code/ch15/ch15.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 15장. 심층 합성곱 신경망으로 이미지 분류"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**아래 링크를 통해 이 노트북을 주피터 노트북 뷰어(nbviewer.jupyter.org)로 보거나 구글 코랩(colab.research.google.com)에서 실행할 수 있습니다.**\n",
    "\n",
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://nbviewer.jupyter.org/github/rickiepark/python-machine-learning-book-2nd-edition/blob/master/code/ch15/ch15.ipynb\"><img src=\"https://jupyter.org/assets/main-logo.svg\" width=\"28\" />주피터 노트북 뷰어로 보기</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/python-machine-learning-book-2nd-edition/blob/master/code/ch15/ch15.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩(Colab)에서 실행하기</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`watermark`는 주피터 노트북에 사용하는 파이썬 패키지를 출력하기 위한 유틸리티입니다. `watermark` 패키지를 설치하려면 다음 셀의 주석을 제거한 뒤 실행하세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-08-10 \n",
      "\n",
      "numpy 1.18.5\n",
      "scipy 1.4.1\n",
      "tensorflow 2.3.0\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -d -p numpy,scipy,tensorflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**이 노트북을 실행하려면 텐서플로 2.0.0-alpha0 버전 이상이 필요합니다.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 합성곱 신경망의 구성 요소"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 이산 합성곱 수행하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 합성곱 출력 크기 계산하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conv1d 구현:  [ 5. 14. 16. 26. 24. 34. 19. 22.]\n",
      "넘파이 결과:  [ 5 14 16 26 24 34 19 22]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "def conv1d(x, w, p=0, s=1):\n",
    "    w_rot = np.array(w[::-1])\n",
    "    x_padded = np.array(x)\n",
    "    if p > 0:\n",
    "        zero_pad = np.zeros(shape=p)\n",
    "        x_padded = np.concatenate([zero_pad, x_padded, zero_pad])\n",
    "    res = []\n",
    "    for i in range(0, int(len(x)/s),s):\n",
    "        res.append(np.sum(x_padded[i:i+w_rot.shape[0]] * w_rot))\n",
    "    return np.array(res)\n",
    "\n",
    "## 테스트:\n",
    "x = [1, 3, 2, 4, 5, 6, 1, 3]\n",
    "w = [1, 0, 3, 1, 2]\n",
    "print('Conv1d 구현: ', \n",
    "      conv1d(x, w, p=2, s=1))\n",
    "print('넘파이 결과: ', \n",
    "      np.convolve(x, w, mode='same'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2D 이산 합성곱 수행하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conv2d 구현: \n",
      " [[11. 25. 32. 13.]\n",
      " [19. 25. 24. 13.]\n",
      " [13. 28. 25. 17.]\n",
      " [11. 17. 14.  9.]]\n",
      "싸이파이 결과: \n",
      " [[11 25 32 13]\n",
      " [19 25 24 13]\n",
      " [13 28 25 17]\n",
      " [11 17 14  9]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy.signal\n",
    "\n",
    "\n",
    "def conv2d(X, W, p=(0,0), s=(1,1)):\n",
    "    W_rot = np.array(W)[::-1,::-1]\n",
    "    X_orig = np.array(X)\n",
    "    n1 = X_orig.shape[0] + 2*p[0]\n",
    "    n2 = X_orig.shape[1] + 2*p[1]\n",
    "    X_padded = np.zeros(shape=(n1,n2))\n",
    "    X_padded[p[0]:p[0] + X_orig.shape[0], \n",
    "             p[1]:p[1] + X_orig.shape[1]] = X_orig\n",
    "\n",
    "    res = []\n",
    "    for i in range(0, int((X_padded.shape[0] - \n",
    "                           W_rot.shape[0])/s[0])+1, s[0]):\n",
    "        res.append([])\n",
    "        for j in range(0, int((X_padded.shape[1] - \n",
    "                               W_rot.shape[1])/s[1])+1, s[1]):\n",
    "            X_sub = X_padded[i:i+W_rot.shape[0], j:j+W_rot.shape[1]]\n",
    "            res[-1].append(np.sum(X_sub * W_rot))\n",
    "    return(np.array(res))\n",
    "    \n",
    "X = [[1, 3, 2, 4], [5, 6, 1, 3], [1 , 2,0, 2], [3, 4, 3, 2]]\n",
    "W = [[1, 0, 3], [1, 2, 1], [0, 1, 1]]\n",
    "print('Conv2d 구현: \\n', \n",
    "      conv2d(X, W, p=(1,1), s=(1,1)))\n",
    "\n",
    "print('싸이파이 결과: \\n', \n",
    "      scipy.signal.convolve2d(X, W, mode='same'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 기본 구성 요소를 사용하여 CNN 구성하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 여러 개의 입력 또는 컬러 채널을 다루기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**코랩을 사용할 때는 다음 셀의 주석을 제거하고 실행하세요.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!wget https://github.com/rickiepark/python-machine-learning-book-2nd-edition/raw/master/code/ch15/example-image.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "이미지 크기: (252, 221, 3)\n",
      "채널 수: 3\n",
      "이미지 데이터 타입: uint8\n",
      "[[[179 134 110]\n",
      "  [182 136 112]]\n",
      "\n",
      " [[180 135 111]\n",
      "  [182 137 113]]]\n"
     ]
    }
   ],
   "source": [
    "import imageio\n",
    "\n",
    "img = imageio.imread('./example-image.png', pilmode='RGB')\n",
    "    \n",
    "print('이미지 크기:', img.shape)\n",
    "print('채널 수:', img.shape[2])\n",
    "print('이미지 데이터 타입:', img.dtype)\n",
    "\n",
    "print(img[100:102, 100:102, :])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 텐서플로를 사용해 심층 합성곱 신경망 구현하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 적재와 전처리"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**코랩을 사용할 때는 다음 셀의 주석을 제거하고 실행하세요.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!wget https://github.com/rickiepark/python-machine-learning-book-2nd-edition/raw/master/code/ch12/t10k-images-idx3-ubyte.gz\n",
    "#!wget https://github.com/rickiepark/python-machine-learning-book-2nd-edition/raw/master/code/ch12/t10k-labels-idx1-ubyte.gz\n",
    "#!wget https://github.com/rickiepark/python-machine-learning-book-2nd-edition/raw/master/code/ch12/train-images-idx3-ubyte.gz\n",
    "#!wget https://github.com/rickiepark/python-machine-learning-book-2nd-edition/raw/master/code/ch12/train-labels-idx1-ubyte.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## MNIST 데이터셋 압축을 풉니다.\n",
    "\n",
    "import sys\n",
    "import gzip\n",
    "import shutil\n",
    "import os\n",
    "\n",
    "\n",
    "if (sys.version_info > (3, 0)):\n",
    "    writemode = 'wb'\n",
    "else:\n",
    "    writemode = 'w'\n",
    "\n",
    "zipped_mnist = [f for f in os.listdir('./')\n",
    "                if f.endswith('ubyte.gz')]\n",
    "for z in zipped_mnist:\n",
    "    with gzip.GzipFile(z, mode='rb') as decompressed, open(z[:-3], writemode) as outfile:\n",
    "        outfile.write(decompressed.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "행: 60000,  열: 784\n",
      "행: 10000,  열: 784\n",
      "훈련 세트:  (50000, 784) (50000,)\n",
      "검증 세트:  (10000, 784) (10000,)\n",
      "테스트 세트:  (10000, 784) (10000,)\n"
     ]
    }
   ],
   "source": [
    "import struct\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def load_mnist(path, kind='train'):\n",
    "    \"\"\"`path`에서 MNIST 데이터셋을 적재합니다.\"\"\"\n",
    "    labels_path = os.path.join(path,\n",
    "                               '%s-labels-idx1-ubyte'\n",
    "                                % kind)\n",
    "    images_path = os.path.join(path,\n",
    "                               '%s-images-idx3-ubyte'\n",
    "                               % kind)\n",
    "\n",
    "    with open(labels_path, 'rb') as lbpath:\n",
    "        magic, n = struct.unpack('>II',\n",
    "                                 lbpath.read(8))\n",
    "        labels = np.fromfile(lbpath,\n",
    "                             dtype=np.uint8)\n",
    "\n",
    "    with open(images_path, 'rb') as imgpath:\n",
    "        magic, num, rows, cols = struct.unpack(\">IIII\",\n",
    "                                               imgpath.read(16))\n",
    "        images = np.fromfile(imgpath,\n",
    "                             dtype=np.uint8).reshape(len(labels), 784)\n",
    "\n",
    "    return images, labels\n",
    "\n",
    "\n",
    "X_data, y_data = load_mnist('./', kind='train')\n",
    "print('행: %d,  열: %d' % (X_data.shape[0], X_data.shape[1]))\n",
    "X_test, y_test = load_mnist('./', kind='t10k')\n",
    "print('행: %d,  열: %d' % (X_test.shape[0], X_test.shape[1]))\n",
    "\n",
    "X_train, y_train = X_data[:50000,:], y_data[:50000]\n",
    "X_valid, y_valid = X_data[50000:,:], y_data[50000:]\n",
    "\n",
    "print('훈련 세트: ', X_train.shape, y_train.shape)\n",
    "print('검증 세트: ', X_valid.shape, y_valid.shape)\n",
    "print('테스트 세트: ', X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_vals = np.mean(X_train, axis=0)\n",
    "std_val = np.std(X_train)\n",
    "\n",
    "X_train_centered = (X_train - mean_vals)/std_val\n",
    "X_valid_centered = (X_valid - mean_vals)/std_val\n",
    "X_test_centered = (X_test - mean_vals)/std_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_centered = X_train_centered.reshape((-1, 28, 28, 1))\n",
    "X_valid_centered = X_valid_centered.reshape((-1, 28, 28, 1))\n",
    "X_test_centered = X_test_centered.reshape((-1, 28, 28, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "y_train_onehot = to_categorical(y_train)\n",
    "y_valid_onehot = to_categorical(y_valid)\n",
    "y_test_onehot = to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "print(y_train[0], y_train_onehot[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 세트:  (50000, 28, 28, 1) (50000, 10)\n",
      "검증 세트:  (10000, 28, 28, 1) (10000, 10)\n",
      "테스트 세트:  (10000, 28, 28, 1) (10000, 10)\n"
     ]
    }
   ],
   "source": [
    "print('훈련 세트: ', X_train_centered.shape, y_train_onehot.shape)\n",
    "print('검증 세트: ', X_valid_centered.shape, y_valid_onehot.shape)\n",
    "print('테스트 세트: ', X_test_centered.shape, y_test_onehot.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 텐서플로 Keras API로 CNN 구성하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import layers, models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = models.Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(layers.Conv2D(32, (5, 5), padding='valid', \n",
    "                        activation='relu', input_shape=(28, 28, 1)))\n",
    "model.add(layers.MaxPool2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, (5, 5), padding='valid', \n",
    "                        activation='relu'))\n",
    "model.add(layers.MaxPool2D((2, 2)))\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(1024, activation='relu'))\n",
    "model.add(layers.Dropout(0.5))\n",
    "model.add(layers.Dense(10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 24, 24, 32)        832       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 12, 12, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 8, 8, 64)          51264     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 4, 4, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1024)              1049600   \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                10250     \n",
      "=================================================================\n",
      "Total params: 1,111,946\n",
      "Trainable params: 1,111,946\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 합성곱 신경망 모델 훈련하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy', \n",
    "              optimizer='adam', metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard\n",
    "\n",
    "callback_list = [ModelCheckpoint(filepath='cnn_checkpoint.h5', \n",
    "                                 monitor='val_loss', \n",
    "                                 save_best_only=True), \n",
    "                 TensorBoard(log_dir=\"logs/{}\".format(time.asctime()))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "  1/782 [..............................] - ETA: 0s - loss: 2.3227 - acc: 0.0938WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/summary_ops_v2.py:1277: stop (from tensorflow.python.eager.profiler) is deprecated and will be removed after 2020-07-01.\n",
      "Instructions for updating:\n",
      "use `tf.profiler.experimental.stop` instead.\n",
      "  2/782 [..............................] - ETA: 44s - loss: 2.2869 - acc: 0.1562WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0062s vs `on_train_batch_end` time: 0.1030s). Check your callbacks.\n",
      "782/782 [==============================] - 4s 5ms/step - loss: 0.1403 - acc: 0.9558 - val_loss: 0.0539 - val_acc: 0.9845\n",
      "Epoch 2/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0497 - acc: 0.9844 - val_loss: 0.0411 - val_acc: 0.9882\n",
      "Epoch 3/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0355 - acc: 0.9888 - val_loss: 0.0563 - val_acc: 0.9847\n",
      "Epoch 4/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0303 - acc: 0.9904 - val_loss: 0.0383 - val_acc: 0.9894\n",
      "Epoch 5/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0228 - acc: 0.9929 - val_loss: 0.0362 - val_acc: 0.9900\n",
      "Epoch 6/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0195 - acc: 0.9938 - val_loss: 0.0313 - val_acc: 0.9926\n",
      "Epoch 7/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0201 - acc: 0.9936 - val_loss: 0.0444 - val_acc: 0.9893\n",
      "Epoch 8/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0174 - acc: 0.9946 - val_loss: 0.0441 - val_acc: 0.9890\n",
      "Epoch 9/20\n",
      "782/782 [==============================] - 3s 3ms/step - loss: 0.0151 - acc: 0.9955 - val_loss: 0.0377 - val_acc: 0.9914\n",
      "Epoch 10/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0133 - acc: 0.9957 - val_loss: 0.0426 - val_acc: 0.9918\n",
      "Epoch 11/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0144 - acc: 0.9956 - val_loss: 0.0354 - val_acc: 0.9920\n",
      "Epoch 12/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0122 - acc: 0.9961 - val_loss: 0.0599 - val_acc: 0.9878\n",
      "Epoch 13/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0117 - acc: 0.9965 - val_loss: 0.0576 - val_acc: 0.9903\n",
      "Epoch 14/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0103 - acc: 0.9969 - val_loss: 0.0631 - val_acc: 0.9899\n",
      "Epoch 15/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0100 - acc: 0.9970 - val_loss: 0.0438 - val_acc: 0.9923\n",
      "Epoch 16/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0089 - acc: 0.9973 - val_loss: 0.0542 - val_acc: 0.9915\n",
      "Epoch 17/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0122 - acc: 0.9966 - val_loss: 0.0539 - val_acc: 0.9914\n",
      "Epoch 18/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0098 - acc: 0.9970 - val_loss: 0.0615 - val_acc: 0.9910\n",
      "Epoch 19/20\n",
      "782/782 [==============================] - 3s 3ms/step - loss: 0.0109 - acc: 0.9972 - val_loss: 0.0674 - val_acc: 0.9897\n",
      "Epoch 20/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0111 - acc: 0.9972 - val_loss: 0.0653 - val_acc: 0.9908\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train_centered, y_train_onehot, \n",
    "                    batch_size=64, epochs=20, \n",
    "                    validation_data=(X_valid_centered, y_valid_onehot),\n",
    "                    callbacks=callback_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "epochs = np.arange(1, 21)\n",
    "plt.plot(epochs, history.history['loss'])\n",
    "plt.plot(epochs, history.history['val_loss'])\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(epochs, history.history['acc'])\n",
    "plt.plot(epochs, history.history['val_acc'])\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 2\r\n",
      "drwxr-xr-x 3 work work        0 Aug 10 14:16  .\r\n",
      "drwxr-xr-x 3 work work 66779599 Aug 10 14:16  ..\r\n",
      "drwxr-xr-x 4 work work    60155 Aug 10 14:16 'Mon Aug 10 14:16:02 2020'\r\n"
     ]
    }
   ],
   "source": [
    "!ls -al logs/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "모델 저장"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('cnn_model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "모델 복원"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "restored_model = load_model('cnn_model.h5')\n",
    "restored_model.load_weights('cnn_checkpoint.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "테스트 정확도 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step - loss: 0.0287 - acc: 0.9915\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.028689252212643623, 0.9915000200271606]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restored_model.evaluate(X_test_centered, y_test_onehot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "테스트 샘플 예측 확인 + 이미지 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 2, 1, 0, 4, 1, 4, 9, 5, 9])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(restored_model.predict(X_test_centered[:10]), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 2, 1, 0, 4, 1, 4, 9, 5, 9], dtype=uint8)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 5))\n",
    "for i in range(10):\n",
    "    fig.add_subplot(2, 5, i+1)\n",
    "    plt.imshow(X_test_centered[i].reshape(28, 28))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 활성화 출력과 필터 시각화하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fb899d26a20>\n"
     ]
    }
   ],
   "source": [
    "first_layer = model.layers[0]\n",
    "print(first_layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"conv2d_input:0\", shape=(None, 28, 28, 1), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "print(model.input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_activation = models.Model(inputs=model.input, \n",
    "                                outputs=first_layer.output)\n",
    "activation = first_activation.predict(X_test_centered[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 24, 24, 32)\n"
     ]
    }
   ],
   "source": [
    "print(activation.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 32 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 15))\n",
    "for i in range(32):\n",
    "    fig.add_subplot(7, 5, i+1)\n",
    "    plt.imshow(activation[0, :, :, i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 32 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 15))\n",
    "for i in range(32):\n",
    "    fig.add_subplot(7, 5, i+1)\n",
    "    plt.imshow(activation[3, :, :, i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 32 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 15))\n",
    "for i in range(32):\n",
    "    fig.add_subplot(7, 5, i+1)\n",
    "    plt.imshow(first_layer.kernel[:, :, 0, i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "20번 더 훈련"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0242 - acc: 0.9929 - val_loss: 0.0330 - val_acc: 0.9920\n",
      "Epoch 2/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0170 - acc: 0.9948 - val_loss: 0.0334 - val_acc: 0.9903\n",
      "Epoch 3/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0150 - acc: 0.9953 - val_loss: 0.0479 - val_acc: 0.9901\n",
      "Epoch 4/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0131 - acc: 0.9961 - val_loss: 0.0518 - val_acc: 0.9906\n",
      "Epoch 5/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0124 - acc: 0.9964 - val_loss: 0.0550 - val_acc: 0.9898\n",
      "Epoch 6/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0139 - acc: 0.9959 - val_loss: 0.0519 - val_acc: 0.9916\n",
      "Epoch 7/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0115 - acc: 0.9966 - val_loss: 0.0404 - val_acc: 0.9933\n",
      "Epoch 8/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0102 - acc: 0.9967 - val_loss: 0.0567 - val_acc: 0.9915\n",
      "Epoch 9/20\n",
      "782/782 [==============================] - 3s 3ms/step - loss: 0.0106 - acc: 0.9969 - val_loss: 0.0608 - val_acc: 0.9914\n",
      "Epoch 10/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0116 - acc: 0.9967 - val_loss: 0.0535 - val_acc: 0.9924\n",
      "Epoch 11/20\n",
      "782/782 [==============================] - 3s 3ms/step - loss: 0.0103 - acc: 0.9973 - val_loss: 0.0619 - val_acc: 0.9904\n",
      "Epoch 12/20\n",
      "782/782 [==============================] - 3s 3ms/step - loss: 0.0105 - acc: 0.9971 - val_loss: 0.0602 - val_acc: 0.9913\n",
      "Epoch 13/20\n",
      "782/782 [==============================] - 3s 3ms/step - loss: 0.0075 - acc: 0.9979 - val_loss: 0.0544 - val_acc: 0.9915\n",
      "Epoch 14/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0087 - acc: 0.9979 - val_loss: 0.0633 - val_acc: 0.9920\n",
      "Epoch 15/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0120 - acc: 0.9970 - val_loss: 0.0514 - val_acc: 0.9928\n",
      "Epoch 16/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0110 - acc: 0.9974 - val_loss: 0.0636 - val_acc: 0.9925\n",
      "Epoch 17/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0075 - acc: 0.9981 - val_loss: 0.0913 - val_acc: 0.9911\n",
      "Epoch 18/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0096 - acc: 0.9978 - val_loss: 0.0797 - val_acc: 0.9917\n",
      "Epoch 19/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0093 - acc: 0.9976 - val_loss: 0.0842 - val_acc: 0.9911\n",
      "Epoch 20/20\n",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.0118 - acc: 0.9973 - val_loss: 0.0881 - val_acc: 0.9904\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fb604267c88>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restored_model.fit(X_train_centered, y_train_onehot, batch_size=64, epochs=20, \n",
    "                   validation_data=(X_valid_centered, y_valid_onehot))"
   ]
  }
 ],
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