EloquentTinyML_ESP32_Ejemplo_01.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "EloquentTinyML-ESP32-Ejemplo-01.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "2o7yBJGt6MyK"
      },
      "outputs": [],
      "source": [
        "%tensorflow_version 2.x"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras"
      ],
      "metadata": {
        "id": "cZMxmxOg61sF"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Cargar dataset\n",
        "df = pd.read_csv('sensor_dataset.csv')\n",
        "# Obtener caracteristicas de entrenamiento y etiquetas\n",
        "x_train = df.iloc[:, [0]]\n",
        "y_train = df.iloc[:, [1]]"
      ],
      "metadata": {
        "id": "EIdiEqK39H6p"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Crear modelo \n",
        "\n",
        "model = keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape=[1]),\n",
        "    keras.layers.Dense(64, activation='relu'),\n",
        "    keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer='rmsprop',             #also try adam\n",
        "              metrics=['accuracy'])\n"
      ],
      "metadata": {
        "id": "G0Kt-F4N9WMM"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Entrenar modelo\n",
        "\n",
        "history = model.fit(x_train, y_train,epochs=1000)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R-DYTTZD9eg6",
        "outputId": "c304556b-c121-4e65-a376-b48e2b9d0a9f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/1000\n",
            "4/4 [==============================] - 1s 4ms/step - loss: 1.1665 - accuracy: 0.4949\n",
            "Epoch 2/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.6654 - accuracy: 0.5051\n",
            "Epoch 3/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.5991 - accuracy: 0.5152\n",
            "Epoch 4/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6853 - accuracy: 0.5455\n",
            "Epoch 5/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6217 - accuracy: 0.5253\n",
            "Epoch 6/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.5907 - accuracy: 0.5354\n",
            "Epoch 7/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6413 - accuracy: 0.5455\n",
            "Epoch 8/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6061 - accuracy: 0.6869\n",
            "Epoch 9/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.5916 - accuracy: 0.5657\n",
            "Epoch 10/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.5712 - accuracy: 0.5354\n",
            "Epoch 11/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.6288 - accuracy: 0.5556\n",
            "Epoch 12/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.5754 - accuracy: 0.6263\n",
            "Epoch 13/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.5881 - accuracy: 0.5455\n",
            "Epoch 14/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.5892 - accuracy: 0.6465\n",
            "Epoch 15/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.6126 - accuracy: 0.6566\n",
            "Epoch 16/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.5558 - accuracy: 0.5960\n",
            "Epoch 17/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.5678 - accuracy: 0.6263\n",
            "Epoch 18/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.5599 - accuracy: 0.7071\n",
            "Epoch 19/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.5612 - accuracy: 0.7273\n",
            "Epoch 20/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.5535 - accuracy: 0.7475\n",
            "Epoch 21/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.5399 - accuracy: 0.5960\n",
            "Epoch 22/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.6139 - accuracy: 0.5152\n",
            "Epoch 23/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.5316 - accuracy: 0.6667\n",
            "Epoch 24/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.5788 - accuracy: 0.6465\n",
            "Epoch 25/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.5289 - accuracy: 0.6162\n",
            "Epoch 26/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.5266 - accuracy: 0.6162\n",
            "Epoch 27/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.5312 - accuracy: 0.6667\n",
            "Epoch 28/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.5253 - accuracy: 0.7576\n",
            "Epoch 29/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.5266 - accuracy: 0.6768\n",
            "Epoch 30/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.5091 - accuracy: 0.6970\n",
            "Epoch 31/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.5203 - accuracy: 0.6465\n",
            "Epoch 32/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.4986 - accuracy: 0.6566\n",
            "Epoch 33/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.5983 - accuracy: 0.6667\n",
            "Epoch 34/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.5006 - accuracy: 0.7071\n",
            "Epoch 35/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.4991 - accuracy: 0.6667\n",
            "Epoch 36/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.4995 - accuracy: 0.6667\n",
            "Epoch 37/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.5463 - accuracy: 0.6263\n",
            "Epoch 38/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.5240 - accuracy: 0.6465\n",
            "Epoch 39/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.4936 - accuracy: 0.8485\n",
            "Epoch 40/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.4992 - accuracy: 0.7273\n",
            "Epoch 41/1000\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.4755 - accuracy: 0.6869\n",
            "Epoch 42/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.4992 - accuracy: 0.8081\n",
            "Epoch 43/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.5124 - accuracy: 0.6768\n",
            "Epoch 44/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.4716 - accuracy: 0.7273\n",
            "Epoch 45/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.4702 - accuracy: 0.7778\n",
            "Epoch 46/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.5429 - accuracy: 0.7374\n",
            "Epoch 47/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.4736 - accuracy: 0.7980\n",
            "Epoch 48/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.4911 - accuracy: 0.8384\n",
            "Epoch 49/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.4741 - accuracy: 0.7273\n",
            "Epoch 50/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.4968 - accuracy: 0.6970\n",
            "Epoch 51/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.4572 - accuracy: 0.7475\n",
            "Epoch 52/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.4729 - accuracy: 0.8283\n",
            "Epoch 53/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.4461 - accuracy: 0.8283\n",
            "Epoch 54/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.4894 - accuracy: 0.7879\n",
            "Epoch 55/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.4436 - accuracy: 0.8081\n",
            "Epoch 56/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.4516 - accuracy: 0.7879\n",
            "Epoch 57/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.4677 - accuracy: 0.7071\n",
            "Epoch 58/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.4487 - accuracy: 0.7879\n",
            "Epoch 59/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.4362 - accuracy: 0.8485\n",
            "Epoch 60/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.5718 - accuracy: 0.6768\n",
            "Epoch 61/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.4352 - accuracy: 0.8788\n",
            "Epoch 62/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.4430 - accuracy: 0.8990\n",
            "Epoch 63/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.4228 - accuracy: 0.8485\n",
            "Epoch 64/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.4196 - accuracy: 0.8586\n",
            "Epoch 65/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.4314 - accuracy: 0.8889\n",
            "Epoch 66/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.4247 - accuracy: 0.8384\n",
            "Epoch 67/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.4535 - accuracy: 0.8182\n",
            "Epoch 68/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.4263 - accuracy: 0.8081\n",
            "Epoch 69/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.4075 - accuracy: 0.8788\n",
            "Epoch 70/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.4400 - accuracy: 0.8485\n",
            "Epoch 71/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.4137 - accuracy: 0.8283\n",
            "Epoch 72/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.4082 - accuracy: 0.9192\n",
            "Epoch 73/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.4092 - accuracy: 0.8889\n",
            "Epoch 74/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.4270 - accuracy: 0.7879\n",
            "Epoch 75/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.4178 - accuracy: 0.8586\n",
            "Epoch 76/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.4260 - accuracy: 0.7677\n",
            "Epoch 77/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.3887 - accuracy: 0.8586\n",
            "Epoch 78/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.4523 - accuracy: 0.8283\n",
            "Epoch 79/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.4252 - accuracy: 0.7778\n",
            "Epoch 80/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.4154 - accuracy: 0.7677\n",
            "Epoch 81/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.3923 - accuracy: 0.9596\n",
            "Epoch 82/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.3934 - accuracy: 0.8485\n",
            "Epoch 83/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.3924 - accuracy: 0.8990\n",
            "Epoch 84/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.3771 - accuracy: 0.8889\n",
            "Epoch 85/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.3761 - accuracy: 0.8485\n",
            "Epoch 86/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.3714 - accuracy: 0.8586\n",
            "Epoch 87/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.4193 - accuracy: 0.8384\n",
            "Epoch 88/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.4121 - accuracy: 0.8788\n",
            "Epoch 89/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.4025 - accuracy: 0.8182\n",
            "Epoch 90/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.3826 - accuracy: 0.8586\n",
            "Epoch 91/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.3706 - accuracy: 0.8990\n",
            "Epoch 92/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.4028 - accuracy: 0.8687\n",
            "Epoch 93/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.4014 - accuracy: 0.8384\n",
            "Epoch 94/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.3620 - accuracy: 0.8788\n",
            "Epoch 95/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.3583 - accuracy: 0.8990\n",
            "Epoch 96/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.3667 - accuracy: 0.8586\n",
            "Epoch 97/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.3752 - accuracy: 0.8283\n",
            "Epoch 98/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.3506 - accuracy: 0.9596\n",
            "Epoch 99/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.3552 - accuracy: 0.8586\n",
            "Epoch 100/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.3781 - accuracy: 0.9091\n",
            "Epoch 101/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.3460 - accuracy: 0.8889\n",
            "Epoch 102/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.3599 - accuracy: 0.9091\n",
            "Epoch 103/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.3440 - accuracy: 0.9192\n",
            "Epoch 104/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.3447 - accuracy: 0.8687\n",
            "Epoch 105/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.3399 - accuracy: 0.9192\n",
            "Epoch 106/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.3830 - accuracy: 0.8384\n",
            "Epoch 107/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.3314 - accuracy: 0.9091\n",
            "Epoch 108/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.3548 - accuracy: 0.8990\n",
            "Epoch 109/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.3446 - accuracy: 0.8586\n",
            "Epoch 110/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.3433 - accuracy: 0.8889\n",
            "Epoch 111/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.3284 - accuracy: 0.9091\n",
            "Epoch 112/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.3255 - accuracy: 0.9091\n",
            "Epoch 113/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.3433 - accuracy: 0.9091\n",
            "Epoch 114/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.3306 - accuracy: 0.9091\n",
            "Epoch 115/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.3552 - accuracy: 0.8283\n",
            "Epoch 116/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.3335 - accuracy: 0.8990\n",
            "Epoch 117/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.3185 - accuracy: 0.9091\n",
            "Epoch 118/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.3311 - accuracy: 0.9091\n",
            "Epoch 119/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.3554 - accuracy: 0.8687\n",
            "Epoch 120/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.3191 - accuracy: 0.8990\n",
            "Epoch 121/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.3471 - accuracy: 0.8485\n",
            "Epoch 122/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.3155 - accuracy: 0.8687\n",
            "Epoch 123/1000\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.3224 - accuracy: 0.8788\n",
            "Epoch 124/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2998 - accuracy: 0.9192\n",
            "Epoch 125/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.3427 - accuracy: 0.8485\n",
            "Epoch 126/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.3119 - accuracy: 0.9192\n",
            "Epoch 127/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.3056 - accuracy: 0.9192\n",
            "Epoch 128/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.3178 - accuracy: 0.9091\n",
            "Epoch 129/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2954 - accuracy: 0.9697\n",
            "Epoch 130/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.3071 - accuracy: 0.9798\n",
            "Epoch 131/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.3020 - accuracy: 0.9192\n",
            "Epoch 132/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2885 - accuracy: 0.9495\n",
            "Epoch 133/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.3042 - accuracy: 0.8990\n",
            "Epoch 134/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.3360 - accuracy: 0.8586\n",
            "Epoch 135/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.3172 - accuracy: 0.9293\n",
            "Epoch 136/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2886 - accuracy: 0.8990\n",
            "Epoch 137/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.2848 - accuracy: 0.9495\n",
            "Epoch 138/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2889 - accuracy: 0.9293\n",
            "Epoch 139/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2785 - accuracy: 0.9192\n",
            "Epoch 140/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.3284 - accuracy: 0.8889\n",
            "Epoch 141/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2842 - accuracy: 0.9495\n",
            "Epoch 142/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2843 - accuracy: 0.9394\n",
            "Epoch 143/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2745 - accuracy: 0.9293\n",
            "Epoch 144/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2799 - accuracy: 0.9394\n",
            "Epoch 145/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.3176 - accuracy: 0.8687\n",
            "Epoch 146/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2818 - accuracy: 0.9293\n",
            "Epoch 147/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2714 - accuracy: 0.9596\n",
            "Epoch 148/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2884 - accuracy: 0.9192\n",
            "Epoch 149/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.3005 - accuracy: 0.8990\n",
            "Epoch 150/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2659 - accuracy: 0.9293\n",
            "Epoch 151/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2923 - accuracy: 0.8889\n",
            "Epoch 152/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2705 - accuracy: 0.9596\n",
            "Epoch 153/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2656 - accuracy: 0.9192\n",
            "Epoch 154/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2732 - accuracy: 0.9091\n",
            "Epoch 155/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2785 - accuracy: 0.8990\n",
            "Epoch 156/1000\n",
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            "Epoch 157/1000\n",
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            "Epoch 857/1000\n",
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            "Epoch 858/1000\n",
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            "Epoch 859/1000\n",
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            "Epoch 860/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.1020 - accuracy: 0.9596\n",
            "Epoch 861/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0837 - accuracy: 0.9798\n",
            "Epoch 862/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0953 - accuracy: 0.9495\n",
            "Epoch 863/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0679 - accuracy: 0.9899\n",
            "Epoch 864/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0785 - accuracy: 0.9798\n",
            "Epoch 865/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0849 - accuracy: 0.9596\n",
            "Epoch 866/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1040 - accuracy: 0.9495\n",
            "Epoch 867/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0684 - accuracy: 0.9596\n",
            "Epoch 868/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0699 - accuracy: 0.9798\n",
            "Epoch 869/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0721 - accuracy: 0.9899\n",
            "Epoch 870/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1069 - accuracy: 0.9596\n",
            "Epoch 871/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0704 - accuracy: 0.9899\n",
            "Epoch 872/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0815 - accuracy: 0.9798\n",
            "Epoch 873/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0670 - accuracy: 0.9899\n",
            "Epoch 874/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0685 - accuracy: 1.0000\n",
            "Epoch 875/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0697 - accuracy: 0.9798\n",
            "Epoch 876/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0938 - accuracy: 0.9394\n",
            "Epoch 877/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0772 - accuracy: 0.9596\n",
            "Epoch 878/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.1038 - accuracy: 0.9596\n",
            "Epoch 879/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0716 - accuracy: 0.9798\n",
            "Epoch 880/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0795 - accuracy: 0.9697\n",
            "Epoch 881/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0699 - accuracy: 1.0000\n",
            "Epoch 882/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0682 - accuracy: 0.9798\n",
            "Epoch 883/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0665 - accuracy: 1.0000\n",
            "Epoch 884/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0878 - accuracy: 0.9596\n",
            "Epoch 885/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0669 - accuracy: 1.0000\n",
            "Epoch 886/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0705 - accuracy: 0.9798\n",
            "Epoch 887/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0963 - accuracy: 0.9697\n",
            "Epoch 888/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0840 - accuracy: 0.9899\n",
            "Epoch 889/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0726 - accuracy: 1.0000\n",
            "Epoch 890/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0676 - accuracy: 0.9798\n",
            "Epoch 891/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0699 - accuracy: 0.9798\n",
            "Epoch 892/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0864 - accuracy: 0.9495\n",
            "Epoch 893/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0716 - accuracy: 1.0000\n",
            "Epoch 894/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0807 - accuracy: 0.9495\n",
            "Epoch 895/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0688 - accuracy: 0.9899\n",
            "Epoch 896/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0939 - accuracy: 0.9596\n",
            "Epoch 897/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0653 - accuracy: 1.0000\n",
            "Epoch 898/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0717 - accuracy: 1.0000\n",
            "Epoch 899/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0657 - accuracy: 0.9899\n",
            "Epoch 900/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0651 - accuracy: 1.0000\n",
            "Epoch 901/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0819 - accuracy: 0.9596\n",
            "Epoch 902/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0774 - accuracy: 0.9798\n",
            "Epoch 903/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0812 - accuracy: 0.9697\n",
            "Epoch 904/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0813 - accuracy: 0.9596\n",
            "Epoch 905/1000\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0834 - accuracy: 0.9697\n",
            "Epoch 906/1000\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0652 - accuracy: 1.0000\n",
            "Epoch 907/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0801 - accuracy: 0.9495\n",
            "Epoch 908/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0679 - accuracy: 0.9798\n",
            "Epoch 909/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0821 - accuracy: 0.9899\n",
            "Epoch 910/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0679 - accuracy: 0.9899\n",
            "Epoch 911/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0821 - accuracy: 0.9798\n",
            "Epoch 912/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0669 - accuracy: 1.0000\n",
            "Epoch 913/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0661 - accuracy: 1.0000\n",
            "Epoch 914/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0728 - accuracy: 1.0000\n",
            "Epoch 915/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0914 - accuracy: 0.9495\n",
            "Epoch 916/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0687 - accuracy: 0.9899\n",
            "Epoch 917/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0794 - accuracy: 0.9697\n",
            "Epoch 918/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0757 - accuracy: 0.9899\n",
            "Epoch 919/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0911 - accuracy: 0.9596\n",
            "Epoch 920/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0675 - accuracy: 0.9798\n",
            "Epoch 921/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0708 - accuracy: 0.9596\n",
            "Epoch 922/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0652 - accuracy: 1.0000\n",
            "Epoch 923/1000\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0892 - accuracy: 0.9697\n",
            "Epoch 924/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0824 - accuracy: 0.9495\n",
            "Epoch 925/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0658 - accuracy: 1.0000\n",
            "Epoch 926/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0656 - accuracy: 0.9798\n",
            "Epoch 927/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0707 - accuracy: 0.9798\n",
            "Epoch 928/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0684 - accuracy: 0.9798\n",
            "Epoch 929/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0659 - accuracy: 1.0000\n",
            "Epoch 930/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0689 - accuracy: 0.9899\n",
            "Epoch 931/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0785 - accuracy: 0.9899\n",
            "Epoch 932/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0936 - accuracy: 0.9596\n",
            "Epoch 933/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0678 - accuracy: 0.9899\n",
            "Epoch 934/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0634 - accuracy: 1.0000\n",
            "Epoch 935/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0663 - accuracy: 0.9899\n",
            "Epoch 936/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0940 - accuracy: 0.9697\n",
            "Epoch 937/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0681 - accuracy: 0.9596\n",
            "Epoch 938/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0649 - accuracy: 0.9899\n",
            "Epoch 939/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0707 - accuracy: 0.9798\n",
            "Epoch 940/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0767 - accuracy: 0.9596\n",
            "Epoch 941/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0775 - accuracy: 1.0000\n",
            "Epoch 942/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0843 - accuracy: 0.9495\n",
            "Epoch 943/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0745 - accuracy: 0.9697\n",
            "Epoch 944/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0683 - accuracy: 0.9899\n",
            "Epoch 945/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0839 - accuracy: 0.9596\n",
            "Epoch 946/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0674 - accuracy: 0.9899\n",
            "Epoch 947/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0754 - accuracy: 0.9697\n",
            "Epoch 948/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0649 - accuracy: 1.0000\n",
            "Epoch 949/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0647 - accuracy: 0.9899\n",
            "Epoch 950/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0699 - accuracy: 0.9899\n",
            "Epoch 951/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0766 - accuracy: 0.9697\n",
            "Epoch 952/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0758 - accuracy: 0.9798\n",
            "Epoch 953/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0623 - accuracy: 1.0000\n",
            "Epoch 954/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0846 - accuracy: 0.9596\n",
            "Epoch 955/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0865 - accuracy: 0.9596\n",
            "Epoch 956/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0660 - accuracy: 0.9899\n",
            "Epoch 957/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0635 - accuracy: 0.9899\n",
            "Epoch 958/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0673 - accuracy: 0.9798\n",
            "Epoch 959/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0624 - accuracy: 0.9899\n",
            "Epoch 960/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.1028 - accuracy: 0.9293\n",
            "Epoch 961/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0653 - accuracy: 0.9899\n",
            "Epoch 962/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0878 - accuracy: 0.9697\n",
            "Epoch 963/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0625 - accuracy: 0.9899\n",
            "Epoch 964/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0641 - accuracy: 1.0000\n",
            "Epoch 965/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0647 - accuracy: 0.9798\n",
            "Epoch 966/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0983 - accuracy: 0.9495\n",
            "Epoch 967/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0709 - accuracy: 0.9899\n",
            "Epoch 968/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0659 - accuracy: 0.9697\n",
            "Epoch 969/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0635 - accuracy: 0.9798\n",
            "Epoch 970/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0871 - accuracy: 0.9697\n",
            "Epoch 971/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0641 - accuracy: 0.9899\n",
            "Epoch 972/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0632 - accuracy: 0.9798\n",
            "Epoch 973/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0686 - accuracy: 0.9798\n",
            "Epoch 974/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0715 - accuracy: 0.9697\n",
            "Epoch 975/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0728 - accuracy: 0.9596\n",
            "Epoch 976/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0681 - accuracy: 0.9697\n",
            "Epoch 977/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0822 - accuracy: 0.9596\n",
            "Epoch 978/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0673 - accuracy: 1.0000\n",
            "Epoch 979/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0752 - accuracy: 0.9697\n",
            "Epoch 980/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0633 - accuracy: 0.9697\n",
            "Epoch 981/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0608 - accuracy: 1.0000\n",
            "Epoch 982/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0612 - accuracy: 1.0000\n",
            "Epoch 983/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0646 - accuracy: 0.9899\n",
            "Epoch 984/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1000 - accuracy: 0.9596\n",
            "Epoch 985/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0657 - accuracy: 1.0000\n",
            "Epoch 986/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0628 - accuracy: 0.9596\n",
            "Epoch 987/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0689 - accuracy: 0.9798\n",
            "Epoch 988/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0799 - accuracy: 0.9596\n",
            "Epoch 989/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0748 - accuracy: 0.9596\n",
            "Epoch 990/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0658 - accuracy: 0.9798\n",
            "Epoch 991/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0627 - accuracy: 0.9899\n",
            "Epoch 992/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0968 - accuracy: 0.9596\n",
            "Epoch 993/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0732 - accuracy: 0.9596\n",
            "Epoch 994/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0640 - accuracy: 1.0000\n",
            "Epoch 995/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0731 - accuracy: 0.9697\n",
            "Epoch 996/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0678 - accuracy: 0.9798\n",
            "Epoch 997/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0624 - accuracy: 1.0000\n",
            "Epoch 998/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0981 - accuracy: 0.9394\n",
            "Epoch 999/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0605 - accuracy: 1.0000\n",
            "Epoch 1000/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0762 - accuracy: 0.9596\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convertir modelo al formato tensorflow lite\n",
        "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "# Guardar modelo en el disco\n",
        "open(\"moisture_sensor_model.tflite\", \"wb\").write(tflite_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pTXapT1E9rYd",
        "outputId": "83f1f128-3f99-47c0-9af3-f7ff0deb9c52"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: /tmp/tmpp5mtznq8/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Buffer deduplication procedure will be skipped when flatbuffer library is not properly loaded\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2500"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tinymlgen"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dETqSZJY-H50",
        "outputId": "f204ff02-12bd-456a-ef4f-535f37378a36"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tinymlgen in /usr/local/lib/python3.7/dist-packages (0.2)\n",
            "Requirement already satisfied: hexdump in /usr/local/lib/python3.7/dist-packages (from tinymlgen) (3.3)\n",
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.7/dist-packages (from tinymlgen) (2.8.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow->tinymlgen) (4.2.0)\n",
            "Requirement already satisfied: libclang>=9.0.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow->tinymlgen) (14.0.1)\n",
            "Requirement already satisfied: keras<2.9,>=2.8.0rc0 in /usr/local/lib/python3.7/dist-packages (from tensorflow->tinymlgen) (2.8.0)\n",
            "Requirement already satisfied: tensorboard<2.9,>=2.8 in /usr/local/lib/python3.7/dist-packages (from tensorflow->tinymlgen) (2.8.0)\n",
            "Requirement already satisfied: gast>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow->tinymlgen) (0.5.3)\n",
            "Requirement already satisfied: absl-py>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow->tinymlgen) (1.0.0)\n",
            "Requirement already satisfied: protobuf>=3.9.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow->tinymlgen) (3.17.3)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow->tinymlgen) (1.44.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow->tinymlgen) (1.6.3)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow->tinymlgen) (1.1.0)\n",
            "Requirement already satisfied: tf-estimator-nightly==2.8.0.dev2021122109 in /usr/local/lib/python3.7/dist-packages (from tensorflow->tinymlgen) (2.8.0.dev2021122109)\n",
            "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow->tinymlgen) (3.1.0)\n",
            "Requirement already satisfied: flatbuffers>=1.12 in /usr/local/lib/python3.7/dist-packages (from tensorflow->tinymlgen) (2.0)\n",
            "Requirement already satisfied: keras-preprocessing>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow->tinymlgen) (1.1.2)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow->tinymlgen) (1.15.0)\n",
            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.7/dist-packages (from tensorflow->tinymlgen) (1.21.6)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow->tinymlgen) (3.3.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow->tinymlgen) (0.2.0)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow->tinymlgen) (0.25.0)\n",
            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow->tinymlgen) (1.14.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from tensorflow->tinymlgen) (57.4.0)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.7/dist-packages (from astunparse>=1.6.0->tensorflow->tinymlgen) (0.37.1)\n",
            "Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py>=2.9.0->tensorflow->tinymlgen) (1.5.2)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow->tinymlgen) (0.4.6)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow->tinymlgen) (2.23.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow->tinymlgen) (3.3.6)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow->tinymlgen) (1.35.0)\n",
            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow->tinymlgen) (0.6.1)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow->tinymlgen) (1.8.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow->tinymlgen) (1.0.1)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow->tinymlgen) (4.2.4)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow->tinymlgen) (0.2.8)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow->tinymlgen) (4.8)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow->tinymlgen) (1.3.1)\n",
            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard<2.9,>=2.8->tensorflow->tinymlgen) (4.11.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.9,>=2.8->tensorflow->tinymlgen) (3.8.0)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow->tinymlgen) (0.4.8)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow->tinymlgen) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow->tinymlgen) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow->tinymlgen) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow->tinymlgen) (2021.10.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow->tinymlgen) (3.2.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tinymlgen import port\n",
        "# Convertir modelo en un array C\n",
        "c_code = port(model, pretty_print=True)\n",
        "print(c_code)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cvYv4vyU-af7",
        "outputId": "30531043-b898-4e6d-8cd0-b22802945422"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Optimization option OPTIMIZE_FOR_SIZE is deprecated, please use optimizations=[Optimize.DEFAULT] instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: /tmp/tmpfwrebam1/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: /tmp/tmpfwrebam1/assets\n",
            "WARNING:absl:Optimization option OPTIMIZE_FOR_SIZE is deprecated, please use optimizations=[Optimize.DEFAULT] instead.\n",
            "WARNING:absl:Optimization option OPTIMIZE_FOR_SIZE is deprecated, please use optimizations=[Optimize.DEFAULT] instead.\n",
            "WARNING:absl:Buffer deduplication procedure will be skipped when flatbuffer library is not properly loaded\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "#ifdef __has_attribute\n",
            "#define HAVE_ATTRIBUTE(x) __has_attribute(x)\n",
            "#else\n",
            "#define HAVE_ATTRIBUTE(x) 0\n",
            "#endif\n",
            "#if HAVE_ATTRIBUTE(aligned) || (defined(__GNUC__) && !defined(__clang__))\n",
            "#define DATA_ALIGN_ATTRIBUTE __attribute__((aligned(4)))\n",
            "#else\n",
            "#define DATA_ALIGN_ATTRIBUTE\n",
            "#endif\n",
            "\n",
            "const unsigned char model_data[] DATA_ALIGN_ATTRIBUTE = {\n",
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            "\t0x2f, 0x6c, 0x00, 0xbe, 0x9a, 0x2e, 0xbd, 0xbd, 0xb4, 0x35, 0x4c, 0xbe, \n",
            "\t0x09, 0x78, 0x9c, 0x3e, 0x85, 0xf0, 0xed, 0xbd, 0xf4, 0xf4, 0x55, 0x3e, \n",
            "\t0x34, 0x8a, 0xb6, 0x3d, 0x9a, 0xa2, 0x8b, 0xbd, 0x9f, 0xc5, 0x53, 0xbe, \n",
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            "\t0x86, 0x36, 0x34, 0xbe, 0x8a, 0x6e, 0x93, 0xbe, 0xcd, 0xc4, 0x82, 0xbe, \n",
            "\t0x00, 0x98, 0xdf, 0xbd, 0x51, 0x9c, 0xa5, 0x3e, 0xf1, 0x1d, 0x2a, 0xbe, \n",
            "\t0x14, 0xb0, 0xef, 0xbe, 0x26, 0x28, 0x0f, 0x3f, 0x27, 0x9d, 0xe3, 0xbe, \n",
            "\t0x94, 0xc8, 0x98, 0x3d, 0xd0, 0xbf, 0xc1, 0xbc, 0xf0, 0x65, 0x68, 0x3d, \n",
            "\t0xf3, 0xa9, 0xa8, 0x3e, 0xd7, 0x82, 0x84, 0xbe, 0x0a, 0x41, 0x2c, 0x3e, \n",
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            "\t0xd4, 0xae, 0x92, 0x3f, 0xb8, 0x4d, 0x90, 0xbf, 0x69, 0xe4, 0x92, 0x3f, \n",
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            "\t0x0a, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, \n",
            "\t0x01, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, \n",
            "\t0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, \n",
            "\t0x09, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, \n",
            "\t0x0a, 0x00, 0x00, 0x00, 0x20, 0x03, 0x00, 0x00, 0xd4, 0x02, 0x00, 0x00, \n",
            "\t0x90, 0x02, 0x00, 0x00, 0x48, 0x02, 0x00, 0x00, 0xf4, 0x01, 0x00, 0x00, \n",
            "\t0xac, 0x01, 0x00, 0x00, 0x54, 0x01, 0x00, 0x00, 0xd0, 0x00, 0x00, 0x00, \n",
            "\t0x5c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x1c, 0xfd, 0xff, 0xff, \n",
            "\t0x44, 0x00, 0x00, 0x00, 0x0a, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, \n",
            "\t0x14, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, \n",
            "\t0xff, 0xff, 0xff, 0xff, 0x01, 0x00, 0x00, 0x00, 0x08, 0xfd, 0xff, 0xff, \n",
            "\t0x19, 0x00, 0x00, 0x00, 0x53, 0x74, 0x61, 0x74, 0x65, 0x66, 0x75, 0x6c, \n",
            "\t0x50, 0x61, 0x72, 0x74, 0x69, 0x74, 0x69, 0x6f, 0x6e, 0x65, 0x64, 0x43, \n",
            "\t0x61, 0x6c, 0x6c, 0x3a, 0x30, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, \n",
            "\t0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x70, 0xfd, 0xff, 0xff, \n",
            "\t0x60, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, \n",
            "\t0x14, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, \n",
            "\t0xff, 0xff, 0xff, 0xff, 0x01, 0x00, 0x00, 0x00, 0x5c, 0xfd, 0xff, 0xff, \n",
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            "\t0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, \n",
            "\t0xe0, 0xfd, 0xff, 0xff, 0x70, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, \n",
            "\t0x1c, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, \n",
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            "\t0x14, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, \n",
            "\t0xff, 0xff, 0xff, 0xff, 0x01, 0x00, 0x00, 0x00, 0x4c, 0xfe, 0xff, 0xff, \n",
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            "\t0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0xf6, 0xfe, 0xff, 0xff, \n",
            "\t0x34, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, \n",
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            "\t0x40, 0x00, 0x00, 0x00, 0x3a, 0xff, 0xff, 0xff, 0x30, 0x00, 0x00, 0x00, \n",
            "\t0x05, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, \n",
            "\t0xd4, 0xfe, 0xff, 0xff, 0x17, 0x00, 0x00, 0x00, 0x73, 0x65, 0x71, 0x75, \n",
            "\t0x65, 0x6e, 0x74, 0x69, 0x61, 0x6c, 0x2f, 0x64, 0x65, 0x6e, 0x73, 0x65, \n",
            "\t0x2f, 0x4d, 0x61, 0x74, 0x4d, 0x75, 0x6c, 0x00, 0x02, 0x00, 0x00, 0x00, \n",
            "\t0x40, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0e, 0x00, \n",
            "\t0x18, 0x00, 0x08, 0x00, 0x07, 0x00, 0x0c, 0x00, 0x10, 0x00, 0x14, 0x00, \n",
            "\t0x0e, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x34, 0x00, 0x00, 0x00, \n",
            "\t0x04, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, \n",
            "\t0x28, 0xff, 0xff, 0xff, 0x18, 0x00, 0x00, 0x00, 0x73, 0x65, 0x71, 0x75, \n",
            "\t0x65, 0x6e, 0x74, 0x69, 0x61, 0x6c, 0x2f, 0x66, 0x6c, 0x61, 0x74, 0x74, \n",
            "\t0x65, 0x6e, 0x2f, 0x43, 0x6f, 0x6e, 0x73, 0x74, 0x00, 0x00, 0x00, 0x00, \n",
            "\t0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0xce, 0xff, 0xff, 0xff, \n",
            "\t0x24, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, \n",
            "\t0x04, 0x00, 0x00, 0x00, 0x68, 0xff, 0xff, 0xff, 0x0a, 0x00, 0x00, 0x00, \n",
            "\t0x64, 0x65, 0x6e, 0x73, 0x65, 0x2f, 0x62, 0x69, 0x61, 0x73, 0x00, 0x00, \n",
            "\t0x01, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0e, 0x00, \n",
            "\t0x14, 0x00, 0x04, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x10, 0x00, \n",
            "\t0x0e, 0x00, 0x00, 0x00, 0x28, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, \n",
            "\t0x0c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xa8, 0xff, 0xff, 0xff, \n",
            "\t0x0c, 0x00, 0x00, 0x00, 0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x31, 0x2f, \n",
            "\t0x62, 0x69, 0x61, 0x73, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, \n",
            "\t0x01, 0x00, 0x00, 0x00, 0x14, 0x00, 0x18, 0x00, 0x04, 0x00, 0x00, 0x00, \n",
            "\t0x08, 0x00, 0x0c, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x14, 0x00, \n",
            "\t0x14, 0x00, 0x00, 0x00, 0x4c, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, \n",
            "\t0x20, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, \n",
            "\t0x02, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff, 0x01, 0x00, 0x00, 0x00, \n",
            "\t0x04, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x1f, 0x00, 0x00, 0x00, \n",
            "\t0x73, 0x65, 0x72, 0x76, 0x69, 0x6e, 0x67, 0x5f, 0x64, 0x65, 0x66, 0x61, \n",
            "\t0x75, 0x6c, 0x74, 0x5f, 0x66, 0x6c, 0x61, 0x74, 0x74, 0x65, 0x6e, 0x5f, \n",
            "\t0x69, 0x6e, 0x70, 0x75, 0x74, 0x3a, 0x30, 0x00, 0x02, 0x00, 0x00, 0x00, \n",
            "\t0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, \n",
            "\t0x40, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, \n",
            "\t0xd8, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x0e, 0x0e, 0x00, 0x00, 0x00, \n",
            "\t0x0c, 0x00, 0x10, 0x00, 0x07, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0c, 0x00, \n",
            "\t0x0c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x09, 0x00, 0x00, 0x00, \n",
            "\t0x09, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x0c, 0x00, 0x07, 0x00, 0x00, 0x00, \n",
            "\t0x00, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x16, \n",
            "\t0x16, 0x00, 0x00, 0x00\n",
            "};\n",
            "const int model_data_len = 2608;\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "humedad = 45\n",
        "y = model.predict([humedad])\n",
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0XsU7yHdGt9c",
        "outputId": "7c6ba3e2-cf61-4d91-ed08-034c1db49bc0"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.4825797]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Obteniendo el array C con xxd\n",
        "# Install xxd if it is not available\n",
        "!apt-get -qq install xxd\n",
        "# Save the file as a C source file\n",
        "!xxd -i moisture_sensor_model.tflite > sensor_model.cc\n",
        "# Print the source file\n",
        "!cat sensor_model.cc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yZJeadBmNcqk",
        "outputId": "dc6739cf-03fc-4483-dc53-204411eb1a71"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "unsigned char moisture_sensor_model_tflite[] = {\n",
            "  0x1c, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x14, 0x00, 0x20, 0x00,\n",
            "  0x04, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x10, 0x00, 0x14, 0x00, 0x00, 0x00,\n",
            "  0x18, 0x00, 0x1c, 0x00, 0x14, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,\n",
            "  0x18, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00, 0x30, 0x01, 0x00, 0x00,\n",
            "  0x24, 0x00, 0x00, 0x00, 0xd8, 0x00, 0x00, 0x00, 0x50, 0x00, 0x00, 0x00,\n",
            "  0x03, 0x00, 0x00, 0x00, 0x0c, 0x04, 0x00, 0x00, 0x40, 0x03, 0x00, 0x00,\n",
            "  0xd4, 0x01, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x30, 0x01, 0x00, 0x00,\n",
            "  0x0c, 0x00, 0x00, 0x00, 0x68, 0x09, 0x00, 0x00, 0x64, 0x09, 0x00, 0x00,\n",
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            "  0x6a, 0xdf, 0x62, 0xbe, 0xfe, 0xfd, 0xff, 0xff, 0x10, 0x00, 0x00, 0x00,\n",
            "  0x05, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x2c, 0x00, 0x00, 0x00,\n",
            "  0x02, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x17, 0x00, 0x00, 0x00, 0x73, 0x65, 0x71, 0x75, 0x65, 0x6e, 0x74, 0x69,\n",
            "  0x61, 0x6c, 0x2f, 0x64, 0x65, 0x6e, 0x73, 0x65, 0x2f, 0x4d, 0x61, 0x74,\n",
            "  0x4d, 0x75, 0x6c, 0x00, 0x8c, 0xfd, 0xff, 0xff, 0x56, 0xfe, 0xff, 0xff,\n",
            "  0x04, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x18, 0x00, 0x08, 0x00,\n",
            "  0x07, 0x00, 0x0c, 0x00, 0x10, 0x00, 0x14, 0x00, 0x0e, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x02, 0x10, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,\n",
            "  0x10, 0x00, 0x00, 0x00, 0x2c, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x02, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x73, 0x65, 0x71, 0x75,\n",
            "  0x65, 0x6e, 0x74, 0x69, 0x61, 0x6c, 0x2f, 0x66, 0x6c, 0x61, 0x74, 0x74,\n",
            "  0x65, 0x6e, 0x2f, 0x43, 0x6f, 0x6e, 0x73, 0x74, 0x00, 0x00, 0x00, 0x00,\n",
            "  0xf4, 0xfd, 0xff, 0xff, 0xbe, 0xfe, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,\n",
            "  0x6a, 0x98, 0x92, 0xbf, 0xdf, 0x35, 0x92, 0x3f, 0x00, 0x00, 0x00, 0x00,\n",
            "  0xba, 0x45, 0x8a, 0xbf, 0x0a, 0x84, 0x93, 0x3f, 0x1c, 0x20, 0x92, 0x3f,\n",
            "  0x72, 0x8b, 0x92, 0xbf, 0x00, 0x95, 0x92, 0x3f, 0xcd, 0xe3, 0x92, 0x3f,\n",
            "  0x96, 0x7a, 0x92, 0x3f, 0x2f, 0x5b, 0x92, 0x3f, 0x8c, 0x48, 0x94, 0xbf,\n",
            "  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5e, 0xdc, 0x92, 0xbf,\n",
            "  0x00, 0x00, 0x00, 0x00, 0x9a, 0xd3, 0x91, 0xbf, 0x13, 0x3e, 0x90, 0xbf,\n",
            "  0xde, 0x37, 0x92, 0xbf, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x00, 0xa1, 0x46, 0x90, 0xbf, 0xc9, 0x77, 0x92, 0x3f,\n",
            "  0x0e, 0xb8, 0x91, 0xbf, 0x56, 0xc8, 0x91, 0x3f, 0x00, 0x00, 0x00, 0x00,\n",
            "  0x31, 0x33, 0x92, 0x3f, 0xd2, 0xd2, 0x91, 0xbf, 0x61, 0xb1, 0x91, 0x3f,\n",
            "  0x00, 0x00, 0x00, 0x00, 0x83, 0xcc, 0x94, 0xbf, 0xe2, 0xa2, 0x90, 0x3f,\n",
            "  0x00, 0x00, 0x00, 0x00, 0xa5, 0x2e, 0x93, 0xbf, 0x3d, 0x57, 0x92, 0x3f,\n",
            "  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,\n",
            "  0xd0, 0x75, 0x92, 0x3f, 0xbf, 0x9a, 0x91, 0x3f, 0x9e, 0xa3, 0x91, 0xbf,\n",
            "  0x8f, 0x12, 0x92, 0x3f, 0xd4, 0xae, 0x92, 0x3f, 0xb8, 0x4d, 0x90, 0xbf,\n",
            "  0x69, 0xe4, 0x92, 0x3f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x00, 0xa6, 0xd8, 0x91, 0xbf, 0xbe, 0xa7, 0x92, 0x3f,\n",
            "  0x00, 0x00, 0x00, 0x00, 0x17, 0xd3, 0x92, 0x3f, 0x00, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,\n",
            "  0x97, 0x77, 0x92, 0xbf, 0xae, 0xad, 0x91, 0x3f, 0xba, 0x79, 0x92, 0xbf,\n",
            "  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xb2, 0xff, 0xff, 0xff,\n",
            "  0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,\n",
            "  0x20, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00,\n",
            "  0x0a, 0x00, 0x00, 0x00, 0x64, 0x65, 0x6e, 0x73, 0x65, 0x2f, 0x62, 0x69,\n",
            "  0x61, 0x73, 0x00, 0x00, 0x04, 0x00, 0x06, 0x00, 0x04, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x06, 0x00, 0x08, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00,\n",
            "  0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x14, 0xd9, 0x92, 0xbf,\n",
            "  0x00, 0x00, 0x0e, 0x00, 0x14, 0x00, 0x04, 0x00, 0x00, 0x00, 0x08, 0x00,\n",
            "  0x0c, 0x00, 0x10, 0x00, 0x0e, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,\n",
            "  0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00,\n",
            "  0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x31, 0x2f, 0x62, 0x69, 0x61, 0x73,\n",
            "  0x00, 0x00, 0x00, 0x00, 0x90, 0xff, 0xff, 0xff, 0x14, 0x00, 0x18, 0x00,\n",
            "  0x04, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x10, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x14, 0x00, 0x14, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x44, 0x00, 0x00, 0x00,\n",
            "  0x34, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x1f, 0x00, 0x00, 0x00, 0x73, 0x65, 0x72, 0x76,\n",
            "  0x69, 0x6e, 0x67, 0x5f, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x5f,\n",
            "  0x66, 0x6c, 0x61, 0x74, 0x74, 0x65, 0x6e, 0x5f, 0x69, 0x6e, 0x70, 0x75,\n",
            "  0x74, 0x3a, 0x30, 0x00, 0x02, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff,\n",
            "  0x01, 0x00, 0x00, 0x00, 0xfc, 0xff, 0xff, 0xff, 0x04, 0x00, 0x04, 0x00,\n",
            "  0x04, 0x00, 0x00, 0x00\n",
            "};\n",
            "unsigned int moisture_sensor_model_tflite_len = 2500;\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Ahora reemplazar el contenido del array y la longitud que se obtuvo con tinymlgen"
      ],
      "metadata": {
        "id": "YhnKKok9S1wS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fuentes:\n",
        "# * https://github.com/eloquentarduino/EloquentTinyML\n",
        "# * https://eloquentarduino.github.io/2020/01/easy-tinyml-on-esp32-and-arduino/\n",
        "# See here for multiple outputs https://smartmicrotech.it/eloquent-tinyml/getting-started/"
      ],
      "metadata": {
        "id": "3rTFLAFw_Bps"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}