MRI_brain_v4_CONV2D_gray_121_no_augmentation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "3lkge0LBhZU9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import glob\n",
    "import h5py\n",
    "import shutil\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import seaborn as sns\n",
    "from os import listdir, makedirs, getcwd, remove\n",
    "from os.path import isfile, join, abspath, exists, isdir, expanduser\n",
    "from PIL import Image\n",
    "from pathlib import Path\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, Callback, EarlyStopping\n",
    "from tensorflow.keras.layers import Dense, Input\n",
    "from sklearn.model_selection import train_test_split\n",
    "from mlxtend.plotting import plot_confusion_matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import cv2\n",
    "import keras\n",
    "import pandas as pd\n",
    "from tensorflow.keras.callbacks import ReduceLROnPlateau\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras.applications import DenseNet169, DenseNet121, DenseNet201\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ylbVCxz3hZU-",
    "outputId": "8ba51200-e72b-4407-f41a-26fef9ba3d83"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All imported!\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import sys\n",
    "import os\n",
    "import PIL\n",
    "import PIL.Image as Image\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "import nibabel as nib\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from dltk.io.preprocessing import whitening\n",
    "from dltk.io.augmentation import flip\n",
    "import time\n",
    "print('All imported!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 333
    },
    "id": "qL90xbPChZU_",
    "outputId": "a9e8f351-caf3-4057-dff8-9249727f1a0d"
   },
   "outputs": [
    {
     "data": {
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       "    }\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>IXI_ID</th>\n",
       "      <th>SEX_ID (1=m, 2=f)</th>\n",
       "      <th>HEIGHT</th>\n",
       "      <th>WEIGHT</th>\n",
       "      <th>ETHNIC_ID</th>\n",
       "      <th>MARITAL_ID</th>\n",
       "      <th>OCCUPATION_ID</th>\n",
       "      <th>QUALIFICATION_ID</th>\n",
       "      <th>DOB</th>\n",
       "      <th>DATE_AVAILABLE</th>\n",
       "      <th>STUDY_DATE</th>\n",
       "      <th>AGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
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       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "      <td>5</td>\n",
       "      <td>1970-01-30</td>\n",
       "      <td>1</td>\n",
       "      <td>2005-11-18</td>\n",
       "      <td>35.800137</td>\n",
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       "      <td>12</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1966-08-20</td>\n",
       "      <td>1</td>\n",
       "      <td>2005-06-01</td>\n",
       "      <td>38.781656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
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       "      <td>2005-06-01</td>\n",
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       "      <td>2</td>\n",
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       "      <td>4</td>\n",
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       "      <td>1971-03-15</td>\n",
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       "      <td>1981-03-11</td>\n",
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       "      <td>2005-06-23</td>\n",
       "      <td>24.284736</td>\n",
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      ],
      "text/plain": [
       "   IXI_ID  SEX_ID (1=m, 2=f)  HEIGHT  WEIGHT  ETHNIC_ID  MARITAL_ID  \\\n",
       "0       2                  2     164      58          1           4   \n",
       "1      12                  1     175      70          1           2   \n",
       "2      13                  1     182      70          1           2   \n",
       "3      14                  2     163      65          1           4   \n",
       "4      15                  1     181      90          2           1   \n",
       "\n",
       "   OCCUPATION_ID  QUALIFICATION_ID         DOB  DATE_AVAILABLE STUDY_DATE  \\\n",
       "0              1                 5  1970-01-30               1 2005-11-18   \n",
       "1              1                 5  1966-08-20               1 2005-06-01   \n",
       "2              1                 5  1958-09-15               1 2005-06-01   \n",
       "3              1                 5  1971-03-15               1 2005-06-09   \n",
       "4              6                 5  1981-03-11               1 2005-06-23   \n",
       "\n",
       "         AGE  \n",
       "0  35.800137  \n",
       "1  38.781656  \n",
       "2  46.710472  \n",
       "3  34.236824  \n",
       "4  24.284736  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_directory = 'dataset'\n",
    "dataset_info = pd.read_excel('IXI.xls')\n",
    "dataset_info.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QLD5SYRAhZVB"
   },
   "source": [
    "### Processing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lQHT8qU9hZVB",
    "outputId": "614c17c6-9f49-41c5-8852-924dbe2730af"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading the MRI images with nibabel and\n",
    "# identifying file names that are not mentioned in the EXCEL file\n",
    "\n",
    "image_files = []\n",
    "labels = []\n",
    "not_included = []\n",
    "for filename in os.listdir(img_directory):\n",
    "    try:\n",
    "        # reading the label from Excel\n",
    "        img_id = int(filename[3:6])\n",
    "        sex = np.array(dataset_info[dataset_info.IXI_ID == img_id])[0][1]\n",
    "        labels.append(sex-1)\n",
    "        # reading images\n",
    "        img = nib.load(img_directory+\"/\"+filename)\n",
    "        image_files.append(img)\n",
    "    except Exception as e:\n",
    "        not_included.append(filename)\n",
    "        \n",
    "len(not_included)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sHuYrVfJAM4G",
    "outputId": "2de003c9-5a2f-449c-bbc0-ffc5c71e387a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(566, 566)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(image_files), len(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "q0mHyqNhUUbl",
    "outputId": "07ad5fc2-3177-4e45-fd05-bd6b165dcbb1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(452, 114)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(image_files, labels, test_size=0.2,\n",
    "                                                    random_state =1, stratify = labels )\n",
    "\n",
    "len(X_train), len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "Q6W2RKM7AM4H"
   },
   "outputs": [],
   "source": [
    "def count_class(labels):\n",
    "    zero = 0\n",
    "    one = 0\n",
    "    for l in labels:\n",
    "        if l == 0:\n",
    "             zero += 1\n",
    "        else:\n",
    "            one += 1\n",
    "    return zero, one, zero/ one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 701
    },
    "id": "XtK3NP2JAM4H",
    "outputId": "1ff17a2c-ec42-4bd0-dd5d-0c17c9a3dca1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "A:\\anaconda3\\envs\\tf\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "A:\\anaconda3\\envs\\tf\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(202, 250, 0.808)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(51, 63, 0.8095238095238095)\n"
     ]
    }
   ],
   "source": [
    "from seaborn import countplot\n",
    "\n",
    "# Plot the results \n",
    "plt.figure(figsize=(4,4))\n",
    "c1 = countplot(y_train)\n",
    "plt.title('Train Data dist', fontsize=14)\n",
    "plt.ylabel('Count', fontsize=12)\n",
    "plt.xlabel('Class', fontsize=12)\n",
    "plt.show()\n",
    "print(count_class(y_train))\n",
    "\n",
    "plt.figure(figsize=(4,4))\n",
    "c2 = countplot(y_test)\n",
    "plt.title('Test Data dist', fontsize=14)\n",
    "plt.ylabel('Count', fontsize=12)\n",
    "plt.xlabel('Class', fontsize=12)\n",
    "plt.show()\n",
    "\n",
    "print(count_class(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5Fk_YX0JZmrt",
    "outputId": "493cb362-a750-48de-a37d-be14ed8abc0f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "A:\\anaconda3\\envs\\tf\\lib\\site-packages\\sklearn\\utils\\validation.py:70: FutureWarning: Pass classes=[0 1], y=[1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.utils import class_weight\n",
    "weights = class_weight.compute_class_weight('balanced', np.unique(y_train), y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 537
    },
    "id": "NbLRBR9abO8F",
    "outputId": "226e909e-a32b-4f8e-de8a-b1a3e33a9b89"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(256, 256, 150)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = np.array(image_files[0].dataobj)\n",
    "print(img.shape)\n",
    "img = flip(img, axis=1)\n",
    "plt.imshow(img[:,:, 50])\n",
    "plt.show()\n",
    "plt.imshow(img[:,:, 80])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# showing a progressbar in output\n",
    "def progressBar(value, endvalue, bar_length=20,job='Job'):\n",
    "\n",
    "    percent = float(value) / endvalue\n",
    "    arrow = '-' * int(round(percent * bar_length)-1) + '>'\n",
    "    spaces = ' ' * (bar_length - len(arrow))\n",
    "\n",
    "    sys.stdout.write(\"\\r{0} Completion: [{1}] {2}%\".format(job,arrow + spaces, int(round(percent * 100))))\n",
    "    sys.stdout.flush()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZbjHrKhwhZVC"
   },
   "source": [
    "### Defining the generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "in_size = 256; in_dim = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_images(images, method = None, channels = 150):#, resize= False, resize_scale = 2):\n",
    "    \n",
    "    data = []\n",
    "    j = 1\n",
    "    \n",
    "    for img_ in images:\n",
    "        try:\n",
    "            progressBar(j, len(images), bar_length=20, job='Job')\n",
    "            \n",
    "##             rescaling the image, although the problem is the output images does not have the same size\n",
    "#             if resize:\n",
    "#                 img = resample_img(img, target_affine=np.eye(3)*resize_scale, interpolation='nearest')\n",
    "#                 print(img.shape)\n",
    "            \n",
    "            img = np.array(img_.dataobj)\n",
    "            dx , dy = img.shape[0], img.shape[1]\n",
    "            \n",
    "            # add zero channels to images with fewer channels than others\n",
    "            if method == 'add_channels':\n",
    "                dz = 150\n",
    "                if img.shape[2] != 150:\n",
    "                    img = np.concatenate((img, np.zeros((img.shape[0],img.shape[1],150-img.shape[2]))), axis =2)\n",
    "            elif method == 'del_channels':\n",
    "                dz = channels\n",
    "                if dz%2 == 0:\n",
    "                    img = img[:,:,75 - (channels//2) :75 + (channels//2)]\n",
    "                else:\n",
    "                    img = img[:,:,74 - (channels//2) +1 :76 + (channels//2)]\n",
    "\n",
    "            img = whitening(img)\n",
    "            \n",
    "            data.append(img)\n",
    "            j += 1\n",
    "        except Exception as e:\n",
    "            print(filename)\n",
    "            print(e)\n",
    "            \n",
    "    return np.array(data).reshape(-1, dx, dy, dz)                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Completion: [------------------->] 100%"
     ]
    }
   ],
   "source": [
    "X_train_processed= process_images(X_train, method = 'del_channels', channels = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(452, 256, 256, 1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_processed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Completion: [------------------->] 100%"
     ]
    }
   ],
   "source": [
    "X_test_processed= process_images(X_test, method = 'del_channels', channels = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(114, 256, 256, 1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_processed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "datagen = ImageDataGenerator(rotation_range=0,\n",
    "    width_shift_range=0.0,\n",
    "    height_shift_range=0.0,\n",
    "    zoom_range=0.0,                         \n",
    "    horizontal_flip=False)\n",
    "\n",
    "train_data_gray = tf.image.grayscale_to_rgb(\n",
    "    tf.constant(X_train_processed),\n",
    "    name='train_data')\n",
    "\n",
    "valid_data_gray = tf.image.grayscale_to_rgb(\n",
    "    tf.constant(X_test_processed),\n",
    "    name='val_data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = np.array(y_train).reshape(len(y_train),1)\n",
    "y_test = np.array(y_test).reshape(len(y_test),1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AcWsR2wXFFfo"
   },
   "source": [
    "### Defining the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "transfer_lr = True; base_weights = 'imagenet' ; bs = 32; reg = False; penalty = 0\n",
    "# transfer_lr = False; base_weights = None; bs = 16.\n",
    "\n",
    "train_gen = datagen.flow(train_data_gray, y_train, batch_size=bs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "HLIVYn7ehZVF"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         [(None, 256, 256, 3)]     0         \n",
      "_________________________________________________________________\n",
      "densenet121 (Model)          (None, 1024)              7037504   \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 1025      \n",
      "=================================================================\n",
      "Total params: 7,038,529\n",
      "Trainable params: 1,025\n",
      "Non-trainable params: 7,037,504\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# if transfer learning is enabled, we freeze everything except the last layer\n",
    "\n",
    "base_model = DenseNet121(weights= base_weights, include_top=False,\n",
    "                         input_shape=(in_size, in_size, in_dim), pooling='avg')\n",
    "\n",
    "base_model.trainable = not transfer_lr\n",
    "\n",
    "inputs = Input(shape=(in_size, in_size, in_dim))\n",
    "    \n",
    "x = base_model(inputs, training = not transfer_lr)\n",
    "\n",
    "outputs = Dense(1, activation='sigmoid')(x)\n",
    "\n",
    "model = Model(inputs, outputs)\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "KEGPBzzVFajv"
   },
   "outputs": [],
   "source": [
    "steps = len(train_data_gray) // bs\n",
    "\n",
    "reduce_lr = ReduceLROnPlateau(\n",
    "    monitor='val_loss', factor=0.1, patience=3, verbose=1,\n",
    "    mode='auto', min_delta=0.001, cooldown=0, min_lr=1e-8)\n",
    "\n",
    "\n",
    "chkpt = ModelCheckpoint(filepath='weights/', save_best_only=True,\n",
    "                        save_weights_only=True, monitor= 'val_accuracy', mode='max')\n",
    "\n",
    "initial_learning_rate = 3e-3\n",
    "lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(\n",
    "    initial_learning_rate,\n",
    "    decay_steps=steps,\n",
    "    decay_rate=0.9,\n",
    "    staircase=True)\n",
    "\n",
    "opt = Adam(learning_rate=lr_schedule) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss= 'binary_crossentropy', metrics=['accuracy'], optimizer=opt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "M-hQlgPBhZVG",
    "outputId": "e600a2d4-d325-4369-f6c4-174851795928",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:sample_weight modes were coerced from\n",
      "  ...\n",
      "    to  \n",
      "  ['...']\n",
      "Train for 14 steps, validate on 114 samples\n",
      "Epoch 1/25\n",
      "14/14 [==============================] - 17s 1s/step - loss: 0.9960 - accuracy: 0.5143 - val_loss: 0.8422 - val_accuracy: 0.5526\n",
      "Epoch 2/25\n",
      "14/14 [==============================] - 2s 155ms/step - loss: 0.6913 - accuracy: 0.5690 - val_loss: 0.6532 - val_accuracy: 0.5877\n",
      "Epoch 3/25\n",
      "14/14 [==============================] - 2s 155ms/step - loss: 0.6181 - accuracy: 0.6357 - val_loss: 0.6052 - val_accuracy: 0.6754\n",
      "Epoch 4/25\n",
      "14/14 [==============================] - 2s 132ms/step - loss: 0.5608 - accuracy: 0.7405 - val_loss: 0.5868 - val_accuracy: 0.6754\n",
      "Epoch 5/25\n",
      "14/14 [==============================] - 2s 157ms/step - loss: 0.5576 - accuracy: 0.7119 - val_loss: 0.5341 - val_accuracy: 0.8070\n",
      "Epoch 6/25\n",
      "14/14 [==============================] - 2s 131ms/step - loss: 0.5149 - accuracy: 0.7643 - val_loss: 0.5095 - val_accuracy: 0.7982\n",
      "Epoch 7/25\n",
      "14/14 [==============================] - 2s 154ms/step - loss: 0.4735 - accuracy: 0.8190 - val_loss: 0.4868 - val_accuracy: 0.8333\n",
      "Epoch 8/25\n",
      "14/14 [==============================] - 2s 131ms/step - loss: 0.4457 - accuracy: 0.8429 - val_loss: 0.4829 - val_accuracy: 0.7807\n",
      "Epoch 9/25\n",
      "14/14 [==============================] - 2s 174ms/step - loss: 0.4494 - accuracy: 0.8571 - val_loss: 0.4558 - val_accuracy: 0.8421\n",
      "Epoch 10/25\n",
      "14/14 [==============================] - 2s 154ms/step - loss: 0.4250 - accuracy: 0.8476 - val_loss: 0.4421 - val_accuracy: 0.8596\n",
      "Epoch 11/25\n",
      "14/14 [==============================] - 2s 132ms/step - loss: 0.4020 - accuracy: 0.8905 - val_loss: 0.4357 - val_accuracy: 0.8509\n",
      "Epoch 12/25\n",
      "14/14 [==============================] - 2s 133ms/step - loss: 0.3966 - accuracy: 0.8690 - val_loss: 0.4237 - val_accuracy: 0.8509\n",
      "Epoch 13/25\n",
      "14/14 [==============================] - 2s 133ms/step - loss: 0.3827 - accuracy: 0.8714 - val_loss: 0.4165 - val_accuracy: 0.8509\n",
      "Epoch 14/25\n",
      "14/14 [==============================] - 2s 138ms/step - loss: 0.3886 - accuracy: 0.8973 - val_loss: 0.4098 - val_accuracy: 0.8509\n",
      "Epoch 15/25\n",
      "14/14 [==============================] - 2s 154ms/step - loss: 0.3689 - accuracy: 0.8833 - val_loss: 0.4030 - val_accuracy: 0.8772\n",
      "Epoch 16/25\n",
      "14/14 [==============================] - 2s 132ms/step - loss: 0.3622 - accuracy: 0.9095 - val_loss: 0.3988 - val_accuracy: 0.8684\n",
      "Epoch 17/25\n",
      "14/14 [==============================] - 2s 132ms/step - loss: 0.3542 - accuracy: 0.8786 - val_loss: 0.3990 - val_accuracy: 0.8772\n",
      "Epoch 18/25\n",
      "14/14 [==============================] - 2s 132ms/step - loss: 0.3540 - accuracy: 0.9000 - val_loss: 0.3901 - val_accuracy: 0.8772\n",
      "Epoch 19/25\n",
      "14/14 [==============================] - 2s 155ms/step - loss: 0.3492 - accuracy: 0.9143 - val_loss: 0.3873 - val_accuracy: 0.8860\n",
      "Epoch 20/25\n",
      "14/14 [==============================] - 2s 134ms/step - loss: 0.3459 - accuracy: 0.9095 - val_loss: 0.3836 - val_accuracy: 0.8860\n",
      "Epoch 21/25\n",
      "14/14 [==============================] - 2s 157ms/step - loss: 0.3438 - accuracy: 0.9119 - val_loss: 0.3810 - val_accuracy: 0.8947\n",
      "Epoch 22/25\n",
      "14/14 [==============================] - 2s 133ms/step - loss: 0.3365 - accuracy: 0.9119 - val_loss: 0.3803 - val_accuracy: 0.8947\n",
      "Epoch 23/25\n",
      "14/14 [==============================] - 2s 133ms/step - loss: 0.3342 - accuracy: 0.9167 - val_loss: 0.3774 - val_accuracy: 0.8947\n",
      "Epoch 24/25\n",
      "14/14 [==============================] - 2s 167ms/step - loss: 0.3468 - accuracy: 0.9152 - val_loss: 0.3732 - val_accuracy: 0.9035\n",
      "Epoch 25/25\n",
      "14/14 [==============================] - 2s 133ms/step - loss: 0.3321 - accuracy: 0.9095 - val_loss: 0.3745 - val_accuracy: 0.8947\n",
      "****************************************\n",
      "Took 1.09 minutes\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "\n",
    "nb_epochs = 25 \n",
    "\n",
    "# Fit the model\n",
    "history= model.fit(train_gen, epochs= nb_epochs, steps_per_epoch= steps,\n",
    "                              validation_data=(valid_data_gray, y_test),\n",
    "                              callbacks=[chkpt], #, [chkpt, clr, es , reduce_lr]\n",
    "                              class_weight=weights)\n",
    "end = time.time()\n",
    "\n",
    "print('*'*40)\n",
    "print('Took {:.2f} minutes'. format((end-start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best train acc: 0.9166666865348816 | Best val acc: 0.9035087823867798 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Best train acc: {} | Best val acc: {} '.format(max(history.history['accuracy']),\n",
    "                                                      max(history.history['val_accuracy'])))\n",
    "plt.plot(history.history['accuracy'],'ro-')\n",
    "plt.plot(history.history['val_accuracy'],'bx-')\n",
    "plt.legend(['train_accuracy','val_accuracy'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best train acc: 0.9357143044471741 | Best val acc: 0.9210526347160339 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Best train acc: {} | Best val acc: {} '.format(max(history.history['accuracy']),\n",
    "                                                      max(history.history['val_accuracy'])))\n",
    "plt.plot(history.history['accuracy'],'ro-')\n",
    "plt.plot(history.history['val_accuracy'],'bx-')\n",
    "plt.legend(['train_accuracy','val_accuracy'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "114/114 [==============================] - 0s 4ms/sample - loss: 0.3732 - accuracy: 0.9035\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.37321004114652934, 0.9035088]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_weights('weights/')\n",
    "model.evaluate(valid_data_gray, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fine tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "bs = bs//2\n",
    "steps = len(X_train_processed) // bs\n",
    "train_gen = datagen.flow(train_data_gray, y_train, batch_size=bs)\n",
    "\n",
    "base_model.trainable = True\n",
    "\n",
    "# in fintuning if you dont lower the LR the weight update \n",
    "# will be very rapid and the result is poor\n",
    "initial_learning_rate = 5e-7\n",
    "lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(\n",
    "    initial_learning_rate,\n",
    "    decay_steps=steps*2,\n",
    "    decay_rate=0.6,\n",
    "    staircase=True)\n",
    "\n",
    "opt = Adam(learning_rate=lr_schedule) \n",
    "\n",
    "model.compile(loss= 'binary_crossentropy', metrics=['accuracy'], optimizer=opt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:sample_weight modes were coerced from\n",
      "  ...\n",
      "    to  \n",
      "  ['...']\n",
      "Train for 113 steps, validate on 114 samples\n",
      "Epoch 1/25\n",
      "113/113 [==============================] - 25s 219ms/step - loss: 0.3250 - accuracy: 0.9204 - val_loss: 0.3284 - val_accuracy: 0.9123\n",
      "Epoch 2/25\n",
      "113/113 [==============================] - 10s 92ms/step - loss: 0.2768 - accuracy: 0.9447 - val_loss: 0.2900 - val_accuracy: 0.9211\n",
      "Epoch 3/25\n",
      "113/113 [==============================] - 10s 86ms/step - loss: 0.2416 - accuracy: 0.9558 - val_loss: 0.2785 - val_accuracy: 0.9211\n",
      "Epoch 4/25\n",
      "113/113 [==============================] - 10s 93ms/step - loss: 0.2215 - accuracy: 0.9513 - val_loss: 0.2572 - val_accuracy: 0.9298\n",
      "Epoch 5/25\n",
      "113/113 [==============================] - 11s 96ms/step - loss: 0.2036 - accuracy: 0.9558 - val_loss: 0.2510 - val_accuracy: 0.9386\n",
      "Epoch 6/25\n",
      "113/113 [==============================] - 10s 87ms/step - loss: 0.1928 - accuracy: 0.9624 - val_loss: 0.2412 - val_accuracy: 0.9211\n",
      "Epoch 7/25\n",
      "113/113 [==============================] - 10s 88ms/step - loss: 0.1840 - accuracy: 0.9668 - val_loss: 0.2395 - val_accuracy: 0.9298\n",
      "Epoch 8/25\n",
      "113/113 [==============================] - 10s 88ms/step - loss: 0.1781 - accuracy: 0.9690 - val_loss: 0.2369 - val_accuracy: 0.9298\n",
      "Epoch 9/25\n",
      "113/113 [==============================] - 10s 88ms/step - loss: 0.1726 - accuracy: 0.9735 - val_loss: 0.2323 - val_accuracy: 0.9298\n",
      "Epoch 10/25\n",
      "113/113 [==============================] - 10s 88ms/step - loss: 0.1690 - accuracy: 0.9712 - val_loss: 0.2293 - val_accuracy: 0.9298\n",
      "Epoch 11/25\n",
      "113/113 [==============================] - 10s 88ms/step - loss: 0.1662 - accuracy: 0.9735 - val_loss: 0.2278 - val_accuracy: 0.9298\n",
      "Epoch 12/25\n",
      "113/113 [==============================] - 10s 88ms/step - loss: 0.1639 - accuracy: 0.9735 - val_loss: 0.2268 - val_accuracy: 0.9298\n",
      "Epoch 13/25\n",
      "113/113 [==============================] - 10s 89ms/step - loss: 0.1623 - accuracy: 0.9735 - val_loss: 0.2259 - val_accuracy: 0.9298\n",
      "Epoch 14/25\n",
      "113/113 [==============================] - 10s 89ms/step - loss: 0.1613 - accuracy: 0.9735 - val_loss: 0.2251 - val_accuracy: 0.9298\n",
      "Epoch 15/25\n",
      "113/113 [==============================] - 10s 89ms/step - loss: 0.1600 - accuracy: 0.9735 - val_loss: 0.2245 - val_accuracy: 0.9298\n",
      "Epoch 16/25\n",
      "113/113 [==============================] - 10s 89ms/step - loss: 0.1594 - accuracy: 0.9735 - val_loss: 0.2241 - val_accuracy: 0.9298\n",
      "Epoch 17/25\n",
      "113/113 [==============================] - 10s 89ms/step - loss: 0.1587 - accuracy: 0.9735 - val_loss: 0.2239 - val_accuracy: 0.9298\n",
      "Epoch 18/25\n",
      "113/113 [==============================] - 10s 89ms/step - loss: 0.1583 - accuracy: 0.9735 - val_loss: 0.2236 - val_accuracy: 0.9298\n",
      "Epoch 19/25\n",
      "113/113 [==============================] - 10s 89ms/step - loss: 0.1579 - accuracy: 0.9735 - val_loss: 0.2234 - val_accuracy: 0.9298\n",
      "Epoch 20/25\n",
      "113/113 [==============================] - 10s 89ms/step - loss: 0.1577 - accuracy: 0.9735 - val_loss: 0.2233 - val_accuracy: 0.9298\n",
      "Epoch 21/25\n",
      "113/113 [==============================] - 10s 89ms/step - loss: 0.1575 - accuracy: 0.9735 - val_loss: 0.2233 - val_accuracy: 0.9298\n",
      "Epoch 22/25\n",
      "113/113 [==============================] - 10s 91ms/step - loss: 0.1574 - accuracy: 0.9735 - val_loss: 0.2232 - val_accuracy: 0.9298\n",
      "Epoch 23/25\n",
      "113/113 [==============================] - 10s 89ms/step - loss: 0.1573 - accuracy: 0.9735 - val_loss: 0.2231 - val_accuracy: 0.9298\n",
      "Epoch 24/25\n",
      "113/113 [==============================] - 10s 89ms/step - loss: 0.1573 - accuracy: 0.9735 - val_loss: 0.2231 - val_accuracy: 0.9298\n",
      "Epoch 25/25\n",
      "113/113 [==============================] - 10s 89ms/step - loss: 0.1572 - accuracy: 0.9735 - val_loss: 0.2231 - val_accuracy: 0.9298\n",
      "****************************************\n",
      "Took 4.45 minutes\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "\n",
    "nb_epochs = 25 \n",
    "\n",
    "# Fit the model\n",
    "history= model.fit(train_gen, epochs= nb_epochs, steps_per_epoch= steps,\n",
    "                              validation_data=(valid_data_gray, y_test),\n",
    "                              callbacks=[chkpt], #, [chkpt, clr, es , reduce_lr]\n",
    "                              class_weight=weights)\n",
    "end = time.time()\n",
    "\n",
    "print('*'*40)\n",
    "print('Took {:.2f} minutes'. format((end-start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "BizXOeDjuLJ7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best train acc: 0.963302731513977 | Best val acc: 0.9298245906829834 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Best train acc: {} | Best val acc: {} '.format(max(history.history['accuracy']),\n",
    "                                                      max(history.history['val_accuracy'])))\n",
    "plt.plot(history.history['accuracy'],'ro-')\n",
    "plt.plot(history.history['val_accuracy'],'bx-')\n",
    "plt.legend(['accuracy','val_accuracy'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "452/452 [==============================] - 2s 4ms/sample - loss: 0.1960 - accuracy: 0.9668\n",
      "114/114 [==============================] - 0s 4ms/sample - loss: 0.2510 - accuracy: 0.9386\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.2509884175501372, 0.9385965]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_weights('weights/')\n",
    "model.evaluate(train_data_gray, y_train)\n",
    "model.evaluate(valid_data_gray, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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