MRI_brain_v4_CONV2D_3channels_169_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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       "    .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",
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       "  </thead>\n",
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       "      <td>1970-01-30</td>\n",
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       "      <td>2005-11-18</td>\n",
       "      <td>35.800137</td>\n",
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       "      <td>2</td>\n",
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       "      <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",
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       "      <th>2</th>\n",
       "      <td>13</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": "stdout",
     "output_type": "stream",
     "text": [
      "(202, 250, 0.808)\n"
     ]
    },
    {
     "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": "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 = in_dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(452, 256, 256, 3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_processed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Completion: [------------------->] 100%"
     ]
    }
   ],
   "source": [
    "X_test_processed= process_images(X_test, method = 'del_channels', channels = in_dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(114, 256, 256, 3)"
      ]
     },
     "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)"
   ]
  },
  {
   "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": 20,
   "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(X_train_processed, y_train, batch_size=bs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "HLIVYn7ehZVF"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_4 (InputLayer)         [(None, 256, 256, 3)]     0         \n",
      "_________________________________________________________________\n",
      "densenet169 (Model)          (None, 1664)              12642880  \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 1665      \n",
      "=================================================================\n",
      "Total params: 12,644,545\n",
      "Trainable params: 1,665\n",
      "Non-trainable params: 12,642,880\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# if transfer learning is enabled, we freeze everything except the last layer\n",
    "\n",
    "base_model = DenseNet169(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": 27,
   "metadata": {
    "id": "KEGPBzzVFajv"
   },
   "outputs": [],
   "source": [
    "steps = len(X_train_processed) // 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": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss= 'binary_crossentropy', metrics=['accuracy'], optimizer=opt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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 [==============================] - 16s 1s/step - loss: 0.6595 - accuracy: 0.5667 - val_loss: 0.6111 - val_accuracy: 0.6404\n",
      "Epoch 2/25\n",
      "14/14 [==============================] - 3s 195ms/step - loss: 0.5492 - accuracy: 0.7167 - val_loss: 0.5523 - val_accuracy: 0.7544\n",
      "Epoch 3/25\n",
      "14/14 [==============================] - 3s 194ms/step - loss: 0.4628 - accuracy: 0.8381 - val_loss: 0.4844 - val_accuracy: 0.8421\n",
      "Epoch 4/25\n",
      "14/14 [==============================] - 2s 161ms/step - loss: 0.3988 - accuracy: 0.8476 - val_loss: 0.4665 - val_accuracy: 0.8246\n",
      "Epoch 5/25\n",
      "14/14 [==============================] - 2s 161ms/step - loss: 0.3631 - accuracy: 0.8857 - val_loss: 0.4315 - val_accuracy: 0.8070\n",
      "Epoch 6/25\n",
      "14/14 [==============================] - 3s 220ms/step - loss: 0.3509 - accuracy: 0.9174 - val_loss: 0.4028 - val_accuracy: 0.8596\n",
      "Epoch 7/25\n",
      "14/14 [==============================] - 2s 162ms/step - loss: 0.3163 - accuracy: 0.9071 - val_loss: 0.3888 - val_accuracy: 0.8596\n",
      "Epoch 8/25\n",
      "14/14 [==============================] - 2s 161ms/step - loss: 0.3113 - accuracy: 0.9214 - val_loss: 0.3867 - val_accuracy: 0.8333\n",
      "Epoch 9/25\n",
      "14/14 [==============================] - 2s 162ms/step - loss: 0.2903 - accuracy: 0.9310 - val_loss: 0.3653 - val_accuracy: 0.8596\n",
      "Epoch 10/25\n",
      "14/14 [==============================] - 3s 196ms/step - loss: 0.2871 - accuracy: 0.9119 - val_loss: 0.3540 - val_accuracy: 0.8684\n",
      "Epoch 11/25\n",
      "14/14 [==============================] - 2s 162ms/step - loss: 0.2730 - accuracy: 0.9262 - val_loss: 0.3475 - val_accuracy: 0.8596\n",
      "Epoch 12/25\n",
      "14/14 [==============================] - 3s 198ms/step - loss: 0.2578 - accuracy: 0.9310 - val_loss: 0.3399 - val_accuracy: 0.8772\n",
      "Epoch 13/25\n",
      "14/14 [==============================] - 2s 163ms/step - loss: 0.2582 - accuracy: 0.9333 - val_loss: 0.3377 - val_accuracy: 0.8684\n",
      "Epoch 14/25\n",
      "14/14 [==============================] - 3s 196ms/step - loss: 0.2486 - accuracy: 0.9452 - val_loss: 0.3280 - val_accuracy: 0.8860\n",
      "Epoch 15/25\n",
      "14/14 [==============================] - 2s 163ms/step - loss: 0.2461 - accuracy: 0.9357 - val_loss: 0.3269 - val_accuracy: 0.8684\n",
      "Epoch 16/25\n",
      "14/14 [==============================] - 2s 163ms/step - loss: 0.2402 - accuracy: 0.9500 - val_loss: 0.3200 - val_accuracy: 0.8860\n",
      "Epoch 17/25\n",
      "14/14 [==============================] - 2s 163ms/step - loss: 0.2386 - accuracy: 0.9381 - val_loss: 0.3179 - val_accuracy: 0.8772\n",
      "Epoch 18/25\n",
      "14/14 [==============================] - 2s 163ms/step - loss: 0.2327 - accuracy: 0.9452 - val_loss: 0.3144 - val_accuracy: 0.8860\n",
      "Epoch 19/25\n",
      "14/14 [==============================] - 3s 204ms/step - loss: 0.2361 - accuracy: 0.9429 - val_loss: 0.3118 - val_accuracy: 0.8947\n",
      "Epoch 20/25\n",
      "14/14 [==============================] - 2s 171ms/step - loss: 0.2375 - accuracy: 0.9420 - val_loss: 0.3080 - val_accuracy: 0.8860\n",
      "Epoch 21/25\n",
      "14/14 [==============================] - 2s 164ms/step - loss: 0.2297 - accuracy: 0.9452 - val_loss: 0.3061 - val_accuracy: 0.8860\n",
      "Epoch 22/25\n",
      "14/14 [==============================] - 3s 198ms/step - loss: 0.2208 - accuracy: 0.9429 - val_loss: 0.3049 - val_accuracy: 0.9035\n",
      "Epoch 23/25\n",
      "14/14 [==============================] - 2s 164ms/step - loss: 0.2256 - accuracy: 0.9429 - val_loss: 0.3018 - val_accuracy: 0.8860\n",
      "Epoch 24/25\n",
      "14/14 [==============================] - 2s 164ms/step - loss: 0.2197 - accuracy: 0.9381 - val_loss: 0.3010 - val_accuracy: 0.9035\n",
      "Epoch 25/25\n",
      "14/14 [==============================] - 2s 164ms/step - loss: 0.2215 - accuracy: 0.9429 - val_loss: 0.2990 - val_accuracy: 0.9035\n",
      "****************************************\n",
      "Took 1.24 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=(X_test_processed, 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": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best train acc: 0.949999988079071 | 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(['accuracy','val_accuracy'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "114/114 [==============================] - 1s 5ms/sample - loss: 0.3049 - accuracy: 0.9035\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.30490694992374956, 0.9035088]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_weights('weights/')\n",
    "model.evaluate(X_test_processed, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fine tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "bs = bs//2\n",
    "steps = len(X_train_processed) // bs\n",
    "train_gen = datagen.flow(X_train_processed, 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": 34,
   "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 28 steps, validate on 114 samples\n",
      "Epoch 1/20\n",
      "28/28 [==============================] - 35s 1s/step - loss: 0.2214 - accuracy: 0.9541 - val_loss: 0.2835 - val_accuracy: 0.8947\n",
      "Epoch 2/20\n",
      "28/28 [==============================] - 9s 307ms/step - loss: 0.1949 - accuracy: 0.9587 - val_loss: 0.2656 - val_accuracy: 0.9035\n",
      "Epoch 3/20\n",
      "28/28 [==============================] - 10s 346ms/step - loss: 0.1797 - accuracy: 0.9587 - val_loss: 0.2558 - val_accuracy: 0.9123\n",
      "Epoch 4/20\n",
      "28/28 [==============================] - 10s 348ms/step - loss: 0.1692 - accuracy: 0.9656 - val_loss: 0.2468 - val_accuracy: 0.9211\n",
      "Epoch 5/20\n",
      "28/28 [==============================] - 9s 309ms/step - loss: 0.1634 - accuracy: 0.9587 - val_loss: 0.2423 - val_accuracy: 0.9211\n",
      "Epoch 6/20\n",
      "28/28 [==============================] - 9s 310ms/step - loss: 0.1555 - accuracy: 0.9656 - val_loss: 0.2370 - val_accuracy: 0.9211\n",
      "Epoch 7/20\n",
      "28/28 [==============================] - 10s 369ms/step - loss: 0.1497 - accuracy: 0.9633 - val_loss: 0.2347 - val_accuracy: 0.9298\n",
      "Epoch 8/20\n",
      "28/28 [==============================] - 9s 311ms/step - loss: 0.1472 - accuracy: 0.9633 - val_loss: 0.2314 - val_accuracy: 0.9211\n",
      "Epoch 9/20\n",
      "28/28 [==============================] - 9s 311ms/step - loss: 0.1459 - accuracy: 0.9679 - val_loss: 0.2297 - val_accuracy: 0.9298\n",
      "Epoch 10/20\n",
      "28/28 [==============================] - 9s 311ms/step - loss: 0.1415 - accuracy: 0.9633 - val_loss: 0.2282 - val_accuracy: 0.9211\n",
      "Epoch 11/20\n",
      "28/28 [==============================] - 9s 312ms/step - loss: 0.1411 - accuracy: 0.9633 - val_loss: 0.2271 - val_accuracy: 0.9211\n",
      "Epoch 12/20\n",
      "28/28 [==============================] - 9s 313ms/step - loss: 0.1358 - accuracy: 0.9702 - val_loss: 0.2263 - val_accuracy: 0.9211\n",
      "Epoch 13/20\n",
      "28/28 [==============================] - 9s 314ms/step - loss: 0.1369 - accuracy: 0.9702 - val_loss: 0.2258 - val_accuracy: 0.9211\n",
      "Epoch 14/20\n",
      "28/28 [==============================] - 9s 315ms/step - loss: 0.1341 - accuracy: 0.9725 - val_loss: 0.2252 - val_accuracy: 0.9298\n",
      "Epoch 15/20\n",
      "28/28 [==============================] - 9s 314ms/step - loss: 0.1342 - accuracy: 0.9702 - val_loss: 0.2248 - val_accuracy: 0.9298\n",
      "Epoch 16/20\n",
      "28/28 [==============================] - 9s 315ms/step - loss: 0.1358 - accuracy: 0.9679 - val_loss: 0.2245 - val_accuracy: 0.9298\n",
      "Epoch 17/20\n",
      "28/28 [==============================] - 9s 315ms/step - loss: 0.1332 - accuracy: 0.9702 - val_loss: 0.2243 - val_accuracy: 0.9298\n",
      "Epoch 18/20\n",
      "28/28 [==============================] - 9s 315ms/step - loss: 0.1358 - accuracy: 0.9702 - val_loss: 0.2242 - val_accuracy: 0.9298\n",
      "Epoch 19/20\n",
      "28/28 [==============================] - 9s 316ms/step - loss: 0.1377 - accuracy: 0.9679 - val_loss: 0.2241 - val_accuracy: 0.9298\n",
      "Epoch 20/20\n",
      "28/28 [==============================] - 9s 316ms/step - loss: 0.1346 - accuracy: 0.9679 - val_loss: 0.2240 - val_accuracy: 0.9298\n",
      "****************************************\n",
      "Took 3.41 minutes\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "\n",
    "nb_epochs = 20 \n",
    "\n",
    "# Fit the model\n",
    "history= model.fit(train_gen, epochs= nb_epochs, steps_per_epoch= steps,\n",
    "                              validation_data=(X_test_processed, 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": 35,
   "metadata": {
    "id": "BizXOeDjuLJ7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best train acc: 0.9724770784378052 | 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": [
      "114/114 [==============================] - 1s 5ms/sample - loss: 0.2347 - accuracy: 0.9298\n",
      "452/452 [==============================] - 2s 4ms/sample - loss: 0.1474 - accuracy: 0.9646\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.14742638342148434, 0.96460176]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_weights('weights/')\n",
    "model.evaluate(X_test_processed, y_test)\n",
    "model.evaluate(X_train_processed, y_train)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "MRI_brain_v2_CONV3D_colab.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}