Keras Post-training quantization.ipynb

keras-post-training-quantization.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/gist/NobuoTsukamoto/0470fa22f3808f305db1fd4fbe01e3e4/keras-post-training-quantization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "MIT License\n",
        "\n",
        "Copyright (c) 2019-2020 Nobuo Tsukamoto\n",
        "\n",
        "Permission is hereby granted, free of charge, to any person obtaining a copy\n",
        "of this software and associated documentation files (the \"Software\"), to deal\n",
        "in the Software without restriction, including without limitation the rights\n",
        "to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n",
        "copies of the Software, and to permit persons to whom the Software is\n",
        "furnished to do so, subject to the following conditions:\n",
        "\n",
        "The above copyright notice and this permission notice shall be included in all\n",
        "copies or substantial portions of the Software.\n",
        "\n",
        "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
        "IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
        "FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n",
        "AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
        "LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n",
        "OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n",
        "SOFTWARE."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vhndS7EZ_Qa5",
        "colab_type": "text"
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      "source": [
        "# Keras - Post-training quantization\n",
        "\n",
        "## 目的\n",
        "KerasのmodelをPost-training quantizationでTF-Lite modelに変換する。<br>\n",
        "Keras applicationのMobileNet V2をfine-tuningしてTF-Lite modelに変換する。\n",
        "\n",
        "*   Weight quantization\n",
        "*   Float16 quantization\n",
        "*   Integer quantization\n",
        "*   Full integer quantization\n",
        "<br>\n",
        "\n",
        "また、Full integer quantizationのmodelをEdge TPU modelに変換する。\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MRhJ335J-HE0",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "**(2019.9.22時点の注意事項)**\n",
        "<br>\n",
        "\n",
        "*   ~~TensorFlow2.0.0rc-1, rc-2では、TFLiteConverter.from_keras_model() からpost-training full integer quantizationで変換した TF-Lite model が Edge TPU modelに変換できない。<br>\n",
        "Edge TPU Compilerでエラーが発生してしまう（原因不明）。<br>\n",
        "回避策として、tf.compat.v1.lite.TFLiteConverter.from_keras_model_file() を使う。~~<br>\n",
        "from_keras_model() の場合、Full integer quantization model にならない (Input / Output がFloatになる)。しかし、Edge TPU Compilerのバージョンアップ (September 2019 Updates - version 2.0.267685300) で、Ege TPU model に変換することができる ()。\n",
        "*   Weight quantization model の精度が他と比べて極端に低くなる。理由は不明。1.14, 1.15.0-rc1, 2.0.0-rc2 も同じ。<br>\n",
        "(Weight quantization model は精度が保たれるはず？)\n",
        "\n",
        "以下のissueも参照。<br>\n",
        "[Quantization-Aware Training support in Keras #27880](https://github.com/tensorflow/tensorflow/issues/27880#issuecomment-531433321)\n",
        "\n",
        "最終目標はTF2.0でKerasのmodelをTF-Lite modelに変換できること。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AV6vGbCfc1p3",
        "colab_type": "text"
      },
      "source": [
        "**(2020.4.13時点の注意事項)**\n",
        "- TensorFlow2.2.0-rc2ではIntger quant modelの変換に失敗するため、tf-nightlyを使用する。\n",
        "  ['TFLiteConverter' object has no attribute 'experimental_new_quantizer' - TF 2.2.0-rc2 #38082](https://github.com/tensorflow/tensorflow/issues/38082)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "22f8e174-6543-4f68-d7b4-98e35dc4d93a",
        "id": "_vCj8b_kyIur",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "!pip uninstall -y tensorflow tensorflow-gpu\n",
        "!pip install -qq tensorflow-gpu==2.2.0-rc3"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Uninstalling tensorflow-2.2.0rc2:\n",
            "  Successfully uninstalled tensorflow-2.2.0rc2\n",
            "\u001b[33mWARNING: Skipping tensorflow-gpu as it is not installed.\u001b[0m\n",
            "\u001b[K     |████████████████████████████████| 516.2MB 30kB/s \n",
            "\u001b[?25h"
          ],
          "name": "stdout"
        }
      ]
    },
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      "metadata": {
        "id": "db00R68VyBaL",
        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 955
        }
      },
      "source": [
        "!cat /proc/cpuinfo"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "processor\t: 0\n",
            "vendor_id\t: GenuineIntel\n",
            "cpu family\t: 6\n",
            "model\t\t: 79\n",
            "model name\t: Intel(R) Xeon(R) CPU @ 2.20GHz\n",
            "stepping\t: 0\n",
            "microcode\t: 0x1\n",
            "cpu MHz\t\t: 2200.000\n",
            "cache size\t: 56320 KB\n",
            "physical id\t: 0\n",
            "siblings\t: 2\n",
            "core id\t\t: 0\n",
            "cpu cores\t: 1\n",
            "apicid\t\t: 0\n",
            "initial apicid\t: 0\n",
            "fpu\t\t: yes\n",
            "fpu_exception\t: yes\n",
            "cpuid level\t: 13\n",
            "wp\t\t: yes\n",
            "flags\t\t: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities\n",
            "bugs\t\t: cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs taa itlb_multihit\n",
            "bogomips\t: 4400.00\n",
            "clflush size\t: 64\n",
            "cache_alignment\t: 64\n",
            "address sizes\t: 46 bits physical, 48 bits virtual\n",
            "power management:\n",
            "\n",
            "processor\t: 1\n",
            "vendor_id\t: GenuineIntel\n",
            "cpu family\t: 6\n",
            "model\t\t: 79\n",
            "model name\t: Intel(R) Xeon(R) CPU @ 2.20GHz\n",
            "stepping\t: 0\n",
            "microcode\t: 0x1\n",
            "cpu MHz\t\t: 2200.000\n",
            "cache size\t: 56320 KB\n",
            "physical id\t: 0\n",
            "siblings\t: 2\n",
            "core id\t\t: 0\n",
            "cpu cores\t: 1\n",
            "apicid\t\t: 1\n",
            "initial apicid\t: 1\n",
            "fpu\t\t: yes\n",
            "fpu_exception\t: yes\n",
            "cpuid level\t: 13\n",
            "wp\t\t: yes\n",
            "flags\t\t: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities\n",
            "bugs\t\t: cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs taa itlb_multihit\n",
            "bogomips\t: 4400.00\n",
            "clflush size\t: 64\n",
            "cache_alignment\t: 64\n",
            "address sizes\t: 46 bits physical, 48 bits virtual\n",
            "power management:\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "miz9lBiLyB56",
        "colab_type": "code",
        "outputId": "85db2c0e-87ff-4333-8028-a8f3e05b60de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Wed Apr 15 13:52:37 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 440.64.00    Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   35C    P8     7W /  75W |      0MiB /  7611MiB |      0%      Default |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                       GPU Memory |\n",
            "|  GPU       PID   Type   Process name                             Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0BLoGtFzmUQA",
        "colab_type": "text"
      },
      "source": [
        "# Import"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2JMioBWJA5Qk",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "*   [TensorFlow Dataset](https://www.tensorflow.org/datasets/api_docs/python/tfds) からデータを取得して学習を行うため、tensorflow_datasetsをimportする。\n",
        "*   tf.kerasからMobileNet V2と必要なモジュールをimportする。\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MX9PXu6Dk8Yj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import sys\n",
        "import os\n",
        "import datetime\n",
        "import time\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "if sys.version_info.major >= 3:\n",
        "    import pathlib\n",
        "else:\n",
        "    import pathlib2 as pathlib\n",
        "\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NW4lZB0vBiYv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "\n",
        "from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2\n",
        "from tensorflow.keras.applications.mobilenet_v2 import preprocess_input, decode_predictions\n",
        "\n",
        "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QX7XB0mHLI5Z",
        "colab_type": "code",
        "outputId": "e917358f-cf4d-42f2-cfab-468c9278e085",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# print version\n",
        "print('tensorflow:', tf.__version__)\n",
        "print('keras     :', tf.keras.__version__)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensorflow: 2.2.0-rc3\n",
            "keras     : 2.3.0-tf\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CjCmZNq9mXiA",
        "colab_type": "text"
      },
      "source": [
        "# Prepare the dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GLZwFywwcty4",
        "colab_type": "text"
      },
      "source": [
        "TensorFlow Datasets （tfds）で '[tf_flowers](https://www.tensorflow.org/datasets/catalog/tf_flowers)' をロードする。<br>\n",
        "'tf_flowers' のtrainからtrain, validation, test用のdatasetを分割する（8：1：1）。<br>\n",
        "（[Split](https://www.tensorflow.org/datasets/catalog/tf_flowers#statistics) に train しかないため分割する。val, test が存在すれば、そちらを使えばよい。<br>\n",
        "(2020.04.12)<br>以前はタプルでの分割ができたが、現在はエラーとなってしまうため修正。詳細は[こちら(stackoverflow)](https://stackoverflow.com/questions/59195322/cannot-split-malaria-dataset-using-tensorflow-datasets)を参照<br>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iw5WcxMLpf-9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "SPLIT_WEIGHTS = (8, 1, 1)\n",
        "#splits = tfds.Split.TRAIN.subsplit(weighted=SPLIT_WEIGHTS)\n",
        "splits = (\"train[:80%]\", \"train[:10%]\", \"train[:10%]\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hzZcApMFos1-",
        "colab_type": "text"
      },
      "source": [
        "[tfds.load](https://www.tensorflow.org/datasets/api_docs/python/tfds/load) でロードする。<br>\n",
        "tf.keras を使うときは引数の as_supervised に True を指定する。これにより (input, label)の tuple にする。\n",
        "\n",
        "as_supervised: `bool`, if `True`, the returned `tf.data.Dataset` will have a 2-tuple structure `(input, label)`  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ke3s_PUTRsUb",
        "colab_type": "code",
        "outputId": "31c66628-5d09-4538-8e35-4f8f245d16aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222,
          "referenced_widgets": [
            "39622acab7804d3a8bdc997a8d9dbf3f",
            "f49eeecf15c946bba64ddc148ad470ca",
            "b20ce05621594fec8565d8b78d3812f9",
            "b74a3286910f42789bd215e7395073ca",
            "b23f0d30f750488897b505c45235bc1f",
            "893964bc1c684058805e1f6439e4b295",
            "499fd93ae616412292f2ba75055a0901",
            "cf7997aa7dca41519275bcd141f44400"
          ]
        }
      },
      "source": [
        "# For tf_flowers\n",
        "(raw_train, raw_validation, raw_test), info = tfds.load(name=\"tf_flowers\",\n",
        "                                                        with_info=True,\n",
        "                                                        split=list(splits),\n",
        "                                                        as_supervised=True)\n",
        "\n",
        "# For cats vs dogs\n",
        "#(raw_train, raw_validation, raw_test), info = tfds.load(name=\"cats_vs_dogs\",\n",
        "#                                                       with_info=True,\n",
        "#                                                       split=list(splits),\n",
        "#                                                       as_supervised=True)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:absl:Dataset tf_flowers is hosted on GCS. It will automatically be downloaded to your\n",
            "local data directory. If you'd instead prefer to read directly from our public\n",
            "GCS bucket (recommended if you're running on GCP), you can instead set\n",
            "data_dir=gs://tfds-data/datasets.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDownloading and preparing dataset tf_flowers/3.0.0 (download: 218.21 MiB, generated: Unknown size, total: 218.21 MiB) to /root/tensorflow_datasets/tf_flowers/3.0.0...\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "39622acab7804d3a8bdc997a8d9dbf3f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Dl Completed...', max=5, style=ProgressStyle(description_widt…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\u001b[1mDataset tf_flowers downloaded and prepared to /root/tensorflow_datasets/tf_flowers/3.0.0. Subsequent calls will reuse this data.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A8YPgX1-f4ZU",
        "colab_type": "text"
      },
      "source": [
        "Datasets の情報を表示。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gkfdy4DKp0Td",
        "colab_type": "code",
        "outputId": "4335be26-db9e-48a1-d767-8dfd04ecbf02",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "info"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tfds.core.DatasetInfo(\n",
              "    name='tf_flowers',\n",
              "    version=3.0.0,\n",
              "    description='A large set of images of flowers',\n",
              "    homepage='https://www.tensorflow.org/tutorials/load_data/images',\n",
              "    features=FeaturesDict({\n",
              "        'image': Image(shape=(None, None, 3), dtype=tf.uint8),\n",
              "        'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=5),\n",
              "    }),\n",
              "    total_num_examples=3670,\n",
              "    splits={\n",
              "        'train': 3670,\n",
              "    },\n",
              "    supervised_keys=('image', 'label'),\n",
              "    citation=\"\"\"@ONLINE {tfflowers,\n",
              "    author = \"The TensorFlow Team\",\n",
              "    title = \"Flowers\",\n",
              "    month = \"jan\",\n",
              "    year = \"2019\",\n",
              "    url = \"http://download.tensorflow.org/example_images/flower_photos.tgz\" }\"\"\",\n",
              "    redistribution_info=,\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PKOj8kvyGaZF",
        "colab_type": "text"
      },
      "source": [
        "データ数の分割は単純に指定した％では分割されない。また、分割した際のデータ数を取得するためのAPIがないため自分で計算、下記のようにカウントが必要.\n",
        "詳細はこの[issueを参照](https://github.com/tensorflow/datasets/issues/292)。\n",
        "\n",
        "```\n",
        "def dataset_length(dataset):\n",
        "    count = 0\n",
        "    for image in dataset:\n",
        "        count += 1\n",
        "    return count\n",
        "\n",
        "print(dataset_length(raw_train))\n",
        "print(dataset_length(raw_validation))\n",
        "print(dataset_length(raw_test))\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KHP-VfOyC2tE",
        "colab_type": "code",
        "outputId": "5c4997a6-7e10-485d-f578-c99d88a271af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "total_num_examples = info.splits['train'].num_examples\n",
        "\n",
        "print('total num examples:', total_num_examples)\n",
        "print('total num examples % 100:', total_num_examples % 100)\n",
        "print('total num examples =', total_num_examples - (total_num_examples % 100), '+', total_num_examples % 100)\n",
        "\n",
        "print('num test :', int(3600 * 0.1 + 7))\n",
        "print('num val  :', int(3600 * 0.1 + 7))\n",
        "print('num train:', int(3600 * 0.8 + (70 - 14)))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total num examples: 3670\n",
            "total num examples % 100: 70\n",
            "total num examples = 3600 + 70\n",
            "num test : 367\n",
            "num val  : 367\n",
            "num train: 2936\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7_Oe-IMtlZ4z",
        "colab": {}
      },
      "source": [
        "IMG_SIZE = 224\n",
        "SHUFFLE_BUFFER_SIZE = 1000\n",
        "BATCH_SIZE = 32"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ed0I5yd-gLiz",
        "colab_type": "text"
      },
      "source": [
        "## Data augumentation\n",
        "train の dataset に data augumantation を追加する。\n",
        "\n",
        "\n",
        "*   左右反転\n",
        "*   コントラストの変更\n",
        "\n",
        "できそうなことは [tf.image](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/image) を見ればよい。\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j8KRH8WKQuGN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def format_example(image, label):\n",
        "    image = tf.cast(image, tf.float32)\n",
        "    # Normalize the pixel values\n",
        "    image = image / 255.0\n",
        "    # image = (image/127.5) - 1\n",
        "\n",
        "    # Resize the image if required\n",
        "    image = tf.image.resize(image, (IMG_SIZE, IMG_SIZE))\n",
        "    \n",
        "    return image, label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U0K3qswHRa1F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = raw_train.map(format_example)\n",
        "validation = raw_validation.map(format_example)\n",
        "test = raw_test.map(format_example)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SPI9EUreqA80",
        "colab_type": "text"
      },
      "source": [
        "多分同じことを以下で実現できるはず（だけど、動作未確認）\n",
        "\n",
        "```\n",
        "builder = tfds.builder('tf_flowers', as_supervised=True)\n",
        "num_train = builder.info.splits['train']\n",
        "print(num_train.num_examples)\n",
        "builder.download_and_prepare()\n",
        "datasets = builder.as_dataset()\n",
        "full_dataset = datasets['train']\n",
        "builder.info\n",
        "train_size = int(0.7 * num_train.num_examples)\n",
        "val_size = int(0.15 * num_train.num_examples)\n",
        "test_size = int(0.15 * num_train.num_examples)\n",
        "print('train_size: ', train_size)\n",
        "print('val_size: ', val_size)\n",
        "print('test_size: ', test_size)\n",
        "train_datasets_unbatched = datasets['train'].map(format_example).shuffle(num_train.num_examples)\n",
        "train_datasets = train_datasets_unbatched.batch(BATCH_SIZE)\n",
        "full_dataset = full_dataset.shuffle(num_train.num_examples)\n",
        "train_dataset = full_dataset.take(train_size)\n",
        "test_dataset = full_dataset.skip(train_size)\n",
        "val_dataset = test_dataset.skip(val_size)\n",
        "test_dataset = test_dataset.take(test_size)\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-4jgVzNLghLb",
        "colab_type": "text"
      },
      "source": [
        "## Input format\n",
        "model に入力する際のフォーマットを定義する。\n",
        "\n",
        "\n",
        "*   0.0 - 1.0の正規化\n",
        "*   input size へのリサイズ\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ib0OO-K9qS_W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def augment_data(image, label):\n",
        "  image = tf.image.random_flip_left_right(image)\n",
        "  image = tf.image.random_contrast(image, lower=0.1, upper=0.6)\n",
        "\n",
        "  return image, label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q0UvV4EmAvKk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = train.map(augment_data)\n",
        "train = train.cache()\n",
        "train = train.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE)\n",
        "validation = validation.batch(BATCH_SIZE)\n",
        "test = test.batch(1)\n",
        "\n",
        "# (Optional) prefetch will enable the input pipeline to asynchronously fetch batches while\n",
        "# your model is training.\n",
        "train = train.prefetch(tf.data.experimental.AUTOTUNE)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZDbIp3fiFz2n",
        "colab_type": "code",
        "outputId": "1346cb30-3981-4dd6-f3ca-039f7369e644",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "print(train)\n",
        "print(validation)\n",
        "print(test)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<DatasetV1Adapter shapes: ((None, 224, 224, 3), (None,)), types: (tf.float32, tf.int64)>\n",
            "<DatasetV1Adapter shapes: ((None, 224, 224, 3), (None,)), types: (tf.float32, tf.int64)>\n",
            "<DatasetV1Adapter shapes: ((None, 224, 224, 3), (None,)), types: (tf.float32, tf.int64)>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m3WIRI-Eh7hD",
        "colab_type": "text"
      },
      "source": [
        "## Display train image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CaZp8d6yGFaE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Get the function which converts label indices to string\n",
        "get_label_name = info.features['label'].int2str"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AJS1EMN_t3rM",
        "colab_type": "code",
        "outputId": "983d4e36-1051-40d7-9554-f4dff54e95ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        }
      },
      "source": [
        "plt.figure(figsize=(12,12)) \n",
        "\n",
        "for batch in train.take(1):\n",
        "  # print(batch)\n",
        "  for i in range(9):\n",
        "    image, label = batch[0][i], batch[1][i]\n",
        "    plt.subplot(3, 3, i+1)\n",
        "    plt.imshow(image.numpy())\n",
        "    plt.title(get_label_name(label.numpy()))\n",
        "    plt.grid(False)    "
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F1-qgK00hYUJ",
        "colab_type": "text"
      },
      "source": [
        "# Train model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ob1OeYHciJqh",
        "colab_type": "text"
      },
      "source": [
        "## Build model\n",
        "MobileNet V2の model を構築する。<br>\n",
        "Fine tuning するため、108層以降を学習するように設定する。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "vptWZq2xnclo",
        "outputId": "4196c393-28a3-4bfe-edab-9dd9f61788d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "base_model = MobileNetV2(include_top=False,\n",
        "                         weights='imagenet',\n",
        "                         input_shape=(IMG_SIZE, IMG_SIZE, 3))\n",
        "base_model.trainable = False\n",
        "\n",
        "model = tf.keras.Sequential([\n",
        "    base_model,\n",
        "   tf.keras.layers.GlobalAveragePooling2D(),\n",
        "   # tf.keras.layers.Dense(1024, activation = 'relu'),\n",
        "   # tf.keras.layers.Dropout(rate=0.2),\n",
        "    tf.keras.layers.Dense(info.features['label'].num_classes, activation='softmax')\n",
        "])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n",
            "9412608/9406464 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AhW4kM6JL5eu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "base_model.trainable = True\n",
        "for layer in base_model.layers[:108]:\n",
        "    layer.trainable = False"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ievZtWcyMKD1",
        "colab_type": "code",
        "outputId": "97cf66be-bae2-4970-88ed-e2e5b1db6e7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for i, layer in enumerate(base_model.layers):\n",
        "  print(i, layer.name, layer.trainable)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 input_1 False\n",
            "1 Conv1_pad False\n",
            "2 Conv1 False\n",
            "3 bn_Conv1 False\n",
            "4 Conv1_relu False\n",
            "5 expanded_conv_depthwise False\n",
            "6 expanded_conv_depthwise_BN False\n",
            "7 expanded_conv_depthwise_relu False\n",
            "8 expanded_conv_project False\n",
            "9 expanded_conv_project_BN False\n",
            "10 block_1_expand False\n",
            "11 block_1_expand_BN False\n",
            "12 block_1_expand_relu False\n",
            "13 block_1_pad False\n",
            "14 block_1_depthwise False\n",
            "15 block_1_depthwise_BN False\n",
            "16 block_1_depthwise_relu False\n",
            "17 block_1_project False\n",
            "18 block_1_project_BN False\n",
            "19 block_2_expand False\n",
            "20 block_2_expand_BN False\n",
            "21 block_2_expand_relu False\n",
            "22 block_2_depthwise False\n",
            "23 block_2_depthwise_BN False\n",
            "24 block_2_depthwise_relu False\n",
            "25 block_2_project False\n",
            "26 block_2_project_BN False\n",
            "27 block_2_add False\n",
            "28 block_3_expand False\n",
            "29 block_3_expand_BN False\n",
            "30 block_3_expand_relu False\n",
            "31 block_3_pad False\n",
            "32 block_3_depthwise False\n",
            "33 block_3_depthwise_BN False\n",
            "34 block_3_depthwise_relu False\n",
            "35 block_3_project False\n",
            "36 block_3_project_BN False\n",
            "37 block_4_expand False\n",
            "38 block_4_expand_BN False\n",
            "39 block_4_expand_relu False\n",
            "40 block_4_depthwise False\n",
            "41 block_4_depthwise_BN False\n",
            "42 block_4_depthwise_relu False\n",
            "43 block_4_project False\n",
            "44 block_4_project_BN False\n",
            "45 block_4_add False\n",
            "46 block_5_expand False\n",
            "47 block_5_expand_BN False\n",
            "48 block_5_expand_relu False\n",
            "49 block_5_depthwise False\n",
            "50 block_5_depthwise_BN False\n",
            "51 block_5_depthwise_relu False\n",
            "52 block_5_project False\n",
            "53 block_5_project_BN False\n",
            "54 block_5_add False\n",
            "55 block_6_expand False\n",
            "56 block_6_expand_BN False\n",
            "57 block_6_expand_relu False\n",
            "58 block_6_pad False\n",
            "59 block_6_depthwise False\n",
            "60 block_6_depthwise_BN False\n",
            "61 block_6_depthwise_relu False\n",
            "62 block_6_project False\n",
            "63 block_6_project_BN False\n",
            "64 block_7_expand False\n",
            "65 block_7_expand_BN False\n",
            "66 block_7_expand_relu False\n",
            "67 block_7_depthwise False\n",
            "68 block_7_depthwise_BN False\n",
            "69 block_7_depthwise_relu False\n",
            "70 block_7_project False\n",
            "71 block_7_project_BN False\n",
            "72 block_7_add False\n",
            "73 block_8_expand False\n",
            "74 block_8_expand_BN False\n",
            "75 block_8_expand_relu False\n",
            "76 block_8_depthwise False\n",
            "77 block_8_depthwise_BN False\n",
            "78 block_8_depthwise_relu False\n",
            "79 block_8_project False\n",
            "80 block_8_project_BN False\n",
            "81 block_8_add False\n",
            "82 block_9_expand False\n",
            "83 block_9_expand_BN False\n",
            "84 block_9_expand_relu False\n",
            "85 block_9_depthwise False\n",
            "86 block_9_depthwise_BN False\n",
            "87 block_9_depthwise_relu False\n",
            "88 block_9_project False\n",
            "89 block_9_project_BN False\n",
            "90 block_9_add False\n",
            "91 block_10_expand False\n",
            "92 block_10_expand_BN False\n",
            "93 block_10_expand_relu False\n",
            "94 block_10_depthwise False\n",
            "95 block_10_depthwise_BN False\n",
            "96 block_10_depthwise_relu False\n",
            "97 block_10_project False\n",
            "98 block_10_project_BN False\n",
            "99 block_11_expand False\n",
            "100 block_11_expand_BN False\n",
            "101 block_11_expand_relu False\n",
            "102 block_11_depthwise False\n",
            "103 block_11_depthwise_BN False\n",
            "104 block_11_depthwise_relu False\n",
            "105 block_11_project False\n",
            "106 block_11_project_BN False\n",
            "107 block_11_add False\n",
            "108 block_12_expand True\n",
            "109 block_12_expand_BN True\n",
            "110 block_12_expand_relu True\n",
            "111 block_12_depthwise True\n",
            "112 block_12_depthwise_BN True\n",
            "113 block_12_depthwise_relu True\n",
            "114 block_12_project True\n",
            "115 block_12_project_BN True\n",
            "116 block_12_add True\n",
            "117 block_13_expand True\n",
            "118 block_13_expand_BN True\n",
            "119 block_13_expand_relu True\n",
            "120 block_13_pad True\n",
            "121 block_13_depthwise True\n",
            "122 block_13_depthwise_BN True\n",
            "123 block_13_depthwise_relu True\n",
            "124 block_13_project True\n",
            "125 block_13_project_BN True\n",
            "126 block_14_expand True\n",
            "127 block_14_expand_BN True\n",
            "128 block_14_expand_relu True\n",
            "129 block_14_depthwise True\n",
            "130 block_14_depthwise_BN True\n",
            "131 block_14_depthwise_relu True\n",
            "132 block_14_project True\n",
            "133 block_14_project_BN True\n",
            "134 block_14_add True\n",
            "135 block_15_expand True\n",
            "136 block_15_expand_BN True\n",
            "137 block_15_expand_relu True\n",
            "138 block_15_depthwise True\n",
            "139 block_15_depthwise_BN True\n",
            "140 block_15_depthwise_relu True\n",
            "141 block_15_project True\n",
            "142 block_15_project_BN True\n",
            "143 block_15_add True\n",
            "144 block_16_expand True\n",
            "145 block_16_expand_BN True\n",
            "146 block_16_expand_relu True\n",
            "147 block_16_depthwise True\n",
            "148 block_16_depthwise_BN True\n",
            "149 block_16_depthwise_relu True\n",
            "150 block_16_project True\n",
            "151 block_16_project_BN True\n",
            "152 Conv_1 True\n",
            "153 Conv_1_bn True\n",
            "154 out_relu True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "na5yKclEBXuw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "base_learning_rate = 0.0001\n",
        "model.compile(\n",
        "    optimizer = tf.keras.optimizers.RMSprop(lr=base_learning_rate),\n",
        "    loss = 'sparse_categorical_crossentropy',\n",
        "    metrics = [\"accuracy\"]\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bAfgmPLBbh9C",
        "colab_type": "code",
        "outputId": "f69f1e17-96d1-4595-ab7a-de922b5d3888",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "base_model.summary()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"mobilenetv2_1.00_224\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "Conv1_pad (ZeroPadding2D)       (None, 225, 225, 3)  0           input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "Conv1 (Conv2D)                  (None, 112, 112, 32) 864         Conv1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "bn_Conv1 (BatchNormalization)   (None, 112, 112, 32) 128         Conv1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "Conv1_relu (ReLU)               (None, 112, 112, 32) 0           bn_Conv1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise (Depthw (None, 112, 112, 32) 288         Conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_BN (Bat (None, 112, 112, 32) 128         expanded_conv_depthwise[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_relu (R (None, 112, 112, 32) 0           expanded_conv_depthwise_BN[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project (Conv2D)  (None, 112, 112, 16) 512         expanded_conv_depthwise_relu[0][0\n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project_BN (Batch (None, 112, 112, 16) 64          expanded_conv_project[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand (Conv2D)         (None, 112, 112, 96) 1536        expanded_conv_project_BN[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_BN (BatchNormali (None, 112, 112, 96) 384         block_1_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_relu (ReLU)      (None, 112, 112, 96) 0           block_1_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_pad (ZeroPadding2D)     (None, 113, 113, 96) 0           block_1_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise (DepthwiseCon (None, 56, 56, 96)   864         block_1_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_BN (BatchNorm (None, 56, 56, 96)   384         block_1_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_relu (ReLU)   (None, 56, 56, 96)   0           block_1_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project (Conv2D)        (None, 56, 56, 24)   2304        block_1_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project_BN (BatchNormal (None, 56, 56, 24)   96          block_1_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand (Conv2D)         (None, 56, 56, 144)  3456        block_1_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_BN (BatchNormali (None, 56, 56, 144)  576         block_2_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_relu (ReLU)      (None, 56, 56, 144)  0           block_2_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise (DepthwiseCon (None, 56, 56, 144)  1296        block_2_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_BN (BatchNorm (None, 56, 56, 144)  576         block_2_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_relu (ReLU)   (None, 56, 56, 144)  0           block_2_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project (Conv2D)        (None, 56, 56, 24)   3456        block_2_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project_BN (BatchNormal (None, 56, 56, 24)   96          block_2_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_add (Add)               (None, 56, 56, 24)   0           block_1_project_BN[0][0]         \n",
            "                                                                 block_2_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand (Conv2D)         (None, 56, 56, 144)  3456        block_2_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_BN (BatchNormali (None, 56, 56, 144)  576         block_3_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_relu (ReLU)      (None, 56, 56, 144)  0           block_3_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_pad (ZeroPadding2D)     (None, 57, 57, 144)  0           block_3_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise (DepthwiseCon (None, 28, 28, 144)  1296        block_3_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_BN (BatchNorm (None, 28, 28, 144)  576         block_3_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_relu (ReLU)   (None, 28, 28, 144)  0           block_3_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project (Conv2D)        (None, 28, 28, 32)   4608        block_3_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project_BN (BatchNormal (None, 28, 28, 32)   128         block_3_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand (Conv2D)         (None, 28, 28, 192)  6144        block_3_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_4_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_4_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise (DepthwiseCon (None, 28, 28, 192)  1728        block_4_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_BN (BatchNorm (None, 28, 28, 192)  768         block_4_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_relu (ReLU)   (None, 28, 28, 192)  0           block_4_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project (Conv2D)        (None, 28, 28, 32)   6144        block_4_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project_BN (BatchNormal (None, 28, 28, 32)   128         block_4_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_add (Add)               (None, 28, 28, 32)   0           block_3_project_BN[0][0]         \n",
            "                                                                 block_4_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand (Conv2D)         (None, 28, 28, 192)  6144        block_4_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_5_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_5_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise (DepthwiseCon (None, 28, 28, 192)  1728        block_5_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_BN (BatchNorm (None, 28, 28, 192)  768         block_5_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_relu (ReLU)   (None, 28, 28, 192)  0           block_5_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project (Conv2D)        (None, 28, 28, 32)   6144        block_5_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project_BN (BatchNormal (None, 28, 28, 32)   128         block_5_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_5_add (Add)               (None, 28, 28, 32)   0           block_4_add[0][0]                \n",
            "                                                                 block_5_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand (Conv2D)         (None, 28, 28, 192)  6144        block_5_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_6_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_6_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_pad (ZeroPadding2D)     (None, 29, 29, 192)  0           block_6_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise (DepthwiseCon (None, 14, 14, 192)  1728        block_6_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_BN (BatchNorm (None, 14, 14, 192)  768         block_6_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_relu (ReLU)   (None, 14, 14, 192)  0           block_6_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project (Conv2D)        (None, 14, 14, 64)   12288       block_6_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project_BN (BatchNormal (None, 14, 14, 64)   256         block_6_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand (Conv2D)         (None, 14, 14, 384)  24576       block_6_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_7_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_7_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_7_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_7_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_7_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project (Conv2D)        (None, 14, 14, 64)   24576       block_7_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project_BN (BatchNormal (None, 14, 14, 64)   256         block_7_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_add (Add)               (None, 14, 14, 64)   0           block_6_project_BN[0][0]         \n",
            "                                                                 block_7_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand (Conv2D)         (None, 14, 14, 384)  24576       block_7_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_8_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_8_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_8_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_8_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_8_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project (Conv2D)        (None, 14, 14, 64)   24576       block_8_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project_BN (BatchNormal (None, 14, 14, 64)   256         block_8_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_8_add (Add)               (None, 14, 14, 64)   0           block_7_add[0][0]                \n",
            "                                                                 block_8_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand (Conv2D)         (None, 14, 14, 384)  24576       block_8_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_9_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_9_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_9_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_9_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_9_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project (Conv2D)        (None, 14, 14, 64)   24576       block_9_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project_BN (BatchNormal (None, 14, 14, 64)   256         block_9_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_9_add (Add)               (None, 14, 14, 64)   0           block_8_add[0][0]                \n",
            "                                                                 block_9_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand (Conv2D)        (None, 14, 14, 384)  24576       block_9_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_BN (BatchNormal (None, 14, 14, 384)  1536        block_10_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_relu (ReLU)     (None, 14, 14, 384)  0           block_10_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise (DepthwiseCo (None, 14, 14, 384)  3456        block_10_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_BN (BatchNor (None, 14, 14, 384)  1536        block_10_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           block_10_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project (Conv2D)       (None, 14, 14, 96)   36864       block_10_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project_BN (BatchNorma (None, 14, 14, 96)   384         block_10_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand (Conv2D)        (None, 14, 14, 576)  55296       block_10_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_11_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_11_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise (DepthwiseCo (None, 14, 14, 576)  5184        block_11_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_BN (BatchNor (None, 14, 14, 576)  2304        block_11_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_relu (ReLU)  (None, 14, 14, 576)  0           block_11_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project (Conv2D)       (None, 14, 14, 96)   55296       block_11_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project_BN (BatchNorma (None, 14, 14, 96)   384         block_11_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_add (Add)              (None, 14, 14, 96)   0           block_10_project_BN[0][0]        \n",
            "                                                                 block_11_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand (Conv2D)        (None, 14, 14, 576)  55296       block_11_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_12_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_12_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise (DepthwiseCo (None, 14, 14, 576)  5184        block_12_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_BN (BatchNor (None, 14, 14, 576)  2304        block_12_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_relu (ReLU)  (None, 14, 14, 576)  0           block_12_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project (Conv2D)       (None, 14, 14, 96)   55296       block_12_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project_BN (BatchNorma (None, 14, 14, 96)   384         block_12_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_12_add (Add)              (None, 14, 14, 96)   0           block_11_add[0][0]               \n",
            "                                                                 block_12_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand (Conv2D)        (None, 14, 14, 576)  55296       block_12_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_13_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_13_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_pad (ZeroPadding2D)    (None, 15, 15, 576)  0           block_13_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise (DepthwiseCo (None, 7, 7, 576)    5184        block_13_pad[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_BN (BatchNor (None, 7, 7, 576)    2304        block_13_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_relu (ReLU)  (None, 7, 7, 576)    0           block_13_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project (Conv2D)       (None, 7, 7, 160)    92160       block_13_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project_BN (BatchNorma (None, 7, 7, 160)    640         block_13_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand (Conv2D)        (None, 7, 7, 960)    153600      block_13_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_14_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_14_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_14_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_14_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_14_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project (Conv2D)       (None, 7, 7, 160)    153600      block_14_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project_BN (BatchNorma (None, 7, 7, 160)    640         block_14_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_add (Add)              (None, 7, 7, 160)    0           block_13_project_BN[0][0]        \n",
            "                                                                 block_14_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand (Conv2D)        (None, 7, 7, 960)    153600      block_14_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_15_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_15_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_15_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_15_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_15_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project (Conv2D)       (None, 7, 7, 160)    153600      block_15_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project_BN (BatchNorma (None, 7, 7, 160)    640         block_15_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_15_add (Add)              (None, 7, 7, 160)    0           block_14_add[0][0]               \n",
            "                                                                 block_15_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand (Conv2D)        (None, 7, 7, 960)    153600      block_15_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_16_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_16_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_16_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_16_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_16_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project (Conv2D)       (None, 7, 7, 320)    307200      block_16_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project_BN (BatchNorma (None, 7, 7, 320)    1280        block_16_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1 (Conv2D)                 (None, 7, 7, 1280)   409600      block_16_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1_bn (BatchNormalization)  (None, 7, 7, 1280)   5120        Conv_1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "out_relu (ReLU)                 (None, 7, 7, 1280)   0           Conv_1_bn[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 2,257,984\n",
            "Trainable params: 1,799,616\n",
            "Non-trainable params: 458,368\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SOWJAj2hiLAn",
        "colab_type": "code",
        "outputId": "ea892a79-c47b-4684-9bc5-4a993e14fca1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "mobilenetv2_1.00_224 (Model) (None, 7, 7, 1280)        2257984   \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d (Gl (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 5)                 6405      \n",
            "=================================================================\n",
            "Total params: 2,264,389\n",
            "Trainable params: 1,806,021\n",
            "Non-trainable params: 458,368\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T-iWG685ijpV",
        "colab_type": "text"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wGHFkOUni8w1",
        "colab_type": "text"
      },
      "source": [
        "ステップ数（steps_per_epoch）、validationステップ数（validation_steps）を dataset から算出する。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NPV54hx47rse",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_train = 2936\n",
        "num_val =  367\n",
        "num_test = 367\n",
        "\n",
        "steps_per_epoch = round(num_train) // BATCH_SIZE\n",
        "validation_steps = round(num_val) // BATCH_SIZE"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ATwWPA66aN2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "log_dir = os.path.join('.', 'logs', datetime.datetime.now().strftime('%Y%m%d-%H%M%S'))\n",
        "os.makedirs(log_dir)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gqpchx8D73yO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
        "\n",
        "model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
        "    'training_checkpoints/weights.{epoch:02d}-{val_loss:.2f}.hdf5',\n",
        "    verbose=1)\n",
        "os.makedirs('training_checkpoints/', exist_ok=True)\n",
        "early_stopping_checkpoint = tf.keras.callbacks.EarlyStopping(patience=5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zVz7pzYDjkaM",
        "colab_type": "text"
      },
      "source": [
        "学習を行う。<br>\n",
        "accuracy は0.99程度、val_accuracy は0.85程度になる。<br>\n",
        "early_stopping_checkpoint を設定しているので10 epoch 前後で学習は終了する。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bIcws2jw8JrH",
        "colab_type": "code",
        "outputId": "4a83bf06-1f19-4651-d860-b1e78e222b30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "history = model.fit(train.repeat(), \n",
        "                    epochs=20, \n",
        "                    steps_per_epoch=steps_per_epoch,\n",
        "                    validation_data=validation.repeat(),\n",
        "                    validation_steps=validation_steps,\n",
        "                    callbacks=[tensorboard_callback,\n",
        "                               model_checkpoint_callback,\n",
        "                               early_stopping_checkpoint])"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "91/91 [==============================] - ETA: 0s - loss: 0.6152 - accuracy: 0.7716\n",
            "Epoch 00001: saving model to training_checkpoints/weights.01-0.67.hdf5\n",
            "91/91 [==============================] - 13s 139ms/step - loss: 0.6152 - accuracy: 0.7716 - val_loss: 0.6651 - val_accuracy: 0.7926\n",
            "Epoch 2/20\n",
            "91/91 [==============================] - ETA: 0s - loss: 0.1880 - accuracy: 0.9370\n",
            "Epoch 00002: saving model to training_checkpoints/weights.02-0.55.hdf5\n",
            "91/91 [==============================] - 9s 96ms/step - loss: 0.1880 - accuracy: 0.9370 - val_loss: 0.5462 - val_accuracy: 0.8125\n",
            "Epoch 3/20\n",
            "91/91 [==============================] - ETA: 0s - loss: 0.0839 - accuracy: 0.9780\n",
            "Epoch 00003: saving model to training_checkpoints/weights.03-0.37.hdf5\n",
            "91/91 [==============================] - 9s 95ms/step - loss: 0.0839 - accuracy: 0.9780 - val_loss: 0.3698 - val_accuracy: 0.8864\n",
            "Epoch 4/20\n",
            "91/91 [==============================] - ETA: 0s - loss: 0.0428 - accuracy: 0.9869\n",
            "Epoch 00004: saving model to training_checkpoints/weights.04-0.41.hdf5\n",
            "91/91 [==============================] - 9s 95ms/step - loss: 0.0428 - accuracy: 0.9869 - val_loss: 0.4117 - val_accuracy: 0.8665\n",
            "Epoch 5/20\n",
            "91/91 [==============================] - ETA: 0s - loss: 0.0221 - accuracy: 0.9935\n",
            "Epoch 00005: saving model to training_checkpoints/weights.05-0.29.hdf5\n",
            "91/91 [==============================] - 9s 95ms/step - loss: 0.0221 - accuracy: 0.9935 - val_loss: 0.2935 - val_accuracy: 0.9205\n",
            "Epoch 6/20\n",
            "91/91 [==============================] - ETA: 0s - loss: 0.0151 - accuracy: 0.9969\n",
            "Epoch 00006: saving model to training_checkpoints/weights.06-0.30.hdf5\n",
            "91/91 [==============================] - 9s 95ms/step - loss: 0.0151 - accuracy: 0.9969 - val_loss: 0.2975 - val_accuracy: 0.9176\n",
            "Epoch 7/20\n",
            "91/91 [==============================] - ETA: 0s - loss: 0.0112 - accuracy: 0.9983\n",
            "Epoch 00007: saving model to training_checkpoints/weights.07-0.30.hdf5\n",
            "91/91 [==============================] - 9s 95ms/step - loss: 0.0112 - accuracy: 0.9983 - val_loss: 0.3045 - val_accuracy: 0.9176\n",
            "Epoch 8/20\n",
            "91/91 [==============================] - ETA: 0s - loss: 0.0070 - accuracy: 0.9990\n",
            "Epoch 00008: saving model to training_checkpoints/weights.08-0.22.hdf5\n",
            "91/91 [==============================] - 9s 95ms/step - loss: 0.0070 - accuracy: 0.9990 - val_loss: 0.2217 - val_accuracy: 0.9403\n",
            "Epoch 9/20\n",
            "91/91 [==============================] - ETA: 0s - loss: 0.0047 - accuracy: 0.9993\n",
            "Epoch 00009: saving model to training_checkpoints/weights.09-0.22.hdf5\n",
            "91/91 [==============================] - 9s 95ms/step - loss: 0.0047 - accuracy: 0.9993 - val_loss: 0.2199 - val_accuracy: 0.9375\n",
            "Epoch 10/20\n",
            "91/91 [==============================] - ETA: 0s - loss: 0.0053 - accuracy: 0.9979\n",
            "Epoch 00010: saving model to training_checkpoints/weights.10-0.17.hdf5\n",
            "91/91 [==============================] - 9s 95ms/step - loss: 0.0053 - accuracy: 0.9979 - val_loss: 0.1670 - val_accuracy: 0.9545\n",
            "Epoch 11/20\n",
            "91/91 [==============================] - ETA: 0s - loss: 0.0056 - accuracy: 0.9979\n",
            "Epoch 00011: saving model to training_checkpoints/weights.11-0.21.hdf5\n",
            "91/91 [==============================] - 9s 96ms/step - loss: 0.0056 - accuracy: 0.9979 - val_loss: 0.2102 - val_accuracy: 0.9347\n",
            "Epoch 12/20\n",
            "91/91 [==============================] - ETA: 0s - loss: 0.0077 - accuracy: 0.9966\n",
            "Epoch 00012: saving model to training_checkpoints/weights.12-0.22.hdf5\n",
            "91/91 [==============================] - 9s 95ms/step - loss: 0.0077 - accuracy: 0.9966 - val_loss: 0.2223 - val_accuracy: 0.9375\n",
            "Epoch 13/20\n",
            "91/91 [==============================] - ETA: 0s - loss: 0.0080 - accuracy: 0.9966\n",
            "Epoch 00013: saving model to training_checkpoints/weights.13-0.24.hdf5\n",
            "91/91 [==============================] - 9s 94ms/step - loss: 0.0080 - accuracy: 0.9966 - val_loss: 0.2380 - val_accuracy: 0.9375\n",
            "Epoch 14/20\n",
            "91/91 [==============================] - ETA: 0s - loss: 0.0034 - accuracy: 0.9990\n",
            "Epoch 00014: saving model to training_checkpoints/weights.14-0.25.hdf5\n",
            "91/91 [==============================] - 9s 95ms/step - loss: 0.0034 - accuracy: 0.9990 - val_loss: 0.2474 - val_accuracy: 0.9347\n",
            "Epoch 15/20\n",
            "91/91 [==============================] - ETA: 0s - loss: 0.0034 - accuracy: 0.9990\n",
            "Epoch 00015: saving model to training_checkpoints/weights.15-0.16.hdf5\n",
            "91/91 [==============================] - 9s 95ms/step - loss: 0.0034 - accuracy: 0.9990 - val_loss: 0.1643 - val_accuracy: 0.9545\n",
            "Epoch 16/20\n",
            "91/91 [==============================] - ETA: 0s - loss: 0.0043 - accuracy: 0.9986\n",
            "Epoch 00016: saving model to training_checkpoints/weights.16-0.11.hdf5\n",
            "91/91 [==============================] - 9s 95ms/step - loss: 0.0043 - accuracy: 0.9986 - val_loss: 0.1068 - val_accuracy: 0.9631\n",
            "Epoch 17/20\n",
            "91/91 [==============================] - ETA: 0s - loss: 0.0055 - accuracy: 0.9983\n",
            "Epoch 00017: saving model to training_checkpoints/weights.17-0.20.hdf5\n",
            "91/91 [==============================] - 9s 95ms/step - loss: 0.0055 - accuracy: 0.9983 - val_loss: 0.1964 - val_accuracy: 0.9545\n",
            "Epoch 18/20\n",
            "91/91 [==============================] - ETA: 0s - loss: 0.0079 - accuracy: 0.9979\n",
            "Epoch 00018: saving model to training_checkpoints/weights.18-0.21.hdf5\n",
            "91/91 [==============================] - 9s 95ms/step - loss: 0.0079 - accuracy: 0.9979 - val_loss: 0.2054 - val_accuracy: 0.9517\n",
            "Epoch 19/20\n",
            "91/91 [==============================] - ETA: 0s - loss: 0.0030 - accuracy: 0.9993\n",
            "Epoch 00019: saving model to training_checkpoints/weights.19-0.19.hdf5\n",
            "91/91 [==============================] - 9s 95ms/step - loss: 0.0030 - accuracy: 0.9993 - val_loss: 0.1921 - val_accuracy: 0.9489\n",
            "Epoch 20/20\n",
            "91/91 [==============================] - ETA: 0s - loss: 0.0013 - accuracy: 0.9997\n",
            "Epoch 00020: saving model to training_checkpoints/weights.20-0.18.hdf5\n",
            "91/91 [==============================] - 9s 95ms/step - loss: 0.0013 - accuracy: 0.9997 - val_loss: 0.1793 - val_accuracy: 0.9460\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zrOFPKnnkAJV",
        "colab_type": "text"
      },
      "source": [
        "Accuracy, Loss を描画する。<br>\n",
        "TF1.xとTF2.xで名前が異なるため注意。\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LdNj7UJ0A1Nv",
        "colab_type": "code",
        "outputId": "368316a1-197d-41d8-9d67-25bcc5a0c68f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        }
      },
      "source": [
        "# For TF2.x\n",
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "\n",
        "# For TF1.x\n",
        "# acc = history.history['acc']\n",
        "# val_acc = history.history['val_acc']\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.subplot(2, 1, 1)\n",
        "plt.plot(acc, label='Training Accuracy')\n",
        "plt.plot(val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.plot(loss, label='Training Loss')\n",
        "plt.plot(val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.ylabel('Cross Entropy')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7L-_7PnWl1dZ",
        "colab_type": "text"
      },
      "source": [
        "## Test Model\n",
        "Test dataset による評価。おおよそ0.80程度のaccuracy。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yAcOD9pwl6ky",
        "colab_type": "code",
        "outputId": "f8485642-5b78-433b-c5dc-e8962c01c784",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "model.evaluate(test)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "367/367 [==============================] - 3s 8ms/step - loss: 0.1724 - accuracy: 0.9482\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.17238318920135498, 0.9482288956642151]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sqAcaeI2lCAq",
        "colab_type": "text"
      },
      "source": [
        "## Save model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FvMcPToThcBw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "models_dir = pathlib.Path(os.path.join('.', 'models'))\n",
        "models_dir.mkdir(exist_ok=True, parents=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UaMvBYYaIIgF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Save keras model\n",
        "model.save(os.path.join(models_dir, 'mobilenet_v2.h5'), include_optimizer=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G9A-pgm-4NjR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tf.keras.backend.clear_session()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-q38_6lClHBv",
        "colab_type": "text"
      },
      "source": [
        "# Convert Keras model to TF-Lite model\n",
        "[TensorFlow Lite converter Python API](https://www.tensorflow.org/lite/convert/python_api) を使って保存した Keras model を TF-Lite モデルに変換する。<br>\n",
        "TF2.xの場合、from_keras_model を使って keras model から converter を得る。<br>\n",
        "TF1.xの場合、from_keras_model_file を使って ファイルから keras model を読み込み、converter を得る。\n",
        "ここでは、いくつかの変換を試してみる。\n",
        "\n",
        "\n",
        "*   [Weight quantization](https://www.tensorflow.org/lite/performance/post_training_quantization#weight_quantization)\n",
        "*   [Float16 quantization of weights](https://www.tensorflow.org/lite/performance/post_training_quantization#float16_quantization_of_weights)\n",
        "*   [Full integer quantization of weights and activations](https://www.tensorflow.org/lite/performance/post_training_quantization#full_integer_quantization_of_weights_and_activations)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lru90cehEZES",
        "colab_type": "text"
      },
      "source": [
        "## TF-Lite Model\n",
        "\n",
        "\n",
        "*   量子化なしのTF-Lite model\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5eSl--KPEgYf",
        "colab_type": "code",
        "outputId": "b967ce9a-4be8-4547-dbc3-823bed883182",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "loaded_model = tf.keras.models.load_model(os.path.join(models_dir, 'mobilenet_v2.h5'))\n",
        "converter = tf.lite.TFLiteConverter.from_keras_model(loaded_model)\n",
        "\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "tflite_file = models_dir/'mobilenet_v2.tflite'\n",
        "tflite_file.write_bytes(tflite_model)\n",
        "\n",
        "tf.keras.backend.clear_session()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IdWSo-FhDqBK",
        "colab_type": "text"
      },
      "source": [
        "## Weight quantization\n",
        "\n",
        "\n",
        "*   \"hybrid\" quantization とも呼ばれる方法。\n",
        "*   重みのみを量子化して、推論時は浮動小数点演算で行われる。\n",
        "*   推論の制度の低下は低い（はず）。\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AapUdYKbpW4f",
        "colab_type": "code",
        "outputId": "2ec760fe-95d5-4078-c06f-df0bca24dbbe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "loaded_model = tf.keras.models.load_model(os.path.join(models_dir, 'mobilenet_v2.h5'))\n",
        "converter = tf.lite.TFLiteConverter.from_keras_model(loaded_model)\n",
        "\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "tflite_model_file = models_dir/'mobilenet_v2.tflite'\n",
        "tflite_model_file.write_bytes(tflite_model)\n",
        "\n",
        "converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]\n",
        "tflite_weight_quant_model = converter.convert()\n",
        "\n",
        "tflite_weight_model_quant_file = models_dir/'mobilenet_v2_weight_quant.tflite'\n",
        "tflite_weight_model_quant_file.write_bytes(tflite_weight_quant_model)\n",
        "\n",
        "tf.keras.backend.clear_session()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fdX2366fpDXG",
        "colab_type": "text"
      },
      "source": [
        "## Float16 quantization\n",
        "\n",
        "\n",
        "*   重みをfloat16に量子化する。\n",
        "*   モデルサイズは最大で半分になる。\n",
        "*   GPU delegate によって、float16の演算が可能な場合、推論速度が向上する。\n",
        "\n",
        "\n",
        "supported_types に指定する tf.lite.constants.FLOAT16 は TF2.0では削除された。<br>\n",
        "このため、[tf.float16](https://www.tensorflow.org/lite/r2/convert/python_api#liteconstants) を指定すればよいはず。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "g8PUvLWDlmmz",
        "outputId": "d3bd70ff-ce1c-4382-9283-34d4ccba9323",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "loaded_model = tf.keras.models.load_model(os.path.join(models_dir, 'mobilenet_v2.h5'))\n",
        "converter = tf.lite.TFLiteConverter.from_keras_model(loaded_model)\n",
        "\n",
        "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
        "converter.target_spec.supported_types = [tf.float16]\n",
        "\n",
        "tflite_fp16_quant_model = converter.convert()\n",
        "\n",
        "tflite_fp16_model_quant_file = models_dir/'mobilenet_v2_fp16_quant.tflite'\n",
        "tflite_fp16_model_quant_file.write_bytes(tflite_fp16_quant_model)\n",
        "\n",
        "tf.keras.backend.clear_session()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9JY42uUqpJHH",
        "colab_type": "text"
      },
      "source": [
        "## Integer quantization\n",
        "\n",
        "\n",
        "*   重みとアクティベーションの完全な整数量子化。\n",
        "*   メモリ使用量の削減、推論時間の高速化。\n",
        "*   入力と出力は float となる。\n",
        "\n",
        "\n",
        "tfds の test dataset を使って入力のキャリブレーションを行う。<br>\n",
        "MobileNet V2の入力 (1, 244, 244, 3) になるようにする。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q1hB-g_qXb6E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def representative_data_gen():\n",
        "  for batch in test.take(255):\n",
        "    yield [batch[0]]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "tRBfI2JwpX0X",
        "outputId": "70d6bce1-0927-4fa3-a977-8c30ebdb3f21",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "loaded_model = tf.keras.models.load_model(os.path.join(models_dir, 'mobilenet_v2.h5'))\n",
        "converter = tf.lite.TFLiteConverter.from_keras_model(loaded_model)\n",
        "\n",
        "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
        "converter.representative_dataset = representative_data_gen\n",
        "\n",
        "tflite_full_integer_quant_model = converter.convert()\n",
        "\n",
        "tflite_full_integer_model_quant_file = models_dir/'mobilenet_v2_integer_quant.tflite'\n",
        "tflite_full_integer_model_quant_file.write_bytes(tflite_full_integer_quant_model)\n",
        "\n",
        "tf.keras.backend.clear_session()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bEpoRU1qpPre",
        "colab_type": "text"
      },
      "source": [
        "## Full integer quantization\n",
        "\n",
        "\n",
        "*   入力 / 出力を含め完全な整数量子化。\n",
        "*   Edge TPU Modelに変換するには Full integer quantizationが必要。\n",
        "*   **from_keras_model() は Full integer quantization model にならないため、TF1.xのAPI(from_keras_model_files)を使う。**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nIiAfyqCTKN8",
        "colab_type": "code",
        "outputId": "0e6c575b-d67a-44ea-ffdb-529475c1bd99",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "converter = tf.compat.v1.lite.TFLiteConverter.from_keras_model_file(os.path.join(models_dir, 'mobilenet_v2.h5'))\n",
        "\n",
        "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
        "converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]\n",
        "converter.inference_input_type = tf.uint8\n",
        "converter.inference_output_type = tf.uint8\n",
        "converter.representative_dataset = representative_data_gen\n",
        "\n",
        "tflite_full_integer_quant_model = converter.convert()\n",
        "\n",
        "tflite_full_integer_model_quant_file = models_dir/'mobilenet_v2_full_integer_quant1.tflite'\n",
        "tflite_full_integer_model_quant_file.write_bytes(tflite_full_integer_quant_model)\n",
        "\n",
        "tf.keras.backend.clear_session()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JDyPf2tp8kS8",
        "colab_type": "text"
      },
      "source": [
        "比較用に **from_keras_model()** で変換したモデルも作る。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bQIvcSnR4o6Q",
        "colab_type": "code",
        "outputId": "25cb7738-18da-451f-f626-be1fadce2e41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "loaded_model = tf.keras.models.load_model(os.path.join(models_dir, 'mobilenet_v2.h5'))\n",
        "converter = tf.lite.TFLiteConverter.from_keras_model(loaded_model)\n",
        "\n",
        "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
        "converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]\n",
        "converter.inference_input_type = tf.uint8\n",
        "converter.inference_output_type = tf.uint8\n",
        "converter.representative_dataset = representative_data_gen\n",
        "\n",
        "tflite_full_integer_quant_model = converter.convert()\n",
        "\n",
        "tflite_full_integer_model_quant_file = models_dir/'mobilenet_v2_full_integer_quant2.tflite'\n",
        "tflite_full_integer_model_quant_file.write_bytes(tflite_full_integer_quant_model)\n",
        "\n",
        "tf.keras.backend.clear_session()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KRVG7xGMmQ9n",
        "colab_type": "text"
      },
      "source": [
        "## Edge TPU Model\n",
        "Full integer quantization で変換した TF-Lite model を Edge TPU Model に変換する。<br>\n",
        "変換は Edge TPU Compiler で行う。\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Egq_bj7Tt7lQ",
        "colab_type": "code",
        "outputId": "819bb017-4d83-41b8-b542-c39b51e5669b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!echo \"deb https://packages.cloud.google.com/apt coral-edgetpu-stable main\" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list\n",
        "!sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys 6A030B21BA07F4FB\n",
        "!sudo apt update\n",
        "!sudo apt install edgetpu-compiler"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main\n",
            "Executing: /tmp/apt-key-gpghome.y8oXaXm4Zl/gpg.1.sh --keyserver keyserver.ubuntu.com --recv-keys 6A030B21BA07F4FB\n",
            "gpg: key 6A030B21BA07F4FB: public key \"Google Cloud Packages Automatic Signing Key <gc-team@google.com>\" imported\n",
            "gpg: Total number processed: 1\n",
            "gpg:               imported: 1\n",
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            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "61 packages can be upgraded. Run 'apt list --upgradable' to see them.\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following additional packages will be installed:\n",
            "  libedgetpu1-std\n",
            "The following NEW packages will be installed:\n",
            "  edgetpu-compiler libedgetpu1-std\n",
            "0 upgraded, 2 newly installed, 0 to remove and 61 not upgraded.\n",
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            "After this operation, 18.2 MB of additional disk space will be used.\n",
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            "Selecting previously unselected package libedgetpu1-std:amd64.\n",
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            "Preparing to unpack .../edgetpu-compiler_14.0_amd64.deb ...\n",
            "Unpacking edgetpu-compiler (14.0) ...\n",
            "Setting up libedgetpu1-std:amd64 (14.0) ...\n",
            "Setting up edgetpu-compiler (14.0) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/python3.6/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xmereY3chztw",
        "colab_type": "code",
        "outputId": "0be83254-5a86-4cfd-85b8-0ac9322372a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!edgetpu_compiler -v"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Edge TPU Compiler version 2.1.302470888\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PNSrRqa28vI2",
        "colab_type": "text"
      },
      "source": [
        "Full integer quantization (from_keras_model_files) を変換。<br>\n",
        "Model はすべて Edge TPU で実行され、CPUにオフロードされた Ope はない。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q3HmbcSkiBMm",
        "colab_type": "code",
        "outputId": "737edf32-e2b8-4a10-c8d5-32adac36d71f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        }
      },
      "source": [
        "!edgetpu_compiler -s --out_dir /content/models /content/models/mobilenet_v2_full_integer_quant1.tflite"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Edge TPU Compiler version 2.1.302470888\n",
            "\n",
            "Model compiled successfully in 437 ms.\n",
            "\n",
            "Input model: /content/models/mobilenet_v2_full_integer_quant1.tflite\n",
            "Input size: 2.74MiB\n",
            "Output model: /content/models/mobilenet_v2_full_integer_quant1_edgetpu.tflite\n",
            "Output size: 2.77MiB\n",
            "On-chip memory used for caching model parameters: 2.71MiB\n",
            "On-chip memory remaining for caching model parameters: 4.21MiB\n",
            "Off-chip memory used for streaming uncached model parameters: 0.00B\n",
            "Number of Edge TPU subgraphs: 1\n",
            "Total number of operations: 72\n",
            "Operation log: /content/models/mobilenet_v2_full_integer_quant1_edgetpu.log\n",
            "\n",
            "Operator                       Count      Status\n",
            "\n",
            "MEAN                           1          Mapped to Edge TPU\n",
            "FULLY_CONNECTED                1          Mapped to Edge TPU\n",
            "SOFTMAX                        1          Mapped to Edge TPU\n",
            "ADD                            10         Mapped to Edge TPU\n",
            "PAD                            5          Mapped to Edge TPU\n",
            "QUANTIZE                       2          Mapped to Edge TPU\n",
            "CONV_2D                        35         Mapped to Edge TPU\n",
            "DEPTHWISE_CONV_2D              17         Mapped to Edge TPU\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HwbXM9Dc9K7H",
        "colab_type": "text"
      },
      "source": [
        "Integer quantization (from_keras_files) を変換。<br>\n",
        "Input / Output がFloatとなっているため、CPU側にオフロードされている。<br>\n",
        "\n",
        "\n",
        "*   DEQUANTIZE\n",
        "*   QUANTIZE\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5S0aLgO_4jd6",
        "colab_type": "code",
        "outputId": "980fbf30-dfb0-4eaa-9696-4ef0bed0c67a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        }
      },
      "source": [
        "!edgetpu_compiler -s --out_dir /content/models /content/models/mobilenet_v2_full_integer_quant2.tflite"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Edge TPU Compiler version 2.1.302470888\n",
            "\n",
            "Model compiled successfully in 447 ms.\n",
            "\n",
            "Input model: /content/models/mobilenet_v2_full_integer_quant2.tflite\n",
            "Input size: 2.74MiB\n",
            "Output model: /content/models/mobilenet_v2_full_integer_quant2_edgetpu.tflite\n",
            "Output size: 2.77MiB\n",
            "On-chip memory used for caching model parameters: 2.71MiB\n",
            "On-chip memory remaining for caching model parameters: 4.21MiB\n",
            "Off-chip memory used for streaming uncached model parameters: 0.00B\n",
            "Number of Edge TPU subgraphs: 1\n",
            "Total number of operations: 72\n",
            "Operation log: /content/models/mobilenet_v2_full_integer_quant2_edgetpu.log\n",
            "\n",
            "Model successfully compiled but not all operations are supported by the Edge TPU. A percentage of the model will instead run on the CPU, which is slower. If possible, consider updating your model to use only operations supported by the Edge TPU. For details, visit g.co/coral/model-reqs.\n",
            "Number of operations that will run on Edge TPU: 70\n",
            "Number of operations that will run on CPU: 2\n",
            "\n",
            "Operator                       Count      Status\n",
            "\n",
            "MEAN                           1          Mapped to Edge TPU\n",
            "FULLY_CONNECTED                1          Mapped to Edge TPU\n",
            "SOFTMAX                        1          Mapped to Edge TPU\n",
            "ADD                            10         Mapped to Edge TPU\n",
            "PAD                            5          Mapped to Edge TPU\n",
            "QUANTIZE                       1          Operation is otherwise supported, but not mapped due to some unspecified limitation\n",
            "CONV_2D                        35         Mapped to Edge TPU\n",
            "DEPTHWISE_CONV_2D              17         Mapped to Edge TPU\n",
            "DEQUANTIZE                     1          Operation is working on an unsupported data type\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0HNSBetBzY2J",
        "colab_type": "text"
      },
      "source": [
        "それぞれのモデルサイズを比較してみる。<br>\n",
        "\n",
        "TF-Lite model > fp16 quant model > integer quant = full integer quant model > weight quant model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RsKFx56OLpuc",
        "colab_type": "code",
        "outputId": "8ff0ff9a-50d9-4c46-ead6-a7812d39c958",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "!ls -alh ./models"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 38M\n",
            "drwxr-xr-x 2 root root 4.0K Apr 15 13:58 .\n",
            "drwxr-xr-x 1 root root 4.0K Apr 15 13:56 ..\n",
            "-rw-r--r-- 1 root root 4.4M Apr 15 13:56 mobilenet_v2_fp16_quant.tflite\n",
            "-rw-r--r-- 1 root root  714 Apr 15 13:58 mobilenet_v2_full_integer_quant1_edgetpu.log\n",
            "-rw-r--r-- 1 root root 2.8M Apr 15 13:58 mobilenet_v2_full_integer_quant1_edgetpu.tflite\n",
            "-rw-r--r-- 1 root root 2.8M Apr 15 13:57 mobilenet_v2_full_integer_quant1.tflite\n",
            "-rw-r--r-- 1 root root  870 Apr 15 13:58 mobilenet_v2_full_integer_quant2_edgetpu.log\n",
            "-rw-r--r-- 1 root root 2.8M Apr 15 13:58 mobilenet_v2_full_integer_quant2_edgetpu.tflite\n",
            "-rw-r--r-- 1 root root 2.8M Apr 15 13:58 mobilenet_v2_full_integer_quant2.tflite\n",
            "-rw-r--r-- 1 root root 9.0M Apr 15 13:56 mobilenet_v2.h5\n",
            "-rw-r--r-- 1 root root 2.8M Apr 15 13:57 mobilenet_v2_integer_quant.tflite\n",
            "-rw-r--r-- 1 root root 8.5M Apr 15 13:56 mobilenet_v2.tflite\n",
            "-rw-r--r-- 1 root root 2.3M Apr 15 13:56 mobilenet_v2_weight_quant.tflite\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "om3tMVRe22QL",
        "colab_type": "code",
        "outputId": "3d729d7d-917e-401c-e333-27da79e593f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "!tar zcvf models.tar.gz ./models"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "./models/\n",
            "./models/mobilenet_v2_full_integer_quant2.tflite\n",
            "./models/mobilenet_v2_full_integer_quant1.tflite\n",
            "./models/mobilenet_v2_weight_quant.tflite\n",
            "./models/mobilenet_v2_full_integer_quant1_edgetpu.tflite\n",
            "./models/mobilenet_v2_full_integer_quant1_edgetpu.log\n",
            "./models/mobilenet_v2_full_integer_quant2_edgetpu.tflite\n",
            "./models/mobilenet_v2_integer_quant.tflite\n",
            "./models/mobilenet_v2.h5\n",
            "./models/mobilenet_v2.tflite\n",
            "./models/mobilenet_v2_fp16_quant.tflite\n",
            "./models/mobilenet_v2_full_integer_quant2_edgetpu.log\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_x3JGrxxzz9V",
        "colab_type": "text"
      },
      "source": [
        "# Inference\n",
        "それぞれの model で推論を行ってみる。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Usme8LYz6mJ",
        "colab_type": "text"
      },
      "source": [
        "## Keras model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1cY9hIRMIb0A",
        "colab_type": "code",
        "outputId": "b8297ad2-fbea-4214-8d9a-484863bd6c84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        }
      },
      "source": [
        "loaded_model = tf.keras.models.load_model(os.path.join(models_dir, 'mobilenet_v2.h5'))\n",
        "total_seen = 0\n",
        "num_correct = 0\n",
        "inference_time = []\n",
        "\n",
        "for batch in test.take(int(num_test)):\n",
        "  image = batch[0].numpy()\n",
        "\n",
        "  start_ms = time.time()\n",
        "\n",
        "  predictions = loaded_model.predict(image)\n",
        "\n",
        "  elapsed_ms = time.time() - start_ms\n",
        "  inference_time.append(elapsed_ms * 1000.0)\n",
        "  \n",
        "  if batch[1].numpy() == predictions.argmax():\n",
        "    num_correct += 1\n",
        "  total_seen += 1\n",
        "\n",
        "  if total_seen % 50 == 0:\n",
        "    print(\"Accuracy after %i images: %f\" %\n",
        "          (total_seen, float(num_correct) / float(total_seen)))\n",
        "        \n",
        "print('Num images: {0:}, Accuracy: {1:.4f}, Latency: {2:.2f} ms'.format(num_test,\n",
        "                                                                        float(num_correct / total_seen),\n",
        "                                                                        np.array(inference_time).mean()))\n",
        "\n",
        "tf.keras.backend.clear_session()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Accuracy after 50 images: 0.880000\n",
            "Accuracy after 100 images: 0.910000\n",
            "Accuracy after 150 images: 0.920000\n",
            "Accuracy after 200 images: 0.920000\n",
            "Accuracy after 250 images: 0.936000\n",
            "Accuracy after 300 images: 0.943333\n",
            "Accuracy after 350 images: 0.945714\n",
            "Num images: 367, Accuracy: 0.9482, Latency: 29.43 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ni9yIBmMz-Vq",
        "colab_type": "text"
      },
      "source": [
        "## TF-Lite Model\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fHmPHvtmmWde",
        "colab_type": "text"
      },
      "source": [
        "TF-Lite, Weight quant, Float16, Integer quant共通の推論関数。<br>\n",
        "inputは学習時と同じ0.0 〜 1.0にnormalizedされた (224, 224, 3) の画像をとなる。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MfebfIHzmNYs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def inference_tflite(mode_path, num_test):\n",
        "  interpreter = tf.lite.Interpreter(model_path=mode_path)\n",
        "\n",
        "  interpreter.allocate_tensors()\n",
        "  input_index = interpreter.get_input_details()[0][\"index\"]\n",
        "  output_index = interpreter.get_output_details()[0][\"index\"]\n",
        "\n",
        "  print('input_details:  ', interpreter.get_input_details())\n",
        "  print('output_details: ', interpreter.get_output_details())\n",
        "\n",
        "  total_seen = 0\n",
        "  num_correct = 0\n",
        "  inference_time = []\n",
        "\n",
        "  for batch in test.take(int(num_test)):\n",
        "    image = batch[0].numpy()\n",
        "\n",
        "    start_ms = time.time()\n",
        "\n",
        "    interpreter.set_tensor(interpreter.get_input_details()[0][\"index\"], image)\n",
        "    interpreter.invoke()\n",
        "    predictions = interpreter.get_tensor(interpreter.get_output_details()[0][\"index\"])\n",
        "\n",
        "    elapsed_ms = time.time() - start_ms\n",
        "    inference_time.append(elapsed_ms * 1000.0)\n",
        "  \n",
        "    if batch[1].numpy() == predictions.argmax():\n",
        "      num_correct += 1\n",
        "    total_seen += 1\n",
        "\n",
        "    if total_seen % 500 == 0:\n",
        "        print(\"Accuracy after %i images: %f\" %\n",
        "              (total_seen, float(num_correct) / float(total_seen)))\n",
        "\n",
        "  print('Num images: {0:}, Accuracy: {1:.4f}, Latency: {2:.2f} ms'.format(num_test,\n",
        "                                                                         float(num_correct / total_seen),\n",
        "                                                                         np.array(inference_time).mean()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mw03lyXmm3VI",
        "colab_type": "text"
      },
      "source": [
        "Full integer quant共通の推論関数。<br>\n",
        "inputは学習時とは異なり、normalizedされない (224, 224, 3) の画像をとなる。<br>\n",
        "（ここでは、testのdatasetはnormalizedされているため、もとに戻している。）"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r_76bGcGhyiO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def inference_tflite_full_integer_quant(mode_path, num_test):\n",
        "  interpreter = tf.lite.Interpreter(model_path=mode_path)\n",
        "\n",
        "  interpreter.allocate_tensors()\n",
        "  input_index = interpreter.get_input_details()[0][\"index\"]\n",
        "  output_index = interpreter.get_output_details()[0][\"index\"]\n",
        "\n",
        "  print('input_details:  ', interpreter.get_input_details())\n",
        "  print('output_details: ', interpreter.get_output_details())\n",
        "\n",
        "  total_seen = 0\n",
        "  num_correct = 0\n",
        "  inference_time = []\n",
        "\n",
        "  for batch in test.take(int(num_test)):\n",
        "    image = batch[0].numpy()\n",
        "    image = batch[0].numpy() * 255\n",
        "    image = image.astype(np.uint8)\n",
        "\n",
        "    start_ms = time.time()\n",
        "\n",
        "    interpreter.set_tensor(interpreter.get_input_details()[0][\"index\"], image)\n",
        "    interpreter.invoke()\n",
        "    predictions = interpreter.get_tensor(interpreter.get_output_details()[0][\"index\"])\n",
        "\n",
        "    elapsed_ms = time.time() - start_ms\n",
        "    inference_time.append(elapsed_ms * 1000.0)\n",
        "  \n",
        "    if batch[1].numpy() == predictions.argmax():\n",
        "      num_correct += 1\n",
        "    total_seen += 1\n",
        "\n",
        "    if total_seen % 500 == 0:\n",
        "        print(\"Accuracy after %i images: %f\" %\n",
        "               (total_seen, float(num_correct) / float(total_seen)))\n",
        "        \n",
        "  print('Num images: {0:}, Accuracy: {1:.4f}, Latency: {2:.2f} ms'.format(num_test,\n",
        "                                                                         float(num_correct / total_seen),\n",
        "                                                                         np.array(inference_time).mean()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u0ueo2iuOAZh",
        "colab_type": "text"
      },
      "source": [
        "### TF-Lite model\n",
        "量子化されていないモデルの推論。<br>\n",
        "Accuracy は Keras modelとほぼ変わらない。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R8Cz5PHdhIzR",
        "colab_type": "code",
        "outputId": "501c8e41-f075-4a84-f4b3-7fc4bd57313c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "model_path = os.path.join(models_dir, 'mobilenet_v2.tflite')\n",
        "inference_tflite(model_path, int(num_test))"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input_details:   [{'name': 'mobilenetv2_1.00_224_input', 'index': 0, 'shape': array([  1, 224, 224,   3], dtype=int32), 'shape_signature': array([  1, 224, 224,   3], dtype=int32), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]\n",
            "output_details:  [{'name': 'Identity', 'index': 178, 'shape': array([1, 5], dtype=int32), 'shape_signature': array([1, 5], dtype=int32), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]\n",
            "Num images: 367, Accuracy: 0.9482, Latency: 33.26 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZRIQb5N80IWA",
        "colab_type": "text"
      },
      "source": [
        "### Weight quantization\n",
        "Weight quantization model の推論。<br>\n",
        "Accuracy はTF-Lite model と変わらないはずが、なぜか落ちている... 原因不明。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4kMNvcMUEWcn",
        "colab_type": "code",
        "outputId": "6c838e2e-f452-4f19-fd68-293e07d33269",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "model_path = os.path.join(models_dir, 'mobilenet_v2_weight_quant.tflite')\n",
        "inference_tflite(model_path, int(num_test))"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input_details:   [{'name': 'mobilenetv2_1.00_224_input', 'index': 0, 'shape': array([  1, 224, 224,   3], dtype=int32), 'shape_signature': array([  1, 224, 224,   3], dtype=int32), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]\n",
            "output_details:  [{'name': 'Identity', 'index': 178, 'shape': array([1, 5], dtype=int32), 'shape_signature': array([1, 5], dtype=int32), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]\n",
            "Num images: 367, Accuracy: 0.8338, Latency: 51.02 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mrMOe5KPd_vM",
        "colab_type": "text"
      },
      "source": [
        "### Float16 quantization\n",
        "Float16 quantization model の推論。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IB8EPjNrIv7S",
        "colab_type": "code",
        "outputId": "0ca96e9e-731b-451c-b25a-fb88689c67bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "model_path = os.path.join(models_dir, 'mobilenet_v2_fp16_quant.tflite')\n",
        "inference_tflite(model_path, int(num_test))"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input_details:   [{'name': 'mobilenetv2_1.00_224_input', 'index': 0, 'shape': array([  1, 224, 224,   3], dtype=int32), 'shape_signature': array([  1, 224, 224,   3], dtype=int32), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]\n",
            "output_details:  [{'name': 'Identity', 'index': 178, 'shape': array([1, 5], dtype=int32), 'shape_signature': array([1, 5], dtype=int32), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]\n",
            "Num images: 367, Accuracy: 0.9510, Latency: 33.64 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nz0d7MRffyCd",
        "colab_type": "text"
      },
      "source": [
        "### Integer quantization\n",
        "Integer quantization model の推論。<br>\n",
        "推論の処理時間がとても遅い...<br>\n",
        "[Post-training integer quantization](https://www.tensorflow.org/lite/performance/post_training_integer_quant) にもあるとおり、Google Colab上で実行する場合、WeightやFloat16のquantization modelより遅い（おそらくx86_64に最適化されていないためか？ARM CPUの場合は別途計測）。\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "VrISWF0YfszY",
        "outputId": "9baad901-ba2f-4182-c49e-1cb7c815ed4c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "model_path = os.path.join(models_dir, 'mobilenet_v2_integer_quant.tflite')\n",
        "inference_tflite(model_path, int(num_test))"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input_details:   [{'name': 'mobilenetv2_1.00_224_input', 'index': 179, 'shape': array([  1, 224, 224,   3], dtype=int32), 'shape_signature': array([  1, 224, 224,   3], dtype=int32), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]\n",
            "output_details:  [{'name': 'Identity', 'index': 180, 'shape': array([1, 5], dtype=int32), 'shape_signature': array([1, 5], dtype=int32), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]\n",
            "Num images: 367, Accuracy: 0.9482, Latency: 865.65 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xVLynyNlKUBG",
        "colab_type": "code",
        "outputId": "1efdec4a-ace8-4873-f70e-a03636a01d45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "model_path = os.path.join(models_dir, 'mobilenet_v2_full_integer_quant1.tflite')\n",
        "inference_tflite_full_integer_quant(model_path, int(num_test))"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input_details:   [{'name': 'mobilenetv2_1.00_224_input', 'index': 179, 'shape': array([  1, 224, 224,   3], dtype=int32), 'shape_signature': array([  1, 224, 224,   3], dtype=int32), 'dtype': <class 'numpy.uint8'>, 'quantization': (0.003921568859368563, 0), 'quantization_parameters': {'scales': array([0.00392157], dtype=float32), 'zero_points': array([0], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]\n",
            "output_details:  [{'name': 'Identity', 'index': 180, 'shape': array([1, 5], dtype=int32), 'shape_signature': array([1, 5], dtype=int32), 'dtype': <class 'numpy.uint8'>, 'quantization': (0.00390625, 0), 'quantization_parameters': {'scales': array([0.00390625], dtype=float32), 'zero_points': array([0], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]\n",
            "Num images: 367, Accuracy: 0.9537, Latency: 853.87 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hx9XcfNJ9ns8",
        "colab_type": "text"
      },
      "source": [
        "### Full integer quantization\n",
        "Full Integer quantization model の推論。<br>\n",
        "推論の処理時間がとても遅い...（同じ理由）"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "rUYAWKO89lSd",
        "outputId": "cc4d5f27-4ff0-4d04-919a-cbcbba94c5ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "model_path = os.path.join(models_dir, 'mobilenet_v2_full_integer_quant2.tflite')\n",
        "inference_tflite(model_path, int(num_test))"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input_details:   [{'name': 'mobilenetv2_1.00_224_input', 'index': 179, 'shape': array([  1, 224, 224,   3], dtype=int32), 'shape_signature': array([  1, 224, 224,   3], dtype=int32), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]\n",
            "output_details:  [{'name': 'Identity', 'index': 180, 'shape': array([1, 5], dtype=int32), 'shape_signature': array([1, 5], dtype=int32), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]\n",
            "Num images: 367, Accuracy: 0.9482, Latency: 853.66 ms\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}