host_dataset_2_new.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# import modules\n",
    "import pandas\n",
    "from sklearn import svm\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from sklearn.decomposition import PCA\n",
    "from pylab import *\n",
    "import struct\n",
    "import keras as ks\n",
    "import logging\n",
    "from keras.layers import Dense, Activation, Flatten, Convolution2D\n",
    "from keras.utils import np_utils\n",
    "from keras.models import model_from_json\n",
    "from keras import backend as K\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from skimage import io\n",
    "import numpy as np\n",
    "from PIL import Image \n",
    "from scipy import misc\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import classification_report\n",
    "# ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据读取和预处理\n",
    "label 3个 => 4个\n",
    "- normal: 2462\n",
    "- cpu   : 373\n",
    "- mem   : 266\n",
    "- io    : 592"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2462\n",
      "373\n",
      "266\n",
      "592\n",
      "(3693,)\n"
     ]
    }
   ],
   "source": [
    "# 3888 * 51 with 4 labels\n",
    "data_raw = pandas.read_csv(\"data/host10280-labeled-2.csv\")\n",
    "data = np.array(data_raw)\n",
    "data_raw = pandas.read_csv(\"data/host10274-labeled-2.csv\")\n",
    "data = np.concatenate((data,np.array(data_raw)),axis=0)\n",
    "data_raw = pandas.read_csv(\"data/host10283-labeled-2.csv\")\n",
    "data = np.concatenate((data,np.array(data_raw)),axis=0)\n",
    "\n",
    "# 50 = 2 ids + 45 features + 3 labels\n",
    "# ids: host + clock\n",
    "# labels: normal, cpu, mem, io\n",
    "data_features = data[:,2:47]\n",
    "data_labels = data[:,46:50]\n",
    "\n",
    "len(data_labels)\n",
    "for i in range(len(data_labels)):\n",
    "    item = data_labels[i]\n",
    "    if item[1] == 0 and item[2] == 0 and item[3] == 0:\n",
    "        data_labels[i][0] = 1\n",
    "    else:\n",
    "        data_labels[i][0] = 0\n",
    "\n",
    "data_class = [] # 0 ~ 3\n",
    "for label in data_labels:\n",
    "    data_class.append(np.dot([0,1,2,3],label))\n",
    "data_class = np.array(data_class,dtype=\"int\")\n",
    "\n",
    "print(sum(data_class == 0))\n",
    "print(sum(data_class == 1))\n",
    "print(sum(data_class == 2))\n",
    "print(sum(data_class == 3))\n",
    "print(data_class.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 1231 abnormal\n",
    "num_sample = sum(data_class != 0)\n",
    "# select 1231 normal samples randomly\n",
    "data_tmp = data_features[data_class == 0]\n",
    "index_tmp = np.arange(0,data_tmp.shape[0],data_tmp.shape[0]/num_sample)[0:num_sample]\n",
    "data_normal_tmp = data_tmp[index_tmp]\n",
    "\n",
    "data_features = np.concatenate((data_normal_tmp,                 data_features[data_class != 0]),axis=0)\n",
    "data_labels   = np.concatenate((np.array([[1,0,0,0]]*num_sample),data_labels[data_class != 0]),  axis=0)\n",
    "data_class    = np.concatenate((np.array([0]*num_sample),        data_class[data_class != 0]),   axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 划分训练集和验证集\n",
    "- 训练集: 80%\n",
    "- 验证集: 20%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1969, 45)\n",
      "(1969, 4)\n",
      "(1969,)\n",
      "(493, 45)\n",
      "(493, 4)\n",
      "(493,)\n"
     ]
    }
   ],
   "source": [
    "# feature scaling\n",
    "for i in range(data_features.shape[1]):\n",
    "    d_min = data_features[:,i].min()\n",
    "    d_max = data_features[:,i].max()\n",
    "    if d_min == d_max:\n",
    "        data_features[:,i] = 1\n",
    "        continue\n",
    "    data_features[:,i] -= d_min\n",
    "    data_features[:,i] /= (d_max - d_min)\n",
    "\n",
    "num_tosample = len(data_class)\n",
    "index_test = np.arange(0,num_tosample,5)\n",
    "index_train = np.array(list(set(np.arange(0,num_tosample,1)) - set(index_test)))\n",
    "\n",
    "data_train = data_features[index_train]\n",
    "labels_train = data_labels[index_train]\n",
    "class_train = data_class[index_train]\n",
    "data_test = data_features[index_test]\n",
    "labels_test = data_labels[index_test]\n",
    "class_test = data_class[index_test]\n",
    "print(data_train.shape)\n",
    "print(labels_train.shape)\n",
    "print(class_train.shape)\n",
    "print(data_test.shape)\n",
    "print(labels_test.shape)\n",
    "print(class_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# save loss and acc\n",
    "class LossHistory(ks.callbacks.Callback):\n",
    "    def on_train_begin(self, logs={}):\n",
    "        self.losses = {'batch':[], 'epoch':[]}\n",
    "        self.accuracy = {'batch':[], 'epoch':[]}\n",
    "        self.val_loss = {'batch':[], 'epoch':[]}\n",
    "        self.val_acc = {'batch':[], 'epoch':[]}\n",
    "\n",
    "    def on_batch_end(self, batch, logs={}):\n",
    "        self.losses['batch'].append(logs.get('loss'))\n",
    "        self.accuracy['batch'].append(logs.get('acc'))\n",
    "        self.val_loss['batch'].append(logs.get('val_loss'))\n",
    "        self.val_acc['batch'].append(logs.get('val_acc'))\n",
    "\n",
    "    def on_epoch_end(self, batch, logs={}):\n",
    "        self.losses['epoch'].append(logs.get('loss'))\n",
    "        self.accuracy['epoch'].append(logs.get('acc'))\n",
    "        self.val_loss['epoch'].append(logs.get('val_loss'))\n",
    "        self.val_acc['epoch'].append(logs.get('val_acc'))\n",
    "\n",
    "    def loss_plot(self, loss_type):\n",
    "        iters = range(len(self.losses[loss_type]))\n",
    "        iindex = np.arange(0,len(self.losses[loss_type]),len(self.losses[loss_type])/200)\n",
    "        iters = np.array(iters)\n",
    "        plt.figure()\n",
    "        # acc\n",
    "        plt.plot(iters[iindex], np.array(self.accuracy[loss_type])[iindex], 'r', label='train acc')\n",
    "        # loss\n",
    "        plt.plot(iters[iindex], np.array(self.losses[loss_type])[iindex], 'g', label='train loss')\n",
    "        if loss_type == 'epoch':\n",
    "            # val_acc\n",
    "            plt.plot(iters[iindex], np.array(self.val_acc[loss_type])[iindex], 'b', label='val acc')\n",
    "            # val_loss\n",
    "            plt.plot(iters[iindex], np.array(self.val_loss[loss_type])[iindex], 'k', label='val loss')\n",
    "        plt.grid(True)\n",
    "        plt.xlabel(loss_type)\n",
    "        plt.ylabel('acc-loss')\n",
    "        ylim(0,1.5)\n",
    "        plt.legend(loc=\"upper right\")\n",
    "        plt.savefig('fig.png')\n",
    "        \n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 神经网络\n",
    "45 => 128 => 64 => 32 => 16 => 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/lyq/Anaconda/anaconda2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:2755: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n",
      "WARNING:tensorflow:From /Users/lyq/Anaconda/anaconda2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:1290: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lyq/Anaconda/anaconda2/lib/python2.7/site-packages/keras/models.py:848: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  warnings.warn('The `nb_epoch` argument in `fit` '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1969 samples, validate on 493 samples\n",
      "Epoch 1/5000\n",
      "1969/1969 [==============================] - 0s - loss: 1.3174 - acc: 0.4261 - val_loss: 1.1665 - val_acc: 0.6633\n",
      "Epoch 2/5000\n",
      "1969/1969 [==============================] - 0s - loss: 1.0472 - acc: 0.6927 - val_loss: 0.9063 - val_acc: 0.6694\n",
      "Epoch 3/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.7714 - acc: 0.7679 - val_loss: 0.6640 - val_acc: 0.7830\n",
      "Epoch 4/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.6034 - acc: 0.7796 - val_loss: 0.4758 - val_acc: 0.8093\n",
      "Epoch 5/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.4395 - acc: 0.8106 - val_loss: 0.4027 - val_acc: 0.8114\n",
      "Epoch 6/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.3812 - acc: 0.8314 - val_loss: 0.3629 - val_acc: 0.8621\n",
      "Epoch 7/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.3470 - acc: 0.8603 - val_loss: 0.3314 - val_acc: 0.8600\n",
      "Epoch 8/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.3228 - acc: 0.8598 - val_loss: 0.3127 - val_acc: 0.8621\n",
      "Epoch 9/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.3044 - acc: 0.8629 - val_loss: 0.3869 - val_acc: 0.8154\n",
      "Epoch 10/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.3023 - acc: 0.8578 - val_loss: 0.2930 - val_acc: 0.8600\n",
      "Epoch 11/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2940 - acc: 0.8614 - val_loss: 0.2983 - val_acc: 0.8661\n",
      "Epoch 12/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2902 - acc: 0.8608 - val_loss: 0.2975 - val_acc: 0.8276\n",
      "Epoch 13/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2893 - acc: 0.8588 - val_loss: 0.2887 - val_acc: 0.8600\n",
      "Epoch 14/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2875 - acc: 0.8649 - val_loss: 0.2824 - val_acc: 0.8621\n",
      "Epoch 15/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2841 - acc: 0.8619 - val_loss: 0.2870 - val_acc: 0.8600\n",
      "Epoch 16/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2880 - acc: 0.8553 - val_loss: 0.2914 - val_acc: 0.8600\n",
      "Epoch 17/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2832 - acc: 0.8619 - val_loss: 0.2947 - val_acc: 0.8600\n",
      "Epoch 18/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2816 - acc: 0.8624 - val_loss: 0.2772 - val_acc: 0.8621\n",
      "Epoch 19/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2788 - acc: 0.8624 - val_loss: 0.2782 - val_acc: 0.8641\n",
      "Epoch 20/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2784 - acc: 0.8614 - val_loss: 0.2859 - val_acc: 0.8600\n",
      "Epoch 21/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2832 - acc: 0.8573 - val_loss: 0.2995 - val_acc: 0.7992\n",
      "Epoch 22/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2828 - acc: 0.8598 - val_loss: 0.2768 - val_acc: 0.8621\n",
      "Epoch 23/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2763 - acc: 0.8583 - val_loss: 0.2741 - val_acc: 0.8621\n",
      "Epoch 24/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2756 - acc: 0.8639 - val_loss: 0.2750 - val_acc: 0.8621\n",
      "Epoch 25/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2780 - acc: 0.8669 - val_loss: 0.2800 - val_acc: 0.8641\n",
      "Epoch 26/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2782 - acc: 0.8654 - val_loss: 0.2720 - val_acc: 0.8621\n",
      "Epoch 27/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2733 - acc: 0.8634 - val_loss: 0.2757 - val_acc: 0.8621\n",
      "Epoch 28/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2733 - acc: 0.8629 - val_loss: 0.2965 - val_acc: 0.8256\n",
      "Epoch 29/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2786 - acc: 0.8563 - val_loss: 0.2723 - val_acc: 0.8600\n",
      "Epoch 30/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2760 - acc: 0.8624 - val_loss: 0.2736 - val_acc: 0.8641\n",
      "Epoch 31/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2737 - acc: 0.8634 - val_loss: 0.2782 - val_acc: 0.8600\n",
      "Epoch 32/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2718 - acc: 0.8608 - val_loss: 0.2715 - val_acc: 0.8621\n",
      "Epoch 33/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2732 - acc: 0.8629 - val_loss: 0.2863 - val_acc: 0.8235\n",
      "Epoch 34/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2736 - acc: 0.8593 - val_loss: 0.2695 - val_acc: 0.8621\n",
      "Epoch 35/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2723 - acc: 0.8624 - val_loss: 0.2696 - val_acc: 0.8621\n",
      "Epoch 36/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2712 - acc: 0.8649 - val_loss: 0.2752 - val_acc: 0.8661\n",
      "Epoch 37/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2711 - acc: 0.8563 - val_loss: 0.2686 - val_acc: 0.8621\n",
      "Epoch 38/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2692 - acc: 0.8624 - val_loss: 0.2744 - val_acc: 0.8905\n",
      "Epoch 39/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2708 - acc: 0.8629 - val_loss: 0.2686 - val_acc: 0.8621\n",
      "Epoch 40/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2679 - acc: 0.8588 - val_loss: 0.2766 - val_acc: 0.8884\n",
      "Epoch 41/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2681 - acc: 0.8669 - val_loss: 0.2686 - val_acc: 0.8621\n",
      "Epoch 42/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2704 - acc: 0.8619 - val_loss: 0.2651 - val_acc: 0.8661\n",
      "Epoch 43/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2680 - acc: 0.8629 - val_loss: 0.2817 - val_acc: 0.8600\n",
      "Epoch 44/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2681 - acc: 0.8598 - val_loss: 0.2685 - val_acc: 0.9026\n",
      "Epoch 45/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2704 - acc: 0.8649 - val_loss: 0.2696 - val_acc: 0.8661\n",
      "Epoch 46/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2669 - acc: 0.8634 - val_loss: 0.2683 - val_acc: 0.8621\n",
      "Epoch 47/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2640 - acc: 0.8659 - val_loss: 0.2618 - val_acc: 0.8621\n",
      "Epoch 48/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2628 - acc: 0.8614 - val_loss: 0.2607 - val_acc: 0.8621\n",
      "Epoch 49/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2638 - acc: 0.8649 - val_loss: 0.2655 - val_acc: 0.9108\n",
      "Epoch 50/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2608 - acc: 0.8695 - val_loss: 0.2636 - val_acc: 0.8864\n",
      "Epoch 51/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2659 - acc: 0.8700 - val_loss: 0.2780 - val_acc: 0.8621\n",
      "Epoch 52/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2641 - acc: 0.8685 - val_loss: 0.2613 - val_acc: 0.8621\n",
      "Epoch 53/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2620 - acc: 0.8598 - val_loss: 0.2591 - val_acc: 0.8702\n",
      "Epoch 54/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2606 - acc: 0.8634 - val_loss: 0.2830 - val_acc: 0.8073\n",
      "Epoch 55/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2628 - acc: 0.8568 - val_loss: 0.2868 - val_acc: 0.8073\n",
      "Epoch 56/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2628 - acc: 0.8756 - val_loss: 0.2669 - val_acc: 0.8742\n",
      "Epoch 57/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2664 - acc: 0.8593 - val_loss: 0.2603 - val_acc: 0.8600\n",
      "Epoch 58/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2596 - acc: 0.8700 - val_loss: 0.2577 - val_acc: 0.8722\n",
      "Epoch 59/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2591 - acc: 0.8680 - val_loss: 0.2680 - val_acc: 0.8276\n",
      "Epoch 60/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2592 - acc: 0.8756 - val_loss: 0.2590 - val_acc: 0.8621\n",
      "Epoch 61/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2571 - acc: 0.8801 - val_loss: 0.2558 - val_acc: 0.8621\n",
      "Epoch 62/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2621 - acc: 0.8659 - val_loss: 0.2552 - val_acc: 0.8621\n",
      "Epoch 63/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2575 - acc: 0.8700 - val_loss: 0.2647 - val_acc: 0.8621\n",
      "Epoch 64/5000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.2578 - acc: 0.8644 - val_loss: 0.2748 - val_acc: 0.8276\n",
      "Epoch 65/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2639 - acc: 0.8603 - val_loss: 0.2930 - val_acc: 0.8600\n",
      "Epoch 66/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2629 - acc: 0.8705 - val_loss: 0.2563 - val_acc: 0.9128\n",
      "Epoch 67/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2584 - acc: 0.8603 - val_loss: 0.2679 - val_acc: 0.8276\n",
      "Epoch 68/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2568 - acc: 0.8674 - val_loss: 0.2603 - val_acc: 0.8600\n",
      "Epoch 69/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2519 - acc: 0.8812 - val_loss: 0.2528 - val_acc: 0.8742\n",
      "Epoch 70/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2554 - acc: 0.8720 - val_loss: 0.2528 - val_acc: 0.9067\n",
      "Epoch 71/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.2535 - acc: 0.8730 - val_loss: 0.2654 - val_acc: 0.8600\n",
      "Epoch 72/5000\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
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     ]
    },
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      "1969/1969 [==============================] - ETA: 0s - loss: 0.1968 - acc: 0.914 - 0s - loss: 0.1498 - acc: 0.9365 - val_loss: 0.1811 - val_acc: 0.9249\n",
      "Epoch 223/5000\n",
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      "Epoch 253/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.1375 - acc: 0.9431 - val_loss: 0.1809 - val_acc: 0.9148\n",
      "Epoch 254/5000\n",
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      "Epoch 303/5000\n",
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      "Epoch 308/5000\n",
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     ]
    },
    {
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     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.1249 - acc: 0.9380 - val_loss: 0.1816 - val_acc: 0.9148\n",
      "Epoch 317/5000\n",
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      "Epoch 330/5000\n",
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      "1969/1969 [==============================] - ETA: 0s - loss: 0.1180 - acc: 0.945 - 0s - loss: 0.1359 - acc: 0.9345 - val_loss: 0.1614 - val_acc: 0.9310\n",
      "Epoch 349/5000\n",
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      "1969/1969 [==============================] - ETA: 0s - loss: 0.1646 - acc: 0.925 - 0s - loss: 0.1637 - acc: 0.9264 - val_loss: 0.2248 - val_acc: 0.9148\n",
      "Epoch 359/5000\n",
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      "Epoch 368/5000\n",
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      "Epoch 370/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.1200 - acc: 0.9411 - val_loss: 0.1603 - val_acc: 0.9249\n",
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      "Epoch 372/5000\n",
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      "Epoch 374/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.1187 - acc: 0.9431 - val_loss: 0.1127 - val_acc: 0.9473\n",
      "Epoch 375/5000\n",
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      "Epoch 376/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.1420 - acc: 0.9299 - val_loss: 0.1514 - val_acc: 0.9290\n",
      "Epoch 377/5000\n",
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      "Epoch 378/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.1372 - acc: 0.9330 - val_loss: 0.1200 - val_acc: 0.9371\n",
      "Epoch 379/5000\n",
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      "Epoch 441/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.1106 - acc: 0.9426 - val_loss: 0.1271 - val_acc: 0.9371\n",
      "Epoch 442/5000\n",
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      "Epoch 504/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.1093 - acc: 0.9441 - val_loss: 0.1616 - val_acc: 0.9209\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.1206 - acc: 0.9426 - val_loss: 0.2422 - val_acc: 0.8986\n",
      "Epoch 568/5000\n",
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      "Epoch 628/5000\n",
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      "Epoch 629/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.1102 - acc: 0.9553 - val_loss: 0.1160 - val_acc: 0.9412\n",
      "Epoch 630/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0939 - acc: 0.9584 - val_loss: 0.1057 - val_acc: 0.9452\n",
      "Epoch 694/5000\n",
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      "Epoch 729/5000\n",
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      "1969/1969 [==============================] - ETA: 0s - loss: 0.1021 - acc: 0.953 - 0s - loss: 0.0977 - acc: 0.9528 - val_loss: 0.1067 - val_acc: 0.9412\n",
      "Epoch 748/5000\n",
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      "Epoch 750/5000\n",
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      "Epoch 751/5000\n",
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      "Epoch 753/5000\n",
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      "Epoch 754/5000\n",
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      "Epoch 755/5000\n",
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      "Epoch 756/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "Epoch 819/5000\n"
     ]
    },
    {
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     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0946 - acc: 0.9553 - val_loss: 0.1848 - val_acc: 0.9249\n",
      "Epoch 820/5000\n",
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      "Epoch 823/5000\n",
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      "1969/1969 [==============================] - 0s - loss: 0.1061 - acc: 0.9487 - val_loss: 0.1057 - val_acc: 0.9473\n",
      "Epoch 873/5000\n",
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      "Epoch 874/5000\n",
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      "Epoch 875/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.1011 - acc: 0.9502 - val_loss: 0.0994 - val_acc: 0.9635\n",
      "Epoch 876/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.1027 - acc: 0.9512 - val_loss: 0.1634 - val_acc: 0.9249\n",
      "Epoch 877/5000\n",
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      "Epoch 878/5000\n",
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      "Epoch 879/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0966 - acc: 0.9492 - val_loss: 0.1114 - val_acc: 0.9594\n",
      "Epoch 880/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0881 - acc: 0.9578 - val_loss: 0.0989 - val_acc: 0.9594\n",
      "Epoch 881/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0989 - acc: 0.9451 - val_loss: 0.1451 - val_acc: 0.9249\n",
      "Epoch 882/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0915 - acc: 0.9558 - val_loss: 0.1011 - val_acc: 0.9412\n",
      "Epoch 883/5000\n",
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      "Epoch 886/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.1029 - acc: 0.9548 - val_loss: 0.1497 - val_acc: 0.9331\n",
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      "Epoch 888/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.1112 - acc: 0.9512 - val_loss: 0.0975 - val_acc: 0.9493\n",
      "Epoch 889/5000\n",
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      "Epoch 894/5000\n",
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      "Epoch 898/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0989 - acc: 0.9543 - val_loss: 0.1958 - val_acc: 0.9270\n",
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      "Epoch 900/5000\n",
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      "Epoch 903/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0906 - acc: 0.9558 - val_loss: 0.1258 - val_acc: 0.9412\n",
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      "Epoch 905/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0938 - acc: 0.9538 - val_loss: 0.1888 - val_acc: 0.9087\n",
      "Epoch 906/5000\n",
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      "Epoch 912/5000\n",
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      "Epoch 915/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0959 - acc: 0.9538 - val_loss: 0.1001 - val_acc: 0.9432\n",
      "Epoch 916/5000\n",
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      "Epoch 931/5000\n",
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      "1969/1969 [==============================] - 0s - loss: 0.1123 - acc: 0.9416 - val_loss: 0.1505 - val_acc: 0.9249\n",
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      "Epoch 943/5000\n",
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      "Epoch 945/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0930 - acc: 0.9518 - val_loss: 0.1254 - val_acc: 0.9331\n",
      "Epoch 946/5000\n",
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      "Epoch 951/5000\n",
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      "Epoch 959/5000\n",
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      "Epoch 961/5000\n",
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      "Epoch 962/5000\n",
      "1969/1969 [==============================] - ETA: 0s - loss: 0.0690 - acc: 0.976 - 0s - loss: 0.0922 - acc: 0.9568 - val_loss: 0.1819 - val_acc: 0.9229\n",
      "Epoch 963/5000\n",
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      "Epoch 966/5000\n",
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      "Epoch 967/5000\n",
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      "Epoch 968/5000\n",
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      "Epoch 969/5000\n",
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      "Epoch 970/5000\n",
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      "Epoch 971/5000\n",
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      "Epoch 975/5000\n",
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      "Epoch 976/5000\n",
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      "Epoch 977/5000\n",
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      "Epoch 978/5000\n",
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      "Epoch 980/5000\n",
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      "Epoch 984/5000\n",
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      "Epoch 985/5000\n",
      "1969/1969 [==============================] - ETA: 0s - loss: 0.2257 - acc: 0.890 - 0s - loss: 0.0953 - acc: 0.9573 - val_loss: 0.1265 - val_acc: 0.9432\n",
      "Epoch 986/5000\n",
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      "Epoch 999/5000\n",
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      "Epoch 1000/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0910 - acc: 0.9558 - val_loss: 0.1098 - val_acc: 0.9391\n",
      "Epoch 1001/5000\n",
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      "Epoch 1002/5000\n",
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      "Epoch 1003/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0902 - acc: 0.9543 - val_loss: 0.1848 - val_acc: 0.9310\n",
      "Epoch 1004/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0975 - acc: 0.9533 - val_loss: 0.1155 - val_acc: 0.9574\n",
      "Epoch 1005/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0904 - acc: 0.9604 - val_loss: 0.1108 - val_acc: 0.9351\n",
      "Epoch 1006/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.1016 - acc: 0.9548 - val_loss: 0.0987 - val_acc: 0.9635\n",
      "Epoch 1007/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0994 - acc: 0.9523 - val_loss: 0.1100 - val_acc: 0.9513\n",
      "Epoch 1008/5000\n",
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      "Epoch 1010/5000\n",
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      "Epoch 1019/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0846 - acc: 0.9634 - val_loss: 0.1208 - val_acc: 0.9371\n",
      "Epoch 1020/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.1013 - acc: 0.9512 - val_loss: 0.0916 - val_acc: 0.9594\n",
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      "Epoch 1022/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.1093 - acc: 0.9502 - val_loss: 0.1013 - val_acc: 0.9473\n",
      "Epoch 1023/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0939 - acc: 0.9568 - val_loss: 0.1105 - val_acc: 0.9473\n",
      "Epoch 1024/5000\n",
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      "Epoch 1025/5000\n",
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      "Epoch 1027/5000\n",
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      "Epoch 1028/5000\n",
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      "Epoch 1029/5000\n",
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      "Epoch 1030/5000\n",
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      "Epoch 1031/5000\n",
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      "Epoch 1032/5000\n",
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      "Epoch 1033/5000\n",
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      "Epoch 1038/5000\n",
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      "Epoch 1039/5000\n",
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      "Epoch 1040/5000\n",
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      "Epoch 1042/5000\n",
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      "Epoch 1069/5000\n",
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      "Epoch 1070/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0891 - acc: 0.9609 - val_loss: 0.1694 - val_acc: 0.9290\n",
      "Epoch 1071/5000\n",
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      "Epoch 1076/5000\n",
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      "Epoch 1080/5000\n",
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      "Epoch 1095/5000\n",
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      "Epoch 1096/5000\n",
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      "Epoch 1100/5000\n",
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      "Epoch 1101/5000\n",
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      "Epoch 1111/5000\n",
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      "Epoch 1120/5000\n",
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      "Epoch 1128/5000\n",
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      "Epoch 1131/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0936 - acc: 0.9553 - val_loss: 0.0957 - val_acc: 0.9473\n",
      "Epoch 1132/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0908 - acc: 0.9563 - val_loss: 0.1279 - val_acc: 0.9351\n",
      "Epoch 1133/5000\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0921 - acc: 0.9533 - val_loss: 0.1349 - val_acc: 0.9351\n",
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      "Epoch 1225/5000\n",
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      "Epoch 1226/5000\n",
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      "Epoch 1227/5000\n",
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      "Epoch 1229/5000\n",
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      "Epoch 1230/5000\n",
      "1969/1969 [==============================] - ETA: 0s - loss: 0.1293 - acc: 0.921 - 0s - loss: 0.0825 - acc: 0.9599 - val_loss: 0.1277 - val_acc: 0.9432\n",
      "Epoch 1231/5000\n",
      "1969/1969 [==============================] - ETA: 0s - loss: 0.1604 - acc: 0.945 - 0s - loss: 0.1020 - acc: 0.9512 - val_loss: 0.1390 - val_acc: 0.9331\n",
      "Epoch 1232/5000\n",
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      "Epoch 1235/5000\n",
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      "Epoch 1238/5000\n",
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      "Epoch 1239/5000\n",
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      "Epoch 1240/5000\n",
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      "Epoch 1246/5000\n",
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      "Epoch 1248/5000\n",
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      "Epoch 1250/5000\n",
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      "Epoch 1252/5000\n",
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      "Epoch 1255/5000\n",
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      "Epoch 1256/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0952 - acc: 0.9543 - val_loss: 0.1596 - val_acc: 0.9310\n",
      "Epoch 1257/5000\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0877 - acc: 0.9644 - val_loss: 0.0932 - val_acc: 0.9635\n",
      "Epoch 1258/5000\n",
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      "Epoch 1260/5000\n",
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      "Epoch 1261/5000\n",
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      "Epoch 1320/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
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     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.1081 - acc: 0.9512 - val_loss: 0.0961 - val_acc: 0.9594\n",
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      "Epoch 1446/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "1969/1969 [==============================] - ETA: 0s - loss: 0.1216 - acc: 0.906 - 0s - loss: 0.0947 - acc: 0.9538 - val_loss: 0.0845 - val_acc: 0.9635\n",
      "Epoch 1468/5000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0879 - acc: 0.9609 - val_loss: 0.1002 - val_acc: 0.9493\n",
      "Epoch 1509/5000\n",
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      "Epoch 1571/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
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     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0952 - acc: 0.9568 - val_loss: 0.0929 - val_acc: 0.9574\n",
      "Epoch 1634/5000\n",
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      "Epoch 1656/5000\n",
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      "Epoch 1680/5000\n",
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      "Epoch 1681/5000\n",
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      "Epoch 1682/5000\n",
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      "Epoch 1684/5000\n",
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      "Epoch 1686/5000\n",
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      "Epoch 1691/5000\n",
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      "Epoch 1694/5000\n",
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      "Epoch 1695/5000\n",
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      "Epoch 1696/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0962 - acc: 0.9563 - val_loss: 0.1295 - val_acc: 0.9533\n",
      "Epoch 1697/5000\n",
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      "Epoch 1719/5000\n",
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      "Epoch 1720/5000\n",
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      "Epoch 1759/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0847 - acc: 0.9604 - val_loss: 0.0968 - val_acc: 0.9635\n",
      "Epoch 1760/5000\n",
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      "Epoch 1764/5000\n",
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      "Epoch 1765/5000\n",
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      "Epoch 1800/5000\n",
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      "Epoch 1808/5000\n",
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      "Epoch 1809/5000\n",
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      "Epoch 1812/5000\n",
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      "Epoch 1813/5000\n",
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      "Epoch 1814/5000\n",
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      "Epoch 1815/5000\n",
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      "Epoch 1816/5000\n",
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      "Epoch 1819/5000\n",
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      "Epoch 1820/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0819 - acc: 0.9599 - val_loss: 0.1543 - val_acc: 0.9270\n",
      "Epoch 1821/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0815 - acc: 0.9584 - val_loss: 0.0982 - val_acc: 0.9513\n",
      "Epoch 1822/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0808 - acc: 0.9619 - val_loss: 0.1120 - val_acc: 0.9493\n",
      "Epoch 1823/5000\n",
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      "Epoch 1884/5000\n",
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      "Epoch 1885/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0771 - acc: 0.9660 - val_loss: 0.1101 - val_acc: 0.9554\n",
      "Epoch 1886/5000\n",
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      "Epoch 1890/5000\n",
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      "Epoch 1891/5000\n",
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      "1969/1969 [==============================] - 0s - loss: 0.0812 - acc: 0.9614 - val_loss: 0.1151 - val_acc: 0.9452\n",
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      "Epoch 1908/5000\n",
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      "Epoch 1909/5000\n",
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      "Epoch 1910/5000\n",
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      "Epoch 1911/5000\n",
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      "Epoch 1921/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0829 - acc: 0.9614 - val_loss: 0.1421 - val_acc: 0.9351\n",
      "Epoch 1922/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0876 - acc: 0.9599 - val_loss: 0.0954 - val_acc: 0.9615\n",
      "Epoch 1923/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0789 - acc: 0.9650 - val_loss: 0.1545 - val_acc: 0.9391\n",
      "Epoch 1924/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0826 - acc: 0.9599 - val_loss: 0.2090 - val_acc: 0.9249\n",
      "Epoch 1925/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0985 - acc: 0.9538 - val_loss: 0.1002 - val_acc: 0.9635\n",
      "Epoch 1926/5000\n",
      "1969/1969 [==============================] - ETA: 0s - loss: 0.1224 - acc: 0.937 - 0s - loss: 0.0761 - acc: 0.9639 - val_loss: 0.0902 - val_acc: 0.9655\n",
      "Epoch 1927/5000\n",
      "1969/1969 [==============================] - ETA: 0s - loss: 0.0476 - acc: 0.984 - 0s - loss: 0.0753 - acc: 0.9665 - val_loss: 0.1729 - val_acc: 0.9371\n",
      "Epoch 1928/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0975 - acc: 0.9558 - val_loss: 0.0869 - val_acc: 0.9716\n",
      "Epoch 1929/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0782 - acc: 0.9639 - val_loss: 0.1158 - val_acc: 0.9432\n",
      "Epoch 1930/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0734 - acc: 0.9680 - val_loss: 0.1673 - val_acc: 0.9290\n",
      "Epoch 1931/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0990 - acc: 0.9528 - val_loss: 0.0955 - val_acc: 0.9675\n",
      "Epoch 1932/5000\n",
      "1969/1969 [==============================] - ETA: 0s - loss: 0.0782 - acc: 0.968 - 0s - loss: 0.0899 - acc: 0.9528 - val_loss: 0.1091 - val_acc: 0.9493\n",
      "Epoch 1933/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0755 - acc: 0.9670 - val_loss: 0.1001 - val_acc: 0.9655\n",
      "Epoch 1934/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0807 - acc: 0.9629 - val_loss: 0.0922 - val_acc: 0.9696\n",
      "Epoch 1935/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0798 - acc: 0.9660 - val_loss: 0.0987 - val_acc: 0.9554\n",
      "Epoch 1936/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0852 - acc: 0.9609 - val_loss: 0.0910 - val_acc: 0.9594\n",
      "Epoch 1937/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0818 - acc: 0.9639 - val_loss: 0.0968 - val_acc: 0.9554\n",
      "Epoch 1938/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0866 - acc: 0.9584 - val_loss: 0.0930 - val_acc: 0.9594\n",
      "Epoch 1939/5000\n",
      "1969/1969 [==============================] - ETA: 0s - loss: 0.0551 - acc: 0.992 - 0s - loss: 0.0720 - acc: 0.9690 - val_loss: 0.1228 - val_acc: 0.9371\n",
      "Epoch 1940/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0811 - acc: 0.9594 - val_loss: 0.1168 - val_acc: 0.9412\n",
      "Epoch 1941/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0841 - acc: 0.9594 - val_loss: 0.1113 - val_acc: 0.9533\n",
      "Epoch 1942/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0882 - acc: 0.9584 - val_loss: 0.1005 - val_acc: 0.9635\n",
      "Epoch 1943/5000\n",
      "1969/1969 [==============================] - ETA: 0s - loss: 0.1018 - acc: 0.953 - 0s - loss: 0.0787 - acc: 0.9634 - val_loss: 0.0959 - val_acc: 0.9554\n",
      "Epoch 1944/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0786 - acc: 0.9655 - val_loss: 0.1210 - val_acc: 0.9473\n",
      "Epoch 1945/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0836 - acc: 0.9578 - val_loss: 0.0847 - val_acc: 0.9635\n",
      "Epoch 1946/5000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0903 - acc: 0.9563 - val_loss: 0.1078 - val_acc: 0.9594\n",
      "Epoch 1947/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0800 - acc: 0.9624 - val_loss: 0.1726 - val_acc: 0.9351\n",
      "Epoch 1948/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0895 - acc: 0.9609 - val_loss: 0.1102 - val_acc: 0.9493\n",
      "Epoch 1949/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0773 - acc: 0.9629 - val_loss: 0.1426 - val_acc: 0.9351\n",
      "Epoch 1950/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0792 - acc: 0.9634 - val_loss: 0.0934 - val_acc: 0.9554\n",
      "Epoch 1951/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0761 - acc: 0.9670 - val_loss: 0.1000 - val_acc: 0.9513\n",
      "Epoch 1952/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0825 - acc: 0.9634 - val_loss: 0.0950 - val_acc: 0.9574\n",
      "Epoch 1953/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0810 - acc: 0.9619 - val_loss: 0.1022 - val_acc: 0.9655\n",
      "Epoch 1954/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0823 - acc: 0.9599 - val_loss: 0.1209 - val_acc: 0.9391\n",
      "Epoch 1955/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0893 - acc: 0.9609 - val_loss: 0.1420 - val_acc: 0.9391\n",
      "Epoch 1956/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0842 - acc: 0.9589 - val_loss: 0.0866 - val_acc: 0.9655\n",
      "Epoch 1957/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0846 - acc: 0.9594 - val_loss: 0.1249 - val_acc: 0.9371\n",
      "Epoch 1958/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0752 - acc: 0.9690 - val_loss: 0.0991 - val_acc: 0.9655\n",
      "Epoch 1959/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0834 - acc: 0.9609 - val_loss: 0.1310 - val_acc: 0.9412\n",
      "Epoch 1960/5000\n",
      "1969/1969 [==============================] - ETA: 0s - loss: 0.0760 - acc: 0.961 - 0s - loss: 0.0737 - acc: 0.9639 - val_loss: 0.4136 - val_acc: 0.8884\n",
      "Epoch 1961/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0926 - acc: 0.9589 - val_loss: 0.1464 - val_acc: 0.9331\n",
      "Epoch 1962/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0869 - acc: 0.9644 - val_loss: 0.0843 - val_acc: 0.9675\n",
      "Epoch 1963/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0761 - acc: 0.9660 - val_loss: 0.0863 - val_acc: 0.9655\n",
      "Epoch 1964/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0813 - acc: 0.9604 - val_loss: 0.1022 - val_acc: 0.9594\n",
      "Epoch 1965/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0829 - acc: 0.9584 - val_loss: 0.1016 - val_acc: 0.9513\n",
      "Epoch 1966/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0710 - acc: 0.9695 - val_loss: 0.1274 - val_acc: 0.9452\n",
      "Epoch 1967/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0855 - acc: 0.9609 - val_loss: 0.1302 - val_acc: 0.9452\n",
      "Epoch 1968/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0872 - acc: 0.9599 - val_loss: 0.1267 - val_acc: 0.9391\n",
      "Epoch 1969/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0782 - acc: 0.9619 - val_loss: 0.1216 - val_acc: 0.9554\n",
      "Epoch 1970/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0847 - acc: 0.9594 - val_loss: 0.0976 - val_acc: 0.9635\n",
      "Epoch 1971/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0861 - acc: 0.9594 - val_loss: 0.1670 - val_acc: 0.9391\n",
      "Epoch 1972/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0816 - acc: 0.9599 - val_loss: 0.0995 - val_acc: 0.9615\n",
      "Epoch 1973/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0840 - acc: 0.9624 - val_loss: 0.0919 - val_acc: 0.9574\n",
      "Epoch 1974/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0876 - acc: 0.9614 - val_loss: 0.0871 - val_acc: 0.9675\n",
      "Epoch 1975/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0811 - acc: 0.9604 - val_loss: 0.1275 - val_acc: 0.9452\n",
      "Epoch 1976/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0857 - acc: 0.9644 - val_loss: 0.1166 - val_acc: 0.9432\n",
      "Epoch 1977/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0761 - acc: 0.9690 - val_loss: 0.0974 - val_acc: 0.9675\n",
      "Epoch 1978/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0825 - acc: 0.9609 - val_loss: 0.0964 - val_acc: 0.9655\n",
      "Epoch 1979/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0755 - acc: 0.9644 - val_loss: 0.1658 - val_acc: 0.9331\n",
      "Epoch 1980/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0853 - acc: 0.9568 - val_loss: 0.1715 - val_acc: 0.9371\n",
      "Epoch 1981/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0840 - acc: 0.9624 - val_loss: 0.1007 - val_acc: 0.9554\n",
      "Epoch 1982/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0814 - acc: 0.9573 - val_loss: 0.1171 - val_acc: 0.9574\n",
      "Epoch 1983/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0792 - acc: 0.9614 - val_loss: 0.1250 - val_acc: 0.9473\n",
      "Epoch 1984/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0774 - acc: 0.9614 - val_loss: 0.1588 - val_acc: 0.9290\n",
      "Epoch 1985/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0901 - acc: 0.9584 - val_loss: 0.1549 - val_acc: 0.9290\n",
      "Epoch 1986/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0924 - acc: 0.9558 - val_loss: 0.1170 - val_acc: 0.9412\n",
      "Epoch 1987/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0851 - acc: 0.9599 - val_loss: 0.0869 - val_acc: 0.9615\n",
      "Epoch 1988/5000\n",
      "1969/1969 [==============================] - ETA: 0s - loss: 0.1008 - acc: 0.976 - 0s - loss: 0.0830 - acc: 0.9624 - val_loss: 0.1284 - val_acc: 0.9371\n",
      "Epoch 1989/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0761 - acc: 0.9660 - val_loss: 0.0933 - val_acc: 0.9655\n",
      "Epoch 1990/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0771 - acc: 0.9690 - val_loss: 0.0939 - val_acc: 0.9594\n",
      "Epoch 1991/5000\n",
      "1969/1969 [==============================] - ETA: 0s - loss: 0.1240 - acc: 0.953 - 0s - loss: 0.0853 - acc: 0.9573 - val_loss: 0.0918 - val_acc: 0.9716\n",
      "Epoch 1992/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0930 - acc: 0.9609 - val_loss: 0.0995 - val_acc: 0.9635\n",
      "Epoch 1993/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0735 - acc: 0.9624 - val_loss: 0.1056 - val_acc: 0.9513\n",
      "Epoch 1994/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0896 - acc: 0.9538 - val_loss: 0.1039 - val_acc: 0.9452\n",
      "Epoch 1995/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0805 - acc: 0.9670 - val_loss: 0.0858 - val_acc: 0.9736\n",
      "Epoch 1996/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0734 - acc: 0.9685 - val_loss: 0.0954 - val_acc: 0.9635\n",
      "Epoch 1997/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0910 - acc: 0.9523 - val_loss: 0.0847 - val_acc: 0.9675\n",
      "Epoch 1998/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0759 - acc: 0.9680 - val_loss: 0.1255 - val_acc: 0.9371\n",
      "Epoch 1999/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0821 - acc: 0.9629 - val_loss: 0.0912 - val_acc: 0.9696\n",
      "Epoch 2000/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0760 - acc: 0.9619 - val_loss: 0.0937 - val_acc: 0.9554\n",
      "Epoch 2001/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0812 - acc: 0.9619 - val_loss: 0.1387 - val_acc: 0.9351\n",
      "Epoch 2002/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0837 - acc: 0.9644 - val_loss: 0.1123 - val_acc: 0.9432\n",
      "Epoch 2003/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0862 - acc: 0.9639 - val_loss: 0.1010 - val_acc: 0.9513\n",
      "Epoch 2004/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0752 - acc: 0.9634 - val_loss: 0.0876 - val_acc: 0.9574\n",
      "Epoch 2005/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0818 - acc: 0.9624 - val_loss: 0.1697 - val_acc: 0.9412\n",
      "Epoch 2006/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0897 - acc: 0.9589 - val_loss: 0.0847 - val_acc: 0.9716\n",
      "Epoch 2007/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0791 - acc: 0.9594 - val_loss: 0.1060 - val_acc: 0.9554\n",
      "Epoch 2008/5000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0769 - acc: 0.9634 - val_loss: 0.0968 - val_acc: 0.9675\n",
      "Epoch 2009/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0780 - acc: 0.9660 - val_loss: 0.1260 - val_acc: 0.9391\n",
      "Epoch 2010/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0858 - acc: 0.9548 - val_loss: 0.1365 - val_acc: 0.9391\n",
      "Epoch 2011/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0831 - acc: 0.9609 - val_loss: 0.1165 - val_acc: 0.9371\n",
      "Epoch 2012/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0766 - acc: 0.9629 - val_loss: 0.1064 - val_acc: 0.9452\n",
      "Epoch 2013/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0795 - acc: 0.9624 - val_loss: 0.1283 - val_acc: 0.9371\n",
      "Epoch 2014/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0753 - acc: 0.9665 - val_loss: 0.0967 - val_acc: 0.9533\n",
      "Epoch 2015/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0827 - acc: 0.9604 - val_loss: 0.1140 - val_acc: 0.9412\n",
      "Epoch 2016/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0782 - acc: 0.9639 - val_loss: 0.1286 - val_acc: 0.9331\n",
      "Epoch 2017/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0897 - acc: 0.9604 - val_loss: 0.1119 - val_acc: 0.9493\n",
      "Epoch 2018/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0857 - acc: 0.9589 - val_loss: 0.1170 - val_acc: 0.9574\n",
      "Epoch 2019/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0858 - acc: 0.9568 - val_loss: 0.1257 - val_acc: 0.9331\n",
      "Epoch 2020/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0880 - acc: 0.9584 - val_loss: 0.1616 - val_acc: 0.9331\n",
      "Epoch 2021/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0892 - acc: 0.9589 - val_loss: 0.0921 - val_acc: 0.9594\n",
      "Epoch 2022/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0810 - acc: 0.9655 - val_loss: 0.1019 - val_acc: 0.9554\n",
      "Epoch 2023/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0771 - acc: 0.9604 - val_loss: 0.0992 - val_acc: 0.9533\n",
      "Epoch 2024/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0813 - acc: 0.9639 - val_loss: 0.1184 - val_acc: 0.9493\n",
      "Epoch 2025/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0820 - acc: 0.9629 - val_loss: 0.3156 - val_acc: 0.9006\n",
      "Epoch 2026/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0862 - acc: 0.9589 - val_loss: 0.0993 - val_acc: 0.9635\n",
      "Epoch 2027/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0893 - acc: 0.9584 - val_loss: 0.0922 - val_acc: 0.9574\n",
      "Epoch 2028/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0745 - acc: 0.9705 - val_loss: 0.2140 - val_acc: 0.9270\n",
      "Epoch 2029/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.1119 - acc: 0.9512 - val_loss: 0.1033 - val_acc: 0.9533\n",
      "Epoch 2030/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0730 - acc: 0.9700 - val_loss: 0.0855 - val_acc: 0.9675\n",
      "Epoch 2031/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0818 - acc: 0.9563 - val_loss: 0.0899 - val_acc: 0.9594\n",
      "Epoch 2032/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0714 - acc: 0.9655 - val_loss: 0.0849 - val_acc: 0.9655\n",
      "Epoch 2033/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0846 - acc: 0.9619 - val_loss: 0.0886 - val_acc: 0.9696\n",
      "Epoch 2034/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0825 - acc: 0.9594 - val_loss: 0.1322 - val_acc: 0.9412\n",
      "Epoch 2035/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0910 - acc: 0.9553 - val_loss: 0.1479 - val_acc: 0.9391\n",
      "Epoch 2036/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0744 - acc: 0.9639 - val_loss: 0.1061 - val_acc: 0.9473\n",
      "Epoch 2037/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0787 - acc: 0.9619 - val_loss: 0.1766 - val_acc: 0.9331\n",
      "Epoch 2038/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0887 - acc: 0.9573 - val_loss: 0.1380 - val_acc: 0.9391\n",
      "Epoch 2039/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0828 - acc: 0.9614 - val_loss: 0.1121 - val_acc: 0.9513\n",
      "Epoch 2040/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0706 - acc: 0.9655 - val_loss: 0.0963 - val_acc: 0.9554\n",
      "Epoch 2041/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0767 - acc: 0.9670 - val_loss: 0.1079 - val_acc: 0.9574\n",
      "Epoch 2042/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0708 - acc: 0.9675 - val_loss: 0.0864 - val_acc: 0.9594\n",
      "Epoch 2043/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0847 - acc: 0.9604 - val_loss: 0.1187 - val_acc: 0.9533\n",
      "Epoch 2044/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0864 - acc: 0.9604 - val_loss: 0.1748 - val_acc: 0.9391\n",
      "Epoch 2045/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0845 - acc: 0.9650 - val_loss: 0.0968 - val_acc: 0.9594\n",
      "Epoch 2046/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0831 - acc: 0.9594 - val_loss: 0.0875 - val_acc: 0.9635\n",
      "Epoch 2047/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0796 - acc: 0.9660 - val_loss: 0.0851 - val_acc: 0.9696\n",
      "Epoch 2048/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0753 - acc: 0.9619 - val_loss: 0.0943 - val_acc: 0.9635\n",
      "Epoch 2049/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0784 - acc: 0.9644 - val_loss: 0.0999 - val_acc: 0.9675\n",
      "Epoch 2050/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0839 - acc: 0.9594 - val_loss: 0.1176 - val_acc: 0.9513\n",
      "Epoch 2051/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0883 - acc: 0.9563 - val_loss: 0.0956 - val_acc: 0.9635\n",
      "Epoch 2052/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0753 - acc: 0.9639 - val_loss: 0.1072 - val_acc: 0.9594\n",
      "Epoch 2053/5000\n",
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      "Epoch 2069/5000\n",
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      "Epoch 2070/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0908 - acc: 0.9563 - val_loss: 0.1489 - val_acc: 0.9473\n",
      "Epoch 2071/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "Epoch 2072/5000\n",
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      "Epoch 2099/5000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0731 - acc: 0.9639 - val_loss: 0.1117 - val_acc: 0.9513\n",
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      "Epoch 2190/5000\n",
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      "Epoch 2191/5000\n",
      "1969/1969 [==============================] - ETA: 0s - loss: 0.0946 - acc: 0.968 - 0s - loss: 0.0898 - acc: 0.9614 - val_loss: 0.1031 - val_acc: 0.9533\n",
      "Epoch 2192/5000\n",
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      "Epoch 2193/5000\n",
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      "1969/1969 [==============================] - 0s - loss: 0.0789 - acc: 0.9584 - val_loss: 0.1307 - val_acc: 0.9473\n",
      "Epoch 2195/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0813 - acc: 0.9614 - val_loss: 0.0888 - val_acc: 0.9696\n",
      "Epoch 2196/5000\n",
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      "Epoch 2258/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0696 - acc: 0.9695 - val_loss: 0.1326 - val_acc: 0.9432\n",
      "Epoch 2259/5000\n",
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      "1969/1969 [==============================] - ETA: 0s - loss: 0.1122 - acc: 0.953 - 0s - loss: 0.0704 - acc: 0.9695 - val_loss: 0.1144 - val_acc: 0.9513\n",
      "Epoch 2302/5000\n",
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      "Epoch 2320/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "Epoch 2351/5000\n",
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      "Epoch 2352/5000\n",
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      "Epoch 2382/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0721 - acc: 0.9690 - val_loss: 0.0991 - val_acc: 0.9615\n",
      "Epoch 2383/5000\n",
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      "Epoch 2404/5000\n",
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      "Epoch 2413/5000\n",
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      "1969/1969 [==============================] - ETA: 0s - loss: 0.1299 - acc: 0.921 - 0s - loss: 0.0746 - acc: 0.9604 - val_loss: 0.1121 - val_acc: 0.9513\n",
      "Epoch 2415/5000\n",
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      "Epoch 2444/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0727 - acc: 0.9670 - val_loss: 0.0989 - val_acc: 0.9675\n",
      "Epoch 2445/5000\n",
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      "Epoch 2474/5000\n",
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      "Epoch 2475/5000\n",
      "1969/1969 [==============================] - ETA: 0s - loss: 0.0650 - acc: 0.970 - 0s - loss: 0.0691 - acc: 0.9690 - val_loss: 0.1160 - val_acc: 0.9452\n",
      "Epoch 2476/5000\n",
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      "Epoch 2496/5000\n",
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      "Epoch 2500/5000\n",
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      "Epoch 2502/5000\n",
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      "Epoch 2503/5000\n",
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      "Epoch 2504/5000\n",
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      "Epoch 2505/5000\n",
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      "Epoch 2506/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0796 - acc: 0.9655 - val_loss: 0.1089 - val_acc: 0.9615\n",
      "Epoch 2507/5000\n",
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      "Epoch 2569/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0747 - acc: 0.9639 - val_loss: 0.1122 - val_acc: 0.9554\n",
      "Epoch 2570/5000\n",
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      "Epoch 2575/5000\n",
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      "Epoch 2624/5000\n",
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      "Epoch 2625/5000\n",
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      "Epoch 2627/5000\n",
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      "Epoch 2628/5000\n",
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      "Epoch 2629/5000\n",
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      "Epoch 2630/5000\n",
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      "Epoch 2631/5000\n",
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      "Epoch 2632/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0803 - acc: 0.9629 - val_loss: 0.1414 - val_acc: 0.9493\n",
      "Epoch 2633/5000\n",
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      "Epoch 2654/5000\n",
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      "1969/1969 [==============================] - ETA: 0s - loss: 0.0411 - acc: 0.976 - 0s - loss: 0.0676 - acc: 0.9685 - val_loss: 0.1646 - val_acc: 0.9412\n",
      "Epoch 2694/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0765 - acc: 0.9655 - val_loss: 0.1093 - val_acc: 0.9513\n",
      "Epoch 2695/5000\n",
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      "Epoch 2708/5000\n",
      "1969/1969 [==============================] - ETA: 0s - loss: 0.0751 - acc: 0.968 - 0s - loss: 0.0675 - acc: 0.9680 - val_loss: 0.1147 - val_acc: 0.9513\n",
      "Epoch 2709/5000\n",
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      "Epoch 2729/5000\n",
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      "Epoch 2730/5000\n",
      "1969/1969 [==============================] - ETA: 0s - loss: 0.0575 - acc: 0.992 - 0s - loss: 0.0752 - acc: 0.9665 - val_loss: 0.1037 - val_acc: 0.9675\n",
      "Epoch 2731/5000\n",
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      "Epoch 2751/5000\n",
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      "Epoch 2753/5000\n",
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      "Epoch 2754/5000\n",
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      "Epoch 2755/5000\n",
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      "Epoch 2756/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0701 - acc: 0.9660 - val_loss: 0.0923 - val_acc: 0.9655\n",
      "Epoch 2757/5000\n",
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      "1969/1969 [==============================] - ETA: 0s - loss: 0.0889 - acc: 0.953 - 0s - loss: 0.0650 - acc: 0.9695 - val_loss: 0.1043 - val_acc: 0.9594\n",
      "Epoch 2810/5000\n",
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      "Epoch 2816/5000\n",
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      "Epoch 2818/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0616 - acc: 0.9705 - val_loss: 0.1426 - val_acc: 0.9351\n",
      "Epoch 2819/5000\n",
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      "Epoch 2827/5000\n",
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      "Epoch 2829/5000\n",
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      "Epoch 2831/5000\n",
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      "Epoch 2832/5000\n",
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      "Epoch 2833/5000\n",
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      "Epoch 2834/5000\n",
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      "Epoch 2835/5000\n",
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      "Epoch 2836/5000\n",
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      "Epoch 2837/5000\n",
      "1969/1969 [==============================] - ETA: 0s - loss: 0.0835 - acc: 0.960 - 0s - loss: 0.0661 - acc: 0.9711 - val_loss: 0.1917 - val_acc: 0.9270\n",
      "Epoch 2838/5000\n",
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      "Epoch 2841/5000\n",
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      "Epoch 2843/5000\n",
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      "Epoch 2849/5000\n",
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      "Epoch 2864/5000\n",
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      "Epoch 2865/5000\n",
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      "Epoch 2866/5000\n",
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      "Epoch 2867/5000\n",
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      "Epoch 2870/5000\n",
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      "Epoch 2872/5000\n",
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      "Epoch 2874/5000\n",
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      "Epoch 2875/5000\n",
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      "Epoch 2876/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0717 - acc: 0.9624 - val_loss: 0.1101 - val_acc: 0.9574\n",
      "Epoch 2877/5000\n",
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      "Epoch 2878/5000\n",
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      "Epoch 2879/5000\n",
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      "Epoch 2880/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0582 - acc: 0.9736 - val_loss: 0.1293 - val_acc: 0.9594\n",
      "Epoch 2881/5000\n",
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      "Epoch 2917/5000\n",
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      "Epoch 2942/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0594 - acc: 0.9726 - val_loss: 0.1136 - val_acc: 0.9675\n",
      "Epoch 2943/5000\n",
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      "Epoch 2968/5000\n",
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      "Epoch 2990/5000\n",
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      "Epoch 2991/5000\n",
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      "Epoch 2994/5000\n",
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      "Epoch 2995/5000\n",
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      "Epoch 2996/5000\n",
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      "Epoch 2997/5000\n",
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      "Epoch 2999/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0639 - acc: 0.9665 - val_loss: 0.1962 - val_acc: 0.9351\n",
      "Epoch 3000/5000\n",
      "1969/1969 [==============================] - ETA: 0s - loss: 0.0662 - acc: 0.976 - 0s - loss: 0.0590 - acc: 0.9756 - val_loss: 0.1128 - val_acc: 0.9757\n",
      "Epoch 3001/5000\n",
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      "Epoch 3002/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0615 - acc: 0.9700 - val_loss: 0.1380 - val_acc: 0.9473\n",
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      "Epoch 3004/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0595 - acc: 0.9726 - val_loss: 0.1321 - val_acc: 0.9574\n",
      "Epoch 3005/5000\n",
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      "Epoch 3049/5000\n",
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      "Epoch 3066/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0596 - acc: 0.9695 - val_loss: 0.1721 - val_acc: 0.9452\n",
      "Epoch 3067/5000\n",
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      "Epoch 3080/5000\n",
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      "Epoch 3089/5000\n",
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      "Epoch 3090/5000\n",
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      "Epoch 3096/5000\n",
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      "Epoch 3128/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0623 - acc: 0.9716 - val_loss: 0.1465 - val_acc: 0.9675\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0509 - acc: 0.9782 - val_loss: 0.1577 - val_acc: 0.9615\n",
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      "Epoch 3252/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0671 - acc: 0.9670 - val_loss: 0.1363 - val_acc: 0.9655\n",
      "Epoch 3253/5000\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0549 - acc: 0.9746 - val_loss: 0.1438 - val_acc: 0.9635\n",
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      "Epoch 3376/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "Epoch 3377/5000\n",
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      "Epoch 3439/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0780 - acc: 0.9655 - val_loss: 0.1598 - val_acc: 0.9635\n",
      "Epoch 3440/5000\n",
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      "Epoch 3453/5000\n",
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      "Epoch 3461/5000\n",
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      "Epoch 3463/5000\n",
      "1969/1969 [==============================] - ETA: 0s - loss: 0.0177 - acc: 1.000 - 0s - loss: 0.0388 - acc: 0.9858 - val_loss: 0.1532 - val_acc: 0.9757\n",
      "Epoch 3464/5000\n",
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      "Epoch 3492/5000\n",
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      "Epoch 3496/5000\n",
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      "Epoch 3497/5000\n",
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      "Epoch 3499/5000\n",
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      "1969/1969 [==============================] - 0s - loss: 0.0656 - acc: 0.9721 - val_loss: 0.1604 - val_acc: 0.9817\n",
      "Epoch 3501/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0425 - acc: 0.9812 - val_loss: 0.1531 - val_acc: 0.9757\n",
      "Epoch 3565/5000\n",
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      "Epoch 3602/5000\n",
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      "Epoch 3623/5000\n",
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      "Epoch 3626/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0545 - acc: 0.9766 - val_loss: 0.1834 - val_acc: 0.9513\n",
      "Epoch 3627/5000\n",
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      "Epoch 3633/5000\n",
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      "Epoch 3651/5000\n",
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      "Epoch 3654/5000\n",
      "1969/1969 [==============================] - ETA: 0s - loss: 0.1278 - acc: 0.945 - 0s - loss: 0.0413 - acc: 0.9853 - val_loss: 0.1649 - val_acc: 0.9574\n",
      "Epoch 3655/5000\n",
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      "Epoch 3665/5000\n",
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      "1969/1969 [==============================] - ETA: 0s - loss: 0.0396 - acc: 0.992 - 0s - loss: 0.0312 - acc: 0.9893 - val_loss: 0.2083 - val_acc: 0.9473\n",
      "Epoch 3677/5000\n",
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      "Epoch 3687/5000\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0343 - acc: 0.9898 - val_loss: 0.1446 - val_acc: 0.9797\n",
      "Epoch 3688/5000\n",
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      "Epoch 3749/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0427 - acc: 0.9827 - val_loss: 0.1618 - val_acc: 0.9696\n",
      "Epoch 3750/5000\n",
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      "Epoch 3752/5000\n",
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      "1969/1969 [==============================] - ETA: 0s - loss: 0.0418 - acc: 0.976 - 0s - loss: 0.0441 - acc: 0.9797 - val_loss: 0.1586 - val_acc: 0.9716\n",
      "Epoch 3765/5000\n",
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      "Epoch 3797/5000\n",
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      "Epoch 3798/5000\n",
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      "Epoch 3799/5000\n",
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      "Epoch 3801/5000\n",
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      "Epoch 3805/5000\n",
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      "Epoch 3806/5000\n",
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      "Epoch 3807/5000\n",
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      "Epoch 3808/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0576 - acc: 0.9741 - val_loss: 0.1979 - val_acc: 0.9493\n",
      "Epoch 3809/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0552 - acc: 0.9741 - val_loss: 0.1541 - val_acc: 0.9797\n",
      "Epoch 3810/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0417 - acc: 0.9837 - val_loss: 0.1658 - val_acc: 0.9716\n",
      "Epoch 3811/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0348 - acc: 0.9868 - val_loss: 0.1627 - val_acc: 0.9675\n",
      "Epoch 3812/5000\n",
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      "Epoch 3813/5000\n",
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      "Epoch 3815/5000\n",
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      "Epoch 3818/5000\n",
      "1969/1969 [==============================] - ETA: 0s - loss: 0.0831 - acc: 0.960 - 0s - loss: 0.0479 - acc: 0.9782 - val_loss: 0.1637 - val_acc: 0.9675\n",
      "Epoch 3819/5000\n",
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      "Epoch 3826/5000\n",
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      "Epoch 3827/5000\n",
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      "Epoch 3828/5000\n",
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      "Epoch 3831/5000\n",
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      "Epoch 3832/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0319 - acc: 0.9878 - val_loss: 0.1567 - val_acc: 0.9696\n",
      "Epoch 3833/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0661 - acc: 0.9716 - val_loss: 0.1517 - val_acc: 0.9777\n",
      "Epoch 3834/5000\n",
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      "Epoch 3835/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0717 - acc: 0.9670 - val_loss: 0.1573 - val_acc: 0.9817\n",
      "Epoch 3836/5000\n",
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      "Epoch 3837/5000\n",
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      "Epoch 3843/5000\n",
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      "Epoch 3844/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0594 - acc: 0.9726 - val_loss: 0.1561 - val_acc: 0.9797\n",
      "Epoch 3845/5000\n",
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      "1969/1969 [==============================] - ETA: 0s - loss: 0.0504 - acc: 0.976 - 0s - loss: 0.0335 - acc: 0.9898 - val_loss: 0.1617 - val_acc: 0.9675\n",
      "Epoch 3853/5000\n",
      "1969/1969 [==============================] - ETA: 0s - loss: 0.0474 - acc: 0.976 - 0s - loss: 0.0598 - acc: 0.9716 - val_loss: 0.1884 - val_acc: 0.9533\n",
      "Epoch 3854/5000\n",
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      "Epoch 3855/5000\n",
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      "Epoch 3861/5000\n",
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      "Epoch 3870/5000\n",
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      "Epoch 3873/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0380 - acc: 0.9843 - val_loss: 0.1683 - val_acc: 0.9675\n",
      "Epoch 3874/5000\n",
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      "Epoch 3877/5000\n",
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      "Epoch 3879/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0308 - acc: 0.9888 - val_loss: 0.1634 - val_acc: 0.9696\n",
      "Epoch 3880/5000\n",
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      "Epoch 3883/5000\n",
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      "Epoch 3891/5000\n",
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      "Epoch 3895/5000\n",
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      "Epoch 3899/5000\n",
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      "Epoch 3904/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0406 - acc: 0.9817 - val_loss: 0.1873 - val_acc: 0.9473\n",
      "Epoch 3905/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0375 - acc: 0.9863 - val_loss: 0.1208 - val_acc: 0.9797\n",
      "Epoch 3906/5000\n",
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      "Epoch 3907/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0448 - acc: 0.9832 - val_loss: 0.1213 - val_acc: 0.9858\n",
      "Epoch 3908/5000\n",
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      "Epoch 3909/5000\n",
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      "Epoch 3910/5000\n",
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      "Epoch 3914/5000\n",
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      "Epoch 3922/5000\n",
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      "Epoch 3926/5000\n",
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      "Epoch 3927/5000\n",
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      "Epoch 3928/5000\n",
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      "Epoch 3929/5000\n",
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      "Epoch 3931/5000\n",
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      "Epoch 3932/5000\n",
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      "Epoch 3933/5000\n",
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      "Epoch 3934/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0558 - acc: 0.9741 - val_loss: 0.1430 - val_acc: 0.9817\n",
      "Epoch 3935/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0533 - acc: 0.9787 - val_loss: 0.1781 - val_acc: 0.9696\n",
      "Epoch 3936/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0513 - acc: 0.9766 - val_loss: 0.1466 - val_acc: 0.9777\n",
      "Epoch 3937/5000\n",
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      "Epoch 3960/5000\n",
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      "Epoch 3999/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0633 - acc: 0.9705 - val_loss: 0.1406 - val_acc: 0.9757\n",
      "Epoch 4000/5000\n",
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      "Epoch 4005/5000\n",
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      "Epoch 4015/5000\n",
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      "1969/1969 [==============================] - ETA: 0s - loss: 0.0140 - acc: 1.000 - 0s - loss: 0.0269 - acc: 0.9909 - val_loss: 0.2851 - val_acc: 0.9371\n",
      "Epoch 4017/5000\n",
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      "Epoch 4031/5000\n",
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      "Epoch 4034/5000\n",
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      "Epoch 4039/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0539 - acc: 0.9761 - val_loss: 0.1447 - val_acc: 0.9716\n",
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      "Epoch 4041/5000\n",
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      "Epoch 4042/5000\n",
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      "Epoch 4048/5000\n",
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      "Epoch 4050/5000\n",
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      "Epoch 4051/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0259 - acc: 0.9909 - val_loss: 0.1026 - val_acc: 0.9777\n",
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      "Epoch 4053/5000\n",
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      "Epoch 4054/5000\n",
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      "Epoch 4055/5000\n",
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      "Epoch 4060/5000\n",
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      "Epoch 4061/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0281 - acc: 0.9898 - val_loss: 0.1145 - val_acc: 0.9878\n",
      "Epoch 4062/5000\n",
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      "Epoch 4069/5000\n",
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      "Epoch 4077/5000\n",
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      "Epoch 4079/5000\n",
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      "Epoch 4084/5000\n",
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      "Epoch 4086/5000\n",
      "1969/1969 [==============================] - ETA: 0s - loss: 0.0122 - acc: 1.000 - 0s - loss: 0.0348 - acc: 0.9868 - val_loss: 0.1344 - val_acc: 0.9777\n",
      "Epoch 4087/5000\n",
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      "1969/1969 [==============================] - ETA: 0s - loss: 0.0164 - acc: 1.000 - 0s - loss: 0.0219 - acc: 0.9944 - val_loss: 0.1414 - val_acc: 0.9777\n",
      "Epoch 4094/5000\n",
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      "Epoch 4108/5000\n",
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      "Epoch 4118/5000\n",
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      "Epoch 4122/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0192 - acc: 0.9959 - val_loss: 0.1205 - val_acc: 0.9817\n",
      "Epoch 4123/5000\n",
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      "Epoch 4175/5000\n",
      "1969/1969 [==============================] - ETA: 0s - loss: 0.0182 - acc: 1.000 - 0s - loss: 0.0220 - acc: 0.9939 - val_loss: 0.1567 - val_acc: 0.9655\n",
      "Epoch 4176/5000\n",
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      "Epoch 4184/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0403 - acc: 0.9863 - val_loss: 0.1245 - val_acc: 0.9858\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0333 - acc: 0.9898 - val_loss: 0.1246 - val_acc: 0.9878\n",
      "Epoch 4248/5000\n",
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      "Epoch 4310/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0369 - acc: 0.9802 - val_loss: 0.1259 - val_acc: 0.9878\n",
      "Epoch 4311/5000\n",
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      "Epoch 4317/5000\n",
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      "Epoch 4320/5000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0255 - acc: 0.9898 - val_loss: 0.1342 - val_acc: 0.9817\n",
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      "Epoch 4435/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "Epoch 4436/5000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0172 - acc: 0.9929 - val_loss: 0.2572 - val_acc: 0.9351\n",
      "Epoch 4499/5000\n",
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      "1969/1969 [==============================] - ETA: 0s - loss: 0.0105 - acc: 0.992 - 0s - loss: 0.0151 - acc: 0.9954 - val_loss: 0.0982 - val_acc: 0.9858\n",
      "Epoch 4541/5000\n",
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      "Epoch 4550/5000\n",
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      "Epoch 4551/5000\n",
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      "Epoch 4552/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0168 - acc: 0.9964 - val_loss: 0.0894 - val_acc: 0.9878\n",
      "Epoch 4553/5000\n",
      "1969/1969 [==============================] - ETA: 0s - loss: 0.0037 - acc: 1.000 - 0s - loss: 0.0122 - acc: 0.9970 - val_loss: 0.0860 - val_acc: 0.9919\n",
      "Epoch 4554/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0327 - acc: 0.9868 - val_loss: 0.1090 - val_acc: 0.9878\n",
      "Epoch 4555/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0133 - acc: 0.9964 - val_loss: 0.0980 - val_acc: 0.9878\n",
      "Epoch 4556/5000\n",
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      "Epoch 4557/5000\n",
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      "Epoch 4558/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0238 - acc: 0.9893 - val_loss: 0.1821 - val_acc: 0.9574\n",
      "Epoch 4559/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0347 - acc: 0.9863 - val_loss: 0.1505 - val_acc: 0.9716\n",
      "Epoch 4560/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0164 - acc: 0.9944 - val_loss: 0.0951 - val_acc: 0.9899\n",
      "Epoch 4561/5000\n",
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      "Epoch 4564/5000\n",
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      "Epoch 4623/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0157 - acc: 0.9954 - val_loss: 0.1126 - val_acc: 0.9858\n",
      "Epoch 4624/5000\n",
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      "Epoch 4659/5000\n",
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      "Epoch 4685/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0149 - acc: 0.9949 - val_loss: 0.1256 - val_acc: 0.9757\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0119 - acc: 0.9985 - val_loss: 0.1145 - val_acc: 0.9878\n",
      "Epoch 4748/5000\n",
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      "Epoch 4790/5000\n",
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      "Epoch 4802/5000\n",
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      "Epoch 4805/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0142 - acc: 0.9954 - val_loss: 0.1095 - val_acc: 0.9878\n",
      "Epoch 4806/5000\n",
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      "Epoch 4807/5000\n",
      "1969/1969 [==============================] - ETA: 0s - loss: 0.0169 - acc: 1.000 - 0s - loss: 0.0154 - acc: 0.9939 - val_loss: 0.1054 - val_acc: 0.9838\n",
      "Epoch 4808/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0087 - acc: 0.9995 - val_loss: 0.0874 - val_acc: 0.9899\n",
      "Epoch 4809/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0151 - acc: 0.9929 - val_loss: 0.0861 - val_acc: 0.9878\n",
      "Epoch 4810/5000\n",
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      "Epoch 4830/5000\n",
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      "Epoch 4831/5000\n",
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      "Epoch 4833/5000\n",
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      "1969/1969 [==============================] - ETA: 0s - loss: 0.0436 - acc: 0.968 - 0s - loss: 0.0324 - acc: 0.9878 - val_loss: 0.1216 - val_acc: 0.9777\n",
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      "Epoch 4871/5000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0100 - acc: 0.9980 - val_loss: 0.0839 - val_acc: 0.9899\n",
      "Epoch 4872/5000\n",
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      "1969/1969 [==============================] - 0s - loss: 0.0741 - acc: 0.9741 - val_loss: 0.0870 - val_acc: 0.9919\n",
      "Epoch 4931/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0098 - acc: 0.9985 - val_loss: 0.0899 - val_acc: 0.9878\n",
      "Epoch 4932/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0095 - acc: 0.9980 - val_loss: 0.1107 - val_acc: 0.9858\n",
      "Epoch 4933/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0185 - acc: 0.9929 - val_loss: 0.0986 - val_acc: 0.9858\n",
      "Epoch 4934/5000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0137 - acc: 0.9959 - val_loss: 0.0842 - val_acc: 0.9899\n",
      "Epoch 4935/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0591 - acc: 0.9787 - val_loss: 0.4869 - val_acc: 0.9108\n",
      "Epoch 4936/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0867 - acc: 0.9761 - val_loss: 0.1151 - val_acc: 0.9777\n",
      "Epoch 4937/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0149 - acc: 0.9954 - val_loss: 0.1108 - val_acc: 0.9858\n",
      "Epoch 4938/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0160 - acc: 0.9949 - val_loss: 0.1050 - val_acc: 0.9899\n",
      "Epoch 4939/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0133 - acc: 0.9954 - val_loss: 0.1022 - val_acc: 0.9878\n",
      "Epoch 4940/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0098 - acc: 0.9975 - val_loss: 0.1036 - val_acc: 0.9878\n",
      "Epoch 4941/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0115 - acc: 0.9959 - val_loss: 0.1047 - val_acc: 0.9899\n",
      "Epoch 4942/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0385 - acc: 0.9848 - val_loss: 0.2625 - val_acc: 0.9452\n",
      "Epoch 4943/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0334 - acc: 0.9904 - val_loss: 0.1186 - val_acc: 0.9817\n",
      "Epoch 4944/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0265 - acc: 0.9893 - val_loss: 0.0973 - val_acc: 0.9858\n",
      "Epoch 4945/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0210 - acc: 0.9909 - val_loss: 0.0984 - val_acc: 0.9878\n",
      "Epoch 4946/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0132 - acc: 0.9970 - val_loss: 0.1133 - val_acc: 0.9777\n",
      "Epoch 4947/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0764 - acc: 0.9726 - val_loss: 0.1474 - val_acc: 0.9675\n",
      "Epoch 4948/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0317 - acc: 0.9853 - val_loss: 0.0968 - val_acc: 0.9858\n",
      "Epoch 4949/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0148 - acc: 0.9939 - val_loss: 0.0987 - val_acc: 0.9838\n",
      "Epoch 4950/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0129 - acc: 0.9949 - val_loss: 0.2072 - val_acc: 0.9452\n",
      "Epoch 4951/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0134 - acc: 0.9975 - val_loss: 0.0906 - val_acc: 0.9878\n",
      "Epoch 4952/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0224 - acc: 0.9914 - val_loss: 0.2382 - val_acc: 0.9493\n",
      "Epoch 4953/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0402 - acc: 0.9848 - val_loss: 0.2015 - val_acc: 0.9473\n",
      "Epoch 4954/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0627 - acc: 0.9741 - val_loss: 0.1013 - val_acc: 0.9878\n",
      "Epoch 4955/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0179 - acc: 0.9924 - val_loss: 0.0887 - val_acc: 0.9899\n",
      "Epoch 4956/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0118 - acc: 0.9964 - val_loss: 0.1071 - val_acc: 0.9817\n",
      "Epoch 4957/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0158 - acc: 0.9934 - val_loss: 0.0944 - val_acc: 0.9899\n",
      "Epoch 4958/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0409 - acc: 0.9853 - val_loss: 0.0983 - val_acc: 0.9838\n",
      "Epoch 4959/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0115 - acc: 0.9970 - val_loss: 0.0937 - val_acc: 0.9899\n",
      "Epoch 4960/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0098 - acc: 0.9980 - val_loss: 0.0980 - val_acc: 0.9878\n",
      "Epoch 4961/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0109 - acc: 0.9975 - val_loss: 0.0961 - val_acc: 0.9878\n",
      "Epoch 4962/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0189 - acc: 0.9924 - val_loss: 0.0833 - val_acc: 0.9919\n",
      "Epoch 4963/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0093 - acc: 0.9980 - val_loss: 0.0859 - val_acc: 0.9919\n",
      "Epoch 4964/5000\n",
      "1969/1969 [==============================] - ETA: 0s - loss: 0.0107 - acc: 1.000 - 0s - loss: 0.0130 - acc: 0.9964 - val_loss: 0.0948 - val_acc: 0.9858\n",
      "Epoch 4965/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0138 - acc: 0.9959 - val_loss: 0.1108 - val_acc: 0.9777\n",
      "Epoch 4966/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0343 - acc: 0.9853 - val_loss: 0.1184 - val_acc: 0.9817\n",
      "Epoch 4967/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0221 - acc: 0.9939 - val_loss: 0.0892 - val_acc: 0.9878\n",
      "Epoch 4968/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0165 - acc: 0.9934 - val_loss: 0.1387 - val_acc: 0.9696\n",
      "Epoch 4969/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0141 - acc: 0.9954 - val_loss: 0.2943 - val_acc: 0.9351\n",
      "Epoch 4970/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0287 - acc: 0.9909 - val_loss: 0.3109 - val_acc: 0.9412\n",
      "Epoch 4971/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0281 - acc: 0.9883 - val_loss: 0.0891 - val_acc: 0.9899\n",
      "Epoch 4972/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0121 - acc: 0.9975 - val_loss: 0.2496 - val_acc: 0.9391\n",
      "Epoch 4973/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0350 - acc: 0.9873 - val_loss: 0.0874 - val_acc: 0.9899\n",
      "Epoch 4974/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0094 - acc: 0.9990 - val_loss: 0.1113 - val_acc: 0.9757\n",
      "Epoch 4975/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0153 - acc: 0.9944 - val_loss: 0.1098 - val_acc: 0.9757\n",
      "Epoch 4976/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0148 - acc: 0.9944 - val_loss: 0.1734 - val_acc: 0.9615\n",
      "Epoch 4977/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0209 - acc: 0.9939 - val_loss: 0.0824 - val_acc: 0.9899\n",
      "Epoch 4978/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0330 - acc: 0.9863 - val_loss: 0.2670 - val_acc: 0.9310\n",
      "Epoch 4979/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0308 - acc: 0.9883 - val_loss: 0.0822 - val_acc: 0.9899\n",
      "Epoch 4980/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0104 - acc: 0.9975 - val_loss: 0.0908 - val_acc: 0.9858\n",
      "Epoch 4981/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0280 - acc: 0.9898 - val_loss: 0.0861 - val_acc: 0.9878\n",
      "Epoch 4982/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0285 - acc: 0.9883 - val_loss: 0.1101 - val_acc: 0.9797\n",
      "Epoch 4983/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0509 - acc: 0.9822 - val_loss: 0.1008 - val_acc: 0.9777\n",
      "Epoch 4984/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0094 - acc: 0.9985 - val_loss: 0.1260 - val_acc: 0.9777\n",
      "Epoch 4985/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0294 - acc: 0.9919 - val_loss: 0.0966 - val_acc: 0.9858\n",
      "Epoch 4986/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0158 - acc: 0.9944 - val_loss: 0.0923 - val_acc: 0.9878\n",
      "Epoch 4987/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0255 - acc: 0.9914 - val_loss: 0.0996 - val_acc: 0.9878\n",
      "Epoch 4988/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0096 - acc: 0.9985 - val_loss: 0.0873 - val_acc: 0.9899\n",
      "Epoch 4989/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0107 - acc: 0.9975 - val_loss: 0.1098 - val_acc: 0.9838\n",
      "Epoch 4990/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0151 - acc: 0.9954 - val_loss: 0.0895 - val_acc: 0.9899\n",
      "Epoch 4991/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0291 - acc: 0.9898 - val_loss: 0.1003 - val_acc: 0.9817\n",
      "Epoch 4992/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0526 - acc: 0.9822 - val_loss: 0.1351 - val_acc: 0.9736\n",
      "Epoch 4993/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0436 - acc: 0.9832 - val_loss: 0.0911 - val_acc: 0.9899\n",
      "Epoch 4994/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0108 - acc: 0.9980 - val_loss: 0.0953 - val_acc: 0.9878\n",
      "Epoch 4995/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0142 - acc: 0.9959 - val_loss: 0.0852 - val_acc: 0.9899\n",
      "Epoch 4996/5000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1969/1969 [==============================] - 0s - loss: 0.0085 - acc: 0.9975 - val_loss: 0.0882 - val_acc: 0.9878\n",
      "Epoch 4997/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0272 - acc: 0.9893 - val_loss: 0.1796 - val_acc: 0.9574\n",
      "Epoch 4998/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0244 - acc: 0.9929 - val_loss: 0.0827 - val_acc: 0.9919\n",
      "Epoch 4999/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0079 - acc: 0.9990 - val_loss: 0.0819 - val_acc: 0.9919\n",
      "Epoch 5000/5000\n",
      "1969/1969 [==============================] - 0s - loss: 0.0088 - acc: 0.9985 - val_loss: 0.0822 - val_acc: 0.9919\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x10b06b5d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ks.models.Sequential()\n",
    "model.add(Dense(128, input_dim=data_features.shape[1]))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(64))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(32))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(16))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(4))\n",
    "model.add(Activation('softmax'))\n",
    "history = LossHistory()\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',optimizer='adadelta',metrics=['accuracy'])\n",
    "model.fit(x=data_train,y=labels_train,batch_size=128,nb_epoch=5000,verbose=1,validation_data=(data_test,labels_test),callbacks=[history])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2aRNcIAtmJcvBXVq9YmNj9RPy73/ruPnMmWJ78CGRG2/UHkdenuzfnC4eHjbp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\nJBKJgjQKEolEIlH4f/0CQzRiMs6gAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c2997d910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "history.loss_plot('epoch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predict(model,data):\n",
    "    tmp_res = model.predict(data)\n",
    "    res = []\n",
    "    for item in tmp_res:\n",
    "        res.append(np.where(item == max(item))[0][0])\n",
    "    return np.array(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train acc: 0.999492127984\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "     normal       1.00      1.00      1.00       984\n",
      "        cpu       1.00      1.00      1.00       299\n",
      "        mem       1.00      1.00      1.00       212\n",
      "         io       1.00      1.00      1.00       474\n",
      "\n",
      "avg / total       1.00      1.00      1.00      1969\n",
      "\n",
      "test acc: 0.991886409736\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "     normal       1.00      1.00      1.00       247\n",
      "        cpu       0.97      1.00      0.99        74\n",
      "        mem       1.00      0.96      0.98        54\n",
      "         io       0.98      0.98      0.98       118\n",
      "\n",
      "avg / total       0.99      0.99      0.99       493\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_true = class_train\n",
    "y_pred = predict(model,data_train)\n",
    "target_names = ['normal', 'cpu', 'mem', 'io']\n",
    "print(\"train acc: {}\".format(sum(y_true == y_pred)/float(len(y_true))))\n",
    "print(classification_report(y_true, y_pred, target_names=target_names))\n",
    "\n",
    "# print(\"test: {}\".format(clf.score(mid_features_test, class_test)))\n",
    "y_true = class_test\n",
    "y_pred = predict(model,data_test)\n",
    "print(\"test acc: {}\".format(sum(y_true == y_pred)/float(len(y_true))))\n",
    "print(classification_report(y_true, y_pred, target_names=target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 神经网络 + SVM\n",
    "45 => 128 => 64 => 32 => 16 => SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train acc: 0.982732351447\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "     normal       1.00      1.00      1.00       984\n",
      "        cpu       0.95      0.94      0.95       299\n",
      "        mem       0.95      0.99      0.97       212\n",
      "         io       0.98      0.97      0.97       474\n",
      "\n",
      "avg / total       0.98      0.98      0.98      1969\n",
      "\n",
      "test acc: 0.975659229209\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "     normal       1.00      1.00      1.00       247\n",
      "        cpu       0.96      0.92      0.94        74\n",
      "        mem       0.93      0.94      0.94        54\n",
      "         io       0.96      0.97      0.97       118\n",
      "\n",
      "avg / total       0.98      0.98      0.98       493\n",
      "\n"
     ]
    }
   ],
   "source": [
    "get_feature = K.function([model.layers[0].input],[model.layers[4].output])\n",
    "mid_features_train = get_feature([data_train])[0]\n",
    "mid_features_test  = get_feature([data_test])[0]\n",
    "clf = svm.SVC()\n",
    "clf.fit(mid_features_train, class_train)\n",
    "\n",
    "print(\"train acc: {}\".format(clf.score(mid_features_train, class_train)))\n",
    "y_true = class_train\n",
    "y_pred = clf.predict(mid_features_train)\n",
    "target_names = ['normal', 'cpu', 'mem', 'io']\n",
    "print(classification_report(y_true, y_pred, target_names=target_names))\n",
    "\n",
    "print(\"test acc: {}\".format(clf.score(mid_features_test, class_test)))\n",
    "y_true = class_test\n",
    "y_pred = clf.predict(mid_features_test)\n",
    "print(classification_report(y_true, y_pred, target_names=target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 其他机器学习算法\n",
    "- KNN\n",
    "- 线性SVM\n",
    "- 基于核函数的SVM (RBF SVM), Radial basis function kernel\n",
    "- 决策树\n",
    "- 随机森林"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nearest Neighbors\n",
      "train acc: 0.973590655155\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "     normal       1.00      1.00      1.00       984\n",
      "        cpu       0.89      0.97      0.93       299\n",
      "        mem       0.95      0.90      0.92       212\n",
      "         io       0.98      0.96      0.97       474\n",
      "\n",
      "avg / total       0.97      0.97      0.97      1969\n",
      "\n",
      "test acc: 0.95537525355\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "     normal       1.00      1.00      1.00       247\n",
      "        cpu       0.84      0.93      0.88        74\n",
      "        mem       0.94      0.83      0.88        54\n",
      "         io       0.95      0.93      0.94       118\n",
      "\n",
      "avg / total       0.96      0.96      0.96       493\n",
      "\n",
      "\n",
      "\n",
      "Linear SVM\n",
      "train acc: 0.857287963433\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "     normal       1.00      1.00      1.00       984\n",
      "        cpu       1.00      0.45      0.63       299\n",
      "        mem       1.00      0.44      0.61       212\n",
      "         io       0.63      1.00      0.77       474\n",
      "\n",
      "avg / total       0.91      0.86      0.85      1969\n",
      "\n",
      "test acc: 0.855983772819\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "     normal       1.00      1.00      1.00       247\n",
      "        cpu       1.00      0.45      0.62        74\n",
      "        mem       1.00      0.44      0.62        54\n",
      "         io       0.62      1.00      0.77       118\n",
      "\n",
      "avg / total       0.91      0.86      0.85       493\n",
      "\n",
      "\n",
      "\n",
      "RBF SVM\n",
      "train acc: 0.858303707466\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "     normal       1.00      1.00      1.00       984\n",
      "        cpu       0.99      0.46      0.63       299\n",
      "        mem       1.00      0.44      0.61       212\n",
      "         io       0.63      1.00      0.77       474\n",
      "\n",
      "avg / total       0.91      0.86      0.85      1969\n",
      "\n",
      "test acc: 0.860040567951\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "     normal       1.00      1.00      1.00       247\n",
      "        cpu       1.00      0.47      0.64        74\n",
      "        mem       1.00      0.44      0.62        54\n",
      "         io       0.63      1.00      0.77       118\n",
      "\n",
      "avg / total       0.91      0.86      0.85       493\n",
      "\n",
      "\n",
      "\n",
      "Decision Tree\n",
      "train acc: 0.861350939563\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "     normal       1.00      1.00      1.00       984\n",
      "        cpu       1.00      0.80      0.89       299\n",
      "        mem       0.00      0.00      0.00       212\n",
      "         io       0.63      1.00      0.78       474\n",
      "\n",
      "avg / total       0.80      0.86      0.82      1969\n",
      "\n",
      "test acc: 0.860040567951\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "     normal       1.00      1.00      1.00       247\n",
      "        cpu       1.00      0.80      0.89        74\n",
      "        mem       0.00      0.00      0.00        54\n",
      "         io       0.63      1.00      0.77       118\n",
      "\n",
      "avg / total       0.80      0.86      0.82       493\n",
      "\n",
      "\n",
      "\n",
      "Random Forest\n",
      "train acc: 0.948704926359\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "     normal       0.95      0.97      0.96       984\n",
      "        cpu       0.95      0.93      0.94       299\n",
      "        mem       0.97      0.84      0.90       212\n",
      "         io       0.94      0.97      0.96       474\n",
      "\n",
      "avg / total       0.95      0.95      0.95      1969\n",
      "\n",
      "test acc: 0.939148073022\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "     normal       0.93      0.97      0.95       247\n",
      "        cpu       0.94      0.91      0.92        74\n",
      "        mem       0.96      0.83      0.89        54\n",
      "         io       0.94      0.94      0.94       118\n",
      "\n",
      "avg / total       0.94      0.94      0.94       493\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classifiers = [\n",
    "    KNeighborsClassifier(4),\n",
    "    svm.SVC(kernel=\"linear\", C=0.025),\n",
    "    svm.SVC(),\n",
    "    DecisionTreeClassifier(max_depth=3),\n",
    "    RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1)\n",
    "    ]\n",
    "names = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Decision Tree\",\n",
    "         \"Random Forest\"]\n",
    "for name, clf in zip(names,classifiers):\n",
    "    clf.fit(data_train,class_train)\n",
    "    print(name)\n",
    "    print(\"train acc: {}\".format(clf.score(data_train, class_train)))\n",
    "    y_true = class_train\n",
    "    y_pred = clf.predict(data_train)\n",
    "    target_names = ['normal', 'cpu', 'mem', 'io']\n",
    "    print(classification_report(y_true, y_pred, target_names=target_names))\n",
    "    \n",
    "    print(\"test acc: {}\".format(clf.score(data_test, class_test)))\n",
    "    y_true = class_test\n",
    "    y_pred = clf.predict(data_test)\n",
    "    print(classification_report(y_true, y_pred, target_names=target_names))\n",
    "    print '\\n'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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