01_CNN_MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import layers\n",
    "from keras import models\n",
    "\n",
    "model = models.Sequential()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MAKE model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(layers.Conv2D(32, (3,3),activation='relu', input_shape = (28,28,1)))\n",
    "model.add(layers.MaxPool2D((2,2)))\n",
    "model.add(layers.Conv2D(64, (3,3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D(2,2))\n",
    "model.add(layers.Conv2D(64, (3,3), activation='relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 11, 11, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 3, 3, 64)          36928     \n",
      "=================================================================\n",
      "Total params: 55,744\n",
      "Trainable params: 55,744\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(64, activation='relu'))\n",
    "model.add(layers.Dense(10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 11, 11, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 3, 3, 64)          36928     \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 576)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                36928     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                650       \n",
      "=================================================================\n",
      "Total params: 93,322\n",
      "Trainable params: 93,322\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MNist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.datasets import mnist\n",
    "from keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 사용할 수 있게 바꾸고"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_images.reshape((60000, 28, 28, 1))\n",
    "train_images = train_images.astype('float32') / 255\n",
    "\n",
    "test_images = test_images.reshape((10000, 28, 28 ,1))\n",
    "test_images = test_images.astype('float32') / 255\n",
    "\n",
    "train_labels = to_categorical(train_labels)\n",
    "test_labels = to_categorical(test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 30s 494us/step - loss: 0.1713 - acc: 0.9454\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 23s 381us/step - loss: 0.0454 - acc: 0.9858\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 22s 371us/step - loss: 0.0324 - acc: 0.9899\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 19s 321us/step - loss: 0.0240 - acc: 0.99271s - loss:\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 19s 316us/step - loss: 0.0188 - acc: 0.99420s - loss: 0.0188 - acc: 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2c587722a20>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(optimizer='rmsprop',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "model.fit(train_images, train_labels, epochs=5, batch_size=64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 성능평가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 2s 250us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9914"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
    "test_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WVhs8d+204nUR4f9I0g1mNs7MLpM0W9LGAvr4DjPrzD6IkZl1SrpP7bf68EZJc7PbcyVtKLCXv9IuKzdXWllaBT937bbidSFf8smmMl6TdKmkle7+zy1vYhBmdr36j/ZS/yKma4vszczekXSX+s/6OibpZ5L+Q9I6SWMk/UnSTHdv+QdvFXq7S/0vXf+ycvO599gt7m2ypA8k7ZV0Ntvco/7314U9d4m+5qiA541v+AFB8Q0/ICjCDwRF+IGgCD8QFOEHgiL8QFCEHwiK8ANB/T//6Td/idYhFQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "예상 정답값 :  0\n",
      "실제 정답값 :  0\n"
     ]
    },
    {
     "data": {
      "image/png": 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cfOE9dpt7u1HS7yW9J2k4W7xWI++vS3vuEn2tUAnPG2f4AUFxhh8QFOEHgiL8QFCEHwiK8ANBEX4gKMIPBEX4gaD+H2UZAktaUoNLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "예상 정답값 :  0\n",
      "실제 정답값 :  0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "예상 정답값 :  7\n",
      "실제 정답값 :  7\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "예상 정답값 :  5\n",
      "실제 정답값 :  5\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "예상 정답값 :  0\n",
      "실제 정답값 :  0\n"
     ]
    },
    {
     "data": {
      "image/png": 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gKMIPBPX/IN8AG1uzca4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "예상 정답값 :  4\n",
      "실제 정답값 :  4\n"
     ]
    },
    {
     "data": {
      "image/png": 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0U0n/Jum3km6R9HtJy9297V+81ejtXg2/df3LzM2XP2O3ubeFkt6R9JGkS9niTRr+fF3atkv0tUIlbDfO8AOC4gw/ICjCDwRF+IGgCD8QFOEHgiL8QFCEHwiK8ANB/T/tAi4vlzi46wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "예상 정답값 :  5\n",
      "실제 정답값 :  5\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "예상 정답값 :  4\n",
      "실제 정답값 :  4\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "예상 정답값 :  1\n",
      "실제 정답값 :  1\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "예상 정답값 :  1\n",
      "실제 정답값 :  1\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    r = random.randint(0, len(test_images) -1)\n",
    "\n",
    "    digit = test_images[r].reshape((28, 28))\n",
    "    digit = digit.astype('float32') * 255\n",
    "    digit = digit.astype('uint8')\n",
    "    digit\n",
    "\n",
    "    plt.imshow(digit, cmap=plt.cm.binary)\n",
    "    plt.show()\n",
    "    print(\"예상 정답값 : \",int(model.predict_classes(test_images[r].reshape((1, 28, 28, 1)))))\n",
    "    print(\"실제 정답값 : \",list(test_labels[r]).index(1.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 틀린 모델 찾기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "예상 정답값 :  0\n",
      "실제 정답값 :  3\n"
     ]
    },
    {
     "data": {
      "image/png": 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AUIQfCIrwA0ERfiAowg8ERfiBoP4fLmoj70kj08IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "예상 정답값 :  5\n",
      "실제 정답값 :  9\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "예상 정답값 :  7\n",
      "실제 정답값 :  2\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "예상 정답값 :  3\n",
      "실제 정답값 :  5\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "예상 정답값 :  2\n",
      "실제 정답값 :  0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "예상 정답값 :  7\n",
      "실제 정답값 :  0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "예상 정답값 :  0\n",
      "실제 정답값 :  8\n"
     ]
    }
   ],
   "source": [
    "for i in range(1000):\n",
    "    r = random.randint(0, len(test_images) -1)\n",
    "    if int(model.predict_classes(test_images[r].reshape((1, 28, 28, 1)))) != list(test_labels[r]).index(1.0):\n",
    "        digit = test_images[r].reshape((28, 28))\n",
    "        digit = digit.astype('float32') * 255\n",
    "        digit = digit.astype('uint8')\n",
    "        digit\n",
    "\n",
    "        plt.imshow(digit, cmap=plt.cm.binary)\n",
    "        plt.show()\n",
    "        print(\"예상 정답값 : \",int(model.predict_classes(test_images[r].reshape((1, 28, 28, 1)))))\n",
    "        print(\"실제 정답값 : \",list(test_labels[r]).index(1.0))"
   ]
  }
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