planet-v2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/home/chicm/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import os\n",
    "import os.path\n",
    "import glob\n",
    "\n",
    "import keras as k\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "\n",
    "import cv2\n",
    "from tqdm import tqdm\n",
    "\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.cross_validation import KFold\n",
    "from sklearn.metrics import fbeta_score\n",
    "import time\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "DATA_DIR = '/home/chicm/data/planet'\n",
    "RESULT_DIR = DATA_DIR + '/resultsv2'\n",
    "\n",
    "\n",
    "df_train = pd.read_csv(DATA_DIR+'/train_v2.csv')\n",
    "df_test = pd.read_csv(DATA_DIR+'/sample_submission_v2.csv')\n",
    "\n",
    "classes = ['clear', 'haze', 'partly_cloudy', 'cloudy', \n",
    "           'primary', 'agriculture', 'water', 'cultivation', 'habitation', 'road',\n",
    "            'slash_burn', 'conventional_mine', 'bare_ground', 'artisinal_mine', \n",
    "           'blooming', 'selective_logging', 'blow_down']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['artisinal_mine', 'blooming', 'blow_down', 'cultivation', 'road', 'primary', 'cloudy', 'water', 'partly_cloudy', 'bare_ground', 'clear', 'habitation', 'slash_burn', 'haze', 'selective_logging', 'agriculture', 'conventional_mine']\n"
     ]
    }
   ],
   "source": [
    "flatten = lambda l:[item for sublist in l for item in sublist]\n",
    "t = list(set(flatten([l.split(' ') for l in df_train['tags'].values])))\n",
    "print(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 'clear', 1: 'haze', 2: 'partly_cloudy', 3: 'cloudy', 4: 'primary', 5: 'agriculture', 6: 'water', 7: 'cultivation', 8: 'habitation', 9: 'road', 10: 'slash_burn', 11: 'conventional_mine', 12: 'bare_ground', 13: 'artisinal_mine', 14: 'blooming', 15: 'selective_logging', 16: 'blow_down'}\n"
     ]
    }
   ],
   "source": [
    "label_map = {l: i for i, l in enumerate(classes)}\n",
    "inv_label_map = {i: l for l, i in label_map.items()}\n",
    "print(inv_label_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 40479/40479 [00:53<00:00, 760.36it/s]\n"
     ]
    }
   ],
   "source": [
    "img_size = (224, 224)\n",
    "x_train_list = []\n",
    "y_train_list = []\n",
    "\n",
    "for f, tags in tqdm(df_train.values, miniters=10000):\n",
    "    fn = DATA_DIR+'/train-jpg/'+f+'.jpg'\n",
    "    img = cv2.imread(fn) \n",
    "    targets = np.zeros(17)\n",
    "    for t in tags.split(' '):\n",
    "        targets[label_map[t]] = 1\n",
    "    x_train_list.append(cv2.resize(img, img_size)) \n",
    "    y_train_list.append(targets)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(40479, 224, 224, 3)\n",
      "(40479, 17)\n"
     ]
    }
   ],
   "source": [
    "x_train = np.array(x_train_list)\n",
    "y_train = np.array(y_train_list)\n",
    "print(x_train.shape) \n",
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del x_train_list\n",
    "del y_train_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  image_name                                       tags\n",
      "0    train_0                               haze primary\n",
      "1    train_1            agriculture clear primary water\n",
      "2    train_2                              clear primary\n",
      "3    train_3                              clear primary\n",
      "4    train_4  agriculture clear habitation primary road\n",
      "[[ 0.  1.  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 1.  0.  0.  0.  1.  1.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 1.  0.  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 1.  0.  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 1.  0.  0.  0.  1.  1.  0.  0.  1.  1.  0.  0.  0.  0.  0.  0.  0.]]\n"
     ]
    }
   ],
   "source": [
    "print(df_train[:5])\n",
    "print(y_train[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fb7bd1d67b8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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QWsc/LAvetPdDFOh9XRajDSlKCsfS/oDTSVGtz2pvdogWG/i9PSuA5bRl+PZ8xtqJrFas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\nqh+Qp5QitKHVJW5iRYFzUVGQb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f5tpmSVM4FOe5s+hkzqsNBhBeh8p4AeP/f0jwIdXpNF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Cm8PUX5sBIgVG9B2sQyf58hhDdIP5SUEt/hg0j+aCrjuLY9oFmgiMPVUE/UKDaDKFyW5d1i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\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CStWsofV5oiW82lCKew3yHGgmsDYqUx1WyBymymjeCYwwbMazQTG8XEXV+w51dy88a+Jp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\nLXyWJ/HJzkzjWs7sqTpRRjYe2FL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VPPegmSIcr89BVrmOndiyb8szng9z/a54fz/T+Ixu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/NVrKP85WfAq5rUjC7kyzjFtHOlhgzPEYYWwpAi4nqQaYtOmu2tCKOItIWj+M06cH8sqYsB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1PNqkMX8MPKSxET5kLIxw/suH6RzamjYgFkJUNZ6lrJb9ln6MkSG4sByx2Jcj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\nJBKJRCKRSCQSiUQikUgkEolXxf8PFJxC8Pve42oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb7bcfe50f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(32,32))\n",
    "plt.subplot(1,4,1)\n",
    "plt.imshow(x_train[26])\n",
    "plt.title(y_train[26])\n",
    "plt.subplot(1,4,2)\n",
    "plt.imshow(x_train[273])\n",
    "plt.title(y_train[273])\n",
    "plt.subplot(1,4,3)\n",
    "plt.imshow(x_train[290])\n",
    "plt.title(y_train[290])\n",
    "plt.subplot(1,4,4)\n",
    "plt.imshow(x_train[412])\n",
    "plt.title(y_train[412])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6072, 224, 224, 3) (6072, 17) (34407, 224, 224, 3) (34407, 17)\n"
     ]
    }
   ],
   "source": [
    "split_percent = 0.85\n",
    "split = int(x_train.shape[0] * split_percent)\n",
    "x_val = x_train[split:]\n",
    "y_val = y_train[split:]\n",
    "x_train = x_train[:split]\n",
    "y_train = y_train[:split]\n",
    "\n",
    "print(x_val.shape, y_val.shape, x_train.shape, y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "81.7656678181\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(x_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data augumentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size=16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "train_datagen = ImageDataGenerator(\n",
    "        width_shift_range=0.05,\n",
    "        height_shift_range=0.05, \n",
    "        horizontal_flip=True,\n",
    "        vertical_flip = True,\n",
    "        rescale = 1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_datagen = ImageDataGenerator(\n",
    "        rescale = 1./255)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.layers import Input, concatenate, Conv2D, MaxPooling2D, UpSampling2D\n",
    "from keras.models import Model\n",
    "from keras.optimizers import Adam\n",
    "from keras.layers.pooling import GlobalAveragePooling2D\n",
    "from keras.layers import Activation\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Activation\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from keras.optimizers import SGD, RMSprop, Adam\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.pooling import GlobalAveragePooling2D\n",
    "from keras import backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras import applications\n",
    "res50 = applications.ResNet50(include_top=False, weights='imagenet',input_shape=(224,224,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_fc_model(input_shape):\n",
    "    fc_model = Sequential()\n",
    "    fc_model.add(Flatten(input_shape=input_shape))\n",
    "    #fc_model.add(Dense(256, activation='relu'))\n",
    "    #fc_model.add(Dropout(0.6))\n",
    "    fc_model.add(Dense(17, activation='sigmoid'))\n",
    "    return fc_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Model(inputs=res50.input, outputs=get_fc_model(res50.output_shape[1:])(res50.output))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss='binary_crossentropy', \n",
    "                      optimizer='adam',\n",
    "                      metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.callbacks import LearningRateScheduler\n",
    "def lr_schedule(epoch):\n",
    "    if epoch <= 10:\n",
    "        return 0.001\n",
    "    elif epoch <= 20:\n",
    "        return 0.0001\n",
    "    else: \n",
    "        return 0.00005\n",
    "    \n",
    "w_filename = RESULT_DIR + '/res50_224.h5'\n",
    "callbacks = [ EarlyStopping(monitor='val_loss', patience=40, verbose=0),\n",
    "              ModelCheckpoint(w_filename, monitor='val_loss', save_best_only=True, verbose=0),\n",
    "              LearningRateScheduler(lr_schedule)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "epochs = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "493s - loss: 0.1483 - acc: 0.9429 - val_loss: 0.1304 - val_acc: 0.9483\n",
      "Epoch 2/100\n",
      "468s - loss: 0.1197 - acc: 0.9539 - val_loss: 0.2921 - val_acc: 0.9172\n",
      "Epoch 3/100\n",
      "468s - loss: 0.1116 - acc: 0.9573 - val_loss: 0.1568 - val_acc: 0.9359\n",
      "Epoch 4/100\n",
      "468s - loss: 0.1070 - acc: 0.9588 - val_loss: 0.1557 - val_acc: 0.9375\n",
      "Epoch 5/100\n",
      "469s - loss: 0.1036 - acc: 0.9601 - val_loss: 0.1599 - val_acc: 0.9365\n",
      "Epoch 6/100\n",
      "469s - loss: 0.1011 - acc: 0.9610 - val_loss: 0.1760 - val_acc: 0.9274\n",
      "Epoch 7/100\n",
      "470s - loss: 0.0987 - acc: 0.9620 - val_loss: 0.1282 - val_acc: 0.9477\n",
      "Epoch 8/100\n",
      "469s - loss: 0.0968 - acc: 0.9624 - val_loss: 0.2690 - val_acc: 0.8961\n",
      "Epoch 9/100\n",
      "502s - loss: 0.0953 - acc: 0.9631 - val_loss: 0.2329 - val_acc: 0.9221\n",
      "Epoch 10/100\n",
      "505s - loss: 0.0941 - acc: 0.9635 - val_loss: 0.3194 - val_acc: 0.8909\n",
      "Epoch 11/100\n",
      "506s - loss: 0.0932 - acc: 0.9638 - val_loss: 0.1475 - val_acc: 0.9420\n",
      "Epoch 12/100\n",
      "495s - loss: 0.0855 - acc: 0.9665 - val_loss: 0.0853 - val_acc: 0.9671\n",
      "Epoch 13/100\n",
      "474s - loss: 0.0839 - acc: 0.9674 - val_loss: 0.0854 - val_acc: 0.9668\n",
      "Epoch 14/100\n",
      "475s - loss: 0.0829 - acc: 0.9676 - val_loss: 0.0857 - val_acc: 0.9666\n",
      "Epoch 15/100\n",
      "475s - loss: 0.0820 - acc: 0.9678 - val_loss: 0.0851 - val_acc: 0.9674\n",
      "Epoch 16/100\n",
      "475s - loss: 0.0816 - acc: 0.9680 - val_loss: 0.0844 - val_acc: 0.9664\n",
      "Epoch 17/100\n",
      "475s - loss: 0.0811 - acc: 0.9683 - val_loss: 0.0830 - val_acc: 0.9673\n",
      "Epoch 18/100\n",
      "475s - loss: 0.0804 - acc: 0.9685 - val_loss: 0.0826 - val_acc: 0.9677\n",
      "Epoch 19/100\n",
      "475s - loss: 0.0807 - acc: 0.9683 - val_loss: 0.0878 - val_acc: 0.9655\n",
      "Epoch 20/100\n",
      "474s - loss: 0.0799 - acc: 0.9687 - val_loss: 0.0847 - val_acc: 0.9668\n",
      "Epoch 21/100\n",
      "475s - loss: 0.0794 - acc: 0.9689 - val_loss: 0.0866 - val_acc: 0.9664\n",
      "Epoch 22/100\n",
      "475s - loss: 0.0786 - acc: 0.9692 - val_loss: 0.0819 - val_acc: 0.9681\n",
      "Epoch 23/100\n",
      "473s - loss: 0.0783 - acc: 0.9692 - val_loss: 0.0849 - val_acc: 0.9673\n",
      "Epoch 24/100\n",
      "474s - loss: 0.0783 - acc: 0.9694 - val_loss: 0.0828 - val_acc: 0.9678\n",
      "Epoch 25/100\n",
      "473s - loss: 0.0781 - acc: 0.9692 - val_loss: 0.0834 - val_acc: 0.9666\n",
      "Epoch 26/100\n",
      "473s - loss: 0.0777 - acc: 0.9694 - val_loss: 0.0839 - val_acc: 0.9674\n",
      "Epoch 27/100\n",
      "474s - loss: 0.0774 - acc: 0.9695 - val_loss: 0.0845 - val_acc: 0.9669\n",
      "Epoch 28/100\n",
      "474s - loss: 0.0773 - acc: 0.9697 - val_loss: 0.0841 - val_acc: 0.9670\n",
      "Epoch 29/100\n",
      "473s - loss: 0.0776 - acc: 0.9697 - val_loss: 0.0828 - val_acc: 0.9675\n",
      "Epoch 30/100\n",
      "473s - loss: 0.0772 - acc: 0.9697 - val_loss: 0.0826 - val_acc: 0.9679\n",
      "Epoch 31/100\n",
      "473s - loss: 0.0768 - acc: 0.9698 - val_loss: 0.0829 - val_acc: 0.9676\n",
      "Epoch 32/100\n",
      "475s - loss: 0.0765 - acc: 0.9699 - val_loss: 0.0841 - val_acc: 0.9672\n",
      "Epoch 33/100\n",
      "476s - loss: 0.0766 - acc: 0.9697 - val_loss: 0.0841 - val_acc: 0.9674\n",
      "Epoch 34/100\n",
      "476s - loss: 0.0760 - acc: 0.9702 - val_loss: 0.0839 - val_acc: 0.9670\n",
      "Epoch 35/100\n",
      "476s - loss: 0.0763 - acc: 0.9699 - val_loss: 0.0832 - val_acc: 0.9673\n",
      "Epoch 36/100\n",
      "476s - loss: 0.0760 - acc: 0.9701 - val_loss: 0.0837 - val_acc: 0.9681\n",
      "Epoch 37/100\n",
      "477s - loss: 0.0760 - acc: 0.9700 - val_loss: 0.0842 - val_acc: 0.9670\n",
      "Epoch 38/100\n",
      "477s - loss: 0.0758 - acc: 0.9700 - val_loss: 0.0845 - val_acc: 0.9673\n",
      "Epoch 39/100\n",
      "477s - loss: 0.0751 - acc: 0.9704 - val_loss: 0.0846 - val_acc: 0.9671\n",
      "Epoch 40/100\n",
      "477s - loss: 0.0754 - acc: 0.9704 - val_loss: 0.0848 - val_acc: 0.9672\n",
      "Epoch 41/100\n",
      "477s - loss: 0.0749 - acc: 0.9704 - val_loss: 0.0833 - val_acc: 0.9679\n",
      "Epoch 42/100\n",
      "477s - loss: 0.0746 - acc: 0.9707 - val_loss: 0.0837 - val_acc: 0.9675\n",
      "Epoch 43/100\n",
      "477s - loss: 0.0745 - acc: 0.9707 - val_loss: 0.0837 - val_acc: 0.9676\n",
      "Epoch 44/100\n",
      "477s - loss: 0.0745 - acc: 0.9706 - val_loss: 0.0845 - val_acc: 0.9677\n",
      "Epoch 45/100\n",
      "477s - loss: 0.0743 - acc: 0.9707 - val_loss: 0.0843 - val_acc: 0.9671\n",
      "Epoch 46/100\n",
      "477s - loss: 0.0739 - acc: 0.9708 - val_loss: 0.0847 - val_acc: 0.9675\n",
      "Epoch 47/100\n",
      "474s - loss: 0.0741 - acc: 0.9707 - val_loss: 0.0853 - val_acc: 0.9672\n",
      "Epoch 48/100\n",
      "474s - loss: 0.0741 - acc: 0.9707 - val_loss: 0.0843 - val_acc: 0.9673\n",
      "Epoch 49/100\n",
      "474s - loss: 0.0737 - acc: 0.9711 - val_loss: 0.0833 - val_acc: 0.9681\n",
      "Epoch 50/100\n",
      "474s - loss: 0.0733 - acc: 0.9711 - val_loss: 0.0856 - val_acc: 0.9668\n",
      "Epoch 51/100\n",
      "474s - loss: 0.0733 - acc: 0.9713 - val_loss: 0.0821 - val_acc: 0.9682\n",
      "Epoch 52/100\n",
      "474s - loss: 0.0734 - acc: 0.9710 - val_loss: 0.0860 - val_acc: 0.9666\n",
      "Epoch 53/100\n",
      "474s - loss: 0.0726 - acc: 0.9713 - val_loss: 0.0834 - val_acc: 0.9679\n",
      "Epoch 54/100\n",
      "484s - loss: 0.0726 - acc: 0.9713 - val_loss: 0.0854 - val_acc: 0.9676\n",
      "Epoch 55/100\n",
      "498s - loss: 0.0727 - acc: 0.9712 - val_loss: 0.0842 - val_acc: 0.9676\n",
      "Epoch 56/100\n",
      "498s - loss: 0.0717 - acc: 0.9718 - val_loss: 0.0858 - val_acc: 0.9669\n",
      "Epoch 57/100\n",
      "498s - loss: 0.0724 - acc: 0.9715 - val_loss: 0.0849 - val_acc: 0.9674\n",
      "Epoch 58/100\n",
      "481s - loss: 0.0717 - acc: 0.9716 - val_loss: 0.0830 - val_acc: 0.9683\n",
      "Epoch 59/100\n",
      "477s - loss: 0.0719 - acc: 0.9715 - val_loss: 0.0867 - val_acc: 0.9666\n",
      "Epoch 60/100\n",
      "475s - loss: 0.0714 - acc: 0.9718 - val_loss: 0.0873 - val_acc: 0.9661\n",
      "Epoch 61/100\n",
      "475s - loss: 0.0711 - acc: 0.9719 - val_loss: 0.0838 - val_acc: 0.9682\n",
      "Epoch 62/100\n",
      "475s - loss: 0.0709 - acc: 0.9720 - val_loss: 0.0855 - val_acc: 0.9666\n",
      "Epoch 63/100\n",
      "475s - loss: 0.0707 - acc: 0.9720 - val_loss: 0.0852 - val_acc: 0.9675\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f9fdc6ebdd8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit_generator(\n",
    "        train_datagen.flow(x_train, y_train, batch_size=batch_size),\n",
    "        steps_per_epoch=x_train.shape[0] // batch_size,\n",
    "        epochs=epochs,\n",
    "        validation_data=test_datagen.flow(x_val, y_val, batch_size=batch_size), \n",
    "        validation_steps=x_val.shape[0] // batch_size,\n",
    "        verbose=2,\n",
    "        callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find best thresholds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model.load_weights(w_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "255\n"
     ]
    }
   ],
   "source": [
    "print(np.max(x_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x_val = x_val / 255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.319779734174\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(x_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred_val = model.predict(x_val, batch_size=batch_size, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  9.99858499e-01   2.93157300e-05   7.23574922e-05   1.17544579e-08\n",
      "    9.99998331e-01   1.88213843e-03   6.66980981e-04   3.63010622e-04\n",
      "    4.47272649e-03   3.47845198e-04   3.45748918e-09   1.20959286e-13\n",
      "    1.48884747e-05   2.49554229e-13   4.61414037e-03   4.72017302e-04\n",
      "    2.39206725e-04]\n",
      " [  5.25961723e-03   2.16855407e-02   5.90177224e-05   9.70001519e-01\n",
      "    3.32388021e-02   4.76877118e-04   5.30947978e-03   3.14057761e-05\n",
      "    3.90282803e-05   4.53111425e-04   4.75164010e-17   2.22561022e-20\n",
      "    5.59842706e-09   2.99019740e-14   3.03582419e-19   7.80039755e-18\n",
      "    1.20124280e-16]\n",
      " [  9.99868631e-01   7.19455193e-06   1.20634577e-04   5.67238478e-10\n",
      "    9.99999166e-01   2.69926153e-03   7.24922458e-04   4.62576427e-04\n",
      "    7.73407752e-04   7.83959695e-04   8.11005396e-09   4.94620458e-13\n",
      "    1.32469304e-05   4.76361843e-13   2.35142233e-03   5.95939055e-04\n",
      "    1.19775638e-03]\n",
      " [  9.98425722e-01   1.87370650e-04   5.06852579e-04   4.28398961e-09\n",
      "    9.99890924e-01   9.09424484e-01   5.21196872e-02   3.13648343e-01\n",
      "    1.61004707e-01   7.54786551e-01   1.45215830e-02   7.61962682e-03\n",
      "    4.92041409e-02   3.36861284e-03   3.32535687e-03   2.16949303e-02\n",
      "    2.44502747e-03]\n",
      " [  1.16124144e-02   9.84637365e-02   8.49351466e-01   8.11157897e-02\n",
      "    9.15218771e-01   8.69075418e-01   1.27939969e-01   9.33428481e-02\n",
      "    2.71210037e-02   7.21017361e-01   6.47328503e-04   2.41169808e-04\n",
      "    7.75933173e-03   1.25175147e-05   1.03614877e-08   1.08533959e-05\n",
      "    3.15787997e-06]]\n"
     ]
    }
   ],
   "source": [
    "print(pred_val[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def optimise_f2_thresholds(y, p, verbose=True, resolution=100):\n",
    "    def mf(x):\n",
    "        p2 = np.zeros_like(p)\n",
    "        for i in range(17):\n",
    "            p2[:, i] = (p[:, i] > x[i]).astype(np.int)\n",
    "        score = fbeta_score(y, p2, beta=2, average='samples')\n",
    "        return score\n",
    "\n",
    "    x = [0.18] * 17\n",
    "    for i in range(17):\n",
    "        best_i2 = 0\n",
    "        best_score = 0\n",
    "        for i2 in range(resolution):\n",
    "            i2 /= resolution\n",
    "            x[i] = i2\n",
    "            score = mf(x)\n",
    "            if score > best_score:\n",
    "                best_i2 = i2\n",
    "                best_score = score\n",
    "        x[i] = best_i2\n",
    "        if verbose:\n",
    "            print(i, best_i2, best_score)\n",
    "\n",
    "    for i in range(17):\n",
    "        best_i2 = 0\n",
    "        best_score = 0\n",
    "        for i2 in range(resolution):\n",
    "            i2 /= resolution\n",
    "            x[i] = i2\n",
    "            score = mf(x)\n",
    "            if score > best_score:\n",
    "                best_i2 = i2\n",
    "                best_score = score\n",
    "        x[i] = best_i2\n",
    "        if verbose:\n",
    "            print(i, best_i2, best_score)\n",
    "            \n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.2 0.927211707958\n",
      "1 0.18 0.927211707958\n",
      "2 0.12 0.927365249266\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chicm/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in samples with no predicted labels.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 0.09 0.928399798706\n",
      "4 0.31 0.928768080386\n",
      "5 0.13 0.928802288443\n",
      "6 0.18 0.928802288443\n",
      "7 0.26 0.929088996515\n",
      "8 0.26 0.929340602807\n",
      "9 0.21 0.929415288289\n",
      "10 0.32 0.929506310529\n",
      "11 0.28 0.929530959567\n",
      "12 0.18 0.929530959567\n",
      "13 0.16 0.929575892269\n",
      "14 0.24 0.92968291064\n",
      "15 0.19 0.929725100231\n",
      "16 0.33 0.929759098814\n",
      "0 0.2 0.929759098814\n",
      "1 0.25 0.929799788434\n",
      "2 0.12 0.929799788434\n",
      "3 0.09 0.929799788434\n",
      "4 0.31 0.929799788434\n",
      "5 0.13 0.929799788434\n",
      "6 0.18 0.929799788434\n",
      "7 0.26 0.929799788434\n",
      "8 0.26 0.929799788434\n",
      "9 0.21 0.929799788434\n",
      "10 0.32 0.929799788434\n",
      "11 0.28 0.929799788434\n",
      "12 0.18 0.929799788434\n",
      "13 0.16 0.929799788434\n",
      "14 0.24 0.929799788434\n",
      "15 0.19 0.929799788434\n",
      "16 0.33 0.929799788434\n"
     ]
    }
   ],
   "source": [
    "thr = optimise_f2_thresholds(y_val, pred_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.2, 0.25, 0.12, 0.09, 0.31, 0.13, 0.18, 0.26, 0.26, 0.21, 0.32, 0.28, 0.18, 0.16, 0.24, 0.19, 0.33]\n"
     ]
    }
   ],
   "source": [
    "print(thr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "thr = [0.2, 0.25, 0.12, 0.09, 0.31, 0.13, 0.18, 0.26, 0.26, 0.21, 0.32, 0.28, 0.18, 0.16, 0.24, 0.19, 0.33]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import bcolz\n",
    "import glob\n",
    "\n",
    "def save_array(fname, arr):\n",
    "    c=bcolz.carray(arr, rootdir=fname, mode='w')\n",
    "    c.flush()\n",
    "def load_array(fname):\n",
    "    return bcolz.open(fname)[:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_test = pd.read_csv(DATA_DIR+'/sample_submission_v2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 61191/61191 [17:03<00:00, 59.79it/s]\n"
     ]
    }
   ],
   "source": [
    "preds = np.zeros((df_test.values.shape[0], 17))\n",
    "index = 0\n",
    "for f, tags in tqdm(df_test.values):\n",
    "    fn = DATA_DIR+'/test-jpg/'+f+'.jpg'\n",
    "    if not os.path.isfile(fn):\n",
    "        fn = DATA_DIR+'/test-jpg-add/'+f+'.jpg'\n",
    "    img = cv2.imread(fn)\n",
    "    #x_test.append(cv2.resize(img, img_size))\n",
    "    #x_test[index] = img / 255.\n",
    "    img = img/255.\n",
    "    x_test = np.expand_dims(img, axis=0)\n",
    "    preds[index] = model.predict(x_test)\n",
    "    index += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(61191, 17)\n"
     ]
    }
   ],
   "source": [
    "print(preds.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "save_array(RESULT_DIR+'/preds.dat', preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  9.98107076e-01   2.44011031e-03   5.99584419e-05   1.28415536e-07\n",
      "    9.99978662e-01   2.97449925e-03   2.20675021e-03   1.14945986e-03\n",
      "    2.19779578e-03   9.84876882e-04   2.00360930e-07   6.58251058e-12\n",
      "    7.10248278e-05   1.66358930e-12   1.98746342e-02   1.35715178e-03\n",
      "    8.27464275e-04]\n",
      " [  9.97804821e-01   2.37056684e-05   2.92682229e-03   6.81408430e-10\n",
      "    9.99999642e-01   5.46942512e-03   4.40920761e-04   1.21302262e-03\n",
      "    4.43664001e-04   4.09760221e-04   9.67624114e-09   1.21644136e-13\n",
      "    1.23169420e-05   5.67540589e-13   1.85405975e-03   3.93186900e-04\n",
      "    9.14004282e-04]\n",
      " [  4.69667975e-06   9.87952603e-07   9.99735653e-01   1.54740574e-05\n",
      "    9.99983191e-01   1.26873711e-02   4.98612262e-02   4.37710294e-03\n",
      "    8.44378883e-05   7.02132331e-03   1.57957595e-06   6.71198375e-11\n",
      "    2.86169161e-05   2.83954166e-10   1.82413942e-08   3.70630232e-06\n",
      "    1.37054678e-06]\n",
      " [  8.63901854e-01   1.41512661e-03   1.51743889e-01   1.84250428e-08\n",
      "    9.99996066e-01   4.71121579e-01   1.48012592e-02   3.74306470e-01\n",
      "    1.92362873e-03   6.80270698e-03   5.29784418e-04   7.68894915e-06\n",
      "    6.00232696e-03   2.39599967e-06   9.63647291e-03   7.82778300e-03\n",
      "    4.01135068e-03]\n",
      " [  5.77710198e-05   1.23814170e-04   8.67280245e-01   1.85358137e-01\n",
      "    8.69696856e-01   1.49797322e-02   2.60736942e-02   1.15838582e-02\n",
      "    3.70711408e-04   3.55642033e-03   5.23659693e-08   1.02979255e-16\n",
      "    3.80869024e-05   1.92731839e-08   2.11820954e-17   8.06281916e-11\n",
      "    4.17334183e-14]]\n"
     ]
    }
   ],
   "source": [
    "print(preds[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_multi_classes(score, threshold, nil=''):\n",
    "    N = len(classes)\n",
    "    s = nil\n",
    "    for n in range(N):\n",
    "        if score[n] > threshold[n]:\n",
    "            if s == nil:\n",
    "                s = classes[n]\n",
    "            else:\n",
    "                s = '%s %s' % (s, classes[n])\n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i, pred in enumerate(preds):\n",
    "    tags = get_multi_classes(pred, thr)\n",
    "    df_test['tags'][i] = tags\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_test.to_csv(RESULT_DIR+'/sub1.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.2, 0.25, 0.12, 0.09, 0.31, 0.13, 0.18, 0.26, 0.26, 0.21, 0.32, 0.28, 0.18, 0.16, 0.24, 0.19, 0.33]\n"
     ]
    }
   ],
   "source": [
    "print(thr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start KFold number 1 from 6\n",
      "Split train:  33732 33732\n",
      "Split valid:  6747 6747\n",
      "Train on 33732 samples, validate on 6747 samples\n",
      "Epoch 1/5\n",
      "38s - loss: 0.4670 - acc: 0.8167 - val_loss: 0.2817 - val_acc: 0.9106\n",
      "Epoch 2/5\n",
      "31s - loss: 0.1969 - acc: 0.9301 - val_loss: 0.3107 - val_acc: 0.9103\n",
      "Epoch 3/5\n",
      "32s - loss: 0.1698 - acc: 0.9366 - val_loss: 0.5406 - val_acc: 0.8727\n",
      "Epoch 4/5\n",
      "32s - loss: 0.1580 - acc: 0.9400 - val_loss: 0.1603 - val_acc: 0.9366\n",
      "Epoch 5/5\n",
      "32s - loss: 0.1482 - acc: 0.9429 - val_loss: 0.1928 - val_acc: 0.9250\n",
      "Train on 33732 samples, validate on 6747 samples\n",
      "Epoch 1/80\n",
      "36s - loss: 0.1416 - acc: 0.9455 - val_loss: 0.1338 - val_acc: 0.9471\n",
      "Epoch 2/80\n",
      "32s - loss: 0.1394 - acc: 0.9460 - val_loss: 0.1316 - val_acc: 0.9484\n",
      "Epoch 3/80\n",
      "32s - loss: 0.1388 - acc: 0.9465 - val_loss: 0.1307 - val_acc: 0.9484\n",
      "Epoch 4/80\n",
      "32s - loss: 0.1380 - acc: 0.9466 - val_loss: 0.1313 - val_acc: 0.9478\n",
      "Epoch 5/80\n",
      "32s - loss: 0.1368 - acc: 0.9470 - val_loss: 0.1302 - val_acc: 0.9488\n",
      "Epoch 6/80\n",
      "32s - loss: 0.1366 - acc: 0.9468 - val_loss: 0.1301 - val_acc: 0.9489\n",
      "Epoch 7/80\n",
      "32s - loss: 0.1370 - acc: 0.9468 - val_loss: 0.1305 - val_acc: 0.9486\n",
      "Epoch 8/80\n",
      "32s - loss: 0.1362 - acc: 0.9472 - val_loss: 0.1296 - val_acc: 0.9489\n",
      "Epoch 9/80\n",
      "32s - loss: 0.1361 - acc: 0.9472 - val_loss: 0.1297 - val_acc: 0.9489\n",
      "Epoch 10/80\n",
      "32s - loss: 0.1355 - acc: 0.9475 - val_loss: 0.1298 - val_acc: 0.9489\n",
      "Epoch 11/80\n",
      "32s - loss: 0.1360 - acc: 0.9474 - val_loss: 0.1294 - val_acc: 0.9491\n",
      "Epoch 12/80\n",
      "32s - loss: 0.1351 - acc: 0.9474 - val_loss: 0.1296 - val_acc: 0.9490\n",
      "Epoch 13/80\n",
      "32s - loss: 0.1350 - acc: 0.9477 - val_loss: 0.1289 - val_acc: 0.9493\n",
      "Epoch 14/80\n",
      "32s - loss: 0.1346 - acc: 0.9478 - val_loss: 0.1289 - val_acc: 0.9493\n",
      "Epoch 15/80\n",
      "32s - loss: 0.1343 - acc: 0.9477 - val_loss: 0.1290 - val_acc: 0.9494\n",
      "Epoch 16/80\n",
      "32s - loss: 0.1346 - acc: 0.9480 - val_loss: 0.1292 - val_acc: 0.9494\n",
      "Epoch 17/80\n",
      "32s - loss: 0.1340 - acc: 0.9480 - val_loss: 0.1290 - val_acc: 0.9494\n",
      "Epoch 18/80\n",
      "32s - loss: 0.1340 - acc: 0.9480 - val_loss: 0.1291 - val_acc: 0.9495\n",
      "Epoch 19/80\n",
      "32s - loss: 0.1344 - acc: 0.9479 - val_loss: 0.1293 - val_acc: 0.9494\n",
      "Epoch 20/80\n",
      "32s - loss: 0.1334 - acc: 0.9482 - val_loss: 0.1285 - val_acc: 0.9494\n",
      "Epoch 21/80\n",
      "32s - loss: 0.1336 - acc: 0.9478 - val_loss: 0.1286 - val_acc: 0.9495\n",
      "Epoch 22/80\n",
      "32s - loss: 0.1333 - acc: 0.9481 - val_loss: 0.1294 - val_acc: 0.9494\n",
      "Epoch 23/80\n",
      "32s - loss: 0.1331 - acc: 0.9484 - val_loss: 0.1282 - val_acc: 0.9498\n",
      "Epoch 24/80\n",
      "32s - loss: 0.1331 - acc: 0.9483 - val_loss: 0.1283 - val_acc: 0.9497\n",
      "Epoch 25/80\n",
      "32s - loss: 0.1328 - acc: 0.9483 - val_loss: 0.1288 - val_acc: 0.9496\n",
      "Epoch 26/80\n",
      "32s - loss: 0.1328 - acc: 0.9485 - val_loss: 0.1287 - val_acc: 0.9496\n",
      "Epoch 27/80\n",
      "32s - loss: 0.1324 - acc: 0.9483 - val_loss: 0.1283 - val_acc: 0.9500\n",
      "Epoch 28/80\n",
      "32s - loss: 0.1324 - acc: 0.9487 - val_loss: 0.1288 - val_acc: 0.9496\n",
      "Epoch 29/80\n",
      "32s - loss: 0.1319 - acc: 0.9488 - val_loss: 0.1283 - val_acc: 0.9500\n",
      "Epoch 30/80\n",
      "32s - loss: 0.1316 - acc: 0.9488 - val_loss: 0.1281 - val_acc: 0.9499\n",
      "Epoch 31/80\n",
      "32s - loss: 0.1321 - acc: 0.9486 - val_loss: 0.1277 - val_acc: 0.9500\n",
      "Epoch 32/80\n",
      "32s - loss: 0.1318 - acc: 0.9486 - val_loss: 0.1281 - val_acc: 0.9500\n",
      "Epoch 33/80\n",
      "32s - loss: 0.1315 - acc: 0.9487 - val_loss: 0.1289 - val_acc: 0.9498\n",
      "Epoch 34/80\n",
      "32s - loss: 0.1319 - acc: 0.9486 - val_loss: 0.1291 - val_acc: 0.9494\n",
      "Epoch 35/80\n",
      "32s - loss: 0.1310 - acc: 0.9489 - val_loss: 0.1281 - val_acc: 0.9501\n",
      "Epoch 36/80\n",
      "32s - loss: 0.1313 - acc: 0.9487 - val_loss: 0.1276 - val_acc: 0.9502\n",
      "Epoch 37/80\n",
      "32s - loss: 0.1307 - acc: 0.9487 - val_loss: 0.1293 - val_acc: 0.9496\n",
      "Epoch 38/80\n",
      "32s - loss: 0.1312 - acc: 0.9488 - val_loss: 0.1277 - val_acc: 0.9504\n",
      "Epoch 39/80\n",
      "32s - loss: 0.1315 - acc: 0.9486 - val_loss: 0.1277 - val_acc: 0.9502\n",
      "Epoch 40/80\n",
      "32s - loss: 0.1304 - acc: 0.9492 - val_loss: 0.1274 - val_acc: 0.9503\n",
      "Epoch 41/80\n",
      "32s - loss: 0.1305 - acc: 0.9492 - val_loss: 0.1274 - val_acc: 0.9506\n",
      "Epoch 42/80\n",
      "32s - loss: 0.1309 - acc: 0.9493 - val_loss: 0.1325 - val_acc: 0.9482\n",
      "Epoch 43/80\n",
      "32s - loss: 0.1303 - acc: 0.9490 - val_loss: 0.1275 - val_acc: 0.9505\n",
      "Epoch 44/80\n",
      "32s - loss: 0.1302 - acc: 0.9492 - val_loss: 0.1279 - val_acc: 0.9503\n",
      "Epoch 45/80\n",
      "32s - loss: 0.1298 - acc: 0.9495 - val_loss: 0.1277 - val_acc: 0.9503\n",
      "Epoch 46/80\n",
      "32s - loss: 0.1295 - acc: 0.9495 - val_loss: 0.1280 - val_acc: 0.9502\n",
      "Epoch 47/80\n",
      "32s - loss: 0.1296 - acc: 0.9492 - val_loss: 0.1274 - val_acc: 0.9506\n",
      "Epoch 48/80\n",
      "32s - loss: 0.1291 - acc: 0.9495 - val_loss: 0.1277 - val_acc: 0.9506\n",
      "Epoch 49/80\n",
      "32s - loss: 0.1299 - acc: 0.9494 - val_loss: 0.1275 - val_acc: 0.9504\n",
      "Epoch 50/80\n",
      "32s - loss: 0.1300 - acc: 0.9494 - val_loss: 0.1274 - val_acc: 0.9505\n",
      "Epoch 51/80\n",
      "32s - loss: 0.1297 - acc: 0.9495 - val_loss: 0.1272 - val_acc: 0.9502\n",
      "Epoch 52/80\n",
      "32s - loss: 0.1293 - acc: 0.9496 - val_loss: 0.1267 - val_acc: 0.9505\n",
      "Epoch 53/80\n",
      "32s - loss: 0.1292 - acc: 0.9498 - val_loss: 0.1267 - val_acc: 0.9504\n",
      "Epoch 54/80\n",
      "32s - loss: 0.1297 - acc: 0.9494 - val_loss: 0.1267 - val_acc: 0.9507\n",
      "Epoch 55/80\n",
      "32s - loss: 0.1294 - acc: 0.9495 - val_loss: 0.1268 - val_acc: 0.9507\n",
      "Epoch 56/80\n",
      "32s - loss: 0.1289 - acc: 0.9496 - val_loss: 0.1274 - val_acc: 0.9506\n",
      "Epoch 57/80\n",
      "32s - loss: 0.1288 - acc: 0.9496 - val_loss: 0.1268 - val_acc: 0.9505\n",
      "Epoch 58/80\n",
      "32s - loss: 0.1280 - acc: 0.9498 - val_loss: 0.1285 - val_acc: 0.9501\n",
      "Epoch 59/80\n",
      "32s - loss: 0.1290 - acc: 0.9499 - val_loss: 0.1266 - val_acc: 0.9509\n",
      "Epoch 60/80\n",
      "32s - loss: 0.1280 - acc: 0.9499 - val_loss: 0.1270 - val_acc: 0.9509\n",
      "Epoch 61/80\n",
      "32s - loss: 0.1286 - acc: 0.9497 - val_loss: 0.1266 - val_acc: 0.9509\n",
      "Epoch 62/80\n",
      "32s - loss: 0.1286 - acc: 0.9497 - val_loss: 0.1277 - val_acc: 0.9505\n",
      "Epoch 63/80\n",
      "32s - loss: 0.1284 - acc: 0.9499 - val_loss: 0.1269 - val_acc: 0.9509\n",
      "Epoch 64/80\n",
      "32s - loss: 0.1274 - acc: 0.9499 - val_loss: 0.1268 - val_acc: 0.9508\n",
      "Epoch 65/80\n",
      "32s - loss: 0.1281 - acc: 0.9499 - val_loss: 0.1277 - val_acc: 0.9507\n",
      "Epoch 66/80\n",
      "32s - loss: 0.1280 - acc: 0.9503 - val_loss: 0.1271 - val_acc: 0.9510\n",
      "Epoch 67/80\n",
      "32s - loss: 0.1278 - acc: 0.9502 - val_loss: 0.1280 - val_acc: 0.9505\n",
      "Epoch 68/80\n",
      "32s - loss: 0.1278 - acc: 0.9504 - val_loss: 0.1260 - val_acc: 0.9509\n",
      "Epoch 69/80\n",
      "32s - loss: 0.1274 - acc: 0.9503 - val_loss: 0.1265 - val_acc: 0.9511\n",
      "Epoch 70/80\n",
      "32s - loss: 0.1278 - acc: 0.9503 - val_loss: 0.1264 - val_acc: 0.9511\n",
      "Epoch 71/80\n",
      "32s - loss: 0.1277 - acc: 0.9501 - val_loss: 0.1263 - val_acc: 0.9510\n",
      "Epoch 72/80\n",
      "32s - loss: 0.1267 - acc: 0.9501 - val_loss: 0.1277 - val_acc: 0.9506\n",
      "Epoch 73/80\n",
      "32s - loss: 0.1271 - acc: 0.9503 - val_loss: 0.1266 - val_acc: 0.9512\n",
      "Epoch 74/80\n",
      "32s - loss: 0.1271 - acc: 0.9502 - val_loss: 0.1279 - val_acc: 0.9509\n",
      "Epoch 75/80\n",
      "32s - loss: 0.1268 - acc: 0.9503 - val_loss: 0.1283 - val_acc: 0.9504\n",
      "Epoch 76/80\n",
      "32s - loss: 0.1264 - acc: 0.9506 - val_loss: 0.1263 - val_acc: 0.9513\n",
      "Epoch 77/80\n",
      "32s - loss: 0.1266 - acc: 0.9507 - val_loss: 0.1261 - val_acc: 0.9513\n",
      "Epoch 78/80\n",
      "32s - loss: 0.1266 - acc: 0.9507 - val_loss: 0.1260 - val_acc: 0.9514\n",
      "Epoch 79/80\n",
      "32s - loss: 0.1264 - acc: 0.9508 - val_loss: 0.1276 - val_acc: 0.9507\n",
      "Epoch 80/80\n",
      "32s - loss: 0.1262 - acc: 0.9507 - val_loss: 0.1255 - val_acc: 0.9512\n",
      "0.891290199721\n",
      "Start KFold number 2 from 6\n",
      "Split train:  33732 33732\n",
      "Split valid:  6747 6747\n",
      "Train on 33732 samples, validate on 6747 samples\n",
      "Epoch 1/5\n",
      "38s - loss: 0.4721 - acc: 0.8154 - val_loss: 0.2706 - val_acc: 0.9094\n",
      "Epoch 2/5\n",
      "32s - loss: 0.1988 - acc: 0.9291 - val_loss: 0.2577 - val_acc: 0.9119\n",
      "Epoch 3/5\n",
      "32s - loss: 0.1707 - acc: 0.9362 - val_loss: 0.2341 - val_acc: 0.9083\n",
      "Epoch 4/5\n",
      "32s - loss: 0.1548 - acc: 0.9411 - val_loss: 0.1794 - val_acc: 0.9339\n",
      "Epoch 5/5\n",
      "32s - loss: 0.1466 - acc: 0.9433 - val_loss: 0.3166 - val_acc: 0.8971\n",
      "Train on 33732 samples, validate on 6747 samples\n",
      "Epoch 1/80\n",
      "36s - loss: 0.1385 - acc: 0.9461 - val_loss: 0.1292 - val_acc: 0.9492\n",
      "Epoch 2/80\n",
      "32s - loss: 0.1371 - acc: 0.9468 - val_loss: 0.1286 - val_acc: 0.9496\n",
      "Epoch 3/80\n",
      "32s - loss: 0.1363 - acc: 0.9468 - val_loss: 0.1278 - val_acc: 0.9499\n",
      "Epoch 4/80\n",
      "32s - loss: 0.1360 - acc: 0.9470 - val_loss: 0.1275 - val_acc: 0.9501\n",
      "Epoch 5/80\n",
      "32s - loss: 0.1351 - acc: 0.9473 - val_loss: 0.1272 - val_acc: 0.9504\n",
      "Epoch 6/80\n",
      "32s - loss: 0.1345 - acc: 0.9475 - val_loss: 0.1268 - val_acc: 0.9505\n",
      "Epoch 7/80\n",
      "32s - loss: 0.1345 - acc: 0.9479 - val_loss: 0.1265 - val_acc: 0.9504\n",
      "Epoch 8/80\n",
      "32s - loss: 0.1340 - acc: 0.9478 - val_loss: 0.1265 - val_acc: 0.9505\n",
      "Epoch 9/80\n",
      "32s - loss: 0.1334 - acc: 0.9481 - val_loss: 0.1260 - val_acc: 0.9507\n",
      "Epoch 10/80\n",
      "32s - loss: 0.1340 - acc: 0.9478 - val_loss: 0.1262 - val_acc: 0.9507\n",
      "Epoch 11/80\n",
      "32s - loss: 0.1331 - acc: 0.9482 - val_loss: 0.1254 - val_acc: 0.9510\n",
      "Epoch 12/80\n",
      "32s - loss: 0.1332 - acc: 0.9480 - val_loss: 0.1258 - val_acc: 0.9509\n",
      "Epoch 13/80\n",
      "32s - loss: 0.1323 - acc: 0.9486 - val_loss: 0.1255 - val_acc: 0.9508\n",
      "Epoch 14/80\n",
      "32s - loss: 0.1326 - acc: 0.9483 - val_loss: 0.1260 - val_acc: 0.9504\n",
      "Epoch 15/80\n",
      "32s - loss: 0.1321 - acc: 0.9489 - val_loss: 0.1258 - val_acc: 0.9506\n",
      "Epoch 16/80\n",
      "32s - loss: 0.1319 - acc: 0.9488 - val_loss: 0.1250 - val_acc: 0.9512\n",
      "Epoch 17/80\n",
      "32s - loss: 0.1319 - acc: 0.9488 - val_loss: 0.1251 - val_acc: 0.9508\n",
      "Epoch 18/80\n",
      "31s - loss: 0.1314 - acc: 0.9488 - val_loss: 0.1249 - val_acc: 0.9512\n",
      "Epoch 19/80\n",
      "31s - loss: 0.1315 - acc: 0.9487 - val_loss: 0.1248 - val_acc: 0.9513\n",
      "Epoch 20/80\n",
      "31s - loss: 0.1321 - acc: 0.9488 - val_loss: 0.1245 - val_acc: 0.9510\n",
      "Epoch 21/80\n",
      "31s - loss: 0.1311 - acc: 0.9490 - val_loss: 0.1251 - val_acc: 0.9510\n",
      "Epoch 22/80\n",
      "31s - loss: 0.1304 - acc: 0.9493 - val_loss: 0.1247 - val_acc: 0.9513\n",
      "Epoch 23/80\n",
      "31s - loss: 0.1306 - acc: 0.9490 - val_loss: 0.1245 - val_acc: 0.9509\n",
      "Epoch 24/80\n",
      "31s - loss: 0.1308 - acc: 0.9491 - val_loss: 0.1253 - val_acc: 0.9509\n",
      "Epoch 25/80\n",
      "31s - loss: 0.1306 - acc: 0.9490 - val_loss: 0.1241 - val_acc: 0.9513\n",
      "Epoch 26/80\n",
      "31s - loss: 0.1302 - acc: 0.9492 - val_loss: 0.1241 - val_acc: 0.9515\n",
      "Epoch 27/80\n",
      "31s - loss: 0.1299 - acc: 0.9494 - val_loss: 0.1239 - val_acc: 0.9514\n",
      "Epoch 28/80\n",
      "31s - loss: 0.1298 - acc: 0.9493 - val_loss: 0.1239 - val_acc: 0.9516\n",
      "Epoch 29/80\n",
      "31s - loss: 0.1296 - acc: 0.9492 - val_loss: 0.1236 - val_acc: 0.9518\n",
      "Epoch 30/80\n",
      "31s - loss: 0.1293 - acc: 0.9498 - val_loss: 0.1248 - val_acc: 0.9512\n",
      "Epoch 31/80\n",
      "31s - loss: 0.1293 - acc: 0.9497 - val_loss: 0.1242 - val_acc: 0.9512\n",
      "Epoch 32/80\n",
      "31s - loss: 0.1292 - acc: 0.9498 - val_loss: 0.1235 - val_acc: 0.9518\n",
      "Epoch 33/80\n",
      "31s - loss: 0.1286 - acc: 0.9499 - val_loss: 0.1236 - val_acc: 0.9520\n",
      "Epoch 34/80\n",
      "31s - loss: 0.1292 - acc: 0.9496 - val_loss: 0.1234 - val_acc: 0.9515\n",
      "Epoch 35/80\n",
      "31s - loss: 0.1287 - acc: 0.9501 - val_loss: 0.1245 - val_acc: 0.9516\n",
      "Epoch 36/80\n",
      "31s - loss: 0.1290 - acc: 0.9496 - val_loss: 0.1232 - val_acc: 0.9518\n",
      "Epoch 37/80\n",
      "31s - loss: 0.1290 - acc: 0.9497 - val_loss: 0.1231 - val_acc: 0.9520\n",
      "Epoch 38/80\n",
      "31s - loss: 0.1282 - acc: 0.9500 - val_loss: 0.1240 - val_acc: 0.9514\n",
      "Epoch 39/80\n",
      "31s - loss: 0.1278 - acc: 0.9502 - val_loss: 0.1230 - val_acc: 0.9520\n",
      "Epoch 40/80\n",
      "31s - loss: 0.1281 - acc: 0.9499 - val_loss: 0.1230 - val_acc: 0.9521\n",
      "Epoch 41/80\n",
      "31s - loss: 0.1280 - acc: 0.9501 - val_loss: 0.1234 - val_acc: 0.9517\n",
      "Epoch 42/80\n",
      "31s - loss: 0.1277 - acc: 0.9502 - val_loss: 0.1232 - val_acc: 0.9521\n",
      "Epoch 43/80\n",
      "31s - loss: 0.1277 - acc: 0.9503 - val_loss: 0.1229 - val_acc: 0.9520\n",
      "Epoch 44/80\n",
      "31s - loss: 0.1280 - acc: 0.9500 - val_loss: 0.1239 - val_acc: 0.9515\n",
      "Epoch 45/80\n",
      "31s - loss: 0.1278 - acc: 0.9501 - val_loss: 0.1227 - val_acc: 0.9522\n",
      "Epoch 46/80\n",
      "31s - loss: 0.1278 - acc: 0.9500 - val_loss: 0.1230 - val_acc: 0.9517\n",
      "Epoch 47/80\n",
      "31s - loss: 0.1275 - acc: 0.9503 - val_loss: 0.1237 - val_acc: 0.9516\n",
      "Epoch 48/80\n",
      "31s - loss: 0.1272 - acc: 0.9504 - val_loss: 0.1229 - val_acc: 0.9522\n",
      "Epoch 49/80\n",
      "32s - loss: 0.1271 - acc: 0.9506 - val_loss: 0.1230 - val_acc: 0.9519\n",
      "Epoch 50/80\n",
      "36s - loss: 0.1273 - acc: 0.9503 - val_loss: 0.1232 - val_acc: 0.9521\n",
      "Epoch 51/80\n",
      "35s - loss: 0.1271 - acc: 0.9504 - val_loss: 0.1224 - val_acc: 0.9521\n",
      "Epoch 52/80\n",
      "34s - loss: 0.1270 - acc: 0.9506 - val_loss: 0.1225 - val_acc: 0.9522\n",
      "Epoch 53/80\n",
      "35s - loss: 0.1263 - acc: 0.9506 - val_loss: 0.1223 - val_acc: 0.9524\n",
      "Epoch 54/80\n",
      "34s - loss: 0.1264 - acc: 0.9506 - val_loss: 0.1223 - val_acc: 0.9522\n",
      "Epoch 55/80\n",
      "34s - loss: 0.1261 - acc: 0.9509 - val_loss: 0.1221 - val_acc: 0.9524\n",
      "Epoch 56/80\n",
      "35s - loss: 0.1259 - acc: 0.9508 - val_loss: 0.1222 - val_acc: 0.9524\n",
      "Epoch 57/80\n",
      "35s - loss: 0.1265 - acc: 0.9504 - val_loss: 0.1223 - val_acc: 0.9523\n",
      "Epoch 58/80\n",
      "33s - loss: 0.1260 - acc: 0.9514 - val_loss: 0.1239 - val_acc: 0.9518\n",
      "Epoch 59/80\n",
      "35s - loss: 0.1257 - acc: 0.9510 - val_loss: 0.1221 - val_acc: 0.9524\n",
      "Epoch 60/80\n",
      "35s - loss: 0.1260 - acc: 0.9511 - val_loss: 0.1225 - val_acc: 0.9522\n",
      "Epoch 61/80\n",
      "32s - loss: 0.1258 - acc: 0.9508 - val_loss: 0.1219 - val_acc: 0.9526\n",
      "Epoch 62/80\n",
      "32s - loss: 0.1253 - acc: 0.9512 - val_loss: 0.1229 - val_acc: 0.9520\n",
      "Epoch 63/80\n",
      "32s - loss: 0.1261 - acc: 0.9508 - val_loss: 0.1234 - val_acc: 0.9518\n",
      "Epoch 64/80\n",
      "32s - loss: 0.1252 - acc: 0.9510 - val_loss: 0.1220 - val_acc: 0.9527\n",
      "Epoch 65/80\n",
      "32s - loss: 0.1256 - acc: 0.9508 - val_loss: 0.1221 - val_acc: 0.9524\n",
      "Epoch 66/80\n",
      "32s - loss: 0.1249 - acc: 0.9513 - val_loss: 0.1216 - val_acc: 0.9527\n",
      "Epoch 67/80\n",
      "32s - loss: 0.1253 - acc: 0.9511 - val_loss: 0.1221 - val_acc: 0.9523\n",
      "Epoch 68/80\n",
      "32s - loss: 0.1247 - acc: 0.9515 - val_loss: 0.1216 - val_acc: 0.9525\n",
      "Epoch 69/80\n",
      "32s - loss: 0.1247 - acc: 0.9514 - val_loss: 0.1217 - val_acc: 0.9525\n",
      "Epoch 70/80\n",
      "32s - loss: 0.1244 - acc: 0.9515 - val_loss: 0.1213 - val_acc: 0.9528\n",
      "Epoch 71/80\n",
      "32s - loss: 0.1242 - acc: 0.9517 - val_loss: 0.1212 - val_acc: 0.9530\n",
      "Epoch 72/80\n",
      "32s - loss: 0.1251 - acc: 0.9511 - val_loss: 0.1215 - val_acc: 0.9529\n",
      "Epoch 73/80\n",
      "31s - loss: 0.1246 - acc: 0.9513 - val_loss: 0.1216 - val_acc: 0.9527\n",
      "Epoch 74/80\n",
      "32s - loss: 0.1249 - acc: 0.9512 - val_loss: 0.1213 - val_acc: 0.9526\n",
      "Epoch 75/80\n",
      "31s - loss: 0.1244 - acc: 0.9514 - val_loss: 0.1212 - val_acc: 0.9527\n",
      "Epoch 76/80\n",
      "32s - loss: 0.1241 - acc: 0.9517 - val_loss: 0.1220 - val_acc: 0.9522\n",
      "Epoch 77/80\n",
      "32s - loss: 0.1247 - acc: 0.9512 - val_loss: 0.1210 - val_acc: 0.9528\n",
      "Epoch 78/80\n",
      "32s - loss: 0.1237 - acc: 0.9517 - val_loss: 0.1206 - val_acc: 0.9531\n",
      "Epoch 79/80\n",
      "32s - loss: 0.1241 - acc: 0.9517 - val_loss: 0.1208 - val_acc: 0.9530\n",
      "Epoch 80/80\n",
      "32s - loss: 0.1245 - acc: 0.9516 - val_loss: 0.1208 - val_acc: 0.9530\n",
      "0.892917218043\n",
      "Start KFold number 3 from 6\n",
      "Split train:  33732 33732\n",
      "Split valid:  6747 6747\n",
      "Train on 33732 samples, validate on 6747 samples\n",
      "Epoch 1/5\n",
      "36s - loss: 0.4751 - acc: 0.8050 - val_loss: 0.3200 - val_acc: 0.9063\n",
      "Epoch 2/5\n",
      "32s - loss: 0.2001 - acc: 0.9294 - val_loss: 0.2074 - val_acc: 0.9182\n",
      "Epoch 3/5\n",
      "32s - loss: 0.1709 - acc: 0.9364 - val_loss: 0.1635 - val_acc: 0.9335\n",
      "Epoch 4/5\n",
      "32s - loss: 0.1560 - acc: 0.9407 - val_loss: 0.2362 - val_acc: 0.9142\n",
      "Epoch 5/5\n",
      "32s - loss: 0.1478 - acc: 0.9430 - val_loss: 0.1490 - val_acc: 0.9436\n",
      "Train on 33732 samples, validate on 6747 samples\n",
      "Epoch 1/80\n",
      "36s - loss: 0.1406 - acc: 0.9455 - val_loss: 0.1306 - val_acc: 0.9482\n",
      "Epoch 2/80\n",
      "32s - loss: 0.1390 - acc: 0.9462 - val_loss: 0.1291 - val_acc: 0.9488\n",
      "Epoch 3/80\n",
      "32s - loss: 0.1381 - acc: 0.9462 - val_loss: 0.1284 - val_acc: 0.9493\n",
      "Epoch 4/80\n",
      "33s - loss: 0.1372 - acc: 0.9469 - val_loss: 0.1278 - val_acc: 0.9498\n",
      "Epoch 5/80\n",
      "32s - loss: 0.1369 - acc: 0.9469 - val_loss: 0.1272 - val_acc: 0.9500\n",
      "Epoch 6/80\n",
      "33s - loss: 0.1366 - acc: 0.9471 - val_loss: 0.1272 - val_acc: 0.9499\n",
      "Epoch 7/80\n",
      "33s - loss: 0.1364 - acc: 0.9470 - val_loss: 0.1271 - val_acc: 0.9499\n",
      "Epoch 8/80\n",
      "32s - loss: 0.1361 - acc: 0.9472 - val_loss: 0.1264 - val_acc: 0.9506\n",
      "Epoch 9/80\n",
      "32s - loss: 0.1358 - acc: 0.9474 - val_loss: 0.1263 - val_acc: 0.9505\n",
      "Epoch 10/80\n",
      "33s - loss: 0.1352 - acc: 0.9477 - val_loss: 0.1262 - val_acc: 0.9503\n",
      "Epoch 11/80\n",
      "33s - loss: 0.1347 - acc: 0.9478 - val_loss: 0.1259 - val_acc: 0.9505\n",
      "Epoch 12/80\n",
      "33s - loss: 0.1348 - acc: 0.9479 - val_loss: 0.1254 - val_acc: 0.9509\n",
      "Epoch 13/80\n",
      "33s - loss: 0.1345 - acc: 0.9477 - val_loss: 0.1256 - val_acc: 0.9508\n",
      "Epoch 14/80\n",
      "31s - loss: 0.1344 - acc: 0.9479 - val_loss: 0.1253 - val_acc: 0.9510\n",
      "Epoch 15/80\n",
      "32s - loss: 0.1344 - acc: 0.9482 - val_loss: 0.1251 - val_acc: 0.9511\n",
      "Epoch 16/80\n",
      "32s - loss: 0.1336 - acc: 0.9482 - val_loss: 0.1253 - val_acc: 0.9510\n",
      "Epoch 17/80\n",
      "32s - loss: 0.1336 - acc: 0.9484 - val_loss: 0.1253 - val_acc: 0.9511\n",
      "Epoch 18/80\n",
      "32s - loss: 0.1334 - acc: 0.9484 - val_loss: 0.1248 - val_acc: 0.9513\n",
      "Epoch 19/80\n",
      "32s - loss: 0.1332 - acc: 0.9486 - val_loss: 0.1247 - val_acc: 0.9512\n",
      "Epoch 20/80\n",
      "32s - loss: 0.1334 - acc: 0.9482 - val_loss: 0.1249 - val_acc: 0.9513\n",
      "Epoch 21/80\n",
      "32s - loss: 0.1328 - acc: 0.9484 - val_loss: 0.1245 - val_acc: 0.9514\n",
      "Epoch 22/80\n",
      "32s - loss: 0.1325 - acc: 0.9489 - val_loss: 0.1243 - val_acc: 0.9515\n",
      "Epoch 23/80\n",
      "32s - loss: 0.1324 - acc: 0.9487 - val_loss: 0.1244 - val_acc: 0.9514\n",
      "Epoch 24/80\n",
      "32s - loss: 0.1325 - acc: 0.9488 - val_loss: 0.1241 - val_acc: 0.9516\n",
      "Epoch 25/80\n",
      "32s - loss: 0.1322 - acc: 0.9488 - val_loss: 0.1240 - val_acc: 0.9519\n",
      "Epoch 26/80\n",
      "32s - loss: 0.1315 - acc: 0.9488 - val_loss: 0.1239 - val_acc: 0.9518\n",
      "Epoch 27/80\n",
      "32s - loss: 0.1317 - acc: 0.9492 - val_loss: 0.1239 - val_acc: 0.9518\n",
      "Epoch 28/80\n",
      "34s - loss: 0.1317 - acc: 0.9489 - val_loss: 0.1234 - val_acc: 0.9520\n",
      "Epoch 29/80\n",
      "32s - loss: 0.1313 - acc: 0.9490 - val_loss: 0.1239 - val_acc: 0.9517\n",
      "Epoch 30/80\n",
      "33s - loss: 0.1309 - acc: 0.9493 - val_loss: 0.1236 - val_acc: 0.9518\n",
      "Epoch 31/80\n",
      "36s - loss: 0.1307 - acc: 0.9491 - val_loss: 0.1236 - val_acc: 0.9520\n",
      "Epoch 32/80\n",
      "33s - loss: 0.1303 - acc: 0.9493 - val_loss: 0.1238 - val_acc: 0.9519\n",
      "Epoch 33/80\n",
      "35s - loss: 0.1307 - acc: 0.9496 - val_loss: 0.1237 - val_acc: 0.9520\n",
      "Epoch 34/80\n",
      "35s - loss: 0.1311 - acc: 0.9490 - val_loss: 0.1239 - val_acc: 0.9517\n",
      "Epoch 35/80\n",
      "35s - loss: 0.1307 - acc: 0.9494 - val_loss: 0.1231 - val_acc: 0.9521\n",
      "Epoch 36/80\n",
      "37s - loss: 0.1302 - acc: 0.9494 - val_loss: 0.1233 - val_acc: 0.9522\n",
      "Epoch 37/80\n",
      "36s - loss: 0.1298 - acc: 0.9495 - val_loss: 0.1232 - val_acc: 0.9520\n",
      "Epoch 38/80\n",
      "37s - loss: 0.1299 - acc: 0.9497 - val_loss: 0.1233 - val_acc: 0.9521\n",
      "Epoch 39/80\n",
      "36s - loss: 0.1301 - acc: 0.9496 - val_loss: 0.1226 - val_acc: 0.9525\n",
      "Epoch 40/80\n",
      "34s - loss: 0.1296 - acc: 0.9497 - val_loss: 0.1228 - val_acc: 0.9524\n",
      "Epoch 41/80\n",
      "34s - loss: 0.1294 - acc: 0.9498 - val_loss: 0.1233 - val_acc: 0.9521\n",
      "Epoch 42/80\n",
      "35s - loss: 0.1293 - acc: 0.9498 - val_loss: 0.1224 - val_acc: 0.9526\n",
      "Epoch 43/80\n",
      "32s - loss: 0.1292 - acc: 0.9496 - val_loss: 0.1232 - val_acc: 0.9522\n",
      "Epoch 44/80\n",
      "32s - loss: 0.1291 - acc: 0.9500 - val_loss: 0.1232 - val_acc: 0.9519\n",
      "Epoch 45/80\n",
      "32s - loss: 0.1291 - acc: 0.9498 - val_loss: 0.1224 - val_acc: 0.9524\n",
      "Epoch 46/80\n",
      "32s - loss: 0.1287 - acc: 0.9502 - val_loss: 0.1223 - val_acc: 0.9525\n",
      "Epoch 47/80\n",
      "32s - loss: 0.1289 - acc: 0.9502 - val_loss: 0.1220 - val_acc: 0.9528\n",
      "Epoch 48/80\n",
      "32s - loss: 0.1285 - acc: 0.9501 - val_loss: 0.1220 - val_acc: 0.9526\n",
      "Epoch 49/80\n",
      "32s - loss: 0.1282 - acc: 0.9501 - val_loss: 0.1222 - val_acc: 0.9526\n",
      "Epoch 50/80\n",
      "32s - loss: 0.1283 - acc: 0.9502 - val_loss: 0.1218 - val_acc: 0.9530\n",
      "Epoch 51/80\n",
      "32s - loss: 0.1281 - acc: 0.9502 - val_loss: 0.1220 - val_acc: 0.9527\n",
      "Epoch 52/80\n",
      "32s - loss: 0.1279 - acc: 0.9505 - val_loss: 0.1220 - val_acc: 0.9527\n",
      "Epoch 53/80\n",
      "32s - loss: 0.1276 - acc: 0.9504 - val_loss: 0.1222 - val_acc: 0.9528\n",
      "Epoch 54/80\n",
      "32s - loss: 0.1280 - acc: 0.9502 - val_loss: 0.1218 - val_acc: 0.9528\n",
      "Epoch 55/80\n",
      "32s - loss: 0.1279 - acc: 0.9503 - val_loss: 0.1217 - val_acc: 0.9529\n",
      "Epoch 56/80\n",
      "32s - loss: 0.1273 - acc: 0.9503 - val_loss: 0.1220 - val_acc: 0.9529\n",
      "Epoch 57/80\n",
      "32s - loss: 0.1274 - acc: 0.9506 - val_loss: 0.1217 - val_acc: 0.9530\n",
      "Epoch 58/80\n",
      "32s - loss: 0.1277 - acc: 0.9505 - val_loss: 0.1223 - val_acc: 0.9528\n",
      "Epoch 59/80\n",
      "32s - loss: 0.1274 - acc: 0.9506 - val_loss: 0.1217 - val_acc: 0.9530\n",
      "Epoch 60/80\n",
      "32s - loss: 0.1272 - acc: 0.9503 - val_loss: 0.1219 - val_acc: 0.9529\n",
      "Epoch 61/80\n",
      "32s - loss: 0.1266 - acc: 0.9510 - val_loss: 0.1221 - val_acc: 0.9527\n",
      "Epoch 62/80\n",
      "32s - loss: 0.1270 - acc: 0.9506 - val_loss: 0.1214 - val_acc: 0.9531\n",
      "Epoch 63/80\n",
      "32s - loss: 0.1266 - acc: 0.9506 - val_loss: 0.1216 - val_acc: 0.9531\n",
      "Epoch 64/80\n",
      "32s - loss: 0.1269 - acc: 0.9506 - val_loss: 0.1217 - val_acc: 0.9531\n",
      "Epoch 65/80\n",
      "32s - loss: 0.1267 - acc: 0.9507 - val_loss: 0.1213 - val_acc: 0.9532\n",
      "Epoch 66/80\n",
      "32s - loss: 0.1262 - acc: 0.9510 - val_loss: 0.1210 - val_acc: 0.9533\n",
      "Epoch 67/80\n",
      "32s - loss: 0.1264 - acc: 0.9509 - val_loss: 0.1215 - val_acc: 0.9532\n",
      "Epoch 68/80\n",
      "33s - loss: 0.1265 - acc: 0.9506 - val_loss: 0.1215 - val_acc: 0.9531\n",
      "Epoch 69/80\n",
      "32s - loss: 0.1267 - acc: 0.9507 - val_loss: 0.1215 - val_acc: 0.9531\n",
      "Epoch 70/80\n",
      "32s - loss: 0.1262 - acc: 0.9512 - val_loss: 0.1217 - val_acc: 0.9530\n",
      "Epoch 71/80\n",
      "32s - loss: 0.1256 - acc: 0.9510 - val_loss: 0.1214 - val_acc: 0.9531\n",
      "Epoch 72/80\n",
      "32s - loss: 0.1258 - acc: 0.9510 - val_loss: 0.1212 - val_acc: 0.9533\n",
      "Epoch 73/80\n",
      "32s - loss: 0.1258 - acc: 0.9511 - val_loss: 0.1209 - val_acc: 0.9534\n",
      "Epoch 74/80\n",
      "32s - loss: 0.1255 - acc: 0.9512 - val_loss: 0.1209 - val_acc: 0.9534\n",
      "Epoch 75/80\n",
      "32s - loss: 0.1253 - acc: 0.9511 - val_loss: 0.1214 - val_acc: 0.9532\n",
      "Epoch 76/80\n",
      "32s - loss: 0.1253 - acc: 0.9515 - val_loss: 0.1212 - val_acc: 0.9534\n",
      "Epoch 77/80\n",
      "32s - loss: 0.1248 - acc: 0.9515 - val_loss: 0.1203 - val_acc: 0.9535\n",
      "Epoch 78/80\n",
      "32s - loss: 0.1254 - acc: 0.9511 - val_loss: 0.1212 - val_acc: 0.9534\n",
      "Epoch 79/80\n",
      "32s - loss: 0.1253 - acc: 0.9510 - val_loss: 0.1211 - val_acc: 0.9532\n",
      "Epoch 80/80\n",
      "32s - loss: 0.1250 - acc: 0.9514 - val_loss: 0.1217 - val_acc: 0.9528\n",
      "0.893018062268\n",
      "Start KFold number 4 from 6\n",
      "Split train:  33733 33733\n",
      "Split valid:  6746 6746\n",
      "Train on 33733 samples, validate on 6746 samples\n",
      "Epoch 1/5\n",
      "37s - loss: 0.4749 - acc: 0.7999 - val_loss: 0.3087 - val_acc: 0.9083\n",
      "Epoch 2/5\n",
      "32s - loss: 0.1929 - acc: 0.9306 - val_loss: 0.2118 - val_acc: 0.9185\n",
      "Epoch 3/5\n",
      "33s - loss: 0.1663 - acc: 0.9377 - val_loss: 0.1712 - val_acc: 0.9320\n",
      "Epoch 4/5\n",
      "33s - loss: 0.1535 - acc: 0.9415 - val_loss: 0.1622 - val_acc: 0.9390\n",
      "Epoch 5/5\n",
      "33s - loss: 0.1477 - acc: 0.9433 - val_loss: 0.1960 - val_acc: 0.9246\n",
      "Train on 33733 samples, validate on 6746 samples\n",
      "Epoch 1/80\n",
      "38s - loss: 0.1407 - acc: 0.9457 - val_loss: 0.1332 - val_acc: 0.9477\n",
      "Epoch 2/80\n",
      "34s - loss: 0.1388 - acc: 0.9466 - val_loss: 0.1319 - val_acc: 0.9481\n",
      "Epoch 3/80\n",
      "33s - loss: 0.1379 - acc: 0.9468 - val_loss: 0.1313 - val_acc: 0.9483\n",
      "Epoch 4/80\n",
      "33s - loss: 0.1375 - acc: 0.9471 - val_loss: 0.1305 - val_acc: 0.9484\n",
      "Epoch 5/80\n",
      "32s - loss: 0.1368 - acc: 0.9473 - val_loss: 0.1305 - val_acc: 0.9484\n",
      "Epoch 6/80\n",
      "34s - loss: 0.1359 - acc: 0.9475 - val_loss: 0.1301 - val_acc: 0.9486\n",
      "Epoch 7/80\n",
      "33s - loss: 0.1351 - acc: 0.9479 - val_loss: 0.1297 - val_acc: 0.9488\n",
      "Epoch 8/80\n",
      "33s - loss: 0.1352 - acc: 0.9479 - val_loss: 0.1293 - val_acc: 0.9490\n",
      "Epoch 9/80\n",
      "33s - loss: 0.1349 - acc: 0.9479 - val_loss: 0.1290 - val_acc: 0.9491\n",
      "Epoch 10/80\n",
      "33s - loss: 0.1346 - acc: 0.9482 - val_loss: 0.1301 - val_acc: 0.9488\n",
      "Epoch 11/80\n",
      "33s - loss: 0.1348 - acc: 0.9481 - val_loss: 0.1289 - val_acc: 0.9492\n",
      "Epoch 12/80\n",
      "33s - loss: 0.1345 - acc: 0.9481 - val_loss: 0.1283 - val_acc: 0.9493\n",
      "Epoch 13/80\n",
      "33s - loss: 0.1338 - acc: 0.9481 - val_loss: 0.1298 - val_acc: 0.9489\n",
      "Epoch 14/80\n",
      "33s - loss: 0.1333 - acc: 0.9486 - val_loss: 0.1285 - val_acc: 0.9493\n",
      "Epoch 15/80\n",
      "33s - loss: 0.1335 - acc: 0.9486 - val_loss: 0.1289 - val_acc: 0.9493\n",
      "Epoch 16/80\n",
      "33s - loss: 0.1336 - acc: 0.9484 - val_loss: 0.1283 - val_acc: 0.9494\n",
      "Epoch 17/80\n",
      "33s - loss: 0.1334 - acc: 0.9486 - val_loss: 0.1277 - val_acc: 0.9497\n",
      "Epoch 18/80\n",
      "33s - loss: 0.1335 - acc: 0.9485 - val_loss: 0.1282 - val_acc: 0.9496\n",
      "Epoch 19/80\n",
      "33s - loss: 0.1327 - acc: 0.9491 - val_loss: 0.1277 - val_acc: 0.9496\n",
      "Epoch 20/80\n",
      "33s - loss: 0.1323 - acc: 0.9488 - val_loss: 0.1284 - val_acc: 0.9496\n",
      "Epoch 21/80\n",
      "34s - loss: 0.1320 - acc: 0.9492 - val_loss: 0.1278 - val_acc: 0.9495\n",
      "Epoch 22/80\n",
      "34s - loss: 0.1315 - acc: 0.9494 - val_loss: 0.1285 - val_acc: 0.9496\n",
      "Epoch 23/80\n",
      "33s - loss: 0.1321 - acc: 0.9492 - val_loss: 0.1279 - val_acc: 0.9497\n",
      "Epoch 24/80\n",
      "33s - loss: 0.1318 - acc: 0.9492 - val_loss: 0.1278 - val_acc: 0.9496\n",
      "Epoch 25/80\n",
      "33s - loss: 0.1315 - acc: 0.9493 - val_loss: 0.1276 - val_acc: 0.9495\n",
      "Epoch 26/80\n",
      "33s - loss: 0.1316 - acc: 0.9491 - val_loss: 0.1273 - val_acc: 0.9497\n",
      "Epoch 27/80\n",
      "33s - loss: 0.1315 - acc: 0.9491 - val_loss: 0.1277 - val_acc: 0.9496\n",
      "Epoch 28/80\n",
      "33s - loss: 0.1313 - acc: 0.9496 - val_loss: 0.1269 - val_acc: 0.9497\n",
      "Epoch 29/80\n",
      "33s - loss: 0.1304 - acc: 0.9494 - val_loss: 0.1276 - val_acc: 0.9495\n",
      "Epoch 30/80\n",
      "33s - loss: 0.1306 - acc: 0.9496 - val_loss: 0.1270 - val_acc: 0.9497\n",
      "Epoch 31/80\n",
      "33s - loss: 0.1301 - acc: 0.9498 - val_loss: 0.1274 - val_acc: 0.9498\n",
      "Epoch 32/80\n",
      "33s - loss: 0.1301 - acc: 0.9497 - val_loss: 0.1266 - val_acc: 0.9499\n",
      "Epoch 33/80\n",
      "33s - loss: 0.1309 - acc: 0.9498 - val_loss: 0.1269 - val_acc: 0.9498\n",
      "Epoch 34/80\n",
      "34s - loss: 0.1301 - acc: 0.9497 - val_loss: 0.1262 - val_acc: 0.9500\n",
      "Epoch 35/80\n",
      "33s - loss: 0.1302 - acc: 0.9499 - val_loss: 0.1263 - val_acc: 0.9500\n",
      "Epoch 36/80\n",
      "33s - loss: 0.1305 - acc: 0.9497 - val_loss: 0.1265 - val_acc: 0.9500\n",
      "Epoch 37/80\n",
      "33s - loss: 0.1294 - acc: 0.9501 - val_loss: 0.1269 - val_acc: 0.9501\n",
      "Epoch 38/80\n",
      "33s - loss: 0.1297 - acc: 0.9499 - val_loss: 0.1263 - val_acc: 0.9502\n",
      "Epoch 39/80\n",
      "33s - loss: 0.1292 - acc: 0.9500 - val_loss: 0.1263 - val_acc: 0.9501\n",
      "Epoch 40/80\n",
      "33s - loss: 0.1292 - acc: 0.9501 - val_loss: 0.1265 - val_acc: 0.9501\n",
      "Epoch 41/80\n",
      "32s - loss: 0.1291 - acc: 0.9500 - val_loss: 0.1269 - val_acc: 0.9498\n",
      "Epoch 42/80\n",
      "33s - loss: 0.1293 - acc: 0.9501 - val_loss: 0.1265 - val_acc: 0.9500\n",
      "Epoch 43/80\n",
      "33s - loss: 0.1287 - acc: 0.9501 - val_loss: 0.1260 - val_acc: 0.9503\n",
      "Epoch 44/80\n",
      "33s - loss: 0.1286 - acc: 0.9502 - val_loss: 0.1256 - val_acc: 0.9503\n",
      "Epoch 45/80\n",
      "33s - loss: 0.1283 - acc: 0.9503 - val_loss: 0.1260 - val_acc: 0.9502\n",
      "Epoch 46/80\n",
      "33s - loss: 0.1283 - acc: 0.9504 - val_loss: 0.1260 - val_acc: 0.9502\n",
      "Epoch 47/80\n",
      "33s - loss: 0.1283 - acc: 0.9499 - val_loss: 0.1259 - val_acc: 0.9502\n",
      "Epoch 48/80\n",
      "33s - loss: 0.1280 - acc: 0.9506 - val_loss: 0.1262 - val_acc: 0.9503\n",
      "Epoch 49/80\n",
      "34s - loss: 0.1283 - acc: 0.9506 - val_loss: 0.1261 - val_acc: 0.9502\n",
      "Epoch 50/80\n",
      "33s - loss: 0.1282 - acc: 0.9502 - val_loss: 0.1258 - val_acc: 0.9504\n",
      "Epoch 51/80\n",
      "33s - loss: 0.1284 - acc: 0.9505 - val_loss: 0.1252 - val_acc: 0.9506\n",
      "Epoch 52/80\n",
      "33s - loss: 0.1277 - acc: 0.9507 - val_loss: 0.1264 - val_acc: 0.9502\n",
      "Epoch 53/80\n",
      "33s - loss: 0.1272 - acc: 0.9506 - val_loss: 0.1252 - val_acc: 0.9504\n",
      "Epoch 54/80\n",
      "33s - loss: 0.1268 - acc: 0.9511 - val_loss: 0.1252 - val_acc: 0.9505\n",
      "Epoch 55/80\n",
      "33s - loss: 0.1270 - acc: 0.9508 - val_loss: 0.1259 - val_acc: 0.9502\n",
      "Epoch 56/80\n",
      "33s - loss: 0.1271 - acc: 0.9508 - val_loss: 0.1255 - val_acc: 0.9505\n",
      "Epoch 57/80\n",
      "33s - loss: 0.1268 - acc: 0.9508 - val_loss: 0.1252 - val_acc: 0.9506\n",
      "Epoch 58/80\n",
      "33s - loss: 0.1274 - acc: 0.9508 - val_loss: 0.1259 - val_acc: 0.9502\n",
      "Epoch 59/80\n",
      "33s - loss: 0.1271 - acc: 0.9507 - val_loss: 0.1257 - val_acc: 0.9505\n",
      "Epoch 60/80\n",
      "34s - loss: 0.1268 - acc: 0.9510 - val_loss: 0.1249 - val_acc: 0.9505\n",
      "Epoch 61/80\n",
      "33s - loss: 0.1265 - acc: 0.9513 - val_loss: 0.1248 - val_acc: 0.9508\n",
      "Epoch 62/80\n",
      "33s - loss: 0.1265 - acc: 0.9512 - val_loss: 0.1253 - val_acc: 0.9504\n",
      "Epoch 63/80\n",
      "33s - loss: 0.1265 - acc: 0.9513 - val_loss: 0.1252 - val_acc: 0.9506\n",
      "Epoch 64/80\n",
      "34s - loss: 0.1261 - acc: 0.9511 - val_loss: 0.1251 - val_acc: 0.9508\n",
      "Epoch 65/80\n",
      "34s - loss: 0.1259 - acc: 0.9510 - val_loss: 0.1249 - val_acc: 0.9505\n",
      "Epoch 66/80\n",
      "33s - loss: 0.1259 - acc: 0.9512 - val_loss: 0.1255 - val_acc: 0.9506\n",
      "Epoch 67/80\n",
      "33s - loss: 0.1261 - acc: 0.9514 - val_loss: 0.1250 - val_acc: 0.9507\n",
      "Epoch 68/80\n",
      "32s - loss: 0.1257 - acc: 0.9515 - val_loss: 0.1248 - val_acc: 0.9507\n",
      "Epoch 69/80\n",
      "33s - loss: 0.1260 - acc: 0.9513 - val_loss: 0.1246 - val_acc: 0.9510\n",
      "Epoch 70/80\n",
      "33s - loss: 0.1265 - acc: 0.9513 - val_loss: 0.1249 - val_acc: 0.9508\n",
      "Epoch 71/80\n",
      "33s - loss: 0.1256 - acc: 0.9517 - val_loss: 0.1251 - val_acc: 0.9508\n",
      "Epoch 72/80\n",
      "33s - loss: 0.1255 - acc: 0.9515 - val_loss: 0.1247 - val_acc: 0.9507\n",
      "Epoch 73/80\n",
      "33s - loss: 0.1256 - acc: 0.9513 - val_loss: 0.1253 - val_acc: 0.9508\n",
      "Epoch 74/80\n",
      "33s - loss: 0.1253 - acc: 0.9515 - val_loss: 0.1249 - val_acc: 0.9507\n",
      "Epoch 75/80\n",
      "33s - loss: 0.1250 - acc: 0.9515 - val_loss: 0.1250 - val_acc: 0.9509\n",
      "Epoch 76/80\n",
      "33s - loss: 0.1253 - acc: 0.9515 - val_loss: 0.1244 - val_acc: 0.9510\n",
      "Epoch 77/80\n",
      "33s - loss: 0.1254 - acc: 0.9512 - val_loss: 0.1254 - val_acc: 0.9506\n",
      "Epoch 78/80\n",
      "33s - loss: 0.1250 - acc: 0.9517 - val_loss: 0.1248 - val_acc: 0.9512\n",
      "Epoch 79/80\n",
      "34s - loss: 0.1248 - acc: 0.9518 - val_loss: 0.1243 - val_acc: 0.9508\n",
      "Epoch 80/80\n",
      "33s - loss: 0.1249 - acc: 0.9517 - val_loss: 0.1248 - val_acc: 0.9510\n",
      "0.892381070803\n",
      "Start KFold number 5 from 6\n",
      "Split train:  33733 33733\n",
      "Split valid:  6746 6746\n",
      "Train on 33733 samples, validate on 6746 samples\n",
      "Epoch 1/5\n",
      "38s - loss: 0.4675 - acc: 0.8129 - val_loss: 0.2977 - val_acc: 0.9063\n",
      "Epoch 2/5\n",
      "33s - loss: 0.1955 - acc: 0.9299 - val_loss: 0.2226 - val_acc: 0.9191\n",
      "Epoch 3/5\n",
      "33s - loss: 0.1707 - acc: 0.9358 - val_loss: 0.1703 - val_acc: 0.9377\n",
      "Epoch 4/5\n",
      "33s - loss: 0.1557 - acc: 0.9405 - val_loss: 0.1604 - val_acc: 0.9388\n",
      "Epoch 5/5\n",
      "33s - loss: 0.1496 - acc: 0.9424 - val_loss: 0.1475 - val_acc: 0.9420\n",
      "Train on 33733 samples, validate on 6746 samples\n",
      "Epoch 1/80\n",
      "39s - loss: 0.1425 - acc: 0.9452 - val_loss: 0.1319 - val_acc: 0.9490\n",
      "Epoch 2/80\n",
      "33s - loss: 0.1404 - acc: 0.9462 - val_loss: 0.1299 - val_acc: 0.9495\n",
      "Epoch 3/80\n",
      "33s - loss: 0.1394 - acc: 0.9459 - val_loss: 0.1292 - val_acc: 0.9498\n",
      "Epoch 4/80\n",
      "33s - loss: 0.1391 - acc: 0.9466 - val_loss: 0.1297 - val_acc: 0.9495\n",
      "Epoch 5/80\n",
      "33s - loss: 0.1376 - acc: 0.9466 - val_loss: 0.1283 - val_acc: 0.9500\n",
      "Epoch 6/80\n",
      "33s - loss: 0.1370 - acc: 0.9470 - val_loss: 0.1277 - val_acc: 0.9504\n",
      "Epoch 7/80\n",
      "34s - loss: 0.1373 - acc: 0.9471 - val_loss: 0.1285 - val_acc: 0.9500\n",
      "Epoch 8/80\n",
      "33s - loss: 0.1368 - acc: 0.9471 - val_loss: 0.1270 - val_acc: 0.9506\n",
      "Epoch 9/80\n",
      "33s - loss: 0.1369 - acc: 0.9470 - val_loss: 0.1275 - val_acc: 0.9502\n",
      "Epoch 10/80\n",
      "34s - loss: 0.1366 - acc: 0.9472 - val_loss: 0.1262 - val_acc: 0.9507\n",
      "Epoch 11/80\n",
      "33s - loss: 0.1363 - acc: 0.9474 - val_loss: 0.1269 - val_acc: 0.9505\n",
      "Epoch 12/80\n",
      "33s - loss: 0.1360 - acc: 0.9474 - val_loss: 0.1269 - val_acc: 0.9507\n",
      "Epoch 13/80\n",
      "34s - loss: 0.1353 - acc: 0.9480 - val_loss: 0.1260 - val_acc: 0.9509\n",
      "Epoch 14/80\n",
      "33s - loss: 0.1356 - acc: 0.9475 - val_loss: 0.1257 - val_acc: 0.9509\n",
      "Epoch 15/80\n",
      "33s - loss: 0.1355 - acc: 0.9477 - val_loss: 0.1258 - val_acc: 0.9509\n",
      "Epoch 16/80\n",
      "33s - loss: 0.1346 - acc: 0.9480 - val_loss: 0.1262 - val_acc: 0.9507\n",
      "Epoch 17/80\n",
      "34s - loss: 0.1341 - acc: 0.9481 - val_loss: 0.1256 - val_acc: 0.9510\n",
      "Epoch 18/80\n",
      "34s - loss: 0.1341 - acc: 0.9481 - val_loss: 0.1250 - val_acc: 0.9512\n",
      "Epoch 19/80\n",
      "33s - loss: 0.1335 - acc: 0.9488 - val_loss: 0.1250 - val_acc: 0.9514\n",
      "Epoch 20/80\n",
      "34s - loss: 0.1337 - acc: 0.9484 - val_loss: 0.1248 - val_acc: 0.9513\n",
      "Epoch 21/80\n",
      "33s - loss: 0.1336 - acc: 0.9481 - val_loss: 0.1255 - val_acc: 0.9513\n",
      "Epoch 22/80\n",
      "33s - loss: 0.1336 - acc: 0.9483 - val_loss: 0.1248 - val_acc: 0.9513\n",
      "Epoch 23/80\n",
      "34s - loss: 0.1329 - acc: 0.9486 - val_loss: 0.1254 - val_acc: 0.9511\n",
      "Epoch 24/80\n",
      "34s - loss: 0.1329 - acc: 0.9486 - val_loss: 0.1246 - val_acc: 0.9514\n",
      "Epoch 25/80\n",
      "33s - loss: 0.1331 - acc: 0.9484 - val_loss: 0.1246 - val_acc: 0.9515\n",
      "Epoch 26/80\n",
      "33s - loss: 0.1327 - acc: 0.9487 - val_loss: 0.1247 - val_acc: 0.9515\n",
      "Epoch 27/80\n",
      "34s - loss: 0.1322 - acc: 0.9487 - val_loss: 0.1240 - val_acc: 0.9520\n",
      "Epoch 28/80\n",
      "33s - loss: 0.1322 - acc: 0.9488 - val_loss: 0.1243 - val_acc: 0.9515\n",
      "Epoch 29/80\n",
      "33s - loss: 0.1323 - acc: 0.9486 - val_loss: 0.1245 - val_acc: 0.9517\n",
      "Epoch 30/80\n",
      "33s - loss: 0.1318 - acc: 0.9490 - val_loss: 0.1243 - val_acc: 0.9517\n",
      "Epoch 31/80\n",
      "33s - loss: 0.1315 - acc: 0.9490 - val_loss: 0.1240 - val_acc: 0.9518\n",
      "Epoch 32/80\n",
      "34s - loss: 0.1309 - acc: 0.9491 - val_loss: 0.1239 - val_acc: 0.9520\n",
      "Epoch 33/80\n",
      "34s - loss: 0.1308 - acc: 0.9491 - val_loss: 0.1242 - val_acc: 0.9516\n",
      "Epoch 34/80\n",
      "34s - loss: 0.1314 - acc: 0.9492 - val_loss: 0.1238 - val_acc: 0.9519\n",
      "Epoch 35/80\n",
      "33s - loss: 0.1311 - acc: 0.9494 - val_loss: 0.1248 - val_acc: 0.9511\n",
      "Epoch 36/80\n",
      "33s - loss: 0.1308 - acc: 0.9497 - val_loss: 0.1245 - val_acc: 0.9515\n",
      "Epoch 37/80\n",
      "34s - loss: 0.1309 - acc: 0.9492 - val_loss: 0.1238 - val_acc: 0.9519\n",
      "Epoch 38/80\n",
      "34s - loss: 0.1302 - acc: 0.9494 - val_loss: 0.1233 - val_acc: 0.9521\n",
      "Epoch 39/80\n",
      "34s - loss: 0.1304 - acc: 0.9497 - val_loss: 0.1239 - val_acc: 0.9519\n",
      "Epoch 40/80\n",
      "33s - loss: 0.1304 - acc: 0.9493 - val_loss: 0.1233 - val_acc: 0.9521\n",
      "Epoch 41/80\n",
      "33s - loss: 0.1305 - acc: 0.9493 - val_loss: 0.1226 - val_acc: 0.9523\n",
      "Epoch 42/80\n",
      "34s - loss: 0.1300 - acc: 0.9497 - val_loss: 0.1232 - val_acc: 0.9520\n",
      "Epoch 43/80\n",
      "34s - loss: 0.1303 - acc: 0.9496 - val_loss: 0.1234 - val_acc: 0.9521\n",
      "Epoch 44/80\n",
      "33s - loss: 0.1294 - acc: 0.9499 - val_loss: 0.1228 - val_acc: 0.9523\n",
      "Epoch 45/80\n",
      "34s - loss: 0.1295 - acc: 0.9499 - val_loss: 0.1232 - val_acc: 0.9522\n",
      "Epoch 46/80\n",
      "34s - loss: 0.1293 - acc: 0.9499 - val_loss: 0.1229 - val_acc: 0.9524\n",
      "Epoch 47/80\n",
      "33s - loss: 0.1293 - acc: 0.9500 - val_loss: 0.1232 - val_acc: 0.9521\n",
      "Epoch 48/80\n",
      "33s - loss: 0.1295 - acc: 0.9501 - val_loss: 0.1233 - val_acc: 0.9524\n",
      "Epoch 49/80\n",
      "34s - loss: 0.1288 - acc: 0.9500 - val_loss: 0.1224 - val_acc: 0.9524\n",
      "Epoch 50/80\n",
      "33s - loss: 0.1294 - acc: 0.9501 - val_loss: 0.1225 - val_acc: 0.9523\n",
      "Epoch 51/80\n",
      "33s - loss: 0.1292 - acc: 0.9499 - val_loss: 0.1228 - val_acc: 0.9522\n",
      "Epoch 52/80\n",
      "33s - loss: 0.1289 - acc: 0.9500 - val_loss: 0.1228 - val_acc: 0.9522\n",
      "Epoch 53/80\n",
      "33s - loss: 0.1285 - acc: 0.9503 - val_loss: 0.1224 - val_acc: 0.9523\n",
      "Epoch 54/80\n",
      "33s - loss: 0.1291 - acc: 0.9497 - val_loss: 0.1234 - val_acc: 0.9521\n",
      "Epoch 55/80\n",
      "34s - loss: 0.1284 - acc: 0.9501 - val_loss: 0.1221 - val_acc: 0.9526\n",
      "Epoch 56/80\n",
      "33s - loss: 0.1281 - acc: 0.9503 - val_loss: 0.1220 - val_acc: 0.9527\n",
      "Epoch 57/80\n",
      "33s - loss: 0.1282 - acc: 0.9503 - val_loss: 0.1222 - val_acc: 0.9527\n",
      "Epoch 58/80\n",
      "33s - loss: 0.1280 - acc: 0.9503 - val_loss: 0.1220 - val_acc: 0.9528\n",
      "Epoch 59/80\n",
      "34s - loss: 0.1280 - acc: 0.9502 - val_loss: 0.1219 - val_acc: 0.9527\n",
      "Epoch 60/80\n",
      "33s - loss: 0.1275 - acc: 0.9504 - val_loss: 0.1223 - val_acc: 0.9525\n",
      "Epoch 61/80\n",
      "33s - loss: 0.1276 - acc: 0.9506 - val_loss: 0.1214 - val_acc: 0.9529\n",
      "Epoch 62/80\n",
      "34s - loss: 0.1273 - acc: 0.9507 - val_loss: 0.1225 - val_acc: 0.9522\n",
      "Epoch 63/80\n",
      "33s - loss: 0.1274 - acc: 0.9506 - val_loss: 0.1217 - val_acc: 0.9529\n",
      "Epoch 64/80\n",
      "33s - loss: 0.1278 - acc: 0.9505 - val_loss: 0.1216 - val_acc: 0.9527\n",
      "Epoch 65/80\n",
      "34s - loss: 0.1274 - acc: 0.9509 - val_loss: 0.1216 - val_acc: 0.9525\n",
      "Epoch 66/80\n",
      "34s - loss: 0.1270 - acc: 0.9505 - val_loss: 0.1211 - val_acc: 0.9528\n",
      "Epoch 67/80\n",
      "34s - loss: 0.1272 - acc: 0.9505 - val_loss: 0.1214 - val_acc: 0.9527\n",
      "Epoch 68/80\n",
      "33s - loss: 0.1260 - acc: 0.9512 - val_loss: 0.1219 - val_acc: 0.9525\n",
      "Epoch 69/80\n",
      "34s - loss: 0.1266 - acc: 0.9507 - val_loss: 0.1214 - val_acc: 0.9527\n",
      "Epoch 70/80\n",
      "33s - loss: 0.1265 - acc: 0.9509 - val_loss: 0.1212 - val_acc: 0.9528\n",
      "Epoch 71/80\n",
      "33s - loss: 0.1264 - acc: 0.9510 - val_loss: 0.1212 - val_acc: 0.9529\n",
      "Epoch 72/80\n",
      "33s - loss: 0.1261 - acc: 0.9512 - val_loss: 0.1219 - val_acc: 0.9527\n",
      "Epoch 73/80\n",
      "33s - loss: 0.1264 - acc: 0.9510 - val_loss: 0.1209 - val_acc: 0.9529\n",
      "Epoch 74/80\n",
      "34s - loss: 0.1259 - acc: 0.9511 - val_loss: 0.1205 - val_acc: 0.9530\n",
      "Epoch 75/80\n",
      "34s - loss: 0.1265 - acc: 0.9511 - val_loss: 0.1208 - val_acc: 0.9530\n",
      "Epoch 76/80\n",
      "34s - loss: 0.1259 - acc: 0.9512 - val_loss: 0.1211 - val_acc: 0.9530\n",
      "Epoch 77/80\n",
      "33s - loss: 0.1258 - acc: 0.9513 - val_loss: 0.1208 - val_acc: 0.9532\n",
      "Epoch 78/80\n",
      "33s - loss: 0.1262 - acc: 0.9511 - val_loss: 0.1211 - val_acc: 0.9531\n",
      "Epoch 79/80\n",
      "33s - loss: 0.1251 - acc: 0.9517 - val_loss: 0.1208 - val_acc: 0.9534\n",
      "Epoch 80/80\n",
      "34s - loss: 0.1255 - acc: 0.9514 - val_loss: 0.1208 - val_acc: 0.9531\n",
      "0.896669254284\n",
      "Start KFold number 6 from 6\n",
      "Split train:  33733 33733\n",
      "Split valid:  6746 6746\n",
      "Train on 33733 samples, validate on 6746 samples\n",
      "Epoch 1/5\n",
      "39s - loss: 0.4715 - acc: 0.8044 - val_loss: 0.2695 - val_acc: 0.9093\n",
      "Epoch 2/5\n",
      "33s - loss: 0.1982 - acc: 0.9288 - val_loss: 0.2477 - val_acc: 0.9170\n",
      "Epoch 3/5\n",
      "33s - loss: 0.1715 - acc: 0.9358 - val_loss: 0.1740 - val_acc: 0.9330\n",
      "Epoch 4/5\n",
      "33s - loss: 0.1580 - acc: 0.9397 - val_loss: 0.1519 - val_acc: 0.9404\n",
      "Epoch 5/5\n",
      "34s - loss: 0.1495 - acc: 0.9425 - val_loss: 0.2179 - val_acc: 0.9254\n",
      "Train on 33733 samples, validate on 6746 samples\n",
      "Epoch 1/80\n",
      "39s - loss: 0.1423 - acc: 0.9446 - val_loss: 0.1330 - val_acc: 0.9474\n",
      "Epoch 2/80\n",
      "33s - loss: 0.1403 - acc: 0.9456 - val_loss: 0.1319 - val_acc: 0.9480\n",
      "Epoch 3/80\n",
      "34s - loss: 0.1402 - acc: 0.9457 - val_loss: 0.1312 - val_acc: 0.9482\n",
      "Epoch 4/80\n",
      "34s - loss: 0.1393 - acc: 0.9460 - val_loss: 0.1312 - val_acc: 0.9482\n",
      "Epoch 5/80\n",
      "34s - loss: 0.1388 - acc: 0.9463 - val_loss: 0.1305 - val_acc: 0.9486\n",
      "Epoch 6/80\n",
      "33s - loss: 0.1387 - acc: 0.9461 - val_loss: 0.1305 - val_acc: 0.9487\n",
      "Epoch 7/80\n",
      "34s - loss: 0.1379 - acc: 0.9467 - val_loss: 0.1304 - val_acc: 0.9486\n",
      "Epoch 8/80\n",
      "33s - loss: 0.1379 - acc: 0.9467 - val_loss: 0.1301 - val_acc: 0.9486\n",
      "Epoch 9/80\n",
      "34s - loss: 0.1375 - acc: 0.9468 - val_loss: 0.1298 - val_acc: 0.9487\n",
      "Epoch 10/80\n",
      "33s - loss: 0.1375 - acc: 0.9467 - val_loss: 0.1300 - val_acc: 0.9489\n",
      "Epoch 11/80\n",
      "34s - loss: 0.1369 - acc: 0.9468 - val_loss: 0.1298 - val_acc: 0.9487\n",
      "Epoch 12/80\n",
      "35s - loss: 0.1364 - acc: 0.9470 - val_loss: 0.1300 - val_acc: 0.9487\n",
      "Epoch 13/80\n",
      "35s - loss: 0.1364 - acc: 0.9470 - val_loss: 0.1293 - val_acc: 0.9490\n",
      "Epoch 14/80\n",
      "35s - loss: 0.1364 - acc: 0.9470 - val_loss: 0.1294 - val_acc: 0.9489\n",
      "Epoch 15/80\n",
      "35s - loss: 0.1362 - acc: 0.9472 - val_loss: 0.1295 - val_acc: 0.9490\n",
      "Epoch 16/80\n",
      "35s - loss: 0.1352 - acc: 0.9475 - val_loss: 0.1292 - val_acc: 0.9491\n",
      "Epoch 17/80\n",
      "35s - loss: 0.1352 - acc: 0.9478 - val_loss: 0.1295 - val_acc: 0.9489\n",
      "Epoch 18/80\n",
      "35s - loss: 0.1362 - acc: 0.9470 - val_loss: 0.1291 - val_acc: 0.9492\n",
      "Epoch 19/80\n",
      "35s - loss: 0.1354 - acc: 0.9472 - val_loss: 0.1294 - val_acc: 0.9490\n",
      "Epoch 20/80\n",
      "36s - loss: 0.1351 - acc: 0.9475 - val_loss: 0.1290 - val_acc: 0.9492\n",
      "Epoch 21/80\n",
      "33s - loss: 0.1356 - acc: 0.9474 - val_loss: 0.1294 - val_acc: 0.9491\n",
      "Epoch 22/80\n",
      "33s - loss: 0.1342 - acc: 0.9478 - val_loss: 0.1289 - val_acc: 0.9493\n",
      "Epoch 23/80\n",
      "33s - loss: 0.1344 - acc: 0.9479 - val_loss: 0.1289 - val_acc: 0.9493\n",
      "Epoch 24/80\n",
      "34s - loss: 0.1347 - acc: 0.9477 - val_loss: 0.1288 - val_acc: 0.9494\n",
      "Epoch 25/80\n",
      "34s - loss: 0.1342 - acc: 0.9478 - val_loss: 0.1291 - val_acc: 0.9493\n",
      "Epoch 26/80\n",
      "33s - loss: 0.1342 - acc: 0.9478 - val_loss: 0.1284 - val_acc: 0.9496\n",
      "Epoch 27/80\n",
      "32s - loss: 0.1341 - acc: 0.9481 - val_loss: 0.1286 - val_acc: 0.9495\n",
      "Epoch 28/80\n",
      "32s - loss: 0.1340 - acc: 0.9479 - val_loss: 0.1290 - val_acc: 0.9494\n",
      "Epoch 29/80\n",
      "33s - loss: 0.1337 - acc: 0.9480 - val_loss: 0.1286 - val_acc: 0.9496\n",
      "Epoch 30/80\n",
      "32s - loss: 0.1337 - acc: 0.9483 - val_loss: 0.1283 - val_acc: 0.9496\n",
      "Epoch 31/80\n",
      "33s - loss: 0.1335 - acc: 0.9481 - val_loss: 0.1290 - val_acc: 0.9495\n",
      "Epoch 32/80\n",
      "32s - loss: 0.1339 - acc: 0.9478 - val_loss: 0.1291 - val_acc: 0.9494\n",
      "Epoch 33/80\n",
      "33s - loss: 0.1329 - acc: 0.9484 - val_loss: 0.1282 - val_acc: 0.9500\n",
      "Epoch 34/80\n",
      "32s - loss: 0.1336 - acc: 0.9481 - val_loss: 0.1286 - val_acc: 0.9496\n",
      "Epoch 35/80\n",
      "32s - loss: 0.1323 - acc: 0.9487 - val_loss: 0.1282 - val_acc: 0.9499\n",
      "Epoch 36/80\n",
      "32s - loss: 0.1320 - acc: 0.9489 - val_loss: 0.1285 - val_acc: 0.9498\n",
      "Epoch 37/80\n",
      "32s - loss: 0.1327 - acc: 0.9485 - val_loss: 0.1285 - val_acc: 0.9500\n",
      "Epoch 38/80\n",
      "32s - loss: 0.1329 - acc: 0.9483 - val_loss: 0.1282 - val_acc: 0.9500\n",
      "Epoch 39/80\n",
      "33s - loss: 0.1320 - acc: 0.9488 - val_loss: 0.1286 - val_acc: 0.9498\n",
      "Epoch 40/80\n",
      "32s - loss: 0.1321 - acc: 0.9485 - val_loss: 0.1279 - val_acc: 0.9497\n",
      "Epoch 41/80\n",
      "33s - loss: 0.1318 - acc: 0.9485 - val_loss: 0.1283 - val_acc: 0.9498\n",
      "Epoch 42/80\n",
      "32s - loss: 0.1315 - acc: 0.9487 - val_loss: 0.1285 - val_acc: 0.9498\n",
      "Epoch 43/80\n",
      "32s - loss: 0.1318 - acc: 0.9487 - val_loss: 0.1277 - val_acc: 0.9500\n",
      "Epoch 44/80\n",
      "32s - loss: 0.1313 - acc: 0.9490 - val_loss: 0.1279 - val_acc: 0.9502\n",
      "Epoch 45/80\n",
      "32s - loss: 0.1315 - acc: 0.9490 - val_loss: 0.1281 - val_acc: 0.9499\n",
      "Epoch 46/80\n",
      "32s - loss: 0.1313 - acc: 0.9488 - val_loss: 0.1287 - val_acc: 0.9497\n",
      "Epoch 47/80\n",
      "32s - loss: 0.1316 - acc: 0.9487 - val_loss: 0.1281 - val_acc: 0.9501\n",
      "Epoch 48/80\n",
      "32s - loss: 0.1313 - acc: 0.9490 - val_loss: 0.1277 - val_acc: 0.9501\n",
      "Epoch 49/80\n",
      "32s - loss: 0.1311 - acc: 0.9492 - val_loss: 0.1280 - val_acc: 0.9500\n",
      "Epoch 50/80\n",
      "32s - loss: 0.1313 - acc: 0.9490 - val_loss: 0.1277 - val_acc: 0.9499\n",
      "Epoch 51/80\n",
      "33s - loss: 0.1304 - acc: 0.9494 - val_loss: 0.1278 - val_acc: 0.9502\n",
      "Epoch 52/80\n",
      "33s - loss: 0.1308 - acc: 0.9492 - val_loss: 0.1277 - val_acc: 0.9501\n",
      "Epoch 53/80\n",
      "32s - loss: 0.1311 - acc: 0.9490 - val_loss: 0.1276 - val_acc: 0.9500\n",
      "Epoch 54/80\n",
      "32s - loss: 0.1307 - acc: 0.9491 - val_loss: 0.1277 - val_acc: 0.9503\n",
      "Epoch 55/80\n",
      "33s - loss: 0.1297 - acc: 0.9495 - val_loss: 0.1273 - val_acc: 0.9501\n",
      "Epoch 56/80\n",
      "33s - loss: 0.1305 - acc: 0.9494 - val_loss: 0.1279 - val_acc: 0.9503\n",
      "Epoch 57/80\n",
      "32s - loss: 0.1302 - acc: 0.9492 - val_loss: 0.1282 - val_acc: 0.9498\n",
      "Epoch 58/80\n",
      "33s - loss: 0.1295 - acc: 0.9498 - val_loss: 0.1275 - val_acc: 0.9503\n",
      "Epoch 59/80\n",
      "35s - loss: 0.1293 - acc: 0.9495 - val_loss: 0.1274 - val_acc: 0.9504\n",
      "Epoch 60/80\n",
      "35s - loss: 0.1297 - acc: 0.9495 - val_loss: 0.1271 - val_acc: 0.9503\n",
      "Epoch 61/80\n",
      "35s - loss: 0.1298 - acc: 0.9495 - val_loss: 0.1274 - val_acc: 0.9502\n",
      "Epoch 62/80\n",
      "35s - loss: 0.1294 - acc: 0.9494 - val_loss: 0.1271 - val_acc: 0.9504\n",
      "Epoch 63/80\n",
      "34s - loss: 0.1290 - acc: 0.9496 - val_loss: 0.1272 - val_acc: 0.9505\n",
      "Epoch 64/80\n",
      "34s - loss: 0.1290 - acc: 0.9501 - val_loss: 0.1273 - val_acc: 0.9505\n",
      "Epoch 65/80\n",
      "34s - loss: 0.1291 - acc: 0.9497 - val_loss: 0.1269 - val_acc: 0.9505\n",
      "Epoch 66/80\n",
      "34s - loss: 0.1292 - acc: 0.9499 - val_loss: 0.1272 - val_acc: 0.9504\n",
      "Epoch 67/80\n",
      "35s - loss: 0.1292 - acc: 0.9500 - val_loss: 0.1270 - val_acc: 0.9506\n",
      "Epoch 68/80\n",
      "34s - loss: 0.1289 - acc: 0.9497 - val_loss: 0.1270 - val_acc: 0.9505\n",
      "Epoch 69/80\n",
      "35s - loss: 0.1293 - acc: 0.9497 - val_loss: 0.1273 - val_acc: 0.9506\n",
      "Epoch 70/80\n",
      "33s - loss: 0.1290 - acc: 0.9497 - val_loss: 0.1268 - val_acc: 0.9505\n",
      "Epoch 71/80\n",
      "33s - loss: 0.1286 - acc: 0.9498 - val_loss: 0.1268 - val_acc: 0.9507\n",
      "Epoch 72/80\n",
      "33s - loss: 0.1280 - acc: 0.9500 - val_loss: 0.1266 - val_acc: 0.9508\n",
      "Epoch 73/80\n",
      "33s - loss: 0.1286 - acc: 0.9502 - val_loss: 0.1266 - val_acc: 0.9507\n",
      "Epoch 74/80\n",
      "35s - loss: 0.1278 - acc: 0.9500 - val_loss: 0.1269 - val_acc: 0.9509\n",
      "Epoch 75/80\n",
      "36s - loss: 0.1282 - acc: 0.9502 - val_loss: 0.1270 - val_acc: 0.9506\n",
      "Epoch 76/80\n",
      "39s - loss: 0.1279 - acc: 0.9500 - val_loss: 0.1267 - val_acc: 0.9506\n",
      "Epoch 77/80\n",
      "37s - loss: 0.1282 - acc: 0.9501 - val_loss: 0.1271 - val_acc: 0.9508\n",
      "Epoch 78/80\n",
      "36s - loss: 0.1273 - acc: 0.9505 - val_loss: 0.1269 - val_acc: 0.9508\n",
      "Epoch 79/80\n",
      "36s - loss: 0.1281 - acc: 0.9502 - val_loss: 0.1264 - val_acc: 0.9509\n",
      "Epoch 80/80\n",
      "35s - loss: 0.1276 - acc: 0.9500 - val_loss: 0.1269 - val_acc: 0.9508\n",
      "0.891764690704\n"
     ]
    }
   ],
   "source": [
    "nfolds = 6\n",
    "batch_size = 128\n",
    "\n",
    "num_fold = 0\n",
    "sum_score = 0\n",
    "\n",
    "kf = KFold(len(y_train), n_folds=nfolds, shuffle=True, random_state=1)\n",
    "\n",
    "for train_index, test_index in kf:\n",
    "        start_time_model_fitting = time.time()\n",
    "        \n",
    "        X_train = x_train[train_index]\n",
    "        Y_train = y_train[train_index]\n",
    "        X_valid = x_train[test_index]\n",
    "        Y_valid = y_train[test_index]\n",
    "\n",
    "        num_fold += 1\n",
    "        print('Start KFold number {} from {}'.format(num_fold, nfolds))\n",
    "        print('Split train: ', len(X_train), len(Y_train))\n",
    "        print('Split valid: ', len(X_valid), len(Y_valid))\n",
    "        \n",
    "        w_filename = RESULT_DIR + '/wconv_' + str(num_fold) + '.h5'\n",
    "        \n",
    "        model = get_model()\n",
    "        \n",
    "        callbacks = [\n",
    "            EarlyStopping(monitor='val_loss', patience=10, verbose=0),\n",
    "            ModelCheckpoint(w_filename, monitor='val_loss', save_best_only=True, verbose=0)]\n",
    "        \n",
    "        model.fit(x = X_train, y= Y_train, validation_data=(X_valid, Y_valid),\n",
    "                  batch_size=batch_size,verbose=2, epochs=5, shuffle=True)\n",
    "        \n",
    "        K.set_value(model.optimizer.lr, 0.00001)\n",
    "        \n",
    "        model.fit(x = X_train, y= Y_train, validation_data=(X_valid, Y_valid),\n",
    "                  batch_size=batch_size, verbose=2, epochs=80, shuffle=True, callbacks = callbacks)\n",
    "        \n",
    "        \n",
    "        p_valid = model.predict(X_valid, batch_size = batch_size, verbose=2)\n",
    "        print(fbeta_score(Y_valid, np.array(p_valid) > 0.18, beta=2, average='samples'))\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import bcolz\n",
    "import glob\n",
    "\n",
    "def save_array(fname, arr):\n",
    "    c=bcolz.carray(arr, rootdir=fname, mode='w')\n",
    "    c.flush()\n",
    "def load_array(fname):\n",
    "    return bcolz.open(fname)[:]\n",
    "\n",
    "\n",
    "PREDICTS_FILE = RESULT_DIR + '/preds'\n",
    "\n",
    "def ensemble():\n",
    "    preds = []\n",
    "    w_files = glob.glob(RESULT_DIR +'/wconv_*.h5')\n",
    "    for fn in w_files:\n",
    "        model = get_model()\n",
    "        print(fn)\n",
    "        model.load_weights(fn)\n",
    "        preds.append(model.predict(x_test, batch_size=128))\n",
    "    m = np.mean(preds, axis=0)\n",
    "    print(m.shape)\n",
    "    save_array(PREDICTS_FILE, m)\n",
    "    return m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/chicm/data/planet/resultsv2/wconv_5.h5\n",
      "/home/chicm/data/planet/resultsv2/wconv_3.h5\n",
      "/home/chicm/data/planet/resultsv2/wconv_4.h5\n",
      "/home/chicm/data/planet/resultsv2/wconv_2.h5\n",
      "/home/chicm/data/planet/resultsv2/wconv_6.h5\n",
      "/home/chicm/data/planet/resultsv2/wconv_1.h5\n",
      "(61191, 17)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>haze</th>\n",
       "      <th>cultivation</th>\n",
       "      <th>blooming</th>\n",
       "      <th>partly_cloudy</th>\n",
       "      <th>habitation</th>\n",
       "      <th>primary</th>\n",
       "      <th>road</th>\n",
       "      <th>agriculture</th>\n",
       "      <th>selective_logging</th>\n",
       "      <th>artisinal_mine</th>\n",
       "      <th>slash_burn</th>\n",
       "      <th>blow_down</th>\n",
       "      <th>cloudy</th>\n",
       "      <th>bare_ground</th>\n",
       "      <th>conventional_mine</th>\n",
       "      <th>clear</th>\n",
       "      <th>water</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.002282</td>\n",
       "      <td>0.001152</td>\n",
       "      <td>0.007417</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.000439</td>\n",
       "      <td>0.999537</td>\n",
       "      <td>0.001956</td>\n",
       "      <td>0.002205</td>\n",
       "      <td>0.000570</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>0.000445</td>\n",
       "      <td>0.000285</td>\n",
       "      <td>0.000360</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>0.999130</td>\n",
       "      <td>0.006381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000715</td>\n",
       "      <td>0.003500</td>\n",
       "      <td>0.012611</td>\n",
       "      <td>0.001527</td>\n",
       "      <td>0.000834</td>\n",
       "      <td>0.999433</td>\n",
       "      <td>0.003365</td>\n",
       "      <td>0.004024</td>\n",
       "      <td>0.001773</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.000178</td>\n",
       "      <td>0.000813</td>\n",
       "      <td>0.000473</td>\n",
       "      <td>0.000362</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.998265</td>\n",
       "      <td>0.010875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.008655</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.999564</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>0.998762</td>\n",
       "      <td>0.008366</td>\n",
       "      <td>0.039851</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000703</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000066</td>\n",
       "      <td>0.036886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.001383</td>\n",
       "      <td>0.007506</td>\n",
       "      <td>0.007806</td>\n",
       "      <td>0.007181</td>\n",
       "      <td>0.001555</td>\n",
       "      <td>0.998597</td>\n",
       "      <td>0.006056</td>\n",
       "      <td>0.011330</td>\n",
       "      <td>0.001824</td>\n",
       "      <td>0.000164</td>\n",
       "      <td>0.000290</td>\n",
       "      <td>0.000800</td>\n",
       "      <td>0.000883</td>\n",
       "      <td>0.000760</td>\n",
       "      <td>0.000085</td>\n",
       "      <td>0.990166</td>\n",
       "      <td>0.017417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.002283</td>\n",
       "      <td>0.001846</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>0.501018</td>\n",
       "      <td>0.000938</td>\n",
       "      <td>0.436492</td>\n",
       "      <td>0.008180</td>\n",
       "      <td>0.018032</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.550392</td>\n",
       "      <td>0.000225</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.001707</td>\n",
       "      <td>0.034938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000326</td>\n",
       "      <td>0.000598</td>\n",
       "      <td>0.002263</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>0.999666</td>\n",
       "      <td>0.000807</td>\n",
       "      <td>0.001365</td>\n",
       "      <td>0.000165</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.999535</td>\n",
       "      <td>0.002520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.085211</td>\n",
       "      <td>0.220040</td>\n",
       "      <td>0.001899</td>\n",
       "      <td>0.254308</td>\n",
       "      <td>0.139351</td>\n",
       "      <td>0.988512</td>\n",
       "      <td>0.312776</td>\n",
       "      <td>0.608390</td>\n",
       "      <td>0.005095</td>\n",
       "      <td>0.003962</td>\n",
       "      <td>0.004658</td>\n",
       "      <td>0.001481</td>\n",
       "      <td>0.004334</td>\n",
       "      <td>0.023026</td>\n",
       "      <td>0.001650</td>\n",
       "      <td>0.643094</td>\n",
       "      <td>0.405633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.002256</td>\n",
       "      <td>0.010192</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.001455</td>\n",
       "      <td>0.887690</td>\n",
       "      <td>0.815533</td>\n",
       "      <td>0.951758</td>\n",
       "      <td>0.225530</td>\n",
       "      <td>0.000574</td>\n",
       "      <td>0.004287</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.002508</td>\n",
       "      <td>0.012728</td>\n",
       "      <td>0.000894</td>\n",
       "      <td>0.993042</td>\n",
       "      <td>0.126930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000587</td>\n",
       "      <td>0.001203</td>\n",
       "      <td>0.004099</td>\n",
       "      <td>0.000170</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>0.999521</td>\n",
       "      <td>0.002168</td>\n",
       "      <td>0.002402</td>\n",
       "      <td>0.000457</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000277</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>0.000262</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.999461</td>\n",
       "      <td>0.005090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.876548</td>\n",
       "      <td>0.042462</td>\n",
       "      <td>0.000698</td>\n",
       "      <td>0.010119</td>\n",
       "      <td>0.010125</td>\n",
       "      <td>0.985020</td>\n",
       "      <td>0.054970</td>\n",
       "      <td>0.158937</td>\n",
       "      <td>0.000601</td>\n",
       "      <td>0.000378</td>\n",
       "      <td>0.000880</td>\n",
       "      <td>0.000701</td>\n",
       "      <td>0.008518</td>\n",
       "      <td>0.003040</td>\n",
       "      <td>0.000350</td>\n",
       "      <td>0.219986</td>\n",
       "      <td>0.115348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.001301</td>\n",
       "      <td>0.090870</td>\n",
       "      <td>0.000417</td>\n",
       "      <td>0.981807</td>\n",
       "      <td>0.013128</td>\n",
       "      <td>0.995158</td>\n",
       "      <td>0.075013</td>\n",
       "      <td>0.278967</td>\n",
       "      <td>0.000623</td>\n",
       "      <td>0.000392</td>\n",
       "      <td>0.001093</td>\n",
       "      <td>0.000348</td>\n",
       "      <td>0.002909</td>\n",
       "      <td>0.002734</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.008693</td>\n",
       "      <td>0.196112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.003492</td>\n",
       "      <td>0.567447</td>\n",
       "      <td>0.010908</td>\n",
       "      <td>0.002211</td>\n",
       "      <td>0.118578</td>\n",
       "      <td>0.997853</td>\n",
       "      <td>0.147888</td>\n",
       "      <td>0.586829</td>\n",
       "      <td>0.043943</td>\n",
       "      <td>0.004523</td>\n",
       "      <td>0.030674</td>\n",
       "      <td>0.006261</td>\n",
       "      <td>0.000962</td>\n",
       "      <td>0.025296</td>\n",
       "      <td>0.001739</td>\n",
       "      <td>0.997377</td>\n",
       "      <td>0.308423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.070330</td>\n",
       "      <td>0.001033</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>0.005971</td>\n",
       "      <td>0.000437</td>\n",
       "      <td>0.085406</td>\n",
       "      <td>0.005642</td>\n",
       "      <td>0.009155</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.930138</td>\n",
       "      <td>0.000185</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.006913</td>\n",
       "      <td>0.035617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.010515</td>\n",
       "      <td>0.340349</td>\n",
       "      <td>0.011088</td>\n",
       "      <td>0.006245</td>\n",
       "      <td>0.090011</td>\n",
       "      <td>0.993104</td>\n",
       "      <td>0.220298</td>\n",
       "      <td>0.522160</td>\n",
       "      <td>0.024826</td>\n",
       "      <td>0.003803</td>\n",
       "      <td>0.012285</td>\n",
       "      <td>0.003992</td>\n",
       "      <td>0.002126</td>\n",
       "      <td>0.026960</td>\n",
       "      <td>0.001675</td>\n",
       "      <td>0.991191</td>\n",
       "      <td>0.247607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.035589</td>\n",
       "      <td>0.015512</td>\n",
       "      <td>0.002285</td>\n",
       "      <td>0.002274</td>\n",
       "      <td>0.002756</td>\n",
       "      <td>0.996990</td>\n",
       "      <td>0.012667</td>\n",
       "      <td>0.027441</td>\n",
       "      <td>0.000833</td>\n",
       "      <td>0.000208</td>\n",
       "      <td>0.000384</td>\n",
       "      <td>0.000447</td>\n",
       "      <td>0.001330</td>\n",
       "      <td>0.001379</td>\n",
       "      <td>0.000109</td>\n",
       "      <td>0.974916</td>\n",
       "      <td>0.039106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.000521</td>\n",
       "      <td>0.000857</td>\n",
       "      <td>0.011472</td>\n",
       "      <td>0.000230</td>\n",
       "      <td>0.000646</td>\n",
       "      <td>0.999521</td>\n",
       "      <td>0.002607</td>\n",
       "      <td>0.001704</td>\n",
       "      <td>0.001012</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.000387</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>0.000231</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.999439</td>\n",
       "      <td>0.006944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.006604</td>\n",
       "      <td>0.218812</td>\n",
       "      <td>0.002468</td>\n",
       "      <td>0.005385</td>\n",
       "      <td>0.462835</td>\n",
       "      <td>0.988320</td>\n",
       "      <td>0.763212</td>\n",
       "      <td>0.828580</td>\n",
       "      <td>0.010658</td>\n",
       "      <td>0.005544</td>\n",
       "      <td>0.009502</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.001015</td>\n",
       "      <td>0.040430</td>\n",
       "      <td>0.003607</td>\n",
       "      <td>0.993012</td>\n",
       "      <td>0.205725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.010343</td>\n",
       "      <td>0.039925</td>\n",
       "      <td>0.007998</td>\n",
       "      <td>0.522593</td>\n",
       "      <td>0.006531</td>\n",
       "      <td>0.992080</td>\n",
       "      <td>0.024966</td>\n",
       "      <td>0.056388</td>\n",
       "      <td>0.004689</td>\n",
       "      <td>0.001810</td>\n",
       "      <td>0.001454</td>\n",
       "      <td>0.002223</td>\n",
       "      <td>0.006916</td>\n",
       "      <td>0.004411</td>\n",
       "      <td>0.000597</td>\n",
       "      <td>0.474095</td>\n",
       "      <td>0.100653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.420794</td>\n",
       "      <td>0.004677</td>\n",
       "      <td>0.000474</td>\n",
       "      <td>0.001671</td>\n",
       "      <td>0.000999</td>\n",
       "      <td>0.991281</td>\n",
       "      <td>0.006052</td>\n",
       "      <td>0.013874</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>0.000092</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000166</td>\n",
       "      <td>0.003779</td>\n",
       "      <td>0.000921</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.726110</td>\n",
       "      <td>0.035345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.010582</td>\n",
       "      <td>0.184261</td>\n",
       "      <td>0.034986</td>\n",
       "      <td>0.006953</td>\n",
       "      <td>0.110204</td>\n",
       "      <td>0.990736</td>\n",
       "      <td>0.301170</td>\n",
       "      <td>0.199586</td>\n",
       "      <td>0.079453</td>\n",
       "      <td>0.028341</td>\n",
       "      <td>0.009593</td>\n",
       "      <td>0.006592</td>\n",
       "      <td>0.003696</td>\n",
       "      <td>0.030841</td>\n",
       "      <td>0.003902</td>\n",
       "      <td>0.991484</td>\n",
       "      <td>0.458204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.005096</td>\n",
       "      <td>0.191217</td>\n",
       "      <td>0.005077</td>\n",
       "      <td>0.005113</td>\n",
       "      <td>0.066773</td>\n",
       "      <td>0.994357</td>\n",
       "      <td>0.191343</td>\n",
       "      <td>0.211980</td>\n",
       "      <td>0.019911</td>\n",
       "      <td>0.020932</td>\n",
       "      <td>0.004275</td>\n",
       "      <td>0.001674</td>\n",
       "      <td>0.001550</td>\n",
       "      <td>0.027007</td>\n",
       "      <td>0.001570</td>\n",
       "      <td>0.992036</td>\n",
       "      <td>0.709238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.052944</td>\n",
       "      <td>0.075446</td>\n",
       "      <td>0.001456</td>\n",
       "      <td>0.017419</td>\n",
       "      <td>0.063481</td>\n",
       "      <td>0.905051</td>\n",
       "      <td>0.209208</td>\n",
       "      <td>0.366283</td>\n",
       "      <td>0.003179</td>\n",
       "      <td>0.008825</td>\n",
       "      <td>0.001651</td>\n",
       "      <td>0.000847</td>\n",
       "      <td>0.015649</td>\n",
       "      <td>0.058427</td>\n",
       "      <td>0.002065</td>\n",
       "      <td>0.911530</td>\n",
       "      <td>0.681233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.033221</td>\n",
       "      <td>0.185116</td>\n",
       "      <td>0.001004</td>\n",
       "      <td>0.025325</td>\n",
       "      <td>0.395753</td>\n",
       "      <td>0.979891</td>\n",
       "      <td>0.718494</td>\n",
       "      <td>0.843155</td>\n",
       "      <td>0.004008</td>\n",
       "      <td>0.002958</td>\n",
       "      <td>0.005375</td>\n",
       "      <td>0.000978</td>\n",
       "      <td>0.001880</td>\n",
       "      <td>0.028006</td>\n",
       "      <td>0.002514</td>\n",
       "      <td>0.947953</td>\n",
       "      <td>0.231440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.058369</td>\n",
       "      <td>0.000599</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.001517</td>\n",
       "      <td>0.000254</td>\n",
       "      <td>0.054618</td>\n",
       "      <td>0.004193</td>\n",
       "      <td>0.006499</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.946299</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.010262</td>\n",
       "      <td>0.033120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.002534</td>\n",
       "      <td>0.083667</td>\n",
       "      <td>0.000291</td>\n",
       "      <td>0.996229</td>\n",
       "      <td>0.039509</td>\n",
       "      <td>0.968338</td>\n",
       "      <td>0.275229</td>\n",
       "      <td>0.636665</td>\n",
       "      <td>0.000569</td>\n",
       "      <td>0.001650</td>\n",
       "      <td>0.001832</td>\n",
       "      <td>0.000739</td>\n",
       "      <td>0.006791</td>\n",
       "      <td>0.020785</td>\n",
       "      <td>0.001745</td>\n",
       "      <td>0.001912</td>\n",
       "      <td>0.311279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.000269</td>\n",
       "      <td>0.000557</td>\n",
       "      <td>0.003743</td>\n",
       "      <td>0.000085</td>\n",
       "      <td>0.000212</td>\n",
       "      <td>0.999681</td>\n",
       "      <td>0.000908</td>\n",
       "      <td>0.001082</td>\n",
       "      <td>0.000324</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000187</td>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.999677</td>\n",
       "      <td>0.002923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.007339</td>\n",
       "      <td>0.103814</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.927489</td>\n",
       "      <td>0.238441</td>\n",
       "      <td>0.976543</td>\n",
       "      <td>0.820471</td>\n",
       "      <td>0.949800</td>\n",
       "      <td>0.000615</td>\n",
       "      <td>0.000499</td>\n",
       "      <td>0.001582</td>\n",
       "      <td>0.000311</td>\n",
       "      <td>0.001187</td>\n",
       "      <td>0.013774</td>\n",
       "      <td>0.001257</td>\n",
       "      <td>0.074911</td>\n",
       "      <td>0.114042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.016853</td>\n",
       "      <td>0.084673</td>\n",
       "      <td>0.001332</td>\n",
       "      <td>0.008299</td>\n",
       "      <td>0.077245</td>\n",
       "      <td>0.887494</td>\n",
       "      <td>0.302885</td>\n",
       "      <td>0.780796</td>\n",
       "      <td>0.001540</td>\n",
       "      <td>0.000957</td>\n",
       "      <td>0.002910</td>\n",
       "      <td>0.001441</td>\n",
       "      <td>0.003496</td>\n",
       "      <td>0.137758</td>\n",
       "      <td>0.002101</td>\n",
       "      <td>0.974687</td>\n",
       "      <td>0.085843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.051484</td>\n",
       "      <td>0.094780</td>\n",
       "      <td>0.002717</td>\n",
       "      <td>0.492594</td>\n",
       "      <td>0.130187</td>\n",
       "      <td>0.900481</td>\n",
       "      <td>0.402611</td>\n",
       "      <td>0.620469</td>\n",
       "      <td>0.003861</td>\n",
       "      <td>0.025751</td>\n",
       "      <td>0.005415</td>\n",
       "      <td>0.002445</td>\n",
       "      <td>0.017114</td>\n",
       "      <td>0.113708</td>\n",
       "      <td>0.010175</td>\n",
       "      <td>0.467514</td>\n",
       "      <td>0.485189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.000546</td>\n",
       "      <td>0.000866</td>\n",
       "      <td>0.002025</td>\n",
       "      <td>0.000463</td>\n",
       "      <td>0.000217</td>\n",
       "      <td>0.999625</td>\n",
       "      <td>0.001040</td>\n",
       "      <td>0.001803</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>0.000099</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.998433</td>\n",
       "      <td>0.003401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61161</th>\n",
       "      <td>0.006643</td>\n",
       "      <td>0.143913</td>\n",
       "      <td>0.019807</td>\n",
       "      <td>0.008290</td>\n",
       "      <td>0.025031</td>\n",
       "      <td>0.994690</td>\n",
       "      <td>0.077086</td>\n",
       "      <td>0.181145</td>\n",
       "      <td>0.020188</td>\n",
       "      <td>0.003734</td>\n",
       "      <td>0.004946</td>\n",
       "      <td>0.004314</td>\n",
       "      <td>0.002348</td>\n",
       "      <td>0.011616</td>\n",
       "      <td>0.001066</td>\n",
       "      <td>0.991551</td>\n",
       "      <td>0.180440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61162</th>\n",
       "      <td>0.016063</td>\n",
       "      <td>0.245630</td>\n",
       "      <td>0.000714</td>\n",
       "      <td>0.006510</td>\n",
       "      <td>0.433627</td>\n",
       "      <td>0.990573</td>\n",
       "      <td>0.728691</td>\n",
       "      <td>0.884454</td>\n",
       "      <td>0.003294</td>\n",
       "      <td>0.001940</td>\n",
       "      <td>0.005716</td>\n",
       "      <td>0.000832</td>\n",
       "      <td>0.000736</td>\n",
       "      <td>0.022988</td>\n",
       "      <td>0.001939</td>\n",
       "      <td>0.984900</td>\n",
       "      <td>0.182388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61163</th>\n",
       "      <td>0.006532</td>\n",
       "      <td>0.047968</td>\n",
       "      <td>0.017807</td>\n",
       "      <td>0.003317</td>\n",
       "      <td>0.005669</td>\n",
       "      <td>0.998014</td>\n",
       "      <td>0.017771</td>\n",
       "      <td>0.057991</td>\n",
       "      <td>0.007034</td>\n",
       "      <td>0.000891</td>\n",
       "      <td>0.001755</td>\n",
       "      <td>0.003165</td>\n",
       "      <td>0.001305</td>\n",
       "      <td>0.005400</td>\n",
       "      <td>0.000436</td>\n",
       "      <td>0.993634</td>\n",
       "      <td>0.074847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61164</th>\n",
       "      <td>0.002931</td>\n",
       "      <td>0.006825</td>\n",
       "      <td>0.006428</td>\n",
       "      <td>0.112246</td>\n",
       "      <td>0.001352</td>\n",
       "      <td>0.997047</td>\n",
       "      <td>0.005685</td>\n",
       "      <td>0.010938</td>\n",
       "      <td>0.001193</td>\n",
       "      <td>0.000247</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>0.000594</td>\n",
       "      <td>0.002542</td>\n",
       "      <td>0.000760</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>0.851954</td>\n",
       "      <td>0.025975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61165</th>\n",
       "      <td>0.004684</td>\n",
       "      <td>0.462931</td>\n",
       "      <td>0.002532</td>\n",
       "      <td>0.004833</td>\n",
       "      <td>0.093993</td>\n",
       "      <td>0.998029</td>\n",
       "      <td>0.183450</td>\n",
       "      <td>0.727333</td>\n",
       "      <td>0.008519</td>\n",
       "      <td>0.000890</td>\n",
       "      <td>0.012195</td>\n",
       "      <td>0.001658</td>\n",
       "      <td>0.000504</td>\n",
       "      <td>0.015808</td>\n",
       "      <td>0.000511</td>\n",
       "      <td>0.992354</td>\n",
       "      <td>0.197197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61166</th>\n",
       "      <td>0.000288</td>\n",
       "      <td>0.000398</td>\n",
       "      <td>0.001514</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>0.999728</td>\n",
       "      <td>0.000523</td>\n",
       "      <td>0.001041</td>\n",
       "      <td>0.000097</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000092</td>\n",
       "      <td>0.000178</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.999514</td>\n",
       "      <td>0.001681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61167</th>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.011833</td>\n",
       "      <td>0.000109</td>\n",
       "      <td>0.999089</td>\n",
       "      <td>0.000754</td>\n",
       "      <td>0.999701</td>\n",
       "      <td>0.006661</td>\n",
       "      <td>0.018473</td>\n",
       "      <td>0.000074</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.000109</td>\n",
       "      <td>0.000192</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000604</td>\n",
       "      <td>0.026923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61168</th>\n",
       "      <td>0.000474</td>\n",
       "      <td>0.000804</td>\n",
       "      <td>0.004393</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.000218</td>\n",
       "      <td>0.999699</td>\n",
       "      <td>0.001216</td>\n",
       "      <td>0.001507</td>\n",
       "      <td>0.000364</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000243</td>\n",
       "      <td>0.000230</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.999472</td>\n",
       "      <td>0.003750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61169</th>\n",
       "      <td>0.004159</td>\n",
       "      <td>0.001518</td>\n",
       "      <td>0.034800</td>\n",
       "      <td>0.000272</td>\n",
       "      <td>0.001252</td>\n",
       "      <td>0.999555</td>\n",
       "      <td>0.004941</td>\n",
       "      <td>0.002574</td>\n",
       "      <td>0.003126</td>\n",
       "      <td>0.000223</td>\n",
       "      <td>0.000178</td>\n",
       "      <td>0.001664</td>\n",
       "      <td>0.000410</td>\n",
       "      <td>0.000572</td>\n",
       "      <td>0.000138</td>\n",
       "      <td>0.999243</td>\n",
       "      <td>0.009403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61170</th>\n",
       "      <td>0.000759</td>\n",
       "      <td>0.001867</td>\n",
       "      <td>0.002890</td>\n",
       "      <td>0.000218</td>\n",
       "      <td>0.000304</td>\n",
       "      <td>0.999502</td>\n",
       "      <td>0.001571</td>\n",
       "      <td>0.003759</td>\n",
       "      <td>0.000289</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.000270</td>\n",
       "      <td>0.000237</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.998984</td>\n",
       "      <td>0.006208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61171</th>\n",
       "      <td>0.015683</td>\n",
       "      <td>0.291986</td>\n",
       "      <td>0.000953</td>\n",
       "      <td>0.006791</td>\n",
       "      <td>0.136120</td>\n",
       "      <td>0.994610</td>\n",
       "      <td>0.377052</td>\n",
       "      <td>0.880040</td>\n",
       "      <td>0.002417</td>\n",
       "      <td>0.000438</td>\n",
       "      <td>0.006375</td>\n",
       "      <td>0.000791</td>\n",
       "      <td>0.000698</td>\n",
       "      <td>0.014911</td>\n",
       "      <td>0.000552</td>\n",
       "      <td>0.981768</td>\n",
       "      <td>0.125875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61172</th>\n",
       "      <td>0.022179</td>\n",
       "      <td>0.311838</td>\n",
       "      <td>0.012979</td>\n",
       "      <td>0.011319</td>\n",
       "      <td>0.146886</td>\n",
       "      <td>0.989758</td>\n",
       "      <td>0.291131</td>\n",
       "      <td>0.616300</td>\n",
       "      <td>0.026096</td>\n",
       "      <td>0.008820</td>\n",
       "      <td>0.017762</td>\n",
       "      <td>0.006506</td>\n",
       "      <td>0.002977</td>\n",
       "      <td>0.035705</td>\n",
       "      <td>0.003555</td>\n",
       "      <td>0.982615</td>\n",
       "      <td>0.249421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61173</th>\n",
       "      <td>0.001342</td>\n",
       "      <td>0.004314</td>\n",
       "      <td>0.041787</td>\n",
       "      <td>0.001403</td>\n",
       "      <td>0.001300</td>\n",
       "      <td>0.999353</td>\n",
       "      <td>0.004585</td>\n",
       "      <td>0.006474</td>\n",
       "      <td>0.003854</td>\n",
       "      <td>0.000436</td>\n",
       "      <td>0.000447</td>\n",
       "      <td>0.002452</td>\n",
       "      <td>0.000558</td>\n",
       "      <td>0.001082</td>\n",
       "      <td>0.000209</td>\n",
       "      <td>0.998492</td>\n",
       "      <td>0.013937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61174</th>\n",
       "      <td>0.000431</td>\n",
       "      <td>0.000540</td>\n",
       "      <td>0.001555</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.999661</td>\n",
       "      <td>0.000661</td>\n",
       "      <td>0.001299</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>0.000219</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.999324</td>\n",
       "      <td>0.002369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61175</th>\n",
       "      <td>0.000301</td>\n",
       "      <td>0.000295</td>\n",
       "      <td>0.001727</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.999731</td>\n",
       "      <td>0.000546</td>\n",
       "      <td>0.000692</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000085</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.999581</td>\n",
       "      <td>0.001717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61176</th>\n",
       "      <td>0.001817</td>\n",
       "      <td>0.008029</td>\n",
       "      <td>0.007882</td>\n",
       "      <td>0.000561</td>\n",
       "      <td>0.000943</td>\n",
       "      <td>0.998893</td>\n",
       "      <td>0.003435</td>\n",
       "      <td>0.015181</td>\n",
       "      <td>0.000769</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.000205</td>\n",
       "      <td>0.000454</td>\n",
       "      <td>0.000617</td>\n",
       "      <td>0.001273</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.997548</td>\n",
       "      <td>0.023273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61177</th>\n",
       "      <td>0.010060</td>\n",
       "      <td>0.191160</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.007336</td>\n",
       "      <td>0.380539</td>\n",
       "      <td>0.994213</td>\n",
       "      <td>0.831137</td>\n",
       "      <td>0.962549</td>\n",
       "      <td>0.000479</td>\n",
       "      <td>0.000255</td>\n",
       "      <td>0.002274</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.000150</td>\n",
       "      <td>0.012154</td>\n",
       "      <td>0.000440</td>\n",
       "      <td>0.976413</td>\n",
       "      <td>0.125246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61178</th>\n",
       "      <td>0.003671</td>\n",
       "      <td>0.002585</td>\n",
       "      <td>0.008010</td>\n",
       "      <td>0.000357</td>\n",
       "      <td>0.001357</td>\n",
       "      <td>0.998729</td>\n",
       "      <td>0.004873</td>\n",
       "      <td>0.003977</td>\n",
       "      <td>0.000999</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>0.000568</td>\n",
       "      <td>0.000844</td>\n",
       "      <td>0.000367</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.997382</td>\n",
       "      <td>0.013635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61179</th>\n",
       "      <td>0.020449</td>\n",
       "      <td>0.203254</td>\n",
       "      <td>0.002491</td>\n",
       "      <td>0.007323</td>\n",
       "      <td>0.266447</td>\n",
       "      <td>0.986135</td>\n",
       "      <td>0.620353</td>\n",
       "      <td>0.836582</td>\n",
       "      <td>0.006761</td>\n",
       "      <td>0.002086</td>\n",
       "      <td>0.007604</td>\n",
       "      <td>0.001580</td>\n",
       "      <td>0.001303</td>\n",
       "      <td>0.027497</td>\n",
       "      <td>0.001923</td>\n",
       "      <td>0.983750</td>\n",
       "      <td>0.153324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61180</th>\n",
       "      <td>0.006434</td>\n",
       "      <td>0.078929</td>\n",
       "      <td>0.000537</td>\n",
       "      <td>0.005722</td>\n",
       "      <td>0.241867</td>\n",
       "      <td>0.912894</td>\n",
       "      <td>0.667396</td>\n",
       "      <td>0.888470</td>\n",
       "      <td>0.001066</td>\n",
       "      <td>0.000680</td>\n",
       "      <td>0.001912</td>\n",
       "      <td>0.000794</td>\n",
       "      <td>0.001387</td>\n",
       "      <td>0.086473</td>\n",
       "      <td>0.002117</td>\n",
       "      <td>0.988617</td>\n",
       "      <td>0.055176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61181</th>\n",
       "      <td>0.000360</td>\n",
       "      <td>0.001327</td>\n",
       "      <td>0.023443</td>\n",
       "      <td>0.000277</td>\n",
       "      <td>0.000687</td>\n",
       "      <td>0.999666</td>\n",
       "      <td>0.002496</td>\n",
       "      <td>0.001911</td>\n",
       "      <td>0.002063</td>\n",
       "      <td>0.000092</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>0.000884</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>0.000332</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.999697</td>\n",
       "      <td>0.005819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61182</th>\n",
       "      <td>0.009579</td>\n",
       "      <td>0.349004</td>\n",
       "      <td>0.002572</td>\n",
       "      <td>0.015793</td>\n",
       "      <td>0.107819</td>\n",
       "      <td>0.995687</td>\n",
       "      <td>0.293947</td>\n",
       "      <td>0.647000</td>\n",
       "      <td>0.007152</td>\n",
       "      <td>0.002460</td>\n",
       "      <td>0.006468</td>\n",
       "      <td>0.001483</td>\n",
       "      <td>0.000937</td>\n",
       "      <td>0.018149</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0.976154</td>\n",
       "      <td>0.295103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61183</th>\n",
       "      <td>0.000332</td>\n",
       "      <td>0.000330</td>\n",
       "      <td>0.002018</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>0.999757</td>\n",
       "      <td>0.000598</td>\n",
       "      <td>0.000750</td>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000102</td>\n",
       "      <td>0.000153</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.999503</td>\n",
       "      <td>0.001990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61184</th>\n",
       "      <td>0.014415</td>\n",
       "      <td>0.164149</td>\n",
       "      <td>0.002126</td>\n",
       "      <td>0.008185</td>\n",
       "      <td>0.035797</td>\n",
       "      <td>0.986634</td>\n",
       "      <td>0.090500</td>\n",
       "      <td>0.320018</td>\n",
       "      <td>0.004861</td>\n",
       "      <td>0.003836</td>\n",
       "      <td>0.003027</td>\n",
       "      <td>0.000994</td>\n",
       "      <td>0.003294</td>\n",
       "      <td>0.026589</td>\n",
       "      <td>0.000696</td>\n",
       "      <td>0.975396</td>\n",
       "      <td>0.566608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61185</th>\n",
       "      <td>0.018936</td>\n",
       "      <td>0.028590</td>\n",
       "      <td>0.001903</td>\n",
       "      <td>0.727663</td>\n",
       "      <td>0.015598</td>\n",
       "      <td>0.757618</td>\n",
       "      <td>0.054407</td>\n",
       "      <td>0.154083</td>\n",
       "      <td>0.001208</td>\n",
       "      <td>0.003537</td>\n",
       "      <td>0.000652</td>\n",
       "      <td>0.000688</td>\n",
       "      <td>0.097835</td>\n",
       "      <td>0.013362</td>\n",
       "      <td>0.001062</td>\n",
       "      <td>0.058266</td>\n",
       "      <td>0.486205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61186</th>\n",
       "      <td>0.004794</td>\n",
       "      <td>0.001374</td>\n",
       "      <td>0.000143</td>\n",
       "      <td>0.066356</td>\n",
       "      <td>0.001046</td>\n",
       "      <td>0.101585</td>\n",
       "      <td>0.009466</td>\n",
       "      <td>0.012827</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.900837</td>\n",
       "      <td>0.000268</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.006458</td>\n",
       "      <td>0.021499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61187</th>\n",
       "      <td>0.012857</td>\n",
       "      <td>0.183846</td>\n",
       "      <td>0.014774</td>\n",
       "      <td>0.012150</td>\n",
       "      <td>0.072243</td>\n",
       "      <td>0.983883</td>\n",
       "      <td>0.179590</td>\n",
       "      <td>0.290149</td>\n",
       "      <td>0.027661</td>\n",
       "      <td>0.022529</td>\n",
       "      <td>0.007612</td>\n",
       "      <td>0.004607</td>\n",
       "      <td>0.004706</td>\n",
       "      <td>0.053727</td>\n",
       "      <td>0.003890</td>\n",
       "      <td>0.983938</td>\n",
       "      <td>0.521697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61188</th>\n",
       "      <td>0.001280</td>\n",
       "      <td>0.015992</td>\n",
       "      <td>0.017316</td>\n",
       "      <td>0.000494</td>\n",
       "      <td>0.002945</td>\n",
       "      <td>0.999001</td>\n",
       "      <td>0.010489</td>\n",
       "      <td>0.020818</td>\n",
       "      <td>0.005164</td>\n",
       "      <td>0.000335</td>\n",
       "      <td>0.000707</td>\n",
       "      <td>0.001881</td>\n",
       "      <td>0.000635</td>\n",
       "      <td>0.001687</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>0.999268</td>\n",
       "      <td>0.029743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61189</th>\n",
       "      <td>0.039465</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.000835</td>\n",
       "      <td>0.000109</td>\n",
       "      <td>0.041994</td>\n",
       "      <td>0.001260</td>\n",
       "      <td>0.001750</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.956142</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.017438</td>\n",
       "      <td>0.014397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61190</th>\n",
       "      <td>0.000996</td>\n",
       "      <td>0.004760</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.001364</td>\n",
       "      <td>0.970812</td>\n",
       "      <td>0.807773</td>\n",
       "      <td>0.987426</td>\n",
       "      <td>0.137013</td>\n",
       "      <td>0.000224</td>\n",
       "      <td>0.001274</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.001329</td>\n",
       "      <td>0.004289</td>\n",
       "      <td>0.000398</td>\n",
       "      <td>0.993089</td>\n",
       "      <td>0.061962</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61191 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           haze  cultivation  blooming  partly_cloudy  habitation   primary  \\\n",
       "0      0.002282     0.001152  0.007417       0.000157    0.000439  0.999537   \n",
       "1      0.000715     0.003500  0.012611       0.001527    0.000834  0.999433   \n",
       "2      0.000020     0.008655  0.000013       0.999564    0.000539  0.998762   \n",
       "3      0.001383     0.007506  0.007806       0.007181    0.001555  0.998597   \n",
       "4      0.002283     0.001846  0.000082       0.501018    0.000938  0.436492   \n",
       "5      0.000326     0.000598  0.002263       0.000087    0.000161  0.999666   \n",
       "6      0.085211     0.220040  0.001899       0.254308    0.139351  0.988512   \n",
       "7      0.002256     0.010192  0.000101       0.001455    0.887690  0.815533   \n",
       "8      0.000587     0.001203  0.004099       0.000170    0.000498  0.999521   \n",
       "9      0.876548     0.042462  0.000698       0.010119    0.010125  0.985020   \n",
       "10     0.001301     0.090870  0.000417       0.981807    0.013128  0.995158   \n",
       "11     0.003492     0.567447  0.010908       0.002211    0.118578  0.997853   \n",
       "12     0.070330     0.001033  0.000111       0.005971    0.000437  0.085406   \n",
       "13     0.010515     0.340349  0.011088       0.006245    0.090011  0.993104   \n",
       "14     0.035589     0.015512  0.002285       0.002274    0.002756  0.996990   \n",
       "15     0.000521     0.000857  0.011472       0.000230    0.000646  0.999521   \n",
       "16     0.006604     0.218812  0.002468       0.005385    0.462835  0.988320   \n",
       "17     0.010343     0.039925  0.007998       0.522593    0.006531  0.992080   \n",
       "18     0.420794     0.004677  0.000474       0.001671    0.000999  0.991281   \n",
       "19     0.010582     0.184261  0.034986       0.006953    0.110204  0.990736   \n",
       "20     0.005096     0.191217  0.005077       0.005113    0.066773  0.994357   \n",
       "21     0.052944     0.075446  0.001456       0.017419    0.063481  0.905051   \n",
       "22     0.033221     0.185116  0.001004       0.025325    0.395753  0.979891   \n",
       "23     0.058369     0.000599  0.000052       0.001517    0.000254  0.054618   \n",
       "24     0.002534     0.083667  0.000291       0.996229    0.039509  0.968338   \n",
       "25     0.000269     0.000557  0.003743       0.000085    0.000212  0.999681   \n",
       "26     0.007339     0.103814  0.000157       0.927489    0.238441  0.976543   \n",
       "27     0.016853     0.084673  0.001332       0.008299    0.077245  0.887494   \n",
       "28     0.051484     0.094780  0.002717       0.492594    0.130187  0.900481   \n",
       "29     0.000546     0.000866  0.002025       0.000463    0.000217  0.999625   \n",
       "...         ...          ...       ...            ...         ...       ...   \n",
       "61161  0.006643     0.143913  0.019807       0.008290    0.025031  0.994690   \n",
       "61162  0.016063     0.245630  0.000714       0.006510    0.433627  0.990573   \n",
       "61163  0.006532     0.047968  0.017807       0.003317    0.005669  0.998014   \n",
       "61164  0.002931     0.006825  0.006428       0.112246    0.001352  0.997047   \n",
       "61165  0.004684     0.462931  0.002532       0.004833    0.093993  0.998029   \n",
       "61166  0.000288     0.000398  0.001514       0.000069    0.000091  0.999728   \n",
       "61167  0.000117     0.011833  0.000109       0.999089    0.000754  0.999701   \n",
       "61168  0.000474     0.000804  0.004393       0.000132    0.000218  0.999699   \n",
       "61169  0.004159     0.001518  0.034800       0.000272    0.001252  0.999555   \n",
       "61170  0.000759     0.001867  0.002890       0.000218    0.000304  0.999502   \n",
       "61171  0.015683     0.291986  0.000953       0.006791    0.136120  0.994610   \n",
       "61172  0.022179     0.311838  0.012979       0.011319    0.146886  0.989758   \n",
       "61173  0.001342     0.004314  0.041787       0.001403    0.001300  0.999353   \n",
       "61174  0.000431     0.000540  0.001555       0.000083    0.000120  0.999661   \n",
       "61175  0.000301     0.000295  0.001727       0.000067    0.000098  0.999731   \n",
       "61176  0.001817     0.008029  0.007882       0.000561    0.000943  0.998893   \n",
       "61177  0.010060     0.191160  0.000057       0.007336    0.380539  0.994213   \n",
       "61178  0.003671     0.002585  0.008010       0.000357    0.001357  0.998729   \n",
       "61179  0.020449     0.203254  0.002491       0.007323    0.266447  0.986135   \n",
       "61180  0.006434     0.078929  0.000537       0.005722    0.241867  0.912894   \n",
       "61181  0.000360     0.001327  0.023443       0.000277    0.000687  0.999666   \n",
       "61182  0.009579     0.349004  0.002572       0.015793    0.107819  0.995687   \n",
       "61183  0.000332     0.000330  0.002018       0.000089    0.000104  0.999757   \n",
       "61184  0.014415     0.164149  0.002126       0.008185    0.035797  0.986634   \n",
       "61185  0.018936     0.028590  0.001903       0.727663    0.015598  0.757618   \n",
       "61186  0.004794     0.001374  0.000143       0.066356    0.001046  0.101585   \n",
       "61187  0.012857     0.183846  0.014774       0.012150    0.072243  0.983883   \n",
       "61188  0.001280     0.015992  0.017316       0.000494    0.002945  0.999001   \n",
       "61189  0.039465     0.000195  0.000057       0.000835    0.000109  0.041994   \n",
       "61190  0.000996     0.004760  0.000024       0.001364    0.970812  0.807773   \n",
       "\n",
       "           road  agriculture  selective_logging  artisinal_mine  slash_burn  \\\n",
       "0      0.001956     0.002205           0.000570        0.000054    0.000096   \n",
       "1      0.003365     0.004024           0.001773        0.000121    0.000178   \n",
       "2      0.008366     0.039851           0.000012        0.000013    0.000016   \n",
       "3      0.006056     0.011330           0.001824        0.000164    0.000290   \n",
       "4      0.008180     0.018032           0.000045        0.000060    0.000022   \n",
       "5      0.000807     0.001365           0.000165        0.000008    0.000026   \n",
       "6      0.312776     0.608390           0.005095        0.003962    0.004658   \n",
       "7      0.951758     0.225530           0.000574        0.004287    0.000098   \n",
       "8      0.002168     0.002402           0.000457        0.000037    0.000065   \n",
       "9      0.054970     0.158937           0.000601        0.000378    0.000880   \n",
       "10     0.075013     0.278967           0.000623        0.000392    0.001093   \n",
       "11     0.147888     0.586829           0.043943        0.004523    0.030674   \n",
       "12     0.005642     0.009155           0.000039        0.000028    0.000023   \n",
       "13     0.220298     0.522160           0.024826        0.003803    0.012285   \n",
       "14     0.012667     0.027441           0.000833        0.000208    0.000384   \n",
       "15     0.002607     0.001704           0.001012        0.000079    0.000071   \n",
       "16     0.763212     0.828580           0.010658        0.005544    0.009502   \n",
       "17     0.024966     0.056388           0.004689        0.001810    0.001454   \n",
       "18     0.006052     0.013874           0.000137        0.000092    0.000100   \n",
       "19     0.301170     0.199586           0.079453        0.028341    0.009593   \n",
       "20     0.191343     0.211980           0.019911        0.020932    0.004275   \n",
       "21     0.209208     0.366283           0.003179        0.008825    0.001651   \n",
       "22     0.718494     0.843155           0.004008        0.002958    0.005375   \n",
       "23     0.004193     0.006499           0.000014        0.000010    0.000010   \n",
       "24     0.275229     0.636665           0.000569        0.001650    0.001832   \n",
       "25     0.000908     0.001082           0.000324        0.000012    0.000031   \n",
       "26     0.820471     0.949800           0.000615        0.000499    0.001582   \n",
       "27     0.302885     0.780796           0.001540        0.000957    0.002910   \n",
       "28     0.402611     0.620469           0.003861        0.025751    0.005415   \n",
       "29     0.001040     0.001803           0.000184        0.000011    0.000031   \n",
       "...         ...          ...                ...             ...         ...   \n",
       "61161  0.077086     0.181145           0.020188        0.003734    0.004946   \n",
       "61162  0.728691     0.884454           0.003294        0.001940    0.005716   \n",
       "61163  0.017771     0.057991           0.007034        0.000891    0.001755   \n",
       "61164  0.005685     0.010938           0.001193        0.000247    0.000283   \n",
       "61165  0.183450     0.727333           0.008519        0.000890    0.012195   \n",
       "61166  0.000523     0.001041           0.000097        0.000004    0.000014   \n",
       "61167  0.006661     0.018473           0.000074        0.000025    0.000055   \n",
       "61168  0.001216     0.001507           0.000364        0.000015    0.000037   \n",
       "61169  0.004941     0.002574           0.003126        0.000223    0.000178   \n",
       "61170  0.001571     0.003759           0.000289        0.000018    0.000055   \n",
       "61171  0.377052     0.880040           0.002417        0.000438    0.006375   \n",
       "61172  0.291131     0.616300           0.026096        0.008820    0.017762   \n",
       "61173  0.004585     0.006474           0.003854        0.000436    0.000447   \n",
       "61174  0.000661     0.001299           0.000101        0.000005    0.000019   \n",
       "61175  0.000546     0.000692           0.000100        0.000005    0.000015   \n",
       "61176  0.003435     0.015181           0.000769        0.000084    0.000205   \n",
       "61177  0.831137     0.962549           0.000479        0.000255    0.002274   \n",
       "61178  0.004873     0.003977           0.000999        0.000104    0.000122   \n",
       "61179  0.620353     0.836582           0.006761        0.002086    0.007604   \n",
       "61180  0.667396     0.888470           0.001066        0.000680    0.001912   \n",
       "61181  0.002496     0.001911           0.002063        0.000092    0.000134   \n",
       "61182  0.293947     0.647000           0.007152        0.002460    0.006468   \n",
       "61183  0.000598     0.000750           0.000116        0.000005    0.000014   \n",
       "61184  0.090500     0.320018           0.004861        0.003836    0.003027   \n",
       "61185  0.054407     0.154083           0.001208        0.003537    0.000652   \n",
       "61186  0.009466     0.012827           0.000062        0.000087    0.000034   \n",
       "61187  0.179590     0.290149           0.027661        0.022529    0.007612   \n",
       "61188  0.010489     0.020818           0.005164        0.000335    0.000707   \n",
       "61189  0.001260     0.001750           0.000010        0.000005    0.000006   \n",
       "61190  0.987426     0.137013           0.000224        0.001274    0.000026   \n",
       "\n",
       "       blow_down    cloudy  bare_ground  conventional_mine     clear     water  \n",
       "0       0.000445  0.000285     0.000360           0.000035  0.999130  0.006381  \n",
       "1       0.000813  0.000473     0.000362           0.000058  0.998265  0.010875  \n",
       "2       0.000012  0.000703     0.000042           0.000003  0.000066  0.036886  \n",
       "3       0.000800  0.000883     0.000760           0.000085  0.990166  0.017417  \n",
       "4       0.000037  0.550392     0.000225           0.000045  0.001707  0.034938  \n",
       "5       0.000139  0.000234     0.000090           0.000006  0.999535  0.002520  \n",
       "6       0.001481  0.004334     0.023026           0.001650  0.643094  0.405633  \n",
       "7       0.000072  0.002508     0.012728           0.000894  0.993042  0.126930  \n",
       "8       0.000277  0.000250     0.000262           0.000022  0.999461  0.005090  \n",
       "9       0.000701  0.008518     0.003040           0.000350  0.219986  0.115348  \n",
       "10      0.000348  0.002909     0.002734           0.000195  0.008693  0.196112  \n",
       "11      0.006261  0.000962     0.025296           0.001739  0.997377  0.308423  \n",
       "12      0.000038  0.930138     0.000185           0.000031  0.006913  0.035617  \n",
       "13      0.003992  0.002126     0.026960           0.001675  0.991191  0.247607  \n",
       "14      0.000447  0.001330     0.001379           0.000109  0.974916  0.039106  \n",
       "15      0.000387  0.000299     0.000231           0.000033  0.999439  0.006944  \n",
       "16      0.001953  0.001015     0.040430           0.003607  0.993012  0.205725  \n",
       "17      0.002223  0.006916     0.004411           0.000597  0.474095  0.100653  \n",
       "18      0.000166  0.003779     0.000921           0.000065  0.726110  0.035345  \n",
       "19      0.006592  0.003696     0.030841           0.003902  0.991484  0.458204  \n",
       "20      0.001674  0.001550     0.027007           0.001570  0.992036  0.709238  \n",
       "21      0.000847  0.015649     0.058427           0.002065  0.911530  0.681233  \n",
       "22      0.000978  0.001880     0.028006           0.002514  0.947953  0.231440  \n",
       "23      0.000016  0.946299     0.000079           0.000016  0.010262  0.033120  \n",
       "24      0.000739  0.006791     0.020785           0.001745  0.001912  0.311279  \n",
       "25      0.000187  0.000236     0.000101           0.000010  0.999677  0.002923  \n",
       "26      0.000311  0.001187     0.013774           0.001257  0.074911  0.114042  \n",
       "27      0.001441  0.003496     0.137758           0.002101  0.974687  0.085843  \n",
       "28      0.002445  0.017114     0.113708           0.010175  0.467514  0.485189  \n",
       "29      0.000118  0.000228     0.000099           0.000008  0.998433  0.003401  \n",
       "...          ...       ...          ...                ...       ...       ...  \n",
       "61161   0.004314  0.002348     0.011616           0.001066  0.991551  0.180440  \n",
       "61162   0.000832  0.000736     0.022988           0.001939  0.984900  0.182388  \n",
       "61163   0.003165  0.001305     0.005400           0.000436  0.993634  0.074847  \n",
       "61164   0.000594  0.002542     0.000760           0.000087  0.851954  0.025975  \n",
       "61165   0.001658  0.000504     0.015808           0.000511  0.992354  0.197197  \n",
       "61166   0.000092  0.000178     0.000058           0.000004  0.999514  0.001681  \n",
       "61167   0.000109  0.000192     0.000081           0.000013  0.000604  0.026923  \n",
       "61168   0.000243  0.000230     0.000110           0.000012  0.999472  0.003750  \n",
       "61169   0.001664  0.000410     0.000572           0.000138  0.999243  0.009403  \n",
       "61170   0.000214  0.000270     0.000237           0.000016  0.998984  0.006208  \n",
       "61171   0.000791  0.000698     0.014911           0.000552  0.981768  0.125875  \n",
       "61172   0.006506  0.002977     0.035705           0.003555  0.982615  0.249421  \n",
       "61173   0.002452  0.000558     0.001082           0.000209  0.998492  0.013937  \n",
       "61174   0.000093  0.000219     0.000072           0.000005  0.999324  0.002369  \n",
       "61175   0.000085  0.000162     0.000057           0.000004  0.999581  0.001717  \n",
       "61176   0.000454  0.000617     0.001273           0.000051  0.997548  0.023273  \n",
       "61177   0.000126  0.000150     0.012154           0.000440  0.976413  0.125246  \n",
       "61178   0.000568  0.000844     0.000367           0.000047  0.997382  0.013635  \n",
       "61179   0.001580  0.001303     0.027497           0.001923  0.983750  0.153324  \n",
       "61180   0.000794  0.001387     0.086473           0.002117  0.988617  0.055176  \n",
       "61181   0.000884  0.000297     0.000332           0.000051  0.999697  0.005819  \n",
       "61182   0.001483  0.000937     0.018149           0.001128  0.976154  0.295103  \n",
       "61183   0.000102  0.000153     0.000058           0.000005  0.999503  0.001990  \n",
       "61184   0.000994  0.003294     0.026589           0.000696  0.975396  0.566608  \n",
       "61185   0.000688  0.097835     0.013362           0.001062  0.058266  0.486205  \n",
       "61186   0.000054  0.900837     0.000268           0.000058  0.006458  0.021499  \n",
       "61187   0.004607  0.004706     0.053727           0.003890  0.983938  0.521697  \n",
       "61188   0.001881  0.000635     0.001687           0.000197  0.999268  0.029743  \n",
       "61189   0.000013  0.956142     0.000038           0.000009  0.017438  0.014397  \n",
       "61190   0.000011  0.001329     0.004289           0.000398  0.993089  0.061962  \n",
       "\n",
       "[61191 rows x 17 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = ensemble()\n",
    "\n",
    "result = pd.DataFrame(result, columns = labels)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 61191/61191 [01:36<00:00, 635.99it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "preds = []\n",
    "for i in tqdm(range(result.shape[0]), miniters=1000):\n",
    "    a = result.ix[[i]]\n",
    "    a = a.apply(lambda x: x > 0.18, axis=1)\n",
    "    a = a.transpose()\n",
    "    a = a.loc[a[i] == True]\n",
    "    ' '.join(list(a.index))\n",
    "    preds.append(' '.join(list(a.index)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_name</th>\n",
       "      <th>tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test_0</td>\n",
       "      <td>primary clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test_1</td>\n",
       "      <td>primary clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>test_2</td>\n",
       "      <td>partly_cloudy primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>test_3</td>\n",
       "      <td>primary clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>test_4</td>\n",
       "      <td>partly_cloudy primary cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>test_5</td>\n",
       "      <td>primary clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>test_6</td>\n",
       "      <td>cultivation partly_cloudy primary road agricul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>test_7</td>\n",
       "      <td>habitation primary road agriculture clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>test_8</td>\n",
       "      <td>primary clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>test_9</td>\n",
       "      <td>haze primary clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>test_10</td>\n",
       "      <td>partly_cloudy primary agriculture water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>test_11</td>\n",
       "      <td>cultivation primary agriculture clear water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>test_12</td>\n",
       "      <td>cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>test_13</td>\n",
       "      <td>cultivation primary road agriculture clear water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>test_14</td>\n",
       "      <td>primary clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>test_15</td>\n",
       "      <td>primary clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>test_16</td>\n",
       "      <td>cultivation habitation primary road agricultur...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>test_17</td>\n",
       "      <td>partly_cloudy primary clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>test_18</td>\n",
       "      <td>haze primary clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>test_19</td>\n",
       "      <td>cultivation primary road agriculture clear water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>test_20</td>\n",
       "      <td>cultivation primary road agriculture clear water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>test_21</td>\n",
       "      <td>primary road agriculture clear water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>test_22</td>\n",
       "      <td>cultivation habitation primary road agricultur...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>test_23</td>\n",
       "      <td>cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>test_24</td>\n",
       "      <td>partly_cloudy primary road agriculture water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>test_25</td>\n",
       "      <td>primary clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>test_26</td>\n",
       "      <td>partly_cloudy habitation primary road agriculture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>test_27</td>\n",
       "      <td>primary road agriculture clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>test_28</td>\n",
       "      <td>partly_cloudy primary road agriculture clear w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>test_29</td>\n",
       "      <td>primary clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61161</th>\n",
       "      <td>file_9972</td>\n",
       "      <td>primary agriculture clear water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61162</th>\n",
       "      <td>file_9973</td>\n",
       "      <td>cultivation habitation primary road agricultur...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61163</th>\n",
       "      <td>file_9974</td>\n",
       "      <td>primary clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61164</th>\n",
       "      <td>file_9975</td>\n",
       "      <td>primary clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61165</th>\n",
       "      <td>file_9976</td>\n",
       "      <td>cultivation primary road agriculture clear water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61166</th>\n",
       "      <td>file_9977</td>\n",
       "      <td>primary clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61167</th>\n",
       "      <td>file_9978</td>\n",
       "      <td>partly_cloudy primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61168</th>\n",
       "      <td>file_9979</td>\n",
       "      <td>primary clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61169</th>\n",
       "      <td>file_998</td>\n",
       "      <td>primary clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61170</th>\n",
       "      <td>file_9980</td>\n",
       "      <td>primary clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61171</th>\n",
       "      <td>file_9981</td>\n",
       "      <td>cultivation primary road agriculture clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61172</th>\n",
       "      <td>file_9982</td>\n",
       "      <td>cultivation primary road agriculture clear water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61173</th>\n",
       "      <td>file_9983</td>\n",
       "      <td>primary clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61174</th>\n",
       "      <td>file_9984</td>\n",
       "      <td>primary clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61175</th>\n",
       "      <td>file_9985</td>\n",
       "      <td>primary clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61176</th>\n",
       "      <td>file_9986</td>\n",
       "      <td>primary clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61177</th>\n",
       "      <td>file_9987</td>\n",
       "      <td>cultivation habitation primary road agricultur...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61178</th>\n",
       "      <td>file_9988</td>\n",
       "      <td>primary clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61179</th>\n",
       "      <td>file_9989</td>\n",
       "      <td>cultivation habitation primary road agricultur...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61180</th>\n",
       "      <td>file_999</td>\n",
       "      <td>habitation primary road agriculture clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61181</th>\n",
       "      <td>file_9990</td>\n",
       "      <td>primary clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61182</th>\n",
       "      <td>file_9991</td>\n",
       "      <td>cultivation primary road agriculture clear water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61183</th>\n",
       "      <td>file_9992</td>\n",
       "      <td>primary clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61184</th>\n",
       "      <td>file_9993</td>\n",
       "      <td>primary agriculture clear water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61185</th>\n",
       "      <td>file_9994</td>\n",
       "      <td>partly_cloudy primary water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61186</th>\n",
       "      <td>file_9995</td>\n",
       "      <td>cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61187</th>\n",
       "      <td>file_9996</td>\n",
       "      <td>cultivation primary agriculture clear water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61188</th>\n",
       "      <td>file_9997</td>\n",
       "      <td>primary clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61189</th>\n",
       "      <td>file_9998</td>\n",
       "      <td>cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61190</th>\n",
       "      <td>file_9999</td>\n",
       "      <td>habitation primary road clear</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61191 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      image_name                                               tags\n",
       "0         test_0                                      primary clear\n",
       "1         test_1                                      primary clear\n",
       "2         test_2                              partly_cloudy primary\n",
       "3         test_3                                      primary clear\n",
       "4         test_4                       partly_cloudy primary cloudy\n",
       "5         test_5                                      primary clear\n",
       "6         test_6  cultivation partly_cloudy primary road agricul...\n",
       "7         test_7          habitation primary road agriculture clear\n",
       "8         test_8                                      primary clear\n",
       "9         test_9                                 haze primary clear\n",
       "10       test_10            partly_cloudy primary agriculture water\n",
       "11       test_11        cultivation primary agriculture clear water\n",
       "12       test_12                                             cloudy\n",
       "13       test_13   cultivation primary road agriculture clear water\n",
       "14       test_14                                      primary clear\n",
       "15       test_15                                      primary clear\n",
       "16       test_16  cultivation habitation primary road agricultur...\n",
       "17       test_17                        partly_cloudy primary clear\n",
       "18       test_18                                 haze primary clear\n",
       "19       test_19   cultivation primary road agriculture clear water\n",
       "20       test_20   cultivation primary road agriculture clear water\n",
       "21       test_21               primary road agriculture clear water\n",
       "22       test_22  cultivation habitation primary road agricultur...\n",
       "23       test_23                                             cloudy\n",
       "24       test_24       partly_cloudy primary road agriculture water\n",
       "25       test_25                                      primary clear\n",
       "26       test_26  partly_cloudy habitation primary road agriculture\n",
       "27       test_27                     primary road agriculture clear\n",
       "28       test_28  partly_cloudy primary road agriculture clear w...\n",
       "29       test_29                                      primary clear\n",
       "...          ...                                                ...\n",
       "61161  file_9972                    primary agriculture clear water\n",
       "61162  file_9973  cultivation habitation primary road agricultur...\n",
       "61163  file_9974                                      primary clear\n",
       "61164  file_9975                                      primary clear\n",
       "61165  file_9976   cultivation primary road agriculture clear water\n",
       "61166  file_9977                                      primary clear\n",
       "61167  file_9978                              partly_cloudy primary\n",
       "61168  file_9979                                      primary clear\n",
       "61169   file_998                                      primary clear\n",
       "61170  file_9980                                      primary clear\n",
       "61171  file_9981         cultivation primary road agriculture clear\n",
       "61172  file_9982   cultivation primary road agriculture clear water\n",
       "61173  file_9983                                      primary clear\n",
       "61174  file_9984                                      primary clear\n",
       "61175  file_9985                                      primary clear\n",
       "61176  file_9986                                      primary clear\n",
       "61177  file_9987  cultivation habitation primary road agricultur...\n",
       "61178  file_9988                                      primary clear\n",
       "61179  file_9989  cultivation habitation primary road agricultur...\n",
       "61180   file_999          habitation primary road agriculture clear\n",
       "61181  file_9990                                      primary clear\n",
       "61182  file_9991   cultivation primary road agriculture clear water\n",
       "61183  file_9992                                      primary clear\n",
       "61184  file_9993                    primary agriculture clear water\n",
       "61185  file_9994                        partly_cloudy primary water\n",
       "61186  file_9995                                             cloudy\n",
       "61187  file_9996        cultivation primary agriculture clear water\n",
       "61188  file_9997                                      primary clear\n",
       "61189  file_9998                                             cloudy\n",
       "61190  file_9999                      habitation primary road clear\n",
       "\n",
       "[61191 rows x 2 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['tags'] = preds\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_test.to_csv(RESULT_DIR+'/v2_sub2.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/chicm/ml/planet'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pwd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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