Plant Pathology 2021 TPU training.ipynb

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    "# Plant Pathology 2021 - FGVC8\n",
    "<img src=\"https://www.researchgate.net/profile/Seung-Yeol-Lee/publication/282210822/figure/fig1/AS:502574476660736@1496834500292/Comparison-of-leaves-with-apple-blotch-disease-and-apple-blotch-like-symptom-A-H-apple.png\" width=\"400\" height=\"400\" />\n",
    "\n",
    "## Description\n",
    "Apples are one of the most important temperate fruit crops in the world. Foliar (leaf) diseases pose a major threat to the overall productivity and quality of apple orchards. The current process for disease diagnosis in apple orchards is based on manual scouting by humans, which is time-consuming and expensive.\n",
    "\n",
    "Although computer vision-based models have shown promise for plant disease identification, there are some limitations that need to be addressed. Large variations in visual symptoms of a single disease across different apple cultivars, or new varieties that originated under cultivation, are major challenges for computer vision-based disease identification. These variations arise from differences in natural and image capturing environments, for example, leaf color and leaf morphology, the age of infected tissues, non-uniform image background, and different light illumination during imaging etc.\n",
    "\n",
    "## About CVPR\n",
    "This competition is part of the Fine-Grained Visual Categorization FGVC8 workshop at the Computer Vision and Pattern Recognition Conference CVPR 2021. A panel will review the top submissions for the competition based on the description of the methods provided. From this, a subset may be invited to present their results at the workshop. Attending the workshop is not required to participate in the competition, however only teams that are attending the workshop will be considered to present their work."
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     "text": [
      "Requirement already satisfied: pip in /opt/conda/lib/python3.7/site-packages (21.0.1)\r\n",
      "Collecting pip\r\n",
      "  Downloading pip-21.1.1-py3-none-any.whl (1.5 MB)\r\n",
      "\u001b[K     |████████████████████████████████| 1.5 MB 2.9 MB/s \r\n",
      "\u001b[?25hInstalling collected packages: pip\r\n",
      "  Attempting uninstall: pip\r\n",
      "    Found existing installation: pip 21.0.1\r\n",
      "    Uninstalling pip-21.0.1:\r\n",
      "      Successfully uninstalled pip-21.0.1\r\n",
      "Successfully installed pip-21.1.1\r\n",
      "\u001b[33mWARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv\u001b[0m\r\n"
     ]
    }
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   "source": [
    "!/opt/conda/bin/python3.7 -m pip install --upgrade pip\n",
    "! pip install -q efficientnet"
   ]
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   "source": [
    "#-------------------\n",
    "# importing libraries\n",
    "#-------------------\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers\n",
    "import efficientnet.tfkeras as efn\n",
    "from sklearn.model_selection import KFold,StratifiedKFold\n",
    "\n",
    "from kaggle_datasets import KaggleDatasets\n",
    "\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import os\n",
    "import shutil\n",
    "import csv\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import PIL"
   ]
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     "text": [
      "Running on TPU  grpc://10.0.0.2:8470\n",
      "REPLICAS:  8\n"
     ]
    }
   ],
   "source": [
    "AUTO = tf.data.experimental.AUTOTUNE\n",
    "\n",
    "try:\n",
    "    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()  \n",
    "    print('Running on TPU ', tpu.master())\n",
    "except ValueError:\n",
    "    tpu = None\n",
    "\n",
    "if tpu:\n",
    "    tf.config.experimental_connect_to_cluster(tpu)\n",
    "    tf.tpu.experimental.initialize_tpu_system(tpu)\n",
    "    strategy = tf.distribute.experimental.TPUStrategy(tpu)\n",
    "else:\n",
    "    strategy = tf.distribute.get_strategy()\n",
    "\n",
    "print(\"REPLICAS: \", strategy.num_replicas_in_sync)"
   ]
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     "text": [
      "gs://kds-044025978685b91e16a595d7ad1cbace6eb4029e76d4f555679a21cd\n",
      "Number of unique labels:  12\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "                  image     labels\n",
       "0  800113bb65efe69e.jpg         []\n",
       "1  8002cb321f8bfcdf.jpg  [0, 1, 3]\n",
       "2  80070f7fb5e2ccaa.jpg        [0]\n",
       "3  80077517781fb94f.jpg        [0]\n",
       "4  800cbf0ff87721f8.jpg        [3]"
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   "source": [
    "GCS_DS_PATH = KaggleDatasets().get_gcs_path()\n",
    "print(GCS_DS_PATH)\n",
    "\n",
    "TRAIN_PATH = GCS_DS_PATH + \"/train_images/\"\n",
    "\n",
    "train_df = pd.read_csv(\"../input/plant-pathology-2021-fgvc8/train.csv\")\n",
    "\n",
    "count_dict = train_df.labels.value_counts()\n",
    "classes = list(count_dict.index)\n",
    "classes_count = list(count_dict.values)\n",
    "print(\"Number of unique labels: \",len(classes))\n",
    "\n",
    "label2id = {\n",
    "    'scab': 0,\n",
    "    'frog_eye_leaf_spot' : 1,\n",
    "    'rust' : 2,\n",
    "    'complex' : 3,\n",
    "    'powdery_mildew' : 4,\n",
    "}\n",
    "NUM_CLASSES = len(label2id)    \n",
    "id2label = dict([(value, key) for key, value in label2id.items()])\n",
    "train_df[\"labels\"] = train_df[\"labels\"].map(lambda x : [i for i in x.split(\" \") if i != \"healthy\"])\n",
    "#train_df[\"labels\"] = train_df[\"labels\"].map(lambda x : x.split(\" \"))\n",
    "train_df[\"labels\"] = train_df[\"labels\"].map(lambda x : [label2id[i] for i in x])\n",
    "\n",
    "train_df.head()"
   ]
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   "source": [
    "# Which type of competition is this ? (Multi class or Multi label)\n",
    "### Many have doubt whether the follwing competition is multi-class or multi-label . Let me clarify this by giving more details.\n",
    "\n",
    "In the details of data it is mentioned as follows:\n",
    "> ## **Unhealthy leaves with too many diseases to classify visually will have the complex class, and may also have a subset of the diseases identified.**\n",
    "\n",
    "Let me show you some labels from `train.csv` file\n",
    "\n",
    "> **`8002cb321f8bfcdf.jpg     scab frog_eye_leaf_spot complex`**\n",
    "\n",
    "> **`801f78399a44e7af.jpg     complex`**\n",
    "\n",
    "> **`80769797ce42f658.jpg      scab frog_eye_leaf_spot`**\n",
    "\n",
    "You can see that the image with complex label may have some other diseases mentioned. But from data I got **number of unique labels as 12**. So I considered it as **`Multi-Class classification`** and proceded. \n",
    "\n",
    "### **In this version I am using MULTI-LABEL CLASSIFICATION**"
   ]
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   "cell_type": "markdown",
   "id": "honest-rings",
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    "tags": []
   },
   "source": [
    "# Data Visualization"
   ]
  },
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\n",
      "text/plain": [
       "<Figure size 2520x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(35,15))\n",
    "plt.bar(classes,classes_count)\n",
    "plt.title(\"Number of instances per class\",fontweight=\"bold\",fontsize=40)\n",
    "plt.xlabel(\"Classes\",fontsize = 30)\n",
    "plt.xticks(rotation=20,fontsize = 20,fontweight = \"bold\")\n",
    "plt.xticks(fontsize = 20,fontweight = \"bold\")\n",
    "plt.ylabel(\"Count\",fontsize=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "optimum-press",
   "metadata": {
    "papermill": {
     "duration": 0.02375,
     "end_time": "2021-05-21T17:15:38.563430",
     "exception": false,
     "start_time": "2021-05-21T17:15:38.539680",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### It's a biased data\n",
    "\n",
    "Also the image size are varying as follows:\n",
    "## height = [1728,4032], width = [2592,5184]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "above-clock",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T17:15:38.617024Z",
     "iopub.status.busy": "2021-05-21T17:15:38.616328Z",
     "iopub.status.idle": "2021-05-21T17:15:38.619471Z",
     "shell.execute_reply": "2021-05-21T17:15:38.618973Z"
    },
    "papermill": {
     "duration": 0.032242,
     "end_time": "2021-05-21T17:15:38.619648",
     "exception": false,
     "start_time": "2021-05-21T17:15:38.587406",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#--------------\n",
    "#initialize constants\n",
    "#--------------\n",
    "HEIGHT,WIDTH = 512,512\n",
    "CHANNELS = 3\n",
    "BATCH_SIZE = 8 * strategy.num_replicas_in_sync\n",
    "SEED = 143\n",
    "AUTO = tf.data.experimental.AUTOTUNE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "expanded-problem",
   "metadata": {
    "papermill": {
     "duration": 0.023889,
     "end_time": "2021-05-21T17:15:38.667970",
     "exception": false,
     "start_time": "2021-05-21T17:15:38.644081",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Data Preprocessing Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "freelance-chinese",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T17:15:38.736117Z",
     "iopub.status.busy": "2021-05-21T17:15:38.735390Z",
     "iopub.status.idle": "2021-05-21T17:15:38.738542Z",
     "shell.execute_reply": "2021-05-21T17:15:38.738039Z"
    },
    "papermill": {
     "duration": 0.046242,
     "end_time": "2021-05-21T17:15:38.738683",
     "exception": false,
     "start_time": "2021-05-21T17:15:38.692441",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def process_img(filepath,label):\n",
    "    image = tf.io.read_file(filepath)\n",
    "    image = tf.image.decode_jpeg(image, channels=CHANNELS)\n",
    "    image = tf.image.convert_image_dtype(image, tf.float32) \n",
    "    image = tf.image.resize(image, [HEIGHT,WIDTH])\n",
    "    return image,label\n",
    "\n",
    "\n",
    "def data_augment(image, label):\n",
    "    p_spatial = tf.random.uniform([], 0, 1.0, dtype=tf.float32)\n",
    "    p_rotate = tf.random.uniform([], 0, 1.0, dtype=tf.float32)\n",
    "    p_pixel_1 = tf.random.uniform([], 0, 1.0, dtype=tf.float32)\n",
    "    p_pixel_2 = tf.random.uniform([], 0, 1.0, dtype=tf.float32)\n",
    "    p_pixel_3 = tf.random.uniform([], 0, 1.0, dtype=tf.float32)\n",
    "    p_crop = tf.random.uniform([], 0, 1.0, dtype=tf.float32)\n",
    "            \n",
    "    # Flips\n",
    "    image = tf.image.random_flip_left_right(image)\n",
    "    image = tf.image.random_flip_up_down(image)\n",
    "    if p_spatial > .75:\n",
    "        image = tf.image.transpose(image)\n",
    "        \n",
    "    # Rotates\n",
    "    if p_rotate > .75:\n",
    "        image = tf.image.rot90(image, k=3) \n",
    "    elif p_rotate > .5:\n",
    "        image = tf.image.rot90(image, k=2) \n",
    "    elif p_rotate > .25:\n",
    "        image = tf.image.rot90(image, k=1) \n",
    "        \n",
    "    \n",
    "    if p_pixel_1 >= .4:\n",
    "        image = tf.image.random_saturation(image, lower=.7, upper=1.3)\n",
    "    if p_pixel_2 >= .4:\n",
    "        image = tf.image.random_contrast(image, lower=.8, upper=1.2)\n",
    "    if p_pixel_3 >= .4:\n",
    "        image = tf.image.random_brightness(image, max_delta=.1)\n",
    "        \n",
    "    \n",
    "    if p_crop > .7:\n",
    "        if p_crop > .9:\n",
    "            image = tf.image.central_crop(image, central_fraction=.7)\n",
    "        elif p_crop > .8:\n",
    "            image = tf.image.central_crop(image, central_fraction=.8)\n",
    "        else:\n",
    "            image = tf.image.central_crop(image, central_fraction=.9)\n",
    "    elif p_crop > .4:\n",
    "        crop_size = tf.random.uniform([], int(HEIGHT*.8), HEIGHT, dtype=tf.int32)\n",
    "        image = tf.image.random_crop(image, size=[crop_size, crop_size, CHANNELS])\n",
    "    \n",
    "    image = tf.image.resize(image, [HEIGHT,WIDTH])\n",
    "    return image,label\n",
    "\n",
    "def get_dataset(filenames,labels, training=True):\n",
    "    dataset = tf.data.Dataset.from_tensor_slices((filenames,labels))\n",
    "    dataset = dataset.map(process_img,num_parallel_calls=AUTO)\n",
    "    dataset = dataset.map(data_augment,num_parallel_calls=AUTO)\n",
    "    dataset = dataset.cache()\n",
    "    dataset = dataset.repeat()\n",
    "    if training:\n",
    "        dataset = dataset.shuffle(2048)\n",
    "    dataset = dataset.batch(BATCH_SIZE)\n",
    "    dataset = dataset.prefetch(AUTO)\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beneficial-rover",
   "metadata": {
    "papermill": {
     "duration": 0.024252,
     "end_time": "2021-05-21T17:15:38.787452",
     "exception": false,
     "start_time": "2021-05-21T17:15:38.763200",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "referenced-bronze",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T17:15:38.847297Z",
     "iopub.status.busy": "2021-05-21T17:15:38.846509Z",
     "iopub.status.idle": "2021-05-21T17:15:50.256577Z",
     "shell.execute_reply": "2021-05-21T17:15:50.256014Z"
    },
    "papermill": {
     "duration": 11.444467,
     "end_time": "2021-05-21T17:15:50.256739",
     "exception": false,
     "start_time": "2021-05-21T17:15:38.812272",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://github.com/qubvel/efficientnet/releases/download/v0.0.1/efficientnet-b7_noisy-student_notop.h5\n",
      "258072576/258068648 [==============================] - 2s 0us/step\n"
     ]
    }
   ],
   "source": [
    "def create_model():\n",
    "    \n",
    "    pretrained = efn.EfficientNetB7(include_top=False, weights='noisy-student',input_shape=[HEIGHT,WIDTH, 3])\n",
    "            \n",
    "    x = pretrained.output\n",
    "    x = tf.keras.layers.GlobalAveragePooling2D() (x)\n",
    "    x = tf.keras.layers.Dense(512, activation = \"relu\") (x)\n",
    "    x = tf.keras.layers.Dropout(0.3) (x)\n",
    "    x = tf.keras.layers.Dense(256, activation = \"relu\") (x)\n",
    "    x = tf.keras.layers.LeakyReLU(alpha=0.2) (x)\n",
    "    x = tf.keras.layers.GaussianDropout(0.4) (x)\n",
    "    outputs = tf.keras.layers.Dense(NUM_CLASSES,activation=\"sigmoid\", dtype='float32')(x)\n",
    "        \n",
    "    model = tf.keras.Model(pretrained.input, outputs)\n",
    "    return model\n",
    "\n",
    "model = create_model()\n",
    "#model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "classical-boutique",
   "metadata": {
    "papermill": {
     "duration": 0.039528,
     "end_time": "2021-05-21T17:15:50.339723",
     "exception": false,
     "start_time": "2021-05-21T17:15:50.300195",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Compiling the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "rolled-stockholm",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T17:15:50.426553Z",
     "iopub.status.busy": "2021-05-21T17:15:50.425826Z",
     "iopub.status.idle": "2021-05-21T17:15:50.556219Z",
     "shell.execute_reply": "2021-05-21T17:15:50.555138Z"
    },
    "papermill": {
     "duration": 0.17666,
     "end_time": "2021-05-21T17:15:50.556388",
     "exception": false,
     "start_time": "2021-05-21T17:15:50.379728",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import tensorflow_addons as tfa\n",
    "\n",
    "def compile_model(model, lr=0.001):\n",
    "    \n",
    "    optimizer = tf.keras.optimizers.Adam(lr=lr)\n",
    "    \n",
    "    loss = tf.keras.losses.BinaryCrossentropy()\n",
    "    #loss =  tfa.losses.SigmoidFocalCrossEntropy(name=\"loss\",alpha=1.0,gamma=2.0)\n",
    "    metrics = [\n",
    "       tfa.metrics.F1Score(num_classes = NUM_CLASSES,average = \"macro\", name = \"f1_score\"),\n",
    "       tf.keras.metrics.BinaryAccuracy(name='acc')\n",
    "    ]\n",
    "\n",
    "    model.compile(optimizer=optimizer, loss=loss, metrics=metrics)\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "focused-hands",
   "metadata": {
    "papermill": {
     "duration": 0.039572,
     "end_time": "2021-05-21T17:15:50.636056",
     "exception": false,
     "start_time": "2021-05-21T17:15:50.596484",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Callbacks Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "increased-abuse",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T17:15:50.723576Z",
     "iopub.status.busy": "2021-05-21T17:15:50.722864Z",
     "iopub.status.idle": "2021-05-21T17:15:50.725957Z",
     "shell.execute_reply": "2021-05-21T17:15:50.725314Z"
    },
    "papermill": {
     "duration": 0.050257,
     "end_time": "2021-05-21T17:15:50.726098",
     "exception": false,
     "start_time": "2021-05-21T17:15:50.675841",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "METRIC = \"val_f1_score\"\n",
    "\n",
    "def create_callbacks(kfold,metric = METRIC):\n",
    "    \n",
    "    cpk_path = f'./best_model_{kfold}.h5'\n",
    "    \n",
    "    checkpoint = tf.keras.callbacks.ModelCheckpoint(\n",
    "        filepath=cpk_path,\n",
    "        monitor= metric,\n",
    "        mode='max',\n",
    "        save_best_only=True,\n",
    "        verbose=1,\n",
    "    )\n",
    "\n",
    "    reducelr = tf.keras.callbacks.ReduceLROnPlateau(\n",
    "        monitor= metric,\n",
    "        mode='max',\n",
    "        factor=0.1,\n",
    "        patience=3,\n",
    "        verbose=0\n",
    "    )\n",
    "\n",
    "    earlystop = tf.keras.callbacks.EarlyStopping(\n",
    "        monitor= metric,\n",
    "        mode='max',\n",
    "        patience=10, \n",
    "        verbose=1\n",
    "    )\n",
    "    \n",
    "    callbacks = [checkpoint, reducelr, earlystop]         \n",
    "    \n",
    "    return callbacks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "quick-shepherd",
   "metadata": {
    "papermill": {
     "duration": 0.039929,
     "end_time": "2021-05-21T17:15:50.805956",
     "exception": false,
     "start_time": "2021-05-21T17:15:50.766027",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "wireless-trial",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T17:15:50.894053Z",
     "iopub.status.busy": "2021-05-21T17:15:50.893360Z",
     "iopub.status.idle": "2021-05-21T17:15:54.511754Z",
     "shell.execute_reply": "2021-05-21T17:15:54.510491Z"
    },
    "papermill": {
     "duration": 3.66474,
     "end_time": "2021-05-21T17:15:54.511920",
     "exception": false,
     "start_time": "2021-05-21T17:15:50.847180",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of Images:  18632\n"
     ]
    }
   ],
   "source": [
    "files_ls = tf.io.gfile.glob(TRAIN_PATH + '*.jpg')\n",
    "files_df = pd.DataFrame(files_ls, columns = [\"filepath\"])\n",
    "\n",
    "labels = np.zeros((len(files_ls),NUM_CLASSES))\n",
    "\n",
    "for i in range(len(files_ls)):\n",
    "    labels[i][train_df.iloc[i][\"labels\"]] = 1\n",
    "    \n",
    "print(\"Total number of Images: \",len(files_ls))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "official-numbers",
   "metadata": {
    "_kg_hide-output": true,
    "execution": {
     "iopub.execute_input": "2021-05-21T17:15:54.605179Z",
     "iopub.status.busy": "2021-05-21T17:15:54.604127Z",
     "iopub.status.idle": "2021-05-21T21:27:49.871443Z",
     "shell.execute_reply": "2021-05-21T21:27:49.870894Z"
    },
    "papermill": {
     "duration": 15115.319389,
     "end_time": "2021-05-21T21:27:49.871622",
     "exception": false,
     "start_time": "2021-05-21T17:15:54.552233",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Training Images:  14905\n",
      "Number of Validation Images:  3727\n",
      "------------------Fold -  1  --------------------------\n",
      "Epoch 1/20\n",
      "232/232 [==============================] - 1128s 4s/step - loss: 0.4612 - f1_score: 0.2354 - acc: 0.8115 - val_loss: 0.2034 - val_f1_score: 0.6215 - val_acc: 0.9186\n",
      "\n",
      "Epoch 00001: val_f1_score improved from -inf to 0.62148, saving model to ./best_model_0.h5\n",
      "Epoch 2/20\n",
      "232/232 [==============================] - 117s 503ms/step - loss: 0.1918 - f1_score: 0.6448 - acc: 0.9254 - val_loss: 0.1317 - val_f1_score: 0.7135 - val_acc: 0.9456\n",
      "\n",
      "Epoch 00002: val_f1_score improved from 0.62148 to 0.71354, saving model to ./best_model_0.h5\n",
      "Epoch 3/20\n",
      "232/232 [==============================] - 116s 502ms/step - loss: 0.1382 - f1_score: 0.7090 - acc: 0.9459 - val_loss: 0.1085 - val_f1_score: 0.7509 - val_acc: 0.9546\n",
      "\n",
      "Epoch 00003: val_f1_score improved from 0.71354 to 0.75093, saving model to ./best_model_0.h5\n",
      "Epoch 4/20\n",
      "232/232 [==============================] - 116s 502ms/step - loss: 0.1270 - f1_score: 0.7200 - acc: 0.9498 - val_loss: 0.1050 - val_f1_score: 0.7658 - val_acc: 0.9603\n",
      "\n",
      "Epoch 00004: val_f1_score improved from 0.75093 to 0.76576, saving model to ./best_model_0.h5\n",
      "Epoch 5/20\n",
      "232/232 [==============================] - 116s 502ms/step - loss: 0.1099 - f1_score: 0.7448 - acc: 0.9556 - val_loss: 0.1058 - val_f1_score: 0.7619 - val_acc: 0.9604\n",
      "\n",
      "Epoch 00005: val_f1_score did not improve from 0.76576\n",
      "Epoch 6/20\n",
      "232/232 [==============================] - 116s 501ms/step - loss: 0.0994 - f1_score: 0.7606 - acc: 0.9603 - val_loss: 0.1067 - val_f1_score: 0.7808 - val_acc: 0.9606\n",
      "\n",
      "Epoch 00006: val_f1_score improved from 0.76576 to 0.78076, saving model to ./best_model_0.h5\n",
      "Epoch 7/20\n",
      "232/232 [==============================] - 117s 504ms/step - loss: 0.0911 - f1_score: 0.7630 - acc: 0.9644 - val_loss: 0.1070 - val_f1_score: 0.7728 - val_acc: 0.9609\n",
      "\n",
      "Epoch 00007: val_f1_score did not improve from 0.78076\n",
      "Epoch 8/20\n",
      "232/232 [==============================] - 117s 502ms/step - loss: 0.0784 - f1_score: 0.7759 - acc: 0.9692 - val_loss: 0.1059 - val_f1_score: 0.7854 - val_acc: 0.9606\n",
      "\n",
      "Epoch 00008: val_f1_score improved from 0.78076 to 0.78543, saving model to ./best_model_0.h5\n",
      "Epoch 9/20\n",
      "232/232 [==============================] - 116s 502ms/step - loss: 0.0692 - f1_score: 0.7834 - acc: 0.9729 - val_loss: 0.1179 - val_f1_score: 0.7849 - val_acc: 0.9605\n",
      "\n",
      "Epoch 00009: val_f1_score did not improve from 0.78543\n",
      "Epoch 10/20\n",
      "232/232 [==============================] - 117s 503ms/step - loss: 0.0578 - f1_score: 0.7990 - acc: 0.9780 - val_loss: 0.1194 - val_f1_score: 0.8014 - val_acc: 0.9589\n",
      "\n",
      "Epoch 00010: val_f1_score improved from 0.78543 to 0.80138, saving model to ./best_model_0.h5\n",
      "Epoch 11/20\n",
      "232/232 [==============================] - 117s 503ms/step - loss: 0.0449 - f1_score: 0.8181 - acc: 0.9828 - val_loss: 0.1244 - val_f1_score: 0.7709 - val_acc: 0.9595\n",
      "\n",
      "Epoch 00011: val_f1_score did not improve from 0.80138\n",
      "Epoch 12/20\n",
      "232/232 [==============================] - 116s 501ms/step - loss: 0.0473 - f1_score: 0.8057 - acc: 0.9819 - val_loss: 0.1443 - val_f1_score: 0.7802 - val_acc: 0.9592\n",
      "\n",
      "Epoch 00012: val_f1_score did not improve from 0.80138\n",
      "Epoch 13/20\n",
      "232/232 [==============================] - 116s 499ms/step - loss: 0.0415 - f1_score: 0.8047 - acc: 0.9847 - val_loss: 0.1298 - val_f1_score: 0.7920 - val_acc: 0.9610\n",
      "\n",
      "Epoch 00013: val_f1_score did not improve from 0.80138\n",
      "Epoch 14/20\n",
      "232/232 [==============================] - 117s 505ms/step - loss: 0.0315 - f1_score: 0.8108 - acc: 0.9892 - val_loss: 0.1251 - val_f1_score: 0.7767 - val_acc: 0.9615\n",
      "\n",
      "Epoch 00014: val_f1_score did not improve from 0.80138\n",
      "Epoch 15/20\n",
      "232/232 [==============================] - 116s 501ms/step - loss: 0.0307 - f1_score: 0.8148 - acc: 0.9894 - val_loss: 0.1252 - val_f1_score: 0.7723 - val_acc: 0.9620\n",
      "\n",
      "Epoch 00015: val_f1_score did not improve from 0.80138\n",
      "Epoch 16/20\n",
      "232/232 [==============================] - 116s 500ms/step - loss: 0.0265 - f1_score: 0.8181 - acc: 0.9901 - val_loss: 0.1252 - val_f1_score: 0.7830 - val_acc: 0.9621\n",
      "\n",
      "Epoch 00016: val_f1_score did not improve from 0.80138\n",
      "Epoch 17/20\n",
      "232/232 [==============================] - 116s 499ms/step - loss: 0.0273 - f1_score: 0.8173 - acc: 0.9906 - val_loss: 0.1256 - val_f1_score: 0.7785 - val_acc: 0.9613\n",
      "\n",
      "Epoch 00017: val_f1_score did not improve from 0.80138\n",
      "Epoch 18/20\n",
      "232/232 [==============================] - 117s 503ms/step - loss: 0.0243 - f1_score: 0.8187 - acc: 0.9910 - val_loss: 0.1264 - val_f1_score: 0.7777 - val_acc: 0.9610\n",
      "\n",
      "Epoch 00018: val_f1_score did not improve from 0.80138\n",
      "Epoch 19/20\n",
      "232/232 [==============================] - 116s 501ms/step - loss: 0.0249 - f1_score: 0.8127 - acc: 0.9911 - val_loss: 0.1268 - val_f1_score: 0.7784 - val_acc: 0.9616\n",
      "\n",
      "Epoch 00019: val_f1_score did not improve from 0.80138\n",
      "Epoch 20/20\n",
      "232/232 [==============================] - 117s 503ms/step - loss: 0.0230 - f1_score: 0.8158 - acc: 0.9917 - val_loss: 0.1270 - val_f1_score: 0.7818 - val_acc: 0.9609\n",
      "\n",
      "Epoch 00020: val_f1_score did not improve from 0.80138\n",
      "Epoch 00020: early stopping\n",
      "Number of Training Images:  14905\n",
      "Number of Validation Images:  3727\n",
      "------------------Fold -  2  --------------------------\n",
      "Epoch 1/20\n",
      "232/232 [==============================] - 758s 2s/step - loss: 0.4535 - f1_score: 0.2359 - acc: 0.8210 - val_loss: 0.2120 - val_f1_score: 0.6125 - val_acc: 0.9184\n",
      "\n",
      "Epoch 00001: val_f1_score improved from -inf to 0.61251, saving model to ./best_model_1.h5\n",
      "Epoch 2/20\n",
      "232/232 [==============================] - 117s 503ms/step - loss: 0.1864 - f1_score: 0.6363 - acc: 0.9248 - val_loss: 0.1149 - val_f1_score: 0.7453 - val_acc: 0.9532\n",
      "\n",
      "Epoch 00002: val_f1_score improved from 0.61251 to 0.74530, saving model to ./best_model_1.h5\n",
      "Epoch 3/20\n",
      "232/232 [==============================] - 116s 502ms/step - loss: 0.1461 - f1_score: 0.6975 - acc: 0.9426 - val_loss: 0.1029 - val_f1_score: 0.7502 - val_acc: 0.9598\n",
      "\n",
      "Epoch 00003: val_f1_score improved from 0.74530 to 0.75018, saving model to ./best_model_1.h5\n",
      "Epoch 4/20\n",
      "232/232 [==============================] - 117s 505ms/step - loss: 0.1264 - f1_score: 0.7157 - acc: 0.9496 - val_loss: 0.1067 - val_f1_score: 0.7504 - val_acc: 0.9572\n",
      "\n",
      "Epoch 00004: val_f1_score improved from 0.75018 to 0.75039, saving model to ./best_model_1.h5\n",
      "Epoch 5/20\n",
      "232/232 [==============================] - 117s 506ms/step - loss: 0.1140 - f1_score: 0.7330 - acc: 0.9555 - val_loss: 0.0977 - val_f1_score: 0.7806 - val_acc: 0.9608\n",
      "\n",
      "Epoch 00005: val_f1_score improved from 0.75039 to 0.78064, saving model to ./best_model_1.h5\n",
      "Epoch 6/20\n",
      "232/232 [==============================] - 116s 501ms/step - loss: 0.0966 - f1_score: 0.7554 - acc: 0.9621 - val_loss: 0.0989 - val_f1_score: 0.7783 - val_acc: 0.9606\n",
      "\n",
      "Epoch 00006: val_f1_score did not improve from 0.78064\n",
      "Epoch 7/20\n",
      "232/232 [==============================] - 116s 500ms/step - loss: 0.0848 - f1_score: 0.7592 - acc: 0.9667 - val_loss: 0.1002 - val_f1_score: 0.7708 - val_acc: 0.9620\n",
      "\n",
      "Epoch 00007: val_f1_score did not improve from 0.78064\n",
      "Epoch 8/20\n",
      "232/232 [==============================] - 118s 507ms/step - loss: 0.0744 - f1_score: 0.7829 - acc: 0.9720 - val_loss: 0.1111 - val_f1_score: 0.7765 - val_acc: 0.9581\n",
      "\n",
      "Epoch 00008: val_f1_score did not improve from 0.78064\n",
      "Epoch 9/20\n",
      "232/232 [==============================] - 117s 505ms/step - loss: 0.0630 - f1_score: 0.7835 - acc: 0.9759 - val_loss: 0.0999 - val_f1_score: 0.7938 - val_acc: 0.9626\n",
      "\n",
      "Epoch 00009: val_f1_score improved from 0.78064 to 0.79376, saving model to ./best_model_1.h5\n",
      "Epoch 10/20\n",
      "232/232 [==============================] - 118s 508ms/step - loss: 0.0590 - f1_score: 0.7936 - acc: 0.9774 - val_loss: 0.1009 - val_f1_score: 0.7906 - val_acc: 0.9632\n",
      "\n",
      "Epoch 00010: val_f1_score did not improve from 0.79376\n",
      "Epoch 11/20\n",
      "232/232 [==============================] - 117s 504ms/step - loss: 0.0510 - f1_score: 0.7946 - acc: 0.9809 - val_loss: 0.1042 - val_f1_score: 0.7878 - val_acc: 0.9625\n",
      "\n",
      "Epoch 00011: val_f1_score did not improve from 0.79376\n",
      "Epoch 12/20\n",
      "232/232 [==============================] - 115s 498ms/step - loss: 0.0524 - f1_score: 0.7950 - acc: 0.9807 - val_loss: 0.1039 - val_f1_score: 0.7878 - val_acc: 0.9629\n",
      "\n",
      "Epoch 00012: val_f1_score did not improve from 0.79376\n",
      "Epoch 13/20\n",
      "232/232 [==============================] - 116s 500ms/step - loss: 0.0479 - f1_score: 0.7959 - acc: 0.9817 - val_loss: 0.1028 - val_f1_score: 0.7859 - val_acc: 0.9630\n",
      "\n",
      "Epoch 00013: val_f1_score did not improve from 0.79376\n",
      "Epoch 14/20\n",
      "232/232 [==============================] - 116s 500ms/step - loss: 0.0458 - f1_score: 0.7979 - acc: 0.9828 - val_loss: 0.1036 - val_f1_score: 0.7857 - val_acc: 0.9630\n",
      "\n",
      "Epoch 00014: val_f1_score did not improve from 0.79376\n",
      "Epoch 15/20\n",
      "232/232 [==============================] - 115s 497ms/step - loss: 0.0477 - f1_score: 0.7987 - acc: 0.9820 - val_loss: 0.1040 - val_f1_score: 0.7858 - val_acc: 0.9629\n",
      "\n",
      "Epoch 00015: val_f1_score did not improve from 0.79376\n",
      "Epoch 16/20\n",
      "232/232 [==============================] - 116s 501ms/step - loss: 0.0479 - f1_score: 0.7936 - acc: 0.9817 - val_loss: 0.1042 - val_f1_score: 0.7844 - val_acc: 0.9628\n",
      "\n",
      "Epoch 00016: val_f1_score did not improve from 0.79376\n",
      "Epoch 17/20\n",
      "232/232 [==============================] - 116s 500ms/step - loss: 0.0498 - f1_score: 0.8006 - acc: 0.9814 - val_loss: 0.1039 - val_f1_score: 0.7834 - val_acc: 0.9629\n",
      "\n",
      "Epoch 00017: val_f1_score did not improve from 0.79376\n",
      "Epoch 18/20\n",
      "232/232 [==============================] - 116s 498ms/step - loss: 0.0450 - f1_score: 0.7994 - acc: 0.9833 - val_loss: 0.1039 - val_f1_score: 0.7853 - val_acc: 0.9629\n",
      "\n",
      "Epoch 00018: val_f1_score did not improve from 0.79376\n",
      "Epoch 19/20\n",
      "232/232 [==============================] - 116s 499ms/step - loss: 0.0460 - f1_score: 0.8032 - acc: 0.9831 - val_loss: 0.1042 - val_f1_score: 0.7847 - val_acc: 0.9629\n",
      "\n",
      "Epoch 00019: val_f1_score did not improve from 0.79376\n",
      "Epoch 00019: early stopping\n",
      "Number of Training Images:  14906\n",
      "Number of Validation Images:  3726\n",
      "------------------Fold -  3  --------------------------\n",
      "Epoch 1/20\n",
      "232/232 [==============================] - 754s 2s/step - loss: 0.4632 - f1_score: 0.2237 - acc: 0.8078 - val_loss: 0.2048 - val_f1_score: 0.6166 - val_acc: 0.9137\n",
      "\n",
      "Epoch 00001: val_f1_score improved from -inf to 0.61656, saving model to ./best_model_2.h5\n",
      "Epoch 2/20\n",
      "232/232 [==============================] - 117s 504ms/step - loss: 0.1916 - f1_score: 0.6380 - acc: 0.9247 - val_loss: 0.1169 - val_f1_score: 0.7522 - val_acc: 0.9513\n",
      "\n",
      "Epoch 00002: val_f1_score improved from 0.61656 to 0.75221, saving model to ./best_model_2.h5\n",
      "Epoch 3/20\n",
      "232/232 [==============================] - 117s 505ms/step - loss: 0.1439 - f1_score: 0.7077 - acc: 0.9436 - val_loss: 0.1072 - val_f1_score: 0.7544 - val_acc: 0.9583\n",
      "\n",
      "Epoch 00003: val_f1_score improved from 0.75221 to 0.75444, saving model to ./best_model_2.h5\n",
      "Epoch 4/20\n",
      "232/232 [==============================] - 117s 506ms/step - loss: 0.1230 - f1_score: 0.7311 - acc: 0.9528 - val_loss: 0.1073 - val_f1_score: 0.7573 - val_acc: 0.9575\n",
      "\n",
      "Epoch 00004: val_f1_score improved from 0.75444 to 0.75727, saving model to ./best_model_2.h5\n",
      "Epoch 5/20\n",
      "232/232 [==============================] - 117s 505ms/step - loss: 0.1142 - f1_score: 0.7438 - acc: 0.9547 - val_loss: 0.1040 - val_f1_score: 0.7677 - val_acc: 0.9598\n",
      "\n",
      "Epoch 00005: val_f1_score improved from 0.75727 to 0.76767, saving model to ./best_model_2.h5\n",
      "Epoch 6/20\n",
      "232/232 [==============================] - 117s 503ms/step - loss: 0.0992 - f1_score: 0.7579 - acc: 0.9611 - val_loss: 0.1006 - val_f1_score: 0.7614 - val_acc: 0.9603\n",
      "\n",
      "Epoch 00006: val_f1_score did not improve from 0.76767\n",
      "Epoch 7/20\n",
      "232/232 [==============================] - 116s 501ms/step - loss: 0.0858 - f1_score: 0.7663 - acc: 0.9666 - val_loss: 0.1044 - val_f1_score: 0.7519 - val_acc: 0.9610\n",
      "\n",
      "Epoch 00007: val_f1_score did not improve from 0.76767\n",
      "Epoch 8/20\n",
      "232/232 [==============================] - 116s 500ms/step - loss: 0.0766 - f1_score: 0.7630 - acc: 0.9697 - val_loss: 0.1199 - val_f1_score: 0.7451 - val_acc: 0.9573\n",
      "\n",
      "Epoch 00008: val_f1_score did not improve from 0.76767\n",
      "Epoch 9/20\n",
      "232/232 [==============================] - 116s 500ms/step - loss: 0.0667 - f1_score: 0.7716 - acc: 0.9737 - val_loss: 0.1048 - val_f1_score: 0.7839 - val_acc: 0.9629\n",
      "\n",
      "Epoch 00009: val_f1_score improved from 0.76767 to 0.78392, saving model to ./best_model_2.h5\n",
      "Epoch 10/20\n",
      "232/232 [==============================] - 117s 502ms/step - loss: 0.0583 - f1_score: 0.7886 - acc: 0.9780 - val_loss: 0.1054 - val_f1_score: 0.7824 - val_acc: 0.9637\n",
      "\n",
      "Epoch 00010: val_f1_score did not improve from 0.78392\n",
      "Epoch 11/20\n",
      "232/232 [==============================] - 117s 503ms/step - loss: 0.0537 - f1_score: 0.7903 - acc: 0.9794 - val_loss: 0.1053 - val_f1_score: 0.7791 - val_acc: 0.9630\n",
      "\n",
      "Epoch 00011: val_f1_score did not improve from 0.78392\n",
      "Epoch 12/20\n",
      "232/232 [==============================] - 116s 502ms/step - loss: 0.0515 - f1_score: 0.7949 - acc: 0.9807 - val_loss: 0.1067 - val_f1_score: 0.7817 - val_acc: 0.9625\n",
      "\n",
      "Epoch 00012: val_f1_score did not improve from 0.78392\n",
      "Epoch 13/20\n",
      "232/232 [==============================] - 116s 500ms/step - loss: 0.0491 - f1_score: 0.7957 - acc: 0.9823 - val_loss: 0.1066 - val_f1_score: 0.7782 - val_acc: 0.9629\n",
      "\n",
      "Epoch 00013: val_f1_score did not improve from 0.78392\n",
      "Epoch 14/20\n",
      "232/232 [==============================] - 116s 500ms/step - loss: 0.0480 - f1_score: 0.7946 - acc: 0.9820 - val_loss: 0.1075 - val_f1_score: 0.7817 - val_acc: 0.9626\n",
      "\n",
      "Epoch 00014: val_f1_score did not improve from 0.78392\n",
      "Epoch 15/20\n",
      "232/232 [==============================] - 116s 499ms/step - loss: 0.0495 - f1_score: 0.7888 - acc: 0.9817 - val_loss: 0.1075 - val_f1_score: 0.7819 - val_acc: 0.9627\n",
      "\n",
      "Epoch 00015: val_f1_score did not improve from 0.78392\n",
      "Epoch 16/20\n",
      "232/232 [==============================] - 115s 498ms/step - loss: 0.0473 - f1_score: 0.7992 - acc: 0.9824 - val_loss: 0.1078 - val_f1_score: 0.7813 - val_acc: 0.9628\n",
      "\n",
      "Epoch 00016: val_f1_score did not improve from 0.78392\n",
      "Epoch 17/20\n",
      "232/232 [==============================] - 116s 499ms/step - loss: 0.0513 - f1_score: 0.7920 - acc: 0.9809 - val_loss: 0.1073 - val_f1_score: 0.7811 - val_acc: 0.9631\n",
      "\n",
      "Epoch 00017: val_f1_score did not improve from 0.78392\n",
      "Epoch 18/20\n",
      "232/232 [==============================] - 116s 499ms/step - loss: 0.0484 - f1_score: 0.7969 - acc: 0.9820 - val_loss: 0.1077 - val_f1_score: 0.7823 - val_acc: 0.9629\n",
      "\n",
      "Epoch 00018: val_f1_score did not improve from 0.78392\n",
      "Epoch 19/20\n",
      "232/232 [==============================] - 116s 500ms/step - loss: 0.0467 - f1_score: 0.7940 - acc: 0.9829 - val_loss: 0.1073 - val_f1_score: 0.7810 - val_acc: 0.9630\n",
      "\n",
      "Epoch 00019: val_f1_score did not improve from 0.78392\n",
      "Epoch 00019: early stopping\n",
      "Number of Training Images:  14906\n",
      "Number of Validation Images:  3726\n",
      "------------------Fold -  4  --------------------------\n",
      "Epoch 1/20\n",
      "232/232 [==============================] - 763s 2s/step - loss: 0.4440 - f1_score: 0.2558 - acc: 0.8238 - val_loss: 0.1980 - val_f1_score: 0.5962 - val_acc: 0.9111\n",
      "\n",
      "Epoch 00001: val_f1_score improved from -inf to 0.59623, saving model to ./best_model_3.h5\n",
      "Epoch 2/20\n",
      "232/232 [==============================] - 116s 501ms/step - loss: 0.1879 - f1_score: 0.6577 - acc: 0.9268 - val_loss: 0.1162 - val_f1_score: 0.7229 - val_acc: 0.9519\n",
      "\n",
      "Epoch 00002: val_f1_score improved from 0.59623 to 0.72290, saving model to ./best_model_3.h5\n",
      "Epoch 3/20\n",
      "232/232 [==============================] - 116s 502ms/step - loss: 0.1443 - f1_score: 0.7263 - acc: 0.9437 - val_loss: 0.1030 - val_f1_score: 0.7484 - val_acc: 0.9567\n",
      "\n",
      "Epoch 00003: val_f1_score improved from 0.72290 to 0.74845, saving model to ./best_model_3.h5\n",
      "Epoch 4/20\n",
      "232/232 [==============================] - 117s 503ms/step - loss: 0.1218 - f1_score: 0.7323 - acc: 0.9522 - val_loss: 0.1015 - val_f1_score: 0.7532 - val_acc: 0.9584\n",
      "\n",
      "Epoch 00004: val_f1_score improved from 0.74845 to 0.75319, saving model to ./best_model_3.h5\n",
      "Epoch 5/20\n",
      "232/232 [==============================] - 117s 502ms/step - loss: 0.1123 - f1_score: 0.7504 - acc: 0.9548 - val_loss: 0.1012 - val_f1_score: 0.7494 - val_acc: 0.9587\n",
      "\n",
      "Epoch 00005: val_f1_score did not improve from 0.75319\n",
      "Epoch 6/20\n",
      "232/232 [==============================] - 115s 497ms/step - loss: 0.0975 - f1_score: 0.7701 - acc: 0.9630 - val_loss: 0.1000 - val_f1_score: 0.7567 - val_acc: 0.9601\n",
      "\n",
      "Epoch 00006: val_f1_score improved from 0.75319 to 0.75671, saving model to ./best_model_3.h5\n",
      "Epoch 7/20\n",
      "232/232 [==============================] - 117s 503ms/step - loss: 0.0842 - f1_score: 0.7728 - acc: 0.9666 - val_loss: 0.0997 - val_f1_score: 0.8090 - val_acc: 0.9619\n",
      "\n",
      "Epoch 00007: val_f1_score improved from 0.75671 to 0.80902, saving model to ./best_model_3.h5\n",
      "Epoch 8/20\n",
      "232/232 [==============================] - 117s 502ms/step - loss: 0.0791 - f1_score: 0.7878 - acc: 0.9690 - val_loss: 0.1093 - val_f1_score: 0.7576 - val_acc: 0.9603\n",
      "\n",
      "Epoch 00008: val_f1_score did not improve from 0.80902\n",
      "Epoch 9/20\n",
      "232/232 [==============================] - 116s 500ms/step - loss: 0.0668 - f1_score: 0.7936 - acc: 0.9750 - val_loss: 0.1060 - val_f1_score: 0.7935 - val_acc: 0.9615\n",
      "\n",
      "Epoch 00009: val_f1_score did not improve from 0.80902\n",
      "Epoch 10/20\n",
      "232/232 [==============================] - 116s 499ms/step - loss: 0.0594 - f1_score: 0.8149 - acc: 0.9780 - val_loss: 0.1167 - val_f1_score: 0.8040 - val_acc: 0.9604\n",
      "\n",
      "Epoch 00010: val_f1_score did not improve from 0.80902\n",
      "Epoch 11/20\n",
      "232/232 [==============================] - 116s 498ms/step - loss: 0.0485 - f1_score: 0.8204 - acc: 0.9826 - val_loss: 0.1150 - val_f1_score: 0.7878 - val_acc: 0.9628\n",
      "\n",
      "Epoch 00011: val_f1_score did not improve from 0.80902\n",
      "Epoch 12/20\n",
      "232/232 [==============================] - 115s 498ms/step - loss: 0.0406 - f1_score: 0.8253 - acc: 0.9854 - val_loss: 0.1209 - val_f1_score: 0.7956 - val_acc: 0.9628\n",
      "\n",
      "Epoch 00012: val_f1_score did not improve from 0.80902\n",
      "Epoch 13/20\n",
      "232/232 [==============================] - 116s 500ms/step - loss: 0.0392 - f1_score: 0.8254 - acc: 0.9861 - val_loss: 0.1228 - val_f1_score: 0.7901 - val_acc: 0.9620\n",
      "\n",
      "Epoch 00013: val_f1_score did not improve from 0.80902\n",
      "Epoch 14/20\n",
      "232/232 [==============================] - 115s 498ms/step - loss: 0.0372 - f1_score: 0.8184 - acc: 0.9863 - val_loss: 0.1240 - val_f1_score: 0.7897 - val_acc: 0.9621\n",
      "\n",
      "Epoch 00014: val_f1_score did not improve from 0.80902\n",
      "Epoch 15/20\n",
      "232/232 [==============================] - 115s 498ms/step - loss: 0.0375 - f1_score: 0.8200 - acc: 0.9863 - val_loss: 0.1240 - val_f1_score: 0.7890 - val_acc: 0.9617\n",
      "\n",
      "Epoch 00015: val_f1_score did not improve from 0.80902\n",
      "Epoch 16/20\n",
      "232/232 [==============================] - 116s 498ms/step - loss: 0.0382 - f1_score: 0.8238 - acc: 0.9862 - val_loss: 0.1237 - val_f1_score: 0.7904 - val_acc: 0.9621\n",
      "\n",
      "Epoch 00016: val_f1_score did not improve from 0.80902\n",
      "Epoch 17/20\n",
      "232/232 [==============================] - 116s 499ms/step - loss: 0.0352 - f1_score: 0.8276 - acc: 0.9877 - val_loss: 0.1240 - val_f1_score: 0.7884 - val_acc: 0.9619\n",
      "\n",
      "Epoch 00017: val_f1_score did not improve from 0.80902\n",
      "Epoch 00017: early stopping\n",
      "Number of Training Images:  14906\n",
      "Number of Validation Images:  3726\n",
      "------------------Fold -  5  --------------------------\n",
      "Epoch 1/20\n",
      "232/232 [==============================] - 747s 2s/step - loss: 0.4652 - f1_score: 0.2269 - acc: 0.8073 - val_loss: 0.1789 - val_f1_score: 0.6391 - val_acc: 0.9271\n",
      "\n",
      "Epoch 00001: val_f1_score improved from -inf to 0.63910, saving model to ./best_model_4.h5\n",
      "Epoch 2/20\n",
      "232/232 [==============================] - 117s 505ms/step - loss: 0.1959 - f1_score: 0.6395 - acc: 0.9230 - val_loss: 0.1179 - val_f1_score: 0.7250 - val_acc: 0.9497\n",
      "\n",
      "Epoch 00002: val_f1_score improved from 0.63910 to 0.72496, saving model to ./best_model_4.h5\n",
      "Epoch 3/20\n",
      "232/232 [==============================] - 118s 507ms/step - loss: 0.1457 - f1_score: 0.7035 - acc: 0.9429 - val_loss: 0.1118 - val_f1_score: 0.7307 - val_acc: 0.9525\n",
      "\n",
      "Epoch 00003: val_f1_score improved from 0.72496 to 0.73067, saving model to ./best_model_4.h5\n",
      "Epoch 4/20\n",
      "232/232 [==============================] - 117s 502ms/step - loss: 0.1286 - f1_score: 0.7295 - acc: 0.9492 - val_loss: 0.1026 - val_f1_score: 0.7233 - val_acc: 0.9581\n",
      "\n",
      "Epoch 00004: val_f1_score did not improve from 0.73067\n",
      "Epoch 5/20\n",
      "232/232 [==============================] - 116s 502ms/step - loss: 0.1135 - f1_score: 0.7337 - acc: 0.9549 - val_loss: 0.0976 - val_f1_score: 0.7665 - val_acc: 0.9608\n",
      "\n",
      "Epoch 00005: val_f1_score improved from 0.73067 to 0.76654, saving model to ./best_model_4.h5\n",
      "Epoch 6/20\n",
      "232/232 [==============================] - 118s 507ms/step - loss: 0.1002 - f1_score: 0.7518 - acc: 0.9600 - val_loss: 0.1035 - val_f1_score: 0.7651 - val_acc: 0.9618\n",
      "\n",
      "Epoch 00006: val_f1_score did not improve from 0.76654\n",
      "Epoch 7/20\n",
      "232/232 [==============================] - 116s 502ms/step - loss: 0.0884 - f1_score: 0.7593 - acc: 0.9655 - val_loss: 0.0993 - val_f1_score: 0.7809 - val_acc: 0.9624\n",
      "\n",
      "Epoch 00007: val_f1_score improved from 0.76654 to 0.78089, saving model to ./best_model_4.h5\n",
      "Epoch 8/20\n",
      "232/232 [==============================] - 117s 504ms/step - loss: 0.0788 - f1_score: 0.7808 - acc: 0.9688 - val_loss: 0.1086 - val_f1_score: 0.7935 - val_acc: 0.9624\n",
      "\n",
      "Epoch 00008: val_f1_score improved from 0.78089 to 0.79354, saving model to ./best_model_4.h5\n",
      "Epoch 9/20\n",
      "232/232 [==============================] - 117s 505ms/step - loss: 0.0683 - f1_score: 0.7831 - acc: 0.9726 - val_loss: 0.1152 - val_f1_score: 0.8105 - val_acc: 0.9624\n",
      "\n",
      "Epoch 00009: val_f1_score improved from 0.79354 to 0.81053, saving model to ./best_model_4.h5\n",
      "Epoch 10/20\n",
      "232/232 [==============================] - 117s 504ms/step - loss: 0.0612 - f1_score: 0.7911 - acc: 0.9775 - val_loss: 0.1261 - val_f1_score: 0.7664 - val_acc: 0.9589\n",
      "\n",
      "Epoch 00010: val_f1_score did not improve from 0.81053\n",
      "Epoch 11/20\n",
      "232/232 [==============================] - 116s 500ms/step - loss: 0.0569 - f1_score: 0.7992 - acc: 0.9790 - val_loss: 0.1330 - val_f1_score: 0.7873 - val_acc: 0.9598\n",
      "\n",
      "Epoch 00011: val_f1_score did not improve from 0.81053\n",
      "Epoch 12/20\n",
      "232/232 [==============================] - 116s 499ms/step - loss: 0.0487 - f1_score: 0.8061 - acc: 0.9822 - val_loss: 0.1209 - val_f1_score: 0.7973 - val_acc: 0.9604\n",
      "\n",
      "Epoch 00012: val_f1_score did not improve from 0.81053\n",
      "Epoch 13/20\n",
      "232/232 [==============================] - 116s 500ms/step - loss: 0.0395 - f1_score: 0.8127 - acc: 0.9863 - val_loss: 0.1257 - val_f1_score: 0.7901 - val_acc: 0.9629\n",
      "\n",
      "Epoch 00013: val_f1_score did not improve from 0.81053\n",
      "Epoch 14/20\n",
      "232/232 [==============================] - 116s 501ms/step - loss: 0.0336 - f1_score: 0.8166 - acc: 0.9878 - val_loss: 0.1288 - val_f1_score: 0.7901 - val_acc: 0.9624\n",
      "\n",
      "Epoch 00014: val_f1_score did not improve from 0.81053\n",
      "Epoch 15/20\n",
      "232/232 [==============================] - 117s 506ms/step - loss: 0.0326 - f1_score: 0.8201 - acc: 0.9884 - val_loss: 0.1272 - val_f1_score: 0.7934 - val_acc: 0.9634\n",
      "\n",
      "Epoch 00015: val_f1_score did not improve from 0.81053\n",
      "Epoch 16/20\n",
      "232/232 [==============================] - 116s 500ms/step - loss: 0.0295 - f1_score: 0.8195 - acc: 0.9900 - val_loss: 0.1297 - val_f1_score: 0.7939 - val_acc: 0.9631\n",
      "\n",
      "Epoch 00016: val_f1_score did not improve from 0.81053\n",
      "Epoch 17/20\n",
      "232/232 [==============================] - 115s 498ms/step - loss: 0.0300 - f1_score: 0.8191 - acc: 0.9893 - val_loss: 0.1307 - val_f1_score: 0.7951 - val_acc: 0.9629\n",
      "\n",
      "Epoch 00017: val_f1_score did not improve from 0.81053\n",
      "Epoch 18/20\n",
      "232/232 [==============================] - 115s 497ms/step - loss: 0.0300 - f1_score: 0.8186 - acc: 0.9892 - val_loss: 0.1305 - val_f1_score: 0.7954 - val_acc: 0.9628\n",
      "\n",
      "Epoch 00018: val_f1_score did not improve from 0.81053\n",
      "Epoch 19/20\n",
      "232/232 [==============================] - 115s 497ms/step - loss: 0.0287 - f1_score: 0.8204 - acc: 0.9900 - val_loss: 0.1303 - val_f1_score: 0.7935 - val_acc: 0.9626\n",
      "\n",
      "Epoch 00019: val_f1_score did not improve from 0.81053\n",
      "Epoch 00019: early stopping\n"
     ]
    }
   ],
   "source": [
    "EPOCHS = 20\n",
    "VERBOSE = 1\n",
    "N_SPLITS = 5\n",
    "\n",
    "kfold = KFold(n_splits=N_SPLITS, shuffle=True, random_state=SEED)\n",
    "history = {}\n",
    "\n",
    "\n",
    "for fold,(tID,vID) in enumerate(kfold.split(files_ls,labels)):\n",
    "    tFiles, tLabels = list(files_df.iloc[tID][\"filepath\"]) , labels[tID]\n",
    "    vFiles, vLabels = list(files_df.iloc[vID][\"filepath\"]) , labels[vID]\n",
    "    print(\"Number of Training Images: \",len(tID))\n",
    "    print(\"Number of Validation Images: \",len(vID))\n",
    "    \n",
    "    STEPS_PER_EPOCH  = len(tID)//BATCH_SIZE\n",
    "    VALID_STEPS = len(vID)//BATCH_SIZE\n",
    "    \n",
    "    tf.keras.backend.clear_session()\n",
    "    \n",
    "    train_ds = get_dataset(tFiles,tLabels, training = True)\n",
    "    val_ds = get_dataset(vFiles, vLabels, training = False)\n",
    "    \n",
    "    with strategy.scope():\n",
    "        model = create_model()\n",
    "        model = compile_model(model, lr=0.0001)\n",
    "        callbacks = create_callbacks(kfold = fold)\n",
    "    \n",
    "        print(\"------------------Fold - \",fold+1,\" --------------------------\")\n",
    "        history[fold] = model.fit(\n",
    "                            train_ds,\n",
    "                            epochs=EPOCHS,\n",
    "                            callbacks=callbacks,\n",
    "                            validation_data = val_ds,\n",
    "                            verbose=VERBOSE,\n",
    "                            steps_per_epoch = STEPS_PER_EPOCH,\n",
    "                            validation_steps=VALID_STEPS\n",
    "                           )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lyric-projector",
   "metadata": {
    "papermill": {
     "duration": 6.725625,
     "end_time": "2021-05-21T21:28:03.358615",
     "exception": false,
     "start_time": "2021-05-21T21:27:56.632990",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# History plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "personalized-edmonton",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T21:28:16.730375Z",
     "iopub.status.busy": "2021-05-21T21:28:16.729250Z",
     "iopub.status.idle": "2021-05-21T21:28:19.183625Z",
     "shell.execute_reply": "2021-05-21T21:28:19.184143Z"
    },
    "papermill": {
     "duration": 9.105237,
     "end_time": "2021-05-21T21:28:19.184316",
     "exception": false,
     "start_time": "2021-05-21T21:28:10.079079",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2880x1728 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8*N_SPLITS,24))\n",
    "\n",
    "for i in range(N_SPLITS):\n",
    "    acc = history[i].history['acc']\n",
    "    val_acc = history[i].history['val_acc']\n",
    "    f1 = history[i].history['f1_score']\n",
    "    val_f1 = history[i].history['val_f1_score']\n",
    "    loss = history[i].history['loss']\n",
    "    val_loss = history[i].history['val_loss']\n",
    "    epochs_range = range(len(history[i].history['val_loss'])) \n",
    "    \n",
    "    plt.subplot(N_SPLITS, 3,i*3+1)\n",
    "    plt.plot(epochs_range, acc, label='Training Accuracy')\n",
    "    plt.plot(epochs_range, val_acc, label='Validation  Accuracy')\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.title(f'FOLD:{str(i)} Training and Validation  Accuracy')\n",
    "    \n",
    "    plt.subplot(N_SPLITS, 3,i*3+2)\n",
    "    plt.plot(epochs_range, f1, label='Training F1 score')\n",
    "    plt.plot(epochs_range, val_f1, label='Validation  F1 score')\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.title(f'FOLD:{str(i)} Training and Validation  F1 score')\n",
    "    \n",
    "    plt.subplot(N_SPLITS, 3, i*3+3)\n",
    "    plt.plot(epochs_range, loss, label='Training Loss')\n",
    "    plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.title(f'FOLD:{str(i)} Training and Validation Loss')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ranging-august",
   "metadata": {
    "papermill": {
     "duration": 6.781047,
     "end_time": "2021-05-21T21:28:32.748603",
     "exception": false,
     "start_time": "2021-05-21T21:28:25.967556",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### If you have any doubts or suggestions feel free to contact me.\n",
    "## Happy coding❤ "
   ]
  }
 ],
 "metadata": {
  "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": "3.7.10"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 15239.354595,
   "end_time": "2021-05-21T21:28:42.220922",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2021-05-21T17:14:42.866327",
   "version": "2.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}