Virtual_Notepad.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0g1pF6RfViPr"
      },
      "outputs": [],
      "source": [
        "SAVED_MODEL_FILENAME = \"saved_model\"\n",
        "FLOAT_TFL_MODEL_FILENAME = \"float_model.tfl\"\n",
        "QUANTIZED_TFL_MODEL_FILENAME = \"quantized_model.tfl\"\n",
        "TFL_CC_MODEL_FILENAME = \"virtual_notepad_model_data.cc\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LqM_kM7iR1Nf",
        "outputId": "8747d988-4982-4d23-9c01-6c185e62b529"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QzxPxsKKTbP4",
        "outputId": "978ffc8b-6bf6-4e9b-8979-9ec5227f1afe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Colab Notebooks\n"
          ]
        }
      ],
      "source": [
        "cd drive/MyDrive/Colab Notebooks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xvPo0OP0TFDq",
        "outputId": "6478247a-fe5b-4f6e-f0d0-d62c1be2cda3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  virtual_notepad_letter_data-1.zip\n",
            "   creating: virtual_notepad_letter_data-1/virtual_notepad_letter_data-1/\n",
            "  inflating: virtual_notepad_letter_data-1/virtual_notepad_letter_data-1/wanddata_A.json  \n",
            "  inflating: virtual_notepad_letter_data-1/virtual_notepad_letter_data-1/wanddata_B.json  \n",
            "  inflating: virtual_notepad_letter_data-1/virtual_notepad_letter_data-1/wanddata_C.json  \n",
            "  inflating: virtual_notepad_letter_data-1/virtual_notepad_letter_data-1/wanddata_D.json  \n",
            "  inflating: virtual_notepad_letter_data-1/virtual_notepad_letter_data-1/wanddata_E.json  \n",
            "  inflating: virtual_notepad_letter_data-1/virtual_notepad_letter_data-1/wanddata_F.json  \n",
            "  inflating: virtual_notepad_letter_data-1/virtual_notepad_letter_data-1/wanddata_G.json  \n",
            "  inflating: virtual_notepad_letter_data-1/virtual_notepad_letter_data-1/wanddata_H.json  \n",
            "  inflating: virtual_notepad_letter_data-1/virtual_notepad_letter_data-1/wanddata_I.json  \n",
            "  inflating: virtual_notepad_letter_data-1/virtual_notepad_letter_data-1/wanddata_J.json  \n",
            "  inflating: virtual_notepad_letter_data-1/virtual_notepad_letter_data-1/wanddata_K.json  \n",
            "  inflating: virtual_notepad_letter_data-1/virtual_notepad_letter_data-1/wanddata_L.json  \n",
            "  inflating: virtual_notepad_letter_data-1/virtual_notepad_letter_data-1/wanddata_M.json  \n",
            "  inflating: virtual_notepad_letter_data-1/virtual_notepad_letter_data-1/wanddata_N.json  \n",
            "  inflating: virtual_notepad_letter_data-1/virtual_notepad_letter_data-1/wanddata_O.json  \n",
            "  inflating: virtual_notepad_letter_data-1/virtual_notepad_letter_data-1/wanddata_P.json  \n",
            "  inflating: virtual_notepad_letter_data-1/virtual_notepad_letter_data-1/wanddata_Q.json  \n",
            "  inflating: virtual_notepad_letter_data-1/virtual_notepad_letter_data-1/wanddata_R.json  \n",
            "  inflating: virtual_notepad_letter_data-1/virtual_notepad_letter_data-1/wanddata_S.json  \n",
            "  inflating: virtual_notepad_letter_data-1/virtual_notepad_letter_data-1/wanddata_T.json  \n",
            "  inflating: virtual_notepad_letter_data-1/virtual_notepad_letter_data-1/wanddata_U.json  \n",
            "  inflating: virtual_notepad_letter_data-1/virtual_notepad_letter_data-1/wanddata_V.json  \n",
            "  inflating: virtual_notepad_letter_data-1/virtual_notepad_letter_data-1/wanddata_W.json  \n",
            "  inflating: virtual_notepad_letter_data-1/virtual_notepad_letter_data-1/wanddata_X.json  \n",
            "  inflating: virtual_notepad_letter_data-1/virtual_notepad_letter_data-1/wanddata_Y.json  \n",
            "  inflating: virtual_notepad_letter_data-1/virtual_notepad_letter_data-1/wanddata_Z.json  \n"
          ]
        }
      ],
      "source": [
        "!unzip virtual_notepad_letter_data-1.zip\n",
        "!rm -rf sample_data\n",
        "!mkdir -p checkpoints"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QruvJjpLZuqQ",
        "outputId": "2d6aab52-0887-4fd6-f1e4-17cef43cea23"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Colab Notebooks/virtual_notepad_letter_data-1\n"
          ]
        }
      ],
      "source": [
        "cd virtual_notepad_letter_data-1/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mWO58-igVFSd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b9234726-9811-4898-985f-e1734feb009d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "virtual_notepad_letter_data-1/wanddata_A.json\n",
            "virtual_notepad_letter_data-1/wanddata_A.json\n",
            "virtual_notepad_letter_data-1/wanddata_A.json\n",
            "virtual_notepad_letter_data-1/wanddata_A.json\n",
            "virtual_notepad_letter_data-1/wanddata_A.json\n",
            "virtual_notepad_letter_data-1/wanddata_A.json\n",
            "virtual_notepad_letter_data-1/wanddata_A.json\n",
            "virtual_notepad_letter_data-1/wanddata_A.json\n",
            "virtual_notepad_letter_data-1/wanddata_A.json\n",
            "virtual_notepad_letter_data-1/wanddata_A.json\n",
            "virtual_notepad_letter_data-1/wanddata_A.json\n",
            "virtual_notepad_letter_data-1/wanddata_A.json\n",
            "virtual_notepad_letter_data-1/wanddata_A.json\n",
            "virtual_notepad_letter_data-1/wanddata_A.json\n",
            "virtual_notepad_letter_data-1/wanddata_A.json\n",
            "virtual_notepad_letter_data-1/wanddata_A.json\n",
            "virtual_notepad_letter_data-1/wanddata_A.json\n",
            "virtual_notepad_letter_data-1/wanddata_A.json\n",
            "virtual_notepad_letter_data-1/wanddata_A.json\n",
            "virtual_notepad_letter_data-1/wanddata_A.json\n",
            "virtual_notepad_letter_data-1/wanddata_A.json\n",
            "virtual_notepad_letter_data-1/wanddata_A.json\n",
            "virtual_notepad_letter_data-1/wanddata_A.json\n",
            "virtual_notepad_letter_data-1/wanddata_B.json\n",
            "virtual_notepad_letter_data-1/wanddata_B.json\n",
            "virtual_notepad_letter_data-1/wanddata_B.json\n",
            "virtual_notepad_letter_data-1/wanddata_B.json\n",
            "virtual_notepad_letter_data-1/wanddata_B.json\n",
            "virtual_notepad_letter_data-1/wanddata_B.json\n",
            "virtual_notepad_letter_data-1/wanddata_B.json\n",
            "virtual_notepad_letter_data-1/wanddata_B.json\n",
            "virtual_notepad_letter_data-1/wanddata_B.json\n",
            "virtual_notepad_letter_data-1/wanddata_B.json\n",
            "virtual_notepad_letter_data-1/wanddata_B.json\n",
            "virtual_notepad_letter_data-1/wanddata_B.json\n",
            "virtual_notepad_letter_data-1/wanddata_B.json\n",
            "virtual_notepad_letter_data-1/wanddata_B.json\n",
            "virtual_notepad_letter_data-1/wanddata_B.json\n",
            "virtual_notepad_letter_data-1/wanddata_B.json\n",
            "virtual_notepad_letter_data-1/wanddata_B.json\n",
            "virtual_notepad_letter_data-1/wanddata_B.json\n",
            "virtual_notepad_letter_data-1/wanddata_B.json\n",
            "virtual_notepad_letter_data-1/wanddata_B.json\n",
            "virtual_notepad_letter_data-1/wanddata_C.json\n",
            "virtual_notepad_letter_data-1/wanddata_C.json\n",
            "virtual_notepad_letter_data-1/wanddata_C.json\n",
            "virtual_notepad_letter_data-1/wanddata_C.json\n",
            "virtual_notepad_letter_data-1/wanddata_C.json\n",
            "virtual_notepad_letter_data-1/wanddata_C.json\n",
            "virtual_notepad_letter_data-1/wanddata_C.json\n",
            "virtual_notepad_letter_data-1/wanddata_C.json\n",
            "virtual_notepad_letter_data-1/wanddata_C.json\n",
            "virtual_notepad_letter_data-1/wanddata_C.json\n",
            "virtual_notepad_letter_data-1/wanddata_C.json\n",
            "virtual_notepad_letter_data-1/wanddata_C.json\n",
            "virtual_notepad_letter_data-1/wanddata_C.json\n",
            "virtual_notepad_letter_data-1/wanddata_C.json\n",
            "virtual_notepad_letter_data-1/wanddata_C.json\n",
            "virtual_notepad_letter_data-1/wanddata_C.json\n",
            "virtual_notepad_letter_data-1/wanddata_C.json\n",
            "virtual_notepad_letter_data-1/wanddata_C.json\n",
            "virtual_notepad_letter_data-1/wanddata_C.json\n",
            "virtual_notepad_letter_data-1/wanddata_C.json\n",
            "virtual_notepad_letter_data-1/wanddata_C.json\n",
            "virtual_notepad_letter_data-1/wanddata_C.json\n",
            "virtual_notepad_letter_data-1/wanddata_C.json\n",
            "virtual_notepad_letter_data-1/wanddata_C.json\n",
            "virtual_notepad_letter_data-1/wanddata_C.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_D.json\n",
            "virtual_notepad_letter_data-1/wanddata_E.json\n",
            "virtual_notepad_letter_data-1/wanddata_E.json\n",
            "virtual_notepad_letter_data-1/wanddata_E.json\n",
            "virtual_notepad_letter_data-1/wanddata_E.json\n",
            "virtual_notepad_letter_data-1/wanddata_E.json\n",
            "virtual_notepad_letter_data-1/wanddata_E.json\n",
            "virtual_notepad_letter_data-1/wanddata_E.json\n",
            "virtual_notepad_letter_data-1/wanddata_E.json\n",
            "virtual_notepad_letter_data-1/wanddata_E.json\n",
            "virtual_notepad_letter_data-1/wanddata_E.json\n",
            "virtual_notepad_letter_data-1/wanddata_E.json\n",
            "virtual_notepad_letter_data-1/wanddata_E.json\n",
            "virtual_notepad_letter_data-1/wanddata_E.json\n",
            "virtual_notepad_letter_data-1/wanddata_E.json\n",
            "virtual_notepad_letter_data-1/wanddata_E.json\n",
            "virtual_notepad_letter_data-1/wanddata_E.json\n",
            "virtual_notepad_letter_data-1/wanddata_E.json\n",
            "virtual_notepad_letter_data-1/wanddata_E.json\n",
            "virtual_notepad_letter_data-1/wanddata_E.json\n",
            "virtual_notepad_letter_data-1/wanddata_E.json\n",
            "virtual_notepad_letter_data-1/wanddata_E.json\n",
            "virtual_notepad_letter_data-1/wanddata_E.json\n",
            "virtual_notepad_letter_data-1/wanddata_E.json\n",
            "virtual_notepad_letter_data-1/wanddata_E.json\n",
            "virtual_notepad_letter_data-1/wanddata_E.json\n",
            "virtual_notepad_letter_data-1/wanddata_E.json\n",
            "virtual_notepad_letter_data-1/wanddata_E.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_F.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_G.json\n",
            "virtual_notepad_letter_data-1/wanddata_H.json\n",
            "virtual_notepad_letter_data-1/wanddata_H.json\n",
            "virtual_notepad_letter_data-1/wanddata_H.json\n",
            "virtual_notepad_letter_data-1/wanddata_H.json\n",
            "virtual_notepad_letter_data-1/wanddata_H.json\n",
            "virtual_notepad_letter_data-1/wanddata_H.json\n",
            "virtual_notepad_letter_data-1/wanddata_H.json\n",
            "virtual_notepad_letter_data-1/wanddata_H.json\n",
            "virtual_notepad_letter_data-1/wanddata_H.json\n",
            "virtual_notepad_letter_data-1/wanddata_H.json\n",
            "virtual_notepad_letter_data-1/wanddata_H.json\n",
            "virtual_notepad_letter_data-1/wanddata_H.json\n",
            "virtual_notepad_letter_data-1/wanddata_H.json\n",
            "virtual_notepad_letter_data-1/wanddata_H.json\n",
            "virtual_notepad_letter_data-1/wanddata_H.json\n",
            "virtual_notepad_letter_data-1/wanddata_H.json\n",
            "virtual_notepad_letter_data-1/wanddata_H.json\n",
            "virtual_notepad_letter_data-1/wanddata_H.json\n",
            "virtual_notepad_letter_data-1/wanddata_H.json\n",
            "virtual_notepad_letter_data-1/wanddata_H.json\n",
            "virtual_notepad_letter_data-1/wanddata_H.json\n",
            "virtual_notepad_letter_data-1/wanddata_H.json\n",
            "virtual_notepad_letter_data-1/wanddata_H.json\n",
            "virtual_notepad_letter_data-1/wanddata_H.json\n",
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            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Y.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n",
            "virtual_notepad_letter_data-1/wanddata_Z.json\n"
          ]
        }
      ],
      "source": [
        "import glob\n",
        "import json\n",
        "\n",
        "strokes = []\n",
        "for filename in glob.glob(\"virtual_notepad_letter_data-1/*.json\"):\n",
        "  with open(filename, \"r\") as file:\n",
        "    file_contents = file.read()\n",
        "  file_data = json.loads(file_contents)\n",
        "  for stroke in file_data[\"strokes\"]:\n",
        "    stroke[\"filename\"] = filename\n",
        "    print(filename)\n",
        "    strokes.append(stroke)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xfLzrpyLVJ5S"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_stroke(stroke):\n",
        "\n",
        "  x_array = []\n",
        "  y_array = []\n",
        "  for coords in stroke[\"strokePoints\"]:\n",
        "    x_array.append(coords[\"x\"])\n",
        "    y_array.append(coords[\"y\"])\n",
        "\n",
        "  fig = plt.figure(figsize=(12.8, 4.8))\n",
        "  fig.suptitle(stroke[\"label\"])\n",
        "\n",
        "  ax = fig.add_subplot(131)\n",
        "  ax.set_xlabel('x')\n",
        "  ax.set_ylabel('y')\n",
        "  ax.set_xlim(-0.4, 0.4)\n",
        "  ax.set_ylim(-0.4, 0.4)\n",
        "  ax.plot(x_array, y_array)\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "dveZd2ZuW-jl",
        "outputId": "020dbf46-009f-4e52-c64b-94bfbfcc3730"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1280x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_stroke(strokes[20])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3FVPj-eqjvoB"
      },
      "outputs": [],
      "source": [
        "import math\n",
        "import numpy as np\n",
        "import PIL\n",
        "\n",
        "FIXED_POINT = 256\n",
        "\n",
        "def mul_fp(a, b):\n",
        "  return (a * b) / FIXED_POINT\n",
        "\n",
        "def div_fp(a, b):\n",
        "  if b == 0:\n",
        "    b = 1\n",
        "  return (a * FIXED_POINT) / b\n",
        "\n",
        "def float_to_fp(a):\n",
        "  return math.floor(a * FIXED_POINT)\n",
        "\n",
        "def norm_to_coord_fp(a, range_fp, half_size_fp):\n",
        "  a_fp = float_to_fp(a)\n",
        "  norm_fp = div_fp(a_fp, range_fp)\n",
        "  return mul_fp(norm_fp, half_size_fp) + half_size_fp\n",
        "\n",
        "def round_fp_to_int(a):\n",
        "  return math.floor((a + (FIXED_POINT / 2)) / FIXED_POINT)\n",
        "\n",
        "def gate(a, min, max):\n",
        "  if a < min:\n",
        "    return min\n",
        "  elif a > max:\n",
        "    return max\n",
        "  else:\n",
        "    return a\n",
        "\n",
        "def rasterize_stroke(stroke_points, x_range, y_range, width, height):\n",
        "  num_channels = 3\n",
        "  buffer_byte_count = height * width * num_channels\n",
        "  buffer = bytearray(buffer_byte_count)\n",
        "\n",
        "  width_fp = width * FIXED_POINT\n",
        "  height_fp = height * FIXED_POINT\n",
        "  half_width_fp = width_fp / 2\n",
        "  half_height_fp = height_fp / 2\n",
        "  x_range_fp = float_to_fp(x_range)\n",
        "  y_range_fp = float_to_fp(y_range)\n",
        "\n",
        "  t_inc_fp = FIXED_POINT / len(stroke_points)\n",
        "\n",
        "  one_half_fp = (FIXED_POINT / 2)\n",
        "\n",
        "  for point_index in range(len(stroke_points) - 1):\n",
        "    start_point = stroke_points[point_index]\n",
        "    end_point = stroke_points[point_index + 1]\n",
        "    start_x_fp = norm_to_coord_fp(start_point[\"x\"], x_range_fp, half_width_fp)\n",
        "    start_y_fp = norm_to_coord_fp(-start_point[\"y\"], y_range_fp, half_height_fp)\n",
        "    end_x_fp = norm_to_coord_fp(end_point[\"x\"], x_range_fp, half_width_fp)\n",
        "    end_y_fp = norm_to_coord_fp(-end_point[\"y\"], y_range_fp, half_height_fp)\n",
        "    delta_x_fp = end_x_fp - start_x_fp\n",
        "    delta_y_fp = end_y_fp - start_y_fp\n",
        "\n",
        "    t_fp = point_index * t_inc_fp\n",
        "    if t_fp < one_half_fp:\n",
        "      local_t_fp = div_fp(t_fp, one_half_fp)\n",
        "      one_minus_t_fp = FIXED_POINT - local_t_fp\n",
        "      red = round_fp_to_int(one_minus_t_fp * 255)\n",
        "      green = round_fp_to_int(local_t_fp * 255)\n",
        "      blue = 0\n",
        "    else:\n",
        "      local_t_fp = div_fp(t_fp - one_half_fp, one_half_fp)\n",
        "      one_minus_t_fp = FIXED_POINT - local_t_fp\n",
        "      red = 0\n",
        "      green = round_fp_to_int(one_minus_t_fp * 255)\n",
        "      blue = round_fp_to_int(local_t_fp * 255)\n",
        "    red = gate(red, 0, 255)\n",
        "    green = gate(green, 0, 255)\n",
        "    blue = gate(blue, 0, 255)\n",
        "\n",
        "    if abs(delta_x_fp) > abs(delta_y_fp):\n",
        "      line_length = abs(round_fp_to_int(delta_x_fp))\n",
        "      if delta_x_fp > 0:\n",
        "        x_inc_fp = 1 * FIXED_POINT\n",
        "        y_inc_fp = div_fp(delta_y_fp, delta_x_fp)\n",
        "      else:\n",
        "        x_inc_fp = -1 * FIXED_POINT\n",
        "        y_inc_fp = -div_fp(delta_y_fp, delta_x_fp)\n",
        "    else:\n",
        "      line_length = abs(round_fp_to_int(delta_y_fp))\n",
        "      if delta_y_fp > 0:\n",
        "        y_inc_fp = 1 * FIXED_POINT\n",
        "        x_inc_fp = div_fp(delta_x_fp, delta_y_fp)\n",
        "      else:\n",
        "        y_inc_fp = -1 * FIXED_POINT\n",
        "        x_inc_fp = -div_fp(delta_x_fp, delta_y_fp)\n",
        "    for i in range(line_length + 1):\n",
        "      x_fp = start_x_fp + (i * x_inc_fp)\n",
        "      y_fp = start_y_fp + (i * y_inc_fp)\n",
        "      x = round_fp_to_int(x_fp)\n",
        "      y = round_fp_to_int(y_fp)\n",
        "      if (x < 0) or (x >= width) or (y < 0) or (y >= height):\n",
        "        continue\n",
        "      buffer_index = (y * width * num_channels) + (x * num_channels)\n",
        "      buffer[buffer_index + 0] = red\n",
        "      buffer[buffer_index + 1] = green\n",
        "      buffer[buffer_index + 2] = blue\n",
        "  \n",
        "  np_buffer = np.frombuffer(buffer, dtype=np.uint8).reshape(height, width, num_channels)\n",
        "\n",
        "  return np_buffer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        },
        "id": "sOaxOIjRskJg",
        "outputId": "8f19fb58-153c-4ff9-badf-97476a9aeb71"
      },
      "outputs": [
        {
           "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=512x512 at 0x7F57EF3C8C40>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 27
          
        }
      ],
      "source": [
        "raster = rasterize_stroke(strokes[300][\"strokePoints\"], 0.5, 0.5, 32, 32)\n",
        "PIL.Image.fromarray(raster).resize((512, 512), PIL.Image.NEAREST)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o-FOVdFpgkdf"
      },
      "outputs": [],
      "source": [
        "from pathlib import Path\n",
        "import shutil\n",
        "\n",
        "X_RANGE = 0.6\n",
        "Y_RANGE = 0.6\n",
        "\n",
        "def ensure_empty_dir(dirname):\n",
        "  dirpath = Path(dirname)\n",
        "  if dirpath.exists() and dirpath.is_dir():\n",
        "    shutil.rmtree(dirpath)\n",
        "  dirpath.mkdir()\n",
        "\n",
        "def augment_points(points, move_range, scale_range, rotate_range):\n",
        "  move_x = np.random.uniform(low=-move_range, high=move_range)\n",
        "  move_y = np.random.uniform(low=-move_range, high=move_range)\n",
        "  scale = np.random.uniform(low=1.0-scale_range, high=1.0+scale_range)\n",
        "  rotate = np.random.uniform(low=-rotate_range, high=rotate_range)\n",
        "\n",
        "  x_axis_x = math.cos(rotate) * scale\n",
        "  x_axis_y = math.sin(rotate) * scale\n",
        "\n",
        "  y_axis_x = -math.sin(rotate) * scale\n",
        "  y_axis_y = math.cos(rotate) * scale\n",
        "\n",
        "  new_points = []\n",
        "  for point in points:\n",
        "    old_x = point[\"x\"]\n",
        "    old_y = point[\"y\"]\n",
        "    new_x = (x_axis_x * old_x) + (x_axis_y * old_y) + move_x\n",
        "    new_y = (y_axis_x * old_x) + (y_axis_y * old_y) + move_y\n",
        "    new_points.append({\"x\": new_x, \"y\": new_y})\n",
        "\n",
        "  return new_points\n",
        "\n",
        "def save_strokes_as_images(strokes, root_folder, width, height, augment_count):\n",
        "  ensure_empty_dir(root_folder)\n",
        "  labels = set()\n",
        "  for stroke in strokes:\n",
        "    labels.add(stroke[\"label\"].lower())\n",
        "  for label in labels:\n",
        "    label_path = Path(root_folder, label)\n",
        "    ensure_empty_dir(label_path)\n",
        "\n",
        "  label_counts = {}\n",
        "  for stroke in strokes:\n",
        "    points = stroke[\"strokePoints\"]\n",
        "    label = stroke[\"label\"].lower()\n",
        "    if label == \"\":\n",
        "      raise Exception(\"Missing label for %s:%d\" % (stroke[\"filename\"], stroke[\"index\"]))\n",
        "    if label not in label_counts:\n",
        "      label_counts[label] = 0\n",
        "    label_count = label_counts[label]\n",
        "    label_counts[label] += 1\n",
        "    raster = rasterize_stroke(points, X_RANGE, Y_RANGE, width, height)\n",
        "    image = PIL.Image.fromarray(raster)\n",
        "    image.save(Path(root_folder, label, str(label_count) + \".png\"))\n",
        "    for i in range(augment_count):\n",
        "      augmented_points = augment_points(points, 0.1, 0.1, 0.3)\n",
        "      raster = rasterize_stroke(augmented_points, X_RANGE, Y_RANGE, width, height)\n",
        "      image = PIL.Image.fromarray(raster)\n",
        "      image.save(Path(root_folder, label, str(label_count) + \"_a\" + str(i) + \".png\"))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2cmmhBwW9d_p"
      },
      "outputs": [],
      "source": [
        "IMAGE_WIDTH = 32\n",
        "IMAGE_HEIGHT = 32\n",
        "\n",
        "shuffled_strokes = strokes\n",
        "np.random.shuffle(shuffled_strokes)\n",
        "\n",
        "test_percentage = 10\n",
        "validation_percentage = 10\n",
        "train_percentage = 100 - (test_percentage + validation_percentage)\n",
        "\n",
        "test_count = math.floor((len(shuffled_strokes) * test_percentage) / 100)\n",
        "validation_count = math.floor((len(shuffled_strokes) * validation_percentage) / 100)\n",
        "test_strokes = shuffled_strokes[0:test_count]\n",
        "validation_strokes = shuffled_strokes[test_count:(test_count + validation_count)]\n",
        "train_strokes = shuffled_strokes[(test_count + validation_count):]\n",
        "\n",
        "save_strokes_as_images(test_strokes, \"test\", IMAGE_WIDTH, IMAGE_HEIGHT, 10)\n",
        "save_strokes_as_images(validation_strokes, \"validation\", IMAGE_WIDTH, IMAGE_HEIGHT, 0)\n",
        "save_strokes_as_images(train_strokes, \"train\", IMAGE_WIDTH, IMAGE_HEIGHT, 10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9-Ttfz7LlPil",
        "outputId": "f578e65e-6f2d-4a99-b23d-dca56ddb0393"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 91 files belonging to 26 classes.\n",
            "Found 8107 files belonging to 26 classes.\n"
          ]
        }
      ],
      "source": [
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.utils import image_dataset_from_directory\n",
        "\n",
        "validation_ds = image_dataset_from_directory(\n",
        "    directory='validation',\n",
        "    labels='inferred',\n",
        "    label_mode='categorical',\n",
        "    batch_size=32,\n",
        "    image_size=(IMAGE_WIDTH, IMAGE_HEIGHT)).prefetch(buffer_size=32)\n",
        "\n",
        "train_ds = image_dataset_from_directory(\n",
        "    directory='train',\n",
        "    labels='inferred',\n",
        "    label_mode='categorical',\n",
        "    batch_size=32,\n",
        "    image_size=(IMAGE_WIDTH, IMAGE_HEIGHT)).prefetch(buffer_size=32)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "Q_vUMVmQn400",
        "outputId": "9414698d-99d8-4cb9-bbd7-ea47198d1dbb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 9 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(10, 10))\n",
        "for images, labels in train_ds.take(1):\n",
        "  for i in range(9):\n",
        "    ax = plt.subplot(3, 3, i + 1)\n",
        "    plt.imshow(images[i].numpy().astype(\"uint8\"))\n",
        "    plt.axis(\"off\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "21qi3bLAo80t"
      },
      "outputs": [],
      "source": [
        "from keras import layers\n",
        "\n",
        "def make_model(input_shape, num_classes):\n",
        "    inputs = keras.Input(shape=input_shape)\n",
        "\n",
        "    # Entry block\n",
        "    x = layers.Rescaling(1.0 / 255)(inputs)\n",
        "    x = layers.Conv2D(16, 3, strides=2, padding=\"same\")(x)\n",
        "    x = layers.BatchNormalization()(x)\n",
        "    x = layers.Activation(\"relu\")(x)\n",
        "    x = layers.Dropout(0.5)(x)\n",
        "\n",
        "    x = layers.Conv2D(32, 3, strides=2, padding=\"same\")(x)\n",
        "    x = layers.BatchNormalization()(x)\n",
        "    x = layers.Activation(\"relu\")(x)\n",
        "    x = layers.Dropout(0.5)(x)\n",
        "\n",
        "    x = layers.Conv2D(64, 3, strides=2, padding=\"same\")(x)\n",
        "    x = layers.BatchNormalization()(x)\n",
        "    x = layers.Activation(\"relu\")(x)\n",
        "    x = layers.Dropout(0.5)(x)\n",
        "\n",
        "    x = layers.GlobalAveragePooling2D()(x)\n",
        "    activation = \"softmax\"\n",
        "    units = num_classes\n",
        "\n",
        "    x = layers.Dropout(0.5)(x)\n",
        "    outputs = layers.Dense(units, activation=activation)(x)\n",
        "    return keras.Model(inputs, outputs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7DjpCkt0qZiy",
        "outputId": "486f8ac7-d4ee-4505-b0f5-22cd8c936bd7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "model = make_model(input_shape=(IMAGE_WIDTH, IMAGE_HEIGHT, 3), num_classes=26)\n",
        "keras.utils.plot_model(model, show_shapes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "287Am2coqkQC",
        "outputId": "8b44096f-b5ec-4c84-dc5e-49eb66db1fa5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/90\n",
            "254/254 [==============================] - 18s 60ms/step - loss: 0.2678 - accuracy: 0.0442 - val_loss: 0.1628 - val_accuracy: 0.0879\n",
            "Epoch 2/90\n",
            "254/254 [==============================] - 15s 60ms/step - loss: 0.1698 - accuracy: 0.0656 - val_loss: 0.1597 - val_accuracy: 0.0879\n",
            "Epoch 3/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.1648 - accuracy: 0.0815 - val_loss: 0.1491 - val_accuracy: 0.1319\n",
            "Epoch 4/90\n",
            "254/254 [==============================] - 15s 60ms/step - loss: 0.1615 - accuracy: 0.0978 - val_loss: 0.1447 - val_accuracy: 0.1538\n",
            "Epoch 5/90\n",
            "254/254 [==============================] - 15s 60ms/step - loss: 0.1578 - accuracy: 0.1137 - val_loss: 0.1395 - val_accuracy: 0.2418\n",
            "Epoch 6/90\n",
            "254/254 [==============================] - 16s 60ms/step - loss: 0.1545 - accuracy: 0.1290 - val_loss: 0.1359 - val_accuracy: 0.3297\n",
            "Epoch 7/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.1505 - accuracy: 0.1576 - val_loss: 0.1302 - val_accuracy: 0.3846\n",
            "Epoch 8/90\n",
            "254/254 [==============================] - 16s 61ms/step - loss: 0.1461 - accuracy: 0.1713 - val_loss: 0.1273 - val_accuracy: 0.3626\n",
            "Epoch 9/90\n",
            "254/254 [==============================] - 17s 65ms/step - loss: 0.1419 - accuracy: 0.2061 - val_loss: 0.1244 - val_accuracy: 0.3956\n",
            "Epoch 10/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.1384 - accuracy: 0.2240 - val_loss: 0.1187 - val_accuracy: 0.5385\n",
            "Epoch 11/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.1353 - accuracy: 0.2530 - val_loss: 0.1197 - val_accuracy: 0.4505\n",
            "Epoch 12/90\n",
            "254/254 [==============================] - 15s 60ms/step - loss: 0.1322 - accuracy: 0.2710 - val_loss: 0.1099 - val_accuracy: 0.6044\n",
            "Epoch 13/90\n",
            "254/254 [==============================] - 16s 61ms/step - loss: 0.1290 - accuracy: 0.2906 - val_loss: 0.1103 - val_accuracy: 0.5165\n",
            "Epoch 14/90\n",
            "254/254 [==============================] - 16s 61ms/step - loss: 0.1258 - accuracy: 0.3174 - val_loss: 0.1561 - val_accuracy: 0.0989\n",
            "Epoch 15/90\n",
            "254/254 [==============================] - 16s 60ms/step - loss: 0.1239 - accuracy: 0.3407 - val_loss: 0.0966 - val_accuracy: 0.6923\n",
            "Epoch 16/90\n",
            "254/254 [==============================] - 16s 60ms/step - loss: 0.1224 - accuracy: 0.3465 - val_loss: 0.0990 - val_accuracy: 0.6484\n",
            "Epoch 17/90\n",
            "254/254 [==============================] - 16s 60ms/step - loss: 0.1199 - accuracy: 0.3701 - val_loss: 0.0934 - val_accuracy: 0.7143\n",
            "Epoch 18/90\n",
            "254/254 [==============================] - 18s 69ms/step - loss: 0.1187 - accuracy: 0.3836 - val_loss: 0.0892 - val_accuracy: 0.7033\n",
            "Epoch 19/90\n",
            "254/254 [==============================] - 16s 61ms/step - loss: 0.1164 - accuracy: 0.3899 - val_loss: 0.1507 - val_accuracy: 0.1099\n",
            "Epoch 20/90\n",
            "254/254 [==============================] - 15s 57ms/step - loss: 0.1158 - accuracy: 0.3945 - val_loss: 0.0899 - val_accuracy: 0.7143\n",
            "Epoch 21/90\n",
            "254/254 [==============================] - 18s 67ms/step - loss: 0.1147 - accuracy: 0.4110 - val_loss: 0.1505 - val_accuracy: 0.1538\n",
            "Epoch 22/90\n",
            "254/254 [==============================] - 15s 57ms/step - loss: 0.1128 - accuracy: 0.4269 - val_loss: 0.1103 - val_accuracy: 0.3626\n",
            "Epoch 23/90\n",
            "254/254 [==============================] - 16s 63ms/step - loss: 0.1125 - accuracy: 0.4204 - val_loss: 0.0802 - val_accuracy: 0.7033\n",
            "Epoch 24/90\n",
            "254/254 [==============================] - 16s 62ms/step - loss: 0.1102 - accuracy: 0.4368 - val_loss: 0.0790 - val_accuracy: 0.7582\n",
            "Epoch 25/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.1101 - accuracy: 0.4412 - val_loss: 0.0834 - val_accuracy: 0.7692\n",
            "Epoch 26/90\n",
            "254/254 [==============================] - 16s 63ms/step - loss: 0.1096 - accuracy: 0.4402 - val_loss: 0.0854 - val_accuracy: 0.7033\n",
            "Epoch 27/90\n",
            "254/254 [==============================] - 17s 65ms/step - loss: 0.1087 - accuracy: 0.4439 - val_loss: 0.0836 - val_accuracy: 0.7473\n",
            "Epoch 28/90\n",
            "254/254 [==============================] - 19s 73ms/step - loss: 0.1078 - accuracy: 0.4527 - val_loss: 0.0732 - val_accuracy: 0.7692\n",
            "Epoch 29/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.1061 - accuracy: 0.4642 - val_loss: 0.0786 - val_accuracy: 0.8352\n",
            "Epoch 30/90\n",
            "254/254 [==============================] - 15s 58ms/step - loss: 0.1059 - accuracy: 0.4721 - val_loss: 0.1473 - val_accuracy: 0.2527\n",
            "Epoch 31/90\n",
            "254/254 [==============================] - 15s 57ms/step - loss: 0.1053 - accuracy: 0.4730 - val_loss: 0.0710 - val_accuracy: 0.9011\n",
            "Epoch 32/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.1052 - accuracy: 0.4749 - val_loss: 0.0717 - val_accuracy: 0.8462\n",
            "Epoch 33/90\n",
            "254/254 [==============================] - 15s 58ms/step - loss: 0.1041 - accuracy: 0.4864 - val_loss: 0.0674 - val_accuracy: 0.8462\n",
            "Epoch 34/90\n",
            "254/254 [==============================] - 15s 58ms/step - loss: 0.1032 - accuracy: 0.4904 - val_loss: 0.0687 - val_accuracy: 0.8791\n",
            "Epoch 35/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.1023 - accuracy: 0.4925 - val_loss: 0.0715 - val_accuracy: 0.9011\n",
            "Epoch 36/90\n",
            "254/254 [==============================] - 15s 58ms/step - loss: 0.1015 - accuracy: 0.5050 - val_loss: 0.0854 - val_accuracy: 0.7143\n",
            "Epoch 37/90\n",
            "254/254 [==============================] - 16s 62ms/step - loss: 0.1019 - accuracy: 0.4980 - val_loss: 0.0667 - val_accuracy: 0.9231\n",
            "Epoch 38/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.1013 - accuracy: 0.4990 - val_loss: 0.0698 - val_accuracy: 0.8901\n",
            "Epoch 39/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.1014 - accuracy: 0.4999 - val_loss: 0.0761 - val_accuracy: 0.7692\n",
            "Epoch 40/90\n",
            "254/254 [==============================] - 15s 58ms/step - loss: 0.1000 - accuracy: 0.5144 - val_loss: 0.0648 - val_accuracy: 0.9231\n",
            "Epoch 41/90\n",
            "254/254 [==============================] - 15s 58ms/step - loss: 0.0993 - accuracy: 0.5170 - val_loss: 0.0625 - val_accuracy: 0.9231\n",
            "Epoch 42/90\n",
            "254/254 [==============================] - 15s 58ms/step - loss: 0.0995 - accuracy: 0.5200 - val_loss: 0.0623 - val_accuracy: 0.9231\n",
            "Epoch 43/90\n",
            "254/254 [==============================] - 15s 57ms/step - loss: 0.0994 - accuracy: 0.5073 - val_loss: 0.0611 - val_accuracy: 0.8791\n",
            "Epoch 44/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0981 - accuracy: 0.5288 - val_loss: 0.0631 - val_accuracy: 0.9341\n",
            "Epoch 45/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0989 - accuracy: 0.5289 - val_loss: 0.0845 - val_accuracy: 0.6813\n",
            "Epoch 46/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0976 - accuracy: 0.5292 - val_loss: 0.1041 - val_accuracy: 0.4615\n",
            "Epoch 47/90\n",
            "254/254 [==============================] - 17s 66ms/step - loss: 0.0976 - accuracy: 0.5233 - val_loss: 0.0686 - val_accuracy: 0.9121\n",
            "Epoch 48/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0974 - accuracy: 0.5309 - val_loss: 0.0803 - val_accuracy: 0.5824\n",
            "Epoch 49/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0968 - accuracy: 0.5406 - val_loss: 0.0602 - val_accuracy: 0.9011\n",
            "Epoch 50/90\n",
            "254/254 [==============================] - 16s 60ms/step - loss: 0.0966 - accuracy: 0.5323 - val_loss: 0.0585 - val_accuracy: 0.9341\n",
            "Epoch 51/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0963 - accuracy: 0.5353 - val_loss: 0.0591 - val_accuracy: 0.8901\n",
            "Epoch 52/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0963 - accuracy: 0.5382 - val_loss: 0.0658 - val_accuracy: 0.8901\n",
            "Epoch 53/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0961 - accuracy: 0.5398 - val_loss: 0.0580 - val_accuracy: 0.9451\n",
            "Epoch 54/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0956 - accuracy: 0.5473 - val_loss: 0.0568 - val_accuracy: 0.9341\n",
            "Epoch 55/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0954 - accuracy: 0.5515 - val_loss: 0.0589 - val_accuracy: 0.9011\n",
            "Epoch 56/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0951 - accuracy: 0.5457 - val_loss: 0.0606 - val_accuracy: 0.9341\n",
            "Epoch 57/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0949 - accuracy: 0.5559 - val_loss: 0.0670 - val_accuracy: 0.8681\n",
            "Epoch 58/90\n",
            "254/254 [==============================] - 18s 70ms/step - loss: 0.0950 - accuracy: 0.5522 - val_loss: 0.0550 - val_accuracy: 0.9231\n",
            "Epoch 59/90\n",
            "254/254 [==============================] - 15s 60ms/step - loss: 0.0946 - accuracy: 0.5510 - val_loss: 0.0697 - val_accuracy: 0.8571\n",
            "Epoch 60/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0941 - accuracy: 0.5606 - val_loss: 0.0598 - val_accuracy: 0.9121\n",
            "Epoch 61/90\n",
            "254/254 [==============================] - 15s 60ms/step - loss: 0.0942 - accuracy: 0.5530 - val_loss: 0.0654 - val_accuracy: 0.8791\n",
            "Epoch 62/90\n",
            "254/254 [==============================] - 15s 60ms/step - loss: 0.0935 - accuracy: 0.5609 - val_loss: 0.0539 - val_accuracy: 0.9231\n",
            "Epoch 63/90\n",
            "254/254 [==============================] - 15s 60ms/step - loss: 0.0932 - accuracy: 0.5598 - val_loss: 0.0526 - val_accuracy: 0.9011\n",
            "Epoch 64/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0932 - accuracy: 0.5630 - val_loss: 0.0631 - val_accuracy: 0.8681\n",
            "Epoch 65/90\n",
            "254/254 [==============================] - 15s 58ms/step - loss: 0.0927 - accuracy: 0.5605 - val_loss: 0.0532 - val_accuracy: 0.9341\n",
            "Epoch 66/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0924 - accuracy: 0.5694 - val_loss: 0.0550 - val_accuracy: 0.8901\n",
            "Epoch 67/90\n",
            "254/254 [==============================] - 15s 60ms/step - loss: 0.0921 - accuracy: 0.5664 - val_loss: 0.0705 - val_accuracy: 0.8352\n",
            "Epoch 68/90\n",
            "254/254 [==============================] - 18s 69ms/step - loss: 0.0918 - accuracy: 0.5725 - val_loss: 0.0554 - val_accuracy: 0.9011\n",
            "Epoch 69/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0920 - accuracy: 0.5665 - val_loss: 0.0715 - val_accuracy: 0.7802\n",
            "Epoch 70/90\n",
            "254/254 [==============================] - 16s 62ms/step - loss: 0.0927 - accuracy: 0.5658 - val_loss: 0.0526 - val_accuracy: 0.9231\n",
            "Epoch 71/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0914 - accuracy: 0.5760 - val_loss: 0.0550 - val_accuracy: 0.9011\n",
            "Epoch 72/90\n",
            "254/254 [==============================] - 16s 60ms/step - loss: 0.0919 - accuracy: 0.5719 - val_loss: 0.0500 - val_accuracy: 0.9011\n",
            "Epoch 73/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0919 - accuracy: 0.5712 - val_loss: 0.0626 - val_accuracy: 0.9011\n",
            "Epoch 74/90\n",
            "254/254 [==============================] - 15s 58ms/step - loss: 0.0904 - accuracy: 0.5869 - val_loss: 0.0648 - val_accuracy: 0.8681\n",
            "Epoch 75/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0907 - accuracy: 0.5779 - val_loss: 0.0511 - val_accuracy: 0.9231\n",
            "Epoch 76/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0917 - accuracy: 0.5775 - val_loss: 0.0513 - val_accuracy: 0.9341\n",
            "Epoch 77/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0911 - accuracy: 0.5751 - val_loss: 0.0494 - val_accuracy: 0.9121\n",
            "Epoch 78/90\n",
            "254/254 [==============================] - 18s 68ms/step - loss: 0.0911 - accuracy: 0.5743 - val_loss: 0.0548 - val_accuracy: 0.9341\n",
            "Epoch 79/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0907 - accuracy: 0.5832 - val_loss: 0.0675 - val_accuracy: 0.8132\n",
            "Epoch 80/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0905 - accuracy: 0.5817 - val_loss: 0.0515 - val_accuracy: 0.9231\n",
            "Epoch 81/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0907 - accuracy: 0.5884 - val_loss: 0.0559 - val_accuracy: 0.9121\n",
            "Epoch 82/90\n",
            "254/254 [==============================] - 15s 58ms/step - loss: 0.0897 - accuracy: 0.5857 - val_loss: 0.0510 - val_accuracy: 0.9231\n",
            "Epoch 83/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0907 - accuracy: 0.5754 - val_loss: 0.0480 - val_accuracy: 0.9121\n",
            "Epoch 84/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0899 - accuracy: 0.5925 - val_loss: 0.0523 - val_accuracy: 0.9231\n",
            "Epoch 85/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0911 - accuracy: 0.5760 - val_loss: 0.0517 - val_accuracy: 0.9451\n",
            "Epoch 86/90\n",
            "254/254 [==============================] - 15s 60ms/step - loss: 0.0898 - accuracy: 0.5859 - val_loss: 0.0516 - val_accuracy: 0.8901\n",
            "Epoch 87/90\n",
            "254/254 [==============================] - 15s 59ms/step - loss: 0.0890 - accuracy: 0.5921 - val_loss: 0.0487 - val_accuracy: 0.9231\n",
            "Epoch 88/90\n",
            "254/254 [==============================] - 17s 67ms/step - loss: 0.0886 - accuracy: 0.5941 - val_loss: 0.0502 - val_accuracy: 0.9341\n",
            "Epoch 89/90\n",
            "254/254 [==============================] - 15s 60ms/step - loss: 0.0896 - accuracy: 0.5831 - val_loss: 0.0490 - val_accuracy: 0.9451\n",
            "Epoch 90/90\n",
            "254/254 [==============================] - 15s 60ms/step - loss: 0.0889 - accuracy: 0.5818 - val_loss: 0.0568 - val_accuracy: 0.8791\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f9c92f8feb0>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "epochs = 90\n",
        "\n",
        "callbacks = [\n",
        "    keras.callbacks.ModelCheckpoint(\"checkpoints/save_at_{epoch}.h5\"),\n",
        "]\n",
        "model.compile(\n",
        "    optimizer=keras.optimizers.Adam(1e-3),\n",
        "    loss=\"binary_crossentropy\",\n",
        "    metrics=[\"accuracy\"],\n",
        ")\n",
        "model.fit(\n",
        "    train_ds, epochs=epochs, callbacks=callbacks, validation_data=validation_ds,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model.evaluate(train_ds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UMUI33IpAw4m",
        "outputId": "efe1e861-1026-4e43-fab3-9a968a5a9b80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "254/254 [==============================] - 13s 50ms/step - loss: 0.0621 - accuracy: 0.8426\n",
            "[0.06213385984301567, 0.8426051735877991]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model.evaluate(validation_ds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VK24dj5ISXDi",
        "outputId": "109fb057-9be4-4b8a-f737-1f88b1d627db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 0s 11ms/step - loss: 0.0568 - accuracy: 0.8791\n",
            "[0.05682911351323128, 0.8791208863258362]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ocE3kudZq24U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b817179-bc14-4da5-860f-05606c9e4343"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 1s 924ms/step\n",
            "c 0.47918314\n"
          ]
        }
      ],
      "source": [
        "import string\n",
        "\n",
        "def predict_image(model, filename):\n",
        "  img = keras.preprocessing.image.load_img(filename, target_size=(IMAGE_WIDTH, IMAGE_HEIGHT))\n",
        "  img_array = keras.preprocessing.image.img_to_array(img)\n",
        "  img_array = tf.expand_dims(img_array, 0)  # Create batch axis\n",
        "  predictions = model.predict(img_array).flatten()\n",
        "  predicted_label_index = np.argmax(predictions)\n",
        "  predicted_score = predictions[predicted_label_index]\n",
        "  letter = string.ascii_lowercase[predicted_label_index]\n",
        "  return (letter, predicted_score)\n",
        "  \n",
        "index, score = predict_image(model, \"test/c/1.png\")\n",
        "\n",
        "print(index, score)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MYyLaqOYtxTH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e12cba88-56c6-44ef-d50b-8078a1dd83ae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "1/1 [==============================] - 0s 25ms/step\n",
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            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 47ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "a expected, g found with score 0.878740\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "99.6% correct (N=241, 760 unknown)\n"
          ]
        }
      ],
      "source": [
        "from IPython.display import Image, display\n",
        "\n",
        "SCORE_THRESHOLD = 0.75\n",
        "\n",
        "correct_count = 0\n",
        "wrong_count = 0\n",
        "discarded_count = 0\n",
        "for label_dir in glob.glob(\"test/*\"):\n",
        "  label = label_dir.split(\"/\")[-1]  # extract the letter from the directory name\n",
        "  # label = int(label_dir.replace(\"test/\", \"\"))\n",
        "  for filename in glob.glob(label_dir + \"/*.png\"):\n",
        "    index, score = predict_image(model, filename)\n",
        "    if score < SCORE_THRESHOLD:\n",
        "      discarded_count += 1\n",
        "      continue\n",
        "    if index == label:\n",
        "      correct_count += 1\n",
        "    else:\n",
        "      wrong_count += 1\n",
        "      print(\"%s expected, %s found with score %f\" % (label, index, score))\n",
        "      display(Image(filename=filename))\n",
        "\n",
        "correct_percentage = (correct_count / (correct_count + wrong_count)) * 100\n",
        "print(\"%.1f%% correct (N=%d, %d unknown)\" % (correct_percentage, (correct_count + wrong_count), discarded_count))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d26wGJn0t20g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f29899f6-2705-49de-defe-30964cd842df"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
          ]
        }
      ],
      "source": [
        "model.save(SAVED_MODEL_FILENAME)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ki3E7lM_Kr0C"
      },
      "outputs": [],
      "source": [
        "#!curl -L https://storage.googleapis.com/download.tensorflow.org/models/tflite/micro/virtual_notepad_saved_model_2021_01_02.tgz -o saved_model.tgz\n",
        "#!tar -xzf saved_model.tgz"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t-hU8aU24gbL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "505683df-c5ec-4805-b52d-ac922f158fd4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32392"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "converter = tf.lite.TFLiteConverter.from_saved_model(SAVED_MODEL_FILENAME)\n",
        "model_no_quant_tflite = converter.convert()\n",
        "\n",
        "# Save the model to disk\n",
        "open(FLOAT_TFL_MODEL_FILENAME, \"wb\").write(model_no_quant_tflite)\n",
        "\n",
        "def representative_dataset():\n",
        "  for filename in glob.glob(\"test/*/*.png\"):\n",
        "    img = keras.preprocessing.image.load_img(filename, target_size=(IMAGE_WIDTH, IMAGE_HEIGHT))\n",
        "    img_array = keras.preprocessing.image.img_to_array(img)\n",
        "    img_array = tf.expand_dims(img_array, 0)  # Create batch axis      for images, labels in train_ds.take(1):\n",
        "    yield([img_array])\n",
        "# Set the optimization flag.\n",
        "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
        "# Enforce integer only quantization\n",
        "converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]\n",
        "converter.inference_input_type = tf.int8\n",
        "converter.inference_output_type = tf.int8\n",
        "# Provide a representative dataset to ensure we quantize correctly.\n",
        "converter.representative_dataset = representative_dataset\n",
        "model_tflite = converter.convert()\n",
        "\n",
        "# Save the model to disk\n",
        "open(QUANTIZED_TFL_MODEL_FILENAME, \"wb\").write(model_tflite)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w5QZTfwRLFAi"
      },
      "outputs": [],
      "source": [
        "def predict_tflite(tflite_model, filename):\n",
        "  img = keras.preprocessing.image.load_img(filename, target_size=(IMAGE_WIDTH, IMAGE_HEIGHT))\n",
        "  img_array = keras.preprocessing.image.img_to_array(img)\n",
        "  img_array = tf.expand_dims(img_array, 0)\n",
        "\n",
        "  # Initialize the TFLite interpreter\n",
        "  interpreter = tf.lite.Interpreter(model_content=tflite_model)\n",
        "  interpreter.allocate_tensors()\n",
        "\n",
        "  input_details = interpreter.get_input_details()[0]\n",
        "  output_details = interpreter.get_output_details()[0]\n",
        "\n",
        "  # If required, quantize the input layer (from float to integer)\n",
        "  input_scale, input_zero_point = input_details[\"quantization\"]\n",
        "  if (input_scale, input_zero_point) != (0.0, 0):\n",
        "    img_array = np.multiply(img_array, 1.0 / input_scale) + input_zero_point\n",
        "    img_array = img_array.astype(input_details[\"dtype\"])\n",
        "  \n",
        "  # Invoke the interpreter\n",
        "  interpreter.set_tensor(input_details[\"index\"], img_array)\n",
        "  interpreter.invoke()\n",
        "  pred = interpreter.get_tensor(output_details[\"index\"])[0]\n",
        "  \n",
        "  # If required, dequantized the output layer (from integer to float)\n",
        "  output_scale, output_zero_point = output_details[\"quantization\"]\n",
        "  if (output_scale, output_zero_point) != (0.0, 0):\n",
        "    pred = pred.astype(np.float32)\n",
        "    pred = np.multiply((pred - output_zero_point), output_scale)\n",
        "  \n",
        "  predicted_label_index = np.argmax(pred)\n",
        "  predicted_score = pred[predicted_label_index]\n",
        "  letter = string.ascii_lowercase[predicted_label_index]\n",
        "\n",
        "  return (letter, predicted_score)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vtee_WxPMgup",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d5b51397-a875-497c-ba84-bf317db8ff34"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('g', 0.51990825)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "predict_tflite(model_no_quant_tflite, \"test/a/2.png\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0rp0LirfN9vB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "949b0402-670e-4fde-d9e0-ac7877d13768"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('g', 0.5859375)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "predict_tflite(model_tflite, \"test/a/2.png\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jdNgTO19PRqO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "f9052089-7af0-4dde-d1f0-bc9c0643b695"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "a expected, g found with score 0.894531\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "99.6% correct (N=231, 770 unknown)\n"
          ]
        }
      ],
      "source": [
        "from IPython.display import Image, display\n",
        "\n",
        "correct_count = 0\n",
        "wrong_count = 0\n",
        "discarded_count = 0\n",
        "for label_dir in glob.glob(\"test/*\"):\n",
        "  label = label_dir.split(\"/\")[-1]  # extract the letter from the directory name\n",
        "  # label = int(label_dir.replace(\"test/\", \"\"))\n",
        "  for filename in glob.glob(label_dir + \"/*.png\"):\n",
        "    index, score = predict_tflite(model_tflite, filename)\n",
        "    if score < 0.75:\n",
        "      discarded_count += 1\n",
        "      continue\n",
        "    if index == label:\n",
        "      correct_count += 1\n",
        "    else:\n",
        "      wrong_count += 1\n",
        "      print(\"%s expected, %s found with score %f\" % (label, index, score))\n",
        "      display(Image(filename=filename))\n",
        "\n",
        "correct_percentage = (correct_count / (correct_count + wrong_count)) * 100\n",
        "\n",
        "print(\"%.1f%% correct (N=%d, %d unknown)\" % (correct_percentage, (correct_count + wrong_count), discarded_count))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NTjGMU8BPpoz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "ec0c7363-a408-43ad-d305-9ebae3a53801"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                    Size                           \n",
              "Model                                                              \n",
              "TensorFlow                  702510 bytes                           \n",
              "TensorFlow Lite            104672 bytes   (reduced by 597838 bytes)\n",
              "TensorFlow Lite Quantized    32392 bytes   (reduced by 72280 bytes)"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1e813cb2-9fa4-4567-9910-983ccfdbdf18\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Size</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>TensorFlow</th>\n",
              "      <td>702510 bytes</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TensorFlow Lite</th>\n",
              "      <td>104672 bytes</td>\n",
              "      <td>(reduced by 597838 bytes)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TensorFlow Lite Quantized</th>\n",
              "      <td>32392 bytes</td>\n",
              "      <td>(reduced by 72280 bytes)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1e813cb2-9fa4-4567-9910-983ccfdbdf18')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1e813cb2-9fa4-4567-9910-983ccfdbdf18 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1e813cb2-9fa4-4567-9910-983ccfdbdf18');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "\n",
        "def get_dir_size(dir):\n",
        "  size = 0\n",
        "  for f in os.scandir(dir):\n",
        "    if f.is_file():\n",
        "      size += f.stat().st_size\n",
        "    elif f.is_dir():\n",
        "      size += get_dir_size(f.path)\n",
        "  return size\n",
        "\n",
        "# Calculate size\n",
        "size_tf = get_dir_size(SAVED_MODEL_FILENAME)\n",
        "size_no_quant_tflite = os.path.getsize(FLOAT_TFL_MODEL_FILENAME)\n",
        "size_tflite = os.path.getsize(QUANTIZED_TFL_MODEL_FILENAME)\n",
        "\n",
        "# Compare size\n",
        "pd.DataFrame.from_records(\n",
        "    [[\"TensorFlow\", f\"{size_tf} bytes\", \"\"],\n",
        "     [\"TensorFlow Lite\", f\"{size_no_quant_tflite} bytes \", f\"(reduced by {size_tf - size_no_quant_tflite} bytes)\"],\n",
        "     [\"TensorFlow Lite Quantized\", f\"{size_tflite} bytes\", f\"(reduced by {size_no_quant_tflite - size_tflite} bytes)\"]],\n",
        "     columns = [\"Model\", \"Size\", \"\"], index=\"Model\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mrvnEJLfR8KU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "481062e4-d094-4d92-f42a-1d3f74b29f7a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r0% [Working]\r            \rGet:1 http://security.ubuntu.com/ubuntu focal-security InRelease [114 kB]\n",
            "\r0% [Connecting to archive.ubuntu.com] [1 InRelease 14.2 kB/114 kB 12%] [Connect\r                                                                               \rGet:2 https://cloud.r-project.org/bin/linux/ubuntu focal-cran40/ InRelease [3,622 B]\n",
            "\r0% [Connecting to archive.ubuntu.com] [1 InRelease 20.0 kB/114 kB 18%] [2 InRel\r0% [Connecting to archive.ubuntu.com] [1 InRelease 54.7 kB/114 kB 48%] [Connect\r0% [Connecting to archive.ubuntu.com (91.189.91.39)] [Connecting to ppa.launchp\r                                                                               \rHit:3 http://archive.ubuntu.com/ubuntu focal InRelease\n",
            "\r                                                                               \r0% [Waiting for headers] [Waiting for headers]\r                                              \rHit:4 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64  InRelease\n",
            "Get:5 http://archive.ubuntu.com/ubuntu focal-updates InRelease [114 kB]\n",
            "Get:6 http://archive.ubuntu.com/ubuntu focal-backports InRelease [108 kB]\n",
            "Hit:7 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu focal InRelease\n",
            "Get:8 https://cloud.r-project.org/bin/linux/ubuntu focal-cran40/ Packages [77.6 kB]\n",
            "Hit:9 http://ppa.launchpad.net/cran/libgit2/ubuntu focal InRelease\n",
            "Hit:10 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu focal InRelease\n",
            "Hit:11 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu focal InRelease\n",
            "Hit:12 http://ppa.launchpad.net/ubuntugis/ppa/ubuntu focal InRelease\n",
            "Get:13 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 Packages [1,334 kB]\n",
            "Get:14 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 Packages [3,150 kB]\n",
            "Fetched 4,901 kB in 2s (2,776 kB/s)\n",
            "Reading package lists... Done\n"
          ]
        }
      ],
      "source": [
        "# Install xxd if it is not available\n",
        "!apt-get update && apt-get -qq install xxd\n",
        "# Convert to a C source file, i.e, a TensorFlow Lite for Microcontrollers model\n",
        "!xxd -i {QUANTIZED_TFL_MODEL_FILENAME} > {TFL_CC_MODEL_FILENAME}\n",
        "# Update variable names\n",
        "REPLACE_TEXT = QUANTIZED_TFL_MODEL_FILENAME.replace('/', '_').replace('.', '_')\n",
        "!sed -i 's/'{REPLACE_TEXT}'/g_virtual_notepad_model_data/g' {TFL_CC_MODEL_FILENAME}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oazLUtBqWzdJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "addd5751-7ca3-4b43-f61d-cb9b0572cf0c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  0x2c, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,\n",
            "  0xd0, 0xff, 0xff, 0xff, 0x19, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x19, 0xe0, 0xff, 0xff, 0xff, 0x09, 0x00, 0x00, 0x00,\n",
            "  0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0xf0, 0xff, 0xff, 0xff,\n",
            "  0x28, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x28,\n",
            "  0x0c, 0x00, 0x10, 0x00, 0x0f, 0x00, 0x00, 0x00, 0x08, 0x00, 0x04, 0x00,\n",
            "  0x0c, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x03\n",
            "};\n",
            "unsigned int g_virtual_notepad_model_data_len = 32392;\n"
          ]
        }
      ],
      "source": [
        "# Print the C source file\n",
        "!tail {TFL_CC_MODEL_FILENAME}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VqN2F42PW-uv"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}