Repository: soskek/attention_is_all_you_need Branch: master Commit: 1c72fd1e50c1 Files: 10 Total size: 435.3 KB Directory structure: gitextract_z4u87utv/ ├── .gitignore ├── .gitmodules ├── LICENSE ├── Observe_Position_Encoding.ipynb ├── README.md ├── download_wmt.sh ├── net.py ├── preprocess.py ├── subfuncs.py └── train.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python env/ build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ *.egg-info/ .installed.cfg *.egg # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover .hypothesis/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # pyenv .python-version # celery beat schedule file celerybeat-schedule # SageMath parsed files *.sage.py # dotenv .env # virtualenv .venv venv/ ENV/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ ================================================ FILE: .gitmodules ================================================ ================================================ FILE: LICENSE ================================================ BSD 3-Clause License Copyright (c) 2017, Sosuke Kobayashi All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ================================================ FILE: Observe_Position_Encoding.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/sos/.pyenv/versions/anaconda3-4.3.1/lib/python3.6/site-packages/matplotlib/__init__.py:1405: UserWarning: \n", "This call to matplotlib.use() has no effect because the backend has already\n", "been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n", "or matplotlib.backends is imported for the first time.\n", "\n", " warnings.warn(_use_error_msg)\n" ] } ], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib\n", "matplotlib.use(\"Agg\")\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def make_position_encoding(xp, batch, length, n_units, f=10000.):\n", " assert(n_units % 2 == 0)\n", " position_block = xp.broadcast_to(\n", " xp.arange(length)[None, None, :],\n", " (batch, n_units // 2, length)).astype('f')\n", " unit_block = xp.broadcast_to(\n", " xp.arange(n_units // 2)[None, :, None],\n", " (batch, n_units // 2, length)).astype('f')\n", " rad_block = position_block / (f * 1.) ** (unit_block / (n_units // 2))\n", " sin_block = xp.sin(rad_block)\n", " cos_block = xp.cos(rad_block)\n", " emb_block = xp.concatenate([sin_block, cos_block], axis=1)\n", " return emb_block" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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rKCiwrVu3mplZVlaWJRIJi8fj9tZbb6VfiAAIV4jTJYsEY+ThJkN3QGPBfVoB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\nrkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set()\n", "a = make_position_encoding(np, 1, 100, 512, 10000)[0]\n", "plt.figure(figsize=(20, 10))\n", "sns.heatmap(a, cmap='afmhot')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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cbEfqyEiZw17rSh/1IsmWARM5I5FlzQGNIYEO/YmpR5s2pAkNkZ4SGR2pHQdt\nCfRfHwJSYVKSQvSBVkE8+VqSGn9QtnLxD+waf+IEkFNNhw61bYgEn0iJpz15s21EdLekWJuUGe1v\nNDoFsu97Gw8ybUhZqnFyrCRWVk8krKRXwIcPfsk2IoXs5LBwItMnkmPihBu1Il8634eMDfk5J2tv\nzJ1X2UakToSUv5DGR6TED2itb2+1u9dbn3NSqkQ+56TGmpSVHbsrOPOelF2Qnimk4TH5oIN6+JaP\n/Ldpk7XPOanfH3It6CFBPugLF9o2Xh90UCZ48xL7+EVyGs2f/mTbkM+5VfJEDvuRpH+zqx+zjUQg\n2Ljxxht1xRVXaNmyZero6FBDQ4NaW1tVV1en7u5udXd3a/jw4brL6gZPLm1t5qTwmXSSIkXqTh/m\n365/q2lDfH3SaJGURpOab3Ks2CkngqDUep7kC0YoXFJnTDZ7wJz87Klppg0hhMjhCaS+dfZs24bU\nI3/w4JdtI8KeWIEOOeqCNB4iEQH4eP/oT/aJGSQeIL0xyMebcGEkcD125l9tIxKREzbMajZBUs2k\nDwCI8K6Yb3/UiY9JCFvSgI3Un5NgaN5I0LWfNHEhtbQkGLJauDtFDd/4tp0JJEQEIQEJAU9KiImD\nvW/VH20j4hiQToGkgRLpC2WoxzpPtY93I/E6cfNIyx4SAx5yiG1Dat3fsgIkVMjzJEkV4uiRfh4g\n+7D6uNIn/pDnSXqlEsEcIQpJawySIJv8OCDpiaNHonpychxx9IBjsHQ/+4QYy80jPdskaelSZpdZ\nDIenUXggghMtekUgXHbZZbr00ktVVVWlQqGgQqGg+vp6NTQ0qL29XWvXrtWuu+6qm24yjtUhl7Yc\nWvJlIZ4CYfZJR0KwYP86/QjThvh9xI8n/SNJbEY+qsRJ2uWFX5U2IJEZ+aCSlCI58YE4vSAo/eZ3\nfZxeQpST5nPkVSZKhvfuDb4I1stMvj7kzE1CGhESEBxM/tt1e5g2xJcgJCA5YZYcv0XiceJIDb7+\nO7aR9TKTaJyApCbBi/zKwXaTO7InE1EA4T9JoENeZUJEvE2gNTZJd1lSDyKxmjDBtiFHC4EI7+pb\nR5g2hFgiRCEBUbiQaq+PHQfOBydEIYnyrKidJGYIAwMaWN8/6J2mDXmNCZ+2fLltQzjSww+3bQhZ\nMWbB/9lG5GUmwa0lGSEZKfAit3/0FNOG5C9ITmrRItuGkImEHCZk/7zmx2wjQj4RpQzpLG2dyEVY\nGkn61remihNgAAAgAElEQVSYXVaRCAQbxx57rBYuXKh+/fqpo6NDhUJBF154oc4880x1bd5cPvCB\nD+izlqSYXNry2sjuQNrhklQg+cIDqWf7h+wz24mzSn46EV+QgJP46cS5+eB7DOeGdDonjg35IpDO\nziT9C+QZr8y1zwcmP53E2iQTM2iQbeNFMhw56/nSBuRDSE4iIJEZcWiJFwAkHIsn2C2HiUNLiELC\nr5CglPjyRL1yQJPRadrLkycMKSGWSB0X8Pzurd3HtCHEEim7INUJhBwmRASI37THRiOD7sWokeiN\nHEFLFGiAgbl9mS03Jrw4kfsTDoYErsTfBzyqJj75i9IG5MxXIr0jLCohn0jEDn74TffZKjVS5k9+\nOsmFEAKZ8G7kmY+4wzixhvxwUipHznEkSSDCnIPs1/d/bJc/kiQQqVQiZT2k+oX8dEJQ1X3PCPzJ\nOdtS+Z/jOMImpd3wyivhrrUV9IpAOOGEE3oaJBaLRRWLRX384x/Xd7/7XXV2dkqSbr75Zk23JHDk\n0lZw4UUgkBVLNisiowZOyf/dYTNZZLMi+zRRM5Iek8QfsxzRnV80joWSGJNOvDHiiBKNHAk+QKT9\n8CD7KBziiJK4i1QNkFibnAxoEUtHzgYqBhJ8EJKBsCuEWCLyQdDlqP39/2HaEH6FcGpEvUKUk8Rx\nsZxV4qgO/DGoLSeOC0kREwkwCVBIZAai8V8sGm/aED+diLVIUEricYuIIMTTWwvguGASlBIZPyGf\nttvOtiGEIyCZn55iE45kGySEI+kjQTKuVmKBtM74wOFAMUEIRyL/IWpUkkwiWXaiXgQb4XeutBst\nkm2QiL5IzG6RFYSjIcqoUfeAkgGyCRJVBSGoCDNHpLqAiP7Dmt1MG7INEj6WENEWB08SD5L0I3Ba\ncqaRCAQbry1hKBaLPaoDSaqvr1dra6uGDh2qu+82nAFyaeuLSTxnQvl5OZAknQOCyT+37m7akL2T\n+FEkmCQZa5Kst76pHzhqgz0I8aKIM0EiKtJ0gDQhI4EF8LBvf26iaUPeC8KvkA8LyWpbSgbiZB69\nH5BreZEMRLZDIm1COBJvCzgcP3tglGlDCEeSESPl8JbDQWIuomiaN/VZ24h4WiTqIuSTV/BBiGjw\nXvzfrfa51KQKkBDRlg9OSAjiiJJ+PPtu94RtRN4LwsYS5RMhJUmNOkkpAoLq+9fYQSnJzVjcHMme\nk7jMya3SHrrfNiIJCiIRIrIwD5ZGQmRF5/s/bNqQT6jlcpN8HSEqiLtN4nVCXE5b/jvbyMuxIsQl\nyd4QiShIYqw++L2mjZW4oj0Q7r2X2WUWO+wQ7lovgx5l2xh9KmHI5TZ1Vf5XQ9TW1uqxx4w6HnJp\n6yPvRSCQNDzRgxLPGCzqpbva2QjynSN+MYmXiECDxNGWI0Ccw91aQZaKREuEXSGdfEj/DBI0kGAS\nfA1vusMOGohzSIh7IuKwSAYP4kmS/v1wQD6RoIF8DUk6h5CSxHu2TjSQULT9lyZb4UJ8JBJTkVjb\nAuFfiOqJBBbjnwHKJxJpezmQJLAgJXVEtwz2lKuvt3u4WJ9iDxJCYqIwoowie8oRc0CWh3yICXFJ\nsuOE0CZyQRKRgz3lwarSvWC8khyEoyENTIl6yonr14h7fmYbEekAYXUJ20+CUkJWGCRW5wkfNIcg\nn2GSAyKuPSGxCLlJPsMkGXLABEB6kz2F9DcgpbwkGWLJTgiTI0lf/SqzyyoSgWBjawRC9eZi+q6u\nLg0ZMkT3WPQ0ubT1BSK7DJGwEh0nSbeSDZhkl4DXe9UP7GOxsnZ7nG6Ncj8A8mevLxRJyZKbQ2Tx\nJCBwendIHE3eHev2eN0ar3cnf+3VtpHXwgr57jjdoKuvzZs21u0p01tT2e+Owf5efZ2d9Q55a0g8\nFfLdqbn+h7YRuUGEqQl1g5xuzg+v93l3Yro1Upm+OyFvDsg4/fBGm/z02ndIQsXr9pB8E3l36n4M\n6gqsG0TeGykK2f02xShbCeqGl8Bx0NsYrgTCoEGDtHHjRnV0dGjAgAE9fRK2CnJpK5NMmH3ycpOV\nT7JCJNNM2DpAZf52yY6mDRFokKQZScQT9a6lUiASVpI52mvoYtuIsP8k3UpYXpKNJjQ4SdcDkqHt\nXUebNkTEQZafxcEQcop8dIkCmGSXSBZh2AKjeZjEvBKSXSI1RqQTKknpk34eQEH1cKF0DSdZeqR+\nkyw9op4iZ5eTrDZRmM/bE5zfTBYfyUARPTFJ+5NeAJYigni8IOt9w8/rTRtCfhKFFWlQTgRo5ERE\nIgoge9PE5aBRGZGgEcfAaqZAsptEq06UnWDxLZ1kn9RAhEZk6RFBCcmOE8WNV97qfSc4+ORemYfY\nfHKwN92+yA4giU9OeteE8skl2y8nPrkk7bUXs8ssEoFg433ve5/uu+8+5XK5fypf2EIqNDU16S7L\nOSaXtjxNEsGQzYrISkkdo8dqlNDH8K9D32raED+BlPN5fQwtP5QEgUS2DGIcDbvvFtuIfAzJbk/q\nIQmI5J1EOsCbWD3HLqMhgaDlbJFqAJIVIr4EkbCSGJp8MCevAQV/XgwfqRkghaekTIuQWBZTAyKh\nH11nq2RIiRbhj8n2TyTSpBs6kcISImKvpqdto5AfAKsMkKghSFkGcfbBB+A3D9mNir04QOLsk1ib\nVCeQ0mgiZJs3Z7VtZH0ASKQd8gPgxeSAD8D96yabNmT7J0RqyO3fIiu8SPr8jeBoSq8EYmwfALBA\nn97+baZNqO2fVIBLLOTLNHa0k7puWAqaj29j9IpAOPHEE3X33Xf3EAhVVVUqbP76bSEQ+vfvr4es\n7Ae5tLUCyAeKbCAky0foPBLgEQccZGvaDznCtCEbCLmFhIMh2RorUerlIJFk17y9QfdncgOJSoE4\nSeQGko8YIbGcSIZXptsZHctJIrePJFJJwoJwgMQHIAkxkvHZZyfwESBeJvECSNNQ4qiTlKuldnBy\nov7aWLoGW2JBoFcMTZL5pCEtiXMID00CxV0EzhwnN9GKdEizHeKJEjmSF1MIbuCD6+1Akdw+whMS\nxQ0h8gmXQxK3VqxNiLAxLwLFBCHyPRQTElMLkqNhSTbEiaz43SK7+7tXLsTqR0GICqKqIDJ+wiWS\nd3DIPSCZRMgKLzaRyOYI20NuIsnIGe/gz26zVWGS9O53I7PsIhEINrYQCFuQz+fV3d2tXC6n6upq\n5XI5FQoFPfGE0QmZXNr68noRCCRDTFIEZGckWRbyYQHOze8et7MsJItHPiwe32bi2BDfkMQnJMCb\nVgDONfmwkDQCcbCJ3p8EeCRKJiQDYGpemVK6eZ9XgEdIBi+OxosnJN/uI98D9klCMngxhR57JXF+\nAu6Tv104zLQh+yTxH71iGK+9khARZK/cucvYK0kE47VPEiaHZKwJGUvYRLBPvjxhH9OG7JWkKSHh\ns0lfPmuv9NonSVb7iMO6bSOyTxJGO9Q+KfntlSTaBnvl7Q+UbtLstU+SxplknyTVVyH3yckbH7GN\nvAizUHsl8Scl6X5wCkqWMWZMuGs9/3y4a20FfSIQthAHWzBu3DitWrVKa9as8WuiaC0kr82esIKE\nWvXy6ohTAr6qTw+yj4P0in9JgtNySkgpN6lXJsQriT2IkmHwfaCDOwneSFEuiT5ICoqkQb1IBiNK\nfnWqfToAeUe9SAaiZvTy+0iSystxGfUiKKnwYhOtVClxWggRRuqnnZic9rl2SQ/x+8h76hWfkKb9\nJJnqQdqSvXRn/cU2IpsBca6JpITImrwYR1LITj5aYDP41R12YzmP3IxXEEhEJ6TFi5NbpbeOBvXH\nXoSZFzNOFLQeZAVR9oB39A+L7OQXeUcJWUFKQIg4youTJCpbknw4YM8W28jjo0WajUvS06DsLstI\nBIKNww47TEuWLFE+n1fXZqlNc3OzXnzxxZ5Shn333VfftQ4PJZe2HFrijREFgteXzmsHIU4vcCY6\nD7SdXuKPkayGB09DbrFX7EtuMSHtD5gDjg8kgZnXTSaRBSHDiNdGbrT1NQQ3ee3Md5g2XkQY+RZ6\nBW+EbySOMeFxyLv8tkmv2kbkXbZuNEmBkrIycpNJCorUDDjd5JdG271ryC0mMTK5zYQwI4pai4gg\nJIQDHylJ2n0ocK68brJXgEdYyVBsj4Ru9F9rp5X8OwnwyC0myQlyiwmIiJSUdzjdYk3uMpS8ErvR\nhPglpBqRplggDaO9bjJghB5ZN9a0CXmLCW9Jkm3kNnskMQjpJoXtMfimYKz9HrnhWXAc6DZGrwiE\nL37xi7r55ptVLBZ7CIRjjjlGN910U09TxRtvvFG77Va6EzciEKxV6xV0kcwuoSBJkTVJoRNJmtMX\n6t7n7Po5wtOQBLqlqCIbJwEh0knMQDIWJCm0WyNwaL2+UF5pdq8I2CIZCJMDshptex9g2pBb7EUy\nEE6SiEVIIp70DiFbClE7EKd38MI/lDbwIn5JUTjp6+DljXmpywARsVh27T0R6JEtxSOAIyQE4XoI\noUYyeGTf3mc6aCToFSV7MZdeiQ6i7rGkfiQbDTaU3/zeVkwQV5BsKcQVJJWERJhCXEGvOJoQyGPW\nAHm9tamQlH8ookJipJsXgQw2lZdH2317yL7tJV4hPL2VbyJJPUlaC9qOZRoVRiAATRPDTTfdJEmq\n3uyInXTSSV5DJyQkJCQkJCQkJCQkJCQkvMkAaZd/Ri6XU319vWpqarRy5aYzrYvFovL5fE8Jw3qS\nWiOw6rZI5ohQwWQcApJmIfJxUiNM0vUg09zcbCsQCFNOsv5WloqIPEiChWQICPNKetCQrNl2B9u1\nUaNIJp6sK/J+kfeUaELJjbYKDIl0F8y3Hvzuo99jp2EGDMgDG9MEKQdItosk0EkNJ9lSSNKHJEEP\nPLB0X4s9TgOlL6Q8xktiTnrgkB9O9mTy0EHtwWSgZJh8lJ0R239/u3M0ye5aiUeSdCRbDklwkn2b\nZPD23LN0wzhJ2n//95k2h3zXtqm58zf2hMikieKSbBhEdmLdaJJKtUpdJR0EJCUHHWOrHV490y4f\nIuIor/IhsqWQxplEBHPJJbbNjBmGaljSfvuVtjn48rPMMcasA82pPTYdiW08ZByy9r7+ddNkB1DC\n/GHQxOvDxwDZyQ9sf+f2u2x1j/XTyZZTESB9RsoIvSphuOGGG3ThhReqtbW1p4RBkmpra1UoFNTd\n3a0RI0boD38wZKzk0tYmQnZyrw8q8eQJPGTfEusU6KTB/+1dNaaNx6MgimTio5OYlDwG0q4i4GNQ\n/X3GmpLYgyBaTrImSMRpBf/kQRA5LdHokwcBZLd3Lxps2hB/w0ulTwIvwhmRthce5RJOalANewZ0\ndfZ6ECQqJQ+ClAYRTSh5EKTAlTwMsCYeXrZDyb97PQYSD3hVaBGikPRHDPgYtOM6UDNPgjNSk2g9\nDPIgCOFN2FjyIEg9gNODWNxhy5nJmvCKkUnChCRnrEdBkk2k0ouUIZH1MLkWSL29yApCunmVIZFy\naa+yO+thkAchSdNK90zJPMaPD3etCBpS9irtvmjRIrW1tf3Tvw8cOFAtLS0qFovq7Ozs8+QkhVMg\nEBvCLhEvnXguJBVPCA0S4CGVgv0xJHuVpVIg5ABJwhMbcotJPEDibBKYEZt3vB18VcnHx0ulQJga\n66ESFQPJ2pJ1RX43eHn2As0CGo+yM7uEOyHvhZcDSVQKHqeNkrmQHhL772/Xk+7zSeAgkRpY4kCS\nOnbyLpON0MtZJS8PCCZ3NwKv3U+w18xjB5cmISQ/ftRrzZD1QB45eb1Io9g997Sd9P33t232+uhH\n7YtZgRf5USRVT24g+RATNpZIEK680jSZDFijyYCs+Pil9rp5+EX7xAKvdWO972TNkEdFepxedJFt\nM3267bvOmfMx02a/U22bt04BRf5eZAVR1pF1QzYVy4Y4MpL0KmjSnGVUmAKhTyUMY8eO1aOPPqpi\nsajq6mqtWrVKNTU1KhaLaiKNSwg8CAQvG6+XgwQ6JAj0KnMAUXLzLHsTJgkA6+NC+A7ys8ktJjaE\noyEfTJJQJNkuIq/fg5RChCQQLJB3lDx0IkMnv4msPUAy7AzUDtsds4ttQ0pkgA3xN0jQTtaEFbd6\nlVOQR75okS3R3G+/Q02bySR76eWlE3KA/HhCznmVeFhzBk7xLmDv2uVwW7p7yCF2yRhpHulFRHhV\nyJBvDeGwiOR95swG02bvvY8o+ff9zyz9d0kasQIoJkI+LK+bTNYVCd6ADHJ3QFbsDlL6n/qQvbYe\nfKZ0WQ95VEQUTIg5InAhRASptCGnc0yaZCsTZ8062rR5+8G2zf4X2POpX3i3bUQemPW+k4eVUHbo\nFYEwdepU3X///Vq0aFFP74Pu7u6eUxmKxaIGkGiIIKYeCMSGRKVeQQzxwom3DwiEunU2c9jcbLPg\nlryNZPzJT/K6fSTOJvEvcSCJSpPV3tsfsWmEZAhFIBCQm0wkJeRBBCT4hr/dDvCOOaZ0PwGJNZom\nNiQZ6FFS4aV0IK+FV0ZszhxbUbLfIR8wbYYTPbuX2oEETCRyJUSE9VDJ2iNBF7g3E/fc07YB2d/D\nDtvZtPGKbQlnRAg+siaIDXl1LAn+jTfaY8yaZash9t7bttmXNO4mD4uUbpCNkmwqXk0/SEROmimA\nUsG3GPvXW4hvceJ+psljfxtm2njdGvKtIdsk4Y/JK/i979k2JGG36657mTZ77mnbvP380n/fufFl\nezKVgApTIPSqB0Jra6ve/e5366WXXlJ3d7fq6urU3t6uXC7Xo0Coq6vTgw8+WHogcmlrhyBfZq/N\nntCdJIghLxnRLZMdhEhzSe04YK9bJuxu2ng8Ti/lFslwkjiR8EokQ0xKmkm5GvCdUX3h+H4v2UYe\nX3DiqXp5zoQcILWFJBonDTRIVps8UBAM/WWFTfAR39kjbvWKWQnHRUg30gCWtKUh3ADxr0kZdt3C\ne20jr2CIsEYWU0O+jYSQJN9GUohNzoMk30bwQNtn2k462Uq9vo9esa0lxCKuDvk2erk6pF8R+TZO\nawJSbLL2QjJLxOEhGRMrIUe+jcTZ8fo2ggf6bJdNDofkdL2UgOT7SBJXlvqCuDqSdMstzC6zmGwf\nreyGxYvDXWsr6JUC4fzzz1dHR4d23nlnPf744+rY7JTX1taqWCwql8tpI9mICLKmQCBfTOIkkUCH\nFPoTL9ypzGEg+Cg0N5fOjhOOxkvxTsgBL0EJmQ/57SS2JTZoScwZZdqMIdGQBbJmvGzIy0P2Lq9m\neWQNOymNdgbPaiQoqSDBtuVwEJ+YkIAeQY7k57B5qR3I/Zkzx+4o/7ZPgoiJBDokjWdFroSEIJsg\nWQ9edQVOiog6EAwdAKLbA06zg6HHXrAVaF55FytzS4hCsq68FBMkQ8yIfJuMnT3bLt/Y+1TbZsfC\n8/aEyAMNFd06nQ6GfhORuAD2aSxgn8YCMvEDJ9nf2NWj7W8s+elkSw71nSVbaUWgwhQIvSIQ7rzz\nTq1atUrL/2GjGDVqlFatWqU1a9ZoyBD7+COEUD0QvEgGLwLBq8yBZH0IyeDWjLHvBAKZCvlJ5NaQ\nWNJrHK+jJ716hqKlNYccT2kYeJEDXnuBlzTFSxERsJxp8Gz7t8/bz3aSRo8uXT9NGq6S2I3EpF6n\nL3rtKX69HWybhx7KmTazZ+9j2rz1k4AotLxeL4+XMDBeHUOJd02uRX4XSXGCBn+7gEBnl/3szO1R\nR9nZNOuRe8WsXmQi2QuIDSFXSF09ydzuuqv9jZ01y7aZc+Z7TZsR60Fnd2sdezBPkp/qkOw75EWd\nP9+2AcqnIeChzwOE47z9gfri8/a+/eDjdo8g6xaSb3VC+aFXBEJDQ4PWrFmjLdUPDQ0N2m677fTc\nc8+psDk43pVorQliOoUhZKNFQiA4dZQPqVIYbGywzc0DzTGIA06yGiFVCmQcYkN+V1ACAdjMnl3a\nuRk1x4lA8PrhxMYr9U3WXkglg9M6382QljYfYwcnRLZMlOpeAYpXctyrUSx5BYkPTu7PAw/YzVst\nImIPQkJ4OfvkR3kREV7SFPJACWtE0pegE/ww4MMdapAVh55pBzmPPWM3a/TqWRiScCR7ClmfRCBE\nCFlyEvL06fYxdLNmlbbZ88x/N8cY0QqOViQP3av8yqsEhBCFxMarqyOQ07wFqC+svhc6EyjdJEkT\noV1GkRQINqqqqtSvXz8Vi0W1trYqn89rjz320PPP/11q9fGPf9xnhjGdwuAVdZGAIGSZg1czRgeV\nQnOz3bQqJIFAbp9XmYPX4yT3x4sL89gvZ82y6w93JCRDSALBi2QI+RKGbMxqvIRDQLOAg/a0nZKd\ndrLl2oSI8Ao+iHKArE/yyAmh4aVqIsGQFbPft6tNQsya9TbTZvZptk3ugftNGxR8eBERXlEpsSHr\nkwRMJICzagJAn4ldQLZ1lz1tVcVxx9mNFr1iUq+jO734KTIfsn8REYxVwkaIiqlT7VO9Zs60bWaf\neqxps2M1aPBHHjr5SHgpK8jmTvYUci3S1dE6ppEwWJJ0P9iXEzKDXhEI/wo/3tzhtbq6Wl1dXTrp\npJN0993gCBELVoQSMjOZxT4JXmUOXgSCEVQNBAXWVhmExKbrJUn2UimQ2I0QEV4y6lAEAnnVC7NI\nPwande7VaILYeEWBXseFeNXsWHN2KokiZ62PO2Y32wbUPRP/0UvtQAIL8jgJEeEVxFj+LCu5sG2I\nSGHGjD1Mm1nvt20angNHDHq9GF7pcS+JC1nn1stDfjeJWkFU2gAyqe8AZMU7TrDJipcKO5g25LXw\nIi5JnEj4bPKpsWJkL6LCKcGuKVPsZ7XrrvYxvrOOsW12/vQGe0IhyYpQLBb5TZWApEB441i7dm3P\n/+7aHOGsJ54LQUwKhNgIBK8yB+IohDoykvRRmFCeBIKXAsGrqWPI3oYWEJ82w25sNZE0fSSBP2ld\nTGy8SAYSTYY829R6UQP2XakBQddegIiYMMFWypDsG1GwegUN5NXxOhbX8kNDNqH0IyLsrPaMA22b\nEfsttS8WkoggUhkPcpOsYa96ABLEkEwpqFEfBZQVowBZcehp9r7z9Arb3/F6dcjasl4dL6KCvMZk\nvoSsIAdHECXbpEl2Oc706faxzLvOtm1mnWzPp+4pQICSD5L18pAHkVB2cCEQBg8erLVr16p6c/Dc\n1dWlhgZ7ISHE1EQxtj4JIVUKXgSC5SyAr08d2O2bm+1g0uNoc4kF9STm8qpsCamI8ABSIDjxaR27\n2s7YNHKOlxeBQM4YJDaEiPA6voS8zFbg4LXnOPVsIJHrcFATftCedtAwaZL9DhKfjiSgPAICySeW\n9CAhJPbISaLeSxFBjvecPt0mn2bMsG0mzp1rXywkEWGxWF4PlDD55EUm8yELizQmIA1aAFkxHpAV\n48HedPS7bLJi8Qv1po21N3klxsnj9CIrCBlLbEj7EXJsKTnpiKgvpk61yc3p0wFJenLpvhZjBq22\nJ1MJSAoEji1HNW7YsEm2M3PmTC3cvLts+bc+w6OEIbYeCLERCF7NGD0cfvJFADvwqBm2zapVdody\nLwKBOM/kMZAg2YtkIPPxIBmCEghoOdSYNjNm2Ufo5b3IAS8bElV5eWTWC+ZFVHidMuNEMhCvFx0Z\n9i6biJgyxX5PveJED4c/1KtFrxXbsZxEETF1Kslw2qdqzPiIbVP3HDhn3HrBvNLIIfcusjeRcUgE\nTF4MouUnZAWQR00GZMVkY/869jB77/rrizZR4bV3eb1eXtWGZByy7xCesLHRtiHqC+v1mjCBnbp3\n8cXILCEj6DWB0Pqar3h1dbUKhYLu29yhprq6Wvl8XosWLdJUsCGVREwKhEouc/AiEKwd1iujaDV9\nkdTcbB99FBuB4NWwkbw6ZD6hSAYCPwLBtiHPc9ddbWZ/MAn8Bw0KZ0Pm46GL9zr71KsRgNdJNOTe\nkMACpLt2BkTElGPsM8dJxtCjSRvJ4MXEcUl+x3uS18KLiCCZSXJ84NSp9kkp02eXthlz4NqSf5cU\nNlIkL6GXasJLQRWyxIOQFVYZCCAqJgLZzkQQOxw511ZePL/GVnx5NaH0UlZ4lZ55fdas307EllIF\nEAiRKBAKhYLOPvtsLV68WLW1tTrvvPM0Zszf450rr7xSv/zlL5XL5XTSSSfpgAMO6NV1ekUgFItF\nFYtF5XKbMrjt7e09Rzpu+Xttba0WLFiw7QmE2BQIIVUKXqnbkM0YrR3Nq1kjIBAGI5LBPlYyNgLB\n67Ug8CAZiJ/lxZXFRjJMn26TWKPmgMCfpBq8bAgRYXlAZA17NY/0qjEi43jtX05qhxzwaHcB3+ip\nx9in43g0VwsZA3oREV78VGxHd3oktUm5ztSpthpi+gm2TcNycDQgeQm9GpB4afC9SjzIvkNsrEVK\niAqS9ib9KgBbNgawZWMAoTHvBJuwXVlrN2z0Kj0LWZ5mvRahkkQJDHfccYc6Ojp0ww03aOHChfrK\nV76iyy67TJK0bt06XXPNNbrtttvU1tamww8/vNcEQq/okn7/UG9b+5qgOZ/Pq66uTl1dXWppaenV\npBISEhISEhISEhISEhISEhgefPBB7bPPJtJ1xowZevw1rFV9fb1GjhyptrY2tbW19QgBeoNeKRBO\nPPFEfe5zn9Mee+yh+++/X/X19ers7FQ+n9egQYO0atUqjRs3ToNIpqqviE05ELKEgSBrfRJIBi9g\ntrV5ip1581IgeDU/9MrEhxK4kFeLIGQvBS8FArGZMsWuL9yZnC4RUqVg2ZBvg1chO8nOkYXlpYsn\n8/Eq5SI1AUClkJ9ip7t2M7J4ux7XdxWDxDJvREocUsng9ZqSbw2xCdWIkjSDI93tyekmkyaNNW2m\nTrVtJh98sH0xLzmNVxrZ63hPjxfVS+ngVZbh1bUQvKjDwIu6L1BE7LufrQrr/ohdYuQluLEeBXlF\nKwKRlDCsX79eA15TjprP59XV1dVz0MEOO+ygefPmqbu7Wx/72Md6fZ1eEQi5XE7FYlEPbe7isWbN\nGkA0/ikAACAASURBVA0bNkwtLS1au3atisWiGhsbNWuWfZ6uiUotYSA2MUWBEnOeLe/G67QHEqCA\nQCgHxmluHmXahCQQQvZJILD8DS/+Kr5mjLaNV7y5bp3NIk+ZAnoyEHIAlP6YNmQMMhevIy69yAGv\nOiQvXbxXuYQDEZEDwdIuwLne5SjbuV68xF4PXkSEV3znRUR49Sf1WDZkeZJ7THo/EMU7Iyvyps2k\nSXZvkSl72jY7HgKajXsREeRGkxfV2i9CERWSXw8JwiaSroXku+bUFDMPXma0n5KGKAeWJodf3sia\nKCaEwYABA153kEGhUOghD+666y4tX75cv/3tbyVJH/7whzVz5kztCk5y+Uf0ikBYsmSJ6urqVFtb\nq87OTrW1tWns2LFavXq1nn76aUnSSy+9pPb29t4M/3p4NFEkJEPIwD+2PgkhI6ZQBIJXczowzsAp\ntk1zs10LWq4EggWvBDGxIdeKbcl48HISW1pTpthk2Ji9QUbHyvqQrBCxIQ4biWJCppG92EQvVVgo\nIoI46SQQAimzycApnnywXff8/Iw604bEZbEREV6cmvV6MfLTtiGvn9fRnV7l+YyssE/VmDDhLabN\nlLm2zYh3gYaWHkQEeZFDFedLfvs2WRDEhryopEkJIdi91BcGobED6dwqSZ/6FLPLKiJRIMycOVO/\n+93vdMghh2jhwoWa9Jrv4eDBg9WvXz/V1tYql8tp4MCBWkfe/3+BXhEIXV1d2rhxozZu3Kiqqirl\ncjkNHTpUDz30kPr166dCoaB58+Zp9uzZvZrU65AUCFtHyJfVK2KyHGPyInsds0S8KKcj9EZMstvU\ntjbbx7KVI4FA4NUHj/xucq2QlUFecaKXM79qgh1UTZq0W8m/N3gRCMSGRASEiCCRmVdzSC9Wzau+\niszHctS9jsr0IiJAvcQYIEkeM8smK2bMsDN0XmUXXkSEh7jHK3Yje6BXV3ryu8lz8EpGE7KCNLwc\nN85OYkyYYB9PPOnA0jYj+gGiwutl96pDIjYhyQqvDzr5XYRVs5Jt5GWXyp9AiAQHHHCA/vznP+u4\n445TsVjU+eefr6uuukrNzc165zvfqQULFuiYY45RVVWVZs6cqbe97W29uk6vCITq6mpVVVX1nLxQ\nLBZ12223KZfLqaOjQ4VCQVdeeaUk6YwzzujVxDBCEghZJBmyVubglUolgT85e4bYOF2rudmuz4yN\nQIiJZCDwujfkNQ15KoRXUjsUfzdhAlE6gMCfkAPESyfjeMh7JeaIeh0TF/IECg9ymDjgXr0fvIgI\nkGoeDjJ0wwFZMXPmjqaNV9xFiAjrdc/icvDaS8mrTO4PeQW9eFQPsmKnnQhRsYdts59ts8NhoAQk\n5ILwYua8yAqvRWEtQLJAKwGRKBCqqqp0zjnnvO7fxo8f3/O/Tz31VJ166ql9vk6vCIR9991XV111\nVc9RjjU1Ners7Hzd8Y61tbUaNmxYnyfoUsIQW+lBSAVCyOMgPaIhL702STV4EQjE5h9OLvlXyAOb\n5mb7mKBKJRDIq+4VT5Hf7TVOTBVG1MbyW8jyXDHOVuRMmAD6OngRCF42Xp35QjqZHiUVXtGbV5lb\nSCLCKY1cD4iIXYjNIbbN0uW20siKqbxiLq8l41W64bVkYmuKScgKK9lMtkAH1fxmG7sEZNy40mo4\nSZowy7YZc3i3PSEvIsKDvZP8yvesF4wsmoSyQ68IhD/96U+qra1Vd3e3ampq1NbWJkk6+uijdc89\n92jp0qVqamrq6YewTZEUCKWRtTIHrygHBONo0yPjBCQZ6sG709xsE3cxEQhePFhIG69yCS+SIbZ+\nC1ay2asKicSJ48bZ62GnmbZNDSEiiGdMxonJOZR8pLlefR28xolNEUHeCxLhORXo70hsppQmImbN\nGmGOETKe8lpWIVUTXiUeofpRkFfdqce1mwCN8Xt2c82ddrJPRhg3zrbZaT97PvWrXrKNyMPwWFxk\n0VQCIlEghEKvCIS9995b3//+91UoFFQoFNTQ0KANGzbopptuUtPmFb1q1SoNHDiw7zP06IHg1UQx\ni40WYytzsK4V6rQHiX29vQgEMo7TOzh4HCEZ7LVZjgqEkMIe8pp6qQtClkt4xHghW52QGJAEBOPG\njTFtRs0BBAIhGUJGTCQA9mgyGTK1G1vdT8gTM4ie3SuqMmwaSKd4YnOgbfPymnrThsRTXoqI2Pi9\nUBVPsTXOJBWmHgcLSWxZke2fERp2GeBOOwGbA+369xEDjFIR8iASyg69IhBuvvnmnh4IxWLxdcdF\nvPrqq5Kk+vp6NRIK0UJMTRTLVYHgVeZA4EEgxNa6PiA54GUzDOgDC0AeGIpAiI0c8LqWVywUsqTC\nY4mGXJ5eJANx5HfayS67aG6eaNoM3xt4mcRpI94qiXQ8iAivI9e8mlCGPJbTa85eRAR55oTEslLA\n5P1z0rPv4GSz5542URgbERFSNWG9grG1ZvEqASFuntfhX4Ss8FJfMEKjtC/Y3GyXEkrSQcwsu0gK\nBBvt7e0qFAoqFouqqqpSVVWVurs31QcVi0Xl83lt3LjR5xhHC7ERCOVKRHiloy2bkF3lIgvqg9qA\nZz4ckQx2nawHQpYnxLYVeMU5XiSDh9ohpnIKyY9k8FI7kNpe4rQNJqksEumQcaxoyEvpUK5EBFlY\nXh3cSZRH7qH1vMi75dUB0ImsyIF3fSz4No6datu07WmXeIQszfBQTXi1Zgl5oIHX98iruSZ5DoTQ\n8CoVsZYoWZ6SdNBBzC4hG+gVgXDfffepUCiourpa3d3dKmz2TIcPH65XX31VxWJRHR0dGjLEPrrI\nRKgShpBlDpXcjNFCTDprKb7A3+vdcXrmI0hgob6TDOVKDoRcwqHIAcleWiF5Qi+SIeTpscQJJ7F2\nc7PdEX00aDI2kHQ0s/aCUBEMtfHShnsREV6RTkzlG16Mmlfa1ouscCoBqQeExmSw9ibPtK+1YY7d\n58WDiAipmPAiK7yqq0IuYTIOmTPZKi2ygigvKgJJgWCjs7NTknpUB1vQ1dWlxsZGrVmzRlVVVZoz\nZ07fZ2ghi1FDbAGnV5G1B0K2pScOUmzvRWxRKcAI0wGyZd9ZJBBi4xJDOjfWMvYiM0JyiV61vSFL\nKryIiNGj7WTA6AmlbRoICREbyeCl+44tivGqnbJsyBhepyqR50AiHZKSjY2sADYNgNDYmdjMKm2z\nYY69V3gtc6+tIOQy91JWeImjPJYoWcIJ5YdeEQhbiIN8Pt/zv8ePH68XX3yxp2xh9OjRmjjRrvU0\nEUqBEFsQGFswGbIZo8cYWSQZstgbw8FmBMjCVFXZKobYlnnIrcDLJpRT4qV0CNk80itp66VkCNlA\nkjjhljM/ciQgIXaybZAawmPCdByvVGkWiQjLxoOEkBgL6HUUgVefiZAF8RGRFQ1EVUFsZthkRhtQ\nVYTcCryET7EpKzwO2KkIJAUCR+E1gdyMGTP0zGua7px//vl9GZojNgKhXEkGr3tInAULIQkEryL1\n2MiemI4ABc9zOCEZmu0u3LERCMQmZI9Or99uLYlQSgc6Tki1g1fjL69YyKulgOX0ejnyI0faZRnE\nZogXERGb2iEmIiK20g2y0L0UHF4NmGMjKywbQlQ4kRn1wGY88B3GT7JtuvfewbSJbbsIxVuS7SSh\n/NAnAuG1mD9//qYBq6vV1dWlk046SXfffXffB87aKQyVTDKQj7P1vLza+nuRDMRxie0djEhdgG0s\ngOc5DDgK1aA5XSUTCORaHhkLr5jBqxQiZIPJkERETMdlEmeWxBXEuWaJVHsvGDkSnJgBjiF0k3l4\nRQ2hIotQKVBqE6p0w9PGq8TD60hqi6wI1bnP0wZsGHlgsyOxIc0+Z9jjrFxvqzJDbSlkjIpAUiD0\nDsViUdKmPgiStD5UUUwlt2cPSTJ4ZestT91DoUCuI/kQHpJfRBBbUB+RAsHLZjAiGQaaNrG1MfEi\nB0KRFWQM8gkhW07I5pEhSyq8CBavpv2WTci+Dl5JUJYotXu4jBy5oz3OLNsmv+pVe0IxkQwhC8c9\nXlLJTyLkRVZ4bRihyIpQRAW18epp4XW2ohOhMczJZpdZtk17/9KlZYSoSCg/uEYI1dXVqt7s0TU0\n2Ox+QkJCQkJCQkJCQkJCQkJCNtAnBUJDQ4M6Ojp6Gie+4x3v0JIlS7R06dKekxr6jHJsohhbwXJM\njRZDHRdJx/Fi/2N7T2NSIHjBSaXQALIIzc1286bY1AUk6eM1jmVDxgiZLIytXCLkOF6nVFj3OVSp\nhMQS3yEV0l7965qahtvj7GTbDBw3zr6Yh0rBq2dDFpUMIcsuQvaIiOnkDS9ZHfkgkdIMr/INr8aZ\nTptTnWEzlmxekjR2D2aXVWTNZ+4jekUg5PN5SdKGDRt6Shck6fbbb5ck5XI51dTYsj4XxBaYlWsJ\nA7EhHx9rPiG9a4Iskgxem1hMm6FXmYOTxrwG2IwePcK0CVl6ENLXssbxmotXnzIvRXJIPz5rDSQ9\nSAgp7FGZGfT1oY2tEG1qGm/ajJhjEBGhOnTScbxIhpAt8GMru7AWehbLMoifTJ55Fss3PDY5snlJ\n0h5lTiBUGHpFIGwpU+jXr5+6urrUsXkR53I59e/fX8ViUW1tbVq0aJGmTp3qN9veIuQpA7GpFGJT\nIFg2Xn0JYiMQCBKBsHWEJBCcIrMcsBkBvP1+/exmSWSZeygHvGxCziUkyRBS7ZA1IiJk80jyXoQk\nIkhs61WGHfLUv+22yxlj2CRq0062zcAJE+zJhFQ7hDzpwssmlGrC67zbmFQV1MaLrPDy7b0+oh6N\nMysBMfnMAdArAmH06NFqaWmR9PemiQMHDlRLS4s2bNigxsZG5XI5LViwoO8EQtZKGGKzCUlEeDRa\n9GjEKMVHMnh56SGRtc0wMpWCl+MyGHjytaDxY9ZKGGKzIb51FtUOoYgIr+t4ETDkHsd0ioUUViHt\nYeNHVNhH9DY1jbHHmWHb1KxfbU8oNrVDSLLCeuG9iIosMrZemxyx8drAiM9tfdhIfJBQdugVgTBz\n5kwtWbJEhUJBhc1eQ3t7u3K5nPL5vFpaWjRt2rQekmGbIrau9LEF/rGRFdbG6EUgeNl4IaRKIWS5\nREyIjUAIeNRXPfDUR4+2a6OzRiB4KT1jIyK8SAYvIsKj/4OXGiJkVVmo/hASey+8SrVJ/Ouhfg5Z\nAuJXSlK647wkNTXZNsMmTbIvFhvJ4EFEZE0xIcVHVoQ8OsjLT0nIns/cR/SKQNh55501dOhQLd/8\nFaqqqtKQIUO0atUqVVdXq729XQMHDtQgD1lLKAVCuaoUylGB4FXmEFKlQBCyXKIcEZJYiu3jTUoq\ngFMyHHjY/fvb2cCsEQheNsTnJeOE9ItDkQxer3oWl7AXEUEIPi8igsR4HsrmkKoKMo4fWVG6vGPT\nODapu932ts2QSd32hEKRDF5ym5A9JGLrRZE1mVrySysSvSIQ3vOe96itrU3nnnuucrmcRo4cqVde\neUWDBg3SypUrJUmPPPKI5s6d6zrZXqOSyYHY5mNtRF5z8Qr8s9iw0QtZY1NjiywiK4Xwynw0AA+7\n3+jSGbpyJRBCkgxeREQofzY2JXFs24XXEiaf4VCKiJBrOCRZEVOZyCabPLCxe01sBxr/Dp5knLAW\nkkCIrYdEbCd4xLS5VwKy5jP3Eb0iEObPn69rr71WxWJR1dXV+tvf/qZjjjlGN954Y4/N4YcfruOP\nP95toltFuZYwlKsCwRrH4yQHyc87rGSVgpdsLdSmGlKBEFtkEZBAIDb5oaUdDqZ0sJtHxkYOeAU6\nXn5oTGqH2EouQpYrh2yTE9PRnSFPfqlksiIs6VH6hDU3omIKUFXERjLERlZ4be6WDSEhEsoOvVYg\nXH/99ZI2ncTQ3t6u66+/XlVVVcrn8+ru7tZ1112nuro6nXHGGX2bYdZKGLzGCakuCHkcpOUBeR0X\nGTJQrGSSwQMhFRxe45SrAiEUEUGUDsDjbRhte7wDBthS4tgIBC+bkESE5UOGVO7GRkSEPDEjpj4S\nIRUTXk3pYzuJL+R+EVPfC6KqaGy0yzuGbgdsQLuK3BrQXDMkgRCTaoKQEJWApECwMX/+fC1dulS5\nXE5tbW3q7t7EFBb+4as0bNiwvs/QA7GRDLGpC0KRA2Qc4rWQucTWaDFkKQRBOZ4cEdupGllUKYQi\nELyiQGAzBHirA5obbJsMBgQhiQjLhwypuA1ZQkzGCbk8YyIiQiomiBtDnlXIE/RiIys89q+Qygty\nLa/eGI2NduPMoUOBTfN406amY4M9oZiICHKdhLJDrxUIN910k9auXatisaiamhp1dnZqyJAham1t\nVXt7u3bYYQc9/fTT3vP9Z2Qx8I/NJmSZg0er7tjKHLxUCgSxnRxB7nNMJANBFgmErJVChDyLD9jU\nAA9yBLAZMMAuu4iNHPCy8SAQYiv9jakJJbWJ6bS52LZSjz7P1Ia8O4RAIG5VyDIQy6Zcidaw5SY2\noY1sdtrRtBk4xehpISUCgSIpEGx0dnZq/fr1KhaLKhaLmjFjhh544AGtXr1aTU1NWr58udasWaOB\nA+2zyfuM1AOhvGy8CI/Y6uEJvMoKskYyeBEMsT3zkAXUsakUPLTNAVUKXtEkKrvY3rYZNMiW78bm\nYFuBfUwlu1J8JEM5lmbE1kPCS1TndVKyl7IiZA7IIitCHonrpc4g+1tspIeXQqOxsXRPi002pctA\nSCmJJDGrhKygVwTCnXfeqVdffVW5XE7FYlGLFy9WTU2NOjo69Oqrr0qS6uvr1UjecAuhGJ1yJRlC\nflmypkBIKoUwsJ6Xl4ohiyUgsZEVodQOIVOyIaM3J7JiIFE7jB5s2sREIHiMEdrG67QCLw4ra0RE\nFks3QvL4Ie9PqPZcoYgKahMbWRFTuYmXDQ315s1jdplFUiDYWLZsmdatW6disaiqqioVi0V1bN7B\nisWi8vm8Nm7cqPb2dtfJ/kuUa+CfRZtQxzh6NVqMTclAQH5XbE0dLXgRQiE9v9hsYiIHiE1M5RRS\nUJUC8rbAOLlBduQ6GFxrULOtFKxUAiEkyZBFtUPWju4MefJGyLZHXp8+j+0/tj7isZWJhOx7EYr0\nIHu7VAEEQoWhVwTCunXr1N3drVwu97rGiVvKF7YQCkOG2A1FgiC2MocskhUxNVEMSQ6EVCkQeM05\ntqaOFryIk5AoVwLBwyayYyeDdtQLSVYgIsL2/oYAm0aDiEgEQjzjhFJEhFxWsRERWTwmNJQrE1JV\nQd6d2MS8XuqLUAoNMkZFIDYfdBujVwTCH//4R0nqKWHYsGGDqqqq1NnZqcbGRq1Zs0ZVVVWaM2dO\n32cY0zGOWQz8YyIHyDghFQix9Vsg8Aomy7FcIjZ9ahZPhQjVbyFrp0ZQm8hKIdxsQORqEREeJASc\nSiYJhNj6NnjYxKSGkLLZzDJrqonYVBUhyzvIOCEPRgtFaBCiIqH80Kso4pVXXpG0iUCQNh3fOHXq\nVLW2tmrN5k5Jo0eP1sSJE52mmZCQkJCQkJCQkJCQkJCQ8GaiVwqELWUL1dXVKhQKKhaLestb3qIn\nn3xS0iZi4eqrr/abZQjEplIIWUgW8myjrJUwEBuvaxHEdsICQTmqFEJK1UKmdEKVS2Tt1AgpPpUC\nKV71SjV7XMtBxSBJg4nNaHu+Letzpk1spQdZUylksa9DyJKK2JQMHmqH2MoyCLwaXhKEVESEsiEu\ncEUglTDYaGjYdP5oR0eHisWiJOmnP/2pisWiqqur1dXVpc9//vO6/PLL/Wa6NWQx8M/a7iCFIyK8\nyhNik3177bAxBeOSD8kQ22+KLWCPjegKRSCEbPoYUttMCkZjK5fwIBDIGCRCdjpzbSAYZ+D29jht\nHfaRm4lA2PY2sZVLhCQrvLY4j3G8tuTYTkGOrSLRK8/hYUNCiITyQ68ee8e/WP1r166VJFVtftN+\n//vf935W3oiNZChXm1DHOIbsgRBbn4SQXzGv9z2RDH1DSCIiVNPQUL0WYrQJSUSE7LdgBe1eZ5N5\nKSZINA7GqSc2jbZNY2OdaRMTOUBsYpqLFHY5xNaXNRRHGtt26/UZCUlExGZjwUudkXkkBYKNLSUM\n/fv3V3t7u7q6utTY2KgNGzaou7tbkjR16lS/WfYVsTVaTETE1lGuRzTGpvEKmWW3nnk5lkpIfu+O\n157iBY/fFdOJENTGy6MlHaeyqJqwohivwD/kgewBr1XnZDN0aINpkwiEbT9OFokIj3FClm6ELAGJ\nTVkRG1mRUHnoFYEwatQovfzyy2ptbe0pYVizZo0GDRqk6upqrV69Wq1ktyLwOIXBC7GVQoQcJ6Ye\nCEmB0HebkLCeRSgVgxTfvSGIraTCQ6+YxeMrvfadkEREyL4NHmd9eVxHYpFZ1g5bh9fKkfINYtNk\n27QPLV2+kUVyIItERExlFzGpITxtsqisCGWTRbdqmyApEGxs/IfVtuU4x9bWVnV3d6uqqkrt7e0u\nE3RBbOqCSiUHJJ9gMotBfRZ7IIS6lhfZ40UyEMT2xcxaSYUXOeBVchEbERFbSYUHWeFFDoQ63Fzy\nIyJCkhUhVRPGtZhiot60ySKBUKkkQ2wlF7E1vIyNrPDgvEO6XgnxoFcEwssvvyxJqqmpeV0jRenv\nJzNUh+qq4cX4xEYgxEYOxFTCEDL4SEREHAgZTGbxxAyCmEoqYurHQK+VxXIJYuNFaFieMQn8vUiG\nRFbEYQMIhBy4zkBiM5SQFTWmTSIQ+mYT01ykRESEGCcRCJuRFAg2NmzYIEnq7Ox83b93bX6Lcrmc\n1qxZ08epOSIF9dmxyaICwWs+BFksYQgFL2IpJLL4rGJSppA9ObbyoZCKm5CKCGuckH0dvMgKMo4X\nyRCSrAg1n8jUGTXAZgix2d62IadzxBZsVyqBEBtZEZuNtXWTMRLKD70iELY0Sqyvr1dXV1fPqQy5\nXE5VVVXK5/P/8qSGzCM2BUIWCQ2PHggxzVcKm7EO2Ywxa5n4LJZCEJQjyRCyeWRIkiHk/hVS7UD2\nHQ8CgVzHi0Ag1/IiEMqVrLDGyRrh4WhT72QzbCggKzbmTJtKJRCy1oSSjhOTTSIQNiMpEGz069dP\nnZ2d2rhxo3K5TRtXVVWVJk6cqKVLl6q1tVXjxo3zmaFHE8WQXcxjIweyRjLEVjLgFdTHRg7EhNhO\nEPAaJxERfUNsR5bGVnaRteaQHiQEtUlkRZhrWTZZIzzoOJFdy4usIP0oPFqdZJEciI1AiImISARC\nZaJXBEJdXZ1aWlokqaf/QWNjo5566qkedcJBBx3kNEUHxKYKiI0ciIlkiInMkMKSA7ERI1kLOMl7\n7BXUx9aTIWtqES/ENt+QZRchlRUe+2lINYTX3u5FMngRCLGN40EgxGaTRULDaZwcGMciIpiqwr6O\nl6oiEQjb/lqJQNiMpECw8dqmiVsUCFOmTNGCBQt6/v3YY4/t49QCIzYCoRzJAWJTyeRApSoQyhVZ\nVCl4IbbA3kJMDSY9bWIquwhZukHGiY2s8CIiQpIMlo1XUB9SwZFFsiKm+Tj9JqSqIL8JlIC0d8RF\nViQCISELcDsqYcGCBcrn8yoWiyoUCvqP//gP/fKXv/QafusIyfjE5hxmjRwgNrGRA15Z5CwqEBL6\nhkoul7BQru9fbGUXMRERXnt7yO9eyFMsypGsiInMoNeqZEIja2UrTteqA+MQGzXZ1+os2M01E4GQ\nUSQFQu+xpXxBklauXOk5dN9QyX0SskZWeDl+IU9PiO2IxpCIKRCMbT14BfUhg9KskU8xzYUi5Jxj\nIiJiO6Eii4oIr29WqBKPkKUkIYmIrJWShLxWTGRGhDbkJJAaMM7ARtumWFtn2iQCIaG3cCUQampq\n1N3drUKhoKamJs+hs4OsBeyeNh6OVEgCIbbSg3IM8BL6jqRk6BvKdT1kjYgIqYaIrbwjNtVEKCIi\nNlVFIjS2/TixqTNim09AG9TTwsMGkBmbYKsvMo2kQPjXeOSRR3ThhRfqhz/8oQqv+fBWVVX1KA92\n3XVXPf7442pvb9fatWt9ZhjTKQyxkQNeDbtCOjehZK4hm4eVqwIha4FXFjfvSlUyZJEIi20+Xgj1\nu2JSQ3jOJ4uqCfK7PIgILwIhNuVFSCIiazZZbAYa2zhZI0/IGJI0ZgyzS8gEEIFwzjnnaP78+Zo0\naZKef/55rV69WtIm8uC1xzg++OCDkqTa2lqtW7duG035TURsJENsZIWHI5DFoD623gXlGOiUa6+T\nkBn/mAiEkL/bK5j0QjmuT6lyiYgskhWhkgZeaojYSIYsEhGhrhUTmSHF12OjHMdJBMImZDGJ1QeY\nv/aKK67Qrbfe2vPf//M//yNp0+kL3d3dPQTCa1UJWxoprlq1ynu+CQkJCQkJCQkJCQkJCQkJbwJM\nSnLJkiUaOHCgNmzYIEl66KGH1L9/f9XV1WndunXq7u5W1WtYl3w+r6qqKuXzebW0tGjo0KHbbvYU\nldwgMaS6wGOc2BQIWSw98EI5ZkrLlSGOrRTC492pZEVOFufsgdiUIFn0HUJ+z635xHbyRmyKiCyq\nJmJqnJlsSiOm41oTyg7mSp87d65mzJihc889V5LU2dmprq4uFYtF1dbWqrOzU9tvv702btyodevW\nqbOzU7lcTk1NTRo4cOA2/wFuiC3wL1eSwUPaHBuBkEWbhL4htjXsRQ54XStrBEK5rr3Y5hMKWSQi\nQhIaoa4VW8lFyH5PsTXCDkVEpPKOyrMp1yTaG0W5Jqi2AnPVvPDCC7r66qtVLBYlbSpPqK+vlyS1\nt7erWCzq0EMP1S9+8Qu1tbWpq6tLNTU16t+/fxj1QWy10V7jxGYTU8YitoAgNhsvlGPwETKjGBKx\nERFZIxBiO30i9W2IAyH7hsQU+FMbjzFiUkzQa8U2jpf6wmM+IRUTMREn9FqxERoe10oEQkUClTBs\nKVF46qmnJEmHHnqofvWrX/WUL5xyyin6wAc+oL333lvSJpVCXZ19/mgwxEYylKuNR/AfWzAegkaL\nsAAAIABJREFUm40XyjEgiC3wz6ICgcBjPiGVDjE1j/S0IYiJiCjHPccTse3/oQiE2IiT2E63im0c\nj8aZsakzskhExDQOGaMSEJsPuo2BShhefPFFvfLKK5owYYIeeeQR3XTTTaqtrVXt5pfqtttu09ln\nn93TSHG33XbTDTfcsG1n7o3YgnGC2ObsYRMTmRHaxgvJUe8bsqhSiI2s8JA2x1ROQcfJog1BIiKy\ng5juT7n2mYhpL5X8CASPa1VymUgWbTxIhkQgVCRQCcPTTz8t6e8KhFwup6qqqp5+BxdffLHWrVun\nXC6nYrGohQsXavLkyfr1r3+t8ePHb9tfUK6I7UMXKiAP6TjHRjIQJCJi64gtqCeoVAUCQWzy8ZCK\niCzaWIiJhAh9rSwipvuTxRIQgtiIiFAEQmyqikR69M0mEQibkEUftA8wn3pzc7OmTZumBQsW9PRB\n6O7u1ujRo7V8+XK1trZq2bJlyufzPQqEXC6nfv36qbGxse8zDJWxrmQbr0A6lAIhZOAf8loEsY1T\njohtfcZm4+GshnSKvQL/RDL0zaYc1RAUsc0nJsRGFIYq76DjxEZoeJSnxaaqiI2IyBqhkQiEigRS\nIDzxxBOSpI0bN/b8+4svvqi2tjbl83n1799fnZ2dGjp0qFavXq1CoaCpU6fq2Wef1bBhw7bd7GNE\nbM5+bDYexzjGFvh7XYsgtnFiQoWxv9sEMSkiEslQXjYxqSGkRERUImK7f5VKVsSmqkiERt/GSQTC\nJlSYD/qGFAiSesoUcrmchg8frpUrV6q6ulrV1dVas2aN8vm8JOnRRx/VuHHjtu3sPRFb3XNsgX8o\nZUBIBUIK/BP+FWJbV7GpCwiypkBIJMO2t4lpLtQmiyQDQRbnHBOyeP9iKt+ITVWRRUIjJtKjwgLn\nhE1ACoTHHnvs7/+H6mp1dnaqra1N7e3tqqmpUW1trTo6Onr+JkkjR45Ud3f3tpv5G0EK/LNjExs5\nEHIcgkqtEU4fqL4jJiIiNqcukQzb3iamuXjaEJQrEWGhHH9TaMR2D0P1Q/EaJ7ZrxfQd9rpW8s82\nocLuAzrGsXpz8NOvXz/V1NRo7dq1KhQKqq+v1+DBg9Xe3q7q6mp1bX5Zq6qqtHz5crW1tW3b2YdG\nyI3Ia5ys2WRRgRCSHAgpFYvNcfFATO96aJtQ6gJiEzKbk0iGvl8rEQjb3saLZChHZUVMcylnZO0+\nZ7EEpByvVWGBc8ImvOFoZMKECXr44YdVKBTU1tamkSNH9pQvDBw4UC0tLSoUCtpuu+18mihayGJQ\n7zVOFm08eiDERjJ4IbaPd2zzSdg6skZExJQ98byW1zhk7cV2rUQgbHubRDJse5Tjb4oNWbzHsZEV\nXuOEKkmpBFTYfUDR0ZZShIkTJ+rEE0/UwoULJUn5fF4zZsxQVVWVbr75ZjU2Nmr9+vUqFovq37+/\nBg0atO1m/mYga4va81pZK2HwIhkIYpuPF7L4kY8JWQvqKWJSIGSRZAhJDoRUO1jjxKSGKGebkCRD\nqLmUK5mRxTlnDVm8xyHXRCIQEnqJNxyxjBw5UhMnTtSTTz6prq4udXd36xOf+IRuueUWLVu2rOeo\nxy9+8Yvuk+01stgDIeS1YrIJqS4IpaqIEbGpL0KhXIN6gpgC6SyqArJIDsSkQAgZ2FYyWUEQ03xi\nI04SWZHQW8TWoyrUu5wIhE2osPuAIoTC5hfsxhtv1OOPP666ujpJmxQIX/7ylyVJxWKxp4FiY2Oj\n9thjj20x322HkIE/QchAhyAmAiG2oD4kWZHF+xMKWQzqCWIjNMqxzKFcx4mpFCJkUB+SrEg2fRsj\nlWWUF9L9iwfpWSRsQ7hEEfPnz1dLS4vq6urU3t6uNWvW6OKLL9Zpp53mMXy2EFugSBDTnGOaC7Up\nx2A8NlQyORBT80OpchUIWRwnpmaMMakhkk0YmywqJhKhkR2kexMH0nPYhApTILj82quuukqS1N7e\n3vNvzz33nMfQCQkJCQkJCQkJCQkJCQkJEQClTmtqav71/3lz5vX444/X2WefrcmTJ+upp55Sd3e3\npk2b5jfLhN4jtmx9OSoQktph2yOLDHds6oKsXSu2bH4WxynHEobYyhOSIqJvyNp8s3qtpHboG9K9\nSUh4HcxoZO7cuaqpqdHNN98sSRo8eLA+97nP6bjjjuuxKRaLyuVyWrlyperq6tTa2qqqUPL62AK8\nZNM3m5jmIsUXsIdsCBfTbye/O+R8Yyup8HJuEjmQnXFiIgfItcq1iWJshEZMNjHNhdoQZLGkgsBj\nzomo6DvS/ckmKqyEwfS4X3jhBd1xxx09//3SSy/plFNOUaFQUGdnp1asWKExY8aoWCxqxYoVkqRc\nLqfZs2dvu1kncMQWkGetiWLIgD22oD62oN1CbB2QvRCSrAhFRMQWjMc2TmynOXgEOiED/3IlK7Jm\nE9NcKCp5zqGC/6zNN/S1YkO5/q6EPsH0/pcsWdJzNKMknXfeeRo6dKjWr18vSbrkkktUVVWlXC6n\nmpoaFYtFdXV16YknntAuu+yy7WYeK2IKxqWwm2dMBAJBFgN/r6Ah5HxCkQwh55JFsiKmQDqmucQ4\nTsjmh6EIjXIN/JMC4c2/TlZtCLI2n9gC/5DjeP2uSiYrsoykQCiNXC6n+vp65fN5FQoFPfroo5o9\ne/brjnEsFotasmSJ+2TfdMS2qGMjIiqVQCBBaWylByGJCOtasR25GXKckNeKaZyY5kLHyVrAHtu1\nKjnwr1SVQkxz8bQhqNQ5xzQXKb6gPrZxEhJ6iV577tXV1Wpra1NXV5d22GEH5XI5SX/vhzBw4EC3\nSQZBFrP5WZxPORIIIa/lRQ7EpEDIonIgi3OOaZzYAvbYVAFZvJY1Tkxz8ZxPbGRFTDYxzaWcbQhi\nmk9Mcwk9ThZJBg9yuBKQFAg21q1bp46Ojh7SIJ/Pq7a2Vvfff79mzZqljo4OTZ482XWifUJswXg5\nBv7UJpbr0HGyls33HCcmBULI313JgX+ooN0reCPIYjBejsF2THPxnE9Im6wpGWKaS2gbgtjmXKnk\nQLleK9Q4iUCoSCBPufCal2PdunVatWrV6/oiHHzwwfra176m3XffXd3d3ZJUnk0UYyMHYgr86Tge\nTmbI+XqNEzKoJ4iJ0IiNHIjpwxx6nJjmnMVgPLbgNqb5xDQXahNbwJ410iOmuVS6DUFMz5ygHO+f\n57W8xvHw2ysBFXYf3lCqrbu7W6+88krPfxeLRa1cuVJNTU2qq6tTS0uLJKmxsVHDhg3znWk5IWvy\nJM9xQqkUynWcLGbrPRQIIT+Wsd2/mBwFz2t5jJHFwD826Xyo+cRGipRjwC7FRXrEdm9i2wPL1SZL\nc/G8Vmzzie1aCWUFF63u/Pnz1dLSorq6OrW3t2vNmjW6+OKLddppp3kMXxpZDKLTONt+LgRpnNKI\nSYFAUK5Bfcg5h7pWTIEtRTkG9XQ+Hu9ObPPNWjBObWIiPWK7N5VsQ5C1Z05QrvOJ6VqJYNiEpEB4\n47jqqqskSe3t7T3/9txzz3kMXZ7IWlDvOU6IMaTKJiK8AulQ14ot+A15b7LmKHhdK6a5SNkM6mML\nbj3I4az9ptDzyZpNbL1Okk0Ym5jmksX3iyCmayUCoSLhQiAcf/zxOvvss3sUCJI0bdo0j6HjQrn2\nQAgZbHtkJglCMoGVGvh7XSu23xSS0CCIjWAJFViEmgu1IYgtuI0pII8tYI/p3pSrTSJg+m5DENOc\nK/V3U5TrfBKSAuGNIpfLadmyZZKkgQMHKpfLaePGjT3NFKNAFgP/kNfKWta/HH8TRTleKyYyQ8pe\nMB7jfFKWKg6bmPoOlOu9STZv/nWSTTibmOZSrns7QUzzSQRDReINEwi5XE6FQqHnCMeGhgY1NDRI\nklasWNFj19TU5DTFMkQWSQava1mo5ICdIOR8Qgb2FmKai1S+KoVQc46NzMiiU5e1YDtl/CvPJqa5\nxGhDENuckwKhbyjH+SQCYRNiixe2MfqsQBg6dKiWLl36T//+1FNP9XXosAgZaBNkLfCXfOYTcgHG\nRlYQxDafmAL72IJ6gqwF/pLPnGNz/LKo4MiaTSpPqEybmOaSbPqOrO3tBGnO234uCWWHXhMIxWJR\n0iZFQk1NjSSpqqpKxWJRxWLxdcc9lg2ySDIQxEZEeCCL5EAW50wQqgeCF7IY1BPEFADHdv9iujfl\napM1xUSy6btNTHMJbUMQ25xjykbH9Jso0nwqG1n0z/uAXv3aXC7XU8Lw3HPPaaeddlI+n9fUqVN7\nbPbff3+fGSYkJCQkJCQkJCQkJCQkJLzpQAqELQoDaZPyYPjw4Vq7dq3a29u1YcMGHXzwwfrmN7+p\nZcuW9SgT5s6du21mHDtiUykQxFY24IEsMoFZnLMHYiqDCI0sZr5jehZZvH+VahPTXJJNGJvYMqAx\n3ZvQNgTpmW97G4KszSe2mCYhCEzva+7cuaqpqdHNN9+sfD6vKVOm6Nlnn1VHR4ckacCAAWpqalJT\nU1NP34P6+np1dHSorq5u284+qyjHgJ2iHIPkciwBiQ1Z7G9QrijHHgjJZtvbxDSXZONjE9NcsmhD\nENucy/G9IEhzTrBQYT686Qm+8MILuuOOO3r++8UXX1ThNS9ToVDQ4sWL9eyzz6qpqUkrVqxQPp/X\n448/rr322mvbzDqBoxxf6Ng2s3K8x+WKlLGOwyamuSSbMDYxzaWcbQjSM+8bYptzTL+rHH8TtSEo\nx/nE5pMnBIHpTS9ZsqSnLEHaVMKQy+XUv39/bdiwQV1dXTrrrLPU3d2t5cuXS5LWr1+vK664IhEI\nCdsGKWBPSEhI8EGohoOxBVRZtCGIac7p3vTdhiBrc87afD2vVY7zSQTCJlRYbNKrUxiKxaJaW1sl\nScOHD9d//ud/6owzzlB7e7u6u7uVy+V0xhln+Mywwh5IQkJCQtkjdcnPlg2B9Uxj+02x2RDENudQ\nmcmYfhO1ISjHOWdtvp42BOU4n0QgVCTeMIHQ3Nysp59+uue/i8WiVqxYodbWVo0dO1bPP/+8CoWC\n/vjHP2rKlCmuk01IqHgkQi3hXyFrAXm5OhxZew6hbSxU8v0jiG3O5ZiNLtdnRRDT3l6O94baEMQ0\nn3L9nr9RVJh//oYJhOXLl+vTn/60Lr/8cq1cuVIrV67UsmXLJEktLS1qbGzUqlWr1N3d7T7ZhDJA\nhS0wd4TcqNOz2vYIGTCVI7IYcIZETPfH6zox/SZPG4LY5lyOBAJBmnP5zMXzWrHNJ9S1YvvuJQQB\nIhBe2zSxublZ11xzjVatWiVp0xGPDQ0NkqQVK1b02DU1NXnOM6G3iC0IzNpGE9v9CwkvZ75SkcUj\nXb0Qk5w9JLIY3BJ4/K5y/E2hbbI45yzNxfNa5TrnLM0lq9eKbT4hxigHVJg//IYVCF1dXXr11Vd7\n/ru2tlZLly79J7stRzom9BJeL2LWFnZsCzBl/EsjkQyVh3IkRrKoBIktmLQQ23zLNWCPbc5Zmovn\ntSp1PjHNJcZrxTYfj2tl7Xuf4II3RCB0d3fr2WeflbRJedDR0aF8Pq+amhpJUlVVlYrFov4/e2ce\nZkV1rf23ztgDjQ00LQKCTEYRx6uoQYwKBoJeZ6OBOFyTaDRfEr25IWpIxDGOERONs6ioAYLggAYR\nFQeMMYIMRgEZZKZbpIEeTp+xvj8ajmCg1gtd57Crav2e5z439FlW7b2rau817bVt20ZNTY37rfUC\nfjX8i2UEmqaAF5NiRtaKidQv09obZLzmHFDDv/WYdAoDg2kGsmntYTCpPSa1xc17mdaeYt3LpLYU\n+16mtce0e/mdgOmyu+VAmD9/PhobG3c41jESieCAAw5AOBzGwQcfjH//+98AgFNOOcXdliqFw49G\nA0NQDXbArDZ7rb1exY/fuRr+rb9XsdoTZKM+qO0xqS0sakwW/l46xv66l0l6g1I0dnsLQzQaRTqd\nzjsRjjnmGAwdOhR33XUXPvvss/zfjz32WHdbagJeVGgZvNZm0wx/09rD4LU2q5PBX3hxLtX2OOO1\nbQ4MxWyPH50MphlCpj0HBj/ey499CvK91IHQQsB00N12IITDYYTDYSSTSWSzWZSVlaG6uhrxeBz1\n9fUAgMrKSnTo0MH1xhaUoCpsLCYZ2ya1BfBvexhMMuxNaotiDl6cS71oABerLcW8lxcNTpP67tcx\n1nt54z5Bv5db11EHgbITdtuBsDMmT56M+vp6xONxJJNJbNq0Cffddx+uvvpqNy6vFBqTFGzTlEwG\nL7bHJCeDX416055DMXHDsPAips1fJt3LpLb4+V4mGeR+7JNX78WgDoTC38ut63jxXn7Hr/rcLnDF\ngTB27FgAQDKZzP/tiy++cOPS/sSLSolbFMuwMG2MGYppcJpk3Aa130rrMe0713sV/j5+NfD8eC8/\n9onFi0ZgscbHi2Pj1r3cuo5JbVYHQyBxxYEwfPhwjB49Op+BAACHHHKIG5dWvITXFFG9lzn3klDD\nX1HMJqjOYdPuZZJB7jXDlr2O154Dex0Gk5xGDF4zxtnrmNRmdSC0EDAdlOrttmMab7zxRgBALpdD\nNpsFALz22mt49913AQCHH344SkpKAABr1qxxvbGKoiiKoiiKoiiKouwdxAyEwYMHo7a2Fh988AHS\n6TQAwLZtVFRUoL6+HkOGDEGPHj3w5ptv4sMPP0Q4HEYkEsk7FYzAi1GEoN6rmFEYBr1X4e9lmtfW\npLHx6r2UXePHedst/Do2pmUXMLhxLy9G4b3YZgaTsk4Y/DrGbl3HpDabtIYoRUN0IAwZMgRTpkzJ\n/9u2baRSqfxWhenTp+OSSy6BZVkIhUIIh8PIZDJIp9PYuHEj2rdvX7jW+xmvKQpu3cu0BcGL9/Ka\nwWmacsigxrgSJNQRUfh7+bHNfjXYg9xmN65hUnuLfR0/9t2kuX9vEjCdT3QgPProo5g3bx4AwLIs\nlJSUoLm5GbFYDKlUCiUlJViwYAEsy0I2m0Uul0N5eXn+WEd1IAQIVTILfy81pFuH19oL+LPNprXX\ni+h8W3hMm7cZTGpzUPvt5nUYTDJcTeu3W9fx4jMv1nVMm7eVoiA6EBYvXgzbtgEAZWVl6Ny5MzZv\n3oxwOAwAOOuss/Dqq6/Ctm1EIhFkMhk0NjaiU6dOqKioKGzri40qQK2/TrHuo4pL66/DYJLCYZpT\nxI+GP2Bem5Vg4cV50i1M67vXCmfqdQp/Ly/2O6iGv1vXMW2e3FsYohvlcjmMHj0aixYtQiwWwy23\n3ILu3bv/h8zll1+OQYMG4Qc/+MEe3We3T2Goq6tDLpeDbdsIhUJ46aWX0LdvX9TW1iKdTiOTyQBo\nyVbQ7INd4EXFxS1MWnwYTFugTDNc/ahwFJMg990NgtpvNwnyeuQGXpzbTXqepvVbr9M6/JoVoNdp\n3TWUojFjxgykUilMmDABc+fOxe23344HH3xwB5kxY8Zgy5YtrboP5UCIxWIAgCVLlqBDhw7YZ599\n8jf+4osv8JOf/AR33HEH+vfvD6ClTkJpaWmrGqYQmDSBeBHTxs+06zB4LQPBtOt4EZOcRoo5mOQo\nVFqPzu16nT25jkltYdHrFOc6fscQnWX27NkYOHAgAOCII47AJ598ssPv06ZNg2VZeZk9hTqFYfXq\n1aipqUHv3r2xaNEiJJNJxGIxZLNZhEIhnHPOOfjZz36GxsbGfIMnTJjQqoYpHsSPi0+Qr8NgkjGp\n1ynOdRTFdLz4XfnxOia1Ra9TnOuY1Ba9TnGuow4Go2hoaECbNm3y/952uEEkEsHixYsxdepU/OlP\nf8IDDzzQqvuIDoSVK1fi888/B9CSgRCJRJDNZpFKpfK1Ea677jq8+eabCIfDyGazmDt3Lr71rW/h\n1VdfRa9evVrVQEVRDEadRnqdPbmOOioURVHMQtdzvc6eXEfX8xYMGYc2bdrkA/pAS72DSKTF3H/h\nhRdQU1ODSy65BGvWrEE0GkWXLl1w4okn7vZ9RAdCt27d0KdPH8yZMyfvMEin07AsC+FwGJFIBK+8\n8gpy33jB4vE4Kisrd7tBu41JH5Fep/XXMaktep3iXEcj48rOMOkd9fN1FKVQePF7CPJ1ioVp/Q7y\ndRhMencUkaOOOgpvvfUWhg0bhrlz5+LAAw/M/zZy5Mj8//7zn/+MqqqqPXIeAGQGwtKlSwEAzc3N\nKC8vRzQaRSaTQS6XQzqdRnl5OdLpNNq1a5cvstivXz8sX74cHTp02KOGKUqr8OJEHtTrmLSdQq9T\nnOuo8WsO+iyUneE1o9Q0vDYnu3Udk+orBf06DCY9c69jyDp56qmnYtasWbjwwgth2zZuu+02jB07\nFt26dcOgQYNcuw+VgXDIIYfg/fffBwAcfvjheO+99wAA0WgU8XgcZWVlSCaT2LRpU/54x/nz56Nn\nz56uNVQpICZNjEFWWkx6DsW8jioc5lzHa+j4mYOmuZpBUNdQ0+ZbLxJUHc60d6eYY2hSEEhxhVAo\nhJtuummHv+2snMDPf/7zVt2HykBYsGBB/t+zZ89GPB5HOp1GOp1GNptFdXU1ampqEIlEkE6nAQCd\nO3dGNpttVeMUAS9O1G5g2iTttcnetOvo+BXnOgxee+amKS5efOYmEdR+K8HEtHWkWHix36a12aS+\nm/Ru7U0CtjaJDoTFixfniy+UlJQgm80imUzmayBEo1HU1dUhEokgk8kAaPF+1NbWIpFIFLb1XsVr\nk72beG0i8uJk7xYmZSB4zUAu9nUYvNhmNwhqvwFvttkNgtpvLxLktbGYmLSeFxN9Vq2/jhv3UXyH\n6ED4JslkEiUlJUilUshms3nngm3bqKioQH19PXK5HKqqqopTRFEpDl6bhL3WXsB7i4Zb9/Jae4t9\nHQavtdlr7XUTbbOiKH7FtDWWQdvc+usogVsDKQfCtq0Iffr0wbx589Dc3IxQKATLsmBZFvr164d/\n/OMfqKysRENDA2zbRllZGdq2bVvQxhcdL35oXmuz19oL6KLR2nt5rb3svfzaZgavbWHwokPDa0a9\n19oLeLPNir/wok5ULLw4Nn7UC0wbY6Uo7HYGAtBSPDGbzcK2baRSKYwaNQrnn38+1q9fnz/q8YYb\nbnC1oYoH8Npk7sWJvJiYlP6mBntx7uU1pxGDOgfMuZcbeK29xUbHR9lTvKinFAuT1k837+UGOp+0\nELBxoHqb2/qiTpw4EQDytQ4AIBKJ4IADDoBt2/kCipWVlTjmmGPcbquiKIqiKIqiKIqiKHsJKgMh\nGo3u8O9YLIZoNIpEIoFQKITZs2ejvr4ePXr0wIoVK5BMJjFjxgyceuqpBWl0IFBv567x69gEORJf\nrPuYFrU17TmYtIXBjfuw9zJpbPx6L9OiMyaNjZv3UhTT8eLa6Fd0fJQ9RHQgDB48GNFoFM8//3z+\nb5lMBul0GrlcDvF4HNdddx0AYPny5QCARCKBJ598Uh0Iyn9SrL1UOik6Y9IC7sVnrkZp6+6lz0Hv\ntTfv41X0mSuKuXhRB3HjXkHWpbcnYPOU6EBYuXIlZsyYscPfLMtCSUkJmpqakM1mcdVVV2HUqFEI\nh8NIpVKwbRsnnXRSodqsKGZh0kTuxXv5sU96r9Zfg8GPBpVf7+XHPhX7XoqiKIpiAKIDYfHixfnC\niNuwbRuJRAIAEI/HsWnTJqTTaXTv3h3Lli2DbdsImbRgek25DvK9/NgnvZd37qP3Kt69JPxqBAb1\nXn7sk97LjPsoirL30G+4hYCNA1UDIRaLAQAuuugiAC0OBMuykMvl0NDQgNdffx0AsHbtWliWBQBo\n165dIdqrKMXFNKPLj/fyY59Y/Gg0sPeSMO15+vVeDH40OP16r6Di1+ep746yp+hWXqWAUDUQVq9e\njQ0bNiCZTAIAQqFQPishl8th2LBhWLp0Kerq6gAA7du3x6OPPopzzjmngE1XjMOP0WjTMM348Nrz\nNO3d8WvfTXIaMfjVIDBpnE3rtxfvxWBS3/06xn5Fx1lR9pyAfRtUDYRFixbt8Ld99tkH3bt3x5w5\nc/JbFbY5D0KhEDZv3oxoNIqNGzeiffv2BWi2QmOawVQsTDKW3LyXaQTVycDgxffLj4YFgx/HuNj3\nkvBrv4PqHHDzXiahY1x4gtpvRfERVA2EcDi8w98aGxsxf/58AC1HPGYyGQAtxRW3ZSZEo1HU19er\nA2FnqMG5a4Lab8CbxhCDSdFok/oNBLfvaoy3Hq/1Paj9Zu/F4MW+S5j2HPxquAa574pSDAL2/VA1\nELanY8eOqK+vzzsNOnbsiB49eiAej6OyshI1NTWwbRulpaWoqKhwvcFKATDN+CgWpvU7qAanaf1m\n8OJzMKnvJrUFCO5zADTrpLWYZpiZZpBL6HPwF6b127T2KIpP2K0iikuWLMHLL7+M008/HUBLxkEq\nlcLkyZMxcOBAvPHGGwBatjGsXbtWsw+U3cckwxYwrz3FxKS+m9QWQA3OYt1LQp9D6zGpPaYp+14z\nxgF/GuQmtQXQ9piCaf3W9gSbgI3lbhVRtG0bd999N3JbX8o2bdogkUjg+uuvx4033pjfvhCPx/HI\nI48UtuWKEkRMMt4As9pjUluAYLcnqCnvDNqeXWNSWwB/GuOAWe0xqS2AtkeiWJlGiqIYDVVE8fPP\nPwfQknFw77334uCDD0YoFEJTUxPKy8tx5ZVX4tNPP0U4HEY2m0UikcCIESPw6quvolevXgXvhCsE\nWdlnMKk9JrUF0PZImFT3wmtjAwS3PUE2Jhn82B6T2gJoeyTUabRrTGsPg0nOCpPaAmh7nPBrFu7u\nEjDnmehA6NatG/r06YM5c+bAtm1cc801sG0bsVgM6XQauVwOq1evzmclbGNbTQRFUWCWYQaY1R6T\n2gJ4sz0MJim0po2ftqf1qDG5a7Q9rcMkYwnwZnsY9HnumiC3R1F2ApWBsHTpUgBAc3Mz7r33Xrz2\n2mtIp9OwbRuhUAht27bFli1b0K5dO9TV1SGXy6Ffv35Yvnw5OnToUPBOKIZgkmJsUlswYNr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Wto3749evbsibKyssL3QFEUxUQ8koameBQfpUIqBhKgVFzFQAKWDq4oXsMxA6G2thbxeBzpdBoA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\ngg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set()\n", "a = make_position_encoding(np, 1, 100, 512, 10000)[0]\n", "plt.figure(figsize=(20, 10))\n", "sns.heatmap(a, cmap=\"bwr\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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zZx8SEdqzZ4+VlJRYJBLxYyCEFlpooYUWWmihhRZaaKGFFlpo/+dWrvR+NBq1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r17AQADAwNoaWnBgQMHUCwWeUboiihGixPWF1CbA4GyUJASGbTlDSoTIprbzJXXbXMgSG74aBMi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RJ0PeC49xP2bs5oB3fRyYNDB6hWZqEYyGdKSQNY4s/+RxqXVw/Xrd3m1mdscdyC1pdcTkVjxk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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# change 10000 -> 100\n", "sns.set()\n", "# a = make_position_encoding(np, 1, 100, 512, 10000)[0]\n", "a = make_position_encoding(np, 1, 100, 512, 100)[0]\n", "plt.figure(figsize=(20, 10))\n", "sns.heatmap(a, cmap=\"bwr\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: README.md ================================================ # Transformer - Attention Is All You Need [Chainer](https://github.com/chainer/chainer/)-based Python implementation of Transformer, an attention-based seq2seq model without convolution and recurrence. If you want to see the architecture, please see [net.py](https://github.com/soskek/attention_is_all_you_need/blob/master/net.py). See "[Attention Is All You Need](https://arxiv.org/abs/1706.03762)", Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin, arxiv, 2017. This repository is partly derived from my [convolutional seq2seq](https://github.com/soskek/convolutional_seq2seq) repo, which is also derived from Chainer's official [seq2seq example](https://github.com/chainer/chainer/tree/seq2seq-europal/examples/seq2seq). ## Requirement - Python 3.6.0+ - [Chainer](https://github.com/chainer/chainer/) 2.0.0+ - [numpy](https://github.com/numpy/numpy) 1.12.1+ - [cupy](https://github.com/cupy/cupy) 1.0.0+ (if using gpu) - nltk - progressbar - (You can install all through `pip`) - and their dependencies ## Prepare Dataset You can use any parallel corpus. For example, run ``` sh download_wmt.sh ``` which downloads and decompresses [training dataset](http://www.statmt.org/europarl/v7/fr-en.tgz) and [development dataset](http://www.statmt.org/wmt15/dev-v2.tgz) from [WMT](http://www.statmt.org/wmt15/translation-task.html#download)/[europal](http://www.statmt.org/europarl/) into your current directory. These files and their paths are set in training script `train.py` as default. ## How to Run ``` PYTHONIOENCODING=utf-8 python -u train.py -g=0 -i DATA_DIR -o SAVE_DIR ``` During training, logs for loss, perplexity, word accuracy and time are printed at a certain internval, in addition to validation tests (perplexity and BLEU for generation) every half epoch. And also, generation test is performed and printed for checking training progress. ### Arguments Some of them is as follows: - `-g`: your gpu id. If cpu, set `-1`. - `-i DATA_DIR`, `-s SOURCE`, `-t TARGET`, `-svalid SVALID`, `-tvalid TVALID`: `DATA_DIR` directory needs to include a pair of training dataset `SOURCE` and `TARGET` with a pair of validation dataset `SVALID` and `TVALID`. Each pair should be parallell corpus with line-by-line sentence alignment. - `-o SAVE_DIR`: JSON log report file and a model snapshot will be saved in `SAVE_DIR` directory (if it does not exist, it will be automatically made). - `-e`: max epochs of training corpus. - `-b`: minibatch size. - `-u`: size of units and word embeddings. - `-l`: number of layers in both the encoder and the decoder. - `--source-vocab`: max size of vocabulary set of source language - `--target-vocab`: max size of vocabulary set of target language Please see the others by `python train.py -h`. ## Note This repository does not aim for complete validation of results in the paper, so I have not eagerly confirmed validity of performance. But, I expect my implementation is almost compatible with a model described in the paper. Some differences where I am aware are as follows: - Optimization/training strategy. Detailed information about batchsize, parameter initialization, etc. is unclear in the paper. Additionally, the learning rate proposed in the paper may work only with a large batchsize (e.g. 4000) for deep layer nets. I changed warmup_step to 32000 from 4000, though there is room for improvement. I also changed `relu` into `leaky relu` in feedforward net layers for easy gradient propagation. - Vocabulary set, dataset, preprocessing and evaluation. This repo uses a common word-based tokenization, although the paper uses byte-pair encoding. Size of token set also differs. Evaluation (validation) is little unfair and incompatible with one in the paper, e.g., even validation set replaces unknown words to a single "unk" token. - Beam search is unused in BLEU calculation. - Model size. The setting of a model in this repo is one of "base model" in the paper, although you can modify some lines for using "big model". - This code follows some settings used in [tensor2tensor repository](https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/models), which includes a Transformer model. For example, positional encoding used in the repository seems to differ from one in the paper. This code follows the former one. ================================================ FILE: download_wmt.sh ================================================ curl -O http://www.statmt.org/europarl/v7/fr-en.tgz curl -O http://www.statmt.org/wmt15/dev-v2.tgz gzip -dc fr-en.tgz | tar xvf - gzip -dc dev-v2.tgz | tar xvf - rm fr-en.tgz rm dev-v2.tgz ================================================ FILE: net.py ================================================ # encoding: utf-8 import numpy as np import chainer from chainer import cuda import chainer.functions as F import chainer.links as L from chainer import reporter from train import source_pad_concat_convert # linear_init = chainer.initializers.GlorotNormal() linear_init = chainer.initializers.LeCunUniform() def sentence_block_embed(embed, x): """ Change implicitly embed_id function's target to ndim=2 Apply embed_id for array of ndim 2, shape (batchsize, sentence_length), instead for array of ndim 1. """ batch, length = x.shape _, units = embed.W.shape e = embed(x.reshape((batch * length, ))) assert(e.shape == (batch * length, units)) e = F.transpose(F.stack(F.split_axis(e, batch, axis=0), axis=0), (0, 2, 1)) assert(e.shape == (batch, units, length)) return e def seq_func(func, x, reconstruct_shape=True): """ Change implicitly function's target to ndim=3 Apply a given function for array of ndim 3, shape (batchsize, dimension, sentence_length), instead for array of ndim 2. """ batch, units, length = x.shape e = F.transpose(x, (0, 2, 1)).reshape(batch * length, units) e = func(e) if not reconstruct_shape: return e out_units = e.shape[1] e = F.transpose(e.reshape((batch, length, out_units)), (0, 2, 1)) assert(e.shape == (batch, out_units, length)) return e class LayerNormalizationSentence(L.LayerNormalization): """ Position-wise Linear Layer for Sentence Block Position-wise layer-normalization layer for array of shape (batchsize, dimension, sentence_length). """ def __init__(self, *args, **kwargs): super(LayerNormalizationSentence, self).__init__(*args, **kwargs) def __call__(self, x): y = seq_func(super(LayerNormalizationSentence, self).__call__, x) return y class ConvolutionSentence(L.Convolution2D): """ Position-wise Linear Layer for Sentence Block Position-wise linear layer for array of shape (batchsize, dimension, sentence_length) can be implemented a convolution layer. """ def __init__(self, in_channels, out_channels, ksize=1, stride=1, pad=0, nobias=False, initialW=None, initial_bias=None): super(ConvolutionSentence, self).__init__( in_channels, out_channels, ksize, stride, pad, nobias, initialW, initial_bias) def __call__(self, x): """Applies the linear layer. Args: x (~chainer.Variable): Batch of input vector block. Its shape is (batchsize, in_channels, sentence_length). Returns: ~chainer.Variable: Output of the linear layer. Its shape is (batchsize, out_channels, sentence_length). """ x = F.expand_dims(x, axis=3) y = super(ConvolutionSentence, self).__call__(x) y = F.squeeze(y, axis=3) return y class MultiHeadAttention(chainer.Chain): """ Multi Head Attention Layer for Sentence Blocks For batch computation efficiency, dot product to calculate query-key scores is performed all heads together. """ def __init__(self, n_units, h=8, dropout=0.1, self_attention=True): super(MultiHeadAttention, self).__init__() with self.init_scope(): if self_attention: self.W_QKV = ConvolutionSentence( n_units, n_units * 3, nobias=True, initialW=linear_init) else: self.W_Q = ConvolutionSentence( n_units, n_units, nobias=True, initialW=linear_init) self.W_KV = ConvolutionSentence( n_units, n_units * 2, nobias=True, initialW=linear_init) self.finishing_linear_layer = ConvolutionSentence( n_units, n_units, nobias=True, initialW=linear_init) self.h = h self.scale_score = 1. / (n_units // h) ** 0.5 self.dropout = dropout self.is_self_attention = self_attention def __call__(self, x, z=None, mask=None): xp = self.xp h = self.h if self.is_self_attention: Q, K, V = F.split_axis(self.W_QKV(x), 3, axis=1) else: Q = self.W_Q(x) K, V = F.split_axis(self.W_KV(z), 2, axis=1) batch, n_units, n_querys = Q.shape _, _, n_keys = K.shape # Calculate Attention Scores with Mask for Zero-padded Areas # Perform Multi-head Attention using pseudo batching # all together at once for efficiency batch_Q = F.concat(F.split_axis(Q, h, axis=1), axis=0) batch_K = F.concat(F.split_axis(K, h, axis=1), axis=0) batch_V = F.concat(F.split_axis(V, h, axis=1), axis=0) assert(batch_Q.shape == (batch * h, n_units // h, n_querys)) assert(batch_K.shape == (batch * h, n_units // h, n_keys)) assert(batch_V.shape == (batch * h, n_units // h, n_keys)) mask = xp.concatenate([mask] * h, axis=0) batch_A = F.batch_matmul(batch_Q, batch_K, transa=True) \ * self.scale_score batch_A = F.where(mask, batch_A, xp.full(batch_A.shape, -np.inf, 'f')) batch_A = F.softmax(batch_A, axis=2) batch_A = F.where( xp.isnan(batch_A.data), xp.zeros(batch_A.shape, 'f'), batch_A) assert(batch_A.shape == (batch * h, n_querys, n_keys)) # Calculate Weighted Sum batch_A, batch_V = F.broadcast( batch_A[:, None], batch_V[:, :, None]) batch_C = F.sum(batch_A * batch_V, axis=3) assert(batch_C.shape == (batch * h, n_units // h, n_querys)) C = F.concat(F.split_axis(batch_C, h, axis=0), axis=1) assert(C.shape == (batch, n_units, n_querys)) C = self.finishing_linear_layer(C) return C class FeedForwardLayer(chainer.Chain): def __init__(self, n_units): super(FeedForwardLayer, self).__init__() n_inner_units = n_units * 4 with self.init_scope(): self.W_1 = ConvolutionSentence(n_units, n_inner_units, initialW=linear_init) self.W_2 = ConvolutionSentence(n_inner_units, n_units, initialW=linear_init) # self.act = F.relu self.act = F.leaky_relu def __call__(self, e): e = self.W_1(e) e = self.act(e) e = self.W_2(e) return e class EncoderLayer(chainer.Chain): def __init__(self, n_units, h=8, dropout=0.1): super(EncoderLayer, self).__init__() with self.init_scope(): self.self_attention = MultiHeadAttention(n_units, h) self.feed_forward = FeedForwardLayer(n_units) self.ln_1 = LayerNormalizationSentence(n_units, eps=1e-6) self.ln_2 = LayerNormalizationSentence(n_units, eps=1e-6) self.dropout = dropout def __call__(self, e, xx_mask): sub = self.self_attention(e, e, xx_mask) e = e + F.dropout(sub, self.dropout) e = self.ln_1(e) sub = self.feed_forward(e) e = e + F.dropout(sub, self.dropout) e = self.ln_2(e) return e class DecoderLayer(chainer.Chain): def __init__(self, n_units, h=8, dropout=0.1): super(DecoderLayer, self).__init__() with self.init_scope(): self.self_attention = MultiHeadAttention(n_units, h) self.source_attention = MultiHeadAttention( n_units, h, self_attention=False) self.feed_forward = FeedForwardLayer(n_units) self.ln_1 = LayerNormalizationSentence(n_units, eps=1e-6) self.ln_2 = LayerNormalizationSentence(n_units, eps=1e-6) self.ln_3 = LayerNormalizationSentence(n_units, eps=1e-6) self.dropout = dropout def __call__(self, e, s, xy_mask, yy_mask): sub = self.self_attention(e, e, yy_mask) e = e + F.dropout(sub, self.dropout) e = self.ln_1(e) sub = self.source_attention(e, s, xy_mask) e = e + F.dropout(sub, self.dropout) e = self.ln_2(e) sub = self.feed_forward(e) e = e + F.dropout(sub, self.dropout) e = self.ln_3(e) return e class Encoder(chainer.Chain): def __init__(self, n_layers, n_units, h=8, dropout=0.1): super(Encoder, self).__init__() self.layer_names = [] for i in range(1, n_layers + 1): name = 'l{}'.format(i) layer = EncoderLayer(n_units, h, dropout) self.add_link(name, layer) self.layer_names.append(name) def __call__(self, e, xx_mask): for name in self.layer_names: e = getattr(self, name)(e, xx_mask) return e class Decoder(chainer.Chain): def __init__(self, n_layers, n_units, h=8, dropout=0.1): super(Decoder, self).__init__() self.layer_names = [] for i in range(1, n_layers + 1): name = 'l{}'.format(i) layer = DecoderLayer(n_units, h, dropout) self.add_link(name, layer) self.layer_names.append(name) def __call__(self, e, source, xy_mask, yy_mask): for name in self.layer_names: e = getattr(self, name)(e, source, xy_mask, yy_mask) return e class Transformer(chainer.Chain): def __init__(self, n_layers, n_source_vocab, n_target_vocab, n_units, h=8, dropout=0.1, max_length=500, use_label_smoothing=False, embed_position=False): super(Transformer, self).__init__() with self.init_scope(): self.embed_x = L.EmbedID(n_source_vocab, n_units, ignore_label=-1, initialW=linear_init) self.embed_y = L.EmbedID(n_target_vocab, n_units, ignore_label=-1, initialW=linear_init) self.encoder = Encoder(n_layers, n_units, h, dropout) self.decoder = Decoder(n_layers, n_units, h, dropout) if embed_position: self.embed_pos = L.EmbedID(max_length, n_units, ignore_label=-1) self.n_layers = n_layers self.n_units = n_units self.n_target_vocab = n_target_vocab self.dropout = dropout self.use_label_smoothing = use_label_smoothing self.initialize_position_encoding(max_length, n_units) self.scale_emb = self.n_units ** 0.5 def initialize_position_encoding(self, length, n_units): xp = self.xp """ # Implementation described in the paper start = 1 # index starts from 1 or 0 posi_block = xp.arange( start, length + start, dtype='f')[None, None, :] unit_block = xp.arange( start, n_units // 2 + start, dtype='f')[None, :, None] rad_block = posi_block / 10000. ** (unit_block / (n_units // 2)) sin_block = xp.sin(rad_block) cos_block = xp.cos(rad_block) self.position_encoding_block = xp.empty((1, n_units, length), 'f') self.position_encoding_block[:, ::2, :] = sin_block self.position_encoding_block[:, 1::2, :] = cos_block """ # Implementation in the Google tensor2tensor repo channels = n_units position = xp.arange(length, dtype='f') num_timescales = channels // 2 log_timescale_increment = ( xp.log(10000. / 1.) / (float(num_timescales) - 1)) inv_timescales = 1. * xp.exp( xp.arange(num_timescales).astype('f') * -log_timescale_increment) scaled_time = \ xp.expand_dims(position, 1) * \ xp.expand_dims(inv_timescales, 0) signal = xp.concatenate( [xp.sin(scaled_time), xp.cos(scaled_time)], axis=1) signal = xp.reshape(signal, [1, length, channels]) self.position_encoding_block = xp.transpose(signal, (0, 2, 1)) def make_input_embedding(self, embed, block): batch, length = block.shape emb_block = sentence_block_embed(embed, block) * self.scale_emb emb_block += self.xp.array(self.position_encoding_block[:, :, :length]) if hasattr(self, 'embed_pos'): emb_block += sentence_block_embed( self.embed_pos, self.xp.broadcast_to( self.xp.arange(length).astype('i')[None, :], block.shape)) emb_block = F.dropout(emb_block, self.dropout) return emb_block def make_attention_mask(self, source_block, target_block): mask = (target_block[:, None, :] >= 0) * \ (source_block[:, :, None] >= 0) # (batch, source_length, target_length) return mask def make_history_mask(self, block): batch, length = block.shape arange = self.xp.arange(length) history_mask = (arange[None, ] <= arange[:, None])[None, ] history_mask = self.xp.broadcast_to( history_mask, (batch, length, length)) return history_mask def output(self, h): return F.linear(h, self.embed_y.W) def output_and_loss(self, h_block, t_block): batch, units, length = h_block.shape # Output (all together at once for efficiency) concat_logit_block = seq_func(self.output, h_block, reconstruct_shape=False) rebatch, _ = concat_logit_block.shape # Make target concat_t_block = t_block.reshape((rebatch)) ignore_mask = (concat_t_block >= 0) n_token = ignore_mask.sum() normalizer = n_token # n_token or batch or 1 # normalizer = 1 if not self.use_label_smoothing: loss = F.softmax_cross_entropy(concat_logit_block, concat_t_block) loss = loss * n_token / normalizer else: log_prob = F.log_softmax(concat_logit_block) broad_ignore_mask = self.xp.broadcast_to( ignore_mask[:, None], concat_logit_block.shape) pre_loss = ignore_mask * \ log_prob[self.xp.arange(rebatch), concat_t_block] loss = - F.sum(pre_loss) / normalizer accuracy = F.accuracy( concat_logit_block, concat_t_block, ignore_label=-1) perp = self.xp.exp(loss.data * normalizer / n_token) # Report the Values reporter.report({'loss': loss.data * normalizer / n_token, 'acc': accuracy.data, 'perp': perp}, self) if self.use_label_smoothing: label_smoothing = broad_ignore_mask * \ - 1. / self.n_target_vocab * log_prob label_smoothing = F.sum(label_smoothing) / normalizer loss = 0.9 * loss + 0.1 * label_smoothing return loss def __call__(self, x_block, y_in_block, y_out_block, get_prediction=False): batch, x_length = x_block.shape batch, y_length = y_in_block.shape # Make Embedding ex_block = self.make_input_embedding(self.embed_x, x_block) ey_block = self.make_input_embedding(self.embed_y, y_in_block) # Make Masks xx_mask = self.make_attention_mask(x_block, x_block) xy_mask = self.make_attention_mask(y_in_block, x_block) yy_mask = self.make_attention_mask(y_in_block, y_in_block) yy_mask *= self.make_history_mask(y_in_block) # Encode Sources z_blocks = self.encoder(ex_block, xx_mask) # [(batch, n_units, x_length), ...] # Encode Targets with Sources (Decode without Output) h_block = self.decoder(ey_block, z_blocks, xy_mask, yy_mask) # (batch, n_units, y_length) if get_prediction: return self.output(h_block[:, :, -1]) else: return self.output_and_loss(h_block, y_out_block) def translate(self, x_block, max_length=50, beam=5): if beam: return self.translate_beam(x_block, max_length, beam) # TODO: efficient inference by re-using result with chainer.no_backprop_mode(): with chainer.using_config('train', False): x_block = source_pad_concat_convert( x_block, device=None) batch, x_length = x_block.shape # y_block = self.xp.zeros((batch, 1), dtype=x_block.dtype) y_block = self.xp.full( (batch, 1), 2, dtype=x_block.dtype) # bos eos_flags = self.xp.zeros((batch, ), dtype=x_block.dtype) result = [] for i in range(max_length): log_prob_tail = self(x_block, y_block, y_block, get_prediction=True) ys = self.xp.argmax(log_prob_tail.data, axis=1).astype('i') result.append(ys) y_block = F.concat([y_block, ys[:, None]], axis=1).data eos_flags += (ys == 0) if self.xp.all(eos_flags): break result = cuda.to_cpu(self.xp.stack(result).T) # Remove EOS taggs outs = [] for y in result: inds = np.argwhere(y == 0) if len(inds) > 0: y = y[:inds[0, 0]] if len(y) == 0: y = np.array([1], 'i') outs.append(y) return outs def translate_beam(self, x_block, max_length=50, beam=5): # TODO: efficient inference by re-using result # TODO: batch processing with chainer.no_backprop_mode(): with chainer.using_config('train', False): x_block = source_pad_concat_convert( x_block, device=None) batch, x_length = x_block.shape assert batch == 1, 'Batch processing is not supported now.' y_block = self.xp.full( (batch, 1), 2, dtype=x_block.dtype) # bos eos_flags = self.xp.zeros( (batch * beam, ), dtype=x_block.dtype) sum_scores = self.xp.zeros(1, 'f') result = [[2]] * batch * beam for i in range(max_length): log_prob_tail = self(x_block, y_block, y_block, get_prediction=True) ys_list, ws_list = get_topk( log_prob_tail.data, beam, axis=1) ys_concat = self.xp.concatenate(ys_list, axis=0) sum_ws_list = [ws + sum_scores for ws in ws_list] sum_ws_concat = self.xp.concatenate(sum_ws_list, axis=0) # Get top-k from total candidates idx_list, sum_w_list = get_topk( sum_ws_concat, beam, axis=0) idx_concat = self.xp.stack(idx_list, axis=0) ys = ys_concat[idx_concat] sum_scores = self.xp.stack(sum_w_list, axis=0) if i != 0: old_idx_list = (idx_concat % beam).tolist() else: old_idx_list = [0] * beam result = [result[idx] + [y] for idx, y in zip(old_idx_list, ys.tolist())] y_block = self.xp.array(result).astype('i') if x_block.shape[0] != y_block.shape[0]: x_block = self.xp.broadcast_to( x_block, (y_block.shape[0], x_block.shape[1])) eos_flags += (ys == 0) if self.xp.all(eos_flags): break outs = [[wi for wi in sent if wi not in [2, 0]] for sent in result] outs = [sent if sent else [0] for sent in outs] return outs def get_topk(x, k=5, axis=1): ids_list = [] scores_list = [] xp = cuda.get_array_module(x) for i in range(k): ids = xp.argmax(x, axis=axis).astype('i') if axis == 0: scores = x[ids] x[ids] = - float('inf') else: scores = x[xp.arange(ids.shape[0]), ids] x[xp.arange(ids.shape[0]), ids] = - float('inf') ids_list.append(ids) scores_list.append(scores) return ids_list, scores_list ================================================ FILE: preprocess.py ================================================ from __future__ import unicode_literals import collections import io import re import numpy import progressbar split_pattern = re.compile(r'([.,!?"\':;)(])') digit_pattern = re.compile(r'\d') def split_sentence(s): s = s.lower() s = s.replace('\u2019', "'") s = digit_pattern.sub('0', s) words = [] for word in s.strip().split(): words.extend(split_pattern.split(word)) words = [w for w in words if w] return words def open_file(path): return io.open(path, encoding='utf-8', errors='ignore') def count_lines(path): with open_file(path) as f: return sum([1 for _ in f]) def read_file(path): n_lines = count_lines(path) bar = progressbar.ProgressBar() with open_file(path) as f: for line in bar(f, max_value=n_lines): words = split_sentence(line) yield words def count_words(path, max_vocab_size=40000): counts = collections.Counter() for words in read_file(path): for word in words: counts[word] += 1 vocab = [word for (word, _) in counts.most_common(max_vocab_size)] return vocab def make_dataset(path, vocab): word_id = {word: index for index, word in enumerate(vocab)} dataset = [] token_count = 0 unknown_count = 0 for words in read_file(path): array = make_array(word_id, words) dataset.append(array) token_count += array.size unknown_count += (array == 1).sum() print('# of tokens: %d' % token_count) print('# of unknown: %d (%.2f %%)' % (unknown_count, 100. * unknown_count / token_count)) return dataset def make_array(word_id, words): ids = [word_id.get(word, 1) for word in words] return numpy.array(ids, 'i') ================================================ FILE: subfuncs.py ================================================ from __future__ import division from chainer.training import extension class VaswaniRule(extension.Extension): """Trainer extension to shift an optimizer attribute magically by Vaswani. Args: attr (str): Name of the attribute to shift. rate (float): Rate of the exponential shift. This value is multiplied to the attribute at each call. init (float): Initial value of the attribute. If it is ``None``, the extension extracts the attribute at the first call and uses it as the initial value. target (float): Target value of the attribute. If the attribute reaches this value, the shift stops. optimizer (~chainer.Optimizer): Target optimizer to adjust the attribute. If it is ``None``, the main optimizer of the updater is used. """ def __init__(self, attr, d, warmup_steps=4000, init=None, target=None, optimizer=None, scale=1.): self._attr = attr self._d_inv05 = d ** (-0.5) * scale self._warmup_steps_inv15 = warmup_steps ** (-1.5) self._init = init self._target = target self._optimizer = optimizer self._t = 0 self._last_value = None def initialize(self, trainer): optimizer = self._get_optimizer(trainer) # ensure that _init is set if self._init is None: # self._init = getattr(optimizer, self._attr) self._init = self._d_inv05 * (1. * self._warmup_steps_inv15) if self._last_value is not None: # resuming from a snapshot self._update_value(optimizer, self._last_value) else: self._update_value(optimizer, self._init) def __call__(self, trainer): self._t += 1 optimizer = self._get_optimizer(trainer) value = self._d_inv05 * \ min(self._t ** (-0.5), self._t * self._warmup_steps_inv15) self._update_value(optimizer, value) def serialize(self, serializer): self._t = serializer('_t', self._t) self._last_value = serializer('_last_value', self._last_value) def _get_optimizer(self, trainer): return self._optimizer or trainer.updater.get_optimizer('main') def _update_value(self, optimizer, value): setattr(optimizer, self._attr, value) self._last_value = value ================================================ FILE: train.py ================================================ # encoding: utf-8 import argparse import json import os.path from nltk.translate import bleu_score import numpy import six import chainer from chainer import cuda from chainer.dataset import convert from chainer import reporter from chainer import training from chainer.training import extensions import preprocess import net from subfuncs import VaswaniRule def seq2seq_pad_concat_convert(xy_batch, device, eos_id=0, bos_id=2): """ Args: xy_batch (list of tuple of two numpy.ndarray-s or cupy.ndarray-s): xy_batch[i][0] is an array of token ids of i-th input sentence in a minibatch. xy_batch[i][1] is an array of token ids of i-th target sentence in a minibatch. The shape of each array is `(sentence length, )`. device (int or None): Device ID to which an array is sent. If it is negative value, an array is sent to CPU. If it is positive, an array is sent to GPU with the given ID. If it is ``None``, an array is left in the original device. Returns: Tuple of Converted array. (input_sent_batch_array, target_sent_batch_input_array, target_sent_batch_output_array). The shape of each array is `(batchsize, max_sentence_length)`. All sentences are padded with -1 to reach max_sentence_length. """ x_seqs, y_seqs = zip(*xy_batch) x_block = convert.concat_examples(x_seqs, device, padding=-1) y_block = convert.concat_examples(y_seqs, device, padding=-1) xp = cuda.get_array_module(x_block) # The paper did not mention eos # add eos x_block = xp.pad(x_block, ((0, 0), (0, 1)), 'constant', constant_values=-1) for i_batch, seq in enumerate(x_seqs): x_block[i_batch, len(seq)] = eos_id x_block = xp.pad(x_block, ((0, 0), (1, 0)), 'constant', constant_values=bos_id) y_out_block = xp.pad(y_block, ((0, 0), (0, 1)), 'constant', constant_values=-1) for i_batch, seq in enumerate(y_seqs): y_out_block[i_batch, len(seq)] = eos_id y_in_block = xp.pad(y_block, ((0, 0), (1, 0)), 'constant', constant_values=bos_id) return (x_block, y_in_block, y_out_block) def source_pad_concat_convert(x_seqs, device, eos_id=0, bos_id=2): x_block = convert.concat_examples(x_seqs, device, padding=-1) xp = cuda.get_array_module(x_block) # add eos x_block = xp.pad(x_block, ((0, 0), (0, 1)), 'constant', constant_values=-1) for i_batch, seq in enumerate(x_seqs): x_block[i_batch, len(seq)] = eos_id x_block = xp.pad(x_block, ((0, 0), (1, 0)), 'constant', constant_values=bos_id) return x_block class CalculateBleu(chainer.training.Extension): trigger = 1, 'epoch' priority = chainer.training.PRIORITY_WRITER def __init__( self, model, test_data, key, batch=50, device=-1, max_length=50): self.model = model self.test_data = test_data self.key = key self.batch = batch self.device = device self.max_length = max_length def __call__(self, trainer): print('## Calculate BLEU') with chainer.no_backprop_mode(): with chainer.using_config('train', False): references = [] hypotheses = [] for i in range(0, len(self.test_data), self.batch): sources, targets = zip(*self.test_data[i:i + self.batch]) references.extend([[t.tolist()] for t in targets]) sources = [ chainer.dataset.to_device(self.device, x) for x in sources] ys = [y.tolist() for y in self.model.translate( sources, self.max_length, beam=False)] # greedy generation for efficiency hypotheses.extend(ys) bleu = bleu_score.corpus_bleu( references, hypotheses, smoothing_function=bleu_score.SmoothingFunction().method1) * 100 print('BLEU:', bleu) reporter.report({self.key: bleu}) def main(): parser = argparse.ArgumentParser( description='Chainer example: convolutional seq2seq') parser.add_argument('--batchsize', '-b', type=int, default=48, help='Number of images in each mini-batch') parser.add_argument('--epoch', '-e', type=int, default=100, help='Number of sweeps over the dataset to train') parser.add_argument('--gpu', '-g', type=int, default=-1, help='GPU ID (negative value indicates CPU)') parser.add_argument('--unit', '-u', type=int, default=512, help='Number of units') parser.add_argument('--layer', '-l', type=int, default=6, help='Number of layers') parser.add_argument('--head', type=int, default=8, help='Number of heads in attention mechanism') parser.add_argument('--dropout', '-d', type=float, default=0.1, help='Dropout rate') parser.add_argument('--input', '-i', type=str, default='./', help='Input directory') parser.add_argument('--source', '-s', type=str, default='europarl-v7.fr-en.en', help='Filename of train data for source language') parser.add_argument('--target', '-t', type=str, default='europarl-v7.fr-en.fr', help='Filename of train data for target language') parser.add_argument('--source-valid', '-svalid', type=str, default='dev/newstest2013.en', help='Filename of validation data for source language') parser.add_argument('--target-valid', '-tvalid', type=str, default='dev/newstest2013.fr', help='Filename of validation data for target language') parser.add_argument('--out', '-o', default='result', help='Directory to output the result') parser.add_argument('--source-vocab', type=int, default=40000, help='Vocabulary size of source language') parser.add_argument('--target-vocab', type=int, default=40000, help='Vocabulary size of target language') parser.add_argument('--no-bleu', '-no-bleu', action='store_true', help='Skip BLEU calculation') parser.add_argument('--use-label-smoothing', action='store_true', help='Use label smoothing for cross entropy') parser.add_argument('--embed-position', action='store_true', help='Use position embedding rather than sinusoid') parser.add_argument('--use-fixed-lr', action='store_true', help='Use fixed learning rate rather than the ' + 'annealing proposed in the paper') args = parser.parse_args() print(json.dumps(args.__dict__, indent=4)) # Check file en_path = os.path.join(args.input, args.source) source_vocab = ['', '', ''] + \ preprocess.count_words(en_path, args.source_vocab) source_data = preprocess.make_dataset(en_path, source_vocab) fr_path = os.path.join(args.input, args.target) target_vocab = ['', '', ''] + \ preprocess.count_words(fr_path, args.target_vocab) target_data = preprocess.make_dataset(fr_path, target_vocab) assert len(source_data) == len(target_data) print('Original training data size: %d' % len(source_data)) train_data = [(s, t) for s, t in six.moves.zip(source_data, target_data) if 0 < len(s) < 50 and 0 < len(t) < 50] print('Filtered training data size: %d' % len(train_data)) en_path = os.path.join(args.input, args.source_valid) source_data = preprocess.make_dataset(en_path, source_vocab) fr_path = os.path.join(args.input, args.target_valid) target_data = preprocess.make_dataset(fr_path, target_vocab) assert len(source_data) == len(target_data) test_data = [(s, t) for s, t in six.moves.zip(source_data, target_data) if 0 < len(s) and 0 < len(t)] source_ids = {word: index for index, word in enumerate(source_vocab)} target_ids = {word: index for index, word in enumerate(target_vocab)} target_words = {i: w for w, i in target_ids.items()} source_words = {i: w for w, i in source_ids.items()} # Define Model model = net.Transformer( args.layer, min(len(source_ids), len(source_words)), min(len(target_ids), len(target_words)), args.unit, h=args.head, dropout=args.dropout, max_length=500, use_label_smoothing=args.use_label_smoothing, embed_position=args.embed_position) if args.gpu >= 0: chainer.cuda.get_device(args.gpu).use() model.to_gpu(args.gpu) # Setup Optimizer optimizer = chainer.optimizers.Adam( alpha=5e-5, beta1=0.9, beta2=0.98, eps=1e-9 ) optimizer.setup(model) # Setup Trainer train_iter = chainer.iterators.SerialIterator(train_data, args.batchsize) test_iter = chainer.iterators.SerialIterator(test_data, args.batchsize, repeat=False, shuffle=False) iter_per_epoch = len(train_data) // args.batchsize print('Number of iter/epoch =', iter_per_epoch) updater = training.StandardUpdater( train_iter, optimizer, converter=seq2seq_pad_concat_convert, device=args.gpu) trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out) # If you want to change a logging interval, change this number log_trigger = (min(200, iter_per_epoch // 2), 'iteration') def floor_step(trigger): floored = trigger[0] - trigger[0] % log_trigger[0] if floored <= 0: floored = trigger[0] return (floored, trigger[1]) # Validation every half epoch eval_trigger = floor_step((iter_per_epoch // 2, 'iteration')) record_trigger = training.triggers.MinValueTrigger( 'val/main/perp', eval_trigger) evaluator = extensions.Evaluator( test_iter, model, converter=seq2seq_pad_concat_convert, device=args.gpu) evaluator.default_name = 'val' trainer.extend(evaluator, trigger=eval_trigger) # Use Vaswan's magical rule of learning rate(Eq. 3 in the paper) # But, the hyperparamter in the paper seems to work well # only with a large batchsize. # If you run on popular setup (e.g. size=48 on 1 GPU), # you may have to change the hyperparamter. # I scaled learning rate by 0.5 consistently. # ("scale" is always multiplied to learning rate.) # If you use a shallow layer network (<=2), # you may not have to change it from the paper setting. if not args.use_fixed_lr: trainer.extend( # VaswaniRule('alpha', d=args.unit, warmup_steps=4000, scale=1.), # VaswaniRule('alpha', d=args.unit, warmup_steps=32000, scale=1.), # VaswaniRule('alpha', d=args.unit, warmup_steps=4000, scale=0.5), # VaswaniRule('alpha', d=args.unit, warmup_steps=16000, scale=1.), VaswaniRule('alpha', d=args.unit, warmup_steps=64000, scale=1.), trigger=(1, 'iteration')) observe_alpha = extensions.observe_value( 'alpha', lambda trainer: trainer.updater.get_optimizer('main').alpha) trainer.extend( observe_alpha, trigger=(1, 'iteration')) # Only if a model gets best validation score, # save (overwrite) the model trainer.extend(extensions.snapshot_object( model, 'best_model.npz'), trigger=record_trigger) def translate_one(source, target): words = preprocess.split_sentence(source) print('# source : ' + ' '.join(words)) x = model.xp.array( [source_ids.get(w, 1) for w in words], 'i') ys = model.translate([x], beam=5)[0] words = [target_words[y] for y in ys] print('# result : ' + ' '.join(words)) print('# expect : ' + target) @chainer.training.make_extension(trigger=(200, 'iteration')) def translate(trainer): translate_one( 'Who are we ?', 'Qui sommes-nous?') translate_one( 'And it often costs over a hundred dollars ' + 'to obtain the required identity card .', 'Or, il en coûte souvent plus de cent dollars ' + 'pour obtenir la carte d\'identité requise.') source, target = test_data[numpy.random.choice(len(test_data))] source = ' '.join([source_words[i] for i in source]) target = ' '.join([target_words[i] for i in target]) translate_one(source, target) # Gereneration Test trainer.extend( translate, trigger=(min(200, iter_per_epoch), 'iteration')) # Calculate BLEU every half epoch if not args.no_bleu: trainer.extend( CalculateBleu( model, test_data, 'val/main/bleu', device=args.gpu, batch=args.batchsize // 4), trigger=floor_step((iter_per_epoch // 2, 'iteration'))) # Log trainer.extend(extensions.LogReport(trigger=log_trigger), trigger=log_trigger) trainer.extend(extensions.PrintReport( ['epoch', 'iteration', 'main/loss', 'val/main/loss', 'main/perp', 'val/main/perp', 'main/acc', 'val/main/acc', 'val/main/bleu', 'alpha', 'elapsed_time']), trigger=log_trigger) print('start training') trainer.run() if __name__ == '__main__': main()