VGGNet_Malaria_Detection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "VGGNet_Malaria-Detection.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NaYOxZ3NNwnm",
        "outputId": "c3ef5d06-b367-425d-9470-890548c66ac3"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5zjzALehOw9r"
      },
      "source": [
        "IMAGE_SIZE = [224,224]\n",
        "train_path = '/content/drive/MyDrive/Dataset/Train'\n",
        "test_set = '/content/drive/MyDrive/Dataset/Test'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JEcAnNToPPrU",
        "outputId": "70f9a746-c563-4416-b756-5c0695bbed3f"
      },
      "source": [
        "from tensorflow.keras.applications.vgg19 import VGG19\n",
        "vgnet = VGG19(input_shape = IMAGE_SIZE +[3], weights='imagenet',include_top=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "80142336/80134624 [==============================] - 1s 0us/step\n",
            "80150528/80134624 [==============================] - 1s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n4uxnYPYP8Oe"
      },
      "source": [
        "for layer in vgnet.layers:\n",
        "  layer.trainable = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SZXsV7_TQOMI"
      },
      "source": [
        "from tensorflow.keras.layers import Flatten, Dense\n",
        "x = Flatten()(vgnet.output)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xG5qPOAQQbQQ"
      },
      "source": [
        "prediction = Dense(2,activation='softmax')(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bi8-qp-aQmHr"
      },
      "source": [
        "from tensorflow.keras.models import Model\n",
        "model = Model(inputs=vgnet.input,outputs=prediction)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lIxk9dZfQ1L_",
        "outputId": "2ceb4052-3255-4095-f477-57c0bad2b9af"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      \n",
            "                                                                 \n",
            " block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     \n",
            "                                                                 \n",
            " block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         \n",
            "                                                                 \n",
            " block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     \n",
            "                                                                 \n",
            " block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    \n",
            "                                                                 \n",
            " block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         \n",
            "                                                                 \n",
            " block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    \n",
            "                                                                 \n",
            " block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " block3_conv4 (Conv2D)       (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         \n",
            "                                                                 \n",
            " block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   \n",
            "                                                                 \n",
            " block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " block4_conv4 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv4 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 25088)             0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 2)                 50178     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 20,074,562\n",
            "Trainable params: 50,178\n",
            "Non-trainable params: 20,024,384\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zsw92s5BQ3Aj"
      },
      "source": [
        "model.compile(\n",
        "    loss='categorical_crossentropy',\n",
        "    optimizer='adam',\n",
        "    metrics=['accuracy']\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yUW7VavHRFfi"
      },
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "train_datagen = ImageDataGenerator(rescale=1./255,\n",
        "                                   shear_range=0.2,\n",
        "                                   zoom_range=0.2,\n",
        "                                   horizontal_flip=True)\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dJ3TcUG_Rk7X",
        "outputId": "016de3e3-168a-4abe-9ceb-91f77f216064"
      },
      "source": [
        "training_set = train_datagen.flow_from_directory('/content/drive/MyDrive/Dataset/Train',\n",
        "                                                 target_size=(224,224),\n",
        "                                                 batch_size=32,\n",
        "                                                 class_mode='categorical')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 416 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xbpoUTIfR2LM",
        "outputId": "bc8a4a6c-113d-4a2f-fc5e-26822cc2e90d"
      },
      "source": [
        "test_set = test_datagen.flow_from_directory('/content/drive/MyDrive/Dataset/Test',\n",
        "                                                 target_size=(224,224),\n",
        "                                                 batch_size=32,\n",
        "                                                 class_mode='categorical')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 134 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NR-wk3UVSDjW",
        "outputId": "a0904288-e78c-46c0-f770-3cb1d4cc153f"
      },
      "source": [
        "ans = model.fit(\n",
        "    training_set,\n",
        "    validation_data = test_set,\n",
        "    epochs=2\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "13/13 [==============================] - 357s 28s/step - loss: 1.2837 - accuracy: 0.5385 - val_loss: 0.5802 - val_accuracy: 0.6940\n",
            "Epoch 2/2\n",
            "13/13 [==============================] - 351s 28s/step - loss: 0.5182 - accuracy: 0.7404 - val_loss: 0.5477 - val_accuracy: 0.7388\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "9dMQsba-SY47",
        "outputId": "a6c0e27f-d6ec-4be1-a88b-ca820a1f3adb"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(ans.history['loss'],label='train loss')\n",
        "plt.plot(ans.history['val_loss'],label='value loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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A59BFZJCIxItIfFJSUkGuutBoH1qOhSOi6ds0iA/j9tJpbByr956yuiylVAEo0EA3xkw2xkQYYyICAwMLctWFSskiXvz7nnC+eqw5aQb6TV7NS99v5vxlfROllDvTs1zcWKsaAfw8vA2PtQ7h698O0mFMHL9uP251WUqpfKKB7ub8vD15+a5QZj3ZiuK+njw6NZ7h03/ntDb7UsrtSG4fHxeRr4G2QABwHHgN8AIwxkwSkfJAPFACSAMuAKHGmHM5PW9ERISJj4/Pa/3qFlxNTWPCkt1MXLqb4r5evN49jG7hFRARq0tTStlJRNYZYyKyXGZVPxANdOtsP3aO52duYmNiMu3qluPtnvUoX9LX6rKUUnbIKdB1yqUQqlO+BN89FclLXeqyfHcS7WNi+fq3g9rsSykXp4FeSHnYhMejqvHzsCjCKpXghe82c99Hazhw6g+rS1NK3SYN9EIuOKAoXz3Wgn/dXZ+Ew8l0HBvHx8v2arMvpVyQBrrCZhPua16FhSOjiKwewNs/beOeD1ay45g2+1LKlWigq+sqlCzCxw9FML5/Iw6dvshd7y1j7OKd2uxLKRehga7+QkTo3qAii0dG06V+BcYu3kW395az4ZA2+1LK2WmgqyyVKerNuH6N+OShCJIvpXDPxBW8/eNWLl3VZl9KOSsNdJWjO+uWY+HIKPo1q8LHy/fRcWwcK/ectLospVQWNNBVrkr4evGvu+vz9eMtsAnc99EaXvhuE+e02ZdSTkUDXdmtZXV/5g+L4omoasxYe4j2MbEs3qrNvpRyFhro6pYU8fbghS51+eHpSEr7efPY5/E88/XvnLpwxerSlCr0NNDVbQmvXIo5Q1ozsn0tfk44SruYWH74/bC2D1DKQhro6rZ5e9oYemdNfhrahqr+RRk+YwMDP4vnyNlLVpemVKGkga7yrFa54sx6shWv3BXKqj2n6DAmji9WHyBN2wcoVaA00JVDeNiEga1DWDA8igZBJXn5hwT6f7SafSe12ZdSBUUDXTlUFX8/vhjYnP/0Cmfr0XN0GhvHh7F7SL2m7QOUym+5BrqITBGREyKSkM1yEZHxIrJbRDaJSGPHl6lciYjQp2kQi0dGE1UrkH/P3849H6xk29EcL2KllMoje/bQpwKdcljeGaiZ8W8Q8EHey1LuoFwJXyY/0IQJ9zXmyNlLdHtvOTELd3AlVdsHKJUfcg10Y0wccDqHIT2Az0261UApEangqAKVaxMRuoZXYNGIaLo3qMj4X3fTdfxy1h04Y3VpSrkdR8yhVwIOZbqdmHGfUteVLupNTN+GfPpIUy5eSeXeSSt5Y+4WLl5Ntbo0pdxGgR4UFZFBIhIvIvFJSUkFuWrlJO6oXZaFI6N5oEVVPl2xnw5j4li+S5t9KeUIjgj0w0BQptuVM+67iTFmsjEmwhgTERgY6IBVK1dUzMeTN3vU45snWuLlYeP+T9bwj5kbSb6kzb6UygtHBPoc4MGMs11aAMnGmKMOeF7l5pqFlGH+sDY82bY6s9Yfpn1MLAu2HLO6LKVclj2nLX4NrAJqi0iiiAwUkcEiMjhjyDxgL7Ab+Ah4Kt+qVW7H18uD5zvV4YenIvEv5sMT09bx9JfrSTqvzb6UulViVTOliIgIEx8fb8m6lXNKuZbG5Li9jFu8iyLeHrx6Vyj3NK6EiFhdmlJOQ0TWGWMislqmnxRVTsPLw8bTd9Rg3rDW1ChbjGe/3cjDn67lsDb7UsouGujK6dQoW5xvn2jJ691CWbv/NB1iYvl81X5t9qVULjTQlVOy2YSHI9ObfTWuWppXZ2+h7+RV7Em6YHVpSjktDXTl1ILK+PH5o834773h7Dh2ns7jljFx6W5StNmXUjfRQFdOT0ToHRHE4mej+Vvtsvzn5x30nLCChMPJVpemlFPRQFcuo2xxXyY90IQPBjTm+Lkr9Jiwgv8u2M7lFG32pRRooCsX1Ll+BRaPjOLuRpWYsGQPXcYvI35/Tv3jlCocNNCVSyrl5827vRvw+aPNuJKSRu8PV/H6nC38cUWbfanCSwNdubSoWoEsHBHFQy2D+WxVerOvuJ3a+E0VThroyuUV9fHk9e5hfPtES3y8bDw45Tf+/u1Gzl68anVpShUoDXTlNiKCyzBvaBuevqM63/9+mHYxcczfrH3iVOGhga7ciq+XB891rMOcIZGUK+HDk1+uZ/C0dZw4d9nq0pTKdxroyi2FVSzJ7Kcjeb5THX7dcYJ2MbF8G38Iq5rRKVUQNNCV2/L0sPFk2+rMH9aG2uWL89zMTTw45TcOnb5odWlK5QsNdOX2qgcWY8aglrzVI4z1B87QcWwcU1fs02Zfyu1ooKtCwWYTHmgZzIIRUTQNLsPrc7fS+8NV7D5x3urSlHIYDXRVqFQu7cfUR5oS06cBe5Iu0GXcct7/dZc2+1Juwa5AF5FOIrJDRHaLyKgsllcVkV9EZJOILBWRyo4vVSnHEBHuaVyZRSOiaR9WjncX7qT7+9rsS7k+e64p6gFMADoDoUB/EQm9Ydi7wOfGmHDgTeDfji5UKUcLLO7DhPsa8+EDTTh5Ib3Z1+j52uxLuS579tCbAbuNMXuNMVeB6UCPG8aEAr9mfL8ki+VKOa2OYeVZPCKaextXZlLsHrqMW8Zv+7TZl3I99gR6JeBQptuJGfdlthG4J+P7u4HiIuJ/4xOJyCARiReR+KQk7behnEdJPy/euTecLwY25+q1NPp8uIpXfkjg/OUUq0tTym6OOij6dyBaRH4HooHDwE3vW40xk40xEcaYiMDAQAetWinHaV0zgIUjong0MoQv1hyg45g4luw4YXVZStnFnkA/DARlul05477rjDFHjDH3GGMaAS9l3HfWYVUqVYD8vD15tVsoMwe3oqiPJ498upaRMzZw5g9t9qWcmz2BvhaoKSIhIuIN9APmZB4gIgEi8udzvQBMcWyZShW8JlVL8+PQ1gz9Ww3mbDxCu5hYftx0RNsHKKeVa6AbY1KBIcACYBvwjTFmi4i8KSLdM4a1BXaIyE6gHPDPfKpXqQLl4+nByA61mftMayqWKsKQr37niWnrOK7NvpQTEqv2NiIiIkx8fLwl61bqdqReS+OT5fuIWbQTb08bL3etS5+IIETE6tJUISIi64wxEVkt00+KKmUnTw8bT0RX5+fhUdStUILnZ23m/k/WcPCUNvtSzkEDXalbFBJQlOmPt+DtnvXYeCiZjmPj+GT5Pq5psy9lMQ10pW6DzSbc36IqC0dE0bK6P2/9uJVeH6xk53Ft9qWso4GuVB5ULFWETx6KYFy/hhw49Qddxy9j/C+7uJqqzb5UwdNAVyqPRIQeDSuxeGQ0nepVIGbRTrq/v5yNh/SjGKpgaaAr5SD+xXx4r38jPnowgjMXr3L3xBX8e942Ll3VZl+qYGigK+Vg7UPLsWhkNH2bBvFh3F46j4tj1Z5TVpelCgENdKXyQQlfL/59TzhfPdacNAP9P1rNi99v5pw2+1L5SANdqXzUqkYAC4ZH8XibEKb/dpAOMXH8uv241WUpN6WBrlQ+K+LtwUtdQ/nuqUhKFvHi0anxDJv+O6cuXLG6NOVmNNCVKiANg0ox95nWDG9Xk3mbj9J+TBxzNmqzL+U4GuhKFSBvTxvD29Xix2faEFTGj6Ff/87jn8dzLFmbfam800BXygK1yxfnuydb8XLXuizffZL2MbF8teYgado+QOWBBrpSFvGwCY+1qcaC4VHUq1SSF7/fzH0fr2b/yT+sLk25KA10pSxW1b8oXz3enNH31GfL4XN0GhfHR3F7tdmXumUa6Eo5ARGhX7MqLBoZTesaAfxz3jbumbiCHce02Zeynwa6Uk6kfElfPnowgvf6NyLxzCXuem8ZYxbt1GZfyi52BbqIdBKRHSKyW0RGZbG8iogsEZHfRWSTiHRxfKlKFQ4iQrcGFVk0Mpqu9Ssw7pdd3PXeMn4/eMbq0pSTyzXQRcQDmAB0BkKB/iISesOwl0m/1mgj0i8iPdHRhSpV2JQp6s3Yfo2Y8nAE5y+ncs8HK3nrx61cvJpqdWnKSdmzh94M2G2M2WuMuQpMB3rcMMYAJTK+LwkccVyJShVuf6tTjoUjohjQvAqfLN9Hp7HLWLn7pNVlKSdkT6BXAg5lup2YcV9mrwP3i0giMA94JqsnEpFBIhIvIvFJSUm3Ua5ShVNxXy/e7lmf6YNaYBO47+M1jJq1ieRL2uxL/Y+jDor2B6YaYyoDXYBpInLTcxtjJhtjIowxEYGBgQ5atVKFR4tq/vw8PIonoqvxTfwhOoyJZdFWbfal0tkT6IeBoEy3K2fcl9lA4BsAY8wqwBcIcESBSqm/8vXy4IXOdfnh6UhK+3nz+OfxDPlqPSe12VehZ0+grwVqikiIiHiTftBzzg1jDgJ3AohIXdIDXedUlMpH4ZVLMWdIa55tX4uFW47TPiaWH34/rM2+CrFcA90YkwoMARYA20g/m2WLiLwpIt0zhj0LPC4iG4GvgYeN/lUple+8PW08c2dNfhramuCAogyfsYFHp67lyNlLVpemLCBW5W5ERISJj4+3ZN1KuaNraYbPVu7nvwt24GETnu9chwHNqmCzidWlKQcSkXXGmIisluknRZVyEx424dHWISwcEUXDoFK88kMC/T5azT5t9lVoaKAr5WaCyvgxbWAz/tMrnG1Hz9FpbByTYveQek3bB7g7DXSl3JCI0KdpEItHRhNdK5DR87dz98SVbD1yzurSVD7SQFfKjZUr4cuHDzRhwn2NOZp8ie7vL+f/Fu7gSuo1q0tT+UADXSk3JyJ0Da/AohHRdG9Ykfd+3U3X8ctZd0CbfbkbDXSlConSRb2J6dOQqY805dLVa9w7aSVvzN3CH1e02Ze70EBXqpBpW7ssC0ZE8UCLqny6Yj8dx8axbJd+DtAdaKArVQgV8/HkzR71+OaJlnh72Hjgk9/4x8yNJF/UZl+uTANdqUKsWUgZ5g1rw5NtqzNr/WHajYnl54RjVpelbpMGulKFnK+XB893qsPspyMJLObD4C/W8fSX60k6r82+XI0GulIKgHqVSjJ7SCTPdazNom3HaRcTy6x1idrsy4VooCulrvPysPH0HTWYN7QNNcoW49lvN/LQp2tJPHPR6tKUHTTQlVI3qVG2GN8+0ZI3uocRv/80HcfE8fmq/aSl6d66M9NAV0plyWYTHmoVzILhUTSuWppXZ2+h7+RV7Em6YHVpKhsa6EqpHAWV8ePzR5vxbu8G7Dx+gc7jljFx6W5StNmX09FAV0rlSkS4t0llFo2Mol3dsvzn5x30nLCChMPJVpemMtFAV0rZrWxxXyYOaMKk+xtz/NwVekxYwX9+3s7lFG325QzsCnQR6SQiO0Rkt4iMymL5GBHZkPFvp4icdXypSiln0aleBX4ZGc09jSoxcekeuoxfRvz+01aXVejlGugi4gFMADoDoUB/EQnNPMYYM8IY09AY0xB4D/guP4pVSjmPkn5e/Ld3Az5/tBlXUtLo/eEqXpudwAVt9mUZe/bQmwG7jTF7jTFXgelAjxzG9yf9QtFKqUIgqlYgC0dE8VDLYD5ffYCOY+KI3anNvqxgT6BXAg5lup2Ycd9NRKQqEAL8ms3yQSISLyLxSUn6C1fKXRT18eT17mHMHNwSXy8bD035jWe/2cjZi1etLq1QcfRB0X7ATGNMlkdIjDGTjTERxpiIwMBAB69aKWW1JlXL8NPQNgy5owazNxymXUws8zYftbqsQsOeQD8MBGW6XTnjvqz0Q6dblCrUfL08+HvH2sweEkn5kr489eV6Bk9bx4lzl60uze3ZE+hrgZoiEiIi3qSH9pwbB4lIHaA0sMqxJSqlXFFYxZL88FQkz3eqw687TtAuJpZv4g9ps698lGugG2NSgSHAAmAb8I0xZouIvCki3TMN7QdMN/rbUkpl8PSw8WTb6vw8rA11ypfgHzM38eCU3zh0Wpt95QexKn8jIiJMfHy8JetWShW8tDTDl2sOMHr+dgzwXMfaPNgyGA+bWF2aSxGRdcaYiKyW6SdFlVIFwmYTHmgZzMKR0TQLKcMbc7fSe9JKdp84b3VpbkMDXSlVoCqVKsKnDzdlTN8G7D35B13GLef9X3dpsy8H0EBXShU4EeHuRpVZPNS5ncIAAAxjSURBVDKa9mHleHfhTrq9t5zNidrsKy800JVSlgko5sOE+xrz4QNNOP3HVXpOXMHo+drs63ZpoCulLNcxrDyLRkZzb+PKTIrdQ+dxy1iz95TVZbkcDXSllFMoWcSLd+4N58vHmpOalkbfyat55YcEzl9Osbo0l6GBrpRyKpE1AlgwPIqBrUP4Yk16s68l209YXZZL0EBXSjkdP29PXrkrlFlPtqKojyePTF3LiBkbOP2HNvvKiQa6UsppNa5Smh+HtmbonTWZu/EI7WNi+XHTEW0fkA0NdKWUU/Px9GBk+1rMfaY1lUoXYchXvzNo2jqOa7Ovm2igK6VcQt0KJfjuyVa82KUOcTuTaBcTy4y1B3VvPRMNdKWUy/D0sDEoqjoLhkcRWqEEz8/azICP13DwlDb7Ag10pZQLCg4oytePt+Bfd9dnU2IyHcbG8vGyvVxLK9x76xroSimXZLMJ9zWvwqKRUbSqHsDbP22j1wcr2Xm88Db70kBXSrm0CiWL8MlDEYzr15CDpy/Sdfwyxi3exdXUwtfsSwNdKeXyRIQeDSuxaEQUnetVYMzinXR/fzkbD521urQCpYGulHIb/sV8GN+/ER8/GMHZiyncPXEF/5q3jUtXC0ezL7sCXUQ6icgOEdktIqOyGdNHRLaKyBYR+cqxZSqllP3ahZZj4cgo+jWrwuS4vXQaF8eqPe7f7CvXQBcRD2AC0BkIBfqLSOgNY2oCLwCRxpgwYHg+1KqUUnYr4evFv+6uz1ePNweg/0ereeG7zZxz42Zf9uyhNwN2G2P2GmOuAtOBHjeMeRyYYIw5A2CM0U46Simn0Kp6AD8Pi2JQVDVmrD1Ih5g4ftl23Oqy8oU9gV4JOJTpdmLGfZnVAmqJyAoRWS0inbJ6IhEZJCLxIhKflJR0exUrpdQtKuLtwYtd6vLdU5GULOLFwM/iGfr175y6cMXq0hzKUQdFPYGaQFugP/CRiJS6cZAxZrIxJsIYExEYGOigVSullH0aBpVi7jOtGdGuFvMTjtJ+TByzNxx2m/YB9gT6YSAo0+3KGfdllgjMMcakGGP2ATtJD3illHIq3p42hrWryU9D21CljB/Dpm/gsc/iOZp8yerS8syeQF8L1BSREBHxBvoBc24Y8wPpe+eISADpUzB7HVinUko5VK1yxZn1ZCte7lqXFXtO0iEmjq/WHCTNhdsHeOY2wBiTKiJDgAWABzDFGLNFRN4E4o0xczKWdRCRrcA14DljTP6cI7T7F1j4Mnh4g6dPpq8+4Ol9w1efLMZ5g6dvNsvseA6RfNkspVTB87AJj7WpRofQ8oz6bhMvfr+ZORsPM/qecIIDilpd3i0Tq+aOIiIiTHx8/K0/8OBqWPkeXLsKqVey+HoFUq/+72vqZTAO/FCBx42Bn/ECkeOLQk4vMFmN87X/sTb9bJhSjmCMYcbaQ/zzp21cvZbGsx1q8WhkCJ4ezvV/TETWGWMislzmcoF+O9KuZR32167Y96Lwl3GX8/AcGV+vOfAyWjZPO188fG/jhSWHcX95Ecu0zCPXN31KObVjyZd5+YcEFm87ToPKJXnn3nDqlC9hdVnXaaA7G2NueAG4fBsvLDmMT718ay8wqVcAB/0diM3Oaa8sXkRyfadzqy9EvuDhpdNk6pYZY/hx01Fen7OF5EspPHVHDZ6+ozo+nh5Wl5ZjoOvulBVE0kPH08fqStIZA2mpuU9dOeQdTsbXixdyHuPQabJbfGHJ7kXk+tg8vsPRaTKnJyJ0a1CRyBoBvPXjVsb/soufE47yTq9wGlUpbXV52dI9dOWcbnWa7Hbeldhz/OXPZWkO/Lh45mkyhx9/8bn1FxidJsvVku0nePH7zRw7d5lHI0N4tkMt/Lyt+bnplItSeZWWlh7ueX13ctMYO6bQLJ8my+34yy2+S8nu+Mufj3HSabLzl1N45+ftfLH6IEFlijD6nnAiawQUeB0a6Eq5m+vTZJfz9gLjiOMvzjBNlh/HX66/wHj/ZZpszd5TjPpuM/tO/kG/pkG80KUuJYt4OW7bc6Fz6Eq5G5H0PVkPL3CSQzHZTpPl+fhLNmeaXU7O+bkcOk3mdT3sm3v68IunN6dLwYmNcCjBmzT/kpQuUcz+dzqVm0LVlo6rL4MGulLKMWwe4O0H+FldSbpbmia7tXc4tmtXCUi9gteFP9h15BTbj1+k3IVLVCnpgWdaSvbvdP7UeoQGulJK2c1mA5svePnm2ypKAg2upfFh7B7G/7Ibv6sevNYtlJ4NKyE3HgcwBq6lpAe75M/pj3r+lFJK5YGXh40hf6vJvGGtqRZQlBEzNvLI1LUcPntDsy+R9CkXn+IZ72QcTwNdKaUcoEbZ4nw7uBWvdQtlzd7TdIiJZdrqAwXa7EsDXSmlHMTDJjwSGcLCEVE0qlKaV35IoN/k1exNulAg69dAV0opBwsq48e0gc34z73hbD92js7jljEpdg+p19Lydb0a6EoplQ9EhD4RQSweGU3b2oGMnr+dnhNXsPXIuXxbpwa6Ukrlo7IlfPnwgQg+GNCYY8lX6P7+cj5Zvi9f1qWnLSqlVAHoXL8CLav789aP26haxsKzXESkk4jsEJHdIjIqi+UPi0iSiGzI+PeY40tVSinXVsrPm//r04B2oeXy5flz3UMXEQ9gAtCe9ItBrxWROcaYrTcMnWGMGZIPNSqllLKDPXvozYDdxpi9xpirwHSgR/6WpZRS6lbZE+iVgEOZbidm3HejXiKySURmikiQQ6pTSillN0ed5TIXCDbGhAOLgM+yGiQig0QkXkTik5KSHLRqpZRSYF+gHwYy73FXzrjvOmPMKWPMn63EPgaaZPVExpjJxpgIY0xEYGDg7dSrlFIqG/YE+lqgpoiEiIg30A+Yk3mAiFTIdLM7sM1xJSqllLJHrme5GGNSRWQIsADwAKYYY7aIyJtAvDFmDjBURLoDqcBp4OF8rFkppVQW9BJ0SinlQpzymqIikgQcuM2HBwAnHViOK9BtLhx0mwuHvGxzVWNMlgchLQv0vBCR+OxeodyVbnPhoNtcOOTXNmtzLqWUchMa6Eop5SZcNdAnW12ABXSbCwfd5sIhX7bZJefQlVJK3cxV99CVUkrdQANdKaXchFMHuh0X1vARkRkZy9eISHDBV+lYdmzzSBHZmtHZ8hcRqWpFnY6U2zZnGtdLRIyIuPwpbvZss4j0yfhdbxGRrwq6Rkez42+7iogsEZHfM/6+u1hRp6OIyBQROSEiCdksFxEZn/Hz2CQijfO8UmOMU/4jvc3AHqAa4A1sBEJvGPMUMCnj+36kX2TD8trzeZvvAPwyvn+yMGxzxrjiQBywGoiwuu4C+D3XBH4HSmfcLmt13QWwzZOBJzO+DwX2W113Hrc5CmgMJGSzvAswHxCgBbAmr+t05j10ey6s0YP/teqdCdwpIlKANTparttsjFlijLmYcXM16d0vXZm9F1B5C3gHuFyQxeUTe7b5cWCCMeYMgDHmRAHX6Gj2bLMBSmR8XxI4UoD1OZwxJo703lbZ6QF8btKtBkrd0OjwljlzoNtzYY3rY4wxqUAy4F8g1eUPey8m8qeBpL/Cu7JctznjrWiQMeangiwsH9nze64F1BKRFSKyWkQ6FVh1+cOebX4duF9EEoF5wDMFU5plbvX/e65y7baonJOI3A9EANFW15KfRMQGxFD4Onh6kj7t0pb0d2FxIlLfGHPW0qryV39gqjHm/0SkJTBNROoZY9KsLsxVOPMeeq4X1sg8RkQ8SX+bdqpAqssf9mwzItIOeAnobv53YRFXlds2FwfqAUtFZD/pc41zXPzAqD2/50RgjjEmxRizD9hJesC7Knu2eSDwDYAxZhXgS3oTK3dl1//3W+HMgZ7rhTUybj+U8f29wK8m42iDi7LnYiKNgA9JD3NXn1eFXLbZGJNsjAkwxgQbY4JJP27Q3Rjjyr2X7fnb/oH0vXNEJID0KZi9BVmkg9mzzQeBOwFEpC7pge7O16qcAzyYcbZLCyDZGHM0T89o9ZHgXI4SdyF9z2QP8FLGfW+S/h8a0n/h3wK7gd+AalbXXADbvBg4DmzI+DfH6prze5tvGLsUFz/Lxc7fs5A+1bQV2Az0s7rmAtjmUGAF6WfAbAA6WF1zHrf3a+AokEL6O66BwGBgcKbf8YSMn8dmR/xd60f/lVLKTTjzlItSSqlboIGulFJuQgNdKaXchAa6Ukq5CQ10pZRyExroSinlJjTQlVLKTfw/R4SymN84TdEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "aCLg05SCV2AQ",
        "outputId": "f6c12d2f-47c9-4af4-c023-6c2b5be5ff4c"
      },
      "source": [
        "plt.plot(ans.history['accuracy'],label='train accuracy')\n",
        "plt.plot(ans.history['val_accuracy'],label='value accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Geui4WWBWAqW"
      },
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "model.save('vgg19.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F62W5xEzWT8r"
      },
      "source": [
        "from tensorflow.keras.preprocessing import image\n",
        "model = load_model('vgg19.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qp3jwznTWkoj"
      },
      "source": [
        "from tensorflow.keras.preprocessing.image import load_img\n",
        "img = load_img('/content/drive/MyDrive/Dataset/Test/Uninfected/2.png',target_size=(224,224))\n",
        "x.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KxmvEJpAW1nE"
      },
      "source": [
        "x = image.img_to_array(img)\n",
        "x = x/255"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UYH4qmTSXBWD"
      },
      "source": [
        "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
        "import numpy as np\n",
        "x= np.expand_dims(x,axis=0)\n",
        "img_data = preprocess_input(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yHrpEtLEXJ-l",
        "outputId": "93adb896-8a17-40e5-aeb2-24b0f1c29711"
      },
      "source": [
        "model.predict(img_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.8837131 , 0.11628681]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0DQG5aV9X5ZY"
      },
      "source": [
        "value = np.argmax(model.predict(img_data),axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zs8Y6oReYT4G",
        "outputId": "0a4cc400-6711-4870-e5eb-43a803249a2f"
      },
      "source": [
        "if value==0:\n",
        "  print(\"Uninfected\")\n",
        "else:\n",
        "  print(\"Infected\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Uninfected\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SQD9544hYf3T"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}