{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Recurrent Neural Networks in Theano\n", "\n", "Credits: Forked from [summerschool2015](https://github.com/mila-udem/summerschool2015) by mila-udem\n", "\n", "First, we import some dependencies:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "from synthetic import mackey_glass\n", "import matplotlib.pyplot as plt\n", "import theano\n", "import theano.tensor as T\n", "import numpy\n", "plt.xkcd()\n", "\n", "floatX = theano.config.floatX" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now define a class that uses `scan` to initialize an RNN and apply it to a sequence of data vectors. The constructor initializes the shared variables after which the instance can be called on a symbolic variable to construct an RNN graph. Note that this class only handles the computation of the hidden layer activations. We'll define a set of output weights later." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class SimpleRNN(object):\n", " def __init__(self, input_dim, recurrent_dim):\n", " w_xh = numpy.random.normal(0, .01, (input_dim, recurrent_dim))\n", " w_hh = numpy.random.normal(0, .02, (recurrent_dim, recurrent_dim))\n", " self.w_xh = theano.shared(numpy.asarray(w_xh, dtype=floatX), name='w_xh')\n", " self.w_hh = theano.shared(numpy.asarray(w_hh, dtype=floatX), name='w_hh')\n", " self.b_h = theano.shared(numpy.zeros((recurrent_dim,), dtype=floatX), name='b_h')\n", " self.parameters = [self.w_xh, self.w_hh, self.b_h]\n", " \n", " def _step(self, input_t, previous):\n", " return T.tanh(T.dot(previous, self.w_hh) + input_t)\n", " \n", " def __call__(self, x):\n", " x_w_xh = T.dot(x, self.w_xh) + self.b_h \n", " result, updates = theano.scan(self._step,\n", " sequences=[x_w_xh],\n", " outputs_info=[T.zeros_like(self.b_h)])\n", " return result" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For visualization purposes and to keep the optimization time managable, we will train the RNN on a short synthetic chaotic time series. Let's first have a look at the data:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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QR1z0Hs990iwsK3hG/NhyBkOSIvWhNdNDHdZA1FLU2bhHzzvp759v4KWX3oDb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\nHD799hdlGsqSzwWpjQMAXLVkX+xhMqpKZ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ZrFhQsXsqqqis3NzXf0b2tro4ODAxsbG8V5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\nRmZc5AIhIyMjIzMucoGQkZGRkRmX/wHaMoHej/p9XwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = numpy.asarray(mackey_glass(2000)[0], dtype=floatX)\n", "plt.plot(data)\n", "plt.show()\n", "data_train = data[:1500]\n", "data_val = data[1500:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To train an RNN model on this sequences, we need to generate a theano graph that computes the cost and its gradient. In this case, the task will be to predict the next time step and the error objective will be the mean squared error (MSE). We also need to define shared variables for the output weights. Finally, we also add a regularization term to the cost." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "w_ho_np = numpy.random.normal(0, .01, (15, 1))\n", "w_ho = theano.shared(numpy.asarray(w_ho_np, dtype=floatX), name='w_ho')\n", "b_o = theano.shared(numpy.zeros((1,), dtype=floatX), name='b_o')\n", "\n", "x = T.matrix('x')\n", "my_rnn = SimpleRNN(1, 15)\n", "hidden = my_rnn(x)\n", "prediction = T.dot(hidden, w_ho) + b_o\n", "parameters = my_rnn.parameters + [w_ho, b_o]\n", "l2 = sum((p**2).sum() for p in parameters)\n", "mse = T.mean((prediction[:-1] - x[1:])**2)\n", "cost = mse + .0001 * l2\n", "gradient = T.grad(cost, wrt=parameters)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now compile the function that will update the parameters of the model using gradient descent. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "lr = .3\n", "updates = [(par, par - lr * gra) for par, gra in zip(parameters, gradient)] \n", "update_model = theano.function([x], cost, updates=updates)\n", "get_cost = theano.function([x], mse)\n", "predict = theano.function([x], prediction)\n", "get_hidden = theano.function([x], hidden)\n", "get_gradient = theano.function([x], gradient)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now train the network by supplying this function with our data and calling it repeatedly." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0: train mse: 0.05159830456706826 validation mse: 0.04688619396459131\n", "Epoch 100: train mse: 0.03308586204754072 validation mse: 0.031912791487249084\n", "Epoch 200: train mse: 0.002231280627935095 validation mse: 0.002057841132534526\n", "Epoch 300: train mse: 0.0018355755403003256 validation mse: 0.0016508816899916982\n", "Epoch 400: train mse: 0.0016013576287368058 validation mse: 0.0014066188731226932\n", "Epoch 500: train mse: 0.0014443176507955217 validation mse: 0.0012405799393282923\n", "Epoch 600: train mse: 0.001330610671787295 validation mse: 0.0011186985124206118\n", "Epoch 700: train mse: 0.001243807104666937 validation mse: 0.0010243720769920668\n", "Epoch 800: train mse: 0.0011748882250026316 validation mse: 0.0009484523467887362\n", "Epoch 900: train mse: 0.0011184538218755669 validation mse: 0.0008854402691881776\n", "Epoch 1000: train mse: 0.001071052484828377 validation mse: 0.0008318068096166963\n" ] } ], "source": [ "for i in range(1001):\n", " mse_train = update_model(data_train)\n", " \n", " if i % 100 == 0:\n", " mse_val = get_cost(data_val)\n", " print('Epoch {}: train mse: {} validation mse: {}'.format(i, mse_train, mse_val))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we're only looking at a very small toy problem here, the model probably already memorized the train data quite well. Let's find out by plotting the predictions of the network:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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WpQq3m4sMzQ/CdN1iyNm5fhCmI4jpzfPiAy/Aqt6JSpGDFytsFhWYOvMp85gL\nlqlGbRkeZRRFuONwE9+4K1HqKx0H5YKZDveHiSCiKFVUtnP0q/hBlCiJ+VPFT3cETiGSDCfiRw9t\n4OsHlgSTKBeOsgOA9tGh0TwDL0zPzs5lIJiEAUzDyIegAhJHgVJNw4A4yBjTmho0US/nzJDz/RVF\nqBSH+3y1yHog5k+Jv2v4elOz76kRREa9qXLgauBzL0XpS29IfwGlvHOmfI+H3hzLQDzvec/DoUOH\n8Gd/9mc4ePAgXve618l8eaVSwfr6Os455xzccsstWFlZweHDh/H2t78dr3jFK/COd7xj6ON0Oh0U\ni0UUCsd3otPX7jiK+9d6ajdoV2uAyZtXNowEkUMpo2E9cUJJzJwsfjrtXANZIiAJR8krG9I4uUEG\n331W4TSKgBs/jPINl2DXgc+qeWGpmOP7yVIXqTpKeVoav4JtHjtSu+EDMP7v2Xhy45uCGjyWlbaD\nWslOK+Zh6i9eT31fFOUreALJ2liI2WfzRpvc68/ILbcHPp5jfB/lA19QHQVKtZBibx9Fcch0iZiM\nxgxCcWp4FJFOiRH6sE0DjX7OIjU5ALTetVSmH4Tpvg7aSzQW1u0KNt48wmuLUSjSk1tIFEWYrxWT\nHghiWNaduWPIwAvVY2ftSQJt3PXlxMEiIdBMzuL28dCbY/dB7N27F29+85tx8sknb/qel7/85Thw\n4AAeeughHDp0CIcOHcIVV1wx9DE8zzvuxgFRhOcVfoRTrnw6zA/8QvIAadFS4S4vdBFIPCMJIxzS\nQBC2fWZ/fC7NXN29YYTEi6cUU3k472GgKw4gu1HuoZuBf/mfwLcvBa55C/DF/8xOIFYgFMUUk8+b\nBjS6gd0SQy+UH7tQPQL44ZUCwfKF1+CMm/5UFllXO44I/3UgwTA3TW+oi9Mlw0qpYCYRxK5fED/z\nGgie8sioGT3z5DJw1SuBf/gvKgSXlOX+p4mf7aNitsUQ4oWRen/y9LronfGhB8s0hqasSAk9A4bM\nCcII19x+BEZ/DXjwe8m5kpKc2isijtCHHeWMXNbZQKYoQslK37MoitBhkGEviHDyfDWJ2k44R/zM\nQSsvEG8sJZXVAEtRKJB2Smmd5AET4PjozW2h2jBNE2eeeaYMdU4++eRcYY/jOCiXj/Nw9B9+Dvib\nl4sH31lK6gWU76NJcoPWCF/ONqBhDd9PQApmOjYQTjtX4TSKojQR25DHHniBWjQ1rOzioQz145QI\nbxQjw0jFC3G5AAAgAElEQVQKm3k8hmGkmoWIpsEyDdUj070zXoT+0eeBn4q6VrPvCT6eFNJsCAOh\nfyb0czVdKVThtFby0lbzYnMWWo6nkvi5kTdLyqpzFJUhvWnRK8MUax4eJl3CAAXbzFWDEGs0fj8Z\nHJYGDcMQL3avgXnpacCnLhBGAkiUZnU+cVzy7k1+v0MfxYL2vO/6CowffAYD18P9q0IfeEEo0FKk\npGUEsXm9SU9b25ahGqcs+LoyNldzSmmd5UQ/HQ+9uSO4mLrdLqrV4eFdQ4lejKbCKC2E2TgiGgXm\nynHdpanhkQdkIBZOFz87y8JDHvawBhiSyEuOzyQMI/zbT9dEKo0t1DCCWjTdDG9N9+cX4pGmHM2h\ne5gslDYNqOSD1QXR/4AYAss9LEfb+EQJse+p8bUJD6zV94WB0BXFMEXPlIHwcjXKVQpWci+m9gIw\nhOIdlpkUUIckZVGEbJZzlpFLDBXubwiDNazw55QLqaYZ3sBFwTRyNcoFYZQYUnJAGJCi8OANIjIl\nIceNFHJ1IdmreTrXe+uq4g19FPh6jyLg798AfOUi7Lr6xXi8IWqRQRRhplJIHEeiyfC6mZHqtw4u\n4/61nrImDcNQYbJZzbc8ktN5ngiUkJPD6XjozR1hIAaDwfGPIHRoH1lw8l5peMsodBdcuebBt9PG\nmdkvoJNuG6Vo+M5gkakhps74vPU0URTilx76JHDpqWLM52ayWS8C3R+Kcvx+kkvXhxOxRW8YhmpM\nDEMUegHUipYK8dVTNRRBUGE7iuAFIfpeIIbA6JQNw8AmqW5B3mvg50ox1UxPKA2zIBQ5KdpNGvWW\nWn3cv9qFGytTPwjVedhZsEmObKL7HvixsjGAE2L0VHdV3MMhxA8j1YEZhVWUYL3+AKWClQuKHURR\nYpzp2LxIrfdoULOlzMMzA9Efvg6g0OgDQOCoRnXQTPbfw98HPvlcYbRDiBoE3f+pE8T1h36mov81\n3IpT/u4FwF+eBHxfpNELpqE6QFkGgu9TVq8IoyiJnnJGEMdDb+4YA1GpjD4PNlN4uqNYT7xnWnQ0\n/nGUFBPH9OeZ9kULtFBNUC05oJOWYSSRAxW6tIKY+d2/Aq57r/jj8K3y9RQr5mapB75R9Z4LPYLQ\nkTkaNJCgmbWSrXaq6jlYytlSZGAV0HF8FCwDFSuMm4jM5JyH6R6ne03nGLi5GuWkQZs+QawdOrbW\nkxGEEZo9B3uWvoPHf/vNKD7wbQBxSm/5zuSNWc+Ze7g0S7m7IpyA2q7Em+0sD43ocbxQrTXlJWQE\nEhSU20W1YB4zgtBReLZhJMhAep78HPQ1Q+c6YL0AVFfLgzBsPKD+7fUFQysJGehCTZxXbxVoPAjD\nBKairohqC1XhpNCe0lkWVu8F/uZ3gSPxONGmWNO2ZaocSlloN77fmM7xg0i99hxyPPTmjjAQvV7v\n+BuIzbpY6WFQ0ai7kp/7nYftmzCTdrK4c0i5FCpJOJkjjLYsI1GmtOD1SOCBG5PfT/7l5LN6Gmwz\nJSsx2XNgAy/EDx09pKO3yJBYRbh+KAOPcsFSQ3BlHkcQf6/B7k8VHcfHbIV5dpW55PjDpJjksJz4\n+QRuZr0nHLTQ6DrC4+dCaQBKAxEKSa9DRCFmrnwBcOXvALf9LXDrpwFAoI6O3Ja8b6tUJtUjegzy\nSMfuraE2BCIHADqOpyrkjAjiwbUerr3jCG55YB13Hs4ovFsl6UUXInfTQXqe68K8+8vw+4nCs9ym\nSMWVprPXqA6qIIQVpR1LU0lktQmj6iXX3IWLv3yH+syaGsmd04HFkV9Ul6ntAk44V/zu98WaoPU4\ntVfkcclo6Q2a+jOMDfh8rQgs35W8ngVm4M+EFcCjKEoiipxF6uOhN3eEgXhUUEx8Y/DQjRbi1F4R\nWQRO6oGudhysd53Nm5O4154Bcb3zkSbqjYPA3f+k/gcpLauYeAs50BJhyKCqdM66geLKky3Koq0t\nhc3gsdxApMjWNEO6mcdTrKHn+vL+la1ILchyY0FRiWkn6b5CBT3HF8VDeT7ziQHSuH0yKanpu0rM\nQOhGcuUgzPefitmrfgtmP8kLR1GkGqb4mgCkagmW0wQO35K8ECvDYut+NUWyFU05RVgUXdV2iTRp\naQaIApjOcAiqrhOoDkwGiOEffvgwnn8y8LSDH8RZ7m0yLSbXThSwKKKXORc6iiIUrno5cPWrYH/3\ng8l/0PMqVIQxtYqqctQbBslRkdF1LYFf6z1LAO443MTnvvcAfve8k2Cv3o2IakJbUmXQs2cd/95A\nrAkyyuRIkoHQ0Uj6vYwNxO6iC6yxelOmgWD7lBk+0zSANkWrw9OEAzsIxXQ8ZNSh25sKV4B8UXJS\nLELhaLnOuaiF+b97Oezr/xJOFgsqNz4ZENezph3gY88A/vYVKj6ak/tRBNFeEth1TQgi6gc+HmkI\nZSemXm0x7pRj3tmCrug57M2UO4X18v+NpAjPN0yhtjm+vjyD9sCX/SfW+n1qXlYZ+UrNVBZ4xDLw\nwpijP1aYlVlhTAxTqUE8vNFDs+cAh24Ajt6efC8djzZ9loK+9+vCs3zwRphX/DqwLDjEHD9MFA41\nE26WdtA99Lk4dcnPBUgK8ZsJKVZaL+TU1BbU12Nx/ABBvwmsH8JaN/F0nUCDM2vrI4oiXPRrjwM+\n+ETgxg8BN/wfuD7NkGbXSKyiXjeTK8wwDOC+b4o/Dv8g+Q9fj3B3KSAOtzCjpluoPsGcA7k3NGoa\n1w9x6T8fxIE/ex6eeOCvgct/Bca/vlP8J60pilZ0b18aPyTPzO/DNg0takZSv9Th2LpTNXOicCaO\n/BiK86SDMAC1bsbmThQQxtGqkZ8bDePrzR1hIFLdzsdD+MPk3D+k5KxisiAYpC2KIlg3f0xALa9/\nH0q3fTbd1MO7l7O6VB/5YfI79+i5t0wK2Gkrk6+iKAJ+8FkUPnoe8JWLYL//VOzriGJnyLu45ThJ\n7fjcu2bXXbRMtcichW4JQ+Dhm+PvLas/ATWAKNU3f25WCW4QJpk77mEDaj6eRwVyE4fwghAL9WLi\n3ZGxsyuKwjlx/d+w+9PPAD7zQuBzL02+lwxEfTcAA3CaaQQSh9s2HgSufRuA2ECQF0gRIq2nVIpJ\nuwdzcSeunjrcijWY0hy6siLnhtVtWn0PpeXbYL3vZOBD/wELVmJ8Gz1P5X3SDJjjh6ri660nkbLF\nFCODmqYoK356vXqvzxK/h2GUOA10nzQOqubAFyM5SeiYckpjWam9cPn2T1bwmd89HbjmrcANHxAv\nHvhH8ZOcvHlqatwE+RYFynWappE8K9ovlJ5mqLzP3XR/2hmYfZxw2h75kfp6VvMtjz644aMoqbZ7\naNockuOhN3eEgRhL3J7w1nQUgxJBMPgY5SLNQuJdsgcahJGKp77+/WkUK38hy0AQhBZQFbE0EAXm\nrbXhsVSWYRjAl/9InMOtnxaK7d6vAxBNPfJ4lCPXw15L7UUgqRQsNaeaVaQ+eE3y+xW/nj5/bowq\n85tPOStW0XeD5DatxOy+pzxL/OTKk0cQjMPHDyPMVgqJEqfz0Gkkrn9/AiflG5O+61jpPFLa5/yu\n+BnTTftBmHjA9Fk7QfZI8R3gkv3qd87GswCof4QgzVulmOjcZQRHhkkfvhPPNPn7mLIhCpUu7b6r\n9btojkAQRioKz2knpHbMQEsD4bRRshOnI4oi4F//dxI9AMDikwAArYGXXlcaMeNK21HhnPK+Osnf\n8tiqkj/npBngqt8Hbv548uIF75XnCSAxLnoEQYaLMwHHVCISEj+1T3tv4lD8/i8uAp95cfJ95Vmg\nMiuKzFQglw5nxrhSvm55BE30/1xnbKPsCANhGAbCHKR1iqzcDXzsP4o5BFx43pOngbhConCUpZjC\nSJtZ3Vs/djNbFoppmimNzcaKysW0jiBgijbrPpzxXADEp6TlbPVU0Sa0w7WyrUJ6sxrs1u5Jv8bf\np0Ug/mYGwq6g7wWJc0352VOeKX7ygiJZkShiBtRCEEaYrRWTZyMjGi3y4c92+sT0uRAiCEhvXLqH\npFS4ASVPnhS1Vdr8PVxoWAzVXE4UVPhbUrpQSosXa4FkjTKMf6lgAas/ST4bpy+iKBIKizsJGhgh\n9cQ2DgmUGaCi1siIuF1xvFgMw0gXhGPHYaPnpgEEWgp2o+eqUQUZNx7ZU6pFiyAWp8rAA99Rv5+o\nOShiIPh6CgHFahB0T6g3hiPWANWYIDaqR34kJr6RxI5AhCh5dsdyBjjUt7OUODB07Jn96c9sIWPp\nTTqtsT69TWKa5ugXutlgdl4U4ugAjoQhD1tBSxjqbNrAgaHDO7lkeeIcqRF7J1HEuZCiRJE0HlRz\nvFmKJKZ6cIMwfTw9L8o9Rsa1VC1a6nQ0u5Qu7tL5nXg+8LTXid9JUQSeCiUsTaVJ5khMC37AuI8I\nwnlaHJW0j6YHHmn3OAipgamhnofeA8GNMfeMeaGRFJKuNOiYZGRjg2EaLO1Ak+B0TxfIzhlXF0Sq\nhQqPxASb5VVyoTWoj3UlI78Z2q00o0RyHddXKFD0cZ9RFOG+lU6iWPn/GUayBmiduW0RsSRfkM6x\nx4ZlteOmkW6aI9LouapTU9QMhF1k9bmj6RSeLnOniL1F0SHxZundyjw6kmsuEHuP1jVB3w3VQDT6\nrpoWY++1TRNYv1+8RuiorCI1X7ehn0Q4cp3lnFmOMfUmfcdYn94mGc9AEO31MXiGagxmxw0EhXVs\nyptpIP2A9bwzV8JbNW3FMwXCCIkHbpiJp9M6jKLNNvExwlPXD9N50JSBYKEsQ1jNKA1nAEwrPcaS\nNs7+/wA89ZXid7qvKwdVj9kqSCoNAOpGNi1ExMcfRQlCZ9cZIgIIvSS9IxW5oxj7IAxFFzUpI/Lg\n9ec8w6KGlbtlOk5JA5KzkFU8VM5BHF9Qg2gwV1KYvFtczxnvPRswDBGFkjGinPhWBoIUHIdCA4mz\nsVm/ji3Gcnq+iNj8IFTPS8uPGoYhQA/TaY81AlivSawk3Z5K8+G00lFxvG6WmhkNYlqE2x74arqF\nrlNG9rYw9IWauNf8mWV1sdN3kddO91un65YGIkpFCFJJT+8Te4I7cojTYsTySnLaswEAlokkgohT\nbZkGQq/50V6k88zZJAf8HBkI27aVGde5RM/ZIlakPGrgm4EbCAmnS7wz02QdwaSU7rtOPSbvsdiK\nSnlVpG1C3ROSdAIrSo73WLBXpQhIorOE8r+ZkZyvFVUlYxVFzpiLvDe8HhAvIX3YkWmr6CtufLqr\nmKsWRR+Y0xJ5+2JdnA/LbdP3wDDFsSmdVJqCH0aYKtksXI/PR8fRE2qI5PMXxnULlrqiyEI3EHSN\nlCaMU3CmabAidazgMvLSKTnvD5LfyQDS+WUYiPbAS/LeJAHLxQNKL0SmdFeAD/0iCh96Cgz5WQ4s\nUNdd2TZx30o3UWb80GGUKDJyFpw2aiVuIDLQWPG5rnfdtAOj7UsFiceF7UsvCJPIjU+J28TAG1GU\noPpIkes9FDKVGaajVkaW18gYjrTWccXzPynpK8IZzwcAWPzzx2L91a+Z0JRdDWKbQ8bSm3RaY316\nm2SsCyXvnnnSjb6reVGb3AZSNmzRm1EYe0hGsln/4b9qJ8w2wVYjAmMET8hTTFHIkBpHUeYhvK6I\niSgO8UQ5ve9BN1DcUynPSKTDdNnWqDAstPppemfxnXZ6XCQZLjqeaasUDLxTun0Uu6dKIvzW+wmk\nRxxvIt6YRAasUEMYAbNVVoOg9+j3eyajuGfayXlzRE5KubFCNiBy72EgEF9y48YKWvc6EReEeaom\nTgUKAxPfL8nt00tRlXTdAJhiEOwoYoOg4jW26bkzWbsXaD4II3CFPeDrU0v52JaJux5pAnvOSn2N\n0mcja2TC2CcXHV/X7jMTQsd477UHXsZY28RR6zq+cN4szYgoYog5JBn1wVTN5xlvwrd/spIAB+xy\nkqrpLqv1PB4V8FpLGCbfW12IDYTqHDX7Hu460lIL3/R83Y54f7GeOBNZkGZdB3k9sTed0bqogYmB\nGE4yhvY0up6aK+ZeFFfSku6Cee2cDoNE59/haaWtqAwevjWenodEyUShUOTFKSBwRfcpie75PP6Z\n8lcxDEZLKekeG/ceS1NSiVd5ugYALDvN80+FNN9NNoPuUTJajD6PIJrMQDhtTJVtkbvWQ2gycHyz\nSSx87HXbRYRhhN1TpeTzFBEyBRRFUTb6ozSV3Aevl4lWE8dlSl9CG10B6+QNegBTKlpIz73FeJ2Y\ngSvuk2mLdVTMVvJhGCX9BnSuvFgLKHDoY8rsyQhLU0K5KhFEKHtqSA43BgnaiokfRsl9oHXWW1Nn\nQ1NkVKwlThjVILpO2kCw9dnzgjhNmhV1J3u05wSZ6K2UZ37S+bj29iOqgZDNhaEajcv16yUUJ2Eg\nlHkUAsU6ohgcoUddrh9iqqyRRtJ94KCCYl39Py46FJLWf5a+GVJ+bgxEoVCA5+WdexuLnE2QbLSO\n46udlcrmYg+Kwl/epUqhnw6n5MK9gYxwOYwYHLX1MAzDEGtOtvDHC1oWT5lnRAuHzo1tMDH3mRkE\nw0wvLK7AjGTYS7WoRxCm6LrlQoiTQSNdLM1IbSkpKl5EDRyUIlc059FzoHSexNezzavk8pOBTAv1\nYqLUKQ3ECtEbPS97vrVhsLRSmyGBNPI3nmqQXEsDsabC2Bum43LHgm6BZahKixQI/SxNq+eiQS9N\nw1AVR3+DGYhYKdPz3aqP4gX/B35McKhEkVGIntbsudZ11JRGXHtxfeaA0L3pNzBTYQqdr4uARZwA\nllsZBqKUDI7qOT4GfpA9BZGhCxt9Lx1pAmkDH/oCgSUL3PHeoDXBmws5fQaPILjBQzwHRD5j8Wz6\nXhDTzrP9Q4qdp7hpnXWW08X1VATRFwVy7fh5ZCy9GcuOMBDlchmDQUaBaxjx00VqLww1qCrL3/Jc\nsuxFYEVoihb4Iib4mpRj90H03ADY/QvJC1Ek4HC6h0FedZspV0kqpnnuWccrVNOeiebh0rjImYqt\nKhnDEikSLpxvSOLSNzmPYlVNUek58n5D0C3LYm+8ebI2L2/uK9YB04JtmZir2IlSp+tmqJj1rpu+\nfoooSszzpjx+CiHGoI9kpAI/HT0AKhw3Fjvy1L4Iula9fiGHU6n3yDINVZEMmklahNapbNA7hoEo\nzwJnXIBM1HEUJp3Sm0m8VzqOnzwnUtiDhvCeSXgjH51TvG7aTgAnMtQ1Wp4RRgFC0aZG08rzTBom\nGz03eX7cqGrd5Ag8cW4EbqA1mhUxWkVIynZ6lmGorM8IMW9YlBgrABi4AaaL2lREckzlALLZmOhv\nSpxzCgKtrVMyatLA5B8bOpbejGVHGIhSqQTHGZ72WhEv7fFbOlabb0wOV8wKY4kltVABLrhE/K4X\n9Ph0sIwIomAZar755k8ID14rhiZdsgxxQYuQzo15HiWdT2kI2mtS4lPlQipET7F0yrTMgIW+FfUn\niVlQYa4ZQ1AMA4xbKN60BBfkm11pxishiiKUCyas3qq415wXiqVP1rsurrn9CPD89yefPzNuZjKZ\nlzi1CfmbyYwCpbk4wmqzmRkkOmyWlAJFpLTpa9kRjG0a6nCfQStNSCiNNktzKpBpAL/yh+Ktlins\nDf+/KEjBmW1D66qPYZ49N0iMGq2t3jpmlBQTOTDVWNEbCON7N3CDOFPElKFdlo6I64exbj6GwTLi\nCXZZQ4N0pRu4IoLQ7xmPhElMk/FpteWxuMHzg0htKI11hW0ZMAfrqvMlB5CxCMIwkpqTzg2lOzK+\nEyP84ppj1vjfLWQsvRnLjjAQxWIRrptz7i2Jw8L5WMII6mbkpF8cH1+oiIUeOMmDlvnMYlJg0/P8\nfHNlpKJKtgUss1kA175VnA+ljehYUnEwAyZDzvh7UwaCLbSsYfBaRLMe8/RUCpZqIKIwzfNfZnBQ\nOS5yKnUe4jgFdHnqgtOLAMCgKUJoutbKHLqOnwkMUMLrUh1hbCAkhfLMiSxSTK55o+fixw81VOVJ\njgHDuicFT03ByNRPW6X8kPUX9myll8qiHT1qcjJSTACLHNUUk2FA9aadFvQCqczX8zkKg4aqrAIX\nfihGqnqBVgQOA6VTH4hZgbmTc9vVAGL+L1rrtFbbRzDPi9S0Luh9dglkfzpOFgy1Jl93g1BAn3l9\nUBqABGXUGnis+52nmDToqu+INCZrsFTOXU8p0jlLiHVBodr2w1CsO19FgylgC3kf2ur5yWhxk54b\nfa9SL4TTFMfZbD7LMWQsvUmnNdant0mq1Sr6/REG9wDJglUaciJ1M/LpcjxqMIykKYc8XZkDLm3e\nhMe9vs0QGTrL69p9ac+GCpQ89SOnccXHZArRtkxVUWcNg9Ew6hsxbK9WslMGwtUjCN4PwAt/QDqn\nalrouYGoi9zwAeDQt9X/J29demizeHijnyhNfi588xTr8MiT4xQIGYPdOwNfKAhuIEhp85zzZn0Q\ndP/7jeT5e71sZImfASHVO4pJ2dDao+NmgSEQp5gUA8GMpqQKSfMCpaYl+o6MEsIIahE48FIzGypF\nS+0p+MFngPuuE7OZyYAXKmKtdZYwXWAGhtYnKeOYdwsA2o4nBgZxI2ra2OiKNekHEXbVi2r0GPcJ\n8T4Fxw+TqJL3IJG3TffV66NW5BFEfFzZaKfdJ16olteTkAT6QSSMgWZwSgUzY450fA16rY7W51az\n7qMwMToaoeGwMpbejGVHGYiRmj5oMzIv1OYwQ0CFyhW1kF0qrHhz84KXq+ZYpfAFlgGhbQ88YP8v\nqi92V9IFR8kmyzxbJ8N7jaVgmWpBN6sHgytLp4OBJxZ7uaDlUMNAcDvRW71AVaoszA7CMH2dhokw\njNFZd30lfR7NuPlQdoruwU9XO8lG4gU/rlDsMrwgRNm2E4+xvpiuZUDc53LBUpXIIIlYAIh7S5/p\nrqmGjozvnV9KXjvwjyyKY06Hz9hGSQjaS8+KzlGmKcvJ+QOpFJfIxzMj7TtI5aqzOrh1GmyvLw1E\nydaciNBLcWbVi3a6DnD0dkxXCsl9c1osl8+UnVSIScROUOq+SwSNPMq1RK0Ios9ijkOXgUTps9qg\nx4vlfD2TgZXTDgeiR4NxeCn/rxsIHSRiWEodxfFDMVhLGg3xXMsFKx29ODoKKV4XlDbWG/UizRmL\neP0j3xwIkrH0Ziw7xkAAGK3gQkqPcb4UbUtd1EqRmvDxrtg4Zc27JANhWokS06Gs3EvPsPyWaaTD\n3UEj2exkYLJQTPr18KKoaUDdfBkNR9xLbh0GIvH+gqUZiMAFDMg6QqPvJcfsbyjG0QuidA0iCoWC\nA7IHu3SpYEsKe1508GbNVdDID70gFF4ubbLa7rSnBpHSmCrZGj69qb7PaYvfCzWhDPj9IcXOBiuh\n+VA2ks3TIiogWVfUU0Dnq0dfRKsSkwGSmHxCIJAxwwDZBkIv1vp9+RzFM2H3M/BSGf9y0VKjYCBR\n1AUG3MiCJKdqfuLboyjCwAvSEUShgqWWOPcgjNTxnkByD9m+9MIoe0CTPjfa7YpI02aODcD2tE7Y\nF+8XOn6pnjzrQhWOH6hRXfz+km2m6nfyntCzpvPNAmEAaepwDsMdoQcCGFNvxrIjDMTUlNjM7fYW\nWO8syVAc1aKlwi75w5Jc747wfHSMOg9XNU9CCn/YGVwxlsk4bei8+o30IBKpxNhC5kVAQNlsqbGh\nWUSBfCE3HpKfKViGGvYGDoqWKQqCAFbbToK26W+w+1pX4Y9SjGSQDL33TbcDT/6d5DuApBO2toAH\n1nrZuH7u8Vo2vCBCtWQBjVihzp7MaiKJ59oe+KKZjisc8jI5NUYUMagrcxYkpNMCfvPPk8/pCh5g\nEYR4LeIUInvPUb+bD4YCkghCq1mYBtTUkVKjiO8tocj8frLW5IyM+Hn5Dtw4GlTWHiDWsLZEZyp2\neloaefU8ws4aiysVarIeIgi0nOMHwjQptCsFrPdc+b7F6bLq2dPetNne4HBuHkGQQiViO68nnAva\nwzpEXE/zyO7wpCEzedYl9NxAjeri+1i0zGT90T2nvxntvqix0frWu/YzIghCL07l52ECxtSbsewI\nA1Gvi1C+09mCEjlL5ANOPFzF+wTUjUmbNgxEvlanguYwQy8jrQBo4XnaQJgGgy/yNJKtoZg4DxGJ\nJKeLFSEvUhcsNWrIGnfKDURnCVbcFGSZppoD910UbRPNuE9io+eqvQAspdNz/TRZGSLYlgkrCsTG\nNEzBpvrk306uF1Bos482B9m5ZZ4qMwXHU9m22KSt/cqzoJx61/VRL1mqYnVaMaWzqXqh5HXyugEv\nXvNnrXmQ4jtUT9EPo6QWQBEEKVlyIEjJZaHlEDOj8uKz203Bab0I6dw5PUdSLF4fQVwHsHUDEbip\niXCzlWKaT4mMrDzXVnb/iISBq/Wv9a4LL4hi4km1MbURG4gwikTBO6sGwSK+gmUyA8/Ok86D6Em8\nPgxwKpW2uG/Eyqqneeg+SifQUGpLjheowIH4/ZUiA3iQcaL1zcANjh9mF9eBDJZmIzNtmkfG0pux\n7AgDUS6LxTBSwUXvPEXcFMYXNZ87zTabmmKiCIKhWUIVDy1F6dJO5/+EgYg/K9vvu+kCFoeVkkga\n7/i8WI7aNjWMeVZzDTeG/fU4LZVBQhg4KNkmmjEMtuMESU4+CpJzrMyL96SMZCDmNfBOUtNMqA7a\nR8Q9l4igWmyEyCNm16yRH7qUC+bjN6ViKsMNxD0ZeKEovuupBHr2EjW2nknMqDRj8chRR8XQewBp\nlMMwSu7RrifEx2kIRaA3u5W0NRaLaWj3weuDp4eCIBT03RyCCyT3nCITry8/Vi3xyXwAfCc1Ea5e\nsthApfh5kcLjqbmsZjUqbpOBiFONR1uD5L5oI2EJxRSFESo2VGYC6sBnCL9aiVGlcGeM1hIZe6cl\nEIN2Sezr0Bf3fjMUExlOunbTUlhkB14IIwpZ2kisy1rJTuo+FC3S2mScZYojxdKuYRSlDbJVYE2k\no5LnWDgAACAASURBVEUQY+nNWHaEgaDB2yNdaMZmrimoA0M8HFrkjILBMMA82vjY3ChshizgHr/G\nrxMfMVkQVGdwO0nRj5R4gXnsgPAmJUVDfF56565W0E2JNsuCZlEbgaeet9NBvWSLsBhAz/UFNxB5\nkJSTrswKFEpGmm2uVlRgxmEYqQiSwBXGxrThGbZIZ8mcOtswvMZjFTHw4lxwi22gblLQow5wxw9F\nTltHmFB6kXvAkj2XcV1Jb7mjpgA4s6i8Xo1Ej/d4TJ0gHIEoEIpWj0A28SpNw9AMRE8p1rYG8Vxv\nnVyODCLVNvobsGLlVy/ZaorHdxLa9VgqRYb1J2+b1qSsmTVZwZWPiKXu6SQStwxDRIdAus/BtGQa\n0+AkiCRksBnCr1q0kvtAUVPgxXUzK/Hiu6sJDYjJDAon4eSeOz07OgerpAwQc/wAhtcX51+oynNQ\nAB40MTCDkK/r+Kk9HjkdmN+/Qkw85GIVkxRlBrPuMDKW3ozl58BApDez7XfEhi7WE4ZFSi2wQqxp\nGOnmGd50Q5uBhexRFKnKLasr1EDyHjIQXk9N4QCsMEeeIfFK1VU6ALouU+uDyGqU48qyfVQgU4B0\nTtTtolqyJQ1Dzw0E2oSiHInQqaDj+nCCSDVIgSvmNciCbg2HVrvwq4viurorbIB9FX0viDH6STES\niO8n/95iTXTcRiEbJr+ooJOoO9z1g5jQLz7OCfHsBSoGc8K+ClN8JLwPgjfH6Y1XQPI8qXnK5DxT\ne1juuZlek5sZCERqqs0bKGnIgR+nQTl3EJDcC4qKnLY0AqITmClFv58yECWbRRD0HbRu+GhVMqo8\nLUfrkZRt6MMyDQmnNnh6FQBMC93YQFQKLPVLxpu8aKbgDQNpo8g5i6gOMGiIbAGg1vfsomqwSWgN\nhCwKYs/KC6LEYFgFic4qWlayN8kBomiGjTL1gjToxbjmrcA1b1GnVMb3JSEJHA3F9HNjIGo1sSi7\n3S34ZrKEz7Il4fAxYkMlpcGge6aBNFKD54D1VAFiSuRgCwMBJB6njCB6qbkDCarJVc+hWFcbvWJJ\ndVKbySIGIO6F0v9xWKSBgHTjjtdFtWjJedgDL4h5fLS0lVVC3w3U5qX4nKfLBSUn3XV9HGl7iYdH\nm8Iuw/HCeGSqmi4xNg4Bt12VfG+xJgwJvxdRKDacYSEoTssIwg8jzJSt9LAY2ni8UC1pVdj94U1N\nQeJJZoo2d8QKvdjjt0UEJOk0NtK0HJrScPyYA+jOv1eP4Q+UYq3nhyrDqixSU2orZpLtrYnIAUBV\nNxBOB5WCpXTN10pWktas7xHX4HbEaxxlRp44j0rpu63EQNimgWZcZ0j1dgDoxGtMQQPNnSKeDzVl\nMmNuwEiTI3LoMCue14rx+1gmwPECBrhgaSY9Ci7PKBmDIGTOn1WUcVCJRxBEJ95bF9EJI+D0w4wa\nxGYzSAxW+C5OZb9nCxlLb8ayIwzE9LTYQCNV43WiLgBYjmcgl2fUnDighICmaaS9ek4LLAvWSXQS\nRlBrEBkjRQ0gWdB0PLedeKy0ePQZA7xQqiOegDhdpHpnvJch1dnbXRVePpBcP6PWnioVZDe1F0TC\nM9dGRFInrKMPK/IHwiNk9ygMIyy1BoliobGbhbKAQPJZAJTL5nN+AcAui6/kDZCy8WwKHTeUyq5o\nmbCcZsLMKzH8HXl/AAglkMWoymsDHkMpZRDzpRoyee3FMFh9aQ16L0NklcR9D1zA66O0fDvwF/uA\nv3udeu2+o3jSPZ0rS55LfGxyfvobmCqLc64ULTVd5gpH4EgjSWXNVotqtMqRZXw/0P1UIp/42iSc\nNoKJUBrtFH1IFKETkzo+ef9MEhVW51XED5v5XeS9HPQMOEca6zWQsyrk3mpp4BN27nrfUGVeiRYN\nA8p9ITCE6IOI99b0fpGGCz1xLWw9RxHSfVWbzppWaT5GkbH0Ziw7wkCMZQmDJCQUX7IK/O3vid+P\n3g5U45tPyorRHhhAmuvG4OG8RntAwr3QzWZO04KmopXTYcNfYq9GjxJ85iVpqRiAUkzaqXCenb6G\nq+6xHC3leolE0OujXDAlft71w5irX/OgrSJ6jh9PAmOejjcQuVnuLRsGltuMKZRSE1YRbhAKA6Gn\nzjKmk5ULjOSvMqcAERwv4RaqFM0keijNsAJrU34XALGZ6xkEbiahwqLk9fIsEiPMSN3oeTIabABJ\nqkR627w5izVw8Z6X+76BTAkcxTEYSFw+GeH4+Us450yskCKYTgthGKFeLqh5d09MhDvCpr2J1GAC\nY072RFuN8LIoQvi6ZL8TcKCgRxCBJ0kEaz+8AvjSG8Uf0/vUgjiLrgtWhtriFDSluliL/gA2PWtG\nyGgayO5H0IEWhbJiIAqWqXT+08z1km2qylw6meuKYbNNA55ZjAvm8ciBjMFMdLwUTUdO+bmJIAiu\nNdKF6gXBH35O/f+bPip+rsdwOlZzMAwj3Yik5D4TfhiSFBpIx5OTkCdCisnrJQuUsPm6l8SpPdiG\noU5Y4R2qdN59j21GmWp5gjj37grmSkaM2Y+Ls5SWcDuolWw5K8ANAlWBk1iieNdxfJW8zu+LCIwp\n4YJliDqGnPWd5Jf9IEoPTQJU1tv4eOWClSjg2i7WtFdB3wvkqNPZSjFJf9QX08N1eEGc2EH10Z2E\nqiKvXKFTj58/M1Dy7uskhLy4ayVIOfE1BktBrScesC4EUACA0EffjdFcoZb+knWfOkP/bGCj54oe\nB66gowB2MBAU37EoKL/KvOr1cqeFeeVS+P5gMzTcIBT9ArrBD1ycuqsmqGa+9ifJ3lm7T31eMkUk\nIp5E4mfgqciixOBTRJJAlv0wSk3iyxxmZKiIr3LBUmpdtM4KRsQM6jQzPssKwqxcsOJ9wogGz3l5\ncrwXXZbMZTfZfJYRDcRYejOWHWEgZmdnYZomlpeXt36zLnpBUefHIU9V8rholNt6XUChw9A8SRCN\nB58fkfFw+ExiSnH5A6Fw9F4ILhyyyzwThUaBo6aiUJ0LTV5OfVGEwlEIs/WQ2DCk0HafKX721jFX\nLUovyQsiNQUEAIaFMJ4c13cDFVbrDWLYLXmRPmzTFPN7SQEyGoUwitH4en5e97DMQpyrZtGQ3EjT\n6Dq+TDEt1ItqM51Op87rDrxLnAshyeQIVBZB0TnKxrdSsiR0ygkeIWQxsPI6hI5aIQURBkou3vEF\nQigFt+aRJkFdO8tw/FAof71LureGziAxGgXLUBtMeWGaGwBJi8K73pmBZ0SCfhCJ79UNROjhpIWq\nmGnOxWmraSBGh7I4XUqjE/XmRTnLJV4bxaTnRWdIGHgBpkwXuPX/qedgFRSDWC9ZSorJp0FLEuI8\nI6I4KlR3llMOZrvvqcbJLgJ7zhZ/7z2bOYMxYpHPHMkpY+nNWHaEgbBtG7t27RrrQqUS15tjSEhB\nShw+RQxaHUDC9zYZxEEwOOVv7UycljBMRbb5JL0Gy1Xrc595NMR6JIgMzeD9FQAQBslMB98FvvYO\n+bospm3cL5QM5X5nTgRgAH4fFRtyE3hBmK4zWEUEYQQ3iOICNivyDRqiMMpSRpZpZDNxGgbCKFK7\nbOmaH/8f1ZtnWsKD5AgPybg5i9bAl13Dc9Uia6bbp8JW4/cDEMZ6s2lfdJ9dXrcghyBS/4/DcXXW\nXe5VSqXB1gb30nc/UT2HZ7wp/kWNaBNaI+aoRFFybLvClHgXfTcQ42t1Wge3qzgSRctUr4k5I+qo\nVi2fDqjGjwANXhdBGMXpMK1uEngCKKHDQu2ymsJiMxwWp8rHMBD6KFbNCx80xd1iEV25/aCo+XAx\nbZZiBBD6KiV+eSa5Z5JUL3YCyAFqH1XuRwSg72UUqvnQLNI7tDZ0Us8ccjz05o4wEIAIl0Yqthha\nGohCzrP/kxjXeeaLxN/Nh0VuVp9opn+ee0j6/wEpymYFx07C85V8wwHZXPWkAHiHJykev6+ycfIN\nGPrJ2M8v/ueEIO+ef06KY82HRSqolaFI3Y5spAtCiiB4n0URfhDBD6g+wUJ0tyPYZdn1FSxDTUWR\nYjFMhGHMB8VghI4fAKc8S713VlF0fXNKB1a877mBvB9T5YI69F3n1eKT3AoZDXrxeYj3sIhSj3Ky\nSNU8jXKCI6KyKFS40qDeAwD4b98DTnxafJ8MxbgYoKZLtjb9AYBIRDOWrXSLdxxfdNvr1zhoCoQN\nnUrBUiMImd4ZKLBNMVa0JJSZPrnR6bD5JgMEUSSerx5B+I4YHTvQUHROS00x0Zr0eqJGojfASoMR\nPy+9iU9HqfG/j/wYKSHHgKVIayU2VKtYTwwE9dVQPVFGbUspkEsQss5uty3uKRWz64uJHqB9rNdF\ncsrIejOWHWMgarXaaLm0zfDSv/xGhK/6CvDyz8cDZwIBeUsxqGreovwzzOxFSCGFsmoQPD2inx9H\nJ8mubS0vbzCqAd9RqRK4gaD6ROADB69Rz4GOQ1QGtMinTlAUOHE1+WEkahUKnXgZbhDCzzIeqTy/\ng6JtioiGFj3DiosZD4y+xC4Jo6N3oltxiomnieiZ2gINFcaKu16ylXkTKb4lliNXaDc4NJhelxxO\nJRZVeuKe8GIirQ+dhoV35LNnJ4UbL977MXOSej4s0rEMQy36mlZGqmVann9ISlqPap22UrcuKSCA\nebXwzCG1hqF8v3K9fp9dZx9RBHW6G0ngYu9MOb1v6otaWi6hYLFM4xgGgnpLtL4WZmxs01AbYCma\nViR+kAzpJmZBJOk7Ocubsg9kGJR6U3LvwiiKm28pshqwCGc2pu+J1zI98xFmUXMZWW/GsqMMRK+X\nwWa5lZga2octpLbj44G1rlLI24ybX4pSsGMwSRKdudTPqCXIFMc0UgZIfmeYTrfwv9mGUfgAeeor\n9IWS7K2mFS0Zn5WfxNfDWGIZKV/RtuJDR6IewWsQpg0/CBFFGfUJJ+2p1Uq2iCB08rRYygVTUax+\nkEFBYMQpJg4jZX0jrh9KVEzqfZSmoDQjn+Bn2SIlE4VqcxopORbZJBtcpA24ty2rUaxwLv6PRQg8\nZUMi71NXrEN/kKQgeeRISrO3LjxaPlLX5NPOYsXJUjOmYQjjqjstblshBSjZFiOK26N2IesOja6I\n+f8XknRtFMWUFHr04g9wwkwlifTOez1wyq+KtBpP++l8VVpjYmqv6Gkcljr2w0j97oUnICV0Q+zE\nkFSL7H7bzECwepOIENj65jXMKAax8HUnaxqxIaB9Q898hFGjXEbWm7HsGAMxNTU1WqhU0Lw15oXX\nS7aK7R+04ia0uMHLG6QbpDg9g5FhIHRaBx0VA6hejZ6mUjagRvTHIwg2TMjku1vpwYjnIWQVymkD\nrdwtNhf3gpmyIkhhFNHZsGMVqkojndKMSDC+UmwEnRamC4aoaRSZBwUIOgbTiDt4kw2YajoEAMMQ\naRLOpMvuS8ispcgZs2hNZ9LUDRWtFT6DXA/x7bKi4E2F0K2U8BqlaJ6pyLqiUrzL47DcPXWpz5wo\n1gfvvGY1haLNPFq9diYjiIRsMkKEetlOOy1xNEIiCBbbYp2VZ9U0qJ4S1WdiW0lhmiv1gmWICEI3\nTt4Ae6ZLScR+8i8Dr/4KcNqzVZI9HZbKO6cVIcxs/H5yBug8By1hJLmRzpj6mMw4ZwV6QNm7EkLO\n+3AGvjr0ihkDyzSEHeOwYUlZH0cf5IiEGihmRBlZb8YytoG48cYb8YlPfGLLkzh48CAuu+wyLC0t\nHfN9m8nMzAyazTS/yZaiT9xiiA/LNEQjGG9uMgw1N6032vGBNlrXLwDmiWd0apLInDWbFEV6jTO4\npiIIbiAoTdNTKaF4+sAfCIWQaSBmARjA+k/F+YS+MDp2STEQ1J1tGFBhqIDM+QOIC9ga2mLQFF4t\nu2elgpUoLVJUkRiHWbTV1IEXZKSY6J5sQn0RRQDB5OdqhYSvaXqfGh2GIVKzJ7Imy+lT+axiuqCd\nRf8tFRgNBSJky5IasZJIdFwnOWdKWcgu7ZIy1MY2DQZ9jh0LnTmAGRTAEEVqnbywvyHSSuxvAHHK\njHctB+kIQucq42y8zIsvFSwsTpXSEYTXVQ15ZRaXfeMevPNLd6hpv4JmALW50Kk9ond5s0iqXLDS\n8HV6PiT0//r1sgFXcoSqrGtU0fN8lbaFrsEfwLZM4UBwZ4AYHGZPUo9DeiqLTy2HjKw3YxnZQAwG\nA1x44YV4xjOegTe84Q04//zzcdddd6XeF0UR3vKWt+Css87CRRddhLPOOgvXX3997uNNT0+PdqGs\nUAYg5bG7fpD2LAvMa5ADgojTpZDQO9Cm4F6u5MGJH7iTYTj7DPWgT7vii0ffBFyYEStyig2eHvEH\nAsmTBZntrccKKAKW7hSvEXZc5sObIu0DUQwVM4lZIbpQlXOnBY+SNqBowNI7AOB0RAGZ90sAoj5h\nmeJc2TU7AUuzkdB98lgKh+Xg/TCSEVW1aCsDiWCXxLMlCnKdWpxtZn6NiliFtHLhPQPsmsR1iGNE\ndkn8f+gnmHn+XPhzJ/I74jrilC7sHOtlm9VftCJ7QTMQThuIKdilAZiJgQrto6J7GhD3m6NqAFX5\nyjpTpNZKdBRR4DLARxNl28Se6XJ6P5CxkmmaGXhBKNaV0iinIb/YpDddwjBSZ5cAyZomo87TZgDw\nwg+Kn3vPTq4VSPWscEMmO9npWdPwLJYG5PegZJsC6ssHGPGxokCSOqN1ru+pnDKy3oxl5KNfdtll\nuOGGG/DiF78YL33pS3HrrbfiBS94AW666Sbs3ZtY42uvvRYf+chH8JKXvATnnHMOFhYW8MIXvhDf\n+c53cO655w59vLm5OTQam9QFjiV696zmlXthlM5X8jBaPnyWaihUhYKRbf7MQMgGuL0AfqwqgSgS\nC4LCyvJMuthmJx6iPGbWZDiGNCrxzlIewtOxsyaReV3hVXeWEk4k2mzc86sIhWAYUHl/4vNy/RAB\n9TDo4zB7qwBOV87V5L0eJG4XtZItZh77MZrKroi0lW4bQ21T2mUgTKaO+UGIQjnm6bdNZbBRx/FR\nL9XFtbvdjB4X5g2v3Qfc8ingri+rxzfMNCUK68OQotUCDMMQ8Mf+evJ/XFlyo0NpSkpn8jXLFNZs\npQisk3GKlRKfmQ4oz7JgmuL+k4Oy5yyBbGsfwQvOPgF44CaBdqP0DEUwxxJ5D2NFy9FBrKN4ulIQ\n0/107iFS+Ax04PphnKphaC9O98GhvEXN2YCB1Y6DRTnGNTZ20gCTgdAUv0QNEeycHEMtS8CaEN1u\n/BkGrBDFFtaVzxogayVb1CC4cdK7pXX4fNbY4Bwyst6MZSQD4fs+Lr/8crz73e/GG98oWuNf/epX\n484778QHPvABXHrppfK9H/7wh/Ha174WH/nIR+RrBw8exJ/+6Z/i2muvVb734osvxrvf/e7U8a67\n7jrU63X0ej2EYQgzg1JiU9G59jW4ahRFKh4eUL0i3TMC1GgCkIvcC0IUyCMgGCkvdv/tK1Q0UWUu\nnZ7guWTeOQ0oxF+yiSf0YUUepCbVIgjxWryAn3AB8LxLgIPXAme+EPiX/wU88kNRhwCSDVlkee4p\ncZ+CUPxLdVJDML0iAtRcF5JUGkvlmIaRXI8R8wINmpgq29g3W2FFu1qsJPQ6jEbBkbGBiJ20GPZj\ngrsKfLOMlcYA9WIdwJJaQAy1+7xyEPjqmzahSTGU+wMgmxJBf3ZAknrwB+J7vL5QrJbNPG8nybMT\nnp4XqVnRtF620yNoedEaUHo/bMsUCqoXe9XE1URe/LcuEQqVlCrti6wBSYBQhtKZ0mofvsu6l9cx\nWy+IVJLeiEj3UMKWK3D8XgxmYKleK+798QfivnGlDCjw26OtARZn474GSjFJr56cCZ2lIH7WtFbJ\n6dObGhkpo088ZywC9cMIYWkWpmEKYyhRXuuoFkwgslVwixyYNa2eh0RRjRdBjKw3YxkpxXTzzTej\n0Wjgla98pXzNMAzs3r0bMzPJJllaWsI3v/lN/NEf/ZHy+cXFReV9W0m/35fDL3LPV9XzxRryyDKM\npMFFn8Pg9bfg/lcL4H4QJZubpojRAg084Cf/rJ5bZY59f/woePFMV4KkgHVvx+0mrK3cQNCmY+Mw\nu/XHAU//70I5UFgrU0yxQmK5+kpB3C/LjBEYGQOQOgM/htpqBkJXnIMmCjYzEPTT66JWtHDiXIWF\n3AuCIlpPMUn+f4IFF1LHPX13XWw8yu9W5+GGUWxwWL1JnylAyq79yOYcWqaldOUCUHstSLKUquzu\n7cQKK0q8eYulHfQ+DR4V6L0cTLEqx7VssSZYw1jJjnPgFE3LNGj8XQpPFBKlmqI3Z8g7vWjNaiQ8\nmqgWbVGk1tGBso6TgA7CMBKzE3SuJ7435NqiyYrJGmj1fYSlWXG+/Q1xT2Vvwop6LbqBoPtFe7yU\nGFgA4LPHJRGmkxSpIwBtN0gcUz+GUAcuCkEPtbLafCfX6PR+sdblCIHjYyBG1puxjGQg7rvvPuzd\nu1eyBQLAHXfcga997Wu48MIL5WuPPPIIHMfBE56QwMiazSY++tGP4vd+7/eGPl6v1xudV0QvRLKQ\n0fECwV+k5ys5pI57+L4D/PP/TAyJRjUQRpyy4onCQ3aaYpG0jyI1d7a6wDZfrKz44tHpxC0t911J\naI2pFqAWqfvq++2y6GQmIQVANAf1RVFLIKqA5sNyXkTJttLDXmLpur6gYNb/T3pvCWpHKRBybqHA\nE0VM5lF1HT9tkKSBYMaT1ZV2TZVwynQAXPZU4KO/LF5vHUa1aIsCojSqPQYy0OczHKN71bTTXiUb\niiQly0BwRIzek8GdjYH2fQoRncYnJdlP4+/7/hXi56FvxxxPbNAOMZvK8ZgnqX/r3r0cdck6tcMw\n+dsw04SSSg0iid6nSjYqNhv/WtD2JVvrfhhzTJEhJiPIe4R4/wCdGwCi1V7uhXENJRLrkCjy/b7Y\njzrBpj6pUZ99LYd2MRg2NY+4ST2kZJto9T0V8MDug22aSrSj9B/JUcOldG1yRBmXj2nkIjWFLQBw\n9OhRvOxlL8Mf//Ef48wzz8SRI0dwzTVJKoVOrt/v42Uvexme+cxn4iUveUnqOy+++GJEUZT6d+GF\nF2JhQSzWlZWV1OeOKXoHpczh+ui6vihkporUvFDMDMSNlwE3fTj93fEGs0yDDYlZVEeIZvG+KxGE\nbiACBRkSUWMSF0ZrLAtmHLFEG4t5yArRmQy7YyVTnsWdh5uJZ9l8SNKBlwumKHAqRG8hDEOkmExO\no6DDOHm9xGZKJQpYn4qDXdxAFOuiC1w3EPQ8uAJmKJuz9k3DPPLjNNw4DOH5kTqlT88v0zM41oAW\nq5CuXehevDiZ9Gez8vMUfXBFpNM8c++ck/oB6noDgLu/qh6TpU9pJoS8x5RiotkF+ghOug+8EY8T\nA+oIJ0DlKmPprVrJFs4YRRCUgiXjxIx0exCIcy1okRrPBqRQTEl3uwEIzi8OjeWpzcBJrytJ9ldL\nrinw1MgOUCJX4ilLzq8K0zAE8R9z3lLU/Dx6YRGzCjBI87yNIiPrzVhGMhDPec5zsLq6it/6rd/C\nm9/8Zpx33nl4wxvegHe+850AgDe96U0SsXT66afjggsuwJ/8yZ/g3HPPxZOe9CR89rOfzX1MutCN\njY0t3qmJTl3BHlbHCTBfK6S9Mt45ymGDbQ2iS15f7AUVTSMJ06f3qVC7rMa76kLa0+QpMC9RPIZh\nAAf+Ufz94I3xtSW8TZJziR/Hj3Pc7BiUMgKQbsIpTeGe5U6Cbmk8JIb+QPQUCFilygRqGQYcLxTo\nDNrkpKxo8TPW1IJlCmCAfr3/n7w3jdUtzcrDnj1/4znnTnVvVTVdZaYG2zSuphlsrGAItpEBW8QQ\nYxsntGXHBCSwhQUkINESCgRCE2QZKfYP5ERujGUZZaLTYDKZ2A4RwdDgbpyGLqimxnvrnukb95gf\n77ve/ay197nDOVdI172k0r23vu/b4/uu8VnPqv0EOsqpb8p62Hkrm4i7zMmoHozx+gDA9r5DZFEO\nf+AdimLIZv33rCS56hB2xxrB5I95gFx0Haw5Mjq2rsBSLOHqF2v3LkLENZKybRtP5+JGv86wd97z\n9sQ9txuf6b63vuu5vxodBQk2n1NwNh1H67Vkrq6mVEi7eZE650QcGDEQEkHRGl3tK/ddRYwJnXKy\nhIvyvHcn+PxPO3LzzQMp3j19zc1I6jLUoVJdm7JwdUJPhQgiRJCH2FWNS42yA0ADjADoNcFpWNXT\nYhpoLymX1pteLmUgnn32WfziL/4iyrLEL//yL+Mf/+N/jL/1t/5WaBL6gR/4AXz4wx9Gnuf4Z//s\nn+Fd73oXPvShD+GHfuiH8OM//uMhL/Y4IqHSarV6yDeN2CI1eTz7usHNRTHkxgmLvFL877j2wvg5\n/Hfi8swtqmzuvEmmCrDhO2AMhI0gCEGVTp1C+3/+vv59YI18o+/qtOepNsq7Ulz6TC7n7/G37658\nBBG51EzmFujBJBty6fhpYWXTutBZPhN4poTPpFAnWeJqNbFRMNXGNcoRV07VdCO4eUNFEEUqLbfg\nbleW9T3XyavSW9S1DuiOdeZDUhL1hH1d65Qw92SIfPS/778vwgpODRCCjqqoM7frOiqMeq+d4bm2\nUY7l/A33fWGHPXvNO0oeBprPXBTaNcCbv+G+w3WU+U3neDCKajBFsVdk+Rv/Gvgn73P//OQvqX6P\na7MMsyLt3+/ydv8sABVJ7+vWIbTsRMWE0q88URAAfuob3J+v/r84muWaNdgOw2pKqtX0bMPhO+yk\ncRNt2xIx5CHQ+Xs/9Y2NuRudu9rXOm09Ns5Wjs9zQ1TN8clEEJfWm14uXQF5z3vegw9/+MOjn332\nZ392+PuLL76If/AP/sFlTxNkuXTK7LG7Ai+cAbBDVXeOSsIyP3IIyGyQmWmmkcUl3kVAnzzT/wZw\nC2OMtC+bkifsFyUv4oCkmPb1BxbCW+9rUVYCs73tFPTuVHnYEXsvdpShpHXSwoXn67uI1/eAVbPH\nWAAAIABJREFUg2fxpZ95Ay/cmAMfIwPRlMjTGHXTap4ayW3L86CU3TRLNC1HOgVw2nPscEqvw7BY\nLEac3xFBP2Pm6WHZvI35wTsNnbr10shbu6gOEUUItOy1T0FWJsXEURYrhQB+2CiEz+B+xMhzRy7Q\n03XMbjp46uYeUZqMdNze/2033nV+y3Fure/2z1fSL7Mbbp2E6X50nMUzuLfa49O4SH0RcOLsNeB/\n+pu6zhZSoG/imYMJNvua+oS8syURr2l2vLUshv0mzFxg5z8Yeet81xshSTcy95atO3EzIjsfrBua\nPQDX+7FvgM+4NQd+42f6vpU4RVP533JD7UVDx3anCKzOaa7TzU+oBnFpvenlqaHauH7dWeR79+49\n5JtG2IsHTFHNvwSLluCXw3j7paEElk2yvud+Y3PHHL2MRRDhOui6uLOao5cxiJp4huevO++bFckB\nwfxsN6iITTFNjsLwIeUBdR1e+Nf/lRuuxN55vUeeJijr1tM++M8kgrD5dW8gOlA9JdBbGMhocYCq\nbTFo8hODzaNJbU1gLILY3nfziRlOaoUVoZ2cJyKbm5ud2KsEtDPAw+gZwjxQftIAuHVKNM7Qza47\nXinF3QXKrb+tu/Kt3PM8W5zuEOCApD/FUEm0x4SLZ6+7CJvToLZjmnP5YyCMOAW2xzjMOs+N5e8l\njEMdMg1UbYs7h1NdI+o67dmzQh+R4w2lhwTWG9bhjq7bUKOkxTilTbXpr90PpoqiqIeIA8DL/xyT\nLHFHZHCMRItiqAZsAIKcevIRxKX1ppenxkDcuuUW+WMXW2yKiRZZYMnODFqC0z6EUBikHYSeoto4\nxWVZHTmNYEn85MUPQnb6OHRcj2x+Pv72RM9ImB5RWuHVi5Wi9ZKziaPSBjRC641fB/6v/xr4uf9M\nN3f5saJl03oeJRp0Dww94XKFWZG45y4KUaIYMQyibKdHLiqynEa2TsSTy2wBm2V77AAJbExseo/f\nxR/5S/1vlWMgHa50HNvLwue/9/H+7wyfHsxE9sflSXnCLVVTHQzQxe7AA+SdHFFoQD8siZ0khlUC\n/RoKA7OWwJ13u7/f+HQ3aY6JCi2NOVNb2B6ZKFL3OZ+kfT3g+qf7axJa/T6CatsONxY5qg66oU1F\n9n2/zJicbKqLncOu0fcEaEeQub2iqHcgJVLIXdNlEhHVCQCcvNLzLbHeEUMl71oM6b5f6+7f9C7H\n5p5fQi6tN708NQYiz3MsFgvcvz/0OB4olo6Yc/wiltCPueV5cIoU7US2xxopIYtfFoR8tiFP70u+\nDXjuJeCvflif09JpdBhv0mOh2RXXZrmmtqbwftA1KmLTEukkcC+pxqL7v033TM/f49vLunUGSrzL\n5bNuo5Xng7kAkyzRnE4UXbhr7L2oLI56Ay0e7xhrqM1Vj6WYdqcORcOFSjtDgDDuuOYpoJ9/L/A3\nP9IfRwyoGKVq7QxvFKOT/8fG/m0yEEyHMTU1CDGYxIMURV7ZlKYIzikLew+8Vk5+1/3JxHVrfz5x\nYuQ9iwOT5MBf+wXgO/8tPrq7iTdOdwTUmNIz8udheKug30TqnVpHaRz3qCsxENXaHYPqZB06PHs4\n8TNG+N1SSpDX+oic76phz4h69wY9GCIjjSCqm653BmTtZVPsqhYHU8NOe/IKijR20TQ7WBYMIUqf\nnQFAI8QSs6YvKZfWm16eGgMBuILLYxdbeLoToLqRAwrhIg80yTRts+VFyWZm80kE4UN6VgKyuN75\nxcB/8n841kpgyL7Jm8AqBsnbipABchBCiTiu0wjNE4yyzgJDmoK0CF3IirV2Rd4Hp3yqDWZ5gq7z\nsxe4XsMGisgQl5PUR9j+PIVJ7xHMFVHU//9D7/EOBsCsh+masQhC0l2MQbf5dI4E5DyL21rhS3pA\nPGM5rsA+AW2I73+i/zvXw3iAkLswfQ0+uuvQqYFEroFshCtMrpFrCJ/4P/t7ANz6FC94fsulE8Xw\nCoNskuFf/M45/vnrCf7nj7ymI8NsMkRscTrm4B1QUq7p3G+5mpqMLZ0c6v1B6aQYMf7Azbnn9xqZ\nRVGunTJNClw0jvNkWw1JEdWsFdN/RKnNATvwYGhUgbJuXLTNa+P8DWRJ7MAcHOXZFKjoHTHWY2ix\nsVECl5RL6U0vT5WByPMcZfmYFjUgfd50mzuM76QHb1lZma6AccpWPu8b+g22emNI2sa5X5sqEbEb\nnOGbNPi+btre6wLcvTApGNArm8mRbsS6KMVkw/OkcGyfgE6ByCQ6QNdS6h0mcYdJFjvaB+I9Uucn\nT05gs0GsoiTYX1m3OirhzxWRm1HwI3Qg/WcUutvZCYG5N9fpOhYxEPL/5R3FaV/ivqiwyNGSTTFZ\nDh5vOLsO/TPI5w5EwEqT5iK8vdoDz767P0aY2UDpDrne5W3HdSVrNRjQBOt9jTiKEEeRa1hrqAZh\nx6sGrzcFrr+o76Ha6EK7Nf48nIsiwjSNsJz4PgNW6hK9SRTi19ibZ7ueZM/L2ZZG29q6VdsM4bq2\nfuil67qR9RWhlTIaU6mcfhLzPPWEfJSVYGcG6A2QdLSPRUFjmY5LyqX0plzGlc/++yiTyeSxW8ZX\nXeEWabN3i4B4lgIUmrsqW8p7J/mwU/VLvtX9+blf65SE6nU41t8Nyo8I2mzrvE0RXFAQPN1WwE0a\nbLI/1/DFpu4X3OSAPPg3xkdcAmiiTCvTJMNCFHjYvPeB49/tv1Ou3PMiTvtrs9yPY5SGoXlvmNVc\n3p1u1AM01Xpdunx7lKBOfHOgeKySEtmfuQ1Ocw4GG1A27Rf+NeBv/CLwBe8DXvorTmnwuFBrtDmi\nCLTOklY0iCdr2HgNjRkoQMOnLQ+UTf+xI0Gph33V6Pw2efS/8/amf+9JAXy5n0FOjKI9/fUzrsZj\niQfjGE3XYZo7OvyDaaaHLjEVOF93kvd9FeG6a5qDcU/P5uA/d6e98m9bl1oE3KNmT1qea9iTbo1+\n4t4aeP4L+vN2nafxN+uC58szdJi/UywVFL7pQAVngcq7yKLtOv2ezl7Dokgc5xSDGC4yEDtrIAhF\nZ+uiV5DL6E2Rf+cNxPG6pFTMfeXZpn5RtoC22AHN1LjFVBxgH/mFJAgdUc7Uzaw2EtDD7M7fHGeF\nBYaejGpK6tNP26rp4aOAKzamhYtSusZ5htLIN7+lr8t6QF52daujiCTHNIudYmYFKAVMkek1ZXQW\nk9QNnt9SBMVzeYlJc5anLo0lkRI3MlL0cLZv9AjSnKKS9V0NPwwpF6Eq6Gsu9w8+B/uv+gBw5w/j\n1ZOtRqixRwto4xwor32B2ob8NBt6IBZwYFOZTUnXeEHdxHv9UQQ1sW5T0chWAUf4z+6e7/po4U98\nD/AljkhTRXPifS/vYFcRY2q4D1f3OJik/ZAfbuZi3D4/jzjpkUkiTd2nT9Z3h9xcfF3BwFKvTgSd\nEhQFahrn3jjd9fsRAM5exbVZNuIYUWRnHTNeC/SuI0DNXnGHcc9ITY/z147diZtDwg6f3du28Czv\njJ/lAMRwefmUMRCXCZVW+1p3Moai8l2NHOWipyyEgNUvevgnFZWdIh1XcACI7vjeECsvwtEKHz/J\nVUGwalqd7rBprNWb/eZd3O4N2ckrw1DbS1m32gOKXbdroyjQT4a0FZMj1aX67OEE88zMiRbFevaa\nMjZHs8yF4JK2kO9tj4myYI5NWevO6uKgVwKnv0cbsBlSMtDm/F8/9qaLvuBHkHLfCafEAA0Y2FA9\nxz8bOab7jlUaca9+LMNs6OIn5tPQ0e3fsTUQ/plFiPR1MQKsa1WdarVvYAkk3T1QR/GuT0NWjVVw\nCMrvaJajSBPM1PzvxYiBoMIqK+k4dQZD3v3udJjG4RoEpX6nPsrUM8qpu9ikGU83xPsEAK/+iuuj\nsOsiSGfIHqHrd5Y+JIALRpwBS11/9rpzljiVGaJkUfamO3owdGmmKWGuKJ8yKaY0TVHXIwXIB8ia\nDcT+VJG1qVGdqglHEDUyVnHRU/tS52rEM4K3J4P2/y71WO56N6RUgB+yQ/lldfxspgyO433x1/uu\nr+4ht9zvwOH/4ae5RXr2ar9RjDeyrxuNeolizPLU5aaZAt0OlJ8c9BHC+Zv4rNvLvkO3ONQe0P5c\nhdjTLHHPPXD3ED00Te8r6xbPHBR9SmTxjOr7UE1nPI0NUB72NE/Q+KghT2OtGHhDApo1NTC0euVq\neZsmlFsHNFzd1iDuv9yfE3DXYJEtNsUUhg9pZZIlMSmwVkWZZd2O97wwkGJ74i52es0ZCAuh7jpE\nkQMdLCcJ0mbj3lU6ddcvgAV5/zXVbYQ+AwD+4k+jjTM9NvSiCGJ9TylhqVPN0lj/Rp4rFe0BOMPI\nAJJ3/lF87PWzIXiBews43dV1un4XYM+1p6cXKGxPxd3Bc69ZA3H+mjNsbCBsOtJO9BvQqif6HV9R\nLqM3RZ4qA5EkCZrm8ar657taI3IkJ7q514/TBHQbv/CtiOcxv4W67XxO3PDQs6dt+WGiqN+ckqYh\nA1E3VO+Q4zG1ceXGRLrB9egXHTe4sYdGRW0kaT9GUQxHrb2Isun0Ao8izIvEI0D8OdZ3+2sSKQ4U\nOuWd12d07pv6GZRrxWw5yz0rrCx8OX+zV7xX+7p1ikIUxMykrTgPnxZuQ7V1T68MAOWm79wGMMsS\nXWcgWg90ncbW2/ywnSxmGYAtZQ5PlxMlyZ6pnXVsIwjvHUdm5nUaR72ibH0KFE6JlY0pYIdr8few\nP3MXWhzgrPTr2U73Q4c4cuSMB5OcyAB9pGrrCIGbiOpOAPDOL3Fd7dyEallvD6h+RySah95AxGjd\ns4oSZ4BCcVf4p5xi3dcNgoX+gm9GN7/p6jFc52lpfG0U9w7A7IZvBm2cc5PmqjAeRbgQ5BFHxDgr\ncu+33DvjWtdgXKqJCkI9hxwUC/++glxGb4o8dQZCGGQfVVZ7YyCIDTMP85YjjW6SBSgvbHYDddM6\nL95OD2MUyF4jPKIo6lNAjPbwUjUdKSmTLw3MqM4jj1U+lp7BrDd4A1y1bGo5R005ePgUE48QbRsc\nTDI3N0EUmMAfWSaHqgnp+aPp8Nx8XdTUlsaRazCSe2BKa0rDbUvP6BmU9lK/R0a3qDm/ehby4TQL\nt1xkia4zEOcRmqqnH89nFI15724w0tNseuvpiQH9qz8H3HqXPydFqRb6aBWBv5+Yob75wildEYbl\nRhGqutWdv15aEEkh4Go8Qvs+gqBJYzc3Qg8kYiOD/hkH5N6R73aWYVrGgdoeK2NTNS2lvt5WkY9Q\nzPcNgqKg/buTZ547wryyZuWfIIoipHHkRtaO8S/FiaYLt4g1CyCQ6FHO4Ukqp1miI7BnPx94x3v9\nd5grzESftuN8ZGwqP5uus97H48ll9KbIU2UgLiP7utFoIsLnz3IKS0P7ux8lmi/7hVgs0MKnhPZG\noXMznFUqwDDHS1QGLsVkIgjb1OMVUaJ45OllM9bb5odZ+WZzzS0FuD6QmAqqXYNby8IVxMXzs/UH\nAHjPf6y6xJ85mOihKVzk3p16/qLeAKedZ5iNU1I8K6XwdnXjahC8ealzfECyxhGLSTGJuNoHgQB4\nZjYDDNrW4faBPmqxhcrQKFaHZ6d4riQFNkbZEUUYFL2tV+mPH0fQzYNMtsidt/ApDzuDHfBoGzIQ\ni2dw73zvrtfyccFDWwEsCqoBiOEJDWp+3XF9QpRwnPaOBw/qCQbCQ2wnJgoEgHqLGwtpTDOgj9YY\n0+IAq12NsmkHlDXzItWkkG3Vv/s41dH+ztyTLSqLkQv0+TVSKeDLmrz+6cDf+OdkIJi2hWhhgB60\nIBI68KlIPb3mfrs7QfQEeiEuK0+VgWjbVm/CR5C66XSuc9ZHEJNs5PYJMcIhXxpHLlUxKLQJHv6e\nC7ejRBuFAQ1zf/1N2/UhuoSqsullgfnfz4pEh60idD8DAzHGHkp9DFXdDhTYreXEoZvGSNCOXgD+\no/8R+KyvVAiUd16f9eeeHLlsix3UxM2IzKPD3lrdK8KqbnF9YfpQ2NBbjn3umqW038GkjyCSOKaU\nyLQnNiwWvfKa33LPqCk9QaN/DnauyEVwZZGLmhMB3Y9jJ5qJMLqMFIea6meovvM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4jE1IzkfZl+GH+8NHJ1riyh+kecaUp+Tr9aAyF7RIxSuDaT+g3Rt3eupH7ADYuEIFv7COJsV/UU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\nATQ3N6ufSCSCcDiMUCiEaDQaZ1jVeswoqzRl3LVjq3gYeb1e5OXlIS8vD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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "predict = theano.function([x], prediction)\n", "prediction_np = predict(data)\n", "plt.plot(data[1:], label='data')\n", "plt.plot(prediction_np, label='prediction')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Small scale optimizations of this type often benefit from more advanced second order methods. The following block defines some functions that allow you to experiment with off-the-shelf optimization routines. In this case we used BFGS." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Warning: Desired error not necessarily achieved due to precision loss.\n", " Current function value: 0.000040\n", " Iterations: 16\n", " Function evaluations: 135\n", " Gradient evaluations: 123\n", "train mse: 3.976745702376021e-05 validation mse: 4.0653327071471766e-05\n" ] } ], "source": [ "def vector_to_params(v):\n", " return_list = []\n", " offset = 0\n", " # note the global variable here\n", " for par in parameters:\n", " par_size = numpy.product(par.get_value().shape)\n", " return_list.append(v[offset:offset+par_size].reshape(par.get_value().shape))\n", " offset += par_size\n", " return return_list\n", " \n", " \n", "def set_params(values):\n", " for parameter, value in zip(parameters, values):\n", " parameter.set_value(numpy.asarray(value, dtype=floatX))\n", " \n", " \n", "def f_obj(x):\n", " values = vector_to_params(x)\n", " set_params(values)\n", " return get_cost(data_train)\n", " \n", " \n", "def f_prime(x):\n", " values = vector_to_params(x)\n", " set_params(values)\n", " grad = get_gradient(data_train)\n", " return numpy.asarray(numpy.concatenate([var.flatten() for var in grad]), dtype='float64')\n", " \n", " \n", "from scipy.optimize import fmin_bfgs\n", "x0 = numpy.asarray(numpy.concatenate([p.get_value().flatten() for p in parameters]), dtype='float64')\n", "result = fmin_bfgs(f_obj, x0, f_prime)\n", "\n", "print('train mse: {} validation mse: {}'.format(get_cost(data_train), get_cost(data_val)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generating sequences\n", "Predicting a single step ahead is a relatively easy task. It would be more intresting to see if the network actually learned how to generate multiple time steps such that it can continue the sequence.\n", "Write code that generates the next 1000 examples after processing the train sequence." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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tHpTlZhC+oJuq3kp28wHr38nyelf0BCFA98J+oyCMRiMAwOPxCHqdoQUmTCi3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\nHvj6Q6D1Iq0/pZdRygME599QAsTZI/KCuYH2MJUAwd9pFTz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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_t = T.vector()\n", "h_p = T.vector()\n", "preactivation = T.dot(x_t, my_rnn.w_xh) + my_rnn.b_h\n", "h_t = my_rnn._step(preactivation, h_p)\n", "o_t = T.dot(h_t, w_ho) + b_o\n", "\n", "single_step = theano.function([x_t, h_p], [o_t, h_t])\n", "\n", "def generate(single_step, x_t, h_p, n_steps):\n", " output = numpy.zeros((n_steps, 1))\n", " for output_t in output:\n", " x_t, h_p = single_step(x_t, h_p)\n", " output_t[:] = x_t\n", " return output\n", "\n", "\n", "output = predict(data_train)\n", "hidden = get_hidden(data_train)\n", "\n", "output = generate(single_step, output[-1], hidden[-1], n_steps=200)\n", "plt.plot(output)\n", "plt.plot(data_val[:200])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "#Things to Try\n", "The quality of the generated sequence is probably not very good. Let's try to improve on it. Things to consider are:\n", "* The initial weight values\n", "* Using L2/L1 regularization\n", "* Using weight noise\n", "* The number of hidden units\n", "* The non-linearity\n", "* Adding direct connections between the input and the output" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }