rainfall_neurals_E.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "mingw_path = 'C:\\Program Files\\mingw-w64\\x86_64-6.3.0-posix-seh-rt_v5-rev2\\mingw64\\bin'\n",
    "os.environ['PATH'] = mingw_path + ';' + os.environ['PATH']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import sklearn\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "train_y = []\n",
    "count = 0\n",
    "with open('CIKM2017_train/train.txt') as fhdl:\n",
    "    for line in fhdl:\n",
    "        count += 1\n",
    "        if count % 50 == 0:\n",
    "            print (float(count) / 10000,end=' '),\n",
    "        linenum,label,datas = line.strip().split(',')\n",
    "        label = float(label)\n",
    "        datas = np.asarray(datas.split(\" \"),dtype=np.int)\n",
    "        datas[datas < 0] = 0\n",
    "        datas = np.asarray(datas,dtype=np.uint8)\n",
    "        train_x.append(datas)\n",
    "        train_y.append(label)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "import pickle\n",
    "with open('datauint8.pkl','wb') as whdl:\n",
    "    pickle.dump((train_x,train_y),whdl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "del train_y\n",
    "    del train_x\n",
    "del test_x\n",
    "del test_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "train_x,train_y = pickle.load(open('datauint8.pkl','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 10000)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_x),len(train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "train_x,test_x,train_y,test_y = train_test_split(train_x,train_y,test_size=0.2,random_state=1120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# do not use this now, will cause future shuffle slow\n",
    "#train_x = np.asarray(train_x,dtype=np.uint8)\n",
    "#test_x = np.asarray(test_x,dtype=np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8000, 8000, 2000, 2000)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_x),len(train_y),len(test_x),len(test_y)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "from sklearn.utils import shuffle\n",
    "train_x,train_y = shuffle(train_x,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "#pickle.dump((train_x,train_y),open('data.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "960000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.getsizeof(train_x[0]) * 10000"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "sample = np.asarray([-2,2],dtype=np.uint8)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "sample[sample < 0] = 0\n",
    "sample"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# process negative value\n",
    "for each_ele in train_x:\n",
    "    each_ele[each_ele < 0] = 0\n",
    "test_x[test_x < 0] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8000, 2000)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_x),len(test_x)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "train_y = [1 if i >= 20 else 0 for i in train_y  ]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "test_y = [1 if i >= 20 else 0 for i in test_y  ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_x = np.asarray(train_x,dtype=np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8000, 612060)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_x = train_x.reshape((-1,60,101,101))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8000, 60, 101, 101)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#train_x = [i[59].reshape(101,101,1) for i in train_x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#train_x = np.asarray(train_x,dtype=np.uint8)\n",
    "#train_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_x = np.asarray(test_x,dtype=np.uint8)\n",
    "test_x = test_x.reshape((-1,60,101,101))\n",
    "#test_x = [i[59].reshape(101,101,1) for i in test_x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 60, 101, 101)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#test_x = np.asarray(test_x,dtype=np.uint8)\n",
    "test_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_y)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "tmp = np.zeros((8000,2))\n",
    "for i,val in enumerate(train_y):\n",
    "    tmp[i][val] = 1\n",
    "train_y = tmp"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "tmp = np.zeros((2000,2))\n",
    "for i,val in enumerate(test_y):\n",
    "    tmp[i][val] = 1\n",
    "test_y = tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8000, 8000, 2000, 2000)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_x),len(train_y),len(test_x),len(test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8000, 60, 101, 101), 8000)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.shape,len(train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_x = np.transpose(train_x,axes=[0,2,3,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_x = np.transpose(test_x,axes=[0,2,3,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8000, 101, 101, 60), (2000, 101, 101, 60))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.shape,test_x.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# keras logistic model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8000 samples, validate on 2000 samples\n",
      "Epoch 1/10000\n",
      "8000/8000 [==============================] - 105s - loss: 115037.5976 - mean_squared_error: 113983.9013 - val_loss: 1240.1870 - val_mean_squared_error: 243.2327\n",
      "Epoch 2/10000\n",
      "8000/8000 [==============================] - 94s - loss: 1191.3272 - mean_squared_error: 292.2041 - val_loss: 1045.2369 - val_mean_squared_error: 269.4739\n",
      "Epoch 3/10000\n",
      "8000/8000 [==============================] - 96s - loss: 944.8637 - mean_squared_error: 309.6236 - val_loss: 832.8390 - val_mean_squared_error: 325.0924\n",
      "Epoch 4/10000\n",
      "8000/8000 [==============================] - 97s - loss: 643.3969 - mean_squared_error: 250.1238 - val_loss: 529.6626 - val_mean_squared_error: 265.0042\n",
      "Epoch 5/10000\n",
      "8000/8000 [==============================] - 97s - loss: 403.4995 - mean_squared_error: 230.0338 - val_loss: 335.6208 - val_mean_squared_error: 226.1757\n",
      "Epoch 6/10000\n",
      "8000/8000 [==============================] - 96s - loss: 263.2493 - mean_squared_error: 183.7486 - val_loss: 328.4739 - val_mean_squared_error: 269.6416\n",
      "Epoch 7/10000\n",
      "8000/8000 [==============================] - 96s - loss: 200.1697 - mean_squared_error: 149.7129 - val_loss: 226.5079 - val_mean_squared_error: 182.0326\n",
      "Epoch 8/10000\n",
      "8000/8000 [==============================] - 97s - loss: 160.8178 - mean_squared_error: 119.3831 - val_loss: 212.6615 - val_mean_squared_error: 173.5447\n",
      "Epoch 9/10000\n",
      "8000/8000 [==============================] - 88s - loss: 130.1389 - mean_squared_error: 92.8479 - val_loss: 283.3609 - val_mean_squared_error: 247.7250\n",
      "Epoch 10/10000\n",
      "8000/8000 [==============================] - 98s - loss: 107.4719 - mean_squared_error: 73.1778 - val_loss: 202.1969 - val_mean_squared_error: 169.1158\n",
      "Epoch 11/10000\n",
      "8000/8000 [==============================] - 90s - loss: 93.5877 - mean_squared_error: 61.9176 - val_loss: 208.2076 - val_mean_squared_error: 177.4877\n",
      "Epoch 12/10000\n",
      "8000/8000 [==============================] - 100s - loss: 82.0073 - mean_squared_error: 52.5441 - val_loss: 193.4784 - val_mean_squared_error: 165.0192\n",
      "Epoch 13/10000\n",
      "8000/8000 [==============================] - 92s - loss: 74.7967 - mean_squared_error: 47.4374 - val_loss: 246.0589 - val_mean_squared_error: 219.5021\n",
      "Epoch 14/10000\n",
      "8000/8000 [==============================] - 92s - loss: 66.0812 - mean_squared_error: 40.6823 - val_loss: 200.0925 - val_mean_squared_error: 175.6656\n",
      "Epoch 15/10000\n",
      "8000/8000 [==============================] - 99s - loss: 64.9882 - mean_squared_error: 41.3481 - val_loss: 185.1996 - val_mean_squared_error: 162.1091\n",
      "Epoch 16/10000\n",
      "8000/8000 [==============================] - 100s - loss: 58.0551 - mean_squared_error: 35.8628 - val_loss: 170.6266 - val_mean_squared_error: 149.1581\n",
      "Epoch 17/10000\n",
      "8000/8000 [==============================] - 94s - loss: 56.4980 - mean_squared_error: 35.6825 - val_loss: 175.1524 - val_mean_squared_error: 154.8065\n",
      "Epoch 18/10000\n",
      "8000/8000 [==============================] - 99s - loss: 51.2866 - mean_squared_error: 31.6419 - val_loss: 170.2000 - val_mean_squared_error: 150.9713\n",
      "Epoch 19/10000\n",
      "8000/8000 [==============================] - 91s - loss: 49.1434 - mean_squared_error: 30.5090 - val_loss: 174.4487 - val_mean_squared_error: 156.2299\n",
      "Epoch 20/10000\n",
      "8000/8000 [==============================] - 92s - loss: 48.3743 - mean_squared_error: 30.5773 - val_loss: 184.0825 - val_mean_squared_error: 166.4671\n",
      "Epoch 21/10000\n",
      "8000/8000 [==============================] - 99s - loss: 44.8830 - mean_squared_error: 27.6973 - val_loss: 166.7302 - val_mean_squared_error: 149.8280\n",
      "Epoch 22/10000\n",
      "8000/8000 [==============================] - 99s - loss: 41.5631 - mean_squared_error: 25.1141 - val_loss: 164.3894 - val_mean_squared_error: 148.2449\n",
      "Epoch 23/10000\n",
      "8000/8000 [==============================] - 91s - loss: 41.1261 - mean_squared_error: 25.3306 - val_loss: 173.0342 - val_mean_squared_error: 157.4761\n",
      "Epoch 24/10000\n",
      "8000/8000 [==============================] - 91s - loss: 41.8626 - mean_squared_error: 26.6014 - val_loss: 169.4682 - val_mean_squared_error: 154.3285\n",
      "Epoch 25/10000\n",
      "8000/8000 [==============================] - 92s - loss: 36.9567 - mean_squared_error: 22.1229 - val_loss: 164.9639 - val_mean_squared_error: 150.3586\n",
      "Epoch 26/10000\n",
      "8000/8000 [==============================] - 101s - loss: 37.7863 - mean_squared_error: 23.4248 - val_loss: 162.2547 - val_mean_squared_error: 147.9758\n",
      "Epoch 27/10000\n",
      "8000/8000 [==============================] - 93s - loss: 35.3138 - mean_squared_error: 21.2935 - val_loss: 164.7378 - val_mean_squared_error: 150.8997\n",
      "Epoch 28/10000\n",
      "8000/8000 [==============================] - 91s - loss: 35.2629 - mean_squared_error: 21.6383 - val_loss: 164.8548 - val_mean_squared_error: 151.4091\n",
      "Epoch 29/10000\n",
      "8000/8000 [==============================] - 92s - loss: 33.2774 - mean_squared_error: 20.0249 - val_loss: 166.3229 - val_mean_squared_error: 153.1894\n",
      "Epoch 30/10000\n",
      "8000/8000 [==============================] - 93s - loss: 32.4671 - mean_squared_error: 19.5426 - val_loss: 166.0177 - val_mean_squared_error: 153.2860\n",
      "Epoch 31/10000\n",
      " 640/8000 [=>............................] - ETA: 70s - loss: 29.4666 - mean_squared_error: 16.7805"
     ]
    },
    {
     "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-27-95280523d492>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0mcheckpoint\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mModelCheckpoint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'models/keras_conv_reg_checkpoint.hdf5'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msave_best_only\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_x\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtrain_y\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_x\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtest_y\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10000\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m64\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcheckpoint\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\keras\\models.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m    851\u001b[0m                               \u001b[0mclass_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    852\u001b[0m                               \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 853\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m    854\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    855\u001b[0m     def evaluate(self, x, y, batch_size=32, verbose=1,\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m   1484\u001b[0m                               \u001b[0mval_f\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mval_f\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval_ins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mval_ins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1485\u001b[0m                               \u001b[0mcallback_metrics\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcallback_metrics\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1486\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1487\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1488\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m32\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch)\u001b[0m\n\u001b[1;32m   1139\u001b[0m                 \u001b[0mbatch_logs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'size'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_ids\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1140\u001b[0m                 \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1141\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1142\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1143\u001b[0m                     \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2101\u001b[0m         \u001b[0msession\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2102\u001b[0m         updated = session.run(self.outputs + [self.updates_op],\n\u001b[0;32m-> 2103\u001b[0;31m                               feed_dict=feed_dict)\n\u001b[0m\u001b[1;32m   2104\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2105\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\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[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\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[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    769\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\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[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\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[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m       \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\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[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\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[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\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;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1020\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1021\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1022\u001b[0;31m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1023\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1024\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\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[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\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[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# set a big learning rate,and early stopping after val_loss stop decreasing 3 rounds\n",
    "\n",
    "from keras.models import Sequential, load_model\n",
    "from keras import backend, regularizers\n",
    "from keras.layers.core import Dense, Dropout, Flatten\n",
    "from keras.layers.pooling import MaxPooling2D\n",
    "from keras.layers.convolutional_recurrent import ConvLSTM2D\n",
    "import keras.layers.recurrent as recurrent_unit\n",
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.layers.wrappers import TimeDistributed\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.callbacks import EarlyStopping\n",
    "from sklearn import metrics, ensemble\n",
    "from sklearn.externals import joblib\n",
    "from keras import optimizers\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "\n",
    "def FuckModel(input_shape, rweight=0.1):\n",
    "    fuckingmodel = Sequential()\n",
    "    fuckingmodel.add(Conv2D(6, (5, 5), padding='same', activation='relu', input_shape=input_shape))\n",
    "    fuckingmodel.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    fuckingmodel.add(Conv2D(16, (5, 5), padding='same', activation='relu'))\n",
    "    fuckingmodel.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    \n",
    "    fuckingmodel.add(Flatten())\n",
    "    fuckingmodel.add(Dense(16*26*26, kernel_regularizer=regularizers.l2(rweight), activation='relu'))\n",
    "    fuckingmodel.add(Dense(512, kernel_regularizer=regularizers.l2(rweight), activation='relu'))\n",
    "    fuckingmodel.add(Dropout(0.4))\n",
    "    fuckingmodel.add(Dense(1))\n",
    "    fuckingmodel.compile(loss='mean_squared_error',\n",
    "                      metrics=['mean_squared_error'],\n",
    "                      optimizer='adadelta')\n",
    "    return fuckingmodel\n",
    "\n",
    "model = FuckModel((101,101,60))\n",
    "#checkpoint = ModelCheckpoint(filepath='models/keras_conv_reg_checkpoint-{epoch:02d}-{val_loss:.2f}.hdf5',save_best_only=True)\n",
    "checkpoint = ModelCheckpoint(filepath='models/keras_conv_reg_checkpoint.hdf5',save_best_only=True)\n",
    "\n",
    "model.fit(train_x,train_y,validation_data=(test_x,test_y),epochs=10000,batch_size=64,callbacks=[checkpoint])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# fine tuning using sgd\n",
    "from keras.models import Sequential, load_model\n",
    "from keras import backend, regularizers\n",
    "from keras.layers.core import Dense, Dropout, Flatten\n",
    "from keras.layers.pooling import MaxPooling2D\n",
    "from keras.layers.convolutional_recurrent import ConvLSTM2D\n",
    "import keras.layers.recurrent as recurrent_unit\n",
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.layers.wrappers import TimeDistributed\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.callbacks import EarlyStopping\n",
    "from sklearn import metrics, ensemble\n",
    "from sklearn.externals import joblib\n",
    "from keras import optimizers\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "sgd = optimizers.SGD(lr=0.0000001, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "\n",
    "def FuckModel(input_shape, rweight=0.1):\n",
    "    fuckingmodel = Sequential()\n",
    "    fuckingmodel.add(Conv2D(6, (5, 5), padding='same', activation='relu', input_shape=input_shape))\n",
    "    fuckingmodel.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    fuckingmodel.add(Conv2D(16, (5, 5), padding='same', activation='relu'))\n",
    "    fuckingmodel.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    \n",
    "    fuckingmodel.add(Flatten())\n",
    "    fuckingmodel.add(Dense(16*26*26, kernel_regularizer=regularizers.l2(rweight), activation='relu'))\n",
    "    fuckingmodel.add(Dense(512, kernel_regularizer=regularizers.l2(rweight), activation='relu'))\n",
    "    fuckingmodel.add(Dropout(0.4))\n",
    "    fuckingmodel.add(Dense(1))\n",
    "    fuckingmodel.compile(loss='mean_squared_error',\n",
    "                      metrics=['mean_squared_error'],\n",
    "                      optimizer=sgd)\n",
    "    return fuckingmodel\n",
    "\n",
    "model = FuckModel((101,101,60))\n",
    "model.load_weights('models/keras_conv_reg_checkpoint.hdf5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8000 samples, validate on 2000 samples\n",
      "Epoch 1/10000\n",
      "8000/8000 [==============================] - 94s - loss: 32.1726 - mean_squared_error: 17.8947 - val_loss: 162.6673 - val_mean_squared_error: 148.3902\n",
      "Epoch 2/10000\n",
      "8000/8000 [==============================] - 90s - loss: 30.0185 - mean_squared_error: 15.7419 - val_loss: 162.1322 - val_mean_squared_error: 147.8562\n",
      "Epoch 3/10000\n",
      "8000/8000 [==============================] - 89s - loss: 29.7050 - mean_squared_error: 15.4294 - val_loss: 161.9334 - val_mean_squared_error: 147.6583\n",
      "Epoch 4/10000\n",
      "8000/8000 [==============================] - 90s - loss: 29.1440 - mean_squared_error: 14.8692 - val_loss: 161.7692 - val_mean_squared_error: 147.4949\n",
      "Epoch 5/10000\n",
      "8000/8000 [==============================] - 85s - loss: 29.1663 - mean_squared_error: 14.8924 - val_loss: 161.8465 - val_mean_squared_error: 147.5732\n",
      "Epoch 6/10000\n",
      "8000/8000 [==============================] - 91s - loss: 29.0106 - mean_squared_error: 14.7376 - val_loss: 161.4111 - val_mean_squared_error: 147.1385\n",
      "Epoch 7/10000\n",
      "8000/8000 [==============================] - 89s - loss: 28.3763 - mean_squared_error: 14.1041 - val_loss: 161.3302 - val_mean_squared_error: 147.0585\n",
      "Epoch 8/10000\n",
      "8000/8000 [==============================] - 84s - loss: 28.9816 - mean_squared_error: 14.7103 - val_loss: 161.6449 - val_mean_squared_error: 147.3740\n",
      "Epoch 9/10000\n",
      "8000/8000 [==============================] - 84s - loss: 28.5313 - mean_squared_error: 14.2607 - val_loss: 161.4922 - val_mean_squared_error: 147.2220\n",
      "Epoch 10/10000\n",
      "8000/8000 [==============================] - 84s - loss: 28.2926 - mean_squared_error: 14.0227 - val_loss: 161.3387 - val_mean_squared_error: 147.0691\n",
      "Epoch 11/10000\n",
      "8000/8000 [==============================] - 90s - loss: 28.3284 - mean_squared_error: 14.0593 - val_loss: 161.2513 - val_mean_squared_error: 146.9826\n",
      "Epoch 12/10000\n",
      "8000/8000 [==============================] - 84s - loss: 27.8652 - mean_squared_error: 13.5969 - val_loss: 161.6661 - val_mean_squared_error: 147.3981\n",
      "Epoch 13/10000\n",
      "8000/8000 [==============================] - 84s - loss: 28.1669 - mean_squared_error: 13.8992 - val_loss: 161.6377 - val_mean_squared_error: 147.3705\n",
      "Epoch 14/10000\n",
      "8000/8000 [==============================] - 85s - loss: 27.8605 - mean_squared_error: 13.5937 - val_loss: 161.2745 - val_mean_squared_error: 147.0079\n",
      "Epoch 15/10000\n",
      "8000/8000 [==============================] - 85s - loss: 28.2642 - mean_squared_error: 13.9980 - val_loss: 161.3614 - val_mean_squared_error: 147.0956\n",
      "Epoch 16/10000\n",
      "8000/8000 [==============================] - 90s - loss: 27.8104 - mean_squared_error: 13.5450 - val_loss: 161.2415 - val_mean_squared_error: 146.9763\n",
      "Epoch 17/10000\n",
      "8000/8000 [==============================] - 85s - loss: 27.6739 - mean_squared_error: 13.4092 - val_loss: 161.3770 - val_mean_squared_error: 147.1126\n",
      "Epoch 18/10000\n",
      "8000/8000 [==============================] - 86s - loss: 27.7537 - mean_squared_error: 13.4897 - val_loss: 161.3871 - val_mean_squared_error: 147.1234\n",
      "Epoch 19/10000\n",
      "8000/8000 [==============================] - 91s - loss: 27.1961 - mean_squared_error: 12.9328 - val_loss: 161.1805 - val_mean_squared_error: 146.9175\n",
      "Epoch 20/10000\n",
      "8000/8000 [==============================] - 91s - loss: 27.2418 - mean_squared_error: 12.9791 - val_loss: 161.1236 - val_mean_squared_error: 146.8613\n",
      "Epoch 21/10000\n",
      "8000/8000 [==============================] - 86s - loss: 27.5924 - mean_squared_error: 13.3304 - val_loss: 161.3210 - val_mean_squared_error: 147.0594\n",
      "Epoch 22/10000\n",
      "8000/8000 [==============================] - 86s - loss: 27.3020 - mean_squared_error: 13.0407 - val_loss: 161.1502 - val_mean_squared_error: 146.8892\n",
      "Epoch 23/10000\n",
      "8000/8000 [==============================] - 86s - loss: 27.0340 - mean_squared_error: 12.7733 - val_loss: 161.1680 - val_mean_squared_error: 146.9077\n",
      "Epoch 24/10000\n",
      "8000/8000 [==============================] - 86s - loss: 26.6991 - mean_squared_error: 12.4392 - val_loss: 161.3082 - val_mean_squared_error: 147.0487\n",
      "Epoch 25/10000\n",
      "8000/8000 [==============================] - 91s - loss: 27.3435 - mean_squared_error: 13.0842 - val_loss: 160.9479 - val_mean_squared_error: 146.6891\n",
      "Epoch 26/10000\n",
      "8000/8000 [==============================] - 91s - loss: 27.2945 - mean_squared_error: 13.0360 - val_loss: 160.7485 - val_mean_squared_error: 146.4902\n",
      "Epoch 27/10000\n",
      "8000/8000 [==============================] - 86s - loss: 26.8844 - mean_squared_error: 12.6267 - val_loss: 161.0486 - val_mean_squared_error: 146.7912\n",
      "Epoch 28/10000\n",
      "8000/8000 [==============================] - 85s - loss: 26.9187 - mean_squared_error: 12.6615 - val_loss: 161.1368 - val_mean_squared_error: 146.8800\n",
      "Epoch 29/10000\n",
      "8000/8000 [==============================] - 91s - loss: 27.3241 - mean_squared_error: 13.0676 - val_loss: 160.7191 - val_mean_squared_error: 146.4629\n",
      "Epoch 30/10000\n",
      "8000/8000 [==============================] - 88s - loss: 27.0307 - mean_squared_error: 12.7749 - val_loss: 161.4617 - val_mean_squared_error: 147.2063\n",
      "Epoch 31/10000\n",
      " 704/8000 [=>............................] - ETA: 64s - loss: 28.2577 - mean_squared_error: 14.0023"
     ]
    },
    {
     "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-32-1cb7cb4f17c9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mcheckpoint\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mModelCheckpoint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'models/keras_conv_reg_sgd_checkpoint.hdf5'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msave_best_only\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_x\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtrain_y\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_x\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtest_y\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10000\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m64\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcheckpoint\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\keras\\models.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m    851\u001b[0m                               \u001b[0mclass_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    852\u001b[0m                               \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 853\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m    854\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    855\u001b[0m     def evaluate(self, x, y, batch_size=32, verbose=1,\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m   1484\u001b[0m                               \u001b[0mval_f\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mval_f\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval_ins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mval_ins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1485\u001b[0m                               \u001b[0mcallback_metrics\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcallback_metrics\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1486\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1487\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1488\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m32\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch)\u001b[0m\n\u001b[1;32m   1139\u001b[0m                 \u001b[0mbatch_logs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'size'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_ids\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1140\u001b[0m                 \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1141\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1142\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1143\u001b[0m                     \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2101\u001b[0m         \u001b[0msession\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2102\u001b[0m         updated = session.run(self.outputs + [self.updates_op],\n\u001b[0;32m-> 2103\u001b[0;31m                               feed_dict=feed_dict)\n\u001b[0m\u001b[1;32m   2104\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2105\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\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[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\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[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    769\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\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[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\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[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m       \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\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[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\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[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\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;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1020\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1021\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1022\u001b[0;31m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1023\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1024\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\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[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\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[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#checkpoint = ModelCheckpoint(filepath='models/keras_conv_reg_checkpoint-{epoch:02d}-{val_loss:.2f}.hdf5',save_best_only=True)\n",
    "checkpoint = ModelCheckpoint(filepath='models/keras_conv_reg_sgd_checkpoint.hdf5',save_best_only=True)\n",
    "\n",
    "model.fit(train_x,train_y,validation_data=(test_x,test_y),epochs=10000,batch_size=64,callbacks=[checkpoint])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "testpredict = model.predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.124450710984904"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(np.average(np.square(testpredict.reshape(-1) - np.asarray(test_y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000270F4A5EEB8>]], dtype=object)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dUZWcUJ2syhkW5ZQs815yughaclpERGRh8l5yWmccREREpG0qHERERKRtKhxKptFoFD2E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\nKhxERESkbf8/UjL0f2gGE54AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26e827a6a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "pd.DataFrame(testpredict).hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.775969"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(testpredict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 5, 60, 6)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "denselayer = model.get_layer(\"Dense\",1)\n",
    "denselayer.get_weights()[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del train_x\n",
    "del train_y\n",
    "del test_x\n",
    "del test_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "with open('onlinedata.plk','rb') as fhdl:\n",
    "    predict_x = pickle.load(fhdl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predict_x = np.asarray(predict_x,dtype=np.uint8)\n",
    "predict_x = predict_x.reshape((-1,60,101,101))\n",
    "predict_x = np.transpose(predict_x,axes=[0,2,3,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predictresult = model.predict(predict_x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000270F5E96B38>]], dtype=object)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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q9z0OZvYrwI3A60MIP8lz25dffjm1Wm3GY9WqVR2+ozpK5wsMbQK+1LZsrLlu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\niEhmGhxEREQkMw0OIiIiktn/B8AZzWqBfJAPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x270f6006cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(predictresult).hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.226006"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(predictresult)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 5, 60, 6)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "denselayer = model.get_layer(\"Dense\",1)\n",
    "denselayer.get_weights()[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26fd0840160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Vi8WCyqyGW4jsuBJtDbNzok32/AOGlryPId5nbcXb8+m8PGzLfsMh659NQcx1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\nxcNTn/pUeepTn7oU97WvfU0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goncqvgQbM1bHszGgMi4idDxPSbh7V7xP7aPmFVhHXZWM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\nLnkUqa94M9LoGcfI4EaPmdsvmytQYVhHwq6ORM5Uy4lAouOtLrL7QfK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jhQsXwtVtBu83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XvVvFVEt+wTBXilQLFmIrLFt77O6FVzKbaPHTt2SGzLyfs4UrSe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NNQGLV9kzZPN7tNpjNvCoEdyREM8uE5Vf0Sptr82r\n7VULcDmXwlvPtRCXDQDbVGaUu1Z0e/Uow1DCu9UeW0a8I/vzlmXAL5FlKe1Aln+ND5S+YXd3+jdi\nsiGX16D9Yt/3adHN16tFj37FV+fVI4FR0FiTRz8XOc8K7yF8ZKs9DjHibbXRXrvdktcT20S5zv4W\nMSTxhIke5fbaeX0tViyil73OBWlzMiax7jcbWA4ldFpskut+BstfWO2DVX7Sd7AfYz/CItvysZa9\neDaUSZdp+voswZ19Dd0T5V7sbT07p6zDeqjLIVu+Q9Bij/OMeOv4Wa5H21rWrYnbY60PMp2msi3v\n+7Mj3l5sZ6Vn40C+XlFuuhxnBLe1LuuVXpdx7pj+senjan3fv4uI3tWyb4aS2I56dqxR7zPXPjXn\n89T2stcIUp0utxPZI1W6MfAaBtnTJNO8CsxGK8W3DDite9D3GgXWcp5hKOF95vrS9phtyL0GNhI9\nrXMdqOuAcsht7GBkgCbT5VyKM09cy7qil4lopmfU6imNnK4nwq0gJUM2f+FYTfbY8gaGF7yVtml/\nQzRrI7oRKolvq3HXveYyv/SH3HhzUMsjCta3AHQwLYM76f/39mZHGHW+Wh+QDQqtdibLgJ2TaXuc\nZ8RbtqdjL1si1BIGznMJ6w5PROT6FFlnrGvT4tuzC75O9qs1r95b/l3el7QfK7jM2vSQH/vL2qQO\numuw2gvt96y40uu4k+30mXuYuj6vHfLSM8vaF2vRref6tXFLZE8mk3DkOxLeUTxck4eZx09av/Vt\nJWuPW1tb4e+5nXOYMV9tWja/7vguCWods1mT7jwnOm37mTcYa9I8oa59Gs/7flpEa8Gt64+sW3K0\nWy5nhffYr5qnKVU+bx+5byTCtQDnh6cFONHshyr4HF7PTuREokZbox12FBx7z4ENho8hRTdvsxy2\nrFBe41W6fu9+W4X3gK8JNdHSkEc9lLVptdt1UDXUupXG9ccT7NLW5LVJ+9JC3HPilpCyHKwlurXY\njjqwsg35PAJpCFqFd+2k80fIhoknzw9a/kD6BRa3kUiw7N/qlMkG0VLscL5IeMtjecut21rsa8jO\nySwtwtsK4KLgrmZeu63UVkt0ndDpmTrF9619niW+ZSeQZw/sR6N4xBPf1j144pvrdIZlttmtr5p7\nnbMay3fo18290XOOq7Qvs+KxmrlO43aVt+lOAWueee1cp1vCm5+RfFbRNqt+MV5a1h6H7AjKMu83\nWea1ldI2PZJdEuH6Ldioo09PfD5PPJdGvDNvPFrttdXO8nNg/adFubWsxXlWeI8+4r0oZEW0RLcW\n3PLhSdHNc6LZ31QxXiNvNW7aQehGjq9NbpOOpbZRt0QPr8u8VqAsnbYU3F5DLQVTqZNBNmZWhahh\nmUElUdura5bYG2rdasyJyt84KE2ZY2jhLG1PrmunK4W4dyy+lqhH1HLC2saiZ1YKrg4yLQ050ayI\n0MvRNokX9HAZe75Qn8vyD9F1y3LTI95Ro10rvK3tXXf2Y5p8fTXPqnZ7lDfDMm249VVzS4Bm0lqO\nUfJ9ch6VkRXQWu24RvtZb/SUyG/3LH+auX++RhkbsP/lbbJznvfPsGzh3TriXWofdBsoy0yOduuO\nExmnRcI7ElE1YkyLb90xYInvzOvm3qi3rv+6LultOsblNF732iSesuWbFUZD0vqqufYxll0OkY/j\nMd0BznZkCXC2Y619LP8jp729vbC9tka3o/XoTcdIePf99Ei3TNPbLMEN4S0oCQX90LTYtgpCH5/n\nnoFZjRyjj8sNqwxYZWWQDaPeP2rU5XptMMgBrL4H7/plo2wFCLrS64qRYZVHvKNJNpbz5I0CyzG2\n6eBRTjIYtJxuKQCWU/RKUq0TrhHhqyRyiM52SGSwfEDLNsbzNZb/0L5E2zI34NY167zydVx+BtoW\nrBFvXdZ8nZbfl+0FC249Ij7vZD1feb9Zltk5Oe+It+dzona1Zd0bjeHrkXMLXYaW8CGiGTuR98xz\n69qsN+7k9ej8WX/qtedWB6p17AzLHGFsHfG23pKReGUmfZL1QTy5P4sa6de0j2vZpvOxfaytrU2J\n6+i33qXfcnuvnfNUGzfI5yGXZewQ0dL+DvmqeZbt7e2mV809EV2zLZOuxbUW2tp/cpr1QTWud177\nzzoj81E17y3H6O1H+aq5p/X4/j1dyGk67tBajX/qk+HAvWreQlTRdc+tfGjew64R4LW96IwVbBHZ\njZ7nfLxjyOvSy94+en/d0Frn5slq/PX5+ZhWBV+1Ee8hhDc3ttFr0NmJj6mDTS8I1YGsLHOdR4so\n7VjkvkTto+3evkT2iHep51PaWKkDwwqOa2hp+IekdcRbYt1DTRrbBi/LNF7Pjnh33dnec6+XWjZy\nPJe/sSrZgSWCpO3rdBmEWMLb6rwpLUs/5gnuVptcpo+cd8TbarNLbXpmWyS6ozab0eWRCXQ11nVZ\ncQifx9u368o/+ykty/uRy6X8NSxTeLe0114nnVWOll1JAa7tQe8XtUm126x02a7L9eiL5jWvmkev\nl+v/8Zb36hHVM55bdUDHoBmWLbyzI96WLxlr0jGlNdIdifGMT97b26sS1J6Yrhn5lu0/P8vaOU/S\nL9eI9aw9Hjjh3ero9f5W4euHxg+TaPrVg9J1RaLBS5N4vck1QUDp3uU8QyS8ZQVlQ4x6xK1KbVWI\nWpY94t06Su+J5Wg9m2YJZD2PtsnGWs6lIMnYerZeRMFv9Hdi0gFbv/OOBLgVLGdYtvBuGdFpxfIl\npTRvVJHRotvzHTyx8JWvfLMfIqJiPZHlLq/Vs3Hdy61HwUpBsF7ngIOvlc9jBZWWSK9hmcK79eNq\nRPP/PKZm8ka/s21tFLTKMtPCIRLQ+q0L3sfLKwNhaUv6+NayXrcEt5dXpul6JPflbctss+cZ8fZ8\nBqNtSgbmPNpNNN2BocvP809DTfp8pd9zW//JbY1o69fM9Tb5fGpiWi9NUhLjtRwW4d26LdrOtsLt\nqiW4Lb/pdXJGMSEL79LotjfibQluL037Yy2gtXiWddnaJmMCPc+OeB+KV82jBtcSiFJ4awFeOo9s\nZLRz0Wkay2F4x9PHiNJa8+pG2AuAdTCpK6F1j1GPWjZIXLbQaeks0AG79dqzJ8K9PN6y9TpQtK63\n/f/svW2sbttVHjbeffY5F3/UVCLXCGQpgO61cdWGCLvGssEWUDmJf7R1ZCHaSCitAkokRJUqFSVq\n86MRaf+AkrTlX6NCSkLyo1IDFaSyKEaXgpCxMYk/ZAxGWMhfGLhg33vO3vvstz98xznP++xnjDnG\nXGvvd7/nvo+0tOaac6655ppzzDHGM+dc7+tlsKJxZEY0ul6aZhZ/460IeDTrmR0o2x3sWx5nVryr\nznhGQqr3sQF2KNKiymC94cbN5R5/xdnzs45SZBgdEK8jPtsdA74fv90cHS53KN+s7/g6InMd7JN4\nn56eTm01j/TK2mmo6xThju43i7/tzs1Xk6MAACAASURBVPqMZU29d+aPoC8S5Xe9PcJIVzH57t4f\nYd9bzWcnynmHzGjSBBciRrsVeNIk0x9VPTM6Tk6u/o939M135XtvRb5xxZt1vrIBDDXGVP5sXI3Q\nJUZrYgnxxvDoejbN5QmJNsdF/iOPh5FOZuKdke1olbvzqWHUBmqschqTdEXKfdy/LIm3IzK6I6Op\nBFKVrRqr6tA6lFIZObQRQYme3yU/6p2VU+HCh7NgStkq0o7lvBxWvBWRvo6zKwDlYKojyod9hoQG\nrzE+I8wjQl2N65DsDtmOxv2hYGZFR7V35cz3V+6p6gbXCQiWQ8+DToEi3qxr1HOwn7GuXoayF0p/\nZWPSnQt3VvjdR+2Cz+hgnzI8s9W8a6OW5h05hVw3ROT8R3okIm5cL7ULj9sH5YhXoZDoc12VLzMb\nNyNb+7TZsz4G2w/uQ0W6kXyzDxnlz3a08SRjRLZVOSgr+Nzqt92dLedq5TsaT94ejmh8dcdZB4e4\n4u3niCBy3Gx+JYvoK/pZEfSKfsWDiXflh9Wqvp/yBdX7czgi15WwWqiq4NYR72zwVu5ho6aMXDZ4\nvRxuGC9n1sFRxHaEkRBznT1cHQQ8ILh+ynl1pT9yXrAMH7B4dLDP1Rx//tIZdDUbh/EzedFYV0h3\nJR9/x8KYJXDds5ld2WYe/WXULBGfNeT7JuozxNssJyaKyOB9ZvHuHC4D5SlCNKZdv+CqnnJ0Uc+N\nHAs/ZySocj86JmqM8gqBnx1eB25jJvkzxHufOnL2x9X8nOmCbpqKUxOOmT1VUA5bNFkyIt1YV0W4\nMd1tM088+aQrP2t0ze8TvVN2bwX7JN4z+pHtBb+3mhRRhNvTzeyK3OGnMopMc5g/dXF9iARITfoh\nCffnRr9inm09563ko6M6Jrk9lXxlRGkG+ybe3R9XM+tPis2msd1CXYNxkc7M+AmfI+IdrX5Hq9yj\nsx84PpFkdwg1r2zjIiRed3DrvvFegkgYkIQjATcbb2/xstCQjEh05zpL43fwM6arvNF9HMfOK7YH\nr/S4EPuAVA4MvkPkmBwq8e7UYbt9/CNqkYMexVXuYUKuiLUKs8JgxyEzcExwlDHtxo3Su9/78ISE\nItos14dIvGe3ms8cXEalzMqKt4eZrLic4QRfdK4QplGbqPeKsN1uwwmfrN1YF2blo57s4NCItyMj\n1nhekicj2yOZZ0R20vuN9WSkS3m8qHbxAye8MazsLMex3CndNbpvRkce6lbz0cQs+5TotGMezusk\nxn0oNamOpBzDaLuZZLvuUXE4UaO+846+/R6tfGe/ap6NzQgRycZzlK+KLjFaEzMr3o4qd5jlGG5z\neSKHd9ZkRDyy9yrOy1KEOVpUWbLy7e+YcT68VotPipQzMX/ZEu/MCfQGwsZUAzsy4OhYRYpBKYPo\nuqJocKKAtzPge3GdWdlHYb5mw4yG1xWxGwd2ZBQix2TWKO4TXUfYibciy/6jD10FkhFwJNPqjP2G\n36OhXGXfp5nVth4vzctp0d+JRcrYw+zQZOT7EIm3v2sHGTFW13xvVFalPAcSa7PHP+jo8ehkZvo8\nMvJ8rpK66r2Xl1/53lvJINcN70VdiO+HaZjn5bDijYh0QFVXjO4ZrXSPyEFGSpEUcR92CbdKV7rL\n9Tc/iycVMT6z8cpWq3MH+ybeMyveaC8y0s2+ZfRNPjrto4l1njTmhQ6cYM/IOJJur8Po18vVj6sp\noq1+ZG0p8c78Rz7zJFIH+17xniHe2ZgbjcfOvWqXhCLY1cOhZODy8rL0N7GdH19j/8/DPo55cSki\n1xGhjuLQ9x7JOeOJIN484DOnLSO6XCaf3UlUTnvkxGcKZBSHz0MjzbPdLOw4WKrbkF2ZsYFHg8Df\nd0dOcOZMHtpqjll/Bj0i3upAslid0XOl4mck1bidDX9x0fOh08aOQzYZpcgEIlI8nXgmad0fVUP5\nYtKt5PBQibdvwe8AxyyvJrsscLrZ450OkT7MdIEyxth2Xi4einTj86q6XtVJ1b9ThsvknTtf+Q/x\nk5OTR6Sbn6F0O5NuhNKXHexTR858442Y1RHV+M7qDIPlNSPhOJGE+bk+/Fwfa54X5Q3HZESKlV7D\n8WRmV+rG7xQR/GgSYYR9TpbPTO5Hk7TqvVlvYr/hIg36VriCHZFwJOi8u5DDrCsx7O/udUC/gH9Y\nbebbbvVXYuob7yr5q/jEKk8Hh0i8bwo4uYOr20jGXZb4umqnHbjiXfkBtY4PrSa0MnliAj5Ki7gT\nc7Bqm1dxY8S7+xJ8HwoGklZUlGprABNYLMsFbqQQeFYZMbqXD3we1mtk0FHp46xrFlYG2A2AH1ym\ncqzV+zKBPySn0p/fNeQj5RApFhU/ujcyrLg6wislTLjx2kw7iMpxi7B0DEfEG9tgRMCjg+Vy30S6\nC3//DniWVulCb3ueoEQSgOgQXUdWbvfsdcVno25C4oJnrLu6L4rzMZKtoKr3xIkOlV/p3q7O26cM\nLyXeZvP6olq26l/Vb5ktwzDbfJxUGREEJNPZWMMxyhOFSJCVzlP1x/cdTSCwH3BIxHt2cl/ZB+V/\noe5Q8bwizXZYkW4/3CdDos2Em6+RbGN9sDz+325FwLtkW201N4t9XwxH48mvWd5Z5rs4dOKd6cel\naSgrvPqd2ffMpnm5HIcLTKNPCJXPVyXjzDMynuVjBFewR2T7iSTeXajOVwfPLKsyuDwUQp5tVmE0\nlI5IyWTleGeywsA6ZuSbSTevivJMqCIkbCCYgEcOZ6QssdwODpl4d36xcRTOfkCCv9fKiDg7GRWj\nlE2wRFhCBrbbbfl/vDPFGzmmLO8d7FseZ4m3mpDDSTeejMS0yKCqMhUiUhrliaDIP08msu5T7xK1\nRVaW2VecKMzDDgi+o8sWGnUk/wjWmTP6Zl9YutWcUdUblXzoPKlJGOU0ZpNFGelGGVMyzuWyTLIz\nmDmLaFP9+a731HuocYzxbJ/VuYND22rOvp2akI38SY5j28o+kLcn73bDONxVExHw7HB9cHIy3mqe\nkezR9nJFvL1NVDgi5dFY5rEXlTFCZJduAjM/rpZNBs6kjfJlZBsnfCKSjWVl6S7jo92M0SJLl4yz\nXVfkWuncCuH2w8vs4Ikg3ohIOZrtrnh3ynDB9MONpIfZAHoZ/Bw1o8dKmYl3VC828CqPIt8ZKdtu\n9RZpteqNA9Gfy+8ake5Dcir9+bPEO1q9zs6VPH52xx63kaORVUQzkkuEGkcVA9lJG90zUtAj0u1n\nnthSRwdLJhTWwMVF/8fVeGdL9A6sW0Z5o8Ms/1GXTlyWR+k3JNz8Xqr+lclJtyOoU6sTj0y6s3sO\nccV7hnjP9PuMPLnNzxzHyqRiRr6x73D8ZPVkx1cRdvVMvnbnMlr9RMKtZA7Dyk6r1aMK9mmzZ+w1\n24jIXrI99HvRL3M/kGUD+wz7DcMYl00OjnSvA3VY55fNK38flhFvbxcVVmlqLEVxalJkhENb8Wa/\nWpHoTlqWx8c7ThrxyndEzEfyx89G4h0R6SrJrqyAr0WuRwe2qcsm+kHsE9064l0xgtE9owNnJ6r3\nZOW4QDqQfKMiNouVCxMjJvjqPSMSVCHc0S9bIvFmw+vfNWbKHxG9I5bZwb5XGGdXoLLtMyPC3Ql7\nP/okiV87Ecf+VRM8KKMOdkpRHqsGdWnebLU7UsjRbg214s3vX8W+iffsig5/YrLdPv7hEQcbECUb\nnA91Dv9egLpXlRsRjlEa6ziXfc97586dkGgx6Va7RvDazHbaMCPRrPeq+hLv7WCfOvL09HQR8VYy\nlsV100dkxZGRU7xW/cXljcYNX2cyH71z5Msgsjp6emSj2bHt4NBWvNVERGQbWI8op57vx2tc0MhI\nhJNwtfLtuiuSZ9ZvM0Q7It0R+R6RLm+HqN1HfXF6eirLqODQiLfZ1V243bhqfifUVWIdyRueOQ51\nlvtx2Y+sjch4RsI9fbTYovT4DOH2cAcd/XTrV7wzosyItgeMyPbIKYoMnCMj3nxwHZDYc91VfUfk\nGxUrG17/FXMn3bziXTX4kSPawb6J91or3hGBVEqlGu+k0om29+Xp6ekVcq2ciUgp48RSlXhXneFq\n3so2pGzFe0S2M+fqNuPi4kLqlgxORtVqsFnc/6p9lK5BnbPdXv19isipzeIreU9OTnZ0GOdFvcnx\nrCMjp9TjuJ0QqOdY3/E3m9h22LYspx3sU4bd8e5AEVqMX/NcXTHM6lqVVfV+DJYhHy9R+0TxTLw5\nD5JBXBBQ76VWutip7WCfK94zPsZIjsz0LgUcwywfUXnZijeSbNatGdlR/W92dWIyIuERGecfUMtI\n+GgM8TWPm2iVO9o90EGXGK2JpSveakIlItHdNLT3PFHcWeHOZBLlgle8IzJd2dU48v3UijfLEduF\nSp5bT7w3m813mNl/Y2ZvMrOvM7P/dLvd/qtuOcVn7ThVTJiZgLuwcX5XpopwszB6GMFGNDN0bPBw\nlU7NZmM9ItLAyrr6IxpeFxZiT0cCrhwYfFc0MpFB72AtIz4rj7Mr3hHhrm6hPj8/3zlns4JOvL3P\nvL2xbz2sjCC00U6feh8rp+26z92Z0QoBx7E3a8TXIjmz8qj0zghIVPFabclWpDCov5zoy0iIMmzV\na5WGq0Z3797deTbrcNb/rCNHTqfZ7rfyqn39wDE52iWkHM196MhZeVyy4q0IbSdcydvZ4dapt5oc\nV2VEkzXV9Oged2Kj56M8RXWLZA9tl9ufDtZY8V6iH9d4vtJ/7CdW7uE0XvF2sq3IuKd5P48mjviM\nE5M8kRjpuohoq5Xue/futYg3n6M8GTGfWYhZY8V7Vh79d0GazxoS3TXTmWT7eI8IeFZfBdQxVf+3\nsgoerX5HK95sj0cEm32bLK6D617xfpWZ/aaZ/W9m9n9WbqgaQL4HCbPHMelG50s5hkgwWMFiWWyE\nUGHgik9EVBT5RtKNhIGdW54BVG2nBEM5l6hQ3Vm8uLiQijpb7fZnc5tkBr2DFVe82/Loz58l3pkS\n4QOJdjUfTpA4kVKGq0OilELmcjqOcBYXpV1eXv2/x9FqN46j6lFpG8ZaxNsm5XHGqWSizVuw+VCz\nv2b5jiA0Zj5mXRcq8s2TjzNxrhMjXYjpKo/Sj9F2Sr/Hzyy/qOd8x1C00h31D5bT7d8VMCWPODHR\ngZKv7Loax9csn5kTWemf6FCTO34vljkKV/OOVrBnJ3zYgZ0hsmsQX5uUxxkfw0xPfGQkY/ZeRbYx\n7GlV8sTPYn909LlhZfu5+vswTkN5xzMC7btfVw/3cXz3UQcrbTWfttdrEu+1D/fxUAdgmqpPFMf1\nZyjirXbXRH5edRU8WvFmXxb1pNKbyBuzOPWuGTr6qS3t2+32F8zsF8zMNt2aNYEk2a/9jA2FK8ae\n7gaMz14m3qcajI19ZrzNdrfUqNVgdtgU4Y4cTeUIK8XLStOfj4LP90UzQhnp5neccSrXIt6z8tit\n83a7lcpEEXBFsjkuu0aiicZJzfZFiIgU9xka1pHDWwmP0mZWudXkVUS2K20T9e8amJXHi4uLqTrz\nuyoDGhFuLwPvxXv4frOr/x0ckZXqBIkbUozHiSYE6j4cv6ifIz2JetFXdHxFN3ofpcejH6WsEKB9\n6MhZeZz9cbXRUc03yp+tEkaOI9d1VG/2HRyZQ5oRqErciHjPku+IgHewxg6MJfZ6lngrIovXUb90\n8iiyzavavJOhutjB12rRhcNLf2TN45HUYH2iccpp7BejTLJ8du1fl/gqzMpj94dQXypf+mSR7orS\nqvFMuhUBV/IXjZMozol3tCilSHdl0SVagGHZQxlFLphdK1LOvkNDHMzsFv642gy8MZXBU2SbFQAa\nMVdWeA+GlbD5bBoS9siYRw6nEiB/Xka+WQHhoHKl62dFuP3wOvkAUCveyolk8s1tzEpzZvZ8LaIz\ni64hR9IYrXY7eUYSHRHs6OzH3bt35YovGzOvmyJQGHaHwMPoxFWcXRXXzbvdbocr/5Ei5jHFBns0\niVXp333CdUMH6l2VU4gEgvubwboGjRTqSuUgsu5T4UqaE2t+L3RsFTnHuqNuZML91FNPPTr7fWzA\ncZwpeVSTlap/WD47WGtycgZLiXc0GdY5sjLYVkWr3lG/cJify/qTZV6VHznQSh9H6SiHql4+RqIJ\ncnyvjHT7zip/Dj5TYbPZrLXiPYUZH2PUD1meSjoeOCnnq6F4RHHRc/k9sG/Q9xt9chh9z52FUV9i\nW0UY2XxPc32G4zr6VK6CmVXytXB2dtbWz6ynovAon+snvoftsyLZXg8l336N9Y3AfZz5spGPVyHd\nKq8izRmhjsj1KDySe8Z1bzW/MeCLu1A5vEE9jI6hD2x0pJCEcxo+w8/YeUi+Vb2UcVRGD5WvCxCu\n3igFxIOxs+LtA+Lu3buPBsSIdGeONbcNO84d3Aai0yXeShmMto9H5ywNVwHZAfW6qLNyKljeopXH\nDnlekrez0p0R8Ggigut1KHAnrAN8Rx67SLazCRa+n8ti48V6GOuh9F+0ayGL80nPSO/xzDe/Q7bi\nfe/evUeHE298B1V332LuO4aiFUcmUEpXdrBP4j2z1TwjzdEYVfGR/cY8yklCZymy11F9o8PlHsHl\ncf/PrGKhU+11M9v9BhYnfCJbHb0DTpDj0cEaK96z6NprM010MI3zVkhPlE9NxmVEW9UhA+rYk5Or\n/+Md+YIVUs6r3JiG+szPyuabjX9Izc9IttmWd8Dj8iZxfn7e1s9KlrIJRJXm+oiJtttmT0P776Rb\nrXwjOTfLd3hw2METe+wnj4i0Wi3PfEDF4dhWMAGPiDi251LifetWvP/e3/t79prXvGYn7j3veY/9\n1b/6V9P72KliRA2Djc5xGVnAjqiQ7qi+yolApZTN5PN7jxAZdh6InaOLQyQ6/+Jf/At7xStesRP3\nlre8xd7ylreU7q84bHxWK3zRqgSu3EX5s+dzPRmj/s76MyJeM1BkTxEonKTy1U7VxhVZ9ImPtd5h\nDcyMoZE+iZCN/Whir+KAVfXISE+6czB6r+o7q/qwTmfnItONFZ3Z1a3ubNwW/JN/8k/sVa961U7c\nO97xDnvnO98Z3oP96H2o5ATJJcuVKjOSF3e2UGbMTIbRMYvuz8pCJx99Ah8zCEyP0hRpyCYS14Aa\n5xE+9alP2e/93u/txM1sr10L/+yf/bMr9vrbvu3b7Nu+7dvCe9AHQp/O7OpktYcjX4rPiih1kemT\nkY7OfkCNSTcusKjPC6P3nTlUmdnqIh8RPvGJT9hv//Zv78TN/J3XWlDPxn/KiODvzjwDJy+YWI/O\nFUSch8kqnlEXbjZXV839zL5rxXarcceT6+jznZ6e7ujO6rhcmjfCH//xH9uf/Mmf7MTdOuL99//+\n37e/8Bf+Qvs+Fio2QEroRmRdpUUKMCuno2gjZ5Xrq/JmxHxE/kaOaddJj+JnDM++ic73fu/32p//\n83++nF8Ri6wNWEF5uNNOUV4sp2MII2M3kpdZOYsUvMqD7TxqC/X+6h0jqC20FxcXVxTpoSBzHpUs\nRMfodx/U88x0n0VxEXHGOOUUVvSMkrNsNt5X1tVvK1Qnuyrtju0fQcnkw4cP7f79++E914nv//7v\nt2eeeaZ1DxNpdw4rTl/37HrLJ+G8fIbSwyotI+pm+pMzPzLbkN0X5feJQfVDlJFji++kHFm/p7q1\n99lnn7Vnn312J+6LX/yi/ezP/uxYEK4B3/M93yPt9WjV0UkEhpHosJxif5rVyDET/GyyeOSTIaJn\nn5ycyFXqDsmOfGcfX0y8WOYiXViRrcxeRXjDG95gb3jDG3biPv/5z9u//Jf/Mn3WdeHevXupPlfg\nPsQzx2dpFTJZObxOjo6/F8lG9N5qXKAuinQ3v1tEvrN2WZI/wtNPP21PP/30TtwLL7xgH/vYx0qy\ncKu3mjMiZTHKo/KqwT+Kz8rmZ2SEgh2FEXmpnrtkKGob1UbYBpHi7GKkoK8TM3XuOFCZ7HSfnckn\n1yuajc/Id5U0z+bDgx0QXP1ScsltqYwRK3Q/d/v2kJGNy8hZrJBwPypjNdIr6l7V337dId3KIeBd\nJtGuEt/6qbbDq98RqDrLWTt3cIj60Syvd6Q3uM9GYWUjo2dF9VRnJt282r3dapLt91fis7jtdnuF\ndI9kUr1fRADxvm7/7vObWrQVXbiNQ9Lt719pi0ivZrYI7Zzboor+4Od4/fmZ+C12tsKdTWIiquRq\ntNMu8l9H78rtXMG+bfZMfTNinJHxCtlWPl7FhkbIZALHVGQfI1uIk1GZLsd3cnnGHQGRjxu14Uy4\ng45+nPkf71eZ2TNm5rX6ps1m8y1m9kfb7fbTwT2LBsnI0aneH+WNjKJ6RpZWqQcLWBSn8iw5+FlY\nfgWR8ZkZ0DPPT+rVlseX7mvVWQ1MvFblZmkzyBy8TImPCFZHdpbkiQ53gCrvh78SGxHukxP9TwU3\ngVl5XPH5ocGL5IFXSTisiPdIjtXYzhxPjI8cRn5uJncR4XbZ4BVv/rbM7xmRPXYUsA+wzX37XAeH\npB/9Hh/HHnZEeoJX0ZSTH8UhofEzx0X15PNmE/8DCuqpSFd1wln6drv7Q0UR+VYyGY191vGO7kTQ\nGsR7Vh6975dATTKzjKu4l+qd6lYvD6+x7b1sJZdKFlD/8W8DbTabKz+ExgQ8+ueaSJfi+0cr3tFY\nVL6Al4XnqD25DapY4ktBGVPyODORmvloUVyHdI98PiW/jMiWYv1QNlg+lJ+AdcKt45xf5cUfLPSx\npNpwFNfJq/z6Cq6VeJvZm83s/zWz7UvHj70U/5Nm9l9OlDdEpQEqhnb0jOjA9C6UMCqFpBy6tYhN\nVE5UB39fDlcciz1gSh59gFXBA75y4LMyY95B5/lV8l2RlbXSlIH2/NjOo/fld8DVyxnivdSpA+xN\nP6pz1cjw5EVlxTvqS5XHw9H9mC9zFvD9orKyVW50ZP3HpUY/4NdZac3ktLsLI2vXBqb146xuqshD\n1l+VA3X3qJ1GdoudSH8HJuKom5aeozSX1+gHKEe7MDLZU45uB135DTAljzPE2/usc3CbdG0+E+/t\n9vEur9HOoY4tj77vHv1wriIV2Zh0mYz0IMshltfpp1l9swJuXD9mRLsal5HuTI68HlUouVA6M/Pt\nWI5xPET5lP5y0l55z9E4qh4ddPTjzP94v9/M9vdzggGWDMKosTNB7T6PnbU1SHR2jLYEqTpFbcNn\nDs8I6VqYlcdZxZnNHkZtg/d3nx3dUzH+URiVGRNvs5gQrRHfdTy4rXHmX82MPnz4cGp1ca0V8pvW\nj9H4HMmGkgNl6DpbzUfgLZceZkSOoho3md5TBNxlx8m3mclVxegX9CvORdTO3RWSbn6FJfI4ox+j\neyr2Sh3ZRIgfKFfo/CjZUjoUw+xUopziNb+n0vMz+bbbbelXfrEd1Dsy4eb28DwdrLHiPSuPLiMd\nRH5bxR5FshzpVJbHTBaVDEYkCyeW0e5FfxmGBDxb9VTtk5GsDvlm+x/1y8vNfxzZtUwGRuHM18v8\n06BNhroa70cZUG3E4wR1knp/11vuy+G/irgeHvncSw7vqy6ue8V7CvsaWBVEnefXnFeFEZEyZ+Md\noUJeOgfWqUO6GSMB72ANR34JunWOlGWkAPgcyVP12VG9s7pkpBuPLoFemtfjovccvROf1d/0dYn0\nPn+xdy1kRqgqC7xS0iXeHR1kdpWMb7f5j6tVnh0RuEhGRqvd2SRm1g+q/TvYp81c4gxX+wn7SvUd\n9geHXTar7xHpZD/YoVR9HJGn7vtn6dut/t/40USQel/X7X5Gx9DTO9jnN96zK94Yxj6uEEQui2WH\ndaoi33id1Y+fo/S0y8Fms0l/yXy06q3GthqTSLyRXCnyXbHv+M6Zj1TFvjnFTH0zn2ZJOCLlI18d\nw9GYUITbw3xvRL7V+GB4nXFruRPwhw8f7vyyeeR3rx3fwa0k3rcBrnC5QTNl4GdlvEfIlNCIKPN9\nI9KzhIRHdVTvOxLaKm6D0kQni9M8jsOdw+9Zs76q/soRiOKrxJuv14zzMJ75XUfvyCsAeJ5Z8T5k\n4l2RjchoVwi4b/EyWzZh5sZWkW1FvLGe+G4MVYYiabjt3GXIzOSKIq8qRkRH9YOS1xniPTPrvhZm\ndbrSexW7lW0r52/u/b9cu/Uwe7yzJRofql6qH7oksAok3iivHM5kkeXPxxvr1e47rLTVfAoVcszw\n90OiULFJiJGjzm3t8hKRC4WK3uDJSPU/3dm33co3iNoYx6O33Yhsj9qz4l/u2yfsYFY/LiXZI6LN\nYS6j4q+P9DXKhaMzEaja0X03L9fHEus/5G/K1x6ldfJ2ca1bzW8aaw5G7zQkUn5Wjb/k+YpgjBy3\nEWEe5Rkpxo5SZFSFtop9K1l06FVdMM5lZmR4KweWHclYxakcOQBsbDOClZFmvl4jD7fx6D0wzD+S\nhUQbZ0e7xPvBgwet/LcRWTvyeUS0MQ5/sA4Nn0PpksgRqxwjZxGfXyVxLjObzeMf6PNy1Hbeyso3\nvzv3gWrvbn/uC0t0etRPfEZnXvVXRD6jLdYKI6JdOZigqmevFeftEa32R/LoqDi5nt4l3vte8Z75\nHMj7T51HpJvLyfSrl4HhqMyKzXbywTrayQn+gjmS7sp33iM9isTH2766CwjLG7WDaosODlE/RrZZ\nhaMV7Mivi66jNs7qPyLbygdgvYxypmyfl4cLJzhBrmwC+uH8jCVxUVoHt27Fe0ZIHVUD68+ZKTtT\nqpg+84zs2bPkuuvIRkQI2yC65vdV7XJoStOf36lDxUHzfHiPKidKy54dnSvHiHhVCfXS6+023nKX\nyZWqMxJv35aEcV2nUv2P8iEiasPRBAw6d4p8u1FVq9XqG1s+c5iBMqOcDaVnlM5U25Zxm7kqW5Hu\nyg9ZKcLDbc9t28E+VxiXOJYRRnarSsC9P7u+QaYf3bFEfZjZSiUDa+TNJiNGxEe9V9QGnckLx77/\nTqxbX7U1NvOP/DkMtumRXo10fHjynwAAIABJREFUQ+RTcX9gmaizmIxvNpuQZHN8pvdU/ZhoeTsq\nmRutfkdQuvzQfMgl+pHtWkSqK2lR/2aTLVndM/8NxxD7V5UVb9RJSg+j3nOfg/Uh+gg3ce7g1hHv\ntTBqDO7wTrmRc6fC3UGnBJnTIqVVIdHKgane122fSptVy9wnuitQ223+/8JKuWE4aqOsHSLZGz2D\nlfJohVPJZpVQd/JWHJCo7vjdD28JViveXeJ99+7dVv7bgIrMRfJakQ1eNZkZs1WdhSQzqndW/ojA\nOXHjss2u/qr5iHz7c7M+idq5g33ryFnHstNfUT+p88XFxc7KB5eZ1Slb+WaH0vtN9XcUF51n8rLD\nmZ3Ve0dOrpM3vL+DQ13xRsc+8n8i+5TZWG7XbDIIy3E95GeUQSzLz27XUIcp/Zx93z3Sp2pcep5s\n4icj3xX5OlQfEsdX557ILldXtSNinfVzpA84zIhsa5Y3emd/tueJ9JGSK47L/OIl4Si9iieGeCvl\nMMqj8o4aUQmfEtzqvVgHVu6eVwlqxTAsPTLMKMpZpXkbsNSxrBz8nJEsZXFR2ZkCV4pZkYEKcV4j\nDd8Ff9wqexdfAXAHhUmiIkxdJ+0Qibcjk5GoPVVc9MNqasU7AusbteI90lEjWR45jW6kFelmp8dM\nf+M92lI5ctSjdu6gm39NrK3Tq/ZpRL69f1TbKz3CcbiyqOKcPHE+fAd+nyitmk9dc5uM4rEdzOwK\nGdxsvkI88f0q9h5xaN94q370o+MXZbo0IhYcZnvN5Bu33PL2WyTid+7cefS8aHeSSlOr3ZEe9TZz\nktUh2ijLIyz1Iffpc87Ud0S22TZV8mSknOO93vgOiI6OZqix5OMMdRLqIqWPOs/l+q993cWt+8Z7\n1oirwauEJUrjPBnhiQ6Vb/S8DCycSmCrA2DtI0PWRmh4Ou2wT3RlcqQMRzKkZGdWlqrPzcg2kquO\nA4nhmXu8/kiE2AFGss0Em7ecKqecV0dc0XMY0w99q3kmcyNjrWSCj6p8ZuPaZUAZKJQp5YBUxisa\naEW4o3LUfyWPvquNMBqDHRwa8e72k+ovNWGCn5RUtporPYtEh+uqtiR7+UrnRXGd9CwPx1eu1ftj\nG1XvzbDPycmZyVTv0yV+D5eH5eL4xrZVdWA5dFlDso0E20m2p+HWWzO7MkGqdDiHMx2IY9Hr5lCr\n3NGkJLZB1r5KRywlPjeJWf2YLYoomxHFKWId3Rs9N4PS0Q6XUc6P9+E7+3mJP4nXlXZfK08HT8yK\ntwMbSHVqJc3Ts8ZWQjkzwNRz0anEOJUvihsZkOpRRWWQVgeyum+f6DrC3nfRYZbvnFgCfgbHq4MV\ndEa4MoW3dhjfgdsNjb7Xl7fdRdvv1LmDQyfeDGzbjiyws1Yh3qhbslXtLA2JjnJGRg4jOgg4iz4i\n4WZX/05Mke6RHlV6gJ2h7orhoRJvDzsyvaCcebXqzQfrEgzjGft8tLU3kiOsr5K3teyykq1RWMkh\nh1Xejh/g2OdWc7N+nXm1m/sYy43Kjmx8ZP8rY1bdH008IRH3o6K/M13vdfCzGpPcjjhGl8jwqC07\n2KcPuUQ/jnw2ZTtUXDaZXrGhUf3dBntYyYci356H3xnDHd2WhW8jnhjizYYD47J4TFPxETlSpEaV\nPxp0mSKPlP7IwEeGYtaYZ3Xk9+X46gC+zZhRnCMllhkSzjdb5+xZUf0wrUO8VVwlHKXzmGSHGIk3\nrgTg6jdvu/Nvu9lR7yrpQ99q3jkqRJvjlMx6nyKh5Dilh9gB9vweVvIaPZ+fiw4iOq28tRMx+r7b\nyxzpVe6LaJxVsU/iPYPMHqi+4jZVJDwi30qXcB9n39KyrlGTfti/qv8rcTP3cZvyuyofhtv9Omzy\nvr/x7k6m4jhEwqBslLqOylNjPNMD6p5sN80ozWz3U5aIZGfky+uI7+6ypo5IF0ar3l5mp58OCTN1\nZpnJ5Cmz2SNbPvIFvS54drAceJyfXT+qe9X7VvJ1UZGrtfJ0cOuI9+zAwoZBo8NxGB8JUhbHAllN\nG9WZr/G5mdJnJRYpw86hylX1iKDagRVAB10jeh3oyCQT2JFSGxm6bh2UwswMe+cwqxHtpeku/+gE\nI+lGJxiJdrbtLlqhfDmveI9kI5KT6PvujHiP4tS1IuocnzknrPcjR1ERMAXfap5tN8/IUdbuS4j3\nPp3RWZvNthMxcuoV0WbS7X0U2ZtIT6rJPdQ73le8YwIJeEQ4MkLSvSd7l2pcN38V+ybe3ofo642I\ncrSjIfKR+H4/j3Qpt6/3JedX8qYItyK7HsZ3Uzq9EheNTQ/jsyL7WvExM12p2raDferHmeeP/LSR\nD1fx7zA981kZzE1wbPnZx5GHqzplRh9lcVjn67zu4tYR7yVAYcDrbpzqtJFizTq9ghEZydIyhVZN\nq9yjwO8dtcOhK82lilO9/6jcikJR+bN7M2eADS8TLfWNt5+vI061mzLqalUgWilgIj5DvA91xTuT\nBWXIR4Y7+pVc1qdIlFG3cBzKV3TNZfi7KIdEgUmc34vXinSjY5n9uFrkaGb6U7V/17Dve8V7VqdX\n+4qJJ8YhEc+2mqvnen+7buHJPQ+jPkGCxnHuhGJ9s/OSPKgj+Z2iOPZlqvd1cRt+XI19pwxRf/Nu\nhqwcbqdsbPMkNoa9DngP1qkjO17eaBGgMnHJbezthu8VyWlGvivI/Mzu/TeN6/AfR6vVGRlXdl6V\nxfWIEI0zJcORDuJ2GtVjpLP8WtUtqm8n3NEtCk8E8faGRsWlrrO47JqfpToY0ziOn9dF5sRxeOZw\nBZ2RIU7PMFIah6Q0zdb/xnukRDI5GkEpsszIqpnPjGwpkuznalw1jeusCE60CqAINud5uRFvR6S3\nKsZb/QJu9o036wqlr9R/e3MaxqGTFzkfGVwO3DCjgzC6b7TVnB3MqP1Hbd015vvUkbM6PbqHdQPb\nKnVEpPvi4uKR7q46ckjAN5ur28+ZoHF/V1YmR9eVvJn8Z3HcD9X7O9j3ire3kdmuA46+IdtK7E/V\ntyP/yMup2F28H+P8zGQb61UluEi8R/6Gilf3qnc2e/xvI6OJSKUfK7pShTvYp37s+o94T0WeRqva\nmT0fPSNC1F9oS/G5rkMjHeTn0bipxHsa1/W6zll7RLh1xHtGyeNLR0oWrz1u5poHvlIEWVrnnfBd\nVHp0YPoob5TPr/GcIXvHzoDuln0T6NaZlWKm2JYYk2p9R8+ubk3CWfqKUlqaB+ukZk/XcHDRQavi\nkLeaK91UkYlINrJfNVfEma/97GkYxjR3Rjkf1mukZ5SuY0cB20bdN9pirpzLkVOJ8j3jpJntd4Vx\nVqdXnDt1ZBNw0Yq36mM88H6OQ9LDcVgnFVedKJzJ5++hJk9xbPiZ/SA13tVZOcsj7JN4I+n061GY\nbYoi3dlYRmRyhv2gVtO97zAcTQawnsn0zshvjewCxnEd8RrrrSYgR76nlxW18VIfcp9Yoh95QaTj\nU1b8udEqd6fNo7GBk9xePk6G8nmkjyL9pNIjH3NJXJTWwa0j3rNAZ8+vWfGw4VHXSsnwczIBrdSR\nwZ0ZdXKktDrhSjlRmjpH76jaJXJsR7gNSnamzjOke9a4qLxRWRUFHcWbzZPs7r1o0L0u6BhFq0Qq\nTqVjWgdP0op3ZPCU0Vekmwk4OunsVEVbybMVb3Q0Mez3mtkVYxs5jVgXPjIZQBmtrniPyLca7/5+\nM8R75p61MOtYKoxslyKjapVtRLx9FQZXY/yIiK4i4JxmZuH9owmD7j0uK+pAOcJxE/kzIye927/7\nnAia0ekVsh35PpGuwXjWTVy+x6He4HxZ/SKiq+ozc638YmwPrP/IBmdtXMGa+uamMFNfHpuKcKpx\nmhH0Cnnn52fAfmPO5elcjstxpItGfscoDd8tsycYXiOtg1v3P94zwAZmpw+vVZiRpXl5mXM3Q5qw\nvlwP1aHs0Pp5JCDdQ5WVIWoPP+OAPyR0V6HY0VMzi0rBqXapyFLU3nyuKPNMoSHZ6Zxn7vE6O8Hm\ns3LE1bmap4NDXvF2REZXGeUK4fbj9PT0kdMb6SmMi0g3hlUcXkfji51FvNf739tCAR1el/2Zb7y5\nDqoPeIKhi33q1Bl7h/fxvdxm2TjO/kbMfwSPV1fMHk9UYBqTcdc1/OONFYJWIdfda47z+kfjUskb\nypga39EnJN3+vQ1bzWfuySbQzLRdM4vtrGpnvJ8P9FFn/DSVxujau0oZSuZ53FbqppARxE4Z+8Js\nfWeOkS+n0hSXyfiNw+VV9SX6wCjXWA7LfNXnqF7jMyrjZWl8B7dyxXuWtKIQoALrhFXZmM/LjggU\nv8fMgI+UfCYIKm72wLK4TlVE5NKvu2XtE9epOD2vuq9bB6U8+Zwp6Exh40qK2dzKduXsYVTqfvbn\nR0Y9M/ajfB0c6op3JEORAc8OXOFWxt0sJs0OTvc+wbD3uYpDfYxyGyHSd8pJj/RiRLz9PNKrUXtz\n23cxc89aWKIf8Rqh+sjP2arxaKu5P8vlRf2wWvTvCdnKs4f9Pq4rEma1U2Imzsx2xiGOE25XdEK5\nD3i8qx9M7MrXbdpqXkFEGHmSpQqUN54U9PSREz/y8arXeK6GZ+5R42GkE7O2zfygQ/Ihr9N/rKz+\njo6MYI98dvTPonRVtutK9Tx8J/V3pdW4LvGeOczm/nXp2oj3ZrP5ETN7j5l9s5m9aGb/n5n98Ha7\n/USnnMbzdjqZw4o8c7wKq+dkhLs7wDLFh3FVZbymQCmlODJAqj0iwlnFGkpziTx264zKY+bAcpai\n89wKyTLrk+jZsx/s/ETGPDPyo3s6WGPFex/6EcNL5EHJRrbiHcmD2mqu0ryvVBy+TzTmGNz/Wbrn\ncdlXZLuz6s19gnUf5c9wG3Rk81nD+zL7pAi3It8XFxePnDDlsEZyj0Qc46oELVqVV6v0nTRFvE9P\nT3eer7Yx4qQp9wM6uerw8jtYg3jPyuOSFW+UrZF/JOpb0qkO9rVuIq6TjsQFw9H9o0kpbj9uy45f\nuQ/MyuOsz1uxw1Vfs0K88dkqjHCZwLCSHS8D+9brxP0f1TfSS6NDLdR0ju59HVznivd3mNn/bGYf\neOne/9HM/p/NZvPG7Xb7YrOsFKqTI6FQ8RzHYX6WcuyisErLwIotiovSWKktOfg5I4PTbacqVlK4\n0/LIBnMEl53s8HKjMD575Jyq+qqyR0q5oqT5mZn8VfNEeaODndy1ZX2ElVa8b0w/KnTkYSQTbOxQ\nh/KZyXO2up3lQSIROR8M7GsO42QByheTqi7h5jbgMa7aPrI9GVb6pnZKJpc4lh5GRPpgtOodkW+l\nj5V8Z6vaqm8jQpHVU00ScJ3VO0TE25+ldpTwuzH55rbg8Xx6ejpNvPcpjzieq8A+y2xE5os5srGN\npIPJCta/cq7mVXpvdDBZ4rAqMyLdI7sbPYfbk9u0g336kDP1ZWJc9eEych3l57pG78BAm8b9hjaM\n09CeqnZSvoXrok7Y64XyyfLXWcRZc/Hm2r7x3m6378brzWbz183s82b2JjN7LrpvRkhZAFAQMM7z\ncjhKx/IjUhTFcXjmfVRc1SisfVSgnEpO35fSnJVHf/5MnbMVuMjxVOlc7ui5qt6ZwlaEKyPeZj1n\noJs/k+MoviL7o3urWIN4L5HHJWC5qhCSaFKGt5v7tbdpRKDVt9p43m6vEuyoLHwHrKfH49mf6Trf\nyQhue/M0JMCe18zCrb8RQfNnZn2B9cd26fbrUtykzQ6evxNWY5ZXmiNiWyXeuLqtwhUSrvo7mxiI\n6pl9r64Os6+snCh52Wx2t8xXVrx9DLPD6w5tB2useM/K48yKN/bjaPIsG5tK97C8Ybqyg+q6Eo7S\nZslF9AyM53sV6eazasuRvov8qJvEUh+ygwrZVvY5y5MR8SXI5EO9l+uj7H2jiX3URx7ma94FFOnq\nSriT1sFNfuP975rZ1sz+aGE5EiPSreIQ1biIQFVIFZbBz4kUaKb4IxLRydfZTlFBZHj2rTQFyvLY\nrTMb2qwNojhVZgeRLEbOZxZGBagM8nXFKecbw2umdXBNP652rfqRoeRitOsh+qaKj5HO8jPKk9+j\ntp37EZHxro7BPnfSjXYBSbc7Cl5eZ0U0kq+IcGP9unLZXZEsoiSTMzo96qeqzRoRbv9hNdxqzvKd\nxVXJN69um2kZybbCX15e7vxNXeW4uLh4JLNKXvxdIrnMHF10aE9PT+3u3bt7Id4CJXmcId5Rf2bj\nGcOZnVX6FZGN9ZEeGN3Lfp4iEXh2mfFrfx98VuRDjsaH8jm5XAa2lfJpqrgmn7Mkj2pleYQugR6t\ndo/SZ2Wwm4a2VuktrKv65IX1Esdhmj+nKvvdscJpHdwI8d58Rer/oZk9t91uPzpbTlL+I2PiDcBx\nEanO0qJnZWRpyQDPHK6RksoclZmjWz+FyADtW2l25VE5xxkyMlCVnxkSnpVROSpK2t8P37UT170n\nkvvrSKti7R9Xu279OHj2o3OVlEREnIn3djte3Var3dvtLsnmPLhVDXV1Nr4qDiw6BZG+ipzMKK6i\nV9U47Mrk2sS7I5OzOj3ScUoHZI79iNz6MzLiXSXZo4nqrM4dUh1dY5jbCtsT34sdRe4DzItjmleU\nOlibeHfk0d+3g1nCLeq5E45s7E2BZRCvUdd5e/m74WQj+tUs4xnpro6RkV/JuuLQfMgl+pHlRsWP\n0lQ6+3TKTno4sqHZmIiuUSdxnqiOTLydcPOZ41yns5zyOK+cK3k6uLat5oSfMLN/z8zePsr4Iz/y\nI/aa17xmJ+69732vvfe9703vuy6HPIpTiiSCUhbRIIhWSrMVVC4vIl6qvaK6Zwpx5BRmimakgF54\n4QV78cXdz2W6Ql1AWR7NzH7iJ37CXvWqV+3Efed3fqd913d9V3jPjJw4VP/5eckRlTF6Jp5HUO+Z\njblqXg57nZB4cXx2f7U/3ve+99n73ve+nbgvf/nL6T0TaMmjwsi4M0FmJ7szU545Auz8uwOnrpfK\nb+X92XnAOLxW+lE9a+ScR/Lm5XC53iZInDebTarzPvvZz9pnP/vZnbiLi4sw/yTKMvljP/Zj9upX\nv3on7l3vepf9pb/0l8J7FGEeEeosPfvu3swksVYEuUIIIp3Rca6VrKgjqhOuTqoVxJEccp3VpAQ7\nvxF+7ud+zn7u535uJ+7P/uzPym1RRFken3vuOXvqqad24p599ll79tlnpx9eIY6jAwmvQ8mM0m2j\nPFEc6xvWv6xnOD2yuVHbRDKt6podaiKx4juZmX34wx+2f/Nv/s1O3P3790v3NlCWx89//vNXxs9r\nXvMa++qv/urwnu6qNR+RTVfxDrSDHkZ5xzREx9dTOiuTE8fIB47eEWWeJ5dUHJ/RNnMcnyM899xz\n9txzu18ivPDCC2F+xhTx3mw2/4uZvdvMvmO73X5mlP9Hf/RH7Vu+5VuuxGeOSGR0orhOOuf17Wv8\nXZbagiDaQg4EdZ8LVLalQv2oQDTA8Nmo2DIFGzmzIwcjG0Aj5fnKV77SXvnKV+7EnZ2d2Re+8IX0\nviq68mhm9gM/8AP2zDPPXImPnN3tdmvn5+ehrCi5iZwmViyRklEKNSIvWE8MK4cPnVu1NViFl6Z3\nnN0laZX07/7u77bv/u7v3on7xCc+Yd///d+f3lfFjDzeu3evvcJ57949u3v37s7B30SxDlGy9FKd\nd8rOZAdlKJo5ztJYN/IzRv2nnIUKQYoICjqyGOZ7mFS78WdDrYz3yJB//dd/vX3913/9Ttyf/umf\n2q/92q8N36uCrkz+4A/+oL3hDW+4En92dhbec3Z2Zufn5zvHxcXFjt5E/am2YndWoen9rvRt5tAp\n5y5yaM12VzOUU4rPjepVqZOZXfENeHJNOaJsD9j+K7KY+V/vfve77d3v3vn81T7ykY8MF0uq6Mrj\nO97xDnvta1/beoayo5HOU+3jnyaM7K3SGfCe8tyJ47QlULLXIdEdGzUi4n7geI/w5je/2d785jfv\nxP3BH/yB/eN//I/rL5/XtSWPX/d1X2eveMUrWs8YkeVqXCWvWbwQiXYuisM0VQbGKb8SJ0WjcZTJ\nuiOqu/IjOqva1XwR3vrWt9pb3/rWnbhPfepT9nf/7t8tyUKbeL8koP+Jmb1zu93+fuUe3EZVhXLc\nOw5+5x52CDIS9VIbpDMzykHA/NGWCvXDAnjOyBifVT0VsjTVJ7cNM/JoZo8cwipYTlhmlMOoBm9k\nzLxvcQtuh4BzXSPSpFaWonGzZrzHKWTyN5LNNZwRs/Vke1Ye79271976+dRTT9m9e/ckAWcSzs57\nVX6yVcnI2GWGMCsT81R1F8P7cSRTyvBjXESsM1KtZtIzkjbCWrI9I5NnZ2f24MGD1nOQcJ+dndnF\nxUVIxiPSPSLgiIwwYFgR3BHhRt3LGOn00bOV34B2O/ILFAFX5UaEksd11yfLiHoHM/LoflAHkQ0d\ntRHbx0xH4oq30iPVcJSOutDDiIp9xCMbMypfRJYjAh7lzfR9hXgrrPXpw4w8IsGtYgnZrsQp4h35\nY5EsZHGRj7fZbHbGy8nJyaNxoya7WO75WZnvenJyciU+8je6/omK66CjT7v/4/0TZvafmdl/bGZf\n3mw2X/tS0vPb7Tbc9zGj5COhydK6+f06Itwjw89OmSLfnN+Jd5V0Rz9+FBlefI56Pteli4xodctZ\nill5NOsTbzO7snKTrdZkcvNS3aXzhcQb+zoygBVEpMdXvN2hGq0yLT34/Tms4mbzrkVaOlgij64P\nOuiQ7dGqd9cIRucKMWc5UwZwFpV+RxlBJ4TbIXKO+ZwRc0XGq+g6dgqzMumkuYPqarfSoa6PlOxE\ntnzw3inpVmkRAXd0bF2mr0f6PCLb0Tge2QMea0gWO1iDeM/Ko7dHB53JRrPHdtLHbUS6Pa+Z7bRl\n1qfdNH9GZOewHhGJyuLx8DyqHqgXfZz4e7N+cgLGZWEZTMLxuqsfuxPVCrPyGE3KZaiS5lmyjdcZ\nR1I6lGVNpUe6OJucxzE08l+zZ+KEPMddV7grj9dGvM3sb5rZ1sx+ieL/CzP7qeimWeLt56jD14pT\n294i8hQpKSRNnM/PrvyjX+1TpDtzmpXhVY6hUvJrYomjvBBT8mj2+POCKrbbbborQslLRDZHTl+V\nKEVKLFK0rBD9HCmdLH42r5LDTG5n4mawkgxPy+Pdu3fbv6zOK92jFTJ0QDuku0Kks36PiFRUbgXV\nvs6c3ezs4Qqpjs5sC7oytgbxtkmZ9FXrDiLCzSvd0Q+LRRM3akJGtWWmW0Y6Nzv8ee5o+zmztdmB\nq0HqWepH0DJ/YOTQev153HX15T515AzRwZ2C3F5mV7eaMxF0Iok/5sf5T09PH91X7X+lf0c2Peor\nJyKVeC+zEq/0pbcN60VPR9KNkxFu9zHs98/ofscaxNsWyGNXP3fJcxRXyW+W+4EKnfHNOnkJ4Vb1\niA6Um8jnXPO6g2sj3tvtdsoTmN1q7md1ZGnde6NfIlVOJQKVqKejMlCGfrOZI94VEl4V7OvASka5\n+8xpz3R2RUet4qhVGyVzZlomUIG6UlyjryMShMrRZVfN+nVmCKtxGRHKlHQ3vYs15HeJPN67d+/K\njweN4ITbCbifIwKOcha1lTLSEcmO0iJCXcnjz8UxwoicxyoyxzaL83OVZEdpnXouxaxMzm41R7Kt\nztGKd+c77wzcfyPyE0164sHlI4E2s51zRV9lzzezHR8g2wEX2QSEGs9IoDroEiOFWXlcstVcbTNn\nEo3tg32L6Wb6HxBcn2L5UVgR2Ogearfye0f60eNYDlU8XnvYz9mkIpJrPCuirXR/Byv9r/yUPEa2\nKUOHPC+9fundJKlEvuJx1CZXrkeHIt2KfCtUysTJMOXXRvd34lVaB9e54j0F1bmVe6rH0vzZ92VR\nR7AijTqL8yHxxpltNxDdVe8ZwrIGuB0PDWuteEc/xjeaNcvIt9nuL1ZHfY1lqfpGSiUjQBEZGqV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nd0sAyM4zSVPkpTDcwKi0k35mOlpYy9+nG16z7zd0j43a5ynNUAw3ZSM23o2HaQ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\nCqFWyrCbH1cWRp2h4jp5/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vbhrQDOC7wz954hI9orVIXv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\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IYCe6pozo9sSJfLbcZrPfkq9ZEl32aRnRHY2CV18/t4JZK6Bt5csKbyvIHBOrjLWnTbfZ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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26fd08402b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find out what cnn sees\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "denselayer = model.get_layer(\"Dense\",1)\n",
    "plt.figure(figsize=(10,10))\n",
    "f,axarr = plt.subplots(4,6,figsize=(10,10))\n",
    "for j in range(4):\n",
    "    for i in range(6):\n",
    "        #plt.subplot(340 + j * 4 + i + 1)\n",
    "        axarr[j][i].imshow(denselayer.get_weights()[0][:,:,i,j],cmap='gray')\n",
    "f.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 5, 6, 16)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_layer(\"Dense\",3).get_weights()[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26fd03ff9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VsAo2Tbx7DHlk8Cwj6znalvwxegl3tAXdI+X6rJbTmwmP0lpxrNss0hyFZdNv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4Ho0rcnpLCWvp7nskz6Rzn6ovclj1HOS77L8vXyWDOs0h5lG5mh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\nItZ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/tdLlM9FtdTRn4c2iWgprK73ko2opH55T5lY5aMEt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\nxTJnZ2dyNbtKuEdknB3ZKjJ9chURtbFK4zEeje8o7v2G/bzd3t+VhaEi052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Ycau50kOR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jeCsv0oP6O6bTb3yTevwJvZzlhWE5nqBxPZd8FzrjP2Qwfd8mtihnhjW/i5h/guHmf/huWj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\nhr3gBS/YSXvnO99p73znO8NrMuUB9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m0XJS2WQNfC21pWYdPCu/da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yaYkk3r+mJbqtpKTCHL/P6bVHor5XKSsCuzUf1RmGoUtw9wyWjbRspurQxfVamc8myhmm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26fd014fe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find out what cnn sees\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "denselayer = model.get_layer(\"Dense\",3)\n",
    "plt.figure(figsize=(10,10))\n",
    "f,axarr = plt.subplots(4,6,figsize=(10,10))\n",
    "for j in range(4):\n",
    "    for i in range(6):\n",
    "        #plt.subplot(340 + j * 4 + i + 1)\n",
    "        axarr[j][i].imshow(denselayer.get_weights()[0][:,:,i,j],cmap='gray')\n",
    "f.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('answers/4_25_lenet_len8.csv','w') as whdl:\n",
    "    for one in predictresult:\n",
    "        whdl.write(\"%s\\n\" % one[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train_x' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-70-3a0cbb163711>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_x\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m15\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m101\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m101\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'matplotlib inline'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1000\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'train_x' is not defined"
     ]
    }
   ],
   "source": [
    "train_x[0].reshape([15,4,101,101])[0][0]\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "index = 1000\n",
    "index += 1\n",
    "\n",
    "print (\n",
    "    [float(sum(train_x[index].reshape([15,4,101,101])[14][i].reshape(-1) == 0)) / (101*101) for i in range(4)]\n",
    ")\n",
    "print (\n",
    "    [np.average(train_x[index].reshape([15,4,101,101])[14][i]).reshape(-1) for i in range(4)]\n",
    ")\n",
    "print (\n",
    "    [np.std(train_x[index].reshape([15,4,101,101])[14][i]).reshape(-1) for i in range(4)]\n",
    ")\n",
    "plt.figure(figsize=(10,10))\n",
    "f,axarr = plt.subplots(3,4,figsize=(10,10))\n",
    "for j in range(5):\n",
    "    for i in range(4):\n",
    "        #plt.subplot(340 + j * 4 + i + 1)\n",
    "        axarr[j][i].imshow(train_x[index].reshape([15,4,101,101])[14-2*j][i],cmap='gray')\n",
    "f.tight_layout()\n",
    "plt.show()\n",
    "print (train_y[index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}