Codelabs/AIparatodxs.ipynb

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  "metadata": {
    "colab": {
      "name": "AIparatodxs.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TIcv7OO7AwCj",
        "colab_type": "text"
      },
      "source": [
        "## **Reconoce imagenes de flores con TensorFlow Lite**\n",
        "\n",
        "Para comenzar, debemos instalar el \"Model Maker\" una libreria que usaremos para este ejercicio desde el repositorio de GitHub\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5K2Df4yFbn6A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4a2f4af9-72b5-4c52-cf48-d1f03d824756"
      },
      "source": [
        "!pip install git+git://github.com/tensorflow/examples.git#egg=tensorflow-examples[model_maker]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "  Stored in directory: /root/.cache/pip/wheels/4f/32/0a/df3b340a82583566975377d65e724895b3fad101a3fb729f68\n",
            "  Building wheel for py-cpuinfo (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Stored in directory: /root/.cache/pip/wheels/f1/93/7b/127daf0c3a5a49feb2fecd468d508067c733fba5192f726ad1\n",
            "Successfully built tensorflow-examples fire tflite-support seqeval py-cpuinfo\n",
            "Installing collected packages: flatbuffers, tf-estimator-nightly, tb-nightly, tf-nightly, seqeval, sentencepiece, tf-slim, py-cpuinfo, opencv-python-headless, tensorflow-model-optimization, tf-models-nightly, fire, pybind11, tflite-support, tensorflow-examples\n",
            "Successfully installed fire-0.3.1 flatbuffers-1.12 opencv-python-headless-4.4.0.40 py-cpuinfo-7.0.0 pybind11-2.5.0 sentencepiece-0.1.91 seqeval-0.0.12 tb-nightly-2.4.0a20200814 tensorflow-examples-b30a40f9416fc38cfa91ca03d835ba1fc432a824- tensorflow-model-optimization-0.4.1 tf-estimator-nightly-2.4.0.dev2020081401 tf-models-nightly-2.3.0.dev20200814 tf-nightly-2.4.0.dev20200813 tf-slim-1.1.0 tflite-support-0.1.0a0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B1c_NY_kDI8u",
        "colab_type": "text"
      },
      "source": [
        "## Importamos, descargamos y descomprimos\n",
        "\n",
        "\n",
        "\n",
        "Necesitamos importar la libreria que acabamos de descargar de github al Colab "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NO4cDaDBdoi_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "import tensorflow as tf\n",
        "assert tf.__version__.startswith('2')\n",
        "\n",
        "from tensorflow_examples.lite.model_maker.core.data_util.image_dataloader import ImageClassifierDataLoader\n",
        "from tensorflow_examples.lite.model_maker.core.task import image_classifier\n",
        "from tensorflow_examples.lite.model_maker.core.task.model_spec import mobilenet_v2_spec\n",
        "from tensorflow_examples.lite.model_maker.core.task.model_spec import ImageModelSpec\n",
        "\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PnHq4jbPDZfF",
        "colab_type": "text"
      },
      "source": [
        "Para comenzar a entrenar nuestro reconocerdor de flores debemos descargar la carpeta de imagenes de nuestras flores, primero nos descargará una carpeta comprimida con terminación de `.tgz` que tendremos que descomprimir desde nuestra computadora y después subir la carpeta de imagenes nuevamente a colab\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6_OWctg5DYyH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "image_path = tf.keras.utils.get_file(\n",
        "      'flower_photos',\n",
        "      'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz',\n",
        "      untar=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QTPQJq0UEdDa",
        "colab_type": "text"
      },
      "source": [
        "Sin embargo es importante que al momento de subir nuestra carpeta descomprimida volvamos a comprimirla en un zip con el sieguiente comando:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R8Yx4X0QErAz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!tar -xf flower_photos.zip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sN-e2jG8FYMM",
        "colab_type": "text"
      },
      "source": [
        "Corremos cuatro de lineas de código que nos ayudará a entrenar y preparar nuestro modelo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uJvrKOZJFxJt",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "1. Entrenamos y testeamos nuestro modelo\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7JIjDRFFFs9n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = ImageClassifierDataLoader.from_folder(image_path)\n",
        "train_data, test_data = data.split(0.9)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NjPb5-DZF4sO",
        "colab_type": "text"
      },
      "source": [
        " 2. Personaliza el modelo de tensor flow ya entrenado"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sq1DdC5jGI5n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = image_classifier.create(train_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P2akuGJtGUqw",
        "colab_type": "text"
      },
      "source": [
        "3. Evalua tu modelo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bj5j3LjyGXG5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loss, accuracy = model.evaluate(test_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rE_AWBjQGcGI",
        "colab_type": "text"
      },
      "source": [
        "4. Exporta a TensorFlow tu modelo.\n",
        "\n",
        "Tendrás que descargarlo desde las barra de carpetas y posteriormente abrirlo en Android Studio"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ipPx0i07HA3E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.export(export_dir='.', with_metadata=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Om2z-s3HCx1",
        "colab_type": "text"
      },
      "source": [
        "### **Ve a Android Studio**\n",
        "\n",
        "En la terminal de Android Studio clona el repositorio de GitHub de TensorFlow"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OPdqOwTOHkvf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "git clone https://github.com/tensorflow/examples.git"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fUBZ3jROHoDv",
        "colab_type": "text"
      },
      "source": [
        "Te saldra una carpeta con el nombre de examples, es importante que trabajemos sobre ella. \n",
        "\n",
        "La dirección con la que empezaremos a usar nuestro modelo de tensor flox sera en esta:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d9PlVg24Hu6X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "examples/lite/codelabs/flower_classification/start"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C2tRf0kpH__I",
        "colab_type": "text"
      },
      "source": [
        "Ve a la carpeta **Build.gradle** y dentro del bloque de ***dependencias*** copia el siguiente código, que te ayudará a implementar dependendicias de TensorFlow a la app"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JueW8yMdIhz0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "implementation('org.tensorflow:tensorflow-lite:0.0.0-nightly') { changing = true }\n",
        "implementation('org.tensorflow:tensorflow-lite-gpu:0.0.0-nightly') { changing = true }\n",
        "implementation('org.tensorflow:tensorflow-lite-support:0.0.0-nightly') { changing = true }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ffcqI66IIzu2",
        "colab_type": "text"
      },
      "source": [
        "Ahora, dentro del bloque de ***android***  debemos  copiar la siguiente línea de código que nos ayudara a prevenir que los archivos del modelo de TensorFlow se amontonen, después damos clic a \"Sync Nox\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wUJF7sfMHm6e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "aaptOptions {\n",
        "  noCompress \"tflite\"\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g-zNA6RqAttq",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    }
  ]
}