Functional Number recognizer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DONE\n"
     ]
    }
   ],
   "source": [
    "#imports\n",
    "import tensorflow as ts\n",
    "import keras as ks\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import time\n",
    "import random\n",
    "\n",
    "from numpy.random import seed\n",
    "from random import randint\n",
    "from keras.datasets import mnist\n",
    "from keras.layers import Dense,Activation\n",
    "from keras.optimizers import Adam\n",
    "from keras.models import Model, Input\n",
    "from keras.utils import to_categorical,np_utils\n",
    "\n",
    "def done():\n",
    "    print(\"DONE\")\n",
    "def nl():\n",
    "    print(\"\")\n",
    "    \n",
    "done()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DONE\n"
     ]
    }
   ],
   "source": [
    "#magic numbers\n",
    "seed(42)\n",
    "nr_classes=10\n",
    "nr_neurons_in_layer=10\n",
    "my_optimizer=Adam()\n",
    "my_batch_size=128\n",
    "pixels_ox=28\n",
    "pixels_oy=28\n",
    "my_epochs=10\n",
    "\n",
    "#want_to_train=False\n",
    "\n",
    "done()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "path: f:\\stuff\\anaconda2\\envs\\tensorflow\\lib\\site-packages\\keras\\datasets\\mnist.py\n",
      "\n",
      "shape of training input: (60000, 28, 28)\n",
      "shape of training output: (60000,)\n",
      "shape of test input: (10000, 28, 28)\n",
      "shape of training output: (10000,)\n",
      "\n",
      "DONE\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADcFJREFUeJzt3X/sVfV9x/HXC0RQpBNqQUBatFpTYjpcv8FlmJbN6XCrQf6wlSYNTZrRLbrYrenm2BLdkmV2qbZmXUxQmbRRW5NWZa1dZaSZNrMIOH+gVKX0O4tQ0LFVqg758d4f30PzFb/3cy/317nwfj4Scu8973vOeXPDi3Pv/ZxzP44IAchnXN0NAKgH4QeSIvxAUoQfSIrwA0kRfiApwg8kRfiBpAg/kNRJ/dzZyZ4YkzS5n7sEUvk/va63Yr9beW5H4be9WNKtksZLuiMibio9f5Im6yJf0skuARRsiPUtP7ftt/22x0v6J0mXS5onaZntee1uD0B/dfKZf4GkbRGxPSLekvQNSUu60xaAXusk/LMl/WzU4x3VsrexvcL2JtubDmh/B7sD0E2dhH+sLxXecX1wRKyKiKGIGJqgiR3sDkA3dRL+HZLmjHp8lqSdnbUDoF86Cf9GSefZPtv2yZKulrS2O20B6LW2h/oi4qDtayV9XyNDfasj4tmudQagpzoa54+IhyQ91KVeAPQRp/cCSRF+ICnCDyRF+IGkCD+QFOEHkiL8QFKEH0iK8ANJEX4gKcIPJEX4gaQIP5AU4QeSIvxAUoQfSIrwA0kRfiApwg8kRfiBpAg/kFRfp+hGb3hi45mQxs86s7juiytmFesHph1sq6cj/uzihxvWbnn094rrnn/dU8V67Gf6t05w5AeSIvxAUoQfSIrwA0kRfiApwg8kRfiBpDoa57c9LGmfpEOSDkbEUDeawtuVxvEl6flbf71h7YUrbut2O2+zPw4U61/ZO79h7X3n7CmuO37q6cX6wZ/vLtZR1o2TfH47Il7twnYA9BFv+4GkOg1/SHrY9mbbK7rREID+6PRt/8KI2Gl7uqR1tn8cEY+MfkL1n8IKSZqkUzvcHYBu6ejIHxE7q9s9ku6XtGCM56yKiKGIGJqg8hdXAPqn7fDbnmx7ypH7ki6TtKVbjQHorU7e9s+QdL/tI9u5JyL+tStdAei5tsMfEdslNR5gRte89IUPF+svXPGPPdv3/a9PK9a/+uefKNZPeeDxhrWJGi6u29kvCaAZhvqApAg/kBThB5Ii/EBShB9IivADSfHT3ceBN+eUL5vtxAce/ONifd6XypfdnrK98VAeBhtHfiApwg8kRfiBpAg/kBThB5Ii/EBShB9IinH+5M69561i/eD24f40gr7jyA8kRfiBpAg/kBThB5Ii/EBShB9IivADSTHOfwIYJ9fdAo5DHPmBpAg/kBThB5Ii/EBShB9IivADSRF+IKmm4be92vYe21tGLZtme53tF6vbqb1tEyWHFW3/QV6tHPnvkrT4qGXXS1ofEedJWl89BnAcaRr+iHhE0t6jFi+RtKa6v0bSlV3uC0CPtfuZf0ZE7JKk6nZ691oC0A89P7ff9gpJKyRpkk7t9e4AtKjdI/9u2zMlqbptOJtjRKyKiKGIGJqgiW3uDkC3tRv+tZKWV/eXS3qwO+0A6JdWhvrulfSYpPNt77D9GUk3SbrU9ouSLq0eAziONP3MHxHLGpQu6XIvGEBvLL2oWH+5yb+Cia+Ob1g75/bh4roHX95Z3jg6whl+QFKEH0iK8ANJEX4gKcIPJEX4gaT46e7krrr94WL9E1N+Uqyf5vJZm6XLhu9bVr4k5Obnf7e87X97d7E+fePrDWt+7Kniuhlw5AeSIvxAUoQfSIrwA0kRfiApwg8kRfiBpBzRv59vfpenxUXmSuBjddLc9xbrl3736Ya1a04vj9N3qtn04HX+PPjj+xv39ref/HR55R81fk0H2YZYr9dib0tztnPkB5Ii/EBShB9IivADSRF+ICnCDyRF+IGkuJ7/OHBw+KVi/atPLWpY+5OPbu9o37sOvVGsX/L1L5Q3UBjm/+QV/15c9a/P2FKsd+Q4HcfvJo78QFKEH0iK8ANJEX4gKcIPJEX4gaQIP5BU03F+26slfUzSnoi4oFp2o6Q/lPRK9bSVEfFQr5pE2bk3H2hY+/sL5nW07XUrP1Ksz/3OY21ve8Nd5xfr//K98vkNf3DqL4r1/3jjA8fcUyatHPnvkrR4jOVfjoj51R+CDxxnmoY/Ih6RtLcPvQDoo04+819r+2nbq21P7VpHAPqi3fDfJun9kuZL2iXp5kZPtL3C9ibbmw5of5u7A9BtbYU/InZHxKGIOCzpdkkLCs9dFRFDETE0QeVJHQH0T1vhtz1z1MOlknp4+RWAXmhlqO9eSYsknWF7h6QbJC2yPV8jF2wOS/psD3sE0ANNwx8Ry8ZYfGcPekGbYvOzDWuPfmhSR9uepMc7Wr/k0LafFuvPvTm7WG82zn/HA5c1rM1V++cnnCg4ww9IivADSRF+ICnCDyRF+IGkCD+QFD/djdqMP/fsYv2CU77f0fanbq1vevDjAUd+ICnCDyRF+IGkCD+QFOEHkiL8QFKEH0iKcX7U5n8/PKNYv/zUfR1t/9fu/lFH65/oOPIDSRF+ICnCDyRF+IGkCD+QFOEHkiL8QFKM86M2s67dVqyPk/vUSU4c+YGkCD+QFOEHkiL8QFKEH0iK8ANJEX4gqabj/LbnSPqapDMlHZa0KiJutT1N0jclzZU0LOnjEfE/vWv1xPXzP/2tYj0+Wn5Z971yWsPaB6//SXHdQ/+9t1jv1LgpUxrWzpxUvl7/sMq/u/+xHy9psvcdTeq5tXLkPyjp8xHxQUm/Keka2/MkXS9pfUScJ2l99RjAcaJp+CNiV0Q8Ud3fJ2mrpNmSlkhaUz1tjaQre9UkgO47ps/8tudKulDSBkkzImKXNPIfhKTp3W4OQO+0HH7bp0n6lqTPRcRrx7DeCtubbG86oP3t9AigB1oKv+0JGgn+3RHx7Wrxbtszq/pMSXvGWjciVkXEUEQMTdDEbvQMoAuaht+2Jd0paWtE3DKqtFbS8ur+ckkPdr89AL3SyiW9CyV9StIztp+slq2UdJOk+2x/RtJLkq7qTYvHv2ZTUX/9uluK9Q+dPKlYPxCHGtZuGLqwuO737ry4WJ/x2C+KdW/9abG+/Y7Gf/e1s/65uG4zB754ZrE+gaG+oqbhj4gfSg0vrL6ku+0A6BfO8AOSIvxAUoQfSIrwA0kRfiApwg8kxU9398Eb559RrL9n3MFi/VAcbnvffzP9P8v1vyzXNzc5I3vjm+cU6390+qPlDRQs3rq0WD/5B08X6+ULgsGRH0iK8ANJEX4gKcIPJEX4gaQIP5AU4QeSckT/RkPf5WlxkbkK+GgnzTmrWH/ur2a1ve0v/s43i/Wlkzv76e5m02iXfn574ZNXF9d998ryaSiHn9parGe0Idbrtdjb0tzmHPmBpAg/kBThB5Ii/EBShB9IivADSRF+ICnG+YETCOP8AJoi/EBShB9IivADSRF+ICnCDyRF+IGkmobf9hzbP7C91faztq+rlt9o+2XbT1Z/fr/37QLollYm7Tgo6fMR8YTtKZI2215X1b4cEV/qXXsAeqVp+CNil6Rd1f19trdKmt3rxgD01jF95rc9V9KFkjZUi661/bTt1banNlhnhe1NtjcdUJO5nwD0Tcvht32apG9J+lxEvCbpNknvlzRfI+8Mbh5rvYhYFRFDETE0QRO70DKAbmgp/LYnaCT4d0fEtyUpInZHxKGIOCzpdkkLetcmgG5r5dt+S7pT0taIuGXU8pmjnrZU0pbutwegV1r5tn+hpE9Jesb2k9WylZKW2Z6vkZmQhyV9ticdAuiJVr7t/6E05o+zP9T9dgD0C2f4AUkRfiApwg8kRfiBpAg/kBThB5Ii/EBShB9IivADSRF+ICnCDyRF+IGkCD+QFOEHkurrFN22X5H0X6MWnSHp1b41cGwGtbdB7Uuit3Z1s7f3RcR7WnliX8P/jp3bmyJiqLYGCga1t0HtS6K3dtXVG2/7gaQIP5BU3eFfVfP+Swa1t0HtS6K3dtXSW62f+QHUp+4jP4Ca1BJ+24ttP297m+3r6+ihEdvDtp+pZh7eVHMvq23vsb1l1LJpttfZfrG6HXOatJp6G4iZmwszS9f62g3ajNd9f9tve7ykFyRdKmmHpI2SlkXEc31tpAHbw5KGIqL2MWHbH5H0S0lfi4gLqmX/IGlvRNxU/cc5NSL+YkB6u1HSL+ueubmaUGbm6JmlJV0p6dOq8bUr9PVx1fC61XHkXyBpW0Rsj4i3JH1D0pIa+hh4EfGIpL1HLV4iaU11f41G/vH0XYPeBkJE7IqIJ6r7+yQdmVm61teu0Fct6gj/bEk/G/V4hwZryu+Q9LDtzbZX1N3MGGZU06YfmT59es39HK3pzM39dNTM0gPz2rUz43W31RH+sWb/GaQhh4UR8RuSLpd0TfX2Fq1paebmfhljZumB0O6M191WR/h3SJoz6vFZknbW0MeYImJndbtH0v0avNmHdx+ZJLW63VNzP78ySDM3jzWztAbgtRukGa/rCP9GSefZPtv2yZKulrS2hj7ewfbk6osY2Z4s6TIN3uzDayUtr+4vl/Rgjb28zaDM3NxoZmnV/NoN2ozXtZzkUw1lfEXSeEmrI+Lv+t7EGGyfo5GjvTQyiek9dfZm+15JizRy1dduSTdIekDSfZLeK+klSVdFRN+/eGvQ2yKNvHX91czNRz5j97m3iyU9KukZSYerxSs18vm6tteu0Ncy1fC6cYYfkBRn+AFJEX4gKcIPJEX4gaQIP5AU4QeSIvxAUoQfSOr/AVWu+GciQH1NAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#loading the data set and veifying shape\n",
    "print(\"path:\",mnist.__file__)\n",
    "nl()\n",
    "(input_train,output_train),(input_test,output_test)=mnist.load_data()\n",
    "print(\"shape of training input:\",input_train.shape)\n",
    "print(\"shape of training output:\",output_train.shape)\n",
    "print(\"shape of test input:\",input_test.shape)\n",
    "print(\"shape of training output:\",output_test.shape)\n",
    "nl()\n",
    "\n",
    "pick_a_random_input=randint(0,len(input_train))\n",
    "plt.imshow(input_train[pick_a_random_input])\n",
    "\n",
    "\n",
    "done()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reshaped input_train: (60000, 784)\n",
      "reshaped input_test: (10000, 784)\n"
     ]
    }
   ],
   "source": [
    "#RESHAPING\n",
    "\n",
    "#difference between the two?\n",
    "\n",
    "#MODEL BASED \n",
    "reshaped=28*28\n",
    "input_train = np.reshape(input_train, (input_train.shape[0],reshaped))\n",
    "input_test = np.reshape(input_test, (input_test.shape[0],reshaped))\n",
    "\n",
    "#FUNCTIONAL\n",
    "#input_train = np.reshape(input_train, (-1,pixels_ox, pixels_oy,1))\n",
    "#input_test = np.reshape(input_test, (-1,pixels_ox, pixels_oy, 1))\n",
    "\n",
    "#input_train = np.reshape(input_train, (-1,pixels_ox, pixels_oy))\n",
    "#input_test = np.reshape(input_test, (-1,pixels_ox, pixels_oy))\n",
    "\n",
    "print(\"reshaped input_train:\",input_train.shape)\n",
    "print(\"reshaped input_test:\",input_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DONE\n"
     ]
    }
   ],
   "source": [
    "#ENSURING FLOAT\n",
    "\n",
    "input_train = input_train.astype('float32')\n",
    "input_test = input_test.astype('float32')\n",
    "\n",
    "done()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DONE\n"
     ]
    }
   ],
   "source": [
    "#NORMALIZING DATA\n",
    "input_train=input_train/255\n",
    "input_test=input_test/255\n",
    "done()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before onehot enconding: 2\n",
      "after onehot encoding: [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
      "DONE\n"
     ]
    }
   ],
   "source": [
    "#ONEHOT ENCODING\n",
    "choose_random_output=randint(0,len(output_train)-1)\n",
    "print(\"before onehot enconding:\",output_train[choose_random_output])\n",
    "output_train_encoded=to_categorical(output_train,nr_classes)\n",
    "output_test_encoded=to_categorical(output_test,nr_classes)\n",
    "print(\"after onehot encoding:\",output_train_encoded[choose_random_output])\n",
    "done()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DONE\n"
     ]
    }
   ],
   "source": [
    "#DEFINING MODEL\n",
    "def my_model(x):\n",
    "    x=Dense(nr_neurons_in_layer)(x)\n",
    "    x=Dense(nr_neurons_in_layer)(x)\n",
    "    x=Dense(nr_classes, activation='softmax')(x)\n",
    "    return x\n",
    "\n",
    "done()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_7 (InputLayer)         (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense_16 (Dense)             (None, 10)                7850      \n",
      "_________________________________________________________________\n",
      "dense_17 (Dense)             (None, 10)                110       \n",
      "_________________________________________________________________\n",
      "dense_18 (Dense)             (None, 10)                110       \n",
      "=================================================================\n",
      "Total params: 8,070\n",
      "Trainable params: 8,070\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#FUNCTIONAL API\n",
    "\n",
    "my_inputs=Input((reshaped,))\n",
    "my_outputs=my_model(my_inputs)\n",
    "\n",
    "model = Model(inputs=my_inputs,outputs=my_outputs)\n",
    "\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DONE\n"
     ]
    }
   ],
   "source": [
    "#COMPILATION\n",
    "model.compile(loss=\"categorical_crossentropy\", optimizer=my_optimizer, metrics=['accuracy'])\n",
    "\n",
    "done()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DONE\n"
     ]
    }
   ],
   "source": [
    "#callbacks?\n",
    "\n",
    "class PlotLosses(ks.callbacks.Callback):\n",
    "    def on_train_begin(self, logs={}):\n",
    "        self.i = 0\n",
    "        self.x = []\n",
    "        self.logs = []\n",
    "        self.losses = []\n",
    "        self.test_losses = []\n",
    "        \n",
    "        self.plot = plt.figure()\n",
    "        \n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        self.x.append(self.i)\n",
    "        self.logs.append(logs)\n",
    "        self.losses.append(logs.get('loss'))\n",
    "        self.test_losses.append(logs.get('val_loss'))\n",
    "        \n",
    "        plt.plot(self.x, self.losses, label='train')\n",
    "        plt.plot(self.x, self.test_losses, label='test')\n",
    "        plt.show()\n",
    "        \n",
    "        self.i += 1\n",
    "losses_train = PlotLosses()\n",
    "done()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 1s 12us/step - loss: 0.2520 - acc: 0.9303 - val_loss: 0.2740 - val_acc: 0.9228\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 1s 12us/step - loss: 0.2503 - acc: 0.9301 - val_loss: 0.2740 - val_acc: 0.9235\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 1s 12us/step - loss: 0.2497 - acc: 0.9301 - val_loss: 0.2710 - val_acc: 0.9257\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 1s 12us/step - loss: 0.2473 - acc: 0.9310 - val_loss: 0.2746 - val_acc: 0.9238\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 1s 12us/step - loss: 0.2469 - acc: 0.9321 - val_loss: 0.2684 - val_acc: 0.9255\n"
     ]
    },
    {
     "data": {
      "image/png": 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Bre6+3d3bgfuBK5IruPsad2+OD9cCNQAxOArc/bFYrympnoiI5JB0AmEGsCvpcUMs68/1wMPx79OAg2b2EzPbaGZ3xR5Hwh1xmOkLZlY8qJaLiMiwSicQLEWZp6xodi1QB9wViwqAC4BPAOcCc4GPxGW3AmfE8inAzf2s8wYzqzez+sbGxjSaKyIiQ5FOIDQAM5Me1wC7+1Yys2XAbcAKd29Leu7GONzUCTwILAFw9zc9aAO+QRiaeht3v9fd69y9rrq6Ot3tEhGRQUonENYB881sjpkVAVcDq5IrmNli4B5CGOzt89xKM0t8kl8CbI7PmR5/G7ASePFENkRERE5MwUAV3L3TzG4EHgHygfvc/SUzux2od/dVhCGicuBH4fOdne6+wt27zOwTwOr4wb8e+Fpc9XdjUBiwCfjj4d44ERFJn7mnPByQk+rq6ry+vj7bzRARGVXMbL271w1UT2cqi4gIoEAQEZFIgSAiIoACQUREIgWCiIgACgQREYkUCCIiAigQREQkUiCIiAigQBARkUiBICIigAJBREQiBYKIiAAKBBERiRQIIiICKBBERCRSIIiICKBAEBGRSIEgIiKAAkFERCIFgoiIAAoEERGJFAgiIgIoEEREJFIgiIgIoEAQEZFIgSAiIkCagWBmy83sFTPbama3pFh+k5ltNrPnzWy1mc1KWlZrZo+a2ZZYZ3Ysn2Nmz5rZr8zsB2ZWNFwbJSIigzdgIJhZPnA3cDmwALjGzBb0qbYRqHP3hcADwJ1Jy74N3OXuZwJLgb2x/HPAF9x9PnAAuP5ENkRERE5MOj2EpcBWd9/u7u3A/cAVyRXcfY27N8eHa4EagBgcBe7+WKzX5O7NZmbAJYTwAPgWsPKEt0ZERIYsnUCYAexKetwQy/pzPfBw/Ps04KCZ/cTMNprZXbHHMRU46O6dA63TzG4ws3ozq29sbEyjuSIiMhTpBIKlKPOUFc2uBeqAu2JRAXAB8AngXGAu8JHBrNPd73X3Onevq66uTqO5IiIyFOkEQgMwM+lxDbC7byUzWwbcBqxw97ak526Mw02dwIPAEmAfUGFmBcdbp4iIZE46gbAOmB9nBRUBVwOrkiuY2WLgHkIY7O3z3EozS3y1vwTY7O4OrAGujOXXAT8b+maIiMiJGjAQ4jf7G4FHgC3AD939JTO73cxWxGp3AeXAj8xsk5mtis/tIgwXrTazFwhDRV+Lz7kZuMnMthKOKXx9GLdLREQGycKX9dGhrq7O6+vrs90MEZFRxczWu3vdQPV0prKIiAAKBBERiRQIIiICKBBERCRSIIiICKBAEBGRSIEgIiKAAkFERCIFgoiIAAoEERGJFAgiIgIoEEREJFIgiIgIoEAQEZFIgSAiIoACQUREIgWCiIgACgQREYkUCCIiAigQREQkUiCIiAigQBARkUiBICIigAJBREQiBYKIiAAKBBERidIKBDNbbmavmNlWM7slxfKbzGyzmT1vZqvNbFbSsi4z2xR/ViWVf9PMXktatmh4NklERIaiYKAKZpYP3A1cBjQA68xslbtvTqq2Eahz92Yz+xhwJ3BVXNbi7v192H/S3R8YevNFRGS4pNNDWApsdfft7t4O3A9ckVzB3de4e3N8uBaoGd5miojISEsnEGYAu5IeN8Sy/lwPPJz0uMTM6s1srZmt7FP3jjjM9AUzK06vySIiMhLSCQRLUeYpK5pdC9QBdyUV17p7HfBh4ItmNi+W3wqcAZwLTAFu7medN8RAqW9sbEyjuSIiMhTpBEIDMDPpcQ2wu28lM1sG3AascPe2RLm7746/twNPAovj4zc9aAO+QRiaeht3v9fd69y9rrq6Oq2NEhGRwUsnENYB881sjpkVAVcDq5IrmNli4B5CGOxNKq9MDAWZWRVwPrA5Pp4efxuwEnjxxDdHRESGasBZRu7eaWY3Ao8A+cB97v6Smd0O1Lv7KsIQUTnwo/D5zk53XwGcCdxjZt2E8Pls0uyk75pZNWFIahPwx8O8bSIiMgjmnvJwQE6qq6vz+vr6bDdDRGRUMbP18VjucelMZRERARQIIiISKRBERARQIIiISKRAEBERQIEgIiKRAkFERAAFgoiIRAoEEREBFAgiIhIpEEREBFAgiIhIpEAQERFAgSAiIpECQUREgDRukDMWvLrnCPl5Ru2UMgrzlYEiIqmMi0C449+38NSrjRTkGbVTy5hbVc686gnMrZ7A3Opy5lZNYMqEIuLd3kRExqVxEQh/tfx0fvPsU9je2MT2xqNs39fE06820t7V3VNncmlhCIiqcuZWT4iBUc6sqWUUF+RnsfUiIpkxLgLhrFMmc9Ypk48p6+p2Gg40s73xKNsam9i+7yjbG5v4xa8a+fGGhp56eQY1lWU9AZEIjXnVE6ieWKxehYiMGeMiEFLJzzNmTZ3ArKkTuPiMk45ZdqS1g9f2HQ29icYmtsW/n9m+n9aO3l5FeXFBDIhjw2JO1QRKi9SrEJHRZdwGwvFMLClkYU0FC2sqjinv7nbePNzaM/S0Lf7+n9fe4sFNu4+pO6Oi9JiwmBcD4+RJJeTlqVchIrlHgTAIeXnGjIpSZlSUcsH86mOWNbd3JvUqwnGK7Y1HeWB9A0fbu3rqlRbmM6eq94D2vESvonoC5cX65xCR7NEn0DApKypIeazC3dl7pK2nN5EIi+cbDvEfL7xJt/fWnTapuOegdmIIal5VOTMqS8lXr0JERpgCYYSZGdMmlTBtUgnvmVd1zLLWji52vtUcjlPEsNjW2MTPn9vN4dbOnnpFBXnMnlrWM+yUHBqTSwszvUkiMkYpELKopDCf06ZN5LRpE48pd3f2H23vOaidmAH1yq+P8NjmPXQmdSuqyouSAqI3LGqnlFGgk/BEZBAUCDnIzKgqL6aqvJilc6Ycs6yjqzv2Ko4ec17FY5v3sP9oe0+9vifh9fQuqsuZMqEo05skIqOAAmGUKczPY16ctQTTjll2sLk99iYSM6BCYPQ9Ca+irPBtU2XnVU+gVifhiYxraQWCmS0H/gnIB/7V3T/bZ/lNwEeBTqAR+AN33xGXdQEvxKo73X1FLJ8D3A9MATYAv+vu7ciQVZQVsaS2iCW1lceU93cS3tOvNvLA+mNPwps5pYy5VROoqSxjcmkhk0sLmVRaEH6XFDKpp6yQicUFmkIrMoaYux+/glk+8CpwGdAArAOucffNSXUuBp5192Yz+xhwkbtfFZc1uXt5ivX+EPiJu99vZl8FnnP3fzleW+rq6ry+vn5wWyjH1d9JeLsPtnCkteOYWVB9mcHE4oLekCh5e4BMLktdPqm0kJJC9UZEMsHM1rt73UD10ukhLAW2uvv2uOL7gSuAnkBw9zVJ9dcC1w7QOAMuAT4ci74FfBo4biDI8OvvJDwIJ+Idbe/kUEsHh1vi79aO+Dj+tHb2PD7U0sH2fU099Vs6ulK8Yq+igrwYEAU9vY5UwaLeiUhmpBMIM4BdSY8bgHcdp/71wMNJj0vMrJ4wnPRZd38QmAocdPfE3MqG+DqSQ/LyjIklhUwsKYTKgev31dbZxZE+gZEcIIeTAuZQSwf7m8LMqsOtYZl6JyKZlU4gpPoalvKtambXAnXAe5OKa919t5nNBZ4wsxeAw4NY5w3ADQC1tbVpNFdyRXFBPsXl+VSVFw/6ubnSO+l93Lt8Yol6JzI2pRMIDcDMpMc1wO6+lcxsGXAb8F53b0uUu/vu+Hu7mT0JLAZ+DFSYWUHsJaRcZ3zevcC9EI4hpNFeGQMy2Ts53NLJ/qZ2Xtt3tGf5QL2T8uLkHkcBJ08qYdHMCpbMquTM6ZN0IyYZldIJhHXA/Dgr6A3ganrH/gEws8XAPcByd9+bVF4JNLt7m5lVAecDd7q7m9ka4ErCTKPrgJ8NxwaJwIn1TtydprbOECDNx/ZMDvXTO3lm+/6eCxyWFOaxsKaCJbWVLKkNITGUdohk2oCzjADM7APAFwnTTu9z9zvM7Hag3t1XmdnjwDuBN+NTdrr7CjN7DyEougn3b/6iu389rnMuvdNONwLXJvcsUtEsI8lluw+2sGHnAdbvOMCGnQfZvPsQHV3h/VU7pYwltRWcM6uSxbWVnHHyRJ1JLhmT7iyjtAIhVygQZDRp7ejixTcOsWHnATbsOMj6nQdoPBK+85QV5bOwZjJLait7QkJnkMtIGc5ppyIyBCWF+dTNnkLd7HD5EXen4UDoRWzceZANOw9w79Pbe65NNadqAotrK3pC4rRpE3WVW8koBYJIhpgZM6eUMXNKGVcsCrOsW9q7eL7hIBtiQDz9aiM/2fAGEA5cnz1zcjwWUcni2goqytSLkJGjQBDJotKifN41dyrvmjsVCL2IXW8lH4s4wFee3EZX7EXMq54QAmJWCIn5J5VrCqwMGx1DEMlxze2dPLcrcSwihMSB5g4gnJy3KA4zLZlVyaKZFbpHhryNjiGIjBFlRQW8e95U3j2vtxfx+v7mnnBYv+MAX3riV3R7OEfi1Opyzok9iCWzKphbpV6EpEc9BJExoKmtk+d2HewJiQ07D3KoJfQiJpUUsDgehzhnViVnz5wcTviTcUM9BJFxpLy4gPNPreL8U8NtWru7ne37jsYZTWHa6xdXv4rHXsTp0ybGkAjnRsypmkC45qSMZ+ohiIwTh1s72BRnM23YeZCNOw9wJN67u7KssCcgltRWcvbMCiYU6/viWKEegogcY1JJIReeVs2Fp1UDoRexrbGpZzbThp0HeeLlcOWZPIMzTp7EklkVPdNeZ00tUy9ijFMPQUR6HGruYOOuEA4bdhxg066DNLWFXsTUCUWhFxFD4uyaCkqLdBnx0UA9BBEZtMllhVx0+klcdPpJQLj96q/2HmHDjoM9014f37IHgPw848zpEzkn6byImspS9SJGMfUQRGRQDhxtD72IGBKbdh2kuT3cf6KqvLjnQPWSWZW8c8Zk3YwoB6iHICIjonJCEZecMY1LzpgGQGdXN6/sORIOVMfjEY9uDr2IwnxjwfRJLI5TXpfMquSUySXqReQo9RBEZNjta2rruYDfhh0HeL7hUM9d7KZNKuasUyZTXlxASWEepYX5lBTlU1KQT2lRPiUFeeF3YfgpLUyU51NalEdxrFcal+sCgANTD0FEsqaqvJjLFkzjsgWhF9HR1c0rvz7SM6Pp1T1NtHZ00dLeRUtHF60dXbR1dg/ptYry8yiOwZIIjpKkYEkERyJcekIoBtHbypLrJT2/uCBvzPdsFAgiMuIK8/N4x4zJvGPGZK57z+yUdbq7nbbOblo6ekOipT38bu3o7i3rSJR10dLeTWtncr2u+PxuWju6eOtoe29Zezdt8e/O490j9TgSwZEcHsnBUdwndJLLQq8n79iyPgGUCKhs3YJVgSAiOSEvz8IHawamsnZ0dfcERVsMm0SotMQASg6flo4uWtu7aO3s7lOvN6wONne8rV5imGyw8vMsKXRCiPzrdXXMmjphmPfEsRQIIjLuFObnUZifN+LXdHIPvZ7k4Ghp76K1MxEcoedybK+ntzxRr6Wji9IMzNZSIIiIjBAz6xlaGg10l28REQEUCCIiEikQREQEUCCIiEikQBAREUCBICIikQJBREQABYKIiESj6mqnZtYI7Bji06uAfcPYnOGidg2O2jU4atfgjNV2zXL36oEqjapAOBFmVp/O5V8zTe0aHLVrcNSuwRnv7dKQkYiIAAoEERGJxlMg3JvtBvRD7RoctWtw1K7BGdftGjfHEERE5PjGUw9BRESOY8wFgpktN7NXzGyrmd2SYnmxmf0gLn/WzGbnSLs+YmaNZrYp/nw0A226z8z2mtmL/Sw3M/vn2ObnzWzJSLcpzXZdZGaHkvbV/81Qu2aa2Roz22JmL5nZn6eok/F9lma7Mr7PzKzEzP7HzJ6L7fpMijoZfz+m2a6Mvx+TXjvfzDaa2UMplo3s/nL3MfMD5APbgLlAEfAcsKBPnY8DX41/Xw38IEfa9RHgyxneXxcCS4AX+1n+AeBhwIDzgGdzpF0XAQ9l4f/XdGBJ/Hsi8GqKf8eM77M025XxfRb3QXn8uxB4FjivT51svB/TaVfG349Jr30T8L1U/14jvb/GWg9hKbDV3be7eztwP3BFnzpXAN+Kfz8AXGpmlgPtyjiso768AAAC3UlEQVR3fxp46zhVrgC+7cFaoMLMpudAu7LC3d909w3x7yPAFmBGn2oZ32dptivj4j5oig8L40/fg5YZfz+m2a6sMLMa4IPAv/ZTZUT311gLhBnArqTHDbz9jdFTx907gUPA1BxoF8Bvx2GGB8xs5gi3KR3ptjsb3h27/A+b2VmZfvHYVV9M+HaZLKv77Djtgizsszj8sQnYCzzm7v3urwy+H9NpF2Tn/fhF4K+A7n6Wj+j+GmuBkCop+yZ/OnWGWzqv+XNgtrsvBB6n91tANmVjX6VjA+FU/LOBLwEPZvLFzawc+DHwF+5+uO/iFE/JyD4boF1Z2Wfu3uXui4AaYKmZvaNPlazsrzTalfH3o5l9CNjr7uuPVy1F2bDtr7EWCA1AcpLXALv7q2NmBcBkRn54YsB2uft+d2+LD78GnDPCbUpHOvsz49z9cKLL7+7/ARSaWVUmXtvMCgkfut9195+kqJKVfTZQu7K5z+JrHgSeBJb3WZSN9+OA7crS+/F8YIWZvU4YVr7EzL7Tp86I7q+xFgjrgPlmNsfMiggHXVb1qbMKuC7+fSXwhMcjNNlsV59x5hWEceBsWwX8Xpw5cx5wyN3fzHajzOzkxLipmS0l/D/en4HXNeDrwBZ3/3w/1TK+z9JpVzb2mZlVm1lF/LsUWAa83Kdaxt+P6bQrG+9Hd7/V3WvcfTbhM+IJd7+2T7UR3V8Fw7WiXODunWZ2I/AIYWbPfe7+kpndDtS7+yrCG+ffzGwrIVmvzpF2/ZmZrQA6Y7s+MtLtMrPvE2afVJlZA/ApwgE23P2rwH8QZs1sBZqB3x/pNqXZriuBj5lZJ9ACXJ2BUIfwDe53gRfi+DPA/wFqk9qWjX2WTruysc+mA98ys3xCAP3Q3R/K9vsxzXZl/P3Yn0zuL52pLCIiwNgbMhIRkSFSIIiICKBAEBGRSIEgIiKAAkFERCIFgoiIAAoEERGJFAgiIgLA/wexcTE5LRxoLQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 1s 12us/step - loss: 0.2459 - acc: 0.9322 - val_loss: 0.2777 - val_acc: 0.9224\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 1s 12us/step - loss: 0.2450 - acc: 0.9320 - val_loss: 0.2719 - val_acc: 0.9245\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 1s 12us/step - loss: 0.2436 - acc: 0.9328 - val_loss: 0.2722 - val_acc: 0.9236\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 1s 12us/step - loss: 0.2425 - acc: 0.9326 - val_loss: 0.2751 - val_acc: 0.9246\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 1s 12us/step - loss: 0.2416 - acc: 0.9327 - val_loss: 0.2731 - val_acc: 0.9250\n"
     ]
    },
    {
     "data": {
      "image/png": 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z8dH295vZ0pivejM7K9o30cxeNLM3zewOMxuAHdjSrxz5Sfj473ovFJrq4N4vRvd3+O/eqaNIP9ZtIJhZKnADcDowHTjfzKZ3KrYEKHX3I4G7gesA3H2hux/l7kcBpwC1QNvNjX8M/MzdpwDbgEt64Xwk2R1xdu+FQltX0Zm/jv96DpEBLJ4WwixglbuvcfdG4HbgzNgC0Rt/bfTwBaCrBfzPBh5y91ozM0JA3B3t+xNw1r6cgMi79EYorH8xrKNTerG6iiRpxBMIY4HymMcV0bY9uQR4qIvt5wF/jf5dAGx39+Y4jynSM/sTCk11cN8Xd98KVCRJxBMIXS0t2OVcVTO7ECgFru+0fQxwBPDwPhzzUjMrM7OyqqqqOKorEtnXUFh4bbhq+8xfqatIkko8gVABFMc8Hgds7FzIzE4DrgbmuntDp93nAPe4e1P0eAuQb2Zt0167PCaAu89z91J3Ly0qKoqjuiIxOoTCJ7sPhfJ/hZsSHfM5mPS+vqihSL8RTyAsAqZEs4IyCF0/C2ILmNkM4CZCGGzu4hjns7u7CA9Xwy0kjCsAfBa4r+fVF4lDeyg8v/dQaJ9VNE5dRZKUug2EqJ//ckJ3zwrgTnd/zcyuMbO5UbHrgRzgrmh6aXtgmNkEQgvjyU6HvhK4wsxWEcYU/rCf5yKyZ/GEwsL/ga1vwtxfQmZe39dRJMG0dIUkl1fuhvmfh5Lj4FN37b6iuXwR3PxBOPoz8NFfJLaOIr0s3qUrdKWyJJeuWgpNdXDvZZB7ULhFqEiS0lpGknyOiIau5n8+hMKow0JX0YXz1VUkSU2BIMkpNhTWPRtWfj341MTWSSTBFAiSvI44G1LSwr0hPqiuIhEFgiS3w84KXyKiQWUREQkUCCIiAigQREQkokAQERFAgSAiIhEFgoiIAAoEERGJKBBERARQIIiISESBICIigAJBREQiCgQREQEUCCIiEokrEMxsjpmtNLNVZnZVF/uvMLPlZrbMzB43s/Ex+0rM7BEzWxGVmRBt/6OZrY3uwbzUzI7qrZMSEZGe6zYQzCwVuAE4HZgOnG9m0zsVWwKUuvuRwN3AdTH7bgWud/dpwCxgc8y+b7n7UdHX0v04DxER2U/xtBBmAavcfY27NwK3A2fGFnD3he5eGz18ARgHEAVHmrs/GpWriSknIiL9SDyBMBYoj3lcEW3bk0uAh6J/HwJsN7P5ZrbEzK6PWhxtro26mX5mZkN6VHMREelV8QSCdbHNuyxodiFQClwfbUoDTgS+CcwEJgEXRfu+A0yNto8ArtzDMS81szIzK6uqqoqjuiIisi/iCYQKoDjm8ThgY+dCZnYacDUw190bYp67JOpuagbuBY4GcPdKDxqAWwhdU+/i7vPcvdTdS4uKiuI9LxER6aF4AmERMMXMJppZBnAesCC2gJnNAG4ihMHmTs8dbmZt7+SnAMuj54yJvhtwFvDq/pyIiIjsn7TuCrh7s5ldDjwMpAI3u/trZnYNUObuCwhdRDnAXeH9nfXuPtfdW8zsm8Dj0Rv/YuB30aFvi4LCgKXAF3r75EREJH7m3uVwQL9UWlrqZWVlia6GiMiAYmaL3b20u3K6UllERAAFgoiIRBQIIiICKBBERCSiQBAREUCBICIiEQWCiIgACgQREYkoEEREBFAgiIhIRIEgIiKAAkFERCIKBBERARQIIiISUSCIiAigQBARkYgCQUREAAWCiIhEFAgiIgLEGQhmNsfMVprZKjO7qov9V5jZcjNbZmaPm9n4mH0lZvaIma2IykyItk80sxfN7E0zu8PMMnrrpEREpOe6DQQzSwVuAE4HpgPnm9n0TsWWAKXufiRwN3BdzL5bgevdfRowC9gcbf8x8DN3nwJsAy7ZnxMREZH9E08LYRawyt3XuHsjcDtwZmwBd1/o7rXRwxeAcQBRcKS5+6NRuRp3rzUzA04hhAfAn4Cz9vtsRERkn8UTCGOB8pjHFdG2PbkEeCj69yHAdjObb2ZLzOz6qMVRAGx39+bujmlml5pZmZmVVVVVxVFdERHZF/EEgnWxzbssaHYhUApcH21KA04EvgnMBCYBF/XkmO4+z91L3b20qKgojuqKiMi+iCcQKoDimMfjgI2dC5nZacDVwFx3b4h57pKou6kZuBc4GtgC5JtZ2t6OKSIifSeeQFgETIlmBWUA5wELYguY2QzgJkIYbO703OFm1vbR/hRgubs7sBA4O9r+WeC+fT8NERHZX90GQvTJ/nLgYWAFcKe7v2Zm15jZ3KjY9UAOcJeZLTWzBdFzWwjdRY+b2SuErqLfRc+5ErjCzFYRxhT+0IvnJSIiPWThw/rAUFpa6mVlZYmuhojIgGJmi929tLtyulJZREQABYKIiEQUCCIiAigQREQkokAQERFAgSAiIhEFgoiIAAoEERGJKBBERARQIIiISESBICIigAJBREQiCgQREQEUCCIiElEgiIgIoEAQEZGIAkFERAAFgoiIROIKBDObY2YrzWyVmV3Vxf4rzGy5mS0zs8fNbHzMvpboPsvt91qOtv/RzNbG7Duqd05JRET2RVp3BcwsFbgB+ABQASwyswXuvjym2BKg1N1rzewy4Drg3Ghfnbvv6c3+W+5+975XX0REeks8LYRZwCp3X+PujcDtwJmxBdx9obvXRg9fAMb1bjVFRORAiycQxgLlMY8rom17cgnwUMzjTDMrM7MXzOysTmWvjbqZfmZmQ+KrsoiIHAjxBIJ1sc27LGh2IVAKXB+zucTdS4ELgJ+b2eRo+3eAqcBMYARw5R6OeWkUKGVVVVVxVFdERPZFPIFQARTHPB4HbOxcyMxOA64G5rp7Q9t2d98YfV8D/BOYET2u9KABuIXQNfUu7j7P3UvdvbSoqCiukxIRkZ6LJxAWAVPMbKKZZQDnAQtiC5jZDOAmQhhsjtk+vK0ryMwKgdnA8ujxmOi7AWcBr+7/6YiIyL7qdpaRuzeb2eXAw0AqcLO7v2Zm1wBl7r6A0EWUA9wV3t9Z7+5zgWnATWbWSgifH8XMTrrNzIoIXVJLgS/08rmJiEgPmHuXwwH9UmlpqZeVlSW6GiIiA4qZLY7GcvdKVyqLiAigQBARkYgCQUREAAWCiIhEFAgiIgIoEEREJKJAEBERQIEgIiIRBYKIiAAKBBERiXS7ltFgcNOTq9m8s4ETDi5k1sQRZA9JitMWEemRpHhnXLtlF/OXbOAPz6wlLcWYUZLP8ZMLOWFKIUcV55OeqoaSiEjSLG5X39TC4nXbeGbVFp5btYVXNlTT6jA0I5X3ThzB7IMLOX5yIVNH55KS0tU9gUREBqZ4F7dLihYCQGZ6KrMPLmT2wYUAVNc28fyarTy3egvPrNrCwgdWAFCQncFxkws4ISpbPGJoIqstItJnkiYQOhs2NJ05h49mzuGjAaisruPZVVt5blUIiPuXVQJQMmIosw8uaG9BjMjOSGS1RUQOmKTpMuoJd2d1VQ3PvLmFZ1dv5YXVW9nZ0AzA9DF57QExa+IIhmYkbaaKyAARb5eRAiEOzS2tvLKhmmdXbeHZVVtZvG4bjS2tpKcaM0qGM3tyISdMKeDIcRqgFpH+R4FwANU1tlC27p1ogHorr26sxh2yM1J576SCaKyigENH5RLdUlREJGF6dVDZzOYAvyDcU/n37v6jTvuvAP4NaAaqgIvdfV20rwV4JSradq9lzGwicDswAngJ+LS7N8ZTn0TLykjlxClFnDilCIDttY08v3prCIjVW3ni9c0AFOYM4fhogPr4gwsYN1wD1CLSf3XbQjCzVOAN4ANABbAION/dl8eUeT/worvXmtllwPvc/dxoX42753Rx3DuB+e5+u5n9FnjZ3W/cW136SwuhOxu21/FsNL31mVVb2VLTAMCEgqFMPyiP0XlZjBmWyZj8zPB9WBYjc4eQpu4mETkAeq3LyMyOA/7L3T8UPf4OgLv/cA/lZwC/dvfZ0eN3BYKFfpQqYLS7N3f+GXsyUAIhlrvz5uYwQP3c6q2s3VJDZXU9tY0tHcqlGIzMzWT0sEwOys9kdF5W+B4FxphhmQoNEdknvdllNBYoj3lcAbx3L+UvAR6KeZxpZmWE7qQfufu9QAGw3d2bY445No66DDhmxiGjcjlkVC4XnzARCCGxo76Zyuo6Kqvrqdxez6bqOjZW17Opup7XN+1k4etV1DV1HRqxLYu2721BUpSj0BCRfRNPIHQ1Ktpls8LMLgRKgZNjNpe4+0YzmwQ8YWavADt6cMxLgUsBSkpK4qhu/2dmDMtKZ1hWOlNH53VZxt3ZUddM5Y46KrfXh+BoC5Dquj2GRmqKMTJ3SAiIKCjawyMKkpG5maTqamwR6SSeQKgAimMejwM2di5kZqcBVwMnu3tD23Z33xh9X2Nm/wRmAH8D8s0sLWoldHnM6HnzgHkQuoziqO+gYGYMG5rOsKHdh8bG6jo2Vdfv/r69nk076lhRuYPHX3+b+qbWDs9LSzFOnFLIOaXFnDptFBlpalGISHyBsAiYEs0K2gCcB1wQWyAaN7gJmOPum2O2Dwdq3b3BzAqB2cB17u5mthA4mzDT6LPAfb1xQskkNjSmjdlzaFTXNXVoYayt2sX9yyq57LaXKMjO4GMzxnLuzGKmjMrt4zMQkf4krusQzOwM4OeEaac3u/u1ZnYNUObuC8zsMeAIoDJ6ynp3n2tmxxOCopVw74Wfu/sfomNOYve00yXAhbEti64MxEHl/qql1XnqjSruWFTOYyveprnVmVGSzzmlxXz0PQeRoyXCRQYNXZgmcdta08A9SzZwx6Jy3txcQ1Z6Kh8+cgznziymdPxwXVwnMsApEKTH3J0l5du5q6ycBUs3squxhUmF2XyytJhPHDOWkbmZia6iiOwDBYLsl9rGZh5YVsmdZeUsemsbqSnG+w8dybkzi3nfoUVas0lkAFEgSK9ZXVXDXWUV/O2lCqp2NlCUO4SPHz2Wc0qLmVz0rovQRaSfUSBIr2tqaeWfK8NA9MKVm2lpdWZOGM45pcWcccQY3atapJ9SIMgBtXlHPfOXbODOReWs2bKL7IxUPvqegzhnZjEzivM1EC3SjygQpE+4O2XrtnHHonIeWFZJXVMLU0bmcE5pMR87eiyFOUMSXUWRpKdAkD5X09DM/S9v5I6ycpas305ainHatFGcO7OYkw4p0nIZIgmiQJCEeuPtndy5qJz5Szbwzq5GRudl8oljwkD0+ILsRFdPJKkoEKRfaGxu5YnX3+aOReU8+UYVrQ7HThrBuTOLmXPYGLIyUhNdRZFBT4Eg/U5ldR1/W1zBnWUVrH+nlqz0VEbmDWFYVjp5mWH117ysdPKy0jpsa9setqWRl5Wu6yBEekCBIP1Wa6vz4tp3eGT5Jt7Z1Uh1XRPVdU3sqGuiuq6ZHXVNNLa07vUY2RmpMSERExh7C5NoX1Z6qmZBSVLp1Xsqi/SmlBTjuMkFHDe5YI9l6ptaYkIiCoz6Jqpro9Cob+qwv2JbLSsqm6mua6KmoXmPxwVIT7X2wMjNSqcgO4MJBdlMHpnNpMIcJo/MpihniEJDko4CQfqlzPRUMtNTGZXX8/WTmlta2Vnf3DFI6prYUdfVtiY2RvfAbmje3SrJHZLGpKJsJhflMKkom0lFOUwuymF8wVAy0zXuIYOTAkEGnbTUFIZnZzA8OyPu57S2Ohur61hTtYvVVTWsqdrFmi01PL9mK/OXbGgvZwbjhmeFlkR7WGRzcFEORblqVcjApkAQIXRjjRs+lHHDh3LSIUUd9u1qaGbtlhAUq6t2sSYKjBfXbu1wN7qc2FZFYdSqGJnNhIJstSpkQFAgiHQje0gah48dxuFjh3XY3trqVO6oZ01VDas317Bmy64QFGu2ck+nVsXY/KxO3U8hOEaqVSH9iAJBZB+lpBhj87MYm5/FiVM6tipqG5ujbqddMWFRw7/WvkNdU0t7ubZWRVuLYmJh6IKaWJjN0Az9eUrf0m+cyAEwNGPPrYpNO+rbxyjawmLRW9u4d+nGDmXHDMtkYmF2FBJt3VDZjM3PIk3XYcgBoEAQ6UMpKcZB+VkclJ/FCVMKO+yra2zhra2h22ntlpr2FsbfX97IjvrdU2nTU42SEUPbQ2JiTOuiMCdDXVCyz+IKBDObA/wCSAWFLimhAAAKl0lEQVR+7+4/6rT/CuDfgGagCrjY3dfF7M8DVgD3uPvl0bZ/AmOAuqjYB919836djcgAlpWRyrQxeUwbk9dhu7vzzq5G1m7Z1T5O0RYYT66s6nARX25m2rtCQl1QEq9uf0PMLBW4AfgAUAEsMrMF7r48ptgSoNTda83sMuA64NyY/d8Hnuzi8J9yd116LLIXZkZBzhAKcoZQOmFEh30trc7G7XWsrqoJgVG1i7V76IIanZfZHg4TC8Og9sTCbMYNVxeUBPF8ZJgFrHL3NQBmdjtwJtAeCO6+MKb8C8CFbQ/M7BhgFPAPoNtLp0UkfqkpRvGIoRSPGMr7Du24r60Lam00oN3WuthTF9TEwjD76dDRucwoGc6EgqHqfkoy8QTCWKA85nEF8N69lL8EeAjAzFKAnwCfBk7touwtZtYC/A34gXexsJKZXQpcClBSUhJHdUUE9t4Fta22qT0k2gJj7ZZdPPXG7i6o4UPTOao4nxklw5lRks97ivPJy0xPxKlIH4knELr6iNDlinhmdiGhFXBytOmLwIPuXt7FJ41PufsGM8slBMKngVvf9YPc5wHzICxuF0d9RWQvzIwR2RmMyB7RZRfUm5t3smT9dpas38aS9dv55xtVuIfrKQ4uymFGSQiJo4rzOWRUrm58NIjEEwgVQHHM43HAxs6FzOw04GrgZHdviDYfB5xoZl8EcoAMM6tx96vcfQOAu+80s78QuqbeFQgi0ndSU4ypo/OYOjqP82eFFvmO+iaWlVeHgCjfzqPL3+bOsgogrDp75Lj8DiFRlKvbpg5U8QTCImCKmU0ENgDnARfEFjCzGcBNwJzYmULu/qmYMhcRBp6vMrM0IN/dt5hZOvAR4LH9PRkR6X15memcMKWwfZqsu7Nuay1LyrdFLYntzHtqDc2toQFfPCKLGcXD20Ni2phchqRp6Y6BoNtAcPdmM7sceJgw7fRmd3/NzK4Bytx9AXA9oQVwV9Q1tN7d5+7lsEOAh6MwSCWEwe/271REpC+YGRMKs5lQmM3HZowDwgD2qxtDK2Jp+XYWvfUOC14OHQkZqSkcNjYvJiTyGZufpQHrfkg3yBGRA6Kyuo6l67ezpDyMRyyrqG5fYrwodwgzYgasjxw3TNdJHEC6QY6IJNSYYVmMOSKL048YA0BTSyuvV+5s72paWr6dR5a/DUCKwdTReRxVkt8eFJMKs0nRgHWfUgtBRBLmnV2NvBy1IJaUb2fp+u3sjO54NzQjlUNH57ZPnZ0+JpdDR+eRM0SfY3tK91QWkQGntdVZXVXDkvXbWV65g+WVO1hRuYOdMRfSlYwYyrQxsUGRx7jhGpPYG3UZiciAk5JiTBmVy5RRue3b3J2N1fWs2BjCYcWmHayo3Mkjy9+m7fNszpA0psa0JqaNyeXQ0bkal+ghtRBEZECqbWxm5aadrKjcyYrKHby+aQevV+5s73IygwkF2aE1MToExdQxuUk5w0ktBBEZ1IZmpEWzlIa3b3N3KrbVtXc1vV65k9c27uDBVza1l8nLTGNq1NXU1vV0yKhc3eYUBYKIDCJmuxf7+9Bho9u31zQ0szLqaloRhcWdZeXUNoa716UYTCzM7jAuMW1MHqPykusWpwoEERn0coakccz4ERwzfvfaTa2tTvm2WlZU7mB5FBQvV2zn/mWV7WXyh6ZzcLRM+MSibCYVhvtil4wYOihbFBpDEBGJsaO+KRqbCC2KtpsRbd7Z0F7GDMbmZ3W6a134ftCwrH53/YTGEERE9kFeZjozJ4xgZqeVYGsamnlry672mxG13ZDo7nXbqGnYPS12SFoKEwqyO9yMqC04hmdn9PXp9IgCQUQkDjlD0jh87DAOHzusw3Z3p6qmof1udW33l1j59k4eXf52+6J/ELqgQosipz0wJhVlM6Egu190QSkQRET2g5kxMjeTkbmZHDupoMO+5pZWyrfVtXc7rdmyi7VVu3h21Rb+9lJFzDHgoGFZHVoVbbc5PSg/q8/uOaFAEBE5QNJSU9rf3E+Z2nHfrobm9hZF7F3r7nlpQ/u1FBBWix1fMJTffvoYJhflHNj6HtCji4hIl7L30gW1paYxCordLYsRQw/8+IMCQUSkHzEzinKHUJQ7hFkTR3T/hF6U0qc/TURE+i0FgoiIAAoEERGJxBUIZjbHzFaa2Sozu6qL/VeY2XIzW2Zmj5vZ+E7788xsg5n9OmbbMWb2SnTMX1oyLRgiItIPdRsIZpYK3ACcDkwHzjez6Z2KLQFK3f1I4G7guk77vw882WnbjcClwJToa06Pay8iIr0mnhbCLGCVu69x90bgduDM2ALuvtDda6OHLwDj2vaZ2THAKOCRmG1jgDx3f97DYkq3Amft15mIiMh+iScQxgLlMY8rom17cgnwEICZpQA/Ab7VxTErYh53d0wRETnA4rkOoau+/S6XSDWzC4FS4ORo0xeBB929vNMQQU+OeSmha4mSkpI4qisiIvsinkCoAIpjHo8DNnYuZGanAVcDJ7t72zqxxwEnmtkXgRwgw8xqgF8Q0620p2MCuPs8YF70M6rMbF0cde5KIbBlH587GOn12E2vRUd6PToaDK/H+O6LxBcIi4ApZjYR2ACcB1wQW8DMZgA3AXPcfXPbdnf/VEyZiwgDz1dFj3ea2bHAi8BngF91VxF3L4qjvl0ys7J41gNPFno9dtNr0ZFej46S6fXodgzB3ZuBy4GHgRXAne7+mpldY2Zzo2LXE1oAd5nZUjNbEMfPvgz4PbAKWE007iAiIokxoO6Ytj+SKeXjoddjN70WHen16CiZXo9kulJ5XqIr0M/o9dhNr0VHej06SprXI2laCCIisnfJ1EIQEZG9SIpA6G4tpmRhZsVmttDMVpjZa2b21UTXqT8ws1QzW2Jm9ye6LolmZvlmdreZvR79nhyX6Dolipl9Pfo7edXM/mpmmYmu04E26AMhzrWYkkUz8A13nwYcC3wpiV+LWF8lzKCTcI3QP9x9KvAekvR1MbOxwFcIU+UPB1IJU+4HtUEfCMSxFlOycPdKd38p+vdOwh97Ui8ZYmbjgA8TpkAnNTPLA04C/gDg7o3uvj2xtUqoNCDLzNKAoezh4tnBJBkCoadrMSUFM5sAzCBcGJjMfg58G2hNdEX6gUlAFXBL1IX2ezPLTnSlEsHdNwD/C6wHKoFqd39k788a+JIhEOJeNylZmFkO8Dfga+6+I9H1SRQz+wiw2d0XJ7ou/UQacDRwo7vPAHYBSTnmZmbDCT0JE4GDgOxorbZBLRkCIa61mJKFmaUTwuA2d5+f6Pok2Gxgrpm9RehKPMXM/pzYKiVUBVDh7m2txrsJAZGMTgPWunuVuzcB84HjE1ynAy4ZAqF9LSYzyyAMDMWztMagE92V7g/ACnf/aaLrk2ju/h13H+fuEwi/F0+4+6D/FLgn7r4JKDezQ6NNpwLLE1ilRFoPHGtmQ6O/m1NJggH2eBa3G9DcvdnM2tZiSgVudvfXElytRJkNfBp4xcyWRtu+6+4PJrBO0r98Gbgt+vC0BvhcguuTEO7+opndDbxEmJ23hCS4YllXKouICJAcXUYiIhIHBYKIiAAKBBERiSgQREQEUCCIiEhEgSAiIoACQUREIgoEEREB4P8Dha/7DYgfeJwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1c631710>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#FITTING \n",
    "\n",
    "model.fit(input_train, output_train_encoded,\n",
    "             epochs=my_epochs,\n",
    "             batch_size=my_batch_size,\n",
    "             validation_data=(input_test, output_test_encoded),\n",
    "             callbacks=[losses_train],\n",
    "             verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 0s 15us/step\n",
      "accuracy:  0.925\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(input_test, output_test_encoded)\n",
    "print(\"accuracy: \",score[1])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}