2 German-Traffic-Sign-Recognition-Benchmark.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "In c:\\python36\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The text.latex.preview rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In c:\\python36\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The mathtext.fallback_to_cm rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In c:\\python36\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: Support for setting the 'mathtext.fallback_to_cm' rcParam is deprecated since 3.3 and will be removed two minor releases later; use 'mathtext.fallback : 'cm' instead.\n",
      "In c:\\python36\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The validate_bool_maybe_none function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In c:\\python36\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The savefig.jpeg_quality rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In c:\\python36\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The keymap.all_axes rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In c:\\python36\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The animation.avconv_path rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In c:\\python36\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The animation.avconv_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "c:\\python36\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "c:\\python36\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "c:\\python36\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "c:\\python36\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "c:\\python36\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "c:\\python36\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "c:\\python36\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "c:\\python36\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "c:\\python36\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "c:\\python36\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "c:\\python36\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "c:\\python36\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import cv2\n",
    "from PIL import Image\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras.utils import to_categorical\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPool2D, Dense, Flatten, Dropout\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from sklearn.metrics import accuracy_score\n",
    "np.random.seed(42)\n",
    "\n",
    "from matplotlib import style\n",
    "style.use('fivethirtyeight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []    #input data list\n",
    "labels = []  # label data list\n",
    "classes = 43 # ouput classes \n",
    "width = 30  # image size\n",
    "height = 30 # image size\n",
    "channels = 3 # represet RGB\n",
    "\n",
    "# current path of working directory \n",
    "cur_path = os.getcwd()\n",
    "\n",
    "#print(\"Current path \",cur_path)\n",
    "\n",
    "# Get the list of all files and directories \n",
    "# in current working directory\n",
    "\n",
    "for i in range(classes):\n",
    "    \n",
    "    path = os.path.join(cur_path,'gstrb-traffic-sign/Train',str(i))\n",
    "    images = os.listdir(path)\n",
    "    #print(images)\n",
    "\n",
    "    for a in images:\n",
    "        try:\n",
    "            image = Image.open(path +'\\\\' + a)\n",
    "            resized_image = image.resize((height, width))\n",
    "            image_array = np.array(resized_image)\n",
    "            data.append(image_array)\n",
    "            labels.append(i)\n",
    "        except:\n",
    "            print('Error Loading images')\n",
    "data = np.array(data)\n",
    "labels = np.array(labels)\n",
    "#print('data is', data)\n",
    "#print('labels are',labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(39209, 30, 30, 3) (39209,)\n",
      "(29406, 30, 30, 3) (29406,) (9803, 30, 30, 3) (9803,)\n"
     ]
    }
   ],
   "source": [
    "print(data.shape, labels.shape)\n",
    "X_train, X_test, y_train, y_test = train_test_split(data, labels, random_state = 42)\n",
    "\n",
    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)\n",
    "\n",
    "# Converting labels to one hot encoding\n",
    "y_train = to_categorical(y_train)\n",
    "y_test = to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\python36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Create CNN-Model\n",
    "model = Sequential()\n",
    "model.add(Conv2D(filters =32, kernel_size =(5,5), activation ='relu', input_shape =X_train.shape[1:]))\n",
    "model.add(Conv2D(filters =32, kernel_size =(5,5), activation ='relu'))\n",
    "model.add(MaxPool2D(pool_size =(2,2)))\n",
    "model.add(Dropout(rate =0.25))\n",
    "model.add(Conv2D(filters =64, kernel_size =(3,3), activation ='relu'))\n",
    "model.add(Conv2D(filters =64, kernel_size =(3,3), activation ='relu'))\n",
    "model.add(MaxPool2D(pool_size =(2,2)))\n",
    "model.add(Dropout(rate =0.25))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(256, activation = 'relu'))\n",
    "model.add(Dropout(rate =0.5))\n",
    "model.add(Dense(43, activation = 'softmax'))\n",
    "\n",
    "#Compilation of the model\n",
    "model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\python36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "Train on 29406 samples, validate on 9803 samples\n",
      "Epoch 1/50\n",
      "29406/29406 [==============================] - 156s 5ms/step - loss: 2.4465 - accuracy: 0.4423 - val_loss: 0.5623 - val_accuracy: 0.8518\n",
      "Epoch 2/50\n",
      "29406/29406 [==============================] - 154s 5ms/step - loss: 0.6408 - accuracy: 0.8203 - val_loss: 0.2017 - val_accuracy: 0.9532\n",
      "Epoch 3/50\n",
      "29406/29406 [==============================] - 152s 5ms/step - loss: 0.3559 - accuracy: 0.9005 - val_loss: 0.1081 - val_accuracy: 0.9734\n",
      "Epoch 4/50\n",
      "29406/29406 [==============================] - 149s 5ms/step - loss: 0.2629 - accuracy: 0.9296 - val_loss: 0.0786 - val_accuracy: 0.9798\n",
      "Epoch 5/50\n",
      "29406/29406 [==============================] - 149s 5ms/step - loss: 0.2240 - accuracy: 0.9382 - val_loss: 0.0671 - val_accuracy: 0.9830\n",
      "Epoch 6/50\n",
      "29406/29406 [==============================] - 149s 5ms/step - loss: 0.1970 - accuracy: 0.9447 - val_loss: 0.0637 - val_accuracy: 0.9846\n",
      "Epoch 7/50\n",
      "29406/29406 [==============================] - 149s 5ms/step - loss: 0.1853 - accuracy: 0.9486 - val_loss: 0.0627 - val_accuracy: 0.9851\n",
      "Epoch 8/50\n",
      "29406/29406 [==============================] - 149s 5ms/step - loss: 0.1871 - accuracy: 0.9484 - val_loss: 0.0523 - val_accuracy: 0.9874\n",
      "Epoch 9/50\n",
      "29406/29406 [==============================] - 149s 5ms/step - loss: 0.1507 - accuracy: 0.9598 - val_loss: 0.0694 - val_accuracy: 0.9814\n",
      "Epoch 10/50\n",
      "29406/29406 [==============================] - 150s 5ms/step - loss: 0.1574 - accuracy: 0.9561 - val_loss: 0.0606 - val_accuracy: 0.9838\n",
      "Epoch 11/50\n",
      "29406/29406 [==============================] - 149s 5ms/step - loss: 0.1439 - accuracy: 0.9619 - val_loss: 0.0591 - val_accuracy: 0.9837\n",
      "Epoch 12/50\n",
      "29406/29406 [==============================] - 150s 5ms/step - loss: 0.1416 - accuracy: 0.9618 - val_loss: 0.0561 - val_accuracy: 0.9853\n",
      "Epoch 13/50\n",
      "29406/29406 [==============================] - 149s 5ms/step - loss: 0.1388 - accuracy: 0.9626 - val_loss: 0.0442 - val_accuracy: 0.9877\n",
      "Epoch 14/50\n",
      "29406/29406 [==============================] - 150s 5ms/step - loss: 0.1315 - accuracy: 0.9664 - val_loss: 0.1109 - val_accuracy: 0.9685\n",
      "Epoch 15/50\n",
      "29406/29406 [==============================] - 151s 5ms/step - loss: 0.1332 - accuracy: 0.9643 - val_loss: 0.0364 - val_accuracy: 0.9903\n",
      "Epoch 16/50\n",
      "29406/29406 [==============================] - 152s 5ms/step - loss: 0.1439 - accuracy: 0.9621 - val_loss: 0.0491 - val_accuracy: 0.9865\n",
      "Epoch 17/50\n",
      "29406/29406 [==============================] - 150s 5ms/step - loss: 0.1433 - accuracy: 0.9644 - val_loss: 0.0310 - val_accuracy: 0.9923\n",
      "Epoch 18/50\n",
      "29406/29406 [==============================] - 150s 5ms/step - loss: 0.1212 - accuracy: 0.9679 - val_loss: 0.0318 - val_accuracy: 0.9921\n",
      "Epoch 19/50\n",
      "29406/29406 [==============================] - 149s 5ms/step - loss: 0.1371 - accuracy: 0.9652 - val_loss: 0.0391 - val_accuracy: 0.9897\n",
      "Epoch 20/50\n",
      "29406/29406 [==============================] - 150s 5ms/step - loss: 0.1439 - accuracy: 0.9658 - val_loss: 0.0377 - val_accuracy: 0.9910\n",
      "Epoch 21/50\n",
      "29406/29406 [==============================] - 152s 5ms/step - loss: 0.1262 - accuracy: 0.9693 - val_loss: 0.0326 - val_accuracy: 0.9921\n",
      "Epoch 22/50\n",
      "29406/29406 [==============================] - 149s 5ms/step - loss: 0.1618 - accuracy: 0.9611 - val_loss: 0.0322 - val_accuracy: 0.9909\n",
      "Epoch 23/50\n",
      "29406/29406 [==============================] - 149s 5ms/step - loss: 0.1232 - accuracy: 0.9707 - val_loss: 0.0447 - val_accuracy: 0.9891\n",
      "Epoch 24/50\n",
      "29406/29406 [==============================] - 149s 5ms/step - loss: 0.1376 - accuracy: 0.9677 - val_loss: 0.0337 - val_accuracy: 0.9919\n",
      "Epoch 25/50\n",
      "29406/29406 [==============================] - 149s 5ms/step - loss: 0.1209 - accuracy: 0.9692 - val_loss: 0.0495 - val_accuracy: 0.9869\n",
      "Epoch 26/50\n",
      "29406/29406 [==============================] - 149s 5ms/step - loss: 0.1388 - accuracy: 0.9665 - val_loss: 0.0345 - val_accuracy: 0.9914\n",
      "Epoch 27/50\n",
      "29406/29406 [==============================] - 149s 5ms/step - loss: 0.1116 - accuracy: 0.9735 - val_loss: 0.0256 - val_accuracy: 0.9922\n",
      "Epoch 28/50\n",
      "29406/29406 [==============================] - 149s 5ms/step - loss: 0.1328 - accuracy: 0.9684 - val_loss: 0.0358 - val_accuracy: 0.9915\n",
      "Epoch 29/50\n",
      "29406/29406 [==============================] - 150s 5ms/step - loss: 0.1215 - accuracy: 0.9719 - val_loss: 0.0297 - val_accuracy: 0.9925\n",
      "Epoch 30/50\n",
      "29406/29406 [==============================] - 149s 5ms/step - loss: 0.1314 - accuracy: 0.9700 - val_loss: 0.0237 - val_accuracy: 0.9943\n",
      "Epoch 31/50\n",
      "29406/29406 [==============================] - 153s 5ms/step - loss: 0.1584 - accuracy: 0.9649 - val_loss: 0.0359 - val_accuracy: 0.9916\n",
      "Epoch 32/50\n",
      "29406/29406 [==============================] - 151s 5ms/step - loss: 0.1221 - accuracy: 0.9724 - val_loss: 0.0375 - val_accuracy: 0.9901\n",
      "Epoch 33/50\n",
      "29406/29406 [==============================] - 151s 5ms/step - loss: 0.1126 - accuracy: 0.9735 - val_loss: 0.0395 - val_accuracy: 0.9901\n",
      "Epoch 34/50\n",
      "29406/29406 [==============================] - 149s 5ms/step - loss: 0.1405 - accuracy: 0.9693 - val_loss: 0.0451 - val_accuracy: 0.9884\n",
      "Epoch 35/50\n",
      "29406/29406 [==============================] - 149s 5ms/step - loss: 0.1417 - accuracy: 0.9701 - val_loss: 0.0352 - val_accuracy: 0.9923\n",
      "Epoch 36/50\n",
      "29406/29406 [==============================] - 148s 5ms/step - loss: 0.1348 - accuracy: 0.9697 - val_loss: 0.0316 - val_accuracy: 0.9933\n",
      "Epoch 37/50\n",
      "29406/29406 [==============================] - 149s 5ms/step - loss: 0.1104 - accuracy: 0.9744 - val_loss: 0.0250 - val_accuracy: 0.9929\n",
      "Epoch 38/50\n",
      "29406/29406 [==============================] - 148s 5ms/step - loss: 0.1347 - accuracy: 0.9709 - val_loss: 0.0252 - val_accuracy: 0.9937\n",
      "Epoch 39/50\n",
      "29406/29406 [==============================] - 148s 5ms/step - loss: 0.1135 - accuracy: 0.9728 - val_loss: 0.0354 - val_accuracy: 0.9908\n",
      "Epoch 40/50\n",
      "29406/29406 [==============================] - 148s 5ms/step - loss: 0.1263 - accuracy: 0.9733 - val_loss: 0.0309 - val_accuracy: 0.9931\n",
      "Epoch 41/50\n",
      "29406/29406 [==============================] - 150s 5ms/step - loss: 0.1403 - accuracy: 0.9709 - val_loss: 0.0373 - val_accuracy: 0.9921\n",
      "Epoch 42/50\n",
      "29406/29406 [==============================] - 149s 5ms/step - loss: 0.1234 - accuracy: 0.9726 - val_loss: 0.0436 - val_accuracy: 0.9913\n",
      "Epoch 43/50\n",
      "29406/29406 [==============================] - 149s 5ms/step - loss: 0.1114 - accuracy: 0.9751 - val_loss: 0.0356 - val_accuracy: 0.9925\n",
      "Epoch 44/50\n",
      "29406/29406 [==============================] - 150s 5ms/step - loss: 0.1228 - accuracy: 0.9752 - val_loss: 0.0467 - val_accuracy: 0.9895\n",
      "Epoch 45/50\n",
      "29406/29406 [==============================] - 153s 5ms/step - loss: 0.1626 - accuracy: 0.9650 - val_loss: 0.0407 - val_accuracy: 0.9912\n",
      "Epoch 46/50\n",
      "29406/29406 [==============================] - 152s 5ms/step - loss: 0.1371 - accuracy: 0.9710 - val_loss: 0.0356 - val_accuracy: 0.9929\n",
      "Epoch 47/50\n",
      "29406/29406 [==============================] - 151s 5ms/step - loss: 0.1421 - accuracy: 0.9711 - val_loss: 0.0328 - val_accuracy: 0.9937\n",
      "Epoch 48/50\n",
      "29406/29406 [==============================] - 149s 5ms/step - loss: 0.1949 - accuracy: 0.9648 - val_loss: 0.0381 - val_accuracy: 0.9911\n",
      "Epoch 49/50\n",
      "29406/29406 [==============================] - 149s 5ms/step - loss: 0.1249 - accuracy: 0.9743 - val_loss: 0.0317 - val_accuracy: 0.9919\n",
      "Epoch 50/50\n",
      "29406/29406 [==============================] - 149s 5ms/step - loss: 0.1307 - accuracy: 0.9744 - val_loss: 0.0302 - val_accuracy: 0.9939\n"
     ]
    }
   ],
   "source": [
    "epochs = 50\n",
    "history = model.fit(X_train, y_train, batch_size=64, epochs = epochs, validation_data = (X_test, y_test), verbose = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e7bd34c978>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Accuracy Figure\n",
    "plt.figure(0)\n",
    "plt.plot(history.history['accuracy'], label = 'training_accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label ='val_accuracy')\n",
    "plt.title('Accuracy')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('accuracy')\n",
    "plt.legend()\n",
    "\n",
    "# Loss Accuracy\n",
    "plt.figure(1)\n",
    "plt.plot(history.history['loss'], label = 'training_loss')\n",
    "plt.plot(history.history['val_loss'], label = 'val_loss')\n",
    "plt.title('loss')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}