diff --git a/repeat-copy/.ipynb_checkpoints/repeat-copy-nn-checkpoint.ipynb b/repeat-copy/.ipynb_checkpoints/repeat-copy-nn-checkpoint.ipynb
index 30a41bc..b547d7c 100644
--- a/repeat-copy/.ipynb_checkpoints/repeat-copy-nn-checkpoint.ipynb
+++ b/repeat-copy/.ipynb_checkpoints/repeat-copy-nn-checkpoint.ipynb
@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "# Repeat Copy Task\n",
-    "### Using a Differentiable Memory Architecture (DNC)\n",
+    "### Differentiable Neural Computer (DNC) using a NN Controller\n",
     "\n",
     "<a href=\"http://www.nature.com/nature/journal/v538/n7626/full/nature20101.html\"><img src=\"../static/dnc_schema.png\" alt=\"DNC schema\" style=\"width: 700px;\"/></a>\n",
     "\n",
@@ -85,9 +85,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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qQ0QcSfHDZOLOv++PiNOBt1JKr1Icd7wpIn5Ecde8P6S4auEfehDuvM3W3sbjZopjbzsb\n8/0Rxb8wm2Yurb9FxFqKy3MuAfZExMSe2u6U0sTdDwcmv3O1t5H7gchvRNxGcZz4FeA9wBXAucCt\njVkGJq8we3sHKa9A9y9Na5xTcD1FR9lLcWLC8l6f1l9ROzdQfBH2UnSm+4GTex1XSW07j+LynUPT\nHusmzfN5iktd3qH4cpzS67iraC/FiTUPUWwQ9gEvAl8B3tvruDtsa7N2HgJ+a9p8A5Hfudo7SPkF\n/qoR/95Gex4GPjGIeZ2rvYOU15SSt0CVJKnurM0uSVLNOZhLklRzDuaSJNWcg7kkSTXnYC5JUs05\nmEuSVHMO5pIk1ZyDuSRJNedgLklSzTmYS5JUcw7mkiTVnIO5JEk1978M4TJVqhYNwgAAAABJRU5E\nrkJggg==\n",
+      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x103e5b650>"
+       "<matplotlib.figure.Figure at 0x109d165d0>"
       ]
      },
      "metadata": {},
@@ -240,7 +240,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "loaded model: models/model.ckpt-7000\n"
+      "loaded model: nn_models/model.ckpt-10000\n"
      ]
     }
    ],
@@ -275,31 +275,7 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Iteration 7053/10000"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-8-6907e51af76f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mfeed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mdnc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdnc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdnc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtsteps\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtsteps\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m     \u001b[0mstep_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m     \u001b[0mloss_history\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep_loss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mglobal_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    765\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 767\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    768\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    769\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    963\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 965\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    966\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1013\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1014\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1015\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1016\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1017\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
-      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1020\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1021\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1022\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1023\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1024\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1002\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1003\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1004\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1005\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "loss_history = []\n",
     "for i in xrange(global_step, FLAGS.iterations + 1):\n",
@@ -334,9 +310,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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R9ZkvjQtl/dvSp93sxbi8u48rPP2ba+D7m/edNjR20LmYantW+qSPvg6ffRBIUcdHN7zN\n7NPAD4ATgfuAqcBNZraHe7+iWwswZs9tGdfRd+9tH93ebxrEj2/WUyCPYuIOnXGK7d/DCd035ouJ\nO/41/PR71riO9I3XoMgvv+iaxohb0XEd61OnrVbZF1rL6IZ3RNlnqdh+NbzIb7N42VclmqjU8ftt\nf+2jiq1nhNiGd0Ta6P2kSCzVONZmuc9W7/jW/9cZu5/ExVLW76HhH8cup47fc0fo2K3vF6O36D8N\nYPV+FhVP24b0hdIelzXr9o2Jo/D0kSNhvwL5DI1o777YEVdD7RCx68XmPXbLwtNHDYWOvO98dVTW\nbIisiIdGnNyP6Ij7Pe5RpNEzahR05JXn6yNj99n06Ue8FHcAjLnY8eK+hTf4yNHGPgX2i7GRhzSP\nuCj2WuTJ/cjxEYnbC08eNRI69i7wxeL0WftOEXEAGyKOKcNejsub/tdKgAHWM6bhHbFNANZFlE97\nxEVIKH6cGGmwX953w/aMyzuq2h4VmXdQso4vZxzvqcDF7v5Ld19EGOJnNfCFMvISERGRxqE6XkRE\nJANRDW8zGwZMBP7QO81Dt+jzgf2rG5qIiIjUiup4ERGR7MTe8R5DeGo3/4n9FwnvgomIiEhzUh0v\nIiKSkax7NQdC5z7to/u+hDFqp5G1WHTdRfSz0OQGx5oOhrUcP2WveodQE+M7B8l6DpLybJxf50PA\nwrxpDf9qd0WmXhLe6c610/b1iaXWPn1UvSOojc4x9Y6gNqZ8st4RZO/jU7LsKLVxdH6s3hHURueR\n9Y6gNj7dILvtnBfCJ9eKiO5kYhveS4FuYIe86TsAhbuIBA6feWjlnRg1qUY5FcxeoZ4dWs9gWMvx\nnTG9mjSvCYNmPdXwrq296X+kWAz8rA6xRCurjp95QuGO1AaDKZ+qdwS10bldvSOojc5BcCHlE50N\n0oLJ2GBpkA6W9ZySZf++ETrfFD65ulbAxD+lmz/qUXN3fwNYAGwcm8bMLPn7rpi8REREpHGojhcR\nEclOOY+aXwjMNrMFbBpqZAtgdhXjEhERkdpTHS8iIpKB6Ia3u19tZmOAGYTHz/4CHOHusSPSiYiI\nSANRHS8iIpKNsjpXc/dZwKwqxyIiIiJ1pjpeRESk+qIb3mZ2MPAtwlif44CPu/u8agcWEVDqpN09\np2YYSHOK2HwAnN6zPptAmljkJmSInZdZ5j09p8XN0DDiVvS7FtE5TOQ2PKMnonvKLLlHJR/Sdn5U\n+p5BcDyM3ISc05Z+v4o5Fi7ueoNLJ8XFUi9l1fFtpD6bWN4+OiqecateTZ94RFTWvDYi/QxjXlgV\nl3mEyN2UGc+mTzvtbyvjMr86fVL7R1zWQ2M3YcSxex1xPS+tay+dptdrbVtF5b3NuvT77Mo3xZ2G\nb7VsQ+q0Q+iOynvBc1HJeUfE7219+rABuH2P9GknvzUub86MSDsqLuvXRqXfsbZZuS4u89jOxSK3\neYwXRuT3v1ncDru8FJX32tfTHxG3iTxd+9/906edfHlExk8BWXSulhhBePTsq8TXFyIiItK4VMeL\niIhkoJx3vG8EboSNvZ2KiIhIC1AdLyIiko1y7niLiIiIiIiISEpqeIuIiIiIiIhkqKxezWPdMvUP\ntI/u2+HA+Cl7MaFzr1osXkREpCYenvMwD899pM+0tcsjO9FpMlN/CqO37DttymTofH9dwhEREcnE\nnLthzj19p61YnX7+mjS8D595KOM6xtZiUSIiInUzvnM84zvH95m2uGsJl066rE4RZW/mSdCxe72j\nEBERyVbn/uGTq+spmDgt3fx61FxEREREREQkQ+WM4z0C2I1NIyy+1cz2BZa5e8TokiIiItJIVMeL\niIhko5xHzScBtxHG93TgB8n0y4EvVCkuERERqT3V8SIiIhkoZxzv/0WPqIuIiLQc1fEiIiLZqEnn\napiFTwo93adGZT1kyHmp0/b0nBaVN+7p42g7PyrrmFhSbrqNTu9+I3Xa7w4ZFpX3GT3p847YfACc\n0xYZS/f6uAU0odjfg/QX83toKJE//J4NkcfOiGNW9LGzQUQfOzek31fOiTp21qaqrZtuYEO6pFt2\nvx6XdVv6tENXRWXNlqvSzxC5K+ERRT569cqovM98OX3ap7aPyppdIvJmp7i8H9stLv1u16VPO5Tu\nqLzbIwYa2GyLuHONnrb0e8tWr6T84ZRheOQ5UsebIhcwvnSSXptHZv22W9KnXbko7vg68vKIbb42\nKmva10Wcb8QOdhFbjUT8JGKPb7u+/GLqtK9u1146UY5tJqXfMBb3s+eQmyMSz4pIuzx90qir2mZ2\nmpndZ2YrzexFM/u1me0Rk4eIiIg0HtXxIiIi2Yl9nOxg4MfAe4DDgGHAzWYWezFLREREGovqeBER\nkYxEPbjg7h/J/dvMjgNeAiYCf6xeWCIiIlJLquNFRESyU2kHKqMJvZ4uq0IsIiIi0jhUx4uIiFRJ\n2Q1vMzPgh8Af3f2R6oUkIiIi9aQ6XkREpLoq6Wp1FrAXcGCphLdMnU/7qL692o3v3IsJnRFdIoqI\niDS8hcknV2TXuI0hdR0/9RIYvUXfaVMOgc7JmcQlIiJSF3OehTnP9Z22IqIz+7Ia3mb2E+AjwMHu\nvrhU+sNnHsa4jrHlLEpERKSJTEg+uRYDl9QhlvLE1vEzT4COyKGiREREmk3njuGTq2s5TLwt3fzR\nDe+kQj4SOMTdn4mdX0RERBqT6ngREZFsRDW8zWwW0Al8DFhlZjskX61w96Z8lk5ERERUx4uIiGQp\ntnO1k4CRwO3ACzmfo6sbloiIiNSY6ngREZGMxI7jXenwYyIiItKAVMeLiIhkJ/ZR85OArwC7JJMe\nBma4+41Vi8gi03tM2pjEMKTt/NRpe7pPjco7UzHbMG6TRG3Cc9qGReV9endEt4CRIlczqjxj9pPM\n8+45LSp9nLitGFOeWe4rsXmf0b0+ddohQ+LKJ+anOWTIeVF5Rx+DYn8UMVnHHCeGxJVPzFb8bmTe\nccegmNKMrdjqo+w6vo3UZxPr2zaLi2nL9DvTVkM3ROW9ZkR76USJoRvWReU9bHX6tMNXRWUNr6dP\n+uaRkXlHxLJhYlzWu90al56IbbiO9GUJsKEtfdr1xO2z7UPT7yvrRsTts+0j0qfdMqarZQi/4wjr\nrkmfdvi+cXm/ZVFE4j3jtiFnRqQdFZd199CIa5cje+IyjxVRnh5b9luUTtNr+Mi4YydvS5/Uj4rL\n2mZFJH4pIm3EMTn26vazwLeBDmAicCswz8z2isxHREREGovqeBERkYzEPmr+u7xJZ5jZV4D3AI9U\nLSoRERGpKdXxIiIi2SlrHG8AMxtC6HClHbizahGJiIhIXamOFxERqa5yxvGeANwNDCe8gXO0uz9W\n7cBERESktlTHi4iIZKOcHkwXAfsC7wZ+Asw1s/2qGpWIiIjUg+p4ERGRDETf8Xb3DcATyZ8PmNm7\nCb2gnlhsnlumzqd9VN8eJ8d37sWEzvGxixcREWlgDwEL86atrUcgZSmnjp/6Uxi9Zd9pUyZD5/sz\nClJERKQO5rwcPrlWdKefv+x3vHMMoUSn9YfPPIxxHWOrsCgREZFGtnfyybUY+FkdYqmKknX8zJOg\nY/caRSMiIlInnduFT66u12Hig+nmjx3H+1zgBuAZYCvgGOB9wHdj8hEREZHGojpeREQkO7F3vLcH\nLgfGASuAvwJHuPtt1Q5MREREakp1vIiISEZix/H+UlaBiIiISP2ojhcREclONd7xLs09fFIYMuT8\njINJz9KFDMCQtri4e3pOS5025abb6Jwhw+JmyIpbVPJz2uLiPqN7fVT6KEPSx97TfWpc1kPT7yux\neTcSixgz4fTuN6LyPmdo+n0lNm8soux7Issn4rfcFnlMiT8GZbdvRWxCTu+JLJ+IbXhO22ZRWccc\ng07vSX/8Wdz1BpdOigqluXQDG9IlXW9xZdLWljJjgPaItMB62ksn6k07PK6+GbYiYkeNrONX7pM+\n7QPr4vI+5Jn0af+xPC7vZR+IS3/gdenTrmbzqLzXjEj/W19tW0TlvVlb+n1ls7Q/nIStiEgclzVv\nrIxLv37z9E2IIQvignls2/Rp97orKuv+fV8OJHIbvta2Veq0W3W/GpW3pz9cAdA9YM8cfQ2L6BwM\noP3hiMQXx+XNO9MnXRZxLAQYdmz6tCNnRWT8NJDyHe9yhhPbyMxONbMeM7uwknxERESksaiOFxER\nqZ6yG95m9i7C8CIp2/giIiLSDFTHi4iIVFdZDW8z2xK4AvgSEPmwkYiIiDQq1fEiIiLVV+4d7/8E\nrnf3W6sZjIiIiNSd6ngREZEqi+5czcymEF59b+WuYkRERAYd1fEiIiLZiGp4m9lbgB8Ch7l76q5o\nb5n6B9pH9+2Ob/yUvZjQuVfM4kVERBraw3Me5uG5j/SZtnZ5ZNfSdVJuHT/1Ehid1/HzlEOgc3JV\nwxMREamrOffAnPv6TluxOv38sXe8JwLbAV1mGweMaQPeZ2ZfB9rd+w9+dfjMQxnXMTZyUSIiIs1l\nfOd4xneO7zNtcdcSLp10WZ0iilJWHT/zBOjYrYZRioiI1EHne8MnV9fTMPHsdPPHNrznA3vnTZsN\nPAqcX6hCFhERkaagOl5ERCQjUQ1vd18F9HmGzsxWAa+4+6PVDExERERqR3W8iIhIdsoexzuHroCL\niIi0JtXxIiIiVRDdq3k+d/9ANQIRERGRxqI6XkREpDos5pUtM5sOTM+bvMjdC3ZPbmYdwIIvLjg+\nfedqWb5CtrGvmJRiYokMe8jQ81On7ek5LSrvRtmEWb8NGFWcscHE7isxMtwwQ9oaZ79qlE2YZRyZ\naqDXaWP2K4jft7LSKMfCxV1L+MXEywAmuntXVjFVqtw6/pb7N2efjrZUy3jadq4syAHs7E9HpX/Q\n9k2d9q3+eFTet/v7U6f90tCTo/Lm0+9On/Z7a+Py3jl997z2jW2isp5/4YFR6SewMHXa++xdcXl7\n+ryX2LiovLdhWeq0yzxuG/7D0/di+Pmh+d01lPCn/J9+CZ+OSHtMXNZ8MaJt8mJc1s8fkH6bP03c\n8WqDpTsOAqz39tKJcmzL0qj0TvpK6j5/T1TeX277ZvrEsQV09+Hp06bf3CGU0T2p0576trNSp13S\ntZjLJv0CUtTx5dzxXggcChtLdEMZeYiIiEjjUR0vIiKSgXIa3hvc/eWqRyIiIiL1pjpeREQkA+V0\nrra7mT1vZo+b2RVmtmPVoxIREZF6UB0vIiKSgdiG9z3AccARwEnArsAdZjaiynGJiIhIbamOFxER\nyUhUw9vdb3L3a9x9obvfAnwE2Bo4OnbBC+c8HDtLU1o455HSiVrA4CnP1l/PwbCOMJjWU8cgSaea\ndfy1c96oenyN6Po5a+odQm34tfWOoCbmzYnsjK4Z+UP1jqAm/JY59Q6hJm6Y81q9Q6iNm1ujPCsa\nTszdV5jZ34EBu1m8Zep82kf17b3vtedfY0Ln+EoW3xQenvsIEzoLdgjbUh6e88jgKM9BsJ6DYR1h\nEK2njkE1tXDOwzycd7Fj3Yp1dYqmMmnr+DOnrmPk6L496C5+vodPdg7LMryGcP3ctXy0c/N6h1ED\n1wKfrHcQmZs3dx0f6xxe7zAythCI7PG8Gc2fC4d31juKzN049zU+3LlVvcPI3s1z4YP1L8+H5yzk\nkbl9L/SvW56+jq+o4W1mWxIq5F8OlO7wmYf1G07svz/2q0oWLSIi0nAmdI7vdwEgZzixppK2jv/3\nme39hhP73JGD5E6wiIgMGuM7JzC+c0KfaTnDiZUU9ai5mV1gZu8zs53N7ADg18AbQGvc/xcRERmk\nVMeLiIhkJ/aO91uAq4BtgZeBPwLvdfdXqh2YiIiI1JTqeBERkYxENbzdPfbh+uEASx9d2u+LdSvW\nsbhrSaGFRC4iglnpNLliYimStNh6DonIuqfQdhoolDpswkLrmWUcA8VSUGwwRTIvut/GyHDDVGO/\nKraOVdqEVRETS8w+21CqtJ+sW175esbsVxB/zKqGWh+DYvbvnDqwoV8eLbeO/8einn5frFzu/LWr\nu9/0xZbdI+grvP/yBvIPez112tVeuLO415b3sLCr/3dP+7L0gfjf0qcFWBZx6vZQZP8CXqxzsZXg\nD/ZN+tKyst2LAAAgAElEQVSoqKz/3rUqKn0P6cvzMYvsYCqiPF+x1VFZj2R96rQrPS7vZ/zV9Il9\ncZEv1hb+7m9dUbFErCbEVgkPpz94F70s+PoKvMA6/XV4+v1qCXHls8HaSidKvOEbovIeTeHf5mvL\ne3i0q/93TvpK6pnoa6uPpk8am/eibQtPf305LMorz/SbO4SyVf+6qpglK4r9fvpbuih9HW+e4ZmJ\nmX0GuDKzBYiIiDSPY9z9qnoHUS2q40VERDYqWcdn3fDeljAe6FNQ5FKNiIhIaxsO7ALc1EqPbauO\nFxERSV/HZ9rwFhERERERERnsono1FxEREREREZE4aniLiIiIiIiIZEgNbxEREREREZEMqeEtIiIi\nIiIikiE1vEVEREREREQyVJeGt5l9zcyeNLM1ZnaPmb2rHnFkxcymm1lP3ueResdVCTM72Mzmmdnz\nyfp8rECaGWb2gpmtNrNbzGy3esRaiVLraWaXFSjb39cr3nKY2Wlmdp+ZrTSzF83s12a2R4F0TV2e\nadazRcrzJDN70MxWJJ+7zOxDeWmauiyh9Hq2Qlm2CtXxzUd1/Mbvm/44ojq+T5pWKE/V8bRGWUId\nGt5m9mngB8B0YD/gQeAmMxtT61gythDYARibfA6qbzgVGwH8Bfgq0G8MOjP7NvB14ETg3cAqQrlu\nVssgq2DA9UzcQN+y7axNaFVzMPBj4D3AYcAw4GYz27w3QYuUZ8n1TDR7eT4LfBvoACYCtwLzzGwv\naJmyhBLrmWj2smx6quOblur4TZr9OKI6vq9mL0/V8Zs0e1mCu9f0A9wD/CjnbwOeA06pdSwZruN0\noKvecWS4fj3Ax/KmvQBMzfl7JLAGOLre8VZ5PS8Drq13bFVezzHJuh7U4uVZaD1brjyT9XoFOL5V\ny7LIerZkWTbbR3V8839Ux7fWcUR1fGuVZ7JequOb9FPTO95mNoxwFeMPvdM8bM35wP61jKUGdk8e\nZXrczK4wsx3rHVBWzGxXwpWn3HJdCdxL65UrwOTksaZFZjbLzLapd0AVGk248r8MWro8+6xnjpYp\nTzMbYmZTgHbgjlYty/z1zPmqZcqyGamOb02tehwZQKsdR1THt0h5qo5v/rIcWuPljQHagBfzpr8I\nvL3GsWTpHuA44G/AOOAswg9kgruvqmNcWRlLONgVKtextQ8nUzcA1wBPAm8DzgN+b2b7JyeYTcXM\nDPgh8Ed3731HseXKs8h6QouUp5lNAO4GhgOrCVe6Hzez/Wmhsiy2nsnXLVGWTU51vOr4ZtdSxxHV\n8a1RnqrjgRYpy1o3vAcFd78p58+FZnYf8DRwNOFRCWlS7n51zp8Pm9lDwOPAZOC2ugRVmVnAXsCB\n9Q4kYwXXs4XKcxGwLzAK+BQw18wOqW9ImSi4nu7+QAuVpTQ41fGtqwWPI6rjN2nm8lQd3yJlWevO\n1ZYC3YQX43PtACypcSw14+4rgL8DTdfLYEpLCO/xDapyBXD3Jwn7ddOVrZn9BPgIMNndF+d81VLl\nOcB69tOs5enuG9z9iaRyOp3wmNlXaLGyHGA9C6VtyrJscqrjW1NLHUdiNPNxRHV8f81anqrjC6Zt\nyrKsacPb3d8AFgCH9k5LHg85FLirlrHUkpltSdgxBjwgNKtk519C33IdSehpsmXLFcDM3gJsS5OV\nbVJRHQm8392fyf2ulcpzoPUskr4py7OAIUBbK5VlEUMIjzb300Jl2TRUx7fmvjYIjiNFNetxRHV8\n0fRNWZ4FqI5v0rKsx6PmFwKzzWwBcB8wFdgCmF2HWDJhZhcA1xMePXszcDbwBjCnnnFVwsxGEE4s\nLJn0VjPbF1jm7s8S3q05w8weA54C/p3Qk+1v6hBu2QZaz+QznfCOyZIk3X8Q7nTc1D+3xmRmswhD\nMHwMWGVmvVdKV7j72uT/TV+epdYzKetWKM9zCe8+PQNsBRwDvA/4bpKk6csSBl7PVinLFqE6vgmp\njlcdT5OVp+p41fE0WVkCtR9OLHn//auEnWMN4SX6SfWII8P1m0PY6dcQdqCrgF3rHVeF63QIYZiG\n7rzPpTlpziIMa7Ca8EPYrd5xV3M9CZ093Ej40a8FngD+C9iu3nFHrmOh9esGPp+XrqnLs9R6tlB5\n/jyJfU2yLjcDH2ilsiy1nq1Slq3yUR3ffB/V8arjm608Vce3TlmWWs9WKUt3x5IVEhEREREREZEM\n1LpzNREREREREZFBRQ1vERERERERkQyp4S0iIiIiIiKSITW8RURERERERDKkhreIiIiIiIhIhtTw\nFhEREREREcmQGt4iIiIiIiIiGVLDW0RERERERCRDaniLiIiIiIiIZEgNbxEREREREZEMqeEtIiIi\nIiIikiE1vEVEREREREQypIa3iIiIiIiISIbU8BYRERERERHJkBreIiIiIiIiIhlSw1tEREREREQk\nQ2p4i4iIiIiIiGRIDW8RERERERGRDKnhLSIiIiIiIpIhNbxFREREREREMqSGt4iIiIiIiEiG1PAW\nERERERERyZAa3iIiIiIiIiIZUsNbREREREREJENqeIuIiIiIiIhkSA1vERERERERkQyp4S0iIiIi\nIiKSITW8RURERERERDKkhreIiIiIiIhIhtTwFhEREREREcmQGt4iIiIiIiIiGVLDW0RERERERCRD\naniLiIiIiIiIZEgNbxEREREREZEMqeEtIiIiIiIikiE1vEVEREREREQypIa3iIiIiIiISIbU8BYR\nERERERHJkBreIiIiIiIiIhlSw1tEREREREQkQ2p4i4iIiIiIiGRIDW8RERERERGRDKnhLSIiIiIi\nIpIhNbxFREREREREMqSGt4iIiIiIiEiG1PCWQcPMjjOzHjPbqd6xVKLS9TCzs5L5t6lXDNVgZqeY\n2SMZL+PLZva0mQ3LcjkiIiIi0trU8JbBxJPPRma2v5lNN7ORdYqpHP3WI4v5S2ybSmOoiJltBZwC\nnJ/xomYDmwFfzng5IiIiItLC1PCWweSXwObu/kzOtAOAacDo+oRUlkLrkYWBtk2tYijmi0AbMDfL\nhbj7OuBy4N+yXI6IiIiItDY1vKXlmdkWAB6sz/+6DiFVpMh6ZKHotqlhDMUcB8yrUQxXA7uY2eQa\nLEtEREREWpAa3tKQzGzv5B3i/5MzrSOZdn9e2hvM7O7k/73vL7/DzK4ys2XAncl3fd5LNrPpwPeS\nbJ5KvuvO+f5NZnapmS0xs7VmttDMjs8q9rTLLPZ+tZlNNrP7zWyNmf3DzE7s3R5FQt3azGab2atm\ntjxZ7vCU2yZ/W/Zu97cVy7PCWHPn3QXYB5hfKm01uHsXsAw4shbLExEREZHWM7TeAYgUsRBYDrwP\n+G0y7WCgB9jXzLZ099fNzID9gZ8maXrfO/4V8HfgNDbduc1/L/kaYA9gCvAN4JVk+stmtj1wL9AN\nXAQsBT4M/MLMtnL3i6ode8QyC72rvh9wA/ACcCbht31mkkehd7GNcCf3CeBUoAM4AXgx2WbXFts2\nRWLo/X9+nl/KybPcWPMdkKTrSpG2WrqAA2u4PBERERFpIWp4S0NydzezPxEarL0OBn5NuPN4AHAz\n8E5gJMld7RwPuPvnSixjoZl1ERqXv8l9X9nMfkxonL7T3Zcnk39mZlcBZ5nZxcn7v9WM/dxylwmc\nDWwADnD3F5N1uBpYNMAmWODuJ+as8xjCu9OnuftDxbZNCUXzrDDWXHsm/z6ZO9HMPgvsCwxz939N\nph0ETHf3w81sc8JFkX9x99+nXFavJ4DPRs4jIiIiIgLoUXNpbHcCHUmDCeAg4PfAg2xq1PbeSf5T\nznwOXFzhsj8JXA+0mdm2vR9Cg3kU4W5utWL/YyXLNLMhwKHAdb0NWQB3f4JwZ7mQQtvoTmBbM9uy\nxLoVUzLPMmPNty2wwd1X904ws7cDawh36nMfzf8nYEny/3WEO/d7p12hHK8Cmxd6bF5EREREpBTd\n8ZZGdicwDNjfzJ4DtkumTWBT4/Ug4JGcO8S9nqRMZrYdoSfvEyk8jJQD25fIJk3sBxNiX1HhMrcH\nNgceK/BdoWm98u9iv5r8uzXw+gDzDaRUnuXGWsqBwP8DvkV4XL/X/iQ9n7t7j5ldQHnHvdzXFURE\nREREoqjhLY3sfmAt4V3pZ4GX3P0xM7sT+IqZbUZovF5bYN41FSy390mQKwhDSRXy1xJ5pIn9IDbF\nXo1lxuouMr2Snt6zyDPfK8BQMxvh7qsA3P1SADP7JDAr+X8bMAn4es68w+j/WkIaWwOrB3jUX0RE\nRESkKDW8pWG5+xtmdh+h8foMmxpMdwLtwDHADsAdlSymwLSXgdeANne/taxM42OvZJkvERr5uxX4\nbvfIvHJlcXe3GrH2vgu+K+GdbQCSx/L3IzzSD+GR8g3uvjBn3h3d/dmoiDct69Ey5hMRERER0Tve\n0vDuBN4DTE7+j7u/Qmh8fZvQOCznDmavVcm/o3snuHsPocfzo8xsfP4MSYdhaaSOvZJlJvPOBz5u\nZmNz5tkN+FDKWAvpt20qVaVY7ybcQZ+UN3034FV3732ne3dyHl9P3rdfn/z/LWb2z2Y2O/n7EDM7\nw8zazOyR5F30XB3AXSnjExERERHpQw1vaXR3Et4J3pG+Dew7CMNdPeXuL1SQ/wJCI+5cM/usmX06\naaCdCiwG7jWzmWZ2gpl9O7L37djYK1nmWYTHqO8ys2+Z2WnA7eTcES5DsW1TqbOoIFZ3fzJJe1je\nV88TOqbrfZLnXUBuZ2gnAlcm/98d+Auh0zqA/0Pokb0bmJdcIADAzCYC2wDXpYlPRERERCSfGt7S\n6O4ivDe8ktAjeK87CXeMK3nMHHe/HzgD2Ae4DLgK2M7dXwLeDVwKfAL4MXAy4e7vKVnEXsky3b2L\ncMd4GTAD+AKhgfsHwqPd0Yptm3LyyiDWS4GPmll7Tr7PAdOAS8zsLGAecKOZ/cjMpgEPJtsYd78N\nOJZNvbAfzKby+Evesv4ZeNrdb0+/liIiIiIim5i7OukVaVVm9mtgL3d/e71jKSUmVjMbCTwOnOLu\nl5W5vN8CnyYMM/YXd59gZqOA/d39xiTNZsBTwLnu/pNyliMiIiIiojveIi0if4xpM9sd+AhwW30i\nKq7SWN19JXABYfiwcl1FuOv9BeCWpEf0T/Q2uhPHE94Lr3RceBEREREZxHTHW6RFmNkLwGzgCWAX\n4CTCu9Qd7v54/SLrr5liFRERERGplIYTE2kdNwBTgLGEx6fvAr7ToA3ZZopVRERERKQiuuMtIiIi\nIiIikqFM73ib2bbAEYTOicrqWVlERKTJDSe8UnGTu79S51hERESkDrJ+1PwINo2bKyIiMpgdQ+jU\nT0RERAaZshreZvY14JuE9zMfBP7F3f9cIOlTEAYkHpP3xU2EVnm+SzixnJAa2I2EIYvztUXk0V2l\nWLJUbD1jxL32cAKXpE5bvf2qGuuZpWrsV42+jrGK7VeFj0KnR+xX5zTF8arVyrOYRl7PpcC1kNSJ\nIiIiMvhEN7zN7NPAD4ATgfuAqcBNZraHuy/NS74WQqN7XN4XwwtMCwpPbV7F1jRm02+oUixZKl6i\n6cU1vOOWVq39qhrrmaVq7FeNvo6xiu1Xhddzp6i8m2E7tVp5FtMU66lXrkRERAapcsbxngpc7O6/\ndPdFhGGAVhPGwhURERERaQpmNsnM/mRmr5tZt5ntU++YmpGZnWJmj0Sk/7KZPW1mw7KMS6SRRDW8\nkx/HROAPvdM8dIs+H9i/uqGJiIiIiGTDzIYC/wNsDfwr8Dng6boGlREz29/MppvZyAzy3go4BTg/\nYrbZwGbAl6sdj0ijir3jPYbwEumLedNfJLzvLSIiIiLSDN5GeMPoAnf/ubtf5e4r6h1URg4ApgGj\nM8j7i4T2wdyBEpnZqWZ2kZn9FmgHLgf+LYN4RBpS1r2aA6ELo+F500bVYsENYUK9A6gRrWfrGAzr\nCFrPVtMo6/kQsDBvml7tFmlQOyT/DtjYNrMt3H11DeLJkmWY93HAPHdfX3ThZocBxwJfAeYAWwJX\nA6eY2WR3vz3D+EQaQuwd76WErpB3yJu+A7Ck2ExHAFPyPh+OXHDz2rveAdSI1rN1DIZ1hMZpqGVt\nsJRno6zn3kBn3qdRe1sXGbzM7DLgdkIPnP9jZj1mdmvyOHaPmb3DzK4ys2XAnTnzvcnMLjWzJWa2\n1swWmtnxBfJPlW6A+Cab2f1mtsbM/mFmJ5rZWWbWk5NmJzObZWaLzGy1mS01s6vNbOe8vKYD30v+\nfCpZv24z26nSWM1sF2AfwmunAzkKuNPdb3f3ce7+grt3AcuAI9MsS6TZRd3xdvc3zGwBcCgwD8DM\nLPn7ouqHJyIiIiJSdT8FngNOB34E/Jnw6uRByfe/Av4OnEZyt9jMtgfuJdyEuohwQ+rDwC/MbCt3\nvygmXTFmth9wA/ACcCbhfP3MJJ/coTreBbyXcAf5OWAX4KvAbWa2l7v3Pm5zDbAH4d7XN4BXkukv\nVxor4RF2B7pSpPthgeldwIEl5hVpCeU8an4hMDtpgPcOJ7YFoZMEEREREZGG5u73mtlwQsP7Tne/\nFsDMehveD7j75/JmO5fQCH+nuy9Ppv3MzK4CzjKzi919XUS6Ys4mjPl5gLu/mMR1NbAoL91v3f2a\n3Almdj1wD+EO85XJui40sy5Cw/s37v5MTvofVxjrnsm/Txb60syOAT5KeBzoo2Y2GTg55136J4DP\nDpC/SMuIHk7M3a8GvgnMAB4gPF5yhLu/XOXYRERERERqzYGLC0z/JHA90GZm2/Z+gJsJnZZ1pEg3\nKiddP2Y2hPAk6XW9jW4Ad3+CcBecnGnrcuYbambbEBqyywdaRsQ6DRhrYltgQ7F34N39SuA84A3g\naHc/Nq8Du1eBzZOLICItrazO1dx9FjCryrGIiIiIiDSCPndwzWw7QuP6RAoPgeXA9mnTDbDc7YHN\ngccKfNdnWtJY/Q6hc7M3s6kDNSdFP8ZViDWtfYFF7r6hUBg5yxJpaTXp1fwSTgDG1WJRTaTQsWew\ni+twcwbTUqedxtmReU+PSt84tF/1F7dfnRSxX10QuV99q2n3KxGRQWdN3t+9T4leQRgGq5C/EobV\nSpOuGn5C6Cl8JuHx8hWEBux/k+6p1rTrNJBXgKFmNsLdVxVJsy/wlyLfbQ2sLvE4u0hLiG54m9nB\nwLeAiYTW9MfdfV61AxMRERERaRAvA68Bbe5+a7FEyaPiJdMN4CXCGIS7Ffhu97y/jwJmu/spOctv\np/BY3YXuKKdapxJ63zvflf5jKfbah7zH5HPsCjxa5rJFmkr0O97ACMJVq6+ix0JEREREpMW5ew+h\nd/CjzGx8/vdmNiYmXYnlzAc+bmZjc+bbjf5jE3bT/1z+ZDbddc/Vezd6Y6O80lgTdxMeLZs0QJp3\nUvyOdwdwV4rliDS96Dve7n4jcCNsHEpMRERERKTVnQpMBu41s0uAR4BtCE+BfgAYE5mumLOADwJ3\nmdl/Ec7Xv0a4o7xvTrrfAp8zs5XJMvYndMy2tECeCwgN5HPNbC6hs7N5lcbq7k+a2ULgMAqMcGRm\nOyb5PVjgu4nJd9cNtAyRVlGTd7xFRERERBpQ6qc33f0lM3s3MA34BPAVwjvODwOnxKYbYDldZvYh\n4PuEUYSeIzTG3558ep1M6NzlM8Bw4I+EBvBN+evl7veb2RnAScARhDvlu7r7M5XEmrgUONvM2gu8\nq90BLHT3VwrM98/A0+5+e8rliDQ1NbxFREREZNBx9/8l77Fsdz8bivec6e5LCQ3ek0vknSrdAPPf\nTt7j22b2a0IjvDfNSuBLBWZ/a5E8zyWMMV7VWAkN79MJFwAuS2I9m/Be9yTgd/kzmNlmwOcLxSPS\nqmrU8L6JcCEu14TkIyIi0ioeon//QmvrEYiINDEzG+7ua3P+3h34CEnDtpG4+0ozu4DQ+fJlybvh\npwH3AgcCXyww2/HAegqPly7SkmrU8D4CDScmIiKtb+/kk2sx8LM6xCIiTewJM5sNPAHsQnhEfC1w\nQR1jKsrdvwd8L/n/UjP7BaHRfaW7P1kg/cWo0S2DjB41FxERERFpLDcAU4CxwDpCz9/fcffH6xpV\nSu7+lXrHINJoyhnHewRhbMHeHs3famb7Asvc/dlqBiciIiIiMti4e6HHs0WkiZUzjvck4AHCsAQO\n/ADoYoCOKERERKR2zGxrM7vSzFaY2atm9vPkwnna+X9qZj1mVm5nSyIiIpKjnHG8/5fyGuwiIiJS\nG1cBOxDG9N2MML7uxcBnS81oZp8A3gM8XyLdtoROXJ5CPciJiMjgNJzQD8NNRYbN2yiq4W1mpxHG\n+NsTWEN43+Tb7v73EnOy6cn0UlIPpwjANGakTjuD6VF5S6NLu0/BvzMtKudzIx7g+I72q5biEfvV\nKZGjoFzOd1KnPVb7lZTJzPYkNIgnuvsDybR/AX5nZt909yUDzPtm4EfJ/L8vsagjgCurE7WIiEhT\nO4Zw0buo2DveBwM/Bu5P5j0PuNnM3uHua8oKUURERKppf+DV3kZ3Yj7hyvZ7gN8UmsnMDPgl8D13\nfzT8OaCnAK7YB/bcsu8XUx+Fme/oP8OkV+9PE/9GX310UulEiaH3Hx2V986Trk6ddlWR6VcSzrTy\nTeOE1HmfyCWp09bLjcCHarzMoyLS9usyu4ST+HaRb67pt+T7r/2PqLyf/2T6tD+4/5yovO+YlH4Y\n3hM5suD0epRlPRRbz+0i8sgfCLmUDxc45hVz+aNxeR87uvD0qa/DzC37T79+efq8R9y/f1Qsj026\nOyp9NTTyfrsUuDb896lSaaMa3u7+kdy/zew44CVgIvDHmLxEREQkE2MJdfNG7t5tZsuS74o5FVjv\n7j9JuZy1EBrdHaP6fjF6WP9pAKzrSJl18KaItMM6Yk6p4W0RaVcWmb4F4fnC/tIPodoMg60Op/Zx\n7hORdlh07jsVmb55v+86xsflvHVE2tEdu8ZlTvqGd7HyqkdZ1kOx9XxzRB6bRy6zY4v0aefH5l1k\nJx89pPB3D0XkvVXBg3VxxS5EZqlJ9tuSr1xVOpzYaMIV9GUV5iMiIiIDMLPzoOitOgj1ccQ9lz55\nTwROBvaLnXfqo6Ghnet5vfEtIiIt5iFgYd60mOqu7IZ38kjaD4E/uvsj5eYjIiIiqXwfuKxEmieA\nJcD2uRPNrA3YJvmukIMIT2E+m/OIeRtwoZn9q7u/tdgCZ76j/93tIxeUiFJERKTJ7J18ci0GfpZy\n/krueM8C9gIOLJ30Rvq/KTGB/qGLiIg0s0qvhxeX9JY6YI+pAGZ2NzDazH5PqKN7gHsIPVLeW2S2\nKwmV8sHAW4DXgS2BnwNpHz0XERGRIspqeJvZT4CPAAe7++LSc3yIpngyX0REpCKVXg+vnLsvMrNX\ngEOArxBeVfwx8Exuj+ZmtogwMslvgPXAjsB3gL8SXlW9A/i4u38jNoYpg6TKf2+9A6iR9G8WN7v0\nnfk1q8FSloNlPae01zuC2miV8oxueCeN7iOBQ9z9meqHJCIiIuVKhhMbA9xAaHD3AH8ADjezsTmN\n792BUQDuvpIwPFhuPkuBHc3sLe7+XEwMnTG9ojWxuL6Am9fgeT6x9Rveg6UsB8t6dsZ2vd6kWqU8\nY8fxngV0Ah8DVpnZDslXK9xdXamIiIjUX+9wYv/UOyF5x3stOcOJuXtbiXyOI7wrFjEwjYiIiBQy\nJDL9ScBI4HbghZxP3OCZIiIikpWCw4kRRiAZaDixjcysHTgfuMrdX696hCIiIoNM7DjesQ11ERER\nqYIshxPLW85Q4FdJfl8tlb7QcGJTxg2ex81FRGRwqNtwYnE8+ZQ2jRlROc9gWhnxyGDjWOlEOU6P\n2K/O5eyovL/D9Kj00ricdVHpj+WXEakfi8r7ochj597aD5tRlsOJ9abrbXTvCHwgzd3uQsOJiYiI\ntJqaDidmZicRekjdJZn0MDDD3W+MyUdERETilDGc2H7AAcA3gTcR6vyiV4uSRvdtwETCq2j3mtk5\n7n55FcIXEREZ1GIfHX+W8JhbB6FivhWYZ2Z7VTswERERiefui4CbgP8BLgSuIPTH8g/gKjMbA2E4\nMTM7Mvn/UKB33O+5wAeAS4Gfm9mHar4SIiIiLSaq4e3uv3P3G939cXd/zN3PAF4j9JIqIiIijeEz\nhM5QAb4OzAfeCawGvpBM3zicGPBm4DDAgGOBO4FzgTbQewkiIiKVKvsdbzMbQujNvJ1QQYuIiEhj\nWAWMBo5y93m9E81sPsnw07nDibn702Z2J7DA3f8tJ/1xwMxaBS0iItKqohveZjYBuBsYTrhyfrS7\nx/UCJCIiIlkaQ7hb/WLe9BeBtxeZZ2yR9CPNrN3d43oTFBERkY3KueO9CNiX8Hjap4C5ZnaIuz9Q\nfJabCO30XBOSj4iISKuodLCR5qPhxEREZDCo+XBi7r6BMFwJwANm9m5CT+cnFp/rCGBc7KJERESa\nTKWDjVTNUqAb2CFv+g4UH1JsSZH0Kwe6263hxEREZDCotIaP7dW8WB5tJVOJiIhITbj7G8ACYKqZ\nPWlma8zsHuDDwF1FZlsKnGBmL5nZCjO7C/g84fUyERERqUBUw9vMzjWzg81sZzObYGbnAe8jDFUi\nIiIijeNuYDJwM/BJQi/n2wHzAMzsPDPLHaN7OTCMMKzYUcAK4P3AdbULWUREpDXF3vHeHric8J73\nfMJY3ke4+23VDkxEREQqsj9wG+F9r2sJDemXgSOT78cCO/YmdvcvAh8kPEl3PbAb8Byh7hcREZEK\nRL3j7e5fyioQERERqQ4zG0a4OJ4/nNhsNg0ndnz+fO5+RzIfZmbAU8Cy7CMWERFpbWWP4w1gZqcC\n5wI/zB33s0DK5FPaDKbFRhGZPoanTjmNGVE5z2B6bDBSQx6xX50euc9ewNmp035L+0mLeTwi7Xui\nct4n8hjUPS79ftu2WPthEypnOLF83wJGAFdXMS4REZFBqeyGt5m9i9CT+YPVC0dERETqzcw+A5wJ\nfEX7W6EAACAASURBVMzdlw6UVsOJiYjIYFDz4cQAzGxLQodqXyJUzCIiItI4yhlODAAzm0IYHeVT\nafpw0XBiIiIyGNRrOLH/BK5391vLnF9EREQyUuZwYphZJ/AL4GzgejPrqknAIiIiLS664Z1cCX8n\ncFr1wxEREZEqiRpOLHm8/HLgdOCrwB3AUDMbWduwRUREWk/Uo+Zm9hbgh8BhydX0lG4EhudNm0D/\nm/UiIiLNrNI3wKoqdzixzwN/YdNwYt8jbzgx4ARCh2wXJn/vTOjB9IfAF2IWPOeFwfGO990kXcS3\nuIcYLGds9wOT6h1EpgZLWQ6W9ZyzFjrzm1gtqFXKM/Yd74mEq+VdyTAjECrp95nZ14F2dy/QFfiH\ngHEVhCkiItIMKn0DrDrKGU7M3d9vZscDXwYOIPThcqS7RzW6AeYuHhwN73sYHA3vhbTGSW9prd/w\nHixlOVjWc+66wdHwbpXyjG14z6f/es8GHgXOL9zoFhERkRqLHk7MzHYnDBF6kLv3bLq+LiIiIpWK\nani7+yrgkdxpZrYKeMXdH61mYCIiIlIbZjYEuBKY7u69A86nankXGk7s+bo9XS8iIpKNugwnlkd3\nuUVERBpL7HBiWxGesX2nmf1nMm0IYGa2Hvigu99eaEGFhhM7ckHZcYuIiDSkSl8mq7jh7e4fqDQP\nERERqR53f8PMFgCHsqkXc0v+vqjALCsJvZ7m+hrwfuAo4KkC8wwHWPR6/y+WvwFdKwrMsTpudLIX\nItIO7Xo5Ku+YE6BVRaavpvCGCadi6aRPWT9rqX2cf41I+2R07s8Umb6m33ddD8fl/HxE2uVdsZGP\nSJ2yWHnVoyzrodh6bojII/bV6a7V6dPG7CcAXUW6tF7eU/i7pyLyHlHwYF1cPfafRt5vl276b8ld\nxrJ8LdvMOoAFcCLZda6WPv5pzIjKeQbTM4kj0LtzjS3mcBv7TOVWEWk3j8z7pcj0UlvpjxM38u9R\nOX+IaZnFouNVpTZeD5/o7jUbF9vMjib0w3IScB8wFfgUsKe7v2xm5wFvcvdji8w/ndC5WkeR7z9D\neDxdRERksDvG3a8aKEHscGLToV9rdJG77xUbmYiIiGTH3a82szHADMIj5n8BjnD33lvD+cOJxboJ\nOIZwc0VvdYuIyGA0HNiFUCcOqJxHzRcSHlXrvQUS89SGiIiI1Ii7zwJmFfnu+ELTc74/Gzh7gO9f\nAQa8ui8iIjII3JUmUTkN7w05V8tFREREREREZABDyphndzN73sweN7MrzKySx9REREREREREWlps\nw/se4DjgCEJnLbsCd5hZ+m4WRURERERE5P+3d+fhcpVVvse/v4QhzCgIUUEBQZQLIkHAMBnFloC3\nQcWLDLaCIjK1XrqVSSQQFBQa9CKdbpwItCKde2kUFIii0BCZJAEkQlTGMCUQwETDnKz7x7sLKnWq\n6tSuU7vq1K7f53nqyTm73tq13rMq59Sq/Q42QHINNY+I6knjcyXdBjwM7A9c2PiR1zB0leitGboT\nmpmZWT+7m7QUSjWvO2ZmZjboRrSPd0QslvQnYPPmLSdT3HZiZmZmo8U2DP1Q+dXtxMzMzGxAtTPH\n+1WS1iQV3aN1T3MzMzPrIklHS3pQ0vOSbpG0Q69j6iRJUyQtr7nd0+u4RkLSbpKuyNbwWS5pnzpt\npkp6XNJzkn4laZiLLqPPcP2UdGGd3F7Vq3jbIelESbdJWiJpoaTLJb29Tru+zmcr/SxJPo+QdJek\nxdntJkmTa9r0dS5h+H6WIZeQs/CWdLak3SW9VdLOwOXAy8BPConOzMzM+oakTwDnAFOA7YC7gJnZ\nfuJlMpe0N/r47LZrb8MZsTVI+7wfBUTtnZKOB44BDgd2BJaS8rpKN4PsgKb9zFzNirk9sDuhdcxu\nwHeAnYAPAisDv5S0WqVBSfI5bD8z/Z7PR4DjgQnA9sBvgCskbQWlySUM089Mv+cy91DzjUh7dq4H\nPAXMAt6b7eVpZmZmg+1Y4IKIuBjSVQzgw8BngLN6GViHlWpr1Yi4hrQgD5JUp8kXgdMj4udZm08B\nC4GPADO6FedItdBPgBf7ObcRsXf195IOAZ4kFTOzssN9n88W+wn9n89f1Bw6WdKRpA8c7qEEuYSW\n+gl9nkvIecU7Ig6MiI0iYrWIeEtEHBQRDxYVnJmZmfUHSSuT3vT+unIsIgK4FpjYq7gKMjBbq0ra\nlHR1qTqvS4BbKV9eASZlQ5fnSZom6fW9DmiE1iVd3X8GSp3PFfpZpTT5lDRG0gHAqqRdpUqZy9p+\nVt3V97nMvbiapDcB3wT2AlYH/gwcGhFzOhxbqxG13HIqp+Q68ymcluPcU3Kd20a7PKsQ167YP5wl\nLbf8d76c68xH5HyNW7e1/vtqMl/Nee7/mbP9FjnafivnuW1ArQ+MJV1tqbYQ2LL74RSmsrXqH0kr\nx55KehO8dUQs7WFcRRlPKmjq5XV898Mp1NXAZcCDwNuAM4GrJE3MPkTqK9lV/W8DsyKictWwdPls\n0E8oST4lbQ3cTHrD+Rywf0TcL2kiJcplo35md5cil7kKb0nrAr8lfbKyJ7CI9O7t2c6HZmZmZja6\ntL+1qo12EVE9NPcPku4G7gcmAdf1JKiRmQZsBezS60AKVrefJcrnPGBbYB3g48Clkt7X25AKUbef\nEXFHWXKZd1XzE4D5EXFYRMyOiIcj4loPNzczMxt4i4BlpMVvqm0ILOh+ON0REYuBFrZW7VsLSMN1\nBiqvANn720X0YW4lnQ/sDUyKiOrdh0qVzyb9HKJf8xkRr0TEA1kB+hXSUPIjKVkum/SzXtu+zGXe\nwvvvgdslzcjG2M+RdFgRgZmZmVn/iIiXgdnAHpVj2RDQPYCbehVX0VTyrVWzN7gLWDGva5MWPSpt\nXgEkbURaULivcpsVo/sC74+I+dX3lSmfzfrZoH1f5rOOMcDYMuWygTGk6UtD9Gsu887x3oz0ycM5\nwNdJy9afJ+nFiPiPTgdnZmZmfeVcYLqk2cBtpFXOVwem9zKoTpJ0NnAlaXj5m4HT6POtVSWtQfrw\noLIQxWaStgWeiYhHSPNnT5Z0H/AQcDrwKPCzHoTbtmb9zG5TSPNIF2TtvkkazTBz6NlGJ0nTSNss\n7QMslVS5Gro4IioL2PR9PofrZ5brMuTzDNL85vnAWsDBwO7A17ImfZ9LaN7PsuQS8hfeY4DbIqKy\n6s9d2UT4I4CchffdwDY5n74fuZ/lMgj9HIQ+wuD0cyZpSY6yG5R8jm4RMSPbs3sqabjjncCe/b4F\nTI0ybq36HtI8ychu52THLwI+ExFnSVoduIC0evSNwF4R8VIvgh2BZv08CngX8ClSHx8n/QI9JRvN\n0S+OIPXt+prjhwIXA5Qkn8P1cxnlyOcGpNfnG4HFwO9Jv1Ovg9LkEpr0U9I4ypHL3IX3E8C9Ncfu\nBT7W/GHXMHTl5yUMxpukubifZTII/RyEPsLg9HNQCu/Rks+7SbFUy7NLQv+LiGmkxY5KKSIO7HUM\nnRYR/80w0w8j4lTSCu59q4V+Tu5WLEWJiJamkfZ7PofrZ3Z1vwz5HHZKb7/nEpr3syy5hPyF928Z\nuiXIlqThVk1MJn2AUa1vR2SZmZk1sA1DPwB4AvhuD2IxMzOz0SLv4mrfAt4r6URJb5N0EHAYcH7n\nQzMzMzMzMzPrf7kK74i4HfgoaTGDu4GvAF+MiEsLiM3MzMzMzMys7+Udak5EXAVc1WLzbGL3ojp3\nvUD3V4CPXK3zRdeodS/62QuD3M9Vcp7jxZZbzs/5mu1MDgY5l6NJ3tzPa3D8bw3uW5rj3KP551Qx\nmvP56t/A2sVOzMzMbEAoIu+buxwnT0PRf1zYE5iZmfWPgyPikl4HYWZmZt1XdOG9Hmk53YcYtGVd\nzczMknHAJsDMPt9yyszMzNpUaOFtZmZmZmZmNujyrmpuZmZmZmZmZjm48DYzMzMzMzMrkAtvMzMz\nMzMzswK58DYzMzMzMzMrkAtvMzMzMzMzswL1pPCWdLSkByU9L+kWSTv0Io6iSJoiaXnN7Z5exzUS\nknaTdIWkx7L+7FOnzVRJj0t6TtKvJG3ei1hHYrh+SrqwTm6v6lW87ZB0oqTbJC2RtFDS5ZLeXqdd\nX+ezlX6WJJ9HSLpL0uLsdpOkyTVt+jqXMHw/y5BLMzMzK6+uF96SPgGcA0wBtgPuAmZKWr/bsRRs\nLrAhMD677drbcEZsDeBO4ChgyB50ko4HjgEOB3YElpLyuko3g+yApv3MXM2KuT2wO6F1zG7Ad4Cd\ngA8CKwO/lLRapUFJ8jlsPzP9ns9HgOOBCcD2wG+AKyRtBaXJJQzTz0y/59LMzMxKquv7eEu6Bbg1\nIr6YfS/SG6rzIuKsrgZTEElTgH0jYkKvYymCpOXARyLiiqpjjwNnR8S3su/XBhYCn46IGb2JdGQa\n9PNCYJ2I+FjvIuus7EOvJ4HdI2JWdqyM+azXz9LlE0DS08CXIuLCMuayoqafpcylmZmZlUNXr3hL\nWpl0peLXlWORKv9rgYndjKULtsiGK98v6UeSNu51QEWRtCnp6lJ1XpcAt1K+vAJMyoYuz5M0TdLr\nex3QCK1Lurr/DJQ6nyv0s0pp8ilpjKQDgFWBG8qay9p+Vt1VmlyamZlZuazU5edbHxhLutpSbSGw\nZZdjKdItwCHAH4E3AqeS3gRvHRFLexhXUcaTCpp6eR3f/XAKdTVwGfAg8DbgTOAqSROj28NHOiAb\ncfJtYFZEVNYhKF0+G/QTSpJPSVsDNwPjgOeA/SPifkkTKVEuG/Uzu7sUuTQzM7Ny6nbhPRAiYmbV\nt3Ml3QY8DOwPXNibqKwTaobm/kHS3cD9wCTgup4ENTLTgK2AXXodSMHq9rNE+ZwHbAusA3wcuFTS\n+3obUiHq9jMi7ihRLs3MzKyEur242iJgGWnxm2obAgu6HEvXRMRi4E9A360k3KIFgBiwvAJExIOk\n13Xf5VbS+cDewKSIeKLqrlLls0k/h+jXfEbEKxHxQFaAfoU0lPxISpbLJv2s17Yvc2lmZmbl1NXC\nOyJeBmYDe1SOZUNA9wBu6mYs3SRpTdKbv6Zv+vtV9gZ3ASvmdW3SatKlzSuApI2A9eiz3GbF6L7A\n+yNifvV9Zcpns342aN+X+axjDDC2TLlsYAxp+tIQJcqlmZmZlUAvhpqfC0yXNBu4DTgWWB2Y3oNY\nCiHpbOBK0vDyNwOnAS8DP+llXCMhaQ3ShwfKDm0maVvgmYh4hDR/9mRJ9wEPAacDjwI/60G4bWvW\nz+w2hTSPdEHW7puk0Qwzh55tdJI0jbTN0j7AUkmVq6GLI+KF7Ou+z+dw/cxyXYZ8nkGa3zwfWAs4\nGNgd+FrWpO9zCc37WZZcmpmZWXl1vfCOiBnZtj5TScMd7wT2jIinuh1LgTYCLiFdbXkKmAW8NyKe\n7mlUI/Me0jzJyG7nZMcvAj4TEWdJWh24gLR69I3AXhHxUi+CHYFm/TwKeBfwKVIfHye9qT8lG83R\nL44g9e36muOHAhcDlCSfw/VzGeXI5wak1+cbgcXA70m/U6+D0uQSmvRT0jjKkUszMzMrqa7v421m\nZmZmZmY2SLq9uJqZmZmZmZnZQHHhbWZmZmZmZlYgF95mZmZmZmZmBXLhbWZmZmZmZlYgF95mZmZm\nZmZmBXLhbWZmZmZmZlYgF95mZmZmZmZmBXLhbWZmZmZmZlYgF95mZmZmZmZmBXLhbWZmZmZmZlYg\nF95mZmZmZmZmBXLhbWZmZmZmZlYgF95mZmZmZmZmBXLhbWZmZmZmZlYgF95mZmZmZmZmBXLhbWZm\nZmZmZlYgF95mZmZmZmZmBXLhbWZmZmZmZlYgF95mZmZmZmZmBXLhbWZmZmZmZlYgF95mZmZmZmZm\nBXLhbWZmZmZmZlYgF95mZmZmZmZmBXLhbWZmZmZmZlYgF95mZmZmZmZmBXLhbWZmZmZmZlYgF95m\nZmZmZmZmBXLhbWZmZmZmZlYgF95mZmZmZmZmBXLhbWZmZmZmZlYgF95mZmZmZmZmBXLhbWZmZmZm\nZlYgF95mZmZmZmZmBXLhbWZmZmZmZlYgF95mZmZmZmZmBXLhbWZmZmZmZlYgF95mZmZmZmZmBXLh\nbWZmZmZmZlYgF95mZmZmZmZmBXLhbZaDpFMlLe9xDIdIWi7pLW0+/tTs8a/vVQydIOk4SfcUcN7P\nS3pY0sqdPreZmZmZDSYX3mb5RHbr5xhaerykiZKmSFq7gBhGRNJawHHANwo4/XRgFeDzBZzbzMzM\nzAaQC2+z/nMxsFpEzC/4eXYGTgHW7WEMjXwWGAtc2ukTR8SLwEXAP3X63GZmZmY2mFx4W1+TtHqv\nY+i2SF7qwlNpFMTQyCHAFQXGMAPYRNKkgs5vZmZmZgPEhbf1jaq5ye+UdImkZ4Abs/veJOmHkhZI\nekHSXEmH1jnHWyRNkzRP0nOSFkmaIemtddruKul3kp6X9GdJh7cY5zZZnP+z6tiE7NjtNW2vlnRz\n1ffD9qPR/GpJkyTdXh3vMHPSXydpuqRnJf0le95x2bmmAGdl7R7Knm9Z5TlrY6jKzdsanXOEsVY/\ndhPgXcC1w7VtV0TMAZ4B9i3qOczMzMxscKzU6wDMcqjMKf6/wJ+AEwFJ2gC4FVgGnAcsAvYCfiBp\nrYg4r+ocOwDvBX4CPApsAhwFXCdpq4h4gXTSrYGZwJOk4dYrA6dm3w9nLvAXYHfg59mx3YDlwLaS\n1oyIv0kSMBH49+w5W+3HkPnVkrYDrgYeB75K+r/91ewc9eZii3RV9wHgBGAC8DlgIenn+l/A24ED\ngC8CT2ePe6pBDJWva895WNU524211s5ZuzkttB2JOcAuBT+HmZmZmQ0AF97Wj+6IiH+ofCPp+6RC\n8t0R8Zfs8HclXQKcKumCbN4uwM8j4rLqk0m6ErgF2A/4cXb49OzfXSPisazdZaSiuqmICEm/JRXb\nFbsBl5OuoO4M/BJ4N7A22VV74Iwc/ah1GvAKsHNELMzinQHMaxLq7Ih49Sq+pPVJc6dPjIi7Jc0h\nFd4/yzGXu+E5RxhrtXdk/z5YfVDSJ4FtgZUj4n9nx3YFpkTE30lajZS/f4yIq1p4ngeAT7YYk5mZ\nmZlZQx5qbv0mgAtqjn0MuBIYK2m9yo1U3K5DuvKaHlxVuEpaKdtS6wHSFeoJ2fExwIeAyytFd/bY\nP5KugrfiRmBCVuwB7ApcBdzFawV55Sr4rLz9qJbFuwfw00ohm8X7AOnKcj31fo43AutJWrPFPuY+\nZ5ux1loPeCUinqsckLQl8DzpSn310PwPAwuyr18kXbnfpsXneRZYrd5QeTMzMzOzPFx4Wz969Uqn\npDeQVt0+nDQMuvr2w6zZBlXtx0maKmk+qRBbRBo+vk52A3gDsBpwX53n/mOLMd5IGp4+UdLbs3Pe\nCNzAioX3PRGxOG8/amzQJN56xypqr2I/m/37uiaPGc5w52w31uHsAlwBvJ80XL9iIvBbgIhYDpxd\nJ8ZGKovL9Xr7ODMzMzPrcx5qbv3o+aqvKx8e/Yi0BVQ9v6/6+nzg08C3SMPLF5MKq/+ksx9E3Q68\nQJrn/QjwZETcJ+lG4EhJq5Cugv9Xm/3ohGUNjjdczbxH56z1NLCSpDUiYilARPwQQNLHgGnZ12OB\n9wDHVD12ZV4b2j+c1wHPNRneb2ZmZmbWEhfe1u+eAv4KjI2I37TQfj9gekQcVzkgaVVW3Kv6KVJx\nv0Wdx7+jzrEhIuJlSbeRCu/5vFbs3QisChwMbEi6At5OP6o9SSryN69zX70+tKqIK72diLUyF3xT\nqubcZ8PytyMN6Yc0pPyViKiel79xRDzS4vNsCtzbYlszMzMzs4Y81Nz6WjZ8+DJgP0n/o/b+bHGv\nassY+rr/AjC25pwzgY9I2qjqXO8kzf1u1Y3ATsCk7Gsi4mlS4Xg8qbCtHM/bj1dlj702i3d81WM2\nBybniLfW0uzfdZu2yqFDsd5MuoL+nprjmwPPRkRlTvcWVA1fz+bbv5R9/QFJx0r6haSjs4Xkak0A\nbmoxJjMzMzOzhnzF28rgBFJxe6uk7wH3AK8Htgc+AFQXrT8H/kHSkqzdRNJiX4tqzjmFVAjOkjSN\nNET5GNIV1ne1GNeNwFeAjVlxePMNwOeBByPi8Tb7UetU0ocCN0n6N9L/7aOzeLdtMd5as0kF7hmS\nLgVeBq6IiOebP2xYI4o1Ih6UNBf4IDC96q7HSAvTrRQRr5C2jqteGO1wXlu1/gnSIm1/ioh/zVbG\nf5Wk7Uk/+5/m7p2ZmZmZWQ0X3tb3IuJJSTuS9tv+KHAkaR7wH4Djapp/gbSV1UGkomwWqYCbSdXQ\n6mw7rQ8B55K2v3o0O/+baL3wvol0hf1vpNXMK24kFYE3VDfO2Q9qHjtH0mTgX4CpWbynAltmt9wi\n4nZJJwNHAHuSRgpsSuuLkxUZ6w+B0yStWpmDHRGPSjoF+J6kh0mLrY2R9H9IP8cbIuLJrO29kr5A\nVojXmcf9v4CHI+L69ntqZmZmZpYowgv2mpWVpMuBrSKireK7m/LEKmlt4H7guIi4sM3nmwPsEBHL\nao6vAjwEnBER57dzbjMzMzOzap7jbVYStftNS9oC2Bu4rjcRNTbSWCNiCWlrsC+3+fzrAX+sLboz\nh5LmgtfuSW5mZmZm1hZf8TYrCUmPk+Y8PwBsQhoivjIwISLu711kQ/VTrGZmZmZmI+U53mblcTVw\nADAeeJE0x/ykUVrI9lOsZmZmZmYj4iveZmZmZmZmZgXyHG8zMzMzMzOzAhU61DxbwGhP0grBLxT5\nXGZmZqPUONJaBjMj4ukex2JmZmY90FbhLelo4Euk+Zl3Af8YEb+r03RPsn1yzczMBtzBwCW9DsLM\nzMy6L3fhLekTwDnA4cBtwLHATElvj4hFNc0fSv98DFi/5q5rgMl5n34UazRXfibp84cVfY7vtXzm\n73F4eyF1VSfyOTZn+3o7QRWt26/bfGswdOZ11Q//N1fP0fbFBsevIu1gVqsXr6tOaPT/p1E/X2n5\nzHleV9Cr31mdeN1unqPtGjnazge+Ca/+TTQzM7NB084V72OBCyLiYgBJRwAfBj4DnFXTNhtevj7w\nxpq7xtU51s8aFUj1+5mv5/3wc+pEPvO+HFsvHDqn26/bfIV3Z15X/fB/c60cbZ9vcHwc8KY6x3vx\nuuqERv9/GvXz5ZbPnP/V0IvXTydet5vlaLt2O0/gKVdmZmYDKtfiapJWBrYHfl05FmlZ9GuBiZ0N\nzczMzMxsKEmHSFou6S31vh+N+iHG0ULScZLu6eD5Pi/p4ayWMeuJvKuar08az7iw5vhC0nxvMzMz\nM7OiBSsOC6v9vickTZQ0RVK9YTGjIsZOGKafIz33WsBxwDc6eNrpwCrA5zt4TrNcCl3V/DXXkIYB\nVlvSnac2MzPrmuuyW7WlvQjEzHpjZ+AU4EKGvtm9GPhJRLzU9ag6r1k/R+qzpAt9l3bqhBHxoqSL\ngH8Czu/Uec3yyFt4LyKtPLRhzfENgQWNHzaZoXPv7s751P1q614H0CXuZ3kMQh8B3tXrALpkUPo5\nWl63789u1f4MHN2DWMysB9Tojmx6ZhmKbmjSzw44BLiigA8oZgDHSZoUEdd3+Nxmw8o11DwiXgZm\nA3tUjklS9v1N+Z56m3zN+9ZoeTNYtEHJ5yD0cxD6CINTkA5KPwfldWtWbpLeImmapHmSnpO0SNIM\nSW9t83zbSbpa0mJJf5V0raSd6rR7k6QfSHpM0guSHsjiWKnVuCRN4bWFhh/K5nMvG24eeisxSjo1\ne+zbJE2X9Kykv0j6oaTaYaWNfhaTJN0u6XlJf5Z0eOW8VW1G3M+qn+cPJS3Ifp5zJR3aQoybkP5w\nXdtKn/KIiDnAM8C+nT63WSvaGWp+LjBd0mxe205sddLcCTMzMzOzdu0AvBf4CfAosAlwFHCdpK0i\nouXdASRtBdwALCbNF36FNMf3ekm7R8TvsnZvBH5H2q7gAuCPwJuBj5Pe4y5pMa7LgLcDBwBfBJ7O\nQnkq+3fIHO9WY6x63AzgAeAEYAJwGGmtpROH+VlsB1wNPA58lVQDfJU0mrU6phH3U9IGwK2kUbLn\nZc+xF/ADSWtFxHlNQt05i2dOs/6MwBxgl4LObdZU7sI7ImZIWh+YShpifiewZ0Q81fyRZmZmZmZN\n/TwiLqs+IOlK4BZgP+DHOc71ddJ73V0i4uHsXP9BKqzP4rV5Id8ANgB2jIg7qh5/ap64ImKupDmk\ngvRnETG/gzFWzI6Iw6tiWJ80J7pp4Q2cRirqd46IhdljZwDzatp1op9nkIaivzsi/pId+66kS4BT\nJV0QES82iPMd2b8PNuqIpAOASaQPBh4FToiIdzRqX+MB4JMttjXrqLyrmgMQEdMiYpOIWC0iJkbE\n7Z0OzMzMzMwGS3VBJmklSa8nFUt/IV3hbYmkMcDfAZdXCtrs/AuAS4BdJa2ZTZnclzSn+I76Z+tc\nXO3EWB0G6Yp8tRuB9Wra1XuePYCfVoru7HkeIF0Fp+pYJ/r5MeBKYKyk9So34JfAOsOcZz3glYh4\nrkFfPg/8M3BURHwN+ADpinqrngVWa3V4vlkndWlV80GQb42JqZxSUBz97JVeBzAK5XtdnZ7jdXUG\np+U690lMydW+WH/N0Tbv39Z+fR3mjbv1rUzzvK4AZuR4be0/ql5XeRb9zDOn3Kuam7UqK4hOIi2w\n9WZe+0MYpKKtVW8gDRP/U5377iVdfNqYNEx6beAPXYqrnRjvrTpee3X52ezf1wF/a/A8GwCrAffV\nuW+FYyPtp6Q3AOsCh1N/667I4sktmxJwLmmkbWVe+lOkq94tn6YqDrOuyl14S9oN+DKwPWmp8o9E\nxBWdDszMzMzMBs75wKeBb5GGNy8mFUn/SZsjNUsW17IGxzu1yvhI+1lp8yPgogZtft/k8U8DK0la\nIyJqP7X8LPDXiJhVdWwXyHUl4XXAc02GupsVpp0r3muQ5nX/APivzoZjZmZmZgNsP2B6RBxXsYI1\nrgAAHAxJREFUOSBpVdJV1DyeAp4Dtqxz3zuB5cAjpCEpSxh+G5pW48pzJbXVGEfqSeAFYPM6921R\n8/1I+/kUaVja2Ij4TRuxVuacbwrMrblvZ+C3VXGNA7atPtaCTVlxBIFZ1+T+hC4iromIUyLiZxS7\nh5+ZmZmZDZZlDH1/+gVgbJ6TZEORfwnsW7PN1YbAgcCNEfG3bG/tnwJ/L6nZ3ONW46pcpR32g4JW\nYxzuPC0+z7XARySNr3qezYHJNc1H1M/suS4D9pP0P2pjyRaDa+ZmUn3xnjr3rcqKw/J3BOZFxJLs\n3O+QNFXS30u6TNK2dc4xgdxbIJt1hud4m5mZmdlo8XPgHyQtAe4BJpIWBsuzgFbFycAHgd9KmkYq\nKg8HVgGOq2p3EmmRsxskfZd0RfRNpO3EdskKu1bjmk0qHM+QdCnwMmnhtudHGONInQp8CLhJ0r+R\naoCjSVeVqwvUTvTzBNKq47dK+l52nteTpql+AGhYfEfEg5Lmkn4m02vuvoE0Lx5JqwCnkxaXQ9Lq\npAXpdouIpZJOzp73VZK2z+L4aaPnNyuSC28zMzMzGy2+QFot8iDS6pizSEXYTHIuiBUR92RrE51J\nKgbHkOYtH1S9I09EPC5pJ1IhdxBpsbXHgKtIQ8Fbjisibs+KviOAPbPn3JShi6LlinGkImKOpMnA\nv5C2BH6UVIxvyYpD3Ufcz4h4UtKOwCnAR4EjSXO3/0BrHyb8EDhN0qo1c7H/Bfi+pBOyWNYiK7xJ\nK6nfmRXd6wF/iYiXa877v4CHI+L6FmIw6zilETZtPlhaTpPF1bIhO7PhLQxdWXhr8q0KWzZ5fu4e\n0W+tUY7X1deZmuvco2tV8zzyrmr+QiFRjD6tf+4qat+7NPefOV5bo2tV8zwa/f2ald2qLSWbUrh9\nRMwpMiozszwkXQ5sFRH15pn3hKS1gfuB4yLiwgZt1gQWAG+LiIWSjgWWR8T/kfRJYBPg1oj4VdZ+\nFeAh4IyIOL8L3TAboktXvCeTFkA3MzMrs12zW7UH6OyIUTOz/CSNi4gXqr7fAtgbqFvc9kpELJF0\nNmkXpQsBJB0NTIiIz2bNvgRcWLUv+aXAcZL2AtbMbtXz0g8FXmLoPuhmXeOh5mZmZmZm5feApOmk\nTwM3IQ0TfwE4u4cx1RURZwFnVR1aA1hL0j+T5t8/GRH/WNX+CeDYJue7ABfd1mPt7OO9Bmk7gsr4\n582yVQOfiYhObHlgZmZmZmaddTVwADAeeJG0uvdJEXF/T6NqQVaIm/W13NuJkZb3v4O0mmEA5wBz\nyLd5vZmZmRVE0usk/VjSYknPSvp+9sF5q4//d0nLJX2hyDjNrHsi4rMRsVlErB4Rr4uID0fEXb2O\ny2xQ5L7iHRH/TXsFu5mZmXXHJcCGpG2AViFty3MB8MnhHijpo8BOpFWdm7Vbj7Sa8UMMzqqEZmZm\n1caRpm7MjIinmzXMtaq5pBNJ2wK8A3ieNETl+Ij4U4P22armh+PF1UYi38rzZ+RYUbh/V6q2kcqz\nAjrAd3Kugn5M37628qyC7lqjnjyvrT/nfF1t3pevqyeA70KXVjWX9A7S/rXbR8Qd2bE9gV8AG0XE\ngiaPfTNwM6mgvgr4VkSc16DtQcCPOxy+mZlZPzo4Ii5p1iDvFe/dgO8At2ePPRP4paR3RsTz7cVo\nZmZmHTQReLZSdGeuJX2KuxPws3oPkiTgYuCsiLg3fdvUQ5A2z12/5o5rSPuZlEWjj5Jmkj6hqPW9\n03Nsv/zV7+aMpvULGZ/LOQvwe3yuwT1De/rPfC/Xuf+aq3VvdPt1m3dD3+8dl+N1dVajdcTqv2oP\nz5nPscM3edWyXGfO93PJ85qF/P0c7Rq9ZnP9DMfl3C7+hTy/s16X69SH8826x+v1c79cZ4Y9G5y7\nnhM4vuW2C0hDysj+JjaTq/COiL2rv5d0CPAksD1DNy41MzOz7htP+tv8qohYJumZ7L5GTgBeyrHH\n7QuQiu7aUnBcnWP9rNGb2Ib93GRCjrPn/Um9tbAzN37E0J5unPPMz+aOpfu6/brNW3izcSdeV/V7\nmbffeQqIV3KeO9/Ppdh+jnaNXrO5foZj87yuaPCMjWzQkTPX6+e7cp0ZYLOWW74l97mBFoZBjnQ7\nsXVJuX1mhOcxMzOzJiSdCU0/hg/gnW2ee3vgC8B2eR97DUMnaCxpJwgzM7NR7HekYd/V8gz5brvw\nzoakfRuYFRH3tHseMzMza8m/ABcO0+YB0si3FS4zSBoLvD67r55dgTcAj1QNMR8LnCvpf0dEw0sF\nkxl6JeInwwRpZmbWb3bIbtXmA99o8fEjueI9DdgK2GX4pvU+D98a2GYET29mZjba3A3MrTnWmUX4\nstVSm66YCiDpZmBdSVeR/kYvB24BBNza4GE/Jv1R3g3YCPgbsCbwfaDVoedmZmbWQFuFt6Tzgb2B\n3SLiieEfUe/zcDMzs7LZhqEfKr+6qnlXRMQ8SU8D7wOOBFYjLYw6v3pFc0nzSDuT/Ax4iTRt9yTg\n96QVcW4APhIRX8wbw9Yj7kV/GJR+DkpP3csyGYx+DkYvy9PP3IV3VnTvC7wvIuZ3PiQzMzNrV7ad\n2PrA1aSCeznwa+DvJI2vKr63ANYBiIgl1CwBLGkRsLGkjSLi0TwxDMp4trK8GRzeYPR0MF63g5HL\nQennYLxmy9PPXIW3pGnAgcA+wFJJG2Z3LY4Ib2hrZmbWe5XtxD5cOZDN8X6Bqu3EImK4HYEOIc0V\n+0sxYZqZmQ2OMTnbHwGsDVwPPF5127+zYZmZmVmb6m4nRtqBpNl2Yq+StCppvZhLIuJvHY/QzMxs\nwOTdxztvoW5mZmYdUOR2YjXPsxLwf7PzHTVcey+famZmg6Bn24nZSLW+tf0ZTM115pM4JW8wNoAC\nDd+oyj/mfF3N4LSW2+7PlFznLlaeWTO15UYnz92/8ry2Ns/9+6r1353LLs33u3PsAXleh3n+fA43\nortlRW4nVmlXKbo3Bj7QytVuL59qZmaDoKvbiUk6grRC6ibZoT8AUyPimjznMTMzs3za2E5sO2Bn\n4EvAm0h/819s8riVgOuA7UlT0W6V9PWIuKgD4ZuZmQ20vEPHHyENc5tA+sP8G+AKSVt1OjAzMzPL\nLyLmATOB/wecC/yItB7Ln4FLJK0PaTsxSftmX68EVPb9vhT4APBD4PuSJne9E2ZmZiWTq/COiF9E\nxDURcX9E3BcRJwN/Ja2SamZmZqPDQaTFUAGOAa4F3g08B3wmO/7qdmLAm4EPAgI+DdwInEEaJz+a\n5oKYmZn1pbbneEsaQ1rNfFXSH2gzMzMbHZYC6wL7RcQVlYOSriVtN7bCdmIR8bCkG4HZEfFPVe0P\nAb7VraDNzMzKKnfhLWlr4GbSqkLPAftHxH2dDszMzMzatj7pavXCmuMLgS0bPGZ8g/ZrS1o1IhrO\nDzczM7Pm2rniPQ/YljQ87ePApZLeFxF3NH6INxsxM7NB8PvsVq3cq9n7L7yZmQ2Crm8nFhGvkLYr\nAbhD0o6klc4Pb/wobzZiZmaD4F3ZrdrjwL91O5BFwDJgw5rjG9J4S7EFDdovaXa123/hzcxsEIx0\nO7G8q5o3OkfHNik1MzOzkYmIl4HZwLGSHpT0vKRbgL2Amxo8bBHwOUlPSlos6SbgU6TpZWZmZjYC\nuQpvSWdI2k3SWyVtLelMYHfSViVmZmY2etwMTAJ+CXyMtMr5G4ArACSdKal6j+6/ACuTthXbD1gM\nvB/4afdCNjMzK6e8V7w3AC4izfO+lrSX954RcV2nAzMzM7MRmQhcB+wJ/BepkH4K2De7fzywcaVx\nRHwW+BBpevaVwObAo6S//WZmZjYCueZ4R8RhRQViZmZmnSFpZdKH47XbiU3nte3EDq19XETckD0O\nSQIeAp4pPmIzM7Nya3sfbwBJJwBnAN+u3vfTWqGWW57EKYWdO79oueXZTM115i8zJW8w1kWR83X1\niRyv25s4Lde5dx41r5W8q1XXrv3cyXP3q7y/r1r/s7XSgfl+d85ZdknLbSeM/XSOMxf5O7mhdrYT\nq/VlYA1gRgfjMjMzG0htF96SdiCtZH5X58IxMzOzXpN0EPBVYJ+IWNSsrbcTMzOzQdD17cQAJK1J\nWlDtMNIfZjMzMxs92tlODABJBwDfBT7eyhou3k7MzMwGQa+2E/tX4MqI+E2bjzczM7OCtLmdGJIO\nBH4AnAZcKWlOVwI2MzMrudyFd/ZJ+LuBEzsfjpmZmXVIru3EsuHlFwFfAY4CbgBWkrR2d8M2MzMr\nn7z7eG8EfBs4OPs03czMzEanXNuJAZ8jLch2LvBW0h7e25D+7udyd9sh95e5vQ6gawajp4Pxuh2M\nXA5KPwfjNVuefuad47096dPyOdk2I5D+SO8u6Rhg1Yios+y1l14xM7NBcBdD3yJ0f4X6drYTi4j3\nSzoU+DywM2kNl30j4jN5n38ug/EXfi7p3Uz5DUZPB+N1Oxi5HJR+DsZrtjz9zFt4X8vQfk8H7gW+\nUb/oBi+9YmZmg2Hb7FbtcWBatwPJvZ2YpC1IW4TuGhHLX/t83czMzEYqV+EdEUuBe6qPSVoKPB0R\n93YyMDMzM+sOSWOAHwNTIuL+yuFWHltvTNuSDsZmZmY2GvRkO7EaDa5ym5mZWY/k3U5sLeA9wLsl\n/Wt2bAwgSS8BH4qI6+s9Ub0xbT9pM2gzM7PRaqTbiY248I6ID4z0HGZmZtY5EfGypNnAHry2irmy\n78+r85AlDJ0QeTRpgbX9gIfqPGYcpAq/1gvAE+0EPko1usLQsJ8P5dmFrbifVP4zN3rE0J4+kvPM\nf80dS/d1+3Wb+8rVI514XdXvZd5+j83RdlnOc+f7uRTbz9Gu0Ws2189wWd5dI/P8FPOtcZInm7/P\ndWaAB1puOT/HWas+ya4d/DWEGk7L7gBJE4DZcDie491Nref0DKbmOvNJTCkkjsTzCUe3YX+f1Mjz\nyzbvufPsbvRkznNbd+X7PbHs705vue3YX51SYCx5fl89AXwXYPuI6Nq+2JL2J63DcgRwG3As8HHg\nHRHxlKQzgTdFxKcbPH4KaXG1CQ3uP4g0PN3MzGzQHRwRlzRrkOuKd/ZHuLbymhcRW+WNzMzMzIoT\nETMkrQ9MJQ0xvxPYMyKeyprUbieW10zgYNLV8O4v3W5mZtZ744BNSH8Tm2pnqPlc0lC1ysf9r7Rx\nDjMzMytYREyjwZLqtduJ1bn/NOC0Jvc/DTT9dN/MzGwA3NRKo3YK71eqPi03MzMzMzMzsybGtPGY\nLSQ9Jul+ST+SNJJhamZmZmZmZmallrfwvgU4BNiTtFjLpsANktbocFxmZmZmZmZmpZBrqHlEVE8a\nnyvpNuBhYH/gwsaPvIahqxZvDWyT5+nNzMxGubtJS6FU87pjZmZmg25E+3hHxGJJfwI2b95yMt5O\nzMzMym8bhn6o/Op2YmZmZjag2pnj/SpJa5KK7rLtR29mZmZtkHS0pAclPS/pFkk79DqmTpI0RdLy\nmts9vY5rJCTtJumKbA2f5ZL2qdNmqqTHJT0n6VeShrnoMvoM109JF9bJ7VW9ircdkk6UdJukJZIW\nSrpc0tvrtOvrfLbSz5Lk8whJd0lanN1ukjS5pk1f5xKG72cZcgk5C29JZ0vaXdJbJe0MXA68DPyk\nkOjMzMysb0j6BHAOMAXYDrgLmJntJ14mc0l7o4/Pbrv2NpwRW4O0z/tRQNTeKel44BjgcGBHYCkp\nr6t0M8gOaNrPzNWsmNsDuxNax+wGfAfYCfggsDLwS0mrVRqUJJ/D9jPT7/l8BDgemABsD/wGuELS\nVlCaXMIw/cz0ey5zDzXfiLRn53rAU8As4L3ZXp5mZmY22I4FLoiIiyFdxQA+DHwGOKuXgXVYqbZW\njYhrSAvyIEl1mnwROD0ifp61+RSwEPgIMKNbcY5UC/0EeLGfcxsRe1d/L+kQ4ElSMTMrO9z3+Wyx\nn9D/+fxFzaGTJR1J+sDhHkqQS2ipn9DnuYScV7wj4sCI2CgiVouIt0TEQRHxYFHBmZmZWX+QtDLp\nTe+vK8ciIoBrgYm9iqsgA7O1qqRNSVeXqvO6BLiV8uUVYFI2dHmepGmSXt/rgEZ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ftuy4qKyjjI+Yv+iRhXDsT4AMdXx0w9vMPgx8CzgBuBuYAdxoZm9z98VFyVcDbLnzFkzo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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x114d59090>"
+       "<matplotlib.figure.Figure at 0x11a7aae10>"
       ]
      },
      "metadata": {},
diff --git a/repeat-copy/.ipynb_checkpoints/repeat-copy-rnn-checkpoint.ipynb b/repeat-copy/.ipynb_checkpoints/repeat-copy-rnn-checkpoint.ipynb
index a6cca09..00167bf 100644
--- a/repeat-copy/.ipynb_checkpoints/repeat-copy-rnn-checkpoint.ipynb
+++ b/repeat-copy/.ipynb_checkpoints/repeat-copy-rnn-checkpoint.ipynb
@@ -45,7 +45,7 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -65,7 +65,7 @@
     "tf.app.flags.DEFINE_float(\"lr\", 1e-4, \"Learning rate (alpha) for the model\")\n",
     "tf.app.flags.DEFINE_float(\"momentum\", .9, \"RMSProp momentum\")\n",
     "tf.app.flags.DEFINE_integer(\"save_every\", 1000, \"Save model after this number of train steps\")\n",
-    "tf.app.flags.DEFINE_string(\"save_path\", \"nn_models/model.ckpt\", \"Where to save checkpoints\")\n",
+    "tf.app.flags.DEFINE_string(\"save_path\", \"rnn_models/model.ckpt\", \"Where to save checkpoints\")\n",
     "FLAGS = tf.app.flags.FLAGS"
    ]
   },
@@ -85,9 +85,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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EHAA29TKuMkTEERRfTCbv5Pv+iDgVeCOl9DLF/47XRcSPKe6a98cUVy38Qw/C7dhc7W08\nrqf4721XY74/ofgVZsvMtfW3iFhPcXnOhcDeiJg8UtuTUpq8++HA5He+9jZyPxD5jYibKP4nfgl4\nN3ApcDZwY2OWgckrzN3eQcor0P1L0xrnFFxN0VH2UZyYsKLXp/VX1M5NFB+EfRSd6W7gxF7HVVLb\nzqG4fGd82mPDlHm+SHGpy1sUH46Teh13Fe2lOLHmPoodwn7geeBrwHt6HXebbZ2tnePA70ybbyDy\nO197Bym/wF834t/XaM/9wCcGMa/ztXeQ8ppS8haokiTVnbXZJUmqOQdzSZJqzsFckqSaczCXJKnm\nHMwlSao5B3NJkmrOwVySpJpzMJckqeYczCVJqjkHc0mSas7BXJKkmnMwlySp5v4PTkxW3yNB1/kA\nAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10964f650>"
+       "<matplotlib.figure.Figure at 0x111a190d0>"
       ]
      },
      "metadata": {},
@@ -160,7 +160,6 @@
      "output_type": "stream",
      "text": [
       "building graph...\n",
-      "LSTMStateTuple(c=<tf.Tensor 'while_loop/while/Identity_1:0' shape=(1, 64) dtype=float32>, h=<tf.Tensor 'while_loop/while/Identity_2:0' shape=(1, 64) dtype=float32>) LSTMStateTuple(c=<tf.Tensor 'while_loop/while/basic_lstm_cell/add_1:0' shape=(1, 64) dtype=float32>, h=<tf.Tensor 'while_loop/while/basic_lstm_cell/mul_2:0' shape=(1, 64) dtype=float32>)\n",
       "defining loss...\n",
       "computing gradients...\n",
       "init variables... \n",
@@ -232,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
@@ -241,7 +240,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "no saved model to load.\n"
+      "loaded model: rnn_models/model.ckpt-7000\n"
      ]
     }
    ],
@@ -272,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
@@ -281,25 +280,41 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iteration 0/40000\n",
-      "\tloss: 0.6937\n",
-      "Iteration 100/40000\n",
-      "\tloss: 0.6921\n",
-      "Iteration 200/40000\n",
-      "\tloss: 0.4698\n",
-      "Iteration 300/40000\n",
-      "\tloss: 0.3195\n",
-      "Iteration 400/40000\n",
-      "\tloss: 0.2620\n",
-      "Iteration 500/40000\n",
-      "\tloss: 0.2429\n",
-      "Iteration 600/40000\n",
-      "\tloss: 0.2198\n",
-      "Iteration 700/40000\n",
-      "\tloss: 0.2103\n",
-      "Iteration 800/40000\n",
-      "\tloss: 0.1918\n",
-      "Iteration 831/40000"
+      "Iteration 7100/10000\n",
+      "\tloss: 0.0758\n",
+      "Iteration 7200/10000\n",
+      "\tloss: 0.0761\n",
+      "Iteration 7300/10000\n",
+      "\tloss: 0.0640\n",
+      "Iteration 7400/10000\n",
+      "\tloss: 0.0725\n",
+      "Iteration 7500/10000\n",
+      "\tloss: 0.0759\n",
+      "Iteration 7600/10000\n",
+      "\tloss: 0.0738\n",
+      "Iteration 7700/10000\n",
+      "\tloss: 0.0689\n",
+      "Iteration 7800/10000\n",
+      "\tloss: 0.0702\n",
+      "Iteration 7900/10000\n",
+      "\tloss: 0.0741\n",
+      "Iteration 7978/10000"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-8-6907e51af76f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mfeed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mdnc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdnc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdnc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtsteps\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtsteps\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m     \u001b[0mstep_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m     \u001b[0mloss_history\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep_loss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mglobal_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    765\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 767\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    768\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    769\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    963\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 965\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    966\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1013\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1014\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1015\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1016\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1017\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
+      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1020\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1021\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1022\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1023\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1024\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1002\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1003\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1004\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1005\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
    ],
@@ -330,11 +345,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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j35s6dvnKoVFlr1/TVDoo0fbiFlFlsyAi9rPnRhV9d+sNqWPHdbxzoKRlpD+G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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x12298d2d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "X, y = next_batch(FLAGS.batch_size, FLAGS.length, FLAGS.reps, FLAGS.xlen)\n",
     "tsteps = FLAGS.reps*(2*FLAGS.length+3)\n",
diff --git a/repeat-copy/nn_controller.py b/repeat-copy/nn_controller.py
index 144c977..1bbe8e6 100755
--- a/repeat-copy/nn_controller.py
+++ b/repeat-copy/nn_controller.py
@@ -28,5 +28,8 @@ class NNController(Controller):
         h2 = tf.nn.elu(z2)
         return h2, state
 
+    def update_state(self, update):
+        return tf.group(tf.zeros(1), tf.zeros(1))
+
     def get_state(self):
         return LSTMStateTuple(tf.zeros(1), tf.zeros(1))
diff --git a/repeat-copy/nn_controller.pyc b/repeat-copy/nn_controller.pyc
index da35d7f..f789e2e 100644
Binary files a/repeat-copy/nn_controller.pyc and b/repeat-copy/nn_controller.pyc differ
diff --git a/repeat-copy/repeat-copy-nn.ipynb b/repeat-copy/repeat-copy-nn.ipynb
index 73dc39f..b547d7c 100644
--- a/repeat-copy/repeat-copy-nn.ipynb
+++ b/repeat-copy/repeat-copy-nn.ipynb
@@ -85,9 +85,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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3ADsKiCVXZvYF4FskX63/EvAnwElgqMi4smBmC0g+mEzcyfc9ZnYR8Ia7v0Lyu+PtZvZj\nkrvm/SnJVQvfLCDcWWvV39pjC8lvbwdr7f6c5FuY3dPX1t3MbCvJ5TnrgaNmNnGkdsTdJ+5+WJn8\nztTfWu4rkV8zu4vkd+KXgXcC1wKXAnfWmlQmr9C6v1XKK9D5S9Nq5xTcQjJQjpGcmLCq6NP6c+rn\nEMk/wjGSwfQgcH7RcWXUt8tILt85XffYNqnNZ0kudXmL5J/jgqLjzqO/JCfWPEyyQzgOvAB8CXhX\n0XGn7Gujfp4Gfq+uXSXyO1N/q5Rf4O9r8R+r9ecR4KNVzOtM/a1SXt1dt0AVEREpO9VmFxERKTlN\n5iIiIiWnyVxERKTkNJmLiIiUnCZzERGRktNkLiIiUnKazEVEREpOk7mIiEjJaTIXEREpOU3mIiIi\nJafJXEREpOQ0mYuIiJTc/wGGNU2mjcmoGQAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10aa5d750>"
+       "<matplotlib.figure.Figure at 0x109d165d0>"
       ]
      },
      "metadata": {},
@@ -240,7 +240,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "loaded model: nn_models/model.ckpt-7000\n"
+      "loaded model: nn_models/model.ckpt-10000\n"
      ]
     }
    ],
@@ -275,80 +275,7 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Iteration 7100/10000\n",
-      "\tloss: 0.0162\n",
-      "Iteration 7200/10000\n",
-      "\tloss: 0.0112\n",
-      "Iteration 7300/10000\n",
-      "\tloss: 0.0105\n",
-      "Iteration 7400/10000\n",
-      "\tloss: 0.0176\n",
-      "Iteration 7500/10000\n",
-      "\tloss: 0.0053\n",
-      "Iteration 7600/10000\n",
-      "\tloss: 0.0180\n",
-      "Iteration 7700/10000\n",
-      "\tloss: 0.0144\n",
-      "Iteration 7800/10000\n",
-      "\tloss: 0.0159\n",
-      "Iteration 7900/10000\n",
-      "\tloss: 0.6691\n",
-      "Iteration 8000/10000\n",
-      "\tloss: 0.3837\n",
-      "\n",
-      "\tSAVING MODEL\n",
-      "Iteration 8100/10000\n",
-      "\tloss: 0.0386\n",
-      "Iteration 8200/10000\n",
-      "\tloss: 0.0129\n",
-      "Iteration 8300/10000\n",
-      "\tloss: 0.0166\n",
-      "Iteration 8400/10000\n",
-      "\tloss: 0.0266\n",
-      "Iteration 8500/10000\n",
-      "\tloss: 0.0160\n",
-      "Iteration 8600/10000\n",
-      "\tloss: 0.0153\n",
-      "Iteration 8700/10000\n",
-      "\tloss: 0.1232\n",
-      "Iteration 8800/10000\n",
-      "\tloss: 0.1623\n",
-      "Iteration 8900/10000\n",
-      "\tloss: 0.0657\n",
-      "Iteration 9000/10000\n",
-      "\tloss: 0.0635\n",
-      "\n",
-      "\tSAVING MODEL\n",
-      "Iteration 9100/10000\n",
-      "\tloss: 0.3011\n",
-      "Iteration 9200/10000\n",
-      "\tloss: 0.1336\n",
-      "Iteration 9300/10000\n",
-      "\tloss: 0.1150\n",
-      "Iteration 9400/10000\n",
-      "\tloss: 0.0853\n",
-      "Iteration 9500/10000\n",
-      "\tloss: 0.0779\n",
-      "Iteration 9600/10000\n",
-      "\tloss: 0.0704\n",
-      "Iteration 9700/10000\n",
-      "\tloss: 0.0385\n",
-      "Iteration 9800/10000\n",
-      "\tloss: 0.0153\n",
-      "Iteration 9900/10000\n",
-      "\tloss: 0.0030\n",
-      "Iteration 10000/10000\n",
-      "\tloss: 0.0191\n",
-      "\n",
-      "\tSAVING MODEL\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "loss_history = []\n",
     "for i in xrange(global_step, FLAGS.iterations + 1):\n",
@@ -383,9 +310,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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+2fMeMS8qFF7ozP7h33le5BiG09MXT10IM4q/KnvisuZfI9Kemz3pY+vhY8uBDHV8dMPb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ftuy4qKyjjI+Yv+iRhXDsT4AMdXx0w9vMPgx8CzgBuBuYAdxoZm9z98VFyVcDbLnzFkzo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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118fe8f10>"
+       "<matplotlib.figure.Figure at 0x11a7aae10>"
       ]
      },
      "metadata": {},
diff --git a/repeat-copy/repeat-copy-rnn.ipynb b/repeat-copy/repeat-copy-rnn.ipynb
index a6cca09..00167bf 100644
--- a/repeat-copy/repeat-copy-rnn.ipynb
+++ b/repeat-copy/repeat-copy-rnn.ipynb
@@ -45,7 +45,7 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -65,7 +65,7 @@
     "tf.app.flags.DEFINE_float(\"lr\", 1e-4, \"Learning rate (alpha) for the model\")\n",
     "tf.app.flags.DEFINE_float(\"momentum\", .9, \"RMSProp momentum\")\n",
     "tf.app.flags.DEFINE_integer(\"save_every\", 1000, \"Save model after this number of train steps\")\n",
-    "tf.app.flags.DEFINE_string(\"save_path\", \"nn_models/model.ckpt\", \"Where to save checkpoints\")\n",
+    "tf.app.flags.DEFINE_string(\"save_path\", \"rnn_models/model.ckpt\", \"Where to save checkpoints\")\n",
     "FLAGS = tf.app.flags.FLAGS"
    ]
   },
@@ -85,9 +85,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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EHAA29TKuMkTEERRfTCbv5Pv+iDgVeCOl9DLF/47XRcSPKe6a98cUVy38Qw/C7dhc7W08\nrqf4721XY74/ofgVZsvMtfW3iFhPcXnOhcDeiJg8UtuTUpq8++HA5He+9jZyPxD5jYibKP4nfgl4\nN3ApcDZwY2OWgckrzN3eQcor0P1L0xrnFFxN0VH2UZyYsKLXp/VX1M5NFB+EfRSd6W7gxF7HVVLb\nzqG4fGd82mPDlHm+SHGpy1sUH46Teh13Fe2lOLHmPoodwn7geeBrwHt6HXebbZ2tnePA70ybbyDy\nO197Bym/wF834t/XaM/9wCcGMa/ztXeQ8ppS8haokiTVnbXZJUmqOQdzSZJqzsFckqSaczCXJKnm\nHMwlSao5B3NJkmrOwVySpJpzMJckqeYczCVJqjkHc0mSas7BXJKkmnMwlySp5v4PTkxW3yNB1/kA\nAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10964f650>"
+       "<matplotlib.figure.Figure at 0x111a190d0>"
       ]
      },
      "metadata": {},
@@ -160,7 +160,6 @@
      "output_type": "stream",
      "text": [
       "building graph...\n",
-      "LSTMStateTuple(c=<tf.Tensor 'while_loop/while/Identity_1:0' shape=(1, 64) dtype=float32>, h=<tf.Tensor 'while_loop/while/Identity_2:0' shape=(1, 64) dtype=float32>) LSTMStateTuple(c=<tf.Tensor 'while_loop/while/basic_lstm_cell/add_1:0' shape=(1, 64) dtype=float32>, h=<tf.Tensor 'while_loop/while/basic_lstm_cell/mul_2:0' shape=(1, 64) dtype=float32>)\n",
       "defining loss...\n",
       "computing gradients...\n",
       "init variables... \n",
@@ -232,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
@@ -241,7 +240,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "no saved model to load.\n"
+      "loaded model: rnn_models/model.ckpt-7000\n"
      ]
     }
    ],
@@ -272,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
@@ -281,25 +280,41 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iteration 0/40000\n",
-      "\tloss: 0.6937\n",
-      "Iteration 100/40000\n",
-      "\tloss: 0.6921\n",
-      "Iteration 200/40000\n",
-      "\tloss: 0.4698\n",
-      "Iteration 300/40000\n",
-      "\tloss: 0.3195\n",
-      "Iteration 400/40000\n",
-      "\tloss: 0.2620\n",
-      "Iteration 500/40000\n",
-      "\tloss: 0.2429\n",
-      "Iteration 600/40000\n",
-      "\tloss: 0.2198\n",
-      "Iteration 700/40000\n",
-      "\tloss: 0.2103\n",
-      "Iteration 800/40000\n",
-      "\tloss: 0.1918\n",
-      "Iteration 831/40000"
+      "Iteration 7100/10000\n",
+      "\tloss: 0.0758\n",
+      "Iteration 7200/10000\n",
+      "\tloss: 0.0761\n",
+      "Iteration 7300/10000\n",
+      "\tloss: 0.0640\n",
+      "Iteration 7400/10000\n",
+      "\tloss: 0.0725\n",
+      "Iteration 7500/10000\n",
+      "\tloss: 0.0759\n",
+      "Iteration 7600/10000\n",
+      "\tloss: 0.0738\n",
+      "Iteration 7700/10000\n",
+      "\tloss: 0.0689\n",
+      "Iteration 7800/10000\n",
+      "\tloss: 0.0702\n",
+      "Iteration 7900/10000\n",
+      "\tloss: 0.0741\n",
+      "Iteration 7978/10000"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-8-6907e51af76f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mfeed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mdnc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdnc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdnc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtsteps\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtsteps\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m     \u001b[0mstep_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m     \u001b[0mloss_history\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep_loss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mglobal_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    765\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 767\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    768\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    769\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    963\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 965\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    966\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1013\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1014\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1015\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1016\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1017\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
+      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1020\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1021\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1022\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1023\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1024\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1002\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1003\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1004\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1005\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
    ],
@@ -330,11 +345,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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j35s6dvnKoVFlr1/TVDoo0fbiFlFlsyAi9rPnRhV9d+sNqWPHdbxzoKRlpD+G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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x12298d2d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "X, y = next_batch(FLAGS.batch_size, FLAGS.length, FLAGS.reps, FLAGS.xlen)\n",
     "tsteps = FLAGS.reps*(2*FLAGS.length+3)\n",
diff --git a/repeat-copy/rnn_models/checkpoint b/repeat-copy/rnn_models/checkpoint
index d87012d..1701b1a 100644
--- a/repeat-copy/rnn_models/checkpoint
+++ b/repeat-copy/rnn_models/checkpoint
@@ -1,2 +1,2 @@
-model_checkpoint_path: "model.ckpt-1000"
-all_model_checkpoint_paths: "model.ckpt-1000"
+model_checkpoint_path: "model.ckpt-7000"
+all_model_checkpoint_paths: "model.ckpt-7000"
diff --git a/repeat-copy/rnn_models/model.ckpt-1000.data-00000-of-00001 b/repeat-copy/rnn_models/model.ckpt-1000.data-00000-of-00001
deleted file mode 100644
index 04457d0..0000000
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diff --git a/repeat-copy/rnn_models/model.ckpt-1000.index b/repeat-copy/rnn_models/model.ckpt-1000.index
deleted file mode 100644
index b65497b..0000000
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diff --git a/repeat-copy/rnn_models/model.ckpt-7000.data-00000-of-00001 b/repeat-copy/rnn_models/model.ckpt-7000.data-00000-of-00001
new file mode 100644
index 0000000..f16742e
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diff --git a/repeat-copy/rnn_models/model.ckpt-7000.index b/repeat-copy/rnn_models/model.ckpt-7000.index
new file mode 100644
index 0000000..de14090
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diff --git a/repeat-copy/rnn_models/model.ckpt-1000.meta b/repeat-copy/rnn_models/model.ckpt-7000.meta
similarity index 98%
rename from repeat-copy/rnn_models/model.ckpt-1000.meta
rename to repeat-copy/rnn_models/model.ckpt-7000.meta
index cc14ddd..55737e6 100644
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