TFXpipeline/TFXpipeline.ipynb

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  "metadata": {
    "colab": {
      "name": "TFXpipeline.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wJ0EPyVI4K9J"
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      "source": [
        "#BackGround\n",
        "\n",
        "Goal : Using transfer learning on a Breast Cancer dataset to create a new model and predict any new test data sample image for cancer.\n",
        "\n",
        "Dataset - We have \"combined.zip\" which contains two folders - train and test. Each folder has two classes \"benign\" and \"malignant\". \n",
        "\n",
        "Procedure : End to End TFX pipeline is created below for training the model. \n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dz6NtoiAlso4"
      },
      "source": [
        "#Installing tfx libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "bTpgCU6ImK3-",
        "outputId": "c7427e87-fe95-4b12-d834-c604043e12ec"
      },
      "source": [
        "!pip install tensorflow_model_analysis\n",
        "!pip install tfx\n",
        "!pip install tflite-support"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fb/48/69046506f6ac61c1eaa9a0d42d22d54673b69e176d30ca98e3f61513e980/pbr-5.5.1-py2.py3-none-any.whl (106kB)\n",
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            "Collecting fasteners>=0.14\n",
            "  Downloading https://files.pythonhosted.org/packages/18/bd/55eb2d6397b9c0e263af9d091ebdb756b15756029b3cededf6461481bc63/fasteners-0.15-py2.py3-none-any.whl\n",
            "Collecting grpc-google-iam-v1<0.13dev,>=0.12.3\n",
            "  Downloading https://files.pythonhosted.org/packages/65/19/2060c8faa325fddc09aa67af98ffcb6813f39a0ad805679fa64815362b3a/grpc-google-iam-v1-0.12.3.tar.gz\n",
            "Collecting libcst>=0.2.5\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ef/af/c1cc1250ec12f30aca0211b0c30d8f8e4a888308e0363fa14f03114adb82/libcst-0.3.15-py3-none-any.whl (504kB)\n",
            "\u001b[K     |████████████████████████████████| 512kB 56.8MB/s \n",
            "\u001b[?25hCollecting proto-plus>=0.4.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e8/e0/59ad1e6a7d62259027e9b48aedfc95ba63f9336b803d1b174769bcb2c7f9/proto-plus-1.13.0.tar.gz (44kB)\n",
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            "Collecting monotonic>=0.1\n",
            "  Downloading https://files.pythonhosted.org/packages/ac/aa/063eca6a416f397bd99552c534c6d11d57f58f2e94c14780f3bbf818c4cf/monotonic-1.5-py2.py3-none-any.whl\n",
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            "Collecting pyyaml>=5.2\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/64/c2/b80047c7ac2478f9501676c988a5411ed5572f35d1beff9cae07d321512c/PyYAML-5.3.1.tar.gz (269kB)\n",
            "\u001b[K     |████████████████████████████████| 276kB 61.1MB/s \n",
            "\u001b[?25hCollecting typing-inspect>=0.4.0\n",
            "  Downloading https://files.pythonhosted.org/packages/42/1c/66402db44184904a2f14722d317a4da0b5c8c78acfc3faf74362566635c5/typing_inspect-0.6.0-py3-none-any.whl\n",
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            "Collecting mypy-extensions>=0.3.0\n",
            "  Downloading https://files.pythonhosted.org/packages/5c/eb/975c7c080f3223a5cdaff09612f3a5221e4ba534f7039db34c35d95fa6a5/mypy_extensions-0.4.3-py2.py3-none-any.whl\n",
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            "Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<8,>=7->tensorflow_model_analysis) (20.7)\n",
            "Building wheels for collected packages: future, hdfs, dill, avro-python3, google-apitools, grpc-google-iam-v1, proto-plus, pyyaml\n",
            "  Building wheel for future (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Stored in directory: /root/.cache/pip/wheels/8b/99/a0/81daf51dcd359a9377b110a8a886b3895921802d2fc1b2397e\n",
            "  Building wheel for hdfs (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for hdfs: filename=hdfs-2.5.8-cp36-none-any.whl size=33213 sha256=e448ac6cadb0a2296dace2685804a42805d50cafd966fcdeca7256da38780a6f\n",
            "  Stored in directory: /root/.cache/pip/wheels/fe/a7/05/23e3699975fc20f8a30e00ac1e515ab8c61168e982abe4ce70\n",
            "  Building wheel for dill (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dill: filename=dill-0.3.1.1-cp36-none-any.whl size=78533 sha256=a146bdf0df4922547846573a251cbfcb6226cf432f6f944c530dc6f7675bacd7\n",
            "  Stored in directory: /root/.cache/pip/wheels/59/b1/91/f02e76c732915c4015ab4010f3015469866c1eb9b14058d8e7\n",
            "  Building wheel for avro-python3 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for avro-python3: filename=avro_python3-1.9.2.1-cp36-none-any.whl size=43516 sha256=d60e28c82789c9d1fee8e1220471af353cfe2084af8cf92019cbf912be12d145\n",
            "  Stored in directory: /root/.cache/pip/wheels/5a/d3/be/86620c9dd3fca68986c33b9c616510289fc0abb81ec9aa70bd\n",
            "  Building wheel for google-apitools (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for google-apitools: filename=google_apitools-0.5.31-cp36-none-any.whl size=131043 sha256=651575f01f1690862ec328d5662107be9b41b728ef3e9c31ab03352ecb912444\n",
            "  Stored in directory: /root/.cache/pip/wheels/3b/43/31/09a9dad88d3aec6fed2d63bd35dfc532fca372e2edec5af5bf\n",
            "  Building wheel for grpc-google-iam-v1 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for grpc-google-iam-v1: filename=grpc_google_iam_v1-0.12.3-cp36-none-any.whl size=18500 sha256=50e02840118df868f9c31d2f546efd21922a9f5c40aab0f138bc27cd2b8302c5\n",
            "  Stored in directory: /root/.cache/pip/wheels/de/3a/83/77a1e18e1a8757186df834b86ce6800120ac9c79cd8ca4091b\n",
            "  Building wheel for proto-plus (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for proto-plus: filename=proto_plus-1.13.0-cp36-none-any.whl size=41593 sha256=a3ffc1fdaeda3c6598f6eed00ff5ced33239c830127468f7f0b8c6b0859e6bd7\n",
            "  Stored in directory: /root/.cache/pip/wheels/65/57/45/b5c1c1c29003f3670581d84356fa449e1133da93c15f1de364\n",
            "  Building wheel for pyyaml (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyyaml: filename=PyYAML-5.3.1-cp36-cp36m-linux_x86_64.whl size=44620 sha256=2bd78837a9df87ea23f03415775055485ff47e59f5d05e9532f9bc4e4e96094f\n",
            "  Stored in directory: /root/.cache/pip/wheels/a7/c1/ea/cf5bd31012e735dc1dfea3131a2d5eae7978b251083d6247bd\n",
            "Successfully built future hdfs dill avro-python3 google-apitools grpc-google-iam-v1 proto-plus pyyaml\n",
            "\u001b[31mERROR: multiprocess 0.70.11.1 has requirement dill>=0.3.3, but you'll have dill 0.3.1.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: jupyter-console 5.2.0 has requirement prompt-toolkit<2.0.0,>=1.0.0, but you'll have prompt-toolkit 3.0.8 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: google-colab 1.0.0 has requirement google-auth~=1.17.2, but you'll have google-auth 1.23.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: google-colab 1.0.0 has requirement ipython~=5.5.0, but you'll have ipython 7.16.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: google-colab 1.0.0 has requirement requests~=2.23.0, but you'll have requests 2.25.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: google-cloud-spanner 1.19.1 has requirement google-cloud-core<2.0dev,>=1.4.1, but you'll have google-cloud-core 1.0.3 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: google-cloud-build 2.0.0 has requirement google-api-core[grpc]<2.0.0dev,>=1.22.0, but you'll have google-api-core 1.16.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: google-cloud-bigtable 1.6.1 has requirement google-cloud-core<2.0dev,>=1.4.1, but you'll have google-cloud-core 1.0.3 which is incompatible.\u001b[0m\n",
            "Installing collected packages: prompt-toolkit, ipython, tensorflow-serving-api, pyarrow, future, fastavro, requests, hdfs, pbr, mock, dill, avro-python3, google-cloud-dlp, google-cloud-videointelligence, monotonic, fasteners, google-apitools, grpcio-gcp, google-auth, grpc-google-iam-v1, google-cloud-spanner, google-cloud-vision, pyyaml, mypy-extensions, typing-inspect, libcst, proto-plus, google-cloud-build, google-cloud-language, google-cloud-pubsub, google-cloud-bigtable, apache-beam, tfx-bsl, tensorflow-model-analysis\n",
            "  Found existing installation: prompt-toolkit 1.0.18\n",
            "    Uninstalling prompt-toolkit-1.0.18:\n",
            "      Successfully uninstalled prompt-toolkit-1.0.18\n",
            "  Found existing installation: ipython 5.5.0\n",
            "    Uninstalling ipython-5.5.0:\n",
            "      Successfully uninstalled ipython-5.5.0\n",
            "  Found existing installation: pyarrow 0.14.1\n",
            "    Uninstalling pyarrow-0.14.1:\n",
            "      Successfully uninstalled pyarrow-0.14.1\n",
            "  Found existing installation: future 0.16.0\n",
            "    Uninstalling future-0.16.0:\n",
            "      Successfully uninstalled future-0.16.0\n",
            "  Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
            "  Found existing installation: dill 0.3.3\n",
            "    Uninstalling dill-0.3.3:\n",
            "      Successfully uninstalled dill-0.3.3\n",
            "  Found existing installation: google-auth 1.17.2\n",
            "    Uninstalling google-auth-1.17.2:\n",
            "      Successfully uninstalled google-auth-1.17.2\n",
            "  Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "  Found existing installation: google-cloud-language 1.2.0\n",
            "    Uninstalling google-cloud-language-1.2.0:\n",
            "      Successfully uninstalled google-cloud-language-1.2.0\n",
            "Successfully installed apache-beam-2.25.0 avro-python3-1.9.2.1 dill-0.3.1.1 fastavro-1.2.1 fasteners-0.15 future-0.18.2 google-apitools-0.5.31 google-auth-1.23.0 google-cloud-bigtable-1.6.1 google-cloud-build-2.0.0 google-cloud-dlp-1.0.0 google-cloud-language-1.3.0 google-cloud-pubsub-1.7.0 google-cloud-spanner-1.19.1 google-cloud-videointelligence-1.16.1 google-cloud-vision-1.0.0 grpc-google-iam-v1-0.12.3 grpcio-gcp-0.2.2 hdfs-2.5.8 ipython-7.16.1 libcst-0.3.15 mock-2.0.0 monotonic-1.5 mypy-extensions-0.4.3 pbr-5.5.1 prompt-toolkit-3.0.8 proto-plus-1.13.0 pyarrow-0.17.1 pyyaml-5.3.1 requests-2.25.0 tensorflow-model-analysis-0.25.0 tensorflow-serving-api-2.3.0 tfx-bsl-0.25.0 typing-inspect-0.6.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "IPython",
                  "google",
                  "prompt_toolkit"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Collecting tfx\n",
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            "Requirement already satisfied: nbconvert in /usr/local/lib/python3.6/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<8,>=7->tensorflow-model-analysis<0.26,>=0.25->tfx) (5.6.1)\n",
            "Requirement already satisfied: Send2Trash in /usr/local/lib/python3.6/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<8,>=7->tensorflow-model-analysis<0.26,>=0.25->tfx) (1.5.0)\n",
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            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<8,>=7->tensorflow-model-analysis<0.26,>=0.25->tfx) (1.4.3)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<8,>=7->tensorflow-model-analysis<0.26,>=0.25->tfx) (3.2.1)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<8,>=7->tensorflow-model-analysis<0.26,>=0.25->tfx) (0.6.0)\n",
            "Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<8,>=7->tensorflow-model-analysis<0.26,>=0.25->tfx) (0.3)\n",
            "Requirement already satisfied: testpath in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<8,>=7->tensorflow-model-analysis<0.26,>=0.25->tfx) (0.4.4)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.6/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<8,>=7->tensorflow-model-analysis<0.26,>=0.25->tfx) (0.5.1)\n",
            "Building wheels for collected packages: keras-tuner, terminaltables\n",
            "  Building wheel for keras-tuner (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-tuner: filename=keras_tuner-1.0.2-cp36-none-any.whl size=78939 sha256=87de45d935d2aaf571ba01ce80fda7dae5bcc90a1e7a0a1fdbd430cc3babbf6d\n",
            "  Stored in directory: /root/.cache/pip/wheels/bb/a1/8a/7c3de0efb3707a1701b36ebbfdbc4e67aedf6d4943a1f463d6\n",
            "  Building wheel for terminaltables (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for terminaltables: filename=terminaltables-3.1.0-cp36-none-any.whl size=15357 sha256=5db1e5626e03a6b196f9a2722170cba129a4956a6139b294b4b6a6e0a183f91c\n",
            "  Stored in directory: /root/.cache/pip/wheels/30/6b/50/6c75775b681fb36cdfac7f19799888ef9d8813aff9e379663e\n",
            "Successfully built keras-tuner terminaltables\n",
            "Installing collected packages: websocket-client, kubernetes, terminaltables, colorama, keras-tuner, tensorflow-datasets, docker, tensorflow-cloud, ml-metadata, tensorflow-hub, tensorflow-transform, joblib, tensorflow-data-validation, tfx\n",
            "  Found existing installation: tensorflow-datasets 4.0.1\n",
            "    Uninstalling tensorflow-datasets-4.0.1:\n",
            "      Successfully uninstalled tensorflow-datasets-4.0.1\n",
            "  Found existing installation: tensorflow-hub 0.10.0\n",
            "    Uninstalling tensorflow-hub-0.10.0:\n",
            "      Successfully uninstalled tensorflow-hub-0.10.0\n",
            "  Found existing installation: joblib 0.17.0\n",
            "    Uninstalling joblib-0.17.0:\n",
            "      Successfully uninstalled joblib-0.17.0\n",
            "Successfully installed colorama-0.4.4 docker-4.4.0 joblib-0.14.1 keras-tuner-1.0.2 kubernetes-11.0.0 ml-metadata-0.25.1 tensorflow-cloud-0.1.10 tensorflow-data-validation-0.25.0 tensorflow-datasets-3.0.0 tensorflow-hub-0.9.0 tensorflow-transform-0.25.0 terminaltables-3.1.0 tfx-0.25.0 websocket-client-0.57.0\n",
            "Collecting tflite-support\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a2/34/21b81b8250eb2f5c98fdc60544b6e9cde42fde50c815f9f1e493a57c1f00/tflite_support-0.1.0rc4-cp36-cp36m-manylinux2010_x86_64.whl (1.0MB)\n",
            "\u001b[K     |████████████████████████████████| 1.0MB 8.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tflite-support) (1.18.5)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tflite-support) (0.10.0)\n",
            "Collecting pybind11>=2.4\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/00/84/fc9dc13ee536ba5e6b8fd10ce368fea5b738fe394c3b296cde7c9b144a92/pybind11-2.6.1-py2.py3-none-any.whl (188kB)\n",
            "\u001b[K     |████████████████████████████████| 194kB 19.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: flatbuffers>=1.12 in /usr/local/lib/python3.6/dist-packages (from tflite-support) (1.12)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from absl-py>=0.7.0->tflite-support) (1.15.0)\n",
            "Installing collected packages: pybind11, tflite-support\n",
            "Successfully installed pybind11-2.6.1 tflite-support-0.1.0rc4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vxHclSW2oLGT"
      },
      "source": [
        "#downloading data set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LP0zFKZR-60X",
        "outputId": "fe02bddd-7680-4e84-9d84-1db7046e82de"
      },
      "source": [
        "!gdown --id 1kLevdRmyykFfaiUt0A3uSFCRog-mOWSJ"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1kLevdRmyykFfaiUt0A3uSFCRog-mOWSJ\n",
            "To: /content/combined.zip\n",
            "4.21GB [00:38, 111MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z8kh98M6oQWd"
      },
      "source": [
        "#unzipping the file we just downloaded"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_tTcdEl3_DP9"
      },
      "source": [
        "import zipfile\n",
        "with zipfile.ZipFile('/content/combined.zip', 'r') as zip_ref:\n",
        "    zip_ref.extractall('/content/combined')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rG71WVvKoVRm"
      },
      "source": [
        "#Resizing all the images in the dataset to a fixed resolution size. In this case (224,224,3)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vIpRD6HlzXx-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8728f33-f9d2-43a7-dbb0-81a4a16c78fe"
      },
      "source": [
        "from PIL import Image\n",
        "import os, sys\n",
        "\n",
        "# path = \"/content/combined_py_torch/train/1/\"\n",
        "# dirs = os.listdir( path )\n",
        " \n",
        "try:\n",
        "  os.mkdir('/content/converted')\n",
        "  os.mkdir('/content/converted/train/')\n",
        "  os.mkdir('/content/converted/test/')\n",
        "  os.mkdir('/content/converted/train/1')\n",
        "  os.mkdir('/content/converted/train/0')\n",
        "  os.mkdir('/content/converted/test/1')\n",
        "  os.mkdir('/content/converted/test/0')\n",
        "\n",
        "except:\n",
        "  pass\n",
        "\n",
        "def resize(p1='/content/combined/combined/',p2='train/1'):\n",
        "    dirs = os.listdir( p1 + p2)\n",
        "    for item in dirs:\n",
        "        if os.path.isfile(p1 + p2+item):\n",
        "            im = Image.open(p1 + p2+item)\n",
        "            f, e = os.path.splitext(p1 + p2 +item)\n",
        "            print(item)\n",
        "            imResize = im.resize((224,224), Image.ANTIALIAS)\n",
        "            imResize.save('/content/converted/'+ p2 + item , quality=90)\n",
        "\n",
        "resize(p2='train/1/')\n",
        "resize(p2='train/0/')\n",
        "resize(p2='test/1/')\n",
        "resize(p2='test/0/')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "SOB_M_PC-14-19440-400-025.png\n",
            "SOB_M_MC-14-19979C-100-005.png\n",
            "SOB_M_PC-14-12465-200-009.png\n",
            "SOB_M_DC-14-14926-40-020.png\n",
            "SOB_M_DC-14-17915-40-013.png\n",
            "SOB_M_DC-14-3909-200-004.png\n",
            "SOB_M_PC-14-9146-200-007.png\n",
            "SOB_M_PC-14-15704-100-024.png\n",
            "SOB_M_DC-14-5694-200-004.png\n",
            "SOB_M_DC-14-10926-200-005.png\n",
            "SOB_M_DC-14-13412-400-025.png\n",
            "SOB_M_PC-14-19440-200-029.png\n",
            "SOB_M_DC-14-20629-400-002.png\n",
            "SOB_M_MC-14-19979C-400-002.png\n",
            "SOB_M_DC-14-2980-400-012.png\n",
            "SOB_M_DC-14-20629-400-001.png\n",
            "SOB_M_DC-14-20636-200-011.png\n",
            "SOB_M_DC-14-15696-100-012.png\n",
            "SOB_M_DC-14-16875-40-007.png\n",
            "SOB_M_MC-14-13413-40-004.png\n",
            "SOB_M_DC-14-2773-100-031.png\n",
            "SOB_M_DC-14-18650-40-011.png\n",
            "SOB_M_DC-14-14015-200-008.png\n",
            "SOB_M_DC-14-15696-40-015.png\n",
            "SOB_M_DC-14-20629-40-005.png\n",
            "SOB_M_PC-14-19440-400-028.png\n",
            "SOB_M_DC-14-14946-40-003.png\n",
            "SOB_M_DC-14-13993-100-026.png\n",
            "SOB_M_DC-14-17915-200-005.png\n",
            "SOB_M_DC-14-13993-400-025.png\n",
            "SOB_M_DC-14-14015-400-007.png\n",
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            "SOB_M_DC-14-4372-40-021.png\n",
            "SOB_M_DC-14-5694-400-001.png\n",
            "SOB_M_MC-14-19979-400-018.png\n",
            "SOB_M_DC-14-14926-200-016.png\n",
            "SOB_M_DC-14-17901-200-021.png\n",
            "SOB_M_DC-14-2773-400-006.png\n",
            "SOB_M_DC-14-20636-200-020.png\n",
            "SOB_M_DC-14-16448-200-004.png\n",
            "SOB_M_MC-14-16456-100-062.png\n",
            "SOB_M_DC-14-16716-40-038.png\n",
            "SOB_M_PC-14-19440-400-002.png\n",
            "SOB_M_PC-14-19440-100-011.png\n",
            "SOB_M_DC-14-20629-100-028.png\n",
            "SOB_M_DC-14-18650-100-008.png\n",
            "SOB_M_DC-14-20629-400-011.png\n",
            "SOB_M_DC-14-8168-200-006.png\n",
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            "SOB_M_DC-14-15572-40-007.png\n",
            "SOB_M_DC-14-13412-40-011.png\n",
            "SOB_M_PC-14-12465-200-008.png\n",
            "SOB_M_PC-14-15704-40-015.png\n",
            "SOB_M_MC-14-16456-400-050.png\n",
            "SOB_M_DC-14-14946-100-019.png\n",
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            "SOB_M_DC-14-11951-400-012.png\n",
            "SOB_M_DC-14-16601-400-012.png\n",
            "SOB_M_MC-14-16456-100-051.png\n",
            "SOB_M_DC-14-5694-100-010.png\n",
            "SOB_M_PC-14-15704-40-022.png\n",
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            "SOB_M_DC-14-17614-400-018.png\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tc1PX6tsotkx"
      },
      "source": [
        "#Importing regular libraries\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sbmYeHH2jgUD",
        "outputId": "862a8d6d-4283-4246-da0c-36f0619afd49"
      },
      "source": [
        "from __future__ import absolute_import, division, print_function\n",
        " \n",
        "import tensorflow as tf\n",
        "print(tf.version)\n",
        "  \n",
        "# tf.enable_eager_execution()\n",
        " \n",
        "AUTOTUNE = tf.data.experimental.AUTOTUNE\n",
        " \n",
        "import IPython.display as display\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<module 'tensorflow._api.v2.version' from '/usr/local/lib/python3.6/dist-packages/tensorflow/_api/v2/version/__init__.py'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B71JOEhMjY_Y"
      },
      "source": [
        "tf.config.run_functions_eagerly(True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UkSZ_TGkZV2s"
      },
      "source": [
        "import numpy as np, pandas as pd, os\n",
        "import matplotlib.pyplot as plt, cv2\n",
        "import tensorflow as tf, re, math\n",
        "import glob"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jDFqWb7aqMKk"
      },
      "source": [
        "#The below functions help in converting train samples to a serialized format. This format makes processing faster and uses gRPC concepts. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mBcmWrnkIgyL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c6e21d6-ae82-426e-d2e4-c3001430cf12"
      },
      "source": [
        "import os  # used for directory operations\n",
        "import tensorflow as tf\n",
        "from PIL import Image  # used to read images from directory\n",
        "\n",
        "# !unzip /content/drive/My\\ Drive/garbage-classification.zip\n",
        "\n",
        "\n",
        "def _bytes_feature(value):\n",
        "  \"\"\"Returns a bytes_list from a string / byte.\"\"\"\n",
        "  if isinstance(value, type(tf.constant(0))):\n",
        "    value = value.numpy() # BytesList won't unpack a string from an EagerTensor.\n",
        "  return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\n",
        "\n",
        "def _float_feature(value):\n",
        "  \"\"\"Returns a float_list from a float / double.\"\"\"\n",
        "  return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\n",
        "\n",
        "def _int64_feature(value):\n",
        "  \"\"\"Returns an int64_list from a bool / enum / int / uint.\"\"\"\n",
        "  return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))\n",
        "  \n",
        "\n",
        "cwd = \"/content/converted/train\"\n",
        "\n",
        "recordPath = \"/content/\"\n",
        "\n",
        "bestNum = 1000\n",
        "\n",
        "num = 0\n",
        "\n",
        "recordFileNum = 0\n",
        "\n",
        "keys = [str(i) for i in list(range(10))]\n",
        "values = [i for i in list(range(10))]\n",
        "\n",
        "classes ={'0': 0, '1': 1} \n",
        "\n",
        "print(classes)\n",
        "\n",
        "recordFileName = (\"train.tfrecords-%.3d\" % recordFileNum)\n",
        "\n",
        "writer = tf.io.TFRecordWriter(recordPath + recordFileName)\n",
        "\n",
        "import cv2\n",
        "print(\"Creating the 000 tfrecord file\")\n",
        "for name, label in classes.items():\n",
        "    print(name)\n",
        "    print(label)\n",
        "    class_path = os.path.join(cwd, name)\n",
        "    print(class_path)\n",
        "    for img_name in os.listdir(class_path):\n",
        "\n",
        "      img_path = os.path.join(class_path, img_name)\n",
        "      image_string = open(img_path, 'rb').read()\n",
        "    \n",
        "      image_shape = tf.image.decode_jpeg(image_string).shape\n",
        "      print(image_shape)\n",
        "      \n",
        "\n",
        "\n",
        "      feature = {\n",
        "      'height': _int64_feature(image_shape[0]),\n",
        "      'width': _int64_feature(image_shape[1]),\n",
        "      'depth': _int64_feature(image_shape[2]),\n",
        "      'label': _int64_feature(label),\n",
        "      'image_raw': _bytes_feature(image_string),\n",
        "      }\n",
        "\n",
        "      example = tf.train.Example(features=tf.train.Features(feature=feature))\n",
        "      writer.write(example.SerializeToString())\n",
        "writer.close()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(224, 224, 3)\n",
            "(224, 224, 3)\n",
            "(224, 224, 3)\n",
            "(224, 224, 3)\n",
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            "(224, 224, 3)\n",
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            "(224, 224, 3)\n",
            "(224, 224, 3)\n",
            "(224, 224, 3)\n",
            "(224, 224, 3)\n",
            "(224, 224, 3)\n",
            "(224, 224, 3)\n",
            "(224, 224, 3)\n",
            "(224, 224, 3)\n",
            "(224, 224, 3)\n",
            "(224, 224, 3)\n",
            "(224, 224, 3)\n",
            "(224, 224, 3)\n",
            "(224, 224, 3)\n",
            "(224, 224, 3)\n",
            "(224, 224, 3)\n",
            "(224, 224, 3)\n",
            "(224, 224, 3)\n",
            "(224, 224, 3)\n",
            "(224, 224, 3)\n",
            "(224, 224, 3)\n",
            "(224, 224, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jKRh80qovQXO"
      },
      "source": [
        "#same for test data\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_iRTlPb8LDNu"
      },
      "source": [
        "import os  # used for directory operations\n",
        "import tensorflow as tf\n",
        "from PIL import Image  # used to read images from directory\n",
        "\n",
        "# !unzip /content/drive/My\\ Drive/garbage-classification.zip\n",
        "\n",
        "\n",
        "def _bytes_feature(value):\n",
        "  \"\"\"Returns a bytes_list from a string / byte.\"\"\"\n",
        "  if isinstance(value, type(tf.constant(0))):\n",
        "    value = value.numpy() # BytesList won't unpack a string from an EagerTensor.\n",
        "  return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\n",
        "\n",
        "def _float_feature(value):\n",
        "  \"\"\"Returns a float_list from a float / double.\"\"\"\n",
        "  return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\n",
        "\n",
        "def _int64_feature(value):\n",
        "  \"\"\"Returns an int64_list from a bool / enum / int / uint.\"\"\"\n",
        "  return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))\n",
        "  \n",
        "\n",
        "cwd = \"/content/converted/test\"\n",
        "\n",
        "recordPath = \"/content/\"\n",
        "\n",
        "bestNum = 1000\n",
        "\n",
        "num = 0\n",
        "\n",
        "recordFileNum = 0\n",
        "\n",
        "keys = [str(i) for i in list(range(10))]\n",
        "values = [i for i in list(range(10))]\n",
        "\n",
        "classes ={'0': 0, '1': 1} \n",
        "\n",
        "print(classes)\n",
        "\n",
        "recordFileName = (\"test.tfrecords-%.3d\" % recordFileNum)\n",
        "\n",
        "writer = tf.io.TFRecordWriter(recordPath + recordFileName)\n",
        "\n",
        "import cv2\n",
        "print(\"Creating the 000 tfrecord file\")\n",
        "for name, label in classes.items():\n",
        "    print(name)\n",
        "    print(label)\n",
        "    class_path = os.path.join(cwd, name)\n",
        "    print(class_path)\n",
        "    for img_name in os.listdir(class_path):\n",
        "\n",
        "      img_path = os.path.join(class_path, img_name)\n",
        "      image_string = open(img_path, 'rb').read()\n",
        "    \n",
        "      image_shape = tf.image.decode_jpeg(image_string).shape\n",
        "      print(image_shape)\n",
        "      \n",
        "\n",
        "\n",
        "      feature = {\n",
        "      'height': _int64_feature(image_shape[0]),\n",
        "      'width': _int64_feature(image_shape[1]),\n",
        "      'depth': _int64_feature(image_shape[2]),\n",
        "      'label': _int64_feature(label),\n",
        "      'image_raw': _bytes_feature(image_string),\n",
        "      }\n",
        "\n",
        "      example = tf.train.Example(features=tf.train.Features(feature=feature))\n",
        "      writer.write(example.SerializeToString())\n",
        "writer.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "irpiiB4Qr8N3"
      },
      "source": [
        "#Making directories to store our newly converted data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rTeEUAGJkkMn"
      },
      "source": [
        "import os\n",
        "os.mkdir('cifar10')\n",
        "os.mkdir('cifar10/data')\n",
        "os.mkdir('cifar10/data/train')\n",
        "os.mkdir('cifar10/data/test')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VQHjPnvLsCY3"
      },
      "source": [
        "#Copying the tf records into respective directories"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EVFUCIEP6g8q"
      },
      "source": [
        "!cp /content/train.tfrecords-000 /content/cifar10/data/train\n",
        "!cp /content/test.tfrecords-000 /content/cifar10/data/test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ud1n8S6cU5va",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "950ad918-01e7-4d21-eb76-667e635e3832"
      },
      "source": [
        "!gdown --id 1e35udggcbKOaPdSW9BfzzU7SEGGyd3GF\n",
        "!gdown --id 1FCqs4035iNAynxkZIV1HI4AY1RlP-me_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1e35udggcbKOaPdSW9BfzzU7SEGGyd3GF\n",
            "To: /content/cifar10_utils_native_keras.py\n",
            "100% 14.3k/14.3k [00:00<00:00, 26.0MB/s]\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1FCqs4035iNAynxkZIV1HI4AY1RlP-me_\n",
            "To: /content/cifar10_pipeline_native_keras_e2e_test.py\n",
            "100% 4.87k/4.87k [00:00<00:00, 7.62MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QtBo64EpU9v8"
      },
      "source": [
        "!cp /content/cifar10_pipeline_native_keras_e2e_test.py /content/cifar10/\n",
        "!cp /content/cifar10_utils_native_keras.py /content/cifar10/"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o1wixcmWLMSh"
      },
      "source": [
        "# Train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1aet63wsGTpl"
      },
      "source": [
        "# import os\n",
        "# os.mkdir('cifar10')\n",
        "# os.mkdir('cifar10/data')\n",
        "# os.mkdir('cifar10/data/train')\n",
        "# os.mkdir('cifar10/data/test')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1eIYGCyWV7wR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1d06703d-cca0-4077-89dd-154e13b47559"
      },
      "source": [
        "from tfx.proto import evaluator_pb2\n",
        "from tfx.proto import example_gen_pb2\n",
        "from tfx.proto import pusher_pb2\n",
        "from tfx.proto import trainer_pb2\n",
        "from tfx.components import ModelValidator\n",
        "import absl\n",
        "import tensorflow_model_analysis as tfma\n",
        "\n",
        "from tfx.components import Evaluator\n",
        "from tfx.components import ExampleValidator\n",
        "from tfx.components import ImportExampleGen\n",
        "from tfx.components import Pusher\n",
        "from tfx.components import SchemaGen\n",
        "from tfx.components import StatisticsGen\n",
        "from tfx.components import Trainer\n",
        "from tfx.components import Transform\n",
        "from tfx.components.base import executor_spec\n",
        "from tfx.components.trainer.executor import GenericExecutor\n",
        "from tfx.orchestration import metadata\n",
        "from tfx.orchestration import pipeline\n",
        "from tfx.orchestration.beam.beam_dag_runner import BeamDagRunner\n",
        "from tfx.proto import pusher_pb2\n",
        "from tfx.proto import trainer_pb2\n",
        "from tfx.utils.dsl_utils import external_input\n",
        "\n",
        "\n",
        "import os\n",
        "import pprint\n",
        "import tempfile\n",
        "import urllib\n",
        "\n",
        "import absl\n",
        "import tensorflow as tf\n",
        "import tensorflow_model_analysis as tfma\n",
        "tf.get_logger().propagate = False\n",
        "pp = pprint.PrettyPrinter()\n",
        "\n",
        "import tfx\n",
        "from tfx.components import CsvExampleGen\n",
        "from tfx.components import Evaluator\n",
        "from tfx.components import ExampleValidator\n",
        "from tfx.components import Pusher\n",
        "from tfx.components import ResolverNode\n",
        "from tfx.components import SchemaGen\n",
        "from tfx.components import StatisticsGen\n",
        "from tfx.components import Trainer\n",
        "from tfx.components import Transform\n",
        "from tfx.components.base import executor_spec\n",
        "from tfx.components.trainer.executor import GenericExecutor\n",
        "from tfx.dsl.experimental import latest_blessed_model_resolver\n",
        "from tfx.orchestration import metadata\n",
        "from tfx.orchestration import pipeline\n",
        "from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext\n",
        "from tfx.proto import pusher_pb2\n",
        "from tfx.proto import trainer_pb2\n",
        "from tfx.types import Channel\n",
        "from tfx.types.standard_artifacts import Model\n",
        "from tfx.types.standard_artifacts import ModelBlessing\n",
        "from tfx.utils.dsl_utils import external_input\n",
        "\n",
        "\n",
        "%load_ext tfx.orchestration.experimental.interactive.notebook_extensions.skip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:absl:RuntimeParameter is only supported on Cloud-based DAG runner currently.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r994myczsWjD"
      },
      "source": [
        "#Assigning interactive context "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VDxqeHF8V8BT",
        "outputId": "df0f7c66-9141-45d8-dca3-1a08b0c93ac4"
      },
      "source": [
        "context = InteractiveContext()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:absl:InteractiveContext pipeline_root argument not provided: using temporary directory /tmp/tfx-interactive-2020-12-11T17_02_33.840705-ou9_6kxs as root for pipeline outputs.\n",
            "WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at /tmp/tfx-interactive-2020-12-11T17_02_33.840705-ou9_6kxs/metadata.sqlite.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tQ15C_qLWMwQ"
      },
      "source": [
        "try:\n",
        "  os.mkdir('tfx_whatever')\n",
        "except:\n",
        "  pass"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oa9_rceZscWZ"
      },
      "source": [
        "#defining pipeline"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nl9yKAgHV-k7",
        "outputId": "5717c681-72d3-4584-f086-e4d148ecdf6f"
      },
      "source": [
        "from pathlib import Path\n",
        "_tfx_ = \"/content/tfx_whatever\"\n",
        "#home = str(Path)\n",
        "_pipeline_name = 'waste_mgm_beam'\n",
        "\n",
        "HOME=os.environ.get\n",
        "_cifar10_root = os.path.join(os.environ['HOME'], 'cifar10')\n",
        "# _cifar10_root = os.path.join(_tfx_, 'waste_mgm')\n",
        "print(_cifar10_root)\n",
        "_data_root = os.path.join(_cifar10_root, 'data')\n",
        "_module_file = os.path.join(_cifar10_root, 'cifar10_utils_native_keras.py')\n",
        "_serving_model_dir = os.path.join(_cifar10_root, 'serving_model',\n",
        "                                  _pipeline_name)\n",
        "print(_data_root)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/root/cifar10\n",
            "/root/cifar10/data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b-z6UdObWeQM",
        "outputId": "92c60afb-c77b-4b1d-95bd-334bb103b3e7"
      },
      "source": [
        "_tfx_dir = \"/content/tfx_whatever\"\n",
        "_tfx_root = os.path.join(_tfx_dir, 'tfx')\n",
        "print(_tfx_root)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/tfx_whatever/tfx\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kpsj1nNPWmCb"
      },
      "source": [
        "_pipeline_root = os.path.join(_tfx_root, 'pipelines', _pipeline_name)\n",
        "\n",
        "_metadata_path = os.path.join(_tfx_root, 'metadata', _pipeline_name,\n",
        "                              'metadata.db')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mWyL_crkWpu7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2e844ed-45b9-4a18-b184-728a9bd1fc21"
      },
      "source": [
        "_module_file = '/content/cifar10/cifar10_utils_native_keras.py'\n",
        "_data_root = '/content/cifar10/data/train'\n",
        "examples = external_input(_data_root)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-20-970e38d5e78f>:3: external_input (from tfx.utils.dsl_utils) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "external_input is deprecated, directly pass the uri to ExampleGen.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bf3fR_2otqCu"
      },
      "source": [
        "#pipeline parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y2Olg8FMW3Hz"
      },
      "source": [
        "pipeline_name=_pipeline_name\n",
        "pipeline_root=_pipeline_root\n",
        "data_root=_data_root\n",
        "module_file=_module_file\n",
        "serving_model_dir=_serving_model_dir\n",
        "metadata_path=_metadata_path"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I79Czg9xtmwl"
      },
      "source": [
        "#TFX interactive context for iterative development."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Iw23aioW5fX",
        "outputId": "714bf4e4-4801-4f56-8cdd-6516b789c5ee"
      },
      "source": [
        "context = InteractiveContext()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:absl:InteractiveContext pipeline_root argument not provided: using temporary directory /tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8 as root for pipeline outputs.\n",
            "WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at /tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/metadata.sqlite.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cNQWNLTJtChH"
      },
      "source": [
        "#The ImportExampleGen component takes TFRecord files with TF Example data format, and generates train and eval examples for downsteam components. This component provides consistent and configurable partition, and it also shuffle the dataset for ML best practice."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mLvZdZ_FW70c",
        "outputId": "8826fbcd-909a-47fb-e354-7afac22558c8"
      },
      "source": [
        "examples = external_input(_data_root)\n",
        "\n",
        "  # Brings data into the pipeline.\n",
        "example_gen = ImportExampleGen(input=examples)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:absl:The \"input\" argument to the ImportExampleGen component has been deprecated by \"input_base\". Please update your usage as support for this argument will be removed soon.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "ewUc5TXmW-H0",
        "outputId": "b3b37106-7d18-4575-8530-9654a441b548"
      },
      "source": [
        "context.run(example_gen)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "        if (typeof window.interactive_beam_jquery == 'undefined') {\n",
              "          var jqueryScript = document.createElement('script');\n",
              "          jqueryScript.src = 'https://code.jquery.com/jquery-3.4.1.slim.min.js';\n",
              "          jqueryScript.type = 'text/javascript';\n",
              "          jqueryScript.onload = function() {\n",
              "            var datatableScript = document.createElement('script');\n",
              "            datatableScript.src = 'https://cdn.datatables.net/1.10.20/js/jquery.dataTables.min.js';\n",
              "            datatableScript.type = 'text/javascript';\n",
              "            datatableScript.onload = function() {\n",
              "              window.interactive_beam_jquery = jQuery.noConflict(true);\n",
              "              window.interactive_beam_jquery(document).ready(function($){\n",
              "                \n",
              "              });\n",
              "            }\n",
              "            document.head.appendChild(datatableScript);\n",
              "          };\n",
              "          document.head.appendChild(jqueryScript);\n",
              "        } else {\n",
              "          window.interactive_beam_jquery(document).ready(function($){\n",
              "            \n",
              "          });\n",
              "        }"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "        var import_html = () => {\n",
              "          ['https://raw.githubusercontent.com/PAIR-code/facets/1.0.0/facets-dist/facets-jupyter.html'].forEach(href => {\n",
              "            var link = document.createElement('link');\n",
              "            link.rel = 'import'\n",
              "            link.href = href;\n",
              "            document.head.appendChild(link);\n",
              "          });\n",
              "        }\n",
              "        if ('import' in document.createElement('link')) {\n",
              "          import_html();\n",
              "        } else {\n",
              "          var webcomponentScript = document.createElement('script');\n",
              "          webcomponentScript.src = 'https://cdnjs.cloudflare.com/ajax/libs/webcomponentsjs/1.3.3/webcomponents-lite.js';\n",
              "          webcomponentScript.type = 'text/javascript';\n",
              "          webcomponentScript.onload = function(){\n",
              "            import_html();\n",
              "          };\n",
              "          document.head.appendChild(webcomponentScript);\n",
              "        }"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">ImportExampleGen</span><span class=\"deemphasize\"> at 0x7f7c622f7a58</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.inputs</td><td class = \"attrvalue\">{}</td></tr><tr><td class=\"attr-name\">.outputs</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">['examples']</td><td class = \"attrvalue\"><style>\n",
              ".tfx-object.expanded {\n",
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              ".tfx-object, .tfx-object * {\n",
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              "}\n",
              ".tfx-object > .title {\n",
              "  cursor: pointer;\n",
              "}\n",
              ".tfx-object .expansion-marker {\n",
              "  color: #999999;\n",
              "}\n",
              ".tfx-object.expanded > .title > .expansion-marker:before {\n",
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              "}\n",
              ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
              "  content: '▶';\n",
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              ".tfx-object .class-name {\n",
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              ".tfx-object .deemphasize {\n",
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              ".tfx-object.collapsed > table.attr-table {\n",
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              ".tfx-object.expanded > table.attr-table {\n",
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              "}\n",
              ".tfx-object table.attr-table {\n",
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            "text/plain": [
              "ExecutionResult(\n",
              "    component_id: ImportExampleGen\n",
              "    execution_id: 1\n",
              "    outputs:\n",
              "        examples: Channel(\n",
              "            type_name: Examples\n",
              "            artifacts: [Artifact(artifact: id: 1\n",
              "        type_id: 5\n",
              "        uri: \"/tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/ImportExampleGen/examples/1\"\n",
              "        properties {\n",
              "          key: \"split_names\"\n",
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              "          }\n",
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              "        custom_properties {\n",
              "          key: \"input_fingerprint\"\n",
              "          value {\n",
              "            string_value: \"split:single_split,num_files:1,total_bytes:633654024,xor_checksum:1607705591,sum_checksum:1607705591\"\n",
              "          }\n",
              "        }\n",
              "        custom_properties {\n",
              "          key: \"payload_format\"\n",
              "          value {\n",
              "            string_value: \"FORMAT_TF_EXAMPLE\"\n",
              "          }\n",
              "        }\n",
              "        custom_properties {\n",
              "          key: \"span\"\n",
              "          value {\n",
              "            string_value: \"0\"\n",
              "          }\n",
              "        }\n",
              "        custom_properties {\n",
              "          key: \"state\"\n",
              "          value {\n",
              "            string_value: \"published\"\n",
              "          }\n",
              "        }\n",
              "        state: LIVE\n",
              "        , artifact_type: id: 5\n",
              "        name: \"Examples\"\n",
              "        properties {\n",
              "          key: \"span\"\n",
              "          value: INT\n",
              "        }\n",
              "        properties {\n",
              "          key: \"split_names\"\n",
              "          value: STRING\n",
              "        }\n",
              "        properties {\n",
              "          key: \"version\"\n",
              "          value: INT\n",
              "        }\n",
              "        )]\n",
              "        ))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3gV-AYaUsle6"
      },
      "source": [
        "#The StatisticsGen TFX pipeline component generates features statistics over both training and serving data, which can be used by other pipeline components. StatisticsGen uses Beam to scale to large datasets.\n",
        "\n",
        "Consumes: datasets created by an ExampleGen pipeline component.\n",
        "Emits: Dataset statistics."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "POpcr9RlXAH2",
        "outputId": "cfef27c0-7bc9-4d7b-83db-684e5b853c18"
      },
      "source": [
        "statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])\n",
        "context.run(statistics_gen)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              ".tfx-object.collapsed > table.attr-table {\n",
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              ".tfx-object table.attr-table {\n",
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              ".tfx-object table.attr-table td.attrvalue {\n",
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              "</style>\n",
              "<script>\n",
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              ".tfx-object.expanded {\n",
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              ".tfx-object table.attr-table td.attrvalue {\n",
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              "</style>\n",
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              "}\n",
              "</script>\n",
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              "  padding: 4px 8px 4px 8px;\n",
              "  background: white;\n",
              "  border: 1px solid #bbbbbb;\n",
              "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
              "}\n",
              ".tfx-object, .tfx-object * {\n",
              "  font-size: 11pt;\n",
              "}\n",
              ".tfx-object > .title {\n",
              "  cursor: pointer;\n",
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              ".tfx-object .expansion-marker {\n",
              "  color: #999999;\n",
              "}\n",
              ".tfx-object.expanded > .title > .expansion-marker:before {\n",
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              "}\n",
              ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
              "  content: '▶';\n",
              "}\n",
              ".tfx-object .class-name {\n",
              "  font-weight: bold;\n",
              "}\n",
              ".tfx-object .deemphasize {\n",
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              ".tfx-object.collapsed > table.attr-table {\n",
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              ".tfx-object.expanded > table.attr-table {\n",
              "  display: block;\n",
              "}\n",
              ".tfx-object table.attr-table {\n",
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              ".tfx-object table.attr-table td.attr-name {\n",
              "  vertical-align: top;\n",
              "  font-weight: bold;\n",
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              ".tfx-object table.attr-table td.attrvalue {\n",
              "  text-align: left;\n",
              "}\n",
              "</style>\n",
              "<script>\n",
              "function toggleTfxObject(element) {\n",
              "  var objElement = element.parentElement;\n",
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              "    objElement.classList.add('collapsed');\n",
              "    objElement.classList.remove('expanded');\n",
              "  }\n",
              "}\n",
              "</script>\n",
              "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'ExampleStatistics'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7f7c618a9160</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">ExampleStatistics</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
              ".tfx-object.expanded {\n",
              "  padding: 4px 8px 4px 8px;\n",
              "  background: white;\n",
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              "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
              "}\n",
              ".tfx-object, .tfx-object * {\n",
              "  font-size: 11pt;\n",
              "}\n",
              ".tfx-object > .title {\n",
              "  cursor: pointer;\n",
              "}\n",
              ".tfx-object .expansion-marker {\n",
              "  color: #999999;\n",
              "}\n",
              ".tfx-object.expanded > .title > .expansion-marker:before {\n",
              "  content: '▼';\n",
              "}\n",
              ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
              "  content: '▶';\n",
              "}\n",
              ".tfx-object .class-name {\n",
              "  font-weight: bold;\n",
              "}\n",
              ".tfx-object .deemphasize {\n",
              "  opacity: 0.5;\n",
              "}\n",
              ".tfx-object.collapsed > table.attr-table {\n",
              "  display: none;\n",
              "}\n",
              ".tfx-object.expanded > table.attr-table {\n",
              "  display: block;\n",
              "}\n",
              ".tfx-object table.attr-table {\n",
              "  border: 2px solid white;\n",
              "  margin-top: 5px;\n",
              "}\n",
              ".tfx-object table.attr-table td.attr-name {\n",
              "  vertical-align: top;\n",
              "  font-weight: bold;\n",
              "}\n",
              ".tfx-object table.attr-table td.attrvalue {\n",
              "  text-align: left;\n",
              "}\n",
              "</style>\n",
              "<script>\n",
              "function toggleTfxObject(element) {\n",
              "  var objElement = element.parentElement;\n",
              "  if (objElement.classList.contains('collapsed')) {\n",
              "    objElement.classList.remove('collapsed');\n",
              "    objElement.classList.add('expanded');\n",
              "  } else {\n",
              "    objElement.classList.add('collapsed');\n",
              "    objElement.classList.remove('expanded');\n",
              "  }\n",
              "}\n",
              "</script>\n",
              "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'ExampleStatistics'</span> (uri: /tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/StatisticsGen/statistics/2)<span class=\"deemphasize\"> at 0x7f7ce7a7c320</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.ExampleStatistics&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/StatisticsGen/statistics/2</td></tr><tr><td class=\"attr-name\">.span</td><td class = \"attrvalue\">0</td></tr><tr><td class=\"attr-name\">.split_names</td><td class = \"attrvalue\">[&quot;train&quot;, &quot;eval&quot;]</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr></table></div>"
            ],
            "text/plain": [
              "ExecutionResult(\n",
              "    component_id: StatisticsGen\n",
              "    execution_id: 2\n",
              "    outputs:\n",
              "        statistics: Channel(\n",
              "            type_name: ExampleStatistics\n",
              "            artifacts: [Artifact(artifact: id: 2\n",
              "        type_id: 7\n",
              "        uri: \"/tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/StatisticsGen/statistics/2\"\n",
              "        properties {\n",
              "          key: \"split_names\"\n",
              "          value {\n",
              "            string_value: \"[\\\"train\\\", \\\"eval\\\"]\"\n",
              "          }\n",
              "        }\n",
              "        custom_properties {\n",
              "          key: \"name\"\n",
              "          value {\n",
              "            string_value: \"statistics\"\n",
              "          }\n",
              "        }\n",
              "        custom_properties {\n",
              "          key: \"producer_component\"\n",
              "          value {\n",
              "            string_value: \"StatisticsGen\"\n",
              "          }\n",
              "        }\n",
              "        custom_properties {\n",
              "          key: \"state\"\n",
              "          value {\n",
              "            string_value: \"published\"\n",
              "          }\n",
              "        }\n",
              "        state: LIVE\n",
              "        , artifact_type: id: 7\n",
              "        name: \"ExampleStatistics\"\n",
              "        properties {\n",
              "          key: \"span\"\n",
              "          value: INT\n",
              "        }\n",
              "        properties {\n",
              "          key: \"split_names\"\n",
              "          value: STRING\n",
              "        }\n",
              "        )]\n",
              "        ))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "iHs4AspUYAB7",
        "outputId": "b2554f37-ff82-45de-c717-13afee6cf042"
      },
      "source": [
        "context.show(statistics_gen.outputs['statistics'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<b>Artifact at /tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/StatisticsGen/statistics/2</b><br/><br/>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div><b>'train' split:</b></div><br/>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_data_validation/utils/stats_util.py:247: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use eager execution and: \n",
            "`tf.data.TFRecordDataset(path)`\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "        facets_iframe.srcdoc = facets_html;\n",
              "         facets_iframe.id = \"\";\n",
              "         setTimeout(() => {\n",
              "           facets_iframe.setAttribute('height', facets_iframe.contentWindow.document.body.offsetHeight + 'px')\n",
              "         }, 1500)\n",
              "         </script>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div><b>'eval' split:</b></div><br/>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<iframe id='facets-iframe' width=\"100%\" height=\"500px\"></iframe>\n",
              "        <script>\n",
              "        facets_iframe = document.getElementById('facets-iframe');\n",
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              "        facets_iframe.srcdoc = facets_html;\n",
              "         facets_iframe.id = \"\";\n",
              "         setTimeout(() => {\n",
              "           facets_iframe.setAttribute('height', facets_iframe.contentWindow.document.body.offsetHeight + 'px')\n",
              "         }, 1500)\n",
              "         </script>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_4Zu3DW1t1sI"
      },
      "source": [
        "#A SchemaGen pipeline component will automatically generate a schema by inferring types, categories, and ranges from the training data.\n",
        "\n",
        "Consumes: statistics from a StatisticsGen component\n",
        "Emits: Data schema proto"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "8hiyC4zKYX3Y",
        "outputId": "1df1c4a5-93c7-410d-e864-714dc97f7306"
      },
      "source": [
        "schema_gen = SchemaGen(\n",
        "      statistics=statistics_gen.outputs['statistics'], infer_feature_shape=True)\n",
        "context.run(schema_gen)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div class=\"tfx-object expanded\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">ExecutionResult</span><span class=\"deemphasize\"> at 0x7f7c4560a9b0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.execution_id</td><td class = \"attrvalue\">3</td></tr><tr><td class=\"attr-name\">.component</td><td class = \"attrvalue\"><style>\n",
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            "text/plain": [
              "ExecutionResult(\n",
              "    component_id: SchemaGen\n",
              "    execution_id: 3\n",
              "    outputs:\n",
              "        schema: Channel(\n",
              "            type_name: Schema\n",
              "            artifacts: [Artifact(artifact: id: 3\n",
              "        type_id: 9\n",
              "        uri: \"/tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/SchemaGen/schema/3\"\n",
              "        custom_properties {\n",
              "          key: \"name\"\n",
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              "        custom_properties {\n",
              "          key: \"producer_component\"\n",
              "          value {\n",
              "            string_value: \"SchemaGen\"\n",
              "          }\n",
              "        }\n",
              "        custom_properties {\n",
              "          key: \"state\"\n",
              "          value {\n",
              "            string_value: \"published\"\n",
              "          }\n",
              "        }\n",
              "        state: LIVE\n",
              "        , artifact_type: id: 9\n",
              "        name: \"Schema\"\n",
              "        )]\n",
              "        ))"
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          "metadata": {
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          "execution_count": 27
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              "<b>Artifact at /tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/SchemaGen/schema/3</b><br/><br/>"
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              "               Type  Presence Valency Domain\n",
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    {
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      "source": [
        "#The ExampleValidator pipeline component identifies anomalies in training and serving data. It can detect different classes of anomalies in the data. For example it can:\n",
        "\n",
        "perform validity checks by comparing data statistics against a schema that codifies expectations of the user\n",
        "detect training-serving skew by comparing training and serving data.\n",
        "detect data drift by looking at a series of data.\n",
        "The ExampleValidator pipeline component identifies any anomalies in the example data by comparing data statistics computed by the StatisticsGen pipeline component against a schema. The inferred schema codifies properties which the input data is expected to satisfy, and can be modified by the developer.\n",
        "\n",
        "Consumes: A schema from a SchemaGen component, and statistics from a StatisticsGen component.\n",
        "Emits: Validation results"
      ]
    },
    {
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          "height": 196
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        "outputId": "9c9fd350-a925-41f0-f770-0fcfbc21b203"
      },
      "source": [
        "example_validator = ExampleValidator(\n",
        "      statistics=statistics_gen.outputs['statistics'],\n",
        "      schema=schema_gen.outputs['schema'])\n",
        "context.run(example_validator)"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "</style>\n",
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              "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'ExampleAnomalies'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7f7c6fa27b70</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">ExampleAnomalies</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
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              ".tfx-object table.attr-table td.attrvalue {\n",
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              "</style>\n",
              "<script>\n",
              "function toggleTfxObject(element) {\n",
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              "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'ExampleAnomalies'</span> (uri: /tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/ExampleValidator/anomalies/4)<span class=\"deemphasize\"> at 0x7f7c62336f98</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.ExampleAnomalies&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/ExampleValidator/anomalies/4</td></tr><tr><td class=\"attr-name\">.span</td><td class = \"attrvalue\">0</td></tr><tr><td class=\"attr-name\">.split_names</td><td class = \"attrvalue\">[&quot;train&quot;, &quot;eval&quot;]</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr><tr><td class=\"attr-name\">.exec_properties</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">['exclude_splits']</td><td class = \"attrvalue\">[]</td></tr></table></td></tr></table></div></td></tr><tr><td class=\"attr-name\">.component.inputs</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">['statistics']</td><td class = \"attrvalue\"><style>\n",
              ".tfx-object.expanded {\n",
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              ".tfx-object.expanded {\n",
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              ".tfx-object .expansion-marker {\n",
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              ".tfx-object.expanded > .title > .expansion-marker:before {\n",
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              ".tfx-object table.attr-table {\n",
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              ".tfx-object table.attr-table td.attr-name {\n",
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              ".tfx-object table.attr-table td.attrvalue {\n",
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              "</style>\n",
              "<script>\n",
              "function toggleTfxObject(element) {\n",
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              "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'ExampleStatistics'</span> (uri: /tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/StatisticsGen/statistics/2)<span class=\"deemphasize\"> at 0x7f7ce7a7c320</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.ExampleStatistics&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/StatisticsGen/statistics/2</td></tr><tr><td class=\"attr-name\">.span</td><td class = \"attrvalue\">0</td></tr><tr><td class=\"attr-name\">.split_names</td><td class = \"attrvalue\">[&quot;train&quot;, &quot;eval&quot;]</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr><tr><td class=\"attr-name\">['schema']</td><td class = \"attrvalue\"><style>\n",
              ".tfx-object.expanded {\n",
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              "  background: white;\n",
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              ".tfx-object, .tfx-object * {\n",
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              "</style>\n",
              "<script>\n",
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              "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'Schema'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7f7ce7a7cf98</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">Schema</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
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              "</style>\n",
              "<script>\n",
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              "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'Schema'</span> (uri: /tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/SchemaGen/schema/3)<span class=\"deemphasize\"> at 0x7f7c43db57f0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.Schema&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/SchemaGen/schema/3</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr><tr><td class=\"attr-name\">.component.outputs</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">['anomalies']</td><td class = \"attrvalue\"><style>\n",
              ".tfx-object.expanded {\n",
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              "</style>\n",
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              "    objElement.classList.remove('expanded');\n",
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              "}\n",
              "</script>\n",
              "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'ExampleAnomalies'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7f7c6fa27b70</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">ExampleAnomalies</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
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              ".tfx-object.expanded > table.attr-table {\n",
              "  display: block;\n",
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              ".tfx-object table.attr-table {\n",
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              ".tfx-object table.attr-table td.attr-name {\n",
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              ".tfx-object table.attr-table td.attrvalue {\n",
              "  text-align: left;\n",
              "}\n",
              "</style>\n",
              "<script>\n",
              "function toggleTfxObject(element) {\n",
              "  var objElement = element.parentElement;\n",
              "  if (objElement.classList.contains('collapsed')) {\n",
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              "    objElement.classList.add('expanded');\n",
              "  } else {\n",
              "    objElement.classList.add('collapsed');\n",
              "    objElement.classList.remove('expanded');\n",
              "  }\n",
              "}\n",
              "</script>\n",
              "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'ExampleAnomalies'</span> (uri: /tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/ExampleValidator/anomalies/4)<span class=\"deemphasize\"> at 0x7f7c62336f98</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.ExampleAnomalies&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/ExampleValidator/anomalies/4</td></tr><tr><td class=\"attr-name\">.span</td><td class = \"attrvalue\">0</td></tr><tr><td class=\"attr-name\">.split_names</td><td class = \"attrvalue\">[&quot;train&quot;, &quot;eval&quot;]</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr></table></div>"
            ],
            "text/plain": [
              "ExecutionResult(\n",
              "    component_id: ExampleValidator\n",
              "    execution_id: 4\n",
              "    outputs:\n",
              "        anomalies: Channel(\n",
              "            type_name: ExampleAnomalies\n",
              "            artifacts: [Artifact(artifact: id: 4\n",
              "        type_id: 11\n",
              "        uri: \"/tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/ExampleValidator/anomalies/4\"\n",
              "        properties {\n",
              "          key: \"split_names\"\n",
              "          value {\n",
              "            string_value: \"[\\\"train\\\", \\\"eval\\\"]\"\n",
              "          }\n",
              "        }\n",
              "        custom_properties {\n",
              "          key: \"name\"\n",
              "          value {\n",
              "            string_value: \"anomalies\"\n",
              "          }\n",
              "        }\n",
              "        custom_properties {\n",
              "          key: \"producer_component\"\n",
              "          value {\n",
              "            string_value: \"ExampleValidator\"\n",
              "          }\n",
              "        }\n",
              "        custom_properties {\n",
              "          key: \"state\"\n",
              "          value {\n",
              "            string_value: \"published\"\n",
              "          }\n",
              "        }\n",
              "        state: LIVE\n",
              "        , artifact_type: id: 11\n",
              "        name: \"ExampleAnomalies\"\n",
              "        properties {\n",
              "          key: \"span\"\n",
              "          value: INT\n",
              "        }\n",
              "        properties {\n",
              "          key: \"split_names\"\n",
              "          value: STRING\n",
              "        }\n",
              "        )]\n",
              "        ))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "5rAs7JjsY9Ie",
        "outputId": "fedd4dee-216f-4d69-a6aa-95f157094a15"
      },
      "source": [
        "context.show(example_validator.outputs['anomalies'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<b>Artifact at /tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/ExampleValidator/anomalies/4</b><br/><br/>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div><b>'train' split:</b></div><br/>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<h4 style=\"color:green;\">No anomalies found.</h4>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div><b>'eval' split:</b></div><br/>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<h4 style=\"color:green;\">No anomalies found.</h4>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "56z2za6Cudvt"
      },
      "source": [
        "#The Transform TFX pipeline component performs feature engineering on tf.Examples emitted from an ExampleGen component, using a data schema created by a SchemaGen component, and emits a SavedModel. When executed, the SavedModel will accept tf.Examples emitted from an ExampleGen component and emit the transformed feature data.\n",
        "\n",
        "Consumes: tf.Examples from an ExampleGen component, and a data schema from a SchemaGen component.\n",
        "Emits: A SavedModel to a Trainer component"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IUz49wYvYmA5"
      },
      "source": [
        "transform = Transform(\n",
        "      examples=example_gen.outputs['examples'],\n",
        "      schema=schema_gen.outputs['schema'],\n",
        "      module_file=module_file)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "sC1xWkDyZSnI",
        "outputId": "11ba2db2-d110-4ed8-d151-79e6f9957947"
      },
      "source": [
        "context.run(transform)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:TFT beam APIs accept both the TFXIO format and the instance dict format now. There is no need to set use_tfxio any more and it will be removed soon.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType]] instead.\n",
            "WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType]] instead.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Tensorflow version (2.3.0) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended. \n",
            "INFO:tensorflow:Assets added to graph.\n",
            "INFO:tensorflow:No assets to write.\n",
            "INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/Transform/transform_graph/23/.temp_path/tftransform_tmp/462ca9db6ae94e8799b5cb666c280904/saved_model.pb\n",
            "WARNING:tensorflow:Tensorflow version (2.3.0) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended. \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>\n",
            "WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>\n",
            "WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>\n",
            "WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>\n",
            "WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>\n",
            "WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Tensorflow version (2.3.0) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended. \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>\n",
            "WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>\n",
            "WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>\n",
            "WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>\n",
            "WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>\n",
            "WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n",
            "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/apache_beam/transforms/util.py:422: RankWarning: Polyfit may be poorly conditioned\n",
            "  a, b = self.linear_regression(xs, ys)\n",
            "/usr/local/lib/python3.6/dist-packages/apache_beam/transforms/util.py:422: RankWarning: Polyfit may be poorly conditioned\n",
            "  a, b = self.linear_regression(xs, ys)\n",
            "/usr/local/lib/python3.6/dist-packages/apache_beam/transforms/util.py:422: RankWarning: Polyfit may be poorly conditioned\n",
            "  a, b = self.linear_regression(xs, ys)\n",
            "/usr/local/lib/python3.6/dist-packages/apache_beam/transforms/util.py:422: RankWarning: Polyfit may be poorly conditioned\n",
            "  a, b = self.linear_regression(xs, ys)\n",
            "/usr/local/lib/python3.6/dist-packages/apache_beam/transforms/util.py:422: RankWarning: Polyfit may be poorly conditioned\n",
            "  a, b = self.linear_regression(xs, ys)\n",
            "/usr/local/lib/python3.6/dist-packages/apache_beam/transforms/util.py:422: RankWarning: Polyfit may be poorly conditioned\n",
            "  a, b = self.linear_regression(xs, ys)\n",
            "/usr/local/lib/python3.6/dist-packages/apache_beam/transforms/util.py:422: RankWarning: Polyfit may be poorly conditioned\n",
            "  a, b = self.linear_regression(xs, ys)\n",
            "/usr/local/lib/python3.6/dist-packages/apache_beam/transforms/util.py:422: RankWarning: Polyfit may be poorly conditioned\n",
            "  a, b = self.linear_regression(xs, ys)\n",
            "/usr/local/lib/python3.6/dist-packages/apache_beam/transforms/util.py:422: RankWarning: Polyfit may be poorly conditioned\n",
            "  a, b = self.linear_regression(xs, ys)\n",
            "/usr/local/lib/python3.6/dist-packages/apache_beam/transforms/util.py:422: RankWarning: Polyfit may be poorly conditioned\n",
            "  a, b = self.linear_regression(xs, ys)\n",
            "/usr/local/lib/python3.6/dist-packages/apache_beam/transforms/util.py:422: RankWarning: Polyfit may be poorly conditioned\n",
            "  a, b = self.linear_regression(xs, ys)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/apache_beam/transforms/util.py:422: RankWarning: Polyfit may be poorly conditioned\n",
            "  a, b = self.linear_regression(xs, ys)\n",
            "/usr/local/lib/python3.6/dist-packages/apache_beam/transforms/util.py:422: RankWarning: Polyfit may be poorly conditioned\n",
            "  a, b = self.linear_regression(xs, ys)\n",
            "/usr/local/lib/python3.6/dist-packages/apache_beam/transforms/util.py:422: RankWarning: Polyfit may be poorly conditioned\n",
            "  a, b = self.linear_regression(xs, ys)\n",
            "/usr/local/lib/python3.6/dist-packages/apache_beam/transforms/util.py:422: RankWarning: Polyfit may be poorly conditioned\n",
            "  a, b = self.linear_regression(xs, ys)\n",
            "/usr/local/lib/python3.6/dist-packages/apache_beam/transforms/util.py:422: RankWarning: Polyfit may be poorly conditioned\n",
            "  a, b = self.linear_regression(xs, ys)\n",
            "/usr/local/lib/python3.6/dist-packages/apache_beam/transforms/util.py:422: RankWarning: Polyfit may be poorly conditioned\n",
            "  a, b = self.linear_regression(xs, ys)\n",
            "/usr/local/lib/python3.6/dist-packages/apache_beam/transforms/util.py:422: RankWarning: Polyfit may be poorly conditioned\n",
            "  a, b = self.linear_regression(xs, ys)\n",
            "/usr/local/lib/python3.6/dist-packages/apache_beam/transforms/util.py:422: RankWarning: Polyfit may be poorly conditioned\n",
            "  a, b = self.linear_regression(xs, ys)\n",
            "/usr/local/lib/python3.6/dist-packages/apache_beam/transforms/util.py:422: RankWarning: Polyfit may be poorly conditioned\n",
            "  a, b = self.linear_regression(xs, ys)\n",
            "/usr/local/lib/python3.6/dist-packages/apache_beam/transforms/util.py:422: RankWarning: Polyfit may be poorly conditioned\n",
            "  a, b = self.linear_regression(xs, ys)\n",
            "/usr/local/lib/python3.6/dist-packages/apache_beam/transforms/util.py:422: RankWarning: Polyfit may be poorly conditioned\n",
            "  a, b = self.linear_regression(xs, ys)\n"
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              "<script>\n",
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              "</style>\n",
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              "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'TransformCache'</span> (uri: /tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/Transform/updated_analyzer_cache/23)<span class=\"deemphasize\"> at 0x7f7c36208198</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.TransformCache&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">/tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/Transform/updated_analyzer_cache/23</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr></table></div>"
            ],
            "text/plain": [
              "ExecutionResult(\n",
              "    component_id: Transform\n",
              "    execution_id: 23\n",
              "    outputs:\n",
              "        transform_graph: Channel(\n",
              "            type_name: TransformGraph\n",
              "            artifacts: [Artifact(artifact: id: 46\n",
              "        type_id: 13\n",
              "        uri: \"/tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/Transform/transform_graph/23\"\n",
              "        custom_properties {\n",
              "          key: \"name\"\n",
              "          value {\n",
              "            string_value: \"transform_graph\"\n",
              "          }\n",
              "        }\n",
              "        custom_properties {\n",
              "          key: \"producer_component\"\n",
              "          value {\n",
              "            string_value: \"Transform\"\n",
              "          }\n",
              "        }\n",
              "        custom_properties {\n",
              "          key: \"state\"\n",
              "          value {\n",
              "            string_value: \"published\"\n",
              "          }\n",
              "        }\n",
              "        state: LIVE\n",
              "        , artifact_type: id: 13\n",
              "        name: \"TransformGraph\"\n",
              "        )]\n",
              "        )\n",
              "        transformed_examples: Channel(\n",
              "            type_name: Examples\n",
              "            artifacts: [Artifact(artifact: id: 47\n",
              "        type_id: 5\n",
              "        uri: \"/tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/Transform/transformed_examples/23\"\n",
              "        properties {\n",
              "          key: \"split_names\"\n",
              "          value {\n",
              "            string_value: \"[\\\"train\\\", \\\"eval\\\"]\"\n",
              "          }\n",
              "        }\n",
              "        custom_properties {\n",
              "          key: \"name\"\n",
              "          value {\n",
              "            string_value: \"transformed_examples\"\n",
              "          }\n",
              "        }\n",
              "        custom_properties {\n",
              "          key: \"producer_component\"\n",
              "          value {\n",
              "            string_value: \"Transform\"\n",
              "          }\n",
              "        }\n",
              "        custom_properties {\n",
              "          key: \"state\"\n",
              "          value {\n",
              "            string_value: \"published\"\n",
              "          }\n",
              "        }\n",
              "        state: LIVE\n",
              "        , artifact_type: id: 5\n",
              "        name: \"Examples\"\n",
              "        properties {\n",
              "          key: \"span\"\n",
              "          value: INT\n",
              "        }\n",
              "        properties {\n",
              "          key: \"split_names\"\n",
              "          value: STRING\n",
              "        }\n",
              "        properties {\n",
              "          key: \"version\"\n",
              "          value: INT\n",
              "        }\n",
              "        )]\n",
              "        )\n",
              "        updated_analyzer_cache: Channel(\n",
              "            type_name: TransformCache\n",
              "            artifacts: [Artifact(artifact: id: 48\n",
              "        type_id: 14\n",
              "        uri: \"/tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/Transform/updated_analyzer_cache/23\"\n",
              "        custom_properties {\n",
              "          key: \"name\"\n",
              "          value {\n",
              "            string_value: \"updated_analyzer_cache\"\n",
              "          }\n",
              "        }\n",
              "        custom_properties {\n",
              "          key: \"producer_component\"\n",
              "          value {\n",
              "            string_value: \"Transform\"\n",
              "          }\n",
              "        }\n",
              "        custom_properties {\n",
              "          key: \"state\"\n",
              "          value {\n",
              "            string_value: \"published\"\n",
              "          }\n",
              "        }\n",
              "        state: LIVE\n",
              "        , artifact_type: id: 14\n",
              "        name: \"TransformCache\"\n",
              "        )]\n",
              "        ))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fd-pMB-YYn4Q",
        "outputId": "a996dc87-e18d-431f-d7fb-8203f7afdc31"
      },
      "source": [
        "transform.outputs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'transform_graph': Channel(\n",
              "    type_name: TransformGraph\n",
              "    artifacts: [Artifact(artifact: id: 34\n",
              "type_id: 13\n",
              "uri: \"/tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/Transform/transform_graph/18\"\n",
              "custom_properties {\n",
              "  key: \"name\"\n",
              "  value {\n",
              "    string_value: \"transform_graph\"\n",
              "  }\n",
              "}\n",
              "custom_properties {\n",
              "  key: \"producer_component\"\n",
              "  value {\n",
              "    string_value: \"Transform\"\n",
              "  }\n",
              "}\n",
              ", artifact_type: id: 13\n",
              "name: \"TransformGraph\"\n",
              ")]\n",
              "), 'transformed_examples': Channel(\n",
              "    type_name: Examples\n",
              "    artifacts: [Artifact(artifact: id: 35\n",
              "type_id: 5\n",
              "uri: \"/tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/Transform/transformed_examples/18\"\n",
              "properties {\n",
              "  key: \"split_names\"\n",
              "  value {\n",
              "    string_value: \"[\\\"train\\\", \\\"eval\\\"]\"\n",
              "  }\n",
              "}\n",
              "custom_properties {\n",
              "  key: \"name\"\n",
              "  value {\n",
              "    string_value: \"transformed_examples\"\n",
              "  }\n",
              "}\n",
              "custom_properties {\n",
              "  key: \"producer_component\"\n",
              "  value {\n",
              "    string_value: \"Transform\"\n",
              "  }\n",
              "}\n",
              ", artifact_type: id: 5\n",
              "name: \"Examples\"\n",
              "properties {\n",
              "  key: \"span\"\n",
              "  value: INT\n",
              "}\n",
              "properties {\n",
              "  key: \"split_names\"\n",
              "  value: STRING\n",
              "}\n",
              "properties {\n",
              "  key: \"version\"\n",
              "  value: INT\n",
              "}\n",
              ")]\n",
              "), 'updated_analyzer_cache': Channel(\n",
              "    type_name: TransformCache\n",
              "    artifacts: [Artifact(artifact: id: 36\n",
              "type_id: 14\n",
              "uri: \"/tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/Transform/updated_analyzer_cache/18\"\n",
              "custom_properties {\n",
              "  key: \"name\"\n",
              "  value {\n",
              "    string_value: \"updated_analyzer_cache\"\n",
              "  }\n",
              "}\n",
              "custom_properties {\n",
              "  key: \"producer_component\"\n",
              "  value {\n",
              "    string_value: \"Transform\"\n",
              "  }\n",
              "}\n",
              ", artifact_type: id: 14\n",
              "name: \"TransformCache\"\n",
              ")]\n",
              ")}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NVQDcIzNuuEk"
      },
      "source": [
        "#The Trainer TFX pipeline component trains a TensorFlow model.\n",
        "\n",
        "Component\n",
        "Trainer takes:\n",
        "* tf.Examples used for training and eval.\n",
        "* A user provided module file that defines the trainer logic.\n",
        "* A data schema created by a SchemaGen pipeline component and optionally altered by the developer.\n",
        "* Protobuf definition of train args and eval args.\n",
        "* (Optional) transform graph produced by an upstream Transform component.\n",
        "* (Optional) pre-trained models used for scenarios such as warmstart.\n",
        "* (Optional) hyperparameters, which will be passed to user module function. \n",
        "\n",
        "Trainer emits: At least one model for inference/serving (typically in SavedModelFormat) and optionally another model for eval (typically an EvalSavedModel)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JkXKinDKYptR"
      },
      "source": [
        "trainer = Trainer(\n",
        "      module_file=module_file,\n",
        "      examples=transform.outputs['transformed_examples'],\n",
        "      schema=schema_gen.outputs['schema'],\n",
        "      transform_graph=transform.outputs['transform_graph'],\n",
        "      train_args=trainer_pb2.TrainArgs(num_steps=1000),\n",
        "      eval_args=trainer_pb2.EvalArgs(num_steps=500))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "raW9snGTYrz4",
        "outputId": "3064c0a7-0711-4f13-b121-307e27036c38"
      },
      "source": [
        "context.run(trainer)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE\n",
            "WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "1/4 [======>.......................] - ETA: 0s - loss: 0.9627 - sparse_categorical_accuracy: 0.5000WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/summary_ops_v2.py:1277: stop (from tensorflow.python.eager.profiler) is deprecated and will be removed after 2020-07-01.\n",
            "Instructions for updating:\n",
            "use `tf.profiler.experimental.stop` instead.\n",
            "4/4 [==============================] - 63s 16s/step - loss: 0.7492 - sparse_categorical_accuracy: 0.6094 - val_loss: 0.7350 - val_sparse_categorical_accuracy: 0.5863\n",
            "Epoch 2/30\n",
            "4/4 [==============================] - 63s 16s/step - loss: 0.7534 - sparse_categorical_accuracy: 0.5938 - val_loss: 0.6709 - val_sparse_categorical_accuracy: 0.6168\n",
            "Epoch 3/30\n",
            "4/4 [==============================] - 63s 16s/step - loss: 0.6787 - sparse_categorical_accuracy: 0.6641 - val_loss: 0.6623 - val_sparse_categorical_accuracy: 0.6143\n",
            "Epoch 4/30\n",
            "4/4 [==============================] - 63s 16s/step - loss: 0.5847 - sparse_categorical_accuracy: 0.6719 - val_loss: 0.6231 - val_sparse_categorical_accuracy: 0.6927\n",
            "Epoch 5/30\n",
            "4/4 [==============================] - 62s 16s/step - loss: 0.6389 - sparse_categorical_accuracy: 0.7031 - val_loss: 0.5826 - val_sparse_categorical_accuracy: 0.6929\n",
            "Epoch 6/30\n",
            "4/4 [==============================] - 62s 16s/step - loss: 0.5573 - sparse_categorical_accuracy: 0.6953 - val_loss: 0.6045 - val_sparse_categorical_accuracy: 0.6801\n",
            "Epoch 7/30\n",
            "4/4 [==============================] - 62s 16s/step - loss: 0.5738 - sparse_categorical_accuracy: 0.6953 - val_loss: 0.5745 - val_sparse_categorical_accuracy: 0.7167\n",
            "Epoch 8/30\n",
            "4/4 [==============================] - 63s 16s/step - loss: 0.6309 - sparse_categorical_accuracy: 0.6641 - val_loss: 0.5586 - val_sparse_categorical_accuracy: 0.7216\n",
            "Epoch 9/30\n",
            "4/4 [==============================] - 63s 16s/step - loss: 0.5945 - sparse_categorical_accuracy: 0.7031 - val_loss: 0.5560 - val_sparse_categorical_accuracy: 0.7214\n",
            "Epoch 10/30\n",
            "4/4 [==============================] - 63s 16s/step - loss: 0.5221 - sparse_categorical_accuracy: 0.7422 - val_loss: 0.5172 - val_sparse_categorical_accuracy: 0.7437\n",
            "Epoch 11/30\n",
            "4/4 [==============================] - 63s 16s/step - loss: 0.5364 - sparse_categorical_accuracy: 0.7500 - val_loss: 0.5247 - val_sparse_categorical_accuracy: 0.7487\n",
            "Epoch 12/30\n",
            "4/4 [==============================] - 64s 16s/step - loss: 0.4908 - sparse_categorical_accuracy: 0.7656 - val_loss: 0.5029 - val_sparse_categorical_accuracy: 0.7584\n",
            "Epoch 13/30\n",
            "4/4 [==============================] - 64s 16s/step - loss: 0.4760 - sparse_categorical_accuracy: 0.7891 - val_loss: 0.4989 - val_sparse_categorical_accuracy: 0.7611\n",
            "Epoch 14/30\n",
            "4/4 [==============================] - 65s 16s/step - loss: 0.5722 - sparse_categorical_accuracy: 0.6875 - val_loss: 0.4932 - val_sparse_categorical_accuracy: 0.7534\n",
            "Epoch 15/30\n",
            "4/4 [==============================] - 64s 16s/step - loss: 0.5307 - sparse_categorical_accuracy: 0.7422 - val_loss: 0.5047 - val_sparse_categorical_accuracy: 0.7673\n",
            "Epoch 16/30\n",
            "4/4 [==============================] - 65s 16s/step - loss: 0.4778 - sparse_categorical_accuracy: 0.7656 - val_loss: 0.5051 - val_sparse_categorical_accuracy: 0.7562\n",
            "Epoch 17/30\n",
            "4/4 [==============================] - 65s 16s/step - loss: 0.5966 - sparse_categorical_accuracy: 0.7188 - val_loss: 0.4708 - val_sparse_categorical_accuracy: 0.7841\n",
            "Epoch 18/30\n",
            "4/4 [==============================] - 65s 16s/step - loss: 0.5375 - sparse_categorical_accuracy: 0.7656 - val_loss: 0.4620 - val_sparse_categorical_accuracy: 0.7931\n",
            "Epoch 19/30\n",
            "4/4 [==============================] - 64s 16s/step - loss: 0.3924 - sparse_categorical_accuracy: 0.8359 - val_loss: 0.4630 - val_sparse_categorical_accuracy: 0.7771\n",
            "Epoch 20/30\n",
            "4/4 [==============================] - 64s 16s/step - loss: 0.4853 - sparse_categorical_accuracy: 0.8047 - val_loss: 0.4651 - val_sparse_categorical_accuracy: 0.7932\n",
            "Epoch 21/30\n",
            "4/4 [==============================] - 64s 16s/step - loss: 0.5108 - sparse_categorical_accuracy: 0.7266 - val_loss: 0.4588 - val_sparse_categorical_accuracy: 0.7984\n",
            "Epoch 22/30\n",
            "4/4 [==============================] - 65s 16s/step - loss: 0.3775 - sparse_categorical_accuracy: 0.8438 - val_loss: 0.4431 - val_sparse_categorical_accuracy: 0.8014\n",
            "Epoch 23/30\n",
            "4/4 [==============================] - 64s 16s/step - loss: 0.5796 - sparse_categorical_accuracy: 0.7031 - val_loss: 0.5341 - val_sparse_categorical_accuracy: 0.7390\n",
            "Epoch 24/30\n",
            "4/4 [==============================] - 64s 16s/step - loss: 0.4929 - sparse_categorical_accuracy: 0.7734 - val_loss: 0.4372 - val_sparse_categorical_accuracy: 0.8086\n",
            "Epoch 25/30\n",
            "4/4 [==============================] - 64s 16s/step - loss: 0.3909 - sparse_categorical_accuracy: 0.8438 - val_loss: 0.4437 - val_sparse_categorical_accuracy: 0.8064\n",
            "Epoch 26/30\n",
            "4/4 [==============================] - 63s 16s/step - loss: 0.4871 - sparse_categorical_accuracy: 0.7812 - val_loss: 0.5623 - val_sparse_categorical_accuracy: 0.7079\n",
            "Epoch 27/30\n",
            "4/4 [==============================] - 63s 16s/step - loss: 0.4171 - sparse_categorical_accuracy: 0.8047 - val_loss: 0.4369 - val_sparse_categorical_accuracy: 0.8136\n",
            "Epoch 28/30\n",
            "4/4 [==============================] - 63s 16s/step - loss: 0.5053 - sparse_categorical_accuracy: 0.7969 - val_loss: 0.4249 - val_sparse_categorical_accuracy: 0.8197\n",
            "Epoch 29/30\n",
            "4/4 [==============================] - 63s 16s/step - loss: 0.4440 - sparse_categorical_accuracy: 0.7578 - val_loss: 0.4364 - val_sparse_categorical_accuracy: 0.8071\n",
            "Epoch 30/30\n",
            "4/4 [==============================] - 63s 16s/step - loss: 0.4423 - sparse_categorical_accuracy: 0.7891 - val_loss: 0.4920 - val_sparse_categorical_accuracy: 0.7664\n",
            "Epoch 31/250\n",
            "4/4 [==============================] - 63s 16s/step - loss: 0.7271 - sparse_categorical_accuracy: 0.6562 - val_loss: 0.4715 - val_sparse_categorical_accuracy: 0.7862\n",
            "Epoch 32/250\n",
            "4/4 [==============================] - 63s 16s/step - loss: 0.5697 - sparse_categorical_accuracy: 0.7031 - val_loss: 0.4586 - val_sparse_categorical_accuracy: 0.7956\n",
            "Epoch 33/250\n",
            "4/4 [==============================] - 63s 16s/step - loss: 0.5236 - sparse_categorical_accuracy: 0.7188 - val_loss: 0.4678 - val_sparse_categorical_accuracy: 0.7888\n",
            "Epoch 34/250\n",
            "4/4 [==============================] - 64s 16s/step - loss: 0.6012 - sparse_categorical_accuracy: 0.7344 - val_loss: 0.4567 - val_sparse_categorical_accuracy: 0.7997\n",
            "Epoch 35/250\n",
            "4/4 [==============================] - 64s 16s/step - loss: 0.6259 - sparse_categorical_accuracy: 0.7031 - val_loss: 0.4437 - val_sparse_categorical_accuracy: 0.8067\n",
            "Epoch 36/250\n",
            "4/4 [==============================] - 64s 16s/step - loss: 0.6786 - sparse_categorical_accuracy: 0.6875 - val_loss: 0.4340 - val_sparse_categorical_accuracy: 0.8121\n",
            "Epoch 37/250\n",
            "4/4 [==============================] - 64s 16s/step - loss: 0.4407 - sparse_categorical_accuracy: 0.8047 - val_loss: 0.4308 - val_sparse_categorical_accuracy: 0.8153\n",
            "Epoch 38/250\n",
            "4/4 [==============================] - 64s 16s/step - loss: 0.5187 - sparse_categorical_accuracy: 0.7500 - val_loss: 0.4316 - val_sparse_categorical_accuracy: 0.8163\n",
            "Epoch 39/250\n",
            "4/4 [==============================] - 64s 16s/step - loss: 0.6511 - sparse_categorical_accuracy: 0.7344 - val_loss: 0.4289 - val_sparse_categorical_accuracy: 0.8165\n",
            "Epoch 40/250\n",
            "4/4 [==============================] - 64s 16s/step - loss: 0.6471 - sparse_categorical_accuracy: 0.6641 - val_loss: 0.4297 - val_sparse_categorical_accuracy: 0.8164\n",
            "Epoch 41/250\n",
            "4/4 [==============================] - 64s 16s/step - loss: 0.7695 - sparse_categorical_accuracy: 0.6719 - val_loss: 0.4269 - val_sparse_categorical_accuracy: 0.8176\n",
            "Epoch 42/250\n",
            "4/4 [==============================] - 64s 16s/step - loss: 0.6236 - sparse_categorical_accuracy: 0.7656 - val_loss: 0.4253 - val_sparse_categorical_accuracy: 0.8181\n",
            "Epoch 43/250\n",
            "4/4 [==============================] - 64s 16s/step - loss: 0.4589 - sparse_categorical_accuracy: 0.7812 - val_loss: 0.4284 - val_sparse_categorical_accuracy: 0.8149\n",
            "Epoch 44/250\n",
            "4/4 [==============================] - 64s 16s/step - loss: 0.5394 - sparse_categorical_accuracy: 0.7266 - val_loss: 0.4267 - val_sparse_categorical_accuracy: 0.8174\n",
            "Epoch 45/250\n",
            "4/4 [==============================] - 64s 16s/step - loss: 0.5140 - sparse_categorical_accuracy: 0.7266 - val_loss: 0.4263 - val_sparse_categorical_accuracy: 0.8156\n",
            "Epoch 46/250\n",
            "4/4 [==============================] - 64s 16s/step - loss: 0.4813 - sparse_categorical_accuracy: 0.7656 - val_loss: 0.4266 - val_sparse_categorical_accuracy: 0.8184\n",
            "Epoch 47/250\n",
            "4/4 [==============================] - 64s 16s/step - loss: 0.5414 - sparse_categorical_accuracy: 0.7188 - val_loss: 0.4265 - val_sparse_categorical_accuracy: 0.8179\n",
            "Epoch 48/250\n",
            "4/4 [==============================] - 64s 16s/step - loss: 0.6442 - sparse_categorical_accuracy: 0.7344 - val_loss: 0.4314 - val_sparse_categorical_accuracy: 0.8127\n",
            "Epoch 49/250\n",
            "4/4 [==============================] - 63s 16s/step - loss: 0.4855 - sparse_categorical_accuracy: 0.7969 - val_loss: 0.4210 - val_sparse_categorical_accuracy: 0.8188\n",
            "Epoch 50/250\n",
            "4/4 [==============================] - 63s 16s/step - loss: 0.5792 - sparse_categorical_accuracy: 0.7266 - val_loss: 0.4276 - val_sparse_categorical_accuracy: 0.8114\n",
            "Epoch 51/250\n",
            "4/4 [==============================] - 64s 16s/step - loss: 0.5321 - sparse_categorical_accuracy: 0.7812 - val_loss: 0.4267 - val_sparse_categorical_accuracy: 0.8127\n",
            "Epoch 52/250\n",
            "4/4 [==============================] - 63s 16s/step - loss: 0.5558 - sparse_categorical_accuracy: 0.7891 - val_loss: 0.4269 - val_sparse_categorical_accuracy: 0.8114\n",
            "Epoch 53/250\n",
            "4/4 [==============================] - 64s 16s/step - loss: 0.5235 - sparse_categorical_accuracy: 0.7969 - val_loss: 0.4209 - val_sparse_categorical_accuracy: 0.8129\n",
            "Epoch 54/250\n",
            "4/4 [==============================] - 63s 16s/step - loss: 0.5623 - sparse_categorical_accuracy: 0.7266 - val_loss: 0.4247 - val_sparse_categorical_accuracy: 0.8112\n",
            "Epoch 55/250\n",
            "4/4 [==============================] - 63s 16s/step - loss: 0.4060 - sparse_categorical_accuracy: 0.8438 - val_loss: 0.4328 - val_sparse_categorical_accuracy: 0.8073\n",
            "Epoch 56/250\n",
            "4/4 [==============================] - 63s 16s/step - loss: 0.5245 - sparse_categorical_accuracy: 0.7812 - val_loss: 0.4276 - val_sparse_categorical_accuracy: 0.8104\n",
            "Epoch 57/250\n",
            "4/4 [==============================] - 62s 16s/step - loss: 0.4339 - sparse_categorical_accuracy: 0.7656 - val_loss: 0.4253 - val_sparse_categorical_accuracy: 0.8121\n",
            "Epoch 58/250\n",
            "4/4 [==============================] - 62s 16s/step - loss: 0.3789 - sparse_categorical_accuracy: 0.8281 - val_loss: 0.4316 - val_sparse_categorical_accuracy: 0.8081\n",
            "Epoch 59/250\n",
            "4/4 [==============================] - 62s 15s/step - loss: 0.4653 - sparse_categorical_accuracy: 0.8125 - val_loss: 0.4279 - val_sparse_categorical_accuracy: 0.8084\n",
            "Epoch 60/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.5035 - sparse_categorical_accuracy: 0.8047 - val_loss: 0.4319 - val_sparse_categorical_accuracy: 0.8072\n",
            "Epoch 61/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.4655 - sparse_categorical_accuracy: 0.8125 - val_loss: 0.4281 - val_sparse_categorical_accuracy: 0.8076\n",
            "Epoch 62/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.4653 - sparse_categorical_accuracy: 0.7578 - val_loss: 0.4283 - val_sparse_categorical_accuracy: 0.8062\n",
            "Epoch 63/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.4314 - sparse_categorical_accuracy: 0.7891 - val_loss: 0.4228 - val_sparse_categorical_accuracy: 0.8132\n",
            "Epoch 64/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.5533 - sparse_categorical_accuracy: 0.7500 - val_loss: 0.4168 - val_sparse_categorical_accuracy: 0.8217\n",
            "Epoch 65/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.4124 - sparse_categorical_accuracy: 0.8203 - val_loss: 0.4157 - val_sparse_categorical_accuracy: 0.8201\n",
            "Epoch 66/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.4932 - sparse_categorical_accuracy: 0.7500 - val_loss: 0.4221 - val_sparse_categorical_accuracy: 0.8146\n",
            "Epoch 67/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.4863 - sparse_categorical_accuracy: 0.7578 - val_loss: 0.4209 - val_sparse_categorical_accuracy: 0.8127\n",
            "Epoch 68/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.4467 - sparse_categorical_accuracy: 0.8047 - val_loss: 0.4224 - val_sparse_categorical_accuracy: 0.8133\n",
            "Epoch 69/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.4920 - sparse_categorical_accuracy: 0.7578 - val_loss: 0.4166 - val_sparse_categorical_accuracy: 0.8189\n",
            "Epoch 70/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.5196 - sparse_categorical_accuracy: 0.7891 - val_loss: 0.4110 - val_sparse_categorical_accuracy: 0.8240\n",
            "Epoch 71/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.4558 - sparse_categorical_accuracy: 0.7812 - val_loss: 0.4144 - val_sparse_categorical_accuracy: 0.8179\n",
            "Epoch 72/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4637 - sparse_categorical_accuracy: 0.7969 - val_loss: 0.4190 - val_sparse_categorical_accuracy: 0.8189\n",
            "Epoch 73/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4607 - sparse_categorical_accuracy: 0.8359 - val_loss: 0.4184 - val_sparse_categorical_accuracy: 0.8181\n",
            "Epoch 74/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4701 - sparse_categorical_accuracy: 0.7812 - val_loss: 0.4170 - val_sparse_categorical_accuracy: 0.8160\n",
            "Epoch 75/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4618 - sparse_categorical_accuracy: 0.8203 - val_loss: 0.4176 - val_sparse_categorical_accuracy: 0.8166\n",
            "Epoch 76/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4991 - sparse_categorical_accuracy: 0.7734 - val_loss: 0.4191 - val_sparse_categorical_accuracy: 0.8171\n",
            "Epoch 77/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3961 - sparse_categorical_accuracy: 0.8438 - val_loss: 0.4140 - val_sparse_categorical_accuracy: 0.8185\n",
            "Epoch 78/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3740 - sparse_categorical_accuracy: 0.8672 - val_loss: 0.4190 - val_sparse_categorical_accuracy: 0.8174\n",
            "Epoch 79/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4907 - sparse_categorical_accuracy: 0.7734 - val_loss: 0.4207 - val_sparse_categorical_accuracy: 0.8134\n",
            "Epoch 80/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4343 - sparse_categorical_accuracy: 0.8125 - val_loss: 0.4176 - val_sparse_categorical_accuracy: 0.8141\n",
            "Epoch 81/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4388 - sparse_categorical_accuracy: 0.7812 - val_loss: 0.4148 - val_sparse_categorical_accuracy: 0.8166\n",
            "Epoch 82/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4999 - sparse_categorical_accuracy: 0.7969 - val_loss: 0.4145 - val_sparse_categorical_accuracy: 0.8198\n",
            "Epoch 83/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3972 - sparse_categorical_accuracy: 0.8281 - val_loss: 0.4112 - val_sparse_categorical_accuracy: 0.8207\n",
            "Epoch 84/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.3582 - sparse_categorical_accuracy: 0.8203 - val_loss: 0.4090 - val_sparse_categorical_accuracy: 0.8195\n",
            "Epoch 85/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4103 - sparse_categorical_accuracy: 0.8438 - val_loss: 0.4102 - val_sparse_categorical_accuracy: 0.8183\n",
            "Epoch 86/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4309 - sparse_categorical_accuracy: 0.7969 - val_loss: 0.4147 - val_sparse_categorical_accuracy: 0.8159\n",
            "Epoch 87/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3570 - sparse_categorical_accuracy: 0.8438 - val_loss: 0.4129 - val_sparse_categorical_accuracy: 0.8170\n",
            "Epoch 88/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.5206 - sparse_categorical_accuracy: 0.7656 - val_loss: 0.4172 - val_sparse_categorical_accuracy: 0.8154\n",
            "Epoch 89/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4935 - sparse_categorical_accuracy: 0.7500 - val_loss: 0.4199 - val_sparse_categorical_accuracy: 0.8160\n",
            "Epoch 90/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4946 - sparse_categorical_accuracy: 0.7812 - val_loss: 0.4243 - val_sparse_categorical_accuracy: 0.8121\n",
            "Epoch 91/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3788 - sparse_categorical_accuracy: 0.8203 - val_loss: 0.4256 - val_sparse_categorical_accuracy: 0.8100\n",
            "Epoch 92/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3912 - sparse_categorical_accuracy: 0.8125 - val_loss: 0.4199 - val_sparse_categorical_accuracy: 0.8152\n",
            "Epoch 93/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4455 - sparse_categorical_accuracy: 0.7578 - val_loss: 0.4219 - val_sparse_categorical_accuracy: 0.8116\n",
            "Epoch 94/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3719 - sparse_categorical_accuracy: 0.8359 - val_loss: 0.4139 - val_sparse_categorical_accuracy: 0.8173\n",
            "Epoch 95/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4234 - sparse_categorical_accuracy: 0.7891 - val_loss: 0.4213 - val_sparse_categorical_accuracy: 0.8124\n",
            "Epoch 96/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4132 - sparse_categorical_accuracy: 0.7969 - val_loss: 0.4214 - val_sparse_categorical_accuracy: 0.8137\n",
            "Epoch 97/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3812 - sparse_categorical_accuracy: 0.8203 - val_loss: 0.4247 - val_sparse_categorical_accuracy: 0.8061\n",
            "Epoch 98/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3897 - sparse_categorical_accuracy: 0.8438 - val_loss: 0.4309 - val_sparse_categorical_accuracy: 0.8033\n",
            "Epoch 99/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3706 - sparse_categorical_accuracy: 0.8516 - val_loss: 0.4336 - val_sparse_categorical_accuracy: 0.8004\n",
            "Epoch 100/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3879 - sparse_categorical_accuracy: 0.8203 - val_loss: 0.4296 - val_sparse_categorical_accuracy: 0.8046\n",
            "Epoch 101/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4084 - sparse_categorical_accuracy: 0.8125 - val_loss: 0.4237 - val_sparse_categorical_accuracy: 0.8080\n",
            "Epoch 102/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3703 - sparse_categorical_accuracy: 0.8359 - val_loss: 0.4297 - val_sparse_categorical_accuracy: 0.8006\n",
            "Epoch 103/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3912 - sparse_categorical_accuracy: 0.8281 - val_loss: 0.4142 - val_sparse_categorical_accuracy: 0.8126\n",
            "Epoch 104/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4095 - sparse_categorical_accuracy: 0.8203 - val_loss: 0.4092 - val_sparse_categorical_accuracy: 0.8191\n",
            "Epoch 105/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3616 - sparse_categorical_accuracy: 0.8672 - val_loss: 0.4049 - val_sparse_categorical_accuracy: 0.8218\n",
            "Epoch 106/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3024 - sparse_categorical_accuracy: 0.8672 - val_loss: 0.4076 - val_sparse_categorical_accuracy: 0.8182\n",
            "Epoch 107/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3288 - sparse_categorical_accuracy: 0.8828 - val_loss: 0.4090 - val_sparse_categorical_accuracy: 0.8198\n",
            "Epoch 108/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3499 - sparse_categorical_accuracy: 0.8438 - val_loss: 0.4086 - val_sparse_categorical_accuracy: 0.8189\n",
            "Epoch 109/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4159 - sparse_categorical_accuracy: 0.8047 - val_loss: 0.4079 - val_sparse_categorical_accuracy: 0.8191\n",
            "Epoch 110/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4296 - sparse_categorical_accuracy: 0.7578 - val_loss: 0.4061 - val_sparse_categorical_accuracy: 0.8222\n",
            "Epoch 111/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4796 - sparse_categorical_accuracy: 0.7656 - val_loss: 0.4122 - val_sparse_categorical_accuracy: 0.8154\n",
            "Epoch 112/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4244 - sparse_categorical_accuracy: 0.8281 - val_loss: 0.4131 - val_sparse_categorical_accuracy: 0.8167\n",
            "Epoch 113/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.5488 - sparse_categorical_accuracy: 0.7109 - val_loss: 0.4128 - val_sparse_categorical_accuracy: 0.8139\n",
            "Epoch 114/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4483 - sparse_categorical_accuracy: 0.7812 - val_loss: 0.4035 - val_sparse_categorical_accuracy: 0.8217\n",
            "Epoch 115/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4626 - sparse_categorical_accuracy: 0.7734 - val_loss: 0.4034 - val_sparse_categorical_accuracy: 0.8234\n",
            "Epoch 116/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3848 - sparse_categorical_accuracy: 0.8438 - val_loss: 0.4067 - val_sparse_categorical_accuracy: 0.8199\n",
            "Epoch 117/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.5139 - sparse_categorical_accuracy: 0.8047 - val_loss: 0.4093 - val_sparse_categorical_accuracy: 0.8166\n",
            "Epoch 118/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4429 - sparse_categorical_accuracy: 0.8047 - val_loss: 0.4056 - val_sparse_categorical_accuracy: 0.8212\n",
            "Epoch 119/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4390 - sparse_categorical_accuracy: 0.8125 - val_loss: 0.4022 - val_sparse_categorical_accuracy: 0.8216\n",
            "Epoch 120/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4705 - sparse_categorical_accuracy: 0.7500 - val_loss: 0.3990 - val_sparse_categorical_accuracy: 0.8267\n",
            "Epoch 121/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3021 - sparse_categorical_accuracy: 0.8984 - val_loss: 0.4048 - val_sparse_categorical_accuracy: 0.8226\n",
            "Epoch 122/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3748 - sparse_categorical_accuracy: 0.8203 - val_loss: 0.3993 - val_sparse_categorical_accuracy: 0.8230\n",
            "Epoch 123/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4677 - sparse_categorical_accuracy: 0.7969 - val_loss: 0.3969 - val_sparse_categorical_accuracy: 0.8278\n",
            "Epoch 124/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3882 - sparse_categorical_accuracy: 0.8125 - val_loss: 0.3982 - val_sparse_categorical_accuracy: 0.8249\n",
            "Epoch 125/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4790 - sparse_categorical_accuracy: 0.7656 - val_loss: 0.3969 - val_sparse_categorical_accuracy: 0.8252\n",
            "Epoch 126/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3342 - sparse_categorical_accuracy: 0.8359 - val_loss: 0.4071 - val_sparse_categorical_accuracy: 0.8199\n",
            "Epoch 127/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4081 - sparse_categorical_accuracy: 0.8203 - val_loss: 0.3964 - val_sparse_categorical_accuracy: 0.8232\n",
            "Epoch 128/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.4274 - sparse_categorical_accuracy: 0.7812 - val_loss: 0.3953 - val_sparse_categorical_accuracy: 0.8256\n",
            "Epoch 129/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3755 - sparse_categorical_accuracy: 0.8359 - val_loss: 0.4047 - val_sparse_categorical_accuracy: 0.8220\n",
            "Epoch 130/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4230 - sparse_categorical_accuracy: 0.8203 - val_loss: 0.4040 - val_sparse_categorical_accuracy: 0.8208\n",
            "Epoch 131/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3673 - sparse_categorical_accuracy: 0.8203 - val_loss: 0.4127 - val_sparse_categorical_accuracy: 0.8121\n",
            "Epoch 132/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3942 - sparse_categorical_accuracy: 0.8516 - val_loss: 0.4045 - val_sparse_categorical_accuracy: 0.8204\n",
            "Epoch 133/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.3518 - sparse_categorical_accuracy: 0.8281 - val_loss: 0.4005 - val_sparse_categorical_accuracy: 0.8238\n",
            "Epoch 134/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3771 - sparse_categorical_accuracy: 0.8672 - val_loss: 0.3969 - val_sparse_categorical_accuracy: 0.8254\n",
            "Epoch 135/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.2895 - sparse_categorical_accuracy: 0.8828 - val_loss: 0.3983 - val_sparse_categorical_accuracy: 0.8246\n",
            "Epoch 136/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3443 - sparse_categorical_accuracy: 0.8359 - val_loss: 0.4102 - val_sparse_categorical_accuracy: 0.8142\n",
            "Epoch 137/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3501 - sparse_categorical_accuracy: 0.8594 - val_loss: 0.4107 - val_sparse_categorical_accuracy: 0.8151\n",
            "Epoch 138/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.3546 - sparse_categorical_accuracy: 0.8203 - val_loss: 0.4060 - val_sparse_categorical_accuracy: 0.8184\n",
            "Epoch 139/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4719 - sparse_categorical_accuracy: 0.8047 - val_loss: 0.4006 - val_sparse_categorical_accuracy: 0.8220\n",
            "Epoch 140/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4263 - sparse_categorical_accuracy: 0.8047 - val_loss: 0.3947 - val_sparse_categorical_accuracy: 0.8254\n",
            "Epoch 141/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3599 - sparse_categorical_accuracy: 0.8672 - val_loss: 0.3973 - val_sparse_categorical_accuracy: 0.8239\n",
            "Epoch 142/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.2963 - sparse_categorical_accuracy: 0.8828 - val_loss: 0.3936 - val_sparse_categorical_accuracy: 0.8248\n",
            "Epoch 143/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.3580 - sparse_categorical_accuracy: 0.8281 - val_loss: 0.3954 - val_sparse_categorical_accuracy: 0.8235\n",
            "Epoch 144/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.5257 - sparse_categorical_accuracy: 0.7734 - val_loss: 0.3964 - val_sparse_categorical_accuracy: 0.8244\n",
            "Epoch 145/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.4794 - sparse_categorical_accuracy: 0.7500 - val_loss: 0.3899 - val_sparse_categorical_accuracy: 0.8262\n",
            "Epoch 146/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4239 - sparse_categorical_accuracy: 0.8438 - val_loss: 0.3947 - val_sparse_categorical_accuracy: 0.8257\n",
            "Epoch 147/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.5018 - sparse_categorical_accuracy: 0.7734 - val_loss: 0.4021 - val_sparse_categorical_accuracy: 0.8189\n",
            "Epoch 148/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.3928 - sparse_categorical_accuracy: 0.8203 - val_loss: 0.3989 - val_sparse_categorical_accuracy: 0.8228\n",
            "Epoch 149/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3655 - sparse_categorical_accuracy: 0.8516 - val_loss: 0.4048 - val_sparse_categorical_accuracy: 0.8182\n",
            "Epoch 150/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4651 - sparse_categorical_accuracy: 0.8125 - val_loss: 0.4020 - val_sparse_categorical_accuracy: 0.8195\n",
            "Epoch 151/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3728 - sparse_categorical_accuracy: 0.8359 - val_loss: 0.4016 - val_sparse_categorical_accuracy: 0.8185\n",
            "Epoch 152/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4443 - sparse_categorical_accuracy: 0.8047 - val_loss: 0.3970 - val_sparse_categorical_accuracy: 0.8202\n",
            "Epoch 153/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.4142 - sparse_categorical_accuracy: 0.8203 - val_loss: 0.3905 - val_sparse_categorical_accuracy: 0.8273\n",
            "Epoch 154/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3582 - sparse_categorical_accuracy: 0.8516 - val_loss: 0.3929 - val_sparse_categorical_accuracy: 0.8251\n",
            "Epoch 155/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4256 - sparse_categorical_accuracy: 0.8125 - val_loss: 0.3893 - val_sparse_categorical_accuracy: 0.8304\n",
            "Epoch 156/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4327 - sparse_categorical_accuracy: 0.8203 - val_loss: 0.3907 - val_sparse_categorical_accuracy: 0.8282\n",
            "Epoch 157/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3965 - sparse_categorical_accuracy: 0.7969 - val_loss: 0.3869 - val_sparse_categorical_accuracy: 0.8304\n",
            "Epoch 158/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.4380 - sparse_categorical_accuracy: 0.7500 - val_loss: 0.3926 - val_sparse_categorical_accuracy: 0.8233\n",
            "Epoch 159/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.3870 - sparse_categorical_accuracy: 0.8125 - val_loss: 0.3925 - val_sparse_categorical_accuracy: 0.8246\n",
            "Epoch 160/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4214 - sparse_categorical_accuracy: 0.8047 - val_loss: 0.3981 - val_sparse_categorical_accuracy: 0.8203\n",
            "Epoch 161/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3924 - sparse_categorical_accuracy: 0.8672 - val_loss: 0.3887 - val_sparse_categorical_accuracy: 0.8278\n",
            "Epoch 162/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3505 - sparse_categorical_accuracy: 0.8672 - val_loss: 0.3859 - val_sparse_categorical_accuracy: 0.8312\n",
            "Epoch 163/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4061 - sparse_categorical_accuracy: 0.8203 - val_loss: 0.3818 - val_sparse_categorical_accuracy: 0.8319\n",
            "Epoch 164/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3473 - sparse_categorical_accuracy: 0.8125 - val_loss: 0.3842 - val_sparse_categorical_accuracy: 0.8301\n",
            "Epoch 165/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3030 - sparse_categorical_accuracy: 0.8984 - val_loss: 0.3898 - val_sparse_categorical_accuracy: 0.8244\n",
            "Epoch 166/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.3815 - sparse_categorical_accuracy: 0.8203 - val_loss: 0.3862 - val_sparse_categorical_accuracy: 0.8301\n",
            "Epoch 167/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.3476 - sparse_categorical_accuracy: 0.8359 - val_loss: 0.3912 - val_sparse_categorical_accuracy: 0.8251\n",
            "Epoch 168/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3691 - sparse_categorical_accuracy: 0.8438 - val_loss: 0.3859 - val_sparse_categorical_accuracy: 0.8263\n",
            "Epoch 169/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.3393 - sparse_categorical_accuracy: 0.8672 - val_loss: 0.3855 - val_sparse_categorical_accuracy: 0.8262\n",
            "Epoch 170/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3171 - sparse_categorical_accuracy: 0.8438 - val_loss: 0.3953 - val_sparse_categorical_accuracy: 0.8216\n",
            "Epoch 171/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4129 - sparse_categorical_accuracy: 0.8672 - val_loss: 0.3954 - val_sparse_categorical_accuracy: 0.8224\n",
            "Epoch 172/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4748 - sparse_categorical_accuracy: 0.7969 - val_loss: 0.4020 - val_sparse_categorical_accuracy: 0.8199\n",
            "Epoch 173/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3683 - sparse_categorical_accuracy: 0.8438 - val_loss: 0.4055 - val_sparse_categorical_accuracy: 0.8204\n",
            "Epoch 174/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4028 - sparse_categorical_accuracy: 0.7812 - val_loss: 0.4164 - val_sparse_categorical_accuracy: 0.8094\n",
            "Epoch 175/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3448 - sparse_categorical_accuracy: 0.8281 - val_loss: 0.3968 - val_sparse_categorical_accuracy: 0.8238\n",
            "Epoch 176/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3692 - sparse_categorical_accuracy: 0.8359 - val_loss: 0.3968 - val_sparse_categorical_accuracy: 0.8249\n",
            "Epoch 177/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.5252 - sparse_categorical_accuracy: 0.7891 - val_loss: 0.3932 - val_sparse_categorical_accuracy: 0.8264\n",
            "Epoch 178/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4121 - sparse_categorical_accuracy: 0.8359 - val_loss: 0.3957 - val_sparse_categorical_accuracy: 0.8247\n",
            "Epoch 179/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3469 - sparse_categorical_accuracy: 0.8594 - val_loss: 0.3800 - val_sparse_categorical_accuracy: 0.8298\n",
            "Epoch 180/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3474 - sparse_categorical_accuracy: 0.8672 - val_loss: 0.3802 - val_sparse_categorical_accuracy: 0.8333\n",
            "Epoch 181/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4975 - sparse_categorical_accuracy: 0.7969 - val_loss: 0.3857 - val_sparse_categorical_accuracy: 0.8273\n",
            "Epoch 182/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.5074 - sparse_categorical_accuracy: 0.7891 - val_loss: 0.3742 - val_sparse_categorical_accuracy: 0.8361\n",
            "Epoch 183/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3756 - sparse_categorical_accuracy: 0.8203 - val_loss: 0.3792 - val_sparse_categorical_accuracy: 0.8310\n",
            "Epoch 184/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3508 - sparse_categorical_accuracy: 0.8438 - val_loss: 0.3798 - val_sparse_categorical_accuracy: 0.8305\n",
            "Epoch 185/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4897 - sparse_categorical_accuracy: 0.7734 - val_loss: 0.3752 - val_sparse_categorical_accuracy: 0.8336\n",
            "Epoch 186/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4185 - sparse_categorical_accuracy: 0.7969 - val_loss: 0.3740 - val_sparse_categorical_accuracy: 0.8328\n",
            "Epoch 187/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.2901 - sparse_categorical_accuracy: 0.8672 - val_loss: 0.3771 - val_sparse_categorical_accuracy: 0.8314\n",
            "Epoch 188/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3334 - sparse_categorical_accuracy: 0.8594 - val_loss: 0.3883 - val_sparse_categorical_accuracy: 0.8293\n",
            "Epoch 189/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3003 - sparse_categorical_accuracy: 0.8594 - val_loss: 0.3930 - val_sparse_categorical_accuracy: 0.8274\n",
            "Epoch 190/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4694 - sparse_categorical_accuracy: 0.8125 - val_loss: 0.3878 - val_sparse_categorical_accuracy: 0.8297\n",
            "Epoch 191/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4237 - sparse_categorical_accuracy: 0.7969 - val_loss: 0.3892 - val_sparse_categorical_accuracy: 0.8267\n",
            "Epoch 192/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3814 - sparse_categorical_accuracy: 0.8281 - val_loss: 0.4032 - val_sparse_categorical_accuracy: 0.8199\n",
            "Epoch 193/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3102 - sparse_categorical_accuracy: 0.8672 - val_loss: 0.3917 - val_sparse_categorical_accuracy: 0.8279\n",
            "Epoch 194/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4144 - sparse_categorical_accuracy: 0.8125 - val_loss: 0.3901 - val_sparse_categorical_accuracy: 0.8291\n",
            "Epoch 195/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3319 - sparse_categorical_accuracy: 0.8672 - val_loss: 0.3857 - val_sparse_categorical_accuracy: 0.8304\n",
            "Epoch 196/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3761 - sparse_categorical_accuracy: 0.8359 - val_loss: 0.3750 - val_sparse_categorical_accuracy: 0.8339\n",
            "Epoch 197/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3818 - sparse_categorical_accuracy: 0.8047 - val_loss: 0.3754 - val_sparse_categorical_accuracy: 0.8327\n",
            "Epoch 198/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3765 - sparse_categorical_accuracy: 0.8281 - val_loss: 0.3740 - val_sparse_categorical_accuracy: 0.8349\n",
            "Epoch 199/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.5196 - sparse_categorical_accuracy: 0.7500 - val_loss: 0.3797 - val_sparse_categorical_accuracy: 0.8335\n",
            "Epoch 200/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3265 - sparse_categorical_accuracy: 0.8359 - val_loss: 0.3831 - val_sparse_categorical_accuracy: 0.8322\n",
            "Epoch 201/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4679 - sparse_categorical_accuracy: 0.7812 - val_loss: 0.3743 - val_sparse_categorical_accuracy: 0.8352\n",
            "Epoch 202/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3148 - sparse_categorical_accuracy: 0.8828 - val_loss: 0.3798 - val_sparse_categorical_accuracy: 0.8329\n",
            "Epoch 203/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3533 - sparse_categorical_accuracy: 0.8672 - val_loss: 0.3774 - val_sparse_categorical_accuracy: 0.8351\n",
            "Epoch 204/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4379 - sparse_categorical_accuracy: 0.7734 - val_loss: 0.3677 - val_sparse_categorical_accuracy: 0.8369\n",
            "Epoch 205/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.2444 - sparse_categorical_accuracy: 0.8984 - val_loss: 0.3768 - val_sparse_categorical_accuracy: 0.8338\n",
            "Epoch 206/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3735 - sparse_categorical_accuracy: 0.8516 - val_loss: 0.3871 - val_sparse_categorical_accuracy: 0.8298\n",
            "Epoch 207/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3349 - sparse_categorical_accuracy: 0.8516 - val_loss: 0.3943 - val_sparse_categorical_accuracy: 0.8268\n",
            "Epoch 208/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4384 - sparse_categorical_accuracy: 0.8047 - val_loss: 0.3925 - val_sparse_categorical_accuracy: 0.8268\n",
            "Epoch 209/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.2845 - sparse_categorical_accuracy: 0.8750 - val_loss: 0.3919 - val_sparse_categorical_accuracy: 0.8269\n",
            "Epoch 210/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3483 - sparse_categorical_accuracy: 0.8516 - val_loss: 0.3893 - val_sparse_categorical_accuracy: 0.8286\n",
            "Epoch 211/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3620 - sparse_categorical_accuracy: 0.8125 - val_loss: 0.3987 - val_sparse_categorical_accuracy: 0.8266\n",
            "Epoch 212/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4131 - sparse_categorical_accuracy: 0.8047 - val_loss: 0.4006 - val_sparse_categorical_accuracy: 0.8254\n",
            "Epoch 213/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4227 - sparse_categorical_accuracy: 0.8047 - val_loss: 0.4005 - val_sparse_categorical_accuracy: 0.8257\n",
            "Epoch 214/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4431 - sparse_categorical_accuracy: 0.8203 - val_loss: 0.3917 - val_sparse_categorical_accuracy: 0.8280\n",
            "Epoch 215/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4425 - sparse_categorical_accuracy: 0.7812 - val_loss: 0.3776 - val_sparse_categorical_accuracy: 0.8338\n",
            "Epoch 216/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3395 - sparse_categorical_accuracy: 0.8594 - val_loss: 0.3792 - val_sparse_categorical_accuracy: 0.8335\n",
            "Epoch 217/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4035 - sparse_categorical_accuracy: 0.8047 - val_loss: 0.3760 - val_sparse_categorical_accuracy: 0.8357\n",
            "Epoch 218/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3104 - sparse_categorical_accuracy: 0.8594 - val_loss: 0.3728 - val_sparse_categorical_accuracy: 0.8367\n",
            "Epoch 219/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3736 - sparse_categorical_accuracy: 0.8281 - val_loss: 0.3622 - val_sparse_categorical_accuracy: 0.8418\n",
            "Epoch 220/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3802 - sparse_categorical_accuracy: 0.8438 - val_loss: 0.3578 - val_sparse_categorical_accuracy: 0.8427\n",
            "Epoch 221/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3622 - sparse_categorical_accuracy: 0.8359 - val_loss: 0.3595 - val_sparse_categorical_accuracy: 0.8407\n",
            "Epoch 222/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3372 - sparse_categorical_accuracy: 0.8438 - val_loss: 0.3554 - val_sparse_categorical_accuracy: 0.8443\n",
            "Epoch 223/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.2482 - sparse_categorical_accuracy: 0.9141 - val_loss: 0.3567 - val_sparse_categorical_accuracy: 0.8441\n",
            "Epoch 224/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3302 - sparse_categorical_accuracy: 0.8359 - val_loss: 0.3621 - val_sparse_categorical_accuracy: 0.8391\n",
            "Epoch 225/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3854 - sparse_categorical_accuracy: 0.8281 - val_loss: 0.3528 - val_sparse_categorical_accuracy: 0.8467\n",
            "Epoch 226/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.4423 - sparse_categorical_accuracy: 0.8125 - val_loss: 0.3587 - val_sparse_categorical_accuracy: 0.8431\n",
            "Epoch 227/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.2915 - sparse_categorical_accuracy: 0.8750 - val_loss: 0.3626 - val_sparse_categorical_accuracy: 0.8403\n",
            "Epoch 228/250\n",
            "4/4 [==============================] - 61s 15s/step - loss: 0.4441 - sparse_categorical_accuracy: 0.8047 - val_loss: 0.3587 - val_sparse_categorical_accuracy: 0.8439\n",
            "Epoch 229/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3433 - sparse_categorical_accuracy: 0.8359 - val_loss: 0.3538 - val_sparse_categorical_accuracy: 0.8449\n",
            "Epoch 230/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3487 - sparse_categorical_accuracy: 0.8594 - val_loss: 0.3617 - val_sparse_categorical_accuracy: 0.8426\n",
            "Epoch 231/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3179 - sparse_categorical_accuracy: 0.8672 - val_loss: 0.3611 - val_sparse_categorical_accuracy: 0.8416\n",
            "Epoch 232/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3008 - sparse_categorical_accuracy: 0.8594 - val_loss: 0.3574 - val_sparse_categorical_accuracy: 0.8434\n",
            "Epoch 233/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3670 - sparse_categorical_accuracy: 0.8359 - val_loss: 0.3619 - val_sparse_categorical_accuracy: 0.8418\n",
            "Epoch 234/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3484 - sparse_categorical_accuracy: 0.8281 - val_loss: 0.3649 - val_sparse_categorical_accuracy: 0.8407\n",
            "Epoch 235/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3264 - sparse_categorical_accuracy: 0.9141 - val_loss: 0.3684 - val_sparse_categorical_accuracy: 0.8394\n",
            "Epoch 236/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.2325 - sparse_categorical_accuracy: 0.9219 - val_loss: 0.3614 - val_sparse_categorical_accuracy: 0.8417\n",
            "Epoch 237/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3362 - sparse_categorical_accuracy: 0.8750 - val_loss: 0.3584 - val_sparse_categorical_accuracy: 0.8404\n",
            "Epoch 238/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3312 - sparse_categorical_accuracy: 0.8516 - val_loss: 0.3584 - val_sparse_categorical_accuracy: 0.8423\n",
            "Epoch 239/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3722 - sparse_categorical_accuracy: 0.8516 - val_loss: 0.3637 - val_sparse_categorical_accuracy: 0.8423\n",
            "Epoch 240/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3822 - sparse_categorical_accuracy: 0.8438 - val_loss: 0.3681 - val_sparse_categorical_accuracy: 0.8382\n",
            "Epoch 241/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4100 - sparse_categorical_accuracy: 0.8203 - val_loss: 0.3605 - val_sparse_categorical_accuracy: 0.8416\n",
            "Epoch 242/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4585 - sparse_categorical_accuracy: 0.8125 - val_loss: 0.3574 - val_sparse_categorical_accuracy: 0.8421\n",
            "Epoch 243/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3496 - sparse_categorical_accuracy: 0.8594 - val_loss: 0.3641 - val_sparse_categorical_accuracy: 0.8426\n",
            "Epoch 244/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3589 - sparse_categorical_accuracy: 0.7812 - val_loss: 0.3657 - val_sparse_categorical_accuracy: 0.8392\n",
            "Epoch 245/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4022 - sparse_categorical_accuracy: 0.8438 - val_loss: 0.3796 - val_sparse_categorical_accuracy: 0.8356\n",
            "Epoch 246/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3136 - sparse_categorical_accuracy: 0.8438 - val_loss: 0.3882 - val_sparse_categorical_accuracy: 0.8302\n",
            "Epoch 247/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4165 - sparse_categorical_accuracy: 0.8750 - val_loss: 0.3826 - val_sparse_categorical_accuracy: 0.8324\n",
            "Epoch 248/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.4549 - sparse_categorical_accuracy: 0.8125 - val_loss: 0.3732 - val_sparse_categorical_accuracy: 0.8350\n",
            "Epoch 249/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3338 - sparse_categorical_accuracy: 0.8359 - val_loss: 0.3762 - val_sparse_categorical_accuracy: 0.8332\n",
            "Epoch 250/250\n",
            "4/4 [==============================] - 60s 15s/step - loss: 0.3465 - sparse_categorical_accuracy: 0.8516 - val_loss: 0.3732 - val_sparse_categorical_accuracy: 0.8343\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
            "INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2020-12-11T17_02_33.881294-yv0ziop8/Trainer/model_run/24/serving_model_dir/temp/assets\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-64-a501b7646f34>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tfx/orchestration/experimental/interactive/interactive_context.py\u001b[0m in \u001b[0;36mrun_if_ipython\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m       \u001b[0;31m# __IPYTHON__ variable is set by IPython, see\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0;31m# https://ipython.org/ipython-doc/rel-0.10.2/html/interactive/reference.html#embedding-ipython.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m       absl.logging.warning(\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tfx/orchestration/experimental/interactive/interactive_context.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, component, enable_cache, beam_pipeline_args)\u001b[0m\n\u001b[1;32m    180\u001b[0m         \u001b[0mtelemetry_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLABEL_TFX_RUNNER\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mrunner_label\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m     }):\n\u001b[0;32m--> 182\u001b[0;31m       \u001b[0mexecution_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlauncher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlaunch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecution_id\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    183\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m     return execution_result.ExecutionResult(\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tfx/orchestration/launcher/base_component_launcher.py\u001b[0m in \u001b[0;36mlaunch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    207\u001b[0m                          \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexecution_decision\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m                          \u001b[0mexecution_decision\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 209\u001b[0;31m                          copy.deepcopy(execution_decision.exec_properties))\n\u001b[0m\u001b[1;32m    210\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m     absl.logging.info('Running publisher for %s',\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tfx/orchestration/launcher/in_process_component_launcher.py\u001b[0m in \u001b[0;36m_run_executor\u001b[0;34m(self, execution_id, input_dict, output_dict, exec_properties)\u001b[0m\n\u001b[1;32m     70\u001b[0m     \u001b[0;31m# output_dict can still be changed, specifically properties.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m     executor.Do(\n\u001b[0;32m---> 72\u001b[0;31m         copy.deepcopy(input_dict), output_dict, copy.deepcopy(exec_properties))\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tfx/components/trainer/executor.py\u001b[0m in \u001b[0;36mDo\u001b[0;34m(self, input_dict, output_dict, exec_properties)\u001b[0m\n\u001b[1;32m    297\u001b[0m     \u001b[0mfn_args\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval_model_dir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpath_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval_model_dir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworking_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    298\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 299\u001b[0;31m     \u001b[0mtraining_spec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrainer_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mschema\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m     \u001b[0;31m# Train the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/cifar10/cifar10_utils_native_keras.py\u001b[0m in \u001b[0;36mtrainer_fn\u001b[0;34m(fn_args, x)\u001b[0m\n\u001b[1;32m    394\u001b[0m       \u001b[0mrewriter_factory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTFLITE_REWRITER\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    395\u001b[0m       \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'tflite_rewriter'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 396\u001b[0;31m       enable_experimental_new_converter=True)\n\u001b[0m\u001b[1;32m    397\u001b[0m   converters.rewrite_saved_model(temp_saving_model_dir,\n\u001b[1;32m    398\u001b[0m                                  \u001b[0mfn_args\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserving_model_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtfrw\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tfx/components/trainer/rewriting/rewriter_factory.py\u001b[0m in \u001b[0;36mcreate_rewriter\u001b[0;34m(rewriter_type, *args, **kwargs)\u001b[0m\n\u001b[1;32m     82\u001b[0m     \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIf\u001b[0m \u001b[0munable\u001b[0m \u001b[0mto\u001b[0m \u001b[0minstantiate\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mrewriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m   \"\"\"\n\u001b[0;32m---> 84\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0m_RewriterFactory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_rewriter_cls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrewriter_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'enable_experimental_new_converter'"
          ]
        }
      ]
    }
  ]
}