Notebooks/ICH Detection-multiclass.ipynb

{
 "cells": [
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   "cell_type": "code",
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   "metadata": {
    "colab": {},
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import csv\n",
    "from PIL import Image\n",
    "import time\n",
    "import cv2\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 79
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 32989,
     "status": "ok",
     "timestamp": 1573943586285,
     "user": {
      "displayName": "Prashanth Reddy Duggirala",
      "photoUrl": "",
      "userId": "15292119504454868818"
     },
     "user_tz": 480
    },
    "id": "tWPPLB0g2w0T",
    "outputId": "dd308076-97d2-46f3-fb7f-cf606a3951e8"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras.models import Model\n",
    "from keras.optimizers import Adam, SGD\n",
    "from keras.layers.core import Dropout, Activation\n",
    "from keras.callbacks import ReduceLROnPlateau\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "from keras.layers import GlobalAveragePooling2D, Dense\n",
    "from keras.initializers import TruncatedNormal\n",
    "from keras.regularizers import l1, l2\n",
    "from keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-jrDtKom2w0W"
   },
   "outputs": [],
   "source": [
    "from keras.applications import InceptionV3\n",
    "from keras.applications import imagenet_utils\n",
    "from keras.preprocessing.image import img_to_array\n",
    "from keras.preprocessing.image import load_img\n",
    "# from keras.applications.inception_v3 import preprocess_input\n",
    "from keras.applications.inception_v3 import preprocess_input\n",
    "# from keras_preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kbe1Fq0H2w0Y"
   },
   "outputs": [],
   "source": [
    "import pydicom\n",
    "from pydicom.data import get_testdata_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 34202,
     "status": "ok",
     "timestamp": 1573943588958,
     "user": {
      "displayName": "Prashanth Reddy Duggirala",
      "photoUrl": "",
      "userId": "15292119504454868818"
     },
     "user_tz": 480
    },
    "id": "Sg8NbBu22w0a",
    "outputId": "ae22f099-9c5e-42de-e2bc-ca5c9c85585e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(674262, 7)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_filepath = \"dataset/RSNA_training_processed.csv\"\n",
    "df = pd.read_csv(train_filepath)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Hy-keO1e2w0f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID_63eb1e259\n",
      "ID_e732e6d17\n",
      "ID_2124519bc\n",
      "ID_fac4426fa\n",
      "ID_5fc337950\n",
      "ID_aeda0804d\n"
     ]
    }
   ],
   "source": [
    "lines = []\n",
    "d = dict()\n",
    "for file in ['RSNA0', 'RSNA1', 'RSNA2', 'RSNA3', 'RSNA4', 'RSNA5']:\n",
    "# for file in ['RSNA0', 'RSNA2']:\n",
    "    with open( 'dataset/' + file + '.csv') as f:\n",
    "        path_lines = f.read().splitlines()\n",
    "        for i in range(len(path_lines)):\n",
    "            path_lines[i] = path_lines[i].split(\".\")[0]\n",
    "    lines = lines + path_lines\n",
    "    d[file] = set(path_lines)\n",
    "    print(path_lines[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['RSNA0', 'RSNA1', 'RSNA2', 'RSNA3', 'RSNA4', 'RSNA5'])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 431,
     "status": "ok",
     "timestamp": 1573943592423,
     "user": {
      "displayName": "Prashanth Reddy Duggirala",
      "photoUrl": "",
      "userId": "15292119504454868818"
     },
     "user_tz": 480
    },
    "id": "-8-Dm_ae2w0h",
    "outputId": "a67e2cd4-5be8-474c-a346-03e7743f2c84"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID_63eb1e259\n",
      "ID_63eb1e259\n"
     ]
    }
   ],
   "source": [
    "line = lines[0]\n",
    "print(line)\n",
    "lin = line.split(\".\")[0]\n",
    "print(lin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "czsw_COe2w0j"
   },
   "outputs": [],
   "source": [
    "for i, line in enumerate(lines):\n",
    "    lines[i] = line.split(\".\")[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HdwEIM-g2w0k",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sample_df = df.loc[df['ID'].isin(lines)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tdf = sample_df[:10]\n",
    "for i, row in sample_df.iterrows():\n",
    "    filename = row['ID']\n",
    "    for key in d.keys():\n",
    "        if filename in d[key]:\n",
    "            sample_df.at[i,'ID'] = key + '/' + filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6000, 7)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_df_shuffled = shuffle(sample_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>ID</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>RSNA0/ID_aa8d466d4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43285</th>\n",
       "      <td>RSNA3/ID_f805c5ba4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30132</th>\n",
       "      <td>RSNA4/ID_5c5bf9ff9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17587</th>\n",
       "      <td>RSNA2/ID_c1f3703f5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32327</th>\n",
       "      <td>RSNA2/ID_1bcd3ba21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       ID   1  2  3  4  5  label\n",
       "286    RSNA0/ID_aa8d466d4   0  0  0  0  0      0\n",
       "43285  RSNA3/ID_f805c5ba4   0  0  1  0  0      1\n",
       "30132  RSNA4/ID_5c5bf9ff9   0  0  0  1  0      1\n",
       "17587  RSNA2/ID_c1f3703f5   0  1  0  0  0      1\n",
       "32327  RSNA2/ID_1bcd3ba21   0  1  0  0  0      1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sample_df_shuffled.reset_index(inplace=True, drop=True)\n",
    "sample_df_shuffled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 412,
     "status": "ok",
     "timestamp": 1573943594428,
     "user": {
      "displayName": "Prashanth Reddy Duggirala",
      "photoUrl": "",
      "userId": "15292119504454868818"
     },
     "user_tz": 480
    },
    "id": "xpZm1jm42w0o",
    "outputId": "bfa4da4b-bd0b-4283-be76-80a6f0f20726"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4800\n"
     ]
    }
   ],
   "source": [
    "splitdf_index1 = round(0.8 * len(sample_df_shuffled))\n",
    "print(splitdf_index1)\n",
    "\n",
    "train_df = sample_df_shuffled[ : splitdf_index1]\n",
    "validate_df = sample_df_shuffled[splitdf_index1 : ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID       RSNA0/ID_8f276c3f7\n",
      " 1                        0\n",
      "2                         0\n",
      "3                         0\n",
      "4                         0\n",
      "5                         0\n",
      "label                     0\n",
      "Name: 1161, dtype: object\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'classfinder' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-17-a88047d2f6cd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msample_df_shuffled\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mclassfinder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'classfinder' is not defined"
     ]
    }
   ],
   "source": [
    "x = sample_df_shuffled.iloc[6]\n",
    "print(x)\n",
    "classfinder(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ILDeqClZ2w0r"
   },
   "outputs": [],
   "source": [
    "# train_list = train_df[train_df.columns[0]].tolist()\n",
    "# validate_list = validate_df[validate_df.columns[0]].tolist()\n",
    "# train_label_list = train_df[train_df.columns[6]].tolist()\n",
    "# validate_label_list = validate_df[validate_df.columns[6]].tolist()"
   ]
  },
  {
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   "execution_count": 19,
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   "outputs": [],
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       "\n",
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       "\n",
       "static PyObject *__pyx_pf_46_cython_magic_09295b4ff42c0799e8202a77c8450998_threshold_fast(CYTHON_UNUSED PyObject *__pyx_self, int __pyx_v_T1, int __pyx_v_T2, __Pyx_memviewslice __pyx_v_image) {\n",
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       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
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       "  __pyx_t_2 = 0;\n",
       "  goto __pyx_L0;\n",
       "\n",
       "  /* function exit code */\n",
       "  __pyx_L1_error:;\n",
       "  __PYX_XDEC_MEMVIEW(&amp;__pyx_t_1, 1);\n",
       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_2);\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_09295b4ff42c0799e8202a77c8450998.threshold_fast\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  __pyx_r = NULL;\n",
       "  __pyx_L0:;\n",
       "  __PYX_XDEC_MEMVIEW(&amp;__pyx_v_image, 1);\n",
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       "}\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">05</span>:     <span class=\"c\"># set the variable extension types</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">06</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">x</span><span class=\"p\">,</span> <span class=\"nf\">y</span><span class=\"p\">,</span> <span class=\"nf\">w</span><span class=\"p\">,</span> <span class=\"nf\">h</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">07</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">08</span>:     <span class=\"c\"># grab the image dimensions</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">09</span>:     <span class=\"n\">h</span> <span class=\"o\">=</span> <span class=\"n\">image</span><span class=\"o\">.</span><span class=\"n\">shape</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_v_h = (__pyx_v_image.shape[0]);\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">10</span>:     <span class=\"n\">w</span> <span class=\"o\">=</span> <span class=\"n\">image</span><span class=\"o\">.</span><span class=\"n\">shape</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_v_w = (__pyx_v_image.shape[1]);\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">11</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">12</span>:     <span class=\"c\"># loop over the image</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">13</span>:     <span class=\"k\">for</span> <span class=\"n\">y</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span> <span class=\"n\">h</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_1 = __pyx_v_h;\n",
       "  __pyx_t_2 = __pyx_t_1;\n",
       "  for (__pyx_t_3 = 0; __pyx_t_3 &lt; __pyx_t_2; __pyx_t_3+=1) {\n",
       "    __pyx_v_y = __pyx_t_3;\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">14</span>:         <span class=\"k\">for</span> <span class=\"n\">x</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span> <span class=\"n\">w</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_4 = __pyx_v_w;\n",
       "    __pyx_t_5 = __pyx_t_4;\n",
       "    for (__pyx_t_6 = 0; __pyx_t_6 &lt; __pyx_t_5; __pyx_t_6+=1) {\n",
       "      __pyx_v_x = __pyx_t_6;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">15</span>:             <span class=\"c\"># threshold the pixel</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">16</span>:             <span class=\"n\">image</span><span class=\"p\">[</span><span class=\"n\">y</span><span class=\"p\">,</span> <span class=\"n\">x</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">image</span><span class=\"p\">[</span><span class=\"n\">y</span><span class=\"p\">,</span> <span class=\"n\">x</span><span class=\"p\">]</span> <span class=\"k\">if</span> <span class=\"p\">(</span><span class=\"n\">image</span><span class=\"p\">[</span><span class=\"n\">y</span><span class=\"p\">,</span> <span class=\"n\">x</span><span class=\"p\">]</span> <span class=\"o\">&gt;=</span> <span class=\"n\">T1</span> <span class=\"ow\">and</span> <span class=\"n\">image</span><span class=\"p\">[</span><span class=\"n\">y</span><span class=\"p\">,</span> <span class=\"n\">x</span><span class=\"p\">]</span> <span class=\"o\">&lt;=</span> <span class=\"n\">T2</span><span class=\"p\">)</span> <span class=\"k\">else</span> <span class=\"mf\">0</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_9 = __pyx_v_y;\n",
       "      __pyx_t_10 = __pyx_v_x;\n",
       "      if (__pyx_t_9 &lt; 0) __pyx_t_9 += __pyx_v_image.shape[0];\n",
       "      if (__pyx_t_10 &lt; 0) __pyx_t_10 += __pyx_v_image.shape[1];\n",
       "      __pyx_t_11 = (((*((float *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_image.data + __pyx_t_9 * __pyx_v_image.strides[0]) ) + __pyx_t_10 * __pyx_v_image.strides[1]) ))) &gt;= __pyx_v_T1) != 0);\n",
       "      if (__pyx_t_11) {\n",
       "      } else {\n",
       "        __pyx_t_8 = __pyx_t_11;\n",
       "        goto __pyx_L7_bool_binop_done;\n",
       "      }\n",
       "      __pyx_t_12 = __pyx_v_y;\n",
       "      __pyx_t_13 = __pyx_v_x;\n",
       "      if (__pyx_t_12 &lt; 0) __pyx_t_12 += __pyx_v_image.shape[0];\n",
       "      if (__pyx_t_13 &lt; 0) __pyx_t_13 += __pyx_v_image.shape[1];\n",
       "      __pyx_t_11 = (((*((float *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_image.data + __pyx_t_12 * __pyx_v_image.strides[0]) ) + __pyx_t_13 * __pyx_v_image.strides[1]) ))) &lt;= __pyx_v_T2) != 0);\n",
       "      __pyx_t_8 = __pyx_t_11;\n",
       "      __pyx_L7_bool_binop_done:;\n",
       "      if (__pyx_t_8) {\n",
       "        __pyx_t_14 = __pyx_v_y;\n",
       "        __pyx_t_15 = __pyx_v_x;\n",
       "        if (__pyx_t_14 &lt; 0) __pyx_t_14 += __pyx_v_image.shape[0];\n",
       "        if (__pyx_t_15 &lt; 0) __pyx_t_15 += __pyx_v_image.shape[1];\n",
       "        __pyx_t_7 = (*((float *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_image.data + __pyx_t_14 * __pyx_v_image.strides[0]) ) + __pyx_t_15 * __pyx_v_image.strides[1]) )));\n",
       "      } else {\n",
       "        __pyx_t_7 = 0.0;\n",
       "      }\n",
       "      __pyx_t_16 = __pyx_v_y;\n",
       "      __pyx_t_17 = __pyx_v_x;\n",
       "      if (__pyx_t_16 &lt; 0) __pyx_t_16 += __pyx_v_image.shape[0];\n",
       "      if (__pyx_t_17 &lt; 0) __pyx_t_17 += __pyx_v_image.shape[1];\n",
       "      *((float *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_image.data + __pyx_t_16 * __pyx_v_image.strides[0]) ) + __pyx_t_17 * __pyx_v_image.strides[1]) )) = __pyx_t_7;\n",
       "    }\n",
       "  }\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">17</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">18</span>:     <span class=\"c\"># return the thresholded image</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">19</span>:     <span class=\"k\">return</span> <span class=\"n\">image</span></pre>\n",
       "<pre class='cython code score-0 '>  __PYX_INC_MEMVIEW(&amp;__pyx_v_image, 0);\n",
       "  __pyx_r = __pyx_v_image;\n",
       "  goto __pyx_L0;\n",
       "</pre></div></body></html>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%cython -a\n",
    "import cython\n",
    "\n",
    "@cython.boundscheck(False)\n",
    "cpdef float[:, :] threshold_fast(int T1, int T2, float [:, :] image):\n",
    "    # set the variable extension types\n",
    "    cdef int x, y, w, h\n",
    "    \n",
    "    # grab the image dimensions\n",
    "    h = image.shape[0]\n",
    "    w = image.shape[1]\n",
    "    \n",
    "    # loop over the image\n",
    "    for y in range(0, h):\n",
    "        for x in range(0, w):\n",
    "            # threshold the pixel\n",
    "            image[y, x] = image[y, x] if (image[y, x] >= T1 and image[y, x] <= T2) else 0\n",
    "    \n",
    "    # return the thresholded image\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "g4A6UDcw2w0t"
   },
   "outputs": [],
   "source": [
    "def classfinder(df):\n",
    "    for i in range(1,6):\n",
    "        if df[i] == 1:\n",
    "            return i\n",
    "    return 0\n",
    "            \n",
    "def window_image_reg(img, window_center,window_width, intercept, slope):\n",
    "\n",
    "    img = (img*slope +intercept)\n",
    "    img_min = window_center - window_width//2\n",
    "    img_max = window_center + window_width//2\n",
    "    img[img<img_min] = img_min\n",
    "    img[img>img_max] = img_max\n",
    "    return img \n",
    "\n",
    "def get_first_of_dicom_field_as_int(x):\n",
    "    #get x[0] as in int is x is a 'pydicom.multival.MultiValue', otherwise get int(x)\n",
    "    if type(x) == pydicom.multival.MultiValue:\n",
    "        return int(x[0])\n",
    "    else:\n",
    "        return int(x)\n",
    "\n",
    "def get_windowing(data):\n",
    "    dicom_fields = [data[('0028','1050')].value, #window center\n",
    "                    data[('0028','1051')].value, #window width\n",
    "                    data[('0028','1052')].value, #intercept\n",
    "                    data[('0028','1053')].value] #slope\n",
    "    return [get_first_of_dicom_field_as_int(x) for x in dicom_fields]\n",
    "\n",
    "# generates batches of images to be fed into the model\n",
    "def myGenerator(image_df, batch_size):\n",
    "    num_samples = image_df.shape[0]\n",
    "    X_return = []\n",
    "    y_return = []\n",
    "    \n",
    "    while True:\n",
    "        for offset in range(0, num_samples, batch_size):\n",
    "            images = []\n",
    "            labels = []\n",
    "            temp_df = image_df[offset:offset+int(batch_size)]\n",
    "\n",
    "            for index in range(0, temp_df.shape[0]):\n",
    "                source_path = temp_df.iloc[index]['ID']\n",
    "                \n",
    "                filename = 'dataset/' + source_path + '.dcm'\n",
    "                \n",
    "                # Load the image\n",
    "                try:\n",
    "                    ds = pydicom.dcmread(filename)\n",
    "                except:\n",
    "                    continue\n",
    "                # get pixel values from the read DICOM file\n",
    "                image_single = ds.pixel_array\n",
    "                \n",
    "                # perform windowing\n",
    "                window_center , window_width, intercept, slope = get_windowing(ds)\n",
    "                image_windowed = window_image(image_single, window_center, window_width, intercept, slope)\n",
    "                \n",
    "                #  resize the image\n",
    "                image_windowed_float = image_windowed.astype(np.float32)\n",
    "                image_windowed_resized = cv2.resize(image_windowed_float, dsize=(224, 224))\n",
    "                \n",
    "                # thresholding to include hounsfield values for blood\n",
    "#                 thresh = threshold_fast(40, 66, image_windowed_resized)\n",
    "                \n",
    "                # stack thrice for 3 channels\n",
    "#                 image = np.stack([thresh, thresh, thresh])\n",
    "                image = np.stack([image_windowed_resized, image_windowed_resized, image_windowed_resized])\n",
    "\n",
    "                \n",
    "                # converting to channels_last\n",
    "                image = np.moveaxis(image, 0, 2)\n",
    "\n",
    "                images.append(image)\n",
    "                \n",
    "                label = to_categorical(classfinder(temp_df.iloc[index]), num_classes=6)\n",
    "                labels.append(label)\n",
    "\n",
    "\n",
    "            X_return = np.asarray(images)\n",
    "            y_return = np.asarray(labels)\n",
    "\n",
    "            yield shuffle(X_return, y_return)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EGrXN8rOHT1x"
   },
   "source": [
    "This cell is for looking at the DICOM files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Constants\n",
    "TEST_SIZE = 0.02\n",
    "HEIGHT = 224\n",
    "WIDTH = 224\n",
    "CHANNELS = 3\n",
    "TRAIN_BATCH_SIZE = 32\n",
    "VALID_BATCH_SIZE = 64\n",
    "SHAPE = (HEIGHT, WIDTH, CHANNELS)\n",
    "\n",
    "def correct_dcm(dcm):\n",
    "    x = dcm.pixel_array + 1000\n",
    "    px_mode = 4096\n",
    "    x[x>=px_mode] = x[x>=px_mode] - px_mode\n",
    "    dcm.PixelData = x.tobytes()\n",
    "    dcm.RescaleIntercept = -1000\n",
    "\n",
    "def window_image(dcm, window_center, window_width):    \n",
    "    if (dcm.BitsStored == 12) and (dcm.PixelRepresentation == 0) and (int(dcm.RescaleIntercept) > -100):\n",
    "        correct_dcm(dcm)\n",
    "    img = dcm.pixel_array * dcm.RescaleSlope + dcm.RescaleIntercept\n",
    "    \n",
    "    # Resize\n",
    "    img = cv2.resize(img, SHAPE[:2], interpolation = cv2.INTER_LINEAR)\n",
    "   \n",
    "    img_min = window_center - window_width // 2\n",
    "    img_max = window_center + window_width // 2\n",
    "    img = np.clip(img, img_min, img_max)\n",
    "    return img\n",
    "\n",
    "def bsb_window(dcm):\n",
    "    brain_img = window_image(dcm, 40, 80)\n",
    "    subdural_img = window_image(dcm, 80, 200)\n",
    "    soft_img = window_image(dcm, 40, 380)\n",
    "    \n",
    "    brain_img = (brain_img - 0) / 80\n",
    "    subdural_img = (subdural_img - (-20)) / 200\n",
    "    soft_img = (soft_img - (-150)) / 380\n",
    "    bsb_img = np.array([brain_img, subdural_img, soft_img]).transpose(1,2,0)\n",
    "    return bsb_img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 344,
     "status": "ok",
     "timestamp": 1573943928984,
     "user": {
      "displayName": "Prashanth Reddy Duggirala",
      "photoUrl": "",
      "userId": "15292119504454868818"
     },
     "user_tz": 480
    },
    "id": "HYdAoQ_-2w0v",
    "outputId": "8be9534b-3066-475a-ff41-6aae4a8d9fd3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset/RSNA3/ID_738db51ab.dcm\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x14cfed1a5f8>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# source_path = train_list[2] # use index 2 to for the image P wants\n",
    "# for line in validate_df[503:504]:\n",
    "line = validate_df.iloc[16]\n",
    "# print(line)\n",
    "filename = 'dataset/' + line[0] + '.dcm'\n",
    "current_path = filename\n",
    "print(current_path)\n",
    "\n",
    "ds = pydicom.dcmread(filename)\n",
    "\n",
    "image_single = ds.pixel_array\n",
    "window_center , window_width, intercept, slope = get_windowing(ds)\n",
    "image_windowed = window_image_reg(image_single, window_center, window_width, intercept, slope)\n",
    "\n",
    "# # Convert to uint\n",
    "# image_2d_scaled = np.uint8(image_2d_scaled)\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(image_windowed, cmap = plt.cm.bone)\n",
    "\n",
    "# image_windowed_float = image_windowed.astype(np.float32)\n",
    "\n",
    "# image_windowed_resized = cv2.resize(image_windowed_float, dsize=(224, 224))\n",
    "\n",
    "# thresholding to include hounsfield values from -150 to +150    \n",
    "\n",
    "# thresh = threshold_fast(40, 66, image_windowed_resized)  \n",
    "# plt.figure()\n",
    "# plt.imshow(thresh, cmap =plt.cm.bone) \n",
    "\n",
    "#     print(type(np.array(thresh)))\n",
    "# thresh1 = cv2.dilate(np.array(thresh), None, iterations=3)\n",
    "# thresh1 = cv2.erode(np.array(thresh1), None, iterations=4)\n",
    "\n",
    "image_bsb = bsb_window(ds)\n",
    "plt.figure()\n",
    "plt.imshow(image_bsb, cmap = plt.cm.bone)\n",
    "\n",
    "# plt.figure()\n",
    "# plt.imshow(thresh1, cmap =plt.cm.bone) \n",
    "\n",
    "#     savefile_name = \"images/\" + source_path + \".png\"\n",
    "#     plt.imsave(savefile_name, ds.pixel_array, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8_S144uO_M9n"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 2., 3.],\n",
       "       [4., 5., 6.],\n",
       "       [7., 8., 9.]], dtype=float32)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[1,2,3],[4,5,6], [7,8,9]])\n",
    "a = a.astype(np.float32)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IfdRJAyk_NLR"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2. , 3.5],\n",
       "       [6.5, 8. ]], dtype=float32)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = cv2.resize(a, dsize=(2, 2))\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eB4Djaau_NQ7"
   },
   "outputs": [],
   "source": [
    "np.savetxt('image_windowed.txt', image_windowed)\n",
    "np.savetxt('image_windowed_single.txt', image_windowed_single)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(image_windowed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(image_windowed_resized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>ID</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3478</th>\n",
       "      <td>RSNA0EXT/ID_d2d243c32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30340</th>\n",
       "      <td>RSNA2/ID_db79c02f9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3331</th>\n",
       "      <td>RSNA0EXT/ID_8301f4b60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7500</th>\n",
       "      <td>RSNA5/ID_2077e39ca</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12025</th>\n",
       "      <td>RSNA5/ID_63b3ee44c</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          ID   1  2  3  4  5  label\n",
       "3478   RSNA0EXT/ID_d2d243c32   0  0  0  0  0      0\n",
       "30340     RSNA2/ID_db79c02f9   0  1  0  0  0      1\n",
       "3331   RSNA0EXT/ID_8301f4b60   0  0  0  0  0      0\n",
       "7500      RSNA5/ID_2077e39ca   0  0  0  0  1      1\n",
       "12025     RSNA5/ID_63b3ee44c   0  0  0  0  1      1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validate_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 507
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 13427,
     "status": "ok",
     "timestamp": 1573943615301,
     "user": {
      "displayName": "Prashanth Reddy Duggirala",
      "photoUrl": "",
      "userId": "15292119504454868818"
     },
     "user_tz": 480
    },
    "id": "l0zcXi52mmSQ",
    "outputId": "63fa4ec0-c4dd-4f57-edd0-27d6fb194664"
   },
   "outputs": [],
   "source": [
    "from keras.applications.vgg16 import VGG16\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JpU4rlFTlzt1"
   },
   "source": [
    "Load a predefined model into 'base_model'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_model = keras.applications.inception_v3.InceptionV3(input_shape=(224, 224, 3),\n",
    "                                                         include_top=False, weights='imagenet', input_tensor=None, \n",
    "                                                         pooling=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 528,
     "status": "ok",
     "timestamp": 1573945610230,
     "user": {
      "displayName": "Prashanth Reddy Duggirala",
      "photoUrl": "",
      "userId": "15292119504454868818"
     },
     "user_tz": 480
    },
    "id": "vk20yOa3mmVn",
    "outputId": "f4787a84-8ad6-4656-e560-1fbae54b29c5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 224, 224, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 111, 111, 32) 864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 111, 111, 32) 96          conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 111, 111, 32) 0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 109, 109, 32) 9216        activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 109, 109, 32) 96          conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 109, 109, 32) 0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 109, 109, 64) 18432       activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 109, 109, 64) 192         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 109, 109, 64) 0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 54, 54, 64)   0           activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 54, 54, 80)   5120        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 54, 54, 80)   240         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 54, 54, 80)   0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 52, 52, 192)  138240      activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 52, 52, 192)  576         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 52, 52, 192)  0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 25, 25, 192)  0           activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 25, 25, 64)   12288       max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 25, 25, 64)   192         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 25, 25, 64)   0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 25, 25, 48)   9216        max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 25, 25, 96)   55296       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 25, 25, 48)   144         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 25, 25, 96)   288         conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 25, 25, 48)   0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 25, 25, 96)   0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_1 (AveragePoo (None, 25, 25, 192)  0           max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 25, 25, 64)   12288       max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 25, 25, 64)   76800       activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 25, 25, 96)   82944       activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 25, 25, 32)   6144        average_pooling2d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 25, 25, 64)   192         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 25, 25, 64)   192         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 25, 25, 96)   288         conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 25, 25, 32)   96          conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 25, 25, 64)   0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 25, 25, 64)   0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 25, 25, 96)   0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 25, 25, 32)   0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed0 (Concatenate)            (None, 25, 25, 256)  0           activation_6[0][0]               \n",
      "                                                                 activation_8[0][0]               \n",
      "                                                                 activation_11[0][0]              \n",
      "                                                                 activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 25, 25, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 25, 25, 64)   192         conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 25, 25, 64)   0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 25, 25, 48)   12288       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 25, 25, 96)   55296       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 25, 25, 48)   144         conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 25, 25, 96)   288         conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 25, 25, 48)   0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 25, 25, 96)   0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_2 (AveragePoo (None, 25, 25, 256)  0           mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 25, 25, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 25, 25, 64)   76800       activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 25, 25, 96)   82944       activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 25, 25, 64)   16384       average_pooling2d_2[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 25, 25, 64)   192         conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 25, 25, 64)   192         conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 25, 25, 96)   288         conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 25, 25, 64)   192         conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 25, 25, 64)   0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 25, 25, 64)   0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 25, 25, 96)   0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 25, 25, 64)   0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed1 (Concatenate)            (None, 25, 25, 288)  0           activation_13[0][0]              \n",
      "                                                                 activation_15[0][0]              \n",
      "                                                                 activation_18[0][0]              \n",
      "                                                                 activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 25, 25, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 25, 25, 64)   192         conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 25, 25, 64)   0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 25, 25, 48)   13824       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 25, 25, 96)   55296       activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 25, 25, 48)   144         conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 25, 25, 96)   288         conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 25, 25, 48)   0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 25, 25, 96)   0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_3 (AveragePoo (None, 25, 25, 288)  0           mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 25, 25, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 25, 25, 64)   76800       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 25, 25, 96)   82944       activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, 25, 25, 64)   18432       average_pooling2d_3[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 25, 25, 64)   192         conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 25, 25, 64)   192         conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 25, 25, 96)   288         conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, 25, 25, 64)   192         conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 25, 25, 64)   0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 25, 25, 64)   0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 25, 25, 96)   0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 25, 25, 64)   0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed2 (Concatenate)            (None, 25, 25, 288)  0           activation_20[0][0]              \n",
      "                                                                 activation_22[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "                                                                 activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, 25, 25, 64)   18432       mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, 25, 25, 64)   192         conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 25, 25, 64)   0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, 25, 25, 96)   55296       activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, 25, 25, 96)   288         conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 25, 25, 96)   0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, 12, 12, 384)  995328      mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, 12, 12, 96)   82944       activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, 12, 12, 384)  1152        conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, 12, 12, 96)   288         conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 12, 12, 384)  0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 12, 12, 96)   0           batch_normalization_30[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 12, 12, 288)  0           mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed3 (Concatenate)            (None, 12, 12, 768)  0           activation_27[0][0]              \n",
      "                                                                 activation_30[0][0]              \n",
      "                                                                 max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)              (None, 12, 12, 128)  98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_35 (BatchNo (None, 12, 12, 128)  384         conv2d_35[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 12, 12, 128)  0           batch_normalization_35[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)              (None, 12, 12, 128)  114688      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_36 (BatchNo (None, 12, 12, 128)  384         conv2d_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 12, 12, 128)  0           batch_normalization_36[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, 12, 12, 128)  98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_37 (Conv2D)              (None, 12, 12, 128)  114688      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_32 (BatchNo (None, 12, 12, 128)  384         conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_37 (BatchNo (None, 12, 12, 128)  384         conv2d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 12, 12, 128)  0           batch_normalization_32[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 12, 12, 128)  0           batch_normalization_37[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, 12, 12, 128)  114688      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_38 (Conv2D)              (None, 12, 12, 128)  114688      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_33 (BatchNo (None, 12, 12, 128)  384         conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_38 (BatchNo (None, 12, 12, 128)  384         conv2d_38[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 12, 12, 128)  0           batch_normalization_33[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 12, 12, 128)  0           batch_normalization_38[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_4 (AveragePoo (None, 12, 12, 768)  0           mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, 12, 12, 192)  147456      mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, 12, 12, 192)  172032      activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_39 (Conv2D)              (None, 12, 12, 192)  172032      activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_40 (Conv2D)              (None, 12, 12, 192)  147456      average_pooling2d_4[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, 12, 12, 192)  576         conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_34 (BatchNo (None, 12, 12, 192)  576         conv2d_34[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_39 (BatchNo (None, 12, 12, 192)  576         conv2d_39[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_40 (BatchNo (None, 12, 12, 192)  576         conv2d_40[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 12, 12, 192)  0           batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 12, 12, 192)  0           batch_normalization_34[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 12, 12, 192)  0           batch_normalization_39[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 12, 12, 192)  0           batch_normalization_40[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed4 (Concatenate)            (None, 12, 12, 768)  0           activation_31[0][0]              \n",
      "                                                                 activation_34[0][0]              \n",
      "                                                                 activation_39[0][0]              \n",
      "                                                                 activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_45 (Conv2D)              (None, 12, 12, 160)  122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_45 (BatchNo (None, 12, 12, 160)  480         conv2d_45[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 12, 12, 160)  0           batch_normalization_45[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_46 (Conv2D)              (None, 12, 12, 160)  179200      activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_46 (BatchNo (None, 12, 12, 160)  480         conv2d_46[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 12, 12, 160)  0           batch_normalization_46[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_42 (Conv2D)              (None, 12, 12, 160)  122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_47 (Conv2D)              (None, 12, 12, 160)  179200      activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_42 (BatchNo (None, 12, 12, 160)  480         conv2d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_47 (BatchNo (None, 12, 12, 160)  480         conv2d_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 12, 12, 160)  0           batch_normalization_42[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 12, 12, 160)  0           batch_normalization_47[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_43 (Conv2D)              (None, 12, 12, 160)  179200      activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_48 (Conv2D)              (None, 12, 12, 160)  179200      activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_43 (BatchNo (None, 12, 12, 160)  480         conv2d_43[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_48 (BatchNo (None, 12, 12, 160)  480         conv2d_48[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 12, 12, 160)  0           batch_normalization_43[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 12, 12, 160)  0           batch_normalization_48[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_5 (AveragePoo (None, 12, 12, 768)  0           mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_41 (Conv2D)              (None, 12, 12, 192)  147456      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_44 (Conv2D)              (None, 12, 12, 192)  215040      activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_49 (Conv2D)              (None, 12, 12, 192)  215040      activation_48[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_50 (Conv2D)              (None, 12, 12, 192)  147456      average_pooling2d_5[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_41 (BatchNo (None, 12, 12, 192)  576         conv2d_41[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_44 (BatchNo (None, 12, 12, 192)  576         conv2d_44[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_49 (BatchNo (None, 12, 12, 192)  576         conv2d_49[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_50 (BatchNo (None, 12, 12, 192)  576         conv2d_50[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 12, 12, 192)  0           batch_normalization_41[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 12, 12, 192)  0           batch_normalization_44[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, 12, 12, 192)  0           batch_normalization_49[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_50 (Activation)      (None, 12, 12, 192)  0           batch_normalization_50[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed5 (Concatenate)            (None, 12, 12, 768)  0           activation_41[0][0]              \n",
      "                                                                 activation_44[0][0]              \n",
      "                                                                 activation_49[0][0]              \n",
      "                                                                 activation_50[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_55 (Conv2D)              (None, 12, 12, 160)  122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_55 (BatchNo (None, 12, 12, 160)  480         conv2d_55[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_55 (Activation)      (None, 12, 12, 160)  0           batch_normalization_55[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_56 (Conv2D)              (None, 12, 12, 160)  179200      activation_55[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_56 (BatchNo (None, 12, 12, 160)  480         conv2d_56[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_56 (Activation)      (None, 12, 12, 160)  0           batch_normalization_56[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_52 (Conv2D)              (None, 12, 12, 160)  122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_57 (Conv2D)              (None, 12, 12, 160)  179200      activation_56[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_52 (BatchNo (None, 12, 12, 160)  480         conv2d_52[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_57 (BatchNo (None, 12, 12, 160)  480         conv2d_57[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_52 (Activation)      (None, 12, 12, 160)  0           batch_normalization_52[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_57 (Activation)      (None, 12, 12, 160)  0           batch_normalization_57[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_53 (Conv2D)              (None, 12, 12, 160)  179200      activation_52[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_58 (Conv2D)              (None, 12, 12, 160)  179200      activation_57[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_53 (BatchNo (None, 12, 12, 160)  480         conv2d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_58 (BatchNo (None, 12, 12, 160)  480         conv2d_58[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_53 (Activation)      (None, 12, 12, 160)  0           batch_normalization_53[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_58 (Activation)      (None, 12, 12, 160)  0           batch_normalization_58[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_6 (AveragePoo (None, 12, 12, 768)  0           mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_51 (Conv2D)              (None, 12, 12, 192)  147456      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_54 (Conv2D)              (None, 12, 12, 192)  215040      activation_53[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_59 (Conv2D)              (None, 12, 12, 192)  215040      activation_58[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_60 (Conv2D)              (None, 12, 12, 192)  147456      average_pooling2d_6[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_51 (BatchNo (None, 12, 12, 192)  576         conv2d_51[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_54 (BatchNo (None, 12, 12, 192)  576         conv2d_54[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_59 (BatchNo (None, 12, 12, 192)  576         conv2d_59[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_60 (BatchNo (None, 12, 12, 192)  576         conv2d_60[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_51 (Activation)      (None, 12, 12, 192)  0           batch_normalization_51[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_54 (Activation)      (None, 12, 12, 192)  0           batch_normalization_54[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_59 (Activation)      (None, 12, 12, 192)  0           batch_normalization_59[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_60 (Activation)      (None, 12, 12, 192)  0           batch_normalization_60[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed6 (Concatenate)            (None, 12, 12, 768)  0           activation_51[0][0]              \n",
      "                                                                 activation_54[0][0]              \n",
      "                                                                 activation_59[0][0]              \n",
      "                                                                 activation_60[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_65 (Conv2D)              (None, 12, 12, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_65 (BatchNo (None, 12, 12, 192)  576         conv2d_65[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_65 (Activation)      (None, 12, 12, 192)  0           batch_normalization_65[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_66 (Conv2D)              (None, 12, 12, 192)  258048      activation_65[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_66 (BatchNo (None, 12, 12, 192)  576         conv2d_66[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_66 (Activation)      (None, 12, 12, 192)  0           batch_normalization_66[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_62 (Conv2D)              (None, 12, 12, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_67 (Conv2D)              (None, 12, 12, 192)  258048      activation_66[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_62 (BatchNo (None, 12, 12, 192)  576         conv2d_62[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_67 (BatchNo (None, 12, 12, 192)  576         conv2d_67[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_62 (Activation)      (None, 12, 12, 192)  0           batch_normalization_62[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_67 (Activation)      (None, 12, 12, 192)  0           batch_normalization_67[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_63 (Conv2D)              (None, 12, 12, 192)  258048      activation_62[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_68 (Conv2D)              (None, 12, 12, 192)  258048      activation_67[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_63 (BatchNo (None, 12, 12, 192)  576         conv2d_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_68 (BatchNo (None, 12, 12, 192)  576         conv2d_68[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_63 (Activation)      (None, 12, 12, 192)  0           batch_normalization_63[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_68 (Activation)      (None, 12, 12, 192)  0           batch_normalization_68[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_7 (AveragePoo (None, 12, 12, 768)  0           mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_61 (Conv2D)              (None, 12, 12, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_64 (Conv2D)              (None, 12, 12, 192)  258048      activation_63[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_69 (Conv2D)              (None, 12, 12, 192)  258048      activation_68[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_70 (Conv2D)              (None, 12, 12, 192)  147456      average_pooling2d_7[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_61 (BatchNo (None, 12, 12, 192)  576         conv2d_61[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_64 (BatchNo (None, 12, 12, 192)  576         conv2d_64[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_69 (BatchNo (None, 12, 12, 192)  576         conv2d_69[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_70 (BatchNo (None, 12, 12, 192)  576         conv2d_70[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_61 (Activation)      (None, 12, 12, 192)  0           batch_normalization_61[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_64 (Activation)      (None, 12, 12, 192)  0           batch_normalization_64[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_69 (Activation)      (None, 12, 12, 192)  0           batch_normalization_69[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_70 (Activation)      (None, 12, 12, 192)  0           batch_normalization_70[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed7 (Concatenate)            (None, 12, 12, 768)  0           activation_61[0][0]              \n",
      "                                                                 activation_64[0][0]              \n",
      "                                                                 activation_69[0][0]              \n",
      "                                                                 activation_70[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_73 (Conv2D)              (None, 12, 12, 192)  147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_73 (BatchNo (None, 12, 12, 192)  576         conv2d_73[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_73 (Activation)      (None, 12, 12, 192)  0           batch_normalization_73[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_74 (Conv2D)              (None, 12, 12, 192)  258048      activation_73[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_74 (BatchNo (None, 12, 12, 192)  576         conv2d_74[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_74 (Activation)      (None, 12, 12, 192)  0           batch_normalization_74[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_71 (Conv2D)              (None, 12, 12, 192)  147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_75 (Conv2D)              (None, 12, 12, 192)  258048      activation_74[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_71 (BatchNo (None, 12, 12, 192)  576         conv2d_71[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_75 (BatchNo (None, 12, 12, 192)  576         conv2d_75[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_71 (Activation)      (None, 12, 12, 192)  0           batch_normalization_71[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_75 (Activation)      (None, 12, 12, 192)  0           batch_normalization_75[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_72 (Conv2D)              (None, 5, 5, 320)    552960      activation_71[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_76 (Conv2D)              (None, 5, 5, 192)    331776      activation_75[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_72 (BatchNo (None, 5, 5, 320)    960         conv2d_72[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_76 (BatchNo (None, 5, 5, 192)    576         conv2d_76[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_72 (Activation)      (None, 5, 5, 320)    0           batch_normalization_72[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_76 (Activation)      (None, 5, 5, 192)    0           batch_normalization_76[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2D)  (None, 5, 5, 768)    0           mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed8 (Concatenate)            (None, 5, 5, 1280)   0           activation_72[0][0]              \n",
      "                                                                 activation_76[0][0]              \n",
      "                                                                 max_pooling2d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_81 (Conv2D)              (None, 5, 5, 448)    573440      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_81 (BatchNo (None, 5, 5, 448)    1344        conv2d_81[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_81 (Activation)      (None, 5, 5, 448)    0           batch_normalization_81[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_78 (Conv2D)              (None, 5, 5, 384)    491520      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_82 (Conv2D)              (None, 5, 5, 384)    1548288     activation_81[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_78 (BatchNo (None, 5, 5, 384)    1152        conv2d_78[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_82 (BatchNo (None, 5, 5, 384)    1152        conv2d_82[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_78 (Activation)      (None, 5, 5, 384)    0           batch_normalization_78[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_82 (Activation)      (None, 5, 5, 384)    0           batch_normalization_82[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_79 (Conv2D)              (None, 5, 5, 384)    442368      activation_78[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_80 (Conv2D)              (None, 5, 5, 384)    442368      activation_78[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_83 (Conv2D)              (None, 5, 5, 384)    442368      activation_82[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_84 (Conv2D)              (None, 5, 5, 384)    442368      activation_82[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_8 (AveragePoo (None, 5, 5, 1280)   0           mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_77 (Conv2D)              (None, 5, 5, 320)    409600      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_79 (BatchNo (None, 5, 5, 384)    1152        conv2d_79[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_80 (BatchNo (None, 5, 5, 384)    1152        conv2d_80[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_83 (BatchNo (None, 5, 5, 384)    1152        conv2d_83[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_84 (BatchNo (None, 5, 5, 384)    1152        conv2d_84[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_85 (Conv2D)              (None, 5, 5, 192)    245760      average_pooling2d_8[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_77 (BatchNo (None, 5, 5, 320)    960         conv2d_77[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_79 (Activation)      (None, 5, 5, 384)    0           batch_normalization_79[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_80 (Activation)      (None, 5, 5, 384)    0           batch_normalization_80[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_83 (Activation)      (None, 5, 5, 384)    0           batch_normalization_83[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_84 (Activation)      (None, 5, 5, 384)    0           batch_normalization_84[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_85 (BatchNo (None, 5, 5, 192)    576         conv2d_85[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_77 (Activation)      (None, 5, 5, 320)    0           batch_normalization_77[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_0 (Concatenate)          (None, 5, 5, 768)    0           activation_79[0][0]              \n",
      "                                                                 activation_80[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 5, 5, 768)    0           activation_83[0][0]              \n",
      "                                                                 activation_84[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_85 (Activation)      (None, 5, 5, 192)    0           batch_normalization_85[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9 (Concatenate)            (None, 5, 5, 2048)   0           activation_77[0][0]              \n",
      "                                                                 mixed9_0[0][0]                   \n",
      "                                                                 concatenate_1[0][0]              \n",
      "                                                                 activation_85[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_90 (Conv2D)              (None, 5, 5, 448)    917504      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_90 (BatchNo (None, 5, 5, 448)    1344        conv2d_90[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_90 (Activation)      (None, 5, 5, 448)    0           batch_normalization_90[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_87 (Conv2D)              (None, 5, 5, 384)    786432      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_91 (Conv2D)              (None, 5, 5, 384)    1548288     activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_87 (BatchNo (None, 5, 5, 384)    1152        conv2d_87[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_91 (BatchNo (None, 5, 5, 384)    1152        conv2d_91[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_87 (Activation)      (None, 5, 5, 384)    0           batch_normalization_87[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_91 (Activation)      (None, 5, 5, 384)    0           batch_normalization_91[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_88 (Conv2D)              (None, 5, 5, 384)    442368      activation_87[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_89 (Conv2D)              (None, 5, 5, 384)    442368      activation_87[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_92 (Conv2D)              (None, 5, 5, 384)    442368      activation_91[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_93 (Conv2D)              (None, 5, 5, 384)    442368      activation_91[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_9 (AveragePoo (None, 5, 5, 2048)   0           mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_86 (Conv2D)              (None, 5, 5, 320)    655360      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_88 (BatchNo (None, 5, 5, 384)    1152        conv2d_88[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_89 (BatchNo (None, 5, 5, 384)    1152        conv2d_89[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_92 (BatchNo (None, 5, 5, 384)    1152        conv2d_92[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_93 (BatchNo (None, 5, 5, 384)    1152        conv2d_93[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_94 (Conv2D)              (None, 5, 5, 192)    393216      average_pooling2d_9[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_86 (BatchNo (None, 5, 5, 320)    960         conv2d_86[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_88 (Activation)      (None, 5, 5, 384)    0           batch_normalization_88[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_89 (Activation)      (None, 5, 5, 384)    0           batch_normalization_89[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_92 (Activation)      (None, 5, 5, 384)    0           batch_normalization_92[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_93 (Activation)      (None, 5, 5, 384)    0           batch_normalization_93[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_94 (BatchNo (None, 5, 5, 192)    576         conv2d_94[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_86 (Activation)      (None, 5, 5, 320)    0           batch_normalization_86[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_1 (Concatenate)          (None, 5, 5, 768)    0           activation_88[0][0]              \n",
      "                                                                 activation_89[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 5, 5, 768)    0           activation_92[0][0]              \n",
      "                                                                 activation_93[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_94 (Activation)      (None, 5, 5, 192)    0           batch_normalization_94[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed10 (Concatenate)           (None, 5, 5, 2048)   0           activation_86[0][0]              \n",
      "                                                                 mixed9_1[0][0]                   \n",
      "                                                                 concatenate_2[0][0]              \n",
      "                                                                 activation_94[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_1 (Glo (None, 2048)         0           mixed10[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 2048)         0           global_average_pooling2d_1[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 500)          1024500     dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_95 (Activation)      (None, 500)          0           dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 500)          0           activation_95[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 200)          100200      dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_96 (Activation)      (None, 200)          0           dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)             (None, 200)          0           activation_96[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 50)           10050       dropout_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_97 (Activation)      (None, 50)           0           dense_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_4 (Dropout)             (None, 50)           0           activation_97[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 6)            306         dropout_4[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 22,937,840\n",
      "Trainable params: 22,903,408\n",
      "Non-trainable params: 34,432\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "for layer in base_model.layers: \n",
    "    layer.trainable = True\n",
    "\n",
    "x = base_model.output\n",
    "\n",
    "x = GlobalAveragePooling2D()(x) # GAP\n",
    "\n",
    "x = Dropout(0.5)(x)\n",
    "\n",
    "x = Dense(500)(x)               # Dense 1\n",
    "x = Activation('relu')(x)\n",
    "\n",
    "x = Dropout(0.5)(x)\n",
    "\n",
    "x = Dense(200)(x)               # Dense 2\n",
    "x = Activation('relu')(x)\n",
    "\n",
    "x = Dropout(0.5)(x)\n",
    "\n",
    "x = Dense(50)(x)               # Dense 3\n",
    "x = Activation('relu')(x)\n",
    "\n",
    "x = Dropout(0.5)(x)\n",
    "\n",
    "predictions = Dense(6, activation='softmax')(x) # Dense 4\n",
    "\n",
    "model_PrashNet = Model(inputs=base_model.input, outputs=predictions)\n",
    "\n",
    "model_PrashNet.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CRqx4_oA2w00"
   },
   "outputs": [],
   "source": [
    "sgd = SGD(lr=0.001, momentum=0.9, decay=1e-6, nesterov=True)\n",
    "\n",
    "adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)\n",
    "\n",
    "model_PrashNet.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])\n",
    "\n",
    "timeasname = time.asctime(time.localtime(time.time())).replace(\" \", \"-\").replace(\":\", \"-\")\n",
    "modelname = \"inception-all\"\n",
    "weights = \"imagenet\"\n",
    "optimizer= \"sgd\"\n",
    "specs = modelname + '_' + weights + '_' + optimizer\n",
    "weight_path=\"weights/{}_{}_weights.best.hdf5\".format(specs, timeasname)\n",
    "\n",
    "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor = 0.1, patience = 2, verbose = 1,min_lr = 1e-7)\n",
    "checkpoint = ModelCheckpoint(weight_path, monitor='val_loss', verbose=1, \n",
    "                             save_best_only=True, mode='min', save_weights_only = False)\n",
    "\n",
    "# tensorboard = keras.callbacks.TensorBoard(log_dir=\"logs/{}\".format(timeasname),\n",
    "#                                           histogram_freq=0, write_graph=True, write_images=True)\n",
    "\n",
    "# csvlogger = keras.callbacks.CSVLogger(\"epochlogs/{}_{}\".format(specs, timeasname), separator=',', append=False)                                          \n",
    "                                          \n",
    "earlyStop = EarlyStopping(monitor='val_acc',min_delta=0,\n",
    "                              patience=5,\n",
    "                              verbose=0, mode='auto')\n",
    "\n",
    "# callbacks_list = [checkpoint, reduce_lr, tensorboard, csvlogger]\n",
    "# callbacks_list = [checkpoint, reduce_lr, csvlogger]\n",
    "callbacks_list = [checkpoint, reduce_lr, earlyStop]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vIkm6mlUsE8A"
   },
   "outputs": [],
   "source": [
    "batch_size = 64\n",
    "\n",
    "train_gen = myGenerator(train_df, batch_size)\n",
    "valid_gen = myGenerator(validate_df, batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 218
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 36585,
     "status": "ok",
     "timestamp": 1573945650464,
     "user": {
      "displayName": "Prashanth Reddy Duggirala",
      "photoUrl": "",
      "userId": "15292119504454868818"
     },
     "user_tz": 480
    },
    "id": "UO-Zt0a2sEvP",
    "outputId": "3ee1479d-6fe2-4897-c9a0-9afaa01db86e",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "75/75 [==============================] - 177s 2s/step - loss: 2.0397 - acc: 0.1790 - val_loss: 1.7800 - val_acc: 0.2127\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 1.78004, saving model to weights/inception-all_imagenet_sgd_Mon-Nov-25-19-54-05-2019_weights.best.hdf5\n",
      "Epoch 2/50\n",
      "75/75 [==============================] - 61s 819ms/step - loss: 1.8117 - acc: 0.1781 - val_loss: 1.7752 - val_acc: 0.2405\n",
      "\n",
      "Epoch 00002: val_loss improved from 1.78004 to 1.77521, saving model to weights/inception-all_imagenet_sgd_Mon-Nov-25-19-54-05-2019_weights.best.hdf5\n",
      "Epoch 3/50\n",
      "75/75 [==============================] - 63s 841ms/step - loss: 1.7783 - acc: 0.2119 - val_loss: 1.7588 - val_acc: 0.2847\n",
      "\n",
      "Epoch 00003: val_loss improved from 1.77521 to 1.75880, saving model to weights/inception-all_imagenet_sgd_Mon-Nov-25-19-54-05-2019_weights.best.hdf5\n",
      "Epoch 4/50\n",
      "75/75 [==============================] - 62s 832ms/step - loss: 1.7484 - acc: 0.2265 - val_loss: 1.7195 - val_acc: 0.3099\n",
      "\n",
      "Epoch 00004: val_loss improved from 1.75880 to 1.71946, saving model to weights/inception-all_imagenet_sgd_Mon-Nov-25-19-54-05-2019_weights.best.hdf5\n",
      "Epoch 5/50\n",
      "75/75 [==============================] - 63s 836ms/step - loss: 1.7229 - acc: 0.2533 - val_loss: 1.6757 - val_acc: 0.3064\n",
      "\n",
      "Epoch 00005: val_loss improved from 1.71946 to 1.67566, saving model to weights/inception-all_imagenet_sgd_Mon-Nov-25-19-54-05-2019_weights.best.hdf5\n",
      "Epoch 6/50\n",
      "75/75 [==============================] - 63s 839ms/step - loss: 1.6985 - acc: 0.2569 - val_loss: 1.6515 - val_acc: 0.3116\n",
      "\n",
      "Epoch 00006: val_loss improved from 1.67566 to 1.65155, saving model to weights/inception-all_imagenet_sgd_Mon-Nov-25-19-54-05-2019_weights.best.hdf5\n",
      "Epoch 7/50\n",
      "75/75 [==============================] - 63s 838ms/step - loss: 1.6624 - acc: 0.2775 - val_loss: 1.6338 - val_acc: 0.3264\n",
      "\n",
      "Epoch 00007: val_loss improved from 1.65155 to 1.63379, saving model to weights/inception-all_imagenet_sgd_Mon-Nov-25-19-54-05-2019_weights.best.hdf5\n",
      "Epoch 8/50\n",
      "75/75 [==============================] - 63s 836ms/step - loss: 1.6401 - acc: 0.2806 - val_loss: 1.6394 - val_acc: 0.3394\n",
      "\n",
      "Epoch 00008: val_loss did not improve from 1.63379\n",
      "Epoch 9/50\n",
      "75/75 [==============================] - 63s 834ms/step - loss: 1.5955 - acc: 0.3113 - val_loss: 1.5946 - val_acc: 0.3568\n",
      "\n",
      "Epoch 00009: val_loss improved from 1.63379 to 1.59462, saving model to weights/inception-all_imagenet_sgd_Mon-Nov-25-19-54-05-2019_weights.best.hdf5\n",
      "Epoch 10/50\n",
      "75/75 [==============================] - 63s 844ms/step - loss: 1.5671 - acc: 0.3237 - val_loss: 1.6233 - val_acc: 0.3559\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 1.59462\n",
      "Epoch 11/50\n",
      "75/75 [==============================] - 63s 837ms/step - loss: 1.5143 - acc: 0.3583 - val_loss: 1.6113 - val_acc: 0.3646\n",
      "\n",
      "Epoch 00011: val_loss did not improve from 1.59462\n",
      "\n",
      "Epoch 00011: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "Epoch 12/50\n",
      "75/75 [==============================] - 63s 837ms/step - loss: 1.4846 - acc: 0.3638 - val_loss: 1.5957 - val_acc: 0.3715\n",
      "\n",
      "Epoch 00012: val_loss did not improve from 1.59462\n",
      "Epoch 13/50\n",
      "75/75 [==============================] - 64s 850ms/step - loss: 1.4597 - acc: 0.3769 - val_loss: 1.5975 - val_acc: 0.3854\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 1.59462\n",
      "\n",
      "Epoch 00013: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
      "Epoch 14/50\n",
      "75/75 [==============================] - 63s 836ms/step - loss: 1.4465 - acc: 0.3779 - val_loss: 1.5872 - val_acc: 0.3872\n",
      "\n",
      "Epoch 00014: val_loss improved from 1.59462 to 1.58717, saving model to weights/inception-all_imagenet_sgd_Mon-Nov-25-19-54-05-2019_weights.best.hdf5\n",
      "Epoch 15/50\n",
      "75/75 [==============================] - 64s 848ms/step - loss: 1.4506 - acc: 0.3792 - val_loss: 1.5855 - val_acc: 0.3872\n",
      "\n",
      "Epoch 00015: val_loss improved from 1.58717 to 1.58547, saving model to weights/inception-all_imagenet_sgd_Mon-Nov-25-19-54-05-2019_weights.best.hdf5\n",
      "Epoch 16/50\n",
      "75/75 [==============================] - 65s 865ms/step - loss: 1.4502 - acc: 0.3754 - val_loss: 1.5842 - val_acc: 0.3906\n",
      "\n",
      "Epoch 00016: val_loss improved from 1.58547 to 1.58417, saving model to weights/inception-all_imagenet_sgd_Mon-Nov-25-19-54-05-2019_weights.best.hdf5\n",
      "Epoch 17/50\n",
      "75/75 [==============================] - 61s 819ms/step - loss: 1.4458 - acc: 0.3765 - val_loss: 1.5848 - val_acc: 0.3915\n",
      "\n",
      "Epoch 00017: val_loss did not improve from 1.58417\n",
      "Epoch 18/50\n",
      "75/75 [==============================] - 61s 820ms/step - loss: 1.4369 - acc: 0.3827 - val_loss: 1.5845 - val_acc: 0.3898\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 1.58417\n",
      "\n",
      "Epoch 00018: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n",
      "Epoch 19/50\n",
      "75/75 [==============================] - 62s 830ms/step - loss: 1.4477 - acc: 0.3894 - val_loss: 1.5839 - val_acc: 0.3906\n",
      "\n",
      "Epoch 00019: val_loss improved from 1.58417 to 1.58387, saving model to weights/inception-all_imagenet_sgd_Mon-Nov-25-19-54-05-2019_weights.best.hdf5\n",
      "Epoch 20/50\n",
      "75/75 [==============================] - 62s 824ms/step - loss: 1.4418 - acc: 0.3817 - val_loss: 1.5836 - val_acc: 0.3906\n",
      "\n",
      "Epoch 00020: val_loss improved from 1.58387 to 1.58356, saving model to weights/inception-all_imagenet_sgd_Mon-Nov-25-19-54-05-2019_weights.best.hdf5\n",
      "Epoch 21/50\n",
      "75/75 [==============================] - 64s 851ms/step - loss: 1.4487 - acc: 0.3733 - val_loss: 1.5835 - val_acc: 0.3906\n",
      "\n",
      "Epoch 00021: val_loss improved from 1.58356 to 1.58349, saving model to weights/inception-all_imagenet_sgd_Mon-Nov-25-19-54-05-2019_weights.best.hdf5\n",
      "Epoch 22/50\n",
      "75/75 [==============================] - 61s 819ms/step - loss: 1.4388 - acc: 0.3846 - val_loss: 1.5834 - val_acc: 0.3906\n",
      "\n",
      "Epoch 00022: val_loss improved from 1.58349 to 1.58344, saving model to weights/inception-all_imagenet_sgd_Mon-Nov-25-19-54-05-2019_weights.best.hdf5\n"
     ]
    }
   ],
   "source": [
    "hist = model_PrashNet.fit_generator(train_gen,\n",
    "                           int((len(train_df))/batch_size),\n",
    "                           epochs = 50, verbose=1, \n",
    "                           callbacks=callbacks_list,\n",
    "                           validation_data = valid_gen,\n",
    "                           validation_steps = int((len(validate_df))/batch_size))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cells below here are for experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save weights if you want"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(hist.history['acc'])\n",
    "plt.plot(hist.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(hist.history['loss'])\n",
    "plt.plot(hist.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'loss'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "timeasname = time.asctime(time.localtime(time.time())).replace(\" \", \"-\").replace(\":\", \"-\")\n",
    "modelname = \"inception-0-1\"\n",
    "weights = \"imagenet\"\n",
    "optimizer= \"adam\"\n",
    "specs = modelname + '_' + weights + '_' + optimizer\n",
    "weight_path=\"weights/{}_{}_weights.best.hdf5\".format(specs, timeasname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MFzDLf71sE_M"
   },
   "outputs": [],
   "source": [
    "model_PrashNet.save_weights(weight_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HhsyiJbJsFCz"
   },
   "source": [
    "load weights - inception"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_PrashNet.load_weights(\"weights/inceptionv3_adam_imagenet_11_22_2019.hdf5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_data = validate_df.\n",
    "testinfo_df = validate_df.copy(deep=True)\n",
    "# testinfo_df['GroundTruth'] = \"\"\n",
    "# testinfo_df['Predicted'] = \"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 7)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testinfo_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FkbQGKW7sFF2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "345 0 400 0.8625\n"
     ]
    }
   ],
   "source": [
    "no_of_lines = len(testinfo_df)\n",
    "# no_of_lines = 200\n",
    "gt = []\n",
    "preds = []\n",
    "probs = []\n",
    "cgt, cprobs = [], []\n",
    "count, c2 = 0, 0\n",
    "for index in range(0, no_of_lines):\n",
    "    source_path = testinfo_df.iloc[index]['ID']\n",
    "    filename = 'dataset/' + source_path + '.dcm'\n",
    "    try:\n",
    "        ds = pydicom.dcmread(filename)\n",
    "    except:\n",
    "        continue\n",
    "    image_single = ds.pixel_array\n",
    "    window_center , window_width, intercept, slope = get_windowing(ds)\n",
    "    image_windowed = window_image(image_single, window_center, window_width, intercept, slope)\n",
    "    image_windowed_float = image_windowed.astype(np.float32)\n",
    "    image_windowed_resized = cv2.resize(image_windowed_float, dsize=(224, 224))\n",
    "#     thresh = threshold_fast(40, 66, image_windowed_resized)\n",
    "    image = np.stack([image_windowed_resized, image_windowed_resized, image_windowed_resized])\n",
    "    image = np.moveaxis(image, 0, 2)            \n",
    "    X = model_PrashNet.predict(np.array(image, ndmin = 4))\n",
    "#     print(X)\n",
    "    predicted_prob = np.max(X)\n",
    "    predicted_class = np.argmax(X)\n",
    "#     print(\"predicted class: \", predicted_class)\n",
    "    actual_class = classfinder(testinfo_df.iloc[index])\n",
    "#     print(\"actual class: \", actual_class)\n",
    "#     testinfo_df.at[index, \"GroundTruth\"] = actual_class\n",
    "#     testinfo_df.at[index, 'Predicted'] = predicted_class\n",
    "    preds.append(predicted_class)\n",
    "    gt.append(actual_class)\n",
    "    probs.append(predicted_prob)\n",
    "    if predicted_class == actual_class:\n",
    "        count = count + 1\n",
    "        cprobs.append(predicted_prob)\n",
    "        cgt.append(actual_class)\n",
    "#         if predicted_prob > 0.75:\n",
    "#             cprobs.append(predicted_prob)\n",
    "#             cgt.append(actual_class)\n",
    "            \n",
    "print(count, c2, no_of_lines, count/no_of_lines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scikitplot as skplt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "400"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.48282772614544456\n"
     ]
    }
   ],
   "source": [
    "fpr, tpr, thresholds = metrics.roc_curve(gt, probs)\n",
    "auc = metrics.roc_auc_score(gt,probs)\n",
    "plt.plot(fpr,tpr,label=\"auc=\"+str(auc))\n",
    "plt.legend(loc=4)\n",
    "plt.show()\n",
    "print(auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnf_matrix = metrics.confusion_matrix(preds, gt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized confusion matrix\n",
      "[[0.82380952 0.17619048]\n",
      " [0.09473684 0.90526316]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "class_list = list(range(2))\n",
    "plot_confusion_matrix(cnf_matrix, classes=class_list, normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array_path=\"{}_{}.txt\".format(specs, timeasname)\n",
    "np.savetxt('arrays/preds' + '_' + array_path, preds)\n",
    "np.savetxt('arrays/probs' + '_' + array_path, probs)\n",
    "np.savetxt('arrays/gt' + '_' + array_path, gt)\n",
    "np.savetxt('arrays/cprobs' + '_' + array_path, cprobs)\n",
    "np.savetxt('arrays/cgt' + '_' + array_path, cgt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving intermediate layer output to a pkl file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer_name = 'global_average_pooling2d_1'\n",
    "intermediate_layer_model = Model(inputs=model_PrashNet.input,\n",
    "                                 outputs=model_PrashNet.get_layer(layer_name).output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "temp_df = validate_df.copy()\n",
    "temp_df['Inter_op'] = \"\"\n",
    "temp_df['class'] = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>ID</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>label</th>\n",
       "      <th>Inter_op</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>RSNA0/ID_ea65a8ad0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26197</th>\n",
       "      <td>RSNA2/ID_20b9eac20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1752</th>\n",
       "      <td>RSNA3/ID_3aaaf8cd6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42863</th>\n",
       "      <td>RSNA3/ID_5fcb747c0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2696</th>\n",
       "      <td>RSNA3/ID_27dd8ab3a</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       ID   1  2  3  4  5  label Inter_op class\n",
       "252    RSNA0/ID_ea65a8ad0   0  0  0  0  0      0               \n",
       "26197  RSNA2/ID_20b9eac20   0  1  0  0  0      1               \n",
       "1752   RSNA3/ID_3aaaf8cd6   0  0  1  0  0      1               \n",
       "42863  RSNA3/ID_5fcb747c0   0  0  1  0  0      1               \n",
       "2696   RSNA3/ID_27dd8ab3a   0  0  1  0  0      1               "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_of_lines = len(temp_df)\n",
    "# no_of_lines = 2\n",
    "\n",
    "for index in range(0, no_of_lines):\n",
    "    source_path = temp_df.iloc[index]['ID']\n",
    "    filename = 'dataset/' + source_path + '.dcm'\n",
    "    try:\n",
    "        ds = pydicom.dcmread(filename)\n",
    "    except:\n",
    "        continue\n",
    "    image_single = ds.pixel_array\n",
    "    window_center , window_width, intercept, slope = get_windowing(ds)\n",
    "    image_windowed = window_image(image_single, window_center, window_width, intercept, slope)\n",
    "    image_windowed_float = image_windowed.astype(np.float32)\n",
    "    image_windowed_resized = cv2.resize(image_windowed_float, dsize=(224, 224))\n",
    "#     thresh = threshold_fast(40, 66, image_windowed_resized)\n",
    "    image = np.stack([image_windowed_resized, image_windowed_resized, image_windowed_resized])\n",
    "    image = np.moveaxis(image, 0, 2)            \n",
    "    X = intermediate_layer_model.predict(np.array(image, ndmin = 4))\n",
    "\n",
    "    actual_class = classfinder(temp_df.iloc[index])\n",
    "#     print(filename, actual_class, type(X), X.size)\n",
    "    temp_df.iat[index, 8] = actual_class\n",
    "    temp_df.iat[index, 7] = X\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "export_df = temp_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>label</th>\n",
       "      <th>Inter_op</th>\n",
       "      <th>class</th>\n",
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       "  </thead>\n",
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       "      <th>35032</th>\n",
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       "      <td>4</td>\n",
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      ],
      "text/plain": [
       "                        ID   1  2  3  4  5  label  \\\n",
       "35032   RSNA4/ID_352b3ae8e   0  0  0  1  0      1   \n",
       "795     RSNA0/ID_82e2004a0   0  0  0  0  0      0   \n",
       "15844   RSNA5/ID_0db6d4271   0  0  0  0  1      1   \n",
       "143678  RSNA1/ID_27dd8ecb4   1  0  0  0  0      1   \n",
       "416928  RSNA1/ID_3b48698b8   1  0  0  0  0      1   \n",
       "\n",
       "                                                 Inter_op class  \n",
       "35032   [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...     4  \n",
       "795     [[0.4198561, 0.0, 0.0, 0.0, 0.44340202, 0.0002...     0  \n",
       "15844   [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...     5  \n",
       "143678  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...     1  \n",
       "416928  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...     1  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "export_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', ' 1', '2', '3', '4', '5', 'label', 'Inter_op', 'class'], dtype='object')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "export_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "export_df = export_df.drop(columns=[' 1','2','3','4','5', 'label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = export_df.to_pickle(\"train_multiclass.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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