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Theano_Tutorial.ipynb
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Theano_Tutorial.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Theano Tutorial @ LTI, Carnegie Mellon University\n",
"=======================\n",
"\n",
"Kazuya Kawakami, clab\n",
"\n",
"# Theano\n",
"\n",
"Theano is a Python library, like numpy, that lets you to define mathematical expressions and run them on GPU. \n",
"In short, Theano is the best prototyping tool!!\n",
"\n",
"**Pros**\n",
"\n",
"- **Python** : Preprocessing, Modeling, Visualization. Numpy like expression.\n",
"- **Easy to access GPU**: You don't need to do anything!!\n",
"- **Execution speed optimizations**: Theano can use g++ or nvcc to compile your expression graph\n",
"- **Symbolic differentiation**: Automatic Differentiation on symbolic expressions\n",
"- **Stability optimizations**: Theano recognize numerically unstable expressions and fix them\n",
"- **Still growing**: Developer communitiy is active\n",
"\n",
"**Cons**\n",
"\n",
"- **Loop** : Restrictions on how the loop interact with the rest of the graph\n",
"- ** Goto/Recursion**: are not supprted\n",
"\n",
"## Contents\n",
"0. **Tools**: You don't need to know theano at all !! ( [nolearn](https://github.com/dnouri/nolearn.git), [Pylearn2](http://deeplearning.net/software/pylearn2/), [sklearn-theano](http://sklearn-theano.github.io/auto_examples/plot_mnist_generator.html#example-plot-mnist-generator-py) ).\n",
"\n",
"1. **Overview**: How theano codes look like??\n",
"2. **Variables**: Symbolic variable, Shared variable\n",
"3. **Function, Computational Graph**: tensor.function, tensor.clone, theano.printing.pp, theano.printing.debugprint\n",
"4. **Math**: Comparison, Condition\n",
"5. **Linear Algebra**\n",
"6. **Gradient**: theano.gradient.grad, theano.gradient.hessian, theano.gradient.jacobian, update\n",
"7. **GPU**: Data type \n",
"8. **Linear Regression**\n",
"9. **Multi Layer Perceptron**\n",
"10. **Convolution**\n",
"11. **Maxpooling**\n",
"12. **Scan**\n",
"13. **Recurrent Neural Networks**\n",
"14. **Tips for debugging**\n",
"15. **Links**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Server\n",
"\n",
"This is a hands-on tutorial, if you sent your public key to Prasanna, you can log in to your server.\n",
"The server has 4 GPUs. To avoid all participants uses one gpu, please specify explicitly the gpu you want to use."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using gpu device 0: GeForce GTX TITAN Black\n"
]
}
],
"source": [
"import theano.sandbox.cuda\n",
"theano.sandbox.cuda.use(\"gpu0\") #gpu1, gpu2, gpu3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you are running command line, run your code with the following options.\n",
"\n",
"```bash\n",
"THEANO_FLAGS=mode=FAST_RUN,device=gpu0,floatX=float32, python your_code.py\n",
"THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32, python your_code.py\n",
"THEANO_FLAGS=mode=FAST_RUN,device=gpu2,floatX=float32, python your_code.py\n",
"THEANO_FLAGS=mode=FAST_RUN,device=gpu3,floatX=float32, python your_code.py\n",
"```\n",
"\n",
"To run your experiments with ipython notebook, you should write this in your ~/.ssh/config \n",
"\n",
"Host tutorial \n",
"HostName [[[IP ADDRESS]]] \n",
"User [[[USER NAME]]] \n",
"LocalForward 8888 127.0.0.1:8888 \n",
"\n",
"```bash\n",
"ssh tutorial\n",
"```\n",
"\n",
"Then launch your ipython server by following this [Instruction](http://ipython.org/ipython-doc/1/interactive/public_server.html)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tools\n",
"\n",
"There are a lots of tools implemented with Theano.\n",
"For example, [nolearn](https://github.com/dnouri/nolearn.git) let you write image classification in 30 lines !! \n",
"\n",
"More examples and tutorials are in [dl_tutorial](https://github.com/oduerr/dl_tutorial/tree/master/lasagne)."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"from collections import OrderedDict\n",
"\n",
"import numpy\n",
"import theano\n",
"import theano.tensor as T\n",
"\n",
"from sklearn.datasets import fetch_mldata\n",
"from sklearn.utils import shuffle\n",
"from sklearn.cross_validation import train_test_split\n",
"from sklearn.metrics import f1_score\n",
"\n",
"#Random Seed\n",
"rng = numpy.random.RandomState(1234)\n",
"\n",
"mnist = fetch_mldata('MNIST original')\n",
"\n",
"# mnist_x is a (n_sample, n_feature=784) matrix\n",
"mnist_x, mnist_y = mnist.data.astype(\"float32\")/255.0, mnist.target.astype(\"int32\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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FVnI6nWZxcZE7d+5w7do1BgcHj9Vjz37g6f8jtQ27XC6sViuxWCwrGqsvQ6oz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jx5gt/vF6GWUnmopZ65UvmjWq3m1KlTjIyMUFNTg9VqFckKQGhuA4EAW1tbuFwuQqEQyWSSqakp\n0bpTmh5RThuuzWZjeHiYtrY2qqqqUKlUomduJBIpuWdZ2iSk3rCSQmHvETiXy5HNZkX5tlQkEo/H\nRTz/9u3b+Hw+MplMya3xRUilzntL7P1+P7OzsyIhmMlkSKVS+P1+tre38fl8ok2j9HsKBAIsLS2h\nVCrRaDQYjUbRECkQCAi1TS6XI5lMiuf5dd/dAze6UvVOVVWVaPhSihnQN0Gr1TI4OMjly5f51a9+\nhdlsplgsCs9VGt0SCARYWVlhenqaf/7nf2Z2drZkjOyLKBQKpNNpoane3NwkHA4L1YU0XBMQzW0W\nFxeZnp7mzp07+Hw+crmcqBYqN6RkU11dHadOnaKzs1McU6PRKJubmwQCgZIzSJLxkYyI9GdSldze\ncuVQKITX6xWjsra3t0VuIZ1Ol81pZC+SjVlaWmJpaeknf7/H4zmAq/qOAze60hjyckqYvCmSsRob\nG2N6elokxHZ2dggGg2xvbx/4jd1P8vk8mUyG2dlZgsEgZrNZjCqXPN1EIvFchZnP5xPTY8vJq92L\nwWCgtbWVM2fOcOnSJVpbW0X80O12c+3aNR4+fFhS3cQANjc3uXbtGo2NjUJlIR2FpQ5aW1tbeL1e\nUQotxSqlcfFHOcvubedQjK6kNw2Hw6yvr5fkkexNyOVyBAIBlpeXqampEZntq1evcu/ePaFAkPqT\nltpL+mNICQiPx1NWm8XPRSaTodPpcDqdDA8Pc/LkSY4dO4ZWqyWRSIiJGRMTE6ysrJTcpiIZ3fr6\nejFANJVK4fP5hN7W6/Xi8XgIBoNvZe+TUkb2I23s3vjsq9Fo0Ov1OJ1OtFqtiINJNe8HTbFY/FEd\nx5uuU6lUisY0UtazUCiIh1yq2pIqt/Z73YexxlLgx9a5X2uUBh0ODw/z0UcfcfLkSXp6ekgmk/h8\nPm7evMmdO3eYmJjA7XaLJuz7wX7cS6k4RUrewnenFUm7m06nSafTZDKZI9k0DuteHiUvW+OBe7rS\nzS3VJsj7geTpHtbYmQoHj1RU0NLSQn19PUqlUmT5t7a2WF1dFQUDpRaXl5LVFUqTF3emrlDhF4zU\njHx7exu9Xi9mZ0mtNguFAtFolEgkQiKRKDmjW6G0qRjdChW+h1wux2QyiSO6pD2ORCK43W6h5ChF\nL7dC6VPaKu8KFY4AqWzdZDKRSqUIh8PI5XLW19eZmZlheXn5RydMVKjwMipGt0KF7yElQW/fvi0k\ncjKZjFgsRigU4smTJ0d9iRXKmANXLxw1v4TM/i9hjfDLznjv5Zewzrd5jZWYboUKFSocIq/0dCtU\nqFChwv5S8XQrVKhQ4RCpGN0KFSpUOEQqRrdChQoVDpGK0a1QoUKFQ6RidCtUqFDhEKkY3QoVKlQ4\nRCpGt0KFChUOkYrRrVChQoVD5JW9F97mUry9lPs6fwlrhF926ehefgnrfJvXWPF0K1SoUOEQqXQZ\nq1DhF4pCocDhcNDR0cGpU6fI5/M8efKExcVFVlZWjvry3loqRrdChV8oCoWCpqYmzp8/z9/93d+R\nTCb56quvSCQSFaN7gFTCCxUq/EJRKpW0t7fT399PVVUVCoXiqC/pF8GRerpGo5GqqirMZjOFQgGP\nx8Pu7m7Zj0BRKBQolUpMJhNmsxm73U6hUCAWixGJRIjFYqTTafL5fNmv9W1Emodms9loa2vDaDQC\nEIlECIVCRKNR4vE4u7u75HK5I77an49cLsdqtVJTU4NGoyGdTuP3+9nd3T3qS3urOVKjW1dXx7Fj\nx+jv7yeXy/HHP/6R1dXVsn6QAdRqNSaTie7ubgYGBjh37hyZTIbFxUWmp6dZXFwkEAi8FRvM24hC\noaCmpoYzZ87wX//rf6WzsxOAqakpxsfHmZub48mTJ6ytrRGPx4/4avePUCjE7Ows29vbR30pbzVH\nanSbmpp45513GBgYIBqNcvPmTTY3N8vWA5Smxfb39zM8PExLSwvt7e10dnaSy+VobW3l2LFjuN1u\n1tfXWV5eZnZ2lkgkQjqdPurLfy2koY06nU4MbJRQqVT09PSg1+tZWFgQXlM2my2rjVSpVNLZ2cmJ\nEyfo6uqitbUVeGaMzWYzTqeTqqoqgsFgWRtdmUyGwWCgqqoKpVJJJpMhEomQSqWO+tLeao7c6J49\ne5bu7m42Nzex2+1oNBqSyWTZGV3JGHV0dPDRRx/xl3/5l1itVvR6PYVCgWKxSEdHB3K5nEwmw/Ly\nMtevXyeRSLC2tkY4HAagWCxSKBTEVykhk8lQqVTU1tbicDhQqVTI5d+lBfR6Pf/pP/0nqqur+T//\n5/8wNTWF1+sVx/FyQalU0tXVxeDgIEajUTyLTU1NOJ1OamtrUSgU3Lt3r6wHVMrlcqqqqrBYLCiV\nSvL5PJlMhnw+f9SX9lZTUS/sA2q1moaGBrq7uxkeHqavrw+r1YpGoyEWi7G0tITX6yUcDjM0NERH\nRwc1NTW8++67OJ1OPB4PW1tbwtNwuVziq5QMr8PhoKWlhQ8++IDBwUF0Oh0KhQKZTEaxWESlUtHW\n1oZWq8VgMLC4uMjs7Cz37t1jYmKCfD5fUuv5OcjlciwWCzabDZVKddSX80YUi0Wy2SypVIrd3V0S\niQSpVKqsTiXlSMXoviEKhQKj0cixY8c4efIkw8PDtLW1oVQqSSQSbGxscPv2bZaWltje3iYcDpNK\npVCr1ej1ei5evMju7i47Ozskk0m8Xi/3798nk8ngdrtLwkipVCq0Wi2dnZ2cPn2aDz74gBMnTqDX\n63+Q8ZbCDY2NjbS3t9Pc3EwoFGJ6elp48eWMXC7HaDRit9uxWCzo9fqyPJkpFArUajUajQaFQkEi\nkSAWi5FIJMhms0d9eW81FaP7huh0Ourq6rh06RIXL16koaEBuVxOIBDA4/EwNTXFF198wfLyMvF4\nnLW1Na5du0ZVVRV9fX1cuXKF+vp6uru7yWQyVFVVsbm5yeLi4nPx0qPEbDbT0tLCpUuX+Pjjj2lt\nbUWv1z8XWngR1dXVjI6OcuvWLVQq1VvhQclkMrRaLTabja6uLra3t1lZWSm7tUlraGlpoaGhgUAg\nIJQZ5ZJfKFcqRvdnolQqUavVDA4OcvbsWU6ePElLSwsqlYqFhQUmJibY2NhgaWmJubk5tre3yWaz\nxONx3G43BoMBr9dLIpGgpaWFuro6Wlpa0Gq1tLa2MjQ0RDQaZWVl5cjihgqFQni4H374IefPn6e3\nt/c5Dzefz4tEWaFQQK/Xi2O3RqNBo9EwNDTElStXmJ+fZ3NzE4BsNks6nRbx7lIin8+zvr7O4uKi\nkIyp1WqxCSoUCkwmE+3t7aysrOByucrO6JrNZhobG7Hb7SiVSoLBIMFgkEwmU3KnESmX0NzcTH19\nPXa7nXw+z87ODvDsfqhUKorFIrFYTNyL6upq7HY7iUSC3d1dYrHYK734WCxGIBAgnU6TzWZFQn+/\nn8+K0f2ZqNVqzGYz77//Pr/73e9obGxEpVIRDAa5ceMG/+N//A92dnbEQyA9yMlkkmQyyc7ODtvb\n20xOTmK1WnE6nfz+97/n3LlztLe3o9frqa+v549//OORGV2lUonFYuHEiRP8l//yX6itrUWn0z3n\ngWezWXZ3d4VKQUqw7eXMmTOYzWb+8R//kTt37gAQj8fZ2dkRD3cpkclkmJycRKPR0N/fj91ux2q1\ninXL5XIMBgOtra3U19eXZVGBzWajvb0dk8lEOp1ma2uLQCBQcvcCvvt9nz17lsuXL3PixAmSySQz\nMzPAs83dZDKRy+VYW1sTOmMp3Of1etnY2GBlZYVYLPbSn7O2tsbDhw8JBoPEYjEymcyBhFqO1Oj6\n/X6Wlpaora09ysv4SSiVSnQ6HcePH+fy5ctcuHABu93O8vKy8HDHx8fZ2toilUr94KZJu2axWCST\nyZDL5chkMsTjcf74xz+ytLTEsWPHaGxs5PTp06ysrLC6uorf7yeRSBzqWvV6PR0dHbS3t2OxWNBq\ntT8Iefh8Ph4/fozf7yeTyfD+++9TVVX13N+xWq309fXxu9/9jnfffRcAl8vFxMQEPp+PnZ0d/H7/\nK1+Iw6RQKBAOh/F6veK6LBaLWLtUPNHc3IzT6fzRMEspIpfLRRI0k8mwtbVFMBgsOS8XEAnq0dFR\n2tvbcTqdyGQyTCYT8MzT1Wg0FAoF2traxDtXV1cnFFF2u52WlpZXhk7C4TAXL14kmUwKVZHH48Hn\n87G9vY3X6xVFTW/CkRrdSCSCx+MhGo1SLBbLIrutVqtxOp2cOnWKv/qrv8JisZBKpZiYmODq1atc\nu3aNaDT6Wp8lycLi8TjxeJzt7W3m5uYIhUJ89NFHnD9/noGBARYWFkin04dmdKW4pcPhYHBwkI6O\nDqHLhWcbRi6XI5FI4HK5ePToEcFgEIATJ0784PMMBgMGg4GGhgbxZ3Nzc9TU1LC8vMzKygrZbLZk\njG6xWGR3d5dwOEwoFHphEYtaraa2tha73V6Wnq5SqRSbaCKREMblp7x/MplMbEQHcQyX6Orq4oMP\nPqCmpgaj0YhKpcJkMmG1WoXjolQqRS+J7zsGZrNZhFNeto7va86l0878/DwrKyvMzs5SLBYJBAKv\n/X6/jCM1upLQPJ1Os7OzI4oESi3GtxeLxcKFCxc4c+YMDoeDlZUVHj16xOeff87ExATJZPKNPj8W\nizExMUFraysffPABXV1dXLx4UcjKDgOVSkVfXx9nzpzh448/ZmBgQIQMJJmR1+vlzp07bGxsADA8\nPExHR4coJPgxGhsb+eijj1hcXGRqaoqtrS3cbvdBLelns/dZ/P6/H6ShOWhqamro6upCLpezubnJ\n7OzsT4pNy2Qy4WFKm/DeMNp+YrfbaW1tZXV1FZ/Ph0qlorq6Gnh2YvJ4PGIDNBgMP0nKJ6k4jEYj\nSuV35lCpVApp5+joKH19fTgcDu7cucPjx4/faD1HYnSlHbKqqora2lry+TyhUEjIVUrxQZbL5Tgc\nDo4dO8b58+fp6emhWCyytLTE9evXmZycxOPxvPHPSaVSrK+vMzc3x9TUFCqVitbWVgwGwz6s4vVQ\nKpW0tbVx4sQJjh079lzcMpfLsbKywszMDA8fPkSpVIpy597eXnQ63Us/d6+RMhqNmEwmMpkMgUAA\nvV5/KGv7OfyYcS3F5/VlSMaytraWzs5OZDIZPp8Pj8fDzs7OS42mFI5wOBw4HA4RZrPZbCKB5fF4\n8Hg8IhG1X/j9fmZnZ5mdnRX5EKvVCjwzuhsbGzidTux2+w+M5/fXvvdeSQk6o9Eo8hXw7BSj1Wqx\nWCzU1dUJyWQul8Pj8ZSv0VUqlZjNZhwOhziq7ke85KBQKBQMDw/zwQcfcPbsWcxmM1tbW0xMTHDr\n1i0CgcC+/JxCoUAikWB+fp7/9b/+F6Ojo5jN5n357NdFLpcLNYXBYHguZplOp/nmm2+4desWuVyO\n4eFh3nnnHZxOJwaD4aVHbSl8JN1f6SXO5XIlfd/fNiTv1Ol00tnZid/vJxAIkEgkXunlKpVK9Ho9\nZ86c4fLly8LgdnR0UCgU2Nra4t/+7d/4/PPP8fv9+2p0x8bGmJiYEGqXL7/8UjxnUrJLpVKhUCh+\nECZ4FVJs3m6309nZKWLENpsNp9PJyMgInZ2dWCwWrFYrAwMDjI2NvfF6jsToSjfQbrdTX1+P3+8X\n0qFS9BrkcjkajUa0wbNYLGxsbPDVV18xPj6O3+/fN22j9DvY3t7m4cOH1NbWYjAY0Gg06HQ68eAd\nJJIHIAnnX/QQWywWent7GRgYoK6uThjcF/3dUChEMBh8Llmm1+sxm83cvn2ba9eu7cspYb+QnAKp\nsu5FCcRyRVLd2Gw2rFYroVCIbDb7UumeJI3r6Oigs7OT48eP09fXRyKRIJPJEAwGqaqqor+/n5WV\nFebn50kmk28c99xLNBrd18+TkJ5zv99PMBhEq9UCz3IQdrudcDhMPB5ndHQUvV5PQ0PDD5LEP4cj\nM7oGg4Hq6mrq6+tLvi5fOko1NTXR2tqKTCZjbm6O//k//ycej+dAElyRSIR4PE3bJOgAACAASURB\nVM7JkyeFhMxoNIoX5KApFAovbDwkl8upqalhaGiITz/99LnkGHy3aez9XilmuLi4iNfrRSaTYbfb\naWxs5IsvvuCzzz478PX8VKSKQaPR+FKjKx3VpR4UpZ4EhmfyKqvVitlsFkmp769NLpeLk0htbS0X\nL17k8uXLXLx4URyzV1dXefLkCbOzs3R2dtLb20tLSwttbW0lGZt/EZKCKJPJEAqFxJ9LHnAikUAu\nl9PV1YXVakWlUr0yfPa6VHS6r4FWq8Vut+N0OtHpdCwuLjI3N4fP53vjxNnLkF7gXC6HTCajtrYW\np9P5nPj7oMjlciwvLzM9PU1TUxMmk0nEydRqNaOjo6TTaSwWy3PfJ2X9JSmg5J08fvxYKBwkDaVG\no8FoNLK+vn6ga/m5SD0JpLDXi9QL1dXV9PT0cOnSJR4/fszTp0+P6GpfH6kpk91u/0EyUDK0DQ0N\ntLa20tfXR39/PwMDA1gsFtbX15mcnOTx48f4fD78fj87Ozt8+OGH/OY3vwHKK779Mux2O83NzZw/\nf16EEgOBAKurq/tyIisJo1vqRzepP66k1ZyammJmZoZIJHLgdeqSl200Gn9QmHBQSJ7M3Nwc58+f\nJ5/Pi9CBlGR72fe5XC5mZma4f/++iHPPzs4yNzdHLpcri9it5KlnMpkXaq3hu6o0p9NJR0cH6+vr\nZWF0dTodtbW1ojE7fBc+q6mpoba2lv7+fgYHBxkZGaG1tRW5XI7X62VhYYFr164xNjYmGrhrtVpO\nnjwp5J6l2B3vdZE2naamJk6ePMmpU6fo6+vDYDCwtrbG9PT0viiIjtTo7tX5lQuRSIS7d+8yMzNz\noAZEkrLY7XYcDodoinMYRksyng6Hg2g0+to/M5VKce3aNT7//HPW1tZE/FaqVisnL0gKr+zVju8t\njpC+stmsSESVKyqVSiTFfvvb39LZ2Ul9fT1yuZzt7W2uXbvG48ePWVxcxOfzEQ6HyefzmEwm+vr6\n6OjoQKlUksvlSKVSZWt0VSoVBoOB48eP8+mnn9Lf34/VakWpVBKJRFhaWtqXhPmRe7r5fJ5AIMDW\n1lZZNNrIZrP4/X7C4fCBGhEp9ma1WjEYDGSzWRKJxKE80FKYwO12MzY2hkql4uTJk6jV6ld+n5TF\nXllZYXNz88BCL4eB5O2m02kymcxL77XBYKCpqYmFhYVDvsL9QaPRUFdXx3vvvYfT6eT8+fMolUqS\nySSLi4tMTk7y7bffii55uVzuOdnfsWPHaG9vRy6XEwqFcLvdJZ+jeRlms5n29nYGBwcZGhoShS+7\nu7t4PB5mZmb2ZarGkRpdSVTtcrlYWloqeW/hMNUVOp0Op9OJ2WwWHbpe9fIfBFtbW/zhD38gm80y\nODiIRqN55d/f6wGW2wnmReTzeXZ3d0mlUi+898VikZqaGs6ePcvi4iK3b98+oiv9eUg9Dbq6uujt\n7cVkMmG325mbm+PBgwd89tlnPHjwQPQh2LvhS2W4AwMDtLe3I5PJ2NraYm5urmyNrtQV79ixY6Ky\nLZVKEQwGWVlZ4fHjx2+HpwuI2FmpHktMJhMNDQ2HUqAglSQ2NTVx5coVkskkX3/9Naurq4c+U61Q\nKAhP73W6gUkecTQa5e7du6Ixu9lsxmAwsLGxITpDlQO7u7ssLCzQ3t7O0NDQCz39XC7H7u4umUzm\nCK7wpyO1F93Z2REN2aWucYFAgLGxMW7fvs2tW7d48uQJkUjkB5VmKpWKzs5O3nnnHQYHBwG4ceOG\nMLjl1nFN6qbX3NzM2bNnaW5uBhBd/r788kuuX7/+k0Jtr+JIjK4k0FYqlc+J5ks15ldVVUVTU9OB\nG929FTJdXV1cunSJR48eiXLbUj+uq1Qqjh8/LmKdKysrJBIJ0Y7v3r17pFKpsimGSCaTrKyssLGx\n8dLrlUrYS/3eSMTjcVwuF+FwWHir+XyeZDKJx+Phyy+/5MaNGzx8+PCF36/RaDCbzQwPD3Pu3Dk6\nOzuFZn1+fr4sQoR7kcvlQoPb29vLyMgItbW1pNNp3G43Dx8+5F//9V9FNdx+OIZHYnQlwbXNZjuK\nH/+TqaqqorGxEYPBcKC7uFKpxG63c+LECd555x16enqYn58/FJXEfiB5Tj09PSiVSkKhEKlUiurq\naoxGIxaLBZPJxMzMjJgJV+6kUil8Pl/ZHKkzmQyxWIx0Oi1CQNFolKmpKa5du8bVq1dfKYtqaWkR\nHfZGR0eRy+WsrKxw/fp11tbWDmkV+4Okx21vb+cv/uIvuHDhAvX19aKf8v/7f/+Pr776ipWVlX2d\nDnIkRlfqFbsf1R2HgbS7q9Vq0Qthc3OT9fX1faszlwxWR0cHFy9epLOzk2AwiNfrZXt7u+QmtEqj\nd6LRqMhox2IxFAoFOp1OGN5sNovJZEKlUpHP51EqlaRSKZaXl4lGoyUbUnpd1Go1VqtVNDo/qKYv\n+0U+nxdNvXd3d9FoNGQyGXw+Hy6Xi6dPnz6XW5HL5aKKrbq6mjNnzvDOO+9w/PhxDAYDs7OzPHjw\ngCdPnpTNxiMh9ZLo6+vj3XffZWhoCJPJxOrqKtPT09y9e5fx8XGSyeS+OltHYnSlzPx+VHccNlar\nlffffx94drTcrzpzuVxOY2Mjo6OjXLlyhVgsxr/8y79w69Yt1tbWSs7Tlap5lpeX+eabb0RxQH19\nPSMjI/z+97+noaGBYrEo6uGlZuaxWIxCocD09HRJG6jXweFwcPbsWRYWFrh7964opihVJP1xOBxm\na2sLh8OBXC5Hp9O9sPJOpVJhtVoZHBzk3Xff5cyZMwwNDZHL5VhaWuIPf/gDd+/eLTmn4MeQQnkd\nHR0MDw/T2toqdPirq6t8++23oiF6ZXLEEeDz+ZienmZgYICmpiaGh4cJh8PCGO7u7r7R2Bmr1Upd\nXR1Xrlzh/fffp66ujo2NDe7fvy+86VIjnU7j9XqZmZnhm2++YXl5Gb/fTzwex2w24/P5qK2txWQy\niYY5krckbbjl0vx7rxrj+/+UkNQlpR6rllpzxuNxQqEQZrMZnU5Ha2srp0+fJhKJsLOzI7xds9lM\nX1+f+KquriadTvPw4UNu377NxMQEm5ubJb/u7+N0Omlvb+f999/n/PnzVFdXEwwGWVpa4saNG9y5\nc4etra0DcQqOxOhKu2253CiPx8O9e/e4fPkyWq2Wnp4ednZ2mJ2dJRwOi7lKP3U9UgVMfX09o6Oj\n/OY3v+HcuXPEYjE2NzeZmZkp2Wx/KpViY2OD6elpbt++TTweR6FQEAqF8Pl8bGxsUF9f/1yXMrlc\njlKpFA2ny43vF0dI3n44HCYajYoS51Jmb+P8UChEXV2dGLKp1+upq6tjc3MTv98PIHovSC0Ot7a2\nWF5e5k9/+hNff/01Pp+v7LxcgObmZi5cuMCHH37IyZMnRZvWzz//nG+++YYHDx4c2M8+EqO7s7PD\n5OQkp0+fPoof/5NJpVKin8DS0hLNzc00NTXx8ccfY7FYsFgsTE1N4fP5XuvzpEYpjY2NnDp1ihMn\nTjAyMkJTUxNut5tvvvmGb775hmg0WnbyG4mX6XRLVaHyOryqoXm5EYvF8Hq9tLW1ifLu6upqobuV\nDKlUAi/NGBsfH2d8fJzFxUUx464cMZvNQgYqTS1ZXV0VnvtBciRGV5KthEIhZDIZGo0GrVZbssfN\ndDpNOBxmamqKmpoacrmcEFJLpYNarVZ0V4pGoyI7LyUhJI2nFPNTqVQMDAyIyQwtLS2kUikWFxdF\ny8j9kqj8XKSCAMk7KhaLz03E1ev1WK1WHA4HMpmMXC5HQ0MDbW1tWK1WNBrNc8Z37+eVk6F6WVFM\nqbYifR22t7eZnZ2lvr4ek8kk7pXBYECv14u+zvF4nKWlJZaXl3n8+DH379/n0aNHJZ8wfBlSV7jq\n6mqam5sxGo3i1La8vMzS0pIYPXVQHHlMV2oVWF9f/6MVT0eFVJk0NjbG1tYWXq+Xs2fPMjo6KuK8\nFy5cIJFIUCwWuXfvHtevX0cul1NbW8u7775LTU0NAEtLS7hcLtRqNT09PaLs0uPxiOnAc3NzeL3e\nknihC4WCaDSey+XEKBStVktLSwsjIyO4XC6mpqbY3t7m008/5eOPPxaJib0b6d5x7aWsy/4lsLCw\nQDgcFj1xpQqsaDRKNpslnU4LY/v06VMCgQCxWEwUS5TrvdNqtdhsNlpaWkTj8lgsxqNHj5iZmSEU\nCh14DuXIjK7k8cB3CZYfq+0/KqRy5b39IbLZLDqdDofDQU1NDXV1dcLAGAwGqqqqyOfz2Gw2Tp06\nhd1uRyaT0dTUhNfrRalUUltbS319PVtbW6yvrzMxMcHDhw/xeDwlIbaXNpvV1VVu3LjBwMAAbW1t\nqNVqMfmjp6eHjz76iO7uboLBIO+//z79/f2iygm+q2yThlhKjVNK3VOSrjuVSokcRDnGol9ENBol\nlUpx+/Zt/H6/mLAbj8dFzmV9fZ2NjQ02NzdLvkT/dZDJZDgcDk6cOCFmnknjiqamplhaWjqUytgj\nMbqSwZUq0aTJni+bbVQq5PN5dnZ2+Pbbb9nd3UUul4s5YjU1Nej1eorFIqOjo/T394vEijQGGp6N\nAjl+/DjwXSItGo2yurrK119/zf3790smTpbNZgmFQmJUyl/8xV9gtVpF6aharaa9vZ2WlhbhuUoG\neS+5XI5YLMb9+/f58ssvuX37NltbWyWfSM3n88TjcWKxGIlE4pXjiMqRTCbDo0ePmJqaei45KHmx\n5d6qcS/S+hobG/n1r3/N8PAwZrOZnZ0dPB4Pk5OTrKysHEoO5UisnBS4XltbY3Jyklwu95NmGx0l\n0kO4trbGF198wdzcHC0tLXR1ddHS0iIG5FVVVeHz+UgkEuTzeSwWC1VVVSiVStLptPAitra2mJ+f\nZ3p6mvX19ZKr4S8Wi4RCIebm5rBYLBQKBd555x26u7vFOJ+9hmjvSG6J5eVlxsfHuXbtGuPj44RC\noZI3uIAYmjkxMcE//uM/cuHCBYaHh1lbW8Pn85HJZMQ06Onp6aO+3J+FNMX3bUfS5TocDoaHh6mr\nqxPl6olEgmg0KsKDB82RGF3pRj958oTbt29jtVrLLhPq8/nw+XxMTk6KoXX9/f309vbS1tZGfX09\ny8vLhMNhbDabaBAtHeGkF3VhYYH19XU2NzdLtqJHql66d+8eu7u7OBwOmpqaUCqVr5X8fPLkCVev\nXuXevXtl0ehbIpfLEQ6HmZ6eJhKJAM/aGT548IDl5WUSiYTYUKTewRVKE0kxZDabaWlpwWq1Ppev\nSCaTh+bwHOl5fnp6Gp/Ph1qtJpVK7UuvysNGmq80OTnJ2toaN2/exGAwoNPpiMfjZLNZ1Gq1UGjA\ns5c5FAoRiUTE0XW/Sw0PAr/fz8LCgtBmvm4DoM3NTTGupxyRTmX/9E//xI0bN9jZ2SEWiz0Xfign\nh+GXiBTSTCaTBAIBtFrtkVXEHqnR3d7eLktDuxepQ1MymSz7tfwYu7u7eL1eJiYmxDTf10l+Pnz4\nELfbXZKVda/D3uGF5RpG+KUjdTP0er3cuXOHM2fO0Nvbi1arFYNWs9ksGxsbB94FT/aqGIZMJitP\nXcgeisXijwaKy32dh7lGqTHPq0auf59wOEwkEnljXeuPrbPc7yP8Mp5XOJp7KQ147ezs5O///u/5\n27/9WzKZDH6/n5mZGb788kv++Z//ed/GL71sjaUtF6hQchQKBXZ2dkq2PLlChZdRLBaJRCI8efKE\nhw8f0tHRIVrMDgwMEAwGWVxcZHZ2FpfLdWDXUZolYBUqVKhwAKRSKQKBAOPj43z++ee43W7UajUN\nDQ309/dz6tQpGhsbD1RNVfF0K1So8ItBSqitrKyIYpdYLCZ6n3z66adCNx8Ohw+kKKRidCtUqPCL\nolgs4vP5iMVi1NTU4HA46O7uprGxEavVyv3797FarSQSiQMxupVEGuW/zl/CGqGSSJP4JazzbV7j\nK41uhQoVKlTYXyqJtAoVKlQ4RCpGt0KFChUOkYrRrVChQoVDpGJ0K1SoUOEQqRjdChUqVDhEKka3\nQoUKFQ6RitGtUKFChUOkYnQrVKhQ4RB5ZRnw21wVspdyX+cvYY3wy65i2ssvYZ1v8xornm6FChUq\nHCIVo1uhQoUKh8iRdxmTyWQolUqUSiUKhYJ8Pk86nX7jKQMVKlSoUIocudFVKpX09/fT1dVFY2Mj\n6+vrjI2NEY1GS24ceYUKFSq8KUdqdFUqFRaLhZGREc6fP09HRweLi4tEIhGWl5fxeDwUCgUKhcJR\nXubPQqFQ0NLSQnV1tRgT4vV6SaVSlcmxFUoapVKJWq3GbDZjMplQq9Ukk0lCoRCJRIJUKnXUl1jW\nHKnRNZvNtLa2cu7cOS5fvozJZKK6upqqqio+++wz/vznP7O7u1uWHq9areav//qv+eSTTygWi9y6\ndYs//OEPrK+vEwgEjvryKlR4KXq9HofDwblz5xgcHMTpdLK2tsb169dZWlrC7XYf9SWWNUdidNVq\nNXq9nuHhYc6fP8/x48dpaGhAoVCI/5dKpVAqlayuruJ2u/H5fGW1wyoUCjo6Ojhz5gzFYpFsNsuT\nJ09Ip9MVo1uhJJHevePHj3Py5EkGBgaoq6sjm82i1+ux2Wzo9fqjvsyy50iMrsFgoL6+ng8//JC/\n/uu/pqqqCqVSKf6fVqvl008/ZXR0lK+//pobN25w+/btsjK6gEgGFotFamtruXDhAuvr68zOzh71\npVWo8AP0ej11dXV88skn/Of//J/JZDJ4PB5u3LjB2toaMpkMhUJx1JdZ9hya0VUoFOLYMjAwwJkz\nZ3jnnXew2+3P3UhJzWA2m1GpVLzzzjtotVpkMhnT09O4XC5yudxhXfa+oVQq0el0YnMpJ+RyOXK5\nnM7OTrq7u7FYLCLep1KpAMjlcqTTaYLBINFolGw2i8/nY21tjWg0eiCzpirsD5IxbW1t5cMPP2R4\neBitVsuNGze4desWCwsLRKNR5HI5tbW1dHV1UVtbi0wm4+nTp6ytreF2u8lkMoeuOFIqlWg0GhwO\nBw6Hg+rqavGVTCZJJBIUi0WhiorFYkQiEXw+H36/n2Qyeeg5lkOxAHK5HJ1OR21tLUNDQ1y+fJnf\n/va32Gw21Go1+XyebDZLPp9HLpejUqlQqVRUVVUxPDyM1WolnU6TzWbxeDxlY3RlMpkY4/z9f5YT\ncrkctVrN4OAgv/3tb2lubqaxsZG6ujp0Oh3wbLR1PB5neXmZzc1NEokEc3NzjI2Nsbq6SjKZfKsl\ngDKZTIztlr6kzUr6kslkFItFMpmMeN5LAblcjkajoaOjg9/85jc0NTURiUS4evUq//f//l+SySQa\njQaz2cyHH37Ir3/9a3p7e5HL5dy4cYOxsTEikQjRaJR0On3g1ys5Zmq1GoPBgMVioa+vj76+Prq7\nu+nq6qK7u5twOEwgEKBQKJDJZIjH42xtbbG+vs78/DwLCwv4/X4ikQi5XO7QEvYHbnSVSiV6vZ4z\nZ85w+vRpRkdHhbekUqnI5/NsbW3h9XrxeDzY7XaOHz+OTqdDoVCgUCiwWq0cP34cl8vFjRs3DvqS\n94294QXJ4JSj4ZHuYUdHB2fPnsVoNKLX61GpVGI9SqUSo9FIe3s7TqeTXC5HS0sLnZ2dfPbZZ/z7\nv/87uVyuZAzNfmMwGKiqqsJoNIovm81GTU0Ndrsdq9WK0WgkEolw8+ZN5ufn2djYKAkHQqlUYrFY\ncDqdNDY24vP5mJqawuVykU6nKRQKOBwORkdHOXPmDP39/WKU+fnz5zEajQDMzMyI0eYH9ZwrFAo0\nGg3d3d2cPXuWtrY2mpqaxO+4qqoKs9mM0WhErVZTVVUFQKFQIJvN0t3dze7uLu+99x5bW1vcu3eP\niYkJlpaWCIfDh/J8HrjRNZvNNDU1cf78ed5//32OHTuGxWIBnv0iEokE8/PzTE5O8vTpUxobG5HL\n5VRXV2MwGEilUsRiMXK5nPAWyhGNRoPVasVms2E2m0kkEmUjHdNqtdTU1NDY2EhzczPJZJJ0Oi1O\nH3u9WLlcjlarFce91tZW1tfXmZiYIBgMsru7e8SreTGSZ6rT6dDpdOK0JXlUGo2GbDYrvFR49ntR\nqVTCMbDZbCL0YrFYcDgc1NfXU1tbS01NDUajEY/HQyAQwOfzsbm5ecSrfoZGo8HpdFJTU4NWq8Xl\ncnH9+nXcbrdYq06nw+l0olarCYfDeL1e5HI5IyMjDA4OkkqlkMvlxONxwuHwgYWTFAoFBoOBrq4u\nPvnkE/r6+mhsbCSfz5PJZMSzub29/dz3Sd68tPkVi0V2d3ex2WwYDAaKxSJPnz4lGAweuMd74Ea3\nq6uL9957j3fffZdjx46JXRGeHUn9fj9//vOfuXr1KtFolJqaGhYXF0WxhMvlYmtri2QyydOnTw/l\n+LJf7A0vSB788PAwT548YWlpiWAweMRX+HpIxzer1SpCCD6fD4BgMCji7DKZDI1GQ0NDAxcuXMDp\ndFJdXU1PTw+jo6NMTEyUrNFVKBSoVCpaWlro6OjAZrOJDVIynqFQiO3tbXZ2dgBoaWkRL61er0ev\n16PT6dBoNMJYa7Va1Go1SqWSbDZLLpcTFZilgl6vp7OzE7vdjtfr5fHjx3z77bfPPZ+xWIyVlRXc\nbje5XI5AIIDNZiMWi3Hs2DF+/etfi1DhxMQELpfrQK5V8sobGxvp6+ujtrYWuVyO3+9nY2OD5eVl\nQqGQqGrdu8b6+no6Ojro6+sTOZZTp05hsVjQ6XSMjY1x+/btA7cxB3bnJc/B6XRy7NgxmpubhYcb\nj8fZ2dnB5XIxPz/Pw4cPWVxcJJfLEQqFCIVCrK+vU1tby+bmJjs7O+TzeSKRSNkdT6Ubr9Fo0Gq1\n2O12EVopF9LpNOFwmOnpaYrFIktLS2xtbQEIr0e6L2q1mp6eHnp6eqiursZoNFJVVYXVakWj0Rzl\nMl6IVARQW1tLQ0MD/f399PX1YTabqaqqwmQyYbPZcDgcRKNRgsEgkUgEgLq6OsxmM3q9HoVCIZ55\nhUKBUqkUJ7NEIkEkEmFtbY2pqSmePn3Kzs5OyRT9KJVKqqqqUKvVxONx/H4/m5ubz12fZHSl2H08\nHsdut4vfwdDQEM3NzdTX17OwsHBg11ooFEgmk6yvr3Pr1i2MRiOFQoGtrS02NzdxuVxEIhGy2exz\nRlc6fQ0ODpLJZGhqahKJt3Q6LdYhlx98O5oDM7pSQsxisVBfXy8SLvDMO3r8+DFjY2PcunWLtbU1\ncrkcxWKReDxOMpnE4/GIXgyFQkHERcvJ6L4ojluOPSUCgQAPHjzA5XLx1Vdf4fV6heEpFAo/uCeJ\nRIJPPvmEjo4ODAaDqCosxXXrdDo6Ojo4efIk58+fp6+vj/b29ucSY1Juoaqqirq6uudCKZKxzWQy\n4ksul2MwGMQLHA6HWVlZ4euvv+bWrVtMT08TCARKIp67FynhJHnke4nFYuKUIt3LSCTC3bt3aWxs\n5Fe/+hUqlerAFTrpdBqv18vXX3/N9PQ0SqWSYrFINBpld3eXbDb7wmdNup/z8/NEIhGuXLmCw+EA\nEHal7BNpWq0Wm81Gc3MzbW1tmEwmEXzf3t7m3r17TE5Osra2Rjwef85A5XK5knsg35S96oVyi0vn\ncjkSiQTb29uEw2Hi8fgLj2AymQyDwSC+1Go1MpmMnZ0dVldXicViR3D1P6Smpgan04nT6aShoYH2\n9nZ6e3sZHBxEp9MRDAZZXV3F5/ORzWZRKBRotVpMJhM6nQ6/3/+DtcRiMVKpFG1tbbS1tdHe3o5W\nqyWdTjM3N8fNmze5f/8+i4uLhMPhknq+tVotLS0tOJ1O5HL5CzfHF5XjFwoF0um0kIqlUikikciB\nVpBKBjIWi4kNTlKEvI4CIZfLodPpUKvV4r93d3fZ2to6lHguHLDRlZIvLS0tAMLohkIhFhYWcLlc\nBIPBkvSA9oPvG9hyM7YS0oMuHStfhlwuF8fxqqoqcbqJRCK43e4jj+dK3k5TUxOjo6MMDg7S2dmJ\nw+HAbrdjs9nwer0sLy8zNjbG3NwcqVQKlUqFyWSirq4Oq9XK4uIiXq/3uc+WtMkff/wxOp2OhoYG\n4NmpbnJykqtXr7KysoLf7z+Kpb8UyStvb2+nvr6eWCz22oZH2owkFVI0GsXn85FMJg/4qvnJjpl0\n741GI/X19cIJTKVS7Ozs4Ha78fv95a9ekHbHQqEgDI7UCOaTTz6hUCjg9XqFlOhtM77fl4tJf/Y2\no1Kp0Gg0qNVqcfR2OBz09fWRzWaP1NvV6XRYLBbOnTvHf/yP/5Hq6mqRRNnY2ODrr79mamqKubk5\ntra2hIRICpXp9XrUajWRSOQHhqWhoYGBgQFGR0cZGBgQSoX79+/z6NEjVlZWSsbTl5DL5UIqVlNT\ng1wux+PxiNDRq1AqlTgcDj788EMGBgbw+/0sLS0xNzdHOBw+hKv/aWg0Gqqrq+nu7ub48eM4nU4y\nmQzT09OMjY0xOzvL9vZ2eXu62WyW3d1dIpEI4XAYg8GARqNBJpPhdDo5d+4coVCIcDjM06dP8fl8\nJRv3+zkUCgV8Ph8bGxtUV1eLJFJ1dTUtLS0sLi4e8RXuP5InYTabxfFNOp7r9fojz9ibzWZ6e3sZ\nHR3l7NmzFAoFUqkUoVCImZkZvvjiCx4/fsyTJ09e+zPlcjlKpVIY3L6+Purq6sjlcmxubnLnzh3m\n5uZKzsOFZ9cuqTOsViuFQoGNjY1XGl2NRoNer6empoahoSEuXrxITU0NCwsLLC4uPiczKwWkkJdU\nCTs8PExbWxvFYhGv18vExAR3795lbW3ttTab/eDA3oJEIoHH42F5eZmFhQVxjAMwGo00Nzfzl3/5\nl5w8eZL/9t/+G1evXj2SMsKDIpfL8e2332I0GvkP/+E/0NDQgEwm49SpU2i1Wubm5lhZWTnqy9xX\npDLRpqYmtFqtqAJaX19nYWHhyD2g+vp6Ll++THd3t5AZLS4ucuPGDSYmZn6YZgAAEn5JREFUJpif\nnxdysNdFEuAfO3aMS5cuicIQKXl29+5dNjY2DmhFb4bk6VZXV6PX6wkGgz/q6dbU1NDR0cGFCxc4\nc+YMg4ODLC8v8+c//5m5ubmSe4eVSiUtLS2cPHmSTz/9lOHhYTQaDUtLS4yPj3P79m1mZmZeGTbb\n92s6qA+WYi4zMzP86U9/YmhoiO7ubpxOJ2azGYPBgMlkwuFwsLKygl6vZ3t7G7fbLdQM5YwkY3G7\n3c816qmurqa1tRWDwXCEV7f/yGQy1Go1zc3NdHR0oNPpyGQyhMNhUQhw1P0XpOz85uYmSqWS2dlZ\nJicnuXfvHi6Xi1Ao9NpemhSvb2ho4MSJE5w8eZK2tjY0Gg2hUIhHjx4xMTHB+vr6oXlQPwdJS6xU\nKsnn8z8o2lGr1VgsFvHV09PDwMAAJ0+epKGhge3tbSYnJ3n06NEPZGZHjV6vx263c+rUKd5//31G\nRkYwmUy4XC7u3r0rFBDb29uH2j72wM974+PjLC0tMTw8zOnTp3nvvffo6elBp9MJ1/+v/uqveO+9\n95iYmOCLL75gc3Oz7I3uXr5fBlxKnsB+IVWiSeJzqZowFAoRDAYJhUJHvm6/38+DBw/w+/3YbDaR\nLHuV1OhlyGQyVCoVPT09/M3f/A0DAwOYTCZyuRxbW1t88cUX3Lx5s+SUCj8VvV5PV1eX0C8PDQ3R\n19eHyWTC6/Vy9epVxsbGWFlZIRqNHvXlPofFYqGzs5MrV66Ift0ej4eJiQn+/Oc/8+///u9CIneY\nz+aBG13J21lcXGR3dxe3201nZyednZ0MDw/T398vWj1KyTSDwcD4+Dizs7Ok0+myfmjhh41vylXF\n8Cr0ej3V1dU0NDTgdDrRaDRsbGwwPj7OxsZGSXhAkUiEhYUFvF4vOp1OxPF+ahJXoVBgs9k4duyY\n0PZWV1eTz+eZmZnh1q1bPH78WPRWOOrN5sf4fjMmqTlVV1cXfX19DA8PY7FYyOVyeDwe1tfXCQaD\nuN1uHj16hMvlEqX6pYDZbKauro7Tp09z/vx5hoaG0Gq1bG5uMjk5yc2bN0V1a7FYRC6XH2o+6VAy\nG/l8no2NDTweD+Pj4zQ0NNDX1yeaokiiaqn8UvKENzY2yt5TgOcfaql+vxSM0H4iSarq6urEiCKf\nz8f9+/dLZtJALBZ7YwWBVOpcV1fHpUuXOH/+PE1NTchkMkKhEA8ePODatWvMzc2VdLN6aWCARqNB\npVKJQg+dTkd1dTVarZYPPviAc+fO0dvbSygUYnx8nOnpaWZmZpibm8Pj8Rz1Ml6I1Wrl2LFj/OpX\nv+KTTz5Bp9Oxu7vL5uYmT548YX5+nkQiQXV1tSjukXS+UpHEQRb0HHo6OZ/PEwgEmJ6exmKxkM1m\nOXfuHJ2dnULn2dTURHd3N/39/czNzR15LPBN2RtSWF5e5s6dO2XTd+F1cTqdDAwMYLVaRTGF1+tl\nfn6+JDP3PxelUklXVxdnz57l3Llz9PT0oFKpGB8f5+bNm9y4cYPJyclDTcz8HOrq6mhvb+f06dMM\nDg4K/eqVK1e4ePEiSqUSu91OPp/nX//1X59rhbizs0MoFDrqJbwUg8FAU1MT1dXVokJOr9fT09Mj\nZjLubdoUi8XY3t5mc3OTjY0NAoEAgUBAzITbbw7V6ErGRxLZ379/X+gdi8Ui9fX1GAwG7HY7PT09\nnDlzhlQqRSKRIB6Pl73HC7C9vc3y8nLJv5Svi9RnoKWlhdHRUex2O9lsVmTC3W53SSeSfgpSUmlo\naEg0cDKZTGxvb/PgwQP+7d/+TYQvSgkpyWkymUQ/iZ6eHvr6+hgcHKS3t1dokPv7+4XHt7Ozw/Ly\nMlevXmVmZobNzU0R+y51pNLgzc1N0YiopqaGuro6hoeHAYRkMBwO4/F4cLlcrK6usrm5yebmppgH\nF4vF9lUGd6TCSa/Xy+7uLoFAgIWFBX7/+9/T3d2NXq+nr68PvV4vjuYzMzNHLjn6uextbP22xXQ1\nGg0Wi4WBgQEuXryI3W4nmUzicrnY2Nhgd3f3rdgsAWw2G52dnVy6dIn33nsPq9WK2+3mzp07jI2N\nMTU1VXKbqZTwq66uZmRkhJGREUZHR0UHOKlDmlarJR6PI5PJmJqa4uHDh7hcLtxut9DuHnbC6eci\nJUylqeJdXV10dnbS3t7O/2/v3J7Sur44/oUDKCAoKAGCKBdviIJGY1rbyUMnkwefOpn+hZ30qTN9\naN5i6pimGIONVhKJAuWm5SZyE7nD+T04e/80MbZJieWY85nxyVHYnMM6a6+91verUCiomDwpFfX3\n90Mul2NoaAhzc3OoVqvI5/NYXl7G06dPsbW19cGthJfRtqArEAhoY7xOp6MiwkSgnMymn4XYOZNs\nlwRaq9UKlUoFsViM2dlZpNNphMNhTgVdoVAIvV6P4eHhc+paGo0GFovlnMQllyE6qwaDAQaDAUKh\nkN7s0Wj0WtSvie7CzMwMFhcX4XQ6odFoUC6X4fP5sLy8jFevXiGTyXRcUGIYBoODg7Db7fjmm29w\n69YtTE5O0uGVs9cml8vhzZs3cLvdePbsGZLJJJVJ5NI1JIpohUIBkUgEPp8PZrMZIyMjVNeYJD8y\nmYzGLTIoIhAIUKlUUKlU0Gw2kc1m0Wg0UCwW2/I5tC3oMgyDgYEBTE5O4u7duzCZTFAqlfj111/x\nyy+/IBQKXWgs2Wq1cHx8jGg0CrfbDZVKhaGhIUilUlqHSSaTWF1dbddbvRIYhoHNZsPc3BwVTQYA\nq9UKlmXx448//sfvsD3I5XIYDAaoVCo6cXZycgKfz4dIJMIpVbj3oVQqYbFYcO/ePTx48AD9/f1o\nNBpIpVJ4/fo1njx50pEBFzgdy7bb7dRmx2g0QiwWI5/PI5vNotlsQiKRUBnV5eVlrK+vY2dn55zC\nH5eoVCqo1Wo4OjpCIBDAxsYG5HI5tFotFeIXiURgGAYGgwEmkwnj4+PQ6/X0UFEsFsPpdEIoFFKx\nplKp1FlBVyKR0CbkqakpKJVKOrd+mfIQ2f5IJJJ3pBvJ78gHwTWIDgHRIABOPydiRcRliOShXq/H\nV199heHhYbRaLRSLRSoaE4vFOB10idC1zWbD0tIS7ty5A41GA4FAgIODAzx69Airq6t0692JsCyL\ncrlMe5EDgQC2t7epTnV3dzeMRiPu3btHxW9EItGVDgu0GxJHiPciAHpolsvlcHBwQH3rent7oVar\nodPpYDabqb+ayWSirXMTExO0Va4dpbK2Bt25uTncv38fWq0WtVoNyWQS9Xqdtn29HWjI3HpfXx80\nGg2USiV1/iVcJBrDFRqNxrk6GKklXQfIA3FwcBBff/01TCYTVZA7ODig0ohcD7q9vb2w2+349ttv\nodfr0d3djUKhgFAohEePHuHly5cd7WZCrkkikUA+n0c0GsXDhw8RCoVwdHQElUqF+fl52Gw2MAyD\nmzdvUjFvrn7vLoLEoVwuh2g0eu53JLMluspLS0vQaDSQyWTo7e3F2NgYfD4fRCJRW671JztII3J4\nOp0OFosFR0dH7xwyDAwM4ObNm3TaZWxsDENDQ9RVgWVZVKtVlMtlTtWUgNPWOOKKcePGDVrDvS43\nskgkgkKhoNdQqVSi1Wrh6OgIiUQClUqFc9fsbRiGOVfv6+7uRr1eh8fjwdraGpLJ5Du2MJ0GMX49\nODhAs9mEWCym/eLFYhH1eh1+vx8ulwsTExOYmZnB3t4eNjY2UC6XOZ3x/lOIhncsFsPa2hrkcjmk\nUimcTieVKlCr1W07AG9r0D3bUEzcYa1WKxYXFy80JSQHTQ6HA6Ojo7QpGzgthqfTaQQCAUQikQvr\nwZ1Mq9VCLBZDOBzm3Hv/J5AdCtGh7erqQqlUQjQaRSAQwMnJSUcHo8sg4+k6nQ42mw1msxk9PT2o\n1Wo4PDzE5uYm3G53R7o/vE2r1UImk0E8HsfR0RGEQiFMJhMdzyYuLZubm1CpVHA4HDAYDJDL5XSQ\n57pDyhEkEzYajbBYLDCZTOjr66Pu1+0qcbYt6LIse27ainhELSwsYHR0FPV6/Z2tZldXF6RSKV0U\nOVEFAL/fj/X1dbjdbmxvb3N6mODtVrHr0DImkUjOZQDkdHd7extut5vTvbkikQhmsxm3b9/G0tIS\nnE4nJBIJQqEQ/vjjD7hcLmxtbXWcPu5FEFcF8rCYmprCd999RwcfwuEwSqUSAoEA7HY75HI5enp6\nIJVKO6797aogdu1kp9bu6bS2Bd16vY69vT24XC5YLBbo9XpoNBooFApoNJoL/4YEH7LlJpbs5XIZ\nXq+XCpJEo9ErUaP/VHA143sfQqGQyhlarVZa6yL1Mi7uTAhEuMdisWB2dhYOhwNarRalUglv3rzB\n06dPsbu7i3Q6zZnrSgYdXrx4QTuCbt++DbFYjI2NDezv70MsFoNl2XPW89chOfgYJBIJenp6IBaL\n0Wg0aOdCxwXdarWKZ8+eIRqNYmJiArOzs1hYWIDBYKAlg8sgT+RcLodEIgGPx4P19XXkcjlOb1WB\n66UyRroWBgYGsLi4CIfDAbFYTMtB6XQauVyOs2sUiUSQy+UYGRnB5OQkVCoVHV3f2trC48ePqeA+\nl8jlclhbW0NPTw/m5+dht9sxPz+PwcFB/P7778jn81AqlZ9toD2LQqGAVquFVCql05XtvKfbFnTJ\nIQo5JYzH4/D7/RgfH4fFYqEnwWQqBDi9ETKZDNLpNGKxGILBIFKpFLLZLDweDzKZTMeJIn8MZ0sL\n+Xwe8Xics5mgQCBAX18f9Ho9jEYj+vv7wTAMFQTPZrOcC0hnmZiYwJ07d/DFF19geHiYHoiurKzg\nt99+uzIPsHZDdpGvX7/G999/jy+//BJOp5NqFNTrdQwODp4r8X1ukEOz8fFxjI2NQalU0onZXC7X\ntvu6rTXdk5MTnJyc0D7NjY0NTE9PY2pqChKJBEajERKJBDqdDgCwv7+PYDAIv9+PV69eYW1tDfF4\n/FoEWuD/9uTkBzjVXggGg5ytlwmFQqjVahgMBmi1WiiVSgBAKpXC7u4uZ2u5pH3R6XTiwYMHsNvt\n6O/vRzKZxNbWFn744QfE43HOXjeyw/L5fPD5fEgkEqhWq7h16xb1dCM95VwciPhYSA89y7JQq9WY\nnJyE3W7H6OgoBAIBDg8PkUgkkMlkOi/ovk2tVkOhUIDX60U8Hqc+WSsrK5DJZACAYrGI4+NjFAoF\nZLNZehp8HS54q9XC4eEhQqEQQqEQzXSfPHmC5eXljrVw+TsYhqGTPWfLRqSey9WgNDAwAKvVSoNQ\nX18fSqUSfD4fAoEAstksZ3cnF7Gzs4Pj42Osr69jZGSEOiMbjUYUi0WUy+WO78z4t5B7WS6Xo1ar\nYXx8HIuLizCbzWAYhiYSbrcbPp+vbZ/HJwu6zWYT5XIZ5XIZsVjsU71Mx8KyLPXJevHiBQ2yq6ur\nWFtb48TJ90WQeXWFQgGGYejUTyqVQjgc5lzQJRmuVqvFzMwMbDYbFdRPJBLwer1UFe46BaFYLIZE\nIoHd3V14vV7EYjFqMBAMBq+VUNH7IMMgZCzYZrNhYWEBg4ODEAgESKfTCAaDCAQCSCQSbXvd/9ae\n9RrDsixtxXn48CHNCtPpNAqFAmcntci6isUifbBmMhmEw2H4fD7OaR+Tswaj0Yjp6WlotVo6zkxE\n99uZ5XQSpCQYCoWQyWTgcrkgk8lweHiI4+Njzt6j/xQiSXr37l3an6xSqSCTyejhKSnDtPV12/rf\neM7RaDRQKBQ6zjvq30DKJpFIBIlEAuVyGcFgkFrfcK00JJVKMTIyAofDgenpady4cQPNZhOxWAx7\ne3v4888/OT/O/D5Ibz2ZTvvcIB1TQqEQw8PDMBgMNKlIp9PY2dmhHnrthA+6PB9Eo9HA/v4+dnZ2\n4Pf70dXVhc3NTYTDYc4FXOBUQWx2dpbaicvlclSrVQSDQXg8Hnp4xsW18VwOuZf9fj8KhQL1uSOd\nVy6X65MM+vBBl+eDYFkWlUoFkUgEP/30ExiGofVALsIwDBQKBRQKBbq6uhCJROD1erGysoKXL19y\nuueY53LIqP7z589Rq9WgVqvRarVQKBSopdinKAXyQZfngyHiID///PN//VbawlnbFo/Hg8ePH+P5\n8+fw+/0drSDG8+9oNptIpVJIpVLY2Ni4stcVXPYUFwgEnH/Esyz7tyM2XF/n57BG4O/X+TFr7Onp\ngdlspvY1qVQKf/31F1KpFM1yrjLT5a/lKdd5jXzQBffX+TmsEfi8v6hn+RzWeZ3XyD07Bh4eHh4O\nc2mmy8PDw8PTXvhMl4eHh+cK4YMuDw8PzxXCB10eHh6eK4QPujw8PDxXCB90eXh4eK6Q/wFD9RPt\nGFO6JQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ff3a9a5f390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## Show Randomly picked Examples\n",
"def plot_sample(x, axis):\n",
" img = x.reshape(28, 28)\n",
" axis.imshow(img, cmap='gray')\n",
" \n",
"fig = plt.figure(figsize=(6, 6))\n",
"for i in range(36):\n",
" ax = fig.add_subplot(6, 6, i + 1, xticks=[], yticks=[])\n",
" plot_sample(mnist_x[numpy.random.randint(0,60000)], ax)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# Neural Network with 648010 learnable parameters\n",
"\n",
"## Layer information\n",
"\n",
" # name size\n",
"--- ------- -------\n",
" 0 input 1x28x28\n",
" 1 hidden1 500\n",
" 2 hidden2 500\n",
" 3 output 10\n",
"\n",
" epoch train loss valid loss train/val valid acc dur\n",
"------- ------------ ------------ ----------- ----------- ------\n",
" 1 \u001b[36m0.86462\u001b[0m \u001b[32m3.96558\u001b[0m 0.21803 0.10426 14.01s\n",
" 2 0.96698 5.56005 0.17392 0.10547 12.81s\n",
" 3 0.92779 \u001b[32m2.41471\u001b[0m 0.38422 0.21967 14.54s\n",
" 4 \u001b[36m0.77710\u001b[0m 3.91885 0.19830 0.15497 13.74s\n",
" 5 0.87891 5.69673 0.15428 0.24744 14.38s\n",
" 6 0.81317 3.49051 0.23297 0.23534 13.49s\n",
" 7 0.87590 4.86817 0.17992 0.27003 11.94s\n",
" 8 1.03506 5.42487 0.19080 0.30234 12.79s\n",
" 9 0.78844 3.54112 0.22265 0.35298 12.21s\n",
" 10 \u001b[36m0.70892\u001b[0m 5.16522 0.13725 0.33487 13.82s\n",
" 11 0.71349 3.07026 0.23239 0.41449 12.17s\n",
" 12 \u001b[36m0.68724\u001b[0m \u001b[32m1.94332\u001b[0m 0.35364 0.46598 14.78s\n",
" 13 \u001b[36m0.66860\u001b[0m 2.76769 0.24157 0.36811 11.77s\n",
" 14 0.71116 \u001b[32m1.21626\u001b[0m 0.58471 0.59569 12.02s\n",
" 15 \u001b[36m0.58038\u001b[0m 1.55759 0.37261 0.56208 12.02s\n",
" 16 0.59189 1.85552 0.31899 0.55433 11.99s\n",
" 17 0.60398 \u001b[32m1.19872\u001b[0m 0.50385 0.60624 10.99s\n",
" 18 \u001b[36m0.57605\u001b[0m 1.47875 0.38955 0.67429 11.05s\n",
" 19 \u001b[36m0.53741\u001b[0m \u001b[32m0.98669\u001b[0m 0.54465 0.65716 12.13s\n",
" 20 \u001b[36m0.51248\u001b[0m 1.03778 0.49383 0.65169 14.63s\n",
" 21 \u001b[36m0.48295\u001b[0m 1.01329 0.47662 0.67664 15.94s\n",
" 22 0.49155 \u001b[32m0.75731\u001b[0m 0.64907 0.75405 15.55s\n",
" 23 \u001b[36m0.39848\u001b[0m 0.77521 0.51402 0.75005 16.36s\n",
" 24 0.40710 0.80753 0.50413 0.76842 13.91s\n",
" 25 \u001b[36m0.38583\u001b[0m \u001b[32m0.60479\u001b[0m 0.63797 0.83047 11.14s\n",
" 26 0.39448 0.63866 0.61767 0.81936 11.22s\n",
" 27 \u001b[36m0.34665\u001b[0m \u001b[32m0.52752\u001b[0m 0.65714 0.84176 12.52s\n",
" 28 \u001b[36m0.31350\u001b[0m \u001b[32m0.43806\u001b[0m 0.71566 0.87792 10.99s\n",
" 29 \u001b[36m0.27955\u001b[0m \u001b[32m0.35839\u001b[0m 0.78004 0.91009 11.51s\n",
" 30 \u001b[36m0.24095\u001b[0m \u001b[32m0.33305\u001b[0m 0.72348 0.92018 13.90s\n",
" 31 \u001b[36m0.22267\u001b[0m \u001b[32m0.31417\u001b[0m 0.70874 0.92778 11.01s\n",
" 32 \u001b[36m0.19776\u001b[0m \u001b[32m0.28278\u001b[0m 0.69936 0.93818 11.53s\n",
" 33 \u001b[36m0.17561\u001b[0m \u001b[32m0.25478\u001b[0m 0.68927 0.94269 11.39s\n",
" 34 \u001b[36m0.15463\u001b[0m \u001b[32m0.22460\u001b[0m 0.68849 0.94880 14.37s\n",
" 35 \u001b[36m0.13811\u001b[0m \u001b[32m0.20975\u001b[0m 0.65846 0.95196 11.18s\n",
" 36 \u001b[36m0.12411\u001b[0m \u001b[32m0.20120\u001b[0m 0.61687 0.95345 10.55s\n",
" 37 \u001b[36m0.11277\u001b[0m \u001b[32m0.19313\u001b[0m 0.58390 0.95458 13.66s\n",
" 38 \u001b[36m0.10248\u001b[0m \u001b[32m0.18233\u001b[0m 0.56203 0.95717 13.31s\n",
" 39 \u001b[36m0.09312\u001b[0m \u001b[32m0.17554\u001b[0m 0.53050 0.95810 12.22s\n",
" 40 \u001b[36m0.08560\u001b[0m \u001b[32m0.16860\u001b[0m 0.50772 0.95945 14.37s\n",
" 41 \u001b[36m0.07922\u001b[0m \u001b[32m0.16403\u001b[0m 0.48295 0.95994 15.73s\n",
" 42 \u001b[36m0.07355\u001b[0m \u001b[32m0.15938\u001b[0m 0.46148 0.96001 12.99s\n",
" 43 \u001b[36m0.06845\u001b[0m \u001b[32m0.15810\u001b[0m 0.43296 0.96087 11.69s\n",
" 44 \u001b[36m0.06378\u001b[0m \u001b[32m0.15369\u001b[0m 0.41502 0.96186 11.49s\n",
" 45 \u001b[36m0.05934\u001b[0m \u001b[32m0.15056\u001b[0m 0.39411 0.96257 14.91s\n",
" 46 \u001b[36m0.05560\u001b[0m \u001b[32m0.14742\u001b[0m 0.37717 0.96321 12.48s\n",
" 47 \u001b[36m0.05196\u001b[0m \u001b[32m0.14555\u001b[0m 0.35697 0.96385 11.46s\n",
" 48 \u001b[36m0.04875\u001b[0m \u001b[32m0.14384\u001b[0m 0.33893 0.96392 11.10s\n",
" 49 \u001b[36m0.04570\u001b[0m \u001b[32m0.14115\u001b[0m 0.32375 0.96456 14.31s\n",
" 50 \u001b[36m0.04289\u001b[0m \u001b[32m0.13898\u001b[0m 0.30861 0.96534 13.89s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/ubuntu/anaconda/lib/python2.7/site-packages/lasagne/init.py:86: UserWarning: The uniform initializer no longer uses Glorot et al.'s approach to determine the bounds, but defaults to the range (-0.01, 0.01) instead. Please use the new GlorotUniform initializer to get the old behavior. GlorotUniform is now the default for all layers.\n",
" warnings.warn(\"The uniform initializer no longer uses Glorot et al.'s \"\n",
"/home/ubuntu/anaconda/lib/python2.7/site-packages/lasagne/layers/helper.py:69: UserWarning: get_all_layers() has been changed to return layers in topological order. The former implementation is still available as get_all_layers_old(), but will be removed before the first release of Lasagne. To ignore this warning, use `warnings.filterwarnings('ignore', '.*topo.*')`.\n",
" warnings.warn(\"get_all_layers() has been changed to return layers in \"\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7ff363134d50>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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GkPXestOiZGtitaqTY8JDPvBtf2FQkd/3AxuipZnzF35PrzKdquzWpEyv48475Eqgm52z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\nB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DISTtKiUGdhjls9I0OfZMMWRQ551F2ydft04gMuxHBxjUqst01E0A1essFWFeqFxuMk\nG9EtW4IxrQq8FHedfz7/L9H42f4iz3ufyTn11ntbjyz7rLDODgXa3OAlTwbe77N+5kTcHMsT99tj\nleb4qDFgm/WSjERztxnTfdxvDEdS3D/wBy0KXe8lg5E0m9Kula3bDakvWuyg3eE1dpy5XGlsKNov\n3ScjvxAUEm/c25HLZOmRr02OLmvs0i9DvHEFWpVolK9dkxKdzXkORRbLjPS6wUv/Z4vn//TqkeW0\nWXYE1zmcsMBquzzsHhn6dMtSoNWf3e6QhaarMZ4a9HDkHnut9HrCle7xRxGB2LnqqJbLs7zqGl3h\nHL8c+6ROuTGFT8Bcxw1Id5MX5GtX5IJbd75suhqtgUJ3etRUtRPHFYsc9Lg7LXVAWFC/NM2Koibr\nvl3SglHtTp0KLyTc4Huhf9Iu39dmf1uqIVWWajfJSnvVmuoarxmWYpqzMRRoSK5OByxVqNlYfkCD\ncgPSwJHi+fqlOWGOlkiRapV+7tNmFR13whwRQeUa/JPv6ZBnuhq7ypbqk2GfFRarct3X3zDdGfdE\nHnaPP3rTRv8Y/30Z+nxv6GsuKPa6q1Sq1hGYpEVhjGEcbXo9lv6BKD9BwGAwxRwnJRgTFLbfcr0y\nFEVavOAm4+Ld5hntQ4Uy9dpjpXHxvtPyzz51/kHFmry099YY/W+qiIA/xn9IkxLl6j0Q/1lBYYVa\nLHTI0cgCv/Qpf3a7nMR2ObrVmmaG06aoU+mE4+ZqTiyQZGSilt2qYOIo5r+6/uIg7fuRz2qLqTYq\n1JvqrGfdao1dBqSa5bRemWpMl6djgqbVIU++VgPSJ57+u6zRLdt1XrbNepO0ecelPuq3NrvSJO16\nZUafFqEFlsYdUOacPVbLi03Fs3QLoF96zEVfq1emcOz50W6SxQ4KC0ozYIvLTVcjXb8+GWpNdbHt\nTppttd1OmaXOlNhrYliJJnMd84zbDIymWZ/4tpaYZny+Y0KCpqr1rFuNSDLLKaUaxRmPNZOidPli\nzQ5baK2dok2iOJ3ydMg1Lt58R+Vp9xsfd7dHbLXBSnucNV2+VvWxM8UEY7L1iAjokSndwASqbrMr\nJRmx2m7nY/niHF1ed6VMfa60OWZDjYJryjXolDvh4BqSrEuuKWq9z/P+3r+5yAszh3MAACAASURB\nVE4lMXZoo9JY/GyRUo3R10inBUTUmjrBHK2yWIE2KYZ0yxYIEw4y3VlbbFCoRbccCTF1TUpMOf/z\nwJf/qoO0+yIPOFs/y40VzzpncgyF2CvZsM6YQywobKW9ttjgbPssCyYddMosk51TNNaqIaEsRvVq\nsMM6ix10tm6GisI6kZRo5TaqOU8RjqUPejtzXJ37ij+9d59fT/6YPxTcpVCLAWluHNjkybRbhAQF\nMMtJW2y02EHT1Hht7BqzE07K1GtcnMYYTLxZkcUOCggbliIoJCIgW0+sSluqVKNWBXplWmeHnS5S\nriEalwtNkhI35Jh5ZjklT4d3rbHKXuPiJRq1L7xc/0CWyoxjhiVrHC+zMn6PYUmWj1V5LOGOiURR\nnJA2+fpkSGobl5ff6rwyc5wQb9yHQ3/wcNw9IgI65YgXkhT7vccJGZMwEScrc15ARENMGvle/Roz\nKo5Z7KDDowsVJLZF9TqxI4wy552LGZg75NroTW/ZoMY0qaFhgbjokcCF3hKfzfyp3eOrvRG4QkdT\nsY+XPuB0MAryaZMf4x1nONdVLj0nirhNNCLJqGHJZjuhT6b9llvqgDghAWFfCTzwPy9H3Bh5wnpv\nG5GkzHmNSp0206W2aVSiSItaU6XEtumJRlWot9NFMvVKMuJKmzUpiVUQO4xIFC/kJdeb57jl9jlt\nllSDMvR63J1KNCnQaqn3HLREjywZ+uTocjiWq+yR5UYvalakSYkLikzSodgFvTIUaHPUfDOdlmhE\nu0mKtGhQPpEVXWWPJsVydGuTr0WhyaJouWPm6ZcuMdanXuqALjnijYn7/0HZ19nhLeuVaVRliQ22\nSDKqW7Y3XGFxDG8431HNCsUJxwDZ0Qxth7zYgGPAUgdiEZUKWXqtFUX1BYXtscoau7TJt88KN3rR\nG66w3lt2WGdUkhRDsmJftKlqY5XS84akytemRaGQOB3yjIm32h5Puc0SB01Xo0G5Aq1SDTprqjGJ\nzppmqlrzHXHIYqkGYnaN2XplmaxBvHHT1E4YWkckOa9MnnYphp02U7cs6QZk69YvzdcC//ZXXXR/\nE7k71oLMmIDSzHTaeZNl65Js2AFLTVcjS4/zJsvQGzXpDo3annKRpQ6oVy7BuAp1E4D+zaNXmpd4\n1K5YCyzaoGp0wFJto/lmJ55UrsHx8HzlwbpYuysag0ozMNEKy9GlR6alqoxKVGWJKwff9Fbqpbpk\nm+W0sXCiU8GZLvGOtMiALYENyp2L6duTrLTHm66wwBED0qy2x37LtCqw1AGnzVBnqmXekxT7nlzc\nudOR3HnqVVhuv8MWOmm2OOMWOmxR+LDE8TGdiTnOmCEwErAsaa+X3TAhuxyT4DJRnfyANH0yDEt2\nk+cdsVC+Nv3SjEmcyPtWqJepV7MimeN9+uLTNSmVr0WBNtOcdUGxZkXSDOiRFTMjJ00Qz0LijIsX\nEHa1133PV11hs/qmaYZKElWo16hUq3wVGiby82+5zAKHlWpSb4oIil2ItfcixiUIiVOkWbNCFRo0\nmCxLr+e8zw1empB93hbY9D8vR8x3xE4X6ZatX7qXXW9VbIfYJ9Nuq6Xrj/1nYUtUxaa5odgXd5rT\nZthrhS45Lii233J7rBIXe0Xd7mL90p1TJs2Aq71mmfe87ir52kzSptgFObo0KYniG6VYokowVkEm\nIiTONV6N7cSmaVDuC4M/Va9CqSbvWe6wBQ6ElzpqvnmOmaRdn0w9skCl49IMeMkN3rNMuQaJRg3E\nXrWiRY4m1SpNc9Yk7TL1mh1rak1XY4uNOuTZbbXv+JrXXBXLuM621rtW2z3RwHnPMiFx5qiWrctR\n81SqNiLZmAQX2SFbtybFCrVINBp9xYztSJsVqTXNWu9abr8paq31rlKNak3VJt8MNfK1Rm2sqlRZ\nYoo6X/Rj/dJ92B+sEj0/u8arTpup3SRJRnTJscJeXXJEBL03tsxKeyckjCMSdcmdGNIciL3WtigU\nELHPCi+7XqsC+62Ieus0xqJuf92rV6azpk3szvql65GpXrkSTXpkucUzFjkkJM5qu/RFMrxnmfaU\nXJWqY2fXiYLCVlyIxrMGBjPMTTzupDkWOCxLj0ItOuRZbbfixAtRv15kujnB6K7xkEUy9UrX7z3L\nYhaDgzrkRZMeljtigTLn1aWWK9JssUMalTo7Hv0MkRDPBd4nV6daUzQqie3SN1plz8RAblycdpNM\n0oZI7B457ZzJdlstLKgxt0iOLrd41iPutsoeS1S534NRZVOg06SB6KYmIKIorkm8kGmxYVy3bJfa\nJiAyAV/Pj7T5qN962fXKIudjlpIiJZpMUWelPbL02GOlQSki8VHQ/Cq7pRoyLFltaKpx8drk65du\nerhGoWY5ukxToyJSb5fVKtRLNaTGNJ/xMymGHOhfLixoknZFms1Q409v36c2PFWRZu/3pIJImzaT\nRB3LnI+Umu3UhB1kmpoYRL3J29bL0S3FkHs8LCxov+VOmP3f3nd/GWJuhTwdsnV7wU0ustOTPjDR\nCY/2v5fYb7k3B68UiQQMSLPAYTm6NJisLjLV+GD0xmw3SfFwi+U9B3zAkwIiLrfFcvvd6lkvuNl8\nRwyPp7gm8qp/9P880XOX05GZTplljhOuGHvTOjs86fbok90GfZFMl3nLL31Sv3R3RR7VIc8Lqdd7\npfEmHXLd7knlzrk0uM3wWPSmblSqLjTFUfONxzxH1Srd5VHpoT790s1zTI4u6+xQY7oTZlvgsNNm\nKtfgCXdM7CAXq/J8z01Omh3LS1b6pF9JM+DN7ivVm+Kk2WpNtcI+l3lLhXovjN6k1lSznLbIIQsc\n0S/dWdMVanbeZJd4xwFLnTFDi0JbbVA+eF6KIQXaVEWWyAt3qlfhWpvc4CWFmrUqMCbRu9YYkGaZ\n/Ta7Qn/MXtEm30ORjxiTqN0kb7hClh4VGiYU5FPVytZtY8Kb6lXYYZ2qnqVKI03uCj1qkUMiAsrC\njXJ12iKKOVzvbYsdlK7fHCdcY5MH3edFN/5frJv/q2uWUw5bKCxOmgGr7RYRcLHtWhVI0+9p7/eC\nmwSFNStUEGjxfk+ZGfvZMQkSjOmR6evFX1fqvD+m3uUxd5rtpPmirr4tLndemZ3WTjSi8gIdgsKy\n9FiiSqsC2botcsha73rU3RP69llOTqQSIgJSDDtjhhRDZiWeFGdcb1ym2z0ZYx8cNCbRYQut97YU\ngxKNKnNes+j8I92ArS4XFDEoTbdsU9TGuAmJimKV/1s8a7fV8rV5yIet97bGQKlf5XxCQESrAq8F\nr9YqGq2qMc2n/XyCKzGn94yLbbchsNUrrnOrZ1377FZT1BqRFJV0ijhhjnjj8rWb7ZRcnVbZI12/\nQakq1CmPa9Co1GW2KnLBcDBZkRbZMcHSLwOf9C++pk+GdP0u6dnjQfcLC7p81msWOCJPh2PmOWGO\nP66/w8frH5Kp1wFLvRC4KfawPCIgIi0wKE+HJiU+6HEHLRYRcMgiq+2KzngssdcqZ02z3D6FWv/b\n++4vLrrT/nTBOtu93BNdDBOMyR9v8726b7i76wm1pvqUX9ry5lVuTX3S9PFa09X4beRjumW71x99\ne+ybMlJ7rbPDpaM7TE8+oy8rTXOo2Bkz5GtTrdILbvKZ0AMWnznuT9332DRyvW/4Z+UJ9eoGp8nU\n65qBNwycz/D02G0qVds/vtyPfdGJnrlaFLkz8phrbfLEyAeUarStd4M7Jz1iNJQkO9Lt8ca77RlZ\nLbehVwD7xlYoaW2xIbLVqEQ/q/+Cp2vviALK4y6osthtg887ar5XXe3ahs1ydJndctYOF5nrmClq\nJwoP55S7L+v3+oay5Ojy67GPT/T0g8GQJiUx08UZLQo96QPRs7XEc746/AOBwTgpT4a8e+oSeeF2\n2aO9dvetVd09L6pM6qrU253t5JH50gzoSs2Sp91iVdZ723Aw2aAUhaEWj4Q+ZJkDemR5zJ1KNXlh\n5GY9sl3vZd9p/LZemY4MLjQ3cNw0Z5VGGn3cf0StAsfPqotMkWbADeMv+XPfB13iHfMct/fVde7K\nekRtYIpPxf1CWMAeq1QGjysdbjZdjVd7r/En9xiPQZ13PXmZL/z5V5Y64EPdj/9frJv/q2u31WY4\nbUCaqOV6jhozpOuLOcxCpjpruhrNCjUrlqXXb33UcF+qvNFoaw3ijcvRZUiq9MiAJQ64oNgzbpMZ\nC8zPc0yrQukGhMR51xphcc4rc16ZMzGo/D4rvGmjpQ4odT6W/7zECXM0KdEm32zV8rUJChuTINmI\n7S423JesTkWsodkdU7snOGIhOGOG9yzVK9OoREtUmeWUkuFmFeqUaPKGK8zvOKFqcGlsgR9SoHXi\n6G2zK9WYZnq4xjRnzXdUuXrXHts6oc6qM8Xv3G+7S4QyI962fqLQUa9C7Y3FEiJj5jou1aAGk81V\nLVu33NGuWBN2hqZY6zEkTosi9SpiTcE5ak11Xpk6U6QYctIsxS542fVOmq1fumNZMzUptt3F1rbt\nmjASz1FtJJJkVIIHpn7csdH5OuUp1CLZsJNmR/GuhvzGx/RJ97orXWL7xFvJYoekhQYscsjqmDZs\nagya899df3HR/dH1n1OqSVNWgRu8pESTDw88rOKJJp3Hc/2o5uu+4Mde3HiN0dYUY2eTlKv3p8jd\n+qXb3HqdZxNvmWj2FHa3WDO+yzaXOh8odZm3POcW13vJpEib8upmwykJvjXpG/427j887F61cVN8\nIO1xh+uWqE6e5aEp9xppTfd3/t0D/Z/z0MhHzMk+5s6xx7wZ3uix4Q+6IeFl/zH2t+7P/LV3ktb5\nyJlHbAtc4tL8t0xLqvFw/l0eD33QooRDfvTyP/lixS98ofMnSvMbHN61xOV/fldInOmRs6ZurfOv\n4S+JCIrPG/Un9/hh2ud9yQ+1DRc6ZZZ3XOKzwz/XLVu6PnVJ5bYOX+Yrcd9XrsGigcM+nPl7W8Mb\n3H7heYXjLYakuNTbZjqtR5avJ39LXWqZ/bcuMmlWsw+FH3UkNF9KxqCF2QfVDM/wkZzf+Nypn7l5\nwVOu9prjB5b4xunvu2n/ZtmB6KtOIMzDcfdq7Cr16hs3uqJ7i3xtVg3udXPSc6aqtXD0qKmDdTZv\nvsG61HcMxRo50wbrRQT9sfdeJQfPKQw0a44U+XT8A27LeNIeq9Sa6v5rfmEolOLA51f73fBHnTLb\nrNHTng7f5rsJ/2hYsoyEfmMdSZb1VGlSIvXGbgkb+u2NrHAku/L/YNn8319nTTMkxV4rpRg0TY3H\n3BXNPCty2ixVljhvsp3W6pEVHS5mLNSZmCVPR+xVN0OBFlUWyx7vRsAcJ6yz3VHzlTmv1lSLHHKh\no8w+KzQpsdcKARGznJKt23h8dNe9RJXTZhqUFjsXHxInJFu3KktsdqUmJVIM6ZOhV6arbHYmaZaD\nlkjXZ5qzOuXqlalXph3WxcSsmZbZ74gFemRFEwrJ+TGW7kxNSjwZf5vu1CzNimLV1oU65U4o3Y+b\n63ywTK0p9ljldHCmR+fdKiwoIKxZkYzYTOc1V2tRGM3Oj+W5oNgfez6iKVA6oYlPMOaoeXZYpz6x\n3KhEbSY5bIGkWKrnPxezHaF1xiTI02lcvFGJWhWqNtc8x4xIjB3LFMYG/J3RVlr+2tiQP1enXEmB\nYWfMlKHPq4lXIwrtjx4vsMUGo+GkWNSvRETAfsu1x2YwVZZojCtxyCKPRD4kMwYEOm3Wf3vP/cVB\n2v3D/6761UWuufkFv/QJs5zW1puvMLPZ1n3X2rJi3UQ1sftCngODS31m+k9lhPvFGfd3bb/wbut6\ns2YelJHcr90kv/ApW20w1JpurCCoRYFcXdIM2GeF9w2/4JaEp3067ueu9prZoZN2xK1TZYnv+pqH\n3atrIMeF+BJZSd3+qepHLl/0qi8Ef+TJ2nvMyK32/qwn/NKn3OVRhy30QM2X7J823+cDP9JQPUNP\nUaqbcl6QZMQih2x3sRu9oNpcN739msL1Ddb2vesP4ft8LOtXrrXJ8fG5vhD/I++ELxUJiu74rLTJ\ntfa+erF1V2yxIn6f6WpssNV5ZT4TesCquD3SQgNuaH/d+sLNOt8sdW7DJFnBaLPma9U/9KvKj9jR\nfYkDW9f4yi3fVHa+RVJZf9SAOjrXjR2vuqX4CZWj1fYnLlM1ttzahB1+4vO6+/OF00NOjs62KPGQ\nW4686k/lt1s/+K67ih90p0e1h/MdDi5Q4oIv7/6pDatf9ednPiR467DDFnrRjfa0r7Wo7pCtr15l\n7Ve3OnBmlTWz3vF661XuLXjYmqb95pYccp8HfdfX9Evz0ROPuH3Ow3r6c8xIP61ZoaWq7LFKhTof\n9VtDUv3U59zlETVmWhI+4CfBz3s8cN9fdZD2QOQ+77jEPEcRMCBVsWaZop6rTrlmOWVYsoMWR9Uw\nkQNeCtzgQ6E/uRBX7KTZ7vSYt1xmu4u931Ma+qdYlHZAVyBXnSlyY8LF5farVmlW5JQzgRlgPEbc\nW223GtPNdMo55eKMx6J3Y3pkq1TtnMnGJJikXY3pUg04YJlbPGtXDFnaEikwIxDNo54wxyTt5jhh\nr5WgQIsSUdh4kxK5MXJZtm7p+l1QbMnYQdsSogTAEk3etNEdHrfVBivsNyhFbrjLueBk8cYlGZGl\n24Pul6vTFd5w1DyZeiUbmYBXJRs2c7RGX2LaxFl6lh49sgSFzVGtSak5TthprbjYv5013ahEV9ps\nqw2SDU8AcFojBSKBgEEpBqWZ6ZSqGO6yUrViF5w1Tb90+eE2kWDAYQvNdtJk56KD6/Aa4WDAfitc\nbPuEPXuGM3J16pJjllO65MT6A9H4X70Kcx2fiMxtc+lEP+AfAz/7n9eAl3z8Rp9d+SOtCnzII67w\npgNdq+Wkd8pL7jQr4ZThuCjHdiAj1cncma7wpnOByTYFrnVyfJbyirMe7PmE6aNnNSaVmBqo82b/\nFfoHM12b+Yr9Vsgbbzc3WO2erkcMZyQaCqZ4a3yDOcETRoNJnnS7Am0e8hGLHbI9fIn3JT/r92c+\nIW/eBV17871Vtl5hcqOC9FaLHfKIu3VG8iwPvOf7uf9gd2CVM4fnu3f+71SPVsoK9vhE3K/kx6Jr\n09W4ZuQNB6fN92DgPrckPevphFt9aPxRw3HJ4oJhBAwFUm1ynTSDpqsRHwmZ216tpWKSPJ1mO+m7\no1/XFFdiQ/At622zK7ja9XGbvJlwuTemXuZscJp5qv3U53y55yfuS/kPF2XsVFZZZ0PvO76e9C1f\nSPqxjHC/jrg8D3R/zjVZr9gztsaUYD3x3O4peTp9J/GrEox5pPZ+Kbl9nim42Q+f+SdH1870JT+0\ny1rFLjgSWGhQqvXtOywv2uPxsjsMxqWpDs6JvnKlVpuecdbMK465K/CYpLxBezrXat9T6kDiErmT\n28wYP2tFcJ9TZjtunu6RPKsz37Wt4XJjuXFRQLxrzHXcZOd1y3FCpUrVPtv8cynpgypDJ9QEp9v3\n7c1/1Rrwl7+V4GM7H3Z5zw7nCouttE+zIgtjE+x4Y67oe8uWpA3mO+rezieUpjRYH3jbrHO1wtnc\n5AXnlLvITv9Q8wsncme49Nx2K04cNHVyjQOWWeCoRQ4bkGa9bTL2jBgsS3b/occoGrfMAX0yfOjC\nUyIZYeUdFxxKXeBjo7/VFldgw+A7KhJqpRk0ItkGb7ml6TVjGQF3Df5ZaCRORmKfOGFzd59iclid\nKRJieM5sPS71juPmmuGsLjlWDh1wdecbAunjzo+U++zZX2rKK4rOTE7sMLv6lEtbd+ovTfXJ879z\nLrNEamjIh157yiszr3bP0GOSukasSNunQ57K6tP+9vBDVk3bocMkf9f5G5c/9Y6Fucf0DmS59dRL\nOkqy3fqDl+Us7HTl/m2Ky8/J1uOm8IsCgYjclh416dPccfg5vYXpPvf7XwsuGVVlqTtHHjcan6hT\nni+/8u8GZiVFk0Mt76pPL/eZ078x/9gxsyedNJaY4FbPCMWoYmu8K82gy4feciiy2Ccjv7I5eKX3\nv/Kisp5Wm8sus942i1VpHi92VfB1xW1tJqc1mDV0WklXm5TQmMNJ893lsZgBOkG9KS4osdhBF4/t\nND2uZqLB99S3T/3PIeZ/jNzmuf5bLEw/ZKW9dlvlIR/2lNujIejBw74z9E2T8pql61OsWalG68Z3\n2Bu/0pNud7PnzFXtnDJvuNIHPOE9y/WF0w0HU1So12Cymc64vutVDTmlfuBL7vWwF90kcWBcOI1d\n3Wt8MftH3nCF8bF4dyQ84Sf+3ndbvuW+yIO+Xvgt2wKXWOY9i3qP+0bmN6TrNzdUrS8u3TRnPRm5\n3bLAe5qUWBw5ZG7gmKHhdL9I/lsPtX7UawUbrbPDfe0Pi88a9vn4n+gJZHrF9VIjg2YHTvpA57P+\nNffvLVGlRJNf+YRP+7mjsfZM9Il30BtjV5LA+z2lUYmFDquNtZSiQJFTnnWr9d72o8gXfTDw+IQC\n/Svj/09a/IC/8QdT1Dppjl6ZQoISYzDoYhccN9e4ODOdUWuqJaoUueD5gff5xeBn1eSXqxpfJhJP\ng3LZY71aEvJl6vXC2M3en/AkTAxXFjrkFde7yutGJeqSo9ZUY+INh1KUxjVqN8ksp5w026SxDtPi\na7wdWK9CnVeabzan6EisSxXvXg97rfdauZntfrfnU25Y9ZR4Ib0yPRW496+60/2HyLedV6pCgzQD\n2uWpdEJxLDLUI0uKIbOdVGWx/VbEkiethiVLNCrZUMy6nCcYibgosFNNaIb8SJuO+GgGOl7In33A\nZd6SaFT2eI+98Sv0yLLRm55wh8Wq1MVoXBn6LHbQsOSJ88pRiWY5Jd64l9wQU8bvFRF0ZHSBjYlb\nYtHHTpO0e9W10vRLMqpHlsnOiTMuIijBWIzOl25cgiMWSDRiTKL5jmiXH/u7TzFNrXcGL3V56pty\ndfr9ib91yZw3rLJXQ6TckcACcx0TFzsTr1QtIqDOFFPUOadMshFnTXOxd9SaJtFoDOtYIMWwI6Lc\ngm7Z5jti98hamUk9zkam2hjYYly8qtAS18S96oQ53nCFOapl6YUYdP6Ml0evNzO+xozgaafMMssp\nVZbolRE7dkhWoskZM6zxrq0ut8Je21wqU5+6sQq3Jzxlr5Xa5Xmf571po7TwgKLghSiiwAVFmh2w\nFKywz7Bkm11puhr52oTE+VLg5//znO5PIx/ztvWWeW8iVhP9osfrku1uj9pvubRY/jLNgD1WOWO6\nhFgraVSiSdpdUCTFMKL5N9juYkFhZc7L1OusaSY7Z7Eq77jEdTbpk+GMGY6Z6xP+w8/8nenOutwW\n260TFFERsxD3ylSt0kV2KNbsFdcZF29IiuJY6D9PhyWqvOwGt3laqwKvusbfeEi/9JhaJdVb1ltj\nd4w2lShLrwFp1tgV6+hHmav/SR3K1ybZsCzduuQ6YoHrvaxDnlpTdcuOYfoKXW6LAWkTOdFu2RKN\nqVduvmNGJU5g+ZY4oEGFehWmOStLj9lOesId/x9x//1nd13n///Xc6b3XjOZSZ/0XgkBEkIJCUgV\nEREVESu6q7u66qqrru6quzZsFBuIICX0lhBCCoSUSU8mmbTJ9N77zDmfH855z+Xy/eG7Xi6ul8u+\n/oNkXufxer0ez/v9dnOlN73hautsExBWY6JEgyZGyUr/LzvbJyXKCo7RFoVH/8VtsnS6xusRh5U4\nq73jkIUqLJar1WIVDlugRI0U/eoVWyKiuI81KiDsrKkKNFnsgHOm6pYmT6sLysRE8ZET1XjGLTZ4\nNWoQKPD9wHf/T4fut8Nfss42r7guCrmfLlFk3bLUAZk6nTRLmQs6ZVk98o5zwckKYxptCV0lPdgt\nyYAJalWZIVdrxDdmqbi2kMk5VcbEmuG08yaP0+dijUo0aJILkqP/p21y7LXcvR5SYbEEQxIMqjdB\num7d0pU7Fc2LxmqWFzVep6pTYkTceJ77gknjZZY+yUJiNCgSa0RQ2HmTrLDXEfMscsg0Z9SY6IRZ\npjkrQ6csHaqjnIZiDXqlGhMjsWfMkbSZFjrktHLTVUWz5pHSz4j48azqKza40+MOWaBYg7Yo97ZU\ntVhjeqTJ1uY9K8ftErtdIoCFDqlVYpILDlpkRpTv26BIjjZNChSrFxYQEjQxGu2MM+o57xv/ekjR\nJ8aYVrkKNBkTo1m+AUlmOanOBFOc1S5Hqh7VJgka0yrPSnuk6rXbarPCJ3UH0p02Q7F6uVqdMc0c\nx8cjhxdM0iNNQMjnAw//7TndkKB7PeR57/Nzn3PGNKl63eYpCx123iRDEhRq1KBo3CSRoUujAivt\nMcMp8x0x2XkDkqwXeWou955rvSZdl0GJ8rS4yWZX2G6v5Zbb5yH3qrBYtja3+4u9lvuEh1ztdXus\ndJmdGhRqjZ48ThUh3J81zQWTtMs2EKVEvWO1SS4I4E1XSo4qVI6Z6x6PGBEv2YAzpsnR5kpvjtsv\nYqPQjUg0pUOlmRY5ZIpzMnW5yWaQaEBFNGe5wSuS9NtivTwtZjsuwbC7POqkWfZZJsaY4+ZGYz4R\nS8Q5Uxyy0AynperVJzUC9HDMLCfttdxLNumPtpymq/Ksm+2zzJCE8X3eRWVa5Y6DQvI1S9ctHG3I\nZejWoFCHLHlalLmoX5JV3vUxvzXZecvtNccxZ00zI5pFXuJA1L4RqTzf5FmDEmx2s1lOEh0six10\nhbfG92FrveWcKV53tXbZf8cR+rddNSZG3pq6T6syXY80BZoVahpXVEXytZHM8h/j7nI4ZoGf+5yy\n0RqVozNl6NQrDWFDEmTpMClUrSSjWrZ2TQpcMAmRr4kYY9Fs9SRN8v3B3Tpl6ojubXdbHQXSTBYQ\n4Tm3yJOhy6CEaMJiqmBrIPqGnSsYijS42mUrGa6TozWSIhhp1y9FYRTQ0ytNmh43eFGXDCn6xBrx\nghtUK0PASza5qEzzWKEzpnp5cJOSUK0Wed61ymhaSLyRqE1mumplzpiqIK14GwAAIABJREFUKVxk\nUJJk/bpkOG+ytbZ72+VytEdNHGkGJGmXY5dL7bDGg+6LHP4KiwmPmajWiFh7LZMwOuyUGa6w3Vgo\nRpXp44eeoegbe4dMjQoNSfSnzo/Yar2ASDM1ICzUF2uv5eP1/BNmC0VBRR0yTXLB62MbnDbDwYGl\nUZ72JdJ1jxdC2mU5E5iqTY6AkDoTNCrULF/jYJEYY/ZYMV7gOvVXTNd/dei2yvHz4fvd7gkbvTyO\nIxyUqE2OYQkCwv6x5ef6JcnQFW14pFnsoL2WK9CsP5ysWYGP+a0Dlgh1xDlsoZXe9XGPuNQu8xwx\nz1FnoqaCl0Y3WWOnwbFErfLst1SmDvstdcASjQo9H3qfREMqzTIo0TZXmuycsVCMU8rFG9ItTYla\nn/RrBy3WNFJgX2iZ3GjNtlCjJ93uBTc4aKEwsnRokS9Dl2QD+iWb7rQNXvOcG33YH6XpcTDa0PqF\nz2iXHaVLHRQQin7i9bvDEw5aqFmBQo1GxFrlXbd5ylvWusoWdVGOWola9YpcZYtt1rnHwwo1usmz\n0RJK2GXeNmnsgqUO2Gu5ac74iN9b663xT8dcbb7uO5IMWOstXdKdj+75/p9TqlCDm2123mT9ku23\n1JLhQ37mfkS+Rra7Qr9kcUbEGIs+wIblaZGpy1vWWtG3X4yQuY46Z4oJ6kxUMw59OW+yZnkumKTO\nBKl6Zen838zLv8sVbxhhvelJ0UjXCSfMEhZwrVfH334aFOmQZbEKZeFq01WZHl9pbew2V3pTszwp\n+sx3WLlK8cFBs2OPS9EvwZBUvf8/WeXJLljtHd0yhESYBznapetSYZFMXdrkqDPBEgeiX1QdGhRZ\na5u13vJC7qao/69DfXCCSc5L1ud8fGQlcN5k++KWGpJglzW2WadMtWL1nnC7OhMst0+lWWKMydNs\nhT1W22WmSodi5os15qbEZxXXt0mO/vte8D6xIiLNcpVGxZio1trANq+Hr5FoUJoezfIdD832dtM6\n501WoMl6W7XJ0SzfG66So90aOyIHYvJlB9pl6DLXcWUu6otNlmgo8nUabDPFOZfaJcGQK2w30UVn\nTRMUkqlTIHPEPEfdZLOQgIlqbEu5wjL7dMi02yUmuaBDtqnO6pHuG+HvyI1p0S5bS1KOsKBZTsrW\nblCiKtNdZqf+aKOuSKPFKpw1NZJESSxUpxgBp5SrNFPe/zanW7l9kbH4CNxiviNmjFVFPznqTe6p\nkTIU+ZQd2JYqzqi5jqo00zL7ounDWnGGBcZEm2TJfvLSv8jOalGiVpNCC0YPy9LhHau9a6VJQ9W2\nvHud46cWenH0ejeMPa96rMwWVzlplpv6ntcwUOKgRUaH41UNzHCwd7EtofVKXYy84QVyDEjyWtNG\nM6OKm1PKI1XjuB6d7xTa6GX7LLV/cKnfj31E81i+NL2Oj81VoMn5psjTbflYBBpSaaaj5qrqKZc4\nFHEhVYZmec9Kee3t2sI5XnS9QxYZHo03MJTsQ3uflmBIeCzohR/dGv2RFtgydLV+yeoVGRKxDs91\nTEDYN8Lf8Zu6T9tSc42BvnRvtFzrwc5PC6NAE0NBOzrWStVruipJBqTq9djoXZbbq1aJjV72dPhW\nKXoVapSvRXoUBdg5kKVAk3Y5jpjvrtCj2kI5ylRLDfdZ6y2HLHR15TYhQafGZrnSm3a7xL0e8rTb\nPNTyKSfMljfY7GcpnzXXMSn6rbMtul6aZqc1AjhsgWu8IcGg83tmiBEyIOnvMjj/N1ehRi9X3OSn\n27/sOTf6L19UZYYu6U6arVeqapNka3fQIn858UGnAzPst9QX3nnAk6EP2Gq9wxYYluDuo084ZJHG\n8xP8ouvz0WZVsqPmedqt5jvsVRtcNNFmN3nga19Ue7hUc0+RLhlqlejqy7Gvf7mwgB3WyNccaU75\nkA5ZNrvZg52fdPjgcum6/f7UPY42zfeSTV5zrfMNU71lrT4p9luiTY4V3pOnRUjQizaZqEadCXa5\nNEIzGw773otf85Z1AnjEx+z+8ULPvXqbn1z4ipvffMZFZbpkytHmsAW2ja1ztm26GCGvuM7rY1c7\nfXCefZY5Zo43XG3zAx/QN5Bm68BVvrLvv/2zH9h+6ip7LVf31chu94xp/tj/YSPhOP869F0v2eSP\nH5vgPcv9cP/XnTZDrFGPj94pJOiP7nJidI4trvJnH7S3biV4xi2G6pL9zP0edZffNn5SrRKpXX1O\nm2GvFc6PTFWt1EtdmzQo8nzbLRKvCEX5FWFnRyM6932Wed3VjpnrlcHr/Md7X7d77+XjQ/g5NypW\nN05xO2eqHS4b50ps3nn7/3jf/dWd7oXuYudPT7Ux/lU/nvFpxxLmWDR80N21f/HGv1/t2MNTvRTY\nZM+Xr7D5PzfI0GVYnOxX+k0srhUcG9G+JE0gHLbZTT7wq82qPl3qER/zxdH/9kbsVfK12Dj0ih8l\nfNE/9v5ES3Ku7wx+XWFyg0/6tQ/7o1s8o06JP534mLyyWpem7LLAIaNitcpTrzjyR2yZ4dP9v/Hw\nxLu9ueM6w6VBr13Y4KfrPuvAT1ZZcf8Oi4MVNoRftfDtk35+xb3Wn93hjql/cLu/+PXQfXLi2lU9\nO9ezt16nwQRzWo5b/cv9YrNHPfKJO/0y4dNaXyk2ef0p5UOnPfz4Z/z6vo9oC+dYFtjn5z6ncF+L\n4LIRi8IHfaT2SbdOfFReqEVvMFV6qNuZ6plGMoN+l3m3J33AhrHXDMfGmdp7wQMpn/Ls4C2+kfRv\nJnVU+3bLt6ybscVNNa94sPDDZsad8kcftsBhFRb7TN9vPJF0q5uCm73oeju7r7B/cIXt+aucMEdG\nuMtH6x+3Z8JCfwzfrXCkSXNcnte/e725HzviksydclNa5Gu21XonzHKj5933pZ/55I9+59sj37Q5\neJNn3Gxd8E1tgVxba68zu+SQ1eFdMgLdMgZ6dcWkCcSHxPeNqEwp92rXdb6f8RUdMi1uPuobed/w\n9sxrrXhvu3/I/LFrAjv/T3e63wx/2TRVUTdaqnpFcrRLMCQg7GpvRLCalpig3uL+w55P3mSNHY6b\na7EK8YYdsESeFtN6z2tNzXS2bbprc15xyCJhgXEX4IBEVWaYoNZg9KFzSrkCTU6b4R6P2OVS01Q5\naZaQGGWq7bdUmh4ZumTqMDSYrDpxoku8Y1sUKPSh8GN+H7jbWdOstW18JZJgyLtWWRzdUcdGo2iD\nEsebWeVOqTFRuu6Ilmmkzrtxq8b/fT8e/AcfT3zIqLioPirZkHjrB7f7QuJ/WeSgqcPnHYyPsKMj\nctQzAt2xCtNr1Eeh4XXR/XSTAvGG5WiLDvH5JocvaAnkKnPRrLGT6mOKxy3DbbLHY1ot8ixy0DlT\nojvsMZ2yIjvy9otOZM9UIJKBH5Robd9Of0i50wKHHbTIBq8aFq9GicnO22e5o+b6uEdUWGw4HK8k\nUGugL0V5ygmvu9ZS+yQY1ipXql4j4sAFk6z1lirTrRne5XD8PLussdpudwWe+dsP0irCM31v73fc\nteRBde0lDuYtNNKaIDG7X3KgT06o3dmh6eqSi3yu6Td+2vRZN89/ypVbd3k97yqPTP2o42cWun/u\nD70bu8JH/d6VY1s9HHOvLB3j4JptI+t8aein9gUWK0m5aO7Lp3x64081y1egyet1G22bcJn+4WTP\nxd5oT/8qn0r9hRJ1JvbXej35amvs9EDos1aeq5A/sV5fQpJ526o8s26jH770dV/Z9G+CxhwfnOOG\n8EseTLrXV33PNFVuannOH/Luttdy67t2eN+ZF9285HEznPbll3/i9NwZSh+u8Z2v/rOfnPuyO+c8\nbLaT0nT7aM2j9mSsIn3UBzqetCrrHY0XSv1XzBdUTJzvSwd+YemSXdJ1+6rvGX4x3anrSwW7g+LT\nB3RL95gPKe2s9YOEf3J38Pd+lPBFBa1dXs+9QpN8H+p4wqmsaV50vVvqntedleJI8jznTXZ8eLZ5\nsUf9eehOL3ifA0mLHAwvcnhwoZI9TZ6e+n5DTyfqvzXRltI18gdbzQhWOjK20N6E5WaHTzodnO61\nwDU+G/qFZwdulh3Xrjy+0mOdd1uWucfzzTd5NP+DYLObrLFL2VCtDyf81ld934needpSM/WOpao6\nM8fHy39pVfdeP0j7osXNh/Q0ZXpn/jJrRnY5fmKhb2Z/y7zSyv/TofvL8N2aFLjZZs+7YXzgPudG\nd3nUGdM0KBIWkKJP71C6nISW8b34IQssclBIjCwdJqpxwSRp4R6xgVFPuc1k5wWErPaOE2ZpVqBE\nrQFJTkcbVHMd9ZoNyp3SFj2byNZmt0utsVONiUpdjKqPmgxIdshCpS5K1i99uEdqfI9dLjXzYJWk\nRb2OmmeWk1HTcLIrvalbmkqzFGrQrECabjnaHTfHUHeivPQm9SZokedjfitVjzGxkn804rkvbZSs\n3zL7bHe5fC2W2h8hd+l3whyXhHbbGlwvW4ccbcICRsR5b2SlrLhIpXdmzxn70xZqk+2Yea6yRaFG\nZ8NT5AdaIrZf9/umb+uU6Um3u9QuE9U4JqI5StODsBHxzpvsam+4YJLzJpvjmFFxMnQ5OjZPOCYw\nntVdbq9zpsgOtwsH6JOqTrFsHWKNuqR7j9fT1yvUZERclI87qE+KAcnGos3K5ujasVi9fZZJMjDO\nRCnU6EjXIn/JvOtvP0j7xtb/cCa+TF5Mi+qsMlfY7q3uK5UFqzUFCgwFE4wlB3y94z9VFkyxeP5e\nH9n6hG+u/6qeBYmGU4M+sfBn3u2+xHpvagoXaFbgT8c/7O2BtdJ1OaXcuo4dRlLJT2l0zFzbN642\nx3Fzuip9pOsPvjjhP1WZLjm+3/WBF5U3VemQ7UXXa+vN89Trd7rtwHMqBherTJ1uICHer0c/6Q/r\nbtcr1ell0yx2wGvVm0xMrPVs0k3SddtjhZ/5vLl5x8b9Rm29mT665JfjP64FCw85Ulbufd98QkzS\nqG1zVsnT6rGROx2y0M0TnzLYn+Du1sfMz4r05pdN2mV70eX2W6Y4o0bzniLLWyscM8f91//AOVOl\nDXR7b2iFDUOvmzJ6ztWZr7m2Z6uPBn7vAZ/zh9zbtcsyFoq1Oe1GbXJUDU9334RfiE8ectIsX6v5\ngZVjexUHG+THNQn0BDQq1D+cYnLSOcvW7vaJop/a+oVLrSh421Per+i9Nr+P/6jm3kJ3eNyumFW+\nf/Eb8trbPNR2r+7kNB+I/bP9lirMrPPYgY8qCdZ6OHSvyXU1CDhjmm8kfFNcY9gf+z/scOpcs3pP\n6Y1JdU/5Lw1I8nL6NTYGXlZecEL9/DwfCj2mJy7Vp+b8zEcnPvh3HaJ/y9WkwDXeUKtEAOm67bXc\nYgfEGbbcXsPizXPEFbb7VM+D5jsiR5vpqix1QF700CoSg4ochDUECuVHwePlThkVZ5dLzVLpBi9E\nYVFpHjr6OROiYPKrbJGi11rbLHJQtg43eF6TAnMcl2Qg6rG7Q0jQFOek6jUkwbn4yQYlGBNjy6J1\nGhRJ1u+I+Qo1Wm+rEXEumDyu36mw2DHzTHHOZXZoqJugRb6rbLHcXvnDLU6abac1vvSl749bMn4a\n/ryV3rPCe961UqVZ4wCm1S0Vak0UZ0SvFGVjF01XZVFcxbg1/OWUa/RKkazffX4tKOS4OcYCMbZZ\n57D5Hqz+nAqLnDEteliWpUeaPC0CQrpkiDMqzrBbPW2HywSFbGx9Q4yQk2YaCcdJielzn99I0yPO\niBYRS8jkwHk52vVL9suWL7pn76Pu9JjT6dMscES3dEMSJBp03hSTQxdMVCNNt1IXxURNIUfNM8Np\nU53RI80q72iTY2bG/9IG3H9lrNsWPm5D/6u+0PFztUosKDmgUYHL7DQWiDEmxhcyfmifZS6Y5Cvr\nv+Vuf4g0UhrGzHPELwbv16DI7sBqv4m5z7xZh8xKOi4kRodMU/JPOxGebb7DpjjnwNgSt3rKSEbQ\n3ozI0yQ2POKXA59RHyhm6pg7Bp4UZ8Su/JVyVzf47pIvW5m8x77CReYMVoqLHfFO3yU2esk7Bcv8\nxif1F8Sb65h7RMDGixxyV/djPuMB71jlspGddhZfYrdLFWqMmHUn7LY5dLMRcQ5aNP55tTTugNlO\n2tZwrcTOUSUxF9Uq8VT4Ni+O3eBgzAIt8myattmG6S8byI3z66FPmRI6r3Jopu6CVK+7xgsJmxyu\nXWyhQzbkv2Bv/DKne8rFG1ERWqIuOMEfRz4cqSXGxbvHIx4a/ITJzjs/MMlQUrxLh99xZeybGvLz\n9Enxev/VJjlvQKIlcQccDs6zLuEtTQq8tORql3nbk1m3Otc/1ahYM3KPSc3udm3eqyYEarUE88wc\nrTRTpfDUUalJPZKD/bZPWKNLhpkqrfOWKRlVwrEBeVrUpBb7kMeEBU1yXt9oquuGXtEePbj45qHv\nqbDI5tgbx9Me/5fXBHVytXgltEGcEdnhdu8P/cWi7iN2uMzDPm5R6JBWeZ4J36IlKddh82VrEwjR\nLzkK9A6oV+xkOCIZHRhKYSwSYxqMUrLmOO60GYYk2OhlRRo8Mu+DCFhnm3OmyNOqV5qegXT1isbb\ncX1SpOp12gy3elq+Zjus0SclArgP51jkkMu9bX7v0XE29K2edsR8z7glytHrMlGNPVaarkq5Uy6Y\nJMaYD1Y85aqxN9UrtmnkZQ+H79EiT9pYr/d70nxHpOv2TN0HjInxpNsljUQSLMMS5HZ3eLngSgsc\nlqzfJNX+oeVnfuEz6kYnGAgnSdKvJRyBfv/uzU96xyWmj51RHyrWJUP52Gn5WjRMzDHfEUNtiVGN\n/Yh0XbqlR/i/td0GRKDmT7nNAodNddaR3FkSDcrRLhgIiTHmI9WPe901QoKmOutUuFy9YqNifO74\ng/blzfOj5ff7Td+nzHNEW5SjnKdFsXrzHLU1fJXcoTaxxhw1zyV2W6wiAjzvO6ZQk/d70kWlVnnX\nBHX/4333Vxtp3/nWoC2u9snYX9ubstRENQIxYfsts95WnTJNUKc8cMoR8230qlyt5o6eUBssUZtS\nYlbgpOG0WLVKLHTYV/r/U3V8mRmqvOYa93rYT33eVYGtYo153bWGwom6gxnmOi5bhxoTPR24zS1x\nzzhppinOSYvrUW+CheHDLiRMcqPnPRK6x68Dn3IgdrEmBa6Nf93rrlWs3k2e1RhbJNGQVH3mRPdE\nefEtTgTmSDKoOSbPrUPPeiT2Y4YkytShUZGzgamuDEaysEvsd8hi3TL0SXVd2osO5s73/qS/mOeY\n0UCcwWCSawOvaZdtsYPCyQEFGk2NPWdT4EWHYhcpVaMrNhO8GNyoPOG0oJAumQbjE9QESlUFpilV\nY0HcQXutcFVgi2fDtyiIa3SdV/0q5z652tTGlNhmnTS9QgJWJO11rdd1yFYf1Zc0KHZH+AlpCT2C\nwiqCi02JPydfi7T4HrHGosHvJTZ5yelgeeQ0N/GknPi2aMj8hCUqxAgp0KgjLttITJwFDqszUY80\nlcrVKNUQLPZE7AfMdVyiQc0FuUoDNTJ1OmyBU//2zP9pI235t66Wok9joEiiQQOBJM8GbrYnYYXp\nzoCY6I83EAhrGCoSSAhLMmhbYB0o1mBQkhGxXglsdIl3nB+aKj5hMMp5TdQjXX8U7D8sQdZIl8aY\nQvFG5GqTok+9YkMSjIqTOtSvJT5PgcYo3HtIhcXS9ApGc7pPnvuQuVlHJRjWE0iT3DYqLmnQ1t5r\nTUuukq9ZrYnKVWqT66DFZjgdtQFHmLjFGux0mQpLXBa3VU1+iWPmGY2JlTww5GR8uaxghz6pijRo\nUOR4+iz9kgxIFhczYrFDumTalrBWi3xJBlSZLlWf86mT3OBFwWBYVqDdXCe0B7IVBpokTukTb0Rt\nsER9oFiXTCXBmghsZmSWYExIZXI5ArJCXY4H5siMDt4J6XXqFQkwvmMNINGQLJ2ytUs0qEapVZm7\nLHBEnBGBEZJiBqKs3KCmpEKHg3MdCC41Jf6s3S4VHx4yIVAvU5eLSg1IMhRM0BGbJdGQsIAapXql\nSTKoM5SlJzZNk0K90oyIk2DIC/929G9vpP1H+H7puuRpjfalE6Tqc9Q8V3lDhcXKnTYs3os22eRl\n7bL1RRMOEfpPudlOOGiRJgUydMrTGu00n1JhiVlOGpQoW7s0PeNA9H2WusLb+qSMv9aPCUY73xET\nQY2JwjhigY1eHteeJOuPMkojw/mI+SY7L1s76JRpQJITZuuQZUHUOFGj1DL7VJppvS2ecEe0zBDh\nfy61z3FztciVpVOzPKn6tIk8od+xyqAka+yIrE68ZVSsE2Yrd8oxc4UELXJQuyzxRqIh/GrzHfFb\nH3OdV8Z75um6o9SrUSvt0S/JbCf9yZ0KNY5LDW/xrKfcpkiDZP3aZYkz6pQZ5jkm3pAcbdL0OmmW\nYfGW22ubtaY5q112NF9b4Zi5eqXqk2KTl5w11XaXK9KoWJ3dVtvkZSfMNsU5b7vcLCcVq/euVfK0\nSNOjW7pMndqizN1i9Y6Yr0y1nwW+8n+6030ifL15Q8dtTrhRqYt6pQgKe8km9/mNCosUazAgSYpe\nU51Tq0S1UktUaFTosAXmOjb+Nn/UPOkjPZaNVKhOnqArOnAXOeiQhbK1O2CJZfaZ77A9Vo7vC/9f\nYSJ2KORCQqkJ6sZhOQOSnDXVLCdt7b/Kv7T/yCslERLZoASrq/fbXLZxHME4yQUzVTpnioMWWaxC\nkn6nleuUaUVor73BZXK16JJh3dDbjiXMFhK03RU+4xcGh5KlJXQaGkx2IbE0AkTv2e9g2gJjgmaE\nzhgMJogzom57mbQrOrTIM9VZZ01VrcwsJyWfHtIzI9lpM6QP9JqYVD1ucknWr1WO2PCYC4Eyix00\naV+d0oVnVcQusiVwtSEJuqVH60HVVl6osG/SAiPi1CuSHAX3J+uP8hCOG5TkgCXW2OFH/skldmuV\nG82Vb/am9da+t8vJFVMtDB8SHgsaik1wMjRLUrBfjJBCDapNkqdF4akWJ8pnjGeRhySY7JxGRTJ1\nGJYw/vtZZq/lgeN/+063QJMjFnjarfolR0hbRn3Cg/ZbNv4D3uVSK72nR5pj5qpVojEKX16swmnT\n1SqJAjYiSu4s7aY7Y65jBiQKRXeFkdB2BKQx3RnvWiVVr7OmGhWjV5pOWU6YHdnfiDMk0Qav6pTp\niPlOm6HSTIcstMsaIUH9klQr06BIqxx/8X4dMi1xwEQ1OmVJ0yvJgFGxFjjsIfdaY6c+KeMqkOfd\nJM6Icqejn359snRYbbezpuiRbqIaw1HbRo+0aEi8zUWlsnQo1OiMabqjQfJUvXqleNRdylzQLN8i\nBy1WIUerac5YZ9u4YWCv5dbYqV6RkKAEIx51l4UORZXTk+Rrcdwcq6KA8gbFmhSqMt1iB2TodNoM\nOdqdNEuvVBnRJ3ylmZbYL1Ont6x1wBIr7DUiVrscK71njxVKVUdrmGd1RoPqV9huqrNOmG231c6F\np0jTa4I6F5WOZ3j/r69RcToTIlXcs6bqke6iUrlavWulbB3Rv1e7iWqFBMUakaNNh0xhAXMdc1Fk\nIEUypkcF4sYcTZglU6d4w6Y664JJylzQoMhGL0vRp9ok263VI810VYrVywh3yUmIGBIaFeqUIUWv\ndD0mO2e6Kp9N/pkvlPynRVGbRFc4U3NZhiUO+GD48ajFtzqqb584Xg7qkC3BkFijLgRLpemxwGFL\nVOhMSJdoQKmL7vYHVaPTBRNGxRsxrfGCJP0aFIlLHTLFOfFGXFW5E4wJGrwi1iELXD7ytjetM8kF\neZql6TY4PU6mTvmaNCblS9LveTdEITgDxsQaCiSY7IJEA15bts72uCv0SzEY/Q31SrHEAWEBiZO6\nnDFtHDhT7pRm+eKMRE3GRYbFGxErR7t1tpmhSmc4yyp7xBiLSBFWNHvLOqOBOAOxSS4d3G1pcJ86\nJSao84h7FEfv2WD5iLejfJYxMdJ1a5WnRZ7XXWuL9VHrcqLdLv0f77u/OnTfsUqabutsM12VQpGD\nrgvKlIarlah1yAKVZipRo1id93nOPEddapcjg/OjmshKix3w6ZaHvNYZ0SRn65BkwJzoCmGO45L1\n22211nCuKtNtG1lnavisCouNRvUymTrNdcw62ySGI7GYCG+zwIAkK+2RM9auL5qRvKjU3vByiQYl\n69Mj1W6XKndKQjQgP80Zq+2KNrOqzXXUcwciGpvCkaZxlFyiAbtGVlvigCEJ3m5e7/G2D3pm5BYN\nivSE0m0Ye9Vye9UoUWtihF3am+SZsVt0yDJNFWFijBoW74aLrzrTP02MUOTw0AlVpnnBDeoUS9Fv\nRvQNuU6xvVbokq5RgRJ1vup7itS710NGxKlW5tvV39UrRf9gsgFJVkTzuwMSjYodX420hPPMHIrk\nmJsUWNm83wmz3eg5PdJl6fCaayO0MzM1KlKkwX5LDUqM9PV7T4g1EuFGhMMumGRQots85TovqwpM\nd1vfs46ZI1+zdtnaZf1dBuf/5trhMi+43o99QZqeKC8iqMZERRr1SHNRqTdcY59leqQ5Hp4rW4d9\nlos35G2XK1ZvQJIzpnnTeqvD7yiNuehP7rTbajus0SbHazYYFaspqj5P1aMsXC3WqN+4z05rPBS4\n17tWecUGF5WOP6iqTHM++tb6lnWmOG+PleINqQ2U2G6tP/mgPwXu1CfZRWWec6McbUJRJ96gREUa\nLLNPuh4T1aiw2E5rvDpynVR9TprlsPl2xV7qVz7l677rF5PuNSBZrFH1gWL7LFNppmdmb/C0Wx01\nX9tQrplOeSju40rUetRdrhna4i3rVAWmG5Q4XpypNdHezhUOW6BZgTY5eqQ5bIE9VpnqnGFxfh24\nT6EmeVrGvYa1SvzH8Ff1SjEsTkwo5CWbjIjTJkerXC/b6LAFpjrn9lOb5WlxxDz3BB4Rb9gvfUaX\nDL/yKc82fdDP3O/HF77k94l3u6g02jBb6XI7tMm1fegKXx3+d8uh0tdcAAAgAElEQVTsR9hR8zQq\nRFibbDNVRuUDhyQYVq/4f7zv/urQTTKgW4ZWOTrqcvxu8KPRvvpGV//lbefbptnbtsKx1UvlavOa\nDZ50uxxt0nX7waGvK+8+G7WZFunNS/CRzN97tWGTf6p/QK0JhiT4x5O/8gcfcYcI9OXFXTf7/Ruf\n8P64J60IvCdbm5dDG93x/GZL7ffL0U/7187vODS8SJZ2TQMFXrXBf9d/yfyWE777s+/a6TKtcn3M\nb80PHPa02wSEnQhFRtt/HPuWBEN+477xQRRr1HaX2+FyJUsuWG+rtcPbffvM93wv9FVxI6P6gin2\nWSbesFUDu5TmVLsz7k/O9U7RHUz32b5fy3u1V2XrPEf655sfOiqUGjbz49V2WBOB5wT6HTMvAk8v\nDfhq8D9c5m0r7TG/47jfvf0ZlecW+oAn/LzrH1wwWb4m11W+Lk+zE6E5rvC2MTGGe5PNcMpWVxqQ\nKMGQrWWXWTx6yFWJb8jRZr+lyp1S7rQdW6/xpis90PlZbYEcqQndgkJCgsbyw6rGZih10abK1/zB\nh/0i9BnXh1701sg6S+030UVtIzmSDWhS4E+pd+iOcll7A8mqR8psdpMHX/68K2y32m7n/jjVWttV\nDC923iT9w8l/p9H5t1+X2RGJCkUB1O2yZen0Kb+WrluuVgWarLZbqWpnh6eLCYdUK4tCmsrkajUi\nTk+0Ypuu24WxybZab5GDFjpsvTcNSrTabtnadcqUpd2LbnCDF/RJcaPnXOZt623RqNCq6BppVJxO\nmSaqUaZakoGo3HNAnBFtcgWNydEmV6vU6BokxphRsXqlmeK8s6aKMeqsqRoVOG+yxuFiHSLaoTlx\nx9SYKCZa377c224ceMEl3nGuboYLyiTrt6TtsBlOu2nwBV0yTXEukqQY63VBmSwdumVY4LAHE+5V\nqNHGoVc1KhBnVLpuF0304cw/mO244+aMryGXOCBfkz1WqjbJBzxhrmPGxCgLXYyu4oasi39TjnYL\nHTYajJWuW5Z2k0YumhU6abm90Resft8t/2eFGqy31WM+pEuGq2yRrd10VSZmV5k/dtSmSc/rkGVM\njBlOSTQojLOmmpdwxIb417TJccw8k52PKrxSNSgWErDQQcfMMyTBdFX/4333V4du/kCrLhneDV9i\nTvZxcbEjEltHXO9FA0OJ0nI6zUg+bdXut1SGy9X1lvia70kb7ZVZ1+/Yyumyt/eoUap9JEe/ZPf2\nPuLGomfcn/kDRRpcVOpXsz7iBi8oG662P7TUj8e+4FtX/4tWef6p+Sc+EXrYs8Gbzbqywg+G/lle\noMW28LX+EPMRZS5aHF/hx/5B2tl+/YlJLv38VrOdcGXPNqdGZ9rYu8Wd/iTOiJuCm/2L73tk7p2W\njlT4ZPg3Jjuv1EXvWuVWz/j+f37b/LHDnnarNcM7/Vvmv8hta5Mf12xxTIU3XO1I6yKXFu1QqMk0\nVb6R+m9WdO1zR/of/WjVZ12T+7KP+Z36YFHEr/VQnId8wryRoxEu6r5evwh8RlwNu8fWyB7ttLZm\nl+8nfcXXLv+6T076qZ8PfN63M77qquGtMnQ7MHORDw8/5vvBf7E1vF4YW0LrtMmNuKn6cgSE7Ri5\nTEVwkdf6rrPxxBue+PlHfaX1v50zxdev+Lorw9ssT98rTY93XOJDPY/TE3AmPFVGTKcnhz9gc8n7\nfLvpu1KDvR4J3uPO3scV9zdGcJLBb+iSYXvU6pw70qZErbHueCc65vqo31m/8UW/dY/UQ/1+dsv9\nftz4j2bEnTY6EutE/Oy/z+T8X1ytcs1UKU2PVrnyNVvgsGJ1apVExagV+iVL02tp/HsGg/FS9Vqk\nwvVtr5rhtESDpjljQm+TS+00HBvriv07ZUdjSb1SzY8e5mToEhD5GtjkJS8FNpnj+Hi19tTYLDcN\nPq9aqcvs1C3NkERpunXIkqYnKpvca3GowkEL1ZpogloJhq0/ts2YGEPiFWlw2gzZ2pxSbkCyUbHj\nXN6L8SUydUoYG5LR3KtIvT4pqkyLqHuCF2TqtGjCPpNdUKDJyZzpumToTkw20hVnnqMuKrW2+h0n\nWuebpFqWDlOdFRCOQJZCsVbYa56j9llqtXcss88ih6zyrjgj+kKpUvXoluGYuYo0mFNbZag/WacM\nNcES051xUZm1rbtMVyVFnyrTlam2ZLTCjPApZ4IRK/lUZ812Ikrmi7XdWl/s/sn4G/KAJB94+xnl\ncaeMxQTN7ThpkgvCglECYFUUxN7jSttcf/FVM1Wapiq6Oy7VL9kn2h+OqJe0K1YnV6tA1Cby/+/6\nq0P316c/7ewPZ3mk+z7PJd5ggcN+Hrg/8kp+3f0ePf8xF3vLfNDj5gROaOwrlt3f44HYz3gl8yoN\n4WLLy9/xx+0f90Ln+/y27+NKn2mU39UhIXnQRWVijMnVKt6Q6aNVYkJj4sqG/fuBb3lm5BZfy/+m\nQ8EF3gut8JnkX/r3+K/ZFPOSP2TdITY4LLOhx+mYGe7e+Sf/vOzfPZ52u/XBLUpddCBpscSRIQ+m\nflSSAVd7w5buq+0Or/bQyL12xF3qsf33mNt60p9DHzDPUQcs9sMvf1ZuTJs8zWb3VyrIbdDfkW63\nSyT1DXis9iPiMwfUxxZbZp/S/c3+Zfg/fL/1az7rAem1vRFDQ/Jh/3Xxy3Z0rBXsDvuxL3gt7lpV\npvt4+a9819ednjjZkt59Lo6UenviJU4MzXFN0xbzgkdsSHpFlwyXVu11LjzFi8PX+3H851Urs/ni\nbR5o+AdZ6e0qzTQ2Fisxpd8nBx60aeQ1fVLFpwxpSs515J5ZpgxWqVdsXu8Jrwau9RkP+NDhJy1w\nxITEGiNJsU4Hyp1rKffZ/l95PPF29zf81Pd8VZ4WdyY8Lm+oxdeHvuO1mGsscMi3e7/tkEUm9DS4\nwlti04fdlP9MdNvV7Fuj31K4sF5s/oB/KvyBb3d/24ShRlee2fF3G55/69Uuy9LQAe/rf977Bl+w\nJHzAdpcr1GiWk+PsiJtstqpvv7TmIUGhSHmhvVdjTqR5FaGGDVjx58jOseBYi9SlXeNS0whsu8AS\n+/VIM3fwhJlOWdFxyEYvRw5qNFgW3mdTzIuKGprMUmm246Y65xOdv/fe8CpF6jXLN7n5YoSPHJxn\no5cVn2qw3VqX2K3gfIsRsapNco3XbfSyMhettjuKqaxUqMldg3/y/vBfzHNUXXiCGwdfRMBCh+Vo\n9+k3HjI5/qzbPOXzeyJ52mztLn37gHKVpp6pMXcoUuu/efRZ7a2pfv2bz0aMDIak6PPd9/27Kc7Z\nl7REjDFLHPCD7n+1pOWQRU2VWuSZ1FbntqbnXRrcKX5wTL4mP236J8vtNWvomJjkYTOdkjrWJyjk\ncm/LOd05jo68/tTLmhTIiW3VdLjYkndeNdl5y0L77LHSnfVPu9RO+ZplpreZFj5rk5dNckHr5Rl+\n4gvyNAs3Bc1zVK0J8rQq7ajzIY9ZPfyOvNOdsk9HWCENihWrt1iFDF1ys5p1ytItXbEGXTJc27bt\nf7zv/urQvWzBNrP/6ZCbM/7i44GHXR/7glB2yGIVJuZeMHvyYT/Nu9/+8FLnTHFfwS+9m7xEfHjE\ntpQr9AZS/Xv5P7v9ikfdmf6oS5J3eeHuq32p7odijToZnuWkmRoUaZfjgeTPCsSGPT95ky8s/KFZ\ncSf9rO/zEdBdMMErwQ1+H/iI3aOr3eHP7g0+pKUo29mWclmzWrXHZDvWs8B0VdrkWB27W39Sgrnh\nY4o0+FDLX2xIf0VBoEm4j7OmWL1sm8dzb7csuF+xegsd9t2Wf/WqDcpclFzUo8p0EyefNyLeupRt\nHii5T3lspa5ghp3W6JqdpDT+oh9M/UcXlUqY269Zvp3WuKx0m1MXZipMrh8/XOmTol+q1bX7JOuz\nK/YyZ5OmeM9y9yQ85EjBXMn6LA5XCAi7a87DQoGgO+L/bECSN12ppm+yN/KvVGGxpfZrjcmVrtuB\npEV2Ja9UEGw0Qa1nJt3o0aQ7TC05JWjMhZjJMnU4HFhgy4Ir1JhoffebkmP7Zep0Y+5TqlKnGulK\ndv3CzZ5uuN2weL9JvteSmEMmJUQIWr3SfP78z93jEUey5/iL9/v0wC8Ni1cRXmzH2OXyBtuVqTbZ\nBcfM9eGM37oydYvDU+b8XQbn/+aaoUpS76juoTzJib06A5mmOuewBUKCUaTgKc95n8axQjH7xlwd\n2ipZn76GZB2ypOrVKdMcJ3SlposR0l+cYkpPTdRdNqhDloFAolNmijesITHfOZPVZ+Y4aaZmeWqU\n2u0SzfI1xBd60u32WKVVrs2ZG82LPyJ7rFOCQWmZ3VL0WeKApuEiK2L3ChpTMzjJmbVTNSuQp9lh\nC/RJFmNMmh5NCsQYk6NVY2K+6kCZY+YqC1YbrkxQrlKDQonDwwI1AYH6WIkdYcdWTpesT69Usckj\n6k3QMy1BR3ymRAMOxS4QnB8WviugTLUZTps3fEz7nZlCgjbVvKa4tyXCqIiN1ZubSOqIVL0COcNq\nWyY4G5pm2kCVkKDjBdMjLL/Ogv+PuLv8kvu8sj3+qapm7lYzqsXMssAgsBVTzLFlO8yMk4kzgYmT\nTJjRzoTJjpnjyLIs2ZYFFrNa3KRmZqiq+6Jq+t1N1srcdfP8AerW6med33nO2fu7nTVFh0luHX3c\nWLxL37L6Us2KPeEWTaklysP12hQazU0Smlpoq7UCHYkSjRrqSJHSHBASttcyfeFMjUqdNl15V4tJ\nLTE2yYzUU06YJdmozOEBNbnTXFRqOClJasmg2svKDEo1zelY7JhWGfp1BXIlGFftvGEpkg1rz8n+\nu/fuH+p0b753njOmxbRv0RHjgUS7AyuttsPl8XTcZKN6A9n2R5aaHzjsMbebHzjiQ37ur65XoNVa\n22xLWGda4KyooFPp06xM3KU5UKI6Dume5rRq5x220LX+6mgwtnG/IekZEUHbXW6RQ4q0mBM84SF3\nytZrgSP609LlpXe6EJqsLLnBwjgRaUiaxQ56JnCDbL0S00fUmOkuD2pPyXeLJ2Xrs9FfRKNBWwPr\nJAjLSe/WKzuGpQtkG5RqZuiUo/EherlGGQZEhCy3R11ipSFptlmnK84NvcQeey1XrlFXSY5VCTud\nMssl9ijRJJg87lDWfPk6HE+b5VrP65EjMWHUk25xUZnEQGwjm2rIHsulGZJmyHFzvK3gNxKDsble\ngVbzHY0J+b3Ju/1KuUbPu85ktdICg27ytMlqPZd8rfVeciiw2Aq7FWrRn5YpIiRTnwcCd3tn8DfG\n0hLk6jYps123XAXa9SZneM71E+Dv5KIhbQocssgU52Ql9prtpPpAhXPBqcaTQs6ZqlCrM9Fp+gOZ\nOuRLDIw7+uWn/qU63Zn33qYhuUwkNepxt+qRrUir86r1ytYlV4d8XfKcT67SOy3DX8PX2xJar7Kw\nVoMKQdE4g3ehovIWJ1NnKOpv9VjuzXZZJU+XQBz72BuPqR+VpFSTgYu59mUt1qDcWVNlBAaMSNaX\nlSGIZCPOmBrHaA7YH1wiIkHb+UKn86Z53QrjoQTn8yZrU2AgId20pNMOWyhLn3OmytLrhNm65AkL\nGZQuU7+j5kk2qsMkCYGw3xe8VSQ5hjlNCQ07tGSejqxcp1KnesHVMYWBZBczy5xJjik90kMDdoQu\nVWOW7JQuNdlTHbDYaTM8EbrF4MUcHVOzXciudDBpgQfdLS+xw5HAAn9LutppM4xJ1FJYoC+QaTA1\nVVdcdrXFVZJLB5xXLVuPC4nVDlosT2c82DOWjRbOCjkenGubdWpzKgQyI46ZpyZ9mlzd6orK/Cbj\nbWY47VWXKws2OGeqKB5NutWjmbeY76iTOTE5WLkGTyfc4JQZokLOh6doS8nTnZAj1bAO+c6ZKsmo\nZ90gOTqiO5Brn6UCKNLqaHCe57984P96t/9h0V127zWGpZjtpMZAuXOmuM5z6lSqUyXd4AR/8ubA\nk9IM6pDvCq943nVmOKVDfvw5UBu3VR4xkJghw4CXrZVq2ICMCRF5UNQhi1S7INWQRuVCIhY7oEeO\nIxZMPOmmO223FbIDvYrjG+dq5z3hVlfbZFiKGjPd5jHTnPWS9aY7Y0yCLpPMUuOIeR5xuyOB+fJ0\n2m+pRQ4q1aTGLPUqzVRjruOIcVETjCnS4pi5irTGAd8xK+WH/UydKl1yDUozWa2l9gogW4/0uF6y\nTaEVXlemUbIxzUokGXHYorj8bKodLpWlV6si6QbVmixHj3mOSjbqiAVxne+cuOMplmjQI8c+S622\nkzjJf0zSRBLtUfPNd9RhC9SarFiLbrlxvWqWOU4YjYM9MvW7yova5SuJA62X2ecl613pJa0KXWWL\nDAN+611SDMfzvcYdtkCiUSlGVAbq4waMFpXqbPny7n9p0f30vSkKetqVJTa6EKw2zVk9siekWK2K\nXBZffs0Zq3E6YaqKUINsPRaNHNGXkGFIqgCGpQiljuk0SWdanozAgBTDxiSqVCdTvzwxo0FaZMhY\nIFFJVqPhvhQzg2cIcaNntCkwJhZFc93Q3xxJnG9+33FpyYOydbuoVFZerw8P/EJdUoXySIP5gcPK\nNQoZl2rYjPg8cqqz8sS0s7d3PqU1NZb4ERKWrTcOvklVoF3eWJfzSdUKtcrVLfdQm0syD6hPKpMW\n19pXqvPmnY+qnVwm1ZDOaL6KYJ2IoJCwtzz1uN2zlsrSZ7k9jhXNsTzpda+4wuW9r1mWtFdVZ73B\nhHR5Td1KkxolJYxK7I2YnXzMLqukGJl4qi8bOWg0IUmjcjmRbjMDpwRE5ek0xdlYXPt4h9FQkmlO\nW9p9QGFqq0p1ltgnJGJV++tS0wY0Kjev4biWrCLhjiSdabmu73re7QmPO2FWjD8c6DQSTdIbyHaJ\nPYaluqx7h+rOWslZww5aNBHK0GmSAm1SA8Oy9FkZ3aU3kKVFsazRPk/919F/3hzx7ehHVKhz0GID\n0i2xz5A0m1xttR22WWOu40o0CQupVyHZiEFp5jiuS+5Enn2CccWa7bXUnf6iRbF0A6IC8ciMXvMc\nNddRW623x3LrvTQBlkgzKMG4kLDzqkGnPJd6bSKpc5WdWhXqlWWqMyJCsvUo1OoX3u8Sryt10bAU\n3bJFBQREVWhQq0qHSSrVWWavR9wuT6eLSiXHA/ZWe81R8wxJi9kKzXSLJyYin4s0SzaqRZFWhdpN\nUqzFsBRtChRok6dTqiFzHPdb71CpXkTQHMftsdwU5xAVFJ0wefTIVuKii8rc6GkvusqIZHMdU6/c\naTOUxG2L9SrjCQ97hSUYk2iO47a7VKphVWphQncYA05v1SXHBZOFJThokUttl6tbp1zt8qXFw/pq\nzDTVWZ1yBUU0K5GvXamLRiVO6EHLNMakQzZpUyDRmFIXjUvQqMy3A/f+S80R/x19iz/sfY+xZVFl\nGpW6qF+Gq2x2xnQBUXstU6hFbe9kA8dyrF3+ov5Ammdab1NQcFFxQrP18Q/Pwz132pj9oGMD8/R3\n56oqOyVL34TJZIZTCrTZY3kMHn7+Nu+tvk+PLGmGnDNFZW+jCzsnu/nqx2yzRpFWQWH5451OJsww\nKlkoGvG30au9OfnPMYLbyAHbktdIMiYyFnQkcb57fCtGzZJkif2ecpMZTkmLy8KKtDoUXWhm4KSV\ndvvGsS+5ZO52k3QYHki1K+0SPd05KnLqVfTVW5h1yEkzPVd7g49X/cA+S1WP1upPSpMaHY69fsJ1\nMhJ6lWm0bWyd1h1VrrnsCa1NZfrzk5UkNzsbrtY2VihhcNzaSdt0RPOdujjXjWWPOhOdZn3gJX3j\nWR5MuFPnq8XWXb7JBpu9bI0ko5oV2f7CBu95w0/1yLap+1pvzvmD09EZRnemG1sdsNDBiTqUbkCv\nLEER+drtsNrxi/N8pfQLfv2JD5r+reNCSePGookSh8atS3rJ7sQVWiJFyoKNTkenmx894sLFGTaW\n/9Hvvd1tHpvgQ9dEZ5kaOGNbZJ2yYIOScLPS0EU/DHzunzdHHLLAX10vKKJSnd94t9dd4i4PGpOo\nQLsXBjb41tg9+mWY54hbPR6TMkmyzxJXRzdZZYdjY3Pd5Clv9oBBaQakOWSBKc5Zap93+J3l9njF\nmgne6k6rPO5W3XIMStMhz5hEH3SfwUi62U4YlaRRmdlOqlAvT6f5jnjJlQal2e0SvbJ0y9Er02EL\nNCgTindd4xKFhBVq9UbPGJbiAXfplQXmOO4mTxkXMiZJWIKVdinT6BKvT0QYdcqTp8s2a62PvKRe\nhZV22ewqXXIs97paVY6YJ8HYRI7Y2yJ/kG7AdpdpUeRVlwkgKKLGTF1ynTbNzZ7yFn9Uq8rJuCnk\nrCmWOOBNHrXAEVut9+LgVeY5Zo9LjEtANG7dTNUp17Pe6Iyp5jvssAWu8bwn3eyIBeY6rk6lj/mx\nHjmW2C/RmCUOKNLil5H3muN4HBs46pBFPjP8HRtsdtIsf/JWTUpscaUaM1Sp1ahMjm6D0tSr0G7S\nP/Sn//84J8w2OpMCbZbaZ+foKhFB50zRokihFlVqTdKps7ZI4apGs4InjIWSlBefUzDWrk2BehUx\nl1I4UZIRff1ZysvOKdCmX7oyjXJ0SzHspFmKNSNgQ/XzsvTqHJ2kToV2+Y5nzZR+da8H3C1Pp/Mm\nO2O6FxOuVKJZtXOCo2H/kfx1/dLNddwLrpZkVDAc1TOcN9EM9UuXpVeTEsvs1axIWIIMA5oVWRA4\nLICmSInRilix2meplPQh5eGL0o6MyQz0OZs8VX18lBLekqrDJAXaFY20yNPlVGCGDwV+rvbEdNUu\nSBAWSIxKXdUpO9Qjp7zVcEqyqYEzsiN9NqS+YGgoJs/MCvRYUrZLZaROfbTCHss8Fr7VJB2WX/7a\nBB9ivyUq1LvMaxa/Yac2BULGzcg+YVd4laTAiISqMcvssdReEUFtCrw4fJVhKYLCalUpddHNRY97\n2Rpj70uUFBw1HEgxL3hU/UNT9SZmydVpYChDsWaN0TINwTJNoWL74r/DkbijtNZkqZFBs52wKHjQ\nG2yekO39vfMPO90Nbc95Y/6T5jqmRaGz4en+OPQWRRktPt/6Hc8WvsHZkelqByt9Lvdrfu1dPuUH\nMvQblWREkrUHd/vjotv9sOvTfpbzfhtHHvKmlEddEX5FZahOtxzTx8+4ZuR5P07/mIfdYefRy711\n3m/98Nw9zleVuif0LQkjEa+FV1uXtsViBz0Wvc3ssZP+duyNrli8xVf3/ZedS5eoDp/z7w3ft77q\nRffs/YFfLHunRmW+/vxX3HXtby1w2HfPfN77pv1EolHHwvPdEnrMPb7tC/7L42611lavusL3+z/j\n9+lv9r2+T5s7fsLXov/pk5nfdmfSg1YO7XXtg5tsfNcfvd6zwoLsg44dW6Jobr3vPfU5f73uKj/Z\n8ynvWn2/M6PTDPWnac3Ld41Nko3Ii3RaEtyv9OkO77nhZ9YHtijQbps1moeLnQzNtmR8v2OnF6hc\ncE6CsNktNcqK6hwML/bHk+/z8bnfdOfIw8LJ7A1fojTU6NtbPu8HV35EdDzkNwlv19uba2rWaWdM\nc5XNXh1ZY1HyAT+NfET18Hn/lvJd3w7e42djH3UqcZpTZsjW46Mtv/DuovvMctLdFx713OQ3+I13\nKlcvKKpSnYtKfdIP/NV1bvGE33t7fAyUrcMkWXo8Eb3Vfds/6tfL3mlHykorArstdtC7Aw/8Szvd\nR6PXWdR/2GBKqqFoqs6EXI2Bsgk3U5oh1c550s1mOG2pfV50lVlOmlxfZ1vFFfplCIpINGZZdK/2\nwCRdfZNMCZ63P32RJKOxeKORMSeSZwiIaGspU1xUb1JLj7bEAmO5DAbSTYmeExyLyg51ezF0lVvH\nnnA6cZoUQ7qieUKBsC65lo/uszUpprJI6h2TkDxuVvJxO8cuE0wY0xfINEuNEbGneYIxZXE5WG20\nynAgZSLPbZYThqTJ3Dno8Kq5hqS4KfKM8LEEQyXpyrNqHUmY52Kw1Kgk1zdssrX8MjOc0jZUpCs1\nW6Z+7T0FpmWfVqtSUFTuYJ/dZ1ZYM22b7rFs+aEO0Yywtrpi0fKo+mCFeUPH9IzlaBgvtyHpRTUZ\nU2XpU9LU6mDJfNObz9pXvFjy0KhZiSecTpguQ59VF/c6XjrdiBQdA/mK02NxROf7p6pKO68mONNi\nB2yz1gq7HLHAiGTJRmTptSy616nADJcf2uGn8z5oaWS/tsT8WJcfSZEYjLkOe2XJ7Bw0kpcgsSes\nPzvVsFR1Ki1yUHI8Qade5cS/nRnu1xAq856/c7f/YdH986k7HK+e7mDCQnm6tPSXuD3jIYNSVfQ2\nOZ4103aXOjKywAeS7xceTrAg5aDF204KzRvSPlbge8mfdsvI435e+H4VoTod8k12Xmlfq8LMmMwi\nZgkc1h4t8PTAjSoy6rzRs6qj5/UFMo0LmRyudd3JzdbM3eyzvuWE2ZZ7XUb7kL3bV3vgxtsMR5Ld\nO/YVV+zcZfmSHY62z/f45JvtT1gkHA0pDLfJDXfZnHyVN/uzUUk+VvszrxRd4faUBxVpMdKa5akn\nbvH792+UUjemqOSiX4Te73hgjs8Gvmnp2AGfGfu20rQGTzbf4pPF31cj9vT77rHPemXuaultIx7K\nv83oQIpPpXzXDxI+KWvHkNbVOT56/j7frf6Eri3Fll65023tT9mcv8706GmtgQIPN9zl5vLH/Pro\nB9T2V7r1kkd8NvIt3w3+m65wrs8Ev2N34BJ/63qj36S/U8JZfjT3Az627z7/Me/LAsmxlI537fmD\nB5bfrkSzIamecYOhaKrhtgzphT3mOqr+fLVV1dsNSwGPNm60tuxF7x37tcOJ8z3hFhsHHje594Jv\npn9GUVajhQ5ZMbTHKy+tF7k+LDISdCJptqxAr8qBeifTZ8rV6RJ7dMtx4MAck/KHzK846J7ot7wp\n8Ih9fZd4MuvOf2nR/Wz0P/1t5Br3Jt/riPkT4YvL7XFRjJKLPKEAACAASURBVHy1zF7HxOhhDSrc\n5CkP2WiJ/ba7zFL7JBqbiOUZiHe27fK1KXDKdInGrbDbDqtl6I9bSPP1yBYyboXXY7InHRKNIWpU\nslaFGpSb7bhE41oVutR2rYokGNesWK8shdEWDwXutNZWuSf6nJ09OR4cOcNMNY6brViLPJ0Twa/Z\nehCVpc/rLrH/yDLL5r9uUJpKdZ5xgzVelqVXzni31oQCrYrMdTT+So112butkGZQnUofdJ/tLtMj\nW4Y+N3Zs8sVJX1Sl1svWmOuYjW2Pebng0tgLIVphSuCsS+1wygzjEmJFbKTChuTNtlk7UcwGpcUD\nIdvUmGWJfQ6LRQqdNVWBViERA9JVqDdTjfOq3V/3UV8q+pKu5Cxno1OVBRqdNVVAVKsCy+013pek\nILNFg3KjkkxxzllTlGtUqyqmPxeUYcBxc6yw21mxpWGlOsWaJRsxJFW/DGdM9UjgHf/8eGG0NKhm\ndJavRb/g813fkBvuktfZ4+HRjaYlnxEStmjksGXJe813xF8id8loH3Lfmnf6eOhH/pR/t8da3+QH\nJR/T3ZCvJNrskW+8zcahh53KnGp4LMU+S70UXq90tFlmoFc4HLT2+R3uP/shHSZp6it362t/lfPc\nsK/P/Xe3jD7le0Of9kTkFgH8MP9j5ty43+LwPh9O+KmClBY/Xf8eP8r5sE+mfU9iwojm5lLroy+Z\nk3DMvuQl9vSu9BE/FRjmktzdLqSUyxgfcLcHTcmoEakJOWCR/0r+vA+6z793f190R4L7Wj5qactB\nG9MekGbQ9IEzcnT7wrlv2zj4qOtmPe3+ng/5RNePrerbZUrGaV8f+KLEmqj6OaVmO+lH1R9xW9+T\nbp36iCq16jPLJRhXP1bhEw/8t87uSeZHjsqb3G7Jgn2Kgi22JKzXOl5oNJKiIVhmRvCU44PzfDnp\nC+6b+y7zHPW5pV92afJrkvvHvFh3jZ8vf58+ma59cIv1h7cJNQS9IfCC3+S9zYf8XH1PlUuqd6h2\n3tsPPSytacRdZX+UYNxbw3/0rOtjz9/0DO8puc9XfFGBdi9ZTzjkzPVVNtjsqeSb3HL4GU1KfLT3\n5wLHQ1oVmdN7ytejnzM0L9fmtCvd/NLzth67zmub11vU/PeZo/8/Tq0qFcn1/uzNogJW2mmBww5a\nFE9AiUGypzorR7cWRZ5znVs97lG3iQqY7YSIgCEpXnW5qIA/d73NAYuUq5do3JBU26yNB1DmKtZs\nrW1W2yEi5DWXqlehXsXEYvMVVyjWZJIOzXFwdyyBO89f3Ok+HzQqSYV6JwJzVLmgXYHs2Z1W2TFh\ngPifD8MFk9WYqUSTM6YZlyB7vNd+S2K64zltZqhBTL+8yEE7rLbVOmMJiRqVK9Dmu/5dSMRey3x9\n+HMS4tl5jcrc4WEvulKrQqOSpab2mqnGKTNc7hXDUmwuWBuHU82XHBiJx7NXG4iPQso0qkqsExQx\n1VlTnZFozEUx+/GLNnjRVbZab6ZTyjXEdzrBiRTsTnk6TNIjW3Z5u2hyRJVaG+sfV69CugED0hXH\nx0ftmXmOm2Oeo6Y5I1eXHjlmOSlPp5DwxEfwCq84Z4pizSbpUBptVK9CWEivTA3KJxbu/7fzDzvd\nG8IPmT9+RFZir/xAuz2W65KrTb47oo94aPROq+00JfmMY+ZqHS90a8Ljvhr5otTokLtCD9riSq8M\nXSHvQp/fzH6rnnC2trECB1IWK9YkT7dJ2uMBfR3OmmrJ6SPunfQFFXn1ZqjRrMhCh50ywywnLQof\n9HjoFu/1K3XRSn8J3GnTszf65bJ3uL/4fVraSq0ueFW/dAQ8dfYO7536UwnGlWhyvw94e+T3Xgi+\nQdV4rW9FP+sLiV+VoV+vLFtcaU30FemBPkPSfHnoXl9P/azdAyt9Ov27rvSiD/m52zwuxbArLu52\nz4VvaF49yc99yMahh3wp9csq1PupjzgUXmTByaMeKNpoRuopa9JfFhT28OBGb077s+qB83anr9Cm\nwMLwIe8O/lpzoNhR88x3JGY9Han11+Tr7LfYOtt89jc/8vJNK3VPynKfDxqS4mu+4EN+5i/u8i2f\nka3XVWNbLEnc50vRL9s9vMKjo3ebu/+kTy39plDWqNOmi0aCpgVPW2GXHD2m9Nb6Tsa/WRg8YLvL\nfcjP/T76NhmBAV85/3W/rH6rrdb5mB/L0O8+H3STp9SqkmxEoVa7rfD1l77qK+v/3e6mS32h5F47\nrDbFOd9yj9cDa/+lne6d0d9Y4HCcq9GhQZlko5qUmO2EeuUShOOwpDQ1TXPNLjli19hKH0i8X6Y+\n+y1RpVZI2KA0L8VhLymGJRh30CIb/UWDCiFhnfLUhSsVhZptGr3GoqSD7vQXPbL80dussNuIZKUu\nOmCxAWne4s8Qt+9OMTieJi+hw+aRN0hKHrHWy9rl65ZjuT1qzBQR1CnPYvvNc0yPbMfi7Iv/idTp\nMMmtHtOk1P19H3Jd5rOalDhkobf4UwymI9tmG8x0Uo5ueboERdww8Lz/Sv+sjR7yrOstcsh1+7b4\n4dIPxFGsMcVRpzwZ+hXFC1ytSnMd1ynXMfN0ynOZ7XplmeO4h2y00CFnTNNukiUOyNcet7lvkGpI\nng5nTBMUVWOmlXaarNaz3qhZsbf4ky2utM5WjUrVqTLVWfnaPO2m+Ew9BvO61eM6TFKrSld4knAo\nIFeXc6YIC0kzGF9kZymPOxVbFMnRbaWdWuIJz5siV5sXPKpAmy45vh742j8/XvhO9MMalekM57ki\nvN204CmPJNwuxbAVdjtkoQp1Hozc7dbg4zEXmCVGJZvrqEfcoVCr2kilQJD1XvKtvnusztxhsgvW\neNmoJAFRT7nJ1f7mUbdbbYcpzvpg7/0+lvVjM5zyYx9z6vX53nXJfaY7ZaZTHnG7QWlGJdngBQXa\n1Zhpdu0Zz1ZdbUCa2zxmszfELZnpVkV3Wht42VZrEbDMHmdN85J1MWi3GIA6LCEGi4nu8t+B92uX\nLzE6piJQb4PNXnWZVMN+MfR+V4a2OBmYZU7iMQXabbXON/yHc+EpngndIL1jWM2kaRZGD8kNdNne\ntM47Sv7bZzq/4915v5Zg3B0eVmOmvkimcDAkX7vjZpvujONmq3ZBolGbXD1xAW7zmJ1Weefw77yW\nsjp+8YqsttNMJ+2wWp4u2boF0C9DvwwHIotVB88p72+WmdElKihHt0p19lvi/ZFfeCi4UedQvs2p\n66VER9wd+bPZoRr7LJngEjwafZPVgR12W2FMol8e/LA/LNro+uhffSBwv0/6gR/5mH/zPe8a+a1l\nyXuVa7DOVjcGXvyXFt3vRz8gK9qrN5AlS68DFsvXPhHmeMp08xxzwizFWpy5MENqRb8LoWoLHJap\nN8ZlVaRMg31HV7o4r8Cc+lMKoq2SK2MxNklG5WuXrz0GzRmerzylzgWTTXdKRMiwFO+L/Lcfjn9S\nUajJaChZ7gSCNHdiNBDrWqvsGL7U2pRthqUo6GqTmjukU57TprvaJvssjTs92xRpscsqSUaFhSxw\n2KsuN91pUWJ0ta5qBbnNCrWYFa7xRONtZoVOCpcxJM2YRGdMs3bvDj3LUjSoUDHYYCwtwYA0eePd\ngq8FtKyZJEO/oIjQf7PorkNaM3MNNWXZXzJfdVO94ZJEpa926r88IRb+2V/tlvRHvRa+TFLCqFaF\nQsKymvqESsZjkPhoqYJAm/mOSG0Y11OeJiisZmC2lak7HQvMldo8ojJU61DhAkWajcThTltG1luV\nvEtYSGG0VXOgWJ4OHfI95SYf8jMdg4VK0hrts1SWXgnGlWtwwGLze47JDXTrzMqKm0yKZel1TrVs\nvdIMGheSJ+YM7Rgo9EDGu/75ovvD6PukGVTlgnOmaFVkn6Xe6o8TRJ5OeWpVWWerY+aqVeUGz2hV\nKF+7WU563rWSjThkoUnajUgx11EXVJvmjAHpIoLSDNprqbmOCWBIakwELWRMonb5ltrnjGmGpWhQ\n7o2etc9SicZUOy9XpwLtDlkg1TDI165RmfOqFWgDXXLNdFKfTDOcVqfCmKT4prRBmgFDUpW6GIcX\nV8jRPRGIFxIxKE23HLd4wtNuUK9Snk63edSTblapXqIx58arJSeMWmK/PJ0CojHHixQBUS2KJBqb\ncEFVu2CrdVbbISwkw4AdVpnviBTD9ltsocMy9YkKaJdvsw0T5PqIoEq1XnC1Qq3ydMaXW3nOmO56\nzzknpk09Z8qEpG+XFQq1udRrGpQhdm+SDUsQNs1p+ywz2QVt8mXGsg7UqRAVtNh+By3SI8dsJ/zV\nda711wkM3mQXjEmUbsAnA7/4lxbdX0XvttU6S4cOGkmNISubFalUKyoo2bCooBlq7LLKOVN83I9s\ncaW5kWMeDbzJ2kAsjSMiZL4jLirVPF7sYkKpVXYq1OqkWYIiE51biSYNyv25623el/QLe9KXKtJi\nniNetnaC7Ruz1I4q0CpBWLYe2bodG54vmhKdAMg0KYlr0R/V2FthOCum8x2TKMmoYs0GpIsKCAsK\nxA0dGfoNSo8FNY5Uqkq+oEe2YSmydUs2ItWw8cYkXWXZlsQZwiHhiayymDTzrIdG7nRiYI635/1W\npj4phk0fOeOV5MvtDq9QFGpR6qKl9jlgsUZlhqQq1qRQmxNmW2K/bjnOmqpSnSw9hqVOzHOLtBiU\npkG5qc5qUuKI+dZ7yR7LjUtQPN4iIWFMpj7pBgxLMav5rF3FS3TLUaDdcXPMd0SHSQ6bL0eP+Y6o\nMTPOBayfkPk1RsusDuxw1lRnTXGFV41IlqfTJB0umOyikvi4o8YU5xw3x28DH/7nZ7pNig1L8boV\ncY5ttrmOOa9aiyLdchRqdanXQI9slWq95lINym3yBvst0ahMtxxTnXWNTco06DRJUlzTez6e33TB\nZEVa1an0qstUqLfQIYVa491FXyxcTqd87W73sNOmm+qMLrm640u5c6qVaPa6S1wwWRT7LbHQI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X9lplWIZyVwzLsMhRbyTnit0K7LBRsU6diqSIusoO+61MRsJOq3NKqgnpRu2y3jEL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C7iru4X5WizPbDCI8nf8iHPmByokh1pdaV3PRm6T4MiSxJ2We9ay+wwr/h9h0MzvO4m0xzX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\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WuGKPXQ64J2oXHpVkjRNGJLtgqXf4sRFJxiTK1CdTnwLtOuSr0OAPfAlhT3unD/mmjY5o\nVGa3Fw1JddxaicFKWrUlJsT7pH8RFpIQGrfVQXEmTYqTYFyvTKudCk62ncYles0202KVa5CpLwrq\nHJQu2YglqgOUeJ8aVdIMqrbUW/3UFgctdEmjMilG7Ajv8we+qEK9jY5oN0OGfm0KLXNeoTYnrHHe\nMtl6fNVHrXVCiWabHLbBMaestsseaQZl6nXYJt/yQRn6dMjTqshjnvVTb/MTb/91P9t3dVe/lbpj\n0a0bmW9Amu9PvseO8f32elC+dodtlBge0xAuj17vH7RXY0yZVkW+5FOmx2KVapSl13b7tYSLpBr2\nuKfdNsNx60GKIV/2CamGXLJIe1yeruDUVabRx7OfVD22xHHrfMqXLHFBWUAoWBJAIetVWKzaI+M/\n947pnyjWrFlpZEegJ8ty5xRpNSHBcufFT046ZLMcPZINSzbi8+N/6rZCH+v5hnQD5rguzkSEHDp9\nxTEbDEtxy8woAaJHtm1eNyBdhfoICTVc4pq5XnOfB7ysS55EY/J1aFXknOVWOuOmImesjPRop8Ma\nlXvWY2Zpdc1cTUrBXNc8OP2S73u3+a5IMWJKjMf92B67bJg86uHJ5w1I84SnTYi3xklnrNRqlk55\n1jrhUnix+LEJb9jsmnnqzfZ7k9/2Qd/0VOi9vuhTQsK+M/p78nWIM+GF8d36Zaozz7RYp63ywZF/\ntchFB9yjRpVfeliTUo94zlHrZQyMmOu64XCKe70qU59FLgUbErOCbItjv94n+67u6rdUdyy6Z8Mr\ndMmzPW6/1xO2SjIi2agZk+1ipsPuD+0zKFWiMcfH1+uVZf7INT3hLH2JmYHNoclNs6wPHdc4VepH\n4Se0KBYW8gHfttPL0evmNq9rSi2Url+rIlNi3J4usDDxsivmqzVPrywJxp2yyg+8y02zrHVCp3zf\niXm/H8S803rHXbZQvg51abPdNkOMacNSNCmxLO6cNIOGpAiFaZis8EDCy8o1uB0zwxonxJiO7u+G\nY0Ky9VgZBHjXmadRuZluaVFsWEp0gEXYLTOlGbTfdmetMC5eqSaHbfKEp6UHp8G3+ql4E86MrxLq\njrHbi1KnhgMjCQ9MvxwJ+4kZ8hf+u4sWG5lO0qRUk9LI6yIuWVzMhPSJweCnFlajSqIxtebb5nXP\neUReqNP2xP2SjRiR7DmPaBos16LEFgeNSXLdbDeSygJU94jNCYdNiDMh3qQ47/GULyd/3LRYi1U7\n37FMgXZbHdQhz7GBDd5IX2eG27JDPTY4Zr/tAUkkwRGbdMsOcnzv6q5+93RHR9r7/kuJC5aZkOBG\nMAQr0upQzFZrY05qUO6auepViI2dts5xodiw6ZhYM7W5YoE5bgiLMSjVvaHXtYRKoiEuV1Q5Ya2Z\nbpnnmgkJfuZRNxXJ1O+AbTaFjoA8XUakOGqjSrV65ZjnmqWqhYRl6jcdE6HOhsUYkaLebHNjr9np\nJf3SJRqXp8vr7pWnK+LACqVZFnNegglrnTCclCTRuJ+E325t6KQQRiVrUGFUkkRjbpshT6ewkFtm\nijPlosUalHlb6KeOBYOvyxZa4YweuSbFa1QW4OS7LQ0iMGdo1x5XICF5VIlmV2KqpBrWLcet0Cwd\nCgxIV6bJlDjZoV4XLJNhQKphWXo1h0rEx046abW+cJb00ICBILayW67d9kg2olO+FCN2e9ErdliQ\ndMU5KxyyRZc8S1x0n1ddtERs8NJ5yIvaFJrnmtbpYlmhXhn6LXbJktSL5rruhLUy9StLbHTJIjGm\nhMVYqEafLCudddrqAPU0bJnzXvjc+buOtLv6ndMdV8aqXDYsxSWL3OdVr9hhoyMy9Ltthjd5Icol\nG5Sm2hJnY1Z6k+ctdtFJa1wz16vuk2pIVyjPE572mnuVafRj7/Bn/t5ZK/zAO02Kt83rrptjthsK\ntYk1KSmYyjeItDNalJjjmnqzhYSjUY5rnbDDK7L0WqJaimGvus+3fcA1c+XottopH/Bti1xy1gq3\ngk2JVU47bZVEYyrVmheqExYSRqXa6OlxSKpNDrtigRzdFqrxqvuETPsj/+SzPuuP/JMccb3BhQAA\nGj9JREFU3V7wkB454kwaluwTnvSiXQq065SrxkLZeoJB42prnDDLzYBqXK9btgoNko14zb3iTYB3\n+b4b5qixQJoB1RYLY1KcWaGbVjinWakFrogxFZAd1poSa5ZWH/ek9/pesMecr0e2t/mpC5ba4RXJ\nhg1Jla/DLYVSDJunzvmYZfpkmBCnS65u2ea6rlWReBMGpLvPfj/zmB7ZPu9Po/j5X2VPhIX0y/w1\nP9p3dVe/nbrjytg7w99UoN15S1WqMyxFQmALblKmXL2Z2rzmXjPcjg5+ljvnJQ8o0aJBuY2OaFKq\nW45Ug1KMOGWVlc7K1xFEJb7iaz6iRLMQMvVKNaxIi1HJYk1pUaxYi2HJeuR4xHP+7/bu69fv+77v\n+OPsvffimTznkDzce0kUqUVFw5JlS7YTO7EbN0VaoEmDtAHSoGjaAmmDBCiSIKNxYyeyZEWyrGFR\ng6IokuIU9zx7L5699zm94Ne/y/ImMgTo9/wDzjk454P373Pen9d42TeUaTMjXp8CSyItBdKmAdlB\n9GOWWQkWRUs3akS6aItSTEg3pt0qPYoc9q4WFVJN6FEoMqiPX+uW4w6YDJx13/SyH/umJ7yrT54E\ns97xpG96yScOKNPmmvVq1avWYFhmSJExLFO0BS941Z/6PZtcFoEcAyItaVYl3qwa9RbESDPqug0G\nZdnpvD75uhQH8ZpZtrugXo0aDVpUyDJoSbQSHVqVu6nOC35iUpL+4MGxQK/r1ks0rUmVx7xvVJoq\nTRbEaFURauP4yMFQDOYv9umf2SbOvDVuaVZlSpIEM8FtOku8OU2q7HfSoGyz4t2y1gbX9MlXrcHv\nRfxVWDIW5kvHfYfu36+8aEyaJNPq1SjV7ojD8vSZlmRZpDVu2+mss3ZLMK1VRVCRk2ujq3oUirAs\n2pJS7WLNG5eiV6HtznvN8yq0SjBtRCZIMeGqjaEEsnu9YaVmxQe5BClq1HvLM9a5KdmEAn2Oeli5\nVrnuGpca6FAjseJu0DnWLy9QLiTqVBLcEGOs1qhJlWyDEk3rlys/qKeJsHKv3diyG+qsdcu0BK0q\nQtXuUKNesU7n7LIgxjo3tSg3Jt2gbFkGtSm33XkxFg3KlqffjATrXfeKF/yKdzWrlGNArLlAAZLp\nST/XrVCJLldtdFeuDa5pUmVMmt3OuGTLvb+dFTXqQ5bke7/HDpdsVeuOetU2u+KaDfL1SQlCfda7\noUqjMekarXbVRpWaTUv0oONBoti4JdFuqLPDeZ940JPe0aNQi3v5EeNSZbtXA3/RZivBfXtcmgrN\nSnT6VxEvh4dumC8d931I+8QBvUG2QrNKJzxgp3NmxUs07SEfq3PDX/q34syZkejhwG0WZUmEFe1K\n9cvTq8B1601K0q7Migif2S7HoKs2WKVToR7VGozIsNsZVRolmzQgW74+12w0JMuAXC/5VXucVqjH\nsCzNKmQZUqzTiHSJpr3iRR855I41BmWLN2uzS1KNG5dyr+VXvq96XaVmue4Gbq0lDzqhW5GoYF87\nLtWn9uqTb5V2U5Ll6dOs0ogM6113yWY/8D131OpU7FVfk+euHoW+6nWf2e4r3rBakwu2u269fH2B\n66tckR4NqiWbUK7Vgljn7bBakxvqHHPQtARXbRRt0az4oLzyotc9Z61bwRNbh355blsjxoJdzuhV\nqNZt82L9ine95zHpRp2yTwRSTFoW6YpNxqTJdTckG8s05E1fEW3RHbUGZQXVP5Vmxftv/tCkZFOS\nNFhtRkIQM7nDXXlKtRuWpUKz69YbDTdHhPmSct+h+5S3QbxZm1yxyxm7nQk5zdqV+tAjsg1KMK1Q\nj2STmlW6rs5pezzhXZtdccBxyyJ96BFFuoNK8hJPetuv+wc/9Zwa9RpU2+4CqFXvio2yDalX6z/6\nE+lGFenyTS+p1KzDKmvdctZuu5zRLz9UpviMN7FiWKbb1oSG6E3r1Ki3zk3bXfBH/qs3fEWxLres\n1a3IpCR31Iq2aI3bNrtsmwu2ueCffd2kZJtdkadfjgEn7XPAcb/jz+QY8ICTtrgs2aStLrpuvRKd\n3nPYoCzPed12F4Kqm2zzYm3zmXx9WlRaFmlGgt/y187aJcug7/qBeHN2OG+fU4ZlytPvrF2iLUk2\nKceAMWkyjCjV7lk/FWtes0o9isyINy3RRtfkGPC0tyyKtiBavRo31XnN85pU2uuUKYmmg/3uLWvt\ndla3YvNivOVpDzgh2YTb1njea7a47KBjRmQo0GdZZJBnMSzNuHVuuWbD53qww4T5onLf9cJ/WPlj\nOQZcssUWl2QYds5OU5LlGBBnNgiOmdesyowEVZqsc8PPPSktiD1sURHcjqfctlayCfud0qFEjyKF\nehTrUqJTvzzNKpVrlaffJx40J85ta/yeP3XSflmGFOg1LMO4NIuBBbhEl2yDocbaeLM6rJJmTJ88\ni2K0qLAsUqpxeUHubILpoKl4q1RjIYtrfLBaeNAnztthSpJkExbEGpYRWG4zTUsMdsUZnvCu0/bY\n55S3PG2ri+6olW7UkMygWHNYmrHAWj3iDc/Z4bzr6hQFqWxFuly0zbe8ZFqiDz0SauOt0KJUu7/1\nfYe964b1lkSJsaBQj1EZuhUoDWrgB+Qo0m1MmkjLQaddnhSTEsy4Za0ybcq0uaNWkypbXHLEYYd8\nJM2YaAuBPC7JPqcMyHHJFgV6xJuTr88xB0O17cW6ZBkyL1a8GZFWnLNDBPL1+YuI3w+vF8J86bjv\nTXdJpFLtNrliVJq2IPTmsCPuqLUgRo9CbcpkGpYcBJ78g9/Qqtx61zVaHYSBjxmT7qCPZBty1MO6\nlNjpnETTMoyYlmhCimJd/mLk3xuXKtmkWfEe9YEJKXLd1aLCEYfNBkN+XpwYiwr0SDStT57poKUt\n3qxqDaFa9ySTNrtkQrK4IJ+gWZVE03Y4H1JdrIhQoFeJTg2qJZmSbNJyEG24SqdybXoUKtTjSe9Y\npUOEZZGWdSmy3XnRFuz1qQjLGlUr1yrBjCs2yTbojN0OO2JAjgkpnvFm8KHS5xlv6laoRbmHHbXZ\nZSnGfWqvv/c9DzuqRJfLNks2qUKzNmXGpKrSbEyacq3mxOpSbEC2ERnizFnrtmQTobLLLsUmJfvQ\nI6E84y0umRejPwhrTzZlrVtmJGhSpdFq/fK1KdWmzKJomYY96n0ZRsRYkG3QCQ8YkOOQYx70iR4F\nv4zzHSbMF477Dt21bjtjlxYVcoKOrj4FGqxWpUmdG+LMm5RsTJo6NxxwXLbBIIjlXoVNtEUtKqzW\n6J993QHHLYkSYdlJ+9WoD0nD1rsuwYynMt4wJMsVm4KKoATveVyefmvcNi5VjXodVsk2qFKzHANe\n87woyza6qlth6IZYrdEBx1VpFhs0VBTrFmMhJP6vDr7ev/O/bXHZOTv8rd9UrlWDapGWVWgxIEeG\nEfn67HFarrv+p9+XbtQbnnNDnV6FlkRpVqU0yO39HX+uTblGq63S4RUvmpYI4sx5yHFn7DYky6f2\n+rkn5Lnrik0+ctCcOBtdtdklUZZctNVlm33FzxTqlmPQYUcMynLdepWatKhwyzqP+NAz3pJp2Gl7\nHHVIpBXpRj3jTa3KzIt10DHveDLUanHZZnHmRFp2zk5LohTrlGLCWrdEWbIsyg7nLYt0R42feMHb\nnpJjwIAcz3jLWrfMi9WrwIxwXU+YLyf3XS/855U/UKbNnDgXbFOjQZw5RbrclWdWvAwjMg0bkine\nXBB/kugtT/uuH/iJFzzrDXHmvOkZL/iJYw6KsWBIlh3Omw1q1ptVGpUuzZjdzvhLv+0pb2tTJsmk\nVhVKtanUEmrgvW69dKNKtUsxYUCOO2ptd8FZOz3qQx874ImgXy1fn3N22Ou0SMshSdMv0rmWROlU\nrEKr9a4Zkq1VmQJ9sg26ZIspSfrkq3NdtiGsuGibhx0VaVmDaiMyZBqSaFq7Mod8pFmls3ZZ56YF\nMQr0mhcb+jke9YGT9rlkq1/zj07bI9a8PP0GZJuSLNmEgUC5cNVGkZbVuuOiLVJNyNenTZmNrqpX\nI8aCQ476c7+rTKsZCb7tH7UpC1qSx0KPcgNypBq32xmved6cuCA8stdFWxXotSjaGrdUaPV+kGFc\npk2+PldtDGSBUza6KtWYXoVGZEg1Lj8wzAzL8KOIfxNeL4T50nHfm26JTm3KVGlSokuLCsc9qE+B\nNGMu2+yird7zuNP2alfqtrVmxKvWoCNonv0wiCevc0OnEjXqxZu13jXpRu8129ql1h0JZmx01Y98\n22Ped0OdHAMSzYixIMFs6KZ9yRYHHRNnNlQZXqbNHqe1KfOkn+tRoM5NSSaDNLQFq4KbWoZhE1Lc\ntkavfCsitCqXa0CCGW/6inc8aTHYD1+xyV6fyjAi26BRGW6o0xHc5q9b7y1Pmxcr07Al0Yp1Wa3R\ny75hlQ4bXTUnToJpd9SKsmhMqnkx/smvyjYo07AzdlutUa8Ck5Ltd0q2QXucuWeC0CPbgCxD7sqR\nblS/PHflWutWoIbottYtHUrtclZ5MBxf8aIEMyKsyNOvXZkFMVZESDTttD2KdIs3a0aCPvny9elR\nIN6sc3a5ZItxqaEWjF/Ee+bpB51KDMuSZCqQ/JX51F6rNSjS87kf7jBhvojcd+ge85AkkyKsmBcr\nw4hf80/e85gb6tS6Y7sLKjV72lv65FsQE4otTDLtkKNiLKjQIs6cm9aFmhRO26tPnh6FyrTpUKJb\nkRblQVj5qBwDUo2bE6dGvWGZ0o05ZS8YlmlerA6rZBixKEqseTMSfOwhg7ItifKJA5qsNiRLrrte\n9XWn7DcuxXf8UJx5WYY84gPtVom0rMYdB3zsASc0qJZiQodVyrRZFK1bkWr1Uo2LMa9cq+f8VLEu\nM+JFWFGv1in75Ot1zEEXbdVotRQT0oyFijgL9Mkw7JIttrtgSZTbah1wHLzuq8akuqNGoR6veV5O\n8Gi4zi3Zhhxw3GqNIi3b4qJhGYZl6nOvUy3KkjVuy9VvVpxB2XoVGJOqR6ESna7aaFS6Ci1Gpcsx\noDjon0s3pliXZZHu1VwWWBAjxrwxaf7Sb8s0pFCPdKOu2WBaolx3JZp2wHGRVmQZ+lwPdpgwX1Tu\nO3QnpZiU4g/8D5tdlmDGkih/6L9rUmVWvGWRRqX7ge961hs2uGZaog2uOeJxV21ywHEfesQuZ9Sr\n0a5UhhHxZqxzy6BsXYrVuSnDsE2uet9jfuqrTtujW5ELtpsTZ52b6oMBOCvepGRr3ZZi3HEP+dRe\nKyI87j3Pe802Fy2KNivOfifl6/O+xzzvNVmGZBsSbzZoL142I9F3/EinEic8qFi3S7boVGKdm7b5\nTKPV8vTb5axcA2Yk2OKym9Z50zNiLEgyJceAs3bJcdcjjirWaZ9T1rmpSZVOJda77l/7G0W6rYg0\nJs1ttVpUmBPvgu02uirTsAQzztvppP2BWSFNrTty3fWhR1y1wSn7AgvvoAgrblrrlH12O2NSskK9\nnvGWFpWGZXjP4wZkq9Fgkyu2ueCQj9yVq0eBT+31uucU6zInTlGQFlagT4pJDzvqis0iLfldfxbK\n3i3V7vv+RrUGN6zzFT/zD37deteVaf1lnO8wYb5w3Hen+0cr/8mgLHfletrbbqgLerp6nbHbAceN\nyHDS/lC8YYQVpdo1q/SwD52xR4lOHzkk1rwHHTcjUaJpURZdsVmJTkOyQvvVX3j0q9WLtKI1uPkO\nyFGvWlKgUNgYRDvOi5VsUp5+UZacsk+5VrfV+oZXjEvVJ1+8Wbn6fWqfCCv2OalDqSZV8vTL1+d1\nX7XfCVmGZRmUZMop+61z03V1nvCui7aFnG4Dcs2JC5V2lmqXYST0QPgLG/KgbI97T6JpR4IHwRoN\nuhTLMOKE/XY767o6k5KtczNoFc5Xr1ahHvn6xJh3Q50nHAk0vll6FUgyJdqiUu3SjJqV4JIt+uV5\nxAfe95jtPjMp2U3rgge3bCc84KCPQJRlH3hUrTt2OGdItlTjWlQEkrNUsRakG9Un3888o1ybZ/zM\nEU/ok+cxH+hUIsWEE/bLc9cWlwKd7phhGaIt+V7YkRbmS8h9b7qpxuUasN51rcoNydSgxl25kkx6\nxYuOeUiyCYV6ZBrWpdjd4EGmXWlwM60xJdFGV0SgS7EuxT7ysGgLYs1bEmmTK6IsydNvVnxIMXDD\nOh94VIdVBuRaFG2H85pVKtEh111bXdSj0BGHFejRpFKyKUcdkuuuZpUarXbeTpCv15Rkqcbscsa0\nBJOSfctLoUe5VBMWxcgwYkWEUenO2CPCsgQz5sUqDfak8WZ0KnHbGssijUsxK16ffIOyPeRjp+12\ny5rAXBDrlrVOeCDUP/eWp5UEWRPXbJRgWoR7QTYpxi2LNCjnXgSlPONSxJu1LNJ610RaMi7Vxw4G\nDrwZD/ok6DE7GQp8nxGvS7EOJfIDV92KSMsibXVRjgFveDYwwUwp1OOYg6IsO2mfazZYECPHoDz9\nPnbQao2+44fmxZoX45oN8oLs3BvqtCt10n4jMkO26TBhvmzcd+gOyzQrXpPVgWD/3uNKmjGTUtS5\nYa3bIq2YkqhLsdUanbbHKfuMyHRHrSpNUkxoVO1l39CjUKZh5Vqs1qhRVXCru5dDm2HEOjdxT0r1\ntLdUaDEjPqi6GXPCA4p061RiVLq/9ltq3PG49xz2nu/7OynG7XfSNRu0KbXVRQ/6xFYXrYg0IUWG\nUQ1qbHJVignveVyfAk0q3bYmCHeZ06JcrXo31Dltr1nxCvWq1mhMmjnx9jitWaVb1sjXb0GMS7bY\n4by3PWU8KHxcrVGdG3Y6Z7PLatSbDR6sVms0IdmKCPVqNatUrEtGsCOdkOKyzVKNOekBF2w3IcUF\nO6Sa8JltCvQ67oB8/Y46ZJV2qcYV6lGuxSHHgn65JTudsyzKOTsJ6pQiLYUMIvceGmvv5QqLkGXY\nPqc0qfKUt21yRa8Cxxx0yVadSjSo8bCjQc5DTVA8utkWl8SZE2v+8z7bYcJ8Ibnv0K3UZK9PPewo\nVqyI1BKEvLSoMCZNZvAokmzKCiIt+21/5VlvmJTsrlw/8BsOO2KLS5ZFSDVmTKqtLkkyrUCfEp2W\nRTroWBAVeS8Va1mky7ZYpUObcgui9cv3Na9JM6ZAn0nJnvO6bEO6FZmW6KxddjurTVnog2JEhk88\nGIj6c01K9prnQ00U9yyr97Sly6Ks0qFQt5lAGhdrzvf8H1tdtCDagBzve0y8WdAvT50bxqRJNyrT\nsK951QceFWXJXp9qVW5GgnN2+pFvWxbpb3zfOjdUq9ejULRFBXod81DI1bXWLQtibHRFnZte8i1P\nekeOAYcc9ZmtFkXZ56QlUbY7b0SGKk3izDvugEhLztklxYQ4c8H3OOiuHJtdlmNQiwqFei2IlmDG\nD33HZlfuZSVb0aPQS74l05DLNlkSpUi3Hc6blOwBJzziw1DYe4wFTao87zV/73ve95g3PPt5nusw\nYb6w3Hen+7srf2xItse8rzOwgHYrCgWrXLTFiMzggaXboGydSjzkY1OStCnztDed8KAcA8alOmun\nh3xsTrxhmR53xD/7ejAc5gJ//5kgBqYgUAg0iLGgWKdeha7YZLsL7qiRaUSiKSW6fOSQDCO2+sxt\na213wZREF22VbUirctt85pItCnXb67QxqSEL85BM5dpEBP9sj8jQpUipDjud1aFUqnGn7HPQMT/2\nTbuCwb4sMqScOOQjLSqV6HTaHinGxQYW3TmxBuUYlO1FL7tlnQUxBuSIN2tCihYVMg1brVGMBbes\nMSzLN7ws0rJ2pbIM6VRijdsWxFgQ7aJtoi3qla/OTQ2qVauXH1hzV0SYE6tJlSrNIizbGrjOrtpk\nSpLDjjhlnySTMo2EtMuRlq2I8LCjXvYNW1wyIUWTKtUaNKtQrdFxB0ItziVB51ynEoui9CoQgUhL\n/lfEfwnvdMN86bjvTTfZlCe9o02pJdEGZXvS2+bEaVbptrXWuhXKQpgVb6Orgd12yrBMo9JVaTIr\n3m6nvegVFVrl67XDeXflOehYoOvMUKRbhxL98sSal2JCkinbXdCjSJ5+yyLdVutJP/eQj4PM2nu5\nB6nGNKtywHFx5ozIFGvBdhcCu222HAMWRbsrx499y6h0ufqVa9OqXIQVC2Lsc8oeZ0RbFGfeCQ+o\nV+1Zb+iT75t+bFyK5/zUC34iy5Bdzrpsi2gLXvV1RbqNyFTnpiaVpiVJN2q/kz7ysGEZhmTpk29a\nomKdNrqqRKclURZFSzNur081Wq1FOdyrxDFiQI4F0X7o1xXoNSLdYz7QL0+lZqMyXLbZbqfl6rfe\nDQX67HA+SIZLMC5NqnEP+dj1ILpyWZRGq1UGf7toiyItOeEBZdoMyg6q1Y+YlGy3MxpUe9HLoFyr\nag2u2aBbkSHZ+hRYFqFK8+d7ssOE+YJy36HbrtT7HrMUJFBFW3LEYbetcdM6iabEmxFnzlGHHHZE\nke5QTOJBx2QYFWlZgV5/5zelGdOuVIqJUA7ARVs97a2QeL9Qr3ItbqhTrEuOAcsi3bTOMQclmvZd\n/1eSSf/kVy2LMihHh1WmJMsw4m1Pua5OvRrwlqfNipevX70aDaoti7LVRW1KfWa7JFMOOO6sncq0\nOWO3GAs2uqrRao97L9RW0abMrDgdSl21wZw4MxJlGjEqPWQ0aFDtUe+LNadQj3alyrV421OWRRqT\nLtugR31gkyuIUKXJbbUu2qpcq8Xg979KhzLtFsRYFqlPgVlx8vV5yts6lKhzw2e2hcLeS7XLMuSa\njS7Y7lxQH3/aHr0KjQfa30xDJqSoc9ObnrHPKUOyfOhRMxLUqA+CdIoMy5RswjafOe4hrcq96uua\nVPlH37bVZ6o1+NTekNRtULavedUu5yyJ+rzPdpgwX0juu174Jf4sYb6EhNcLYb5s/H+HbpgwYcKE\n+ZflvuuFMGHChAnzL0d46IYJEybML5Hw0A0TJkyYXyLhoRsmTJgwv0TCQzdMmDBhfon8Py16ADCn\nIjIaAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ff36c7741d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from lasagne import layers\n",
"from lasagne import nonlinearities\n",
"from nolearn.lasagne import NeuralNet\n",
"\n",
"network = NeuralNet(\n",
" #Define Layers\n",
" layers=[\n",
" ('input', layers.InputLayer),\n",
" #('conv', layers.Conv2DLayer),\n",
" #('pool', layers.MaxPool2DLayer),\n",
" ('hidden1', layers.DenseLayer),\n",
" #('dropout', layers.DropoutLayer),\n",
" ('hidden2', layers.DenseLayer),\n",
" ('output', layers.DenseLayer),\n",
" ],\n",
" #Hidden Unit Size, Activation Function\n",
" input_shape=(None, 1, 28, 28),\n",
" #conv_num_filters=5, conv_filter_size=(3, 3), pool_pool_size=(2, 2),\n",
" hidden1_num_units=500, #854 \n",
" hidden1_nonlinearity=nonlinearities.tanh,\n",
" #dropout_p = 0.5,\n",
" hidden2_num_units=500,\n",
" output_num_units=10, \n",
" output_nonlinearity=nonlinearities.softmax,\n",
"\n",
" # learning rate parameters\n",
" update_learning_rate=0.01,\n",
" update_momentum=0.9,\n",
" regression=False,\n",
" max_epochs=50,\n",
" verbose=1,\n",
"\n",
" # Training test-set split\n",
" eval_size = 0.2\n",
")\n",
"\n",
"#Learn Parameters\n",
"X = mnist_x.reshape(-1, 1, 28, 28) # Reshape sample to 2D image to feed in nolearn.\n",
"y = mnist_y\n",
"net = network.fit(X, y)\n",
"\n",
"#Visualize weights\n",
"weights = network.get_all_params()\n",
"hidden1_weight = weights[0].get_value()\n",
"hidden2_weight = weights[2].get_value()\n",
"#plt.imshow(hidden1_weight)\n",
"fig = plt.figure(figsize=(6, 6))\n",
"fig.add_subplot(121, xticks=[], yticks=[]).imshow(hidden1_weight)\n",
"fig.add_subplot(122, xticks=[], yticks=[]).imshow(hidden2_weight)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cool!! however your research is not just simple classification/regression. You will need to implement your algorithm by yourself.\n",
"\n",
"For example, following contents are **not** coverd in several libraries.\n",
"- **Layers**: Embedding Layers, Maxout Layer, Recurrent Layer\n",
"- **Customized convolution**: k-max pooling\n",
"- **Different cost function**: hinge loss, huber loss, cosine similarity\n",
"- **Pretraining strategies**: pre-train network with different dataset\n",
"- **Transfer learning**: multiple objective function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Overview of Theano code\n",
"\n",
"1. Define Symbolic/Shared variables (**Variables**)\n",
"2. Construct a computational graph (**Math**)\n",
"3. Compile the graph (**Function**)\n",
"4. Run!!"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch:: 0, cost:: 0.295\n",
"epoch:: 1, cost:: 0.106\n",
"epoch:: 2, cost:: 0.038\n",
"epoch:: 3, cost:: 0.014\n",
"epoch:: 4, cost:: 0.005\n"
]
}
],
"source": [
"# Linear Regression\n",
"import numpy\n",
"import theano\n",
"import theano.tensor as T\n",
"\n",
"## Step1. Define Symbolic / Shared Variables\n",
"x, t = T.fvector(\"x\"), T.fvector(\"t\") #input\n",
"\n",
"W = theano.shared(rng.uniform(low=-0.08,high=0.08, size=(5, 3)), name=\"W\") #variables that are shared over iteration: weight, bias\n",
"b = theano.shared(numpy.zeros(3), name=\"bias\")\n",
"\n",
"\n",
"## Step2. Define graph\n",
"y = T.dot(x, W) + b\n",
"\n",
"cost = T.sum((y - t)**2) #Cost function\n",
"\n",
"gW, gb = T.grad(cost, [W, b]) # Take gradient\n",
"\n",
"updates = OrderedDict({W: W-0.01*gW, b: b-0.01*gb}) # Set update expression in OrderedDict\n",
"\n",
"\n",
"## Step3. Compile graph\n",
"f = theano.function(inputs=[x, t], outputs=[cost, gW, gb], updates=updates, allow_input_downcast=True)\n",
"\n",
"\n",
"## Step4. Run!!\n",
"for epoch in range(5):\n",
" cost, gW, gb = f([-2., -1., 1., 2., 3.], [.4, .3, .5])\n",
" print \"epoch:: %d, cost:: %.3f\"%(epoch, cost)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Variables\n",
"\n",
"In addition to numpy.ndarray, you need to know **Symbolic Variable** and **Shared Variable**.\n",
"\n",
"- **Symbolic Variable** is a symbolic representation of quantities you want to use in functions. (Inputs)\n",
"- **Shared Variable** is a variable with **storage** that is shared between functions. (Weights, Data)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0. 1. 2. 3. 4.]\n",
"((x \\dot HostFromGpu(W)) + bias)\n",
"(a + ((x \\dot HostFromGpu(W)) + bias))\n"
]
}
],
"source": [
"## Symbolic Variables \n",
"a = T.iscalar(\"a\") # integer\n",
"b = T.fscalar(\"b\") # float scalar\n",
"\n",
"x = T.fvector(\"x\") # float vector\n",
"X = T.fmatrix(\"X\") # float matrix\n",
"\n",
"## Shared Variable, store variables on cpu/gpu memory\n",
"W = theano.shared(numpy.array([0., 1., 2., 3., 4.]).astype(\"float32\"), name=\"W\")\n",
"bias = theano.shared(numpy.float32(5), name=\"bias\")\n",
"\n",
"# Get Value from shared variable\n",
"print W.get_value() \n",
"\n",
"## Define symbolic graph\n",
"c = a + b\n",
"y = T.dot(x, W) + bias\n",
"\n",
"## Print symbolic graph\n",
"print theano.pp(y)\n",
"\n",
"## Advanced:: You can replace some parts of computation graph with different variable\n",
"d = theano.clone(output=c, replace={b: y}) #replace \"b\" with \"y\"\n",
"print theano.pp(d)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"## Function\n",
"\n",
"Compile symbolic graph into a function\n",
"\n",
"** BE CAREFUL**:: YOU NEED TO USE int32 or float32, only computations with float32 data-type can be accelerated."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6.0\n",
"9.0\n",
"HostFromGpu [@A] '' 7\n",
" |GpuElemwise{Add}[(0, 0)] [@B] '' 6\n",
" |GpuDimShuffle{} [@C] '' 5\n",
" | |GpuGemv{inplace} [@D] '' 4\n",
" | |GpuAlloc{memset_0=True} [@E] '' 2\n",
" | | |CudaNdarrayConstant{0.0} [@F]\n",
" | | |TensorConstant{1} [@G]\n",
" | |TensorConstant{1.0} [@H]\n",
" | |GpuDimShuffle{x,0} [@I] '' 1\n",
" | | |W [@J]\n",
" | |GpuFromHost [@K] '' 0\n",
" | | |x [@L]\n",
" | |TensorConstant{0.0} [@M]\n",
" |GpuFromHost [@N] '' 3\n",
" |bias [@O]\n",
"28.1000003815\n"
]
}
],
"source": [
"## Compile symbplic graph into callable functions\n",
"add = theano.function(inputs=[a, b], outputs=c)\n",
"linear = theano.function(inputs=[x], outputs=y)\n",
"\n",
"## Call Functions\n",
"print add(1, 5)\n",
"print linear([0., 0., 0., 0., 1.]).astype(\"float32\")\n",
"\n",
"##Print function\n",
"theano.printing.debugprint(linear)\n",
"\n",
"## Advanced :: You can evaluate symbolic graph without compilation\n",
"print c.eval({\n",
" a : numpy.int32(16), \n",
" b : numpy.float32(12.10)\n",
" })"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Math\n",
"\n",
"Most mathmatic operations are similar to numpy. However, comparison operations have special form. [[Basics](http://deeplearning.net/software/theano/library/tensor/basic.html)]\n",
"\n",
" Condition operation is \n",
" ```python\n",
" T.switch(condition, if true, if false)\n",
" ```\n",
" \n",
" Comparison is \n",
" \n",
" ```python\n",
" T.gt(a, b) #Greater Than\n",
" ```"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[array([ 0.13533528, 0.36787945, 2.71828175, 7.38905621, 20.08553696], dtype=float32),\n",
" array([ 0.11920292, 0.26894143, 0.7310586 , 0.88079703, 0.95257413], dtype=float32),\n",
" array([-0.96402758, -0.76159418, 0.76159418, 0.96402758, 0.99505478], dtype=float32),\n",
" array([ 0., 0., 1., 2., 3.], dtype=float32)]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = T.fvector(\"x\")\n",
"\n",
"### Basic Math operation & Activation funcsions\n",
"exp_x = T.exp(x)\n",
"sigmoid_x = T.nnet.sigmoid(x)\n",
"tanh_x = T.tanh(x)\n",
"\n",
"### Advanced:: condition and comparison\n",
"relu_x = T.switch(T.gt(x, 0), x, 0)\n",
"\n",
"f = theano.function([x], [exp_x, sigmoid_x, tanh_x, relu_x])\n",
"f(numpy.array([-2., -1., 1., 2., 3.]).astype(\"float32\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Gradient (Automatic Differentiation)\n",
"\n",
"You can define gradient symbolically. Amazing!!\n",
"\n",
"If you want to use Jacobian or Hessian, use theano.gradient.jacobian, theano.gradient.hessian."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[array(100.0, dtype=float32), array(20.0, dtype=float32)]\n"
]
}
],
"source": [
"# y = x ** 2\n",
"x = T.fscalar(\"x\")\n",
"y = x ** 2\n",
"gy = theano.grad(cost=y, wrt=x) ## 2x\n",
"\n",
"f = theano.function([x], [y, gy]) ## x**2, 2x\n",
"print f(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Updateing your parameters (shared variables)** is the key process, but it's a bit complicated.\n",
"\n",
"Let's start from a toy example."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"1\n",
"2\n",
"3\n",
"4\n"
]
}
],
"source": [
"##Define a function which update t by 1 for each call.\n",
"\n",
"t = theano.shared(numpy.int32(0))\n",
"increment = theano.function([], t, updates=OrderedDict({t: t+1}) ) #OrderedDict({before update: after update})\n",
"for i in range(5):\n",
" t = increment()\n",
" print t"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch:: 0, cost:: 0.295\n",
"epoch:: 1, cost:: 0.106\n",
"epoch:: 2, cost:: 0.038\n",
"epoch:: 3, cost:: 0.014\n",
"epoch:: 4, cost:: 0.005\n"
]
}
],
"source": [
"# Linear Regression\n",
"from collections import OrderedDict\n",
"rng = numpy.random.RandomState(1234)\n",
"\n",
"## Step1. Define Symbolic / Shared Variables\n",
"x, t = T.fvector(\"x\"), T.fvector(\"t\") #inputs\n",
"\n",
"\n",
"W = theano.shared(rng.uniform(low=-0.08,high=0.08, size=(5, 3)), name=\"W\") #variables that are shared over iterations\n",
"b = theano.shared(numpy.zeros(3), name=\"bias\")\n",
"\n",
"\n",
"## Step2. Define graph\n",
"y = T.dot(x, W) + b\n",
"#y = T.nnet.sigmoid(T.dot(x, W) + b)\n",
"#y = T.tanh(T.dot(x, W) + b)\n",
"cost = T.sum((y - t)**2)\n",
"\n",
"\n",
"gW, gb = T.grad(cost, [W, b]) # Take gradient\n",
"\n",
"updates = OrderedDict({W: W-0.01*gW, b: b-0.01*gb}) # Set update expression in OrderedDict\n",
"\n",
"\n",
"## Step3. Compile graph\n",
"f = theano.function(inputs=[x, t], outputs=[cost, gW, gb], updates=updates, allow_input_downcast=True)\n",
"\n",
"## Step4. Run!!\n",
"for epoch in range(5):\n",
" cost, gW, gb = f([-2., -1., 1., 2., 3.], [.4, .3, .5])\n",
" print \"epoch:: %d, cost:: %.3f\"%(epoch, cost)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Since we have Autodiff, it's easy to change linear regression to non-linear regression.** Try it!!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Multi Layer Perceptron (MLP) \n",
"\n",
"<img src=\"http://k-kawakami.com/img/mlp.png\">"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Multi Layer Perceptron\n",
"class Layer:\n",
" def __init__(self, in_dim, out_dim, function):\n",
" self.in_dim = in_dim\n",
" self.out_dim = out_dim\n",
" self.function = function\n",
"\n",
" self.W = theano.shared(\n",
" rng.uniform(\n",
" low=-0.08, \n",
" high=0.08, \n",
" size=(in_dim, out_dim)\n",
" ).astype(\"float32\"), name=\"W\")\n",
" self.b = theano.shared(numpy.zeros(out_dim).astype(\"float32\"), name=\"bias\")\n",
" \n",
" self.params = [ self.W, self.b ]\n",
"\n",
" def fprop(self, x):\n",
" h = self.function(T.dot(x, self.W)+self.b)\n",
" self.h = h\n",
" return h "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_x, valid_x, train_y, valid_y = train_test_split(mnist_x, mnist_y, test_size=0.2, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPOCH:: 10, Validation cost: 0.105, Validation F1: 0.970\n",
"EPOCH:: 20, Validation cost: 0.100, Validation F1: 0.974\n",
"EPOCH:: 30, Validation cost: 0.095, Validation F1: 0.979\n",
"EPOCH:: 40, Validation cost: 0.098, Validation F1: 0.980\n",
"EPOCH:: 50, Validation cost: 0.103, Validation F1: 0.980\n"
]
}
],
"source": [
"x, t = T.fmatrix(\"x\"), T.ivector(\"t\")\n",
"activation = T.nnet.sigmoid #T.tanh\n",
"\n",
"layers = [\n",
" Layer(784, 500, activation),\n",
" Layer(500, 500, activation),\n",
" Layer(500, 500, activation),\n",
" Layer(500, 10, T.nnet.softmax)\n",
"]\n",
"\n",
"## Collect Parameters and Symbolic output\n",
"params = []\n",
"for i, layer in enumerate(layers):\n",
" params += layer.params\n",
" if i == 0:\n",
" layer_out = layer.fprop(x)\n",
" else:\n",
" layer_out = layer.fprop(layer_out)\n",
"\n",
" \n",
"## Cost Function (Negative Log Likelihood)\n",
"y = layers[-1].h\n",
"cost = - T.mean((T.log(y))[T.arange(x.shape[0]), t])\n",
"\n",
"\n",
"## Gradient\n",
"gparams = T.grad(cost, params)\n",
"gmomentums = [theano.shared(numpy.zeros_like(param.get_value(borrow=True)).astype(\"float32\")) for param in params]\n",
"updates = OrderedDict()\n",
"\n",
"\n",
"## Defile Learning Rule, you can add Adagrad, Adadelta etc.\n",
"lr, momentum = numpy.float32(0.1), numpy.float32(0.9)\n",
"\n",
"for param, gparam, gmomentum in zip(params, gparams, gmomentums,):\n",
" #Clip gradient\n",
" #gparam = theano.gradient.grad_clip(gparam, lower_bound=-5, upper_bound=5)\n",
"\n",
" # sgd\n",
" #updates[param] = param - lr * gparam\n",
"\n",
" # momentum\n",
" updates[gmomentum] = momentum * gmomentum - lr * gparam\n",
" updates[param] = param + updates[gmomentum]\n",
"\n",
" \n",
"## Compile \n",
"train = theano.function([x,t],cost,updates=updates)\n",
"test = theano.function([x,t],[cost, T.argmax(y, axis=1)])\n",
"\n",
"\n",
"## Iterate\n",
"batch_size = 100\n",
"nbatches = train_x.shape[0]//batch_size\n",
"for epoch in range(50):\n",
" train_x, train_y = shuffle(train_x, train_y) # Shuffle Samples !!\n",
" for i in range(nbatches):\n",
" start = i * batch_size\n",
" end = start + batch_size\n",
" train(train_x[start:end], train_y[start:end])\n",
" valid_cost, pred = test(valid_x, valid_y)\n",
" if (epoch + 1) % 10 == 0:\n",
" print \"EPOCH:: %i, Validation cost: %.3f, Validation F1: %.3f\"%(epoch+1, valid_cost, f1_score(valid_y, pred, average=\"macro\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####Exercises: Squeeze out the last few percent!!\n",
"1. Try other objectives, MSE, Cross Entropy. \n",
"2. Try symmetric activation functions (tanh, softsign).\n",
"3. Implement ReLU (Hint. look at Math section).\n",
"4. Add Dropout (Hint. apply random mask to inputs in fprop).\n",
"5. Add learning rate adjuster (Hint. make learning rate as shared variable)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"https://imiloainf.files.wordpress.com/2013/11/activation_funcs1.png\"> [source](\"https://imiloainf.wordpress.com/2013/11/06/rectifier-nonlinearities/\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Convolution\n",
"\n",
"1D convolution.\n",
"$$(f*g)(n) = \\sum_{u=-\\infty}^{+\\infty} f(u) g(n-u) = \\sum_{u=-\\infty}^{+\\infty} f(n-u) g(u)$$\n",
"\n",
"<!--img src=\"https://upload.wikimedia.org/wikipedia/commons/6/6a/Convolution_of_box_signal_with_itself2.gif\"-->\n",
"\n",
"2D convolution.\n",
"$$(f*g)(m,n) = \\sum_{u=-\\infty}^{+\\infty} \\sum_{v=-\\infty}^{+\\infty} f(u,v) g(m-u,n-v)$$\n",
"\n",
"Narrow Convolution\n",
"<img src=\"http://ufldl.stanford.edu/wiki/images/6/6c/Convolution_schematic.gif\">\n",
"\n",
"Images :: [wiki](https://en.wikipedia.org/wiki/Convolution), [Stanford UFLDL Tutorial](http://ufldl.stanford.edu/wiki/index.php/Feature_extraction_using_convolution)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 1. 1. 1. 0. 0.]\n",
" [ 0. 1. 1. 1. 0.]\n",
" [ 0. 0. 1. 1. 1.]\n",
" [ 0. 0. 1. 1. 0.]\n",
" [ 0. 1. 1. 0. 0.]]\n",
"[[ 4. 3. 4.]\n",
" [ 2. 4. 3.]\n",
" [ 2. 3. 4.]]\n",
"[[ 1. 1. 2. 1. 1. 0. 0.]\n",
" [ 0. 2. 2. 3. 1. 1. 0.]\n",
" [ 1. 1. 4. 3. 4. 1. 1.]\n",
" [ 0. 1. 2. 4. 3. 3. 0.]\n",
" [ 0. 1. 2. 3. 4. 1. 1.]\n",
" [ 0. 0. 2. 2. 1. 1. 0.]\n",
" [ 0. 1. 1. 1. 1. 0. 0.]]\n"
]
}
],
"source": [
"from theano.tensor.nnet import conv\n",
"\n",
"x = T.fmatrix('x')\n",
"\n",
"##Prepare 3 * 3 line detector and reshape it to (number of filters=1, num input feature maps=1, filter height=3, filter width=3)\n",
"W = numpy.array([[1,0,1],[0,1,0],[1,0,1]]).astype(\"float32\").reshape(1, 1, 3, 3)\n",
"\n",
"##Reshape x to 2 * 2 image and convert it to (batch size=1, num input feature maps=1, image height, image width)\n",
"## if your image is RGB, num input feature maps=3\n",
"\n",
"#Narrow Convolution\n",
"narrow_convoluted_image = conv.conv2d(x.reshape((1, 1, 5, 5)), W, border_mode=\"valid\")\n",
"#Wide Convolution\n",
"wide_convoluted_image = conv.conv2d(x.reshape((1, 1, 5, 5)), W, border_mode=\"full\")\n",
"\n",
"## Convolution Function\n",
"narrow_convolution = theano.function([x], narrow_convoluted_image)\n",
"wide_convolution = theano.function([x], wide_convoluted_image)\n",
"\n",
"## Sample Image\n",
"sample_image = numpy.array([[1., 1., 1., 0., 0.], [0., 1., 1., 1., 0.], [0., 0., 1., 1., 1.], [0., 0., 1., 1., 0.], [0., 1., 1., 0., 0.]]).astype(\"float32\")\n",
"\n",
"#Original Image\n",
"print sample_image\n",
"\n",
"#Convolved Image\n",
"narrow_convoluted_image = narrow_convolution(sample_image).reshape(3, 3)\n",
"wide_convoluted_image = wide_convolution(sample_image).reshape(7, 7)\n",
"print narrow_convoluted_image\n",
"print wide_convoluted_image"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Maxpooling\n",
"\n",
"Maxpooling provides robustness to position and reduce dimensions."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 4.]\n",
" [ 4.]\n",
" [ 4.]]\n",
"[[ 4. 4.]\n",
" [ 4. 3.]\n",
" [ 3. 4.]]\n",
"[[ 4.]]\n",
"[[ 3.66666675]\n",
" [ 3. ]\n",
" [ 3. ]]\n"
]
}
],
"source": [
"from theano.tensor.nnet import conv\n",