9807 lines (9806 with data), 1.2 MB

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Classification of Cancer or Non Cancer with hand picked features \n",
    "\n",
    "## Summary\n",
    "\n",
    "* Find largest nodule in each patient sample and make a 64x64 crop over it\n",
    "* Label as cancer or non-cancer. Alternatively, create random labels with same ratio of classes\n",
    "* Use stratified K-fold to split data for cross validation\n",
    "* Perform cross validation with CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#EDIT HERE##############################\n",
    "\n",
    "#input list of image files, mask files, and tables generated from TrainUnet.ipynb\n",
    "\n",
    "imageslist=['DSBNoduleImages.npy','DSBNoduleImages369-629.npy','DSBNoduleImages630-1199.npy','DSBNoduleImages1200-1594.npy', 'DSBNoduleImagesTest.npy']\n",
    "maskslist=['DSBNoduleMasks.npy','DSBNoduleMasks369-629.npy','DSBNoduleMasks630-1199.npy','DSBNoduleMasks1200-1594.npy', 'DSBNoduleMasksTest.npy']\n",
    "tablelist=['DSBPatientNoduleIndex.csv','DSBPatientNoduleIndex369-629.csv','DSBPatientNoduleIndex630-1199.csv','DSBPatientNoduleIndex1200-1594.csv', 'DSBPatientNoduleIndexTest.csv']\n",
    "INPUT_FOLDER = 'stage1/'\n",
    "\n",
    "########################################\n",
    "\n",
    "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 dicom\n",
    "import os\n",
    "import scipy.ndimage\n",
    "import time\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "import h5py\n",
    "from sklearn.cluster import KMeans\n",
    "from skimage import measure, morphology\n",
    "from mpl_toolkits.mplot3d.art3d import Poly3DCollection\n",
    "\n",
    "\n",
    "import random\n",
    "from sklearn.model_selection import train_test_split, StratifiedKFold\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier, ExtraTreesClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix, classification_report,log_loss\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from scipy.ndimage.measurements import center_of_mass, label\n",
    "from skimage.measure import regionprops\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "import keras\n",
    "from keras.utils import to_categorical\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D, BatchNormalization\n",
    "from keras import backend as K\n",
    "from keras.optimizers import Adam\n",
    "from keras.utils import plot_model\n",
    "#from keras.utils import multi_gpu_model\n",
    "import scipy as sp\n",
    "\n",
    "def processimagenomask(img):\n",
    "    #Standardize the pixel values\n",
    "    mean = np.mean(img)\n",
    "    std = np.std(img)\n",
    "    img = img-mean\n",
    "    img = img/std\n",
    "    #plt.hist(img.flatten(),bins=200)\n",
    "    #plt.show()\n",
    "    #print(thresh_img[366][280:450])\n",
    "    middle = img[100:400,100:400] \n",
    "    mean = np.mean(middle)  \n",
    "    max = np.max(img)\n",
    "    min = np.min(img)\n",
    "    #move the underflow bins\n",
    "    img[img==max]=mean\n",
    "    img[img==min]=mean\n",
    "    return img\n",
    "\n",
    "def largestnodulecoordinates(mask):\n",
    "    #mask=nodulemasks[indx,0][0]\n",
    "    mask[mask>0.5]=1\n",
    "    mask[mask<0.5]=0\n",
    "    labeled_array,nf=label(mask)\n",
    "    areasinslice=[]\n",
    "    if nf>1:\n",
    "        for n in range(nf):\n",
    "            lab=np.array(labeled_array)\n",
    "            lab[lab!=(n+1)]=0\n",
    "            lab[lab==(n+1)]=1\n",
    "            areasinslice.append(np.sum(lab))\n",
    "        nlargest=areasinslice.index(max(areasinslice))\n",
    "        labeled_array[labeled_array!=(nlargest+1)]=0\n",
    "        com=center_of_mass(labeled_array)\n",
    "    else:\n",
    "        com=center_of_mass(mask)\n",
    "    return [int(com[0]),int(com[1])]\n",
    "\n",
    "def largestnodulearea(mask,table,i):\n",
    "    #mask=nodulemasks[indx,0][0]\n",
    "    mask[mask>0.5]=1\n",
    "    mask[mask<0.5]=0\n",
    "    labeled_array,nf=label(mask)\n",
    "    areasinslice=[]\n",
    "    if nf>1:\n",
    "        for n in range(nf):\n",
    "            lab=np.array(labeled_array)\n",
    "            lab[lab!=(n+1)]=0\n",
    "            lab[lab==(n+1)]=1\n",
    "            areasinslice.append(np.sum(lab))\n",
    "        #nlargest=areasinslice.index(max(areasinslice))\n",
    "        #labeled_array[labeled_array!=(nlargest+1)]=0\n",
    "        return max(areasinslice)\n",
    "    else:\n",
    "        return table[\"Area\"][i]\n",
    "\n",
    "def crop_nodule(coord,image):\n",
    "    dim=32\n",
    "    return image[coord[0]-dim:coord[0]+dim,coord[1]-dim:coord[1]+dim]\n",
    "#output: 64x64 images of the nodules with malignancy labels from the patient\n",
    "\n",
    "    \n",
    "patients = os.listdir(INPUT_FOLDER)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading file DSBNoduleImages.npy\n",
      "loading file DSBNoduleImages369-629.npy\n",
      "loading file DSBNoduleImages630-1199.npy\n",
      "loading file DSBNoduleImages1200-1594.npy\n",
      "loading file DSBNoduleImagesTest.npy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:46: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "#Get largest nodule for each patient, make a 64x64 crop over processed image, label with malignancy label\n",
    "\n",
    "\n",
    "table=pd.read_csv(tablelist[0])\n",
    "if len(tablelist)>1:\n",
    "    for file in tablelist[1:]:\n",
    "        temptable=pd.read_csv(file)\n",
    "        table=pd.concat([table,temptable])\n",
    "table=table.reset_index()\n",
    "\n",
    "table[:10]\n",
    "malignantlabel=[]\n",
    "malignancytable=pd.concat([pd.read_csv(\"stage1_labels.csv\"),pd.read_csv(\"stage1_solution.csv\")])\n",
    "patients=malignancytable[\"id\"].values\n",
    "index=0\n",
    "noduleexists=[]\n",
    "nodulecrops=np.ndarray([len(patients),1,64,64])\n",
    "indicies=[]\n",
    "for i in range(len(imageslist)):\n",
    "    print(\"loading file\",imageslist[i])\n",
    "    #del noduleimages, nodulemasks\n",
    "    noduleimages=np.load(imageslist[i])\n",
    "    nodulemasks=np.load(maskslist[i])\n",
    "    tabletemp=pd.read_csv(tablelist[i])\n",
    "    biggestnodulearea=[]\n",
    "    for j in range(nodulemasks.shape[0]):\n",
    "        biggestnodulearea.append(largestnodulearea(nodulemasks[j,0],tabletemp,j))\n",
    "    tabletemp[\"LargestNoduleArea\"]=pd.Series(biggestnodulearea)\n",
    "    for patient in patients:\n",
    "        nodulearea=tabletemp[[\"LargestNoduleArea\"]].loc[tabletemp[\"Patient\"]==patient]\n",
    "        if len(nodulearea)>0:# and len(malignancytable[\"cancer\"].loc[malignancytable[\"id\"]==patient])>0:\n",
    "            malignantlabel.append(malignancytable[\"cancer\"].loc[malignancytable[\"id\"]==patient].values[0].astype(np.bool))\n",
    "            noduleexists.append(1)\n",
    "            indx=nodulearea.loc[nodulearea[\"LargestNoduleArea\"]==max(nodulearea[\"LargestNoduleArea\"])].index[0]\n",
    "            indicies.append(indx)\n",
    "            nodcrop=crop_nodule(largestnodulecoordinates(nodulemasks[indx,0]),processimagenomask(noduleimages[indx,0]))\n",
    "            if nodcrop.shape[0]*nodcrop.shape[1]<64**2:\n",
    "                nodulecrops[index,0]=np.zeros([64,64])\n",
    "                nodulecrops[index,0][0:nodcrop.shape[0],0:nodcrop.shape[1]]=nodcrop\n",
    "            else:\n",
    "                nodulecrops[index,0]=nodcrop\n",
    "            index+=1\n",
    "nodulecrops=nodulecrops[:index]\n",
    "nodulecrops=nodulecrops.reshape(nodulecrops.shape[0],64,64,1)\n",
    "features=table.iloc[indicies]\n",
    "features[\"label\"]=malignantlabel\n",
    "TFratio=len([a for a in malignantlabel if a==True])/len(malignantlabel)\n",
    "TFratio\n",
    "randomlabel=np.random.choice([0, 1], size=(len(malignantlabel),), p=[(1-TFratio), TFratio])\n",
    "malignantlabelcat=to_categorical(malignantlabel,2)\n",
    "randomlabelcat=to_categorical(randomlabel,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent labels True: 0.2656546489563567\n"
     ]
    },
    {
     "data": {
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JtMuK+gz2srN1khbFwczmFJgUjDekkHWhsGI/Bx+xR6W1avSIst1aAsYVLvY7HI4d4Ivf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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd0ae6e3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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x6c+mxh5ESpjZ1ApOkiL0ShcGu6xySuTmuNbh2HXWmlr2LotC2c7JGMd1NuZj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\nAkYU/x/8uwEqOCOtXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd0ae0bbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CeBDAm83su2V7Qs1UhZk9ZmZPmdlTw0m90mwgEFgs7orqSyndBvAJAO8A8IKZ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\n15myU1ox1ZUUB5CoToBpnUCm3Ji2VHH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      "text/plain": [
       "<matplotlib.figure.Figure at 0xd0acd40b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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lcDjePJjE7O8C+EAI4YcAPAzgQyLyfgCfAPB0COFBAE+PPjscjrcIJqnVFwAc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\nYvKyqc/nDi2ju0j6ePY6VZR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W83U4HG8RTDL5KwD+IYD/GkJ4L4B9GBM/DAuOH7vUICJP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\njhmFT36HY0bhk9/hmFH45Hc4ZhT/Hx82Slr7ohYtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd0acf9d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LUfacv8sRXImLrKvc0WvJ7tv2WWyyTpRY+bU9176eYPMvzpt91St6AeV7lBW378p6dZmq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\nCnf13Pt5fR5/sHfNHNeg5+W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5qR2KYntCBR/6zv0o3qYyWU7QhDMs2Dx2epLIUTkBLlEG\nWLqJKaqqy3bjaLTC2vEUXpvYTi/AkidXwrsEXH4tIf2LtMtQZFPfi5Z0KkxpcpSdE/PQwEiUVs0u\nFNf1eWnNWlM8RY8xt9GecxGPNFY7zeOp7PkX9QJ89CmXoMeM23eQiXl6pi+++SMiJhVx8kdETCjG\navZLq4vcG+sAgP6ytVsGWTWn8m/YBMHkoprOvRJFOS3a7ndo5V6cqZy/qfZZZ1q/Vz/vdNJYp84F\nn+XmNJKvva/99WWm3spp+a5By9mo9HPL0YQAEFgH74EyF/lt9xtNh7FOPwCU7uv29Ft6zdsftaYm\nr6wXH5hdxybDlO/a42qXdLt+2dqb2V1ib2g1vrRhj6svMZPjknKoLBezMsV15xbS8HedOc+JYOk2\nuxh23KpP0HHN48dbBvZ7Ocqr4tX4jlvtz5BLZxgl2PHukTCMN/sLm/q9+mWnmXjgLbwHYh4RERH/\niSFO/oiICUWc/BERE4oxU30phOLQlw2J/d1hgY1Ux9JjTAMy7Vdac8KW5Pz5bKvCPRUI7c5qpF5u\n12W0kUhl/YKjg2rqQKapVlpwPn/qrvrrg4rlnqRI0WJV274Rr2gyvWmvhUUeOjZpEH1K6zORdS5T\nrXxP/WaObgNsNiDTb76sNfejaHVK0Vii9igLr+NqFRQoCtGVAjS1FgaUsdmac+s5HHl4gs/La0IH\nJa0PkKV1lbwTJtm/SjUl3BJObo/uJwmJpJ2oSGmVaNEtx8fR6erLtKblMh57lB0ZEttGZlQr0Yu2\nnIT45o+ImFDEyR8RMaEYb2JPJoXu8lA8I9W2NuTsz5QzSe3ZMLDOxbnD7faM2l1ZZzanekcLKwBA\nZ0Ftpsy22rKlNWuyM91Ud9STVHW4pKj8TOKSPaZuaT+YVgSAzhSXY7J9TEjLrbBOpcfy9jimgLyV\nG2gfX4s3Idt9+t13r4AeJfMwjZZz0XNZEs7wIhcs/DEglrE9Y48bUNUpdlkAq+/H7l7ljh241qw+\nEx2XqJUlKo7N7daMvWhTotuZ9ryv7VyOpKMHc7Rffsu2kSMRGu/6MH3IdKGnI3l8fNmzdPPhvj4K\n8c0fETGhiJM/ImJCESd/RMSEYuxiHr3i0EcKFXvqwp2qfmja9C6m+li8sTVj2yivEu1Stw5quqo+\nunRJNMP59VwGrrBmfa7WsjpUM1Ma6rubs3UHm0ucTedCOUmIMlM1u9AlLVGm2wrrto3ONLXhtN0z\nNaIqz5Ev7GoQZNgn9/QVZfxxe3239pDbI1/1pHospG/amTmeiyuvurBd6ldC8bInUZN+HaVDY9qd\n0gYTq7n6kFCJAXX5oXUa6ldz4XjRGbMvOZ2Iqa8RwCHZ/ZIdq359NK9ieG9ERMSjECd/RMSEYsxm\nP9AvDH9vCmvWtE9tkw1cLJh9uds7h9uzbbXj+q7sUfWC8lJGTw2AEA3YW9b229OOAqPP3sQrv6Xn\ne5DR0LrEReC1ycTuF6x5Vtoh07PtohCJ3kuI5sm40thshnodfI5+6xc4K872o7h6vH3I5mWO9P3E\n6fY3qSz33Ctts681p2PVWNRr9rQluxj+XnBEIWsyNuftfedoPV9bgF0Co9foXnvswtTP251M4bE2\noe8zU59eS5ApvNKqi/qkJrmeRem+tfu3P6qRhq2OHauDPh5dLvdonOrNLyIzIvIvROQVEXlZRH5Z\nROZE5Lsi8vro/9lHtxQREfF+wWnN/v8FwL8OIXwEw9JdLwP4GoDnQwg3ADw/+hwREfEBwWmq9E4D\n+MsA/ksACCF0AHRE5IsAnhkd9k0A3wfw1RPbCio60HZaaN3KyuH23lVrM3FyCa8Az75qbbxBRr/n\nI844CaVXIB29+vFmnAebcizgkam771CTKVc1ldtIuxVnjlBMk6nvk5Q40sszBikSimDWoXjPRdbR\nna9f9MIQZL7SeKdchB+PXf2cvWesnceCIH5VnVfLq1dc5BvrEaa0w76cFjNAXsOP+8jme+OcHw8y\n3/dc+/Qs+ShEdvFyO7S9Z90sU9F4x/qT1csaAsn9bS24MWX9G/fIdWaG3/PMzUk4zZv/GoAHAP4P\nEflTEfnfR6W6l0MIB1KHaxhW842IiPiA4DSTPw3gFwH8byGETwOow5n4IYSAh8oUDCEiz4rICyLy\nQrdTP+qQiIiIM8BpJv9dAHdDCD8Yff4XGP4YrIvICgCM/t846sshhOdCCE+HEJ7OZEtHHRIREXEG\neKTPH0JYE5E7IvLhEMKrAD4H4KXRvy8D+Mbo/28/qq1Uu4/SW0PuKKSdL5zTrhS2rOPCEVAD2t7+\nmA0rKz5QP4szwgCguUDtb6rPlalaQZB+Vtv0GVJ1apN12FtXLCVTfF3XM7h81vB7uu2zENl3ZZ16\n9keH+4i2tKyoyVxL2seXna4v676UHQKkiEbiDD9Pc2WrVAKtYu8nU5DlO+R3u2vmLD/fD44obFG5\nqxH9gjkAAAWZSURBVLBjj2Obs7nisgtJFIVFOuZecYsDhO4JYXJ991wVKfqS1wN81B6ve3g6rrBF\nJbou8ANij2PqM9Xx7Y8afYwIv9Py/H8PwO+JSBbAWwD+q1HXviUiXwFwC8CXTn/aiIiIs8apJn8I\n4ScAnj5i1+fe3e5ERESMC+PV8BM5NPd7FRvqJX01Ib1ZxGWb2CJrOM39IunDw1VyZXMoQ6XCJDia\ni0z94oar9NunRBmiBGXLUjJ50lcfOGooTVSOd0046aW1oNszr1v/gyPV2rM+0utoc/6h5B2KivOV\nirtUT4DNfh8V2C0dzytxdCSb9iyuAVhXrVu27Q3IMmeK0Fvl/LzkXMVhjiisk0uQdI63jwcu8aaw\nrX2sXnB9pBnENJ0fU9bfY71KAOhnjz6ub9lwQw17SnNwcGxM7ImIiHgU4uSPiJhQxMkfETGhGG9W\nX6uN8NKbww//2cfMvgHVXS6s2wyxIOr87D3BWXG2fc6E6zoKzJwrr230s45yTPO2CxGu0boEOdGD\nrF036ExzuXF7bg7zZL8esGsF+9f17xtPe3FM/VxwdfaYUtq/ROG9a/Y49vkrbzvREsrW4/Eoujp7\ne9eo/pyjRTNcw44ENVpuDSRPPnrllm0kv6mLFvtX1TFuLDufnMYt5UKh526qc9wtc1i3E8Pgege+\nVB+dru+eK5NFyN1y48HCHBz6DNgS5mbNwkeNU7+StltTmHoM5c6DPj32NyIiIv6TQJz8ERETCgnh\nyJD89+ZkIg8wDAhaALA5thMfj9gPi9gPi/dDPx63D1dCCIunOXCsk//wpCIvhBCOChqK/Yj9iP0Y\nUx+i2R8RMaGIkz8iYkJxVpP/uTM6r0fsh0Xsh8X7oR/vWR/OxOePiIg4e0SzPyJiQjHWyS8iXxCR\nV0XkDREZm9qviPyuiGyIyM/ob2OXHheRSyLyPRF5SUReFJHfOou+iEheRP5ERP5s1I9/dBb9oP4k\nI33IPzirfojITRH5cxH5iYi8cIb9GJtM/tgmv4gkAP5XAH8VwFMAflNEnhrT6f8JgC+4v52F9HgP\nwD8IITwF4JcA/J3RGIy7L20AvxZC+CSATwH4goj80hn04wC/haEc/AHOqh+fDSF8iqi1s+jH+GTy\nQwhj+QfglwH8G/r8dQBfH+P5rwL4GX1+FcDKaHsFwKvj6gv14dsAPn+WfQFQBPBjAH/pLPoB4OLo\ngf41AH9wVvcGwE0AC+5vY+0HgGkAb2O0Fvde92OcZv8FAHfo893R384KZyo9LiJXAXwawA/Ooi8j\nU/snGAqvfjcMBVrPYkx+B8A/hE2FOYt+BAB/JCI/EpFnz6gfY5XJjwt+OFl6/L2AiJQB/EsAfz+E\nYLRtxtWXEEI/hPApDN+8nxGRj4+7HyLy1wFshBB+dEI/x3VvfnU0Hn8VQ3fsL59BP96RTP7jYpyT\n/x6AS/T54uhvZ4VTSY+/2xCRDIYT//dCCP/qLPsCACGEXQDfw3BNZNz9+BUAf0NEbgL45wB+TUT+\n6Rn0AyGEe6P/NwD8PoDPnEE/3pFM/uNinJP/hwBuiMi1kQrw3wTwnTGe3+M7GEqOA6eUHn+nEBEB\n8I8BvBxC+O2z6ouILIrIzGi7gOG6wyvj7kcI4eshhIshhKsYPg//Xwjhb4+7HyJSEpGpg20AfwXA\nz8bdjxDCGoA7IvLh0Z8OZPLfm3681wspbuHiNwC8BuBNAP/dGM/7zwCsAuhi+Ov6FQDzGC40vQ7g\njwDMjaEfv4qhyfZTAD8Z/fuNcfcFwCcA/OmoHz8D8N+P/j72MaE+PQNd8Bv3eDwB4M9G/148eDbP\n6Bn5FIAXRvfm/wYw+171I0b4RURMKOKCX0TEhCJO/oiICUWc/BERE4o4+SMiJhRx8kdETCji5I+I\nmFDEyR8RMaGIkz8iYkLx/wPcAjG+XbAgYgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd0b369b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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c7njcuusf8M8u/3x7e3Fbz13JrAk2WtP5j1ZtllaJVBi+t/Yms+9ESbP67q2q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\n5Rt2HtRO2q/V9rj6j+sndLu6ZI+rzOs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      "text/plain": [
       "<matplotlib.figure.Figure at 0xd0b01d668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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N8WbXX8eEXbMwSZZVz4/wO8HhvABAFdFVOC+g5weY59uoYqGb0Lml+3/yvmfz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd0b37bb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JSgwW5G7/XS20i/9Eaqvoa2N6fDPMfP/ZrKzE++4oJRxtdY+LvkemWAmmkiGi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\neQ90yvJu3FXhmoa3xCSrwd2db4y/S/RxJobLda/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      "text/plain": [
       "<matplotlib.figure.Figure at 0xd0b369470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ppea8M+HZBdARKLgjLALCCh5pzZurw2P5clqOvimZqAXdfkcbNaLu\naJ9M9oa5yia2tmEZk13AZmkSU9aJhbArohRYqUxWRgCjIT6S7aFHVSjz7WhAdZGqQjRkDmp+u0hA\nofA4O5L6WNL+V9fkYPxTqQaDIGL7A4GOIiZ/INBRxOQPBDqK5dXqa/jWlLFUuI59d2VkrBCay+G+\nRuKbjQrdtKagfUy59QApNz46Rn3cWHPH0qvkJ5ey3VShJ4OGXzjiem5chjt/r4ZXz5QYhdU2KDDu\no/i4aUz7BcUft680JYf0cmir0oolOjLnAzeoskItQNqutER3Lpy6VAJcawEyShmKBaHSI1Tmrpu7\ngWsCgcA/AMTkDwQ6ioWb/dWMgjOJTGMBTxVdTNkdD2cNTuR3jTMDx2SWS6KUs86ELmQRUI4g1PNG\nx2n7zg13bPBNilQT0zAbdVbSwG9bnknNUC47XdCwd+6CmKslk71thmI2Qg6AHT9W77DZL7SiG/1i\n+W4SVm24B/OzEIF5LiqBXSTKsExKCU7y2XrOhGcRFLmVra/X2xs+m/MgW9LebP88jyd/INBRxOQP\nBDqKhZv9B5r8vYK5WmmyCpldkwlHsBX0/RXURurnT0wU/VcV9NSYdTA5jxN9ds9482xAlW1NV+DZ\n7HX6+/lSWA2TncxjA5nK4jpwxJ+6H+7ebDY33AOuQSAuQSGxxYGv02voWOoXyl1lGI79Fwol3Aic\nSNXr+ezT0li5BCy+VyORqlA9OJf4VIomVBfpcCOq9AYCgesgJn8g0FHE5A8EOooFl+i2Q195qqF1\nriy093VYYGPCWvrqkhPl1sjqG5P/yOF+0g8+z0RstOoTHaRUImGyVp+3e8qfd2KtjhCbvnFJLsz4\nhepnsminRJzZKh1jH1SzwNiPVdGInCCIZhfydUqP8dqDioy6Rqbzt6WP/CmlkdY/LPjTDPanNcqu\nl197cPeW7EhXEr1tVGZJY79QRtzdOyfaEVl9gUDgeojJHwh0FAsu15UOabEkZa2ZOqtUaOEoGusH\nt9KknLYXFk5kS9FFgakHs1IfG56U93LMR/xl78WlmbSLrCOv5l+aH51XLOVdSDRx9yol5fQKnxH3\nsZFQw/fy78V2dutjXEJMI+Tamv3c30IJNHV7EvVZXSQeV6b9GlRnqf5BS0xzZdRAn0VbsRHEkz8Q\n6Cxi8gcCHUVM/kCgo1h4eO+Be1mSF09Cv02YpuOQXv3pouumYznoyne3W0OYTiUklgVBSqG/RDOO\njvlj41N1ZlZvzdN0jjZiVqpRH47DgIUe2639ZJc9VhDHbNBDGfGNhjZ/Py9Ums1Q1HvxfkFEoxi2\nSqIfet+cQGhD3IR9ZRlvpk9tvO6OgenIW+DX+/qK+ZBpXfdIs9E6iqhHPPkDgY4iJn8g0FEsTcNP\nTXs2xRvUlov+I5O0UuEGNu2lDRXmOEDR/ZB9zSI87IecyGb/pj+2e1dtQh7f9LTfhCO42Bxe8xFy\n2VJYcp2j+kp0mPpgbDqzST0q0IU6WBzht0Y6hmqWcw0CjSDM6dkXaDQdD2NTX/ufu5e6BBRFWZ08\n4a/bo89sxNl/hWjFRvk1/t5ShOmquh91Pxp05w2g1ZPfzE6Z2e+Z2ZfN7Hkz+wEzO21mz5rZC7P/\nd9x0bwKBwMLQ1uz/zwD+OKX03dgv3fU8gMcBPJdSug/Ac7P9QCDwFkGbKr0nAfwwgH8OACmlIYCh\nmT0C4MHZaU8B+BSAj95oR4ySZrSCL5vzvQGZcWKG8+p802RneWc+UBIVEZEOYgw4sadUImmy4c2/\n7Ttrk+/YHd6EtK1tvlm2TVcaa+BFCN0qc04cBGJ6lhJvSivkrAOYb8H3YyBfOS0j1qYfeszpDKo7\neXRR60Y0ZK9mUFhHDwCMGBuvJbiLHBome0YH0LlLgBu7SkuWzdwP1qe8Hto8+d8F4DUA/93M/trM\n/tusVPfZlNLLs3NewX4130Ag8BZBm8nfB/BPAfzXlNJ7AWxBTPy0v1oy9yfWzB4zs/Nmdn5ydetm\n+xsIBG4R2kz+bwH4Vkrp07P938P+j8GrZnYOAGb/L8y7OKX0ZErpgZTSA73jm/NOCQQCS8B1ff6U\n0itm9k0z+66U0lcAPATgS7O/RwE8Mfv/dJsb5iL7ev28j8sCHq50UklPXUt5Ef3mKEGl7zJ0HuDL\ng/ukOL0Z3Wvdt7dzV/1exqc81dd/iSIUOZtO+uFopJT3mV20m/rCtN+IRqMPydFjmv1XyrZ0WY+F\nfvC9df0iIxDaiOJjYRKNQmxbC8FRghr9V1iLYTGVqjCm/F4KtQVcf1f8eEyP1+sNlYqz7LVcOyG0\n5fn/FYDfNrMVAH8P4F9g32r4uJl9GMDXAXzoyHcPBAJLQ6vJn1L6HIAH5hx66NZ2JxAILAqLj/A7\nsGqKpXjVrKtNNEe3DSSKqpS8k0vEKfRjKi4Am/esM1gV6gBM+r6Pw5P1uXtnfGJPnxM3mAJbKUX4\nFRJq2CxXkzoXgQeh9Fwl3nxSTkr5xCEr0JauyrCYuU4oo2AOu/eiYhvsIhWELhz1afnzpjuewjPu\nI5n6jYrGbNortcrj4xKuRNxkmxK/tnbcsYPvS+O+BURsfyDQUcTkDwQ6ipj8gUBHsQQxj1mJ7pLe\nY8Nfz5wnPrnbF4FQ43fKFGGDR6s3pyMVU2AdfKblpI1pfu1hcrz2467e47O7Ns+cqncuX6GLhBpy\nSqJ5vXwXfiu0KPv5acP7/MZUF4tG7uRFLxtlrZk6o8y9hkgnr1+UhETZZ1ZdfRZF0dLgFDLtMggV\nLWsG6vqFoxlp3EzDmAshzu798HhIKXJcvXa4Ob3mA+YO1kSOInUbT/5AoKOIyR8IdBR2FGrgpm9m\n9hr2A4LuBHBxYTfOI/rhEf3w+Hbox1H78M6U0l1tTlzo5D+8qdn5lNK8oKHoR/Qj+rGgPoTZHwh0\nFDH5A4GOYlmT/8kl3VcR/fCIfnh8O/TjtvVhKT5/IBBYPsLsDwQ6ioVOfjN72My+YmZfNbOFqf2a\n2W+Y2QUz+wK9tnDpcTO7x8w+aWZfMrMvmtlHltEXM1szs780s8/P+vEry+gH9ac304f8xLL6YWYv\nmtnfmtnnzOz8EvuxMJn8hU1+M+sB+C8AfhzAewD8rJm9Z0G3/00AD8try5AeHwP4xZTSewC8H8DP\nz8Zg0X3ZA/CBlNL3AbgfwMNm9v4l9OMAH8G+HPwBltWPH0kp3U/U2jL6sTiZ/JTSQv4A/ACAP6H9\njwH42ALvfy+AL9D+VwCcm22fA/CVRfWF+vA0gA8usy8ANgD8FYDvX0Y/ANw9+0J/AMAnlvXZAHgR\nwJ3y2kL7AeAkgP+H2Vrc7e7HIs3+dwD4Ju1/a/basrBU6XEzuxfAewF8ehl9mZnan8O+8OqzaV+g\ndRlj8msAfgm+cNoy+pEA/KmZfdbMHltSPxYqkx8LfihLj98OmNkxAL8P4BdSSlf42KL6klKapJTu\nx/6T931m9j2L7oeZ/RSACymlzxb6uajP5odm4/Hj2HfHfngJ/bgpmfyjYpGT/yUA99D+3bPXloVW\n0uO3GmY2wP7E/+2U0h8ssy8AkFK6BOCT2F8TWXQ/fhDAT5vZiwB+F8AHzOy3ltAPpJRemv2/AOAP\nAbxvCf24KZn8o2KRk/8zAO4zs3fNVIB/BsAzC7y/4hnsS44DR5AevxnYftL1rwN4PqX0q8vqi5nd\nZWanZtvr2F93+PKi+5FS+lhK6e6U0r3Y/z78WUrp5xbdDzPbNLPjB9sAfgzAFxbdj5TSKwC+aWbf\nNXvpQCb/9vTjdi+kyMLFTwD4OwBfA/BvF3jf3wHwMoAR9n9dPwzgDPYXml4A8KcATi+gHz+EfZPt\nbwB8bvb3E4vuC4DvBfDXs358AcC/m72+8DGhPj2IesFv0ePxnQA+P/v74sF3c0nfkfsBnJ99Nv8L\nwB23qx8R4RcIdBSx4BcIdBQx+QOBjiImfyDQUcTkDwQ6ipj8gUBHEZM/EOgoYvIHAh1FTP5AoKP4\n/2Hidz0Rb+yIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd0b08cc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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A9zWxTYUPM3Vj5w04XNNQPnkn+mC9JdJrb+v9GmtD2qavc2sp+oWDOW0sNbLJ\n/bcUG693Q8Ns40y72N6iyepr0hyAlSWZp+teLOTyRXC473qux6BJ/ejQkObGJ10LkY5siR6rDmUK\ndkiMxAqT3NGOYcFbR/W2nOZjsm6cHxFbDl2VXzfPREn4rQX78jY0V4WKm6w+pvRkM45HmNPzBoGe\npUJ4fH8Pyp0jTMPznwDwpIjUsG0pfCaE8FkR+SKAz4jIJwC8AeDjez67w+HYN0wz2//XAD404e+X\nATx4KzrlcDhuPWYa4Zf1Axbf2jZxWpd1RpiQFhqbPgC0qUXmWjA0nRJhsGYdm/PcXiFTjdszWXeN\nyVMkhQxC5VYYk53Ms6yn2+/nk3UBrZm7TFF8ZeIdANCnUta5Ua1rKCqunEJqk+ldEOwgfbyGoQs3\nwuTlvgmRY2t1MdOuSYP6zLeibcZjgbUKD2nqc+O2mPm59DcUvdnVz5gaAWuy07Njy8Cp/aw5z9vq\nvM3UpWDqjyJdxTybSvQjlV04JTy23+GoKPzldzgqipmb/XPntk3WQrkhipwK/UQ5Iz7GmuyclGNN\nWTK/VcklW0VXJX8kzk3t5Q0TTajMRH1c1ovH1Tf0cRv92K+cTPZhKO/IvDGBh2TAzqsZcn0cR8/Z\nXJWNEP/Sp5nuljnXIkXrLZqZ+ja1sU7LqWSgdWNSs6gIe09D48JsUCRjo6FnwXuL5CZSebeCtDYn\n5ZgoPvWc1Se7ZgAgm+WuazJxiBinwDL1RiRGUmIeO/ve7Ag/h8Px7Qd/+R2OisJffoejopipzy/D\nHLW1EoEJLlNkaTrOlqKoJ1suWUVi2Wg/VWqLfEZDxXH0lS3jzDQP1wEINUPJkEBoZqLFGmvx3HMX\ndfsXLsXyVBdORNHSe1rn1X49+s1eDyZSkrxq9o0z4233VPafiWSk9hcp6nDRZuSRf3kl1+PNZ+PW\nawlGqm98eaYBWWDk/GBJ7fe1tZjJt3Fdi7pyQHmYT5Rw5+fDRNYlwZGjPM9ky8B1idq281H0zAln\nA9qyXvS8K9EP0FyVl+h2OBy7wV9+h6OimK1ufx4gG9t0SEGTnE19I2LAyTeKFilE55WLeSjzjGjG\nQgIG6+/Z9reiqaV0+mrltpYYt6J+Pbo9C2e1WbfyVjT5vnH3sfHy93a+qfZjnT5L9S1nbPZHbAQ9\nHpwcdNT0X0f8xT7apBzGvIkwY9qOzXmbpMRJP32zjb9Mb+fxPr3UPan2++YaKcgNLO0al3MSdJGs\no/ZLuoLUo+ZoAAAabklEQVTcntXfI5M965EWXyE5LS5aCk8ll9E4WrcTHCVo2hjrB+4h0M+//A5H\nReEvv8NRUfjL73BUFLP1+YGiLz6CytCzVB9nOnXLs56UbvqUogtWTJH7Ycsxc6hubTP6dHlDU0O5\nFYdktHhOQW9qXotj87fXDo+Xrx3WCkjvb0TRJBbRBIBrRNv16bfd+tqLqlafofpo7Hhb29TZG9J+\nmRH9yGjuYZUpUmjU6Hnom3vGXi3TmxzOayFt3Y/+Qryf/QMUBrxSXmdQElmOtlaEUC9TtRx0Vl85\nVH0J83yocgrm2UTYvrZUZqGFf/kdjorCX36Ho6KYcYnuMI58sqZVzvSeCQOTXokZZrOehkSTWHED\nOl/oRErGCnaoQ6xOGpdmYvPdmoJsDpuyYfkc1QwwJbobJEf/9sUY4ff8sfeq/U42ombdnXJVbWMB\nD47waxuhDF67bqLzWI9vPotu0RHjjvXJ1N8wbfAaD3HCIULX0JHnh9Hd+cpmHIM/u/g+td8bbx2J\n7V/ULkFjjWoobE1Zss1s4udArMAL04dMSZvIzpxetWHHitCUPIPG7OfnqiAqsnO6PYh6+Jff4ago\n/OV3OCqK2c/2l4BNmmCrdZWZaNb0ydgs0ptCiQS1JIQyiu1T8hFF+9VhkpU4Samn2YRsIxrctTU9\n4xxq0cztHonm658v3a324wq7P7z0dbXtu5sxCej2Wrlpy6W3rLldJqKxFrTroBN2jAtDyzyLv2GG\ntEsuxrVcsyZfo0i+333z742Xz79wTO03dzm20ViD2RbHn8c72zD3LCHPrUxsu40FZNrlpd9q672J\ny4AWnmF3Mrdlw1gCvWkiDUdqLamSXhb+5Xc4Kgp/+R2OisJffoejopitz1/LEJYWABT9GUn4XCr6\nLxHBpOYGChF+7DCxrr6ZC2A6K3kuOs5mklE0V6H0E4lDytCUpzobjzs+iHTk5a4Wr/jjre8aL9fv\n1f0/thz5wobE5bYJF+uQb9gxbiKX4eL5gK4ZUx4dO7vAAqGcJZiZNroh+q4vbp1S2/7XWfLzX4x+\n/uLf2vJoFJG4aTIU10rKjdnnI1XeXd3PEhENQD1LKSrRitAoCo+jTxvlpdIyE0W68z6l6goU2ph2\nx1GZ7r8Skc+O1g+JyDMi8sro/4O7teFwOL51sBez/5MAXqL1xwA8G0K4B8Czo3WHw/EuwVRmv4ic\nAvAPAfw7AP9i9OeHATwwWn4SwBcAfGqXhsYmFEe6AUYfr2uSbdhKIoGDQlKOMuWMLcvmfEn5L8Do\npFnTkE0+SiKySRsqMtC2QZGMheNIAGLuzbh8ZLCg9rtEQhR/tvAdats9cxfGy53stfHycqbppUOk\nx7eQaW27LaL0Nqgk17q5Ftb+2wraDNWJOJHCuzhcVPu92r1tvPx/zn+X2nbmq3Hb0stUj+CCvu+M\nbGBckx5H9U2+fwAQ6qyPV+4SBBtVymY60cbB6vuzad+zQh+UHMTPX0L0w7YhI5rxVpj9vwrgl6Bd\nu+MhhHOj5fMAjk99VofDse/Y9eUXkZ8EcDGE8HzZPmH7kzvxJ0dEHhWR0yJyujfYeOc9dTgcNxXT\nmP0/COCnROQnALQBHBCR3wRwQUROhBDOicgJABcnHRxCeALAEwCw1Ll9D8LCDofjVmLXlz+E8GkA\nnwYAEXkAwL8MIfysiPwHAI8AeHz0/1N7ObHNuhvSHEBY0FRIY41CYq+S9WAzmOqU0WX9JfbNmIax\nPhz59QWR0ZL2rPnEWVqhEKJZQjlCCzbaORHG3IX4G3r59WW17U8PvX+8vHgkCn1+d+us2u8C4hzA\n2eG62sbCH4s0xk0pDwO+PNSCmKc3Ykjy89ffM15mkRIAuHwlzmc0X9fhvYdfjcuL34zhuDUzJ5Q3\nyjMssy3aN1UoMFGaHTmFa9vwWQ7NrZVTgql5JrXO7VnBDr5OEzYuO9c5o1p9jwP4mIi8AuBHR+sO\nh+Ndgj0F+YQQvoDtWX2EEC4DePDmd8nhcMwCM43wCxLpLWu61ShLrndQm3/DduxmLVVKeRBN2QLV\nwhQbmWSyZiYhWS+wo0s/qTLLgwSdRzSazbKSRCQZUztsrtZNpOH8eSqv/YK+hc81I/W3dm8cx+8/\n9Lra7972ufFyZlIgr5GIRh44Uk+bsuf60eX400v3qG2vvhZpuuaF2MfWVX3Nxy7Ec3cu6vvZvBzd\nlow1EztGw48oU2XmA5BueclrBTb1C1l9iWg9fq7ITSzEl5ZEAgKaWlTugokOVc+SjSDc6bOX6HY4\nHLvBX36Ho6KYbZXeEEUwpKvFFNjcafV1RNtggU0rmvHcsmIYJVF8gK6ayjOqpiKrKJ0+zTrktJ4v\nRZegIMvMGn42sYej/0zVWzbz2MzNTGXjBolXtC7r/s9djrPuZ165c7z88qn3qP1wJLZpy43lPXJb\nOGnJiH7IRtyvfUmPwcG3Y5utlbjcvqLN8taF6HbVrmvWAZvdeGp2iaAZDn4+2EUEgBpH65FXUahy\ny+Ie1uxnU9ya26r6M0X42Rn9svYA/Wym3Fo+l2WibNTgFPAvv8NRUfjL73BUFP7yOxwVxewFPHd+\nbmxp7Fp59FJtkyKsWEPdRHMlSxXR+ZSQiPHXVXSeiSAMDS6RHBczq++fgIoCM/5jxtfdL89cY6qo\nfrGrNh24Gv3m+bfi3MnmcZ25N6C5jkImXH8yXZQ3ystfB7HRinG51mPtfFPWi0tZb2yqbWFANF2b\nai1YipTmSqywJVN4tQ1qb8VQgjze1ifn+QFTPl49I0whFxRkE5mkPO/E74XN0KPrLtCdo7kOL9fl\ncDh2hb/8DkdFsW9Vei2NxqacpWEYw/lo7mTW/GO6w5YDI1Ofk2YKZtI0JZEA9bOZW1EODuLrW823\nvHSbMg1TSUV8iDEhmUaqX1wZLy+saPdguBBNzYL70S0xgc1Y8bltEspwnnUXyfQ2tQq46nLoW5GL\nyeNhE2/qq0TTDcuj85iStUlbyuwPxuViMY917ZqoWg4NumZ7/xIJXRLY1UzUD9hMJJONxt9W9k3B\nv/wOR0XhL7/DUVH4y+9wVBSzzerLZOyz2+yrlNAC18VTReCMr523iM6y4ZUcHkpls5WPBeOvbxkf\nlPw29pnzpjkXUWe2chrPZxSyxdjHY3/X7sf14eYM9UQ+b7YR/XwxvmqN/XybAcmUFYdhm3BqFcbc\n0HM42SqFZLeJyjL+usqUzHR9AjX+MpnuBQxNnAjrlmG5z19KtwFaNMZQsFKW3WnvGfXRzkeVtVco\nM8/7bZpag71s8nkT8C+/w1FR+MvvcFQUszf7S7TpVFkr+5PElgzTNYUTECVjLSaOrGPT3kq0c4Sf\niaJi0QV2Hbjs9nab5XwLm6gWnFlWyPxisClro8WYEmPz1Zr2nIFmqUo+9x504Mugyq/DRgnSfbHm\nNrsmXObMZruxG2Tb4NJYCe381HgrWtq6kxzJx9SnpZATWX4oK9dlqWDus3mOpFleHrwM/uV3OCoK\nf/kdjopitmIeeUBtFKVkZzIzti7NhGXeIRENSq6xFXALwhkl4FNZ84xn7sUmvOQUYUVsRaESrzqZ\nidJKyIazKacKj03pAgAmUo2TmRa1HiGzFda0r/P1sBlt7pmkGAPWtuOZ+k3DGCSgXJj5udL9UhGh\nKqqP3Zs8USnXJJapKMdCBV9iGhJVotV9SZThUmNsXQVOKrL92ENCz7j5PR/hcDi+LeAvv8NRUfjL\n73BUFLPN6hPyTczPzrBD/l0qsilBPeW2vBFBRRRS+zY6j/38+nUdFaeol5SGOsP6wupcNqtvsoBH\ngb5K0EYc8TekuZJCjQAWRTH9V74xnbtwV3iOwt4z8mslldHGY5AQuRgcjj7/sKXHtHmVKFLrr/N8\nA81lcHYoAPSW43qtq/vYuB4jJVlYFTC+vCrXbZ7TYUkkIADU6f5SH0NPZ2IKP0sFaq/8OSvDVC+/\niLwOYBXAEMAghHC/iBwC8LsA7gTwOoCPhxCu7rkHDodjX7AXs/9HQgj3hRDuH60/BuDZEMI9AJ4d\nrTscjncJbsTsfxjAA6PlJ7Fdw+9Tux20Y7YXguCUmEd5sk2ol7sEin4z7gGbfEw91ex+LLbRNYk9\nZWIblmZJUD4qss6abrRteCAmKeXGzC0TFQGAYYOjzOJ1Wl2+Bo1VtmrMy5LkEksn8X4FybpBiVth\ntQlJp69gynIbfF/seNO9Vfr7gI66a8f2rb5/bzGOccsIgiSr+5I5H9bWJ/4dAGSeqhi3da2FshJb\nhSc9kRAUtyX2MZj2yx8AfE5EnheRR0d/Ox5C2Cn4dh7A8anP6nA49h3Tfvl/KITwlogcA/CMiHyd\nN4YQgshkAaHRj8WjANBuLU3axeFw7AOm+vKHEN4a/X8RwB8C+DCACyJyAgBG/18sOfaJEML9IYT7\nG435Sbs4HI59wK5ffhGZB5CFEFZHyz8G4N8CeBrAIwAeH/3/1K5nC5FiCkZfnf1CGy6rs85obsBW\nxmY6z/hRnJnFGX/sSwJGO79QerssVFTvlvRxia4ZHNY/hqyt31uI7TfX9AnaV6iMuAlBrpGvyeHO\nYvTyFW1p6MjAoa50LVY4JKd1G7bLwpxJv57XExmP9auxpl/hoeW5AZOtxzSp9OOYNlZ1fzO6ztqK\n2bZONQNt/9uThVDDpqlBUEZ92jZTwq18bYYWHY5qRxbqRiYwjdl/HMAfjjpcB/A/Qwh/LCLPAfiM\niHwCwBsAPj71WR0Ox75j15c/hPAagA9O+PtlAA/eik45HI5bj9lG+IUwpuAKkXpcNttG6tG+XHIJ\niWy60NSXxtr6XF4rJbxRiKSrT9ZhkzyRdWcj/PhaTOnt+oFoRrPZb/tYvxpNymxN03RMxwXKAktp\n2xXMfo4SZHps3uj0dalGgKXY+HzNcjEMNnmHSzpzj58DHitL5ylz25rlfG10zdmKoZO3yD2w2v/s\nVpioTBXZyC5G8wDKUIjY5G30nOZzpkR8PY7PYEFv21revs78FS/X5XA4doG//A5HReEvv8NRUczW\n569lGCxu+5M2M0vK/HoANaLfmLKytI6aN7D+OrFU7KcVtPN53fjCw0WmdYgOs2HAqaxEzvza0P56\n53oMD22fiTRgsGO1kdDSJ1ot60QfUenjAzpU1PixStWG/O6sl6BFLVKhqKoRumeGpuovUKYnhXU3\n7HlJw96eVdcFoPZN+G1G9J6dN1Bt2LHi9VRdw4QikqKQWdDUzD1kNLfUvKbvRX1t+zh7j1LwL7/D\nUVH4y+9wVBSz1e0HkI8yzYKtjE1mYt1m2tksq7L2WbDSZG2xImZKEITppeG8zr7qHovrWS+2MWfL\ncHO0mzEvWfDRRnPJWoxiy67G8tphoYNSWJEOFobgqLJuuXBmSAiOZESx1a6u643kwhQi2oTM1w6V\nUSuUnY7t1+210H1iqjY3LkxG+0mqnBYSlCwfY9oIiRJYQdWDSJTkSowxR+7VaDxSopw283X8d6v1\nn4B/+R2OisJffoejopitbj+AbGTCi9FJU6a4MZkGCxypFv+ebRmzmSL+akZrTZnA7EZYky4rj6zL\nG7FfvcW4X32zrfZrktlf0Mdj3bu2iUbLKNEnpdXPsNFiZcelZt9t9F+ZEIdlFrj5lhGoYDOXZ9lt\nohObzcY1qVE/MhorMRWeVSVhG0GYMre5jYQOo3o2UxGhKSTup3JNNqmysk104rJk1iWYll0h+Jff\n4ago/OV3OCoKf/kdjopithF+eUBtfdsXL4h0coZeoQ4Z6+zHLu9EC8Y2OGPOCDIMJgtsFHxy8qFr\nJCABAAvURvdojJ7LjahovhjnALINKzZJ/nS3XANe+XSJen8Fn5Zrx7EvaX3VhI+oS3QnaK5BeYQf\na8znVGfP1tVTfr7dxn2ulUfnBfL5pWkiGZkWzMvnetR4p8qo2zmLMtj6BFymPOWvKw1/cy4aKlua\nfTw+e5iS8C+/w1FR+MvvcFQU+ybmUdBCV4kmJoGEknk44swmT+SdJi1bTXxK5ulRIoXtR+LnULrR\nDGu/HSkZLhsOADnrE4qmwGSLTH17bjJnlXmZonFMEgq7RZy0VCgjnjLtlbAKtWfMYSmj82BKbR0k\nkQ4zvo1LMWpQVkwEIVGLKgHLjoeiEo0rtVpCIVsBkxQlyCZ7IilHnbdfbn8XaEV296j/BdOead0y\nenYPVKR/+R2OisJffoejovCX3+GoKGZcolvGZbQlM1RIj0s168NCjfxOFnIwYh7Cop3NdxiGqc5r\nfNyMsse2OBPLHEfiG4X6dizcUDNZbIFoqT7RjAnt/4K4BM9tsJ9fKAeeyGzkPnN/G8bfHUzeD9Di\nkyzEERLzF8H462oOhI9Laf8n5gNU3UFbL4+yBguZcQm6k++FEpCxYdc8f2HvBdOziXLm6h5aunNn\nX/f5HQ7HbvCX3+GoKGZr9qNcJ5/NwQLFweYPb7PmnzqRiboj+q3GZaysqUkUjcCY26GENrKRaVsJ\nE5X18Wyfy/TghuVmYuHcmyUmaqFUeK18G483LwfTYx4D696Q6dy4SuWuaonIQmuKMyWWKm1O67mp\n1zDkWgiLcb/BnHW54nL7sn4mGpejC1bQCCyh+tKl3iYfYtsPvUQWpRU+2YlsTOhHWkz15ReRZRH5\nPRH5uoi8JCI/ICKHROQZEXll9P/Bqc/qcDj2HdOa/f8RwB+HEL4T26W7XgLwGIBnQwj3AHh2tO5w\nON4lmKZK7xKAHwbwjwAghNAD0BORhwE8MNrtSQBfAPCpZGMhFKrFjjc1Js/KAkBGkXUqgquQHBTb\nrtlKvzwDzyaTKQ2mhD6mnSG3M7scIZeomlowGamSMAtZFEQ09iLMMW7QRLS1iEExYyVdjv6LywVz\nO5l8ROuceJNKFLLj2KTrLDOvASPUYiS5NymJqxPb73eMLLvyOExZsl5M1Kqtarn1QpTm+GTGLOf7\nuWH0DjmCULVtxpTdPZt8NJ7tL+nPBEzz5b8LwCUA/11E/kpE/tuoVPfxEMK50T7nsV3N1+FwvEsw\nzctfB/B3AfyXEMKHAKzDmPhh+6dr4m+OiDwqIqdF5HR/sDFpF4fDsQ+Y5uU/A+BMCOFLo/Xfw/aP\nwQUROQEAo/8vTjo4hPBECOH+EML9jXpCgtrhcMwUu/r8IYTzIvKmiNwbQngZwIMAvjb69wiAx0f/\nP7VbWxKiT2a1+PM6ZeTZUke1EhrQRrdRxF+hlHKITh1r89soPtZ2l0JxgRKqz/rgLNhhWtflmCyF\nR/Me7enKa4udDyC6LJBefoFiU1RlomRZytdW9JXZj8uZKWrS9IOpT1t+rSSzsVB6jINDTQm07GIc\nn9qV+PFpXtcltAc0H1DbslmllHXXLJ/fURmbG+a+0L0OhfGeTqw19GluI0XdTolpef5/BuC3RKQJ\n4DUA/xjbVsNnROQTAN4A8PE9n93hcOwbpnr5QwhfBnD/hE0P3tzuOByOWWG25boyKZTA2oGqDFsw\ni6gNNi9rxvzj9ixNp4Qh6LIbljKh5VRFVtV2IgHDRr6VCGUAAIZsHid0+vi4ZCkpOpc1V9lsLNQP\nIFOWNPasWZ6iTEFJUOCkrYRwSAElYiEpjf2C+TsXXR/W369f1sIh9euJiEd2P2wCU2eyO4mOec67\n1H/7HAk9x0rUxpQN4+PqlhYd3cObHeHncDi+/eAvv8NRUfjL73BUFLP3+UeUihW9rFHY7462/w44\nQ4zpsWD8zHxhbuIxgKEBmZKx/hf70wXKkdYTGvCKjrQ+Ls9t2FLQiW1qvxb5iNbH5VBRChfuLZmy\n1kzFDUwYqapNR7EZxj/PNuI4ZqsmgKtMLNPOXyT8aQX2w41PruZEbFgtzw+wOIjV7Q8kPmLCmDNu\ng0poA0CN+p9zKXJLfSZKs6tnkH12KybD111WMyFRft7Cv/wOR0XhL7/DUVFI2IPm1w2fTOQStgOC\njgB4e2YnLof3Q8P7ofGt0I+99uG9IYSj0+w405d/fFKR0yGESUFD3g/vh/djRn1ws9/hqCj85Xc4\nKor9evmf2KfzWng/NLwfGt8K/bhlfdgXn9/hcOw/3Ox3OCqKmb78IvKQiLwsIq+KyMzUfkXk10Xk\nooi8QH+bufS4iNwhIp8Xka+JyIsi8sn96IuItEXkL0XkK6N+/PJ+9IP6UxvpQ352v/ohIq+LyFdF\n5Msicnof+zEzmfyZvfwiUgPwnwD8OIAPAPgZEfnAjE7/GwAeMn/bD+nxAYBfDCF8AMBHAPz8aAxm\n3ZctAB8NIXwQwH0AHhKRj+xDP3bwSWzLwe9gv/rxIyGE+4ha249+zE4mP4Qwk38AfgDAn9D6pwF8\neobnvxPAC7T+MoATo+UTAF6eVV+oD08B+Nh+9gVAB8D/A/D9+9EPAKdGD/RHAXx2v+4NgNcBHDF/\nm2k/ACwB+FuM5uJudT9mafafBPAmrZ8Z/W2/sK/S4yJyJ4APAfjSfvRlZGp/GdvCq8+EbYHW/RiT\nXwXwS9AyKvvRjwDgcyLyvIg8uk/9mKlMvk/4IS09fisgIgsAfh/AL4QQVvajLyGEYQjhPmx/eT8s\nIt8z636IyE8CuBhCeD7Rz1ndmx8ajcePY9sd++F96McNyeTvFbN8+d8CcAetnxr9bb8wlfT4zYaI\nNLD94v9WCOEP9rMvABBCuAbg89ieE5l1P34QwE+JyOsAfgfAR0XkN/ehHwghvDX6/yKAPwTw4X3o\nxw3J5O8Vs3z5nwNwj4jcNVIB/mkAT8/w/BZPY1tyHJhSevxGIdsCfr8G4KUQwq/sV19E5KiILI+W\n57A97/D1WfcjhPDpEMKpEMKd2H4e/m8I4Wdn3Q8RmReRxZ1lAD8G4IVZ9yOEcB7AmyJy7+hPOzL5\nt6Yft3oixUxc/ASAbwD4GwD/eobn/W0A5wD0sf3r+gkAh7E90fQKgM8BODSDfvwQtk22vwbw5dG/\nn5h1XwB8L4C/GvXjBQD/ZvT3mY8J9ekBxAm/WY/H3QC+Mvr34s6zuU/PyH0ATo/uzf8GcPBW9cMj\n/ByOisIn/ByOisJffoejovCX3+GoKPzldzgqCn/5HY6Kwl9+h6Oi8Jff4ago/OV3OCqK/w+SDn9a\nWHEBJgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd0b4c9ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Percent labels True:\", TFratio)\n",
    "for i in range(10,20):\n",
    "    plt.imshow(nodulecrops[i,:,:,0])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial CNN and Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1264 samples, validate on 317 samples\n",
      "Epoch 1/135\n",
      "1264/1264 [==============================] - 2s - loss: 0.6892 - acc: 0.5570 - val_loss: 0.6483 - val_acc: 0.7003\n",
      "Epoch 2/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.6323 - acc: 0.6820 - val_loss: 0.6086 - val_acc: 0.7350\n",
      "Epoch 3/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.6040 - acc: 0.7263 - val_loss: 0.5908 - val_acc: 0.7350\n",
      "Epoch 4/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5988 - acc: 0.7334 - val_loss: 0.5855 - val_acc: 0.7350\n",
      "Epoch 5/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5965 - acc: 0.7342 - val_loss: 0.5847 - val_acc: 0.7350\n",
      "Epoch 6/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5990 - acc: 0.7342 - val_loss: 0.5842 - val_acc: 0.7350\n",
      "Epoch 7/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5926 - acc: 0.7334 - val_loss: 0.5833 - val_acc: 0.7350\n",
      "Epoch 8/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5946 - acc: 0.7326 - val_loss: 0.5824 - val_acc: 0.7350\n",
      "Epoch 9/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5868 - acc: 0.7326 - val_loss: 0.5817 - val_acc: 0.7350\n",
      "Epoch 10/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5879 - acc: 0.7358 - val_loss: 0.5814 - val_acc: 0.7350\n",
      "Epoch 11/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5916 - acc: 0.7326 - val_loss: 0.5812 - val_acc: 0.7350\n",
      "Epoch 12/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5874 - acc: 0.7342 - val_loss: 0.5810 - val_acc: 0.7350\n",
      "Epoch 13/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5868 - acc: 0.7334 - val_loss: 0.5807 - val_acc: 0.7350\n",
      "Epoch 14/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5808 - acc: 0.7342 - val_loss: 0.5803 - val_acc: 0.7350\n",
      "Epoch 15/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5899 - acc: 0.7342 - val_loss: 0.5800 - val_acc: 0.7350\n",
      "Epoch 16/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5755 - acc: 0.7366 - val_loss: 0.5794 - val_acc: 0.7350\n",
      "Epoch 17/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5870 - acc: 0.7310 - val_loss: 0.5791 - val_acc: 0.7350\n",
      "Epoch 18/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5818 - acc: 0.7342 - val_loss: 0.5789 - val_acc: 0.7350\n",
      "Epoch 19/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5804 - acc: 0.7326 - val_loss: 0.5786 - val_acc: 0.7350\n",
      "Epoch 20/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5839 - acc: 0.7334 - val_loss: 0.5784 - val_acc: 0.7350\n",
      "Epoch 21/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5799 - acc: 0.7318 - val_loss: 0.5782 - val_acc: 0.7350\n",
      "Epoch 22/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5860 - acc: 0.7310 - val_loss: 0.5778 - val_acc: 0.7350\n",
      "Epoch 23/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5816 - acc: 0.7350 - val_loss: 0.5773 - val_acc: 0.7350\n",
      "Epoch 24/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5788 - acc: 0.7358 - val_loss: 0.5768 - val_acc: 0.7350\n",
      "Epoch 25/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5792 - acc: 0.7342 - val_loss: 0.5764 - val_acc: 0.7350\n",
      "Epoch 26/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5755 - acc: 0.7326 - val_loss: 0.5761 - val_acc: 0.7350\n",
      "Epoch 27/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5756 - acc: 0.7342 - val_loss: 0.5758 - val_acc: 0.7350\n",
      "Epoch 28/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5766 - acc: 0.7342 - val_loss: 0.5757 - val_acc: 0.7350\n",
      "Epoch 29/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5657 - acc: 0.7350 - val_loss: 0.5757 - val_acc: 0.7350\n",
      "Epoch 30/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5770 - acc: 0.7358 - val_loss: 0.5755 - val_acc: 0.7350\n",
      "Epoch 31/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5741 - acc: 0.7326 - val_loss: 0.5753 - val_acc: 0.7350\n",
      "Epoch 32/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5737 - acc: 0.7350 - val_loss: 0.5752 - val_acc: 0.7350\n",
      "Epoch 33/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5687 - acc: 0.7350 - val_loss: 0.5749 - val_acc: 0.7350\n",
      "Epoch 34/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5684 - acc: 0.7373 - val_loss: 0.5745 - val_acc: 0.7350\n",
      "Epoch 35/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5639 - acc: 0.7366 - val_loss: 0.5740 - val_acc: 0.7350\n",
      "Epoch 36/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5750 - acc: 0.7310 - val_loss: 0.5738 - val_acc: 0.7350\n",
      "Epoch 37/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5688 - acc: 0.7334 - val_loss: 0.5736 - val_acc: 0.7350\n",
      "Epoch 38/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5718 - acc: 0.7310 - val_loss: 0.5734 - val_acc: 0.7350\n",
      "Epoch 39/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5678 - acc: 0.7350 - val_loss: 0.5732 - val_acc: 0.7350\n",
      "Epoch 40/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5716 - acc: 0.7373 - val_loss: 0.5731 - val_acc: 0.7350\n",
      "Epoch 41/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5664 - acc: 0.7318 - val_loss: 0.5731 - val_acc: 0.7350\n",
      "Epoch 42/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5664 - acc: 0.7350 - val_loss: 0.5730 - val_acc: 0.7350\n",
      "Epoch 43/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5673 - acc: 0.7381 - val_loss: 0.5729 - val_acc: 0.7350\n",
      "Epoch 44/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5612 - acc: 0.7350 - val_loss: 0.5726 - val_acc: 0.7350\n",
      "Epoch 45/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5614 - acc: 0.7373 - val_loss: 0.5722 - val_acc: 0.7350\n",
      "Epoch 46/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5672 - acc: 0.7342 - val_loss: 0.5718 - val_acc: 0.7350\n",
      "Epoch 47/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5612 - acc: 0.7366 - val_loss: 0.5715 - val_acc: 0.7350\n",
      "Epoch 48/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5602 - acc: 0.7397 - val_loss: 0.5711 - val_acc: 0.7350\n",
      "Epoch 49/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5610 - acc: 0.7397 - val_loss: 0.5708 - val_acc: 0.7350\n",
      "Epoch 50/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5684 - acc: 0.7389 - val_loss: 0.5706 - val_acc: 0.7350\n",
      "Epoch 51/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5511 - acc: 0.7373 - val_loss: 0.5704 - val_acc: 0.7319\n",
      "Epoch 52/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5632 - acc: 0.7381 - val_loss: 0.5703 - val_acc: 0.7319\n",
      "Epoch 53/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5624 - acc: 0.7318 - val_loss: 0.5701 - val_acc: 0.7319\n",
      "Epoch 54/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5592 - acc: 0.7358 - val_loss: 0.5700 - val_acc: 0.7319\n",
      "Epoch 55/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5622 - acc: 0.7389 - val_loss: 0.5700 - val_acc: 0.7319\n",
      "Epoch 56/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5681 - acc: 0.7342 - val_loss: 0.5702 - val_acc: 0.7319\n",
      "Epoch 57/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5660 - acc: 0.7381 - val_loss: 0.5703 - val_acc: 0.7319\n",
      "Epoch 58/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5585 - acc: 0.7413 - val_loss: 0.5701 - val_acc: 0.7319\n",
      "Epoch 59/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5602 - acc: 0.7342 - val_loss: 0.5702 - val_acc: 0.7319\n",
      "Epoch 60/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5605 - acc: 0.7342 - val_loss: 0.5702 - val_acc: 0.7319\n",
      "Epoch 61/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5571 - acc: 0.7326 - val_loss: 0.5703 - val_acc: 0.7319\n",
      "Epoch 62/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5634 - acc: 0.7373 - val_loss: 0.5702 - val_acc: 0.7382\n",
      "Epoch 63/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5549 - acc: 0.7381 - val_loss: 0.5701 - val_acc: 0.7319\n",
      "Epoch 64/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5598 - acc: 0.7453 - val_loss: 0.5701 - val_acc: 0.7350\n",
      "Epoch 65/135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1264/1264 [==============================] - 0s - loss: 0.5510 - acc: 0.7381 - val_loss: 0.5699 - val_acc: 0.7350\n",
      "Epoch 66/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5630 - acc: 0.7397 - val_loss: 0.5697 - val_acc: 0.7350\n",
      "Epoch 67/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5500 - acc: 0.7373 - val_loss: 0.5698 - val_acc: 0.7350\n",
      "Epoch 68/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5557 - acc: 0.7413 - val_loss: 0.5698 - val_acc: 0.7350\n",
      "Epoch 69/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5530 - acc: 0.7373 - val_loss: 0.5697 - val_acc: 0.7413\n",
      "Epoch 70/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5577 - acc: 0.7389 - val_loss: 0.5696 - val_acc: 0.7413\n",
      "Epoch 71/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5531 - acc: 0.7421 - val_loss: 0.5695 - val_acc: 0.7382\n",
      "Epoch 72/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5484 - acc: 0.7437 - val_loss: 0.5692 - val_acc: 0.7382\n",
      "Epoch 73/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5560 - acc: 0.7421 - val_loss: 0.5690 - val_acc: 0.7413\n",
      "Epoch 74/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5587 - acc: 0.7318 - val_loss: 0.5687 - val_acc: 0.7413\n",
      "Epoch 75/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5512 - acc: 0.7373 - val_loss: 0.5683 - val_acc: 0.7382\n",
      "Epoch 76/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5544 - acc: 0.7381 - val_loss: 0.5682 - val_acc: 0.7382\n",
      "Epoch 77/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5566 - acc: 0.7437 - val_loss: 0.5680 - val_acc: 0.7382\n",
      "Epoch 78/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5449 - acc: 0.7445 - val_loss: 0.5679 - val_acc: 0.7413\n",
      "Epoch 79/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5514 - acc: 0.7437 - val_loss: 0.5676 - val_acc: 0.7413\n",
      "Epoch 80/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5550 - acc: 0.7334 - val_loss: 0.5676 - val_acc: 0.7413\n",
      "Epoch 81/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5560 - acc: 0.7413 - val_loss: 0.5677 - val_acc: 0.7413\n",
      "Epoch 82/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5565 - acc: 0.7429 - val_loss: 0.5677 - val_acc: 0.7413\n",
      "Epoch 83/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5484 - acc: 0.7437 - val_loss: 0.5678 - val_acc: 0.7413\n",
      "Epoch 84/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5565 - acc: 0.7381 - val_loss: 0.5678 - val_acc: 0.7413\n",
      "Epoch 85/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5449 - acc: 0.7468 - val_loss: 0.5676 - val_acc: 0.7413\n",
      "Epoch 86/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5477 - acc: 0.7413 - val_loss: 0.5675 - val_acc: 0.7413\n",
      "Epoch 87/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5543 - acc: 0.7405 - val_loss: 0.5676 - val_acc: 0.7413\n",
      "Epoch 88/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5537 - acc: 0.7397 - val_loss: 0.5675 - val_acc: 0.7413\n",
      "Epoch 89/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5478 - acc: 0.7453 - val_loss: 0.5674 - val_acc: 0.7413\n",
      "Epoch 90/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5483 - acc: 0.7389 - val_loss: 0.5675 - val_acc: 0.7445\n",
      "Epoch 91/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5477 - acc: 0.7413 - val_loss: 0.5674 - val_acc: 0.7445\n",
      "Epoch 92/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5527 - acc: 0.7484 - val_loss: 0.5672 - val_acc: 0.7445\n",
      "Epoch 93/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5453 - acc: 0.7405 - val_loss: 0.5669 - val_acc: 0.7445\n",
      "Epoch 94/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5507 - acc: 0.7445 - val_loss: 0.5665 - val_acc: 0.7445\n",
      "Epoch 95/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5453 - acc: 0.7429 - val_loss: 0.5660 - val_acc: 0.7445\n",
      "Epoch 96/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5488 - acc: 0.7397 - val_loss: 0.5658 - val_acc: 0.7445\n",
      "Epoch 97/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5456 - acc: 0.7445 - val_loss: 0.5657 - val_acc: 0.7476\n",
      "Epoch 98/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5460 - acc: 0.7421 - val_loss: 0.5656 - val_acc: 0.7476\n",
      "Epoch 99/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5474 - acc: 0.7492 - val_loss: 0.5655 - val_acc: 0.7476\n",
      "Epoch 100/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5434 - acc: 0.7508 - val_loss: 0.5653 - val_acc: 0.7476\n",
      "Epoch 101/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5438 - acc: 0.7508 - val_loss: 0.5652 - val_acc: 0.7476\n",
      "Epoch 102/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5433 - acc: 0.7492 - val_loss: 0.5651 - val_acc: 0.7476\n",
      "Epoch 103/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5401 - acc: 0.7540 - val_loss: 0.5649 - val_acc: 0.7476\n",
      "Epoch 104/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5473 - acc: 0.7516 - val_loss: 0.5647 - val_acc: 0.7476\n",
      "Epoch 105/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5434 - acc: 0.7476 - val_loss: 0.5646 - val_acc: 0.7476\n",
      "Epoch 106/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5430 - acc: 0.7445 - val_loss: 0.5643 - val_acc: 0.7476\n",
      "Epoch 107/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5378 - acc: 0.7468 - val_loss: 0.5643 - val_acc: 0.7445\n",
      "Epoch 108/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5422 - acc: 0.7445 - val_loss: 0.5642 - val_acc: 0.7445\n",
      "Epoch 109/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5385 - acc: 0.7492 - val_loss: 0.5641 - val_acc: 0.7445\n",
      "Epoch 110/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5405 - acc: 0.7492 - val_loss: 0.5642 - val_acc: 0.7445\n",
      "Epoch 111/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5418 - acc: 0.7508 - val_loss: 0.5643 - val_acc: 0.7476\n",
      "Epoch 112/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5433 - acc: 0.7437 - val_loss: 0.5641 - val_acc: 0.7445\n",
      "Epoch 113/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5458 - acc: 0.7429 - val_loss: 0.5640 - val_acc: 0.7382\n",
      "Epoch 114/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5442 - acc: 0.7460 - val_loss: 0.5640 - val_acc: 0.7382\n",
      "Epoch 115/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5380 - acc: 0.7563 - val_loss: 0.5640 - val_acc: 0.7445\n",
      "Epoch 116/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5431 - acc: 0.7508 - val_loss: 0.5639 - val_acc: 0.7413\n",
      "Epoch 117/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5354 - acc: 0.7508 - val_loss: 0.5639 - val_acc: 0.7413\n",
      "Epoch 118/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5325 - acc: 0.7492 - val_loss: 0.5639 - val_acc: 0.7445\n",
      "Epoch 119/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5350 - acc: 0.7516 - val_loss: 0.5640 - val_acc: 0.7413\n",
      "Epoch 120/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5380 - acc: 0.7492 - val_loss: 0.5639 - val_acc: 0.7382\n",
      "Epoch 121/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5349 - acc: 0.7500 - val_loss: 0.5640 - val_acc: 0.7350\n",
      "Epoch 122/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5417 - acc: 0.7460 - val_loss: 0.5641 - val_acc: 0.7350\n",
      "Epoch 123/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5278 - acc: 0.7500 - val_loss: 0.5641 - val_acc: 0.7319\n",
      "Epoch 124/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5316 - acc: 0.7445 - val_loss: 0.5641 - val_acc: 0.7319\n",
      "Epoch 125/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5381 - acc: 0.7540 - val_loss: 0.5638 - val_acc: 0.7319\n",
      "Epoch 126/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5324 - acc: 0.7595 - val_loss: 0.5638 - val_acc: 0.7319\n",
      "Epoch 127/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5252 - acc: 0.7516 - val_loss: 0.5636 - val_acc: 0.7319\n",
      "Epoch 128/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5347 - acc: 0.7524 - val_loss: 0.5636 - val_acc: 0.7319\n",
      "Epoch 129/135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1264/1264 [==============================] - 0s - loss: 0.5283 - acc: 0.7508 - val_loss: 0.5637 - val_acc: 0.7319\n",
      "Epoch 130/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5376 - acc: 0.7421 - val_loss: 0.5637 - val_acc: 0.7319\n",
      "Epoch 131/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5271 - acc: 0.7595 - val_loss: 0.5633 - val_acc: 0.7319\n",
      "Epoch 132/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5254 - acc: 0.7587 - val_loss: 0.5634 - val_acc: 0.7319\n",
      "Epoch 133/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5243 - acc: 0.7619 - val_loss: 0.5634 - val_acc: 0.7319\n",
      "Epoch 134/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5256 - acc: 0.7532 - val_loss: 0.5636 - val_acc: 0.7319\n",
      "Epoch 135/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5268 - acc: 0.7682 - val_loss: 0.5641 - val_acc: 0.7319\n",
      "317/317 [==============================] - 0s     \n",
      " 32/317 [==>...........................] - ETA: 0ssTrain on 1265 samples, validate on 316 samples\n",
      "Epoch 1/135\n",
      "1265/1265 [==============================] - 2s - loss: 0.7065 - acc: 0.4964 - val_loss: 0.6510 - val_acc: 0.7184\n",
      "Epoch 2/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.6456 - acc: 0.6545 - val_loss: 0.6044 - val_acc: 0.7342\n",
      "Epoch 3/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.6050 - acc: 0.7257 - val_loss: 0.5809 - val_acc: 0.7342\n",
      "Epoch 4/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5918 - acc: 0.7352 - val_loss: 0.5731 - val_acc: 0.7342\n",
      "Epoch 5/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5862 - acc: 0.7328 - val_loss: 0.5723 - val_acc: 0.7342\n",
      "Epoch 6/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5879 - acc: 0.7344 - val_loss: 0.5727 - val_acc: 0.7342\n",
      "Epoch 7/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5881 - acc: 0.7344 - val_loss: 0.5721 - val_acc: 0.7342\n",
      "Epoch 8/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5781 - acc: 0.7344 - val_loss: 0.5713 - val_acc: 0.7342\n",
      "Epoch 9/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5861 - acc: 0.7344 - val_loss: 0.5706 - val_acc: 0.7342\n",
      "Epoch 10/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5846 - acc: 0.7336 - val_loss: 0.5703 - val_acc: 0.7342\n",
      "Epoch 11/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5780 - acc: 0.7344 - val_loss: 0.5702 - val_acc: 0.7342\n",
      "Epoch 12/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5863 - acc: 0.7336 - val_loss: 0.5701 - val_acc: 0.7342\n",
      "Epoch 13/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5760 - acc: 0.7344 - val_loss: 0.5700 - val_acc: 0.7342\n",
      "Epoch 14/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5858 - acc: 0.7352 - val_loss: 0.5697 - val_acc: 0.7342\n",
      "Epoch 15/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5746 - acc: 0.7352 - val_loss: 0.5693 - val_acc: 0.7342\n",
      "Epoch 16/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5805 - acc: 0.7336 - val_loss: 0.5687 - val_acc: 0.7342\n",
      "Epoch 17/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5845 - acc: 0.7336 - val_loss: 0.5683 - val_acc: 0.7342\n",
      "Epoch 18/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5818 - acc: 0.7320 - val_loss: 0.5678 - val_acc: 0.7342\n",
      "Epoch 19/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5840 - acc: 0.7344 - val_loss: 0.5675 - val_acc: 0.7342\n",
      "Epoch 20/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5773 - acc: 0.7344 - val_loss: 0.5672 - val_acc: 0.7342\n",
      "Epoch 21/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5707 - acc: 0.7344 - val_loss: 0.5669 - val_acc: 0.7342\n",
      "Epoch 22/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5824 - acc: 0.7336 - val_loss: 0.5666 - val_acc: 0.7342\n",
      "Epoch 23/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5775 - acc: 0.7352 - val_loss: 0.5665 - val_acc: 0.7342\n",
      "Epoch 24/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5757 - acc: 0.7344 - val_loss: 0.5664 - val_acc: 0.7342\n",
      "Epoch 25/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5767 - acc: 0.7344 - val_loss: 0.5663 - val_acc: 0.7342\n",
      "Epoch 26/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5806 - acc: 0.7336 - val_loss: 0.5663 - val_acc: 0.7342\n",
      "Epoch 27/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5814 - acc: 0.7344 - val_loss: 0.5663 - val_acc: 0.7342\n",
      "Epoch 28/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5764 - acc: 0.7352 - val_loss: 0.5662 - val_acc: 0.7342\n",
      "Epoch 29/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5770 - acc: 0.7344 - val_loss: 0.5662 - val_acc: 0.7342\n",
      "Epoch 30/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5700 - acc: 0.7352 - val_loss: 0.5658 - val_acc: 0.7342\n",
      "Epoch 31/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5716 - acc: 0.7352 - val_loss: 0.5655 - val_acc: 0.7342\n",
      "Epoch 32/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5734 - acc: 0.7344 - val_loss: 0.5651 - val_acc: 0.7342\n",
      "Epoch 33/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5728 - acc: 0.7344 - val_loss: 0.5649 - val_acc: 0.7342\n",
      "Epoch 34/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5702 - acc: 0.7336 - val_loss: 0.5648 - val_acc: 0.7342\n",
      "Epoch 35/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5747 - acc: 0.7352 - val_loss: 0.5646 - val_acc: 0.7342\n",
      "Epoch 36/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5733 - acc: 0.7352 - val_loss: 0.5645 - val_acc: 0.7342\n",
      "Epoch 37/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5651 - acc: 0.7352 - val_loss: 0.5644 - val_acc: 0.7342\n",
      "Epoch 38/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5787 - acc: 0.7344 - val_loss: 0.5642 - val_acc: 0.7342\n",
      "Epoch 39/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5662 - acc: 0.7344 - val_loss: 0.5643 - val_acc: 0.7342\n",
      "Epoch 40/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5736 - acc: 0.7344 - val_loss: 0.5642 - val_acc: 0.7342\n",
      "Epoch 41/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5652 - acc: 0.7352 - val_loss: 0.5642 - val_acc: 0.7342\n",
      "Epoch 42/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5698 - acc: 0.7352 - val_loss: 0.5643 - val_acc: 0.7342\n",
      "Epoch 43/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5611 - acc: 0.7344 - val_loss: 0.5641 - val_acc: 0.7342\n",
      "Epoch 44/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5629 - acc: 0.7360 - val_loss: 0.5639 - val_acc: 0.7342\n",
      "Epoch 45/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5575 - acc: 0.7352 - val_loss: 0.5637 - val_acc: 0.7342\n",
      "Epoch 46/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5697 - acc: 0.7375 - val_loss: 0.5634 - val_acc: 0.7342\n",
      "Epoch 47/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5714 - acc: 0.7336 - val_loss: 0.5634 - val_acc: 0.7342\n",
      "Epoch 48/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5626 - acc: 0.7352 - val_loss: 0.5632 - val_acc: 0.7342\n",
      "Epoch 49/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5674 - acc: 0.7375 - val_loss: 0.5631 - val_acc: 0.7342\n",
      "Epoch 50/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5661 - acc: 0.7368 - val_loss: 0.5631 - val_acc: 0.7342\n",
      "Epoch 51/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5631 - acc: 0.7352 - val_loss: 0.5632 - val_acc: 0.7342\n",
      "Epoch 52/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5626 - acc: 0.7360 - val_loss: 0.5629 - val_acc: 0.7342\n",
      "Epoch 53/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5657 - acc: 0.7368 - val_loss: 0.5627 - val_acc: 0.7342\n",
      "Epoch 54/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5663 - acc: 0.7352 - val_loss: 0.5623 - val_acc: 0.7342\n",
      "Epoch 55/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5541 - acc: 0.7360 - val_loss: 0.5621 - val_acc: 0.7342\n",
      "Epoch 56/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5655 - acc: 0.7352 - val_loss: 0.5621 - val_acc: 0.7342\n",
      "Epoch 57/135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1265/1265 [==============================] - 0s - loss: 0.5603 - acc: 0.7336 - val_loss: 0.5620 - val_acc: 0.7342\n",
      "Epoch 58/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5633 - acc: 0.7336 - val_loss: 0.5620 - val_acc: 0.7342\n",
      "Epoch 59/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5600 - acc: 0.7320 - val_loss: 0.5618 - val_acc: 0.7342\n",
      "Epoch 60/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5647 - acc: 0.7375 - val_loss: 0.5616 - val_acc: 0.7342\n",
      "Epoch 61/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5587 - acc: 0.7360 - val_loss: 0.5612 - val_acc: 0.7342\n",
      "Epoch 62/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5590 - acc: 0.7336 - val_loss: 0.5610 - val_acc: 0.7342\n",
      "Epoch 63/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5538 - acc: 0.7360 - val_loss: 0.5609 - val_acc: 0.7342\n",
      "Epoch 64/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5629 - acc: 0.7328 - val_loss: 0.5609 - val_acc: 0.7342\n",
      "Epoch 65/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5590 - acc: 0.7368 - val_loss: 0.5608 - val_acc: 0.7342\n",
      "Epoch 66/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5599 - acc: 0.7368 - val_loss: 0.5606 - val_acc: 0.7342\n",
      "Epoch 67/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5564 - acc: 0.7375 - val_loss: 0.5606 - val_acc: 0.7342\n",
      "Epoch 68/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5583 - acc: 0.7391 - val_loss: 0.5606 - val_acc: 0.7342\n",
      "Epoch 69/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5550 - acc: 0.7375 - val_loss: 0.5604 - val_acc: 0.7342\n",
      "Epoch 70/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5583 - acc: 0.7352 - val_loss: 0.5604 - val_acc: 0.7342\n",
      "Epoch 71/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5633 - acc: 0.7352 - val_loss: 0.5603 - val_acc: 0.7342\n",
      "Epoch 72/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5561 - acc: 0.7391 - val_loss: 0.5602 - val_acc: 0.7342\n",
      "Epoch 73/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5621 - acc: 0.7352 - val_loss: 0.5603 - val_acc: 0.7342\n",
      "Epoch 74/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5496 - acc: 0.7407 - val_loss: 0.5603 - val_acc: 0.7342\n",
      "Epoch 75/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5548 - acc: 0.7391 - val_loss: 0.5604 - val_acc: 0.7342\n",
      "Epoch 76/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5535 - acc: 0.7352 - val_loss: 0.5601 - val_acc: 0.7342\n",
      "Epoch 77/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5548 - acc: 0.7383 - val_loss: 0.5601 - val_acc: 0.7342\n",
      "Epoch 78/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5557 - acc: 0.7368 - val_loss: 0.5602 - val_acc: 0.7342\n",
      "Epoch 79/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5474 - acc: 0.7368 - val_loss: 0.5605 - val_acc: 0.7342\n",
      "Epoch 80/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5451 - acc: 0.7391 - val_loss: 0.5603 - val_acc: 0.7342\n",
      "Epoch 81/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5463 - acc: 0.7375 - val_loss: 0.5602 - val_acc: 0.7342\n",
      "Epoch 82/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5496 - acc: 0.7383 - val_loss: 0.5601 - val_acc: 0.7342\n",
      "Epoch 83/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5531 - acc: 0.7375 - val_loss: 0.5600 - val_acc: 0.7342\n",
      "Epoch 84/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5494 - acc: 0.7447 - val_loss: 0.5597 - val_acc: 0.7342\n",
      "Epoch 85/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5504 - acc: 0.7352 - val_loss: 0.5596 - val_acc: 0.7342\n",
      "Epoch 86/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5463 - acc: 0.7391 - val_loss: 0.5594 - val_acc: 0.7342\n",
      "Epoch 87/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5522 - acc: 0.7423 - val_loss: 0.5594 - val_acc: 0.7342\n",
      "Epoch 88/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5449 - acc: 0.7399 - val_loss: 0.5593 - val_acc: 0.7342\n",
      "Epoch 89/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5482 - acc: 0.7407 - val_loss: 0.5593 - val_acc: 0.7342\n",
      "Epoch 90/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5458 - acc: 0.7439 - val_loss: 0.5594 - val_acc: 0.7342\n",
      "Epoch 91/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5520 - acc: 0.7431 - val_loss: 0.5594 - val_acc: 0.7342\n",
      "Epoch 92/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5517 - acc: 0.7447 - val_loss: 0.5593 - val_acc: 0.7342\n",
      "Epoch 93/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5411 - acc: 0.7462 - val_loss: 0.5593 - val_acc: 0.7342\n",
      "Epoch 94/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5478 - acc: 0.7360 - val_loss: 0.5590 - val_acc: 0.7342\n",
      "Epoch 95/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5478 - acc: 0.7415 - val_loss: 0.5590 - val_acc: 0.7342\n",
      "Epoch 96/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5496 - acc: 0.7447 - val_loss: 0.5589 - val_acc: 0.7342\n",
      "Epoch 97/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5521 - acc: 0.7391 - val_loss: 0.5591 - val_acc: 0.7342\n",
      "Epoch 98/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5491 - acc: 0.7399 - val_loss: 0.5591 - val_acc: 0.7342\n",
      "Epoch 99/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5539 - acc: 0.7439 - val_loss: 0.5592 - val_acc: 0.7342\n",
      "Epoch 100/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5468 - acc: 0.7431 - val_loss: 0.5592 - val_acc: 0.7342\n",
      "Epoch 101/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5474 - acc: 0.7391 - val_loss: 0.5592 - val_acc: 0.7373\n",
      "Epoch 102/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5424 - acc: 0.7399 - val_loss: 0.5593 - val_acc: 0.7373\n",
      "Epoch 103/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5454 - acc: 0.7415 - val_loss: 0.5593 - val_acc: 0.7373\n",
      "Epoch 104/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5448 - acc: 0.7470 - val_loss: 0.5594 - val_acc: 0.7373\n",
      "Epoch 105/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5435 - acc: 0.7447 - val_loss: 0.5594 - val_acc: 0.7373\n",
      "Epoch 106/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5448 - acc: 0.7415 - val_loss: 0.5593 - val_acc: 0.7373\n",
      "Epoch 107/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5429 - acc: 0.7494 - val_loss: 0.5591 - val_acc: 0.7373\n",
      "Epoch 108/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5474 - acc: 0.7423 - val_loss: 0.5592 - val_acc: 0.7373\n",
      "Epoch 109/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5370 - acc: 0.7478 - val_loss: 0.5591 - val_acc: 0.7373\n",
      "Epoch 110/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5396 - acc: 0.7455 - val_loss: 0.5591 - val_acc: 0.7373\n",
      "Epoch 111/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5379 - acc: 0.7510 - val_loss: 0.5589 - val_acc: 0.7373\n",
      "Epoch 112/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5418 - acc: 0.7431 - val_loss: 0.5589 - val_acc: 0.7373\n",
      "Epoch 113/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5380 - acc: 0.7431 - val_loss: 0.5590 - val_acc: 0.7373\n",
      "Epoch 114/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5452 - acc: 0.7439 - val_loss: 0.5592 - val_acc: 0.7373\n",
      "Epoch 115/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5384 - acc: 0.7462 - val_loss: 0.5594 - val_acc: 0.7373\n",
      "Epoch 116/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5444 - acc: 0.7455 - val_loss: 0.5596 - val_acc: 0.7373\n",
      "Epoch 117/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5336 - acc: 0.7462 - val_loss: 0.5597 - val_acc: 0.7373\n",
      "Epoch 118/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5448 - acc: 0.7518 - val_loss: 0.5597 - val_acc: 0.7373\n",
      "Epoch 119/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5358 - acc: 0.7510 - val_loss: 0.5599 - val_acc: 0.7373\n",
      "Epoch 120/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5343 - acc: 0.7534 - val_loss: 0.5600 - val_acc: 0.7373\n",
      "Epoch 121/135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1265/1265 [==============================] - 0s - loss: 0.5379 - acc: 0.7526 - val_loss: 0.5600 - val_acc: 0.7373\n",
      "Epoch 122/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5368 - acc: 0.7462 - val_loss: 0.5602 - val_acc: 0.7373\n",
      "Epoch 123/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5332 - acc: 0.7518 - val_loss: 0.5602 - val_acc: 0.7373\n",
      "Epoch 124/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5422 - acc: 0.7470 - val_loss: 0.5603 - val_acc: 0.7373\n",
      "Epoch 125/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5382 - acc: 0.7510 - val_loss: 0.5604 - val_acc: 0.7373\n",
      "Epoch 126/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5331 - acc: 0.7573 - val_loss: 0.5603 - val_acc: 0.7342\n",
      "Epoch 127/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5325 - acc: 0.7470 - val_loss: 0.5602 - val_acc: 0.7342\n",
      "Epoch 128/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5288 - acc: 0.7526 - val_loss: 0.5601 - val_acc: 0.7342\n",
      "Epoch 129/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5352 - acc: 0.7470 - val_loss: 0.5602 - val_acc: 0.7342\n",
      "Epoch 130/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5374 - acc: 0.7399 - val_loss: 0.5605 - val_acc: 0.7342\n",
      "Epoch 131/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5255 - acc: 0.7573 - val_loss: 0.5605 - val_acc: 0.7342\n",
      "Epoch 132/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5381 - acc: 0.7486 - val_loss: 0.5604 - val_acc: 0.7342\n",
      "Epoch 133/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5338 - acc: 0.7486 - val_loss: 0.5604 - val_acc: 0.7342\n",
      "Epoch 134/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5313 - acc: 0.7510 - val_loss: 0.5604 - val_acc: 0.7342\n",
      "Epoch 135/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5268 - acc: 0.7502 - val_loss: 0.5604 - val_acc: 0.7342\n",
      "316/316 [==============================] - 0s     \n",
      " 32/316 [==>...........................] - ETA: 0ssTrain on 1265 samples, validate on 316 samples\n",
      "Epoch 1/135\n",
      "1265/1265 [==============================] - 2s - loss: 0.6717 - acc: 0.5905 - val_loss: 0.6404 - val_acc: 0.7342\n",
      "Epoch 2/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.6257 - acc: 0.6925 - val_loss: 0.6057 - val_acc: 0.7342\n",
      "Epoch 3/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.6064 - acc: 0.7257 - val_loss: 0.5893 - val_acc: 0.7342\n",
      "Epoch 4/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5930 - acc: 0.7336 - val_loss: 0.5850 - val_acc: 0.7342\n",
      "Epoch 5/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5972 - acc: 0.7344 - val_loss: 0.5851 - val_acc: 0.7342\n",
      "Epoch 6/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5919 - acc: 0.7344 - val_loss: 0.5855 - val_acc: 0.7342\n",
      "Epoch 7/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5825 - acc: 0.7344 - val_loss: 0.5847 - val_acc: 0.7342\n",
      "Epoch 8/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5873 - acc: 0.7344 - val_loss: 0.5834 - val_acc: 0.7342\n",
      "Epoch 9/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5883 - acc: 0.7360 - val_loss: 0.5824 - val_acc: 0.7342\n",
      "Epoch 10/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5890 - acc: 0.7336 - val_loss: 0.5818 - val_acc: 0.7342\n",
      "Epoch 11/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5883 - acc: 0.7336 - val_loss: 0.5813 - val_acc: 0.7342\n",
      "Epoch 12/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5767 - acc: 0.7352 - val_loss: 0.5810 - val_acc: 0.7342\n",
      "Epoch 13/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5803 - acc: 0.7344 - val_loss: 0.5807 - val_acc: 0.7342\n",
      "Epoch 14/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5799 - acc: 0.7336 - val_loss: 0.5804 - val_acc: 0.7342\n",
      "Epoch 15/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5833 - acc: 0.7328 - val_loss: 0.5800 - val_acc: 0.7342\n",
      "Epoch 16/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5794 - acc: 0.7344 - val_loss: 0.5796 - val_acc: 0.7342\n",
      "Epoch 17/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5792 - acc: 0.7344 - val_loss: 0.5793 - val_acc: 0.7342\n",
      "Epoch 18/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5737 - acc: 0.7352 - val_loss: 0.5790 - val_acc: 0.7342\n",
      "Epoch 19/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5809 - acc: 0.7344 - val_loss: 0.5786 - val_acc: 0.7342\n",
      "Epoch 20/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5714 - acc: 0.7336 - val_loss: 0.5783 - val_acc: 0.7342\n",
      "Epoch 21/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5772 - acc: 0.7336 - val_loss: 0.5781 - val_acc: 0.7342\n",
      "Epoch 22/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5750 - acc: 0.7344 - val_loss: 0.5779 - val_acc: 0.7342\n",
      "Epoch 23/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5746 - acc: 0.7360 - val_loss: 0.5777 - val_acc: 0.7342\n",
      "Epoch 24/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5762 - acc: 0.7352 - val_loss: 0.5775 - val_acc: 0.7342\n",
      "Epoch 25/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5784 - acc: 0.7352 - val_loss: 0.5773 - val_acc: 0.7342\n",
      "Epoch 26/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5764 - acc: 0.7344 - val_loss: 0.5771 - val_acc: 0.7342\n",
      "Epoch 27/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5689 - acc: 0.7360 - val_loss: 0.5770 - val_acc: 0.7342\n",
      "Epoch 28/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5646 - acc: 0.7336 - val_loss: 0.5767 - val_acc: 0.7342\n",
      "Epoch 29/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5698 - acc: 0.7352 - val_loss: 0.5764 - val_acc: 0.7342\n",
      "Epoch 30/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5687 - acc: 0.7368 - val_loss: 0.5761 - val_acc: 0.7342\n",
      "Epoch 31/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5742 - acc: 0.7360 - val_loss: 0.5759 - val_acc: 0.7342\n",
      "Epoch 32/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5669 - acc: 0.7360 - val_loss: 0.5756 - val_acc: 0.7342\n",
      "Epoch 33/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5688 - acc: 0.7368 - val_loss: 0.5754 - val_acc: 0.7342\n",
      "Epoch 34/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5691 - acc: 0.7368 - val_loss: 0.5752 - val_acc: 0.7342\n",
      "Epoch 35/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5675 - acc: 0.7352 - val_loss: 0.5750 - val_acc: 0.7342\n",
      "Epoch 36/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5704 - acc: 0.7368 - val_loss: 0.5747 - val_acc: 0.7342\n",
      "Epoch 37/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5631 - acc: 0.7360 - val_loss: 0.5745 - val_acc: 0.7342\n",
      "Epoch 38/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5659 - acc: 0.7336 - val_loss: 0.5743 - val_acc: 0.7342\n",
      "Epoch 39/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5654 - acc: 0.7360 - val_loss: 0.5741 - val_acc: 0.7342\n",
      "Epoch 40/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5732 - acc: 0.7360 - val_loss: 0.5740 - val_acc: 0.7342\n",
      "Epoch 41/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5621 - acc: 0.7360 - val_loss: 0.5738 - val_acc: 0.7342\n",
      "Epoch 42/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5660 - acc: 0.7352 - val_loss: 0.5734 - val_acc: 0.7342\n",
      "Epoch 43/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5644 - acc: 0.7375 - val_loss: 0.5731 - val_acc: 0.7342\n",
      "Epoch 44/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5666 - acc: 0.7352 - val_loss: 0.5729 - val_acc: 0.7342\n",
      "Epoch 45/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5619 - acc: 0.7368 - val_loss: 0.5727 - val_acc: 0.7342\n",
      "Epoch 46/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5615 - acc: 0.7375 - val_loss: 0.5724 - val_acc: 0.7342\n",
      "Epoch 47/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5651 - acc: 0.7336 - val_loss: 0.5722 - val_acc: 0.7342\n",
      "Epoch 48/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5657 - acc: 0.7352 - val_loss: 0.5722 - val_acc: 0.7342\n",
      "Epoch 49/135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1265/1265 [==============================] - 0s - loss: 0.5575 - acc: 0.7344 - val_loss: 0.5720 - val_acc: 0.7342\n",
      "Epoch 50/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5678 - acc: 0.7391 - val_loss: 0.5718 - val_acc: 0.7342\n",
      "Epoch 51/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5646 - acc: 0.7423 - val_loss: 0.5719 - val_acc: 0.7342\n",
      "Epoch 52/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5613 - acc: 0.7375 - val_loss: 0.5715 - val_acc: 0.7342\n",
      "Epoch 53/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5631 - acc: 0.7383 - val_loss: 0.5712 - val_acc: 0.7342\n",
      "Epoch 54/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5596 - acc: 0.7360 - val_loss: 0.5710 - val_acc: 0.7342\n",
      "Epoch 55/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5620 - acc: 0.7407 - val_loss: 0.5706 - val_acc: 0.7342\n",
      "Epoch 56/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5600 - acc: 0.7391 - val_loss: 0.5705 - val_acc: 0.7342\n",
      "Epoch 57/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5553 - acc: 0.7383 - val_loss: 0.5702 - val_acc: 0.7342\n",
      "Epoch 58/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5590 - acc: 0.7407 - val_loss: 0.5699 - val_acc: 0.7342\n",
      "Epoch 59/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5553 - acc: 0.7383 - val_loss: 0.5696 - val_acc: 0.7342\n",
      "Epoch 60/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5606 - acc: 0.7368 - val_loss: 0.5694 - val_acc: 0.7342\n",
      "Epoch 61/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5562 - acc: 0.7368 - val_loss: 0.5694 - val_acc: 0.7342\n",
      "Epoch 62/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5625 - acc: 0.7344 - val_loss: 0.5692 - val_acc: 0.7342\n",
      "Epoch 63/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5578 - acc: 0.7447 - val_loss: 0.5691 - val_acc: 0.7342\n",
      "Epoch 64/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5463 - acc: 0.7423 - val_loss: 0.5689 - val_acc: 0.7342\n",
      "Epoch 65/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5562 - acc: 0.7423 - val_loss: 0.5686 - val_acc: 0.7342\n",
      "Epoch 66/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5529 - acc: 0.7407 - val_loss: 0.5683 - val_acc: 0.7342\n",
      "Epoch 67/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5553 - acc: 0.7407 - val_loss: 0.5681 - val_acc: 0.7342\n",
      "Epoch 68/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5621 - acc: 0.7360 - val_loss: 0.5679 - val_acc: 0.7342\n",
      "Epoch 69/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5540 - acc: 0.7391 - val_loss: 0.5678 - val_acc: 0.7373\n",
      "Epoch 70/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5516 - acc: 0.7375 - val_loss: 0.5677 - val_acc: 0.7342\n",
      "Epoch 71/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5526 - acc: 0.7360 - val_loss: 0.5674 - val_acc: 0.7373\n",
      "Epoch 72/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5516 - acc: 0.7415 - val_loss: 0.5673 - val_acc: 0.7373\n",
      "Epoch 73/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5473 - acc: 0.7391 - val_loss: 0.5671 - val_acc: 0.7373\n",
      "Epoch 74/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5576 - acc: 0.7368 - val_loss: 0.5669 - val_acc: 0.7405\n",
      "Epoch 75/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5497 - acc: 0.7391 - val_loss: 0.5669 - val_acc: 0.7405\n",
      "Epoch 76/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5555 - acc: 0.7462 - val_loss: 0.5668 - val_acc: 0.7437\n",
      "Epoch 77/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5539 - acc: 0.7431 - val_loss: 0.5667 - val_acc: 0.7405\n",
      "Epoch 78/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5450 - acc: 0.7439 - val_loss: 0.5663 - val_acc: 0.7405\n",
      "Epoch 79/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5483 - acc: 0.7447 - val_loss: 0.5661 - val_acc: 0.7405\n",
      "Epoch 80/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5487 - acc: 0.7455 - val_loss: 0.5659 - val_acc: 0.7405\n",
      "Epoch 81/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5504 - acc: 0.7502 - val_loss: 0.5659 - val_acc: 0.7405\n",
      "Epoch 82/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5464 - acc: 0.7439 - val_loss: 0.5657 - val_acc: 0.7468\n",
      "Epoch 83/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5453 - acc: 0.7470 - val_loss: 0.5659 - val_acc: 0.7373\n",
      "Epoch 84/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5539 - acc: 0.7415 - val_loss: 0.5656 - val_acc: 0.7437\n",
      "Epoch 85/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5479 - acc: 0.7415 - val_loss: 0.5653 - val_acc: 0.7468\n",
      "Epoch 86/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5537 - acc: 0.7415 - val_loss: 0.5651 - val_acc: 0.7437\n",
      "Epoch 87/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5432 - acc: 0.7486 - val_loss: 0.5648 - val_acc: 0.7373\n",
      "Epoch 88/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5412 - acc: 0.7470 - val_loss: 0.5645 - val_acc: 0.7373\n",
      "Epoch 89/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5454 - acc: 0.7415 - val_loss: 0.5641 - val_acc: 0.7373\n",
      "Epoch 90/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5489 - acc: 0.7431 - val_loss: 0.5641 - val_acc: 0.7373\n",
      "Epoch 91/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5509 - acc: 0.7502 - val_loss: 0.5640 - val_acc: 0.7373\n",
      "Epoch 92/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5502 - acc: 0.7455 - val_loss: 0.5638 - val_acc: 0.7373\n",
      "Epoch 93/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5416 - acc: 0.7486 - val_loss: 0.5636 - val_acc: 0.7373\n",
      "Epoch 94/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5423 - acc: 0.7439 - val_loss: 0.5634 - val_acc: 0.7373\n",
      "Epoch 95/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5429 - acc: 0.7462 - val_loss: 0.5633 - val_acc: 0.7373\n",
      "Epoch 96/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5401 - acc: 0.7534 - val_loss: 0.5631 - val_acc: 0.7405\n",
      "Epoch 97/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5487 - acc: 0.7510 - val_loss: 0.5629 - val_acc: 0.7405\n",
      "Epoch 98/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5414 - acc: 0.7470 - val_loss: 0.5627 - val_acc: 0.7405\n",
      "Epoch 99/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5421 - acc: 0.7518 - val_loss: 0.5626 - val_acc: 0.7373\n",
      "Epoch 100/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5401 - acc: 0.7526 - val_loss: 0.5625 - val_acc: 0.7373\n",
      "Epoch 101/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5410 - acc: 0.7447 - val_loss: 0.5622 - val_acc: 0.7373\n",
      "Epoch 102/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5423 - acc: 0.7557 - val_loss: 0.5620 - val_acc: 0.7373\n",
      "Epoch 103/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5392 - acc: 0.7510 - val_loss: 0.5618 - val_acc: 0.7373\n",
      "Epoch 104/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5435 - acc: 0.7502 - val_loss: 0.5618 - val_acc: 0.7373\n",
      "Epoch 105/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5420 - acc: 0.7534 - val_loss: 0.5618 - val_acc: 0.7342\n",
      "Epoch 106/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5381 - acc: 0.7557 - val_loss: 0.5615 - val_acc: 0.7342\n",
      "Epoch 107/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5329 - acc: 0.7407 - val_loss: 0.5613 - val_acc: 0.7342\n",
      "Epoch 108/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5297 - acc: 0.7510 - val_loss: 0.5610 - val_acc: 0.7373\n",
      "Epoch 109/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5373 - acc: 0.7542 - val_loss: 0.5609 - val_acc: 0.7373\n",
      "Epoch 110/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5312 - acc: 0.7557 - val_loss: 0.5610 - val_acc: 0.7342\n",
      "Epoch 111/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5367 - acc: 0.7470 - val_loss: 0.5610 - val_acc: 0.7342\n",
      "Epoch 112/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5358 - acc: 0.7494 - val_loss: 0.5611 - val_acc: 0.7342\n",
      "Epoch 113/135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1265/1265 [==============================] - 0s - loss: 0.5295 - acc: 0.7565 - val_loss: 0.5611 - val_acc: 0.7342\n",
      "Epoch 114/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5375 - acc: 0.7565 - val_loss: 0.5609 - val_acc: 0.7342\n",
      "Epoch 115/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5337 - acc: 0.7494 - val_loss: 0.5608 - val_acc: 0.7342\n",
      "Epoch 116/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5278 - acc: 0.7549 - val_loss: 0.5608 - val_acc: 0.7342\n",
      "Epoch 117/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5329 - acc: 0.7549 - val_loss: 0.5608 - val_acc: 0.7342\n",
      "Epoch 118/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5279 - acc: 0.7565 - val_loss: 0.5605 - val_acc: 0.7342\n",
      "Epoch 119/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5336 - acc: 0.7581 - val_loss: 0.5604 - val_acc: 0.7342\n",
      "Epoch 120/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5294 - acc: 0.7573 - val_loss: 0.5604 - val_acc: 0.7342\n",
      "Epoch 121/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5318 - acc: 0.7478 - val_loss: 0.5599 - val_acc: 0.7342\n",
      "Epoch 122/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5330 - acc: 0.7510 - val_loss: 0.5598 - val_acc: 0.7342\n",
      "Epoch 123/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5321 - acc: 0.7573 - val_loss: 0.5597 - val_acc: 0.7310\n",
      "Epoch 124/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5307 - acc: 0.7502 - val_loss: 0.5596 - val_acc: 0.7310\n",
      "Epoch 125/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5272 - acc: 0.7518 - val_loss: 0.5597 - val_acc: 0.7310\n",
      "Epoch 126/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5343 - acc: 0.7557 - val_loss: 0.5597 - val_acc: 0.7310\n",
      "Epoch 127/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5299 - acc: 0.7542 - val_loss: 0.5598 - val_acc: 0.7310\n",
      "Epoch 128/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5266 - acc: 0.7565 - val_loss: 0.5597 - val_acc: 0.7310\n",
      "Epoch 129/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5311 - acc: 0.7542 - val_loss: 0.5596 - val_acc: 0.7310\n",
      "Epoch 130/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5293 - acc: 0.7605 - val_loss: 0.5597 - val_acc: 0.7310\n",
      "Epoch 131/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5268 - acc: 0.7565 - val_loss: 0.5597 - val_acc: 0.7310\n",
      "Epoch 132/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5329 - acc: 0.7526 - val_loss: 0.5596 - val_acc: 0.7310\n",
      "Epoch 133/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5239 - acc: 0.7573 - val_loss: 0.5598 - val_acc: 0.7310\n",
      "Epoch 134/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5272 - acc: 0.7542 - val_loss: 0.5598 - val_acc: 0.7310\n",
      "Epoch 135/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5218 - acc: 0.7573 - val_loss: 0.5596 - val_acc: 0.7310\n",
      " 32/316 [==>...........................] - ETA: 0ssTrain on 1265 samples, validate on 316 samples\n",
      "Epoch 1/135\n",
      "1265/1265 [==============================] - 2s - loss: 0.7535 - acc: 0.4221 - val_loss: 0.6729 - val_acc: 0.6772\n",
      "Epoch 2/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.6703 - acc: 0.5953 - val_loss: 0.6162 - val_acc: 0.7342\n",
      "Epoch 3/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.6161 - acc: 0.6964 - val_loss: 0.5870 - val_acc: 0.7342\n",
      "Epoch 4/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.6058 - acc: 0.7265 - val_loss: 0.5780 - val_acc: 0.7342\n",
      "Epoch 5/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.6052 - acc: 0.7304 - val_loss: 0.5791 - val_acc: 0.7342\n",
      "Epoch 6/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.6026 - acc: 0.7344 - val_loss: 0.5811 - val_acc: 0.7342\n",
      "Epoch 7/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5945 - acc: 0.7336 - val_loss: 0.5809 - val_acc: 0.7342\n",
      "Epoch 8/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5944 - acc: 0.7352 - val_loss: 0.5795 - val_acc: 0.7342\n",
      "Epoch 9/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5959 - acc: 0.7344 - val_loss: 0.5773 - val_acc: 0.7342\n",
      "Epoch 10/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5831 - acc: 0.7352 - val_loss: 0.5762 - val_acc: 0.7342\n",
      "Epoch 11/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5778 - acc: 0.7344 - val_loss: 0.5758 - val_acc: 0.7342\n",
      "Epoch 12/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5833 - acc: 0.7328 - val_loss: 0.5755 - val_acc: 0.7342\n",
      "Epoch 13/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5882 - acc: 0.7320 - val_loss: 0.5752 - val_acc: 0.7342\n",
      "Epoch 14/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5864 - acc: 0.7352 - val_loss: 0.5748 - val_acc: 0.7342\n",
      "Epoch 15/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5910 - acc: 0.7352 - val_loss: 0.5745 - val_acc: 0.7342\n",
      "Epoch 16/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5942 - acc: 0.7336 - val_loss: 0.5743 - val_acc: 0.7342\n",
      "Epoch 17/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5853 - acc: 0.7336 - val_loss: 0.5740 - val_acc: 0.7342\n",
      "Epoch 18/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5905 - acc: 0.7336 - val_loss: 0.5738 - val_acc: 0.7342\n",
      "Epoch 19/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5874 - acc: 0.7352 - val_loss: 0.5736 - val_acc: 0.7342\n",
      "Epoch 20/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5868 - acc: 0.7336 - val_loss: 0.5734 - val_acc: 0.7342\n",
      "Epoch 21/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5778 - acc: 0.7336 - val_loss: 0.5732 - val_acc: 0.7342\n",
      "Epoch 22/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5880 - acc: 0.7336 - val_loss: 0.5730 - val_acc: 0.7342\n",
      "Epoch 23/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5803 - acc: 0.7344 - val_loss: 0.5728 - val_acc: 0.7342\n",
      "Epoch 24/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5769 - acc: 0.7344 - val_loss: 0.5727 - val_acc: 0.7342\n",
      "Epoch 25/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5770 - acc: 0.7336 - val_loss: 0.5724 - val_acc: 0.7342\n",
      "Epoch 26/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5815 - acc: 0.7352 - val_loss: 0.5721 - val_acc: 0.7342\n",
      "Epoch 27/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5808 - acc: 0.7344 - val_loss: 0.5720 - val_acc: 0.7342\n",
      "Epoch 28/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5855 - acc: 0.7336 - val_loss: 0.5719 - val_acc: 0.7342\n",
      "Epoch 29/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5836 - acc: 0.7320 - val_loss: 0.5718 - val_acc: 0.7342\n",
      "Epoch 30/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5752 - acc: 0.7352 - val_loss: 0.5717 - val_acc: 0.7342\n",
      "Epoch 31/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5702 - acc: 0.7336 - val_loss: 0.5714 - val_acc: 0.7342\n",
      "Epoch 32/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5825 - acc: 0.7344 - val_loss: 0.5712 - val_acc: 0.7342\n",
      "Epoch 33/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5747 - acc: 0.7344 - val_loss: 0.5708 - val_acc: 0.7342\n",
      "Epoch 34/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5782 - acc: 0.7336 - val_loss: 0.5705 - val_acc: 0.7342\n",
      "Epoch 35/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5809 - acc: 0.7352 - val_loss: 0.5702 - val_acc: 0.7342\n",
      "Epoch 36/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5715 - acc: 0.7352 - val_loss: 0.5701 - val_acc: 0.7342\n",
      "Epoch 37/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5739 - acc: 0.7336 - val_loss: 0.5702 - val_acc: 0.7342\n",
      "Epoch 38/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5786 - acc: 0.7344 - val_loss: 0.5698 - val_acc: 0.7342\n",
      "Epoch 39/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5789 - acc: 0.7344 - val_loss: 0.5696 - val_acc: 0.7342\n",
      "Epoch 40/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5733 - acc: 0.7344 - val_loss: 0.5696 - val_acc: 0.7342\n",
      "Epoch 41/135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1265/1265 [==============================] - 0s - loss: 0.5668 - acc: 0.7352 - val_loss: 0.5695 - val_acc: 0.7342\n",
      "Epoch 42/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5733 - acc: 0.7304 - val_loss: 0.5694 - val_acc: 0.7342\n",
      "Epoch 43/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5731 - acc: 0.7328 - val_loss: 0.5691 - val_acc: 0.7342\n",
      "Epoch 44/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5698 - acc: 0.7360 - val_loss: 0.5688 - val_acc: 0.7342\n",
      "Epoch 45/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5746 - acc: 0.7360 - val_loss: 0.5687 - val_acc: 0.7342\n",
      "Epoch 46/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5746 - acc: 0.7344 - val_loss: 0.5686 - val_acc: 0.7342\n",
      "Epoch 47/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5664 - acc: 0.7352 - val_loss: 0.5685 - val_acc: 0.7342\n",
      "Epoch 48/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5714 - acc: 0.7344 - val_loss: 0.5684 - val_acc: 0.7342\n",
      "Epoch 49/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5728 - acc: 0.7320 - val_loss: 0.5682 - val_acc: 0.7342\n",
      "Epoch 50/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5675 - acc: 0.7352 - val_loss: 0.5682 - val_acc: 0.7342\n",
      "Epoch 51/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5689 - acc: 0.7391 - val_loss: 0.5680 - val_acc: 0.7342\n",
      "Epoch 52/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5649 - acc: 0.7336 - val_loss: 0.5678 - val_acc: 0.7342\n",
      "Epoch 53/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5729 - acc: 0.7344 - val_loss: 0.5677 - val_acc: 0.7342\n",
      "Epoch 54/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5717 - acc: 0.7352 - val_loss: 0.5676 - val_acc: 0.7342\n",
      "Epoch 55/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5751 - acc: 0.7344 - val_loss: 0.5674 - val_acc: 0.7342\n",
      "Epoch 56/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5682 - acc: 0.7344 - val_loss: 0.5674 - val_acc: 0.7342\n",
      "Epoch 57/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5677 - acc: 0.7352 - val_loss: 0.5672 - val_acc: 0.7342\n",
      "Epoch 58/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5642 - acc: 0.7368 - val_loss: 0.5671 - val_acc: 0.7342\n",
      "Epoch 59/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5622 - acc: 0.7320 - val_loss: 0.5670 - val_acc: 0.7342\n",
      "Epoch 60/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5650 - acc: 0.7312 - val_loss: 0.5668 - val_acc: 0.7342\n",
      "Epoch 61/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5638 - acc: 0.7344 - val_loss: 0.5665 - val_acc: 0.7342\n",
      "Epoch 62/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5626 - acc: 0.7344 - val_loss: 0.5665 - val_acc: 0.7342\n",
      "Epoch 63/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5658 - acc: 0.7360 - val_loss: 0.5665 - val_acc: 0.7342\n",
      "Epoch 64/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5711 - acc: 0.7328 - val_loss: 0.5666 - val_acc: 0.7342\n",
      "Epoch 65/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5651 - acc: 0.7360 - val_loss: 0.5667 - val_acc: 0.7342\n",
      "Epoch 66/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5716 - acc: 0.7360 - val_loss: 0.5667 - val_acc: 0.7342\n",
      "Epoch 67/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5686 - acc: 0.7360 - val_loss: 0.5665 - val_acc: 0.7342\n",
      "Epoch 68/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5720 - acc: 0.7360 - val_loss: 0.5663 - val_acc: 0.7342\n",
      "Epoch 69/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5640 - acc: 0.7352 - val_loss: 0.5661 - val_acc: 0.7342\n",
      "Epoch 70/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5643 - acc: 0.7368 - val_loss: 0.5660 - val_acc: 0.7342\n",
      "Epoch 71/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5704 - acc: 0.7344 - val_loss: 0.5658 - val_acc: 0.7342\n",
      "Epoch 72/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5689 - acc: 0.7336 - val_loss: 0.5656 - val_acc: 0.7342\n",
      "Epoch 73/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5697 - acc: 0.7352 - val_loss: 0.5658 - val_acc: 0.7342\n",
      "Epoch 74/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5573 - acc: 0.7368 - val_loss: 0.5660 - val_acc: 0.7342\n",
      "Epoch 75/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5643 - acc: 0.7360 - val_loss: 0.5661 - val_acc: 0.7342\n",
      "Epoch 76/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5665 - acc: 0.7360 - val_loss: 0.5660 - val_acc: 0.7342\n",
      "Epoch 77/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5605 - acc: 0.7328 - val_loss: 0.5660 - val_acc: 0.7342\n",
      "Epoch 78/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5620 - acc: 0.7336 - val_loss: 0.5660 - val_acc: 0.7342\n",
      "Epoch 79/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5592 - acc: 0.7352 - val_loss: 0.5660 - val_acc: 0.7342\n",
      "Epoch 80/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5522 - acc: 0.7407 - val_loss: 0.5658 - val_acc: 0.7342\n",
      "Epoch 81/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5589 - acc: 0.7368 - val_loss: 0.5655 - val_acc: 0.7342\n",
      "Epoch 82/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5624 - acc: 0.7383 - val_loss: 0.5653 - val_acc: 0.7342\n",
      "Epoch 83/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5588 - acc: 0.7352 - val_loss: 0.5653 - val_acc: 0.7342\n",
      "Epoch 84/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5491 - acc: 0.7391 - val_loss: 0.5654 - val_acc: 0.7342\n",
      "Epoch 85/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5617 - acc: 0.7320 - val_loss: 0.5652 - val_acc: 0.7342\n",
      "Epoch 86/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5547 - acc: 0.7399 - val_loss: 0.5654 - val_acc: 0.7342\n",
      "Epoch 87/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5544 - acc: 0.7375 - val_loss: 0.5653 - val_acc: 0.7342\n",
      "Epoch 88/135\n",
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      "Epoch 89/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5539 - acc: 0.7352 - val_loss: 0.5650 - val_acc: 0.7342\n",
      "Epoch 90/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5584 - acc: 0.7352 - val_loss: 0.5649 - val_acc: 0.7342\n",
      "Epoch 91/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5582 - acc: 0.7399 - val_loss: 0.5649 - val_acc: 0.7342\n",
      "Epoch 92/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5572 - acc: 0.7352 - val_loss: 0.5649 - val_acc: 0.7310\n",
      "Epoch 93/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5579 - acc: 0.7368 - val_loss: 0.5649 - val_acc: 0.7310\n",
      "Epoch 94/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5564 - acc: 0.7368 - val_loss: 0.5648 - val_acc: 0.7310\n",
      "Epoch 95/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5592 - acc: 0.7336 - val_loss: 0.5648 - val_acc: 0.7310\n",
      "Epoch 96/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5580 - acc: 0.7375 - val_loss: 0.5647 - val_acc: 0.7310\n",
      "Epoch 97/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5517 - acc: 0.7368 - val_loss: 0.5647 - val_acc: 0.7342\n",
      "Epoch 98/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5434 - acc: 0.7399 - val_loss: 0.5646 - val_acc: 0.7342\n",
      "Epoch 99/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5484 - acc: 0.7447 - val_loss: 0.5647 - val_acc: 0.7342\n",
      "Epoch 100/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5549 - acc: 0.7368 - val_loss: 0.5646 - val_acc: 0.7342\n",
      "Epoch 101/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5564 - acc: 0.7375 - val_loss: 0.5645 - val_acc: 0.7342\n",
      "Epoch 102/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5537 - acc: 0.7360 - val_loss: 0.5644 - val_acc: 0.7342\n",
      "Epoch 103/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5465 - acc: 0.7368 - val_loss: 0.5644 - val_acc: 0.7342\n",
      "Epoch 104/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5589 - acc: 0.7431 - val_loss: 0.5643 - val_acc: 0.7342\n",
      "Epoch 105/135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1265/1265 [==============================] - 0s - loss: 0.5493 - acc: 0.7375 - val_loss: 0.5642 - val_acc: 0.7342\n",
      "Epoch 106/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5488 - acc: 0.7439 - val_loss: 0.5643 - val_acc: 0.7342\n",
      "Epoch 107/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5427 - acc: 0.7431 - val_loss: 0.5642 - val_acc: 0.7342\n",
      "Epoch 108/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5448 - acc: 0.7360 - val_loss: 0.5642 - val_acc: 0.7342\n",
      "Epoch 109/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5540 - acc: 0.7344 - val_loss: 0.5643 - val_acc: 0.7373\n",
      "Epoch 110/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5523 - acc: 0.7399 - val_loss: 0.5643 - val_acc: 0.7373\n",
      "Epoch 111/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5469 - acc: 0.7431 - val_loss: 0.5644 - val_acc: 0.7373\n",
      "Epoch 112/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5427 - acc: 0.7439 - val_loss: 0.5641 - val_acc: 0.7373\n",
      "Epoch 113/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5463 - acc: 0.7360 - val_loss: 0.5640 - val_acc: 0.7373\n",
      "Epoch 114/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5533 - acc: 0.7431 - val_loss: 0.5638 - val_acc: 0.7373\n",
      "Epoch 115/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5430 - acc: 0.7423 - val_loss: 0.5637 - val_acc: 0.7373\n",
      "Epoch 116/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5375 - acc: 0.7415 - val_loss: 0.5636 - val_acc: 0.7373\n",
      "Epoch 117/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5512 - acc: 0.7415 - val_loss: 0.5635 - val_acc: 0.7373\n",
      "Epoch 118/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5452 - acc: 0.7407 - val_loss: 0.5636 - val_acc: 0.7373\n",
      "Epoch 119/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5476 - acc: 0.7415 - val_loss: 0.5639 - val_acc: 0.7373\n",
      "Epoch 120/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5419 - acc: 0.7423 - val_loss: 0.5642 - val_acc: 0.7373\n",
      "Epoch 121/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5465 - acc: 0.7431 - val_loss: 0.5644 - val_acc: 0.7373\n",
      "Epoch 122/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5392 - acc: 0.7431 - val_loss: 0.5647 - val_acc: 0.7373\n",
      "Epoch 123/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5460 - acc: 0.7383 - val_loss: 0.5647 - val_acc: 0.7373\n",
      "Epoch 124/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5438 - acc: 0.7391 - val_loss: 0.5646 - val_acc: 0.7373\n",
      "Epoch 125/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5528 - acc: 0.7368 - val_loss: 0.5646 - val_acc: 0.7373\n",
      "Epoch 126/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5437 - acc: 0.7439 - val_loss: 0.5646 - val_acc: 0.7342\n",
      "Epoch 127/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5459 - acc: 0.7470 - val_loss: 0.5646 - val_acc: 0.7310\n",
      "Epoch 128/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5419 - acc: 0.7383 - val_loss: 0.5645 - val_acc: 0.7310\n",
      "Epoch 129/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5448 - acc: 0.7391 - val_loss: 0.5642 - val_acc: 0.7310\n",
      "Epoch 130/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5402 - acc: 0.7407 - val_loss: 0.5643 - val_acc: 0.7342\n",
      "Epoch 131/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5448 - acc: 0.7407 - val_loss: 0.5643 - val_acc: 0.7373\n",
      "Epoch 132/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5408 - acc: 0.7462 - val_loss: 0.5645 - val_acc: 0.7373\n",
      "Epoch 133/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5394 - acc: 0.7486 - val_loss: 0.5646 - val_acc: 0.7373\n",
      "Epoch 134/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5412 - acc: 0.7439 - val_loss: 0.5646 - val_acc: 0.7310\n",
      "Epoch 135/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5395 - acc: 0.7407 - val_loss: 0.5647 - val_acc: 0.7310\n",
      "316/316 [==============================] - 0s     \n",
      " 32/316 [==>...........................] - ETA: 0ssTrain on 1265 samples, validate on 316 samples\n",
      "Epoch 1/135\n",
      "1265/1265 [==============================] - 2s - loss: 0.6771 - acc: 0.5858 - val_loss: 0.6363 - val_acc: 0.7342\n",
      "Epoch 2/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.6255 - acc: 0.6996 - val_loss: 0.6059 - val_acc: 0.7342\n",
      "Epoch 3/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.6040 - acc: 0.7336 - val_loss: 0.5915 - val_acc: 0.7342\n",
      "Epoch 4/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5935 - acc: 0.7328 - val_loss: 0.5880 - val_acc: 0.7342\n",
      "Epoch 5/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5951 - acc: 0.7360 - val_loss: 0.5890 - val_acc: 0.7342\n",
      "Epoch 6/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5888 - acc: 0.7344 - val_loss: 0.5902 - val_acc: 0.7342\n",
      "Epoch 7/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5798 - acc: 0.7344 - val_loss: 0.5899 - val_acc: 0.7342\n",
      "Epoch 8/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5941 - acc: 0.7336 - val_loss: 0.5886 - val_acc: 0.7342\n",
      "Epoch 9/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5930 - acc: 0.7344 - val_loss: 0.5872 - val_acc: 0.7342\n",
      "Epoch 10/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5851 - acc: 0.7344 - val_loss: 0.5865 - val_acc: 0.7342\n",
      "Epoch 11/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5829 - acc: 0.7344 - val_loss: 0.5861 - val_acc: 0.7342\n",
      "Epoch 12/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5847 - acc: 0.7344 - val_loss: 0.5858 - val_acc: 0.7342\n",
      "Epoch 13/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5914 - acc: 0.7344 - val_loss: 0.5856 - val_acc: 0.7342\n",
      "Epoch 14/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5763 - acc: 0.7336 - val_loss: 0.5855 - val_acc: 0.7342\n",
      "Epoch 15/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5867 - acc: 0.7344 - val_loss: 0.5853 - val_acc: 0.7342\n",
      "Epoch 16/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5823 - acc: 0.7344 - val_loss: 0.5851 - val_acc: 0.7342\n",
      "Epoch 17/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5711 - acc: 0.7344 - val_loss: 0.5849 - val_acc: 0.7342\n",
      "Epoch 18/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5820 - acc: 0.7344 - val_loss: 0.5848 - val_acc: 0.7342\n",
      "Epoch 19/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5825 - acc: 0.7336 - val_loss: 0.5846 - val_acc: 0.7342\n",
      "Epoch 20/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5836 - acc: 0.7344 - val_loss: 0.5844 - val_acc: 0.7342\n",
      "Epoch 21/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5679 - acc: 0.7336 - val_loss: 0.5841 - val_acc: 0.7342\n",
      "Epoch 22/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5811 - acc: 0.7344 - val_loss: 0.5839 - val_acc: 0.7342\n",
      "Epoch 23/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5736 - acc: 0.7344 - val_loss: 0.5839 - val_acc: 0.7342\n",
      "Epoch 24/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5746 - acc: 0.7336 - val_loss: 0.5838 - val_acc: 0.7342\n",
      "Epoch 25/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5746 - acc: 0.7328 - val_loss: 0.5836 - val_acc: 0.7342\n",
      "Epoch 26/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5690 - acc: 0.7336 - val_loss: 0.5836 - val_acc: 0.7342\n",
      "Epoch 27/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5758 - acc: 0.7344 - val_loss: 0.5834 - val_acc: 0.7342\n",
      "Epoch 28/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5777 - acc: 0.7344 - val_loss: 0.5833 - val_acc: 0.7342\n",
      "Epoch 29/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5681 - acc: 0.7352 - val_loss: 0.5832 - val_acc: 0.7342\n",
      "Epoch 30/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5660 - acc: 0.7344 - val_loss: 0.5830 - val_acc: 0.7342\n",
      "Epoch 31/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5740 - acc: 0.7344 - val_loss: 0.5828 - val_acc: 0.7342\n",
      "Epoch 32/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5711 - acc: 0.7352 - val_loss: 0.5827 - val_acc: 0.7342\n",
      "Epoch 33/135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1265/1265 [==============================] - 0s - loss: 0.5665 - acc: 0.7352 - val_loss: 0.5825 - val_acc: 0.7342\n",
      "Epoch 34/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5696 - acc: 0.7344 - val_loss: 0.5824 - val_acc: 0.7342\n",
      "Epoch 35/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5662 - acc: 0.7336 - val_loss: 0.5824 - val_acc: 0.7342\n",
      "Epoch 36/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5703 - acc: 0.7344 - val_loss: 0.5823 - val_acc: 0.7342\n",
      "Epoch 37/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5725 - acc: 0.7344 - val_loss: 0.5822 - val_acc: 0.7342\n",
      "Epoch 38/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5677 - acc: 0.7360 - val_loss: 0.5821 - val_acc: 0.7342\n",
      "Epoch 39/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5615 - acc: 0.7352 - val_loss: 0.5820 - val_acc: 0.7342\n",
      "Epoch 40/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5673 - acc: 0.7352 - val_loss: 0.5820 - val_acc: 0.7342\n",
      "Epoch 41/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5639 - acc: 0.7344 - val_loss: 0.5820 - val_acc: 0.7342\n",
      "Epoch 42/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5613 - acc: 0.7336 - val_loss: 0.5820 - val_acc: 0.7342\n",
      "Epoch 43/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5642 - acc: 0.7360 - val_loss: 0.5819 - val_acc: 0.7342\n",
      "Epoch 44/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5603 - acc: 0.7344 - val_loss: 0.5820 - val_acc: 0.7342\n",
      "Epoch 45/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5619 - acc: 0.7368 - val_loss: 0.5821 - val_acc: 0.7342\n",
      "Epoch 46/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5678 - acc: 0.7344 - val_loss: 0.5821 - val_acc: 0.7342\n",
      "Epoch 47/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5607 - acc: 0.7360 - val_loss: 0.5820 - val_acc: 0.7342\n",
      "Epoch 48/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5581 - acc: 0.7344 - val_loss: 0.5820 - val_acc: 0.7342\n",
      "Epoch 49/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5651 - acc: 0.7360 - val_loss: 0.5820 - val_acc: 0.7342\n",
      "Epoch 50/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5551 - acc: 0.7375 - val_loss: 0.5821 - val_acc: 0.7342\n",
      "Epoch 51/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5587 - acc: 0.7328 - val_loss: 0.5823 - val_acc: 0.7342\n",
      "Epoch 52/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5584 - acc: 0.7368 - val_loss: 0.5824 - val_acc: 0.7342\n",
      "Epoch 53/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5673 - acc: 0.7383 - val_loss: 0.5824 - val_acc: 0.7342\n",
      "Epoch 54/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5597 - acc: 0.7391 - val_loss: 0.5825 - val_acc: 0.7342\n",
      "Epoch 55/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5620 - acc: 0.7391 - val_loss: 0.5826 - val_acc: 0.7342\n",
      "Epoch 56/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5600 - acc: 0.7344 - val_loss: 0.5826 - val_acc: 0.7342\n",
      "Epoch 57/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5640 - acc: 0.7360 - val_loss: 0.5826 - val_acc: 0.7342\n",
      "Epoch 58/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5521 - acc: 0.7399 - val_loss: 0.5828 - val_acc: 0.7342\n",
      "Epoch 59/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5523 - acc: 0.7344 - val_loss: 0.5829 - val_acc: 0.7342\n",
      "Epoch 60/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5550 - acc: 0.7344 - val_loss: 0.5831 - val_acc: 0.7342\n",
      "Epoch 61/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5525 - acc: 0.7399 - val_loss: 0.5831 - val_acc: 0.7310\n",
      "Epoch 62/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5519 - acc: 0.7431 - val_loss: 0.5830 - val_acc: 0.7310\n",
      "Epoch 63/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5576 - acc: 0.7336 - val_loss: 0.5831 - val_acc: 0.7310\n",
      "Epoch 64/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5532 - acc: 0.7368 - val_loss: 0.5832 - val_acc: 0.7342\n",
      "Epoch 65/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5544 - acc: 0.7360 - val_loss: 0.5833 - val_acc: 0.7310\n",
      "Epoch 66/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5515 - acc: 0.7439 - val_loss: 0.5833 - val_acc: 0.7278\n",
      "Epoch 67/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5529 - acc: 0.7399 - val_loss: 0.5835 - val_acc: 0.7278\n",
      "Epoch 68/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5534 - acc: 0.7383 - val_loss: 0.5838 - val_acc: 0.7247\n",
      "Epoch 69/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5517 - acc: 0.7415 - val_loss: 0.5840 - val_acc: 0.7247\n",
      "Epoch 70/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5486 - acc: 0.7447 - val_loss: 0.5841 - val_acc: 0.7215\n",
      "Epoch 71/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5467 - acc: 0.7447 - val_loss: 0.5842 - val_acc: 0.7215\n",
      "Epoch 72/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5505 - acc: 0.7415 - val_loss: 0.5844 - val_acc: 0.7184\n",
      "Epoch 73/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5502 - acc: 0.7423 - val_loss: 0.5845 - val_acc: 0.7215\n",
      "Epoch 74/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5512 - acc: 0.7462 - val_loss: 0.5846 - val_acc: 0.7278\n",
      "Epoch 75/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5489 - acc: 0.7407 - val_loss: 0.5847 - val_acc: 0.7278\n",
      "Epoch 76/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5493 - acc: 0.7470 - val_loss: 0.5848 - val_acc: 0.7278\n",
      "Epoch 77/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5421 - acc: 0.7470 - val_loss: 0.5850 - val_acc: 0.7278\n",
      "Epoch 78/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5567 - acc: 0.7478 - val_loss: 0.5851 - val_acc: 0.7278\n",
      "Epoch 79/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5471 - acc: 0.7375 - val_loss: 0.5853 - val_acc: 0.7278\n",
      "Epoch 80/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5425 - acc: 0.7462 - val_loss: 0.5853 - val_acc: 0.7278\n",
      "Epoch 81/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5496 - acc: 0.7344 - val_loss: 0.5855 - val_acc: 0.7278\n",
      "Epoch 82/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5528 - acc: 0.7375 - val_loss: 0.5856 - val_acc: 0.7278\n",
      "Epoch 83/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5456 - acc: 0.7462 - val_loss: 0.5857 - val_acc: 0.7247\n",
      "Epoch 84/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5455 - acc: 0.7478 - val_loss: 0.5859 - val_acc: 0.7247\n",
      "Epoch 85/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5439 - acc: 0.7494 - val_loss: 0.5859 - val_acc: 0.7247\n",
      "Epoch 86/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5428 - acc: 0.7431 - val_loss: 0.5861 - val_acc: 0.7247\n",
      "Epoch 87/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5440 - acc: 0.7542 - val_loss: 0.5862 - val_acc: 0.7247\n",
      "Epoch 88/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5368 - acc: 0.7526 - val_loss: 0.5866 - val_acc: 0.7247\n",
      "Epoch 89/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5390 - acc: 0.7415 - val_loss: 0.5870 - val_acc: 0.7247\n",
      "Epoch 90/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5457 - acc: 0.7431 - val_loss: 0.5871 - val_acc: 0.7278\n",
      "Epoch 91/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5410 - acc: 0.7542 - val_loss: 0.5873 - val_acc: 0.7278\n",
      "Epoch 92/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5511 - acc: 0.7462 - val_loss: 0.5874 - val_acc: 0.7278\n",
      "Epoch 93/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5361 - acc: 0.7478 - val_loss: 0.5876 - val_acc: 0.7278\n",
      "Epoch 94/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5444 - acc: 0.7455 - val_loss: 0.5879 - val_acc: 0.7278\n",
      "Epoch 95/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5397 - acc: 0.7494 - val_loss: 0.5880 - val_acc: 0.7278\n",
      "Epoch 96/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5414 - acc: 0.7455 - val_loss: 0.5882 - val_acc: 0.7278\n",
      "Epoch 97/135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1265/1265 [==============================] - 0s - loss: 0.5424 - acc: 0.7439 - val_loss: 0.5884 - val_acc: 0.7278\n",
      "Epoch 98/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5350 - acc: 0.7518 - val_loss: 0.5885 - val_acc: 0.7278\n",
      "Epoch 99/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5402 - acc: 0.7455 - val_loss: 0.5885 - val_acc: 0.7278\n",
      "Epoch 100/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5358 - acc: 0.7470 - val_loss: 0.5886 - val_acc: 0.7215\n",
      "Epoch 101/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5361 - acc: 0.7494 - val_loss: 0.5887 - val_acc: 0.7247\n",
      "Epoch 102/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5356 - acc: 0.7542 - val_loss: 0.5891 - val_acc: 0.7247\n",
      "Epoch 103/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5345 - acc: 0.7478 - val_loss: 0.5896 - val_acc: 0.7215\n",
      "Epoch 104/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5351 - acc: 0.7502 - val_loss: 0.5899 - val_acc: 0.7247\n",
      "Epoch 105/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5406 - acc: 0.7447 - val_loss: 0.5900 - val_acc: 0.7247\n",
      "Epoch 106/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5331 - acc: 0.7542 - val_loss: 0.5902 - val_acc: 0.7247\n",
      "Epoch 107/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5396 - acc: 0.7502 - val_loss: 0.5905 - val_acc: 0.7215\n",
      "Epoch 108/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5363 - acc: 0.7478 - val_loss: 0.5905 - val_acc: 0.7247\n",
      "Epoch 109/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5332 - acc: 0.7502 - val_loss: 0.5905 - val_acc: 0.7247\n",
      "Epoch 110/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5349 - acc: 0.7526 - val_loss: 0.5904 - val_acc: 0.7247\n",
      "Epoch 111/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5403 - acc: 0.7447 - val_loss: 0.5903 - val_acc: 0.7278\n",
      "Epoch 112/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5349 - acc: 0.7494 - val_loss: 0.5905 - val_acc: 0.7278\n",
      "Epoch 113/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5337 - acc: 0.7534 - val_loss: 0.5905 - val_acc: 0.7278\n",
      "Epoch 114/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5332 - acc: 0.7542 - val_loss: 0.5906 - val_acc: 0.7278\n",
      "Epoch 115/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5419 - acc: 0.7494 - val_loss: 0.5907 - val_acc: 0.7278\n",
      "Epoch 116/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5367 - acc: 0.7565 - val_loss: 0.5908 - val_acc: 0.7278\n",
      "Epoch 117/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5320 - acc: 0.7478 - val_loss: 0.5911 - val_acc: 0.7247\n",
      "Epoch 118/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5290 - acc: 0.7589 - val_loss: 0.5914 - val_acc: 0.7247\n",
      "Epoch 119/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5292 - acc: 0.7510 - val_loss: 0.5918 - val_acc: 0.7247\n",
      "Epoch 120/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5241 - acc: 0.7621 - val_loss: 0.5923 - val_acc: 0.7278\n",
      "Epoch 121/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5353 - acc: 0.7549 - val_loss: 0.5923 - val_acc: 0.7278\n",
      "Epoch 122/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5292 - acc: 0.7581 - val_loss: 0.5923 - val_acc: 0.7247\n",
      "Epoch 123/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5260 - acc: 0.7542 - val_loss: 0.5926 - val_acc: 0.7247\n",
      "Epoch 124/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5228 - acc: 0.7581 - val_loss: 0.5930 - val_acc: 0.7215\n",
      "Epoch 125/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5272 - acc: 0.7613 - val_loss: 0.5931 - val_acc: 0.7184\n",
      "Epoch 126/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5286 - acc: 0.7581 - val_loss: 0.5932 - val_acc: 0.7247\n",
      "Epoch 127/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5260 - acc: 0.7628 - val_loss: 0.5936 - val_acc: 0.7247\n",
      "Epoch 128/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5260 - acc: 0.7636 - val_loss: 0.5940 - val_acc: 0.7215\n",
      "Epoch 129/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5284 - acc: 0.7636 - val_loss: 0.5941 - val_acc: 0.7215\n",
      "Epoch 130/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5239 - acc: 0.7581 - val_loss: 0.5943 - val_acc: 0.7215\n",
      "Epoch 131/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5205 - acc: 0.7494 - val_loss: 0.5945 - val_acc: 0.7247\n",
      "Epoch 132/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5274 - acc: 0.7502 - val_loss: 0.5946 - val_acc: 0.7215\n",
      "Epoch 133/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5239 - acc: 0.7557 - val_loss: 0.5947 - val_acc: 0.7247\n",
      "Epoch 134/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5202 - acc: 0.7660 - val_loss: 0.5950 - val_acc: 0.7247\n",
      "Epoch 135/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5216 - acc: 0.7676 - val_loss: 0.5954 - val_acc: 0.7215\n",
      "316/316 [==============================] - 0s     \n",
      " 32/316 [==>...........................] - ETA: 0ssTrain on 1264 samples, validate on 317 samples\n",
      "Epoch 1/135\n",
      "1264/1264 [==============================] - 2s - loss: 0.6915 - acc: 0.5498 - val_loss: 0.5861 - val_acc: 0.7445\n",
      "Epoch 2/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.6100 - acc: 0.7350 - val_loss: 0.5902 - val_acc: 0.7445\n",
      "Epoch 3/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.6224 - acc: 0.7445 - val_loss: 0.5950 - val_acc: 0.7445\n",
      "Epoch 4/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.6070 - acc: 0.7445 - val_loss: 0.5813 - val_acc: 0.7445\n",
      "Epoch 5/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.6026 - acc: 0.7421 - val_loss: 0.5746 - val_acc: 0.7445\n",
      "Epoch 6/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5879 - acc: 0.7381 - val_loss: 0.5759 - val_acc: 0.7445\n",
      "Epoch 7/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5903 - acc: 0.7373 - val_loss: 0.5766 - val_acc: 0.7445\n",
      "Epoch 8/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5852 - acc: 0.7421 - val_loss: 0.5760 - val_acc: 0.7445\n",
      "Epoch 9/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5839 - acc: 0.7437 - val_loss: 0.5749 - val_acc: 0.7445\n",
      "Epoch 10/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5799 - acc: 0.7429 - val_loss: 0.5749 - val_acc: 0.7445\n",
      "Epoch 11/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5803 - acc: 0.7413 - val_loss: 0.5751 - val_acc: 0.7445\n",
      "Epoch 12/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5818 - acc: 0.7421 - val_loss: 0.5754 - val_acc: 0.7445\n",
      "Epoch 13/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5789 - acc: 0.7405 - val_loss: 0.5755 - val_acc: 0.7445\n",
      "Epoch 14/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5782 - acc: 0.7429 - val_loss: 0.5763 - val_acc: 0.7445\n",
      "Epoch 15/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5772 - acc: 0.7421 - val_loss: 0.5759 - val_acc: 0.7445\n",
      "Epoch 16/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5826 - acc: 0.7437 - val_loss: 0.5757 - val_acc: 0.7445\n",
      "Epoch 17/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5769 - acc: 0.7460 - val_loss: 0.5757 - val_acc: 0.7445\n",
      "Epoch 18/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5792 - acc: 0.7413 - val_loss: 0.5752 - val_acc: 0.7445\n",
      "Epoch 19/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5782 - acc: 0.7437 - val_loss: 0.5757 - val_acc: 0.7445\n",
      "Epoch 20/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5744 - acc: 0.7421 - val_loss: 0.5759 - val_acc: 0.7445\n",
      "Epoch 21/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5730 - acc: 0.7445 - val_loss: 0.5763 - val_acc: 0.7445\n",
      "Epoch 22/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5804 - acc: 0.7445 - val_loss: 0.5767 - val_acc: 0.7445\n",
      "Epoch 23/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5779 - acc: 0.7445 - val_loss: 0.5772 - val_acc: 0.7445\n",
      "Epoch 24/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5787 - acc: 0.7437 - val_loss: 0.5775 - val_acc: 0.7445\n",
      "Epoch 25/135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1264/1264 [==============================] - 0s - loss: 0.5789 - acc: 0.7437 - val_loss: 0.5774 - val_acc: 0.7445\n",
      "Epoch 26/135\n",
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      "Epoch 27/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5671 - acc: 0.7437 - val_loss: 0.5774 - val_acc: 0.7445\n",
      "Epoch 28/135\n",
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      "Epoch 29/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5743 - acc: 0.7429 - val_loss: 0.5769 - val_acc: 0.7445\n",
      "Epoch 30/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5759 - acc: 0.7445 - val_loss: 0.5772 - val_acc: 0.7445\n",
      "Epoch 31/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5690 - acc: 0.7437 - val_loss: 0.5774 - val_acc: 0.7445\n",
      "Epoch 32/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5685 - acc: 0.7453 - val_loss: 0.5780 - val_acc: 0.7445\n",
      "Epoch 33/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5738 - acc: 0.7437 - val_loss: 0.5784 - val_acc: 0.7445\n",
      "Epoch 34/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5683 - acc: 0.7445 - val_loss: 0.5782 - val_acc: 0.7445\n",
      "Epoch 35/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5742 - acc: 0.7445 - val_loss: 0.5778 - val_acc: 0.7445\n",
      "Epoch 36/135\n",
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      "Epoch 37/135\n",
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      "Epoch 38/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5724 - acc: 0.7453 - val_loss: 0.5776 - val_acc: 0.7445\n",
      "Epoch 39/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5690 - acc: 0.7453 - val_loss: 0.5776 - val_acc: 0.7445\n",
      "Epoch 40/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5721 - acc: 0.7445 - val_loss: 0.5768 - val_acc: 0.7445\n",
      "Epoch 41/135\n",
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      "Epoch 42/135\n",
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      "Epoch 43/135\n",
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      "Epoch 44/135\n",
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      "Epoch 45/135\n",
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      "Epoch 46/135\n",
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      "Epoch 47/135\n",
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      "Epoch 48/135\n",
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      "Epoch 49/135\n",
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      "Epoch 50/135\n",
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      "Epoch 51/135\n",
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      "Epoch 52/135\n",
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      "Epoch 53/135\n",
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      "Epoch 54/135\n",
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      "Epoch 55/135\n",
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      "Epoch 56/135\n",
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      "Epoch 57/135\n",
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      "Epoch 58/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5613 - acc: 0.7445 - val_loss: 0.5755 - val_acc: 0.7445\n",
      "Epoch 59/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5614 - acc: 0.7453 - val_loss: 0.5753 - val_acc: 0.7445\n",
      "Epoch 60/135\n",
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      "Epoch 61/135\n",
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      "Epoch 62/135\n",
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      "Epoch 63/135\n",
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      "Epoch 64/135\n",
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      "Epoch 65/135\n",
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      "Epoch 66/135\n",
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      "Epoch 67/135\n",
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      "Epoch 68/135\n",
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      "Epoch 69/135\n",
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      "Epoch 70/135\n",
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      "Epoch 71/135\n",
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      "Epoch 72/135\n",
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      "Epoch 73/135\n",
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      "Epoch 74/135\n",
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      "Epoch 75/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5508 - acc: 0.7468 - val_loss: 0.5748 - val_acc: 0.7445\n",
      "Epoch 76/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5606 - acc: 0.7437 - val_loss: 0.5747 - val_acc: 0.7445\n",
      "Epoch 77/135\n",
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      "Epoch 78/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5521 - acc: 0.7460 - val_loss: 0.5744 - val_acc: 0.7445\n",
      "Epoch 79/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5516 - acc: 0.7453 - val_loss: 0.5740 - val_acc: 0.7445\n",
      "Epoch 80/135\n",
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      "Epoch 81/135\n",
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      "Epoch 82/135\n",
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      "Epoch 83/135\n",
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      "Epoch 84/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5529 - acc: 0.7460 - val_loss: 0.5737 - val_acc: 0.7445\n",
      "Epoch 85/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5557 - acc: 0.7437 - val_loss: 0.5739 - val_acc: 0.7445\n",
      "Epoch 86/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5531 - acc: 0.7476 - val_loss: 0.5746 - val_acc: 0.7445\n",
      "Epoch 87/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5500 - acc: 0.7437 - val_loss: 0.5752 - val_acc: 0.7445\n",
      "Epoch 88/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5520 - acc: 0.7453 - val_loss: 0.5750 - val_acc: 0.7445\n",
      "Epoch 89/135\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1264/1264 [==============================] - 0s - loss: 0.5489 - acc: 0.7476 - val_loss: 0.5749 - val_acc: 0.7445\n",
      "Epoch 90/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5590 - acc: 0.7445 - val_loss: 0.5749 - val_acc: 0.7445\n",
      "Epoch 91/135\n",
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      "Epoch 92/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5524 - acc: 0.7476 - val_loss: 0.5747 - val_acc: 0.7445\n",
      "Epoch 93/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5475 - acc: 0.7453 - val_loss: 0.5745 - val_acc: 0.7445\n",
      "Epoch 94/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5571 - acc: 0.7445 - val_loss: 0.5742 - val_acc: 0.7445\n",
      "Epoch 95/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5502 - acc: 0.7445 - val_loss: 0.5743 - val_acc: 0.7445\n",
      "Epoch 96/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5544 - acc: 0.7445 - val_loss: 0.5744 - val_acc: 0.7445\n",
      "Epoch 97/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5452 - acc: 0.7460 - val_loss: 0.5742 - val_acc: 0.7445\n",
      "Epoch 98/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5519 - acc: 0.7413 - val_loss: 0.5743 - val_acc: 0.7445\n",
      "Epoch 99/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5513 - acc: 0.7421 - val_loss: 0.5743 - val_acc: 0.7445\n",
      "Epoch 100/135\n",
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      "Epoch 101/135\n",
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      "Epoch 102/135\n",
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      "Epoch 103/135\n",
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      "Epoch 104/135\n",
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      "Epoch 105/135\n",
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      "Epoch 106/135\n",
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      "Epoch 107/135\n",
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      "Epoch 108/135\n",
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      "Epoch 109/135\n",
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      "Epoch 110/135\n",
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      "Epoch 111/135\n",
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      "Epoch 112/135\n",
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      "Epoch 113/135\n",
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      "Epoch 114/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5403 - acc: 0.7468 - val_loss: 0.5744 - val_acc: 0.7445\n",
      "Epoch 115/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5387 - acc: 0.7476 - val_loss: 0.5746 - val_acc: 0.7445\n",
      "Epoch 116/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5392 - acc: 0.7460 - val_loss: 0.5749 - val_acc: 0.7445\n",
      "Epoch 117/135\n",
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      "Epoch 118/135\n",
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      "Epoch 119/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5330 - acc: 0.7453 - val_loss: 0.5749 - val_acc: 0.7445\n",
      "Epoch 120/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5353 - acc: 0.7476 - val_loss: 0.5749 - val_acc: 0.7445\n",
      "Epoch 121/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5370 - acc: 0.7468 - val_loss: 0.5744 - val_acc: 0.7445\n",
      "Epoch 122/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5333 - acc: 0.7492 - val_loss: 0.5742 - val_acc: 0.7445\n",
      "Epoch 123/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5353 - acc: 0.7476 - val_loss: 0.5745 - val_acc: 0.7445\n",
      "Epoch 124/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5346 - acc: 0.7500 - val_loss: 0.5747 - val_acc: 0.7445\n",
      "Epoch 125/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5281 - acc: 0.7508 - val_loss: 0.5750 - val_acc: 0.7445\n",
      "Epoch 126/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5382 - acc: 0.7500 - val_loss: 0.5758 - val_acc: 0.7445\n",
      "Epoch 127/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5336 - acc: 0.7460 - val_loss: 0.5752 - val_acc: 0.7445\n",
      "Epoch 128/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5329 - acc: 0.7508 - val_loss: 0.5753 - val_acc: 0.7445\n",
      "Epoch 129/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5344 - acc: 0.7524 - val_loss: 0.5753 - val_acc: 0.7445\n",
      "Epoch 130/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5206 - acc: 0.7571 - val_loss: 0.5755 - val_acc: 0.7445\n",
      "Epoch 131/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5325 - acc: 0.7540 - val_loss: 0.5756 - val_acc: 0.7445\n",
      "Epoch 132/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5381 - acc: 0.7468 - val_loss: 0.5761 - val_acc: 0.7445\n",
      "Epoch 133/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5252 - acc: 0.7524 - val_loss: 0.5760 - val_acc: 0.7445\n",
      "Epoch 134/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5302 - acc: 0.7508 - val_loss: 0.5756 - val_acc: 0.7445\n",
      "Epoch 135/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5313 - acc: 0.7476 - val_loss: 0.5758 - val_acc: 0.7445\n",
      "317/317 [==============================] - 0s     \n",
      " 32/317 [==>...........................] - ETA: 0ssTrain on 1264 samples, validate on 317 samples\n",
      "Epoch 1/135\n",
      "1264/1264 [==============================] - 2s - loss: 0.6384 - acc: 0.6788 - val_loss: 0.5970 - val_acc: 0.7445\n",
      "Epoch 2/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5935 - acc: 0.7429 - val_loss: 0.5778 - val_acc: 0.7445\n",
      "Epoch 3/135\n",
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      "Epoch 4/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5804 - acc: 0.7445 - val_loss: 0.5807 - val_acc: 0.7445\n",
      "Epoch 5/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5859 - acc: 0.7445 - val_loss: 0.5793 - val_acc: 0.7445\n",
      "Epoch 6/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5831 - acc: 0.7445 - val_loss: 0.5786 - val_acc: 0.7445\n",
      "Epoch 7/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5789 - acc: 0.7445 - val_loss: 0.5792 - val_acc: 0.7445\n",
      "Epoch 8/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5743 - acc: 0.7445 - val_loss: 0.5802 - val_acc: 0.7445\n",
      "Epoch 9/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5740 - acc: 0.7445 - val_loss: 0.5807 - val_acc: 0.7445\n",
      "Epoch 10/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5770 - acc: 0.7445 - val_loss: 0.5812 - val_acc: 0.7445\n",
      "Epoch 11/135\n",
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      "Epoch 12/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5694 - acc: 0.7445 - val_loss: 0.5815 - val_acc: 0.7445\n",
      "Epoch 13/135\n",
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      "Epoch 14/135\n",
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      "Epoch 15/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5728 - acc: 0.7445 - val_loss: 0.5825 - val_acc: 0.7445\n",
      "Epoch 16/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5715 - acc: 0.7445 - val_loss: 0.5827 - val_acc: 0.7445\n",
      "Epoch 17/135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1264/1264 [==============================] - 0s - loss: 0.5689 - acc: 0.7445 - val_loss: 0.5829 - val_acc: 0.7445\n",
      "Epoch 18/135\n",
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      "Epoch 19/135\n",
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      "Epoch 20/135\n",
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      "Epoch 21/135\n",
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      "Epoch 22/135\n",
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      "Epoch 23/135\n",
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      "Epoch 24/135\n",
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      "Epoch 25/135\n",
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      "Epoch 26/135\n",
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      "Epoch 27/135\n",
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      "Epoch 28/135\n",
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      "Epoch 29/135\n",
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      "Epoch 30/135\n",
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      "Epoch 31/135\n",
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      "Epoch 32/135\n",
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      "Epoch 33/135\n",
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      "Epoch 34/135\n",
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      "Epoch 35/135\n",
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      "Epoch 37/135\n",
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      "Epoch 38/135\n",
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      "Epoch 39/135\n",
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      "Epoch 40/135\n",
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      "Epoch 41/135\n",
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      "Epoch 42/135\n",
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      "Epoch 43/135\n",
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      "Epoch 44/135\n",
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      "Epoch 45/135\n",
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      "Epoch 46/135\n",
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      "Epoch 47/135\n",
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      "Epoch 48/135\n",
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      "Epoch 49/135\n",
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      "Epoch 50/135\n",
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      "Epoch 51/135\n",
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      "Epoch 53/135\n",
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      "Epoch 54/135\n",
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      "Epoch 55/135\n",
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      "Epoch 56/135\n",
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      "Epoch 58/135\n",
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      "Epoch 59/135\n",
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      "Epoch 60/135\n",
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      "Epoch 64/135\n",
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      "Epoch 65/135\n",
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      "Epoch 66/135\n",
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      "Epoch 67/135\n",
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      "Epoch 68/135\n",
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      "Epoch 69/135\n",
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      "Epoch 70/135\n",
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      "Epoch 71/135\n",
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      "Epoch 72/135\n",
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      "Epoch 73/135\n",
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      "Epoch 74/135\n",
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      "Epoch 75/135\n",
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      "Epoch 76/135\n",
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      "Epoch 77/135\n",
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      "Epoch 78/135\n",
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      "Epoch 79/135\n",
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      "Epoch 80/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5442 - acc: 0.7460 - val_loss: 0.5941 - val_acc: 0.7445\n",
      "Epoch 81/135\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1264/1264 [==============================] - 0s - loss: 0.5349 - acc: 0.7445 - val_loss: 0.5943 - val_acc: 0.7445\n",
      "Epoch 82/135\n",
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      "Epoch 83/135\n",
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      "Epoch 84/135\n",
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      "Epoch 85/135\n",
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      "Epoch 86/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5318 - acc: 0.7468 - val_loss: 0.5957 - val_acc: 0.7445\n",
      "Epoch 87/135\n",
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      "Epoch 88/135\n",
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      "Epoch 89/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5265 - acc: 0.7476 - val_loss: 0.5963 - val_acc: 0.7445\n",
      "Epoch 90/135\n",
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      "Epoch 91/135\n",
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      "Epoch 92/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5312 - acc: 0.7453 - val_loss: 0.5983 - val_acc: 0.7445\n",
      "Epoch 93/135\n",
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      "Epoch 94/135\n",
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      "Epoch 95/135\n",
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      "Epoch 96/135\n",
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      "Epoch 97/135\n",
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      "Epoch 98/135\n",
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      "Epoch 99/135\n",
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      "Epoch 100/135\n",
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      "Epoch 101/135\n",
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      "Epoch 102/135\n",
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      "Epoch 103/135\n",
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      "Epoch 104/135\n",
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      "Epoch 105/135\n",
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      "Epoch 106/135\n",
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      "Epoch 107/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5212 - acc: 0.7571 - val_loss: 0.6027 - val_acc: 0.7413\n",
      "Epoch 108/135\n",
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      "Epoch 109/135\n",
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      "Epoch 110/135\n",
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      "Epoch 111/135\n",
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      "Epoch 112/135\n",
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      "Epoch 113/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5219 - acc: 0.7508 - val_loss: 0.6044 - val_acc: 0.7445\n",
      "Epoch 114/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5122 - acc: 0.7532 - val_loss: 0.6049 - val_acc: 0.7445\n",
      "Epoch 115/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5243 - acc: 0.7484 - val_loss: 0.6058 - val_acc: 0.7413\n",
      "Epoch 116/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5195 - acc: 0.7532 - val_loss: 0.6050 - val_acc: 0.7413\n",
      "Epoch 117/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5142 - acc: 0.7555 - val_loss: 0.6049 - val_acc: 0.7445\n",
      "Epoch 118/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5135 - acc: 0.7516 - val_loss: 0.6051 - val_acc: 0.7445\n",
      "Epoch 119/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5176 - acc: 0.7555 - val_loss: 0.6053 - val_acc: 0.7445\n",
      "Epoch 120/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5200 - acc: 0.7555 - val_loss: 0.6057 - val_acc: 0.7445\n",
      "Epoch 121/135\n",
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      "Epoch 122/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5123 - acc: 0.7508 - val_loss: 0.6059 - val_acc: 0.7445\n",
      "Epoch 123/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5136 - acc: 0.7532 - val_loss: 0.6058 - val_acc: 0.7445\n",
      "Epoch 124/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5156 - acc: 0.7532 - val_loss: 0.6058 - val_acc: 0.7445\n",
      "Epoch 125/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5031 - acc: 0.7516 - val_loss: 0.6066 - val_acc: 0.7445\n",
      "Epoch 126/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5192 - acc: 0.7547 - val_loss: 0.6071 - val_acc: 0.7445\n",
      "Epoch 127/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5125 - acc: 0.7563 - val_loss: 0.6079 - val_acc: 0.7445\n",
      "Epoch 128/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5139 - acc: 0.7516 - val_loss: 0.6079 - val_acc: 0.7413\n",
      "Epoch 129/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5130 - acc: 0.7587 - val_loss: 0.6082 - val_acc: 0.7413\n",
      "Epoch 130/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5078 - acc: 0.7563 - val_loss: 0.6088 - val_acc: 0.7413\n",
      "Epoch 131/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5158 - acc: 0.7571 - val_loss: 0.6094 - val_acc: 0.7413\n",
      "Epoch 132/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5063 - acc: 0.7563 - val_loss: 0.6094 - val_acc: 0.7413\n",
      "Epoch 133/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.4978 - acc: 0.7603 - val_loss: 0.6096 - val_acc: 0.7413\n",
      "Epoch 134/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5021 - acc: 0.7603 - val_loss: 0.6099 - val_acc: 0.7413\n",
      "Epoch 135/135\n",
      "1264/1264 [==============================] - 0s - loss: 0.5000 - acc: 0.7555 - val_loss: 0.6104 - val_acc: 0.7413\n",
      " 32/317 [==>...........................] - ETA: 0ssTrain on 1265 samples, validate on 316 samples\n",
      "Epoch 1/135\n",
      "1265/1265 [==============================] - 2s - loss: 0.6454 - acc: 0.6530 - val_loss: 0.5973 - val_acc: 0.7437\n",
      "Epoch 2/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.6048 - acc: 0.7407 - val_loss: 0.5821 - val_acc: 0.7437\n",
      "Epoch 3/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5855 - acc: 0.7447 - val_loss: 0.5858 - val_acc: 0.7437\n",
      "Epoch 4/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5857 - acc: 0.7447 - val_loss: 0.5871 - val_acc: 0.7437\n",
      "Epoch 5/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5877 - acc: 0.7447 - val_loss: 0.5833 - val_acc: 0.7437\n",
      "Epoch 6/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5792 - acc: 0.7439 - val_loss: 0.5799 - val_acc: 0.7437\n",
      "Epoch 7/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5800 - acc: 0.7455 - val_loss: 0.5785 - val_acc: 0.7437\n",
      "Epoch 8/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5773 - acc: 0.7447 - val_loss: 0.5780 - val_acc: 0.7437\n",
      "Epoch 9/135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1265/1265 [==============================] - 0s - loss: 0.5751 - acc: 0.7447 - val_loss: 0.5775 - val_acc: 0.7437\n",
      "Epoch 10/135\n",
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      "Epoch 11/135\n",
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      "Epoch 12/135\n",
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      "Epoch 13/135\n",
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      "Epoch 14/135\n",
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      "Epoch 15/135\n",
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      "Epoch 16/135\n",
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      "Epoch 17/135\n",
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      "Epoch 18/135\n",
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      "Epoch 19/135\n",
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      "Epoch 20/135\n",
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      "Epoch 21/135\n",
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      "Epoch 22/135\n",
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      "Epoch 23/135\n",
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      "Epoch 24/135\n",
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      "Epoch 25/135\n",
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      "Epoch 26/135\n",
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      "Epoch 27/135\n",
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      "Epoch 30/135\n",
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      "Epoch 31/135\n",
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      "Epoch 32/135\n",
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      "Epoch 33/135\n",
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      "Epoch 34/135\n",
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      "Epoch 39/135\n",
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      "Epoch 48/135\n",
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      "Epoch 50/135\n",
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      "Epoch 51/135\n",
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      "Epoch 60/135\n",
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      "Epoch 61/135\n",
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      "Epoch 62/135\n",
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      "Epoch 63/135\n",
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      "Epoch 64/135\n",
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      "Epoch 65/135\n",
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      "Epoch 66/135\n",
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      "Epoch 68/135\n",
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      "Epoch 69/135\n",
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      "Epoch 70/135\n",
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      "Epoch 71/135\n",
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      "Epoch 72/135\n",
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      "Epoch 73/135\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1265/1265 [==============================] - 0s - loss: 0.5517 - acc: 0.7455 - val_loss: 0.5829 - val_acc: 0.7437\n",
      "Epoch 74/135\n",
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      "Epoch 75/135\n",
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      "Epoch 76/135\n",
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      "Epoch 77/135\n",
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      "Epoch 78/135\n",
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      "Epoch 79/135\n",
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      "Epoch 80/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5375 - acc: 0.7439 - val_loss: 0.5854 - val_acc: 0.7437\n",
      "Epoch 81/135\n",
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      "Epoch 82/135\n",
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      "Epoch 83/135\n",
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      "Epoch 84/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5374 - acc: 0.7470 - val_loss: 0.5854 - val_acc: 0.7437\n",
      "Epoch 85/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5308 - acc: 0.7462 - val_loss: 0.5845 - val_acc: 0.7437\n",
      "Epoch 86/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5369 - acc: 0.7510 - val_loss: 0.5853 - val_acc: 0.7437\n",
      "Epoch 87/135\n",
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      "Epoch 88/135\n",
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      "Epoch 89/135\n",
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      "Epoch 90/135\n",
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      "Epoch 91/135\n",
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      "Epoch 92/135\n",
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      "Epoch 93/135\n",
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      "Epoch 94/135\n",
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      "Epoch 95/135\n",
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      "Epoch 96/135\n",
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      "Epoch 97/135\n",
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      "Epoch 98/135\n",
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      "Epoch 99/135\n",
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      "Epoch 100/135\n",
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      "Epoch 101/135\n",
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      "Epoch 102/135\n",
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      "Epoch 103/135\n",
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      "Epoch 104/135\n",
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      "Epoch 105/135\n",
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      "Epoch 106/135\n",
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      "Epoch 107/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5283 - acc: 0.7526 - val_loss: 0.5920 - val_acc: 0.7437\n",
      "Epoch 108/135\n",
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      "Epoch 109/135\n",
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      "Epoch 110/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5209 - acc: 0.7502 - val_loss: 0.5918 - val_acc: 0.7405\n",
      "Epoch 111/135\n",
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      "Epoch 112/135\n",
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      "Epoch 113/135\n",
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      "Epoch 114/135\n",
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      "Epoch 115/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5153 - acc: 0.7502 - val_loss: 0.5951 - val_acc: 0.7437\n",
      "Epoch 116/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5158 - acc: 0.7557 - val_loss: 0.5949 - val_acc: 0.7437\n",
      "Epoch 117/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5141 - acc: 0.7565 - val_loss: 0.5955 - val_acc: 0.7405\n",
      "Epoch 118/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5213 - acc: 0.7542 - val_loss: 0.5958 - val_acc: 0.7405\n",
      "Epoch 119/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5217 - acc: 0.7526 - val_loss: 0.5969 - val_acc: 0.7405\n",
      "Epoch 120/135\n",
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      "Epoch 121/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5158 - acc: 0.7534 - val_loss: 0.5990 - val_acc: 0.7405\n",
      "Epoch 122/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5164 - acc: 0.7494 - val_loss: 0.5995 - val_acc: 0.7405\n",
      "Epoch 123/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5169 - acc: 0.7510 - val_loss: 0.5999 - val_acc: 0.7405\n",
      "Epoch 124/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5137 - acc: 0.7542 - val_loss: 0.5982 - val_acc: 0.7405\n",
      "Epoch 125/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5076 - acc: 0.7573 - val_loss: 0.5983 - val_acc: 0.7405\n",
      "Epoch 126/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5056 - acc: 0.7510 - val_loss: 0.5988 - val_acc: 0.7405\n",
      "Epoch 127/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5099 - acc: 0.7644 - val_loss: 0.5992 - val_acc: 0.7405\n",
      "Epoch 128/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5099 - acc: 0.7549 - val_loss: 0.6003 - val_acc: 0.7405\n",
      "Epoch 129/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5049 - acc: 0.7597 - val_loss: 0.6004 - val_acc: 0.7405\n",
      "Epoch 130/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5001 - acc: 0.7573 - val_loss: 0.6000 - val_acc: 0.7405\n",
      "Epoch 131/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5037 - acc: 0.7621 - val_loss: 0.5999 - val_acc: 0.7373\n",
      "Epoch 132/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5019 - acc: 0.7676 - val_loss: 0.6004 - val_acc: 0.7373\n",
      "Epoch 133/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5117 - acc: 0.7581 - val_loss: 0.6012 - val_acc: 0.7373\n",
      "Epoch 134/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.4955 - acc: 0.7652 - val_loss: 0.6019 - val_acc: 0.7373\n",
      "Epoch 135/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5020 - acc: 0.7684 - val_loss: 0.6007 - val_acc: 0.7342\n",
      "316/316 [==============================] - 0s     \n",
      " 32/316 [==>...........................] - ETA: 0ssTrain on 1265 samples, validate on 316 samples\n",
      "Epoch 1/135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1265/1265 [==============================] - 2s - loss: 0.6344 - acc: 0.6704 - val_loss: 0.5802 - val_acc: 0.7437\n",
      "Epoch 2/135\n",
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      "Epoch 3/135\n",
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      "Epoch 4/135\n",
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      "Epoch 5/135\n",
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      "Epoch 7/135\n",
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      "Epoch 8/135\n",
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      "Epoch 9/135\n",
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      "Epoch 11/135\n",
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      "Epoch 13/135\n",
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      "Epoch 15/135\n",
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      "Epoch 16/135\n",
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      "Epoch 17/135\n",
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      "Epoch 64/135\n",
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      "Epoch 65/135\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1265/1265 [==============================] - 0s - loss: 0.5588 - acc: 0.7455 - val_loss: 0.5718 - val_acc: 0.7437\n",
      "Epoch 66/135\n",
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      "Epoch 69/135\n",
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      "Epoch 71/135\n",
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      "Epoch 72/135\n",
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      "Epoch 73/135\n",
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      "Epoch 75/135\n",
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      "Epoch 76/135\n",
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      "Epoch 77/135\n",
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      "Epoch 78/135\n",
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      "Epoch 79/135\n",
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      "Epoch 80/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5491 - acc: 0.7423 - val_loss: 0.5741 - val_acc: 0.7437\n",
      "Epoch 81/135\n",
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      "Epoch 82/135\n",
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      "Epoch 83/135\n",
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      "Epoch 84/135\n",
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      "Epoch 85/135\n",
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      "Epoch 86/135\n",
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      "Epoch 87/135\n",
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      "Epoch 88/135\n",
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      "Epoch 89/135\n",
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      "Epoch 90/135\n",
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      "Epoch 91/135\n",
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      "Epoch 92/135\n",
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      "Epoch 93/135\n",
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      "Epoch 94/135\n",
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      "Epoch 95/135\n",
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      "Epoch 96/135\n",
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      "Epoch 97/135\n",
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      "Epoch 98/135\n",
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      "Epoch 99/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5312 - acc: 0.7494 - val_loss: 0.5789 - val_acc: 0.7437\n",
      "Epoch 100/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5355 - acc: 0.7486 - val_loss: 0.5789 - val_acc: 0.7437\n",
      "Epoch 101/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5316 - acc: 0.7470 - val_loss: 0.5792 - val_acc: 0.7437\n",
      "Epoch 102/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5311 - acc: 0.7502 - val_loss: 0.5796 - val_acc: 0.7437\n",
      "Epoch 103/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5335 - acc: 0.7470 - val_loss: 0.5799 - val_acc: 0.7437\n",
      "Epoch 104/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5293 - acc: 0.7439 - val_loss: 0.5805 - val_acc: 0.7437\n",
      "Epoch 105/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5339 - acc: 0.7534 - val_loss: 0.5806 - val_acc: 0.7437\n",
      "Epoch 106/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5272 - acc: 0.7534 - val_loss: 0.5808 - val_acc: 0.7437\n",
      "Epoch 107/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5265 - acc: 0.7502 - val_loss: 0.5809 - val_acc: 0.7437\n",
      "Epoch 108/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5235 - acc: 0.7526 - val_loss: 0.5813 - val_acc: 0.7437\n",
      "Epoch 109/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5318 - acc: 0.7518 - val_loss: 0.5817 - val_acc: 0.7437\n",
      "Epoch 110/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5293 - acc: 0.7470 - val_loss: 0.5821 - val_acc: 0.7437\n",
      "Epoch 111/135\n",
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      "Epoch 112/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5233 - acc: 0.7502 - val_loss: 0.5825 - val_acc: 0.7437\n",
      "Epoch 113/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5257 - acc: 0.7518 - val_loss: 0.5824 - val_acc: 0.7437\n",
      "Epoch 114/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5331 - acc: 0.7526 - val_loss: 0.5825 - val_acc: 0.7405\n",
      "Epoch 115/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5303 - acc: 0.7494 - val_loss: 0.5829 - val_acc: 0.7405\n",
      "Epoch 116/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5215 - acc: 0.7502 - val_loss: 0.5828 - val_acc: 0.7405\n",
      "Epoch 117/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5224 - acc: 0.7628 - val_loss: 0.5827 - val_acc: 0.7405\n",
      "Epoch 118/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5258 - acc: 0.7526 - val_loss: 0.5829 - val_acc: 0.7437\n",
      "Epoch 119/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5147 - acc: 0.7549 - val_loss: 0.5833 - val_acc: 0.7437\n",
      "Epoch 120/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5152 - acc: 0.7589 - val_loss: 0.5838 - val_acc: 0.7437\n",
      "Epoch 121/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5143 - acc: 0.7597 - val_loss: 0.5843 - val_acc: 0.7405\n",
      "Epoch 122/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5182 - acc: 0.7621 - val_loss: 0.5846 - val_acc: 0.7405\n",
      "Epoch 123/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5160 - acc: 0.7510 - val_loss: 0.5847 - val_acc: 0.7405\n",
      "Epoch 124/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5125 - acc: 0.7581 - val_loss: 0.5851 - val_acc: 0.7405\n",
      "Epoch 125/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5172 - acc: 0.7470 - val_loss: 0.5850 - val_acc: 0.7405\n",
      "Epoch 126/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5115 - acc: 0.7526 - val_loss: 0.5853 - val_acc: 0.7405\n",
      "Epoch 127/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5097 - acc: 0.7510 - val_loss: 0.5855 - val_acc: 0.7405\n",
      "Epoch 128/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5136 - acc: 0.7565 - val_loss: 0.5859 - val_acc: 0.7405\n",
      "Epoch 129/135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1265/1265 [==============================] - 0s - loss: 0.5188 - acc: 0.7534 - val_loss: 0.5866 - val_acc: 0.7405\n",
      "Epoch 130/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5002 - acc: 0.7621 - val_loss: 0.5872 - val_acc: 0.7405\n",
      "Epoch 131/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5063 - acc: 0.7573 - val_loss: 0.5880 - val_acc: 0.7405\n",
      "Epoch 132/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5135 - acc: 0.7573 - val_loss: 0.5879 - val_acc: 0.7405\n",
      "Epoch 133/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5159 - acc: 0.7542 - val_loss: 0.5879 - val_acc: 0.7405\n",
      "Epoch 134/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5091 - acc: 0.7621 - val_loss: 0.5879 - val_acc: 0.7405\n",
      "Epoch 135/135\n",
      "1265/1265 [==============================] - 0s - loss: 0.5142 - acc: 0.7644 - val_loss: 0.5885 - val_acc: 0.7405\n",
      "316/316 [==============================] - 0s     \n",
      " 32/316 [==>...........................] - ETA: 0ssTrain on 1266 samples, validate on 315 samples\n",
      "Epoch 1/135\n",
      "1266/1266 [==============================] - 2s - loss: 0.6732 - acc: 0.5821 - val_loss: 0.6029 - val_acc: 0.7460\n",
      "Epoch 2/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5993 - acc: 0.7291 - val_loss: 0.5770 - val_acc: 0.7460\n",
      "Epoch 3/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5859 - acc: 0.7441 - val_loss: 0.5838 - val_acc: 0.7460\n",
      "Epoch 4/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5938 - acc: 0.7441 - val_loss: 0.5848 - val_acc: 0.7460\n",
      "Epoch 5/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5888 - acc: 0.7441 - val_loss: 0.5799 - val_acc: 0.7460\n",
      "Epoch 6/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5832 - acc: 0.7449 - val_loss: 0.5771 - val_acc: 0.7460\n",
      "Epoch 7/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5776 - acc: 0.7433 - val_loss: 0.5773 - val_acc: 0.7460\n",
      "Epoch 8/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5782 - acc: 0.7441 - val_loss: 0.5779 - val_acc: 0.7460\n",
      "Epoch 9/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5742 - acc: 0.7433 - val_loss: 0.5782 - val_acc: 0.7460\n",
      "Epoch 10/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5758 - acc: 0.7441 - val_loss: 0.5777 - val_acc: 0.7460\n",
      "Epoch 11/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5734 - acc: 0.7433 - val_loss: 0.5771 - val_acc: 0.7460\n",
      "Epoch 12/135\n",
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      "Epoch 13/135\n",
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      "Epoch 14/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5726 - acc: 0.7441 - val_loss: 0.5770 - val_acc: 0.7460\n",
      "Epoch 15/135\n",
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      "Epoch 16/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5760 - acc: 0.7441 - val_loss: 0.5771 - val_acc: 0.7460\n",
      "Epoch 17/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5705 - acc: 0.7441 - val_loss: 0.5774 - val_acc: 0.7460\n",
      "Epoch 18/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5672 - acc: 0.7441 - val_loss: 0.5773 - val_acc: 0.7460\n",
      "Epoch 19/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5741 - acc: 0.7441 - val_loss: 0.5774 - val_acc: 0.7460\n",
      "Epoch 20/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5728 - acc: 0.7441 - val_loss: 0.5775 - val_acc: 0.7460\n",
      "Epoch 21/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5717 - acc: 0.7441 - val_loss: 0.5776 - val_acc: 0.7460\n",
      "Epoch 22/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5745 - acc: 0.7441 - val_loss: 0.5778 - val_acc: 0.7460\n",
      "Epoch 23/135\n",
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      "Epoch 24/135\n",
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      "Epoch 25/135\n",
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      "Epoch 26/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5677 - acc: 0.7433 - val_loss: 0.5784 - val_acc: 0.7460\n",
      "Epoch 27/135\n",
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      "Epoch 28/135\n",
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      "Epoch 29/135\n",
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      "Epoch 30/135\n",
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      "Epoch 31/135\n",
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      "Epoch 32/135\n",
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      "Epoch 33/135\n",
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      "Epoch 34/135\n",
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      "Epoch 35/135\n",
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      "Epoch 36/135\n",
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      "Epoch 38/135\n",
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      "Epoch 39/135\n",
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      "Epoch 40/135\n",
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      "Epoch 41/135\n",
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      "Epoch 43/135\n",
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      "Epoch 44/135\n",
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      "Epoch 45/135\n",
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      "Epoch 46/135\n",
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      "Epoch 47/135\n",
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      "Epoch 48/135\n",
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      "Epoch 49/135\n",
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      "Epoch 50/135\n",
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      "Epoch 51/135\n",
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      "Epoch 52/135\n",
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      "Epoch 53/135\n",
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      "Epoch 54/135\n",
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      "Epoch 55/135\n",
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      "Epoch 56/135\n",
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      "Epoch 57/135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1266/1266 [==============================] - 0s - loss: 0.5584 - acc: 0.7441 - val_loss: 0.5838 - val_acc: 0.7460\n",
      "Epoch 58/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5543 - acc: 0.7449 - val_loss: 0.5840 - val_acc: 0.7460\n",
      "Epoch 59/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5575 - acc: 0.7441 - val_loss: 0.5842 - val_acc: 0.7460\n",
      "Epoch 60/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5570 - acc: 0.7441 - val_loss: 0.5843 - val_acc: 0.7460\n",
      "Epoch 61/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5515 - acc: 0.7425 - val_loss: 0.5846 - val_acc: 0.7460\n",
      "Epoch 62/135\n",
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      "Epoch 63/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5550 - acc: 0.7433 - val_loss: 0.5855 - val_acc: 0.7460\n",
      "Epoch 64/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5521 - acc: 0.7441 - val_loss: 0.5861 - val_acc: 0.7460\n",
      "Epoch 65/135\n",
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      "Epoch 66/135\n",
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      "Epoch 67/135\n",
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      "Epoch 68/135\n",
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      "Epoch 69/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5540 - acc: 0.7425 - val_loss: 0.5887 - val_acc: 0.7460\n",
      "Epoch 70/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5447 - acc: 0.7449 - val_loss: 0.5892 - val_acc: 0.7460\n",
      "Epoch 71/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5534 - acc: 0.7449 - val_loss: 0.5895 - val_acc: 0.7460\n",
      "Epoch 72/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5472 - acc: 0.7457 - val_loss: 0.5895 - val_acc: 0.7460\n",
      "Epoch 73/135\n",
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      "Epoch 74/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5486 - acc: 0.7449 - val_loss: 0.5899 - val_acc: 0.7460\n",
      "Epoch 75/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5483 - acc: 0.7449 - val_loss: 0.5901 - val_acc: 0.7460\n",
      "Epoch 76/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5390 - acc: 0.7472 - val_loss: 0.5904 - val_acc: 0.7460\n",
      "Epoch 77/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5505 - acc: 0.7457 - val_loss: 0.5908 - val_acc: 0.7460\n",
      "Epoch 78/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5479 - acc: 0.7449 - val_loss: 0.5911 - val_acc: 0.7460\n",
      "Epoch 79/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5478 - acc: 0.7433 - val_loss: 0.5915 - val_acc: 0.7460\n",
      "Epoch 80/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5451 - acc: 0.7449 - val_loss: 0.5919 - val_acc: 0.7460\n",
      "Epoch 81/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5405 - acc: 0.7425 - val_loss: 0.5927 - val_acc: 0.7460\n",
      "Epoch 82/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5405 - acc: 0.7457 - val_loss: 0.5933 - val_acc: 0.7460\n",
      "Epoch 83/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5402 - acc: 0.7472 - val_loss: 0.5943 - val_acc: 0.7460\n",
      "Epoch 84/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5406 - acc: 0.7449 - val_loss: 0.5944 - val_acc: 0.7460\n",
      "Epoch 85/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5353 - acc: 0.7464 - val_loss: 0.5944 - val_acc: 0.7460\n",
      "Epoch 86/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5406 - acc: 0.7480 - val_loss: 0.5951 - val_acc: 0.7460\n",
      "Epoch 87/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5382 - acc: 0.7472 - val_loss: 0.5961 - val_acc: 0.7460\n",
      "Epoch 88/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5368 - acc: 0.7433 - val_loss: 0.5964 - val_acc: 0.7460\n",
      "Epoch 89/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5340 - acc: 0.7457 - val_loss: 0.5962 - val_acc: 0.7460\n",
      "Epoch 90/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5345 - acc: 0.7457 - val_loss: 0.5963 - val_acc: 0.7460\n",
      "Epoch 91/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5344 - acc: 0.7472 - val_loss: 0.5968 - val_acc: 0.7460\n",
      "Epoch 92/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5365 - acc: 0.7480 - val_loss: 0.5973 - val_acc: 0.7460\n",
      "Epoch 93/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5300 - acc: 0.7472 - val_loss: 0.5976 - val_acc: 0.7460\n",
      "Epoch 94/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5320 - acc: 0.7433 - val_loss: 0.5983 - val_acc: 0.7460\n",
      "Epoch 95/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5296 - acc: 0.7480 - val_loss: 0.5987 - val_acc: 0.7460\n",
      "Epoch 96/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5279 - acc: 0.7496 - val_loss: 0.5994 - val_acc: 0.7460\n",
      "Epoch 97/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5359 - acc: 0.7433 - val_loss: 0.5994 - val_acc: 0.7460\n",
      "Epoch 98/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5241 - acc: 0.7536 - val_loss: 0.5998 - val_acc: 0.7460\n",
      "Epoch 99/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5274 - acc: 0.7488 - val_loss: 0.6000 - val_acc: 0.7460\n",
      "Epoch 100/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5297 - acc: 0.7472 - val_loss: 0.5999 - val_acc: 0.7460\n",
      "Epoch 101/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5236 - acc: 0.7512 - val_loss: 0.6011 - val_acc: 0.7460\n",
      "Epoch 102/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5251 - acc: 0.7472 - val_loss: 0.6012 - val_acc: 0.7460\n",
      "Epoch 103/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5195 - acc: 0.7449 - val_loss: 0.6017 - val_acc: 0.7460\n",
      "Epoch 104/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5303 - acc: 0.7464 - val_loss: 0.6016 - val_acc: 0.7460\n",
      "Epoch 105/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5239 - acc: 0.7441 - val_loss: 0.6012 - val_acc: 0.7429\n",
      "Epoch 106/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5272 - acc: 0.7504 - val_loss: 0.6026 - val_acc: 0.7460\n",
      "Epoch 107/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5242 - acc: 0.7559 - val_loss: 0.6025 - val_acc: 0.7429\n",
      "Epoch 108/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5170 - acc: 0.7520 - val_loss: 0.6031 - val_acc: 0.7429\n",
      "Epoch 109/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5209 - acc: 0.7551 - val_loss: 0.6038 - val_acc: 0.7429\n",
      "Epoch 110/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5213 - acc: 0.7536 - val_loss: 0.6039 - val_acc: 0.7397\n",
      "Epoch 111/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5221 - acc: 0.7567 - val_loss: 0.6037 - val_acc: 0.7397\n",
      "Epoch 112/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5170 - acc: 0.7472 - val_loss: 0.6045 - val_acc: 0.7397\n",
      "Epoch 113/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5172 - acc: 0.7488 - val_loss: 0.6062 - val_acc: 0.7429\n",
      "Epoch 114/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5240 - acc: 0.7528 - val_loss: 0.6067 - val_acc: 0.7460\n",
      "Epoch 115/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5156 - acc: 0.7528 - val_loss: 0.6049 - val_acc: 0.7397\n",
      "Epoch 116/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5130 - acc: 0.7615 - val_loss: 0.6048 - val_acc: 0.7397\n",
      "Epoch 117/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5178 - acc: 0.7536 - val_loss: 0.6060 - val_acc: 0.7397\n",
      "Epoch 118/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5173 - acc: 0.7528 - val_loss: 0.6071 - val_acc: 0.7397\n",
      "Epoch 119/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5135 - acc: 0.7567 - val_loss: 0.6067 - val_acc: 0.7397\n",
      "Epoch 120/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5194 - acc: 0.7512 - val_loss: 0.6062 - val_acc: 0.7397\n",
      "Epoch 121/135\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1266/1266 [==============================] - 0s - loss: 0.5155 - acc: 0.7551 - val_loss: 0.6066 - val_acc: 0.7397\n",
      "Epoch 122/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5205 - acc: 0.7559 - val_loss: 0.6071 - val_acc: 0.7397\n",
      "Epoch 123/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5162 - acc: 0.7622 - val_loss: 0.6072 - val_acc: 0.7397\n",
      "Epoch 124/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5163 - acc: 0.7575 - val_loss: 0.6081 - val_acc: 0.7397\n",
      "Epoch 125/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5077 - acc: 0.7520 - val_loss: 0.6090 - val_acc: 0.7397\n",
      "Epoch 126/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5101 - acc: 0.7559 - val_loss: 0.6097 - val_acc: 0.7397\n",
      "Epoch 127/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5119 - acc: 0.7528 - val_loss: 0.6094 - val_acc: 0.7397\n",
      "Epoch 128/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5091 - acc: 0.7607 - val_loss: 0.6089 - val_acc: 0.7397\n",
      "Epoch 129/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5081 - acc: 0.7591 - val_loss: 0.6095 - val_acc: 0.7397\n",
      "Epoch 130/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5098 - acc: 0.7567 - val_loss: 0.6101 - val_acc: 0.7397\n",
      "Epoch 131/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5031 - acc: 0.7615 - val_loss: 0.6107 - val_acc: 0.7397\n",
      "Epoch 132/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5083 - acc: 0.7583 - val_loss: 0.6118 - val_acc: 0.7397\n",
      "Epoch 133/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.4997 - acc: 0.7583 - val_loss: 0.6125 - val_acc: 0.7397\n",
      "Epoch 134/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5014 - acc: 0.7646 - val_loss: 0.6137 - val_acc: 0.7397\n",
      "Epoch 135/135\n",
      "1266/1266 [==============================] - 0s - loss: 0.5028 - acc: 0.7528 - val_loss: 0.6123 - val_acc: 0.7397\n",
      "315/315 [==============================] - 0s     \n",
      " 32/315 [==>...........................] - ETA: 0ss"
     ]
    }
   ],
   "source": [
    "n_splits=5\n",
    "kfold=StratifiedKFold(n_splits=n_splits, shuffle=True)\n",
    "#classify as nodule or non-nodule\n",
    "input_shape=(64,64,1)\n",
    "num_classes=2\n",
    "width=32\n",
    "epochs=135\n",
    "batch_size=256\n",
    "cvscores=[]\n",
    "cvscoresrandom=[]\n",
    "history=[]\n",
    "historyrandom=[]\n",
    "aucscores=[]\n",
    "aucscoresrandom=[]\n",
    "predicted=[]\n",
    "malignantlabeltest=[]\n",
    "predictedrandom=[]\n",
    "randomlabeltest=[]\n",
    "for train,test in kfold.split(nodulecrops,malignantlabel):\n",
    "    model = Sequential()\n",
    "    \n",
    "    model.add(Conv2D(width, kernel_size=(3, 3),\n",
    "                     activation='relu', padding='same',\n",
    "                     input_shape=input_shape))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Conv2D(width*2, (3, 3), activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Dropout(0.50))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(width*4, activation='relu'))\n",
    "    model.add(Dropout(0.50))\n",
    "    model.add(Dense(num_classes, activation='softmax'))\n",
    "    #model.summary()\n",
    "    model.compile(loss=keras.losses.categorical_crossentropy,\n",
    "                  optimizer=Adam(lr=1e-5),\n",
    "                  metrics=['accuracy'])\n",
    "    histor=model.fit(nodulecrops[train],malignantlabelcat[train], batch_size=batch_size, epochs=epochs, verbose=1, shuffle=True, validation_data=(nodulecrops[test],malignantlabelcat[test]))\n",
    "    scores=model.evaluate(nodulecrops[test],malignantlabelcat[test])\n",
    "    cvscores.append(scores)\n",
    "    predicted.append(model.predict_proba(nodulecrops[test])[:,1])\n",
    "    malignantlabeltest.append([malignantlabel[i] for i in test])\n",
    "    aucscores.append(roc_auc_score(malignantlabelcat[test],model.predict_proba(nodulecrops[test])))\n",
    "    history.append(histor)\n",
    "predicted=np.concatenate(np.array(predicted),axis=0)\n",
    "malignantlabeltest=np.concatenate(np.array(malignantlabeltest),axis=0)\n",
    "roc=roc_curve(malignantlabeltest,predicted)\n",
    "    \n",
    "for train,test in kfold.split(nodulecrops,randomlabel):\n",
    "    model = Sequential()\n",
    "    model.add(Conv2D(width, kernel_size=(3, 3),\n",
    "                     activation='relu', padding='same',\n",
    "                     input_shape=input_shape))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Conv2D(width*2, (3, 3), activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Dropout(0.50))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(width*4, activation='relu'))\n",
    "    model.add(Dropout(0.50))\n",
    "    model.add(Dense(num_classes, activation='softmax'))\n",
    "    model.compile(loss=keras.losses.categorical_crossentropy,\n",
    "                  optimizer=Adam(lr=2e-5),\n",
    "                  metrics=['accuracy'])\n",
    "    historrandom=model.fit(nodulecrops[train],randomlabelcat[train], batch_size=batch_size, epochs=epochs, verbose=1, shuffle=True, validation_data=(nodulecrops[test],randomlabelcat[test]))\n",
    "    coresrandom=model.evaluate(nodulecrops[test],randomlabelcat[test])\n",
    "    cvscoresrandom.append(coresrandom)\n",
    "    aucscoresrandom.append(roc_auc_score(randomlabelcat[test],model.predict_proba(nodulecrops[test])))\n",
    "    historyrandom.append(historrandom)\n",
    "    predictedrandom.append(model.predict_proba(nodulecrops[test])[:,1])\n",
    "    randomlabeltest.append(randomlabel[test])\n",
    "predictedrandom=np.concatenate(np.array(predictedrandom),axis=0)\n",
    "randomlabeltest=np.concatenate(np.array(randomlabeltest),axis=0)\n",
    "rocrandom=roc_curve(randomlabeltest,predictedrandom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean loss across all CV sets with true labels: 0.568389013583\n",
      "Mean loss across all CV sets with random labels: 0.636227802017\n",
      "Mean accuracy across all CV sets with true labels: 0.739410216015\n",
      "Mean accuracy across all CV sets with random labels: 0.722336503171\n",
      "Lowest val_loss of 0.564570076191 at epoch [126] with true labels\n",
      "Lowest val_loss of 0.576504066628 at epoch [1] with random labels\n",
      "Average AUC across CV sets with true labels: 0.623053044198\n",
      "Average AUC across CV sets with random labels: 0.465063403498\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ad1cVsWxrBdGhNvwaFueWs62snp6JxpVMWYXV9EuOJNR2etzHIYQ4sx1pH8ZY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\nSZUiGwxJkiSpUv4fpLy3ZxJTrd4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd1ddf5320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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f0HXb8UWLFhEerv0R1tXVsWjRIsaOHcuPf/zjEFO+efPmERsbS0REBGPGjOHo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jCli4quulGtKIOHjMsvJ3XObPwTJ5E6fTqedBuMcqa0lObS3QSmTUP8fvwTJ9JcdgBjDMe+uYbm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\nOQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd371a4ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QDj/mwP/u8gLhuIHQeaiGr5CUFs0oqRXVBVjjnPsCwMyeAc4CVkYcdw3wIpDl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd3238c5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Mean loss across all CV sets with true labels:\", np.mean([cvscores[i][0] for i in range(len(cvscores))]))\n",
    "print(\"Mean loss across all CV sets with random labels:\", np.mean([cvscoresrandom[i][0] for i in range(len(cvscoresrandom))]))\n",
    "print(\"Mean accuracy across all CV sets with true labels:\", np.mean([cvscores[i][1] for i in range(len(cvscores))]))\n",
    "print(\"Mean accuracy across all CV sets with random labels:\", np.mean([cvscoresrandom[i][1] for i in range(len(cvscoresrandom))]))\n",
    "\n",
    "print(\"Lowest val_loss of\", min(np.mean([history[i].history['val_loss'] for i in range(n_splits)],axis=0)), \"at epoch\", np.where(np.mean([history[i].history['val_loss'] for i in range(n_splits)],axis=0)==min(np.mean([history[i].history['val_loss'] for i in range(n_splits)],axis=0)))[0], \"with true labels\")\n",
    "print(\"Lowest val_loss of\", min(np.mean([historyrandom[i].history['val_loss'] for i in range(n_splits)],axis=0)), \"at epoch\", np.where(np.mean([historyrandom[i].history['val_loss'] for i in range(n_splits)],axis=0)==min(np.mean([historyrandom[i].history['val_loss'] for i in range(n_splits)],axis=0)))[0],\"with random labels\")\n",
    "print(\"Average AUC across CV sets with true labels:\",np.mean(aucscores))\n",
    "print(\"Average AUC across CV sets with random labels:\",np.mean(aucscoresrandom))\n",
    "acc=np.mean([history[i].history['acc'] for i in range(n_splits)], axis=0)\n",
    "valacc=np.mean([history[i].history['val_acc'] for i in range(n_splits)], axis=0)\n",
    "loss=np.mean([history[i].history['loss'] for i in range(n_splits)], axis=0)\n",
    "valloss=np.mean([history[i].history['val_loss'] for i in range(n_splits)], axis=0)\n",
    "randacc=np.mean([historyrandom[i].history['acc'] for i in range(n_splits)], axis=0)\n",
    "randvalacc=np.mean([historyrandom[i].history['val_acc'] for i in range(n_splits)], axis=0)\n",
    "randloss=np.mean([historyrandom[i].history['loss'] for i in range(n_splits)], axis=0)\n",
    "randvalloss=np.mean([historyrandom[i].history['val_loss'] for i in range(n_splits)], axis=0)\n",
    "\n",
    "# summarize history for loss\n",
    "plt.plot(loss)\n",
    "plt.plot(valloss)\n",
    "plt.plot(randloss)\n",
    "plt.plot(randvalloss)\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train truelabel', 'validation truelabel', 'train randomlabel', 'val randomlabel'], loc='best')\n",
    "plt.ylim([0.45,0.7])\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "# summarize history for accuracy\n",
    "plt.plot(acc)\n",
    "plt.plot(valacc)\n",
    "plt.plot(randacc)\n",
    "plt.plot(randvalacc)\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train truelabel', 'validation truelabel', 'train randomlabel', 'val randomlabel'], loc='best')\n",
    "plt.ylim([0.725,0.78])\n",
    "plt.show()\n",
    "\n",
    "#ROC curve\n",
    "plt.plot(roc[0],roc[1])\n",
    "plt.plot(rocrandom[0],rocrandom[1])\n",
    "plt.title('ROC')\n",
    "plt.ylabel('TPrate')\n",
    "plt.xlabel('FPrate')\n",
    "plt.legend(['true label', 'random label'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Increased dropout rate to 0.75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1264 samples, validate on 317 samples\n",
      "Epoch 1/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.6196 - acc: 0.7008Epoch 00000: loss improved from inf to 0.62190, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 4s - loss: 0.6219 - acc: 0.6986 - val_loss: 0.5855 - val_acc: 0.7350\n",
      "Epoch 2/30\n",
      "1245/1264 [============================>.] - ETA: 0s - loss: 0.5838 - acc: 0.7373Epoch 00001: loss improved from 0.62190 to 0.58765, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5877 - acc: 0.7342 - val_loss: 0.5851 - val_acc: 0.7350\n",
      "Epoch 3/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5787 - acc: 0.7360Epoch 00002: loss improved from 0.58765 to 0.58036, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5804 - acc: 0.7342 - val_loss: 0.5835 - val_acc: 0.7350\n",
      "Epoch 4/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5819 - acc: 0.7352Epoch 00003: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5832 - acc: 0.7342 - val_loss: 0.5826 - val_acc: 0.7350\n",
      "Epoch 5/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5827 - acc: 0.7352Epoch 00004: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5836 - acc: 0.7342 - val_loss: 0.5819 - val_acc: 0.7350\n",
      "Epoch 6/30\n",
      "1260/1264 [============================>.] - ETA: 0s - loss: 0.5768 - acc: 0.7341Epoch 00005: loss improved from 0.58036 to 0.57657, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5766 - acc: 0.7342 - val_loss: 0.5811 - val_acc: 0.7350\n",
      "Epoch 7/30\n",
      "1260/1264 [============================>.] - ETA: 0s - loss: 0.5827 - acc: 0.7349Epoch 00006: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5831 - acc: 0.7342 - val_loss: 0.5802 - val_acc: 0.7350\n",
      "Epoch 8/30\n",
      "1245/1264 [============================>.] - ETA: 0s - loss: 0.5779 - acc: 0.7349Epoch 00007: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5784 - acc: 0.7342 - val_loss: 0.5792 - val_acc: 0.7350\n",
      "Epoch 9/30\n",
      "1240/1264 [============================>.] - ETA: 0s - loss: 0.5736 - acc: 0.7339Epoch 00008: loss improved from 0.57657 to 0.57266, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5727 - acc: 0.7342 - val_loss: 0.5779 - val_acc: 0.7350\n",
      "Epoch 10/30\n",
      "1240/1264 [============================>.] - ETA: 0s - loss: 0.5675 - acc: 0.7363Epoch 00009: loss improved from 0.57266 to 0.56981, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5698 - acc: 0.7342 - val_loss: 0.5769 - val_acc: 0.7350\n",
      "Epoch 11/30\n",
      "1255/1264 [============================>.] - ETA: 0s - loss: 0.5727 - acc: 0.7355Epoch 00010: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5737 - acc: 0.7342 - val_loss: 0.5767 - val_acc: 0.7350\n",
      "Epoch 12/30\n",
      "1260/1264 [============================>.] - ETA: 0s - loss: 0.5682 - acc: 0.7333Epoch 00011: loss improved from 0.56981 to 0.56735, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5674 - acc: 0.7342 - val_loss: 0.5748 - val_acc: 0.7350\n",
      "Epoch 13/30\n",
      "1255/1264 [============================>.] - ETA: 0s - loss: 0.5684 - acc: 0.7331Epoch 00012: loss improved from 0.56735 to 0.56726, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5673 - acc: 0.7342 - val_loss: 0.5739 - val_acc: 0.7350\n",
      "Epoch 14/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5664 - acc: 0.7344Epoch 00013: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5675 - acc: 0.7342 - val_loss: 0.5726 - val_acc: 0.7350\n",
      "Epoch 15/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5684 - acc: 0.7344Epoch 00014: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5684 - acc: 0.7342 - val_loss: 0.5743 - val_acc: 0.7350\n",
      "Epoch 16/30\n",
      "1240/1264 [============================>.] - ETA: 0s - loss: 0.5638 - acc: 0.7347Epoch 00015: loss improved from 0.56726 to 0.56429, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5643 - acc: 0.7342 - val_loss: 0.5698 - val_acc: 0.7350\n",
      "Epoch 17/30\n",
      "1240/1264 [============================>.] - ETA: 0s - loss: 0.5685 - acc: 0.7315Epoch 00016: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5654 - acc: 0.7342 - val_loss: 0.5691 - val_acc: 0.7350\n",
      "Epoch 18/30\n",
      "1255/1264 [============================>.] - ETA: 0s - loss: 0.5679 - acc: 0.7339Epoch 00017: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5678 - acc: 0.7334 - val_loss: 0.5683 - val_acc: 0.7350\n",
      "Epoch 19/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5593 - acc: 0.7344Epoch 00018: loss improved from 0.56429 to 0.55974, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5597 - acc: 0.7342 - val_loss: 0.5678 - val_acc: 0.7350\n",
      "Epoch 20/30\n",
      "1245/1264 [============================>.] - ETA: 0s - loss: 0.5580 - acc: 0.7357Epoch 00019: loss improved from 0.55974 to 0.55681, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5568 - acc: 0.7366 - val_loss: 0.5664 - val_acc: 0.7350\n",
      "Epoch 21/30\n",
      "1255/1264 [============================>.] - ETA: 0s - loss: 0.5571 - acc: 0.7363Epoch 00020: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5579 - acc: 0.7350 - val_loss: 0.5658 - val_acc: 0.7350\n",
      "Epoch 22/30\n",
      "1255/1264 [============================>.] - ETA: 0s - loss: 0.5591 - acc: 0.7331Epoch 00021: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5596 - acc: 0.7342 - val_loss: 0.5646 - val_acc: 0.7350\n",
      "Epoch 23/30\n",
      "1260/1264 [============================>.] - ETA: 0s - loss: 0.5517 - acc: 0.7365Epoch 00022: loss improved from 0.55681 to 0.55140, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5514 - acc: 0.7366 - val_loss: 0.5665 - val_acc: 0.7350\n",
      "Epoch 24/30\n",
      "1240/1264 [============================>.] - ETA: 0s - loss: 0.5569 - acc: 0.7363Epoch 00023: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5562 - acc: 0.7366 - val_loss: 0.5642 - val_acc: 0.7350\n",
      "Epoch 25/30\n",
      "1260/1264 [============================>.] - ETA: 0s - loss: 0.5519 - acc: 0.7389Epoch 00024: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5521 - acc: 0.7389 - val_loss: 0.5635 - val_acc: 0.7350\n",
      "Epoch 26/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5430 - acc: 0.7392Epoch 00025: loss improved from 0.55140 to 0.54610, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5461 - acc: 0.7366 - val_loss: 0.5628 - val_acc: 0.7350\n",
      "Epoch 27/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5482 - acc: 0.7392Epoch 00026: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5493 - acc: 0.7373 - val_loss: 0.5628 - val_acc: 0.7382\n",
      "Epoch 28/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5535 - acc: 0.7416Epoch 00027: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5528 - acc: 0.7421 - val_loss: 0.5621 - val_acc: 0.7350\n",
      "Epoch 29/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5463 - acc: 0.7392Epoch 00028: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5462 - acc: 0.7389 - val_loss: 0.5618 - val_acc: 0.7350\n",
      "Epoch 30/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5450 - acc: 0.7384Epoch 00029: loss improved from 0.54610 to 0.54395, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5439 - acc: 0.7389 - val_loss: 0.5616 - val_acc: 0.7350\n",
      "288/317 [==========================>...] - ETA: 0sTrain on 1265 samples, validate on 316 samples\n",
      "Epoch 1/30\n",
      "1260/1265 [============================>.] - ETA: 0s - loss: 0.6309 - acc: 0.6690Epoch 00000: loss improved from inf to 0.63007, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 4s - loss: 0.6301 - acc: 0.6696 - val_loss: 0.5887 - val_acc: 0.7342\n",
      "Epoch 2/30\n",
      "1245/1265 [============================>.] - ETA: 0s - loss: 0.5844 - acc: 0.7341Epoch 00001: loss improved from 0.63007 to 0.58445, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5844 - acc: 0.7344 - val_loss: 0.5887 - val_acc: 0.7342\n",
      "Epoch 3/30\n",
      "1250/1265 [============================>.] - ETA: 0s - loss: 0.5805 - acc: 0.7352Epoch 00002: loss improved from 0.58445 to 0.58164, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5816 - acc: 0.7344 - val_loss: 0.5876 - val_acc: 0.7342\n",
      "Epoch 4/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5785 - acc: 0.7347Epoch 00003: loss improved from 0.58164 to 0.57857, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5786 - acc: 0.7344 - val_loss: 0.5874 - val_acc: 0.7342\n",
      "Epoch 5/30\n",
      "1250/1265 [============================>.] - ETA: 0s - loss: 0.5866 - acc: 0.7352Epoch 00004: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5877 - acc: 0.7344 - val_loss: 0.5864 - val_acc: 0.7342\n",
      "Epoch 6/30\n",
      "1240/1265 [============================>.] - ETA: 0s - loss: 0.5836 - acc: 0.7331Epoch 00005: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5818 - acc: 0.7344 - val_loss: 0.5864 - val_acc: 0.7342\n",
      "Epoch 7/30\n",
      "1260/1265 [============================>.] - ETA: 0s - loss: 0.5728 - acc: 0.7333Epoch 00006: loss improved from 0.57857 to 0.57175, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5717 - acc: 0.7344 - val_loss: 0.5885 - val_acc: 0.7342\n",
      "Epoch 8/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5812 - acc: 0.7347Epoch 00007: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5819 - acc: 0.7344 - val_loss: 0.5862 - val_acc: 0.7342\n",
      "Epoch 9/30\n",
      "1260/1265 [============================>.] - ETA: 0s - loss: 0.5714 - acc: 0.7341Epoch 00008: loss improved from 0.57175 to 0.57089, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5709 - acc: 0.7344 - val_loss: 0.5868 - val_acc: 0.7342\n",
      "Epoch 10/30\n",
      "1250/1265 [============================>.] - ETA: 0s - loss: 0.5692 - acc: 0.7360Epoch 00009: loss improved from 0.57089 to 0.57034, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5703 - acc: 0.7344 - val_loss: 0.5860 - val_acc: 0.7342\n",
      "Epoch 11/30\n",
      "1245/1265 [============================>.] - ETA: 0s - loss: 0.5664 - acc: 0.7373Epoch 00010: loss improved from 0.57034 to 0.56921, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5692 - acc: 0.7344 - val_loss: 0.5861 - val_acc: 0.7342\n",
      "Epoch 12/30\n",
      "1250/1265 [============================>.] - ETA: 0s - loss: 0.5759 - acc: 0.7328Epoch 00011: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5750 - acc: 0.7344 - val_loss: 0.5864 - val_acc: 0.7342\n",
      "Epoch 13/30\n",
      "1260/1265 [============================>.] - ETA: 0s - loss: 0.5723 - acc: 0.7333Epoch 00012: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5718 - acc: 0.7336 - val_loss: 0.5869 - val_acc: 0.7342\n",
      "Epoch 14/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5647 - acc: 0.7363Epoch 00013: loss improved from 0.56921 to 0.56598, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5660 - acc: 0.7360 - val_loss: 0.5868 - val_acc: 0.7342\n",
      "Epoch 15/30\n",
      "1250/1265 [============================>.] - ETA: 0s - loss: 0.5643 - acc: 0.7360Epoch 00014: loss improved from 0.56598 to 0.56539, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5654 - acc: 0.7352 - val_loss: 0.5872 - val_acc: 0.7342\n",
      "Epoch 16/30\n",
      "1250/1265 [============================>.] - ETA: 0s - loss: 0.5664 - acc: 0.7352Epoch 00015: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5656 - acc: 0.7360 - val_loss: 0.5872 - val_acc: 0.7342\n",
      "Epoch 17/30\n",
      "1260/1265 [============================>.] - ETA: 0s - loss: 0.5687 - acc: 0.7310Epoch 00016: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5677 - acc: 0.7320 - val_loss: 0.5880 - val_acc: 0.7342\n",
      "Epoch 18/30\n",
      "1260/1265 [============================>.] - ETA: 0s - loss: 0.5676 - acc: 0.7333Epoch 00017: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5667 - acc: 0.7344 - val_loss: 0.5876 - val_acc: 0.7342\n",
      "Epoch 19/30\n",
      "1245/1265 [============================>.] - ETA: 0s - loss: 0.5613 - acc: 0.7341Epoch 00018: loss improved from 0.56539 to 0.56173, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5617 - acc: 0.7344 - val_loss: 0.5891 - val_acc: 0.7342\n",
      "Epoch 20/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5553 - acc: 0.7355Epoch 00019: loss improved from 0.56173 to 0.55564, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5556 - acc: 0.7352 - val_loss: 0.5942 - val_acc: 0.7342\n",
      "Epoch 21/30\n",
      "1240/1265 [============================>.] - ETA: 0s - loss: 0.5545 - acc: 0.7355Epoch 00020: loss improved from 0.55564 to 0.55298, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5530 - acc: 0.7360 - val_loss: 0.5914 - val_acc: 0.7342\n",
      "Epoch 22/30\n",
      "1250/1265 [============================>.] - ETA: 0s - loss: 0.5638 - acc: 0.7368Epoch 00021: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5638 - acc: 0.7368 - val_loss: 0.5898 - val_acc: 0.7342\n",
      "Epoch 23/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5536 - acc: 0.7339Epoch 00022: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5541 - acc: 0.7336 - val_loss: 0.5900 - val_acc: 0.7342\n",
      "Epoch 24/30\n",
      "1240/1265 [============================>.] - ETA: 0s - loss: 0.5519 - acc: 0.7331Epoch 00023: loss improved from 0.55298 to 0.55171, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5517 - acc: 0.7336 - val_loss: 0.5895 - val_acc: 0.7278\n",
      "Epoch 25/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5523 - acc: 0.7347Epoch 00024: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5538 - acc: 0.7336 - val_loss: 0.5910 - val_acc: 0.7310\n",
      "Epoch 26/30\n",
      "1245/1265 [============================>.] - ETA: 0s - loss: 0.5520 - acc: 0.7406Epoch 00025: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5535 - acc: 0.7383 - val_loss: 0.5922 - val_acc: 0.7310\n",
      "Epoch 27/30\n",
      "1260/1265 [============================>.] - ETA: 0s - loss: 0.5521 - acc: 0.7333Epoch 00026: loss improved from 0.55171 to 0.55098, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5510 - acc: 0.7344 - val_loss: 0.5903 - val_acc: 0.7310\n",
      "Epoch 28/30\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1240/1265 [============================>.] - ETA: 0s - loss: 0.5480 - acc: 0.7411Epoch 00027: loss improved from 0.55098 to 0.54825, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5483 - acc: 0.7407 - val_loss: 0.5905 - val_acc: 0.7310\n",
      "Epoch 29/30\n",
      "1260/1265 [============================>.] - ETA: 0s - loss: 0.5455 - acc: 0.7413Epoch 00028: loss improved from 0.54825 to 0.54510, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5451 - acc: 0.7415 - val_loss: 0.5899 - val_acc: 0.7310\n",
      "Epoch 30/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5385 - acc: 0.7450Epoch 00029: loss improved from 0.54510 to 0.53728, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5373 - acc: 0.7455 - val_loss: 0.5922 - val_acc: 0.7310\n",
      "288/316 [==========================>...] - ETA: 0sTrain on 1265 samples, validate on 316 samples\n",
      "Epoch 1/30\n",
      "1250/1265 [============================>.] - ETA: 0s - loss: 0.6049 - acc: 0.7152Epoch 00000: loss improved from inf to 0.60344, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 4s - loss: 0.6034 - acc: 0.7170 - val_loss: 0.5738 - val_acc: 0.7342\n",
      "Epoch 2/30\n",
      "1250/1265 [============================>.] - ETA: 0s - loss: 0.5915 - acc: 0.7360Epoch 00001: loss improved from 0.60344 to 0.59255, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5925 - acc: 0.7344 - val_loss: 0.5724 - val_acc: 0.7342\n",
      "Epoch 3/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5835 - acc: 0.7331Epoch 00002: loss improved from 0.59255 to 0.58174, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5817 - acc: 0.7344 - val_loss: 0.5708 - val_acc: 0.7342\n",
      "Epoch 4/30\n",
      "1240/1265 [============================>.] - ETA: 0s - loss: 0.5852 - acc: 0.7355Epoch 00003: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5856 - acc: 0.7344 - val_loss: 0.5706 - val_acc: 0.7342\n",
      "Epoch 5/30\n",
      "1250/1265 [============================>.] - ETA: 0s - loss: 0.5872 - acc: 0.7344Epoch 00004: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5871 - acc: 0.7344 - val_loss: 0.5692 - val_acc: 0.7342\n",
      "Epoch 6/30\n",
      "1245/1265 [============================>.] - ETA: 0s - loss: 0.5800 - acc: 0.7390Epoch 00005: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5854 - acc: 0.7344 - val_loss: 0.5674 - val_acc: 0.7342\n",
      "Epoch 7/30\n",
      "1240/1265 [============================>.] - ETA: 0s - loss: 0.5769 - acc: 0.7323Epoch 00006: loss improved from 0.58174 to 0.57505, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5750 - acc: 0.7344 - val_loss: 0.5661 - val_acc: 0.7342\n",
      "Epoch 8/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5786 - acc: 0.7363Epoch 00007: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5797 - acc: 0.7344 - val_loss: 0.5649 - val_acc: 0.7342\n",
      "Epoch 9/30\n",
      "1245/1265 [============================>.] - ETA: 0s - loss: 0.5786 - acc: 0.7341Epoch 00008: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5782 - acc: 0.7344 - val_loss: 0.5635 - val_acc: 0.7342\n",
      "Epoch 10/30\n",
      "1260/1265 [============================>.] - ETA: 0s - loss: 0.5769 - acc: 0.7357Epoch 00009: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5785 - acc: 0.7344 - val_loss: 0.5625 - val_acc: 0.7342\n",
      "Epoch 11/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5685 - acc: 0.7378Epoch 00010: loss improved from 0.57505 to 0.57147, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5715 - acc: 0.7344 - val_loss: 0.5606 - val_acc: 0.7342\n",
      "Epoch 12/30\n",
      "1245/1265 [============================>.] - ETA: 0s - loss: 0.5711 - acc: 0.7333Epoch 00011: loss improved from 0.57147 to 0.57038, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5704 - acc: 0.7336 - val_loss: 0.5599 - val_acc: 0.7342\n",
      "Epoch 13/30\n",
      "1250/1265 [============================>.] - ETA: 0s - loss: 0.5651 - acc: 0.7384Epoch 00012: loss improved from 0.57038 to 0.56672, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5667 - acc: 0.7368 - val_loss: 0.5590 - val_acc: 0.7342\n",
      "Epoch 14/30\n",
      "1260/1265 [============================>.] - ETA: 0s - loss: 0.5724 - acc: 0.7357Epoch 00013: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5735 - acc: 0.7344 - val_loss: 0.5594 - val_acc: 0.7342\n",
      "Epoch 15/30\n",
      "1240/1265 [============================>.] - ETA: 0s - loss: 0.5652 - acc: 0.7363Epoch 00014: loss improved from 0.56672 to 0.56490, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5649 - acc: 0.7344 - val_loss: 0.5581 - val_acc: 0.7342\n",
      "Epoch 16/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5665 - acc: 0.7331Epoch 00015: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5669 - acc: 0.7336 - val_loss: 0.5593 - val_acc: 0.7342\n",
      "Epoch 17/30\n",
      "1250/1265 [============================>.] - ETA: 0s - loss: 0.5607 - acc: 0.7328Epoch 00016: loss improved from 0.56490 to 0.55972, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5597 - acc: 0.7336 - val_loss: 0.5571 - val_acc: 0.7342\n",
      "Epoch 18/30\n",
      "1260/1265 [============================>.] - ETA: 0s - loss: 0.5654 - acc: 0.7349Epoch 00017: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5658 - acc: 0.7344 - val_loss: 0.5569 - val_acc: 0.7342\n",
      "Epoch 19/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5605 - acc: 0.7331Epoch 00018: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5603 - acc: 0.7336 - val_loss: 0.5575 - val_acc: 0.7342\n",
      "Epoch 20/30\n",
      "1250/1265 [============================>.] - ETA: 0s - loss: 0.5637 - acc: 0.7320Epoch 00019: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5619 - acc: 0.7328 - val_loss: 0.5576 - val_acc: 0.7342\n",
      "Epoch 21/30\n",
      "1250/1265 [============================>.] - ETA: 0s - loss: 0.5652 - acc: 0.7376Epoch 00020: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5654 - acc: 0.7375 - val_loss: 0.5575 - val_acc: 0.7342\n",
      "Epoch 22/30\n",
      "1250/1265 [============================>.] - ETA: 0s - loss: 0.5593 - acc: 0.7360Epoch 00021: loss improved from 0.55972 to 0.55852, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5585 - acc: 0.7360 - val_loss: 0.5552 - val_acc: 0.7342\n",
      "Epoch 23/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5587 - acc: 0.7339Epoch 00022: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5604 - acc: 0.7328 - val_loss: 0.5545 - val_acc: 0.7342\n",
      "Epoch 24/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5552 - acc: 0.7355Epoch 00023: loss improved from 0.55852 to 0.55422, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5542 - acc: 0.7368 - val_loss: 0.5553 - val_acc: 0.7342\n",
      "Epoch 25/30\n",
      "1245/1265 [============================>.] - ETA: 0s - loss: 0.5494 - acc: 0.7357Epoch 00024: loss improved from 0.55422 to 0.55111, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5511 - acc: 0.7336 - val_loss: 0.5547 - val_acc: 0.7342\n",
      "Epoch 26/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5553 - acc: 0.7331Epoch 00025: loss did not improve\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1265/1265 [==============================] - 3s - loss: 0.5538 - acc: 0.7344 - val_loss: 0.5542 - val_acc: 0.7342\n",
      "Epoch 27/30\n",
      "1245/1265 [============================>.] - ETA: 0s - loss: 0.5492 - acc: 0.7373Epoch 00026: loss improved from 0.55111 to 0.54833, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5483 - acc: 0.7391 - val_loss: 0.5568 - val_acc: 0.7310\n",
      "Epoch 28/30\n",
      "1260/1265 [============================>.] - ETA: 0s - loss: 0.5473 - acc: 0.7413Epoch 00027: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5491 - acc: 0.7399 - val_loss: 0.5520 - val_acc: 0.7342\n",
      "Epoch 29/30\n",
      "1240/1265 [============================>.] - ETA: 0s - loss: 0.5423 - acc: 0.7411Epoch 00028: loss improved from 0.54833 to 0.54321, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5432 - acc: 0.7399 - val_loss: 0.5516 - val_acc: 0.7310\n",
      "Epoch 30/30\n",
      "1260/1265 [============================>.] - ETA: 0s - loss: 0.5465 - acc: 0.7405Epoch 00029: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5475 - acc: 0.7407 - val_loss: 0.5525 - val_acc: 0.7310\n",
      "288/316 [==========================>...] - ETA: 0sTrain on 1265 samples, validate on 316 samples\n",
      "Epoch 1/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.6318 - acc: 0.6789Epoch 00000: loss improved from inf to 0.63064, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 4s - loss: 0.6306 - acc: 0.6798 - val_loss: 0.5758 - val_acc: 0.7342\n",
      "Epoch 2/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5980 - acc: 0.7347Epoch 00001: loss improved from 0.63064 to 0.59820, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5982 - acc: 0.7344 - val_loss: 0.5740 - val_acc: 0.7342\n",
      "Epoch 3/30\n",
      "1260/1265 [============================>.] - ETA: 0s - loss: 0.5874 - acc: 0.7333Epoch 00002: loss improved from 0.59820 to 0.58739, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5874 - acc: 0.7336 - val_loss: 0.5714 - val_acc: 0.7342\n",
      "Epoch 4/30\n",
      "1245/1265 [============================>.] - ETA: 0s - loss: 0.5849 - acc: 0.7333Epoch 00003: loss improved from 0.58739 to 0.58353, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5835 - acc: 0.7344 - val_loss: 0.5715 - val_acc: 0.7342\n",
      "Epoch 5/30\n",
      "1240/1265 [============================>.] - ETA: 0s - loss: 0.5891 - acc: 0.7347Epoch 00004: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5884 - acc: 0.7344 - val_loss: 0.5726 - val_acc: 0.7342\n",
      "Epoch 6/30\n",
      "1240/1265 [============================>.] - ETA: 0s - loss: 0.5890 - acc: 0.7363Epoch 00005: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5915 - acc: 0.7344 - val_loss: 0.5682 - val_acc: 0.7342\n",
      "Epoch 7/30\n",
      "1250/1265 [============================>.] - ETA: 0s - loss: 0.5795 - acc: 0.7368Epoch 00006: loss improved from 0.58353 to 0.58086, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5809 - acc: 0.7344 - val_loss: 0.5689 - val_acc: 0.7342\n",
      "Epoch 8/30\n",
      "1245/1265 [============================>.] - ETA: 0s - loss: 0.5842 - acc: 0.7349Epoch 00007: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5849 - acc: 0.7344 - val_loss: 0.5710 - val_acc: 0.7342\n",
      "Epoch 9/30\n",
      "1240/1265 [============================>.] - ETA: 0s - loss: 0.5811 - acc: 0.7347Epoch 00008: loss improved from 0.58086 to 0.58073, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5807 - acc: 0.7344 - val_loss: 0.5658 - val_acc: 0.7342\n",
      "Epoch 10/30\n",
      "1245/1265 [============================>.] - ETA: 0s - loss: 0.5811 - acc: 0.7349Epoch 00009: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5815 - acc: 0.7344 - val_loss: 0.5662 - val_acc: 0.7342\n",
      "Epoch 11/30\n",
      "1260/1265 [============================>.] - ETA: 0s - loss: 0.5759 - acc: 0.7349Epoch 00010: loss improved from 0.58073 to 0.57673, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5767 - acc: 0.7344 - val_loss: 0.5658 - val_acc: 0.7342\n",
      "Epoch 12/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5693 - acc: 0.7363Epoch 00011: loss improved from 0.57673 to 0.57070, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5707 - acc: 0.7344 - val_loss: 0.5652 - val_acc: 0.7342\n",
      "Epoch 13/30\n",
      "1245/1265 [============================>.] - ETA: 0s - loss: 0.5811 - acc: 0.7325Epoch 00012: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5791 - acc: 0.7344 - val_loss: 0.5653 - val_acc: 0.7342\n",
      "Epoch 14/30\n",
      "1245/1265 [============================>.] - ETA: 0s - loss: 0.5763 - acc: 0.7333Epoch 00013: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5748 - acc: 0.7344 - val_loss: 0.5666 - val_acc: 0.7342\n",
      "Epoch 15/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5753 - acc: 0.7355Epoch 00014: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5755 - acc: 0.7344 - val_loss: 0.5643 - val_acc: 0.7342\n",
      "Epoch 16/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5700 - acc: 0.7363Epoch 00015: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5711 - acc: 0.7352 - val_loss: 0.5640 - val_acc: 0.7342\n",
      "Epoch 17/30\n",
      "1245/1265 [============================>.] - ETA: 0s - loss: 0.5673 - acc: 0.7309Epoch 00016: loss improved from 0.57070 to 0.56527, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5653 - acc: 0.7336 - val_loss: 0.5636 - val_acc: 0.7342\n",
      "Epoch 18/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5700 - acc: 0.7339Epoch 00017: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5693 - acc: 0.7352 - val_loss: 0.5634 - val_acc: 0.7342\n",
      "Epoch 19/30\n",
      "1240/1265 [============================>.] - ETA: 0s - loss: 0.5612 - acc: 0.7339Epoch 00018: loss improved from 0.56527 to 0.56456, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5646 - acc: 0.7336 - val_loss: 0.5649 - val_acc: 0.7342\n",
      "Epoch 20/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5643 - acc: 0.7355Epoch 00019: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5656 - acc: 0.7344 - val_loss: 0.5612 - val_acc: 0.7342\n",
      "Epoch 21/30\n",
      "1250/1265 [============================>.] - ETA: 0s - loss: 0.5636 - acc: 0.7352Epoch 00020: loss improved from 0.56456 to 0.56415, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5641 - acc: 0.7352 - val_loss: 0.5621 - val_acc: 0.7342\n",
      "Epoch 22/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5676 - acc: 0.7339Epoch 00021: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5666 - acc: 0.7352 - val_loss: 0.5625 - val_acc: 0.7342\n",
      "Epoch 23/30\n",
      "1245/1265 [============================>.] - ETA: 0s - loss: 0.5633 - acc: 0.7349Epoch 00022: loss improved from 0.56415 to 0.56108, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5611 - acc: 0.7383 - val_loss: 0.5603 - val_acc: 0.7342\n",
      "Epoch 24/30\n",
      "1240/1265 [============================>.] - ETA: 0s - loss: 0.5619 - acc: 0.7323Epoch 00023: loss improved from 0.56108 to 0.55812, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5581 - acc: 0.7368 - val_loss: 0.5618 - val_acc: 0.7342\n",
      "Epoch 25/30\n",
      "1240/1265 [============================>.] - ETA: 0s - loss: 0.5624 - acc: 0.7339Epoch 00024: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5602 - acc: 0.7352 - val_loss: 0.5604 - val_acc: 0.7342\n",
      "Epoch 26/30\n",
      "1240/1265 [============================>.] - ETA: 0s - loss: 0.5545 - acc: 0.7371Epoch 00025: loss improved from 0.55812 to 0.55266, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5527 - acc: 0.7368 - val_loss: 0.5600 - val_acc: 0.7373\n",
      "Epoch 27/30\n",
      "1240/1265 [============================>.] - ETA: 0s - loss: 0.5595 - acc: 0.7387Epoch 00026: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5610 - acc: 0.7375 - val_loss: 0.5613 - val_acc: 0.7405\n",
      "Epoch 28/30\n",
      "1250/1265 [============================>.] - ETA: 0s - loss: 0.5624 - acc: 0.7368Epoch 00027: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5616 - acc: 0.7368 - val_loss: 0.5590 - val_acc: 0.7342\n",
      "Epoch 29/30\n",
      "1260/1265 [============================>.] - ETA: 0s - loss: 0.5481 - acc: 0.7373Epoch 00028: loss improved from 0.55266 to 0.54844, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5484 - acc: 0.7375 - val_loss: 0.5584 - val_acc: 0.7342\n",
      "Epoch 30/30\n",
      "1245/1265 [============================>.] - ETA: 0s - loss: 0.5597 - acc: 0.7373Epoch 00029: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5611 - acc: 0.7360 - val_loss: 0.5580 - val_acc: 0.7342\n",
      "288/316 [==========================>...] - ETA: 0sTrain on 1265 samples, validate on 316 samples\n",
      "Epoch 1/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.6093 - acc: 0.7100Epoch 00000: loss improved from inf to 0.60958, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 5s - loss: 0.6096 - acc: 0.7099 - val_loss: 0.5759 - val_acc: 0.7342\n",
      "Epoch 2/30\n",
      "1245/1265 [============================>.] - ETA: 0s - loss: 0.5977 - acc: 0.7253Epoch 00001: loss improved from 0.60958 to 0.59480, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5948 - acc: 0.7289 - val_loss: 0.5777 - val_acc: 0.7342\n",
      "Epoch 3/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5948 - acc: 0.7355Epoch 00002: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5956 - acc: 0.7344 - val_loss: 0.5764 - val_acc: 0.7342\n",
      "Epoch 4/30\n",
      "1245/1265 [============================>.] - ETA: 0s - loss: 0.5863 - acc: 0.7325Epoch 00003: loss improved from 0.59480 to 0.58618, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5862 - acc: 0.7320 - val_loss: 0.5761 - val_acc: 0.7342\n",
      "Epoch 5/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5863 - acc: 0.7347Epoch 00004: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5867 - acc: 0.7344 - val_loss: 0.5741 - val_acc: 0.7342\n",
      "Epoch 6/30\n",
      "1245/1265 [============================>.] - ETA: 0s - loss: 0.5805 - acc: 0.7333Epoch 00005: loss improved from 0.58618 to 0.58049, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5805 - acc: 0.7328 - val_loss: 0.5735 - val_acc: 0.7342\n",
      "Epoch 7/30\n",
      "1240/1265 [============================>.] - ETA: 0s - loss: 0.5743 - acc: 0.7387Epoch 00006: loss improved from 0.58049 to 0.57744, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5774 - acc: 0.7352 - val_loss: 0.5731 - val_acc: 0.7342\n",
      "Epoch 8/30\n",
      "1240/1265 [============================>.] - ETA: 0s - loss: 0.5759 - acc: 0.7347Epoch 00007: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5784 - acc: 0.7328 - val_loss: 0.5711 - val_acc: 0.7342\n",
      "Epoch 9/30\n",
      "1260/1265 [============================>.] - ETA: 0s - loss: 0.5812 - acc: 0.7341Epoch 00008: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5804 - acc: 0.7352 - val_loss: 0.5713 - val_acc: 0.7342\n",
      "Epoch 10/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5717 - acc: 0.7331Epoch 00009: loss improved from 0.57744 to 0.57166, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5717 - acc: 0.7336 - val_loss: 0.5712 - val_acc: 0.7342\n",
      "Epoch 11/30\n",
      "1240/1265 [============================>.] - ETA: 0s - loss: 0.5706 - acc: 0.7371Epoch 00010: loss improved from 0.57166 to 0.56917, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5692 - acc: 0.7375 - val_loss: 0.5698 - val_acc: 0.7342\n",
      "Epoch 12/30\n",
      "1245/1265 [============================>.] - ETA: 0s - loss: 0.5723 - acc: 0.7373Epoch 00011: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5719 - acc: 0.7375 - val_loss: 0.5689 - val_acc: 0.7342\n",
      "Epoch 13/30\n",
      "1240/1265 [============================>.] - ETA: 0s - loss: 0.5723 - acc: 0.7298Epoch 00012: loss improved from 0.56917 to 0.56910, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5691 - acc: 0.7336 - val_loss: 0.5699 - val_acc: 0.7342\n",
      "Epoch 14/30\n",
      "1245/1265 [============================>.] - ETA: 0s - loss: 0.5712 - acc: 0.7325Epoch 00013: loss improved from 0.56910 to 0.56878, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5688 - acc: 0.7344 - val_loss: 0.5698 - val_acc: 0.7342\n",
      "Epoch 15/30\n",
      "1245/1265 [============================>.] - ETA: 0s - loss: 0.5620 - acc: 0.7341Epoch 00014: loss improved from 0.56878 to 0.56193, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5619 - acc: 0.7336 - val_loss: 0.5680 - val_acc: 0.7342\n",
      "Epoch 16/30\n",
      "1260/1265 [============================>.] - ETA: 0s - loss: 0.5585 - acc: 0.7325Epoch 00015: loss improved from 0.56193 to 0.55896, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5590 - acc: 0.7328 - val_loss: 0.5669 - val_acc: 0.7342\n",
      "Epoch 17/30\n",
      "1245/1265 [============================>.] - ETA: 0s - loss: 0.5605 - acc: 0.7390Epoch 00016: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5638 - acc: 0.7383 - val_loss: 0.5677 - val_acc: 0.7342\n",
      "Epoch 18/30\n",
      "1260/1265 [============================>.] - ETA: 0s - loss: 0.5607 - acc: 0.7365Epoch 00017: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5596 - acc: 0.7375 - val_loss: 0.5661 - val_acc: 0.7342\n",
      "Epoch 19/30\n",
      "1260/1265 [============================>.] - ETA: 0s - loss: 0.5550 - acc: 0.7341Epoch 00018: loss improved from 0.55896 to 0.55462, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5546 - acc: 0.7344 - val_loss: 0.5659 - val_acc: 0.7342\n",
      "Epoch 20/30\n",
      "1250/1265 [============================>.] - ETA: 0s - loss: 0.5583 - acc: 0.7376Epoch 00019: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5593 - acc: 0.7375 - val_loss: 0.5659 - val_acc: 0.7342\n",
      "Epoch 21/30\n",
      "1240/1265 [============================>.] - ETA: 0s - loss: 0.5562 - acc: 0.7363Epoch 00020: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5568 - acc: 0.7360 - val_loss: 0.5663 - val_acc: 0.7342\n",
      "Epoch 22/30\n",
      "1260/1265 [============================>.] - ETA: 0s - loss: 0.5611 - acc: 0.7357Epoch 00021: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5610 - acc: 0.7360 - val_loss: 0.5662 - val_acc: 0.7405\n",
      "Epoch 23/30\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5545 - acc: 0.7418Epoch 00022: loss improved from 0.55462 to 0.55389, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5539 - acc: 0.7423 - val_loss: 0.5659 - val_acc: 0.7437\n",
      "Epoch 24/30\n",
      "1240/1265 [============================>.] - ETA: 0s - loss: 0.5447 - acc: 0.7347Epoch 00023: loss improved from 0.55389 to 0.54615, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5462 - acc: 0.7352 - val_loss: 0.5658 - val_acc: 0.7437\n",
      "Epoch 25/30\n",
      "1250/1265 [============================>.] - ETA: 0s - loss: 0.5380 - acc: 0.7416Epoch 00024: loss improved from 0.54615 to 0.54068, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5407 - acc: 0.7383 - val_loss: 0.5661 - val_acc: 0.7437\n",
      "Epoch 26/30\n",
      "1240/1265 [============================>.] - ETA: 0s - loss: 0.5400 - acc: 0.7452Epoch 00025: loss improved from 0.54068 to 0.53871, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5387 - acc: 0.7455 - val_loss: 0.5654 - val_acc: 0.7437\n",
      "Epoch 27/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5390 - acc: 0.7474Epoch 00026: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5395 - acc: 0.7462 - val_loss: 0.5670 - val_acc: 0.7468\n",
      "Epoch 28/30\n",
      "1240/1265 [============================>.] - ETA: 0s - loss: 0.5486 - acc: 0.7395Epoch 00027: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5499 - acc: 0.7368 - val_loss: 0.5653 - val_acc: 0.7468\n",
      "Epoch 29/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5343 - acc: 0.7498Epoch 00028: loss improved from 0.53871 to 0.53422, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1265/1265 [==============================] - 3s - loss: 0.5342 - acc: 0.7502 - val_loss: 0.5658 - val_acc: 0.7405\n",
      "Epoch 30/30\n",
      "1255/1265 [============================>.] - ETA: 0s - loss: 0.5438 - acc: 0.7402Epoch 00029: loss did not improve\n",
      "1265/1265 [==============================] - 3s - loss: 0.5430 - acc: 0.7415 - val_loss: 0.5655 - val_acc: 0.7468\n",
      "256/316 [=======================>......] - ETA: 0sTrain on 1264 samples, validate on 317 samples\n",
      "Epoch 1/30\n",
      "1245/1264 [============================>.] - ETA: 0s - loss: 0.5953 - acc: 0.7261Epoch 00000: loss improved from inf to 0.59570, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 4s - loss: 0.5957 - acc: 0.7263 - val_loss: 0.5879 - val_acc: 0.7445\n",
      "Epoch 2/30\n",
      "1255/1264 [============================>.] - ETA: 0s - loss: 0.5929 - acc: 0.7442Epoch 00001: loss improved from 0.59570 to 0.59167, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5917 - acc: 0.7453 - val_loss: 0.5867 - val_acc: 0.7445\n",
      "Epoch 3/30\n",
      "1240/1264 [============================>.] - ETA: 0s - loss: 0.5864 - acc: 0.7452Epoch 00002: loss improved from 0.59167 to 0.58687, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5869 - acc: 0.7453 - val_loss: 0.5850 - val_acc: 0.7445\n",
      "Epoch 4/30\n",
      "1260/1264 [============================>.] - ETA: 0s - loss: 0.5865 - acc: 0.7452Epoch 00003: loss improved from 0.58687 to 0.58641, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5864 - acc: 0.7453 - val_loss: 0.5856 - val_acc: 0.7445\n",
      "Epoch 5/30\n",
      "1255/1264 [============================>.] - ETA: 0s - loss: 0.5745 - acc: 0.7458Epoch 00004: loss improved from 0.58641 to 0.57454, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5745 - acc: 0.7453 - val_loss: 0.5847 - val_acc: 0.7445\n",
      "Epoch 6/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5819 - acc: 0.7448Epoch 00005: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5816 - acc: 0.7453 - val_loss: 0.5846 - val_acc: 0.7445\n",
      "Epoch 7/30\n",
      "1260/1264 [============================>.] - ETA: 0s - loss: 0.5815 - acc: 0.7452Epoch 00006: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5813 - acc: 0.7453 - val_loss: 0.5850 - val_acc: 0.7445\n",
      "Epoch 8/30\n",
      "1260/1264 [============================>.] - ETA: 0s - loss: 0.5711 - acc: 0.7444Epoch 00007: loss improved from 0.57454 to 0.57037, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5704 - acc: 0.7453 - val_loss: 0.5845 - val_acc: 0.7445\n",
      "Epoch 9/30\n",
      "1260/1264 [============================>.] - ETA: 0s - loss: 0.5764 - acc: 0.7468Epoch 00008: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5774 - acc: 0.7453 - val_loss: 0.5886 - val_acc: 0.7445\n",
      "Epoch 10/30\n",
      "1260/1264 [============================>.] - ETA: 0s - loss: 0.5767 - acc: 0.7444Epoch 00009: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5758 - acc: 0.7453 - val_loss: 0.5847 - val_acc: 0.7445\n",
      "Epoch 11/30\n",
      "1255/1264 [============================>.] - ETA: 0s - loss: 0.5665 - acc: 0.7466Epoch 00010: loss improved from 0.57037 to 0.56802, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5680 - acc: 0.7453 - val_loss: 0.5850 - val_acc: 0.7445\n",
      "Epoch 12/30\n",
      "1260/1264 [============================>.] - ETA: 0s - loss: 0.5731 - acc: 0.7444Epoch 00011: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5722 - acc: 0.7453 - val_loss: 0.5848 - val_acc: 0.7445\n",
      "Epoch 13/30\n",
      "1255/1264 [============================>.] - ETA: 0s - loss: 0.5796 - acc: 0.7450Epoch 00012: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5799 - acc: 0.7453 - val_loss: 0.5836 - val_acc: 0.7445\n",
      "Epoch 14/30\n",
      "1245/1264 [============================>.] - ETA: 0s - loss: 0.5664 - acc: 0.7494Epoch 00013: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5693 - acc: 0.7453 - val_loss: 0.5839 - val_acc: 0.7445\n",
      "Epoch 15/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5666 - acc: 0.7440Epoch 00014: loss improved from 0.56802 to 0.56530, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5653 - acc: 0.7453 - val_loss: 0.5837 - val_acc: 0.7445\n",
      "Epoch 16/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5699 - acc: 0.7440Epoch 00015: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5688 - acc: 0.7453 - val_loss: 0.5827 - val_acc: 0.7445\n",
      "Epoch 17/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5740 - acc: 0.7456Epoch 00016: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5739 - acc: 0.7453 - val_loss: 0.5820 - val_acc: 0.7445\n",
      "Epoch 18/30\n",
      "1260/1264 [============================>.] - ETA: 0s - loss: 0.5687 - acc: 0.7452Epoch 00017: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5687 - acc: 0.7453 - val_loss: 0.5823 - val_acc: 0.7445\n",
      "Epoch 19/30\n",
      "1260/1264 [============================>.] - ETA: 0s - loss: 0.5658 - acc: 0.7460Epoch 00018: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5672 - acc: 0.7453 - val_loss: 0.5822 - val_acc: 0.7445\n",
      "Epoch 20/30\n",
      "1255/1264 [============================>.] - ETA: 0s - loss: 0.5630 - acc: 0.7450Epoch 00019: loss improved from 0.56530 to 0.56323, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5632 - acc: 0.7453 - val_loss: 0.5820 - val_acc: 0.7445\n",
      "Epoch 21/30\n",
      "1240/1264 [============================>.] - ETA: 0s - loss: 0.5594 - acc: 0.7435Epoch 00020: loss improved from 0.56323 to 0.55793, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1264/1264 [==============================] - 3s - loss: 0.5579 - acc: 0.7453 - val_loss: 0.5814 - val_acc: 0.7445\n",
      "Epoch 22/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5609 - acc: 0.7456Epoch 00021: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5612 - acc: 0.7453 - val_loss: 0.5816 - val_acc: 0.7445\n",
      "Epoch 23/30\n",
      "1245/1264 [============================>.] - ETA: 0s - loss: 0.5580 - acc: 0.7446Epoch 00022: loss improved from 0.55793 to 0.55702, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5570 - acc: 0.7453 - val_loss: 0.5807 - val_acc: 0.7445\n",
      "Epoch 24/30\n",
      "1255/1264 [============================>.] - ETA: 0s - loss: 0.5634 - acc: 0.7450Epoch 00023: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5632 - acc: 0.7453 - val_loss: 0.5802 - val_acc: 0.7445\n",
      "Epoch 25/30\n",
      "1245/1264 [============================>.] - ETA: 0s - loss: 0.5586 - acc: 0.7454Epoch 00024: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5582 - acc: 0.7460 - val_loss: 0.5806 - val_acc: 0.7445\n",
      "Epoch 26/30\n",
      "1255/1264 [============================>.] - ETA: 0s - loss: 0.5587 - acc: 0.7442Epoch 00025: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5577 - acc: 0.7453 - val_loss: 0.5789 - val_acc: 0.7445\n",
      "Epoch 27/30\n",
      "1245/1264 [============================>.] - ETA: 0s - loss: 0.5545 - acc: 0.7430Epoch 00026: loss improved from 0.55702 to 0.55358, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5536 - acc: 0.7445 - val_loss: 0.5790 - val_acc: 0.7445\n",
      "Epoch 28/30\n",
      "1255/1264 [============================>.] - ETA: 0s - loss: 0.5525 - acc: 0.7442Epoch 00027: loss improved from 0.55358 to 0.55132, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5513 - acc: 0.7453 - val_loss: 0.5785 - val_acc: 0.7445\n",
      "Epoch 29/30\n",
      "1245/1264 [============================>.] - ETA: 0s - loss: 0.5467 - acc: 0.7486Epoch 00028: loss improved from 0.55132 to 0.55091, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5509 - acc: 0.7453 - val_loss: 0.5792 - val_acc: 0.7445\n",
      "Epoch 30/30\n",
      "1260/1264 [============================>.] - ETA: 0s - loss: 0.5515 - acc: 0.7444Epoch 00029: loss improved from 0.55091 to 0.55063, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5506 - acc: 0.7453 - val_loss: 0.5784 - val_acc: 0.7445\n",
      "288/317 [==========================>...] - ETA: 0sTrain on 1264 samples, validate on 317 samples\n",
      "Epoch 1/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.6224 - acc: 0.6840Epoch 00000: loss improved from inf to 0.62178, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 4s - loss: 0.6218 - acc: 0.6851 - val_loss: 0.5760 - val_acc: 0.7445\n",
      "Epoch 2/30\n",
      "1245/1264 [============================>.] - ETA: 0s - loss: 0.5752 - acc: 0.7478Epoch 00001: loss improved from 0.62178 to 0.57947, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5795 - acc: 0.7445 - val_loss: 0.5750 - val_acc: 0.7445\n",
      "Epoch 3/30\n",
      "1255/1264 [============================>.] - ETA: 0s - loss: 0.5869 - acc: 0.7450Epoch 00002: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5866 - acc: 0.7453 - val_loss: 0.5754 - val_acc: 0.7445\n",
      "Epoch 4/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5827 - acc: 0.7448Epoch 00003: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5829 - acc: 0.7453 - val_loss: 0.5752 - val_acc: 0.7445\n",
      "Epoch 5/30\n",
      "1245/1264 [============================>.] - ETA: 0s - loss: 0.5892 - acc: 0.7430Epoch 00004: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5873 - acc: 0.7453 - val_loss: 0.5760 - val_acc: 0.7445\n",
      "Epoch 6/30\n",
      "1245/1264 [============================>.] - ETA: 0s - loss: 0.5887 - acc: 0.7446Epoch 00005: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5881 - acc: 0.7453 - val_loss: 0.5753 - val_acc: 0.7445\n",
      "Epoch 7/30\n",
      "1245/1264 [============================>.] - ETA: 0s - loss: 0.5747 - acc: 0.7446Epoch 00006: loss improved from 0.57947 to 0.57372, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5737 - acc: 0.7460 - val_loss: 0.5754 - val_acc: 0.7445\n",
      "Epoch 8/30\n",
      "1245/1264 [============================>.] - ETA: 0s - loss: 0.5810 - acc: 0.7446Epoch 00007: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5800 - acc: 0.7453 - val_loss: 0.5756 - val_acc: 0.7445\n",
      "Epoch 9/30\n",
      "1240/1264 [============================>.] - ETA: 0s - loss: 0.5765 - acc: 0.7452Epoch 00008: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5768 - acc: 0.7453 - val_loss: 0.5753 - val_acc: 0.7445\n",
      "Epoch 10/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5846 - acc: 0.7440Epoch 00009: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5840 - acc: 0.7453 - val_loss: 0.5754 - val_acc: 0.7445\n",
      "Epoch 11/30\n",
      "1245/1264 [============================>.] - ETA: 0s - loss: 0.5745 - acc: 0.7462Epoch 00010: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5748 - acc: 0.7453 - val_loss: 0.5757 - val_acc: 0.7445\n",
      "Epoch 12/30\n",
      "1245/1264 [============================>.] - ETA: 0s - loss: 0.5680 - acc: 0.7486Epoch 00011: loss improved from 0.57372 to 0.57082, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5708 - acc: 0.7453 - val_loss: 0.5742 - val_acc: 0.7445\n",
      "Epoch 13/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5778 - acc: 0.7456Epoch 00012: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5782 - acc: 0.7453 - val_loss: 0.5738 - val_acc: 0.7445\n",
      "Epoch 14/30\n",
      "1260/1264 [============================>.] - ETA: 0s - loss: 0.5780 - acc: 0.7452Epoch 00013: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5775 - acc: 0.7453 - val_loss: 0.5737 - val_acc: 0.7445\n",
      "Epoch 15/30\n",
      "1240/1264 [============================>.] - ETA: 0s - loss: 0.5739 - acc: 0.7452Epoch 00014: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5735 - acc: 0.7453 - val_loss: 0.5730 - val_acc: 0.7445\n",
      "Epoch 16/30\n",
      "1260/1264 [============================>.] - ETA: 0s - loss: 0.5706 - acc: 0.7452Epoch 00015: loss improved from 0.57082 to 0.57042, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5704 - acc: 0.7453 - val_loss: 0.5742 - val_acc: 0.7445\n",
      "Epoch 17/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5756 - acc: 0.7448Epoch 00016: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5748 - acc: 0.7453 - val_loss: 0.5732 - val_acc: 0.7445\n",
      "Epoch 18/30\n",
      "1245/1264 [============================>.] - ETA: 0s - loss: 0.5703 - acc: 0.7454Epoch 00017: loss improved from 0.57042 to 0.57040, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5704 - acc: 0.7453 - val_loss: 0.5732 - val_acc: 0.7445\n",
      "Epoch 19/30\n",
      "1255/1264 [============================>.] - ETA: 0s - loss: 0.5742 - acc: 0.7466Epoch 00018: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5762 - acc: 0.7453 - val_loss: 0.5721 - val_acc: 0.7445\n",
      "Epoch 20/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5646 - acc: 0.7456Epoch 00019: loss improved from 0.57040 to 0.56419, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5642 - acc: 0.7453 - val_loss: 0.5718 - val_acc: 0.7445\n",
      "Epoch 21/30\n",
      "1245/1264 [============================>.] - ETA: 0s - loss: 0.5661 - acc: 0.7454Epoch 00020: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5658 - acc: 0.7453 - val_loss: 0.5715 - val_acc: 0.7445\n",
      "Epoch 22/30\n",
      "1240/1264 [============================>.] - ETA: 0s - loss: 0.5794 - acc: 0.7452Epoch 00021: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5780 - acc: 0.7460 - val_loss: 0.5715 - val_acc: 0.7445\n",
      "Epoch 23/30\n",
      "1255/1264 [============================>.] - ETA: 0s - loss: 0.5653 - acc: 0.7442Epoch 00022: loss improved from 0.56419 to 0.56378, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5638 - acc: 0.7453 - val_loss: 0.5712 - val_acc: 0.7445\n",
      "Epoch 24/30\n",
      "1240/1264 [============================>.] - ETA: 0s - loss: 0.5666 - acc: 0.7427Epoch 00023: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5644 - acc: 0.7453 - val_loss: 0.5707 - val_acc: 0.7445\n",
      "Epoch 25/30\n",
      "1260/1264 [============================>.] - ETA: 0s - loss: 0.5671 - acc: 0.7460Epoch 00024: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5683 - acc: 0.7453 - val_loss: 0.5704 - val_acc: 0.7445\n",
      "Epoch 26/30\n",
      "1255/1264 [============================>.] - ETA: 0s - loss: 0.5700 - acc: 0.7442Epoch 00025: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5686 - acc: 0.7453 - val_loss: 0.5705 - val_acc: 0.7445\n",
      "Epoch 27/30\n",
      "1245/1264 [============================>.] - ETA: 0s - loss: 0.5648 - acc: 0.7446Epoch 00026: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5654 - acc: 0.7453 - val_loss: 0.5708 - val_acc: 0.7445\n",
      "Epoch 28/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5606 - acc: 0.7472Epoch 00027: loss improved from 0.56378 to 0.56221, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5622 - acc: 0.7453 - val_loss: 0.5709 - val_acc: 0.7445\n",
      "Epoch 29/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5629 - acc: 0.7448Epoch 00028: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5627 - acc: 0.7453 - val_loss: 0.5705 - val_acc: 0.7445\n",
      "Epoch 30/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5567 - acc: 0.7472Epoch 00029: loss improved from 0.56221 to 0.55832, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5583 - acc: 0.7453 - val_loss: 0.5705 - val_acc: 0.7445\n",
      "256/317 [=======================>......] - ETA: 0sTrain on 1264 samples, validate on 317 samples\n",
      "Epoch 1/30\n",
      "1260/1264 [============================>.] - ETA: 0s - loss: 0.6131 - acc: 0.7056Epoch 00000: loss improved from inf to 0.61308, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 5s - loss: 0.6131 - acc: 0.7057 - val_loss: 0.5744 - val_acc: 0.7445\n",
      "Epoch 2/30\n",
      "1245/1264 [============================>.] - ETA: 0s - loss: 0.5828 - acc: 0.7406Epoch 00001: loss improved from 0.61308 to 0.58146, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5815 - acc: 0.7421 - val_loss: 0.5731 - val_acc: 0.7445\n",
      "Epoch 3/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5943 - acc: 0.7464Epoch 00002: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5945 - acc: 0.7445 - val_loss: 0.5733 - val_acc: 0.7445\n",
      "Epoch 4/30\n",
      "1255/1264 [============================>.] - ETA: 0s - loss: 0.5812 - acc: 0.7474Epoch 00003: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5831 - acc: 0.7453 - val_loss: 0.5749 - val_acc: 0.7445\n",
      "Epoch 5/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5765 - acc: 0.7448Epoch 00004: loss improved from 0.58146 to 0.57600, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5760 - acc: 0.7453 - val_loss: 0.5767 - val_acc: 0.7445\n",
      "Epoch 6/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5879 - acc: 0.7440Epoch 00005: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5865 - acc: 0.7453 - val_loss: 0.5765 - val_acc: 0.7445\n",
      "Epoch 7/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5900 - acc: 0.7432Epoch 00006: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5875 - acc: 0.7453 - val_loss: 0.5742 - val_acc: 0.7445\n",
      "Epoch 8/30\n",
      "1245/1264 [============================>.] - ETA: 0s - loss: 0.5789 - acc: 0.7470Epoch 00007: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5804 - acc: 0.7453 - val_loss: 0.5787 - val_acc: 0.7445\n",
      "Epoch 9/30\n",
      "1240/1264 [============================>.] - ETA: 0s - loss: 0.5837 - acc: 0.7419Epoch 00008: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5800 - acc: 0.7453 - val_loss: 0.5772 - val_acc: 0.7445\n",
      "Epoch 10/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5761 - acc: 0.7456Epoch 00009: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5769 - acc: 0.7453 - val_loss: 0.5767 - val_acc: 0.7445\n",
      "Epoch 11/30\n",
      "1255/1264 [============================>.] - ETA: 0s - loss: 0.5790 - acc: 0.7442Epoch 00010: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5793 - acc: 0.7445 - val_loss: 0.5765 - val_acc: 0.7445\n",
      "Epoch 12/30\n",
      "1255/1264 [============================>.] - ETA: 0s - loss: 0.5669 - acc: 0.7458Epoch 00011: loss improved from 0.57600 to 0.56751, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5675 - acc: 0.7453 - val_loss: 0.5767 - val_acc: 0.7445\n",
      "Epoch 13/30\n",
      "1245/1264 [============================>.] - ETA: 0s - loss: 0.5784 - acc: 0.7462Epoch 00012: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5791 - acc: 0.7453 - val_loss: 0.5755 - val_acc: 0.7445\n",
      "Epoch 14/30\n",
      "1245/1264 [============================>.] - ETA: 0s - loss: 0.5736 - acc: 0.7470Epoch 00013: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5762 - acc: 0.7453 - val_loss: 0.5758 - val_acc: 0.7445\n",
      "Epoch 15/30\n",
      "1245/1264 [============================>.] - ETA: 0s - loss: 0.5682 - acc: 0.7454Epoch 00014: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5687 - acc: 0.7453 - val_loss: 0.5737 - val_acc: 0.7445\n",
      "Epoch 16/30\n",
      "1240/1264 [============================>.] - ETA: 0s - loss: 0.5708 - acc: 0.7419Epoch 00015: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5680 - acc: 0.7453 - val_loss: 0.5741 - val_acc: 0.7445\n",
      "Epoch 17/30\n",
      "1245/1264 [============================>.] - ETA: 0s - loss: 0.5681 - acc: 0.7454Epoch 00016: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5681 - acc: 0.7453 - val_loss: 0.5743 - val_acc: 0.7445\n",
      "Epoch 18/30\n",
      "1240/1264 [============================>.] - ETA: 0s - loss: 0.5679 - acc: 0.7468Epoch 00017: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5688 - acc: 0.7453 - val_loss: 0.5745 - val_acc: 0.7445\n",
      "Epoch 19/30\n",
      "1255/1264 [============================>.] - ETA: 0s - loss: 0.5708 - acc: 0.7450Epoch 00018: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5706 - acc: 0.7453 - val_loss: 0.5777 - val_acc: 0.7445\n",
      "Epoch 20/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5729 - acc: 0.7448Epoch 00019: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5734 - acc: 0.7453 - val_loss: 0.5747 - val_acc: 0.7445\n",
      "Epoch 21/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5723 - acc: 0.7440Epoch 00020: loss did not improve\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1264/1264 [==============================] - 3s - loss: 0.5710 - acc: 0.7453 - val_loss: 0.5751 - val_acc: 0.7445\n",
      "Epoch 22/30\n",
      "1260/1264 [============================>.] - ETA: 0s - loss: 0.5688 - acc: 0.7452Epoch 00021: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5688 - acc: 0.7453 - val_loss: 0.5756 - val_acc: 0.7445\n",
      "Epoch 23/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5639 - acc: 0.7456Epoch 00022: loss improved from 0.56751 to 0.56435, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5644 - acc: 0.7453 - val_loss: 0.5737 - val_acc: 0.7445\n",
      "Epoch 24/30\n",
      "1240/1264 [============================>.] - ETA: 0s - loss: 0.5694 - acc: 0.7444Epoch 00023: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5683 - acc: 0.7453 - val_loss: 0.5743 - val_acc: 0.7445\n",
      "Epoch 25/30\n",
      "1240/1264 [============================>.] - ETA: 0s - loss: 0.5587 - acc: 0.7452Epoch 00024: loss improved from 0.56435 to 0.55837, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5584 - acc: 0.7453 - val_loss: 0.5730 - val_acc: 0.7445\n",
      "Epoch 26/30\n",
      "1255/1264 [============================>.] - ETA: 0s - loss: 0.5670 - acc: 0.7458Epoch 00025: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5675 - acc: 0.7453 - val_loss: 0.5741 - val_acc: 0.7445\n",
      "Epoch 27/30\n",
      "1250/1264 [============================>.] - ETA: 0s - loss: 0.5579 - acc: 0.7448Epoch 00026: loss improved from 0.55837 to 0.55697, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5570 - acc: 0.7453 - val_loss: 0.5723 - val_acc: 0.7445\n",
      "Epoch 28/30\n",
      "1260/1264 [============================>.] - ETA: 0s - loss: 0.5656 - acc: 0.7444Epoch 00027: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5650 - acc: 0.7453 - val_loss: 0.5765 - val_acc: 0.7445\n",
      "Epoch 29/30\n",
      "1260/1264 [============================>.] - ETA: 0s - loss: 0.5604 - acc: 0.7444Epoch 00028: loss did not improve\n",
      "1264/1264 [==============================] - 3s - loss: 0.5594 - acc: 0.7453 - val_loss: 0.5722 - val_acc: 0.7445\n",
      "Epoch 30/30\n",
      "1245/1264 [============================>.] - ETA: 0s - loss: 0.5570 - acc: 0.7430Epoch 00029: loss improved from 0.55697 to 0.55479, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1264/1264 [==============================] - 3s - loss: 0.5548 - acc: 0.7453 - val_loss: 0.5726 - val_acc: 0.7445\n",
      "288/317 [==========================>...] - ETA: 0sTrain on 1266 samples, validate on 315 samples\n",
      "Epoch 1/30\n",
      "1260/1266 [============================>.] - ETA: 0s - loss: 0.6007 - acc: 0.7294Epoch 00000: loss improved from inf to 0.60009, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 4s - loss: 0.6001 - acc: 0.7299 - val_loss: 0.5722 - val_acc: 0.7460\n",
      "Epoch 2/30\n",
      "1250/1266 [============================>.] - ETA: 0s - loss: 0.5922 - acc: 0.7416Epoch 00001: loss improved from 0.60009 to 0.58996, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5900 - acc: 0.7441 - val_loss: 0.5715 - val_acc: 0.7460\n",
      "Epoch 3/30\n",
      "1245/1266 [============================>.] - ETA: 0s - loss: 0.5902 - acc: 0.7438Epoch 00002: loss improved from 0.58996 to 0.58989, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5899 - acc: 0.7449 - val_loss: 0.5725 - val_acc: 0.7460\n",
      "Epoch 4/30\n",
      "1265/1266 [============================>.] - ETA: 0s - loss: 0.5837 - acc: 0.7447Epoch 00003: loss improved from 0.58989 to 0.58334, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5833 - acc: 0.7449 - val_loss: 0.5713 - val_acc: 0.7460\n",
      "Epoch 5/30\n",
      "1250/1266 [============================>.] - ETA: 0s - loss: 0.5895 - acc: 0.7424Epoch 00004: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5878 - acc: 0.7449 - val_loss: 0.5717 - val_acc: 0.7460\n",
      "Epoch 6/30\n",
      "1260/1266 [============================>.] - ETA: 0s - loss: 0.5801 - acc: 0.7437Epoch 00005: loss improved from 0.58334 to 0.57888, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5789 - acc: 0.7449 - val_loss: 0.5730 - val_acc: 0.7460\n",
      "Epoch 7/30\n",
      "1255/1266 [============================>.] - ETA: 0s - loss: 0.5832 - acc: 0.7434Epoch 00006: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5813 - acc: 0.7449 - val_loss: 0.5703 - val_acc: 0.7460\n",
      "Epoch 8/30\n",
      "1265/1266 [============================>.] - ETA: 0s - loss: 0.5756 - acc: 0.7455Epoch 00007: loss improved from 0.57888 to 0.57590, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5759 - acc: 0.7449 - val_loss: 0.5702 - val_acc: 0.7460\n",
      "Epoch 9/30\n",
      "1250/1266 [============================>.] - ETA: 0s - loss: 0.5736 - acc: 0.7456Epoch 00008: loss improved from 0.57590 to 0.57395, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5739 - acc: 0.7449 - val_loss: 0.5707 - val_acc: 0.7460\n",
      "Epoch 10/30\n",
      "1255/1266 [============================>.] - ETA: 0s - loss: 0.5808 - acc: 0.7426Epoch 00009: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5791 - acc: 0.7449 - val_loss: 0.5712 - val_acc: 0.7460\n",
      "Epoch 11/30\n",
      "1255/1266 [============================>.] - ETA: 0s - loss: 0.5780 - acc: 0.7434Epoch 00010: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5768 - acc: 0.7449 - val_loss: 0.5713 - val_acc: 0.7460\n",
      "Epoch 12/30\n",
      "1260/1266 [============================>.] - ETA: 0s - loss: 0.5743 - acc: 0.7437Epoch 00011: loss improved from 0.57395 to 0.57321, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5732 - acc: 0.7449 - val_loss: 0.5706 - val_acc: 0.7460\n",
      "Epoch 13/30\n",
      "1250/1266 [============================>.] - ETA: 0s - loss: 0.5745 - acc: 0.7448Epoch 00012: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5738 - acc: 0.7449 - val_loss: 0.5716 - val_acc: 0.7460\n",
      "Epoch 14/30\n",
      "1265/1266 [============================>.] - ETA: 0s - loss: 0.5706 - acc: 0.7447Epoch 00013: loss improved from 0.57321 to 0.57042, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5704 - acc: 0.7449 - val_loss: 0.5701 - val_acc: 0.7460\n",
      "Epoch 15/30\n",
      "1250/1266 [============================>.] - ETA: 0s - loss: 0.5681 - acc: 0.7456Epoch 00014: loss improved from 0.57042 to 0.56910, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5691 - acc: 0.7449 - val_loss: 0.5702 - val_acc: 0.7460\n",
      "Epoch 16/30\n",
      "1260/1266 [============================>.] - ETA: 0s - loss: 0.5721 - acc: 0.7444Epoch 00015: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5718 - acc: 0.7449 - val_loss: 0.5700 - val_acc: 0.7460\n",
      "Epoch 17/30\n",
      "1245/1266 [============================>.] - ETA: 0s - loss: 0.5637 - acc: 0.7470Epoch 00016: loss improved from 0.56910 to 0.56558, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5656 - acc: 0.7449 - val_loss: 0.5716 - val_acc: 0.7460\n",
      "Epoch 18/30\n",
      "1265/1266 [============================>.] - ETA: 0s - loss: 0.5655 - acc: 0.7447Epoch 00017: loss improved from 0.56558 to 0.56522, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5652 - acc: 0.7449 - val_loss: 0.5694 - val_acc: 0.7460\n",
      "Epoch 19/30\n",
      "1245/1266 [============================>.] - ETA: 0s - loss: 0.5640 - acc: 0.7438Epoch 00018: loss improved from 0.56522 to 0.56318, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1266/1266 [==============================] - 3s - loss: 0.5632 - acc: 0.7449 - val_loss: 0.5722 - val_acc: 0.7460\n",
      "Epoch 20/30\n",
      "1265/1266 [============================>.] - ETA: 0s - loss: 0.5699 - acc: 0.7455Epoch 00019: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5703 - acc: 0.7449 - val_loss: 0.5704 - val_acc: 0.7460\n",
      "Epoch 21/30\n",
      "1260/1266 [============================>.] - ETA: 0s - loss: 0.5574 - acc: 0.7452Epoch 00020: loss improved from 0.56318 to 0.55779, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5578 - acc: 0.7449 - val_loss: 0.5704 - val_acc: 0.7460\n",
      "Epoch 22/30\n",
      "1250/1266 [============================>.] - ETA: 0s - loss: 0.5669 - acc: 0.7432Epoch 00021: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5650 - acc: 0.7449 - val_loss: 0.5726 - val_acc: 0.7460\n",
      "Epoch 23/30\n",
      "1250/1266 [============================>.] - ETA: 0s - loss: 0.5637 - acc: 0.7480Epoch 00022: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5666 - acc: 0.7449 - val_loss: 0.5706 - val_acc: 0.7460\n",
      "Epoch 24/30\n",
      "1250/1266 [============================>.] - ETA: 0s - loss: 0.5590 - acc: 0.7448Epoch 00023: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5598 - acc: 0.7449 - val_loss: 0.5704 - val_acc: 0.7460\n",
      "Epoch 25/30\n",
      "1265/1266 [============================>.] - ETA: 0s - loss: 0.5597 - acc: 0.7447Epoch 00024: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5595 - acc: 0.7449 - val_loss: 0.5706 - val_acc: 0.7460\n",
      "Epoch 26/30\n",
      "1255/1266 [============================>.] - ETA: 0s - loss: 0.5604 - acc: 0.7466Epoch 00025: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5621 - acc: 0.7449 - val_loss: 0.5710 - val_acc: 0.7460\n",
      "Epoch 27/30\n",
      "1265/1266 [============================>.] - ETA: 0s - loss: 0.5606 - acc: 0.7455Epoch 00026: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5614 - acc: 0.7449 - val_loss: 0.5710 - val_acc: 0.7460\n",
      "Epoch 28/30\n",
      "1265/1266 [============================>.] - ETA: 0s - loss: 0.5564 - acc: 0.7447Epoch 00027: loss improved from 0.55779 to 0.55610, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5561 - acc: 0.7449 - val_loss: 0.5715 - val_acc: 0.7460\n",
      "Epoch 29/30\n",
      "1250/1266 [============================>.] - ETA: 0s - loss: 0.5505 - acc: 0.7456Epoch 00028: loss improved from 0.55610 to 0.55147, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5515 - acc: 0.7449 - val_loss: 0.5721 - val_acc: 0.7460\n",
      "Epoch 30/30\n",
      "1265/1266 [============================>.] - ETA: 0s - loss: 0.5539 - acc: 0.7447Epoch 00029: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5538 - acc: 0.7449 - val_loss: 0.5727 - val_acc: 0.7460\n",
      "288/315 [==========================>...] - ETA: 0sTrain on 1266 samples, validate on 315 samples\n",
      "Epoch 1/30\n",
      "1260/1266 [============================>.] - ETA: 0s - loss: 0.6027 - acc: 0.7294Epoch 00000: loss improved from inf to 0.60451, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 5s - loss: 0.6045 - acc: 0.7283 - val_loss: 0.5742 - val_acc: 0.7460\n",
      "Epoch 2/30\n",
      "1265/1266 [============================>.] - ETA: 0s - loss: 0.5848 - acc: 0.7478Epoch 00001: loss improved from 0.60451 to 0.58591, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5859 - acc: 0.7472 - val_loss: 0.5740 - val_acc: 0.7460\n",
      "Epoch 3/30\n",
      "1265/1266 [============================>.] - ETA: 0s - loss: 0.5886 - acc: 0.7462Epoch 00002: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5885 - acc: 0.7464 - val_loss: 0.5745 - val_acc: 0.7460\n",
      "Epoch 4/30\n",
      "1260/1266 [============================>.] - ETA: 0s - loss: 0.5908 - acc: 0.7452Epoch 00003: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5913 - acc: 0.7449 - val_loss: 0.5742 - val_acc: 0.7460\n",
      "Epoch 5/30\n",
      "1250/1266 [============================>.] - ETA: 0s - loss: 0.5869 - acc: 0.7456Epoch 00004: loss improved from 0.58591 to 0.58556, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5856 - acc: 0.7457 - val_loss: 0.5741 - val_acc: 0.7460\n",
      "Epoch 6/30\n",
      "1250/1266 [============================>.] - ETA: 0s - loss: 0.5823 - acc: 0.7424Epoch 00005: loss improved from 0.58556 to 0.57963, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5796 - acc: 0.7449 - val_loss: 0.5746 - val_acc: 0.7460\n",
      "Epoch 7/30\n",
      "1245/1266 [============================>.] - ETA: 0s - loss: 0.5821 - acc: 0.7430Epoch 00006: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5799 - acc: 0.7449 - val_loss: 0.5746 - val_acc: 0.7460\n",
      "Epoch 8/30\n",
      "1260/1266 [============================>.] - ETA: 0s - loss: 0.5859 - acc: 0.7444Epoch 00007: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5853 - acc: 0.7449 - val_loss: 0.5749 - val_acc: 0.7460\n",
      "Epoch 9/30\n",
      "1265/1266 [============================>.] - ETA: 0s - loss: 0.5811 - acc: 0.7447Epoch 00008: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5809 - acc: 0.7449 - val_loss: 0.5783 - val_acc: 0.7460\n",
      "Epoch 10/30\n",
      "1250/1266 [============================>.] - ETA: 0s - loss: 0.5852 - acc: 0.7424Epoch 00009: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5826 - acc: 0.7449 - val_loss: 0.5754 - val_acc: 0.7460\n",
      "Epoch 11/30\n",
      "1260/1266 [============================>.] - ETA: 0s - loss: 0.5778 - acc: 0.7452Epoch 00010: loss improved from 0.57963 to 0.57786, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5779 - acc: 0.7449 - val_loss: 0.5758 - val_acc: 0.7460\n",
      "Epoch 12/30\n",
      "1250/1266 [============================>.] - ETA: 0s - loss: 0.5830 - acc: 0.7432Epoch 00011: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5826 - acc: 0.7449 - val_loss: 0.5769 - val_acc: 0.7460\n",
      "Epoch 13/30\n",
      "1255/1266 [============================>.] - ETA: 0s - loss: 0.5759 - acc: 0.7458Epoch 00012: loss improved from 0.57786 to 0.57688, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5769 - acc: 0.7449 - val_loss: 0.5761 - val_acc: 0.7460\n",
      "Epoch 14/30\n",
      "1245/1266 [============================>.] - ETA: 0s - loss: 0.5717 - acc: 0.7470Epoch 00013: loss improved from 0.57688 to 0.57331, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5733 - acc: 0.7449 - val_loss: 0.5765 - val_acc: 0.7460\n",
      "Epoch 15/30\n",
      "1255/1266 [============================>.] - ETA: 0s - loss: 0.5771 - acc: 0.7442Epoch 00014: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5772 - acc: 0.7449 - val_loss: 0.5777 - val_acc: 0.7460\n",
      "Epoch 16/30\n",
      "1250/1266 [============================>.] - ETA: 0s - loss: 0.5689 - acc: 0.7472Epoch 00015: loss improved from 0.57331 to 0.57101, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5710 - acc: 0.7449 - val_loss: 0.5767 - val_acc: 0.7460\n",
      "Epoch 17/30\n",
      "1245/1266 [============================>.] - ETA: 0s - loss: 0.5755 - acc: 0.7422Epoch 00016: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5728 - acc: 0.7449 - val_loss: 0.5767 - val_acc: 0.7460\n",
      "Epoch 18/30\n",
      "1245/1266 [============================>.] - ETA: 0s - loss: 0.5689 - acc: 0.7454Epoch 00017: loss improved from 0.57101 to 0.56936, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5694 - acc: 0.7449 - val_loss: 0.5775 - val_acc: 0.7460\n",
      "Epoch 19/30\n",
      "1265/1266 [============================>.] - ETA: 0s - loss: 0.5711 - acc: 0.7447Epoch 00018: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5710 - acc: 0.7449 - val_loss: 0.5782 - val_acc: 0.7460\n",
      "Epoch 20/30\n",
      "1245/1266 [============================>.] - ETA: 0s - loss: 0.5685 - acc: 0.7454Epoch 00019: loss improved from 0.56936 to 0.56913, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5691 - acc: 0.7449 - val_loss: 0.5790 - val_acc: 0.7460\n",
      "Epoch 21/30\n",
      "1245/1266 [============================>.] - ETA: 0s - loss: 0.5692 - acc: 0.7446Epoch 00020: loss improved from 0.56913 to 0.56839, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5684 - acc: 0.7449 - val_loss: 0.5783 - val_acc: 0.7460\n",
      "Epoch 22/30\n",
      "1255/1266 [============================>.] - ETA: 0s - loss: 0.5662 - acc: 0.7442Epoch 00021: loss improved from 0.56839 to 0.56585, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5659 - acc: 0.7449 - val_loss: 0.5786 - val_acc: 0.7460\n",
      "Epoch 23/30\n",
      "1255/1266 [============================>.] - ETA: 0s - loss: 0.5649 - acc: 0.7450Epoch 00022: loss improved from 0.56585 to 0.56488, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5649 - acc: 0.7449 - val_loss: 0.5797 - val_acc: 0.7460\n",
      "Epoch 24/30\n",
      "1265/1266 [============================>.] - ETA: 0s - loss: 0.5614 - acc: 0.7455Epoch 00023: loss improved from 0.56488 to 0.56180, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5618 - acc: 0.7449 - val_loss: 0.5795 - val_acc: 0.7460\n",
      "Epoch 25/30\n",
      "1260/1266 [============================>.] - ETA: 0s - loss: 0.5637 - acc: 0.7452Epoch 00024: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5640 - acc: 0.7449 - val_loss: 0.5796 - val_acc: 0.7460\n",
      "Epoch 26/30\n",
      "1265/1266 [============================>.] - ETA: 0s - loss: 0.5599 - acc: 0.7447Epoch 00025: loss improved from 0.56180 to 0.55961, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5596 - acc: 0.7449 - val_loss: 0.5809 - val_acc: 0.7460\n",
      "Epoch 27/30\n",
      "1260/1266 [============================>.] - ETA: 0s - loss: 0.5545 - acc: 0.7460Epoch 00026: loss improved from 0.55961 to 0.55555, saving model to malignantnodule-cnn-weights-weightedlogloss2.hdf5\n",
      "1266/1266 [==============================] - 3s - loss: 0.5556 - acc: 0.7449 - val_loss: 0.5806 - val_acc: 0.7460\n",
      "Epoch 28/30\n",
      "1255/1266 [============================>.] - ETA: 0s - loss: 0.5576 - acc: 0.7458Epoch 00027: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5593 - acc: 0.7449 - val_loss: 0.5814 - val_acc: 0.7460\n",
      "Epoch 29/30\n",
      "1245/1266 [============================>.] - ETA: 0s - loss: 0.5603 - acc: 0.7454Epoch 00028: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5601 - acc: 0.7449 - val_loss: 0.5832 - val_acc: 0.7460\n",
      "Epoch 30/30\n",
      "1255/1266 [============================>.] - ETA: 0s - loss: 0.5616 - acc: 0.7434Epoch 00029: loss did not improve\n",
      "1266/1266 [==============================] - 3s - loss: 0.5604 - acc: 0.7449 - val_loss: 0.5844 - val_acc: 0.7460\n",
      "288/315 [==========================>...] - ETA: 0s"
     ]
    }
   ],
   "source": [
    "n_splits=5\n",
    "kfold=StratifiedKFold(n_splits=n_splits, shuffle=True)\n",
    "#classify as nodule or non-nodule\n",
    "input_shape=(64,64,1)\n",
    "num_classes=2\n",
    "width=16\n",
    "epochs=30\n",
    "batch_size=5\n",
    "cvscores=[]\n",
    "cvscoresrandom=[]\n",
    "history=[]\n",
    "historyrandom=[]\n",
    "for train,test in kfold.split(nodulecrops,malignantlabel):\n",
    "    model = Sequential()\n",
    "    model.add(Conv2D(width, kernel_size=(3, 3),\n",
    "                     activation='relu',\n",
    "                     input_shape=input_shape))\n",
    "    #model.add(BatchNormalization(axis=1))\n",
    "    model.add(Conv2D(width, kernel_size=(3, 3),\n",
    "                     activation='relu',\n",
    "                     input_shape=input_shape))\n",
    "    model.add(Conv2D(width*2, (3, 3), activation='relu'))\n",
    "    #model.add(BatchNormalization(axis=1))\n",
    "    model.add(Conv2D(width*2, (3, 3), activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Dropout(0.75))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(width*4, activation='relu'))\n",
    "    model.add(Dropout(0.50))\n",
    "    model.add(Dense(num_classes, activation='softmax'))\n",
    "    model.compile(loss=keras.losses.categorical_crossentropy,\n",
    "                  optimizer=Adam(lr=1e-5),\n",
    "                  metrics=['accuracy'])\n",
    "    histor=model.fit(nodulecrops[train],malignantlabelcat[train], batch_size=batch_size, epochs=epochs, verbose=1, shuffle=True, validation_data=(nodulecrops[test],malignantlabelcat[test]))\n",
    "    scores=model.evaluate(nodulecrops[test],malignantlabelcat[test])\n",
    "    cvscores.append(scores)\n",
    "    history.append(histor)\n",
    "    \n",
    "for train,test in kfold.split(nodulecrops,randomlabel):\n",
    "    model = Sequential()\n",
    "    model.add(Conv2D(width, kernel_size=(3, 3),\n",
    "                     activation='relu',\n",
    "                     input_shape=input_shape))\n",
    "    #model.add(BatchNormalization(axis=1))\n",
    "    model.add(Conv2D(width, kernel_size=(3, 3),\n",
    "                     activation='relu',\n",
    "                     input_shape=input_shape))\n",
    "    model.add(Conv2D(width*2, (3, 3), activation='relu'))\n",
    "    #model.add(BatchNormalization(axis=1))\n",
    "    model.add(Conv2D(width*2, (3, 3), activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Dropout(0.75))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(width*4, activation='relu'))\n",
    "    model.add(Dropout(0.50))\n",
    "    model.add(Dense(num_classes, activation='softmax'))\n",
    "    model.compile(loss=keras.losses.categorical_crossentropy,\n",
    "                  optimizer=Adam(lr=1e-5),\n",
    "                  metrics=['accuracy'])\n",
    "    historrandom=model.fit(nodulecrops[train],randomlabelcat[train], batch_size=batch_size, epochs=epochs, verbose=1, shuffle=True, validation_data=(nodulecrops[test],randomlabelcat[test]))\n",
    "    scoresrandom=model.evaluate(nodulecrops[test],randomlabelcat[test])\n",
    "    cvscoresrandom.append(scoresrandom)\n",
    "    historyrandom.append(historrandom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean loss across all CV sets with true labels: 0.565945509407\n",
      "Mean loss across all CV sets with random labels: 0.57571187416\n",
      "Mean accuracy across all CV sets with true labels: 0.735610751248\n",
      "Mean accuracy across all CV sets with random labels: 0.745100395732\n",
      "Lowest val_loss of 0.565504729975 at epoch [28] with true labels\n",
      "Lowest val_loss of 0.574752183049 at epoch [26] with random labels\n"
     ]
    },
    {
     "data": {
      "image/png": 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OCUxOT+PigV2Ji26+kdFen5+H393GG+v3MiU9jf/5xRCio0LUCIyBH1bC589Yf2OT4YIH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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6a6eca58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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G45Mp+auRUoqidG8qYDTDRe9CiE9Io6G16uY9RVG6MxUwWhDuezxFSHCAOwUV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\nkSH+vFnL4UIOlVZw5/vruHbqSorLnG5K3VEaMJRSqpEFB/g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      "text/plain": [
       "<matplotlib.figure.Figure at 0x65899e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Mean loss across all CV sets with true labels:\", np.mean([cvscores[i][0] for i in range(len(cvscores))]))\n",
    "print(\"Mean loss across all CV sets with random labels:\", np.mean([cvscoresrandom[i][0] for i in range(len(cvscoresrandom))]))\n",
    "print(\"Mean accuracy across all CV sets with true labels:\", np.mean([cvscores[i][1] for i in range(len(cvscores))]))\n",
    "print(\"Mean accuracy across all CV sets with random labels:\", np.mean([cvscoresrandom[i][1] for i in range(len(cvscoresrandom))]))\n",
    "\n",
    "print(\"Lowest val_loss of\", min(np.mean([history[i].history['val_loss'] for i in range(n_splits)],axis=0)), \"at epoch\", np.where(np.mean([history[i].history['val_loss'] for i in range(n_splits)],axis=0)==min(np.mean([history[i].history['val_loss'] for i in range(n_splits)],axis=0)))[0], \"with true labels\")\n",
    "print(\"Lowest val_loss of\", min(np.mean([historyrandom[i].history['val_loss'] for i in range(n_splits)],axis=0)), \"at epoch\", np.where(np.mean([historyrandom[i].history['val_loss'] for i in range(n_splits)],axis=0)==min(np.mean([historyrandom[i].history['val_loss'] for i in range(n_splits)],axis=0)))[0],\"with random labels\")\n",
    "acc=np.mean([history[i].history['acc'] for i in range(n_splits)], axis=0)\n",
    "valacc=np.mean([history[i].history['val_acc'] for i in range(n_splits)], axis=0)\n",
    "loss=np.mean([history[i].history['loss'] for i in range(n_splits)], axis=0)\n",
    "valloss=np.mean([history[i].history['val_loss'] for i in range(n_splits)], axis=0)\n",
    "randacc=np.mean([historyrandom[i].history['acc'] for i in range(n_splits)], axis=0)\n",
    "randvalacc=np.mean([historyrandom[i].history['val_acc'] for i in range(n_splits)], axis=0)\n",
    "randloss=np.mean([historyrandom[i].history['loss'] for i in range(n_splits)], axis=0)\n",
    "randvalloss=np.mean([historyrandom[i].history['val_loss'] for i in range(n_splits)], axis=0)\n",
    "\n",
    "# summarize history for accuracy\n",
    "plt.plot(acc)\n",
    "plt.plot(valacc)\n",
    "plt.plot(randacc)\n",
    "plt.plot(randvalacc)\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train truelabel', 'validation truelabel', 'train randomlabel', 'val randomlabel'], loc='best')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(loss)\n",
    "plt.plot(valloss)\n",
    "plt.plot(randloss)\n",
    "plt.plot(randvalloss)\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train truelabel', 'validation truelabel', 'train randomlabel', 'val randomlabel'], loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1264 samples, validate on 317 samples\n",
      "Epoch 1/250\n",
      "1264/1264 [==============================] - 2s - loss: 0.6817 - acc: 0.5672 - val_loss: 0.6277 - val_acc: 0.7350\n",
      "Epoch 2/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.6180 - acc: 0.7033 - val_loss: 0.6014 - val_acc: 0.7350\n",
      "Epoch 3/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.6105 - acc: 0.7223 - val_loss: 0.5956 - val_acc: 0.7350\n",
      "Epoch 4/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5932 - acc: 0.7358 - val_loss: 0.5945 - val_acc: 0.7350\n",
      "Epoch 5/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5911 - acc: 0.7350 - val_loss: 0.5935 - val_acc: 0.7350\n",
      "Epoch 6/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.6022 - acc: 0.7342 - val_loss: 0.5920 - val_acc: 0.7350\n",
      "Epoch 7/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5918 - acc: 0.7334 - val_loss: 0.5908 - val_acc: 0.7350\n",
      "Epoch 8/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5903 - acc: 0.7334 - val_loss: 0.5897 - val_acc: 0.7350\n",
      "Epoch 9/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5976 - acc: 0.7342 - val_loss: 0.5887 - val_acc: 0.7350\n",
      "Epoch 10/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5953 - acc: 0.7318 - val_loss: 0.5880 - val_acc: 0.7350\n",
      "Epoch 11/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5889 - acc: 0.7326 - val_loss: 0.5871 - val_acc: 0.7350\n",
      "Epoch 12/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5820 - acc: 0.7342 - val_loss: 0.5866 - val_acc: 0.7350\n",
      "Epoch 13/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5898 - acc: 0.7334 - val_loss: 0.5855 - val_acc: 0.7350\n",
      "Epoch 14/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5877 - acc: 0.7326 - val_loss: 0.5849 - val_acc: 0.7350\n",
      "Epoch 15/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5884 - acc: 0.7326 - val_loss: 0.5842 - val_acc: 0.7350\n",
      "Epoch 16/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5884 - acc: 0.7334 - val_loss: 0.5834 - val_acc: 0.7350\n",
      "Epoch 17/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5789 - acc: 0.7326 - val_loss: 0.5826 - val_acc: 0.7350\n",
      "Epoch 18/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5939 - acc: 0.7358 - val_loss: 0.5819 - val_acc: 0.7350\n",
      "Epoch 19/250\n",
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      "Epoch 129/250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 130/250\n",
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      "Epoch 149/250\n",
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      "Epoch 151/250\n",
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      "Epoch 152/250\n",
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      "Epoch 153/250\n",
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      "Epoch 154/250\n",
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      "Epoch 155/250\n",
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      "Epoch 156/250\n",
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      "Epoch 157/250\n",
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      "Epoch 158/250\n",
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      "Epoch 159/250\n",
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      "Epoch 160/250\n",
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      "Epoch 161/250\n",
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      "Epoch 162/250\n",
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      "Epoch 163/250\n",
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      "Epoch 164/250\n",
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      "Epoch 165/250\n",
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      "Epoch 166/250\n",
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      "Epoch 167/250\n",
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      "Epoch 168/250\n",
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      "Epoch 169/250\n",
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      "Epoch 170/250\n",
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      "Epoch 171/250\n",
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      "Epoch 172/250\n",
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      "Epoch 173/250\n",
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      "Epoch 174/250\n",
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      "Epoch 175/250\n",
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      "Epoch 176/250\n",
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      "Epoch 178/250\n",
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      "Epoch 179/250\n",
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      "Epoch 180/250\n",
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      "Epoch 181/250\n",
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      "Epoch 182/250\n",
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      "Epoch 183/250\n",
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      "Epoch 184/250\n",
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      "Epoch 185/250\n",
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      "Epoch 186/250\n",
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      "Epoch 187/250\n",
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      "Epoch 188/250\n",
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      "Epoch 189/250\n",
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      "Epoch 190/250\n",
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      "Epoch 191/250\n",
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      "Epoch 192/250\n",
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      "Epoch 193/250\n",
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      "Epoch 194/250\n",
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      "Epoch 195/250\n",
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      "Epoch 196/250\n",
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      "Epoch 197/250\n",
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      "Epoch 198/250\n",
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      "Epoch 199/250\n",
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      "Epoch 200/250\n",
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      "Epoch 201/250\n",
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      "Epoch 202/250\n",
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      "Epoch 204/250\n",
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      "Epoch 205/250\n",
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      "Epoch 206/250\n",
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      "Epoch 207/250\n",
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      "Epoch 208/250\n",
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      "Epoch 209/250\n",
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      "Epoch 210/250\n",
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      "Epoch 211/250\n",
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      "Epoch 212/250\n",
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      "Epoch 213/250\n",
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      "Epoch 214/250\n",
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      "Epoch 215/250\n",
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      "Epoch 216/250\n",
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      "Epoch 217/250\n",
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      "Epoch 218/250\n",
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      "Epoch 219/250\n",
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      "Epoch 220/250\n",
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      "Epoch 221/250\n",
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      "Epoch 222/250\n",
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      "Epoch 223/250\n",
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      "Epoch 224/250\n",
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      "Epoch 225/250\n",
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      "Epoch 226/250\n",
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      "Epoch 227/250\n",
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      "Epoch 228/250\n",
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      "Epoch 229/250\n",
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      "Epoch 230/250\n",
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      "Epoch 231/250\n",
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      "Epoch 232/250\n",
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      "Epoch 233/250\n",
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      "Epoch 234/250\n",
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      "Epoch 235/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4517 - acc: 0.7848 - val_loss: 0.5855 - val_acc: 0.7350\n",
      "Epoch 236/250\n",
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      "Epoch 237/250\n",
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      "Epoch 238/250\n",
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      "Epoch 239/250\n",
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      "Epoch 240/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4560 - acc: 0.7903 - val_loss: 0.5808 - val_acc: 0.7319\n",
      "Epoch 241/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4548 - acc: 0.7809 - val_loss: 0.5814 - val_acc: 0.7319\n",
      "Epoch 242/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4545 - acc: 0.7959 - val_loss: 0.5833 - val_acc: 0.7319\n",
      "Epoch 243/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4606 - acc: 0.7840 - val_loss: 0.5843 - val_acc: 0.7319\n",
      "Epoch 244/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4609 - acc: 0.7880 - val_loss: 0.5833 - val_acc: 0.7319\n",
      "Epoch 245/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4403 - acc: 0.7975 - val_loss: 0.5834 - val_acc: 0.7319\n",
      "Epoch 246/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4548 - acc: 0.7880 - val_loss: 0.5864 - val_acc: 0.7319\n",
      "Epoch 247/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4462 - acc: 0.7943 - val_loss: 0.5863 - val_acc: 0.7319\n",
      "Epoch 248/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4606 - acc: 0.7856 - val_loss: 0.5861 - val_acc: 0.7350\n",
      "Epoch 249/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4455 - acc: 0.7975 - val_loss: 0.5856 - val_acc: 0.7350\n",
      "Epoch 250/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4486 - acc: 0.7967 - val_loss: 0.5877 - val_acc: 0.7350\n",
      "288/317 [==========================>...] - ETA: 0sTrain on 1265 samples, validate on 316 samples\n",
      "Epoch 1/250\n",
      "1265/1265 [==============================] - 2s - loss: 0.7031 - acc: 0.4561 - val_loss: 0.6698 - val_acc: 0.7278\n",
      "Epoch 2/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.6640 - acc: 0.6767 - val_loss: 0.6389 - val_acc: 0.7342\n",
      "Epoch 3/250\n",
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      "Epoch 4/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.6125 - acc: 0.7344 - val_loss: 0.5950 - val_acc: 0.7342\n",
      "Epoch 5/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5935 - acc: 0.7344 - val_loss: 0.5844 - val_acc: 0.7342\n",
      "Epoch 6/250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1265/1265 [==============================] - 0s - loss: 0.5891 - acc: 0.7344 - val_loss: 0.5788 - val_acc: 0.7342\n",
      "Epoch 7/250\n",
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      "Epoch 8/250\n",
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      "Epoch 9/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5887 - acc: 0.7344 - val_loss: 0.5757 - val_acc: 0.7342\n",
      "Epoch 10/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5825 - acc: 0.7344 - val_loss: 0.5752 - val_acc: 0.7342\n",
      "Epoch 11/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5852 - acc: 0.7344 - val_loss: 0.5750 - val_acc: 0.7342\n",
      "Epoch 12/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5852 - acc: 0.7344 - val_loss: 0.5747 - val_acc: 0.7342\n",
      "Epoch 13/250\n",
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      "Epoch 14/250\n",
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      "Epoch 15/250\n",
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      "Epoch 16/250\n",
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      "Epoch 17/250\n",
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      "Epoch 18/250\n",
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      "Epoch 19/250\n",
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      "Epoch 20/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5841 - acc: 0.7344 - val_loss: 0.5726 - val_acc: 0.7342\n",
      "Epoch 21/250\n",
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      "Epoch 22/250\n",
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      "Epoch 23/250\n",
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      "Epoch 24/250\n",
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      "Epoch 25/250\n",
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      "Epoch 26/250\n",
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      "Epoch 27/250\n",
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      "Epoch 28/250\n",
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      "Epoch 134/250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1265/1265 [==============================] - 0s - loss: 0.5230 - acc: 0.7557 - val_loss: 0.5524 - val_acc: 0.7532\n",
      "Epoch 135/250\n",
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      "Epoch 159/250\n",
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      "Epoch 160/250\n",
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      "Epoch 161/250\n",
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      "Epoch 162/250\n",
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      "Epoch 163/250\n",
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      "Epoch 164/250\n",
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      "Epoch 165/250\n",
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      "Epoch 166/250\n",
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      "Epoch 167/250\n",
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      "Epoch 168/250\n",
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      "Epoch 169/250\n",
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      "Epoch 170/250\n",
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      "Epoch 171/250\n",
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      "Epoch 172/250\n",
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      "Epoch 173/250\n",
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      "Epoch 174/250\n",
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      "Epoch 175/250\n",
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      "Epoch 176/250\n",
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      "Epoch 177/250\n",
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      "Epoch 178/250\n",
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      "Epoch 179/250\n",
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      "Epoch 180/250\n",
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      "Epoch 181/250\n",
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      "Epoch 182/250\n",
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      "Epoch 183/250\n",
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      "Epoch 184/250\n",
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      "Epoch 185/250\n",
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      "Epoch 186/250\n",
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      "Epoch 187/250\n",
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      "Epoch 189/250\n",
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      "Epoch 190/250\n",
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      "Epoch 191/250\n",
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      "Epoch 192/250\n",
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      "Epoch 193/250\n",
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      "Epoch 194/250\n",
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      "Epoch 195/250\n",
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      "Epoch 196/250\n",
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      "Epoch 197/250\n",
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      "Epoch 198/250\n",
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      "Epoch 199/250\n",
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      "Epoch 200/250\n",
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      "Epoch 201/250\n",
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      "Epoch 202/250\n",
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      "Epoch 203/250\n",
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      "Epoch 205/250\n",
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      "Epoch 206/250\n",
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      "Epoch 207/250\n",
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      "Epoch 209/250\n",
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      "Epoch 210/250\n",
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      "Epoch 211/250\n",
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      "Epoch 212/250\n",
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      "Epoch 213/250\n",
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      "Epoch 214/250\n",
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      "Epoch 215/250\n",
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      "Epoch 216/250\n",
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      "Epoch 217/250\n",
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      "Epoch 218/250\n",
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      "Epoch 219/250\n",
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      "Epoch 220/250\n",
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      "Epoch 221/250\n",
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      "Epoch 222/250\n",
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      "Epoch 223/250\n",
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      "Epoch 224/250\n",
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      "Epoch 225/250\n",
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      "Epoch 226/250\n",
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      "Epoch 227/250\n",
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      "Epoch 228/250\n",
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      "Epoch 229/250\n",
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      "Epoch 230/250\n",
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      "Epoch 231/250\n",
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      "Epoch 232/250\n",
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      "Epoch 233/250\n",
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      "Epoch 234/250\n",
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      "Epoch 235/250\n",
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      "Epoch 236/250\n",
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      "Epoch 237/250\n",
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      "Epoch 238/250\n",
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      "Epoch 239/250\n",
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      "Epoch 240/250\n",
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      "Epoch 241/250\n",
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      "Epoch 242/250\n",
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      "Epoch 243/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4649 - acc: 0.7826 - val_loss: 0.5698 - val_acc: 0.7532\n",
      "Epoch 244/250\n",
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      "Epoch 245/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4679 - acc: 0.7889 - val_loss: 0.5692 - val_acc: 0.7468\n",
      "Epoch 246/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4644 - acc: 0.7834 - val_loss: 0.5679 - val_acc: 0.7278\n",
      "Epoch 247/250\n",
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      "Epoch 248/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4610 - acc: 0.7889 - val_loss: 0.5695 - val_acc: 0.7405\n",
      "Epoch 249/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4668 - acc: 0.7874 - val_loss: 0.5712 - val_acc: 0.7500\n",
      "Epoch 250/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4580 - acc: 0.7937 - val_loss: 0.5707 - val_acc: 0.7437\n",
      "256/316 [=======================>......] - ETA: 0sTrain on 1265 samples, validate on 316 samples\n",
      "Epoch 1/250\n",
      "1265/1265 [==============================] - 2s - loss: 0.6665 - acc: 0.6245 - val_loss: 0.6129 - val_acc: 0.7342\n",
      "Epoch 2/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.6140 - acc: 0.7154 - val_loss: 0.5855 - val_acc: 0.7342\n",
      "Epoch 3/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5965 - acc: 0.7360 - val_loss: 0.5817 - val_acc: 0.7342\n",
      "Epoch 4/250\n",
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      "Epoch 5/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5898 - acc: 0.7360 - val_loss: 0.5805 - val_acc: 0.7342\n",
      "Epoch 6/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5905 - acc: 0.7344 - val_loss: 0.5804 - val_acc: 0.7342\n",
      "Epoch 7/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5874 - acc: 0.7344 - val_loss: 0.5802 - val_acc: 0.7342\n",
      "Epoch 8/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5846 - acc: 0.7344 - val_loss: 0.5798 - val_acc: 0.7342\n",
      "Epoch 9/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5760 - acc: 0.7328 - val_loss: 0.5794 - val_acc: 0.7342\n",
      "Epoch 10/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5850 - acc: 0.7336 - val_loss: 0.5790 - val_acc: 0.7342\n",
      "Epoch 11/250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1265/1265 [==============================] - 0s - loss: 0.5884 - acc: 0.7352 - val_loss: 0.5788 - val_acc: 0.7342\n",
      "Epoch 12/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5839 - acc: 0.7336 - val_loss: 0.5791 - val_acc: 0.7342\n",
      "Epoch 13/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5893 - acc: 0.7344 - val_loss: 0.5791 - val_acc: 0.7342\n",
      "Epoch 14/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5759 - acc: 0.7344 - val_loss: 0.5784 - val_acc: 0.7342\n",
      "Epoch 15/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5878 - acc: 0.7344 - val_loss: 0.5781 - val_acc: 0.7342\n",
      "Epoch 16/250\n",
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      "Epoch 17/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5800 - acc: 0.7336 - val_loss: 0.5779 - val_acc: 0.7342\n",
      "Epoch 18/250\n",
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      "Epoch 19/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5822 - acc: 0.7344 - val_loss: 0.5781 - val_acc: 0.7342\n",
      "Epoch 20/250\n",
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      "Epoch 21/250\n",
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      "Epoch 22/250\n",
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      "Epoch 23/250\n",
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      "Epoch 24/250\n",
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      "Epoch 25/250\n",
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      "Epoch 26/250\n",
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      "Epoch 27/250\n",
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      "Epoch 28/250\n",
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      "Epoch 29/250\n",
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      "Epoch 30/250\n",
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      "Epoch 31/250\n",
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      "Epoch 32/250\n",
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      "Epoch 33/250\n",
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      "Epoch 34/250\n",
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      "Epoch 35/250\n",
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      "Epoch 36/250\n",
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      "Epoch 37/250\n",
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      "Epoch 38/250\n",
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      "Epoch 39/250\n",
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      "Epoch 139/250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1265/1265 [==============================] - 0s - loss: 0.5385 - acc: 0.7494 - val_loss: 0.5668 - val_acc: 0.7278\n",
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      "Epoch 174/250\n",
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      "Epoch 176/250\n",
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      "Epoch 177/250\n",
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      "Epoch 179/250\n",
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      "Epoch 180/250\n",
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      "Epoch 181/250\n",
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      "Epoch 182/250\n",
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      "Epoch 183/250\n",
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      "Epoch 184/250\n",
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      "Epoch 185/250\n",
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      "Epoch 186/250\n",
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      "Epoch 187/250\n",
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      "Epoch 188/250\n",
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      "Epoch 199/250\n",
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      "Epoch 200/250\n",
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      "Epoch 201/250\n",
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      "Epoch 202/250\n",
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      "Epoch 203/250\n",
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      "Epoch 206/250\n",
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      "Epoch 209/250\n",
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      "Epoch 215/250\n",
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      "Epoch 218/250\n",
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      "Epoch 219/250\n",
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      "Epoch 220/250\n",
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      "Epoch 221/250\n",
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      "Epoch 223/250\n",
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      "Epoch 224/250\n",
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      "Epoch 225/250\n",
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      "Epoch 226/250\n",
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      "Epoch 227/250\n",
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      "Epoch 228/250\n",
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      "Epoch 229/250\n",
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      "Epoch 230/250\n",
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      "Epoch 231/250\n",
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      "Epoch 232/250\n",
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      "Epoch 233/250\n",
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      "Epoch 234/250\n",
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      "Epoch 235/250\n",
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      "Epoch 236/250\n",
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      "Epoch 237/250\n",
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      "Epoch 238/250\n",
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      "Epoch 239/250\n",
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      "Epoch 240/250\n",
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      "Epoch 241/250\n",
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      "Epoch 242/250\n",
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      "Epoch 243/250\n",
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      "Epoch 244/250\n",
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      "Epoch 245/250\n",
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      "Epoch 246/250\n",
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      "Epoch 247/250\n",
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      "Epoch 248/250\n",
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      "Epoch 249/250\n",
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      "Epoch 250/250\n",
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      "256/316 [=======================>......] - ETA: 0sTrain on 1265 samples, validate on 316 samples\n",
      "Epoch 1/250\n",
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      "Epoch 2/250\n",
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      "Epoch 3/250\n",
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      "Epoch 4/250\n",
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      "Epoch 5/250\n",
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      "Epoch 6/250\n",
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      "Epoch 7/250\n",
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      "Epoch 8/250\n",
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      "Epoch 9/250\n",
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      "Epoch 10/250\n",
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      "Epoch 11/250\n",
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      "Epoch 12/250\n",
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      "Epoch 13/250\n",
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      "Epoch 14/250\n",
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      "Epoch 15/250\n",
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      "Epoch 16/250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1265/1265 [==============================] - 0s - loss: 0.5805 - acc: 0.7344 - val_loss: 0.5750 - val_acc: 0.7342\n",
      "Epoch 17/250\n",
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      "Epoch 18/250\n",
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      "Epoch 19/250\n",
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      "Epoch 20/250\n",
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      "Epoch 21/250\n",
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      "Epoch 22/250\n",
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      "Epoch 23/250\n",
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      "Epoch 24/250\n",
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      "Epoch 26/250\n",
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      "Epoch 27/250\n",
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      "Epoch 28/250\n",
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      "Epoch 30/250\n",
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      "Epoch 31/250\n",
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      "Epoch 32/250\n",
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      "Epoch 33/250\n",
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      "Epoch 40/250\n",
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      "Epoch 45/250\n",
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      "Epoch 46/250\n",
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      "Epoch 47/250\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1265/1265 [==============================] - 0s - loss: 0.5345 - acc: 0.7431 - val_loss: 0.5563 - val_acc: 0.7437\n",
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      "Epoch 179/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5207 - acc: 0.7573 - val_loss: 0.5567 - val_acc: 0.7532\n",
      "Epoch 180/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5191 - acc: 0.7565 - val_loss: 0.5568 - val_acc: 0.7532\n",
      "Epoch 181/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5200 - acc: 0.7549 - val_loss: 0.5572 - val_acc: 0.7532\n",
      "Epoch 182/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5235 - acc: 0.7510 - val_loss: 0.5569 - val_acc: 0.7532\n",
      "Epoch 183/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5236 - acc: 0.7494 - val_loss: 0.5569 - val_acc: 0.7532\n",
      "Epoch 184/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5135 - acc: 0.7581 - val_loss: 0.5569 - val_acc: 0.7532\n",
      "Epoch 185/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5205 - acc: 0.7518 - val_loss: 0.5582 - val_acc: 0.7532\n",
      "Epoch 186/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5122 - acc: 0.7557 - val_loss: 0.5576 - val_acc: 0.7532\n",
      "Epoch 187/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5248 - acc: 0.7652 - val_loss: 0.5570 - val_acc: 0.7532\n",
      "Epoch 188/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5179 - acc: 0.7573 - val_loss: 0.5579 - val_acc: 0.7532\n",
      "Epoch 189/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5205 - acc: 0.7557 - val_loss: 0.5578 - val_acc: 0.7563\n",
      "Epoch 190/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5154 - acc: 0.7644 - val_loss: 0.5573 - val_acc: 0.7532\n",
      "Epoch 191/250\n",
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      "Epoch 192/250\n",
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      "Epoch 193/250\n",
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      "Epoch 194/250\n",
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      "Epoch 195/250\n",
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      "Epoch 196/250\n",
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      "Epoch 197/250\n",
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      "Epoch 198/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5140 - acc: 0.7636 - val_loss: 0.5585 - val_acc: 0.7532\n",
      "Epoch 199/250\n",
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      "Epoch 200/250\n",
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      "Epoch 201/250\n",
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      "Epoch 202/250\n",
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      "Epoch 203/250\n",
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      "Epoch 204/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5074 - acc: 0.7526 - val_loss: 0.5587 - val_acc: 0.7532\n",
      "Epoch 205/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5086 - acc: 0.7700 - val_loss: 0.5588 - val_acc: 0.7532\n",
      "Epoch 206/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5012 - acc: 0.7652 - val_loss: 0.5592 - val_acc: 0.7532\n",
      "Epoch 207/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4975 - acc: 0.7636 - val_loss: 0.5600 - val_acc: 0.7532\n",
      "Epoch 208/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5097 - acc: 0.7652 - val_loss: 0.5602 - val_acc: 0.7532\n",
      "Epoch 209/250\n",
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      "Epoch 210/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5026 - acc: 0.7700 - val_loss: 0.5598 - val_acc: 0.7532\n",
      "Epoch 211/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5004 - acc: 0.7676 - val_loss: 0.5603 - val_acc: 0.7532\n",
      "Epoch 212/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5061 - acc: 0.7660 - val_loss: 0.5604 - val_acc: 0.7532\n",
      "Epoch 213/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5037 - acc: 0.7652 - val_loss: 0.5598 - val_acc: 0.7532\n",
      "Epoch 214/250\n",
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      "Epoch 215/250\n",
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      "Epoch 216/250\n",
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      "Epoch 217/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5024 - acc: 0.7723 - val_loss: 0.5591 - val_acc: 0.7532\n",
      "Epoch 218/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5049 - acc: 0.7692 - val_loss: 0.5593 - val_acc: 0.7532\n",
      "Epoch 219/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4999 - acc: 0.7700 - val_loss: 0.5600 - val_acc: 0.7532\n",
      "Epoch 220/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4977 - acc: 0.7708 - val_loss: 0.5598 - val_acc: 0.7532\n",
      "Epoch 221/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5030 - acc: 0.7636 - val_loss: 0.5607 - val_acc: 0.7532\n",
      "Epoch 222/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4960 - acc: 0.7668 - val_loss: 0.5600 - val_acc: 0.7500\n",
      "Epoch 223/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4984 - acc: 0.7684 - val_loss: 0.5600 - val_acc: 0.7532\n",
      "Epoch 224/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5024 - acc: 0.7644 - val_loss: 0.5605 - val_acc: 0.7532\n",
      "Epoch 225/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4974 - acc: 0.7652 - val_loss: 0.5600 - val_acc: 0.7532\n",
      "Epoch 226/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4943 - acc: 0.7771 - val_loss: 0.5597 - val_acc: 0.7500\n",
      "Epoch 227/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4996 - acc: 0.7652 - val_loss: 0.5602 - val_acc: 0.7500\n",
      "Epoch 228/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4995 - acc: 0.7534 - val_loss: 0.5606 - val_acc: 0.7532\n",
      "Epoch 229/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4926 - acc: 0.7723 - val_loss: 0.5611 - val_acc: 0.7500\n",
      "Epoch 230/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4956 - acc: 0.7692 - val_loss: 0.5607 - val_acc: 0.7500\n",
      "Epoch 231/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4950 - acc: 0.7715 - val_loss: 0.5604 - val_acc: 0.7500\n",
      "Epoch 232/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4916 - acc: 0.7676 - val_loss: 0.5604 - val_acc: 0.7500\n",
      "Epoch 233/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4940 - acc: 0.7684 - val_loss: 0.5613 - val_acc: 0.7500\n",
      "Epoch 234/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4861 - acc: 0.7731 - val_loss: 0.5616 - val_acc: 0.7500\n",
      "Epoch 235/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4868 - acc: 0.7739 - val_loss: 0.5615 - val_acc: 0.7532\n",
      "Epoch 236/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4844 - acc: 0.7676 - val_loss: 0.5618 - val_acc: 0.7500\n",
      "Epoch 237/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4962 - acc: 0.7763 - val_loss: 0.5614 - val_acc: 0.7500\n",
      "Epoch 238/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4857 - acc: 0.7715 - val_loss: 0.5622 - val_acc: 0.7500\n",
      "Epoch 239/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4787 - acc: 0.7708 - val_loss: 0.5614 - val_acc: 0.7500\n",
      "Epoch 240/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4867 - acc: 0.7747 - val_loss: 0.5616 - val_acc: 0.7405\n",
      "Epoch 241/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4855 - acc: 0.7771 - val_loss: 0.5623 - val_acc: 0.7500\n",
      "Epoch 242/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4892 - acc: 0.7613 - val_loss: 0.5651 - val_acc: 0.7500\n",
      "Epoch 243/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4804 - acc: 0.7739 - val_loss: 0.5634 - val_acc: 0.7500\n",
      "Epoch 244/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4827 - acc: 0.7668 - val_loss: 0.5632 - val_acc: 0.7500\n",
      "Epoch 245/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4940 - acc: 0.7636 - val_loss: 0.5622 - val_acc: 0.7532\n",
      "Epoch 246/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4767 - acc: 0.7763 - val_loss: 0.5634 - val_acc: 0.7500\n",
      "Epoch 247/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4894 - acc: 0.7684 - val_loss: 0.5624 - val_acc: 0.7532\n",
      "Epoch 248/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4825 - acc: 0.7818 - val_loss: 0.5636 - val_acc: 0.7532\n",
      "Epoch 249/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4954 - acc: 0.7605 - val_loss: 0.5649 - val_acc: 0.7500\n",
      "Epoch 250/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4861 - acc: 0.7779 - val_loss: 0.5634 - val_acc: 0.7532\n",
      "288/316 [==========================>...] - ETA: 0sTrain on 1265 samples, validate on 316 samples\n",
      "Epoch 1/250\n",
      "1265/1265 [==============================] - 2s - loss: 0.7760 - acc: 0.4925 - val_loss: 0.5921 - val_acc: 0.7247\n",
      "Epoch 2/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.6513 - acc: 0.6846 - val_loss: 0.5818 - val_acc: 0.7342\n",
      "Epoch 3/250\n",
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      "Epoch 4/250\n",
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      "Epoch 5/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.6128 - acc: 0.7209 - val_loss: 0.5776 - val_acc: 0.7342\n",
      "Epoch 6/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.6057 - acc: 0.7273 - val_loss: 0.5761 - val_acc: 0.7342\n",
      "Epoch 7/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.6125 - acc: 0.7178 - val_loss: 0.5757 - val_acc: 0.7342\n",
      "Epoch 8/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.6137 - acc: 0.7051 - val_loss: 0.5758 - val_acc: 0.7342\n",
      "Epoch 9/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.6188 - acc: 0.7091 - val_loss: 0.5764 - val_acc: 0.7342\n",
      "Epoch 10/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.6037 - acc: 0.7241 - val_loss: 0.5776 - val_acc: 0.7342\n",
      "Epoch 11/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5912 - acc: 0.7304 - val_loss: 0.5785 - val_acc: 0.7342\n",
      "Epoch 12/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5859 - acc: 0.7304 - val_loss: 0.5782 - val_acc: 0.7342\n",
      "Epoch 13/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.6076 - acc: 0.7289 - val_loss: 0.5794 - val_acc: 0.7342\n",
      "Epoch 14/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5829 - acc: 0.7265 - val_loss: 0.5803 - val_acc: 0.7342\n",
      "Epoch 15/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5818 - acc: 0.7304 - val_loss: 0.5804 - val_acc: 0.7342\n",
      "Epoch 16/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5846 - acc: 0.7249 - val_loss: 0.5812 - val_acc: 0.7342\n",
      "Epoch 17/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5965 - acc: 0.7233 - val_loss: 0.5811 - val_acc: 0.7342\n",
      "Epoch 18/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5750 - acc: 0.7320 - val_loss: 0.5814 - val_acc: 0.7342\n",
      "Epoch 19/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5806 - acc: 0.7360 - val_loss: 0.5820 - val_acc: 0.7342\n",
      "Epoch 20/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5788 - acc: 0.7296 - val_loss: 0.5825 - val_acc: 0.7342\n",
      "Epoch 21/250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1265/1265 [==============================] - 0s - loss: 0.5854 - acc: 0.7304 - val_loss: 0.5834 - val_acc: 0.7342\n",
      "Epoch 22/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5878 - acc: 0.7273 - val_loss: 0.5845 - val_acc: 0.7342\n",
      "Epoch 23/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5786 - acc: 0.7360 - val_loss: 0.5847 - val_acc: 0.7342\n",
      "Epoch 24/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5799 - acc: 0.7328 - val_loss: 0.5845 - val_acc: 0.7342\n",
      "Epoch 25/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5790 - acc: 0.7407 - val_loss: 0.5852 - val_acc: 0.7342\n",
      "Epoch 26/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5771 - acc: 0.7281 - val_loss: 0.5851 - val_acc: 0.7342\n",
      "Epoch 27/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5781 - acc: 0.7344 - val_loss: 0.5850 - val_acc: 0.7342\n",
      "Epoch 28/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5817 - acc: 0.7281 - val_loss: 0.5850 - val_acc: 0.7342\n",
      "Epoch 29/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5944 - acc: 0.7328 - val_loss: 0.5852 - val_acc: 0.7342\n",
      "Epoch 30/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5748 - acc: 0.7320 - val_loss: 0.5853 - val_acc: 0.7342\n",
      "Epoch 31/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5678 - acc: 0.7289 - val_loss: 0.5860 - val_acc: 0.7342\n",
      "Epoch 32/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5773 - acc: 0.7320 - val_loss: 0.5868 - val_acc: 0.7342\n",
      "Epoch 33/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5681 - acc: 0.7320 - val_loss: 0.5873 - val_acc: 0.7342\n",
      "Epoch 34/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5813 - acc: 0.7304 - val_loss: 0.5877 - val_acc: 0.7342\n",
      "Epoch 35/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5725 - acc: 0.7312 - val_loss: 0.5875 - val_acc: 0.7342\n",
      "Epoch 36/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5733 - acc: 0.7360 - val_loss: 0.5888 - val_acc: 0.7342\n",
      "Epoch 37/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5779 - acc: 0.7344 - val_loss: 0.5892 - val_acc: 0.7342\n",
      "Epoch 38/250\n",
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      "Epoch 39/250\n",
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      "Epoch 40/250\n",
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      "Epoch 41/250\n",
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      "Epoch 42/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5626 - acc: 0.7320 - val_loss: 0.5890 - val_acc: 0.7342\n",
      "Epoch 43/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5771 - acc: 0.7328 - val_loss: 0.5894 - val_acc: 0.7342\n",
      "Epoch 44/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5624 - acc: 0.7352 - val_loss: 0.5898 - val_acc: 0.7342\n",
      "Epoch 45/250\n",
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      "Epoch 46/250\n",
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      "Epoch 47/250\n",
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      "Epoch 48/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5663 - acc: 0.7368 - val_loss: 0.5883 - val_acc: 0.7342\n",
      "Epoch 49/250\n",
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      "Epoch 50/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5687 - acc: 0.7312 - val_loss: 0.5880 - val_acc: 0.7342\n",
      "Epoch 51/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5704 - acc: 0.7304 - val_loss: 0.5883 - val_acc: 0.7342\n",
      "Epoch 52/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5661 - acc: 0.7352 - val_loss: 0.5886 - val_acc: 0.7342\n",
      "Epoch 53/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5670 - acc: 0.7360 - val_loss: 0.5883 - val_acc: 0.7342\n",
      "Epoch 54/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5655 - acc: 0.7352 - val_loss: 0.5881 - val_acc: 0.7342\n",
      "Epoch 55/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5649 - acc: 0.7375 - val_loss: 0.5884 - val_acc: 0.7342\n",
      "Epoch 56/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5706 - acc: 0.7375 - val_loss: 0.5888 - val_acc: 0.7342\n",
      "Epoch 57/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5625 - acc: 0.7352 - val_loss: 0.5896 - val_acc: 0.7342\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 189/250\n",
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      "Epoch 190/250\n",
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      "Epoch 191/250\n",
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      "Epoch 192/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.5021 - acc: 0.7613 - val_loss: 0.6012 - val_acc: 0.7278\n",
      "Epoch 193/250\n",
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      "Epoch 194/250\n",
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      "Epoch 195/250\n",
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      "Epoch 196/250\n",
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      "Epoch 198/250\n",
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      "Epoch 199/250\n",
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      "Epoch 200/250\n",
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      "Epoch 201/250\n",
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      "Epoch 202/250\n",
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      "Epoch 204/250\n",
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      "Epoch 205/250\n",
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      "Epoch 206/250\n",
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      "Epoch 207/250\n",
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      "Epoch 208/250\n",
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      "Epoch 209/250\n",
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      "Epoch 210/250\n",
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      "Epoch 211/250\n",
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      "Epoch 212/250\n",
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      "Epoch 213/250\n",
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      "Epoch 214/250\n",
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      "Epoch 215/250\n",
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      "Epoch 216/250\n",
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      "Epoch 217/250\n",
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      "Epoch 218/250\n",
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      "Epoch 219/250\n",
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      "Epoch 220/250\n",
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      "Epoch 221/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4848 - acc: 0.7763 - val_loss: 0.6119 - val_acc: 0.7278\n",
      "Epoch 222/250\n",
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      "Epoch 223/250\n",
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      "Epoch 224/250\n",
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      "Epoch 225/250\n",
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      "Epoch 226/250\n",
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      "Epoch 227/250\n",
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      "Epoch 228/250\n",
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      "Epoch 229/250\n",
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      "Epoch 230/250\n",
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      "Epoch 231/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4823 - acc: 0.7834 - val_loss: 0.6120 - val_acc: 0.7184\n",
      "Epoch 232/250\n",
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      "Epoch 233/250\n",
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      "Epoch 234/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4725 - acc: 0.7929 - val_loss: 0.6152 - val_acc: 0.7278\n",
      "Epoch 235/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4708 - acc: 0.7826 - val_loss: 0.6154 - val_acc: 0.7184\n",
      "Epoch 236/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4736 - acc: 0.7850 - val_loss: 0.6153 - val_acc: 0.7120\n",
      "Epoch 237/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4711 - acc: 0.7834 - val_loss: 0.6157 - val_acc: 0.7089\n",
      "Epoch 238/250\n",
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      "Epoch 239/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4742 - acc: 0.7802 - val_loss: 0.6151 - val_acc: 0.7184\n",
      "Epoch 240/250\n",
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      "Epoch 241/250\n",
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      "Epoch 242/250\n",
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      "Epoch 243/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4620 - acc: 0.7874 - val_loss: 0.6146 - val_acc: 0.7152\n",
      "Epoch 244/250\n",
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      "Epoch 245/250\n",
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      "Epoch 246/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4640 - acc: 0.7937 - val_loss: 0.6142 - val_acc: 0.7120\n",
      "Epoch 247/250\n",
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      "Epoch 248/250\n",
      "1265/1265 [==============================] - 0s - loss: 0.4640 - acc: 0.7913 - val_loss: 0.6194 - val_acc: 0.7152\n",
      "Epoch 249/250\n",
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      "Epoch 250/250\n",
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      "256/316 [=======================>......] - ETA: 0sTrain on 1264 samples, validate on 317 samples\n",
      "Epoch 1/250\n",
      "1264/1264 [==============================] - 2s - loss: 0.6918 - acc: 0.5293 - val_loss: 0.6390 - val_acc: 0.7445\n",
      "Epoch 2/250\n",
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      "Epoch 3/250\n",
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      "Epoch 4/250\n",
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      "Epoch 5/250\n",
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      "Epoch 6/250\n",
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      "Epoch 7/250\n",
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      "Epoch 8/250\n",
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      "Epoch 9/250\n",
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      "Epoch 10/250\n",
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      "Epoch 11/250\n",
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      "Epoch 12/250\n",
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      "Epoch 13/250\n",
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      "Epoch 14/250\n",
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      "Epoch 15/250\n",
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      "Epoch 16/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5818 - acc: 0.7453 - val_loss: 0.5755 - val_acc: 0.7445\n",
      "Epoch 17/250\n",
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      "Epoch 18/250\n",
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      "Epoch 19/250\n",
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      "Epoch 20/250\n",
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      "Epoch 21/250\n",
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      "Epoch 22/250\n",
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      "Epoch 23/250\n",
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      "Epoch 24/250\n",
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      "Epoch 25/250\n",
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      "Epoch 26/250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1264/1264 [==============================] - 0s - loss: 0.5751 - acc: 0.7453 - val_loss: 0.5768 - val_acc: 0.7445\n",
      "Epoch 27/250\n",
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      "Epoch 28/250\n",
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      "Epoch 29/250\n",
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      "Epoch 30/250\n",
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      "Epoch 31/250\n",
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      "Epoch 32/250\n",
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      "Epoch 33/250\n",
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      "Epoch 34/250\n",
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      "Epoch 35/250\n",
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      "Epoch 36/250\n",
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      "Epoch 37/250\n",
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      "Epoch 38/250\n",
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      "Epoch 39/250\n",
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      "Epoch 40/250\n",
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      "Epoch 41/250\n",
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      "Epoch 42/250\n",
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      "Epoch 43/250\n",
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      "Epoch 44/250\n",
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      "Epoch 46/250\n",
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      "Epoch 47/250\n",
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      "Epoch 48/250\n",
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      "Epoch 49/250\n",
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      "Epoch 50/250\n",
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      "Epoch 51/250\n",
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      "Epoch 52/250\n",
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      "Epoch 53/250\n",
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      "Epoch 55/250\n",
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      "Epoch 56/250\n",
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      "Epoch 57/250\n",
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      "Epoch 58/250\n",
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      "Epoch 59/250\n",
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      "Epoch 60/250\n",
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      "Epoch 61/250\n",
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      "Epoch 62/250\n",
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      "Epoch 63/250\n",
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      "Epoch 64/250\n",
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      "Epoch 65/250\n",
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      "Epoch 66/250\n",
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      "Epoch 67/250\n",
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      "Epoch 68/250\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 200/250\n",
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      "Epoch 201/250\n",
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      "Epoch 203/250\n",
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      "Epoch 212/250\n",
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      "Epoch 213/250\n",
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      "Epoch 214/250\n",
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      "Epoch 215/250\n",
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      "Epoch 216/250\n",
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      "Epoch 217/250\n",
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      "Epoch 218/250\n",
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      "Epoch 219/250\n",
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      "Epoch 220/250\n",
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      "Epoch 221/250\n",
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      "Epoch 222/250\n",
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      "Epoch 223/250\n",
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      "Epoch 224/250\n",
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      "Epoch 225/250\n",
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      "Epoch 226/250\n",
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      "Epoch 227/250\n",
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      "Epoch 228/250\n",
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      "Epoch 229/250\n",
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      "Epoch 230/250\n",
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      "Epoch 231/250\n",
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      "Epoch 232/250\n",
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      "Epoch 233/250\n",
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      "Epoch 234/250\n",
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      "Epoch 235/250\n",
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      "Epoch 236/250\n",
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      "Epoch 237/250\n",
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      "Epoch 238/250\n",
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      "Epoch 239/250\n",
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      "Epoch 240/250\n",
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      "Epoch 241/250\n",
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      "Epoch 242/250\n",
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      "Epoch 243/250\n",
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      "Epoch 244/250\n",
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      "Epoch 245/250\n",
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      "Epoch 246/250\n",
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      "Epoch 247/250\n",
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      "Epoch 248/250\n",
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      "Epoch 249/250\n",
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      "Epoch 250/250\n",
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      "256/317 [=======================>......] - ETA: 0sTrain on 1264 samples, validate on 317 samples\n",
      "Epoch 1/250\n",
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      "Epoch 2/250\n",
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      "Epoch 4/250\n",
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      "Epoch 5/250\n",
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      "Epoch 6/250\n",
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      "Epoch 7/250\n",
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      "Epoch 8/250\n",
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      "Epoch 9/250\n",
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      "Epoch 10/250\n",
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      "Epoch 11/250\n",
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      "Epoch 14/250\n",
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      "Epoch 15/250\n",
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      "Epoch 16/250\n",
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      "Epoch 18/250\n",
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      "Epoch 19/250\n",
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      "Epoch 20/250\n",
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      "Epoch 21/250\n",
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      "Epoch 22/250\n",
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      "Epoch 23/250\n",
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      "Epoch 24/250\n",
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      "Epoch 25/250\n",
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      "Epoch 26/250\n",
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      "Epoch 27/250\n",
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      "Epoch 28/250\n",
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      "Epoch 29/250\n",
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      "Epoch 30/250\n",
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      "Epoch 31/250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1264/1264 [==============================] - 0s - loss: 0.5795 - acc: 0.7453 - val_loss: 0.5787 - val_acc: 0.7445\n",
      "Epoch 32/250\n",
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      "Epoch 33/250\n",
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      "Epoch 34/250\n",
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      "Epoch 35/250\n",
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      "Epoch 36/250\n",
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      "Epoch 37/250\n",
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      "Epoch 38/250\n",
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      "Epoch 39/250\n",
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      "Epoch 40/250\n",
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      "Epoch 41/250\n",
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      "Epoch 42/250\n",
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      "Epoch 43/250\n",
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      "Epoch 45/250\n",
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      "Epoch 46/250\n",
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      "Epoch 47/250\n",
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      "Epoch 48/250\n",
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      "Epoch 49/250\n",
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      "Epoch 51/250\n",
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      "Epoch 52/250\n",
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      "Epoch 53/250\n",
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      "Epoch 54/250\n",
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      "Epoch 55/250\n",
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      "Epoch 56/250\n",
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      "Epoch 58/250\n",
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      "Epoch 59/250\n",
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      "Epoch 60/250\n",
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      "Epoch 61/250\n",
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      "Epoch 62/250\n",
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      "Epoch 63/250\n",
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      "Epoch 64/250\n",
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      "Epoch 65/250\n",
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      "Epoch 66/250\n",
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      "Epoch 67/250\n",
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      "Epoch 68/250\n",
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      "Epoch 69/250\n",
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      "Epoch 70/250\n",
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      "Epoch 71/250\n",
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      "Epoch 72/250\n",
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      "Epoch 73/250\n",
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      "Epoch 74/250\n",
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      "Epoch 75/250\n",
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      "Epoch 76/250\n",
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      "Epoch 77/250\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 211/250\n",
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      "Epoch 214/250\n",
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      "Epoch 216/250\n",
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      "Epoch 217/250\n",
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      "Epoch 218/250\n",
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      "Epoch 219/250\n",
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      "Epoch 220/250\n",
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      "Epoch 221/250\n",
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      "Epoch 222/250\n",
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      "Epoch 223/250\n",
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      "Epoch 224/250\n",
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      "Epoch 225/250\n",
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      "Epoch 226/250\n",
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      "Epoch 227/250\n",
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      "Epoch 228/250\n",
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      "Epoch 229/250\n",
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      "Epoch 230/250\n",
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      "Epoch 231/250\n",
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      "Epoch 232/250\n",
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      "Epoch 233/250\n",
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      "Epoch 234/250\n",
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      "Epoch 235/250\n",
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      "Epoch 236/250\n",
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      "Epoch 237/250\n",
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      "Epoch 238/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5160 - acc: 0.7500 - val_loss: 0.5948 - val_acc: 0.7445\n",
      "Epoch 239/250\n",
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      "Epoch 240/250\n",
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      "Epoch 241/250\n",
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      "Epoch 242/250\n",
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      "Epoch 243/250\n",
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      "Epoch 244/250\n",
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      "Epoch 245/250\n",
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      "Epoch 246/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5203 - acc: 0.7547 - val_loss: 0.5972 - val_acc: 0.7445\n",
      "Epoch 247/250\n",
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      "Epoch 248/250\n",
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      "Epoch 249/250\n",
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      "Epoch 250/250\n",
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      "256/317 [=======================>......] - ETA: 0sTrain on 1264 samples, validate on 317 samples\n",
      "Epoch 1/250\n",
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      "Epoch 2/250\n",
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      "Epoch 3/250\n",
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      "Epoch 4/250\n",
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      "Epoch 5/250\n",
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      "Epoch 6/250\n",
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      "Epoch 7/250\n",
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      "Epoch 8/250\n",
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      "Epoch 9/250\n",
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      "Epoch 10/250\n",
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      "Epoch 11/250\n",
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      "Epoch 12/250\n",
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      "Epoch 13/250\n",
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      "Epoch 14/250\n",
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      "Epoch 15/250\n",
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      "Epoch 16/250\n",
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      "Epoch 17/250\n",
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      "Epoch 18/250\n",
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      "Epoch 19/250\n",
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      "Epoch 20/250\n",
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      "Epoch 21/250\n",
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      "Epoch 22/250\n",
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      "Epoch 23/250\n",
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      "Epoch 24/250\n",
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      "Epoch 25/250\n",
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      "Epoch 26/250\n",
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      "Epoch 27/250\n",
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      "Epoch 28/250\n",
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      "Epoch 29/250\n",
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      "Epoch 30/250\n",
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      "Epoch 31/250\n",
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      "Epoch 32/250\n",
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      "Epoch 33/250\n",
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      "Epoch 34/250\n",
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      "Epoch 35/250\n",
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      "Epoch 36/250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1264/1264 [==============================] - 0s - loss: 0.5761 - acc: 0.7453 - val_loss: 0.5760 - val_acc: 0.7445\n",
      "Epoch 37/250\n",
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      "Epoch 38/250\n",
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      "Epoch 39/250\n",
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      "Epoch 40/250\n",
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      "Epoch 41/250\n",
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      "Epoch 42/250\n",
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      "Epoch 43/250\n",
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      "Epoch 44/250\n",
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      "Epoch 45/250\n",
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      "Epoch 46/250\n",
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      "Epoch 47/250\n",
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      "Epoch 48/250\n",
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      "Epoch 49/250\n",
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      "Epoch 50/250\n",
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      "Epoch 51/250\n",
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      "Epoch 52/250\n",
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      "Epoch 53/250\n",
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      "Epoch 54/250\n",
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      "Epoch 55/250\n",
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      "Epoch 56/250\n",
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      "Epoch 57/250\n",
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      "Epoch 58/250\n",
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      "Epoch 59/250\n",
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      "Epoch 60/250\n",
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      "Epoch 61/250\n",
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      "Epoch 62/250\n",
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      "Epoch 63/250\n",
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      "Epoch 64/250\n",
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      "Epoch 65/250\n",
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      "Epoch 66/250\n",
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      "Epoch 67/250\n",
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      "Epoch 68/250\n",
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      "Epoch 69/250\n",
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      "Epoch 70/250\n",
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      "Epoch 71/250\n",
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      "Epoch 72/250\n",
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      "Epoch 73/250\n",
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      "Epoch 74/250\n",
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      "Epoch 75/250\n",
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      "Epoch 76/250\n",
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      "Epoch 77/250\n",
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      "Epoch 78/250\n",
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      "Epoch 79/250\n",
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      "Epoch 80/250\n",
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      "Epoch 81/250\n",
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      "Epoch 82/250\n",
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      "Epoch 83/250\n",
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      "Epoch 84/250\n",
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      "Epoch 85/250\n",
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      "Epoch 86/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5621 - acc: 0.7453 - val_loss: 0.5735 - val_acc: 0.7445\n",
      "Epoch 87/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5645 - acc: 0.7453 - val_loss: 0.5734 - val_acc: 0.7445\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 219/250\n",
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      "Epoch 220/250\n",
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      "Epoch 221/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4940 - acc: 0.7603 - val_loss: 0.5740 - val_acc: 0.7382\n",
      "Epoch 222/250\n",
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      "Epoch 223/250\n",
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      "Epoch 224/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5024 - acc: 0.7595 - val_loss: 0.5747 - val_acc: 0.7350\n",
      "Epoch 225/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4999 - acc: 0.7627 - val_loss: 0.5747 - val_acc: 0.7350\n",
      "Epoch 226/250\n",
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      "Epoch 227/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5109 - acc: 0.7666 - val_loss: 0.5746 - val_acc: 0.7287\n",
      "Epoch 228/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5044 - acc: 0.7714 - val_loss: 0.5748 - val_acc: 0.7287\n",
      "Epoch 229/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5059 - acc: 0.7658 - val_loss: 0.5754 - val_acc: 0.7319\n",
      "Epoch 230/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5003 - acc: 0.7682 - val_loss: 0.5752 - val_acc: 0.7287\n",
      "Epoch 231/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4999 - acc: 0.7650 - val_loss: 0.5750 - val_acc: 0.7287\n",
      "Epoch 232/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5002 - acc: 0.7761 - val_loss: 0.5752 - val_acc: 0.7287\n",
      "Epoch 233/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5037 - acc: 0.7634 - val_loss: 0.5760 - val_acc: 0.7287\n",
      "Epoch 234/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4851 - acc: 0.7682 - val_loss: 0.5776 - val_acc: 0.7382\n",
      "Epoch 235/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4996 - acc: 0.7674 - val_loss: 0.5763 - val_acc: 0.7287\n",
      "Epoch 236/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4988 - acc: 0.7698 - val_loss: 0.5767 - val_acc: 0.7287\n",
      "Epoch 237/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4976 - acc: 0.7698 - val_loss: 0.5766 - val_acc: 0.7287\n",
      "Epoch 238/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5017 - acc: 0.7650 - val_loss: 0.5764 - val_acc: 0.7287\n",
      "Epoch 239/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4921 - acc: 0.7729 - val_loss: 0.5772 - val_acc: 0.7287\n",
      "Epoch 240/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4871 - acc: 0.7722 - val_loss: 0.5774 - val_acc: 0.7287\n",
      "Epoch 241/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4949 - acc: 0.7769 - val_loss: 0.5786 - val_acc: 0.7287\n",
      "Epoch 242/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5011 - acc: 0.7666 - val_loss: 0.5782 - val_acc: 0.7287\n",
      "Epoch 243/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4909 - acc: 0.7682 - val_loss: 0.5788 - val_acc: 0.7287\n",
      "Epoch 244/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4949 - acc: 0.7777 - val_loss: 0.5787 - val_acc: 0.7287\n",
      "Epoch 245/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4848 - acc: 0.7745 - val_loss: 0.5790 - val_acc: 0.7287\n",
      "Epoch 246/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4810 - acc: 0.7801 - val_loss: 0.5788 - val_acc: 0.7287\n",
      "Epoch 247/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4909 - acc: 0.7666 - val_loss: 0.5791 - val_acc: 0.7256\n",
      "Epoch 248/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.5014 - acc: 0.7698 - val_loss: 0.5788 - val_acc: 0.7287\n",
      "Epoch 249/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4879 - acc: 0.7737 - val_loss: 0.5794 - val_acc: 0.7287\n",
      "Epoch 250/250\n",
      "1264/1264 [==============================] - 0s - loss: 0.4830 - acc: 0.7809 - val_loss: 0.5804 - val_acc: 0.7287\n",
      "256/317 [=======================>......] - ETA: 0sTrain on 1266 samples, validate on 315 samples\n",
      "Epoch 1/250\n",
      "1266/1266 [==============================] - 3s - loss: 0.6672 - acc: 0.6161 - val_loss: 0.6215 - val_acc: 0.7460\n",
      "Epoch 2/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.6120 - acc: 0.7314 - val_loss: 0.5849 - val_acc: 0.7460\n",
      "Epoch 3/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5971 - acc: 0.7441 - val_loss: 0.5727 - val_acc: 0.7460\n",
      "Epoch 4/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5908 - acc: 0.7449 - val_loss: 0.5712 - val_acc: 0.7460\n",
      "Epoch 5/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5869 - acc: 0.7449 - val_loss: 0.5712 - val_acc: 0.7460\n",
      "Epoch 6/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5902 - acc: 0.7449 - val_loss: 0.5715 - val_acc: 0.7460\n",
      "Epoch 7/250\n",
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      "Epoch 8/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5820 - acc: 0.7449 - val_loss: 0.5716 - val_acc: 0.7460\n",
      "Epoch 9/250\n",
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      "Epoch 10/250\n",
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      "Epoch 11/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5848 - acc: 0.7449 - val_loss: 0.5723 - val_acc: 0.7460\n",
      "Epoch 12/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5867 - acc: 0.7457 - val_loss: 0.5727 - val_acc: 0.7460\n",
      "Epoch 13/250\n",
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      "Epoch 14/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5838 - acc: 0.7449 - val_loss: 0.5733 - val_acc: 0.7460\n",
      "Epoch 15/250\n",
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      "Epoch 16/250\n",
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      "Epoch 17/250\n",
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      "Epoch 18/250\n",
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      "Epoch 19/250\n",
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      "Epoch 20/250\n",
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      "Epoch 21/250\n",
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      "Epoch 22/250\n",
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      "Epoch 23/250\n",
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      "Epoch 24/250\n",
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      "Epoch 25/250\n",
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      "Epoch 26/250\n",
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      "Epoch 27/250\n",
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      "Epoch 28/250\n",
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      "Epoch 29/250\n",
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      "Epoch 30/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5724 - acc: 0.7449 - val_loss: 0.5742 - val_acc: 0.7460\n",
      "Epoch 31/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5770 - acc: 0.7449 - val_loss: 0.5741 - val_acc: 0.7460\n",
      "Epoch 32/250\n",
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      "Epoch 33/250\n",
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      "Epoch 34/250\n",
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      "Epoch 35/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5728 - acc: 0.7449 - val_loss: 0.5743 - val_acc: 0.7460\n",
      "Epoch 36/250\n",
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      "Epoch 37/250\n",
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      "Epoch 38/250\n",
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      "Epoch 39/250\n",
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      "Epoch 40/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5707 - acc: 0.7449 - val_loss: 0.5744 - val_acc: 0.7460\n",
      "Epoch 41/250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1266/1266 [==============================] - 0s - loss: 0.5750 - acc: 0.7449 - val_loss: 0.5742 - val_acc: 0.7460\n",
      "Epoch 42/250\n",
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      "Epoch 43/250\n",
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      "Epoch 44/250\n",
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      "Epoch 45/250\n",
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      "Epoch 46/250\n",
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      "Epoch 47/250\n",
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      "Epoch 48/250\n",
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      "Epoch 49/250\n",
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      "Epoch 50/250\n",
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      "Epoch 51/250\n",
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      "Epoch 52/250\n",
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      "Epoch 53/250\n",
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      "Epoch 54/250\n",
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      "Epoch 55/250\n",
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      "Epoch 56/250\n",
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      "Epoch 57/250\n",
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      "Epoch 58/250\n",
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      "Epoch 59/250\n",
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      "Epoch 60/250\n",
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      "Epoch 61/250\n",
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      "Epoch 62/250\n",
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      "Epoch 63/250\n",
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      "Epoch 64/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5619 - acc: 0.7457 - val_loss: 0.5740 - val_acc: 0.7460\n",
      "Epoch 65/250\n",
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      "Epoch 66/250\n",
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      "Epoch 67/250\n",
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      "Epoch 68/250\n",
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      "Epoch 69/250\n",
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      "Epoch 70/250\n",
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      "Epoch 71/250\n",
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      "Epoch 72/250\n",
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      "Epoch 73/250\n",
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      "Epoch 74/250\n",
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      "Epoch 75/250\n",
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      "Epoch 76/250\n",
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      "Epoch 77/250\n",
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      "Epoch 78/250\n",
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      "Epoch 79/250\n",
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      "Epoch 80/250\n",
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      "Epoch 81/250\n",
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      "Epoch 82/250\n",
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      "Epoch 83/250\n",
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      "Epoch 84/250\n",
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      "Epoch 85/250\n",
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      "Epoch 86/250\n",
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      "Epoch 87/250\n",
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      "Epoch 88/250\n",
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      "Epoch 89/250\n",
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      "Epoch 90/250\n",
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      "Epoch 91/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5525 - acc: 0.7457 - val_loss: 0.5755 - val_acc: 0.7460\n",
      "Epoch 92/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5533 - acc: 0.7464 - val_loss: 0.5753 - val_acc: 0.7460\n",
      "Epoch 93/250\n",
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      "Epoch 94/250\n",
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      "Epoch 95/250\n",
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      "Epoch 96/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5510 - acc: 0.7464 - val_loss: 0.5757 - val_acc: 0.7460\n",
      "Epoch 97/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5551 - acc: 0.7449 - val_loss: 0.5757 - val_acc: 0.7460\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 229/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.4893 - acc: 0.7733 - val_loss: 0.5927 - val_acc: 0.7429\n",
      "Epoch 230/250\n",
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      "Epoch 231/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.4822 - acc: 0.7780 - val_loss: 0.5950 - val_acc: 0.7492\n",
      "Epoch 232/250\n",
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      "Epoch 233/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.4709 - acc: 0.7930 - val_loss: 0.5960 - val_acc: 0.7397\n",
      "Epoch 234/250\n",
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      "Epoch 235/250\n",
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      "Epoch 236/250\n",
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      "Epoch 237/250\n",
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      "Epoch 238/250\n",
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      "Epoch 239/250\n",
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      "Epoch 240/250\n",
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      "Epoch 241/250\n",
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      "Epoch 242/250\n",
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      "Epoch 243/250\n",
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      "Epoch 244/250\n",
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      "Epoch 245/250\n",
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      "Epoch 246/250\n",
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      "Epoch 247/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.4839 - acc: 0.7780 - val_loss: 0.5970 - val_acc: 0.7429\n",
      "Epoch 248/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.4796 - acc: 0.7859 - val_loss: 0.5968 - val_acc: 0.7429\n",
      "Epoch 249/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.4769 - acc: 0.7788 - val_loss: 0.5992 - val_acc: 0.7460\n",
      "Epoch 250/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.4822 - acc: 0.7765 - val_loss: 0.5979 - val_acc: 0.7460\n",
      "256/315 [=======================>......] - ETA: 0sTrain on 1266 samples, validate on 315 samples\n",
      "Epoch 1/250\n",
      "1266/1266 [==============================] - 3s - loss: 0.6789 - acc: 0.5711 - val_loss: 0.6261 - val_acc: 0.7460\n",
      "Epoch 2/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.6325 - acc: 0.7212 - val_loss: 0.5931 - val_acc: 0.7460\n",
      "Epoch 3/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5962 - acc: 0.7449 - val_loss: 0.5779 - val_acc: 0.7460\n",
      "Epoch 4/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5945 - acc: 0.7449 - val_loss: 0.5737 - val_acc: 0.7460\n",
      "Epoch 5/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5874 - acc: 0.7449 - val_loss: 0.5730 - val_acc: 0.7460\n",
      "Epoch 6/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5907 - acc: 0.7449 - val_loss: 0.5728 - val_acc: 0.7460\n",
      "Epoch 7/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5835 - acc: 0.7449 - val_loss: 0.5727 - val_acc: 0.7460\n",
      "Epoch 8/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5774 - acc: 0.7449 - val_loss: 0.5725 - val_acc: 0.7460\n",
      "Epoch 9/250\n",
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      "Epoch 10/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5923 - acc: 0.7449 - val_loss: 0.5726 - val_acc: 0.7460\n",
      "Epoch 11/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5825 - acc: 0.7449 - val_loss: 0.5729 - val_acc: 0.7460\n",
      "Epoch 12/250\n",
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      "Epoch 13/250\n",
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      "Epoch 14/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5840 - acc: 0.7449 - val_loss: 0.5725 - val_acc: 0.7460\n",
      "Epoch 15/250\n",
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      "Epoch 16/250\n",
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      "Epoch 17/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5833 - acc: 0.7449 - val_loss: 0.5726 - val_acc: 0.7460\n",
      "Epoch 18/250\n",
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      "Epoch 19/250\n",
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      "Epoch 20/250\n",
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      "Epoch 21/250\n",
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      "Epoch 22/250\n",
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      "Epoch 23/250\n",
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      "Epoch 24/250\n",
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      "Epoch 25/250\n",
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      "Epoch 26/250\n",
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      "Epoch 27/250\n",
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      "Epoch 28/250\n",
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      "Epoch 29/250\n",
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      "Epoch 30/250\n",
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      "Epoch 31/250\n",
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      "Epoch 32/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5818 - acc: 0.7449 - val_loss: 0.5724 - val_acc: 0.7460\n",
      "Epoch 33/250\n",
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      "Epoch 34/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5801 - acc: 0.7449 - val_loss: 0.5722 - val_acc: 0.7460\n",
      "Epoch 35/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5733 - acc: 0.7449 - val_loss: 0.5722 - val_acc: 0.7460\n",
      "Epoch 36/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5813 - acc: 0.7449 - val_loss: 0.5720 - val_acc: 0.7460\n",
      "Epoch 37/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5779 - acc: 0.7449 - val_loss: 0.5719 - val_acc: 0.7460\n",
      "Epoch 38/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5817 - acc: 0.7449 - val_loss: 0.5721 - val_acc: 0.7460\n",
      "Epoch 39/250\n",
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      "Epoch 40/250\n",
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      "Epoch 41/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5840 - acc: 0.7449 - val_loss: 0.5723 - val_acc: 0.7460\n",
      "Epoch 42/250\n",
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      "Epoch 43/250\n",
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      "Epoch 44/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5711 - acc: 0.7449 - val_loss: 0.5719 - val_acc: 0.7460\n",
      "Epoch 45/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5742 - acc: 0.7449 - val_loss: 0.5722 - val_acc: 0.7460\n",
      "Epoch 46/250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1266/1266 [==============================] - 0s - loss: 0.5747 - acc: 0.7449 - val_loss: 0.5721 - val_acc: 0.7460\n",
      "Epoch 47/250\n",
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      "Epoch 48/250\n",
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      "Epoch 49/250\n",
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      "Epoch 50/250\n",
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      "Epoch 51/250\n",
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      "Epoch 52/250\n",
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      "Epoch 53/250\n",
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      "Epoch 54/250\n",
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      "Epoch 55/250\n",
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      "Epoch 56/250\n",
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      "Epoch 57/250\n",
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      "Epoch 58/250\n",
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      "Epoch 59/250\n",
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      "Epoch 60/250\n",
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      "Epoch 61/250\n",
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      "Epoch 62/250\n",
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      "Epoch 63/250\n",
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      "Epoch 64/250\n",
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      "Epoch 65/250\n",
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      "Epoch 66/250\n",
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      "Epoch 67/250\n",
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      "Epoch 68/250\n",
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      "Epoch 69/250\n",
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      "Epoch 70/250\n",
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      "Epoch 71/250\n",
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      "Epoch 72/250\n",
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      "Epoch 73/250\n",
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      "Epoch 74/250\n",
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      "Epoch 75/250\n",
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      "Epoch 76/250\n",
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      "Epoch 77/250\n",
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      "Epoch 78/250\n",
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      "Epoch 79/250\n",
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      "Epoch 80/250\n",
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      "Epoch 81/250\n",
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      "Epoch 82/250\n",
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      "Epoch 83/250\n",
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      "Epoch 84/250\n",
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      "Epoch 85/250\n",
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      "Epoch 86/250\n",
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      "Epoch 87/250\n",
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      "Epoch 88/250\n",
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      "Epoch 89/250\n",
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      "Epoch 90/250\n",
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      "Epoch 91/250\n",
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      "Epoch 92/250\n",
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      "Epoch 93/250\n",
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      "Epoch 94/250\n",
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      "Epoch 95/250\n",
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      "Epoch 96/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5651 - acc: 0.7449 - val_loss: 0.5701 - val_acc: 0.7460\n",
      "Epoch 97/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5635 - acc: 0.7449 - val_loss: 0.5703 - val_acc: 0.7460\n",
      "Epoch 98/250\n",
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      "Epoch 99/250\n",
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      "Epoch 100/250\n",
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      "Epoch 101/250\n",
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      "Epoch 102/250\n",
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      "Epoch 103/250\n",
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      "Epoch 104/250\n",
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      "Epoch 105/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5617 - acc: 0.7449 - val_loss: 0.5699 - val_acc: 0.7460\n",
      "Epoch 106/250\n",
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      "Epoch 107/250\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "1266/1266 [==============================] - 0s - loss: 0.5187 - acc: 0.7504 - val_loss: 0.5705 - val_acc: 0.7460\n",
      "Epoch 239/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5243 - acc: 0.7512 - val_loss: 0.5704 - val_acc: 0.7460\n",
      "Epoch 240/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5231 - acc: 0.7512 - val_loss: 0.5708 - val_acc: 0.7460\n",
      "Epoch 241/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5115 - acc: 0.7496 - val_loss: 0.5708 - val_acc: 0.7460\n",
      "Epoch 242/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5156 - acc: 0.7536 - val_loss: 0.5703 - val_acc: 0.7460\n",
      "Epoch 243/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5238 - acc: 0.7551 - val_loss: 0.5702 - val_acc: 0.7460\n",
      "Epoch 244/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5181 - acc: 0.7567 - val_loss: 0.5701 - val_acc: 0.7460\n",
      "Epoch 245/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5178 - acc: 0.7512 - val_loss: 0.5703 - val_acc: 0.7460\n",
      "Epoch 246/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5220 - acc: 0.7528 - val_loss: 0.5712 - val_acc: 0.7460\n",
      "Epoch 247/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5217 - acc: 0.7480 - val_loss: 0.5708 - val_acc: 0.7460\n",
      "Epoch 248/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5207 - acc: 0.7512 - val_loss: 0.5711 - val_acc: 0.7460\n",
      "Epoch 249/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5186 - acc: 0.7504 - val_loss: 0.5706 - val_acc: 0.7460\n",
      "Epoch 250/250\n",
      "1266/1266 [==============================] - 0s - loss: 0.5210 - acc: 0.7607 - val_loss: 0.5707 - val_acc: 0.7460\n",
      "288/315 [==========================>...] - ETA: 0s"
     ]
    }
   ],
   "source": [
    "from keras import regularizers\n",
    "n_splits=5\n",
    "kfold=StratifiedKFold(n_splits=n_splits, shuffle=True)\n",
    "#classify as nodule or non-nodule\n",
    "input_shape=(64,64,1)\n",
    "num_classes=2\n",
    "width=16\n",
    "epochs=250\n",
    "batch_size=96\n",
    "cvscores=[]\n",
    "cvscoresrandom=[]\n",
    "history=[]\n",
    "historyrandom=[]\n",
    "for train,test in kfold.split(nodulecrops,malignantlabel):\n",
    "    model = Sequential()\n",
    "    model.add(Conv2D(width, kernel_size=(3, 3),\n",
    "                     activation='relu',\n",
    "                     input_shape=input_shape))\n",
    "    #model.add(BatchNormalization())\n",
    "    model.add(Conv2D(width, kernel_size=(3, 3),\n",
    "                     activation='relu',\n",
    "                     input_shape=input_shape))\n",
    "    model.add(Conv2D(width*2, (3, 3), activation='relu'))\n",
    "    #model.add(BatchNormalization())\n",
    "    model.add(Conv2D(width*2, (3, 3), activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Dropout(0.75))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(width*4, activation='relu'))\n",
    "    model.add(Dropout(0.50))\n",
    "    model.add(Dense(num_classes, activation='softmax'))\n",
    "    model.compile(loss=keras.losses.categorical_crossentropy,\n",
    "                  optimizer=Adam(lr=1e-5),\n",
    "                  metrics=['accuracy'])\n",
    "    histor=model.fit(nodulecrops[train],malignantlabelcat[train], batch_size=batch_size, epochs=epochs, verbose=1, shuffle=True, validation_data=(nodulecrops[test],malignantlabelcat[test]))\n",
    "    scores=model.evaluate(nodulecrops[test],malignantlabelcat[test])\n",
    "    cvscores.append(scores)\n",
    "    history.append(histor)\n",
    "    \n",
    "for train,test in kfold.split(nodulecrops,randomlabel):\n",
    "    model = Sequential()\n",
    "    model.add(Conv2D(width, kernel_size=(3, 3),\n",
    "                     activation='relu',\n",
    "                     input_shape=input_shape))\n",
    "    #model.add(BatchNormalization())\n",
    "    model.add(Conv2D(width, kernel_size=(3, 3),\n",
    "                     activation='relu',\n",
    "                     input_shape=input_shape))\n",
    "    model.add(Conv2D(width*2, (3, 3), activation='relu'))\n",
    "    #model.add(BatchNormalization())\n",
    "    model.add(Conv2D(width*2, (3, 3), activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Dropout(0.75))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(width*4, activation='relu'))\n",
    "    model.add(Dropout(0.50))\n",
    "    model.add(Dense(num_classes, activation='softmax'))\n",
    "    model.compile(loss=keras.losses.categorical_crossentropy,\n",
    "                  optimizer=Adam(lr=1e-5),\n",
    "                  metrics=['accuracy'])\n",
    "    historrandom=model.fit(nodulecrops[train],randomlabelcat[train], batch_size=batch_size, epochs=epochs, verbose=1, shuffle=True, validation_data=(nodulecrops[test],randomlabelcat[test]))\n",
    "    scoresrandom=model.evaluate(nodulecrops[test],randomlabelcat[test])\n",
    "    cvscoresrandom.append(scoresrandom)\n",
    "    historyrandom.append(historrandom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean loss across all CV sets with true labels: 0.581052138289\n",
      "Mean loss across all CV sets with random labels: 0.588065568571\n",
      "Mean accuracy across all CV sets with true labels: 0.736243661584\n",
      "Mean accuracy across all CV sets with random labels: 0.741945823707\n",
      "Lowest val_loss of 0.568163756725 at epoch [130] with true labels\n",
      "Lowest val_loss of 0.575047499944 at epoch [8] with random labels\n"
     ]
    },
    {
     "data": {
      "image/png": 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eKnQWHrdltMXxc7I0Sdkwnd7DgExMp/fVWuuNh+wXDewCmmuty/zfhQMWrXWJ\n/+dZwNNa6xl1nfNYA0b+J5+ivd6qz1nlWewvy8KqbNitQdgsNuwWOw6PA43pMCx1l1DuNp17Lq8L\nuzWIco8Dp8dBqC0UhYWwoFDcPjdur/uwc9qtdqwWKw63E6j738DpdeLVXsJsYahaXgep0Tg8DhQW\nQm2Hv0s4yBpEkCXIXIPWhNhC8WkfLm/F0d0swIfG4S7Hq73YLOa4FV4nobZQrMpadW2lrjIcnnKC\nrcF4fB40EGoLqSrYKvPrw0uJq5T4kLiqAtvlc+H0OKvOGWILodxdjtvnJs5f0Dvc5ZS6Swm2BpMU\nnozb6yLIamd/mZm9HBYUhsvrItQWxrr0IppGR9M8Poyt+VvYVZxGqDWMxLAEHJ5yCpyFaO0jKTyJ\nElcpJa4StC+YRmHx5FbsxaZseLQH7fNXyi2eQ29LwIIsQXRP6M6mvM2Ue8sAiLJH0iG2AzuLduHy\nVlDiLgUgPiSeFpHNKXGXEBccR1J4EilRKYTYQskpz2bK9qk0Dk+md6PexIfGU+YpJz4kDup4bWih\nswC39pAYmnjM1/B7hXbrSmivXjX2rVRs345j/QYizhmELT6+htSiPp0UAcOfkQuB/2GG1X6otX5W\nKXU7gNb6bf8+4zB9HVdWS9camOr/aAMmaa2fPdL5jjVgbOnZC+1wHHU6IcSxU0FBYDl8JJOu8D/E\nKIWy249zrgx7q1bEXH4ZuW+/ja+4hNCuXYm7+SYizz33QD5rKTsDWcPrZHLSBIzj7VgDhre0tOrn\n+375K7uKd/H28Lfx+Dw4fRU4PQ7KXOXEhERjtZgnzNjgWKLskewr3UeEPRKHu5wIeyRRwVE4POV4\nfF6KKgoJtgYTFnRwzUCjKXOV4fa5iQ6JxnKEN+WG2EKxKAsOT3mdv6ShtjB82ofTc3jwK3eX4/A4\niAmJRSkodhZjtViJsEfUWmupTeW5lFL4tA+Pz43dGkyFx4nb5666tvjQeEKDwnB7XVX3zeEurzpO\nWFA4Pu1D48NmCaLC46waURNsC8ZmOTBHwON1Y7FY0VqRXVJMdJitKg8ubwX7y/YTHhRBqauEJhFN\nsFisTFuTzrM/pnJl/0ZMXLwLrA4+uKEPq3Z6efOXNOIj7JRWeHjkgo7klbgY0Caepbvy+XHdXrxa\nc8NZKTz1/SbQMKBNPH/u1YzB7RPxas20tZkUlLm5pn8LosOOXKht2VdM+6TIgIayHrPV/wfL34cb\nf4Kgw2sX+5ugAAAgAElEQVSZJwvtdlM6dx6unTtq3G5r1IjQ7t0pXbjw4Ac5rxuyN0Nie6ihFl1v\n+fNpir77Dm9eHiFduhDWpzclc+fh3rOH6NGjsSUno50OimfNwrN332Hpoy68gOSn/4U1IhztcuEt\nK6Nk9mwsIaFEjhiOOzOTPbfcSsyllxJ/802okBCUxYLP5YJqLR0H5cntpnT+fLTLTeTIkebYHg/a\nfaD1whJ6bJMIJWAco72lezn/m/O5rftt3NnjznrMmagP363J5MlpGymr8DL93kFVncK1Gf3mItam\nF2KzKELtVkqcHga2TWDxjlxGdWvCvcPaMeKV+Rz6X8BmUfz9wk7cNLAVZRUeylweGkU2YAHs84Ky\ngFKQtQFSZ0KvcRB+FM0xPi+81gMK98DYT6Dz6GPLS2k2bPwWeo8DW4BP9z5fjTWF3614L2yaBs36\nmD8/PgjL34PYVnDtNxDX2ly31QZam/tXT9z791M6bz7RY0ZjCQ5Gu1zs//d/KPzqK7TPB0oR3rcv\noT17HnReb0EBBZ9/jjU+juC2bSlfuszcHz9bcjLWuFgqtqWCxzwcWRMTCG7TlvKlSznsl7EGKiyM\nsD69KV++oiqgWhMSaL/w12O61lNxlNRJYWbaTDSaMW3HnOisnJZ8Pk2hw01kiI1nftjE2L7N6dIk\n+sgJMaOWnvlxM0lRIezOK+eln7diUYqB7RLo2jSaGRuySIoOIbPAQfdm0SRFh7A2vZDQICsOt5dz\nOzQio6CchdtzGdA6nhcu7Up4sI3H/K/rHNw+kVW7CxjSMfGg4BAebKtz2YzfzVEAH10I9nBo1AlW\nfWK+37cOxn5cc5rtcyBvO/S9BSqHo6b+bIKFssDaLw8EjD1LIWsd9LnpwL6VPK6Dg4LW8O0dsH02\n7F8PF71mCuSyHAiJBvvho53YOQ++GgeXvg/thpvvnMWw4kMTdELr6CTXGtJ+hbSFMOAuCIk68P2K\nD+CnR8DnBosNulwK67+CDqNgz2KYehtEN4PU2dC4O2SugN43wnn/AusRZq87i8FdDuGNag10QUlJ\nxF5xYBSXsttJfvwxku+/Axb9D9wOOPMOiG9zWNqoUReS9+57uHbtIm7cOGzx8YT164uvpIR9j/+T\nik2bafzss1iiInGlpeFYucrse9ON2GJjDztepZAuXbAE2ymYMpXyxUuIGnUhwSkpJn/HWLs4WhIw\nqskuzyY8KJymEU1PdFZOS89O38z/Ld3NuLNa8fGS3WzaV8zk2wawOr2Q7ftLuax3s8OabNxeH09M\n20iF20dOSQX/vbw787fm8OEiM2fgx/X7CLZZqPCYpzilDjyk2a0Wnr3kDO6fvJaz28YzpEMnih3u\ng969cPPAA4vmHanGUm/2rYNdC6D/7TD5BshNBVswZCw3Bac1CBa+Yv70uflAQQqw6FWY9U/zc+os\nuOJTc8Fzn4PIJtD5Ylj+AXx7JyR1Md+7SmDbDLj0PQiLM/v/+ABsnAJXTzYBqNtY2LfGBIsmvUzg\najMM1n0JW6dDaCyMmw5J1d6HvW8tTL4enEUmr5UB49eXTKG6Yw60Ogfi20F8W9g4FdqNgOb9wVkI\nU24ztSmA8jwY9RKkLYLpD0H2Rmh3Hgx93Bx7xy/Q4ky49B3YMh2mjjf3q9U5UJIFrc+FpW/B5u+h\n57XQ6zoTUDwVYAkyATVnMyR2Mnn2VsBZd8N5z5ig/etL0GoItDn38MBayVMBX14D6cvMZ1cpXPL2\nYbuFde9K2LMPQlj8YQGz1TdfU77sNyJsK1Fn/BlGjIBbq+3gKoeg0MNrS24HfHwx2MMJfebbun+/\nGpA0SVXzz0X/ZFHmIuaMnVOPuTq1HM2yEZWKHG525JTSs3kMSimcbi/fr93LhswiRnROpk9KLMVO\nNwP/PReXv2CvfPLvmBzJlqwSAN65rjchQVbmbsnGpzWD2iVS4nRz/+S1gCnQZ913DjklFdzzxWqu\nH5DC1yszKHG6eePqXri9PhpFhrB4Ry5bs0poGR/GyC7JzNuaw9ltE7DbGqDZ5Gg4CiBnK3xxDZTn\nmkJu51y4+HVIGWSaYFLONk/+n11qnr6Dwsx+4fHQegh8fTN0ughSBsJPf4OOo0xhsuMXuHISJLSH\nL66C8nxzjsjG0P82+OVZiEw2TTvOYlOYW+3gdZm8dboIcrebGspt8+GNvqbgzNsOZ1xmagFWOwx/\nArLWQ0EabJtpAkmnP8Gyd+GOxeap/dVuprDO3Xbg2i02qJzx3eVS2LsaijJgxFOQv9MEueu/g2l3\nm4B2zoPQ87qaawBaw/f3mHMNfexA4brtZ1j6trkXtmCTftXHEBoHpVkH0iedYQrzzFXwwGaY9wIs\necNsi25+4NxluRDpX859zefwy7+gOBMu+9DUrDZMgb+uN8F4xy+grNDnRvPvsm8N2CPMv8mOX6D7\nlaYGCTD7SRMEg8JhzATo4m/RWPkxTH8Qhj8J/e+A3K1QmA6bv4P9m2DvKrAGw6MZgTcXBkD6MI7R\nA/MeILUwlWljptVjrmqmtcbr09isDVuIaa3ZW+Qk3G4l5pDOWY/Xx74iJ01jQrFYFDklFYz/dAUW\npfjs5v6E2g88aTlcXspcHhIigskrrWBrVgldmkQzbW0mT32/CY9P8+RFnWnTKIK/T11Per6DIKvC\n7dXYLIq4cDu5pRXcMaQNb83bwbvX9eGpH0x/xL3D2vHBwl1UeLzsL64gNMiKRUGZy0t0aBCNIoN5\ncGQHWsaH0TE56tBLrF95O8wTc2IH00ykNZTuh4ik2tvIHQVmvzD/nIWSLCjKNIWy9sHgv0HOFvhg\nJFQUmeadmJamqajNMNMef+ixtTYF2qqJsHuJCSbuMlOLuHOpqXUs/B/MfsJ0AJ/3DPS79eD06UtN\nwIhtCRkrYMYj4Cg029ucC92vMrUVe7gp9ABGv2me0Je8CTP/bp7O799kCsovrjF/W2ymHyGxA/zp\nFfP55c6mFmEPM0/+dy4z9y26Gaz61Fz/ec/A2s9h/n/M/Rz7CbToDxUl8PYgE0B8blPraT/y2P8N\nC9Lgu7tMwG11jgm68W2hUWdYP9lcY0kWvD/M1DKWvW8Cb6c/wW9vQ/pvJhA6i+GmGSb4fn4lNOsL\ngx82taS0hTBxlNnPUWDus9thak7KAsOfMkG0KN3kqeXZcNY9puaWOhM6XGjykLEMzr7X3I+ZfzdB\nOTwRmvYytSWAkBiIaGSuYet0GD/P5G3dlwf+PUOi4ZJjWypFAsaxKMvj9jl/ochTzue9H/3dedlb\n6GDR9jxaJ4bRq0Vs1VC7jAIHDreXTxanUeJ08/SYM5i5YT/ntE8gKarujtXSCg8zNmSREBGMx2dm\nIgfbrOzIKSXCbqN/mzhaxoUD4PH5WLargElLd7O/uIIgq+KCro25ql9zbBbThPPU9xtJ3V9KSJCF\nprGh7Cty4vH6cHs1zWPDsFqgcXQoHZIj+WnDPvLLXPRqGcuynfn4NCRGBlNQ7qJzE1OIr88sYZ23\nJU0S4nhqdBf6psQxb2sO6zIK2bC3mJ7NY7hvRHsKylzEhtspLHcRZLUQHmzj82V7eHTKenq1iGHS\nrWditSju+GwVszfv58XLunF5n7rfHBcwtwPWfmH+w7c4E7I3mQKz7QhTaP42wexnj4Sul5nCbMPX\npoM1tpV5SoxsDHmppklp8RumENI+OOPP5mn+wwtMk0elHteYJiivC0Y+Z57yvR5T2I98zhToR5K3\nA35+zJyz9WDzndawZ4kpCOvqKziSgt2mwzwkxgSHoFBTCL5yhilIL/WvyuP1mAIuvq0pwKpLnWVq\nPz63KZDPuLT28+1bB1FNILzaSrI52+D94ZDQDm6Z/fs7sL0eEzBbnFlzE5PW8N5Q89SurHDXctMf\n4fPB4lchc6UJ2CjTh9Ookxl9VtmP4/PB6z1NwT1mArQ/39RIZvwN2gw1QXf/JhOQo5qYmo6ymsCQ\n3NU0ZdnDTRBf8aE5Zsc/QbcrYPJ15nO/26D9eZByjqlRFO6B/3WFdiNN0AmJMTUigLBYuOH7Y7pV\nEjCOls8H75zDdZZsgrXm/azs+s/cH0SFNZygJl2xRDUxBeP2OaaQrhQWZzpkbSHmackeARFJ+Pau\nYWnwADoMuYq4de9DcQau1uexOddF16axWJr3Nk9RtUlfZgr74U/CyokQHAVn/9U0aWhtmlGK95pm\nhf0bzLGcRebp0+s60FzS91bzVLp1umlz91SYJ/e8HeDIN8fxecyTt89tCoHKzuffJpgmg+BIuOhV\n04cw7wVY9wXEppimjKa9G+7m/x7z/2MKtp7XHvgub4cJDMEBvm+7JMvcy5hjXKKneJ8JVr8n+B2N\nkizTDxPVxBTih9o2EyaNhVaD4bKPDh+1VrzPXyM4wmg2txPe6GP2vWX2gZpopTWTTBPXqJfN9b/W\n09Qgb517cAe+1vDfdiaARTWFu1fVy/BpCRhHa+O38NUNXNq+O82jmvNyx1uZsiqdX7Zk06lJFHef\n267qgWdtRhEv/bwVr09Xzc+2WxUur8YCDO6QyJasErw+zWOjOrM6vYDMAgfFDjdb95dwbodGNI0J\npXViBF+u2MNvO/M5r3MS6zOLiAqxYVGKLVklaKBZTChNYkLZW+ggo9A08dwztB0JEcGEBFkARbnL\nQ8u4cMrdHmZv2k+Rw1PVFNS5SRS9WsRUvWvh+7V7+WJ5Ou0bRRAebKNvqzgGt697tq/WsK/IQXSY\nnbAgK2UuLxHB5omtxOnBp/WBdyq4yk01uijdPLmX55mn0cjGBw6YtwNK9pqfQ6JNgexxmiev0v2m\nANZeU5BXm7NBUJhpn2/UyRRqC140BX6zvqZJZNl7pgAPjjbNPgBxbcx/PGeRaScH81R2yTumySN7\nkync3A7zc2gcNO524JyOQlPDiKlWuynNgYpiU6OY+5wZfdRqkNk2798w7zn48wemdgLmSTd7IyR1\nbZihp6Jh5e0wzYfW3zk+qCzXPCQFBzCwoiTLBJdDAwvApCtMTXjo46avpR5IwDgaPi+8dRZoHyOT\nY+mT3Jf48ut57ZftdG8WzdqMIv52fkfuGNKG/cVOhr00n+ZxYXx5m3lh+9Kd+fy8MYseLWLYllXC\n58vTcXl8fHxTvyMWxkUON4u253J+l+SDOpldHh8Ol5fosANPFxUeLz4fB/UrHItyl4cw+3EYHOep\nMG3Sca0Pbl7weU3bvc9rOh89TnCVmae83YvMU33Ls0wVf/8mQJvtm7+Hwt2wc74JDBFJJnjsXuJv\nDrrUNJ98fbMZ8dO8P2z5wZzTYoO2w8354tse3fyGo6E1lOwz1yJEQ1j8Bsx7Hu5ZAxH1s7SLBIyj\nUVFi2hHbDmfg+pfolziMKbP6cXnvZvznsm7c9flqZm7I4ts7z+b9X3cyfX0WP993DikJ4TUerqjc\nze78Mro1k0XaGkT6MjMe/5yHTHOJs8gEjFD/+PWyPPNkdootzyBEQLwe8ztfjw89MnHvaARHwug3\n0VpTtvJp8kpMQfPohZ1QSvHcmK4s35XP2HeWUO7ycvfQtrUGC4DosCC6hUmwaDDN+5k/lQ7t12io\n2oMQJwOr7YT+jkujqp/L58KjPeSXKJrHhRIXboagRocF8coVPejcOIrHRnXinmHtTnBOhRDixJAa\nhl+pyyxAmFWg6XdIc9LZbRM4u21CTcmEEOIPQ2oYfuX+ETkFZRa6NwtsfSMhhPgjkYDhV+YxL7LB\nFywd1kIIUQMJGH6VTVLaG8IZTaWGIYQQh5KA4VfuMU1SEfZwIhpyOWshhDhFScDwq6xhRAUyE1MI\nIf6AJGD4VfZhxIZIwBBCiJpIwPArc5mAERfawMtnCyHEKUoChl9lDSMuNMCVOYUQ4g9GAoZfmbsM\n7QsmPiL4RGdFCCFOShIw/IorStHe4MPeSieEEMKQgOFX6CxB+4KJlYAhhBA1koDhV+QsBV8wceFB\nR95ZCCH+gCRg+Dk8FWhtkyYpIYSohQQMP5fXC1pJk5QQQtRCAoaf2+sFFLFh0iQlhBA1kYDh5/Z6\nAIs0SQkhRC0kYPi5vT6syoLdJrdECCFq0qClo1LqfKXUVqXUdqXUIzVsf0gptcb/Z4NSyquUigsk\nbX3z+LwEWa0NfRohhDhlNVjAUEpZgTeBC4DOwFVKqc7V99Fav6i17qG17gE8CszXWucHkra+eXw+\ngiwSMIQQojYNWcPoB2zXWu/UWruAL4DRdex/FfD5Mab93Xzai9UizVFCCFGbhiwhmwLp1T5n+L87\njFIqDDgf+OYY0o5XSq1QSq3Iyck55sxqNAp1zOmFEOJ0d7I8Ul8ELNJa5x9tQq31u1rrPlrrPomJ\nicecAY1GqZPldgghxMmnIUvITKB5tc/N/N/V5EoONEcdbdp64kOdNPFTCCFOPg1ZQi4H2imlWiml\n7JigMO3QnZRS0cBg4LujTVuftJYmKSGEqIutoQ6stfYope4CZgJW4EOt9Ual1O3+7W/7d70E+Flr\nXXaktA2VVwCND4s0SQkhRK0aLGAAaK2nA9MP+e7tQz5PBCYGkrZhSR+GEELURUpIP40PizRJCSFE\nrSRg+MkoKSGEqJuUkFW0jJISQog6SAlZRTq9hRCiLlJC+mk0FiV9GEIIUZuAAoZSaopSapQ6rRv5\nfShk8UEhhKhNoAFgAnA1kKqUekEp1aEB83RCSA1DCCHqFlDA0FrP1lpfA/QC0oDZSqnFSqkblVKn\nxztNlYySEkKIugRcQiql4oFxwC3AauBVTACZ1SA5O+58UsMQQog6BDTTWyk1FegAfApcpLXe59/0\npVJqRUNl7vjSWKQPQwghahXo0iCvaa3n1rRBa92nHvNzAkkfhhBC1CXQJqnOSqmYyg9KqVil1F8a\nKE8niE/6MIQQog6BlpC3aq0LKz9orQuAWxsmSyeGVhqrTEsRQohaBVpCWpU60F6jlLIC9obJ0omi\nscg7vYUQolaB9mHMwHRwv+P/fJv/u9OIDKsVQoi6BBow/oYJEnf4P88C3m+QHJ0AWmuUNEkJIUSd\nAgoYWmsf8Jb/z2nHp30A0iQlhBB1CHQeRjvgeaAzEFL5vda6dQPl67jy+kzAkOXNhRCidoGWkB9h\nahce4FzgE+CzhsrU8eb2eQGwyjwMIYSoVaABI1RrPQdQWuvdWusngVENl63jy+01AUPehyGEELUL\ntNO7wr+0eapS6i4gE4houGwdX96qPgxZGkQIIWoT6CP1vUAYcA/QG7gWuKGhMnW8VdYwrFLDEEKI\nWh2xhuGfpHeF1vpBoBS4scFzdZx5qjq9pQ9DCCFqc8RHaq21Fxh4HPJywngrO72lSUoIIWoVaB/G\naqXUNOAroKzyS631lAbJ1XFWWcOQJikhhKhdoAEjBMgDhlb7TgOnScCQUVJCCHEkgc70Pu36Laqr\nmochM72FEKJWgc70/ghToziI1vqmes/RCVA501tqGEIIUbtAm6R+qPZzCHAJsLf+s3NieKUPQwgh\njijQJqlvqn9WSn0OLDxSOqXU+cCrgBV4X2v9Qg37DAH+BwQBuVrrwf7v04ASwAt4GvJVsG6vB5CA\nIYQQdQm0hnGodkCjunbwz994ExgBZADLlVLTtNabqu0TA0wAztda71FKHXrMc7XWuceYx4B5ZLVa\nIYQ4okD7MEo4uA8jC/OOjLr0A7ZrrXf6j/EFMBrYVG2fq4EpWus9AFrr7ADzXa+83somKZmHIYQQ\ntQm0SSryGI7dFEiv9jkD6H/IPu2BIKXUPCASeFVr/UnlaYHZSikv8I7W+t2aTqKUGg+MB2jRosUx\nZPPAWlIySkoIIWoXUAmplLpEKRVd7XOMUmpMPZzfhlmbahQwEnhcKdXev22g1roHcAFwp1LqnJoO\noLV+V2vdR2vdJzEx8ZgycWAtKVkaRAghahPoI/UTWuuiyg9a60LgiSOkyQSaV/vczP9ddRnATK11\nmb+vYgHQ3X+OTP/f2cBUTBNXg/DqyoAhTVJCCFGbQANGTfsdqTlrOdBOKdVKKWUHrgSmHbLPd8BA\npZRNKRWGabLarJQKV0pFAiilwoHzgA0B5vWoVQ2rlSYpIYSoVaCjpFYopV7GjHoCuBNYWVcCrbXH\n/+6MmZhhtR9qrTcqpW73b39ba71ZKTUDWAf4MENvNyilWgNTlWkisgGTtNYzjvbiAuWR92EIIcQR\nBRow7gYeB77EdEbPwgSNOmmtpwPTD/nu7UM+vwi8eMh3O/E3TR0PHnkfhhBCHFGgo6TKgEcaOC8n\njM9fw7BJH4YQQtQq0FFSs/yT7Co/xyqlZjZcto6vyuXNLRYZJSWEELUJtA0mwT8yCgCtdQFHmOl9\nKql8gZJNmqSEEKJWgZaQPqVU1aw4pVQKNaxee6qqeoGSdHoLIUStAu30/gewUCk1H1DAIPyzq08H\nVfMwJGAIIUStAu30nqGU6oMJEquBbwFHQ2bsePLJ0iBCCHFEgS4+eAtwL2a29hrgTGAJB7+y9ZRV\n2SQlfRhCCFG7QEvIe4G+wG6t9blAT6C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e7vri9fDwsFfzfeqppxg3bhylpaXs3LnTXrQR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\nUsohmjCUUko5RBOGUkoph/w/wzGMlzg0pR4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5bd514a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kyJFMmDCBPn36AHDttdcyYMAAEhMTmT9/fsO69XflLUk7DlqW2LvuuovBgwfzyCOPsHnz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb570d278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Mean loss across all CV sets with true labels:\", np.mean([cvscores[i][0] for i in range(len(cvscores))]))\n",
    "print(\"Mean loss across all CV sets with random labels:\", np.mean([cvscoresrandom[i][0] for i in range(len(cvscoresrandom))]))\n",
    "print(\"Mean accuracy across all CV sets with true labels:\", np.mean([cvscores[i][1] for i in range(len(cvscores))]))\n",
    "print(\"Mean accuracy across all CV sets with random labels:\", np.mean([cvscoresrandom[i][1] for i in range(len(cvscoresrandom))]))\n",
    "\n",
    "print(\"Lowest val_loss of\", min(np.mean([history[i].history['val_loss'] for i in range(n_splits)],axis=0)), \"at epoch\", np.where(np.mean([history[i].history['val_loss'] for i in range(n_splits)],axis=0)==min(np.mean([history[i].history['val_loss'] for i in range(n_splits)],axis=0)))[0], \"with true labels\")\n",
    "print(\"Lowest val_loss of\", min(np.mean([historyrandom[i].history['val_loss'] for i in range(n_splits)],axis=0)), \"at epoch\", np.where(np.mean([historyrandom[i].history['val_loss'] for i in range(n_splits)],axis=0)==min(np.mean([historyrandom[i].history['val_loss'] for i in range(n_splits)],axis=0)))[0],\"with random labels\")\n",
    "acc=np.mean([history[i].history['acc'] for i in range(n_splits)], axis=0)\n",
    "valacc=np.mean([history[i].history['val_acc'] for i in range(n_splits)], axis=0)\n",
    "loss=np.mean([history[i].history['loss'] for i in range(n_splits)], axis=0)\n",
    "valloss=np.mean([history[i].history['val_loss'] for i in range(n_splits)], axis=0)\n",
    "randacc=np.mean([historyrandom[i].history['acc'] for i in range(n_splits)], axis=0)\n",
    "randvalacc=np.mean([historyrandom[i].history['val_acc'] for i in range(n_splits)], axis=0)\n",
    "randloss=np.mean([historyrandom[i].history['loss'] for i in range(n_splits)], axis=0)\n",
    "randvalloss=np.mean([historyrandom[i].history['val_loss'] for i in range(n_splits)], axis=0)\n",
    "\n",
    "# summarize history for accuracy\n",
    "plt.plot(acc)\n",
    "plt.plot(valacc)\n",
    "plt.plot(randacc)\n",
    "plt.plot(randvalacc)\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train truelabel', 'validation truelabel', 'train randomlabel', 'val randomlabel'], loc='best')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(loss)\n",
    "plt.plot(valloss)\n",
    "plt.plot(randloss)\n",
    "plt.plot(randvalloss)\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train truelabel', 'validation truelabel', 'train randomlabel', 'val randomlabel'], loc='best')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
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