7-3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "7-3 신경망 모델 훈련.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "TensorFlow 2.3 on Python 3.6 (CUDA 10.1)",
      "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": "3.6.9"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pqxnslml3tu6"
      },
      "source": [
        "# 신경망 모델 훈련"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fN7zF6Zy3tvE"
      },
      "source": [
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/hg-mldl/blob/master/7-3.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩에서 실행하기</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XGP-X65EmJBg"
      },
      "source": [
        "## 손실 곡선"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hZsGl9udlqZk",
        "outputId": "dfb7c783-8276-4c75-9a1c-6825310e29d8"
      },
      "source": [
        "from tensorflow import keras\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "(train_input, train_target), (test_input, test_target) = \\\n",
        "    keras.datasets.fashion_mnist.load_data()\n",
        "\n",
        "train_scaled = train_input / 255.0\n",
        "\n",
        "train_scaled, val_scaled, train_target, val_target = train_test_split(\n",
        "    train_scaled, train_target, test_size=0.2, random_state=42)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "40960/29515 [=========================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "26435584/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "16384/5148 [===============================================================================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n",
            "4431872/4422102 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iogH7o0Ll6uL"
      },
      "source": [
        "def model_fn(a_layer=None):\n",
        "    model = keras.Sequential()\n",
        "    model.add(keras.layers.Flatten(input_shape=(28, 28)))\n",
        "    model.add(keras.layers.Dense(100, activation='relu'))\n",
        "    if a_layer:\n",
        "        model.add(a_layer)\n",
        "    model.add(keras.layers.Dense(10, activation='softmax'))\n",
        "    return model"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Eh6hM4DNdzu",
        "outputId": "0e2c29b8-8fd5-4432-a667-020a01c36fa7"
      },
      "source": [
        "model = model_fn()\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten (Flatten)            (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 100)               78500     \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                1010      \n",
            "=================================================================\n",
            "Total params: 79,510\n",
            "Trainable params: 79,510\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P-UK21N_mCM0"
      },
      "source": [
        "model.compile(loss='sparse_categorical_crossentropy', metrics='accuracy')\n",
        "\n",
        "history = model.fit(train_scaled, train_target, epochs=5, verbose=0)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ix_EA2LpaR0",
        "outputId": "63049ab7-b817-4fcd-9f8a-ab73d73f89cd"
      },
      "source": [
        "print(history.history.keys())"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['loss', 'accuracy'])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "uz_TCdfPmG6e",
        "outputId": "033f08e3-9dee-4e4a-85b6-dacbfdc8ae38"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('loss')\n",
        "plt.savefig('7_3-01', dpi=300)\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "CpmK9lXQcBe9",
        "outputId": "dbbe0523-1ac8-47ab-b655-60ae9de9ea08"
      },
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('accuracy')\n",
        "plt.savefig('7_3-02', dpi=300)\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1Sg6DurWPOppIo6UCIY3arrIjPPl2Ib/5YBPlldVcfkYPbpkygAFdVBhETpcKhDRK2/cd5sm3Cvnth5upqna+OKon3zq3P/0y20UdTaTJUIGQRmVr6WEe/78CXlhYRLU7XxqTxTfP7U/vzqlRRxNpclQgpFHYsucQv/i/An6/qAiAL+f24p8+159endpGnEyk6VKBkAZtY8lBHptXwB8XbyXJjKvHZfOPn+tPj/Q2UUcTafJUIKRBWl98gMfmFfDykm0ktzCum9ibb5zTn25pKVFHE2k2VCCkQcnfuZ+fzyvgT0u30Sq5BV8/uw83n9OPLu1VGETqmwqENAhrdpTxszcLmLN8O21aJjH9nP7cNLkvGe1aRx1NpNlSgZBIrdy2j5/NLeAvK3fQrnUy3/r8AL4+qS+dUltFHU2k2VOBkEgsKyrl0bkF/HX1TtqnJHPbeTl8/ew+pLdVYRBpKFQgpF59tHkvP5ubz7y1xaS1acl3LxjI9Wf1Ia1Ny6ijiUgtKhBSL/I27uGRufnMzy+hY9uW/MsXBnHdxN60T1FhEGmoVCAkVO8X7ubRufm8u343nVNb8f2LBnPthN6kttY/PZGGTp9SqXPuzrvrd/PI3Hw+3LCHzPatueeSIfzD+N60aZUUdTwRSVCoBcLMpgKPAEnA0+7+YK3t2cCzQHrQ5k53nxNsGwk8CXQAqoEz3f1ImHnl9Lg7b+eX8OjcfBZt2kvXDq2577KhXDUum5SWKgwijU1oBcLMkoDHgAuAImChmc1291U1mt0DvODuj5vZUGAO0MfMkoFfA19196Vm1hmoCCurnB53Z97aXTwyt4ClW0rpkZbCA18czpfHZqkwiDRiYR5BjAMK3L0QwMyeB6YBNQuEEztCAEgDtgWPLwSWuftSAHffHWJOOUXuzhurdvLom/ms2FpGVsc2/PvfjeBLY7Joldwi6ngicprCLBA9gS01louA8bXa3Ae8bma3AqnA+cH6gYCb2WtAJvC8u/+09huY2XRgOkB2dnadhpfjq652Xl+1g0fmFrB6exm9O7flp1eO5IrRPWmZpMIg0lREPUh9NTDT3R8ys4nALDMbHuSaBJwJHALmmtkid59b88nuPgOYAZCbm+v1G7152neogmuefp+V28rom5HKQ18+g2mjepCswiDS5IRZILYCvWosZwXraroRmArg7u+ZWQqQQexo4213LwEwsznAGGAuEqkH/7KGNTv289CXz+CLo3uS1MKijiQiIQnzz76FQI6Z9TWzVsBVwOxabTYD5wGY2RAgBSgGXgNGmFnbYMD6c/zt2IVEYNGmPTz34Wa+dlYfvjQ2S8VBpIkL7QjC3SvN7BZiX/ZJwDPuvtLM7gfy3H02cAfwlJndTmzA+gZ3d2CvmT1MrMg4MMfd/xxWVjmxiqpq7n5xBd3TUvjOBQOjjiMi9SDUMYjgmoY5tdbdW+PxKuDs4zz318ROdZUG4JfvbGDNjv08ce1Y2ukqaJFmIaEuJjP7o5ldYmYaiWyGtpYe5r/eyOf8IV34wrCuUccRkXqS6Bf+L4BrgHwze9DMBoWYSRqYH768EoD7Lh+GmcYdRJqLhAqEu//V3f+B2JlEG4G/mtm7ZvY1M9N0nE3Y6yt38NfVO/nO+TlkdWwbdRwRqUcJdxkF013cANwELCY2x9IY4I1QkknkDh6t5L7ZKxnUtT1fn9Q36jgiUs8SGm00sxeBQcAs4DJ33x5s+p2Z5YUVTqL1339dx7Z9R/jDNaN1hbRIM5To6SiPuvu8eBvcPbcO80gDsWpbGc+8s5Grx/VibO9OUccRkQgk+mfhUDNLP7ZgZh3N7JshZZKIVVc7d7+0nPQ2Lfne1MFRxxGRiCRaIG5299JjC+6+F7g5nEgStecWbmbx5lLuvmQI6W1bRR1HRCKSaIFIshrnNwb3etA3RxNUvP8o//HqGib068QVo3tGHUdEIpToGMRfiA1IPxksfyNYJ03Mj/+8isMVVfzbF0fomgeRZi7RAvE9YkXhn4LlN4CnQ0kkkXmnoISXlmzjtikDGNClXdRxRCRiCRUId68GHg9+pAk6UlHFPS+toHfntnzz3AFRxxGRBiDR6yBygH8HhhKbkhsAd+8XUi6pZ0+8tZ4NJQf51dfH6T7SIgIkPkj9S2JHD5XAucCv0EyrTcaGkoP8Yt56LjujB+cMzIw6jog0EIkWiDbB7T7N3Te5+33AJeHFkvri7tzz0nJat2zBDy4dEnUcEWlAEh2kPhpM9Z0f3ARoK6BRzCZg9tJtvFOwmwemDaNL+5QTP0FEmo1EjyC+DbQFbgPGAtcC14cVSurHvkMVPPDKKs7ISuOa8b2jjiMiDcwJjyCCi+K+4u7/DBwAvhZ6KqkXP31tDXsOljPza+N0f2kR+ZQTHkG4exUw6VRe3MymmtlaMyswszvjbM82s3lmttjMlpnZxcH6PmZ22MyWBD9PnMr7y/F9tHkvv/1wMzec1ZfhPdOijiMiDVCiYxCLzWw28L/AwWMr3f2Px3tCcOTxGHABUAQsNLPZwX2oj7kHeMHdHzezocTuX90n2Lbe3Ucl/JtIwiqrqrn7xRV0bZ/Cdy8cGHUcEWmgEi0QKcBuYEqNdQ4ct0AA44ACdy8EMLPngWlAzQLhQIfgcRqwLcE8chpmvruR1dvLeOLaMbRrneg/ARFpbhK9kvpUxh16AltqLBcB42u1uQ943cxuBVKB82ts62tmi4Ey4B53n1/7DcxsOjAdIDs7+xQiNj9bSw/z8BvrmDK4C18Y1i3qOCLSgCV6JfUvif21/zfc/eun+f5XAzPd/SEzmwjMMrPhwHYg2913m9lY4CUzG+buZbXefwYwAyA3N/dT+eTTfjR7JdXu/OjyYZqMT0Q+U6L9C6/UeJwCXMGJu4O2Ar1qLGcF62q6EZgK4O7vmVkKkOHuu4CjwfpFZrYeGAjo9qan4Y1VO3l91U6+N3UwvTq1jTqOiDRwiXYx/aHmspk9Byw4wdMWAjlm1pdYYbgKuKZWm83AecBMMxtCrPgUm1kmsMfdq8ysH5ADFCaSVeI7VF7JfbNXMrBrO26a3DfqOCLSCJzqCGUO0OWzGrh7ZXDV9WtAEvCMu680s/uBPHefDdwBPGVmtxPrwrrB3d3MzgHuN7MKoBr4R3ffc4pZBXjkr/lsLT3M//7jRFomJXp9pIg0Z4mOQeznb8cgdhC7R8Rncvc5xE5drbnu3hqPVwFnx3neH4A/1F4vp2b19jKeXrCBr+T24sw+naKOIyKNRKJdTO3DDiLhqK527n5xOWltWnLnRYOjjiMijUhCfQ1mdoWZpdVYTjezL4YXS+rK7/K28NHmUu66eAgdU3UbcRFJXKKd0T90933HFty9FPhhOJGkrpQcOMqDr65hfN9OfGlMz6jjiEgjk2iBiNdOl+A2cD/582oOlVfy4yuG65oHETlpiRaIPDN72Mz6Bz8PA4vCDCan592CEv64eCvfOKc/A7poCElETl6iBeJWoBz4HfA8cAT4Vlih5PQcrazinpdWkN2pLbdMGRB1HBFppBI9i+kg8KnpuqVhevKtQgpLDvLs18eR0jIp6jgi0kglehbTG2aWXmO5o5m9Fl4sOVUbSg7y83kFXDqyO58bmBl1HBFpxBLtYsoIzlwCwN33coIrqaX+uTv3vryC1kkt+MGlQ6OOIyKNXKIFotrMPp5P28z6EGd2V4nW7KXbmJ9fwj9/YRBdO6REHUdEGrlET1W9G1hgZm8BBkwmuA+DNAz7DlfwwCurGZmVxrUTekcdR0SagEQHqf9iZrnEisJi4CXgcJjB5OT852tr2XPwKDO/diZJLXTNg4icvkQn67sJ+DaxezosASYA7/G3tyCViCzZUsqvP9jEDWf1YXjPtBM/QUQkAYmOQXwbOBPY5O7nAqOB0s9+itSHyqpq7vrjcrq0b813LxgYdRwRaUISLRBH3P0IgJm1dvc1wKDwYkmiZr67kVXby/jhZcNon9Iy6jgi0oQkOkhdFFwH8RLwhpntBTaFF0sSsa30MA+/sY5zB2Vy0fBuUccRkSYm0UHqK4KH95nZPCAN+EtoqSQh9/9pFdXu3D9Nk/GJSN076RlZ3f2tMILIyZm7eid/WbmDf506iF6d2kYdR0SaoFBvTmxmU81srZkVmNmn5nIys2wzm2dmi81smZldHGf7ATP75zBzNjaHyiu59+WV5HRpx02T+kUdR0SaqNAKhJklAY8BFwFDgavNrPb8D/cAL7j7aOAq4Be1tj8MvBpWxsbqkbn5bC09zI+vGEGr5FBrvIg0Y2F+u4wDCty90N3LiU0TPq1WGwc6BI/TgG3HNgS3NN0ArAwxY6OzZkcZ/zN/A3+fm8W4vp2ijiMiTViYBaInsKXGclGwrqb7gGvNrAiYQ+y+E5hZO+B7wI9CzNfoVFc797y4gvYpydx50ZCo44hIExd1/8TVwEx3zwIuBmaZWQtiheO/3P3AZz3ZzKabWZ6Z5RUXF4efNmIv5G0hb9Ne7rp4CJ1SW0UdR0SauDDvK70V6FVjOStYV9ONwFQAd3/PzFKADGA8cKWZ/RRIJzab7BF3/3nNJ7v7DGAGQG5ubpOeXXb3gaP8+6trGNe3E1eOzYo6jog0A2EWiIVAjpn1JVYYrgKuqdVmM3AeMNPMhgApQLG7Tz7WwMzuAw7ULg7NzY/nrOZQeSU/uULXPIhI/Qiti8ndK4FbgNeA1cTOVlppZveb2eVBszuAm81sKfAccIO7N+kjgVPx7voS/vjRVqaf048BXdpHHUdEmglrKt/Hubm5npeXF3WMOne0soqLHplPRVU1r3/nc7RppXtMi0jdMbNF7p4bb1uYXUxSB2a8VUhh8UFmfu1MFQcRqVdRn8Ukn2FjyUF+Nq+AS0Z05/ODdAtwEalfKhANlLvzg5dX0CqpBfdeVvsCdBGR8KlANFCvLNvO/PwS/vnCgXTtkBJ1HBFphlQgGqCyIxXc/8oqRvRM46sT+0QdR0SaKQ1SN0D/+dpadh84yjPXn0lSC13zICLR0BFEA7N0Symz3t/EdRP7MCIrLeo4ItKMqUA0IJVV1dz14nIy27XmjgsHRh1HRJo5FYgG5FfvbWLltjJ+eNkw2qe0jDqOiDRzKhANxI59R3jo9bV8bmAmF4/oFnUcEREViIbiR39aSWW188A0TcYnIg2DCkQD8Oaanby6Yge3nZdDdue2UccREQFUICJ3uLyKe19eyYAu7bh5cr+o44iIfEzXQUTs0TfzKdp7mN9Nn0CrZNVrEWk49I0UoXU79/PU24VcOTaL8f06Rx1HRORvqEBEpLraufvF5bRLSeaui4dEHUdE5FNUICLy+0VFLNy4l7suGkKn1FZRxxER+RQViAjsOVjOT15dzbg+nbhybFbUcURE4lKBiMBP5qzmwJFK/u2K4bTQZHwi0kCFWiDMbKqZrTWzAjO7M872bDObZ2aLzWyZmV0crB9nZkuCn6VmdkWYOevT+4W7+f2iIm4+px8Du7aPOo6IyHGFdpqrmSUBjwEXAEXAQjOb7e6rajS7B3jB3R83s6HAHKAPsALIdfdKM+sOLDWzP7l7ZVh560N5ZTX3vLSCrI5tuG1KTtRxREQ+U5hHEOOAAncvdPdy4HlgWq02DnQIHqcB2wDc/VCNYpAStGv0nppfSMGuAzwwbThtWiVFHUdE5DOFWSB6AltqLBcF62q6D7jWzIqIHT3cemyDmY03s5XAcuAf4x09mNl0M8szs7zi4uK6zl+nNu8+xKNz87l4RDfOHdwl6jgiIicU9SD11cBMd88CLgZmmVkLAHf/wN2HAWcC3zezT92Y2d1nuHuuu+dmZmbWa/CT4e784OUVJLcw7r10WNRxREQSEmaB2Ar0qrGcFayr6UbgBQB3f49Yd1JGzQbuvho4AAwPLWnI5izfwVvrirnjwkF0S/tUnRMRaZDCLBALgRwz62tmrYCrgNm12mwGzgMwsyHECkRx8JzkYH1vYDCwMcSsoSk7UsGP/rSS4T07cN3E3lHHERFJWGhnMQVnIN0CvAYkAc+4+0ozux/Ic/fZwB3AU2Z2O7GB6Bvc3c1sEnCnmVUA1cA33b0krKxhevj1dRQfOMpT1+WSnBR1j56ISOJCnc3V3ecQG3yuueOAqYcAAAsTSURBVO7eGo9XAWfHed4sYFaY2erDsqJSfvXeRq6b0JszeqVHHUdE5KToT9qQVFU7d7+4gox2rbnjC4OijiMictJUIEIy672NLN+6j3svG0qHlJZRxxEROWkqECHYse8I//n6Os4ZmMklI7pHHUdE5JSoQITggVdWUVFVzQPThmGmyfhEpHFSgahj89bu4s/Lt3PrlAH07pwadRwRkVOmAlGHDpdXce/LK+ifmcrN5/SLOo6IyGkJ9TTX5ubn8/LZsucwz0+fQOtkTcYnIo2bjiDqSP7O/cx4u5AvjcliQr/OUccRETltKhB1wD12zUNq62Tuunhw1HFEROqECkQd+P2iIj7cuIfvXzSYzu1aRx1HRKROqECcpj0Hy/nJnNXk9u7Il8f2OvETREQaCRWI0/Tgq6vZf6SSH18xghYtdM2DiDQdKhCn4cMNe3ghr4ibJvdjULf2UccREalTKhCnqLyymrtfXE7P9Dbcdt6AqOOIiNQ5XQdxip5eUEj+rgM8c0MubVtpN4pI06MjiFOwZc8hHp2bz9Rh3ZgyuGvUcUREQqECcZLcnR+8vIIkM354+dCo44iIhEYF4iS9umIH/7e2mO9eOIjuaW2ijiMiEppQC4SZTTWztWZWYGZ3xtmebWbzzGyxmS0zs4uD9ReY2SIzWx78d0qYORO1/0gFP/rTSoZ278D1E3tHHUdEJFShja6aWRLwGHABUAQsNLPZwX2oj7kHeMHdHzezocTuX90HKAEuc/dtZjYceA3oGVbWRD38xjp27T/Kk1/NJTlJB18i0rSF+S03Dihw90J3LweeB6bVauNAh+BxGrANwN0Xu/u2YP1KoI2ZRTqHxYqt+3j23Y1cO743o3qlRxlFRKRehHl+Zk9gS43lImB8rTb3Aa+b2a1AKnB+nNf5EvCRux8NI2Qiqqqdu15cTud2rfmXqYOiiiEiUq+i7ie5Gpjp7lnAxcAsM/s4k5kNA/4D+Ea8J5vZdDPLM7O84uLi0EL++v1NLCvaxw8uHUqHlJahvY+ISEMSZoHYCtScvS4rWFfTjcALAO7+HpACZACYWRbwInCdu6+P9wbuPsPdc909NzMzs47jx+wsO8L/e20tk3MyuGxk91DeQ0SkIQqzQCwEcsysr5m1Aq4CZtdqsxk4D8DMhhArEMVmlg78GbjT3d8JMeMJPfDKKsqrqnlg2nDMNBmfiDQfoRUId68EbiF2BtJqYmcrrTSz+83s8qDZHcDNZrYUeA64wd09eN4A4F4zWxL8dAkr6/G8ta6YV5Zt55ZzB9AnI7W+315EJFIW+z5u/HJzcz0vL6/OXu9IRRUX/tfbJCcZr357su4xLSJNkpktcvfceNs0y9xx/PzNAjbvOcRvbx6v4iAizVLUZzE1SAW79vPk2+v5u9E9Oat/RtRxREQioQJRi7tz94sraNsqmbsuGRJ1HBGRyKhA1PKHj7bywYY93HnRYDLaRXrxtohIpFQgath7sJyfzFnN2N4d+UpurxM/QUSkCVOBqOHBV9dQdriCH18xnBYtdM2DiDRvKhCBhRv38Lu8Ldw4uS+Du3U48RNERJo4FQigoqqau19cTs/0Nnz7vJyo44iINAi6DgJ4ev4G1u08wNPX5dK2lXaJiAjoCIItew7xyNx1fGFYV84f2jXqOCIiDUazLxCV1c6ZfTrxw8uGRR1FRKRBafb9KX0zUpl1Y+37GImISLM/ghARkfhUIEREJC4VCBERiUsFQkRE4lKBEBGRuFQgREQkLhUIERGJSwVCRETiMnePOkOdMLNiYNNpvEQGUFJHceqScp0c5To5ynVymmKu3u6eGW9DkykQp8vM8tw9N+octSnXyVGuk6NcJ6e55VIXk4iIxKUCISIicalAfGJG1AGOQ7lOjnKdHOU6Oc0ql8YgREQkLh1BiIhIXCoQIiISV7MqEGY21czWmlmBmd0ZZ3trM/tdsP0DM+vTQHLdYGbFZrYk+LmpnnI9Y2a7zGzFcbabmT0a5F5mZmMaSK7Pm9m+Gvvr3nrK1cvM5pnZKjNbaWbfjtOm3vdZgrnqfZ+ZWYqZfWhmS4NcP4rTpt4/kwnmiuozmWRmi83slTjb6n5fuXuz+AGSgPVAP6AVsBQYWqvNN4EngsdXAb9rILluAH4ewT47BxgDrDjO9ouBVwEDJgAfNJBcnwdeiWB/dQfGBI/bA+vi/L+s932WYK5632fBPmgXPG4JfABMqNUmis9kIrmi+kx+F/htvP9XYeyr5nQEMQ4ocPdCdy8Hngem1WozDXg2ePx74DwzswaQKxLu/jaw5zOaTAN+5THvA+lm1r0B5IqEu29394+Cx/uB1UDPWs3qfZ8lmKveBfvgQLDYMvipfdZMvX8mE8xV78wsC7gEePo4Tep8XzWnAtET2FJjuYhPf0g+buPulcA+oHMDyAXwpaBL4vdm1ivkTIlKNHsUJgZdBK+a2bD6fvPg8H40sb8+a4p0n31GLohgnwVdJkuAXcAb7n7c/VWPn8lEckH9fyb/G/hXoPo42+t8XzWnAtGY/Qno4+4jgTf45K8Eie8jYvPLnAH8DHipPt/czNoBfwC+4+5l9fnen+UEuSLZZ+5e5e6jgCxgnJkNr4/3PZEEctXrZ9LMLgV2ufuiMN+ntuZUILYCNat8VrAubhszSwbSgN1R53L33e5+NFh8GhgbcqZEJbJP6527lx3rInD3OUBLM8uoj/c2s5bEvoR/4+5/jNMkkn12olxR7rPgPUuBecDUWpui+EyeMFcEn8mzgcvNbCOxbugpZvbrWm3qfF81pwKxEMgxs75m1orYIM7sWm1mA9cHj68E3vRgxCfKXLX6qC8n1ofcEMwGrgvOzJkA7HP37VGHMrNux/pezWwcsX/noX+pBO/5P8Bqd3/4OM3qfZ8lkiuKfWZmmWaWHjxuA1wArKnVrN4/k4nkqu/PpLt/392z3L0Pse+IN9392lrN6nxfJZ/OkxsTd680s1uA14idOfSMu680s/uBPHefTexDNMvMCogNgl7VQHLdZmaXA5VBrhvCzgVgZs8RO7slw8yKgB8SG7DD3Z8A5hA7K6cAOAR8rYHkuhL4JzOrBA4DV9VDoYfYX3lfBZYH/dcAdwHZNbJFsc8SyRXFPusOPGtmScQK0gvu/krUn8kEc0Xymawt7H2lqTZERCSu5tTFJCIiJ0EFQkRE4lKBEBGRuFQgREQkLhUIERGJSwVCpAGw2Gyqn5qhUyRKKhAiIhKXCoTISTCza4N7BSwxsyeDSd0OmNl/BfcOmGtmmUHbUWb2fjCh24tm1jFYP8DM/hpMjPeRmfUPXr5dMPHbGjP7TT3MJCzymVQgRBJkZkOArwBnBxO5VQH/AKQSu5p1GPAWsSu7AX4FfC+Y0G15jfW/AR4LJsY7Czg21cZo4DvAUGL3Bzk79F9K5DM0m6k2ROrAecQmZVsY/HHfhth00NXA74I2vwb+aGZpQLq7vxWsfxb4XzNrD/R09xcB3P0IQPB6H7p7UbC8BOgDLAj/1xKJTwVCJHEGPOvu3/+blWY/qNXuVOevOVrjcRX6fErE1MUkkri5wJVm1gXAzDqZWW9in6MrgzbXAAvcfR+w18wmB+u/CrwV3NGtyMy+GLxGazNrW6+/hUiC9BeKSILcfZWZ3QO8bmYtgArgW8BBYjeVuYdYl9NXgqdcDzwRFIBCPpm59avAk8FMnBXAl+vx1xBJmGZzFTlNZnbA3dtFnUOkrqmLSURE4tIRhIiIxKUjCBERiUsFQkRE4lKBEBGRuFQgREQkLhUIERGJ6/8D5KCxEoHjhSQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5cJlWITXqJWr"
      },
      "source": [
        "model = model_fn()\n",
        "model.compile(loss='sparse_categorical_crossentropy', metrics='accuracy')\n",
        "\n",
        "history = model.fit(train_scaled, train_target, epochs=20, verbose=0)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "YT87Fjo2qKPC",
        "outputId": "1b1831ef-8c0b-4c47-d842-d6e32eb7449c"
      },
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('loss')\n",
        "plt.savefig('7_3-03', dpi=300)\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bGqf6ceRr3zO"
      },
      "source": [
        "## 검증 손실"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4_iHvMxwu2D2"
      },
      "source": [
        "model = model_fn()\n",
        "model.compile(loss='sparse_categorical_crossentropy', metrics='accuracy')\n",
        "\n",
        "history = model.fit(train_scaled, train_target, epochs=20, verbose=0, \n",
        "                    validation_data=(val_scaled, val_target))"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nJ5RGEmLu5KI",
        "outputId": "2a59e1ef-9850-4fe1-db3b-a8eaf7d14876"
      },
      "source": [
        "print(history.history.keys())"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "zcpm7CpXu5vC",
        "outputId": "0aa51ffc-0966-44d4-e513-0d7db84c87ce"
      },
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('loss')\n",
        "plt.legend(['train', 'val'])\n",
        "plt.savefig('7_3-04', dpi=300)\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qC8gDwo3qcJv"
      },
      "source": [
        "model = model_fn()\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', \n",
        "              metrics='accuracy')\n",
        "\n",
        "history = model.fit(train_scaled, train_target, epochs=20, verbose=0, \n",
        "                    validation_data=(val_scaled, val_target))"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "k8wWnyFzsLKb",
        "outputId": "140306b0-3e43-4178-8941-79ec92023510"
      },
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('loss')\n",
        "plt.legend(['train', 'val'])\n",
        "plt.savefig('7_3-05', dpi=300)\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F-dFY8lrYXm3"
      },
      "source": [
        "## 드롭아웃"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AppFtFKgsk--",
        "outputId": "64f0076d-94a0-4003-c907-e8e149a2458f"
      },
      "source": [
        "model = model_fn(keras.layers.Dropout(0.3))\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten_4 (Flatten)          (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 100)               78500     \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 10)                1010      \n",
            "=================================================================\n",
            "Total params: 79,510\n",
            "Trainable params: 79,510\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_TSe7oM9v1lW"
      },
      "source": [
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', \n",
        "              metrics='accuracy')\n",
        "\n",
        "history = model.fit(train_scaled, train_target, epochs=20, verbose=0, \n",
        "                    validation_data=(val_scaled, val_target))"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "Rj_syB_iv30l",
        "outputId": "71f26e56-f44f-44c9-ce4b-fc6b60b469ce"
      },
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('loss')\n",
        "plt.legend(['train', 'val'])\n",
        "plt.savefig('7_3-06', dpi=300)\n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hQi91PCQorHr"
      },
      "source": [
        "## 모델 저장과 복원"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "npsYdX3rv6Oa"
      },
      "source": [
        "model = model_fn(keras.layers.Dropout(0.3))\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', \n",
        "              metrics='accuracy')\n",
        "\n",
        "history = model.fit(train_scaled, train_target, epochs=10, verbose=0, \n",
        "                    validation_data=(val_scaled, val_target))"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hWVYzt0Y2FPm"
      },
      "source": [
        "model.save_weights('model-weights.h5')"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NissHzcq3xbN"
      },
      "source": [
        "model.save('model-whole.h5')"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2vljkGGu3AUl",
        "scrolled": true,
        "outputId": "27ddb7a3-99c7-476d-c2b4-ccdb8432a981"
      },
      "source": [
        "!ls -al *.h5"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-rw-r--r-- 1 root root 332480 Aug 18 14:46 model-weights.h5\n",
            "-rw-r--r-- 1 root root 981736 Aug 18 14:46 model-whole.h5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7-t6gC5Z3GCM"
      },
      "source": [
        "model = model_fn(keras.layers.Dropout(0.3))\n",
        "\n",
        "model.load_weights('model-weights.h5')"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pHV9tBnzi8St",
        "outputId": "7da9201e-32c9-4802-dad4-9801c4cb50fc"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "val_labels = np.argmax(model.predict(val_scaled), axis=-1)\n",
        "print(np.mean(val_labels == val_target))"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.88125\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sRM3Vpki4QyH",
        "outputId": "69127611-2c5f-4452-b716-fb4a8de59c4c"
      },
      "source": [
        "model = keras.models.load_model('model-whole.h5')\n",
        "\n",
        "model.evaluate(val_scaled, val_target)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3307 - accuracy: 0.8813\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.33071279525756836, 0.8812500238418579]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4NTCF3YD3EyA"
      },
      "source": [
        "## 콜백"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L2lKN_934VB4",
        "outputId": "29d7e4a2-5363-4e8d-b700-a531bb4759df"
      },
      "source": [
        "model = model_fn(keras.layers.Dropout(0.3))\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', \n",
        "              metrics='accuracy')\n",
        "\n",
        "checkpoint_cb = keras.callbacks.ModelCheckpoint('best-model.h5', \n",
        "                                                save_best_only=True)\n",
        "\n",
        "model.fit(train_scaled, train_target, epochs=20, verbose=0, \n",
        "          validation_data=(val_scaled, val_target),\n",
        "          callbacks=[checkpoint_cb])"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7efeb58a2a90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qYISeH6U5oh9",
        "outputId": "1f4391a4-6dab-403f-aa44-a00cb8b6dba8"
      },
      "source": [
        "model = keras.models.load_model('best-model.h5')\n",
        "\n",
        "model.evaluate(val_scaled, val_target)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3230 - accuracy: 0.8865\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3230079412460327, 0.8865000009536743]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HLLlkR0s5Nd8"
      },
      "source": [
        "model = model_fn(keras.layers.Dropout(0.3))\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', \n",
        "              metrics='accuracy')\n",
        "\n",
        "checkpoint_cb = keras.callbacks.ModelCheckpoint('best-model.h5', \n",
        "                                                save_best_only=True)\n",
        "early_stopping_cb = keras.callbacks.EarlyStopping(patience=2,\n",
        "                                                  restore_best_weights=True)\n",
        "\n",
        "history = model.fit(train_scaled, train_target, epochs=20, verbose=0, \n",
        "                    validation_data=(val_scaled, val_target),\n",
        "                    callbacks=[checkpoint_cb, early_stopping_cb])"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b6TazMcDxqXD",
        "outputId": "3f559a9c-3ba2-478a-b607-d8ac6ea4d55a"
      },
      "source": [
        "print(early_stopping_cb.stopped_epoch)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "QrUNYGPB6Kq7",
        "outputId": "13ccaee5-3d8b-421b-8636-90cf23dbacba"
      },
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('loss')\n",
        "plt.legend(['train', 'val'])\n",
        "plt.savefig('7_3-07', dpi=300)\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H-7y1qlg5yqO",
        "outputId": "f374810c-71af-4690-a90d-7571e734e62e"
      },
      "source": [
        "model.evaluate(val_scaled, val_target)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3262 - accuracy: 0.8786\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3261638879776001, 0.8785833120346069]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    }
  ]
}