUgYx9VfvFbzEgjC6LQYrgCdbBdsZJBGjA7qQGijZZsRyD4pAj\nTGON+jEWTSUHeJjD01LpUfUXtq/Cs+cDXt61v7UfT9uXQ4ZgdDhxjIgahGvbhFMS4X7XZBO9mwLx\nsnZT03uCRv7wd/6N95Yp92KU6MQTQJlnVruup9Nl/CbEmRgDEXW/k3gGK7UT5o7+zsfPZmWnLEI0\nDnqwcxF1GA6PJtgobCD0DA6Yx8bQzu0nalQ5rK5DNq2rBbC9Y9FAgjPdLCw7dVVsIdPYqHljY6Z6\n5xEOBjtwYpGxJEC+FllPkUPOBMMCCkyQ3kRZYqKYdgUkkz7KJsx2WiI76AT2xLrTkVLg2/8WioqZ\nIiE+mRaT/PXLtXmSd2Cesxaw2WS5yVvDWnWce21HPKoqEvZURBBQu2x98I6iSB3hby5++JQGFJiO\ncQsWP7YEunG4vLzalwdMjIly68r6NnVwkHKPk7OTs0utjUY4hJ4zTwIP5gEynOVb6pEaaD4qLG2P\nsjPOdml3FsHt+WeEfzkh0I8iDWlisY08z66P/9+PBSAenfTYBhcZTNyEGIhCNSZQvuA91ILkPUHB\nDIEKTnTAsFX5Ymwz29HT+4IOu5pHpDniENEQSdsbGa7xegbji+Zbe2QP+oiC/1VAv7gJZzCw+fnc\nPEAajF19pp1B0IBhDvAIOBOxWpMDcF37WUswZoDR5R/8oHjM7NFabjBvAKedv44cJMVMWm8C6TWJ\nwqbVbz9MAGNwuVicZdiAkDBW3ThhSTOoEqUA9zNLJIExyCQpOV8fT088v/zTxwQriMhyotpZyAiq\nW+e7uZgQjVXf7pSAwMZoqiOH6uBY0jGZoubBoVLqwUBU8sE7lBgGOAHHQntzb5/TMAhFqT95e3ZT\nq5H5fs+WruwLPmCOY3LzOADKXoklfHOfJkZuX27Kd+L9x7lo8+6v/scRHCNXEmgQYX7GUqJIT6Ik\ng66UpIV+EpcVE4CYNenpwhM6Ngpiy2mOQN7woh4jzEhTv6sJ0Yaw3jc+UlELGQuXbmIi37aUeeMR\nDn1aG4D+BEAP1agnHrU+WC1npl6fnXTF6mp8h68ggewmm1cNxBO1d3t0+b6Lct/Rh4zeAx3bTLvJ\ngAaiXJRwrqSZaE/O8dUN1Gk4Yp4FQl1Q2Ebm+he/nbJl2ipwFLkfzX8OVBimSyyykEgxHztXm8U6\nBuJ/PFsffk2TyaxuThKU4Y+XLu3EB/T4AlgewW0DCQ0+SrCJGDVMCqIh6iSQMo69paMB5VHCQRkM\niKdDeSb2QIOuGk4CLm+GtUmECSR0HuZnu34FvjLiaofDzhJvOQ1DhEFbHDh0UwjTuiUhhtiivhXn\nrtck8Mny02dXGQFovyKEAO9RcbFQ/K7cxUIu1UEcygiGQC0kWdy0NuQRRoG0Jpl5WOWdgGmLjjlR\nMQouBI3nffXm3/0XbByftHVOO3Vx6OMIL6ym7P3lpow5wrxEo8pH+PVjUme/8NBInbmTk+gxwQN0\nBv+zP/jZ5xDtZ2xtaeiJEk0brRjX0HEbLr/3dwz0eJRDrMNgPfaVJCNhPvNusI0roV80d5HQwVYm\nEMYZI32tGZDo8mcMPBuhrTMjiI9Q0COKmNc2iZP52AALosE6x1ihbpyLGk0lZN/JuuLjhwD5sQ1u\nOfYkQsk882EpTM1Pit2LL8oAWb07l8yoVvGoiIhrbV9kYYySUNxBw4wMgPxAwUgvOMwv/+b7E5kk\nveupW+FtzqN5hJpVfiU6gcJIkyD06pDC8/gRyDNlUr2DkSB6BqNZu+rZ5ifHEnju6wYtItUEV7uY\nIDa3OxjGkwfeQMAaCuyHTHQeuNckctbQweUaCAypoxJ5XCCFqUPgAwZJHEKEAwSWa6sV5sgF3HcO\n4wEzGNPdVVSBi1s0AI2HMehEdHaChEVPZ8NC7Ad4S4TefXQIMfv5G2HJ8xVdN5Y+NOwjYaDl77Xe\n1hxxxoaU+4cHdBYPsj2OFQWMNadMeCp7ZKPEovfQB7OPcqp6PqEDwlka+WyJhwdb4xF7h6I8K4mO\nBrjJ+aerg7guS0nPcjsq5tHYC/s5Xq6B9NSPExppsiSOQEuneP22fLV/UkwzwH79ZHlnMXWRJgYH\ncE57jBBujQc4SI1B8ZgHAmj0DPGMgiU0fv4ADkV2W08sHgeJR8ycs8Wgrc5vBgHJ3fde1UiDdTrO\n7J4usNvSwTYsSRWcb79LWDM9HNpf/503F3/1W1NNhLp99TonzRI8Mkk2NEFmgMZR3Pxkf7kAtYaJ\n+4oDyG5ceOaxB8QTX9L0hZpMVWtQ1tKV7tSjuksuGWXdVwTHwoPr7bv9xCPg0/TtfJXBW4uK+Kys\nEVWMpSfrr35c7KGKVWFyiaKAklTvOxb8rvr5H33+vX/5xQhpS4f7akmNpVQjS+lo7CAEFVhzmJYm\n6WITWx8BNThYgilQ5l6nSIBxD9hYIx2RgkoIvmPGNAFnIemh4jRiJowHKqZ0wKMbTcx6Nx4EwnCT\nP4rsPkFlDc+jq0tpxkC6l140PkmBWX2CIG09Mx1k5pd/dqiT96P0eEQhAN+PNB0cg5GAM9aMWYFw\na4WVLabQlLOde9P0j/CcOM3qE6f8zrq+khXQrz6+ba+qfRMnfDoeBlGNyGXPm8OGA2BnEhQYGb3n\nRB9Hp52rNrvvnsZ0Bw6l5vXMC9IGFAVvRoopj+QRE7uEOh0HXeAOYyYc895polgWNt3IPdh5Ew/q\neJJhJ/yoAcl+GBpmf/PeWQ0kqolhxjE0Qc75EQ0UF6OCto0ILOPtFZzY6AO7enHIXshr0mqq40f7\nZk5AJw340fIINSpq+7tXf6Ke/jiZXhuRZKCygZlkAOsIQogoZCjue2YZ8kB7O0/m29f/4Pyjx2Li\nGosE7TdYyILwE2jFdf+3fjebH3b7R7puSGX7Bl2m0ebAphAlwY3xqHsvqbp3uRyMavnHT5PQIhji\nBFseKkG99lxRHGILylgb5g3Md3uyQhJp4ykDptDo26szy+r9VQ1FvSr8uy3LMNaq94R2HYsv0R1r\nxwjioTc4bhGwUYbGS8b4hFalriaVA90Ud2+5jN9/8ujwoOW44TSZs0ny0/s2gSHDtzavkkwZhCWN\njNvuN5/oVNWkA6dnrCNsHGHkEjMLmJoOCB7tKGmcrsWjyU9e/ubzDz9d5lMT42rTXtzGcwYIFodv\nxjf7qciLdr1zVtcn86f7wzGeNItb0CV6//2HDneD7EqfZL616Gm3WJ2+aKdQ4OihqGcD0IAMRJYw\nqkiZGuI4gzWXnNs43oUw9Nhx3BianXidrNoU2mlP8PMIajawUXjQNo/iaB2neRXBflIloasFxbhT\niEtBzU50cXq5jhiQCX71c+Of9dfeM0ltlCZXaWP7aSZGEM7kXZc67Q1gkqbp0/MoGVLI7uUAEO8j\n3uQMee8H7D0yHe0YYQdcdLRNYXzdDcPXd3fbH3x/WVk1vhPtY3loigE6nN6boxgm8+MNKsh00zxN\nvtnt1F99dHSAZseH/OOXdyQ+ITBl9DDwqw/nPy2iBnMwUuFDZL112FoinQ9NMKGbmy5EINJxmRqm\nwEps2yxCJ1s3vMnlyb3DkFTJbkIyH0ajIowwTAlUFVz0pAeQYWRHpXI+BeppoLQ96IjJVPvBg9lc\n7HX32dsv+IurPOOZSJ6c7969rmY4akBlmWO1X8CYaAjy6nd/kJ/7THz4DdVzwHaGdCBRZawnhCHr\nPRLDWnDeSdpHS/WT5dnv/XjToy7tb1f2oAmlLsqVg/rjwyfl8R+fPTnev/nRdz7e/AP6ofmb/8FX\nf7k7+/p90DaM6+2vbrJvnd3fi/dfV33+iNZPgCdbBaTLchVA9o4QqMNJ44hLB//1Jec9UCjIw6pJ\ncR4pjwAZ5IewuaVqnO6gtZFgvNz4TE9E0/NlcXdnTrTMw8QBMO8i+6FXvehkYG5XMxJNb/YIDED7\n/PZcXLtvXv/Ok29398NU3/29J8uwuSge3p5B1qnVrjlBeXrgTnXLj2yTBwvD1mYtTCwgA43PBIZe\nQWL8cT59l2ER7xN64pphOZjz+pEVdJCLYTyIx1N8JE2/gidvsvL0JvtD+/WE04s3L3/wO735LH/S\np7fLO4hDcrgPO7VZVvjdi85tvkx+MY0Sv6yWAH3cDf3kyNfsnA2F8Q1ODNrbrJUtg6RCJu6GaLCd\np34nsOCtMsfVZhoSyEvcFf4YPWveIqcOkE4OcayL1nmjQKJU92dx39Y+uanm8TMl49WdO2QTWIN8\nN0fJbPVEX3yUb96dvVwPEeye/u7mgXsKzJzZ9LGIWWDddD8sntk+ok7dq1nA4KySuUtso1LR+kft\naFw3ptZKM3G0q9nFGp3CLGmn+8PJx4ubrW0yi/S8VXCw+7PDHy5K/Lfix7P7ycx9M7lbXz5ZPnxs\nK9CMSWm+XyfFZnf6sy8/uMAYTUV9sTkNJUTWsIua6fjGLkMV4lljtZ7Q6WGICFift7QefzUx+uPR\nyBj2w5z6eU/lzQqOBJFvAJ+92yVdNdC4/TV4AnsSzDAH6zo83ZmmO69b/KFhnyHOXw06ASUkzLyJ\n7PrFoTB3R/g9Sa6gF+g1HkPBMPRRrcJlWW35lC9X/c/FSOCa6AfhUQkj5haED5KgDRc3EJDYCxMo\ntJD8F/0G/ssP/XtklzTzI5lqckhxLc18x2zOoFKJ/Be/RSNi8g7padPNJbH3y9Z3LKQcl4v/6G9c\nvJuyYFkZaC+zEZBU5MDYfANR/bf/t8JNtwsjO8GvCy7Gyi43MqW0TW7j/9H/6aRxQgU0kBDmQDd5\nQ9OjTGnX48Vf/2sbMbknPMeDzloZBlTsBDeqUCg5/OnXe0n6wvVugfs+sf1qz8OkGaYo9NH7v2R2\nwE3EMXOaCoeOxQ5hFuNRDPA7/8p8Z5a/naMoPWTJuzQ6HvBH3ZGP6FLDv/WdUfSzLSks6fJWhmbS\nxEFnaJ+4+EirxXKXBWHb+rTgaREcPtmREvwsrTDIyJeowMQf2+WpLh9YU6yjp9eFJRkA3RS+V+59\nSds1Yiz5sDQPs032wEeXQUyOa96Kcrhsl6W+e3b+i8/O/U0c0Lhk4EuqcFyvtjg/bFS2O0lHq2Qb\n/JQi2JygO0qSpFn4XVv5ocsnh8LBaGSnwRfVzA8RCDVahFnqp4m3eXqc9cUAYU8SBB2nI8OV92Bp\n4rzbxTQoCXbfRK/zq80XT2plT/UfbIZRf3JrHg6ujwFhHu9zehRHXomsQxVvJ0wFptkhpWxoab0K\nBRE+ikdSvZJJdEDxInQGgRB3K7GeQnTsH3SUXJJuefrl9iQYayoQuR+70+HhlMWNv9wcf51nj/vh\nYU4NSQCTRKF8XTTbM8s2i+yXr5wBPXtMms0ESJB8Z3MiaTJM54nIX/6HQ3whn/T1dAuFfzgvtXnH\nnOk3UIQFI8VvaMETVChAgA1mbup2Olksvz7KMwgJ39HajQy0CDpR1PVtjPJxN1V3zyO3JhbaqQXZ\nnfgxqe4X18fs8Xb6t79i+fcPp18/adX6CiB0uXcp7a7KqXHydN/biiPakGQO1EYuZu17E8/KY7ny\n8SqZhNVdrJdqDlCjyztOtamns4IXhxxF11+EK+lV7FYg3Q6K2RGbyvqPiTrk9O761Uv0o0udBZCD\nWXVrfhdhX3Qf833x7SZ/Eoi+mZcMYksYbzdiwtspKVUyyh/dH5R+G3G0BFIvnY2oOlpOL57Ivu+v\ncvVyszLTwMHI+R5xWDuZ5nSTkVAn8BClE1oSB2mbVa4XvbTNpEMIz3Ix+FN9XURGwpZH1+kHl3/5\n/4n48xt/ny32mz/OVt//J2dfL68BJtAmsamzJibc2IdpSHh6HKbZYbSUjSu7T8b24To+iSMB1PS5\nzj5J2v0OQ0rgvM3sFvA5lKWH1KBz5KIGJfgIJS80usvWpWseVwzN0yg60GdZ/e77VQ8ucWEGPrl8\n5tdDPCQ82++/XH+wmF+TAroCO1ruzXz20aw33lft+UV3uNdWkAQOhcm5cMt0XXgGCseZ2WYfk3oe\n1RY60PnasKIeZqEmwDlTOPUlR7ICaNGOlFJut+SJv1AJQQEQVWEyT7oAZ+61T67wy2O6eTdPl3u2\n6jhOfvD05Y0wENmOFdYkcnTNOI2Iz6zzWRRVTOW0n5s2HGz5i92jHz6VrH3A2WjS/nSn+RLq7O53\n5p6qHPw7LNp+wgylkxvdX/kpzEl1AcXs/Gbz6DS6r0SeOZleve+NyTEcTlJVipt2dvS9v6ZMlg24\n6bZ+ESMFFPU73/3cnX0Agw3EGRLlQD77+xfvPQOIOe0UplN8HtV+ZNzxwZBEXEBoCaQ8vSOSoakj\nHluOlHcGY6pNNS0A21kLzcOnoFJlvGaJ0yxg/4QdUAc3p8l+HF5t0vll2XbN8nK2v//69Yth7c8N\n7OJUDgjybOjCioGnHLmSYBX6BNG4NOQof7r+mVjQSHbKcoFdg+DNb2Y5FNLcvNjiSRffpB8vosPL\nLaEEZfvjy1MP6+mxlw8X6517etHUOynC6Ex1+0R0Kof50dCdWutNPktI4K6iLEw5s6VzDgp6P0YP\nf8KXiXeaoDBqUrjy9tc/+xsZwIBcx2VJcq6J0w6b3jpXCG/f9jlofFyu3XDiEARPuQsqYdrKcByY\nBXkMetrROywR9r3BjiHt0dQ11rRweffe4eejuYuu9NgB5tnEbKT/u//R5/yf+y5MPQ1x7Gtpo5jb\nEHRwlIs2tIsO03F+78zrL1/dfFucPu1vH7BAxqPhXZlkLeQNuTWBm9jk3/kbPw7/KPqZFqkffHzT\nPYZVx76cnI/GT6dwbEgMo2qMfPytzaurCtSkvj3+xsWHSdwoj61JBHZg7Pgun0OVP69a83jy+Gri\nLEJu6BnNkUu6Fk4h8tUoEJW+jRPcjdQoHCmP0NguDcTWZBtnx4CAIO9RYBJzQwexMhmQZdWn5zBi\nRIwdgIYwEKyP1NBYAWLsSV918+lYRrGeBFIZsjLyt2/i+1PYsWZBsBuPlBrrIHgA4IyMhMcldRvL\nGqN2H3743ed6fXRZErwXrTPQelg/2HV3UuduQNNV9vbNlxs0Q36WwPWTl3CX4seXxbZNpmN5z2cs\ngpA+ffR7375/3P8K+E6wzVf7HwlktodJaNTZWflgnBePZAwns/x7b+9R8uEzcWhFzoxDaPbiBZHf\nz2bQuh22qR4S7xUwKRwER71tW6Ic1NQfXB4HEFgBJygXWls3z6PlhoLKheQq5aN3SGopHJISekmP\nzRWFUHSPlrvfzM92NO8muvpCzZ6elbdPkqffCeA9r1e28wb6QJF3WDPwDpEJH3d0VsYtqnaffPw7\nvyfuSpNHQXvKoQ7cF2D7F+MTlMnjhl3/0T/44sttL8FSjwTXH3422w3Rd8ZdzMc3xi/z1kBHTh7P\n3i/6hwbgbLCdkfE0rg8oYW765NGbG5WmJG17OE7i7MNXfPUhOd6Q0zlSndFo+a3nV8/ub2C+1u6S\nktAzDCqKGj0hY2d80FGwEDo3cKAUEzeiLhMJvLk/5O+fZ4cIgaC+qFwiguozPovVQBLsCG3yabSB\n+++I5HSjyuMwhzN/22zCqgDPzobF8j8FyVUirHQ4BEdF6MdhSvyYEGg3M9ojur7rPjn/9g/Su00j\nrdKWJNy6bqU8rLaQf3DI+uCPrw/r+zCbB19jZrKpNtCUmw+evjK2CG3/KG4aENoJom/+4T9caIgP\nyo1FczJFSkb5gK4+jK4rNhu3p8kBim0Ap5+dTXb348nJxa5s8m57wNM8+zLALovjy0K4gAJiftcU\nj4qH1w5leTl9ACKwjgwFZ8Az6im7/vnPj58+4nZrMdidxDveU18HEbDSOkK2G1GSrTsL/PRVHovZ\nbjifJrbsoh9i9kbI1cX7Q3sAaUO4T0TgKkRnOTL3B8SMjlhQ04qqgJG/unr00XT70JKoBxMkADWk\nTwS0F12i0MOg4xhv8WUUo9L2kc6hbBgky5JsK5V4oGD7wLBDHKoH9Nu3uxh2oeey+/CTaamW0h2B\n9O0tiuX4ZZyuwXSujRznba1GxHdf7EJob3aWtPffRDBZ4+cpZlwhB8yZ5KO/dPLT423GZ6pC4AQy\np4cBc4cTbBjU33z+t+7/NcBAdQpa27OFiQbDrVUoQxiGsQdwG0SnMNy+tKXL37hwaDJxytLd2qMZ\n/ea7Z19QaMfWIU9ybNiLFxeT33Ytz5qxnoqyFXS1Vsn30sllsX9oTCGx5oH7TpOTox1hYM2H5qjT\nSU8xuBxsdLKFTjb9DsVAOzZ8GRxhuYWHKM/bFslhF14dFH8AF4sv+eIHC6wjrPcdPf6q/MJdxsdw\nfypB2U16GfTobUrZXVkJKnxHM7H1Eww+Ns/7rssi7Cph6JO/8l9Xv+4OS8wTj4BQXjXepkQL0CgE\njKMfTOYYHIcGZD83zCGLURIYophg7qJk2ZVzZeHy/NCesO3SnvbIB9vt6zKerMbdvc0tJC2+X7xH\nsFKTy2//i3+374IkgK01e3tBD0n/w8T4aL3tgKfceAbEN3zVFJMjrHp/B+10QqjzRFS9YCR3mn0l\nJiyGZLw6vj1dTectcygShzuX+LQa7dfHMw6a3opl/jRywJ3LAuoeqm7JD5v4yDBUaTS90aYlhnHR\nhwyTgB3BzTYkHfAuWhcqaIS9HUfF/cOgOkQokm8jcEcYuwlPlLcjkiJG77Fv+WI5Wm0mcATkBsMF\njv3QTIkHYNQCSs+qAPD6k8UGy6hZzCLcN4di6luWc/ce+RVYQGQ2JomoDMyK2Z+8KhXAaACkj5Cn\naQvFtOqaSinBw1CPCR+VipIN7nM49qB30cxZMnjNImO6EeUh3s5yXsGDVH05QZncbd3qtNrs4yKy\nXuu8mBzhshNN8tFKUKxHjRakOo6TS/Yw6FwEiOhuwZxzZR+tWE05eNNzHCRX4KGSvb0eHTjqgtFh\n+yefjW9M2ucVDw0wzrYCJchxaw06Krl4OjxYCPWw2gPFR2ys5twPPQIoIc5drUp8s3tuQdSzN8ND\nW6JOWudOfth0fRpe0tlXu+8IUFRNsG/KkNC3VbS+61gYQUs0VX6kOj2Y+ogbC5KwCGlGg1aQAu6b\nJeTxeG4koBYByWQQnVE5547rlN1CPB9RUWVcDa08Lw7RIuGaqBMR3+wHaAQXl7nHylU6xNMwivTs\n9H4fzg52DjDMdTelCDBzZgBDqFUkF6g/PL+GWOOvUrVkGo1xBq77le3amEIpJrMOYq0mVlgEicXM\nu15lkqx2lEV3XQZZk6lhZgePOJWk9VHeHr8MetSXDODRV4/L4d6k6leFJfEVZWz+zK+TH/cPpzuY\nSNf5vh8YPGzAOhWYsyowBWOxpUP2cbHXphaYE15DrINxEfSe9O95oOH906/jQRsACV0WuSbxBuvJ\n4fQwQnK8ieYDjF5H04nLZ7ZUjjs+4G9+vwCGFsvZFnkUMCLcD25BeV+HSeLKDpTMh12cuQACGoUp\nMGmd5371xCTQsTYnCWeMggyYOdsPNJGxabF8gLo4kggjjULExuCDMm3hfHDxVBswuXp4DMhoJCNm\nvJz3n3/5ZyV/8mSoJqDYq8koQub311z4tfLqxeVX33x/c/L7yIAeFnHtCEW4BzeiSJKAYkjDN4XK\nqITACK2D5kwoRQUoT6iEUJIGw5DKRLbMUsuUCbw3qdVySzgudQ7t5Ob20amC1hAacOE9GZgmnt9B\njIDe80nnBcU+Xfj64CR3231L+nO/zuFsa0JEGeaMtjVPkLWSBdGx+TVmMCE5nQyABHA+AozTeQUF\nM/UbJBjMynAXfYi3GGEfEB/cOFimRWV6nQPsYDr6XJYIdR7wuf38H37xnz57LHpnGgjbc5l8IHA4\ndVNvb38jotnXt4vpazPPb6ERRFpHCivQ6DsvvI9iF9BudzyRlO7R2bNdhTEittWT4DSmnElI/0Sv\nwO8L5XVPIfKehhYVocE9tPR4KKYQa+gP527feOZ3mS5RPEfHoi9D+kCgnM60IMS1Lo4VrqWUTYMA\nE/Hk3oGO015SoLG1XiYCEHgvFK2P68se2mg+Oqqk7L1BzHERDUabvhSUE9DZXj+RAwswUEMRSkln\nKcG4KlgCVkadJzgwYwgzctrv7//+ybJIQeQSZujkJjtTc4fCLPv53QbH10Y+JofHtmwBZyZQTxlC\nHgBxHyRGeETUIgLUw3Q+7TIICTaMx9pK50LduxIhDwZDHXQ7QwQJSXpjbGg5w7Z4iwOgxMdh9AEh\nZhTwoCBFjDPbPp4yWGyWz1oINujIgceEMe94gKmrIYIuxE6YkWMbHHXGYMDSWowOq0HBvMyVwcgb\nSyjXmNh9HVvK/TCej9DS6IxUmCEInnGaGMcY1j11gisYSLao/SBiJF3fCz+gUL2XP05zYQDC3eym\nfJrEU7GLfvsnd9PhXZV/+8w2B1fNYNrpFHsryBAgxFQjwlwgZnJlU0W5m0o0eBIoBETBBUpr3yGm\n6bQDHmcuwZLHxHLCaa0IT3G8tIu98rAr+u17PlnrTJgm2IDMoEfvs4IaDePI8JYzlzEHliI/WJRK\ncHx7ezFAyN6833MVlPMWA0aBcz9Qd+y2n95DyWo1pc4gbrB2DRod9xaFkFxqBJEknmodMUx9CNaM\nOEYGOxEPUEHcy9L21iNwDEzTQjH9fnGygHgxxBBmX/7ZX6wjueuuH9ZNsSzbk8UPTrQ36iaDitp1\njCSmVksSOUKD1l4WtDXVSEeym1FLiPfGYWgARcoEO97GqQegdEPaCZ0R7z13I6AhgjOZyPX5lxgW\nND64VHSvzaTEicQqKN5ubHL2Nc9hLxxrJ5xx1vpIMaWLiPMByWFxeQ/FoEowdlAYe0wtksyVfYgp\n+M8ugWS9AkccNdjKUQISRpggcn3xzQDpLkv8EUNgasTAR8qNVcSjMh4xeBwGQ1GHOBGa0D65/MGF\nnNBVBuQIKr1rfvr+2XU1lpV8OpOn32uX0kzm8NwLmLTRGk6wt8ARGgLSzhKRQe3FjlIc9A04kvQG\nSa1AMD0Qm6JpIIcEOrcNacs6xD03xiHHqMSDu6lPLYaSXTxduhPb2i5gPqhOeBSpzbYaugJCfh9/\ntOkc9glo3hOrCtxvDczb7RoaaxPdCyscQwwhq13doi6jPOVHEIokIeRjL1Bwknjd8p669nhul1vY\nLA6Mh4hg5cEyCzrgXgU+hg4KMGE/MAc0EOywJMZPX3zLv5saaayF11fi9GK7WgJ6DIGQmCcHb36+\nmMynKIMRcukmrsTEwUg9FZ7zaBi5pYJTbu7ZV4LUqhc4ABoHHrW+n+6ms4YCGq6+HEEOnHHFvQcZ\nctioHNlfPCuhwPWz7bRfwFDtx531bS9TNP21WT4q7wEdUXW+PzDvEAEfKcJRPYab/UXMTwFnGeGJ\nc4oGwNoYjzrpUVWKqU6BjuTiIY4d4KBgLo44Xo3b1w9VXRAGkzHs5zNfRw4CGqWFwE4bfUyxRAzE\nEOaTcHCe2DodXZ/Gi6V/0020kRk82l0tchU9/VFfm7rTflc1DIpbx1/1FlqGL3xYkJqQQIglniDf\n2TXvcdRR6/PtTRGpikCMCPaKSaVZd8Z4MDBxUIAzd/HEchuzSKqB6CUmA9kMgAdA5LfdSbrISYWc\n72gnHh8/WFM7MoibeX//hjb7ItYdSXl1vwTCxvdVSvag+3lI6uM0xkNvERZxKMaGpkM2OAEPKzik\n0QAp18bVQrOFzfsoSxIpR/CauJH6OHe9VN7iYCjhklO6iwgESe2DJNTgXvTBxL499sV3mzVYzCHq\nrlL9cv/2fRa19fjm1+yji6v6Fw2djC+OIH0Xdy1tCVMEI5JY4g/MuL5Ijpi6UZOE7x2bhi6SeI15\nLslNVcTjxAHt6JW5yA8meP0wSn3ipq8HiB5fr9YGqiphApfaiH0dTNTfN+ftX1g+TTRBU8CiF/eT\nex9PLQkxG6S083z7Xpx4l4A+hy/fR4j7w7FjaMUJF7fDKlwOTt9BviOZk3XotZWkkqVIN7OIcxFI\npYGr1IQ1B+qCtjYaMG5rB+TwaOZbcCPFE0+6VoMHMSlMRebhl9OTEeEGfiM+Pxd/+B/+Jz/58Q90\ntbubXJ1Mfj3+pS2/LmoJCfE+UWEY5khG7SiyNdQ4Tw3fI6B2PrTOLgHzobCNM3Os32Y59w+F3MID\nKaX+fLbZzhYSO3VVlUNbp+aaV0hCx2K2X0682h3HhO8Wn7onc5RO+oOY7AFwVtF4HFfT6zgxwt0X\n5HgQ000BLYOZw4VqnInPpRrsFl+ep83rWzllA5xC5xndL0GriUc6NmdtJbpGxyFjiQaT1tZkkOiH\nbhp89ZxuvRbEiYY7C2NmotGTFi3zjTmcQ8Mm9f5iPGSh40CfU2+vL8mv+Xv6JPt75R+oj65H/M/8\ntp27LVjUOlHzeKgEMgM3XdfFyzIrR05HOuQ8mUVm73W6x42YtW5DSjnkXLMJTA7LbrSfHZS4d3W4\nUMRt4ydvV70gsYELe3+6Gr6eyW3P6Ery6Dxub0vVuH3A4MQYNbuzm0MpQyL5XTIddxlHU3JcjtBM\nO1+tcswkjy1tc38okzSBdeAhwIwYyPtBTqounoyb8TiJ5sxnfD9mMyBAUdqyvVzKYZqr1uPJgLVa\ntkmbwYkJ+8l8l93N6CntsU42u9YIO1fpiKBYX7wCYuMfn30d/GuR/OxH7cfv4r9TlMvPXgBoG9pH\nvpNUrGONN1EgtEk9CQb9V7PhPxf6y0ca2To+bfD2yXGMQieFGGJ1OFeC/e2/mnsv1DAk+dAuWgVp\ntEm9SrzsAv/b/7oSNuIbmxqfsV3MeAc0bSf3y20m/if//iGcrRfQJeM4DVUo+lbyAGpyOLnL/vlf\nHTFXxeASFUZmyCRUk9oRZrMDgz/4X0ZISyN9M2l9Wk00SEuqGS8VWb371/85HHwMPSVKRFUws8NC\nK510CCNM/l//Pa7ZEPDpOuWIHcf5UYlRnoeb03ty8r/5fzuXKWZNDkqYEekCuqjP60zrtPkX/3cH\nGWe/+vAdgWgkKtq/+P9T6189u655gtj1v/Kd7/uJb35XXmun2qFSV3V1nulptwdLljDIYgyIOcWW\njEQQQkKWgAPgxBLGJ8ZItmwLELasgYn09HSq6q6qXbXz3muv/Ob3yc9z5yvzMcb+fY7f8mAR+dqL\nm4OApgpYFnrubeL3t7qmabcay2Do+HwItjZhFQFbNeN4mdjU6DtdQ1hjhG0hLE2O5nQ0x2yI96oU\n+Kp8bSCyiO7AjxcWvBLzyK7neEDciMXcrZrxmjnYeAHOC0wtSmkgg3wt07BkzSr3DoZNWKeu57Rd\nxRfMWM1tSvsNctN+CqIZwhz7SrAVT70/R3437Lep6KdbAhNbUvNAQdcd5PFbF8OvtpOqG367fxvV\nGiSjJMAmRaiQiAyl1lSA56Z3kEJ3/O36SWT46c3+oJ7t/viX26P8zQ+uXt83yyO644ws87D1bbYd\nBtqxApftxK/WDDWAO7SLdqAm2xU+8+x2/MjOds7QNq0LWB11hIYEZxFuj7J3dfDiP/rkR++tqRlg\nBr0EezyjZbAlwSD4PmoUnbGdhEWcryGvWwGGW8VbKTqd1L10iHiVloMGqnwTbDLejANyFp9t86PR\ngf4zP5wMJJHghaqHykbcSfJFEfivld87fbzF+Zw3sEwVGjT9lZTLH90b6E/DB4O3/uIqQlSGJVAP\nWmjsbSvxyWjP5rhpm3YDntcBbOh9sFpO5Nt3Qe//J8/1R5X7br76wy/H42cUTDkerCoxi2Z3p/H1\noGzCn9BhfYFC20A/4UuqYk/v773kyo5OXLUv5r4frJGFyWqMtttVzx+OfDA+mmyev7eXnL0a343L\nAQS+7dpgXtTVPcfd120c04crKXleswAIJgwbJUNp5iSi56HfCJGXvot6B/kMj66gGR1aFl6/J44S\nfv3ldn3z+JQbDrIbon4Lw5XI5RG9kxy+hNPi0WdrD7SFfD2dxYuzcnk92r55PP7u1t8sHpyMlnyD\nQyAK+zbZSdHowQZWWy9FC5EnWAcA0cXR2YfyV514NB8UH+f1dx/9fPuT+9M/f5Nt7lAQfRM/uO4G\nkOTuxuCkkcPwZl7hzSQB1pOxS7ZbnMwRGozkrG+nA7/M96NNDz7rzO6rFBcFFKxddPPqYLS/+oeb\nRt5RQCIenkeKR0Ww23VDTmzcbzs1qbHFIBUz/rRxra4nhEnNk067Ne7lUcugns6XD9QYz8XOTOKM\ny3VVfvrmR9/2gzgBw6re5XG2nzQ1mVZPxY9yMf78WTl+f5GD2NPHm5ffrPDxQbz69eEYlBV6tj+8\n8F6DTqVrOz9MVAXxriMBWNwKUhQ3NQM1dHe27w5/KnbyuBxDIq4t+6L99D9+iJ/cUN7Ylazj0pAE\nyt7EQrbr2UZ2ulm/A56iZhAiGIdoxXiHiQgBpdMMk/gWdjraXfsyYG1hNrhsd/S4dcm7Z6+5uwOV\nim7FaosL3KggH9i2VtseD0VwaR1wGndjJzuS0Q7AYrTzbMN9Ub2ZeFA5GkIU9bxZxHlcsJuajh/e\nfPvdhaMMxrchJCZsItdWDCrz0X//bbS+nfGH7EDB7HC9z7KHh6f3jwjU7QszesDWx/brv4yHh5C3\nhGqEgbrQNUZ7I7TixK/aTloYysOzs+8uvnf7FOy2TwbRq/1+ZZb483eev0NdnZLEYAimWaQblxvC\nTajtaP95+w0MTTke9iq2G6+wA0+8txYTWfXbPfDTmy3BB4c43ZWpvrQqCCOCGoJ/9KaGfC28f3i1\nFsilnBqrOAJHg53xLASUlAFqKjnxHRbWgfEIjbRVcVIJcHJSX4zeEXYXQs9lp8Nper2/vXfqbjDU\n6ZqFmwbpZe0SoxvPCvj1V/C97173EljZ7Ip3N9Of7Fno3nz8qbmXOC3a23jtb2DDg5oiHPMtCxQC\nijzwjruVBazh1XepDNKHn8/JEHEjVJM11Nbt9y+++Cin3Z7YJKwN7gf9rlrmPnSRoHa3T7vlEawP\nCHmWtAXpNOHCGw3EbQTUNdMEjEG3ex8eocVmFbPmZuX3JwNq9u1wxBxstGWJDFngMy+xYB4xFjT9\n1SRNS2giGdwmLKQbwMAEYIypahhFkgQQRvTrUEaT11jjKGWryyJBxcA2cYUNCJzMaEZNp0jAtGXb\nZ7T7pn/n8ebaCcjo0VaNT3/0vc16sbz6+k2KdsYeKpz9SLz6kxhJRWysdyFgAIaQJT4LZNzSpIEf\nbD+evjVeFo+reuNNtAYebUzQzfev/sVdWi4/8j3GTwJQouQhc51MxGKVvLgIEezNR1Dmo7DPXemM\nM1JgCqC0iskLEMulndzFDWnDY3a7nEsLveDRHcpXDgLdKhTFQxNa6Q0l1OmOIRxO2kkAvgw84DBu\nBDFRj4jGhGphFJqLGKzZnpy+e7JrUcSHMa5LAvH0/v4E5ZseKtnfqpgrSqjynovZxzfs5XBoPp6b\nMbR7UfrumcS/7i5fvGxem8njaehHXcguD+4BxDtbBxy1mFpEPBiqKOqV50VYQ4oendSurz1l2MTE\ntWnNFHtwtH7Y95SdbJLSxE2bxFakKZJzz7BJ4Sf51ekLGRsyCN3WRY4S6hn11EdWhT6YRZCS7eFJ\ntLMmKKYtPbgTKi+ryYRfv8kW0HonnRcBcoExzrUqdr0d2PeL6+cZOD7s2rjbkBg5RyKPrO9Fw2L3\nqJPA1dtocDSYYYV9E3iU5YTs/y4PDi8SCjVJ9W6ZM4YV6UiK6uezYBvZm29dgaBIwrf3P5O/clcv\nb8PhvdPfPM6VpFZNIXDQxduuS6n2mBjClCdeeOxssuu2DDbo++3leoz83OT7zDVGRuEwfi9V/yxV\ntIi3Uc9w75jIrfRNbUVM8+Hxd47eXACx2+TQV1vivEldYJBBILmPEjG6hc3Dfn9fxmCjRM/I4/fR\n05c9EUO5GO+XEG7TVMqIKIwwDsHRsOlxG1cZda/AhKZYm9B4S3tHkcMOoA98Y0EJUHSR8Eou1j3z\nDWbB3UnYTwPD4yPooGgu71GNpqoPojVGljXM4CBAH1wfdvB6EN+sb/Wte96Hj/b37JTNyiajlgXl\nFsIuQsFd1GGwFLwHCsh5sC5TQQ1f/S206NCLibE+vteueBWehr4Ppv/3f/QTSoOdkKFb8ngzGNV6\nG+FRlMiDNGxexBVI614/CFsA0mEJfWwtAqywCXYRhlQNLbSoUai7vD0+SFZ1q/cGxgL5JoRq2tHd\nCnPqMHGEOiItIgTtimIbwUDLfYooMr4yyHLqGFJlwTd9Qw1IIbf38tc3tQ65bHMu9A30qOvs5mAG\nGakz4DGzkVEmphpRBQHdrtJsVsD9V4CjNxH00Z29BMfLzbKrquAwWr53lgHxhQ+VBQYWYW8RVRiA\naIulSODJl3/7+jJ2i7g6Hr38xpX5996/fvY6ki+LUUq3DrPAJwDdBnfchqguRrxw+7uBe/CrTmDt\nm4hihsDIwHswGPsuXlLUQlnADWriOjSbBbhZf3WFRnlU2pzWPRTrAjUBR8YgTZF0FJssJUFrmqQD\nh8xq2DHGCMiQUGINEok18bCMMUzkrnD+5cwwlgxFW+0USVhj3LLbFWBdUA6aSY0d29pEE4I4iXdy\nude7COT+i1BeTkdDOCG4qc0yJTyXmB2tjiT0OcotNoRqJ7HHRDOCrYklawmGm+PZT6PsdKYCfvaz\ni9O3fvJ31Me/xFd7H164C8qW+Cgn2Xq9k204dN2tGd/bf++r80y9aGE0D1YyEIQZ3jjjnZeMMg+e\n6ZkHErVSNzQiymZBcLMyEY3kupXh42AOsZ8JcwcB6zHxJXfGhTGGjfxWxj3UnKmaacMUZl5QrUxI\n440NI9Y4KGfDerFZE0jiQdjZuuIxaOUqKCyFjjqLqELWBXhoehUAQp3mk+z1HoOVGvBvqqMg8Nq1\ngOOY4xg3cL45WmrQMi9pB5ZoD4YQRBD2CsWtLWgLFfzFiu0P1Qyj3Qru/OAPkv/qZ+tTcoNp0VEv\nTIWfJJVIl/I0IPVV0A3ffdu6lWqHoBJrexNzaxqE0hgkeAp9oqwkDtSF6KRtCuyjaKCaPvaGqDkA\nRjcEWoEjOtRbhk0AnoQdJh3lkpC9WgDhQSScJo5ogpF3gDXxHgWRDgHCxFRf5BTHvsVL3aooziyy\nwqMNs1AdHbZVDK4Bkdjae2Qptihw5V4hgHvzpN7e4bi+4n1fcKtopIv2pvvDhQPgJgKiMGXOWIQR\neI8pcQ57y+Bkef8Fu9PiExO28aPf/iH/xa9f+TCVM3+7oTWN5eLdcDrZpooGQAvpZxfCaNMhC2Xs\nu2UCDHXa+8hqIOCtw8SfzAwMAhGYGNEQu8huFywH53axEUTcXULNQvNI96gKnCaUsdqjKLNCz5BC\noHUATBpiBGOgHUUUe8qsaV2wA0zZeZbZviGBNJQfABOKs5DLQS3Ar9I7jYVdhyTqCSkMcRRzKpcR\nuQXcjim+P2WNRtyGpA6J26CBUIKd7wHuDcXaUhTUOEKAPQYKnQWbKwldjB/sqx3Zz5qv0idT+emf\n3QaG79PL6x8tqcX19W9EC8JrCnodFbhZfzb/ZvGF+yJKgfZ2XFlEBQHkXQ86YMZxUmdieQBldfzh\nayds7bu6a2Ek2zAaCU1BUQuRj8ffMot4qBqfsroPpqP6Sn5WTo4UhLwM4533RPdAwHtMqHHCusTH\nFdjJ2XElsIpcZFHBuTYdNVpCvEw0IB80k+JSU4EQYkgbwXoveKqvegIioaM08I3veIEaHPcisG3k\njjfb+xoQg83IEO96jz0QYgm2iEuOo66FIX0di6a/iunKF9OT1a83J3uvZHd9O28iypDlwknRXXSx\nd91kDNfbl1G77BZ3esiN7gnaxZyEfKsEjkhvOa0aivAOwHMe3A5VKz0E2YBoi22scVVvigxKQZyy\n2DFLCYvandibwsWvXvcHJFRQhXqwk5wGbczrPnRArGcI9Q23EQTLSJGOBw5xzZS2bS/4uivS4e2S\nQ+Gre3tHpbXQIsSNColuDLQTNhheAMPTs+Qap7ObUWq7qOfY9CPKRtH4cgeihsBYgnrrMHNcI0l7\n6o0bB5EAskzG6axrPn97Y9mdZLaiD6LmaX3yqHyTUYpu3GqFttVqsN8s2zwUkSqJDrrj5BowDmqf\nrZEMoq7lHBupFGddAq8MAdIJPHgZRYJTkeeNdgLSEsq1HwgPUToPfT+RGPfS9GgyLsw3f/rpzQ8R\n9xMYlcEt50FEMNdtzLTzRhoOjijMYWaPXwfbERPdDpBLnHaBjtBguj9/E0LLN+MHwbZOu64LGpYx\n1WMsKUX+eQ+tPE5edeF6h71pZUWsJy6QNozkxgMGiDSJjXLehIwYjbTVSiVpcN7A4lrsvXM552+N\n1UGnZldeTCy5vLotfvVvAi1E4ybmct7fyZPOeOPifaVpzB+/0QG0fUds1BvUNBwPA0UMiShLnBZ7\nDWjaB30SxL0LgfUrwyK/U3ZxNT8i+2Dmt48OQ42lDFDZv/s+ufn2r3760z/ENAwVrNnaxEG9dCzz\nIqI76ZjyKpJNLQIYr0e6cTbtJEqU9YqEAgPKRvHg0RrCrVm2O4RL1XqmYq86HRsxFE48rD8TmyLu\nZ3aShVHVpkWFsSaLrWmY2FtAY5gDFhiMaJj0SiWo1+BF7C3FMBicbQ1fD4d9LNXZ7e06mcty8/Lw\nfrgbUKijOIVFmR8Gm7VPfedz6GXEByVbwI6lBE7vrUEvu3DXCyKMI8bp2myHMFzncUkjZb0SrqkI\nLVFMl2s+HG446CIUXJPWmUDsJvfT5tW/+PzTg5SGY1NDooNd5bs3EovDt0ZUYaSoaF2sl3gMyOXo\nynqKdMSYUtRij2RNzaYuAGLYFjdbVmwFUQgpj3yY+K3rWfY6BMj1pa/IfiZla0OLcO6Xi76LRue0\ng8QjE6mWaEaz9rIaxIxQ57vI3RAKa/0guZhM0NK53c6a2gQGhcPx996/vS3pxqKKSJO9vVfOllmO\nOjsmdrfLZrNgDJlwITq6L9rr599Ws/Q4k51DMde2Ga+hpnHtHMqlpBEoIL4LIYhPHwYL6YAqGu+c\nt4xTnO0P8M2Xz5/BY9HF7s4cutS4brH8+Yt4/6P9Y4WZpBSoR5blBsi+nh33rXIMe+swgOv0vJa7\nJ8sIwU2hFqK2UQje2aC3mEbYsy3h5voU/BgRTFfzoQNIkUUctOWJ3z279+UR4D7x3sIWU3b77Gx7\nD6WSgqBR1y6mMHdRfrR+aGxbv+riJL4XReHD8Kz/5fX3LigKNm6j3GCfbOtoklQV6fX6tRQTfSqB\nyAIm+797sPh2xD9ZYk6bXsuhRDHES2hGJZZ+p6hzYHwqeonHo8CkTXbeg6WxRK3wJkJSDHy1vFlc\nfJQN9xi5oJAgLk2w/8H1L39cLa9MTDk1GCrQjMSgar6luWCK2J77qEcysdqkk7WWc+BBWn6BkgJC\noZm1IRGqtb0uUHmAAGsfn65mUnVZZkpsQUo/mI7/VO4pCZYv0Ng0mpDN119+/vrvHuSEWT0eVbHy\nUBxLq69gLzrfZjyj749DU4A2qr169ISmxFy/IX2/rbdBTKTE5Y2uNnBPZ7MYAqkm7vxP3nr8jpAX\nszyhrnmV8DT1bZvA8WWAdbXNx6nZOUcQ1qxPaF9fQz0A3G0lWiEeYCmRaVtHVu/fT0ej6YoANBbt\nXWeJ/TsfDO7FnQ2BSRNFZREFXkLHZNiuh8sOR0hLQSHMyCTZIxd9o8GuOlDz/biViWNW2kwrUybx\nNuQigFshp9G7OendNrdOOkgnvLwN77hzOAGE0rZ3OkmSGppPBunJnTOUBqcbI42COy+/e1ZdrPbv\nyOMn7ioZ+lfD4en1Qor/nvuW2qU5Ta7ygyXQw7by69rd6tH7NlfSrWFleeyr+vm93499Z8UIn1fe\nxmG0pEbD9V47ID/oeaCu6nK6H6FBGBVzV0HUNqAGVJuhg0G6drLsvcOj6fBOHHQhq8AiIoYsT07s\nQdFUESJz32O2EYJ1GPq9XdEnNMYtd3U/jBtlp+malDpbYAi6kEWD/YevlXOdWpcPwqa0BPwiKwyw\nvfJ2/wF/2hVhxDTzEA6aWZQMn92/WkGLLYNhKYeBlPa3gyN+O8+KaF0bhwl88+SC6qezpx/1g5/o\nlX35sn+vh1dKjvSlowk1W3T3zt4/XRzj49sLE3BH30nKzt+iGBLnVmF80WrR/Ke/mLr4zcu6FhXz\nJW9zSPi2E/ce7coFzmD/A/+8meT1Brm+wRTGmzipo0Wci0TXenp4ePB3Lo4GIWECJpAoqlAHOjy9\n280ypKvGAIdpOVCegZ8NFvXYoIqRpJFpOMft2F94EeJhB1XEdMLpp/0jUS5evCh7UoekQiTx1sD4\nnLG//Ecnv3p7squGri7xnk6G618qvUwsCNAMOj6Y4K/l6TvofrYdjvSaBVWXInAvCh3B7W/1n92B\n90ZDdpgHb56HH14M2wfP6VJG+3vogxd4ZQ6ra3ucbrbV0/FRuTm8pYAdNYZU6e5nTz/L3z1Z9wim\nudhyX04raNOY3extwusXkzv6KW1mcYR/FaksxGsGDfI6sqnadZtJjjm2B+VPmli4gbZr4FqadLtl\nU9GsAJydIRU3qUBbxzUEaafV4gQ1GtDWrW9bNvjKrAfBCvEaRl2bj+PZ8smEF/RnEs+hiIa9D/O2\nMlDfXVZ3lqtAnK5m526+O0xv5F6QNqUcAniZqYGou0u7C+Ng+lYQH/UbzZd+oDsYtzfi0R+f5sVO\nDGHS2NlfP4bi2dt3EJ0d0HaI5Rt6RdBvRi/fhI+zysjonl4uivVAwWJSoWB5CLtd9aEZs1u6F6Ns\nM0J97AjYOi7vX6wrM5A1y8i6JjtAWfMmpAEBMdipHaftNivHNLxNb+ydEEl9KHmLYYfSuk1JMO9E\n2kVkGQQqGeiGxR2REDUdG3ObnPq9xZTslN/rnCR8E/TtAMrJVaW68+bye+UZoPujaqzyJmlMHYYV\nULx8+1Wkvzcph+/9/MuvDillbtVdDgJnSmDgOicIb/uhNMavd7itEXe0lVEIF/mQzH4Z4zNcftlN\nxjh9a72P8l9+L5rtb/5lv4H/5kP/XnaZjtbjWbQ52OAxerF3w0jueVssh+jffQ3GWRu0CEVEEdvg\nINqE1lMt+OrDf2+yOtikZn7HXBl7b2mi5PzBdnobmRmf/jufxNY6FTdAdEwaoiNLNsIlygTK/Mbf\nHwf+Mnsgd/lGCwHV9r11cH6or9755sD97/9tE6KqaFxiFEMAtKcUd0HnESLw7/+pYlK0Pgj8Dusw\n7KPS2omTVczc9t/4vTata5qSbhdZHJkGsE3DTYGtofOn/whHSlhLldNRi7MG2aSlpE2sZ+0f/VVv\nUSADxHrAfSRZTxASruNtoTuqBRTP734e1ao6E4NPdtAsf7BEem/WLzFg2fmBqVivTQOHJjd8a5Xw\nMmvrMRRw+ConZ4/l47uvbfh+j9Dp8CYKvz5Z75+B6zgFKaERtHejrevA56apDMWKwnd34vo99kkK\n5T2cXqIZw3feZNNg4QVLwKS6Z41nLkh2fh2xQG6hWCWh8w6A9lbmzaDammiNOhiIeHB9eVxUDeUM\nHMxTETEYkoItmGUoTxZ5W7q474+eIsW0kHRDKj6lsZWbzHbMcUs3EQdChktDODMJCVBNC2wc3g4r\nkzKTUnbhrqfVyWWVox9nCb1z9TIO8x3dX6hpCdusC6Sl7c7R40S4Xg+TGVzTId8mDnb3SnP/84K9\ndR695y9+OR2M7hL/0nZvjl8OgWINhluOq8DojVC7GK1EQFS4KzR89fYbdFk/0BePf4H1+NnxBz/6\ntt719PBksVkCaaOWBpZEuQdyr59LWRIU1IoHOoDVwOU3YUMZ7o5V5/f72VbcnXSnq6BJYE0jYaqx\nZftWAj3otzb6/vKksg0TNSSmNb3scZ+Z5QAtdmKjeDhjw61oMTi0oKMlEmQXBSWQPpRVOLitpl77\nkHbJno6er0sStrezWTg/+M3Dwfznt2dbphQEBLzYyHCPaaGNJ3zJ0uroyt/GPYZ7n4X69d3Vt8dO\nn/XP1csD45999fPk5LG59xyA6R5V5uDqJO4PDi5cgK49dRR2rEwh66fNYog++q0Hp683r6d39V+M\n9j4evhs+DTkHmW0jEzfRth4dq+UF7trdNhB51uwbA/EuYCm93jM8YFlpGjvwo0EVkV2PdhAmejvc\nw5Av+nkfu6BYb1b+VHY6VgpaK2zIRbJwJe/sIO7qEKsRWdsRIGgFZbO7YXaVOpn0ndIhF1qX8aRS\nN1Sq5fA72ZU/837eI05UpWSLXyn+WxWHjjMWW9E3AQLNQ4+pUlOX7woSbmEeP3m2UvT9o3sXF7t4\nL4VcvpfEyxj9arIHdkWiFvHNKGkNPisJU4eBN1gwDxaGq/RNXN7hp1fr5sbGLCIkO7z+5z85te0L\nCOtAsS1tc0bWm9Wsa25vVfBRiHBnFJCBxevdMFw7GHpEKVa6Iv4TOrjzIgBc0nh2sBmm1uy3ppTW\nm0rKbnRSb4bAqYlW+1MttjEOmkbw2MZtJfaaxbADD0E8JKySvZSh05i6oXJHR6QxTNCo8O8q9Tb5\n8E/O14O0Vw397EH2GG6/+HY4hLASrh92Ok97xJy3HWVsR+LQNbSAqndmLCT73LO+mCgI+sS9P2ou\nrsu3ehBKcxtIZhbc3hrqwBviSuZZTwVc5LuT5ajcnf168zJ+e8JnfZB/tHf94I/rPz0CRlCAzS4V\nxhqSFgRJTHnI+e6rqQeigzkP/Na7pAeDgYaBMuqOL47aLfBGJuHr4dZk9RLhhHuto7Fk3SY5vgFq\n2Uglzc4EEvVeIAhIqxKWpGAw5G0h1tqXNQ9MS7hnFGkToAtdoSPa0S4Cc1vYEBdJ0vfNC/XNOw8f\nsacXVQGG+QJnmrptgBQCZDViFLGgkjMBwZHWX3TPH/z+cG6TvZivm133VXH65C9GwgDkfRkVPb3F\n1HnGtFKRZpEwYbvVoML+Z8eavfrkywv24wM0e7oqTod3s/Yz5W9BC6NFGASySoiPsPABWBaqsg7n\nx1AZH83ToUTGCWWAIqexE41L9cCCH4VX2BOSiziw3LjWCsZ6CNqXTwrYeKGoJgGxWhPXW+yBWxxu\nO15cQ28rwGYrPSaxUtYSrKme1y9NPDqjb2+tL79OaOdHaAps3ZlqN52IZVD1GogMb4fVBlvvAVMM\ngBADGQctSgLwf0Mvrz/zb7OmQ9SxxUtgZ68PUp+9pz8BtvaR74M+CRR1oRUGIcpCWumSRzA02btn\nND2Bvj5Nz9+8MBjLu5M3T//8sw8fgPLE1zkhDWiPwXhkHdEbbyyKOqDJmmPTh8muWzOHXNvSUdLt\nEinrOVTxOtgNBuVOEN41nfO0Jso6E56spyB8oPAqFlh7T41RQB3OfO9xfx1ADiiXHVXEU4AQADtO\nZy8vOpf7U1q2b1/tmkeNr+J8n8/t8KBfq3P1xTzdzUAhE41WUvAAGQccQGLjaa+bDHawPthuj2D6\nnR93v8TMfP7m6fFb7aU6/tY9zBCQiYU6RI54jYgzCBNijUK+xZmAydnbl1t1wg7fyYq+7MdPoi2l\nIhfo/b+VQq+SjnVxKbkB7wxGCDnmvURBroHm01dllg8l4QJ0zLc6YNC0YTXuUxjXe5QkZcVl4DrF\nGGp421YwaOi1gmGrQ9h1A+o0tZZx0EhgaQS1lAOvJnSISuYwBghWiqWDevHpG3aXCEMv3lP07D0k\nbnkR7bqb/NHby7KbfV299yxvId0OQiFyzt1sZQZ7VGmgRmunKHMwmb2Xfu+foh8dnE91Il/8Wj+M\nx/eGbjh/3eSw8Ux71LVcE4sRIg5RI+mO6sQ2EL999fPPH++PhHptRyE7eXv76fwVuppP7+//AmK9\n4rogLaUYQHnBNUWB07QmzAI1swACKhWh1PKYSTsNy3lX3Ru5GkpRCS6WWFJT+5ABpbWxQ0iGu32A\n21RPrhFQhb0l1HvMpOKWGakiBcvjWd4RT7x0OPG1ztKsLJ/+2X8nGgdjOnX5XaHkts/RHHfD+7/L\np+35M7X3Bx/pCzABkhVhgp198bp7J0iNRw6zViN/fQiDO3cfjAsx6s+68WhbRdlhcIeTw9nLQY9A\neTtO7QIRA1gybjBDxBjimy5H8MmPLzr6nePIr2Sah3Aw2Nl+dyOjOL9BIEoPOQcUMu01I8QS5pWD\n+rZYMFALRmJrOgsY74gxuyjunn1d3hvd+h6om41QE1hvvRXgbC2ojZgi/HjRAcZp32DBOGDtsXLE\nYWSJp2ZHKVDndIUC4gkg3+i4COrSCFfXAxTQAI9LFd3cTrOrHWNH9yy0i7OL2YNm1SGoUwj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hMZ7SruN9RsMWd5ttFvQCuC9b/sN/DffOh9FQQ69EtalEFYN7yokCijpN8UbfTz/7HxxGGMW5eU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\nfH6b+D/17PLjQWPlKq7DNIQot3HaIdqMsCh9axv7P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"text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 重みの可視化(utils.pyのtile_raster_imagesを利用)\n", "Image.fromarray(tile_raster_images(\n", " X=dA.W.get_value(borrow=True).T,\n", " img_shape=(28, 28), tile_shape=(10, 10),\n", " tile_spacing=(1, 1)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\t

復元されたデータの比較

\n", "\t

\n", "\t\tオートエンコーダとDenoisingオートエンコーダで復元された数字画像を比較してみます。\n", "\t

\n", "\t

\n", "\t\tオートエンコーダよりもDenoisingオートエンコーダの数字の方がはっきりしているように思います。\n", "\t

\n", "" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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j8MI5mbOCGvRmjWBbJVsQATWIIK9KKtkDAGgAJ76vKxrUx4IoHCqRQCLRQOCD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\nivgWueSbxiTY/NY0nqNIn4hYbY+oFDrDOsf0/DgV99z3VD4WQbFfpvy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AycqJnduVv+xg\ng/uUaTavilTc8vEzJotW/857FNnZDv02oYD5M3KD9x4wiCGAAsCy02y8g2dAUXffHe353A1q+Xdj\nE1gkELPYO3KD24mw1P9bMwJHAECuggTTZwcpbRHErQ1m9eiKG+/EteGc+nVqVUWZUaMtIUqbavLD\n3ZdnJ4bo7gMjXHaORhs3rV9TaSoloyqYpA0FAJp5Kn7oxV603Ru/nmXnKCX+/beY2Q8BRD8YfLob\n7bjELHoOiMe2DWFCUck8BAUASmPAxC4EQOA7UkmFubRPeufdrmCdmAsbG1YBcJ0ixHRCDGwbedZ3\nLR0HPh1vMdm1vWt/AQCYC4+/b/qeBcCYHfBaY1ND7c77s3qtLyQnJBjf0aiThiYuuviBmcbguCdg\n8PqPTSLnImb0w0eDwXfcBpnwrwOmcE049r3xCsDC70v0zV18de9TAIQwRj/vpvgzgFhGGGmWUOhE\neUqKMfoQAJcz8d4Hi81C8P2qTl35yyLH7vwj7Tv+sw7AP68r6kWHgvXBUH8jmMvB/gRT+P8Aq/eK\nPdYOxwwAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# DenoisingAutoencoderで復元された画像\n", "y = dA.get_hidden_values(test_set_x[:100])\n", "z = dA.get_reconstructed_input(y).eval()\n", "\n", "Image.fromarray(tile_raster_images(\n", " X=z,\n", " img_shape=(28, 28), tile_shape=(10, 10),\n", " tile_spacing=(1, 1)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 0 }