Modelo_K_128.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7xXSKt4mbBIL"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import cv2\n",
        "import os\n",
        "import zipfile\n",
        "from PIL import Image\n",
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pD-8crTwB-Z0",
        "outputId": "65a01605-80fb-4995-e07f-9e55e35f213b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KhaCvu2h3nCb",
        "outputId": "a74260f5-29e7-4d5f-dd57-4045198d565c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (2.31.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests) (2023.11.17)\n"
          ]
        }
      ],
      "source": [
        "%pip install requests"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9zuSL3Asb2wh",
        "outputId": "b5a2cb6c-5b39-4805-b0dc-2e357dad1264"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2023-11-30 19:49:40--  https://data.lhncbc.nlm.nih.gov/public/Malaria/cell_images.zip\n",
            "Resolving data.lhncbc.nlm.nih.gov (data.lhncbc.nlm.nih.gov)... 108.138.94.6, 108.138.94.36, 108.138.94.100, ...\n",
            "Connecting to data.lhncbc.nlm.nih.gov (data.lhncbc.nlm.nih.gov)|108.138.94.6|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 353452851 (337M) [application/zip]\n",
            "Saving to: ‘/content/drive/MyDrive/ELT579/Problema4/cell_images.zip’\n",
            "\n",
            "cell_images.zip     100%[===================>] 337.08M  41.4MB/s    in 7.0s    \n",
            "\n",
            "2023-11-30 19:49:48 (48.1 MB/s) - ‘/content/drive/MyDrive/ELT579/Problema4/cell_images.zip’ saved [353452851/353452851]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Definir o endereço para baixar arquivo\n",
        "!wget -P /content/drive/MyDrive/ELT579/Problema4 https://data.lhncbc.nlm.nih.gov/public/Malaria/cell_images.zip\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Rxh8sYBx50Ve"
      },
      "outputs": [],
      "source": [
        "# Extrair conteúdo dos arquivos comprimidos\n",
        "local_zip = '/content/drive/MyDrive/ELT579/Problema4/cell_images.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/content/drive/MyDrive/ELT579/Problema4')\n",
        "zip_ref.close()"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5M6hp0wXFG6R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xzVO5Nz79fTs"
      },
      "outputs": [],
      "source": [
        "pasta = '/content/drive/MyDrive/ELT579/Problema4/cell_images/'\n",
        "\n",
        "positivas = os.listdir(pasta + 'Parasitized/')\n",
        "\n",
        "negativas = os.listdir(pasta + 'Uninfected/')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m_6LEadddCbM"
      },
      "outputs": [],
      "source": [
        "dataset = list()\n",
        "\n",
        "label = list()\n",
        "\n",
        "SIZE = 64\n",
        "\n",
        "for i, image_name in enumerate(positivas):\n",
        "  if(image_name.split('.')[1] == 'png'):\n",
        "    img = cv2.imread(pasta + 'Parasitized/' + image_name)\n",
        "    img = Image.fromarray(img, 'RGB')\n",
        "    img = img.resize((SIZE, SIZE))\n",
        "    dataset.append(np.array(img))\n",
        "    label.append(1)\n",
        "\n",
        "for i, image_name in enumerate(negativas):\n",
        "  if(image_name.split('.')[1] == 'png'):\n",
        "    img = cv2.imread(pasta + 'Uninfected/' + image_name)\n",
        "    img = Image.fromarray(img, 'RGB')\n",
        "    img = img.resize((SIZE, SIZE))\n",
        "    dataset.append(np.array(img))\n",
        "    label.append(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "FloEBnQSxT3K",
        "outputId": "3ba6185d-9712-495f-83f6-6f9acb020732"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "label =  0\n",
            "imagem\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7d5c70dae860>"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print('label = ', label[-2])\n",
        "\n",
        "print('imagem')\n",
        "plt.imshow(dataset[-2])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aAWghSEVKMhB"
      },
      "outputs": [],
      "source": [
        "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)\n",
        "from keras.utils import to_categorical\n",
        "from keras.applications.vgg16 import VGG16, preprocess_input\n",
        "from keras.models import Model, Sequential\n",
        "from keras.layers import Lambda, Input\n",
        "from keras.optimizers import Adam\n",
        "from keras.layers import Dense, Dropout, Flatten\n",
        "from keras.preprocessing.image import ImageDataGenerator\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jV7EYjmWyoky"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import tensorflow.keras as K\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "def preprocess_data(X, Y):\n",
        "    \"\"\" This method has the preprocess to train a model \"\"\"\n",
        "    X = X.astype('float32')\n",
        "    X_p = K.applications.vgg16.preprocess_input(X)\n",
        "    Y_p = K.utils.to_categorical(Y, 2)  # Assuming binary classification, adjust accordingly\n",
        "    return (X_p, Y_p)\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    # Load your custom dataset\n",
        "    dataset = np.array(dataset)\n",
        "    label = np.array(label)\n",
        "\n",
        "    # Divisão dos dados em treinamento, validação e teste\n",
        "\n",
        "    X_train, X_temp, Y_train, Y_temp = train_test_split(dataset, label, test_size=0.4, random_state=42)\n",
        "    X_val, X_test, Y_val, Y_test = train_test_split(X_temp, Y_temp, test_size=0.5, random_state=42)\n",
        "\n",
        "    # Split the dataset into training and validation sets\n",
        "    #Xt, X, Yt, Y = train_test_split(dataset, label, test_size=0.2, random_state=42)\n",
        "\n",
        "    X_train_p, Y_train_p = preprocess_data(X_train, Y_train)\n",
        "    X_val_p, Y_val_p = preprocess_data(X_val, Y_val)\n",
        "    X_test_p, Y_test_p = preprocess_data(X_test, Y_test)\n",
        "\n",
        "    # Now you can use Xt, Yt for training and X, Y for validation\n",
        "    base_model = K.applications.vgg16.VGG16(include_top=False,\n",
        "                                            weights='imagenet',\n",
        "                                            pooling='avg',\n",
        "                                            input_shape=(128, 128,3))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "YGhk8szjIO2n",
        "outputId": "c6ef1084-a4e2-4224-9d7a-4e21fe43f5c4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 1: LearningRateScheduler setting learning rate to 3.2258064516129034e-05.\n",
            "Epoch 1/20\n",
            "281/281 [==============================] - ETA: 0s - loss: 0.0262 - accuracy: 0.9933"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "281/281 [==============================] - 49s 159ms/step - loss: 0.0262 - accuracy: 0.9933 - val_loss: 0.0941 - val_accuracy: 0.9669 - lr: 3.2258e-05\n",
            "\n",
            "Epoch 2: LearningRateScheduler setting learning rate to 3.2258064516129034e-05.\n",
            "Epoch 2/20\n",
            "281/281 [==============================] - 44s 155ms/step - loss: 0.0098 - accuracy: 0.9971 - val_loss: 0.0951 - val_accuracy: 0.9873 - lr: 3.2258e-05\n",
            "\n",
            "Epoch 3: LearningRateScheduler setting learning rate to 3.2258064516129034e-05.\n",
            "Epoch 3/20\n",
            "281/281 [==============================] - 43s 154ms/step - loss: 0.0019 - accuracy: 0.9996 - val_loss: 0.1517 - val_accuracy: 0.9840 - lr: 3.2258e-05\n",
            "\n",
            "Epoch 4: LearningRateScheduler setting learning rate to 3.2258064516129034e-05.\n",
            "Epoch 4/20\n",
            "281/281 [==============================] - 43s 153ms/step - loss: 0.0045 - accuracy: 0.9991 - val_loss: 0.1031 - val_accuracy: 0.9833 - lr: 3.2258e-05\n",
            "\n",
            "Epoch 5: LearningRateScheduler setting learning rate to 3.2258064516129034e-05.\n",
            "Epoch 5/20\n",
            "281/281 [==============================] - 44s 156ms/step - loss: 0.0134 - accuracy: 0.9957 - val_loss: 0.0788 - val_accuracy: 0.9866 - lr: 3.2258e-05\n",
            "\n",
            "Epoch 6: LearningRateScheduler setting learning rate to 3.2258064516129034e-05.\n",
            "Epoch 6/20\n",
            "281/281 [==============================] - 43s 154ms/step - loss: 0.0032 - accuracy: 0.9991 - val_loss: 0.0969 - val_accuracy: 0.9863 - lr: 3.2258e-05\n",
            "\n",
            "Epoch 7: LearningRateScheduler setting learning rate to 3.2258064516129034e-05.\n",
            "Epoch 7/20\n",
            "281/281 [==============================] - 43s 155ms/step - loss: 0.0104 - accuracy: 0.9970 - val_loss: 0.0850 - val_accuracy: 0.9856 - lr: 3.2258e-05\n",
            "\n",
            "Epoch 8: LearningRateScheduler setting learning rate to 3.2258064516129034e-05.\n",
            "Epoch 8/20\n",
            "281/281 [==============================] - 43s 154ms/step - loss: 8.9692e-04 - accuracy: 0.9997 - val_loss: 0.1396 - val_accuracy: 0.9873 - lr: 3.2258e-05\n",
            "\n",
            "Epoch 9: LearningRateScheduler setting learning rate to 3.2258064516129034e-05.\n",
            "Epoch 9/20\n",
            "281/281 [==============================] - 43s 152ms/step - loss: 1.1135e-04 - accuracy: 1.0000 - val_loss: 0.1462 - val_accuracy: 0.9883 - lr: 3.2258e-05\n",
            "\n",
            "Epoch 10: LearningRateScheduler setting learning rate to 3.2258064516129034e-05.\n",
            "Epoch 10/20\n",
            "281/281 [==============================] - 43s 152ms/step - loss: 2.6331e-05 - accuracy: 1.0000 - val_loss: 0.1589 - val_accuracy: 0.9880 - lr: 3.2258e-05\n",
            "\n",
            "Epoch 11: LearningRateScheduler setting learning rate to 3.2258064516129034e-05.\n",
            "Epoch 11/20\n",
            "281/281 [==============================] - 43s 152ms/step - loss: 1.3121e-05 - accuracy: 1.0000 - val_loss: 0.1660 - val_accuracy: 0.9880 - lr: 3.2258e-05\n",
            "\n",
            "Epoch 12: LearningRateScheduler setting learning rate to 3.2258064516129034e-05.\n",
            "Epoch 12/20\n",
            "281/281 [==============================] - 43s 152ms/step - loss: 6.0097e-06 - accuracy: 1.0000 - val_loss: 0.1734 - val_accuracy: 0.9880 - lr: 3.2258e-05\n",
            "\n",
            "Epoch 13: LearningRateScheduler setting learning rate to 3.2258064516129034e-05.\n",
            "Epoch 13/20\n",
            "281/281 [==============================] - 43s 151ms/step - loss: 2.8381e-06 - accuracy: 1.0000 - val_loss: 0.1781 - val_accuracy: 0.9880 - lr: 3.2258e-05\n",
            "\n",
            "Epoch 14: LearningRateScheduler setting learning rate to 3.2258064516129034e-05.\n",
            "Epoch 14/20\n",
            "281/281 [==============================] - 43s 151ms/step - loss: 2.3883e-06 - accuracy: 1.0000 - val_loss: 0.1828 - val_accuracy: 0.9880 - lr: 3.2258e-05\n",
            "\n",
            "Epoch 15: LearningRateScheduler setting learning rate to 3.2258064516129034e-05.\n",
            "Epoch 15/20\n",
            "281/281 [==============================] - 43s 151ms/step - loss: 2.4318e-06 - accuracy: 1.0000 - val_loss: 0.1894 - val_accuracy: 0.9880 - lr: 3.2258e-05\n",
            "\n",
            "Epoch 16: LearningRateScheduler setting learning rate to 3.2258064516129034e-05.\n",
            "Epoch 16/20\n",
            "281/281 [==============================] - 43s 152ms/step - loss: 1.4038e-06 - accuracy: 1.0000 - val_loss: 0.1947 - val_accuracy: 0.9876 - lr: 3.2258e-05\n",
            "\n",
            "Epoch 17: LearningRateScheduler setting learning rate to 3.2258064516129034e-05.\n",
            "Epoch 17/20\n",
            "281/281 [==============================] - 42s 151ms/step - loss: 9.8387e-07 - accuracy: 1.0000 - val_loss: 0.1994 - val_accuracy: 0.9876 - lr: 3.2258e-05\n",
            "\n",
            "Epoch 18: LearningRateScheduler setting learning rate to 3.2258064516129034e-05.\n",
            "Epoch 18/20\n",
            "281/281 [==============================] - 43s 151ms/step - loss: 1.1040e-06 - accuracy: 1.0000 - val_loss: 0.2025 - val_accuracy: 0.9876 - lr: 3.2258e-05\n",
            "\n",
            "Epoch 19: LearningRateScheduler setting learning rate to 3.2258064516129034e-05.\n",
            "Epoch 19/20\n",
            "281/281 [==============================] - 42s 151ms/step - loss: 8.9063e-07 - accuracy: 1.0000 - val_loss: 0.2073 - val_accuracy: 0.9876 - lr: 3.2258e-05\n",
            "\n",
            "Epoch 20: LearningRateScheduler setting learning rate to 3.2258064516129034e-05.\n",
            "Epoch 20/20\n",
            "281/281 [==============================] - 43s 151ms/step - loss: 1.8821e-07 - accuracy: 1.0000 - val_loss: 0.2094 - val_accuracy: 0.9876 - lr: 3.2258e-05\n"
          ]
        }
      ],
      "source": [
        "model= K.Sequential()\n",
        "model.add(K.layers.UpSampling2D())\n",
        "model.add(base_model)\n",
        "model.add(K.layers.Flatten())\n",
        "model.add(K.layers.Dense(512, activation=('relu')))\n",
        "model.add(K.layers.Dropout(0.2))\n",
        "model.add(K.layers.Dense(256, activation=('relu')))\n",
        "model.add(K.layers.Dropout(0.2))\n",
        "model.add(K.layers.Dense(2, activation=('sigmoid')))\n",
        "callback = []\n",
        "def decay(epoch):\n",
        "    \"\"\" This method create the alpha\"\"\"\n",
        "    return 0.001 / (1 + 1 * 20)\n",
        "callback += [K.callbacks.LearningRateScheduler(decay, verbose=1)]\n",
        "callback += [K.callbacks.ModelCheckpoint('malaria_K2.h5',\n",
        "                                          save_best_only=True,\n",
        "                                          mode='min'\n",
        "                                          )]\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(x=X_train_p, y=Y_train_p,\n",
        "          batch_size=32,\n",
        "          validation_data=(X_val_p, Y_val_p),\n",
        "          epochs=20, shuffle=True,\n",
        "          callbacks=callback,\n",
        "          verbose=1\n",
        "          )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JAWyeLrBX4Gy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "44d477ed-439e-43cc-80d4-5fa3b3d2767a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#plotar gráficos\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "loss = history.history['accuracy']\n",
        "val_loss = history.history['val_accuracy']\n",
        "\n",
        "epochs = range(1, len(loss)+ 1)\n",
        "\n",
        "plt.plot(epochs, loss, 'y', label = \"Treinamento\")\n",
        "plt.plot(epochs, val_loss, 'r', label = \"Validação\")\n",
        "\n",
        "plt.title(\"Treinamento versus validação\")\n",
        "plt.xlabel(\"Epocas\")\n",
        "plt.ylabel(\"Acurácia Global\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UYapfZC7tknV"
      },
      "outputs": [],
      "source": [
        "model.save('malaria_K2.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2BIdmQfFkRm6"
      },
      "outputs": [],
      "source": [
        "from keras.models import load_model\n",
        "\n",
        "model = load_model('malaria_K2.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HwtNXJsLyjbf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2753fba1-e650-42bf-eb9b-83d1c3a4146c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "94/94 [==============================] - 4s 40ms/step - loss: 0.2463 - accuracy: 0.9870\n",
            "Test loss: 0.24626491963863373\n",
            "Test accuracy: 0.9869695901870728\n"
          ]
        }
      ],
      "source": [
        "score = model.evaluate(X_test_p, Y_test_p)\n",
        "print('Test loss:', score[0])\n",
        "print('Test accuracy:', score[1])"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "machine_shape": "hm",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}