rnn-old.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "#Source code with the blog post at http://monik.in/a-noobs-guide-to-implementing-rnn-lstm-using-tensorflow/\n",
    "import numpy as np\n",
    "import random\n",
    "from random import shuffle\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.special as sps\n",
    "from IPython import display\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 104627 events, 120 removed\n",
      "Loaded 1805 events, 77 removed\n"
     ]
    }
   ],
   "source": [
    "def load_keystrokes(log_file):\n",
    "    with open(log_file, 'r') as f:\n",
    "        data = [[int(i) for i in line.split(',')] for line in f]\n",
    "    data_count = len(data)\n",
    "    data = [x for x in data if x[0] > 0]\n",
    "    clean_data_count = len(data)\n",
    "\n",
    "    print(\"Loaded {} events, {} removed\".format(clean_data_count, data_count - clean_data_count))\n",
    "    return data\n",
    "\n",
    "david_data = load_keystrokes(\"key_logging/david_keystroke.log\")\n",
    "robert_data = load_keystrokes(\"key_logging/keystroke.log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# muddle positive time_delta data to generate negative samples\n",
    "NUM_EXAMPLES = 10000\n",
    "SEQ_LENGTH = 200\n",
    "\n",
    "assert all(len(data) >= SEQ_LENGTH for data in [david_data, robert_data]), 'need at least SEQ_LENGTH events'\n",
    "\n",
    "def random_sub_seq(xs):\n",
    "    # TODO extend to support variable size sequences\n",
    "    start = int(np.random.uniform(0, len(xs) - SEQ_LENGTH))\n",
    "    end = start + SEQ_LENGTH\n",
    "    return xs[start:end]\n",
    "\n",
    "train_input = []\n",
    "train_output = []\n",
    "\n",
    "for i in range(NUM_EXAMPLES * 10):\n",
    "    if np.random.rand() < 0.5:\n",
    "        # positive sample\n",
    "        train_input.append(random_sub_seq(david_data))\n",
    "        train_output.append([1, 0])\n",
    "    else:\n",
    "        # negative sample\n",
    "        train_input.append(random_sub_seq(robert_data))\n",
    "        train_output.append([0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test and training data loaded\n"
     ]
    }
   ],
   "source": [
    "test_input = train_input[NUM_EXAMPLES:]\n",
    "test_output = train_output[NUM_EXAMPLES:]\n",
    "train_input = train_input[:NUM_EXAMPLES]\n",
    "train_output = train_output[:NUM_EXAMPLES]\n",
    "\n",
    "print(\"test and training data loaded\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/site-packages/tensorflow/python/ops/gradients_impl.py:91: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    }
   ],
   "source": [
    "data = tf.placeholder(tf.float32, [None, SEQ_LENGTH,2]) #Number of examples, number of input, dimension of each input\n",
    "target = tf.placeholder(tf.float32, [None, 2])\n",
    "num_hidden = 24\n",
    "cell = tf.nn.rnn_cell.LSTMCell(num_hidden,state_is_tuple=True)\n",
    "val, _ = tf.nn.dynamic_rnn(cell, data, dtype=tf.float32)\n",
    "val = tf.transpose(val, [1, 0, 2])\n",
    "last = tf.gather(val, int(val.get_shape()[0]) - 1)\n",
    "weight = tf.Variable(tf.truncated_normal([num_hidden, int(target.get_shape()[1])]))\n",
    "bias = tf.Variable(tf.constant(0.1, shape=[target.get_shape()[1]]))\n",
    "prediction = tf.nn.softmax(tf.matmul(last, weight) + bias)\n",
    "cross_entropy = -tf.reduce_sum(target * tf.log(prediction))\n",
    "optimizer = tf.train.AdamOptimizer()\n",
    "minimize = optimizer.minimize(cross_entropy)\n",
    "mistakes = tf.not_equal(tf.argmax(target, 1), tf.argmax(prediction, 1))\n",
    "error = tf.reduce_mean(tf.cast(mistakes, tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sess = tf.Session()\n",
    "saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model restored.\n"
     ]
    }
   ],
   "source": [
    "saver.restore(sess, \"./Models/old/model_old.ckpt\")\n",
    "print(\"Model restored.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# CAREFUL!!!! only run this cell if you want to clear the model\n",
    "\n",
    "init_op = tf.initialize_all_variables()\n",
    "sess.run(init_op)\n",
    "print(\"inited vars\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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6amjbFp59NurKRNKTQoeISCV23BFGjw6DTXffPaxo2r07/OMfUVcmkl4UOkRE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\niIiISEr8PzASWLZLE3uaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a1bf278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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-16-892712d72b1d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0minp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_input\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mptr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mptr\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_output\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mptr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mptr\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0mptr\u001b[0m\u001b[0;34m+=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m         \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mminimize\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0minp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/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    764\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    765\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 766\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    767\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    768\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/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    962\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    963\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 964\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    965\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    966\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/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   1012\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1013\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1014\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1015\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1016\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/usr/local/lib/python3.5/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   1019\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1020\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1021\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1022\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1023\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/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   1001\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1002\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1003\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1004\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1005\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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6amjbFp59NurKRNKTQoeISCV23BFGjw6DTXffPaxo2r07/OMfUVcmkl4UOkRE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\niIiISEr8PzASWLZLE3uaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a1bf278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch_size = 10\n",
    "no_of_batches = int(len(train_input)) // batch_size\n",
    "epoch = 10000\n",
    "print(\"Batch size: {} || batches: {} || epochs: {}\".format(batch_size, no_of_batches, epoch))\n",
    "\n",
    "error_per_epoch = []\n",
    "\n",
    "for i in range(epoch):\n",
    "    ptr = 0\n",
    "    for j in range(no_of_batches):\n",
    "        inp, out = train_input[ptr:ptr+batch_size], train_output[ptr:ptr+batch_size]\n",
    "        ptr+=batch_size\n",
    "        sess.run(minimize,{data: inp, target: out})\n",
    "    \n",
    " \n",
    "    if i % 1 == 0:\n",
    "        incorrect = sess.run(error,{data: test_input, target: test_output})\n",
    "        error_per_epoch.append(incorrect)\n",
    "        plt.plot(error_per_epoch, color='b')\n",
    "        display.clear_output(wait=True)\n",
    "        display.display(plt.gcf())\n",
    "    \n",
    "    # --- comment this out if you don't want to overwrite existing model\n",
    "    save_path = saver.save(sess, \"./Models/old/model_old.ckpt\")\n",
    "\n",
    "incorrect = sess.run(error,{data: test_input, target: test_output})\n",
    "print('Epoch {:2d} error {:3.1f}%'.format(i + 1, 100 * incorrect))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "good = [test_input[i] for i in range(len(test_output)) if test_output[i] == [1, 0]]\n",
    "bad = [test_input[i] for i in range(len(test_output)) if test_output[i] == [0, 1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "true positive:  0.933469\n",
      "true negative:  0.944045\n"
     ]
    }
   ],
   "source": [
    "print(\"true positive: \", sess.run(prediction,{data: good}).mean(axis=0)[0])\n",
    "print(\"true negative: \", sess.run(prediction,{data: bad}).mean(axis=0)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45141 44859\n"
     ]
    }
   ],
   "source": [
    "print(len(good), len(bad))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "save_path = saver.save(sess, \"./Models/old/model_old.ckpt\")\n",
    "print(\"Model saved in file: %s\" % save_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5rTd3ugSp5QwZklQCe+21Fxe99aKaj3kKq7qV18mQpJI4dK9DOXSvQztdhtQyjmRIkqQs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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11826bc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processed 29200 keystrokes. Running time: 146 seconds\n"
     ]
    },
    {
     "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-21-513add5d84b7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0mdisplay\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'processed {} keystrokes. Running time: {} seconds'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_len\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m200\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcounter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m     \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;31m# --- can retrain model with this data ---\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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W7OdSSj1zAz4I3FV1/++AR4C5VcvOBH7e6Vpr1H4dcE7V/QB+DZzQ6do2oC/b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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11826bc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- classify the latest keystrokes ----\n",
    "history = []\n",
    "counter = 0\n",
    "while 1:\n",
    "    current_keystrokes = load_keystrokes('./key_logging/keystroke.log')[-SEQ_LENGTH:]\n",
    "    inp, out = [current_keystrokes], [[1,0]]\n",
    "    result = sess.run(prediction,{data: inp}).mean(axis=0)\n",
    "    history.append((result[0] - 0.5) * 2)\n",
    "    data_len = len(history)\n",
    "    plt.scatter(range(data_len), history, color=['g' if h > 0 else 'r' for h in history])\n",
    "    plt.plot([0] * data_len, color='b')\n",
    "    counter += 1  \n",
    "    display.clear_output(wait=True)\n",
    "    display.display(plt.gcf())\n",
    "    print('processed {} keystrokes. Running time: {} seconds'.format(data_len * 200, counter))\n",
    "    time.sleep(1)\n",
    "    \n",
    "# --- can retrain model with this data --- \n",
    "#sess.run(minimize,{data: inp, target: out})\n",
    "#print(sess.run(prediction,{data: [current_keystrokes]}).mean(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# don't run this before you save the model if you want to save the model\n",
    "sess.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}