src/04.OptimisationKeras.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Deep Learning\n",
    "\n",
    "<p align=\"center\">\n",
    "    <img width=\"699\" alt=\"image\" src=\"https://user-images.githubusercontent.com/49638680/159042792-8510fbd1-c4ac-4a48-8320-bc6c1a49cdae.png\">\n",
    "</p>\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep Neural Network optimisation\n",
    "\n",
    "We will build a deep network, and study several optimisation methods to avoid overfit and other issues affecting deep learning applications. \n",
    "\n",
    "**After this assignment you will be able to:**\n",
    "- Build and apply a deep neural network to supervised learning, having a method to understand and face issues.\n",
    "\n",
    "Let's get started!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy\n",
    "from PIL import Image\n",
    "from scipy import ndimage\n",
    "from utils.dnn_app_utils_v3 import *\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import make_moons\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from sklearn.preprocessing import Normalizer, OneHotEncoder\n",
    "\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.layers import Input, Add, Dense, Activation, Dropout\n",
    "from tensorflow.keras.models import Model, load_model, Sequential\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.applications.imagenet_utils import preprocess_input\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from IPython.display import SVG\n",
    "from tensorflow.keras.utils import plot_model\n",
    "from utils.resnets_utils import *\n",
    "from tensorflow.keras.initializers import glorot_uniform\n",
    "from tensorflow.keras.regularizers import l2\n",
    "import scipy.misc\n",
    "from matplotlib.pyplot import imshow\n",
    "\n",
    "import tensorflow.keras.backend as K\n",
    "K.set_image_data_format('channels_last')\n",
    "\n",
    "import skimage\n",
    "import imageio\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = \"retina\"\n",
    "plt.rcParams['figure.figsize'] = (10, 15) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "np.random.seed(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of training examples = 120\n",
      "number of test examples = 30\n",
      "X_train shape: (120, 2)\n",
      "Y_train shape: (120, 2)\n",
      "X_test shape: (30, 2)\n",
      "Y_test shape: (30, 2)\n"
     ]
    }
   ],
   "source": [
    "# generate 2d classification dataset\n",
    "X, Y = make_moons(n_samples=150, noise=0.2, random_state=42)\n",
    "\n",
    "X_train, X_test, Y_train_orig, Y_test_orig = train_test_split(X, Y, test_size=0.2)\n",
    "\n",
    "# Convert training and test labels to one hot matrices\n",
    "Y_train = convert_to_one_hot(Y_train_orig, 2).T\n",
    "Y_test = convert_to_one_hot(Y_test_orig, 2).T\n",
    "\n",
    "print(\"number of training examples = \" + str(X_train.shape[0]))\n",
    "print(\"number of test examples = \" + str(X_test.shape[0]))\n",
    "print(\"X_train shape: \" + str(X_train.shape))\n",
    "print(\"Y_train shape: \" + str(Y_train.shape))\n",
    "print(\"X_test shape: \" + str(X_test.shape))\n",
    "print(\"Y_test shape: \" + str(Y_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just for sake of clariness, let's plot our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABNIAAAa3CAYAAAC0qUYTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAABYlAAAWJQFJUiTwAACTzUlEQVR4nOz9f2zsa34f9r2/i+tsRM6V5F3G2dra4cZrrXZLBHFJo15LddIdIxs3RU0VdREysBLJ6B8pGUm2ZXSMKoBWqNUQU9uy5JBWgsKSsw7IAm4hIkVrb+FZ54d+wiTsOqyklXdzz0h2twq9lnTn0FrAud/+8Rzu3nv3DM+X58zMd368XgDx7H2eZ4afyz33cOY9z4+qrusAAAAAAA97T9sFAAAAAMAyEKQBAAAAQAOCNAAAAABoQJAGAAAAAA0I0gAAAACgAUEaAAAAADQgSAMAAACABgRpAAAAANCAIA0AAAAAGhCkAQAAAEADgjQAAAAAaECQBgAAAAANvNZ2AUxWVdV/m+Trk7zRcikAAAAAq+JDSX6zrut/6bEPFKQttq//uq/7uvd97GMfe1/bhQAAAACsgl/4hV/IP/2n//SlHitIW2xvfOxjH3vf1dVV23UAAAAArIS9vb1cX1+/8TKPdUYaAAAAADQgSAMAAACABgRpAAAAANCAIA0AAAAAGhCkAQAAAEADgjQAAAAAaECQBgAAAAANvNZ2AQAAAADr6K233sqXvvSlvPnmm/nyl7+cuq7bLmnpVFWV9773vXn99dfzvve9L+95z2zXjAnSAAAAAObsrbfeyq/8yq/k7u6u7VKWWl3X+a3f+q381m/9Vp4+fZoPfvCDMw3TBGkAAAAAc/alL30pd3d3ee211/KBD3wgm5ubM19NtYreeuutPH36NF/84hdzd3eXL33pS9na2prZ9/P/EAAAAMCcvfnmm0mSD3zgA3n99deFaC/pPe95T15//fV84AMfSPLVn+vMvt9Mnx0AAACAr/HlL385SbK5udlyJavh/ud4/3OdFUEaAAAAwJzdXyxgJdp0VFWVJDO/sMH/WwAAAAAstfsgbdbWLkirquqPVlX1l6qq+q+qqvrNqqrqqqr+2ks8zxvPHvu8ry/OonYAAAAA2rOOt3b+B0n+lSTjJL+a5KOv8Fy/keQvPqd//ArPCQAAAMACWscg7U+mBGj/IMm/luSzr/Bcv17X9aemURQAAAAAi23ttnbWdf3Zuq5/uZ716XMAAAAANParv/qr+eN//I/nd/7O35n3vve9+dCHPpQ/8Sf+RP7JP/knbZf2Feu4Im2a3ltV1R9L0k3yNMn/O8l/Wdf1f99uWQAAAADL4/Of/3y+9Vu/Nb/2a7+W/f39fPSjH83P//zP50d+5EfyN/7G38hP/dRP5f3vf3/bZQrSXtEHknz6XX3/bVVV31XX9X/R9EmqqrqaMPQq57cBAAAALIWjo6P82q/9Wn70R3803/3d3/2V/j/1p/5UfviHfzjf//3fnx/7sR9rscJi7bZ2TtGPJ/lDKWHaZpJ/Ocl/nORDSf6fVVX9K+2VBgAAALAcPv/5z+czn/lMPvShD+X4+PgdYz/4gz+Yzc3NfPrTn87Tp09bqvCrBGkvqa7rH6zreljX9f+vruu7uq7/m7qu/70kfyHJ1yX51COea+95X0l+cUblAwAAAOtgNEr6/WR7O9ncLG2/X/oXxGc/W+6B/OQnP5n3vOedUdXrr7+eb/u2b8vd3V1+9md/to3y3kGQNn336wz/1VarAAAAANbbcJjs7CSDQQnO7u5KOxiU/uGw7QqTJL/0S7+UJPnIRz7y3PFv/uZvTpJ87nOfm1tNkwjSpu+/e9ZutloFAAAAsL5Go2R/PxmPnz8+HpfxBViZ9hu/8RtJkm/4hm947vh9/6//+q/Pq6SJBGnT9/Fn7RdarQIAAABYX6enk0O0e+NxcnY2n3pWhCDtAVVV/baqqj5aVdWH39X/saqqvmbFWVVVH0ryHz37x782hxIBAAAAvtbFRbN55+ezraOB+xVn9yvT3u2+/xu/8RvnVdJEr7VdwLxVVfXtSb792T9+4Fn7B6qq+oln//u2rus//ex//64kv5DkScptnPf+rSTfV1XVf/ls7M0kH07yP0/yzyf5fyT5czP5FwAAAAB4kdvb6c6boW/5lm9JMvkMtF/+5V9OMvkMtXlauyAtye9N8u++q+93P/tKSjD2p/Owzyb5liT/oyTflnIe2q8n+a+TfDrJp+u6rqdTLgAAAMAjbW01O/9sa2v2tbzAJz7xiSTJZz7zmbz11lvvuLnzzTffzE/91E9lY2MjH//4xyc9xdys3dbOuq4/Vdd19cDXh94294139z3r/y/quj6s6/qjdV1/Y13Xv62u63+hrut/va7r/1SIBgAAALTq4KDZvMPD2dbRwIc//OF88pOfzBtvvJHT09N3jP3AD/xAnj59mu/4ju/I5mb79zqu44o0AAAAgNV2fFwuEnjowoFOJzk6ml9NDzg7O8u3fuu35nu+53vyt/7W38rHPvax/NzP/Vw++9nP5iMf+Uh+6Id+qO0Sk6zhijQAAACAldftJpeXJSx7nk6njHe7861rgg9/+MP5O3/n7+Q7v/M783M/93P583/+z+fzn/98vvd7vzc/+7M/m/e///1tl5jEijQAAACA1dTrJTc3ZWXa+Xm5WGBrq2znPDpamBDt3gc/+MH8+I//eNtlPEiQBgAAALCqut3k5KR88cps7QQAAACABgRpAAAAANCAIA0AAAAAGhCkAQAAAEADgjQAAAAAaECQBgAAAAANCNIAAAAAoAFBGgAAAAA0IEgDAAAAgAYEaQAAAADQgCANAAAAABoQpAEAAABAA4I0AAAAAGhAkAYAAABAa/76X//r+e7v/u78wT/4B/P1X//1qaoqf+yP/bG2y3qu19ouAAAAAID19Wf/7J/N3/t7fy+dTiff9E3flF/8xV9su6SJrEgDAAAAoDU//MM/nM997nP5zd/8zfzlv/yX2y7nQVakAQAAANCaT3ziE22X0JgVaQAAAAArajRK+v1kezvZ3Cxtv1/6eTxBGgAAAMAKGg6TnZ1kMCjB2d1daQeD0j8ctl3h8hGkAQAAAKyY0SjZ30/G4+ePj8dl3Mq0xxGkAQAAAKyY09PJIdq98Tg5O5tPPatCkAYAAACwYi4ums07P59tHatGkAYAAACwYm5vpzuPQpAGAAAAsGK2tqY7j0KQBgAAALBiDg6azTs8nG0dq+a1tgsAAAAAYLqOj8tFAg9dONDpJEdH86tpkp/8yZ/MT/7kTyZJvvjFLyZJfuZnfibf+Z3fmSTZ2trKn/tzf66l6t5JkAYAAACwYrrd5PIy2d9/fpjW6ZTxbnf+tb3b3/27fzd/9a/+1Xf0feELX8gXvvCFJMn29vbCBGm2dgIAAACsoF4vublJ+v0SmG1slLbfL/29XtsVFp/61KdS1/XErzfeeKPtEr/CijQAAACAFdXtJicn5YtXZ0UaAAAAADQgSAMAAACABgRpAAAAANCAIA0AAAAAGhCkAQAAAEADgjQAeFWjUblDfHs72dwsbb9f+gEAgJmr63ou30eQBgCvYjhMdnaSwaAEZ3d3pR0MSv9w2HaFAAAsoKqqkiRvvfVWy5Wshvsg7f7nOiuCNAB4WaNRsr+fjMfPHx+Py7iVaQAAvMt73/veJMnTp09brmQ13P8c73+usyJIA4CXdXo6OUS7Nx4nZ2fzqQcAgKXx+uuvJ0m++MUv5s0338xbb701t+2Jq6Ku67z11lt5880388UvfjHJV3+us/LaTJ8dAFbZxUWzeefnycnJbGsBAGCpvO9978vTp09zd3eXX/3VX227nJWwsbGR973vfTP9HoI0AHhZt7fTnQcAwNp4z3vekw9+8IP50pe+lDfffDNf/vKXrUh7CVVV5b3vfW9ef/31vO9978t73jPbzZeCNAB4WVtbzc4/29qafS0AACyd97znPdna2sqW14tLwxlpAPCyDg6azTs8nG0dAADAXAjSAOBlHR8nnc7Dczqd5OhoPvUAAAAzJUgDgJfV7SaXl5PDtE6njHe7860LAACYCUEaALyKXi+5uUn6/RKYbWyUtt8v/b1e2xUCAABT4rIBAHhV3W5yclK+AACAlWVFGgAAAAA0IEgDAAAAgAYEaQAAAADQgCANAAAAABoQpAEAAABAA4I0AAAAAGhAkAYAAAAADQjSAAAAAKABQRoAAAAANCBIAwAAAIAGBGkAAAAA0IAgDQAAAAAaEKQBAAAAQAOCNAAAAABoQJAGAAAAAA0I0gAAAACgAUEaAAAAADQgSAMAAACABgRpAAAAANCAIA0AAAAAGhCkAQAAAEADgjQAAAAAaECQBgAAAAANCNIAAAAAoAFBGgAAAAA0IEgDAAAAgAYEaQAAAADQgCANAAAAABoQpAEAAABAA4I0AAAAAGhAkAYAAAAADQjSAAAAAKABQRoAAAAANCBIAwAAAIAGBGkAAAAA0IAgDQAAAAAaEKQBAAAAQAOCNAAAAABoQJAGAAAAAA0I0gAAAACgAUEaAAAAADQgSAMAAACABgRpAAAAANCAIA0AAAAAGhCkAQAAAEADgjQAAAAAaECQBgAAAAANCNIAAAAAoAFBGgAAAAA0IEgDAAAAgAYEaQAAAADQgCANAAAAABoQpAEAAABAA4I0AAAAAGhAkAYAAAAADQjSAAAAAKABQRoAAAAANCBIAwAAAIAGBGkAAAAA0IAgDYDHG42Sfj/Z3k42N0vb75d+AACAFSVIA+BxhsNkZycZDEpwdndX2sGg9A+HbVcIAAAwE4I0AJobjZL9/WQ8fv74eFzGrUwDAABWkCANgOZOTyeHaPfG4+TsbD71AAAAzJEgDYDmLi6azTs/n20dAAAALRCkAdDc7e105wEAACwRQRoAzW1tTXceAADAEhGkAdDcwUGzeYeHs60DAACgBYI0AJo7Pk46nYfndDrJ0dF86gEAAJgjQRoAzXW7yeXl5DCt0ynj3e586wIAAJgDQRoAj9PrJTc3Sb9fArONjdL2+6W/12u7QgAAgJkQpAHweN1ucnKSPHmSPH1a2pOT9VqJNhqV8HB7O9ncLG2/X/oBAICVJEgDgMcaDpOdnWQwKMHZ3V1pB4PSPxy2XSEAADADgjQAeIzRKNnfT8bj54+Px2XcyjQAAFg5gjQAeIzT08kh2r3xODk7m089AADA3AjSAOAxLi6azTs/n20dAADA3AnSAOAxbm+nOw8AAFgagjQAeIytrenOAwAAloYgDQAe4+Cg2bzDw9nWAQAAzJ0gDQAe4/g46XQentPpJEdH86kHAACYG0EaADxGt5tcXk4O0zqdMt7tzrcuAABg5gRpAPBYvV5yc5P0+yUw29gobb9f+nu9tisEAABm4LW2CwCApdTtJicn5QsAAFgLVqQBAAAAQAOCNAAAAABoQJAGAAAAAA0I0gAAAACgAUEaAAAAADQgSAMAAACABgRpAAAAANCAIA0AAAAAGhCkAQAAAEADgjQAAAAAaECQBgAAAAANCNIAAAAAoAFBGgAAAAA0IEgDAAAAgAYEaQAAAADQgCANAAAAABoQpAEAAABAA4I0AAAAAGhAkAYAAAAADQjSAAAAAKABQRoAAAAANCBIAwAAAIAGBGkAAAAA0IAgDQAAAAAaEKQBAAAAQAOCNAAAAABoQJAGAAAAAA0I0gAAAACgAUEaAAAAADQgSAMAAACABgRpAAAAANCAIA0AAAAAGhCkAQAAAEADgjQAAAAAaECQBgAAAAANCNIAAAAAoAFBGgAAAAA0IEgDAAAAgAYEaQAAAADQgCANAGDeRqOk30+2t5PNzdL2+6UfAICFJUgDAJin4TDZ2UkGgxKc3d2VdjAo/cNh2xUCADCBIA0AYF5Go2R/PxmPnz8+HpdxK9MAABaSIA0AYF5OTyeHaPfG4+TsbD71AADwKII0AIB5ubhoNu/8fLZ1AADwUgRpAADzcns73XkAAMyVIA0AYF62tqY7DwCAuRKkAQDMy8FBs3mHh7OtAwCAlyJIAwCYl+PjpNN5eE6nkxwdzaceAAAeRZAGADAv3W5yeTk5TOt0yni3O9+6AABoRJAGADBPvV5yc5P0+yUw29gobb9f+nu9tisEAGCC19ouAABg7XS7yclJ+QIAYGlYkQYAAAAADQjSAAAAAKABQRoAAAAANCBIAwAAAIAGBGkAAAAA0IAgDQAAAAAaEKQBAAAAQAOCNAAAAABoQJAGAAAAAA0I0gAAAACgAUEaAAAAADQgSAMAAACABgRpAAAAANCAIA0AAAAAGhCkAQAAAEADgjQAAAAAaECQBgAAAAANCNIAAAAAoAFBGgAAAAA0IEgDAAAAgAYEaQAAAADQgCANAAAAABoQpAEAAABAA4I0AAAAAGhAkAYAAAAADQjSAAAAAKABQRoAAAAANCBIAwAAAIAGBGkAAAAA0IAgDQAAAAAaEKQBAAAAQAOCNAAAAABoQJAGAAAAAA0I0gAAAACgAUEaAAAAADQgSAMAFsNolPT7yfZ2srlZ2n6/9AMAwAIQpAEA7RsOk52dZDAowdndXWkHg9I/HLZdIQAACNIAgJaNRsn+fjIeP398PC7jVqYBANAyQRoA0K7T08kh2r3xODk7m089AAAwgSANAGjXxUWzeefns60DAABeQJAGALTr9na68wAAYEYEaQBAu7a2pjsPAABmRJAGALTr4KDZvMPD2dYBAAAvIEgDANp1fJx0Og/P6XSSo6P51AMAABMI0gCAdnW7yeXl5DCt0ynj3e586wIAgHcRpAEA7ev1kpubpN8vgdnGRmn7/dLf67VdIQAA5LW2CwAASFKCs5OT8gUAAAvIijQAAAAAaECQBgAAAAANCNIAAAAAoAFBGgAAAAA0IEgDAAAAgAYEaQAAAADQgCANAAAAABoQpAEAAABAA4I0AAAAAGhAkAYAAAAADQjSAAAAAKABQRoAAAAANCBIAwAAAIAGBGkAAAAA0IAgDQAAAAAaEKQBAAAAQAOCNAAAAABoQJAGAAAAAA0I0gAAAACgAUEaAAAAADQgSAMAAACABgRpAAAAANCAIA0AAAAAGhCkAQAAAEADgjQAAAAAaECQBgAAAAANCNIAAAAAoAFBGgAAAAA0IEgDAAAAgAYEaQAAAADQgCANAAAAABoQpAEAAABAA4I0AFhXo1HS7yfb28nmZmn7/dJPM36GAABrparruu0amKCqqqvd3d3dq6urtksBYNUMh8n+fjIef+1Yp5NcXia93vzrWiZ+hgAAS2lvby/X19fXdV3vPfaxVqQBwLoZjSYHQEnp39+3quohfoYAAGtJkAYA6+b0dHIAdG88Ts7O5lPPMvIzBABYS4I0AFg3FxfN5p2fz7aOZeZnCACwlgRpALBubm+nO28d+RkCAKwlQRoArJutrenOW0d+hgAAa0mQBgDr5uCg2bzDw9nWscz8DAEA1pIgDQDWzfFx0uk8PKfTSY6O5lPPMvIzBABYS4I0AFg33W5yeTk5COp0yni3O9+6lomfIQDAWhKkAcA66vWSm5uk3y9hz8ZGafv90t/rtV3h4vMzBABYO1Vd123XwARVVV3t7u7uXl1dtV0KAAAAwErY29vL9fX1dV3Xe499rBVpAAAAANCAIA0AAAAAGhCkAQAAAEADgjQAAAAAaECQBgAAAAANCNIAAAAAoAFBGgAAAAA0IEgDAAAAgAYEaQAAAADQgCANAAAAABoQpAEAAABAA4I0AIBpGI2Sfj/Z3k42N0vb75d+AABWgiANAOBVDYfJzk4yGJTg7O6utINB6R8O264QAIApEKQBALyK0SjZ30/G4+ePj8dl3Mo0AIClJ0gDAHgVp6eTQ7R743FydjafegAAmBlBGgDAq7i4aDbv/Hy2dTB/zsUDgLUjSAMAeBW3t9Odx3JwLh4ArCVBGgDAq9jamu48Fp9z8QBgbQnSAABexcFBs3mHh7Otg/lxLh4ArC1BGgDAqzg+Tjqdh+d0OsnR0XzqYfaciwcAa0uQBgDwKrrd5PJycpjW6ZTxbne+dTE7zsUDgLUlSAMAeFW9XnJzU25s7HaTjY3S9vulv9dru0Kmybl4ALC2BGkAANPQ7SYnJ8mTJ8nTp6U9ObESbRU5Fw8A1pYgDQAAHsO5eACwtgRpAADwGM7FA4C1JUgDAIDHci4eAKyl19ouAAAAltL9uXgnJ21XAgDMiRVpAAAAANCAIA0AAAAAGhCkAQAAAEADgjQAAAAAaECQBgAAAAANCNIAAAAAoAFBGgAAAAA0IEgDAAAAgAYEaQAAAADQgCANAAAAABoQpAEAAABAA4I0AAAAAGhAkAYAAAAADQjSAAAAAKABQRoAANMzGiX9frK9nWxulrbfL/0AAEtu7YK0qqr+aFVVf6mqqv+qqqrfrKqqrqrqr73kc31TVVV/paqqf1RV1Zerqnqjqqq/WFXVb5923QAAC284THZ2ksGgBGd3d6UdDEr/cNh2hQAAr2TtgrQk/0GSfz/J703yD1/2Saqq+nCSqyTfleTnk/xwki8k+d4kP1NV1ftfuVIAgGUxGiX7+8l4/Pzx8biMW5kGACyxdQzS/mSSjyT5+iT/21d4nrMkvyPJ99R1/e11Xf+Zuq57KYHatyT5oVeuFABgWZyeTg7R7o3HydnZfOoBAJiBtQvS6rr+bF3Xv1zXdf2yz/FsNdonk7yR5PRdwz+Q5GmS76iqavOlCwUAWCYXF83mnZ/Ptg4AgBlauyBtSj7xrP1MXddvvX2grus3k/xUko0kH593YQAArbi9ne48AIAFJEh7Od/yrP3chPFfftZ+pMmTVVV19byvJB991UIBAOZia2u683g8N6YCwMwJ0l7ONzxrf2PC+H3/N86+FACABXBw0Gze4eFs61hXbkwFgLkQpC2Auq73nveV5Bfbrg0AoJHj46TTeXhOp5McHc2nnnXixlQAmBtB2su5X3H2DRPG7/t/ffalAAAsgG43ubycHKZ1OmW8251vXevAjakAMDeCtJfzS8/aSWegffOzdtIZagAAq6fXS25uyrlc3W6ysVHafr/093ptV7ia3JgKAHPzWtsFLKnPPms/WVXVe95+c2dVVa8n+bYkd0l+to3iAABa0+0mJyfli/lwYyoAzI0VaQ+oquq3VVX10aqqPvz2/rquP5/kM0k+lOT4XQ/7wSSbST5d1/XTuRQKAMD6cmMqAMzN2q1Iq6rq25N8+7N//MCz9g9UVfUTz/73bV3Xf/rZ//5dSX4hyZOU0OztjpL8dJIfrarqDz2b9/uTfCJlS+f3T796AAB4l4ODcjvni7gxFQBe2TquSPu9Sf7dZ1//xrO+3/22vj/a5EmerUr7fUl+IiVA+74kH07yI0k+Xtf1P55m0QAA8FxuTAWAuVm7FWl1XX8qyacazn0jSfXA+K8k+a5p1AUAAC/l/sbU/f3n397pxlQAmJp1XJEGAACrxY2pADAXa7ciDQAAVpIbUwFg5qxIAwAAAIAGBGkAAAAA0IAgDQAAAAAaEKQBAAAAQAOCNAAAAABoQJAGAAAAAA0I0gAAAACgAUEaAAAAADQgSAMAAACABgRpAKtsNEr6/WR7O9ncLG2/X/oBAAB4FEEawKoaDpOdnWQwKMHZ3V1pB4PSPxy2XSEAAMBSEaQBrKLRKNnfT8bj54+Px2V8EVamWTUHAAAsCUEawCo6PZ0cot0bj5Ozs/nUM4lVcwAAwBIRpAGsoouLZvPOz2dbx0OWadUcAABABGkAq+n2drrzZmFZVs0BAAA8I0gDWEVbW9OdNwvLsGoOAADgbQRpAKvo4KDZvMPD2dbxkGVYNQcAAPA2gjSAVXR8nHQ6D8/pdJKjo/nU8zzLsGoOAADgbQRpAKuo200uLyeHaZ1OGe9251vX2y3DqjkAAIC3EaQBrKpeL7m5Sfr9EphtbJS23y/9vV679S3DqjkAAIC3EaQBrLJuNzk5SZ48SZ4+Le3JSbsr0d5e26KvmgOWy2hUPizY3k42N0vb75d+AIApEKQB0J5FXzUHLI/hMNnZSQaDEpzd3ZV2MCj9w2HbFQIAK+C1tgsAYM3dr5o7OWm7EmBZjUbJ/n4yHj9/fDwu4zc3VrkCAK/EijQAgGVmO2Nyejo5RLs3HidnZ/OpBwBYWYI0AIBlZTtjcXHRbN75+WzrAABWniANAGAZNd3OuA4r025vpzsPAGACQRoAwDKynfGrtramOw8AYAJBGgDAMrKd8asODprNOzycbR0AwMoTpAEALCPbGb/q+DjpdB6e0+kkR0fzqQcAWFmCNACAZWQ741d1u8nl5eQwrdMp493ufOsCAFaOIA0AYBnZzvhOvV5yc5P0+yUw29gobb9f+nu9tisEAFaAIA0AeDWjUQkrtreTzc3S9vvrcVtkm2xn/FrdbnJykjx5kjx9WtqTEyvRAICpEaQBAC9vOEx2dpLBoARnd3elHQxK/3DYdoWry3ZGAIC5E6QBi8OqFlguo1Gyv5+Mx88fH4/LuP+GZ8d2RgCAuarqum67Biaoqupqd3d39+rqqu1SYPaGw8lvyO9XVXhDCIul3y8rz17k678++Wf/rBx6f3BQtiRaJQUAQEv29vZyfX19Xdf13mMfa0Ua0D6rWmA5XVw0m/ebv2nLJwAAK0GQBrTv9HRyiHZvPE7OzuZTD9DM7e3LPU44DgDAkhKkAe1ruqrl/Hy2dQCPs7X18o8VjgMAsIQEaUD7mq5qednVL8BsHBy82uOF4wAALBlBGtC+pqtaXmX1CzB9x8flMpCXJRxvxo3GAAALQ5AGtK/pqpbDw9nWATxOt1tu1H3ZME04/mLDYbmcYTAowZlLGwAAWiVIA9rXZFVLp5McHc2nHqC5Xi+5uSkrpLrdZGMj+fqvb/ZY4fjD3GgMALBwBGlA+160qqXTKePd7nzrgllZta163W5ycpI8eZI8fZr8/b8vHJ8GNxoDACwcQRqwGJ63qqXbLf98c1PGYRWsw1Y94fh0uNEYAGDhVHVdt10DE1RVdbW7u7t7dXXVdikATMNoVMKyh1YZdTolPF6FkGk0Kqulzs/LxQJbW2U759HRavz7zdrmZglaX2Rjo6wEBACgkb29vVxfX1/Xdb332MdakQYA87JuW/XeveXzyZPyz0K0ZtxoDACwcARpADAvturxGG40BgBYOII0AJiX29vpzmO1udEYAGDhCNIAYF5s1eMxXNoAALBwBGkAMC+26vFYbjQGAFgobu1cYG7tBFgx63ZrJwAALCC3dgLAMrBVDwAAlpogDWCZjEZlS9f2drK5Wdp+v/SzHGzVAwCApWVr5wKztRN4h+Ew2d9//rbA+5VMQhgAAIAH2doJsOpGo8khWlL69/etTAMAAJghQRrAMjg9ffiA+qSMn53Npx4AAIA1JEgDmIdXPdvs4qLZvPPzl68RAACABwnSAGZtOEx2dpLBoARnd3elHQxK/3D44ue4vW32vZrOAwAA4NEEaQCzNK2zzba2mn2/pvMAAAB4NEEawCxN62yzg4Nm3+/wsNk8AAAAHk2QBjBL0zrb7Pg46XQentPpJEdHzb4fAAAAjyZIA5ilaZ1t1u0ml5eTw7ROp4x3u4+rD4Cv9aoXxAAAK0uQBjBL0zzbrNdLbm7Km7luN9nYKG2/X/p7vVerFYDpXBADAKwsQRrALE37bLNuNzk5SZ48SZ4+Le3JiZVoANMwrQtiAGCBWXj9agRpALPkbDOA5TGtC2IAYEFZeP3qBGkAs+RsM4DlMa0LYgBgAVl4PR2CNIBZc7YZFPYRsOimdUEMACwgC6+no6rruu0amKCqqqvd3d3dq6urtksBgFczHE7+CPR+ZaZQmbZtbzcLdrvdckYlACwRv+a+am9vL9fX19d1Xe899rFWpAEAs2UfActi2hfEAMACsfB6OgRpAMBsrcs+AltXl58LYgBYYVtb0523rgRpACwGIcTqWocD3F2BtRpcEAPACrPwejoEaQC0Twix2lZ9H4Gtq6vFBTEArCgLr6dDkAZAu4QQq2/V9xGsy9bVddLtJicn5aTlp09Le3JiJRoAS83C6+kQpAHQLiHE6lv1fQTrsHUVAFgJFl6/OkEaAO0SQqy+Vd9HsOpbVwGApfPQ8cMWXr8aQRoA7RJCrL5V30ew6ltXAYCl4vjh2RKkAdAuIcR6WOV9BKu+dRUAWBqOH549QRoA7RJCrI9V3Uew6ltXAYCl4fjh2ROkAdAuIQTLbtW3rgIAS8Pxw7MnSAOgXUIIVsEqb10FAJaG44dn77W2CwCAr4QQZ2fl47Hb23Im2uFhWYkmRGMZ3G9dPTlpuxIAYE1tbTU7/8zxwy/PijSAdfDQ/deLYlXPzwIAgDlx/PDsCdIAVp37rwEAYC04fnj2BGkAq8z91wAAsDYcPzx7gjSAVeb+awAAWCvuQJqtqq7rtmtggqqqrnZ3d3evrq7aLgVYVtvbzVabdbvlTDIAAIAVt7e3l+vr6+u6rvce+1gr0gBWmfuvAQAApkaQBrDKmt5r7f5rAACAFxKkAawy918DAABMjSANYJW5/xpYVKNROfV4ezvZ3Cxtv+8WYQBgoQnSAFaZ+69XlxCCZTYcJjs7yWBQ/sze3ZV2MCj9w2HbFQIAPJcgDWDVuf969QghWGajUbK/n4zHzx8fj8u4UBgAWECCNIB10O0mJyfJkyfJ06elPTmxEm0ZCSFYdqenk//83huPk7Oz+dQDAPAIgjSAx7KljjYJIVh2FxfN5p2fz7YOAICXIEgDeAxb6mibEIJld3s73XkAAHMkSANoypY6FoEQgmW3tTXdeQAAcyRIA2jKljoWgRCCZXdw0Gze4eFs6wAAeAmCNICmbKljEQghWHbHx0mn8/CcTic5OppPPQAAjyBIA2jKljoWgRCCZdftJpeXk/8cdzpl3K3CAMACEqQBNGVLHYtACMEq6PWSm5ty43G3m2xslLbfL/29XtsVAgA8lyANoClb6lgUQghWQbebnJwkT54kT5+W9uRECAwALLSqruu2a2CCqqqudnd3d6+urtouBUjKbZw7Ow9fONDplCDDG0EAAICFtLe3l+vr6+u6rvce+1gr0gCasqUOAABgrQnSAB7DljoAAIC1JUgDeCzn+gA0NxqVDxu2t5PNzdL2+6UfAGDJCNIAAJiN4bCcLTkYlODs7q60g0HpHw7brhAA4FEEaQAATN9olOzvT76gZTwu41amAQBLRJAGAMD0nZ4+fMtxUsbPzuZTDwDAFAjSAACYvouLZvPOz2dbBwDAFAnSAACYvtvb6c4DAFgAgjQAAKZva2u68wAAFoAgDQCA6Ts4aDbv8HC2dQAATJEgDQCA6Ts+Tjqdh+d0OsnR0XzqAQCYAkEaAADT1+0ml5eTw7ROp4x3u/OtCwDgFQjSAFhco1HS7yfb28nmZmn7/dIPLL5eL7m5Kf/ddrvJxkZp+/3S3+u1XSEAwKNUdV23XQMTVFV1tbu7u3t1ddV2KQDzNxwm+/vJePy1Y/crWbwJBwAAHmlvby/X19fXdV3vPfaxVqQBsHhGo8khWlL69/etTAMAAOZKkAbA4jk9nRyi3RuPk7Oz+dQDAAAQQRoAi+jiotm88/PZ1gEAAPA2gjQAFs/t7XTnAQAATIEgDYDFs7U13XkAAABTIEgDYPEcHDSbd3g42zoAAADeRpAGwOI5Pk46nYfndDrJ0dF86gEAAIggDYBF1O0ml5eTw7ROp4x3u/OtCwAAWGuCNAAWU6+X3Nwk/X4JzDY2Stvvl/5er+0KAQCANSNIA3hVo1EJd7a3k83N0vb7pZ9X0+0mJyfJkyfJ06elPTmxEg0AAGiFIA3gVQyHyc5OMhiU4OzurrSDQekfDtuuEAAAgCkRpAG8rNEo2d9PxuPnj4/HZdzKNAAAgJUgSAN4Waenk0O0e+NxcnY2n3oAAACYKUEawMu6uGg27/x8tnUAAAAwF4I0gJd1ezvdeQAAACw0QRrAy9ramu48AAAAFpogDeBlHRw0m3d4ONs6AAAAmAtBGsDLOj5OOp2H53Q6ydHRfOoBAABgpgRpAC+r200uLyeHaZ1OGe9251sXAAAAMyFIA3gVvV5yc5P0+yUw29gobb9f+nu9tisEAABgSl5ruwCApdftJicn5QsAAICVZUUaAAAAADQgSAMAAACABgRpAAAAANCAIA0AAAAAGhCkAQAAAEADgjQAAAAAaECQBgAAAAANCNIAAAAAoAFBGgAAAAA0IEgDAAAAgAYEaQAAAADQgCANAAAAABoQpAEAAABAA4I0AAAAAGhAkAYAUzYaJf1+sr2dbG6Wtt8v/QAAwPISpAHAFA2Hyc5OMhiU4OzurrSDQekfDtuuEAAAeFmCNACYktEo2d9PxuPnj4/HZdzKNAAAWE6CNACYktPTySHavfE4OTubTz0AAMB0CdIAYEouLprNOz+fbR0AAMBsCNIAYEpub6c7DwAAWCyCNACYkq2t6c4DAAAWiyANAKbk4KDZvMPD2dYBAADMhiANAKbk+DjpdB6e0+kkR0fzqQcAAJguQRoATEm3m1xeTg7TOp0y3u3Oty4AAGA6BGkAMEW9XnJzk/T7JTDb2Chtv1/6e722KwQAAF7Wa20XAACrpttNTk7KFwAAsDqsSAMAAACABgRpAAAAANCAIA0AAAAAGhCkAQAAAEADgjQAAAAAaECQBgAAAAANCNIAAAAAoAFBGgAAAAA0IEgDAAAAgAYEaQAAAADQgCANAAAAABoQpAEAAABAA4I04NWNRkm/n2xvJ5ubpe33Sz8A7fB3MwDA1FV1XbddAxNUVXW1u7u7e3V11XYpMNlwmOzvJ+Px1451OsnlZdLrzb8ugHXm72YAgIn29vZyfX19Xdf13mMfa0Ua8PJGo8lv1JLSv79v9QPAPPm7GQBgZgRpwMs7PZ38Ru3eeJycnc2nHgD83QwAMEOCNODlXVw0m3d+Pts6APgqfzcDAMyMIA14ebe3050HwKvzdzMAwMwI0oCXt7U13XkAvDp/NwMAzIwgDXh5BwfN5h0ezrYOAL7K380AADMjSANe3vFx0uk8PKfTSY6O5lMPAP5uBgCYIUEa8PK63eTycvIbtk6njHe7860LYJ35uxkAYGYEacCr6fWSm5uk3y9vyjY2Stvvl/5er+0KAdaPv5sBAGaiquu67RqYoKqqq93d3d2rq6u2SwEAAABYCXt7e7m+vr6u63rvsY+1Ig0AAAAAGhCkAQAAAEADgjQAANozGpWz27a3k83N0vb7pR8AYMEI0gAAaMdwmOzsJINBCc7u7ko7GJT+4bDtCgEA3kGQBgDA/I1Gyf5+Mh4/f3w8LuNWpgEAC0SQBgDLypY4ltnp6eQQ7d54nJydzaceAIAGBGkAsIxsiWPZXVw0m3d+Pts6AAAeQZAGAMvGljhWwe3tdOcBAMyBIA0Alo0tcayCra3pzgMAmANBGgAsG1viWAUHB83mHR7Otg4AgEcQpAHAsrEljlVwfJx0Og/P6XSSo6P51AMA0IAgDQCWjS1xrIJuN7m8nBymdTplvNudb10AAA8QpAHAsrEljlXR6yU3N0m/XwKzjY3S9vulv9dru0IAgHeo6rpuuwYmqKrqand3d/fq6qrtUgBYJKNRsrPz8IUDnU4JIqzmAQCAd9jb28v19fV1Xdd7j32sFWkAsGxsiQMAgFYI0gBmbTQq25S2t5PNzdL2+6UfXpYtcQAAMHe2di4wWzthBQyHyf7+87fg3a8aEngAAADMja2dAItoNJocoiWlf3/fyjQAAIAlIUgDmJXT04cPg0/K+NnZfOoBAADglQjSAGbl4qLZvPPz2dYBAADAVAjSAGbl9na68wAAAGiVIA1gVra2pjsPAACAVgnSAGbl4KDZvMPD2dYBAADAVAjSAGbl+DjpdB6e0+kkR0fzqQcAAIBXIkgDmJVuN7m8nBymdTplvNudb10AAAC8FEEawCz1esnNTdLvl8BsY6O0/X7p7/XarhAAAICGBGkAs9btJicnyZMnydOnpT05sRINYBWMRuXDke3tZHOztP1+6QcAVo4gDQAAXsZwmOzsJINBCc7u7ko7GJT+4bDtCgGAKROkAQDAY41Gyf5+Mh4/f3w8LuNWpgHAShGkAQDAY52eTg7R7o3HydnZfOoBAOZCkAYAAI91cdFs3vn5bOsAAOZKkAYAAI91ezvdeQDAUhCkAQDAY21tTXceALAUBGkAAPBYBwfN5h0ezrYOAGCuBGkAAPBYx8dJp/PwnE4nOTqaTz0AwFwI0gAA4LG63eTycnKY1umU8W53vnUBADMlSAMAgJfR6yU3N0m/XwKzjY3S9vulv9dru0IAYMpea7sAAABYWt1ucnJSvgCAlWdFGgAAAAA0IEgDAAAAgAYEaQAAAADQgCANAAAAABoQpAEAAABAA4I0AAAAAGhAkAYAAAAADQjSAAAAAKABQRoAAAAANCBIAwAAAIAGBGkAAPB2o1HS7yfb28nmZmn7/dIPAKw1QRoAANwbDpOdnWQwKMHZ3V1pB4PSPxy2XSEA0CJBGgAAJCUw299PxuPnj4/HZdzKNABYW4I0AABIktPTySHavfE4OTubTz0AwMIRpAEAQJJcXDSbd34+2zoAgIUlSAMAgCS5vZ3uPABg5QjSAAAgSba2pjsPAFg5gjQAAEiSg4Nm8w4PZ1sHALCwBGkAAJAkx8dJp/PwnE4nOTqaTz0AwMIRpAEPG42Sfj/Z3k42N0vb75d+AFgl3W5yeTk5TOt0yni3O9+6AICFIUgDJhsOk52dZDAowdndXWkHg9I/HLZdIbDE5PQspF4vubkpfxi73WRjo7T9funv9dquEGBh+d3OOqjqum67Biaoqupqd3d39+rqqu1SWEejUQnLxuPJczqd8qbCJ/PAIw2Hyf7+8/+KuV/0I68AgOXhdzvLZG9vL9fX19d1Xe899rFWpAHPd3r6cIiWlPGzs/nUA6yM0WjyC+2k9O/v+/QaAJaF3+2sE0Ea8HwXF83mnZ/Ptg5g5cjpAWC1+N3OOhGkAc93ezvdeQDPyOkBYLX43c46EaQBz7e1Nd15AM/I6QFgtfjdzjoRpAHPd3DQbN7h4WzrAFaOnB4AVovf7awTQRrwfMfH5Xqdh3Q6ydHRfOoBltNoVO69395ONjeT7e0cfOBvN3qonB4AloPP4FkngjTg+brdckf1pDDt/g7rbne+dQHLYzhMdnaSwaAEand3yWiU45//d9LJwycSy+kBYHn4DJ51IkgDJuv1kpubspqk2002Nkrb75f+Xq/tCoFFNRqVe+6fc4VXN7+Sy/yRiWGanB4AlovP4FkngjTgYd1ucnKSPHmSPH1a2pMTvwWBh52ePjdEu9fLZ3OT/2H6v/9vy+kBYAX4DJ51UdV13XYNTFBV1dXu7u7u1dVV26UAwONsb5dVaS/S7ZaAHgAA5mRvby/X19fXdV3vPfaxVqQBANPX9H77pvMAAGABCNIAgOlrer9903kAALAABGkAwPQdHDSbd3g42zoAAGCKBGkAwPQdH0++uutep5McHc2nHgAAmAJBGgAwfd1uued+UpjW6ZRxNwADALBEBGkAwGz0euW++36/BGYbG6Xt90t/r9d2hQAA8CiCNGB9jUblDf32drK5Wdp+v/QD09HtJicnyZMnydOnpT05sRINAIClJEgD1tNwmOzsJINBCc7u7ko7GJT+4bDtCgEAAFgwgjRg/YxGyf5+Mh4/f3w8LuNWpgEAAPA2gjRg/ZyeTg7R7o3HydnZfOoBAABgKQjSgPVzcdFs3vn5bOsAAABgqQjSgPVzezvdeQAAAKwFQRqwfra2pjsPAACAtSBIA9bPwUGzeYeHs60DAACApSJIA2ZvNEr6/WR7O9ncLG2/396tmMfHSafz8JxOJzk6mk89AAAALAVBGjBbw2Gys5MMBiU4u7sr7WBQ+ofD+dfU7SaXl5PDtE6njHe7860LAACAhSZIA2ZnNEr295Px+Pnj43EZb2NlWq+X3NyUlXHdbrKxUdp+v/T3evOvCQAWwaKtJAeABSJIA2bn9HRyiHZvPE7OzuZTz7t1u8nJSfLkSfL0aWlPTqxEA2AxtBFoLeJKcgBYIII0YHYuLprNOz+fbR0AsGzaCLQWeSU5ACwIQRqLyZaC1XB7O915ALAO2gq0Fn0lOQAsAEEai8eWgtWxtTXdeQCwDtoKtKwkB4AXEqSxWGwpWC0HB83mHR7Otg4AWCZtBVpWkgPACwnSWCy2FKyW4+Ok03l4TqeTHB3Npx4AWAZtBVpWkgPACwnSWCy2FKyWbje5vJwcpnU6ZdwtmQDwVW0FWlaSA8ALCdJYLLYUrJ5eL7m5KZdFdLvJxkZp+/3S3+u1XSEALJa2Ai0ryQHghQRpLBZbClZTt5ucnCRPniRPn5b25MRKNAB4nrYCLSvJAeCFBGksFlsKAIB112agZSU5ADxIkMZisaWgudGovKjd3k42N0vb77vRFABWQZuB1rquJPfaCoAGqrqu266BCaqqutrd3d29urpqu5T5Gg6T/f3n3955/wnsun8a6mcEADA9XlsBrJW9vb1cX19f13W999jHWpHG4rGl4GGj0eQXeknp39/36SkAQBNeWwHwCII0FtO6bilo4vR08gu9e+NxcnY2n3oAAJaZ11YAPIIgDZbNxUWzeefns60DAGAVeG0FwCMI0mDZ3N5Odx4AwDrz2gqARxCk0T43JD3O1tZ05wEArDOvrQB4BEEa7RoOk52dZDAowdndXWkHg9I/HLZd4eI5OGg27/BwtnUAAKwCr60AeARBGu1xQ9LLOT4u17A/pNNJjo7mUw8AwDLz2gqARxCk0R43JL2cbje5vJz8gq/TKeNuOAUAeDGvrQB4BEEa7XFD0svr9ZKbm3KWXLebbGyUtt8v/b1e2xUCACwPr60AaKiq67rtGpigqqqr3d3d3aurq7ZLmY3NzXIm2otsbCRPn86+HgAAAGDl7e3t5fr6+rqu673HPtaKNNrjhiQAAABgiQjSaI8bkgAAAIAlIkijPW5IAgAAAJaIII32uCEJAAAAWCKCNNrlhiQAAABgSQjSaF+3m5ycJE+elNs5nzwp/2wlGsBCG43K5x7b2+Ui5u3t8s+jUduVAQDAbAjSAIBHGw6TnZ1kMCjB2d1daQeD0j8ctl0hAABMnyANAHiU0SjZ30/G4+ePj8dl3Mo0AABWjSANAHiU09PJIdq98Tg5O5tPPQAAMC+CNADgUS4ums07P59tHQAAMG+CNADgUW5vpzsPAACWhSANAHiUra3pzgMAgGUhSAMAHuXgoNm8w8PZ1gEAAPMmSAMAHuX4OOl0Hp7T6SRHR/OpBwAA5kWQBgA8SrebXF5ODtM6nTLe7c63LgAAmDVBGgDwaL1ecnOT9PslMNvYKG2/X/p7vbYrBACA6Xut7QIAgOXU7SYnJ+ULAADWgRVpALCiRqOyQmx7O9ncLG2/X/oBAIDHW9sgraqqb6qq6q9UVfWPqqr6clVVb1RV9Rerqvrtj3iOv11VVf3A1z8/y38HAJhkOEx2dpLBoARnd3elHQxK/3DYdoWzI0AEAGBW1nJrZ1VVH07y00l+R5LLJL+Y5H+c5HuT/OGqqr6trut//Iin/MEJ/f/slQoFgJcwGiX7+8l4/Pzx8biM39ys3oUAw+HX/rvfB4hnZ+USBOe3AQDwstYySEtylhKifU9d13/pvrOqqr+Q5E8m+aEk/17TJ6vr+lPTLhAAXtbp6eQQ7d54XIKlVTrfbJ0DRAAA5mPttnY+W432ySRvJDl91/APJHma5Duqqtqcc2kAMBUXF83mnZ/Pto55e0yACAAAL2PtgrQkn3jWfqau67fePlDX9ZtJfirJRpKPN33Cqqr+raqq/kxVVX+qqqr/WVVV751euQDwOLe30523LNY1QAQAYH7WcWvntzxrPzdh/JdTVqx9JMnfavic737p/mtVVR3Xdf3Xmzy4qqqrCUMfbfj9YbLRqCzTuLgo75q3tpKDg+T42N4mWFFbW80O1t/amn0t87SuASIAAPOzjivSvuFZ+xsTxu/7v7HBc10m+V8k+aYkX5cSfP2Hzx77f6mq6g+/dJUwDet8bR+ssYODZvMOD2dbx7w1DQZXLUAEAGB+1jFIm5q6rn+4ruv/e13X/7Cu69+q6/qX6rr+3yf5vpSf7X/Y8Hn2nveVcpsovJymp243WbYCLJXj46TTeXhOp5McHc2nnnlZ1wARAID5Wccg7X7F2TdMGL/v//VX+B7/5yT/LMnvrarq9Vd4Hnh5Tt2GtdXtJpeXk8O0TqeMr9ru7nUNEAEAmJ91DNJ+6Vn7kQnj3/ysnXSG2gvVdf1bSd589o9u/6QdTt2GtdbrJTc3Sb9fArONjdL2+6W/12u7wulb1wARAID5WcfLBj77rP1kVVXvefvNnc9Wj31bkrskP/uy36Cqqm9J8ttTwjRHGtMOp27D2ut2k5OT8rUu7gPEs7PyOcH9HSuHh2UlmhANAIBXsXYr0uq6/nySzyT5UJLjdw3/YMoKsk/Xdf30vrOqqo9WVfWOGzSrqvqXqqp637ufv6qqfyHJjz/7x4u6rv/ZFMuH5py6Dayp+wDxyZPk6dPSnpwI0QAAeHXruCItSY6S/HSSH62q6g8l+YUkvz/JJ1K2dH7/u+b/wrO2elvfv5bkx6qq+q+TfCHJl5J0k/ybKees/Z0k/7tZ/QvACx0clNs5X8Sp2wAAANDI2q1IS76yKu33JfmJlADt+5J8OMmPJPl4Xdf/uMHTXCW5SPIvJvlfPXuOP5zk7yf5niTfVtf1r0+7dmjMqdsAAAAwVeu6Ii11Xf9Kku9qOLd6Tt/fT/KdUy4Lpuf+1O39/eff3unUbQAAAHiUtVyRBmtjHa/tAwAAgBlZ2xVpsDbW8do+AAAAmAEr0gAAAACgAUEaAAAAADQgSANe3mhUzlvb3k42N0vb75d+AAAAWDGCNODlDIfJzk4yGJTg7O6utINB6R8O264QAAAApkqQBjzeaJTs7yfj8fPHx+MybmUaAAAAK0SQBjze6enkEO3eeJycnc2nHgAAAJgDQRrweBcXzeadn8+2DgAAAJgjQRrweLe3050HAAAAS0CQBjze1tZ05wEAAMASEKQBj3dw0Gze4eFs6wAAAIA5EqQBj3d8nHQ6D8/pdJKjo/nUAwAAAHMgSAMer9tNLi8nh2mdThnvdudbFwAAAMyQIA14Ob1ecnOT9PslMNvYKG2/X/p7vbYrBAAAgKl6re0CgCXW7SYnJ+ULAAAAVpwVaQAAwOONRmUl+vZ2srlZ2n6/9APAihKkAQAAjzMcJjs7yWBQgrO7u9IOBqV/OGy7QgCYCUEaAADQ3GiU7O8n4/Hzx8fjMm5lGgArSJAGAAA0d3o6OUS7Nx4nZ2fzqQcA5kiQBgAANHdx0Wze+fls6wCAFgjSAACA5m5vpzsPAJaIIA0AAGhua2u68wBgiQjSAACA5g4Oms07PJxtHQDQAkEaAADQ3PFx0uk8PKfTSY6O5lMPAMyRIA2WzWiU9PvJ9nayuVnaft8V8wDAfHS7yeXl5DCt0ynj3e586wKAORCkwTIZDpOdnWQwKMHZ3V1pB4PSPxy2XSEAsA56veTmpnyY1+0mGxul7fdLf6/XdoUAMBOCNFgWo1Gyv5+Mx88fH4/LuJVpAMDLeszK9243OTlJnjxJnj4t7cmJlWgArDRBGiyL09PJIdq98Tg5O5tPPQDAarHyHQBeSJAGy+Liotm88/PZ1gEArB4r3wGgEUEaLIvb2+nOAwC4Z+U7ADQiSINlsbU13XkAAPesfAeARgRpsCwODprNOzycbR0AwOqx8h0AGhGkwbI4Pk46nYfndDrJ0dF86gEAVoeV7wDQiCANlkW3m1xeTg7TOp0y7sp5AOCxrHwHgEYEabBMer3k5ibp90tgtrFR2n6/9Pd6bVcIACwjK98BoJHX2i4AeKRuNzk5KV8AANNwv/J9f//5t3da+Q4ASaxIAwAAEivfAXiu0aj8KtjeTjY3S9vvl/51VNV13XYNTFBV1dXu7u7u1dVV26UAAAAAa2Y4fPFi5WX8nGVvby/X19fXdV3vPfaxVqQBAAAA8A6j0eQQLSn9+/vrtzJNkAYAAADAO5yeTg7R7o3HydnZfOpZFII0AAAAAN7h4qLZvPPz2daxaARpAAAAALzD7e10560KQRoAAAAA77C1Nd15q0KQBgAAAMA7HBw0m3d4ONs6Fo0gDQAAAIB3OD5OOp2H53Q6ydHRfOpZFII0AAAAAN6h200uLyeHaZ1OGe9251tX2wRpAAAAAHyNXi+5uUn6/RKYbWyUtt8v/b1e2xXO32ttFwAAAADAYup2k5OT8oUVaQAAAADQiCANAAAAABoQpAEAAABAA4I0AAAAAGhAkAYAAAAADQjSAAAAAKABQRoAAAAANCBIAwAAAIAGBGkAAAAA0IAgDQAAANbQaJT0+8n2drK5Wdp+v/QDzydIA1h0XuEAADBlw2Gys5MMBuVl5d1daQeD0j8ctl0hLCZBGsAi8woHYPZ8YAGsmdEo2d9PxuPnj4/HZdxfg/C1BGkAi8orHIDZ84EFsIZOTye/xLw3HidnZ/OpB5aJIA1gUXmFAzBbPrAA1tTFRbN55+ezrQOWkSANYFF5hQMwWz6wANbU7e1058E6EaQBLCqvcABmywcWwJra2pruPFgngjSAReUVDsBs+cACWFMHB83mHR7Otg5YRoI0gEXlFQ7AbPnAAlhTx8dJp/PwnE4nOTqaTz2wTARpAIvKKxyA2fKBBbCmut3k8nLyS81Op4x3u/Ot61WMRkm/n2xvJ5ubpe333RfD9AnSABbVKr7CAVgkPrDgZXnHzgro9ZKbm/JHt9tNNjZK2++X/l6v7QqbGw6TnZ1kMCj/Gd7dlXYwKP3DYdsVskqquq7broEJqqq62t3d3b26umq7FKBNo1G5Me78vJzTs7VVVkccHQnRAF7VcJjs7z//9s77DyyW6d0ks+fPDCyU0aiEZQ9dwtzplHDQS2fu7e3t5fr6+rqu673HPtaKNIBF1+0mJyfJkyfJ06elPTnxSgBgGlZpSQazNxpNDtGS0r+/b2UazNHp6cMhWlLGz87mUw+rT5AGAMB684EFTXnHDgvn4qLZvPPz2dbB+hCkAQAANOEdOyyc29vpzoMXEaQBAAA04R07LJytrenOgxcRpAEAADThHTssnIODZvMOD2dbB+tDkAYAANCEd+ywcI6Py62cD+l0yoX3MA2CNAAAgCa8Y4eF0+0ml5eT/9PsdMq4+2OYFkEaAABAE96xw0Lq9ZKbm6TfL//5bWyUtt8v/b1e2xWySl5ruwAAAIClcf+O/eys3M55e1vORDs8LCvRhGjQim43OTkpXzBLgjQAAIDH8I4dYG3Z2gkAAAAADQjSAAAAAKABQRoAAAAANCBIAwAAAIAGBGkAAAAA0IAgDQAAAAAaEKQBAAAAQAOCNAAAAFgTo1HS7yfb28nmZmn7/dIPvJggDYCV4YUhAMBkw2Gys5MMBuX10d1daQeD0j8ctl0hLD5BGgArwQtDAIDJRqNkfz8Zj58/Ph6XcR9AwsMEaQAsPS8MAQAedno6+bXSvfE4OTubTz2wrARpACw9LwwBAB52cdFs3vn5bOuAZSdIA2DpeWEIAPCw29vpzoN1JUgDYOl5YQgA8LCtrenOg3UlSANg6XlhCADwsIODZvMOD2dbByw7QRoAS88LQwCAhx0fJ53Ow3M6neToaD71wLISpAGw9LwwBAB4WLebXF5Ofs3U6ZTxbne+dcGyEaQBsPS8MAQAeLFeL7m5Sfr98rpoY6O0/X7p7/XarhAW32ttFwAA03D/wvDkJPlrfy0Zj0v/668n//a/nfye39NufQAAi6DbLa+XTk7argSWkxVpAKyMf/APkk9/OnnzzaSuy9dv/mbyYz+W7Owkw2HbFQIAAMtMkAbAShiNkv39r65Ee7fxuIyPRvOtCwAAWB2CNBbbaFQ27G9vJ5ubpe33vRMGvsbp6eQQ7d54nJydzaceAABg9QjSWFzDYdmLNRiU4OzurrSDgT1awNe4uGg27/x8tnUAAACrS5DGYrJHC3ik29vpzgMAAHg3QRqLyR4t4JG2tqY7DwAA4N0EaSwme7SARzo4aDbv8HC2dQAAAKtLkMZiskcLeKTj46TTeXhOp5McHc2nHgAAYPUI0lhM9mgBj9TtJpeXk8O0TqeMd7vzrQsAAFgdgjQWkz1awEvo9ZKbm6TfL4HZxkZp+/3S3+u1XSHrajQqfw63t5PNzdL2++7MAQBYNlVd123XwARVVV3t7u7uXl1dtV3K/I1Gyc7OwxcOdDrlnbHlJQAssOFw8kXU9yslhbwAAPOzt7eX6+vr67qu9x77WCvSWEz2aAGwAkajySFaUvr3961MAwBYFoI0Fpc9WgAsudPThxdXJ2X87Gw+9QAA8Gps7Vxga721EwBWwPZ2s9Vm3W7y5Mns6wEAwNZOAICFdHs73XkAALRLkAYAMCNbW9OdBwBAuwRpAAAzcnDQbN7h4WzrAABgOgRpAAAzcnw8+QLqe51OcnQ0n3oAAHg1gjQAgBnpdpPLy8lhWqdTxrvd+dYFAMDLmVqQVlXVvzit5wIAWBW9XnJzk/T7JTDb2Chtv1/6e722KwQAoKnXpvhco6qqfjLJf1zX9XCKzwsAsNS63eTkpHwBALC8prm183NJ/tdJ/l9VVX2uqqrvq6rq/VN8fgAAAABozdSCtLqu/+Uk/5Mkn07yu5L8n5L8alVV/1lVVf/qtL4PAAAAALRhqpcN1HX903Vdf2eS35nke5P8gySHST5bVdX/p6qq762q6rdP83sCAABvMxqVQ/i2t5PNzdL2+6UfAHglM7m1s67r36jr+i+9bZXaf5pkO8lfSFml9hNVVf2+WXxvAABYW8NhsrOTDAYlOLu7K+1gUPqHjjIGgFcxkyDtXW6T/JMkv5WkSvLeJP9Okp+rquonq6p63xxqAACA1TYaJfv7yXj8/PHxuIxbmQYAL20mQVpVVb+tqqqDqqo+m+QXkvyJJP9dkj+VZCtJL8nfTPJHkpzOogYAAFgrp6eTQ7R743FydjafegBgBU01SKuq6vdUVTVI8g+T/Gcp2zp/Msm/Xtf1R+u6/ot1Xf96Xdd/u67rfzPJ/y3JH55mDQAAsJYuLprNOz+fbR0AsMJem9YTVVX1t5L8T1O2b/5/k/wfkvwndV3/owcedpXkfzmtGgAAYG3d3k53HgDwNaYWpCX5RJLPJjlL8pN1Xf/3DR7znyd5KGgDAACa2Npqdv7Z1tbsawGAFTXNrZ0fq+v6D9V1/X9tGKKlruv/pq7rvzrFGlg0rl8HAJiPg4Nm8w4PZ1sHAKywqQVpdV3/0rSeixXh+nUAgPk5Pk46nYfndDrJ0dF86gGAFTSTWzvB9esAAHPW7SaXl5PDtE6njHe7860LAFaIII3ZcP06AMD89XrJzU05SqPbTTY2Stvvl/5er+0KAWCpVXVdt10DE1RVdbW7u7t7dXXVdimPt73dbLVZt5s8eTL7egAAAACS7O3t5fr6+rqu673HPtaKNGbD9evQOnd9AAAATJcgjdloeq2669dhJtz1AQAAMH2CNGbD9evQGnd9AAAAzIYgjdlw/Tq0xl0fAAAAsyFIYzZcvw6tubhoNu/8fLZ1AAAArBpBGrPj+nVohbs+AAAAZuO1tgtgxXW7yclJ+QLmYmur2fln7voAAAB4HCvSAFaMuz4AAABmQ5AGsGLc9QEAADAbgjSAFeOuDwAAgNkQpAGsIHd9AAAATJ/LBgBWlLs+AAAApsuKNAAAAABoQJAGAAAAAA0I0gAAAACgAUEaAAAAADQgSAMAAACABgRpAAAAANCAIA0AAAAAGhCkAQAAAEADgjQAAAAAaECQBgAAAAANCNIAAAAAoAFBGgAAAAA0IEgDAIB1Nxol/X6yvZ1sbpa23y/9AMBXCNIAAGCdDYfJzk4yGJTg7O6utINB6R8O264QABaGIA0AANbVaJTs7yfj8fPHx+MybmUaACQRpAEAwPo6PZ0cot0bj5Ozs/nUAwALTpAGAADr6uKi2bzz89nWAQBLQpAGAADr6vZ2uvMAYMUJ0gAAYF1tbU13HgCsOEEaAACsq4ODZvMOD2dbBwAsCUEaAACsq+PjpNN5eE6nkxwdzaceAFhwgjQAAFhX3W5yeTk5TOt0yni3O9+6AGBBCdIAAGCd9XrJzU3S75fAbGOjtP1+6e/12q4QABbGa20XAAAAtKzbTU5OyhcAMJEVaQAAAADQgCANAAAAABoQpAEAAABAA4I0AAAAAGhAkAYAAAAADQjSAAAAAKABQRoAAAAANCBIgxcZjZJ+P9neTjY3S9vvl34AAABgbQjS4CHDYbKzkwwGJTi7uyvtYFD6h8O2KwQAAADmRJAGk4xGyf5+Mh4/f3w8LuNWpgEAAMBaEKTBJKenk0O0e+NxcnY2n3oAAACAVgnSYJKLi2bzzs9nWwcAtMQxoQAA7yRIg0lub6c7DwCWiGNCAQC+liANJtnamu48AFgSjgkFAHg+QRpMcnDQbN7h4WzrAIA5c0woAMDzCdJgkuPjpNN5eE6nkxwdzaceAJgTx4QCADyfIA0m6XaTy8vJYVqnU8a73fnWBQAz5phQWuF2CwCWgCANHtLrJTc35UVct5tsbJS23y/9vd5svq8XkgC0yDGhLVnn3/9utwBgSQjS4EW63eTkJHnyJHn6tLQnJ7NbieaF5HSt85sSgJfkmNAWrPPvf7dbADyKtzjtEqTBIvFCcrrW+U0JwCtwTOicrfvvf7dbADTmLU77BGmwSLyQnJ51f1MC8AocEzpn6/773+0WAI14i7MYBGmwSLyQnJ51f1MC8IraOiZ0La3773+3WwA04i3OYhCkwSLxQnJ61v1NCcAUzPuY0LW17r//3W4B0Ii3OItBkAaLxAvJ6Vn3NyUALI91//3vdguARrzFWQyCNFgkXkhOz7q/KQGgXY+5Um3df/+73QKgEW9xFoMgDRaJF5LTs+5vSgBoz2OvVFv33/9utwBoxFucxSBIg0XiheT0rPubEgDa8TJXqvn973YLgAa8xVkMgjRYNF5IToc3JQC04WWvVPP73+0WAC/gLc5iqOq6brsGJqiq6mp3d3f36uqq7VJgeY1G5c3K+Xk5dXNrq6x1PjryGwaA6dvefv45aO/W7ZagCAAeyVucV7e3t5fr6+vruq73HvtYQdoCE6QBACyZzc1yJtqLbGyUVVcAwNy9SpBmaycAAEzL1lZG+WD6Ocl23shmxtnOG+nnJKN88B3zAIDlI0gDAIApGf6B789ObjJIP6Ns5y6bGWU7g/Szk5sM84ky0ZVqALCUBGkAADAFo1Gy/5//bzLO688dH+f17Ocyo42PulINAJaUIA0AAKbg9DQZ3z388nqc13P2R/6G06ABYEkJ0gAAYAouLprNO//p7dkWAgDMjCANAACm4PZ2uvMAgMUjSAMAgCloehGnCzsBYHkJ0gAAYAoODprNc2EnACwvQRoAAEzB8XHS6Tw8p9NxYScALDNBGgAATEG3m1xeTg7TOp0y7sJOAFhegjQAAJiSXi+5uUn6/RKYbWyUtt8v/b1e2xUCAK/itbYLAACAVdLtJicn5QsAWC1WpAEAAABAA4I0AAAAAGhAkAYAAAAADQjSAAAAAKABQRoAALAeRqNyher2drK5Wdp+v/QDQAOCNAAAYPUNh8nOTjIYlODs7q60g0HpHw7brhCAJSBIAwAAVttolOzvJ+Px88fH4zJuZRoALyBIAwAAVtvp6eQQ7d54nJydzaceAJaWIA0AAFhtFxfN5p2fz7YOAJaeIA0AAFhtt7fTnQfA2hKkAcCacWkdsHa2tqY7D4C1JUgDgDXi0jpgLR0cNJt3eDjbOgBYeoI0AFgTLq0D1tbxcdLpPDyn00mOjuZTDywgK9ahGUEaAKwJl9YBa6vbTS4vJ4dpnU4Z73bnWxcsCCvWoTlBGgCsCZfWAWut10tubsoSm2432dgobb9f+nu9tiuEVlixDo8jSAOANeHSOmDtdbvJyUny5Eny9GlpT06sRGOtWbEOjyNIA4A14dI6AODdrFiHxxGkAcCacGkdAPBuVqzD4wjSAGBNuLQOAHg3K9bhcQRpALAmXFoHALybFevwOII0AFgjLq0DAN7OinV4HEEaAKwZl9YBAPdeZsX6aFQ+hNveTjY3S9vvl35YdYI0AAAAWGOPWbE+HCY7O8lgUIKzu7vSDgalfzhs798D5uG1tgsAAAAA2nW/Yv3kZPKc0SjZ30/G4+ePj8dl/ObGSndWlxVpAAAAwAudnk4O0e6Nx8nZ2XzqgTYI0gAAAIAXurhoNu/8fLZ1QJsEaQAAAMAL3d5Odx4sI0EaAAAA8EJbW9OdB8tIkAYAAAC80MFBs3mHh7OtA9okSAMAAABe6Pg46XQentPpJEdH86kH2iBIAwAAAF6o200uLyeHaZ1OGe9251sXzJMgDQAAAGik10tubpJ+vwRmGxul7fdLf6/XdoUwW6+1XQAAAACwPLrd5OSkfMG6sSINAAAAABoQpAEAAABAA4I0AAAAAGhAkAYAAAAADQjSAAAAAKABQRoAAAAANCBIA17OaJT0+8n2drK5Wdp+v/QDAADAChKkAY83HCY7O8lgUIKzu7vSDgalfzhsu0IAAACYOkEa8DijUbK/n4zHzx8fj8u4lWkAAACsGEEa8Dinp5NDtHvjcXJ2Np96AAAAYE4EacDjXFw0m3d+Pts6AACAheIYZdaBIA14nNvb6c4DAACWnmOUWReCNOBxtramOw8AAFhqjlFmnQjSgMc5OGg27/BwtnUAAAALwTHKrBNBGvA4x8dJp/PwnE4nOTqaTz0AAECrFuUYZWe0MQ+CNOBxut3k8nJymNbplPFud751AbDSvDkCWFyLcIyyM9qYF0Ea8Hi9XnJzU97BdLvJxkZp+/3S3+u1XSEAK8SbI4DF1vYxys5oY54EacDL6XaTk5PkyZPk6dPSnpxYiQbAVHlzBLD42j5G2RltzJMgDWBR2ccE4M0RwBJo+xjlRTmjjfUgSANYRPYxASTx5ghgGbR9jPIinNHG+hCkASwa+5gAvsKbI4Dl0OYxym2f0cZ6EaQBLBr7mAC+wpsjgOXR1jHKbZ/RxnoRpAEsGvuYAL7CmyMAXqTtM9pYL4I0gEVjHxPAV3hzBMCLtH1GG+tFkAawaOxjAvgKb44AaKLNM9pYL4I0gEVjHxPAO3hzBEATbZ3Rxnqp6rpuuwYmqKrqand3d/fq6qrtUoB5Go2SnZ2HLxzodMq7R68KAAAAHmVvby/X19fXdV3vPfaxVqQBLBr7mAAAABaSIA1gEdnHBAAAsHBea7sAACa4P+Th5KTtSgAAAIgVaQAAAADQiCANAAAAABoQpAEAAAAvNBqVI3u3t5PNzdL2+6Uf1oUgDQAAAHjQcJjs7CSDQQnO7u5KOxiU/uGw7QphPgRpAAAAwESjUbK/n4zHzx8fj8u4lWmsA0EaAAAAMNHp6eQQ7d54nJydzaceaJMgDQBolfNWAGCxXVw0m3d+Pts6YBEI0gCA1jhvBQAW3+3tdOfBMhOkAQCtcN4KACyHra3pzoNlJkgDAFrhvBUAWA4HB83mHR7Otg5YBII0AKAVzlsBgOVwfJx0Og/P6XSSo6P51ANtEqQBAK1w3goALIduN7m8nBymdTplvNudb13QBkEaANAK560AwPLo9ZKbm3KzdrebbGyUtt8v/b1e2xXCfAjSAIBWOG8FAJZLt5ucnCRPniRPn5b25MRKNNaLIA0AaIXzVgBYV6NRWcm1vZ1sbpa233dTNSwDQRoA0ArnrQCwjobDZGcnGQxKcHZ3V9rBoPQPh21XCDxEkAYAtMZ5K19ldQLA6huNkv39ZDx+/vh4XMb93Q+LS5AGALTKeStWJwCsi9PTySHavfE4OTubTz3A4wnSAABaZHUCwPq4uGg27/x8tnUAL0+QBgDQIqsTANbH7e105wHzJ0gDAGiR1QlMjYP2YOFtbU13HjB/gjQAgBZZncBUOGgPlsLBQbN5h4ezrQN4eYI0AIAWWZ3AK3PQHiyN4+Ok03l4TqeTHB3Npx7g8QRpAAAtsjqBV+agPVga3W5yeTk5TOt0yvg63VwNy0aQBgDQIqsTeGUO2oOl0uslNzflCMNuN9nYKG2/X/p7vbYrBB7yWtsFAACss/vVCZN25lmdwAs5aA+WTrebnJyUL2C5WJEGAKylRbrg0OoEXomD9gBgbgRpAMDaWcQLDu9XJzx5kjx9WtqTEyvRaMBBe6yhRfowBFgvgjQAYK244JCV46A91swifhgCrA9BGgCwVlxwyMpxDSBrxIchQNsEaQDAWnHBISvJQXusCR+GAG2r6rpuuwYmqKrqand3d/fq6qrtUgBgZWxulm1AL7KxUc4qA2BxbG83W23W7ZazJgGeZ29vL9fX19d1Xe899rFWpAEAa8UFhwDL6/Z2uvMAHkuQBgCsFRccAiwvH4YAbROkAQBrxQWHAMvLhyFA2wRpAMBaccEhwPLyYQjQNkEaALB2XHAIsJx8GAK07bW2CwAAaEO3m5yclC8Alsf9hyFnZ8n5eblYYGurbOc8OhKiAbMlSAMAAGCp+DAEaIutnQAAAADQgCANAAAAABpYyyCtqqpvqqrqr1RV9Y+qqvpyVVVvVFX1F6uq+u2PfJ73PXvcG8+e5x89e95vmlXtAAAAALRj7YK0qqo+nOQqyXcl+fkkP5zkC0m+N8nPVFX1/obP8/4kP/PscZ9/9jw//+x5r6qq+t3Trx4AAGC5jUblluTt7WRzs7T9fukHWHRrF6QlOUvyO5J8T13X317X9Z+p67qXEoR9S5Ifavg8/8ckH0nyF+q6/kPPnufbU4K13/Hs+wAAAPDMcJjs7CSDQQnO7u5KOxiU/uGw7QoBHrZWQdqz1WifTPJGktN3Df9AkqdJvqOqqs0XPE8nyXc8m/+pdw3/R0meJPk3rEoDAAAoRqNkfz8Zj58/Ph6XcSvTgEW2VkFakk88az9T1/Vbbx+o6/rNJD+VZCPJx1/wPB9P8nVJfurZ497+PG8l+Zvv+n4AAABftYb7G09PJ4do98bj5MzeHmCBrVuQ9i3P2s9NGP/lZ+1H5vQ8SZKqqq6e95Xko00eDwAALJE13d94cdFs3vn5bOsAeBXrFqR9w7P2NyaM3/d/45yeBwAAWCdrvL/x9na68wDasG5B2kKq63rveV9JfrHt2gAAgCla4/2NW1vTnQfQhnUL0u5Xin3DhPH7/l+f0/MAAADrZI33Nx4cNJt3eDjbOgBexboFab/0rJ10dtk3P2snnX027ecBAADWyRrvbzw+Tjqdh+d0OsnR0XzqAXgZ6xakffZZ+8mqqt7x715V1etJvi3JXZKffcHz/GySf5rk25497u3P854kn3zX9wMAAFjr/Y3dbnJ5OTlM63TKeLc737oAHmOtgrS6rj+f5DNJPpTk+F3DP5hkM8mn67p+et9ZVdVHq6p6x+2ZdV2Pk3z62fxPvet5/v1nz/8367r+whTLBwAAlt2a72/s9ZKbm6TfL4HZxkZp+/3S3+u1XSHAw6q6rtuuYa6qqvpwkp9O8juSXCb5hSS/P8knUrZifmtd1//4bfPrJKnrunrX87z/2fN8JMkwyc8n+ViS/SS/9ux5Pv+KtV7t7u7uXl1dvcrTAAAAi2I0SnZ2Hr5woNMpqZKlWQAzsbe3l+vr6+tnFz0+ylqtSEu+sirt9yX5iZQA7fuSfDjJjyT5+NtDtBc8zz9O8geS/GiS3/PseX5/kh9PsveqIRoAALCC7G8EWGprF6QlSV3Xv1LX9XfVdf0/qOv6n6vreruu6z9R1/U/ec7c6t2r0d429qW6rr/32eP/uWfP98fruv7V2f9bAEC7RqOyFWd7O9ncLG2/X/oBeID9jQBLa+22di4TWzsBWFTDYbK///ydSfeLKbwPBABgEdnaCQDMzWg0OURLSv/+vpVpAACsHkEaAPAop6cPn5GdlPGzs/nUAwAA8yJIAwAe5eKi2bzz89nWAQAA8yZIAwAe5fZ2uvMAAGBZCNIAgEfZ2pruPAAAWBaCNADgUQ4Oms07PJxtHQAAMG+CNADgUY6Pk07n4TmdTnJ0NJ96AABgXgRpAMCjdLvJ5eXkMK3TKePd7nzrAgCAWROkAQCP1uslNzdJv18Cs42N0vb7pb/Xa7tCAACYvtfaLgAAWE7dbnJyUr4AAGAdWJEGAAAAAA0I0gAAAFh5o1E5gmB7O9ncLG2/X/oBmhKkAQAAsNKGw2RnJxkMSnB2d1fawaD0D4dtVwgsC0EaAACw3Cw14gGjUbK/n4zHzx8fj8u4Py5AE4I0AFgy3i8CvI2lRrzA6enkEO3eeJycnc2nHmC5CdIAYIl4vwjwNpYa0cDFRbN55+ezrYPJfEjIMhGkAcCS8H4R4F0sNaKB29vpzmO6fEjIshGkAcCS8H4R4F0sNaKBra3pzmN6fEjIMhKkAcCS8H4R4F0sNaKBg4Nm8w4PZ1sHX8uHhCwjQRoALAnvFwHexVIjGjg+Tjqdh+d0OsnR0Xzq4at8SMgyEqQBwJLwfhHgXSw1ooFuN7m8nBymdTplvNudb134kJDlJEgDgCXh/SLAu1hqREO9XnJzU26C7HaTjY3S9vulv9dru8L15ENClpEgDQCWhPeLAO+yJkuNRqMS+GxvJ5ubpe33HcD+WN1ucnKSPHmSPH1a2pOTpf/jsdR8SMgyEqQBwJJYk/eLAI+z4kuNhsNkZycZDEpwdndX2sGg9A+HbVcIL8+HhCyjqq7rtmtggqqqrnZ3d3evrq7aLgWA/3979x9c6XfXh/194EuxpesfATkhE3JlQwAzWwzsAgXTgn2ZEiBDlBTcSB1o0pROipQCA8zcTBKKaZNBo2mAJpUwAxQ6ZkaiQ1or7fCrM8L8cmnr3dAkKhB+eW8oECK7gO+ubULy9I9Hitf7lbRHq/v7vl4zd85X5zn30Udzn+9d6X3Pc84MGQza3asOD9s1Q9bW2k9qt7eFaACLZDBow7LrdjXsdNq80Ps/8+rkJNnYuPw6v/iQcM7zcGbQvXv38uDBgwdN09y76XPNSAOAOePWFIDlsL9/fYiWtMcPDiZTD4zDgk8qZQGZkTbDzEgDAIDltb5etw5at9t+qAJAHTPSAAAAFszZ2WjHAXB7gjQAAIAZtLY22nEA3J4gDQAAYAZtbtaN29oabx0AfJAgDQAAYAbt7LS7Fl6n02l3bQZgMgRpAAAAM6jbTd785uvHvPnNdm0GmCRBGgAAwAwaDOqCtJqdPQEYDUEaAADADNrfT4bD68cMh8nBwWTqAUCQBgAAMJOOjurGHR6Otw4APkiQBgAAMIPOzkY7DoDbE6QBAADMoLW10Y4D4PYEaQAAADNoc7Nu3NbWeOsA4IMEaQAAADNoZyfpdK4f0+kk29uTqQcAQRoAAMBM6naT4+Orw7ROpz3e7U62LoBlJkgDAACYUb1ecnqa9PttYLay0rb9ftvf6027QoDl8sK0CwAAAOBq3W6yu9s+AJguM9IAAAAAoIIgDQAmYDBob8NZX09WV9u232/7AQCA+SBIA4AxOzlJ7txJ9vba4Ozx47bd22v7T06mXSEAAFBDkAYAYzQYJBsbyXB4+fHhsD1uZhoAAMw+QRoAjNH+/tUh2oXhMDk4mEw9AADA8xOkAcAYHR3VjTs8HG8dAADA7QnSAGCMzs5GOw4AAJgeQRoAjNHa2mjHAQAA0yNIA4Ax2tysG7e1Nd46AACA2xOkAcAY7ewknc71YzqdZHt7MvUAAADPT5AGAGPU7SbHx1eHaZ1Oe7zbnWxdAMDlBoOk30/W15PV1bbt99t+AEEaAIxZr5ecnra/hHe7ycpK2/b7bX+vN+0KAYAkOTlJ7txJ9vba4Ozx47bd22v7T06mXSEwbS9MuwAAWAbdbrK72z4AgNkzGCQbG8lwePnx4bA9fnpqJjksMzPSAABYKm7bAi6zv391iHZhOEwODiZTDzCbBGkAACwNt20BVzk6qht3eDjeOoDZJkgDAGAp1N62ZWYaLKezs9GOAxaTIA0AgKXgti3gOmtrox0HLCZBGgAAS8FtW8B1Njfrxm1tjbcOYLYJ0gAAWApu2wKus7OTdDrXj+l0ku3tydQDzCZBGgAAS8FtW7C4RrEbb7ebHB9fHaZ1Ou3xbnc0NQPzSZAGAMBScNsWLKZR7sbb6yWnp20I1+0mKytt2++3/b3e+H4OYD6UpmmmXQNXKKXcv3v37t379+9PuxQAgLk3GLR/VF+34UCn0/6xbMYJzAf/XwPP4969e3nw4MGDpmnu3fS5ZqQBALAU3LYFi8duvMCkCdIAAFgabtuCxWI33vk0ijXtYFrc2jnD3NoJAABwtdXVdk20Z1lZSR49Gn89PNvJSbKxcflMwouZwT7UYNzc2gkAAMDSsRvvfBkMrg7RkrZ/Y8PMNGabIA0AAIC5ZDfe+WJNOxaBIA0AAIC5tLNz9QYiFzqdZHt7MvVwPWvasQgEaQAAAMwlu/HOl7Oz0Y6DaRCkAQAAMLfsxjs/rGnHIhCkAQAAMNe63WR3N3n4sN2d8+HD9msz0WaLNe1YBII0AAAAYOysacciEKQBAAAAY2dNOxaBIA0AAACYCGvaMe9emHYBAAAAwPK4WNNud3falcDNmZEGAAAAABUEaQAAAABQQZAGAAAAABUEaQAAAABQQZAGAAAAABUEaQAAAABQQZAGAAAAABUEaQAAAABQQZAGAAAAABUEaQAAAABQQZAGAMyEwSDp95P19WR1tW37/bYfAABmgSANAJi6k5Pkzp1kb68Nzh4/btu9vbb/5GTaFQIAgCANAJiywSDZ2EiGw8uPD4ftcTPTAACYNkEaADBV+/tXh2gXhsPk4GAy9QAAwFUEaQDAVB0d1Y07PBxvHQAA8CyCNABgqs7ORjsOAADGRZAGAEzV2tpoxwEAwLgI0gCAqdrcrBu3tTXeOgAA4FkEaQDAVO3sJJ3O9WM6nWR7ezL1AADAVQRpAMBUdbvJ8fHVYVqn0x7vdidbFwAAPE2QBgBMXa+XnJ4m/X4bmK2stG2/3/b3etOukFkxGLTXxfp6srratv1+2w8AMG6laZpp18AVSin37969e/f+/fvTLgUAYOpOTpKNjWQ4fPGxi5mLQlcA4Fnu3buXBw8ePGia5t5Nn2tGGgAAM28wuDpES9r+jQ0z0wCA8RKkAQAw8/b3rw7RLgyHycHBZOoBAJaTIA0AgJl3dFQ37vBwvHUAAMtNkAYAwMw7OxvtOACA5yFIAwBg5q2tjXYcAMDzEKQBADDzNjfrxm1tjbcOAGC5CdIAAJh5OztJp3P9mE4n2d6eTD0AwHISpAEAMPO63eT4+OowrdNpj3e7k60LAFgugjQAAOZCr5ecnib9fhuYray0bb/f9vd6064QAFh0L0y7AAAAqNXtJru77QMAYNLMSAMAAACACoI0AAAAAKggSAMAAACACoI0AAAAAKggSAMAAACACoI0AAAAAKggSAMAAACACoI0AIA5Nhgk/X6yvp6srrZtv9/2AwAwWoI0AIA5dXKS3LmT7O21wdnjx227t9f2n5xMu0IAgMUiSAMAmEODQbKxkQyHlx8fDtvjZqYBAIyOIA0AYA7t718dol0YDpODg8nUAwCwDARpAABz6Oiobtzh4XjrAGB2WUcTRk+QBgAwh87ORjsOgMViHU0YD0EaAMAcWlsb7TgAFod1NGF8BGkAAHNoc7Nu3NbWeOsAYPZYRxPGR5AGADCHdnaSTuf6MZ1Osr09mXoAmB3W0YTxEaQBAMyhbjc5Pr46TOt02uPd7mTrAmD6rKMJ4yNIAwCYU71ecnra7sDW7SYrK23b77f9vd60KwRgGqyjCeMjSAMAmGPdbrK7mzx8mDx61La7u2aiASwz62jC+AjSAADgCYNBO6tvfT1ZXW3bft/udsD8sI4mjI8gDQAAzp2cJHfuJHt7bXD2+HHb7u21/Scn064Q4NmsownjI0gDAIC0gdnGRjIcXn58OGyPm5kGzAPraMJ4CNIAACDJ/v7VIdqF4TA5OJhMPQC3ZR1NGD1BGgAAJDk6qht3eDjeOgCA2SVIAwCAJGdnox0HACweQRoAACRZWxvtOABg8QjSAAAgyeZm3bitrfHWAQDMLkEaAAAk2dlJOp3rx3Q6yfb2ZOoBAGaPIA0AANLuYnd8fHWY1um0x+12BwDLS5AGAADner3k9DTp99vAbGWlbfv9tr/Xm3aFAMA0vTDtAgAAYJZ0u8nubvsAAHiSGWkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAABUGg6TfT9bXk9XVtu33235gOQjSAAAA4BlOTpI7d5K9vTY4e/y4bff22v6Tk2lXCEyCIA0AAACuMRgkGxvJcHj58eGwPW5mGiw+QRoAAABcY3//6hDtwnCYHBxMph5gegRpAAAAcI2jo7pxh4fjrQOYPkEaAAAAXOPsbLTjgPklSAMAAIBrrK2NdhwwvwRpUMM+1wAAsLQ2N+vGbW2Ntw5g+gRp8Cz2uQYAgKW2s5N0OteP6XSS7e3J1ANMjyANrmOfawCA52ZSP4ui202Oj68O0zqd9ni3O9m6gMkTpMF17HMNwJwQWDBrTOpn0fR6yelp+97a7SYrK23b77f9vd60KwQmoTRNM+0auEIp5f7du3fv3r9/f9qlLK/19bq/QLrd5OHD8dcDAJc4Obl6AvXFLAl/4DFJg0Ebll33eWSn04YPZvAAMGn37t3LgwcPHjRNc++mzzUjDa5jn2sAZpxVCJhFJvUDsKgEaXAd+1wDMOMEFsyio6O6cYeH460DAEZNkAbXsc81ADNOYMEsMqkfgEUlSIPr2OcagBknsGAWmdQPwKISpMF17HMNwIwTWDCLTOoHYFEJ0uBZ7HMNwAwTWDCLTOoHYFEJ0qBGt5vs7iYPHyaPHrXt7q6ZaABMncCCWWRSPwCLSpAGADDHBBbMKpP658tg0L426+vJ6mrb9vttPwAfVJqmmXYNXKGUcv/u3bt379+/P+1SAIAZNxgkBwft7pxnZ+2aaFtb7Uw0IRpwnZOTZGMjGQ5ffOwijBd8Aovk3r17efDgwYOmae7d9LkvjKMgAAAm62IVgt3daVcCzJPB4OoQLWn7NzbaWYRCeQC3dgIAACyt/f2rQ7QLw2E74xUAQRoAAMDSOjqqG3d4ON46AOaFIA0AAGBJnZ2NdhzAohOkAQAALKm1tdGOA1h0gjQAAIAltblZN25ra7x1AMwLQRoAAMCS2tlJOp3rx3Q6yfb2ZOoBmHWCNAAAgCXV7SbHx1eHaZ1Oe7zbnWxdALNKkAYAAFBpMEj6/WR9PVldbdt+v+2fV71ecnra/hzdbrKy0rb9ftvf6027QoDZUZqmmXYNXKGUcv/u3bt379+/P+1SAABg6Z2cJBsbyXD44mMXM7eETgCz7969e3nw4MGDpmnu3fS5ZqQBAAA8w2BwdYiWtP0bG/M9Mw2AZxOkAQAAPMP+/tUh2oXhMDk4mEw9AEyHIA0AAOAZjo7qxh0ejrcOAKZLkAYAAPAMZ2ejHQfAfBKkAQAAPMPa2mjHATCfBGkAAADPsLlZN25ra7x1ADBdgjQAALihwSDp95P19WR1tW37fTs2LrKdnaTTuX5Mp5Nsb0+mHgCmQ5AGAAA3cHKS3LmT7O21wdnjx227t9f2n5xMu0LGodtNjo+vDtM6nfZ4tytoBVhkgjQAAKg0GCQbG8lwePnx4bA9LjBZTL1ecnrahmLdbrKy0rb9ftvf6wlaARadIA0AACrt718dol0YDpODg8nUw+R1u8nubvLwYfLoUdvu7n5wJpqgFWCxCdIAAKDS0VHduMPD8dbBbBK0Aiw+QRoAAFQ6OxvtOBaLoBVg8QnSAACg0traaMexWAStAItPkAYAAJU2N+vGbW2Ntw5mk6AVYPEJ0gAAoNLOTtLpXD+m00m2tydTD7NF0Aqw+ARpAABQqdtNjo+vDtM6nfZ4tzvZupgNglaAxSdIAwCAG/hTfyr5iq9IXv7ypJT28bKXJV/91cnpadLrTbtCpkXQCrD4BGkAAFDp5CS5cyd5y1uS3//9pGnax3vfm7z1rcmv/Mq0K2Taer02UO3328BsZaVt+31BK8AiEKQBAECFwSDZ2EiGw8uPD4ft8cFgsnUxe7rdZHc3efgwefSobXd3F3sm2mDQhoXr68nqatv2+/5/ABaPIA0AACrs718dol0YDpODg8nUA7PiYqbm3l4bnD1+3LZ7e23/ycm0KwQYHUEaAABUODqqG3d4ON46YJaYqQksG0EaAABUODsb7ThYBGZqAstGkAYAABXW1kY7DhaBmZrAshGkAQBAhc3NunFbW+OtA2aJmZrAshGkAQBAhZ2dpNO5fkynk2xvT6YemAVmagLLRpAGAAAVut3k+PjqMK3TaY93u5OtC6bJTE1g2QjSAACgUq+XnJ4m/X4bmK2stG2/3/b3etOuECbLTE1g2QjSAADgBrrdZHc3efgwefSobXd3zUTjeoNBG7iuryerq23b77f988xMTWDZCNIAAADG6OQkuXMn2dtrg7PHj9t2b6/tPzmZdoW3Y6YmsExemHYBAAAAi2owSDY2kuHw8uPDYXv89HS+Z21dzNTc3Z12JQDjZUYaAADAmOzvXx2iXRgOk4ODydQDwO0I0gAAAMbk6Khu3OHheOuYZYu6fhywmARpAAAAY3J2Ntpxi2bR148DFo8gDQAAYEzW1kY7bpHUrh9nZhowSwRpAAAAY7K5WTdua2u8dcwi68cB80iQBgAAMCY7O0mnc/2YTifZ3p5MPbPE+nHAPBKkAQAAjEm3mxwfXx2mdTrt8W53snXNAuvHAfNIkAYAADBGvV5yetruRNntJisrbdvvt/293rQrnA7rxwHzSJAGAAAwZt1usrubPHyYPHrUtru7yzkT7YL144B5JEgDAABg4qwfB8wjQRoAAAATZ/04YB4J0gAAAJgK68cB8+aFaRcAAADA8rpYP253d9qVADybGWkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAAAAVBGkAAAAAUEGQBgAAMCWDQdLvJ+vryepq2/b7bT8As0eQBgAAMAUnJ8mdO8neXhucPX7ctnt7bf/JybQrvJ4QEFhGgjQAAIAJGwySjY1kOLz8+HDYHp/VUGreQ0CA5yVIAwAAmLD9/atDtAvDYXJwMJl6bmLeQ0CA2xCkAQDMGbdTwfw7Oqobd3g43jqexzyHgAC3JUgDAJgjbqeCxXB2NtpxkzTPISDAbQnSAADmhNupYHGsrY123CTNcwgIcFuCNACAOeF2Klgcm5t147a2xlvH85jnEBDgtgRpAABzwu1UsDh2dpJO5/oxnU6yvT2Zem5inkNAgNsSpAEAzAm3U8Hi6HaT4+Orw7ROpz3e7U62rhrzHAIC3JYgDQBgTridChZLr5ecnra77na7ycpK2/b7bX+vN+0KLzfPISDAbQnSAADmhNupYPF0u8nubvLwYfLoUdvu7s5+CDWvISDAbZWmaaZdA1copdy/e/fu3fv370+7FABgBgwGyZ0712840Om0f8TO+h/hAADTcu/evTx48OBB0zT3bvpcM9IAAOaE26kAAKZLkAYAMEfcTgUAMD0vTLsAAABu5mJNpd3daVcCALBczEgDAAAAgAqCNAAAAACoIEgDAADm3mDQrhW4vp6srrZtv9/2A8CoCNIAAIC5dnKS3LmT7O21wdnjx227t9f2n5xMu0IAFoUgDQAAmFuDQbKxkQyHlx8fDtvjZqYBMAqCNAAAYG7t718dol0YDpODg8nUA8BiE6QBAABz6+iobtzh4XjrgCdZsw8WlyANAACYW2dnox0Ht2XNPlhsgjQAAGBura2NdhzchjX7YPEJ0gAAgLm1uVk3bmtrvHVAYs0+WAaCNAAAYG7t7CSdzvVjOp1ke3sy9bDcrNkHi0+QBgAAzK1uNzk+vjpM63Ta493uZOtiOVmzDxafIA0AAJhrvV5yetruitjtJisrbdvvt/293rQrZFlYsw8WnyANAACYe91usrubPHyYPHrUtru7ZqIxWdbsg8UnSAMAAIARsGYfLD5BGgAAAIyANftg8QnSAAAAYESs2QeL7YVpFwAAAACL5GLNvt3daVcCjJoZaQAAAABQQZAGAAAAABUEaQAAAABQQZAGAAAAABUEaQAAAABQQZAGAAAAABUEaQAAAABQYSmDtFLK60spP1xKeU8p5X2llH9USvm6UsqH3/A8zTWPnxtX/QAA82IwSPr9ZH09WV1t236/7QcAmDcvTLuASSulbCT5+0nen+QHk7wnyZcm+fYkn5vkTTc85cMk339J/288f5UAAPPv5CTZ2EiGww/2DQbJ3l5ycJAcHye93vTqAwC4qaUK0kopL0/y3Un+VZI3NE3zzvP+b0pykuTLSymbTdMc3eC072qa5s0jLxYAYI4NBi8O0Z40HLbHT0+TbneytQEAPK9lu7Xzy5O8KsnRRYiWJE3TvD/J3zz/8qunURgAwCLZ3786RLswHLYz0wAA5sVSzUhLcnHzwI9ecuynkjxO8vpSykc2TfOBynO+spTyl5N8TJLfS3K/aZobrY9WSrl/xaHX3uQ8AACz4qhyfv/hYbK7O95aAABGZdmCtE86b//p0weapvnDUsqvJ7mT5OOS/ELlOT81yfc+2VFK+b+TfGXTNP/4FrUCAMyts7PRjgMAmAXLdmvnK87b37vi+EX/KyvP921pNyh4VZKXJfnMJD+UNlw7KaX8iZqTNE1z77JHkl+srAMAYKasrY12HADALJi7IK2U8q5SSnODxw+Mq5amab6haZp3NE1z1jTNsGmadzZN86a0u4KuJfnGcX1vAIBZtrlZN25ra7x1AACM0jze2vmrSd5/g/G/+cR/X8w4e8VlA5/o/90b1vS0tyT5siSfd8vzAADMpZ2ddiOB6zYc6HSS7e3J1QQAcFtzF6Q1TfMFt3j6LyX5jCSfmORDFvgvpbyQ5DVJ/jDJr93ieyTJvzhvV295HgCAudTtJsfHycbG5WFap9Me73YnXxsAwPOau1s7b+nkvP2iS459XpKVJO+4wY6dV/ns8/a2gRwAwNzq9ZLT06TfbwOzlZW27ffb/l7v2ecAAJglyxak/VCSsySbpZTPuOgspbwkyd86//I7n3xCKWWllPLaUkr3qf7XlVI+4ulvUEp5XZK/ff7l2NZnAwCYB91usrubPHyYPHrUtru7ZqIBAPNp7m7tvI2maX6/lPKfpQ3U3l5KOUryniR/Nsknnff/4FNP+6wkP5HkJ5O84Yn+r0/ypaWUn07yz5J8IMlr0852+/Ak353kcGw/DAAAAAATtVRBWpI0TfO2UsrnJ/kbaTcEeEmSX0kbjP3dpmmaylO9LcnLk7wuSe/8PO9O8iNJvrtpmn8w4tIBAAAAmKKlC9KSpGman03yJZVj356kXNL/trRhGgAAAABLYNnWSAMAAACA5yJIAwAAAIAKgjQAAAAAqCBIAwAAAIAKgjQAAAAAqCBIAwBgqQ0GSb+frK8nq6tt2++3/QAATxKkAQCwtE5Okjt3kr29Njh7/Lht9/ba/pOTaVcIAMwSQRoAAEtpMEg2NpLh8PLjw2F73Mw0AOCCIA0AgKW0v391iHZhOEwODiZTD8CzuBUdpk+QBgDAUjo6qht3eDjeOgBquBUdZoMgDQCApXR2NtpxAOPiVnSYHYI0AACW0traaMcBjItb0WF2CNIAAFhKm5t147a2xlsHl7MWFHyQW9FhdgjSAABYSjs7Sadz/ZhOJ9nenkw9fJC1oOBDuRUdZocgDQCApdTtJsfHV4dpnU57vNudbF3LzlpQ8GJuRYfZIUgDAGBp9XrJ6Wl7y2C3m6ystG2/3/b3etOucPlYCwpezK3oMDtK0zTTroErlFLu37179+79+/enXQoAAEzE+nrdbLNuN3n4cPz1wCwYDNrbmq8LmTud9gMAs2jh2e7du5cHDx48aJrm3k2fa0YaAAAwM6wFBS/mVnSYHYI0AABgZlgLCi7nVnSYDYI0AABgZlgLCq7W7Sa7u+1tzY8ete3urploMEmCNAAAYGbs7Fx9+9qFTifZ3p5MPQDwJEEaAADMgcGgvYVrfT1ZXW3bfr9uYf55Mu9rQS3L6wSwrOzaOcPs2gkAQJKcnCQbG5fv2HcRLC3a+kiDQXJwkBwethsLrK21t3Nub89uiLaMrxPAPLrNrp2CtBkmSAMAYDBI7ty5PJy50Om0i43PasC0DLxOAPPjNkGaWzsBAGCG7e9fH84k7fGDg8nUw+W8TgDLQZAGAAAz7Oiobtzh4Xjr4HpeJ4DlIEgDAIAZdnY22nGMh9cJYDkI0gAAYIatrY12HOPhdQJYDoI0AACYYZubdeO2tsZbB9fzOgEsB0EaAADMsJ2ddrfH63Q6yfb2ZOrhcl4ngOUgSAMAgBnW7SbHx1eHNJ1Oe7zbnWxdfCivE8ByEKQBAMCM6/WS09Ok32+DmJWVtu332/5eb9oVknidAJZBaZpm2jVwhVLK/bt37969f//+tEsBAAAAWAj37t3LgwcPHjRNc++mzzUjDQAAAAAqCNIAAAAAoIIgDQAAAAAqCNIAAAAAoIIgDQAAAAAqCNIAAAAAoIIgDQAAAAAqCNIAAAAAoIIgDQBgBAaDpN9P1teT1dW27ffbfgAAFoMgDQDglk5Okjt3kr29Njh7/Lht9/ba/pOTaVcIAMAoCNIAAG5hMEg2NpLh8PLjw2F73Mw0AID5J0gDALiF/f2rQ7QLw2FycDCZegAAGB9BGgDALRwd1Y07PBxvHQAAjJ8gDQDgFs7ORjsOAIDZJUgDALiFtbXRjgNml915ARCkAQDcwuZm3bitrfHWAYyX3XkBSARpAAC3srOTdDrXj+l0ku3tF/eb3QLzwe68AFwQpAEA3EK3mxwfXx2mdTrt8W73Q/vNboH5YXdeAC4I0gAAbqnXS05P29lk3W6ystK2/X7b3+t96HizW2C+2J0XgAuCNACAEeh2k93d5OHD5NGjtt3dffFMtMTsFpg3ducF4IIgDQBgwsxugflid14ALgjSAAAmzOwWmC925wXggiANAGDCzG6B+XKb3XkBWCyCNACACTO7BebL8+7OC8DiEaQBAEyY2S0wf266Oy8Ai+mFaRcAALBsLma3bGxcvnun2S0wmy52593dnXYlAEyLGWkAAFNgdgsAwPwxIw0AYErMbgEAmC9mpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAAABABUEaAAAAAFQQpAEAwDUGg6TfT9bXk9XVtu33234AYLkI0gAA4AonJ8mdO8neXhucPX7ctnt7bf/JybQrBAAmSZAGAACXGAySjY1kOLz8+HDYHjczDQCWhyANAAAusb9/dYh2YThMDg4mUw8AMH2CNAAAuMTRUd24w8Px1gEAzA5BGgAAXOLsbLTjAID5J0gDAIBLrK2NdhwAMP8EaQAAcInNzbpxW1vjrQMAmB2CNAAAuMTOTtLpXD+m00m2tydTDwAwfYI0AAC4RLebHB9fHaZ1Ou3xbneydQEA0yNIAwCAK/R6yelp0u+3gdnKStv2+21/rzftCgGASXph2gUAAMAs63aT3d32AQAsNzPSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAFh6g0HS7yfr68nqatv2+20/AFwQpAEAAEvt5CS5cyfZ22uDs8eP23Zvr+0/OZl2hQDMCkEaAACwtAaDZGMjGQ4vPz4ctsfNTAMgEaQBAABLbH//6hDtwnCYHBxMph4AZpsgDQAAWFpHR3XjDg/HWwcA80GQBgAALK2zs9GOA2CxCdIAAICltbY22nEALDZBGgAAsLQ2N+vGbW2Ntw4A5oMgDQAAWFo7O0mnc/2YTifZ3p5MPQDMNkEaAACwtLrd5Pj46jCt02mPd7uTrQuA2SRIAwAAllqvl5yeJv1+G5itrLRtv9/293rTrhCAWfHCtAsAAACYtm432d1tHwBwFTPSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKBCaZpm2jVwhVLKu1/60pd+1Cd/8idPuxQAAACAhfALv/ALed/73veepmk++qbPFaTNsFLKryd5eZJ3TbkUpu+15+0vTrUK5olrhptyzXBTrhmeh+uGm3LNcFOuGWq8OsnvN03zmps+UZAGc6CUcj9Jmqa5N+1amA+uGW7KNcNNuWZ4Hq4bbso1w025Zhg3a6QBAAAAQAVBGgAAAABUEKQBAAAAQAVBGgAAAABUEKQBAAAAQAW7dgIAAABABTPSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA0AAAAAKgjSAAAAAKCCIA1mRCnlI0opX1tK+b5Sys+XUv6glNKUUr7qFud8fSnlh0sp7ymlvK+U8o9KKV9XSvnwUdbO9I3qtT6/5q56/Ny46mf0SikfW0r570spv1lK+UAp5V2llO8opfyRG57no86f967z8/zm+Xk/dly1Mz2juG5KKW9/xnvJS8b5MzA5pZQvL6X8vVLKT5dSfv/89f2B5zzXSN6zmG2jumbOr4+r3mN+exy1Mx2llI8upXxVKeV/LqX8yvnvub9XSvmZUsp/Wkq5UabhvYZReGHaBQD/xmqS7zj/73+e5LeT/MnnPVkpZSPJ30/y/iQ/mOQ9Sb40ybcn+dwkb7pFrcyQMbzWD5N8/yX9v/H8VTJJpZSPT/KOJH80yXGSX0zyWUm+NskXlVI+t2mad1ec56PPz/OJSU6SHCV5bZL/JMmfKaV8TtM0vzaen4JJG9V184RvuaL/D29VKLPkbyb51CTDtP9GvPZ5TjKGa4/ZNZJr5tzv5YO/Oz9peItzMnvelOQ7k/xWkp9IMkjyx5L8B0m+J8kXl1Le1DRN86wTea9hZJqm8fDwmIFHkn8ryRcn+ePnX785SZPkq57jXC9P8jtJPpDkM57of0nafzyaJJvT/pk9RnLdjPS1Ph//9mn/XB63vi5+7Py1/C+e6v+28/63VJ7nu87H/52n+r/mvP9Hp/2zeszkdfP29lfM6f9MHuN9JHljkk9IUpK84fw6+YHnOM9Irj2P2X+M8Jp5V5J3Tfvn8Rj/I0kv7QfEH/ZU/8ekDdWaJF9WeS7vNR4jebi1E2ZE0zR/0DTNjzRN81sjON2XJ3lVkqOmad75xPd4f9pPApPkq0fwfZg+rzUf4vzT1i9M+0fG/lOHvznJoyRfWUpZfcZ5Okm+8nz8m586/N+lnbn4p0spH3f7qpm2UV03LJemaX6iaZpfbprmmTNBruLaWy6juGZYLk3TnDRN8780TfOvn+r/7SRvOf/yDc86j/caRkmQBoupd97+6CXHfirJ4ySvL6V85ORKYkzG8Vq/spTyl0spf72UslNK+exbV8kkvfG8/fFLful8b5KfTbKS5Fmv62cneWmSnz1/3pPn+ddpP9V98vsx30Z13fwbpZS/UEr5a6WUry+lfLF/c7jCyK89lsZHllK+4vz3la8tpbzxpmvDMvf+5Xlbs2SA9xpGRpAGi+mTztt/+vSBpmn+MMmvp10j0UyS+TeO1/pTk3xvkr+ddubR/17aDTA+5Za1MhlXXhPnfvm8/cQJnYf5MI7X+yjJtyb5O0l+OMmglPLlz1ceC8x7Dc/rY5K8Ne3vK9+Rdi3PXy6lfP40i2IySikvJPmPz7+87APlp3mvYWQEabCYXnHe/t4Vxy/6Xzn+UhizUb/W35Z2g4JXJXlZks9M8kNpw7WTUsqfeL4ymaBRXRPeR5bLKF/v47Tr2Xxs2lmNr00bqL0yyQ+WUr7ouatkEXmv4Xl8X5IvSBumrSb5lLTrer46yY+UUj51eqUxIbtJ/u0kP9w0zY89a3C81zBCgjQYoWdsxX3Z47m2iGexzNJ10zTNNzRN846mac6aphk2TfPOpmnelHZX0LUk3ziu7w0shqZpvr1pmv+1aZr/t2ma9zdN80tN0/z1JN+Q9nfPb51yicCca5rmW87XzvrnTdM8bprmnzRN85+n/UDwpXnx2p4skFLK16T9N+UX067nChP1wrQLgAXzq0nef4PxvzmmOi4+UXnFFccv+n93TN+fm7nNdTOp1/otSb4syefd8jyM36iuCe8jy2USr/f3JPn2JJ9WSnnZ02vvsbS81zBKb0kbsPh9ZUGVUv5qkv82yf+T5AuapnlP5VO91zAygjQYoaZpvmDaNZz7pSSfkfYe//tPHjhfT+A1aRfl/LXJl8bTbnndTOq1/hfnrZ2MZt8vnbdXrfHxCeftVWuEjPo8zIexv95N07y/lPLeJH8k7XuJII3Eew2j5feVBVZK+bq0H8j8k7Qh2u/c4OneaxgZt3bCYjo5by9bh+bz0u5I846maT4wuZIYk0m91hc7GAlfZ99PnLdfWEr5kH/nSykvS7sG3uMkP/eM8/xckvcl+dzz5z15ng9Lu4X8k9+P+Taq6+ZKpZRPShuivTfJ2fOeh4Uz9muPpeL3lQVVSumnDdF+PskbbxiiJd5rGCFBGsyxUsorSimvLaX88acO/VDaP1I2Symf8cT4lyT5W+dffueEymS8bvxal1JWzq+b7lP9ryulfMTT36CU8rq0O2IliXX9ZlzTNL+a5MfTLri889Thb0n7Kf1bm6Z5dNF5fj289qnzDNPuhraaF68181fPz/9jTdP4Y2UBjOq6KaW8ppTyUU+fv5TyqrSLgyfJ0fmuwiyRUspHnF8zH/9k//NceyyHq66ZUsonl1JeNOOslPLqtLuNJ35fWSillG9Ku7nA/bQz0a78MMZ7DZNQmqaZdg3AuVLKX0u7u1mSfFranRLfkQ9ux/wzTdN8zxPj/1LaP0z+h6Zp/tJT5/pzaUOW9yc5SvKeJH827dbPP5TkP2y8ASyEm77WpZQ3pP1U7iebpnnDE/3fn3anvZ9O8s+SfCDt9fhFST48yXcn+Suum9l3/svjO5L80bQ7KP5Ckn8nyRvT3rLw+qZp3v3E+CZJmqYpT53no8/P84lpZz/+n0k+OclGkt85P8+vjvvnYTJGcd2c/7v0liQ/k3ZGyHuSdJN8Sdr1Z96Z5N9vmuZ3x/4DMXbn//78ufMvPybJn077uv/0ed9Z0zTfeD721Ul+PcnDpmle/dR5bnTtMb9Gcc2UUt6cdh20n0ryMO0s149P8meSvCTJDyf5803T/ME4fxYmo5TyF5N8f5J/leTv5fJdN9/VNM33n49/dbzXMGbWSIPZ8kVJPv+pvtefPy58Tyo0TfO2UsrnJ/kbaReJf0mSX0ny9Un+rjBkcYzwtX5bkpcneV2S3vl53p3kR5J8d9M0/2DEpTMmTdP86vkMxf8q7fvKlyT5rbSL835L0zT/X+V53l1K+Zwk35z2D59/L+018X1J/sumaX5jDOUzJSO6bu6nDfTvJfn0tO8p703yj5P8j0m+yx+3C+XTkvzFp/o+7vyRtCHHM3d7HtV7FnPh03L7a+Yn0n5Y+Olpb8dbTbtA/M+knUn9Vr/nLpTXnLcfnuTrrhjzk2nDtmt5r2FUzEgDAAAAgArWSAMAAACACoI0AAAAAKggSAMAAACACoI0AAAAAKggSAMAAACACoI0AAAAAKggSAMAAACACoI0AAAAAKggSAMAAACACoI0AAAAAKggSAMAAACACoI0AAAAAKggSAMAAACACoI0AAAAAKggSAMAAACACoI0AACeSynlbaWUppTyNZcc+6/Pj33vNGoDABiH0jTNtGsAAGAOlVI+Ksk/TPLHknxO0zT/8Lz/C5L8eJJfTPKZTdM8nl6VAACjI0gDAOC5lVJen+Qnk/x6krtJVpP8fJJXpA3RTqdXHQDAaLm1EwCA59Y0zTuSfFOST0jyXUnemuRjknyNEA0AWDRmpAEAcCullJLkR5N84XnXYdM0/9EUSwIAGAsz0gAAuJWm/WT2f3qi6zumVAoAwFiZkQYAwK2UUj4hyYMk/zLt2minST6raZr3T7UwAIARMyMNAIDnVkr5yCQ/mHaTgb+Q5FuTfErMSgMAFpAgDQCA2/hvknx6kr2maf63JN+c5GeT/JVSypumWhkAwIi5tRMAgOdSSvnzaddG+z+S/LtN0/zhef+fTPLzSV5I8ulN0/za1IoEABghQRoAADdWSummDcs+LMmnNU3zrqeObyR5W5L/K23I9gcTLhEAYOQEaQAAAABQwRppAAAAAFBBkAYAAAAAFQRpAAAAAFBBkAYAAAAAFQRpAAAAAFBBkAYAAAAAFQRpAAAAAFBBkAYAAAAAFQRpAAAAAFBBkAYAAAAAFQRpAAAAAFBBkAYAAAAAFQRpAAAAAFBBkAYAAAAAFQRpAAAAAFBBkAYAAAAAFQRpAAAAAFDh/wckZ04rLUefLQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 859,
       "width": 617
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot, dots colored by class value\n",
    "df = pd.DataFrame(dict(x=X[:,0], y=X[:,1], label=Y))\n",
    "colors = {0:'red', 1:'blue'}\n",
    "fig, ax = plt.subplots()\n",
    "grouped = df.groupby('label')\n",
    "for key, group in grouped:\n",
    "    group.plot(ax=ax, kind='scatter', x='x', y='y', label=key, color=colors[key])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the dataset cannot be separated by a line, so a Neural Network, being highly non linear may be what we need for this task."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build a 3-Dense layer model\n",
    "\n",
    "_DENSE -> RELU -> DENSE -> RELU -> DENSE -> SIGMOID_\n",
    "\n",
    "and units: `1024` -> `512` -> `2`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# model building\n",
    "n_x = X_train.shape[1] # number of features\n",
    "model = Sequential()\n",
    "\n",
    "# YOUR CODE HERE\n",
    "\n",
    "opt = Adam(lr=0.0001)\n",
    "model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 84 samples, validate on 36 samples\n",
      "Epoch 1/2000\n",
      "84/84 [==============================] - 0s 5ms/step - loss: 0.6826 - accuracy: 0.6190 - val_loss: 0.6706 - val_accuracy: 0.7778\n",
      "Epoch 2/2000\n",
      "84/84 [==============================] - 0s 616us/step - loss: 0.6666 - accuracy: 0.7679 - val_loss: 0.6541 - val_accuracy: 0.8194\n",
      "Epoch 3/2000\n",
      "84/84 [==============================] - 0s 270us/step - loss: 0.6516 - accuracy: 0.7798 - val_loss: 0.6384 - val_accuracy: 0.8056\n",
      "Epoch 4/2000\n",
      "84/84 [==============================] - 0s 262us/step - loss: 0.6357 - accuracy: 0.7798 - val_loss: 0.6234 - val_accuracy: 0.8056\n",
      "Epoch 5/2000\n",
      "84/84 [==============================] - 0s 240us/step - loss: 0.6210 - accuracy: 0.7857 - val_loss: 0.6092 - val_accuracy: 0.8056\n",
      "Epoch 6/2000\n",
      "84/84 [==============================] - 0s 277us/step - loss: 0.6061 - accuracy: 0.7857 - val_loss: 0.5955 - val_accuracy: 0.8056\n",
      "Epoch 7/2000\n",
      "84/84 [==============================] - 0s 308us/step - loss: 0.5921 - accuracy: 0.7857 - val_loss: 0.5822 - val_accuracy: 0.8056\n",
      "Epoch 8/2000\n",
      "84/84 [==============================] - 0s 353us/step - loss: 0.5787 - accuracy: 0.7857 - val_loss: 0.5693 - val_accuracy: 0.8056\n",
      "Epoch 9/2000\n",
      "84/84 [==============================] - 0s 358us/step - loss: 0.5650 - accuracy: 0.7857 - val_loss: 0.5567 - val_accuracy: 0.8056\n",
      "Epoch 10/2000\n",
      "84/84 [==============================] - 0s 272us/step - loss: 0.5522 - accuracy: 0.7917 - val_loss: 0.5443 - val_accuracy: 0.8056\n",
      "Epoch 11/2000\n",
      "84/84 [==============================] - 0s 299us/step - loss: 0.5401 - accuracy: 0.7976 - val_loss: 0.5324 - val_accuracy: 0.8056\n",
      "Epoch 12/2000\n",
      "84/84 [==============================] - 0s 378us/step - loss: 0.5259 - accuracy: 0.7976 - val_loss: 0.5214 - val_accuracy: 0.8056\n",
      "Epoch 13/2000\n",
      "84/84 [==============================] - 0s 333us/step - loss: 0.5140 - accuracy: 0.7976 - val_loss: 0.5107 - val_accuracy: 0.8056\n",
      "Epoch 14/2000\n",
      "84/84 [==============================] - 0s 317us/step - loss: 0.5029 - accuracy: 0.7976 - val_loss: 0.5004 - val_accuracy: 0.8056\n",
      "Epoch 15/2000\n",
      "84/84 [==============================] - 0s 315us/step - loss: 0.4912 - accuracy: 0.7976 - val_loss: 0.4906 - val_accuracy: 0.7917\n",
      "Epoch 16/2000\n",
      "84/84 [==============================] - 0s 304us/step - loss: 0.4799 - accuracy: 0.8095 - val_loss: 0.4812 - val_accuracy: 0.7917\n",
      "Epoch 17/2000\n",
      "84/84 [==============================] - 0s 342us/step - loss: 0.4692 - accuracy: 0.8095 - val_loss: 0.4727 - val_accuracy: 0.7778\n",
      "Epoch 18/2000\n",
      "84/84 [==============================] - 0s 311us/step - loss: 0.4594 - accuracy: 0.8155 - val_loss: 0.4645 - val_accuracy: 0.7778\n",
      "Epoch 19/2000\n",
      "84/84 [==============================] - 0s 309us/step - loss: 0.4491 - accuracy: 0.8214 - val_loss: 0.4571 - val_accuracy: 0.7778\n",
      "Epoch 20/2000\n",
      "84/84 [==============================] - 0s 360us/step - loss: 0.4398 - accuracy: 0.8214 - val_loss: 0.4501 - val_accuracy: 0.7778\n",
      "Epoch 21/2000\n",
      "84/84 [==============================] - 0s 329us/step - loss: 0.4315 - accuracy: 0.8214 - val_loss: 0.4436 - val_accuracy: 0.7778\n",
      "Epoch 22/2000\n",
      "84/84 [==============================] - 0s 312us/step - loss: 0.4239 - accuracy: 0.8214 - val_loss: 0.4375 - val_accuracy: 0.7778\n",
      "Epoch 23/2000\n",
      "84/84 [==============================] - 0s 232us/step - loss: 0.4154 - accuracy: 0.8214 - val_loss: 0.4322 - val_accuracy: 0.7778\n",
      "Epoch 24/2000\n",
      "84/84 [==============================] - 0s 275us/step - loss: 0.4080 - accuracy: 0.8214 - val_loss: 0.4276 - val_accuracy: 0.7778\n",
      "Epoch 25/2000\n",
      "84/84 [==============================] - 0s 237us/step - loss: 0.4012 - accuracy: 0.8214 - val_loss: 0.4234 - val_accuracy: 0.7778\n",
      "Epoch 26/2000\n",
      "84/84 [==============================] - 0s 298us/step - loss: 0.3952 - accuracy: 0.8214 - val_loss: 0.4198 - val_accuracy: 0.7778\n",
      "Epoch 27/2000\n",
      "84/84 [==============================] - 0s 301us/step - loss: 0.3890 - accuracy: 0.8214 - val_loss: 0.4167 - val_accuracy: 0.7778\n",
      "Epoch 28/2000\n",
      "84/84 [==============================] - 0s 268us/step - loss: 0.3834 - accuracy: 0.8214 - val_loss: 0.4140 - val_accuracy: 0.7778\n",
      "Epoch 29/2000\n",
      "84/84 [==============================] - 0s 298us/step - loss: 0.3786 - accuracy: 0.8393 - val_loss: 0.4115 - val_accuracy: 0.7778\n",
      "Epoch 30/2000\n",
      "84/84 [==============================] - 0s 321us/step - loss: 0.3741 - accuracy: 0.8452 - val_loss: 0.4097 - val_accuracy: 0.7778\n",
      "Epoch 31/2000\n",
      "84/84 [==============================] - 0s 286us/step - loss: 0.3692 - accuracy: 0.8452 - val_loss: 0.4080 - val_accuracy: 0.7778\n",
      "Epoch 32/2000\n",
      "84/84 [==============================] - 0s 312us/step - loss: 0.3650 - accuracy: 0.8393 - val_loss: 0.4065 - val_accuracy: 0.7778\n",
      "Epoch 33/2000\n",
      "84/84 [==============================] - 0s 297us/step - loss: 0.3619 - accuracy: 0.8393 - val_loss: 0.4051 - val_accuracy: 0.7778\n",
      "Epoch 34/2000\n",
      "84/84 [==============================] - 0s 278us/step - loss: 0.3582 - accuracy: 0.8512 - val_loss: 0.4041 - val_accuracy: 0.7778\n",
      "Epoch 35/2000\n",
      "84/84 [==============================] - 0s 320us/step - loss: 0.3550 - accuracy: 0.8571 - val_loss: 0.4032 - val_accuracy: 0.7778\n",
      "Epoch 36/2000\n",
      "84/84 [==============================] - 0s 322us/step - loss: 0.3519 - accuracy: 0.8571 - val_loss: 0.4027 - val_accuracy: 0.7778\n",
      "Epoch 37/2000\n",
      "84/84 [==============================] - 0s 356us/step - loss: 0.3488 - accuracy: 0.8571 - val_loss: 0.4025 - val_accuracy: 0.7778\n",
      "Epoch 38/2000\n",
      "84/84 [==============================] - 0s 355us/step - loss: 0.3465 - accuracy: 0.8571 - val_loss: 0.4025 - val_accuracy: 0.7778\n",
      "Epoch 39/2000\n",
      "84/84 [==============================] - 0s 315us/step - loss: 0.3444 - accuracy: 0.8571 - val_loss: 0.4027 - val_accuracy: 0.7778\n",
      "Epoch 40/2000\n",
      "84/84 [==============================] - 0s 456us/step - loss: 0.3417 - accuracy: 0.8571 - val_loss: 0.4029 - val_accuracy: 0.7778\n",
      "Epoch 41/2000\n",
      "84/84 [==============================] - 0s 356us/step - loss: 0.3398 - accuracy: 0.8571 - val_loss: 0.4031 - val_accuracy: 0.7778\n",
      "Epoch 42/2000\n",
      "84/84 [==============================] - 0s 276us/step - loss: 0.3381 - accuracy: 0.8571 - val_loss: 0.4036 - val_accuracy: 0.7778\n",
      "Epoch 43/2000\n",
      "84/84 [==============================] - 0s 290us/step - loss: 0.3359 - accuracy: 0.8571 - val_loss: 0.4039 - val_accuracy: 0.7778\n",
      "Epoch 44/2000\n",
      "84/84 [==============================] - 0s 290us/step - loss: 0.3343 - accuracy: 0.8571 - val_loss: 0.4040 - val_accuracy: 0.7778\n",
      "Epoch 45/2000\n",
      "84/84 [==============================] - 0s 247us/step - loss: 0.3328 - accuracy: 0.8571 - val_loss: 0.4042 - val_accuracy: 0.7778\n",
      "Epoch 46/2000\n",
      "84/84 [==============================] - 0s 249us/step - loss: 0.3311 - accuracy: 0.8571 - val_loss: 0.4051 - val_accuracy: 0.7778\n",
      "Epoch 47/2000\n",
      "84/84 [==============================] - 0s 354us/step - loss: 0.3298 - accuracy: 0.8571 - val_loss: 0.4060 - val_accuracy: 0.7917\n",
      "Epoch 48/2000\n",
      "84/84 [==============================] - 0s 240us/step - loss: 0.3283 - accuracy: 0.8571 - val_loss: 0.4066 - val_accuracy: 0.7917\n",
      "Epoch 49/2000\n",
      "84/84 [==============================] - 0s 244us/step - loss: 0.3270 - accuracy: 0.8571 - val_loss: 0.4078 - val_accuracy: 0.8056\n",
      "Epoch 50/2000\n",
      "84/84 [==============================] - 0s 300us/step - loss: 0.3262 - accuracy: 0.8571 - val_loss: 0.4081 - val_accuracy: 0.8056\n",
      "Epoch 51/2000\n",
      "84/84 [==============================] - 0s 303us/step - loss: 0.3248 - accuracy: 0.8571 - val_loss: 0.4086 - val_accuracy: 0.8056\n",
      "Epoch 52/2000\n",
      "84/84 [==============================] - 0s 282us/step - loss: 0.3234 - accuracy: 0.8571 - val_loss: 0.4097 - val_accuracy: 0.8056\n",
      "Epoch 53/2000\n",
      "84/84 [==============================] - 0s 308us/step - loss: 0.3223 - accuracy: 0.8571 - val_loss: 0.4101 - val_accuracy: 0.8056\n",
      "Epoch 54/2000\n",
      "84/84 [==============================] - 0s 275us/step - loss: 0.3214 - accuracy: 0.8571 - val_loss: 0.4107 - val_accuracy: 0.8056\n",
      "Epoch 55/2000\n",
      "84/84 [==============================] - 0s 199us/step - loss: 0.3203 - accuracy: 0.8571 - val_loss: 0.4115 - val_accuracy: 0.8056\n",
      "Epoch 56/2000\n",
      "84/84 [==============================] - 0s 205us/step - loss: 0.3195 - accuracy: 0.8571 - val_loss: 0.4121 - val_accuracy: 0.8056\n",
      "Epoch 57/2000\n",
      "84/84 [==============================] - 0s 209us/step - loss: 0.3186 - accuracy: 0.8571 - val_loss: 0.4126 - val_accuracy: 0.8056\n",
      "Epoch 58/2000\n",
      "84/84 [==============================] - 0s 214us/step - loss: 0.3175 - accuracy: 0.8571 - val_loss: 0.4123 - val_accuracy: 0.8056\n",
      "Epoch 59/2000\n",
      "84/84 [==============================] - 0s 229us/step - loss: 0.3166 - accuracy: 0.8571 - val_loss: 0.4117 - val_accuracy: 0.8056\n",
      "Epoch 60/2000\n",
      "84/84 [==============================] - 0s 247us/step - loss: 0.3156 - accuracy: 0.8571 - val_loss: 0.4115 - val_accuracy: 0.8056\n",
      "Epoch 61/2000\n",
      "84/84 [==============================] - 0s 242us/step - loss: 0.3147 - accuracy: 0.8571 - val_loss: 0.4112 - val_accuracy: 0.8056\n",
      "Epoch 62/2000\n",
      "84/84 [==============================] - 0s 248us/step - loss: 0.3140 - accuracy: 0.8571 - val_loss: 0.4110 - val_accuracy: 0.8056\n",
      "Epoch 63/2000\n",
      "84/84 [==============================] - 0s 252us/step - loss: 0.3131 - accuracy: 0.8571 - val_loss: 0.4101 - val_accuracy: 0.8056\n",
      "Epoch 64/2000\n",
      "84/84 [==============================] - 0s 255us/step - loss: 0.3124 - accuracy: 0.8571 - val_loss: 0.4099 - val_accuracy: 0.8056\n",
      "Epoch 65/2000\n",
      "84/84 [==============================] - 0s 235us/step - loss: 0.3119 - accuracy: 0.8571 - val_loss: 0.4096 - val_accuracy: 0.8056\n",
      "Epoch 66/2000\n",
      "84/84 [==============================] - 0s 230us/step - loss: 0.3109 - accuracy: 0.8571 - val_loss: 0.4101 - val_accuracy: 0.8056\n",
      "Epoch 67/2000\n",
      "84/84 [==============================] - 0s 532us/step - loss: 0.3102 - accuracy: 0.8571 - val_loss: 0.4108 - val_accuracy: 0.8056\n",
      "Epoch 68/2000\n",
      "84/84 [==============================] - 0s 377us/step - loss: 0.3094 - accuracy: 0.8571 - val_loss: 0.4111 - val_accuracy: 0.8056\n",
      "Epoch 69/2000\n",
      "84/84 [==============================] - 0s 296us/step - loss: 0.3087 - accuracy: 0.8571 - val_loss: 0.4109 - val_accuracy: 0.8056\n",
      "Epoch 70/2000\n",
      "84/84 [==============================] - 0s 402us/step - loss: 0.3081 - accuracy: 0.8571 - val_loss: 0.4119 - val_accuracy: 0.8056\n",
      "Epoch 71/2000\n",
      "84/84 [==============================] - 0s 273us/step - loss: 0.3072 - accuracy: 0.8571 - val_loss: 0.4122 - val_accuracy: 0.8056\n",
      "Epoch 72/2000\n",
      "84/84 [==============================] - 0s 255us/step - loss: 0.3064 - accuracy: 0.8571 - val_loss: 0.4124 - val_accuracy: 0.8056\n",
      "Epoch 73/2000\n",
      "84/84 [==============================] - 0s 360us/step - loss: 0.3058 - accuracy: 0.8571 - val_loss: 0.4126 - val_accuracy: 0.8056\n",
      "Epoch 74/2000\n",
      "84/84 [==============================] - 0s 250us/step - loss: 0.3051 - accuracy: 0.8571 - val_loss: 0.4131 - val_accuracy: 0.8056\n",
      "Epoch 75/2000\n",
      "84/84 [==============================] - 0s 240us/step - loss: 0.3047 - accuracy: 0.8571 - val_loss: 0.4137 - val_accuracy: 0.8056\n",
      "Epoch 76/2000\n",
      "84/84 [==============================] - 0s 218us/step - loss: 0.3037 - accuracy: 0.8571 - val_loss: 0.4133 - val_accuracy: 0.8056\n",
      "Epoch 77/2000\n",
      "84/84 [==============================] - 0s 274us/step - loss: 0.3035 - accuracy: 0.8571 - val_loss: 0.4119 - val_accuracy: 0.8056\n",
      "Epoch 78/2000\n",
      "84/84 [==============================] - 0s 243us/step - loss: 0.3032 - accuracy: 0.8571 - val_loss: 0.4111 - val_accuracy: 0.8056\n",
      "Epoch 79/2000\n",
      "84/84 [==============================] - 0s 223us/step - loss: 0.3017 - accuracy: 0.8571 - val_loss: 0.4118 - val_accuracy: 0.8056\n",
      "Epoch 80/2000\n",
      "84/84 [==============================] - 0s 234us/step - loss: 0.3016 - accuracy: 0.8571 - val_loss: 0.4130 - val_accuracy: 0.8056\n",
      "Epoch 81/2000\n",
      "84/84 [==============================] - 0s 230us/step - loss: 0.3006 - accuracy: 0.8571 - val_loss: 0.4131 - val_accuracy: 0.8056\n",
      "Epoch 82/2000\n",
      "84/84 [==============================] - 0s 241us/step - loss: 0.3000 - accuracy: 0.8571 - val_loss: 0.4128 - val_accuracy: 0.8056\n",
      "Epoch 83/2000\n",
      "84/84 [==============================] - 0s 231us/step - loss: 0.2993 - accuracy: 0.8571 - val_loss: 0.4128 - val_accuracy: 0.8056\n",
      "Epoch 84/2000\n",
      "84/84 [==============================] - 0s 227us/step - loss: 0.2988 - accuracy: 0.8571 - val_loss: 0.4124 - val_accuracy: 0.8056\n",
      "Epoch 85/2000\n",
      "84/84 [==============================] - 0s 235us/step - loss: 0.2980 - accuracy: 0.8571 - val_loss: 0.4117 - val_accuracy: 0.8056\n",
      "Epoch 86/2000\n",
      "84/84 [==============================] - 0s 247us/step - loss: 0.2975 - accuracy: 0.8571 - val_loss: 0.4109 - val_accuracy: 0.8056\n",
      "Epoch 87/2000\n",
      "84/84 [==============================] - 0s 245us/step - loss: 0.2968 - accuracy: 0.8571 - val_loss: 0.4097 - val_accuracy: 0.8056\n",
      "Epoch 88/2000\n",
      "84/84 [==============================] - 0s 262us/step - loss: 0.2961 - accuracy: 0.8690 - val_loss: 0.4093 - val_accuracy: 0.8056\n",
      "Epoch 89/2000\n",
      "84/84 [==============================] - 0s 242us/step - loss: 0.2956 - accuracy: 0.8690 - val_loss: 0.4095 - val_accuracy: 0.8056\n",
      "Epoch 90/2000\n",
      "84/84 [==============================] - 0s 224us/step - loss: 0.2949 - accuracy: 0.8690 - val_loss: 0.4095 - val_accuracy: 0.8056\n",
      "Epoch 91/2000\n",
      "84/84 [==============================] - 0s 224us/step - loss: 0.2942 - accuracy: 0.8690 - val_loss: 0.4095 - val_accuracy: 0.8056\n",
      "Epoch 92/2000\n",
      "84/84 [==============================] - 0s 227us/step - loss: 0.2936 - accuracy: 0.8690 - val_loss: 0.4102 - val_accuracy: 0.7917\n",
      "Epoch 93/2000\n",
      "84/84 [==============================] - 0s 230us/step - loss: 0.2929 - accuracy: 0.8690 - val_loss: 0.4105 - val_accuracy: 0.7917\n",
      "Epoch 94/2000\n",
      "84/84 [==============================] - 0s 234us/step - loss: 0.2928 - accuracy: 0.8690 - val_loss: 0.4110 - val_accuracy: 0.7917\n",
      "Epoch 95/2000\n",
      "84/84 [==============================] - 0s 249us/step - loss: 0.2918 - accuracy: 0.8690 - val_loss: 0.4095 - val_accuracy: 0.7778\n",
      "Epoch 96/2000\n",
      "84/84 [==============================] - 0s 232us/step - loss: 0.2910 - accuracy: 0.8690 - val_loss: 0.4092 - val_accuracy: 0.7778\n",
      "Epoch 97/2000\n",
      "84/84 [==============================] - 0s 440us/step - loss: 0.2905 - accuracy: 0.8690 - val_loss: 0.4091 - val_accuracy: 0.7778\n",
      "Epoch 98/2000\n",
      "84/84 [==============================] - 0s 229us/step - loss: 0.2898 - accuracy: 0.8690 - val_loss: 0.4086 - val_accuracy: 0.7778\n",
      "Epoch 99/2000\n",
      "84/84 [==============================] - 0s 241us/step - loss: 0.2894 - accuracy: 0.8690 - val_loss: 0.4083 - val_accuracy: 0.7778\n",
      "Epoch 100/2000\n",
      "84/84 [==============================] - 0s 250us/step - loss: 0.2890 - accuracy: 0.8690 - val_loss: 0.4072 - val_accuracy: 0.7778\n",
      "Epoch 101/2000\n",
      "84/84 [==============================] - 0s 275us/step - loss: 0.2882 - accuracy: 0.8690 - val_loss: 0.4075 - val_accuracy: 0.7778\n",
      "Epoch 102/2000\n",
      "84/84 [==============================] - 0s 265us/step - loss: 0.2874 - accuracy: 0.8690 - val_loss: 0.4062 - val_accuracy: 0.7778\n",
      "Epoch 103/2000\n",
      "84/84 [==============================] - 0s 266us/step - loss: 0.2867 - accuracy: 0.8690 - val_loss: 0.4058 - val_accuracy: 0.7778\n",
      "Epoch 104/2000\n",
      "84/84 [==============================] - 0s 410us/step - loss: 0.2862 - accuracy: 0.8690 - val_loss: 0.4056 - val_accuracy: 0.7778\n",
      "Epoch 105/2000\n",
      "84/84 [==============================] - 0s 558us/step - loss: 0.2857 - accuracy: 0.8690 - val_loss: 0.4051 - val_accuracy: 0.7778\n",
      "Epoch 106/2000\n",
      "84/84 [==============================] - 0s 586us/step - loss: 0.2852 - accuracy: 0.8690 - val_loss: 0.4062 - val_accuracy: 0.7778\n",
      "Epoch 107/2000\n",
      "84/84 [==============================] - 0s 530us/step - loss: 0.2841 - accuracy: 0.8690 - val_loss: 0.4060 - val_accuracy: 0.7778\n",
      "Epoch 108/2000\n",
      "84/84 [==============================] - 0s 330us/step - loss: 0.2837 - accuracy: 0.8690 - val_loss: 0.4064 - val_accuracy: 0.7778\n",
      "Epoch 109/2000\n",
      "84/84 [==============================] - 0s 401us/step - loss: 0.2829 - accuracy: 0.8690 - val_loss: 0.4067 - val_accuracy: 0.7778\n",
      "Epoch 110/2000\n",
      "84/84 [==============================] - 0s 344us/step - loss: 0.2823 - accuracy: 0.8690 - val_loss: 0.4060 - val_accuracy: 0.7778\n",
      "Epoch 111/2000\n",
      "84/84 [==============================] - 0s 284us/step - loss: 0.2818 - accuracy: 0.8690 - val_loss: 0.4059 - val_accuracy: 0.7778\n",
      "Epoch 112/2000\n",
      "84/84 [==============================] - 0s 260us/step - loss: 0.2810 - accuracy: 0.8690 - val_loss: 0.4056 - val_accuracy: 0.7778\n",
      "Epoch 113/2000\n",
      "84/84 [==============================] - 0s 303us/step - loss: 0.2803 - accuracy: 0.8690 - val_loss: 0.4048 - val_accuracy: 0.7778\n",
      "Epoch 114/2000\n",
      "84/84 [==============================] - 0s 266us/step - loss: 0.2796 - accuracy: 0.8690 - val_loss: 0.4042 - val_accuracy: 0.7778\n",
      "Epoch 115/2000\n",
      "84/84 [==============================] - 0s 267us/step - loss: 0.2793 - accuracy: 0.8690 - val_loss: 0.4038 - val_accuracy: 0.7778\n",
      "Epoch 116/2000\n",
      "84/84 [==============================] - 0s 257us/step - loss: 0.2784 - accuracy: 0.8690 - val_loss: 0.4043 - val_accuracy: 0.7778\n",
      "Epoch 117/2000\n",
      "84/84 [==============================] - 0s 299us/step - loss: 0.2779 - accuracy: 0.8690 - val_loss: 0.4047 - val_accuracy: 0.7778\n",
      "Epoch 118/2000\n",
      "84/84 [==============================] - 0s 289us/step - loss: 0.2770 - accuracy: 0.8690 - val_loss: 0.4037 - val_accuracy: 0.7778\n",
      "Epoch 119/2000\n",
      "84/84 [==============================] - 0s 280us/step - loss: 0.2767 - accuracy: 0.8690 - val_loss: 0.4015 - val_accuracy: 0.7778\n",
      "Epoch 120/2000\n",
      "84/84 [==============================] - 0s 291us/step - loss: 0.2758 - accuracy: 0.8690 - val_loss: 0.4012 - val_accuracy: 0.7778\n",
      "Epoch 121/2000\n",
      "84/84 [==============================] - 0s 281us/step - loss: 0.2755 - accuracy: 0.8690 - val_loss: 0.3996 - val_accuracy: 0.7778\n",
      "Epoch 122/2000\n",
      "84/84 [==============================] - 0s 265us/step - loss: 0.2744 - accuracy: 0.8690 - val_loss: 0.4001 - val_accuracy: 0.7778\n",
      "Epoch 123/2000\n",
      "84/84 [==============================] - 0s 295us/step - loss: 0.2740 - accuracy: 0.8690 - val_loss: 0.4006 - val_accuracy: 0.7778\n",
      "Epoch 124/2000\n",
      "84/84 [==============================] - 0s 333us/step - loss: 0.2730 - accuracy: 0.8690 - val_loss: 0.4005 - val_accuracy: 0.7778\n",
      "Epoch 125/2000\n",
      "84/84 [==============================] - 0s 243us/step - loss: 0.2725 - accuracy: 0.8690 - val_loss: 0.4010 - val_accuracy: 0.7778\n",
      "Epoch 126/2000\n",
      "84/84 [==============================] - 0s 286us/step - loss: 0.2716 - accuracy: 0.8690 - val_loss: 0.3998 - val_accuracy: 0.7778\n",
      "Epoch 127/2000\n",
      "84/84 [==============================] - 0s 253us/step - loss: 0.2709 - accuracy: 0.8690 - val_loss: 0.3993 - val_accuracy: 0.7778\n",
      "Epoch 128/2000\n",
      "84/84 [==============================] - 0s 240us/step - loss: 0.2706 - accuracy: 0.8690 - val_loss: 0.3999 - val_accuracy: 0.7778\n",
      "Epoch 129/2000\n",
      "84/84 [==============================] - 0s 253us/step - loss: 0.2696 - accuracy: 0.8690 - val_loss: 0.3985 - val_accuracy: 0.7778\n",
      "Epoch 130/2000\n",
      "84/84 [==============================] - 0s 323us/step - loss: 0.2689 - accuracy: 0.8690 - val_loss: 0.3981 - val_accuracy: 0.7778\n",
      "Epoch 131/2000\n",
      "84/84 [==============================] - 0s 309us/step - loss: 0.2680 - accuracy: 0.8690 - val_loss: 0.3982 - val_accuracy: 0.7778\n",
      "Epoch 132/2000\n",
      "84/84 [==============================] - 0s 261us/step - loss: 0.2675 - accuracy: 0.8690 - val_loss: 0.3985 - val_accuracy: 0.7778\n",
      "Epoch 133/2000\n",
      "84/84 [==============================] - 0s 259us/step - loss: 0.2665 - accuracy: 0.8690 - val_loss: 0.3997 - val_accuracy: 0.7778\n",
      "Epoch 134/2000\n",
      "84/84 [==============================] - 0s 240us/step - loss: 0.2658 - accuracy: 0.8690 - val_loss: 0.4001 - val_accuracy: 0.7778\n",
      "Epoch 135/2000\n",
      "84/84 [==============================] - 0s 252us/step - loss: 0.2652 - accuracy: 0.8690 - val_loss: 0.4005 - val_accuracy: 0.7778\n",
      "Epoch 136/2000\n",
      "84/84 [==============================] - 0s 238us/step - loss: 0.2645 - accuracy: 0.8690 - val_loss: 0.3997 - val_accuracy: 0.7778\n",
      "Epoch 137/2000\n",
      "84/84 [==============================] - 0s 250us/step - loss: 0.2636 - accuracy: 0.8690 - val_loss: 0.3991 - val_accuracy: 0.7778\n",
      "Epoch 138/2000\n",
      "84/84 [==============================] - 0s 251us/step - loss: 0.2629 - accuracy: 0.8690 - val_loss: 0.3979 - val_accuracy: 0.7778\n",
      "Epoch 139/2000\n",
      "84/84 [==============================] - 0s 273us/step - loss: 0.2620 - accuracy: 0.8690 - val_loss: 0.3959 - val_accuracy: 0.7778\n",
      "Epoch 140/2000\n",
      "84/84 [==============================] - 0s 218us/step - loss: 0.2615 - accuracy: 0.8690 - val_loss: 0.3945 - val_accuracy: 0.7778\n",
      "Epoch 141/2000\n",
      "84/84 [==============================] - 0s 216us/step - loss: 0.2605 - accuracy: 0.8690 - val_loss: 0.3941 - val_accuracy: 0.7778\n",
      "Epoch 142/2000\n",
      "84/84 [==============================] - 0s 232us/step - loss: 0.2597 - accuracy: 0.8690 - val_loss: 0.3933 - val_accuracy: 0.7778\n",
      "Epoch 143/2000\n",
      "84/84 [==============================] - 0s 298us/step - loss: 0.2588 - accuracy: 0.8690 - val_loss: 0.3925 - val_accuracy: 0.7778\n",
      "Epoch 144/2000\n",
      "84/84 [==============================] - 0s 501us/step - loss: 0.2582 - accuracy: 0.8690 - val_loss: 0.3915 - val_accuracy: 0.7778\n",
      "Epoch 145/2000\n",
      "84/84 [==============================] - 0s 370us/step - loss: 0.2575 - accuracy: 0.8690 - val_loss: 0.3910 - val_accuracy: 0.7778\n",
      "Epoch 146/2000\n",
      "84/84 [==============================] - 0s 278us/step - loss: 0.2564 - accuracy: 0.8690 - val_loss: 0.3912 - val_accuracy: 0.7778\n",
      "Epoch 147/2000\n",
      "84/84 [==============================] - 0s 296us/step - loss: 0.2556 - accuracy: 0.8690 - val_loss: 0.3925 - val_accuracy: 0.7917\n",
      "Epoch 148/2000\n",
      "84/84 [==============================] - 0s 320us/step - loss: 0.2557 - accuracy: 0.8690 - val_loss: 0.3940 - val_accuracy: 0.8056\n",
      "Epoch 149/2000\n",
      "84/84 [==============================] - 0s 297us/step - loss: 0.2542 - accuracy: 0.8690 - val_loss: 0.3925 - val_accuracy: 0.8056\n",
      "Epoch 150/2000\n",
      "84/84 [==============================] - 0s 281us/step - loss: 0.2532 - accuracy: 0.8690 - val_loss: 0.3923 - val_accuracy: 0.8056\n",
      "Epoch 151/2000\n",
      "84/84 [==============================] - 0s 284us/step - loss: 0.2527 - accuracy: 0.8690 - val_loss: 0.3909 - val_accuracy: 0.8056\n",
      "Epoch 152/2000\n",
      "84/84 [==============================] - 0s 296us/step - loss: 0.2517 - accuracy: 0.8690 - val_loss: 0.3904 - val_accuracy: 0.8056\n",
      "Epoch 153/2000\n",
      "84/84 [==============================] - 0s 301us/step - loss: 0.2506 - accuracy: 0.8690 - val_loss: 0.3890 - val_accuracy: 0.8056\n",
      "Epoch 154/2000\n",
      "84/84 [==============================] - 0s 297us/step - loss: 0.2497 - accuracy: 0.8690 - val_loss: 0.3878 - val_accuracy: 0.8056\n",
      "Epoch 155/2000\n",
      "84/84 [==============================] - 0s 282us/step - loss: 0.2489 - accuracy: 0.8750 - val_loss: 0.3863 - val_accuracy: 0.8056\n",
      "Epoch 156/2000\n",
      "84/84 [==============================] - 0s 269us/step - loss: 0.2480 - accuracy: 0.8750 - val_loss: 0.3859 - val_accuracy: 0.8056\n",
      "Epoch 157/2000\n",
      "84/84 [==============================] - 0s 237us/step - loss: 0.2470 - accuracy: 0.8810 - val_loss: 0.3853 - val_accuracy: 0.8056\n",
      "Epoch 158/2000\n",
      "84/84 [==============================] - 0s 262us/step - loss: 0.2462 - accuracy: 0.8810 - val_loss: 0.3846 - val_accuracy: 0.7917\n",
      "Epoch 159/2000\n",
      "84/84 [==============================] - 0s 235us/step - loss: 0.2453 - accuracy: 0.8810 - val_loss: 0.3837 - val_accuracy: 0.8056\n",
      "Epoch 160/2000\n",
      "84/84 [==============================] - 0s 233us/step - loss: 0.2443 - accuracy: 0.8810 - val_loss: 0.3823 - val_accuracy: 0.8194\n",
      "Epoch 161/2000\n",
      "84/84 [==============================] - 0s 233us/step - loss: 0.2440 - accuracy: 0.8810 - val_loss: 0.3802 - val_accuracy: 0.8333\n",
      "Epoch 162/2000\n",
      "84/84 [==============================] - 0s 242us/step - loss: 0.2428 - accuracy: 0.8810 - val_loss: 0.3810 - val_accuracy: 0.8333\n",
      "Epoch 163/2000\n",
      "84/84 [==============================] - 0s 232us/step - loss: 0.2419 - accuracy: 0.8810 - val_loss: 0.3817 - val_accuracy: 0.8333\n",
      "Epoch 164/2000\n",
      "84/84 [==============================] - 0s 234us/step - loss: 0.2408 - accuracy: 0.8810 - val_loss: 0.3802 - val_accuracy: 0.8333\n",
      "Epoch 165/2000\n",
      "84/84 [==============================] - 0s 264us/step - loss: 0.2397 - accuracy: 0.8810 - val_loss: 0.3803 - val_accuracy: 0.8333\n",
      "Epoch 166/2000\n",
      "84/84 [==============================] - 0s 280us/step - loss: 0.2388 - accuracy: 0.8810 - val_loss: 0.3798 - val_accuracy: 0.8333\n",
      "Epoch 167/2000\n",
      "84/84 [==============================] - 0s 275us/step - loss: 0.2377 - accuracy: 0.8810 - val_loss: 0.3798 - val_accuracy: 0.8333\n",
      "Epoch 168/2000\n",
      "84/84 [==============================] - 0s 276us/step - loss: 0.2367 - accuracy: 0.8810 - val_loss: 0.3799 - val_accuracy: 0.8194\n",
      "Epoch 169/2000\n",
      "84/84 [==============================] - 0s 247us/step - loss: 0.2358 - accuracy: 0.8810 - val_loss: 0.3805 - val_accuracy: 0.7917\n",
      "Epoch 170/2000\n",
      "84/84 [==============================] - 0s 259us/step - loss: 0.2349 - accuracy: 0.8810 - val_loss: 0.3805 - val_accuracy: 0.7778\n",
      "Epoch 171/2000\n",
      "84/84 [==============================] - 0s 253us/step - loss: 0.2340 - accuracy: 0.8810 - val_loss: 0.3806 - val_accuracy: 0.7778\n",
      "Epoch 172/2000\n",
      "84/84 [==============================] - 0s 281us/step - loss: 0.2331 - accuracy: 0.8810 - val_loss: 0.3809 - val_accuracy: 0.7778\n",
      "Epoch 173/2000\n",
      "84/84 [==============================] - 0s 278us/step - loss: 0.2321 - accuracy: 0.8810 - val_loss: 0.3809 - val_accuracy: 0.7778\n",
      "Epoch 174/2000\n",
      "84/84 [==============================] - 0s 267us/step - loss: 0.2313 - accuracy: 0.8810 - val_loss: 0.3805 - val_accuracy: 0.7778\n",
      "Epoch 175/2000\n",
      "84/84 [==============================] - 0s 264us/step - loss: 0.2302 - accuracy: 0.8810 - val_loss: 0.3800 - val_accuracy: 0.7778\n",
      "Epoch 176/2000\n",
      "84/84 [==============================] - 0s 280us/step - loss: 0.2293 - accuracy: 0.8810 - val_loss: 0.3769 - val_accuracy: 0.8333\n",
      "Epoch 177/2000\n",
      "84/84 [==============================] - 0s 264us/step - loss: 0.2283 - accuracy: 0.8810 - val_loss: 0.3746 - val_accuracy: 0.8333\n",
      "Epoch 178/2000\n",
      "84/84 [==============================] - 0s 463us/step - loss: 0.2269 - accuracy: 0.8810 - val_loss: 0.3746 - val_accuracy: 0.8333\n",
      "Epoch 179/2000\n",
      "84/84 [==============================] - 0s 268us/step - loss: 0.2260 - accuracy: 0.8810 - val_loss: 0.3758 - val_accuracy: 0.8333\n",
      "Epoch 180/2000\n",
      "84/84 [==============================] - 0s 287us/step - loss: 0.2251 - accuracy: 0.8810 - val_loss: 0.3755 - val_accuracy: 0.8333\n",
      "Epoch 181/2000\n",
      "84/84 [==============================] - 0s 263us/step - loss: 0.2242 - accuracy: 0.8810 - val_loss: 0.3752 - val_accuracy: 0.8333\n",
      "Epoch 182/2000\n",
      "84/84 [==============================] - 0s 257us/step - loss: 0.2229 - accuracy: 0.8810 - val_loss: 0.3733 - val_accuracy: 0.8333\n",
      "Epoch 183/2000\n",
      "84/84 [==============================] - 0s 311us/step - loss: 0.2219 - accuracy: 0.8810 - val_loss: 0.3718 - val_accuracy: 0.8333\n",
      "Epoch 184/2000\n",
      "84/84 [==============================] - 0s 341us/step - loss: 0.2208 - accuracy: 0.8750 - val_loss: 0.3692 - val_accuracy: 0.8333\n",
      "Epoch 185/2000\n",
      "84/84 [==============================] - 0s 337us/step - loss: 0.2199 - accuracy: 0.8690 - val_loss: 0.3675 - val_accuracy: 0.8333\n",
      "Epoch 186/2000\n",
      "84/84 [==============================] - 0s 283us/step - loss: 0.2189 - accuracy: 0.8690 - val_loss: 0.3672 - val_accuracy: 0.8333\n",
      "Epoch 187/2000\n",
      "84/84 [==============================] - 0s 282us/step - loss: 0.2178 - accuracy: 0.8690 - val_loss: 0.3664 - val_accuracy: 0.8333\n",
      "Epoch 188/2000\n",
      "84/84 [==============================] - 0s 307us/step - loss: 0.2168 - accuracy: 0.8690 - val_loss: 0.3657 - val_accuracy: 0.8333\n",
      "Epoch 189/2000\n",
      "84/84 [==============================] - 0s 298us/step - loss: 0.2157 - accuracy: 0.8690 - val_loss: 0.3651 - val_accuracy: 0.8333\n",
      "Epoch 190/2000\n",
      "84/84 [==============================] - 0s 285us/step - loss: 0.2146 - accuracy: 0.8690 - val_loss: 0.3658 - val_accuracy: 0.8333\n",
      "Epoch 191/2000\n",
      "84/84 [==============================] - 0s 335us/step - loss: 0.2137 - accuracy: 0.8750 - val_loss: 0.3663 - val_accuracy: 0.8333\n",
      "Epoch 192/2000\n",
      "84/84 [==============================] - 0s 533us/step - loss: 0.2126 - accuracy: 0.8750 - val_loss: 0.3640 - val_accuracy: 0.8333\n",
      "Epoch 193/2000\n",
      "84/84 [==============================] - 0s 322us/step - loss: 0.2113 - accuracy: 0.8810 - val_loss: 0.3635 - val_accuracy: 0.8333\n",
      "Epoch 194/2000\n",
      "84/84 [==============================] - 0s 304us/step - loss: 0.2102 - accuracy: 0.8869 - val_loss: 0.3636 - val_accuracy: 0.8333\n",
      "Epoch 195/2000\n",
      "84/84 [==============================] - 0s 297us/step - loss: 0.2091 - accuracy: 0.8869 - val_loss: 0.3631 - val_accuracy: 0.8333\n",
      "Epoch 196/2000\n",
      "84/84 [==============================] - 0s 329us/step - loss: 0.2080 - accuracy: 0.8869 - val_loss: 0.3619 - val_accuracy: 0.8333\n",
      "Epoch 197/2000\n",
      "84/84 [==============================] - 0s 268us/step - loss: 0.2069 - accuracy: 0.8929 - val_loss: 0.3613 - val_accuracy: 0.8333\n",
      "Epoch 198/2000\n",
      "84/84 [==============================] - 0s 223us/step - loss: 0.2059 - accuracy: 0.8929 - val_loss: 0.3609 - val_accuracy: 0.8333\n",
      "Epoch 199/2000\n",
      "84/84 [==============================] - 0s 307us/step - loss: 0.2048 - accuracy: 0.8929 - val_loss: 0.3598 - val_accuracy: 0.8333\n",
      "Epoch 200/2000\n",
      "84/84 [==============================] - 0s 258us/step - loss: 0.2039 - accuracy: 0.8929 - val_loss: 0.3595 - val_accuracy: 0.8333\n",
      "Epoch 201/2000\n",
      "84/84 [==============================] - 0s 267us/step - loss: 0.2025 - accuracy: 0.8929 - val_loss: 0.3569 - val_accuracy: 0.8333\n",
      "Epoch 202/2000\n",
      "84/84 [==============================] - 0s 276us/step - loss: 0.2015 - accuracy: 0.8929 - val_loss: 0.3540 - val_accuracy: 0.8333\n",
      "Epoch 203/2000\n",
      "84/84 [==============================] - 0s 295us/step - loss: 0.2005 - accuracy: 0.8929 - val_loss: 0.3533 - val_accuracy: 0.8333\n",
      "Epoch 204/2000\n",
      "84/84 [==============================] - 0s 289us/step - loss: 0.1994 - accuracy: 0.8929 - val_loss: 0.3531 - val_accuracy: 0.8333\n",
      "Epoch 205/2000\n",
      "84/84 [==============================] - 0s 291us/step - loss: 0.1987 - accuracy: 0.8929 - val_loss: 0.3533 - val_accuracy: 0.8333\n",
      "Epoch 206/2000\n",
      "84/84 [==============================] - 0s 302us/step - loss: 0.1974 - accuracy: 0.8929 - val_loss: 0.3508 - val_accuracy: 0.8333\n",
      "Epoch 207/2000\n",
      "84/84 [==============================] - 0s 250us/step - loss: 0.1960 - accuracy: 0.8929 - val_loss: 0.3507 - val_accuracy: 0.8333\n",
      "Epoch 208/2000\n",
      "84/84 [==============================] - 0s 222us/step - loss: 0.1954 - accuracy: 0.8929 - val_loss: 0.3515 - val_accuracy: 0.8333\n",
      "Epoch 209/2000\n",
      "84/84 [==============================] - 0s 209us/step - loss: 0.1940 - accuracy: 0.8929 - val_loss: 0.3501 - val_accuracy: 0.8333\n",
      "Epoch 210/2000\n",
      "84/84 [==============================] - 0s 314us/step - loss: 0.1929 - accuracy: 0.8929 - val_loss: 0.3492 - val_accuracy: 0.8333\n",
      "Epoch 211/2000\n",
      "84/84 [==============================] - 0s 277us/step - loss: 0.1919 - accuracy: 0.8929 - val_loss: 0.3487 - val_accuracy: 0.8333\n",
      "Epoch 212/2000\n",
      "84/84 [==============================] - 0s 496us/step - loss: 0.1910 - accuracy: 0.8929 - val_loss: 0.3491 - val_accuracy: 0.8333\n",
      "Epoch 213/2000\n",
      "84/84 [==============================] - 0s 922us/step - loss: 0.1899 - accuracy: 0.8929 - val_loss: 0.3476 - val_accuracy: 0.8333\n",
      "Epoch 214/2000\n",
      "84/84 [==============================] - 0s 1ms/step - loss: 0.1888 - accuracy: 0.8929 - val_loss: 0.3479 - val_accuracy: 0.8333\n",
      "Epoch 215/2000\n",
      "84/84 [==============================] - 0s 895us/step - loss: 0.1879 - accuracy: 0.8929 - val_loss: 0.3471 - val_accuracy: 0.8333\n",
      "Epoch 216/2000\n",
      "84/84 [==============================] - 0s 383us/step - loss: 0.1865 - accuracy: 0.8929 - val_loss: 0.3459 - val_accuracy: 0.8333\n",
      "Epoch 217/2000\n",
      "84/84 [==============================] - 0s 238us/step - loss: 0.1855 - accuracy: 0.8929 - val_loss: 0.3431 - val_accuracy: 0.8333\n",
      "Epoch 218/2000\n",
      "84/84 [==============================] - 0s 266us/step - loss: 0.1845 - accuracy: 0.8929 - val_loss: 0.3414 - val_accuracy: 0.8333\n",
      "Epoch 219/2000\n",
      "84/84 [==============================] - 0s 230us/step - loss: 0.1836 - accuracy: 0.8988 - val_loss: 0.3400 - val_accuracy: 0.8333\n",
      "Epoch 220/2000\n",
      "84/84 [==============================] - 0s 229us/step - loss: 0.1828 - accuracy: 0.9048 - val_loss: 0.3395 - val_accuracy: 0.8333\n",
      "Epoch 221/2000\n",
      "84/84 [==============================] - 0s 255us/step - loss: 0.1817 - accuracy: 0.9048 - val_loss: 0.3403 - val_accuracy: 0.8333\n",
      "Epoch 222/2000\n",
      "84/84 [==============================] - 0s 239us/step - loss: 0.1805 - accuracy: 0.8988 - val_loss: 0.3413 - val_accuracy: 0.8333\n",
      "Epoch 223/2000\n",
      "84/84 [==============================] - 0s 260us/step - loss: 0.1794 - accuracy: 0.8988 - val_loss: 0.3417 - val_accuracy: 0.8333\n",
      "Epoch 224/2000\n",
      "84/84 [==============================] - 0s 302us/step - loss: 0.1787 - accuracy: 0.8929 - val_loss: 0.3420 - val_accuracy: 0.8333\n",
      "Epoch 225/2000\n",
      "84/84 [==============================] - 0s 259us/step - loss: 0.1776 - accuracy: 0.8929 - val_loss: 0.3404 - val_accuracy: 0.8333\n",
      "Epoch 226/2000\n",
      "84/84 [==============================] - 0s 257us/step - loss: 0.1765 - accuracy: 0.8988 - val_loss: 0.3415 - val_accuracy: 0.8333\n",
      "Epoch 227/2000\n",
      "84/84 [==============================] - 0s 256us/step - loss: 0.1755 - accuracy: 0.9048 - val_loss: 0.3404 - val_accuracy: 0.8333\n",
      "Epoch 228/2000\n",
      "84/84 [==============================] - 0s 257us/step - loss: 0.1748 - accuracy: 0.9048 - val_loss: 0.3380 - val_accuracy: 0.8333\n",
      "Epoch 229/2000\n",
      "84/84 [==============================] - 0s 254us/step - loss: 0.1735 - accuracy: 0.9048 - val_loss: 0.3373 - val_accuracy: 0.8333\n",
      "Epoch 230/2000\n",
      "84/84 [==============================] - 0s 288us/step - loss: 0.1726 - accuracy: 0.9048 - val_loss: 0.3372 - val_accuracy: 0.8333\n",
      "Epoch 231/2000\n",
      "84/84 [==============================] - 0s 225us/step - loss: 0.1715 - accuracy: 0.9048 - val_loss: 0.3357 - val_accuracy: 0.8333\n",
      "Epoch 232/2000\n",
      "84/84 [==============================] - 0s 237us/step - loss: 0.1707 - accuracy: 0.9048 - val_loss: 0.3348 - val_accuracy: 0.8333\n",
      "Epoch 233/2000\n",
      "84/84 [==============================] - 0s 288us/step - loss: 0.1697 - accuracy: 0.9048 - val_loss: 0.3338 - val_accuracy: 0.8333\n",
      "Epoch 234/2000\n",
      "84/84 [==============================] - 0s 241us/step - loss: 0.1688 - accuracy: 0.9048 - val_loss: 0.3318 - val_accuracy: 0.8333\n",
      "Epoch 235/2000\n",
      "84/84 [==============================] - 0s 262us/step - loss: 0.1679 - accuracy: 0.9048 - val_loss: 0.3297 - val_accuracy: 0.8333\n",
      "Epoch 236/2000\n",
      "84/84 [==============================] - 0s 266us/step - loss: 0.1671 - accuracy: 0.9048 - val_loss: 0.3286 - val_accuracy: 0.8333\n",
      "Epoch 237/2000\n",
      "84/84 [==============================] - 0s 260us/step - loss: 0.1662 - accuracy: 0.9048 - val_loss: 0.3259 - val_accuracy: 0.8333\n",
      "Epoch 238/2000\n",
      "84/84 [==============================] - 0s 266us/step - loss: 0.1651 - accuracy: 0.9048 - val_loss: 0.3234 - val_accuracy: 0.8333\n",
      "Epoch 239/2000\n",
      "84/84 [==============================] - 0s 270us/step - loss: 0.1646 - accuracy: 0.9048 - val_loss: 0.3218 - val_accuracy: 0.8333\n",
      "Epoch 240/2000\n",
      "84/84 [==============================] - 0s 252us/step - loss: 0.1634 - accuracy: 0.9048 - val_loss: 0.3220 - val_accuracy: 0.8333\n",
      "Epoch 241/2000\n",
      "84/84 [==============================] - 0s 222us/step - loss: 0.1624 - accuracy: 0.9048 - val_loss: 0.3220 - val_accuracy: 0.8333\n",
      "Epoch 242/2000\n",
      "84/84 [==============================] - 0s 280us/step - loss: 0.1615 - accuracy: 0.9048 - val_loss: 0.3218 - val_accuracy: 0.8333\n",
      "Epoch 243/2000\n",
      "84/84 [==============================] - 0s 241us/step - loss: 0.1606 - accuracy: 0.9048 - val_loss: 0.3220 - val_accuracy: 0.8333\n",
      "Epoch 244/2000\n",
      "84/84 [==============================] - 0s 269us/step - loss: 0.1596 - accuracy: 0.9048 - val_loss: 0.3224 - val_accuracy: 0.8333\n",
      "Epoch 245/2000\n",
      "84/84 [==============================] - 0s 330us/step - loss: 0.1588 - accuracy: 0.9107 - val_loss: 0.3241 - val_accuracy: 0.8333\n",
      "Epoch 246/2000\n",
      "84/84 [==============================] - 0s 281us/step - loss: 0.1578 - accuracy: 0.9226 - val_loss: 0.3234 - val_accuracy: 0.8333\n",
      "Epoch 247/2000\n",
      "84/84 [==============================] - 0s 280us/step - loss: 0.1569 - accuracy: 0.9226 - val_loss: 0.3239 - val_accuracy: 0.8333\n",
      "Epoch 248/2000\n",
      "84/84 [==============================] - 0s 339us/step - loss: 0.1560 - accuracy: 0.9226 - val_loss: 0.3234 - val_accuracy: 0.8333\n",
      "Epoch 249/2000\n",
      "84/84 [==============================] - 0s 359us/step - loss: 0.1551 - accuracy: 0.9286 - val_loss: 0.3235 - val_accuracy: 0.8333\n",
      "Epoch 250/2000\n",
      "84/84 [==============================] - 0s 341us/step - loss: 0.1544 - accuracy: 0.9286 - val_loss: 0.3211 - val_accuracy: 0.8333\n",
      "Epoch 251/2000\n",
      "84/84 [==============================] - 0s 387us/step - loss: 0.1534 - accuracy: 0.9286 - val_loss: 0.3196 - val_accuracy: 0.8333\n",
      "Epoch 252/2000\n",
      "84/84 [==============================] - 0s 360us/step - loss: 0.1527 - accuracy: 0.9286 - val_loss: 0.3173 - val_accuracy: 0.8333\n",
      "Epoch 253/2000\n",
      "84/84 [==============================] - 0s 375us/step - loss: 0.1516 - accuracy: 0.9286 - val_loss: 0.3165 - val_accuracy: 0.8333\n",
      "Epoch 254/2000\n",
      "84/84 [==============================] - 0s 260us/step - loss: 0.1509 - accuracy: 0.9286 - val_loss: 0.3157 - val_accuracy: 0.8333\n",
      "Epoch 255/2000\n",
      "84/84 [==============================] - 0s 371us/step - loss: 0.1500 - accuracy: 0.9286 - val_loss: 0.3167 - val_accuracy: 0.8333\n",
      "Epoch 256/2000\n",
      "84/84 [==============================] - 0s 391us/step - loss: 0.1491 - accuracy: 0.9286 - val_loss: 0.3180 - val_accuracy: 0.8333\n",
      "Epoch 257/2000\n",
      "84/84 [==============================] - 0s 355us/step - loss: 0.1482 - accuracy: 0.9286 - val_loss: 0.3187 - val_accuracy: 0.8333\n",
      "Epoch 258/2000\n",
      "84/84 [==============================] - 0s 350us/step - loss: 0.1476 - accuracy: 0.9286 - val_loss: 0.3186 - val_accuracy: 0.8333\n",
      "Epoch 259/2000\n",
      "84/84 [==============================] - 0s 358us/step - loss: 0.1468 - accuracy: 0.9286 - val_loss: 0.3160 - val_accuracy: 0.8333\n",
      "Epoch 260/2000\n",
      "84/84 [==============================] - 0s 381us/step - loss: 0.1458 - accuracy: 0.9286 - val_loss: 0.3154 - val_accuracy: 0.8333\n",
      "Epoch 261/2000\n",
      "84/84 [==============================] - 0s 396us/step - loss: 0.1449 - accuracy: 0.9286 - val_loss: 0.3125 - val_accuracy: 0.8333\n",
      "Epoch 262/2000\n",
      "84/84 [==============================] - 0s 385us/step - loss: 0.1439 - accuracy: 0.9286 - val_loss: 0.3110 - val_accuracy: 0.8333\n",
      "Epoch 263/2000\n",
      "84/84 [==============================] - 0s 443us/step - loss: 0.1431 - accuracy: 0.9286 - val_loss: 0.3103 - val_accuracy: 0.8333\n",
      "Epoch 264/2000\n",
      "84/84 [==============================] - 0s 252us/step - loss: 0.1422 - accuracy: 0.9286 - val_loss: 0.3094 - val_accuracy: 0.8333\n",
      "Epoch 265/2000\n",
      "84/84 [==============================] - 0s 265us/step - loss: 0.1414 - accuracy: 0.9286 - val_loss: 0.3092 - val_accuracy: 0.8333\n",
      "Epoch 266/2000\n",
      "84/84 [==============================] - 0s 240us/step - loss: 0.1406 - accuracy: 0.9286 - val_loss: 0.3081 - val_accuracy: 0.8333\n",
      "Epoch 267/2000\n",
      "84/84 [==============================] - 0s 362us/step - loss: 0.1398 - accuracy: 0.9286 - val_loss: 0.3086 - val_accuracy: 0.8333\n",
      "Epoch 268/2000\n",
      "84/84 [==============================] - 0s 378us/step - loss: 0.1391 - accuracy: 0.9345 - val_loss: 0.3096 - val_accuracy: 0.8333\n",
      "Epoch 269/2000\n",
      "84/84 [==============================] - 0s 388us/step - loss: 0.1385 - accuracy: 0.9345 - val_loss: 0.3096 - val_accuracy: 0.8472\n",
      "Epoch 270/2000\n",
      "84/84 [==============================] - 0s 371us/step - loss: 0.1373 - accuracy: 0.9345 - val_loss: 0.3073 - val_accuracy: 0.8472\n",
      "Epoch 271/2000\n",
      "84/84 [==============================] - 0s 269us/step - loss: 0.1365 - accuracy: 0.9345 - val_loss: 0.3059 - val_accuracy: 0.8472\n",
      "Epoch 272/2000\n",
      "84/84 [==============================] - 0s 264us/step - loss: 0.1359 - accuracy: 0.9345 - val_loss: 0.3037 - val_accuracy: 0.8472\n",
      "Epoch 273/2000\n",
      "84/84 [==============================] - 0s 265us/step - loss: 0.1352 - accuracy: 0.9345 - val_loss: 0.3030 - val_accuracy: 0.8472\n",
      "Epoch 274/2000\n",
      "84/84 [==============================] - 0s 285us/step - loss: 0.1344 - accuracy: 0.9345 - val_loss: 0.3008 - val_accuracy: 0.8472\n",
      "Epoch 275/2000\n",
      "84/84 [==============================] - 0s 285us/step - loss: 0.1337 - accuracy: 0.9345 - val_loss: 0.3019 - val_accuracy: 0.8472\n",
      "Epoch 276/2000\n",
      "84/84 [==============================] - 0s 347us/step - loss: 0.1331 - accuracy: 0.9345 - val_loss: 0.3019 - val_accuracy: 0.8611\n",
      "Epoch 277/2000\n",
      "84/84 [==============================] - 0s 363us/step - loss: 0.1322 - accuracy: 0.9345 - val_loss: 0.3018 - val_accuracy: 0.8611\n",
      "Epoch 278/2000\n",
      "84/84 [==============================] - 0s 363us/step - loss: 0.1316 - accuracy: 0.9345 - val_loss: 0.3016 - val_accuracy: 0.8750\n",
      "Epoch 279/2000\n",
      "84/84 [==============================] - 0s 403us/step - loss: 0.1309 - accuracy: 0.9405 - val_loss: 0.3011 - val_accuracy: 0.8750\n",
      "Epoch 280/2000\n",
      "84/84 [==============================] - 0s 384us/step - loss: 0.1299 - accuracy: 0.9524 - val_loss: 0.3016 - val_accuracy: 0.8750\n",
      "Epoch 281/2000\n",
      "84/84 [==============================] - 0s 387us/step - loss: 0.1291 - accuracy: 0.9524 - val_loss: 0.3039 - val_accuracy: 0.8889\n",
      "Epoch 282/2000\n",
      "84/84 [==============================] - 0s 382us/step - loss: 0.1282 - accuracy: 0.9405 - val_loss: 0.3045 - val_accuracy: 0.8889\n",
      "Epoch 283/2000\n",
      "84/84 [==============================] - 0s 384us/step - loss: 0.1274 - accuracy: 0.9405 - val_loss: 0.3041 - val_accuracy: 0.8889\n",
      "Epoch 284/2000\n",
      "84/84 [==============================] - 0s 385us/step - loss: 0.1268 - accuracy: 0.9405 - val_loss: 0.3022 - val_accuracy: 0.8889\n",
      "Epoch 285/2000\n",
      "84/84 [==============================] - 0s 264us/step - loss: 0.1261 - accuracy: 0.9405 - val_loss: 0.3034 - val_accuracy: 0.8889\n",
      "Epoch 286/2000\n",
      "84/84 [==============================] - 0s 252us/step - loss: 0.1254 - accuracy: 0.9405 - val_loss: 0.3042 - val_accuracy: 0.8889\n",
      "Epoch 287/2000\n",
      "84/84 [==============================] - 0s 239us/step - loss: 0.1245 - accuracy: 0.9405 - val_loss: 0.3032 - val_accuracy: 0.8889\n",
      "Epoch 288/2000\n",
      "84/84 [==============================] - 0s 281us/step - loss: 0.1239 - accuracy: 0.9405 - val_loss: 0.3038 - val_accuracy: 0.8889\n",
      "Epoch 289/2000\n",
      "84/84 [==============================] - 0s 212us/step - loss: 0.1230 - accuracy: 0.9524 - val_loss: 0.3032 - val_accuracy: 0.8889\n",
      "Epoch 290/2000\n",
      "84/84 [==============================] - 0s 228us/step - loss: 0.1223 - accuracy: 0.9524 - val_loss: 0.3018 - val_accuracy: 0.8889\n",
      "Epoch 291/2000\n",
      "84/84 [==============================] - 0s 268us/step - loss: 0.1216 - accuracy: 0.9524 - val_loss: 0.3002 - val_accuracy: 0.8889\n",
      "Epoch 292/2000\n",
      "84/84 [==============================] - 0s 231us/step - loss: 0.1213 - accuracy: 0.9524 - val_loss: 0.3015 - val_accuracy: 0.8889\n",
      "Epoch 293/2000\n",
      "84/84 [==============================] - 0s 209us/step - loss: 0.1202 - accuracy: 0.9524 - val_loss: 0.2999 - val_accuracy: 0.8889\n",
      "Epoch 294/2000\n",
      "84/84 [==============================] - 0s 243us/step - loss: 0.1196 - accuracy: 0.9524 - val_loss: 0.2982 - val_accuracy: 0.8889\n",
      "Epoch 295/2000\n",
      "84/84 [==============================] - 0s 279us/step - loss: 0.1189 - accuracy: 0.9524 - val_loss: 0.2983 - val_accuracy: 0.8889\n",
      "Epoch 296/2000\n",
      "84/84 [==============================] - 0s 232us/step - loss: 0.1183 - accuracy: 0.9524 - val_loss: 0.2981 - val_accuracy: 0.8889\n",
      "Epoch 297/2000\n",
      "84/84 [==============================] - 0s 313us/step - loss: 0.1176 - accuracy: 0.9524 - val_loss: 0.2954 - val_accuracy: 0.8889\n",
      "Epoch 298/2000\n",
      "84/84 [==============================] - 0s 255us/step - loss: 0.1169 - accuracy: 0.9524 - val_loss: 0.2948 - val_accuracy: 0.8889\n",
      "Epoch 299/2000\n",
      "84/84 [==============================] - 0s 233us/step - loss: 0.1163 - accuracy: 0.9524 - val_loss: 0.2940 - val_accuracy: 0.8889\n",
      "Epoch 300/2000\n",
      "84/84 [==============================] - 0s 258us/step - loss: 0.1156 - accuracy: 0.9524 - val_loss: 0.2949 - val_accuracy: 0.8889\n",
      "Epoch 301/2000\n",
      "84/84 [==============================] - 0s 267us/step - loss: 0.1149 - accuracy: 0.9524 - val_loss: 0.2987 - val_accuracy: 0.8889\n",
      "Epoch 302/2000\n",
      "84/84 [==============================] - 0s 280us/step - loss: 0.1144 - accuracy: 0.9524 - val_loss: 0.3016 - val_accuracy: 0.8889\n",
      "Epoch 303/2000\n",
      "84/84 [==============================] - 0s 293us/step - loss: 0.1136 - accuracy: 0.9524 - val_loss: 0.3026 - val_accuracy: 0.8889\n",
      "Epoch 304/2000\n",
      "84/84 [==============================] - 0s 230us/step - loss: 0.1130 - accuracy: 0.9524 - val_loss: 0.3037 - val_accuracy: 0.8889\n",
      "Epoch 305/2000\n",
      "84/84 [==============================] - 0s 345us/step - loss: 0.1125 - accuracy: 0.9583 - val_loss: 0.3030 - val_accuracy: 0.8889\n",
      "Epoch 306/2000\n",
      "84/84 [==============================] - 0s 331us/step - loss: 0.1119 - accuracy: 0.9583 - val_loss: 0.3020 - val_accuracy: 0.8889\n",
      "Epoch 307/2000\n",
      "84/84 [==============================] - 0s 386us/step - loss: 0.1113 - accuracy: 0.9583 - val_loss: 0.3035 - val_accuracy: 0.8889\n",
      "Epoch 308/2000\n",
      "84/84 [==============================] - 0s 289us/step - loss: 0.1107 - accuracy: 0.9583 - val_loss: 0.3030 - val_accuracy: 0.8889\n",
      "Epoch 309/2000\n",
      "84/84 [==============================] - 0s 284us/step - loss: 0.1101 - accuracy: 0.9583 - val_loss: 0.3028 - val_accuracy: 0.8889\n",
      "Epoch 310/2000\n",
      "84/84 [==============================] - 0s 256us/step - loss: 0.1097 - accuracy: 0.9583 - val_loss: 0.3012 - val_accuracy: 0.8889\n",
      "Epoch 311/2000\n",
      "84/84 [==============================] - 0s 255us/step - loss: 0.1088 - accuracy: 0.9583 - val_loss: 0.3015 - val_accuracy: 0.8889\n",
      "Epoch 312/2000\n",
      "84/84 [==============================] - 0s 279us/step - loss: 0.1084 - accuracy: 0.9643 - val_loss: 0.3002 - val_accuracy: 0.8889\n",
      "Epoch 313/2000\n",
      "84/84 [==============================] - 0s 361us/step - loss: 0.1077 - accuracy: 0.9643 - val_loss: 0.3004 - val_accuracy: 0.8889\n",
      "Epoch 314/2000\n",
      "84/84 [==============================] - 0s 265us/step - loss: 0.1072 - accuracy: 0.9643 - val_loss: 0.3001 - val_accuracy: 0.8889\n",
      "Epoch 315/2000\n",
      "84/84 [==============================] - 0s 278us/step - loss: 0.1066 - accuracy: 0.9643 - val_loss: 0.2999 - val_accuracy: 0.8889\n",
      "Epoch 316/2000\n",
      "84/84 [==============================] - 0s 273us/step - loss: 0.1062 - accuracy: 0.9643 - val_loss: 0.2984 - val_accuracy: 0.8889\n",
      "Epoch 317/2000\n",
      "84/84 [==============================] - 0s 347us/step - loss: 0.1058 - accuracy: 0.9643 - val_loss: 0.2987 - val_accuracy: 0.8889\n",
      "Epoch 318/2000\n",
      "84/84 [==============================] - 0s 306us/step - loss: 0.1052 - accuracy: 0.9643 - val_loss: 0.2982 - val_accuracy: 0.8889\n",
      "Epoch 319/2000\n",
      "84/84 [==============================] - 0s 316us/step - loss: 0.1045 - accuracy: 0.9643 - val_loss: 0.2969 - val_accuracy: 0.8889\n",
      "Epoch 320/2000\n",
      "84/84 [==============================] - 0s 357us/step - loss: 0.1038 - accuracy: 0.9643 - val_loss: 0.2947 - val_accuracy: 0.8889\n",
      "Epoch 321/2000\n",
      "84/84 [==============================] - 0s 370us/step - loss: 0.1033 - accuracy: 0.9643 - val_loss: 0.2920 - val_accuracy: 0.8889\n",
      "Epoch 322/2000\n",
      "84/84 [==============================] - 0s 275us/step - loss: 0.1026 - accuracy: 0.9643 - val_loss: 0.2909 - val_accuracy: 0.8889\n",
      "Epoch 323/2000\n",
      "84/84 [==============================] - 0s 267us/step - loss: 0.1021 - accuracy: 0.9643 - val_loss: 0.2894 - val_accuracy: 0.8889\n",
      "Epoch 324/2000\n",
      "84/84 [==============================] - 0s 266us/step - loss: 0.1017 - accuracy: 0.9643 - val_loss: 0.2893 - val_accuracy: 0.8889\n",
      "Epoch 325/2000\n",
      "84/84 [==============================] - 0s 280us/step - loss: 0.1012 - accuracy: 0.9643 - val_loss: 0.2864 - val_accuracy: 0.8889\n",
      "Epoch 326/2000\n",
      "84/84 [==============================] - 0s 315us/step - loss: 0.1005 - accuracy: 0.9643 - val_loss: 0.2859 - val_accuracy: 0.8889\n",
      "Epoch 327/2000\n",
      "84/84 [==============================] - 0s 277us/step - loss: 0.0999 - accuracy: 0.9643 - val_loss: 0.2880 - val_accuracy: 0.8889\n",
      "Epoch 328/2000\n",
      "84/84 [==============================] - 0s 278us/step - loss: 0.0996 - accuracy: 0.9643 - val_loss: 0.2899 - val_accuracy: 0.8889\n",
      "Epoch 329/2000\n",
      "84/84 [==============================] - 0s 260us/step - loss: 0.0989 - accuracy: 0.9583 - val_loss: 0.2919 - val_accuracy: 0.8889\n",
      "Epoch 330/2000\n",
      "84/84 [==============================] - 0s 249us/step - loss: 0.0986 - accuracy: 0.9583 - val_loss: 0.2927 - val_accuracy: 0.8889\n",
      "Epoch 331/2000\n",
      "84/84 [==============================] - 0s 323us/step - loss: 0.0979 - accuracy: 0.9524 - val_loss: 0.2948 - val_accuracy: 0.8889\n",
      "Epoch 332/2000\n",
      "84/84 [==============================] - 0s 364us/step - loss: 0.0978 - accuracy: 0.9524 - val_loss: 0.2947 - val_accuracy: 0.8889\n",
      "Epoch 333/2000\n",
      "84/84 [==============================] - 0s 369us/step - loss: 0.0973 - accuracy: 0.9583 - val_loss: 0.2921 - val_accuracy: 0.8889\n",
      "Epoch 334/2000\n",
      "84/84 [==============================] - 0s 314us/step - loss: 0.0967 - accuracy: 0.9524 - val_loss: 0.2907 - val_accuracy: 0.8889\n",
      "Epoch 335/2000\n",
      "84/84 [==============================] - 0s 382us/step - loss: 0.0961 - accuracy: 0.9524 - val_loss: 0.2868 - val_accuracy: 0.8889\n",
      "Epoch 336/2000\n",
      "84/84 [==============================] - 0s 313us/step - loss: 0.0955 - accuracy: 0.9524 - val_loss: 0.2837 - val_accuracy: 0.8889\n",
      "Epoch 337/2000\n",
      "84/84 [==============================] - 0s 241us/step - loss: 0.0953 - accuracy: 0.9524 - val_loss: 0.2802 - val_accuracy: 0.8889\n",
      "Epoch 338/2000\n",
      "84/84 [==============================] - 0s 300us/step - loss: 0.0945 - accuracy: 0.9583 - val_loss: 0.2800 - val_accuracy: 0.8889\n",
      "Epoch 339/2000\n",
      "84/84 [==============================] - 0s 308us/step - loss: 0.0941 - accuracy: 0.9524 - val_loss: 0.2805 - val_accuracy: 0.8889\n",
      "Epoch 340/2000\n",
      "84/84 [==============================] - 0s 267us/step - loss: 0.0937 - accuracy: 0.9524 - val_loss: 0.2802 - val_accuracy: 0.8889\n",
      "Epoch 341/2000\n",
      "84/84 [==============================] - 0s 237us/step - loss: 0.0932 - accuracy: 0.9524 - val_loss: 0.2811 - val_accuracy: 0.8889\n",
      "Epoch 342/2000\n",
      "84/84 [==============================] - 0s 237us/step - loss: 0.0929 - accuracy: 0.9524 - val_loss: 0.2800 - val_accuracy: 0.8889\n",
      "Epoch 343/2000\n",
      "84/84 [==============================] - 0s 307us/step - loss: 0.0926 - accuracy: 0.9524 - val_loss: 0.2790 - val_accuracy: 0.8889\n",
      "Epoch 344/2000\n",
      "84/84 [==============================] - 0s 311us/step - loss: 0.0922 - accuracy: 0.9524 - val_loss: 0.2805 - val_accuracy: 0.8889\n",
      "Epoch 345/2000\n",
      "84/84 [==============================] - 0s 314us/step - loss: 0.0914 - accuracy: 0.9524 - val_loss: 0.2808 - val_accuracy: 0.8889\n",
      "Epoch 346/2000\n",
      "84/84 [==============================] - 0s 530us/step - loss: 0.0910 - accuracy: 0.9583 - val_loss: 0.2813 - val_accuracy: 0.8889\n",
      "Epoch 347/2000\n",
      "84/84 [==============================] - 0s 371us/step - loss: 0.0905 - accuracy: 0.9583 - val_loss: 0.2810 - val_accuracy: 0.8889\n",
      "Epoch 348/2000\n",
      "84/84 [==============================] - 0s 229us/step - loss: 0.0904 - accuracy: 0.9583 - val_loss: 0.2818 - val_accuracy: 0.8889\n",
      "Epoch 349/2000\n",
      "84/84 [==============================] - 0s 341us/step - loss: 0.0900 - accuracy: 0.9583 - val_loss: 0.2793 - val_accuracy: 0.8889\n",
      "Epoch 350/2000\n",
      "84/84 [==============================] - 0s 381us/step - loss: 0.0896 - accuracy: 0.9524 - val_loss: 0.2776 - val_accuracy: 0.8889\n",
      "Epoch 351/2000\n",
      "84/84 [==============================] - 0s 370us/step - loss: 0.0891 - accuracy: 0.9524 - val_loss: 0.2772 - val_accuracy: 0.8889\n",
      "Epoch 352/2000\n",
      "84/84 [==============================] - 0s 302us/step - loss: 0.0885 - accuracy: 0.9524 - val_loss: 0.2775 - val_accuracy: 0.8889\n",
      "Epoch 353/2000\n",
      "84/84 [==============================] - 0s 244us/step - loss: 0.0882 - accuracy: 0.9583 - val_loss: 0.2771 - val_accuracy: 0.8889\n",
      "Epoch 354/2000\n",
      "84/84 [==============================] - 0s 326us/step - loss: 0.0879 - accuracy: 0.9583 - val_loss: 0.2807 - val_accuracy: 0.8889\n",
      "Epoch 355/2000\n",
      "84/84 [==============================] - 0s 281us/step - loss: 0.0873 - accuracy: 0.9583 - val_loss: 0.2816 - val_accuracy: 0.8889\n",
      "Epoch 356/2000\n",
      "84/84 [==============================] - 0s 308us/step - loss: 0.0871 - accuracy: 0.9583 - val_loss: 0.2810 - val_accuracy: 0.8889\n",
      "Epoch 357/2000\n",
      "84/84 [==============================] - 0s 294us/step - loss: 0.0865 - accuracy: 0.9583 - val_loss: 0.2802 - val_accuracy: 0.8889\n",
      "Epoch 358/2000\n",
      "84/84 [==============================] - 0s 296us/step - loss: 0.0863 - accuracy: 0.9583 - val_loss: 0.2791 - val_accuracy: 0.8889\n",
      "Epoch 359/2000\n",
      "84/84 [==============================] - 0s 274us/step - loss: 0.0860 - accuracy: 0.9583 - val_loss: 0.2790 - val_accuracy: 0.8889\n",
      "Epoch 360/2000\n",
      "84/84 [==============================] - 0s 266us/step - loss: 0.0854 - accuracy: 0.9583 - val_loss: 0.2788 - val_accuracy: 0.8889\n",
      "Epoch 361/2000\n",
      "84/84 [==============================] - 0s 255us/step - loss: 0.0851 - accuracy: 0.9583 - val_loss: 0.2786 - val_accuracy: 0.8889\n",
      "Epoch 362/2000\n",
      "84/84 [==============================] - 0s 268us/step - loss: 0.0847 - accuracy: 0.9583 - val_loss: 0.2788 - val_accuracy: 0.8889\n",
      "Epoch 363/2000\n",
      "84/84 [==============================] - 0s 246us/step - loss: 0.0846 - accuracy: 0.9583 - val_loss: 0.2772 - val_accuracy: 0.8889\n",
      "Epoch 364/2000\n",
      "84/84 [==============================] - 0s 271us/step - loss: 0.0840 - accuracy: 0.9643 - val_loss: 0.2762 - val_accuracy: 0.8889\n",
      "Epoch 365/2000\n",
      "84/84 [==============================] - 0s 337us/step - loss: 0.0836 - accuracy: 0.9583 - val_loss: 0.2758 - val_accuracy: 0.8889\n",
      "Epoch 366/2000\n",
      "84/84 [==============================] - 0s 302us/step - loss: 0.0835 - accuracy: 0.9583 - val_loss: 0.2775 - val_accuracy: 0.8889\n",
      "Epoch 367/2000\n",
      "84/84 [==============================] - 0s 246us/step - loss: 0.0832 - accuracy: 0.9583 - val_loss: 0.2745 - val_accuracy: 0.8889\n",
      "Epoch 368/2000\n",
      "84/84 [==============================] - 0s 214us/step - loss: 0.0830 - accuracy: 0.9583 - val_loss: 0.2758 - val_accuracy: 0.8889\n",
      "Epoch 369/2000\n",
      "84/84 [==============================] - 0s 230us/step - loss: 0.0824 - accuracy: 0.9583 - val_loss: 0.2740 - val_accuracy: 0.8889\n",
      "Epoch 370/2000\n",
      "84/84 [==============================] - 0s 323us/step - loss: 0.0820 - accuracy: 0.9643 - val_loss: 0.2742 - val_accuracy: 0.8889\n",
      "Epoch 371/2000\n",
      "84/84 [==============================] - 0s 309us/step - loss: 0.0818 - accuracy: 0.9583 - val_loss: 0.2754 - val_accuracy: 0.8889\n",
      "Epoch 372/2000\n",
      "84/84 [==============================] - 0s 254us/step - loss: 0.0814 - accuracy: 0.9583 - val_loss: 0.2750 - val_accuracy: 0.8889\n",
      "Epoch 373/2000\n",
      "84/84 [==============================] - 0s 270us/step - loss: 0.0810 - accuracy: 0.9643 - val_loss: 0.2746 - val_accuracy: 0.8889\n",
      "Epoch 374/2000\n",
      "84/84 [==============================] - 0s 228us/step - loss: 0.0811 - accuracy: 0.9583 - val_loss: 0.2766 - val_accuracy: 0.8889\n",
      "Epoch 375/2000\n",
      "84/84 [==============================] - 0s 237us/step - loss: 0.0803 - accuracy: 0.9643 - val_loss: 0.2771 - val_accuracy: 0.8889\n",
      "Epoch 376/2000\n",
      "84/84 [==============================] - 0s 214us/step - loss: 0.0800 - accuracy: 0.9583 - val_loss: 0.2773 - val_accuracy: 0.8889\n",
      "Epoch 377/2000\n",
      "84/84 [==============================] - 0s 257us/step - loss: 0.0797 - accuracy: 0.9643 - val_loss: 0.2748 - val_accuracy: 0.8889\n",
      "Epoch 378/2000\n",
      "84/84 [==============================] - 0s 261us/step - loss: 0.0793 - accuracy: 0.9643 - val_loss: 0.2755 - val_accuracy: 0.8889\n",
      "Epoch 379/2000\n",
      "84/84 [==============================] - 0s 315us/step - loss: 0.0790 - accuracy: 0.9702 - val_loss: 0.2754 - val_accuracy: 0.8889\n",
      "Epoch 380/2000\n",
      "84/84 [==============================] - 0s 377us/step - loss: 0.0787 - accuracy: 0.9762 - val_loss: 0.2751 - val_accuracy: 0.8889\n",
      "Epoch 381/2000\n",
      "84/84 [==============================] - 0s 395us/step - loss: 0.0784 - accuracy: 0.9762 - val_loss: 0.2750 - val_accuracy: 0.8889\n",
      "Epoch 382/2000\n",
      "84/84 [==============================] - 0s 407us/step - loss: 0.0783 - accuracy: 0.9762 - val_loss: 0.2757 - val_accuracy: 0.8889\n",
      "Epoch 383/2000\n",
      "84/84 [==============================] - 0s 328us/step - loss: 0.0780 - accuracy: 0.9762 - val_loss: 0.2780 - val_accuracy: 0.8889\n",
      "Epoch 384/2000\n",
      "84/84 [==============================] - 0s 224us/step - loss: 0.0777 - accuracy: 0.9762 - val_loss: 0.2784 - val_accuracy: 0.8889\n",
      "Epoch 385/2000\n",
      "84/84 [==============================] - 0s 225us/step - loss: 0.0773 - accuracy: 0.9762 - val_loss: 0.2751 - val_accuracy: 0.8889\n",
      "Epoch 386/2000\n",
      "84/84 [==============================] - 0s 240us/step - loss: 0.0769 - accuracy: 0.9762 - val_loss: 0.2741 - val_accuracy: 0.8889\n",
      "Epoch 387/2000\n",
      "84/84 [==============================] - 0s 229us/step - loss: 0.0766 - accuracy: 0.9762 - val_loss: 0.2723 - val_accuracy: 0.8889\n",
      "Epoch 388/2000\n",
      "84/84 [==============================] - 0s 229us/step - loss: 0.0764 - accuracy: 0.9762 - val_loss: 0.2719 - val_accuracy: 0.8889\n",
      "Epoch 389/2000\n",
      "84/84 [==============================] - 0s 239us/step - loss: 0.0761 - accuracy: 0.9762 - val_loss: 0.2719 - val_accuracy: 0.8889\n",
      "Epoch 390/2000\n",
      "84/84 [==============================] - 0s 238us/step - loss: 0.0757 - accuracy: 0.9762 - val_loss: 0.2722 - val_accuracy: 0.8889\n",
      "Epoch 391/2000\n",
      "84/84 [==============================] - 0s 242us/step - loss: 0.0756 - accuracy: 0.9762 - val_loss: 0.2709 - val_accuracy: 0.8889\n",
      "Epoch 392/2000\n",
      "84/84 [==============================] - 0s 329us/step - loss: 0.0753 - accuracy: 0.9762 - val_loss: 0.2712 - val_accuracy: 0.8889\n",
      "Epoch 393/2000\n",
      "84/84 [==============================] - 0s 367us/step - loss: 0.0749 - accuracy: 0.9762 - val_loss: 0.2695 - val_accuracy: 0.8889\n",
      "Epoch 394/2000\n",
      "84/84 [==============================] - 0s 372us/step - loss: 0.0747 - accuracy: 0.9762 - val_loss: 0.2682 - val_accuracy: 0.8889\n",
      "Epoch 395/2000\n",
      "84/84 [==============================] - 0s 304us/step - loss: 0.0743 - accuracy: 0.9762 - val_loss: 0.2697 - val_accuracy: 0.8889\n",
      "Epoch 396/2000\n",
      "84/84 [==============================] - 0s 314us/step - loss: 0.0740 - accuracy: 0.9762 - val_loss: 0.2707 - val_accuracy: 0.8889\n",
      "Epoch 397/2000\n",
      "84/84 [==============================] - 0s 289us/step - loss: 0.0737 - accuracy: 0.9762 - val_loss: 0.2702 - val_accuracy: 0.8889\n",
      "Epoch 398/2000\n",
      "84/84 [==============================] - 0s 278us/step - loss: 0.0734 - accuracy: 0.9762 - val_loss: 0.2701 - val_accuracy: 0.8889\n",
      "Epoch 399/2000\n",
      "84/84 [==============================] - 0s 225us/step - loss: 0.0731 - accuracy: 0.9762 - val_loss: 0.2701 - val_accuracy: 0.8889\n",
      "Epoch 400/2000\n",
      "84/84 [==============================] - 0s 209us/step - loss: 0.0729 - accuracy: 0.9762 - val_loss: 0.2701 - val_accuracy: 0.8889\n",
      "Epoch 401/2000\n",
      "84/84 [==============================] - 0s 239us/step - loss: 0.0726 - accuracy: 0.9762 - val_loss: 0.2686 - val_accuracy: 0.8889\n",
      "Epoch 402/2000\n",
      "84/84 [==============================] - 0s 258us/step - loss: 0.0725 - accuracy: 0.9762 - val_loss: 0.2669 - val_accuracy: 0.8889\n",
      "Epoch 403/2000\n",
      "84/84 [==============================] - 0s 263us/step - loss: 0.0724 - accuracy: 0.9762 - val_loss: 0.2672 - val_accuracy: 0.8889\n",
      "Epoch 404/2000\n",
      "84/84 [==============================] - 0s 313us/step - loss: 0.0719 - accuracy: 0.9762 - val_loss: 0.2662 - val_accuracy: 0.8889\n",
      "Epoch 405/2000\n",
      "84/84 [==============================] - 0s 281us/step - loss: 0.0716 - accuracy: 0.9762 - val_loss: 0.2666 - val_accuracy: 0.8889\n",
      "Epoch 406/2000\n",
      "84/84 [==============================] - 0s 350us/step - loss: 0.0716 - accuracy: 0.9762 - val_loss: 0.2665 - val_accuracy: 0.8889\n",
      "Epoch 407/2000\n",
      "84/84 [==============================] - 0s 899us/step - loss: 0.0713 - accuracy: 0.9762 - val_loss: 0.2661 - val_accuracy: 0.8889\n",
      "Epoch 408/2000\n",
      "84/84 [==============================] - 0s 718us/step - loss: 0.0719 - accuracy: 0.9762 - val_loss: 0.2731 - val_accuracy: 0.8889\n",
      "Epoch 409/2000\n",
      "84/84 [==============================] - 0s 297us/step - loss: 0.0706 - accuracy: 0.9762 - val_loss: 0.2721 - val_accuracy: 0.8889\n",
      "Epoch 410/2000\n",
      "84/84 [==============================] - 0s 310us/step - loss: 0.0705 - accuracy: 0.9762 - val_loss: 0.2726 - val_accuracy: 0.8889\n",
      "Epoch 411/2000\n",
      "84/84 [==============================] - 0s 337us/step - loss: 0.0703 - accuracy: 0.9762 - val_loss: 0.2721 - val_accuracy: 0.8889\n",
      "Epoch 412/2000\n",
      "84/84 [==============================] - 0s 360us/step - loss: 0.0699 - accuracy: 0.9762 - val_loss: 0.2726 - val_accuracy: 0.8889\n",
      "Epoch 413/2000\n",
      "84/84 [==============================] - 0s 393us/step - loss: 0.0698 - accuracy: 0.9762 - val_loss: 0.2713 - val_accuracy: 0.8889\n",
      "Epoch 414/2000\n",
      "84/84 [==============================] - 0s 402us/step - loss: 0.0695 - accuracy: 0.9762 - val_loss: 0.2701 - val_accuracy: 0.8889\n",
      "Epoch 415/2000\n",
      "84/84 [==============================] - 0s 402us/step - loss: 0.0694 - accuracy: 0.9762 - val_loss: 0.2697 - val_accuracy: 0.8889\n",
      "Epoch 416/2000\n",
      "84/84 [==============================] - 0s 412us/step - loss: 0.0690 - accuracy: 0.9762 - val_loss: 0.2661 - val_accuracy: 0.8889\n",
      "Epoch 417/2000\n",
      "84/84 [==============================] - 0s 342us/step - loss: 0.0688 - accuracy: 0.9762 - val_loss: 0.2623 - val_accuracy: 0.8889\n",
      "Epoch 418/2000\n",
      "84/84 [==============================] - 0s 316us/step - loss: 0.0686 - accuracy: 0.9762 - val_loss: 0.2610 - val_accuracy: 0.8889\n",
      "Epoch 419/2000\n",
      "84/84 [==============================] - 0s 395us/step - loss: 0.0685 - accuracy: 0.9702 - val_loss: 0.2605 - val_accuracy: 0.8889\n",
      "Epoch 420/2000\n",
      "84/84 [==============================] - 0s 317us/step - loss: 0.0684 - accuracy: 0.9643 - val_loss: 0.2625 - val_accuracy: 0.8889\n",
      "Epoch 421/2000\n",
      "84/84 [==============================] - 0s 222us/step - loss: 0.0682 - accuracy: 0.9702 - val_loss: 0.2670 - val_accuracy: 0.8889\n",
      "Epoch 422/2000\n",
      "84/84 [==============================] - 0s 212us/step - loss: 0.0678 - accuracy: 0.9702 - val_loss: 0.2692 - val_accuracy: 0.8889\n",
      "Epoch 423/2000\n",
      "84/84 [==============================] - 0s 244us/step - loss: 0.0676 - accuracy: 0.9702 - val_loss: 0.2708 - val_accuracy: 0.8889\n",
      "Epoch 424/2000\n",
      "84/84 [==============================] - 0s 289us/step - loss: 0.0675 - accuracy: 0.9702 - val_loss: 0.2739 - val_accuracy: 0.8889\n",
      "Epoch 425/2000\n",
      "84/84 [==============================] - 0s 256us/step - loss: 0.0674 - accuracy: 0.9762 - val_loss: 0.2756 - val_accuracy: 0.8889\n",
      "Epoch 426/2000\n",
      "84/84 [==============================] - 0s 278us/step - loss: 0.0671 - accuracy: 0.9762 - val_loss: 0.2752 - val_accuracy: 0.8889\n",
      "Epoch 427/2000\n",
      "84/84 [==============================] - 0s 231us/step - loss: 0.0668 - accuracy: 0.9762 - val_loss: 0.2766 - val_accuracy: 0.8889\n",
      "Epoch 428/2000\n",
      "84/84 [==============================] - 0s 236us/step - loss: 0.0666 - accuracy: 0.9762 - val_loss: 0.2761 - val_accuracy: 0.8889\n",
      "Epoch 429/2000\n",
      "84/84 [==============================] - 0s 215us/step - loss: 0.0664 - accuracy: 0.9762 - val_loss: 0.2744 - val_accuracy: 0.8889\n",
      "Epoch 430/2000\n",
      "84/84 [==============================] - 0s 213us/step - loss: 0.0663 - accuracy: 0.9762 - val_loss: 0.2720 - val_accuracy: 0.8889\n",
      "Epoch 431/2000\n",
      "84/84 [==============================] - 0s 251us/step - loss: 0.0665 - accuracy: 0.9643 - val_loss: 0.2663 - val_accuracy: 0.8889\n",
      "Epoch 432/2000\n",
      "84/84 [==============================] - 0s 212us/step - loss: 0.0658 - accuracy: 0.9643 - val_loss: 0.2663 - val_accuracy: 0.8889\n",
      "Epoch 433/2000\n",
      "84/84 [==============================] - 0s 219us/step - loss: 0.0658 - accuracy: 0.9702 - val_loss: 0.2667 - val_accuracy: 0.8889\n",
      "Epoch 434/2000\n",
      "84/84 [==============================] - 0s 226us/step - loss: 0.0654 - accuracy: 0.9762 - val_loss: 0.2670 - val_accuracy: 0.8889\n",
      "Epoch 435/2000\n",
      "84/84 [==============================] - 0s 266us/step - loss: 0.0651 - accuracy: 0.9762 - val_loss: 0.2685 - val_accuracy: 0.8889\n",
      "Epoch 436/2000\n",
      "84/84 [==============================] - 0s 227us/step - loss: 0.0650 - accuracy: 0.9762 - val_loss: 0.2688 - val_accuracy: 0.8889\n",
      "Epoch 437/2000\n",
      "84/84 [==============================] - 0s 244us/step - loss: 0.0651 - accuracy: 0.9643 - val_loss: 0.2681 - val_accuracy: 0.8889\n",
      "Epoch 438/2000\n",
      "84/84 [==============================] - 0s 215us/step - loss: 0.0645 - accuracy: 0.9702 - val_loss: 0.2727 - val_accuracy: 0.8889\n",
      "Epoch 439/2000\n",
      "84/84 [==============================] - 0s 228us/step - loss: 0.0643 - accuracy: 0.9762 - val_loss: 0.2740 - val_accuracy: 0.8889\n",
      "Epoch 440/2000\n",
      "84/84 [==============================] - 0s 241us/step - loss: 0.0640 - accuracy: 0.9762 - val_loss: 0.2782 - val_accuracy: 0.8889\n",
      "Epoch 441/2000\n",
      "84/84 [==============================] - 0s 229us/step - loss: 0.0640 - accuracy: 0.9762 - val_loss: 0.2801 - val_accuracy: 0.8889\n",
      "Epoch 442/2000\n",
      "84/84 [==============================] - 0s 305us/step - loss: 0.0639 - accuracy: 0.9762 - val_loss: 0.2818 - val_accuracy: 0.8889\n",
      "Epoch 443/2000\n",
      "84/84 [==============================] - 0s 268us/step - loss: 0.0639 - accuracy: 0.9762 - val_loss: 0.2822 - val_accuracy: 0.8889\n",
      "Epoch 444/2000\n",
      "84/84 [==============================] - 0s 257us/step - loss: 0.0637 - accuracy: 0.9762 - val_loss: 0.2815 - val_accuracy: 0.8889\n",
      "Epoch 445/2000\n",
      "84/84 [==============================] - 0s 282us/step - loss: 0.0637 - accuracy: 0.9762 - val_loss: 0.2809 - val_accuracy: 0.8889\n",
      "Epoch 446/2000\n",
      "84/84 [==============================] - 0s 325us/step - loss: 0.0632 - accuracy: 0.9762 - val_loss: 0.2759 - val_accuracy: 0.8889\n",
      "Epoch 447/2000\n",
      "84/84 [==============================] - 0s 335us/step - loss: 0.0634 - accuracy: 0.9762 - val_loss: 0.2715 - val_accuracy: 0.8889\n",
      "Epoch 448/2000\n",
      "84/84 [==============================] - 0s 248us/step - loss: 0.0631 - accuracy: 0.9762 - val_loss: 0.2695 - val_accuracy: 0.8889\n",
      "Epoch 449/2000\n",
      "84/84 [==============================] - 0s 230us/step - loss: 0.0625 - accuracy: 0.9762 - val_loss: 0.2712 - val_accuracy: 0.8889\n",
      "Epoch 450/2000\n",
      "84/84 [==============================] - 0s 289us/step - loss: 0.0624 - accuracy: 0.9762 - val_loss: 0.2735 - val_accuracy: 0.8889\n",
      "Epoch 451/2000\n",
      "84/84 [==============================] - 0s 308us/step - loss: 0.0622 - accuracy: 0.9762 - val_loss: 0.2742 - val_accuracy: 0.8889\n",
      "Epoch 452/2000\n",
      "84/84 [==============================] - 0s 332us/step - loss: 0.0621 - accuracy: 0.9762 - val_loss: 0.2775 - val_accuracy: 0.8889\n",
      "Epoch 453/2000\n",
      "84/84 [==============================] - 0s 217us/step - loss: 0.0618 - accuracy: 0.9762 - val_loss: 0.2781 - val_accuracy: 0.8889\n",
      "Epoch 454/2000\n",
      "84/84 [==============================] - 0s 249us/step - loss: 0.0619 - accuracy: 0.9762 - val_loss: 0.2755 - val_accuracy: 0.8889\n",
      "Epoch 455/2000\n",
      "84/84 [==============================] - 0s 308us/step - loss: 0.0620 - accuracy: 0.9762 - val_loss: 0.2717 - val_accuracy: 0.8889\n",
      "Epoch 456/2000\n",
      "84/84 [==============================] - 0s 348us/step - loss: 0.0616 - accuracy: 0.9762 - val_loss: 0.2724 - val_accuracy: 0.8889\n",
      "Epoch 457/2000\n",
      "84/84 [==============================] - 0s 425us/step - loss: 0.0611 - accuracy: 0.9762 - val_loss: 0.2712 - val_accuracy: 0.8889\n",
      "Epoch 458/2000\n",
      "84/84 [==============================] - 0s 442us/step - loss: 0.0612 - accuracy: 0.9762 - val_loss: 0.2683 - val_accuracy: 0.8889\n",
      "Epoch 459/2000\n",
      "84/84 [==============================] - 0s 447us/step - loss: 0.0608 - accuracy: 0.9762 - val_loss: 0.2681 - val_accuracy: 0.8889\n",
      "Epoch 460/2000\n",
      "84/84 [==============================] - 0s 493us/step - loss: 0.0609 - accuracy: 0.9762 - val_loss: 0.2679 - val_accuracy: 0.8889\n",
      "Epoch 461/2000\n",
      "84/84 [==============================] - 0s 393us/step - loss: 0.0605 - accuracy: 0.9762 - val_loss: 0.2723 - val_accuracy: 0.8889\n",
      "Epoch 462/2000\n",
      "84/84 [==============================] - 0s 406us/step - loss: 0.0605 - accuracy: 0.9762 - val_loss: 0.2738 - val_accuracy: 0.8889\n",
      "Epoch 463/2000\n",
      "84/84 [==============================] - 0s 384us/step - loss: 0.0605 - accuracy: 0.9762 - val_loss: 0.2769 - val_accuracy: 0.8889\n",
      "Epoch 464/2000\n",
      "84/84 [==============================] - 0s 410us/step - loss: 0.0609 - accuracy: 0.9762 - val_loss: 0.2790 - val_accuracy: 0.8889\n",
      "Epoch 465/2000\n",
      "84/84 [==============================] - 0s 353us/step - loss: 0.0601 - accuracy: 0.9762 - val_loss: 0.2740 - val_accuracy: 0.8889\n",
      "Epoch 466/2000\n",
      "84/84 [==============================] - 0s 372us/step - loss: 0.0603 - accuracy: 0.9762 - val_loss: 0.2668 - val_accuracy: 0.8889\n",
      "Epoch 467/2000\n",
      "84/84 [==============================] - 0s 378us/step - loss: 0.0602 - accuracy: 0.9762 - val_loss: 0.2633 - val_accuracy: 0.8889\n",
      "Epoch 468/2000\n",
      "84/84 [==============================] - 0s 400us/step - loss: 0.0596 - accuracy: 0.9762 - val_loss: 0.2637 - val_accuracy: 0.8889\n",
      "Epoch 469/2000\n",
      "84/84 [==============================] - 0s 269us/step - loss: 0.0594 - accuracy: 0.9762 - val_loss: 0.2640 - val_accuracy: 0.8889\n",
      "Epoch 470/2000\n",
      "84/84 [==============================] - 0s 282us/step - loss: 0.0594 - accuracy: 0.9762 - val_loss: 0.2679 - val_accuracy: 0.8889\n",
      "Epoch 471/2000\n",
      "84/84 [==============================] - 0s 320us/step - loss: 0.0593 - accuracy: 0.9762 - val_loss: 0.2696 - val_accuracy: 0.8889\n",
      "Epoch 472/2000\n",
      "84/84 [==============================] - 0s 650us/step - loss: 0.0589 - accuracy: 0.9762 - val_loss: 0.2674 - val_accuracy: 0.8889\n",
      "Epoch 473/2000\n",
      "84/84 [==============================] - 0s 452us/step - loss: 0.0590 - accuracy: 0.9762 - val_loss: 0.2664 - val_accuracy: 0.8889\n",
      "Epoch 474/2000\n",
      "84/84 [==============================] - 0s 404us/step - loss: 0.0587 - accuracy: 0.9762 - val_loss: 0.2684 - val_accuracy: 0.8889\n",
      "Epoch 475/2000\n",
      "84/84 [==============================] - 0s 342us/step - loss: 0.0585 - accuracy: 0.9762 - val_loss: 0.2687 - val_accuracy: 0.8889\n",
      "Epoch 476/2000\n",
      "84/84 [==============================] - 0s 389us/step - loss: 0.0583 - accuracy: 0.9762 - val_loss: 0.2717 - val_accuracy: 0.8889\n",
      "Epoch 477/2000\n",
      "84/84 [==============================] - 0s 370us/step - loss: 0.0584 - accuracy: 0.9762 - val_loss: 0.2751 - val_accuracy: 0.8889\n",
      "Epoch 478/2000\n",
      "84/84 [==============================] - 0s 383us/step - loss: 0.0581 - accuracy: 0.9762 - val_loss: 0.2743 - val_accuracy: 0.8889\n",
      "Epoch 479/2000\n",
      "84/84 [==============================] - 0s 298us/step - loss: 0.0588 - accuracy: 0.9762 - val_loss: 0.2689 - val_accuracy: 0.8889\n",
      "Epoch 480/2000\n",
      "84/84 [==============================] - 0s 260us/step - loss: 0.0581 - accuracy: 0.9762 - val_loss: 0.2702 - val_accuracy: 0.8889\n",
      "Epoch 481/2000\n",
      "84/84 [==============================] - 0s 235us/step - loss: 0.0579 - accuracy: 0.9762 - val_loss: 0.2693 - val_accuracy: 0.8889\n",
      "Epoch 482/2000\n",
      "84/84 [==============================] - 0s 292us/step - loss: 0.0577 - accuracy: 0.9762 - val_loss: 0.2698 - val_accuracy: 0.8889\n",
      "Epoch 483/2000\n",
      "84/84 [==============================] - 0s 290us/step - loss: 0.0576 - accuracy: 0.9702 - val_loss: 0.2695 - val_accuracy: 0.8889\n",
      "Epoch 484/2000\n",
      "84/84 [==============================] - 0s 305us/step - loss: 0.0576 - accuracy: 0.9643 - val_loss: 0.2713 - val_accuracy: 0.8889\n",
      "Epoch 485/2000\n",
      "84/84 [==============================] - 0s 309us/step - loss: 0.0573 - accuracy: 0.9643 - val_loss: 0.2719 - val_accuracy: 0.8889\n",
      "Epoch 486/2000\n",
      "84/84 [==============================] - 0s 371us/step - loss: 0.0573 - accuracy: 0.9702 - val_loss: 0.2706 - val_accuracy: 0.8889\n",
      "Epoch 487/2000\n",
      "84/84 [==============================] - 0s 379us/step - loss: 0.0571 - accuracy: 0.9762 - val_loss: 0.2726 - val_accuracy: 0.8889\n",
      "Epoch 488/2000\n",
      "84/84 [==============================] - 0s 386us/step - loss: 0.0570 - accuracy: 0.9762 - val_loss: 0.2731 - val_accuracy: 0.8889\n",
      "Epoch 489/2000\n",
      "84/84 [==============================] - 0s 393us/step - loss: 0.0577 - accuracy: 0.9762 - val_loss: 0.2791 - val_accuracy: 0.8889\n",
      "Epoch 490/2000\n",
      "84/84 [==============================] - 0s 385us/step - loss: 0.0568 - accuracy: 0.9762 - val_loss: 0.2778 - val_accuracy: 0.8889\n",
      "Epoch 491/2000\n",
      "84/84 [==============================] - 0s 392us/step - loss: 0.0568 - accuracy: 0.9762 - val_loss: 0.2745 - val_accuracy: 0.8889\n",
      "Epoch 492/2000\n",
      "84/84 [==============================] - 0s 377us/step - loss: 0.0566 - accuracy: 0.9762 - val_loss: 0.2730 - val_accuracy: 0.8889\n",
      "Epoch 493/2000\n",
      "84/84 [==============================] - 0s 410us/step - loss: 0.0565 - accuracy: 0.9762 - val_loss: 0.2704 - val_accuracy: 0.8889\n",
      "Epoch 494/2000\n",
      "84/84 [==============================] - 0s 346us/step - loss: 0.0563 - accuracy: 0.9762 - val_loss: 0.2682 - val_accuracy: 0.8889\n",
      "Epoch 495/2000\n",
      "84/84 [==============================] - 0s 292us/step - loss: 0.0564 - accuracy: 0.9762 - val_loss: 0.2638 - val_accuracy: 0.8889\n",
      "Epoch 496/2000\n",
      "84/84 [==============================] - 0s 310us/step - loss: 0.0559 - accuracy: 0.9762 - val_loss: 0.2638 - val_accuracy: 0.8889\n",
      "Epoch 497/2000\n",
      "84/84 [==============================] - 0s 343us/step - loss: 0.0559 - accuracy: 0.9762 - val_loss: 0.2642 - val_accuracy: 0.8889\n",
      "Epoch 498/2000\n",
      "84/84 [==============================] - 0s 384us/step - loss: 0.0558 - accuracy: 0.9762 - val_loss: 0.2654 - val_accuracy: 0.8889\n",
      "Epoch 499/2000\n",
      "84/84 [==============================] - 0s 628us/step - loss: 0.0557 - accuracy: 0.9762 - val_loss: 0.2667 - val_accuracy: 0.8889\n",
      "Epoch 500/2000\n",
      "84/84 [==============================] - 0s 449us/step - loss: 0.0555 - accuracy: 0.9762 - val_loss: 0.2690 - val_accuracy: 0.8889\n",
      "Epoch 501/2000\n",
      "84/84 [==============================] - 0s 392us/step - loss: 0.0555 - accuracy: 0.9762 - val_loss: 0.2702 - val_accuracy: 0.8889\n",
      "Epoch 502/2000\n",
      "84/84 [==============================] - 0s 392us/step - loss: 0.0552 - accuracy: 0.9762 - val_loss: 0.2701 - val_accuracy: 0.8889\n",
      "Epoch 503/2000\n",
      "84/84 [==============================] - 0s 410us/step - loss: 0.0553 - accuracy: 0.9762 - val_loss: 0.2687 - val_accuracy: 0.8889\n",
      "Epoch 504/2000\n",
      "84/84 [==============================] - 0s 410us/step - loss: 0.0550 - accuracy: 0.9762 - val_loss: 0.2698 - val_accuracy: 0.8889\n",
      "Epoch 505/2000\n",
      "84/84 [==============================] - 0s 461us/step - loss: 0.0551 - accuracy: 0.9762 - val_loss: 0.2728 - val_accuracy: 0.8889\n",
      "Epoch 506/2000\n",
      "84/84 [==============================] - 0s 358us/step - loss: 0.0549 - accuracy: 0.9762 - val_loss: 0.2758 - val_accuracy: 0.8889\n",
      "Epoch 507/2000\n",
      "84/84 [==============================] - 0s 366us/step - loss: 0.0547 - accuracy: 0.9762 - val_loss: 0.2774 - val_accuracy: 0.8889\n",
      "Epoch 508/2000\n",
      "84/84 [==============================] - 0s 242us/step - loss: 0.0548 - accuracy: 0.9762 - val_loss: 0.2790 - val_accuracy: 0.8889\n",
      "Epoch 509/2000\n",
      "84/84 [==============================] - 0s 277us/step - loss: 0.0546 - accuracy: 0.9762 - val_loss: 0.2765 - val_accuracy: 0.8889\n",
      "Epoch 510/2000\n",
      "84/84 [==============================] - 0s 294us/step - loss: 0.0547 - accuracy: 0.9702 - val_loss: 0.2717 - val_accuracy: 0.8889\n",
      "Epoch 511/2000\n",
      "84/84 [==============================] - 0s 343us/step - loss: 0.0545 - accuracy: 0.9643 - val_loss: 0.2694 - val_accuracy: 0.8889\n",
      "Epoch 512/2000\n",
      "84/84 [==============================] - 0s 261us/step - loss: 0.0542 - accuracy: 0.9643 - val_loss: 0.2665 - val_accuracy: 0.8889\n",
      "Epoch 513/2000\n",
      "84/84 [==============================] - 0s 276us/step - loss: 0.0544 - accuracy: 0.9643 - val_loss: 0.2648 - val_accuracy: 0.8889\n",
      "Epoch 514/2000\n",
      "84/84 [==============================] - 0s 287us/step - loss: 0.0544 - accuracy: 0.9702 - val_loss: 0.2627 - val_accuracy: 0.8889\n",
      "Epoch 515/2000\n",
      "84/84 [==============================] - 0s 271us/step - loss: 0.0542 - accuracy: 0.9702 - val_loss: 0.2636 - val_accuracy: 0.8889\n",
      "Epoch 516/2000\n",
      "84/84 [==============================] - 0s 264us/step - loss: 0.0547 - accuracy: 0.9643 - val_loss: 0.2708 - val_accuracy: 0.8889\n",
      "Epoch 517/2000\n",
      "84/84 [==============================] - 0s 278us/step - loss: 0.0538 - accuracy: 0.9643 - val_loss: 0.2727 - val_accuracy: 0.8889\n",
      "Epoch 518/2000\n",
      "84/84 [==============================] - 0s 268us/step - loss: 0.0535 - accuracy: 0.9702 - val_loss: 0.2742 - val_accuracy: 0.8889\n",
      "Epoch 519/2000\n",
      "84/84 [==============================] - 0s 292us/step - loss: 0.0535 - accuracy: 0.9702 - val_loss: 0.2757 - val_accuracy: 0.8889\n",
      "Epoch 520/2000\n",
      "84/84 [==============================] - 0s 333us/step - loss: 0.0533 - accuracy: 0.9762 - val_loss: 0.2763 - val_accuracy: 0.8889\n",
      "Epoch 521/2000\n",
      "84/84 [==============================] - 0s 341us/step - loss: 0.0532 - accuracy: 0.9762 - val_loss: 0.2765 - val_accuracy: 0.8889\n",
      "Epoch 522/2000\n",
      "84/84 [==============================] - 0s 251us/step - loss: 0.0532 - accuracy: 0.9762 - val_loss: 0.2758 - val_accuracy: 0.8889\n",
      "Epoch 523/2000\n",
      "84/84 [==============================] - 0s 258us/step - loss: 0.0533 - accuracy: 0.9762 - val_loss: 0.2766 - val_accuracy: 0.8889\n",
      "Epoch 524/2000\n",
      "84/84 [==============================] - 0s 372us/step - loss: 0.0530 - accuracy: 0.9762 - val_loss: 0.2743 - val_accuracy: 0.8889\n",
      "Epoch 525/2000\n",
      "84/84 [==============================] - 0s 312us/step - loss: 0.0531 - accuracy: 0.9762 - val_loss: 0.2744 - val_accuracy: 0.8889\n",
      "Epoch 526/2000\n",
      "84/84 [==============================] - 0s 306us/step - loss: 0.0527 - accuracy: 0.9762 - val_loss: 0.2700 - val_accuracy: 0.8889\n",
      "Epoch 527/2000\n",
      "84/84 [==============================] - 0s 326us/step - loss: 0.0537 - accuracy: 0.9643 - val_loss: 0.2656 - val_accuracy: 0.8889\n",
      "Epoch 528/2000\n",
      "84/84 [==============================] - 0s 253us/step - loss: 0.0529 - accuracy: 0.9643 - val_loss: 0.2705 - val_accuracy: 0.8889\n",
      "Epoch 529/2000\n",
      "84/84 [==============================] - 0s 233us/step - loss: 0.0526 - accuracy: 0.9643 - val_loss: 0.2708 - val_accuracy: 0.8889\n",
      "Epoch 530/2000\n",
      "84/84 [==============================] - 0s 270us/step - loss: 0.0524 - accuracy: 0.9702 - val_loss: 0.2735 - val_accuracy: 0.8889\n",
      "Epoch 531/2000\n",
      "84/84 [==============================] - 0s 233us/step - loss: 0.0523 - accuracy: 0.9762 - val_loss: 0.2774 - val_accuracy: 0.8889\n",
      "Epoch 532/2000\n",
      "84/84 [==============================] - 0s 284us/step - loss: 0.0522 - accuracy: 0.9762 - val_loss: 0.2792 - val_accuracy: 0.8889\n",
      "Epoch 533/2000\n",
      "84/84 [==============================] - 0s 244us/step - loss: 0.0529 - accuracy: 0.9762 - val_loss: 0.2837 - val_accuracy: 0.8889\n",
      "Epoch 534/2000\n",
      "84/84 [==============================] - 0s 328us/step - loss: 0.0523 - accuracy: 0.9762 - val_loss: 0.2810 - val_accuracy: 0.8889\n",
      "Epoch 535/2000\n",
      "84/84 [==============================] - 0s 334us/step - loss: 0.0521 - accuracy: 0.9762 - val_loss: 0.2799 - val_accuracy: 0.8889\n",
      "Epoch 536/2000\n",
      "84/84 [==============================] - 0s 328us/step - loss: 0.0522 - accuracy: 0.9762 - val_loss: 0.2789 - val_accuracy: 0.8889\n",
      "Epoch 537/2000\n",
      "84/84 [==============================] - 0s 394us/step - loss: 0.0521 - accuracy: 0.9762 - val_loss: 0.2771 - val_accuracy: 0.8889\n",
      "Epoch 538/2000\n",
      "84/84 [==============================] - 0s 344us/step - loss: 0.0522 - accuracy: 0.9762 - val_loss: 0.2691 - val_accuracy: 0.8889\n",
      "Epoch 539/2000\n",
      "84/84 [==============================] - 0s 278us/step - loss: 0.0515 - accuracy: 0.9762 - val_loss: 0.2665 - val_accuracy: 0.8889\n",
      "Epoch 540/2000\n",
      "84/84 [==============================] - 0s 319us/step - loss: 0.0515 - accuracy: 0.9762 - val_loss: 0.2655 - val_accuracy: 0.8889\n",
      "Epoch 541/2000\n",
      "84/84 [==============================] - 0s 315us/step - loss: 0.0516 - accuracy: 0.9762 - val_loss: 0.2659 - val_accuracy: 0.8889\n",
      "Epoch 542/2000\n",
      "84/84 [==============================] - 0s 319us/step - loss: 0.0514 - accuracy: 0.9762 - val_loss: 0.2655 - val_accuracy: 0.8889\n",
      "Epoch 543/2000\n",
      "84/84 [==============================] - 0s 318us/step - loss: 0.0515 - accuracy: 0.9762 - val_loss: 0.2634 - val_accuracy: 0.8889\n",
      "Epoch 544/2000\n",
      "84/84 [==============================] - 0s 434us/step - loss: 0.0513 - accuracy: 0.9702 - val_loss: 0.2634 - val_accuracy: 0.8889\n",
      "Epoch 545/2000\n",
      "84/84 [==============================] - 0s 415us/step - loss: 0.0514 - accuracy: 0.9702 - val_loss: 0.2644 - val_accuracy: 0.8889\n",
      "Epoch 546/2000\n",
      "84/84 [==============================] - 0s 404us/step - loss: 0.0512 - accuracy: 0.9702 - val_loss: 0.2655 - val_accuracy: 0.8889\n",
      "Epoch 547/2000\n",
      "84/84 [==============================] - 0s 335us/step - loss: 0.0511 - accuracy: 0.9643 - val_loss: 0.2667 - val_accuracy: 0.8889\n",
      "Epoch 548/2000\n",
      "84/84 [==============================] - 0s 391us/step - loss: 0.0511 - accuracy: 0.9643 - val_loss: 0.2666 - val_accuracy: 0.8889\n",
      "Epoch 549/2000\n",
      "84/84 [==============================] - 0s 400us/step - loss: 0.0508 - accuracy: 0.9643 - val_loss: 0.2705 - val_accuracy: 0.8889\n",
      "Epoch 550/2000\n",
      "84/84 [==============================] - 0s 379us/step - loss: 0.0508 - accuracy: 0.9643 - val_loss: 0.2724 - val_accuracy: 0.8889\n",
      "Epoch 551/2000\n",
      "84/84 [==============================] - 0s 319us/step - loss: 0.0507 - accuracy: 0.9643 - val_loss: 0.2760 - val_accuracy: 0.8889\n",
      "Epoch 552/2000\n",
      "84/84 [==============================] - 0s 377us/step - loss: 0.0504 - accuracy: 0.9702 - val_loss: 0.2793 - val_accuracy: 0.8889\n",
      "Epoch 553/2000\n",
      "84/84 [==============================] - 0s 254us/step - loss: 0.0509 - accuracy: 0.9762 - val_loss: 0.2838 - val_accuracy: 0.8889\n",
      "Epoch 554/2000\n",
      "84/84 [==============================] - 0s 282us/step - loss: 0.0505 - accuracy: 0.9762 - val_loss: 0.2846 - val_accuracy: 0.8889\n",
      "Epoch 555/2000\n",
      "84/84 [==============================] - 0s 303us/step - loss: 0.0504 - accuracy: 0.9762 - val_loss: 0.2816 - val_accuracy: 0.8889\n",
      "Epoch 556/2000\n",
      "84/84 [==============================] - 0s 383us/step - loss: 0.0504 - accuracy: 0.9762 - val_loss: 0.2790 - val_accuracy: 0.8889\n",
      "Epoch 557/2000\n",
      "84/84 [==============================] - 0s 356us/step - loss: 0.0501 - accuracy: 0.9762 - val_loss: 0.2759 - val_accuracy: 0.8889\n",
      "Epoch 558/2000\n",
      "84/84 [==============================] - 0s 291us/step - loss: 0.0501 - accuracy: 0.9762 - val_loss: 0.2745 - val_accuracy: 0.8889\n",
      "Epoch 559/2000\n",
      "84/84 [==============================] - 0s 240us/step - loss: 0.0500 - accuracy: 0.9762 - val_loss: 0.2706 - val_accuracy: 0.8889\n",
      "Epoch 560/2000\n",
      "84/84 [==============================] - 0s 261us/step - loss: 0.0499 - accuracy: 0.9762 - val_loss: 0.2701 - val_accuracy: 0.8889\n",
      "Epoch 561/2000\n",
      "84/84 [==============================] - 0s 244us/step - loss: 0.0499 - accuracy: 0.9702 - val_loss: 0.2685 - val_accuracy: 0.8889\n",
      "Epoch 562/2000\n",
      "84/84 [==============================] - 0s 228us/step - loss: 0.0497 - accuracy: 0.9702 - val_loss: 0.2675 - val_accuracy: 0.8889\n",
      "Epoch 563/2000\n",
      "84/84 [==============================] - 0s 232us/step - loss: 0.0499 - accuracy: 0.9643 - val_loss: 0.2662 - val_accuracy: 0.8889\n",
      "Epoch 564/2000\n",
      "84/84 [==============================] - 0s 200us/step - loss: 0.0498 - accuracy: 0.9643 - val_loss: 0.2671 - val_accuracy: 0.8889\n",
      "Epoch 565/2000\n",
      "84/84 [==============================] - 0s 231us/step - loss: 0.0496 - accuracy: 0.9643 - val_loss: 0.2675 - val_accuracy: 0.8889\n",
      "Epoch 566/2000\n",
      "84/84 [==============================] - 0s 229us/step - loss: 0.0499 - accuracy: 0.9702 - val_loss: 0.2714 - val_accuracy: 0.8889\n",
      "Epoch 567/2000\n",
      "84/84 [==============================] - 0s 239us/step - loss: 0.0496 - accuracy: 0.9702 - val_loss: 0.2699 - val_accuracy: 0.8889\n",
      "Epoch 568/2000\n",
      "84/84 [==============================] - 0s 250us/step - loss: 0.0494 - accuracy: 0.9702 - val_loss: 0.2696 - val_accuracy: 0.8889\n",
      "Epoch 569/2000\n",
      "84/84 [==============================] - 0s 252us/step - loss: 0.0491 - accuracy: 0.9762 - val_loss: 0.2715 - val_accuracy: 0.8889\n",
      "Epoch 570/2000\n",
      "84/84 [==============================] - 0s 266us/step - loss: 0.0493 - accuracy: 0.9762 - val_loss: 0.2765 - val_accuracy: 0.8889\n",
      "Epoch 571/2000\n",
      "84/84 [==============================] - 0s 260us/step - loss: 0.0493 - accuracy: 0.9762 - val_loss: 0.2796 - val_accuracy: 0.8889\n",
      "Epoch 572/2000\n",
      "84/84 [==============================] - 0s 270us/step - loss: 0.0492 - accuracy: 0.9762 - val_loss: 0.2809 - val_accuracy: 0.8889\n",
      "Epoch 573/2000\n",
      "84/84 [==============================] - 0s 248us/step - loss: 0.0491 - accuracy: 0.9762 - val_loss: 0.2780 - val_accuracy: 0.8889\n",
      "Epoch 574/2000\n",
      "84/84 [==============================] - 0s 276us/step - loss: 0.0489 - accuracy: 0.9762 - val_loss: 0.2765 - val_accuracy: 0.8889\n",
      "Epoch 575/2000\n",
      "84/84 [==============================] - 0s 255us/step - loss: 0.0490 - accuracy: 0.9762 - val_loss: 0.2764 - val_accuracy: 0.8889\n",
      "Epoch 576/2000\n",
      "84/84 [==============================] - 0s 327us/step - loss: 0.0487 - accuracy: 0.9762 - val_loss: 0.2747 - val_accuracy: 0.8889\n",
      "Epoch 577/2000\n",
      "84/84 [==============================] - 0s 255us/step - loss: 0.0486 - accuracy: 0.9762 - val_loss: 0.2721 - val_accuracy: 0.9028\n",
      "Epoch 578/2000\n",
      "84/84 [==============================] - 0s 266us/step - loss: 0.0491 - accuracy: 0.9643 - val_loss: 0.2681 - val_accuracy: 0.9028\n",
      "Epoch 579/2000\n",
      "84/84 [==============================] - 0s 270us/step - loss: 0.0487 - accuracy: 0.9643 - val_loss: 0.2677 - val_accuracy: 0.9028\n",
      "Epoch 580/2000\n",
      "84/84 [==============================] - 0s 313us/step - loss: 0.0484 - accuracy: 0.9643 - val_loss: 0.2667 - val_accuracy: 0.9167\n",
      "Epoch 581/2000\n",
      "84/84 [==============================] - 0s 315us/step - loss: 0.0491 - accuracy: 0.9643 - val_loss: 0.2694 - val_accuracy: 0.9028\n",
      "Epoch 582/2000\n",
      "84/84 [==============================] - 0s 304us/step - loss: 0.0485 - accuracy: 0.9702 - val_loss: 0.2677 - val_accuracy: 0.9028\n",
      "Epoch 583/2000\n",
      "84/84 [==============================] - 0s 269us/step - loss: 0.0483 - accuracy: 0.9643 - val_loss: 0.2683 - val_accuracy: 0.9028\n",
      "Epoch 584/2000\n",
      "84/84 [==============================] - 0s 232us/step - loss: 0.0487 - accuracy: 0.9702 - val_loss: 0.2722 - val_accuracy: 0.9028\n",
      "Epoch 585/2000\n",
      "84/84 [==============================] - 0s 260us/step - loss: 0.0482 - accuracy: 0.9762 - val_loss: 0.2740 - val_accuracy: 0.9028\n",
      "Epoch 586/2000\n",
      "84/84 [==============================] - 0s 376us/step - loss: 0.0480 - accuracy: 0.9762 - val_loss: 0.2721 - val_accuracy: 0.9028\n",
      "Epoch 587/2000\n",
      "84/84 [==============================] - 0s 254us/step - loss: 0.0480 - accuracy: 0.9762 - val_loss: 0.2676 - val_accuracy: 0.9167\n",
      "Epoch 588/2000\n",
      "84/84 [==============================] - 0s 275us/step - loss: 0.0480 - accuracy: 0.9762 - val_loss: 0.2665 - val_accuracy: 0.9167\n",
      "Epoch 589/2000\n",
      "84/84 [==============================] - 0s 273us/step - loss: 0.0482 - accuracy: 0.9643 - val_loss: 0.2658 - val_accuracy: 0.9167\n",
      "Epoch 590/2000\n",
      "84/84 [==============================] - 0s 370us/step - loss: 0.0483 - accuracy: 0.9643 - val_loss: 0.2715 - val_accuracy: 0.9028\n",
      "Epoch 591/2000\n",
      "84/84 [==============================] - 0s 339us/step - loss: 0.0477 - accuracy: 0.9702 - val_loss: 0.2733 - val_accuracy: 0.8889\n",
      "Epoch 592/2000\n",
      "84/84 [==============================] - 0s 370us/step - loss: 0.0478 - accuracy: 0.9643 - val_loss: 0.2730 - val_accuracy: 0.8889\n",
      "Epoch 593/2000\n",
      "84/84 [==============================] - 0s 380us/step - loss: 0.0476 - accuracy: 0.9643 - val_loss: 0.2734 - val_accuracy: 0.8889\n",
      "Epoch 594/2000\n",
      "84/84 [==============================] - 0s 394us/step - loss: 0.0476 - accuracy: 0.9643 - val_loss: 0.2731 - val_accuracy: 0.8889\n",
      "Epoch 595/2000\n",
      "84/84 [==============================] - 0s 377us/step - loss: 0.0477 - accuracy: 0.9702 - val_loss: 0.2741 - val_accuracy: 0.8889\n",
      "Epoch 596/2000\n",
      "84/84 [==============================] - 0s 380us/step - loss: 0.0478 - accuracy: 0.9643 - val_loss: 0.2726 - val_accuracy: 0.8889\n",
      "Epoch 597/2000\n",
      "84/84 [==============================] - 0s 381us/step - loss: 0.0476 - accuracy: 0.9702 - val_loss: 0.2760 - val_accuracy: 0.8889\n",
      "Epoch 598/2000\n",
      "84/84 [==============================] - 0s 336us/step - loss: 0.0474 - accuracy: 0.9762 - val_loss: 0.2766 - val_accuracy: 0.8889\n",
      "Epoch 599/2000\n",
      "84/84 [==============================] - 0s 362us/step - loss: 0.0473 - accuracy: 0.9762 - val_loss: 0.2756 - val_accuracy: 0.9028\n",
      "Epoch 600/2000\n",
      "84/84 [==============================] - 0s 371us/step - loss: 0.0472 - accuracy: 0.9762 - val_loss: 0.2749 - val_accuracy: 0.9028\n",
      "Epoch 601/2000\n",
      "84/84 [==============================] - 0s 424us/step - loss: 0.0472 - accuracy: 0.9762 - val_loss: 0.2770 - val_accuracy: 0.9028\n",
      "Epoch 602/2000\n",
      "84/84 [==============================] - 0s 364us/step - loss: 0.0469 - accuracy: 0.9762 - val_loss: 0.2792 - val_accuracy: 0.9028\n",
      "Epoch 603/2000\n",
      "84/84 [==============================] - 0s 360us/step - loss: 0.0473 - accuracy: 0.9762 - val_loss: 0.2851 - val_accuracy: 0.8889\n",
      "Epoch 604/2000\n",
      "84/84 [==============================] - 0s 393us/step - loss: 0.0474 - accuracy: 0.9762 - val_loss: 0.2888 - val_accuracy: 0.8889\n",
      "Epoch 605/2000\n",
      "84/84 [==============================] - 0s 391us/step - loss: 0.0469 - accuracy: 0.9762 - val_loss: 0.2855 - val_accuracy: 0.8889\n",
      "Epoch 606/2000\n",
      "84/84 [==============================] - 0s 399us/step - loss: 0.0468 - accuracy: 0.9762 - val_loss: 0.2783 - val_accuracy: 0.9028\n",
      "Epoch 607/2000\n",
      "84/84 [==============================] - 0s 432us/step - loss: 0.0471 - accuracy: 0.9643 - val_loss: 0.2723 - val_accuracy: 0.9028\n",
      "Epoch 608/2000\n",
      "84/84 [==============================] - 0s 397us/step - loss: 0.0468 - accuracy: 0.9643 - val_loss: 0.2722 - val_accuracy: 0.9028\n",
      "Epoch 609/2000\n",
      "84/84 [==============================] - 0s 309us/step - loss: 0.0474 - accuracy: 0.9702 - val_loss: 0.2714 - val_accuracy: 0.9028\n",
      "Epoch 610/2000\n",
      "84/84 [==============================] - 0s 313us/step - loss: 0.0473 - accuracy: 0.9643 - val_loss: 0.2758 - val_accuracy: 0.8889\n",
      "Epoch 611/2000\n",
      "84/84 [==============================] - 0s 337us/step - loss: 0.0472 - accuracy: 0.9643 - val_loss: 0.2805 - val_accuracy: 0.8889\n",
      "Epoch 612/2000\n",
      "84/84 [==============================] - 0s 628us/step - loss: 0.0467 - accuracy: 0.9643 - val_loss: 0.2828 - val_accuracy: 0.8889\n",
      "Epoch 613/2000\n",
      "84/84 [==============================] - 0s 355us/step - loss: 0.0465 - accuracy: 0.9762 - val_loss: 0.2812 - val_accuracy: 0.8889\n",
      "Epoch 614/2000\n",
      "84/84 [==============================] - 0s 296us/step - loss: 0.0468 - accuracy: 0.9762 - val_loss: 0.2843 - val_accuracy: 0.8889\n",
      "Epoch 615/2000\n",
      "84/84 [==============================] - 0s 351us/step - loss: 0.0463 - accuracy: 0.9762 - val_loss: 0.2833 - val_accuracy: 0.8889\n",
      "Epoch 616/2000\n",
      "84/84 [==============================] - 0s 271us/step - loss: 0.0465 - accuracy: 0.9762 - val_loss: 0.2783 - val_accuracy: 0.9028\n",
      "Epoch 617/2000\n",
      "84/84 [==============================] - 0s 280us/step - loss: 0.0462 - accuracy: 0.9762 - val_loss: 0.2792 - val_accuracy: 0.9028\n",
      "Epoch 618/2000\n",
      "84/84 [==============================] - 0s 417us/step - loss: 0.0461 - accuracy: 0.9762 - val_loss: 0.2785 - val_accuracy: 0.9028\n",
      "Epoch 619/2000\n",
      "84/84 [==============================] - 0s 266us/step - loss: 0.0461 - accuracy: 0.9762 - val_loss: 0.2764 - val_accuracy: 0.9028\n",
      "Epoch 620/2000\n",
      "84/84 [==============================] - 0s 239us/step - loss: 0.0464 - accuracy: 0.9762 - val_loss: 0.2751 - val_accuracy: 0.9167\n",
      "Epoch 621/2000\n",
      "84/84 [==============================] - 0s 253us/step - loss: 0.0464 - accuracy: 0.9762 - val_loss: 0.2801 - val_accuracy: 0.9167\n",
      "Epoch 622/2000\n",
      "84/84 [==============================] - 0s 250us/step - loss: 0.0458 - accuracy: 0.9762 - val_loss: 0.2808 - val_accuracy: 0.9167\n",
      "Epoch 623/2000\n",
      "84/84 [==============================] - 0s 244us/step - loss: 0.0459 - accuracy: 0.9762 - val_loss: 0.2800 - val_accuracy: 0.9167\n",
      "Epoch 624/2000\n",
      "84/84 [==============================] - 0s 239us/step - loss: 0.0459 - accuracy: 0.9762 - val_loss: 0.2757 - val_accuracy: 0.9167\n",
      "Epoch 625/2000\n",
      "84/84 [==============================] - 0s 249us/step - loss: 0.0458 - accuracy: 0.9762 - val_loss: 0.2765 - val_accuracy: 0.9167\n",
      "Epoch 626/2000\n",
      "84/84 [==============================] - 0s 295us/step - loss: 0.0461 - accuracy: 0.9643 - val_loss: 0.2739 - val_accuracy: 0.9167\n",
      "Epoch 627/2000\n",
      "84/84 [==============================] - 0s 267us/step - loss: 0.0456 - accuracy: 0.9643 - val_loss: 0.2736 - val_accuracy: 0.9167\n",
      "Epoch 628/2000\n",
      "84/84 [==============================] - 0s 252us/step - loss: 0.0457 - accuracy: 0.9643 - val_loss: 0.2755 - val_accuracy: 0.9167\n",
      "Epoch 629/2000\n",
      "84/84 [==============================] - 0s 286us/step - loss: 0.0456 - accuracy: 0.9643 - val_loss: 0.2747 - val_accuracy: 0.9167\n",
      "Epoch 630/2000\n",
      "84/84 [==============================] - 0s 321us/step - loss: 0.0455 - accuracy: 0.9643 - val_loss: 0.2769 - val_accuracy: 0.9167\n",
      "Epoch 631/2000\n",
      "84/84 [==============================] - 0s 254us/step - loss: 0.0455 - accuracy: 0.9702 - val_loss: 0.2786 - val_accuracy: 0.9167\n",
      "Epoch 632/2000\n",
      "84/84 [==============================] - 0s 239us/step - loss: 0.0455 - accuracy: 0.9762 - val_loss: 0.2788 - val_accuracy: 0.9167\n",
      "Epoch 633/2000\n",
      "84/84 [==============================] - 0s 314us/step - loss: 0.0453 - accuracy: 0.9762 - val_loss: 0.2800 - val_accuracy: 0.9167\n",
      "Epoch 634/2000\n",
      "84/84 [==============================] - 0s 383us/step - loss: 0.0452 - accuracy: 0.9762 - val_loss: 0.2790 - val_accuracy: 0.9167\n",
      "Epoch 635/2000\n",
      "84/84 [==============================] - 0s 290us/step - loss: 0.0451 - accuracy: 0.9762 - val_loss: 0.2802 - val_accuracy: 0.9167\n",
      "Epoch 636/2000\n",
      "84/84 [==============================] - 0s 402us/step - loss: 0.0451 - accuracy: 0.9762 - val_loss: 0.2826 - val_accuracy: 0.9167\n",
      "Epoch 637/2000\n",
      "84/84 [==============================] - 0s 292us/step - loss: 0.0451 - accuracy: 0.9762 - val_loss: 0.2836 - val_accuracy: 0.9167\n",
      "Epoch 638/2000\n",
      "84/84 [==============================] - 0s 232us/step - loss: 0.0451 - accuracy: 0.9762 - val_loss: 0.2818 - val_accuracy: 0.9167\n",
      "Epoch 639/2000\n",
      "84/84 [==============================] - 0s 236us/step - loss: 0.0449 - accuracy: 0.9762 - val_loss: 0.2813 - val_accuracy: 0.9167\n",
      "Epoch 640/2000\n",
      "84/84 [==============================] - 0s 221us/step - loss: 0.0450 - accuracy: 0.9762 - val_loss: 0.2805 - val_accuracy: 0.9167\n",
      "Epoch 641/2000\n",
      "84/84 [==============================] - 0s 224us/step - loss: 0.0447 - accuracy: 0.9762 - val_loss: 0.2794 - val_accuracy: 0.9167\n",
      "Epoch 642/2000\n",
      "84/84 [==============================] - 0s 230us/step - loss: 0.0449 - accuracy: 0.9762 - val_loss: 0.2771 - val_accuracy: 0.9167\n",
      "Epoch 643/2000\n",
      "84/84 [==============================] - 0s 219us/step - loss: 0.0447 - accuracy: 0.9762 - val_loss: 0.2761 - val_accuracy: 0.9167\n",
      "Epoch 644/2000\n",
      "84/84 [==============================] - 0s 239us/step - loss: 0.0448 - accuracy: 0.9762 - val_loss: 0.2773 - val_accuracy: 0.9167\n",
      "Epoch 645/2000\n",
      "84/84 [==============================] - 0s 234us/step - loss: 0.0446 - accuracy: 0.9762 - val_loss: 0.2756 - val_accuracy: 0.9167\n",
      "Epoch 646/2000\n",
      "84/84 [==============================] - 0s 259us/step - loss: 0.0446 - accuracy: 0.9762 - val_loss: 0.2754 - val_accuracy: 0.9167\n",
      "Epoch 647/2000\n",
      "84/84 [==============================] - 0s 373us/step - loss: 0.0446 - accuracy: 0.9762 - val_loss: 0.2738 - val_accuracy: 0.9167\n",
      "Epoch 648/2000\n",
      "84/84 [==============================] - 0s 373us/step - loss: 0.0444 - accuracy: 0.9762 - val_loss: 0.2761 - val_accuracy: 0.9167\n",
      "Epoch 649/2000\n",
      "84/84 [==============================] - 0s 369us/step - loss: 0.0445 - accuracy: 0.9643 - val_loss: 0.2784 - val_accuracy: 0.9167\n",
      "Epoch 650/2000\n",
      "84/84 [==============================] - 0s 383us/step - loss: 0.0443 - accuracy: 0.9702 - val_loss: 0.2816 - val_accuracy: 0.9167\n",
      "Epoch 651/2000\n",
      "84/84 [==============================] - 0s 381us/step - loss: 0.0444 - accuracy: 0.9762 - val_loss: 0.2854 - val_accuracy: 0.9167\n",
      "Epoch 652/2000\n",
      "84/84 [==============================] - 0s 320us/step - loss: 0.0442 - accuracy: 0.9762 - val_loss: 0.2879 - val_accuracy: 0.9167\n",
      "Epoch 653/2000\n",
      "84/84 [==============================] - 0s 368us/step - loss: 0.0441 - accuracy: 0.9762 - val_loss: 0.2907 - val_accuracy: 0.9167\n",
      "Epoch 654/2000\n",
      "84/84 [==============================] - 0s 357us/step - loss: 0.0442 - accuracy: 0.9762 - val_loss: 0.2930 - val_accuracy: 0.9167\n",
      "Epoch 655/2000\n",
      "84/84 [==============================] - 0s 401us/step - loss: 0.0441 - accuracy: 0.9762 - val_loss: 0.2933 - val_accuracy: 0.9167\n",
      "Epoch 656/2000\n",
      "84/84 [==============================] - 0s 353us/step - loss: 0.0444 - accuracy: 0.9762 - val_loss: 0.2957 - val_accuracy: 0.9167\n",
      "Epoch 657/2000\n",
      "84/84 [==============================] - 0s 312us/step - loss: 0.0442 - accuracy: 0.9762 - val_loss: 0.2918 - val_accuracy: 0.9167\n",
      "Epoch 658/2000\n",
      "84/84 [==============================] - 0s 307us/step - loss: 0.0444 - accuracy: 0.9762 - val_loss: 0.2919 - val_accuracy: 0.9167\n",
      "Epoch 659/2000\n",
      "84/84 [==============================] - 0s 304us/step - loss: 0.0442 - accuracy: 0.9762 - val_loss: 0.2853 - val_accuracy: 0.9167\n",
      "Epoch 660/2000\n",
      "84/84 [==============================] - 0s 362us/step - loss: 0.0441 - accuracy: 0.9762 - val_loss: 0.2838 - val_accuracy: 0.9167\n",
      "Epoch 661/2000\n",
      "84/84 [==============================] - 0s 329us/step - loss: 0.0437 - accuracy: 0.9762 - val_loss: 0.2809 - val_accuracy: 0.9167\n",
      "Epoch 662/2000\n",
      "84/84 [==============================] - 0s 335us/step - loss: 0.0435 - accuracy: 0.9762 - val_loss: 0.2782 - val_accuracy: 0.9167\n",
      "Epoch 663/2000\n",
      "84/84 [==============================] - 0s 288us/step - loss: 0.0437 - accuracy: 0.9762 - val_loss: 0.2753 - val_accuracy: 0.9167\n",
      "Epoch 664/2000\n",
      "84/84 [==============================] - 0s 238us/step - loss: 0.0438 - accuracy: 0.9762 - val_loss: 0.2739 - val_accuracy: 0.9167\n",
      "Epoch 665/2000\n",
      "84/84 [==============================] - 0s 245us/step - loss: 0.0434 - accuracy: 0.9762 - val_loss: 0.2713 - val_accuracy: 0.9167\n",
      "Epoch 666/2000\n",
      "84/84 [==============================] - 0s 296us/step - loss: 0.0434 - accuracy: 0.9762 - val_loss: 0.2693 - val_accuracy: 0.9167\n",
      "Epoch 667/2000\n",
      "84/84 [==============================] - 0s 280us/step - loss: 0.0436 - accuracy: 0.9762 - val_loss: 0.2671 - val_accuracy: 0.9167\n",
      "Epoch 668/2000\n",
      "84/84 [==============================] - 0s 310us/step - loss: 0.0437 - accuracy: 0.9702 - val_loss: 0.2682 - val_accuracy: 0.9167\n",
      "Epoch 669/2000\n",
      "84/84 [==============================] - 0s 267us/step - loss: 0.0436 - accuracy: 0.9702 - val_loss: 0.2675 - val_accuracy: 0.9167\n",
      "Epoch 670/2000\n",
      "84/84 [==============================] - 0s 286us/step - loss: 0.0434 - accuracy: 0.9643 - val_loss: 0.2696 - val_accuracy: 0.9167\n",
      "Epoch 671/2000\n",
      "84/84 [==============================] - 0s 289us/step - loss: 0.0434 - accuracy: 0.9643 - val_loss: 0.2760 - val_accuracy: 0.9167\n",
      "Epoch 672/2000\n",
      "84/84 [==============================] - 0s 292us/step - loss: 0.0431 - accuracy: 0.9702 - val_loss: 0.2818 - val_accuracy: 0.9167\n",
      "Epoch 673/2000\n",
      "84/84 [==============================] - 0s 299us/step - loss: 0.0437 - accuracy: 0.9762 - val_loss: 0.2884 - val_accuracy: 0.9167\n",
      "Epoch 674/2000\n",
      "84/84 [==============================] - 0s 266us/step - loss: 0.0431 - accuracy: 0.9762 - val_loss: 0.2885 - val_accuracy: 0.9167\n",
      "Epoch 675/2000\n",
      "84/84 [==============================] - 0s 297us/step - loss: 0.0432 - accuracy: 0.9762 - val_loss: 0.2877 - val_accuracy: 0.9167\n",
      "Epoch 676/2000\n",
      "84/84 [==============================] - 0s 280us/step - loss: 0.0430 - accuracy: 0.9762 - val_loss: 0.2871 - val_accuracy: 0.9167\n",
      "Epoch 677/2000\n",
      "84/84 [==============================] - 0s 295us/step - loss: 0.0432 - accuracy: 0.9643 - val_loss: 0.2818 - val_accuracy: 0.9167\n",
      "Epoch 678/2000\n",
      "84/84 [==============================] - 0s 312us/step - loss: 0.0430 - accuracy: 0.9643 - val_loss: 0.2795 - val_accuracy: 0.9167\n",
      "Epoch 679/2000\n",
      "84/84 [==============================] - 0s 332us/step - loss: 0.0430 - accuracy: 0.9643 - val_loss: 0.2799 - val_accuracy: 0.9167\n",
      "Epoch 680/2000\n",
      "84/84 [==============================] - 0s 245us/step - loss: 0.0429 - accuracy: 0.9643 - val_loss: 0.2814 - val_accuracy: 0.9167\n",
      "Epoch 681/2000\n",
      "84/84 [==============================] - 0s 291us/step - loss: 0.0428 - accuracy: 0.9643 - val_loss: 0.2852 - val_accuracy: 0.9167\n",
      "Epoch 682/2000\n",
      "84/84 [==============================] - 0s 265us/step - loss: 0.0432 - accuracy: 0.9702 - val_loss: 0.2914 - val_accuracy: 0.9167\n",
      "Epoch 683/2000\n",
      "84/84 [==============================] - 0s 263us/step - loss: 0.0429 - accuracy: 0.9762 - val_loss: 0.2954 - val_accuracy: 0.9167\n",
      "Epoch 684/2000\n",
      "84/84 [==============================] - 0s 354us/step - loss: 0.0430 - accuracy: 0.9762 - val_loss: 0.2960 - val_accuracy: 0.9167\n",
      "Epoch 685/2000\n",
      "84/84 [==============================] - 0s 706us/step - loss: 0.0427 - accuracy: 0.9762 - val_loss: 0.2944 - val_accuracy: 0.9167\n",
      "Epoch 686/2000\n",
      "84/84 [==============================] - 0s 412us/step - loss: 0.0427 - accuracy: 0.9762 - val_loss: 0.2932 - val_accuracy: 0.9167\n",
      "Epoch 687/2000\n",
      "84/84 [==============================] - 0s 289us/step - loss: 0.0425 - accuracy: 0.9762 - val_loss: 0.2910 - val_accuracy: 0.9167\n",
      "Epoch 688/2000\n",
      "84/84 [==============================] - 0s 302us/step - loss: 0.0425 - accuracy: 0.9762 - val_loss: 0.2901 - val_accuracy: 0.9167\n",
      "Epoch 689/2000\n",
      "84/84 [==============================] - 0s 260us/step - loss: 0.0425 - accuracy: 0.9762 - val_loss: 0.2903 - val_accuracy: 0.9167\n",
      "Epoch 690/2000\n",
      "84/84 [==============================] - 0s 307us/step - loss: 0.0423 - accuracy: 0.9762 - val_loss: 0.2912 - val_accuracy: 0.9167\n",
      "Epoch 691/2000\n",
      "84/84 [==============================] - 0s 305us/step - loss: 0.0424 - accuracy: 0.9762 - val_loss: 0.2949 - val_accuracy: 0.9167\n",
      "Epoch 692/2000\n",
      "84/84 [==============================] - 0s 314us/step - loss: 0.0423 - accuracy: 0.9762 - val_loss: 0.2957 - val_accuracy: 0.9167\n",
      "Epoch 693/2000\n",
      "84/84 [==============================] - 0s 272us/step - loss: 0.0423 - accuracy: 0.9762 - val_loss: 0.2954 - val_accuracy: 0.9167\n",
      "Epoch 694/2000\n",
      "84/84 [==============================] - 0s 244us/step - loss: 0.0425 - accuracy: 0.9762 - val_loss: 0.2902 - val_accuracy: 0.9167\n",
      "Epoch 695/2000\n",
      "84/84 [==============================] - 0s 273us/step - loss: 0.0421 - accuracy: 0.9762 - val_loss: 0.2883 - val_accuracy: 0.9167\n",
      "Epoch 696/2000\n",
      "84/84 [==============================] - 0s 265us/step - loss: 0.0422 - accuracy: 0.9762 - val_loss: 0.2886 - val_accuracy: 0.9167\n",
      "Epoch 697/2000\n",
      "84/84 [==============================] - 0s 239us/step - loss: 0.0421 - accuracy: 0.9702 - val_loss: 0.2856 - val_accuracy: 0.9167\n",
      "Epoch 698/2000\n",
      "84/84 [==============================] - 0s 213us/step - loss: 0.0421 - accuracy: 0.9643 - val_loss: 0.2861 - val_accuracy: 0.9167\n",
      "Epoch 699/2000\n",
      "84/84 [==============================] - 0s 200us/step - loss: 0.0419 - accuracy: 0.9643 - val_loss: 0.2854 - val_accuracy: 0.9167\n",
      "Epoch 700/2000\n",
      "84/84 [==============================] - 0s 218us/step - loss: 0.0418 - accuracy: 0.9643 - val_loss: 0.2857 - val_accuracy: 0.9167\n",
      "Epoch 701/2000\n",
      "84/84 [==============================] - 0s 227us/step - loss: 0.0418 - accuracy: 0.9643 - val_loss: 0.2874 - val_accuracy: 0.9167\n",
      "Epoch 702/2000\n",
      "84/84 [==============================] - 0s 309us/step - loss: 0.0419 - accuracy: 0.9643 - val_loss: 0.2879 - val_accuracy: 0.9167\n",
      "Epoch 703/2000\n",
      "84/84 [==============================] - 0s 282us/step - loss: 0.0416 - accuracy: 0.9643 - val_loss: 0.2905 - val_accuracy: 0.9167\n",
      "Epoch 704/2000\n",
      "84/84 [==============================] - 0s 316us/step - loss: 0.0416 - accuracy: 0.9762 - val_loss: 0.2903 - val_accuracy: 0.9167\n",
      "Epoch 705/2000\n",
      "84/84 [==============================] - 0s 390us/step - loss: 0.0416 - accuracy: 0.9762 - val_loss: 0.2904 - val_accuracy: 0.9167\n",
      "Epoch 706/2000\n",
      "84/84 [==============================] - 0s 287us/step - loss: 0.0415 - accuracy: 0.9762 - val_loss: 0.2919 - val_accuracy: 0.9167\n",
      "Epoch 707/2000\n",
      "84/84 [==============================] - 0s 267us/step - loss: 0.0418 - accuracy: 0.9762 - val_loss: 0.2973 - val_accuracy: 0.9167\n",
      "Epoch 708/2000\n",
      "84/84 [==============================] - 0s 320us/step - loss: 0.0416 - accuracy: 0.9762 - val_loss: 0.2976 - val_accuracy: 0.9167\n",
      "Epoch 709/2000\n",
      "84/84 [==============================] - 0s 318us/step - loss: 0.0421 - accuracy: 0.9762 - val_loss: 0.2990 - val_accuracy: 0.9167\n",
      "Epoch 710/2000\n",
      "84/84 [==============================] - 0s 302us/step - loss: 0.0416 - accuracy: 0.9762 - val_loss: 0.2960 - val_accuracy: 0.9167\n",
      "Epoch 711/2000\n",
      "84/84 [==============================] - 0s 315us/step - loss: 0.0420 - accuracy: 0.9762 - val_loss: 0.2899 - val_accuracy: 0.9167\n",
      "Epoch 712/2000\n",
      "84/84 [==============================] - 0s 608us/step - loss: 0.0415 - accuracy: 0.9762 - val_loss: 0.2870 - val_accuracy: 0.9167\n",
      "Epoch 713/2000\n",
      "84/84 [==============================] - 0s 424us/step - loss: 0.0414 - accuracy: 0.9762 - val_loss: 0.2807 - val_accuracy: 0.9167\n",
      "Epoch 714/2000\n",
      "84/84 [==============================] - 0s 463us/step - loss: 0.0416 - accuracy: 0.9643 - val_loss: 0.2786 - val_accuracy: 0.9167\n",
      "Epoch 715/2000\n",
      "84/84 [==============================] - 0s 480us/step - loss: 0.0412 - accuracy: 0.9643 - val_loss: 0.2809 - val_accuracy: 0.9167\n",
      "Epoch 716/2000\n",
      "84/84 [==============================] - 0s 470us/step - loss: 0.0414 - accuracy: 0.9643 - val_loss: 0.2855 - val_accuracy: 0.9167\n",
      "Epoch 717/2000\n",
      "84/84 [==============================] - 0s 403us/step - loss: 0.0412 - accuracy: 0.9643 - val_loss: 0.2873 - val_accuracy: 0.9167\n",
      "Epoch 718/2000\n",
      "84/84 [==============================] - 0s 284us/step - loss: 0.0410 - accuracy: 0.9702 - val_loss: 0.2878 - val_accuracy: 0.9167\n",
      "Epoch 719/2000\n",
      "84/84 [==============================] - 0s 291us/step - loss: 0.0410 - accuracy: 0.9702 - val_loss: 0.2872 - val_accuracy: 0.9167\n",
      "Epoch 720/2000\n",
      "84/84 [==============================] - 0s 308us/step - loss: 0.0408 - accuracy: 0.9643 - val_loss: 0.2872 - val_accuracy: 0.9167\n",
      "Epoch 721/2000\n",
      "84/84 [==============================] - 0s 309us/step - loss: 0.0408 - accuracy: 0.9643 - val_loss: 0.2882 - val_accuracy: 0.9167\n",
      "Epoch 722/2000\n",
      "84/84 [==============================] - 0s 445us/step - loss: 0.0412 - accuracy: 0.9643 - val_loss: 0.2895 - val_accuracy: 0.9167\n",
      "Epoch 723/2000\n",
      "84/84 [==============================] - 0s 368us/step - loss: 0.0408 - accuracy: 0.9762 - val_loss: 0.2874 - val_accuracy: 0.9167\n",
      "Epoch 724/2000\n",
      "84/84 [==============================] - 0s 351us/step - loss: 0.0408 - accuracy: 0.9643 - val_loss: 0.2866 - val_accuracy: 0.9167\n",
      "Epoch 725/2000\n",
      "84/84 [==============================] - 0s 358us/step - loss: 0.0407 - accuracy: 0.9643 - val_loss: 0.2856 - val_accuracy: 0.9167\n",
      "Epoch 726/2000\n",
      "84/84 [==============================] - 0s 367us/step - loss: 0.0410 - accuracy: 0.9643 - val_loss: 0.2861 - val_accuracy: 0.9167\n",
      "Epoch 727/2000\n",
      "84/84 [==============================] - 0s 372us/step - loss: 0.0415 - accuracy: 0.9643 - val_loss: 0.2812 - val_accuracy: 0.9167\n",
      "Epoch 728/2000\n",
      "84/84 [==============================] - 0s 338us/step - loss: 0.0409 - accuracy: 0.9762 - val_loss: 0.2815 - val_accuracy: 0.9167\n",
      "Epoch 729/2000\n",
      "84/84 [==============================] - 0s 329us/step - loss: 0.0409 - accuracy: 0.9643 - val_loss: 0.2866 - val_accuracy: 0.9167\n",
      "Epoch 730/2000\n",
      "84/84 [==============================] - 0s 337us/step - loss: 0.0405 - accuracy: 0.9643 - val_loss: 0.2891 - val_accuracy: 0.9167\n",
      "Epoch 731/2000\n",
      "84/84 [==============================] - 0s 306us/step - loss: 0.0408 - accuracy: 0.9762 - val_loss: 0.2929 - val_accuracy: 0.9167\n",
      "Epoch 732/2000\n",
      "84/84 [==============================] - 0s 372us/step - loss: 0.0408 - accuracy: 0.9762 - val_loss: 0.2938 - val_accuracy: 0.9167\n",
      "Epoch 733/2000\n",
      "84/84 [==============================] - 0s 366us/step - loss: 0.0408 - accuracy: 0.9762 - val_loss: 0.2895 - val_accuracy: 0.9167\n",
      "Epoch 734/2000\n",
      "84/84 [==============================] - 0s 279us/step - loss: 0.0404 - accuracy: 0.9762 - val_loss: 0.2895 - val_accuracy: 0.9167\n",
      "Epoch 735/2000\n",
      "84/84 [==============================] - 0s 267us/step - loss: 0.0403 - accuracy: 0.9762 - val_loss: 0.2894 - val_accuracy: 0.9167\n",
      "Epoch 736/2000\n",
      "84/84 [==============================] - 0s 637us/step - loss: 0.0403 - accuracy: 0.9762 - val_loss: 0.2897 - val_accuracy: 0.9167\n",
      "Epoch 737/2000\n",
      "84/84 [==============================] - 0s 355us/step - loss: 0.0403 - accuracy: 0.9762 - val_loss: 0.2910 - val_accuracy: 0.9167\n",
      "Epoch 738/2000\n",
      "84/84 [==============================] - 0s 446us/step - loss: 0.0402 - accuracy: 0.9762 - val_loss: 0.2898 - val_accuracy: 0.9167\n",
      "Epoch 739/2000\n",
      "84/84 [==============================] - 0s 397us/step - loss: 0.0402 - accuracy: 0.9762 - val_loss: 0.2902 - val_accuracy: 0.9167\n",
      "Epoch 740/2000\n",
      "84/84 [==============================] - 0s 556us/step - loss: 0.0402 - accuracy: 0.9702 - val_loss: 0.2910 - val_accuracy: 0.9167\n",
      "Epoch 741/2000\n",
      "84/84 [==============================] - 0s 414us/step - loss: 0.0400 - accuracy: 0.9762 - val_loss: 0.2940 - val_accuracy: 0.9167\n",
      "Epoch 742/2000\n",
      "84/84 [==============================] - 0s 273us/step - loss: 0.0399 - accuracy: 0.9762 - val_loss: 0.2972 - val_accuracy: 0.9167\n",
      "Epoch 743/2000\n",
      "84/84 [==============================] - 0s 335us/step - loss: 0.0403 - accuracy: 0.9762 - val_loss: 0.3023 - val_accuracy: 0.9167\n",
      "Epoch 744/2000\n",
      "84/84 [==============================] - 0s 308us/step - loss: 0.0401 - accuracy: 0.9762 - val_loss: 0.3022 - val_accuracy: 0.9167\n",
      "Epoch 745/2000\n",
      "84/84 [==============================] - 0s 294us/step - loss: 0.0401 - accuracy: 0.9762 - val_loss: 0.3009 - val_accuracy: 0.9167\n",
      "Epoch 746/2000\n",
      "84/84 [==============================] - 0s 454us/step - loss: 0.0402 - accuracy: 0.9762 - val_loss: 0.2955 - val_accuracy: 0.9167\n",
      "Epoch 747/2000\n",
      "84/84 [==============================] - 0s 364us/step - loss: 0.0401 - accuracy: 0.9762 - val_loss: 0.2932 - val_accuracy: 0.9167\n",
      "Epoch 748/2000\n",
      "84/84 [==============================] - 0s 352us/step - loss: 0.0398 - accuracy: 0.9762 - val_loss: 0.2914 - val_accuracy: 0.9167\n",
      "Epoch 749/2000\n",
      "84/84 [==============================] - 0s 330us/step - loss: 0.0403 - accuracy: 0.9762 - val_loss: 0.2877 - val_accuracy: 0.9167\n",
      "Epoch 750/2000\n",
      "84/84 [==============================] - 0s 388us/step - loss: 0.0397 - accuracy: 0.9762 - val_loss: 0.2885 - val_accuracy: 0.9167\n",
      "Epoch 751/2000\n",
      "84/84 [==============================] - 0s 357us/step - loss: 0.0397 - accuracy: 0.9762 - val_loss: 0.2912 - val_accuracy: 0.9167\n",
      "Epoch 752/2000\n",
      "84/84 [==============================] - 0s 306us/step - loss: 0.0396 - accuracy: 0.9762 - val_loss: 0.2945 - val_accuracy: 0.9167\n",
      "Epoch 753/2000\n",
      "84/84 [==============================] - 0s 304us/step - loss: 0.0396 - accuracy: 0.9762 - val_loss: 0.2963 - val_accuracy: 0.9167\n",
      "Epoch 754/2000\n",
      "84/84 [==============================] - 0s 275us/step - loss: 0.0399 - accuracy: 0.9762 - val_loss: 0.2952 - val_accuracy: 0.9167\n",
      "Epoch 755/2000\n",
      "84/84 [==============================] - 0s 253us/step - loss: 0.0402 - accuracy: 0.9762 - val_loss: 0.2978 - val_accuracy: 0.9167\n",
      "Epoch 756/2000\n",
      "84/84 [==============================] - 0s 318us/step - loss: 0.0396 - accuracy: 0.9762 - val_loss: 0.2971 - val_accuracy: 0.9167\n",
      "Epoch 757/2000\n",
      "84/84 [==============================] - 0s 361us/step - loss: 0.0394 - accuracy: 0.9762 - val_loss: 0.2953 - val_accuracy: 0.9167\n",
      "Epoch 758/2000\n",
      "84/84 [==============================] - 0s 314us/step - loss: 0.0395 - accuracy: 0.9762 - val_loss: 0.2922 - val_accuracy: 0.9167\n",
      "Epoch 759/2000\n",
      "84/84 [==============================] - 0s 263us/step - loss: 0.0396 - accuracy: 0.9762 - val_loss: 0.2880 - val_accuracy: 0.9167\n",
      "Epoch 760/2000\n",
      "84/84 [==============================] - 0s 286us/step - loss: 0.0394 - accuracy: 0.9762 - val_loss: 0.2869 - val_accuracy: 0.9167\n",
      "Epoch 761/2000\n",
      "84/84 [==============================] - 0s 336us/step - loss: 0.0396 - accuracy: 0.9762 - val_loss: 0.2887 - val_accuracy: 0.9167\n",
      "Epoch 762/2000\n",
      "84/84 [==============================] - 0s 287us/step - loss: 0.0392 - accuracy: 0.9762 - val_loss: 0.2885 - val_accuracy: 0.9167\n",
      "Epoch 763/2000\n",
      "84/84 [==============================] - 0s 449us/step - loss: 0.0395 - accuracy: 0.9762 - val_loss: 0.2904 - val_accuracy: 0.9167\n",
      "Epoch 764/2000\n",
      "84/84 [==============================] - 0s 261us/step - loss: 0.0393 - accuracy: 0.9762 - val_loss: 0.2906 - val_accuracy: 0.9167\n",
      "Epoch 765/2000\n",
      "84/84 [==============================] - 0s 243us/step - loss: 0.0392 - accuracy: 0.9762 - val_loss: 0.2942 - val_accuracy: 0.9167\n",
      "Epoch 766/2000\n",
      "84/84 [==============================] - 0s 266us/step - loss: 0.0395 - accuracy: 0.9762 - val_loss: 0.2986 - val_accuracy: 0.9167\n",
      "Epoch 767/2000\n",
      "84/84 [==============================] - 0s 349us/step - loss: 0.0392 - accuracy: 0.9762 - val_loss: 0.2987 - val_accuracy: 0.9167\n",
      "Epoch 768/2000\n",
      "84/84 [==============================] - 0s 312us/step - loss: 0.0396 - accuracy: 0.9762 - val_loss: 0.2969 - val_accuracy: 0.9167\n",
      "Epoch 769/2000\n",
      "84/84 [==============================] - 0s 361us/step - loss: 0.0392 - accuracy: 0.9762 - val_loss: 0.2987 - val_accuracy: 0.9167\n",
      "Epoch 770/2000\n",
      "84/84 [==============================] - 0s 344us/step - loss: 0.0390 - accuracy: 0.9762 - val_loss: 0.2983 - val_accuracy: 0.9167\n",
      "Epoch 771/2000\n",
      "84/84 [==============================] - 0s 260us/step - loss: 0.0389 - accuracy: 0.9762 - val_loss: 0.2994 - val_accuracy: 0.9167\n",
      "Epoch 772/2000\n",
      "84/84 [==============================] - 0s 206us/step - loss: 0.0394 - accuracy: 0.9643 - val_loss: 0.2967 - val_accuracy: 0.9167\n",
      "Epoch 773/2000\n",
      "84/84 [==============================] - 0s 241us/step - loss: 0.0388 - accuracy: 0.9643 - val_loss: 0.2965 - val_accuracy: 0.9167\n",
      "Epoch 774/2000\n",
      "84/84 [==============================] - 0s 220us/step - loss: 0.0389 - accuracy: 0.9643 - val_loss: 0.2951 - val_accuracy: 0.9167\n",
      "Epoch 775/2000\n",
      "84/84 [==============================] - 0s 241us/step - loss: 0.0387 - accuracy: 0.9643 - val_loss: 0.2945 - val_accuracy: 0.9167\n",
      "Epoch 776/2000\n",
      "84/84 [==============================] - 0s 303us/step - loss: 0.0390 - accuracy: 0.9643 - val_loss: 0.2936 - val_accuracy: 0.9167\n",
      "Epoch 777/2000\n",
      "84/84 [==============================] - 0s 281us/step - loss: 0.0387 - accuracy: 0.9643 - val_loss: 0.2947 - val_accuracy: 0.9167\n",
      "Epoch 778/2000\n",
      "84/84 [==============================] - 0s 240us/step - loss: 0.0389 - accuracy: 0.9643 - val_loss: 0.2969 - val_accuracy: 0.9167\n",
      "Epoch 779/2000\n",
      "84/84 [==============================] - 0s 269us/step - loss: 0.0386 - accuracy: 0.9643 - val_loss: 0.2964 - val_accuracy: 0.9167\n",
      "Epoch 780/2000\n",
      "84/84 [==============================] - 0s 316us/step - loss: 0.0388 - accuracy: 0.9643 - val_loss: 0.2942 - val_accuracy: 0.9167\n",
      "Epoch 781/2000\n",
      "84/84 [==============================] - 0s 300us/step - loss: 0.0390 - accuracy: 0.9643 - val_loss: 0.2920 - val_accuracy: 0.9167\n",
      "Epoch 782/2000\n",
      "84/84 [==============================] - 0s 354us/step - loss: 0.0387 - accuracy: 0.9643 - val_loss: 0.2927 - val_accuracy: 0.9167\n",
      "Epoch 783/2000\n",
      "84/84 [==============================] - 0s 344us/step - loss: 0.0387 - accuracy: 0.9643 - val_loss: 0.2949 - val_accuracy: 0.9167\n",
      "Epoch 784/2000\n",
      "84/84 [==============================] - 0s 233us/step - loss: 0.0389 - accuracy: 0.9643 - val_loss: 0.2978 - val_accuracy: 0.9167\n",
      "Epoch 785/2000\n",
      "84/84 [==============================] - 0s 230us/step - loss: 0.0385 - accuracy: 0.9762 - val_loss: 0.2962 - val_accuracy: 0.9167\n",
      "Epoch 786/2000\n",
      "84/84 [==============================] - 0s 332us/step - loss: 0.0385 - accuracy: 0.9643 - val_loss: 0.2958 - val_accuracy: 0.9167\n",
      "Epoch 787/2000\n",
      "84/84 [==============================] - 0s 266us/step - loss: 0.0386 - accuracy: 0.9702 - val_loss: 0.2969 - val_accuracy: 0.9167\n",
      "Epoch 788/2000\n",
      "84/84 [==============================] - 0s 343us/step - loss: 0.0385 - accuracy: 0.9762 - val_loss: 0.2974 - val_accuracy: 0.9167\n",
      "Epoch 789/2000\n",
      "84/84 [==============================] - 0s 267us/step - loss: 0.0383 - accuracy: 0.9762 - val_loss: 0.3005 - val_accuracy: 0.9167\n",
      "Epoch 790/2000\n",
      "84/84 [==============================] - 0s 275us/step - loss: 0.0386 - accuracy: 0.9762 - val_loss: 0.3026 - val_accuracy: 0.9167\n",
      "Epoch 791/2000\n",
      "84/84 [==============================] - 0s 305us/step - loss: 0.0391 - accuracy: 0.9762 - val_loss: 0.2991 - val_accuracy: 0.9167\n",
      "Epoch 792/2000\n",
      "84/84 [==============================] - 0s 358us/step - loss: 0.0383 - accuracy: 0.9762 - val_loss: 0.3025 - val_accuracy: 0.9167\n",
      "Epoch 793/2000\n",
      "84/84 [==============================] - 0s 376us/step - loss: 0.0383 - accuracy: 0.9762 - val_loss: 0.3016 - val_accuracy: 0.9167\n",
      "Epoch 794/2000\n",
      "84/84 [==============================] - 0s 362us/step - loss: 0.0383 - accuracy: 0.9762 - val_loss: 0.3025 - val_accuracy: 0.9167\n",
      "Epoch 795/2000\n",
      "84/84 [==============================] - 0s 255us/step - loss: 0.0382 - accuracy: 0.9762 - val_loss: 0.3002 - val_accuracy: 0.9167\n",
      "Epoch 796/2000\n",
      "84/84 [==============================] - 0s 253us/step - loss: 0.0384 - accuracy: 0.9762 - val_loss: 0.3019 - val_accuracy: 0.9167\n",
      "Epoch 797/2000\n",
      "84/84 [==============================] - 0s 253us/step - loss: 0.0385 - accuracy: 0.9762 - val_loss: 0.2989 - val_accuracy: 0.9167\n",
      "Epoch 798/2000\n",
      "84/84 [==============================] - 0s 280us/step - loss: 0.0382 - accuracy: 0.9643 - val_loss: 0.2964 - val_accuracy: 0.9167\n",
      "Epoch 799/2000\n",
      "84/84 [==============================] - 0s 349us/step - loss: 0.0381 - accuracy: 0.9643 - val_loss: 0.2952 - val_accuracy: 0.9167\n",
      "Epoch 800/2000\n",
      "84/84 [==============================] - 0s 363us/step - loss: 0.0381 - accuracy: 0.9643 - val_loss: 0.2969 - val_accuracy: 0.9167\n",
      "Epoch 801/2000\n",
      "84/84 [==============================] - 0s 292us/step - loss: 0.0381 - accuracy: 0.9643 - val_loss: 0.3004 - val_accuracy: 0.9167\n",
      "Epoch 802/2000\n",
      "84/84 [==============================] - 0s 283us/step - loss: 0.0378 - accuracy: 0.9762 - val_loss: 0.3037 - val_accuracy: 0.9167\n",
      "Epoch 803/2000\n",
      "84/84 [==============================] - 0s 306us/step - loss: 0.0387 - accuracy: 0.9881 - val_loss: 0.3095 - val_accuracy: 0.9167\n",
      "Epoch 804/2000\n",
      "84/84 [==============================] - 0s 297us/step - loss: 0.0381 - accuracy: 0.9881 - val_loss: 0.3074 - val_accuracy: 0.9167\n",
      "Epoch 805/2000\n",
      "84/84 [==============================] - 0s 347us/step - loss: 0.0384 - accuracy: 0.9881 - val_loss: 0.3034 - val_accuracy: 0.9167\n",
      "Epoch 806/2000\n",
      "84/84 [==============================] - 0s 322us/step - loss: 0.0381 - accuracy: 0.9881 - val_loss: 0.3041 - val_accuracy: 0.9167\n",
      "Epoch 807/2000\n",
      "84/84 [==============================] - 0s 268us/step - loss: 0.0382 - accuracy: 0.9881 - val_loss: 0.3051 - val_accuracy: 0.9167\n",
      "Epoch 808/2000\n",
      "84/84 [==============================] - 0s 274us/step - loss: 0.0379 - accuracy: 0.9881 - val_loss: 0.3023 - val_accuracy: 0.9167\n",
      "Epoch 809/2000\n",
      "84/84 [==============================] - 0s 255us/step - loss: 0.0377 - accuracy: 0.9881 - val_loss: 0.3012 - val_accuracy: 0.9167\n",
      "Epoch 810/2000\n",
      "84/84 [==============================] - 0s 221us/step - loss: 0.0377 - accuracy: 0.9881 - val_loss: 0.3010 - val_accuracy: 0.9167\n",
      "Epoch 811/2000\n",
      "84/84 [==============================] - 0s 231us/step - loss: 0.0377 - accuracy: 0.9762 - val_loss: 0.2983 - val_accuracy: 0.9167\n",
      "Epoch 812/2000\n",
      "84/84 [==============================] - 0s 205us/step - loss: 0.0376 - accuracy: 0.9762 - val_loss: 0.2985 - val_accuracy: 0.9167\n",
      "Epoch 813/2000\n",
      "84/84 [==============================] - 0s 205us/step - loss: 0.0375 - accuracy: 0.9762 - val_loss: 0.2987 - val_accuracy: 0.9167\n",
      "Epoch 814/2000\n",
      "84/84 [==============================] - 0s 206us/step - loss: 0.0375 - accuracy: 0.9762 - val_loss: 0.2998 - val_accuracy: 0.9167\n",
      "Epoch 815/2000\n",
      "84/84 [==============================] - 0s 255us/step - loss: 0.0380 - accuracy: 0.9643 - val_loss: 0.2984 - val_accuracy: 0.9167\n",
      "Epoch 816/2000\n",
      "84/84 [==============================] - 0s 280us/step - loss: 0.0381 - accuracy: 0.9643 - val_loss: 0.3033 - val_accuracy: 0.9167\n",
      "Epoch 817/2000\n",
      "84/84 [==============================] - 0s 279us/step - loss: 0.0376 - accuracy: 0.9762 - val_loss: 0.3016 - val_accuracy: 0.9167\n",
      "Epoch 818/2000\n",
      "84/84 [==============================] - 0s 288us/step - loss: 0.0373 - accuracy: 0.9762 - val_loss: 0.3025 - val_accuracy: 0.9167\n",
      "Epoch 819/2000\n",
      "84/84 [==============================] - 0s 311us/step - loss: 0.0378 - accuracy: 0.9762 - val_loss: 0.3033 - val_accuracy: 0.9167\n",
      "Epoch 820/2000\n",
      "84/84 [==============================] - 0s 406us/step - loss: 0.0373 - accuracy: 0.9881 - val_loss: 0.3010 - val_accuracy: 0.9167\n",
      "Epoch 821/2000\n",
      "84/84 [==============================] - 0s 406us/step - loss: 0.0373 - accuracy: 0.9643 - val_loss: 0.2991 - val_accuracy: 0.9167\n",
      "Epoch 822/2000\n",
      "84/84 [==============================] - 0s 387us/step - loss: 0.0372 - accuracy: 0.9643 - val_loss: 0.2987 - val_accuracy: 0.9167\n",
      "Epoch 823/2000\n",
      "84/84 [==============================] - 0s 376us/step - loss: 0.0373 - accuracy: 0.9643 - val_loss: 0.2970 - val_accuracy: 0.9167\n",
      "Epoch 824/2000\n",
      "84/84 [==============================] - 0s 269us/step - loss: 0.0375 - accuracy: 0.9643 - val_loss: 0.2969 - val_accuracy: 0.9167\n",
      "Epoch 825/2000\n",
      "84/84 [==============================] - 0s 239us/step - loss: 0.0372 - accuracy: 0.9762 - val_loss: 0.2962 - val_accuracy: 0.9167\n",
      "Epoch 826/2000\n",
      "84/84 [==============================] - 0s 282us/step - loss: 0.0374 - accuracy: 0.9643 - val_loss: 0.2965 - val_accuracy: 0.9167\n",
      "Epoch 827/2000\n",
      "84/84 [==============================] - 0s 302us/step - loss: 0.0377 - accuracy: 0.9643 - val_loss: 0.3024 - val_accuracy: 0.9167\n",
      "Epoch 828/2000\n",
      "84/84 [==============================] - 0s 418us/step - loss: 0.0371 - accuracy: 0.9762 - val_loss: 0.3029 - val_accuracy: 0.9167\n",
      "Epoch 829/2000\n",
      "84/84 [==============================] - 0s 317us/step - loss: 0.0373 - accuracy: 0.9881 - val_loss: 0.3045 - val_accuracy: 0.9167\n",
      "Epoch 830/2000\n",
      "84/84 [==============================] - 0s 369us/step - loss: 0.0375 - accuracy: 0.9643 - val_loss: 0.2999 - val_accuracy: 0.9167\n",
      "Epoch 831/2000\n",
      "84/84 [==============================] - 0s 875us/step - loss: 0.0370 - accuracy: 0.9643 - val_loss: 0.3001 - val_accuracy: 0.9167\n",
      "Epoch 832/2000\n",
      "84/84 [==============================] - 0s 577us/step - loss: 0.0371 - accuracy: 0.9762 - val_loss: 0.3020 - val_accuracy: 0.9167\n",
      "Epoch 833/2000\n",
      "84/84 [==============================] - 0s 309us/step - loss: 0.0369 - accuracy: 0.9762 - val_loss: 0.3016 - val_accuracy: 0.9167\n",
      "Epoch 834/2000\n",
      "84/84 [==============================] - 0s 263us/step - loss: 0.0368 - accuracy: 0.9762 - val_loss: 0.3006 - val_accuracy: 0.9167\n",
      "Epoch 835/2000\n",
      "84/84 [==============================] - 0s 285us/step - loss: 0.0368 - accuracy: 0.9702 - val_loss: 0.3014 - val_accuracy: 0.9167\n",
      "Epoch 836/2000\n",
      "84/84 [==============================] - 0s 321us/step - loss: 0.0370 - accuracy: 0.9643 - val_loss: 0.3009 - val_accuracy: 0.9167\n",
      "Epoch 837/2000\n",
      "84/84 [==============================] - 0s 299us/step - loss: 0.0371 - accuracy: 0.9643 - val_loss: 0.3010 - val_accuracy: 0.9167\n",
      "Epoch 838/2000\n",
      "84/84 [==============================] - 0s 351us/step - loss: 0.0369 - accuracy: 0.9643 - val_loss: 0.2993 - val_accuracy: 0.9167\n",
      "Epoch 839/2000\n",
      "84/84 [==============================] - 0s 288us/step - loss: 0.0368 - accuracy: 0.9643 - val_loss: 0.2981 - val_accuracy: 0.9167\n",
      "Epoch 840/2000\n",
      "84/84 [==============================] - 0s 300us/step - loss: 0.0367 - accuracy: 0.9643 - val_loss: 0.2976 - val_accuracy: 0.9167\n",
      "Epoch 841/2000\n",
      "84/84 [==============================] - 0s 291us/step - loss: 0.0367 - accuracy: 0.9702 - val_loss: 0.3003 - val_accuracy: 0.9167\n",
      "Epoch 842/2000\n",
      "84/84 [==============================] - 0s 304us/step - loss: 0.0367 - accuracy: 0.9643 - val_loss: 0.3015 - val_accuracy: 0.9167\n",
      "Epoch 843/2000\n",
      "84/84 [==============================] - 0s 256us/step - loss: 0.0367 - accuracy: 0.9643 - val_loss: 0.3021 - val_accuracy: 0.9167\n",
      "Epoch 844/2000\n",
      "84/84 [==============================] - 0s 271us/step - loss: 0.0367 - accuracy: 0.9881 - val_loss: 0.3065 - val_accuracy: 0.9167\n",
      "Epoch 845/2000\n",
      "84/84 [==============================] - 0s 261us/step - loss: 0.0366 - accuracy: 0.9881 - val_loss: 0.3082 - val_accuracy: 0.9167\n",
      "Epoch 846/2000\n",
      "84/84 [==============================] - 0s 278us/step - loss: 0.0366 - accuracy: 0.9881 - val_loss: 0.3085 - val_accuracy: 0.9167\n",
      "Epoch 847/2000\n",
      "84/84 [==============================] - 0s 223us/step - loss: 0.0365 - accuracy: 0.9881 - val_loss: 0.3065 - val_accuracy: 0.9167\n",
      "Epoch 848/2000\n",
      "84/84 [==============================] - 0s 292us/step - loss: 0.0364 - accuracy: 0.9881 - val_loss: 0.3049 - val_accuracy: 0.9167\n",
      "Epoch 849/2000\n",
      "84/84 [==============================] - 0s 283us/step - loss: 0.0366 - accuracy: 0.9762 - val_loss: 0.3031 - val_accuracy: 0.9167\n",
      "Epoch 850/2000\n",
      "84/84 [==============================] - 0s 282us/step - loss: 0.0363 - accuracy: 0.9881 - val_loss: 0.3044 - val_accuracy: 0.9167\n",
      "Epoch 851/2000\n",
      "84/84 [==============================] - 0s 277us/step - loss: 0.0364 - accuracy: 0.9881 - val_loss: 0.3024 - val_accuracy: 0.9167\n",
      "Epoch 852/2000\n",
      "84/84 [==============================] - 0s 255us/step - loss: 0.0363 - accuracy: 0.9821 - val_loss: 0.3015 - val_accuracy: 0.9167\n",
      "Epoch 853/2000\n",
      "84/84 [==============================] - 0s 280us/step - loss: 0.0364 - accuracy: 0.9881 - val_loss: 0.3036 - val_accuracy: 0.9167\n",
      "Epoch 854/2000\n",
      "84/84 [==============================] - 0s 249us/step - loss: 0.0363 - accuracy: 0.9881 - val_loss: 0.3040 - val_accuracy: 0.9167\n",
      "Epoch 855/2000\n",
      "84/84 [==============================] - 0s 234us/step - loss: 0.0364 - accuracy: 0.9881 - val_loss: 0.3026 - val_accuracy: 0.9167\n",
      "Epoch 856/2000\n",
      "84/84 [==============================] - 0s 244us/step - loss: 0.0364 - accuracy: 0.9762 - val_loss: 0.2983 - val_accuracy: 0.9167\n",
      "Epoch 857/2000\n",
      "84/84 [==============================] - 0s 274us/step - loss: 0.0365 - accuracy: 0.9762 - val_loss: 0.2960 - val_accuracy: 0.9167\n",
      "Epoch 858/2000\n",
      "84/84 [==============================] - 0s 250us/step - loss: 0.0362 - accuracy: 0.9643 - val_loss: 0.2962 - val_accuracy: 0.9167\n",
      "Epoch 859/2000\n",
      "84/84 [==============================] - 0s 302us/step - loss: 0.0363 - accuracy: 0.9643 - val_loss: 0.2973 - val_accuracy: 0.9167\n",
      "Epoch 860/2000\n",
      "84/84 [==============================] - 0s 278us/step - loss: 0.0360 - accuracy: 0.9762 - val_loss: 0.3007 - val_accuracy: 0.9167\n",
      "Epoch 861/2000\n",
      "84/84 [==============================] - 0s 275us/step - loss: 0.0364 - accuracy: 0.9762 - val_loss: 0.3073 - val_accuracy: 0.9167\n",
      "Epoch 862/2000\n",
      "84/84 [==============================] - 0s 315us/step - loss: 0.0359 - accuracy: 0.9881 - val_loss: 0.3089 - val_accuracy: 0.9167\n",
      "Epoch 863/2000\n",
      "84/84 [==============================] - 0s 282us/step - loss: 0.0361 - accuracy: 0.9881 - val_loss: 0.3122 - val_accuracy: 0.9167\n",
      "Epoch 864/2000\n",
      "84/84 [==============================] - 0s 328us/step - loss: 0.0361 - accuracy: 0.9881 - val_loss: 0.3122 - val_accuracy: 0.9167\n",
      "Epoch 865/2000\n",
      "84/84 [==============================] - 0s 266us/step - loss: 0.0359 - accuracy: 0.9881 - val_loss: 0.3077 - val_accuracy: 0.9167\n",
      "Epoch 866/2000\n",
      "84/84 [==============================] - 0s 263us/step - loss: 0.0359 - accuracy: 0.9762 - val_loss: 0.3058 - val_accuracy: 0.9167\n",
      "Epoch 867/2000\n",
      "84/84 [==============================] - 0s 248us/step - loss: 0.0366 - accuracy: 0.9762 - val_loss: 0.2994 - val_accuracy: 0.9167\n",
      "Epoch 868/2000\n",
      "84/84 [==============================] - 0s 270us/step - loss: 0.0361 - accuracy: 0.9643 - val_loss: 0.2995 - val_accuracy: 0.9167\n",
      "Epoch 869/2000\n",
      "84/84 [==============================] - 0s 262us/step - loss: 0.0359 - accuracy: 0.9702 - val_loss: 0.2988 - val_accuracy: 0.9167\n",
      "Epoch 870/2000\n",
      "84/84 [==============================] - 0s 267us/step - loss: 0.0359 - accuracy: 0.9643 - val_loss: 0.2987 - val_accuracy: 0.9167\n",
      "Epoch 871/2000\n",
      "84/84 [==============================] - 0s 272us/step - loss: 0.0359 - accuracy: 0.9643 - val_loss: 0.2995 - val_accuracy: 0.9167\n",
      "Epoch 872/2000\n",
      "84/84 [==============================] - 0s 275us/step - loss: 0.0360 - accuracy: 0.9643 - val_loss: 0.3020 - val_accuracy: 0.9167\n",
      "Epoch 873/2000\n",
      "84/84 [==============================] - 0s 303us/step - loss: 0.0358 - accuracy: 0.9643 - val_loss: 0.3027 - val_accuracy: 0.9167\n",
      "Epoch 874/2000\n",
      "84/84 [==============================] - 0s 289us/step - loss: 0.0358 - accuracy: 0.9643 - val_loss: 0.3034 - val_accuracy: 0.9167\n",
      "Epoch 875/2000\n",
      "84/84 [==============================] - 0s 212us/step - loss: 0.0356 - accuracy: 0.9643 - val_loss: 0.3052 - val_accuracy: 0.9167\n",
      "Epoch 876/2000\n",
      "84/84 [==============================] - 0s 207us/step - loss: 0.0357 - accuracy: 0.9762 - val_loss: 0.3108 - val_accuracy: 0.9167\n",
      "Epoch 877/2000\n",
      "84/84 [==============================] - 0s 245us/step - loss: 0.0355 - accuracy: 0.9762 - val_loss: 0.3126 - val_accuracy: 0.9167\n",
      "Epoch 878/2000\n",
      "84/84 [==============================] - 0s 653us/step - loss: 0.0357 - accuracy: 0.9762 - val_loss: 0.3150 - val_accuracy: 0.9167\n",
      "Epoch 879/2000\n",
      "84/84 [==============================] - 0s 789us/step - loss: 0.0355 - accuracy: 0.9881 - val_loss: 0.3146 - val_accuracy: 0.9167\n",
      "Epoch 880/2000\n",
      "84/84 [==============================] - 0s 301us/step - loss: 0.0354 - accuracy: 0.9881 - val_loss: 0.3156 - val_accuracy: 0.9167\n",
      "Epoch 881/2000\n",
      "84/84 [==============================] - 0s 294us/step - loss: 0.0356 - accuracy: 0.9881 - val_loss: 0.3159 - val_accuracy: 0.9167\n",
      "Epoch 882/2000\n",
      "84/84 [==============================] - 0s 307us/step - loss: 0.0353 - accuracy: 0.9881 - val_loss: 0.3139 - val_accuracy: 0.9167\n",
      "Epoch 883/2000\n",
      "84/84 [==============================] - 0s 278us/step - loss: 0.0354 - accuracy: 0.9821 - val_loss: 0.3117 - val_accuracy: 0.9167\n",
      "Epoch 884/2000\n",
      "84/84 [==============================] - 0s 280us/step - loss: 0.0361 - accuracy: 0.9762 - val_loss: 0.3087 - val_accuracy: 0.9167\n",
      "Epoch 885/2000\n",
      "84/84 [==============================] - 0s 364us/step - loss: 0.0352 - accuracy: 0.9762 - val_loss: 0.3109 - val_accuracy: 0.9167\n",
      "Epoch 886/2000\n",
      "84/84 [==============================] - 0s 387us/step - loss: 0.0353 - accuracy: 0.9762 - val_loss: 0.3131 - val_accuracy: 0.9167\n",
      "Epoch 887/2000\n",
      "84/84 [==============================] - 0s 289us/step - loss: 0.0353 - accuracy: 0.9762 - val_loss: 0.3130 - val_accuracy: 0.9167\n",
      "Epoch 888/2000\n",
      "84/84 [==============================] - 0s 367us/step - loss: 0.0352 - accuracy: 0.9762 - val_loss: 0.3152 - val_accuracy: 0.9167\n",
      "Epoch 889/2000\n",
      "84/84 [==============================] - 0s 253us/step - loss: 0.0353 - accuracy: 0.9881 - val_loss: 0.3142 - val_accuracy: 0.9167\n",
      "Epoch 890/2000\n",
      "84/84 [==============================] - 0s 283us/step - loss: 0.0351 - accuracy: 0.9881 - val_loss: 0.3108 - val_accuracy: 0.9167\n",
      "Epoch 891/2000\n",
      "84/84 [==============================] - 0s 271us/step - loss: 0.0351 - accuracy: 0.9881 - val_loss: 0.3094 - val_accuracy: 0.9167\n",
      "Epoch 892/2000\n",
      "84/84 [==============================] - 0s 332us/step - loss: 0.0352 - accuracy: 0.9881 - val_loss: 0.3100 - val_accuracy: 0.9167\n",
      "Epoch 893/2000\n",
      "84/84 [==============================] - 0s 395us/step - loss: 0.0351 - accuracy: 0.9881 - val_loss: 0.3078 - val_accuracy: 0.9167\n",
      "Epoch 894/2000\n",
      "84/84 [==============================] - 0s 384us/step - loss: 0.0350 - accuracy: 0.9762 - val_loss: 0.3096 - val_accuracy: 0.9167\n",
      "Epoch 895/2000\n",
      "84/84 [==============================] - 0s 400us/step - loss: 0.0350 - accuracy: 0.9762 - val_loss: 0.3100 - val_accuracy: 0.9167\n",
      "Epoch 896/2000\n",
      "84/84 [==============================] - 0s 385us/step - loss: 0.0352 - accuracy: 0.9762 - val_loss: 0.3138 - val_accuracy: 0.9167\n",
      "Epoch 897/2000\n",
      "84/84 [==============================] - 0s 347us/step - loss: 0.0351 - accuracy: 0.9762 - val_loss: 0.3132 - val_accuracy: 0.9167\n",
      "Epoch 898/2000\n",
      "84/84 [==============================] - 0s 388us/step - loss: 0.0350 - accuracy: 0.9762 - val_loss: 0.3136 - val_accuracy: 0.9167\n",
      "Epoch 899/2000\n",
      "84/84 [==============================] - 0s 395us/step - loss: 0.0349 - accuracy: 0.9762 - val_loss: 0.3142 - val_accuracy: 0.9167\n",
      "Epoch 900/2000\n",
      "84/84 [==============================] - 0s 383us/step - loss: 0.0348 - accuracy: 0.9762 - val_loss: 0.3128 - val_accuracy: 0.9167\n",
      "Epoch 901/2000\n",
      "84/84 [==============================] - 0s 251us/step - loss: 0.0351 - accuracy: 0.9762 - val_loss: 0.3123 - val_accuracy: 0.9167\n",
      "Epoch 902/2000\n",
      "84/84 [==============================] - 0s 479us/step - loss: 0.0348 - accuracy: 0.9762 - val_loss: 0.3109 - val_accuracy: 0.9167\n",
      "Epoch 903/2000\n",
      "84/84 [==============================] - 0s 415us/step - loss: 0.0347 - accuracy: 0.9762 - val_loss: 0.3125 - val_accuracy: 0.9167\n",
      "Epoch 904/2000\n",
      "84/84 [==============================] - 0s 403us/step - loss: 0.0350 - accuracy: 0.9762 - val_loss: 0.3160 - val_accuracy: 0.9167\n",
      "Epoch 905/2000\n",
      "84/84 [==============================] - 0s 415us/step - loss: 0.0347 - accuracy: 0.9821 - val_loss: 0.3162 - val_accuracy: 0.9167\n",
      "Epoch 906/2000\n",
      "84/84 [==============================] - 0s 409us/step - loss: 0.0346 - accuracy: 0.9762 - val_loss: 0.3156 - val_accuracy: 0.9167\n",
      "Epoch 907/2000\n",
      "84/84 [==============================] - 0s 408us/step - loss: 0.0349 - accuracy: 0.9762 - val_loss: 0.3145 - val_accuracy: 0.9167\n",
      "Epoch 908/2000\n",
      "84/84 [==============================] - 0s 386us/step - loss: 0.0347 - accuracy: 0.9762 - val_loss: 0.3140 - val_accuracy: 0.9167\n",
      "Epoch 909/2000\n",
      "84/84 [==============================] - 0s 355us/step - loss: 0.0346 - accuracy: 0.9762 - val_loss: 0.3134 - val_accuracy: 0.9167\n",
      "Epoch 910/2000\n",
      "84/84 [==============================] - 0s 359us/step - loss: 0.0346 - accuracy: 0.9762 - val_loss: 0.3120 - val_accuracy: 0.9167\n",
      "Epoch 911/2000\n",
      "84/84 [==============================] - 0s 399us/step - loss: 0.0346 - accuracy: 0.9762 - val_loss: 0.3120 - val_accuracy: 0.9167\n",
      "Epoch 912/2000\n",
      "84/84 [==============================] - 0s 278us/step - loss: 0.0346 - accuracy: 0.9762 - val_loss: 0.3108 - val_accuracy: 0.9167\n",
      "Epoch 913/2000\n",
      "84/84 [==============================] - 0s 325us/step - loss: 0.0345 - accuracy: 0.9762 - val_loss: 0.3090 - val_accuracy: 0.9167\n",
      "Epoch 914/2000\n",
      "84/84 [==============================] - 0s 363us/step - loss: 0.0346 - accuracy: 0.9762 - val_loss: 0.3102 - val_accuracy: 0.9167\n",
      "Epoch 915/2000\n",
      "84/84 [==============================] - 0s 307us/step - loss: 0.0346 - accuracy: 0.9881 - val_loss: 0.3104 - val_accuracy: 0.9167\n",
      "Epoch 916/2000\n",
      "84/84 [==============================] - 0s 311us/step - loss: 0.0345 - accuracy: 0.9762 - val_loss: 0.3141 - val_accuracy: 0.9167\n",
      "Epoch 917/2000\n",
      "84/84 [==============================] - 0s 281us/step - loss: 0.0343 - accuracy: 0.9762 - val_loss: 0.3174 - val_accuracy: 0.9167\n",
      "Epoch 918/2000\n",
      "84/84 [==============================] - 0s 282us/step - loss: 0.0342 - accuracy: 0.9762 - val_loss: 0.3212 - val_accuracy: 0.9167\n",
      "Epoch 919/2000\n",
      "84/84 [==============================] - 0s 285us/step - loss: 0.0342 - accuracy: 0.9762 - val_loss: 0.3251 - val_accuracy: 0.8889\n",
      "Epoch 920/2000\n",
      "84/84 [==============================] - 0s 251us/step - loss: 0.0342 - accuracy: 0.9881 - val_loss: 0.3261 - val_accuracy: 0.8889\n",
      "Epoch 921/2000\n",
      "84/84 [==============================] - 0s 248us/step - loss: 0.0345 - accuracy: 0.9881 - val_loss: 0.3257 - val_accuracy: 0.8889\n",
      "Epoch 922/2000\n",
      "84/84 [==============================] - 0s 277us/step - loss: 0.0342 - accuracy: 0.9881 - val_loss: 0.3211 - val_accuracy: 0.9167\n",
      "Epoch 923/2000\n",
      "84/84 [==============================] - 0s 316us/step - loss: 0.0345 - accuracy: 0.9881 - val_loss: 0.3169 - val_accuracy: 0.9167\n",
      "Epoch 924/2000\n",
      "84/84 [==============================] - 0s 257us/step - loss: 0.0341 - accuracy: 0.9881 - val_loss: 0.3167 - val_accuracy: 0.9167\n",
      "Epoch 925/2000\n",
      "84/84 [==============================] - 0s 251us/step - loss: 0.0341 - accuracy: 0.9881 - val_loss: 0.3167 - val_accuracy: 0.9167\n",
      "Epoch 926/2000\n",
      "84/84 [==============================] - 0s 300us/step - loss: 0.0342 - accuracy: 0.9881 - val_loss: 0.3174 - val_accuracy: 0.9167\n",
      "Epoch 927/2000\n",
      "84/84 [==============================] - 0s 268us/step - loss: 0.0340 - accuracy: 0.9881 - val_loss: 0.3157 - val_accuracy: 0.9167\n",
      "Epoch 928/2000\n",
      "84/84 [==============================] - 0s 277us/step - loss: 0.0341 - accuracy: 0.9762 - val_loss: 0.3128 - val_accuracy: 0.9167\n",
      "Epoch 929/2000\n",
      "84/84 [==============================] - 0s 445us/step - loss: 0.0339 - accuracy: 0.9762 - val_loss: 0.3108 - val_accuracy: 0.9167\n",
      "Epoch 930/2000\n",
      "84/84 [==============================] - 0s 329us/step - loss: 0.0343 - accuracy: 0.9762 - val_loss: 0.3088 - val_accuracy: 0.9167\n",
      "Epoch 931/2000\n",
      "84/84 [==============================] - 0s 489us/step - loss: 0.0342 - accuracy: 0.9762 - val_loss: 0.3102 - val_accuracy: 0.9167\n",
      "Epoch 932/2000\n",
      "84/84 [==============================] - 0s 422us/step - loss: 0.0339 - accuracy: 0.9762 - val_loss: 0.3140 - val_accuracy: 0.9167\n",
      "Epoch 933/2000\n",
      "84/84 [==============================] - 0s 378us/step - loss: 0.0338 - accuracy: 0.9762 - val_loss: 0.3176 - val_accuracy: 0.9167\n",
      "Epoch 934/2000\n",
      "84/84 [==============================] - 0s 393us/step - loss: 0.0339 - accuracy: 0.9762 - val_loss: 0.3190 - val_accuracy: 0.9167\n",
      "Epoch 935/2000\n",
      "84/84 [==============================] - 0s 392us/step - loss: 0.0343 - accuracy: 0.9762 - val_loss: 0.3232 - val_accuracy: 0.8889\n",
      "Epoch 936/2000\n",
      "84/84 [==============================] - 0s 326us/step - loss: 0.0337 - accuracy: 0.9881 - val_loss: 0.3225 - val_accuracy: 0.8889\n",
      "Epoch 937/2000\n",
      "84/84 [==============================] - 0s 362us/step - loss: 0.0339 - accuracy: 0.9881 - val_loss: 0.3208 - val_accuracy: 0.9167\n",
      "Epoch 938/2000\n",
      "84/84 [==============================] - 0s 347us/step - loss: 0.0339 - accuracy: 0.9881 - val_loss: 0.3223 - val_accuracy: 0.8889\n",
      "Epoch 939/2000\n",
      "84/84 [==============================] - 0s 395us/step - loss: 0.0337 - accuracy: 0.9881 - val_loss: 0.3196 - val_accuracy: 0.8889\n",
      "Epoch 940/2000\n",
      "84/84 [==============================] - 0s 394us/step - loss: 0.0338 - accuracy: 0.9762 - val_loss: 0.3213 - val_accuracy: 0.8889\n",
      "Epoch 941/2000\n",
      "84/84 [==============================] - 0s 244us/step - loss: 0.0339 - accuracy: 0.9762 - val_loss: 0.3190 - val_accuracy: 0.8889\n",
      "Epoch 942/2000\n",
      "84/84 [==============================] - 0s 248us/step - loss: 0.0338 - accuracy: 0.9762 - val_loss: 0.3177 - val_accuracy: 0.9167\n",
      "Epoch 943/2000\n",
      "84/84 [==============================] - 0s 349us/step - loss: 0.0337 - accuracy: 0.9762 - val_loss: 0.3180 - val_accuracy: 0.9167\n",
      "Epoch 944/2000\n",
      "84/84 [==============================] - 0s 331us/step - loss: 0.0338 - accuracy: 0.9762 - val_loss: 0.3161 - val_accuracy: 0.9167\n",
      "Epoch 945/2000\n",
      "84/84 [==============================] - 0s 264us/step - loss: 0.0335 - accuracy: 0.9762 - val_loss: 0.3187 - val_accuracy: 0.9167\n",
      "Epoch 946/2000\n",
      "84/84 [==============================] - 0s 283us/step - loss: 0.0339 - accuracy: 0.9881 - val_loss: 0.3219 - val_accuracy: 0.9028\n",
      "Epoch 947/2000\n",
      "84/84 [==============================] - 0s 309us/step - loss: 0.0335 - accuracy: 0.9881 - val_loss: 0.3202 - val_accuracy: 0.9167\n",
      "Epoch 948/2000\n",
      "84/84 [==============================] - 0s 268us/step - loss: 0.0337 - accuracy: 0.9881 - val_loss: 0.3194 - val_accuracy: 0.9167\n",
      "Epoch 949/2000\n",
      "84/84 [==============================] - 0s 266us/step - loss: 0.0339 - accuracy: 0.9881 - val_loss: 0.3162 - val_accuracy: 0.9167\n",
      "Epoch 950/2000\n",
      "84/84 [==============================] - 0s 253us/step - loss: 0.0342 - accuracy: 0.9762 - val_loss: 0.3149 - val_accuracy: 0.9167\n",
      "Epoch 951/2000\n",
      "84/84 [==============================] - 0s 206us/step - loss: 0.0333 - accuracy: 0.9762 - val_loss: 0.3197 - val_accuracy: 0.9167\n",
      "Epoch 952/2000\n",
      "84/84 [==============================] - 0s 223us/step - loss: 0.0333 - accuracy: 0.9881 - val_loss: 0.3239 - val_accuracy: 0.9167\n",
      "Epoch 953/2000\n",
      "84/84 [==============================] - 0s 223us/step - loss: 0.0334 - accuracy: 0.9881 - val_loss: 0.3252 - val_accuracy: 0.9167\n",
      "Epoch 954/2000\n",
      "84/84 [==============================] - 0s 233us/step - loss: 0.0334 - accuracy: 0.9881 - val_loss: 0.3254 - val_accuracy: 0.8889\n",
      "Epoch 955/2000\n",
      "84/84 [==============================] - 0s 253us/step - loss: 0.0335 - accuracy: 0.9881 - val_loss: 0.3230 - val_accuracy: 0.9167\n",
      "Epoch 956/2000\n",
      "84/84 [==============================] - 0s 254us/step - loss: 0.0333 - accuracy: 0.9881 - val_loss: 0.3250 - val_accuracy: 0.8889\n",
      "Epoch 957/2000\n",
      "84/84 [==============================] - 0s 233us/step - loss: 0.0334 - accuracy: 0.9881 - val_loss: 0.3263 - val_accuracy: 0.8889\n",
      "Epoch 958/2000\n",
      "84/84 [==============================] - 0s 265us/step - loss: 0.0332 - accuracy: 0.9881 - val_loss: 0.3260 - val_accuracy: 0.8889\n",
      "Epoch 959/2000\n",
      "84/84 [==============================] - 0s 295us/step - loss: 0.0331 - accuracy: 0.9881 - val_loss: 0.3248 - val_accuracy: 0.8889\n",
      "Epoch 960/2000\n",
      "84/84 [==============================] - 0s 266us/step - loss: 0.0332 - accuracy: 0.9881 - val_loss: 0.3201 - val_accuracy: 0.9167\n",
      "Epoch 961/2000\n",
      "84/84 [==============================] - 0s 505us/step - loss: 0.0333 - accuracy: 0.9881 - val_loss: 0.3163 - val_accuracy: 0.9167\n",
      "Epoch 962/2000\n",
      "84/84 [==============================] - 0s 251us/step - loss: 0.0336 - accuracy: 0.9881 - val_loss: 0.3184 - val_accuracy: 0.9167\n",
      "Epoch 963/2000\n",
      "84/84 [==============================] - 0s 341us/step - loss: 0.0330 - accuracy: 0.9881 - val_loss: 0.3169 - val_accuracy: 0.9167\n",
      "Epoch 964/2000\n",
      "84/84 [==============================] - 0s 245us/step - loss: 0.0330 - accuracy: 0.9881 - val_loss: 0.3179 - val_accuracy: 0.9167\n",
      "Epoch 965/2000\n",
      "84/84 [==============================] - 0s 312us/step - loss: 0.0329 - accuracy: 0.9881 - val_loss: 0.3171 - val_accuracy: 0.9167\n",
      "Epoch 966/2000\n",
      "84/84 [==============================] - 0s 345us/step - loss: 0.0334 - accuracy: 0.9762 - val_loss: 0.3143 - val_accuracy: 0.9167\n",
      "Epoch 967/2000\n",
      "84/84 [==============================] - 0s 390us/step - loss: 0.0328 - accuracy: 0.9762 - val_loss: 0.3170 - val_accuracy: 0.9167\n",
      "Epoch 968/2000\n",
      "84/84 [==============================] - 0s 357us/step - loss: 0.0330 - accuracy: 0.9762 - val_loss: 0.3192 - val_accuracy: 0.9167\n",
      "Epoch 969/2000\n",
      "84/84 [==============================] - 0s 319us/step - loss: 0.0331 - accuracy: 0.9762 - val_loss: 0.3249 - val_accuracy: 0.8889\n",
      "Epoch 970/2000\n",
      "84/84 [==============================] - 0s 284us/step - loss: 0.0328 - accuracy: 0.9881 - val_loss: 0.3277 - val_accuracy: 0.8889\n",
      "Epoch 971/2000\n",
      "84/84 [==============================] - 0s 235us/step - loss: 0.0331 - accuracy: 0.9881 - val_loss: 0.3300 - val_accuracy: 0.8889\n",
      "Epoch 972/2000\n",
      "84/84 [==============================] - 0s 312us/step - loss: 0.0328 - accuracy: 0.9881 - val_loss: 0.3291 - val_accuracy: 0.8889\n",
      "Epoch 973/2000\n",
      "84/84 [==============================] - 0s 283us/step - loss: 0.0330 - accuracy: 0.9881 - val_loss: 0.3278 - val_accuracy: 0.8889\n",
      "Epoch 974/2000\n",
      "84/84 [==============================] - 0s 354us/step - loss: 0.0328 - accuracy: 0.9881 - val_loss: 0.3282 - val_accuracy: 0.8889\n",
      "Epoch 975/2000\n",
      "84/84 [==============================] - 0s 804us/step - loss: 0.0329 - accuracy: 0.9881 - val_loss: 0.3260 - val_accuracy: 0.8889\n",
      "Epoch 976/2000\n",
      "84/84 [==============================] - 0s 328us/step - loss: 0.0327 - accuracy: 0.9881 - val_loss: 0.3250 - val_accuracy: 0.8889\n",
      "Epoch 977/2000\n",
      "84/84 [==============================] - 0s 470us/step - loss: 0.0326 - accuracy: 0.9881 - val_loss: 0.3270 - val_accuracy: 0.8889\n",
      "Epoch 978/2000\n",
      "84/84 [==============================] - 0s 304us/step - loss: 0.0327 - accuracy: 0.9881 - val_loss: 0.3253 - val_accuracy: 0.8889\n",
      "Epoch 979/2000\n",
      "84/84 [==============================] - 0s 396us/step - loss: 0.0324 - accuracy: 0.9881 - val_loss: 0.3268 - val_accuracy: 0.8889\n",
      "Epoch 980/2000\n",
      "84/84 [==============================] - 0s 401us/step - loss: 0.0324 - accuracy: 0.9881 - val_loss: 0.3267 - val_accuracy: 0.8889\n",
      "Epoch 981/2000\n",
      "84/84 [==============================] - 0s 408us/step - loss: 0.0324 - accuracy: 0.9881 - val_loss: 0.3272 - val_accuracy: 0.8889\n",
      "Epoch 982/2000\n",
      "84/84 [==============================] - 0s 316us/step - loss: 0.0325 - accuracy: 0.9881 - val_loss: 0.3252 - val_accuracy: 0.8889\n",
      "Epoch 983/2000\n",
      "84/84 [==============================] - 0s 358us/step - loss: 0.0325 - accuracy: 0.9881 - val_loss: 0.3258 - val_accuracy: 0.8889\n",
      "Epoch 984/2000\n",
      "84/84 [==============================] - 0s 303us/step - loss: 0.0325 - accuracy: 0.9881 - val_loss: 0.3252 - val_accuracy: 0.8889\n",
      "Epoch 985/2000\n",
      "84/84 [==============================] - 0s 299us/step - loss: 0.0324 - accuracy: 0.9881 - val_loss: 0.3271 - val_accuracy: 0.8889\n",
      "Epoch 986/2000\n",
      "84/84 [==============================] - 0s 261us/step - loss: 0.0325 - accuracy: 0.9881 - val_loss: 0.3258 - val_accuracy: 0.8889\n",
      "Epoch 987/2000\n",
      "84/84 [==============================] - 0s 294us/step - loss: 0.0322 - accuracy: 0.9881 - val_loss: 0.3224 - val_accuracy: 0.8889\n",
      "Epoch 988/2000\n",
      "84/84 [==============================] - 0s 298us/step - loss: 0.0327 - accuracy: 0.9762 - val_loss: 0.3191 - val_accuracy: 0.9167\n",
      "Epoch 989/2000\n",
      "84/84 [==============================] - 0s 232us/step - loss: 0.0323 - accuracy: 0.9762 - val_loss: 0.3198 - val_accuracy: 0.9167\n",
      "Epoch 990/2000\n",
      "84/84 [==============================] - 0s 233us/step - loss: 0.0322 - accuracy: 0.9762 - val_loss: 0.3205 - val_accuracy: 0.9167\n",
      "Epoch 991/2000\n",
      "84/84 [==============================] - 0s 358us/step - loss: 0.0322 - accuracy: 0.9762 - val_loss: 0.3213 - val_accuracy: 0.8889\n",
      "Epoch 992/2000\n",
      "84/84 [==============================] - 0s 323us/step - loss: 0.0322 - accuracy: 0.9762 - val_loss: 0.3243 - val_accuracy: 0.8889\n",
      "Epoch 993/2000\n",
      "84/84 [==============================] - 0s 254us/step - loss: 0.0323 - accuracy: 0.9762 - val_loss: 0.3281 - val_accuracy: 0.8889\n",
      "Epoch 994/2000\n",
      "84/84 [==============================] - 0s 273us/step - loss: 0.0322 - accuracy: 0.9762 - val_loss: 0.3286 - val_accuracy: 0.8889\n",
      "Epoch 995/2000\n",
      "84/84 [==============================] - 0s 269us/step - loss: 0.0320 - accuracy: 0.9762 - val_loss: 0.3314 - val_accuracy: 0.8889\n",
      "Epoch 996/2000\n",
      "84/84 [==============================] - 0s 248us/step - loss: 0.0320 - accuracy: 0.9881 - val_loss: 0.3323 - val_accuracy: 0.8889\n",
      "Epoch 997/2000\n",
      "84/84 [==============================] - 0s 311us/step - loss: 0.0321 - accuracy: 0.9881 - val_loss: 0.3335 - val_accuracy: 0.8889\n",
      "Epoch 998/2000\n",
      "84/84 [==============================] - 0s 204us/step - loss: 0.0320 - accuracy: 0.9881 - val_loss: 0.3309 - val_accuracy: 0.8889\n",
      "Epoch 999/2000\n",
      "84/84 [==============================] - 0s 231us/step - loss: 0.0320 - accuracy: 0.9881 - val_loss: 0.3301 - val_accuracy: 0.8889\n",
      "Epoch 1000/2000\n",
      "84/84 [==============================] - 0s 254us/step - loss: 0.0321 - accuracy: 0.9881 - val_loss: 0.3264 - val_accuracy: 0.8889\n",
      "Epoch 1001/2000\n",
      "84/84 [==============================] - 0s 422us/step - loss: 0.0322 - accuracy: 0.9881 - val_loss: 0.3230 - val_accuracy: 0.8889\n",
      "Epoch 1002/2000\n",
      "84/84 [==============================] - 0s 268us/step - loss: 0.0319 - accuracy: 0.9881 - val_loss: 0.3225 - val_accuracy: 0.8889\n",
      "Epoch 1003/2000\n",
      "84/84 [==============================] - 0s 275us/step - loss: 0.0318 - accuracy: 0.9881 - val_loss: 0.3239 - val_accuracy: 0.8889\n",
      "Epoch 1004/2000\n",
      "84/84 [==============================] - 0s 267us/step - loss: 0.0318 - accuracy: 0.9881 - val_loss: 0.3244 - val_accuracy: 0.8889\n",
      "Epoch 1005/2000\n",
      "84/84 [==============================] - 0s 280us/step - loss: 0.0319 - accuracy: 0.9881 - val_loss: 0.3245 - val_accuracy: 0.8889\n",
      "Epoch 1006/2000\n",
      "84/84 [==============================] - 0s 280us/step - loss: 0.0318 - accuracy: 0.9881 - val_loss: 0.3254 - val_accuracy: 0.8889\n",
      "Epoch 1007/2000\n",
      "84/84 [==============================] - 0s 330us/step - loss: 0.0320 - accuracy: 0.9881 - val_loss: 0.3289 - val_accuracy: 0.8889\n",
      "Epoch 1008/2000\n",
      "84/84 [==============================] - 0s 285us/step - loss: 0.0316 - accuracy: 0.9881 - val_loss: 0.3294 - val_accuracy: 0.8889\n",
      "Epoch 1009/2000\n",
      "84/84 [==============================] - 0s 264us/step - loss: 0.0316 - accuracy: 0.9881 - val_loss: 0.3302 - val_accuracy: 0.8889\n",
      "Epoch 1010/2000\n",
      "84/84 [==============================] - 0s 315us/step - loss: 0.0322 - accuracy: 0.9762 - val_loss: 0.3273 - val_accuracy: 0.8889\n",
      "Epoch 1011/2000\n",
      "84/84 [==============================] - 0s 284us/step - loss: 0.0317 - accuracy: 0.9762 - val_loss: 0.3305 - val_accuracy: 0.8889\n",
      "Epoch 1012/2000\n",
      "84/84 [==============================] - 0s 244us/step - loss: 0.0318 - accuracy: 0.9881 - val_loss: 0.3313 - val_accuracy: 0.8889\n",
      "Epoch 1013/2000\n",
      "84/84 [==============================] - 0s 250us/step - loss: 0.0318 - accuracy: 0.9821 - val_loss: 0.3354 - val_accuracy: 0.8889\n",
      "Epoch 1014/2000\n",
      "84/84 [==============================] - 0s 290us/step - loss: 0.0316 - accuracy: 0.9762 - val_loss: 0.3385 - val_accuracy: 0.8889\n",
      "Epoch 1015/2000\n",
      "84/84 [==============================] - 0s 255us/step - loss: 0.0315 - accuracy: 0.9762 - val_loss: 0.3386 - val_accuracy: 0.8889\n",
      "Epoch 1016/2000\n",
      "84/84 [==============================] - 0s 321us/step - loss: 0.0315 - accuracy: 0.9762 - val_loss: 0.3378 - val_accuracy: 0.8889\n",
      "Epoch 1017/2000\n",
      "84/84 [==============================] - 0s 305us/step - loss: 0.0317 - accuracy: 0.9762 - val_loss: 0.3365 - val_accuracy: 0.8889\n",
      "Epoch 1018/2000\n",
      "84/84 [==============================] - 0s 368us/step - loss: 0.0315 - accuracy: 0.9762 - val_loss: 0.3392 - val_accuracy: 0.8889\n",
      "Epoch 1019/2000\n",
      "84/84 [==============================] - 0s 297us/step - loss: 0.0314 - accuracy: 0.9821 - val_loss: 0.3404 - val_accuracy: 0.8889\n",
      "Epoch 1020/2000\n",
      "84/84 [==============================] - 0s 307us/step - loss: 0.0319 - accuracy: 0.9762 - val_loss: 0.3370 - val_accuracy: 0.8889\n",
      "Epoch 1021/2000\n",
      "84/84 [==============================] - 0s 260us/step - loss: 0.0315 - accuracy: 0.9762 - val_loss: 0.3374 - val_accuracy: 0.8889\n",
      "Epoch 1022/2000\n",
      "84/84 [==============================] - 0s 290us/step - loss: 0.0315 - accuracy: 0.9881 - val_loss: 0.3433 - val_accuracy: 0.8889\n",
      "Epoch 1023/2000\n",
      "84/84 [==============================] - 0s 476us/step - loss: 0.0320 - accuracy: 0.9881 - val_loss: 0.3446 - val_accuracy: 0.8889\n",
      "Epoch 1024/2000\n",
      "84/84 [==============================] - 0s 410us/step - loss: 0.0317 - accuracy: 0.9881 - val_loss: 0.3407 - val_accuracy: 0.8889\n",
      "Epoch 1025/2000\n",
      "84/84 [==============================] - 0s 365us/step - loss: 0.0314 - accuracy: 0.9881 - val_loss: 0.3377 - val_accuracy: 0.8889\n",
      "Epoch 1026/2000\n",
      "84/84 [==============================] - 0s 349us/step - loss: 0.0319 - accuracy: 0.9881 - val_loss: 0.3311 - val_accuracy: 0.8889\n",
      "Epoch 1027/2000\n",
      "84/84 [==============================] - 0s 320us/step - loss: 0.0315 - accuracy: 0.9881 - val_loss: 0.3317 - val_accuracy: 0.8889\n",
      "Epoch 1028/2000\n",
      "84/84 [==============================] - 0s 434us/step - loss: 0.0313 - accuracy: 0.9881 - val_loss: 0.3301 - val_accuracy: 0.8889\n",
      "Epoch 1029/2000\n",
      "84/84 [==============================] - 0s 516us/step - loss: 0.0315 - accuracy: 0.9881 - val_loss: 0.3305 - val_accuracy: 0.8889\n",
      "Epoch 1030/2000\n",
      "84/84 [==============================] - 0s 393us/step - loss: 0.0311 - accuracy: 0.9881 - val_loss: 0.3267 - val_accuracy: 0.8889\n",
      "Epoch 1031/2000\n",
      "84/84 [==============================] - 0s 481us/step - loss: 0.0310 - accuracy: 0.9881 - val_loss: 0.3239 - val_accuracy: 0.8889\n",
      "Epoch 1032/2000\n",
      "84/84 [==============================] - 0s 517us/step - loss: 0.0310 - accuracy: 0.9762 - val_loss: 0.3238 - val_accuracy: 0.8889\n",
      "Epoch 1033/2000\n",
      "84/84 [==============================] - 0s 350us/step - loss: 0.0311 - accuracy: 0.9762 - val_loss: 0.3252 - val_accuracy: 0.8889\n",
      "Epoch 1034/2000\n",
      "84/84 [==============================] - 0s 542us/step - loss: 0.0314 - accuracy: 0.9762 - val_loss: 0.3240 - val_accuracy: 0.8889\n",
      "Epoch 1035/2000\n",
      "84/84 [==============================] - 0s 389us/step - loss: 0.0311 - accuracy: 0.9881 - val_loss: 0.3246 - val_accuracy: 0.8889\n",
      "Epoch 1036/2000\n",
      "84/84 [==============================] - 0s 255us/step - loss: 0.0314 - accuracy: 0.9881 - val_loss: 0.3306 - val_accuracy: 0.8889\n",
      "Epoch 1037/2000\n",
      "84/84 [==============================] - 0s 276us/step - loss: 0.0310 - accuracy: 0.9762 - val_loss: 0.3338 - val_accuracy: 0.8889\n",
      "Epoch 1038/2000\n",
      "84/84 [==============================] - 0s 376us/step - loss: 0.0311 - accuracy: 0.9762 - val_loss: 0.3354 - val_accuracy: 0.8889\n",
      "Epoch 1039/2000\n",
      "84/84 [==============================] - 0s 356us/step - loss: 0.0315 - accuracy: 0.9762 - val_loss: 0.3331 - val_accuracy: 0.8889\n",
      "Epoch 1040/2000\n",
      "84/84 [==============================] - 0s 366us/step - loss: 0.0310 - accuracy: 0.9762 - val_loss: 0.3388 - val_accuracy: 0.8889\n",
      "Epoch 1041/2000\n",
      "84/84 [==============================] - 0s 413us/step - loss: 0.0307 - accuracy: 0.9762 - val_loss: 0.3422 - val_accuracy: 0.8889\n",
      "Epoch 1042/2000\n",
      "84/84 [==============================] - 0s 409us/step - loss: 0.0310 - accuracy: 0.9762 - val_loss: 0.3452 - val_accuracy: 0.8889\n",
      "Epoch 1043/2000\n",
      "84/84 [==============================] - 0s 454us/step - loss: 0.0307 - accuracy: 0.9881 - val_loss: 0.3438 - val_accuracy: 0.8889\n",
      "Epoch 1044/2000\n",
      "84/84 [==============================] - 0s 270us/step - loss: 0.0309 - accuracy: 0.9762 - val_loss: 0.3418 - val_accuracy: 0.8889\n",
      "Epoch 1045/2000\n",
      "84/84 [==============================] - 0s 256us/step - loss: 0.0309 - accuracy: 0.9762 - val_loss: 0.3424 - val_accuracy: 0.8889\n",
      "Epoch 1046/2000\n",
      "84/84 [==============================] - 0s 253us/step - loss: 0.0307 - accuracy: 0.9762 - val_loss: 0.3382 - val_accuracy: 0.8889\n",
      "Epoch 1047/2000\n",
      "84/84 [==============================] - 0s 281us/step - loss: 0.0308 - accuracy: 0.9762 - val_loss: 0.3363 - val_accuracy: 0.8889\n",
      "Epoch 1048/2000\n",
      "84/84 [==============================] - 0s 269us/step - loss: 0.0305 - accuracy: 0.9762 - val_loss: 0.3342 - val_accuracy: 0.8889\n",
      "Epoch 1049/2000\n",
      "84/84 [==============================] - 0s 260us/step - loss: 0.0306 - accuracy: 0.9762 - val_loss: 0.3313 - val_accuracy: 0.8889\n",
      "Epoch 1050/2000\n",
      "84/84 [==============================] - 0s 251us/step - loss: 0.0307 - accuracy: 0.9881 - val_loss: 0.3320 - val_accuracy: 0.8889\n",
      "Epoch 1051/2000\n",
      "84/84 [==============================] - 0s 278us/step - loss: 0.0305 - accuracy: 0.9881 - val_loss: 0.3342 - val_accuracy: 0.8889\n",
      "Epoch 1052/2000\n",
      "84/84 [==============================] - 0s 261us/step - loss: 0.0308 - accuracy: 0.9762 - val_loss: 0.3332 - val_accuracy: 0.8889\n",
      "Epoch 1053/2000\n",
      "84/84 [==============================] - 0s 248us/step - loss: 0.0303 - accuracy: 0.9762 - val_loss: 0.3366 - val_accuracy: 0.8889\n",
      "Epoch 1054/2000\n",
      "84/84 [==============================] - 0s 257us/step - loss: 0.0302 - accuracy: 0.9762 - val_loss: 0.3427 - val_accuracy: 0.8889\n",
      "Epoch 1055/2000\n",
      "84/84 [==============================] - 0s 462us/step - loss: 0.0302 - accuracy: 0.9881 - val_loss: 0.3465 - val_accuracy: 0.8889\n",
      "Epoch 1056/2000\n",
      "84/84 [==============================] - 0s 550us/step - loss: 0.0304 - accuracy: 0.9881 - val_loss: 0.3491 - val_accuracy: 0.8889\n",
      "Epoch 1057/2000\n",
      "84/84 [==============================] - 0s 488us/step - loss: 0.0309 - accuracy: 0.9881 - val_loss: 0.3513 - val_accuracy: 0.8889\n",
      "Epoch 1058/2000\n",
      "84/84 [==============================] - 0s 738us/step - loss: 0.0311 - accuracy: 0.9881 - val_loss: 0.3536 - val_accuracy: 0.8889\n",
      "Epoch 1059/2000\n",
      "84/84 [==============================] - 0s 329us/step - loss: 0.0309 - accuracy: 0.9881 - val_loss: 0.3491 - val_accuracy: 0.8889\n",
      "Epoch 1060/2000\n",
      "84/84 [==============================] - 0s 311us/step - loss: 0.0304 - accuracy: 0.9881 - val_loss: 0.3465 - val_accuracy: 0.8889\n",
      "Epoch 1061/2000\n",
      "84/84 [==============================] - 0s 337us/step - loss: 0.0307 - accuracy: 0.9881 - val_loss: 0.3426 - val_accuracy: 0.8889\n",
      "Epoch 1062/2000\n",
      "84/84 [==============================] - 0s 347us/step - loss: 0.0306 - accuracy: 0.9881 - val_loss: 0.3429 - val_accuracy: 0.8889\n",
      "Epoch 1063/2000\n",
      "84/84 [==============================] - 0s 319us/step - loss: 0.0301 - accuracy: 0.9881 - val_loss: 0.3392 - val_accuracy: 0.8889\n",
      "Epoch 1064/2000\n",
      "84/84 [==============================] - 0s 329us/step - loss: 0.0304 - accuracy: 0.9762 - val_loss: 0.3339 - val_accuracy: 0.8889\n",
      "Epoch 1065/2000\n",
      "84/84 [==============================] - 0s 339us/step - loss: 0.0301 - accuracy: 0.9762 - val_loss: 0.3312 - val_accuracy: 0.8889\n",
      "Epoch 1066/2000\n",
      "84/84 [==============================] - 0s 326us/step - loss: 0.0304 - accuracy: 0.9762 - val_loss: 0.3308 - val_accuracy: 0.8889\n",
      "Epoch 1067/2000\n",
      "84/84 [==============================] - 0s 346us/step - loss: 0.0304 - accuracy: 0.9762 - val_loss: 0.3349 - val_accuracy: 0.8889\n",
      "Epoch 1068/2000\n",
      "84/84 [==============================] - 0s 295us/step - loss: 0.0300 - accuracy: 0.9762 - val_loss: 0.3363 - val_accuracy: 0.8889\n",
      "Epoch 1069/2000\n",
      "84/84 [==============================] - 0s 338us/step - loss: 0.0304 - accuracy: 0.9762 - val_loss: 0.3376 - val_accuracy: 0.8889\n",
      "Epoch 1070/2000\n",
      "84/84 [==============================] - 0s 372us/step - loss: 0.0303 - accuracy: 0.9762 - val_loss: 0.3458 - val_accuracy: 0.8889\n",
      "Epoch 1071/2000\n",
      "84/84 [==============================] - 0s 356us/step - loss: 0.0303 - accuracy: 0.9881 - val_loss: 0.3521 - val_accuracy: 0.8889\n",
      "Epoch 1072/2000\n",
      "84/84 [==============================] - 0s 422us/step - loss: 0.0303 - accuracy: 0.9881 - val_loss: 0.3535 - val_accuracy: 0.8889\n",
      "Epoch 1073/2000\n",
      "84/84 [==============================] - 0s 443us/step - loss: 0.0299 - accuracy: 0.9881 - val_loss: 0.3495 - val_accuracy: 0.8889\n",
      "Epoch 1074/2000\n",
      "84/84 [==============================] - 0s 414us/step - loss: 0.0298 - accuracy: 0.9881 - val_loss: 0.3442 - val_accuracy: 0.8889\n",
      "Epoch 1075/2000\n",
      "84/84 [==============================] - 0s 370us/step - loss: 0.0300 - accuracy: 0.9762 - val_loss: 0.3408 - val_accuracy: 0.8889\n",
      "Epoch 1076/2000\n",
      "84/84 [==============================] - 0s 275us/step - loss: 0.0301 - accuracy: 0.9762 - val_loss: 0.3406 - val_accuracy: 0.8889\n",
      "Epoch 1077/2000\n",
      "84/84 [==============================] - 0s 492us/step - loss: 0.0298 - accuracy: 0.9762 - val_loss: 0.3393 - val_accuracy: 0.8889\n",
      "Epoch 1078/2000\n",
      "84/84 [==============================] - 0s 875us/step - loss: 0.0297 - accuracy: 0.9762 - val_loss: 0.3380 - val_accuracy: 0.8889\n",
      "Epoch 1079/2000\n",
      "84/84 [==============================] - 0s 836us/step - loss: 0.0305 - accuracy: 0.9762 - val_loss: 0.3339 - val_accuracy: 0.8889\n",
      "Epoch 1080/2000\n",
      "84/84 [==============================] - 0s 515us/step - loss: 0.0300 - accuracy: 0.9881 - val_loss: 0.3379 - val_accuracy: 0.8889\n",
      "Epoch 1081/2000\n",
      "84/84 [==============================] - 0s 478us/step - loss: 0.0299 - accuracy: 0.9762 - val_loss: 0.3412 - val_accuracy: 0.8889\n",
      "Epoch 1082/2000\n",
      "84/84 [==============================] - 0s 454us/step - loss: 0.0298 - accuracy: 0.9762 - val_loss: 0.3431 - val_accuracy: 0.8889\n",
      "Epoch 1083/2000\n",
      "84/84 [==============================] - 0s 602us/step - loss: 0.0299 - accuracy: 0.9821 - val_loss: 0.3458 - val_accuracy: 0.8889\n",
      "Epoch 1084/2000\n",
      "84/84 [==============================] - 0s 389us/step - loss: 0.0297 - accuracy: 0.9821 - val_loss: 0.3461 - val_accuracy: 0.8889\n",
      "Epoch 1085/2000\n",
      "84/84 [==============================] - 0s 727us/step - loss: 0.0296 - accuracy: 0.9881 - val_loss: 0.3450 - val_accuracy: 0.8889\n",
      "Epoch 1086/2000\n",
      "84/84 [==============================] - 0s 295us/step - loss: 0.0296 - accuracy: 0.9881 - val_loss: 0.3462 - val_accuracy: 0.8889\n",
      "Epoch 1087/2000\n",
      "84/84 [==============================] - 0s 353us/step - loss: 0.0298 - accuracy: 0.9762 - val_loss: 0.3465 - val_accuracy: 0.8889\n",
      "Epoch 1088/2000\n",
      "84/84 [==============================] - 0s 315us/step - loss: 0.0296 - accuracy: 0.9762 - val_loss: 0.3456 - val_accuracy: 0.8889\n",
      "Epoch 1089/2000\n",
      "84/84 [==============================] - 0s 233us/step - loss: 0.0298 - accuracy: 0.9762 - val_loss: 0.3426 - val_accuracy: 0.8889\n",
      "Epoch 1090/2000\n",
      "84/84 [==============================] - 0s 460us/step - loss: 0.0298 - accuracy: 0.9881 - val_loss: 0.3397 - val_accuracy: 0.8889\n",
      "Epoch 1091/2000\n",
      "84/84 [==============================] - 0s 229us/step - loss: 0.0298 - accuracy: 0.9881 - val_loss: 0.3417 - val_accuracy: 0.8889\n",
      "Epoch 1092/2000\n",
      "84/84 [==============================] - 0s 202us/step - loss: 0.0299 - accuracy: 0.9762 - val_loss: 0.3467 - val_accuracy: 0.8889\n",
      "Epoch 1093/2000\n",
      "84/84 [==============================] - 0s 215us/step - loss: 0.0294 - accuracy: 0.9762 - val_loss: 0.3496 - val_accuracy: 0.8889\n",
      "Epoch 1094/2000\n",
      "84/84 [==============================] - 0s 252us/step - loss: 0.0294 - accuracy: 0.9762 - val_loss: 0.3504 - val_accuracy: 0.8889\n",
      "Epoch 1095/2000\n",
      "84/84 [==============================] - 0s 374us/step - loss: 0.0293 - accuracy: 0.9762 - val_loss: 0.3515 - val_accuracy: 0.8889\n",
      "Epoch 1096/2000\n",
      "84/84 [==============================] - 0s 363us/step - loss: 0.0295 - accuracy: 0.9762 - val_loss: 0.3495 - val_accuracy: 0.8889\n",
      "Epoch 1097/2000\n",
      "84/84 [==============================] - 0s 402us/step - loss: 0.0293 - accuracy: 0.9881 - val_loss: 0.3500 - val_accuracy: 0.8889\n",
      "Epoch 1098/2000\n",
      "84/84 [==============================] - 0s 524us/step - loss: 0.0296 - accuracy: 0.9881 - val_loss: 0.3465 - val_accuracy: 0.8889\n",
      "Epoch 1099/2000\n",
      "84/84 [==============================] - 0s 836us/step - loss: 0.0295 - accuracy: 0.9881 - val_loss: 0.3479 - val_accuracy: 0.8889\n",
      "Epoch 1100/2000\n",
      "84/84 [==============================] - 0s 335us/step - loss: 0.0293 - accuracy: 0.9881 - val_loss: 0.3470 - val_accuracy: 0.8889\n",
      "Epoch 1101/2000\n",
      "84/84 [==============================] - 0s 343us/step - loss: 0.0293 - accuracy: 0.9881 - val_loss: 0.3455 - val_accuracy: 0.8889\n",
      "Epoch 1102/2000\n",
      "84/84 [==============================] - 0s 392us/step - loss: 0.0292 - accuracy: 0.9881 - val_loss: 0.3438 - val_accuracy: 0.8889\n",
      "Epoch 1103/2000\n",
      "84/84 [==============================] - 0s 400us/step - loss: 0.0294 - accuracy: 0.9881 - val_loss: 0.3468 - val_accuracy: 0.8889\n",
      "Epoch 1104/2000\n",
      "84/84 [==============================] - 0s 438us/step - loss: 0.0294 - accuracy: 0.9881 - val_loss: 0.3461 - val_accuracy: 0.8889\n",
      "Epoch 1105/2000\n",
      "84/84 [==============================] - 0s 237us/step - loss: 0.0292 - accuracy: 0.9881 - val_loss: 0.3497 - val_accuracy: 0.8889\n",
      "Epoch 1106/2000\n",
      "84/84 [==============================] - 0s 264us/step - loss: 0.0290 - accuracy: 0.9881 - val_loss: 0.3508 - val_accuracy: 0.8889\n",
      "Epoch 1107/2000\n",
      "84/84 [==============================] - 0s 283us/step - loss: 0.0294 - accuracy: 0.9881 - val_loss: 0.3508 - val_accuracy: 0.8889\n",
      "Epoch 1108/2000\n",
      "84/84 [==============================] - 0s 254us/step - loss: 0.0292 - accuracy: 0.9881 - val_loss: 0.3567 - val_accuracy: 0.8889\n",
      "Epoch 1109/2000\n",
      "84/84 [==============================] - 0s 271us/step - loss: 0.0293 - accuracy: 0.9881 - val_loss: 0.3571 - val_accuracy: 0.8889\n",
      "Epoch 1110/2000\n",
      "84/84 [==============================] - 0s 272us/step - loss: 0.0290 - accuracy: 0.9881 - val_loss: 0.3524 - val_accuracy: 0.8889\n",
      "Epoch 1111/2000\n",
      "84/84 [==============================] - 0s 319us/step - loss: 0.0292 - accuracy: 0.9821 - val_loss: 0.3502 - val_accuracy: 0.8889\n",
      "Epoch 1112/2000\n",
      "84/84 [==============================] - 0s 219us/step - loss: 0.0291 - accuracy: 0.9881 - val_loss: 0.3518 - val_accuracy: 0.8889\n",
      "Epoch 1113/2000\n",
      "84/84 [==============================] - 0s 224us/step - loss: 0.0289 - accuracy: 0.9881 - val_loss: 0.3520 - val_accuracy: 0.8889\n",
      "Epoch 1114/2000\n",
      "84/84 [==============================] - 0s 210us/step - loss: 0.0288 - accuracy: 0.9881 - val_loss: 0.3516 - val_accuracy: 0.8889\n",
      "Epoch 1115/2000\n",
      "84/84 [==============================] - 0s 241us/step - loss: 0.0291 - accuracy: 0.9762 - val_loss: 0.3528 - val_accuracy: 0.8889\n",
      "Epoch 1116/2000\n",
      "84/84 [==============================] - 0s 207us/step - loss: 0.0288 - accuracy: 0.9762 - val_loss: 0.3500 - val_accuracy: 0.8889\n",
      "Epoch 1117/2000\n",
      "84/84 [==============================] - 0s 209us/step - loss: 0.0289 - accuracy: 0.9762 - val_loss: 0.3495 - val_accuracy: 0.8889\n",
      "Epoch 1118/2000\n",
      "84/84 [==============================] - 0s 214us/step - loss: 0.0294 - accuracy: 0.9762 - val_loss: 0.3462 - val_accuracy: 0.8889\n",
      "Epoch 1119/2000\n",
      "84/84 [==============================] - 0s 250us/step - loss: 0.0288 - accuracy: 0.9881 - val_loss: 0.3456 - val_accuracy: 0.8889\n",
      "Epoch 1120/2000\n",
      "84/84 [==============================] - 0s 280us/step - loss: 0.0290 - accuracy: 0.9881 - val_loss: 0.3462 - val_accuracy: 0.8889\n",
      "Epoch 1121/2000\n",
      "84/84 [==============================] - 0s 279us/step - loss: 0.0291 - accuracy: 0.9762 - val_loss: 0.3512 - val_accuracy: 0.8889\n",
      "Epoch 1122/2000\n",
      "84/84 [==============================] - 0s 271us/step - loss: 0.0287 - accuracy: 0.9762 - val_loss: 0.3533 - val_accuracy: 0.8889\n",
      "Epoch 1123/2000\n",
      "84/84 [==============================] - 0s 246us/step - loss: 0.0286 - accuracy: 0.9762 - val_loss: 0.3530 - val_accuracy: 0.8889\n",
      "Epoch 1124/2000\n",
      "84/84 [==============================] - 0s 296us/step - loss: 0.0288 - accuracy: 0.9762 - val_loss: 0.3531 - val_accuracy: 0.8889\n",
      "Epoch 1125/2000\n",
      "84/84 [==============================] - 0s 284us/step - loss: 0.0285 - accuracy: 0.9881 - val_loss: 0.3540 - val_accuracy: 0.8889\n",
      "Epoch 1126/2000\n",
      "84/84 [==============================] - 0s 256us/step - loss: 0.0287 - accuracy: 0.9762 - val_loss: 0.3544 - val_accuracy: 0.8889\n",
      "Epoch 1127/2000\n",
      "84/84 [==============================] - 0s 313us/step - loss: 0.0288 - accuracy: 0.9881 - val_loss: 0.3568 - val_accuracy: 0.8889\n",
      "Epoch 1128/2000\n",
      "84/84 [==============================] - 0s 284us/step - loss: 0.0289 - accuracy: 0.9881 - val_loss: 0.3534 - val_accuracy: 0.8889\n",
      "Epoch 1129/2000\n",
      "84/84 [==============================] - 0s 290us/step - loss: 0.0287 - accuracy: 0.9762 - val_loss: 0.3533 - val_accuracy: 0.8889\n",
      "Epoch 1130/2000\n",
      "84/84 [==============================] - 0s 283us/step - loss: 0.0284 - accuracy: 0.9762 - val_loss: 0.3551 - val_accuracy: 0.8889\n",
      "Epoch 1131/2000\n",
      "84/84 [==============================] - 0s 302us/step - loss: 0.0287 - accuracy: 0.9881 - val_loss: 0.3575 - val_accuracy: 0.8889\n",
      "Epoch 1132/2000\n",
      "84/84 [==============================] - 0s 297us/step - loss: 0.0285 - accuracy: 0.9881 - val_loss: 0.3559 - val_accuracy: 0.8889\n",
      "Epoch 1133/2000\n",
      "84/84 [==============================] - 0s 305us/step - loss: 0.0285 - accuracy: 0.9881 - val_loss: 0.3556 - val_accuracy: 0.8889\n",
      "Epoch 1134/2000\n",
      "84/84 [==============================] - 0s 254us/step - loss: 0.0284 - accuracy: 0.9881 - val_loss: 0.3592 - val_accuracy: 0.8889\n",
      "Epoch 1135/2000\n",
      "84/84 [==============================] - 0s 255us/step - loss: 0.0284 - accuracy: 0.9881 - val_loss: 0.3586 - val_accuracy: 0.8889\n",
      "Epoch 1136/2000\n",
      "84/84 [==============================] - 0s 240us/step - loss: 0.0288 - accuracy: 0.9881 - val_loss: 0.3607 - val_accuracy: 0.8889\n",
      "Epoch 1137/2000\n",
      "84/84 [==============================] - 0s 229us/step - loss: 0.0285 - accuracy: 0.9881 - val_loss: 0.3579 - val_accuracy: 0.8889\n",
      "Epoch 1138/2000\n",
      "84/84 [==============================] - 0s 208us/step - loss: 0.0286 - accuracy: 0.9881 - val_loss: 0.3543 - val_accuracy: 0.8889\n",
      "Epoch 1139/2000\n",
      "84/84 [==============================] - 0s 221us/step - loss: 0.0285 - accuracy: 0.9881 - val_loss: 0.3521 - val_accuracy: 0.8889\n",
      "Epoch 1140/2000\n",
      "84/84 [==============================] - 0s 242us/step - loss: 0.0285 - accuracy: 0.9881 - val_loss: 0.3526 - val_accuracy: 0.8889\n",
      "Epoch 1141/2000\n",
      "84/84 [==============================] - 0s 256us/step - loss: 0.0283 - accuracy: 0.9881 - val_loss: 0.3512 - val_accuracy: 0.8889\n",
      "Epoch 1142/2000\n",
      "84/84 [==============================] - 0s 273us/step - loss: 0.0282 - accuracy: 0.9881 - val_loss: 0.3497 - val_accuracy: 0.8889\n",
      "Epoch 1143/2000\n",
      "84/84 [==============================] - 0s 264us/step - loss: 0.0289 - accuracy: 0.9881 - val_loss: 0.3469 - val_accuracy: 0.8889\n",
      "Epoch 1144/2000\n",
      "84/84 [==============================] - 0s 298us/step - loss: 0.0281 - accuracy: 0.9881 - val_loss: 0.3505 - val_accuracy: 0.8889\n",
      "Epoch 1145/2000\n",
      "84/84 [==============================] - 0s 311us/step - loss: 0.0283 - accuracy: 0.9881 - val_loss: 0.3537 - val_accuracy: 0.8889\n",
      "Epoch 1146/2000\n",
      "84/84 [==============================] - 0s 368us/step - loss: 0.0283 - accuracy: 0.9881 - val_loss: 0.3558 - val_accuracy: 0.8889\n",
      "Epoch 1147/2000\n",
      "84/84 [==============================] - 0s 383us/step - loss: 0.0282 - accuracy: 0.9881 - val_loss: 0.3571 - val_accuracy: 0.8889\n",
      "Epoch 1148/2000\n",
      "84/84 [==============================] - 0s 355us/step - loss: 0.0281 - accuracy: 0.9881 - val_loss: 0.3591 - val_accuracy: 0.8889\n",
      "Epoch 1149/2000\n",
      "84/84 [==============================] - 0s 386us/step - loss: 0.0281 - accuracy: 0.9881 - val_loss: 0.3581 - val_accuracy: 0.8889\n",
      "Epoch 1150/2000\n",
      "84/84 [==============================] - 0s 373us/step - loss: 0.0281 - accuracy: 0.9881 - val_loss: 0.3571 - val_accuracy: 0.8889\n",
      "Epoch 1151/2000\n",
      "84/84 [==============================] - 0s 359us/step - loss: 0.0283 - accuracy: 0.9881 - val_loss: 0.3586 - val_accuracy: 0.8889\n",
      "Epoch 1152/2000\n",
      "84/84 [==============================] - 0s 231us/step - loss: 0.0282 - accuracy: 0.9881 - val_loss: 0.3553 - val_accuracy: 0.8889\n",
      "Epoch 1153/2000\n",
      "84/84 [==============================] - 0s 245us/step - loss: 0.0279 - accuracy: 0.9881 - val_loss: 0.3539 - val_accuracy: 0.8889\n",
      "Epoch 1154/2000\n",
      "84/84 [==============================] - 0s 305us/step - loss: 0.0280 - accuracy: 0.9881 - val_loss: 0.3529 - val_accuracy: 0.8889\n",
      "Epoch 1155/2000\n",
      "84/84 [==============================] - 0s 296us/step - loss: 0.0280 - accuracy: 0.9881 - val_loss: 0.3504 - val_accuracy: 0.8889\n",
      "Epoch 1156/2000\n",
      "84/84 [==============================] - 0s 537us/step - loss: 0.0284 - accuracy: 0.9762 - val_loss: 0.3476 - val_accuracy: 0.8889\n",
      "Epoch 1157/2000\n",
      "84/84 [==============================] - 0s 355us/step - loss: 0.0280 - accuracy: 0.9762 - val_loss: 0.3504 - val_accuracy: 0.8889\n",
      "Epoch 1158/2000\n",
      "84/84 [==============================] - 0s 391us/step - loss: 0.0279 - accuracy: 0.9762 - val_loss: 0.3520 - val_accuracy: 0.8889\n",
      "Epoch 1159/2000\n",
      "84/84 [==============================] - 0s 409us/step - loss: 0.0277 - accuracy: 0.9762 - val_loss: 0.3508 - val_accuracy: 0.8889\n",
      "Epoch 1160/2000\n",
      "84/84 [==============================] - 0s 377us/step - loss: 0.0278 - accuracy: 0.9881 - val_loss: 0.3494 - val_accuracy: 0.8889\n",
      "Epoch 1161/2000\n",
      "84/84 [==============================] - 0s 236us/step - loss: 0.0279 - accuracy: 0.9881 - val_loss: 0.3478 - val_accuracy: 0.8889\n",
      "Epoch 1162/2000\n",
      "84/84 [==============================] - 0s 217us/step - loss: 0.0285 - accuracy: 0.9881 - val_loss: 0.3482 - val_accuracy: 0.8889\n",
      "Epoch 1163/2000\n",
      "84/84 [==============================] - 0s 237us/step - loss: 0.0281 - accuracy: 0.9881 - val_loss: 0.3527 - val_accuracy: 0.8889\n",
      "Epoch 1164/2000\n",
      "84/84 [==============================] - 0s 297us/step - loss: 0.0283 - accuracy: 0.9881 - val_loss: 0.3590 - val_accuracy: 0.8889\n",
      "Epoch 1165/2000\n",
      "84/84 [==============================] - 0s 420us/step - loss: 0.0277 - accuracy: 0.9881 - val_loss: 0.3615 - val_accuracy: 0.8889\n",
      "Epoch 1166/2000\n",
      "84/84 [==============================] - 0s 343us/step - loss: 0.0280 - accuracy: 0.9762 - val_loss: 0.3641 - val_accuracy: 0.8889\n",
      "Epoch 1167/2000\n",
      "84/84 [==============================] - 0s 328us/step - loss: 0.0277 - accuracy: 0.9821 - val_loss: 0.3613 - val_accuracy: 0.8889\n",
      "Epoch 1168/2000\n",
      "84/84 [==============================] - 0s 315us/step - loss: 0.0279 - accuracy: 0.9762 - val_loss: 0.3630 - val_accuracy: 0.8889\n",
      "Epoch 1169/2000\n",
      "84/84 [==============================] - 0s 329us/step - loss: 0.0276 - accuracy: 0.9881 - val_loss: 0.3601 - val_accuracy: 0.8889\n",
      "Epoch 1170/2000\n",
      "84/84 [==============================] - 0s 467us/step - loss: 0.0280 - accuracy: 0.9762 - val_loss: 0.3576 - val_accuracy: 0.8889\n",
      "Epoch 1171/2000\n",
      "84/84 [==============================] - 0s 264us/step - loss: 0.0278 - accuracy: 0.9762 - val_loss: 0.3606 - val_accuracy: 0.8889\n",
      "Epoch 1172/2000\n",
      "84/84 [==============================] - 0s 212us/step - loss: 0.0275 - accuracy: 0.9762 - val_loss: 0.3610 - val_accuracy: 0.8889\n",
      "Epoch 1173/2000\n",
      "84/84 [==============================] - 0s 242us/step - loss: 0.0277 - accuracy: 0.9762 - val_loss: 0.3609 - val_accuracy: 0.8889\n",
      "Epoch 1174/2000\n",
      "84/84 [==============================] - 0s 245us/step - loss: 0.0274 - accuracy: 0.9762 - val_loss: 0.3632 - val_accuracy: 0.8889\n",
      "Epoch 1175/2000\n",
      "84/84 [==============================] - 0s 252us/step - loss: 0.0273 - accuracy: 0.9881 - val_loss: 0.3680 - val_accuracy: 0.8889\n",
      "Epoch 1176/2000\n",
      "84/84 [==============================] - 0s 274us/step - loss: 0.0279 - accuracy: 0.9881 - val_loss: 0.3726 - val_accuracy: 0.8889\n",
      "Epoch 1177/2000\n",
      "84/84 [==============================] - 0s 267us/step - loss: 0.0277 - accuracy: 0.9881 - val_loss: 0.3723 - val_accuracy: 0.8889\n",
      "Epoch 1178/2000\n",
      "84/84 [==============================] - 0s 217us/step - loss: 0.0276 - accuracy: 0.9881 - val_loss: 0.3659 - val_accuracy: 0.8889\n",
      "Epoch 1179/2000\n",
      "84/84 [==============================] - 0s 200us/step - loss: 0.0273 - accuracy: 0.9881 - val_loss: 0.3641 - val_accuracy: 0.8889\n",
      "Epoch 1180/2000\n",
      "84/84 [==============================] - 0s 218us/step - loss: 0.0277 - accuracy: 0.9762 - val_loss: 0.3619 - val_accuracy: 0.8889\n",
      "Epoch 1181/2000\n",
      "84/84 [==============================] - 0s 263us/step - loss: 0.0275 - accuracy: 0.9762 - val_loss: 0.3607 - val_accuracy: 0.8889\n",
      "Epoch 1182/2000\n",
      "84/84 [==============================] - 0s 284us/step - loss: 0.0274 - accuracy: 0.9762 - val_loss: 0.3619 - val_accuracy: 0.8889\n",
      "Epoch 1183/2000\n",
      "84/84 [==============================] - 0s 243us/step - loss: 0.0275 - accuracy: 0.9762 - val_loss: 0.3596 - val_accuracy: 0.8889\n",
      "Epoch 1184/2000\n",
      "84/84 [==============================] - 0s 231us/step - loss: 0.0274 - accuracy: 0.9762 - val_loss: 0.3617 - val_accuracy: 0.8889\n",
      "Epoch 1185/2000\n",
      "84/84 [==============================] - 0s 329us/step - loss: 0.0274 - accuracy: 0.9762 - val_loss: 0.3622 - val_accuracy: 0.8889\n",
      "Epoch 1186/2000\n",
      "84/84 [==============================] - 0s 263us/step - loss: 0.0273 - accuracy: 0.9762 - val_loss: 0.3623 - val_accuracy: 0.8889\n",
      "Epoch 1187/2000\n",
      "84/84 [==============================] - 0s 311us/step - loss: 0.0273 - accuracy: 0.9762 - val_loss: 0.3659 - val_accuracy: 0.8889\n",
      "Epoch 1188/2000\n",
      "84/84 [==============================] - 0s 329us/step - loss: 0.0271 - accuracy: 0.9762 - val_loss: 0.3685 - val_accuracy: 0.8889\n",
      "Epoch 1189/2000\n",
      "84/84 [==============================] - 0s 324us/step - loss: 0.0271 - accuracy: 0.9762 - val_loss: 0.3689 - val_accuracy: 0.8889\n",
      "Epoch 1190/2000\n",
      "84/84 [==============================] - 0s 369us/step - loss: 0.0274 - accuracy: 0.9762 - val_loss: 0.3718 - val_accuracy: 0.8889\n",
      "Epoch 1191/2000\n",
      "84/84 [==============================] - 0s 396us/step - loss: 0.0274 - accuracy: 0.9881 - val_loss: 0.3741 - val_accuracy: 0.8889\n",
      "Epoch 1192/2000\n",
      "84/84 [==============================] - 0s 431us/step - loss: 0.0271 - accuracy: 0.9881 - val_loss: 0.3713 - val_accuracy: 0.8889\n",
      "Epoch 1193/2000\n",
      "84/84 [==============================] - 0s 407us/step - loss: 0.0270 - accuracy: 0.9762 - val_loss: 0.3716 - val_accuracy: 0.8889\n",
      "Epoch 1194/2000\n",
      "84/84 [==============================] - 0s 338us/step - loss: 0.0273 - accuracy: 0.9762 - val_loss: 0.3745 - val_accuracy: 0.8889\n",
      "Epoch 1195/2000\n",
      "84/84 [==============================] - 0s 329us/step - loss: 0.0269 - accuracy: 0.9762 - val_loss: 0.3726 - val_accuracy: 0.8889\n",
      "Epoch 1196/2000\n",
      "84/84 [==============================] - 0s 338us/step - loss: 0.0269 - accuracy: 0.9762 - val_loss: 0.3719 - val_accuracy: 0.8889\n",
      "Epoch 1197/2000\n",
      "84/84 [==============================] - 0s 272us/step - loss: 0.0271 - accuracy: 0.9762 - val_loss: 0.3718 - val_accuracy: 0.8889\n",
      "Epoch 1198/2000\n",
      "84/84 [==============================] - 0s 292us/step - loss: 0.0269 - accuracy: 0.9762 - val_loss: 0.3693 - val_accuracy: 0.8889\n",
      "Epoch 1199/2000\n",
      "84/84 [==============================] - 0s 313us/step - loss: 0.0269 - accuracy: 0.9881 - val_loss: 0.3675 - val_accuracy: 0.8889\n",
      "Epoch 1200/2000\n",
      "84/84 [==============================] - 0s 365us/step - loss: 0.0275 - accuracy: 0.9881 - val_loss: 0.3659 - val_accuracy: 0.8889\n",
      "Epoch 1201/2000\n",
      "84/84 [==============================] - 0s 335us/step - loss: 0.0272 - accuracy: 0.9881 - val_loss: 0.3724 - val_accuracy: 0.8889\n",
      "Epoch 1202/2000\n",
      "84/84 [==============================] - 0s 409us/step - loss: 0.0271 - accuracy: 0.9762 - val_loss: 0.3749 - val_accuracy: 0.8889\n",
      "Epoch 1203/2000\n",
      "84/84 [==============================] - 0s 362us/step - loss: 0.0270 - accuracy: 0.9762 - val_loss: 0.3758 - val_accuracy: 0.8889\n",
      "Epoch 1204/2000\n",
      "84/84 [==============================] - 0s 413us/step - loss: 0.0269 - accuracy: 0.9881 - val_loss: 0.3799 - val_accuracy: 0.8889\n",
      "Epoch 1205/2000\n",
      "84/84 [==============================] - 0s 386us/step - loss: 0.0268 - accuracy: 0.9881 - val_loss: 0.3796 - val_accuracy: 0.8889\n",
      "Epoch 1206/2000\n",
      "84/84 [==============================] - 0s 357us/step - loss: 0.0270 - accuracy: 0.9881 - val_loss: 0.3764 - val_accuracy: 0.8889\n",
      "Epoch 1207/2000\n",
      "84/84 [==============================] - 0s 333us/step - loss: 0.0267 - accuracy: 0.9881 - val_loss: 0.3721 - val_accuracy: 0.8889\n",
      "Epoch 1208/2000\n",
      "84/84 [==============================] - 0s 325us/step - loss: 0.0267 - accuracy: 0.9881 - val_loss: 0.3680 - val_accuracy: 0.8889\n",
      "Epoch 1209/2000\n",
      "84/84 [==============================] - 0s 225us/step - loss: 0.0269 - accuracy: 0.9762 - val_loss: 0.3650 - val_accuracy: 0.8889\n",
      "Epoch 1210/2000\n",
      "84/84 [==============================] - 0s 251us/step - loss: 0.0269 - accuracy: 0.9762 - val_loss: 0.3655 - val_accuracy: 0.8889\n",
      "Epoch 1211/2000\n",
      "84/84 [==============================] - 0s 282us/step - loss: 0.0266 - accuracy: 0.9762 - val_loss: 0.3636 - val_accuracy: 0.8889\n",
      "Epoch 1212/2000\n",
      "84/84 [==============================] - 0s 246us/step - loss: 0.0266 - accuracy: 0.9762 - val_loss: 0.3634 - val_accuracy: 0.8889\n",
      "Epoch 1213/2000\n",
      "84/84 [==============================] - 0s 241us/step - loss: 0.0269 - accuracy: 0.9881 - val_loss: 0.3631 - val_accuracy: 0.8889\n",
      "Epoch 1214/2000\n",
      "84/84 [==============================] - 0s 349us/step - loss: 0.0266 - accuracy: 0.9881 - val_loss: 0.3663 - val_accuracy: 0.8889\n",
      "Epoch 1215/2000\n",
      "84/84 [==============================] - 0s 241us/step - loss: 0.0266 - accuracy: 0.9881 - val_loss: 0.3707 - val_accuracy: 0.8889\n",
      "Epoch 1216/2000\n",
      "84/84 [==============================] - 0s 307us/step - loss: 0.0272 - accuracy: 0.9762 - val_loss: 0.3760 - val_accuracy: 0.8889\n",
      "Epoch 1217/2000\n",
      "84/84 [==============================] - 0s 295us/step - loss: 0.0267 - accuracy: 0.9881 - val_loss: 0.3768 - val_accuracy: 0.8889\n",
      "Epoch 1218/2000\n",
      "84/84 [==============================] - 0s 244us/step - loss: 0.0265 - accuracy: 0.9881 - val_loss: 0.3740 - val_accuracy: 0.8889\n",
      "Epoch 1219/2000\n",
      "84/84 [==============================] - 0s 341us/step - loss: 0.0269 - accuracy: 0.9762 - val_loss: 0.3716 - val_accuracy: 0.8889\n",
      "Epoch 1220/2000\n",
      "84/84 [==============================] - 0s 332us/step - loss: 0.0268 - accuracy: 0.9762 - val_loss: 0.3734 - val_accuracy: 0.8889\n",
      "Epoch 1221/2000\n",
      "84/84 [==============================] - 0s 333us/step - loss: 0.0265 - accuracy: 0.9762 - val_loss: 0.3730 - val_accuracy: 0.8889\n",
      "Epoch 1222/2000\n",
      "84/84 [==============================] - 0s 369us/step - loss: 0.0264 - accuracy: 0.9762 - val_loss: 0.3714 - val_accuracy: 0.8889\n",
      "Epoch 1223/2000\n",
      "84/84 [==============================] - 0s 400us/step - loss: 0.0264 - accuracy: 0.9762 - val_loss: 0.3730 - val_accuracy: 0.8889\n",
      "Epoch 1224/2000\n",
      "84/84 [==============================] - 0s 343us/step - loss: 0.0264 - accuracy: 0.9762 - val_loss: 0.3724 - val_accuracy: 0.8889\n",
      "Epoch 1225/2000\n",
      "84/84 [==============================] - 0s 291us/step - loss: 0.0265 - accuracy: 0.9762 - val_loss: 0.3759 - val_accuracy: 0.8889\n",
      "Epoch 1226/2000\n",
      "84/84 [==============================] - 0s 220us/step - loss: 0.0265 - accuracy: 0.9762 - val_loss: 0.3771 - val_accuracy: 0.8889\n",
      "Epoch 1227/2000\n",
      "84/84 [==============================] - 0s 259us/step - loss: 0.0264 - accuracy: 0.9762 - val_loss: 0.3819 - val_accuracy: 0.8889\n",
      "Epoch 1228/2000\n",
      "84/84 [==============================] - 0s 370us/step - loss: 0.0265 - accuracy: 0.9762 - val_loss: 0.3835 - val_accuracy: 0.8889\n",
      "Epoch 1229/2000\n",
      "84/84 [==============================] - 0s 239us/step - loss: 0.0270 - accuracy: 0.9762 - val_loss: 0.3793 - val_accuracy: 0.8889\n",
      "Epoch 1230/2000\n",
      "84/84 [==============================] - 0s 232us/step - loss: 0.0262 - accuracy: 0.9762 - val_loss: 0.3813 - val_accuracy: 0.8889\n",
      "Epoch 1231/2000\n",
      "84/84 [==============================] - 0s 241us/step - loss: 0.0263 - accuracy: 0.9762 - val_loss: 0.3835 - val_accuracy: 0.8889\n",
      "Epoch 1232/2000\n",
      "84/84 [==============================] - 0s 314us/step - loss: 0.0262 - accuracy: 0.9881 - val_loss: 0.3851 - val_accuracy: 0.8889\n",
      "Epoch 1233/2000\n",
      "84/84 [==============================] - 0s 241us/step - loss: 0.0262 - accuracy: 0.9881 - val_loss: 0.3832 - val_accuracy: 0.8889\n",
      "Epoch 1234/2000\n",
      "84/84 [==============================] - 0s 235us/step - loss: 0.0262 - accuracy: 0.9881 - val_loss: 0.3811 - val_accuracy: 0.8889\n",
      "Epoch 1235/2000\n",
      "84/84 [==============================] - 0s 238us/step - loss: 0.0261 - accuracy: 0.9881 - val_loss: 0.3801 - val_accuracy: 0.8889\n",
      "Epoch 1236/2000\n",
      "84/84 [==============================] - 0s 241us/step - loss: 0.0267 - accuracy: 0.9881 - val_loss: 0.3765 - val_accuracy: 0.8889\n",
      "Epoch 1237/2000\n",
      "84/84 [==============================] - 0s 234us/step - loss: 0.0262 - accuracy: 0.9881 - val_loss: 0.3797 - val_accuracy: 0.8889\n",
      "Epoch 1238/2000\n",
      "84/84 [==============================] - 0s 256us/step - loss: 0.0261 - accuracy: 0.9881 - val_loss: 0.3799 - val_accuracy: 0.8889\n",
      "Epoch 1239/2000\n",
      "84/84 [==============================] - 0s 260us/step - loss: 0.0263 - accuracy: 0.9881 - val_loss: 0.3795 - val_accuracy: 0.8889\n",
      "Epoch 1240/2000\n",
      "84/84 [==============================] - 0s 281us/step - loss: 0.0262 - accuracy: 0.9881 - val_loss: 0.3772 - val_accuracy: 0.8889\n",
      "Epoch 1241/2000\n",
      "84/84 [==============================] - 0s 274us/step - loss: 0.0263 - accuracy: 0.9881 - val_loss: 0.3785 - val_accuracy: 0.8889\n",
      "Epoch 1242/2000\n",
      "84/84 [==============================] - 0s 284us/step - loss: 0.0260 - accuracy: 0.9881 - val_loss: 0.3779 - val_accuracy: 0.8889\n",
      "Epoch 1243/2000\n",
      "84/84 [==============================] - 0s 289us/step - loss: 0.0262 - accuracy: 0.9881 - val_loss: 0.3787 - val_accuracy: 0.8889\n",
      "Epoch 1244/2000\n",
      "84/84 [==============================] - 0s 320us/step - loss: 0.0260 - accuracy: 0.9881 - val_loss: 0.3782 - val_accuracy: 0.8889\n",
      "Epoch 1245/2000\n",
      "84/84 [==============================] - 0s 292us/step - loss: 0.0262 - accuracy: 0.9881 - val_loss: 0.3763 - val_accuracy: 0.8889\n",
      "Epoch 1246/2000\n",
      "84/84 [==============================] - 0s 314us/step - loss: 0.0259 - accuracy: 0.9881 - val_loss: 0.3753 - val_accuracy: 0.8889\n",
      "Epoch 1247/2000\n",
      "84/84 [==============================] - 0s 278us/step - loss: 0.0259 - accuracy: 0.9762 - val_loss: 0.3752 - val_accuracy: 0.8889\n",
      "Epoch 1248/2000\n",
      "84/84 [==============================] - 0s 274us/step - loss: 0.0261 - accuracy: 0.9762 - val_loss: 0.3760 - val_accuracy: 0.8889\n",
      "Epoch 1249/2000\n",
      "84/84 [==============================] - 0s 288us/step - loss: 0.0259 - accuracy: 0.9762 - val_loss: 0.3776 - val_accuracy: 0.8889\n",
      "Epoch 1250/2000\n",
      "84/84 [==============================] - 0s 234us/step - loss: 0.0259 - accuracy: 0.9762 - val_loss: 0.3792 - val_accuracy: 0.8889\n",
      "Epoch 1251/2000\n",
      "84/84 [==============================] - 0s 266us/step - loss: 0.0257 - accuracy: 0.9881 - val_loss: 0.3831 - val_accuracy: 0.8889\n",
      "Epoch 1252/2000\n",
      "84/84 [==============================] - 0s 265us/step - loss: 0.0258 - accuracy: 0.9881 - val_loss: 0.3858 - val_accuracy: 0.8889\n",
      "Epoch 1253/2000\n",
      "84/84 [==============================] - 0s 239us/step - loss: 0.0266 - accuracy: 0.9881 - val_loss: 0.3883 - val_accuracy: 0.8889\n",
      "Epoch 1254/2000\n",
      "84/84 [==============================] - 0s 240us/step - loss: 0.0262 - accuracy: 0.9881 - val_loss: 0.3863 - val_accuracy: 0.8889\n",
      "Epoch 1255/2000\n",
      "84/84 [==============================] - 0s 312us/step - loss: 0.0260 - accuracy: 0.9881 - val_loss: 0.3831 - val_accuracy: 0.8889\n",
      "Epoch 1256/2000\n",
      "84/84 [==============================] - 0s 276us/step - loss: 0.0260 - accuracy: 0.9881 - val_loss: 0.3811 - val_accuracy: 0.8889\n",
      "Epoch 1257/2000\n",
      "84/84 [==============================] - 0s 346us/step - loss: 0.0257 - accuracy: 0.9881 - val_loss: 0.3772 - val_accuracy: 0.8889\n",
      "Epoch 1258/2000\n",
      "84/84 [==============================] - 0s 341us/step - loss: 0.0257 - accuracy: 0.9881 - val_loss: 0.3743 - val_accuracy: 0.8889\n",
      "Epoch 1259/2000\n",
      "84/84 [==============================] - 0s 350us/step - loss: 0.0256 - accuracy: 0.9762 - val_loss: 0.3724 - val_accuracy: 0.8889\n",
      "Epoch 1260/2000\n",
      "84/84 [==============================] - 0s 351us/step - loss: 0.0260 - accuracy: 0.9881 - val_loss: 0.3717 - val_accuracy: 0.8889\n",
      "Epoch 1261/2000\n",
      "84/84 [==============================] - 0s 393us/step - loss: 0.0257 - accuracy: 0.9881 - val_loss: 0.3741 - val_accuracy: 0.8889\n",
      "Epoch 1262/2000\n",
      "84/84 [==============================] - 0s 384us/step - loss: 0.0261 - accuracy: 0.9762 - val_loss: 0.3807 - val_accuracy: 0.8889\n",
      "Epoch 1263/2000\n",
      "84/84 [==============================] - 0s 405us/step - loss: 0.0258 - accuracy: 0.9762 - val_loss: 0.3847 - val_accuracy: 0.8889\n",
      "Epoch 1264/2000\n",
      "84/84 [==============================] - 0s 244us/step - loss: 0.0256 - accuracy: 0.9762 - val_loss: 0.3852 - val_accuracy: 0.8889\n",
      "Epoch 1265/2000\n",
      "84/84 [==============================] - 0s 243us/step - loss: 0.0255 - accuracy: 0.9762 - val_loss: 0.3839 - val_accuracy: 0.8889\n",
      "Epoch 1266/2000\n",
      "84/84 [==============================] - 0s 304us/step - loss: 0.0260 - accuracy: 0.9762 - val_loss: 0.3823 - val_accuracy: 0.8889\n",
      "Epoch 1267/2000\n",
      "84/84 [==============================] - 0s 390us/step - loss: 0.0256 - accuracy: 0.9762 - val_loss: 0.3833 - val_accuracy: 0.8889\n",
      "Epoch 1268/2000\n",
      "84/84 [==============================] - 0s 375us/step - loss: 0.0254 - accuracy: 0.9762 - val_loss: 0.3866 - val_accuracy: 0.8889\n",
      "Epoch 1269/2000\n",
      "84/84 [==============================] - 0s 371us/step - loss: 0.0254 - accuracy: 0.9881 - val_loss: 0.3904 - val_accuracy: 0.8889\n",
      "Epoch 1270/2000\n",
      "84/84 [==============================] - 0s 283us/step - loss: 0.0265 - accuracy: 0.9881 - val_loss: 0.3941 - val_accuracy: 0.8889\n",
      "Epoch 1271/2000\n",
      "84/84 [==============================] - 0s 309us/step - loss: 0.0258 - accuracy: 0.9881 - val_loss: 0.3907 - val_accuracy: 0.8889\n",
      "Epoch 1272/2000\n",
      "84/84 [==============================] - 0s 353us/step - loss: 0.0256 - accuracy: 0.9881 - val_loss: 0.3862 - val_accuracy: 0.8889\n",
      "Epoch 1273/2000\n",
      "84/84 [==============================] - 0s 386us/step - loss: 0.0255 - accuracy: 0.9881 - val_loss: 0.3830 - val_accuracy: 0.8889\n",
      "Epoch 1274/2000\n",
      "84/84 [==============================] - 0s 383us/step - loss: 0.0255 - accuracy: 0.9881 - val_loss: 0.3800 - val_accuracy: 0.8889\n",
      "Epoch 1275/2000\n",
      "84/84 [==============================] - 0s 389us/step - loss: 0.0255 - accuracy: 0.9881 - val_loss: 0.3790 - val_accuracy: 0.8889\n",
      "Epoch 1276/2000\n",
      "84/84 [==============================] - 0s 270us/step - loss: 0.0259 - accuracy: 0.9881 - val_loss: 0.3757 - val_accuracy: 0.8889\n",
      "Epoch 1277/2000\n",
      "84/84 [==============================] - 0s 311us/step - loss: 0.0253 - accuracy: 0.9881 - val_loss: 0.3773 - val_accuracy: 0.8889\n",
      "Epoch 1278/2000\n",
      "84/84 [==============================] - 0s 385us/step - loss: 0.0255 - accuracy: 0.9881 - val_loss: 0.3790 - val_accuracy: 0.8889\n",
      "Epoch 1279/2000\n",
      "84/84 [==============================] - 0s 445us/step - loss: 0.0254 - accuracy: 0.9881 - val_loss: 0.3819 - val_accuracy: 0.8889\n",
      "Epoch 1280/2000\n",
      "84/84 [==============================] - 0s 383us/step - loss: 0.0258 - accuracy: 0.9881 - val_loss: 0.3847 - val_accuracy: 0.8889\n",
      "Epoch 1281/2000\n",
      "84/84 [==============================] - 0s 400us/step - loss: 0.0255 - accuracy: 0.9881 - val_loss: 0.3851 - val_accuracy: 0.8889\n",
      "Epoch 1282/2000\n",
      "84/84 [==============================] - 0s 340us/step - loss: 0.0252 - accuracy: 0.9881 - val_loss: 0.3816 - val_accuracy: 0.8889\n",
      "Epoch 1283/2000\n",
      "84/84 [==============================] - 0s 350us/step - loss: 0.0252 - accuracy: 0.9881 - val_loss: 0.3779 - val_accuracy: 0.8889\n",
      "Epoch 1284/2000\n",
      "84/84 [==============================] - 0s 298us/step - loss: 0.0252 - accuracy: 0.9881 - val_loss: 0.3752 - val_accuracy: 0.8889\n",
      "Epoch 1285/2000\n",
      "84/84 [==============================] - 0s 351us/step - loss: 0.0253 - accuracy: 0.9881 - val_loss: 0.3759 - val_accuracy: 0.8889\n",
      "Epoch 1286/2000\n",
      "84/84 [==============================] - 0s 295us/step - loss: 0.0255 - accuracy: 0.9881 - val_loss: 0.3756 - val_accuracy: 0.8889\n",
      "Epoch 1287/2000\n",
      "84/84 [==============================] - 0s 264us/step - loss: 0.0252 - accuracy: 0.9881 - val_loss: 0.3798 - val_accuracy: 0.8889\n",
      "Epoch 1288/2000\n",
      "84/84 [==============================] - 0s 260us/step - loss: 0.0252 - accuracy: 0.9881 - val_loss: 0.3841 - val_accuracy: 0.8889\n",
      "Epoch 1289/2000\n",
      "84/84 [==============================] - 0s 257us/step - loss: 0.0252 - accuracy: 0.9762 - val_loss: 0.3882 - val_accuracy: 0.8889\n",
      "Epoch 1290/2000\n",
      "84/84 [==============================] - 0s 290us/step - loss: 0.0250 - accuracy: 0.9762 - val_loss: 0.3910 - val_accuracy: 0.8889\n",
      "Epoch 1291/2000\n",
      "84/84 [==============================] - 0s 265us/step - loss: 0.0251 - accuracy: 0.9881 - val_loss: 0.3913 - val_accuracy: 0.8889\n",
      "Epoch 1292/2000\n",
      "84/84 [==============================] - 0s 278us/step - loss: 0.0256 - accuracy: 0.9881 - val_loss: 0.3965 - val_accuracy: 0.8889\n",
      "Epoch 1293/2000\n",
      "84/84 [==============================] - 0s 248us/step - loss: 0.0252 - accuracy: 0.9881 - val_loss: 0.3985 - val_accuracy: 0.8889\n",
      "Epoch 1294/2000\n",
      "84/84 [==============================] - 0s 251us/step - loss: 0.0253 - accuracy: 0.9881 - val_loss: 0.3975 - val_accuracy: 0.8889\n",
      "Epoch 1295/2000\n",
      "84/84 [==============================] - 0s 222us/step - loss: 0.0253 - accuracy: 0.9881 - val_loss: 0.3937 - val_accuracy: 0.8889\n",
      "Epoch 1296/2000\n",
      "84/84 [==============================] - 0s 300us/step - loss: 0.0251 - accuracy: 0.9881 - val_loss: 0.3911 - val_accuracy: 0.8889\n",
      "Epoch 1297/2000\n",
      "84/84 [==============================] - 0s 263us/step - loss: 0.0251 - accuracy: 0.9881 - val_loss: 0.3890 - val_accuracy: 0.8889\n",
      "Epoch 1298/2000\n",
      "84/84 [==============================] - 0s 249us/step - loss: 0.0251 - accuracy: 0.9881 - val_loss: 0.3877 - val_accuracy: 0.8889\n",
      "Epoch 1299/2000\n",
      "84/84 [==============================] - 0s 259us/step - loss: 0.0256 - accuracy: 0.9881 - val_loss: 0.3857 - val_accuracy: 0.8889\n",
      "Epoch 1300/2000\n",
      "84/84 [==============================] - 0s 282us/step - loss: 0.0255 - accuracy: 0.9881 - val_loss: 0.3909 - val_accuracy: 0.8889\n",
      "Epoch 1301/2000\n",
      "84/84 [==============================] - 0s 299us/step - loss: 0.0252 - accuracy: 0.9881 - val_loss: 0.3899 - val_accuracy: 0.8889\n",
      "Epoch 1302/2000\n",
      "84/84 [==============================] - 0s 246us/step - loss: 0.0250 - accuracy: 0.9881 - val_loss: 0.3885 - val_accuracy: 0.8889\n",
      "Epoch 1303/2000\n",
      "84/84 [==============================] - 0s 274us/step - loss: 0.0250 - accuracy: 0.9881 - val_loss: 0.3842 - val_accuracy: 0.8889\n",
      "Epoch 1304/2000\n",
      "84/84 [==============================] - 0s 284us/step - loss: 0.0249 - accuracy: 0.9762 - val_loss: 0.3861 - val_accuracy: 0.8889\n",
      "Epoch 1305/2000\n",
      "84/84 [==============================] - 0s 309us/step - loss: 0.0250 - accuracy: 0.9762 - val_loss: 0.3876 - val_accuracy: 0.8889\n",
      "Epoch 1306/2000\n",
      "84/84 [==============================] - 0s 265us/step - loss: 0.0248 - accuracy: 0.9762 - val_loss: 0.3862 - val_accuracy: 0.8889\n",
      "Epoch 1307/2000\n",
      "84/84 [==============================] - 0s 325us/step - loss: 0.0249 - accuracy: 0.9881 - val_loss: 0.3855 - val_accuracy: 0.8889\n",
      "Epoch 1308/2000\n",
      "84/84 [==============================] - 0s 386us/step - loss: 0.0249 - accuracy: 0.9881 - val_loss: 0.3856 - val_accuracy: 0.8889\n",
      "Epoch 1309/2000\n",
      "84/84 [==============================] - 0s 334us/step - loss: 0.0249 - accuracy: 0.9881 - val_loss: 0.3893 - val_accuracy: 0.8889\n",
      "Epoch 1310/2000\n",
      "84/84 [==============================] - 0s 301us/step - loss: 0.0249 - accuracy: 0.9881 - val_loss: 0.3964 - val_accuracy: 0.8889\n",
      "Epoch 1311/2000\n",
      "84/84 [==============================] - 0s 267us/step - loss: 0.0249 - accuracy: 0.9881 - val_loss: 0.4002 - val_accuracy: 0.8889\n",
      "Epoch 1312/2000\n",
      "84/84 [==============================] - 0s 237us/step - loss: 0.0247 - accuracy: 0.9762 - val_loss: 0.4051 - val_accuracy: 0.8889\n",
      "Epoch 1313/2000\n",
      "84/84 [==============================] - 0s 210us/step - loss: 0.0247 - accuracy: 0.9881 - val_loss: 0.4081 - val_accuracy: 0.8889\n",
      "Epoch 1314/2000\n",
      "84/84 [==============================] - 0s 234us/step - loss: 0.0247 - accuracy: 0.9881 - val_loss: 0.4064 - val_accuracy: 0.8889\n",
      "Epoch 1315/2000\n",
      "84/84 [==============================] - 0s 247us/step - loss: 0.0249 - accuracy: 0.9881 - val_loss: 0.4041 - val_accuracy: 0.8889\n",
      "Epoch 1316/2000\n",
      "84/84 [==============================] - 0s 289us/step - loss: 0.0246 - accuracy: 0.9881 - val_loss: 0.4066 - val_accuracy: 0.8889\n",
      "Epoch 1317/2000\n",
      "84/84 [==============================] - 0s 297us/step - loss: 0.0251 - accuracy: 0.9881 - val_loss: 0.4098 - val_accuracy: 0.8889\n",
      "Epoch 1318/2000\n",
      "84/84 [==============================] - 0s 284us/step - loss: 0.0247 - accuracy: 0.9881 - val_loss: 0.4065 - val_accuracy: 0.8889\n",
      "Epoch 1319/2000\n",
      "84/84 [==============================] - 0s 293us/step - loss: 0.0250 - accuracy: 0.9881 - val_loss: 0.4048 - val_accuracy: 0.8889\n",
      "Epoch 1320/2000\n",
      "84/84 [==============================] - 0s 273us/step - loss: 0.0256 - accuracy: 0.9881 - val_loss: 0.3976 - val_accuracy: 0.8889\n",
      "Epoch 1321/2000\n",
      "84/84 [==============================] - 0s 277us/step - loss: 0.0245 - accuracy: 0.9881 - val_loss: 0.3991 - val_accuracy: 0.8889\n",
      "Epoch 1322/2000\n",
      "84/84 [==============================] - 0s 259us/step - loss: 0.0247 - accuracy: 0.9881 - val_loss: 0.3993 - val_accuracy: 0.8889\n",
      "Epoch 1323/2000\n",
      "84/84 [==============================] - 0s 255us/step - loss: 0.0244 - accuracy: 0.9881 - val_loss: 0.3968 - val_accuracy: 0.8889\n",
      "Epoch 1324/2000\n",
      "84/84 [==============================] - 0s 284us/step - loss: 0.0244 - accuracy: 0.9762 - val_loss: 0.3932 - val_accuracy: 0.8889\n",
      "Epoch 1325/2000\n",
      "84/84 [==============================] - 0s 276us/step - loss: 0.0245 - accuracy: 0.9762 - val_loss: 0.3913 - val_accuracy: 0.8889\n",
      "Epoch 1326/2000\n",
      "84/84 [==============================] - 0s 257us/step - loss: 0.0246 - accuracy: 0.9762 - val_loss: 0.3928 - val_accuracy: 0.8889\n",
      "Epoch 1327/2000\n",
      "84/84 [==============================] - 0s 246us/step - loss: 0.0245 - accuracy: 0.9881 - val_loss: 0.3953 - val_accuracy: 0.8889\n",
      "Epoch 1328/2000\n",
      "84/84 [==============================] - 0s 276us/step - loss: 0.0244 - accuracy: 0.9762 - val_loss: 0.3981 - val_accuracy: 0.8889\n",
      "Epoch 1329/2000\n",
      "84/84 [==============================] - 0s 309us/step - loss: 0.0251 - accuracy: 0.9762 - val_loss: 0.4036 - val_accuracy: 0.8889\n",
      "Epoch 1330/2000\n",
      "84/84 [==============================] - 0s 246us/step - loss: 0.0246 - accuracy: 0.9881 - val_loss: 0.4041 - val_accuracy: 0.8889\n",
      "Epoch 1331/2000\n",
      "84/84 [==============================] - 0s 258us/step - loss: 0.0244 - accuracy: 0.9881 - val_loss: 0.4020 - val_accuracy: 0.8889\n",
      "Epoch 1332/2000\n",
      "84/84 [==============================] - 0s 280us/step - loss: 0.0243 - accuracy: 0.9881 - val_loss: 0.3988 - val_accuracy: 0.8889\n",
      "Epoch 1333/2000\n",
      "84/84 [==============================] - 0s 280us/step - loss: 0.0244 - accuracy: 0.9881 - val_loss: 0.3962 - val_accuracy: 0.8889\n",
      "Epoch 1334/2000\n",
      "84/84 [==============================] - 0s 234us/step - loss: 0.0245 - accuracy: 0.9762 - val_loss: 0.3954 - val_accuracy: 0.8889\n",
      "Epoch 1335/2000\n",
      "84/84 [==============================] - 0s 250us/step - loss: 0.0243 - accuracy: 0.9762 - val_loss: 0.3942 - val_accuracy: 0.8889\n",
      "Epoch 1336/2000\n",
      "84/84 [==============================] - 0s 261us/step - loss: 0.0245 - accuracy: 0.9762 - val_loss: 0.3910 - val_accuracy: 0.8889\n",
      "Epoch 1337/2000\n",
      "84/84 [==============================] - 0s 273us/step - loss: 0.0243 - accuracy: 0.9881 - val_loss: 0.3902 - val_accuracy: 0.8889\n",
      "Epoch 1338/2000\n",
      "84/84 [==============================] - 0s 275us/step - loss: 0.0245 - accuracy: 0.9881 - val_loss: 0.3913 - val_accuracy: 0.8889\n",
      "Epoch 1339/2000\n",
      "84/84 [==============================] - 0s 271us/step - loss: 0.0245 - accuracy: 0.9881 - val_loss: 0.3946 - val_accuracy: 0.8889\n",
      "Epoch 1340/2000\n",
      "84/84 [==============================] - 0s 278us/step - loss: 0.0246 - accuracy: 0.9881 - val_loss: 0.3946 - val_accuracy: 0.8889\n",
      "Epoch 1341/2000\n",
      "84/84 [==============================] - 0s 302us/step - loss: 0.0242 - accuracy: 0.9881 - val_loss: 0.4004 - val_accuracy: 0.8889\n",
      "Epoch 1342/2000\n",
      "84/84 [==============================] - 0s 333us/step - loss: 0.0241 - accuracy: 0.9762 - val_loss: 0.4050 - val_accuracy: 0.8889\n",
      "Epoch 1343/2000\n",
      "84/84 [==============================] - 0s 239us/step - loss: 0.0238 - accuracy: 0.9881 - val_loss: 0.4123 - val_accuracy: 0.8889\n",
      "Epoch 1344/2000\n",
      "84/84 [==============================] - 0s 341us/step - loss: 0.0241 - accuracy: 0.9881 - val_loss: 0.4216 - val_accuracy: 0.8889\n",
      "Epoch 1345/2000\n",
      "84/84 [==============================] - 0s 519us/step - loss: 0.0244 - accuracy: 0.9881 - val_loss: 0.4215 - val_accuracy: 0.8889\n",
      "Epoch 1346/2000\n",
      "84/84 [==============================] - 0s 401us/step - loss: 0.0244 - accuracy: 0.9881 - val_loss: 0.4201 - val_accuracy: 0.8889\n",
      "Epoch 1347/2000\n",
      "84/84 [==============================] - 0s 362us/step - loss: 0.0248 - accuracy: 0.9881 - val_loss: 0.4183 - val_accuracy: 0.8889\n",
      "Epoch 1348/2000\n",
      "84/84 [==============================] - 0s 500us/step - loss: 0.0245 - accuracy: 0.9881 - val_loss: 0.4085 - val_accuracy: 0.8889\n",
      "Epoch 1349/2000\n",
      "84/84 [==============================] - 0s 402us/step - loss: 0.0247 - accuracy: 0.9881 - val_loss: 0.4014 - val_accuracy: 0.8889\n",
      "Epoch 1350/2000\n",
      "84/84 [==============================] - 0s 429us/step - loss: 0.0243 - accuracy: 0.9881 - val_loss: 0.4023 - val_accuracy: 0.8889\n",
      "Epoch 1351/2000\n",
      "84/84 [==============================] - 0s 406us/step - loss: 0.0240 - accuracy: 0.9881 - val_loss: 0.4007 - val_accuracy: 0.8889\n",
      "Epoch 1352/2000\n",
      "84/84 [==============================] - 0s 377us/step - loss: 0.0245 - accuracy: 0.9881 - val_loss: 0.3972 - val_accuracy: 0.8889\n",
      "Epoch 1353/2000\n",
      "84/84 [==============================] - 0s 392us/step - loss: 0.0240 - accuracy: 0.9881 - val_loss: 0.3973 - val_accuracy: 0.8889\n",
      "Epoch 1354/2000\n",
      "84/84 [==============================] - 0s 211us/step - loss: 0.0239 - accuracy: 0.9881 - val_loss: 0.3979 - val_accuracy: 0.8889\n",
      "Epoch 1355/2000\n",
      "84/84 [==============================] - 0s 222us/step - loss: 0.0241 - accuracy: 0.9881 - val_loss: 0.3979 - val_accuracy: 0.8889\n",
      "Epoch 1356/2000\n",
      "84/84 [==============================] - 0s 234us/step - loss: 0.0241 - accuracy: 0.9881 - val_loss: 0.4009 - val_accuracy: 0.8889\n",
      "Epoch 1357/2000\n",
      "84/84 [==============================] - 0s 213us/step - loss: 0.0241 - accuracy: 0.9881 - val_loss: 0.4010 - val_accuracy: 0.8889\n",
      "Epoch 1358/2000\n",
      "84/84 [==============================] - 0s 278us/step - loss: 0.0239 - accuracy: 0.9881 - val_loss: 0.4011 - val_accuracy: 0.8889\n",
      "Epoch 1359/2000\n",
      "84/84 [==============================] - 0s 283us/step - loss: 0.0241 - accuracy: 0.9881 - val_loss: 0.4051 - val_accuracy: 0.8889\n",
      "Epoch 1360/2000\n",
      "84/84 [==============================] - 0s 230us/step - loss: 0.0239 - accuracy: 0.9881 - val_loss: 0.4049 - val_accuracy: 0.8889\n",
      "Epoch 1361/2000\n",
      "84/84 [==============================] - 0s 237us/step - loss: 0.0238 - accuracy: 0.9881 - val_loss: 0.4026 - val_accuracy: 0.8889\n",
      "Epoch 1362/2000\n",
      "84/84 [==============================] - 0s 263us/step - loss: 0.0240 - accuracy: 0.9762 - val_loss: 0.4037 - val_accuracy: 0.8889\n",
      "Epoch 1363/2000\n",
      "84/84 [==============================] - 0s 244us/step - loss: 0.0241 - accuracy: 0.9762 - val_loss: 0.4015 - val_accuracy: 0.8889\n",
      "Epoch 1364/2000\n",
      "84/84 [==============================] - 0s 324us/step - loss: 0.0239 - accuracy: 0.9762 - val_loss: 0.4053 - val_accuracy: 0.8889\n",
      "Epoch 1365/2000\n",
      "84/84 [==============================] - 0s 312us/step - loss: 0.0238 - accuracy: 0.9881 - val_loss: 0.4092 - val_accuracy: 0.8889\n",
      "Epoch 1366/2000\n",
      "84/84 [==============================] - 0s 343us/step - loss: 0.0238 - accuracy: 0.9881 - val_loss: 0.4120 - val_accuracy: 0.8889\n",
      "Epoch 1367/2000\n",
      "84/84 [==============================] - 0s 311us/step - loss: 0.0238 - accuracy: 0.9881 - val_loss: 0.4128 - val_accuracy: 0.8889\n",
      "Epoch 1368/2000\n",
      "84/84 [==============================] - 0s 362us/step - loss: 0.0242 - accuracy: 0.9881 - val_loss: 0.4119 - val_accuracy: 0.8889\n",
      "Epoch 1369/2000\n",
      "84/84 [==============================] - 0s 361us/step - loss: 0.0241 - accuracy: 0.9881 - val_loss: 0.4083 - val_accuracy: 0.8889\n",
      "Epoch 1370/2000\n",
      "84/84 [==============================] - 0s 333us/step - loss: 0.0241 - accuracy: 0.9881 - val_loss: 0.4059 - val_accuracy: 0.8889\n",
      "Epoch 1371/2000\n",
      "84/84 [==============================] - 0s 426us/step - loss: 0.0239 - accuracy: 0.9881 - val_loss: 0.4086 - val_accuracy: 0.8889\n",
      "Epoch 1372/2000\n",
      "84/84 [==============================] - 0s 394us/step - loss: 0.0244 - accuracy: 0.9762 - val_loss: 0.4053 - val_accuracy: 0.8889\n",
      "Epoch 1373/2000\n",
      "84/84 [==============================] - 0s 322us/step - loss: 0.0237 - accuracy: 0.9881 - val_loss: 0.4087 - val_accuracy: 0.8889\n",
      "Epoch 1374/2000\n",
      "84/84 [==============================] - 0s 327us/step - loss: 0.0236 - accuracy: 0.9881 - val_loss: 0.4127 - val_accuracy: 0.8889\n",
      "Epoch 1375/2000\n",
      "84/84 [==============================] - 0s 244us/step - loss: 0.0241 - accuracy: 0.9881 - val_loss: 0.4175 - val_accuracy: 0.8889\n",
      "Epoch 1376/2000\n",
      "84/84 [==============================] - 0s 289us/step - loss: 0.0240 - accuracy: 0.9881 - val_loss: 0.4146 - val_accuracy: 0.8889\n",
      "Epoch 1377/2000\n",
      "84/84 [==============================] - 0s 323us/step - loss: 0.0239 - accuracy: 0.9881 - val_loss: 0.4129 - val_accuracy: 0.8889\n",
      "Epoch 1378/2000\n",
      "84/84 [==============================] - 0s 358us/step - loss: 0.0245 - accuracy: 0.9881 - val_loss: 0.4187 - val_accuracy: 0.8889\n",
      "Epoch 1379/2000\n",
      "84/84 [==============================] - 0s 303us/step - loss: 0.0237 - accuracy: 0.9881 - val_loss: 0.4162 - val_accuracy: 0.8889\n",
      "Epoch 1380/2000\n",
      "84/84 [==============================] - 0s 332us/step - loss: 0.0239 - accuracy: 0.9881 - val_loss: 0.4110 - val_accuracy: 0.8889\n",
      "Epoch 1381/2000\n",
      "84/84 [==============================] - 0s 295us/step - loss: 0.0238 - accuracy: 0.9881 - val_loss: 0.4086 - val_accuracy: 0.8889\n",
      "Epoch 1382/2000\n",
      "84/84 [==============================] - 0s 307us/step - loss: 0.0235 - accuracy: 0.9881 - val_loss: 0.4095 - val_accuracy: 0.8889\n",
      "Epoch 1383/2000\n",
      "84/84 [==============================] - 0s 309us/step - loss: 0.0235 - accuracy: 0.9881 - val_loss: 0.4098 - val_accuracy: 0.8889\n",
      "Epoch 1384/2000\n",
      "84/84 [==============================] - 0s 292us/step - loss: 0.0237 - accuracy: 0.9881 - val_loss: 0.4117 - val_accuracy: 0.8889\n",
      "Epoch 1385/2000\n",
      "84/84 [==============================] - 0s 314us/step - loss: 0.0236 - accuracy: 0.9881 - val_loss: 0.4108 - val_accuracy: 0.8889\n",
      "Epoch 1386/2000\n",
      "84/84 [==============================] - 0s 301us/step - loss: 0.0237 - accuracy: 0.9881 - val_loss: 0.4103 - val_accuracy: 0.8889\n",
      "Epoch 1387/2000\n",
      "84/84 [==============================] - 0s 288us/step - loss: 0.0234 - accuracy: 0.9881 - val_loss: 0.4127 - val_accuracy: 0.8889\n",
      "Epoch 1388/2000\n",
      "84/84 [==============================] - 0s 288us/step - loss: 0.0234 - accuracy: 0.9881 - val_loss: 0.4170 - val_accuracy: 0.8889\n",
      "Epoch 1389/2000\n",
      "84/84 [==============================] - 0s 303us/step - loss: 0.0238 - accuracy: 0.9881 - val_loss: 0.4212 - val_accuracy: 0.8889\n",
      "Epoch 1390/2000\n",
      "84/84 [==============================] - 0s 363us/step - loss: 0.0237 - accuracy: 0.9881 - val_loss: 0.4199 - val_accuracy: 0.8889\n",
      "Epoch 1391/2000\n",
      "84/84 [==============================] - 0s 402us/step - loss: 0.0234 - accuracy: 0.9881 - val_loss: 0.4209 - val_accuracy: 0.8889\n",
      "Epoch 1392/2000\n",
      "84/84 [==============================] - 0s 416us/step - loss: 0.0236 - accuracy: 0.9762 - val_loss: 0.4191 - val_accuracy: 0.8889\n",
      "Epoch 1393/2000\n",
      "84/84 [==============================] - 0s 437us/step - loss: 0.0236 - accuracy: 0.9762 - val_loss: 0.4213 - val_accuracy: 0.8889\n",
      "Epoch 1394/2000\n",
      "84/84 [==============================] - 0s 432us/step - loss: 0.0235 - accuracy: 0.9881 - val_loss: 0.4242 - val_accuracy: 0.8889\n",
      "Epoch 1395/2000\n",
      "84/84 [==============================] - 0s 447us/step - loss: 0.0235 - accuracy: 0.9881 - val_loss: 0.4239 - val_accuracy: 0.8889\n",
      "Epoch 1396/2000\n",
      "84/84 [==============================] - 0s 381us/step - loss: 0.0233 - accuracy: 0.9881 - val_loss: 0.4260 - val_accuracy: 0.8889\n",
      "Epoch 1397/2000\n",
      "84/84 [==============================] - 0s 375us/step - loss: 0.0233 - accuracy: 0.9881 - val_loss: 0.4260 - val_accuracy: 0.8889\n",
      "Epoch 1398/2000\n",
      "84/84 [==============================] - 0s 370us/step - loss: 0.0234 - accuracy: 0.9881 - val_loss: 0.4248 - val_accuracy: 0.8889\n",
      "Epoch 1399/2000\n",
      "84/84 [==============================] - 0s 277us/step - loss: 0.0236 - accuracy: 0.9881 - val_loss: 0.4242 - val_accuracy: 0.8889\n",
      "Epoch 1400/2000\n",
      "84/84 [==============================] - 0s 265us/step - loss: 0.0232 - accuracy: 0.9881 - val_loss: 0.4194 - val_accuracy: 0.8889\n",
      "Epoch 1401/2000\n",
      "84/84 [==============================] - 0s 276us/step - loss: 0.0235 - accuracy: 0.9762 - val_loss: 0.4150 - val_accuracy: 0.8889\n",
      "Epoch 1402/2000\n",
      "84/84 [==============================] - 0s 269us/step - loss: 0.0237 - accuracy: 0.9762 - val_loss: 0.4171 - val_accuracy: 0.8889\n",
      "Epoch 1403/2000\n",
      "84/84 [==============================] - 0s 256us/step - loss: 0.0233 - accuracy: 0.9762 - val_loss: 0.4167 - val_accuracy: 0.8889\n",
      "Epoch 1404/2000\n",
      "84/84 [==============================] - 0s 272us/step - loss: 0.0233 - accuracy: 0.9762 - val_loss: 0.4141 - val_accuracy: 0.8889\n",
      "Epoch 1405/2000\n",
      "84/84 [==============================] - 0s 302us/step - loss: 0.0236 - accuracy: 0.9881 - val_loss: 0.4114 - val_accuracy: 0.8889\n",
      "Epoch 1406/2000\n",
      "84/84 [==============================] - 0s 348us/step - loss: 0.0235 - accuracy: 0.9881 - val_loss: 0.4131 - val_accuracy: 0.8889\n",
      "Epoch 1407/2000\n",
      "84/84 [==============================] - 0s 319us/step - loss: 0.0232 - accuracy: 0.9881 - val_loss: 0.4108 - val_accuracy: 0.8889\n",
      "Epoch 1408/2000\n",
      "84/84 [==============================] - 0s 362us/step - loss: 0.0233 - accuracy: 0.9881 - val_loss: 0.4085 - val_accuracy: 0.8889\n",
      "Epoch 1409/2000\n",
      "84/84 [==============================] - 0s 322us/step - loss: 0.0233 - accuracy: 0.9881 - val_loss: 0.4129 - val_accuracy: 0.8889\n",
      "Epoch 1410/2000\n",
      "84/84 [==============================] - 0s 371us/step - loss: 0.0232 - accuracy: 0.9881 - val_loss: 0.4161 - val_accuracy: 0.8889\n",
      "Epoch 1411/2000\n",
      "84/84 [==============================] - 0s 312us/step - loss: 0.0233 - accuracy: 0.9881 - val_loss: 0.4188 - val_accuracy: 0.8889\n",
      "Epoch 1412/2000\n",
      "84/84 [==============================] - 0s 388us/step - loss: 0.0233 - accuracy: 0.9881 - val_loss: 0.4233 - val_accuracy: 0.8889\n",
      "Epoch 1413/2000\n",
      "84/84 [==============================] - 0s 770us/step - loss: 0.0231 - accuracy: 0.9881 - val_loss: 0.4250 - val_accuracy: 0.8889\n",
      "Epoch 1414/2000\n",
      "84/84 [==============================] - 0s 361us/step - loss: 0.0234 - accuracy: 0.9762 - val_loss: 0.4271 - val_accuracy: 0.8889\n",
      "Epoch 1415/2000\n",
      "84/84 [==============================] - 0s 587us/step - loss: 0.0230 - accuracy: 0.9762 - val_loss: 0.4253 - val_accuracy: 0.8889\n",
      "Epoch 1416/2000\n",
      "84/84 [==============================] - 0s 852us/step - loss: 0.0233 - accuracy: 0.9762 - val_loss: 0.4256 - val_accuracy: 0.8889\n",
      "Epoch 1417/2000\n",
      "84/84 [==============================] - 0s 882us/step - loss: 0.0230 - accuracy: 0.9881 - val_loss: 0.4221 - val_accuracy: 0.8889\n",
      "Epoch 1418/2000\n",
      "84/84 [==============================] - 0s 733us/step - loss: 0.0231 - accuracy: 0.9881 - val_loss: 0.4177 - val_accuracy: 0.8889\n",
      "Epoch 1419/2000\n",
      "84/84 [==============================] - 0s 818us/step - loss: 0.0233 - accuracy: 0.9881 - val_loss: 0.4167 - val_accuracy: 0.8889\n",
      "Epoch 1420/2000\n",
      "84/84 [==============================] - 0s 542us/step - loss: 0.0236 - accuracy: 0.9881 - val_loss: 0.4170 - val_accuracy: 0.8889\n",
      "Epoch 1421/2000\n",
      "84/84 [==============================] - 0s 322us/step - loss: 0.0232 - accuracy: 0.9881 - val_loss: 0.4220 - val_accuracy: 0.8889\n",
      "Epoch 1422/2000\n",
      "84/84 [==============================] - 0s 361us/step - loss: 0.0231 - accuracy: 0.9881 - val_loss: 0.4262 - val_accuracy: 0.8889\n",
      "Epoch 1423/2000\n",
      "84/84 [==============================] - 0s 313us/step - loss: 0.0230 - accuracy: 0.9881 - val_loss: 0.4331 - val_accuracy: 0.8889\n",
      "Epoch 1424/2000\n",
      "84/84 [==============================] - 0s 328us/step - loss: 0.0229 - accuracy: 0.9881 - val_loss: 0.4373 - val_accuracy: 0.8889\n",
      "Epoch 1425/2000\n",
      "84/84 [==============================] - 0s 329us/step - loss: 0.0230 - accuracy: 0.9881 - val_loss: 0.4387 - val_accuracy: 0.8889\n",
      "Epoch 1426/2000\n",
      "84/84 [==============================] - 0s 341us/step - loss: 0.0231 - accuracy: 0.9881 - val_loss: 0.4354 - val_accuracy: 0.8889\n",
      "Epoch 1427/2000\n",
      "84/84 [==============================] - 0s 296us/step - loss: 0.0231 - accuracy: 0.9881 - val_loss: 0.4336 - val_accuracy: 0.8889\n",
      "Epoch 1428/2000\n",
      "84/84 [==============================] - 0s 291us/step - loss: 0.0231 - accuracy: 0.9881 - val_loss: 0.4299 - val_accuracy: 0.8889\n",
      "Epoch 1429/2000\n",
      "84/84 [==============================] - 0s 254us/step - loss: 0.0230 - accuracy: 0.9881 - val_loss: 0.4282 - val_accuracy: 0.8889\n",
      "Epoch 1430/2000\n",
      "84/84 [==============================] - 0s 285us/step - loss: 0.0232 - accuracy: 0.9881 - val_loss: 0.4305 - val_accuracy: 0.8889\n",
      "Epoch 1431/2000\n",
      "84/84 [==============================] - 0s 305us/step - loss: 0.0230 - accuracy: 0.9881 - val_loss: 0.4325 - val_accuracy: 0.8889\n",
      "Epoch 1432/2000\n",
      "84/84 [==============================] - 0s 318us/step - loss: 0.0231 - accuracy: 0.9881 - val_loss: 0.4329 - val_accuracy: 0.8889\n",
      "Epoch 1433/2000\n",
      "84/84 [==============================] - 0s 397us/step - loss: 0.0230 - accuracy: 0.9881 - val_loss: 0.4361 - val_accuracy: 0.8889\n",
      "Epoch 1434/2000\n",
      "84/84 [==============================] - 0s 394us/step - loss: 0.0231 - accuracy: 0.9881 - val_loss: 0.4361 - val_accuracy: 0.8889\n",
      "Epoch 1435/2000\n",
      "84/84 [==============================] - 0s 319us/step - loss: 0.0233 - accuracy: 0.9881 - val_loss: 0.4350 - val_accuracy: 0.8889\n",
      "Epoch 1436/2000\n",
      "84/84 [==============================] - 0s 318us/step - loss: 0.0231 - accuracy: 0.9881 - val_loss: 0.4314 - val_accuracy: 0.8889\n",
      "Epoch 1437/2000\n",
      "84/84 [==============================] - 0s 290us/step - loss: 0.0229 - accuracy: 0.9881 - val_loss: 0.4268 - val_accuracy: 0.8889\n",
      "Epoch 1438/2000\n",
      "84/84 [==============================] - 0s 306us/step - loss: 0.0237 - accuracy: 0.9881 - val_loss: 0.4208 - val_accuracy: 0.8889\n",
      "Epoch 1439/2000\n",
      "84/84 [==============================] - 0s 323us/step - loss: 0.0228 - accuracy: 0.9881 - val_loss: 0.4232 - val_accuracy: 0.8889\n",
      "Epoch 1440/2000\n",
      "84/84 [==============================] - 0s 315us/step - loss: 0.0228 - accuracy: 0.9881 - val_loss: 0.4244 - val_accuracy: 0.8889\n",
      "Epoch 1441/2000\n",
      "84/84 [==============================] - 0s 295us/step - loss: 0.0227 - accuracy: 0.9881 - val_loss: 0.4266 - val_accuracy: 0.8889\n",
      "Epoch 1442/2000\n",
      "84/84 [==============================] - 0s 475us/step - loss: 0.0227 - accuracy: 0.9881 - val_loss: 0.4277 - val_accuracy: 0.8889\n",
      "Epoch 1443/2000\n",
      "84/84 [==============================] - 0s 352us/step - loss: 0.0229 - accuracy: 0.9881 - val_loss: 0.4273 - val_accuracy: 0.8889\n",
      "Epoch 1444/2000\n",
      "84/84 [==============================] - 0s 275us/step - loss: 0.0229 - accuracy: 0.9881 - val_loss: 0.4241 - val_accuracy: 0.8889\n",
      "Epoch 1445/2000\n",
      "84/84 [==============================] - 0s 272us/step - loss: 0.0228 - accuracy: 0.9881 - val_loss: 0.4247 - val_accuracy: 0.8889\n",
      "Epoch 1446/2000\n",
      "84/84 [==============================] - 0s 252us/step - loss: 0.0226 - accuracy: 0.9881 - val_loss: 0.4236 - val_accuracy: 0.8889\n",
      "Epoch 1447/2000\n",
      "84/84 [==============================] - 0s 304us/step - loss: 0.0227 - accuracy: 0.9881 - val_loss: 0.4243 - val_accuracy: 0.8889\n",
      "Epoch 1448/2000\n",
      "84/84 [==============================] - 0s 330us/step - loss: 0.0227 - accuracy: 0.9881 - val_loss: 0.4245 - val_accuracy: 0.8889\n",
      "Epoch 1449/2000\n",
      "84/84 [==============================] - 0s 318us/step - loss: 0.0226 - accuracy: 0.9881 - val_loss: 0.4239 - val_accuracy: 0.8889\n",
      "Epoch 1450/2000\n",
      "84/84 [==============================] - 0s 362us/step - loss: 0.0228 - accuracy: 0.9762 - val_loss: 0.4215 - val_accuracy: 0.8889\n",
      "Epoch 1451/2000\n",
      "84/84 [==============================] - 0s 297us/step - loss: 0.0228 - accuracy: 0.9881 - val_loss: 0.4212 - val_accuracy: 0.8889\n",
      "Epoch 1452/2000\n",
      "84/84 [==============================] - 0s 326us/step - loss: 0.0227 - accuracy: 0.9881 - val_loss: 0.4235 - val_accuracy: 0.8889\n",
      "Epoch 1453/2000\n",
      "84/84 [==============================] - 0s 342us/step - loss: 0.0226 - accuracy: 0.9881 - val_loss: 0.4276 - val_accuracy: 0.8889\n",
      "Epoch 1454/2000\n",
      "84/84 [==============================] - 0s 334us/step - loss: 0.0225 - accuracy: 0.9762 - val_loss: 0.4317 - val_accuracy: 0.8889\n",
      "Epoch 1455/2000\n",
      "84/84 [==============================] - 0s 311us/step - loss: 0.0226 - accuracy: 0.9762 - val_loss: 0.4326 - val_accuracy: 0.8889\n",
      "Epoch 1456/2000\n",
      "84/84 [==============================] - 0s 349us/step - loss: 0.0225 - accuracy: 0.9881 - val_loss: 0.4375 - val_accuracy: 0.8889\n",
      "Epoch 1457/2000\n",
      "84/84 [==============================] - 0s 390us/step - loss: 0.0226 - accuracy: 0.9881 - val_loss: 0.4417 - val_accuracy: 0.8889\n",
      "Epoch 1458/2000\n",
      "84/84 [==============================] - 0s 294us/step - loss: 0.0226 - accuracy: 0.9881 - val_loss: 0.4400 - val_accuracy: 0.8889\n",
      "Epoch 1459/2000\n",
      "84/84 [==============================] - 0s 301us/step - loss: 0.0225 - accuracy: 0.9881 - val_loss: 0.4394 - val_accuracy: 0.8889\n",
      "Epoch 1460/2000\n",
      "84/84 [==============================] - 0s 319us/step - loss: 0.0227 - accuracy: 0.9881 - val_loss: 0.4373 - val_accuracy: 0.8889\n",
      "Epoch 1461/2000\n",
      "84/84 [==============================] - 0s 365us/step - loss: 0.0226 - accuracy: 0.9881 - val_loss: 0.4352 - val_accuracy: 0.8889\n",
      "Epoch 1462/2000\n",
      "84/84 [==============================] - 0s 398us/step - loss: 0.0229 - accuracy: 0.9881 - val_loss: 0.4324 - val_accuracy: 0.8889\n",
      "Epoch 1463/2000\n",
      "84/84 [==============================] - 0s 381us/step - loss: 0.0228 - accuracy: 0.9881 - val_loss: 0.4351 - val_accuracy: 0.8889\n",
      "Epoch 1464/2000\n",
      "84/84 [==============================] - 0s 368us/step - loss: 0.0226 - accuracy: 0.9881 - val_loss: 0.4314 - val_accuracy: 0.8889\n",
      "Epoch 1465/2000\n",
      "84/84 [==============================] - 0s 278us/step - loss: 0.0225 - accuracy: 0.9881 - val_loss: 0.4316 - val_accuracy: 0.8889\n",
      "Epoch 1466/2000\n",
      "84/84 [==============================] - 0s 317us/step - loss: 0.0224 - accuracy: 0.9881 - val_loss: 0.4305 - val_accuracy: 0.8889\n",
      "Epoch 1467/2000\n",
      "84/84 [==============================] - 0s 277us/step - loss: 0.0225 - accuracy: 0.9881 - val_loss: 0.4302 - val_accuracy: 0.8889\n",
      "Epoch 1468/2000\n",
      "84/84 [==============================] - 0s 263us/step - loss: 0.0226 - accuracy: 0.9881 - val_loss: 0.4293 - val_accuracy: 0.8889\n",
      "Epoch 1469/2000\n",
      "84/84 [==============================] - 0s 347us/step - loss: 0.0226 - accuracy: 0.9762 - val_loss: 0.4291 - val_accuracy: 0.8889\n",
      "Epoch 1470/2000\n",
      "84/84 [==============================] - 0s 295us/step - loss: 0.0223 - accuracy: 0.9762 - val_loss: 0.4333 - val_accuracy: 0.8889\n",
      "Epoch 1471/2000\n",
      "84/84 [==============================] - 0s 387us/step - loss: 0.0224 - accuracy: 0.9762 - val_loss: 0.4362 - val_accuracy: 0.8889\n",
      "Epoch 1472/2000\n",
      "84/84 [==============================] - 0s 412us/step - loss: 0.0230 - accuracy: 0.9881 - val_loss: 0.4425 - val_accuracy: 0.8889\n",
      "Epoch 1473/2000\n",
      "84/84 [==============================] - 0s 403us/step - loss: 0.0224 - accuracy: 0.9881 - val_loss: 0.4421 - val_accuracy: 0.8889\n",
      "Epoch 1474/2000\n",
      "84/84 [==============================] - 0s 397us/step - loss: 0.0227 - accuracy: 0.9881 - val_loss: 0.4364 - val_accuracy: 0.8889\n",
      "Epoch 1475/2000\n",
      "84/84 [==============================] - 0s 371us/step - loss: 0.0227 - accuracy: 0.9762 - val_loss: 0.4336 - val_accuracy: 0.8889\n",
      "Epoch 1476/2000\n",
      "84/84 [==============================] - 0s 360us/step - loss: 0.0223 - accuracy: 0.9881 - val_loss: 0.4364 - val_accuracy: 0.8889\n",
      "Epoch 1477/2000\n",
      "84/84 [==============================] - 0s 368us/step - loss: 0.0225 - accuracy: 0.9881 - val_loss: 0.4414 - val_accuracy: 0.8889\n",
      "Epoch 1478/2000\n",
      "84/84 [==============================] - 0s 393us/step - loss: 0.0224 - accuracy: 0.9881 - val_loss: 0.4418 - val_accuracy: 0.8889\n",
      "Epoch 1479/2000\n",
      "84/84 [==============================] - 0s 400us/step - loss: 0.0228 - accuracy: 0.9762 - val_loss: 0.4460 - val_accuracy: 0.8889\n",
      "Epoch 1480/2000\n",
      "84/84 [==============================] - 0s 276us/step - loss: 0.0222 - accuracy: 0.9881 - val_loss: 0.4446 - val_accuracy: 0.8889\n",
      "Epoch 1481/2000\n",
      "84/84 [==============================] - 0s 270us/step - loss: 0.0227 - accuracy: 0.9762 - val_loss: 0.4413 - val_accuracy: 0.8889\n",
      "Epoch 1482/2000\n",
      "84/84 [==============================] - 0s 269us/step - loss: 0.0222 - accuracy: 0.9762 - val_loss: 0.4425 - val_accuracy: 0.8889\n",
      "Epoch 1483/2000\n",
      "84/84 [==============================] - 0s 230us/step - loss: 0.0221 - accuracy: 0.9881 - val_loss: 0.4450 - val_accuracy: 0.8889\n",
      "Epoch 1484/2000\n",
      "84/84 [==============================] - 0s 261us/step - loss: 0.0221 - accuracy: 0.9881 - val_loss: 0.4453 - val_accuracy: 0.8889\n",
      "Epoch 1485/2000\n",
      "84/84 [==============================] - 0s 258us/step - loss: 0.0224 - accuracy: 0.9881 - val_loss: 0.4430 - val_accuracy: 0.8889\n",
      "Epoch 1486/2000\n",
      "84/84 [==============================] - 0s 266us/step - loss: 0.0224 - accuracy: 0.9881 - val_loss: 0.4445 - val_accuracy: 0.8889\n",
      "Epoch 1487/2000\n",
      "84/84 [==============================] - 0s 306us/step - loss: 0.0223 - accuracy: 0.9881 - val_loss: 0.4435 - val_accuracy: 0.8889\n",
      "Epoch 1488/2000\n",
      "84/84 [==============================] - 0s 272us/step - loss: 0.0224 - accuracy: 0.9881 - val_loss: 0.4412 - val_accuracy: 0.8889\n",
      "Epoch 1489/2000\n",
      "84/84 [==============================] - 0s 239us/step - loss: 0.0227 - accuracy: 0.9881 - val_loss: 0.4440 - val_accuracy: 0.8889\n",
      "Epoch 1490/2000\n",
      "84/84 [==============================] - 0s 219us/step - loss: 0.0221 - accuracy: 0.9881 - val_loss: 0.4417 - val_accuracy: 0.8889\n",
      "Epoch 1491/2000\n",
      "84/84 [==============================] - 0s 261us/step - loss: 0.0220 - accuracy: 0.9881 - val_loss: 0.4383 - val_accuracy: 0.8889\n",
      "Epoch 1492/2000\n",
      "84/84 [==============================] - 0s 253us/step - loss: 0.0222 - accuracy: 0.9881 - val_loss: 0.4350 - val_accuracy: 0.8889\n",
      "Epoch 1493/2000\n",
      "84/84 [==============================] - 0s 276us/step - loss: 0.0220 - accuracy: 0.9881 - val_loss: 0.4341 - val_accuracy: 0.8889\n",
      "Epoch 1494/2000\n",
      "84/84 [==============================] - 0s 248us/step - loss: 0.0220 - accuracy: 0.9881 - val_loss: 0.4336 - val_accuracy: 0.8889\n",
      "Epoch 1495/2000\n",
      "84/84 [==============================] - 0s 295us/step - loss: 0.0222 - accuracy: 0.9881 - val_loss: 0.4365 - val_accuracy: 0.8889\n",
      "Epoch 1496/2000\n",
      "84/84 [==============================] - 0s 310us/step - loss: 0.0223 - accuracy: 0.9881 - val_loss: 0.4389 - val_accuracy: 0.8889\n",
      "Epoch 1497/2000\n",
      "84/84 [==============================] - 0s 293us/step - loss: 0.0223 - accuracy: 0.9881 - val_loss: 0.4448 - val_accuracy: 0.8889\n",
      "Epoch 1498/2000\n",
      "84/84 [==============================] - 0s 252us/step - loss: 0.0222 - accuracy: 0.9881 - val_loss: 0.4453 - val_accuracy: 0.8889\n",
      "Epoch 1499/2000\n",
      "84/84 [==============================] - 0s 311us/step - loss: 0.0220 - accuracy: 0.9881 - val_loss: 0.4485 - val_accuracy: 0.8889\n",
      "Epoch 1500/2000\n",
      "84/84 [==============================] - 0s 281us/step - loss: 0.0223 - accuracy: 0.9881 - val_loss: 0.4501 - val_accuracy: 0.8889\n",
      "Epoch 1501/2000\n",
      "84/84 [==============================] - 0s 313us/step - loss: 0.0223 - accuracy: 0.9881 - val_loss: 0.4464 - val_accuracy: 0.8889\n",
      "Epoch 1502/2000\n",
      "84/84 [==============================] - 0s 240us/step - loss: 0.0222 - accuracy: 0.9881 - val_loss: 0.4481 - val_accuracy: 0.8889\n",
      "Epoch 1503/2000\n",
      "84/84 [==============================] - 0s 263us/step - loss: 0.0220 - accuracy: 0.9881 - val_loss: 0.4463 - val_accuracy: 0.8889\n",
      "Epoch 1504/2000\n",
      "84/84 [==============================] - 0s 290us/step - loss: 0.0221 - accuracy: 0.9881 - val_loss: 0.4468 - val_accuracy: 0.8889\n",
      "Epoch 1505/2000\n",
      "84/84 [==============================] - 0s 271us/step - loss: 0.0219 - accuracy: 0.9881 - val_loss: 0.4434 - val_accuracy: 0.8889\n",
      "Epoch 1506/2000\n",
      "84/84 [==============================] - 0s 213us/step - loss: 0.0225 - accuracy: 0.9881 - val_loss: 0.4391 - val_accuracy: 0.8889\n",
      "Epoch 1507/2000\n",
      "84/84 [==============================] - 0s 229us/step - loss: 0.0221 - accuracy: 0.9881 - val_loss: 0.4410 - val_accuracy: 0.8889\n",
      "Epoch 1508/2000\n",
      "84/84 [==============================] - 0s 261us/step - loss: 0.0218 - accuracy: 0.9881 - val_loss: 0.4409 - val_accuracy: 0.8889\n",
      "Epoch 1509/2000\n",
      "84/84 [==============================] - 0s 259us/step - loss: 0.0219 - accuracy: 0.9881 - val_loss: 0.4411 - val_accuracy: 0.8889\n",
      "Epoch 1510/2000\n",
      "84/84 [==============================] - 0s 282us/step - loss: 0.0220 - accuracy: 0.9881 - val_loss: 0.4409 - val_accuracy: 0.8889\n",
      "Epoch 1511/2000\n",
      "84/84 [==============================] - 0s 222us/step - loss: 0.0221 - accuracy: 0.9762 - val_loss: 0.4384 - val_accuracy: 0.8889\n",
      "Epoch 1512/2000\n",
      "84/84 [==============================] - 0s 229us/step - loss: 0.0220 - accuracy: 0.9762 - val_loss: 0.4409 - val_accuracy: 0.8889\n",
      "Epoch 1513/2000\n",
      "84/84 [==============================] - 0s 216us/step - loss: 0.0218 - accuracy: 0.9762 - val_loss: 0.4413 - val_accuracy: 0.8889\n",
      "Epoch 1514/2000\n",
      "84/84 [==============================] - 0s 221us/step - loss: 0.0222 - accuracy: 0.9762 - val_loss: 0.4444 - val_accuracy: 0.8889\n",
      "Epoch 1515/2000\n",
      "84/84 [==============================] - 0s 304us/step - loss: 0.0219 - accuracy: 0.9762 - val_loss: 0.4416 - val_accuracy: 0.8889\n",
      "Epoch 1516/2000\n",
      "84/84 [==============================] - 0s 286us/step - loss: 0.0217 - accuracy: 0.9881 - val_loss: 0.4396 - val_accuracy: 0.8889\n",
      "Epoch 1517/2000\n",
      "84/84 [==============================] - 0s 277us/step - loss: 0.0219 - accuracy: 0.9881 - val_loss: 0.4403 - val_accuracy: 0.8889\n",
      "Epoch 1518/2000\n",
      "84/84 [==============================] - 0s 290us/step - loss: 0.0219 - accuracy: 0.9881 - val_loss: 0.4399 - val_accuracy: 0.8889\n",
      "Epoch 1519/2000\n",
      "84/84 [==============================] - 0s 270us/step - loss: 0.0217 - accuracy: 0.9881 - val_loss: 0.4405 - val_accuracy: 0.8889\n",
      "Epoch 1520/2000\n",
      "84/84 [==============================] - 0s 222us/step - loss: 0.0218 - accuracy: 0.9881 - val_loss: 0.4416 - val_accuracy: 0.8889\n",
      "Epoch 1521/2000\n",
      "84/84 [==============================] - 0s 230us/step - loss: 0.0220 - accuracy: 0.9881 - val_loss: 0.4421 - val_accuracy: 0.8889\n",
      "Epoch 1522/2000\n",
      "84/84 [==============================] - 0s 240us/step - loss: 0.0219 - accuracy: 0.9881 - val_loss: 0.4457 - val_accuracy: 0.8889\n",
      "Epoch 1523/2000\n",
      "84/84 [==============================] - 0s 253us/step - loss: 0.0217 - accuracy: 0.9881 - val_loss: 0.4491 - val_accuracy: 0.8889\n",
      "Epoch 1524/2000\n",
      "84/84 [==============================] - 0s 270us/step - loss: 0.0222 - accuracy: 0.9762 - val_loss: 0.4527 - val_accuracy: 0.8889\n",
      "Epoch 1525/2000\n",
      "84/84 [==============================] - 0s 256us/step - loss: 0.0217 - accuracy: 0.9881 - val_loss: 0.4520 - val_accuracy: 0.8889\n",
      "Epoch 1526/2000\n",
      "84/84 [==============================] - 0s 241us/step - loss: 0.0216 - accuracy: 0.9881 - val_loss: 0.4495 - val_accuracy: 0.8889\n",
      "Epoch 1527/2000\n",
      "84/84 [==============================] - 0s 249us/step - loss: 0.0217 - accuracy: 0.9881 - val_loss: 0.4481 - val_accuracy: 0.8889\n",
      "Epoch 1528/2000\n",
      "84/84 [==============================] - 0s 246us/step - loss: 0.0219 - accuracy: 0.9881 - val_loss: 0.4498 - val_accuracy: 0.8889\n",
      "Epoch 1529/2000\n",
      "84/84 [==============================] - 0s 238us/step - loss: 0.0218 - accuracy: 0.9881 - val_loss: 0.4498 - val_accuracy: 0.8889\n",
      "Epoch 1530/2000\n",
      "84/84 [==============================] - 0s 236us/step - loss: 0.0218 - accuracy: 0.9881 - val_loss: 0.4503 - val_accuracy: 0.8889\n",
      "Epoch 1531/2000\n",
      "84/84 [==============================] - 0s 237us/step - loss: 0.0225 - accuracy: 0.9881 - val_loss: 0.4467 - val_accuracy: 0.8889\n",
      "Epoch 1532/2000\n",
      "84/84 [==============================] - 0s 214us/step - loss: 0.0219 - accuracy: 0.9881 - val_loss: 0.4508 - val_accuracy: 0.8889\n",
      "Epoch 1533/2000\n",
      "84/84 [==============================] - 0s 245us/step - loss: 0.0218 - accuracy: 0.9881 - val_loss: 0.4550 - val_accuracy: 0.8889\n",
      "Epoch 1534/2000\n",
      "84/84 [==============================] - 0s 230us/step - loss: 0.0217 - accuracy: 0.9881 - val_loss: 0.4574 - val_accuracy: 0.8889\n",
      "Epoch 1535/2000\n",
      "84/84 [==============================] - 0s 265us/step - loss: 0.0222 - accuracy: 0.9881 - val_loss: 0.4607 - val_accuracy: 0.8889\n",
      "Epoch 1536/2000\n",
      "84/84 [==============================] - 0s 276us/step - loss: 0.0215 - accuracy: 0.9881 - val_loss: 0.4575 - val_accuracy: 0.8889\n",
      "Epoch 1537/2000\n",
      "84/84 [==============================] - 0s 261us/step - loss: 0.0217 - accuracy: 0.9881 - val_loss: 0.4561 - val_accuracy: 0.8889\n",
      "Epoch 1538/2000\n",
      "84/84 [==============================] - 0s 459us/step - loss: 0.0218 - accuracy: 0.9881 - val_loss: 0.4544 - val_accuracy: 0.8889\n",
      "Epoch 1539/2000\n",
      "84/84 [==============================] - 0s 302us/step - loss: 0.0222 - accuracy: 0.9762 - val_loss: 0.4605 - val_accuracy: 0.8889\n",
      "Epoch 1540/2000\n",
      "84/84 [==============================] - 0s 279us/step - loss: 0.0216 - accuracy: 0.9881 - val_loss: 0.4607 - val_accuracy: 0.8889\n",
      "Epoch 1541/2000\n",
      "84/84 [==============================] - 0s 344us/step - loss: 0.0218 - accuracy: 0.9762 - val_loss: 0.4568 - val_accuracy: 0.8889\n",
      "Epoch 1542/2000\n",
      "84/84 [==============================] - 0s 297us/step - loss: 0.0217 - accuracy: 0.9881 - val_loss: 0.4580 - val_accuracy: 0.8889\n",
      "Epoch 1543/2000\n",
      "84/84 [==============================] - 0s 288us/step - loss: 0.0215 - accuracy: 0.9881 - val_loss: 0.4590 - val_accuracy: 0.8889\n",
      "Epoch 1544/2000\n",
      "84/84 [==============================] - 0s 309us/step - loss: 0.0215 - accuracy: 0.9762 - val_loss: 0.4597 - val_accuracy: 0.8889\n",
      "Epoch 1545/2000\n",
      "84/84 [==============================] - 0s 432us/step - loss: 0.0219 - accuracy: 0.9881 - val_loss: 0.4660 - val_accuracy: 0.8611\n",
      "Epoch 1546/2000\n",
      "84/84 [==============================] - 0s 610us/step - loss: 0.0215 - accuracy: 0.9881 - val_loss: 0.4681 - val_accuracy: 0.8611\n",
      "Epoch 1547/2000\n",
      "84/84 [==============================] - 0s 330us/step - loss: 0.0216 - accuracy: 0.9881 - val_loss: 0.4671 - val_accuracy: 0.8611\n",
      "Epoch 1548/2000\n",
      "84/84 [==============================] - 0s 266us/step - loss: 0.0215 - accuracy: 0.9881 - val_loss: 0.4652 - val_accuracy: 0.8611\n",
      "Epoch 1549/2000\n",
      "84/84 [==============================] - 0s 254us/step - loss: 0.0217 - accuracy: 0.9881 - val_loss: 0.4611 - val_accuracy: 0.8611\n",
      "Epoch 1550/2000\n",
      "84/84 [==============================] - 0s 281us/step - loss: 0.0215 - accuracy: 0.9881 - val_loss: 0.4592 - val_accuracy: 0.8889\n",
      "Epoch 1551/2000\n",
      "84/84 [==============================] - 0s 286us/step - loss: 0.0219 - accuracy: 0.9881 - val_loss: 0.4549 - val_accuracy: 0.8889\n",
      "Epoch 1552/2000\n",
      "84/84 [==============================] - 0s 242us/step - loss: 0.0218 - accuracy: 0.9881 - val_loss: 0.4585 - val_accuracy: 0.8611\n",
      "Epoch 1553/2000\n",
      "84/84 [==============================] - 0s 242us/step - loss: 0.0217 - accuracy: 0.9881 - val_loss: 0.4595 - val_accuracy: 0.8611\n",
      "Epoch 1554/2000\n",
      "84/84 [==============================] - 0s 257us/step - loss: 0.0215 - accuracy: 0.9881 - val_loss: 0.4598 - val_accuracy: 0.8611\n",
      "Epoch 1555/2000\n",
      "84/84 [==============================] - 0s 273us/step - loss: 0.0217 - accuracy: 0.9881 - val_loss: 0.4590 - val_accuracy: 0.8611\n",
      "Epoch 1556/2000\n",
      "84/84 [==============================] - 0s 295us/step - loss: 0.0216 - accuracy: 0.9881 - val_loss: 0.4583 - val_accuracy: 0.8611\n",
      "Epoch 1557/2000\n",
      "84/84 [==============================] - 0s 329us/step - loss: 0.0217 - accuracy: 0.9881 - val_loss: 0.4584 - val_accuracy: 0.8611\n",
      "Epoch 1558/2000\n",
      "84/84 [==============================] - 0s 331us/step - loss: 0.0215 - accuracy: 0.9881 - val_loss: 0.4570 - val_accuracy: 0.8611\n",
      "Epoch 1559/2000\n",
      "84/84 [==============================] - 0s 294us/step - loss: 0.0217 - accuracy: 0.9881 - val_loss: 0.4522 - val_accuracy: 0.8889\n",
      "Epoch 1560/2000\n",
      "84/84 [==============================] - 0s 290us/step - loss: 0.0219 - accuracy: 0.9881 - val_loss: 0.4479 - val_accuracy: 0.8889\n",
      "Epoch 1561/2000\n",
      "84/84 [==============================] - 0s 317us/step - loss: 0.0212 - accuracy: 0.9881 - val_loss: 0.4504 - val_accuracy: 0.8889\n",
      "Epoch 1562/2000\n",
      "84/84 [==============================] - 0s 267us/step - loss: 0.0216 - accuracy: 0.9881 - val_loss: 0.4536 - val_accuracy: 0.8889\n",
      "Epoch 1563/2000\n",
      "84/84 [==============================] - 0s 250us/step - loss: 0.0217 - accuracy: 0.9881 - val_loss: 0.4493 - val_accuracy: 0.8889\n",
      "Epoch 1564/2000\n",
      "84/84 [==============================] - 0s 302us/step - loss: 0.0212 - accuracy: 0.9881 - val_loss: 0.4491 - val_accuracy: 0.8889\n",
      "Epoch 1565/2000\n",
      "84/84 [==============================] - 0s 354us/step - loss: 0.0212 - accuracy: 0.9881 - val_loss: 0.4477 - val_accuracy: 0.8889\n",
      "Epoch 1566/2000\n",
      "84/84 [==============================] - 0s 372us/step - loss: 0.0217 - accuracy: 0.9762 - val_loss: 0.4453 - val_accuracy: 0.8889\n",
      "Epoch 1567/2000\n",
      "84/84 [==============================] - 0s 409us/step - loss: 0.0211 - accuracy: 0.9762 - val_loss: 0.4509 - val_accuracy: 0.8889\n",
      "Epoch 1568/2000\n",
      "84/84 [==============================] - 0s 383us/step - loss: 0.0212 - accuracy: 0.9881 - val_loss: 0.4575 - val_accuracy: 0.8889\n",
      "Epoch 1569/2000\n",
      "84/84 [==============================] - 0s 370us/step - loss: 0.0211 - accuracy: 0.9881 - val_loss: 0.4621 - val_accuracy: 0.8611\n",
      "Epoch 1570/2000\n",
      "84/84 [==============================] - 0s 322us/step - loss: 0.0212 - accuracy: 0.9881 - val_loss: 0.4644 - val_accuracy: 0.8611\n",
      "Epoch 1571/2000\n",
      "84/84 [==============================] - 0s 292us/step - loss: 0.0212 - accuracy: 0.9881 - val_loss: 0.4657 - val_accuracy: 0.8611\n",
      "Epoch 1572/2000\n",
      "84/84 [==============================] - 0s 347us/step - loss: 0.0213 - accuracy: 0.9881 - val_loss: 0.4677 - val_accuracy: 0.8611\n",
      "Epoch 1573/2000\n",
      "84/84 [==============================] - 0s 405us/step - loss: 0.0211 - accuracy: 0.9881 - val_loss: 0.4711 - val_accuracy: 0.8611\n",
      "Epoch 1574/2000\n",
      "84/84 [==============================] - 0s 385us/step - loss: 0.0211 - accuracy: 0.9881 - val_loss: 0.4752 - val_accuracy: 0.8611\n",
      "Epoch 1575/2000\n",
      "84/84 [==============================] - 0s 408us/step - loss: 0.0216 - accuracy: 0.9881 - val_loss: 0.4765 - val_accuracy: 0.8611\n",
      "Epoch 1576/2000\n",
      "84/84 [==============================] - 0s 359us/step - loss: 0.0216 - accuracy: 0.9881 - val_loss: 0.4755 - val_accuracy: 0.8611\n",
      "Epoch 1577/2000\n",
      "84/84 [==============================] - 0s 420us/step - loss: 0.0216 - accuracy: 0.9881 - val_loss: 0.4711 - val_accuracy: 0.8611\n",
      "Epoch 1578/2000\n",
      "84/84 [==============================] - 0s 373us/step - loss: 0.0212 - accuracy: 0.9881 - val_loss: 0.4708 - val_accuracy: 0.8611\n",
      "Epoch 1579/2000\n",
      "84/84 [==============================] - 0s 550us/step - loss: 0.0214 - accuracy: 0.9881 - val_loss: 0.4685 - val_accuracy: 0.8611\n",
      "Epoch 1580/2000\n",
      "84/84 [==============================] - 0s 465us/step - loss: 0.0214 - accuracy: 0.9881 - val_loss: 0.4647 - val_accuracy: 0.8611\n",
      "Epoch 1581/2000\n",
      "84/84 [==============================] - 0s 370us/step - loss: 0.0213 - accuracy: 0.9881 - val_loss: 0.4645 - val_accuracy: 0.8611\n",
      "Epoch 1582/2000\n",
      "84/84 [==============================] - 0s 447us/step - loss: 0.0215 - accuracy: 0.9881 - val_loss: 0.4578 - val_accuracy: 0.8889\n",
      "Epoch 1583/2000\n",
      "84/84 [==============================] - 0s 418us/step - loss: 0.0210 - accuracy: 0.9881 - val_loss: 0.4569 - val_accuracy: 0.8889\n",
      "Epoch 1584/2000\n",
      "84/84 [==============================] - 0s 431us/step - loss: 0.0212 - accuracy: 0.9881 - val_loss: 0.4605 - val_accuracy: 0.8889\n",
      "Epoch 1585/2000\n",
      "84/84 [==============================] - 0s 376us/step - loss: 0.0209 - accuracy: 0.9881 - val_loss: 0.4598 - val_accuracy: 0.8889\n",
      "Epoch 1586/2000\n",
      "84/84 [==============================] - 0s 446us/step - loss: 0.0211 - accuracy: 0.9762 - val_loss: 0.4604 - val_accuracy: 0.8889\n",
      "Epoch 1587/2000\n",
      "84/84 [==============================] - 0s 379us/step - loss: 0.0210 - accuracy: 0.9881 - val_loss: 0.4587 - val_accuracy: 0.8889\n",
      "Epoch 1588/2000\n",
      "84/84 [==============================] - 0s 396us/step - loss: 0.0210 - accuracy: 0.9881 - val_loss: 0.4592 - val_accuracy: 0.8889\n",
      "Epoch 1589/2000\n",
      "84/84 [==============================] - 0s 398us/step - loss: 0.0212 - accuracy: 0.9881 - val_loss: 0.4637 - val_accuracy: 0.8611\n",
      "Epoch 1590/2000\n",
      "84/84 [==============================] - 0s 399us/step - loss: 0.0212 - accuracy: 0.9881 - val_loss: 0.4658 - val_accuracy: 0.8611\n",
      "Epoch 1591/2000\n",
      "84/84 [==============================] - 0s 389us/step - loss: 0.0210 - accuracy: 0.9881 - val_loss: 0.4664 - val_accuracy: 0.8611\n",
      "Epoch 1592/2000\n",
      "84/84 [==============================] - 0s 365us/step - loss: 0.0212 - accuracy: 0.9881 - val_loss: 0.4678 - val_accuracy: 0.8611\n",
      "Epoch 1593/2000\n",
      "84/84 [==============================] - 0s 337us/step - loss: 0.0211 - accuracy: 0.9881 - val_loss: 0.4688 - val_accuracy: 0.8611\n",
      "Epoch 1594/2000\n",
      "84/84 [==============================] - 0s 399us/step - loss: 0.0213 - accuracy: 0.9881 - val_loss: 0.4744 - val_accuracy: 0.8611\n",
      "Epoch 1595/2000\n",
      "84/84 [==============================] - 0s 357us/step - loss: 0.0210 - accuracy: 0.9881 - val_loss: 0.4798 - val_accuracy: 0.8611\n",
      "Epoch 1596/2000\n",
      "84/84 [==============================] - 0s 291us/step - loss: 0.0214 - accuracy: 0.9762 - val_loss: 0.4839 - val_accuracy: 0.8611\n",
      "Epoch 1597/2000\n",
      "84/84 [==============================] - 0s 356us/step - loss: 0.0210 - accuracy: 0.9762 - val_loss: 0.4820 - val_accuracy: 0.8611\n",
      "Epoch 1598/2000\n",
      "84/84 [==============================] - 0s 321us/step - loss: 0.0209 - accuracy: 0.9881 - val_loss: 0.4784 - val_accuracy: 0.8611\n",
      "Epoch 1599/2000\n",
      "84/84 [==============================] - 0s 309us/step - loss: 0.0211 - accuracy: 0.9881 - val_loss: 0.4801 - val_accuracy: 0.8611\n",
      "Epoch 1600/2000\n",
      "84/84 [==============================] - 0s 302us/step - loss: 0.0209 - accuracy: 0.9881 - val_loss: 0.4754 - val_accuracy: 0.8611\n",
      "Epoch 1601/2000\n",
      "84/84 [==============================] - 0s 326us/step - loss: 0.0212 - accuracy: 0.9881 - val_loss: 0.4730 - val_accuracy: 0.8611\n",
      "Epoch 1602/2000\n",
      "84/84 [==============================] - 0s 325us/step - loss: 0.0209 - accuracy: 0.9881 - val_loss: 0.4732 - val_accuracy: 0.8611\n",
      "Epoch 1603/2000\n",
      "84/84 [==============================] - 0s 329us/step - loss: 0.0209 - accuracy: 0.9881 - val_loss: 0.4742 - val_accuracy: 0.8611\n",
      "Epoch 1604/2000\n",
      "84/84 [==============================] - 0s 330us/step - loss: 0.0208 - accuracy: 0.9881 - val_loss: 0.4768 - val_accuracy: 0.8611\n",
      "Epoch 1605/2000\n",
      "84/84 [==============================] - 0s 349us/step - loss: 0.0210 - accuracy: 0.9881 - val_loss: 0.4777 - val_accuracy: 0.8611\n",
      "Epoch 1606/2000\n",
      "84/84 [==============================] - 0s 367us/step - loss: 0.0208 - accuracy: 0.9881 - val_loss: 0.4802 - val_accuracy: 0.8611\n",
      "Epoch 1607/2000\n",
      "84/84 [==============================] - 0s 268us/step - loss: 0.0212 - accuracy: 0.9762 - val_loss: 0.4823 - val_accuracy: 0.8611\n",
      "Epoch 1608/2000\n",
      "84/84 [==============================] - 0s 366us/step - loss: 0.0207 - accuracy: 0.9881 - val_loss: 0.4777 - val_accuracy: 0.8611\n",
      "Epoch 1609/2000\n",
      "84/84 [==============================] - 0s 281us/step - loss: 0.0209 - accuracy: 0.9762 - val_loss: 0.4745 - val_accuracy: 0.8611\n",
      "Epoch 1610/2000\n",
      "84/84 [==============================] - 0s 262us/step - loss: 0.0212 - accuracy: 0.9881 - val_loss: 0.4697 - val_accuracy: 0.8611\n",
      "Epoch 1611/2000\n",
      "84/84 [==============================] - 0s 350us/step - loss: 0.0210 - accuracy: 0.9881 - val_loss: 0.4703 - val_accuracy: 0.8611\n",
      "Epoch 1612/2000\n",
      "84/84 [==============================] - 0s 299us/step - loss: 0.0212 - accuracy: 0.9881 - val_loss: 0.4684 - val_accuracy: 0.8889\n",
      "Epoch 1613/2000\n",
      "84/84 [==============================] - 0s 256us/step - loss: 0.0210 - accuracy: 0.9881 - val_loss: 0.4708 - val_accuracy: 0.8611\n",
      "Epoch 1614/2000\n",
      "84/84 [==============================] - 0s 275us/step - loss: 0.0211 - accuracy: 0.9881 - val_loss: 0.4708 - val_accuracy: 0.8611\n",
      "Epoch 1615/2000\n",
      "84/84 [==============================] - 0s 385us/step - loss: 0.0214 - accuracy: 0.9881 - val_loss: 0.4707 - val_accuracy: 0.8611\n",
      "Epoch 1616/2000\n",
      "84/84 [==============================] - 0s 333us/step - loss: 0.0209 - accuracy: 0.9881 - val_loss: 0.4754 - val_accuracy: 0.8611\n",
      "Epoch 1617/2000\n",
      "84/84 [==============================] - 0s 325us/step - loss: 0.0204 - accuracy: 0.9881 - val_loss: 0.4869 - val_accuracy: 0.8611\n",
      "Epoch 1618/2000\n",
      "84/84 [==============================] - 0s 353us/step - loss: 0.0202 - accuracy: 1.0000 - val_loss: 0.4968 - val_accuracy: 0.8611\n",
      "Epoch 1619/2000\n",
      "84/84 [==============================] - 0s 324us/step - loss: 0.0207 - accuracy: 0.9881 - val_loss: 0.5037 - val_accuracy: 0.8611\n",
      "Epoch 1620/2000\n",
      "84/84 [==============================] - 0s 389us/step - loss: 0.0208 - accuracy: 0.9881 - val_loss: 0.5038 - val_accuracy: 0.8611\n",
      "Epoch 1621/2000\n",
      "84/84 [==============================] - 0s 414us/step - loss: 0.0211 - accuracy: 0.9881 - val_loss: 0.5015 - val_accuracy: 0.8611\n",
      "Epoch 1622/2000\n",
      "84/84 [==============================] - 0s 421us/step - loss: 0.0219 - accuracy: 0.9881 - val_loss: 0.5015 - val_accuracy: 0.8611\n",
      "Epoch 1623/2000\n",
      "84/84 [==============================] - 0s 529us/step - loss: 0.0219 - accuracy: 0.9881 - val_loss: 0.4961 - val_accuracy: 0.8611\n",
      "Epoch 1624/2000\n",
      "84/84 [==============================] - 0s 471us/step - loss: 0.0217 - accuracy: 0.9881 - val_loss: 0.4865 - val_accuracy: 0.8611\n",
      "Epoch 1625/2000\n",
      "84/84 [==============================] - 0s 609us/step - loss: 0.0214 - accuracy: 0.9881 - val_loss: 0.4774 - val_accuracy: 0.8611\n",
      "Epoch 1626/2000\n",
      "84/84 [==============================] - 0s 311us/step - loss: 0.0211 - accuracy: 0.9881 - val_loss: 0.4692 - val_accuracy: 0.8889\n",
      "Epoch 1627/2000\n",
      "84/84 [==============================] - 0s 467us/step - loss: 0.0209 - accuracy: 0.9881 - val_loss: 0.4661 - val_accuracy: 0.8889\n",
      "Epoch 1628/2000\n",
      "84/84 [==============================] - 0s 434us/step - loss: 0.0207 - accuracy: 0.9881 - val_loss: 0.4617 - val_accuracy: 0.8889\n",
      "Epoch 1629/2000\n",
      "84/84 [==============================] - 0s 310us/step - loss: 0.0209 - accuracy: 0.9881 - val_loss: 0.4615 - val_accuracy: 0.8889\n",
      "Epoch 1630/2000\n",
      "84/84 [==============================] - 0s 384us/step - loss: 0.0206 - accuracy: 0.9881 - val_loss: 0.4621 - val_accuracy: 0.8889\n",
      "Epoch 1631/2000\n",
      "84/84 [==============================] - 0s 290us/step - loss: 0.0210 - accuracy: 0.9762 - val_loss: 0.4639 - val_accuracy: 0.8889\n",
      "Epoch 1632/2000\n",
      "84/84 [==============================] - 0s 291us/step - loss: 0.0206 - accuracy: 0.9881 - val_loss: 0.4692 - val_accuracy: 0.8750\n",
      "Epoch 1633/2000\n",
      "84/84 [==============================] - 0s 346us/step - loss: 0.0204 - accuracy: 0.9881 - val_loss: 0.4770 - val_accuracy: 0.8611\n",
      "Epoch 1634/2000\n",
      "84/84 [==============================] - 0s 270us/step - loss: 0.0205 - accuracy: 0.9881 - val_loss: 0.4838 - val_accuracy: 0.8611\n",
      "Epoch 1635/2000\n",
      "84/84 [==============================] - 0s 242us/step - loss: 0.0217 - accuracy: 0.9881 - val_loss: 0.4940 - val_accuracy: 0.8611\n",
      "Epoch 1636/2000\n",
      "84/84 [==============================] - 0s 237us/step - loss: 0.0208 - accuracy: 0.9881 - val_loss: 0.4919 - val_accuracy: 0.8611\n",
      "Epoch 1637/2000\n",
      "84/84 [==============================] - 0s 218us/step - loss: 0.0209 - accuracy: 0.9881 - val_loss: 0.4888 - val_accuracy: 0.8611\n",
      "Epoch 1638/2000\n",
      "84/84 [==============================] - 0s 271us/step - loss: 0.0206 - accuracy: 0.9881 - val_loss: 0.4798 - val_accuracy: 0.8611\n",
      "Epoch 1639/2000\n",
      "84/84 [==============================] - 0s 302us/step - loss: 0.0201 - accuracy: 0.9881 - val_loss: 0.4737 - val_accuracy: 0.8611\n",
      "Epoch 1640/2000\n",
      "84/84 [==============================] - 0s 324us/step - loss: 0.0211 - accuracy: 0.9762 - val_loss: 0.4655 - val_accuracy: 0.8889\n",
      "Epoch 1641/2000\n",
      "84/84 [==============================] - 0s 285us/step - loss: 0.0207 - accuracy: 0.9881 - val_loss: 0.4641 - val_accuracy: 0.8889\n",
      "Epoch 1642/2000\n",
      "84/84 [==============================] - 0s 232us/step - loss: 0.0208 - accuracy: 0.9881 - val_loss: 0.4667 - val_accuracy: 0.8889\n",
      "Epoch 1643/2000\n",
      "84/84 [==============================] - 0s 241us/step - loss: 0.0212 - accuracy: 0.9881 - val_loss: 0.4715 - val_accuracy: 0.8889\n",
      "Epoch 1644/2000\n",
      "84/84 [==============================] - 0s 250us/step - loss: 0.0207 - accuracy: 0.9881 - val_loss: 0.4786 - val_accuracy: 0.8611\n",
      "Epoch 1645/2000\n",
      "84/84 [==============================] - 0s 366us/step - loss: 0.0210 - accuracy: 0.9881 - val_loss: 0.4910 - val_accuracy: 0.8611\n",
      "Epoch 1646/2000\n",
      "84/84 [==============================] - 0s 281us/step - loss: 0.0207 - accuracy: 0.9762 - val_loss: 0.4980 - val_accuracy: 0.8611\n",
      "Epoch 1647/2000\n",
      "84/84 [==============================] - 0s 221us/step - loss: 0.0211 - accuracy: 0.9881 - val_loss: 0.5025 - val_accuracy: 0.8611\n",
      "Epoch 1648/2000\n",
      "84/84 [==============================] - 0s 265us/step - loss: 0.0207 - accuracy: 0.9881 - val_loss: 0.4957 - val_accuracy: 0.8611\n",
      "Epoch 1649/2000\n",
      "84/84 [==============================] - 0s 213us/step - loss: 0.0205 - accuracy: 0.9881 - val_loss: 0.4942 - val_accuracy: 0.8611\n",
      "Epoch 1650/2000\n",
      "84/84 [==============================] - 0s 224us/step - loss: 0.0206 - accuracy: 0.9881 - val_loss: 0.4893 - val_accuracy: 0.8611\n",
      "Epoch 1651/2000\n",
      "84/84 [==============================] - 0s 243us/step - loss: 0.0207 - accuracy: 0.9881 - val_loss: 0.4870 - val_accuracy: 0.8611\n",
      "Epoch 1652/2000\n",
      "84/84 [==============================] - 0s 260us/step - loss: 0.0206 - accuracy: 0.9881 - val_loss: 0.4927 - val_accuracy: 0.8611\n",
      "Epoch 1653/2000\n",
      "84/84 [==============================] - 0s 305us/step - loss: 0.0209 - accuracy: 0.9881 - val_loss: 0.4975 - val_accuracy: 0.8611\n",
      "Epoch 1654/2000\n",
      "84/84 [==============================] - 0s 264us/step - loss: 0.0206 - accuracy: 0.9881 - val_loss: 0.4967 - val_accuracy: 0.8611\n",
      "Epoch 1655/2000\n",
      "84/84 [==============================] - 0s 281us/step - loss: 0.0212 - accuracy: 0.9881 - val_loss: 0.4882 - val_accuracy: 0.8611\n",
      "Epoch 1656/2000\n",
      "84/84 [==============================] - 0s 292us/step - loss: 0.0204 - accuracy: 0.9881 - val_loss: 0.4878 - val_accuracy: 0.8611\n",
      "Epoch 1657/2000\n",
      "84/84 [==============================] - 0s 330us/step - loss: 0.0203 - accuracy: 0.9881 - val_loss: 0.4856 - val_accuracy: 0.8611\n",
      "Epoch 1658/2000\n",
      "84/84 [==============================] - 0s 300us/step - loss: 0.0211 - accuracy: 0.9881 - val_loss: 0.4773 - val_accuracy: 0.8611\n",
      "Epoch 1659/2000\n",
      "84/84 [==============================] - 0s 308us/step - loss: 0.0209 - accuracy: 0.9881 - val_loss: 0.4756 - val_accuracy: 0.8889\n",
      "Epoch 1660/2000\n",
      "84/84 [==============================] - 0s 296us/step - loss: 0.0210 - accuracy: 0.9881 - val_loss: 0.4829 - val_accuracy: 0.8611\n",
      "Epoch 1661/2000\n",
      "84/84 [==============================] - 0s 292us/step - loss: 0.0204 - accuracy: 0.9881 - val_loss: 0.4864 - val_accuracy: 0.8611\n",
      "Epoch 1662/2000\n",
      "84/84 [==============================] - 0s 338us/step - loss: 0.0208 - accuracy: 0.9762 - val_loss: 0.4918 - val_accuracy: 0.8611\n",
      "Epoch 1663/2000\n",
      "84/84 [==============================] - 0s 289us/step - loss: 0.0202 - accuracy: 0.9881 - val_loss: 0.4894 - val_accuracy: 0.8611\n",
      "Epoch 1664/2000\n",
      "84/84 [==============================] - 0s 265us/step - loss: 0.0202 - accuracy: 0.9881 - val_loss: 0.4901 - val_accuracy: 0.8611\n",
      "Epoch 1665/2000\n",
      "84/84 [==============================] - 0s 236us/step - loss: 0.0205 - accuracy: 0.9762 - val_loss: 0.4921 - val_accuracy: 0.8611\n",
      "Epoch 1666/2000\n",
      "84/84 [==============================] - 0s 260us/step - loss: 0.0205 - accuracy: 0.9881 - val_loss: 0.4910 - val_accuracy: 0.8611\n",
      "Epoch 1667/2000\n",
      "84/84 [==============================] - 0s 237us/step - loss: 0.0204 - accuracy: 0.9881 - val_loss: 0.4931 - val_accuracy: 0.8611\n",
      "Epoch 1668/2000\n",
      "84/84 [==============================] - 0s 223us/step - loss: 0.0204 - accuracy: 0.9881 - val_loss: 0.4922 - val_accuracy: 0.8611\n",
      "Epoch 1669/2000\n",
      "84/84 [==============================] - 0s 212us/step - loss: 0.0202 - accuracy: 0.9881 - val_loss: 0.4902 - val_accuracy: 0.8611\n",
      "Epoch 1670/2000\n",
      "84/84 [==============================] - 0s 264us/step - loss: 0.0203 - accuracy: 0.9881 - val_loss: 0.4911 - val_accuracy: 0.8611\n",
      "Epoch 1671/2000\n",
      "84/84 [==============================] - 0s 242us/step - loss: 0.0203 - accuracy: 0.9881 - val_loss: 0.4910 - val_accuracy: 0.8611\n",
      "Epoch 1672/2000\n",
      "84/84 [==============================] - 0s 462us/step - loss: 0.0203 - accuracy: 0.9881 - val_loss: 0.4918 - val_accuracy: 0.8611\n",
      "Epoch 1673/2000\n",
      "84/84 [==============================] - 0s 323us/step - loss: 0.0203 - accuracy: 0.9881 - val_loss: 0.4911 - val_accuracy: 0.8611\n",
      "Epoch 1674/2000\n",
      "84/84 [==============================] - 0s 485us/step - loss: 0.0207 - accuracy: 0.9881 - val_loss: 0.4868 - val_accuracy: 0.8611\n",
      "Epoch 1675/2000\n",
      "84/84 [==============================] - 0s 747us/step - loss: 0.0206 - accuracy: 0.9881 - val_loss: 0.4897 - val_accuracy: 0.8611\n",
      "Epoch 1676/2000\n",
      "84/84 [==============================] - 0s 328us/step - loss: 0.0201 - accuracy: 0.9881 - val_loss: 0.4900 - val_accuracy: 0.8611\n",
      "Epoch 1677/2000\n",
      "84/84 [==============================] - 0s 285us/step - loss: 0.0203 - accuracy: 0.9881 - val_loss: 0.4915 - val_accuracy: 0.8611\n",
      "Epoch 1678/2000\n",
      "84/84 [==============================] - 0s 336us/step - loss: 0.0201 - accuracy: 0.9881 - val_loss: 0.4923 - val_accuracy: 0.8611\n",
      "Epoch 1679/2000\n",
      "84/84 [==============================] - 0s 386us/step - loss: 0.0201 - accuracy: 0.9881 - val_loss: 0.4936 - val_accuracy: 0.8611\n",
      "Epoch 1680/2000\n",
      "84/84 [==============================] - 0s 315us/step - loss: 0.0204 - accuracy: 0.9881 - val_loss: 0.4949 - val_accuracy: 0.8611\n",
      "Epoch 1681/2000\n",
      "84/84 [==============================] - 0s 333us/step - loss: 0.0204 - accuracy: 0.9762 - val_loss: 0.4914 - val_accuracy: 0.8611\n",
      "Epoch 1682/2000\n",
      "84/84 [==============================] - 0s 262us/step - loss: 0.0200 - accuracy: 0.9881 - val_loss: 0.4942 - val_accuracy: 0.8611\n",
      "Epoch 1683/2000\n",
      "84/84 [==============================] - 0s 242us/step - loss: 0.0203 - accuracy: 0.9881 - val_loss: 0.4976 - val_accuracy: 0.8611\n",
      "Epoch 1684/2000\n",
      "84/84 [==============================] - 0s 275us/step - loss: 0.0200 - accuracy: 1.0000 - val_loss: 0.4985 - val_accuracy: 0.8611\n",
      "Epoch 1685/2000\n",
      "84/84 [==============================] - 0s 327us/step - loss: 0.0200 - accuracy: 1.0000 - val_loss: 0.4973 - val_accuracy: 0.8611\n",
      "Epoch 1686/2000\n",
      "84/84 [==============================] - 0s 279us/step - loss: 0.0202 - accuracy: 0.9881 - val_loss: 0.4979 - val_accuracy: 0.8611\n",
      "Epoch 1687/2000\n",
      "84/84 [==============================] - 0s 327us/step - loss: 0.0204 - accuracy: 0.9881 - val_loss: 0.5000 - val_accuracy: 0.8611\n",
      "Epoch 1688/2000\n",
      "84/84 [==============================] - 0s 319us/step - loss: 0.0200 - accuracy: 0.9881 - val_loss: 0.4960 - val_accuracy: 0.8611\n",
      "Epoch 1689/2000\n",
      "84/84 [==============================] - 0s 345us/step - loss: 0.0200 - accuracy: 1.0000 - val_loss: 0.4952 - val_accuracy: 0.8611\n",
      "Epoch 1690/2000\n",
      "84/84 [==============================] - 0s 300us/step - loss: 0.0205 - accuracy: 0.9881 - val_loss: 0.4933 - val_accuracy: 0.8611\n",
      "Epoch 1691/2000\n",
      "84/84 [==============================] - 0s 602us/step - loss: 0.0202 - accuracy: 0.9881 - val_loss: 0.4950 - val_accuracy: 0.8611\n",
      "Epoch 1692/2000\n",
      "84/84 [==============================] - 0s 285us/step - loss: 0.0199 - accuracy: 1.0000 - val_loss: 0.5020 - val_accuracy: 0.8611\n",
      "Epoch 1693/2000\n",
      "84/84 [==============================] - 0s 303us/step - loss: 0.0199 - accuracy: 0.9881 - val_loss: 0.5083 - val_accuracy: 0.8611\n",
      "Epoch 1694/2000\n",
      "84/84 [==============================] - 0s 309us/step - loss: 0.0207 - accuracy: 0.9881 - val_loss: 0.5117 - val_accuracy: 0.8611\n",
      "Epoch 1695/2000\n",
      "84/84 [==============================] - 0s 343us/step - loss: 0.0200 - accuracy: 0.9881 - val_loss: 0.5060 - val_accuracy: 0.8611\n",
      "Epoch 1696/2000\n",
      "84/84 [==============================] - 0s 332us/step - loss: 0.0203 - accuracy: 0.9881 - val_loss: 0.4985 - val_accuracy: 0.8611\n",
      "Epoch 1697/2000\n",
      "84/84 [==============================] - 0s 360us/step - loss: 0.0201 - accuracy: 0.9881 - val_loss: 0.4968 - val_accuracy: 0.8611\n",
      "Epoch 1698/2000\n",
      "84/84 [==============================] - 0s 358us/step - loss: 0.0199 - accuracy: 0.9881 - val_loss: 0.4951 - val_accuracy: 0.8611\n",
      "Epoch 1699/2000\n",
      "84/84 [==============================] - 0s 392us/step - loss: 0.0207 - accuracy: 0.9762 - val_loss: 0.4884 - val_accuracy: 0.8611\n",
      "Epoch 1700/2000\n",
      "84/84 [==============================] - 0s 394us/step - loss: 0.0203 - accuracy: 0.9881 - val_loss: 0.4866 - val_accuracy: 0.8611\n",
      "Epoch 1701/2000\n",
      "84/84 [==============================] - 0s 244us/step - loss: 0.0203 - accuracy: 0.9881 - val_loss: 0.4877 - val_accuracy: 0.8611\n",
      "Epoch 1702/2000\n",
      "84/84 [==============================] - 0s 280us/step - loss: 0.0204 - accuracy: 0.9881 - val_loss: 0.4953 - val_accuracy: 0.8611\n",
      "Epoch 1703/2000\n",
      "84/84 [==============================] - 0s 348us/step - loss: 0.0200 - accuracy: 0.9881 - val_loss: 0.4987 - val_accuracy: 0.8611\n",
      "Epoch 1704/2000\n",
      "84/84 [==============================] - 0s 354us/step - loss: 0.0198 - accuracy: 0.9881 - val_loss: 0.5042 - val_accuracy: 0.8611\n",
      "Epoch 1705/2000\n",
      "84/84 [==============================] - 0s 363us/step - loss: 0.0198 - accuracy: 0.9881 - val_loss: 0.5102 - val_accuracy: 0.8611\n",
      "Epoch 1706/2000\n",
      "84/84 [==============================] - 0s 283us/step - loss: 0.0200 - accuracy: 0.9881 - val_loss: 0.5119 - val_accuracy: 0.8611\n",
      "Epoch 1707/2000\n",
      "84/84 [==============================] - 0s 256us/step - loss: 0.0200 - accuracy: 0.9881 - val_loss: 0.5161 - val_accuracy: 0.8611\n",
      "Epoch 1708/2000\n",
      "84/84 [==============================] - 0s 265us/step - loss: 0.0201 - accuracy: 0.9881 - val_loss: 0.5208 - val_accuracy: 0.8611\n",
      "Epoch 1709/2000\n",
      "84/84 [==============================] - 0s 281us/step - loss: 0.0202 - accuracy: 0.9881 - val_loss: 0.5198 - val_accuracy: 0.8611\n",
      "Epoch 1710/2000\n",
      "84/84 [==============================] - 0s 255us/step - loss: 0.0202 - accuracy: 0.9881 - val_loss: 0.5142 - val_accuracy: 0.8611\n",
      "Epoch 1711/2000\n",
      "84/84 [==============================] - 0s 239us/step - loss: 0.0201 - accuracy: 0.9881 - val_loss: 0.5109 - val_accuracy: 0.8611\n",
      "Epoch 1712/2000\n",
      "84/84 [==============================] - 0s 247us/step - loss: 0.0202 - accuracy: 0.9881 - val_loss: 0.5066 - val_accuracy: 0.8611\n",
      "Epoch 1713/2000\n",
      "84/84 [==============================] - 0s 267us/step - loss: 0.0201 - accuracy: 0.9881 - val_loss: 0.5044 - val_accuracy: 0.8611\n",
      "Epoch 1714/2000\n",
      "84/84 [==============================] - 0s 313us/step - loss: 0.0198 - accuracy: 0.9881 - val_loss: 0.4984 - val_accuracy: 0.8611\n",
      "Epoch 1715/2000\n",
      "84/84 [==============================] - 0s 375us/step - loss: 0.0198 - accuracy: 0.9881 - val_loss: 0.4957 - val_accuracy: 0.8611\n",
      "Epoch 1716/2000\n",
      "84/84 [==============================] - 0s 421us/step - loss: 0.0198 - accuracy: 0.9881 - val_loss: 0.4887 - val_accuracy: 0.8611\n",
      "Epoch 1717/2000\n",
      "84/84 [==============================] - 0s 397us/step - loss: 0.0198 - accuracy: 0.9881 - val_loss: 0.4854 - val_accuracy: 0.8889\n",
      "Epoch 1718/2000\n",
      "84/84 [==============================] - 0s 390us/step - loss: 0.0198 - accuracy: 0.9881 - val_loss: 0.4861 - val_accuracy: 0.8889\n",
      "Epoch 1719/2000\n",
      "84/84 [==============================] - 0s 402us/step - loss: 0.0201 - accuracy: 0.9881 - val_loss: 0.4888 - val_accuracy: 0.8611\n",
      "Epoch 1720/2000\n",
      "84/84 [==============================] - 0s 256us/step - loss: 0.0200 - accuracy: 0.9881 - val_loss: 0.4883 - val_accuracy: 0.8611\n",
      "Epoch 1721/2000\n",
      "84/84 [==============================] - 0s 339us/step - loss: 0.0200 - accuracy: 0.9881 - val_loss: 0.4909 - val_accuracy: 0.8611\n",
      "Epoch 1722/2000\n",
      "84/84 [==============================] - 0s 284us/step - loss: 0.0201 - accuracy: 0.9881 - val_loss: 0.4952 - val_accuracy: 0.8611\n",
      "Epoch 1723/2000\n",
      "84/84 [==============================] - 0s 235us/step - loss: 0.0200 - accuracy: 0.9881 - val_loss: 0.5001 - val_accuracy: 0.8611\n",
      "Epoch 1724/2000\n",
      "84/84 [==============================] - 0s 280us/step - loss: 0.0201 - accuracy: 0.9881 - val_loss: 0.5038 - val_accuracy: 0.8611\n",
      "Epoch 1725/2000\n",
      "84/84 [==============================] - 0s 345us/step - loss: 0.0200 - accuracy: 0.9881 - val_loss: 0.5035 - val_accuracy: 0.8611\n",
      "Epoch 1726/2000\n",
      "84/84 [==============================] - 0s 383us/step - loss: 0.0199 - accuracy: 0.9881 - val_loss: 0.5067 - val_accuracy: 0.8611\n",
      "Epoch 1727/2000\n",
      "84/84 [==============================] - 0s 268us/step - loss: 0.0196 - accuracy: 0.9881 - val_loss: 0.5123 - val_accuracy: 0.8611\n",
      "Epoch 1728/2000\n",
      "84/84 [==============================] - 0s 261us/step - loss: 0.0194 - accuracy: 1.0000 - val_loss: 0.5181 - val_accuracy: 0.8611\n",
      "Epoch 1729/2000\n",
      "84/84 [==============================] - 0s 259us/step - loss: 0.0195 - accuracy: 0.9881 - val_loss: 0.5234 - val_accuracy: 0.8611\n",
      "Epoch 1730/2000\n",
      "84/84 [==============================] - 0s 284us/step - loss: 0.0197 - accuracy: 0.9881 - val_loss: 0.5263 - val_accuracy: 0.8611\n",
      "Epoch 1731/2000\n",
      "84/84 [==============================] - 0s 277us/step - loss: 0.0199 - accuracy: 0.9881 - val_loss: 0.5267 - val_accuracy: 0.8611\n",
      "Epoch 1732/2000\n",
      "84/84 [==============================] - 0s 217us/step - loss: 0.0203 - accuracy: 0.9881 - val_loss: 0.5276 - val_accuracy: 0.8611\n",
      "Epoch 1733/2000\n",
      "84/84 [==============================] - 0s 413us/step - loss: 0.0200 - accuracy: 0.9881 - val_loss: 0.5228 - val_accuracy: 0.8611\n",
      "Epoch 1734/2000\n",
      "84/84 [==============================] - 0s 437us/step - loss: 0.0199 - accuracy: 0.9881 - val_loss: 0.5175 - val_accuracy: 0.8611\n",
      "Epoch 1735/2000\n",
      "84/84 [==============================] - 0s 415us/step - loss: 0.0197 - accuracy: 0.9881 - val_loss: 0.5145 - val_accuracy: 0.8611\n",
      "Epoch 1736/2000\n",
      "84/84 [==============================] - 0s 227us/step - loss: 0.0201 - accuracy: 0.9881 - val_loss: 0.5092 - val_accuracy: 0.8611\n",
      "Epoch 1737/2000\n",
      "84/84 [==============================] - 0s 251us/step - loss: 0.0200 - accuracy: 0.9881 - val_loss: 0.5080 - val_accuracy: 0.8611\n",
      "Epoch 1738/2000\n",
      "84/84 [==============================] - 0s 353us/step - loss: 0.0199 - accuracy: 0.9881 - val_loss: 0.5127 - val_accuracy: 0.8611\n",
      "Epoch 1739/2000\n",
      "84/84 [==============================] - 0s 262us/step - loss: 0.0198 - accuracy: 0.9881 - val_loss: 0.5124 - val_accuracy: 0.8611\n",
      "Epoch 1740/2000\n",
      "84/84 [==============================] - 0s 271us/step - loss: 0.0199 - accuracy: 0.9881 - val_loss: 0.5074 - val_accuracy: 0.8611\n",
      "Epoch 1741/2000\n",
      "84/84 [==============================] - 0s 262us/step - loss: 0.0198 - accuracy: 0.9881 - val_loss: 0.5051 - val_accuracy: 0.8611\n",
      "Epoch 1742/2000\n",
      "84/84 [==============================] - 0s 249us/step - loss: 0.0195 - accuracy: 1.0000 - val_loss: 0.5044 - val_accuracy: 0.8611\n",
      "Epoch 1743/2000\n",
      "84/84 [==============================] - 0s 265us/step - loss: 0.0197 - accuracy: 1.0000 - val_loss: 0.5048 - val_accuracy: 0.8611\n",
      "Epoch 1744/2000\n",
      "84/84 [==============================] - 0s 266us/step - loss: 0.0198 - accuracy: 0.9881 - val_loss: 0.5058 - val_accuracy: 0.8611\n",
      "Epoch 1745/2000\n",
      "84/84 [==============================] - 0s 241us/step - loss: 0.0195 - accuracy: 1.0000 - val_loss: 0.5105 - val_accuracy: 0.8611\n",
      "Epoch 1746/2000\n",
      "84/84 [==============================] - 0s 234us/step - loss: 0.0205 - accuracy: 0.9881 - val_loss: 0.5180 - val_accuracy: 0.8611\n",
      "Epoch 1747/2000\n",
      "84/84 [==============================] - 0s 232us/step - loss: 0.0195 - accuracy: 0.9881 - val_loss: 0.5157 - val_accuracy: 0.8611\n",
      "Epoch 1748/2000\n",
      "84/84 [==============================] - 0s 239us/step - loss: 0.0198 - accuracy: 0.9881 - val_loss: 0.5111 - val_accuracy: 0.8611\n",
      "Epoch 1749/2000\n",
      "84/84 [==============================] - 0s 274us/step - loss: 0.0197 - accuracy: 0.9881 - val_loss: 0.5108 - val_accuracy: 0.8611\n",
      "Epoch 1750/2000\n",
      "84/84 [==============================] - 0s 299us/step - loss: 0.0194 - accuracy: 0.9881 - val_loss: 0.5074 - val_accuracy: 0.8611\n",
      "Epoch 1751/2000\n",
      "84/84 [==============================] - 0s 267us/step - loss: 0.0194 - accuracy: 0.9881 - val_loss: 0.5050 - val_accuracy: 0.8611\n",
      "Epoch 1752/2000\n",
      "84/84 [==============================] - 0s 302us/step - loss: 0.0194 - accuracy: 1.0000 - val_loss: 0.5026 - val_accuracy: 0.8611\n",
      "Epoch 1753/2000\n",
      "84/84 [==============================] - 0s 271us/step - loss: 0.0195 - accuracy: 0.9881 - val_loss: 0.5037 - val_accuracy: 0.8611\n",
      "Epoch 1754/2000\n",
      "84/84 [==============================] - 0s 385us/step - loss: 0.0197 - accuracy: 0.9881 - val_loss: 0.5045 - val_accuracy: 0.8611\n",
      "Epoch 1755/2000\n",
      "84/84 [==============================] - 0s 238us/step - loss: 0.0197 - accuracy: 0.9881 - val_loss: 0.5099 - val_accuracy: 0.8611\n",
      "Epoch 1756/2000\n",
      "84/84 [==============================] - 0s 321us/step - loss: 0.0195 - accuracy: 1.0000 - val_loss: 0.5127 - val_accuracy: 0.8611\n",
      "Epoch 1757/2000\n",
      "84/84 [==============================] - 0s 288us/step - loss: 0.0195 - accuracy: 0.9881 - val_loss: 0.5137 - val_accuracy: 0.8611\n",
      "Epoch 1758/2000\n",
      "84/84 [==============================] - 0s 301us/step - loss: 0.0194 - accuracy: 0.9881 - val_loss: 0.5180 - val_accuracy: 0.8611\n",
      "Epoch 1759/2000\n",
      "84/84 [==============================] - 0s 219us/step - loss: 0.0194 - accuracy: 0.9881 - val_loss: 0.5191 - val_accuracy: 0.8611\n",
      "Epoch 1760/2000\n",
      "84/84 [==============================] - 0s 231us/step - loss: 0.0194 - accuracy: 0.9881 - val_loss: 0.5195 - val_accuracy: 0.8611\n",
      "Epoch 1761/2000\n",
      "84/84 [==============================] - 0s 273us/step - loss: 0.0194 - accuracy: 0.9881 - val_loss: 0.5207 - val_accuracy: 0.8611\n",
      "Epoch 1762/2000\n",
      "84/84 [==============================] - 0s 344us/step - loss: 0.0196 - accuracy: 0.9881 - val_loss: 0.5224 - val_accuracy: 0.8611\n",
      "Epoch 1763/2000\n",
      "84/84 [==============================] - 0s 342us/step - loss: 0.0197 - accuracy: 0.9881 - val_loss: 0.5190 - val_accuracy: 0.8611\n",
      "Epoch 1764/2000\n",
      "84/84 [==============================] - 0s 347us/step - loss: 0.0194 - accuracy: 0.9881 - val_loss: 0.5208 - val_accuracy: 0.8611\n",
      "Epoch 1765/2000\n",
      "84/84 [==============================] - 0s 398us/step - loss: 0.0198 - accuracy: 0.9881 - val_loss: 0.5242 - val_accuracy: 0.8611\n",
      "Epoch 1766/2000\n",
      "84/84 [==============================] - 0s 243us/step - loss: 0.0194 - accuracy: 0.9881 - val_loss: 0.5186 - val_accuracy: 0.8611\n",
      "Epoch 1767/2000\n",
      "84/84 [==============================] - 0s 254us/step - loss: 0.0196 - accuracy: 0.9881 - val_loss: 0.5132 - val_accuracy: 0.8611\n",
      "Epoch 1768/2000\n",
      "84/84 [==============================] - 0s 262us/step - loss: 0.0193 - accuracy: 1.0000 - val_loss: 0.5087 - val_accuracy: 0.8611\n",
      "Epoch 1769/2000\n",
      "84/84 [==============================] - 0s 331us/step - loss: 0.0196 - accuracy: 0.9881 - val_loss: 0.5075 - val_accuracy: 0.8611\n",
      "Epoch 1770/2000\n",
      "84/84 [==============================] - 0s 323us/step - loss: 0.0193 - accuracy: 0.9881 - val_loss: 0.5118 - val_accuracy: 0.8611\n",
      "Epoch 1771/2000\n",
      "84/84 [==============================] - 0s 269us/step - loss: 0.0193 - accuracy: 0.9881 - val_loss: 0.5186 - val_accuracy: 0.8611\n",
      "Epoch 1772/2000\n",
      "84/84 [==============================] - 0s 262us/step - loss: 0.0198 - accuracy: 0.9881 - val_loss: 0.5243 - val_accuracy: 0.8611\n",
      "Epoch 1773/2000\n",
      "84/84 [==============================] - 0s 284us/step - loss: 0.0194 - accuracy: 0.9881 - val_loss: 0.5243 - val_accuracy: 0.8611\n",
      "Epoch 1774/2000\n",
      "84/84 [==============================] - 0s 353us/step - loss: 0.0196 - accuracy: 1.0000 - val_loss: 0.5189 - val_accuracy: 0.8611\n",
      "Epoch 1775/2000\n",
      "84/84 [==============================] - 0s 231us/step - loss: 0.0195 - accuracy: 0.9881 - val_loss: 0.5201 - val_accuracy: 0.8611\n",
      "Epoch 1776/2000\n",
      "84/84 [==============================] - 0s 229us/step - loss: 0.0199 - accuracy: 0.9762 - val_loss: 0.5251 - val_accuracy: 0.8611\n",
      "Epoch 1777/2000\n",
      "84/84 [==============================] - 0s 259us/step - loss: 0.0194 - accuracy: 0.9881 - val_loss: 0.5241 - val_accuracy: 0.8611\n",
      "Epoch 1778/2000\n",
      "84/84 [==============================] - 0s 309us/step - loss: 0.0197 - accuracy: 0.9881 - val_loss: 0.5213 - val_accuracy: 0.8611\n",
      "Epoch 1779/2000\n",
      "84/84 [==============================] - 0s 289us/step - loss: 0.0195 - accuracy: 1.0000 - val_loss: 0.5265 - val_accuracy: 0.8611\n",
      "Epoch 1780/2000\n",
      "84/84 [==============================] - 0s 307us/step - loss: 0.0193 - accuracy: 0.9881 - val_loss: 0.5302 - val_accuracy: 0.8611\n",
      "Epoch 1781/2000\n",
      "84/84 [==============================] - 0s 282us/step - loss: 0.0193 - accuracy: 0.9881 - val_loss: 0.5319 - val_accuracy: 0.8611\n",
      "Epoch 1782/2000\n",
      "84/84 [==============================] - 0s 285us/step - loss: 0.0192 - accuracy: 0.9881 - val_loss: 0.5317 - val_accuracy: 0.8611\n",
      "Epoch 1783/2000\n",
      "84/84 [==============================] - 0s 339us/step - loss: 0.0192 - accuracy: 0.9881 - val_loss: 0.5336 - val_accuracy: 0.8611\n",
      "Epoch 1784/2000\n",
      "84/84 [==============================] - 0s 231us/step - loss: 0.0194 - accuracy: 0.9881 - val_loss: 0.5328 - val_accuracy: 0.8611\n",
      "Epoch 1785/2000\n",
      "84/84 [==============================] - 0s 269us/step - loss: 0.0192 - accuracy: 0.9881 - val_loss: 0.5302 - val_accuracy: 0.8611\n",
      "Epoch 1786/2000\n",
      "84/84 [==============================] - 0s 306us/step - loss: 0.0196 - accuracy: 0.9881 - val_loss: 0.5303 - val_accuracy: 0.8611\n",
      "Epoch 1787/2000\n",
      "84/84 [==============================] - 0s 345us/step - loss: 0.0192 - accuracy: 0.9881 - val_loss: 0.5248 - val_accuracy: 0.8611\n",
      "Epoch 1788/2000\n",
      "84/84 [==============================] - 0s 251us/step - loss: 0.0195 - accuracy: 0.9881 - val_loss: 0.5221 - val_accuracy: 0.8611\n",
      "Epoch 1789/2000\n",
      "84/84 [==============================] - 0s 391us/step - loss: 0.0193 - accuracy: 0.9881 - val_loss: 0.5268 - val_accuracy: 0.8611\n",
      "Epoch 1790/2000\n",
      "84/84 [==============================] - 0s 389us/step - loss: 0.0191 - accuracy: 0.9881 - val_loss: 0.5302 - val_accuracy: 0.8611\n",
      "Epoch 1791/2000\n",
      "84/84 [==============================] - 0s 364us/step - loss: 0.0193 - accuracy: 0.9881 - val_loss: 0.5299 - val_accuracy: 0.8611\n",
      "Epoch 1792/2000\n",
      "84/84 [==============================] - 0s 370us/step - loss: 0.0193 - accuracy: 0.9881 - val_loss: 0.5282 - val_accuracy: 0.8611\n",
      "Epoch 1793/2000\n",
      "84/84 [==============================] - 0s 283us/step - loss: 0.0194 - accuracy: 0.9881 - val_loss: 0.5323 - val_accuracy: 0.8611\n",
      "Epoch 1794/2000\n",
      "84/84 [==============================] - 0s 302us/step - loss: 0.0195 - accuracy: 0.9881 - val_loss: 0.5338 - val_accuracy: 0.8611\n",
      "Epoch 1795/2000\n",
      "84/84 [==============================] - 0s 545us/step - loss: 0.0191 - accuracy: 0.9881 - val_loss: 0.5293 - val_accuracy: 0.8611\n",
      "Epoch 1796/2000\n",
      "84/84 [==============================] - 0s 415us/step - loss: 0.0190 - accuracy: 0.9881 - val_loss: 0.5221 - val_accuracy: 0.8611\n",
      "Epoch 1797/2000\n",
      "84/84 [==============================] - 0s 396us/step - loss: 0.0195 - accuracy: 0.9881 - val_loss: 0.5161 - val_accuracy: 0.8611\n",
      "Epoch 1798/2000\n",
      "84/84 [==============================] - 0s 375us/step - loss: 0.0193 - accuracy: 0.9881 - val_loss: 0.5156 - val_accuracy: 0.8611\n",
      "Epoch 1799/2000\n",
      "84/84 [==============================] - 0s 350us/step - loss: 0.0195 - accuracy: 0.9881 - val_loss: 0.5135 - val_accuracy: 0.8611\n",
      "Epoch 1800/2000\n",
      "84/84 [==============================] - 0s 240us/step - loss: 0.0193 - accuracy: 0.9881 - val_loss: 0.5174 - val_accuracy: 0.8611\n",
      "Epoch 1801/2000\n",
      "84/84 [==============================] - 0s 287us/step - loss: 0.0194 - accuracy: 0.9881 - val_loss: 0.5224 - val_accuracy: 0.8611\n",
      "Epoch 1802/2000\n",
      "84/84 [==============================] - 0s 805us/step - loss: 0.0192 - accuracy: 0.9881 - val_loss: 0.5275 - val_accuracy: 0.8611\n",
      "Epoch 1803/2000\n",
      "84/84 [==============================] - 0s 420us/step - loss: 0.0196 - accuracy: 0.9881 - val_loss: 0.5333 - val_accuracy: 0.8611\n",
      "Epoch 1804/2000\n",
      "84/84 [==============================] - 0s 337us/step - loss: 0.0190 - accuracy: 1.0000 - val_loss: 0.5312 - val_accuracy: 0.8611\n",
      "Epoch 1805/2000\n",
      "84/84 [==============================] - 0s 467us/step - loss: 0.0192 - accuracy: 1.0000 - val_loss: 0.5328 - val_accuracy: 0.8611\n",
      "Epoch 1806/2000\n",
      "84/84 [==============================] - 0s 337us/step - loss: 0.0190 - accuracy: 1.0000 - val_loss: 0.5323 - val_accuracy: 0.8611\n",
      "Epoch 1807/2000\n",
      "84/84 [==============================] - 0s 237us/step - loss: 0.0194 - accuracy: 0.9881 - val_loss: 0.5295 - val_accuracy: 0.8611\n",
      "Epoch 1808/2000\n",
      "84/84 [==============================] - 0s 465us/step - loss: 0.0190 - accuracy: 1.0000 - val_loss: 0.5344 - val_accuracy: 0.8611\n",
      "Epoch 1809/2000\n",
      "84/84 [==============================] - 0s 298us/step - loss: 0.0190 - accuracy: 1.0000 - val_loss: 0.5359 - val_accuracy: 0.8611\n",
      "Epoch 1810/2000\n",
      "84/84 [==============================] - 0s 322us/step - loss: 0.0190 - accuracy: 0.9881 - val_loss: 0.5393 - val_accuracy: 0.8611\n",
      "Epoch 1811/2000\n",
      "84/84 [==============================] - 0s 263us/step - loss: 0.0193 - accuracy: 0.9881 - val_loss: 0.5412 - val_accuracy: 0.8611\n",
      "Epoch 1812/2000\n",
      "84/84 [==============================] - 0s 371us/step - loss: 0.0192 - accuracy: 0.9881 - val_loss: 0.5365 - val_accuracy: 0.8611\n",
      "Epoch 1813/2000\n",
      "84/84 [==============================] - 0s 398us/step - loss: 0.0189 - accuracy: 0.9881 - val_loss: 0.5352 - val_accuracy: 0.8611\n",
      "Epoch 1814/2000\n",
      "84/84 [==============================] - 0s 296us/step - loss: 0.0193 - accuracy: 0.9881 - val_loss: 0.5347 - val_accuracy: 0.8611\n",
      "Epoch 1815/2000\n",
      "84/84 [==============================] - 0s 376us/step - loss: 0.0198 - accuracy: 0.9881 - val_loss: 0.5272 - val_accuracy: 0.8611\n",
      "Epoch 1816/2000\n",
      "84/84 [==============================] - 0s 364us/step - loss: 0.0191 - accuracy: 0.9881 - val_loss: 0.5307 - val_accuracy: 0.8611\n",
      "Epoch 1817/2000\n",
      "84/84 [==============================] - 0s 382us/step - loss: 0.0189 - accuracy: 0.9881 - val_loss: 0.5335 - val_accuracy: 0.8611\n",
      "Epoch 1818/2000\n",
      "84/84 [==============================] - 0s 480us/step - loss: 0.0191 - accuracy: 0.9881 - val_loss: 0.5370 - val_accuracy: 0.8611\n",
      "Epoch 1819/2000\n",
      "84/84 [==============================] - 0s 271us/step - loss: 0.0193 - accuracy: 0.9881 - val_loss: 0.5320 - val_accuracy: 0.8611\n",
      "Epoch 1820/2000\n",
      "84/84 [==============================] - 0s 301us/step - loss: 0.0192 - accuracy: 0.9881 - val_loss: 0.5288 - val_accuracy: 0.8611\n",
      "Epoch 1821/2000\n",
      "84/84 [==============================] - 0s 259us/step - loss: 0.0191 - accuracy: 0.9881 - val_loss: 0.5298 - val_accuracy: 0.8611\n",
      "Epoch 1822/2000\n",
      "84/84 [==============================] - 0s 223us/step - loss: 0.0189 - accuracy: 0.9881 - val_loss: 0.5335 - val_accuracy: 0.8611\n",
      "Epoch 1823/2000\n",
      "84/84 [==============================] - 0s 231us/step - loss: 0.0188 - accuracy: 0.9881 - val_loss: 0.5358 - val_accuracy: 0.8611\n",
      "Epoch 1824/2000\n",
      "84/84 [==============================] - 0s 290us/step - loss: 0.0189 - accuracy: 0.9881 - val_loss: 0.5368 - val_accuracy: 0.8611\n",
      "Epoch 1825/2000\n",
      "84/84 [==============================] - 0s 291us/step - loss: 0.0195 - accuracy: 0.9881 - val_loss: 0.5424 - val_accuracy: 0.8611\n",
      "Epoch 1826/2000\n",
      "84/84 [==============================] - 0s 331us/step - loss: 0.0190 - accuracy: 0.9881 - val_loss: 0.5404 - val_accuracy: 0.8611\n",
      "Epoch 1827/2000\n",
      "84/84 [==============================] - 0s 233us/step - loss: 0.0197 - accuracy: 0.9881 - val_loss: 0.5315 - val_accuracy: 0.8611\n",
      "Epoch 1828/2000\n",
      "84/84 [==============================] - 0s 278us/step - loss: 0.0191 - accuracy: 0.9881 - val_loss: 0.5282 - val_accuracy: 0.8611\n",
      "Epoch 1829/2000\n",
      "84/84 [==============================] - 0s 320us/step - loss: 0.0191 - accuracy: 0.9881 - val_loss: 0.5311 - val_accuracy: 0.8611\n",
      "Epoch 1830/2000\n",
      "84/84 [==============================] - 0s 274us/step - loss: 0.0192 - accuracy: 0.9881 - val_loss: 0.5314 - val_accuracy: 0.8611\n",
      "Epoch 1831/2000\n",
      "84/84 [==============================] - 0s 318us/step - loss: 0.0191 - accuracy: 1.0000 - val_loss: 0.5395 - val_accuracy: 0.8611\n",
      "Epoch 1832/2000\n",
      "84/84 [==============================] - 0s 270us/step - loss: 0.0188 - accuracy: 0.9881 - val_loss: 0.5449 - val_accuracy: 0.8611\n",
      "Epoch 1833/2000\n",
      "84/84 [==============================] - 0s 287us/step - loss: 0.0189 - accuracy: 0.9881 - val_loss: 0.5479 - val_accuracy: 0.8611\n",
      "Epoch 1834/2000\n",
      "84/84 [==============================] - 0s 301us/step - loss: 0.0189 - accuracy: 0.9881 - val_loss: 0.5506 - val_accuracy: 0.8611\n",
      "Epoch 1835/2000\n",
      "84/84 [==============================] - 0s 617us/step - loss: 0.0196 - accuracy: 0.9881 - val_loss: 0.5525 - val_accuracy: 0.8611\n",
      "Epoch 1836/2000\n",
      "84/84 [==============================] - 0s 278us/step - loss: 0.0190 - accuracy: 0.9881 - val_loss: 0.5490 - val_accuracy: 0.8611\n",
      "Epoch 1837/2000\n",
      "84/84 [==============================] - 0s 551us/step - loss: 0.0192 - accuracy: 0.9881 - val_loss: 0.5448 - val_accuracy: 0.8611\n",
      "Epoch 1838/2000\n",
      "84/84 [==============================] - 0s 363us/step - loss: 0.0190 - accuracy: 0.9881 - val_loss: 0.5436 - val_accuracy: 0.8611\n",
      "Epoch 1839/2000\n",
      "84/84 [==============================] - 0s 353us/step - loss: 0.0188 - accuracy: 0.9881 - val_loss: 0.5412 - val_accuracy: 0.8611\n",
      "Epoch 1840/2000\n",
      "84/84 [==============================] - 0s 291us/step - loss: 0.0192 - accuracy: 0.9881 - val_loss: 0.5375 - val_accuracy: 0.8611\n",
      "Epoch 1841/2000\n",
      "84/84 [==============================] - 0s 289us/step - loss: 0.0187 - accuracy: 0.9881 - val_loss: 0.5381 - val_accuracy: 0.8611\n",
      "Epoch 1842/2000\n",
      "84/84 [==============================] - 0s 305us/step - loss: 0.0188 - accuracy: 0.9881 - val_loss: 0.5381 - val_accuracy: 0.8611\n",
      "Epoch 1843/2000\n",
      "84/84 [==============================] - 0s 284us/step - loss: 0.0188 - accuracy: 0.9881 - val_loss: 0.5358 - val_accuracy: 0.8611\n",
      "Epoch 1844/2000\n",
      "84/84 [==============================] - 0s 253us/step - loss: 0.0187 - accuracy: 0.9881 - val_loss: 0.5363 - val_accuracy: 0.8611\n",
      "Epoch 1845/2000\n",
      "84/84 [==============================] - 0s 254us/step - loss: 0.0187 - accuracy: 0.9881 - val_loss: 0.5377 - val_accuracy: 0.8611\n",
      "Epoch 1846/2000\n",
      "84/84 [==============================] - 0s 250us/step - loss: 0.0191 - accuracy: 0.9881 - val_loss: 0.5407 - val_accuracy: 0.8611\n",
      "Epoch 1847/2000\n",
      "84/84 [==============================] - 0s 264us/step - loss: 0.0187 - accuracy: 0.9881 - val_loss: 0.5369 - val_accuracy: 0.8611\n",
      "Epoch 1848/2000\n",
      "84/84 [==============================] - 0s 281us/step - loss: 0.0195 - accuracy: 0.9881 - val_loss: 0.5308 - val_accuracy: 0.8611\n",
      "Epoch 1849/2000\n",
      "84/84 [==============================] - 0s 270us/step - loss: 0.0188 - accuracy: 0.9881 - val_loss: 0.5346 - val_accuracy: 0.8611\n",
      "Epoch 1850/2000\n",
      "84/84 [==============================] - 0s 275us/step - loss: 0.0188 - accuracy: 1.0000 - val_loss: 0.5382 - val_accuracy: 0.8611\n",
      "Epoch 1851/2000\n",
      "84/84 [==============================] - 0s 257us/step - loss: 0.0190 - accuracy: 0.9881 - val_loss: 0.5464 - val_accuracy: 0.8611\n",
      "Epoch 1852/2000\n",
      "84/84 [==============================] - 0s 281us/step - loss: 0.0186 - accuracy: 0.9881 - val_loss: 0.5483 - val_accuracy: 0.8611\n",
      "Epoch 1853/2000\n",
      "84/84 [==============================] - 0s 263us/step - loss: 0.0188 - accuracy: 0.9881 - val_loss: 0.5475 - val_accuracy: 0.8611\n",
      "Epoch 1854/2000\n",
      "84/84 [==============================] - 0s 280us/step - loss: 0.0186 - accuracy: 0.9881 - val_loss: 0.5473 - val_accuracy: 0.8611\n",
      "Epoch 1855/2000\n",
      "84/84 [==============================] - 0s 284us/step - loss: 0.0188 - accuracy: 0.9881 - val_loss: 0.5481 - val_accuracy: 0.8611\n",
      "Epoch 1856/2000\n",
      "84/84 [==============================] - 0s 309us/step - loss: 0.0186 - accuracy: 0.9881 - val_loss: 0.5440 - val_accuracy: 0.8611\n",
      "Epoch 1857/2000\n",
      "84/84 [==============================] - 0s 359us/step - loss: 0.0187 - accuracy: 1.0000 - val_loss: 0.5431 - val_accuracy: 0.8611\n",
      "Epoch 1858/2000\n",
      "84/84 [==============================] - 0s 213us/step - loss: 0.0187 - accuracy: 1.0000 - val_loss: 0.5450 - val_accuracy: 0.8611\n",
      "Epoch 1859/2000\n",
      "84/84 [==============================] - 0s 300us/step - loss: 0.0188 - accuracy: 1.0000 - val_loss: 0.5454 - val_accuracy: 0.8611\n",
      "Epoch 1860/2000\n",
      "84/84 [==============================] - 0s 413us/step - loss: 0.0189 - accuracy: 1.0000 - val_loss: 0.5483 - val_accuracy: 0.8611\n",
      "Epoch 1861/2000\n",
      "84/84 [==============================] - 0s 382us/step - loss: 0.0186 - accuracy: 1.0000 - val_loss: 0.5476 - val_accuracy: 0.8611\n",
      "Epoch 1862/2000\n",
      "84/84 [==============================] - 0s 376us/step - loss: 0.0188 - accuracy: 1.0000 - val_loss: 0.5449 - val_accuracy: 0.8611\n",
      "Epoch 1863/2000\n",
      "84/84 [==============================] - 0s 582us/step - loss: 0.0189 - accuracy: 1.0000 - val_loss: 0.5475 - val_accuracy: 0.8611\n",
      "Epoch 1864/2000\n",
      "84/84 [==============================] - 0s 391us/step - loss: 0.0188 - accuracy: 1.0000 - val_loss: 0.5462 - val_accuracy: 0.8611\n",
      "Epoch 1865/2000\n",
      "84/84 [==============================] - 0s 356us/step - loss: 0.0188 - accuracy: 0.9881 - val_loss: 0.5470 - val_accuracy: 0.8611\n",
      "Epoch 1866/2000\n",
      "84/84 [==============================] - 0s 356us/step - loss: 0.0194 - accuracy: 0.9881 - val_loss: 0.5548 - val_accuracy: 0.8611\n",
      "Epoch 1867/2000\n",
      "84/84 [==============================] - 0s 334us/step - loss: 0.0185 - accuracy: 0.9881 - val_loss: 0.5526 - val_accuracy: 0.8611\n",
      "Epoch 1868/2000\n",
      "84/84 [==============================] - 0s 333us/step - loss: 0.0185 - accuracy: 0.9881 - val_loss: 0.5528 - val_accuracy: 0.8611\n",
      "Epoch 1869/2000\n",
      "84/84 [==============================] - 0s 354us/step - loss: 0.0187 - accuracy: 0.9881 - val_loss: 0.5504 - val_accuracy: 0.8611\n",
      "Epoch 1870/2000\n",
      "84/84 [==============================] - 0s 342us/step - loss: 0.0187 - accuracy: 1.0000 - val_loss: 0.5493 - val_accuracy: 0.8611\n",
      "Epoch 1871/2000\n",
      "84/84 [==============================] - 0s 351us/step - loss: 0.0185 - accuracy: 0.9881 - val_loss: 0.5512 - val_accuracy: 0.8611\n",
      "Epoch 1872/2000\n",
      "84/84 [==============================] - 0s 328us/step - loss: 0.0188 - accuracy: 0.9881 - val_loss: 0.5552 - val_accuracy: 0.8611\n",
      "Epoch 1873/2000\n",
      "84/84 [==============================] - 0s 246us/step - loss: 0.0185 - accuracy: 0.9881 - val_loss: 0.5564 - val_accuracy: 0.8611\n",
      "Epoch 1874/2000\n",
      "84/84 [==============================] - 0s 294us/step - loss: 0.0187 - accuracy: 0.9881 - val_loss: 0.5540 - val_accuracy: 0.8611\n",
      "Epoch 1875/2000\n",
      "84/84 [==============================] - 0s 273us/step - loss: 0.0188 - accuracy: 0.9881 - val_loss: 0.5538 - val_accuracy: 0.8611\n",
      "Epoch 1876/2000\n",
      "84/84 [==============================] - 0s 263us/step - loss: 0.0185 - accuracy: 0.9881 - val_loss: 0.5493 - val_accuracy: 0.8611\n",
      "Epoch 1877/2000\n",
      "84/84 [==============================] - 0s 283us/step - loss: 0.0185 - accuracy: 0.9881 - val_loss: 0.5477 - val_accuracy: 0.8611\n",
      "Epoch 1878/2000\n",
      "84/84 [==============================] - 0s 296us/step - loss: 0.0184 - accuracy: 0.9881 - val_loss: 0.5480 - val_accuracy: 0.8611\n",
      "Epoch 1879/2000\n",
      "84/84 [==============================] - 0s 310us/step - loss: 0.0184 - accuracy: 0.9881 - val_loss: 0.5473 - val_accuracy: 0.8611\n",
      "Epoch 1880/2000\n",
      "84/84 [==============================] - 0s 334us/step - loss: 0.0186 - accuracy: 1.0000 - val_loss: 0.5472 - val_accuracy: 0.8611\n",
      "Epoch 1881/2000\n",
      "84/84 [==============================] - 0s 338us/step - loss: 0.0184 - accuracy: 1.0000 - val_loss: 0.5482 - val_accuracy: 0.8611\n",
      "Epoch 1882/2000\n",
      "84/84 [==============================] - 0s 282us/step - loss: 0.0186 - accuracy: 1.0000 - val_loss: 0.5507 - val_accuracy: 0.8611\n",
      "Epoch 1883/2000\n",
      "84/84 [==============================] - 0s 281us/step - loss: 0.0186 - accuracy: 1.0000 - val_loss: 0.5503 - val_accuracy: 0.8611\n",
      "Epoch 1884/2000\n",
      "84/84 [==============================] - 0s 318us/step - loss: 0.0185 - accuracy: 1.0000 - val_loss: 0.5510 - val_accuracy: 0.8611\n",
      "Epoch 1885/2000\n",
      "84/84 [==============================] - 0s 321us/step - loss: 0.0186 - accuracy: 1.0000 - val_loss: 0.5526 - val_accuracy: 0.8611\n",
      "Epoch 1886/2000\n",
      "84/84 [==============================] - 0s 269us/step - loss: 0.0186 - accuracy: 0.9881 - val_loss: 0.5554 - val_accuracy: 0.8611\n",
      "Epoch 1887/2000\n",
      "84/84 [==============================] - 0s 245us/step - loss: 0.0186 - accuracy: 0.9881 - val_loss: 0.5526 - val_accuracy: 0.8611\n",
      "Epoch 1888/2000\n",
      "84/84 [==============================] - 0s 237us/step - loss: 0.0185 - accuracy: 1.0000 - val_loss: 0.5524 - val_accuracy: 0.8611\n",
      "Epoch 1889/2000\n",
      "84/84 [==============================] - 0s 245us/step - loss: 0.0185 - accuracy: 0.9881 - val_loss: 0.5548 - val_accuracy: 0.8611\n",
      "Epoch 1890/2000\n",
      "84/84 [==============================] - 0s 292us/step - loss: 0.0183 - accuracy: 0.9881 - val_loss: 0.5607 - val_accuracy: 0.8611\n",
      "Epoch 1891/2000\n",
      "84/84 [==============================] - 0s 228us/step - loss: 0.0185 - accuracy: 0.9881 - val_loss: 0.5646 - val_accuracy: 0.8611\n",
      "Epoch 1892/2000\n",
      "84/84 [==============================] - 0s 292us/step - loss: 0.0185 - accuracy: 0.9881 - val_loss: 0.5682 - val_accuracy: 0.8611\n",
      "Epoch 1893/2000\n",
      "84/84 [==============================] - 0s 310us/step - loss: 0.0186 - accuracy: 0.9881 - val_loss: 0.5672 - val_accuracy: 0.8611\n",
      "Epoch 1894/2000\n",
      "84/84 [==============================] - 0s 322us/step - loss: 0.0189 - accuracy: 0.9881 - val_loss: 0.5671 - val_accuracy: 0.8611\n",
      "Epoch 1895/2000\n",
      "84/84 [==============================] - 0s 670us/step - loss: 0.0185 - accuracy: 0.9881 - val_loss: 0.5592 - val_accuracy: 0.8611\n",
      "Epoch 1896/2000\n",
      "84/84 [==============================] - 0s 364us/step - loss: 0.0183 - accuracy: 0.9881 - val_loss: 0.5534 - val_accuracy: 0.8611\n",
      "Epoch 1897/2000\n",
      "84/84 [==============================] - 0s 398us/step - loss: 0.0183 - accuracy: 1.0000 - val_loss: 0.5476 - val_accuracy: 0.8611\n",
      "Epoch 1898/2000\n",
      "84/84 [==============================] - 0s 330us/step - loss: 0.0187 - accuracy: 0.9881 - val_loss: 0.5441 - val_accuracy: 0.8611\n",
      "Epoch 1899/2000\n",
      "84/84 [==============================] - 0s 321us/step - loss: 0.0185 - accuracy: 0.9881 - val_loss: 0.5445 - val_accuracy: 0.8611\n",
      "Epoch 1900/2000\n",
      "84/84 [==============================] - 0s 330us/step - loss: 0.0187 - accuracy: 0.9881 - val_loss: 0.5477 - val_accuracy: 0.8611\n",
      "Epoch 1901/2000\n",
      "84/84 [==============================] - 0s 285us/step - loss: 0.0186 - accuracy: 0.9881 - val_loss: 0.5525 - val_accuracy: 0.8611\n",
      "Epoch 1902/2000\n",
      "84/84 [==============================] - 0s 305us/step - loss: 0.0185 - accuracy: 0.9881 - val_loss: 0.5569 - val_accuracy: 0.8611\n",
      "Epoch 1903/2000\n",
      "84/84 [==============================] - 0s 327us/step - loss: 0.0192 - accuracy: 0.9881 - val_loss: 0.5687 - val_accuracy: 0.8611\n",
      "Epoch 1904/2000\n",
      "84/84 [==============================] - 0s 295us/step - loss: 0.0184 - accuracy: 0.9881 - val_loss: 0.5700 - val_accuracy: 0.8611\n",
      "Epoch 1905/2000\n",
      "84/84 [==============================] - 0s 306us/step - loss: 0.0187 - accuracy: 0.9881 - val_loss: 0.5721 - val_accuracy: 0.8611\n",
      "Epoch 1906/2000\n",
      "84/84 [==============================] - 0s 268us/step - loss: 0.0183 - accuracy: 0.9881 - val_loss: 0.5660 - val_accuracy: 0.8611\n",
      "Epoch 1907/2000\n",
      "84/84 [==============================] - 0s 281us/step - loss: 0.0182 - accuracy: 1.0000 - val_loss: 0.5622 - val_accuracy: 0.8611\n",
      "Epoch 1908/2000\n",
      "84/84 [==============================] - 0s 310us/step - loss: 0.0182 - accuracy: 1.0000 - val_loss: 0.5588 - val_accuracy: 0.8611\n",
      "Epoch 1909/2000\n",
      "84/84 [==============================] - 0s 278us/step - loss: 0.0184 - accuracy: 1.0000 - val_loss: 0.5587 - val_accuracy: 0.8611\n",
      "Epoch 1910/2000\n",
      "84/84 [==============================] - 0s 233us/step - loss: 0.0185 - accuracy: 1.0000 - val_loss: 0.5568 - val_accuracy: 0.8611\n",
      "Epoch 1911/2000\n",
      "84/84 [==============================] - 0s 291us/step - loss: 0.0185 - accuracy: 1.0000 - val_loss: 0.5620 - val_accuracy: 0.8611\n",
      "Epoch 1912/2000\n",
      "84/84 [==============================] - 0s 398us/step - loss: 0.0184 - accuracy: 1.0000 - val_loss: 0.5624 - val_accuracy: 0.8611\n",
      "Epoch 1913/2000\n",
      "84/84 [==============================] - 0s 355us/step - loss: 0.0183 - accuracy: 1.0000 - val_loss: 0.5622 - val_accuracy: 0.8611\n",
      "Epoch 1914/2000\n",
      "84/84 [==============================] - 0s 373us/step - loss: 0.0184 - accuracy: 0.9881 - val_loss: 0.5634 - val_accuracy: 0.8611\n",
      "Epoch 1915/2000\n",
      "84/84 [==============================] - 0s 360us/step - loss: 0.0182 - accuracy: 0.9881 - val_loss: 0.5602 - val_accuracy: 0.8611\n",
      "Epoch 1916/2000\n",
      "84/84 [==============================] - 0s 309us/step - loss: 0.0184 - accuracy: 0.9881 - val_loss: 0.5594 - val_accuracy: 0.8611\n",
      "Epoch 1917/2000\n",
      "84/84 [==============================] - 0s 243us/step - loss: 0.0185 - accuracy: 0.9881 - val_loss: 0.5601 - val_accuracy: 0.8611\n",
      "Epoch 1918/2000\n",
      "84/84 [==============================] - 0s 267us/step - loss: 0.0183 - accuracy: 0.9881 - val_loss: 0.5636 - val_accuracy: 0.8611\n",
      "Epoch 1919/2000\n",
      "84/84 [==============================] - 0s 255us/step - loss: 0.0183 - accuracy: 0.9881 - val_loss: 0.5658 - val_accuracy: 0.8611\n",
      "Epoch 1920/2000\n",
      "84/84 [==============================] - 0s 290us/step - loss: 0.0184 - accuracy: 0.9881 - val_loss: 0.5675 - val_accuracy: 0.8611\n",
      "Epoch 1921/2000\n",
      "84/84 [==============================] - 0s 295us/step - loss: 0.0182 - accuracy: 1.0000 - val_loss: 0.5711 - val_accuracy: 0.8611\n",
      "Epoch 1922/2000\n",
      "84/84 [==============================] - 0s 250us/step - loss: 0.0181 - accuracy: 1.0000 - val_loss: 0.5734 - val_accuracy: 0.8611\n",
      "Epoch 1923/2000\n",
      "84/84 [==============================] - 0s 336us/step - loss: 0.0187 - accuracy: 0.9881 - val_loss: 0.5775 - val_accuracy: 0.8611\n",
      "Epoch 1924/2000\n",
      "84/84 [==============================] - 0s 323us/step - loss: 0.0183 - accuracy: 0.9881 - val_loss: 0.5765 - val_accuracy: 0.8611\n",
      "Epoch 1925/2000\n",
      "84/84 [==============================] - 0s 635us/step - loss: 0.0189 - accuracy: 0.9881 - val_loss: 0.5695 - val_accuracy: 0.8611\n",
      "Epoch 1926/2000\n",
      "84/84 [==============================] - 0s 597us/step - loss: 0.0184 - accuracy: 0.9881 - val_loss: 0.5673 - val_accuracy: 0.8611\n",
      "Epoch 1927/2000\n",
      "84/84 [==============================] - 0s 298us/step - loss: 0.0183 - accuracy: 0.9881 - val_loss: 0.5683 - val_accuracy: 0.8611\n",
      "Epoch 1928/2000\n",
      "84/84 [==============================] - 0s 365us/step - loss: 0.0187 - accuracy: 1.0000 - val_loss: 0.5728 - val_accuracy: 0.8611\n",
      "Epoch 1929/2000\n",
      "84/84 [==============================] - 0s 381us/step - loss: 0.0186 - accuracy: 0.9881 - val_loss: 0.5678 - val_accuracy: 0.8611\n",
      "Epoch 1930/2000\n",
      "84/84 [==============================] - 0s 418us/step - loss: 0.0181 - accuracy: 1.0000 - val_loss: 0.5693 - val_accuracy: 0.8611\n",
      "Epoch 1931/2000\n",
      "84/84 [==============================] - 0s 286us/step - loss: 0.0181 - accuracy: 1.0000 - val_loss: 0.5714 - val_accuracy: 0.8611\n",
      "Epoch 1932/2000\n",
      "84/84 [==============================] - 0s 326us/step - loss: 0.0180 - accuracy: 1.0000 - val_loss: 0.5752 - val_accuracy: 0.8611\n",
      "Epoch 1933/2000\n",
      "84/84 [==============================] - 0s 322us/step - loss: 0.0181 - accuracy: 0.9881 - val_loss: 0.5766 - val_accuracy: 0.8611\n",
      "Epoch 1934/2000\n",
      "84/84 [==============================] - 0s 329us/step - loss: 0.0185 - accuracy: 0.9881 - val_loss: 0.5825 - val_accuracy: 0.8611\n",
      "Epoch 1935/2000\n",
      "84/84 [==============================] - 0s 346us/step - loss: 0.0185 - accuracy: 0.9881 - val_loss: 0.5796 - val_accuracy: 0.8611\n",
      "Epoch 1936/2000\n",
      "84/84 [==============================] - 0s 332us/step - loss: 0.0181 - accuracy: 0.9881 - val_loss: 0.5794 - val_accuracy: 0.8611\n",
      "Epoch 1937/2000\n",
      "84/84 [==============================] - 0s 313us/step - loss: 0.0181 - accuracy: 0.9881 - val_loss: 0.5779 - val_accuracy: 0.8611\n",
      "Epoch 1938/2000\n",
      "84/84 [==============================] - 0s 224us/step - loss: 0.0181 - accuracy: 0.9881 - val_loss: 0.5769 - val_accuracy: 0.8611\n",
      "Epoch 1939/2000\n",
      "84/84 [==============================] - 0s 236us/step - loss: 0.0182 - accuracy: 0.9881 - val_loss: 0.5752 - val_accuracy: 0.8611\n",
      "Epoch 1940/2000\n",
      "84/84 [==============================] - 0s 308us/step - loss: 0.0182 - accuracy: 0.9881 - val_loss: 0.5736 - val_accuracy: 0.8611\n",
      "Epoch 1941/2000\n",
      "84/84 [==============================] - 0s 229us/step - loss: 0.0180 - accuracy: 0.9881 - val_loss: 0.5669 - val_accuracy: 0.8611\n",
      "Epoch 1942/2000\n",
      "84/84 [==============================] - 0s 226us/step - loss: 0.0180 - accuracy: 1.0000 - val_loss: 0.5583 - val_accuracy: 0.8611\n",
      "Epoch 1943/2000\n",
      "84/84 [==============================] - 0s 230us/step - loss: 0.0180 - accuracy: 1.0000 - val_loss: 0.5580 - val_accuracy: 0.8611\n",
      "Epoch 1944/2000\n",
      "84/84 [==============================] - 0s 247us/step - loss: 0.0181 - accuracy: 0.9881 - val_loss: 0.5586 - val_accuracy: 0.8611\n",
      "Epoch 1945/2000\n",
      "84/84 [==============================] - 0s 250us/step - loss: 0.0184 - accuracy: 0.9881 - val_loss: 0.5620 - val_accuracy: 0.8611\n",
      "Epoch 1946/2000\n",
      "84/84 [==============================] - 0s 249us/step - loss: 0.0184 - accuracy: 0.9881 - val_loss: 0.5621 - val_accuracy: 0.8611\n",
      "Epoch 1947/2000\n",
      "84/84 [==============================] - 0s 275us/step - loss: 0.0184 - accuracy: 0.9881 - val_loss: 0.5697 - val_accuracy: 0.8611\n",
      "Epoch 1948/2000\n",
      "84/84 [==============================] - 0s 277us/step - loss: 0.0186 - accuracy: 0.9881 - val_loss: 0.5715 - val_accuracy: 0.8611\n",
      "Epoch 1949/2000\n",
      "84/84 [==============================] - 0s 311us/step - loss: 0.0186 - accuracy: 0.9881 - val_loss: 0.5813 - val_accuracy: 0.8611\n",
      "Epoch 1950/2000\n",
      "84/84 [==============================] - 0s 342us/step - loss: 0.0181 - accuracy: 0.9881 - val_loss: 0.5836 - val_accuracy: 0.8611\n",
      "Epoch 1951/2000\n",
      "84/84 [==============================] - 0s 299us/step - loss: 0.0185 - accuracy: 1.0000 - val_loss: 0.5872 - val_accuracy: 0.8611\n",
      "Epoch 1952/2000\n",
      "84/84 [==============================] - 0s 668us/step - loss: 0.0185 - accuracy: 1.0000 - val_loss: 0.5818 - val_accuracy: 0.8611\n",
      "Epoch 1953/2000\n",
      "84/84 [==============================] - 0s 583us/step - loss: 0.0180 - accuracy: 1.0000 - val_loss: 0.5838 - val_accuracy: 0.8611\n",
      "Epoch 1954/2000\n",
      "84/84 [==============================] - 0s 327us/step - loss: 0.0179 - accuracy: 1.0000 - val_loss: 0.5878 - val_accuracy: 0.8611\n",
      "Epoch 1955/2000\n",
      "84/84 [==============================] - 0s 382us/step - loss: 0.0179 - accuracy: 0.9881 - val_loss: 0.5904 - val_accuracy: 0.8611\n",
      "Epoch 1956/2000\n",
      "84/84 [==============================] - 0s 345us/step - loss: 0.0182 - accuracy: 0.9881 - val_loss: 0.5906 - val_accuracy: 0.8611\n",
      "Epoch 1957/2000\n",
      "84/84 [==============================] - 0s 309us/step - loss: 0.0182 - accuracy: 0.9881 - val_loss: 0.5869 - val_accuracy: 0.8611\n",
      "Epoch 1958/2000\n",
      "84/84 [==============================] - 0s 292us/step - loss: 0.0180 - accuracy: 0.9881 - val_loss: 0.5868 - val_accuracy: 0.8611\n",
      "Epoch 1959/2000\n",
      "84/84 [==============================] - 0s 302us/step - loss: 0.0182 - accuracy: 0.9881 - val_loss: 0.5850 - val_accuracy: 0.8611\n",
      "Epoch 1960/2000\n",
      "84/84 [==============================] - 0s 234us/step - loss: 0.0178 - accuracy: 0.9881 - val_loss: 0.5771 - val_accuracy: 0.8611\n",
      "Epoch 1961/2000\n",
      "84/84 [==============================] - 0s 306us/step - loss: 0.0187 - accuracy: 0.9881 - val_loss: 0.5682 - val_accuracy: 0.8611\n",
      "Epoch 1962/2000\n",
      "84/84 [==============================] - 0s 250us/step - loss: 0.0179 - accuracy: 1.0000 - val_loss: 0.5685 - val_accuracy: 0.8611\n",
      "Epoch 1963/2000\n",
      "84/84 [==============================] - 0s 233us/step - loss: 0.0181 - accuracy: 1.0000 - val_loss: 0.5698 - val_accuracy: 0.8611\n",
      "Epoch 1964/2000\n",
      "84/84 [==============================] - 0s 504us/step - loss: 0.0184 - accuracy: 1.0000 - val_loss: 0.5753 - val_accuracy: 0.8611\n",
      "Epoch 1965/2000\n",
      "84/84 [==============================] - 0s 466us/step - loss: 0.0178 - accuracy: 1.0000 - val_loss: 0.5728 - val_accuracy: 0.8611\n",
      "Epoch 1966/2000\n",
      "84/84 [==============================] - 0s 541us/step - loss: 0.0178 - accuracy: 1.0000 - val_loss: 0.5733 - val_accuracy: 0.8611\n",
      "Epoch 1967/2000\n",
      "84/84 [==============================] - 0s 370us/step - loss: 0.0183 - accuracy: 0.9881 - val_loss: 0.5730 - val_accuracy: 0.8611\n",
      "Epoch 1968/2000\n",
      "84/84 [==============================] - 0s 286us/step - loss: 0.0185 - accuracy: 1.0000 - val_loss: 0.5814 - val_accuracy: 0.8611\n",
      "Epoch 1969/2000\n",
      "84/84 [==============================] - 0s 426us/step - loss: 0.0178 - accuracy: 1.0000 - val_loss: 0.5816 - val_accuracy: 0.8611\n",
      "Epoch 1970/2000\n",
      "84/84 [==============================] - 0s 305us/step - loss: 0.0178 - accuracy: 1.0000 - val_loss: 0.5811 - val_accuracy: 0.8611\n",
      "Epoch 1971/2000\n",
      "84/84 [==============================] - 0s 322us/step - loss: 0.0181 - accuracy: 0.9881 - val_loss: 0.5825 - val_accuracy: 0.8611\n",
      "Epoch 1972/2000\n",
      "84/84 [==============================] - 0s 374us/step - loss: 0.0177 - accuracy: 1.0000 - val_loss: 0.5818 - val_accuracy: 0.8611\n",
      "Epoch 1973/2000\n",
      "84/84 [==============================] - 0s 296us/step - loss: 0.0179 - accuracy: 1.0000 - val_loss: 0.5794 - val_accuracy: 0.8611\n",
      "Epoch 1974/2000\n",
      "84/84 [==============================] - 0s 349us/step - loss: 0.0178 - accuracy: 1.0000 - val_loss: 0.5770 - val_accuracy: 0.8611\n",
      "Epoch 1975/2000\n",
      "84/84 [==============================] - 0s 317us/step - loss: 0.0181 - accuracy: 0.9881 - val_loss: 0.5770 - val_accuracy: 0.8611\n",
      "Epoch 1976/2000\n",
      "84/84 [==============================] - 0s 285us/step - loss: 0.0182 - accuracy: 0.9881 - val_loss: 0.5761 - val_accuracy: 0.8611\n",
      "Epoch 1977/2000\n",
      "84/84 [==============================] - 0s 312us/step - loss: 0.0181 - accuracy: 0.9881 - val_loss: 0.5795 - val_accuracy: 0.8611\n",
      "Epoch 1978/2000\n",
      "84/84 [==============================] - 0s 264us/step - loss: 0.0178 - accuracy: 0.9881 - val_loss: 0.5877 - val_accuracy: 0.8611\n",
      "Epoch 1979/2000\n",
      "84/84 [==============================] - 0s 240us/step - loss: 0.0185 - accuracy: 0.9881 - val_loss: 0.5949 - val_accuracy: 0.8611\n",
      "Epoch 1980/2000\n",
      "84/84 [==============================] - 0s 223us/step - loss: 0.0177 - accuracy: 0.9881 - val_loss: 0.5953 - val_accuracy: 0.8611\n",
      "Epoch 1981/2000\n",
      "84/84 [==============================] - 0s 284us/step - loss: 0.0181 - accuracy: 0.9881 - val_loss: 0.5970 - val_accuracy: 0.8611\n",
      "Epoch 1982/2000\n",
      "84/84 [==============================] - 0s 287us/step - loss: 0.0177 - accuracy: 0.9881 - val_loss: 0.5912 - val_accuracy: 0.8611\n",
      "Epoch 1983/2000\n",
      "84/84 [==============================] - 0s 271us/step - loss: 0.0180 - accuracy: 1.0000 - val_loss: 0.5852 - val_accuracy: 0.8611\n",
      "Epoch 1984/2000\n",
      "84/84 [==============================] - 0s 236us/step - loss: 0.0180 - accuracy: 1.0000 - val_loss: 0.5843 - val_accuracy: 0.8611\n",
      "Epoch 1985/2000\n",
      "84/84 [==============================] - 0s 264us/step - loss: 0.0177 - accuracy: 1.0000 - val_loss: 0.5891 - val_accuracy: 0.8611\n",
      "Epoch 1986/2000\n",
      "84/84 [==============================] - 0s 328us/step - loss: 0.0177 - accuracy: 1.0000 - val_loss: 0.5925 - val_accuracy: 0.8611\n",
      "Epoch 1987/2000\n",
      "84/84 [==============================] - 0s 233us/step - loss: 0.0178 - accuracy: 0.9881 - val_loss: 0.5943 - val_accuracy: 0.8611\n",
      "Epoch 1988/2000\n",
      "84/84 [==============================] - 0s 313us/step - loss: 0.0181 - accuracy: 0.9881 - val_loss: 0.6002 - val_accuracy: 0.8611\n",
      "Epoch 1989/2000\n",
      "84/84 [==============================] - 0s 356us/step - loss: 0.0179 - accuracy: 0.9881 - val_loss: 0.6007 - val_accuracy: 0.8611\n",
      "Epoch 1990/2000\n",
      "84/84 [==============================] - 0s 323us/step - loss: 0.0180 - accuracy: 0.9881 - val_loss: 0.6001 - val_accuracy: 0.8611\n",
      "Epoch 1991/2000\n",
      "84/84 [==============================] - 0s 252us/step - loss: 0.0182 - accuracy: 0.9881 - val_loss: 0.6026 - val_accuracy: 0.8611\n",
      "Epoch 1992/2000\n",
      "84/84 [==============================] - 0s 221us/step - loss: 0.0179 - accuracy: 0.9881 - val_loss: 0.5974 - val_accuracy: 0.8611\n",
      "Epoch 1993/2000\n",
      "84/84 [==============================] - 0s 302us/step - loss: 0.0181 - accuracy: 0.9881 - val_loss: 0.5935 - val_accuracy: 0.8611\n",
      "Epoch 1994/2000\n",
      "84/84 [==============================] - 0s 248us/step - loss: 0.0178 - accuracy: 0.9881 - val_loss: 0.5918 - val_accuracy: 0.8611\n",
      "Epoch 1995/2000\n",
      "84/84 [==============================] - 0s 293us/step - loss: 0.0181 - accuracy: 0.9881 - val_loss: 0.5873 - val_accuracy: 0.8611\n",
      "Epoch 1996/2000\n",
      "84/84 [==============================] - 0s 352us/step - loss: 0.0178 - accuracy: 0.9881 - val_loss: 0.5864 - val_accuracy: 0.8611\n",
      "Epoch 1997/2000\n",
      "84/84 [==============================] - 0s 1ms/step - loss: 0.0177 - accuracy: 0.9881 - val_loss: 0.5890 - val_accuracy: 0.8611\n",
      "Epoch 1998/2000\n",
      "84/84 [==============================] - 0s 525us/step - loss: 0.0177 - accuracy: 0.9881 - val_loss: 0.5903 - val_accuracy: 0.8611\n",
      "Epoch 1999/2000\n",
      "84/84 [==============================] - 0s 324us/step - loss: 0.0181 - accuracy: 0.9881 - val_loss: 0.5888 - val_accuracy: 0.8611\n",
      "Epoch 2000/2000\n",
      "84/84 [==============================] - 0s 282us/step - loss: 0.0183 - accuracy: 0.9881 - val_loss: 0.5922 - val_accuracy: 0.8611\n"
     ]
    }
   ],
   "source": [
    "# fit model\n",
    "history = model.fit(X_train, Y_train, epochs=2000, validation_split=0.3, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_loss', 'val_accuracy', 'loss', 'accuracy'])\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(history.history.keys())\n",
    "\n",
    "plt.figure(figsize=(15,10))\n",
    "\n",
    "# summarize history for accuracy\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(15,10))\n",
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training loss: 0.1900218392411868 \n",
      "Training accuracy: 0.949999988079071\n",
      "===================================\n",
      "Test loss: 0.5222154855728149 \n",
      "Test accuracy: 0.9666666388511658\n"
     ]
    }
   ],
   "source": [
    "train_metr = model.evaluate(X_train, Y_train, verbose=0)\n",
    "results = model.evaluate(X_test, Y_test, verbose=0)\n",
    "\n",
    "print(f'Training {model.metrics_names[0]}: {train_metr[0]} \\nTraining {model.metrics_names[1]}: {train_metr[1]}' )\n",
    "print('='*35)\n",
    "print(f'Test {model.metrics_names[0]}: {results[0]} \\nTest {model.metrics_names[1]}: {results[1]}' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the model has better performance on the training dataset than the test dataset, one possible sign of __overfitting__."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot decision boundaries to have a clearer picture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_decision_boundary(X, y, model, steps=1000, cmap='Paired'):\n",
    "    cmap = plt.get_cmap(cmap)\n",
    "\n",
    "    # Define region of interest by data limits\n",
    "    xmin, xmax = X[:,0].min() - 1, X[:,0].max() + 1\n",
    "    ymin, ymax = X[:,1].min() - 1, X[:,1].max() + 1\n",
    "    steps = 1000\n",
    "    x_span = np.linspace(xmin, xmax, steps)\n",
    "    y_span = np.linspace(ymin, ymax, steps)\n",
    "    xx, yy = np.meshgrid(x_span, y_span)\n",
    "\n",
    "    # Make predictions across region of interest\n",
    "    labels = np.argmax(model.predict(np.c_[xx.ravel(), yy.ravel()]), axis=-1)\n",
    "\n",
    "    \n",
    "    # Plot decision boundary in region of interest\n",
    "    z = labels.reshape(xx.shape)\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.contourf(xx, yy, z, cmap=cmap, alpha=0.5)\n",
    "\n",
    "    # Get predicted labels on training data and plot\n",
    "    train_labels = model.predict(X)\n",
    "    ax.scatter(X[:,0], X[:,1], c=y, cmap=cmap, lw=0)\n",
    "\n",
    "    return fig, ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlsAAANOCAYAAAAmqcntAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdeYyceX7f98/veZ46u/punk3OcDg8Vruzo53VeL1XrF2PZsyRpShBPIZs+oABR4jhAEmQvxIwIRMsMAGChLEjx4JCC6ZCwgaEOPK1S7Xi0BKpXUmmpV3uQe4s2XPwmmGzu/qq+znyR3c1+6jqs556qp56v4DZnX66Wc+Pw2LVp37H92uCIBAAAADCYUU9AAAAgDgjbAEAAISIsAUAABAiwhYAAECICFsAAAAhcqIewGaGR0aDw0dfiHoYAAAAW/rR7e8+C4Jg3/rrHR22Dh99Qf/02r+NehgAAABbevXw0IeNrrOMCAAAECLCFgAAQIgIWwAAACEibAEAAISIsAUAABAiwhYAAECICFsAAAAhImwBAACEiLAFAAAQIsIWAABAiAhbAAAAISJsAQAAhIiwBQAAECLCFgAAQIgIWwAAACEibAEAAISIsAUAABAiwhYAAECICFsAAAAhImwBAACEiLAFAAAQIsIWAABAiAhbAAAAISJsAQAAhIiwBQAAECLCFgAAQIgIWwAAACEibAEAAISIsAUAABAiwhYAAECICFsAAAAhImwBAACEiLAFAAAQIsIWAABAiAhbAAAAISJsAQAAhIiwBQAAECLCFgAAQIgIWwAAACEibAEAAISIsAUAABAiwhYAAECICFsAAAAhImwBAACEiLAFAAAQIsIWAABAiAhbAAAAISJsAQAAhIiwBQAAECLCFgAAQIgIWwAAACEibAEAAISIsAUAABAiwhYAAECICFsAAAAhImwBAACEiLAFAAAQIsIWAABAiAhbAAAAISJsAQAAhIiwBQAAECLCFgAAQIgIWwAAACEibAEAAISIsAUAABAiwhYAAECICFsAAAAhImwBAACEiLAFAAAQIsIWAABAiAhbAAAAISJsAQAAhIiwBQAAECLCFgAAQIgIWwAAACEibAEAAIRoz2HLGHPUGHPdGHPHGPNDY8x/0eBnjDHm7xtj7hljbhtjPr/X+wIAAHQDpwWP4Ur6r4Mg+BNjTL+kf2+M+d0gCH606mfelnRy+Z8/K+kfLv8/AABA18vdmWj6vT3PbAVB8CQIgj9Z/vcFSXckja/7sV+S9JvBkj+UNGSMObTXewMAAERt/O4l3b56uen3W7pnyxhzTNJrkv5o/TgkPVj19UNtDGT1x/gVY8wtY8yt/PR0K4cHAADQcjeuXJd18NWm329Z2DLG5CT935L+yyAI5td/u8EvCRo9ThAEvx4EwetBELw+PDraquEBAAC03OS5szKSTr52uOnPtCRsGWMSWgpaV4Mg+GcNfuShpKOrvj4i6XEr7g0AABCFE9fekSSdevvMpj/XitOIRtI/knQnCIL/tcmP/QtJf2P5VOIXJc0FQfBkr/cGAACIysTNpE5vEbSk1pxG/Iqkvy7p+8aY7y5f+28lvSBJQRD8mqRvSvp5SfckFSX9rRbcFwAAIBKT587KmO3NWe05bAVBcFON92St/plA0t/d670AAAA6xakzb23r56ggDwAAsAOT587u6OcJWwAAANtUL166nb1adYQtAACAbbp99bKs9MCOfg1hCwAAYBvqy4cnv/7lHf06whYAAMAWxu9ekrSz5cM6whYAAMAWtmrJsxnCFgAAwCZOXHtny5Y8myFsAQAAbGLiZlJmh5viVyNsAQAANHH/3fOSdr4pfjXCFgAAQAN595ZM4d6uNsWvRtgCAABoIH/hooyT2PPjELYAAADWqdfUOvXmG3t+LMIWAADAKrtpybMZwhYAAMAqu2nJsxnCFgAAwLIT196RtLfTh+sRtgAAAJZN3Ey2dFZLImwBAABICmdWSyJsAQAASFqa1WrVpvjVCFsAAKDnTZ47K2PCiUWELQAA0NPqLXlOnXkrlMcnbAEAgJ7VqpY8myFsAQCAntWqljybIWwBAICeVD992IqWPJshbAEAgJ40cTMp6+Crod+HsAUAAHrO+N1LkqSTrx0O/V6ELQAA0HNuXLnellktibAFAAB6zOS5szJqz6yWRNgCAAA9ZGVTfIilHtYjbAEAgJ4RVkuezRC2AABATwizJc9mCFsAAKBnhNWSZzOELQAAEHuT585Gdm/CFgAAiLXcnQlJavterTrCFgAAiLXbVy/LSg9Edn/CFgAAiK368uHJr385sjEQtgAAQCzVW/JEtXxYR9gCAACx1M6WPJshbAEAgNhpd0uezRC2AABALLWzJc9mCFsAACBW7r97PuohrEHYAgAAsZF3b8kU7kW+KX41whYAAIiN/IWLMk4i6mGsQdgCAACxUK+pderNNyIeyVqELQAA0PWibsmzGcIWAADoelG35NkMYQsAAHS1E9fekRRtS57NELYAAEBXm7iZ7NhZLYmwBQAAulinz2pJhC0AANDFJm4mO3JT/GqELQAA0JUmz52VMZ0fZTp/hAAAAOvUW/KcOvNWxCPZGmELAAB0lU5sybMZwhYAAOgqndiSZzOELQAA0DXqpw87rSXPZghbAACga3R6Ta1GCFsAAKArjN+9JKmza2o1QtgCAABd4caV67IOvhr1MHaMsAUAADre5LmzMpJOvnY46qHsGGELAAB0tJVN8V1S6mE9whYAAOhoEzeTXbl8WEfYAgAAHavekqcblw/rCFsAAKCjdUNLns0QtgAAQEeaPHc26iG0BGELAAB0nNydCUnqmv6HmyFsAQCAjnP76uWuqxTfDGELAAB0lPryYbdVim+GsAUAADpGvSVPHJYP6whbAACgY9y4cj02y4d1hC0AANARVlryxGT5sI6wBQAAOka3tuTZDGELAABE7v6756MeQmgIWwAAIFJ595ZM4V6sNsWvRtgCAACRyl+4KOMkoh5GaAhbAAAgMvXlw1NvvhHxSMJD2AIAAJHI3ZmI9fJhHWELAABE4vbVy7FePqwjbAEAgLY7ce0dSfFePqwjbAEAgLabuJmMXaX4ZghbAACgreqzWnGrFN8MYQsAALTVxM2krIOvRj2MtiFsAQCAtpk8d1bGWDr52uGoh9I2hC0AANAWK5viz7wV8Ujai7AFAABCl3dvaeJmMvY1tRohbAEAgNDlL1yUMb0ZO3rzdw0AANqmV5cP6whbAAAgVL1UU6sRwhYAAAjN+N1LknqnplYjLQlbxpjfMMY8Ncb8oMn3v2aMmTPGfHf5n/++FfcFAACd7caV6z09qyVJTose5x9L+lVJv7nJz9wIguAXWnQ/AADQ4SbPnZVRb89qSS2a2QqC4PclzbTisQAAQPerLx+e6sFSD+u1c8/Wl4wx3zPGfMsY85k23hcAALTZjSvXe6olz2ZatYy4lT+R9GIQBIvGmJ+X9NuSTjb6QWPMr0j6FUk6NH60TcMDAACtsrJ82EMteTbTlpmtIAjmgyBYXP73b0pKGGPGmvzsrwdB8HoQBK8Pj462Y3gAAKDFWD58ri1hyxhz0Bhjlv/9C8v3nW7HvQEAQPvcf/d81EPoOC1ZRjTG/BNJX5M0Zox5KOm8pIQkBUHwa5L+kqS/Y4xxJZUk/XIQBEEr7g0AADpD7s6ETOFeT/Y/3ExLwlYQBH9li+//qpZKQwAAgJi6ffWyjJOIehgdhwryAABgzybPnZUknXrzjYhH0nkIWwAAYE9ydyYkieXDJghbAABgT25fvdzzLXk2Q9gCAAC7RqPprRG2AADArtFoemuELQAAsCsnrr0jiVmtrRC2gBjzg0CLFVelmhf1UADETN69pYmbSTbFb0O7eiMCaLNi1dOHsyV5/lL94GzC1ovDGdmWiXhkAOIgf+GijGHOZjv4rwTEUBAEejj3PGhJUrHmaWqxEuGoAMRFvSXPqTNvRTyS7kDYAmKo6gWqehs7Yi1WWU4EsDd59xYteXaIsAXEkGMZNVosTNgsIQLYm/yFi7Tk2SHCFhBDtmU02pdcc81IGlt3DQB2on76kJY8O8MGeSCmDvanlHYszZdd2ZbRSDahTMKOelgAutjEzSQ1tXaBsAXE2FAmoaEM0/0A9m787iVNippau8EyIgAA2NKNK9dlHXw16mF0JcIWAADY1OS5szKSTr52OOqhdCXCFgAAaGplUzylHnaNsAUAAJqiJc/eEbYAAEBDk+fO0pKnBfgvCAAAmqIlz94RtgAAwAaT585GPYTYIGwBAIA1xu9ekiT2arUIYQsAAKxx48p1KsW3EGELAACsqC8fUim+dQhbAABAEsuHYSFsAQAASbTkCQthCwAA0JInRE7UAwB6VRAEmi27KlRcJWxLI9mEEjaffwBEh5Y84SBsARF5NFfWbNld+Tpfqun4aFZJAheANrv/7nmZqAcRY7yqAxGouP6aoCVJrh9opliLaEQAelXevSVTuMem+BAxswVEoOr5ja+7ja/vxmLF1cLyEuVQJiHH4nMrgI3yFy7KOImohxFrhC0gApmELSMpWHe9L2m35PE/XqjoWaG68vWzQlUvj2bZEwZgjXpNrVNvvhHxSOKNV14gAo5ldHggvWaPRC5lazi790+XNc9fE7SkpSXK9dcA9LbcnQlJ1NRqB2a2gIgMZxPqT9sqVD0lbUuZRGtmtZotUVZauEQJoPvdvnqZljxtwswWECHHsjSYTrQsaElS2rFlGmzPyrZoiRJA96tXiqclT3sQtoCYsS2jQ/2pNdfSjqXRbDKiEQHoNDSabi+WEYEYGskmlUs6Wqy6SliWcilbptF0F4Cec+LaO5pUklmtNiJsATGVdCyNOMxmAVhr4maSTfFtxjIiAAA9YvLcWRnDW3+78V8cAIAecP/d85KkU2feingkvYewBQBAzNGSJ1qELQAAYo6WPNEibAEA0ANoyRMdwhYAADE2ee6sKPwSLcIWAAAxVa8Uf4q9WpGizhaAjuYHgaYLVS1WPSVso7FsUukWtjcC4uzGleuyDr4a9TB6HmELQEf7aLakxYq38vV82dXLo1mlHAIXsJn68uHJ1w5HPZSexzIigI5VrnlrgpYk+YE0XaxFNCKgO5y49o4klg87BWELQMdy/aDxda/xdQBLaMnTWQhbADpWNmHLanCMqj/FDgigGVrydB7+NAB0LMsyOjqUkb0qcQ1nEhrKELaAzdCSp7PwigWgo/WnHJ3e16dyzVfCNkrYfEYEmpk8dzbqIaABwhaAjmcZo2yS04fAZnJ3JiSJvVodiI+IAADEwO2rl2WlB6IeBhogbAEA0OXqy4cnv/7liEeCRghbAAB0sXpLHpYPOxdhCwCALkZLns5H2AIAoEuN371ES54uQNgCAKBL3bhyXXISUQ8DW6D0A4CWqbq+niyUtVjxlLCN9vWlNJzljQAIw/13z8tIOvXmG1EPBVsgbAFoiSAI9OFsSRXXlyRVvUCP5stK2EY52usALZV3b8kU7rEpvkuwjAigJUo1fyVorZYv1SIYDRBv+QsXZVg+7BqELQAAuki9phbLh92DsAWgJTIJS6kGfQuHMnz6BlqFljzdibAFoCWMMXpxOKNcaqmHYcI2Gh9Iq5/9WkDL0JKnO/EqCKBlko6lY8PZqIcBxBoteboPM1sAAHSByXNnZaIeBHaFsAUAQIc7ce0dSdIp9mp1JcIWAAAdbuJmkk3xXYywBQBAB5s8d1bG8HbdzfjTAwCgQ91/97wk6dSZtyIeCfaCsAVgUzXPV9n1FARBW+7ner4WKq5q3sZq9EAvoSVPfFD6AUBDfhDo4VxZ82VXkpSyLR0dSiudsEO759RiRU8Xq6rHun19SR3oT4V2P6CTHf3JjGZZPowF/hQBNDS1WF0JWpJU8Xw9mCuHdr9yzdMnq4KWJE0VqipU3aa/Boiz21cvy6RyUQ8DLUDYAtDQQmVjyKm4vqoNmk23wmLVa3y90vg6EGf1/ocUMI0HwhaAhmxrY/lE0+R6KyTsxo/b7DoQV+N3L0mi/2GcELYANDTWl9xwbSiTCC1s9accpZ21L0lJ22gwTSNr9JYbV67LOvhq1MNAC7FBHkBD/SlHx4Yzmi5W5fnSQNrRaDa84GMZo5dGspouVlWq+Uo7lkb7wgt3QCeqt+Q5+drhqIeCFiJsAWgql3KUS7XvZcK2jPbnUqp5vhYrnopVX/0pI2MIXIi/vHtLEi154oiwBaCjzJVqejhXXjmVmHIsvTSckWN37q4Hzw9UrHlK2pZSTueOE51t5n/+l+ztiSnCFoCO4QeBHs9X1pR/qLi+pgpVHRpIRzauzeRLNT1eFQ6HMo7GB9LMxmFH6gVMmdWKJ0I0gI5RdX15DSrVF2udWf7B9fw1QUuSZkuu5srUBsPO5C9clHE4DBJXhC0AHSNhW2o0IZTq0CXEQs1ToyZGixRixQ7Ua2qdevONiEeCsHTmKxiAnmRbRvvXlZywjbQv15ktexJW45fQZteB9XJ3JiRRUyvu2LMFoKPsy6WUTdqaL7tyLKOhTEKJDp3ZyiZt5ZL2mur3tmU0EmKJDMTL7auXZaUHoh4GQkbYAtBx+pKO+pLd8fL0wnBG+WJNi1VPKdtopC/ZseEQnYWWPL2jO17NAKBDWcZotC+p0b6oR4JuxPJhbyBsAegZQRBouljTXLm2FJKySQ2keRlE+5249o4mtbElFuKpJXPdxpjfMMY8Ncb8oMn3jTHm7xtj7hljbhtjPt+K+wLATny8UNHHCxWVar4KVU8fzZY0V65FMpYgCOT6voIGpS4QfxM3k8xq9ZBWbSz4x5I2e9a8Lenk8j+/Iukftui+ALAtnh9oprgxWE0Xqm0fy1y5pvemCrr7tKD3pgqajyjwIRqT587KGPb19ZKW/GkHQfD7kmY2+ZFfkvSbwZI/lDRkjDnUinsDwHYEQdCwJpbrt3ccFdfXg9myav7SaGp+oAezZVXbPRBE4v675yVJp868FfFI0E7titbjkh6s+vrh8rUNjDG/Yoy5ZYy5lZ+ebsvgAMSfY1vKJDa+5LV7z9Z8ZWPB06DJdcRLvSUPy4e9p11hq1GTsIYbFYIg+PUgCF4PguD14dHRkIcFoJccGcwovapR9EDK0f5cezcpN3vRpZVi/NGSp3e16yPdQ0lHV319RNLjNt0bACRJKcfSibE+VVxfllEk9bAGM46eLlbkrfq4aRujwTRvwr2Aljy9qV2vNP9C0t9YPpX4RUlzQRA8adO9AWCNlGNFVnjUsSy9NJJVf8pRwjLqTzl6aSQjx2JqK84mz51tuMSD3tCSmS1jzD+R9DVJY8aYh5LOS0pIUhAEvybpm5J+XtI9SUVJf6sV9wU6WRAsnX6bLdUkIw1nEhrJbn/JyvV9Vb1AaceSxRpTrKQTtl4czkQ9DLTJ+N1LmpR0ir1aPaslYSsIgr+yxfcDSX+3FfcCusVUoaqni8/LCpRqFfmBNNa3deD6eKGi6UJVgZYaMR8eSGswwzIT0I1uXLku6+CrUQ8DEaLQBxCS6cLG2knPtlHTaaHs6tly0JIkL5AezpXl+pQGALpNffnw5GuHox4KIkSfCiAkfoPK4I2urbfQpDTAYsXTUIbPR+00U6wqX6zJlzSUdjTWl5RhSRfbVG/Jw/IhCFtASAbSjubK7oZrW7GbbJRmA/VG8+WaPlmsquL66kvaOtSfUjpht+Sxp4tVPZmvrHz9yWJVrh/o0EC6JY+P+Ju4mWT5EJJYRgRCc2ggrVzy+Rt/f8rWof6t36hHsgnZ63JVxrHUl2xNiIiLUs3TR7NlVZYrrxeqnj7Il7Y1e7gdMw2WgfOlGr0MsS25OxOSWD7EEma20DPqb5LtWgZyLKNjI1m53lIYcLZZaiBhWzo+2qdnheczNp22fBUEgeYrrqqur76ko2wEQXC2tDEMuX6gxYqrgRbUrPIaLgMvLel2zp8EOtXtq5cpYIoVhC3Enuv5ejxf0XzFlWWk0WxS+3PtCy/bDVmrpRxL44OduVzl+4HezxdVqtU37FeVSVgazSY1kHZiU6ZiMO1oel3j6v6UHZvfH8Izee6sJAqY4jmWERF7D+bKK33n/GCpJMP6N1FsX75UWxW0lpRqvh7OlfXBTOuW8TZT83ylnI0vX45llEu15jPkgf6UBlftscslbY2zXwtbqC8f0v8QqzGzhVireb4KVW/D9dlSbVv1ruKm5vmaL7syRhpIJ3a16b5U2/jfs65Y8zRfdjUUYk2wJ/PllbBstHSgwPWDlQ3yrZp5sozR0aGMDvuBAgVyLD6bYmu3r16WlR6IehjoMIQtxFqz991eXAlaqLj6KF9aqd/1yUJFx0ayyuzw9F46YUvljeUp6spuePXA5svumlnJQEv7tE7v6wut/c7S6dAefMJgx+rLhye//uWIR4JOw0c1xJpjWepvsKw0kum9Wa0n8xWtXuDzgqXAtVPDmYTSDZbw6rKJ8F5WFquNQ95ig9lLoJ3G716SxPIhGiNsIfaODKY1nEnINkZJ2+hQf0rD2XCWuTw/0HzZ3XSpLQqeH6jqbZxxKu1iFsq2jI6PZnVkML0hdPWnnIbhtlWaLXsmqEGGiP3+//OAmlpoimVExJ5tGY0PpjU+2LrHrLi+yq6njGMruRw45so1PZwrq74/vD9l6+hQpiNOr1lGStpGVW/t5vXNZqg2fzyjoUxCQ5mEilVPpZqnlGO1bHN6MyOZhGaKNbn+899HJkENMkQr796SKdzTyT/HrBYaI2wBO7R6g7Yk7etLaqwvqUergpYkLVQ8zRQ7YyO+MUYH+9N6MPt8z5Zllk7c7VU2abetzpZjWzo+mtX0cg2ybNLWaLazapCh9+QvXJQxLBShOcIWsAOLFXdD2YipQlWOZeQ3qHhQqLodEbakpVZBJ8b6NF+uyRijwbQT2qbyMCVtK/KWOfPlmqaLNflBoMF0QqPZBIGvR91/97yMpFNn3gr1PuXZKdUKc8qMHJKT6Qv1Xmg9whawA43KSEhSzWtcW6rTwkzKsbQvt/fZrPX8INBixZMxS/Wo4hw85so1PZgtr3xdqlVU9XwdpgZXz6kvH4a5KT7wfT3549/R4uPJpQvG0v5Xv6qhlz8b2j3Rep31TgB0uGST8JRNWhpa12TaXq5Wvxc1z1ep5nV0P75SzdN7UwV9NFvSh/mS3ntWUHWP5R+qnq98sabFittxv/fpQnXDtXyxJq/R1CZiLX/hYugteeY/+vHzoCVJga+nt2/KLRVCvS9ai5ktYAcGM46eFa2V5sfS0gbt+im8vqSrxaqrhGU0kk2ubJ7fqSAI9Hi+ovxy/z/HMjoymA59A/puPJ4vr9mwXvMCfbxQ0QvDmV093kyxqsfzz0tSZBKWjg1nl+tdRa/BoU4Fqvfe7IwxInwnrr2jSSVDb8lTevZo48XAV2n6ifqPnAj13midznvlBjqYZYyOj2Q1U6qpXPOUSdgayTzfrzOcTbSkrES+VFsJWtJS4c4HsyWd3p/riNONdX4QbGjdIzWvh7UVzw/0ZF3tr1LN13Sxqv0hLH/uRn/aUWXd7FYmYe2qBya618TNZKiV4n23pqkffEcLj+43/H4i18Lj1QgdYQvYIdsy2rfDTe+u58uyzLaD0kJl494wL5CKVa+jZreMlmbd3HVLaKldBo+lJdON14sdVLR0fy6p6nLbI2mpfMaRwd3N4qE7jd+9pEmFWyn+kz/9t1p48F7D7+UOH1d6aF9o90brdc6rNhBDhaqrx3MVVTxftpH25VLbOp3YrHjnbnoZhskYowO5lB7Nl9dc3+0sVKPm0ptdj4JljF4Yysj1fPmBdr1UjO5148r1UAuY+m5VCw/vbbhuJZLa99mvauCFU6HdG+EgbAEh8f1AH+VLqh9U9ALp44WK0tso/jmaTWi2XFszy5NL2Ut9CTvMcDahpGM0W1pqcD2cSey432JdwrY0mk2sKa/hWEajHVI+YzWWDXvT5LmzMpJOvnY4tHsEQf1/1nLSfRo89lOh3RfhIWwBIVmsumpUEWKu7G4ZttIJW8dHsnpWqKrmBcql7FDrdXl+oLlyTV4gDaScHc8k9SUd9SVb83JyaGDpIMBCZemgwXA2IccKJ9gEQaDZsqtC1VPSNhoJ8V7ofiub4kPuf2gnksodfmntKURJAy+cDvW+CA9hC9hEEASar7gqVpfa0QylE7K2uZTXbH/WdlcCM4mldj9hq7i+JmeKK6ULPlmo6MhgWkOZcI+0bybsHot1D2bLmq8838yfL9b08liWwIWGJm4m29Zo+sDnvy5j2Vp4dF+W7Wjw+Gc0fOq1ttwbrUfYAjbxYK68shFakmaKNb00sr0yBH1JWynbUmVVrQCjpWW2TvJ0sbKhRtTHCxUNpp1YFyct1bw1QUuSan6gmWKtY04+onNMnjvb1pY8djKtQ194SwcDX5KJ9d/FXsDHN6CJYtVbE7Qkqez6mi3XmvyKtYwxOjaS0VDGUcI26kvaOjaS6bh9V6XaxpN+rh+oFvMinc0Kr+61ICviK+yWPI0YYxG0YoCZLaCJSpM33WbXG0nYnV8WIJOwVfXWhkrHMkp02MnHdmnV3jPEx+S5s1EPAV2OVxWgiUyySWueDpuZ2qv9uZQWq96apcSD/anYfpoOgkAf5ktabFC7qz/laCjDyyKey92ZkKS27dVCPPGqAjSRdpZOAD5bVS08l7I1mI7XX5uUY+nUWN/SaUQ/0EA60VF1rVpttuw2DFpHhtIaSnfWfjpE7/bVy6FWikdviNe7BtBiB/tTGko7KtY8Je2t62OFoVTztFBxZRujoUwilB6B9nIvx15QalKNPmCrFtapLx+GWSkevYGwBWwhnYiumOh0obqmV+BUoarjI1mqlu9BN1SpR/RYPkQr8eoCdCjPD/TJ4tqmzK4faGpdE2TszFBm4zLpQNpRNhmvvXjYm4Mf/p9tLfWAeGNmC9gGPwi0uFyTKZdytt1Qei9qy7331iu7ndOUuRvZltHLI1nNlmuquL6ySVsDHdTcG9HLu7eWC5i2v9QD4olXGGAL5ZqnD/IlucvJx7GMjg2HXy8r6ViyjDYErt32HcRzVg/tUcPO5S9cZFYLLcWzCdjCk4XKStCSlnp7q1oAACAASURBVJbyVu+jCotljA71p9dcS9hG+zqwKTMQF/ffPS8pmgKmiC9mtoAtFBqcXis2OdHWasPZhLJJWwsVV45lNJBuzxIm0Ivy7i2Zwj02xaPlCFvAFpK2paq3ti5AO08DphxLKYfZLCBs+QsXZRxqraH1WEYEtnCgf2PQOZAj/ABxkndvSZJOvflGxCNBHDGzBWxhMJ1QctTSbGmpAfVQJsEmdSBm8hcuigV6hIWwBWxDJmETsICYGr97SZOSTrFXCyEhbAExV5t+pPmbvyU3/0SZk39G/V/8j2Rs/uoDdTeuXJd18NWoh4EY4xUXiLHa9GM9vvi35JfmJUnF7/+eSvf+vQ78zXcjHhnQGSbPnZWRdPK1w1EPBTHGBnkgxub/4LdWglZd8fZ1VT9+P6IRAZ3jxLV3JLF8iPARtoAYc/MfN74+2/g60EuWWvIQtBA+whYQY5lTf3bDNZPMKH2M/Snobbk7E1EPAT2EsAXEWP8XfkF9n/u5la9NKqt9v3xOVrovwlEB0bt99TIFTNE2bJAHYszYjvb/9W+o+hf+U7n5j5V+8RWCFnre5LmzkihgivYhbCE2giDQXNlVseYpZVsayiRkW5QplKTk/heV3P9i1MMITc3zNbVYVanmKZ2wta8v2daWSuge9eVD9mqhnQhbiI0Hc2XNl92Vr2eKNR0fzRK4Ys4PAk3OFFXzAklSyfW1UHF1cqyPP3tscPvqZVnpgaiHgR7DRz/EQrHmrQlaklTxfOWXW+wgvubK7krQqnP9QLNl/uyxVr3Uw8mvfznikaDXELYQCxXX39F1xIfnBzu6jt41cTPJrBYiQdhCLGSb9C3MJulnGHf9qca7IZpdX8/1fU0tVvR4vrxhdhTxcf/d85KY1UI02LOFWEg5lvbnknq6WF251p+yNZTmKb4dhaqr6UJNrh9oIO1oNJuQMd2x3ynlWBofSOvjhbK8QLKMdCCX2lbj8Jrn6/50Ue7yLNhMsaaRbEKHB9JhDxttlHdvyRTusSkekeGdCLGxP5fSYDqhYtVT0jHqS/L03o5C1dX7M6WVr4s1T6Wap6NDmQhHtTPD2YQGM46qrq+kbcna5sb46UJ1JWjVzRRrGutLKmkz8R8X+QsXZQx/nogO70aIlZRjKcWR/x2ZLmzcSD5XdnXQ85XoosBhGaP0NmazVqt4jff01UMbut/9d8/LSDp15q2oh4IeRtgCdsAPAj0rVDVfdmVbRqPZpAa6fKly/czO6us7zC5dJ5uwtVDx1lwz0raWINEdWD5EJ+CjG7ADj+fKerpYVdn1Vah6+mi21PWbqvsbhMWEbZTugRnCkWxyw+GKQwMp6nPFxPjdS1EPAZDEzBawba7va7ZBsJouVrt6dms0m1C55mlu+feWsIyODma6ZoP8XtiW0UsjGRWqnmp+oFzS7qqlU2zuxpXrsg7SdB3R6953CKDN/CYlu7q9npNljI4OZXTQ8+X6gdKO1RNBq84Yo9w2y0Q0U655minV5PuBBjOJbZedQHgmz52VkXTytcNRDwUgbAHblXQspR1L5XWFUrt5Vmu1hG3Ffo9WGApVVx/MlFSP3LNlVwdySe3LpSIdVy87ce0dTSqpU+zVQodgvhzYgaNDmTV7mYYyjvb1JSMcEaI2tVjV+rnNqUJVftDdM57dbOJmkuVDdJR4fCQH2iTlWDox1qeq68uyjBw2Uve8aoPyEX6wtLxs2Tw/2i3v3pLE8iE6CzNbwC4kHYugBUlqWDw3ZVtstI9I/sJFGScR9TCANXg1AIA9OJBLrllato3R4UH2a0Vh8txZSdKpN9+IeCTAWiwjAghd/U1Qkt76alX3zvxWhKNpjWLVU8X1lU3aenk0q2LNk+9LfSlbVg+d5uwUuTsTkkQBU3QkwhaA0OTdW8pfuCjp+ZvgxLeuSTfPavjCf6Vh5/Uoh7crQRDoo9myFirPa67t60vqQD+zWVG6ffWyrPRA1MMAGmIZEUAoxu9eWmkAvHq24fTbZ2SchPIXLurEtXciHOHuzJXdNUFLWjp9WHGbFGJD6OrPo5Nf/3LEIwEaY2YLXa3senpWqKrqBupL2hrrS9JqJUK16Ufyiwv69A/O6Xf/ICXr4KsNT4XV99RMfOua3tI7XbWsWKx5ja9XPZqgR2TiZrKls1qlmY/l16rKjB2WZfM2ib3jWYSuVXV9TU4XVS/gXqx5KlQ9HR/NRjuwHuRXy5r6v86p+KObkqRHxpHJvbzl8XsrPaCJm/MK/vS8Xv5v/od2DHXPUk1OGRK0olEvYNqKWS2vUtbDb/9LVfJPJUl2KqPDX/x5ZUYP7vmx0dt4dUDXminVtL5TTrHmqVhtPPOAcPhBoE/++He0MDuzUtzTBK6Serrlrz359S8vLSsW7mny3NmVGkmdbCiT2BCs+lOOsknK70dh4mayZZvin93545WgJUlepaSP//2/UUCBWuwRYQtdy23SrLDWrIkhWq5Y9fTjqYKmT/x5lc/+PZX/+j9QkBmUJFUX8qouzm7rcVbv46qfKutUtmV0fCSrQ/0pjWQTOjKY1gtD6aiH1ZMmz52VMa17GytNPdxwrbY4K7e02LJ7oDcRttC1GjX7NUbKNSgyiXA8miuvacTtHzyl6lf/piTJWJbs5PZDyKk335Axlm5fvdzycbaabRmN9iV1eCCtoUyipxp3d4p6KD915q2WPaaT6d9wzTiJHT2PgUYIW+hag+mERrMJ1d/mbGN0dDC9pw3yQRCoXPMatmDBWjXPV6XBfyfvhdckSQPHPrPjN6lTZ96SMdaaulxAI7evXm7prJYkjXzqZ2SstY85cvI1WVSkxx4xBYCudmggrX19SVX9QGnH2lMxyXLN00ez5ZWgNZBydGQoTYHKJmzLyAmqcs3aRtxOeVb7P/ezGnzpM7t63FNn3tKPv3VN99/tnk3zaK8wZrUkKTt2WEe/9pc09/6P5LsV5Q6fUP/48ZbeA72JmS10Pce2lE3svWr3g7nymhmt+YqrqcXqXocXS3n3lj747/6azB9+c811E/h6MZjV0PFX9rS0Vt803w0b5tF+YRYwTQ/t04HXflaH/sxbBC20DGELsVSqefowX9SPny7qo3xJZXfzE4pV129YlHJ98UoszSrkL1yUkfTKfun4kz/U8MIDjc29r1MPf0/95WctuU99w3wvcv1gzV44PFdfYqaAKboJy4jYsSAIVKh6KtU8pR1buZTdURuEa56v92ee19+qVVwVap5OjfU13c9lW0ZG0vq3NwqkrnXi2juauJmUcRIrhUkHix9rsPhxy+916s03YrOcuFBx9XSxoqoXKJe0daA/pWSDel2uH+jRXEkLlaUPBwMpR+N73IcYJ+N3L2lS9D9E92FmCzv2YK6sD/IlfbJY1YezJX2YL3VUHZq5sruh/pbnB5ovN5+lsi2j4czGTbBjfckGP92b7r97fqVSdz1oha2+nNjp5SA2szTLWlKp5svzA82VXX0w0/jvzJP58krQkpaWsj9eqLRzuB3txpXrsg6+GvUwgB1jZgs7slhxN4SWxaqn+YqrwXTrT+wEQaBA2tF+LL9J8Gt2ve7QQEpJx9J82V062p9NKNegvESvqTeTNopmRsE4CQ2aj9StlY7ypdqGa1XPV6HqrXl+BUHjDwTz5ZrGByk9UN+/t1VXAqAT8U6CHSk3abZbrvlq9fvB1GJFzwpVeYGUTdgaH0xvqyXKYNrR03Ub242kgfTmT3djjMb6ksxmrTJ+95Imr1yXMVbLT35tlxn7Kd24cl3Hv/G3I7n/XjXL+OsvG2NkzMafb8Vp2Irr6cl8RYWqp6Rj6UAuqYEQPhyFKX/hogwlGNClWEbEjjTbOpJOtPapNFuq6ZPFpaAlLbXh+Wh2e8uVKcfW0cG0nOXBJmyjo0MZJZr0tENjk+fOrizbRBW0pKWZDCN1be2twQYh37GM+hq09xnNbgz6o3sM/34Q6IOZkharngJJFdfXR7NllZo01O5E9989L0ltW74GWo13H2zbk/myHs9v3D+SS9oaaPFy22x549JLxfWbzqytN5hJ6PS+Pn1qf59OjfVtOauFtepvbqffPtMRyzanlpcvT1x7J+KR7Fwu5ejwQGol/GccSy8OZxrOWO3PJXWwP6W0YyntWDo0kNrzTGuh4qnW4GRjo+XNTmUK99gUj67GOxC2pVB1NV3c+OK8vy+pfblky08jWmr8eDu5jTFGTgedkuwWk+fOykgdtxHZOviqJm7e1vEufM8dySY1nEnIDzY/4RrGUnbnHF3ZnfoJRKCbMbOFbSlWmyw5GIVS9mE4u3FvRjZhK+1sXHpBa4zfvbTS2Hc7M1q+194aZCdfO9zVrXyMMZGUcMil7JVZtdWGumTPFicQEQfMbGFbkk02pjeqFdQK/SlHRwfTmipU5fqBcilHB/vZuB6G+mnDSS3NHm0VsorPHmvqe7+vyty0En0DGnvlS+ofP9GWsdZb+Zy49o7unfmtttyz21nG6NhwRk8WljfI20YHcillG+wZ6zT1WdZOWMoG9oKwhW0ZSDnKJCyVas/3TGUSVqh7oQYzCQ02qH2F1lkpUrrN04ZetazH3/5X8t2lJeVaYV5P/nhCyT8/rNTgaNjDlbS0j2ziW9c0/HO3NOy83pZ7drt0wtZLI9moh7EjJ669o0klV/brAd2MZURsizFGL41kdWggpeFMQof6U3ppJEuT5i62pkjpNk8bLj75YCVorQgCLTz8SesHuAljrJ5t5dMrJm4mWT5EbBC2sG2WMRrNJjU+mNZoX5Kg1cXG715aOeG1vsecVylr9v0fanbyB3IrpTXfM1aTlwyz9UuJWylp9v0fau7Du/Jre2vwXQ+H9VOTiBcKmCJuWrIGZIw5I+nvSbIlXQqC4H9a9/2vSfrnkt5fvvTPgiD4H1txbwA7M3nu7NL+rPTAhu+VZj7Ro5v/Qr67FIamfvBtHfnKLyozekiSlDt0THYqI29VCDOWrYEXTm96z+LUQz369r9WsLyp/tkPvq0jX/2lPS09nn77jH78rWvKuywnxg0FTBE3e57ZMsbYkv6BpLclfVrSXzHGfLrBj94IguBzy/8QtLAjQRBoarGie88KmpwuaraLagR1ig2nDdfNaEnSs+//wUrQkqTArWnq9s2Vry0nqSP/wS+p78CLspIpZUYPafwrv6hkbnDTez/93o2VoCVJXqWkZz/8zp5/T8ZJsJwYM/XTphQwRZy0YmbrC5LuBUEwKUnGmH8q6Zck/agFjw1Ikp4sVDSzqs5XcW6pGnaj5tHY6P675zVZuCcrPdAwZNWV808bXJta83VqYFTjX/mFbd/bd2uqzs9sfNyZT7b9GM2cevMN/fhb1zR57qyOf+Pqnh8PnYECpoibVuzZGpf0YNXXD5evrfclY8z3jDHfMsZ8ptmDGWN+xRhzyxhzKz893YLhodv5QaB8g4Kq04W97fvpFZPnzsoU7i2VddgkaElScmDjsl5qcGRP9ze2o0R245Jlo3vtRv2NOXdnoiWPh+iM370U9RCAULQibDXaJb2+aPGfSHoxCIKflvS/S/rtZg8WBMGvB0HwehAErw+PtucoOTpbEDSugu1vo09ir9tp2519n/2SjPW8/pKxbI298pU9jcEYo7HPfnlN+X9jOxr7zJ/d0+OuZqUHdPvq5ZY9HqJBAVPEVSuWER9KOrrq6yOSHq/+gSAI5lf9+zeNMf+HMWYsCIJnLbg/Ys5ebtpbWFfFfmBVBWw/CDRTrGmx4iphWxrtS/R0tfncnQndvnpZlrSjOkXZfUd07K2/qoUHS6UcckdOKNm3+X6s7egff1nJN35ZCw9/srKhPpHt3/Pj1p38+pdZTuxy9989L0ucQEQ8tSJs/TtJJ40xL0l6JOmXJf3V1T9gjDko6ZMgCAJjzBe0NKPGGiG2bXwwrYezZRVrS4FrMO1of+55RfkHs2UtVOobsD3NlWt6ebRPqSaV7+NspVCpk9jVJuNEdkAjp3+m5eNKDYwo9enWzWatVz+dmLszocWf2l7dMHQOU7gnw6wWYmrPYSsIAtcY859L+h0tlX74jSAIfmiM+c+Wv/9rkv6SpL9jjHEllST9chCwBoTtS9qWjo9mVfP8pQbTq3q9lV1vVdBa4gfSdLGqwwPpdg81Urk7EyuFSrfanxVH9eXE498gbHWT+glEZrUQVy2psxUEwTclfXPdtV9b9e+/KulXW3Ev9LZEg16Mrtc4tze7Hlf33z2/tBG+R4OWFM/lxIrrq1TzlE5YsVwarx9s4AQi4qz31lgQO9mELavBMY1cKn5vTI3k7kxo8txZWU0qwvea+pt2HE62PZkv6yfPCno4V9a9Z0U9ni9HPaSWu331MgVMEXuELXQ9yzI6MpiRvSpwDaWdnqjBdeLaO0tvVsaiYe8q1sFXdePK9aiHsSfFqqfpdSVPZoo1Fapuk1/RvShgirgjbKEj1TxfT+bLen+mqI8XKnL9zZcEB9KOTu/P6aWRjE6N9enIUEYm5r0bV+/P2m4j6V5x8rXDMlraC9SN9bd8P9BipXGXhPWncrvZ5LmzDWsHAXFD2ELH8fxAk9NFTRdrKlQ9PStU9f5Mccu6WpYx6ks6SvbACcT7755fWX7p9WXDZk69fabr6m+Vap7uPyvoR08X9axBIV9p6bBIHJy49o6knZUmAbpVPP7WIlZmSzXV1s1kVVx/w4nDXpR3b61UhD/99hmWX7ZQD6L1026dzA8CfZgvqeT6y19v/Jm0Y2kg3ZJzTZGbuJlkUzx6BmELHafZkmGvnS5cb/zuJeUvXFxpJI3t6ZYN84WK1/C5n0lYGso4OtSf0ksjWVkxWB7vxqXdThP4nkrPHqu6OBv1ULAN8fiIhFjJpWxNFRpd792na969pckr13ddqLTX1TfMH//G3456KE01y1CZhB27enGcQNyb4rPHevJH1+RVSpKkvkMv6dAX3pJl9+5rZKdjZgsdpy+5VB2+/t5jjHRoINWT1eClpb0t+QsXCVp7sHrDfKfqS9pK2hsTV9xO1db/DHgu707g+/r4jydWgpYkFZ68r9l734twVNhKb757oePtz6V0en+fXhrJ6FP7chrNJrf+RTFT359V39vCm9PenOrw5URjjI6NZDWQdmRbRpmEpReHM8ok4lcvjmXw3avMT8stb5z6L3zyUQSjwXYRttCxHMtSX3LpjafX5O5MLM1miTemVur0+ltJ29ILQxn91P6cXh7tU3/Mls7rJxCxe04qKzUomOGk+9o/GGwbYQvoMHn3FoVKQ9INy4lxxgnEvXMyfRp48VNrrhnL1vDJz0U0ImwHYQvoIGv2Z1GoNBT1AMssS3uN370kYzrnLcf3XPled5aTOfD5r2n/535WfQde1MALn9LRr/0nSg/vj3pY2ES85qiBiARBoOliTYsVV45lNNqX3PFem8lzZzWppKyDr+rka4dDGimkpaXZiW9d0/FdTLJ4fqD5iisFUn/akdODy9y7cePKdVnpgaiHId+t6el3f1/zD96TJPWPv6z9r31NdqJ79oUaY2no+CsaOv5K1EPBNnXOxwygiz2cK+vjhYoWq55my64mp4sq1bbXVqXeSFpaCgEErfYwxtrxcmKp5um9qYIezZX1aL6s96YWY9mrsNXqs4id0O1g6gff0fxHd6XAlwJfCw9/oqnv3Yh6WIg5whawR1XP11x57RtuIGm6UN3y147fvbSyP4u9LO1VX6a9/+75bf+ajxcq8la1jfID6cl8peVja5ea58vbou9oK0zcXJqx7QQLyzNaa649/ImCLdqBNeJ7nhYeTWr+wXvyat37PED4WEYE9qhZZfv1LYfWO3HtHU3cTFI/K0Kn3z6jH3/rmvLuLQ07r2/5841mK8uuryAIuqrxedn19HC2rLLry0gaySZ0sD8Vyu/hxLV3NKlkx8zYGtuR1gUjY9s7/r1XF+f08MZvyy0tSpIsJ6nDX/6Lyo51xu8TnYWZLWCPMgmr4b6dzY7t1+tnWQdfJWhFzDgJ5S9c3NaG+UaFdZO21VVBKwgCfZRfClrS8ixssaaZJo2v96qTZrUkaeilz2y4Ntjg2lae/fA7K0FLkny3qqff/b09jQ3xxcwWsEfGGB0ZTOvhXHmlt91g2tFodmPl77x7S/kLFyVRP6tT1MPudjbMH8il9GG+pPqcpZF0sD8V6vhareL6qnr+huvzFVejfa3dJJ53b0lSx8xqSdLIp16XsWzNffgjKQg08MKnNHL6Z3b8OKXpJxuuVedn5FUrspPd9ZxA+AhbQAvkUo5O7etTuebLsYySTVoL1QuVUj+r89Q3zB//xtWmP5NLOTo51qfZck1BIA1lHKWc7qrwbjU5PRlGg+t6GZNOYozRyOnPa+T05/f0OMncsErl4pprdrpPVqKzfr/oDCwjAi1iGaNs0m4YtMbvXtLkubMUKu1g9Q3zWy0nJh1L+3MpHehPdV3QkpaWPRstcTeaiW2FuC6Tj376CzLW2j//sc98saNqiaFzMLMFtEG9xlArjr57tYoKj99XICl36CWWLFqoXn9r+Oe2t2G+Wx0dSuvpYlULy3XhxrJJ5VrcGmjy3NkGTWWiV5l9pvmHP5Fl2+p/4bSSfYO7epzs2GG9+HO/rPkP78r3XPUfOanMyIEWjxZxQdgCQlZ/02lF0Crnp/Tw5j+Xv3yaaiqR0vhXfpEX+RYyxlL+wkUNb7Kc2O0sY3SwPxX6frNOm8Wdf/CePv53/6+0vOtu5r0/1ZGv/ofKjB7a1eMlc0Ma+8wXWzhCxBXznUCIcncmJLXuTWfq+zdXgpYk+bWKpm7fbMljY8lu6m9hrfG7l6IewgZBEOjZD74j6XlJlsBz9exHfxTdoNAzCFtAiL7323/Q0j0c5ZlPtnUNe3P67TMyhXsrp+mwM53Smmc1362tKdVQV52fiWA06DWELexKuebp0VxZH+VLmi3VdlV9Oe5ydyZkCvda2lA60T+84ZrFnq1Q1OtvYXc6oTXPanYiqeTAyIbr6ZGDEYwGvYawhR0rVj3dny4qX6ppvuKu9AXEWrevXm75sff+wy9vuOZXyyrnp1p6Hzw/RTd57iwzXDuwneKwUdn/039uzd9JO53Vvle+FOGI0CsIW9ixZ4Wq1s9jzRRrKwU98Vyrj737XuMq36Xpxy29T6eoFRc085M/Vf4n35VbKrT9/qffPrOyYR7b02kV41fL7hvX8TN/Qwd+5g0d+sJbeumtv6Zkg9lioNUIW9ixmr+x+nQgyW1wvVeFdew9mRva0fVuVnz6QB9MXNWz739bU9//A70/caVh1e6wsWF+5zqpYvx6djKtwRc/pf4jJ2V1WMFVxBdhCzuWS26sGJKwjFI2T6fVwjj23n/0pFKDY2uuZfaNK3vghZbfK2pT3/8DBf7zxs+B52rq+9+OZCxsmAewF9TZwo6N9SVVrHkqVJfeCG1jdGQo3VXNeMN0/93zoRVztGxHR3/2P9bch3dVnZtWeuSA+o+ejt1/+yAIVJmb3nC9MvcsgtEsqW+Yj3P9LQDhYCoCO2ZbRi+NZPXyaFbHhjM6vb9PfQ1mu3pR3r0lU7gXapNpy0lq+OVXdeDzX9fgsU/LsruvZcxWjDEbZvAkKTW0L4LRLFm9Yb4VgiDQQsVVvlhTrUFjaADxQdjCrmUStnIpJ5QGtt3qlXvf7cgWJd1o36tflbGfh3jLSWjfK9GWE6iH6Hqx2t1y/UD3p4v6MF/So/my3psqaLbU+PADgO7HdAQ25QeB5kquKp6vvqStXNKO3ZJVK924cr1jT2J1m+y+cb301l/TwqN7kjHqHz8hJ52NelgyTkK3r17W8W/svn7as0JVZff5bFYg6cl8WQNpPrwAccTMFprylj99P5ov61mhqg/zJT2ep55WM+N3Ly31QFx3EsurlNds9Mb2OZk+DZ/4aQ2//GpHBC2pNfW3itWNzwcvkCouy4lAHBG20FS+VNvw4p8v1VSuERwauXHlurTqKHllblofXv8t3f/X/0j3/9VvaPouJ9ni4vTbZ/ZUYT7lbHzpNZISnOgFYom/2Wiq3ORTdrPreD7rEQS+Hn/nm6rkn0qSfLeq6R/9keYf/CTK4aGF6n/Wu6m/NdaXlL1uuXBfLinHYgkRiCPCFprKJho/PTKJ+J1+26v1J9TKM09VK85v+LmFh4StONlt/a2UY+nEWFb7c0mNZhM6NpzR/hw9LoG4ImyhqaFMQtl1wWqsL9lwCQRaU+7BshufPbEczqTEzW6XExO2pf25lA4NpJVL8bwA4ox3TTRlGaOXRjJ6cTijg/0pvTya1cF+Pn2v16jxbmpoTOmRg+uuGg2+9Ep7BoW2aXX9LQDxQ9jCpowx6k85GutLsnzYxMTNZMMipuNf+osafOkzcrL9So8c1OEv/byyY53bMw6716r6WwDiiblrYA82e3O1U2kdeO1r7RsMImWlB/Zcf0taauhuZGSzWR6IDWa2gD24ffWyrPRA1MNABzj59aXq9rutv1XzfL0/U9TdpwXdfbqoR3Nl+UHQ6mECiABhC9ij+psssJf6Ww/nyivN3QMt1bSbWqy2eIQAokDYAnZp8txZGcNfIay1mw3zru+vBK3V5stuy8YFIDq8UwB7cOrM3vbnIJ52umHeyDRsYG7xCg3EAn+VgV3gmD+2Ut8wvx22ZTSUSWy4PpJNtnpYACJA2ELogiBQvlTTw7myphYrcv14tPtpVO4BqFu9YX47Dg2ktK8vqZRtKZOwND6Y1nCDAAag+xC2ELoHs2U9mitrtlTTJ4tV3Z8udnXgalTEFJ2rnH+qxSfvy6tW2n7veiAfv3tpy5+1jNGB/pRO7uvTy6N9BC0gRqizhVCVap7mK2s3+da8QDPFWtf2gvudP/207IPZqIeBLfieq8ff+ZaKTz+SJBnb0cHXf0794y+3dRxWekA3rlzX8W/87VAePwgCGUNNLqCTMbOFUFXdxjNYVa/x9VLN3PR5ywAAIABJREFU05P5sp7Ml1WqbTydFZaa56vsegq2qGuUd2/JFO7p5GtUgu90s/dvrwQtSQo8V5/8yf8n3621dRxH3voLqn3hF/Tjf/abTZ/3u1GqeZqcLuiHnyzqx1OLmi219/eFcAS+p9LMxw0b2aN7MbOFUGWTjVv89CU2PvXmy64+mi2tfD1drOmFobQG0uEtpwRBsLTEuXzEPmkbHR3KNG1NlL9wUcZheacbFJ8+2nDNr1VVnp1qW9ukfN9hfXDgdemFpc+1954VdGw42/TvxXb5QaAP8yW5/tKHg5oX6OFcWUnb2vNjIzrFpw/05N/9rrzK0utg/9FTOvgzb8hwLLXr8SeIUCVsa0Pz6v6Uo6HMxrD1yeLGPTWfhFzU8VmhuhK0JKnqBXowW9p0hqteRwmdLdHX3+CqUSLb6HprVJysHo6+oskDX9DUwDE9Gn1FWlWLzQ8aP893arHirQSt1WbLzG51K9/z1gQtSVp48J7m3v9hhKNCqzCzhdCN9SU1kHZUrHpKOVbTWaNGS46VJsuQrbJQ2Vg0suoFqri+0uvGmXdvNayFhM40fPJzWnh4T37tebgZOPZToYWtipPVj498TZ69VK5hLtd49qwVz2m2aMVPJf/JmqBVt/jxBxp6+bMRjAitRNhCWyRtS8nM5hOp2aS9oYp2X8hLIrZlSdr45teoCXD+wkUqxneRZG5IL/75v6zZyR/ILS+q78CL6j96KrT7PU0dkP07/5tS9/9QQXZItS/8Zbmf3VgeJJPY+3Mol7SVsIxq62a3OMHYvex040M3TrqvzSNBGAhb6BiH+lP6YNU+FNsyG5YgW200m9gwuzWYdpSw174h1iuBUzG+uyT6BrTvs+3pXVn6N5eUeHxXkmTKC0pd+18UpPvlv/xFBdbyh4bivA6MHdzzvYwxOjaS1ccLS/0Uk7al/blU01ljdL5kbki58Ze1+Oj+yjVjOxo+8WqEo0KrELbQMdIJW6f29a2En/6UIyvk9ZJcytGx4YymizW5vq+BlKPRvo1Vu29fvcysFpqqLs5Ky0FrtcT3/rWOJwpayO7TJ7dvy77/J0qf/42W3DPlWHpxmBIkcXLoz7yp2ZGDKnzyoZx0TsMnflqpwbGoh4UWIGyho1jGaDDE04eN5FKOcqnmfxXG717SpJjVQnNBkyK96XJeKa+o1MKHmr77HR3/xtU2jwzdxFi2hk9+TsMnPxf1UNBifFQHtpD54Hco94BNpQZGlBrat+H66Pix9g9mnarnq1j15G9RQw5AeAhbwBYmbiYp94AtHf7i28oeeEGSZCfTGvvMFzXwwunIxhMES2VM3psqaHKmqPemClpscPoWQPhYRgQ2Ud8Y34t8t6bFR/dVKy2q78ALSg/vj3pIHS2R7deRr/yiAt+TjLWmhc5P/vRx28czU6xpblUNOdcP9GCurNP7+kLfCwlgLcIWsInbVy/LSg9EPYy28yol/f/t3Xls3Oed5/nP86uL9ykeOqjLFHVal+8rsePYkTOdpDs97u60Zja7O7PBAtPALrBYTAYRYM9iAHVjgDWwmMb2ZL2D9cCePmYmnqQ7iUw7UWLJt2zHuqiDoi5SlHgV77Oqnv2Dh0VVUSJZ9eOvflXvFxBI9bBYv29QMuvD53l+z/fab3+iqeF+SVLv2Y+0auejqtr6gMeVZT/jpL4j0BY3rmgdqc6QiyesxqcSeX/KfCIeU8/pDzR0/YLkBFSxaYeqtj1Ej0m4hmVE4B62PLMyRwdkk2jrF3NBa1Zvy8eKjY96VJG/JW6eXPFrBlOcFXe38XzS/cUx9V86qfjkuOLjI+pt+UR95z/1uizkMMIWsIC2Qwfz9sT48f7upDGbSGhyqM+DanLDff/qX6/o9aqLw0n/fssKggoH8/vHvk3ENXjtfNL4wOWzMw2ghzyoCrmOZUQghWjshCSp6YXkE8DzQUFFjUZvXZs3ZhxH4dIqjyrCUhWGAtpcXaTekUlNJaxKI0FVFXFXraSUvU9jY0O6/pv/KsmobMM21e1/mrP1kDH8SwJS6Pu3f5+3s1qSVNm4R6GSinlj1dsfVnCBliLIToWhgNZVFGpTVZFWFYfZGK/pPXWl6+62f85q8GqLBq8kH1ILLBczW0AKZqQ1b2e1JCkQKdSGZ/9Ywx2XFBsbVlHtehVUJp8jhXu7ePR9Pf/kpFq9LgRz6vZ+VcY4Gmq/KGl6afFOI7euqnzTjpUuDTmKsAXcYfbE+HznBIKenhMFuMUJhVX/4LOqe+Brmhzq09V3/ibpOQs1hgaWg2VE4A7HXj8qp57mr8gMOz6osY3f8LqMFeG3c+mMMYqUVau4fuP88UBQFZvv96Yo5CRmtpATxqfi6hya0MhkXJGAo9rS8LJ6LM5ujN+yb02mS0SespI6tv1zr8tYEX49l271I99Q/6WTGr11XcGiElU27lWkjJtBkDmELfhewlpdiY4plpi+w2gintD1/nGFqx0VhpZ2eOND7/yF3g4Wu1EmkBf8eC6dEwiqqmm/qpr2q//yGXWeeFs2NqWStY2q3v6QnAAflUgP/4Lge8MTsbmgdbv+saklh63m42E5BYWZKg0+lJia1Hi0S8HiUoWLy9N6rQtH/LWslu8GrpxV1+e/mXscvfCZ4uOjqn8wdW/U2NiIBq62KDY+qpL6DSqu37BClcJvCFvAjEuHX5LR8n4zHwuXqa9knYysqoauq2BqOPMFwnVD7Rd187OjsrEpSVLZ+m2qe+BrabVx2X3w+8qHfw2zS/B+1t92Omls8PoF1e55Sk4oPG98cmRA14/+F8UnxyVJA22nVNm0XzW7HluRWuEvbJCH75VEgilbkFQULm3PlhlpXdbG+P6i1Tq37ml1VTbpVuVWnWv4mgYLadrsN/HJcd389NdzQUuSBq+d01CK08aR7KF3/sL3Z9PZeHI/SdmEbCKRNBy98Plc0Jobu/g7WlohJcIWfM8xRhsrC1UcDshIigQdra8oWNIS4qXDL0la3sb4G9U7pNtOmrbGmR6Dr4z1dKb8sB3pupbi2YtjbfKHdK5qPh72/dl0peu2JI0V1TYoEClIGp+8o3eoJMkmNDU66EZp8DmWEZETCkIBbapa/rk4ZqRVW5fxQWElTYRLk8bHQyXLrgXeCBamfs+Chcnv71IMb38+re/3g5KW5pxobVO1db9iE6MavNIim4irqG696h/4WsrnFlat1lh3x7wxJxRWpKx6JUqFzxC2kPfS2WtiJBWP92qkYP4P2JJxGjb7TUFljYrq1s/rCemEI6rYvNPDqvzBr0c+zErEY9PdEsZHVLFpl2ruf0I2kVDgjn1at6ts2quRW9c00d81PWAc1e75ipwg/SeRjLCFvPfQO3+ht7XwD9V7WddzSq2rH1c8MP0awdi41vYmb7TNB/GJcQ13tsk4AZWs3pS0qTjbrXn0BQ1cPqPRrnaFistU0bhboaLlhYgLvzwiW3y3Hny5oe3QQUn+PPJBkuITY7r2259oamZZsOf0B1p1/+Oq2rLvrt8XCEW0/pl/rNGudsXHR1RU26BgIcfGIDXCFvJe8/HwspYQZxVN9Gvn1WYNFNfLWKuy0ZsK2ORea7lutOeGOt7/h7kN5oFIodY9+R1Fyv2zrOIEgqps3KPKxj0Zeb2q//1bGXmdbDXb2iqd/368Fm39Yi5ozeo985HK129PuVfrdsYYFdc1uFkecoT/F9mBNKw992pG9poEbExVw+2qHOnIy6AlSd1fvDvvTr74xJi6T7/vYUVw27HXj/p6+VCSxqNdSWM2EdfEEFsBkDnMbCGvHXv9qAx7LNKWiMc1MdCbND7edytj17DWavTWNU2NDqmotkHhkvQOHHWblVQZfNDrMlwRjZ1Q9OVXln0uXSbZRFw9Zz/W0PULMsZR+aYdqmzav+iz0SIVqzTadX3emHEchUsr3SgXeYqwhbzVeORFtSmspudSnw6NxXMCAYWKyzU1MjBvPJyh/nKJqUm1H//pvFmImj1PqfI+/zYMn4onNBFLqCAUSHlOXDabDVrZcNRD18n3NNB2au5xz5kPJUlVWx9Y1PdXNu7VcMclTY18eWRD9faHFYzQSQKZQ9hC3ppuzePvJZBssmrX4+r8+C1p5mwpEwhq1c5HM/La/W2nk5Z7ek69r7J1WxTw4YfizaEJ9YxMSpq+o7W+NKLq4uy/mWB2RkvKjqBlEwkNXm1JGu+/fGbRYStYUKQNz/6xhtpbFRsbUXH9BhVUcigxMouwhbzm9RJILildu1nhZ/9YQ+0XZYyjsvVbFSpeOMyO9d7UxECPCipr7/nhNtZ3M2nMJuIa7+9Wcd36tGvPtAu/PLLg14YnYnNBS5pebuwcmlBJJKhIMHu30TYeeVHNx8MywVDGZ4MTsSkZx5FxltbLVLIpT3dPNXY3TjCs8o0cRAz3ELaQl2b7ICKzImVViux45K7Psdbq5om3NXT94txY+cYdqtv/zMKvW1qpkc7Ld4yarN1XYyVt/jdvpPza8GSKljAz45Fgds5utR06qDaF5dTvXlaXhYVMjQ7p1me/1mhXu5xgSBWb71f1zkcXvd/KOAGVrmvU0PUL88bLGpoyViOQCdn7axTgouWeGI/0jd66Ni9oSdLAlbMa67mx4PdUNO5WsKg0aSxUlN7p7m5a6LDckJP6x+5C415be+5VSdPHO2QyaElS58dvabSrXdL07Fbfhc80kKIZ9N3U7v2qShuaJOPIBIIq37RL1fcI/MBKY2YLeWf2bCB4I9WS4Ox44arUH+bBgmJt+NofafDaecVm7kYsrt/gZplpMcZR9OVXVJlidquiMKSekUlNJezcWEHQUWlkqUto7prdn9UmLatB+71MjQymvFt18PoFVdx3/6JfJxAKa/VDz8201TEyWRpakd8IW8g7x14/6sqHBxYnssAdiuHSu9+5GAgXZOywUbc1HXhe5xfYtxVwjDZXF6l3dEoTsbgKQwFVF4UXvXS2Etaee1Vtrx+VMY6aDrjU23GBUGQCywudS9/vBaycjPwKYIw5YIw5b4xpNcb8MMXXjTHm/5r5+kljzP5MXBdYqtmlnUwvh2DxStZsVkH16nljRbXrsnqmarlmW9ncKRRwVF8a0YbKItWWRBTIoqMf2g4d1Ls/+VRm9d6MBS1rrQautqjj/Z/r5ol3NN53S6HCEhXXb0x6bsUmelEi96Q9s2WMCUj6S0nPSWqX9Ikx5mfW2rO3Pe0FSVtm/veIpP975k9gRUVffiUjJ8Zj+YwT0Lonv6Oh9ovTdyNW1Kh0XWNWzexkwtYXDuj8L49o7blX1bHtn3tdzj1FYyfU8+9/psmDL8tW1isQn1RX9LxqBy6l/drdp95Tf+sXc48Hr1/Uuqe+o/qHnlPPmQ80cuOynHCBKrfsUem6LWlfD8g2mVhGfFhSq7W2TZKMMX8j6TuSbg9b35H0H621VtKHxpgKY8xqa21nBq4PLIlryyJYNCcQUPmGbV6X4TqnoEzHXj8q6eiCdydmg5KWZl3627/WxH9/WCqYbqYcD4TVsep+FU4OqHSsZ9mvHZ+cSN70bhOKXvhMax//PdXt/aq096vplA9kvUz8ir9W0u29Dtpnxpb6HEmSMeYHxpgTxpgT0d7k9h/Aci20pIOls4mEJgZ7FZ8c97qUrLblmcfn7notaWn2uJrUGo+8qJNvvKbExvvngtbtoiXr0nr9+OSYbCK5X2hsbCSt1wX8JBMzW6nm/u0ynjM9aO2PJf1Yknbu2ZfyOcBycdxD+kZuXdPNT3+t+PiIjBNQZeMerdr1mNdlZTWnoEwn33hNm/9N9syqXjr8ksxI69z5Wat371CqBUMnkfpcsMUKFZenbOVUlIWH0QJuycTMVrukhtser5N054E5i3kO4JrGIy96XUJOSExNqvOjtxQfn56VsIm4+i58pqEODtO4m9lOBdkwu3rp8EtqO3RQZqRVTv3uufOzSse6VTA5OO+5JhFX9eDVtK5njFH9Q19X4LZZs6LaBlVvW1w7HSAXZGJm6xNJW4wxmyR1SPoTSX96x3N+JunPZvZzPSJpgP1aWEn0QcyM0e4OJWKTSeMjnW0qXbvZg4r8Y3bDfNuhgyuyfysaO6GGi32SpPqr/4+aj0+fTm+UeobXSGq88b5uVG3XcOEqRaaGVR89r8KpobRrKayq1+YD/1TjfbfkhAsWPP4DyFVphy1rbcwY82eS3pIUkPQfrLVnjDH/88zX/0rSLyR9U1KrpFFJ/0O61wUWa3avDH0Q0xeIFKQcd8KpxzHfbOC6dPglfeUPGjJ6l2JJS7PKzbW5x22vH1X/zJ23p1SgrS/cewkzFB/Xhu7PM1bT7YwTWPDQ2jsl4nFNDvUpVFjiy0bjwJ0ycqiptfYXmg5Ut4/91W1/t5L+RSauBSzVyTdeY1brHqZGBzXa1a5gYYmKahsWPIahsHq1Cqrq5p38bQJBVWzatVKl+t7WFw7owtu/0rHXW2WLr+sb+86q9cB/XvLrXDr80rzHZqRVJhhSvH6zYpv3yjz2+9qyylEkNpqp0lfEcOdl3frsqOITYzKOo4rGParZ5f0vSvGJMckJKBDKzv6VyG6cII+8wKzWwvrbTqvri3clO30/SkFVndY98W05C3yorH38W+q78KlGuzoUKi5T1db9CpdWrGTJvtf03LO6+Pn0ttXm42Hp+NL3chnd0UandLcqvvqE2mu+PGX/XCKmxhvHVTzRn27JKyI+OaHOj5tl49Ob8m0ioeiFz1VYVa+SNd4sU8fGRtR54m2NdXdIxlHZ+ibV7n1azjJPukd+Imwhp7UdOpjyVlhMi0+Oq/vUe3NBS5LG+26pv+2Uqram3sAcCEeyYqbB777sYpCZbgYJOTpdtX3+mBPUzcptuu/mh8t+XZtIaGKgV8GCIgULk4+GyKTR7va5oHW74c7LnoWtm5++Mx20JMkmNHj1nIIFJVq1k3O5sXiELeSsxiMvqk1hNXHcw4Im+rtTfriN9aZuFo3sFQuEFQ8kz0ZOhEqW/Zqj3R3q/OTt6btPjVHZhu2q2/dV17owLLQ/KxApcuV69xKfHNdoV3vS+FBHK2ELS0LfEuSs5uNhztW6h1BJhVIdgxcurVz5YpCWUHxc4ankg0KLx5d3OHQiHlfnx18e8yFrNXjlrAYun737N6Zhdk/g7ZxgWOWbdrh2zbsxTkAmRcNsJxjyoBr4GWELOSlbT+vONqGiUlU07p43FiwoThpD9jOSGrp/N+8Q0vDUsFb3nVvW641Hb01vCr/DyM0ry6zw3owxWvvEt1S19QEVVNWrbP1WNTz9XYWLy1275t04wZDK1ie3laq4734PqoGfsYyInMQdiItXu/tJFdWu0+it6woWlqh8wzZut/epsrFu7bz6lgaL6hRIxFQ2eksmdbOOewqEU/8bMI67HxuBUESrdj7q6jWWonbvVxQsLNZQ+/TdnhWb78+Lvp7ILMIWcs7sKd3cgbh4JfUbVVK/0esykAHBxJSqhpP3GS1VYjJ5VkuSTDC/PjaME1D19odVvf1hr0uBj7GMiJwyu3zIXi0gPbGJ1E3GDff3AkuWX7+iIOexfJiaTSTUc+ZDDVw5K5uIq6yhSTW7n2SjLxZUVLtWJhiSjU3NGy9Zs8mjigD/YmYLOYflw2S95z5R9OLnSkxNyMZjGrhyVl2/e9frspDFAqGIVj/0/Nz+PeMEVNm037PzrgA/Y2YLOaPt0EHXzv/xu8GryXekDbVfUN3+p2UcTsJGaiWrN6r4he9rciiqYGGJAh70wIxPTWi445JsIqGSNZsULHD3YFXADYQt5IRLh1+SkdR04N7NdvNSyl6H7L1ZikQ8ruEblzQ13K/CVWtUVLPO65JWhHECipSv8uTaEwO9un7svykxOb1/rPvUe1rz2DdVXNvgST3AchG24HvR2AmZkVY2xd9F+Ybt6m35eN5YaUMTs1qLlIhN6fq7b2qiv3turHzz/arb+xUPq8p9Pac/mAtakmTjMXV/cUzFz/2ph1UBS0fYgu9FX35Fho3ed1W17QHZRHxmg3xCpQ1NqrmfvW2LNXC1ZV7QkqSBtlOq2LxLkbIqj6rKHfHJcUVbT2oi2qVwebUqG/coWFCk8f6upOdODkWViE3KCaZulA5kI8IWfG1u+fC5Z70uJasZ42jVzkez6rBIP7kzaM2ND/QQttJkE3Fdf/dNTQ72SZJGbl3VcEerNnztjxUuq/qyCfSMUHGZTGBlfrlKxGMavHZe49EuRcqqVL5hu5wQIQ9Lx25i+BbLh1gpBRW1C4zXrHAluWf4xuW5oDVramRQg+0XtWrno/NnrY2jVbuekEm5BzGzrLXqeO/v1fX5bzR45ay6Tx7X9Xd/okSKxu3AvTCzBd9i+RArpWzDNg1eP6/xvltzY5WNe2jYnQGxseHU46PDqti0Uxuf+1MNXb8gG4+rdN0WhUsrVqSu0VvXNNZzY97YxECvhq5fVPnG7StSA3IHYQu+xvIhVoITDKnhK9/VcOdlTQ0PqLBmjQqr6r0uKycU1a2XTr2XNF5cv16SFCosUVXT/pUuS5PD/UsaB+6GsAVfajt0kIMLsKKM46h07X1el5FzImVVqrn/CfWc+VA2EZeMo6qtD6iwerWndRWuWpNyvGiBceBuCFvwncYjL6pNYTWxVwvICZVb9qps/VZNDPYqXFqZFQeXFlTUqLJpv6IXPpsbK9uwbXomDlgiwhZ8p/l4WE79bq/LwF3ExkflhCJyApzjhcUJRAqz7qDYml2PqXzDtpm7EasVqfDmcFf4H2ELvjLbkmfLPqbys9F4tFs3P/uVJgd65YTCqmrar6qtD3hdFrBs4dJKboRA2jj6Ab7ReORFSbTkyVY2EVfHBz/X5ECvJCkxNameMx9qqKPN48oAwFuELfhG8/EwZ2plsbGeTsXHR5LGhzsuelBNehLxuCYGehWfmvS6FAA5gGVE+MLs8iGylwmm/nGyUqd9Z8pQR6u6fveu4hNjMoGgqrc9yFIogLTw6YWsd+nwS5JYPsx2hVX1itx50rpxVL5ppzcFLUNsfEQ3P3lb8YkxSdONj3vOfKjRO1rGAMBSELaQ1WjJ4y9rn/g9lW/coVBRmQpXrdHax/+RCqvqvC5r0UZuXpNNJJLGhzsve1ANgFzBMiKyGi15/CUYKVTd/me8LmPZApGC1OPh1OMAsBjMbCFrtR06KImWPFg5xXUbkm7zd8IRlW+gFx6A5WNmC1mppKVZklg+xF1NjQ5r4PJpTY0Nq7i2QaUNTTJm+Y2cjONo3VO/r74Ln2mst1OR0kpVbX1AwULvTzQH4F+ELWSlk2+8xvIh7mpqdFDXfv2fFZ8clyQNXTuv0a521T+Y3kxosKBItbufzESJyCKj3R0auNoi2YTK1m9TMW13sIIIW8g6LB9iMfpbT84FrVmD186patuDCpeUe1QVstFQ+0V1ftz85ePrF1W772lV+OhOWfgbe7aQVVg+xGJNjgymHJ9aYDzT4pMTGo92KxGbWpHrYfl6z51IGutr+cSDSpCvmNlCVjn5xmtyCsq8LgM+UFSzViN3HMlgAkEVVNUu8B2Z03f+M/We+0Q2HpMTCqtm95Nsos9isdGh5LHxEVmb4LBkrAj+lSFrzC4fbnnmcY8rgR+Ub9qpotv23RgnoLp9TysQirh63bHeTvWc+UA2HpM03QPy1mdHV2xGDUtXVNuQPFazjqCFFcPMFrLC2nOvqk0sH2LxnEBQ6574lsajXZoaHVLRqjUKRApdv+5I55XkQWs1cvOqKu673/XrY+lqdj+pyaGoJoeikqRQcZlq937V46qQTwhbyArHXj8qp36312XAhwoqa1VQ6f7S4ayFAl2goGjFasDShIpKteHr39N4301Za1VYvTqtI0KApWIOFZ5rO3RQRtKWfWu8LgW4p7L1WxWIzA9WoZIKlaze6E1BWBRjjAqrV6to1RqCFlYcM1vwVOORF9WmsJpYPoRPBCKFWv/0H6rvwmeaGOxTYWWtKpv2yzgBr0vLaVOjQ+pt+WT6sNmyKlVte1AFFTVelwUsCmELnrl0+CW1jYRZPoTvhIrLVLfvaa/LyBs2EVf7sf82dxPC1HC/Rruua8PXv6dQUanH1QH3xjIiVsToVFw3BsfVOTiusam41p57VWakVU79bpYPgUWy1ubluV7DnVeT7vZMxKY0ePWcRxUBS8PMFlw3MDal6wNfnvTdOzql68d7FS4oI2ghLTYRV8+ZDzV47bxkjMo37FD19odknNz7PXL4Rpu6T72nqZFBhUsrVbPnKRWnONIgFyViEwuMT65wJcDy5N5PJGSdW8PJPyhjj36b87SQtu5T7yl68XeKT4wpPj6qvvMn1JuDJ4NPDvXrxkdvzc3uTA5FdeODXyg2PuJxZSujuH5jyj1xJWvu86AaYOkIW3CVtVaTcZs8XlXvQTXIJdZaDVxpSRofuHLWg2rcNdTRKtnEvDEbj2m4o82jiuaLT4xrrO+mElPuzDQFI4Va/cgBBQtLJElOKKLaPV9RYTU/R+APLCPCVcYYFYUCGp2KzxsvGe/1qCLkFJsc5KVUY/620LKoCXh/B2TvuRPqO3dCNhGXCYZUc/8TrjR4Llm9UcX16xUbG1YgUiQnwMcX/IOZLbhuTVlEQefLc21CsTGt6znlYUXIBcYYla3fmjSeaszvShuaZIKheWNOuMDzZbSx3k71nv1INjH9y5SNTanr899qcnjAlesZ4yhUVEbQgu/wLxauKwgF9MLn/5Peat+ndfv3qXS0S44S9/5G4B5qdj8pK2loboP8dlXveNTrsjIuVFiidU9+Wz1nPtTkQK8ilbWq2fWYAuHl94Ec67mhyeF+FVTVK1JWtazXSNm6SFYjt64qXMKRLsAswhZWxK+OOdr5wmpp9KbXpSCHOMGQ6vc/M3fmVS6fDF5YVa+Gp34/7dexiYRufPTLeUGpausDWrVz6SF1oRZFwYLiZVYH5CaFkrucAAAdr0lEQVSWEeG6tkMHZQz/1OAeY4ynQctaq/jk+L2fmAWGOlqTZqT6zn8616R5KcrWb00KVuHSSloXAXdgZguuunT4JRlJTQee97oUwBWD186r5/QHio2PKFRSodq9X8nq86/GelPPLt888Y6KaterYvMuBQsXNzMVCBeo4ek/VPTi55oY6FNBVa2qtuyjdRFwB8IWXBONnZAZadVW+h4iR0309+jmiV9p9g7IqeF+3fjgF9p84L9TIFLobXELCJdWpBwfj3ZpPNqlgast2vC1P1JwgSXCO4WKSlW75yuZLBHIOaztwDXRl19JuoMKyCVDHa2686gJG49puPOyNwUtQvn6bQrfZUN8fHxEA5dPr2BFQO5jZguumFs+fO5Zr0sBXJR6n5jJ4qMJnFBY65/+Qw1ePa/hzisa7bqW9JypkaG0rxOfmtTITOgsXr1RgdDy75wE/C57fyLAt0pamlk+RF6YGEo+nNcEQipZvcmDahbPCYZVcd/9Klm7WW2//I9Jp9MX1axN6/XHo91qf++nSkxOt+pyQhGte+JbKqiqS+t1Ab9iGREZd/KN11g+RM6ziYRGOq8mjTuBoByf/PsPFhSrds9T806oL13XqNKGprRet/vU8bmgJUmJqQl1nXovrdcE/IywBVewfIicZ1K30cmGFjpLUbF5lzYd+L4qGvcoUl6ticGo+s5/Oncq/HKM9yXf8Ti+wF2QQD5gGREZ1Xbo4AK7WIDcYoyjsg3bNNA2fzN5uQt9Ad022nVd/a1fzD3uHexVbGxYdfufWdbrhUurNDHQM3+srEqx8RFFW09qcqhPhVX1qth8v5xQOK3aAT9gZgsZ03jkRUlSE3u1kCdqdz+pyqZ9ChQUKVhYouodj6hq6wNel7Vk/ZeSe5UOXjunxNTksl5v1a7H5s/6GUdVWx/QtaP/RdELn2mk84p6znyo9uM/lbW07kLuY2YLGdN8PCynnn5oyB/GCahm1+Oq2fW416WkJRFLDlU2kVAiEV/Wb+TFdeu14dnvafD6eUlSWUOTRm5dU2xseN7zxqNdGr11XcX1G5ZTNuAbzGwhI2Zb8mzZt8brUgAsUcna+5LGCletUTCNg1nDpRVateMRrdrxiMKllYqNpj5OYmqBcSCXELaQtrnlQ1ryAL5UtfVBla3fJs30MC2sXq36B7+e0WsU1a1PPZ7FrY2ATGEZEWmJxk6o+XiYM7UAH3MCAdU/+Kxqdj8pm4glNZfOhOK69apo3KP+1pOSrIwT0Kr7H1e4pDzj1wKyDWELaYm+/IqMYYIUyAWBcESSeye91+5+UpWNezQ51K+CilVZ2z8SyDTCFpZtriUPy4cAFilUVKpQUanXZQAriikJLEs0doKWPAAALAJhC8sSffkVWvIAALAIhC0sWduhg5JoyQMAwGIQtrAkJS3NksTyIQAAi0TYwpKcfOM1OQVlXpcBAIBvELawaLPLh1ue8XdrEgAAVhJhC4vC8iEAAMtD2MKisHwIAMDyELZwTywfAgCwfIQt3NXac69KYvkQAIDlImzhro69flRO/W6vywAAwLfojYgFtR06KCNpy741XpcCYIVMDvert+UTTfR3K1JRo+rtDylcUrG01xjq1+DVFiUScZWua1RhVb1L1QL+QNhCSo1HXlSbwmpi+RBZampkUIPXzssmEiptaFSkrNrrknwvPjmh67/9ieITY5KkyaGoRruua+NzBxUIRxb1GqM9N9Rx/Geyibgkqb/1C9Xtf0blG3e4VjeQ7VhGRErNx8MsHyJrjfXc0JV3/lq9LR+r7/wJXf3V32qoo9XrsnxvqP3iXNCaFZ8Y01D7xUW/Rm/Lx3NBa1bPmY9kbSIjNQJ+RNhCkrZDB2WMw/IhslbP2Y9k47EvB6xVz6kPZK31rqgckIhNLmk8lamh/qSx+MSoElOLfw0g1xC2ME/jkRclSU0Hnve4EmBhE4N9SWNTo4PzAxiWrGT1Zklm/qAxM+OLU1C9OmksXFqpQLggzeoA/yJsYU40dkLNx8Mc84CsV1hVlzQWLq+WEwx5UE12Gb7Rpo73f66O9/5BQ+1LW1oNl1ao/sFnFYgUSpICkULVP/CswqWL3yBfs+sxBYtK5x47wbBq9z29pDqAXMMGecyJvvyKjCF/I/ut2vWYxqNdc/uLTDCk2t1PeVyV9/ovn1HX57+Zezxy66pi4yOqbNyz6NcoW79VpesaFRsbUbCwWMYJLKmGUHGZNj1/UCM3rykRj6m4foMCofCSXmMx4hNjGuq4JGsTKl1zn4KFxRm/BpAphC1Iki4dfklGLB/CHyJl1dr0jX+i4RuXZRNxlazerECEZarohc+SxvoufL6ksCVJxgkoVLz89lzGCahkzaZlf/+9jEe71H78p3P7wHpOf6C1j31TRbUNrl0TSAfTGFA0dkJmpJXlQ/iKEwyrbP1WlW/cQdCaERsfTRqLTySP+V336ffnbbi38Zi6Tr7nYUXA3RG2ML18yF4XwPeK6zcmj9VtWPlCXDYR7UoamxzsTTpyAsgWhK08N9tkuum5Zz2uBEC6anc/qYLKL28eiFTUqHbvVz2syB2R8lVJY+HSyiXvLwNWCnu28lhJS7MkmkwDuSJYWKz1z/zj6aMxrFWkPDdP1V+163G1v/cz2diUpOk9YjX3P+FxVcDC0gpbxpgqSX8raaOkK5L+yFobTfG8K5KGJMUlxay1D6ZzXWTGyTdek1Ow/E2wgF9NDPSq99wJTQ71qaCqXtXbHlKoqMTrsjImUlbldQmuKqyu16bnDmqo/eL03YhrG9Pa0A+4Ld2ZrR9K+pW19s+NMT+cefwvF3juM9banjSvhwyZXT7c8szjHlcCrKypsWFdf/dNJaYmJEmTg30a627Xhq//qZwAy1B+ESwsVuWWvV6XASxKunu2viPptZm/vybp99N8PawAlg+RzwavnpsLWrOmRgY1cvOKNwUByHnphq06a22nJM38WbvA86ykZmPMp8aYH9ztBY0xPzDGnDDGnIj29qZZHlJh+RD5bKEefXcGMADIlHsuIxpj3pFUn+JLP1rCdZ6w1t4wxtRKetsYc85a+26qJ1prfyzpx5K0c88+uspmGMuHyHclazcrevHzeWPGCai4PveOSACQHe4Ztqy1X1/oa8aYW8aY1dbaTmPMaknJh59Mv8aNmT+7jDFvSnpYUsqwBfesPfeq2sTyIfJbYVW9avY8pd6zHysxNaFAQbHq9n5FwYLFt3uZGh2ScZwlfQ+A/JXuBvmfSfq+pD+f+fOndz7BGFMsybHWDs38/XlJ/0ea18UyHHv9qJz63V6XAXiu8r7dKt+4Q7GxEYWKSmWcxe2oiI2NqPOTZo313JAkFa/eqPoHn3Ol9x/Sk4jHNN53U4FwYc4egQH/SDds/bmkvzPG/DNJ1yS9KEnGmDWSXrXWflNSnaQ3jTGz1/tP1tojaV4XS9R26KCMpC371nhdCpAVnEBQ4ZLyJX3Prc+PzgUtSRrpvKKe0x+obp+3B4fGxkYkiWbMM0a7O9T50RHFJ8clSUW1DVrz6Aty6JQBj6QVtqy1vZKSjh6fWTb85szf2yQtrQsqMqrxyItqU1hNLB8Cy5aIxzVy81rS+PCNS56FrfjkuDo/eVujt6brKqpt0OqHns/rXpHWJnTzxDtzQUuSRruuq+/C51q142EPK0M+o11PHmg+Hmb5EEiTcUzKmZFA2Ltg03Xy+FzQkqZDRdcX+b0ddnIwqtjYcNL4aFdyUAZWCmErx7UdOihjHJYPgTQZ46jivvuTxivuW/wvMrHxUQ1eO6+RW9dkbfo3Ww93XEoeu9G2pNeYHIqq+/QH6j55XOMpGjz7TaCgSDLJH23BwtzpEAD/oTdiDptbPjzwvNelADmhescjChQUaejaBRnHUfmmnSpbv3VR3zvU0aqbn7wjm4hLmm6mvO6p76Q1MxYIRRSLx+aNOUvYrD/a1a6O9/9eNpGQJEVbT6r+wWcX/f8pGwUjharYtFP9bafmxowTUNWWfR5WhXxH2MpR0dgJNR8Pc8wDkEHGGFXet1uVS5jNkqbvjLv1+W/ngpYkTQz0qO/C56rZ9diy66lo3KOe0+/PG6tsXPwW2Z6zH80FrWlWPWc/UmlDk2ZuavKlmj1PKVJZo+Ebl6fD1+b7FalY5XVZyGOErRwVffkVmRRT6QBW3uRQVInbNmzPuv3OxuWoatonJxTW4NVzkqzK1m9TxeZdi69ruD9pLDY6JBuPyfj4zj1jjMo3bFf5hu1elwJIImzlpEuHX5KRWD4EssT0WV6BeTNbkhQurUj7tSs27VTFpp3L+t7C6nqNdF6ZNxapqOGIBCDDmPrIQWakleVDIIsEwgWqbJq/Z8gJRVTVtN+jiqbV7Hpi3in4Tiii2j1f8bAiIDcxs5VjZlvyAH42cvOqRrvbFSoqU+n6JgVCEa9LStuqHY+osHq1hjsvKxgpUtmG7QoVeXuHXLi0Qhu/8U80cvOqZBMqrtuwpA32ABaHsJVjjr1+VE5BmddlAMt263fvauC2O8mirV9o/dN/qECk0MOqMqO4br2K69Z7XcY8TiCo0rX3eV0GkNMIWzlkriXPM497XQqwLJPD/fOCliRNjQyov+20ShuaZOMx+tylKT45ru5T72uk87ICkUJVNu5V+aYdXpcF5DTCVo6YXT6kJQ/8bHIomnK8v+2Uels+liSFy6q15tEXltzXENM6P27WaNd1SdPB69bnR+WEQipdt8XjyoDcxQb5HHHs9aO05IHvFVTUpjz9Oz4xNvf3ycFe3frs1ytZVs6YGh2cC1q3G7h81oNqgPxB2MoBc8uHtOSBzwULi7Vq5yPzB53kH1NjPTcUn5pcoapyh02kbhFkbSLlOIDMYBnR5+Za8rB8iBxR1bRfJas3abS7Q6GiUvWc/UgT/d3znuMEw3ICAY8q9K9wSbkKquo03ndr3nhZg3/b8wB+wMyWz9GSB7koXFqpis27VFy/QZUpetpVNO6WcQhby7H64QMqrtsgafpcrertD7NBHnAZM1s+1nboIC15kPPKGrbICYY0cPmMbDym0oYtKt9IOFiuUFGJ1j7xe9On2RvH1z0QAb8gbPkULXmQT0pWb1TJ6o1el5FTmBkEVg7TIj4UjZ2gJQ8AAD5B2PKh6MuvyNAoFgAAXyBs+cylwy9Jkpqee9bjSgAAwGIQtnykpKWZ5UMAAHyGsOUjJ994jeVDAAB8hrDlE22HDkpi+RAAAL8hbPlASUuzJLF8CACADxG2fODkG6/JKSjzugwAALAMhC2f2PLM416XAAAAloGwleVoyQMAgL/xKZ7FGo+8KImWPAAA+BlhK4s1Hw+zKR4AAJ8jbGUplg8BAMgNfJpnobmWPCwfAgDge4StLBONnaAlDwAAOYSwlWWiL79CSx4AAHIIYSuLzC0f0pIHAICcQdjKEiUtzSwfAgCQgwhbWeLkG6+xfAgAQA4ibGWBtkMHJbF8CABALgp6XUC+K2lpliSWDwGfScSm1NvysYY72uSEwqpo3K3yDdu9LgtAFiJseezkG6/JKSjzugwAS3Trs6Maar/45eNPfy3jBFTW0ORhVQCyEcuIHppdPtzyzOMeVwJgKeIT4xpqb00a72877UE1ALIdYctjLB8C/mMTcUk2eTw2tfLFAMh6hC2PzJ6pBcB/goXFKqxenTRe2rDFg2oAZDvClgdoyQP4X/1Dz6uodp0kyQSCqrhvtyq37PW4KgDZiA3yHoi+/IqMIecCfhYqKtG6J7+j+NSEjBOQE+DHKYDU+Omwwi4dfklGUtOB570uBUAGBEIRr0sAkOWYXllBLB8CAJB/CFsrKPryK7TkAQAgzxC2VggteQAAyE+ErRVASx4AAPIXYWsF0JIHAID8RdhyGS15AADIb4QtF60996oklg8BAMhnnLPlomOvH2X5EEBWmBod0lBHq4xxVLpui4IFRV6XBOQNwpZL2g4dlBHLhwC8N3Lzqm58+MuZBtpS79mPte7Jb6ugqs7jyoD8wDKii5pYPgSQBbpPvTcXtCQpEZtU95kPPKwIyC+ELRdcOvyS1yUAgCQpEY9pciiaND7R3+NBNUB+ImxlGC15AGQTJxBUuLQyaTxSscqDaoD8RNjKMFryAMg2NbuflHECc4+dYFg1Ox/zsCIgv7BBPoMuHX5JRrTkAZBdiuvWa+PzBzXccUlyHJWubeRuRGAFEbYypKSlmeVDAFkrVFSqyi17vS4DyEssI2bIyTdeY/kQAAAkIWxlwGxLHpYPAQDAnQhbaSppaZZESx4AAJAaYStNJ994jZY8AABgQYStNMwuH9KSBwAALISwtUxrz70qieVDAABwd4StZTr2+lE59bu9LgMAAGQ5wtYyrD33qoykLfvWeF0KAADIcoStZTj2+lEZNsUDAIBFIGwt0aXDL0liUzwAAFgcwtYSRGMnaMkDAACWhLC1BNGXX6ElDwAAWBLC1iLRkgcAACwHYWsRaMkDAACWi7C1CLTkAQAAy0XYugda8gAAgHQQtu6CljwAACBdhK27OPb6UZYPAQBAWghbC2g7dHC6JQ/LhwAAIA2ErRRmlw+bWD4EAABpImylcOz1o3Lqd3tdBgAAyAGErTtEYyckSVv2rfG4EgAAkAsIW3egJQ8AAMgkwtZtLh1+SRIteQAAQOYQtmaUtDTLjLRyphYAAMgowtaMk2+8xvIhAADIOMKWvmzJw/IhAADItLTCljHmRWPMGWNMwhjz4F2ed8AYc94Y02qM+WE618y0kpZmSbTkAQAA7kh3Zuu0pO9KenehJxhjApL+UtILknZI+p4xZkea182Yk2+8RkseAADgmmA632ytbZEkY8zdnvawpFZrbdvMc/9G0ncknU3n2pkwu3xISx4AAOCWldiztVbS9dset8+MpWSM+YEx5oQx5kS0t9e9omZa8rB8CAAA3HTPmS1jzDuS6lN86UfW2p8u4hqppr3sQk+21v5Y0o8laeeefQs+L1205AEAACvhnmHLWvv1NK/RLqnhtsfrJN1I8zXT0nbooIxoyQMAANy3EsuIn0jaYozZZIwJS/oTST9bgeveVRPLhwAAYAWke/TDHxhj2iU9Junnxpi3ZsbXGGN+IUnW2pikP5P0lqQWSX9nrT2TXtnLd+nwSynXNQEAANyQ7t2Ib0p6M8X4DUnfvO3xLyT9Ip1rZUI0dkJmpJVZLQAAsGLy6gT56Muv0JIHAACsqLwJW7TkAQAAXsiLsEVLHgAA4JW8CFu05AEAAF7J+bBFSx4AAOClnA5bLB8CAACv5XTYYvkQAAB4LWfDFsuHAAAgG+Rk2Fp77lVJLB8CAADv5WTYevfN6ywfAgCArJBzYWu2JQ/LhwAAIBvkXtiiJQ8AAMgiORW2Lh1+SRIteQAAQPbImbA1u3zIpngAAJBNcidssXwIAACyUE6ErdkztVg+BAAA2cb3YYuWPAAAIJv5PmzRkgcAAGQzX4ctWvIAAIBs5+uwJbF8CAAAsptvw1bjkRe9LgEAAOCefBu2mo+HmdUCAABZz5dhq+3QQRnjy9IBAECe8V1imWvJc+B5jysBAAC4N1+FLVryAAAAv/FX2KIlDwAA8BnfhK255UNa8gAAAB/xRdgqaWlm+RAAAPiSL8LWyTdeY/kQAAD4UtaHrdmWPCwfAgAAP8r6sCXRkgcAAPhXVoetyMAlr0sAAABIS1aHrcFhh1ktAADga1kdtmS8LgAAACA9WR22CsrKvC4BAAAgLVkdtgAAAPyOsAUAAOAiwhYAAICLCFsAAAAuImwBAAC4iLAFAADgIsIWAACAiwhbAAAALiJsAQAAuIiwBQAA4CLCFgAAgIsIWwAAAC4ibAEAALiIsAUAAOAiwhYAAICLCFsAAAAuImwBAAC4iLAFAADgIsIWAACAiwhbAAAALiJsAQAAuIiwBQAA4CLCFgAAgIsIWwAAAC4ibAEAALiIsAUAAOAiwhYAAICLCFsAAAAuImwBAAC4iLAFAADgIsIWAACAiwhbAAAALiJsAQAAuIiwBQAA4CLCFgAAgIsIWwAAAC4ibAEAALiIsAUAAOAiwhYAAICLCFsAAAAuImwBAAC4iLAFAADgIsIWAACAiwhbAAAALiJsAQAAuIiwBQAA4CLCFgAAgIsIWwAAAC4ibAEAALiIsAUAAOAiwhYAAICLCFsAAAAuSitsGWNeNMacMcYkjDEP3uV5V4wxp4wxvzPGnEjnmgAAAH4STPP7T0v6rqR/v4jnPmOt7UnzegAAAL6SVtiy1rZIkjEmM9UAAADkmJXas2UlNRtjPjXG/OBuTzTG/MAYc8IYcyI6OLxC5QEAALjjnjNbxph3JNWn+NKPrLU/XeR1nrDW3jDG1Ep62xhzzlr7bqonWmt/LOnHkrTrvvV2ka8PAACQle4Ztqy1X0/3ItbaGzN/dhlj3pT0sKSUYQsAACCXuL6MaIwpNsaUzv5d0vOa3lgPAACQ89I9+uEPjDHtkh6T9HNjzFsz42uMMb+YeVqdpOPGmC8kfSzp59baI+lcFwAAwC/SvRvxTUlvphi/IembM39vk7QnnesAAAD4FSfIAwAAuIiwBQAA4CLCFgAAgIsIWwAAAC4ibAEAALiIsAUAAOAiwhYAAICLCFsAAAAuImwBAAC4iLAFAADgIsIWAACAiwhbAAAALiJsAQAAuIiwBQAA4CLCFgAAgIsIWwAAAC4ibAEAALiIsAUAAOAiwhYAAICLCFsAAAAuImwBAAC4iLAFAADgIsIWAACAiwhbAAAALiJsAQAAuIiwBQAA4CLCFgAAgIsIWwAAAC4ibAEAALiIsAUAAOAiwhYAAICLCFsAAAAuImwBAAC4iLAFAADgIsIWAACAiwhbAAAALiJsAQAAuIiwBQAA4CLCFgAAgIsIWwAAAC4ibAEAALiIsAUAAOAiwhYAAICLCFsAAAAuImwBAAC4iLAFAADgIsIWAACAiwhbAAAALiJsAQAAuIiwBQAA4CLCFgAAgIsIWwAAAC4ibAEAALiIsAUAAOAiwhYAAICLCFsAAAAuMtZar2tYkDGmW9LVFbzkKkk9K3g93B3vR/bgvcguvB/Zhfcju3j5fmyw1tbcOZjVYWulGWNOWGsf9LoOTOP9yB68F9mF9yO78H5kl2x8P1hGBAAAcBFhCwAAwEWErfl+7HUBmIf3I3vwXmQX3o/swvuRXbLu/WDPFgAAgIuY2QIAAHARYQsAAMBFhK3bGGP+rTHmnDHmpDHmTWNMhdc15TNjzIvGmDPGmIQxJqtu480nxpgDxpjzxphWY8wPva4nnxlj/oMxpssYc9rrWiAZYxqMMUeNMS0zP6v+F69rylfGmAJjzMfGmC9m3ot/7XVNtyNszfe2pF3W2t2SLkj6Vx7Xk+9OS/qupHe9LiRfGWMCkv5S0guSdkj6njFmh7dV5bX/T9IBr4vAnJik/81au13So5L+Bf99eGZC0testXsk7ZV0wBjzqMc1zSFs3cZa22ytjc08/FDSOi/ryXfW2hZr7Xmv68hzD0tqtda2WWsnJf2NpO94XFPesta+K6nP6zowzVrbaa39bObvQ5JaJK31tqr8ZKcNzzwMzfwva+4AJGwt7H+U9EuviwA8tlbS9dset4sPEyCJMWajpH2SPvK2kvxljAkYY34nqUvS29barHkvgl4XsNKMMe9Iqk/xpR9Za38685wfaXp6+I2VrC0fLeb9gKdMirGs+W0RyAbGmBJJ/1XS/2qtHfS6nnxlrY1L2juz3/pNY8wua21W7G/Mu7Blrf363b5ujPm+pN+T9KzlEDLX3ev9gOfaJTXc9nidpBse1QJkHWNMSNNB6w1r7U+8rgeStbbfGPMbTe9vzIqwxTLibYwxByT9S0nfttaOel0PkAU+kbTFGLPJGBOW9CeSfuZxTUBWMMYYSf+vpBZr7f/pdT35zBhTM3uCgDGmUNLXJZ3ztqovEbbm+3eSSiW9bYz5nTHmr7wuKJ8ZY/7AGNMu6TFJPzfGvOV1Tflm5oaRP5P0lqY3//6dtfaMt1XlL2PMX0v6QNJWY0y7MeafeV1TnntC0j+V9LWZz4zfGWO+6XVReWq1pKPGmJOa/iXxbWvtP3hc0xza9QAAALiImS0AAAAXEbYAAABcRNgCAABwEWELAADARYQtAAAAFxG2AAAAXETYAgAAcNH/D+7qgnno1ZMSAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(X, Y, model, steps=1000, cmap='Paired');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A much nicer figure (😉) can be seen [here](https://jamboard.google.com/d/1UmIdOUFZoUcjpPSOq_ryOyAjj5Houy87a--YxZSNi2g/edit?usp=sharing)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The crucial question\n",
    "\n",
    "> How do we fight overfit?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regularisation\n",
    "\n",
    "As we have seen above, by an example, a central problem in machine learning is how to make an algorithm that will perform well not just on the training data, but also on new inputs. \n",
    "Many strategies used in machine learning are explicitly designed to reduce the test error, possibly at the expense of increased training error. These strategies are known collectively as _regularization_.\n",
    "\n",
    "### Penalties\n",
    "\n",
    "One of the most simple way to reduce overfit is to add a penalty term to the cost function.\n",
    "Euristically, this _penalises_ the high-valued terms, reducing parameters' magnitude (each single node weight) and \"simplifying\" the model.\n",
    "\n",
    "$$ J(\\beta) = \\frac{1}{n} \\sum_k \\mathcal{L}(\\hat{y}^{(k)}, {y}^{(k)}) + \\frac{\\lambda}{p n} \\Vert \\beta \\Vert_p^p $$\n",
    "\n",
    "### Dropout\n",
    "\n",
    "![](https://cdn-images-1.medium.com/max/1000/0*ZRRL-z_7ZxWpuryM.gif)\n",
    "\n",
    "In a neural network, while training, we set a probability (can be different for each layer) of deleting some of the units of the layer. If $p = 1/2$, at each step we train the network on half of the units.\n",
    "The guide principle is \n",
    "> _a smaller network is less prone to overfit_.\n",
    "\n",
    "### Data augmentation\n",
    "\n",
    "Another technique, really used in computer vision is _data augmentation_. \n",
    "\n",
    "With this, we mean a slight transformation of each training example (rotation, change of colour, blur, etc.) in order to have much more data and let the deep learning algorithm to have more experience of what an object is.\n",
    "\n",
    "![](data:image/jpeg;base64,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)\n",
    "\n",
    "## Application: a guided exercise\n",
    "\n",
    "We will use a famous dataset about a glass classification problem.\n",
    "\n",
    "We have data about glass chemical compositions and we want to classify the type of glass.\n",
    "\n",
    "Here the description of the dataset\n",
    "\n",
    "__Attribute Information__:\n",
    "\n",
    "* Id number: 1 to 214\n",
    "* RI: refractive index\n",
    "* Na: Sodium (unit measurement: weight percent in corresponding oxide, as are attributes 4-10)\n",
    "* Mg: Magnesium\n",
    "* Al: Aluminum\n",
    "* Si: Silicon\n",
    "* K: Potassium\n",
    "* Ca: Calcium\n",
    "* Ba: Barium\n",
    "* Fe: Iron\n",
    "* Type of glass: (class attribute)\n",
    "   1. building_windows_float_processed\n",
    "   2. building_windows_non_float_processed\n",
    "   3. vehicle_windows_float_processed\n",
    "   4. vehicle_windows_non_float_processed (none in this database)\n",
    "   5. containers\n",
    "   6. tableware\n",
    "   7. headlamps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell loads the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the dataset\n",
    "col_names = ['Id','RI','Na','Mg','Al','Si','K','Ca','Ba','Fe','Type']\n",
    "df = pd.read_csv(\"datasets/glass.data\", names=col_names, index_col='Id')\n",
    "colsx = df.columns[:-1]\n",
    "colsy = 'Type'\n",
    "\n",
    "X = df[colsx]\n",
    "X = X.values\n",
    "Y = df[colsy].values\n",
    "Y = Y.reshape(-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Some preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of training examples = 171\n",
      "number of test examples = 43\n",
      "X_train shape: (171, 9)\n",
      "Y_train shape: (171, 7)\n",
      "X_test shape: (43, 9)\n",
      "Y_test shape: (43, 7)\n"
     ]
    }
   ],
   "source": [
    "X = StandardScaler().fit_transform(X)\n",
    "\n",
    "Y = np.eye(7)[Y - 1] # we have 7 classes = 7 types of glass\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)\n",
    "\n",
    "n_x = X_train.shape[1] # number of features\n",
    "n_c = Y_train.shape[1] # number of classes\n",
    "\n",
    "print (\"number of training examples = \" + str(X_train.shape[0]))\n",
    "print (\"number of test examples = \" + str(X_test.shape[0]))\n",
    "print (\"X_train shape: \" + str(X_train.shape))\n",
    "print (\"Y_train shape: \" + str(Y_train.shape))\n",
    "print (\"X_test shape: \" + str(X_test.shape))\n",
    "print (\"Y_test shape: \" + str(Y_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build a 2-Dense layer model\n",
    "\n",
    "_DENSE -> RELU -> DENSE -> SOFTMAX_\n",
    "\n",
    "and units: `10` ->  `n_c`.\n",
    "\n",
    "Use `kernel_initializer='normal'` for the first layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_4 (Dense)              (None, 10)                100       \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 7)                 77        \n",
      "=================================================================\n",
      "Total params: 177\n",
      "Trainable params: 177\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# create model\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "# YOUR CODE HERE\n",
    "\n",
    "# Compile model\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 153 samples, validate on 18 samples\n",
      "Epoch 1/300\n",
      "153/153 [==============================] - 0s 1ms/step - loss: 1.9391 - accuracy: 0.0980 - val_loss: 1.9195 - val_accuracy: 0.1667\n",
      "Epoch 2/300\n",
      "153/153 [==============================] - 0s 204us/step - loss: 1.9260 - accuracy: 0.2026 - val_loss: 1.9057 - val_accuracy: 0.2778\n",
      "Epoch 3/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 1.9134 - accuracy: 0.3399 - val_loss: 1.8926 - val_accuracy: 0.2778\n",
      "Epoch 4/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 1.9009 - accuracy: 0.3987 - val_loss: 1.8788 - val_accuracy: 0.2778\n",
      "Epoch 5/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 1.8890 - accuracy: 0.4052 - val_loss: 1.8645 - val_accuracy: 0.3889\n",
      "Epoch 6/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 1.8773 - accuracy: 0.4575 - val_loss: 1.8497 - val_accuracy: 0.4444\n",
      "Epoch 7/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 1.8653 - accuracy: 0.4575 - val_loss: 1.8354 - val_accuracy: 0.4444\n",
      "Epoch 8/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 1.8533 - accuracy: 0.4641 - val_loss: 1.8200 - val_accuracy: 0.4444\n",
      "Epoch 9/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 1.8412 - accuracy: 0.4641 - val_loss: 1.8041 - val_accuracy: 0.4444\n",
      "Epoch 10/300\n",
      "153/153 [==============================] - 0s 49us/step - loss: 1.8279 - accuracy: 0.4706 - val_loss: 1.7885 - val_accuracy: 0.5000\n",
      "Epoch 11/300\n",
      "153/153 [==============================] - 0s 49us/step - loss: 1.8144 - accuracy: 0.4837 - val_loss: 1.7712 - val_accuracy: 0.5000\n",
      "Epoch 12/300\n",
      "153/153 [==============================] - 0s 49us/step - loss: 1.8009 - accuracy: 0.5098 - val_loss: 1.7539 - val_accuracy: 0.5556\n",
      "Epoch 13/300\n",
      "153/153 [==============================] - 0s 49us/step - loss: 1.7863 - accuracy: 0.5359 - val_loss: 1.7367 - val_accuracy: 0.5556\n",
      "Epoch 14/300\n",
      "153/153 [==============================] - 0s 49us/step - loss: 1.7713 - accuracy: 0.5359 - val_loss: 1.7185 - val_accuracy: 0.6111\n",
      "Epoch 15/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 1.7558 - accuracy: 0.5490 - val_loss: 1.6998 - val_accuracy: 0.6111\n",
      "Epoch 16/300\n",
      "153/153 [==============================] - 0s 49us/step - loss: 1.7395 - accuracy: 0.5621 - val_loss: 1.6808 - val_accuracy: 0.6111\n",
      "Epoch 17/300\n",
      "153/153 [==============================] - 0s 55us/step - loss: 1.7226 - accuracy: 0.5621 - val_loss: 1.6630 - val_accuracy: 0.6111\n",
      "Epoch 18/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 1.7044 - accuracy: 0.5556 - val_loss: 1.6448 - val_accuracy: 0.6111\n",
      "Epoch 19/300\n",
      "153/153 [==============================] - 0s 55us/step - loss: 1.6869 - accuracy: 0.5556 - val_loss: 1.6239 - val_accuracy: 0.6111\n",
      "Epoch 20/300\n",
      "153/153 [==============================] - 0s 61us/step - loss: 1.6676 - accuracy: 0.5621 - val_loss: 1.6041 - val_accuracy: 0.6111\n",
      "Epoch 21/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 1.6493 - accuracy: 0.5621 - val_loss: 1.5831 - val_accuracy: 0.6111\n",
      "Epoch 22/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 1.6299 - accuracy: 0.5621 - val_loss: 1.5628 - val_accuracy: 0.6111\n",
      "Epoch 23/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 1.6094 - accuracy: 0.5621 - val_loss: 1.5418 - val_accuracy: 0.6111\n",
      "Epoch 24/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 1.5894 - accuracy: 0.5621 - val_loss: 1.5204 - val_accuracy: 0.6111\n",
      "Epoch 25/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 1.5693 - accuracy: 0.5621 - val_loss: 1.4997 - val_accuracy: 0.6111\n",
      "Epoch 26/300\n",
      "153/153 [==============================] - 0s 56us/step - loss: 1.5484 - accuracy: 0.5621 - val_loss: 1.4772 - val_accuracy: 0.6667\n",
      "Epoch 27/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 1.5277 - accuracy: 0.5621 - val_loss: 1.4562 - val_accuracy: 0.6667\n",
      "Epoch 28/300\n",
      "153/153 [==============================] - 0s 64us/step - loss: 1.5074 - accuracy: 0.5621 - val_loss: 1.4352 - val_accuracy: 0.6667\n",
      "Epoch 29/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 1.4852 - accuracy: 0.5556 - val_loss: 1.4153 - val_accuracy: 0.6667\n",
      "Epoch 30/300\n",
      "153/153 [==============================] - 0s 105us/step - loss: 1.4647 - accuracy: 0.5556 - val_loss: 1.3959 - val_accuracy: 0.6667\n",
      "Epoch 31/300\n",
      "153/153 [==============================] - 0s 62us/step - loss: 1.4446 - accuracy: 0.5556 - val_loss: 1.3772 - val_accuracy: 0.6667\n",
      "Epoch 32/300\n",
      "153/153 [==============================] - 0s 79us/step - loss: 1.4243 - accuracy: 0.5686 - val_loss: 1.3577 - val_accuracy: 0.6667\n",
      "Epoch 33/300\n",
      "153/153 [==============================] - 0s 74us/step - loss: 1.4038 - accuracy: 0.5686 - val_loss: 1.3391 - val_accuracy: 0.6667\n",
      "Epoch 34/300\n",
      "153/153 [==============================] - 0s 101us/step - loss: 1.3836 - accuracy: 0.5686 - val_loss: 1.3218 - val_accuracy: 0.6667\n",
      "Epoch 35/300\n",
      "153/153 [==============================] - 0s 75us/step - loss: 1.3639 - accuracy: 0.5686 - val_loss: 1.3046 - val_accuracy: 0.6667\n",
      "Epoch 36/300\n",
      "153/153 [==============================] - 0s 96us/step - loss: 1.3456 - accuracy: 0.5752 - val_loss: 1.2895 - val_accuracy: 0.6667\n",
      "Epoch 37/300\n",
      "153/153 [==============================] - 0s 71us/step - loss: 1.3262 - accuracy: 0.5817 - val_loss: 1.2738 - val_accuracy: 0.6667\n",
      "Epoch 38/300\n",
      "153/153 [==============================] - 0s 73us/step - loss: 1.3082 - accuracy: 0.5948 - val_loss: 1.2600 - val_accuracy: 0.6667\n",
      "Epoch 39/300\n",
      "153/153 [==============================] - 0s 78us/step - loss: 1.2910 - accuracy: 0.5882 - val_loss: 1.2468 - val_accuracy: 0.6667\n",
      "Epoch 40/300\n",
      "153/153 [==============================] - 0s 70us/step - loss: 1.2739 - accuracy: 0.6013 - val_loss: 1.2353 - val_accuracy: 0.6667\n",
      "Epoch 41/300\n",
      "153/153 [==============================] - 0s 70us/step - loss: 1.2570 - accuracy: 0.6078 - val_loss: 1.2224 - val_accuracy: 0.6667\n",
      "Epoch 42/300\n",
      "153/153 [==============================] - 0s 70us/step - loss: 1.2414 - accuracy: 0.6013 - val_loss: 1.2118 - val_accuracy: 0.6667\n",
      "Epoch 43/300\n",
      "153/153 [==============================] - 0s 70us/step - loss: 1.2260 - accuracy: 0.6013 - val_loss: 1.2028 - val_accuracy: 0.6667\n",
      "Epoch 44/300\n",
      "153/153 [==============================] - 0s 70us/step - loss: 1.2107 - accuracy: 0.6144 - val_loss: 1.1929 - val_accuracy: 0.6667\n",
      "Epoch 45/300\n",
      "153/153 [==============================] - 0s 76us/step - loss: 1.1973 - accuracy: 0.6144 - val_loss: 1.1837 - val_accuracy: 0.6111\n",
      "Epoch 46/300\n",
      "153/153 [==============================] - 0s 70us/step - loss: 1.1840 - accuracy: 0.6144 - val_loss: 1.1755 - val_accuracy: 0.5000\n",
      "Epoch 47/300\n",
      "153/153 [==============================] - 0s 122us/step - loss: 1.1710 - accuracy: 0.6340 - val_loss: 1.1682 - val_accuracy: 0.5000\n",
      "Epoch 48/300\n",
      "153/153 [==============================] - 0s 66us/step - loss: 1.1582 - accuracy: 0.6340 - val_loss: 1.1603 - val_accuracy: 0.5556\n",
      "Epoch 49/300\n",
      "153/153 [==============================] - 0s 67us/step - loss: 1.1473 - accuracy: 0.6275 - val_loss: 1.1555 - val_accuracy: 0.5556\n",
      "Epoch 50/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 1.1359 - accuracy: 0.6340 - val_loss: 1.1487 - val_accuracy: 0.5556\n",
      "Epoch 51/300\n",
      "153/153 [==============================] - 0s 72us/step - loss: 1.1251 - accuracy: 0.6340 - val_loss: 1.1416 - val_accuracy: 0.5556\n",
      "Epoch 52/300\n",
      "153/153 [==============================] - 0s 72us/step - loss: 1.1149 - accuracy: 0.6340 - val_loss: 1.1383 - val_accuracy: 0.5556\n",
      "Epoch 53/300\n",
      "153/153 [==============================] - 0s 83us/step - loss: 1.1055 - accuracy: 0.6536 - val_loss: 1.1345 - val_accuracy: 0.5556\n",
      "Epoch 54/300\n",
      "153/153 [==============================] - 0s 64us/step - loss: 1.0960 - accuracy: 0.6601 - val_loss: 1.1321 - val_accuracy: 0.5556\n",
      "Epoch 55/300\n",
      "153/153 [==============================] - 0s 70us/step - loss: 1.0873 - accuracy: 0.6667 - val_loss: 1.1293 - val_accuracy: 0.5556\n",
      "Epoch 56/300\n",
      "153/153 [==============================] - 0s 72us/step - loss: 1.0788 - accuracy: 0.6601 - val_loss: 1.1242 - val_accuracy: 0.5556\n",
      "Epoch 57/300\n",
      "153/153 [==============================] - 0s 69us/step - loss: 1.0710 - accuracy: 0.6536 - val_loss: 1.1214 - val_accuracy: 0.5556\n",
      "Epoch 58/300\n",
      "153/153 [==============================] - 0s 73us/step - loss: 1.0634 - accuracy: 0.6536 - val_loss: 1.1199 - val_accuracy: 0.5556\n",
      "Epoch 59/300\n",
      "153/153 [==============================] - 0s 72us/step - loss: 1.0558 - accuracy: 0.6536 - val_loss: 1.1160 - val_accuracy: 0.5556\n",
      "Epoch 60/300\n",
      "153/153 [==============================] - 0s 69us/step - loss: 1.0486 - accuracy: 0.6601 - val_loss: 1.1132 - val_accuracy: 0.5556\n",
      "Epoch 61/300\n",
      "153/153 [==============================] - 0s 66us/step - loss: 1.0420 - accuracy: 0.6601 - val_loss: 1.1113 - val_accuracy: 0.5556\n",
      "Epoch 62/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 1.0352 - accuracy: 0.6601 - val_loss: 1.1085 - val_accuracy: 0.5556\n",
      "Epoch 63/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 1.0287 - accuracy: 0.6601 - val_loss: 1.1069 - val_accuracy: 0.5556\n",
      "Epoch 64/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 1.0225 - accuracy: 0.6601 - val_loss: 1.1060 - val_accuracy: 0.5556\n",
      "Epoch 65/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 1.0162 - accuracy: 0.6536 - val_loss: 1.1058 - val_accuracy: 0.5556\n",
      "Epoch 66/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 1.0103 - accuracy: 0.6601 - val_loss: 1.1038 - val_accuracy: 0.5556\n",
      "Epoch 67/300\n",
      "153/153 [==============================] - 0s 169us/step - loss: 1.0041 - accuracy: 0.6601 - val_loss: 1.1029 - val_accuracy: 0.5556\n",
      "Epoch 68/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 0.9986 - accuracy: 0.6667 - val_loss: 1.1056 - val_accuracy: 0.5000\n",
      "Epoch 69/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 0.9928 - accuracy: 0.6667 - val_loss: 1.1052 - val_accuracy: 0.5000\n",
      "Epoch 70/300\n",
      "153/153 [==============================] - 0s 61us/step - loss: 0.9873 - accuracy: 0.6667 - val_loss: 1.1035 - val_accuracy: 0.5556\n",
      "Epoch 71/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.9818 - accuracy: 0.6732 - val_loss: 1.1037 - val_accuracy: 0.5556\n",
      "Epoch 72/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.9764 - accuracy: 0.6732 - val_loss: 1.1040 - val_accuracy: 0.5556\n",
      "Epoch 73/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.9714 - accuracy: 0.6732 - val_loss: 1.1026 - val_accuracy: 0.6111\n",
      "Epoch 74/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.9662 - accuracy: 0.6863 - val_loss: 1.1043 - val_accuracy: 0.6111\n",
      "Epoch 75/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.9614 - accuracy: 0.6928 - val_loss: 1.1049 - val_accuracy: 0.5556\n",
      "Epoch 76/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.9564 - accuracy: 0.6928 - val_loss: 1.1044 - val_accuracy: 0.6111\n",
      "Epoch 77/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.9517 - accuracy: 0.6863 - val_loss: 1.1042 - val_accuracy: 0.6111\n",
      "Epoch 78/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.9469 - accuracy: 0.6863 - val_loss: 1.1034 - val_accuracy: 0.6111\n",
      "Epoch 79/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.9424 - accuracy: 0.6863 - val_loss: 1.1045 - val_accuracy: 0.6111\n",
      "Epoch 80/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.9380 - accuracy: 0.6863 - val_loss: 1.1055 - val_accuracy: 0.6111\n",
      "Epoch 81/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.9337 - accuracy: 0.6863 - val_loss: 1.1041 - val_accuracy: 0.6111\n",
      "Epoch 82/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.9293 - accuracy: 0.6863 - val_loss: 1.1041 - val_accuracy: 0.6111\n",
      "Epoch 83/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.9252 - accuracy: 0.6863 - val_loss: 1.1043 - val_accuracy: 0.6111\n",
      "Epoch 84/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.9209 - accuracy: 0.6863 - val_loss: 1.1061 - val_accuracy: 0.6111\n",
      "Epoch 85/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.9169 - accuracy: 0.6863 - val_loss: 1.1062 - val_accuracy: 0.6111\n",
      "Epoch 86/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.9129 - accuracy: 0.6863 - val_loss: 1.1058 - val_accuracy: 0.6111\n",
      "Epoch 87/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.9090 - accuracy: 0.6863 - val_loss: 1.1034 - val_accuracy: 0.6111\n",
      "Epoch 88/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.9053 - accuracy: 0.6863 - val_loss: 1.1059 - val_accuracy: 0.5556\n",
      "Epoch 89/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.9012 - accuracy: 0.6928 - val_loss: 1.1059 - val_accuracy: 0.5556\n",
      "Epoch 90/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8977 - accuracy: 0.6928 - val_loss: 1.1067 - val_accuracy: 0.5556\n",
      "Epoch 91/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 0.8940 - accuracy: 0.6993 - val_loss: 1.1073 - val_accuracy: 0.5556\n",
      "Epoch 92/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 0.8901 - accuracy: 0.6993 - val_loss: 1.1072 - val_accuracy: 0.5556\n",
      "Epoch 93/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.8866 - accuracy: 0.6993 - val_loss: 1.1072 - val_accuracy: 0.5556\n",
      "Epoch 94/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 0.8832 - accuracy: 0.6993 - val_loss: 1.1055 - val_accuracy: 0.5556\n",
      "Epoch 95/300\n",
      "153/153 [==============================] - 0s 58us/step - loss: 0.8795 - accuracy: 0.6993 - val_loss: 1.1057 - val_accuracy: 0.5556\n",
      "Epoch 96/300\n",
      "153/153 [==============================] - 0s 58us/step - loss: 0.8761 - accuracy: 0.6993 - val_loss: 1.1059 - val_accuracy: 0.5556\n",
      "Epoch 97/300\n",
      "153/153 [==============================] - 0s 63us/step - loss: 0.8728 - accuracy: 0.7059 - val_loss: 1.1073 - val_accuracy: 0.5556\n",
      "Epoch 98/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 0.8692 - accuracy: 0.7059 - val_loss: 1.1094 - val_accuracy: 0.5556\n",
      "Epoch 99/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 0.8660 - accuracy: 0.7124 - val_loss: 1.1099 - val_accuracy: 0.5556\n",
      "Epoch 100/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.8629 - accuracy: 0.7124 - val_loss: 1.1083 - val_accuracy: 0.5556\n",
      "Epoch 101/300\n",
      "153/153 [==============================] - 0s 47us/step - loss: 0.8598 - accuracy: 0.7124 - val_loss: 1.1109 - val_accuracy: 0.5556\n",
      "Epoch 102/300\n",
      "153/153 [==============================] - 0s 47us/step - loss: 0.8568 - accuracy: 0.7124 - val_loss: 1.1117 - val_accuracy: 0.5556\n",
      "Epoch 103/300\n",
      "153/153 [==============================] - 0s 47us/step - loss: 0.8535 - accuracy: 0.7124 - val_loss: 1.1112 - val_accuracy: 0.5556\n",
      "Epoch 104/300\n",
      "153/153 [==============================] - 0s 46us/step - loss: 0.8503 - accuracy: 0.7124 - val_loss: 1.1112 - val_accuracy: 0.5556\n",
      "Epoch 105/300\n",
      "153/153 [==============================] - 0s 71us/step - loss: 0.8475 - accuracy: 0.7190 - val_loss: 1.1091 - val_accuracy: 0.5556\n",
      "Epoch 106/300\n",
      "153/153 [==============================] - 0s 73us/step - loss: 0.8447 - accuracy: 0.7190 - val_loss: 1.1098 - val_accuracy: 0.5556\n",
      "Epoch 107/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.8415 - accuracy: 0.7190 - val_loss: 1.1125 - val_accuracy: 0.5556\n",
      "Epoch 108/300\n",
      "153/153 [==============================] - 0s 46us/step - loss: 0.8387 - accuracy: 0.7190 - val_loss: 1.1114 - val_accuracy: 0.5556\n",
      "Epoch 109/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.8359 - accuracy: 0.7190 - val_loss: 1.1126 - val_accuracy: 0.5556\n",
      "Epoch 110/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 0.8331 - accuracy: 0.7190 - val_loss: 1.1117 - val_accuracy: 0.5556\n",
      "Epoch 111/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 0.8304 - accuracy: 0.7190 - val_loss: 1.1110 - val_accuracy: 0.5556\n",
      "Epoch 112/300\n",
      "153/153 [==============================] - 0s 49us/step - loss: 0.8276 - accuracy: 0.7190 - val_loss: 1.1120 - val_accuracy: 0.5556\n",
      "Epoch 113/300\n",
      "153/153 [==============================] - 0s 49us/step - loss: 0.8249 - accuracy: 0.7190 - val_loss: 1.1119 - val_accuracy: 0.5556\n",
      "Epoch 114/300\n",
      "153/153 [==============================] - 0s 49us/step - loss: 0.8223 - accuracy: 0.7190 - val_loss: 1.1135 - val_accuracy: 0.5556\n",
      "Epoch 115/300\n",
      "153/153 [==============================] - 0s 49us/step - loss: 0.8199 - accuracy: 0.7190 - val_loss: 1.1166 - val_accuracy: 0.5556\n",
      "Epoch 116/300\n",
      "153/153 [==============================] - 0s 49us/step - loss: 0.8174 - accuracy: 0.7190 - val_loss: 1.1192 - val_accuracy: 0.5556\n",
      "Epoch 117/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.8147 - accuracy: 0.7190 - val_loss: 1.1169 - val_accuracy: 0.5556\n",
      "Epoch 118/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.8123 - accuracy: 0.7190 - val_loss: 1.1166 - val_accuracy: 0.5556\n",
      "Epoch 119/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.8098 - accuracy: 0.7190 - val_loss: 1.1158 - val_accuracy: 0.5556\n",
      "Epoch 120/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 0.8076 - accuracy: 0.7190 - val_loss: 1.1157 - val_accuracy: 0.5556\n",
      "Epoch 121/300\n",
      "153/153 [==============================] - 0s 49us/step - loss: 0.8052 - accuracy: 0.7190 - val_loss: 1.1179 - val_accuracy: 0.5556\n",
      "Epoch 122/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.8026 - accuracy: 0.7190 - val_loss: 1.1181 - val_accuracy: 0.5556\n",
      "Epoch 123/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 0.8005 - accuracy: 0.7190 - val_loss: 1.1205 - val_accuracy: 0.5556\n",
      "Epoch 124/300\n",
      "153/153 [==============================] - 0s 73us/step - loss: 0.7983 - accuracy: 0.7190 - val_loss: 1.1222 - val_accuracy: 0.5556\n",
      "Epoch 125/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.7959 - accuracy: 0.7190 - val_loss: 1.1219 - val_accuracy: 0.5556\n",
      "Epoch 126/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 0.7938 - accuracy: 0.7320 - val_loss: 1.1219 - val_accuracy: 0.5556\n",
      "Epoch 127/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.7915 - accuracy: 0.7255 - val_loss: 1.1213 - val_accuracy: 0.5556\n",
      "Epoch 128/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.7893 - accuracy: 0.7255 - val_loss: 1.1254 - val_accuracy: 0.5556\n",
      "Epoch 129/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.7873 - accuracy: 0.7386 - val_loss: 1.1282 - val_accuracy: 0.6111\n",
      "Epoch 130/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 0.7850 - accuracy: 0.7320 - val_loss: 1.1289 - val_accuracy: 0.6111\n",
      "Epoch 131/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.7829 - accuracy: 0.7320 - val_loss: 1.1298 - val_accuracy: 0.6111\n",
      "Epoch 132/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.7809 - accuracy: 0.7320 - val_loss: 1.1319 - val_accuracy: 0.6111\n",
      "Epoch 133/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 0.7788 - accuracy: 0.7320 - val_loss: 1.1313 - val_accuracy: 0.6111\n",
      "Epoch 134/300\n",
      "153/153 [==============================] - 0s 91us/step - loss: 0.7768 - accuracy: 0.7320 - val_loss: 1.1307 - val_accuracy: 0.6111\n",
      "Epoch 135/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.7747 - accuracy: 0.7320 - val_loss: 1.1309 - val_accuracy: 0.6111\n",
      "Epoch 136/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 0.7726 - accuracy: 0.7320 - val_loss: 1.1343 - val_accuracy: 0.6111\n",
      "Epoch 137/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.7706 - accuracy: 0.7320 - val_loss: 1.1371 - val_accuracy: 0.6111\n",
      "Epoch 138/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 0.7688 - accuracy: 0.7320 - val_loss: 1.1404 - val_accuracy: 0.6111\n",
      "Epoch 139/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.7669 - accuracy: 0.7320 - val_loss: 1.1392 - val_accuracy: 0.6111\n",
      "Epoch 140/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.7652 - accuracy: 0.7320 - val_loss: 1.1410 - val_accuracy: 0.6111\n",
      "Epoch 141/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.7630 - accuracy: 0.7320 - val_loss: 1.1451 - val_accuracy: 0.6111\n",
      "Epoch 142/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 0.7613 - accuracy: 0.7320 - val_loss: 1.1467 - val_accuracy: 0.6111\n",
      "Epoch 143/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.7592 - accuracy: 0.7320 - val_loss: 1.1468 - val_accuracy: 0.6111\n",
      "Epoch 144/300\n",
      "153/153 [==============================] - 0s 55us/step - loss: 0.7577 - accuracy: 0.7320 - val_loss: 1.1523 - val_accuracy: 0.6111\n",
      "Epoch 145/300\n",
      "153/153 [==============================] - 0s 47us/step - loss: 0.7559 - accuracy: 0.7320 - val_loss: 1.1544 - val_accuracy: 0.6111\n",
      "Epoch 146/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.7539 - accuracy: 0.7320 - val_loss: 1.1528 - val_accuracy: 0.6111\n",
      "Epoch 147/300\n",
      "153/153 [==============================] - 0s 49us/step - loss: 0.7522 - accuracy: 0.7386 - val_loss: 1.1520 - val_accuracy: 0.6111\n",
      "Epoch 148/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.7503 - accuracy: 0.7451 - val_loss: 1.1530 - val_accuracy: 0.6111\n",
      "Epoch 149/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.7489 - accuracy: 0.7451 - val_loss: 1.1541 - val_accuracy: 0.6111\n",
      "Epoch 150/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.7469 - accuracy: 0.7451 - val_loss: 1.1587 - val_accuracy: 0.6111\n",
      "Epoch 151/300\n",
      "153/153 [==============================] - 0s 98us/step - loss: 0.7452 - accuracy: 0.7451 - val_loss: 1.1579 - val_accuracy: 0.6111\n",
      "Epoch 152/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.7442 - accuracy: 0.7451 - val_loss: 1.1658 - val_accuracy: 0.6111\n",
      "Epoch 153/300\n",
      "153/153 [==============================] - 0s 64us/step - loss: 0.7418 - accuracy: 0.7451 - val_loss: 1.1669 - val_accuracy: 0.6111\n",
      "Epoch 154/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.7402 - accuracy: 0.7451 - val_loss: 1.1656 - val_accuracy: 0.6111\n",
      "Epoch 155/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.7385 - accuracy: 0.7451 - val_loss: 1.1660 - val_accuracy: 0.6111\n",
      "Epoch 156/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.7369 - accuracy: 0.7451 - val_loss: 1.1666 - val_accuracy: 0.6111\n",
      "Epoch 157/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.7356 - accuracy: 0.7451 - val_loss: 1.1683 - val_accuracy: 0.6111\n",
      "Epoch 158/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.7338 - accuracy: 0.7582 - val_loss: 1.1679 - val_accuracy: 0.6111\n",
      "Epoch 159/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.7322 - accuracy: 0.7582 - val_loss: 1.1687 - val_accuracy: 0.6111\n",
      "Epoch 160/300\n",
      "153/153 [==============================] - 0s 99us/step - loss: 0.7307 - accuracy: 0.7582 - val_loss: 1.1692 - val_accuracy: 0.6111\n",
      "Epoch 161/300\n",
      "153/153 [==============================] - 0s 58us/step - loss: 0.7293 - accuracy: 0.7582 - val_loss: 1.1715 - val_accuracy: 0.6111\n",
      "Epoch 162/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.7278 - accuracy: 0.7516 - val_loss: 1.1717 - val_accuracy: 0.6111\n",
      "Epoch 163/300\n",
      "153/153 [==============================] - 0s 68us/step - loss: 0.7261 - accuracy: 0.7582 - val_loss: 1.1732 - val_accuracy: 0.6111\n",
      "Epoch 164/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 0.7246 - accuracy: 0.7582 - val_loss: 1.1749 - val_accuracy: 0.6111\n",
      "Epoch 165/300\n",
      "153/153 [==============================] - 0s 47us/step - loss: 0.7232 - accuracy: 0.7582 - val_loss: 1.1767 - val_accuracy: 0.6111\n",
      "Epoch 166/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.7218 - accuracy: 0.7582 - val_loss: 1.1777 - val_accuracy: 0.6111\n",
      "Epoch 167/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.7203 - accuracy: 0.7647 - val_loss: 1.1796 - val_accuracy: 0.6111\n",
      "Epoch 168/300\n",
      "153/153 [==============================] - 0s 47us/step - loss: 0.7187 - accuracy: 0.7647 - val_loss: 1.1778 - val_accuracy: 0.6111\n",
      "Epoch 169/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 0.7176 - accuracy: 0.7582 - val_loss: 1.1743 - val_accuracy: 0.6111\n",
      "Epoch 170/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.7158 - accuracy: 0.7582 - val_loss: 1.1781 - val_accuracy: 0.6111\n",
      "Epoch 171/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.7143 - accuracy: 0.7582 - val_loss: 1.1787 - val_accuracy: 0.6111\n",
      "Epoch 172/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.7130 - accuracy: 0.7582 - val_loss: 1.1814 - val_accuracy: 0.6111\n",
      "Epoch 173/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.7115 - accuracy: 0.7582 - val_loss: 1.1832 - val_accuracy: 0.6111\n",
      "Epoch 174/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7102 - accuracy: 0.7582 - val_loss: 1.1830 - val_accuracy: 0.6667\n",
      "Epoch 175/300\n",
      "153/153 [==============================] - 0s 47us/step - loss: 0.7090 - accuracy: 0.7582 - val_loss: 1.1823 - val_accuracy: 0.6667\n",
      "Epoch 176/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 0.7077 - accuracy: 0.7647 - val_loss: 1.1825 - val_accuracy: 0.6667\n",
      "Epoch 177/300\n",
      "153/153 [==============================] - 0s 47us/step - loss: 0.7063 - accuracy: 0.7647 - val_loss: 1.1859 - val_accuracy: 0.7222\n",
      "Epoch 178/300\n",
      "153/153 [==============================] - 0s 47us/step - loss: 0.7050 - accuracy: 0.7647 - val_loss: 1.1866 - val_accuracy: 0.7222\n",
      "Epoch 179/300\n",
      "153/153 [==============================] - 0s 47us/step - loss: 0.7036 - accuracy: 0.7647 - val_loss: 1.1855 - val_accuracy: 0.7222\n",
      "Epoch 180/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.7027 - accuracy: 0.7647 - val_loss: 1.1885 - val_accuracy: 0.7222\n",
      "Epoch 181/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 0.7012 - accuracy: 0.7647 - val_loss: 1.1857 - val_accuracy: 0.6667\n",
      "Epoch 182/300\n",
      "153/153 [==============================] - 0s 47us/step - loss: 0.6998 - accuracy: 0.7647 - val_loss: 1.1827 - val_accuracy: 0.6667\n",
      "Epoch 183/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.6986 - accuracy: 0.7647 - val_loss: 1.1828 - val_accuracy: 0.6667\n",
      "Epoch 184/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.6974 - accuracy: 0.7582 - val_loss: 1.1840 - val_accuracy: 0.6667\n",
      "Epoch 185/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 0.6962 - accuracy: 0.7647 - val_loss: 1.1860 - val_accuracy: 0.6667\n",
      "Epoch 186/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.6948 - accuracy: 0.7647 - val_loss: 1.1847 - val_accuracy: 0.6667\n",
      "Epoch 187/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.6940 - accuracy: 0.7582 - val_loss: 1.1892 - val_accuracy: 0.7222\n",
      "Epoch 188/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 0.6926 - accuracy: 0.7647 - val_loss: 1.1885 - val_accuracy: 0.6667\n",
      "Epoch 189/300\n",
      "153/153 [==============================] - 0s 49us/step - loss: 0.6914 - accuracy: 0.7582 - val_loss: 1.1884 - val_accuracy: 0.6667\n",
      "Epoch 190/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 0.6903 - accuracy: 0.7582 - val_loss: 1.1930 - val_accuracy: 0.7222\n",
      "Epoch 191/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.6889 - accuracy: 0.7647 - val_loss: 1.1908 - val_accuracy: 0.7222\n",
      "Epoch 192/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.6880 - accuracy: 0.7647 - val_loss: 1.1907 - val_accuracy: 0.7222\n",
      "Epoch 193/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 0.6871 - accuracy: 0.7647 - val_loss: 1.1963 - val_accuracy: 0.7222\n",
      "Epoch 194/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.6857 - accuracy: 0.7647 - val_loss: 1.1984 - val_accuracy: 0.7222\n",
      "Epoch 195/300\n",
      "153/153 [==============================] - 0s 119us/step - loss: 0.6844 - accuracy: 0.7647 - val_loss: 1.2007 - val_accuracy: 0.7222\n",
      "Epoch 196/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.6835 - accuracy: 0.7647 - val_loss: 1.2008 - val_accuracy: 0.7222\n",
      "Epoch 197/300\n",
      "153/153 [==============================] - 0s 62us/step - loss: 0.6824 - accuracy: 0.7647 - val_loss: 1.2000 - val_accuracy: 0.7222\n",
      "Epoch 198/300\n",
      "153/153 [==============================] - 0s 80us/step - loss: 0.6813 - accuracy: 0.7582 - val_loss: 1.2004 - val_accuracy: 0.6667\n",
      "Epoch 199/300\n",
      "153/153 [==============================] - 0s 73us/step - loss: 0.6802 - accuracy: 0.7647 - val_loss: 1.2029 - val_accuracy: 0.6667\n",
      "Epoch 200/300\n",
      "153/153 [==============================] - 0s 75us/step - loss: 0.6792 - accuracy: 0.7712 - val_loss: 1.2028 - val_accuracy: 0.6667\n",
      "Epoch 201/300\n",
      "153/153 [==============================] - 0s 82us/step - loss: 0.6780 - accuracy: 0.7712 - val_loss: 1.2045 - val_accuracy: 0.6667\n",
      "Epoch 202/300\n",
      "153/153 [==============================] - 0s 81us/step - loss: 0.6772 - accuracy: 0.7712 - val_loss: 1.2037 - val_accuracy: 0.6667\n",
      "Epoch 203/300\n",
      "153/153 [==============================] - 0s 84us/step - loss: 0.6759 - accuracy: 0.7712 - val_loss: 1.2072 - val_accuracy: 0.6667\n",
      "Epoch 204/300\n",
      "153/153 [==============================] - 0s 61us/step - loss: 0.6752 - accuracy: 0.7647 - val_loss: 1.2103 - val_accuracy: 0.7222\n",
      "Epoch 205/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 0.6740 - accuracy: 0.7647 - val_loss: 1.2123 - val_accuracy: 0.7222\n",
      "Epoch 206/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 0.6730 - accuracy: 0.7647 - val_loss: 1.2164 - val_accuracy: 0.7222\n",
      "Epoch 207/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.6722 - accuracy: 0.7647 - val_loss: 1.2149 - val_accuracy: 0.6667\n",
      "Epoch 208/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.6708 - accuracy: 0.7647 - val_loss: 1.2165 - val_accuracy: 0.6667\n",
      "Epoch 209/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.6703 - accuracy: 0.7647 - val_loss: 1.2207 - val_accuracy: 0.7222\n",
      "Epoch 210/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.6690 - accuracy: 0.7647 - val_loss: 1.2183 - val_accuracy: 0.7222\n",
      "Epoch 211/300\n",
      "153/153 [==============================] - 0s 62us/step - loss: 0.6679 - accuracy: 0.7647 - val_loss: 1.2196 - val_accuracy: 0.7222\n",
      "Epoch 212/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.6671 - accuracy: 0.7647 - val_loss: 1.2234 - val_accuracy: 0.7222\n",
      "Epoch 213/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.6661 - accuracy: 0.7647 - val_loss: 1.2262 - val_accuracy: 0.7222\n",
      "Epoch 214/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.6649 - accuracy: 0.7647 - val_loss: 1.2231 - val_accuracy: 0.7222\n",
      "Epoch 215/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.6639 - accuracy: 0.7647 - val_loss: 1.2225 - val_accuracy: 0.7222\n",
      "Epoch 216/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.6629 - accuracy: 0.7712 - val_loss: 1.2236 - val_accuracy: 0.6667\n",
      "Epoch 217/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.6618 - accuracy: 0.7712 - val_loss: 1.2260 - val_accuracy: 0.6667\n",
      "Epoch 218/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.6610 - accuracy: 0.7712 - val_loss: 1.2260 - val_accuracy: 0.6667\n",
      "Epoch 219/300\n",
      "153/153 [==============================] - 0s 68us/step - loss: 0.6600 - accuracy: 0.7712 - val_loss: 1.2274 - val_accuracy: 0.6667\n",
      "Epoch 220/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.6591 - accuracy: 0.7712 - val_loss: 1.2292 - val_accuracy: 0.7222\n",
      "Epoch 221/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 0.6581 - accuracy: 0.7712 - val_loss: 1.2321 - val_accuracy: 0.7222\n",
      "Epoch 222/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.6571 - accuracy: 0.7712 - val_loss: 1.2375 - val_accuracy: 0.7222\n",
      "Epoch 223/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.6567 - accuracy: 0.7712 - val_loss: 1.2368 - val_accuracy: 0.7222\n",
      "Epoch 224/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.6552 - accuracy: 0.7647 - val_loss: 1.2367 - val_accuracy: 0.7222\n",
      "Epoch 225/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.6542 - accuracy: 0.7647 - val_loss: 1.2393 - val_accuracy: 0.7222\n",
      "Epoch 226/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.6536 - accuracy: 0.7712 - val_loss: 1.2388 - val_accuracy: 0.7222\n",
      "Epoch 227/300\n",
      "153/153 [==============================] - 0s 73us/step - loss: 0.6524 - accuracy: 0.7712 - val_loss: 1.2401 - val_accuracy: 0.7222\n",
      "Epoch 228/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.6517 - accuracy: 0.7712 - val_loss: 1.2425 - val_accuracy: 0.7222\n",
      "Epoch 229/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.6510 - accuracy: 0.7712 - val_loss: 1.2406 - val_accuracy: 0.6667\n",
      "Epoch 230/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.6497 - accuracy: 0.7712 - val_loss: 1.2425 - val_accuracy: 0.6667\n",
      "Epoch 231/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.6488 - accuracy: 0.7712 - val_loss: 1.2426 - val_accuracy: 0.6667\n",
      "Epoch 232/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.6479 - accuracy: 0.7712 - val_loss: 1.2417 - val_accuracy: 0.6667\n",
      "Epoch 233/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.6471 - accuracy: 0.7712 - val_loss: 1.2424 - val_accuracy: 0.6667\n",
      "Epoch 234/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.6462 - accuracy: 0.7712 - val_loss: 1.2466 - val_accuracy: 0.7222\n",
      "Epoch 235/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.6451 - accuracy: 0.7712 - val_loss: 1.2482 - val_accuracy: 0.7222\n",
      "Epoch 236/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.6446 - accuracy: 0.7712 - val_loss: 1.2492 - val_accuracy: 0.7222\n",
      "Epoch 237/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.6435 - accuracy: 0.7712 - val_loss: 1.2517 - val_accuracy: 0.7222\n",
      "Epoch 238/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.6431 - accuracy: 0.7712 - val_loss: 1.2534 - val_accuracy: 0.6667\n",
      "Epoch 239/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.6421 - accuracy: 0.7712 - val_loss: 1.2545 - val_accuracy: 0.6667\n",
      "Epoch 240/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.6412 - accuracy: 0.7712 - val_loss: 1.2540 - val_accuracy: 0.6667\n",
      "Epoch 241/300\n",
      "153/153 [==============================] - 0s 74us/step - loss: 0.6402 - accuracy: 0.7712 - val_loss: 1.2543 - val_accuracy: 0.7222\n",
      "Epoch 242/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.6394 - accuracy: 0.7712 - val_loss: 1.2534 - val_accuracy: 0.7222\n",
      "Epoch 243/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.6388 - accuracy: 0.7712 - val_loss: 1.2539 - val_accuracy: 0.7222\n",
      "Epoch 244/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.6379 - accuracy: 0.7712 - val_loss: 1.2552 - val_accuracy: 0.7222\n",
      "Epoch 245/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.6369 - accuracy: 0.7712 - val_loss: 1.2590 - val_accuracy: 0.6667\n",
      "Epoch 246/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.6361 - accuracy: 0.7712 - val_loss: 1.2615 - val_accuracy: 0.6667\n",
      "Epoch 247/300\n",
      "153/153 [==============================] - 0s 78us/step - loss: 0.6353 - accuracy: 0.7712 - val_loss: 1.2619 - val_accuracy: 0.6667\n",
      "Epoch 248/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.6345 - accuracy: 0.7712 - val_loss: 1.2627 - val_accuracy: 0.6667\n",
      "Epoch 249/300\n",
      "153/153 [==============================] - 0s 65us/step - loss: 0.6335 - accuracy: 0.7712 - val_loss: 1.2595 - val_accuracy: 0.6667\n",
      "Epoch 250/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.6329 - accuracy: 0.7712 - val_loss: 1.2590 - val_accuracy: 0.6667\n",
      "Epoch 251/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.6319 - accuracy: 0.7712 - val_loss: 1.2595 - val_accuracy: 0.6667\n",
      "Epoch 252/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.6314 - accuracy: 0.7712 - val_loss: 1.2597 - val_accuracy: 0.6667\n",
      "Epoch 253/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.6305 - accuracy: 0.7712 - val_loss: 1.2620 - val_accuracy: 0.6667\n",
      "Epoch 254/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.6297 - accuracy: 0.7712 - val_loss: 1.2621 - val_accuracy: 0.7222\n",
      "Epoch 255/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.6288 - accuracy: 0.7712 - val_loss: 1.2647 - val_accuracy: 0.6667\n",
      "Epoch 256/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.6280 - accuracy: 0.7712 - val_loss: 1.2696 - val_accuracy: 0.6667\n",
      "Epoch 257/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.6272 - accuracy: 0.7712 - val_loss: 1.2701 - val_accuracy: 0.6667\n",
      "Epoch 258/300\n",
      "153/153 [==============================] - ETA: 0s - loss: 0.6991 - accuracy: 0.71 - 0s 52us/step - loss: 0.6264 - accuracy: 0.7712 - val_loss: 1.2720 - val_accuracy: 0.6667\n",
      "Epoch 259/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.6257 - accuracy: 0.7712 - val_loss: 1.2743 - val_accuracy: 0.6667\n",
      "Epoch 260/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.6251 - accuracy: 0.7712 - val_loss: 1.2764 - val_accuracy: 0.6667\n",
      "Epoch 261/300\n",
      "153/153 [==============================] - ETA: 0s - loss: 0.5323 - accuracy: 0.84 - 0s 66us/step - loss: 0.6241 - accuracy: 0.7712 - val_loss: 1.2766 - val_accuracy: 0.6667\n",
      "Epoch 262/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.6233 - accuracy: 0.7712 - val_loss: 1.2742 - val_accuracy: 0.6667\n",
      "Epoch 263/300\n",
      "153/153 [==============================] - 0s 90us/step - loss: 0.6225 - accuracy: 0.7712 - val_loss: 1.2734 - val_accuracy: 0.6667\n",
      "Epoch 264/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.6219 - accuracy: 0.7712 - val_loss: 1.2745 - val_accuracy: 0.6667\n",
      "Epoch 265/300\n",
      "153/153 [==============================] - 0s 55us/step - loss: 0.6212 - accuracy: 0.7712 - val_loss: 1.2767 - val_accuracy: 0.6667\n",
      "Epoch 266/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.6204 - accuracy: 0.7712 - val_loss: 1.2805 - val_accuracy: 0.6667\n",
      "Epoch 267/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.6196 - accuracy: 0.7712 - val_loss: 1.2792 - val_accuracy: 0.6667\n",
      "Epoch 268/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.6190 - accuracy: 0.7712 - val_loss: 1.2755 - val_accuracy: 0.6667\n",
      "Epoch 269/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 0.6182 - accuracy: 0.7712 - val_loss: 1.2788 - val_accuracy: 0.6667\n",
      "Epoch 270/300\n",
      "153/153 [==============================] - 0s 101us/step - loss: 0.6174 - accuracy: 0.7712 - val_loss: 1.2819 - val_accuracy: 0.6667\n",
      "Epoch 271/300\n",
      "153/153 [==============================] - 0s 72us/step - loss: 0.6166 - accuracy: 0.7712 - val_loss: 1.2824 - val_accuracy: 0.6667\n",
      "Epoch 272/300\n",
      "153/153 [==============================] - 0s 45us/step - loss: 0.6157 - accuracy: 0.7712 - val_loss: 1.2825 - val_accuracy: 0.6667\n",
      "Epoch 273/300\n",
      "153/153 [==============================] - 0s 44us/step - loss: 0.6151 - accuracy: 0.7712 - val_loss: 1.2848 - val_accuracy: 0.6667\n",
      "Epoch 274/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.6143 - accuracy: 0.7712 - val_loss: 1.2876 - val_accuracy: 0.6667\n",
      "Epoch 275/300\n",
      "153/153 [==============================] - 0s 116us/step - loss: 0.6136 - accuracy: 0.7712 - val_loss: 1.2901 - val_accuracy: 0.6667\n",
      "Epoch 276/300\n",
      "153/153 [==============================] - 0s 55us/step - loss: 0.6130 - accuracy: 0.7712 - val_loss: 1.2895 - val_accuracy: 0.6667\n",
      "Epoch 277/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.6124 - accuracy: 0.7712 - val_loss: 1.2890 - val_accuracy: 0.6667\n",
      "Epoch 278/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 0.6116 - accuracy: 0.7712 - val_loss: 1.2897 - val_accuracy: 0.6667\n",
      "Epoch 279/300\n",
      "153/153 [==============================] - 0s 55us/step - loss: 0.6110 - accuracy: 0.7712 - val_loss: 1.2917 - val_accuracy: 0.6667\n",
      "Epoch 280/300\n",
      "153/153 [==============================] - 0s 65us/step - loss: 0.6101 - accuracy: 0.7712 - val_loss: 1.2911 - val_accuracy: 0.6667\n",
      "Epoch 281/300\n",
      "153/153 [==============================] - 0s 79us/step - loss: 0.6094 - accuracy: 0.7712 - val_loss: 1.2932 - val_accuracy: 0.6667\n",
      "Epoch 282/300\n",
      "153/153 [==============================] - 0s 62us/step - loss: 0.6088 - accuracy: 0.7712 - val_loss: 1.2945 - val_accuracy: 0.6667\n",
      "Epoch 283/300\n",
      "153/153 [==============================] - 0s 77us/step - loss: 0.6082 - accuracy: 0.7712 - val_loss: 1.2952 - val_accuracy: 0.6667\n",
      "Epoch 284/300\n",
      "153/153 [==============================] - 0s 143us/step - loss: 0.6073 - accuracy: 0.7712 - val_loss: 1.2960 - val_accuracy: 0.6667\n",
      "Epoch 285/300\n",
      "153/153 [==============================] - 0s 66us/step - loss: 0.6068 - accuracy: 0.7712 - val_loss: 1.2982 - val_accuracy: 0.6667\n",
      "Epoch 286/300\n",
      "153/153 [==============================] - 0s 114us/step - loss: 0.6063 - accuracy: 0.7712 - val_loss: 1.2971 - val_accuracy: 0.6667\n",
      "Epoch 287/300\n",
      "153/153 [==============================] - 0s 65us/step - loss: 0.6055 - accuracy: 0.7712 - val_loss: 1.2972 - val_accuracy: 0.6667\n",
      "Epoch 288/300\n",
      "153/153 [==============================] - 0s 106us/step - loss: 0.6049 - accuracy: 0.7712 - val_loss: 1.3031 - val_accuracy: 0.6667\n",
      "Epoch 289/300\n",
      "153/153 [==============================] - 0s 66us/step - loss: 0.6042 - accuracy: 0.7712 - val_loss: 1.3065 - val_accuracy: 0.6667\n",
      "Epoch 290/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.6034 - accuracy: 0.7712 - val_loss: 1.3094 - val_accuracy: 0.6667\n",
      "Epoch 291/300\n",
      "153/153 [==============================] - 0s 49us/step - loss: 0.6028 - accuracy: 0.7712 - val_loss: 1.3067 - val_accuracy: 0.6667\n",
      "Epoch 292/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 0.6023 - accuracy: 0.7778 - val_loss: 1.3102 - val_accuracy: 0.6667\n",
      "Epoch 293/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.6015 - accuracy: 0.7778 - val_loss: 1.3097 - val_accuracy: 0.6667\n",
      "Epoch 294/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 0.6009 - accuracy: 0.7712 - val_loss: 1.3072 - val_accuracy: 0.6667\n",
      "Epoch 295/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 0.6001 - accuracy: 0.7712 - val_loss: 1.3101 - val_accuracy: 0.6667\n",
      "Epoch 296/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.5999 - accuracy: 0.7712 - val_loss: 1.3134 - val_accuracy: 0.6667\n",
      "Epoch 297/300\n",
      "153/153 [==============================] - 0s 69us/step - loss: 0.5988 - accuracy: 0.7712 - val_loss: 1.3119 - val_accuracy: 0.6667\n",
      "Epoch 298/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.5983 - accuracy: 0.7712 - val_loss: 1.3122 - val_accuracy: 0.6667\n",
      "Epoch 299/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.5976 - accuracy: 0.7712 - val_loss: 1.3121 - val_accuracy: 0.6667\n",
      "Epoch 300/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.5972 - accuracy: 0.7712 - val_loss: 1.3125 - val_accuracy: 0.6667\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, Y_train, batch_size=32, epochs=300, validation_split=0.1,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "# summarize history for loss\n",
    "#plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['no_reg loss'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.645904405172481 \n",
      "Test accuracy: 0.7209302186965942\n"
     ]
    }
   ],
   "source": [
    "results = model.evaluate(X_test, Y_test, verbose=0)\n",
    "\n",
    "print(f'Test {model.metrics_names[0]}: {results[0]} \\nTest {model.metrics_names[1]}: {results[1]}' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how to get better from this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Penalities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build a 2-Dense layer model\n",
    "\n",
    "_DENSE -> RELU -> DENSE -> SOFTMAX_\n",
    "\n",
    "and units: `10` ->  `n_c`.\n",
    "\n",
    "Use `l2(0.01)` as `kernel_regularizer` for the first layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a model with a regularisation term\n",
    "model_pen = Sequential()\n",
    "\n",
    "# YOUR CODE HERE\n",
    "\n",
    "model_pen.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 153 samples, validate on 18 samples\n",
      "Epoch 1/300\n",
      "153/153 [==============================] - 0s 2ms/step - loss: 1.9289 - accuracy: 0.1699 - val_loss: 1.9410 - val_accuracy: 0.2778\n",
      "Epoch 2/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 1.9076 - accuracy: 0.3072 - val_loss: 1.9201 - val_accuracy: 0.3333\n",
      "Epoch 3/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 1.8859 - accuracy: 0.4248 - val_loss: 1.8986 - val_accuracy: 0.3889\n",
      "Epoch 4/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 1.8659 - accuracy: 0.4706 - val_loss: 1.8776 - val_accuracy: 0.4444\n",
      "Epoch 5/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 1.8451 - accuracy: 0.5294 - val_loss: 1.8575 - val_accuracy: 0.3889\n",
      "Epoch 6/300\n",
      "153/153 [==============================] - 0s 98us/step - loss: 1.8244 - accuracy: 0.5556 - val_loss: 1.8385 - val_accuracy: 0.3889\n",
      "Epoch 7/300\n",
      "153/153 [==============================] - 0s 65us/step - loss: 1.8045 - accuracy: 0.5817 - val_loss: 1.8190 - val_accuracy: 0.4444\n",
      "Epoch 8/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 1.7832 - accuracy: 0.6013 - val_loss: 1.7996 - val_accuracy: 0.4444\n",
      "Epoch 9/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 1.7629 - accuracy: 0.5948 - val_loss: 1.7806 - val_accuracy: 0.4444\n",
      "Epoch 10/300\n",
      "153/153 [==============================] - 0s 49us/step - loss: 1.7415 - accuracy: 0.6078 - val_loss: 1.7611 - val_accuracy: 0.5000\n",
      "Epoch 11/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 1.7212 - accuracy: 0.6078 - val_loss: 1.7407 - val_accuracy: 0.5000\n",
      "Epoch 12/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 1.6995 - accuracy: 0.6078 - val_loss: 1.7205 - val_accuracy: 0.5000\n",
      "Epoch 13/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 1.6778 - accuracy: 0.6013 - val_loss: 1.7010 - val_accuracy: 0.5000\n",
      "Epoch 14/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 1.6570 - accuracy: 0.6013 - val_loss: 1.6804 - val_accuracy: 0.5000\n",
      "Epoch 15/300\n",
      "153/153 [==============================] - 0s 55us/step - loss: 1.6345 - accuracy: 0.5948 - val_loss: 1.6593 - val_accuracy: 0.5000\n",
      "Epoch 16/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 1.6124 - accuracy: 0.5882 - val_loss: 1.6389 - val_accuracy: 0.4444\n",
      "Epoch 17/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 1.5904 - accuracy: 0.5882 - val_loss: 1.6184 - val_accuracy: 0.4444\n",
      "Epoch 18/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 1.5672 - accuracy: 0.5948 - val_loss: 1.5980 - val_accuracy: 0.4444\n",
      "Epoch 19/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 1.5454 - accuracy: 0.5948 - val_loss: 1.5770 - val_accuracy: 0.4444\n",
      "Epoch 20/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 1.5231 - accuracy: 0.6013 - val_loss: 1.5560 - val_accuracy: 0.4444\n",
      "Epoch 21/300\n",
      "153/153 [==============================] - 0s 56us/step - loss: 1.5005 - accuracy: 0.6013 - val_loss: 1.5352 - val_accuracy: 0.4444\n",
      "Epoch 22/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 1.4785 - accuracy: 0.6013 - val_loss: 1.5150 - val_accuracy: 0.4444\n",
      "Epoch 23/300\n",
      "153/153 [==============================] - 0s 55us/step - loss: 1.4562 - accuracy: 0.6013 - val_loss: 1.4946 - val_accuracy: 0.4444\n",
      "Epoch 24/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 1.4346 - accuracy: 0.6013 - val_loss: 1.4747 - val_accuracy: 0.4444\n",
      "Epoch 25/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 1.4135 - accuracy: 0.6078 - val_loss: 1.4566 - val_accuracy: 0.4444\n",
      "Epoch 26/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 1.3931 - accuracy: 0.6078 - val_loss: 1.4379 - val_accuracy: 0.4444\n",
      "Epoch 27/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 1.3726 - accuracy: 0.6144 - val_loss: 1.4199 - val_accuracy: 0.5000\n",
      "Epoch 28/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 1.3523 - accuracy: 0.6275 - val_loss: 1.4024 - val_accuracy: 0.5000\n",
      "Epoch 29/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 1.3339 - accuracy: 0.6340 - val_loss: 1.3856 - val_accuracy: 0.5000\n",
      "Epoch 30/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 1.3148 - accuracy: 0.6275 - val_loss: 1.3694 - val_accuracy: 0.5000\n",
      "Epoch 31/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 1.2969 - accuracy: 0.6340 - val_loss: 1.3536 - val_accuracy: 0.5000\n",
      "Epoch 32/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 1.2795 - accuracy: 0.6340 - val_loss: 1.3380 - val_accuracy: 0.5000\n",
      "Epoch 33/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 1.2628 - accuracy: 0.6340 - val_loss: 1.3244 - val_accuracy: 0.5000\n",
      "Epoch 34/300\n",
      "153/153 [==============================] - 0s 56us/step - loss: 1.2472 - accuracy: 0.6275 - val_loss: 1.3114 - val_accuracy: 0.4444\n",
      "Epoch 35/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 1.2325 - accuracy: 0.6405 - val_loss: 1.2990 - val_accuracy: 0.4444\n",
      "Epoch 36/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 1.2177 - accuracy: 0.6471 - val_loss: 1.2868 - val_accuracy: 0.4444\n",
      "Epoch 37/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 1.2043 - accuracy: 0.6536 - val_loss: 1.2762 - val_accuracy: 0.4444\n",
      "Epoch 38/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 1.1919 - accuracy: 0.6536 - val_loss: 1.2659 - val_accuracy: 0.4444\n",
      "Epoch 39/300\n",
      "153/153 [==============================] - 0s 72us/step - loss: 1.1796 - accuracy: 0.6536 - val_loss: 1.2576 - val_accuracy: 0.3889\n",
      "Epoch 40/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 1.1674 - accuracy: 0.6536 - val_loss: 1.2486 - val_accuracy: 0.3889\n",
      "Epoch 41/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 1.1567 - accuracy: 0.6536 - val_loss: 1.2409 - val_accuracy: 0.3889\n",
      "Epoch 42/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 1.1464 - accuracy: 0.6536 - val_loss: 1.2335 - val_accuracy: 0.3889\n",
      "Epoch 43/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 1.1361 - accuracy: 0.6601 - val_loss: 1.2264 - val_accuracy: 0.3889\n",
      "Epoch 44/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 1.1265 - accuracy: 0.6601 - val_loss: 1.2198 - val_accuracy: 0.3889\n",
      "Epoch 45/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 1.1177 - accuracy: 0.6601 - val_loss: 1.2132 - val_accuracy: 0.3889\n",
      "Epoch 46/300\n",
      "153/153 [==============================] - 0s 50us/step - loss: 1.1090 - accuracy: 0.6667 - val_loss: 1.2068 - val_accuracy: 0.3889\n",
      "Epoch 47/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 1.1007 - accuracy: 0.6667 - val_loss: 1.2015 - val_accuracy: 0.3889\n",
      "Epoch 48/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 1.0928 - accuracy: 0.6732 - val_loss: 1.1962 - val_accuracy: 0.3889\n",
      "Epoch 49/300\n",
      "153/153 [==============================] - 0s 91us/step - loss: 1.0850 - accuracy: 0.6732 - val_loss: 1.1918 - val_accuracy: 0.3889\n",
      "Epoch 50/300\n",
      "153/153 [==============================] - 0s 61us/step - loss: 1.0783 - accuracy: 0.6732 - val_loss: 1.1872 - val_accuracy: 0.3889\n",
      "Epoch 51/300\n",
      "153/153 [==============================] - 0s 69us/step - loss: 1.0710 - accuracy: 0.6732 - val_loss: 1.1831 - val_accuracy: 0.3889\n",
      "Epoch 52/300\n",
      "153/153 [==============================] - 0s 72us/step - loss: 1.0642 - accuracy: 0.6732 - val_loss: 1.1797 - val_accuracy: 0.3889\n",
      "Epoch 53/300\n",
      "153/153 [==============================] - 0s 73us/step - loss: 1.0580 - accuracy: 0.6732 - val_loss: 1.1768 - val_accuracy: 0.3889\n",
      "Epoch 54/300\n",
      "153/153 [==============================] - 0s 75us/step - loss: 1.0523 - accuracy: 0.6863 - val_loss: 1.1743 - val_accuracy: 0.3889\n",
      "Epoch 55/300\n",
      "153/153 [==============================] - 0s 74us/step - loss: 1.0461 - accuracy: 0.6863 - val_loss: 1.1709 - val_accuracy: 0.3889\n",
      "Epoch 56/300\n",
      "153/153 [==============================] - 0s 74us/step - loss: 1.0405 - accuracy: 0.6863 - val_loss: 1.1686 - val_accuracy: 0.3889\n",
      "Epoch 57/300\n",
      "153/153 [==============================] - 0s 73us/step - loss: 1.0352 - accuracy: 0.6863 - val_loss: 1.1658 - val_accuracy: 0.3889\n",
      "Epoch 58/300\n",
      "153/153 [==============================] - 0s 73us/step - loss: 1.0300 - accuracy: 0.6797 - val_loss: 1.1636 - val_accuracy: 0.4444\n",
      "Epoch 59/300\n",
      "153/153 [==============================] - 0s 69us/step - loss: 1.0250 - accuracy: 0.6797 - val_loss: 1.1602 - val_accuracy: 0.4444\n",
      "Epoch 60/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 1.0199 - accuracy: 0.6732 - val_loss: 1.1585 - val_accuracy: 0.4444\n",
      "Epoch 61/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 1.0152 - accuracy: 0.6732 - val_loss: 1.1564 - val_accuracy: 0.4444\n",
      "Epoch 62/300\n",
      "153/153 [==============================] - 0s 56us/step - loss: 1.0106 - accuracy: 0.6732 - val_loss: 1.1541 - val_accuracy: 0.4444\n",
      "Epoch 63/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 1.0064 - accuracy: 0.6732 - val_loss: 1.1511 - val_accuracy: 0.4444\n",
      "Epoch 64/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 1.0019 - accuracy: 0.6732 - val_loss: 1.1488 - val_accuracy: 0.4444\n",
      "Epoch 65/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.9978 - accuracy: 0.6732 - val_loss: 1.1496 - val_accuracy: 0.4444\n",
      "Epoch 66/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.9936 - accuracy: 0.6732 - val_loss: 1.1474 - val_accuracy: 0.4444\n",
      "Epoch 67/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.9897 - accuracy: 0.6732 - val_loss: 1.1471 - val_accuracy: 0.4444\n",
      "Epoch 68/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 0.9859 - accuracy: 0.6732 - val_loss: 1.1450 - val_accuracy: 0.4444\n",
      "Epoch 69/300\n",
      "153/153 [==============================] - ETA: 0s - loss: 0.9192 - accuracy: 0.78 - 0s 55us/step - loss: 0.9820 - accuracy: 0.6732 - val_loss: 1.1435 - val_accuracy: 0.4444\n",
      "Epoch 70/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.9784 - accuracy: 0.6732 - val_loss: 1.1423 - val_accuracy: 0.4444\n",
      "Epoch 71/300\n",
      "153/153 [==============================] - 0s 116us/step - loss: 0.9748 - accuracy: 0.6732 - val_loss: 1.1414 - val_accuracy: 0.4444\n",
      "Epoch 72/300\n",
      "153/153 [==============================] - 0s 63us/step - loss: 0.9715 - accuracy: 0.6732 - val_loss: 1.1397 - val_accuracy: 0.4444\n",
      "Epoch 73/300\n",
      "153/153 [==============================] - 0s 69us/step - loss: 0.9680 - accuracy: 0.6797 - val_loss: 1.1380 - val_accuracy: 0.4444\n",
      "Epoch 74/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 0.9649 - accuracy: 0.6797 - val_loss: 1.1359 - val_accuracy: 0.4444\n",
      "Epoch 75/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 0.9616 - accuracy: 0.6797 - val_loss: 1.1348 - val_accuracy: 0.4444\n",
      "Epoch 76/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 0.9583 - accuracy: 0.6797 - val_loss: 1.1335 - val_accuracy: 0.4444\n",
      "Epoch 77/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.9550 - accuracy: 0.6797 - val_loss: 1.1325 - val_accuracy: 0.5000\n",
      "Epoch 78/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 0.9521 - accuracy: 0.6797 - val_loss: 1.1315 - val_accuracy: 0.5000\n",
      "Epoch 79/300\n",
      "153/153 [==============================] - 0s 64us/step - loss: 0.9494 - accuracy: 0.6863 - val_loss: 1.1329 - val_accuracy: 0.4444\n",
      "Epoch 80/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 0.9464 - accuracy: 0.6928 - val_loss: 1.1329 - val_accuracy: 0.4444\n",
      "Epoch 81/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 0.9436 - accuracy: 0.6928 - val_loss: 1.1332 - val_accuracy: 0.4444\n",
      "Epoch 82/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.9410 - accuracy: 0.6993 - val_loss: 1.1344 - val_accuracy: 0.4444\n",
      "Epoch 83/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.9381 - accuracy: 0.6993 - val_loss: 1.1338 - val_accuracy: 0.4444\n",
      "Epoch 84/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.9352 - accuracy: 0.6993 - val_loss: 1.1325 - val_accuracy: 0.4444\n",
      "Epoch 85/300\n",
      "153/153 [==============================] - 0s 55us/step - loss: 0.9326 - accuracy: 0.6993 - val_loss: 1.1320 - val_accuracy: 0.4444\n",
      "Epoch 86/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 0.9302 - accuracy: 0.7059 - val_loss: 1.1308 - val_accuracy: 0.4444\n",
      "Epoch 87/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.9278 - accuracy: 0.7059 - val_loss: 1.1288 - val_accuracy: 0.5000\n",
      "Epoch 88/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.9256 - accuracy: 0.7059 - val_loss: 1.1285 - val_accuracy: 0.5000\n",
      "Epoch 89/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.9229 - accuracy: 0.6993 - val_loss: 1.1296 - val_accuracy: 0.4444\n",
      "Epoch 90/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.9204 - accuracy: 0.6993 - val_loss: 1.1303 - val_accuracy: 0.4444\n",
      "Epoch 91/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.9181 - accuracy: 0.6993 - val_loss: 1.1317 - val_accuracy: 0.4444\n",
      "Epoch 92/300\n",
      "153/153 [==============================] - 0s 73us/step - loss: 0.9160 - accuracy: 0.6993 - val_loss: 1.1324 - val_accuracy: 0.4444\n",
      "Epoch 93/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 0.9139 - accuracy: 0.6993 - val_loss: 1.1309 - val_accuracy: 0.4444\n",
      "Epoch 94/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.9115 - accuracy: 0.7059 - val_loss: 1.1306 - val_accuracy: 0.4444\n",
      "Epoch 95/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.9096 - accuracy: 0.7059 - val_loss: 1.1319 - val_accuracy: 0.4444\n",
      "Epoch 96/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.9074 - accuracy: 0.7059 - val_loss: 1.1339 - val_accuracy: 0.4444\n",
      "Epoch 97/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.9055 - accuracy: 0.7059 - val_loss: 1.1342 - val_accuracy: 0.4444\n",
      "Epoch 98/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.9036 - accuracy: 0.7059 - val_loss: 1.1363 - val_accuracy: 0.4444\n",
      "Epoch 99/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 0.9012 - accuracy: 0.7059 - val_loss: 1.1364 - val_accuracy: 0.4444\n",
      "Epoch 100/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.8995 - accuracy: 0.7059 - val_loss: 1.1359 - val_accuracy: 0.4444\n",
      "Epoch 101/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.8974 - accuracy: 0.7059 - val_loss: 1.1355 - val_accuracy: 0.4444\n",
      "Epoch 102/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8958 - accuracy: 0.7059 - val_loss: 1.1379 - val_accuracy: 0.4444\n",
      "Epoch 103/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.8938 - accuracy: 0.7059 - val_loss: 1.1379 - val_accuracy: 0.4444\n",
      "Epoch 104/300\n",
      "153/153 [==============================] - ETA: 0s - loss: 0.8992 - accuracy: 0.71 - 0s 52us/step - loss: 0.8920 - accuracy: 0.7059 - val_loss: 1.1361 - val_accuracy: 0.4444\n",
      "Epoch 105/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8902 - accuracy: 0.7059 - val_loss: 1.1359 - val_accuracy: 0.4444\n",
      "Epoch 106/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8884 - accuracy: 0.7059 - val_loss: 1.1374 - val_accuracy: 0.4444\n",
      "Epoch 107/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8867 - accuracy: 0.7059 - val_loss: 1.1378 - val_accuracy: 0.4444\n",
      "Epoch 108/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8849 - accuracy: 0.7059 - val_loss: 1.1387 - val_accuracy: 0.4444\n",
      "Epoch 109/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.8831 - accuracy: 0.7059 - val_loss: 1.1394 - val_accuracy: 0.4444\n",
      "Epoch 110/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.8815 - accuracy: 0.7059 - val_loss: 1.1381 - val_accuracy: 0.4444\n",
      "Epoch 111/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.8800 - accuracy: 0.6993 - val_loss: 1.1403 - val_accuracy: 0.4444\n",
      "Epoch 112/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 0.8784 - accuracy: 0.6993 - val_loss: 1.1403 - val_accuracy: 0.4444\n",
      "Epoch 113/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.8771 - accuracy: 0.6993 - val_loss: 1.1409 - val_accuracy: 0.4444\n",
      "Epoch 114/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 0.8755 - accuracy: 0.6993 - val_loss: 1.1389 - val_accuracy: 0.4444\n",
      "Epoch 115/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.8737 - accuracy: 0.6993 - val_loss: 1.1412 - val_accuracy: 0.4444\n",
      "Epoch 116/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.8722 - accuracy: 0.7059 - val_loss: 1.1428 - val_accuracy: 0.4444\n",
      "Epoch 117/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.8707 - accuracy: 0.7059 - val_loss: 1.1442 - val_accuracy: 0.4444\n",
      "Epoch 118/300\n",
      "153/153 [==============================] - 0s 61us/step - loss: 0.8693 - accuracy: 0.7059 - val_loss: 1.1434 - val_accuracy: 0.4444\n",
      "Epoch 119/300\n",
      "153/153 [==============================] - 0s 62us/step - loss: 0.8678 - accuracy: 0.7059 - val_loss: 1.1456 - val_accuracy: 0.4444\n",
      "Epoch 120/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.8663 - accuracy: 0.7059 - val_loss: 1.1456 - val_accuracy: 0.4444\n",
      "Epoch 121/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.8650 - accuracy: 0.7059 - val_loss: 1.1479 - val_accuracy: 0.4444\n",
      "Epoch 122/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.8635 - accuracy: 0.7059 - val_loss: 1.1481 - val_accuracy: 0.4444\n",
      "Epoch 123/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.8622 - accuracy: 0.7059 - val_loss: 1.1500 - val_accuracy: 0.4444\n",
      "Epoch 124/300\n",
      "153/153 [==============================] - 0s 61us/step - loss: 0.8608 - accuracy: 0.7059 - val_loss: 1.1506 - val_accuracy: 0.4444\n",
      "Epoch 125/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8595 - accuracy: 0.7059 - val_loss: 1.1507 - val_accuracy: 0.4444\n",
      "Epoch 126/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8583 - accuracy: 0.7059 - val_loss: 1.1493 - val_accuracy: 0.4444\n",
      "Epoch 127/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8570 - accuracy: 0.7059 - val_loss: 1.1511 - val_accuracy: 0.4444\n",
      "Epoch 128/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.8555 - accuracy: 0.7059 - val_loss: 1.1524 - val_accuracy: 0.4444\n",
      "Epoch 129/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.8545 - accuracy: 0.7059 - val_loss: 1.1520 - val_accuracy: 0.4444\n",
      "Epoch 130/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8532 - accuracy: 0.7124 - val_loss: 1.1508 - val_accuracy: 0.4444\n",
      "Epoch 131/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.8519 - accuracy: 0.7124 - val_loss: 1.1501 - val_accuracy: 0.4444\n",
      "Epoch 132/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 0.8507 - accuracy: 0.7124 - val_loss: 1.1512 - val_accuracy: 0.4444\n",
      "Epoch 133/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.8494 - accuracy: 0.7124 - val_loss: 1.1532 - val_accuracy: 0.4444\n",
      "Epoch 134/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 0.8484 - accuracy: 0.7124 - val_loss: 1.1540 - val_accuracy: 0.4444\n",
      "Epoch 135/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8472 - accuracy: 0.7124 - val_loss: 1.1574 - val_accuracy: 0.4444\n",
      "Epoch 136/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.8461 - accuracy: 0.7124 - val_loss: 1.1591 - val_accuracy: 0.4444\n",
      "Epoch 137/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 0.8451 - accuracy: 0.7059 - val_loss: 1.1627 - val_accuracy: 0.4444\n",
      "Epoch 138/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 0.8438 - accuracy: 0.7124 - val_loss: 1.1622 - val_accuracy: 0.4444\n",
      "Epoch 139/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 0.8424 - accuracy: 0.7124 - val_loss: 1.1606 - val_accuracy: 0.4444\n",
      "Epoch 140/300\n",
      "153/153 [==============================] - 0s 91us/step - loss: 0.8418 - accuracy: 0.7124 - val_loss: 1.1584 - val_accuracy: 0.4444\n",
      "Epoch 141/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.8405 - accuracy: 0.7124 - val_loss: 1.1592 - val_accuracy: 0.4444\n",
      "Epoch 142/300\n",
      "153/153 [==============================] - 0s 73us/step - loss: 0.8393 - accuracy: 0.7124 - val_loss: 1.1596 - val_accuracy: 0.4444\n",
      "Epoch 143/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8384 - accuracy: 0.7124 - val_loss: 1.1621 - val_accuracy: 0.4444\n",
      "Epoch 144/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8374 - accuracy: 0.7124 - val_loss: 1.1647 - val_accuracy: 0.4444\n",
      "Epoch 145/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8363 - accuracy: 0.7124 - val_loss: 1.1668 - val_accuracy: 0.4444\n",
      "Epoch 146/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8351 - accuracy: 0.7124 - val_loss: 1.1648 - val_accuracy: 0.4444\n",
      "Epoch 147/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8340 - accuracy: 0.7124 - val_loss: 1.1655 - val_accuracy: 0.4444\n",
      "Epoch 148/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8332 - accuracy: 0.7124 - val_loss: 1.1645 - val_accuracy: 0.5000\n",
      "Epoch 149/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8321 - accuracy: 0.7124 - val_loss: 1.1653 - val_accuracy: 0.5000\n",
      "Epoch 150/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8311 - accuracy: 0.7124 - val_loss: 1.1653 - val_accuracy: 0.5000\n",
      "Epoch 151/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8300 - accuracy: 0.7190 - val_loss: 1.1657 - val_accuracy: 0.5000\n",
      "Epoch 152/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8291 - accuracy: 0.7190 - val_loss: 1.1669 - val_accuracy: 0.5000\n",
      "Epoch 153/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8282 - accuracy: 0.7255 - val_loss: 1.1675 - val_accuracy: 0.5000\n",
      "Epoch 154/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.8272 - accuracy: 0.7190 - val_loss: 1.1696 - val_accuracy: 0.5000\n",
      "Epoch 155/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8262 - accuracy: 0.7190 - val_loss: 1.1715 - val_accuracy: 0.5000\n",
      "Epoch 156/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8253 - accuracy: 0.7190 - val_loss: 1.1737 - val_accuracy: 0.5000\n",
      "Epoch 157/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8243 - accuracy: 0.7190 - val_loss: 1.1756 - val_accuracy: 0.5000\n",
      "Epoch 158/300\n",
      "153/153 [==============================] - 0s 76us/step - loss: 0.8234 - accuracy: 0.7190 - val_loss: 1.1772 - val_accuracy: 0.5000\n",
      "Epoch 159/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.8225 - accuracy: 0.7255 - val_loss: 1.1763 - val_accuracy: 0.5000\n",
      "Epoch 160/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8218 - accuracy: 0.7255 - val_loss: 1.1763 - val_accuracy: 0.5000\n",
      "Epoch 161/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8206 - accuracy: 0.7255 - val_loss: 1.1773 - val_accuracy: 0.5000\n",
      "Epoch 162/300\n",
      "153/153 [==============================] - 0s 76us/step - loss: 0.8198 - accuracy: 0.7255 - val_loss: 1.1807 - val_accuracy: 0.5000\n",
      "Epoch 163/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8188 - accuracy: 0.7255 - val_loss: 1.1828 - val_accuracy: 0.5000\n",
      "Epoch 164/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8180 - accuracy: 0.7255 - val_loss: 1.1841 - val_accuracy: 0.5000\n",
      "Epoch 165/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8172 - accuracy: 0.7255 - val_loss: 1.1842 - val_accuracy: 0.5000\n",
      "Epoch 166/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8163 - accuracy: 0.7255 - val_loss: 1.1861 - val_accuracy: 0.5000\n",
      "Epoch 167/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.8159 - accuracy: 0.7255 - val_loss: 1.1831 - val_accuracy: 0.5000\n",
      "Epoch 168/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8145 - accuracy: 0.7255 - val_loss: 1.1837 - val_accuracy: 0.5000\n",
      "Epoch 169/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8138 - accuracy: 0.7255 - val_loss: 1.1863 - val_accuracy: 0.5000\n",
      "Epoch 170/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8128 - accuracy: 0.7255 - val_loss: 1.1861 - val_accuracy: 0.5000\n",
      "Epoch 171/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8120 - accuracy: 0.7255 - val_loss: 1.1891 - val_accuracy: 0.5556\n",
      "Epoch 172/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8110 - accuracy: 0.7320 - val_loss: 1.1888 - val_accuracy: 0.5556\n",
      "Epoch 173/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8103 - accuracy: 0.7386 - val_loss: 1.1894 - val_accuracy: 0.5556\n",
      "Epoch 174/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.8096 - accuracy: 0.7386 - val_loss: 1.1865 - val_accuracy: 0.5556\n",
      "Epoch 175/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8087 - accuracy: 0.7386 - val_loss: 1.1872 - val_accuracy: 0.5556\n",
      "Epoch 176/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8077 - accuracy: 0.7386 - val_loss: 1.1879 - val_accuracy: 0.5556\n",
      "Epoch 177/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8069 - accuracy: 0.7386 - val_loss: 1.1902 - val_accuracy: 0.5556\n",
      "Epoch 178/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.8062 - accuracy: 0.7386 - val_loss: 1.1928 - val_accuracy: 0.5556\n",
      "Epoch 179/300\n",
      "153/153 [==============================] - 0s 62us/step - loss: 0.8054 - accuracy: 0.7386 - val_loss: 1.1937 - val_accuracy: 0.5556\n",
      "Epoch 180/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 0.8047 - accuracy: 0.7386 - val_loss: 1.1939 - val_accuracy: 0.5556\n",
      "Epoch 181/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.8038 - accuracy: 0.7386 - val_loss: 1.1950 - val_accuracy: 0.5556\n",
      "Epoch 182/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.8031 - accuracy: 0.7386 - val_loss: 1.1956 - val_accuracy: 0.5556\n",
      "Epoch 183/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 0.8022 - accuracy: 0.7386 - val_loss: 1.1964 - val_accuracy: 0.5556\n",
      "Epoch 184/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8015 - accuracy: 0.7386 - val_loss: 1.1990 - val_accuracy: 0.5556\n",
      "Epoch 185/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8007 - accuracy: 0.7386 - val_loss: 1.2007 - val_accuracy: 0.5556\n",
      "Epoch 186/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.8000 - accuracy: 0.7386 - val_loss: 1.2019 - val_accuracy: 0.5556\n",
      "Epoch 187/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7992 - accuracy: 0.7386 - val_loss: 1.2020 - val_accuracy: 0.5556\n",
      "Epoch 188/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.7985 - accuracy: 0.7386 - val_loss: 1.2044 - val_accuracy: 0.5556\n",
      "Epoch 189/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7978 - accuracy: 0.7386 - val_loss: 1.2051 - val_accuracy: 0.5556\n",
      "Epoch 190/300\n",
      "153/153 [==============================] - ETA: 0s - loss: 0.8430 - accuracy: 0.78 - 0s 52us/step - loss: 0.7970 - accuracy: 0.7386 - val_loss: 1.2039 - val_accuracy: 0.5556\n",
      "Epoch 191/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7962 - accuracy: 0.7386 - val_loss: 1.2038 - val_accuracy: 0.5556\n",
      "Epoch 192/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7955 - accuracy: 0.7386 - val_loss: 1.2025 - val_accuracy: 0.5556\n",
      "Epoch 193/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7949 - accuracy: 0.7386 - val_loss: 1.2017 - val_accuracy: 0.5556\n",
      "Epoch 194/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7942 - accuracy: 0.7386 - val_loss: 1.2024 - val_accuracy: 0.5556\n",
      "Epoch 195/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7935 - accuracy: 0.7386 - val_loss: 1.2036 - val_accuracy: 0.5556\n",
      "Epoch 196/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7931 - accuracy: 0.7386 - val_loss: 1.2081 - val_accuracy: 0.5556\n",
      "Epoch 197/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.7923 - accuracy: 0.7386 - val_loss: 1.2095 - val_accuracy: 0.5556\n",
      "Epoch 198/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 0.7914 - accuracy: 0.7386 - val_loss: 1.2108 - val_accuracy: 0.5556\n",
      "Epoch 199/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.7907 - accuracy: 0.7386 - val_loss: 1.2116 - val_accuracy: 0.5556\n",
      "Epoch 200/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 0.7899 - accuracy: 0.7386 - val_loss: 1.2118 - val_accuracy: 0.5556\n",
      "Epoch 201/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7894 - accuracy: 0.7386 - val_loss: 1.2100 - val_accuracy: 0.5556\n",
      "Epoch 202/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7887 - accuracy: 0.7386 - val_loss: 1.2108 - val_accuracy: 0.5556\n",
      "Epoch 203/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7880 - accuracy: 0.7386 - val_loss: 1.2132 - val_accuracy: 0.5556\n",
      "Epoch 204/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7873 - accuracy: 0.7386 - val_loss: 1.2117 - val_accuracy: 0.5556\n",
      "Epoch 205/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7866 - accuracy: 0.7386 - val_loss: 1.2111 - val_accuracy: 0.5556\n",
      "Epoch 206/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 0.7860 - accuracy: 0.7320 - val_loss: 1.2108 - val_accuracy: 0.5556\n",
      "Epoch 207/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.7853 - accuracy: 0.7320 - val_loss: 1.2118 - val_accuracy: 0.5556\n",
      "Epoch 208/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7847 - accuracy: 0.7320 - val_loss: 1.2132 - val_accuracy: 0.5556\n",
      "Epoch 209/300\n",
      "153/153 [==============================] - ETA: 0s - loss: 0.7167 - accuracy: 0.75 - 0s 52us/step - loss: 0.7839 - accuracy: 0.7320 - val_loss: 1.2155 - val_accuracy: 0.5556\n",
      "Epoch 210/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.7834 - accuracy: 0.7386 - val_loss: 1.2177 - val_accuracy: 0.5556\n",
      "Epoch 211/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.7830 - accuracy: 0.7386 - val_loss: 1.2206 - val_accuracy: 0.5556\n",
      "Epoch 212/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7822 - accuracy: 0.7386 - val_loss: 1.2177 - val_accuracy: 0.5556\n",
      "Epoch 213/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7813 - accuracy: 0.7386 - val_loss: 1.2175 - val_accuracy: 0.5556\n",
      "Epoch 214/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7807 - accuracy: 0.7386 - val_loss: 1.2194 - val_accuracy: 0.5556\n",
      "Epoch 215/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7801 - accuracy: 0.7386 - val_loss: 1.2214 - val_accuracy: 0.5556\n",
      "Epoch 216/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7794 - accuracy: 0.7386 - val_loss: 1.2207 - val_accuracy: 0.5556\n",
      "Epoch 217/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.7790 - accuracy: 0.7386 - val_loss: 1.2225 - val_accuracy: 0.5556\n",
      "Epoch 218/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7784 - accuracy: 0.7386 - val_loss: 1.2207 - val_accuracy: 0.5556\n",
      "Epoch 219/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 0.7776 - accuracy: 0.7320 - val_loss: 1.2203 - val_accuracy: 0.5556\n",
      "Epoch 220/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7770 - accuracy: 0.7320 - val_loss: 1.2217 - val_accuracy: 0.5556\n",
      "Epoch 221/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7766 - accuracy: 0.7320 - val_loss: 1.2248 - val_accuracy: 0.5556\n",
      "Epoch 222/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7758 - accuracy: 0.7320 - val_loss: 1.2242 - val_accuracy: 0.5556\n",
      "Epoch 223/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7752 - accuracy: 0.7320 - val_loss: 1.2265 - val_accuracy: 0.5556\n",
      "Epoch 224/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7745 - accuracy: 0.7320 - val_loss: 1.2267 - val_accuracy: 0.5556\n",
      "Epoch 225/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7739 - accuracy: 0.7320 - val_loss: 1.2265 - val_accuracy: 0.5556\n",
      "Epoch 226/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7735 - accuracy: 0.7320 - val_loss: 1.2268 - val_accuracy: 0.5556\n",
      "Epoch 227/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.7731 - accuracy: 0.7320 - val_loss: 1.2258 - val_accuracy: 0.5556\n",
      "Epoch 228/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.7725 - accuracy: 0.7320 - val_loss: 1.2299 - val_accuracy: 0.5556\n",
      "Epoch 229/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7717 - accuracy: 0.7320 - val_loss: 1.2334 - val_accuracy: 0.5556\n",
      "Epoch 230/300\n",
      "153/153 [==============================] - 0s 62us/step - loss: 0.7711 - accuracy: 0.7320 - val_loss: 1.2343 - val_accuracy: 0.5556\n",
      "Epoch 231/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.7705 - accuracy: 0.7386 - val_loss: 1.2316 - val_accuracy: 0.5556\n",
      "Epoch 232/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7699 - accuracy: 0.7320 - val_loss: 1.2313 - val_accuracy: 0.5556\n",
      "Epoch 233/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.7692 - accuracy: 0.7320 - val_loss: 1.2319 - val_accuracy: 0.5556\n",
      "Epoch 234/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7687 - accuracy: 0.7320 - val_loss: 1.2340 - val_accuracy: 0.5556\n",
      "Epoch 235/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7681 - accuracy: 0.7320 - val_loss: 1.2341 - val_accuracy: 0.5556\n",
      "Epoch 236/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7675 - accuracy: 0.7386 - val_loss: 1.2332 - val_accuracy: 0.5556\n",
      "Epoch 237/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7671 - accuracy: 0.7386 - val_loss: 1.2314 - val_accuracy: 0.5556\n",
      "Epoch 238/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7662 - accuracy: 0.7386 - val_loss: 1.2339 - val_accuracy: 0.5556\n",
      "Epoch 239/300\n",
      "153/153 [==============================] - 0s 68us/step - loss: 0.7658 - accuracy: 0.7386 - val_loss: 1.2367 - val_accuracy: 0.5556\n",
      "Epoch 240/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7650 - accuracy: 0.7386 - val_loss: 1.2369 - val_accuracy: 0.5556\n",
      "Epoch 241/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7645 - accuracy: 0.7386 - val_loss: 1.2372 - val_accuracy: 0.5556\n",
      "Epoch 242/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7639 - accuracy: 0.7386 - val_loss: 1.2378 - val_accuracy: 0.5556\n",
      "Epoch 243/300\n",
      "153/153 [==============================] - 0s 105us/step - loss: 0.7633 - accuracy: 0.7516 - val_loss: 1.2402 - val_accuracy: 0.5556\n",
      "Epoch 244/300\n",
      "153/153 [==============================] - 0s 85us/step - loss: 0.7628 - accuracy: 0.7516 - val_loss: 1.2412 - val_accuracy: 0.5556\n",
      "Epoch 245/300\n",
      "153/153 [==============================] - 0s 72us/step - loss: 0.7621 - accuracy: 0.7516 - val_loss: 1.2403 - val_accuracy: 0.5556\n",
      "Epoch 246/300\n",
      "153/153 [==============================] - 0s 136us/step - loss: 0.7617 - accuracy: 0.7386 - val_loss: 1.2382 - val_accuracy: 0.5556\n",
      "Epoch 247/300\n",
      "153/153 [==============================] - 0s 62us/step - loss: 0.7612 - accuracy: 0.7451 - val_loss: 1.2427 - val_accuracy: 0.5556\n",
      "Epoch 248/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.7604 - accuracy: 0.7516 - val_loss: 1.2434 - val_accuracy: 0.5000\n",
      "Epoch 249/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 0.7598 - accuracy: 0.7516 - val_loss: 1.2434 - val_accuracy: 0.5000\n",
      "Epoch 250/300\n",
      "153/153 [==============================] - 0s 55us/step - loss: 0.7595 - accuracy: 0.7516 - val_loss: 1.2399 - val_accuracy: 0.5556\n",
      "Epoch 251/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 0.7588 - accuracy: 0.7516 - val_loss: 1.2417 - val_accuracy: 0.5000\n",
      "Epoch 252/300\n",
      "153/153 [==============================] - ETA: 0s - loss: 0.6190 - accuracy: 0.90 - 0s 93us/step - loss: 0.7582 - accuracy: 0.7516 - val_loss: 1.2419 - val_accuracy: 0.5556\n",
      "Epoch 253/300\n",
      "153/153 [==============================] - 0s 174us/step - loss: 0.7576 - accuracy: 0.7516 - val_loss: 1.2421 - val_accuracy: 0.5000\n",
      "Epoch 254/300\n",
      "153/153 [==============================] - 0s 101us/step - loss: 0.7571 - accuracy: 0.7516 - val_loss: 1.2428 - val_accuracy: 0.5000\n",
      "Epoch 255/300\n",
      "153/153 [==============================] - 0s 110us/step - loss: 0.7565 - accuracy: 0.7516 - val_loss: 1.2423 - val_accuracy: 0.5000\n",
      "Epoch 256/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7559 - accuracy: 0.7516 - val_loss: 1.2434 - val_accuracy: 0.5000\n",
      "Epoch 257/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7554 - accuracy: 0.7516 - val_loss: 1.2418 - val_accuracy: 0.5556\n",
      "Epoch 258/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 0.7548 - accuracy: 0.7516 - val_loss: 1.2439 - val_accuracy: 0.5000\n",
      "Epoch 259/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7544 - accuracy: 0.7516 - val_loss: 1.2436 - val_accuracy: 0.5556\n",
      "Epoch 260/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.7538 - accuracy: 0.7516 - val_loss: 1.2434 - val_accuracy: 0.5556\n",
      "Epoch 261/300\n",
      "153/153 [==============================] - 0s 52us/step - loss: 0.7532 - accuracy: 0.7516 - val_loss: 1.2436 - val_accuracy: 0.5556\n",
      "Epoch 262/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.7527 - accuracy: 0.7516 - val_loss: 1.2431 - val_accuracy: 0.5556\n",
      "Epoch 263/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.7521 - accuracy: 0.7516 - val_loss: 1.2453 - val_accuracy: 0.5556\n",
      "Epoch 264/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.7518 - accuracy: 0.7516 - val_loss: 1.2476 - val_accuracy: 0.5556\n",
      "Epoch 265/300\n",
      "153/153 [==============================] - 0s 61us/step - loss: 0.7511 - accuracy: 0.7516 - val_loss: 1.2472 - val_accuracy: 0.5556\n",
      "Epoch 266/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 0.7506 - accuracy: 0.7516 - val_loss: 1.2502 - val_accuracy: 0.5556\n",
      "Epoch 267/300\n",
      "153/153 [==============================] - 0s 77us/step - loss: 0.7501 - accuracy: 0.7516 - val_loss: 1.2501 - val_accuracy: 0.5556\n",
      "Epoch 268/300\n",
      "153/153 [==============================] - 0s 56us/step - loss: 0.7496 - accuracy: 0.7582 - val_loss: 1.2524 - val_accuracy: 0.5556\n",
      "Epoch 269/300\n",
      "153/153 [==============================] - 0s 63us/step - loss: 0.7491 - accuracy: 0.7451 - val_loss: 1.2533 - val_accuracy: 0.5556\n",
      "Epoch 270/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 0.7485 - accuracy: 0.7451 - val_loss: 1.2533 - val_accuracy: 0.5556\n",
      "Epoch 271/300\n",
      "153/153 [==============================] - 0s 55us/step - loss: 0.7479 - accuracy: 0.7451 - val_loss: 1.2523 - val_accuracy: 0.5556\n",
      "Epoch 272/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.7476 - accuracy: 0.7451 - val_loss: 1.2506 - val_accuracy: 0.5556\n",
      "Epoch 273/300\n",
      "153/153 [==============================] - 0s 63us/step - loss: 0.7469 - accuracy: 0.7516 - val_loss: 1.2509 - val_accuracy: 0.5556\n",
      "Epoch 274/300\n",
      "153/153 [==============================] - 0s 65us/step - loss: 0.7465 - accuracy: 0.7516 - val_loss: 1.2507 - val_accuracy: 0.5556\n",
      "Epoch 275/300\n",
      "153/153 [==============================] - 0s 63us/step - loss: 0.7460 - accuracy: 0.7516 - val_loss: 1.2508 - val_accuracy: 0.5556\n",
      "Epoch 276/300\n",
      "153/153 [==============================] - 0s 63us/step - loss: 0.7454 - accuracy: 0.7451 - val_loss: 1.2519 - val_accuracy: 0.5556\n",
      "Epoch 277/300\n",
      "153/153 [==============================] - 0s 119us/step - loss: 0.7449 - accuracy: 0.7451 - val_loss: 1.2529 - val_accuracy: 0.5556\n",
      "Epoch 278/300\n",
      "153/153 [==============================] - 0s 70us/step - loss: 0.7446 - accuracy: 0.7451 - val_loss: 1.2525 - val_accuracy: 0.5556\n",
      "Epoch 279/300\n",
      "153/153 [==============================] - 0s 63us/step - loss: 0.7440 - accuracy: 0.7451 - val_loss: 1.2543 - val_accuracy: 0.5556\n",
      "Epoch 280/300\n",
      "153/153 [==============================] - 0s 74us/step - loss: 0.7436 - accuracy: 0.7451 - val_loss: 1.2546 - val_accuracy: 0.5556\n",
      "Epoch 281/300\n",
      "153/153 [==============================] - 0s 118us/step - loss: 0.7433 - accuracy: 0.7516 - val_loss: 1.2528 - val_accuracy: 0.5556\n",
      "Epoch 282/300\n",
      "153/153 [==============================] - 0s 75us/step - loss: 0.7426 - accuracy: 0.7516 - val_loss: 1.2546 - val_accuracy: 0.5556\n",
      "Epoch 283/300\n",
      "153/153 [==============================] - 0s 91us/step - loss: 0.7421 - accuracy: 0.7451 - val_loss: 1.2565 - val_accuracy: 0.5556\n",
      "Epoch 284/300\n",
      "153/153 [==============================] - 0s 117us/step - loss: 0.7418 - accuracy: 0.7451 - val_loss: 1.2595 - val_accuracy: 0.5556\n",
      "Epoch 285/300\n",
      "153/153 [==============================] - 0s 46us/step - loss: 0.7413 - accuracy: 0.7516 - val_loss: 1.2618 - val_accuracy: 0.5556\n",
      "Epoch 286/300\n",
      "153/153 [==============================] - 0s 43us/step - loss: 0.7407 - accuracy: 0.7516 - val_loss: 1.2604 - val_accuracy: 0.5556\n",
      "Epoch 287/300\n",
      "153/153 [==============================] - 0s 46us/step - loss: 0.7403 - accuracy: 0.7451 - val_loss: 1.2602 - val_accuracy: 0.5556\n",
      "Epoch 288/300\n",
      "153/153 [==============================] - 0s 46us/step - loss: 0.7397 - accuracy: 0.7451 - val_loss: 1.2591 - val_accuracy: 0.5556\n",
      "Epoch 289/300\n",
      "153/153 [==============================] - 0s 43us/step - loss: 0.7392 - accuracy: 0.7451 - val_loss: 1.2619 - val_accuracy: 0.5556\n",
      "Epoch 290/300\n",
      "153/153 [==============================] - 0s 99us/step - loss: 0.7388 - accuracy: 0.7516 - val_loss: 1.2618 - val_accuracy: 0.5556\n",
      "Epoch 291/300\n",
      "153/153 [==============================] - 0s 45us/step - loss: 0.7385 - accuracy: 0.7516 - val_loss: 1.2605 - val_accuracy: 0.5556\n",
      "Epoch 292/300\n",
      "153/153 [==============================] - 0s 49us/step - loss: 0.7379 - accuracy: 0.7516 - val_loss: 1.2624 - val_accuracy: 0.5556\n",
      "Epoch 293/300\n",
      "153/153 [==============================] - 0s 44us/step - loss: 0.7375 - accuracy: 0.7516 - val_loss: 1.2644 - val_accuracy: 0.5556\n",
      "Epoch 294/300\n",
      "153/153 [==============================] - 0s 44us/step - loss: 0.7373 - accuracy: 0.7451 - val_loss: 1.2649 - val_accuracy: 0.5556\n",
      "Epoch 295/300\n",
      "153/153 [==============================] - ETA: 0s - loss: 0.6095 - accuracy: 0.81 - 0s 46us/step - loss: 0.7367 - accuracy: 0.7516 - val_loss: 1.2660 - val_accuracy: 0.5556\n",
      "Epoch 296/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.7361 - accuracy: 0.7516 - val_loss: 1.2654 - val_accuracy: 0.5556\n",
      "Epoch 297/300\n",
      "153/153 [==============================] - 0s 56us/step - loss: 0.7360 - accuracy: 0.7451 - val_loss: 1.2624 - val_accuracy: 0.5556\n",
      "Epoch 298/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 0.7353 - accuracy: 0.7451 - val_loss: 1.2656 - val_accuracy: 0.5556\n",
      "Epoch 299/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 0.7349 - accuracy: 0.7451 - val_loss: 1.2667 - val_accuracy: 0.5556\n",
      "Epoch 300/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.7343 - accuracy: 0.7451 - val_loss: 1.2683 - val_accuracy: 0.5556\n"
     ]
    }
   ],
   "source": [
    "history_pen = model_pen.fit(X_train, Y_train, batch_size=32, epochs=300, validation_split=0.1,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "# summarize history for loss\n",
    "#plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.plot(history_pen.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['Naive model', 'Regularised model'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropout"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Insert now a Dropout layer with `rate = 0.3`, meaning a $30$% probability of dropping a unit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a model with a dropout layer \n",
    "model_drp = Sequential()\n",
    "model_drp.add(Dense(10, kernel_initializer='normal',input_dim=n_x, activation='relu',\n",
    "                    kernel_regularizer= l2(0.01)))\n",
    "# YOUR CODE HERE\n",
    "\n",
    "model_drp.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 153 samples, validate on 18 samples\n",
      "Epoch 1/300\n",
      "153/153 [==============================] - 0s 2ms/step - loss: 1.9364 - accuracy: 0.2876 - val_loss: 1.9149 - val_accuracy: 0.5000\n",
      "Epoch 2/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 1.9214 - accuracy: 0.3333 - val_loss: 1.8954 - val_accuracy: 0.4444\n",
      "Epoch 3/300\n",
      "153/153 [==============================] - 0s 58us/step - loss: 1.9109 - accuracy: 0.3464 - val_loss: 1.9169 - val_accuracy: 0.5000\n",
      "Epoch 4/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 1.8983 - accuracy: 0.3987 - val_loss: 1.8780 - val_accuracy: 0.5000\n",
      "Epoch 5/300\n",
      "153/153 [==============================] - 0s 58us/step - loss: 1.8891 - accuracy: 0.3725 - val_loss: 1.8759 - val_accuracy: 0.5000\n",
      "Epoch 6/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 1.8745 - accuracy: 0.3856 - val_loss: 1.8413 - val_accuracy: 0.6111\n",
      "Epoch 7/300\n",
      "153/153 [==============================] - 0s 109us/step - loss: 1.8601 - accuracy: 0.3660 - val_loss: 1.8470 - val_accuracy: 0.5556\n",
      "Epoch 8/300\n",
      "153/153 [==============================] - 0s 58us/step - loss: 1.8512 - accuracy: 0.3987 - val_loss: 1.8246 - val_accuracy: 0.5000\n",
      "Epoch 9/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 1.8265 - accuracy: 0.4248 - val_loss: 1.8276 - val_accuracy: 0.4444\n",
      "Epoch 10/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 1.8211 - accuracy: 0.3725 - val_loss: 1.7866 - val_accuracy: 0.5556\n",
      "Epoch 11/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 1.7938 - accuracy: 0.3987 - val_loss: 1.7346 - val_accuracy: 0.6111\n",
      "Epoch 12/300\n",
      "153/153 [==============================] - 0s 58us/step - loss: 1.7801 - accuracy: 0.3922 - val_loss: 1.7450 - val_accuracy: 0.5556\n",
      "Epoch 13/300\n",
      "153/153 [==============================] - 0s 58us/step - loss: 1.7605 - accuracy: 0.4052 - val_loss: 1.7217 - val_accuracy: 0.6111\n",
      "Epoch 14/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 1.7397 - accuracy: 0.3856 - val_loss: 1.7017 - val_accuracy: 0.6111\n",
      "Epoch 15/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 1.7259 - accuracy: 0.3856 - val_loss: 1.7299 - val_accuracy: 0.5556\n",
      "Epoch 16/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 1.7099 - accuracy: 0.4183 - val_loss: 1.6425 - val_accuracy: 0.5556\n",
      "Epoch 17/300\n",
      "153/153 [==============================] - 0s 58us/step - loss: 1.6843 - accuracy: 0.4118 - val_loss: 1.6329 - val_accuracy: 0.5556\n",
      "Epoch 18/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 1.6638 - accuracy: 0.3922 - val_loss: 1.6160 - val_accuracy: 0.6111\n",
      "Epoch 19/300\n",
      "153/153 [==============================] - 0s 121us/step - loss: 1.6434 - accuracy: 0.3856 - val_loss: 1.6541 - val_accuracy: 0.6111\n",
      "Epoch 20/300\n",
      "153/153 [==============================] - 0s 62us/step - loss: 1.6317 - accuracy: 0.3856 - val_loss: 1.5539 - val_accuracy: 0.6111\n",
      "Epoch 21/300\n",
      "153/153 [==============================] - 0s 70us/step - loss: 1.6070 - accuracy: 0.4052 - val_loss: 1.5117 - val_accuracy: 0.6111\n",
      "Epoch 22/300\n",
      "153/153 [==============================] - 0s 75us/step - loss: 1.5932 - accuracy: 0.4118 - val_loss: 1.4749 - val_accuracy: 0.6111\n",
      "Epoch 23/300\n",
      "153/153 [==============================] - 0s 72us/step - loss: 1.5660 - accuracy: 0.4444 - val_loss: 1.4683 - val_accuracy: 0.6111\n",
      "Epoch 24/300\n",
      "153/153 [==============================] - 0s 72us/step - loss: 1.5559 - accuracy: 0.4248 - val_loss: 1.4413 - val_accuracy: 0.6111\n",
      "Epoch 25/300\n",
      "153/153 [==============================] - 0s 71us/step - loss: 1.5361 - accuracy: 0.4248 - val_loss: 1.3940 - val_accuracy: 0.6667\n",
      "Epoch 26/300\n",
      "153/153 [==============================] - 0s 72us/step - loss: 1.5287 - accuracy: 0.4183 - val_loss: 1.3504 - val_accuracy: 0.7222\n",
      "Epoch 27/300\n",
      "153/153 [==============================] - 0s 72us/step - loss: 1.5025 - accuracy: 0.4837 - val_loss: 1.3814 - val_accuracy: 0.7222\n",
      "Epoch 28/300\n",
      "153/153 [==============================] - 0s 71us/step - loss: 1.4796 - accuracy: 0.4248 - val_loss: 1.3728 - val_accuracy: 0.7222\n",
      "Epoch 29/300\n",
      "153/153 [==============================] - 0s 77us/step - loss: 1.4457 - accuracy: 0.4771 - val_loss: 1.3061 - val_accuracy: 0.7222\n",
      "Epoch 30/300\n",
      "153/153 [==============================] - 0s 72us/step - loss: 1.4432 - accuracy: 0.4837 - val_loss: 1.3890 - val_accuracy: 0.5556\n",
      "Epoch 31/300\n",
      "153/153 [==============================] - 0s 71us/step - loss: 1.4189 - accuracy: 0.4641 - val_loss: 1.2875 - val_accuracy: 0.6667\n",
      "Epoch 32/300\n",
      "153/153 [==============================] - 0s 74us/step - loss: 1.4069 - accuracy: 0.4902 - val_loss: 1.2656 - val_accuracy: 0.7222\n",
      "Epoch 33/300\n",
      "153/153 [==============================] - 0s 77us/step - loss: 1.3724 - accuracy: 0.4771 - val_loss: 1.2324 - val_accuracy: 0.7222\n",
      "Epoch 34/300\n",
      "153/153 [==============================] - 0s 72us/step - loss: 1.3601 - accuracy: 0.4706 - val_loss: 1.2484 - val_accuracy: 0.7222\n",
      "Epoch 35/300\n",
      "153/153 [==============================] - 0s 75us/step - loss: 1.3606 - accuracy: 0.4902 - val_loss: 1.2714 - val_accuracy: 0.6111\n",
      "Epoch 36/300\n",
      "153/153 [==============================] - 0s 76us/step - loss: 1.3180 - accuracy: 0.4967 - val_loss: 1.2346 - val_accuracy: 0.7222\n",
      "Epoch 37/300\n",
      "153/153 [==============================] - 0s 77us/step - loss: 1.3053 - accuracy: 0.5294 - val_loss: 1.2263 - val_accuracy: 0.6667\n",
      "Epoch 38/300\n",
      "153/153 [==============================] - 0s 73us/step - loss: 1.2931 - accuracy: 0.4902 - val_loss: 1.2297 - val_accuracy: 0.7222\n",
      "Epoch 39/300\n",
      "153/153 [==============================] - 0s 77us/step - loss: 1.2966 - accuracy: 0.5163 - val_loss: 1.2007 - val_accuracy: 0.6667\n",
      "Epoch 40/300\n",
      "153/153 [==============================] - 0s 76us/step - loss: 1.2586 - accuracy: 0.5359 - val_loss: 1.1893 - val_accuracy: 0.6111\n",
      "Epoch 41/300\n",
      "153/153 [==============================] - 0s 73us/step - loss: 1.2655 - accuracy: 0.5294 - val_loss: 1.1813 - val_accuracy: 0.5556\n",
      "Epoch 42/300\n",
      "153/153 [==============================] - 0s 76us/step - loss: 1.2132 - accuracy: 0.5948 - val_loss: 1.1578 - val_accuracy: 0.6667\n",
      "Epoch 43/300\n",
      "153/153 [==============================] - 0s 106us/step - loss: 1.1775 - accuracy: 0.5752 - val_loss: 1.1034 - val_accuracy: 0.6111\n",
      "Epoch 44/300\n",
      "153/153 [==============================] - 0s 132us/step - loss: 1.1844 - accuracy: 0.5556 - val_loss: 1.1622 - val_accuracy: 0.6111\n",
      "Epoch 45/300\n",
      "153/153 [==============================] - 0s 61us/step - loss: 1.1848 - accuracy: 0.5621 - val_loss: 1.1207 - val_accuracy: 0.6667\n",
      "Epoch 46/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 1.1757 - accuracy: 0.5359 - val_loss: 1.1211 - val_accuracy: 0.7222\n",
      "Epoch 47/300\n",
      "153/153 [==============================] - 0s 61us/step - loss: 1.1685 - accuracy: 0.5752 - val_loss: 1.2852 - val_accuracy: 0.5000\n",
      "Epoch 48/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 1.1568 - accuracy: 0.5948 - val_loss: 1.1478 - val_accuracy: 0.4444\n",
      "Epoch 49/300\n",
      "153/153 [==============================] - 0s 58us/step - loss: 1.1591 - accuracy: 0.6340 - val_loss: 1.1586 - val_accuracy: 0.5556\n",
      "Epoch 50/300\n",
      "153/153 [==============================] - 0s 58us/step - loss: 1.1261 - accuracy: 0.5882 - val_loss: 1.1523 - val_accuracy: 0.4444\n",
      "Epoch 51/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 1.1413 - accuracy: 0.5882 - val_loss: 1.1255 - val_accuracy: 0.6111\n",
      "Epoch 52/300\n",
      "153/153 [==============================] - 0s 55us/step - loss: 1.1146 - accuracy: 0.5882 - val_loss: 1.1736 - val_accuracy: 0.3889\n",
      "Epoch 53/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 1.0961 - accuracy: 0.5817 - val_loss: 1.1006 - val_accuracy: 0.5556\n",
      "Epoch 54/300\n",
      "153/153 [==============================] - 0s 56us/step - loss: 1.0912 - accuracy: 0.5948 - val_loss: 1.0962 - val_accuracy: 0.5000\n",
      "Epoch 55/300\n",
      "153/153 [==============================] - 0s 70us/step - loss: 1.0633 - accuracy: 0.6144 - val_loss: 1.2217 - val_accuracy: 0.4444\n",
      "Epoch 56/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 1.0742 - accuracy: 0.6078 - val_loss: 1.1089 - val_accuracy: 0.5556\n",
      "Epoch 57/300\n",
      "153/153 [==============================] - 0s 56us/step - loss: 1.0723 - accuracy: 0.6078 - val_loss: 1.0413 - val_accuracy: 0.5556\n",
      "Epoch 58/300\n",
      "153/153 [==============================] - 0s 55us/step - loss: 1.0386 - accuracy: 0.6340 - val_loss: 1.0725 - val_accuracy: 0.6667\n",
      "Epoch 59/300\n",
      "153/153 [==============================] - 0s 64us/step - loss: 1.1048 - accuracy: 0.5817 - val_loss: 1.0422 - val_accuracy: 0.6111\n",
      "Epoch 60/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 1.0382 - accuracy: 0.5752 - val_loss: 1.0148 - val_accuracy: 0.6111\n",
      "Epoch 61/300\n",
      "153/153 [==============================] - 0s 56us/step - loss: 1.0520 - accuracy: 0.5686 - val_loss: 1.1636 - val_accuracy: 0.4444\n",
      "Epoch 62/300\n",
      "153/153 [==============================] - 0s 55us/step - loss: 1.0341 - accuracy: 0.5948 - val_loss: 1.1651 - val_accuracy: 0.5556\n",
      "Epoch 63/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 1.0474 - accuracy: 0.6013 - val_loss: 0.9853 - val_accuracy: 0.6111\n",
      "Epoch 64/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 1.0329 - accuracy: 0.5490 - val_loss: 1.1156 - val_accuracy: 0.5000\n",
      "Epoch 65/300\n",
      "153/153 [==============================] - 0s 55us/step - loss: 1.0196 - accuracy: 0.6078 - val_loss: 0.9895 - val_accuracy: 0.7222\n",
      "Epoch 66/300\n",
      "153/153 [==============================] - 0s 63us/step - loss: 1.0055 - accuracy: 0.6340 - val_loss: 1.0447 - val_accuracy: 0.5000\n",
      "Epoch 67/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 1.0064 - accuracy: 0.6144 - val_loss: 1.0459 - val_accuracy: 0.5000\n",
      "Epoch 68/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 1.0449 - accuracy: 0.5621 - val_loss: 1.0816 - val_accuracy: 0.5556\n",
      "Epoch 69/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 1.0614 - accuracy: 0.5948 - val_loss: 1.0608 - val_accuracy: 0.5556\n",
      "Epoch 70/300\n",
      "153/153 [==============================] - 0s 58us/step - loss: 0.9977 - accuracy: 0.6144 - val_loss: 1.1562 - val_accuracy: 0.5000\n",
      "Epoch 71/300\n",
      "153/153 [==============================] - 0s 56us/step - loss: 1.0217 - accuracy: 0.6013 - val_loss: 1.0745 - val_accuracy: 0.6111\n",
      "Epoch 72/300\n",
      "153/153 [==============================] - 0s 56us/step - loss: 1.0402 - accuracy: 0.5948 - val_loss: 0.9775 - val_accuracy: 0.6111\n",
      "Epoch 73/300\n",
      "153/153 [==============================] - 0s 114us/step - loss: 0.9916 - accuracy: 0.6471 - val_loss: 1.1103 - val_accuracy: 0.5000\n",
      "Epoch 74/300\n",
      "153/153 [==============================] - 0s 55us/step - loss: 0.9980 - accuracy: 0.6275 - val_loss: 1.1115 - val_accuracy: 0.6111\n",
      "Epoch 75/300\n",
      "153/153 [==============================] - 0s 55us/step - loss: 0.9670 - accuracy: 0.6275 - val_loss: 1.0936 - val_accuracy: 0.3889\n",
      "Epoch 76/300\n",
      "153/153 [==============================] - 0s 56us/step - loss: 1.0044 - accuracy: 0.6209 - val_loss: 1.0247 - val_accuracy: 0.5556\n",
      "Epoch 77/300\n",
      "153/153 [==============================] - 0s 68us/step - loss: 0.9571 - accuracy: 0.6013 - val_loss: 1.1789 - val_accuracy: 0.4444\n",
      "Epoch 78/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 0.9819 - accuracy: 0.6471 - val_loss: 1.0153 - val_accuracy: 0.7222\n",
      "Epoch 79/300\n",
      "153/153 [==============================] - 0s 55us/step - loss: 0.9331 - accuracy: 0.6078 - val_loss: 0.9397 - val_accuracy: 0.6667\n",
      "Epoch 80/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 1.0242 - accuracy: 0.5490 - val_loss: 1.0814 - val_accuracy: 0.6667\n",
      "Epoch 81/300\n",
      "153/153 [==============================] - 0s 138us/step - loss: 0.9376 - accuracy: 0.6340 - val_loss: 0.9597 - val_accuracy: 0.6111\n",
      "Epoch 82/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.9367 - accuracy: 0.6209 - val_loss: 1.2191 - val_accuracy: 0.3333\n",
      "Epoch 83/300\n",
      "153/153 [==============================] - 0s 49us/step - loss: 1.0036 - accuracy: 0.5686 - val_loss: 1.0547 - val_accuracy: 0.5000\n",
      "Epoch 84/300\n",
      "153/153 [==============================] - 0s 48us/step - loss: 0.9594 - accuracy: 0.6471 - val_loss: 1.2672 - val_accuracy: 0.5556\n",
      "Epoch 85/300\n",
      "153/153 [==============================] - 0s 51us/step - loss: 0.9433 - accuracy: 0.6209 - val_loss: 1.0202 - val_accuracy: 0.6667\n",
      "Epoch 86/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.9437 - accuracy: 0.6144 - val_loss: 1.0848 - val_accuracy: 0.5000\n",
      "Epoch 87/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.9687 - accuracy: 0.6144 - val_loss: 1.1420 - val_accuracy: 0.5000\n",
      "Epoch 88/300\n",
      "153/153 [==============================] - 0s 55us/step - loss: 1.0008 - accuracy: 0.6078 - val_loss: 1.1407 - val_accuracy: 0.5556\n",
      "Epoch 89/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.9356 - accuracy: 0.6340 - val_loss: 0.9663 - val_accuracy: 0.6111\n",
      "Epoch 90/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.9581 - accuracy: 0.6275 - val_loss: 1.1003 - val_accuracy: 0.4444\n",
      "Epoch 91/300\n",
      "153/153 [==============================] - 0s 88us/step - loss: 0.9382 - accuracy: 0.6275 - val_loss: 1.1868 - val_accuracy: 0.5000\n",
      "Epoch 92/300\n",
      "153/153 [==============================] - 0s 54us/step - loss: 0.9503 - accuracy: 0.6209 - val_loss: 1.0761 - val_accuracy: 0.5556\n",
      "Epoch 93/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 0.9217 - accuracy: 0.6013 - val_loss: 1.0342 - val_accuracy: 0.5556\n",
      "Epoch 94/300\n",
      "153/153 [==============================] - 0s 69us/step - loss: 0.9278 - accuracy: 0.6536 - val_loss: 1.0711 - val_accuracy: 0.5556\n",
      "Epoch 95/300\n",
      "153/153 [==============================] - 0s 63us/step - loss: 0.9301 - accuracy: 0.6601 - val_loss: 1.0533 - val_accuracy: 0.5556\n",
      "Epoch 96/300\n",
      "153/153 [==============================] - 0s 62us/step - loss: 0.9365 - accuracy: 0.6078 - val_loss: 0.9752 - val_accuracy: 0.6111\n",
      "Epoch 97/300\n",
      "153/153 [==============================] - 0s 64us/step - loss: 0.9522 - accuracy: 0.6536 - val_loss: 0.9275 - val_accuracy: 0.6667\n",
      "Epoch 98/300\n",
      "153/153 [==============================] - 0s 64us/step - loss: 0.9150 - accuracy: 0.6471 - val_loss: 0.9962 - val_accuracy: 0.5556\n",
      "Epoch 99/300\n",
      "153/153 [==============================] - 0s 62us/step - loss: 1.0055 - accuracy: 0.5882 - val_loss: 1.2012 - val_accuracy: 0.4444\n",
      "Epoch 100/300\n",
      "153/153 [==============================] - 0s 63us/step - loss: 0.9087 - accuracy: 0.6340 - val_loss: 1.0943 - val_accuracy: 0.5000\n",
      "Epoch 101/300\n",
      "153/153 [==============================] - ETA: 0s - loss: 1.0156 - accuracy: 0.56 - 0s 69us/step - loss: 0.9222 - accuracy: 0.6536 - val_loss: 1.0408 - val_accuracy: 0.5556\n",
      "Epoch 102/300\n",
      "153/153 [==============================] - 0s 66us/step - loss: 0.9334 - accuracy: 0.6209 - val_loss: 1.0515 - val_accuracy: 0.6111\n",
      "Epoch 103/300\n",
      "153/153 [==============================] - 0s 63us/step - loss: 0.9838 - accuracy: 0.6078 - val_loss: 1.1519 - val_accuracy: 0.5000\n",
      "Epoch 104/300\n",
      "153/153 [==============================] - 0s 67us/step - loss: 0.9237 - accuracy: 0.6275 - val_loss: 1.0913 - val_accuracy: 0.3889\n",
      "Epoch 105/300\n",
      "153/153 [==============================] - 0s 66us/step - loss: 0.9239 - accuracy: 0.6405 - val_loss: 1.2129 - val_accuracy: 0.5000\n",
      "Epoch 106/300\n",
      "153/153 [==============================] - 0s 63us/step - loss: 0.9498 - accuracy: 0.6144 - val_loss: 1.4185 - val_accuracy: 0.3333\n",
      "Epoch 107/300\n",
      "153/153 [==============================] - 0s 71us/step - loss: 0.8704 - accuracy: 0.6667 - val_loss: 1.1242 - val_accuracy: 0.5000\n",
      "Epoch 108/300\n",
      "153/153 [==============================] - 0s 63us/step - loss: 0.9190 - accuracy: 0.6144 - val_loss: 1.0543 - val_accuracy: 0.5000\n",
      "Epoch 109/300\n",
      "153/153 [==============================] - 0s 62us/step - loss: 0.9533 - accuracy: 0.6405 - val_loss: 1.2303 - val_accuracy: 0.5556\n",
      "Epoch 110/300\n",
      "153/153 [==============================] - 0s 63us/step - loss: 0.9485 - accuracy: 0.6275 - val_loss: 0.9886 - val_accuracy: 0.5556\n",
      "Epoch 111/300\n",
      "153/153 [==============================] - 0s 61us/step - loss: 0.8945 - accuracy: 0.6667 - val_loss: 1.2667 - val_accuracy: 0.5556\n",
      "Epoch 112/300\n",
      "153/153 [==============================] - 0s 64us/step - loss: 0.9405 - accuracy: 0.6144 - val_loss: 1.1922 - val_accuracy: 0.3889\n",
      "Epoch 113/300\n",
      "153/153 [==============================] - 0s 63us/step - loss: 0.9460 - accuracy: 0.6471 - val_loss: 1.0869 - val_accuracy: 0.5000\n",
      "Epoch 114/300\n",
      "153/153 [==============================] - 0s 62us/step - loss: 0.9152 - accuracy: 0.6732 - val_loss: 1.2131 - val_accuracy: 0.5000\n",
      "Epoch 115/300\n",
      "153/153 [==============================] - 0s 62us/step - loss: 0.9088 - accuracy: 0.6144 - val_loss: 1.1499 - val_accuracy: 0.4444\n",
      "Epoch 116/300\n",
      "153/153 [==============================] - 0s 62us/step - loss: 0.9123 - accuracy: 0.6471 - val_loss: 1.2313 - val_accuracy: 0.3333\n",
      "Epoch 117/300\n",
      "153/153 [==============================] - ETA: 0s - loss: 1.2215 - accuracy: 0.59 - 0s 67us/step - loss: 0.9352 - accuracy: 0.6667 - val_loss: 1.3569 - val_accuracy: 0.3333\n",
      "Epoch 118/300\n",
      "153/153 [==============================] - 0s 64us/step - loss: 0.9529 - accuracy: 0.6275 - val_loss: 1.2758 - val_accuracy: 0.5000\n",
      "Epoch 119/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 0.9045 - accuracy: 0.5948 - val_loss: 1.0528 - val_accuracy: 0.6111\n",
      "Epoch 120/300\n",
      "153/153 [==============================] - 0s 70us/step - loss: 0.8847 - accuracy: 0.6797 - val_loss: 1.2625 - val_accuracy: 0.5000\n",
      "Epoch 121/300\n",
      "153/153 [==============================] - 0s 62us/step - loss: 0.8949 - accuracy: 0.6732 - val_loss: 1.2233 - val_accuracy: 0.5000\n",
      "Epoch 122/300\n",
      "153/153 [==============================] - 0s 63us/step - loss: 0.9567 - accuracy: 0.6536 - val_loss: 1.0719 - val_accuracy: 0.5000\n",
      "Epoch 123/300\n",
      "153/153 [==============================] - 0s 72us/step - loss: 0.9152 - accuracy: 0.6601 - val_loss: 1.4043 - val_accuracy: 0.4444\n",
      "Epoch 124/300\n",
      "153/153 [==============================] - 0s 62us/step - loss: 0.9074 - accuracy: 0.6340 - val_loss: 0.9550 - val_accuracy: 0.6111\n",
      "Epoch 125/300\n",
      "153/153 [==============================] - 0s 63us/step - loss: 0.9016 - accuracy: 0.6536 - val_loss: 1.1042 - val_accuracy: 0.6111\n",
      "Epoch 126/300\n",
      "153/153 [==============================] - 0s 61us/step - loss: 0.8911 - accuracy: 0.6209 - val_loss: 0.9725 - val_accuracy: 0.5000\n",
      "Epoch 127/300\n",
      "153/153 [==============================] - ETA: 0s - loss: 0.7489 - accuracy: 0.75 - 0s 62us/step - loss: 0.9260 - accuracy: 0.6797 - val_loss: 1.0858 - val_accuracy: 0.6111\n",
      "Epoch 128/300\n",
      "153/153 [==============================] - 0s 62us/step - loss: 0.8583 - accuracy: 0.6536 - val_loss: 1.0745 - val_accuracy: 0.5000\n",
      "Epoch 129/300\n",
      "153/153 [==============================] - 0s 63us/step - loss: 0.8868 - accuracy: 0.6863 - val_loss: 0.9564 - val_accuracy: 0.5556\n",
      "Epoch 130/300\n",
      "153/153 [==============================] - 0s 61us/step - loss: 0.9442 - accuracy: 0.6275 - val_loss: 1.2075 - val_accuracy: 0.5556\n",
      "Epoch 131/300\n",
      "153/153 [==============================] - 0s 63us/step - loss: 0.8787 - accuracy: 0.6732 - val_loss: 1.1098 - val_accuracy: 0.5556\n",
      "Epoch 132/300\n",
      "153/153 [==============================] - 0s 142us/step - loss: 0.8938 - accuracy: 0.6405 - val_loss: 1.1354 - val_accuracy: 0.5000\n",
      "Epoch 133/300\n",
      "153/153 [==============================] - 0s 102us/step - loss: 0.9287 - accuracy: 0.6405 - val_loss: 1.1534 - val_accuracy: 0.5000\n",
      "Epoch 134/300\n",
      "153/153 [==============================] - 0s 88us/step - loss: 0.9011 - accuracy: 0.6405 - val_loss: 1.2142 - val_accuracy: 0.3889\n",
      "Epoch 135/300\n",
      "153/153 [==============================] - 0s 89us/step - loss: 0.8989 - accuracy: 0.6405 - val_loss: 1.2829 - val_accuracy: 0.4444\n",
      "Epoch 136/300\n",
      "153/153 [==============================] - 0s 86us/step - loss: 0.8884 - accuracy: 0.6601 - val_loss: 1.0555 - val_accuracy: 0.6111\n",
      "Epoch 137/300\n",
      "153/153 [==============================] - 0s 88us/step - loss: 0.8362 - accuracy: 0.7255 - val_loss: 1.1561 - val_accuracy: 0.4444\n",
      "Epoch 138/300\n",
      "153/153 [==============================] - 0s 86us/step - loss: 0.8980 - accuracy: 0.6275 - val_loss: 0.9915 - val_accuracy: 0.6111\n",
      "Epoch 139/300\n",
      "153/153 [==============================] - 0s 85us/step - loss: 0.8757 - accuracy: 0.6536 - val_loss: 0.9773 - val_accuracy: 0.5556\n",
      "Epoch 140/300\n",
      "153/153 [==============================] - 0s 113us/step - loss: 0.8770 - accuracy: 0.6601 - val_loss: 0.9134 - val_accuracy: 0.6111\n",
      "Epoch 141/300\n",
      "153/153 [==============================] - 0s 53us/step - loss: 0.8917 - accuracy: 0.6340 - val_loss: 1.2456 - val_accuracy: 0.5556\n",
      "Epoch 142/300\n",
      "153/153 [==============================] - 0s 64us/step - loss: 0.9075 - accuracy: 0.6601 - val_loss: 1.1368 - val_accuracy: 0.4444\n",
      "Epoch 143/300\n",
      "153/153 [==============================] - 0s 61us/step - loss: 0.8911 - accuracy: 0.6732 - val_loss: 0.8762 - val_accuracy: 0.6111\n",
      "Epoch 144/300\n",
      "153/153 [==============================] - 0s 65us/step - loss: 0.8919 - accuracy: 0.6797 - val_loss: 1.0897 - val_accuracy: 0.6667\n",
      "Epoch 145/300\n",
      "153/153 [==============================] - 0s 70us/step - loss: 0.9281 - accuracy: 0.6601 - val_loss: 1.0783 - val_accuracy: 0.5000\n",
      "Epoch 146/300\n",
      "153/153 [==============================] - 0s 70us/step - loss: 0.8882 - accuracy: 0.6732 - val_loss: 1.3840 - val_accuracy: 0.3889\n",
      "Epoch 147/300\n",
      "153/153 [==============================] - 0s 86us/step - loss: 0.9238 - accuracy: 0.6405 - val_loss: 1.2563 - val_accuracy: 0.5556\n",
      "Epoch 148/300\n",
      "153/153 [==============================] - 0s 70us/step - loss: 0.9186 - accuracy: 0.6275 - val_loss: 1.1283 - val_accuracy: 0.4444\n",
      "Epoch 149/300\n",
      "153/153 [==============================] - 0s 77us/step - loss: 0.8670 - accuracy: 0.6797 - val_loss: 1.4070 - val_accuracy: 0.3889\n",
      "Epoch 150/300\n",
      "153/153 [==============================] - 0s 107us/step - loss: 0.9229 - accuracy: 0.6078 - val_loss: 1.1708 - val_accuracy: 0.5000\n",
      "Epoch 151/300\n",
      "153/153 [==============================] - 0s 61us/step - loss: 0.8903 - accuracy: 0.6928 - val_loss: 1.2167 - val_accuracy: 0.5000\n",
      "Epoch 152/300\n",
      "153/153 [==============================] - 0s 58us/step - loss: 0.8857 - accuracy: 0.6471 - val_loss: 1.0929 - val_accuracy: 0.5000\n",
      "Epoch 153/300\n",
      "153/153 [==============================] - 0s 90us/step - loss: 0.8809 - accuracy: 0.6732 - val_loss: 1.1560 - val_accuracy: 0.5000\n",
      "Epoch 154/300\n",
      "153/153 [==============================] - 0s 69us/step - loss: 0.9002 - accuracy: 0.6405 - val_loss: 0.9795 - val_accuracy: 0.5000\n",
      "Epoch 155/300\n",
      "153/153 [==============================] - 0s 70us/step - loss: 0.8411 - accuracy: 0.6863 - val_loss: 1.1841 - val_accuracy: 0.5000\n",
      "Epoch 156/300\n",
      "153/153 [==============================] - 0s 76us/step - loss: 0.8376 - accuracy: 0.6797 - val_loss: 1.1970 - val_accuracy: 0.5000\n",
      "Epoch 157/300\n",
      "153/153 [==============================] - 0s 74us/step - loss: 0.8392 - accuracy: 0.6732 - val_loss: 1.1201 - val_accuracy: 0.5556\n",
      "Epoch 158/300\n",
      "153/153 [==============================] - 0s 78us/step - loss: 0.8570 - accuracy: 0.6209 - val_loss: 1.2234 - val_accuracy: 0.5000\n",
      "Epoch 159/300\n",
      "153/153 [==============================] - 0s 78us/step - loss: 0.8781 - accuracy: 0.6797 - val_loss: 1.3164 - val_accuracy: 0.3889\n",
      "Epoch 160/300\n",
      "153/153 [==============================] - 0s 75us/step - loss: 0.8638 - accuracy: 0.6863 - val_loss: 1.3834 - val_accuracy: 0.3333\n",
      "Epoch 161/300\n",
      "153/153 [==============================] - 0s 73us/step - loss: 0.8439 - accuracy: 0.6732 - val_loss: 1.1207 - val_accuracy: 0.5000\n",
      "Epoch 162/300\n",
      "153/153 [==============================] - 0s 73us/step - loss: 0.8663 - accuracy: 0.6667 - val_loss: 1.1001 - val_accuracy: 0.5556\n",
      "Epoch 163/300\n",
      "153/153 [==============================] - 0s 74us/step - loss: 0.8686 - accuracy: 0.6732 - val_loss: 1.2271 - val_accuracy: 0.5000\n",
      "Epoch 164/300\n",
      "153/153 [==============================] - 0s 72us/step - loss: 0.8733 - accuracy: 0.6405 - val_loss: 1.2137 - val_accuracy: 0.5000\n",
      "Epoch 165/300\n",
      "153/153 [==============================] - 0s 75us/step - loss: 0.9059 - accuracy: 0.6405 - val_loss: 1.1400 - val_accuracy: 0.5000\n",
      "Epoch 166/300\n",
      "153/153 [==============================] - 0s 75us/step - loss: 0.8946 - accuracy: 0.6471 - val_loss: 0.9444 - val_accuracy: 0.6111\n",
      "Epoch 167/300\n",
      "153/153 [==============================] - 0s 73us/step - loss: 0.9398 - accuracy: 0.6340 - val_loss: 1.0995 - val_accuracy: 0.5556\n",
      "Epoch 168/300\n",
      "153/153 [==============================] - 0s 83us/step - loss: 0.8545 - accuracy: 0.6601 - val_loss: 1.2558 - val_accuracy: 0.5000\n",
      "Epoch 169/300\n",
      "153/153 [==============================] - 0s 77us/step - loss: 0.8701 - accuracy: 0.6471 - val_loss: 1.0827 - val_accuracy: 0.5000\n",
      "Epoch 170/300\n",
      "153/153 [==============================] - 0s 79us/step - loss: 0.8202 - accuracy: 0.6993 - val_loss: 1.2749 - val_accuracy: 0.3889\n",
      "Epoch 171/300\n",
      "153/153 [==============================] - 0s 80us/step - loss: 0.8424 - accuracy: 0.6863 - val_loss: 1.2371 - val_accuracy: 0.5000\n",
      "Epoch 172/300\n",
      "153/153 [==============================] - 0s 82us/step - loss: 0.8652 - accuracy: 0.6667 - val_loss: 1.3906 - val_accuracy: 0.5000\n",
      "Epoch 173/300\n",
      "153/153 [==============================] - 0s 79us/step - loss: 0.8098 - accuracy: 0.6993 - val_loss: 1.1796 - val_accuracy: 0.5000\n",
      "Epoch 174/300\n",
      "153/153 [==============================] - 0s 80us/step - loss: 0.8645 - accuracy: 0.6471 - val_loss: 1.0329 - val_accuracy: 0.5000\n",
      "Epoch 175/300\n",
      "153/153 [==============================] - 0s 80us/step - loss: 0.8569 - accuracy: 0.6928 - val_loss: 1.0497 - val_accuracy: 0.5556\n",
      "Epoch 176/300\n",
      "153/153 [==============================] - 0s 80us/step - loss: 0.8534 - accuracy: 0.6667 - val_loss: 0.9459 - val_accuracy: 0.5556\n",
      "Epoch 177/300\n",
      "153/153 [==============================] - 0s 81us/step - loss: 0.8625 - accuracy: 0.6863 - val_loss: 1.2179 - val_accuracy: 0.5000\n",
      "Epoch 178/300\n",
      "153/153 [==============================] - 0s 79us/step - loss: 0.8567 - accuracy: 0.6863 - val_loss: 1.4879 - val_accuracy: 0.5000\n",
      "Epoch 179/300\n",
      "153/153 [==============================] - 0s 80us/step - loss: 0.8455 - accuracy: 0.6797 - val_loss: 1.2332 - val_accuracy: 0.3889\n",
      "Epoch 180/300\n",
      "153/153 [==============================] - 0s 80us/step - loss: 0.8959 - accuracy: 0.6078 - val_loss: 1.0385 - val_accuracy: 0.5556\n",
      "Epoch 181/300\n",
      "153/153 [==============================] - 0s 81us/step - loss: 0.8133 - accuracy: 0.6797 - val_loss: 1.2051 - val_accuracy: 0.5000\n",
      "Epoch 182/300\n",
      "153/153 [==============================] - 0s 82us/step - loss: 0.9358 - accuracy: 0.6471 - val_loss: 1.1960 - val_accuracy: 0.4444\n",
      "Epoch 183/300\n",
      "153/153 [==============================] - 0s 82us/step - loss: 0.8822 - accuracy: 0.6601 - val_loss: 1.1636 - val_accuracy: 0.5000\n",
      "Epoch 184/300\n",
      "153/153 [==============================] - 0s 83us/step - loss: 0.8660 - accuracy: 0.6601 - val_loss: 1.1765 - val_accuracy: 0.3889\n",
      "Epoch 185/300\n",
      "153/153 [==============================] - 0s 81us/step - loss: 0.8539 - accuracy: 0.6928 - val_loss: 1.1744 - val_accuracy: 0.4444\n",
      "Epoch 186/300\n",
      "153/153 [==============================] - 0s 81us/step - loss: 0.9076 - accuracy: 0.6601 - val_loss: 0.9281 - val_accuracy: 0.6111\n",
      "Epoch 187/300\n",
      "153/153 [==============================] - 0s 85us/step - loss: 0.8662 - accuracy: 0.6405 - val_loss: 1.0494 - val_accuracy: 0.6111\n",
      "Epoch 188/300\n",
      "153/153 [==============================] - 0s 82us/step - loss: 0.9030 - accuracy: 0.6732 - val_loss: 1.2932 - val_accuracy: 0.5556\n",
      "Epoch 189/300\n",
      "153/153 [==============================] - 0s 82us/step - loss: 0.8406 - accuracy: 0.6863 - val_loss: 1.2377 - val_accuracy: 0.5000\n",
      "Epoch 190/300\n",
      "153/153 [==============================] - 0s 86us/step - loss: 0.8206 - accuracy: 0.7190 - val_loss: 1.2754 - val_accuracy: 0.5000\n",
      "Epoch 191/300\n",
      "153/153 [==============================] - 0s 76us/step - loss: 0.8731 - accuracy: 0.6993 - val_loss: 0.9528 - val_accuracy: 0.6111\n",
      "Epoch 192/300\n",
      "153/153 [==============================] - 0s 79us/step - loss: 0.8729 - accuracy: 0.6993 - val_loss: 0.8976 - val_accuracy: 0.6111\n",
      "Epoch 193/300\n",
      "153/153 [==============================] - 0s 79us/step - loss: 0.8194 - accuracy: 0.6667 - val_loss: 0.9891 - val_accuracy: 0.5556\n",
      "Epoch 194/300\n",
      "153/153 [==============================] - 0s 78us/step - loss: 0.8262 - accuracy: 0.6928 - val_loss: 0.9658 - val_accuracy: 0.6667\n",
      "Epoch 195/300\n",
      "153/153 [==============================] - 0s 85us/step - loss: 0.8453 - accuracy: 0.6732 - val_loss: 0.8996 - val_accuracy: 0.6111\n",
      "Epoch 196/300\n",
      "153/153 [==============================] - 0s 81us/step - loss: 0.8192 - accuracy: 0.6732 - val_loss: 1.1925 - val_accuracy: 0.5556\n",
      "Epoch 197/300\n",
      "153/153 [==============================] - 0s 81us/step - loss: 0.8554 - accuracy: 0.6732 - val_loss: 1.0114 - val_accuracy: 0.5556\n",
      "Epoch 198/300\n",
      "153/153 [==============================] - 0s 85us/step - loss: 0.8665 - accuracy: 0.6340 - val_loss: 1.2136 - val_accuracy: 0.6111\n",
      "Epoch 199/300\n",
      "153/153 [==============================] - 0s 82us/step - loss: 0.9115 - accuracy: 0.6471 - val_loss: 1.2117 - val_accuracy: 0.5000\n",
      "Epoch 200/300\n",
      "153/153 [==============================] - 0s 77us/step - loss: 0.8698 - accuracy: 0.6536 - val_loss: 1.4086 - val_accuracy: 0.3889\n",
      "Epoch 201/300\n",
      "153/153 [==============================] - 0s 84us/step - loss: 0.8091 - accuracy: 0.6993 - val_loss: 1.1655 - val_accuracy: 0.5000\n",
      "Epoch 202/300\n",
      "153/153 [==============================] - 0s 80us/step - loss: 0.8831 - accuracy: 0.6601 - val_loss: 1.2044 - val_accuracy: 0.4444\n",
      "Epoch 203/300\n",
      "153/153 [==============================] - 0s 82us/step - loss: 0.8272 - accuracy: 0.6732 - val_loss: 1.0902 - val_accuracy: 0.6111\n",
      "Epoch 204/300\n",
      "153/153 [==============================] - 0s 98us/step - loss: 0.8248 - accuracy: 0.6993 - val_loss: 1.0721 - val_accuracy: 0.6111\n",
      "Epoch 205/300\n",
      "153/153 [==============================] - 0s 64us/step - loss: 0.8762 - accuracy: 0.6405 - val_loss: 1.3650 - val_accuracy: 0.5556\n",
      "Epoch 206/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.8343 - accuracy: 0.6863 - val_loss: 1.3269 - val_accuracy: 0.5000\n",
      "Epoch 207/300\n",
      "153/153 [==============================] - 0s 62us/step - loss: 0.8447 - accuracy: 0.6928 - val_loss: 1.2382 - val_accuracy: 0.3889\n",
      "Epoch 208/300\n",
      "153/153 [==============================] - 0s 72us/step - loss: 0.8204 - accuracy: 0.6863 - val_loss: 1.1515 - val_accuracy: 0.5556\n",
      "Epoch 209/300\n",
      "153/153 [==============================] - 0s 71us/step - loss: 0.8461 - accuracy: 0.7059 - val_loss: 1.4926 - val_accuracy: 0.5000\n",
      "Epoch 210/300\n",
      "153/153 [==============================] - 0s 78us/step - loss: 0.8375 - accuracy: 0.6993 - val_loss: 1.0247 - val_accuracy: 0.6111\n",
      "Epoch 211/300\n",
      "153/153 [==============================] - 0s 70us/step - loss: 0.7812 - accuracy: 0.7059 - val_loss: 1.1839 - val_accuracy: 0.6111\n",
      "Epoch 212/300\n",
      "153/153 [==============================] - 0s 70us/step - loss: 0.8483 - accuracy: 0.6863 - val_loss: 1.0933 - val_accuracy: 0.5556\n",
      "Epoch 213/300\n",
      "153/153 [==============================] - 0s 73us/step - loss: 0.8082 - accuracy: 0.6863 - val_loss: 1.1023 - val_accuracy: 0.5556\n",
      "Epoch 214/300\n",
      "153/153 [==============================] - 0s 70us/step - loss: 0.8655 - accuracy: 0.6471 - val_loss: 1.1130 - val_accuracy: 0.5556\n",
      "Epoch 215/300\n",
      "153/153 [==============================] - 0s 71us/step - loss: 0.8287 - accuracy: 0.6863 - val_loss: 0.9899 - val_accuracy: 0.6111\n",
      "Epoch 216/300\n",
      "153/153 [==============================] - 0s 61us/step - loss: 0.8517 - accuracy: 0.6667 - val_loss: 1.4812 - val_accuracy: 0.4444\n",
      "Epoch 217/300\n",
      "153/153 [==============================] - 0s 58us/step - loss: 0.7815 - accuracy: 0.6993 - val_loss: 1.0134 - val_accuracy: 0.6667\n",
      "Epoch 218/300\n",
      "153/153 [==============================] - 0s 62us/step - loss: 0.8294 - accuracy: 0.6601 - val_loss: 1.2886 - val_accuracy: 0.5000\n",
      "Epoch 219/300\n",
      "153/153 [==============================] - 0s 62us/step - loss: 0.8312 - accuracy: 0.6797 - val_loss: 0.9337 - val_accuracy: 0.6111\n",
      "Epoch 220/300\n",
      "153/153 [==============================] - 0s 69us/step - loss: 0.8617 - accuracy: 0.6797 - val_loss: 1.1770 - val_accuracy: 0.5000\n",
      "Epoch 221/300\n",
      "153/153 [==============================] - 0s 68us/step - loss: 0.8357 - accuracy: 0.6797 - val_loss: 1.1755 - val_accuracy: 0.6667\n",
      "Epoch 222/300\n",
      "153/153 [==============================] - 0s 62us/step - loss: 0.8567 - accuracy: 0.6471 - val_loss: 1.0693 - val_accuracy: 0.5556\n",
      "Epoch 223/300\n",
      "153/153 [==============================] - 0s 63us/step - loss: 0.8290 - accuracy: 0.6797 - val_loss: 1.3435 - val_accuracy: 0.4444\n",
      "Epoch 224/300\n",
      "153/153 [==============================] - 0s 63us/step - loss: 0.8872 - accuracy: 0.6732 - val_loss: 0.9338 - val_accuracy: 0.6111\n",
      "Epoch 225/300\n",
      "153/153 [==============================] - 0s 65us/step - loss: 0.8216 - accuracy: 0.6732 - val_loss: 1.3224 - val_accuracy: 0.5000\n",
      "Epoch 226/300\n",
      "153/153 [==============================] - 0s 65us/step - loss: 0.8361 - accuracy: 0.6863 - val_loss: 1.1178 - val_accuracy: 0.6111\n",
      "Epoch 227/300\n",
      "153/153 [==============================] - 0s 100us/step - loss: 0.8450 - accuracy: 0.7059 - val_loss: 1.1740 - val_accuracy: 0.3889\n",
      "Epoch 228/300\n",
      "153/153 [==============================] - 0s 61us/step - loss: 0.8362 - accuracy: 0.7059 - val_loss: 1.2612 - val_accuracy: 0.5556\n",
      "Epoch 229/300\n",
      "153/153 [==============================] - 0s 65us/step - loss: 0.8226 - accuracy: 0.7190 - val_loss: 0.9490 - val_accuracy: 0.7222\n",
      "Epoch 230/300\n",
      "153/153 [==============================] - 0s 70us/step - loss: 0.8674 - accuracy: 0.6471 - val_loss: 1.5796 - val_accuracy: 0.5000\n",
      "Epoch 231/300\n",
      "153/153 [==============================] - 0s 70us/step - loss: 0.8694 - accuracy: 0.6471 - val_loss: 1.6314 - val_accuracy: 0.6111\n",
      "Epoch 232/300\n",
      "153/153 [==============================] - 0s 73us/step - loss: 0.8103 - accuracy: 0.7059 - val_loss: 1.0228 - val_accuracy: 0.6111\n",
      "Epoch 233/300\n",
      "153/153 [==============================] - 0s 72us/step - loss: 0.8203 - accuracy: 0.6863 - val_loss: 1.0464 - val_accuracy: 0.5556\n",
      "Epoch 234/300\n",
      "153/153 [==============================] - 0s 73us/step - loss: 0.8441 - accuracy: 0.6667 - val_loss: 1.1528 - val_accuracy: 0.6111\n",
      "Epoch 235/300\n",
      "153/153 [==============================] - 0s 73us/step - loss: 0.7840 - accuracy: 0.6928 - val_loss: 1.1233 - val_accuracy: 0.6111\n",
      "Epoch 236/300\n",
      "153/153 [==============================] - 0s 66us/step - loss: 0.8358 - accuracy: 0.6993 - val_loss: 1.2923 - val_accuracy: 0.5556\n",
      "Epoch 237/300\n",
      "153/153 [==============================] - 0s 66us/step - loss: 0.8414 - accuracy: 0.6797 - val_loss: 1.2882 - val_accuracy: 0.4444\n",
      "Epoch 238/300\n",
      "153/153 [==============================] - 0s 73us/step - loss: 0.8549 - accuracy: 0.6667 - val_loss: 1.1031 - val_accuracy: 0.5000\n",
      "Epoch 239/300\n",
      "153/153 [==============================] - 0s 73us/step - loss: 0.8560 - accuracy: 0.6797 - val_loss: 1.2781 - val_accuracy: 0.4444\n",
      "Epoch 240/300\n",
      "153/153 [==============================] - 0s 65us/step - loss: 0.8275 - accuracy: 0.6928 - val_loss: 1.1213 - val_accuracy: 0.5556\n",
      "Epoch 241/300\n",
      "153/153 [==============================] - 0s 66us/step - loss: 0.8756 - accuracy: 0.6928 - val_loss: 1.1321 - val_accuracy: 0.5000\n",
      "Epoch 242/300\n",
      "153/153 [==============================] - 0s 66us/step - loss: 0.8007 - accuracy: 0.7059 - val_loss: 1.1635 - val_accuracy: 0.6111\n",
      "Epoch 243/300\n",
      "153/153 [==============================] - 0s 66us/step - loss: 0.7640 - accuracy: 0.7255 - val_loss: 1.0463 - val_accuracy: 0.5556\n",
      "Epoch 244/300\n",
      "153/153 [==============================] - 0s 64us/step - loss: 0.8415 - accuracy: 0.6667 - val_loss: 1.3112 - val_accuracy: 0.5556\n",
      "Epoch 245/300\n",
      "153/153 [==============================] - 0s 65us/step - loss: 0.8363 - accuracy: 0.7124 - val_loss: 1.0898 - val_accuracy: 0.5556\n",
      "Epoch 246/300\n",
      "153/153 [==============================] - 0s 73us/step - loss: 0.8610 - accuracy: 0.6601 - val_loss: 1.1867 - val_accuracy: 0.4444\n",
      "Epoch 247/300\n",
      "153/153 [==============================] - 0s 65us/step - loss: 0.8689 - accuracy: 0.6471 - val_loss: 1.5774 - val_accuracy: 0.5000\n",
      "Epoch 248/300\n",
      "153/153 [==============================] - 0s 71us/step - loss: 0.8198 - accuracy: 0.6993 - val_loss: 1.4582 - val_accuracy: 0.4444\n",
      "Epoch 249/300\n",
      "153/153 [==============================] - 0s 74us/step - loss: 0.8081 - accuracy: 0.6471 - val_loss: 1.0137 - val_accuracy: 0.6111\n",
      "Epoch 250/300\n",
      "153/153 [==============================] - 0s 73us/step - loss: 0.8421 - accuracy: 0.6732 - val_loss: 1.2287 - val_accuracy: 0.5000\n",
      "Epoch 251/300\n",
      "153/153 [==============================] - 0s 73us/step - loss: 0.8491 - accuracy: 0.6536 - val_loss: 1.0648 - val_accuracy: 0.6111\n",
      "Epoch 252/300\n",
      "153/153 [==============================] - 0s 72us/step - loss: 0.7846 - accuracy: 0.6863 - val_loss: 1.2742 - val_accuracy: 0.4444\n",
      "Epoch 253/300\n",
      "153/153 [==============================] - 0s 72us/step - loss: 0.8249 - accuracy: 0.6797 - val_loss: 1.1974 - val_accuracy: 0.5556\n",
      "Epoch 254/300\n",
      "153/153 [==============================] - 0s 65us/step - loss: 0.8012 - accuracy: 0.6863 - val_loss: 1.4138 - val_accuracy: 0.4444\n",
      "Epoch 255/300\n",
      "153/153 [==============================] - 0s 65us/step - loss: 0.8309 - accuracy: 0.6797 - val_loss: 0.9235 - val_accuracy: 0.6111\n",
      "Epoch 256/300\n",
      "153/153 [==============================] - 0s 73us/step - loss: 0.7882 - accuracy: 0.7124 - val_loss: 1.2594 - val_accuracy: 0.6111\n",
      "Epoch 257/300\n",
      "153/153 [==============================] - 0s 74us/step - loss: 0.8486 - accuracy: 0.6863 - val_loss: 0.9166 - val_accuracy: 0.6667\n",
      "Epoch 258/300\n",
      "153/153 [==============================] - 0s 65us/step - loss: 0.8135 - accuracy: 0.6993 - val_loss: 1.0397 - val_accuracy: 0.5000\n",
      "Epoch 259/300\n",
      "153/153 [==============================] - 0s 65us/step - loss: 0.8168 - accuracy: 0.6797 - val_loss: 1.3882 - val_accuracy: 0.5556\n",
      "Epoch 260/300\n",
      "153/153 [==============================] - 0s 68us/step - loss: 0.7990 - accuracy: 0.6797 - val_loss: 1.0999 - val_accuracy: 0.3889\n",
      "Epoch 261/300\n",
      "153/153 [==============================] - 0s 65us/step - loss: 0.8083 - accuracy: 0.6863 - val_loss: 1.4043 - val_accuracy: 0.5000\n",
      "Epoch 262/300\n",
      "153/153 [==============================] - 0s 66us/step - loss: 0.7899 - accuracy: 0.6863 - val_loss: 1.2875 - val_accuracy: 0.5556\n",
      "Epoch 263/300\n",
      "153/153 [==============================] - 0s 65us/step - loss: 0.7636 - accuracy: 0.6601 - val_loss: 1.2652 - val_accuracy: 0.4444\n",
      "Epoch 264/300\n",
      "153/153 [==============================] - 0s 78us/step - loss: 0.8194 - accuracy: 0.7255 - val_loss: 1.6505 - val_accuracy: 0.5556\n",
      "Epoch 265/300\n",
      "153/153 [==============================] - 0s 63us/step - loss: 0.7553 - accuracy: 0.6928 - val_loss: 1.1533 - val_accuracy: 0.5556\n",
      "Epoch 266/300\n",
      "153/153 [==============================] - 0s 65us/step - loss: 0.7937 - accuracy: 0.7190 - val_loss: 1.1340 - val_accuracy: 0.5000\n",
      "Epoch 267/300\n",
      "153/153 [==============================] - 0s 73us/step - loss: 0.8106 - accuracy: 0.6928 - val_loss: 1.2634 - val_accuracy: 0.5000\n",
      "Epoch 268/300\n",
      "153/153 [==============================] - 0s 106us/step - loss: 0.7865 - accuracy: 0.7516 - val_loss: 1.2015 - val_accuracy: 0.5556\n",
      "Epoch 269/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.8556 - accuracy: 0.6928 - val_loss: 1.0169 - val_accuracy: 0.5000\n",
      "Epoch 270/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.8170 - accuracy: 0.7059 - val_loss: 1.4968 - val_accuracy: 0.5000\n",
      "Epoch 271/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.8138 - accuracy: 0.6863 - val_loss: 0.9902 - val_accuracy: 0.6111\n",
      "Epoch 272/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.8097 - accuracy: 0.6863 - val_loss: 1.2916 - val_accuracy: 0.6111\n",
      "Epoch 273/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.8423 - accuracy: 0.6732 - val_loss: 1.1455 - val_accuracy: 0.6111\n",
      "Epoch 274/300\n",
      "153/153 [==============================] - 0s 58us/step - loss: 0.7762 - accuracy: 0.6797 - val_loss: 1.1227 - val_accuracy: 0.5556\n",
      "Epoch 275/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.7831 - accuracy: 0.6863 - val_loss: 1.2988 - val_accuracy: 0.6111\n",
      "Epoch 276/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.7890 - accuracy: 0.6993 - val_loss: 1.2134 - val_accuracy: 0.5556\n",
      "Epoch 277/300\n",
      "153/153 [==============================] - 0s 58us/step - loss: 0.8274 - accuracy: 0.6667 - val_loss: 1.2715 - val_accuracy: 0.4444\n",
      "Epoch 278/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.7516 - accuracy: 0.7255 - val_loss: 1.4158 - val_accuracy: 0.3889\n",
      "Epoch 279/300\n",
      "153/153 [==============================] - 0s 65us/step - loss: 0.8159 - accuracy: 0.6667 - val_loss: 0.9076 - val_accuracy: 0.6111\n",
      "Epoch 280/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 0.7867 - accuracy: 0.6863 - val_loss: 1.1574 - val_accuracy: 0.6667\n",
      "Epoch 281/300\n",
      "153/153 [==============================] - 0s 58us/step - loss: 0.7744 - accuracy: 0.7255 - val_loss: 1.1207 - val_accuracy: 0.5556\n",
      "Epoch 282/300\n",
      "153/153 [==============================] - 0s 76us/step - loss: 0.7961 - accuracy: 0.6863 - val_loss: 1.4503 - val_accuracy: 0.4444\n",
      "Epoch 283/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.8294 - accuracy: 0.6667 - val_loss: 1.0524 - val_accuracy: 0.6111\n",
      "Epoch 284/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 0.7760 - accuracy: 0.7320 - val_loss: 1.4887 - val_accuracy: 0.3889\n",
      "Epoch 285/300\n",
      "153/153 [==============================] - 0s 93us/step - loss: 0.8393 - accuracy: 0.6993 - val_loss: 1.3743 - val_accuracy: 0.4444\n",
      "Epoch 286/300\n",
      "153/153 [==============================] - 0s 65us/step - loss: 0.8800 - accuracy: 0.6405 - val_loss: 1.1692 - val_accuracy: 0.4444\n",
      "Epoch 287/300\n",
      "153/153 [==============================] - 0s 69us/step - loss: 0.7863 - accuracy: 0.7059 - val_loss: 1.2763 - val_accuracy: 0.5000\n",
      "Epoch 288/300\n",
      "153/153 [==============================] - 0s 59us/step - loss: 0.8634 - accuracy: 0.6667 - val_loss: 1.0866 - val_accuracy: 0.6667\n",
      "Epoch 289/300\n",
      "153/153 [==============================] - 0s 60us/step - loss: 0.8245 - accuracy: 0.6732 - val_loss: 1.2964 - val_accuracy: 0.5556\n",
      "Epoch 290/300\n",
      "153/153 [==============================] - 0s 61us/step - loss: 0.7644 - accuracy: 0.7059 - val_loss: 1.2935 - val_accuracy: 0.5000\n",
      "Epoch 291/300\n",
      "153/153 [==============================] - 0s 61us/step - loss: 0.8172 - accuracy: 0.6732 - val_loss: 1.4300 - val_accuracy: 0.3889\n",
      "Epoch 292/300\n",
      "153/153 [==============================] - 0s 61us/step - loss: 0.8063 - accuracy: 0.6863 - val_loss: 1.8128 - val_accuracy: 0.5000\n",
      "Epoch 293/300\n",
      "153/153 [==============================] - 0s 58us/step - loss: 0.7465 - accuracy: 0.7516 - val_loss: 1.3895 - val_accuracy: 0.6111\n",
      "Epoch 294/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 0.7975 - accuracy: 0.7124 - val_loss: 1.1843 - val_accuracy: 0.5556\n",
      "Epoch 295/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 0.7860 - accuracy: 0.7320 - val_loss: 1.1392 - val_accuracy: 0.6111\n",
      "Epoch 296/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 0.7409 - accuracy: 0.7451 - val_loss: 1.0410 - val_accuracy: 0.5556\n",
      "Epoch 297/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 0.8103 - accuracy: 0.6732 - val_loss: 1.4854 - val_accuracy: 0.5000\n",
      "Epoch 298/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 0.7949 - accuracy: 0.7124 - val_loss: 0.9934 - val_accuracy: 0.5000\n",
      "Epoch 299/300\n",
      "153/153 [==============================] - 0s 105us/step - loss: 0.7913 - accuracy: 0.6863 - val_loss: 1.0420 - val_accuracy: 0.6111\n",
      "Epoch 300/300\n",
      "153/153 [==============================] - 0s 57us/step - loss: 0.7428 - accuracy: 0.7516 - val_loss: 1.0687 - val_accuracy: 0.6111\n"
     ]
    }
   ],
   "source": [
    "history_drp = model_drp.fit(X_train, Y_train, batch_size=32, epochs=300, validation_split=0.1,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "# summarize history for loss\n",
    "#plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.plot(history_pen.history['val_loss'])\n",
    "plt.plot(history_drp.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['Naive model', 'Regularised model', 'Dropout model'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Another regularisation technique: Early Stopping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Have a look at the plot above. All the losses are decresing until aroung epoch $75$. Stopping at this step (_i.e._ in general reducing epochs) might reduce overfitting issues."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://miro.medium.com/max/1400/1*2zD7wwIwhIrHdr8F4urF2w.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What you should remember\n",
    "- Very deep \"plain\" networks don't work in practice because they are hard to train due to overfit and misprediction problems. \n",
    "- Fighting overfit means keeping a bit less attention on training set, to generalise better on new data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References  \n",
    "\n",
    "- A quite nice and not too technical reading is [this medium blog post](https://towardsdatascience.com/handling-overfitting-in-deep-learning-models-c760ee047c6e)\n",
    "- Francois Chollet's GitHub repository: https://github.com/fchollet/deep-learning-models/blob/master/resnet50.py\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "lectures",
   "language": "python",
   "name": "lectures"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}