03_i__MRI_dense-450.ipynb

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   "source": [
    "# MRI STUFF\n",
    "# Test increase in layers \n",
    "# Increase in layer architectures\n",
    "# Increase in drop out \n",
    "# With class weights, without class weights "
   ]
  },
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras.backend as K\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras import datasets, layers, models\n",
    "from sklearn.metrics import (classification_report, confusion_matrix, balanced_accuracy_score, \n",
    "accuracy_score, roc_auc_score, auc)\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "import plotly.graph_objects as go\n",
    "import glob\n"
   ]
  },
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   "source": [
    "#drive.mount('/content/drive')\n",
    "\n",
    "#ROOT_DIR = \"/content/drive/MyDrive/Spring 2022/DTSC 870/Code\" # HC Directory\n",
    "#ROOT_DIR = \"/content/drive/MyDrive/DTSC 870/Code\" #MT Directory\n",
    "KAGGLE_START = \"/kaggle/input\"\n",
    "ROOT_DIR = KAGGLE_START\n",
    "DATASET_01_TEST = KAGGLE_START + \"/mri-test-set/test\"\n",
    "DATASET_01_TRAIN = KAGGLE_START + \"/mri-trainset/train\"\n",
    "KAGGLE_OUT = \"/kaggle/working\"\n",
    "\n"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 121 images belonging to 2 classes.\n",
      "Found 279 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "datagen = ImageDataGenerator()\n",
    "image_size = (256, 256)\n",
    "test_set = datagen.flow_from_directory(DATASET_01_TEST, target_size=image_size, batch_size=32, class_mode='binary', shuffle=False)\n",
    "train_set = datagen.flow_from_directory(DATASET_01_TRAIN, target_size=image_size, batch_size=32, class_mode='binary')\n"
   ]
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   "source": [
    "def create_model_V1():\n",
    "  model = models.Sequential()\n",
    "  model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(256, 256, 3)))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  #model.add(layers.Dropout(0.4))\n",
    "  model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  #model.add(layers.Dropout(0.4))\n",
    "  model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "  #model.add(layers.MaxPooling2D((2, 2)))\n",
    "  #model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "  model.add(layers.Flatten())\n",
    "  model.add(layers.Dense(128, activation='relu'))\n",
    "  model.add(layers.Dense(1, activation='sigmoid'))\n",
    "  return model\n"
   ]
  },
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    "tags": []
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   "outputs": [],
   "source": [
    "def create_model_V1_02():\n",
    "  model = models.Sequential()\n",
    "  model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(256, 256, 3)))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  model.add(layers.Dropout(0.2))\n",
    "  model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  model.add(layers.Dropout(0.2))\n",
    "  model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "  #model.add(layers.MaxPooling2D((2, 2)))\n",
    "  #model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "  model.add(layers.Flatten())\n",
    "  model.add(layers.Dense(128, activation='relu'))\n",
    "  model.add(layers.Dense(1, activation='sigmoid'))\n",
    "  return model"
   ]
  },
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   "source": [
    "def create_model_V1_04():\n",
    "  model = models.Sequential()\n",
    "  model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(256, 256, 3)))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  model.add(layers.Dropout(0.4))\n",
    "  model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  model.add(layers.Dropout(0.4))\n",
    "  model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "  #model.add(layers.MaxPooling2D((2, 2)))\n",
    "  #model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "  model.add(layers.Flatten())\n",
    "  model.add(layers.Dense(128, activation='relu'))\n",
    "  model.add(layers.Dense(1, activation='sigmoid'))\n",
    "  return model"
   ]
  },
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   "source": [
    "def create_model_V2():\n",
    "  model = models.Sequential()\n",
    "  model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(256, 256, 3)))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  #model.add(layers.Dropout(0.4))\n",
    "  model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "  #model.add(layers.MaxPooling2D((2, 2)))\n",
    "  #model.add(layers.Dropout(0.4))\n",
    "  #model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "  #model.add(layers.MaxPooling2D((2, 2)))\n",
    "  #model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "  model.add(layers.Flatten())\n",
    "  model.add(layers.Dense(64, activation='relu'))\n",
    "  model.add(layers.Dense(1, activation='sigmoid'))\n",
    "  return model"
   ]
  },
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   "outputs": [],
   "source": [
    "def create_model_V2_02():\n",
    "  model = models.Sequential()\n",
    "  model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(256, 256, 3)))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  model.add(layers.Dropout(0.2))\n",
    "  model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "  #model.add(layers.MaxPooling2D((2, 2)))\n",
    "  #model.add(layers.Dropout(0.4))\n",
    "  #model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "  #model.add(layers.MaxPooling2D((2, 2)))\n",
    "  #model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "  model.add(layers.Flatten())\n",
    "  model.add(layers.Dense(64, activation='relu'))\n",
    "  model.add(layers.Dense(1, activation='sigmoid'))\n",
    "  return model"
   ]
  },
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   "source": [
    "def create_model_V2_04():\n",
    "  model = models.Sequential()\n",
    "  model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(256, 256, 3)))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  model.add(layers.Dropout(0.4))\n",
    "  model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "  #model.add(layers.MaxPooling2D((2, 2)))\n",
    "  #model.add(layers.Dropout(0.4))\n",
    "  #model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "  #model.add(layers.MaxPooling2D((2, 2)))\n",
    "  #model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "  model.add(layers.Flatten())\n",
    "  model.add(layers.Dense(64, activation='relu'))\n",
    "  model.add(layers.Dense(1, activation='sigmoid'))\n",
    "  return model"
   ]
  },
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   "source": [
    "def create_model_V3():\n",
    "  model = models.Sequential()\n",
    "  model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(256, 256, 3)))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  #model.add(layers.Dropout(0.4))\n",
    "  model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  #model.add(layers.Dropout(0.4))\n",
    "  model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  #model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "  model.add(layers.Conv2D(256, (3, 3), activation='relu'))\n",
    "  model.add(layers.Flatten())\n",
    "  model.add(layers.Dense(256, activation='relu'))\n",
    "  model.add(layers.Dense(1, activation='sigmoid'))\n",
    "  return model"
   ]
  },
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    "tags": []
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   "outputs": [],
   "source": [
    "def create_model_V3_02():\n",
    "  model = models.Sequential()\n",
    "  model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(256, 256, 3)))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  model.add(layers.Dropout(0.2))\n",
    "  model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  model.add(layers.Dropout(0.2))\n",
    "  model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  model.add(layers.Dropout(0.2))\n",
    "  #model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "  model.add(layers.Conv2D(256, (3, 3), activation='relu'))\n",
    "  model.add(layers.Flatten())\n",
    "  model.add(layers.Dense(256, activation='relu'))\n",
    "  model.add(layers.Dense(1, activation='sigmoid'))\n",
    "  return model"
   ]
  },
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   "source": [
    "def create_model_V3_04():\n",
    "  model = models.Sequential()\n",
    "  model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(256, 256, 3)))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  model.add(layers.Dropout(0.4))\n",
    "  model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  model.add(layers.Dropout(0.4))\n",
    "  model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  model.add(layers.Dropout(0.4))\n",
    "  #model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "  model.add(layers.Conv2D(256, (3, 3), activation='relu'))\n",
    "  model.add(layers.Flatten())\n",
    "  model.add(layers.Dense(256, activation='relu'))\n",
    "  model.add(layers.Dense(1, activation='sigmoid'))\n",
    "  return model"
   ]
  },
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     "status": "completed"
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   "outputs": [],
   "source": [
    "def create_model_V4():\n",
    "  model = models.Sequential()\n",
    "  model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(256, 256, 3)))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  #model.add(layers.Dropout(0.4))\n",
    "  model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  #model.add(layers.Dropout(0.4))\n",
    "  model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  #model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "  model.add(layers.Conv2D(256, (3, 3), activation='relu'))\n",
    "  model.add(layers.Flatten())\n",
    "  model.add(layers.Dense(256, activation='relu'))\n",
    "  model.add(layers.Dense(1, activation='sigmoid'))\n",
    "  return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "54837669",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "def create_model_V4_02():\n",
    "  model = models.Sequential()\n",
    "  model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(256, 256, 3)))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  model.add(layers.Dropout(0.2))\n",
    "  model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  model.add(layers.Dropout(0.2))\n",
    "  model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  model.add(layers.Dropout(0.2))\n",
    "  model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  #model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "  model.add(layers.Conv2D(256, (3, 3), activation='relu'))\n",
    "  model.add(layers.Flatten())\n",
    "  model.add(layers.Dense(256, activation='relu'))\n",
    "  model.add(layers.Dense(1, activation='sigmoid'))\n",
    "  return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f8db19e2",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def create_model_V4_04():\n",
    "  model = models.Sequential()\n",
    "  model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(256, 256, 3)))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  model.add(layers.Dropout(0.4))\n",
    "  model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  model.add(layers.Dropout(0.4))\n",
    "  model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  model.add(layers.Dropout(0.4))\n",
    "  model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "  model.add(layers.MaxPooling2D((2, 2)))\n",
    "  #model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "  model.add(layers.Conv2D(256, (3, 3), activation='relu'))\n",
    "  model.add(layers.Flatten())\n",
    "  model.add(layers.Dense(256, activation='relu'))\n",
    "  model.add(layers.Dense(1, activation='sigmoid'))\n",
    "  return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "decf6fcd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-10T03:53:45.536046Z",
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   "outputs": [],
   "source": [
    "#modify this \n",
    "norm_weight = 400/(2*(118+52))\n",
    "tumor_weight = 400/(2*(161+69))\n",
    "\n",
    "class_weights = {0:norm_weight, 1:tumor_weight}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cebe0aa5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-10T03:53:45.595318Z",
     "iopub.status.busy": "2022-05-10T03:53:45.594970Z",
     "iopub.status.idle": "2022-05-10T03:53:45.600345Z",
     "shell.execute_reply": "2022-05-10T03:53:45.599717Z"
    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "\n",
    "#borrowed from svm code\n",
    "def get_data_df(dir):\n",
    "  # modified code from: https://www.kaggle.com/namgalielei/simple-load-images-and-count-number-of-each-class\n",
    "\n",
    "  train_df = pd.DataFrame()\n",
    "\n",
    "  trainset = glob.glob(dir)\n",
    "  #print(trainset)\n",
    "  train_df['filename'] = [img.split(\"/\")[-4] + \"/\" + img.split(\"/\")[-3] + \"/\" + img.split(\"/\")[-2]+\"/\"+img.split(\"/\")[-1] for img in trainset]\n",
    "  train_df['label'] = [img.split(\"/\")[-2] for img in trainset]\n",
    "\n",
    "  return train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d14d789e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-10T03:53:45.656793Z",
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     "shell.execute_reply": "2022-05-10T03:53:45.680702Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "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>filename</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mri-trainset/train/Tumor/Tumor (77).jpg</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mri-trainset/train/Tumor/Tumor (116).jpg</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mri-trainset/train/Tumor/Tumor (86).jpg</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mri-trainset/train/Tumor/Tumor (49).jpg</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mri-trainset/train/Tumor/Tumor (40).jpg</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   filename  label\n",
       "0   mri-trainset/train/Tumor/Tumor (77).jpg  Tumor\n",
       "1  mri-trainset/train/Tumor/Tumor (116).jpg  Tumor\n",
       "2   mri-trainset/train/Tumor/Tumor (86).jpg  Tumor\n",
       "3   mri-trainset/train/Tumor/Tumor (49).jpg  Tumor\n",
       "4   mri-trainset/train/Tumor/Tumor (40).jpg  Tumor"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_train = get_data_df(DATASET_01_TRAIN+\"/*/*.jpg\")\n",
    "test_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "eebfc2fc",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "\n",
    "train_data = get_data_df(DATASET_01_TRAIN+\"/*/*.jpg\")\n",
    "Y = train_data[['label']]\n",
    "\n",
    "#Y = final_train[['label']]\n",
    "#n = len(final_train)   \n",
    "\n",
    "skf = StratifiedKFold(n_splits = 5, random_state = 7, shuffle = True) \n",
    "\n",
    "#Un needed preprocessing aside from rescale\n",
    "idg = ImageDataGenerator()\n",
    "\n",
    "#model name\n",
    "def get_model_name(k):\n",
    "    return 'model_'+str(k)+'.h5'\n",
    "def model_test(fold_count, epoch, model_method, image_size):\n",
    "  skf = StratifiedKFold(n_splits = fold_count, random_state = 7, shuffle = True)\n",
    "  train_data = get_data_df(DATASET_01_TRAIN+\"/*/*.jpg\")\n",
    "  Y = train_data[['label']]\n",
    "  n = len (train_data)\n",
    "  #Incorperate preprocessing in the method \n",
    "  #####################################################################\n",
    "  #aug_train = get_data_df(DATASET_01_ROOT+\"/Aug_train/*/*.jpg\")\n",
    "  #aug_train['filename'] = aug_train['filename'].str[7:]\n",
    "  #final_train = test_train.append(aug_train, sort=False, ignore_index=True)\n",
    "\n",
    "\n",
    "  #######################################################################\n",
    "  idg = ImageDataGenerator()\n",
    "  #idg = ImageDataGenerator()\n",
    "  #Y = final_train[['label']]\n",
    "  #n = len(final_train)    \n",
    "  VALIDATION_ACCURACY = []\n",
    "  VALIDATION_LOSS = []\n",
    "\n",
    "  #create a directory\n",
    "  save_dir = KAGGLE_OUT+'/datasets/01_MRI/Saved_CNN_Models/Testing/'+model_method.__name__+'/AugTest'+'/'+str(epoch)+'epochs/'\n",
    "  img_dir = ROOT_DIR\n",
    "\n",
    "  fold_var = 1\n",
    "\n",
    "  #n is number of samples, doesn't care about x values for split, just y\n",
    "  for train_index, val_index in skf.split(np.zeros(n),Y):\n",
    "\n",
    "    #Index value for fold\n",
    "    training_data = train_data.iloc[train_index]\n",
    "    validation_data = train_data.iloc[val_index]\n",
    "    #generator for train\n",
    "    #print(\"hi\")\n",
    "    #Modify the training size, have an input adjustability \n",
    "    train_data_generator = idg.flow_from_dataframe(training_data, directory = img_dir,\n",
    "                      target_size=image_size, batch_size=32,\n",
    "                    x_col = \"filename\", y_col = \"label\",\n",
    "                    class_mode = \"binary\", shuffle = True)\n",
    "    #print(\"hi\")\n",
    "    #generator for validation\n",
    "    valid_data_generator  = idg.flow_from_dataframe(validation_data, directory = img_dir,\n",
    "                target_size=image_size, batch_size=32,\n",
    "                x_col = \"filename\", y_col = \"label\",\n",
    "                class_mode = \"binary\", shuffle = True)\n",
    "    #print(\"hi\")\n",
    "    #create a method for this\n",
    "    # CREATE NEW MODEL\n",
    "    model = model_method()\n",
    "    # COMPILE NEW MODEL\n",
    "    #Binary cross entropy, opt will be adam, metric will be binary accuracy?\n",
    "    model.compile(loss=tf.keras.losses.BinaryCrossentropy(),\n",
    "            optimizer= tf.keras.optimizers.Adam(learning_rate = 0.0001),\n",
    "            metrics=['binary_accuracy'])\n",
    "    \n",
    "    # CREATE CALLBACKS\n",
    "    #Unsure about checpoint\n",
    "    checkpoint = tf.keras.callbacks.ModelCheckpoint(save_dir+model_method.__name__+\"_\"+get_model_name(fold_var), \n",
    "                monitor='val_binary_accuracy', verbose=1, \n",
    "                save_best_only=True, mode='max')\n",
    "  \n",
    "    callbacks_list = [checkpoint]\n",
    "    # There can be other callbacks, but just showing one because it involves the model name\n",
    "    # This saves the best model\n",
    "    # FIT THE MODEL\n",
    "    #Include the weights for classes\n",
    "    history = model.fit(train_data_generator,\n",
    "            epochs=epoch,\n",
    "            callbacks=callbacks_list,\n",
    "            validation_data=valid_data_generator,\n",
    "                       class_weight = class_weights)\n",
    "  \n",
    "    #PLOT HISTORY\n",
    "    #Plots for whichever\n",
    "    #\t\t:\n",
    "    #plt.plot(history.history['binary_accuracy'], label='accuracy')\n",
    "    #plt.plot(history.history['val_binary_accuracy'], label = 'val_accuracy')\n",
    "    #plt.xlabel('Epoch')\n",
    "    #plt.ylabel('Accuracy')\n",
    "    #plt.ylim([0.3, 1])\n",
    "    #plt.legend(loc='lower right')\n",
    "    #plt.show()\n",
    "    #frame = {'Accuracy': history.history['binary_accuracy'], 'Val_Accuracy': history.history['val_binary_accuracy']}\n",
    "    #fig = px.line(frame)\n",
    "    #fig.show()\n",
    "    fig = go.Figure()\n",
    "    fig.add_trace(go.Scatter(y= history.history['binary_accuracy'],\n",
    "                      mode='lines+markers',\n",
    "                      name='Training Accuracy'))\n",
    "    fig.add_trace(go.Scatter(y= history.history['val_binary_accuracy'],\n",
    "                      mode='lines+markers',\n",
    "                      name='Validation Accuracy'))\n",
    "    fig.update_yaxes(range=[0.40, 1])\n",
    "    fig.update_layout(\n",
    "      title=\"Fold \"+ str(fold_var)+ \"'s performance\",\n",
    "      title_x=0.5,\n",
    "      xaxis_title=\"Epochs\",\n",
    "      yaxis_title=\"Accuracy\",\n",
    "      autosize=False,\n",
    "      width=800,\n",
    "      height=400,\n",
    "      margin=dict(l=20, r=20, t=40, b=20),)\n",
    "    fig.show()\n",
    "    #\t\t:\n",
    "    # LOAD BEST MODEL to evaluate the performance of the model\n",
    "    model.load_weights(save_dir+model_method.__name__+\"_\" + \"model_\"+str(fold_var)+\".h5\")\n",
    "    \n",
    "    results = model.evaluate(test_set)\n",
    "    \n",
    "    results = dict(zip(model.metrics_names,results))\n",
    "    \n",
    "    VALIDATION_ACCURACY.append(results['binary_accuracy'])\n",
    "    VALIDATION_LOSS.append(results['loss'])\n",
    "    \n",
    "    tf.keras.backend.clear_session()\n",
    "    \n",
    "    fold_var += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5faf1f8b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-10T03:53:45.823057Z",
     "iopub.status.busy": "2022-05-10T03:53:45.822385Z",
     "iopub.status.idle": "2022-05-10T03:53:45.825901Z",
     "shell.execute_reply": "2022-05-10T03:53:45.825247Z"
    },
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     "end_time": "2022-05-10T03:53:45.827554",
     "exception": false,
     "start_time": "2022-05-10T03:53:45.794120",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#model_test(5, 30, create_model_V1, (256,256))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b2526733",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-10T03:53:45.883040Z",
     "iopub.status.busy": "2022-05-10T03:53:45.882487Z",
     "iopub.status.idle": "2022-05-10T03:53:45.885474Z",
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    },
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     "duration": 0.03223,
     "end_time": "2022-05-10T03:53:45.887081",
     "exception": false,
     "start_time": "2022-05-10T03:53:45.854851",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#model_test(5, 30, create_model_V1_02, (256,256))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a23e7b02",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-10T03:53:45.942502Z",
     "iopub.status.busy": "2022-05-10T03:53:45.941946Z",
     "iopub.status.idle": "2022-05-10T03:53:45.944941Z",
     "shell.execute_reply": "2022-05-10T03:53:45.944338Z"
    },
    "papermill": {
     "duration": 0.032572,
     "end_time": "2022-05-10T03:53:45.946637",
     "exception": false,
     "start_time": "2022-05-10T03:53:45.914065",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#model_test(5, 30, create_model_V1_04, (256,256))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "909983b9",
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     "end_time": "2022-05-10T03:53:46.011821",
     "exception": false,
     "start_time": "2022-05-10T03:53:45.975752",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#model_test(5, 30, create_model_V2, (256,256))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "26c74222",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-10T03:53:46.073611Z",
     "iopub.status.busy": "2022-05-10T03:53:46.072927Z",
     "iopub.status.idle": "2022-05-10T03:53:46.076580Z",
     "shell.execute_reply": "2022-05-10T03:53:46.075913Z"
    },
    "papermill": {
     "duration": 0.035819,
     "end_time": "2022-05-10T03:53:46.078292",
     "exception": false,
     "start_time": "2022-05-10T03:53:46.042473",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#model_test(5, 30, create_model_V2_02, (256,256))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3ff1af25",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-10T03:53:46.140874Z",
     "iopub.status.busy": "2022-05-10T03:53:46.140243Z",
     "iopub.status.idle": "2022-05-10T03:53:46.143686Z",
     "shell.execute_reply": "2022-05-10T03:53:46.142966Z"
    },
    "papermill": {
     "duration": 0.036622,
     "end_time": "2022-05-10T03:53:46.145449",
     "exception": false,
     "start_time": "2022-05-10T03:53:46.108827",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#model_test(5, 30, create_model_V2_04, (256,256))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6ffe6225",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-10T03:53:46.206696Z",
     "iopub.status.busy": "2022-05-10T03:53:46.206158Z",
     "iopub.status.idle": "2022-05-10T03:53:46.209315Z",
     "shell.execute_reply": "2022-05-10T03:53:46.208587Z"
    },
    "papermill": {
     "duration": 0.035457,
     "end_time": "2022-05-10T03:53:46.211005",
     "exception": false,
     "start_time": "2022-05-10T03:53:46.175548",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#model_test(5, 30, create_model_V3, (256,256))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dc270464",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-10T03:53:46.273160Z",
     "iopub.status.busy": "2022-05-10T03:53:46.272612Z",
     "iopub.status.idle": "2022-05-10T03:53:46.275702Z",
     "shell.execute_reply": "2022-05-10T03:53:46.275013Z"
    },
    "papermill": {
     "duration": 0.036313,
     "end_time": "2022-05-10T03:53:46.277442",
     "exception": false,
     "start_time": "2022-05-10T03:53:46.241129",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#model_test(5, 30, create_model_V3_02, (256,256))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2d6aa6aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-10T03:53:46.336125Z",
     "iopub.status.busy": "2022-05-10T03:53:46.335605Z",
     "iopub.status.idle": "2022-05-10T03:53:46.338541Z",
     "shell.execute_reply": "2022-05-10T03:53:46.337883Z"
    },
    "papermill": {
     "duration": 0.034129,
     "end_time": "2022-05-10T03:53:46.340138",
     "exception": false,
     "start_time": "2022-05-10T03:53:46.306009",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#model_test(5, 30, create_model_V3_04, (256,256))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "be90baf6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-10T03:53:46.399178Z",
     "iopub.status.busy": "2022-05-10T03:53:46.398652Z",
     "iopub.status.idle": "2022-05-10T03:53:46.401826Z",
     "shell.execute_reply": "2022-05-10T03:53:46.401031Z"
    },
    "papermill": {
     "duration": 0.033931,
     "end_time": "2022-05-10T03:53:46.403389",
     "exception": false,
     "start_time": "2022-05-10T03:53:46.369458",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#model_test(5, 30, create_model_V4, (256,256))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "96d72cdb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-10T03:53:46.460582Z",
     "iopub.status.busy": "2022-05-10T03:53:46.460136Z",
     "iopub.status.idle": "2022-05-10T03:53:46.463223Z",
     "shell.execute_reply": "2022-05-10T03:53:46.462598Z"
    },
    "papermill": {
     "duration": 0.033176,
     "end_time": "2022-05-10T03:53:46.464807",
     "exception": false,
     "start_time": "2022-05-10T03:53:46.431631",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#model_test(5, 30, create_model_V4_02, (256,256))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fada7ccb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-10T03:53:46.522522Z",
     "iopub.status.busy": "2022-05-10T03:53:46.522050Z",
     "iopub.status.idle": "2022-05-10T03:53:46.525015Z",
     "shell.execute_reply": "2022-05-10T03:53:46.524385Z"
    },
    "papermill": {
     "duration": 0.033647,
     "end_time": "2022-05-10T03:53:46.526656",
     "exception": false,
     "start_time": "2022-05-10T03:53:46.493009",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#model_test(5, 30, create_model_V4_04, (256,256))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b4ae173f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-10T03:53:46.584597Z",
     "iopub.status.busy": "2022-05-10T03:53:46.584058Z",
     "iopub.status.idle": "2022-05-10T03:53:46.590682Z",
     "shell.execute_reply": "2022-05-10T03:53:46.590058Z"
    },
    "papermill": {
     "duration": 0.037499,
     "end_time": "2022-05-10T03:53:46.592269",
     "exception": false,
     "start_time": "2022-05-10T03:53:46.554770",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def transfer_learning_model_dense169_2():\n",
    "\n",
    "  IMG_SHAPE = (256,256,3,)\n",
    " \n",
    "  inputs = tf.keras.Input(shape=IMG_SHAPE)\n",
    "  x = tf.keras.applications.densenet.preprocess_input(inputs)\n",
    "  base_model = tf.keras.applications.densenet.DenseNet169(input_shape=IMG_SHAPE,\n",
    "                                               include_top=False,\n",
    "                                               weights='imagenet')\n",
    "  #print(len(base_model.layers))\n",
    "  base_model.trainable = True\n",
    "\n",
    "  fine_tune_at = 450\n",
    "  for layer in base_model.layers[:fine_tune_at]:\n",
    "    layer.trainable = False\n",
    "\n",
    "  x = base_model(x)\n",
    "  global_average_layer = tf.keras.layers.GlobalAveragePooling2D()\n",
    "  x = global_average_layer(x)\n",
    "  x = tf.keras.layers.Dropout(0.2)(x)\n",
    "  prediction_layer = tf.keras.layers.Dense(1)\n",
    "  outputs = prediction_layer(x)\n",
    "  model = tf.keras.Model(inputs, outputs)\n",
    "  #print(len(model.trainable_variables))\n",
    "  return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "509040e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-10T03:53:46.663311Z",
     "iopub.status.busy": "2022-05-10T03:53:46.663118Z",
     "iopub.status.idle": "2022-05-10T03:59:45.396591Z",
     "shell.execute_reply": "2022-05-10T03:59:45.395735Z"
    },
    "papermill": {
     "duration": 358.778587,
     "end_time": "2022-05-10T03:59:45.399153",
     "exception": false,
     "start_time": "2022-05-10T03:53:46.620566",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 223 validated image filenames belonging to 2 classes.\n",
      "Found 56 validated image filenames belonging to 2 classes.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-10 03:53:46.760068: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-10 03:53:46.852514: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-10 03:53:46.853327: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-10 03:53:46.854487: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-05-10 03:53:46.854751: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-10 03:53:46.855455: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-10 03:53:46.856103: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-10 03:53:48.519266: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-10 03:53:48.520141: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-10 03:53:48.520802: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-10 03:53:48.522079: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15403 MB memory:  -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/densenet/densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "51879936/51877672 [==============================] - 0s 0us/step\n",
      "51888128/51877672 [==============================] - 0s 0us/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-10 03:53:54.368384: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-10 03:54:04.066921: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8005\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 21s 868ms/step - loss: 2.2011 - binary_accuracy: 0.7265 - val_loss: 0.9267 - val_binary_accuracy: 0.7500\n",
      "\n",
      "Epoch 00001: val_binary_accuracy improved from -inf to 0.75000, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_1.h5\n",
      "Epoch 2/30\n",
      "7/7 [==============================] - 1s 176ms/step - loss: 1.2921 - binary_accuracy: 0.8655 - val_loss: 0.4224 - val_binary_accuracy: 0.9286\n",
      "\n",
      "Epoch 00002: val_binary_accuracy improved from 0.75000 to 0.92857, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_1.h5\n",
      "Epoch 3/30\n",
      "7/7 [==============================] - 1s 158ms/step - loss: 0.7357 - binary_accuracy: 0.9058 - val_loss: 0.5765 - val_binary_accuracy: 0.9643\n",
      "\n",
      "Epoch 00003: val_binary_accuracy improved from 0.92857 to 0.96429, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_1.h5\n",
      "Epoch 4/30\n",
      "7/7 [==============================] - 1s 159ms/step - loss: 0.6259 - binary_accuracy: 0.9372 - val_loss: 0.3694 - val_binary_accuracy: 0.9107\n",
      "\n",
      "Epoch 00004: val_binary_accuracy did not improve from 0.96429\n",
      "Epoch 5/30\n",
      "7/7 [==============================] - 1s 154ms/step - loss: 0.2447 - binary_accuracy: 0.9641 - val_loss: 0.3886 - val_binary_accuracy: 0.8929\n",
      "\n",
      "Epoch 00005: val_binary_accuracy did not improve from 0.96429\n",
      "Epoch 6/30\n",
      "7/7 [==============================] - 1s 159ms/step - loss: 0.2836 - binary_accuracy: 0.9776 - val_loss: 0.3737 - val_binary_accuracy: 0.9286\n",
      "\n",
      "Epoch 00006: val_binary_accuracy did not improve from 0.96429\n",
      "Epoch 7/30\n",
      "7/7 [==============================] - 1s 195ms/step - loss: 0.1718 - binary_accuracy: 0.9731 - val_loss: 0.3485 - val_binary_accuracy: 0.9464\n",
      "\n",
      "Epoch 00007: val_binary_accuracy did not improve from 0.96429\n",
      "Epoch 8/30\n",
      "7/7 [==============================] - 1s 163ms/step - loss: 0.0925 - binary_accuracy: 0.9865 - val_loss: 0.3376 - val_binary_accuracy: 0.9464\n",
      "\n",
      "Epoch 00008: val_binary_accuracy did not improve from 0.96429\n",
      "Epoch 9/30\n",
      "7/7 [==============================] - 1s 162ms/step - loss: 0.1607 - binary_accuracy: 0.9731 - val_loss: 0.3330 - val_binary_accuracy: 0.9643\n",
      "\n",
      "Epoch 00009: val_binary_accuracy did not improve from 0.96429\n",
      "Epoch 10/30\n",
      "7/7 [==============================] - 1s 172ms/step - loss: 0.1503 - binary_accuracy: 0.9865 - val_loss: 0.3215 - val_binary_accuracy: 0.9643\n",
      "\n",
      "Epoch 00010: val_binary_accuracy did not improve from 0.96429\n",
      "Epoch 11/30\n",
      "7/7 [==============================] - 1s 159ms/step - loss: 0.0956 - binary_accuracy: 0.9865 - val_loss: 0.3089 - val_binary_accuracy: 0.9643\n",
      "\n",
      "Epoch 00011: val_binary_accuracy did not improve from 0.96429\n",
      "Epoch 12/30\n",
      "7/7 [==============================] - 1s 162ms/step - loss: 0.0873 - binary_accuracy: 0.9910 - val_loss: 0.2984 - val_binary_accuracy: 0.9643\n",
      "\n",
      "Epoch 00012: val_binary_accuracy did not improve from 0.96429\n",
      "Epoch 13/30\n",
      "7/7 [==============================] - 1s 154ms/step - loss: 0.0871 - binary_accuracy: 0.9910 - val_loss: 0.2955 - val_binary_accuracy: 0.9821\n",
      "\n",
      "Epoch 00013: val_binary_accuracy improved from 0.96429 to 0.98214, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_1.h5\n",
      "Epoch 14/30\n",
      "7/7 [==============================] - 1s 163ms/step - loss: 0.0857 - binary_accuracy: 0.9910 - val_loss: 0.0575 - val_binary_accuracy: 0.9821\n",
      "\n",
      "Epoch 00014: val_binary_accuracy did not improve from 0.98214\n",
      "Epoch 15/30\n",
      "7/7 [==============================] - 1s 160ms/step - loss: 0.0804 - binary_accuracy: 0.9955 - val_loss: 0.0374 - val_binary_accuracy: 0.9821\n",
      "\n",
      "Epoch 00015: val_binary_accuracy did not improve from 0.98214\n",
      "Epoch 16/30\n",
      "7/7 [==============================] - 1s 165ms/step - loss: 0.0825 - binary_accuracy: 0.9955 - val_loss: 0.0315 - val_binary_accuracy: 1.0000\n",
      "\n",
      "Epoch 00016: val_binary_accuracy improved from 0.98214 to 1.00000, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_1.h5\n",
      "Epoch 17/30\n",
      "7/7 [==============================] - 1s 196ms/step - loss: 0.0804 - binary_accuracy: 0.9955 - val_loss: 0.0288 - val_binary_accuracy: 1.0000\n",
      "\n",
      "Epoch 00017: val_binary_accuracy did not improve from 1.00000\n",
      "Epoch 18/30\n",
      "7/7 [==============================] - 1s 170ms/step - loss: 0.0955 - binary_accuracy: 0.9865 - val_loss: 0.0227 - val_binary_accuracy: 1.0000\n",
      "\n",
      "Epoch 00018: val_binary_accuracy did not improve from 1.00000\n",
      "Epoch 19/30\n",
      "7/7 [==============================] - 1s 160ms/step - loss: 0.0813 - binary_accuracy: 0.9955 - val_loss: 0.0186 - val_binary_accuracy: 1.0000\n",
      "\n",
      "Epoch 00019: val_binary_accuracy did not improve from 1.00000\n",
      "Epoch 20/30\n",
      "7/7 [==============================] - 1s 159ms/step - loss: 0.0883 - binary_accuracy: 0.9910 - val_loss: 0.0194 - val_binary_accuracy: 1.0000\n",
      "\n",
      "Epoch 00020: val_binary_accuracy did not improve from 1.00000\n",
      "Epoch 21/30\n",
      "7/7 [==============================] - 1s 196ms/step - loss: 0.0808 - binary_accuracy: 0.9955 - val_loss: 0.0195 - val_binary_accuracy: 1.0000\n",
      "\n",
      "Epoch 00021: val_binary_accuracy did not improve from 1.00000\n",
      "Epoch 22/30\n",
      "7/7 [==============================] - 1s 158ms/step - loss: 0.0817 - binary_accuracy: 0.9955 - val_loss: 0.0202 - val_binary_accuracy: 1.0000\n",
      "\n",
      "Epoch 00022: val_binary_accuracy did not improve from 1.00000\n",
      "Epoch 23/30\n",
      "7/7 [==============================] - 1s 160ms/step - loss: 0.0809 - binary_accuracy: 0.9955 - val_loss: 0.0211 - val_binary_accuracy: 1.0000\n",
      "\n",
      "Epoch 00023: val_binary_accuracy did not improve from 1.00000\n",
      "Epoch 24/30\n",
      "7/7 [==============================] - 1s 153ms/step - loss: 0.0806 - binary_accuracy: 0.9955 - val_loss: 0.0210 - val_binary_accuracy: 1.0000\n",
      "\n",
      "Epoch 00024: val_binary_accuracy did not improve from 1.00000\n",
      "Epoch 25/30\n",
      "7/7 [==============================] - 1s 160ms/step - loss: 0.0804 - binary_accuracy: 0.9955 - val_loss: 0.0210 - val_binary_accuracy: 1.0000\n",
      "\n",
      "Epoch 00025: val_binary_accuracy did not improve from 1.00000\n",
      "Epoch 26/30\n",
      "7/7 [==============================] - 1s 150ms/step - loss: 0.0806 - binary_accuracy: 0.9955 - val_loss: 0.0201 - val_binary_accuracy: 1.0000\n",
      "\n",
      "Epoch 00026: val_binary_accuracy did not improve from 1.00000\n",
      "Epoch 27/30\n",
      "7/7 [==============================] - 1s 157ms/step - loss: 0.0853 - binary_accuracy: 0.9955 - val_loss: 0.0173 - val_binary_accuracy: 1.0000\n",
      "\n",
      "Epoch 00027: val_binary_accuracy did not improve from 1.00000\n",
      "Epoch 28/30\n",
      "7/7 [==============================] - 1s 156ms/step - loss: 0.1618 - binary_accuracy: 0.9910 - val_loss: 0.0152 - val_binary_accuracy: 1.0000\n",
      "\n",
      "Epoch 00028: val_binary_accuracy did not improve from 1.00000\n",
      "Epoch 29/30\n",
      "7/7 [==============================] - 1s 161ms/step - loss: 0.0808 - binary_accuracy: 0.9955 - val_loss: 0.0144 - val_binary_accuracy: 1.0000\n",
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      "Epoch 00029: val_binary_accuracy did not improve from 1.00000\n",
      "Epoch 30/30\n",
      "7/7 [==============================] - 1s 191ms/step - loss: 0.0804 - binary_accuracy: 0.9955 - val_loss: 0.0143 - val_binary_accuracy: 1.0000\n",
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      "Epoch 00030: val_binary_accuracy did not improve from 1.00000\n"
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      "4/4 [==============================] - 1s 392ms/step - loss: 0.5487 - binary_accuracy: 0.9421\n",
      "Found 223 validated image filenames belonging to 2 classes.\n",
      "Found 56 validated image filenames belonging to 2 classes.\n",
      "Epoch 1/30\n",
      "7/7 [==============================] - 13s 596ms/step - loss: 2.2390 - binary_accuracy: 0.7892 - val_loss: 1.4710 - val_binary_accuracy: 0.8571\n",
      "\n",
      "Epoch 00001: val_binary_accuracy improved from -inf to 0.85714, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_2.h5\n",
      "Epoch 2/30\n",
      "7/7 [==============================] - 1s 170ms/step - loss: 0.8979 - binary_accuracy: 0.9103 - val_loss: 1.5062 - val_binary_accuracy: 0.8214\n",
      "\n",
      "Epoch 00002: val_binary_accuracy did not improve from 0.85714\n",
      "Epoch 3/30\n",
      "7/7 [==============================] - 1s 160ms/step - loss: 0.8090 - binary_accuracy: 0.8879 - val_loss: 1.9849 - val_binary_accuracy: 0.8214\n",
      "\n",
      "Epoch 00003: val_binary_accuracy did not improve from 0.85714\n",
      "Epoch 4/30\n",
      "7/7 [==============================] - 1s 161ms/step - loss: 0.4157 - binary_accuracy: 0.9327 - val_loss: 2.0010 - val_binary_accuracy: 0.8214\n",
      "\n",
      "Epoch 00004: val_binary_accuracy did not improve from 0.85714\n",
      "Epoch 5/30\n",
      "7/7 [==============================] - 1s 161ms/step - loss: 0.5654 - binary_accuracy: 0.9417 - val_loss: 1.9952 - val_binary_accuracy: 0.8214\n",
      "\n",
      "Epoch 00005: val_binary_accuracy did not improve from 0.85714\n",
      "Epoch 6/30\n",
      "7/7 [==============================] - 1s 158ms/step - loss: 0.3619 - binary_accuracy: 0.9596 - val_loss: 1.4795 - val_binary_accuracy: 0.8750\n",
      "\n",
      "Epoch 00006: val_binary_accuracy improved from 0.85714 to 0.87500, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_2.h5\n",
      "Epoch 7/30\n",
      "7/7 [==============================] - 1s 156ms/step - loss: 0.2585 - binary_accuracy: 0.9507 - val_loss: 1.2119 - val_binary_accuracy: 0.8750\n",
      "\n",
      "Epoch 00007: val_binary_accuracy did not improve from 0.87500\n",
      "Epoch 8/30\n",
      "7/7 [==============================] - 1s 189ms/step - loss: 0.0747 - binary_accuracy: 0.9910 - val_loss: 0.6452 - val_binary_accuracy: 0.9107\n",
      "\n",
      "Epoch 00008: val_binary_accuracy improved from 0.87500 to 0.91071, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_2.h5\n",
      "Epoch 9/30\n",
      "7/7 [==============================] - 1s 159ms/step - loss: 0.0657 - binary_accuracy: 0.9955 - val_loss: 0.5807 - val_binary_accuracy: 0.9464\n",
      "\n",
      "Epoch 00009: val_binary_accuracy improved from 0.91071 to 0.94643, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_2.h5\n",
      "Epoch 10/30\n",
      "7/7 [==============================] - 1s 166ms/step - loss: 0.0617 - binary_accuracy: 0.9955 - val_loss: 0.3265 - val_binary_accuracy: 0.9464\n",
      "\n",
      "Epoch 00010: val_binary_accuracy did not improve from 0.94643\n",
      "Epoch 11/30\n",
      "7/7 [==============================] - 1s 163ms/step - loss: 0.0661 - binary_accuracy: 0.9955 - val_loss: 0.3160 - val_binary_accuracy: 0.9464\n",
      "\n",
      "Epoch 00011: val_binary_accuracy did not improve from 0.94643\n",
      "Epoch 12/30\n",
      "7/7 [==============================] - 1s 156ms/step - loss: 0.0967 - binary_accuracy: 0.9865 - val_loss: 0.3133 - val_binary_accuracy: 0.9464\n",
      "\n",
      "Epoch 00012: val_binary_accuracy did not improve from 0.94643\n",
      "Epoch 13/30\n",
      "7/7 [==============================] - 1s 185ms/step - loss: 0.0074 - binary_accuracy: 0.9955 - val_loss: 0.1128 - val_binary_accuracy: 0.9464\n",
      "\n",
      "Epoch 00013: val_binary_accuracy did not improve from 0.94643\n",
      "Epoch 14/30\n",
      "7/7 [==============================] - 1s 157ms/step - loss: 0.0036 - binary_accuracy: 0.9955 - val_loss: 0.0872 - val_binary_accuracy: 0.9643\n",
      "\n",
      "Epoch 00014: val_binary_accuracy improved from 0.94643 to 0.96429, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_2.h5\n",
      "Epoch 15/30\n",
      "7/7 [==============================] - 1s 157ms/step - loss: 0.0058 - binary_accuracy: 0.9955 - val_loss: 0.0789 - val_binary_accuracy: 0.9643\n",
      "\n",
      "Epoch 00015: val_binary_accuracy did not improve from 0.96429\n",
      "Epoch 16/30\n",
      "7/7 [==============================] - 2s 219ms/step - loss: 0.0784 - binary_accuracy: 0.9821 - val_loss: 0.0692 - val_binary_accuracy: 0.9643\n",
      "\n",
      "Epoch 00016: val_binary_accuracy did not improve from 0.96429\n",
      "Epoch 17/30\n",
      "7/7 [==============================] - 1s 172ms/step - loss: 9.2533e-04 - binary_accuracy: 1.0000 - val_loss: 0.0604 - val_binary_accuracy: 0.9643\n",
      "\n",
      "Epoch 00017: val_binary_accuracy did not improve from 0.96429\n",
      "Epoch 18/30\n",
      "7/7 [==============================] - 1s 158ms/step - loss: 0.0615 - binary_accuracy: 0.9955 - val_loss: 0.0559 - val_binary_accuracy: 0.9643\n",
      "\n",
      "Epoch 00018: val_binary_accuracy did not improve from 0.96429\n",
      "Epoch 19/30\n",
      "7/7 [==============================] - 1s 162ms/step - loss: 0.0066 - binary_accuracy: 0.9955 - val_loss: 0.0443 - val_binary_accuracy: 0.9821\n",
      "\n",
      "Epoch 00019: val_binary_accuracy improved from 0.96429 to 0.98214, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_2.h5\n",
      "Epoch 20/30\n",
      "7/7 [==============================] - 1s 158ms/step - loss: 0.0033 - binary_accuracy: 0.9955 - val_loss: 0.0390 - val_binary_accuracy: 0.9821\n",
      "\n",
      "Epoch 00020: val_binary_accuracy did not improve from 0.98214\n",
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      "7/7 [==============================] - 1s 160ms/step - loss: 0.0000e+00 - binary_accuracy: 1.0000 - val_loss: 0.0363 - val_binary_accuracy: 0.9821\n",
      "\n",
      "Epoch 00021: val_binary_accuracy did not improve from 0.98214\n",
      "Epoch 22/30\n",
      "7/7 [==============================] - 1s 158ms/step - loss: 0.0021 - binary_accuracy: 1.0000 - val_loss: 0.0337 - val_binary_accuracy: 0.9821\n",
      "\n",
      "Epoch 00022: val_binary_accuracy did not improve from 0.98214\n",
      "Epoch 23/30\n",
      "7/7 [==============================] - 1s 159ms/step - loss: 0.0000e+00 - binary_accuracy: 1.0000 - val_loss: 0.0314 - val_binary_accuracy: 0.9821\n",
      "\n",
      "Epoch 00023: val_binary_accuracy did not improve from 0.98214\n",
      "Epoch 24/30\n",
      "7/7 [==============================] - 1s 164ms/step - loss: 0.0000e+00 - binary_accuracy: 1.0000 - val_loss: 0.0303 - val_binary_accuracy: 0.9821\n",
      "\n",
      "Epoch 00024: val_binary_accuracy did not improve from 0.98214\n",
      "Epoch 25/30\n",
      "7/7 [==============================] - 1s 158ms/step - loss: 0.0000e+00 - binary_accuracy: 1.0000 - val_loss: 0.0300 - val_binary_accuracy: 0.9821\n",
      "\n",
      "Epoch 00025: val_binary_accuracy did not improve from 0.98214\n",
      "Epoch 26/30\n",
      "7/7 [==============================] - 1s 159ms/step - loss: 0.0020 - binary_accuracy: 1.0000 - val_loss: 0.0294 - val_binary_accuracy: 0.9821\n",
      "\n",
      "Epoch 00026: val_binary_accuracy did not improve from 0.98214\n",
      "Epoch 27/30\n",
      "7/7 [==============================] - 1s 158ms/step - loss: 0.0000e+00 - binary_accuracy: 1.0000 - val_loss: 0.0286 - val_binary_accuracy: 0.9821\n",
      "\n",
      "Epoch 00027: val_binary_accuracy did not improve from 0.98214\n",
      "Epoch 28/30\n",
      "7/7 [==============================] - 1s 192ms/step - loss: 0.0012 - binary_accuracy: 1.0000 - val_loss: 0.0278 - val_binary_accuracy: 0.9821\n",
      "\n",
      "Epoch 00028: val_binary_accuracy did not improve from 0.98214\n",
      "Epoch 29/30\n",
      "7/7 [==============================] - 1s 164ms/step - loss: 3.0727e-04 - binary_accuracy: 1.0000 - val_loss: 0.0281 - val_binary_accuracy: 0.9821\n",
      "\n",
      "Epoch 00029: val_binary_accuracy did not improve from 0.98214\n",
      "Epoch 30/30\n",
      "7/7 [==============================] - 1s 164ms/step - loss: 4.4721e-04 - binary_accuracy: 1.0000 - val_loss: 0.0283 - val_binary_accuracy: 0.9821\n",
      "\n",
      "Epoch 00030: val_binary_accuracy did not improve from 0.98214\n"
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      "4/4 [==============================] - 1s 109ms/step - loss: 0.2929 - binary_accuracy: 0.9504\n",
      "Found 223 validated image filenames belonging to 2 classes.\n",
      "Found 56 validated image filenames belonging to 2 classes.\n",
      "Epoch 1/30\n",
      "7/7 [==============================] - 14s 623ms/step - loss: 4.8704 - binary_accuracy: 0.5605 - val_loss: 8.8143 - val_binary_accuracy: 0.4286\n",
      "\n",
      "Epoch 00001: val_binary_accuracy improved from -inf to 0.42857, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_3.h5\n",
      "Epoch 2/30\n",
      "7/7 [==============================] - 1s 169ms/step - loss: 3.1429 - binary_accuracy: 0.7175 - val_loss: 8.8143 - val_binary_accuracy: 0.4286\n",
      "\n",
      "Epoch 00002: val_binary_accuracy did not improve from 0.42857\n",
      "Epoch 3/30\n",
      "7/7 [==============================] - 1s 160ms/step - loss: 2.7589 - binary_accuracy: 0.7713 - val_loss: 8.8143 - val_binary_accuracy: 0.4286\n",
      "\n",
      "Epoch 00003: val_binary_accuracy did not improve from 0.42857\n",
      "Epoch 4/30\n",
      "7/7 [==============================] - 1s 168ms/step - loss: 2.1023 - binary_accuracy: 0.7937 - val_loss: 8.8143 - val_binary_accuracy: 0.4286\n",
      "\n",
      "Epoch 00004: val_binary_accuracy did not improve from 0.42857\n",
      "Epoch 5/30\n",
      "7/7 [==============================] - 1s 159ms/step - loss: 1.9340 - binary_accuracy: 0.8117 - val_loss: 8.8143 - val_binary_accuracy: 0.4286\n",
      "\n",
      "Epoch 00005: val_binary_accuracy did not improve from 0.42857\n",
      "Epoch 6/30\n",
      "7/7 [==============================] - 1s 164ms/step - loss: 1.6483 - binary_accuracy: 0.8251 - val_loss: 8.8143 - val_binary_accuracy: 0.4286\n",
      "\n",
      "Epoch 00006: val_binary_accuracy did not improve from 0.42857\n",
      "Epoch 7/30\n",
      "7/7 [==============================] - 1s 171ms/step - loss: 1.6454 - binary_accuracy: 0.8386 - val_loss: 8.8143 - val_binary_accuracy: 0.4286\n",
      "\n",
      "Epoch 00007: val_binary_accuracy did not improve from 0.42857\n",
      "Epoch 8/30\n",
      "7/7 [==============================] - 1s 175ms/step - loss: 1.5276 - binary_accuracy: 0.8655 - val_loss: 8.8143 - val_binary_accuracy: 0.4286\n",
      "\n",
      "Epoch 00008: val_binary_accuracy did not improve from 0.42857\n",
      "Epoch 9/30\n",
      "7/7 [==============================] - 1s 171ms/step - loss: 1.2023 - binary_accuracy: 0.8969 - val_loss: 8.8143 - val_binary_accuracy: 0.4286\n",
      "\n",
      "Epoch 00009: val_binary_accuracy did not improve from 0.42857\n",
      "Epoch 10/30\n",
      "7/7 [==============================] - 1s 160ms/step - loss: 1.4311 - binary_accuracy: 0.8655 - val_loss: 8.3686 - val_binary_accuracy: 0.4286\n",
      "\n",
      "Epoch 00010: val_binary_accuracy did not improve from 0.42857\n",
      "Epoch 11/30\n",
      "7/7 [==============================] - 1s 161ms/step - loss: 1.1733 - binary_accuracy: 0.8655 - val_loss: 8.2902 - val_binary_accuracy: 0.4286\n",
      "\n",
      "Epoch 00011: val_binary_accuracy did not improve from 0.42857\n",
      "Epoch 12/30\n",
      "7/7 [==============================] - 1s 157ms/step - loss: 0.9438 - binary_accuracy: 0.8969 - val_loss: 8.0869 - val_binary_accuracy: 0.4643\n",
      "\n",
      "Epoch 00012: val_binary_accuracy improved from 0.42857 to 0.46429, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_3.h5\n",
      "Epoch 13/30\n",
      "7/7 [==============================] - 1s 155ms/step - loss: 1.0799 - binary_accuracy: 0.8969 - val_loss: 8.0054 - val_binary_accuracy: 0.4643\n",
      "\n",
      "Epoch 00013: val_binary_accuracy did not improve from 0.46429\n",
      "Epoch 14/30\n",
      "7/7 [==============================] - 1s 158ms/step - loss: 1.0543 - binary_accuracy: 0.8924 - val_loss: 7.9934 - val_binary_accuracy: 0.4821\n",
      "\n",
      "Epoch 00014: val_binary_accuracy improved from 0.46429 to 0.48214, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_3.h5\n",
      "Epoch 15/30\n",
      "7/7 [==============================] - 1s 163ms/step - loss: 0.8057 - binary_accuracy: 0.9103 - val_loss: 7.9879 - val_binary_accuracy: 0.4821\n",
      "\n",
      "Epoch 00015: val_binary_accuracy did not improve from 0.48214\n",
      "Epoch 16/30\n",
      "7/7 [==============================] - 1s 158ms/step - loss: 0.7086 - binary_accuracy: 0.9193 - val_loss: 7.9879 - val_binary_accuracy: 0.4821\n",
      "\n",
      "Epoch 00016: val_binary_accuracy did not improve from 0.48214\n",
      "Epoch 17/30\n",
      "7/7 [==============================] - 1s 165ms/step - loss: 0.6520 - binary_accuracy: 0.9283 - val_loss: 7.2426 - val_binary_accuracy: 0.4821\n",
      "\n",
      "Epoch 00017: val_binary_accuracy did not improve from 0.48214\n",
      "Epoch 18/30\n",
      "7/7 [==============================] - 1s 158ms/step - loss: 0.6918 - binary_accuracy: 0.9462 - val_loss: 6.5393 - val_binary_accuracy: 0.5000\n",
      "\n",
      "Epoch 00018: val_binary_accuracy improved from 0.48214 to 0.50000, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_3.h5\n",
      "Epoch 19/30\n",
      "7/7 [==============================] - 1s 182ms/step - loss: 0.6998 - binary_accuracy: 0.9193 - val_loss: 5.5111 - val_binary_accuracy: 0.5357\n",
      "\n",
      "Epoch 00019: val_binary_accuracy improved from 0.50000 to 0.53571, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_3.h5\n",
      "Epoch 20/30\n",
      "7/7 [==============================] - 1s 162ms/step - loss: 0.6175 - binary_accuracy: 0.9327 - val_loss: 5.3591 - val_binary_accuracy: 0.5714\n",
      "\n",
      "Epoch 00020: val_binary_accuracy improved from 0.53571 to 0.57143, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_3.h5\n",
      "Epoch 21/30\n",
      "7/7 [==============================] - 1s 161ms/step - loss: 0.5555 - binary_accuracy: 0.9327 - val_loss: 5.1163 - val_binary_accuracy: 0.6250\n",
      "\n",
      "Epoch 00021: val_binary_accuracy improved from 0.57143 to 0.62500, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_3.h5\n",
      "Epoch 22/30\n",
      "7/7 [==============================] - 1s 160ms/step - loss: 0.6208 - binary_accuracy: 0.9327 - val_loss: 5.1435 - val_binary_accuracy: 0.5893\n",
      "\n",
      "Epoch 00022: val_binary_accuracy did not improve from 0.62500\n",
      "Epoch 23/30\n",
      "7/7 [==============================] - 1s 154ms/step - loss: 0.5158 - binary_accuracy: 0.9507 - val_loss: 4.8627 - val_binary_accuracy: 0.6071\n",
      "\n",
      "Epoch 00023: val_binary_accuracy did not improve from 0.62500\n",
      "Epoch 24/30\n",
      "7/7 [==============================] - 1s 158ms/step - loss: 0.4913 - binary_accuracy: 0.9372 - val_loss: 4.7915 - val_binary_accuracy: 0.6250\n",
      "\n",
      "Epoch 00024: val_binary_accuracy did not improve from 0.62500\n",
      "Epoch 25/30\n",
      "7/7 [==============================] - 1s 158ms/step - loss: 0.4285 - binary_accuracy: 0.9552 - val_loss: 4.0634 - val_binary_accuracy: 0.6607\n",
      "\n",
      "Epoch 00025: val_binary_accuracy improved from 0.62500 to 0.66071, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_3.h5\n",
      "Epoch 26/30\n",
      "7/7 [==============================] - 1s 160ms/step - loss: 0.2401 - binary_accuracy: 0.9731 - val_loss: 3.9677 - val_binary_accuracy: 0.6786\n",
      "\n",
      "Epoch 00026: val_binary_accuracy improved from 0.66071 to 0.67857, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_3.h5\n",
      "Epoch 27/30\n",
      "7/7 [==============================] - 1s 166ms/step - loss: 0.5651 - binary_accuracy: 0.9462 - val_loss: 3.7587 - val_binary_accuracy: 0.6964\n",
      "\n",
      "Epoch 00027: val_binary_accuracy improved from 0.67857 to 0.69643, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_3.h5\n",
      "Epoch 28/30\n",
      "7/7 [==============================] - 1s 168ms/step - loss: 0.3399 - binary_accuracy: 0.9731 - val_loss: 3.4290 - val_binary_accuracy: 0.7321\n",
      "\n",
      "Epoch 00028: val_binary_accuracy improved from 0.69643 to 0.73214, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_3.h5\n",
      "Epoch 29/30\n",
      "7/7 [==============================] - 1s 157ms/step - loss: 0.3074 - binary_accuracy: 0.9686 - val_loss: 3.1276 - val_binary_accuracy: 0.7500\n",
      "\n",
      "Epoch 00029: val_binary_accuracy improved from 0.73214 to 0.75000, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_3.h5\n",
      "Epoch 30/30\n",
      "7/7 [==============================] - 1s 160ms/step - loss: 0.3184 - binary_accuracy: 0.9596 - val_loss: 1.9196 - val_binary_accuracy: 0.7500\n",
      "\n",
      "Epoch 00030: val_binary_accuracy did not improve from 0.75000\n"
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      "4/4 [==============================] - 1s 106ms/step - loss: 2.5629 - binary_accuracy: 0.7769\n",
      "Found 223 validated image filenames belonging to 2 classes.\n",
      "Found 56 validated image filenames belonging to 2 classes.\n",
      "Epoch 1/30\n",
      "7/7 [==============================] - 13s 555ms/step - loss: 4.2586 - binary_accuracy: 0.5830 - val_loss: 8.8143 - val_binary_accuracy: 0.4286\n",
      "\n",
      "Epoch 00001: val_binary_accuracy improved from -inf to 0.42857, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_4.h5\n",
      "Epoch 2/30\n",
      "7/7 [==============================] - 1s 165ms/step - loss: 2.4882 - binary_accuracy: 0.7309 - val_loss: 8.8143 - val_binary_accuracy: 0.4286\n",
      "\n",
      "Epoch 00002: val_binary_accuracy did not improve from 0.42857\n",
      "Epoch 3/30\n",
      "7/7 [==============================] - 1s 158ms/step - loss: 1.5822 - binary_accuracy: 0.8341 - val_loss: 8.8143 - val_binary_accuracy: 0.4286\n",
      "\n",
      "Epoch 00003: val_binary_accuracy did not improve from 0.42857\n",
      "Epoch 4/30\n",
      "7/7 [==============================] - 1s 194ms/step - loss: 1.1994 - binary_accuracy: 0.8520 - val_loss: 8.0168 - val_binary_accuracy: 0.4821\n",
      "\n",
      "Epoch 00004: val_binary_accuracy improved from 0.42857 to 0.48214, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_4.h5\n",
      "Epoch 5/30\n",
      "7/7 [==============================] - 1s 156ms/step - loss: 1.0832 - binary_accuracy: 0.8924 - val_loss: 6.7363 - val_binary_accuracy: 0.5179\n",
      "\n",
      "Epoch 00005: val_binary_accuracy improved from 0.48214 to 0.51786, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_4.h5\n",
      "Epoch 6/30\n",
      "7/7 [==============================] - 1s 171ms/step - loss: 0.4573 - binary_accuracy: 0.9148 - val_loss: 6.4297 - val_binary_accuracy: 0.5179\n",
      "\n",
      "Epoch 00006: val_binary_accuracy did not improve from 0.51786\n",
      "Epoch 7/30\n",
      "7/7 [==============================] - 1s 170ms/step - loss: 0.7525 - binary_accuracy: 0.9193 - val_loss: 5.9141 - val_binary_accuracy: 0.5536\n",
      "\n",
      "Epoch 00007: val_binary_accuracy improved from 0.51786 to 0.55357, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_4.h5\n",
      "Epoch 8/30\n",
      "7/7 [==============================] - 1s 154ms/step - loss: 0.5788 - binary_accuracy: 0.9103 - val_loss: 4.9581 - val_binary_accuracy: 0.5893\n",
      "\n",
      "Epoch 00008: val_binary_accuracy improved from 0.55357 to 0.58929, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_4.h5\n",
      "Epoch 9/30\n",
      "7/7 [==============================] - 1s 196ms/step - loss: 0.3758 - binary_accuracy: 0.9552 - val_loss: 4.0732 - val_binary_accuracy: 0.6250\n",
      "\n",
      "Epoch 00009: val_binary_accuracy improved from 0.58929 to 0.62500, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_4.h5\n",
      "Epoch 10/30\n",
      "7/7 [==============================] - 1s 207ms/step - loss: 0.1909 - binary_accuracy: 0.9731 - val_loss: 1.8365 - val_binary_accuracy: 0.8036\n",
      "\n",
      "Epoch 00010: val_binary_accuracy improved from 0.62500 to 0.80357, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_4.h5\n",
      "Epoch 11/30\n",
      "7/7 [==============================] - 1s 193ms/step - loss: 0.2792 - binary_accuracy: 0.9641 - val_loss: 1.4503 - val_binary_accuracy: 0.8750\n",
      "\n",
      "Epoch 00011: val_binary_accuracy improved from 0.80357 to 0.87500, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_4.h5\n",
      "Epoch 12/30\n",
      "7/7 [==============================] - 1s 163ms/step - loss: 0.1490 - binary_accuracy: 0.9686 - val_loss: 1.1946 - val_binary_accuracy: 0.8929\n",
      "\n",
      "Epoch 00012: val_binary_accuracy improved from 0.87500 to 0.89286, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_4.h5\n",
      "Epoch 13/30\n",
      "7/7 [==============================] - 1s 192ms/step - loss: 0.0895 - binary_accuracy: 0.9821 - val_loss: 0.4663 - val_binary_accuracy: 0.8929\n",
      "\n",
      "Epoch 00013: val_binary_accuracy did not improve from 0.89286\n",
      "Epoch 14/30\n",
      "7/7 [==============================] - 1s 158ms/step - loss: 0.0176 - binary_accuracy: 0.9865 - val_loss: 0.4187 - val_binary_accuracy: 0.8929\n",
      "\n",
      "Epoch 00014: val_binary_accuracy did not improve from 0.89286\n",
      "Epoch 15/30\n",
      "7/7 [==============================] - 1s 169ms/step - loss: 0.0073 - binary_accuracy: 0.9955 - val_loss: 0.1771 - val_binary_accuracy: 0.9107\n",
      "\n",
      "Epoch 00015: val_binary_accuracy improved from 0.89286 to 0.91071, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_4.h5\n",
      "Epoch 16/30\n",
      "7/7 [==============================] - 1s 154ms/step - loss: 0.0037 - binary_accuracy: 0.9955 - val_loss: 0.1418 - val_binary_accuracy: 0.9107\n",
      "\n",
      "Epoch 00016: val_binary_accuracy did not improve from 0.91071\n",
      "Epoch 17/30\n",
      "7/7 [==============================] - 1s 159ms/step - loss: 0.0734 - binary_accuracy: 0.9865 - val_loss: 0.1223 - val_binary_accuracy: 0.9107\n",
      "\n",
      "Epoch 00017: val_binary_accuracy did not improve from 0.91071\n",
      "Epoch 18/30\n",
      "7/7 [==============================] - 1s 154ms/step - loss: 0.0274 - binary_accuracy: 0.9865 - val_loss: 0.1142 - val_binary_accuracy: 0.9107\n",
      "\n",
      "Epoch 00018: val_binary_accuracy did not improve from 0.91071\n",
      "Epoch 19/30\n",
      "7/7 [==============================] - 1s 165ms/step - loss: 0.0142 - binary_accuracy: 0.9910 - val_loss: 0.0993 - val_binary_accuracy: 0.9464\n",
      "\n",
      "Epoch 00019: val_binary_accuracy improved from 0.91071 to 0.94643, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_4.h5\n",
      "Epoch 20/30\n",
      "7/7 [==============================] - 1s 205ms/step - loss: 0.0021 - binary_accuracy: 1.0000 - val_loss: 0.0891 - val_binary_accuracy: 0.9464\n",
      "\n",
      "Epoch 00020: val_binary_accuracy did not improve from 0.94643\n",
      "Epoch 21/30\n",
      "7/7 [==============================] - 1s 174ms/step - loss: 0.0119 - binary_accuracy: 0.9910 - val_loss: 0.0869 - val_binary_accuracy: 0.9643\n",
      "\n",
      "Epoch 00021: val_binary_accuracy improved from 0.94643 to 0.96429, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_4.h5\n",
      "Epoch 22/30\n",
      "7/7 [==============================] - 1s 160ms/step - loss: 0.0171 - binary_accuracy: 0.9910 - val_loss: 0.1016 - val_binary_accuracy: 0.9286\n",
      "\n",
      "Epoch 00022: val_binary_accuracy did not improve from 0.96429\n",
      "Epoch 23/30\n",
      "7/7 [==============================] - 1s 164ms/step - loss: 0.0036 - binary_accuracy: 1.0000 - val_loss: 0.1135 - val_binary_accuracy: 0.9286\n",
      "\n",
      "Epoch 00023: val_binary_accuracy did not improve from 0.96429\n",
      "Epoch 24/30\n",
      "7/7 [==============================] - 1s 156ms/step - loss: 0.0031 - binary_accuracy: 1.0000 - val_loss: 0.1214 - val_binary_accuracy: 0.9464\n",
      "\n",
      "Epoch 00024: val_binary_accuracy did not improve from 0.96429\n",
      "Epoch 25/30\n",
      "7/7 [==============================] - 1s 165ms/step - loss: 0.0036 - binary_accuracy: 0.9955 - val_loss: 0.1341 - val_binary_accuracy: 0.9464\n",
      "\n",
      "Epoch 00025: val_binary_accuracy did not improve from 0.96429\n",
      "Epoch 26/30\n",
      "7/7 [==============================] - 1s 152ms/step - loss: 0.0000e+00 - binary_accuracy: 1.0000 - val_loss: 0.1565 - val_binary_accuracy: 0.9464\n",
      "\n",
      "Epoch 00026: val_binary_accuracy did not improve from 0.96429\n",
      "Epoch 27/30\n",
      "7/7 [==============================] - 1s 166ms/step - loss: 0.0045 - binary_accuracy: 1.0000 - val_loss: 0.1534 - val_binary_accuracy: 0.9464\n",
      "\n",
      "Epoch 00027: val_binary_accuracy did not improve from 0.96429\n",
      "Epoch 28/30\n",
      "7/7 [==============================] - 2s 238ms/step - loss: 0.0000e+00 - binary_accuracy: 1.0000 - val_loss: 0.1443 - val_binary_accuracy: 0.9464\n",
      "\n",
      "Epoch 00028: val_binary_accuracy did not improve from 0.96429\n",
      "Epoch 29/30\n",
      "7/7 [==============================] - 1s 159ms/step - loss: 0.0136 - binary_accuracy: 0.9910 - val_loss: 0.3310 - val_binary_accuracy: 0.9464\n",
      "\n",
      "Epoch 00029: val_binary_accuracy did not improve from 0.96429\n",
      "Epoch 30/30\n",
      "7/7 [==============================] - 1s 162ms/step - loss: 2.7436e-04 - binary_accuracy: 1.0000 - val_loss: 0.3274 - val_binary_accuracy: 0.9464\n",
      "\n",
      "Epoch 00030: val_binary_accuracy did not improve from 0.96429\n"
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      "4/4 [==============================] - 1s 145ms/step - loss: 0.8271 - binary_accuracy: 0.9256\n",
      "Found 224 validated image filenames belonging to 2 classes.\n",
      "Found 55 validated image filenames belonging to 2 classes.\n",
      "Epoch 1/30\n",
      "7/7 [==============================] - 13s 679ms/step - loss: 2.6089 - binary_accuracy: 0.7143 - val_loss: 1.3591 - val_binary_accuracy: 0.7818\n",
      "\n",
      "Epoch 00001: val_binary_accuracy improved from -inf to 0.78182, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_5.h5\n",
      "Epoch 2/30\n",
      "7/7 [==============================] - 1s 201ms/step - loss: 1.2357 - binary_accuracy: 0.8750 - val_loss: 0.7208 - val_binary_accuracy: 0.8727\n",
      "\n",
      "Epoch 00002: val_binary_accuracy improved from 0.78182 to 0.87273, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_5.h5\n",
      "Epoch 3/30\n",
      "7/7 [==============================] - 1s 160ms/step - loss: 1.2796 - binary_accuracy: 0.8661 - val_loss: 0.5930 - val_binary_accuracy: 0.8545\n",
      "\n",
      "Epoch 00003: val_binary_accuracy did not improve from 0.87273\n",
      "Epoch 4/30\n",
      "7/7 [==============================] - 1s 167ms/step - loss: 1.4178 - binary_accuracy: 0.8527 - val_loss: 0.8112 - val_binary_accuracy: 0.8364\n",
      "\n",
      "Epoch 00004: val_binary_accuracy did not improve from 0.87273\n",
      "Epoch 5/30\n",
      "7/7 [==============================] - 1s 162ms/step - loss: 0.8630 - binary_accuracy: 0.9152 - val_loss: 0.7887 - val_binary_accuracy: 0.8364\n",
      "\n",
      "Epoch 00005: val_binary_accuracy did not improve from 0.87273\n",
      "Epoch 6/30\n",
      "7/7 [==============================] - 2s 260ms/step - loss: 0.9573 - binary_accuracy: 0.9286 - val_loss: 0.7611 - val_binary_accuracy: 0.8364\n",
      "\n",
      "Epoch 00006: val_binary_accuracy did not improve from 0.87273\n",
      "Epoch 7/30\n",
      "7/7 [==============================] - 1s 172ms/step - loss: 1.1092 - binary_accuracy: 0.8973 - val_loss: 0.7355 - val_binary_accuracy: 0.8545\n",
      "\n",
      "Epoch 00007: val_binary_accuracy did not improve from 0.87273\n",
      "Epoch 8/30\n",
      "7/7 [==============================] - 1s 172ms/step - loss: 1.0544 - binary_accuracy: 0.9062 - val_loss: 0.7097 - val_binary_accuracy: 0.8727\n",
      "\n",
      "Epoch 00008: val_binary_accuracy did not improve from 0.87273\n",
      "Epoch 9/30\n",
      "7/7 [==============================] - 1s 160ms/step - loss: 0.9253 - binary_accuracy: 0.9107 - val_loss: 0.6798 - val_binary_accuracy: 0.8727\n",
      "\n",
      "Epoch 00009: val_binary_accuracy did not improve from 0.87273\n",
      "Epoch 10/30\n",
      "7/7 [==============================] - 1s 177ms/step - loss: 0.6757 - binary_accuracy: 0.9464 - val_loss: 0.6594 - val_binary_accuracy: 0.8727\n",
      "\n",
      "Epoch 00010: val_binary_accuracy did not improve from 0.87273\n",
      "Epoch 11/30\n",
      "7/7 [==============================] - 1s 154ms/step - loss: 0.4813 - binary_accuracy: 0.9375 - val_loss: 0.6502 - val_binary_accuracy: 0.8727\n",
      "\n",
      "Epoch 00011: val_binary_accuracy did not improve from 0.87273\n",
      "Epoch 12/30\n",
      "7/7 [==============================] - 1s 160ms/step - loss: 0.4262 - binary_accuracy: 0.9554 - val_loss: 0.6419 - val_binary_accuracy: 0.9273\n",
      "\n",
      "Epoch 00012: val_binary_accuracy improved from 0.87273 to 0.92727, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_5.h5\n",
      "Epoch 13/30\n",
      "7/7 [==============================] - 1s 161ms/step - loss: 0.4975 - binary_accuracy: 0.9509 - val_loss: 0.6334 - val_binary_accuracy: 0.9273\n",
      "\n",
      "Epoch 00013: val_binary_accuracy did not improve from 0.92727\n",
      "Epoch 14/30\n",
      "7/7 [==============================] - 1s 161ms/step - loss: 0.3010 - binary_accuracy: 0.9509 - val_loss: 0.6367 - val_binary_accuracy: 0.9273\n",
      "\n",
      "Epoch 00014: val_binary_accuracy did not improve from 0.92727\n",
      "Epoch 15/30\n",
      "7/7 [==============================] - 1s 159ms/step - loss: 0.4019 - binary_accuracy: 0.9643 - val_loss: 0.4194 - val_binary_accuracy: 0.9455\n",
      "\n",
      "Epoch 00015: val_binary_accuracy improved from 0.92727 to 0.94545, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_5.h5\n",
      "Epoch 16/30\n",
      "7/7 [==============================] - 1s 180ms/step - loss: 0.3542 - binary_accuracy: 0.9554 - val_loss: 0.3959 - val_binary_accuracy: 0.9455\n",
      "\n",
      "Epoch 00016: val_binary_accuracy did not improve from 0.94545\n",
      "Epoch 17/30\n",
      "7/7 [==============================] - 1s 165ms/step - loss: 0.3272 - binary_accuracy: 0.9688 - val_loss: 0.3783 - val_binary_accuracy: 0.9455\n",
      "\n",
      "Epoch 00017: val_binary_accuracy did not improve from 0.94545\n",
      "Epoch 18/30\n",
      "7/7 [==============================] - 1s 159ms/step - loss: 0.1121 - binary_accuracy: 0.9821 - val_loss: 0.3624 - val_binary_accuracy: 0.9455\n",
      "\n",
      "Epoch 00018: val_binary_accuracy did not improve from 0.94545\n",
      "Epoch 19/30\n",
      "7/7 [==============================] - 1s 160ms/step - loss: 0.1142 - binary_accuracy: 0.9777 - val_loss: 0.1372 - val_binary_accuracy: 0.9455\n",
      "\n",
      "Epoch 00019: val_binary_accuracy did not improve from 0.94545\n",
      "Epoch 20/30\n",
      "7/7 [==============================] - 1s 162ms/step - loss: 0.1080 - binary_accuracy: 0.9821 - val_loss: 0.0998 - val_binary_accuracy: 0.9636\n",
      "\n",
      "Epoch 00020: val_binary_accuracy improved from 0.94545 to 0.96364, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_5.h5\n",
      "Epoch 21/30\n",
      "7/7 [==============================] - 1s 176ms/step - loss: 0.1035 - binary_accuracy: 0.9821 - val_loss: 0.0791 - val_binary_accuracy: 0.9636\n",
      "\n",
      "Epoch 00021: val_binary_accuracy did not improve from 0.96364\n",
      "Epoch 22/30\n",
      "7/7 [==============================] - 1s 164ms/step - loss: 0.1910 - binary_accuracy: 0.9821 - val_loss: 0.0752 - val_binary_accuracy: 0.9636\n",
      "\n",
      "Epoch 00022: val_binary_accuracy did not improve from 0.96364\n",
      "Epoch 23/30\n",
      "7/7 [==============================] - 1s 183ms/step - loss: 0.1025 - binary_accuracy: 0.9821 - val_loss: 0.0781 - val_binary_accuracy: 0.9636\n",
      "\n",
      "Epoch 00023: val_binary_accuracy did not improve from 0.96364\n",
      "Epoch 24/30\n",
      "7/7 [==============================] - 1s 163ms/step - loss: 0.1183 - binary_accuracy: 0.9866 - val_loss: 0.0568 - val_binary_accuracy: 0.9636\n",
      "\n",
      "Epoch 00024: val_binary_accuracy did not improve from 0.96364\n",
      "Epoch 25/30\n",
      "7/7 [==============================] - 1s 157ms/step - loss: 0.1563 - binary_accuracy: 0.9866 - val_loss: 0.0458 - val_binary_accuracy: 0.9818\n",
      "\n",
      "Epoch 00025: val_binary_accuracy improved from 0.96364 to 0.98182, saving model to /kaggle/working/datasets/01_MRI/Saved_CNN_Models/Testing/transfer_learning_model_dense169_2/AugTest/30epochs/transfer_learning_model_dense169_2_model_5.h5\n",
      "Epoch 26/30\n",
      "7/7 [==============================] - 1s 182ms/step - loss: 0.0960 - binary_accuracy: 0.9866 - val_loss: 0.0445 - val_binary_accuracy: 0.9636\n",
      "\n",
      "Epoch 00026: val_binary_accuracy did not improve from 0.98182\n",
      "Epoch 27/30\n",
      "7/7 [==============================] - 1s 171ms/step - loss: 0.1093 - binary_accuracy: 0.9911 - val_loss: 0.0514 - val_binary_accuracy: 0.9455\n",
      "\n",
      "Epoch 00027: val_binary_accuracy did not improve from 0.98182\n",
      "Epoch 28/30\n",
      "7/7 [==============================] - 1s 160ms/step - loss: 0.0937 - binary_accuracy: 0.9911 - val_loss: 0.0718 - val_binary_accuracy: 0.9455\n",
      "\n",
      "Epoch 00028: val_binary_accuracy did not improve from 0.98182\n",
      "Epoch 29/30\n",
      "7/7 [==============================] - 1s 165ms/step - loss: 0.1588 - binary_accuracy: 0.9821 - val_loss: 0.0812 - val_binary_accuracy: 0.9455\n",
      "\n",
      "Epoch 00029: val_binary_accuracy did not improve from 0.98182\n",
      "Epoch 30/30\n",
      "7/7 [==============================] - 1s 161ms/step - loss: 0.0941 - binary_accuracy: 0.9911 - val_loss: 0.0826 - val_binary_accuracy: 0.9455\n",
      "\n",
      "Epoch 00030: val_binary_accuracy did not improve from 0.98182\n"
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