bailulu
/
mindspore_yolo

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2.0\n",
      "sys.version_info(major=3, minor=6, micro=9, releaselevel='final', serial=0)\n",
      "matplotlib 3.3.4\n",
      "numpy 1.19.5\n",
      "pandas 1.1.5\n",
      "sklearn 0.24.2\n",
      "tensorflow 2.2.0\n",
      "tensorflow.keras 2.3.0-tf\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow import keras\n",
    "\n",
    "print(tf.__version__)\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, tf, keras:\n",
    "    print(module.__name__, module.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _california_housing_dataset:\n",
      "\n",
      "California Housing dataset\n",
      "--------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 20640\n",
      "\n",
      "    :Number of Attributes: 8 numeric, predictive attributes and the target\n",
      "\n",
      "    :Attribute Information:\n",
      "        - MedInc        median income in block\n",
      "        - HouseAge      median house age in block\n",
      "        - AveRooms      average number of rooms\n",
      "        - AveBedrms     average number of bedrooms\n",
      "        - Population    block population\n",
      "        - AveOccup      average house occupancy\n",
      "        - Latitude      house block latitude\n",
      "        - Longitude     house block longitude\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "This dataset was obtained from the StatLib repository.\n",
      "http://lib.stat.cmu.edu/datasets/\n",
      "\n",
      "The target variable is the median house value for California districts.\n",
      "\n",
      "This dataset was derived from the 1990 U.S. census, using one row per census\n",
      "block group. A block group is the smallest geographical unit for which the U.S.\n",
      "Census Bureau publishes sample data (a block group typically has a population\n",
      "of 600 to 3,000 people).\n",
      "\n",
      "It can be downloaded/loaded using the\n",
      ":func:`sklearn.datasets.fetch_california_housing` function.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "    - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\n",
      "      Statistics and Probability Letters, 33 (1997) 291-297\n",
      "\n",
      "(20640, 8)\n",
      "(20640,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "\n",
    "housing = fetch_california_housing()\n",
    "print(housing.DESCR)\n",
    "print(housing.data.shape)\n",
    "print(housing.target.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array([[ 8.32520000e+00,  4.10000000e+01,  6.98412698e+00,\n",
      "         1.02380952e+00,  3.22000000e+02,  2.55555556e+00,\n",
      "         3.78800000e+01, -1.22230000e+02],\n",
      "       [ 8.30140000e+00,  2.10000000e+01,  6.23813708e+00,\n",
      "         9.71880492e-01,  2.40100000e+03,  2.10984183e+00,\n",
      "         3.78600000e+01, -1.22220000e+02],\n",
      "       [ 7.25740000e+00,  5.20000000e+01,  8.28813559e+00,\n",
      "         1.07344633e+00,  4.96000000e+02,  2.80225989e+00,\n",
      "         3.78500000e+01, -1.22240000e+02],\n",
      "       [ 5.64310000e+00,  5.20000000e+01,  5.81735160e+00,\n",
      "         1.07305936e+00,  5.58000000e+02,  2.54794521e+00,\n",
      "         3.78500000e+01, -1.22250000e+02],\n",
      "       [ 3.84620000e+00,  5.20000000e+01,  6.28185328e+00,\n",
      "         1.08108108e+00,  5.65000000e+02,  2.18146718e+00,\n",
      "         3.78500000e+01, -1.22250000e+02]])\n",
      "--------------------------------------------------\n",
      "array([4.526, 3.585, 3.521, 3.413, 3.422])\n"
     ]
    }
   ],
   "source": [
    "# print(housing.data[0:5])\n",
    "import pprint  #打印的格式比较 好看\n",
    "\n",
    "pprint.pprint(housing.data[0:5])\n",
    "print('-'*50)\n",
    "pprint.pprint(housing.target[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11610, 8) (11610,)\n",
      "(3870, 8) (3870,)\n",
      "(5160, 8) (5160,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#拆分训练集和测试集，random_state是随机种子,同样的随机数种子，是为了得到同样的随机值\n",
    "x_train_all, x_test, y_train_all, y_test = train_test_split(\n",
    "    housing.data, housing.target, random_state = 7)\n",
    "x_train, x_valid, y_train, y_valid = train_test_split(\n",
    "    x_train_all, y_train_all, random_state = 11)\n",
    "# 训练集\n",
    "print(x_train.shape, y_train.shape)\n",
    "# 验证集\n",
    "print(x_valid.shape, y_valid.shape)\n",
    "# 测试集\n",
    "print(x_test.shape, y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "x_train_scaled = scaler.fit_transform(x_train)\n",
    "x_valid_scaled = scaler.transform(x_valid)\n",
    "x_test_scaled = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8,)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#看特征数目\n",
    "x_train_scaled.shape[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.29807281,  0.35226166, -0.10920508, -0.25055521, -0.03406402,\n",
       "       -0.006034  ,  1.08055484, -1.06113817])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_scaled[1] #标准化后的值看一下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_2 (Dense)              (None, 30)                270       \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 31        \n",
      "=================================================================\n",
      "Total params: 301\n",
      "Trainable params: 301\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#回归模型我们只需要1个数\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.Dense(30, activation='relu',\n",
    "                       input_shape=x_train.shape[1:]),\n",
    "    keras.layers.Dense(1),#结果层只有1个节点\n",
    "])\n",
    "model.summary()\n",
    "#mean_squared_error是均方差\n",
    "model.compile(loss=\"mean_squared_error\", optimizer= \"sgd\")\n",
    "callbacks = [keras.callbacks.EarlyStopping(\n",
    "    patience=5, min_delta=1e-3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tensorflow.python.keras.layers.core.Dense at 0x7fd59002a898>,\n",
       " <tensorflow.python.keras.layers.core.Dense at 0x7fd59002ab00>]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "363/363 [==============================] - 0s 875us/step - loss: 0.8912 - val_loss: 0.6639\n",
      "Epoch 2/100\n",
      "363/363 [==============================] - 0s 811us/step - loss: 0.7128 - val_loss: 0.4861\n",
      "Epoch 3/100\n",
      "363/363 [==============================] - 0s 820us/step - loss: 0.4428 - val_loss: 0.4569\n",
      "Epoch 4/100\n",
      "363/363 [==============================] - 0s 812us/step - loss: 0.4231 - val_loss: 0.4379\n",
      "Epoch 5/100\n",
      "363/363 [==============================] - 0s 814us/step - loss: 0.4111 - val_loss: 0.4254\n",
      "Epoch 6/100\n",
      "363/363 [==============================] - 0s 812us/step - loss: 0.4003 - val_loss: 0.4203\n",
      "Epoch 7/100\n",
      "363/363 [==============================] - 0s 824us/step - loss: 0.3940 - val_loss: 0.4087\n",
      "Epoch 8/100\n",
      "363/363 [==============================] - 0s 813us/step - loss: 0.3890 - val_loss: 0.4104\n",
      "Epoch 9/100\n",
      "363/363 [==============================] - 0s 819us/step - loss: 0.3826 - val_loss: 0.4032\n",
      "Epoch 10/100\n",
      "363/363 [==============================] - 0s 819us/step - loss: 0.3787 - val_loss: 0.3991\n",
      "Epoch 11/100\n",
      "363/363 [==============================] - 0s 817us/step - loss: 0.3742 - val_loss: 0.3897\n",
      "Epoch 12/100\n",
      "363/363 [==============================] - 0s 804us/step - loss: 0.3733 - val_loss: 0.3891\n",
      "Epoch 13/100\n",
      "363/363 [==============================] - 0s 806us/step - loss: 0.3686 - val_loss: 0.3868\n",
      "Epoch 14/100\n",
      "363/363 [==============================] - 0s 821us/step - loss: 0.3655 - val_loss: 0.3836\n",
      "Epoch 15/100\n",
      "363/363 [==============================] - 0s 814us/step - loss: 0.3632 - val_loss: 0.3758\n",
      "Epoch 16/100\n",
      "363/363 [==============================] - 0s 814us/step - loss: 0.3616 - val_loss: 0.3914\n",
      "Epoch 17/100\n",
      "363/363 [==============================] - 0s 826us/step - loss: 0.3585 - val_loss: 0.3732\n",
      "Epoch 18/100\n",
      "363/363 [==============================] - 0s 811us/step - loss: 0.3682 - val_loss: 0.3766\n",
      "Epoch 19/100\n",
      "363/363 [==============================] - 0s 808us/step - loss: 0.3540 - val_loss: 0.3707\n",
      "Epoch 20/100\n",
      "363/363 [==============================] - 0s 829us/step - loss: 0.3562 - val_loss: 0.3750\n",
      "Epoch 21/100\n",
      "363/363 [==============================] - 0s 830us/step - loss: 0.3507 - val_loss: 0.3659\n",
      "Epoch 22/100\n",
      "363/363 [==============================] - 0s 813us/step - loss: 0.3581 - val_loss: 0.3752\n",
      "Epoch 23/100\n",
      "363/363 [==============================] - 0s 819us/step - loss: 0.3482 - val_loss: 0.3663\n",
      "Epoch 24/100\n",
      "363/363 [==============================] - 0s 816us/step - loss: 0.3469 - val_loss: 0.3658\n",
      "Epoch 25/100\n",
      "363/363 [==============================] - 0s 807us/step - loss: 0.3441 - val_loss: 0.3635\n",
      "Epoch 26/100\n",
      "363/363 [==============================] - 0s 814us/step - loss: 0.3434 - val_loss: 0.3640\n",
      "Epoch 27/100\n",
      "363/363 [==============================] - 0s 805us/step - loss: 0.3428 - val_loss: 0.3643\n",
      "Epoch 28/100\n",
      "363/363 [==============================] - 0s 804us/step - loss: 0.3422 - val_loss: 0.3575\n",
      "Epoch 29/100\n",
      "363/363 [==============================] - 0s 826us/step - loss: 0.3395 - val_loss: 0.3584\n",
      "Epoch 30/100\n",
      "363/363 [==============================] - 0s 822us/step - loss: 0.3397 - val_loss: 0.3564\n",
      "Epoch 31/100\n",
      "363/363 [==============================] - 0s 813us/step - loss: 0.3380 - val_loss: 0.3563\n",
      "Epoch 32/100\n",
      "363/363 [==============================] - 0s 810us/step - loss: 0.3362 - val_loss: 0.3541\n",
      "Epoch 33/100\n",
      "363/363 [==============================] - 0s 826us/step - loss: 0.3366 - val_loss: 0.3518\n",
      "Epoch 34/100\n",
      "363/363 [==============================] - 0s 824us/step - loss: 0.3525 - val_loss: 0.3794\n",
      "Epoch 35/100\n",
      "363/363 [==============================] - 0s 813us/step - loss: 0.3368 - val_loss: 0.3485\n",
      "Epoch 36/100\n",
      "363/363 [==============================] - 0s 826us/step - loss: 0.3393 - val_loss: 0.3552\n",
      "Epoch 37/100\n",
      "363/363 [==============================] - 0s 824us/step - loss: 0.3458 - val_loss: 0.3571\n",
      "Epoch 38/100\n",
      "363/363 [==============================] - 0s 818us/step - loss: 0.3453 - val_loss: 0.3681\n",
      "Epoch 39/100\n",
      "363/363 [==============================] - 0s 815us/step - loss: 0.3352 - val_loss: 0.3475\n",
      "Epoch 40/100\n",
      "363/363 [==============================] - 0s 821us/step - loss: 0.3623 - val_loss: 0.4131\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x_train_scaled, y_train,\n",
    "                    validation_data = (x_valid_scaled, y_valid),\n",
    "                    epochs = 100,\n",
    "                    callbacks = callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.177860</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.029153</td>\n",
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       "      <th>3</th>\n",
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       "      <th>5</th>\n",
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       "      <th>6</th>\n",
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       "      <th>7</th>\n",
       "      <td>0.001715</td>\n",
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       "      <th>8</th>\n",
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       "      <th>9</th>\n",
       "      <td>-0.004029</td>\n",
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       "      <th>10</th>\n",
       "      <td>-0.009408</td>\n",
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       "      <th>11</th>\n",
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       "      <th>12</th>\n",
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       "      <th>13</th>\n",
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       "      <th>14</th>\n",
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       "      <th>15</th>\n",
       "      <td>0.015586</td>\n",
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       "      <th>16</th>\n",
       "      <td>-0.018196</td>\n",
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       "      <th>17</th>\n",
       "      <td>0.003385</td>\n",
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       "      <th>18</th>\n",
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       "      <th>19</th>\n",
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       "      <th>21</th>\n",
       "      <td>0.009357</td>\n",
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       "      <th>22</th>\n",
       "      <td>-0.008908</td>\n",
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       "      <th>23</th>\n",
       "      <td>-0.000567</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-0.002293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.000529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.000278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.006812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.000943</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-0.002013</td>\n",
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       "      <th>30</th>\n",
       "      <td>-0.000127</td>\n",
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       "      <th>31</th>\n",
       "      <td>-0.002156</td>\n",
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       "      <th>32</th>\n",
       "      <td>-0.002337</td>\n",
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       "      <th>34</th>\n",
       "      <td>-0.030894</td>\n",
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       "      <th>35</th>\n",
       "      <td>0.006756</td>\n",
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       "      <th>36</th>\n",
       "      <td>0.001852</td>\n",
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       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.011062</td>\n",
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       "      <th>38</th>\n",
       "      <td>-0.020612</td>\n",
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       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.065578</td>\n",
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       "           0\n",
       "0        NaN\n",
       "1  -0.177860\n",
       "2  -0.029153\n",
       "3  -0.019061\n",
       "4  -0.012509\n",
       "5  -0.005047\n",
       "6  -0.011595\n",
       "7   0.001715\n",
       "8  -0.007277\n",
       "9  -0.004029\n",
       "10 -0.009408\n",
       "11 -0.000596\n",
       "12 -0.002325\n",
       "13 -0.003157\n",
       "14 -0.007837\n",
       "15  0.015586\n",
       "16 -0.018196\n",
       "17  0.003385\n",
       "18 -0.005923\n",
       "19  0.004336\n",
       "20 -0.009119\n",
       "21  0.009357\n",
       "22 -0.008908\n",
       "23 -0.000567\n",
       "24 -0.002293\n",
       "25  0.000529\n",
       "26  0.000278\n",
       "27 -0.006812\n",
       "28  0.000943\n",
       "29 -0.002013\n",
       "30 -0.000127\n",
       "31 -0.002156\n",
       "32 -0.002337\n",
       "33  0.027607\n",
       "34 -0.030894\n",
       "35  0.006756\n",
       "36  0.001852\n",
       "37  0.011062\n",
       "38 -0.020612\n",
       "39  0.065578"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(history.history['val_loss']).diff(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_learning_curves(history):\n",
    "    pd.DataFrame(history.history).plot(figsize=(8, 5))\n",
    "    plt.grid(True)\n",
    "    plt.gca().set_ylim(0, 1.4)\n",
    "    plt.show()\n",
    "plot_learning_curves(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.37843212485313416"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_test_scaled, y_test, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.tanh(-1000)"
   ]
  }
 ],
 "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}