BuildingaMedicalImage AIModel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e94181fb-9dbb-46ec-b35b-ab4b0caa6332",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9912824d-0b24-416b-b08c-e20fcc72f01d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Building on Cardiomegaly\n",
    "finding = \"Cardiomegaly\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5107c0ad-e44c-44e2-b2ea-226449105631",
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading dataset\n",
    "df = pd.read_csv(\"./labels.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aeb2aa25-4f2a-4ef7-93d0-9fa3d3015675",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>height</th>\n",
       "      <th>width</th>\n",
       "      <th>label</th>\n",
       "      <th>xmin</th>\n",
       "      <th>ymin</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00013118_008.jpg</td>\n",
       "      <td>2992</td>\n",
       "      <td>2991</td>\n",
       "      <td>Atelectasis</td>\n",
       "      <td>225.0</td>\n",
       "      <td>547.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>626.0</td>\n",
       "      <td>PA</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00014716_007.jpg</td>\n",
       "      <td>3056</td>\n",
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       "      <td>Atelectasis</td>\n",
       "      <td>686.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>871.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00029817_009.jpg</td>\n",
       "      <td>3056</td>\n",
       "      <td>2544</td>\n",
       "      <td>Atelectasis</td>\n",
       "      <td>221.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>376.0</td>\n",
       "      <td>533.0</td>\n",
       "      <td>AP</td>\n",
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       "      <th>3</th>\n",
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       "      <td>2500</td>\n",
       "      <td>2048</td>\n",
       "      <td>Atelectasis</td>\n",
       "      <td>726.0</td>\n",
       "      <td>494.0</td>\n",
       "      <td>867.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>AP</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00017877_001.jpg</td>\n",
       "      <td>2500</td>\n",
       "      <td>2048</td>\n",
       "      <td>Atelectasis</td>\n",
       "      <td>660.0</td>\n",
       "      <td>569.0</td>\n",
       "      <td>860.0</td>\n",
       "      <td>647.0</td>\n",
       "      <td>AP</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>1959</th>\n",
       "      <td>00000506_028.jpg</td>\n",
       "      <td>2500</td>\n",
       "      <td>2048</td>\n",
       "      <td>No Finding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>00000506_029.jpg</td>\n",
       "      <td>2500</td>\n",
       "      <td>2048</td>\n",
       "      <td>No Finding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>00000506_030.jpg</td>\n",
       "      <td>2500</td>\n",
       "      <td>2048</td>\n",
       "      <td>No Finding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AP</td>\n",
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       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>00000507_000.jpg</td>\n",
       "      <td>2766</td>\n",
       "      <td>2801</td>\n",
       "      <td>No Finding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2500</td>\n",
       "      <td>2048</td>\n",
       "      <td>No Finding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AP</td>\n",
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       "<p>1964 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "              filename  height  width        label   xmin   ymin   xmax  \\\n",
       "0     00013118_008.jpg    2992   2991  Atelectasis  225.0  547.0  311.0   \n",
       "1     00014716_007.jpg    3056   2544  Atelectasis  686.0  131.0  871.0   \n",
       "2     00029817_009.jpg    3056   2544  Atelectasis  221.0  317.0  376.0   \n",
       "3     00014687_001.jpg    2500   2048  Atelectasis  726.0  494.0  867.0   \n",
       "4     00017877_001.jpg    2500   2048  Atelectasis  660.0  569.0  860.0   \n",
       "...                ...     ...    ...          ...    ...    ...    ...   \n",
       "1959  00000506_028.jpg    2500   2048   No Finding    NaN    NaN    NaN   \n",
       "1960  00000506_029.jpg    2500   2048   No Finding    NaN    NaN    NaN   \n",
       "1961  00000506_030.jpg    2500   2048   No Finding    NaN    NaN    NaN   \n",
       "1962  00000507_000.jpg    2766   2801   No Finding    NaN    NaN    NaN   \n",
       "1963  00000508_002.jpg    2500   2048   No Finding    NaN    NaN    NaN   \n",
       "\n",
       "       ymax view  \n",
       "0     626.0   PA  \n",
       "1     444.0   AP  \n",
       "2     533.0   AP  \n",
       "3     549.0   AP  \n",
       "4     647.0   AP  \n",
       "...     ...  ...  \n",
       "1959    NaN   AP  \n",
       "1960    NaN   AP  \n",
       "1961    NaN   AP  \n",
       "1962    NaN   PA  \n",
       "1963    NaN   AP  \n",
       "\n",
       "[1964 rows x 9 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "91cc25c0-0d82-4065-aab1-91f5d8fc76a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "146"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding the number of positives and negatives of Cardiomegaly on the dataset\n",
    "\n",
    "positives = df.loc[df[\"label\"] == finding]\n",
    "negatives = df.loc[df[\"label\"] != finding]\n",
    "n = len(positives)\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "01f907e2-f507-4e01-8a88-513690167788",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "116 29\n"
     ]
    }
   ],
   "source": [
    "#Defining train and test ratios\n",
    "\n",
    "TRAIN_RATIO = 0.8\n",
    "TEST_RATIO = 0.2\n",
    "TRAIN_N = int(n*TRAIN_RATIO)\n",
    "TEST_N = int(n*TEST_RATIO)\n",
    "print(TRAIN_N, TEST_N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "04268b70-d0ce-43bc-89d1-2ce003809799",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Making the number of positives and negatives equal for better accuracy\n",
    "train_labels = pd.concat([positives[:TRAIN_N], negatives[:TRAIN_N]])\n",
    "test_labels = pd.concat([positives[TRAIN_N:], negatives[TRAIN_N:n]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "508b9f45-5a97-4fbd-9ce3-9dfca3f96366",
   "metadata": {},
   "source": [
    "## Preparing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a82a72f4-5240-4d7c-a1f4-946995a10f60",
   "metadata": {},
   "outputs": [],
   "source": [
    "#making the directories\n",
    "import os\n",
    "rootdir = \"./images/\"\n",
    "os.makedirs(rootdir+finding+\"/test/positive\")\n",
    "os.makedirs(rootdir+finding+\"/train/positive\")\n",
    "os.makedirs(rootdir+finding+\"/test/negative\")\n",
    "os.makedirs(rootdir+finding+\"/train/negative\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "feb781a8-8c1b-4168-89ea-1d027e2eb60c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#transfer images to folders\n",
    "import shutil\n",
    "for idx, image in positives[:TRAIN_N].iterrows():\n",
    "    source = rootdir+image[\"filename\"]\n",
    "    dst = rootdir+finding+\"/train/positive/\"+image[\"filename\"]\n",
    "    shutil.copy(source,dst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "92d5a19d-b0f7-4549-a0f7-dafa28ace376",
   "metadata": {},
   "outputs": [],
   "source": [
    "for idx, image in positives[TRAIN_N:].iterrows():\n",
    "    source = rootdir+image[\"filename\"]\n",
    "    dst = rootdir+finding+\"/test/positive/\"+image[\"filename\"]\n",
    "    shutil.copy(source,dst)\n",
    "\n",
    "for idx, image in negatives[:TRAIN_N].iterrows():\n",
    "    source = rootdir+image[\"filename\"]\n",
    "    dst = rootdir+finding+\"/train/negative/\"+image[\"filename\"]\n",
    "    shutil.copy(source,dst)\n",
    "\n",
    "for idx, image in negatives[TRAIN_N:n].iterrows():\n",
    "    source = rootdir+image[\"filename\"]\n",
    "    dst = rootdir+finding+\"/test/negative/\"+image[\"filename\"]\n",
    "    shutil.copy(source,dst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "489999cb-504a-40f0-98e1-46d1e86f4096",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Loading first six images\n",
    "from PIL import Image, ImageDraw, ImageFont\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "positive_imgs, negative_imgs = [], []\n",
    "IMAGE_HEIGHT, IMAGE_WIDTH = 75,75\n",
    "\n",
    "def load_image_into_numpy_array(image):\n",
    "    image = image.convert('RGB')\n",
    "    (im_width, im_height) = image.size\n",
    "    return np.array(image.getdata()).reshape(\n",
    "        (im_height, im_width, 3)).astype(np.uint8)\n",
    "\n",
    "for idx, row in positives[:6].iterrows():\n",
    "    image_path = rootdir+row[\"filename\"]\n",
    "    image = Image.open(image_path).resize((IMAGE_WIDTH, IMAGE_HEIGHT))\n",
    "    positive_imgs.append(load_image_into_numpy_array(image))\n",
    "\n",
    "for idx, row in negatives[:6].iterrows():\n",
    "    image_path = rootdir+row[\"filename\"]\n",
    "    image = Image.open(image_path).resize((IMAGE_WIDTH, IMAGE_HEIGHT))\n",
    "    negative_imgs.append(load_image_into_numpy_array(image))\n",
    "\n",
    "for idx, img in enumerate(positive_imgs[:6]):\n",
    "    plt.subplot(2,3,idx+1)\n",
    "    plt.title(finding)\n",
    "    plt.imshow(positive_imgs[idx])\n",
    "plt.show()\n",
    "for idx, img in enumerate(negative_imgs[:6]):\n",
    "    plt.subplot(2,3,idx+1)\n",
    "    plt.title(\"No Finding\")\n",
    "    plt.imshow(negative_imgs[idx])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37c8905f-125d-4b32-b611-8f734cdcc475",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ad09c296-386e-464d-a634-43f220c74c49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 75, 75, 3)]  0           []                               \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)                (None, 37, 37, 32)   864         ['input_1[0][0]']                \n",
      "                                                                                                  \n",
      " batch_normalization (BatchNorm  (None, 37, 37, 32)  96          ['conv2d[0][0]']                 \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " activation (Activation)        (None, 37, 37, 32)   0           ['batch_normalization[0][0]']    \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)              (None, 35, 35, 32)   9216        ['activation[0][0]']             \n",
      "                                                                                                  \n",
      " batch_normalization_1 (BatchNo  (None, 35, 35, 32)  96          ['conv2d_1[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_1 (Activation)      (None, 35, 35, 32)   0           ['batch_normalization_1[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_2 (Conv2D)              (None, 35, 35, 64)   18432       ['activation_1[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_2 (BatchNo  (None, 35, 35, 64)  192         ['conv2d_2[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_2 (Activation)      (None, 35, 35, 64)   0           ['batch_normalization_2[0][0]']  \n",
      "                                                                                                  \n",
      " max_pooling2d (MaxPooling2D)   (None, 17, 17, 64)   0           ['activation_2[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_3 (Conv2D)              (None, 17, 17, 80)   5120        ['max_pooling2d[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_3 (BatchNo  (None, 17, 17, 80)  240         ['conv2d_3[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_3 (Activation)      (None, 17, 17, 80)   0           ['batch_normalization_3[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_4 (Conv2D)              (None, 15, 15, 192)  138240      ['activation_3[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_4 (BatchNo  (None, 15, 15, 192)  576        ['conv2d_4[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_4 (Activation)      (None, 15, 15, 192)  0           ['batch_normalization_4[0][0]']  \n",
      "                                                                                                  \n",
      " max_pooling2d_1 (MaxPooling2D)  (None, 7, 7, 192)   0           ['activation_4[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_8 (Conv2D)              (None, 7, 7, 64)     12288       ['max_pooling2d_1[0][0]']        \n",
      "                                                                                                  \n",
      " batch_normalization_8 (BatchNo  (None, 7, 7, 64)    192         ['conv2d_8[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_8 (Activation)      (None, 7, 7, 64)     0           ['batch_normalization_8[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_6 (Conv2D)              (None, 7, 7, 48)     9216        ['max_pooling2d_1[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_9 (Conv2D)              (None, 7, 7, 96)     55296       ['activation_8[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_6 (BatchNo  (None, 7, 7, 48)    144         ['conv2d_6[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " batch_normalization_9 (BatchNo  (None, 7, 7, 96)    288         ['conv2d_9[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_6 (Activation)      (None, 7, 7, 48)     0           ['batch_normalization_6[0][0]']  \n",
      "                                                                                                  \n",
      " activation_9 (Activation)      (None, 7, 7, 96)     0           ['batch_normalization_9[0][0]']  \n",
      "                                                                                                  \n",
      " average_pooling2d (AveragePool  (None, 7, 7, 192)   0           ['max_pooling2d_1[0][0]']        \n",
      " ing2D)                                                                                           \n",
      "                                                                                                  \n",
      " conv2d_5 (Conv2D)              (None, 7, 7, 64)     12288       ['max_pooling2d_1[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_7 (Conv2D)              (None, 7, 7, 64)     76800       ['activation_6[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_10 (Conv2D)             (None, 7, 7, 96)     82944       ['activation_9[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_11 (Conv2D)             (None, 7, 7, 32)     6144        ['average_pooling2d[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_5 (BatchNo  (None, 7, 7, 64)    192         ['conv2d_5[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " batch_normalization_7 (BatchNo  (None, 7, 7, 64)    192         ['conv2d_7[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " batch_normalization_10 (BatchN  (None, 7, 7, 96)    288         ['conv2d_10[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_11 (BatchN  (None, 7, 7, 32)    96          ['conv2d_11[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_5 (Activation)      (None, 7, 7, 64)     0           ['batch_normalization_5[0][0]']  \n",
      "                                                                                                  \n",
      " activation_7 (Activation)      (None, 7, 7, 64)     0           ['batch_normalization_7[0][0]']  \n",
      "                                                                                                  \n",
      " activation_10 (Activation)     (None, 7, 7, 96)     0           ['batch_normalization_10[0][0]'] \n",
      "                                                                                                  \n",
      " activation_11 (Activation)     (None, 7, 7, 32)     0           ['batch_normalization_11[0][0]'] \n",
      "                                                                                                  \n",
      " mixed0 (Concatenate)           (None, 7, 7, 256)    0           ['activation_5[0][0]',           \n",
      "                                                                  'activation_7[0][0]',           \n",
      "                                                                  'activation_10[0][0]',          \n",
      "                                                                  'activation_11[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_15 (Conv2D)             (None, 7, 7, 64)     16384       ['mixed0[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_15 (BatchN  (None, 7, 7, 64)    192         ['conv2d_15[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_15 (Activation)     (None, 7, 7, 64)     0           ['batch_normalization_15[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_13 (Conv2D)             (None, 7, 7, 48)     12288       ['mixed0[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_16 (Conv2D)             (None, 7, 7, 96)     55296       ['activation_15[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_13 (BatchN  (None, 7, 7, 48)    144         ['conv2d_13[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_16 (BatchN  (None, 7, 7, 96)    288         ['conv2d_16[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_13 (Activation)     (None, 7, 7, 48)     0           ['batch_normalization_13[0][0]'] \n",
      "                                                                                                  \n",
      " activation_16 (Activation)     (None, 7, 7, 96)     0           ['batch_normalization_16[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_1 (AveragePo  (None, 7, 7, 256)   0           ['mixed0[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_12 (Conv2D)             (None, 7, 7, 64)     16384       ['mixed0[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_14 (Conv2D)             (None, 7, 7, 64)     76800       ['activation_13[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_17 (Conv2D)             (None, 7, 7, 96)     82944       ['activation_16[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_18 (Conv2D)             (None, 7, 7, 64)     16384       ['average_pooling2d_1[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_12 (BatchN  (None, 7, 7, 64)    192         ['conv2d_12[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_14 (BatchN  (None, 7, 7, 64)    192         ['conv2d_14[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_17 (BatchN  (None, 7, 7, 96)    288         ['conv2d_17[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_18 (BatchN  (None, 7, 7, 64)    192         ['conv2d_18[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_12 (Activation)     (None, 7, 7, 64)     0           ['batch_normalization_12[0][0]'] \n",
      "                                                                                                  \n",
      " activation_14 (Activation)     (None, 7, 7, 64)     0           ['batch_normalization_14[0][0]'] \n",
      "                                                                                                  \n",
      " activation_17 (Activation)     (None, 7, 7, 96)     0           ['batch_normalization_17[0][0]'] \n",
      "                                                                                                  \n",
      " activation_18 (Activation)     (None, 7, 7, 64)     0           ['batch_normalization_18[0][0]'] \n",
      "                                                                                                  \n",
      " mixed1 (Concatenate)           (None, 7, 7, 288)    0           ['activation_12[0][0]',          \n",
      "                                                                  'activation_14[0][0]',          \n",
      "                                                                  'activation_17[0][0]',          \n",
      "                                                                  'activation_18[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_22 (Conv2D)             (None, 7, 7, 64)     18432       ['mixed1[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_22 (BatchN  (None, 7, 7, 64)    192         ['conv2d_22[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_22 (Activation)     (None, 7, 7, 64)     0           ['batch_normalization_22[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_20 (Conv2D)             (None, 7, 7, 48)     13824       ['mixed1[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_23 (Conv2D)             (None, 7, 7, 96)     55296       ['activation_22[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_20 (BatchN  (None, 7, 7, 48)    144         ['conv2d_20[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_23 (BatchN  (None, 7, 7, 96)    288         ['conv2d_23[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_20 (Activation)     (None, 7, 7, 48)     0           ['batch_normalization_20[0][0]'] \n",
      "                                                                                                  \n",
      " activation_23 (Activation)     (None, 7, 7, 96)     0           ['batch_normalization_23[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_2 (AveragePo  (None, 7, 7, 288)   0           ['mixed1[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_19 (Conv2D)             (None, 7, 7, 64)     18432       ['mixed1[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_21 (Conv2D)             (None, 7, 7, 64)     76800       ['activation_20[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_24 (Conv2D)             (None, 7, 7, 96)     82944       ['activation_23[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_25 (Conv2D)             (None, 7, 7, 64)     18432       ['average_pooling2d_2[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_19 (BatchN  (None, 7, 7, 64)    192         ['conv2d_19[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_21 (BatchN  (None, 7, 7, 64)    192         ['conv2d_21[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_24 (BatchN  (None, 7, 7, 96)    288         ['conv2d_24[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_25 (BatchN  (None, 7, 7, 64)    192         ['conv2d_25[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_19 (Activation)     (None, 7, 7, 64)     0           ['batch_normalization_19[0][0]'] \n",
      "                                                                                                  \n",
      " activation_21 (Activation)     (None, 7, 7, 64)     0           ['batch_normalization_21[0][0]'] \n",
      "                                                                                                  \n",
      " activation_24 (Activation)     (None, 7, 7, 96)     0           ['batch_normalization_24[0][0]'] \n",
      "                                                                                                  \n",
      " activation_25 (Activation)     (None, 7, 7, 64)     0           ['batch_normalization_25[0][0]'] \n",
      "                                                                                                  \n",
      " mixed2 (Concatenate)           (None, 7, 7, 288)    0           ['activation_19[0][0]',          \n",
      "                                                                  'activation_21[0][0]',          \n",
      "                                                                  'activation_24[0][0]',          \n",
      "                                                                  'activation_25[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_27 (Conv2D)             (None, 7, 7, 64)     18432       ['mixed2[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_27 (BatchN  (None, 7, 7, 64)    192         ['conv2d_27[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_27 (Activation)     (None, 7, 7, 64)     0           ['batch_normalization_27[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_28 (Conv2D)             (None, 7, 7, 96)     55296       ['activation_27[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_28 (BatchN  (None, 7, 7, 96)    288         ['conv2d_28[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_28 (Activation)     (None, 7, 7, 96)     0           ['batch_normalization_28[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_26 (Conv2D)             (None, 3, 3, 384)    995328      ['mixed2[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_29 (Conv2D)             (None, 3, 3, 96)     82944       ['activation_28[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_26 (BatchN  (None, 3, 3, 384)   1152        ['conv2d_26[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_29 (BatchN  (None, 3, 3, 96)    288         ['conv2d_29[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_26 (Activation)     (None, 3, 3, 384)    0           ['batch_normalization_26[0][0]'] \n",
      "                                                                                                  \n",
      " activation_29 (Activation)     (None, 3, 3, 96)     0           ['batch_normalization_29[0][0]'] \n",
      "                                                                                                  \n",
      " max_pooling2d_2 (MaxPooling2D)  (None, 3, 3, 288)   0           ['mixed2[0][0]']                 \n",
      "                                                                                                  \n",
      " mixed3 (Concatenate)           (None, 3, 3, 768)    0           ['activation_26[0][0]',          \n",
      "                                                                  'activation_29[0][0]',          \n",
      "                                                                  'max_pooling2d_2[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_34 (Conv2D)             (None, 3, 3, 128)    98304       ['mixed3[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_34 (BatchN  (None, 3, 3, 128)   384         ['conv2d_34[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_34 (Activation)     (None, 3, 3, 128)    0           ['batch_normalization_34[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_35 (Conv2D)             (None, 3, 3, 128)    114688      ['activation_34[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_35 (BatchN  (None, 3, 3, 128)   384         ['conv2d_35[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_35 (Activation)     (None, 3, 3, 128)    0           ['batch_normalization_35[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_31 (Conv2D)             (None, 3, 3, 128)    98304       ['mixed3[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_36 (Conv2D)             (None, 3, 3, 128)    114688      ['activation_35[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_31 (BatchN  (None, 3, 3, 128)   384         ['conv2d_31[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_36 (BatchN  (None, 3, 3, 128)   384         ['conv2d_36[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_31 (Activation)     (None, 3, 3, 128)    0           ['batch_normalization_31[0][0]'] \n",
      "                                                                                                  \n",
      " activation_36 (Activation)     (None, 3, 3, 128)    0           ['batch_normalization_36[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_32 (Conv2D)             (None, 3, 3, 128)    114688      ['activation_31[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_37 (Conv2D)             (None, 3, 3, 128)    114688      ['activation_36[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_32 (BatchN  (None, 3, 3, 128)   384         ['conv2d_32[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_37 (BatchN  (None, 3, 3, 128)   384         ['conv2d_37[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_32 (Activation)     (None, 3, 3, 128)    0           ['batch_normalization_32[0][0]'] \n",
      "                                                                                                  \n",
      " activation_37 (Activation)     (None, 3, 3, 128)    0           ['batch_normalization_37[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_3 (AveragePo  (None, 3, 3, 768)   0           ['mixed3[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_30 (Conv2D)             (None, 3, 3, 192)    147456      ['mixed3[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_33 (Conv2D)             (None, 3, 3, 192)    172032      ['activation_32[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_38 (Conv2D)             (None, 3, 3, 192)    172032      ['activation_37[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_39 (Conv2D)             (None, 3, 3, 192)    147456      ['average_pooling2d_3[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_30 (BatchN  (None, 3, 3, 192)   576         ['conv2d_30[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_33 (BatchN  (None, 3, 3, 192)   576         ['conv2d_33[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_38 (BatchN  (None, 3, 3, 192)   576         ['conv2d_38[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_39 (BatchN  (None, 3, 3, 192)   576         ['conv2d_39[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_30 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_30[0][0]'] \n",
      "                                                                                                  \n",
      " activation_33 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_33[0][0]'] \n",
      "                                                                                                  \n",
      " activation_38 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_38[0][0]'] \n",
      "                                                                                                  \n",
      " activation_39 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_39[0][0]'] \n",
      "                                                                                                  \n",
      " mixed4 (Concatenate)           (None, 3, 3, 768)    0           ['activation_30[0][0]',          \n",
      "                                                                  'activation_33[0][0]',          \n",
      "                                                                  'activation_38[0][0]',          \n",
      "                                                                  'activation_39[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_44 (Conv2D)             (None, 3, 3, 160)    122880      ['mixed4[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_44 (BatchN  (None, 3, 3, 160)   480         ['conv2d_44[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_44 (Activation)     (None, 3, 3, 160)    0           ['batch_normalization_44[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_45 (Conv2D)             (None, 3, 3, 160)    179200      ['activation_44[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_45 (BatchN  (None, 3, 3, 160)   480         ['conv2d_45[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_45 (Activation)     (None, 3, 3, 160)    0           ['batch_normalization_45[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_41 (Conv2D)             (None, 3, 3, 160)    122880      ['mixed4[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_46 (Conv2D)             (None, 3, 3, 160)    179200      ['activation_45[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_41 (BatchN  (None, 3, 3, 160)   480         ['conv2d_41[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_46 (BatchN  (None, 3, 3, 160)   480         ['conv2d_46[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_41 (Activation)     (None, 3, 3, 160)    0           ['batch_normalization_41[0][0]'] \n",
      "                                                                                                  \n",
      " activation_46 (Activation)     (None, 3, 3, 160)    0           ['batch_normalization_46[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_42 (Conv2D)             (None, 3, 3, 160)    179200      ['activation_41[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_47 (Conv2D)             (None, 3, 3, 160)    179200      ['activation_46[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_42 (BatchN  (None, 3, 3, 160)   480         ['conv2d_42[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_47 (BatchN  (None, 3, 3, 160)   480         ['conv2d_47[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_42 (Activation)     (None, 3, 3, 160)    0           ['batch_normalization_42[0][0]'] \n",
      "                                                                                                  \n",
      " activation_47 (Activation)     (None, 3, 3, 160)    0           ['batch_normalization_47[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_4 (AveragePo  (None, 3, 3, 768)   0           ['mixed4[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_40 (Conv2D)             (None, 3, 3, 192)    147456      ['mixed4[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_43 (Conv2D)             (None, 3, 3, 192)    215040      ['activation_42[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_48 (Conv2D)             (None, 3, 3, 192)    215040      ['activation_47[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_49 (Conv2D)             (None, 3, 3, 192)    147456      ['average_pooling2d_4[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_40 (BatchN  (None, 3, 3, 192)   576         ['conv2d_40[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_43 (BatchN  (None, 3, 3, 192)   576         ['conv2d_43[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_48 (BatchN  (None, 3, 3, 192)   576         ['conv2d_48[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_49 (BatchN  (None, 3, 3, 192)   576         ['conv2d_49[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_40 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_40[0][0]'] \n",
      "                                                                                                  \n",
      " activation_43 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_43[0][0]'] \n",
      "                                                                                                  \n",
      " activation_48 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_48[0][0]'] \n",
      "                                                                                                  \n",
      " activation_49 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_49[0][0]'] \n",
      "                                                                                                  \n",
      " mixed5 (Concatenate)           (None, 3, 3, 768)    0           ['activation_40[0][0]',          \n",
      "                                                                  'activation_43[0][0]',          \n",
      "                                                                  'activation_48[0][0]',          \n",
      "                                                                  'activation_49[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_54 (Conv2D)             (None, 3, 3, 160)    122880      ['mixed5[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_54 (BatchN  (None, 3, 3, 160)   480         ['conv2d_54[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_54 (Activation)     (None, 3, 3, 160)    0           ['batch_normalization_54[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_55 (Conv2D)             (None, 3, 3, 160)    179200      ['activation_54[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_55 (BatchN  (None, 3, 3, 160)   480         ['conv2d_55[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_55 (Activation)     (None, 3, 3, 160)    0           ['batch_normalization_55[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_51 (Conv2D)             (None, 3, 3, 160)    122880      ['mixed5[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_56 (Conv2D)             (None, 3, 3, 160)    179200      ['activation_55[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_51 (BatchN  (None, 3, 3, 160)   480         ['conv2d_51[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_56 (BatchN  (None, 3, 3, 160)   480         ['conv2d_56[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_51 (Activation)     (None, 3, 3, 160)    0           ['batch_normalization_51[0][0]'] \n",
      "                                                                                                  \n",
      " activation_56 (Activation)     (None, 3, 3, 160)    0           ['batch_normalization_56[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_52 (Conv2D)             (None, 3, 3, 160)    179200      ['activation_51[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_57 (Conv2D)             (None, 3, 3, 160)    179200      ['activation_56[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_52 (BatchN  (None, 3, 3, 160)   480         ['conv2d_52[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_57 (BatchN  (None, 3, 3, 160)   480         ['conv2d_57[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_52 (Activation)     (None, 3, 3, 160)    0           ['batch_normalization_52[0][0]'] \n",
      "                                                                                                  \n",
      " activation_57 (Activation)     (None, 3, 3, 160)    0           ['batch_normalization_57[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_5 (AveragePo  (None, 3, 3, 768)   0           ['mixed5[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_50 (Conv2D)             (None, 3, 3, 192)    147456      ['mixed5[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_53 (Conv2D)             (None, 3, 3, 192)    215040      ['activation_52[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_58 (Conv2D)             (None, 3, 3, 192)    215040      ['activation_57[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_59 (Conv2D)             (None, 3, 3, 192)    147456      ['average_pooling2d_5[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_50 (BatchN  (None, 3, 3, 192)   576         ['conv2d_50[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_53 (BatchN  (None, 3, 3, 192)   576         ['conv2d_53[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_58 (BatchN  (None, 3, 3, 192)   576         ['conv2d_58[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_59 (BatchN  (None, 3, 3, 192)   576         ['conv2d_59[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_50 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_50[0][0]'] \n",
      "                                                                                                  \n",
      " activation_53 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_53[0][0]'] \n",
      "                                                                                                  \n",
      " activation_58 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_58[0][0]'] \n",
      "                                                                                                  \n",
      " activation_59 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_59[0][0]'] \n",
      "                                                                                                  \n",
      " mixed6 (Concatenate)           (None, 3, 3, 768)    0           ['activation_50[0][0]',          \n",
      "                                                                  'activation_53[0][0]',          \n",
      "                                                                  'activation_58[0][0]',          \n",
      "                                                                  'activation_59[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_64 (Conv2D)             (None, 3, 3, 192)    147456      ['mixed6[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_64 (BatchN  (None, 3, 3, 192)   576         ['conv2d_64[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_64 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_64[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_65 (Conv2D)             (None, 3, 3, 192)    258048      ['activation_64[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_65 (BatchN  (None, 3, 3, 192)   576         ['conv2d_65[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_65 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_65[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_61 (Conv2D)             (None, 3, 3, 192)    147456      ['mixed6[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_66 (Conv2D)             (None, 3, 3, 192)    258048      ['activation_65[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_61 (BatchN  (None, 3, 3, 192)   576         ['conv2d_61[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_66 (BatchN  (None, 3, 3, 192)   576         ['conv2d_66[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_61 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_61[0][0]'] \n",
      "                                                                                                  \n",
      " activation_66 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_66[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_62 (Conv2D)             (None, 3, 3, 192)    258048      ['activation_61[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_67 (Conv2D)             (None, 3, 3, 192)    258048      ['activation_66[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_62 (BatchN  (None, 3, 3, 192)   576         ['conv2d_62[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_67 (BatchN  (None, 3, 3, 192)   576         ['conv2d_67[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_62 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_62[0][0]'] \n",
      "                                                                                                  \n",
      " activation_67 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_67[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_6 (AveragePo  (None, 3, 3, 768)   0           ['mixed6[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_60 (Conv2D)             (None, 3, 3, 192)    147456      ['mixed6[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_63 (Conv2D)             (None, 3, 3, 192)    258048      ['activation_62[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_68 (Conv2D)             (None, 3, 3, 192)    258048      ['activation_67[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_69 (Conv2D)             (None, 3, 3, 192)    147456      ['average_pooling2d_6[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_60 (BatchN  (None, 3, 3, 192)   576         ['conv2d_60[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_63 (BatchN  (None, 3, 3, 192)   576         ['conv2d_63[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_68 (BatchN  (None, 3, 3, 192)   576         ['conv2d_68[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_69 (BatchN  (None, 3, 3, 192)   576         ['conv2d_69[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_60 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_60[0][0]'] \n",
      "                                                                                                  \n",
      " activation_63 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_63[0][0]'] \n",
      "                                                                                                  \n",
      " activation_68 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_68[0][0]'] \n",
      "                                                                                                  \n",
      " activation_69 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_69[0][0]'] \n",
      "                                                                                                  \n",
      " mixed7 (Concatenate)           (None, 3, 3, 768)    0           ['activation_60[0][0]',          \n",
      "                                                                  'activation_63[0][0]',          \n",
      "                                                                  'activation_68[0][0]',          \n",
      "                                                                  'activation_69[0][0]']          \n",
      "                                                                                                  \n",
      " flatten (Flatten)              (None, 6912)         0           ['mixed7[0][0]']                 \n",
      "                                                                                                  \n",
      " dense (Dense)                  (None, 1024)         7078912     ['flatten[0][0]']                \n",
      "                                                                                                  \n",
      " dropout (Dropout)              (None, 1024)         0           ['dense[0][0]']                  \n",
      "                                                                                                  \n",
      " dense_1 (Dense)                (None, 1)            1025        ['dropout[0][0]']                \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 16,055,201\n",
      "Trainable params: 7,079,937\n",
      "Non-trainable params: 8,975,264\n",
      "__________________________________________________________________________________________________\n",
      "None\n",
      "You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) for plot_model to work.\n"
     ]
    }
   ],
   "source": [
    "#Building the model\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
    "from tensorflow.keras import layers, Model\n",
    "from tensorflow.keras.optimizers import RMSprop\n",
    "from keras.utils.vis_utils import plot_model\n",
    "\n",
    "pre_trained_model = InceptionV3(input_shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3), weights='imagenet', include_top=False)\n",
    "\n",
    "for layer in pre_trained_model.layers:\n",
    "    layer.trainable = False\n",
    "\n",
    "last_layer = pre_trained_model.get_layer('mixed7')\n",
    "last_ouptut = last_layer.output\n",
    "\n",
    "x = layers.Flatten()(last_ouptut)\n",
    "x = layers.Dense(1024, activation='relu')(x)\n",
    "x = layers.Dropout(0.2)(x)\n",
    "x = layers.Dense(1, activation='sigmoid')(x)\n",
    "\n",
    "model = Model(pre_trained_model.input, x)\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['acc'])\n",
    "print(model.summary())\n",
    "plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "29b34abb-ba6f-4a1f-b735-f763ac37e738",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_dir = rootdir = \"./images/\"\n",
    "train_dir = os.path.join(base_dir, finding, 'train')\n",
    "test_dir = os.path.join(base_dir, finding, 'test')\n",
    "\n",
    "train_pos_dir = os.path.join(train_dir, 'positive')\n",
    "train_neg_dir = os.path.join(train_dir, 'negative')\n",
    "test_pos_dir = os.path.join(test_dir, 'positive')\n",
    "test_neg_dir = os.path.join(test_dir, 'negative')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ed842c4f-0dbc-4be4-ac32-4fca6bd90cff",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Augmentation and Filtering \n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1./255,\n",
    "    rotation_range=40,\n",
    "    width_shift_range=0.2,\n",
    "    height_shift_range=0.2,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=False,\n",
    ")\n",
    "val_datagen = ImageDataGenerator(rescale=1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dd8f6b43-4914-4978-85c3-9f64eb2015a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 232 images belonging to 2 classes.\n",
      "Found 60 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_generator = train_datagen.flow_from_directory(\n",
    "    train_dir, \n",
    "    target_size=(IMAGE_WIDTH, IMAGE_HEIGHT),\n",
    "    batch_size=1,\n",
    "    class_mode='binary'\n",
    ")\n",
    "test_generator = val_datagen.flow_from_directory(\n",
    "    test_dir, \n",
    "    target_size=(IMAGE_WIDTH, IMAGE_HEIGHT),\n",
    "    batch_size=1,\n",
    "    class_mode='binary'\n",
    ")\n",
    "\n",
    "train_step= len(os.listdir(train_pos_dir))*2\n",
    "test_step = len(os.listdir(test_pos_dir))*2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a71d9c80-7cc2-49ae-999e-27abb4ddc1ff",
   "metadata": {},
   "source": [
    "# Run Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "964d9a30-9988-4cfc-b928-d0d000ccf2a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "232/232 - 20s - loss: 2.0562 - acc: 0.4784 - val_loss: 0.6012 - val_acc: 0.6667 - 20s/epoch - 86ms/step\n",
      "Epoch 2/20\n",
      "232/232 - 4s - loss: 0.9191 - acc: 0.5819 - val_loss: 0.6789 - val_acc: 0.6500 - 4s/epoch - 19ms/step\n",
      "Epoch 3/20\n",
      "232/232 - 5s - loss: 0.7285 - acc: 0.6164 - val_loss: 0.9381 - val_acc: 0.5333 - 5s/epoch - 19ms/step\n",
      "Epoch 4/20\n",
      "232/232 - 4s - loss: 0.7248 - acc: 0.6207 - val_loss: 0.6766 - val_acc: 0.6167 - 4s/epoch - 19ms/step\n",
      "Epoch 5/20\n",
      "232/232 - 5s - loss: 0.6359 - acc: 0.6681 - val_loss: 0.6946 - val_acc: 0.6167 - 5s/epoch - 20ms/step\n",
      "Epoch 6/20\n",
      "232/232 - 4s - loss: 0.6743 - acc: 0.6250 - val_loss: 0.8166 - val_acc: 0.5167 - 4s/epoch - 19ms/step\n",
      "Epoch 7/20\n",
      "232/232 - 5s - loss: 0.6575 - acc: 0.6379 - val_loss: 0.6532 - val_acc: 0.6000 - 5s/epoch - 20ms/step\n",
      "Epoch 8/20\n",
      "232/232 - 5s - loss: 0.6529 - acc: 0.6595 - val_loss: 0.6378 - val_acc: 0.6167 - 5s/epoch - 20ms/step\n",
      "Epoch 9/20\n",
      "232/232 - 5s - loss: 0.6468 - acc: 0.6767 - val_loss: 0.6408 - val_acc: 0.6000 - 5s/epoch - 20ms/step\n",
      "Epoch 10/20\n",
      "232/232 - 4s - loss: 0.6116 - acc: 0.6681 - val_loss: 1.0779 - val_acc: 0.5000 - 4s/epoch - 19ms/step\n",
      "Epoch 11/20\n",
      "232/232 - 4s - loss: 0.6410 - acc: 0.6250 - val_loss: 0.6661 - val_acc: 0.7167 - 4s/epoch - 19ms/step\n",
      "Epoch 12/20\n",
      "232/232 - 5s - loss: 0.6066 - acc: 0.6595 - val_loss: 0.6237 - val_acc: 0.6167 - 5s/epoch - 22ms/step\n",
      "Epoch 13/20\n",
      "232/232 - 5s - loss: 0.6356 - acc: 0.6724 - val_loss: 0.6040 - val_acc: 0.6667 - 5s/epoch - 22ms/step\n",
      "Epoch 14/20\n",
      "232/232 - 5s - loss: 0.6283 - acc: 0.6681 - val_loss: 0.8565 - val_acc: 0.5833 - 5s/epoch - 21ms/step\n",
      "Epoch 15/20\n",
      "232/232 - 5s - loss: 0.6273 - acc: 0.6724 - val_loss: 0.6473 - val_acc: 0.6167 - 5s/epoch - 21ms/step\n",
      "Epoch 16/20\n",
      "232/232 - 5s - loss: 0.5224 - acc: 0.7155 - val_loss: 0.6973 - val_acc: 0.6833 - 5s/epoch - 23ms/step\n",
      "Epoch 17/20\n",
      "232/232 - 5s - loss: 0.6372 - acc: 0.6724 - val_loss: 0.6421 - val_acc: 0.6500 - 5s/epoch - 20ms/step\n",
      "Epoch 18/20\n",
      "232/232 - 5s - loss: 0.6103 - acc: 0.6552 - val_loss: 0.6015 - val_acc: 0.6500 - 5s/epoch - 21ms/step\n",
      "Epoch 19/20\n",
      "232/232 - 5s - loss: 0.5899 - acc: 0.6853 - val_loss: 0.5741 - val_acc: 0.7000 - 5s/epoch - 20ms/step\n",
      "Epoch 20/20\n",
      "232/232 - 5s - loss: 0.6139 - acc: 0.6638 - val_loss: 0.5801 - val_acc: 0.6500 - 5s/epoch - 20ms/step\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    train_generator, \n",
    "    steps_per_epoch=train_step,\n",
    "    epochs=20,\n",
    "    validation_data=test_generator,\n",
    "    validation_steps=test_step,\n",
    "    verbose=2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "69073ffa-17dc-47ef-9d5e-63b532a4ce94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x228425b1040>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting training and testing accuracy \n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['loss']\n",
    "\n",
    "epoch = range(len(acc))\n",
    "\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(epoch, acc, label =\"train\")\n",
    "plt.plot(epoch, val_acc,label=\"test\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.title(\"Training and Testing Accuracy\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(epoch, loss, label =\"train\")\n",
    "plt.plot(epoch, val_loss,label=\"test\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.title(\"Training and Testing Loss\")\n",
    "plt.legend(loc=\"lower right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11093b1d-5d27-4953-a680-b54bd41e13cc",
   "metadata": {},
   "source": [
    "# Evaluating Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "94a7aa8b-1783-49b9-bf08-7d285d8d0d32",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_image(filename):\n",
    "  image = Image.open(filename).resize((IMAGE_HEIGHT, IMAGE_WIDTH))\n",
    "  image_np = load_image_into_numpy_array(image)\n",
    "  exp = np.true_divide(image_np, 255.0)\n",
    "  expanded = np.expand_dims(exp, axis=0)\n",
    "  return model.predict(expanded)[0][0]\n",
    "\n",
    "def show_df_row(row):\n",
    "    image_path = row['filepath']\n",
    "    image = Image.open(filename).resize([IMAGE_HEIGHT, IMAGE_WIDTH])\n",
    "    img = load_image_into_numpy_array(image)\n",
    "    exp = np.true_divide(image, 255.0)\n",
    "    exp = np.stack((exp,)*3, axis=-1)\n",
    "    expanded = np.expand_dims(exp, axis=0)\n",
    "    pred = model.predict(expanded)[0][0]\n",
    "    guess = \"neg\"\n",
    "    if pred>0.5: guess=\"pos\"\n",
    "    title = \"Image: \" +row[\"filename\"]+ \"Lable: \"+row[\"label\"]+\"Guess: \"+guess+ \"Score: \" +str(pred)\n",
    "    plt.title(title)\n",
    "    plt.imshow(img)\n",
    "    plt.show()\n",
    "    return\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e79cb176-f827-4b24-8202-f23c59c28b40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 31ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 31ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 37ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
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      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "for image in os.listdir(test_neg_dir):\n",
    "    filename = test_neg_dir+\"/\"+image\n",
    "    confidence = predict_image(filename)\n",
    "    guess = \"pos\" if confidence>0.5 else \"neg\"\n",
    "    results.append([filename, image, \"neg\",guess, confidence])\n",
    "\n",
    "for image in os.listdir(test_pos_dir):\n",
    "    filename = test_pos_dir+\"/\"+image\n",
    "    confidence = predict_image(filename)\n",
    "    guess = \"pos\" if confidence>0.5 else \"neg\"\n",
    "    results.append([filename, image, \"pos\",guess, confidence])\n",
    "\n",
    "sorted_results = sorted(results, key=lambda x:x[4], reverse=True)\n",
    "df = pd.DataFrame(data=sorted_results, columns=[\"filepath\", \"filename\", \"label\", \"guess\", \"confidence\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ecbfa8de-7815-481d-b34a-0da7e25440c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filepath</th>\n",
       "      <th>filename</th>\n",
       "      <th>label</th>\n",
       "      <th>guess</th>\n",
       "      <th>confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>./images/Cardiomegaly\\test\\positive/00008522_0...</td>\n",
       "      <td>00008522_032.jpg</td>\n",
       "      <td>pos</td>\n",
       "      <td>pos</td>\n",
       "      <td>0.923971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>./images/Cardiomegaly\\test\\positive/00013615_0...</td>\n",
       "      <td>00013615_052.jpg</td>\n",
       "      <td>pos</td>\n",
       "      <td>pos</td>\n",
       "      <td>0.912980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>./images/Cardiomegaly\\test\\positive/00022706_0...</td>\n",
       "      <td>00022706_001.jpg</td>\n",
       "      <td>pos</td>\n",
       "      <td>pos</td>\n",
       "      <td>0.880570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>./images/Cardiomegaly\\test\\negative/00028625_0...</td>\n",
       "      <td>00028625_000.jpg</td>\n",
       "      <td>neg</td>\n",
       "      <td>pos</td>\n",
       "      <td>0.835476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>./images/Cardiomegaly\\test\\positive/00004822_0...</td>\n",
       "      <td>00004822_051.jpg</td>\n",
       "      <td>pos</td>\n",
       "      <td>pos</td>\n",
       "      <td>0.800895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            filepath          filename label  \\\n",
       "0  ./images/Cardiomegaly\\test\\positive/00008522_0...  00008522_032.jpg   pos   \n",
       "1  ./images/Cardiomegaly\\test\\positive/00013615_0...  00013615_052.jpg   pos   \n",
       "2  ./images/Cardiomegaly\\test\\positive/00022706_0...  00022706_001.jpg   pos   \n",
       "3  ./images/Cardiomegaly\\test\\negative/00028625_0...  00028625_000.jpg   neg   \n",
       "4  ./images/Cardiomegaly\\test\\positive/00004822_0...  00004822_051.jpg   pos   \n",
       "\n",
       "  guess  confidence  \n",
       "0   pos    0.923971  \n",
       "1   pos    0.912980  \n",
       "2   pos    0.880570  \n",
       "3   pos    0.835476  \n",
       "4   pos    0.800895  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "75d92c80-5335-401a-89e4-09f663c4bc3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 31ms/step\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "n = random.randint(0 , len(df)-1)\n",
    "show_df_row(df.iloc[n])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0b86c1c-d7e3-4590-bde7-27a89276b87b",
   "metadata": {},
   "source": [
    "# Plotting and Evaluating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3b3a8802-d84b-4f49-8ae0-b22a37a294e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import FormatStrFormatter\n",
    "pos = df.loc[df['label'] == \"pos\"][\"confidence\"]\n",
    "neg = df.loc[df['label'] == \"neg\"][\"confidence\"]\n",
    "fig, ax = plt.subplots()\n",
    "n, bins, patches = plt.hist([pos,neg], np.arange(0.0,1.1,0.1).tolist(),\\\n",
    "                            edgecolor=\"black\", linewidth=0.5, density=False, histtype=\"bar\", \\\n",
    "                            stacked=True, color=[\"green\",\"red\"], label=[finding, \"No finding\"])\n",
    "plt.xlabel('Confidence')\n",
    "plt.ylabel('N')\n",
    "plt.xticks(bins)\n",
    "ax.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "plt.title(\"Confidence Score for different images\")\n",
    "plt.legend(loc='lower right', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c47c711f-e1a0-48b5-a42e-d34fec39948a",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (1265231363.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[51], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m    cutoff = 0.79 @param {type:\"slider\", min:0, max:1, step:0.01}\u001b[0m\n\u001b[1;37m                          ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "cutoff = 0.79 @param {type:\"slider\", min:0, max:1, step:0.01}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b35ba854-473c-4d12-8bdb-5c2a063eff3e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def create_with_cutoff(cutoff):\n",
    "  __, ax = plt.subplots()\n",
    "  TP = df.loc[(df['label'] == \"pos\") & (df[\"confidence\"] > cutoff)][\"confidence\"]\n",
    "  FP = df.loc[(df['label'] == \"neg\") & (df[\"confidence\"] > cutoff)][\"confidence\"]\n",
    "  FN = df.loc[(df['label'] == \"pos\") & (df[\"confidence\"] < cutoff)][\"confidence\"]\n",
    "  TN = df.loc[(df['label'] == \"neg\") & (df[\"confidence\"] < cutoff)][\"confidence\"]\n",
    "  plt.hist([TP,FP,TN,FN], np.arange(0.0, 1.1, 0.1).tolist(), \\\n",
    "           edgecolor='black', linewidth=0.5, density=False, histtype='bar', \\\n",
    "           stacked=True, color=['limegreen','forestgreen','orangered','salmon'], \\\n",
    "           label=['TP','FP','TN','FN'])\n",
    "  plt.xlabel('Confidence')\n",
    "  plt.ylabel('N')\n",
    "  plt.xticks(bins)\n",
    "  ax.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "  plt.title('Confidence scores for different values')\n",
    "  plt.axvline(cutoff, color='k', linestyle='dashed', linewidth=2)\n",
    "  plt.legend(loc=\"lower right\", fontsize=16)\n",
    "  sens = round(len(TP)/(len(TP)+len(FN)),2)\n",
    "  spec = round(len(TN)/(len(TN)+len(FP)),2)\n",
    "  stats = \"sensitivity: \"+str(sens)+\"\\n\"+\"specificity: \"+str(spec)+\"\\n\\n\"+\"TP: \"+str(len(TP))+\"\\n\"+\"FP: \"+str(len(FP))+\"\\n\"+\"TN: \"+str(len(TN))+\"\\n\"+\"FN: \"+str(len(FN))\n",
    "  plt.text(0.05, 0.05, stats, fontsize=14, transform=ax.transAxes)\n",
    "  plt.show()\n",
    "\n",
    "create_with_cutoff(cutoff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3eaf65c5-803a-42cf-b6ee-c30677afd601",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import auc\n",
    "\n",
    "def create_auc_curve(classifications):\n",
    "  squares = {}\n",
    "  for x in classifications:\n",
    "    conf = x[4]\n",
    "    TP, FP, TN, FN = 0, 0, 0, 0\n",
    "    for row in classifications:\n",
    "      assert (row[2] == \"neg\" or row[2] == \"pos\")\n",
    "      if row[2] == \"neg\":\n",
    "        if float(row[4]) < conf: TN += 1\n",
    "        else: FP += 1\n",
    "      else:\n",
    "        if float(row[4]) > conf: TP += 1\n",
    "        else: FN += 1\n",
    "    squares[conf] = [TP, FP, TN, FN]\n",
    "  # now we have a list of stuff: convert to\n",
    "  sens_spec = {}\n",
    "  for entry in squares:\n",
    "    sens = squares[entry][0] / float(squares[entry][0] + squares[entry][3])\n",
    "    spec = squares[entry][2] / float(squares[entry][2] + squares[entry][1])\n",
    "    sens_spec[entry] = (1-spec, sens)\n",
    "  return squares, sens_spec\n",
    "\n",
    "squares, sens_spec = create_auc_curve(sorted_results)\n",
    "\n",
    "x = []\n",
    "y = []\n",
    "for point in sens_spec.keys():\n",
    "  x.append(sens_spec[point][0])\n",
    "  y.append(sens_spec[point][1])\n",
    "\n",
    "auc = auc(x, y)\n",
    "\n",
    "plt.figure()\n",
    "lw = 2\n",
    "plt.plot(x, y, color='darkorange', lw=lw, label='ROC curve (area = %0.3f)' % auc)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.ylabel('Sensitivity')\n",
    "plt.xlabel('1-specificity')\n",
    "plt.title('Receiver operating characteristic')\n",
    "plt.legend(loc=\"lower right\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d37e53d2-e29c-4837-9a4b-e3eb0f676687",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 70). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: ./export/Cardiomegaly\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: ./export/Cardiomegaly\\assets\n"
     ]
    }
   ],
   "source": [
    "model.save('./export/'+finding)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "316e80be-899c-4a01-b865-ecad219bcf0d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c01b4d18-f4dd-488f-abc6-af3639bed619",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}