09_Advanced_Keras_Functional_API.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 간단한 입출력 API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import Input, layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_tensor  = Input(shape=(32,))\n",
    "dense = layers.Dense(32, activation='relu')\n",
    "\n",
    "output_tensor = dense(input_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential, Model\n",
    "from keras import layers\n",
    "from keras import Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_4 (InputLayer)         (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_13 (Dense)             (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "dense_14 (Dense)             (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_15 (Dense)             (None, 10)                330       \n",
      "=================================================================\n",
      "Total params: 3,466\n",
      "Trainable params: 3,466\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "seq_model = Sequential()\n",
    "seq_model.add(layers.Dense(32, activation='relu', input_shape=(64,)))\n",
    "seq_model.add(layers.Dense(32, activation='relu'))\n",
    "seq_model.add(layers.Dense(10, activation='softmax'))\n",
    "\n",
    "input_tensor = Input(shape=(64,))\n",
    "x = layers.Dense(32, activation='relu')(input_tensor)\n",
    "x = layers.Dense(32, activation='relu')(x)\n",
    "output_tensor = layers.Dense(10, activation='softmax')(x)\n",
    "\n",
    "model = Model(input_tensor, output_tensor)\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1000/1000 [==============================] - 1s 554us/step - loss: 11.7218\n",
      "Epoch 2/10\n",
      "1000/1000 [==============================] - 0s 49us/step - loss: 11.6106\n",
      "Epoch 3/10\n",
      "1000/1000 [==============================] - 0s 49us/step - loss: 11.5933\n",
      "Epoch 4/10\n",
      "1000/1000 [==============================] - 0s 45us/step - loss: 11.5854\n",
      "Epoch 5/10\n",
      "1000/1000 [==============================] - 0s 43us/step - loss: 11.5799\n",
      "Epoch 6/10\n",
      "1000/1000 [==============================] - 0s 40us/step - loss: 11.5766\n",
      "Epoch 7/10\n",
      "1000/1000 [==============================] - 0s 43us/step - loss: 11.5737\n",
      "Epoch 8/10\n",
      "1000/1000 [==============================] - 0s 43us/step - loss: 11.5717\n",
      "Epoch 9/10\n",
      "1000/1000 [==============================] - 0s 41us/step - loss: 11.5693\n",
      "Epoch 10/10\n",
      "1000/1000 [==============================] - 0s 44us/step - loss: 11.5673\n",
      "1000/1000 [==============================] - 0s 117us/step\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='rmsprop', loss='categorical_crossentropy')\n",
    "import numpy as np\n",
    "x_train = np.random.random((1000, 64))\n",
    "y_train = np.random.random((1000, 10))\n",
    "\n",
    "model.fit(x_train, y_train, epochs=10, batch_size=128)\n",
    "\n",
    "score = model.evaluate(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.564283615112304"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.utils.vis_utils import plot_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_model(model, to_file='model_images/model_plot1.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](model_images/model_plot1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 다중 입력 모델"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Model\n",
    "from keras import layers\n",
    "from keras import Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_vocabulary_size = 10000\n",
    "question_vocabulary_size = 10000\n",
    "answer_vocabulabry_size = 500\n",
    "\n",
    "text_input = Input(shape=(None,), dtype='int32', name='text')\n",
    "\n",
    "embedded_text = layers.Embedding(\n",
    "    text_vocabulary_size, 64)(text_input)\n",
    "\n",
    "encoded_text = layers.LSTM(32)(embedded_text)\n",
    "\n",
    "question_input = Input(shape=(None,),\n",
    "                       dtype='int32',\n",
    "                       name ='question')\n",
    "\n",
    "embedded_question = layers.Embedding(\n",
    "    question_vocabulary_size, 32)(question_input)\n",
    "encoded_question = layers.LSTM(16)(embedded_question)\n",
    "\n",
    "concatenated = layers.concatenate([encoded_text, encoded_question],\n",
    "                                   axis=-1)\n",
    "\n",
    "answer = layers.Dense(answer_vocabulabry_size,\n",
    "                      activation='softmax')(concatenated)\n",
    "\n",
    "model = Model([text_input, question_input], answer)\n",
    "model.compile(optimizer='rmsprop',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1000/1000 [==============================] - 3s 3ms/step - loss: 6.2146 - acc: 0.0030\n",
      "Epoch 2/10\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 6.1997 - acc: 0.0300\n",
      "Epoch 3/10\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 6.1661 - acc: 0.0150\n",
      "Epoch 4/10\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 6.0765 - acc: 0.0070\n",
      "Epoch 5/10\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 6.0153 - acc: 0.0080\n",
      "Epoch 6/10\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 5.9581 - acc: 0.0100\n",
      "Epoch 7/10\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 5.8600 - acc: 0.0150\n",
      "Epoch 8/10\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 5.7649 - acc: 0.0120\n",
      "Epoch 9/10\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 5.6831 - acc: 0.0170\n",
      "Epoch 10/10\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 5.6127 - acc: 0.0200\n",
      "Epoch 1/10\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 5.5357 - acc: 0.0400\n",
      "Epoch 2/10\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 5.4811 - acc: 0.0430\n",
      "Epoch 3/10\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 5.4176 - acc: 0.0520\n",
      "Epoch 4/10\n",
      "1000/1000 [==============================] - 3s 3ms/step - loss: 5.3485 - acc: 0.0630\n",
      "Epoch 5/10\n",
      "1000/1000 [==============================] - 3s 3ms/step - loss: 5.2940 - acc: 0.0630\n",
      "Epoch 6/10\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 5.2528 - acc: 0.0740\n",
      "Epoch 7/10\n",
      "1000/1000 [==============================] - 3s 3ms/step - loss: 5.1975 - acc: 0.0920\n",
      "Epoch 8/10\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 5.1196 - acc: 0.1020\n",
      "Epoch 9/10\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 5.0881 - acc: 0.1010\n",
      "Epoch 10/10\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 5.0144 - acc: 0.1180\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1bb82ee06d8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_samples = 1000\n",
    "max_length = 100\n",
    "\n",
    "text = np.random.randint(1, text_vocabulary_size,\n",
    "                         size=(num_samples, max_length))\n",
    "question = np.random.randint(1, question_vocabulary_size,\n",
    "                             size=(num_samples, max_length))\n",
    "\n",
    "answers = np.random.randint(0, answer_vocabulabry_size, size=num_samples)\n",
    "\n",
    "answers = to_categorical(answers)\n",
    "\n",
    "model.fit([text,question], answers, epochs=10, batch_size=128)\n",
    "\n",
    "model.fit({'text':text, 'question': question}, answers,\n",
    "           epochs=10, batch_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "text (InputLayer)               (None, None)         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "question (InputLayer)           (None, None)         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_3 (Embedding)         (None, None, 64)     640000      text[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "embedding_4 (Embedding)         (None, None, 32)     320000      question[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "lstm_3 (LSTM)                   (None, 32)           12416       embedding_3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "lstm_4 (LSTM)                   (None, 16)           3136        embedding_4[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 48)           0           lstm_3[0][0]                     \n",
      "                                                                 lstm_4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "dense_16 (Dense)                (None, 500)          24500       concatenate_2[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 1,000,052\n",
      "Trainable params: 1,000,052\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_model(model, to_file='model_images/model_plot2.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](model_images/model_plot2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 다중 출력 모델"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Model\n",
    "from keras import layers\n",
    "from keras import Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocabulay_size= 50000\n",
    "num_income_groups = 10\n",
    "\n",
    "posts_input = Input(shape=(None,), dtype = 'int32', name='posts')\n",
    "embedded_posts = layers.Embedding(vocabulay_size, 256)(posts_input)\n",
    "x = layers.Conv1D(128, 5, activation='relu')(embedded_posts)\n",
    "x = layers.MaxPool1D(5)(x)\n",
    "x = layers.Conv1D(256, 5, activation='relu')(x)\n",
    "x = layers.Conv1D(256, 5, activation='relu')(x)\n",
    "x = layers.MaxPool1D(5)(x)\n",
    "x = layers.Conv1D(256, 5, activation='relu')(x)\n",
    "x = layers.Conv1D(256, 5, activation='relu')(x)\n",
    "x = layers.GlobalMaxPool1D()(x)\n",
    "x = layers.Dense(128,activation='relu')(x)\n",
    "\n",
    "age_prediction = layers.Dense(1, name='age')(x)\n",
    "income_prediction = layers.Dense(num_income_groups,\n",
    "                                 activation='softmax',\n",
    "                                 name='income')(x)\n",
    "gender_prediction = layers.Dense(1, activation='sigmoid', name='gender')(x)\n",
    "\n",
    "model = Model(posts_input,\n",
    "              [age_prediction, income_prediction, gender_prediction])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='rmsprop',\n",
    "              loss=['mse', 'categorical_crossentropy', 'binary_crossentropy'])\n",
    "\n",
    "model.compile(optimizer='rmsprop',\n",
    "              loss={'age': 'mse',\n",
    "                    'income' : 'categorical_crossentropy',\n",
    "                    'gender' : 'binary_crossentropy'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='rmsprop',\n",
    "              loss=['mse', 'categorical_crossentropy', 'binary_crossentropy'],\n",
    "              loss_weights=[0.25,1.,10.])\n",
    "\n",
    "model.compile(optimizer='rmsprop',\n",
    "              loss={'age': 'mse',\n",
    "                    'income' : 'categorical_crossentropy',\n",
    "                    'gender' : 'binary_crossentropy'},\n",
    "              loss_weights={'age':0.25,\n",
    "                            'income':1.,\n",
    "                            'gender':10.})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(posts, [age_prediction, income_prediction, gender_prediction],\n",
    "          epochs=10, batch_size=64)\n",
    "\n",
    "model.fit(posts, {'age':age_targets,\n",
    "                  'income':income_targets,\n",
    "                  'gender':gender_targets},\n",
    "          epochs=10, batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "posts (InputLayer)              (None, None)         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_5 (Embedding)         (None, None, 256)    12800000    posts[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1 (Conv1D)               (None, None, 128)    163968      embedding_5[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1D)  (None, None, 128)    0           conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_2 (Conv1D)               (None, None, 256)    164096      max_pooling1d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_3 (Conv1D)               (None, None, 256)    327936      conv1d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1D)  (None, None, 256)    0           conv1d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_4 (Conv1D)               (None, None, 256)    327936      max_pooling1d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_5 (Conv1D)               (None, None, 256)    327936      conv1d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_1 (GlobalM (None, 256)          0           conv1d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_17 (Dense)                (None, 128)          32896       global_max_pooling1d_1[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "age (Dense)                     (None, 1)            129         dense_17[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "income (Dense)                  (None, 10)           1290        dense_17[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "gender (Dense)                  (None, 1)            129         dense_17[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 14,146,316\n",
      "Trainable params: 14,146,316\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_model(model, to_file='model_images/model_plot3.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](model_images/model_plot3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 비순환 유향 그래프"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 인셉션"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'embedding_10/embedding_lookup:0' shape=(?, ?, ?, 128) dtype=float32>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocabulay_size= 50000\n",
    "num_income_groups = 10\n",
    "\n",
    "posts_input = Input(shape=(None,None,), dtype = 'int32', name='posts')\n",
    "x = layers.Embedding(vocabulay_size, 128)(posts_input)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "brahch_a = layers.Conv2D(128,1,\n",
    "                         activation='relu', strides=2)(x)\n",
    "\n",
    "brahch_b = layers.Conv2D(128, 1, activation='relu')(x)\n",
    "brahch_b = layers.Conv2D(128, 1, activation='relu', strides=2)(x)\n",
    "\n",
    "brahch_c = layers.AveragePooling2D(3, strides=2)(x)\n",
    "brahch_c = layers.Conv2D(128, 3, activation='relu')(brahch_c)\n",
    "\n",
    "brahch_d = layers.Conv2D(128, 3, activation='relu')(x)\n",
    "brahch_d = layers.Conv2D(128, 3, activation='relu')(brahch_d)\n",
    "brahch_d = layers.Conv2D(128, 3, activation='relu', strides=2)(brahch_d)\n",
    "output = layers.concatenate(\n",
    "    [brahch_a, brahch_b, brahch_c, brahch_d], axis=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 전차 연결"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = layers.Conv2D(128,3,activation='relu', padding='same')(x)\n",
    "y = layers.Conv2D(128,3,activation='relu', padding='same')(y)\n",
    "y = layers.Conv2D(128,3,activation='relu', padding='same')(y)\n",
    "\n",
    "y = layers.add([y,x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = layers.Conv2D(128,3,activation='relu', padding='same')(x)\n",
    "y = layers.Conv2D(128,3,activation='relu', padding='same')(y)\n",
    "y = layers.MaxPool2D(2, strides=2)(y)\n",
    "\n",
    "residual = layers.Conv2D(128, 1, strides=2, padding='same')(x)\n",
    "\n",
    "y = layers.add([y,residual])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 층 가중치 공유"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import layers\n",
    "from keras import Input\n",
    "from keras.models import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "lstm = layers.LSTM(32)\n",
    "left_input = Input(shape=(None, 128))\n",
    "left_output = lstm(left_input)\n",
    "\n",
    "right_input = Input(shape=(None, 128))\n",
    "rigth_output = lstm (right_input)\n",
    "\n",
    "merged = layers.concatenate([left_output, rigth_output], axis=-1)\n",
    "predictions = layers.Dense(1, activation='sigmoid')(merged)\n",
    "\n",
    "model = Model([left_input, right_input], predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit([left_input, right_input], targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_12 (InputLayer)           (None, None, 128)    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_13 (InputLayer)           (None, None, 128)    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "lstm_8 (LSTM)                   (None, 32)           20608       input_12[0][0]                   \n",
      "                                                                 input_13[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_8 (Concatenate)     (None, 64)           0           lstm_8[0][0]                     \n",
      "                                                                 lstm_8[1][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "dense_28 (Dense)                (None, 1)            65          concatenate_8[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 20,673\n",
      "Trainable params: 20,673\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_model(model, to_file='model_images/model_plot4.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](model_images/model_plot4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 층과 모델"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = model(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y1, y2 = model([x1, x2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import layers\n",
    "from keras import applications\n",
    "from keras import Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "xception_base = applications.Xception(weights=None,\n",
    "                                      include_top=False)\n",
    "left_input = Input(shape=(250, 250, 3))\n",
    "rigth_input = Input(shape=(250, 250, 3))\n",
    "\n",
    "left_features = xception_base(left_input)\n",
    "rigth_input = xception_base(rigth_input)\n",
    "\n",
    "merged_features = layers.concatenate(\n",
    "    [left_features, rigth_input], axis=-1)"
   ]
  }
 ],
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