material/deep_learning/dnn_mnist.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "dnn_mnist.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "pH--ADDvhXQM"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Input, Dense, Flatten\n",
        "\n",
        "import time"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iY37KleXmoaY"
      },
      "source": [
        "# 데이터 준비"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5BEmcWKsmj6V"
      },
      "source": [
        "## 데이터 다운로드"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "46F5aLlSlz6G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a00c6db-dc8d-4cf7-c3d6-485fe3987226"
      },
      "source": [
        "(raw_train_x, raw_train_y), (raw_test_x, raw_test_y) = tf.keras.datasets.mnist.load_data()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "11501568/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZGtQOq_Pl_Hp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a006818b-3535-4247-fb48-ff6229a079f1"
      },
      "source": [
        "print(raw_train_x.shape)\n",
        "print(raw_train_y.shape)\n",
        "print(raw_test_x.shape)\n",
        "print(raw_test_y.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(60000, 28, 28)\n",
            "(60000,)\n",
            "(10000, 28, 28)\n",
            "(10000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a8jaT4ZYRfKg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba168c69-0900-42f2-d241-6af9a5dfe7f1"
      },
      "source": [
        "np.set_printoptions(linewidth=4*29)\n",
        "print(raw_train_x[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[  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]\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]\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   3  18  18  18 126 136 175  26 166 255 247 127   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0  30  36  94 154 170 253 253 253 253 253 225 172 253 242 195  64   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0  49 238 253 253 253 253 253 253 253 253 251  93  82  82  56  39   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0  18 219 253 253 253 253 253 198 182 247 241   0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0  80 156 107 253 253 205  11   0  43 154   0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0  14   1 154 253  90   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   0 139 253 190   2   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  11 190 253  70   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  35 241 225 160 108   1   0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0  81 240 253 253 119  25   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  45 186 253 253 150  27   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  16  93 252 253 187   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   0 249 253 249  64   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  46 130 183 253 253 207   2   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0  39 148 229 253 253 253 250 182   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0  24 114 221 253 253 253 253 201  78   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0  23  66 213 253 253 253 253 198  81   2   0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0  18 171 219 253 253 253 253 195  80   9   0   0   0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0  55 172 226 253 253 253 253 244 133  11   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0 136 253 253 253 212 135 132  16   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]\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]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CoIUBiMQNt7J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "b2b13bd1-3515-48d3-a9fd-ccea19e0c0bb"
      },
      "source": [
        "plt.imshow(raw_train_x[0], cmap=plt.cm.binary)\n",
        "print(raw_train_y[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xOgua7Rb1PUXvkbQv+7i76McuMVdLHjd+XRYIghfogCAoOxAEZQeCoOxAEJQdCIKyA0FQdiCI/wfvpjt5Q0mdXQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pRvTcjrwyMAB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2f470646-f7b7-4d5d-cdf4-9b94d88ed795"
      },
      "source": [
        "for i in range(4):\n",
        "  print(raw_train_y[i])\n",
        "  plt.imshow(raw_train_x[i], cmap=plt.cm.binary)\n",
        "  plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xOgua7Rb1PUXvkbQv+7i76McuMVdLHjd+XRYIghfogCAoOxAEZQeCoOxAEJQdCIKyA0FQdiCI/wfvpjt5Q0mdXQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/or8vaSi7zSv7vUv01ZH3jZ/LAkFwgA4IgrADQRB2IAjCDgRB2IEgCDsQBGEHgvh/HY9V64R+SmQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAMl0lEQVR4nO3db6hc9Z3H8c9n77Y+MEVjM1yjDaYWMchC0zLExWrNKhvUB8b6QJoHNYo0BaOkUGSDK9YHPojL2lJhKaSbkHTpWgqtGkS0MdQ/eVK8StZEZVdXbmhiTOaiEvvErrfffXBPym28c+7NnHPmzM33/YJhZs535vy+nNxPzsw5M/NzRAjA2e9v2m4AwHAQdiAJwg4kQdiBJAg7kMTfDnOwZcuWxcqVK4c5JJDK5OSkpqamPFetUtht3yDpJ5LGJP17RGwre/zKlSs1MTFRZUgAJbrdbt/awC/jbY9J+jdJN0q6QtIG21cMuj4Azarynn2NpHci4t2I+JOkX0paX09bAOpWJewXS/rDrPtHimV/xfYm2xO2J3q9XoXhAFTR+NH4iNgeEd2I6HY6naaHA9BHlbAflbRi1v0vFcsAjKAqYX9F0mW2v2z785K+LWlPPW0BqNvAp94i4lPb90h6TjOn3nZGxBu1dQagVpXOs0fEM5KeqakXAA3i47JAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kUWkWV6BJDz/8cGn9wQcfLK1HRN/aCy+8UPrca6+9trS+GFUKu+1JSR9Lmpb0aUR062gKQP3q2LP/Q0RM1bAeAA3iPTuQRNWwh6Tf2n7V9qa5HmB7k+0J2xO9Xq/icAAGVTXsV0fE1yXdKGmz7W+e/oCI2B4R3YjodjqdisMBGFSlsEfE0eL6hKQnJK2poykA9Rs47LbPtf2FU7clrZN0qK7GANSrytH4cUlP2D61nv+MiGdr6Qop7Nq1q7S+bdu20vrY2FhpfXp6um+t+LtNZeCwR8S7kr5aYy8AGsSpNyAJwg4kQdiBJAg7kARhB5LgK65ozeHDh0vrn3zyyZA6yYE9O5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kwXl2NOr555/vW3vssccqrXvVqlWl9aeffrpvbXx8vNLYixF7diAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgvPsqGT//v2l9TvuuKNv7eTJk5XGvu+++0rrl1xySaX1n23YswNJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEpxnRyW7d+8urb/33nsDr3vt2rWl9dtvv33gdWc0757d9k7bJ2wfmrXsAtt7bb9dXC9ttk0AVS3kZfwuSTectmyrpH0RcZmkfcV9ACNs3rBHxEuSPjht8XpJp16/7ZZ0S819AajZoAfoxiPiWHH7fUl9f9DL9ibbE7Yner3egMMBqKry0fiICElRUt8eEd2I6HY6narDARjQoGE/bnu5JBXXJ+prCUATBg37Hkkbi9sbJT1VTzsAmjLveXbbj0taK2mZ7SOSfihpm6Rf2b5L0mFJtzXZJNozNTVVWt+xY0dpfWxsrG/t/PPPL33uAw88UFrHmZk37BGxoU/p+pp7AdAgPi4LJEHYgSQIO5AEYQeSIOxAEnzFNbnJycnS+q233trY2Pfee29p/brrrmts7IzYswNJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEpxnT+7ZZ58trR88eLDS+q+/vv+XI7ds2VJp3Tgz7NmBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnOs5/lnnzyydL61q3V5uS85pprSutlUzqfd955lcbGmWHPDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJcJ79LFD22+9N/u67JF166aWl9fHx8UbHx8LNu2e3vdP2CduHZi17yPZR2weKy03NtgmgqoW8jN8l6YY5lv84IlYXl2fqbQtA3eYNe0S8JOmDIfQCoEFVDtDdY/v14mX+0n4Psr3J9oTtiV6vV2E4AFUMGvafSvqKpNWSjkl6tN8DI2J7RHQjotvpdAYcDkBVA4U9Io5HxHRE/FnSzyStqbctAHUbKOy2l8+6+y1Jh/o9FsBomPc8u+3HJa2VtMz2EUk/lLTW9mpJIWlS0vca7BHzeOSRR/rWxsbGGh276vfhMTzzhj0iNsyxeEcDvQBoEB+XBZIg7EAShB1IgrADSRB2IAm+4roIHDhwoLT+3HPPNTb2zTffXFq//PLLGxsb9WLPDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJcJ59EVi3bl1p/cMPPxx43VdeeWVpvWzKZSwu7NmBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnOsy8CU1NTpfUqPxe9efPm0vqSJUsGXjdGC3t2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiC8+wj4M477yytR0RpfXp6euCxr7rqqoGfi8Vl3j277RW2f2f7Tdtv2N5SLL/A9l7bbxfXS5tvF8CgFvIy/lNJP4iIKyT9vaTNtq+QtFXSvoi4TNK+4j6AETVv2CPiWES8Vtz+WNJbki6WtF7Sqd8s2i3plqaaBFDdGR2gs71S0tck/V7SeEQcK0rvSxrv85xNtidsT/R6vQqtAqhiwWG3vUTSryV9PyJOzq7FzBGkOY8iRcT2iOhGRLfT6VRqFsDgFhR225/TTNB/ERG/KRYft728qC+XdKKZFgHUYd5Tb7YtaYektyLiR7NKeyRtlLStuH6qkQ7PAvNNubx3797S+sw/QX/nnHNO39rdd99d+tzx8TnffeEstJDz7N+Q9B1JB22f+qu9XzMh/5XtuyQdlnRbMy0CqMO8YY+I/ZL67Vqur7cdAE3h47JAEoQdSIKwA0kQdiAJwg4kwVdch+Cjjz4qrR8/frzS+i+66KK+tUcffbTSunH2YM8OJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSfB99iFYtWpVaX2+aZNffvnlOttBUuzZgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiCJhczPvkLSzyWNSwpJ2yPiJ7YfkvRdSb3iofdHxDNNNbqYXXjhhaX1F198cUidILOFfKjmU0k/iIjXbH9B0qu29xa1H0fEvzbXHoC6LGR+9mOSjhW3P7b9lqSLm24MQL3O6D277ZWSvibp98Wie2y/bnun7aV9nrPJ9oTtiV6vN9dDAAzBgsNue4mkX0v6fkSclPRTSV+RtFoze/45JxWLiO0R0Y2IbqfTqaFlAINYUNhtf04zQf9FRPxGkiLieERMR8SfJf1M0prm2gRQ1bxht21JOyS9FRE/mrV8+ayHfUvSofrbA1CXhRyN/4ak70g6aPtAsex+SRtsr9bM6bhJSd9rpEMAtVjI0fj9kjxHiXPqwCLCJ+iAJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJOCKGN5jdk3R41qJlkqaG1sCZGdXeRrUvid4GVWdvl0TEnL//NtSwf2ZweyIiuq01UGJUexvVviR6G9SweuNlPJAEYQeSaDvs21sev8yo9jaqfUn0Nqih9Nbqe3YAw9P2nh3AkBB2IIlWwm77Btv/bfsd21vb6KEf25O2D9o+YHui5V522j5h+9CsZRfY3mv77eJ6zjn2WurtIdtHi213wPZNLfW2wvbvbL9p+w3bW4rlrW67kr6Gst2G/p7d9pik/5H0j5KOSHpF0oaIeHOojfRhe1JSNyJa/wCG7W9K+qOkn0fE3xXL/kXSBxGxrfiPcmlE/NOI9PaQpD+2PY13MVvR8tnTjEu6RdIdanHblfR1m4aw3drYs6+R9E5EvBsRf5L0S0nrW+hj5EXES5I+OG3xekm7i9u7NfPHMnR9ehsJEXEsIl4rbn8s6dQ0461uu5K+hqKNsF8s6Q+z7h/RaM33HpJ+a/tV25vabmYO4xFxrLj9vqTxNpuZw7zTeA/TadOMj8y2G2T686o4QPdZV0fE1yXdKGlz8XJ1JMXMe7BROne6oGm8h2WOacb/os1tN+j051W1EfajklbMuv+lYtlIiIijxfUJSU9o9KaiPn5qBt3i+kTL/fzFKE3jPdc04xqBbdfm9OdthP0VSZfZ/rLtz0v6tqQ9LfTxGbbPLQ6cyPa5ktZp9Kai3iNpY3F7o6SnWuzlr4zKNN79phlXy9uu9enPI2LoF0k3aeaI/P9K+uc2eujT16WS/qu4vNF2b5Ie18zLuv/TzLGNuyR9UdI+SW9Lel7SBSPU239IOijpdc0Ea3lLvV2tmZfor0s6UFxuanvblfQ1lO3Gx2WBJDhAByRB2IEkCDuQBGEHkiDsQBKEHUiCsANJ/D8K28WFOQm56wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V6AV1QI6mewE"
      },
      "source": [
        "## Normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nBZsXbAjmNiC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a34a3c54-8b8e-4600-d039-72d9a7e85c17"
      },
      "source": [
        "print(np.max(raw_train_x[:,]))\n",
        "print(np.max(raw_test_x[:,]))\n",
        "\n",
        "train_x = raw_train_x/255.\n",
        "test_x = raw_test_x/255.\n",
        "\n",
        "train_y = raw_train_y\n",
        "test_y = raw_test_y\n",
        "\n",
        "print(np.max(train_x[:,]))\n",
        "print(np.max(test_x[:,]))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "255\n",
            "255\n",
            "1.0\n",
            "1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_38v16ommum-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e873a4bc-873b-4540-9287-d04ec00c60c7"
      },
      "source": [
        "train_x = train_x.reshape((60000, 28*28))\n",
        "test_x = test_x.reshape((10000, 28*28))\n",
        "\n",
        "print(train_x.shape)\n",
        "print(test_x.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(60000, 784)\n",
            "(10000, 784)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PeG7foCfu1PF"
      },
      "source": [
        "# 모델 준비"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X08W3R4_ldKc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3735d93d-c862-4f43-936f-5b254cbd4338"
      },
      "source": [
        "model = keras.Sequential()\n",
        "# model.add(Input(4))\n",
        "model.add(Input(28*28))\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dense(10, activation='relu'))\n",
        "# model.add(Dense(3, activation='softmax'))\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 10)                7850      \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                110       \n",
            "=================================================================\n",
            "Total params: 8,070\n",
            "Trainable params: 8,070\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "igZrWG5nu7FK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3952ad8e-0eae-493d-afc8-f86cb70d3c46"
      },
      "source": [
        "model.fit(train_x, train_y, epochs=5, verbose=1, batch_size=128)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "469/469 [==============================] - 3s 2ms/step - loss: 0.9919 - accuracy: 0.6919\n",
            "Epoch 2/5\n",
            "469/469 [==============================] - 1s 2ms/step - loss: 0.3849 - accuracy: 0.8931\n",
            "Epoch 3/5\n",
            "469/469 [==============================] - 1s 2ms/step - loss: 0.3202 - accuracy: 0.9095\n",
            "Epoch 4/5\n",
            "469/469 [==============================] - 1s 2ms/step - loss: 0.2909 - accuracy: 0.9171\n",
            "Epoch 5/5\n",
            "469/469 [==============================] - 1s 2ms/step - loss: 0.2714 - accuracy: 0.9234\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fd2201ce790>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CiIqivhwu9Vi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "78485c1a-9ee5-447f-9dc1-38241541c681"
      },
      "source": [
        "loss, acc = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\",loss)\n",
        "print(\"acc=\",acc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.2597 - accuracy: 0.9258\n",
            "loss= 0.25973793864250183\n",
            "acc= 0.9258000254631042\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8GCmG_ZVurfb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5ab8e3ff-654e-49f6-a382-8eabb7eba1d9"
      },
      "source": [
        "y_ = model.predict(test_x)\n",
        "print(y_)\n",
        "predicted = np.argmax(y_, axis=1)\n",
        "print(predicted)\n",
        "\n",
        "for i in range(4):\n",
        "  print(predicted[i])\n",
        "  plt.imshow(raw_test_x[i], cmap=plt.cm.binary)\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[6.56820339e-05 1.68437664e-09 3.23392946e-04 ... 9.89847243e-01\n",
            "  3.35700929e-06 1.30865152e-03]\n",
            " [1.27114035e-05 3.02180077e-08 9.91940916e-01 ... 4.18439043e-08\n",
            "  1.08434906e-05 8.08591319e-11]\n",
            " [1.85470617e-05 9.83916938e-01 6.82074390e-03 ... 2.37226416e-03\n",
            "  4.08818526e-03 1.37320400e-04]\n",
            " ...\n",
            " [4.01944760e-07 1.22368908e-06 6.18901569e-04 ... 2.07928801e-03\n",
            "  1.08163469e-02 6.32119924e-02]\n",
            " [1.34790052e-05 1.65809997e-08 7.29791691e-06 ... 4.65090144e-09\n",
            "  6.46029636e-02 8.57091618e-06]\n",
            " [5.35054869e-06 2.80236891e-15 1.98382422e-05 ... 5.37024536e-10\n",
            "  6.32482344e-10 9.36655267e-12]]\n",
            "[7 2 1 ... 4 5 6]\n",
            "7\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c503DSvxvOU5"
      },
      "source": [
        "# 한눈에 모아보면"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nSNPBq4kvR9u"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Input, Dense\n",
        "\n",
        "import time"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AeBqmrvcwXDE"
      },
      "source": [
        "(raw_train_x, raw_train_y), (raw_test_x, raw_test_y) = tf.keras.datasets.mnist.load_data()\n",
        "\n",
        "train_x = raw_train_x/255.\n",
        "test_x = raw_test_x/255.\n",
        "\n",
        "train_y = raw_train_y\n",
        "test_y = raw_test_y\n",
        "\n",
        "train_x = train_x.reshape((60000, 28*28))\n",
        "test_x = test_x.reshape((10000, 28*28))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tksmYhZswCac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e938bc23-7547-42c8-80d2-4b586d4122a0"
      },
      "source": [
        "model = keras.Sequential()\n",
        "model.add(Input(28*28))\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "model.fit(train_x, train_y, epochs=5, verbose=1, batch_size=128)\n",
        "\n",
        "\n",
        "loss, acc = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\",loss)\n",
        "print(\"acc=\",acc)\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "predicted = np.argmax(y_, axis=1)\n",
        "\n",
        "print(predicted)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_3 (Dense)              (None, 10)                7850      \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 10)                110       \n",
            "=================================================================\n",
            "Total params: 8,070\n",
            "Trainable params: 8,070\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/5\n",
            "469/469 [==============================] - 1s 2ms/step - loss: 0.8484 - accuracy: 0.7392\n",
            "Epoch 2/5\n",
            "469/469 [==============================] - 1s 2ms/step - loss: 0.3584 - accuracy: 0.8970\n",
            "Epoch 3/5\n",
            "469/469 [==============================] - 1s 2ms/step - loss: 0.3043 - accuracy: 0.9128\n",
            "Epoch 4/5\n",
            "469/469 [==============================] - 1s 2ms/step - loss: 0.2795 - accuracy: 0.9204\n",
            "Epoch 5/5\n",
            "469/469 [==============================] - 1s 2ms/step - loss: 0.2629 - accuracy: 0.9251\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.2577 - accuracy: 0.9246\n",
            "loss= 0.25765565037727356\n",
            "acc= 0.9246000051498413\n",
            "[7 2 1 ... 4 5 6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "63C6TPhfxCHQ"
      },
      "source": [
        "# Flatten 레이어 사용\n",
        "\n",
        "(None, 28, 28) shape의 train_x를 그대로 사용.\n",
        "\n",
        "모델의 처음에 Flatten() 레이어를 두어, 입력 모양을 변경한다.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G8M2QsSUxGZn"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense, Input, Flatten\n",
        "\n",
        "import time"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "98Ly9UIFxIjO"
      },
      "source": [
        "(raw_train_x, raw_train_y), (raw_test_x, raw_test_y) = tf.keras.datasets.mnist.load_data()\n",
        "\n",
        "train_x = raw_train_x/255.\n",
        "test_x = raw_test_x/255.\n",
        "\n",
        "train_y = raw_train_y\n",
        "test_y = raw_test_y\n",
        "\n",
        "\n",
        "# train_x = train_x.reshape((60000, 28*28)) # COMMENT OUT\n",
        "# test_x = test_x.reshape((10000, 28*28)) # COMMENT OUT"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gZ-hK0S5xIl7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "09b8d619-68f0-4cb0-ebd7-85cec8a757df"
      },
      "source": [
        "\n",
        "model = keras.Sequential()\n",
        "# model.add(Input(28*28))\n",
        "model.add(Input((28,28)))  # ADD\n",
        "model.add(Flatten())       # ADD\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "model.fit(train_x, train_y, epochs=5, verbose=1, batch_size=128)\n",
        "\n",
        "\n",
        "loss, acc = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\",loss)\n",
        "print(\"acc=\",acc)\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "predicted = np.argmax(y_, axis=1)\n",
        "\n",
        "print(predicted)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten (Flatten)            (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 10)                7850      \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 10)                110       \n",
            "=================================================================\n",
            "Total params: 8,070\n",
            "Trainable params: 8,070\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/5\n",
            "469/469 [==============================] - 1s 2ms/step - loss: 0.8509 - accuracy: 0.7332\n",
            "Epoch 2/5\n",
            "469/469 [==============================] - 1s 2ms/step - loss: 0.3914 - accuracy: 0.8869\n",
            "Epoch 3/5\n",
            "469/469 [==============================] - 1s 2ms/step - loss: 0.3249 - accuracy: 0.9073\n",
            "Epoch 4/5\n",
            "469/469 [==============================] - 1s 2ms/step - loss: 0.2865 - accuracy: 0.9173\n",
            "Epoch 5/5\n",
            "469/469 [==============================] - 1s 2ms/step - loss: 0.2625 - accuracy: 0.9241\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.2618 - accuracy: 0.9240\n",
            "loss= 0.2617749869823456\n",
            "acc= 0.9240000247955322\n",
            "[7 2 1 ... 4 5 6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CXuXR4tSBRPu"
      },
      "source": [
        "# DNN classification Template"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QfdEDwzKBUnT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8cc885bb-1d2c-4533-cf6d-a33db1346235"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Input, Dense, Flatten\n",
        "\n",
        "import time\n",
        "\n",
        "\n",
        "(train_x, train_y), (test_x, test_y) = tf.keras.datasets.mnist.load_data()\n",
        "\n",
        "train_x = train_x/255.\n",
        "test_x = test_x/255.\n",
        "\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Input((28,28)))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "model.fit(train_x, train_y, epochs=5, verbose=1, batch_size=128)\n",
        "\n",
        "\n",
        "loss, acc = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\",loss)\n",
        "print(\"acc=\",acc)\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "predicted = np.argmax(y_, axis=1)\n",
        "\n",
        "print(predicted)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten_1 (Flatten)          (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 10)                7850      \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 10)                110       \n",
            "=================================================================\n",
            "Total params: 8,070\n",
            "Trainable params: 8,070\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/5\n",
            "469/469 [==============================] - 1s 2ms/step - loss: 0.8826 - accuracy: 0.7219\n",
            "Epoch 2/5\n",
            "469/469 [==============================] - 1s 2ms/step - loss: 0.3662 - accuracy: 0.8964\n",
            "Epoch 3/5\n",
            "469/469 [==============================] - 1s 2ms/step - loss: 0.3062 - accuracy: 0.9132\n",
            "Epoch 4/5\n",
            "469/469 [==============================] - 1s 2ms/step - loss: 0.2691 - accuracy: 0.9240\n",
            "Epoch 5/5\n",
            "469/469 [==============================] - 1s 2ms/step - loss: 0.2496 - accuracy: 0.9299\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.2506 - accuracy: 0.9282\n",
            "loss= 0.2506183981895447\n",
            "acc= 0.9282000064849854\n",
            "[7 2 1 ... 4 5 6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qtntCeHWW42I"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}