movie_review.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 영화 리뷰를 사용한 텍스트 분류"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.6.0\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": 4,
   "metadata": {},
   "outputs": [],
   "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": 6,
   "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)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(218, 189)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_data[0]), len(train_data[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 단어와 정수 인덱스를 매핑한 딕셔너리\n",
    "word_index = imdb.get_word_index()\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",
    "\n",
    "def decode_review(text):\n",
    "    return ' '.join([reverse_word_index.get(i, '?') for i in text])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decode_review(train_data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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",
    "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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(256, 256)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_data[0]), len(train_data[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 20,
   "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": [
    "# 입력 크기는 영화 리뷰 데이터셋에 적용된 어휘 사전의 크기입니다(10,000개의 단어)\n",
    "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": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "30/30 [==============================] - 2s 19ms/step - loss: 0.6924 - accuracy: 0.5596 - val_loss: 0.6910 - val_accuracy: 0.6892\n",
      "Epoch 2/40\n",
      "30/30 [==============================] - 0s 7ms/step - loss: 0.6876 - accuracy: 0.7429 - val_loss: 0.6836 - val_accuracy: 0.7448\n",
      "Epoch 3/40\n",
      "30/30 [==============================] - 0s 7ms/step - loss: 0.6768 - accuracy: 0.7527 - val_loss: 0.6703 - val_accuracy: 0.7406\n",
      "Epoch 4/40\n",
      "30/30 [==============================] - 0s 7ms/step - loss: 0.6583 - accuracy: 0.7718 - val_loss: 0.6490 - val_accuracy: 0.7626\n",
      "Epoch 5/40\n",
      "30/30 [==============================] - 0s 8ms/step - loss: 0.6304 - accuracy: 0.7826 - val_loss: 0.6200 - val_accuracy: 0.7847\n",
      "Epoch 6/40\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.5942 - accuracy: 0.8072 - val_loss: 0.5836 - val_accuracy: 0.7834\n",
      "Epoch 7/40\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.5525 - accuracy: 0.8245 - val_loss: 0.5438 - val_accuracy: 0.8108\n",
      "Epoch 8/40\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.5084 - accuracy: 0.8400 - val_loss: 0.5042 - val_accuracy: 0.8263\n",
      "Epoch 9/40\n",
      "30/30 [==============================] - 0s 7ms/step - loss: 0.4657 - accuracy: 0.8554 - val_loss: 0.4679 - val_accuracy: 0.8363\n",
      "Epoch 10/40\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.4265 - accuracy: 0.8659 - val_loss: 0.4356 - val_accuracy: 0.8466\n",
      "Epoch 11/40\n",
      "30/30 [==============================] - 0s 7ms/step - loss: 0.3925 - accuracy: 0.8734 - val_loss: 0.4090 - val_accuracy: 0.8531\n",
      "Epoch 12/40\n",
      "30/30 [==============================] - 0s 7ms/step - loss: 0.3627 - accuracy: 0.8841 - val_loss: 0.3853 - val_accuracy: 0.8591\n",
      "Epoch 13/40\n",
      "30/30 [==============================] - 0s 7ms/step - loss: 0.3374 - accuracy: 0.8894 - val_loss: 0.3666 - val_accuracy: 0.8650\n",
      "Epoch 14/40\n",
      "30/30 [==============================] - 0s 7ms/step - loss: 0.3155 - accuracy: 0.8966 - val_loss: 0.3523 - val_accuracy: 0.8638\n",
      "Epoch 15/40\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.2969 - accuracy: 0.9015 - val_loss: 0.3397 - val_accuracy: 0.8711\n",
      "Epoch 16/40\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.2801 - accuracy: 0.9055 - val_loss: 0.3289 - val_accuracy: 0.8740\n",
      "Epoch 17/40\n",
      "30/30 [==============================] - 0s 7ms/step - loss: 0.2655 - accuracy: 0.9092 - val_loss: 0.3201 - val_accuracy: 0.8773\n",
      "Epoch 18/40\n",
      "30/30 [==============================] - 0s 7ms/step - loss: 0.2521 - accuracy: 0.9149 - val_loss: 0.3126 - val_accuracy: 0.8777\n",
      "Epoch 19/40\n",
      "30/30 [==============================] - 0s 7ms/step - loss: 0.2404 - accuracy: 0.9186 - val_loss: 0.3067 - val_accuracy: 0.8790\n",
      "Epoch 20/40\n",
      "30/30 [==============================] - 0s 8ms/step - loss: 0.2287 - accuracy: 0.9228 - val_loss: 0.3021 - val_accuracy: 0.8811\n",
      "Epoch 21/40\n",
      "30/30 [==============================] - 0s 7ms/step - loss: 0.2189 - accuracy: 0.9247 - val_loss: 0.2976 - val_accuracy: 0.8809\n",
      "Epoch 22/40\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.2092 - accuracy: 0.9287 - val_loss: 0.2939 - val_accuracy: 0.8829\n",
      "Epoch 23/40\n",
      "30/30 [==============================] - 0s 7ms/step - loss: 0.2005 - accuracy: 0.9329 - val_loss: 0.2911 - val_accuracy: 0.8837\n",
      "Epoch 24/40\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.1924 - accuracy: 0.9353 - val_loss: 0.2894 - val_accuracy: 0.8830\n",
      "Epoch 25/40\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.1843 - accuracy: 0.9393 - val_loss: 0.2876 - val_accuracy: 0.8844\n",
      "Epoch 26/40\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.1773 - accuracy: 0.9422 - val_loss: 0.2858 - val_accuracy: 0.8851\n",
      "Epoch 27/40\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.1705 - accuracy: 0.9450 - val_loss: 0.2853 - val_accuracy: 0.8848\n",
      "Epoch 28/40\n",
      "30/30 [==============================] - 0s 7ms/step - loss: 0.1637 - accuracy: 0.9482 - val_loss: 0.2846 - val_accuracy: 0.8847\n",
      "Epoch 29/40\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.1577 - accuracy: 0.9509 - val_loss: 0.2845 - val_accuracy: 0.8857\n",
      "Epoch 30/40\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.1519 - accuracy: 0.9537 - val_loss: 0.2846 - val_accuracy: 0.8851\n",
      "Epoch 31/40\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.1468 - accuracy: 0.9560 - val_loss: 0.2853 - val_accuracy: 0.8858\n",
      "Epoch 32/40\n",
      "30/30 [==============================] - 0s 7ms/step - loss: 0.1414 - accuracy: 0.9573 - val_loss: 0.2861 - val_accuracy: 0.8849\n",
      "Epoch 33/40\n",
      "30/30 [==============================] - 0s 7ms/step - loss: 0.1360 - accuracy: 0.9597 - val_loss: 0.2867 - val_accuracy: 0.8860\n",
      "Epoch 34/40\n",
      "30/30 [==============================] - 0s 9ms/step - loss: 0.1312 - accuracy: 0.9620 - val_loss: 0.2916 - val_accuracy: 0.8828\n",
      "Epoch 35/40\n",
      "30/30 [==============================] - 0s 7ms/step - loss: 0.1265 - accuracy: 0.9633 - val_loss: 0.2898 - val_accuracy: 0.8842\n",
      "Epoch 36/40\n",
      "30/30 [==============================] - 0s 7ms/step - loss: 0.1222 - accuracy: 0.9655 - val_loss: 0.2911 - val_accuracy: 0.8854\n",
      "Epoch 37/40\n",
      "30/30 [==============================] - 0s 7ms/step - loss: 0.1176 - accuracy: 0.9679 - val_loss: 0.2926 - val_accuracy: 0.8846\n",
      "Epoch 38/40\n",
      "30/30 [==============================] - 0s 7ms/step - loss: 0.1135 - accuracy: 0.9695 - val_loss: 0.2950 - val_accuracy: 0.8837\n",
      "Epoch 39/40\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.1096 - accuracy: 0.9708 - val_loss: 0.2969 - val_accuracy: 0.8841\n",
      "Epoch 40/40\n",
      "30/30 [==============================] - 0s 7ms/step - loss: 0.1062 - accuracy: 0.9723 - val_loss: 0.2997 - val_accuracy: 0.8835\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": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "782/782 - 2s - loss: 0.3205 - accuracy: 0.8732\n",
      "[0.32045501470565796, 0.8731600046157837]\n"
     ]
    }
   ],
   "source": [
    "results = model.evaluate(test_data,  test_labels, verbose=2)\n",
    "\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history_dict = history.history\n",
    "history_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pOOuss3jkkUfYsWMHAKtXr+aDDz7gyCOP5JJLLqGoqIiDDz6YKVOmxH19nz59WL9+PQDTpk2jf//+HH/88bunqoYwRmDYsGEMGjSIM888k88//5yXXnqJefPmce2111JYWMi///1vJkyYwF//+lcAnnrqKQYPHsyhhx7KBRdcsDu+Pn36MGXKFIYMGcKhhx7KW2+9lfTzZXq66ryYfbSx9O4NDzwQxhmcfz48+GAYZDZ7duhO+u674Zhp01R1JLnrqqtg6dLUnrOwEG65JfHzXbt2Zfjw4Tz22GOcfvrpzJkzh7PPPhszY9q0aXTp0oVdu3Zx3HHHsWzZMgYOHBj3PIsXL2bOnDm88sorlJeXM2TIEIYOHQrA2LFjueiiiwD40Y9+xO9//3suv/xyTjvtNE499VTOOuusKufavn07EyZM4KmnnuLAAw/kvPPO4/bbb+eqq64CoFu3bixZsoTbbruN6dOnc/fddyf8fFOmTGHw4MHMnTuXp59+mvPOO4+lS5cyffp0br31VkaOHMnWrVtp06YNM2fO5MQTT+SGG25g165dfF69/3o9qESQYsccA9Onw9y58LOfaSlMkVSJrR6KrRZ64IEHGDJkCIMHD2b58uVVqnGqe/755znjjDNo164dnTp14rTTTtv93Ouvv87XvvY1Dj30UGbPns3y5cuTxrNixQr69u3LgQceCMD555/Pc889t/v5sWPHAjB06NDdE9Ul8sILLzB+/Hgg/nTVM2bMYOPGjbRo0YJhw4Zx7733MnXqVF577TU6duyY9Ny1oRJBGlx5JSxeDDfeCN26JR5wplKB5KJkv9zTacyYMVxzzTUsWbKEbdu2MWTIEP7zn/8wffp0Fi1axJ577smECRMSTj9dwczi7p8wYQJz585l0KBB3HfffSxcuDDpeWqalaFiKutEU13XdK6K6apHjx7N/PnzGTFiBE8++eTu6ar//ve/M378eK699lrOO++8pOeviUoEaWAGd94ZirsacCaSGh06dGDUqFFccMEFu0sDmzdvpn379nTu3JmPPvqIRx99NOk5jjrqKB566CG2bdvGli1bePjhh3c/t2XLFvbdd1/Kysp2Tx0N0LFjR7Zs2fKlcw0YMIDVq1ezcuVKAP74xz9y9NFH1+uzZXq6apUI0qRdO/jb3+CAA8I6B9VpwJlI3Y0bN46xY8furiIaNGgQgwcP5uCDD2b//fdn5MiRSV8/ZMgQzj77bAoLCykoKOBrX/va7ud++tOfcthhh1FQUMChhx66+8v/nHPO4aKLLmLGjBm7G4kB2rRpw7333ss3v/lNysvLGTZsGJMmTfrSe9bG1KlT+fa3v83AgQNp165dlemqn3nmGZo3b85BBx3EySefzJw5c7j55ptp2bIlHTp0SMkCNpp0Ls2uvz6saxBLk9JJrtGkc7lFk85lmZ/9DM49t/JxYy3SISJSW6oaagSzZsEnn4SJ6p57TtVCIpJdVCJoBGZhTiJ3uPTScCuSa3KtGjlf1efvpETQSPr0gZ/+FB55BGLam0RyQps2bdiwYYOSQZZzdzZs2ECbRNMiJ5DWxmIzOwn4X6A5cLe7/yLOMaOAW4CWwHp3T9r/Ktcai2OVl8OIEWFVszffhD33zHREIrVTVlZGaWlpjX30JfPatGlDz549admyZZX9yRqL09ZGYGbNgVuBE4BSYJGZzXP3N2KO2QO4DTjJ3d81s73SFU82aNEC7roLhg2D664LjcYiuaBly5b07ds302FImqSzamg4sNLdV7n7TmAOcHq1Y84F/ubu7wK4+8dpjCcrDB4clre86y549lktfC8imZfOXkM9gPdiHpcCh1U75kCgpZktBDoC/+vuXxodYWYTgYkAvZtAl5upU8OEdOPGwcaNsG1b2F8xDxGoe6mINJ50lgjiTehRvUGiBTAUGA2cCPzYzA780ovcZ7p7kbsXde/ePfWRNrL27UMvorVrK5NABS18LyKNLZ0lglKgV8zjnsAHcY5Z7+6fAZ+Z2XPAIODtNMaVFU48MfFzmodIRBpTOksEi4B+ZtbXzFoB5wDzqh3zf8DXzKyFmbUjVB29mcaYskrPnvH3N4HaLxHJIWlLBO5eDlwGLCB8uT/g7svNbJKZTYqOeRN4DFgGvEzoYvp6umLKNr/4RVjRLJYWvheRxqZJ5zJs1iy46CLYvh169IBf/lINxSKSepp0Lot961vw2muhZHDccUoCItL4lAiywFe+AtdcA/ffD//6V6ajEZF8o0SQJa6/HvbdFy6/PP5CNiIi6aJEkCU6dgztA4sWhZKBiEhjUSLIIsXFYVK6yZNh8+ZMRyMi+UKJIIs0awYzZsBHH8FNN2U6GhHJF0oEWWbYMPj2t+GWW+DtJj++WkSygRJBFvrZz6BNm9CTSLOTiki6ac3iLLTPPnDjjXDttfDkk7BjR9iv2UlFJB1UIshSV1wRFrKpSAIVNDupiKSaEkGWatUqLG0Zj2YnFZFUUiLIYgUF8fdrdlIRSSUlgiw2bVpoNI6l2UlFJNWUCLJYcTHcfTd06hQe77NPWPBeDcUikkpKBFmuuBjeew/23jt0Hx03LtMRiUhTo0SQAzp1CvMQ/fOfYf0CEZFUUiLIEePHw/DhcN11sGVLpqMRkaZEiSBHNGsGv/0tfPih5iESkdRSIsghw4eHeYh+8xvNQyQiqaNEkGMq5iG6+upMRyIiTYUSQY7ZZx+YMgXmz4e//z3T0YhIU6BEkIMuvxz69w+lgupzEYmI1JUSQQ5q1SqsV/DOOzBhgqapFpGG0TTUOeqkk2DwYJgzp3KfpqkWkfpQiSCHffTRl/dpmmoRqau0JgIzO8nMVpjZSjObHOf5UWa2ycyWRtuN6YynqVm7Nv5+TVMtInWRtqohM2sO3AqcAJQCi8xsnru/Ue3Q59391HTF0ZT17h2qg+LtFxGprXSWCIYDK919lbvvBOYAp6fx/fLOtGlhWupYmqZaROoqnYmgB/BezOPSaF91h5vZq2b2qJkdHO9EZjbRzErMrGTdunXpiDUnFReHaakrSgBmYfoJNRSLSF2kMxFYnH1e7fESoMDdBwG/BebGO5G7z3T3Incv6t69e2qjzHHFxaF66N13oX17WLAAvPpVFhFJIp2JoBToFfO4J/BB7AHuvtndt0b35wMtzaxbGmNqsnr1CtNPLFgAf/5zpqMRkVySzkSwCOhnZn3NrBVwDjAv9gAz28fMLLo/PIpnQxpjatK++1047DC46irYoKsoIrWUtkTg7uXAZcAC4E3gAXdfbmaTzGxSdNhZwOtm9iowAzjHXRUb9dW8Odx1F3z6KXzve5mORkRyheXa925RUZGXlJRkOoysdsMNoZroiSfg+OMzHY2IZAMzW+zuRfGe08jiJujHP4Z+/eDii8NIYxGRZJQImqA2bUK30lWr4L//O9PRiEi2UyJookaNggsvhF//Gl55JdPRiEg2UyJowm6+GTp0gBEjwmAzTVMtIvEoETRh8+fDtm2wc2d4XDFNtZKBiMRSImjCbrihMglU0DTVIlKdEkETlmg6ak1TLSKxlAiasETTUWuaahGJpUTQhMWbphrgjDMaPxYRyV5KBE1YxTTVBQWh11Dv3qHn0P33w/vvZzo6EckWSgRNXHExrF4NX3wReg099hhs3w7nnRf2iYgoEeSZ/v1hxgx4+mmYPj3T0YhINlAiyEMXXABnnRW6kWr+PhFRIshDZqHtYN994dxzYevWTEckIpmkRJCn9twT/vhHWLkSrrwy09GISCYpEeSxo4+G66+He+4JSUFE8pMSQZ7r1w9atw69iPbZR/MQieQjJYI8Nnt2WOd4x47w+KOP4DvfUTIQyTdKBHnshhu+vILZ9u0weXJm4hGRzFAiyGOJJp8rLYXPPmvcWEQkc2qVCMysvZk1i+4faGanmVnL9IYm6ZZs8rlx42DXrsaLRUQyp7YlgueANmbWA3gK+DZwX7qCksYRb1K6du3g/PPh4YfhqqvAPSOhiUgjqm0iMHf/HBgL/NbdzwAOSl9Y0hiqT0pXUBAe33cffO978LvfwW9+k+koRSTdWtTyODOzw4Fi4MI6vlayWHFx2Kr71a/CZHXf/35IEGee2eihiUgjqe2X+VXAD4GH3H25me0PPJO2qCTjmjULg8w++AC+9S3o1i0MQBORpqdWVUPu/qy7n+buv4wajde7+xU1vc7MTjKzFWa20swSdko0s2FmtsvMzqpD7JJmbdvCvHmw//4wejS8+GKmIxKRdKhtr6E/mVknM2sPvAGsMLNra3hNc+BW4GRCe8I4M/tSu0J03C+BBXUNXtKvWzd48knYbz84+WR4+eVMRyQiqVbbxuKD3H0zMAaYD/QGxtfwmuHASndf5e47gTnA6XGOuxx4EPi4lrFII5k9O6xo1qNHGHjWti2ceCIsWZLpyEQklWqbCFpG4wbGAP/n7mVATR0LewDvxTwujfbtFnVHPQO4I9mJzGyimZWYWcm6detqGbI0xOzZMHFiWNXMPSxtuXkzNG8OJ5wAy5ZlOkIRSZXaJoI7gdVAe+A5MysANtfwGouzr3ryuAW4zt2TDl1y95nuXuTuRd27d69dxNIgiaafaN06lAyOPx7eeCMzsYlIatW2sXiGu/dw91M8WAMcU8PLSoFeMY97Ah9UO6YImGNmq4GzgNvMbEytIpe0SjT9xNq1YZnL5s3huOPg7bcbNy4RSb3aNhZ3NrP/qaieMbNfE0oHySwC+plZXzNrBZwDzIs9wN37unsfd+8D/BX4rrvPrfOnkJRLNP1E795w4IHw1FNhCopjj4VVqxo3NhFJrdpWDd0DbAH+K9o2A/cme4G7lwOXEXoDvQk8EI1BmGRmk+ofsjSGRNNPTJsW7h90UOhNtG0bjBoFS5c2doQikirmtZhMxsyWunthTfsaQ1FRkZdoxfVGMXt2aCt4991QEpg27cujkJcuhW98AzZsgHvvhbPPzkioIlIDM1vs7kXxnqttiWCbmR0Zc8KRwLZUBCfZq7g4TDPxxRfhNt5UFIWFUFICQ4fCOeeEtQw0a6lIbqntFBOTgPvNrHP0+FPg/PSEJLlm771Dm8GVV8Ivfwmvvgp/+hPsuWemIxOR2qhtr6FX3X0QMBAY6O6DgWPTGpnklFat4Pbb4c47Q1IYPhyWL890VCJSG3VaoczdN0cjjAGuSUM8kkMqRh43axZuKwahPfMMbNkCI0bA3LkZDlJEatSQpSrjDRiTPFF95PGaNeHx7NkwciQsXhx6Fp1xBlx/fRiMJiLZqSGJQGtX5bF4I48//zzshzA/0bPPwoUXws9/DoceCk880fhxikjNkiYCM9tiZpvjbFuA/RopRslCiUYex+5v0wbuvjskADP4+tfh3HPhww8bJ0YRqZ2kicDdO7p7pzhbR3fXCmV5LNnI4+qOPz5MUjd1Kjz4IAwYALfdpm6mItmiIVVDksdqGnlcXZs2MGUKvPYaDBsGl14KRxwBr7yS/lhFJDklAqmXRAvfxxt0FuvAA+Hxx8M4gzVroKgoJIWPtRqFSMbUaoqJbKIpJpqOjRvhRz+CO+4IU1v/4AdwzTXQvqbpDEWkzlIxxYRIyu2xB/zud2Hg2de/DjfeCP36wV13QXl5pqMTyR9KBJI28QacxdO/f2hEfvFF2H//MB5h4EB4+OEwRkFE0kuJQNIi2YCzRI44Ap5/Hh56KEx0d9ppcPTRsHChEoJIOikRSFrUNOAsETMYMwZefz20HbzzDhxzDBx+eGWCEJHUUiKQtKjNgLNkWrSAiy8Oq5/dfjusWwdjx8LBB8M998DOnamLVSTfKRFIWtRlwFkybdvCpEmwYgXMmRPGI1x4YWhL+PWvw+R2ItIwSgSSFnUdcFaTFi3C6mdLlsCCBWE8wve/HxLLZZeFSe7UjiBSP0oEkhb1HXBWk4o5i55+Gv71Lzj55DCfUVERDBoEv/lNqEYSkdpTIpC0qWmpy9p2L01k+PAwQnnt2jB3UZs2YUDafvuF6a/nzYOystR8FpGmTIlAMqI+3UsT2XNPuOQSePnl0NvoyivhpZfg9NPDdNgTJ4bqJDUwi8SnKSYkI/r0CV/+1RUUhNJDQ5WVwaOPhsQyfz5s3RpGMp92Wuh99PWvh4ZokXyRbIoJJQLJiGbN4jfumqV+rMD27WGiuwcfDNVFGzeG+YxGjw6lhmOOgX33Te17imSbZIlAawpIRvTuHb9EUNfupbXRpk0oCZx2WigpPPNMSApz58IDD4Rj+vcPCeGYY2DUKNhrr9THIZKt0tpGYGYnmdkKM1tpZpPjPH+6mS0zs6VmVmJmR6YzHskeqe5eWlstW4ZqoTvvhA8+gJISuPlmOOAAmDUrdFHde2845BC4/PIwduGddzSiWZq2tFUNmVlz4G3gBKAUWASMc/c3Yo7pAHzm7m5mA4EH3H1AsvOqaqjpmD07TDnx7ruhJDBtWsO7lzZEeXkYj/DMM2F74YXKaTI6dYKhQ6tuBxwQqrhEckFG2gjM7HBgqrufGD3+IYC7/zzJ8fe4+1eTnVeJIH9kOlHs3BmmyF68uHJ79dXK3kedO8PgwSEpDBkStn79oHnzxotR8sfGjaFk2qVL/V6fqTaCHsB7MY9LgcOqH2RmZwA/B/YCRqcxHskhFd1LK36RV3QvhcZLBq1ahS/6wYPhO98J+6onhyVLwpoKO3aE59u3h8LCkBwGDw5zIw0YAB07Nk7MkpvKy2H9enj//fBvffXqsFXcX7MGNm0KP4xuuin175/OEsE3gRPd/TvR4/HAcHe/PMHxRwE3uvvxcZ6bCEwE6N2799A18VoZpUlJd/fSVCorgzffDEmhYlu6FD77rPKYHj3gq1+tuvXvH9ojVL3UNJWVhcGO779fua1dG77w162revvpp19+fceO4f9Bnz7h332fPvC1r4WBlPWRE1VD0TH/AYa5+/pEx6hqKD80ZvfSdNi1C1auDAmi+habIFq3hl69QtVXQUG4rbi/337QtWuoCmih/n0NtnEjLFoUrn+3bpXbnnsmr87btQs2bw7bpk1h+/TTcL7Y7dNP4ZNPwhd+aWlYh7v6v+GWLcN7du9eeRt7f999K7/899gj/HtPlUxVDS0C+plZX+B94Bzg3GqBfQX4d9RYPARoBWxIY0ySIxqze2k6NG8efvH37x/WV6jgHr4k3nwT3n47tH9UbAsWhF+M8RJg586VSaFr17BV/0KJve3aNb/bKnbuhGXLwnxUL78cblesiH+sWbiuFdetvLzyS3/z5qqJO5EOHcIXd5cuIYEXFoZSYI8e0LNn5f2uXVP75Z4qaUsE7l5uZpcBC4DmhIbg5WY2KXr+DuBM4DwzKwO2AWd7ro1wk7SYNq1qGwFU7V6a6Ybk+jILJYBevUI31up27gy/KN99N3Rv3bAhbJ98Unl/w4bQpXX9+vBFleh99tijMmlU37p0CeMrWrYMbSGtWlW936pVeD7R1tDqrC++CO0q27eHLfb+tm2V2+eff/n+9u3hOlXfysrCbWkpvPJKZbvN3nvDYYfBeeeFapUuXcI1XL8+/tayZfhF3qlT2Dp3rrzfqVMoQeyxR+XWuXPul9g0sliyVqIv++oNyRCSRCpmN801O3ZUfqnF1juvW1c1ccQmk1Ss4dCsWUg2iTaoLNm4f/l+eXn939ssVKnFS16tWoVEN2xY+PI/7LCQdLPxV3hj0xQT0qTkUkNyNtq5M9RnJ/pFvXNnSDCxv9Jjt23bwrEVX+rxtoov3tjb2PutWoW5nuKVNlq3Dom9bdvK29j7LVvqi70+NMWENCkNXQYz37VqFapLRCqo45rknFQtgykigRKB5JzazFPU0EVvRPKJEoHknJqWwUzlojci+UCNxdLkqDFZ5MuSNRarRCBNjhqTRepGiUCaHDUmi9SNEoE0OWpMFqkbJQJpctSYLFI3aiyWvKPGZMlHaiwWiaHGZJGqlAgk79SmMVltCJJPlAgk79TUmKw2BMk3SgSSd2pqTL7hhqpTXEN4fMMNjR+rSGNQY7FINbm+TKZIPGosFqkDtSFIvlEiEKlGbQiSb5QIRKpRG4LkGyUCkTiKi8Pgsi++CLexayHXZhyCqo4klygRiNRRTW0IqjqSXKNEIFJHNbUhqOpIco0SgUgd1dSGUFPVkaqNJNu0yHQAIrmouLhqu0Gs3r3jT2rXu3dltVFFiaGi2qjinCKZoBKBSIolqzpStZFko7QmAjM7ycxWmNlKM5sc5/liM1sWbS+Z2aB0xiPSGJJVHanHkWSjtFUNmVlz4FbgBKAUWGRm89z9jZjD/gMc7e6fmtnJwEzgsHTFJNJYElUdJas2AlUdSWaks0QwHFjp7qvcfScwBzg99gB3f8ndP40e/hPomcZ4RDJOPY4kG6UzEfQA3ot5XBrtS+RC4NF4T5jZRDMrMbOSdevWpTBEkcbV0B5HoKojSb109hqyOPviTnVqZscQEsGR8Z5395mEaiOKiopya7pUkWrq2+MIVHUk6ZHOEkEp0CvmcU/gg+oHmdlA4G7gdHffkMZ4RLKeqo4kE9KZCBYB/cysr5m1As4B5sUeYGa9gb8B49397TTGIpITVHUkmZC2qiF3Lzezy4AFQHPgHndfbmaToufvAG4EugK3mRlAeaKFE0TyhaqOpLGldRyBu8939wPd/QB3nxbtuyNKArj7d9x9T3cvjDYlAZEkUlF1pBKDVKeRxSI5JBXzHGlmVKlOiUAkxyRbK6GmKbJrKjGotJCflAhEmpCaqo6SlRhUWshfSgQiTUhNVUfJSgzqmpq/lAhEmphkVUfJSgzqmpq/lAhE8kiyEoOW4MxfSgQieSZRiUFdU/OXEoGIAOqams+UCERkt3R2TQWVGLKVEoGI1EpDuqZC7UoMShSZoUQgIrXSkK6pULvBbKpaygwlAhGptfp2TYWaSwwa9Zw5SgQikhINLTFo1HPmKBGISMo0pMTQ0FHPKjHUnxKBiDSKmkoMDRn1rIbohjH33FoCuKioyEtKSjIdhoikwezZ4Vf+u++GksC0aSFR9OkTf0GegoJQ8qjp+eoL9kBIMrGJqKkzs8WJ1nxRiUBEskZ9Rz03tCEa8rvEoEQgIlkvnQ3RoMZoJQIRyQnpaogGlRiUCEQk5zWkIRoaXmLI+STh7jm1DR061EVE6mrWLPeCAnezcDtrVuVzBQXu4Su+6lZQUPPzs2a5t2tXdX+7dlXPn+y9GwtQ4gm+V9VrSETyXk29ipo1C1/x1ZmF6qVc6LGkXkMiIkk0pDG6KfRYUiIQEaH+jdGN0WMp3YlCiUBEpAbJSgzp7rHUGF1b05oIzOwkM1thZivNbHKc5weY2T/MbIeZfT+dsYiINESiEkO6eyzVpmqpoVqk7lRVmVlz4FbgBKAUWGRm89z9jZjDPgGuAMakKw4RkXQrLk7c8FuxP97UGZC4sbm2VUupkM4SwXBgpbuvcvedwBzg9NgD3P1jd18ElKUxDhGRjErnYLhUSGci6AG8F/O4NNpXZ2Y20cxKzKxk3bp1KQlORCQbNLRqKRXSmQgszr56DVpw95nuXuTuRd27d29gWCIi2SVZiaGmRJEKaWsjIJQAesU87gl8kMb3ExFpkpK1QaRCOksEi4B+ZtbXzFoB5wDz0vh+IiJSD2krEbh7uZldBiwAmgP3uPtyM5sUPX+Hme0DlACdgC/M7CrgIHffnK64RESkqnRWDeHu84H51fbdEXP/Q0KVkYiIZIhGFouI5DklAhGRPJdz01Cb2Togzji83boB6xspnLpSbPWj2OpHsdVPU42twN3j9r/PuURQEzMrSTTndqYptvpRbPWj2OonH2NT1ZCISJ5TIhARyXNNMRHMzHQASSi2+lFs9aPY6ifvYmtybQQiIlI3TbFEICIidaBEICKS55pMIqhpWcxMMrPVZvaamS01s5IMx3KPmX1sZq/H7OtiZk+Y2TvR7Z5ZFNtUM3s/unZLzeyUDMXWy8yeMbM3zWy5mV0Z7c/4tUsSW8avnZm1MbOXzezVKLb/jvZnw3VLFFvGr1tMjM3N7BUzeyR6nJbr1iTaCKJlMd8mZllMYFy1ZTEzxsxWA0XunvFBKmZ2FLAVuN/dD4n2/Qr4xN1/ESXRPd39uiyJbSqw1d2nN3Y81WLbF9jX3ZeYWUdgMWGJ1Qlk+Nolie2/yPC1MzMD2rv7VjNrCbwAXAmMJfPXLVFsJ5EF/+YAzOwaoAjo5O6npuv/alMpEdS4LKYE7v4cYa3oWKcDf4ju/4EMrSGdILas4O5r3X1JdH8L8CZhxb2MX7sksWWcB1ujhy2jzcmO65YotqxgZj2B0cDdMbvTct2aSiJI2bKYaeLA42a22MwmZjqYOPZ297UQvlSAvTIcT3WXmdmyqOooI9VWscysDzAY+BdZdu2qxQZZcO2i6o2lwMfAE+6eNdctQWyQBdcNuAX4AfBFzL60XLemkghStixmmox09yHAycClURWI1M7twAFAIbAW+HUmgzGzDsCDwFXZtm5GnNiy4tq5+y53LyRMOT/czA7JRBzxJIgt49fNzE4FPnb3xY3xfk0lEWT1spju/kF0+zHwEKEqK5t8FNUzV9Q3f5zheHZz94+i/6xfAHeRwWsX1SM/CMx2979Fu7Pi2sWLLZuuXRTPRmAhoQ4+K65bhdjYsuS6jQROi9oX5wDHmtks0nTdmkoiyNplMc2sfdSAh5m1B74OvJ78VY1uHnB+dP984P8yGEsVFf/oI2eQoWsXNSz+HnjT3f8n5qmMX7tEsWXDtTOz7ma2R3S/LXA88BbZcd3ixpYN183df+juPd29D+H77Gl3/xbpum7u3iQ24BRCz6F/AzdkOp6YuPYHXo225ZmODfgzobhbRihJXQh0BZ4C3oluu2RRbH8EXgOWRf8J9s1QbEcSqhuXAUuj7ZRsuHZJYsv4tQMGAq9EMbwO3Bjtz4brlii2jF+3anGOAh5J53VrEt1HRUSk/ppK1ZCIiNSTEoGISJ5TIhARyXNKBCIieU6JQEQkzykRiETMbFfMjJNLLYWz2JpZH4uZVVUkm7TIdAAiWWSbh+kGRPKKSgQiNbCwnsQvo7nrXzazr0T7C8zsqWhysqfMrHe0f28zeyia5/5VMzsiOlVzM7srmvv+8Wg0K2Z2hZm9EZ1nToY+puQxJQKRSm2rVQ2dHfPcZncfDvyOMCsk0f373X0gMBuYEe2fATzr7oOAIYQR5QD9gFvd/WBgI3BmtH8yMDg6z6T0fDSRxDSyWCRiZlvdvUOc/auBY919VTS524fu3tXM1hOmHyiL9q91925mtg7o6e47Ys7RhzDNcb/o8XVAS3e/ycweIyzIMxeY65Vz5Is0CpUIRGrHE9xPdEw8O2Lu76KyjW40cCswFFhsZmq7k0alRCBSO2fH3P4juv8SYWZIgGLCUocQJgO7BHYvfNIp0UnNrBnQy92fISxCsgfwpVKJSDrpl4dIpbbRalUVHnP3ii6krc3sX4QfT+OifVcA95jZtcA64NvR/iuBmWZ2IeGX/yWEWVXjaQ7MMrPOhAWWfuNhbnyRRqM2ApEaRG0ERe6+PtOxiKSDqoZERPKcSgQiInlOJQIRkTynRCAikueUCERE8pwSgYhInlMiEBHJc/8fpUT5eDVLplQAAAAASUVORK5CYII=",
      "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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.13 ('readvice')",
   "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": "3.8.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
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