maleria_detection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "maleria_detection.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNFWy077McNThISEgJAaGnL",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/CaptaiN785/Maleria-detection/blob/main/maleria_detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "QQW7NHoUqks4"
      },
      "outputs": [],
      "source": [
        "import zipfile\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import joblib\n",
        "from glob import glob\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras import layers, models\n",
        "from tensorflow.keras.applications import vgg19\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "file = zipfile.ZipFile(\"Dataset.zip\")\n",
        "file.extractall()\n",
        "file.close()"
      ],
      "metadata": {
        "id": "RN4DGnN1rJZD"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dir = \"/content/Dataset/Train\"\n",
        "test_dir = \"/content/Dataset/Test\""
      ],
      "metadata": {
        "id": "uy0dk9bMrQhR"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## There is two category\n",
        "par = 'Parasite'\n",
        "non_par = \"Uninfected\""
      ],
      "metadata": {
        "id": "I6AJZzR2rZDr"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Plotting the some images of these category\n",
        "\n",
        "## Category is parasite\n",
        "\n",
        "category = par\n",
        "cate_path = os.path.join(train_dir, category)\n",
        "plt.figure(figsize=(12, 6))\n",
        "for i, img_name in enumerate(os.listdir(cate_path)[:5]):\n",
        "    plt.subplot(1, 5, i+1)\n",
        "    img = plt.imread(os.path.join(cate_path, img_name)) \n",
        "    plt.imshow(img)\n",
        "    plt.axis(\"off\")\n",
        "    plt.title(category)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "id": "cMlqTr3-rr0c",
        "outputId": "02103bd5-969c-4136-8572-df04ca89cfe1"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Category is non parasite\n",
        "\n",
        "category = non_par\n",
        "cate_path = os.path.join(train_dir, category)\n",
        "plt.figure(figsize=(12, 6))\n",
        "for i, img_name in enumerate(os.listdir(cate_path)[:5]):\n",
        "    plt.subplot(1, 5, i+1)\n",
        "    img = plt.imread(os.path.join(cate_path, img_name)) \n",
        "    plt.imshow(img)\n",
        "    plt.axis(\"off\")\n",
        "    plt.title(category)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "id": "N9RoaxY8snIs",
        "outputId": "48bd260f-72e2-4e20-cf9a-92b5622d826f"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Finding the total image for training set\n",
        "images = glob(train_dir+\"/*/*\", recursive=True)\n",
        "len(images)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "erWDh-P2tP3A",
        "outputId": "84cdc4dc-469a-43fb-f5e2-dcbeeef864e6"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "416"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Checking the testing set\n",
        "test_image = glob(test_dir + \"/*/*\")\n",
        "len(test_image)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZlmJR1SWtkkY",
        "outputId": "55428e21-fda1-4fc3-e94c-3a767e4be6eb"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "134"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Creating the data generator\n",
        "\n",
        "train_gen = ImageDataGenerator(\n",
        "    rescale = 1.0/255.0,\n",
        "    horizontal_flip=True,\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    shear_range=0.2\n",
        ")\n",
        "\n",
        "test_gen = ImageDataGenerator(\n",
        "    rescale=1.0/255.0\n",
        ")"
      ],
      "metadata": {
        "id": "xBlbW2spuxsA"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Generating the training and testing set\n",
        "\n",
        "## Defining some parameters\n",
        "batch_size = 16\n",
        "st_per_ep = len(images)/batch_size\n",
        "val_step = len(test_image)/batch_size\n",
        "\n",
        "train_set = train_gen.flow_from_directory(\n",
        "    train_dir,\n",
        "    target_size=(96, 96),\n",
        "    class_mode = 'binary',\n",
        "    batch_size = batch_size\n",
        ")\n",
        "test_set = test_gen.flow_from_directory(\n",
        "    test_dir,\n",
        "    target_size=(96, 96),\n",
        "    class_mode = 'binary',\n",
        "    batch_size=batch_size\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H3Y6X821uxmq",
        "outputId": "b99a4cc0-7334-4eba-8ac6-95313783ab2c"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 416 images belonging to 2 classes.\n",
            "Found 134 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Designing the model\n",
        "\n",
        "model = models.Sequential([\n",
        "    layers.Input(shape=(96, 96, 3)),\n",
        "    layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same', activation='relu'),\n",
        "    layers.Conv2D(64, (5, 5), padding='same', activation='relu'),\n",
        "    layers.BatchNormalization(),\n",
        "    layers.MaxPooling2D((2, 2)),\n",
        "    layers.Dropout(0.2),\n",
        "\n",
        "    layers.Conv2D(128, (3, 3), padding = \"same\", activation = 'relu'),\n",
        "    layers.Conv2D(128, (3, 3), padding = \"same\", activation = 'relu'),\n",
        "    layers.Conv2D(128, (3, 3), padding = \"same\", activation = 'relu'),\n",
        "    layers.BatchNormalization(),\n",
        "    layers.MaxPooling2D((2, 2)),\n",
        "    layers.Dropout(0.2),\n",
        "\n",
        "    layers.Conv2D(256, (3, 3), activation = 'relu', padding = \"same\"),\n",
        "    layers.Conv2D(256, (3, 3), activation = 'relu', padding = \"same\"),\n",
        "    layers.Conv2D(256, (3, 3), activation = 'relu', padding = \"same\"),\n",
        "    layers.Conv2D(256, (3, 3), activation = 'relu', padding = \"same\"),\n",
        "    layers.Conv2D(256, (3, 3), activation = 'relu', padding = \"same\"),\n",
        "    layers.BatchNormalization(),\n",
        "    layers.MaxPooling2D((2, 2)),\n",
        "    \n",
        "    layers.Flatten(),\n",
        "    layers.Dropout(0.2),\n",
        "    layers.Dense(512, activation = 'relu'),\n",
        "    layers.Dense(1, activation = 'sigmoid')\n",
        "])\n",
        "model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y9a5eq9Wt666",
        "outputId": "63f82d98-094b-48de-a584-686f6a340919"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_20 (Conv2D)          (None, 48, 48, 64)        4864      \n",
            "                                                                 \n",
            " conv2d_21 (Conv2D)          (None, 48, 48, 64)        102464    \n",
            "                                                                 \n",
            " batch_normalization_6 (Batc  (None, 48, 48, 64)       256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_6 (MaxPooling  (None, 24, 24, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_6 (Dropout)         (None, 24, 24, 64)        0         \n",
            "                                                                 \n",
            " conv2d_22 (Conv2D)          (None, 24, 24, 128)       73856     \n",
            "                                                                 \n",
            " conv2d_23 (Conv2D)          (None, 24, 24, 128)       147584    \n",
            "                                                                 \n",
            " conv2d_24 (Conv2D)          (None, 24, 24, 128)       147584    \n",
            "                                                                 \n",
            " batch_normalization_7 (Batc  (None, 24, 24, 128)      512       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_7 (MaxPooling  (None, 12, 12, 128)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_7 (Dropout)         (None, 12, 12, 128)       0         \n",
            "                                                                 \n",
            " conv2d_25 (Conv2D)          (None, 12, 12, 256)       295168    \n",
            "                                                                 \n",
            " conv2d_26 (Conv2D)          (None, 12, 12, 256)       590080    \n",
            "                                                                 \n",
            " conv2d_27 (Conv2D)          (None, 12, 12, 256)       590080    \n",
            "                                                                 \n",
            " conv2d_28 (Conv2D)          (None, 12, 12, 256)       590080    \n",
            "                                                                 \n",
            " conv2d_29 (Conv2D)          (None, 12, 12, 256)       590080    \n",
            "                                                                 \n",
            " batch_normalization_8 (Batc  (None, 12, 12, 256)      1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_8 (MaxPooling  (None, 6, 6, 256)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten_2 (Flatten)         (None, 9216)              0         \n",
            "                                                                 \n",
            " dropout_8 (Dropout)         (None, 9216)              0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 512)               4719104   \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 1)                 513       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 7,853,249\n",
            "Trainable params: 7,852,353\n",
            "Non-trainable params: 896\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.optimizers import Adam\n",
        "adam = Adam(learning_rate = 0.0001)\n",
        "model.compile(optimizer = adam, loss = 'binary_crossentropy', metrics = ['accuracy'])"
      ],
      "metadata": {
        "id": "UwosnP8YxwoH"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    train_set, \n",
        "    steps_per_epoch=st_per_ep,\n",
        "    validation_data = test_set,\n",
        "    validation_steps=val_step,\n",
        "    epochs = 100,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kAaT1Mtex8ve",
        "outputId": "3496f3c8-9df6-4bb4-d798-44f67c943ddf"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "26/26 [==============================] - 3s 83ms/step - loss: 1.2379 - accuracy: 0.5168 - val_loss: 0.6725 - val_accuracy: 0.6791\n",
            "Epoch 2/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.6906 - accuracy: 0.6082 - val_loss: 0.6324 - val_accuracy: 0.6791\n",
            "Epoch 3/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.5507 - accuracy: 0.7212 - val_loss: 0.6577 - val_accuracy: 0.6791\n",
            "Epoch 4/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.5694 - accuracy: 0.7308 - val_loss: 0.7126 - val_accuracy: 0.6791\n",
            "Epoch 5/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.5064 - accuracy: 0.7596 - val_loss: 0.9897 - val_accuracy: 0.6791\n",
            "Epoch 6/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.4113 - accuracy: 0.8077 - val_loss: 1.4386 - val_accuracy: 0.6791\n",
            "Epoch 7/100\n",
            "26/26 [==============================] - 2s 80ms/step - loss: 0.4366 - accuracy: 0.7812 - val_loss: 1.6579 - val_accuracy: 0.6791\n",
            "Epoch 8/100\n",
            "26/26 [==============================] - 3s 96ms/step - loss: 0.3959 - accuracy: 0.8125 - val_loss: 1.6573 - val_accuracy: 0.6791\n",
            "Epoch 9/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.4439 - accuracy: 0.8029 - val_loss: 1.9904 - val_accuracy: 0.6791\n",
            "Epoch 10/100\n",
            "26/26 [==============================] - 2s 71ms/step - loss: 0.4640 - accuracy: 0.7716 - val_loss: 1.8658 - val_accuracy: 0.6791\n",
            "Epoch 11/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.4156 - accuracy: 0.8077 - val_loss: 1.7957 - val_accuracy: 0.6791\n",
            "Epoch 12/100\n",
            "26/26 [==============================] - 2s 72ms/step - loss: 0.3746 - accuracy: 0.8341 - val_loss: 0.8714 - val_accuracy: 0.4627\n",
            "Epoch 13/100\n",
            "26/26 [==============================] - 2s 72ms/step - loss: 0.3738 - accuracy: 0.8149 - val_loss: 0.8537 - val_accuracy: 0.6045\n",
            "Epoch 14/100\n",
            "26/26 [==============================] - 2s 72ms/step - loss: 0.3460 - accuracy: 0.8365 - val_loss: 1.5971 - val_accuracy: 0.6791\n",
            "Epoch 15/100\n",
            "26/26 [==============================] - 2s 75ms/step - loss: 0.3437 - accuracy: 0.8317 - val_loss: 1.0724 - val_accuracy: 0.6493\n",
            "Epoch 16/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.3131 - accuracy: 0.8558 - val_loss: 1.2515 - val_accuracy: 0.6418\n",
            "Epoch 17/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.3149 - accuracy: 0.8510 - val_loss: 1.8532 - val_accuracy: 0.6642\n",
            "Epoch 18/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.4052 - accuracy: 0.8341 - val_loss: 2.0742 - val_accuracy: 0.6567\n",
            "Epoch 19/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.3606 - accuracy: 0.8462 - val_loss: 3.0128 - val_accuracy: 0.4104\n",
            "Epoch 20/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.3050 - accuracy: 0.8702 - val_loss: 1.5301 - val_accuracy: 0.6343\n",
            "Epoch 21/100\n",
            "26/26 [==============================] - 2s 72ms/step - loss: 0.3372 - accuracy: 0.8558 - val_loss: 0.8016 - val_accuracy: 0.6269\n",
            "Epoch 22/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.2277 - accuracy: 0.8990 - val_loss: 1.6600 - val_accuracy: 0.6493\n",
            "Epoch 23/100\n",
            "26/26 [==============================] - 2s 75ms/step - loss: 0.2258 - accuracy: 0.9038 - val_loss: 2.3761 - val_accuracy: 0.5000\n",
            "Epoch 24/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.2389 - accuracy: 0.8942 - val_loss: 3.4496 - val_accuracy: 0.5000\n",
            "Epoch 25/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.1936 - accuracy: 0.9231 - val_loss: 0.6971 - val_accuracy: 0.7015\n",
            "Epoch 26/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.1671 - accuracy: 0.9327 - val_loss: 3.5066 - val_accuracy: 0.5299\n",
            "Epoch 27/100\n",
            "26/26 [==============================] - 2s 76ms/step - loss: 0.1660 - accuracy: 0.9351 - val_loss: 0.4605 - val_accuracy: 0.7537\n",
            "Epoch 28/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.1592 - accuracy: 0.9303 - val_loss: 2.1281 - val_accuracy: 0.5746\n",
            "Epoch 29/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.1907 - accuracy: 0.9375 - val_loss: 0.9256 - val_accuracy: 0.6791\n",
            "Epoch 30/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.1780 - accuracy: 0.9495 - val_loss: 0.6656 - val_accuracy: 0.7836\n",
            "Epoch 31/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.1672 - accuracy: 0.9231 - val_loss: 2.7980 - val_accuracy: 0.6269\n",
            "Epoch 32/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.1568 - accuracy: 0.9447 - val_loss: 3.3421 - val_accuracy: 0.6791\n",
            "Epoch 33/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.1295 - accuracy: 0.9495 - val_loss: 0.8441 - val_accuracy: 0.7463\n",
            "Epoch 34/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.1500 - accuracy: 0.9423 - val_loss: 0.1709 - val_accuracy: 0.9403\n",
            "Epoch 35/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.1459 - accuracy: 0.9351 - val_loss: 0.3973 - val_accuracy: 0.8209\n",
            "Epoch 36/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.1094 - accuracy: 0.9591 - val_loss: 0.7805 - val_accuracy: 0.6791\n",
            "Epoch 37/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.1064 - accuracy: 0.9567 - val_loss: 0.3250 - val_accuracy: 0.8881\n",
            "Epoch 38/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.1453 - accuracy: 0.9471 - val_loss: 1.7547 - val_accuracy: 0.5299\n",
            "Epoch 39/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.1383 - accuracy: 0.9255 - val_loss: 0.2084 - val_accuracy: 0.9104\n",
            "Epoch 40/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.1555 - accuracy: 0.9255 - val_loss: 1.7625 - val_accuracy: 0.6567\n",
            "Epoch 41/100\n",
            "26/26 [==============================] - 3s 108ms/step - loss: 0.1128 - accuracy: 0.9615 - val_loss: 0.1780 - val_accuracy: 0.9328\n",
            "Epoch 42/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.1034 - accuracy: 0.9615 - val_loss: 0.6874 - val_accuracy: 0.8657\n",
            "Epoch 43/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.1430 - accuracy: 0.9543 - val_loss: 0.3777 - val_accuracy: 0.9030\n",
            "Epoch 44/100\n",
            "26/26 [==============================] - 2s 72ms/step - loss: 0.1047 - accuracy: 0.9519 - val_loss: 4.4682 - val_accuracy: 0.4776\n",
            "Epoch 45/100\n",
            "26/26 [==============================] - 2s 72ms/step - loss: 0.1066 - accuracy: 0.9519 - val_loss: 1.4636 - val_accuracy: 0.7836\n",
            "Epoch 46/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.1009 - accuracy: 0.9639 - val_loss: 0.1486 - val_accuracy: 0.9627\n",
            "Epoch 47/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.0718 - accuracy: 0.9760 - val_loss: 0.0933 - val_accuracy: 0.9701\n",
            "Epoch 48/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.0471 - accuracy: 0.9832 - val_loss: 0.0766 - val_accuracy: 0.9776\n",
            "Epoch 49/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.0791 - accuracy: 0.9808 - val_loss: 0.6409 - val_accuracy: 0.8134\n",
            "Epoch 50/100\n",
            "26/26 [==============================] - 2s 72ms/step - loss: 0.1168 - accuracy: 0.9639 - val_loss: 0.2915 - val_accuracy: 0.8507\n",
            "Epoch 51/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.0816 - accuracy: 0.9688 - val_loss: 1.0369 - val_accuracy: 0.8209\n",
            "Epoch 52/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.0965 - accuracy: 0.9615 - val_loss: 0.1759 - val_accuracy: 0.9403\n",
            "Epoch 53/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.1019 - accuracy: 0.9663 - val_loss: 1.4865 - val_accuracy: 0.8284\n",
            "Epoch 54/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.1384 - accuracy: 0.9567 - val_loss: 0.1552 - val_accuracy: 0.9403\n",
            "Epoch 55/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.1143 - accuracy: 0.9519 - val_loss: 0.5067 - val_accuracy: 0.8358\n",
            "Epoch 56/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.1376 - accuracy: 0.9399 - val_loss: 0.2748 - val_accuracy: 0.8582\n",
            "Epoch 57/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.0820 - accuracy: 0.9688 - val_loss: 0.2201 - val_accuracy: 0.9179\n",
            "Epoch 58/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.0860 - accuracy: 0.9688 - val_loss: 0.1463 - val_accuracy: 0.9478\n",
            "Epoch 59/100\n",
            "26/26 [==============================] - 3s 114ms/step - loss: 0.0596 - accuracy: 0.9784 - val_loss: 0.1014 - val_accuracy: 0.9776\n",
            "Epoch 60/100\n",
            "26/26 [==============================] - 2s 83ms/step - loss: 0.1709 - accuracy: 0.9351 - val_loss: 0.2016 - val_accuracy: 0.9478\n",
            "Epoch 61/100\n",
            "26/26 [==============================] - 2s 75ms/step - loss: 0.0898 - accuracy: 0.9663 - val_loss: 0.1368 - val_accuracy: 0.9552\n",
            "Epoch 62/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.0784 - accuracy: 0.9760 - val_loss: 0.4166 - val_accuracy: 0.8806\n",
            "Epoch 63/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.0694 - accuracy: 0.9736 - val_loss: 0.0718 - val_accuracy: 0.9851\n",
            "Epoch 64/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.0873 - accuracy: 0.9663 - val_loss: 0.3053 - val_accuracy: 0.9328\n",
            "Epoch 65/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.0639 - accuracy: 0.9760 - val_loss: 0.1097 - val_accuracy: 0.9627\n",
            "Epoch 66/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.0507 - accuracy: 0.9808 - val_loss: 0.4789 - val_accuracy: 0.9030\n",
            "Epoch 67/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.0729 - accuracy: 0.9808 - val_loss: 0.2071 - val_accuracy: 0.9403\n",
            "Epoch 68/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.0618 - accuracy: 0.9712 - val_loss: 0.1540 - val_accuracy: 0.9179\n",
            "Epoch 69/100\n",
            "26/26 [==============================] - 2s 75ms/step - loss: 0.0689 - accuracy: 0.9760 - val_loss: 0.0716 - val_accuracy: 0.9776\n",
            "Epoch 70/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.0622 - accuracy: 0.9832 - val_loss: 0.1565 - val_accuracy: 0.9627\n",
            "Epoch 71/100\n",
            "26/26 [==============================] - 2s 76ms/step - loss: 0.0817 - accuracy: 0.9760 - val_loss: 0.5738 - val_accuracy: 0.9179\n",
            "Epoch 72/100\n",
            "26/26 [==============================] - 3s 97ms/step - loss: 0.0862 - accuracy: 0.9688 - val_loss: 0.1429 - val_accuracy: 0.9701\n",
            "Epoch 73/100\n",
            "26/26 [==============================] - 2s 85ms/step - loss: 0.0692 - accuracy: 0.9784 - val_loss: 0.0641 - val_accuracy: 0.9776\n",
            "Epoch 74/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.0493 - accuracy: 0.9856 - val_loss: 0.1856 - val_accuracy: 0.9478\n",
            "Epoch 75/100\n",
            "26/26 [==============================] - 2s 75ms/step - loss: 0.0667 - accuracy: 0.9856 - val_loss: 0.0732 - val_accuracy: 0.9851\n",
            "Epoch 76/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.0455 - accuracy: 0.9832 - val_loss: 0.1423 - val_accuracy: 0.9776\n",
            "Epoch 77/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.0556 - accuracy: 0.9808 - val_loss: 0.0662 - val_accuracy: 0.9776\n",
            "Epoch 78/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.0503 - accuracy: 0.9808 - val_loss: 0.0363 - val_accuracy: 0.9851\n",
            "Epoch 79/100\n",
            "26/26 [==============================] - 2s 72ms/step - loss: 0.0547 - accuracy: 0.9760 - val_loss: 0.0656 - val_accuracy: 0.9851\n",
            "Epoch 80/100\n",
            "26/26 [==============================] - 2s 72ms/step - loss: 0.1374 - accuracy: 0.9495 - val_loss: 0.1581 - val_accuracy: 0.9478\n",
            "Epoch 81/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.0643 - accuracy: 0.9712 - val_loss: 0.1530 - val_accuracy: 0.9552\n",
            "Epoch 82/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.0419 - accuracy: 0.9832 - val_loss: 0.2536 - val_accuracy: 0.9403\n",
            "Epoch 83/100\n",
            "26/26 [==============================] - 2s 72ms/step - loss: 0.0540 - accuracy: 0.9856 - val_loss: 0.2372 - val_accuracy: 0.9179\n",
            "Epoch 84/100\n",
            "26/26 [==============================] - 3s 108ms/step - loss: 0.0681 - accuracy: 0.9784 - val_loss: 0.0807 - val_accuracy: 0.9776\n",
            "Epoch 85/100\n",
            "26/26 [==============================] - 2s 81ms/step - loss: 0.0726 - accuracy: 0.9712 - val_loss: 0.1353 - val_accuracy: 0.9403\n",
            "Epoch 86/100\n",
            "26/26 [==============================] - 2s 75ms/step - loss: 0.0795 - accuracy: 0.9736 - val_loss: 0.3646 - val_accuracy: 0.9030\n",
            "Epoch 87/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.0421 - accuracy: 0.9808 - val_loss: 0.9241 - val_accuracy: 0.8582\n",
            "Epoch 88/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.1043 - accuracy: 0.9663 - val_loss: 0.1186 - val_accuracy: 0.9403\n",
            "Epoch 89/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.0489 - accuracy: 0.9808 - val_loss: 0.1097 - val_accuracy: 0.9701\n",
            "Epoch 90/100\n",
            "26/26 [==============================] - 2s 75ms/step - loss: 0.1028 - accuracy: 0.9663 - val_loss: 0.3620 - val_accuracy: 0.8955\n",
            "Epoch 91/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.0676 - accuracy: 0.9760 - val_loss: 0.0828 - val_accuracy: 0.9851\n",
            "Epoch 92/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.0384 - accuracy: 0.9880 - val_loss: 0.1393 - val_accuracy: 0.9701\n",
            "Epoch 93/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.0245 - accuracy: 0.9928 - val_loss: 0.7360 - val_accuracy: 0.9104\n",
            "Epoch 94/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.0341 - accuracy: 0.9880 - val_loss: 0.2296 - val_accuracy: 0.9552\n",
            "Epoch 95/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.0390 - accuracy: 0.9808 - val_loss: 0.4388 - val_accuracy: 0.9328\n",
            "Epoch 96/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.0410 - accuracy: 0.9856 - val_loss: 0.5340 - val_accuracy: 0.8582\n",
            "Epoch 97/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.0616 - accuracy: 0.9688 - val_loss: 0.2560 - val_accuracy: 0.8806\n",
            "Epoch 98/100\n",
            "26/26 [==============================] - 2s 73ms/step - loss: 0.0555 - accuracy: 0.9808 - val_loss: 0.1002 - val_accuracy: 0.9701\n",
            "Epoch 99/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.0626 - accuracy: 0.9736 - val_loss: 0.1091 - val_accuracy: 0.9776\n",
            "Epoch 100/100\n",
            "26/26 [==============================] - 2s 74ms/step - loss: 0.0442 - accuracy: 0.9832 - val_loss: 0.0906 - val_accuracy: 0.9701\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Plottting the history\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title(\"Loss graph\")\n",
        "plt.legend(['loss', 'val_loss'])\n",
        "plt.show()\n",
        "\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title(\"Accurac graph\")\n",
        "plt.legend(['accuracy', 'val_accuracy'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "tEQqtAryyUqe",
        "outputId": "a57fed1b-05ea-4b0d-d619-d01448993c0b"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Building the function for predicting the images\n",
        "import cv2 as cv"
      ],
      "metadata": {
        "id": "OVxae3P_3F8B"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img_path = \"/content/Dataset/Test/Parasite/C39P4thinF_original_IMG_20150622_105554_cell_10.png\"\n",
        "par_img = cv.imread(img_path)\n",
        "par_img.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OObS9cqk3OtQ",
        "outputId": "b0a4db75-278c-4293-a2ea-2df3778e3a34"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(115, 124, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img_rgb = cv.cvtColor(par_img,cv.COLOR_BGR2RGB)\n",
        "plt.imshow(img_rgb)\n",
        "plt.show()\n",
        "img_rgb = cv.resize(par_img, (96, 96))\n",
        "plt.imshow(img_rgb)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "M5CCfU193dpT",
        "outputId": "1a2d12d7-70ab-4a9b-e12e-e96e523b4aa6"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img = np.expand_dims(img_rgb, 0)"
      ],
      "metadata": {
        "id": "k9VeODy23_vS"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(img)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DY85vHiS4MPy",
        "outputId": "b5e9fb77-cd3b-4aa0-9839-ded0f7b01d37"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_set.class_indices"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IL4FZUlKIOeN",
        "outputId": "c9776c99-fead-4a51-f038-813200fb9dcf"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Parasite': 0, 'Uninfected': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def Predict(image_path):\n",
        "    images = []\n",
        "    corrupted_index = []\n",
        "    for i, img_path in enumerate(image_path):\n",
        "        try:\n",
        "            img = cv.imread(img_path)\n",
        "            img = cv.resize(img, (96, 96), interpolation=cv.INTER_AREA)/255.0\n",
        "            images.append(img)\n",
        "        except:\n",
        "            corrupted_index.append(i)\n",
        "    images = np.array(images)\n",
        "    print(\"Shape of array is : \", images.shape)\n",
        "    result = model.predict(images)\n",
        "    return {\"result\":np.round(result[:, 0]), \"error_index\":corrupted_index}"
      ],
      "metadata": {
        "id": "Wa1ozOkn4Ye5"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "par_dir = os.path.join(test_dir, par)\n",
        "par_images = os.listdir(par_dir)\n",
        "\n",
        "par_dirs = []\n",
        "for img_name in par_images:\n",
        "    par_dirs.append(os.path.join(par_dir, img_name))\n",
        "\n",
        "non_par_dir = os.path.join(test_dir, non_par)\n",
        "non_par_images = os.listdir(non_par_dir)\n",
        "\n",
        "non_par_dirs = []\n",
        "for img_name in non_par_images:\n",
        "    non_par_dirs.append(os.path.join(non_par_dir, img_name))\n"
      ],
      "metadata": {
        "id": "O-O88mnYHGcy"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result = Predict(par_dirs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VH07UpBiQ2hX",
        "outputId": "c22c7935-402d-4e6a-e3fc-dd5073bce3da"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of array is :  (91, 96, 96, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result['result'], result['error_index']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "THk04wWpR4Me",
        "outputId": "0d11b509-966d-47cf-b7ff-207e3a624c2c"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0., 0., 0.], dtype=float32), [])"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "1 - (result['result'].sum()/len(par_dirs))\n",
        "## 97% accuracy is achived in case of having Parasite"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SmJa2MVVSL3d",
        "outputId": "19816b12-62b2-4263-a01b-b37320e33207"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.978021978021978"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result = Predict(non_par_dirs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1i6CGsrDRF4e",
        "outputId": "b346db54-de1a-40f4-b213-237933bed427"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of array is :  (43, 96, 96, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result['result'].sum()/len(non_par_dirs)\n",
        "## 97% accuracy is achived in case of not having Parasite"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kncPbePNRSEb",
        "outputId": "dc69c6e6-9dca-48c1-db71-a32dcac75b43"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9767441860465116"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Which is a good model"
      ],
      "metadata": {
        "id": "C1SR3gnGSlNI"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def Predict_One(image_path):\n",
        "\n",
        "    label = {0:\"Parasite\", 1:\"Uninfected\"}\n",
        "\n",
        "    img = cv.imread(image_path)\n",
        "    img = cv.resize(img, (96, 96), interpolation=cv.INTER_AREA)/255.0\n",
        "    img = np.expand_dims(img, 0)\n",
        "\n",
        "    print(\"Shape of array is : \", img.shape)\n",
        "    result = model.predict(img)\n",
        "    return label[np.round(result[0][0])]"
      ],
      "metadata": {
        "id": "Mn9m_NIIS32P"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Predict_One(\"/content/Dataset/Test/Parasite/C39P4thinF_original_IMG_20150622_105554_cell_13.png\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "3zE2AmY8TT8n",
        "outputId": "dd37dcd7-9331-44b6-9686-2728bf314335"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of array is :  (1, 96, 96, 3)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Parasite'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Predict_One(\"/content/Dataset/Test/Uninfected/C3thin_original_IMG_20150608_163002_cell_159.png\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "wkVe4uRJTa8C",
        "outputId": "9b0ec0d9-58a8-4252-d2d3-ddfd11a4e3b7"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of array is :  (1, 96, 96, 3)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Uninfected'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save(\"model.hd5\")"
      ],
      "metadata": {
        "id": "-qvsYVtpBipe"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "md = models.load_model(\"model.hd5\")"
      ],
      "metadata": {
        "id": "x4Wlyh6OEBu5"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def Predict_One_saved(image_path):\n",
        "\n",
        "    label = {0:\"Parasite\", 1:\"Uninfected\"}\n",
        "\n",
        "    img = cv.imread(image_path)\n",
        "    img = cv.resize(img, (96, 96), interpolation=cv.INTER_AREA)/255.0\n",
        "    img = np.expand_dims(img, 0)\n",
        "\n",
        "    print(\"Shape of array is : \", img.shape)\n",
        "    result = md.predict(img)\n",
        "    return label[np.round(result[0][0])]"
      ],
      "metadata": {
        "id": "waBJIoBCWoBM"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Predict_One_saved(\"/content/Dataset/Test/Parasite/C39P4thinF_original_IMG_20150622_105554_cell_13.png\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "dSnZ-aqUWsoe",
        "outputId": "b87e9d91-2be6-4668-f03c-101201640765"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of array is :  (1, 96, 96, 3)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Parasite'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Predict_One_saved(\"/content/Dataset/Test/Uninfected/C3thin_original_IMG_20150608_163002_cell_159.png\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "54t5t1X_Wv8N",
        "outputId": "8bc1d31d-323e-45c4-89e9-257085c1220d"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of array is :  (1, 96, 96, 3)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Uninfected'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "f8FA49qxEqQn"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}