diff --git a/neural_nets_intro.ipynb b/neural_nets_intro.ipynb
index 027c378b7b3df6ccd9d68709a01a7c6876e454ba..f7c3cf25c7b1bc74cdba4f212317719b08a01c0e 100644
--- a/neural_nets_intro.ipynb
+++ b/neural_nets_intro.ipynb
@@ -114,20 +114,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -140,12 +129,7 @@
     "    output = 0\n",
     "    if linear_sum >= threshold:\n",
     "        output = 1\n",
-    "    return output\n",
-    "\n",
-    "\n",
-    "X = [1, 0]\n",
-    "w = [1, 1]\n",
-    "perceptron(X, w)"
+    "    return output"
    ]
   },
   {
@@ -164,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -197,11 +181,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def perceptron_DB(X, w):\n",
+    "def perceptron_DB(X, w, threshold):\n",
     "    # Plotting the decision boundary\n",
     "    for i in X:\n",
     "        plt.plot(i, \"o\", color=\"b\")\n",
@@ -218,14 +202,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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PZvny5fh8Plq3bk3fvn35z3/+43U0SQAqGJEE8Otf/5qNGzfy4IMPsmDBAhWMRIQKRiRBlChRgieffJIdO3bkDJ45atQo9u3b53U0iVMqGJEEU65cOcA/eObQoUNJTk5m3rx5OtpMQk4FI5KgUlNTWb9+PbVr1+a2226jc+fO7Ny50+tYEkdUMCIJLDk5mX/84x+MHz+e5cuX065dOw2eKSGj82BEElxSUhKDBg2iS5cuOYNnHjt2jJ07d1KrVi2v40kM0xaMiABwySWXcOWVVwL+82bq1avHU089xfHjxz1OJrFKBSMiP9OjRw/at2/PQw89RPPmzdm4caPXkSQGqWBE5GcqV67Ma6+9xoIFC9i5cyfp6ek8++yzXseSGKOCEZF8mRk33ngjWVlZ/O53v6Nx48aABs+U4Gknv4icUfny5Zk1a1bOdP/+/SlWrBjDhw+nZMmS3gWTqKctGBEJ2smTJylcuDATJkwgNTWVpUuXeh1JopgKRkSC5vP5ePrpp/nwww8pUqQI7dq1o1evXhw6dMjraBKFClQwZnaJmb1vZtvNbKyZFcv13OrQxxORaNSyZUs2btzI4MGDef311/nmm2+8jiRRqKBbMJOB14AbgQuB982sVOC5wqEMJiLRrVixYowcOZIdO3bwy1/+EuccI0eO5KuvvvI6mkSJghbMRc65Sc65tc65O4BF+EumDBCyQ0vMrL2ZfWpm28xscD7P32Fm+81sQ+B2V6h+t4gUTNmyZQHYsmULw4YNo27dusyZM0dHm0mBC6Zo7gnn3EhgAfA+UCrfVxSQmSUBk4AOQDJwi5kl5zPrS865tMDtuVD8bhE5d/Xq1WPDhg3UrVuXnj170rFjR/797397HUs8VNCC+czM2uZ+wDk3GvgbEKpBi5oC25xz251zx4D5QNcQvbeIhFGdOnVYsWIFEydOZMWKFVx77bUaPDOBFbRgbgY+zPugc24sUC0kiaAKkHvM8C8Dj+V1vZltMrNXzCzf321mfc0sw8wy9u/fH6J4InImPp+P3//+92RmZvLcc8/lDJ65detWr6NJhJ21YMys/6n7zrmjzrmjuZ4ramaTAs/tClEmy+exvF/mvgXUcM7VB/4OzM7vjZxz05xz6c659IoVK4YonogEo0aNGlxxxRUAjBkzhtTUVEaOHMmPP/7ocTKJlGC2YJ4xs9fN7MLcD5pZPWAt0CPEmb7kp1tDVYHduWdwzh3MVXTTgcYhziAiIXTnnXfym9/8hiFDhtCsWTPWr1/vdSSJgGAKpgPQHNhoZq0AzOxeYDVwlND/cV8D1A6cc1ME/9dyC3PPYGYX55rsAmSHOIOIhFClSpV4+eWXefXVV9m9ezdNmjRh6tSpXseSMDtrwTjn3gMaAFuAv5vZWmAsMAVo7pz7LJSBnHPHgYHAEvzFscA5t8XMnjCzLoHZ7jWzLWa2EbgXuCOUGUQkPLp37052djY9e/akSZMmgAbPjGcW7Mo1szb4z3spiv+rsbbOuf+EMVtIpaenu4yMDK9jiEgeffv2pVixYowYMUKDZ0YhM1vrnEs/l9cGs5M/ycxG4N+i+AC4Ff8+kg1mduW5/FIREfAPnlm8eHGeeeYZ6tWrx5IlS7yOJCEUzD6YlcD9wAPOuU7OuflAGvApsMzMhoUzoIjEL5/Px4QJE1ixYgXFixenffv23HHHHXz99ddeR5MQCKZgSuPf1zL+1APOub3OuWuBwcBD4QonIonhiiuuYP369Tz88MMsXLiQ7777zutIEgLBFExj59yG/J5wzo0BWoQ2kogkolMXMduxYwfVq1fHOcfw4cPZs2eP19HkHAVzFNmRszyfb/mIiJyLMmXKAP7BM4cPH05ycjLPP/+8jjaLQbrgmIhEpXr16rFp0yZSU1Pp1asX1157LV988YXXsaQAVDAiErV+9atfsXz5ciZPnsxHH31E+/btNXhmDCnkdQARkTPx+Xz079+fTp06sWvXrpzBM3fs2MFll13mdTw5A23BiEhMqF69Oi1a+I8pGjNmDPXr1+cvf/mLBs+MYioYEYk5vXr1olu3bjzyyCM0adKEtWvXeh1J8qGCEZGYc9FFF/HSSy/x+uuvs2/fPpo1a8bkyZO9jiV5qGBEJGZ169aNrKwsevXqRbNmzQB0EEAU0U5+EYlpZcuWZdq0aTnT/fr1o3Dhwvz1r3+ldOnSHiYTbcGISNxwzlGqVCmmTp1KvXr1WLx4sdeREpoKRkTihpkxduxYVq5cSalSpejUqRO33347Bw8e9DpaQlLBiEjcad68OevWrWPo0KG88847HDlyxhGvJExUMCISl4oWLcqwYcPYsWMH1apVwznHE088we7du72OljBUMCIS10qVKgVAVlYWI0eOJDk5mRkzZmjwzAhQwYhIQkhJSWHz5s2kpaVx1113cc0117B9+3avY8U1FYyIJIxLL72UDz74gKlTp7JmzRo6duyo82bCSAUjIgnF5/PRr18/srKymDVrVs7gmdnZ2V5HizsqGBFJSFWrVqV58+YAjB49mgYNGjBs2DCOHTvmcbL4oYIRkYTXp08frr/+eh5//HHS09NZs2aN15HiggpGRBJexYoVefHFF3nzzTc5ePAgzZs355lnnvE6VsxTwYiIBHTp0oWsrCz69u3LFVdcAWjwzPOhwS5FRHIpU6YMU6ZMyZnu06cPhQsXZtSoUZQpU8bDZLFHWzAiIqfhnOPCCy9k+vTppKSk8Pbbb3sdKaZEZcGYWXsz+9TMtpnZ4HyeL2pmLwWeX2VmNSKfUkTinZnx1FNP8dFHH1GuXDk6d+7M7373Ow4cOOB1tJgQdQVjZknAJKADkAzcYmbJeWbrDRxyzl0KjANGRTalRMqAAVCoEJj5fw4Y4HUiCVY8rbumTZuydu1ahg0bxpIlS/jvf//rdaSYEHUFAzQFtjnntjvnjgHzga555ukKzA7cfwVoY2YWwYwSAQMGwJQpcOKEf/rECf90LP+hShTxuO6KFCnC0KFDfzJ45uOPP86uXbu8jha1orFgqgA7c01/GXgs33mcc8eBw0D5iKSTiMl1kcKgHpfoEc/rLvfgmU8++STJyclMmzZNR5vlIxoLJr8tkbzDngYzD2bW18wyzCxj//79IQknkXPq02+wj0v0SIR1d2rwzMaNG9OvXz/atGnDtm3bvI4VVaKxYL4EquWargrkvYBDzjxmVggoA3yd942cc9Occ+nOufSKFSuGKa6ES1JSwR6X6JEo665WrVq8//77TJ8+nXXr1tGpUydtyeQSjQWzBqhtZpeYWRHgZmBhnnkWAj0D928APnC6uEPc6du3YI9L9EikdWdm3HXXXWRlZTFnzhx8Ph9Hjx4lKyvL62iei7qCCexTGQgsAbKBBc65LWb2hJl1Ccw2AyhvZtuA+4GfHcossW/yZOjf/3+fepOS/NOTJ3ubS84uEdddlSpVaNasGQBjxoyhQYMGPPbYYxw9etTjZN6xRPngn56e7jIyMryOISIJ4MCBA9x333288MILpKSkMGPGjJzyiTVmttY5l34ur426LRgRkVhXoUIF5s6dy6JFizh8+DAtWrTg6aef9jpWxKlgRETCpFOnTmzZsoX+/fvTsmVLILEGz9RglyIiYVS6dGkmTZqUM92nTx98Ph9PPfUUZcuW9TBZ+GkLRkQkQpxzVKhQgZkzZ5KSksLChXkPkI0vKhgRkQgxM0aNGsWqVauoUKECXbt25eabbyZeTwRXwYiIRFh6ejoZGRn8+c9/5oMPPojbQ5lVMCIiHihcuDCPPPIIO3bsoGrVqjjnGDp0KDt37jz7i2OECkZExEMXXHABANnZ2YwZM4aUlBSmTJkSF0ebqWBERKJAcnIymZmZNGvWjAEDBnD11VezdetWr2OdFxWMiEiUuOSSS3jvvfeYOXMmmzZtonPnzjG9JaOCERGJImbGnXfeSVZWFi+88ELO4JmZmZleRyswFYyISBS6+OKLadKkCeAfPLNhw4Y8+uijMXXEmQpGRCTK9evXj1tvvZXhw4fTsGFDVq5c6XWkoKhgRESiXPny5Zk9ezbvvPMO33//PVdeeSUTJkzwOtZZqWBERGJE+/btyczMZODAgbRq1QqAE1F8HWoNdikiEkNKlSrFxIkTc6Z79+5NUlISo0ePply5ch4m+zltwYiIxCjnHJUrV2b27NkkJyfz+uuvex3pJ1QwIiIxyswYMWIEq1evplKlSnTv3p0bb7yRffv2eR0NUMGIiMS8Ro0asXr1akaMGMGHH37IsWPHvI4EqGBEROJC4cKF+b//+7+fDJ758MMP869//cuzTCoYEZE4UqJECcA/eOaECROoV68ekyZN8mTIGRWMiEgcOjV45uWXX87AgQO56qqr+OSTTyKaQQUjIhKnatSowbvvvsusWbPIysqiS5cuET1vRgUjIhLHzIyePXuSlZXFvHnzSEpK4ujRo2zatCnsv1sFIyKSACpVqvSTwTMbNWrEkCFD+OGHH8L2O1UwIiIJpn///vTo0YORI0fSoEED/vGPf4Tl96hgREQSTLly5Zg5cybvvfcex44do2XLlowbNy7kvyeqCsbMLjSzpWa2NfAz34F1zOyEmW0I3BZGOqeISDxo27YtmzdvZtCgQbRu3RoI7eCZ5pwL2ZudLzN7EvjaOfdXMxsMlHPO/Smf+b5zzpUsyHunp6e7jIyMUEUVEYlLPXv2BGDcuHFceOGFmNla51z6ubxXVG3BAF2B2YH7s4FuHmYREUkozjmqVavG3/72N+rWrcsrr7xyXu8XbQVzkXNuD0Dg5y9OM18xM8sws4/NTCUkIhICZsbw4cNZs2YNVatW5cYbbzyv94t4wZjZ380sM59b1wK8TfXAJtutwHgzq3Wa39U3UEQZ+/fvD0l+EZF4l5aWxqpVqxg1atR5vU+07YP5FGjlnNtjZhcDy51zl53lNbOARc65M27LaR+MiEjBxdM+mIVAz8D9nsCbeWcws3JmVjRwvwJwBZAVsYQiIhKUaCuYvwJtzWwr0DYwjZmlm9lzgXnqAhlmthFYBvzVOaeCERGJMoW8DpCbc+4g0CafxzOAuwL3VwKpEY4mIiIFFG1bMCIiEidUMCIiEhYqGBERCQsVjIiIhIUKRkREwkIFIyIiYaGCERGRsFDBiIhIWKhgREQkLFQwIiISFioYEREJCxWMiIiEhQpGRETCQgUjIiJhoYIREZGwUMGIiEhYqGBERCQsVDAiIhIWKhgREQkLFYyIiISFCkZERMJCBSMiImGhghERkbBQwYiISFioYEREJCxUMCIiEhYqGBERCQsVjIiIhEVUFYyZ3WhmW8zspJmln2G+9mb2qZltM7PBkcwoIiLBiaqCATKB7sCHp5vBzJKASUAHIBm4xcySIxNPRESCVcjrALk557IBzOxMszUFtjnntgfmnQ90BbLCHlBERIIWVQUTpCrAzlzTXwLN8pvRzPoCfQOTR80sM8zZvFQBOOB1iDDS8sWueF42iP/lu+xcXxjxgjGzvwOV8nnqYefcm8G8RT6PufxmdM5NA6YFfm+Gc+60+3VinZYvtsXz8sXzskFiLN+5vjbiBeOcu+Y83+JLoFqu6arA7vN8TxERCbFo28kfjDVAbTO7xMyKADcDCz3OJCIieURVwZjZdWb2JdACeNvMlgQer2xmiwGcc8eBgcASIBtY4JzbEsTbTwtT7Gih5Ytt8bx88bxsoOU7LXMu390XIiIi5yWqtmBERCR+qGBERCQs4rZg4n3YGTO70MyWmtnWwM9yp5nvhJltCNyi+mCIs60LMytqZi8Fnl9lZjUin/LcBbF8d5jZ/lzr6y4vcp4rM5tpZvtOd76Z+U0MLP8mM2sU6YznKohla2Vmh3Otu6GRzng+zKyamS0zs+zA381B+cxT8PXnnIvLG1AX/wlCy4H008yTBHwO1ASKABuBZK+zB7l8TwKDA/cHA6NOM993XmcNcnnOui6AAcDUwP2bgZe8zh3i5bsDeMbrrOexjFcBjYDM0zzfEXgH/7lszYFVXmcO4bK1AhZ5nfM8lu9ioFHgfings3z+fRZ4/cXtFoxzLts59+lZZssZdsY5dww4NexMLOgKzA7cnw108zBLKASzLnIv8ytAGzvLuEJRJJaWYL1PAAAEAElEQVT/rQXFOfch8PUZZukKzHF+HwNlzeziyKQ7P0EsW0xzzu1xzq0L3P8W/xG6VfLMVuD1F7cFE6T8hp3J+x81Wl3knNsD/n8cwC9OM18xM8sws4/NLJpLKJh1kTOP8x+ufhgoH5F05y/Yf2vXB75+eMXMquXzfCyL5f/fgtHCzDaa2TtmluJ1mHMV+Oq5IbAqz1MFXn+xOBZZjkgOO+OFMy1fAd6munNut5nVBD4ws83Ouc9DkzCkglkXUb2+ziKY7G8BLzrnjprZ3fi31lqHPVnkxPL6O5t1wC+dc9+ZWUfgDaC2x5kKzMxKAq8Cf3DOfZP36Xxecsb1F9MF4+J82JkzLZ+ZfWVmFzvn9gQ2U/ed5j12B35uN7Pl+D+ZRGPBBLMuTs3zpZkVAsoQO19bnHX5nHMHc01OB0ZFIFckRfX/b+cj9x9j59xiM5tsZhWcczEzCKaZFcZfLvOcc6/lM0uB11+if0UWy8POLAR6Bu73BH62xWZm5cysaOB+BeAKoveyBsGsi9zLfAPwgQvsfYwBZ12+PN9nd8H/PXg8WQj0CByN1Bw4fOpr3lhnZpVO7Q80s6b4/7YePPOrokcg+wwg2zk39jSzFXz9eX30QhiPirgOf+MeBb4ClgQerwwsznNkxGf4P9U/7HXuAixfeeB9YGvg54WBx9OB5wL3Lwc24z9iaTPQ2+vcZ1mmn60L4AmgS+B+MeBlYBuwGqjpdeYQL99IYEtgfS0D6niduYDL9yKwB/gx8P9eb+Bu4O7A84b/YoGfB/495nt0ZzTegli2gbnW3cfA5V5nLuDyXYn/665NwIbAreP5rj8NFSMiImGR6F+RiYhImKhgREQkLFQwIiISFioYEREJCxWMiIiEhQpGRETCQgUjEgFmdoGZfWJmqwNnTJ96vF3gkhL3eJlPJBx0HoxIhJhZQ/wn4Y1zzg02s1/gP7FttXOui7fpREJPBSMSQWZ2HzAGaAc8AKQCDVwMjVklEix9RSYSWeOBd4FF+EumR+5yMbMhgatenozyyyuInJUKRiSCnP8rg7lAUWCjc+79PLO8j38MqA8jnU0k1FQwIhFkZpXwb8WsAxrkvfa5c26Vi87r9YgUmApGJEICQ6LPBo4BbfEXzSgzq+9pMJEwUcGIRM79wDXAbc65r4HB+K/P86KZFfc0mUgYqGBEIiBwiPIIYKRz7v8BOOeOAbcANYDTXeRJJGbF9CWTRWKFc249/h37eR//FLgg8olEwk/nwYhEETN7BP9VBCsC3wI/4L9y4F5Pg4mcAxWMiIiEhfbBiIhIWKhgREQkLFQwIiISFioYEREJCxWMiIiEhQpGRETCQgUjIiJhoYIREZGwUMGIiEhY/H8qu0izlFXxGAAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f4e04b13ac8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -235,7 +219,7 @@
     }
    ],
    "source": [
-    "perceptron_DB(X, w)"
+    "perceptron_DB(X, w, threshold)"
    ]
   },
   {
@@ -273,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -288,9 +272,9 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f4e04d79e10>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -302,7 +286,6 @@
    "source": [
     "# Solution\n",
     "# Calculating Boolean OR using a perceptron\n",
-    "import matplotlib.pyplot as plt\n",
     "threshold=0.6\n",
     "w=[1,1]\n",
     "X=[[0,0],[1,0],[0,1],[1,1]]\n",
@@ -337,6 +320,47 @@
     "# Calculating Boolean NAND using a perceptron"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Perceptron output for x1, x2 =  [0, 0]  is  1\n",
+      "Perceptron output for x1, x2 =  [1, 0]  is  1\n",
+      "Perceptron output for x1, x2 =  [0, 1]  is  1\n",
+      "Perceptron output for x1, x2 =  [1, 1]  is  0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "# Calculating Boolean OR using a perceptron\n",
+    "import matplotlib.pyplot as plt\n",
+    "threshold=-1.5\n",
+    "w=[-1,-1]\n",
+    "X=[[0,0],[1,0],[0,1],[1,1]]\n",
+    "for i in X:\n",
+    "    print(\"Perceptron output for x1, x2 = \" , i , \" is \" , perceptron(i,w,threshold))\n",
+    "# Plotting the decision boundary\n",
+    "perceptron_DB(X,w)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -577,11 +601,26 @@
     "model.summary()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### XOR using neural networks"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
@@ -593,14 +632,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f4e04ab1320>"
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -611,6 +650,7 @@
    ],
    "source": [
     "# Creating a network to solve the XOR problem\n",
+    "\n",
     "# Loading and plotting the data\n",
     "xor = pd.read_csv(\"xor.csv\")\n",
     "\n",
@@ -626,12 +666,12 @@
     "plt.title(\"Blue points are False\")\n",
     "\n",
     "\n",
-    "plt.scatter(features[\"x\"], features[\"y\"], color=colors, marker=\"o\");"
+    "plt.scatter(features[\"x\"], features[\"y\"], color=colors, marker=\"o\") ;"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -654,248 +694,674 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Train on 210 samples, validate on 90 samples\n",
-      "Epoch 1/100\n",
-      "210/210 [==============================] - 0s 2ms/step - loss: 1.0174 - acc: 0.3333 - val_loss: 0.9333 - val_acc: 0.3667\n",
-      "Epoch 2/100\n",
-      "210/210 [==============================] - 0s 134us/step - loss: 0.9745 - acc: 0.3429 - val_loss: 0.9086 - val_acc: 0.3667\n",
-      "Epoch 3/100\n",
-      "210/210 [==============================] - 0s 115us/step - loss: 0.9442 - acc: 0.3381 - val_loss: 0.8877 - val_acc: 0.3556\n",
-      "Epoch 4/100\n",
-      "210/210 [==============================] - 0s 145us/step - loss: 0.9182 - acc: 0.3429 - val_loss: 0.8708 - val_acc: 0.3667\n",
-      "Epoch 5/100\n",
-      "210/210 [==============================] - 0s 72us/step - loss: 0.8967 - acc: 0.3381 - val_loss: 0.8546 - val_acc: 0.3667\n",
-      "Epoch 6/100\n",
-      "210/210 [==============================] - 0s 104us/step - loss: 0.8763 - acc: 0.3476 - val_loss: 0.8392 - val_acc: 0.3667\n",
-      "Epoch 7/100\n",
-      "210/210 [==============================] - 0s 130us/step - loss: 0.8572 - acc: 0.3429 - val_loss: 0.8256 - val_acc: 0.3556\n",
-      "Epoch 8/100\n",
-      "210/210 [==============================] - 0s 113us/step - loss: 0.8399 - acc: 0.3286 - val_loss: 0.8133 - val_acc: 0.3556\n",
-      "Epoch 9/100\n",
-      "210/210 [==============================] - 0s 115us/step - loss: 0.8239 - acc: 0.3286 - val_loss: 0.8018 - val_acc: 0.3444\n",
-      "Epoch 10/100\n",
-      "210/210 [==============================] - 0s 107us/step - loss: 0.8096 - acc: 0.3286 - val_loss: 0.7911 - val_acc: 0.3222\n",
-      "Epoch 11/100\n",
-      "210/210 [==============================] - 0s 108us/step - loss: 0.7964 - acc: 0.3286 - val_loss: 0.7811 - val_acc: 0.3333\n",
-      "Epoch 12/100\n",
-      "210/210 [==============================] - 0s 110us/step - loss: 0.7841 - acc: 0.3190 - val_loss: 0.7717 - val_acc: 0.3444\n",
-      "Epoch 13/100\n",
-      "210/210 [==============================] - 0s 99us/step - loss: 0.7728 - acc: 0.3381 - val_loss: 0.7631 - val_acc: 0.3444\n",
-      "Epoch 14/100\n",
-      "210/210 [==============================] - 0s 105us/step - loss: 0.7624 - acc: 0.3429 - val_loss: 0.7549 - val_acc: 0.3556\n",
-      "Epoch 15/100\n",
-      "210/210 [==============================] - 0s 112us/step - loss: 0.7528 - acc: 0.3619 - val_loss: 0.7473 - val_acc: 0.3667\n",
-      "Epoch 16/100\n",
-      "210/210 [==============================] - 0s 93us/step - loss: 0.7437 - acc: 0.3952 - val_loss: 0.7400 - val_acc: 0.3778\n",
-      "Epoch 17/100\n",
-      "210/210 [==============================] - 0s 108us/step - loss: 0.7351 - acc: 0.4190 - val_loss: 0.7334 - val_acc: 0.3889\n",
-      "Epoch 18/100\n",
-      "210/210 [==============================] - 0s 104us/step - loss: 0.7270 - acc: 0.4000 - val_loss: 0.7271 - val_acc: 0.3889\n",
-      "Epoch 19/100\n",
-      "210/210 [==============================] - 0s 122us/step - loss: 0.7191 - acc: 0.4048 - val_loss: 0.7215 - val_acc: 0.3778\n",
-      "Epoch 20/100\n",
-      "210/210 [==============================] - 0s 103us/step - loss: 0.7120 - acc: 0.4286 - val_loss: 0.7167 - val_acc: 0.3333\n",
-      "Epoch 21/100\n",
-      "210/210 [==============================] - 0s 93us/step - loss: 0.7059 - acc: 0.4524 - val_loss: 0.7124 - val_acc: 0.3556\n",
-      "Epoch 22/100\n",
-      "210/210 [==============================] - 0s 84us/step - loss: 0.7002 - acc: 0.4667 - val_loss: 0.7083 - val_acc: 0.4000\n",
-      "Epoch 23/100\n",
-      "210/210 [==============================] - 0s 151us/step - loss: 0.6947 - acc: 0.5286 - val_loss: 0.7042 - val_acc: 0.4444\n",
-      "Epoch 24/100\n",
-      "210/210 [==============================] - 0s 132us/step - loss: 0.6894 - acc: 0.5476 - val_loss: 0.7002 - val_acc: 0.4444\n",
-      "Epoch 25/100\n",
-      "210/210 [==============================] - 0s 104us/step - loss: 0.6842 - acc: 0.5810 - val_loss: 0.6963 - val_acc: 0.5000\n",
-      "Epoch 26/100\n",
-      "210/210 [==============================] - 0s 86us/step - loss: 0.6792 - acc: 0.6095 - val_loss: 0.6930 - val_acc: 0.5111\n",
-      "Epoch 27/100\n",
-      "210/210 [==============================] - 0s 93us/step - loss: 0.6746 - acc: 0.6476 - val_loss: 0.6897 - val_acc: 0.5444\n",
-      "Epoch 28/100\n",
-      "210/210 [==============================] - 0s 78us/step - loss: 0.6702 - acc: 0.6952 - val_loss: 0.6865 - val_acc: 0.5667\n",
-      "Epoch 29/100\n",
-      "210/210 [==============================] - 0s 128us/step - loss: 0.6659 - acc: 0.7095 - val_loss: 0.6835 - val_acc: 0.6000\n",
-      "Epoch 30/100\n",
-      "210/210 [==============================] - 0s 100us/step - loss: 0.6617 - acc: 0.7190 - val_loss: 0.6808 - val_acc: 0.6222\n",
-      "Epoch 31/100\n",
-      "210/210 [==============================] - 0s 109us/step - loss: 0.6579 - acc: 0.7429 - val_loss: 0.6782 - val_acc: 0.6556\n",
-      "Epoch 32/100\n",
-      "210/210 [==============================] - 0s 128us/step - loss: 0.6542 - acc: 0.7619 - val_loss: 0.6757 - val_acc: 0.6778\n",
-      "Epoch 33/100\n",
-      "210/210 [==============================] - 0s 89us/step - loss: 0.6507 - acc: 0.7810 - val_loss: 0.6733 - val_acc: 0.6778\n",
-      "Epoch 34/100\n",
-      "210/210 [==============================] - 0s 128us/step - loss: 0.6473 - acc: 0.7905 - val_loss: 0.6711 - val_acc: 0.6778\n",
-      "Epoch 35/100\n",
-      "210/210 [==============================] - 0s 134us/step - loss: 0.6441 - acc: 0.7905 - val_loss: 0.6691 - val_acc: 0.6778\n",
-      "Epoch 36/100\n",
-      "210/210 [==============================] - 0s 150us/step - loss: 0.6412 - acc: 0.7905 - val_loss: 0.6671 - val_acc: 0.6778\n",
-      "Epoch 37/100\n",
-      "210/210 [==============================] - 0s 129us/step - loss: 0.6383 - acc: 0.7905 - val_loss: 0.6652 - val_acc: 0.6778\n",
-      "Epoch 38/100\n",
-      "210/210 [==============================] - 0s 98us/step - loss: 0.6355 - acc: 0.7905 - val_loss: 0.6634 - val_acc: 0.6778\n",
-      "Epoch 39/100\n",
-      "210/210 [==============================] - 0s 113us/step - loss: 0.6329 - acc: 0.7905 - val_loss: 0.6616 - val_acc: 0.6778\n",
-      "Epoch 40/100\n",
-      "210/210 [==============================] - 0s 155us/step - loss: 0.6304 - acc: 0.7905 - val_loss: 0.6601 - val_acc: 0.6778\n",
-      "Epoch 41/100\n",
-      "210/210 [==============================] - 0s 84us/step - loss: 0.6281 - acc: 0.7905 - val_loss: 0.6586 - val_acc: 0.6778\n",
-      "Epoch 42/100\n",
-      "210/210 [==============================] - 0s 117us/step - loss: 0.6259 - acc: 0.7905 - val_loss: 0.6571 - val_acc: 0.6778\n",
-      "Epoch 43/100\n",
-      "210/210 [==============================] - 0s 114us/step - loss: 0.6236 - acc: 0.7905 - val_loss: 0.6557 - val_acc: 0.6778\n",
-      "Epoch 44/100\n",
-      "210/210 [==============================] - 0s 93us/step - loss: 0.6215 - acc: 0.7905 - val_loss: 0.6544 - val_acc: 0.6778\n",
-      "Epoch 45/100\n",
-      "210/210 [==============================] - 0s 100us/step - loss: 0.6195 - acc: 0.7905 - val_loss: 0.6533 - val_acc: 0.6778\n",
-      "Epoch 46/100\n",
-      "210/210 [==============================] - 0s 144us/step - loss: 0.6176 - acc: 0.7905 - val_loss: 0.6522 - val_acc: 0.6778\n",
-      "Epoch 47/100\n",
-      "210/210 [==============================] - 0s 122us/step - loss: 0.6158 - acc: 0.7905 - val_loss: 0.6511 - val_acc: 0.6778\n",
-      "Epoch 48/100\n",
-      "210/210 [==============================] - 0s 142us/step - loss: 0.6140 - acc: 0.7905 - val_loss: 0.6502 - val_acc: 0.6778\n",
-      "Epoch 49/100\n",
-      "210/210 [==============================] - 0s 129us/step - loss: 0.6123 - acc: 0.7905 - val_loss: 0.6492 - val_acc: 0.6778\n",
-      "Epoch 50/100\n",
-      "210/210 [==============================] - 0s 105us/step - loss: 0.6106 - acc: 0.7905 - val_loss: 0.6483 - val_acc: 0.6778\n",
-      "Epoch 51/100\n",
-      "210/210 [==============================] - 0s 81us/step - loss: 0.6090 - acc: 0.7905 - val_loss: 0.6475 - val_acc: 0.6778\n",
-      "Epoch 52/100\n",
-      "210/210 [==============================] - 0s 154us/step - loss: 0.6075 - acc: 0.7905 - val_loss: 0.6467 - val_acc: 0.6778\n",
-      "Epoch 53/100\n",
-      "210/210 [==============================] - 0s 174us/step - loss: 0.6060 - acc: 0.7905 - val_loss: 0.6459 - val_acc: 0.6778\n",
-      "Epoch 54/100\n",
-      "210/210 [==============================] - 0s 84us/step - loss: 0.6044 - acc: 0.7905 - val_loss: 0.6451 - val_acc: 0.6778\n",
-      "Epoch 55/100\n",
-      "210/210 [==============================] - 0s 124us/step - loss: 0.6030 - acc: 0.7905 - val_loss: 0.6444 - val_acc: 0.6778\n",
-      "Epoch 56/100\n",
-      "210/210 [==============================] - 0s 131us/step - loss: 0.6015 - acc: 0.7905 - val_loss: 0.6437 - val_acc: 0.6778\n",
-      "Epoch 57/100\n",
-      "210/210 [==============================] - 0s 126us/step - loss: 0.6001 - acc: 0.7905 - val_loss: 0.6431 - val_acc: 0.6778\n",
-      "Epoch 58/100\n",
-      "210/210 [==============================] - 0s 123us/step - loss: 0.5988 - acc: 0.7905 - val_loss: 0.6425 - val_acc: 0.6778\n",
-      "Epoch 59/100\n",
-      "210/210 [==============================] - 0s 106us/step - loss: 0.5975 - acc: 0.7905 - val_loss: 0.6419 - val_acc: 0.6778\n",
-      "Epoch 60/100\n",
-      "210/210 [==============================] - 0s 123us/step - loss: 0.5962 - acc: 0.7905 - val_loss: 0.6414 - val_acc: 0.6778\n",
-      "Epoch 61/100\n"
+      "Train on 350 samples, validate on 150 samples\n",
+      "Epoch 1/300\n",
+      "350/350 [==============================] - 1s 3ms/step - loss: 0.7098 - acc: 0.5257 - val_loss: 0.7071 - val_acc: 0.4267\n",
+      "Epoch 2/300\n",
+      "350/350 [==============================] - 0s 90us/step - loss: 0.7017 - acc: 0.5457 - val_loss: 0.7006 - val_acc: 0.4467\n",
+      "Epoch 3/300\n",
+      "350/350 [==============================] - 0s 116us/step - loss: 0.6960 - acc: 0.5657 - val_loss: 0.6954 - val_acc: 0.4667\n",
+      "Epoch 4/300\n",
+      "350/350 [==============================] - 0s 129us/step - loss: 0.6909 - acc: 0.5857 - val_loss: 0.6901 - val_acc: 0.4733\n",
+      "Epoch 5/300\n",
+      "350/350 [==============================] - 0s 102us/step - loss: 0.6861 - acc: 0.5971 - val_loss: 0.6854 - val_acc: 0.5000\n",
+      "Epoch 6/300\n",
+      "350/350 [==============================] - 0s 99us/step - loss: 0.6815 - acc: 0.6143 - val_loss: 0.6808 - val_acc: 0.5133\n",
+      "Epoch 7/300\n",
+      "350/350 [==============================] - 0s 113us/step - loss: 0.6767 - acc: 0.6314 - val_loss: 0.6739 - val_acc: 0.5533\n",
+      "Epoch 8/300\n",
+      "350/350 [==============================] - 0s 112us/step - loss: 0.6690 - acc: 0.6629 - val_loss: 0.6616 - val_acc: 0.6467\n",
+      "Epoch 9/300\n",
+      "350/350 [==============================] - 0s 111us/step - loss: 0.6581 - acc: 0.6857 - val_loss: 0.6500 - val_acc: 0.6467\n",
+      "Epoch 10/300\n",
+      "350/350 [==============================] - 0s 100us/step - loss: 0.6476 - acc: 0.7086 - val_loss: 0.6407 - val_acc: 0.6267\n",
+      "Epoch 11/300\n",
+      "350/350 [==============================] - 0s 90us/step - loss: 0.6383 - acc: 0.7229 - val_loss: 0.6315 - val_acc: 0.6267\n",
+      "Epoch 12/300\n",
+      "350/350 [==============================] - 0s 74us/step - loss: 0.6296 - acc: 0.7171 - val_loss: 0.6234 - val_acc: 0.6200\n",
+      "Epoch 13/300\n",
+      "350/350 [==============================] - 0s 80us/step - loss: 0.6218 - acc: 0.7086 - val_loss: 0.6159 - val_acc: 0.6133\n",
+      "Epoch 14/300\n",
+      "350/350 [==============================] - 0s 95us/step - loss: 0.6137 - acc: 0.6971 - val_loss: 0.6084 - val_acc: 0.6067\n",
+      "Epoch 15/300\n",
+      "350/350 [==============================] - 0s 108us/step - loss: 0.6061 - acc: 0.7029 - val_loss: 0.6020 - val_acc: 0.6067\n",
+      "Epoch 16/300\n",
+      "350/350 [==============================] - 0s 104us/step - loss: 0.5993 - acc: 0.7000 - val_loss: 0.5966 - val_acc: 0.6067\n",
+      "Epoch 17/300\n",
+      "350/350 [==============================] - 0s 93us/step - loss: 0.5927 - acc: 0.6971 - val_loss: 0.5917 - val_acc: 0.6067\n",
+      "Epoch 18/300\n",
+      "350/350 [==============================] - 0s 93us/step - loss: 0.5863 - acc: 0.7000 - val_loss: 0.5869 - val_acc: 0.6067\n",
+      "Epoch 19/300\n",
+      "350/350 [==============================] - 0s 97us/step - loss: 0.5800 - acc: 0.7000 - val_loss: 0.5822 - val_acc: 0.6000\n",
+      "Epoch 20/300\n",
+      "350/350 [==============================] - 0s 96us/step - loss: 0.5735 - acc: 0.7000 - val_loss: 0.5776 - val_acc: 0.6000\n",
+      "Epoch 21/300\n",
+      "350/350 [==============================] - 0s 97us/step - loss: 0.5675 - acc: 0.7000 - val_loss: 0.5732 - val_acc: 0.6067\n",
+      "Epoch 22/300\n",
+      "350/350 [==============================] - 0s 103us/step - loss: 0.5616 - acc: 0.6971 - val_loss: 0.5696 - val_acc: 0.6067\n",
+      "Epoch 23/300\n",
+      "350/350 [==============================] - 0s 103us/step - loss: 0.5560 - acc: 0.7000 - val_loss: 0.5658 - val_acc: 0.6067\n",
+      "Epoch 24/300\n",
+      "350/350 [==============================] - 0s 101us/step - loss: 0.5501 - acc: 0.7086 - val_loss: 0.5618 - val_acc: 0.6133\n",
+      "Epoch 25/300\n",
+      "350/350 [==============================] - 0s 98us/step - loss: 0.5445 - acc: 0.7114 - val_loss: 0.5579 - val_acc: 0.6133\n",
+      "Epoch 26/300\n",
+      "350/350 [==============================] - 0s 106us/step - loss: 0.5385 - acc: 0.7114 - val_loss: 0.5539 - val_acc: 0.6133\n",
+      "Epoch 27/300\n",
+      "350/350 [==============================] - 0s 101us/step - loss: 0.5326 - acc: 0.7171 - val_loss: 0.5499 - val_acc: 0.6200\n",
+      "Epoch 28/300\n",
+      "350/350 [==============================] - 0s 105us/step - loss: 0.5269 - acc: 0.7200 - val_loss: 0.5458 - val_acc: 0.6200\n",
+      "Epoch 29/300\n",
+      "350/350 [==============================] - 0s 98us/step - loss: 0.5212 - acc: 0.7229 - val_loss: 0.5418 - val_acc: 0.6200\n",
+      "Epoch 30/300\n",
+      "350/350 [==============================] - 0s 99us/step - loss: 0.5155 - acc: 0.7286 - val_loss: 0.5375 - val_acc: 0.6200\n",
+      "Epoch 31/300\n",
+      "350/350 [==============================] - 0s 97us/step - loss: 0.5101 - acc: 0.7486 - val_loss: 0.5337 - val_acc: 0.7600\n",
+      "Epoch 32/300\n",
+      "350/350 [==============================] - 0s 101us/step - loss: 0.5047 - acc: 0.8543 - val_loss: 0.5293 - val_acc: 0.7667\n",
+      "Epoch 33/300\n",
+      "350/350 [==============================] - 0s 102us/step - loss: 0.4993 - acc: 0.8543 - val_loss: 0.5250 - val_acc: 0.7733\n",
+      "Epoch 34/300\n",
+      "350/350 [==============================] - 0s 97us/step - loss: 0.4938 - acc: 0.8571 - val_loss: 0.5209 - val_acc: 0.7733\n",
+      "Epoch 35/300\n",
+      "350/350 [==============================] - 0s 105us/step - loss: 0.4885 - acc: 0.8600 - val_loss: 0.5167 - val_acc: 0.7733\n",
+      "Epoch 36/300\n",
+      "350/350 [==============================] - 0s 109us/step - loss: 0.4830 - acc: 0.8629 - val_loss: 0.5126 - val_acc: 0.7800\n",
+      "Epoch 37/300\n",
+      "350/350 [==============================] - 0s 103us/step - loss: 0.4776 - acc: 0.8743 - val_loss: 0.5087 - val_acc: 0.7933\n",
+      "Epoch 38/300\n",
+      "350/350 [==============================] - 0s 96us/step - loss: 0.4721 - acc: 0.8743 - val_loss: 0.5047 - val_acc: 0.7933\n",
+      "Epoch 39/300\n",
+      "350/350 [==============================] - 0s 127us/step - loss: 0.4665 - acc: 0.8771 - val_loss: 0.5003 - val_acc: 0.7933\n",
+      "Epoch 40/300\n",
+      "350/350 [==============================] - 0s 100us/step - loss: 0.4611 - acc: 0.8800 - val_loss: 0.4963 - val_acc: 0.8133\n",
+      "Epoch 41/300\n",
+      "350/350 [==============================] - 0s 108us/step - loss: 0.4560 - acc: 0.8800 - val_loss: 0.4922 - val_acc: 0.8267\n",
+      "Epoch 42/300\n",
+      "350/350 [==============================] - 0s 94us/step - loss: 0.4507 - acc: 0.8829 - val_loss: 0.4879 - val_acc: 0.8200\n",
+      "Epoch 43/300\n",
+      "350/350 [==============================] - 0s 97us/step - loss: 0.4459 - acc: 0.8829 - val_loss: 0.4846 - val_acc: 0.8267\n",
+      "Epoch 44/300\n",
+      "350/350 [==============================] - 0s 116us/step - loss: 0.4412 - acc: 0.8771 - val_loss: 0.4803 - val_acc: 0.8267\n",
+      "Epoch 45/300\n",
+      "350/350 [==============================] - 0s 103us/step - loss: 0.4365 - acc: 0.8829 - val_loss: 0.4766 - val_acc: 0.8333\n",
+      "Epoch 46/300\n",
+      "350/350 [==============================] - 0s 112us/step - loss: 0.4315 - acc: 0.8886 - val_loss: 0.4725 - val_acc: 0.8400\n",
+      "Epoch 47/300\n",
+      "350/350 [==============================] - 0s 99us/step - loss: 0.4267 - acc: 0.8943 - val_loss: 0.4687 - val_acc: 0.8400\n",
+      "Epoch 48/300\n",
+      "350/350 [==============================] - 0s 106us/step - loss: 0.4219 - acc: 0.8971 - val_loss: 0.4654 - val_acc: 0.8400\n",
+      "Epoch 49/300\n",
+      "350/350 [==============================] - 0s 106us/step - loss: 0.4173 - acc: 0.8943 - val_loss: 0.4615 - val_acc: 0.8400\n",
+      "Epoch 50/300\n",
+      "350/350 [==============================] - 0s 98us/step - loss: 0.4130 - acc: 0.8971 - val_loss: 0.4586 - val_acc: 0.8400\n",
+      "Epoch 51/300\n",
+      "350/350 [==============================] - 0s 96us/step - loss: 0.4087 - acc: 0.8971 - val_loss: 0.4554 - val_acc: 0.8400\n",
+      "Epoch 52/300\n",
+      "350/350 [==============================] - 0s 91us/step - loss: 0.4046 - acc: 0.8971 - val_loss: 0.4516 - val_acc: 0.8400\n",
+      "Epoch 53/300\n",
+      "350/350 [==============================] - 0s 95us/step - loss: 0.4009 - acc: 0.8971 - val_loss: 0.4489 - val_acc: 0.8400\n",
+      "Epoch 54/300\n",
+      "350/350 [==============================] - 0s 100us/step - loss: 0.3969 - acc: 0.9000 - val_loss: 0.4459 - val_acc: 0.8400\n",
+      "Epoch 55/300\n",
+      "350/350 [==============================] - 0s 89us/step - loss: 0.3930 - acc: 0.9000 - val_loss: 0.4423 - val_acc: 0.8400\n",
+      "Epoch 56/300\n",
+      "350/350 [==============================] - 0s 84us/step - loss: 0.3889 - acc: 0.9000 - val_loss: 0.4392 - val_acc: 0.8400\n",
+      "Epoch 57/300\n",
+      "350/350 [==============================] - 0s 97us/step - loss: 0.3851 - acc: 0.9029 - val_loss: 0.4358 - val_acc: 0.8400\n",
+      "Epoch 58/300\n",
+      "350/350 [==============================] - 0s 88us/step - loss: 0.3809 - acc: 0.9057 - val_loss: 0.4324 - val_acc: 0.8467\n",
+      "Epoch 59/300\n",
+      "350/350 [==============================] - 0s 91us/step - loss: 0.3773 - acc: 0.9086 - val_loss: 0.4292 - val_acc: 0.8467\n",
+      "Epoch 60/300\n",
+      "350/350 [==============================] - 0s 89us/step - loss: 0.3738 - acc: 0.9086 - val_loss: 0.4260 - val_acc: 0.8467\n",
+      "Epoch 61/300\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "350/350 [==============================] - 0s 85us/step - loss: 0.3703 - acc: 0.9086 - val_loss: 0.4221 - val_acc: 0.8533\n",
+      "Epoch 62/300\n",
+      "350/350 [==============================] - 0s 101us/step - loss: 0.3672 - acc: 0.9114 - val_loss: 0.4195 - val_acc: 0.8533\n",
+      "Epoch 63/300\n",
+      "350/350 [==============================] - 0s 98us/step - loss: 0.3640 - acc: 0.9086 - val_loss: 0.4162 - val_acc: 0.8533\n",
+      "Epoch 64/300\n",
+      "350/350 [==============================] - 0s 88us/step - loss: 0.3609 - acc: 0.9114 - val_loss: 0.4136 - val_acc: 0.8533\n",
+      "Epoch 65/300\n",
+      "350/350 [==============================] - 0s 107us/step - loss: 0.3577 - acc: 0.9114 - val_loss: 0.4111 - val_acc: 0.8533\n",
+      "Epoch 66/300\n",
+      "350/350 [==============================] - 0s 119us/step - loss: 0.3542 - acc: 0.9143 - val_loss: 0.4084 - val_acc: 0.8533\n",
+      "Epoch 67/300\n",
+      "350/350 [==============================] - 0s 101us/step - loss: 0.3508 - acc: 0.9143 - val_loss: 0.4053 - val_acc: 0.8600\n",
+      "Epoch 68/300\n",
+      "350/350 [==============================] - 0s 100us/step - loss: 0.3475 - acc: 0.9143 - val_loss: 0.4026 - val_acc: 0.8600\n",
+      "Epoch 69/300\n",
+      "350/350 [==============================] - 0s 88us/step - loss: 0.3444 - acc: 0.9143 - val_loss: 0.4002 - val_acc: 0.8600\n",
+      "Epoch 70/300\n",
+      "350/350 [==============================] - 0s 94us/step - loss: 0.3413 - acc: 0.9143 - val_loss: 0.3971 - val_acc: 0.8667\n",
+      "Epoch 71/300\n",
+      "350/350 [==============================] - 0s 101us/step - loss: 0.3385 - acc: 0.9171 - val_loss: 0.3952 - val_acc: 0.8667\n",
+      "Epoch 72/300\n",
+      "350/350 [==============================] - 0s 104us/step - loss: 0.3355 - acc: 0.9143 - val_loss: 0.3926 - val_acc: 0.8667\n",
+      "Epoch 73/300\n",
+      "350/350 [==============================] - 0s 89us/step - loss: 0.3325 - acc: 0.9229 - val_loss: 0.3894 - val_acc: 0.8667\n",
+      "Epoch 74/300\n",
+      "350/350 [==============================] - 0s 97us/step - loss: 0.3295 - acc: 0.9200 - val_loss: 0.3872 - val_acc: 0.8667\n",
+      "Epoch 75/300\n",
+      "350/350 [==============================] - 0s 91us/step - loss: 0.3267 - acc: 0.9229 - val_loss: 0.3848 - val_acc: 0.8667\n",
+      "Epoch 76/300\n",
+      "350/350 [==============================] - 0s 82us/step - loss: 0.3243 - acc: 0.9229 - val_loss: 0.3825 - val_acc: 0.8667\n",
+      "Epoch 77/300\n",
+      "350/350 [==============================] - 0s 110us/step - loss: 0.3214 - acc: 0.9229 - val_loss: 0.3803 - val_acc: 0.8667\n",
+      "Epoch 78/300\n",
+      "350/350 [==============================] - 0s 86us/step - loss: 0.3188 - acc: 0.9229 - val_loss: 0.3782 - val_acc: 0.8667\n",
+      "Epoch 79/300\n",
+      "350/350 [==============================] - 0s 85us/step - loss: 0.3163 - acc: 0.9257 - val_loss: 0.3750 - val_acc: 0.8667\n",
+      "Epoch 80/300\n",
+      "350/350 [==============================] - 0s 79us/step - loss: 0.3138 - acc: 0.9257 - val_loss: 0.3726 - val_acc: 0.8667\n",
+      "Epoch 81/300\n",
+      "350/350 [==============================] - 0s 78us/step - loss: 0.3114 - acc: 0.9286 - val_loss: 0.3702 - val_acc: 0.8667\n",
+      "Epoch 82/300\n",
+      "350/350 [==============================] - 0s 98us/step - loss: 0.3091 - acc: 0.9286 - val_loss: 0.3674 - val_acc: 0.8667\n",
+      "Epoch 83/300\n",
+      "350/350 [==============================] - 0s 92us/step - loss: 0.3065 - acc: 0.9286 - val_loss: 0.3654 - val_acc: 0.8667\n",
+      "Epoch 84/300\n",
+      "350/350 [==============================] - 0s 92us/step - loss: 0.3041 - acc: 0.9286 - val_loss: 0.3632 - val_acc: 0.8667\n",
+      "Epoch 85/300\n",
+      "350/350 [==============================] - 0s 91us/step - loss: 0.3020 - acc: 0.9286 - val_loss: 0.3609 - val_acc: 0.8667\n",
+      "Epoch 86/300\n",
+      "350/350 [==============================] - 0s 104us/step - loss: 0.2994 - acc: 0.9286 - val_loss: 0.3588 - val_acc: 0.8800\n",
+      "Epoch 87/300\n",
+      "350/350 [==============================] - 0s 107us/step - loss: 0.2972 - acc: 0.9314 - val_loss: 0.3564 - val_acc: 0.8800\n",
+      "Epoch 88/300\n",
+      "350/350 [==============================] - 0s 97us/step - loss: 0.2948 - acc: 0.9314 - val_loss: 0.3543 - val_acc: 0.8800\n",
+      "Epoch 89/300\n",
+      "350/350 [==============================] - 0s 99us/step - loss: 0.2927 - acc: 0.9314 - val_loss: 0.3523 - val_acc: 0.8800\n",
+      "Epoch 90/300\n",
+      "350/350 [==============================] - 0s 101us/step - loss: 0.2904 - acc: 0.9314 - val_loss: 0.3503 - val_acc: 0.8800\n",
+      "Epoch 91/300\n",
+      "350/350 [==============================] - 0s 102us/step - loss: 0.2883 - acc: 0.9314 - val_loss: 0.3484 - val_acc: 0.8800\n",
+      "Epoch 92/300\n",
+      "350/350 [==============================] - 0s 99us/step - loss: 0.2861 - acc: 0.9314 - val_loss: 0.3457 - val_acc: 0.8800\n",
+      "Epoch 93/300\n",
+      "350/350 [==============================] - 0s 90us/step - loss: 0.2839 - acc: 0.9314 - val_loss: 0.3429 - val_acc: 0.8800\n",
+      "Epoch 94/300\n",
+      "350/350 [==============================] - 0s 77us/step - loss: 0.2818 - acc: 0.9343 - val_loss: 0.3405 - val_acc: 0.8800\n",
+      "Epoch 95/300\n",
+      "350/350 [==============================] - 0s 79us/step - loss: 0.2794 - acc: 0.9343 - val_loss: 0.3385 - val_acc: 0.8800\n",
+      "Epoch 96/300\n",
+      "350/350 [==============================] - 0s 79us/step - loss: 0.2774 - acc: 0.9314 - val_loss: 0.3366 - val_acc: 0.8800\n",
+      "Epoch 97/300\n",
+      "350/350 [==============================] - 0s 78us/step - loss: 0.2754 - acc: 0.9314 - val_loss: 0.3340 - val_acc: 0.8800\n",
+      "Epoch 98/300\n",
+      "350/350 [==============================] - 0s 87us/step - loss: 0.2733 - acc: 0.9314 - val_loss: 0.3317 - val_acc: 0.8800\n",
+      "Epoch 99/300\n",
+      "350/350 [==============================] - 0s 82us/step - loss: 0.2712 - acc: 0.9371 - val_loss: 0.3301 - val_acc: 0.8800\n",
+      "Epoch 100/300\n",
+      "350/350 [==============================] - 0s 82us/step - loss: 0.2694 - acc: 0.9314 - val_loss: 0.3280 - val_acc: 0.8800\n",
+      "Epoch 101/300\n",
+      "350/350 [==============================] - 0s 90us/step - loss: 0.2674 - acc: 0.9343 - val_loss: 0.3262 - val_acc: 0.8800\n",
+      "Epoch 102/300\n",
+      "350/350 [==============================] - 0s 85us/step - loss: 0.2655 - acc: 0.9400 - val_loss: 0.3242 - val_acc: 0.8800\n",
+      "Epoch 103/300\n",
+      "350/350 [==============================] - 0s 82us/step - loss: 0.2636 - acc: 0.9400 - val_loss: 0.3226 - val_acc: 0.8867\n",
+      "Epoch 104/300\n",
+      "350/350 [==============================] - 0s 85us/step - loss: 0.2616 - acc: 0.9400 - val_loss: 0.3198 - val_acc: 0.8933\n",
+      "Epoch 105/300\n",
+      "350/350 [==============================] - 0s 84us/step - loss: 0.2599 - acc: 0.9400 - val_loss: 0.3176 - val_acc: 0.8933\n",
+      "Epoch 106/300\n",
+      "350/350 [==============================] - 0s 84us/step - loss: 0.2582 - acc: 0.9400 - val_loss: 0.3156 - val_acc: 0.8933\n",
+      "Epoch 107/300\n",
+      "350/350 [==============================] - 0s 75us/step - loss: 0.2564 - acc: 0.9400 - val_loss: 0.3133 - val_acc: 0.8933\n",
+      "Epoch 108/300\n",
+      "350/350 [==============================] - 0s 95us/step - loss: 0.2545 - acc: 0.9429 - val_loss: 0.3105 - val_acc: 0.8933\n",
+      "Epoch 109/300\n",
+      "350/350 [==============================] - 0s 96us/step - loss: 0.2527 - acc: 0.9429 - val_loss: 0.3083 - val_acc: 0.9000\n",
+      "Epoch 110/300\n",
+      "350/350 [==============================] - 0s 84us/step - loss: 0.2513 - acc: 0.9429 - val_loss: 0.3071 - val_acc: 0.9067\n",
+      "Epoch 111/300\n",
+      "350/350 [==============================] - 0s 91us/step - loss: 0.2494 - acc: 0.9457 - val_loss: 0.3051 - val_acc: 0.9067\n",
+      "Epoch 112/300\n",
+      "350/350 [==============================] - 0s 97us/step - loss: 0.2476 - acc: 0.9457 - val_loss: 0.3026 - val_acc: 0.9067\n",
+      "Epoch 113/300\n",
+      "350/350 [==============================] - 0s 93us/step - loss: 0.2462 - acc: 0.9514 - val_loss: 0.3008 - val_acc: 0.9000\n",
+      "Epoch 114/300\n",
+      "350/350 [==============================] - 0s 90us/step - loss: 0.2444 - acc: 0.9514 - val_loss: 0.2991 - val_acc: 0.9067\n",
+      "Epoch 115/300\n",
+      "350/350 [==============================] - 0s 91us/step - loss: 0.2427 - acc: 0.9514 - val_loss: 0.2963 - val_acc: 0.9067\n",
+      "Epoch 116/300\n",
+      "350/350 [==============================] - 0s 90us/step - loss: 0.2414 - acc: 0.9514 - val_loss: 0.2947 - val_acc: 0.9067\n",
+      "Epoch 117/300\n",
+      "350/350 [==============================] - 0s 90us/step - loss: 0.2395 - acc: 0.9514 - val_loss: 0.2930 - val_acc: 0.9067\n",
+      "Epoch 118/300\n",
+      "350/350 [==============================] - 0s 85us/step - loss: 0.2380 - acc: 0.9514 - val_loss: 0.2913 - val_acc: 0.9133\n",
+      "Epoch 119/300\n",
+      "350/350 [==============================] - 0s 77us/step - loss: 0.2365 - acc: 0.9571 - val_loss: 0.2894 - val_acc: 0.9133\n",
+      "Epoch 120/300\n",
+      "350/350 [==============================] - 0s 88us/step - loss: 0.2351 - acc: 0.9514 - val_loss: 0.2876 - val_acc: 0.9133\n",
+      "Epoch 121/300\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "210/210 [==============================] - 0s 113us/step - loss: 0.5950 - acc: 0.7905 - val_loss: 0.6409 - val_acc: 0.6778\n",
-      "Epoch 62/100\n",
-      "210/210 [==============================] - 0s 163us/step - loss: 0.5938 - acc: 0.7905 - val_loss: 0.6404 - val_acc: 0.6778\n",
-      "Epoch 63/100\n",
-      "210/210 [==============================] - 0s 132us/step - loss: 0.5926 - acc: 0.7905 - val_loss: 0.6399 - val_acc: 0.6778\n",
-      "Epoch 64/100\n",
-      "210/210 [==============================] - 0s 57us/step - loss: 0.5914 - acc: 0.7905 - val_loss: 0.6395 - val_acc: 0.6778\n",
-      "Epoch 65/100\n",
-      "210/210 [==============================] - 0s 90us/step - loss: 0.5902 - acc: 0.7905 - val_loss: 0.6390 - val_acc: 0.6778\n",
-      "Epoch 66/100\n",
-      "210/210 [==============================] - 0s 115us/step - loss: 0.5890 - acc: 0.7905 - val_loss: 0.6385 - val_acc: 0.6778\n",
-      "Epoch 67/100\n",
-      "210/210 [==============================] - 0s 99us/step - loss: 0.5877 - acc: 0.7905 - val_loss: 0.6380 - val_acc: 0.6778\n",
-      "Epoch 68/100\n",
-      "210/210 [==============================] - 0s 137us/step - loss: 0.5864 - acc: 0.7905 - val_loss: 0.6375 - val_acc: 0.6778\n",
-      "Epoch 69/100\n",
-      "210/210 [==============================] - 0s 85us/step - loss: 0.5852 - acc: 0.7905 - val_loss: 0.6368 - val_acc: 0.6778\n",
-      "Epoch 70/100\n",
-      "210/210 [==============================] - 0s 140us/step - loss: 0.5838 - acc: 0.7905 - val_loss: 0.6362 - val_acc: 0.6778\n",
-      "Epoch 71/100\n",
-      "210/210 [==============================] - 0s 79us/step - loss: 0.5824 - acc: 0.7905 - val_loss: 0.6356 - val_acc: 0.6778\n",
-      "Epoch 72/100\n",
-      "210/210 [==============================] - 0s 101us/step - loss: 0.5810 - acc: 0.7905 - val_loss: 0.6348 - val_acc: 0.6778\n",
-      "Epoch 73/100\n",
-      "210/210 [==============================] - 0s 136us/step - loss: 0.5793 - acc: 0.7905 - val_loss: 0.6339 - val_acc: 0.6778\n",
-      "Epoch 74/100\n",
-      "210/210 [==============================] - 0s 95us/step - loss: 0.5777 - acc: 0.7905 - val_loss: 0.6330 - val_acc: 0.6778\n",
-      "Epoch 75/100\n",
-      "210/210 [==============================] - 0s 113us/step - loss: 0.5759 - acc: 0.7905 - val_loss: 0.6320 - val_acc: 0.6778\n",
-      "Epoch 76/100\n",
-      "210/210 [==============================] - 0s 129us/step - loss: 0.5741 - acc: 0.7905 - val_loss: 0.6309 - val_acc: 0.6778\n",
-      "Epoch 77/100\n",
-      "210/210 [==============================] - 0s 113us/step - loss: 0.5721 - acc: 0.7905 - val_loss: 0.6297 - val_acc: 0.6778\n",
-      "Epoch 78/100\n",
-      "210/210 [==============================] - 0s 88us/step - loss: 0.5699 - acc: 0.7905 - val_loss: 0.6286 - val_acc: 0.6778\n",
-      "Epoch 79/100\n",
-      "210/210 [==============================] - 0s 109us/step - loss: 0.5677 - acc: 0.7905 - val_loss: 0.6274 - val_acc: 0.6778\n",
-      "Epoch 80/100\n",
-      "210/210 [==============================] - 0s 85us/step - loss: 0.5654 - acc: 0.7905 - val_loss: 0.6263 - val_acc: 0.6778\n",
-      "Epoch 81/100\n",
-      "210/210 [==============================] - 0s 109us/step - loss: 0.5632 - acc: 0.7905 - val_loss: 0.6251 - val_acc: 0.6778\n",
-      "Epoch 82/100\n",
-      "210/210 [==============================] - 0s 76us/step - loss: 0.5610 - acc: 0.7905 - val_loss: 0.6239 - val_acc: 0.6778\n",
-      "Epoch 83/100\n",
-      "210/210 [==============================] - 0s 88us/step - loss: 0.5586 - acc: 0.7905 - val_loss: 0.6227 - val_acc: 0.6778\n",
-      "Epoch 84/100\n",
-      "210/210 [==============================] - 0s 120us/step - loss: 0.5563 - acc: 0.7905 - val_loss: 0.6214 - val_acc: 0.6778\n",
-      "Epoch 85/100\n",
-      "210/210 [==============================] - 0s 94us/step - loss: 0.5538 - acc: 0.7905 - val_loss: 0.6202 - val_acc: 0.6778\n",
-      "Epoch 86/100\n",
-      "210/210 [==============================] - 0s 73us/step - loss: 0.5514 - acc: 0.7905 - val_loss: 0.6189 - val_acc: 0.6778\n",
-      "Epoch 87/100\n",
-      "210/210 [==============================] - 0s 80us/step - loss: 0.5489 - acc: 0.7905 - val_loss: 0.6177 - val_acc: 0.6778\n",
-      "Epoch 88/100\n",
-      "210/210 [==============================] - 0s 111us/step - loss: 0.5465 - acc: 0.7905 - val_loss: 0.6165 - val_acc: 0.6778\n",
-      "Epoch 89/100\n",
-      "210/210 [==============================] - 0s 126us/step - loss: 0.5440 - acc: 0.7905 - val_loss: 0.6153 - val_acc: 0.6778\n",
-      "Epoch 90/100\n",
-      "210/210 [==============================] - 0s 102us/step - loss: 0.5415 - acc: 0.7905 - val_loss: 0.6142 - val_acc: 0.6778\n",
-      "Epoch 91/100\n",
-      "210/210 [==============================] - 0s 119us/step - loss: 0.5391 - acc: 0.7905 - val_loss: 0.6131 - val_acc: 0.6778\n",
-      "Epoch 92/100\n",
-      "210/210 [==============================] - 0s 125us/step - loss: 0.5366 - acc: 0.7905 - val_loss: 0.6119 - val_acc: 0.6778\n",
-      "Epoch 93/100\n",
-      "210/210 [==============================] - 0s 97us/step - loss: 0.5341 - acc: 0.7905 - val_loss: 0.6109 - val_acc: 0.6778\n",
-      "Epoch 94/100\n",
-      "210/210 [==============================] - 0s 84us/step - loss: 0.5316 - acc: 0.7905 - val_loss: 0.6097 - val_acc: 0.6778\n",
-      "Epoch 95/100\n",
-      "210/210 [==============================] - 0s 96us/step - loss: 0.5291 - acc: 0.7905 - val_loss: 0.6088 - val_acc: 0.6778\n",
-      "Epoch 96/100\n",
-      "210/210 [==============================] - 0s 106us/step - loss: 0.5268 - acc: 0.7905 - val_loss: 0.6077 - val_acc: 0.6778\n",
-      "Epoch 97/100\n",
-      "210/210 [==============================] - 0s 109us/step - loss: 0.5243 - acc: 0.7905 - val_loss: 0.6067 - val_acc: 0.6778\n",
-      "Epoch 98/100\n",
-      "210/210 [==============================] - 0s 88us/step - loss: 0.5218 - acc: 0.7905 - val_loss: 0.6058 - val_acc: 0.6778\n",
-      "Epoch 99/100\n",
-      "210/210 [==============================] - 0s 98us/step - loss: 0.5196 - acc: 0.7905 - val_loss: 0.6050 - val_acc: 0.6778\n",
-      "Epoch 100/100\n",
-      "210/210 [==============================] - 0s 93us/step - loss: 0.5173 - acc: 0.7905 - val_loss: 0.6042 - val_acc: 0.6778\n"
+      "350/350 [==============================] - 0s 78us/step - loss: 0.2336 - acc: 0.9571 - val_loss: 0.2857 - val_acc: 0.9133\n",
+      "Epoch 122/300\n",
+      "350/350 [==============================] - 0s 90us/step - loss: 0.2318 - acc: 0.9543 - val_loss: 0.2843 - val_acc: 0.9200\n",
+      "Epoch 123/300\n",
+      "350/350 [==============================] - 0s 77us/step - loss: 0.2304 - acc: 0.9571 - val_loss: 0.2827 - val_acc: 0.9200\n",
+      "Epoch 124/300\n",
+      "350/350 [==============================] - 0s 76us/step - loss: 0.2295 - acc: 0.9543 - val_loss: 0.2813 - val_acc: 0.9200\n",
+      "Epoch 125/300\n",
+      "350/350 [==============================] - 0s 79us/step - loss: 0.2276 - acc: 0.9571 - val_loss: 0.2796 - val_acc: 0.9200\n",
+      "Epoch 126/300\n",
+      "350/350 [==============================] - 0s 83us/step - loss: 0.2262 - acc: 0.9571 - val_loss: 0.2783 - val_acc: 0.9200\n",
+      "Epoch 127/300\n",
+      "350/350 [==============================] - 0s 82us/step - loss: 0.2247 - acc: 0.9571 - val_loss: 0.2757 - val_acc: 0.9200\n",
+      "Epoch 128/300\n",
+      "350/350 [==============================] - 0s 84us/step - loss: 0.2234 - acc: 0.9543 - val_loss: 0.2744 - val_acc: 0.9200\n",
+      "Epoch 129/300\n",
+      "350/350 [==============================] - 0s 88us/step - loss: 0.2218 - acc: 0.9571 - val_loss: 0.2722 - val_acc: 0.9200\n",
+      "Epoch 130/300\n",
+      "350/350 [==============================] - 0s 79us/step - loss: 0.2205 - acc: 0.9543 - val_loss: 0.2708 - val_acc: 0.9333\n",
+      "Epoch 131/300\n",
+      "350/350 [==============================] - 0s 82us/step - loss: 0.2192 - acc: 0.9571 - val_loss: 0.2695 - val_acc: 0.9333\n",
+      "Epoch 132/300\n",
+      "350/350 [==============================] - 0s 100us/step - loss: 0.2174 - acc: 0.9571 - val_loss: 0.2684 - val_acc: 0.9333\n",
+      "Epoch 133/300\n",
+      "350/350 [==============================] - 0s 116us/step - loss: 0.2163 - acc: 0.9600 - val_loss: 0.2671 - val_acc: 0.9333\n",
+      "Epoch 134/300\n",
+      "350/350 [==============================] - 0s 93us/step - loss: 0.2146 - acc: 0.9600 - val_loss: 0.2656 - val_acc: 0.9333\n",
+      "Epoch 135/300\n",
+      "350/350 [==============================] - 0s 83us/step - loss: 0.2134 - acc: 0.9629 - val_loss: 0.2639 - val_acc: 0.9333\n",
+      "Epoch 136/300\n",
+      "350/350 [==============================] - 0s 89us/step - loss: 0.2122 - acc: 0.9600 - val_loss: 0.2620 - val_acc: 0.9333\n",
+      "Epoch 137/300\n",
+      "350/350 [==============================] - 0s 80us/step - loss: 0.2107 - acc: 0.9600 - val_loss: 0.2608 - val_acc: 0.9333\n",
+      "Epoch 138/300\n",
+      "350/350 [==============================] - 0s 74us/step - loss: 0.2095 - acc: 0.9600 - val_loss: 0.2590 - val_acc: 0.9400\n",
+      "Epoch 139/300\n",
+      "350/350 [==============================] - 0s 81us/step - loss: 0.2080 - acc: 0.9600 - val_loss: 0.2579 - val_acc: 0.9400\n",
+      "Epoch 140/300\n",
+      "350/350 [==============================] - 0s 73us/step - loss: 0.2069 - acc: 0.9600 - val_loss: 0.2566 - val_acc: 0.9400\n",
+      "Epoch 141/300\n",
+      "350/350 [==============================] - 0s 80us/step - loss: 0.2056 - acc: 0.9600 - val_loss: 0.2553 - val_acc: 0.9400\n",
+      "Epoch 142/300\n",
+      "350/350 [==============================] - 0s 82us/step - loss: 0.2041 - acc: 0.9600 - val_loss: 0.2538 - val_acc: 0.9400\n",
+      "Epoch 143/300\n",
+      "350/350 [==============================] - 0s 83us/step - loss: 0.2030 - acc: 0.9600 - val_loss: 0.2516 - val_acc: 0.9400\n",
+      "Epoch 144/300\n",
+      "350/350 [==============================] - 0s 83us/step - loss: 0.2017 - acc: 0.9629 - val_loss: 0.2507 - val_acc: 0.9400\n",
+      "Epoch 145/300\n",
+      "350/350 [==============================] - 0s 83us/step - loss: 0.2007 - acc: 0.9600 - val_loss: 0.2494 - val_acc: 0.9400\n",
+      "Epoch 146/300\n",
+      "350/350 [==============================] - 0s 84us/step - loss: 0.1994 - acc: 0.9600 - val_loss: 0.2480 - val_acc: 0.9400\n",
+      "Epoch 147/300\n",
+      "350/350 [==============================] - 0s 87us/step - loss: 0.1982 - acc: 0.9600 - val_loss: 0.2468 - val_acc: 0.9400\n",
+      "Epoch 148/300\n",
+      "350/350 [==============================] - 0s 91us/step - loss: 0.1973 - acc: 0.9600 - val_loss: 0.2456 - val_acc: 0.9400\n",
+      "Epoch 149/300\n",
+      "350/350 [==============================] - 0s 101us/step - loss: 0.1960 - acc: 0.9629 - val_loss: 0.2439 - val_acc: 0.9400\n",
+      "Epoch 150/300\n",
+      "350/350 [==============================] - 0s 96us/step - loss: 0.1949 - acc: 0.9600 - val_loss: 0.2429 - val_acc: 0.9400\n",
+      "Epoch 151/300\n",
+      "350/350 [==============================] - 0s 116us/step - loss: 0.1937 - acc: 0.9629 - val_loss: 0.2418 - val_acc: 0.9400\n",
+      "Epoch 152/300\n",
+      "350/350 [==============================] - 0s 97us/step - loss: 0.1925 - acc: 0.9600 - val_loss: 0.2403 - val_acc: 0.9333\n",
+      "Epoch 153/300\n",
+      "350/350 [==============================] - 0s 99us/step - loss: 0.1916 - acc: 0.9629 - val_loss: 0.2391 - val_acc: 0.9333\n",
+      "Epoch 154/300\n",
+      "350/350 [==============================] - 0s 94us/step - loss: 0.1904 - acc: 0.9629 - val_loss: 0.2376 - val_acc: 0.9333\n",
+      "Epoch 155/300\n",
+      "350/350 [==============================] - 0s 84us/step - loss: 0.1895 - acc: 0.9629 - val_loss: 0.2364 - val_acc: 0.9333\n",
+      "Epoch 156/300\n",
+      "350/350 [==============================] - 0s 82us/step - loss: 0.1886 - acc: 0.9629 - val_loss: 0.2349 - val_acc: 0.9333\n",
+      "Epoch 157/300\n",
+      "350/350 [==============================] - 0s 92us/step - loss: 0.1873 - acc: 0.9629 - val_loss: 0.2343 - val_acc: 0.9333\n",
+      "Epoch 158/300\n",
+      "350/350 [==============================] - 0s 82us/step - loss: 0.1864 - acc: 0.9629 - val_loss: 0.2329 - val_acc: 0.9333\n",
+      "Epoch 159/300\n",
+      "350/350 [==============================] - 0s 84us/step - loss: 0.1854 - acc: 0.9629 - val_loss: 0.2319 - val_acc: 0.9333\n",
+      "Epoch 160/300\n",
+      "350/350 [==============================] - 0s 78us/step - loss: 0.1845 - acc: 0.9629 - val_loss: 0.2307 - val_acc: 0.9333\n",
+      "Epoch 161/300\n",
+      "350/350 [==============================] - 0s 85us/step - loss: 0.1833 - acc: 0.9629 - val_loss: 0.2297 - val_acc: 0.9333\n",
+      "Epoch 162/300\n",
+      "350/350 [==============================] - 0s 83us/step - loss: 0.1824 - acc: 0.9629 - val_loss: 0.2285 - val_acc: 0.9333\n",
+      "Epoch 163/300\n",
+      "350/350 [==============================] - 0s 85us/step - loss: 0.1815 - acc: 0.9629 - val_loss: 0.2272 - val_acc: 0.9333\n",
+      "Epoch 164/300\n",
+      "350/350 [==============================] - 0s 90us/step - loss: 0.1805 - acc: 0.9629 - val_loss: 0.2265 - val_acc: 0.9333\n",
+      "Epoch 165/300\n",
+      "350/350 [==============================] - 0s 85us/step - loss: 0.1796 - acc: 0.9629 - val_loss: 0.2256 - val_acc: 0.9333\n",
+      "Epoch 166/300\n",
+      "350/350 [==============================] - 0s 84us/step - loss: 0.1791 - acc: 0.9629 - val_loss: 0.2245 - val_acc: 0.9333\n",
+      "Epoch 167/300\n",
+      "350/350 [==============================] - 0s 110us/step - loss: 0.1781 - acc: 0.9629 - val_loss: 0.2234 - val_acc: 0.9333\n",
+      "Epoch 168/300\n",
+      "350/350 [==============================] - 0s 100us/step - loss: 0.1772 - acc: 0.9629 - val_loss: 0.2228 - val_acc: 0.9333\n",
+      "Epoch 169/300\n",
+      "350/350 [==============================] - 0s 101us/step - loss: 0.1761 - acc: 0.9629 - val_loss: 0.2214 - val_acc: 0.9333\n",
+      "Epoch 170/300\n",
+      "350/350 [==============================] - 0s 98us/step - loss: 0.1754 - acc: 0.9657 - val_loss: 0.2207 - val_acc: 0.9333\n",
+      "Epoch 171/300\n",
+      "350/350 [==============================] - 0s 97us/step - loss: 0.1745 - acc: 0.9629 - val_loss: 0.2198 - val_acc: 0.9333\n",
+      "Epoch 172/300\n",
+      "350/350 [==============================] - 0s 87us/step - loss: 0.1740 - acc: 0.9629 - val_loss: 0.2183 - val_acc: 0.9333\n",
+      "Epoch 173/300\n",
+      "350/350 [==============================] - 0s 78us/step - loss: 0.1727 - acc: 0.9629 - val_loss: 0.2173 - val_acc: 0.9400\n",
+      "Epoch 174/300\n",
+      "350/350 [==============================] - 0s 83us/step - loss: 0.1720 - acc: 0.9629 - val_loss: 0.2163 - val_acc: 0.9400\n",
+      "Epoch 175/300\n",
+      "350/350 [==============================] - 0s 85us/step - loss: 0.1711 - acc: 0.9629 - val_loss: 0.2155 - val_acc: 0.9400\n",
+      "Epoch 176/300\n",
+      "350/350 [==============================] - 0s 88us/step - loss: 0.1702 - acc: 0.9629 - val_loss: 0.2143 - val_acc: 0.9400\n",
+      "Epoch 177/300\n",
+      "350/350 [==============================] - 0s 92us/step - loss: 0.1693 - acc: 0.9629 - val_loss: 0.2130 - val_acc: 0.9467\n",
+      "Epoch 178/300\n",
+      "350/350 [==============================] - 0s 85us/step - loss: 0.1687 - acc: 0.9657 - val_loss: 0.2124 - val_acc: 0.9400\n",
+      "Epoch 179/300\n",
+      "350/350 [==============================] - 0s 77us/step - loss: 0.1677 - acc: 0.9629 - val_loss: 0.2111 - val_acc: 0.9533\n",
+      "Epoch 180/300\n",
+      "350/350 [==============================] - 0s 81us/step - loss: 0.1671 - acc: 0.9629 - val_loss: 0.2098 - val_acc: 0.9533\n",
+      "Epoch 181/300\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "350/350 [==============================] - 0s 81us/step - loss: 0.1664 - acc: 0.9629 - val_loss: 0.2093 - val_acc: 0.9533\n",
+      "Epoch 182/300\n",
+      "350/350 [==============================] - 0s 84us/step - loss: 0.1658 - acc: 0.9629 - val_loss: 0.2082 - val_acc: 0.9533\n",
+      "Epoch 183/300\n",
+      "350/350 [==============================] - 0s 85us/step - loss: 0.1650 - acc: 0.9629 - val_loss: 0.2071 - val_acc: 0.9533\n",
+      "Epoch 184/300\n",
+      "350/350 [==============================] - 0s 82us/step - loss: 0.1643 - acc: 0.9629 - val_loss: 0.2063 - val_acc: 0.9533\n",
+      "Epoch 185/300\n",
+      "350/350 [==============================] - 0s 78us/step - loss: 0.1635 - acc: 0.9629 - val_loss: 0.2052 - val_acc: 0.9533\n",
+      "Epoch 186/300\n",
+      "350/350 [==============================] - 0s 92us/step - loss: 0.1629 - acc: 0.9629 - val_loss: 0.2044 - val_acc: 0.9533\n",
+      "Epoch 187/300\n",
+      "350/350 [==============================] - 0s 81us/step - loss: 0.1621 - acc: 0.9629 - val_loss: 0.2037 - val_acc: 0.9533\n",
+      "Epoch 188/300\n",
+      "350/350 [==============================] - 0s 80us/step - loss: 0.1615 - acc: 0.9629 - val_loss: 0.2028 - val_acc: 0.9533\n",
+      "Epoch 189/300\n",
+      "350/350 [==============================] - 0s 84us/step - loss: 0.1605 - acc: 0.9629 - val_loss: 0.2024 - val_acc: 0.9533\n",
+      "Epoch 190/300\n",
+      "350/350 [==============================] - 0s 81us/step - loss: 0.1602 - acc: 0.9657 - val_loss: 0.2011 - val_acc: 0.9533\n",
+      "Epoch 191/300\n",
+      "350/350 [==============================] - 0s 78us/step - loss: 0.1593 - acc: 0.9629 - val_loss: 0.2007 - val_acc: 0.9533\n",
+      "Epoch 192/300\n",
+      "350/350 [==============================] - 0s 83us/step - loss: 0.1587 - acc: 0.9629 - val_loss: 0.2001 - val_acc: 0.9533\n",
+      "Epoch 193/300\n",
+      "350/350 [==============================] - 0s 91us/step - loss: 0.1579 - acc: 0.9657 - val_loss: 0.1995 - val_acc: 0.9533\n",
+      "Epoch 194/300\n",
+      "350/350 [==============================] - 0s 90us/step - loss: 0.1574 - acc: 0.9629 - val_loss: 0.1989 - val_acc: 0.9533\n",
+      "Epoch 195/300\n",
+      "350/350 [==============================] - 0s 93us/step - loss: 0.1569 - acc: 0.9629 - val_loss: 0.1986 - val_acc: 0.9533\n",
+      "Epoch 196/300\n",
+      "350/350 [==============================] - 0s 80us/step - loss: 0.1561 - acc: 0.9657 - val_loss: 0.1979 - val_acc: 0.9533\n",
+      "Epoch 197/300\n",
+      "350/350 [==============================] - 0s 82us/step - loss: 0.1555 - acc: 0.9629 - val_loss: 0.1967 - val_acc: 0.9533\n",
+      "Epoch 198/300\n",
+      "350/350 [==============================] - 0s 90us/step - loss: 0.1548 - acc: 0.9629 - val_loss: 0.1957 - val_acc: 0.9533\n",
+      "Epoch 199/300\n",
+      "350/350 [==============================] - 0s 76us/step - loss: 0.1543 - acc: 0.9629 - val_loss: 0.1950 - val_acc: 0.9533\n",
+      "Epoch 200/300\n",
+      "350/350 [==============================] - 0s 87us/step - loss: 0.1538 - acc: 0.9629 - val_loss: 0.1945 - val_acc: 0.9533\n",
+      "Epoch 201/300\n",
+      "350/350 [==============================] - 0s 81us/step - loss: 0.1528 - acc: 0.9629 - val_loss: 0.1937 - val_acc: 0.9533\n",
+      "Epoch 202/300\n",
+      "350/350 [==============================] - 0s 75us/step - loss: 0.1522 - acc: 0.9629 - val_loss: 0.1926 - val_acc: 0.9533\n",
+      "Epoch 203/300\n",
+      "350/350 [==============================] - 0s 86us/step - loss: 0.1519 - acc: 0.9629 - val_loss: 0.1927 - val_acc: 0.9533\n",
+      "Epoch 204/300\n",
+      "350/350 [==============================] - 0s 76us/step - loss: 0.1511 - acc: 0.9629 - val_loss: 0.1919 - val_acc: 0.9533\n",
+      "Epoch 205/300\n",
+      "350/350 [==============================] - 0s 76us/step - loss: 0.1504 - acc: 0.9657 - val_loss: 0.1907 - val_acc: 0.9533\n",
+      "Epoch 206/300\n",
+      "350/350 [==============================] - 0s 82us/step - loss: 0.1499 - acc: 0.9629 - val_loss: 0.1893 - val_acc: 0.9533\n",
+      "Epoch 207/300\n",
+      "350/350 [==============================] - 0s 91us/step - loss: 0.1496 - acc: 0.9657 - val_loss: 0.1886 - val_acc: 0.9533\n",
+      "Epoch 208/300\n",
+      "350/350 [==============================] - 0s 89us/step - loss: 0.1486 - acc: 0.9657 - val_loss: 0.1875 - val_acc: 0.9533\n",
+      "Epoch 209/300\n",
+      "350/350 [==============================] - 0s 93us/step - loss: 0.1481 - acc: 0.9657 - val_loss: 0.1866 - val_acc: 0.9533\n",
+      "Epoch 210/300\n",
+      "350/350 [==============================] - 0s 79us/step - loss: 0.1475 - acc: 0.9657 - val_loss: 0.1858 - val_acc: 0.9533\n",
+      "Epoch 211/300\n",
+      "350/350 [==============================] - 0s 74us/step - loss: 0.1468 - acc: 0.9657 - val_loss: 0.1856 - val_acc: 0.9533\n",
+      "Epoch 212/300\n",
+      "350/350 [==============================] - 0s 86us/step - loss: 0.1465 - acc: 0.9657 - val_loss: 0.1844 - val_acc: 0.9533\n",
+      "Epoch 213/300\n",
+      "350/350 [==============================] - 0s 79us/step - loss: 0.1458 - acc: 0.9657 - val_loss: 0.1840 - val_acc: 0.9533\n",
+      "Epoch 214/300\n",
+      "350/350 [==============================] - 0s 75us/step - loss: 0.1452 - acc: 0.9657 - val_loss: 0.1838 - val_acc: 0.9533\n",
+      "Epoch 215/300\n",
+      "350/350 [==============================] - 0s 81us/step - loss: 0.1451 - acc: 0.9629 - val_loss: 0.1833 - val_acc: 0.9533\n",
+      "Epoch 216/300\n",
+      "350/350 [==============================] - 0s 91us/step - loss: 0.1442 - acc: 0.9657 - val_loss: 0.1820 - val_acc: 0.9533\n",
+      "Epoch 217/300\n",
+      "350/350 [==============================] - 0s 84us/step - loss: 0.1437 - acc: 0.9657 - val_loss: 0.1812 - val_acc: 0.9533\n",
+      "Epoch 218/300\n",
+      "350/350 [==============================] - 0s 93us/step - loss: 0.1432 - acc: 0.9657 - val_loss: 0.1812 - val_acc: 0.9533\n",
+      "Epoch 219/300\n",
+      "350/350 [==============================] - 0s 81us/step - loss: 0.1426 - acc: 0.9657 - val_loss: 0.1808 - val_acc: 0.9533\n",
+      "Epoch 220/300\n",
+      "350/350 [==============================] - 0s 96us/step - loss: 0.1419 - acc: 0.9629 - val_loss: 0.1800 - val_acc: 0.9533\n",
+      "Epoch 221/300\n",
+      "350/350 [==============================] - 0s 91us/step - loss: 0.1417 - acc: 0.9657 - val_loss: 0.1796 - val_acc: 0.9533\n",
+      "Epoch 222/300\n",
+      "350/350 [==============================] - 0s 94us/step - loss: 0.1412 - acc: 0.9657 - val_loss: 0.1784 - val_acc: 0.9533\n",
+      "Epoch 223/300\n",
+      "350/350 [==============================] - 0s 105us/step - loss: 0.1404 - acc: 0.9657 - val_loss: 0.1780 - val_acc: 0.9533\n",
+      "Epoch 224/300\n",
+      "350/350 [==============================] - 0s 77us/step - loss: 0.1397 - acc: 0.9657 - val_loss: 0.1771 - val_acc: 0.9533\n",
+      "Epoch 225/300\n",
+      "350/350 [==============================] - 0s 88us/step - loss: 0.1395 - acc: 0.9657 - val_loss: 0.1764 - val_acc: 0.9533\n",
+      "Epoch 226/300\n",
+      "350/350 [==============================] - 0s 78us/step - loss: 0.1389 - acc: 0.9657 - val_loss: 0.1765 - val_acc: 0.9533\n",
+      "Epoch 227/300\n",
+      "350/350 [==============================] - 0s 88us/step - loss: 0.1382 - acc: 0.9657 - val_loss: 0.1760 - val_acc: 0.9533\n",
+      "Epoch 228/300\n",
+      "350/350 [==============================] - 0s 83us/step - loss: 0.1381 - acc: 0.9657 - val_loss: 0.1747 - val_acc: 0.9533\n",
+      "Epoch 229/300\n",
+      "350/350 [==============================] - 0s 90us/step - loss: 0.1372 - acc: 0.9657 - val_loss: 0.1740 - val_acc: 0.9533\n",
+      "Epoch 230/300\n",
+      "350/350 [==============================] - 0s 89us/step - loss: 0.1367 - acc: 0.9657 - val_loss: 0.1737 - val_acc: 0.9533\n",
+      "Epoch 231/300\n",
+      "350/350 [==============================] - 0s 93us/step - loss: 0.1361 - acc: 0.9657 - val_loss: 0.1730 - val_acc: 0.9533\n",
+      "Epoch 232/300\n",
+      "350/350 [==============================] - 0s 93us/step - loss: 0.1358 - acc: 0.9657 - val_loss: 0.1723 - val_acc: 0.9533\n",
+      "Epoch 233/300\n",
+      "350/350 [==============================] - 0s 93us/step - loss: 0.1352 - acc: 0.9657 - val_loss: 0.1713 - val_acc: 0.9533\n",
+      "Epoch 234/300\n",
+      "350/350 [==============================] - 0s 92us/step - loss: 0.1347 - acc: 0.9657 - val_loss: 0.1705 - val_acc: 0.9600\n",
+      "Epoch 235/300\n",
+      "350/350 [==============================] - 0s 90us/step - loss: 0.1342 - acc: 0.9657 - val_loss: 0.1703 - val_acc: 0.9600\n",
+      "Epoch 236/300\n",
+      "350/350 [==============================] - 0s 93us/step - loss: 0.1336 - acc: 0.9657 - val_loss: 0.1692 - val_acc: 0.9600\n",
+      "Epoch 237/300\n",
+      "350/350 [==============================] - 0s 89us/step - loss: 0.1331 - acc: 0.9657 - val_loss: 0.1689 - val_acc: 0.9600\n",
+      "Epoch 238/300\n",
+      "350/350 [==============================] - 0s 90us/step - loss: 0.1327 - acc: 0.9657 - val_loss: 0.1687 - val_acc: 0.9600\n",
+      "Epoch 239/300\n",
+      "350/350 [==============================] - 0s 90us/step - loss: 0.1321 - acc: 0.9657 - val_loss: 0.1679 - val_acc: 0.9600\n",
+      "Epoch 240/300\n",
+      "350/350 [==============================] - 0s 78us/step - loss: 0.1316 - acc: 0.9657 - val_loss: 0.1670 - val_acc: 0.9600\n",
+      "Epoch 241/300\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "350/350 [==============================] - 0s 86us/step - loss: 0.1311 - acc: 0.9657 - val_loss: 0.1665 - val_acc: 0.9600\n",
+      "Epoch 242/300\n",
+      "350/350 [==============================] - 0s 88us/step - loss: 0.1305 - acc: 0.9657 - val_loss: 0.1661 - val_acc: 0.9600\n",
+      "Epoch 243/300\n",
+      "350/350 [==============================] - 0s 81us/step - loss: 0.1303 - acc: 0.9657 - val_loss: 0.1655 - val_acc: 0.9600\n",
+      "Epoch 244/300\n",
+      "350/350 [==============================] - 0s 78us/step - loss: 0.1296 - acc: 0.9657 - val_loss: 0.1655 - val_acc: 0.9600\n",
+      "Epoch 245/300\n",
+      "350/350 [==============================] - 0s 76us/step - loss: 0.1294 - acc: 0.9657 - val_loss: 0.1653 - val_acc: 0.9600\n",
+      "Epoch 246/300\n",
+      "350/350 [==============================] - 0s 84us/step - loss: 0.1289 - acc: 0.9686 - val_loss: 0.1642 - val_acc: 0.9600\n",
+      "Epoch 247/300\n",
+      "350/350 [==============================] - 0s 81us/step - loss: 0.1286 - acc: 0.9657 - val_loss: 0.1639 - val_acc: 0.9600\n",
+      "Epoch 248/300\n",
+      "350/350 [==============================] - 0s 84us/step - loss: 0.1281 - acc: 0.9657 - val_loss: 0.1634 - val_acc: 0.9600\n",
+      "Epoch 249/300\n",
+      "350/350 [==============================] - 0s 85us/step - loss: 0.1279 - acc: 0.9657 - val_loss: 0.1629 - val_acc: 0.9600\n",
+      "Epoch 250/300\n",
+      "350/350 [==============================] - 0s 76us/step - loss: 0.1274 - acc: 0.9657 - val_loss: 0.1621 - val_acc: 0.9600\n",
+      "Epoch 251/300\n",
+      "350/350 [==============================] - 0s 87us/step - loss: 0.1270 - acc: 0.9657 - val_loss: 0.1615 - val_acc: 0.9600\n",
+      "Epoch 252/300\n",
+      "350/350 [==============================] - 0s 79us/step - loss: 0.1264 - acc: 0.9657 - val_loss: 0.1615 - val_acc: 0.9600\n",
+      "Epoch 253/300\n",
+      "350/350 [==============================] - 0s 75us/step - loss: 0.1263 - acc: 0.9657 - val_loss: 0.1610 - val_acc: 0.9600\n",
+      "Epoch 254/300\n",
+      "350/350 [==============================] - 0s 79us/step - loss: 0.1257 - acc: 0.9657 - val_loss: 0.1600 - val_acc: 0.9600\n",
+      "Epoch 255/300\n",
+      "350/350 [==============================] - 0s 82us/step - loss: 0.1253 - acc: 0.9657 - val_loss: 0.1598 - val_acc: 0.9600\n",
+      "Epoch 256/300\n",
+      "350/350 [==============================] - 0s 79us/step - loss: 0.1251 - acc: 0.9657 - val_loss: 0.1590 - val_acc: 0.9600\n",
+      "Epoch 257/300\n",
+      "350/350 [==============================] - 0s 90us/step - loss: 0.1244 - acc: 0.9657 - val_loss: 0.1587 - val_acc: 0.9600\n",
+      "Epoch 258/300\n",
+      "350/350 [==============================] - 0s 76us/step - loss: 0.1243 - acc: 0.9657 - val_loss: 0.1586 - val_acc: 0.9600\n",
+      "Epoch 259/300\n",
+      "350/350 [==============================] - 0s 86us/step - loss: 0.1238 - acc: 0.9657 - val_loss: 0.1581 - val_acc: 0.9600\n",
+      "Epoch 260/300\n",
+      "350/350 [==============================] - 0s 79us/step - loss: 0.1233 - acc: 0.9657 - val_loss: 0.1575 - val_acc: 0.9600\n",
+      "Epoch 261/300\n",
+      "350/350 [==============================] - 0s 75us/step - loss: 0.1230 - acc: 0.9657 - val_loss: 0.1568 - val_acc: 0.9600\n",
+      "Epoch 262/300\n",
+      "350/350 [==============================] - 0s 81us/step - loss: 0.1226 - acc: 0.9657 - val_loss: 0.1569 - val_acc: 0.9600\n",
+      "Epoch 263/300\n",
+      "350/350 [==============================] - 0s 77us/step - loss: 0.1223 - acc: 0.9657 - val_loss: 0.1555 - val_acc: 0.9600\n",
+      "Epoch 264/300\n",
+      "350/350 [==============================] - 0s 80us/step - loss: 0.1217 - acc: 0.9657 - val_loss: 0.1547 - val_acc: 0.9600\n",
+      "Epoch 265/300\n",
+      "350/350 [==============================] - 0s 82us/step - loss: 0.1215 - acc: 0.9657 - val_loss: 0.1551 - val_acc: 0.9600\n",
+      "Epoch 266/300\n",
+      "350/350 [==============================] - 0s 86us/step - loss: 0.1211 - acc: 0.9657 - val_loss: 0.1548 - val_acc: 0.9600\n",
+      "Epoch 267/300\n",
+      "350/350 [==============================] - 0s 86us/step - loss: 0.1206 - acc: 0.9657 - val_loss: 0.1540 - val_acc: 0.9600\n",
+      "Epoch 268/300\n",
+      "350/350 [==============================] - 0s 84us/step - loss: 0.1206 - acc: 0.9657 - val_loss: 0.1533 - val_acc: 0.9600\n",
+      "Epoch 269/300\n",
+      "350/350 [==============================] - 0s 84us/step - loss: 0.1202 - acc: 0.9686 - val_loss: 0.1534 - val_acc: 0.9600\n",
+      "Epoch 270/300\n",
+      "350/350 [==============================] - 0s 83us/step - loss: 0.1195 - acc: 0.9657 - val_loss: 0.1528 - val_acc: 0.9600\n",
+      "Epoch 271/300\n",
+      "350/350 [==============================] - 0s 83us/step - loss: 0.1197 - acc: 0.9657 - val_loss: 0.1524 - val_acc: 0.9600\n",
+      "Epoch 272/300\n",
+      "350/350 [==============================] - 0s 75us/step - loss: 0.1190 - acc: 0.9657 - val_loss: 0.1518 - val_acc: 0.9600\n",
+      "Epoch 273/300\n",
+      "350/350 [==============================] - 0s 81us/step - loss: 0.1188 - acc: 0.9657 - val_loss: 0.1520 - val_acc: 0.9600\n",
+      "Epoch 274/300\n",
+      "350/350 [==============================] - 0s 84us/step - loss: 0.1184 - acc: 0.9657 - val_loss: 0.1519 - val_acc: 0.9600\n",
+      "Epoch 275/300\n",
+      "350/350 [==============================] - 0s 79us/step - loss: 0.1181 - acc: 0.9657 - val_loss: 0.1510 - val_acc: 0.9600\n",
+      "Epoch 276/300\n",
+      "350/350 [==============================] - 0s 85us/step - loss: 0.1178 - acc: 0.9657 - val_loss: 0.1501 - val_acc: 0.9600\n",
+      "Epoch 277/300\n",
+      "350/350 [==============================] - 0s 81us/step - loss: 0.1173 - acc: 0.9657 - val_loss: 0.1496 - val_acc: 0.9600\n",
+      "Epoch 278/300\n",
+      "350/350 [==============================] - 0s 74us/step - loss: 0.1169 - acc: 0.9657 - val_loss: 0.1495 - val_acc: 0.9600\n",
+      "Epoch 279/300\n",
+      "350/350 [==============================] - 0s 86us/step - loss: 0.1168 - acc: 0.9657 - val_loss: 0.1489 - val_acc: 0.9600\n",
+      "Epoch 280/300\n",
+      "350/350 [==============================] - 0s 75us/step - loss: 0.1165 - acc: 0.9657 - val_loss: 0.1488 - val_acc: 0.9600\n",
+      "Epoch 281/300\n",
+      "350/350 [==============================] - 0s 78us/step - loss: 0.1162 - acc: 0.9657 - val_loss: 0.1476 - val_acc: 0.9600\n",
+      "Epoch 282/300\n",
+      "350/350 [==============================] - 0s 87us/step - loss: 0.1158 - acc: 0.9657 - val_loss: 0.1474 - val_acc: 0.9600\n",
+      "Epoch 283/300\n",
+      "350/350 [==============================] - 0s 83us/step - loss: 0.1155 - acc: 0.9657 - val_loss: 0.1473 - val_acc: 0.9600\n",
+      "Epoch 284/300\n",
+      "350/350 [==============================] - 0s 85us/step - loss: 0.1153 - acc: 0.9657 - val_loss: 0.1474 - val_acc: 0.9600\n",
+      "Epoch 285/300\n",
+      "350/350 [==============================] - 0s 74us/step - loss: 0.1152 - acc: 0.9686 - val_loss: 0.1472 - val_acc: 0.9600\n",
+      "Epoch 286/300\n",
+      "350/350 [==============================] - 0s 84us/step - loss: 0.1146 - acc: 0.9657 - val_loss: 0.1465 - val_acc: 0.9600\n",
+      "Epoch 287/300\n",
+      "350/350 [==============================] - 0s 83us/step - loss: 0.1145 - acc: 0.9657 - val_loss: 0.1459 - val_acc: 0.9600\n",
+      "Epoch 288/300\n",
+      "350/350 [==============================] - 0s 85us/step - loss: 0.1142 - acc: 0.9657 - val_loss: 0.1457 - val_acc: 0.9600\n",
+      "Epoch 289/300\n",
+      "350/350 [==============================] - 0s 83us/step - loss: 0.1139 - acc: 0.9657 - val_loss: 0.1454 - val_acc: 0.9600\n",
+      "Epoch 290/300\n",
+      "350/350 [==============================] - 0s 79us/step - loss: 0.1136 - acc: 0.9657 - val_loss: 0.1454 - val_acc: 0.9600\n",
+      "Epoch 291/300\n",
+      "350/350 [==============================] - 0s 85us/step - loss: 0.1133 - acc: 0.9657 - val_loss: 0.1448 - val_acc: 0.9600\n",
+      "Epoch 292/300\n",
+      "350/350 [==============================] - 0s 87us/step - loss: 0.1129 - acc: 0.9657 - val_loss: 0.1443 - val_acc: 0.9600\n",
+      "Epoch 293/300\n",
+      "350/350 [==============================] - 0s 88us/step - loss: 0.1127 - acc: 0.9657 - val_loss: 0.1440 - val_acc: 0.9600\n",
+      "Epoch 294/300\n",
+      "350/350 [==============================] - 0s 79us/step - loss: 0.1124 - acc: 0.9657 - val_loss: 0.1439 - val_acc: 0.9600\n",
+      "Epoch 295/300\n",
+      "350/350 [==============================] - 0s 86us/step - loss: 0.1121 - acc: 0.9657 - val_loss: 0.1436 - val_acc: 0.9600\n",
+      "Epoch 296/300\n",
+      "350/350 [==============================] - 0s 78us/step - loss: 0.1116 - acc: 0.9657 - val_loss: 0.1429 - val_acc: 0.9600\n",
+      "Epoch 297/300\n",
+      "350/350 [==============================] - 0s 81us/step - loss: 0.1118 - acc: 0.9657 - val_loss: 0.1420 - val_acc: 0.9600\n",
+      "Epoch 298/300\n",
+      "350/350 [==============================] - 0s 85us/step - loss: 0.1113 - acc: 0.9657 - val_loss: 0.1418 - val_acc: 0.9600\n",
+      "Epoch 299/300\n",
+      "350/350 [==============================] - 0s 86us/step - loss: 0.1110 - acc: 0.9657 - val_loss: 0.1414 - val_acc: 0.9600\n",
+      "Epoch 300/300\n",
+      "350/350 [==============================] - 0s 94us/step - loss: 0.1106 - acc: 0.9657 - val_loss: 0.1417 - val_acc: 0.9600\n"
      ]
     }
    ],
    "source": [
+    "# Instantiating the model\n",
     "model = a_simple_NN()\n",
     "\n",
-    "# Here we split the dataset into training (80%) and validation sets (20%)\n",
+    "# Splitting the dataset into training (70%) and validation sets (30%)\n",
     "X_train, X_test, y_train, y_test = train_test_split(\n",
     "    features, labels, test_size=0.3)\n",
     "\n",
-    "num_epochs = 100\n",
+    "# Setting the number of passes through the entire training set\n",
+    "num_epochs = 300\n",
     "\n",
     "# We can pass validation data while training\n",
-    "\n",
     "model_run = model.fit(X_train, y_train, epochs=num_epochs,\n",
     "                      validation_data=(X_test, y_test))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The history has the following data:  dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f4dff647390>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -905,24 +1371,35 @@
     }
    ],
    "source": [
+    "# Looking at the loss and accuracy on the training and validation sets during the training\n",
+    "# This can be done by using Keras callback \"history\" which is applied by default\n",
     "history_model = model_run.history\n",
     "\n",
-    "plt.plot(np.arange(1,num_epochs+1), history_model[\"acc\"], \"blue\") ;\n",
+    "print(\"The history has the following data: \", history_model.keys())\n",
+    "\n",
+    "# Plotting the training and validation accuracy during the training\n",
+    "plt.plot(np.arange(1, num_epochs+1), history_model[\"acc\"], \"blue\") ;\n",
     "\n",
-    "plt.plot(np.arange(1,num_epochs+1), history_model[\"val_acc\"], \"red\") ;"
+    "plt.plot(np.arange(1, num_epochs+1), history_model[\"val_acc\"], \"red\") ;"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We know from previous chapters that to more robustly calculate accuracy we can use **K-fold crossvalidation**.\n",
+    "**Here we dont't really see a big difference between the training and validation data because the function we are trying to fit is quiet simple and there is not too much noise. We will come back to these curves in a later example**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the example above we splitted our dataset into a 70-30 train-validation set. We know from previous chapters that to more robustly calculate accuracy we can use **K-fold crossvalidation**.\n",
     "This is even more important when we have small datasets and cannot afford to reserve a validation set!\n",
-    "This is also the case in the example above.\n",
     "\n",
-    "One way to do the cross validation here would be to write our own function to do this. However, we know that **SciKit learn** provides such a function. So the question is:\n",
+    "One way to do the cross validation here would be to write our own function to do this. However, we also know that **SciKit learn** provides several handy functions to evaluate and tune the models. So the question is:\n",
     "\n",
-    "Can we somehow use the handy functions which **SciKit learn** provides to evaluate and tune our Keras models?\n",
+    "Can we somehow use these **Scikit learn** functions or ones we wrote ourselves for **Scikit learn** models to evaluate and tune our Keras models?\n",
     "\n",
     "The Answer is **YES !**\n",
     "\n",
@@ -935,9 +1412,9 @@
    "source": [
     "## Using SciKit learn functions on Keras models\n",
     "\n",
-    "Keras offers wrappers which allow its Sequential models to be used with SciKit learn. \n",
+    "Keras offers 2 wrappers which allow its Sequential models to be used with SciKit learn. \n",
     "\n",
-    "There 2 such wrappers: **KerasClassifier** and **KerasRegressor**.\n",
+    "There are: **KerasClassifier** and **KerasRegressor**.\n",
     "\n",
     "For more information:\n",
     "https://keras.io/scikit-learn-api/\n",
@@ -947,62 +1424,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 42,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.61428571 0.6        0.88571429 0.7        0.67142857]\n",
-      "0.6942857147966113\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# We wrap the Keras model we created above with KerasClassifier\n",
-    "from keras.wrappers.scikit_learn import KerasClassifier \n",
+    "from keras.wrappers.scikit_learn import KerasClassifier\n",
     "from sklearn.model_selection import cross_val_score\n",
-    "model_scikit = KerasClassifier(build_fn=a_simple_NN, **{\"epochs\":num_epochs, \"verbose\":0})\n",
-    "cross_validation = cross_val_score(model_scikit, X_train, y_train, cv=5, verbose=0)\n",
-    "print(cross_validation)\n",
-    "print(np.mean(cross_validation))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from keras.models import Sequential\n",
-    "from keras.layers import Dense\n",
-    "import numpy as np\n",
-    "# We wrap the Keras model we created above with KerasClassifier\n",
-    "from keras.wrappers.scikit_learn import KerasClassifier "
+    "# Wrapping Keras model\n",
+    "# NOTE: We pass verbose=0 to suppress the model output\n",
+    "num_epochs = 400\n",
+    "model_scikit = KerasClassifier(\n",
+    "    build_fn=a_simple_NN, **{\"epochs\": num_epochs, \"verbose\": 0})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
+    "# Let's reuse the function to visualize the decision boundary which we saw in chapter 2 with minimal change\n",
+    "\n",
     "def list_flatten(list_of_list):\n",
     "    flattened_list = [i for j in list_of_list for i in j]\n",
     "    return flattened_list\n",
     "\n",
+    "def plot_points(plt=plt, marker='o'):\n",
+    "    colors = [[\"steelblue\", \"chocolate\"][i] for i in labels]\n",
+    "    plt.scatter(features.iloc[:, 0], features.iloc[:, 1], color=colors, marker=marker);\n",
+    "\n",
     "def train_and_plot_decision_surface(\n",
     "    name, classifier, features_2d, labels, preproc=None, plt=plt, marker='o', N=400\n",
     "):\n",
@@ -1056,47 +1507,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def a_simple_NN():\n",
-    "    \n",
-    "    model = Sequential()\n",
-    "\n",
-    "    model.add(Dense(8, input_shape = (2,), activation = \"relu\"))\n",
-    "\n",
-    "    model.add(Dense(2, activation = \"relu\"))\n",
-    "\n",
-    "    model.add(Dense(1, activation = \"sigmoid\"))\n",
-    "\n",
-    "    model.compile(loss=\"binary_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
-    "    \n",
-    "    return model\n",
-    "\n",
-    "model = a_simple_NN()\n",
-    "\n",
-    "num_epochs = 400\n",
-    "model_scikit = KerasClassifier(build_fn=a_simple_NN, epochs=num_epochs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Neural Net:\t 487 / 500 correct\n"
+      "Neural Net:\t 484 / 500 correct\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f4e05a6ec18>"
+       "<Figure size 432x432 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -1106,21 +1531,85 @@
     }
    ],
    "source": [
-    "def plot_points(plt=plt, marker='o'):\n",
-    "    colors = [[\"steelblue\", \"chocolate\"][i] for i in labels]\n",
-    "    plt.scatter(features.iloc[:, 0], features.iloc[:, 1], color=colors, marker=marker);\n",
-    "\n",
     "_, ax = plt.subplots(figsize=(6, 6))\n",
     "\n",
-    "xor = pd.read_csv(\"xor.csv\")\n",
-    "features = xor.iloc[:, :-1]\n",
-    "# Convert boolean to integer values (True->1 and False->0)\n",
-    "labels = xor.iloc[:, -1]\n",
-    "\n",
     "train_and_plot_decision_surface(\"Neural Net\", model_scikit, features, labels, plt=ax)\n",
     "plot_points(plt=ax)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The acuracy on the  5  validation folds: [0.72 0.94 0.95 0.75 0.94]\n",
+      "The Average acuracy on the  5  validation folds: 0.86\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Applying K-fold cross-validation\n",
+    "# Here we pass the whole dataset, i.e. features and labels, instead of splitting it.\n",
+    "num_folds = 5\n",
+    "cross_validation = cross_val_score(\n",
+    "    model_scikit, features, labels, cv=num_folds, verbose=0)\n",
+    "\n",
+    "print(\"The acuracy on the \", num_folds, \" validation folds:\", cross_validation)\n",
+    "print(\"The Average acuracy on the \", num_folds, \" validation folds:\", np.mean(cross_validation))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### NOTE: The above code took quiet long even though we used only 5  CV folds and the neural network and data size are very small!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Hyperparameter optimization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We know from chapter 6 that there are 2 types of parameters which need to be tuned for a machine learning model.\n",
+    "* Normal model parameters which can be learned for e.g. by gradient-descent\n",
+    "* Hyperparameters\n",
+    "\n",
+    "In the model which we created above we made some arbitrary choices like which optimizer we use, what is its learning rate, number of hidden units and so on ...\n",
+    "\n",
+    "Now that we have the keras model wrapped as a scikit model we can use the grid search functions we have seen in chapter 6."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9580000002384186 {'epochs': 500}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import GridSearchCV\n",
+    "HP_grid = dict(epochs=[300, 500, 1000])\n",
+    "search = GridSearchCV(estimator=model_scikit, param_grid=HP_grid)\n",
+    "search.fit(features, labels)\n",
+    "print(search.best_score_, search.best_params_)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},