1DCNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ==== 1D CNN 모델링 ===="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# 벡터화된 데이터 불러오기\n",
    "data = pd.read_json(\"./labeled_data.json\")\n",
    "data.columns = [\"label\", \"trigram\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3 x 50 -> 150 차원으로 flastten해주는 과정\n",
    "data['trigram'] = data['trigram'].apply(lambda x: (np.array(x).reshape(-1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train/test 데이터 분리\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "y = data.pop('label')\n",
    "X = data\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# keras 데이터 형식에 맞게 변환\n",
    "X_train = np.array(X_train['trigram'].tolist())\n",
    "X_test = np.array(X_test['trigram'].tolist())\n",
    "y_train = y_train.values\n",
    "y_test = y_test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9779, 150)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape # row개수 x embeedding차원"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9779,)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape # row개수 x 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# keras 모델에 넣기 위한 reshape 과정\n",
    "def reshape(df, dim):\n",
    "    return df.reshape(df.shape[0], dim, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9779, 150, 1)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = reshape(X_train, 150)\n",
    "X_test = reshape(X_test, 150)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ==== CNN ===="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# f1 score를 알기 위해 정의한 함수 \n",
    "# 출처: https://datascience.stackexchange.com/questions/45165/how-to-get-accuracy-f1-precision-and-recall-for-a-keras-model\n",
    "from keras import backend as K\n",
    "\n",
    "def recall_m(y_true, y_pred):\n",
    "        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
    "        possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
    "        recall = true_positives / (possible_positives + K.epsilon())\n",
    "        return recall\n",
    "\n",
    "def precision_m(y_true, y_pred):\n",
    "        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
    "        predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
    "        precision = true_positives / (predicted_positives + K.epsilon())\n",
    "        return precision\n",
    "\n",
    "def f1_m(y_true, y_pred):\n",
    "    precision = precision_m(y_true, y_pred)\n",
    "    recall = recall_m(y_true, y_pred)\n",
    "    return 2*((precision*recall)/(precision+recall+K.epsilon()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 150, 150)          300       \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 150, 150)          600       \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 146, 100)          75100     \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 144, 100)          30100     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 48, 100)           0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 48, 100)           0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 4800)              0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 50)                240050    \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 346,201\n",
      "Trainable params: 345,901\n",
      "Non-trainable params: 300\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, Conv1D\n",
    "from keras.layers import MaxPooling1D\n",
    "from keras.layers import Activation, Dropout, Flatten, Dense\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras import backend as K\n",
    "\n",
    "K.clear_session() # session 초기화\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(150, input_shape=(X_train.shape[1], 1), activation=\"relu\"))\n",
    "model.add(BatchNormalization()) \n",
    "model.add(Conv1D(filters=100, kernel_size=5, activation='relu'))\n",
    "model.add(Conv1D(filters=100, kernel_size=3, activation='sigmoid'))\n",
    "model.add(MaxPooling1D(pool_size=3))\n",
    "model.add(Dropout(0.4))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(50, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy', f1_m])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 6845 samples, validate on 2934 samples\n",
      "Epoch 1/50\n",
      "6845/6845 [==============================] - 42s 6ms/step - loss: 0.7094 - acc: 0.6936 - f1_m: 0.8004 - val_loss: 0.5816 - val_acc: 0.7035 - val_f1_m: 0.8249\n",
      "Epoch 2/50\n",
      "6845/6845 [==============================] - 39s 6ms/step - loss: 0.4939 - acc: 0.7157 - f1_m: 0.8330 - val_loss: 0.4598 - val_acc: 0.7035 - val_f1_m: 0.8249\n",
      "Epoch 3/50\n",
      "6845/6845 [==============================] - 38s 6ms/step - loss: 0.4410 - acc: 0.7234 - f1_m: 0.8367 - val_loss: 0.4408 - val_acc: 0.8054 - val_f1_m: 0.8685\n",
      "Epoch 4/50\n",
      "6845/6845 [==============================] - 40s 6ms/step - loss: 0.4249 - acc: 0.8183 - f1_m: 0.8788 - val_loss: 0.4292 - val_acc: 0.8030 - val_f1_m: 0.8674\n",
      "Epoch 5/50\n",
      "6845/6845 [==============================] - 39s 6ms/step - loss: 0.4133 - acc: 0.8250 - f1_m: 0.8824 - val_loss: 0.4192 - val_acc: 0.8108 - val_f1_m: 0.8719\n",
      "Epoch 6/50\n",
      "6845/6845 [==============================] - 39s 6ms/step - loss: 0.4040 - acc: 0.8324 - f1_m: 0.8864 - val_loss: 0.4098 - val_acc: 0.8211 - val_f1_m: 0.8746\n",
      "Epoch 7/50\n",
      "6845/6845 [==============================] - 40s 6ms/step - loss: 0.3929 - acc: 0.8365 - f1_m: 0.8888 - val_loss: 0.4214 - val_acc: 0.8132 - val_f1_m: 0.8604\n",
      "Epoch 8/50\n",
      "6845/6845 [==============================] - 39s 6ms/step - loss: 0.3864 - acc: 0.8375 - f1_m: 0.8888 - val_loss: 0.4015 - val_acc: 0.8248 - val_f1_m: 0.8785\n",
      "Epoch 9/50\n",
      "6845/6845 [==============================] - 38s 6ms/step - loss: 0.3757 - acc: 0.8405 - f1_m: 0.8901 - val_loss: 0.3932 - val_acc: 0.8306 - val_f1_m: 0.8804\n",
      "Epoch 10/50\n",
      "6845/6845 [==============================] - 39s 6ms/step - loss: 0.3698 - acc: 0.8437 - f1_m: 0.8924 - val_loss: 0.4015 - val_acc: 0.8255 - val_f1_m: 0.8816\n",
      "Epoch 11/50\n",
      "6845/6845 [==============================] - 39s 6ms/step - loss: 0.3666 - acc: 0.8504 - f1_m: 0.8962 - val_loss: 0.3895 - val_acc: 0.8330 - val_f1_m: 0.8858\n",
      "Epoch 12/50\n",
      "6845/6845 [==============================] - 38s 6ms/step - loss: 0.3523 - acc: 0.8545 - f1_m: 0.8993 - val_loss: 0.3833 - val_acc: 0.8265 - val_f1_m: 0.8760\n",
      "Epoch 13/50\n",
      "6845/6845 [==============================] - 37s 5ms/step - loss: 0.3490 - acc: 0.8583 - f1_m: 0.9019 - val_loss: 0.3811 - val_acc: 0.8378 - val_f1_m: 0.8841\n",
      "Epoch 14/50\n",
      "6845/6845 [==============================] - 39s 6ms/step - loss: 0.3513 - acc: 0.8581 - f1_m: 0.9019 - val_loss: 0.3754 - val_acc: 0.8354 - val_f1_m: 0.8853\n",
      "Epoch 15/50\n",
      "6845/6845 [==============================] - 39s 6ms/step - loss: 0.3403 - acc: 0.8587 - f1_m: 0.9029 - val_loss: 0.3722 - val_acc: 0.8364 - val_f1_m: 0.8826\n",
      "Epoch 16/50\n",
      "6845/6845 [==============================] - 38s 6ms/step - loss: 0.3386 - acc: 0.8600 - f1_m: 0.9029 - val_loss: 0.3734 - val_acc: 0.8371 - val_f1_m: 0.8876\n",
      "Epoch 17/50\n",
      "6845/6845 [==============================] - 39s 6ms/step - loss: 0.3316 - acc: 0.8659 - f1_m: 0.9067 - val_loss: 0.3803 - val_acc: 0.8323 - val_f1_m: 0.8858\n",
      "Epoch 18/50\n",
      "6845/6845 [==============================] - 38s 6ms/step - loss: 0.3252 - acc: 0.8675 - f1_m: 0.9075 - val_loss: 0.3826 - val_acc: 0.8374 - val_f1_m: 0.8892\n",
      "Epoch 19/50\n",
      "6845/6845 [==============================] - 37s 5ms/step - loss: 0.3212 - acc: 0.8647 - f1_m: 0.9064 - val_loss: 0.3712 - val_acc: 0.8436 - val_f1_m: 0.8912\n",
      "Epoch 20/50\n",
      "6845/6845 [==============================] - 40s 6ms/step - loss: 0.3176 - acc: 0.8691 - f1_m: 0.9082 - val_loss: 0.3647 - val_acc: 0.8412 - val_f1_m: 0.8853\n",
      "Epoch 21/50\n",
      "6845/6845 [==============================] - 38s 6ms/step - loss: 0.3157 - acc: 0.8684 - f1_m: 0.9079 - val_loss: 0.3687 - val_acc: 0.8449 - val_f1_m: 0.8922\n",
      "Epoch 22/50\n",
      "6845/6845 [==============================] - 39s 6ms/step - loss: 0.3114 - acc: 0.8730 - f1_m: 0.9112 - val_loss: 0.3650 - val_acc: 0.8436 - val_f1_m: 0.8876\n",
      "Epoch 23/50\n",
      "6845/6845 [==============================] - 40s 6ms/step - loss: 0.3041 - acc: 0.8774 - f1_m: 0.9147 - val_loss: 0.3630 - val_acc: 0.8432 - val_f1_m: 0.8888\n",
      "Epoch 24/50\n",
      "6845/6845 [==============================] - 39s 6ms/step - loss: 0.3020 - acc: 0.8764 - f1_m: 0.9139 - val_loss: 0.3635 - val_acc: 0.8456 - val_f1_m: 0.8919\n",
      "Epoch 25/50\n",
      "6845/6845 [==============================] - 38s 6ms/step - loss: 0.3023 - acc: 0.8777 - f1_m: 0.9144 - val_loss: 0.3614 - val_acc: 0.8446 - val_f1_m: 0.8899\n",
      "Epoch 26/50\n",
      "6845/6845 [==============================] - 38s 6ms/step - loss: 0.3011 - acc: 0.8771 - f1_m: 0.9139 - val_loss: 0.3597 - val_acc: 0.8449 - val_f1_m: 0.8875\n",
      "Epoch 27/50\n",
      "6845/6845 [==============================] - 38s 6ms/step - loss: 0.2972 - acc: 0.8792 - f1_m: 0.9154 - val_loss: 0.3562 - val_acc: 0.8449 - val_f1_m: 0.8865\n",
      "Epoch 28/50\n",
      "6845/6845 [==============================] - 39s 6ms/step - loss: 0.2857 - acc: 0.8834 - f1_m: 0.9182 - val_loss: 0.3552 - val_acc: 0.8476 - val_f1_m: 0.8913\n",
      "Epoch 29/50\n",
      "6845/6845 [==============================] - 39s 6ms/step - loss: 0.2885 - acc: 0.8827 - f1_m: 0.9175 - val_loss: 0.3692 - val_acc: 0.8371 - val_f1_m: 0.8869\n",
      "Epoch 30/50\n",
      "6845/6845 [==============================] - 38s 6ms/step - loss: 0.2931 - acc: 0.8801 - f1_m: 0.9164 - val_loss: 0.3617 - val_acc: 0.8449 - val_f1_m: 0.8886\n",
      "Epoch 31/50\n",
      "6845/6845 [==============================] - 38s 6ms/step - loss: 0.2857 - acc: 0.8811 - f1_m: 0.9163 - val_loss: 0.3647 - val_acc: 0.8476 - val_f1_m: 0.8923\n",
      "Epoch 32/50\n",
      "6845/6845 [==============================] - 38s 6ms/step - loss: 0.2800 - acc: 0.8871 - f1_m: 0.9199 - val_loss: 0.3956 - val_acc: 0.8357 - val_f1_m: 0.8888\n",
      "Epoch 33/50\n",
      "6845/6845 [==============================] - 38s 6ms/step - loss: 0.2806 - acc: 0.8858 - f1_m: 0.9200 - val_loss: 0.3623 - val_acc: 0.8408 - val_f1_m: 0.8827\n",
      "Epoch 34/50\n",
      "6845/6845 [==============================] - 38s 5ms/step - loss: 0.2854 - acc: 0.8846 - f1_m: 0.9191 - val_loss: 0.3729 - val_acc: 0.8459 - val_f1_m: 0.8925\n",
      "Epoch 35/50\n",
      "6845/6845 [==============================] - 39s 6ms/step - loss: 0.2783 - acc: 0.8866 - f1_m: 0.9209 - val_loss: 0.3636 - val_acc: 0.8480 - val_f1_m: 0.8913\n",
      "Epoch 36/50\n",
      "6845/6845 [==============================] - 38s 6ms/step - loss: 0.2770 - acc: 0.8877 - f1_m: 0.9213 - val_loss: 0.3598 - val_acc: 0.8507 - val_f1_m: 0.8937\n",
      "Epoch 37/50\n",
      "6845/6845 [==============================] - 39s 6ms/step - loss: 0.2695 - acc: 0.8903 - f1_m: 0.9231 - val_loss: 0.3582 - val_acc: 0.8490 - val_f1_m: 0.8919\n",
      "Epoch 38/50\n",
      "6845/6845 [==============================] - 38s 6ms/step - loss: 0.2660 - acc: 0.8936 - f1_m: 0.9258 - val_loss: 0.3578 - val_acc: 0.8497 - val_f1_m: 0.8945\n",
      "Epoch 39/50\n",
      "6845/6845 [==============================] - 39s 6ms/step - loss: 0.2736 - acc: 0.8897 - f1_m: 0.9226 - val_loss: 0.3613 - val_acc: 0.8490 - val_f1_m: 0.8914\n",
      "Epoch 40/50\n",
      "6845/6845 [==============================] - 38s 6ms/step - loss: 0.2655 - acc: 0.8916 - f1_m: 0.9236 - val_loss: 0.3636 - val_acc: 0.8436 - val_f1_m: 0.8852\n",
      "Epoch 41/50\n",
      "6845/6845 [==============================] - 38s 6ms/step - loss: 0.2612 - acc: 0.8960 - f1_m: 0.9271 - val_loss: 0.3805 - val_acc: 0.8490 - val_f1_m: 0.8957\n",
      "Epoch 42/50\n",
      "6845/6845 [==============================] - 38s 6ms/step - loss: 0.2553 - acc: 0.8991 - f1_m: 0.9292 - val_loss: 0.3678 - val_acc: 0.8483 - val_f1_m: 0.8930\n",
      "Epoch 43/50\n",
      "6845/6845 [==============================] - 38s 6ms/step - loss: 0.2566 - acc: 0.8972 - f1_m: 0.9275 - val_loss: 0.3662 - val_acc: 0.8497 - val_f1_m: 0.8950\n",
      "Epoch 44/50\n",
      "6845/6845 [==============================] - 39s 6ms/step - loss: 0.2638 - acc: 0.8951 - f1_m: 0.9258 - val_loss: 0.3665 - val_acc: 0.8480 - val_f1_m: 0.8926\n",
      "Epoch 45/50\n",
      "6845/6845 [==============================] - 39s 6ms/step - loss: 0.2535 - acc: 0.8969 - f1_m: 0.9275 - val_loss: 0.3712 - val_acc: 0.8494 - val_f1_m: 0.8944\n",
      "Epoch 46/50\n",
      "6845/6845 [==============================] - 38s 6ms/step - loss: 0.2553 - acc: 0.8941 - f1_m: 0.9259 - val_loss: 0.3795 - val_acc: 0.8398 - val_f1_m: 0.8891\n",
      "Epoch 47/50\n",
      "6845/6845 [==============================] - 38s 6ms/step - loss: 0.2480 - acc: 0.9002 - f1_m: 0.9303 - val_loss: 0.3641 - val_acc: 0.8538 - val_f1_m: 0.8952\n",
      "Epoch 48/50\n",
      "6845/6845 [==============================] - 38s 6ms/step - loss: 0.2496 - acc: 0.8977 - f1_m: 0.9277 - val_loss: 0.3657 - val_acc: 0.8504 - val_f1_m: 0.8942\n",
      "Epoch 49/50\n",
      "6845/6845 [==============================] - 39s 6ms/step - loss: 0.2513 - acc: 0.9005 - f1_m: 0.9299 - val_loss: 0.3588 - val_acc: 0.8463 - val_f1_m: 0.8896\n",
      "Epoch 50/50\n",
      "6845/6845 [==============================] - 38s 6ms/step - loss: 0.2577 - acc: 0.8960 - f1_m: 0.9259 - val_loss: 0.3680 - val_acc: 0.8517 - val_f1_m: 0.8961\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, epochs=50, batch_size=64, validation_split=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4191/4191 [==============================] - 7s 2ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.3625960384382891, 0.857790503502461, 0.9014614264113441]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4191/4191 [==============================] - 7s 2ms/step\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "must be real number, not list",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-62-663b71f29ba7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\n 테스트 정확도: %.4f\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: must be real number, not list"
     ]
    }
   ],
   "source": [
    "print(\"\\n 테스트 정확도: %.4f\" % (model.evaluate(X_test, y_test)[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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",
    "epochs = range(1, len(history.history['acc']) + 1)\n",
    "plt.plot(epochs, history.history['acc'])\n",
    "plt.plot(epochs, history.history['val_acc'])\n",
    "plt.plot(epochs, history.history['val_f1_m'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('score')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train_acc', 'val_acc', 'f1score'], loc='upper left')\n",
    "plt.savefig(\"./1DCNN reuslt.png\")\n",
    "plt.show()\n",
    "# train에 대해서 오버피팅 된다. val은 조금 증가하고 왔다갔다한다. epoch은 20정도만해도  충분한 것 같다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"./cnn_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}