Node 23. Understanding_of_Layers(Linear, Convolution).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "specialized-cherry",
   "metadata": {},
   "source": [
    "## 학습 목표\n",
    "- 레이어의 개념을 이해한다.\n",
    "- 딥러닝 모델 속 각 레이어(Linear, Convolution)의 동작 방식을 이해한다.\n",
    "- 데이터의 특성을 고려한 레이어를 설계하고, 이를 Tensorflow로 정의하는 법을 배운다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interesting-genre",
   "metadata": {},
   "source": [
    "## 데이터의 형태"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mysterious-egypt",
   "metadata": {},
   "source": [
    "#### Q1. 해상도가 1280 x 1024(30fps)이고 러닝타임(T)이 90분인 흑백 영화 데이터는 어떻게 표현할 수 있을까요? 표기는 (T, C, H, W)를 따라주세요.\n",
    "\n",
    "> (162000, 1, 1024, 1280)  \n",
    "> Time: 90분 x 60초 x 30fps Channel: 흑백 영화는 단일 채널로 이루어진다. Height: 1024 Width: 1280"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "angry-quantum",
   "metadata": {},
   "source": [
    "이외에 테이블 데이터나 이미지의 경우 RGB 3채널로서 (C, W, H) 혹은 (W, H, C)로 표현하기도 한다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "logical-decline",
   "metadata": {},
   "source": [
    "## Linear 레이어"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "governmental-laugh",
   "metadata": {},
   "source": [
    "- 데이터를 선형변환을 통해 특정 차원으로 변환시켜준다.\n",
    "\n",
    "#### 예) 2차원의 점 4개(x,y로 표현 가능)를 통해 결국 1개의 정수(클래스)를 나타내는 선형 변환을 만들어본다.\n",
    "> <식1>  \n",
    "> 1단계: (4, 2) x (2, 1) 행렬 = (4, )  \n",
    "> 2단계: (4, ) x (4, 1) 행렬 = (1, )  \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "armed-spank",
   "metadata": {},
   "source": [
    "- 위와 같이 하면 점 4개, 즉 4각형을 하나의 정수로 표현할 수 있게 된다.\n",
    "- 2차원을 1차원으로 변환하는 데에 (2, 1) 혹은 (2 x 1) 행렬이 하나 선언되고,  \n",
    "- 4차원을 1차원으로 변환하는 데에 (4, 1) 혹은 (4 x 1) 행렬이 하나 선언됨에 유의한다.\n",
    "- #### 그리고 여기서 행렬들은 Weight을 나타낸다.\n",
    "- #### Linear 레이어는 `(입력의 차원, 출력의 차원)`에 해당하는 Weight을 가지는 특성이 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "colored-bryan",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1단계 연산 준비: (64, 4, 2)\n",
      "1단계 연산 결과: (64, 4)\n",
      "1단계 Linear Layer의 Weight 형태: (2, 1)\n",
      "\n",
      "2단계 연산 준비: (64, 4)\n",
      "2단계 연산 결과: (64,)\n",
      "2단계 Linear Layer의 Weight 형태: (4, 1)\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "batch_size = 64\n",
    "boxes = tf.zeros((batch_size, 4, 2))     # Tensorflow는 Batch를 기반으로 동작하기에,\n",
    "                                         # 우리는 사각형 2개 세트를 batch_size개만큼\n",
    "                                         # 만든 후 처리를 하게 됩니다.\n",
    "print(\"1단계 연산 준비:\", boxes.shape)\n",
    "\n",
    "first_linear = tf.keras.layers.Dense(units=1, use_bias=False) \n",
    "# units은 출력 차원 수를 의미합니다.\n",
    "# Weight 행렬 속 실수를 인간의 뇌 속 하나의 뉴런 '유닛' 취급을 하는 거죠!\n",
    "\n",
    "first_out = first_linear(boxes)\n",
    "first_out = tf.squeeze(first_out, axis=-1) # (4, 1)을 (4,)로 변환해줍니다.\n",
    "                                           # (불필요한 차원 축소)\n",
    "\n",
    "print(\"1단계 연산 결과:\", first_out.shape)\n",
    "print(\"1단계 Linear Layer의 Weight 형태:\", first_linear.weights[0].shape)\n",
    "\n",
    "print(\"\\n2단계 연산 준비:\", first_out.shape)\n",
    "\n",
    "second_linear = tf.keras.layers.Dense(units=1, use_bias=False)\n",
    "second_out = second_linear(first_out)\n",
    "second_out = tf.squeeze(second_out, axis=-1)\n",
    "\n",
    "print(\"2단계 연산 결과:\", second_out.shape)\n",
    "print(\"2단계 Linear Layer의 Weight 형태:\", second_linear.weights[0].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fourth-wrestling",
   "metadata": {},
   "source": [
    "![](https://images.velog.io/images/moondeokjong/post/35f1218f-6c3b-4f34-aa50-faf69faf77a3/Screenshot%202022-02-07%20at%2010.56.56%20AM.png) _- 출처 : 아이펠_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "discrete-marks",
   "metadata": {},
   "source": [
    "근데 이렇게 보면 2개의 다른 사각형이 첫번째 선형변환 후 같아져 버리기 때문에 저 첫번째 weight은 올바르지 않은 weight임"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "reported-spare",
   "metadata": {},
   "source": [
    "그래서 데이터를 집약하기 전에 한 번 풍부하게 해본다!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "saved-civilian",
   "metadata": {},
   "source": [
    "-----------------------------------\n",
    "-----------------------------------\n",
    "-----------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "complicated-protein",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(24,), dtype=float32, numpy=\n",
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0.], dtype=float32)>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.zeros((24,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "trained-satellite",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(24,), dtype=float32, numpy=\n",
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0.], dtype=float32)>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.zeros((24))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "administrative-retention",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(24, 1), dtype=float32, numpy=\n",
       "array([[0.],\n",
       "       [0.],\n",
       "       [0.],\n",
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       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.]], dtype=float32)>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.zeros((24, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "contained-scope",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(1, 24), dtype=float32, numpy=\n",
       "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0.]], dtype=float32)>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.zeros((1, 24))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "scientific-switch",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(24, 1), dtype=float32, numpy=\n",
       "array([[0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
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       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.]], dtype=float32)>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.keras.layers.Dense(1)(tf.zeros((24,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "latin-lawyer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(1, 1), dtype=float32, numpy=array([[0.]], dtype=float32)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.keras.layers.Dense(1)(tf.zeros((1,24)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "composed-institute",
   "metadata": {},
   "source": [
    "#### 위와 같이 테스트하면서 squeeze 안 해보기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "conservative-occupation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t_boxes.shape: (64, 4, 2)\n",
      "===========================\n",
      "out_1.shape: (64, 4, 1)\n",
      "weight 형태: (2, 1)\n",
      "===========================\n",
      "out_2.shape: (64, 4, 1)\n",
      "weight 형태: (1, 1)\n",
      "===========================\n"
     ]
    }
   ],
   "source": [
    "t_boxes = tf.zeros((64, 4, 2))\n",
    "print(f't_boxes.shape: {t_boxes.shape}')\n",
    "print('===========================')\n",
    "\n",
    "linear_1 = tf.keras.layers.Dense(1)\n",
    "out_1 = linear_1(t_boxes)\n",
    "print(f'out_1.shape: {out_1.shape}')\n",
    "print(f'weight 형태: {linear_1.weights[0].shape}')\n",
    "print('===========================')\n",
    "\n",
    "linear_2 = tf.keras.layers.Dense(1)\n",
    "out_2 = linear_2(out_1)\n",
    "print(f'out_2.shape: {out_2.shape}')\n",
    "print(f'weight 형태: {linear_2.weights[0].shape}')\n",
    "print('===========================')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "solid-history",
   "metadata": {},
   "source": [
    "#### flatten()해서 할 경우"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "lasting-mouth",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f_boxes.shape: (64, 4, 2)\n",
      "===================================\n",
      "After flatten - out_1.shape: (64, 8)\n",
      "===========================\n",
      "After dense - out_2.shape: (64, 1)\n",
      "weight 형태: (8, 1)\n",
      "===========================\n"
     ]
    }
   ],
   "source": [
    "f_boxes = tf.zeros((64, 4, 2))\n",
    "print(f'f_boxes.shape: {f_boxes.shape}')\n",
    "print('===================================')\n",
    "fl = tf.keras.layers.Flatten()\n",
    "out_1 = fl(f_boxes)\n",
    "print(f'After flatten - out_1.shape: {out_1.shape}')\n",
    "print('===========================')\n",
    "\n",
    "linear_1 = tf.keras.layers.Dense(1)\n",
    "out_2 = linear_1(out_1)\n",
    "print(f'After dense - out_2.shape: {out_2.shape}')\n",
    "print(f'weight 형태: {linear_1.weights[0].shape}')\n",
    "print('===========================')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aerial-mistake",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 6220800)\n"
     ]
    }
   ],
   "source": [
    "# 4차원 flatten해보기\n",
    "\n",
    "im = tf.zeros((64, 1920, 1080, 3))\n",
    "im_out = tf.keras.layers.Flatten()(im)\n",
    "print(im_out.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "mobile-advisory",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before flat - shape: (64, 8)\n",
      "======================================\n",
      "After flat - shape: (64, 8)\n"
     ]
    }
   ],
   "source": [
    "# (64,8)에서 flatten을 한 번 더 하면?\n",
    "\n",
    "t = tf.zeros((64, 8))\n",
    "print(f'Before flat - shape: {t.shape}')\n",
    "print('======================================')\n",
    "\n",
    "ff = tf.keras.layers.Flatten()\n",
    "double_flat = ff(t)\n",
    "print(f'After flat - shape: {double_flat.shape}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "another-answer",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 8, 10)\n"
     ]
    }
   ],
   "source": [
    "# 그냥 3차원만 2차원으로 줄여주나?\n",
    "z = tf.zeros((64, 8, 10))\n",
    "print(z.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fourth-sample",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 80)\n"
     ]
    }
   ],
   "source": [
    "fff = tf.keras.layers.Flatten()\n",
    "zz = fff(z)\n",
    "print(zz.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "early-legend",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 80)\n"
     ]
    }
   ],
   "source": [
    "ffff = tf.keras.layers.Flatten()\n",
    "zzz = ffff(zz)\n",
    "print(zzz.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "normal-habitat",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 1)\n"
     ]
    }
   ],
   "source": [
    "# 혹시 2차원을 Dense(1)에 넣으면?\n",
    "d = tf.keras.layers.Dense(1)\n",
    "a = d(zzz)\n",
    "print(a.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "charged-identity",
   "metadata": {},
   "source": [
    "#### 여튼 벡터인데, 행렬의 모습을 가진 벡터로 나온다..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "digital-andrew",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64,)\n"
     ]
    }
   ],
   "source": [
    "# squeeze 해줘보자\n",
    "aa = tf.squeeze(a)\n",
    "print(aa.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "compound-istanbul",
   "metadata": {},
   "source": [
    "#### 차원에 정보가 있는데 그냥 squeeze를 한다면..?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "federal-customer",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 80)\n",
      "(64, 80)\n"
     ]
    }
   ],
   "source": [
    "print(zz.shape)\n",
    "szz = tf.squeeze(zz)\n",
    "print(szz.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fuzzy-grave",
   "metadata": {},
   "source": [
    "-----------------------------------\n",
    "-----------------------------------\n",
    "-----------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "technological-sweden",
   "metadata": {},
   "source": [
    "#### 데이터를 한 번 풍부하게 늘린 후에 다시 집약하는 형태로 가보자!\n",
    "> <식2>  \n",
    "> 1단계: (4, 2) x (2 x 3) 행렬 = (4, 3)  \n",
    "> 2단계: (4, 3) x (3 x 1) 행렬 = (4, )  \n",
    "> 3단계: (4, ) x (4 x 1) 행렬 = (1, )  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "administrative-crisis",
   "metadata": {},
   "source": [
    "![](https://images.velog.io/images/moondeokjong/post/db6f3b15-9ff6-4159-913e-84967aea6b10/Screenshot%202022-02-07%20at%2011.00.19%20AM.png) _- 출처 : 아이펠_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "elder-interest",
   "metadata": {},
   "source": [
    "이렇게 하니 조금씩 독립적인 정보가 생겨난다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "literary-green",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1단계 연산 준비: (64, 4, 2)\n",
      "1단계 연산 결과: (64, 4, 3)\n",
      "1단계 Linear Layer의 Weight 형태: (2, 3)\n",
      "\n",
      "2단계 연산 준비: (64, 4, 3)\n",
      "2단계 연산 결과: (64, 4)\n",
      "2단계 Linear Layer의 Weight 형태: (3, 1)\n",
      "\n",
      "3단계 연산 준비: (64, 4)\n",
      "3단계 연산 결과: (64,)\n",
      "3단계 Linear Layer의 Weight 형태: (4, 1)\n",
      "총 Parameters: 13\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "batch_size = 64\n",
    "boxes = tf.zeros((batch_size, 4, 2))\n",
    "\n",
    "print(\"1단계 연산 준비:\", boxes.shape)\n",
    "\n",
    "########\n",
    "# Step 1: (4,2)차원인 boxes를 (4,3)으로 확장시키는 Linear Layer를 활용하세요.\n",
    "# Enter Your Source\n",
    "first_linear = tf.keras.layers.Dense(3, use_bias=False)\n",
    "first_out = first_linear(boxes)\n",
    "\n",
    "########\n",
    "\n",
    "print(\"1단계 연산 결과:\", first_out.shape)\n",
    "print(\"1단계 Linear Layer의 Weight 형태:\", first_linear.weights[0].shape)\n",
    "\n",
    "print(\"\\n2단계 연산 준비:\", first_out.shape)\n",
    "\n",
    "# Dense = Linear\n",
    "second_linear = tf.keras.layers.Dense(units=1, use_bias=False)\n",
    "second_out = second_linear(first_out)\n",
    "second_out = tf.squeeze(second_out, axis=-1)\n",
    "\n",
    "print(\"2단계 연산 결과:\", second_out.shape)\n",
    "print(\"2단계 Linear Layer의 Weight 형태:\", second_linear.weights[0].shape)\n",
    "\n",
    "print(\"\\n3단계 연산 준비:\", second_out.shape)\n",
    "\n",
    "########\n",
    "# Step 2: 4차원인 second_out을 하나의 실수으로 집약시키는 Linear Layer를 활용하세요.\n",
    "# Enter Your Source\n",
    "third_linear = tf.keras.layers.Dense(units=1, use_bias=False)\n",
    "third_out = third_linear(second_out)\n",
    "third_out = tf.squeeze(third_out, axis=-1)\n",
    "########\n",
    "\n",
    "print(\"3단계 연산 결과:\", third_out.shape)\n",
    "print(\"3단계 Linear Layer의 Weight 형태:\", third_linear.weights[0].shape)\n",
    "\n",
    "########\n",
    "# Step 3: 모든 params를 더하여 total_parmams를 구하세요.\n",
    "# Enter Your Source\n",
    "total_params = first_linear.count_params() + second_linear.count_params() + third_linear.count_params()\n",
    "\n",
    "########\n",
    "\n",
    "print(\"총 Parameters:\", total_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "powered-shelter",
   "metadata": {},
   "source": [
    "> 이런 식으로 적절한 Weight의 형태를 선언해주면, 컴퓨터가 해당 Weight을 임의의 실수로 채우면서 적절한 Weight을 찾아가는게 바로 훈련  \n",
    "> 적절한 파라미터는 결국 훈련 데이터의 분포에 따라 달라진다.  \n",
    "> 즉, test 데이터와 다른 분포로 훈련된다면 적절하지 않은 파라미터 값으로 수렴할 것이다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wicked-soldier",
   "metadata": {},
   "source": [
    "## Convolutional 레이어"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "speaking-cheese",
   "metadata": {},
   "source": [
    "> <식 4>  \n",
    "> 1단계: (1920, 1080, 3) x [3 x 16 x 5 x 5 Weight & Stride 5] = (384, 216, 16)  \n",
    "> 2단계: (384, 216, 16) → (384 x 216 x 16, )  \n",
    "> 3단계: (1327104, ) x [1327104 x 1 Weight] = (1, )  \n",
    "> <식 4>는 16개의 5 x 5 필터를 가진 Convolution 레이어를 정의하여 이미지를 Stride 5로 훑었고, 그렇게 생성된 출력을 1차원으로 펼쳐 Linear 레이어로 정보를 집약함"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "civil-concord",
   "metadata": {},
   "source": [
    "#### Q4. 하지만 그럼에도 <식 4>를 따라 정의한 신경망은 손을 찾는 데에 적합하지 않을 수도 있습니다. 이유가 무엇일까요? (힌트는 필터에 있습니다!)\n",
    "> 1) 1920 x 1080 인 입력 데이터 크기에 비해 5 x 5는 손이라는 객체를 판별해 낼 수 있을 만큼 유의미한 정보를 담기에는 작은 필터 사이즈일 가능성이 높다. 입력의 크기를 줄이거나 필터 크기를 키울 필요가 있다.  \n",
    "> 2) 필터의 크기와 Stride를 같게 하면 손이 필터의 경계선에 걸리는 경우, 정상적으로 인식하지 못할 수 있다. Stride를 줄여 이미지를 더 자세하게 살피도록 설계하자."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "emotional-burner",
   "metadata": {},
   "source": [
    "> 그렇다면 필터 사이즈를 훨씬 크게 하는 게 답? 그렇지 않다.  \n",
    "> 만약 극단적으로 필터 사이즈를 이미지 사이즈와 동일하게 한다면,  \n",
    "> 그 순간 우리의 Convolution 레이어는 완벽하게 Linear 레이어와 같아지게 된다.  \n",
    "> 필터 사이즈를 키우게 되면 파라미터 사이즈와 연산량이 커질 뿐 아니라, Accuracy도 떨어지게 될 가능성이 높다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "naval-mobile",
   "metadata": {},
   "outputs": [
    {
     "ename": "ResourceExhaustedError",
     "evalue": "OOM when allocating tensor with shape[64,1920,1080,3] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [Op:Fill]",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mResourceExhaustedError\u001b[0m                    Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-46-20691cb0eec2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m64\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mpic\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1920\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1080\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\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[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"입력 이미지 데이터:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpic\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    199\u001b[0m     \u001b[0;34m\"\"\"Call target, and fall back on dispatchers if there is a TypeError.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 201\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    202\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\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[1;32m    203\u001b[0m       \u001b[0;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/array_ops.py\u001b[0m in \u001b[0;36mwrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   2817\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2818\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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[0;32m-> 2819\u001b[0;31m     \u001b[0mtensor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2820\u001b[0m     \u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_zeros_tensor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2821\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/array_ops.py\u001b[0m in \u001b[0;36mzeros\u001b[0;34m(shape, dtype, name)\u001b[0m\n\u001b[1;32m   2878\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_shape_tuple\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[1;32m   2879\u001b[0m       \u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Ensure it's a vector\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2880\u001b[0;31m     \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconstant\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzero\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2881\u001b[0m   \u001b[0;32massert\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase_dtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2882\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    199\u001b[0m     \u001b[0;34m\"\"\"Call target, and fall back on dispatchers if there is a TypeError.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 201\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    202\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\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[1;32m    203\u001b[0m       \u001b[0;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/array_ops.py\u001b[0m in \u001b[0;36mfill\u001b[0;34m(dims, value, name)\u001b[0m\n\u001b[1;32m    237\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mend_compatibility\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m   \"\"\"\n\u001b[0;32m--> 239\u001b[0;31m   \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgen_array_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    240\u001b[0m   \u001b[0mtensor_util\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_set_static_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    241\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/gen_array_ops.py\u001b[0m in \u001b[0;36mfill\u001b[0;34m(dims, value, name)\u001b[0m\n\u001b[1;32m   3346\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0m_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3347\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3348\u001b[0;31m       \u001b[0m_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from_not_ok_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3349\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_FallbackException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3350\u001b[0m       \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mraise_from_not_ok_status\u001b[0;34m(e, name)\u001b[0m\n\u001b[1;32m   6860\u001b[0m   \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\" name: \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\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[1;32m   6861\u001b[0m   \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6862\u001b[0;31m   \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6863\u001b[0m   \u001b[0;31m# pylint: enable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6864\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/six.py\u001b[0m in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n",
      "\u001b[0;31mResourceExhaustedError\u001b[0m: OOM when allocating tensor with shape[64,1920,1080,3] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [Op:Fill]"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "batch_size = 64\n",
    "pic = tf.zeros((batch_size, 1920, 1080, 3))\n",
    "\n",
    "print(\"입력 이미지 데이터:\", pic.shape)\n",
    "conv_layer = tf.keras.layers.Conv2D(filters=16,\n",
    "                                    kernel_size=(5, 5),\n",
    "                                    strides=5,\n",
    "                                    use_bias=False)\n",
    "conv_out = conv_layer(pic)\n",
    "\n",
    "print(\"\\nConvolution 결과:\", conv_out.shape)\n",
    "print(\"Convolution Layer의 Parameter 수:\", conv_layer.count_params())\n",
    "\n",
    "flatten_out = tf.keras.layers.Flatten()(conv_out)\n",
    "print(\"\\n1차원으로 펼친 데이터:\", flatten_out.shape)\n",
    "\n",
    "linear_layer = tf.keras.layers.Dense(units=1, use_bias=False)\n",
    "linear_out = linear_layer(flatten_out)\n",
    "\n",
    "print(\"\\nLinear 결과:\", linear_out.shape)\n",
    "print(\"Linear Layer의 Parameter 수:\", linear_layer.count_params())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fancy-invalid",
   "metadata": {},
   "source": [
    "## Pooling 레이어"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "suitable-justice",
   "metadata": {},
   "source": [
    "## Deconvolution 레이어"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "graphic-scout",
   "metadata": {},
   "source": [
    "- Convolution으로 집약된 데이터가 정보 손실 측면에서 정말 유의미한 데이터가 되는지 확인해보자\n",
    "\n",
    "> - 패키지 임포트 및 MNIST 데이터셋 로딩  \n",
    "> - AutoEncoder 모델 구성  \n",
    "> - AutoEncoder 모델 훈련  \n",
    "> - AutoEncoder Reconstruction Test  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "varied-convention",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "11493376/11490434 [==============================] - 0s 0us/step\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from tensorflow.keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.datasets import mnist\n",
    "import json\n",
    "import matplotlib.pyplot as plt #for plotting\n",
    "\n",
    "# MNIST 데이터 로딩\n",
    "(x_train, _), (x_test, _) = mnist.load_data()    # y_train, y_test는 사용하지 않습니다.\n",
    "\n",
    "x_train = np.expand_dims(x_train, axis=3)\n",
    "x_test = np.expand_dims(x_test, axis=3)\n",
    "\n",
    "x_train = x_train.astype('float32') / 255.\n",
    "x_test = x_test.astype('float32') / 255."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "filled-wilson",
   "metadata": {},
   "source": [
    "> 라벨 정보를 버리는데, 이유는 Image Reconstruction 시 라벨은 사실 x 자신이 되기 때문이다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "inappropriate-graph",
   "metadata": {},
   "source": [
    "----------------------\n",
    "----------------------\n",
    "----------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "apparent-practice",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "(xx, yy), (x, y) = mnist.load_data()\n",
    "\n",
    "print(xx.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "medieval-onion",
   "metadata": {},
   "source": [
    "----------------------\n",
    "----------------------\n",
    "----------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "animal-collectible",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 28, 28, 1)]       0         \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 28, 28, 16)        160       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 14, 14, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 14, 14, 8)         1160      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 7, 7, 8)           0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 7, 7, 4)           292       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 4, 4, 4)           0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 4, 4, 4)           148       \n",
      "_________________________________________________________________\n",
      "up_sampling2d (UpSampling2D) (None, 8, 8, 4)           0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 8, 8, 8)           296       \n",
      "_________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2 (None, 16, 16, 8)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 14, 14, 16)        1168      \n",
      "_________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2 (None, 28, 28, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 28, 28, 1)         145       \n",
      "=================================================================\n",
      "Total params: 3,369\n",
      "Trainable params: 3,369\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# AutoEncoder 모델 구성 - Input 부분\n",
    "input_shape = x_train.shape[1:]\n",
    "input_img = Input(shape=input_shape)\n",
    "\n",
    "# AutoEncoder 모델 구성 - Encoder 부분\n",
    "encode_conv_layer_1 = Conv2D(16, (3, 3), activation='relu', padding='same')\n",
    "encode_pool_layer_1 = MaxPooling2D((2, 2), padding='same')\n",
    "encode_conv_layer_2 = Conv2D(8, (3, 3), activation='relu', padding='same')\n",
    "encode_pool_layer_2 = MaxPooling2D((2, 2), padding='same')\n",
    "encode_conv_layer_3 = Conv2D(4, (3, 3), activation='relu', padding='same')\n",
    "encode_pool_layer_3 = MaxPooling2D((2, 2), padding='same')\n",
    "\n",
    "encoded = encode_conv_layer_1(input_img)\n",
    "encoded = encode_pool_layer_1(encoded)\n",
    "encoded = encode_conv_layer_2(encoded)\n",
    "encoded = encode_pool_layer_2(encoded)\n",
    "encoded = encode_conv_layer_3(encoded)\n",
    "encoded = encode_pool_layer_3(encoded)\n",
    "\n",
    "# AutoEncoder 모델 구성 - Decoder 부분\n",
    "decode_conv_layer_1 = Conv2D(4, (3, 3), activation='relu', padding='same')\n",
    "decode_upsample_layer_1 = UpSampling2D((2, 2))\n",
    "decode_conv_layer_2 = Conv2D(8, (3, 3), activation='relu', padding='same')\n",
    "decode_upsample_layer_2 = UpSampling2D((2, 2))\n",
    "decode_conv_layer_3 = Conv2D(16, (3, 3), activation='relu')\n",
    "decode_upsample_layer_3 = UpSampling2D((2, 2))\n",
    "decode_conv_layer_4 = Conv2D(1, (3, 3), activation='sigmoid', padding='same')\n",
    "\n",
    "decoded = decode_conv_layer_1(encoded)   # Decoder는 Encoder의 출력을 입력으로 받습니다.\n",
    "decoded = decode_upsample_layer_1(decoded)\n",
    "decoded = decode_conv_layer_2(decoded)\n",
    "decoded = decode_upsample_layer_2(decoded)\n",
    "decoded = decode_conv_layer_3(decoded)\n",
    "decoded = decode_upsample_layer_3(decoded)\n",
    "decoded = decode_conv_layer_4(decoded)\n",
    "\n",
    "# AutoEncoder 모델 정의\n",
    "autoencoder = Model(input_img, decoded)\n",
    "autoencoder.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "requested-stomach",
   "metadata": {},
   "source": [
    "#### 설명\n",
    "\n",
    "1. y_train이 들어가야 할 자리에 x_train이 들어가 있음을 유의해 주십시오.\n",
    "2. 그리고 loss로 주로 분류 모델에서 사용되는 binary_crossentropy를 사용해서 다소 의아할 것입니다.\n",
    "3. 그러나 모든 dim에서 0~1 사이의 값을 가지는 입력 데이터와 출력 데이터(마지막 출력이 sigmoid로 제어되고 있음에 유의합시다) 사이의 분포가 최대한 유사하기를 바라기 때문에 이 loss는 타당합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "continuing-jimmy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "235/235 [==============================] - 15s 42ms/step - loss: 0.6972 - val_loss: 0.6958\n",
      "Epoch 2/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.6953 - val_loss: 0.6938\n",
      "Epoch 3/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.6933 - val_loss: 0.6916\n",
      "Epoch 4/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.6910 - val_loss: 0.6891\n",
      "Epoch 5/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.6884 - val_loss: 0.6862\n",
      "Epoch 6/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.6853 - val_loss: 0.6825\n",
      "Epoch 7/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.6814 - val_loss: 0.6776\n",
      "Epoch 8/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.6759 - val_loss: 0.6704\n",
      "Epoch 9/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.6677 - val_loss: 0.6585\n",
      "Epoch 10/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.6539 - val_loss: 0.6369\n",
      "Epoch 11/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.6279 - val_loss: 0.5942\n",
      "Epoch 12/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.5771 - val_loss: 0.5242\n",
      "Epoch 13/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.5080 - val_loss: 0.4820\n",
      "Epoch 14/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4770 - val_loss: 0.4752\n",
      "Epoch 15/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4718 - val_loss: 0.4724\n",
      "Epoch 16/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4684 - val_loss: 0.4697\n",
      "Epoch 17/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4655 - val_loss: 0.4669\n",
      "Epoch 18/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4626 - val_loss: 0.4642\n",
      "Epoch 19/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4605 - val_loss: 0.4613\n",
      "Epoch 20/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4576 - val_loss: 0.4584\n",
      "Epoch 21/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4545 - val_loss: 0.4555\n",
      "Epoch 22/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4515 - val_loss: 0.4524\n",
      "Epoch 23/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4488 - val_loss: 0.4491\n",
      "Epoch 24/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4448 - val_loss: 0.4458\n",
      "Epoch 25/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4419 - val_loss: 0.4422\n",
      "Epoch 26/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4381 - val_loss: 0.4384\n",
      "Epoch 27/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4346 - val_loss: 0.4344\n",
      "Epoch 28/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4305 - val_loss: 0.4301\n",
      "Epoch 29/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4266 - val_loss: 0.4254\n",
      "Epoch 30/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4219 - val_loss: 0.4204\n",
      "Epoch 31/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4169 - val_loss: 0.4151\n",
      "Epoch 32/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4118 - val_loss: 0.4095\n",
      "Epoch 33/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4068 - val_loss: 0.4037\n",
      "Epoch 34/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4006 - val_loss: 0.3976\n",
      "Epoch 35/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.3941 - val_loss: 0.3913\n",
      "Epoch 36/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.3883 - val_loss: 0.3848\n",
      "Epoch 37/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.3810 - val_loss: 0.3783\n",
      "Epoch 38/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.3752 - val_loss: 0.3718\n",
      "Epoch 39/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.3689 - val_loss: 0.3655\n",
      "Epoch 40/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.3633 - val_loss: 0.3597\n",
      "Epoch 41/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.3567 - val_loss: 0.3544\n",
      "Epoch 42/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.3524 - val_loss: 0.3495\n",
      "Epoch 43/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.3474 - val_loss: 0.3451\n",
      "Epoch 44/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.3431 - val_loss: 0.3410\n",
      "Epoch 45/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.3395 - val_loss: 0.3372\n",
      "Epoch 46/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.3354 - val_loss: 0.3337\n",
      "Epoch 47/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.3318 - val_loss: 0.3303\n",
      "Epoch 48/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.3286 - val_loss: 0.3271\n",
      "Epoch 49/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.3255 - val_loss: 0.3240\n",
      "Epoch 50/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.3225 - val_loss: 0.3211\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f263d0e0e10>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')\n",
    "\n",
    "autoencoder.fit(x_train, x_train,\n",
    "                epochs=50,\n",
    "                batch_size=256,\n",
    "                shuffle=True,\n",
    "                validation_data=(x_test, x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "transparent-resident",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_test_10 = x_test[:10]       # 테스트 데이터셋에서 10개만 골라서\n",
    "x_test_hat = autoencoder.predict(x_test_10)    # AutoEncoder 모델의 이미지 복원생성\n",
    "x_test_imgs = x_test_10.reshape(-1, 28, 28)\n",
    "x_test_hat_imgs = x_test_hat.reshape(-1, 28, 28)\n",
    "\n",
    "plt.figure(figsize=(12,5))  # 이미지 사이즈 지정\n",
    "for i in range(10):  \n",
    "    # 원본이미지 출력\n",
    "    plt.subplot(2, 10, i+1)\n",
    "    plt.imshow(x_test_imgs[i])\n",
    "    # 생성된 이미지 출력\n",
    "    plt.subplot(2, 10, i+11)\n",
    "    plt.imshow(x_test_hat_imgs[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interstate-monthly",
   "metadata": {},
   "source": [
    "> 결과가 정확하진 않지만, 동일한 구조의 decoder로 훌륭하게 deconvolution할 수 있기 때문에 다른 요인이 있다는 정도로만 알고 넘어가자"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "confirmed-snake",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 28, 28)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test_imgs.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "phantom-humor",
   "metadata": {},
   "source": [
    "---------------------\n",
    "---------------------\n",
    "---------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "functional-cinema",
   "metadata": {},
   "source": [
    "#### `padding='valid'`, `padding='same'` 차이 살펴보기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "canadian-proportion",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = tf.constant([[1, 2, 3],\n",
    "                [4, 5, 6],\n",
    "                [7, 8, 9]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "underlying-arbitration",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3, 3), dtype=int32, numpy=\n",
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]], dtype=int32)>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "placed-switzerland",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = tf.reshape(x, (1, 3, 3, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "graphic-sandwich",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(1, 3, 3, 1), dtype=int32, numpy=\n",
       "array([[[[1],\n",
       "         [2],\n",
       "         [3]],\n",
       "\n",
       "        [[4],\n",
       "         [5],\n",
       "         [6]],\n",
       "\n",
       "        [[7],\n",
       "         [8],\n",
       "         [9]]]], dtype=int32)>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "japanese-rings",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(1, 1, 1, 1), dtype=int32, numpy=array([[[[5]]]], dtype=int32)>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tm = tf.keras.layers.MaxPooling2D((2,2), strides=2, padding='valid')\n",
    "tm(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "constitutional-diesel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(1, 2, 2, 1), dtype=int32, numpy=\n",
       "array([[[[5],\n",
       "         [6]],\n",
       "\n",
       "        [[8],\n",
       "         [9]]]], dtype=int32)>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tm = tf.keras.layers.MaxPooling2D((2,2), strides=2, padding='same')\n",
    "tm(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bound-invention",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "primary-heater",
   "metadata": {},
   "source": [
    "### Transposed Convolution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "latest-amplifier",
   "metadata": {},
   "source": [
    "- 사실 위의 모델은 deconvolution이 아니라, 엄밀히 따지면\n",
    "- 인코더와 동일한 convolution 모델을 사용했기에 정방향의 convolution 연산을 통해 이미지 재생산을 흉내낸 것임.\n",
    "- 진짜는 수학적 역연산이 되어야 함.\n",
    "- 그래서 진짜 deconvolution 하는 아래의 모델로 확인해보자"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "sustainable-socket",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         [(None, 28, 28, 1)]       0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 26, 26, 16)        160       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 13, 13, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 11, 11, 8)         1160      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 5, 5, 8)           0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 3, 3, 4)           292       \n",
      "_________________________________________________________________\n",
      "conv2d_transpose (Conv2DTran (None, 3, 3, 4)           148       \n",
      "_________________________________________________________________\n",
      "up_sampling2d_3 (UpSampling2 (None, 6, 6, 4)           0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTr (None, 6, 6, 8)           296       \n",
      "_________________________________________________________________\n",
      "up_sampling2d_4 (UpSampling2 (None, 12, 12, 8)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_2 (Conv2DTr (None, 14, 14, 16)        1168      \n",
      "_________________________________________________________________\n",
      "up_sampling2d_5 (UpSampling2 (None, 28, 28, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_3 (Conv2DTr (None, 28, 28, 1)         145       \n",
      "=================================================================\n",
      "Total params: 3,369\n",
      "Trainable params: 3,369\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.layers import Conv2DTranspose\n",
    "\n",
    "# Conv2DTranspose를 활용한  AutoEncoder 모델\n",
    "# AutoEncoder 모델 구성 - Input 부분\n",
    "input_shape = x_train.shape[1:]\n",
    "input_img = Input(shape=input_shape)\n",
    "\n",
    "# AutoEncoder 모델 구성 - Encoder 부분\n",
    "encode_conv_layer_1 = Conv2D(16, (3, 3), activation='relu')\n",
    "encode_pool_layer_1 = MaxPooling2D((2, 2))\n",
    "encode_conv_layer_2 = Conv2D(8, (3, 3), activation='relu')\n",
    "encode_pool_layer_2 = MaxPooling2D((2, 2))\n",
    "encode_conv_layer_3 = Conv2D(4, (3, 3), activation='relu')\n",
    "\n",
    "encoded = encode_conv_layer_1(input_img)\n",
    "encoded = encode_pool_layer_1(encoded)\n",
    "encoded = encode_conv_layer_2(encoded)\n",
    "encoded = encode_pool_layer_2(encoded)\n",
    "encoded = encode_conv_layer_3(encoded)\n",
    "\n",
    "# AutoEncoder 모델 구성 - Decoder 부분  - \n",
    "decode_conv_layer_1 = Conv2DTranspose(4, (3, 3), activation='relu', padding='same')\n",
    "decode_upsample_layer_1 = UpSampling2D((2, 2))\n",
    "decode_conv_layer_2 = Conv2DTranspose(8, (3, 3), activation='relu', padding='same')\n",
    "decode_upsample_layer_2 = UpSampling2D((2, 2))\n",
    "decode_conv_layer_3 = Conv2DTranspose(16, (3, 3), activation='relu')\n",
    "decode_upsample_layer_3 = UpSampling2D((2, 2))\n",
    "decode_conv_layer_4 = Conv2DTranspose(1, (3, 3), activation='sigmoid', padding='same')\n",
    "\n",
    "decoded = decode_conv_layer_1(encoded)   # Decoder는 Encoder의 출력을 입력으로 받습니다.\n",
    "decoded = decode_upsample_layer_1(decoded)\n",
    "decoded = decode_conv_layer_2(decoded)\n",
    "decoded = decode_upsample_layer_2(decoded)\n",
    "decoded = decode_conv_layer_3(decoded)\n",
    "decoded = decode_upsample_layer_3(decoded)\n",
    "decoded = decode_conv_layer_4(decoded)\n",
    "\n",
    "# AutoEncoder 모델 정의\n",
    "autoencoder = Model(input_img, decoded)\n",
    "autoencoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "subjective-justice",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_test_10 = x_test[:10]       # 테스트 데이터셋에서 10개만 골라서\n",
    "x_test_hat = autoencoder.predict(x_test_10)    # AutoEncoder 모델의 이미지 복원생성\n",
    "x_test_imgs = x_test_10.reshape(-1, 28, 28)\n",
    "x_test_hat_imgs = x_test_hat.reshape(-1, 28, 28)\n",
    "\n",
    "plt.figure(figsize=(12,5))  # 이미지 사이즈 지정\n",
    "for i in range(10):  \n",
    "    # 원본이미지 출력\n",
    "    plt.subplot(2, 10, i+1)\n",
    "    plt.imshow(x_test_imgs[i])\n",
    "    # 생성된 이미지 출력\n",
    "    plt.subplot(2, 10, i+11)\n",
    "    plt.imshow(x_test_hat_imgs[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "imperial-toddler",
   "metadata": {},
   "source": [
    "> 위는 내가 실수로 훈련하지 않고 그냥 predict했을 때의 모습이다.  \n",
    "> 아마 weight이 임의로 선정되어 있는 초기값 상태이지 않을까 싶다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "superb-korea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "235/235 [==============================] - 60s 155ms/step - loss: 0.6909 - val_loss: 0.6903\n",
      "Epoch 2/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.6901 - val_loss: 0.6895\n",
      "Epoch 3/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.6892 - val_loss: 0.6884\n",
      "Epoch 4/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.6881 - val_loss: 0.6871\n",
      "Epoch 5/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.6867 - val_loss: 0.6855\n",
      "Epoch 6/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.6850 - val_loss: 0.6835\n",
      "Epoch 7/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.6829 - val_loss: 0.6810\n",
      "Epoch 8/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.6801 - val_loss: 0.6777\n",
      "Epoch 9/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.6765 - val_loss: 0.6731\n",
      "Epoch 10/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.6715 - val_loss: 0.6664\n",
      "Epoch 11/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.6640 - val_loss: 0.6560\n",
      "Epoch 12/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.6521 - val_loss: 0.6387\n",
      "Epoch 13/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.6323 - val_loss: 0.6120\n",
      "Epoch 14/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.6039 - val_loss: 0.5863\n",
      "Epoch 15/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.5810 - val_loss: 0.5749\n",
      "Epoch 16/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.5711 - val_loss: 0.5698\n",
      "Epoch 17/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.5659 - val_loss: 0.5655\n",
      "Epoch 18/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.5614 - val_loss: 0.5614\n",
      "Epoch 19/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.5573 - val_loss: 0.5573\n",
      "Epoch 20/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.5532 - val_loss: 0.5533\n",
      "Epoch 21/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.5494 - val_loss: 0.5493\n",
      "Epoch 22/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.5456 - val_loss: 0.5454\n",
      "Epoch 23/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.5417 - val_loss: 0.5415\n",
      "Epoch 24/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.5379 - val_loss: 0.5374\n",
      "Epoch 25/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.5335 - val_loss: 0.5334\n",
      "Epoch 26/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.5296 - val_loss: 0.5292\n",
      "Epoch 27/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.5256 - val_loss: 0.5249\n",
      "Epoch 28/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.5212 - val_loss: 0.5204\n",
      "Epoch 29/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.5166 - val_loss: 0.5158\n",
      "Epoch 30/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.5118 - val_loss: 0.5108\n",
      "Epoch 31/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.5071 - val_loss: 0.5057\n",
      "Epoch 32/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.5018 - val_loss: 0.5002\n",
      "Epoch 33/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.4961 - val_loss: 0.4945\n",
      "Epoch 34/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4902 - val_loss: 0.4884\n",
      "Epoch 35/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4840 - val_loss: 0.4819\n",
      "Epoch 36/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4779 - val_loss: 0.4752\n",
      "Epoch 37/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4712 - val_loss: 0.4681\n",
      "Epoch 38/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4636 - val_loss: 0.4604\n",
      "Epoch 39/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4560 - val_loss: 0.4522\n",
      "Epoch 40/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4476 - val_loss: 0.4432\n",
      "Epoch 41/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.4393 - val_loss: 0.4334\n",
      "Epoch 42/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.4290 - val_loss: 0.4227\n",
      "Epoch 43/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.4182 - val_loss: 0.4108\n",
      "Epoch 44/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.4065 - val_loss: 0.3978\n",
      "Epoch 45/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.3930 - val_loss: 0.3832\n",
      "Epoch 46/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.3783 - val_loss: 0.3675\n",
      "Epoch 47/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.3628 - val_loss: 0.3522\n",
      "Epoch 48/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.3490 - val_loss: 0.3387\n",
      "Epoch 49/50\n",
      "235/235 [==============================] - 3s 13ms/step - loss: 0.3357 - val_loss: 0.3274\n",
      "Epoch 50/50\n",
      "235/235 [==============================] - 3s 12ms/step - loss: 0.3251 - val_loss: 0.3182\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f25d09a3d90>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')\n",
    "\n",
    "autoencoder.fit(x_train, x_train,\n",
    "                epochs=50,\n",
    "                batch_size=256,\n",
    "                shuffle=True,\n",
    "                validation_data=(x_test, x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "baking-repair",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_test_10 = x_test[:10]       # 테스트 데이터셋에서 10개만 골라서\n",
    "x_test_hat = autoencoder.predict(x_test_10)    # AutoEncoder 모델의 이미지 복원생성\n",
    "x_test_imgs = x_test_10.reshape(-1, 28, 28)\n",
    "x_test_hat_imgs = x_test_hat.reshape(-1, 28, 28)\n",
    "\n",
    "plt.figure(figsize=(12,5))  # 이미지 사이즈 지정\n",
    "for i in range(10):  \n",
    "    # 원본이미지 출력\n",
    "    plt.subplot(2, 10, i+1)\n",
    "    plt.imshow(x_test_imgs[i])\n",
    "    # 생성된 이미지 출력\n",
    "    plt.subplot(2, 10, i+11)\n",
    "    plt.imshow(x_test_hat_imgs[i])"
   ]
  }
 ],
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