{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-WXqA_23DfeC"
      },
      "source": [
        "Modelo de clasificación de imágenes con Data Augmentation\n",
        "========================================================="
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "55hWDeBfwEZz"
      },
      "source": [
        "<div class=\"admonition warning\">PRECAUCIÓN 😱: El tema presentado en esta sección está clasificado como avanzado. El entendimiento de este contenido es totalmente opcional.</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j3576kEYCwpp"
      },
      "source": [
        "## Introducción"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o-aoxMmlCwpp"
      },
      "source": [
        "Como análistas o científicos de datos, en general una gran cantidad del tiempo que se invierte en el desarrollo de un modelo de aprendizaje automático está dedicado a la preparación, limpieza y reorganización de los datos. Los sistemas de visión por computadora no son la excepción.\n",
        "\n",
        "Dependiendo del problema que estamos intentando resolver, será el tipo de preprocesamiento a realizar. Entre la tareas más comunes están:\n",
        "\n",
        "Estandarización:\n",
        "\n",
        "- Ajuste del tamaño de la imágen a un tamaño estandard.\n",
        "\n",
        "- Recorte de las imágenes.\n",
        "\n",
        "- Ajuste de colores (escala de grises, reducción de contraste, saturación).\n",
        "\n",
        "- Transformaciones específicas como reducciones de ruido.\n",
        "\n",
        "Aumento o augmentation del conjunto de datos:\n",
        "\n",
        "- Rotaciones.\n",
        "\n",
        "- Translaciones.\n",
        "\n",
        "- Escalamiento.\n",
        "\n",
        "- Modificacione de colores (escala de grises, HUE, saturación, brillo).\n",
        "\n",
        "- Filtros específicos (borronear, ruido).\n",
        "\n",
        "- Combinar imagenes o recortarlas.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TghjzJl0wKId"
      },
      "source": [
        "### Preparación del ambiente"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ez9d-jEjDfeV"
      },
      "source": [
        "Intalamos las librerias necesarias"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mrs7T2yDDfeW"
      },
      "outputs": [],
      "source": [
        "!wget https://raw.githubusercontent.com/santiagxf/M72109/master/docs/vision/tasks/classification/code/cnn_class.txt \\\n",
        "    --quiet --no-clobber\n",
        "!pip install -r cnn_class.txt --quiet"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "avCIgEVTCwpt"
      },
      "source": [
        "### Sobre el conjunto de datos de este ejemplo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZJ1mR6CBCwpt"
      },
      "source": [
        "Para ejemplificar esta técnica utilizaremos un conjunto de datos muy popular llamado CIFAR-10. CIFAR-10 es un conjunto de datos que consiste en 60.000 imagenes a color de 32x32 agrupadas en 10 clases, con 6000 imagenes cada una. Hay alrededor de 50000 imagenes para entrenamiento y 10000 para testing.\n",
        "\n",
        "Las categorias son:\n",
        "\n",
        "* airplane\n",
        "* automobile\n",
        "* bird\n",
        "* cat\n",
        "* deer\n",
        "* dog\n",
        "* frog\n",
        "* horse\n",
        "* ship\n",
        "* truck"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ehq4pYRGCwpu"
      },
      "source": [
        "Podemos cargar este conjunto de datos facilmente utilizando `tensorflow-datasets`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "-ToQd6ybDfef",
        "outputId": "87d4156f-fc68-45d0-c337-d11e892570a0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1mDownloading and preparing dataset cifar10/3.0.2 (download: 162.17 MiB, generated: 132.40 MiB, total: 294.58 MiB) to /root/tensorflow_datasets/cifar10/3.0.2...\u001b[0m\n"
          ]
        },
        {
          "data": {
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              "model_id": "e328bfa551f04d4bb3cb3520707e9243",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Dl Completed...: 0 url [00:00, ? url/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b052930bd8554e938a0ce0b5d96d867a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b6873cd2cc8748e9b9808d82677609bc",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Extraction completed...: 0 file [00:00, ? file/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "aa9f159877804aceba0b6c700dee63a8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Shuffling and writing examples to /root/tensorflow_datasets/cifar10/3.0.2.incomplete8HYSX7/cifar10-train.tfrecord\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ffe3d8d35a5b4e3b830576bcc20e17f0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/50000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "76fd40af0a19496b9bd60fb36fdc3d03",
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              "version_minor": 0
            },
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Shuffling and writing examples to /root/tensorflow_datasets/cifar10/3.0.2.incomplete8HYSX7/cifar10-test.tfrecord\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "eae04a0c434a4a209694768b98233c27",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/10000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1mDataset cifar10 downloaded and prepared to /root/tensorflow_datasets/cifar10/3.0.2. Subsequent calls will reuse this data.\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow.keras as keras\n",
        "import tensorflow_datasets as tfds\n",
        "\n",
        "ds, info = tfds.load('cifar10', split='train', shuffle_files=True, with_info=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pl6tmETmDfei",
        "outputId": "a476fb26-ae59-4422-cffc-d9c3deb81ecc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Numero de clases:  10\n",
            "Clases disponibles:  ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n"
          ]
        }
      ],
      "source": [
        "print(\"Numero de clases: \", info.features[\"label\"].num_classes)\n",
        "print(\"Clases disponibles: \", info.features[\"label\"].names)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "StbfaIWWDfel"
      },
      "source": [
        "Veamos algunos ejemplos:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "tnIfBNDhDfem",
        "outputId": "82de8509-6dbb-4006-96db-65d238d05067"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 648x648 with 9 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 648x648 with 9 Axes>"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tfds.show_examples(ds, info)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2U9-305aDfeq"
      },
      "source": [
        "## Construcción de una red neuronal convolucional"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c2ydVcyxCwpx"
      },
      "source": [
        "Antes de comenzar necesitaremos verificar que tenemos el runtime correcto en nuestro ambiente. Esta tarea se beneficiará mucho de una GPU."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ABQ6LdkQDfeZ",
        "outputId": "da9a5dbc-5d03-4426-c3c4-2832e7626f55"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "GPUs disponibles:  1\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "print(\"GPUs disponibles: \", len(tf.config.experimental.list_physical_devices('GPU')))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JOkYNmVAta_n"
      },
      "source": [
        "### Especificando las transformaciones de aumento de datos a realizar"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-vp215uiDfer"
      },
      "source": [
        "En este primer intento haremos las siguientes transformaciones:\n",
        "\n",
        "- Rotar horizontalmente la imagen\n",
        "- Rotar la imagen de forma aleatoria con una probabilidad de 0.1\n",
        "- Agrandar (zoom) la imagen de forma aleatroia con una probabilidad de 0.1\n",
        "\n",
        "Para todas estas tareas `keras` dispone de una capa que nos permite realizar esto automáticamente:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kYDkDZ5treqQ"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.layers.experimental import preprocessing\n",
        "\n",
        "data_augmentation = keras.Sequential(\n",
        "    [\n",
        "        preprocessing.RandomFlip(\"horizontal\", input_shape=(32, 32, 3)),\n",
        "        preprocessing.RandomRotation(0.1),\n",
        "        preprocessing.RandomZoom(0.1),\n",
        "    ]\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EPJxI0JJtFpB"
      },
      "source": [
        "Algunas cosas a notar:\n",
        "\n",
        "- Note como pudimos empaquetar todas las transformaciones como un modelo secuencial.\n",
        "- Note como `input_shape` solo aparece en la primera transformación que aplicamos. Esto se debe a que el mismo debe ser especificado en la primera capa de la red secuencial."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PziBXrSoDfeq"
      },
      "source": [
        "### Aplicando data augmentation a una arquitectura CNN estandar"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ABF9i2EzDfev"
      },
      "source": [
        "Instanciamos nuestro modelo y verificamos su arquitectura"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4QC17MxkDfes"
      },
      "outputs": [],
      "source": [
        "model = keras.models.Sequential(layers=\n",
        "  [\n",
        "    data_augmentation,\n",
        "    keras.layers.Conv2D(32, (3, 3), padding='same', activation='relu', ),\n",
        "    keras.layers.Conv2D(32, (3, 3), padding='same', activation='relu'),\n",
        "    keras.layers.MaxPooling2D((2, 2)),\n",
        "    keras.layers.Flatten(),\n",
        "    keras.layers.Dropout(0.5),\n",
        "    keras.layers.Dense(64, activation='relu'),\n",
        "    keras.layers.Dense(10, activation='softmax')\n",
        "  ])\n",
        "\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "          loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "          metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X46DBEQnDfev",
        "outputId": "b91ce991-8ae4-42c8-d315-0885c1c3a407"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "sequential_2 (Sequential)    (None, 32, 32, 3)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 8192)              0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 8192)              0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 64)                524352    \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 535,146\n",
            "Trainable params: 535,146\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cfrHfmyPDfey"
      },
      "source": [
        "Antes de comenzar el entrenamiento, configuraremos nuestro conjunto de datos para una tarea supervisada. Esto lo hacemos especificando el parametro *as_supervised=True* lo cual nos da la posibilidad de acceder a las anotaciones del conjunto de datos."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rF2k-8vvDfey"
      },
      "outputs": [],
      "source": [
        "(training_set, validation_set) = tfds.load('cifar10', split=['train', 'test'], as_supervised=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rzxxC_l2Cwp0"
      },
      "source": [
        "Configuramos los parametros de entrenamiento:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9ejNMk7MDfe1"
      },
      "outputs": [],
      "source": [
        "BATCH_SIZE=64\n",
        "EPOCHS = 3\n",
        "\n",
        "training_set_batch = training_set.batch(batch_size=BATCH_SIZE).cache().repeat()\n",
        "validation_set_batch = validation_set.batch(batch_size=BATCH_SIZE).cache().repeat()\n",
        "train_size = info.splits['train'].num_examples\n",
        "test_size = info.splits['test'].num_examples"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wUr_78wVDfe4"
      },
      "source": [
        "Comenzamos el entrenamiento"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GKNXfxsGDfe7",
        "outputId": "5087fcf6-835b-43b3-8c4a-27275cc652dd",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/3\n",
            "50000/50000 [==============================] - 597s 11ms/step - loss: 1.2633 - accuracy: 0.5600 - val_loss: 1.1100 - val_accuracy: 0.6278\n",
            "Epoch 2/3\n",
            "50000/50000 [==============================] - 557s 11ms/step - loss: 1.1140 - accuracy: 0.6154 - val_loss: 1.1587 - val_accuracy: 0.6220\n",
            "Epoch 3/3\n",
            "50000/50000 [==============================] - 558s 11ms/step - loss: 1.0796 - accuracy: 0.6291 - val_loss: 1.2239 - val_accuracy: 0.6159\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(training_set_batch,\n",
        "                    epochs=EPOCHS,\n",
        "                    steps_per_epoch=train_size,\n",
        "                    validation_data=validation_set_batch,\n",
        "                    validation_steps=test_size)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6z33mGnmCwp1"
      },
      "source": [
        "> Dado que estamos utilizando un objeto de tipo `tf.Dataset` como argumento de `validation_data` de `Model.fit`, Keras no sabe por cuantos etapas hay que validar. Por este motivo el argumento `validation_steps` fue configurado."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "p1-69LFhCwp1",
        "outputId": "c10f4176-6f1d-4382-a8e9-d637e08b76bd"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_history(history):\n",
        "  # summarize history for accuracy\n",
        "  plt.plot(history.history['accuracy'])\n",
        "  plt.plot(history.history['val_accuracy'])\n",
        "  plt.title('model accuracy')\n",
        "  plt.ylabel('accuracy')\n",
        "  plt.xlabel('epoch')\n",
        "  plt.legend(['train', 'test'], loc='upper left')\n",
        "  plt.show()\n",
        "\n",
        "  # summarize history for loss\n",
        "  plt.plot(history.history['loss'])\n",
        "  plt.plot(history.history['val_loss'])\n",
        "  plt.title('model loss')\n",
        "  plt.ylabel('loss')\n",
        "  plt.xlabel('epoch')\n",
        "  plt.legend(['train', 'test'], loc='upper left')\n",
        "  plt.show()\n",
        "\n",
        "plot_history(history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PNwqjaIr0cvP"
      },
      "source": [
        "> ¿Que le sugiere este gráfico? ¿Porque le parece que la técnica no dio resultados?"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "CNNs.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.2"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
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