pr3-MaxPooling_benchmark.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jeongwhanchoi/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\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 matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "\n",
    "%matplotlib inline\n",
    "np.random.seed(666)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Load data\n",
    "train = pd.read_json(\"input/train.json\")\n",
    "test = pd.read_json(\"input/test.json\")\n",
    "\n",
    "# replace NAs with 0 in inc_angle column.\n",
    "train.inc_angle = train.inc_angle.replace('na', 0)\n",
    "train.inc_angle = train.inc_angle.astype(float).fillna(0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explatory data analysis\n",
    "### Plotting Statistics\n",
    "Now, I will look at some global properties of the images. These are things like the minimum and maximum values, the means, medians, and the 50% mid range of the signal values. There are two different bands used in the file, so I'll do this separately for each band."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_stats(train,label=1):\n",
    "    train['max'+str(label)] = [np.max(np.array(x)) for x in train['band_'+str(label)] ]\n",
    "    train['maxpos'+str(label)] = [np.argmax(np.array(x)) for x in train['band_'+str(label)] ]\n",
    "    train['min'+str(label)] = [np.min(np.array(x)) for x in train['band_'+str(label)] ]\n",
    "    train['minpos'+str(label)] = [np.argmin(np.array(x)) for x in train['band_'+str(label)] ]\n",
    "    train['med'+str(label)] = [np.median(np.array(x)) for x in train['band_'+str(label)] ]\n",
    "    train['std'+str(label)] = [np.std(np.array(x)) for x in train['band_'+str(label)] ]\n",
    "    train['mean'+str(label)] = [np.mean(np.array(x)) for x in train['band_'+str(label)] ]\n",
    "    train['p25_'+str(label)] = [np.sort(np.array(x))[int(0.25*75*75)] for x in train['band_'+str(label)] ]\n",
    "    train['p75_'+str(label)] = [np.sort(np.array(x))[int(0.75*75*75)] for x in train['band_'+str(label)] ]\n",
    "    train['mid50_'+str(label)] = train['p75_'+str(label)]-train['p25_'+str(label)]\n",
    "\n",
    "    return train\n",
    "train = get_stats(train,1)\n",
    "train = get_stats(train,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can make some histograms of these variables. I'll make histograms of both classes to see if there are any differences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_var(name,nbins=50):\n",
    "    minval = train[name].min()\n",
    "    maxval = train[name].max()\n",
    "    plt.hist(train.loc[train.is_iceberg==1,name],range=[minval,maxval],\n",
    "             bins=nbins,color='b',alpha=0.5,label='Boat')\n",
    "    plt.hist(train.loc[train.is_iceberg==0,name],range=[minval,maxval],\n",
    "             bins=nbins,color='r',alpha=0.5,label='Iceberg')\n",
    "    plt.legend()\n",
    "    plt.xlim([minval,maxval])\n",
    "    plt.xlabel(name)\n",
    "    plt.ylabel('Number')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Histograms for band_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x3ddf856d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x3ddf85fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x17d388f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x17d5a9898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x17d7183c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x17d646eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FJGk7YhhIkqpewlpj0BGrvjbAnrPqWoek5mfPQJJkGEiSDANJEoaBJAnDQJKEYSBJwjCQ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17d6e1da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in ['inc_angle','min1','max1','std1','med1','mean1','mid50_1']:\n",
    "    plot_var(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Histograms for band_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x17d3a16d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x17d431b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x17d73b5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x17d5c19b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x17d490c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x17d3a8b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in ['min2','max2','std2','med2','mean2','mid50_2']:\n",
    "    plot_var(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get similar results for band2.\n",
    "\n",
    "### Correlations Between Features\n",
    "Now that we've established that these variables may have some use, we should look at the correlations between them. For this, I'll just plot the correlation matrix. It would also be good to look at scatter plots of each pair of variables (this can be done easily in Seaborn), but i won't do that here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_stats = train.drop(['id','is_iceberg','band_1','band_2'],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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viceoaWYPmVm3fD+WiIiIrF9xpiBvX5VBXgomdz8mH3nXMLPmwHNAm3w+joiI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\nYBIREREpQA0mERERkQLUYBIREREp4P8BZk8dZOLdCTEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14e117ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = train_stats.corr()\n",
    "fig = plt.figure(1, figsize=(10,10))\n",
    "plt.imshow(corr,cmap='viridis')\n",
    "labels = np.arange(len(train_stats.columns))\n",
    "plt.xticks(labels,train_stats.columns,rotation=90)\n",
    "plt.yticks(labels,train_stats.columns)\n",
    "plt.title('Correlation Matrix of Global Variables')\n",
    "cbar = plt.colorbar(shrink=0.85,pad=0.02)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that there are large correlations between some of the variables. In particular, the mean, median, 25% signal, and 75% signal are all closely related, with nearly 75% correlation. The min and max are also pretty highly correlated for band 1, as are the min and median for both bands, suggesting that the signals have maybe been scaled in some way to force this correlation. There are also some correlations between the two bands. Finally, we see an anticorrelation of around -0.5 between the mean of band 2 and the angle, with a weaker correlation for band 1.\n",
    "\n",
    "### Plotting iceberg and ship Images of both bands\n",
    "It's good to plot some images before we do too much analysis. That way, we can get some sense of what we're looking at. The images are 75 x 75 pixels each with two bands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "icebergs = train[train.is_iceberg==1].sample(n=3,random_state=123)\n",
    "ships = train[train.is_iceberg==0].sample(n=3,random_state=456)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### images of icebergs in band_1 :-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m/oy30Jkml6dt34VTmjK3WZQ2OfQBOTFtKHhZoiiNMlK2lYxXFMGE7O6dZX1N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\npEuT1KvTNmqld5Yi8ndmz8uyFurDv5ceb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vhOQHmSrROuU6msj+ZixeWtbA5GPksqd5fi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\nKsU3QwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x143744198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot band_1\n",
    "fig = plt.figure(1,figsize=(15,15))\n",
    "for i in range(3):\n",
    "    ax = fig.add_subplot(1,3,i+1)\n",
    "    arr = np.reshape(np.array(icebergs.iloc[i,0]),(75,75))\n",
    "    plt.title('Icebergs in band_1')\n",
    "    ax.imshow(arr,cmap='viridis')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### images of ships in band_1 :-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TIbJNplbpwZEhWmd3VsaDkpw2MvMcuoDbq3Re551tDiav3TznLfe0F/QUDQeS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\nblH6IImeVdHdvT3eY26eLlMc0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BKQGzHe1GceOOUOuk5utHV6hCjw/J2oRvdn3ojxBoIX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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17d416208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot band_1\n",
    "fig = plt.figure(1,figsize=(15,15))\n",
    "for i in range(3):\n",
    "    ax = fig.add_subplot(1,3,i+1)\n",
    "    arr = np.reshape(np.array(ships.iloc[i,0]),(75,75))\n",
    "    plt.title('ships in band_1')\n",
    "    ax.imshow(arr,cmap='viridis')\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The next set show the same set of icebergs in band 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nndgsxxKqAFB11G2DwOPXjOg2cPwhxwSWMnr++h9s9TexAuT3dLfj+WfpiYrl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EKbO3RWFlZQmEE63bTTiHqHzlgPtwSd2aTFhdb66MUTUQBCsdSMOeVPc79UgM5qytE3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\nMAzDMAzDMPaQ/x+JjV6XlW8KZgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17d3860f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1,figsize=(15,15))\n",
    "for i in range(3):\n",
    "    ax = fig.add_subplot(1,3,i+1)\n",
    "    arr = np.reshape(np.array(icebergs.iloc[i,1]),(75,75))\n",
    "    plt.title('icebergs in band_2')\n",
    "    ax.imshow(arr,cmap='viridis')\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Same set of ships in band 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fcxoKjgedmeQ+yLJJPcY4PkRxMNPqi/7dmZaDJ8mKP6Okn429bj+2qp866Qat\njhMj+bDqz1pax3UnlstQzZIjaSO3Ldt0a1l4JM/NBKG4Jo6dgJTJbR+iQUZl19KhNsX9TJGWS6io\nNg/cK8sut66Ty6Jk6d7VpHsrS2C1JJcl33zv4mONznf9oA25P1VoHBSXZWzo51z5eLmD9rRa+oBi\nHPdjjrERkRSO7xX5Gi91oGRvMdexW9k+4q4lmJF1wxb0G7e4sSZle5J0J/7zTlVtR/fEynE32KYe\nk+2YZLfOcjV+1gHk2OX9taSQrfMbi+68557vdIDtOXneHFmic59tLMk6YYv86qmA3rttTn+HWmaG\nAni2zrJNJNLaQ/FjS9Zd6az7P0sE+XmkJ4c7po67V7b/b+yX27FsWyx1M4GkvjF1Qjby9uF4efaY\nVJZs+XmpAX5GLi1DwP2Qly3IdOIlhvntEG2qhzDgZ2d53dsvugVxZGUIG8lUATnGdP+M7l39XEos\n7cAyx9a8PHb9YGwRBDZTZhiGYRiGYRiGsYPYjzLDMAzDMAzDMIwd5IrIF7XbYRLscNhaLCZux3IV\nlpdkWlK70q3Euz62ZpRDFzlx8VR2hiQz7TlZVSwPYYcuLX/rTGdj92HYvRGQDkIpmnrWLjhhmmQf\n5HwWuRsCQE5JNVPdeGmHdv8Tq5LXWWqS3Jal867uWiTp275OygCiae3WXFo4amnaJIHoFl09Vk+6\n82hZE0tgM1W3T2NRyVyHh071Zb+benhLbNYru7psLbEkkJwY07JOuI54RXgtw42kX9lWqCQd7GKl\nnOHovFp2JcrA5aPiteZkfQXVgTZ14+aSkGGwvEg79vULSdZcu5PZL6+N3pfWZAxpzZLcjN1ARf0q\nyWNIMheS9/WzyRLhyOEyHYTIK3loa86VoX6QtcT0Vsn2MuToyq6B7AwIKGe04fF6xbSQL2q4f3Nf\nbau4qqWgES2Sfut4GUlOerkUgvnkWDN1yklqmgsUY5XiiGP9/NdcUNu+3tVjO0FepWNipiS3K5D0\nvj0bL1PlWD4oX/w1ldbkOO8OnV7TnVD0Jy3rFLGHi0D9s7432fWzetbVIzsP62253BxL81uyTrgf\nllbdsVvq/sl9MqB7pJbXAk4+z1I37lvaDba5NzmlYbfRK5P8dkr2EU7dSHLv1XTJLa+04o5XXk5O\n6eiRE2vlrNyuvs9913BmwMJ1ML+Z/GzB8jx2zgNke7PMWUj9VLoIs+9B92UwK/vf1nUcU13QKJ2n\nsXEy3k34zA/1AAAgAElEQVSwegITn1u2r6dUBJIsls8l7xNyygrdXnsluU/p/OA13ZH12J7Xz50s\nK493u9RtyeMoIMk7yxUB19e6pRTqh5KlwuysGGY4jSPeiREAOtX4Oto+pJ7zhpLDfi6FME3Py+w6\nrJ6XOe0lyZESkJLFAjlPaxfR6Hj9LBDMssyd2lK1nx7DcdhMmWEYhmEYhmEYxg5iP8oMwzAMwzAM\nwzB2EPtRZhiGYRiGYRiGsYNckZwy1t5rLW5ivhlt1le5WNm6O57I7bnI3DWty+fcjsaSE/SWlp1H\naHFF+oXmN11eU3OJ8hNm5e9ctqqvnHb7sG1vfkNZLbP+mTTTxTUkwjbcmQmWwB3KDyotUz7BlFo5\nfoHsWsnaOKUPzXkMaVd32o6UYctpzvXT+nDO9SpQzhxbOqdV6kuXtNDch/R2naGMvJ8Byudc29aP\nTMkN6TLSQbwOWX/OeuyA6jUdxOv2M60+upS71C249yVlZ81jge36OZcOkP2Yr13nHkWa7n4WSNGp\nhIW1uuzpr6ziWmLtW51vbV3lo7A9MedtdDg3Q+UDcb5Zn3NfVOzrk7a+PZMZvRa2ZCNx+8nlFtx7\ntmcffuv24UtSbcl5RFGuWDCVETGksCI9n8O9Lt5xf5w6Lr2Fe2UXxzoJ+b2T6BWTY8jGTS4GTD/h\nKqKfTbbfrh125eY60eXh+MT5c4GKkcV1d959D7gxESw5X+f2rMzHrJx1HYqXDND5xlE+Vmm1K3Lw\ndFk5r1Tk59GGRdXvOHeE8/GmH1fe0HB9gs8b2VEDQLckHyHChHSuMuUbA0Cd8p6F1f32eIyNPhNL\nGrQ5j0SNqyvyVHNlKJ51F5NflZXb2kPxnyza+T6q6yK/STkyW8l5o5xHxNbm67eqpSnylDOrLNEj\ndJ4n5zVxv5pob0/XwcfTlurM+We7DXV+EZchV4vfv7gR/xyl8+9qB1TMEXXi3tfUGO8V43OhMq3k\nuNca5tm25lPimvS443wuXt6Al5zQz2jNPXwviN8HcMuJFFdDsdRUZyo5TyvboOd0Orau4eIK5+NT\n3R2S27UXBt/VD4aonqB9NpPLwM+QOkeNEcsJUB3pdk6CcwI5fw4AstsXvvnZTJlhGIZhGIZhGMYO\nclF/U/I87/kA3uX7/os9z7sZwH0Y/M31KwB+2vf9a2e1RsMwdhUWnwzDuBqx2GQYxqVwwR9lnue9\nCcCdAOrDj44B+AXf9//a87zfAvByAH806RgsrekqSQpbdLMUhuUzWh7Wz7GcjuynlY0zSyXLJ+uj\n9wt/L+er2/N65fbxY2eUPKxHFu0ss9GWviwP2rzRnad8wpVn+yYpmWvTSvZsZ55ryGNPPR7vB9st\nkxSuIqdcWSbTIulKfkveG3h1d5ZtdUtiMyHvYikCWygXV5Vsr50avkrpibYc7ZIVb0Byuqkn3HVv\nHZFLJ0hrefc+qMp+F039d6op1A66+eapE1Jmw7DEM7ft+kNHWWqz/Kx43pW1p6Q+kdVwr5gWstAM\n1YmQY2LcQjyClyAYg3bJ15VF67CdMgGkVJPOk9+U8tqricsRn4SVu9IOBLQsA/fvAskktM15nvqG\nlrwx3Pej8wTTKRTkqgxCRp1bc9qKrVtd3NDyFW6/kIJIX61mwBKRSAqX7oZIt901cKwDgFzd9Yf6\nNEmEDsmxyPKYPC3NwZJAfT9gaV22kSyNEnbZJBfVMZKPz5JscU4lEY7kcgM7eypPU9mBL7lCNRec\nBLY/QbrdqbgG4HrQ1vnbh3KjV64TluAA48sQRORELE1esqNH9Vo7MH4fLAzvCxxX+R6SbcrYEGZY\n9uY6ZVBVDUhFEstGxIWx4dcsJ2tRmkC6q6SfK1fH75zLEZsYthUHpN06jweW5qVV6OZ7LEtXtbxP\n7EOS0ryKTWyxr7+L6FTl/xv7eckXOs+GknqNq2kBAIX15LI2F+M/Z0khABTX3Lnmv8rPE258Bure\nG9VRYzE9Zt/PVE6577Zuojg8JjF0x2epZnHN7dPYl3webbfO8Lk45tcO09ilfgLIGJTfdJ/PPCI3\nbO4fxPl8vY/iKteRug9Sf+AxuXWEpPVaXrsWf026H7fLQ0lzORQxnsut0wXEvYX61vYR/ezrjsdt\n2VWrdLUWB8dr7A9F/8oHHB/lPmH7wilWFyNf/DqA76P/3w7ggeH7TwB46UUcwzAM46nA4pNhGFcj\nFpsMw7gkUmGY/Gs7wvO8IwB+z/f9OzzPO+37/oHh598J4Md833/1pP2/fnIlvOnQnstRXsMwri4u\n0l3nqeMbjU+PnF4Jbz5g8ckwriWed+d78ZmP3LOj8ekbjU0PnV8Jb91rsckwriWe/gvvw9d++e7Y\n2PRkfIp4rm8KwMaFdrjz6H0AgE///hvw7NcfS9yutO4Ondty85Xdqpz3DdPuWlgCpJ3lWM4YSUoe\n+MSb8aLvfpfYjlesZ1g2qZ0dWbKUbSTLJVh+1hm6GH72w0dxxw+/d/T5uLSO/k9vtcwmV4uX/jUW\nk1cuj6RQf/WXb8ELv+c9rmxqqp6lPp1pKo66VHaqEY6ZJEMprSh5UK2PT33sDXjBq+4V0tacqsfC\nWrw7JLtBBhVZdzwdznIzUacYyEL/4d1345ve9D4hwdTSF65zbnNdVkbIqWrJDledagaf/oM34Nu/\n/17pIsrNr/5mwv2d+11xRWo9+ll38Vs3Os0pS1GBgXToM/ffg+fd9V5UTjstCY8J7ebJcs/Pfvho\n7LXtIJccn175ax8BAHzp/7obz7vzveI7lqL1KQyxTK56WsqI129xOgeWXWjnWX3sz3/g6CA+qlDN\nshJ2nRMuj1reR7KgJEkvIKWWvUIan/rDN+AFr7wXeYq/jb3y4OzSlW1MkNDweak87E7FsRwAUmGI\nL/6Ho3jWTx8bK2sS7DaXUVJ3drllORI7WuaUbLpTSePBj96DO37kvUIao50PuV1Yci5lwHIfloLx\nONfui9l2OCoDH7t8XpaVy8SxOUcOxZEUcnQuUimGKXfsgpKwf+b+e/Cil70bANCac52f3ZTLxzfF\nPs3rZkbv+Z6i61hcA0utlPTzbz7+RnzbD947drz2bLy7HODcUK9CF7NLjk3f++uD2PTVX70bt/yq\nfHZiZzd2ZtZxhmHJmnxmUK6K1A7R8b7w/qO4/XWyDJEbIACUz1G6B5WB4wUg5YwcC8bkdHRLSypD\nWppiizhYOesGH0s1ASnXFKka9Jyg5WrdMuC//W54b1fPDOdlffPxWC6YJO8c7EOSxSW3z/Rj8tiN\npdTouYVlifq5jB0lA3p+43uBrm+OVfzcyBJ+ADjw/67hz7/47/Avn/U2rD3TWWbqexD/vz0X/7eR\nYEZeX2HVbbf/Uy7F59zzK2K75lKIR958FDe/65hwsSwuu75WPquelxvxfbI1LzYT8ahymqXj8nj1\nfWn8/bG78S1H34fa9a4ByqfI+VbJMZt7B9c36S9FTyZufcHzvBcP3383gE8+iWMYhmE8FVh8Mgzj\nasRik2EYE3kyM2X3APiA53l5AF8D8LHLWyTDMIwnjcUnwzCuRiw2GYYxkYv6Ueb7/nEAdwzfPwTg\nRU9hmQzDMC4ai0+GYVyNWGwyDONSeDIzZZcM24dnVV4U5xR0KN+ln3WCVG2bzFb1pWU6trLE5zyd\nAuXc6DwGkQMQsJbWiUuzbZXvtO4EuBmyj063lMg5TQrRvjtGnmzT012pIm0lqEq1pSrXSzbBarOj\ncq7SPdfknJ+kRa5J+SBaw90j3XWK82fovbbRj3ID6ktpoV0PU0rXnnF9gHND+hN6rbDbJt14T7k9\ncy6cyB1UVc85ZWmqr17ebVg6L5MaGktO7M3LDuicwCi/q1tKifygIuVs8JIKANChpQEylGPRL8hK\nLp5ziTZsOZ1T1ttRvlhxrSfyyHiM1PdLsXhhOzlPbjeSaSXnYJTPu47cK1CeJY2rrlrqgPMNRF5C\nKAcZ28RHFvbl1R7aaokFbj/ug13KcewqW35Qf+f98xfZds09rs11TgHHCl4OZCxnjsYiyFCKbYL7\nOblPlBuRaY/ngTKc88DV2lK5C9UztA/l0HQT8ojHoOL18vpLym3mfFoaO7mavG8kLWmhc0K4jjlX\nujkvyz3nu8rsF1y/Efc+lStW3xu/TEMwNV7f9X2Dvl3ckLmHEau3L8QeC5BxonBOJrCkeu675mG3\ntAMvIRIR5e9yLOQ8Mh4TwNgw29Xs+bK74RZX5IVxrhDHmZBy1HWf5bym1gItWTAtt+MYxhb0nEMG\nyPs850LxMgU6r7KwHp8/pbdLylflXCVdHs5r4/uZXm6Hl1WKXYYhpjxR3encMB0feb/FL7r2i8ZS\nRO1QfH3xNWzdEN+Zu2VZDv1MxO05/49urPFSEprt6925tm929RWmlSfA1iCPbOU5c4n5eBruh5z/\nqJc34LZYfYZ7gNP9k+GlIaon6P6olnng3FzRt1Qf4njLOfg63zXKKc42Q1GG9gLXl1ryJWGZB+Yq\nzIU1DMMwDMMwDMP4p4P9KDMMwzAMwzAMw9hBroh8sT1HEi5lX5urubne2kG3XcAlU7PYuQbLQ9z+\n6UBKc1qLbk6/Wy7H7gMAnVR8NbCtq5a7sORNTGtWlfVqhSSQZFPcz8dLXwAp8eTpUy0dzNfddq0Z\nV74elSenLKJZ5rR1vStrmFVT8HTtqT5LtWQZuG3YIlpP9TKR/XMvnxKzu1pG0CMdSs65oyK/7abW\nU33ZLizxZAlPp6ple2RVW+NrlWXolhOkpPRxe14WnNuzy/JMdahI5pDuSitvlkZqyaOQ5tD7bkEe\nPJh1egE+RjCt5JDlQYW15jPCHru4zvJaVe4gXlayWyluukbXlup6qYgIlgR2qnKbqRNOgsVSVi0P\n65PkOBqXQSWN6ikZJAtntkfvW4ecjoMlr7rfssW6WG5hwtIekbSFl8MAxpcaYXjM67rjJQQ6UzyW\nafwqrVkUX/o5uTyFlv01yNKYr0lvV9sfL9Xjvi7s7OGkkWEKKK2RtfxBeSwhiU4YEmNW99RveDkQ\n3Tei62hPp1BaJVv/Kdk2mzdTQKYysDS2p2MxHYJlPGwhHlFaHVy/iKtsw6/WOeXr4yU7Wgekloil\niLxPe0HpzDG+JAcAVM67dtH9szNBRrXbOPccd2/pKYt2IZNmi/eENAINS950aoRYTqaUXJ/CYj3G\nwn7wXu1Dt8s9XyFZrBorbFvO5+Gxoi3HAxrLwQwto6PqjuFlKng8RPblo2MMh1p7Tlqlzz4s4/Xa\nba4Ps2RRH08sBUGftxbcdvp5ObKQ75VCtDn+6zEw7Y649jR3Ir5P6OeRMENpDhu0nEVNlrt2KHpN\nifLp4zWX3Mmy9MzOsva+eu5sO4d9UT9s8Q9gtBxAqiclkDwOuB4H53LvWUKp5ajF1Ytb2iF6xu3n\nUqI/6PMyegzHYTNlhmEYhmEYhmEYO4j9KDMMwzAMwzAMw9hBroh8kZ3zKqek/Qi7vOXq5AZEDkJZ\n5ViSa7AzDDkNFeXlsLsgOzOyeyMgpYSJTFBs9UokQ1FyJ5ayZUmCV1xx877BtJS/9XKuHnJU1pxy\nEGJJJUtAeEpZS4pY8pYho6vmrNyM63XMGYxgaQI7ovFUsZ7OHdVJGshvJDsfdmgqunKa2899rmWl\nvOq6cANSTRzMhaPX0jI5H61KvUefHe6EO6HbRrt9FdZcpfSWSH6iHPKyzcGFpDuhcPNkdzPdV8sk\nMWouOZmEdijtaze+mHIDTgLXz6SEMyOPHS1t2T58RcLGFYMlvVpKzLA0ridkqUqCMUvOsQntAIw7\n6QFAeyaNfk5apm1d7xzuhBSIu7fqJ5kER1HdlhzXoriRCYAehSQtK555zPVBPh7L1QCgRq6dLGVk\ntzKWJXNZw7Ts0/Nf2hbbNeedjFNI5pSTJo9NPi/L+7Q7YWt2UNh+NoXWnCv4JBkW9xsuQacq279D\ncnaWTY25MqbcK8vHdCzuVNz7LBscsiOl6mdCmk6ypzhnSNev+MZB55zgXMrtr2U7xTW6h8crTEe0\n5gfxZvoJd8Pi/t6p6Hh07fytefqx5PplSRcP0U7Vvddub5Wzrt7r+5LrSbsVR+hnMb7PC4khjbW0\nkuAJd8HrKL1DOQOzfJnj7SRJJssPcyxF1NKzBHdpRkvmItfIyulQPGf01DNInr4LZpLjvxx79D4Y\n39YdOzV65fJVT0v9YmPRlWnjaSQXp80Ka7LcC18hGTGVmyWFgIsz2YbsD7XDKvaWKF2nQ3GUnCa1\nayTLAJlgSv4/ihlhRjszUowfc8l071vz7v3S5zpqO1fWJAktM/jcnXffgy5OBbNyJ+7vSVw70csw\nDMMwDMMwDGMXYj/KDMMwDMMwDMMwdpArokMqrLrpwfaclObkN913ldNu3jZPLoZ6UWimM+W2y23L\nee0MLficabhjd7RDopABkuMXSYC0PIinrNlVMaXc+liCw06MLFnUbpACmv0uqgU423uc81bxrNMB\n9apOe1A7LHUjLGWqnHPnDWalRinJrWjMIZGas0WubcI1UM3gZ4YyhUwrFK5sWprDErqtI+5ExXVq\nV7Vgbe0A1TFJMtltEQAKQ/ed4vkUck2W0igZIP2fJVgZmvHONmXBWQZUJPe2/Ea8LqF8uiUWfi2e\nd+0czMmG6NF2+Q2Skc0nD2XernxCLs66fttgTj/Vl5LhMOXOox0gEbPI7G6GnSZ5UXdALgzf3OP6\noOyrWjpIMaRHshs5fFElyerWddnhccOxMcZyFparScfU5DYpLScvmiyc+UL3yv07rcIvLz7M8jXt\n8seyNJZTTpIfRW5q2lVNw9I/RvdVduJjeR+3X/lxqXPpVAaBI9MJxZgvJ8gc9Xm6VVwULHnUUqko\nTmv5GTvPDv7v3rN7XWnZdZr2dPI9IJPkIjzaePBSX3LXWl6Z4HyY4FaaUm3eJhfY4ga76cbuDgAI\nZsgtmNwzdfy9kBxyN1EiOT0vJK3ZvJEk09ohOQGWC+oFkHms8HjVzwVTJ+MbjMfDuGu024cXcdbu\nn+u3umviMrAUtqVdo2k7li/qhe35uYzjLcsS49xIAWDqRFe4KrZn5L2XxxFL+tjdEABKZ921cz1u\nH6KxoeJgVKY9X+6IMmhZHEvt8ut0rXmqE/U4snWE0nBm3HZzX1X3t2Fd6rbXMmn+ecF9YFKd8CLM\n4pyqrKVzqdErp81wP9ZywyT54coz5A1XOI+WOQVKpeEML79XBPrUVWoH3XNCSaXDVM5e+NnJZsoM\nwzAMwzAMwzB2EPtRZhiGYRiGYRiGsYPYjzLDMAzDMAzDMIwd5IrklHHelybVJT05yS+zZDPNeR0A\nkG6R5XjR+aaP5X2RtXhryW2n84Y4B4jzyDjfrbFPesSybpht3Wf/YVNsFyy489YOyXw6dyz527i5\nx9UXa/6zDVkGzrnavoUsosmaWufICLvPBcrH25J1l+pxLgbZDyuLZ9Z398TlUa6Ykmb3hnruXjEl\nrE55ZXYAKJK+u01a4aBKNqUq/4ZXdOfzZlQZIj1wqq/av6N10mTrzTa6bB+t0lv4GMV6ch5AZ2pQ\nYb1SVuiYe2VXkd2q1Ip3qU9zGpHWzBcpp4St8zdviPc67mdlfggvv6Ct/JPyeXYr3JZdlRPK44Xt\n7ftxuVjRd5TnkKtTzpWqx8ai61DTJ7qjV92W2ZrrQxu3ug7eLbItszx2hnK9OFdQW/5zjlI3Wtoj\nJa3kx7T4dIzYPKSo3GKJhfgcDp3LUjkzXCaiG6JIS0ts3ywTtaYeb47e1w+6IJQKVe5IOf7vjmnK\n4dq8Ta4HEl17mJLLHbSnVLISnUra6lNu7YQlSRidGxvFu14BQEC5npmLG3t1ul/pZTCS0PmGAEbl\n5bwvcY9UebL9rGsLzs/mHDAAyG/R0h6LKogrovt1Y5biPjXFzHGV13QNhSfOd+FYBIzncEbwUjeR\njXsE53o1luJzjQAgvcXW4skVmpQ7JpajUfCYXPiyS15bf3pZbMfPFhVq49J5V57GPpUDTs8ga0/n\nHHdZhuIq3/Pd55xXlWkpq/thPXAuFzA5BvYoRoc52U/btFRQthWfX9ZR9Xj2+bnRK7cZPwtq0hQ/\n+L1e3oCXIODlEloLcru5hwZjN7/VS8z7A4AiLTUUzPK9Krms3Qr7ONA1dGU9LP1tffRau94lUTb3\n0u8GdX2cJ8n9pHJK5fpRHhgv/6PrIcq7C2ZC5LZTtB2XVfYVnWMWh82UGYZhGIZhGIZh7CD2o8ww\nDMMwDMMwDGMHuSLyxXTAlrdyqjCYzcd+l9tKln01DjlfaJYi9lpyer8z7S6Pp/o7ShImLKzp/dYR\nmj+f4GTJq3b3isl+yCyTqu+nfZSqkaVIOed0j8KG/A3dnnPHYIlSgSQ3uZqUL7KkZPq4m99duy3Z\nNpllV9qile13efV6ljxpO9N+tFko5VR65fjWojte9Qn3Oe+jZRJJtqfaljeSFWWCUFhJ97NKmkD/\nDRP6QF/J0oIZmtIP2FpeyhciWW6m3UO3FO/jnFbyp3Q23uY3ty3bmZee4HGVr8vjRdeXVVbifaoT\nlosCQHqCVGK3oyVBSfbtPGa1NIb7J48dLUVhyVsvnx695mpywLT2OilaadW1M9uPBxWxizhvjuTM\nWibHfTraLtfoI6Bja2kkx4Y8SYy1dT7LyPI1tsF2x2vm5bG7pdTolZcuYet9AGgvuDrpkByqFa/O\nHZSP2rJNsUqXO5JdhhkgmLD8A4+DwnmWElF9q/HLY6lHZe2qGBLFtW45Je4bOgaxvJLjGMdlLVMS\n8tPshBsbUaRlbVoLJKmbkv7rRVrmoUXLdOi43J52A4gt7Nuz4+WJJGU5sv9f+Kq7qOy2HC+Flbgr\n2P2wtA6Q0kROMWCZFstOAblkTGeax6Q8V65G7UArRuj7P99XObZwGcbu0TT+T73EBS69HAIvE9Hc\n4669dojGrk6NoDGVdQrnMRmnkLLRtWvJ28XAdQ8A29ezhI5koOfkIMjLlTjc/mSJ35WKztG9JtOW\n44ZliYMyuffthXjpYD+v94mXrLbmk2NE5Wyy1JL7nliqiM7TU/GaUyiSlmUAnNR1/ellsfQBS1tz\nql04lWf1NophM/r6KG7x74apCTLeKe778VJ9QKYMJWEzZYZhGIZhGIZhGDuI/SgzDMMwDMMwDMPY\nQa6M++J0/HQgICVY2YabXuwXyPVMSbt4n5CmO3vKPSrTjHfEEs55gJBzdCvuXNVTThbBMhEA6JAT\nF0tA2rNKGkmysD67PrK0R7nZZckFh88b6JXj6b8s7+nRlGleS81I/9ImZ8HKOanh2T7gDh7SebpS\nrZIIS7jY1Qdw9T91sof1W0hOMadWjicpQZum0MtnqE5VD2YJhJhSbmqZY2r02qUp9OKmrIdM2/Ub\nrmNuc+0uyU5I2aY7HruGAkA/Pyx8PxQyXJZt5deV9hPkzFhyZesoZziWPYZZlnsol81hfWWCUPbJ\nDXdRmaYcV3xN1wKZhrvWgmp/lmCVl8mVqeDGThivPAUgJWVaQsuSsMbe/Gj7zSNSSswuZCzJYWmc\nlqWGLGeifltfUhIokh9G19EtpqRENdTyI/ee3ROzy1razDKV+NhX2FSaJdqmPe3KyrLNwbHddzxm\n60uyMbi+hFSTZOFaQhNJCbuFFEprJG1X0s/SOadbDebix2VzMfnvnvmYuo+D5UOpnnKEY1koxcIc\nSX/YBRMYtG9Ecb1H242P6+hamvPxcvt8TR67vo86B32lXTG573LMHnOrhBs3hY34+2I/r1wJS1fk\nseaKwFJhfZ9heRc7KbLEcPqL58Q+QWX/6H3lZLJMTp8rYtztmF2a4/fpynAm5LTcZ7UDZIoc99iR\nkNMh9POIdCdlOW9yDONxyLGW5ZOAk8NpWdyYRJlcMlt76JyqW/L/p59wY4+l49qtL5K/VU+Eos3D\nCV0+Q88+6QlOruxk2S2HidtFjoTbh7OYfdg1RueIzMNhySJLPJuUlhLMqmdDukeGGVcPKRWaIkl9\nc28KzYTcoqAlPy+ddwef5HCp+3hERj1DRvLffi5EcYXuR+TsKZ0Y5XdJ2EyZYRiGYRiGYRjGDmI/\nygzDMAzDMAzDMHYQ+1FmGIZhGIZhGIaxg1wR8XVuy+liy6eb4rvWohPxci4No63EewX3W7I9Szk/\nSjvPORucs6NtgFP0X7ZXZvvijMoHEbk5tH9hQ4pfOb+I88vKy267TEvZmVOuF+dBpJQclW2UWZdf\nPR1/3QDQpbw7/q41K3+f9ynPgnXtOr+E81VylBs3dSI57yiyaM82+sjVKTdP2UJ3aHV3oVefUCfF\nFfdBZ4qOpVYqyA1zIdLdEDnqN+VTsn+mlpxonduJcw81oq/lqL7T+bjNEWaT/zbCS0YAMm8yqQ8C\nygaf+mQqwc6+dC4Q/a5NOSRTD0vv3l45/jp2K819rrNry1rORWjNJojNFbMPuaSJ9ae5/tPLx2vf\nAaBTSY1eJ+UXcX4G51ZoK3/OjWgsxevdAWkNHxFUUyLvI6OWS+B8Ho4NY/bxYcJ7Qlsd54dLeBQ3\nemjscW2xdZ1sl041vi5LK8ma/Q6XT+SexpcpmE2hTLlsU18+L4+3b8btQ/lmnGPS7cpysm04t7PO\nY+jn3Wt+g3JeVP5bmGA9nqNcL32/mzrpcuG6lH/VWBof11G453iS7tGYUHbZnMPH92O9jAbntU3q\n7wBQPjdoLO4rzUXKhz4p7zXpbkKe4i6kesLV2+yj8jrXb42vOM55Ov5DB8R3SZbx8/+YfL+uPrI5\neq89ATgecZtyjppeEofHyvRj7nOdf8P9h+3Mp4/zskV6mQD3PqBxrfO5gmn3nmNJp0rPD+qZoTN8\nHtw6khYW+2WVSzv3kHv+WinSPVXlzHdpGZNe0bXl4T9dG70//Z3zcp9hvdYOp5CmetU5gOxTMPuo\nq6+VbyZr+gU5TvhZpbjs6qR+WG4X3U8aSylkG8nPAtXTrs24r0rb++R7Ii+BoXMeo/tOqB6duE70\nvabMEFQAACAASURBVK520JV15pGEBEjInNSNW9w+uqzp7uDkhbU0Kqfcudhiv7FfloFzDJOwmTLD\nMAzDMAzDMIwdZOJMmed5OQAfBHAEQAHALwP4KoD7MPjb51cA/LTv+9fOn6YMw9gVWHwyDONqxGKT\nYRhPhgvJF18NYNX3/Ts9z5sH8MXhv1/wff+vPc/7LQAvB/BHkw5S+tqZ0XttX5vfdFO9bbIV5tXG\ntQQvyf5TT62zHLI1586rJTuZdrxdM0uXMsoOuXLCTX+m1513avOmhfjCqXKzHISnjYGBrC9uO7Za\nHnwXP/XbnnUnyrZUzKddhIREK48S5lBZrghAyICkzTTZnlblwSJp1fbhrJCu8HQ1AOQ3SY5DK8yz\nZCKr7Gm3bnTvub4rJ+V2C19pjl65n7TnpT6oS3LWXjG+n4RKdsntd7GwrFAs7aDaRUhESMabVRIz\nXg6i8Njy6H3ngJRDtJYG19teyAnpLp8nfUTqu3LbV40l/mWJTyyN0fbGLBFtzVO7sHJUNffGrSR5\ndUqxMUv15h4nbYnkzOXl3pjMUdjqU3lYuqvli7ycRGnFXV/luPR5bh5yGprO0Iq5uBEKabSOBcV1\numAqQ3tOS4ncdkWS0Nb20xIpE+5AfK29QrLMhdESKI4VXF/CHl9ZX0fLBJSWQ7RpqYn2C/aL7Viu\nyXXCts45tVQBy3AmqMJRGEpvCqshClvu2Lm67GyteVc+YRmfSW6/TiW+0uPaojVsU21JnURPyN5d\nGQpbKj5RvOKyxsmro21Zel+gGMTL51xFXJbYxP1Zpxhwv2WpXXNPsgCqWyFreVruRR+bl7pozc6N\n3uvlbfhZip+/yudIzqviHscqlnqNSQxpfC0/y8VKliyybEwfj+WuWv7WpdSIDNnEl8+yNFfuE0kb\nO9VQ9G29ZFN+i/opyRz1skrteVeXbVfFOPkv3In1s275bDh61UusMFs3kIRuKX58ZOpqCalWfIzN\nbevnG/fKsuvmXm0t744/95Abrw1aJqR+QD3bk/0+L+3Q1ClHw7ZNB1LWWzrTQhKbN7tOsHx7JXE7\n7oeTlkjIrrm24H5XP5i8nED10Qvfxy4kX/wDAG8bvk8B6AK4HcADw88+AeClFzyLYRjG5cfik2EY\nVyMWmwzDuGRSYTj+lymN53lTAP4bgA8AuNf3/QPDz78TwI/5vv/qSfsf98+ER7z9kzYxDGN3cnFT\nGE8h32h8euz4cnjDkcWnvqCGYVwxXvJd78Rf/cVbdjQ+faOx6eEzK+Et+y/CHcAwjF3Dt/7UMXzh\n/UdjY9MF3Rc9zzuMwRT7b/q+/1HP895NX08B2LjQMX7ih94PAPjzL7wDL33hr4jvWotOspjk6jUJ\nlvZoCQhLujpD6d9nP3wUz33tMbkdSfJYPsHT8XpqnaeOWRajnSJZmhFJyv7Xn7wJL/ye97hrKOrp\n4Xh5kD52h6Q17EiZCUj+qFx5IonSg797D577GlcPuaZy2EmQQIxNmdNm7HzDkoWulh6lgL/7naO4\n/XXH0J5LduFiCZZwb2O3TOWQFMzE/5GBpZDAwNXqsx8+iue+5pgoq5Z79vLuArkPFFddxWpZGveb\n3JarsNZeKY3sVNL42/98D57/6vcilyB57CvpbnsmvjxaJlVcY3lPspQ02wzx6T94A779++8V5Wbp\nSCTnishvumt/4BNvji33leJyxKfv/6WPAAA+96GjuOOH35u4HUs6ub10223c5DQdGYot7IgFjLsx\nfu5DR/GcHz02Fmsq5+PdMwMa/10VQ1iGma+58pVO1iE3dPs195fxyf/6Rrzw5e9Bcz5ZEsbHLq67\nsrFEHABKNEYai+6iWBZe3JB1l6v18cCfvgkvetm7R7EKGJeIdsosFyGJsTbI5PhEsYvvFTqmlc+H\n+Ox9g/tEe5ad2eR2LE2tnqKYm+BwCkgpUY+MyzJKdZNph/j8B47i2a8/Jtz0tNts+ZwLuq35eHdQ\n3c+4/fg7LRH93AeP4o4fGYwH3b9GqPJISS1/IbcrbLr6ai64RsopU7TP3jcYExq+tyfG38WLc0t9\nqrgcsem1P3sfAOBTH3sDXvCqe8V3AY0Pljnmtyb0v33xzzT9fPI+kevc1375btz+OtkW7L6ZlFbS\nLcn/V06TJHyB+5/cjqVfkUzu6288itve+r7R59rRMFuPly82l2QAyW+4ulv8kothPB60DLSfBf7+\n2N34lqPvE59rB1ndH901pBK3Ey6/eyi+rsh99ny5jb/6y7fgJS99JwJKUxlzz6auz8fmNtftxc/c\nvF17r9Qu5zbSePitR3HLrx5Drkbtp4wYq5QywjFs+xC5py8k97s0yUqnHpfb5RohHvzoPbjjR96L\n6uPORpRlifnN5GNzv9Ou7VMnXIOyG7N2yE13MHqGZPkiS+Z1HY/ifCkhnuIC8kXP85YA/A8Ab/Z9\n/4PDj7/ged6Lh++/G8AnJx3DMAzjqcDik2EYVyMWmwzDeDJcaKbsrQDmALzN87xIH/1zAP6953l5\nAF8D8LGnsHyGYRhJWHwyDONqxGKTYRiXzMQfZb7v/xwGgUTzoks5SfOg03009sk5apZCFc84e5Pe\nP/ij9+2XPVfsw45TY26AhFxcOV6iCACVU0470trj5mB5wUuWselya3dIpkOywhSVhxe9ZikOABTI\naYanuINpKQ/iKWaWg/Aix9rZka+JZXt6yj1NM9aTHNJ4ypslRjyd3lfT2pHULswMHM4ieNoXALoV\ndnaic3J1qebPNuPlrFoOG8mS2rMplGiRwcpj0gKyO33hxc1ZRgoAaZIHRe6GwHgfimQP6a6UouTJ\nYU3LhtgFrbSZvN02LbbL8qzSmpQisDySpRssew0qun9fkTXnL8jlik/lFXex0eLFEex4OnUy3u5K\nuGVCyiG4vbREuD1LC6cPx3wwnRpzFGVZ2dRDbiHvMi2Su/I86frKUmCWInYq0klzTFo83J7jaqAW\namZZIcsXC1uy7toz7rxiQXXqPtzvASA7rKMwoxxAlfsfHy9NMsLcBOkWx5MsyX3HXNEoNvA4yMp1\n5UVMYUc5NtzqKplKadm1Gcc0vcBzFNfSXXkefXXBDMlCqb5Y3qfvkUnxnGWuY7CMnuKblpyHqXj5\nmF48mvsQL1Te2DveHyOpEbux8T1Ty4JZ0rlTXK7YxExafH76CXKQJQn+6m1Sc9U8EG+jmdtQzzcb\nNF7p/q0XCxdtTO3I9/JeUS3ie5j2oabSTnVMYT0+tYUXOdZw2SYtUsxugDxetQw0kuBVzvbFGNi4\nUT2XkXNlekJXDCmusrPf9KPuPS9yDQDnn10Qr6NzqmcsfhbjGCYcKZPXfZZy1p5+BkmNXkvn+flU\nyzPde3bp5PGabel9WNpKZVXPyNPHBxVbOt8Rrooc27TjZnmZ45tr89pheezGErkik4uojuVRXQYz\nKeHUyYula1m6lkrGYYtHG4ZhGIZhGIZh7CD2o8wwDMMwDMMwDGMHsR9lhmEYhmEYhmEYO8gVSQ7h\nnIucytlId53GcvOb3LLmqac9f/ReW4myfp+17jml1+T/c35R+ZzMDQlm4q1z06TLL6y2xXec08B2\n9Bq2p+ecItb2FjdkuUUOAZ1nzDKeLiMpj6yrcjZE7hHnCSgr+HRA+SBslaqqirW+bGnLZdX7NIZa\n6sZSSuhv8xvJelthYU11p3PFuhXO56EcC2W1HGmX051QHCNYkELkDuXxcX8QuXpark7/Zz13aUMK\nzJt7naib88jKp1zySv86WR7WTLP9tM4HSfeor5FOu/rwptiuMz84ful8gHC/a0DOIysvqzHbmZB7\nsgvplOPrCgBSpLvnOMZ29Lm6slsmzXye8qw4xwoAChvcn4afbYYiXwGQS2T0qpSjWHKdgXNKAaA9\nTbmsdDidHybi5zBnIhOEYkkEnYNU2HLn2rwh+RbCY47LkKvz56q+e/I1opdLLndp1ZVH509laMzW\n9rv65/yHjMr7yNVC8Rox9Yi8b/DSB5yzzHmJ3ZKsH84L4uU3dE5ZlBOWbYfiHqnrRZTvZDv28342\n+f7UmqectJj8wv6wyoqbdE2F+L4FxCxJMETn7YnrEPfm8XvA0t8N4mEwfXEW9/pcuxnOd9K55wzf\nvzn3XFtyc+4Y541pOEeJx7G2f2d4yQi2wdfLWXCOWfUEnVONcT4e5+YUKQd8bIkegp87KqfksZu0\nLCXHOo7dOZXbG9V/p5wSOVK6TtjmPdNM3o7rla+VY5OOvb1SODoHW8bPPCLLunkz5atRmjyXIb8F\nAVvIN5bc/pzjBrh7pD5nT3kCJMFtpvOndX5XBOdsAcDKMwqjV87T4jxAneMmnnHpuXjyUgxkda9y\n1LrlwXaNfbItKqcunDc2CZspMwzDMAzDMAzD2EHsR5lhGIZhGIZhGMYOckXkiyzt6FSlhKdNq5Kz\nFKJ81kkxaoflvChPS6f6bJUqpw0La26utk0yjdaClEGw/IinKydJRViyyJboWgLC18TT+Fmyx87W\n5YlYstituGOXzsu5+nTb7dchaUd9v7tWvco6S4ekxFD+Pme7fCGhnKAM4e9yJAPQ8oXIVjfTwsQ/\nC/Axcgkzwp0pWSAh/cwm215HMoX8diim9LUUNaiS3CN0F8J9I6OszrnNWK7UK8h+Fy3FUDnVAmiM\n9PPjVukRLGXi6Xi2btdwn972ZsV30dhsqzHBErXCutR3XayMaLeQI3lgoJY3YIlZa47aRYwJWfcs\nbWnOu7qaeVzJpikWRpLFTCDlagDQLbNs0umCSivuePktqY0J0y4GtGfIwn5D9tVszLWn+lIOqZf8\n6FMVZeIVc4PtKPaw/IjrW0s1menHnIy3sV/eA1hyymOkrSQrwkqZbaLpvZYpRnKWXgEon3cbNhfk\n7ZKlTnwd9SW3nZYfcVlZDltcVzFkeB/pZ+S16u04DtUOkPyY6mH6cdU3qP0mScEBFwc4DrJElOX5\nANCiZR6CKbdPaU3dBKjKub9nVSwFgObioCOxFFXcu1SMbCekI+xG2Ia7sU/dw0h+tn2Y+hzF+8K6\n7Nul8/w/svvW0kgaryz10xIuluDxswb3Sy0drB9ELNXT8jkoSZIZLZEAjD9bcJxhWZtebqdyOj7u\nFEjC19yjngWG9vSNfSl0ptx2pXPyGPlNV75OleR0c/KcxWV3/PLZeEmmTv3YvMW9n/R8Wlp27xv7\n3LHbNG4Kq7JdqqfpOXht0vOEe+V+o/saM/uwe4bYuMV1FL3EQvVEvISS64fLoC3meYxoWSIvD9FP\n6KsAkNtOlo8yUQwKMzIecblnH5UHz29M0P8OsZkywzAMwzAMwzCMHcR+lBmGYRiGYRiGYewgV0S+\nyFIf7VSX7pFzCk3tpQM37acd0dIddrThaWQps2LZDZdBy4MYlvOwJEw7NKao3JPcnlhywdIMloPo\n6VN2emQpnJZd8jR5pu2OXT3l5r+1XJSlMExjr9yOp6X5WrXbEU8fC+kAXTfLuQAg3XOvKWqyfl5u\nx848QmZDm5WWleyHrq89T651RSVrGspCuqWU7JMpWQ+lZXfixl5X/6UV97muY3a4TCe4jAFAc6kw\neuVr4n4yJmXjY3d5qj7ZoZSn8XnsAEBp3RVQyE9IFqzlw+woeC3AskTdRuu3JEihaLupE8r5shNf\n9815GW5ZmhK1f1BJobwqj8dtyc5+wbQ73iRntvx2vOx2UD6WAbpXbn/taNinc5XWyPU1m9wHO+Tm\nyW6zdRV3onFeX8qi7eXGPo8okAyztZD8t0Xer1uha6Cm6KrYEEkR0x2gsZdke02xmaiX2gFljTtk\nTBo55d4399H7PXL/SJrWnpXX1muoslLc5zpmtg/Kfseuvdw3dD0AQH3foFxpitMsyW/NJT9CVM67\nvqFl79wuXO4wPd6WUQwUjqI0RLTzbNx1XAtEbm8Odhem93RP1o6GLFnke3lpJdlRlyVh7HwIAOXl\n+P1a1G+1NHLqePx5eur+35plSWb8PvzMAQCVs64824foWWBBljukWMXHCKaS+0507cXVUNxvt2+Q\ndcDyN24XfQ1aXhe3nX7emnmYX5OfY4XjMjlctueS3QSXn+liEDsr6nJHZUp3pGOjPh47SgfUlizv\n03FdHyNCyxwjl9ZsQ5aP5bRaeqidSEfl3Ir/HABae+j5XcXe6Pk7v5kSMlV+jl17mozr5bMXngez\nmTLDMAzDMAzDMIwdxH6UGYZhGIZhGIZh7CD2o8wwDMMwDMMwDGMHuSI5Zblacg5K+YzLn+I8sm7F\nCUU5zwuQ2nLO2dF25tm6E6yytbXOG2E4j4x1wzq3hzXtbPmfVzkEnDvWKzh9Kett0x15fcGcS+5h\nW1i2UAa0jT1ZWFOOG1vqAtLqnnM+tB1ypsW5cKRDV01ZO0jacefWjaBHx1bpFqO6C4EiWSXXDslC\ntGfJbnXNfT512rXr6m2yCwez8RrgwrosQ3S9YVrm2WhLZtZmcx6ZyOWYis+LAYAC5UnqZQei/Ld+\nNiX6F+fwcU7i4BjUttQftDabj8G5Pn21REKUY9gpp4V+vUl5OqlQ5XR2r62/5bD9u4ZzafhPWJUz\nlEul8jZ4jOSpf+u2ZKKcsGwrFO0KyLbk7zim1Q5K4X+aLNo5ViXlMQAu77afTYlcsVxNdq7mgjtI\nJkgOpvN/e3b0fvNbl0bv81uuo7GFOuDsrttzKdEfdV6xzHl1n+s4xtfLOZO8NEh5Of7+VNjsozU3\noa9TkTjGZUSerNxFLOfSTc434fjEdvs6n5Nt+tuUd8H9trCh8mnokni5BG0vDrglDzgn7GKXRWE6\nFZUbR/Uy97B7Bshuy7xwACg/Pmis/o3V2GPrXKRJVuG7jekn3MVwbhAAtOfc+x6l/XLuo84V4zwr\nzt8priXnc/N5OWcHADZupJz3xfj4Nv2o/Hzhy+4gTVrqgo+l4fsb5wDpuMD567xP6ZzKd6X64tyj\nKFcpjuqpQd+cfThAnZafCAsqp7Hv6jhXI3t0Xx/RnWvDc59mWm6ffX8rk8qi8Z7uJOfZAfK5j5/f\nKqcoD/awqhOq/q0byI4+oU76OZnrpe3fOa+Ql+jo52mJrGn1vEztVyRbfx1Ho7y0xlJKLE3C16pz\ngDmXbVLumehrG24ffrYEgOKKW9qJny/rB/ANcW09XRmGYRiGYRiGYewy7EeZYRiGYRiGYRjGDnJF\n5IuMto+vH3TzyLlttsEnOV5G7lPYipcijlnGk1yMj11Yk3IctutkGRnLjdimHgDCLFlJ5915WW4I\nADzryjIntuhPKVkbyyF5n56SSbGVa/HEpttnmnSEKWlnvnWdu1aWG2r5G9s9l8+5KXTdFs0Fd4Vi\nOp1XQVCymOhc6a68poysYmkZTj21NePm2QM1/d2tunbOtNx2Wn4aTW1nm/I8wZSWiJId+QbLF915\nKidUf5rjOuGlD2QZ8pvd0WuPZLPNReqPyhJXQHXM/VuTJTmEbr9I+pPqS1v1XJ3GgZKI6PGz22Hp\nWWFdXhv/P9N072uHafAodUecDAwAcnXZRluHXceL+mAwlRLyN2B8GYOI0orbMNNKtttnm2ct6RCy\n2eFmqT7QWHSV0t8vZUVsdS6klqoeeqfOjN4Xr58fvd88ImMSE1nI52ohSrQ0wPSXzontzr94v9uH\n+qq2TubxU9ig89A+ejmJSPrTnk0L6S9LOgGgsYclvu7z6ceTx0ez6eqyvj/ZGrq43h+9ssV7a162\nRY6k/YVVV57OFMdVJb0XlvY05mvj5Z1+dNBhOtPxWqlUX5YnqJLch6T7WmbG/bC5xx07NT9+nsb1\ng7UMWK4bsGRc9btONX687EZ4qQUtk05RnxFLP5B0LZIDj/6/yGkO7vNMIO97efc4gVwj+V7QWqTz\nzrlCpKif6yUrTr/IrU3BZdB2+wWyW6/vcxu2XCgZk7WlKG2itIxE8ufjLd9Z4qapHR5IFs/eUUCw\nQM+qTVV36/EpHby0ACAldFmWU5NccPuwjOu1w4PXDU9ea+W0TjFw78VzZ45lqXKfzZvjU050CgyG\n18SSRGBcRsz9ZutGLg+VUy/RQzJTlhVq2/rOUMkcZpMt9vV9uEgpMME0vZ9R8u4MPfevubYsn1Vl\nHUqDK2f7E59jGV1ncdhMmWEYhmEYhmEYxg5iP8oMwzAMwzAMwzB2kCsiX2QJl3Znyk1wIUz6vFN1\nxWZJg5ao5TfGnZwAIL8qLYSCWTeXyfI1lhGFWWVbRqdKkzxEy8Mqx7dH75sHnXsUu1711BQ8n7dy\n1s3HprPy+jpVN8e89c0Lo/fseqOdJsvkxlTYindiBIAw5f7frbjzsDslAMw86vRB7ELYmk2ezo3k\nM/mtUDgs8urwgHIaFCu90+fTUveTmXHl6dVcebSkZeTSk5J1rK+P27NL9c2fsxMjIKU13L81katd\nrtZFiqyPhCxlrO/HSzK1NJIpbro66Zak3CicidycQqSpvovrTp7BcloAaO2ZYPu0C2GJWrcs64fH\nKeCuu3zeNdLmEVkfLJsorbh6bCzKcNutsFseOSwquUiK3KrKy+7gjb3k0qrGGMeQiY6LQp7XHb1u\nkWymn0+WXJTPuRM1Vb/Y/L5vHb3n8SelHrJvRdeRacv+3Sb5IyAd9vjYWp7ZXGIXSm7nZOnPKLaH\nkPJ45cTI8srIxRSQEnF9ffVD1ObUZpXTcpwXNwYXWFzvobafJOfF5LaoUhyrpd0++Zo69rLTvQcV\nJyWLc3NrLQ46TyaId/fNK2flTsWdl11NS+daYjuW/+qYm4R0m6XxUriwJGi3wrJP7RLHkv7sdnz8\n0BKu3BZJ6yrxUkYAmHnEPSMt3+76SHOPvj/S81fdxc5sPd7pTsP3dS3VZ6fRrSPxTsP5Ldn27EjZ\nJGmldl9mZPmS76MsBc1uu/LkN2UZlj7jBva557ng21ySjVF93B1j+rH482qHxMhVNR2khIth/YB6\n7jwdf89giSHXKSBdEZnmkvx/1Fca+0PRzrpvsPMnO3hqSS3DElbuG7oPRf8vnQ+FLJTHiE4DSGI8\n3SC+fPr5VHxX52ul3xBK7rt96MKxzmbKDMMwDMMwDMMwdhD7UWYYhmEYhmEYhrGD2I8ywzAMwzAM\nwzCMHeSK5JRxXkxpuZP4Xa9Alr7TrmjaarN0nvKYpjO0nfyNWSfdOluG149Mie2a8/G/TdvTrM2W\nZWCdrrDbf2JNbNe4dc/o/fYhd00yD0Jbcrr3XcpV4PwPQGvp4+1M9T6cD8C292ytDEjLfs6f0vpb\ntm/nvKZJVu6ZYf5MJghRWiE7a5WzATqXrBPaJpus8+1Ou3bprcuuHuV6ZNqhWMaA61vDeT9cJ8GM\nPDaXtVOhZRlUftn29cXRK7cT50uwnT2XG5BW3npZBfEdtUt+Uwqto3zBdDdErsljMb5vAUC2kZwn\ntxvhcTRu3e3+31h0ddxYcvlcekxkyQq4uZAcYrk/pXv0qnJjgxmqf7J5Zr06Wy9rOI9X5xd1KK9t\n8/qceI0oLct9uhSnu5TXllZ5jY2leLt8tkrW8v0o/61XkPl8W4eljX4qJJtuqkde1gGQFss8Djhf\nlffn/xc2+6KOa/tkvmFaWJLzNbljszU9APQpXvXokpqLcvxGsblbTIvciEDbvdN/25SXWl52sS+U\nxRb3ALm8wXguRZRLxvtEubDAeA4m1yXnd2zdIDtoluJYq5x8fwGAYBhD08mrfshjN5PvCbsNtvvu\nqZUkpo9zTiO1HVV1RqbyoXKK64at85PLwLlBOq+tuOL6bYls5rvl8b4UMf9Vl3O1cYuLH9ouvLhB\n905aroFzZLtl7SNA+UWUq6/LzTFo80Z3HrZH53wpwOUopfoyp1XD18S5eulAHq9+yNVrtum+2/cX\nbimRPi39Abi8T7bNB4D6AVkGfv5ijwGxRMi6zhWPXyZAE43Rfj5ESM8nuo65T/LyBpwfJr0C5P85\nRgczcruoT+YaYWK+mj52PqEMQUPl7VFc576v88GC2cHr6jenUD1B56WcTt2nqycvHJtspswwDMMw\nDMMwDGMHueBMmed5GQAfAOBh8PfOnwTQAnDf8P9fAfDTvu9fW38+NwzjqsZik2EYVyMWmwzDeDJc\njHzxXwOA7/sv8DzvxQB+BYN571/wff+vPc/7LQAvB/BHSQeYZNfN8odeKVmqx7Tnqdh06PEpahfv\n8ltu3lZLlPgYLJ9gS18toeQpynSXrNKPSOtmlsOxVKR8zh07pYpTPuOm94MZN4/cnZFlEHIGvgae\nWld2+605kiWyc766NbAUiaUrGiF75GUCqAxBRe4fDKelg2pKyAhY9gVIe2uWYDVJ0oG0Wsk+TW2W\nJctf1Tciu938Vg89slrW9sopUa+80rtro15RHrxACtYmybsae6XUJ5ILtWbT0t6YrlvLJFhilN8i\nCW1NLQ1AsoJs0x2ktUdqYCI76m4pLZaXYCmwbj8t99pBvuHYpOkpCVeWJJ0zJBfavIGW5VB2vTwm\nWAY6Jg9jWeHQ0jq/HaK+TwoYWIJUX3J9aO4Rp2vTMa2+RJb22WSJCRNJ5rKtcKL1OseNNsUkLbXl\n8czxkutE2wxHMswwDdQP0FIqStMRuFVM0Nnj+n75uNTdsHQvSzKVNEkC6/vluIyWpGgsZjBz3N03\n8tuyrNuH3H4s92H5XKjuG+kOSRuLJD+bir+/dEspEVe1FLGTi7+mVJ8su7dlEAlmSV5Fxytsjmuy\n8uvjvtLNJRdDeipeshyWpZZazs7SeY53WqIHuP7LfYDrWy87kKtfFfHpssQmllKlZIgXS0tw/QpZ\nrYoLWzfQOCyRBFgtJbH+dKf94nt05ax8UOAyJB27sCr7yNk7nP6Qnzv09bFcnGNJknU7ALQXWB5M\ntvCnlF07pcCs3+rKI46t5IuR9LNyKpxo696mZ6w4Oe7oux7FZarH9eftG73X8rdIYlg7LMfuzMNq\nDCTIM7k8LAkFgMIax2j3OS+dIMsvx66+t2zd6N6nA1eGuYfc8TKbcp/q4+6Ap1/klmLQy0Z1BdFf\neQAAIABJREFUqoPjbR1Ji37DMXrPl2XsCmZdAWsHVCBNgPt3X5Uhup9kGymxFEOnmtw/m3svvHzH\nBeWLvu//MYB/M/zv9QA2ANwO4IHhZ58A8NILnskwDOMyYrHJMIyrEYtNhmE8GVJheHF/VfI878MA\nXgHgVQDu833/wPDz7wTwY77vvzpp38eOL4c3HFlM+towjN3Ljq/c+o3EJgD4+smV8KZDeyZtYhjG\nLuMFr7wX/z97bxolyXWdB97IjNwzK2uv6qpudKOxJAiKICGKBCVCIKWhKFPWZmtkaSRRtHmkOaI0\n45lukJBE07KORfNwSKIxo3NGokwOzWUsn7GoxbJs6mjjThHcSUAAE0ujgV5rr8o1MnKbH5mR97s3\nI7ILQLML3ed+fzIy48WLF2+570W+737383/81gO1T8/XNj2+ttm/Zclsk8FwPeH2tz9Aj7zrRKht\n2rf6YrlcflOpVPp1InqQhL4PFWjwL1Ak3vRr/4GIiD7z3+6j177+3eIcbqfiFu4kypwPiotIL9MU\nF6RjBRSQz/3p2+ieH32PSOfNcH5IuxMqjwW53dmc5e+Zbb5I0wU6QMkMaBaf/fO30Sv++anR712l\ndDPzmJJMCtKl5AN6c3whqoS5dS7P7k1yzzVQLfvG/32SXvVz94feh0iqifmgIJhUCoJIjUKaG9JL\nYoqWQH2ir33gJH33L58SNJa4orikN8MlHDfu5L316q1KzTPHN+tVuRD507KrL361RZ/8m9+gH3jd\nuwWFVtMcm9A3UoIuGC2/hH0A1UV3SoqbA/WA4yANtNnUtny+1iw/E6oqaUphZoPrQdAz07IPua0e\nfe5P3kZ3/9P3UquASqacRisxYVm//v6TdNB4PraJiOhnfuujRET0lQ+dpO/7Z++LTJcA5cL6MtAD\nVf2kdjkd9v1kRQ6E1gxQIJs9+sx/vY/u+bH3UHNWdkK0YzjOcexpmitSICcphcWZhUvZzU5oGWJK\nDRK/V24EO7gu07WmUZGKf09W+FjT1fxinx57x0m69Z2nqLXCfT87I/lVdx46PzqeTdZHx//1Gy8V\n6eI73DioxJg/D2OsIiuovuTSVz94kl7+S6dE3bmKJof0PKQZ9SaMnV4iXN0tvSnzdlt9+tJH76VX\n/uL9gg6Jaq5Ecu5A+5k/D9TWjqIfAQ3b6QHdKyP73Sf/+jfoDTe/jYiIdl++NPq9Oc9lSCsFt9Qu\nqDnC3IfXEElVs8I/bI6O914qX0D+/v9762ieQvo40ia1a8GIypc/eB2z52ub/tFHB7bpybedpNJv\nPyDOIQ0M1yrbt/P8WDui1iNznM6pc3vnzsq6QnW/wOZ88Q/vpTvfcoqigOu3+irMgUp1MFHlY5xv\n9foN6XDtqcGznj55L734Pq4HPV+jncGx28nJeph9OOwJpNqlpiu7DaIvf+QkveJNp4SyH1KzdbkR\nzWW1dgJ6ZXKL7Wjh6WgVxOoxoid+/STd/H/IdkjuyTKgsiJSW9FOhVGFA2jVTo2HTp2gl5x8QOQh\nlT2j63LuW8z3rx2Vqqyo2IjlxryIBsqK5d8+MTYmUKlWz0dRipLYb4lk+2E7twsyv35sMC5veu8p\nSu1wHjOPRU+4O7denjZ5WatVKpXeWCqVfnP4tUEDofKvDHnSRERvIKLPXvZOBoPBcAVhtslgMLwQ\nYbbJYDA8F+xnp+xPiOg/lEqlzxBRgoj+dyJ6lIg+UCqVksPjj3/nimgwGAyhMNtkMBheiDDbZDAY\nnjUu+1JWLpfrRPTPQk69Zr83ibeQzqO2FFEZDALgIj1BB6tNAt2kB9QFTe9D1SoMEKzVIDGgKpan\nscz7nTooKW7HIqUEaQREsoKRJon0sIRSHawdYfoB3jdZU1SRHaZDYRBnPw+UnW1Zd0gxxLx1GVB9\nLQ2qXHWlIJiqIBWGf0f6KVJkiJhq57b6Ioh2Z2zXl5+jAfSX6s383IliS1zR3uX99PyTXCfFMzJz\nbz4x+kTahKC8kgzCihB9dYJSHfZ3TYcIaDdxX9LSEkBzS57bEdf0Y6zu2c5y/Wh1M1QybYMCaF8V\ntd8OlM0cETwaN9E1rVS350HhStgmIkk31fWD9OjqEe5PSFnRqqGZCNpt6ol18b111+roOAgy3Zxz\nx2g8GKgceyPStrTKF9LDEJr+hrTXoB5i7b6wic1ZeU1zMZxgoRWykEoUFfhXXxNQTHoJIgLFrpXp\nikh3V/Gp0fFL0hw9tP5dKZGuvLs4Or6QYr/m1B7QO5WkYWALE40+eUDB1IrAWP9IA0Uvy/0EYSWS\nlGUipdSJcYE3NRec4QP9uDnPN04qqjXOhckNpn7GmuN5t44O7I1QoYWidhX1J7XJNNPaHYXIsram\nuP5rr2NqpJ6viHi+xvtODBD9AjBPV8o2dUH5TqsOirUB0NLRHmlamwMdEul93oKi6nnhNDI9z2D/\nzl+AdZnLfTFZiW6QxjKstw7L/hdrwRwE5dFBgRG5C3yv6ceZy4aUzkEe4J5RCJ+/sxt67QTq4Ino\nOR8VIPMvZinmmZRUA1z/IgeGxnJjnWi7XjjTH35KdUNNN8Syo5sDQttepP7h+Fr8e7kGqd08iOQ8\n9UxX0PFQfXMcfG7tLlZVTG/tb7AWzsmKCPp+/lyfvFlwe4IFd3pXXoNlRYVErZ6M35Fqr2m4o/ts\nOMJNAe1rV7mLTKI2ju5z2RQGg8FgMBgMBoPBYPiOwV7KDAaDwWAwGAwGg+EAYS9lBoPBYDAYDAaD\nwXCA2Lck/vMByoKP+X0BsmdZK7WfhOjbN0gScbw17gdBRBT3FCcZzqHsuZa3R78flDrHiOcoeTnI\nm5+pk+P8ULJc542SyuirlL0g5Z7bBSb7ol9bT6lp4n074IOHkswo405E5Of5GpT+dJsyHdYd8mIL\n52Qd4zn0hRG+YsrnSvqxwAlFL8b8UHoVueYdT9V3jjP05kHOfk/50gw52J2UI3xfeq72ueFjrGP0\nv+rF5fOh7GwafCTG/Esc+IRnF+EIDs+IS3D8pCEUg6/6dAfSYf1rnwC8MfLXUSZch3mYxKe/FoF8\ncPR10Sie5s7QmkYfTnkN+lOiP1j9jkMiHfp0BL5Cmc0ONRaizTLmh+XWfmhR5dFt2QYfU78w6KB7\nxxLCh0j77yQrfE0KfKGaC9Fjp52DE9DPdH8MfEfinkPt4vizBFhJ7IT+/qbFz4nvXyscGx3/Wexl\no+OLe9wWmXVZ7mCc1lZiwlejsSzv1c1wvej5YQRl05xueNgQ7beHPr7oEzrmwwX+vug7hnL03rS0\nDVNPccO053huRT/ZUXmH/qOZDb6m57J/jpajrx3lhkY731J+Lb042B2IlerNjNdjqzi4Fvt4eoc7\nke6f15N9Sq1z22Uv6ecK98dBX5rGslrrgF8M+kH6RSVnvgx+UeyyKfx3iKR/lzfH91r9JPsqNg9J\n7XX0cRJ+glVZVvShQ9+4SSE1UnvhdaJ9bDFEA8r/Y8if2kq4fLlfdMif4u8x6Som7Pqtcxuj4zun\nzop0fzDNvpTFJ8BHWYQPUb7+Q1+qXsKhNGc9JsO/fVv4Oi+uyorAPoBzxvr3yjVIYLO96RgtfYk7\n0fnXSL89bL+e8l/jssn+FBaKgYho77i0H4FPYOV4tA9YYyF6UuxCOIJxv19Yv6HPm1rHBr6Drbk+\nxZt8LvAPJxoPIXVFJPENBoPBYDAYDAaDwfCdg72UGQwGg8FgMBgMBsMB4qrQF5uLvHeJtB8iSdXq\n3sgSug5Qc9JbkuOCkvbtAufnNqTcpF8ESXugGOktU6SfIe0rBvdJVGXevSSn8wvwbqt2z1Pb/CDe\nHJQH5FV3bsvLa+BeguKk6sGfxm1S2I5Fys1C9HapC6zJMansFlIH+BkaS5qeCdeEyGsPflf39fqj\nT5T11e2CFJ4EhAPIXARqTlvui/cW+GZ9F8qj5EwDKqnb6ou606EPhPy7Ey5V63aUnLUfXufpLdmH\nOkNaUqLeE+Ogned27asRGoN2QcqiDjUhaLPQH/qKahlIwPcdKXstZNjVNTE/nCJyzQIfb8LfVNsl\npmegbdCSurVV7pNdGC5aFj5Z5X4TtKVfiI9RPXygYyFVC2laYxRTGEuTqI04NrvDx3P60u7oMSHp\n4/x7elOWAaWmkQaIfbM1LfNuT/VGn0vHWE76l498RqT7ydzu6LgCBi8bk/ap139mdPxnxPTF/Mu2\nRsd7VUmP71YHeVRLbTr0SaABJWVFxjpA/QQ6E46jTkbWSXoTqJ870IcUBW9kI5t9as7D/KRojlkI\nwYJUcqQ86hAwzSXmfGHf0DRcRPUw932kZ2vJ7gSMheAZiIhiESEaiCS9WtOwiLhu8F4deNbMlqTU\n9yOkq69FIB1Lh95A7B3nft8BSp+uT6QsijmsHl1nSFFObys6nRd+3TP/iGmsbjM0yRi0fD/K9KOL\nAY4V7ItERPVl7hfJCh+3iopmewTGHlAy4zB34z2JiDrD8ATVYz1BQ9bAeq21edxkFc/xjpdxWI/H\n126CcnMaTa0L2rPnyrWBo6JZIO0Ow7fges1fje5PQgo+PMILERFVjvFklVKMct02o7Jth/5MRJKy\niFRtTRF1h5O223CoXYC1KlDrMSQCEVFzEUJFrWOIBbU2hHGGdN12dIQPsQbw5mBeKMq1oA5dEAbb\nKTMYDAaDwWAwGAyGA4S9lBkMBoPBYDAYDAbDAeLqqC8CXaWvmF1Ijer1wik32bNy/7u5wnQ/pPC0\n5tReL+zOJkGlSitYIX0JKReoQINKh0REmTXmAaDSlaZaIvAcUoD6SkFo7yg3S2qX0yEtiogoWQNl\nP2hJDyguSPsjkpTA9A7USSp6Ox5pkoFK3KhMK0CbAMoMUgwy24r66UbTXxCo7FU4y/f1p8fkckbo\nN5DSBxQ8VcfusPn6MUlRTG/JAiUrqOYI9Q/VFVfKlXlQQauvAJVN1XHQ15w+UXodeCXQ7yo3yoL3\nC+FUn9yarOPkDmzdQ35aYc0vDjKJdfrk1rtjvxNJ+gMRUSwZ3VeuRWTXuK70eBH0OjjEPuxrlcCI\nPu3uyRNo44LjfkxSB4nkGBGUI7BVmp6DlGy0NZpKjDYgeKbMZk9QyiaNUVlQ+TVRD7cBgZpeWN79\n2OBcajNG2xWmFT7VWhTpzqTOj45vSvB88LdNaaf/y853j45vKDBvppYGpbCi5N18/dFjo+PKMVCX\n1dMGlD3BYnPUmkblWqV2CRQYpIxp+mKgHNZYiFEXxp9bl+nqh1CdN3xc9ibYdq3OqzGiLUZloRVz\nIV32Itu0+mFpx1BJGPsnKgcGQNXiAKgOq8um1SavZTSX+Nkby/JBkWIWpQY4/7A0JrvHw+vGVfQ3\nVMFDpUKtvijKU0V3D6SHyXRIoUPal56jkzt8X7RbLVDo1OtJrIfqEZ7DJlHwUEVV0/sRqe3Y6HP5\ni9y3114pVQfbec7vVbNPURRWsnuj44dnud/nz3IZkApHpOiLMH/oUuO6GNcJWMepLeXKAOmSoGKZ\nPy8njd1bksN7yHqNqT6EbYZlRXqtpsNiX2uDV4+mkgY0d2+hR/0E0un5er1exrrEPqnnIFQOn3mM\nx4+nFGSDtfT8N/vUgjUArpdQxZRovL+GwXbKDAaDwWAwGAwGg+EAYS9lBoPBYDAYDAaDwXCAsJcy\ng8FgMBgMBoPBYDhAXB2fMpC51Lx3lFHPbDBBtQ1y3/2kLCbyfoV/kua3wytnF6Tunb6SdU2h/wX/\njj5gblPJmWficI5Jqd68kmR2w7m9k7jLGH0e02mfBsFxhXPIW9VcavStQxn8rvITQmnSJPD3WzOy\nLRLoC4F+VtCuXSUlHdRDN+nI8ql2QTlYb44fqrEMvnRFSWR2M/y9TcwpRh8pIuaydxMOdROcd3FN\n6pu72+wskiYmOddXmRit+zT2wxTUN/oeErEUdCftUAPyw/ALKJtOJEMcYF/tqfbrZvh5sc16yp8y\nf3ag0Zre9Cle4wz78RykUrKuIT4e1zIaELLDm5FtlNlG2Xr+HW2LrlP020S/T+1/43p8LrCRma3O\nWH4o/53e5gHjtHuQRpLuO9NwW+if6T1pxzIXub/Xj3CbC3/TtCxPc5b7A/qKOT2ZDvt+5Ui4H2h6\nW/bvdnaQt9sgap1j4v9fzt0u0lXBOeLnpx8cHX+u9j0i3W6b62U60Qw9bnal74FbaI8+MeSK9gfI\nPRNuw5sLoT8TEYcdICLqwbEOlzD1TGf0ib66HdUWaaj/eAv6Qxak/FWoEZkOQ8WMlze4H/qLRM1P\nRNH+h4Uz0q6iv3ZjhduyVRh3uvCH8xTaVT8X7VCTXVfOLdcw4s3ocAHoc4Oy4GibdpXtxvAomN/M\nY3KhUF/mk94s3keVD+Yg9KVBX6G9OXmNV+Q+61a5fOhLRUTkgO8R+gDlzvdDf9dlQEydkX5RlePg\n650Orzu9dgrsm9Njv6owtA/zve7Mnhkd35bYFOmOJjdGx3+5yPYtDmF+dDiBYG5wPaIedHPdN3CM\noobC3s3Qn5IqjA6EkvDBR7m7JefEYO3kNsYl+xFCph/K54PPrQ/zFBHR3MOwlgZtBH9aGha3MShf\n3HPIAd/c2hFOk72kfTDxeaPX3+jfe+51KEwhO0Tq/KCdtl8Uo9lHuXzZDVgzTD17/1bbKTMYDAaD\nwWAwGAyGA4S9lBkMBoPBYDAYDAbDAeKq0BeRtqW3m5GSgJRFvwDXHEcqlaTapYDqpelB3QRSFvF3\nJfGNsp7dcHlUb0FRXGqKbzLKS20JQziAdjb8HTilpLLbeeRdQnTxGVnudi5cGjazztcI6WAiSq8z\njQTLJqS/iagjuJJIhZFlR6oW0rGQXqLluoOs/bwzaReZ2hBpvfIi3gufWqxxObuyThtr3Fdmv4Fb\nx7JdAnpl3O9Tos7lrh+RNLDYIeZQONAWcT+aloZUWZSz1vRaBFKMvDmuZC2Pnt3kH2JAI9T0Wiwr\nUjp0328sp0afboO5CNhP+jE5aDGEwPWA+iGQPVdsg/oShJcAOfIUSAZr2m16h/tqYwHaUlG7Yt3x\nzq+pi0RESbBxSEtD+V9td1Kg8o7S+doGNQ9x2wbP3o8TtaajxzxSfNpg2zNbkjaG1DiUXkdJ/Pqy\nrPDAFsfaRIkap3vmHw6JdK9+7enR8R9s3TM6fnD9qEi3kGX68ZTLXKsYNMZ0QlLrXnH06dFnBah1\nW005DuobS6Njv8h1jGM27mm6MH/PXQQal6c6h8Of2gYgsI5xvkJqus7bA/opUv06IdL5AW0R6eho\nqwQ9l4hSWy0KA9IViYi8xVR4uhC2T2849QrXAqDGJvdkv+umrh9J/JnHuH5d2U2FhDlSz/rxaHof\nAimpSFfUSHMkCcpd0C4GfF+0QZMk6MX1QKGrr8pzmTUMSQShgY7w78XTsv+hXH56i38/+0Ny/Yb1\nld4MD8XkLci8E7VwqfrenVWR7v982R+PjruQIYbuICLy+0xnzOa5czfn2eagxD+RDIMw/y2+5sLd\ncjxt3AljdC98kaXDdaCd8af4ms07JEcxMVx++UVHSOfrOagHeeDch3WP7UokJfGx37kNOW8FIRtm\nHhE/q74hO+HecX4ODA2A62UiSclMbsM8mJB2JRgLuQt9sa7CZ8D2IpIhLqJgO2UGg8FgMBgMBoPB\ncICwlzKDwWAwGAwGg8FgOEBcFfoiQlMxkNYQpUioaXu4FZpdj1aCSnT5HFICkYpBJBWt8DgFCkJd\nFVG8B4qCmfNMp3M8uWXqeOF0juI3eet676Xz4lwLt32hvgRlingbmUgqMYmt9aTccu3BFjrS6cZU\nfjAKPEaHV2o7SDFEOgTSAPrq1T9QK9SqYPovAtFXOkDN6QCFK6YVhDiTqae5LbpK+bAxP8jDLzjk\nF8IVDYmIHGA9YB0hZWaMlobKk0AV0uqZAa0s3upHUht13SFVsgd0o1ZGNkwKVPqQvqaplgiso26W\nH1zTFScph16LQKWq9I5sTOyjSHONTRB4a+egf0If1tRRoSA3hDcdp2Q9QsKOiFqg5oR0wfwFqS4W\na/GNa0DJ1RRDrYzK5ZigTuWF/z42ngGJRm9f6eLDx0jW+oTc5uSuLOd//ML38jVF7uvJlLS/q/m9\n0XGlwxSfpZSkHCFeOnVu9Ik0x0utokj331/F3LDWNvNh4hU2ntquIq0YqV9eRtrpYB6qHXKFLU7v\nKqoNUhZnwue4Mbo+dIFWke8b1u9GNG+gJoXRHAOgrWjNcn37SlUR06E90WqzRETukCKPlKoEKK65\nu9Jod5cmcPauMdSXo/83R3U7pCt3stHtk6yGU7CRckU0TpuLzA/yQKok9pf5b8prmvPcofH6yo3K\nPkIZkJKJtML0lnKNgMdoLIGqthqHkhoXPinqemwu9UafiQq4Z+xJ6uBulyvitaC++HlP9ss3f/lX\nRsf5v2O3Cw/UKoN7BnB6g/u280SXXsX31UqKLowPqfoNLhiKWo30zKi1JRG3iz9FlNyD3wvR6/TU\nDrhaAA13+7tk3ngvpBXOPiLnt3hz0PmLTzTIn0FqKj9sfIy2DfeZ6kE62fenngEXEXARSKh+EqiS\nF852aOfWcBnKMXef6uXXTrZTZjAYDAaDwWAwGAwHCHspMxgMBoPBYDAYDIYDxFWhL6IS1ICWwtBU\nwgCo3pPeUdfA1jgGwmzn5eNg0FMMmqwDdeI2Kx4HW6RhqB/C7Urec0UqIxFRb4qpQx6o6rRWmQpT\neKournGOgiIa0jvVzmcb2DTebPjWs6a/ofoaqlhmNpRyGrQZ0lVSFdkWWF/JXQjcDDQrHVw5oEqg\n0iURkSurTqhD1iucn5fj7epEWpYbKR21VW4jTTcLnr2bdISqXqIpt7y1otCoDBAkXKfBoOPpHYpE\nEPA51u2LdnbrEOA7H60kFm9G09xEfkAdi7VVJxp+jXX7gnaJQXNjvrwPjrnrAVoZDpGFcZGo8XFz\nHgJOT8tBhjQH7MM9RYdxQH0xUJ3rphzq+jId0s9QSdODoOC585JTiJSO/Fnm7rTmJNUG6RWCzgzZ\ntVW5e0CBye1gX5X1gM+ONEmk03YVFc4d9mm32aO+w9dU55WK4RkueAcCpcdakmf+6Cv4XotTbGB6\nYBjvnn5cXJMeTj5LiT3a7rBtn1EqjcvTIK0Fx2fOLHJeW5LWgoHXsV4xYCmRVMIUc+aY0isqLvLv\naNM0XRTtvqbkaAT2MIpy2lYU3HgrPKiunucdUCztgTnBgLEBWsPxlVvjvobzWuVWGdEY58LrCX21\nWnMiggcjPUzTp9C1Ael9Wtmxk0W6WXR9YpBhpAGi4rJWlM1fCJcTdRtyrvMWwql2qN5XX5F5Zzge\ns6CrBWp9o/KBSwwGHJZKheHBlWO+I9wc4ruykr9WP8b5gQ9GTPk5dNqo7Mf3nf02X7PTk3US9/gT\n1zQxP7qNMOA3zjkJtd7C/oQ0wriiq+cuDZ6jeLonaK+6f+J1OG91wERPnVZrQOhDSKf2p2Xm8fTg\nvv5MUtAUkZ6vr0HKahzcXNJbsgzoVjApOHZzqJLdnJMUc2wXTfWPVSfbWyLbKTMYDAaDwWAwGAyG\nA4W9lBkMBoPBYDAYDAbDAWJf9MVSqbRIRF8loh8iog4RfZgGe7sPE9Gvlcvl64vPZDAYrhmYfTIY\nDC9EmG0yGAzPBpd9KSuVSgki+gMiChwTThHRO8rl8qdKpdL7iegniOhPJ+WB8reBjOTo+w5LXTZW\n2OfKBb8V7fOB+TUXmcMeV74v6OPUTYM0udofRL57sgL3dTlhrC3zjpIZb65K/dBOFvMAjjT4qzWX\npB9EqwA8XShrXKo9U98B6Xwogz/Dx6lt5bMB/Ft8bi1zjn5gbgu53eG+D0REbZA9Rn6yq9olsxl8\nSk7yWKgC8JdAnm4HZEp9X/rIpGvgM5fCaPMiGWWHMujZzR61ge+s+xqGSMA6Rqp3JyU7VHoLjjeY\nVKzDE/jFAWE55vcFd9lthHPuB88BbYZhBybI1CPneixEQhCeIO5QF3yCEjXw31B5+xP83K42roR9\nQl9N9LchIupAmIDkTvj6SY/L/AV2OGgshvvYEBEqvo98M50ekV+Q/Qn5+AnwLyo8zY4gu7fkxDVd\nNqWURDlyTWnHxx36ePYd7WsWzYNHSXUN9CnDvorPpzn7gY9UN+VQC2T5MxuyDE3wh0FZ5txF2Ubr\nc6yrfWaW7ezGDNvp2/KXxDWJoR/IWrtI2x2u14Jyrnj1wunR8berS6Pj7Yf5ocblpMGmQb/RoWKC\n70533N8MIXw6oCmwH2ufQHEfDAeTGifO1A4NMpVS6lzHOlxGoiKlq0flycilRmsa6gjmSOzro2uH\nJt4rQhiabS6D25SV13NfGPbpStgmnPdmHpPPuXNr+HOmwX9qtyTP4XjLnwX/1FmZLnspvM/osDyt\nCL92fxrTKZ8d8DcX/vzK7xv9u9DnbYqHnZCPH+TNx0tf4QGWOSdDYOzdPj06rnbC/aKmy6o8Qx+n\n5Qd7Yny1lCz/f5m6c3T8ieLto+PX3ih9V+Nn2Ug3lrmOsH60r1/g/5Te6lN9FcZ4Ptz/jYho9lGu\n5O0XRZPjehOmKkT+6ebos5tg/YPmgqwHbx7DVfHv6B9YlFUifMLw3WDvZhlOoD7s+5t3JMSzJ3fD\n14wa2NfQz5JISuLXjnDB0QeTiP0UO1lpH4XftxpX8Qm+f6NyXzYF0fuI6P1EdGH4/eVE9Onh8SeI\n6HX7yMNgMBi+EzD7ZDAYXogw22QwGJ4VnP6Ef0FLpdI/J6LD5XL5naVS6VNE9CtE9HflcnlleP4H\niejN5XL5Fybd5Mlzm/2bDs9PSmIwGK5NHJjUmdkng8EQhVe+8X760sfuPRD7dKVs0xMXNvs3r5ht\nMhiuJ9z+9gfokXedCLVNl6MvvpmI+qVS6XVE9DIi+igRLcL5AhHtXq4Abzz5YSIi+sJ/fit978/e\nL84JmgzsziNVTEvBI5Aig5QrIind7c0N9u2/8EdvpVe86ZRIhzLoSM9DakZP0ZpQ4lnBCbg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P0I5oaYL2mheM0A4ZRMf4qPmwvK9aOL38OfYZBff/TZTXO69Eb0Hk9qJ/KUgF8Mn1s0hbJyQ3z0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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3de076f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot band_1\n",
    "fig = plt.figure(1,figsize=(15,15))\n",
    "for i in range(3):\n",
    "    ax = fig.add_subplot(1,3,i+1)\n",
    "    arr = np.reshape(np.array(ships.iloc[i,1]),(75,75))\n",
    "    plt.title('ships in band_2')\n",
    "    ax.imshow(arr,cmap='viridis')\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, we see that everything looks pretty blob-like. It also appears that the background is not really random noise but rather has some spatial correlations. If the background is dominated by things like waves rather than noise, then spatial correlations would clearly be expected. The ships seem to have a more regular structure, with a pronounced skewness in the blobs for larger signals.\n",
    "Some of these blobs are not that high above noise, and in the last set there are even two images where the signal cannot even be seen by eye"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benchmark model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Xtrain: (1604, 75, 75, 2)\n",
      "Ytrain: (1604,)\n",
      "Xtest: (8424, 75, 75, 2)\n"
     ]
    }
   ],
   "source": [
    "# Train data\n",
    "x_band1 = np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in train[\"band_1\"]])\n",
    "x_band2 = np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in train[\"band_2\"]])\n",
    "X_train = np.concatenate([x_band1[:, :, :, np.newaxis], x_band2[:, :, :, np.newaxis]], axis=-1)\n",
    "y_train = np.array(train[\"is_iceberg\"])\n",
    "print(\"Xtrain:\", X_train.shape)\n",
    "print(\"Ytrain:\", y_train.shape)\n",
    "# Test data\n",
    "x_band1 = np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in test[\"band_1\"]])\n",
    "x_band2 = np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in test[\"band_2\"]])\n",
    "X_test = np.concatenate([x_band1[:, :, :, np.newaxis], x_band2[:, :, :, np.newaxis]], axis=-1)\n",
    "print(\"Xtest:\", X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Convolution2D, GlobalAveragePooling2D, GlobalMaxPooling2D, MaxPooling2D, Dense, Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_25 (Conv2D)           (None, 73, 73, 32)        608       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 36, 36, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_26 (Conv2D)           (None, 34, 34, 64)        18496     \n",
      "_________________________________________________________________\n",
      "global_max_pooling2d_8 (Glob (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dropout_9 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 19,169\n",
      "Trainable params: 19,169\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Convolution2D(32, 3, activation=\"relu\", input_shape=(75, 75, 2)))\n",
    "model.add(MaxPooling2D())\n",
    "model.add(Convolution2D(64, 3, activation=\"relu\", input_shape=(75, 75, 2)))\n",
    "model.add(GlobalMaxPooling2D())\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(1, activation=\"sigmoid\"))\n",
    "model.compile(\"adam\", \"binary_crossentropy\", metrics=[\"accuracy\"])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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  {
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   "execution_count": 30,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "text": [
      "Train on 1203 samples, validate on 401 samples\n",
      "Epoch 1/20\n",
      "1203/1203 [==============================] - 13s 11ms/step - loss: 1.2911 - acc: 0.6276 - val_loss: 0.5843 - val_acc: 0.6484\n",
      "Epoch 2/20\n",
      "1203/1203 [==============================] - 12s 10ms/step - loss: 0.4966 - acc: 0.7506 - val_loss: 0.5629 - val_acc: 0.6484\n",
      "Epoch 3/20\n",
      "1203/1203 [==============================] - 14s 11ms/step - loss: 0.4196 - acc: 0.8071 - val_loss: 0.4558 - val_acc: 0.7232\n",
      "Epoch 4/20\n",
      "1203/1203 [==============================] - 14s 12ms/step - loss: 0.3940 - acc: 0.8113 - val_loss: 0.4045 - val_acc: 0.8155\n",
      "Epoch 5/20\n",
      "1203/1203 [==============================] - 15s 12ms/step - loss: 0.4098 - acc: 0.8047 - val_loss: 0.4262 - val_acc: 0.7481\n",
      "Epoch 6/20\n",
      "1203/1203 [==============================] - 13s 11ms/step - loss: 0.3721 - acc: 0.8238 - val_loss: 0.5173 - val_acc: 0.7057\n",
      "Epoch 7/20\n",
      "1203/1203 [==============================] - 14s 11ms/step - loss: 0.3576 - acc: 0.8288 - val_loss: 0.3936 - val_acc: 0.7681\n",
      "Epoch 8/20\n",
      "1203/1203 [==============================] - 13s 11ms/step - loss: 0.3486 - acc: 0.8271 - val_loss: 0.3594 - val_acc: 0.8130\n",
      "Epoch 9/20\n",
      "1203/1203 [==============================] - 14s 11ms/step - loss: 0.3318 - acc: 0.8487 - val_loss: 0.5137 - val_acc: 0.7032\n",
      "Epoch 10/20\n",
      "1203/1203 [==============================] - 14s 11ms/step - loss: 0.3301 - acc: 0.8512 - val_loss: 0.3877 - val_acc: 0.7805\n",
      "Epoch 11/20\n",
      "1203/1203 [==============================] - 14s 11ms/step - loss: 0.3336 - acc: 0.8296 - val_loss: 0.5044 - val_acc: 0.6883\n",
      "Epoch 12/20\n",
      "1203/1203 [==============================] - 14s 12ms/step - loss: 0.3222 - acc: 0.8446 - val_loss: 0.3414 - val_acc: 0.8304\n",
      "Epoch 13/20\n",
      "1203/1203 [==============================] - 14s 12ms/step - loss: 0.3147 - acc: 0.8529 - val_loss: 0.3233 - val_acc: 0.8678\n",
      "Epoch 14/20\n",
      "1203/1203 [==============================] - 13s 11ms/step - loss: 0.2988 - acc: 0.8670 - val_loss: 0.3289 - val_acc: 0.8479\n",
      "Epoch 15/20\n",
      "1203/1203 [==============================] - 15s 12ms/step - loss: 0.3001 - acc: 0.8520 - val_loss: 0.3814 - val_acc: 0.7955\n",
      "Epoch 16/20\n",
      "1203/1203 [==============================] - 13s 11ms/step - loss: 0.2931 - acc: 0.8587 - val_loss: 0.3582 - val_acc: 0.8155\n",
      "Epoch 17/20\n",
      "1203/1203 [==============================] - 13s 11ms/step - loss: 0.2644 - acc: 0.8770 - val_loss: 0.3784 - val_acc: 0.8030\n",
      "Epoch 18/20\n",
      "1203/1203 [==============================] - 13s 11ms/step - loss: 0.2788 - acc: 0.8678 - val_loss: 0.4412 - val_acc: 0.7656\n",
      "Epoch 19/20\n",
      "1203/1203 [==============================] - 13s 11ms/step - loss: 0.2849 - acc: 0.8612 - val_loss: 0.3234 - val_acc: 0.8354\n",
      "Epoch 20/20\n",
      "1203/1203 [==============================] - 14s 12ms/step - loss: 0.2673 - acc: 0.8678 - val_loss: 0.3868 - val_acc: 0.7880\n"
     ]
    }
   ],
   "source": [
    "xz = model.fit(X_train, y_train, validation_split=0.25, epochs =20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7695760604114901\n",
      "0.41863389164952564\n"
     ]
    }
   ],
   "source": [
    "from statistics import mean\n",
    "print(mean(xz.history[\"val_acc\"]))\n",
    "# xz.history[\"val_acc\"]\n",
    "print(mean(xz.history[\"val_loss\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The benchmark model gives validation accuracy of 76.96%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Imporved model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "batch_normalization_1 (Batch (None, 75, 75, 2)         8         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 73, 73, 8)         152       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 36, 36, 8)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 34, 34, 16)        1168      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 17, 17, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 15, 15, 32)        4640      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 7, 7, 32)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 5, 5, 64)          18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 2, 2, 64)          0         \n",
      "_________________________________________________________________\n",
      "global_max_pooling2d_1 (Glob (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 30,737.0\n",
      "Trainable params: 30,733.0\n",
      "Non-trainable params: 4.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, BatchNormalization, Dropout, MaxPooling2D, GlobalMaxPooling2D, Dense\n",
    "improved_cnn = Sequential()\n",
    "improved_cnn.add(BatchNormalization(input_shape = (75, 75, 2)))\n",
    "for i in range(4):\n",
    "    improved_cnn.add(Conv2D(8*2**i, kernel_size = (3,3)))\n",
    "    improved_cnn.add(MaxPooling2D((2,2)))\n",
    "improved_cnn.add(GlobalMaxPooling2D())\n",
    "improved_cnn.add(Dropout(0.2))\n",
    "improved_cnn.add(Dense(64))\n",
    "improved_cnn.add(Dropout(0.2))\n",
    "improved_cnn.add(Dense(32))\n",
    "improved_cnn.add(Dense(1, activation = 'sigmoid'))\n",
    "improved_cnn.compile(optimizer='adam', loss = 'binary_crossentropy', metrics = ['accuracy'])\n",
    "improved_cnn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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     "text": [
      "Train on 1283 samples, validate on 321 samples\n",
      "Epoch 1/10\n",
      "1283/1283 [==============================] - 4s - loss: 0.7441 - acc: 0.5908 - val_loss: 1.7118 - val_acc: 0.4112\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 2/10\n",
      "1283/1283 [==============================] - 4s - loss: 0.5421 - acc: 0.7147 - val_loss: 0.5823 - val_acc: 0.6729\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 3/10\n",
      "1283/1283 [==============================] - 4s - loss: 0.4478 - acc: 0.7841 - val_loss: 0.3976 - val_acc: 0.7944\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 4/10\n",
      "1283/1283 [==============================] - 5s - loss: 0.4116 - acc: 0.8012 - val_loss: 0.6015 - val_acc: 0.6729\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 5/10\n",
      "1283/1283 [==============================] - 4s - loss: 0.3494 - acc: 0.8441 - val_loss: 0.3544 - val_acc: 0.8318\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 6/10\n",
      "1283/1283 [==============================] - 4s - loss: 0.3078 - acc: 0.8714 - val_loss: 0.3908 - val_acc: 0.8162\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 7/10\n",
      "1283/1283 [==============================] - 3s - loss: 0.2945 - acc: 0.8675 - val_loss: 0.3428 - val_acc: 0.8349\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 8/10\n",
      "1283/1283 [==============================] - 3s - loss: 0.2757 - acc: 0.8831 - val_loss: 0.3281 - val_acc: 0.8536\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 9/10\n",
      "1283/1283 [==============================] - 4s - loss: 0.2509 - acc: 0.8948 - val_loss: 0.4323 - val_acc: 0.7632\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 10/10\n",
      "1283/1283 [==============================] - 4s - loss: 0.2085 - acc: 0.9150 - val_loss: 0.2874 - val_acc: 0.8567\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"
     ]
    }
   ],
   "source": [
    "history1 = improved_cnn.fit(X_train, y_train, validation_split=0.2, epochs = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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3Sr6o2sTopAJum3QTVvPIbEpHXsoSg/J6+dtUNB8gx6JY9bqTrm43Ny4Yz91L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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b4579588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0I9L12Nd8kN/ve4IeXw/nzT6L6xdehc0ysUtMev293LfnEQ60HGJx9kI2n3Yr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\nVlVXBNc0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b59a99e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list all data in history\n",
    "print(history1.history.keys())\n",
    "# summarize history for accuracy\n",
    "plt.plot(history1.history['acc'])\n",
    "plt.plot(history1.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(history1.history['loss'])\n",
    "plt.plot(history1.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8416/8424 [============================>.] - ETA: 0s 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- ETA: 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     ]
    }
   ],
   "source": [
    "# Make predictions\n",
    "prediction = improved_cnn.predict(X_test, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Mandatory imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.model_selection import StratifiedKFold, StratifiedShuffleSplit\n",
    "from os.path import join as opj\n",
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import pylab\n",
    "plt.rcParams['figure.figsize'] = 10, 10\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_json(\"input/train.json\")\n",
    "target_train=train['is_iceberg']\n",
    "test = pd.read_json(\"input/test.json\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test['inc_angle']=pd.to_numeric(test['inc_angle'], errors='coerce')\n",
    "train['inc_angle']=pd.to_numeric(train['inc_angle'], errors='coerce')#We have only 133 NAs.\n",
    "train['inc_angle']=train['inc_angle'].fillna(method='pad')\n",
    "X_angle=train['inc_angle']\n",
    "test['inc_angle']=pd.to_numeric(test['inc_angle'], errors='coerce')\n",
    "X_test_angle=test['inc_angle']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Generate the training data\n",
    "X_band_1=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in train[\"band_1\"]])\n",
    "X_band_2=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in train[\"band_2\"]])\n",
    "X_band_3=(X_band_1+X_band_2)/2\n",
    "#X_band_3=np.array([np.full((75, 75), angel).astype(np.float32) for angel in train[\"inc_angle\"]])\n",
    "X_train = np.concatenate([X_band_1[:, :, :, np.newaxis]\n",
    "                          , X_band_2[:, :, :, np.newaxis]\n",
    "                         , X_band_3[:, :, :, np.newaxis]], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_band_test_1=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in test[\"band_1\"]])\n",
    "X_band_test_2=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in test[\"band_2\"]])\n",
    "X_band_test_3=(X_band_test_1+X_band_test_2)/2\n",
    "#X_band_test_3=np.array([np.full((75, 75), angel).astype(np.float32) for angel in test[\"inc_angle\"]])\n",
    "X_test = np.concatenate([X_band_test_1[:, :, :, np.newaxis]\n",
    "                          , X_band_test_2[:, :, :, np.newaxis]\n",
    "                         , X_band_test_3[:, :, :, np.newaxis]], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Import Keras.\n",
    "from matplotlib import pyplot\n",
    "from keras.optimizers import RMSprop\n",
    "# from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Input, Flatten, Activation\n",
    "from keras.layers import GlobalMaxPooling2D\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.merge import Concatenate\n",
    "from keras.models import Model\n",
    "from keras import initializers\n",
    "from keras.optimizers import Adam\n",
    "from keras.optimizers import rmsprop\n",
    "from keras.layers.advanced_activations import LeakyReLU, PReLU\n",
    "from keras.optimizers import SGD\n",
    "from keras.callbacks import ModelCheckpoint, Callback, EarlyStopping\n",
    "\n",
    "from keras.datasets import cifar10\n",
    "from keras.applications.inception_v3 import InceptionV3\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from keras.applications.xception import Xception\n",
    "from keras.applications.mobilenet import MobileNet\n",
    "from keras.applications.vgg19 import VGG19\n",
    "from keras.layers import Concatenate, Dense, LSTM, Input, concatenate\n",
    "from keras.preprocessing import image\n",
    "from keras.applications.vgg16 import preprocess_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Data Aug for multi-input\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "batch_size=64\n",
    "# Define the image transformations here\n",
    "gen = ImageDataGenerator(horizontal_flip = True,\n",
    "                         vertical_flip = True,\n",
    "                         width_shift_range = 0.,\n",
    "                         height_shift_range = 0.,\n",
    "                         channel_shift_range=0,\n",
    "                         zoom_range = 0.2,\n",
    "                         rotation_range = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Here is the function that merges our two generators\n",
    "# We use the exact same generator with the same random seed for both the y and angle arrays\n",
    "def gen_flow_for_two_inputs(X1, X2, y):\n",
    "    genX1 = gen.flow(X1,y,  batch_size=batch_size,seed=55)\n",
    "    genX2 = gen.flow(X1,X2, batch_size=batch_size,seed=55)\n",
    "    while True:\n",
    "            X1i = genX1.next()\n",
    "            X2i = genX2.next()\n",
    "            #Assert arrays are equal - this was for peace of mind, but slows down training\n",
    "            #np.testing.assert_array_equal(X1i[0],X2i[0])\n",
    "            yield [X1i[0], X2i[1]], X1i[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Finally create generator\n",
    "def get_callbacks(filepath, patience=2):\n",
    "    es = EarlyStopping('val_loss', patience=10, mode=\"min\")\n",
    "    msave = ModelCheckpoint(filepath, save_best_only=True)\n",
    "    return [es, msave]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getVggAngleModel():\n",
    "    input_2 = Input(shape=[1], name=\"angle\")\n",
    "    angle_layer = Dense(1, )(input_2)\n",
    "    base_model = VGG16(weights='imagenet', include_top=False, \n",
    "                 input_shape=X_train.shape[1:], classes=1)\n",
    "    x = base_model.get_layer('block5_pool').output\n",
    "    \n",
    "\n",
    "    x = GlobalMaxPooling2D()(x)\n",
    "    merge_one = concatenate([x, angle_layer])\n",
    "    merge_one = Dense(512, activation='relu', name='fc2')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    merge_one = Dense(512, activation='relu', name='fc3')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    \n",
    "    predictions = Dense(1, activation='sigmoid')(merge_one)\n",
    "    \n",
    "    model = Model(inputs=[base_model.input, input_2], outputs=predictions)\n",
    "    \n",
    "    sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "    model.compile(loss='binary_crossentropy',\n",
    "                  optimizer=sgd,\n",
    "                  metrics=['accuracy'])\n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===================FOLD= 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jeongwhanchoi/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:18: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=[<tf.Tenso..., outputs=Tensor(\"de...)`\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "24/24 [==============================] - 405s 17s/step - loss: 1.1966 - acc: 0.5367 - val_loss: 8.4629 - val_acc: 0.4692\n",
      "Epoch 2/10\n",
      "24/24 [==============================] - 408s 17s/step - loss: 8.6810 - acc: 0.4555 - val_loss: 8.4629 - val_acc: 0.4692\n",
      "Epoch 3/10\n",
      "24/24 [==============================] - 374s 16s/step - loss: 8.4536 - acc: 0.4697 - val_loss: 8.4629 - val_acc: 0.4692\n",
      "Epoch 4/10\n",
      "24/24 [==============================] - 373s 16s/step - loss: 8.6265 - acc: 0.4589 - val_loss: 8.4629 - val_acc: 0.4692\n",
      "Epoch 5/10\n",
      "24/24 [==============================] - 367s 15s/step - loss: 8.2413 - acc: 0.4831 - val_loss: 8.4629 - val_acc: 0.4692\n",
      "Epoch 6/10\n",
      "24/24 [==============================] - 406s 17s/step - loss: 8.5084 - acc: 0.4663 - val_loss: 8.4629 - val_acc: 0.4692\n",
      "Epoch 7/10\n",
      "24/24 [==============================] - 998s 42s/step - loss: 8.4957 - acc: 0.4671 - val_loss: 8.4629 - val_acc: 0.4692\n",
      "Epoch 8/10\n",
      "24/24 [==============================] - 368s 15s/step - loss: 8.3388 - acc: 0.4769 - val_loss: 8.4629 - val_acc: 0.4692\n",
      "Epoch 9/10\n",
      "24/24 [==============================] - 414s 17s/step - loss: 8.4500 - acc: 0.4700 - val_loss: 8.4629 - val_acc: 0.4692\n",
      "Epoch 10/10\n",
      "24/24 [==============================] - 363s 15s/step - loss: 8.4845 - acc: 0.4678 - val_loss: 8.4629 - val_acc: 0.4692\n",
      "Train loss: 8.455876601088036\n",
      "Train accuracy: 0.4695977549111319\n",
      "Test loss: 8.462873325169644\n",
      "Test accuracy: 0.4691588785046729\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-0e6ae2bc147a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m     \u001b[0;31m#Getting Test Scores\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m     \u001b[0mtemp_test\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgalaxyModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test_angle\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m     \u001b[0my_test_pred_log\u001b[0m\u001b[0;34m+=\u001b[0m\u001b[0mtemp_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, x, batch_size, verbose, steps)\u001b[0m\n\u001b[1;32m   1798\u001b[0m         \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1799\u001b[0m         return self._predict_loop(f, ins, batch_size=batch_size,\n\u001b[0;32m-> 1800\u001b[0;31m                                   verbose=verbose, steps=steps)\n\u001b[0m\u001b[1;32m   1801\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1802\u001b[0m     def train_on_batch(self, x, y,\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_predict_loop\u001b[0;34m(self, f, ins, batch_size, verbose, steps)\u001b[0m\n\u001b[1;32m   1299\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1301\u001b[0;31m                 \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1302\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1303\u001b[0m                     \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2473\u001b[0m         \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2474\u001b[0m         updated = session.run(fetches=fetches, feed_dict=feed_dict,\n\u001b[0;32m-> 2475\u001b[0;31m                               **self.session_kwargs)\n\u001b[0m\u001b[1;32m   2476\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2477\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    776\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    777\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 778\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    779\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    780\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    980\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    981\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 982\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    983\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    984\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1030\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1031\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1032\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1033\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1034\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1037\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1040\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1019\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1020\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1021\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1023\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#def myAngleCV(X_train, X_angle, X_test):\n",
    "K=3\n",
    "folds = list(StratifiedKFold(n_splits=K, shuffle=True, random_state=16).split(X_train, target_train))\n",
    "y_test_pred_log = 0\n",
    "y_train_pred_log=0\n",
    "y_valid_pred_log = 0.0*target_train\n",
    "for j, (train_idx, test_idx) in enumerate(folds):\n",
    "    print('\\n===================FOLD=',j)\n",
    "    X_train_cv = X_train[train_idx]\n",
    "    y_train_cv = target_train[train_idx]\n",
    "    X_holdout = X_train[test_idx]\n",
    "    Y_holdout= target_train[test_idx]\n",
    "    \n",
    "    #Angle\n",
    "    X_angle_cv=X_angle[train_idx]\n",
    "    X_angle_hold=X_angle[test_idx]\n",
    "    #define file path and get callbacks\n",
    "    file_path = \"%s_aug_model_weights.hdf5\"%j\n",
    "    callbacks = get_callbacks(filepath=file_path, patience=5)\n",
    "    gen_flow = gen_flow_for_two_inputs(X_train_cv, X_angle_cv, y_train_cv)\n",
    "    galaxyModel= getVggAngleModel()\n",
    "    histr = galaxyModel.fit_generator(\n",
    "            gen_flow,\n",
    "            steps_per_epoch=24,\n",
    "            epochs=10,\n",
    "            shuffle=True,\n",
    "            verbose=1,\n",
    "            validation_data=([X_holdout,X_angle_hold], Y_holdout),\n",
    "            callbacks=callbacks)\n",
    "     #Getting the Best Model\n",
    "    galaxyModel.load_weights(filepath=file_path)\n",
    "    #Getting Training Score\n",
    "    score = galaxyModel.evaluate([X_train_cv,X_angle_cv], y_train_cv, verbose=0)\n",
    "    print('Train loss:', score[0])\n",
    "    print('Train accuracy:', score[1])\n",
    "    #Getting Test Score\n",
    "    score = galaxyModel.evaluate([X_holdout,X_angle_hold], Y_holdout, verbose=0)\n",
    "    print('Test loss:', score[0])\n",
    "    print('Test accuracy:', score[1])\n",
    "\n",
    "    #Getting validation Score.\n",
    "    pred_valid=galaxyModel.predict([X_holdout,X_angle_hold])\n",
    "    y_valid_pred_log[test_idx] = pred_valid.reshape(pred_valid.shape[0])\n",
    "\n",
    "    #Getting Test Scores\n",
    "    temp_test=galaxyModel.predict([X_test, X_test_angle])\n",
    "    y_test_pred_log+=temp_test.reshape(temp_test.shape[0])\n",
    "\n",
    "    #Getting Train Scores\n",
    "    temp_train=galaxyModel.predict([X_train, X_angle])\n",
    "    y_train_pred_log+=temp_train.reshape(temp_train.shape[0])\n",
    "\n",
    "y_test_pred_log=y_test_pred_log/K\n",
    "y_train_pred_log=y_train_pred_log/K\n",
    "\n",
    "print('\\n Train Log Loss Validation= ',log_loss(target_train, y_train_pred_log))\n",
    "print(' Test Log Loss Validation= ',log_loss(target_train, y_valid_pred_log))\n",
    "#return y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#preds=myAngleCV(X_train, X_angle, X_test)\n",
    "preds = y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Submission to kaggle\n",
    "submission = pd.DataFrame()\n",
    "submission['id']=test['id']\n",
    "submission['is_iceberg']=preds\n",
    "submission.to_csv('sub_03.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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oFxsj/GlUH45on8yf31/GZZO+5flrc+mYGl6F2O7/YAWAJ1Vlm5qI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\nliyMMcYE9f8BW2XVRM+P0gkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c48b3cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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quW5aRGReCXOWzi+BS8J6/Olq78pwflMtZclYfMdMROS0Sj4FO9IZLYksIkKJ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\nX0QkJhT4IiIx8f8BaZmVesVNzJkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cb88f080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list all data in history\n",
    "print(histr.history.keys())\n",
    "# summarize history for accuracy\n",
    "plt.plot(histr.history['acc'])\n",
    "plt.plot(histr.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(histr.history['loss'])\n",
    "plt.plot(histr.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}