fix_train_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "labels_df = pd.read_pickle('../data/labels.pickle')\n",
    "\n",
    "data_dir = \"../data/\"\n",
    "pickle_file = 'tf_data_2.pickle'\n",
    "with open(data_dir + pickle_file, 'rb') as f:\n",
    "    save = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "  '3mlkv',\n",
       "  ...],\n",
       " 'test_data': matrix([[ 0.81689453,  1.07226562,  0.2388916 , ...,  0.61767578,\n",
       "           0.73144531,  0.5078125 ],\n",
       "         [ 0.796875  ,  1.07421875,  0.21826172, ...,  0.58349609,\n",
       "           0.41918945,  0.44360352],\n",
       "         [ 0.96044922,  1.0390625 ,  0.24304199, ...,  0.80957031,\n",
       "           0.54882812,  0.54345703],\n",
       "         ..., \n",
       "         [ 0.92138672,  1.14355469,  0.23620605, ...,  0.54492188,\n",
       "           0.64013672,  0.44165039],\n",
       "         [ 0.98535156,  1.08984375,  0.21728516, ...,  0.72851562,\n",
       "           0.53222656,  0.59130859],\n",
       "         [ 0.75146484,  0.85595703,  0.4699707 , ...,  0.60742188,\n",
       "           0.28881836,  0.51367188]], dtype=float16),\n",
       " 'train_data': matrix([[ 1.43261719,  0.27954102,  0.42163086, ...,  0.60009766,\n",
       "           0.33666992,  0.30712891],\n",
       "         [ 1.01660156,  1.07519531,  0.19970703, ...,  0.54150391,\n",
       "           0.79150391,  0.4050293 ],\n",
       "         [ 0.81542969,  1.84277344,  0.29345703, ...,  0.41235352,\n",
       "           1.15820312,  0.71972656],\n",
       "         ..., \n",
       "         [ 0.72119141,  1.27148438,  0.26855469, ...,  0.36669922,\n",
       "           0.22827148,  0.44165039],\n",
       "         [ 0.67138672,  1.18847656,  0.26831055, ...,  0.40234375,\n",
       "           0.73632812,  0.56884766],\n",
       "         [ 0.70214844,  0.41064453,  0.23352051, ...,  0.37866211,\n",
       "           0.26464844,  0.62744141]], dtype=float16),\n",
       " 'train_labels': matrix([[ 1.,  1.,  0., ...,  0.,  0.,  0.],\n",
       "         [ 1.,  1.,  1., ...,  0.,  0.,  0.],\n",
       "         [ 1.,  1.,  1., ...,  0.,  0.,  0.],\n",
       "         ..., \n",
       "         [ 1.,  1.,  1., ...,  0.,  0.,  0.],\n",
       "         [ 1.,  1.,  1., ...,  0.,  0.,  0.],\n",
       "         [ 1.,  0.,  0., ...,  0.,  0.,  0.]], dtype=float16),\n",
       " 'validate_data': matrix([[ 0.65136719,  1.26953125,  0.18884277, ...,  0.60693359,\n",
       "           0.37304688,  0.41601562],\n",
       "         [ 1.01171875,  0.68798828,  0.03851318, ...,  0.33544922,\n",
       "           0.10107422,  0.70654297],\n",
       "         [ 0.70214844,  1.0625    ,  0.20544434, ...,  0.22509766,\n",
       "           0.30664062,  0.29882812],\n",
       "         ..., \n",
       "         [ 0.65869141,  0.84570312,  0.08081055, ...,  0.87060547,\n",
       "           0.12854004,  0.46704102],\n",
       "         [ 0.69384766,  1.14453125,  0.19750977, ...,  0.24169922,\n",
       "           0.28979492,  0.20788574],\n",
       "         [ 1.07617188,  0.18762207,  0.12188721, ...,  1.47167969,\n",
       "           0.00659561,  0.10015869]], dtype=float16),\n",
       " 'validate_labels': matrix([[ 1.,  1.,  1., ...,  0.,  0.,  0.],\n",
       "         [ 1.,  1.,  1., ...,  1.,  0.,  0.],\n",
       "         [ 1.,  1.,  1., ...,  0.,  0.,  0.],\n",
       "         ..., \n",
       "         [ 1.,  1.,  1., ...,  0.,  0.,  0.],\n",
       "         [ 1.,  1.,  1., ...,  0.,  0.,  0.],\n",
       "         [ 1.,  1.,  1., ...,  0.,  0.,  0.]], dtype=float16)}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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