AT07_CNN_Aprendizado_Profundo.ipynb

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  "metadata": {
    "colab": {
      "name": "AT7 - CNN - Aprendizado Profundo.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/barauna-lo/CAP4213-Deep-Learning/blob/main/AT7_CNN_Aprendizado_Profundo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fd1Hh5A-VEeq"
      },
      "source": [
        "<img src=https://raw.githubusercontent.com/barauna-lo/CAP4213-Deep-Learning/main/logoinpe.png>\n",
        "\n",
        "\n",
        "# CAP-421-3: Aprendizado Profundo\n",
        "## Atividade 7 :  **Uso de Rede Convolucional na classificação de extração e não extração de árvores na Floresta Amazônica.**\n",
        "\n",
        "* Luan Orion Baraúna \n",
        "\n",
        "* Vinicius Monego"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rm_uPRruHjMd"
      },
      "source": [
        "## Introdução\n",
        "\n",
        "Neste código, iremos constuir uma Rede Neural Convolucional para prever a classificação de extração e não extração de arvores na floresta amazônica. \n",
        "\n",
        "Para modelos de redes neurais a _arquitetura_ da rede, ou seja, a quantidade de camadas e os hiperparâmetros de cada camada possuem um papel crucial na previsibilidade do modelo. A diferença entre o número de camadas e/ou o número de neurônios entre duas redes por definição, caracteriza redes com arquiteturas diferentes. \n",
        "\n",
        "Desta forma, neste código buscaremos explorar diferentes arquiteturas para redes neurais convulocionais com intuito de buscar aquela que melhor se preve o problema em questão. \n",
        "\n",
        "E este código esta dividito em duas partes.\n",
        "\n",
        "* 1 - Processamento de Imagens\n",
        "\n",
        "* 2 - Modelo de Redes Neurais Convolucionais"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mjcoANGJHkKc"
      },
      "source": [
        "## Processamento de Imagens\n",
        "\n",
        "Nesta primeira célula célula iremos importar as bibliotecas necessárias e em seguidas iremos acessar uma pasta do drive atravéz do ```!gdonw``` e extrairemos parte dos arquivos que estão dentro destas pastas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jlHKsJt-bL2x"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras import layers\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "import cv2\n",
        "\n",
        "#!gdown --id 16qpj4czPFN4GXfTsbNH7hNYMz2LjLmf3\n",
        "\n",
        "#!unrar x \"/content/covmingrad.rar\" \"/content/sample_data/treinamento/\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4sMSoX6zgCmA"
      },
      "source": [
        "Nesta célula iremos crirar novas pastas usando o comando ```mkdir``` e iremos redimensionar todas as imagens para imagens do mesmo tamanho.\n",
        "\n",
        "Vale ressaltar que existe um comando ```!rm``` para remover um arquivo que esta dentro da pasta e não será útil para as análises (pelo menos foi isso que eu entendi)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wA6nKjla8mEI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "71b40ad3-4e00-472b-e6e0-fb4187046217"
      },
      "source": [
        "#!/usr/bin/python\n",
        "from PIL import Image\n",
        "import os, sys\n",
        "\n",
        "path = \"/content/sample_data/treinamento/non-logging/\"\n",
        "dirs = os.listdir( path )\n",
        "\n",
        "!mkdir \"/content/sample_data/treinamento/non-logging-resized/\"\n",
        "path2 = \"/content/sample_data/treinamento/non-logging-resized/\"\n",
        "dirs2 = os.listdir( path2 )\n",
        "\n",
        "def resize():\n",
        "    for item in dirs:\n",
        "        if os.path.isfile(path+item):\n",
        "            im = Image.open(path+item)\n",
        "            f, e = os.path.splitext(path2+item)\n",
        "            imResize = im.resize((50,50), Image.ANTIALIAS)\n",
        "            imResize.save(f + ' resized.tiff', 'tiff', quality=90)\n",
        "\n",
        "resize()\n",
        "\n",
        "\n",
        "#!/usr/bin/python\n",
        "from PIL import Image\n",
        "import os, sys\n",
        "\n",
        "!rm \"/content/sample_data/treinamento/selective-logging/extrac_Id_1112.tif.aux.xml\"\n",
        "\n",
        "path = \"/content/sample_data/treinamento/selective-logging/\"\n",
        "dirs = os.listdir( path )\n",
        "\n",
        "!mkdir \"/content/sample_data/treinamento/selective-logging-resized/\"\n",
        "path2 = \"/content/sample_data/treinamento/selective-logging-resized/\"\n",
        "dirs2 = os.listdir( path2 )\n",
        "\n",
        "def resize():\n",
        "    for item in dirs:\n",
        "        if os.path.isfile(path+item):\n",
        "            im = Image.open(path+item)\n",
        "            f, e = os.path.splitext(path2+item)\n",
        "            imResize = im.resize((50,50), Image.ANTIALIAS)\n",
        "            imResize.save(f + ' resized.tiff', 'tiff', quality=90)\n",
        "\n",
        "resize()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mkdir: cannot create directory ‘/content/sample_data/treinamento/non-logging-resized/’: File exists\n",
            "rm: cannot remove '/content/sample_data/treinamento/selective-logging/extrac_Id_1112.tif.aux.xml': No such file or directory\n",
            "mkdir: cannot create directory ‘/content/sample_data/treinamento/selective-logging-resized/’: File exists\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KB130G9KgeKO"
      },
      "source": [
        "Nesta célula iremos criar pasta para treinamos, validação e iremos converter os aquivos de ```.tif``` para ```.jpg```. Este mudança de formato faz com que os dados de localização geoespaciais sejam perdidos. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zLLtE_nk-7vB",
        "outputId": "1fe6fea1-0a3a-452c-f038-9bb1b07a8e6b"
      },
      "source": [
        "import os\n",
        "from PIL import Image\n",
        "!mkdir \"/content/sample_data/treinamento/train/\"\n",
        "!mkdir \"/content/sample_data/treinamento/train/non-logging-resized\"\n",
        "#yourpath = os.getcwd()\n",
        "yourpath = \"/content/sample_data/treinamento/non-logging-resized\"\n",
        "\n",
        "root1 = \"/content/sample_data/treinamento/non-logging-resized\"\n",
        "root2 = \"/content/sample_data/treinamento/train/non-logging-resized\"\n",
        "\n",
        "for root1, dirs, files in os.walk(yourpath, topdown=False):\n",
        "    for name in files:\n",
        "        print(os.path.join(root1, name))\n",
        "        if os.path.splitext(os.path.join(root1, name))[1].lower() == \".tiff\":\n",
        "            if os.path.isfile(os.path.splitext(os.path.join(root1, name))[0] + \".jpg\"):\n",
        "                print (\"A jpeg file already exists for %s\" % name)\n",
        "            # If a jpeg is *NOT* present, create one from the tiff.\n",
        "            else:\n",
        "                outfile = os.path.splitext(os.path.join(root2,name))[0] + \".jpg\"\n",
        "                try:\n",
        "                    im = Image.open(os.path.join(root1, name))\n",
        "                    print (\"Generating jpeg for %s\" % name)\n",
        "                    im.thumbnail(im.size)\n",
        "                    im.save(outfile, \"JPEG\", quality=100)\n",
        "                except (Exception, e):\n",
        "                    print (e)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "import os\n",
        "from PIL import Image\n",
        "!mkdir \"/content/sample_data/treinamento/train/selective-loggin-resized/\"\n",
        "#yourpath = os.getcwd()\n",
        "yourpath = \"/content/sample_data/treinamento/selective-logging-resized\"\n",
        "\n",
        "root1 = \"/content/sample_data/treinamento/selective-logging-resized\"\n",
        "root2 = \"/content/sample_data/treinamento/train/selective-loggin-resized\"\n",
        "\n",
        "for root1, dirs, files in os.walk(yourpath, topdown=False):\n",
        "    for name in files:\n",
        "        print(os.path.join(root1, name))\n",
        "        if os.path.splitext(os.path.join(root1, name))[1].lower() == \".tiff\":\n",
        "            if os.path.isfile(os.path.splitext(os.path.join(root1, name))[0] + \".jpg\"):\n",
        "                print (\"A jpeg file already exists for %s\" % name)\n",
        "            # If a jpeg is *NOT* present, create one from the tiff.\n",
        "            else:\n",
        "                outfile = os.path.splitext(os.path.join(root2,name))[0] + \".jpg\"\n",
        "                try:\n",
        "                    im = Image.open(os.path.join(root1, name))\n",
        "                    print (\"Generating jpeg for %s\" % name)\n",
        "                    im.thumbnail(im.size)\n",
        "                    im.save(outfile, \"JPEG\", quality=100)\n",
        "                except (Exception, e):\n",
        "                    print (e)                    "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mA saída de streaming foi truncada nas últimas 5000 linhas.\u001b[0m\n",
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            "mkdir: cannot create directory ‘/content/sample_data/treinamento/train/selective-loggin-resized/’: File exists\n",
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            "Generating jpeg for extrac_Id_89886 resized.tiff\n",
            "/content/sample_data/treinamento/selective-logging-resized/extrac_Id_90139 resized.tiff\n",
            "Generating jpeg for extrac_Id_90139 resized.tiff\n",
            "/content/sample_data/treinamento/selective-logging-resized/extrac_Id_1163 resized.tiff\n",
            "Generating jpeg for extrac_Id_1163 resized.tiff\n",
            "/content/sample_data/treinamento/selective-logging-resized/extrac_Id_89617 resized.tiff\n",
            "Generating jpeg for extrac_Id_89617 resized.tiff\n",
            "/content/sample_data/treinamento/selective-logging-resized/extrac_Id_153829 resized.tiff\n",
            "Generating jpeg for extrac_Id_153829 resized.tiff\n",
            "/content/sample_data/treinamento/selective-logging-resized/extrac_Id_88918 resized.tiff\n",
            "Generating jpeg for extrac_Id_88918 resized.tiff\n",
            "/content/sample_data/treinamento/selective-logging-resized/extrac_Id_45445 resized.tiff\n",
            "Generating jpeg for extrac_Id_45445 resized.tiff\n",
            "/content/sample_data/treinamento/selective-logging-resized/extrac_Id_45024 resized.tiff\n",
            "Generating jpeg for extrac_Id_45024 resized.tiff\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mPkQIWgIhIwx"
      },
      "source": [
        "Nesta célula iremos dividir as imagens entre conjunto de traino, teste e validação."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z6JR7NW4A7sC",
        "outputId": "03ecfd60-bd27-48fc-f740-b541a570e431"
      },
      "source": [
        "!pip install split-folders\n",
        "import splitfolders\n",
        "\n",
        "!mkdir \"/content/sample_data/train&test\"\n",
        "!mkdir \"/content/sample_data/train&test/non-logging-resized\"\n",
        "\n",
        "input_folder = \"/content/sample_data/treinamento/train/\"\n",
        "output_folder = \"/content/sample_data/treinamento/train&test\" #where you want the split datasets saved. one will be created if none is set\n",
        "\n",
        "splitfolders.ratio(input_folder, output_folder, seed=42, ratio=(.8, .1, .1)) # ratio of split are in order of train/val/test. You can change to whatever you want. For train/val sets only, you could do .75, .25 for example.\n",
        "\n",
        "#Defing the size of tthe image\n",
        "image_size = (50, 50)\n",
        "batch_size = 32\n",
        "\n",
        "#Using Keras library for slipt data set\n",
        "train_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "    \"/content/sample_data/treinamento/train&test/train\",\n",
        "    validation_split=0.2,\n",
        "    subset=\"training\",\n",
        "    seed=1337,\n",
        "    image_size=image_size,\n",
        "    batch_size=batch_size,\n",
        ")\n",
        "\n",
        "val_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "    \"/content/sample_data/treinamento/train&test/val\",\n",
        "    validation_split=0.2,\n",
        "    subset=\"validation\",\n",
        "    seed=1337,\n",
        "    image_size=image_size,\n",
        "    batch_size=batch_size,\n",
        ")\n",
        "\n",
        "#??????\n",
        "class_names = train_ds.class_names\n",
        "print(class_names)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: split-folders in /usr/local/lib/python3.7/dist-packages (0.4.3)\n",
            "mkdir: cannot create directory ‘/content/sample_data/train&test’: File exists\n",
            "mkdir: cannot create directory ‘/content/sample_data/train&test/non-logging-resized’: File exists\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Copying files: 4324 files [00:00, 7039.81 files/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 3459 files belonging to 2 classes.\n",
            "Using 2768 files for training.\n",
            "Found 432 files belonging to 2 classes.\n",
            "Using 86 files for validation.\n",
            "['non-logging-resized', 'selective-loggin-resized']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YTvz7jvLhmWi"
      },
      "source": [
        "Nesa célula iremos realiza um leve aumento no conjunto de dados. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dA0NU8-1EnoJ"
      },
      "source": [
        "data_augmentation = keras.Sequential(\n",
        "    [\n",
        "     layers.experimental.preprocessing.RandomFlip(\"horizontal\"),\n",
        "     layers.experimental.preprocessing.RandomRotation(0.05),\n",
        "     layers.experimental.preprocessing.RandomZoom(0.10)\n",
        "    ]\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FTNvRkCihszC"
      },
      "source": [
        "## Modelo de Redes Neurais Convolucionais\n",
        "\n",
        "Uma vez feita o tratamento de dados das imagens, iremos utilizar três modelos as redes neurais convocionais para previsão de extração ilegal"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B8x3spVqkGrf"
      },
      "source": [
        "### Modelo 0: Modelo Padrão\n",
        "\n",
        "Este primeiro modelo é o modelo _standar_ do exercício. Utilizaremos ele como um modelo para comparação\n",
        "\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/barauna-lo/CAP4213-Deep-Learning/840ae3ac76a6d1a9480a891e8e924f8046573175/figures/modelo1.svg\">\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AbnOAnCcvnDS"
      },
      "source": [
        "#### Passo 1 \n",
        "\n",
        "Este modelo utiliza uma primeira camada com 32 filtros e janela de tamanho 3x3, com função de ativação ReLU. A segunda camada possui 64 filtros, com o mesmo tamanho de janela e função de ativação. A rede termina com uma camada de um vetor 1d para gerar os classificadores."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2H2JLAOXFbxf"
      },
      "source": [
        "keras.backend.clear_session()\n",
        "\n",
        "def make_model(input_shape, num_classes):\n",
        "    inputs = keras.Input(shape=input_shape)\n",
        "    # Image augmentation block\n",
        "    x = data_augmentation(inputs)\n",
        "\n",
        "    x = layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\")(x)\n",
        "    x = layers.MaxPooling2D(pool_size=(2, 2))(x)\n",
        "    x = layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\")(x)\n",
        "    x = layers.MaxPooling2D(pool_size=(2, 2))(x)\n",
        "    x = layers.Flatten()(x)\n",
        "    x = layers.Dropout(0.5)(x)\n",
        "\n",
        "    if num_classes == 2:\n",
        "        activation = \"sigmoid\"\n",
        "        units = 1\n",
        "    else:\n",
        "        activation = \"softmax\"\n",
        "        units = num_classes\n",
        "\n",
        "    outputs = layers.Dense(units, activation=activation)(x)\n",
        "    return keras.Model(inputs, outputs)\n",
        "\n",
        "model0 = make_model(       input_shape=image_size + (3,), num_classes=2) \n",
        "\n",
        "#printig the model sumaridez \n",
        "#keras.utils.plot_model(model0, show_shapes=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OlMDbR6qosxp"
      },
      "source": [
        "#### Passo 2\n",
        "\n",
        "* Defindo as condições de paradas para o modelo\n",
        "* Defindo o numero de épocas\n",
        "* Compilando o modelo\n",
        "* Definindo um vetor com os parâmetros de Treinamento"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TP1sdKopHMEx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ac1466c-c5a2-4e10-ed26-6b66630faa82"
      },
      "source": [
        "checkpoint_filepath = '/content/content'\n",
        "# model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
        "#     filepath=checkpoint_filepath,\n",
        "#     save_weights_only=True,\n",
        "#     monitor='val_accuracy',\n",
        "#     mode='max',\n",
        "#     save_best_only=True)\n",
        "\n",
        "early_stop = tf.keras.callbacks.EarlyStopping(\n",
        "    monitor=\"val_loss\",\n",
        "    min_delta=0,\n",
        "    patience=6,\n",
        "    verbose=0,\n",
        "    restore_best_weights = True)\n",
        "\n",
        "def scheduler(epoch, lr):\n",
        "  if epoch < 9:\n",
        "    return lr\n",
        "  else:\n",
        "    return lr * tf.math.exp(-0.008)\n",
        "\n",
        "lr_callback = tf.keras.callbacks.LearningRateScheduler(scheduler)\n",
        "\n",
        "# DEFING THE EPOCH NUMBERS\n",
        "epochs = 100\n",
        "\n",
        "# COMPILING THE MODEL\n",
        "model0.compile(optimizer= keras.optimizers.Adam(),\n",
        "              loss = 'BinaryCrossentropy',\n",
        "              metrics = [\"accuracy\"])\n",
        "\n",
        "# Creating a dataframe with a the model paramentes\n",
        "history0 = model0.fit(\n",
        "    train_ds, epochs = epochs, \n",
        "    callbacks = [early_stop, lr_callback],\n",
        "    validation_data = val_ds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "87/87 [==============================] - 25s 112ms/step - loss: 2.6720 - accuracy: 0.7782 - val_loss: 0.3776 - val_accuracy: 0.8372\n",
            "Epoch 2/100\n",
            "87/87 [==============================] - 10s 111ms/step - loss: 0.4018 - accuracy: 0.8537 - val_loss: 0.3011 - val_accuracy: 0.8605\n",
            "Epoch 3/100\n",
            "87/87 [==============================] - 10s 111ms/step - loss: 0.3550 - accuracy: 0.8652 - val_loss: 0.3322 - val_accuracy: 0.8837\n",
            "Epoch 4/100\n",
            "87/87 [==============================] - 10s 110ms/step - loss: 0.3130 - accuracy: 0.8696 - val_loss: 0.3880 - val_accuracy: 0.8837\n",
            "Epoch 5/100\n",
            "87/87 [==============================] - 10s 110ms/step - loss: 0.2854 - accuracy: 0.8837 - val_loss: 0.3709 - val_accuracy: 0.8837\n",
            "Epoch 6/100\n",
            "87/87 [==============================] - 10s 110ms/step - loss: 0.2763 - accuracy: 0.8862 - val_loss: 0.3914 - val_accuracy: 0.9070\n",
            "Epoch 7/100\n",
            "87/87 [==============================] - 10s 110ms/step - loss: 0.2630 - accuracy: 0.8895 - val_loss: 0.4585 - val_accuracy: 0.8372\n",
            "Epoch 8/100\n",
            "87/87 [==============================] - 10s 110ms/step - loss: 0.2704 - accuracy: 0.8895 - val_loss: 0.4285 - val_accuracy: 0.8605\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tuKucWJXoytl"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F-RYA6OepJ4t"
      },
      "source": [
        "Imprimindo o sumário do modelo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "agRDjfQfIs5e"
      },
      "source": [
        "#Model Summary\n",
        "\n",
        "#model0.summary()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xXgvjzGspjA9"
      },
      "source": [
        "#### Passo 3\n",
        "\n",
        "Alocando os valores de cada um dos dataframes e gerando os gráficos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "tRfmjA4tI2Hh",
        "outputId": "24e7efe3-2172-477e-c7e9-9a0b3d88267d"
      },
      "source": [
        "acc0 =      history0.history['accuracy']\n",
        "val_acc0 =  history0.history['val_accuracy']\n",
        "loss0 =     history0.history['loss']\n",
        "val_loss0 = history0.history['val_loss']\n",
        "epochs0=range(len(acc0))\n",
        "\n",
        "plt.figure(figsize=(15, 5))\n",
        "\n",
        "plt.subplot(121)\n",
        "plt.plot(epochs0,acc0,    label='Trainin_acc',   color='blue')\n",
        "plt.plot(epochs0,val_acc0,label='Validation_acc',color='red')\n",
        "plt.legend()\n",
        "plt.title(\"Training and Validation Accuracy\")\n",
        "\n",
        "plt.subplot(122)\n",
        "plt.plot(epochs0,loss0,    label='Training_loss',  color='blue')\n",
        "plt.plot(epochs0,val_loss0,label='Validation_loss',color='red')\n",
        "plt.legend()\n",
        "plt.title(\"Training and Validation loss\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bjIvsARYw9Hx"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YvUK6LEWw9EY"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c_lWVE98w9Ce"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2u9JVFHgw9Ah"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "__RfuNouw_UI"
      },
      "source": [
        "### Modelo 1: _Simple is Best_\n",
        "\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/barauna-lo/CAP4213-Deep-Learning/840ae3ac76a6d1a9480a891e8e924f8046573175/figures/modelo3.svg\">\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8SyeZENjw_UK"
      },
      "source": [
        "#### Passo 1 \n",
        "\n",
        "Esta rede apenas exclui uma camada convolucional para testar a influência da segunda camada."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j9N2JhQ5w_UN"
      },
      "source": [
        "keras.backend.clear_session()\n",
        "\n",
        "def make_fist_model(input_shape, num_classes):\n",
        "    inputs = keras.Input(shape=input_shape)\n",
        "    # Image augmentation block\n",
        "    x = data_augmentation(inputs)\n",
        "\n",
        "    x = layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\")(x)\n",
        "    x = layers.MaxPooling2D(pool_size=(2, 2))(x)\n",
        "    x = layers.Flatten()(x)\n",
        "    x = layers.Dropout(0.5)(x)\n",
        "\n",
        "    if num_classes == 2:\n",
        "        activation = \"sigmoid\"\n",
        "        units = 1\n",
        "    else:\n",
        "        activation = \"softmax\"\n",
        "        units = num_classes\n",
        "\n",
        "    outputs = layers.Dense(units, activation=activation)(x)\n",
        "    return keras.Model(inputs, outputs)\n",
        "\n",
        "model1 = make_fist_model(       input_shape=image_size + (3,), num_classes=2) \n",
        "\n",
        "#printig the model sumaridez \n",
        "#keras.utils.plot_model(model0, show_shapes=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7YiUbAhgw_UR"
      },
      "source": [
        "#### Passo 2\n",
        "\n",
        "* Defindo as condições de paradas para o modelo\n",
        "* Defindo o numero de épocas\n",
        "* Compilando o modelo\n",
        "* Dedinindo um vetor com os parâmetros de Treinamento"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v2AiUowSw_UT",
        "outputId": "4e1f3728-b9d6-4167-be69-280014694c68"
      },
      "source": [
        "checkpoint_filepath = '/content/content'\n",
        "# model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
        "#     filepath=checkpoint_filepath,\n",
        "#     save_weights_only=True,\n",
        "#     monitor='val_accuracy',\n",
        "#     mode='max',\n",
        "#     save_best_only=True)\n",
        "\n",
        "early_stop = tf.keras.callbacks.EarlyStopping(\n",
        "    monitor=\"val_loss\",\n",
        "    min_delta=0,\n",
        "    patience=6,\n",
        "    verbose=0,\n",
        "    restore_best_weights = True)\n",
        "\n",
        "def scheduler(epoch, lr):\n",
        "  if epoch < 9:\n",
        "    return lr\n",
        "  else:\n",
        "    return lr * tf.math.exp(-0.008)\n",
        "\n",
        "lr_callback = tf.keras.callbacks.LearningRateScheduler(scheduler)\n",
        "\n",
        "# DEFING THE EPOCH NUMBERS\n",
        "epochs = 100\n",
        "\n",
        "# COMPILING THE MODEL\n",
        "model1.compile(optimizer= keras.optimizers.Adam(),\n",
        "              loss = 'BinaryCrossentropy',\n",
        "              metrics = [\"accuracy\"])\n",
        "\n",
        "# Creating a dataframe with a the model paramentes\n",
        "history1 = model1.fit(\n",
        "    train_ds, epochs = epochs, \n",
        "    callbacks = [early_stop, lr_callback],\n",
        "    validation_data = val_ds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "87/87 [==============================] - 7s 69ms/step - loss: 10.7036 - accuracy: 0.7944 - val_loss: 1.1652 - val_accuracy: 0.7674\n",
            "Epoch 2/100\n",
            "87/87 [==============================] - 6s 66ms/step - loss: 0.7146 - accuracy: 0.8316 - val_loss: 0.5360 - val_accuracy: 0.8721\n",
            "Epoch 3/100\n",
            "87/87 [==============================] - 6s 66ms/step - loss: 0.3988 - accuracy: 0.8642 - val_loss: 0.5096 - val_accuracy: 0.8488\n",
            "Epoch 4/100\n",
            "87/87 [==============================] - 6s 65ms/step - loss: 0.3266 - accuracy: 0.8746 - val_loss: 0.3985 - val_accuracy: 0.8605\n",
            "Epoch 5/100\n",
            "87/87 [==============================] - 6s 65ms/step - loss: 0.2949 - accuracy: 0.8757 - val_loss: 0.4838 - val_accuracy: 0.8372\n",
            "Epoch 6/100\n",
            "87/87 [==============================] - 6s 65ms/step - loss: 0.2959 - accuracy: 0.8829 - val_loss: 0.4753 - val_accuracy: 0.8488\n",
            "Epoch 7/100\n",
            "87/87 [==============================] - 6s 65ms/step - loss: 0.2754 - accuracy: 0.8819 - val_loss: 0.5276 - val_accuracy: 0.8372\n",
            "Epoch 8/100\n",
            "87/87 [==============================] - 6s 65ms/step - loss: 0.2815 - accuracy: 0.8804 - val_loss: 0.4340 - val_accuracy: 0.8605\n",
            "Epoch 9/100\n",
            "87/87 [==============================] - 6s 65ms/step - loss: 0.2797 - accuracy: 0.8855 - val_loss: 0.4157 - val_accuracy: 0.8605\n",
            "Epoch 10/100\n",
            "87/87 [==============================] - 6s 66ms/step - loss: 0.2682 - accuracy: 0.8945 - val_loss: 0.3966 - val_accuracy: 0.8605\n",
            "Epoch 11/100\n",
            "87/87 [==============================] - 6s 65ms/step - loss: 0.2651 - accuracy: 0.8974 - val_loss: 0.4041 - val_accuracy: 0.8721\n",
            "Epoch 12/100\n",
            "87/87 [==============================] - 6s 65ms/step - loss: 0.2686 - accuracy: 0.8923 - val_loss: 0.4623 - val_accuracy: 0.8605\n",
            "Epoch 13/100\n",
            "87/87 [==============================] - 6s 65ms/step - loss: 0.2656 - accuracy: 0.8862 - val_loss: 0.4642 - val_accuracy: 0.8605\n",
            "Epoch 14/100\n",
            "87/87 [==============================] - 6s 65ms/step - loss: 0.2699 - accuracy: 0.8887 - val_loss: 0.4676 - val_accuracy: 0.8721\n",
            "Epoch 15/100\n",
            "87/87 [==============================] - 6s 65ms/step - loss: 0.2652 - accuracy: 0.8895 - val_loss: 0.4471 - val_accuracy: 0.8605\n",
            "Epoch 16/100\n",
            "87/87 [==============================] - 6s 66ms/step - loss: 0.2455 - accuracy: 0.8999 - val_loss: 0.4029 - val_accuracy: 0.8953\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1qhmJdchw_UV"
      },
      "source": [
        "Como visto acima, a acurácia aumenta com menos camadas convolucionais."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aK_VA--Qw_UX"
      },
      "source": [
        "Imprimindo o sumário do modelo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vons9lDUw_UX"
      },
      "source": [
        "#Model Summary\n",
        "\n",
        "#model0.summary()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QpG1KZoqw_UZ"
      },
      "source": [
        "#### Passo 3\n",
        "\n",
        "Alocando os valores de cada um dos dataframes e gerando os gráficos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "3s9ssjpOw_Ua",
        "outputId": "97020fd3-55d1-4afb-fe70-8f363bf1c03d"
      },
      "source": [
        "acc1 =      history1.history['accuracy']\n",
        "val_acc1 =  history1.history['val_accuracy']\n",
        "loss1 =     history1.history['loss']\n",
        "val_loss1 = history1.history['val_loss']\n",
        "epochs1=range(len(acc1))\n",
        "\n",
        "plt.figure(figsize=(15, 5))\n",
        "\n",
        "plt.subplot(121)\n",
        "plt.plot(epochs1,acc1,    label='Trainin_acc',   color='blue')\n",
        "plt.plot(epochs1,val_acc1,label='Validation_acc',color='red')\n",
        "plt.legend()\n",
        "plt.title(\"Training and Validation Accuracy\")\n",
        "\n",
        "plt.subplot(122)\n",
        "plt.plot(epochs1,loss1,    label='Training_loss',  color='blue')\n",
        "plt.plot(epochs1,val_loss1,label='Validation_loss',color='red')\n",
        "plt.legend()\n",
        "plt.title(\"Training and Validation loss\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fm13U583stNX"
      },
      "source": [
        "### Modelo 2: _More is less_\n",
        "\n",
        "\n",
        "\n",
        "<img src =\"https://raw.githubusercontent.com/barauna-lo/CAP4213-Deep-Learning/840ae3ac76a6d1a9480a891e8e924f8046573175/figures/modelo2.svg\">\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xkCHaOi1vvjB"
      },
      "source": [
        "\n",
        "#### Passo 1 "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RHLEQx8kst4C"
      },
      "source": [
        "keras.backend.clear_session()\n",
        "\n",
        "def make_second_model(input_shape, num_classes):\n",
        "    inputs = keras.Input(shape=input_shape)\n",
        "    # Image augmentation block\n",
        "    x = data_augmentation(inputs)\n",
        "\n",
        "    x = layers.Conv2D(32, kernel_size=(5, 5), activation=\"LeakyReLU\")(x)\n",
        "    x = layers.MaxPooling2D(pool_size=(2, 2))(x)\n",
        "    x = layers.Conv2D(64, kernel_size=(5, 5), activation=\"LeakyReLU\")(x)\n",
        "    x = layers.MaxPooling2D(pool_size=(2, 2))(x)\n",
        "    x = layers.Conv2D(128, kernel_size=(3, 3), activation=\"relu\")(x)\n",
        "    x = layers.MaxPooling2D(pool_size=(2, 2))(x)\n",
        "    x = layers.Flatten()(x)\n",
        "    x = layers.Dropout(0.5)(x)\n",
        "\n",
        "    if num_classes == 2:\n",
        "        activation = \"sigmoid\"\n",
        "        units = 1\n",
        "    else:\n",
        "        activation = \"softmax\"\n",
        "        units = num_classes\n",
        "\n",
        "    outputs = layers.Dense(units, activation=activation)(x)\n",
        "    return keras.Model(inputs, outputs)\n",
        "\n",
        "#defing the model\n",
        "\n",
        "model2 = make_second_model(input_shape=image_size + (3,), num_classes=2)\n",
        "#printig the model sumaridez \n",
        "#keras.utils.plot_model(model0, show_shapes=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J_nIb_01tDAS"
      },
      "source": [
        "#### Passo 2\n",
        "\n",
        "* Defindo as condições de paradas para o modelo\n",
        "* Defindo o numero de épocas\n",
        "* Compilando o modelo\n",
        "* Definindo um vetor com os parâmetros de Treinamento\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zNn5B0WYtJOM",
        "outputId": "0f319290-7fb9-44a5-ee4c-2c611c6fec8b"
      },
      "source": [
        "checkpoint_filepath = '/content/content'\n",
        "# model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
        "#     filepath=checkpoint_filepath,\n",
        "#     save_weights_only=True,\n",
        "#     monitor='val_accuracy',\n",
        "#     mode='max',\n",
        "#     save_best_only=True)\n",
        "\n",
        "early_stop = tf.keras.callbacks.EarlyStopping(\n",
        "    monitor=\"val_loss\",\n",
        "    min_delta=0,\n",
        "    patience=6,\n",
        "    verbose=0,\n",
        "    restore_best_weights = True)\n",
        "\n",
        "def scheduler(epoch, lr):\n",
        "  if epoch < 9:\n",
        "    return lr\n",
        "  else:\n",
        "    return lr * tf.math.exp(-0.008)\n",
        "\n",
        "lr_callback = tf.keras.callbacks.LearningRateScheduler(scheduler)\n",
        "\n",
        "# DEFING THE EPOCH NUMBERS\n",
        "epochs = 100\n",
        "\n",
        "# COMPILING THE MODEL\n",
        "model2.compile(optimizer= keras.optimizers.Adam(),\n",
        "              loss = 'BinaryCrossentropy',\n",
        "              metrics = [\"accuracy\"])\n",
        "\n",
        "# Creating a dataframe with a the model paramentes\n",
        "history2 = model2.fit(\n",
        "    train_ds, epochs = epochs, \n",
        "    callbacks = [early_stop, lr_callback],\n",
        "    validation_data = val_ds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "87/87 [==============================] - 17s 185ms/step - loss: 1.9529 - accuracy: 0.7858 - val_loss: 0.3206 - val_accuracy: 0.8488\n",
            "Epoch 2/100\n",
            "87/87 [==============================] - 16s 184ms/step - loss: 0.3215 - accuracy: 0.8721 - val_loss: 0.3137 - val_accuracy: 0.8721\n",
            "Epoch 3/100\n",
            "87/87 [==============================] - 16s 183ms/step - loss: 0.3210 - accuracy: 0.8699 - val_loss: 0.3780 - val_accuracy: 0.8721\n",
            "Epoch 4/100\n",
            "87/87 [==============================] - 16s 184ms/step - loss: 0.2793 - accuracy: 0.8902 - val_loss: 0.4456 - val_accuracy: 0.8605\n",
            "Epoch 5/100\n",
            "87/87 [==============================] - 16s 185ms/step - loss: 0.2955 - accuracy: 0.8869 - val_loss: 0.3478 - val_accuracy: 0.9070\n",
            "Epoch 6/100\n",
            "87/87 [==============================] - 16s 185ms/step - loss: 0.2745 - accuracy: 0.8905 - val_loss: 0.2842 - val_accuracy: 0.8953\n",
            "Epoch 7/100\n",
            "87/87 [==============================] - 16s 185ms/step - loss: 0.2795 - accuracy: 0.8873 - val_loss: 0.4125 - val_accuracy: 0.8953\n",
            "Epoch 8/100\n",
            "87/87 [==============================] - 16s 186ms/step - loss: 0.2744 - accuracy: 0.8923 - val_loss: 0.3414 - val_accuracy: 0.8953\n",
            "Epoch 9/100\n",
            "87/87 [==============================] - 16s 184ms/step - loss: 0.2979 - accuracy: 0.8858 - val_loss: 0.3917 - val_accuracy: 0.8605\n",
            "Epoch 10/100\n",
            "87/87 [==============================] - 16s 185ms/step - loss: 0.2727 - accuracy: 0.8927 - val_loss: 0.3850 - val_accuracy: 0.8605\n",
            "Epoch 11/100\n",
            "87/87 [==============================] - 16s 185ms/step - loss: 0.2816 - accuracy: 0.8848 - val_loss: 0.3281 - val_accuracy: 0.9302\n",
            "Epoch 12/100\n",
            "87/87 [==============================] - 16s 185ms/step - loss: 0.2742 - accuracy: 0.8913 - val_loss: 0.3256 - val_accuracy: 0.9302\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mk6GF3P6bLYG"
      },
      "source": [
        "A acurácia com este modelo também apresentou melhora, apenas de menor, em relação ao modelo original, mesmo com um número maior de camadas. Possivelmente pela alteração dos tamanhos de janela e da função de ativação."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5K9do0r2tH6N"
      },
      "source": [
        "#Model Summary\n",
        "\n",
        "#model0.summary()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HzUJmHD3tiOp"
      },
      "source": [
        "#### Passo 3\n",
        "\n",
        "Alocando os valores de cada um dos dataframes e gerando os gráficos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mV9kRQfjtH2z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "outputId": "c04523a9-0ef2-42cb-9540-31160736560e"
      },
      "source": [
        "acc2 =      history2.history['accuracy']\n",
        "val_acc2 =  history2.history['val_accuracy']\n",
        "loss2 =     history2.history['loss']\n",
        "val_loss2 = history2.history['val_loss']\n",
        "epochs2=range(len(acc2))\n",
        "\n",
        "plt.figure(figsize=(15, 5))\n",
        "\n",
        "plt.subplot(121)\n",
        "plt.plot(epochs2,acc2,    label='Trainin_acc',   color='blue')\n",
        "plt.plot(epochs2,val_acc2,label='Validation_acc',color='red')\n",
        "plt.legend()\n",
        "plt.title(\"Training and Validation Accuracy\")\n",
        "\n",
        "plt.subplot(122)\n",
        "plt.plot(epochs2,loss2,    label='Training_loss',  color='blue')\n",
        "plt.plot(epochs2,val_loss2,label='Validation_loss',color='red')\n",
        "plt.legend()\n",
        "plt.title(\"Training and Validation loss\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mj_oyF0dtH0b"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ITFiHCn-tHym"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bDHpjjAxMRFx"
      },
      "source": [
        "### Modelo 3: _The Funnels_ ⏳\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/barauna-lo/CAP4213-Deep-Learning/64bf338cbb90fd7a870897320dd6fe85020cb20b/figures/modelo3.svg\"> \n",
        "\n",
        "Este modelo adiciona mais uma camada, mantendo a mesma função de ativação, alterando as janelas mas diminuindo o número de filtros ao invés de aumentar.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "htBntgg5MRFy"
      },
      "source": [
        "\n",
        "#### Passo 1 "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UJkVv7noMRFz"
      },
      "source": [
        "keras.backend.clear_session()\n",
        "\n",
        "def make_terceiro_model(input_shape, num_classes):\n",
        "    inputs = keras.Input(shape=input_shape)\n",
        "    # Image augmentation block\n",
        "    x = data_augmentation(inputs)\n",
        "\n",
        "    x = layers.Conv2D(128, kernel_size=(5, 5), activation=\"relu\")(x)\n",
        "    x = layers.MaxPooling2D(pool_size=(2, 2))(x)\n",
        "    x = layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\")(x)\n",
        "    x = layers.MaxPooling2D(pool_size=(2, 2))(x)\n",
        "    x = layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\")(x)\n",
        "    x = layers.MaxPooling2D(pool_size=(2, 2))(x)\n",
        "    x = layers.Conv2D(6, kernel_size=(3, 3), activation=\"relu\")(x)\n",
        "    x = layers.MaxPooling2D(pool_size=(2, 2))(x)\n",
        "    x = layers.Flatten()(x)\n",
        "    x = layers.Dropout(0.5)(x)\n",
        "\n",
        "    if num_classes == 2:\n",
        "        activation = \"sigmoid\"\n",
        "        units = 1\n",
        "    else:\n",
        "        activation = \"softmax\"\n",
        "        units = num_classes\n",
        "\n",
        "    outputs = layers.Dense(units, activation=activation)(x)\n",
        "    return keras.Model(inputs, outputs)\n",
        "\n",
        "#defing the model\n",
        "\n",
        "model3 = make_terceiro_model(input_shape=image_size + (3,), num_classes=2)\n",
        "#printig the model sumaridez \n",
        "#keras.utils.plot_model(model0, show_shapes=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UKYx6cNhMRF0"
      },
      "source": [
        "#### Passo 2\n",
        "\n",
        "* Defindo as condições de paradas para o modelo\n",
        "* Defindo o numero de épocas\n",
        "* Compilando o modelo\n",
        "* Dedinindo um vetor com os parâmetros de Treinamento\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5pdFGsWpMRF1",
        "outputId": "a07cbd08-a366-4f7a-f89f-8f6c8e4dc6cf"
      },
      "source": [
        "checkpoint_filepath = '/content/content'\n",
        "# model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
        "#     filepath=checkpoint_filepath,\n",
        "#     save_weights_only=True,\n",
        "#     monitor='val_accuracy',\n",
        "#     mode='max',\n",
        "#     save_best_only=True)\n",
        "\n",
        "early_stop = tf.keras.callbacks.EarlyStopping(\n",
        "    monitor=\"val_loss\",\n",
        "    min_delta=0,\n",
        "    patience=6,\n",
        "    verbose=0,\n",
        "    restore_best_weights = True)\n",
        "\n",
        "def scheduler(epoch, lr):\n",
        "  if epoch < 9:\n",
        "    return lr\n",
        "  else:\n",
        "    return lr * tf.math.exp(-0.008)\n",
        "\n",
        "lr_callback = tf.keras.callbacks.LearningRateScheduler(scheduler)\n",
        "\n",
        "# DEFING THE EPOCH NUMBERS\n",
        "epochs = 100\n",
        "\n",
        "# COMPILING THE MODEL\n",
        "model3.compile(optimizer= keras.optimizers.Adam(),\n",
        "              loss = 'BinaryCrossentropy',\n",
        "              metrics = [\"accuracy\"])\n",
        "\n",
        "# Creating a dataframe with a the model paramentes\n",
        "history3 = model3.fit(\n",
        "    train_ds, epochs = epochs, \n",
        "    callbacks = [early_stop, lr_callback],\n",
        "    validation_data = val_ds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "87/87 [==============================] - 27s 303ms/step - loss: 1.0016 - accuracy: 0.7605 - val_loss: 0.4642 - val_accuracy: 0.6744\n",
            "Epoch 2/100\n",
            "87/87 [==============================] - 26s 299ms/step - loss: 0.5109 - accuracy: 0.7699 - val_loss: 0.4313 - val_accuracy: 0.6744\n",
            "Epoch 3/100\n",
            "87/87 [==============================] - 26s 299ms/step - loss: 0.4906 - accuracy: 0.7699 - val_loss: 0.4233 - val_accuracy: 0.6744\n",
            "Epoch 4/100\n",
            "87/87 [==============================] - 26s 300ms/step - loss: 0.5185 - accuracy: 0.7699 - val_loss: 0.4633 - val_accuracy: 0.6744\n",
            "Epoch 5/100\n",
            "87/87 [==============================] - 26s 300ms/step - loss: 0.5072 - accuracy: 0.7695 - val_loss: 0.4736 - val_accuracy: 0.6744\n",
            "Epoch 6/100\n",
            "87/87 [==============================] - 26s 298ms/step - loss: 0.4999 - accuracy: 0.7699 - val_loss: 0.4891 - val_accuracy: 0.6744\n",
            "Epoch 7/100\n",
            "87/87 [==============================] - 26s 299ms/step - loss: 0.4983 - accuracy: 0.7699 - val_loss: 0.4776 - val_accuracy: 0.6744\n",
            "Epoch 8/100\n",
            "87/87 [==============================] - 26s 299ms/step - loss: 0.4834 - accuracy: 0.7695 - val_loss: 0.4589 - val_accuracy: 0.6744\n",
            "Epoch 9/100\n",
            "87/87 [==============================] - 26s 297ms/step - loss: 0.4692 - accuracy: 0.7699 - val_loss: 0.8235 - val_accuracy: 0.6744\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rASQsxf8b2Vm"
      },
      "source": [
        "Como visto, diminuir o número de filtros piora consideravelmente o desempenho da rede."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NBhl4gI3MRF2"
      },
      "source": [
        "#Model Summary\n",
        "\n",
        "#model0.summary()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HmKk7kuOMRF3"
      },
      "source": [
        "#### Passo 3\n",
        "\n",
        "Alocando os valores de cada um dos dataframes e gerando os gráficos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "YisawWapMRF3",
        "outputId": "8381c9bb-0aa7-40fb-cbf7-0b3cb4728c69"
      },
      "source": [
        "acc3 =      history3.history['accuracy']\n",
        "val_acc3 =  history3.history['val_accuracy']\n",
        "loss3 =     history3.history['loss']\n",
        "val_loss3 = history3.history['val_loss']\n",
        "epochs3=range(len(acc3))\n",
        "\n",
        "plt.figure(figsize=(15, 5))\n",
        "\n",
        "plt.subplot(121)\n",
        "plt.plot(epochs3,acc3,    label='Trainin_acc',   color='blue')\n",
        "plt.plot(epochs3,val_acc3,label='Validation_acc',color='red')\n",
        "plt.legend()\n",
        "plt.title(\"Training and Validation Accuracy\")\n",
        "\n",
        "plt.subplot(122)\n",
        "plt.plot(epochs3,loss3,    label='Training_loss',  color='blue')\n",
        "plt.plot(epochs3,val_loss3,label='Validation_loss',color='red')\n",
        "plt.legend()\n",
        "plt.title(\"Training and Validation loss\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SilWuO5Pc2JL"
      },
      "source": [
        "## Comparação Entre os Modelos\n",
        "\n",
        "Desenhar a arquitetura de uma rede é crucial para a validação de um determinado modelo. Aqui realizamos o teste com 4 diferentes arquiteturas. Estas arquiteturas foram propostas pensando em estudar o comportamento de diferentes redes neurais. \n",
        "\n",
        "O modelo 0 é o modelo inicialmente proposto pelo professor para que possamos comparar com os demais modelos\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6P9xW0gwMRF4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "5a59b828-a881-4c28-813b-9b4208d0d1b2"
      },
      "source": [
        "plt.figure(figsize=(15, 10))\n",
        "\n",
        "linestyles = ['-', '--', '-.', ':']\n",
        "\n",
        "# TREINO DA ACURACIA\n",
        "plt.subplot(221)\n",
        "plt.plot(epochs0,acc0,    label='Model 0',   color='blue', linestyle = linestyles[0])\n",
        "plt.plot(epochs1,acc1,    label='Model 1',   color='blue', linestyle = linestyles[1])\n",
        "plt.plot(epochs2,acc2,    label='Model 2',   color='blue', linestyle = linestyles[2])\n",
        "plt.plot(epochs3,acc3,    label='Model 3',   color='blue', linestyle = linestyles[3])\n",
        "plt.legend()\n",
        "plt.title(\"Training Acuracy\")\n",
        "\n",
        "# VALIDAÇÃO DA ACURACIA\n",
        "plt.subplot(222)\n",
        "plt.plot(epochs0,val_acc0,label='Model 0',color='red',linestyle = linestyles[0])\n",
        "plt.plot(epochs1,val_acc1,label='Model 1',color='red',linestyle = linestyles[1])\n",
        "plt.plot(epochs2,val_acc2,label='Model 2',color='red',linestyle = linestyles[2])\n",
        "plt.plot(epochs3,val_acc3,label='Model 3',color='red',linestyle = linestyles[3])\n",
        "plt.legend()\n",
        "plt.title(\"Validation Accuracy\")\n",
        "\n",
        "# TREINO DE LOSS\n",
        "plt.subplot(223)\n",
        "plt.plot(epochs0,loss0,    label='Model 0',   color='blue', linestyle = linestyles[0])\n",
        "plt.plot(epochs1,loss1,    label='Model 1',   color='blue', linestyle = linestyles[1])\n",
        "plt.plot(epochs2,loss2,    label='Model 2',   color='blue', linestyle = linestyles[2])\n",
        "plt.plot(epochs3,loss3,    label='Model 3',   color='blue', linestyle = linestyles[3])\n",
        "plt.legend()\n",
        "plt.title(\"Training Loss\")\n",
        "\n",
        "#VALIDAÇÃO DA PERDA\n",
        "plt.subplot(224)\n",
        "plt.plot(epochs0,val_loss0,label='Model 0',color='red',linestyle = linestyles[0])\n",
        "plt.plot(epochs1,val_loss1,label='Model 1',color='red',linestyle = linestyles[1])\n",
        "plt.plot(epochs2,val_loss2,label='Model 2',color='red',linestyle = linestyles[2])\n",
        "plt.plot(epochs3,val_loss3,label='Model 3',color='red',linestyle = linestyles[3])\n",
        "plt.legend()\n",
        "plt.title(\"Validation loss\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zudYv01RMRF5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed118c5d-d18c-40ad-8a16-1a00c69eb134"
      },
      "source": [
        "print(\"\\n===============================\\nMáxima Acuracia de Cada Modelo\\n===============================\")\n",
        "print('Model 0 =', round(max(acc0),3))\n",
        "print('Model 1 =', round(max(acc1),3))\n",
        "print('Model 2 =', round(max(acc2),3))\n",
        "print('Model 3 =', round(max(acc3),3))\n",
        "\n",
        "\n",
        "print(\"\\n===============================\\nMínimo Loss de Cada Modelo\\n===============================\")\n",
        "print('Model 0 =', round(min(loss0),3))\n",
        "print('Model 1 =', round(min(loss1),3))\n",
        "print('Model 2 =', round(min(loss2),3))\n",
        "print('Model 3 =', round(min(loss3),3))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "===============================\n",
            "Máxima Acuracia de Cada Modelo\n",
            "===============================\n",
            "Model 0 = 0.889\n",
            "Model 1 = 0.9\n",
            "Model 2 = 0.893\n",
            "Model 3 = 0.77\n",
            "\n",
            "===============================\n",
            "Mínimo Loss de Cada Modelo\n",
            "===============================\n",
            "Model 0 = 0.263\n",
            "Model 1 = 0.246\n",
            "Model 2 = 0.273\n",
            "Model 3 = 0.469\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pEtq7Azmpo3L"
      },
      "source": [
        "Como visto na comparação entre os modelos, houve pouca diferença entre todas as arquiteturas. O modelo 1, com menos camadas, foi o que apressentou a melhor acurácia e o menor loss. O modelo 2, com mais camadas, apresentou uma acurácia levemente melhor mas um loss maior. Já o modelo 3 apresentou um loss muito maior e acurácia muito menor.\n",
        "\n",
        "Conclui-se que para este problema é suficiente apenas uma camada convolucional."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GpuGcu2ZtHkk"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cK4I0rfOtHid"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3pBHeABttHgN"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d2_IAyr1JMMk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a48ba2cb-409d-425c-dd67-13fe3b0c3ad5"
      },
      "source": [
        "image_path = \"/content/sample_data/treinamento/train&test/test/selective-loggin-resized/extrac_Id_100019 resized.jpg\"\n",
        "image_path2 = \"/content/sample_data/treinamento/train&test/test/selective-loggin-resized/extrac_Id_100092 resized.jpg\"\n",
        "image_path3 = \"/content/sample_data/treinamento/train&test/test/non-logging-resized/_Id_100003 resized.jpg\"\n",
        "merged_path = [image_path,image_path2,image_path3]\n",
        "\n",
        "for img_path in merged_path:\n",
        "    img = keras.preprocessing.image.load_img(\n",
        "        img_path, target_size=image_size)\n",
        "    img_array = keras.preprocessing.image.img_to_array(img)\n",
        "    img_array = tf.expand_dims(img_array, 0) # Create a batch\n",
        "    \n",
        "    predictions = model1.predict(img_array)\n",
        "    score = tf.nn.softmax(predictions[0])\n",
        "    pred_class = np.argmax(predictions)\n",
        "\n",
        "    pred_class = class_names[pred_class]\n",
        "\n",
        "    \n",
        "    print(\n",
        "        \"This image most likely belongs to {} with a {:.2f} percent confidence.\"\n",
        "        .format(class_names[np.argmax(score)], 100 * np.max(score)))\n",
        "    print('prediction: ',pred_class)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This image most likely belongs to non-logging-resized with a 100.00 percent confidence.\n",
            "prediction:  non-logging-resized\n",
            "This image most likely belongs to non-logging-resized with a 100.00 percent confidence.\n",
            "prediction:  non-logging-resized\n",
            "This image most likely belongs to non-logging-resized with a 100.00 percent confidence.\n",
            "prediction:  non-logging-resized\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fW9IDjv5ns8D"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}