04_modern_net.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modern Net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
    "x_train = x_train / 255\n",
    "x_test = x_test / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(28*28, activation='relu'),\n",
    "    tf.keras.layers.Dense(300, activation='relu'),\n",
    "    tf.keras.layers.Dense(300, activation='relu'),\n",
    "    tf.keras.layers.Dropout(0.3),\n",
    "    tf.keras.layers.Dense(10, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    optimizer=tf.keras.optimizers.SGD(lr=0.01),\n",
    "    loss=tf.keras.losses.sparse_categorical_crossentropy,\n",
    "    metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 12s 201us/sample - loss: 0.6152 - accuracy: 0.8290 - val_loss: 0.2665 - val_accuracy: 0.9241\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 12s 194us/sample - loss: 0.2628 - accuracy: 0.9237 - val_loss: 0.1975 - val_accuracy: 0.9413\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 12s 201us/sample - loss: 0.2018 - accuracy: 0.9414 - val_loss: 0.1568 - val_accuracy: 0.9522\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 13s 217us/sample - loss: 0.1640 - accuracy: 0.9520 - val_loss: 0.1359 - val_accuracy: 0.9580\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 15s 245us/sample - loss: 0.1400 - accuracy: 0.9588 - val_loss: 0.1227 - val_accuracy: 0.9639\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 12s 204us/sample - loss: 0.1198 - accuracy: 0.9654 - val_loss: 0.1095 - val_accuracy: 0.9666\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 13s 212us/sample - loss: 0.1060 - accuracy: 0.9691 - val_loss: 0.1006 - val_accuracy: 0.9693\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 13s 213us/sample - loss: 0.0933 - accuracy: 0.9728 - val_loss: 0.0920 - val_accuracy: 0.9709\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 14s 225us/sample - loss: 0.0829 - accuracy: 0.9761 - val_loss: 0.0856 - val_accuracy: 0.9733\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 13s 219us/sample - loss: 0.0744 - accuracy: 0.9782 - val_loss: 0.0802 - val_accuracy: 0.9752\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x_train, y_train,\n",
    "                    validation_data=(x_test, y_test),\n",
    "                    epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x15f19b128>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(history.history['loss'], label='loss')\n",
    "plt.plot(history.history['val_loss'], label='val loss')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1305b3a20>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cbr75ZpuiuTf+Dqdrb8fqQMh/6EyTC85MiHmnwIyLIH825J7c5yH8Le1+1m6r6W7Jf76zDlVn/vbZRVlcOauA0ya6OGZMml0WaYaNXkNfRGKBx4BzgEpgrYi8rKobgw57BHhKVf8oImcBDwHXBPY9Bfyrqr4lIqnAISatHtpmzJhBQ0MDubm5jB3rtBivuuoqLrjgAo477jiKi4s55phjDvscB5ueGbApmg+ltQ4q1u5vxVetg45mZ9/IAiia7wR8wRwYNR1i+raUXYe/k/WV+5yWvNvLxzv20e7vJD5WmFmQyW1nT2HupGxOyM8g3k64mmGq16mVReRU4D5VPTdw/y4AVX0o6JgNwEJVrRDn79o6VU0XkenAUlWdF2pBNrVyeIUyRfOA/Huowr4dTj98V3/8ng04XTUxTldN/hwn4AvmOP3wfdTZqWzZ08BKt5cPvqzmw/Jqmtr9iMCMcenMnejitEkuTinMJDlhiHd1majXn1Mr5wIVQfcrgdk9jvkUuBinC+giIE1EsoEpwD4ReQEoAt4G7lTVAxaxFJEbgRsBCgoKQijJDIRBnaLZ74M9nzldNDtWOSHfsMvZl5AG+afAtAuhYDbkFvfbbIs7qptZ+aWXlW4vq76spjow5/sEVwoXnZTL3Iku5kzIJjNlaK56ZExf9Vfz5Q7gP0TkOuB9oArwB55/PjAT2AH8GbgO+EPwg1V1KbAUnJZ+P9VkjtCATtHcWg+Va/afdK1cBx3OiVBG5sP4uYETrrNh9Iw+d9V08Ta28cGX1awsc66yqax1ruQZnT6CM6bkcNokF6dNzGZcRlK/vJ4xQ10ooV+FcxK2S15gWzdV3YnT0ifQb3+Jqu4TkUrgE1UtD+x7CZhDj9APharaFRFDQEgrrXVdVVOxJtBd8yHs3eAskiExMPpYmHnV/qtqRub2W31NbT7WbKthZZmXEreXzbsbAEhPjOPUidncePoETpvoYmJOiv1/MlEplNBfC0wWkSKcsL8cuDL4ABFxATWq2gnchXMlT9djM0QkR1U9wFnAgR32IUhMTKS6uprs7Gz7QQ0jVaW6uprExB7Xnfva9g+AqljthH1jYBWNhDTIOxnO+KHTis8rhhEHv9roaHT4O/m0Yv/J14921OLrVBLiYjilMJN/OXcq8ya5ODZ3pE1jYAwhhL6q+kRkCbAc55LNx1V1g4jcD5Sq6svAAuAhEVGc7p1bAo/1i8gdwDuBE7zrgP860iLz8vKorKzE4/Ec6UNNP0tMTCQvMwk2/zVwwnUN7Px4/wLUmYXOknb5s5yQ74eraoKpKl/saaTE7eUDt5fVQSdfj8sdyXfnT2DeJBfFhZkkxvff6xoTKYbFwugmjDo7wbMp0IoPdNfUlDv7YhOcqQryZzldNXmznIWn+1nVvhZn5Kvby0p3Nd5G5xdMkSuFuZOcQVGnTswmI9lOvproZQujm6PT1uBMXdAV8JVroS2w+lJKjtN6P/l65/vYEyC+/6cYqGvuYFW50ye/0l3NVq9zwteVmsDcSa7ur1w7+WrMEbPQj2bBJ1y7umq6TrgizlU0x/2dE/D5syCzKOR5aY5Ea4ef0m213ZdSflbljHxNSYhl9oRsrp4znnmTXEwZnWrndIzpIwv9aOJrg12fBrpqPjzICddiOP0HTsDnFTtrow4Af6fyeVVdd8iv3VZLu6+TuBhhZkEGt549mXmTXDby1ZgBYKEfyRr37u+mGeQTrsFUla3epu4++Q++9FLf6gPgmDFpXBNoyc8qyiJlhP2XNGYg2U9YpOn0w5bX4MPfwbYVzrauE66zFge6amYPyAnXnso9jTyzegdvfL6LnXX7px0+79ixnDYpm9MmushJG9HLsxhj+pOFfqRoroGPnoK1f4C6Hc4o1zN/BEVnDNgJ14Px+Tt5Z/Nenlm9nRVlXuJjhbOOGcXNZ05i3iQX47Nt2mFjwslCf7jb/ZnTqv/sf5xFtwvnw8KfwZTzIHbw/nk9DW38ee0O/vvDHeysa2XsyETu+PoULjulwFrzxgwhFvrDkd8Hm1+FNUth+0qIS4ITLodZNzpX3AwSVaV0ey1Pr9rO65/vosOvzJ/s4t4LZ3D2MaNsvVdjhiAL/eGkqRo+ehLWPg71lZBRAOc8ADOvdpb5G6wy2ny89EkVT6/azubdDaQlxnHNnEKumlPAxJz+mQ3TGDMwLPSHg52fOK36z5Y5V99MWADn/xtMOXfArrg5GPfeRp5ZvZ2/rKukoc3H9LHpPHzxcVx44jibb96YYcJ+Uocqfwdsehk+XOpMYhaf4rToZ90Iow6/Qld/8vk7eWvjHp5evZ0PvqwmITaG848bwzWnFnJSQYadlDVmmLHQH2oaPbDuSSj9g7OoSGYhnPszOPEqSMoYtDL21rfy7JoKnl2zg931reRmJPEv507lslPycaXaiVljhisL/aGiap3Tqt/wAvjbYeJZcMGvYdI5EDM4J0RVlTVba3hq9XaWf74bX6dy+pQcHvzWsZx5zCibmtiYCGChH06+dtj4v7Dmd87EZgmpcPJ1TheOa/KgldHY5uPFj6t4ZtV2tuxpID0xjutOK+TqOeMpdKUMWh3GmIFnoR8ODXtg3RNQ+rgz903WRFj4/+DEKyExfdDK+GJPA0+v2s4LH1XS1O7n2Nx0fn7J8VxwwjiSEmwuemMikYX+YKoshQ9/Cxtegs4Op+tm9k1OV84gdeF0+Dt5c8Menl69jdXlNSTExfDN48dyzZzxnJhvJ2aNiXQW+gPN1wYbXnRGze78yJnN8pTvOvPgZE8ctDJ217Xy7JodPLtmB3sb2sjLTOLO847h74vzyUqxxUeMiRYhhb6ILAR+jbNc4u9V9eEe+8fjrIubA9QAV6tqZdD+dGAj8JKqLumn2oe2+l1O9826J6DJA64pcP4jzsjZflwj9nBUlVXl1TyzejvLN+yhU5UzpuTw8KnjOWOKnZg1Jhr1GvoiEgs8BpwDVAJrReRlVd0YdNgjwFOq+kcROQt4CLgmaP8DOGvnRr7a7fDOT50TtJ1+ZwDV7O/BhDMHZAGSQ3ln0x4een0z7r2NZCTHc8O8Iq6aXcD4bDsxa0w0C6WlPwtwq2o5gIg8ByzCabl3mQ78c+D2u8BLXTtE5GRgNPAG0Ov6jcOWqtOqf/PHzv3ZN8EpN0DWhEEto7HNx4OvbuS5tRVMGZ3Kv/2dc2LWFgk3xkBooZ8LVATdrwRm9zjmU+BinC6gi4A0EckGaoFfAFcDX+tztUNVXSX87xIofxeKTodFjznz4gyytdtq+OfnP6GqtoV/WDCR2742mRFxFvbGmP3660TuHcB/iMh1ON04VYAfuBl4TVUrD3dViIjcCNwIUFAw+GF51FThkz/BG3dBp8/psy++YdCuxOnS5vPz72+V8bv3vyQ/M5nnv3cqxYWDNwGbMWb4CCX0q4D8oPt5gW3dVHUnTksfEUkFLlHVfSJyKjBfRG4GUoEEEWlU1Tt7PH4psBSguLhYj/bNDKr6XfDKrVC2HMbPdVr3WUWDXsbm3fXc9twnbN7dwBWz8vnRN6aTaksOGmMOIZR0WAtMFpEinLC/HLgy+AARcQE1qtoJ3IVzJQ+qelXQMdcBxT0Df9hRhfXPw+v/4oyoXfgwzPreoLfu/Z3KH0rKeWT5F6QnxfOHa4s5e9rAL4FojBneeg19VfWJyBJgOc4lm4+r6gYRuR8oVdWXgQXAQyKiON07twxgzeHTuBde/SdnAZO8WfCt/wTXpEEvo6Kmmdv/51PWbK3h3Bmj+dlFx5Ftk6AZY0IgqkOrN6W4uFhLS0vDXcZXff4C/PV2aG+Cs+6BU28Z1Lnswbnuftm6Sn76inPh1H0XzuCSk3JtFK0xBhFZp6q9XiFpnb+9aaqGv/4zbHwJxp3ktO4HcT77LtWNbdz1wme8uXEPs4uyeOTSE8jPSh70Oowxw5uF/uFsesXpzmnZB2f9GObeNqiLjXd5e+Me7nxhPfUtPn50/jRumFdEjI2mNcYcBQv9g2mugdd/CJ89D2OOh2//76AuON6lsc3HA69s5M+lFUwbm86fvnsiU8cMzhQOxpjIZKHf0xfL4eV/hGYvLLgL5t8OsfGDXkbwQKubF0zktq9NISFucK8QMsZEHgv9Lq118Mbd8MkzMGoGXPlnGHfioJfR5vPzy7e+YOn75TbQyhjT7yz0AdzvwMvfh4bdMP8OOOMHEDf4l0D2HGh1zzemk2IDrYwx/Si6E6WtAd68x1mI3DUVvvsW5J486GX4O5XfryjnF2/aQCtjzMCK3tAv/5szSVpdBZz2j3DmjyA+cdDLqKhp5vbnP2XNNhtoZYwZeNEX+u1N8Na9sPa/nLVpv7McCnpOGjrwVJX/Ka3kp69sQER45NITbKCVMWbARVfob/8AXvoHZ6GTOTc7194nDP4AJ29goNVbG/cwZ4Iz0Cov0wZaGWMGXnSEfkcLvPMArP4NZI6H6/4KhXPDUsqbG3Zz1wuf0dDm455vTOM7c22glTFm8ER+6FeshZdugmq3syD5134KI1IHvYyG1g4eeHUjz5dWMn1sOv99mQ20MsYMvsgN/Y5WeO8h+OBRSM91RtVOWBCWUtZsdQZa7dxnA62MMeEVmaFf9ZHTd+/ZDCddC19/EBLTB72MNp+fX775BUtX2EArY8zQEFmh72uH938OK34JqaPh6r/ApPAszbtpVz3/9GcbaGWMGVoiJ4X27YBnr4A9n8OJV8G5P4OkjEEvQ1X53fvl/OLNLYxMSrCBVsaYISVyQj8lB5Kz4IrnYOp5YSvjvS0eHn59sw20MsYMSZET+vFJcO0r4a6C97bsJSk+lkevmMmIuMFdWcsYY3oT0iUkIrJQRLaIiFtEvrKwuYiMF5F3RGS9iLwnInmB7SeKyCoR2RDYd1l/v4GhZoXby6yiLAt8Y8yQ1Gvoi0gs8BhwHjAduEJEpvc47BHgKVU9HrgfeCiwvRn4tqrOABYCvxKRwe9oHyQ797VQ7mli/mRXuEsxxpiDCqWlPwtwq2q5qrYDzwGLehwzHfi/wO13u/ar6heqWha4vRPYC+T0R+FDUUmZF4B5FvrGmCEqlNDPBSqC7lcGtgX7FLg4cPsiIE1EsoMPEJFZQALwZc8XEJEbRaRUREo9Hk+otQ85K9xectJGMHW0jbQ1xgxN/TUs9A7gDBH5GDgDqAL8XTtFZCzwNHC9qnb2fLCqLlXVYlUtzskZnn8IdHYqK91e5k1y2UyZxpghK5Srd6qA/KD7eYFt3QJdNxcDiEgqcImq7gvcTwf+CvxIVVf3R9FD0cZd9dQ0tTNvknXtGGOGrlBa+muBySJSJCIJwOXAy8EHiIhLRLqe6y7g8cD2BOBFnJO8y/qv7KGnxG39+caYoa/X0FdVH7AEWA5sAp5X1Q0icr+IXBg4bAGwRUS+AEYD/xrY/vfA6cB1IvJJ4GvwVxsfBCVlXqaMTmV0+uCvvmWMMaEKaXCWqr4GvNZj20+Cbi8DvtKSV9VngGf6WOOQ19rhZ822Gq6ePT7cpRhjzGHZ/L79YO22Gtp9nXZ9vjFmyLPQ7wclZV7iY4XZE2zaZGPM0Gah3w9WlHk5qSCT5ITImcrIGBOZLPT7yNvYxsZd9da1Y4wZFiz0+2hl96Waw3NQmTEmuljo91FJmZeRSfEclzsy3KUYY0yvLPT7QFUpcXs5bWI2sTE29YIxZuiz0O+DLz1N7KprtVG4xphhw0K/D0rKnBlB50+y/nxjzPBgod8HJW4vBVnJFGQnh7sUY4wJiYX+Uerwd7K6vMa6dowxw4qF/lH6pGIfjW0+5ttUysaYYcRC/yitKPMSI3DaRAt9Y8zwYaF/lErKPByXl8HI5Phwl2KMMSGz0D8K9a0dfFpZZ107xphhx0L/KKz6shp/p9pJXGPMsGOhfxRKyrwkJ8RyUkFmuEsxxpgjElLoi8hCEdkiIm4RufMg+8eLyDsisl5E3hORvKB914pIWeDr2v4sPlxK3F5mF2WREGe/M40xw0uvqSUiscBjwHnAdOAKEZne47BHcBY/Px64H3go8Ngs4F5gNjALuFdEhnXzuLK2ma3eJptV0xgzLIXSVJ0FuFW1XFXbgeeART2OmQ78X+D2u0H7zwXeUtUaVa0F3gIW9r2cRmGLAAAOp0lEQVTs8Ckpc6ZStvnzjTHDUSihnwtUBN2vDGwL9ilwceD2RUCaiGSH+NhhZYXby+j0EUwelRruUowx5oj1V6f0HcAZIvIxcAZQBfhDfbCI3CgipSJS6vF4+qmk/tfZqXzg9jJ3kgsRm0rZGDP8hBL6VUB+0P28wLZuqrpTVS9W1ZnAjwLb9oXy2MCxS1W1WFWLc3KGbl/5hp311DZ3WNeOMWbYCiX01wKTRaRIRBKAy4GXgw8QEZeIdD3XXcDjgdvLga+LSGbgBO7XA9uGpRVu56+QuTYoyxgzTPUa+qrqA5bghPUm4HlV3SAi94vIhYHDFgBbROQLYDTwr4HH1gAP4PziWAvcH9g2LJWUeTlmTBqj0hLDXYoxxhyVuFAOUtXXgNd6bPtJ0O1lwLJDPPZx9rf8h62Wdj+l22r59qnjw12KMcYcNRtdFKI122po93fa1AvGmGHNQj9EJWUeEmJjmF2UHe5SjDHmqFnoh2hFmZeTx2eSlBAb7lKMMeaoWeiHwNPQxubdDda1Y4wZ9iz0Q7DSbVMvGGMig4V+CFaUeclIjmfGuJHhLsUYY/rEQr8XqkqJ28PciS5iY2zqBWPM8Gah3wv33kb21LdZf74xJiJY6PdiRWAq5Xk29YIxJgJY6PeixO2lMDuZ/KzkcJdijDF9ZqF/GO2+TlaXV1vXjjEmYljoH8bHO2ppbvczb9LQne7ZGGOOhIX+YZS4vcQInDrRpl4wxkQGC/3DWFHm5YT8DEYmxYe7FGOM6RcW+odQ19zB+sp9zJ9sXTvGmMhhoX8Iq8q9dKpNvWCMiSwW+oewosxL6og4TszPCHcpxhjTbyz0D6HE7WXOhCziY+0jMsZEjpASTUQWisgWEXGLyJ0H2V8gIu+KyMcisl5Ezg9sjxeRP4rIZyKySUTu6u83MBAqaprZXt1so3CNMRGn19AXkVjgMeA8YDpwhYhM73HYPTgLps8ELgd+E9h+KTBCVY8DTga+JyKF/VP6wOmeesFO4hpjIkwoLf1ZgFtVy1W1HXgOWNTjGAXSA7dHAjuDtqeISByQBLQD9X2ueoCVuD2MHZnIxJyUcJdijDH9KpTQzwUqgu5XBrYFuw+4WkQqgdeA7we2LwOagF3ADuARVa3p+QIicqOIlIpIqcfjObJ30M/8ncpKdzXzJrkQsamUjTGRpb/OUl4BPKmqecD5wNMiEoPzV4IfGAcUAbeLyISeD1bVpaparKrFOTnh7VL5vKqOupYOm2/HGBORQgn9KiA/6H5eYFuwG4DnAVR1FZAIuIArgTdUtUNV9wIrgeK+Fj2QSgJLI861k7jGmAgUSuivBSaLSJGIJOCcqH25xzE7gLMBRGQaTuh7AtvPCmxPAeYAm/un9IGxoszD9LHpuFJHhLsUY4zpd72Gvqr6gCXAcmATzlU6G0TkfhG5MHDY7cBiEfkUeBa4TlUV56qfVBHZgPPL4wlVXT8Qb6Q/NLf7WLe91kbhGmMiVlwoB6nqazgnaIO3/STo9kZg7kEe14hz2eaw8OHWGjr8av35xpiIZcNNg5SUeUmIi+GUwqxwl2KMMQPCQj9ISZmXWYVZJMbHhrsUY4wZEBb6AXvrW9myp8G6dowxEc1CP6DrUk2bb8cYE8ks9ANKyrxkpyQwfWx67wcbY8wwZaEPqColbi+nTXIRE2NTLxhjIpeFPvDFnkb2NrQx37p2jDERzkIfZxQuYCdxjTERz0If5yTuhJwUxmUkhbsUY4wZUFEf+m0+Px+W11jXjjEmKkR96H+0fR8tHX5bJcsYExWiPvRL3B5iY4Q5E2zqBWNM5LPQL/MyMz+DtMT4cJdijDEDLqpDf19zO+ur6uyqHWNM1Ijq0P/gy2pUsfnzjTFRI6pDf0WZl7QRcZyQlxHuUowxZlBEdeiXuD3MmZhNXGxUfwzGmCgSUtqJyEIR2SIibhG58yD7C0TkXRH5WETWi8j5QfuOF5FVIrJBRD4TkcT+fANHa3t1ExU1Lda1Y4yJKr0ulygisThr3Z4DVAJrReTlwBKJXe7BWTv3P0VkOs7SioUiEgc8A1yjqp+KSDbQ0e/v4iisKLOplI0x0SeUlv4swK2q5araDjwHLOpxjAJdcxKPBHYGbn8dWK+qnwKoarWq+vtedt+VlHnJzUiiyJUS7lKMMWbQhBL6uUBF0P3KwLZg9wFXi0glTiv/+4HtUwAVkeUi8pGI/KCP9fYLf6fywZde5k1yIWJTKRtjokd/ncG8AnhSVfOA84GnRSQGp/toHnBV4PtFInJ2zweLyI0iUioipR6Pp59KOrT1lfuob/XZ9fnGmKgTSuhXAflB9/MC24LdADwPoKqrgETAhfNXwfuq6lXVZpy/Ak7q+QKqulRVi1W1OCdn4OfAKSnzIgJzrT/fGBNlQgn9tcBkESkSkQTgcuDlHsfsAM4GEJFpOKHvAZYDx4lIcuCk7hnARsJsRZmXGePSyUpJCHcpxhgzqHoNfVX1AUtwAnwTzlU6G0TkfhG5MHDY7cBiEfkUeBa4Th21wC9xfnF8Anykqn8diDcSqsY2Hx/tqGXeJJtV0xgTfXq9ZBNAVV/D6ZoJ3vaToNsbgbmHeOwzOJdtDgkfllfj61S7Pt8YE5WibijqijIvI+JiOHl8ZrhLMcaYQRd1oV/i9jKrKIvE+Nhwl2KMMYMuqkJ/V10L7r2N1rVjjIlaURX6Jd1TL9hJXGNMdIqu0Hd7caUmcMyYtHCXYowxYRE1od/Zqax0e5k7yUVMjE29YIyJTlET+pt3N+BtbLdZNY0xUS1qQr/E7czpM3+y9ecbY6JX1IT+ijIvk0alMmbkkFjDxRhjwiIqQr+1w8+arTXWtWOMiXpREfrrttfS5uu06/ONMVEvKkJ/RZmXuBhh9oTscJdijDFhFRWhX+L2cFJBJqkjQppfzhhjIlbEh35NUzsbdtbbKlnGGEMUhP5KtxdVLPSNMYYoCP2SMi9piXEcnzsy3KUYY0zYRXToqyolbi+nTcwmLjai36oxxoQkopNwq7eJqn0tzLNRuMYYA4QY+iKyUES2iIhbRO48yP4CEXlXRD4WkfUicv5B9jeKyB39VXgoStzOVMqnW3++McYAIYS+iMQCjwHnAdOBK0Rkeo/D7sFZMH0mcDnwmx77fwm83vdyj8yKMi/5WUmMz04Z7Jc2xpghKZSW/izArarlqtoOPAcs6nGMAumB2yOBnV07RORbwFZgQ9/LDZ3P38nqL6ttwRRjjAkSSujnAhVB9ysD24LdB1wtIpXAa8D3AUQkFfgh8NPDvYCI3CgipSJS6vF4Qiz98D6t3EdDm8+mXjDGmCD9dSL3CuBJVc0DzgeeFpEYnF8G/66qjYd7sKouVdViVS3OyemflvmKMi8icNpEm3rBGGO6hDIvQRWQH3Q/L7At2A3AQgBVXSUiiYALmA38nYj8HMgAOkWkVVX/o8+V96KkzMvxuSPJSE4Y6JcyxphhI5SW/lpgsogUiUgCzonal3scswM4G0BEpgGJgEdV56tqoaoWAr8CfjYYgd/Q2sHHFftsFK4xxvTQa+irqg9YAiwHNuFcpbNBRO4XkQsDh90OLBaRT4FngetUVQeq6N6sLq/B36l2EtcYY3oIadpJVX0N5wRt8LafBN3eCMzt5TnuO4r6jkpJmYek+FhOGp8xWC9pjDHDQkSOyF3h9jJ7QhYj4mLDXYoxxgwpERf6O/e1UO5psqURjTHmICIu9EvKnKkX5tt8O8YY8xURF/or3F5GpY1gyujUcJdijDFDTkSFfmenstLtZd4kFyIS7nKMMWbIiajQ37irnpqmdrs+3xhjDiGiQr9rKmU7iWuMMQcXWaFf5mXq6DRGpSeGuxRjjBmSIib0Wzv8rNlWY107xhhzGBET+vWtHSycMYazp40KdynGGDNkhTQNw3AwKi2RR6+YGe4yjDFmSIuYlr4xxpjeWegbY0wUsdA3xpgoYqFvjDFRxELfGGOiiIW+McZEEQt9Y4yJIhb6xhgTRSSM65cflIh4gO19eAoX4O2ncoY7+ywOZJ/Hgezz2C8SPovxqtrr6lFDLvT7SkRKVbU43HUMBfZZHMg+jwPZ57FfNH0W1r1jjDFRxELfGGOiSCSG/tJwFzCE2GdxIPs8DmSfx35R81lEXJ++McaYQ4vElr4xxphDiJjQF5GFIrJFRNwicme46wknEckXkXdFZKOIbBCRW8NdU7iJSKyIfCwir4a7lnATkQwRWSYim0Vkk4icGu6awklE/inwc/K5iDwrIhG93mpEhL6IxAKPAecB04ErRGR6eKsKKx9wu6pOB+YAt0T55wFwK7Ap3EUMEb8G3lDVY4ATiOLPRURygX8EilX1WCAWuDy8VQ2siAh9YBbgVtVyVW0HngMWhbmmsFHVXar6UeB2A84PdW54qwofEckDvgH8Pty1hJuIjAROB/4AoKrtqrovvFWFXRyQJCJxQDKwM8z1DKhICf1coCLofiVRHHLBRKQQmAl8GN5KwupXwA+AznAXMgQUAR7giUB31+9FJCXcRYWLqlYBjwA7gF1Anaq+Gd6qBlakhL45CBFJBf4C3Kaq9eGuJxxE5JvAXlVdF+5ahog44CTgP1V1JtAERO05MBHJxOkVKALGASkicnV4qxpYkRL6VUB+0P28wLaoJSLxOIH/J1V9Idz1hNFc4EIR2YbT7XeWiDwT3pLCqhKoVNWuv/yW4fwSiFZfA7aqqkdVO4AXgNPCXNOAipTQXwtMFpEiEUnAORHzcphrChsREZw+202q+stw1xNOqnqXquapaiHO/4v/U9WIbskdjqruBipEZGpg09nAxjCWFG47gDkikhz4uTmbCD+xHRfuAvqDqvpEZAmwHOfs++OquiHMZYXTXOAa4DMR+SSw7W5VfS2MNZmh4/vAnwINpHLg+jDXEzaq+qGILAM+wrnq7WMifHSujcg1xpgoEindO8YYY0JgoW+MMVHEQt8YY6KIhb4xxkQRC31jjIkiFvrGGBNFLPSNMSaKWOgbY0wU+f9c9bszJTU4wgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'], label='accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='val accuracy')\n",
    "plt.legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}