lstm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import scipy\n",
    "import math\n",
    "import numpy as np\n",
    "from scipy.io import arff\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.utils import np_utils\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "from scipy.spatial.distance import cdist\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "import sklearn.preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset_filename = 'IanArffDataset.arff'\n",
    "dataset, meta = arff.loadarff(dataset_filename)\n",
    "\n",
    "feature_names = meta.names()[:-3]\n",
    "label_name    = 'binary result'\n",
    "cat_name      = 'categorized result'\n",
    "\n",
    "features   = dataset[feature_names]\n",
    "labels     = dataset[label_name].astype(np.float).reshape((-1, 1))\n",
    "categories = dataset[cat_name].astype(np.float)\n",
    "\n",
    "cat_encoder = LabelEncoder()\n",
    "categories = encoder.fit_transform(categories)\n",
    "categories = np_utils.to_categorical(categories)\n",
    "\n",
    "addresses = preprocessing.label_binarize(features['address'], classes=[4])\n",
    "\n",
    "encoder = LabelEncoder()\n",
    "functions = encoder.fit_transform(features['function'])\n",
    "functions = np_utils.to_categorical(functions)\n",
    "\n",
    "responses = dataset['command response'].astype(np.float).reshape((-1, 1))\n",
    "\n",
    "timestamp_diffs = np.diff(features['time'])\n",
    "timestamp_diffs = np.insert(timestamp_diffs, 0, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 1.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 1.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       ..., \n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 1.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 1.,  0.,  0., ...,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/site-packages/ipykernel_launcher.py:3: FutureWarning: Numpy has detected that you may be viewing or writing to an array returned by selecting multiple fields in a structured array. \n",
      "\n",
      "This code may break in numpy 1.13 because this will return a view instead of a copy -- see release notes for details.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "remaining_feature_names = meta.names()[2:14]\n",
    "remaining_features = dataset[remaining_feature_names]\n",
    "remaining_features = remaining_features \\\n",
    "    .view(np.float64) \\\n",
    "    .reshape(remaining_features.shape + (-1,))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[      nan       nan       nan       nan       nan       nan       nan\n",
      "       nan       nan       nan  0.689655]\n"
     ]
    }
   ],
   "source": [
    "#NEW - Getting Length from Remaining_features\n",
    "lengths = remaining_features[:,0].reshape((-1,1))\n",
    "remaining_features = remaining_features[:,1:]\n",
    "print(remaining_features[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(X_train_rf, Y_train_rf), (X_valid_rf, Y_valid_rf), (X_test_rf, Y_test_rf) = split_dataset(remaining_features, labels, .6 , .2, .2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(_, Y_train_cat), (_, Y_valid_cat), (_, Y_test_cat) = split_dataset(remaining_features, categories, .6 , .2, .2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
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       " 1916,\n",
       " 1919,\n",
       " 1920,\n",
       " ...]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#NEW - Classifying Payload (Remaining features)\n",
    "def ClassifyPayload(X_rf,Y_rf):\n",
    "    IndexesNanPayload = []\n",
    "    IndexesOnlyPressure = []\n",
    "    IndexesNoPressure = []\n",
    "    i = 0\n",
    "    for rf in X_rf:\n",
    "        Nan = all(str(i) == 'nan' for i in rf[len(rf)-2:len(rf)])\n",
    "        OnlyPressure = str(rf[len(rf)-2])=='nan' and str(len(rf)!='nan')\n",
    "    #     #print (Nan)\n",
    "        if (Nan):\n",
    "            IndexesNanPayload.append(i)\n",
    "        elif (OnlyPressure):\n",
    "            IndexesOnlyPressure.append(i)\n",
    "        else:\n",
    "            IndexesNoPressure.append(i)\n",
    "        i += 1\n",
    "    #print(IndexesNoPressure)\n",
    "\n",
    "    assert(len(IndexesNanPayload)+len(IndexesOnlyPressure)+len(IndexesNoPressure)) == X_rf.shape[0]\n",
    "\n",
    "    assert(set(IndexesNanPayload)!=set(IndexesOnlyPressure))\n",
    "    assert(set(IndexesOnlyPressure)!=set(IndexesNoPressure))\n",
    "    assert(set(IndexesNoPressure)!=set(IndexesNanPayload))\n",
    "\n",
    "    X_rf_OnlyPressure = X_rf[IndexesOnlyPressure]\n",
    "    Y_rf_OnlyPressure = Y_rf[IndexesOnlyPressure]\n",
    "    X_rf_OnlyPressure = np.array(X_rf_OnlyPressure[:,X_rf_OnlyPressure.shape[1]-1].reshape((-1,1)))\n",
    "    X_rf_NoPressure   = X_rf[IndexesNoPressure]\n",
    "    Y_rf_NoPressure   = Y_rf[IndexesNoPressure]\n",
    "    X_rf_NoPressure   = np.array(X_rf_NoPressure[:,0:X_rf_NoPressure.shape[1]-1])\n",
    "\n",
    "    # IndexesNanPayload   = [str(x) for x in IndexesNanPayload]\n",
    "    # IndexesOnlyPressure = [str(x) for x in IndexesOnlyPressure]\n",
    "    # IndexesNoPressure   = [str(x) for x in IndexesNoPressure]\n",
    "    #print (Y_rf_OnlyPressure)\n",
    "    #print(IndexesNoPressure)\n",
    "    return X_rf_OnlyPressure,X_rf_NoPressure,Y_rf_OnlyPressure,Y_rf_NoPressure,IndexesNanPayload,IndexesOnlyPressure,IndexesNoPressure\n",
    "\n",
    "X_train_rf_OnlyPressure,X_train_rf_NoPressure,Y_train_rf_OnlyPressure,Y_train_rf_NoPressure,IndexesNan_train,IndexesOP_train,IndexesNP_train = ClassifyPayload(X_train_rf,Y_train_rf)\n",
    "X_val_rf_OnlyPressure,X_val_rf_NoPressure, Y_val_rf_OnlyPressure,Y_val_rf_NoPressure,IndexesNan_val,IndexesOP_val,IndexesNP_val  = ClassifyPayload(X_valid_rf,Y_valid_rf)\n",
    "X_test_rf_OnlyPressure, X_test_rf_NoPressure, Y_test_rf_OnlyPressure, Y_test_rf_NoPressure,IndexesNan_test,IndexesOP_test,IndexesNP_test = ClassifyPayload(X_test_rf, Y_test_rf)\n",
    "\n",
    "IndexesNan_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Clustering - looking for the best number of K\n",
    "\n",
    "def clustering_getK(X, samplesize, max_k = 10):\n",
    "    clusters=range(2,max_k)\n",
    "    samplesize = samplesize\n",
    "    best_score_k = 0\n",
    "    \n",
    "    meandist=[]\n",
    "    kmeans = KMeans(n_clusters=clusters[0], random_state=0).fit(X)\n",
    "    labels = kmeans.labels_\n",
    "    #Average distance measure: calculate the difference of each observation \n",
    "    #with all the centroids. Then get which observation is nearest to which centroids,\n",
    "    #after that add all of them and divide by no of observations. \n",
    "    predictions = kmeans.predict(X)\n",
    "    centers = kmeans.cluster_centers_\n",
    "    print(X.shape)\n",
    "    meandist.append(sum(np.min(cdist(X, centers, 'euclidean'), axis=1))/ X.shape[0])\n",
    "    #Calculate silhouette_score (value from [-1,1]) to choose best K--> 1 means the best\n",
    "    #silhouetteScore = silhouette_score(X_train_rf_OnlyPressure, labels, metric='euclidean', sample_size=samplesize, random_state=0)\n",
    "    #best_score_k = silhouetteScore\n",
    "    n_k = 2\n",
    "    #print (best_score_k)\n",
    "\n",
    "    for k in range(clusters[1],len(clusters)+clusters[0]):  \n",
    "        kmeans = KMeans(n_clusters=k, random_state=0).fit(X)\n",
    "        labels = kmeans.labels_\n",
    "        predictions = kmeans.predict(X)\n",
    "        centers = kmeans.cluster_centers_\n",
    "        meandist.append(sum(np.min(cdist(X, centers, 'euclidean'), axis=1))/ X.shape[0])\n",
    "        #Calculate silhouette_score ([-1,1]) to choose best K--> 1 means the best\n",
    "        #silhouetteScore = silhouette_score(X_train_rf_OnlyPressure, labels, metric='euclidean', sample_size=samplesize, random_state=0)\n",
    "#         print(k)\n",
    "#         print (silhouetteScore)\n",
    "#         if(silhouetteScore>best_score_k):\n",
    "#             best_score_k = silhouetteScore\n",
    "#             n_k = k\n",
    "#     print (best_score_k)\n",
    "    #Plot average distance from observations from the cluster centroid \n",
    "    #to use the Elbow Method to identify number of clusters to choose\n",
    "    print(meandist)\n",
    "    plt.plot(clusters, meandist)\n",
    "    plt.xlabel('Number of clusters')\n",
    "    plt.ylabel('Average distance')\n",
    "    plt.title('Selecting k with the Elbow Method')\n",
    "    plt.show()\n",
    "    return n_k, best_score_k\n",
    "#From Elbow I got K=5 as the best\n",
    "k, bestscore = clustering_getK(X_train_rf_OnlyPressure,X_train_rf_OnlyPressure.shape[0],10)\n",
    "#From Elbow I got K=4 as the best\n",
    "k, bestscore = clustering_getK(X_train_rf_NoPressure,X_train_rf_NoPressure.shape[0],10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Once we know the best number of clusters (k) we perform the clustering\n",
    "def clustering(X, k, indexes):\n",
    "    kmeans = KMeans(n_clusters=k, random_state=0, n_init=100).fit(X)\n",
    "    labels = kmeans.labels_\n",
    "    print(set(labels))\n",
    "    #Average distance measure: calculate the difference of each observation \n",
    "    #with all the centroids. Then get which observation is nearest to which centroids,\n",
    "    #after that add all of them and divide by no of observations. \n",
    "    predictions = kmeans.predict(X)\n",
    "    centers = kmeans.cluster_centers_\n",
    "    print(centers)\n",
    "    return [(a,b) for a,b in zip(indexes, predictions)]\n",
    "    \n",
    "#From Elbow I got K=5 as the best\n",
    "#clustering(X_train_rf_OnlyPressure,5,IndexesOnlyPressure)\n",
    "#From Elbow I got K=4 as the best\n",
    "#clustering(X_train_rf_NoPressure,4,IndexesNoPressure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Calculate Log of pressures - OPTIONAL\n",
    "def convertToLog(X_rf):\n",
    "    for i in range(0,X_rf.shape[0]):\n",
    "        for j in range(0,X_rf.shape[1]):\n",
    "            if X_rf[i][j] > 0:\n",
    "                X_rf[i][j] = math.log(X_rf[i][j])\n",
    "    return X_rf\n",
    "        \n",
    "X_train_rf_OnlyPressure = convertToLog(X_train_rf_OnlyPressure)\n",
    "X_val_rf_OnlyPressure   = convertToLog(X_val_rf_OnlyPressure)\n",
    "X_test_rf_OnlyPressure  = convertToLog(X_test_rf_OnlyPressure)\n",
    "X_train_rf_NoPressure   = convertToLog(X_train_rf_NoPressure)\n",
    "X_val_rf_NoPressure     = convertToLog(X_val_rf_NoPressure)\n",
    "X_test_rf_NoPressure    = convertToLog(X_test_rf_NoPressure)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n",
      "138952.769639\n",
      "[[  1.72970378e+33]\n",
      " [  1.28889942e+38]\n",
      " [  6.65694646e+27]\n",
      " [  7.50742754e+00]\n",
      " [  1.90704331e+21]\n",
      " [  8.93054503e+07]\n",
      " [  5.40549509e+30]]\n",
      "full Accuray for training set = null\n",
      "full Accuracy for validation set = null\n",
      "full Predictions for training set = [3 3 3 ..., 3 5 3]\n",
      "full Predictions for validation set = [5 3 3 ..., 3 3 3]\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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xzrng+K678eE1h3DL8V2YuXQtJz7yBRc9lc3MpWsTHVql50OfAx/6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\nc3H3fxFyCVvAiLazAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f03c6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "-237792.527004\n",
      "[[  9.67676789e+00   1.14010701e+02   2.89108555e-01   5.14330182e-01\n",
      "    9.52024219e-01   5.08965712e-01   5.32963504e-01   6.85698173e-01\n",
      "    3.82656649e-01   6.48924132e-01]\n",
      " [  1.04296935e+01   1.14298563e+02   3.01190060e-01   4.76997582e-01\n",
      "    9.54187187e-01   9.72163498e-07   1.99920886e-01   1.00000000e+00\n",
      "    1.00000000e+00   5.14434621e-01]\n",
      " [  7.61308897e+00   1.15045993e+02   7.67049247e-01   4.84148155e-01\n",
      "    9.85167731e-01   4.77719974e-16   6.66880926e-01   6.29894600e-01\n",
      "    3.69761195e-01   4.95053341e-01]\n",
      " [  9.49803030e+00   6.59185606e+01   2.98830019e-01   4.90189394e-01\n",
      "    9.57935606e-01   1.02657911e-37   5.27462121e-01   6.25000000e-01\n",
      "    3.80681818e-01   4.84848485e-01]\n",
      " [  9.26117973e+00   1.14402027e+02   2.99860736e-01   4.58610026e-01\n",
      "    9.48428779e-01   5.08231011e-08   1.97477489e-01   0.00000000e+00\n",
      "    1.00000000e+00   0.00000000e+00]\n",
      " [  1.02159324e+01   1.14215011e+02   3.03076008e-01   4.84372054e-01\n",
      "    9.80347284e-01   4.34648728e-07   8.47826218e-01   1.00000000e+00\n",
      "    0.00000000e+00   4.54543646e-01]\n",
      " [  1.01474451e+01   1.14440445e+02   3.02619535e-01   4.76160545e-01\n",
      "    9.59990939e-01   1.24426723e-07   0.00000000e+00   0.00000000e+00\n",
      "    2.92605431e-01   6.54485938e-01]\n",
      " [  1.04728315e+01   1.14807335e+02   3.08288344e-01   4.11790369e-01\n",
      "    3.63842346e+00   5.59314197e-21   1.03668580e-01   4.20485788e-01\n",
      "    3.65265919e-01   6.13892254e-01]]\n",
      "full Accuray for training set = null\n",
      "full Accuracy for validation set = null\n",
      "full Predictions for training set = [5 5 5 ..., 3 3 3]\n",
      "full Predictions for validation set = [3 3 3 ..., 5 5 5]\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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FU4iLsa66/sKSkYkYUVHChKxBTMgaxCdmjARg78Hj7wyKKNlVx0+Wv/sw7u2Xj+WLV4zz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\nGpqaWba+Z5/Cn9u8n+MNTRT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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c289c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n",
      "136507.0033\n",
      "[[  1.72970378e+33]\n",
      " [  1.28889942e+38]\n",
      " [  6.65694646e+27]\n",
      " [  7.50742754e+00]\n",
      " [  1.90704331e+21]\n",
      " [  8.93054503e+07]\n",
      " [  5.40549509e+30]]\n",
      "full Accuray for training set = null\n",
      "full Accuracy for validation set = null\n",
      "full Predictions for training set = [3 3 3 ..., 3 5 3]\n",
      "full Predictions for validation set = [3 3 3 ..., 3 3 3]\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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NuNrMcoDmwMKE3ReFsubhdd5ygCZmtjS8XgY0KSCWwZKyJGWtXOmL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\nBwBgTpQklwseAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111e7cf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "9.30244234622e+12\n",
      "[[  9.67676789e+00   1.14010701e+02   2.89108555e-01   5.14330182e-01\n",
      "    9.52024219e-01   5.08965712e-01   5.32963504e-01   6.85698173e-01\n",
      "    3.82656649e-01   6.48924132e-01]\n",
      " [  1.04296935e+01   1.14298563e+02   3.01190060e-01   4.76997582e-01\n",
      "    9.54187187e-01   9.72163498e-07   1.99920886e-01   1.00000000e+00\n",
      "    1.00000000e+00   5.14434621e-01]\n",
      " [  7.61308897e+00   1.15045993e+02   7.67049247e-01   4.84148155e-01\n",
      "    9.85167731e-01   4.77719974e-16   6.66880926e-01   6.29894600e-01\n",
      "    3.69761195e-01   4.95053341e-01]\n",
      " [  9.49803030e+00   6.59185606e+01   2.98830019e-01   4.90189394e-01\n",
      "    9.57935606e-01   1.02657911e-37   5.27462121e-01   6.25000000e-01\n",
      "    3.80681818e-01   4.84848485e-01]\n",
      " [  9.26117973e+00   1.14402027e+02   2.99860736e-01   4.58610026e-01\n",
      "    9.48428779e-01   5.08231011e-08   1.97477489e-01   0.00000000e+00\n",
      "    1.00000000e+00   0.00000000e+00]\n",
      " [  1.02159324e+01   1.14215011e+02   3.03076008e-01   4.84372054e-01\n",
      "    9.80347284e-01   4.34648728e-07   8.47826218e-01   1.00000000e+00\n",
      "    0.00000000e+00   4.54543646e-01]\n",
      " [  1.01474451e+01   1.14440445e+02   3.02619535e-01   4.76160545e-01\n",
      "    9.59990939e-01   1.24426723e-07   0.00000000e+00   0.00000000e+00\n",
      "    2.92605431e-01   6.54485938e-01]\n",
      " [  1.04728315e+01   1.14807335e+02   3.08288344e-01   4.11790369e-01\n",
      "    3.63842346e+00   5.59314197e-21   1.03668580e-01   4.20485788e-01\n",
      "    3.65265919e-01   6.13892254e-01]]\n",
      "full Accuray for training set = null\n",
      "full Accuracy for validation set = null\n",
      "full Predictions for training set = [5 5 5 ..., 3 3 3]\n",
      "full Predictions for validation set = [5 5 5 ..., 1 1 1]\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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UWX3amiSGWNnrAbLSkzl7TGa/Xrd0XhGHahv49+t2d2fix49f2MmDq/ZyW+kkPn7R5CE9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ca1c208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n",
      "8\n",
      "7\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "from sklearn.mixture import GaussianMixture\n",
    "\n",
    "def gmm(X, Y_train, X_val,Y_val, k, cov_type = \"full\", strategy = \"None\"):\n",
    "    #If the strategy we want is supervised\n",
    "    if strategy == \"S\":\n",
    "        #If the covariance type we want is a specific one (by default it is \"full\")\n",
    "        if cov_type != \"None\":\n",
    "            estimator = GaussianMixture(n_components=k, init_params='random', covariance_type=cov_type,\n",
    "                                                         max_iter=200, random_state=0)\n",
    "\n",
    "            n_classes = len(np.unique(Y_train_rf))\n",
    "            estimator.means_init = np.array([X[Y_train.reshape(-1) == i].mean(axis=0)\n",
    "                                           for i in range(n_classes)])\n",
    "            estimator.fit(X)\n",
    "            train_pred = estimator.predict(X)\n",
    "            train_accuracy = np.mean(train_pred.ravel() == Y_train.ravel()) * 100\n",
    "\n",
    "            val_pred = estimator.predict(X_val)\n",
    "            test_accuracy = np.mean(val_pred.ravel() == Y_val.ravel()) * 100\n",
    "            name = cov_type\n",
    "            printGMM(name, train_accuracy,test_accuracy,train_pred,val_pred)\n",
    "\n",
    "        else:\n",
    "            # Try GMMs using different types of covariances.\n",
    "            estimators = dict((cov_type, GaussianMixture(n_components=k, init_params='random', covariance_type=cov_type,\n",
    "                                                         max_iter=200, random_state=0))\n",
    "                              for cov_type in ['spherical', 'diag', 'tied', 'full'])\n",
    "            n_classes = len(np.unique(Y_train_rf))\n",
    "            print(n_classes)\n",
    "\n",
    "            for index, (name, estimator) in enumerate(estimators.items()):\n",
    "                # Since we have class labels for the training data, we can\n",
    "                # initialize the GMM parameters in a supervised manner.\n",
    "\n",
    "\n",
    "                estimator.means_init = np.array([X[Y_train.reshape(-1) == i].mean(axis=0)\n",
    "                                           for i in range(n_classes)])\n",
    "\n",
    "                estimator.fit(X)\n",
    "                train_pred = estimator.predict(X)\n",
    "                train_accuracy = np.mean(train_pred.ravel() == Y_train.ravel()) * 100\n",
    "\n",
    "                val_pred = estimator.predict(X_val)\n",
    "                test_accuracy = np.mean(val_pred.ravel() == Y_val.ravel()) * 100\n",
    "                printGMM(name, train_accuracy,test_accuracy,train_pred,val_pred)\n",
    "        \n",
    "    else:\n",
    "        aic_list = []\n",
    "        clusters = range(2,k+1)\n",
    "        aic_value = float(\"inf\")\n",
    "        best_k = 0\n",
    "        for k1 in range(2,k+1):\n",
    "            estimator = GaussianMixture(n_components=k1, init_params='random', covariance_type=cov_type,\n",
    "                                                                 max_iter=200, random_state=0)\n",
    "            \n",
    "            estimator = estimator.fit(X)\n",
    "            aic = estimator.aic(X_val)\n",
    "            aic_list.append(aic)\n",
    "            if aic <= aic_value:\n",
    "                best_k = k1\n",
    "                aic_value = aic\n",
    "        estimator = GaussianMixture(n_components=k, init_params='random', covariance_type=cov_type,\n",
    "                                                                 max_iter=200, random_state=0)\n",
    "        estimator = estimator.fit(X)\n",
    "        aic = estimator.aic(X_val)\n",
    "        print(best_k)\n",
    "        print (aic)\n",
    "        print(estimator.means_)\n",
    "        train_pred = estimator.predict(X)\n",
    "        val_pred = estimator.predict(X_val)\n",
    "        name = cov_type\n",
    "        train_accuracy = \"null\"\n",
    "        test_accuracy  = \"null\"\n",
    "        printGMM(name, train_accuracy,test_accuracy,train_pred,val_pred)\n",
    "        \n",
    "    plt.plot(clusters, aic_list)\n",
    "    plt.xlabel('Number of clusters')\n",
    "    plt.ylabel('AIC value')\n",
    "    plt.title('Selecting k with the AIC Method')\n",
    "    plt.show()\n",
    "    return train_pred, val_pred, best_k\n",
    "\n",
    "def printGMM(name, train_accuracy,test_accuracy,train_pred,val_pred):\n",
    "    \n",
    "    print(name+\" Accuray for training set = \"+str(train_accuracy))\n",
    "    print(name+\" Accuracy for validation set = \"+str(test_accuracy))\n",
    "    print(name+\" Predictions for training set = \"+str(train_pred))\n",
    "    print(name+\" Predictions for validation set = \"+str(val_pred)+\"\\n\")\n",
    "    \n",
    "train_pred_OnlyPress, val_pred_OnlyPress, L = gmm(X_train_rf_OnlyPressure, Y_train_rf_OnlyPressure, X_val_rf_OnlyPressure, Y_val_rf_OnlyPressure, 7, \"full\", \"None\")\n",
    "train_pred_NoPress,   val_pred_NoPress,   K = gmm(X_train_rf_NoPressure,   Y_train_rf_NoPressure,   X_val_rf_NoPressure,   Y_val_rf_NoPressure,   8, \"full\", \"None\")\n",
    "tr, test_pred_OnlyPress, TL = gmm(X_train_rf_OnlyPressure,Y_train_rf_OnlyPressure,X_test_rf_OnlyPressure,Y_test_rf_OnlyPressure,  7, \"full\", \"None\" )\n",
    "tr, test_pred_NoPress ,  TK = gmm(X_train_rf_NoPressure,   Y_train_rf_NoPressure,   X_test_rf_NoPressure,   Y_test_rf_NoPressure, 8, \"full\", \"None\")\n",
    "print(L)\n",
    "print(K)\n",
    "print(TL)\n",
    "print(TK)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4 4 4 ..., 4 6 4]\n",
      "7\n",
      "[6 4 4 ..., 4 4 4]\n",
      "7\n",
      "[4 4 4 ..., 4 4 4]\n",
      "7\n",
      "[13 13 13 ..., 11 11 11]\n",
      "8\n",
      "[11 11 11 ..., 13 13 13]\n",
      "8\n",
      "[13 13 13 ...,  9  9  9]\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "def defineClusters(train_set,val_set,test_set, i):\n",
    "    train_set = train_set + i\n",
    "    val_set   = val_set   + i\n",
    "    test_set  = test_set  + i\n",
    "    return train_set, val_set, test_set\n",
    "\n",
    "train_OnlyPress_Clusters, val_OnlyPress_Clusters, test_OnlyPress_Clusters = defineClusters(train_pred_OnlyPress,val_pred_OnlyPress,test_pred_OnlyPress,1)\n",
    "train_NoPress_Clusters, val_NoPress_Clusters, test_NoPress_Clusters = defineClusters(train_pred_NoPress  ,val_pred_NoPress,test_pred_NoPress,  L+1)\n",
    "\n",
    "#THIS IS ONLY PRINTING TO CHECK ALL GOES WELL\n",
    "print(train_OnlyPress_Clusters)\n",
    "\n",
    "n_classes = len(np.unique(train_OnlyPress_Clusters))\n",
    "print(n_classes)\n",
    "\n",
    "print(val_OnlyPress_Clusters)\n",
    "\n",
    "n_classes = len(np.unique(val_OnlyPress_Clusters))\n",
    "print(n_classes)\n",
    "\n",
    "print(test_OnlyPress_Clusters)\n",
    "\n",
    "n_classes = len(np.unique(test_OnlyPress_Clusters))\n",
    "print(n_classes)\n",
    "print(train_NoPress_Clusters)\n",
    "n_classes = len(np.unique(train_NoPress_Clusters))\n",
    "print(n_classes)\n",
    "print(val_NoPress_Clusters)\n",
    "n_classes = len(np.unique(val_NoPress_Clusters))\n",
    "print(n_classes)\n",
    "print(test_NoPress_Clusters)\n",
    "n_classes = len(np.unique(test_NoPress_Clusters))\n",
    "print(n_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#One hot encoding (7 clusters)\n",
    "label_binarizer = sklearn.preprocessing.LabelBinarizer()\n",
    "label_binarizer.fit(range(L+K+1))\n",
    "\n",
    "train_OP_Encoder = label_binarizer.transform(train_OnlyPress_Clusters)\n",
    "val_OP_Encoder   = label_binarizer.transform(val_OnlyPress_Clusters)\n",
    "test_OP_Encoder  = label_binarizer.transform(test_OnlyPress_Clusters)\n",
    "\n",
    "train_NP_Encoder = label_binarizer.transform(train_NoPress_Clusters)\n",
    "val_NP_Encoder   = label_binarizer.transform(val_NoPress_Clusters)\n",
    "test_NP_Encoder  = label_binarizer.transform(test_NoPress_Clusters)\n",
    "\n",
    "pred_nan_train = np.zeros(len(IndexesNan_train))\n",
    "pred_nan_val = np.zeros(len(IndexesNan_val))\n",
    "pred_nan_test = np.zeros(len(IndexesNan_test))\n",
    "\n",
    "train_Nan_Encoder = label_binarizer.transform(pred_nan_train)\n",
    "val_Nan_Encoder   = label_binarizer.transform(pred_nan_val)\n",
    "test_Nan_Encoder  = label_binarizer.transform(pred_nan_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "zip argument #1 must support iteration",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-551-69c8172944dd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mlala\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0mlala\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: zip argument #1 must support iteration"
     ]
    }
   ],
   "source": [
    "#TESTING --- DIRTY CODE\n",
    "allIndexes = IndexesOP_train + IndexesNP_train + IndexesNan_train\n",
    "allEncoders = np.concatenate((train_OP_Encoder,train_NP_Encoder,train_Nan_Encoder))\n",
    "allEncoders.shape[0]\n",
    "train  = dict(zip(allIndexes,allEncoders))\n",
    "#train = sorted(train, key=lambda x: x[0])\n",
    "sorted(train.items(), key=lambda x: x[0])\n",
    "values = train.values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# def createZipIndexesPredictions(indexes,predictions):\n",
    "#     return [(a,b) for a,b in zip(indexes, predictions)]\n",
    "indexes_train = IndexesOP_train + IndexesNP_train + IndexesNan_train\n",
    "\n",
    "encoders_train = np.concatenate((train_OP_Encoder,train_NP_Encoder,train_Nan_Encoder))\n",
    "payload_trainSet = sorted(zip(indexes_train,encoders_train), key=lambda x: x[0])\n",
    "payload_trainSet = np.array([x[1] for x in payload_trainSet])\n",
    "\n",
    "indexes_val  = IndexesOP_val + IndexesNP_val + IndexesNan_val\n",
    "encoders_val = np.concatenate((val_OP_Encoder,val_NP_Encoder,val_Nan_Encoder))\n",
    "payload_valSet = sorted(zip(indexes_val,encoders_val), key=lambda x: x[0])\n",
    "payload_valSet = np.array([x[1] for x in payload_valSet])\n",
    "\n",
    "\n",
    "indexes_test = IndexesOP_test + IndexesNP_test + IndexesNan_test\n",
    "encoders_test = np.concatenate((test_OP_Encoder,test_NP_Encoder,test_Nan_Encoder))\n",
    "payload_testSet = sorted(zip(indexes_test,encoders_test), key=lambda x: x[0])\n",
    "payload_testSet = np.array([x[1] for x in payload_testSet])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Join all the features for final training, validation and testing sets\n",
    "def createFinalSets(start,end,X_rf):\n",
    "    new_features = np.column_stack((\n",
    "    addresses[start:end],\n",
    "    functions[start:end],\n",
    "    responses[start:end],\n",
    "    timestamp_diffs[start:end],\n",
    "    lengths[start:end],\n",
    "    X_rf\n",
    "    ))\n",
    "    return new_features\n",
    "\n",
    "train_end = len(payload_trainSet)\n",
    "valid_end = train_end + len(payload_valSet)\n",
    "test_end  = valid_end + len(payload_testSet)\n",
    "    \n",
    "trainSet_features = createFinalSets(0,train_end, payload_trainSet)\n",
    "validSet_features = createFinalSets(train_end, valid_end, payload_valSet)\n",
    "testSet_features  = createFinalSets(valid_end, test_end, payload_testSet)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "assert trainSet_features.shape[0] + validSet_features.shape[0] + testSet_features.shape[0] == dataset.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((164726, 50, 48), (164726, 50, 8))"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def make_sequences(Xs, Ys, maxlen):\n",
    "    X_seq, Y_seq = [], []\n",
    "    for i in range(0, Xs.shape[0] - maxlen):\n",
    "        X_seq.append(Xs[i: i+maxlen])\n",
    "        Y_seq.append(Ys[i+1: i+maxlen+1])\n",
    "    return np.array(X_seq), np.array(Y_seq)\n",
    "\n",
    "maxlen = 50\n",
    "X_train_seq, Y_train_seq = make_sequences(trainSet_features, Y_train_cat, maxlen)\n",
    "X_val_seq,   Y_val_seq   = make_sequences(validSet_features, Y_valid_cat, maxlen)\n",
    "X_test_seq,  Y_test_seq  = make_sequences(testSet_features,  Y_test_cat,  maxlen)\n",
    "X_train_seq.shape, Y_train_seq.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_7 (LSTM)                (None, 50, 256)           312320    \n",
      "_________________________________________________________________\n",
      "time_distributed_4 (TimeDist (None, 50, 8)             2056      \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 50, 8)             0         \n",
      "=================================================================\n",
      "Total params: 314,376\n",
      "Trainable params: 314,376\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "\n",
      "--------------------------------------------------\n",
      "Iteration 0\n",
      "Epoch 1/1\n",
      "  5248/164726 [..............................] - ETA: 1201s - loss: 3.3791 - acc: 0.7699"
     ]
    },
    {
     "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-102-6369ea9efd04>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'-'\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Iteration'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miteration\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train_seq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train_seq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\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     13\u001b[0m     \u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_val_seq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_val_seq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\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[1;32m     14\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.4/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    854\u001b[0m                               \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    855\u001b[0m                               \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 856\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m    857\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m     def evaluate(self, x, y, batch_size=32, verbose=1,\n",
      "\u001b[0;32m/usr/local/lib/python3.4/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   1496\u001b[0m                               \u001b[0mval_f\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_ins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_ins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1497\u001b[0m                               \u001b[0mcallback_metrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallback_metrics\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1498\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\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/python3.4/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   1150\u001b[0m                 \u001b[0mbatch_logs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'size'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1151\u001b[0m                 \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1152\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1153\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1154\u001b[0m                     \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.4/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   2227\u001b[0m         \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2228\u001b[0m         updated = session.run(self.outputs + [self.updates_op],\n\u001b[0;32m-> 2229\u001b[0;31m                               feed_dict=feed_dict)\n\u001b[0m\u001b[1;32m   2230\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.4/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    776\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    777\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 778\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    779\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    780\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/python3.4/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    980\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    981\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 982\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    983\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    984\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/python3.4/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   1030\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   1031\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1032\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1033\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1034\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/usr/local/lib/python3.4/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   1037\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   1038\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\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   1040\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   1041\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/python3.4/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   1019\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1020\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-> 1021\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1023\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: "
     ]
    }
   ],
   "source": [
    "import importlib, Algorithm\n",
    "Algorithm = importlib.reload(Algorithm)\n",
    "\n",
    "model = Algorithm.one_layer_lstm(maxlen)\n",
    "\n",
    "iters = 1\n",
    "\n",
    "for iteration in range(iters):\n",
    "    print()\n",
    "    print('-' * 50)\n",
    "    print('Iteration', iteration)\n",
    "    model.fit(X_train_seq, Y_train_seq, batch_size=128, epochs=1)\n",
    "    loss, metrics = model.evaluate(X_val_seq, Y_val_seq, batch_size=128, verbose=1)\n",
    "    print(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 7936/54877 [===>..........................] - ETA: 58s"
     ]
    },
    {
     "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-66-433a77b33de1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_metrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test_seq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_test_seq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\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[0;32m/usr/local/lib/python3.4/site-packages/keras/models.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, x, y, batch_size, verbose, sample_weight)\u001b[0m\n\u001b[1;32m    883\u001b[0m                                    \u001b[0mbatch_size\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    884\u001b[0m                                    \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 885\u001b[0;31m                                    sample_weight=sample_weight)\n\u001b[0m\u001b[1;32m    886\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    887\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\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/python3.4/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, x, y, batch_size, verbose, sample_weight)\u001b[0m\n\u001b[1;32m   1540\u001b[0m         return self._test_loop(f, ins,\n\u001b[1;32m   1541\u001b[0m                                \u001b[0mbatch_size\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[0;32m-> 1542\u001b[0;31m                                verbose=verbose)\n\u001b[0m\u001b[1;32m   1543\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1544\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\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/python3.4/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_test_loop\u001b[0;34m(self, f, ins, batch_size, verbose)\u001b[0m\n\u001b[1;32m   1264\u001b[0m                 \u001b[0mins_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_slice_arrays\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1266\u001b[0;31m             \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1267\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1268\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mbatch_index\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.4/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   2227\u001b[0m         \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2228\u001b[0m         updated = session.run(self.outputs + [self.updates_op],\n\u001b[0;32m-> 2229\u001b[0;31m                               feed_dict=feed_dict)\n\u001b[0m\u001b[1;32m   2230\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.4/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    776\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    777\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 778\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    779\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    780\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/python3.4/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    980\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    981\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 982\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    983\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    984\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/python3.4/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   1030\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   1031\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1032\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1033\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1034\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/usr/local/lib/python3.4/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   1037\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   1038\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\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   1040\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   1041\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/python3.4/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   1019\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1020\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-> 1021\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1023\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: "
     ]
    }
   ],
   "source": [
    "test_loss, test_metrics = model.evaluate(X_test_seq, Y_test_seq, batch_size=128, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "164726/164726 [==============================] - 194s   \n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'numpy.ndarray'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-67-8aef69593f43>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train_seq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\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[0;32m----> 2\u001b[0;31m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\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      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_val_seq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\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[0;31mTypeError\u001b[0m: unhashable type: 'numpy.ndarray'"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(X_train_seq, batch_size=128, verbose=1)\n",
    "predictions = set(np.argmax(predictions[:2], axis=2))\n",
    "print(predictions)\n",
    "\n",
    "predictions = model.predict(X_val_seq, batch_size=128, verbose=1)\n",
    "predictions = set(np.argmax(predictions[:2], axis=2))\n",
    "print(predictions)\n",
    "\n",
    "predictions = model.predict(X_test_seq, batch_size=128, verbose=1)\n",
    "predictions = set(np.argmax(predictions[:2], axis=2))\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0}"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(np.argmax(predictions, axis=2).reshape(-1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  1.00000000e+00   5.09227012e-30   3.22149059e-14   5.53286270e-29\n",
      "    7.63134995e-30   1.06552285e-29   1.30755928e-29   4.79785336e-30]\n",
      " [  1.00000000e+00   5.09141548e-30   3.22120802e-14   5.53201830e-29\n",
      "    7.62995215e-30   1.06533590e-29   1.30735976e-29   4.79708486e-30]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0, 0])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(predictions[:2])\n",
    "np.argmax(predictions[:2], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 633,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "###\n",
    "###\n",
    "###\n",
    "### OLD CODE BELOW\n",
    "###\n",
    "###\n",
    "###\n",
    "###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_features = np.column_stack((\n",
    "    addresses,\n",
    "    functions,\n",
    "    responses,\n",
    "    timestamp_diffs,\n",
    "    lengths,\n",
    "    remaining_features\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.,   0.,   0.,   0.,   1.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   1.,   0.,  16.,  nan,\n",
       "        nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_features[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x122ec78d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x122e3e240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x103fd4ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x122d81438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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ZcaOTpLMPmNXwfmaWtawjaRJwIrB/hLbtyvcDUzNGu75eR9I7gB8CX4yI+4bLI+KpKBwC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\nEQPATcBtkgaBZyn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      "text/plain": [
       "<matplotlib.figure.Figure at 0x111768b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x122d91470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1161aa6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x122d91748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MvIiqWvcbo19yfgvYH9gN+Bpw4Ip9Xg28q7t/JPDRWdS6juN9DrB7d/9VLY936Ji7/fYE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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111a21cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x111a25e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12273ddd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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98oa8JG1dLz/xKknqxpCXpAEz5CVpwAx5SRow7ycvSQPmTF6SBqyXIe/95CWpm16GvPeT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1228e14a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "def make_histograms(features, feature_names):\n",
    "    n_bins = 10\n",
    "    for n in range(features.shape[1]):\n",
    "        xs = features[:, n]\n",
    "        xs = xs[~np.isnan(xs)]\n",
    "        plt.hist(xs, n_bins, normed=1, histtype='bar')\n",
    "        plt.axvline(xs.mean(), color='b', linestyle='dashed', linewidth=2)\n",
    "        plt.title(feature_names[n])\n",
    "        plt.show()\n",
    "\n",
    "make_histograms(remaining_features, remaining_feature_names)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def split_dataset(data, labels, train_per_split, val_per_split, test_per_split):\n",
    "    train_percentage_split      = train_per_split\n",
    "    validation_percentage_split = val_per_split\n",
    "    test_percentage_split       = test_per_split\n",
    "    assert (train_percentage_split + validation_percentage_split + test_percentage_split) == 1.0\n",
    "    \n",
    "    train_end_index = int(data.shape[0] * train_percentage_split)\n",
    "    valid_end_index = int(data.shape[0] * validation_percentage_split) + train_end_index\n",
    "\n",
    "    X_train, Y_train = data[:train_end_index], labels[:train_end_index]\n",
    "    X_valid, Y_valid = data[train_end_index:valid_end_index], labels[train_end_index:valid_end_index]\n",
    "    X_test,  Y_test  = data[valid_end_index:], labels[valid_end_index:]\n",
    "    assert X_train.shape[0] + X_valid.shape[0] + X_test.shape[0] == data.shape[0]\n",
    "    \n",
    "    return (X_train, Y_train), (X_valid, Y_valid), (X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(X_train, Y_train), (X_valid, Y_valid), (X_test, Y_test) = split_dataset(new_features, labels, .1 , .1, .8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(32, 43)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range(X_train.shape[1] - 11, X_train.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(164776, 43)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def merge_two_dicts(x, y):\n",
    "    \"\"\"Given two dicts, merge them into a new dict as a shallow copy.\"\"\"\n",
    "    z = x.copy()\n",
    "    z.update(y)\n",
    "    return z\n",
    "\n",
    "def impute_simple(Xs, strategy = 'mean', stats = None):\n",
    "    if not stats:\n",
    "        Xs_imputer = preprocessing.Imputer(missing_values='NaN', strategy=strategy, axis=0)\n",
    "        Xs  = Xs_imputer.fit_transform(Xs)\n",
    "        Xs_stats = {}\n",
    "        Xs_stats['impute'] = Xs_imputer.statistics_\n",
    "        return Xs, Xs_stats\n",
    "    else:\n",
    "        for f_index in range(Xs.shape[1]):\n",
    "            feature = Xs[:, f_index]\n",
    "            feature[np.isnan(feature)] = stats['impute'][f_index]\n",
    "            Xs[:, f_index] = feature\n",
    "        return Xs, None\n",
    "\n",
    "def normalize(Xs, stats = None):\n",
    "    if not stats:\n",
    "        Xs_scaler = preprocessing.StandardScaler()\n",
    "        Xs = Xs_scaler.fit_transform(Xs)\n",
    "        Xs_stats = {\n",
    "            'mean'  : Xs_scaler.mean_,\n",
    "            'scale' : Xs_scaler.scale_\n",
    "        }\n",
    "        return Xs, Xs_stats\n",
    "    else:\n",
    "        for f_index in range(Xs.shape[1]):\n",
    "            feature = Xs[:, f_index]\n",
    "            feature -= stats['mean'][f_index]\n",
    "            feature /= stats['scale'][f_index]\n",
    "            Xs[:, f_index] = feature\n",
    "        return Xs, None\n",
    "\n",
    "def preprocess_data(Xs, train_data_stats = None):\n",
    "    #split again to pre-process each column adequately\n",
    "    #no need to further pre-process these features\n",
    "    addresses = Xs[:, 0]\n",
    "    functions = Xs[:, 1:29]\n",
    "    responses = Xs[:, 29]\n",
    "    \n",
    "    # features to be further pre-processed\n",
    "    time_diffs   = Xs[:, 30].reshape((-1, 1))\n",
    "    payload_feas = Xs[:, 31:-1]\n",
    "    pressures    = Xs[:, -1].reshape((-1, 1))\n",
    "\n",
    "    stats = None\n",
    "    if not train_data_stats:\n",
    "        stats = {}\n",
    "        time_diffs, stats['time_diffs'] = normalize(time_diffs)\n",
    "        \n",
    "        payload_feas, payload_feas_impute_stats = impute_simple(payload_feas)\n",
    "        payload_feas, payload_feas_norm_stats   = normalize(payload_feas)\n",
    "        stats['payload'] = merge_two_dicts(payload_feas_impute_stats, payload_feas_norm_stats)\n",
    "        \n",
    "        pressures, pressures_impute_stats = impute_simple(pressures)\n",
    "        pressures, pressures_norm_stats   = normalize(pressures)\n",
    "        stats['pressures'] = merge_two_dicts(pressures_impute_stats, pressures_norm_stats)\n",
    "        \n",
    "    else:\n",
    "        time_diffs, _ = normalize(time_diffs, train_data_stats['time_diffs'])\n",
    "\n",
    "        payload_feas, _ = impute_simple(payload_feas, stats = train_data_stats['payload'])\n",
    "        payload_feas, _ = normalize(payload_feas, train_data_stats['payload'])\n",
    "\n",
    "        pressures, _ = impute_simple(pressures, strategy = 'median', stats = train_data_stats['pressures'])\n",
    "        pressures, _ = normalize(pressures, train_data_stats['pressures'])\n",
    "\n",
    "\n",
    "    Xs_preprocessed = np.column_stack((\n",
    "        addresses,\n",
    "        functions,\n",
    "        responses,\n",
    "        time_diffs,\n",
    "        payload_feas,\n",
    "        pressures\n",
    "    ))\n",
    "        \n",
    "    return Xs_preprocessed, stats\n",
    "    \n",
    "X_train_preprocessed, X_train_stats = preprocess_data(X_train)\n",
    "X_val_preprocessed,  _ = preprocess_data(X_valid, X_train_stats)\n",
    "X_test_preprocessed, _ = preprocess_data(X_test, X_train_stats)\n",
    "X_train_preprocessed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((164769, 7, 43), (164769, 7, 1))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def make_sequences(Xs, Ys, maxlen):\n",
    "    X_seq, Y_seq = [], []\n",
    "    for i in range(0, Xs.shape[0] - maxlen):\n",
    "        X_seq.append(Xs[i: i+maxlen])\n",
    "        Y_seq.append(Ys[i: i+maxlen])\n",
    "    return np.array(X_seq), np.array(Y_seq)\n",
    "\n",
    "maxlen = 7\n",
    "X_train_seq, Y_train_seq = make_sequences(X_train_preprocessed, Y_train, maxlen)\n",
    "X_val_seq, Y_val_seq     = make_sequences(X_val_preprocessed,   Y_valid, maxlen)\n",
    "X_test_seq, Y_test_seq   = make_sequences(X_test_preprocessed,  Y_test, maxlen)\n",
    "X_train_seq.shape, Y_train_seq.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 650,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_8 (LSTM)                (None, 50, 128)           86016     \n",
      "_________________________________________________________________\n",
      "time_distributed_8 (TimeDist (None, 50, 1)             129       \n",
      "_________________________________________________________________\n",
      "activation_8 (Activation)    (None, 50, 1)             0         \n",
      "=================================================================\n",
      "Total params: 86,145\n",
      "Trainable params: 86,145\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "\n",
      "--------------------------------------------------\n",
      "Iteration 0\n",
      "Epoch 1/1\n",
      " 36352/164726 [=====>........................] - ETA: 482s - loss: 12.5117"
     ]
    },
    {
     "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-650-b78f8c1a1565>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'-'\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Iteration'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miteration\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train_seq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train_seq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\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     13\u001b[0m     \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_val_seq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_val_seq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\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[1;32m     14\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.4/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    854\u001b[0m                               \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    855\u001b[0m                               \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 856\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m    857\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m     def evaluate(self, x, y, batch_size=32, verbose=1,\n",
      "\u001b[0;32m/usr/local/lib/python3.4/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   1496\u001b[0m                               \u001b[0mval_f\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_ins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_ins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1497\u001b[0m                               \u001b[0mcallback_metrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallback_metrics\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1498\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\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/python3.4/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   1150\u001b[0m                 \u001b[0mbatch_logs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'size'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1151\u001b[0m                 \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1152\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1153\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1154\u001b[0m                     \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.4/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   2227\u001b[0m         \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2228\u001b[0m         updated = session.run(self.outputs + [self.updates_op],\n\u001b[0;32m-> 2229\u001b[0;31m                               feed_dict=feed_dict)\n\u001b[0m\u001b[1;32m   2230\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.4/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    776\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    777\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 778\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    779\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    780\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/python3.4/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    980\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    981\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 982\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    983\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    984\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/python3.4/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   1030\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   1031\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1032\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1033\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1034\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/usr/local/lib/python3.4/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   1037\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   1038\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\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   1040\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   1041\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/python3.4/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   1019\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1020\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-> 1021\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1023\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: "
     ]
    }
   ],
   "source": [
    "import importlib, Algorithm\n",
    "Algorithm = importlib.reload(Algorithm)\n",
    "\n",
    "model = Algorithm.one_layer_lstm(maxlen)\n",
    "\n",
    "iters = 1\n",
    "\n",
    "for iteration in range(60):\n",
    "    print()\n",
    "    print('-' * 50)\n",
    "    print('Iteration', iteration)\n",
    "    model.fit(X_train_seq, Y_train_seq, batch_size=128, epochs=1)\n",
    "    score = model.evaluate(X_val_seq, Y_val_seq, batch_size=128, verbose=1)\n",
    "    print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20500/54827 [==========>...................] - ETA: 12s"
     ]
    },
    {
     "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-51-1cccb3311775>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test_batched\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_test_batched\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\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[0;32m/mnt/gaiagpfs/users/homedirs/mzurad/tensorflow/lib/python3.4/site-packages/keras/models.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, x, y, batch_size, verbose, sample_weight)\u001b[0m\n\u001b[1;32m    883\u001b[0m                                    \u001b[0mbatch_size\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    884\u001b[0m                                    \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 885\u001b[0;31m                                    sample_weight=sample_weight)\n\u001b[0m\u001b[1;32m    886\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    887\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/gaiagpfs/users/homedirs/mzurad/tensorflow/lib/python3.4/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, x, y, batch_size, verbose, sample_weight)\u001b[0m\n\u001b[1;32m   1540\u001b[0m         return self._test_loop(f, ins,\n\u001b[1;32m   1541\u001b[0m                                \u001b[0mbatch_size\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[0;32m-> 1542\u001b[0;31m                                verbose=verbose)\n\u001b[0m\u001b[1;32m   1543\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1544\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/gaiagpfs/users/homedirs/mzurad/tensorflow/lib/python3.4/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_test_loop\u001b[0;34m(self, f, ins, batch_size, verbose)\u001b[0m\n\u001b[1;32m   1264\u001b[0m                 \u001b[0mins_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_slice_arrays\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1266\u001b[0;31m             \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1267\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1268\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mbatch_index\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/gaiagpfs/users/homedirs/mzurad/tensorflow/lib/python3.4/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   2227\u001b[0m         \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2228\u001b[0m         updated = session.run(self.outputs + [self.updates_op],\n\u001b[0;32m-> 2229\u001b[0;31m                               feed_dict=feed_dict)\n\u001b[0m\u001b[1;32m   2230\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/users/mzurad/.local/lib/python3.4/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    776\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    777\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 778\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    779\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    780\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/home/users/mzurad/.local/lib/python3.4/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    980\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    981\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 982\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    983\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    984\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/home/users/mzurad/.local/lib/python3.4/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   1030\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   1031\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1032\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1033\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1034\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/home/users/mzurad/.local/lib/python3.4/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   1037\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   1038\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\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   1040\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   1041\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/home/users/mzurad/.local/lib/python3.4/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   1019\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1020\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-> 1021\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1023\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: "
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_test_batched, Y_test_batched, batch_size=100, verbose=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54827/54827 [==============================] - 18s    \n"
     ]
    }
   ],
   "source": [
    "predicted = model.predict(X_test_batched, batch_size=100, verbose=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False],\n",
       "       [ True],\n",
       "       [False],\n",
       "       ..., \n",
       "       [ True],\n",
       "       [False],\n",
       "       [False]], dtype=bool)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted == Y_test_batched.astype(np.float).reshape((-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([b'0', b'0', b'0', ..., b'1', b'0', b'0'], \n",
       "      dtype='|S1')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.],\n",
       "       [ 0.],\n",
       "       [ 1.],\n",
       "       ..., \n",
       "       [ 0.],\n",
       "       [ 1.],\n",
       "       [ 1.]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train_batched"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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