MNIST_Classification_Test.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "aa46be9a",
   "metadata": {},
   "source": [
    "## 케라스 이용해서 분류 해보기.(테스트 버전)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a227c440",
   "metadata": {},
   "source": [
    "#### ML 라이브러리에 이미 Fashion-MNIST 데이터/API 가 포함되어 있음.\n",
    "#### 각 훈련 및 테스트 예제의 레이블\n",
    "> Lable 0. T-shirt/top </br>\n",
    "> Lable 1. Trouser </br>\n",
    "> Lable 2. Pullover </br>\n",
    "> Lable 3. Dress </br>\n",
    "> Lable 4. Coat </br>\n",
    "> Lable 5. Sandal </br>\n",
    "> Lable 6. Shirt </br>\n",
    "> Lable 7. Sneaker </br>\n",
    "> Lable 8. Bag </br>\n",
    "> Lable 9. Ankle boot </br>\n",
    "\n",
    "\n",
    "#### 텐서플로우의 고수준 API인 케라스(tf.keras)를 사용하여 패션 아이템 분류 신경망 학습\n",
    "#### 학습 과정 : 1. 데이터셋 준비 -> 2. 모델 생성 -> 3. 학습 -> 4. 예측\n",
    "#### 패션 MNIST 데이터 셋 : 70000개 그레이스케일 이미지와 각 이미지의 품목 이름을 나타내는 라벨로 구성된다. </br> 하나의 이미지는 10가지 카테고리 중 하나를 나타내며 어떤 품목인지는 라벨에 0~9 사이 정수로 기록되어 있다.\n",
    "\n",
    "\n",
    "##### 학습 참고 영상. https://youtu.be/VKBSqg57oec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "10225e7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0ac8ba88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
      "32768/29515 [=================================] - 0s 1us/step\n",
      "40960/29515 [=========================================] - 0s 1us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
      "26427392/26421880 [==============================] - 3s 0us/step\n",
      "26435584/26421880 [==============================] - 3s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
      "16384/5148 [===============================================================================================] - 0s 0s/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
      "4423680/4422102 [==============================] - 1s 0us/step\n",
      "4431872/4422102 [==============================] - 1s 0us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## train 데이터 셋과 test 데이터 셋 \n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels)=tf.keras.datasets.fashion_mnist.load_data()\n",
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "02687d7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n",
      "(60000,)\n",
      "()\n",
      "(10000, 28, 28)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "print(train_images[0].shape)\n",
    "print(train_labels.shape)\n",
    "print(train_labels[0].shape)\n",
    "print(test_images.shape)\n",
    "print(test_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "80168519",
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.set_printoptions(linewidth=150)\n",
    "train_images[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dd7aaea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "58369c22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Angle boot'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_names=['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt','Sneaker','Bag','Angle boot']\n",
    "class_names[train_labels[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8be2aef6",
   "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.figure()\n",
    "plt.imshow(train_images[0], cmap='gray')\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dc4025eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_images/255.0\n",
    "test_images = test_images/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b52dc73e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        0.87058824, 0.89411765, 0.88235294, 0.        ],\n",
       "       [0.38431373, 0.91372549, 0.77647059, 0.82352941, 0.87058824, 0.89803922, 0.89803922, 0.91764706, 0.97647059, 0.8627451 , 0.76078431, 0.84313725, 0.85098039, 0.94509804, 0.25490196, 0.28627451, 0.41568627, 0.45882353, 0.65882353, 0.85882353, 0.86666667, 0.84313725, 0.85098039, 0.8745098 ,\n",
       "        0.8745098 , 0.87843137, 0.89803922, 0.11372549],\n",
       "       [0.29411765, 0.8       , 0.83137255, 0.8       , 0.75686275, 0.80392157, 0.82745098, 0.88235294, 0.84705882, 0.7254902 , 0.77254902, 0.80784314, 0.77647059, 0.83529412, 0.94117647, 0.76470588, 0.89019608, 0.96078431, 0.9372549 , 0.8745098 , 0.85490196, 0.83137255, 0.81960784, 0.87058824,\n",
       "        0.8627451 , 0.86666667, 0.90196078, 0.2627451 ],\n",
       "       [0.18823529, 0.79607843, 0.71764706, 0.76078431, 0.83529412, 0.77254902, 0.7254902 , 0.74509804, 0.76078431, 0.75294118, 0.79215686, 0.83921569, 0.85882353, 0.86666667, 0.8627451 , 0.9254902 , 0.88235294, 0.84705882, 0.78039216, 0.80784314, 0.72941176, 0.70980392, 0.69411765, 0.6745098 ,\n",
       "        0.70980392, 0.80392157, 0.80784314, 0.45098039],\n",
       "       [0.        , 0.47843137, 0.85882353, 0.75686275, 0.70196078, 0.67058824, 0.71764706, 0.76862745, 0.8       , 0.82352941, 0.83529412, 0.81176471, 0.82745098, 0.82352941, 0.78431373, 0.76862745, 0.76078431, 0.74901961, 0.76470588, 0.74901961, 0.77647059, 0.75294118, 0.69019608, 0.61176471,\n",
       "        0.65490196, 0.69411765, 0.82352941, 0.36078431],\n",
       "       [0.        , 0.        , 0.29019608, 0.74117647, 0.83137255, 0.74901961, 0.68627451, 0.6745098 , 0.68627451, 0.70980392, 0.7254902 , 0.7372549 , 0.74117647, 0.7372549 , 0.75686275, 0.77647059, 0.8       , 0.81960784, 0.82352941, 0.82352941, 0.82745098, 0.7372549 , 0.7372549 , 0.76078431,\n",
       "        0.75294118, 0.84705882, 0.66666667, 0.        ],\n",
       "       [0.00784314, 0.        , 0.        , 0.        , 0.25882353, 0.78431373, 0.87058824, 0.92941176, 0.9372549 , 0.94901961, 0.96470588, 0.95294118, 0.95686275, 0.86666667, 0.8627451 , 0.75686275, 0.74901961, 0.70196078, 0.71372549, 0.71372549, 0.70980392, 0.69019608, 0.65098039, 0.65882353,\n",
       "        0.38823529, 0.22745098, 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.15686275, 0.23921569, 0.17254902, 0.28235294, 0.16078431, 0.1372549 , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        ]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.set_printoptions(linewidth=300)\n",
    "train_images[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0dbf8779",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       [0.  , 0.22, 0.73, 0.82, 0.88, 0.87, 0.88, 0.82, 0.8 , 0.84, 0.82, 0.82, 0.78, 0.62, 0.96, 0.76, 0.81, 0.87, 1.  , 1.  , 0.87, 0.92, 0.87, 0.83, 0.86, 0.91, 0.96, 0.  ],\n",
       "       [0.01, 0.79, 0.89, 0.88, 0.87, 0.83, 0.83, 0.84, 0.8 , 0.8 , 0.8 , 0.86, 0.94, 0.31, 0.59, 1.  , 0.9 , 0.87, 0.74, 0.6 , 0.75, 0.82, 0.8 , 0.82, 0.87, 0.89, 0.88, 0.  ],\n",
       "       [0.38, 0.91, 0.78, 0.82, 0.87, 0.9 , 0.9 , 0.92, 0.98, 0.86, 0.76, 0.84, 0.85, 0.95, 0.25, 0.29, 0.42, 0.46, 0.66, 0.86, 0.87, 0.84, 0.85, 0.87, 0.87, 0.88, 0.9 , 0.11],\n",
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       "       [0.01, 0.  , 0.  , 0.  , 0.26, 0.78, 0.87, 0.93, 0.94, 0.95, 0.96, 0.95, 0.96, 0.87, 0.86, 0.76, 0.75, 0.7 , 0.71, 0.71, 0.71, 0.69, 0.65, 0.66, 0.39, 0.23, 0.  , 0.  ],\n",
       "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.16, 0.24, 0.17, 0.28, 0.16, 0.14, 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
       "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 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.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 신경망 모델 생성\n",
    "\n",
    "\n",
    "np.round(train_images[0], 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6cc02a0a",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (Temp/ipykernel_11140/332957726.py, line 4)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"C:\\Users\\chaechae\\AppData\\Local\\Temp/ipykernel_11140/332957726.py\"\u001b[1;36m, line \u001b[1;32m4\u001b[0m\n\u001b[1;33m    model = tf.keras.Sequential([\u001b[0m\n\u001b[1;37m          ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "## What is .....\n",
    "model = keras.Sequential([\n",
    "    keras.\n",
    "\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f0e76e86",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Flatten(input_shape=(28,28)),\n",
    "    tf.keras.layers.Dense(128, activation='relu'),\n",
    "    tf.keras.layers.Dense(10, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "63d207e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "             loss = 'sparse_categorical_crossentropy',\n",
    "             metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "14d3c631",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 1s 585us/step - loss: 0.5021 - accuracy: 0.8241\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 1s 576us/step - loss: 0.3787 - accuracy: 0.8634\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 1s 576us/step - loss: 0.3398 - accuracy: 0.8772\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 1s 580us/step - loss: 0.3152 - accuracy: 0.8833\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 1s 585us/step - loss: 0.2980 - accuracy: 0.8896\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 1s 578us/step - loss: 0.2804 - accuracy: 0.8965\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 1s 581us/step - loss: 0.2697 - accuracy: 0.9000\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 1s 581us/step - loss: 0.2573 - accuracy: 0.9043\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 1s 579us/step - loss: 0.2481 - accuracy: 0.9079\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 1s 580us/step - loss: 0.2390 - accuracy: 0.9116\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1961dc18f08>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fb231d6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 435us/step - loss: 0.3352 - accuracy: 0.8843\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images, test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ccd385f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.33520829677581787, 0.8842999935150146)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_loss, test_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bdf7294a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = test_images[32]\n",
    "label = test_labels[32]\n",
    "img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0b7e2966",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 28, 28)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = np.expand_dims(img, 0)\n",
    "img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c0201184",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "67b12bc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bad40e68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.15195394e-03, 5.75529374e-02, 5.44720562e-04, 9.24333155e-01, 2.22515533e-04, 1.20231976e-04, 3.34650744e-04, 5.91348362e-06, 1.57139469e-02, 1.99942224e-05]], dtype=float32)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6e105f13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Dress'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(np.argmax(predictions))\n",
    "print(label)\n",
    "class_names[np.argmax(predictions)]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
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 },
 "nbformat": 4,
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