기본 텍스트 분류.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "located-bunch",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "driving-chuck",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<string>:6: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "C:\\Users\\kbs77\\anaconda3\\envs\\tensorf\\lib\\site-packages\\tensorflow\\python\\keras\\datasets\\imdb.py:159: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])\n",
      "C:\\Users\\kbs77\\anaconda3\\envs\\tensorf\\lib\\site-packages\\tensorflow\\python\\keras\\datasets\\imdb.py:160: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])\n"
     ]
    }
   ],
   "source": [
    "imdb = keras.datasets.imdb\n",
    "\n",
    "(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "refined-motel",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 샘플: 25000, 레이블: 25000\n"
     ]
    }
   ],
   "source": [
    "print(\"훈련 샘플: {}, 레이블: {}\".format(len(train_data), len(train_labels)))\n",
    "\n",
    "# 부정0, 긍정1로 이미 전처리 됌"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "mineral-cornell",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 4468, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 4536, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 4613, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 2, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 5244, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 2, 8, 4, 107, 117, 5952, 15, 256, 4, 2, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 2, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 7486, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 5535, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 4472, 113, 103, 32, 15, 16, 5345, 19, 178, 32]\n"
     ]
    }
   ],
   "source": [
    "print(train_data[0])\n",
    "# 추가적인 딕셔너리에 각 숫자, 단어가 매칭되어 있기 때문"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "understood-section",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(218, 189)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_data[0]), len(train_data[1]) # 리뷰들의 길이가 다르다.\n",
    "# ML모델에 넣을때 인풋의 길이는 모두 같아야 함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "lovely-calcium",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 단어와 정수 인덱스를 매핑한 딕셔너리\n",
    "word_index = imdb.get_word_index() # 숫자 -> 단어\n",
    "\n",
    "# 처음 몇 개 인덱스는 사전에 정의되어 있습니다\n",
    "word_index = {k:(v+3) for k,v in word_index.items()}\n",
    "word_index[\"<PAD>\"] = 0\n",
    "word_index[\"<START>\"] = 1\n",
    "word_index[\"<UNK>\"] = 2  # unknown\n",
    "word_index[\"<UNUSED>\"] = 3\n",
    "\n",
    "reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])\n",
    "# reverse_word_index 딕셔너리 구성 : 숫자는 key, 단어는 value\n",
    "\n",
    "def decode_review(text):\n",
    "    return ' '.join([reverse_word_index.get(i, '?') for i in text])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "closing-wallpaper",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"<START> this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert <UNK> is an amazing actor and now the same being director <UNK> father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for <UNK> and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also <UNK> to the two little boy's that played the <UNK> of norman and paul they were just brilliant children are often left out of the <UNK> list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decode_review(train_data[0]) # 입력데이터가 문자로 잘 바꼈나?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "conceptual-voluntary",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = keras.preprocessing.sequence.pad_sequences(train_data,\n",
    "                                                        value=word_index[\"<PAD>\"],\n",
    "                                                        padding='post',\n",
    "                                                        maxlen=256)\n",
    "\n",
    "# \n",
    "\n",
    "test_data = keras.preprocessing.sequence.pad_sequences(test_data,\n",
    "                                                       value=word_index[\"<PAD>\"],\n",
    "                                                       padding='post',\n",
    "                                                       maxlen=256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "inside-steps",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(256, 256)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_data[0]), len(train_data[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "accomplished-arnold",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   1   14   22   16   43  530  973 1622 1385   65  458 4468   66 3941\n",
      "    4  173   36  256    5   25  100   43  838  112   50  670    2    9\n",
      "   35  480  284    5  150    4  172  112  167    2  336  385   39    4\n",
      "  172 4536 1111   17  546   38   13  447    4  192   50   16    6  147\n",
      " 2025   19   14   22    4 1920 4613  469    4   22   71   87   12   16\n",
      "   43  530   38   76   15   13 1247    4   22   17  515   17   12   16\n",
      "  626   18    2    5   62  386   12    8  316    8  106    5    4 2223\n",
      " 5244   16  480   66 3785   33    4  130   12   16   38  619    5   25\n",
      "  124   51   36  135   48   25 1415   33    6   22   12  215   28   77\n",
      "   52    5   14  407   16   82    2    8    4  107  117 5952   15  256\n",
      "    4    2    7 3766    5  723   36   71   43  530  476   26  400  317\n",
      "   46    7    4    2 1029   13  104   88    4  381   15  297   98   32\n",
      " 2071   56   26  141    6  194 7486   18    4  226   22   21  134  476\n",
      "   26  480    5  144   30 5535   18   51   36   28  224   92   25  104\n",
      "    4  226   65   16   38 1334   88   12   16  283    5   16 4472  113\n",
      "  103   32   15   16 5345   19  178   32    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0]\n"
     ]
    }
   ],
   "source": [
    "print(train_data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "industrial-advocate",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding (Embedding)        (None, None, 16)          160000    \n",
      "_________________________________________________________________\n",
      "global_average_pooling1d (Gl (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 16)                272       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 160,289\n",
      "Trainable params: 160,289\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "vocab_size = 10000\n",
    "\n",
    "model = keras.Sequential()\n",
    "model.add(keras.layers.Embedding(vocab_size, 16, input_shape=(None,)))\n",
    "model.add(keras.layers.GlobalAveragePooling1D())\n",
    "model.add(keras.layers.Dense(16, activation='relu'))\n",
    "model.add(keras.layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "requested-reference",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "integrated-religion",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_val = train_data[:10000]\n",
    "partial_x_train = train_data[10000:]\n",
    "\n",
    "y_val = train_labels[:10000]\n",
    "partial_y_train = train_labels[10000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "possible-junction",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "30/30 [==============================] - 1s 19ms/step - loss: 0.6919 - accuracy: 0.5036 - val_loss: 0.6902 - val_accuracy: 0.4949\n",
      "Epoch 2/40\n",
      "30/30 [==============================] - 0s 16ms/step - loss: 0.6866 - accuracy: 0.5197 - val_loss: 0.6832 - val_accuracy: 0.6172\n",
      "Epoch 3/40\n",
      "30/30 [==============================] - 0s 15ms/step - loss: 0.6752 - accuracy: 0.6636 - val_loss: 0.6688 - val_accuracy: 0.7019\n",
      "Epoch 4/40\n",
      "30/30 [==============================] - 0s 14ms/step - loss: 0.6550 - accuracy: 0.7394 - val_loss: 0.6460 - val_accuracy: 0.7379\n",
      "Epoch 5/40\n",
      "30/30 [==============================] - 0s 16ms/step - loss: 0.6251 - accuracy: 0.7736 - val_loss: 0.6143 - val_accuracy: 0.7842\n",
      "Epoch 6/40\n",
      "30/30 [==============================] - 0s 13ms/step - loss: 0.5857 - accuracy: 0.8054 - val_loss: 0.5757 - val_accuracy: 0.7950\n",
      "Epoch 7/40\n",
      "30/30 [==============================] - 0s 13ms/step - loss: 0.5400 - accuracy: 0.8281 - val_loss: 0.5315 - val_accuracy: 0.8210\n",
      "Epoch 8/40\n",
      "30/30 [==============================] - 0s 13ms/step - loss: 0.4927 - accuracy: 0.8453 - val_loss: 0.4894 - val_accuracy: 0.8342\n",
      "Epoch 9/40\n",
      "30/30 [==============================] - 0s 12ms/step - loss: 0.4476 - accuracy: 0.8603 - val_loss: 0.4511 - val_accuracy: 0.8433\n",
      "Epoch 10/40\n",
      "30/30 [==============================] - 0s 13ms/step - loss: 0.4072 - accuracy: 0.8717 - val_loss: 0.4190 - val_accuracy: 0.8502\n",
      "Epoch 11/40\n",
      "30/30 [==============================] - 0s 13ms/step - loss: 0.3726 - accuracy: 0.8808 - val_loss: 0.3922 - val_accuracy: 0.8578\n",
      "Epoch 12/40\n",
      "30/30 [==============================] - 0s 13ms/step - loss: 0.3434 - accuracy: 0.8878 - val_loss: 0.3708 - val_accuracy: 0.8644\n",
      "Epoch 13/40\n",
      "30/30 [==============================] - 0s 13ms/step - loss: 0.3185 - accuracy: 0.8953 - val_loss: 0.3529 - val_accuracy: 0.8678\n",
      "Epoch 14/40\n",
      "30/30 [==============================] - 0s 13ms/step - loss: 0.2967 - accuracy: 0.9009 - val_loss: 0.3390 - val_accuracy: 0.8708\n",
      "Epoch 15/40\n",
      "30/30 [==============================] - 0s 14ms/step - loss: 0.2778 - accuracy: 0.9050 - val_loss: 0.3270 - val_accuracy: 0.8736\n",
      "Epoch 16/40\n",
      "30/30 [==============================] - 0s 15ms/step - loss: 0.2616 - accuracy: 0.9111 - val_loss: 0.3180 - val_accuracy: 0.8781\n",
      "Epoch 17/40\n",
      "30/30 [==============================] - 0s 14ms/step - loss: 0.2468 - accuracy: 0.9163 - val_loss: 0.3101 - val_accuracy: 0.8787\n",
      "Epoch 18/40\n",
      "30/30 [==============================] - 0s 13ms/step - loss: 0.2341 - accuracy: 0.9199 - val_loss: 0.3041 - val_accuracy: 0.8799\n",
      "Epoch 19/40\n",
      "30/30 [==============================] - 0s 14ms/step - loss: 0.2223 - accuracy: 0.9233 - val_loss: 0.2992 - val_accuracy: 0.8816\n",
      "Epoch 20/40\n",
      "30/30 [==============================] - 0s 13ms/step - loss: 0.2111 - accuracy: 0.9279 - val_loss: 0.2954 - val_accuracy: 0.8815\n",
      "Epoch 21/40\n",
      "30/30 [==============================] - 0s 13ms/step - loss: 0.2014 - accuracy: 0.9315 - val_loss: 0.2920 - val_accuracy: 0.8833\n",
      "Epoch 22/40\n",
      "30/30 [==============================] - 0s 15ms/step - loss: 0.1926 - accuracy: 0.9355 - val_loss: 0.2899 - val_accuracy: 0.8847\n",
      "Epoch 23/40\n",
      "30/30 [==============================] - 0s 14ms/step - loss: 0.1836 - accuracy: 0.9398 - val_loss: 0.2888 - val_accuracy: 0.8839\n",
      "Epoch 24/40\n",
      "30/30 [==============================] - 0s 16ms/step - loss: 0.1758 - accuracy: 0.9435 - val_loss: 0.2871 - val_accuracy: 0.8846\n",
      "Epoch 25/40\n",
      "30/30 [==============================] - 0s 14ms/step - loss: 0.1680 - accuracy: 0.9467 - val_loss: 0.2866 - val_accuracy: 0.8850\n",
      "Epoch 26/40\n",
      "30/30 [==============================] - 0s 13ms/step - loss: 0.1608 - accuracy: 0.9495 - val_loss: 0.2857 - val_accuracy: 0.8856\n",
      "Epoch 27/40\n",
      "30/30 [==============================] - 0s 14ms/step - loss: 0.1541 - accuracy: 0.9519 - val_loss: 0.2857 - val_accuracy: 0.8859\n",
      "Epoch 28/40\n",
      "30/30 [==============================] - 0s 17ms/step - loss: 0.1477 - accuracy: 0.9546 - val_loss: 0.2863 - val_accuracy: 0.8866\n",
      "Epoch 29/40\n",
      "30/30 [==============================] - 0s 13ms/step - loss: 0.1420 - accuracy: 0.9574 - val_loss: 0.2869 - val_accuracy: 0.8870\n",
      "Epoch 30/40\n",
      "30/30 [==============================] - 0s 13ms/step - loss: 0.1362 - accuracy: 0.9583 - val_loss: 0.2879 - val_accuracy: 0.8877\n",
      "Epoch 31/40\n",
      "30/30 [==============================] - 0s 13ms/step - loss: 0.1306 - accuracy: 0.9612 - val_loss: 0.2896 - val_accuracy: 0.8866\n",
      "Epoch 32/40\n",
      "30/30 [==============================] - 0s 13ms/step - loss: 0.1257 - accuracy: 0.9633 - val_loss: 0.2913 - val_accuracy: 0.8852\n",
      "Epoch 33/40\n",
      "30/30 [==============================] - 0s 12ms/step - loss: 0.1205 - accuracy: 0.9657 - val_loss: 0.2937 - val_accuracy: 0.8859\n",
      "Epoch 34/40\n",
      "30/30 [==============================] - 0s 14ms/step - loss: 0.1161 - accuracy: 0.9666 - val_loss: 0.2959 - val_accuracy: 0.8840\n",
      "Epoch 35/40\n",
      "30/30 [==============================] - 0s 15ms/step - loss: 0.1109 - accuracy: 0.9689 - val_loss: 0.2978 - val_accuracy: 0.8862\n",
      "Epoch 36/40\n",
      "30/30 [==============================] - 0s 16ms/step - loss: 0.1074 - accuracy: 0.9706 - val_loss: 0.3005 - val_accuracy: 0.8841\n",
      "Epoch 37/40\n",
      "30/30 [==============================] - 0s 14ms/step - loss: 0.1027 - accuracy: 0.9715 - val_loss: 0.3034 - val_accuracy: 0.8836\n",
      "Epoch 38/40\n",
      "30/30 [==============================] - 0s 17ms/step - loss: 0.0987 - accuracy: 0.9735 - val_loss: 0.3061 - val_accuracy: 0.8836\n",
      "Epoch 39/40\n",
      "30/30 [==============================] - 1s 17ms/step - loss: 0.0948 - accuracy: 0.9750 - val_loss: 0.3100 - val_accuracy: 0.8831\n",
      "Epoch 40/40\n",
      "30/30 [==============================] - 0s 14ms/step - loss: 0.0912 - accuracy: 0.9770 - val_loss: 0.3132 - val_accuracy: 0.8818\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(partial_x_train,\n",
    "                    partial_y_train,\n",
    "                    epochs=40,\n",
    "                    batch_size=512,\n",
    "                    validation_data=(x_val, y_val),\n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "rocky-founder",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "782/782 - 0s - loss: 0.3354 - accuracy: 0.8706\n",
      "[0.33538582921028137, 0.870639979839325]\n"
     ]
    }
   ],
   "source": [
    "results = model.evaluate(test_data,  test_labels, verbose=2)\n",
    "\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "young-cornwall",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history_dict = history.history\n",
    "history_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "focused-berlin",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "acc = history_dict['accuracy']\n",
    "val_acc = history_dict['val_accuracy']\n",
    "loss = history_dict['loss']\n",
    "val_loss = history_dict['val_loss']\n",
    "\n",
    "epochs = range(1, len(acc) + 1)\n",
    "\n",
    "# \"bo\"는 \"파란색 점\"입니다\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "# b는 \"파란 실선\"입니다\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "minor-escape",
   "metadata": {},
   "outputs": [
    {
     "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.clf()   # 그림을 초기화합니다\n",
    "\n",
    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fifteen-portal",
   "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}