MLP.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gabrieladamasceno/Attacks_5G/blob/main/MLP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cuurjZdWJk6h",
        "outputId": "7f6ae088-53c8-4158-a76d-5f2cfb5b5f2f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "import pandas as pd\n",
        "\n",
        "# Acessar dataset\n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "# Carregar o dataset\n",
        "encoded = pd.read_csv(\"/content/gdrive/MyDrive/Datasets/Attacks/Encoded/Encoded.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install imbalanced-learn\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8S3nVM7mjjwA",
        "outputId": "045e9f06-fb00-4e63-b63d-91e13b24b1e4"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting imbalanced-learn\n",
            "  Downloading imbalanced_learn-0.12.4-py3-none-any.whl.metadata (8.3 kB)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.26.4)\n",
            "Requirement already satisfied: scipy>=1.5.0 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.13.1)\n",
            "Requirement already satisfied: scikit-learn>=1.0.2 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.5.2)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (3.5.0)\n",
            "Downloading imbalanced_learn-0.12.4-py3-none-any.whl (258 kB)\n",
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/258.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.9/258.3 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m258.3/258.3 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: imbalanced-learn\n",
            "Successfully installed imbalanced-learn-0.12.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "from imblearn.pipeline import Pipeline\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Input\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, precision_score, recall_score, f1_score\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "metadata": {
        "id": "7VCtN5o8jsGr"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "biNnSG49XmU0",
        "outputId": "c87d12fc-e8b2-4e93-b41e-2c7fc753fe44"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Número de amostras: 1215890\n",
            "Número de amostras (usando len): 1215890\n"
          ]
        }
      ],
      "source": [
        "# Usando shape\n",
        "num_amostras = encoded.shape[0]\n",
        "print(f\"Número de amostras: {num_amostras}\")\n",
        "\n",
        "# Usando len()\n",
        "num_amostras_len = len(encoded)\n",
        "print(f\"Número de amostras (usando len): {num_amostras_len}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ULAbhjQIYl8b",
        "outputId": "9654e647-607d-47aa-e4ca-87d8ffd4d366"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nomes das colunas:\n",
            "Unnamed: 0\n",
            "Seq\n",
            "Dur\n",
            "RunTime\n",
            "Mean\n",
            "Sum\n",
            "Min\n",
            "Max\n",
            "sTos\n",
            "dTos\n",
            "sTtl\n",
            "dTtl\n",
            "sHops\n",
            "dHops\n",
            "TotPkts\n",
            "SrcPkts\n",
            "DstPkts\n",
            "TotBytes\n",
            "SrcBytes\n",
            "DstBytes\n",
            "Offset\n",
            "sMeanPktSz\n",
            "dMeanPktSz\n",
            "Load\n",
            "SrcLoad\n",
            "DstLoad\n",
            "Loss\n",
            "SrcLoss\n",
            "DstLoss\n",
            "pLoss\n",
            "SrcGap\n",
            "DstGap\n",
            "Rate\n",
            "SrcRate\n",
            "DstRate\n",
            "SrcWin\n",
            "DstWin\n",
            "sVid\n",
            "dVid\n",
            "SrcTCPBase\n",
            "DstTCPBase\n",
            "TcpRtt\n",
            "SynAck\n",
            "AckDat\n",
            "Label\n",
            "Attack Type\n",
            "Attack Tool\n",
            " *        \n",
            " *    V   \n",
            " *    f   \n",
            " e        \n",
            " e    f   \n",
            " e &      \n",
            " e *      \n",
            " e d      \n",
            " e g      \n",
            " e i      \n",
            " e r      \n",
            " e s      \n",
            " eU       \n",
            "e        \n",
            "arp\n",
            "icmp\n",
            "ipv6-icmp\n",
            "llc\n",
            "lldp\n",
            "sctp\n",
            "tcp\n",
            "udp\n",
            "ACC\n",
            "CON\n",
            "ECO\n",
            "FIN\n",
            "INT\n",
            "NRS\n",
            "REQ\n",
            "RSP\n",
            "RST\n",
            "TST\n",
            "URP\n",
            "Shutdown\n",
            "Start\n",
            "Status\n",
            "39\n",
            "4\n",
            "52\n",
            "54\n",
            "af11\n",
            "af12\n",
            "af41\n",
            "cs0\n",
            "cs4\n",
            "cs6\n",
            "cs7\n",
            "ef\n",
            "nan\n"
          ]
        }
      ],
      "source": [
        "# Exibir os nomes de todas as colunas\n",
        "colunas = encoded.columns.tolist()  # Converte o Index em uma lista\n",
        "print(\"Nomes das colunas:\")\n",
        "for coluna in colunas:\n",
        "    print(coluna)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Selecionar apenas colunas numéricas\n",
        "numeric_cols = encoded.select_dtypes(include=['number']).columns\n",
        "\n",
        "# Aplicar a suavização apenas nas colunas numéricas\n",
        "for col in numeric_cols:\n",
        "    encoded[col] = encoded[col].fillna(encoded[col].mean())"
      ],
      "metadata": {
        "id": "F7xFWWJ6f72N"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# columns with null values\n",
        "\n",
        "columns_null = []\n",
        "columns = encoded.columns\n",
        "for column in columns:\n",
        "    c = encoded[column].isnull().sum()\n",
        "    if c != 0:\n",
        "        print(column, 'has {} null values'.format(c))\n",
        "        columns_null.append(column)"
      ],
      "metadata": {
        "id": "MTktQ-fDi4cS"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "REG482ke4ujI"
      },
      "source": [
        "Features\n",
        "* Seq: Sequência de pacotes.\n",
        "* Offset: Deslocamento em bytes.\n",
        "* sTtl: Time to Live da fonte.\n",
        "* tcp: Indica se o protocolo TCP está sendo usado.\n",
        "* AckDat: Dados de reconhecimento.\n",
        "* RST: Sinalizador de reinício TCP.\n",
        "* INT: Interrupções no fluxo de dados.\n",
        "* TcpRtt: Tempo de ida e volta do TCP.\n",
        "* icmp: Indica se o protocolo ICMP está presente.\n",
        "* sMeanPktSz: Tamanho médio do pacote da fonte.\n",
        "* FIN: Sinalizador de finalização de conexão.\n",
        "* sHops: Número de saltos até o destino.\n",
        "* Mean: Valor médio de um conjunto de dados.\n",
        "* dTtl: Time to Live do destino.\n",
        "* SrcBytes: Bytes enviados da fonte.\n",
        "* TotBytes: Total de bytes.\n",
        "* dMeanPktSz: Tamanho médio do pacote do destino.\n",
        "* Status: Estado da conexão.\n",
        "* cs0: Parâmetro de controle.\n",
        "* SrcWin: Janela de origem TCP.\n",
        "* sTos: Tipo de serviço.\n",
        "* CON: Conexões.\n",
        "* REQ: Requisições.\n",
        "* Loss: Taxa de perda de pacotes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "iV383A-eKYaC"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Input, Dense\n",
        "from tensorflow.keras.layers import Dropout\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "\n",
        "# Definir as features que você deseja usar\n",
        "features = [\n",
        "    'Seq', 'Offset', 'sTtl', 'e        ', 'tcp', 'AckDat', 'RST', 'INT', 'TcpRtt', 'icmp',\n",
        "    'sMeanPktSz', 'FIN', 'sHops', 'Mean', 'dTtl', 'SrcBytes', 'TotBytes',\n",
        "    'dMeanPktSz', 'Status', 'cs0', 'SrcWin', 'sTos', 'CON', 'REQ', 'Loss'\n",
        "]\n",
        "\n",
        "# Separar as features e os rótulos\n",
        "X = encoded[features]  # Usar apenas as colunas selecionadas\n",
        "y = encoded['Label'].values\n",
        "\n",
        "# Codificar os rótulos para valores numéricos\n",
        "label_encoder = LabelEncoder()\n",
        "y = label_encoder.fit_transform(y)  # Transforma 'Benign' e 'Malign' em 0 e 1\n",
        "\n",
        "# Identificar colunas categóricas (se houver)\n",
        "categorical_cols = X.select_dtypes(include=['object']).columns.tolist()\n",
        "\n",
        "# Aplicar OneHotEncoder nas colunas categóricas\n",
        "transformer = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('cat', OneHotEncoder(), categorical_cols)  # Codificar colunas categóricas\n",
        "    ],\n",
        "    remainder='passthrough'  # Manter as colunas numéricas\n",
        ")\n",
        "\n",
        "X = transformer.fit_transform(X)  # Transformar as features\n",
        "\n",
        "# Normalização\n",
        "scaler = StandardScaler()\n",
        "X = scaler.fit_transform(X)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "print(pd.Series(y).value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9EfnQ6RlhiOG",
        "outputId": "6017530c-a164-4b9f-dd28-0028e2c308ba"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1    738153\n",
            "0    477737\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(pd.Series(y_resampled).value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3SHGFWvIh_YQ",
        "outputId": "0a8b4ee9-448f-429d-c046-63fe56a91d27"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    477737\n",
            "1    477737\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Criar o objeto de undersampling\n",
        "undersampler = RandomUnderSampler(sampling_strategy='auto', random_state=42)\n",
        "\n",
        "# Aplicar o undersampling\n",
        "X_resampled, y_resampled = undersampler.fit_resample(X, y)"
      ],
      "metadata": {
        "id": "rGBQHSuch5lR"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "7ZERRU38MEHy"
      },
      "outputs": [],
      "source": [
        "# Dividir os dados em treino e teste\n",
        "#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=42)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Amostras de treino:\", len(X_train))\n",
        "print(\"Amostras de teste:\", len(X_test))"
      ],
      "metadata": {
        "id": "_4OhbeqtppjF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40551130-3e6f-471e-d1f9-c6c5dea9f8ef"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Amostras de treino: 764379\n",
            "Amostras de teste: 191095\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import psutil\n",
        "import time\n",
        "from keras.callbacks import Callback\n",
        "\n",
        "# Função para monitorar recursos\n",
        "def monitor_resource_usage():\n",
        "    process = psutil.Process()\n",
        "    memory_info = process.memory_info()\n",
        "    cpu_percent = psutil.cpu_percent(interval=0.1)\n",
        "    memory_usage_mb = memory_info.rss / (1024 * 1024)\n",
        "    return cpu_percent, memory_usage_mb\n",
        "\n",
        "# Callback personalizado para monitorar CPU e memória\n",
        "class ResourceMonitor(Callback):\n",
        "    def on_epoch_begin(self, epoch, logs=None):\n",
        "        # Antes da época começar\n",
        "        self.start_time = time.time()\n",
        "        self.start_cpu, self.start_memory = monitor_resource_usage()\n",
        "        print(f\"\\n[Início da Época {epoch + 1}]\")\n",
        "        print(f\"CPU inicial: {self.start_cpu:.2f}%, Memória inicial: {self.start_memory:.2f} MB\")\n",
        "\n",
        "    def on_epoch_end(self, epoch, logs=None):\n",
        "        # Após a época terminar\n",
        "        end_time = time.time()\n",
        "        end_cpu, end_memory = monitor_resource_usage()\n",
        "        elapsed_time = end_time - self.start_time\n",
        "        memory_diff = end_memory - self.start_memory\n",
        "\n",
        "        print(f\"[Fim da Época {epoch + 1}]\")\n",
        "        print(f\"CPU final: {end_cpu:.2f}%, Memória consumida: {memory_diff:.2f} MB, Tempo gasto: {elapsed_time:.2f} segundos\")\n",
        "\n",
        "# Instanciar o callback\n",
        "resource_monitor = ResourceMonitor()"
      ],
      "metadata": {
        "id": "s4yCHc3I-s6b"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from keras.regularizers import l2\n",
        "from keras.layers import Dropout\n",
        "from keras.callbacks import EarlyStopping\n",
        "from keras.layers import BatchNormalization\n",
        "\n",
        "early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n",
        "\n",
        "\n",
        "# Definir a estrutura da MLP Relu\n",
        "model = Sequential()\n",
        "model.add(Input(shape=(X_train.shape[1],)))\n",
        "model.add(Dense(96, activation='relu', kernel_regularizer=l2(0.01))),  # Camada de entrada com 96 neurônios\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(64, activation='relu', kernel_regularizer=l2(0.01))),  # Primeira camada oculta\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(64, activation='relu', kernel_regularizer=l2(0.01))),  # Segunda camada oculta\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(64, activation='relu', kernel_regularizer=l2(0.01))),  # Terceira camada oculta\n",
        "model.add(Dense(1, activation='sigmoid'))  # Camada de saída para classificação binária\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "# Compilar o modelo\n",
        "model.compile(optimizer=Adam(learning_rate=0.0001, clipnorm=1.0), loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Treinar o modelo\n",
        "history = model.fit(X_train, y_train, epochs=20, batch_size=32, validation_data=(X_test, y_test), callbacks=[early_stopping, resource_monitor]) #coloquei o batch size como 32\n",
        "monitor_resource_usage()\n",
        "time.sleep(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O7dONqFBnQ2e",
        "outputId": "b11ba6d6-f9d1-4d19-9886-2b9e76d0a4c5"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "[Início da Época 1]\n",
            "CPU inicial: 0.20%, Memória inicial: 8222.29 MB\n",
            "Epoch 1/20\n",
            "23883/23887 [============================>.] - ETA: 0s - loss: 0.5389 - accuracy: 0.9695[Fim da Época 1]\n",
            "CPU final: 0.30%, Memória consumida: 22.86 MB, Tempo gasto: 61.48 segundos\n",
            "23887/23887 [==============================] - 61s 3ms/step - loss: 0.5389 - accuracy: 0.9695 - val_loss: 0.1076 - val_accuracy: 0.9920\n",
            "\n",
            "[Início da Época 2]\n",
            "CPU inicial: 0.20%, Memória inicial: 8245.15 MB\n",
            "Epoch 2/20\n",
            "23877/23887 [============================>.] - ETA: 0s - loss: 0.1139 - accuracy: 0.9900[Fim da Época 2]\n",
            "CPU final: 0.10%, Memória consumida: 15.63 MB, Tempo gasto: 60.47 segundos\n",
            "23887/23887 [==============================] - 60s 3ms/step - loss: 0.1139 - accuracy: 0.9900 - val_loss: 0.1049 - val_accuracy: 0.9911\n",
            "\n",
            "[Início da Época 3]\n",
            "CPU inicial: 0.70%, Memória inicial: 8260.78 MB\n",
            "Epoch 3/20\n",
            "23872/23887 [============================>.] - ETA: 0s - loss: 0.1133 - accuracy: 0.9905[Fim da Época 3]\n",
            "CPU final: 0.20%, Memória consumida: 6.56 MB, Tempo gasto: 60.20 segundos\n",
            "23887/23887 [==============================] - 60s 3ms/step - loss: 0.1133 - accuracy: 0.9905 - val_loss: 0.1037 - val_accuracy: 0.9925\n",
            "\n",
            "[Início da Época 4]\n",
            "CPU inicial: 0.50%, Memória inicial: 8267.34 MB\n",
            "Epoch 4/20\n",
            "23869/23887 [============================>.] - ETA: 0s - loss: 0.1126 - accuracy: 0.9908[Fim da Época 4]\n",
            "CPU final: 0.20%, Memória consumida: 6.52 MB, Tempo gasto: 60.85 segundos\n",
            "23887/23887 [==============================] - 61s 3ms/step - loss: 0.1125 - accuracy: 0.9908 - val_loss: 0.0990 - val_accuracy: 0.9924\n",
            "\n",
            "[Início da Época 5]\n",
            "CPU inicial: 0.10%, Memória inicial: 8273.87 MB\n",
            "Epoch 5/20\n",
            "23881/23887 [============================>.] - ETA: 0s - loss: 0.1103 - accuracy: 0.9909[Fim da Época 5]\n",
            "CPU final: 0.50%, Memória consumida: 0.89 MB, Tempo gasto: 60.31 segundos\n",
            "23887/23887 [==============================] - 60s 3ms/step - loss: 0.1103 - accuracy: 0.9909 - val_loss: 0.0998 - val_accuracy: 0.9921\n",
            "\n",
            "[Início da Época 6]\n",
            "CPU inicial: 0.50%, Memória inicial: 8274.76 MB\n",
            "Epoch 6/20\n",
            "23878/23887 [============================>.] - ETA: 0s - loss: 0.1099 - accuracy: 0.9911[Fim da Época 6]\n",
            "CPU final: 1.50%, Memória consumida: 0.59 MB, Tempo gasto: 60.42 segundos\n",
            "23887/23887 [==============================] - 60s 3ms/step - loss: 0.1099 - accuracy: 0.9911 - val_loss: 0.1035 - val_accuracy: 0.9925\n",
            "\n",
            "[Início da Época 7]\n",
            "CPU inicial: 1.60%, Memória inicial: 8275.36 MB\n",
            "Epoch 7/20\n",
            "23881/23887 [============================>.] - ETA: 0s - loss: 0.1091 - accuracy: 0.9910[Fim da Época 7]\n",
            "CPU final: 0.20%, Memória consumida: 6.56 MB, Tempo gasto: 58.46 segundos\n",
            "23887/23887 [==============================] - 58s 2ms/step - loss: 0.1092 - accuracy: 0.9910 - val_loss: 0.1026 - val_accuracy: 0.9932\n",
            "\n",
            "[Início da Época 8]\n",
            "CPU inicial: 0.60%, Memória inicial: 8281.92 MB\n",
            "Epoch 8/20\n",
            "23869/23887 [============================>.] - ETA: 0s - loss: 0.1107 - accuracy: 0.9910[Fim da Época 8]\n",
            "CPU final: 0.20%, Memória consumida: 6.70 MB, Tempo gasto: 59.58 segundos\n",
            "23887/23887 [==============================] - 60s 2ms/step - loss: 0.1107 - accuracy: 0.9910 - val_loss: 0.0983 - val_accuracy: 0.9933\n",
            "\n",
            "[Início da Época 9]\n",
            "CPU inicial: 0.10%, Memória inicial: 8288.62 MB\n",
            "Epoch 9/20\n",
            "23876/23887 [============================>.] - ETA: 0s - loss: 0.1102 - accuracy: 0.9911[Fim da Época 9]\n",
            "CPU final: 0.00%, Memória consumida: 6.50 MB, Tempo gasto: 60.43 segundos\n",
            "23887/23887 [==============================] - 60s 3ms/step - loss: 0.1102 - accuracy: 0.9911 - val_loss: 0.1030 - val_accuracy: 0.9928\n",
            "\n",
            "[Início da Época 10]\n",
            "CPU inicial: 0.10%, Memória inicial: 8295.12 MB\n",
            "Epoch 10/20\n",
            "23881/23887 [============================>.] - ETA: 0s - loss: 0.1087 - accuracy: 0.9911[Fim da Época 10]\n",
            "CPU final: 2.10%, Memória consumida: 2.00 MB, Tempo gasto: 58.88 segundos\n",
            "23887/23887 [==============================] - 59s 2ms/step - loss: 0.1086 - accuracy: 0.9911 - val_loss: 0.1009 - val_accuracy: 0.9928\n",
            "\n",
            "[Início da Época 11]\n",
            "CPU inicial: 1.90%, Memória inicial: 8297.12 MB\n",
            "Epoch 11/20\n",
            "23865/23887 [============================>.] - ETA: 0s - loss: 0.1069 - accuracy: 0.9912[Fim da Época 11]\n",
            "CPU final: 0.40%, Memória consumida: 3.30 MB, Tempo gasto: 58.14 segundos\n",
            "23887/23887 [==============================] - 58s 2ms/step - loss: 0.1070 - accuracy: 0.9912 - val_loss: 0.0965 - val_accuracy: 0.9926\n",
            "\n",
            "[Início da Época 12]\n",
            "CPU inicial: 0.20%, Memória inicial: 8300.43 MB\n",
            "Epoch 12/20\n",
            "23879/23887 [============================>.] - ETA: 0s - loss: 0.1052 - accuracy: 0.9912[Fim da Época 12]\n",
            "CPU final: 0.30%, Memória consumida: 1.16 MB, Tempo gasto: 57.60 segundos\n",
            "23887/23887 [==============================] - 58s 2ms/step - loss: 0.1053 - accuracy: 0.9912 - val_loss: 0.0991 - val_accuracy: 0.9917\n",
            "\n",
            "[Início da Época 13]\n",
            "CPU inicial: 0.10%, Memória inicial: 8301.59 MB\n",
            "Epoch 13/20\n",
            "23869/23887 [============================>.] - ETA: 0s - loss: 0.1072 - accuracy: 0.9912[Fim da Época 13]\n",
            "CPU final: 1.60%, Memória consumida: 0.45 MB, Tempo gasto: 58.12 segundos\n",
            "23887/23887 [==============================] - 58s 2ms/step - loss: 0.1072 - accuracy: 0.9912 - val_loss: 0.1144 - val_accuracy: 0.9908\n",
            "\n",
            "[Início da Época 14]\n",
            "CPU inicial: 1.60%, Memória inicial: 8302.03 MB\n",
            "Epoch 14/20\n",
            "23875/23887 [============================>.] - ETA: 0s - loss: 0.1074 - accuracy: 0.9911[Fim da Época 14]\n",
            "CPU final: 0.00%, Memória consumida: 0.69 MB, Tempo gasto: 58.42 segundos\n",
            "23887/23887 [==============================] - 58s 2ms/step - loss: 0.1073 - accuracy: 0.9911 - val_loss: 0.0998 - val_accuracy: 0.9922\n",
            "\n",
            "[Início da Época 15]\n",
            "CPU inicial: 0.30%, Memória inicial: 8302.72 MB\n",
            "Epoch 15/20\n",
            "23873/23887 [============================>.] - ETA: 0s - loss: 0.1068 - accuracy: 0.9912[Fim da Época 15]\n",
            "CPU final: 0.10%, Memória consumida: 0.49 MB, Tempo gasto: 57.73 segundos\n",
            "23887/23887 [==============================] - 58s 2ms/step - loss: 0.1067 - accuracy: 0.9912 - val_loss: 0.0943 - val_accuracy: 0.9935\n",
            "\n",
            "[Início da Época 16]\n",
            "CPU inicial: 0.00%, Memória inicial: 8303.21 MB\n",
            "Epoch 16/20\n",
            "23876/23887 [============================>.] - ETA: 0s - loss: 0.1057 - accuracy: 0.9911[Fim da Época 16]\n",
            "CPU final: 1.70%, Memória consumida: 0.28 MB, Tempo gasto: 58.37 segundos\n",
            "23887/23887 [==============================] - 58s 2ms/step - loss: 0.1057 - accuracy: 0.9911 - val_loss: 0.0985 - val_accuracy: 0.9934\n",
            "\n",
            "[Início da Época 17]\n",
            "CPU inicial: 1.60%, Memória inicial: 8303.49 MB\n",
            "Epoch 17/20\n",
            "23887/23887 [==============================] - ETA: 0s - loss: 0.1044 - accuracy: 0.9912[Fim da Época 17]\n",
            "CPU final: 0.10%, Memória consumida: 6.29 MB, Tempo gasto: 59.82 segundos\n",
            "23887/23887 [==============================] - 60s 3ms/step - loss: 0.1044 - accuracy: 0.9912 - val_loss: 0.0986 - val_accuracy: 0.9922\n",
            "\n",
            "[Início da Época 18]\n",
            "CPU inicial: 0.60%, Memória inicial: 8309.78 MB\n",
            "Epoch 18/20\n",
            "23865/23887 [============================>.] - ETA: 0s - loss: 0.1071 - accuracy: 0.9911[Fim da Época 18]\n",
            "CPU final: 0.00%, Memória consumida: 6.46 MB, Tempo gasto: 60.26 segundos\n",
            "23887/23887 [==============================] - 60s 3ms/step - loss: 0.1071 - accuracy: 0.9911 - val_loss: 0.0947 - val_accuracy: 0.9932\n",
            "\n",
            "[Início da Época 19]\n",
            "CPU inicial: 0.30%, Memória inicial: 8316.24 MB\n",
            "Epoch 19/20\n",
            "23877/23887 [============================>.] - ETA: 0s - loss: 0.1067 - accuracy: 0.9910[Fim da Época 19]\n",
            "CPU final: 0.10%, Memória consumida: 0.62 MB, Tempo gasto: 60.76 segundos\n",
            "23887/23887 [==============================] - 61s 3ms/step - loss: 0.1067 - accuracy: 0.9910 - val_loss: 0.0985 - val_accuracy: 0.9918\n",
            "\n",
            "[Início da Época 20]\n",
            "CPU inicial: 0.30%, Memória inicial: 8316.86 MB\n",
            "Epoch 20/20\n",
            "23879/23887 [============================>.] - ETA: 0s - loss: 0.1067 - accuracy: 0.9911[Fim da Época 20]\n",
            "CPU final: 0.40%, Memória consumida: 0.50 MB, Tempo gasto: 59.59 segundos\n",
            "23887/23887 [==============================] - 60s 2ms/step - loss: 0.1067 - accuracy: 0.9911 - val_loss: 0.0973 - val_accuracy: 0.9926\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LnQuMfoFCD99",
        "outputId": "27d79578-7082-45c6-fdca-103db81c264e"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/bin/bash: line 1: nvidia-smi: command not found\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Matriz de Confusão\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "y_pred_classes = (y_pred > 0.5).astype(int)\n",
        "\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred_classes)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[\"Benign\", \"Malign\"])\n",
        "disp.plot(cmap='Blues')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "RnbqL9ni_iYc",
        "outputId": "021fc68b-2fc8-41ba-cfc1-0d88644bd8e6"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5972/5972 [==============================] - 9s 1ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7c7da8636080>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Maligno\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score\n",
        "\n",
        "precision = precision_score(y_test, y_pred_classes, pos_label=1)\n",
        "recall = recall_score(y_test, y_pred_classes, pos_label=1)\n",
        "f1 = f1_score(y_test, y_pred_classes, pos_label=1)\n",
        "\n",
        "print(f\"Precisão: {precision}\")\n",
        "print(f\"Recall: {recall}\")\n",
        "print(f\"F1 Score: {f1}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HO9JKPy5CO3o",
        "outputId": "44daf617-8f86-461c-83c9-0ec2240e5ff6"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisão: 0.9910731976932142\n",
            "Recall: 0.9942043540574752\n",
            "F1 Score: 0.9926363066638814\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Benigno\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score\n",
        "\n",
        "precision = precision_score(y_test, y_pred_classes, pos_label=0)\n",
        "recall = recall_score(y_test, y_pred_classes, pos_label=0)\n",
        "f1 = f1_score(y_test, y_pred_classes, pos_label=0)\n",
        "\n",
        "print(f\"Precisão: {precision}\")\n",
        "print(f\"Recall: {recall}\")\n",
        "print(f\"F1 Score: {f1}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4E6uKrnYCh46",
        "outputId": "e36d884d-e90c-4626-83d5-531f2c06b569"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisão: 0.994180916768203\n",
            "Recall: 0.9910372123217389\n",
            "F1 Score: 0.9926065754286613\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Gráfico de Acurácia\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Acurácia de treino\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(history.history['accuracy'], label='Treinamento')\n",
        "plt.plot(history.history['val_accuracy'], label='Validação')\n",
        "plt.title('Acurácia por Época')\n",
        "plt.xlabel('Épocas')\n",
        "plt.ylabel('Acurácia')\n",
        "plt.legend()\n",
        "\n",
        "# Gráfico de Perda\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(history.history['loss'], label='Treinamento')\n",
        "plt.plot(history.history['val_loss'], label='Validação')\n",
        "plt.title('Perda por Época')\n",
        "plt.xlabel('Épocas')\n",
        "plt.ylabel('Perda')\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "NbJqXntM-C0q",
        "outputId": "e93fc2a5-9250-43a0-ac91-dbd3751e301f"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import roc_curve, auc\n",
        "\n",
        "# Supondo que 'model' seja seu modelo treinado\n",
        "y_pred_prob = model.predict(X_test).flatten()  # Usando .flatten() para transformar em um vetor 1D"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oUVls-ta6sY3",
        "outputId": "f55b6b46-d266-46a4-8fe0-1b2a8815df7e"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5972/5972 [==============================] - 7s 1ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)"
      ],
      "metadata": {
        "id": "ZNVcNyk-6x7o"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "roc_auc = auc(fpr, tpr)"
      ],
      "metadata": {
        "id": "nmo7Pmob6zJq"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import roc_curve, auc\n",
        "\n",
        "# Calcular a curva ROC\n",
        "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\n",
        "\n",
        "# Calcular a AUC\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "# Plotar a curva ROC\n",
        "plt.figure()\n",
        "plt.plot(fpr, tpr, color='darkorange', lw=2, label='Curva ROC (AUC = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')  # Linha diagonal (chance aleatória)\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('FPR (Taxa de Falsos Positivos)')\n",
        "plt.ylabel('TPR (Taxa de Verdadeiros Positivos)')\n",
        "plt.title('Curva ROC')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "TsQ7649G7qjl",
        "outputId": "6b385aa5-0a66-4544-9034-c6132100d8f1"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "accelerator": "TPU",
    "colab": {
      "gpuType": "V28",
      "provenance": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}