EXP1_SEGNET.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiclass Semantic Segmentation using WeedMap dataset and SegNet Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Segmentation Models: using `keras` framework.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import os\n",
    "import random\n",
    "import re\n",
    "import cv2\n",
    "from pathlib import Path\n",
    "import pathlib\n",
    "from PIL import Image\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score, accuracy_score\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pylab import *\n",
    "\n",
    "from tqdm import tqdm\n",
    "from tqdm import tqdm_gui\n",
    "import segmentation_models as sm\n",
    "sm.set_framework('tf.keras')\n",
    "#from DATASETS.RedEdge.io_class import HDF5DatasetGenerator\n",
    "from PREPROCESSING.aspectawarepreprocessor import AspectAwarePreprocessor\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.regularizers import l2\n",
    "from tensorflow.keras.layers import *\n",
    "#from tensorflow.keras.engine import Layer\n",
    "from tensorflow.keras.applications.vgg16 import *\n",
    "from tensorflow.keras.models import *\n",
    "#from tensorflow.keras.applications.imagenet_utils import _obtain_input_shape\n",
    "import tensorflow.keras.backend as K\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.layers import Convolution2D, ZeroPadding2D, MaxPooling2D, Cropping2D, Conv2D\n",
    "from tensorflow.keras.layers import Input, Add, Dropout, Permute, add\n",
    "from tensorflow.compat.v1.layers import conv2d_transpose\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.python.keras.callbacks import TensorBoard, ModelCheckpoint, EarlyStopping, ReduceLROnPlateau"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Environment checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.0\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Is there a GPU available: \n",
      "True\n",
      "Is the Tensor on GPU #0:  \n",
      "True\n",
      "Device name: /job:localhost/replica:0/task:0/device:GPU:0\n"
     ]
    }
   ],
   "source": [
    "x = tf.random.uniform([3, 3])\n",
    "\n",
    "print(\"Is there a GPU available: \"),\n",
    "print(tf.test.is_gpu_available())\n",
    "\n",
    "print(\"Is the Tensor on GPU #0:  \"),\n",
    "print(x.device.endswith('GPU:0'))\n",
    "\n",
    "print(\"Device name: {}\".format((x.device)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(tf.executing_eagerly())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract Target Class definitions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to parse the file \"label_colors.txt\" which contains the class definitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMG_DIR = Path.home() / Path(r\"WeedMapSemanticSegmentation/DATASETS/RedEdge/\")\n",
    "\n",
    "def parse_code(l):\n",
    "    '''Function to parse lines in a text file, returns separated elements (label codes and names in this case)\n",
    "    '''\n",
    "    if len(l.strip().split(\"\\t\")) == 2:\n",
    "        a, b = l.strip().split(\"\\t\")\n",
    "        return tuple(int(i) for i in a.split(' ')), b\n",
    "    else:\n",
    "        a, b, c = l.strip().split(\"\\t\")\n",
    "        return tuple(int(i) for i in a.split(' ')), c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parse and extract label names and codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([(0, 0, 0), (255, 0, 0), (0, 255, 0)], ['Background', 'weed', 'crop'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_codes, label_names = zip(*[parse_code(l) for l in open(str(IMG_DIR / \"label_colors.txt\"))])\n",
    "label_codes, label_names = list(label_codes), list(label_names)\n",
    "label_codes[:], label_names[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([(0, 0, 0), (255, 0, 0), (0, 255, 0)], ['Background', 'weed', 'crop'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_codes, label_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create useful label and code conversion dictionaries\n",
    "_These will be used for:_\n",
    "- One hot encoding the mask labels for model training\n",
    "- Decoding the predicted labels for interpretation and visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "code2id = {v:k for k,v in enumerate(label_codes)}\n",
    "id2code = {k:v for k,v in enumerate(label_codes)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "name2id = {v:k for k,v in enumerate(label_names)}\n",
    "id2name = {k:v for k,v in enumerate(label_names)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: (0, 0, 0), 1: (255, 0, 0), 2: (0, 255, 0)}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id2code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'Background', 1: 'weed', 2: 'crop'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id2name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define functions for one hot encoding rgb labels, and decoding encoded predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rgb_to_onehot(rgb_image, colormap = id2code):\n",
    "    '''Function to one hot encode RGB mask labels\n",
    "        Inputs: v\n",
    "            rgb_image - image matrix (eg. 256 x 256 x 3 dimension numpy ndarray)\n",
    "            colormap - dictionary of color to label id\n",
    "        Output: One hot encoded image of dimensions (height x width x num_classes) where num_classes = len(colormap)\n",
    "    '''\n",
    "    num_classes = len(colormap)\n",
    "    shape = rgb_image.shape[:2]+(num_classes,)\n",
    "    encoded_image = np.zeros( shape, dtype=np.int8 )\n",
    "    for i, cls in enumerate(colormap):\n",
    "        encoded_image[:,:,i] = np.all(rgb_image.reshape( (-1,3) ) == colormap[i], axis=1).reshape(shape[:2])\n",
    "    return encoded_image\n",
    "\n",
    "\n",
    "def onehot_to_rgb(onehot, colormap = id2code):\n",
    "    '''Function to decode encoded mask labels\n",
    "        Inputs: \n",
    "            onehot - one hot encoded image matrix (height x width x num_classes)\n",
    "            colormap - dictionary of color to label id\n",
    "        Output: Decoded RGB image (height x width x 3) \n",
    "    '''\n",
    "    single_layer = np.argmax(onehot, axis=-1)\n",
    "    output = np.zeros( onehot.shape[:2]+(3,) )\n",
    "    for k in colormap.keys():\n",
    "        output[single_layer==k] = colormap[k]\n",
    "    return np.uint8(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import h5py\n",
    "\n",
    "class HDF5DatasetGenerator:\n",
    "    def __init__(self, dbPath, batchSize, preprocessors=None, aug=None, classes=3, encoded=True):\n",
    "        # store the batch size, preprocessors, and data augmentor,\n",
    "        # along with the total number of classes\n",
    "        self.batchSize = batchSize\n",
    "        self.preprocessors = preprocessors\n",
    "        self.aug = aug\n",
    "        self.classes = classes\n",
    "        self.encoded = encoded\n",
    "        # Open the HDF5 dataset for reading and determnine the total\n",
    "        # number of entries in the database\n",
    "        self.db = h5py.File(dbPath, \"r\")\n",
    "        self.numImages = self.db[\"labels\"].shape[0]\n",
    "        print(\"[INFO] The number of images is: {}\".format(self.numImages))\n",
    "        \n",
    "    def generator(self, passes=np.inf):\n",
    "        # intialize the epoch count\n",
    "        epochs = 0\n",
    "\n",
    "        # keep looping infinitely -- the mode will stop once we have\n",
    "        # to reach the disered number of epochs\n",
    "        while epochs < passes:\n",
    "            # loop over the HDF5 dataset\n",
    "            for i in range(0, self.numImages, self.batchSize):\n",
    "                # extract the images and labels\n",
    "                images = self.db[\"images\"][i: i + self.batchSize]\n",
    "                masks = self.db[\"labels\"][i: i + self.batchSize]\n",
    "                \n",
    "                # check to see if our preprocessors are not None\n",
    "                if self.preprocessors is not None:\n",
    "                    # initialize the list of pocessed images\n",
    "                    procImages = list()\n",
    "                    procMasks = list()\n",
    "                    \n",
    "                    # loop over the image\n",
    "                    for i in range(len(images)):\n",
    "                        image = images[i]\n",
    "                        mask = masks[i]\n",
    "                        # loop over the preprocessors and apply each\n",
    "                        # to the image/mask\n",
    "                        for p in self.preprocessors:\n",
    "                            image = p.preprocess(image)\n",
    "                            mask = p.preprocess(mask)\n",
    "                        # update the list of processed images/masks\n",
    "                        if (len(image.shape) < 3):\n",
    "                            procImages.append(np.expand_dims(image, axis=-1))\n",
    "                        else:\n",
    "                            procImages.append(image)\n",
    "                        \n",
    "                        if (len(mask.shape) < 3):\n",
    "                            procMasks.append(np.expand_dims(mask, axis=-1))\n",
    "                        else: \n",
    "                            procMasks.append(mask)\n",
    "                    \n",
    "                    # update the images/masks arrays to be the processed\n",
    "                    # images\n",
    "                    images = np.asarray(procImages)\n",
    "                    masks = np.asarray(procMasks)\n",
    "                 \n",
    "                # if the data augmentator exists, apply it\n",
    "                if self.aug is not None:\n",
    "                    (images, masks) = next(self.aug.flow(\n",
    "                            images, masks, batch_size=self.batchSize))  \n",
    "                # yield a tuple of images and masks\n",
    "                if self.encoded:\n",
    "                    mask_encoded = [rgb_to_onehot(masks[x,:,:,:], id2code) for x in range(masks.shape[0])]\n",
    "                    yield images, np.asarray(mask_encoded)\n",
    "                else:\n",
    "                    yield images, masks\n",
    "                \n",
    "                \n",
    "            epochs +=1\n",
    "\n",
    "    def close(self):\n",
    "        # close the database\n",
    "        self.db.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] The number of images is: 613\n"
     ]
    }
   ],
   "source": [
    "# construct the training image generator for data augmentation\n",
    "IMG_DIR =  Path.home() / Path(r\"WeedMapSemanticSegmentation/DATASETS/RedEdge\")\n",
    "TRAIN_HDF5 = Path.home() / Path(r\"WeedMapSemanticSegmentation/DATASETS/RedEdge/EXP4_RGB_BS5/train.hdf5\")\n",
    "VAL_HDF5 = Path.home() / Path(r\"WeedMapSemanticSegmentation/DATASETS/RedEdge/EXP4_RGB_BS5/val.hdf5\")\n",
    "\n",
    "\n",
    "aug = ImageDataGenerator(rescale=1./255, fill_mode=\"nearest\")\n",
    "awp = AspectAwarePreprocessor(224, 224)\n",
    "\n",
    "# initialize the training and validation dataset generators\n",
    "trainGen_test = HDF5DatasetGenerator(TRAIN_HDF5, 5, aug=aug, preprocessors=[awp], classes=3, encoded=False)\n",
    "#valGen_test = HDF5DatasetGenerator(VAL_HDF5, 5, aug=aug, preprocessors=[awp], classes=3, encoded=False)\n",
    "#trainGen = HDF5DatasetGenerator(TRAIN_HDF5, 5, aug=None, preprocessors=None, classes=3)\n",
    "#valGen = HDF5DatasetGenerator(VAL_HDF5, 5, aug=None, preprocessors=None, classes=3)\n",
    "# test the generator\n",
    "image_test, mask_test = next(trainGen_test.generator())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_images_to_show = 5\n",
    "\n",
    "for i in range(n_images_to_show):\n",
    "    \n",
    "    # Get the next image from iterator\n",
    "    frame = image_test[i]\n",
    "    mask = mask_test[i]\n",
    "    \n",
    "    #Plot the corresponding frames and masks\n",
    "    fig = plt.figure(figsize=(20,7))\n",
    "    fig.add_subplot(1,2,1)\n",
    "    plt.grid(b=None)\n",
    "    plt.imshow(np.squeeze(frame))\n",
    "    fig.add_subplot(1,2,2)\n",
    "    plt.grid(b=None)\n",
    "    plt.imshow(mask)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] The number of images is: 613\n",
      "[INFO] The number of images is: 153\n"
     ]
    }
   ],
   "source": [
    "trainGen = HDF5DatasetGenerator(TRAIN_HDF5, 5, aug=aug, preprocessors=[awp], classes=3)\n",
    "valGen = HDF5DatasetGenerator(VAL_HDF5, 5, aug=aug, preprocessors=[awp], classes=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defining VGG-16 SegNet model for semantic segmentation\n",
    "\n",
    "<div style=\"text-align: center;\">\n",
    "        \n",
    "![alt text](https://www.researchgate.net/profile/Vijay_Badrinarayanan/publication/283471087/figure/fig1/AS:391733042008065@1470407843299/An-illustration-of-the-SegNet-architecture-There-are-no-fully-connected-layers-and-hence.png)\n",
    "\n",
    "[Picture Credits]https://www.researchgate.net/profile/Vijay_Badrinarayanan/publication/283471087/figure/fig1/AS:391733042008065@1470407843299/An-illustration-of-the-SegNet-architecture-There-are-no-fully-connected-layers-and-hence.png)<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function SegNet model using VGG-16 pretrained"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def CUSTOM_VGG16_SEGNET(num_classes, input_height, input_width, input_depth, vgg_level=-1):\n",
    "    VGG_Weights_path = \"/home/lisi2020/weedmap_multispectral_image_segmentation/pretrained_weights/vgg16_weights_tf_dim_ordering_tf_kernels.h5\"\n",
    "    \n",
    "    inputModel = Input(shape=(input_height,input_width, input_depth))\n",
    "    x = Conv2D(3, (1, 1), activation=\"relu\", padding='same', data_format='channels_last', name='block_init')(inputModel)\n",
    "    \n",
    "    x = Conv2D(64, (3, 3), activation='relu', padding='same',\n",
    "               name='block1_conv1', data_format='channels_last')(x)\n",
    "    x = Conv2D(64, (3, 3), activation='relu', padding='same',\n",
    "               name='block1_conv2', data_format='channels_last')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool',\n",
    "                     data_format='channels_last')(x)\n",
    "    f1 = x\n",
    "    # Block 2\n",
    "    x = Conv2D(128, (3, 3), activation='relu', padding='same',\n",
    "               name='block2_conv1', data_format='channels_last')(x)\n",
    "    x = Conv2D(128, (3, 3), activation='relu', padding='same',\n",
    "               name='block2_conv2', data_format='channels_last')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool',\n",
    "                     data_format='channels_last')(x)\n",
    "    f2 = x\n",
    "\n",
    "    # Block 3\n",
    "    x = Conv2D(256, (3, 3), activation='relu', padding='same',\n",
    "               name='block3_conv1', data_format='channels_last')(x)\n",
    "    x = Conv2D(256, (3, 3), activation='relu', padding='same',\n",
    "               name='block3_conv2', data_format='channels_last')(x)\n",
    "    x = Conv2D(256, (3, 3), activation='relu', padding='same',\n",
    "               name='block3_conv3', data_format='channels_last')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool',\n",
    "                     data_format='channels_last')(x)\n",
    "    f3 = x\n",
    "\n",
    "    # Block 4\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same',\n",
    "               name='block4_conv1', data_format='channels_last')(x)\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same',\n",
    "               name='block4_conv2', data_format='channels_last')(x)\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same',\n",
    "               name='block4_conv3', data_format='channels_last')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool',\n",
    "                     data_format='channels_last')(x)\n",
    "    f4 = x\n",
    "\n",
    "    # Block 5\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same',\n",
    "               name='block5_conv1', data_format='channels_last')(x)\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same',\n",
    "               name='block5_conv2', data_format='channels_last')(x)\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same',\n",
    "               name='block5_conv3', data_format='channels_last')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool',\n",
    "                     data_format='channels_last')(x)\n",
    "    f5 = x\n",
    "\n",
    "    x = Flatten(name='flatten')(x)\n",
    "    x = Dense(4096, activation='relu', name='fc1')(x)\n",
    "    x = Dense(4096, activation='relu', name='fc2')(x)\n",
    "    x = Dense(1000, activation='softmax', name='predictions')(x)\n",
    "\n",
    "    vgg = Model(inputModel, x)\n",
    "    vgg.load_weights(VGG_Weights_path, by_name=True)\n",
    "    levels = [f1, f2, f3, f4, f5]\n",
    "\n",
    "    o = levels[vgg_level]\n",
    "\n",
    "    #o = ( UpSampling2D( (2,2), data_format='channels_last'))(o)\n",
    "    o = (ZeroPadding2D((1, 1), data_format='channels_last'))(o)\n",
    "    o = (Conv2D(512, (3, 3), activation='relu',\n",
    "                padding='valid', data_format='channels_last'))(o)\n",
    "    o = (BatchNormalization())(o)\n",
    "\n",
    "    o = (UpSampling2D((2, 2), data_format='channels_last'))(o)\n",
    "    o = (ZeroPadding2D((1, 1), data_format='channels_last'))(o)\n",
    "    o = (Conv2D(512, (3, 3), activation='relu',\n",
    "                padding='valid', data_format='channels_last'))(o)\n",
    "    o = (BatchNormalization())(o)\n",
    "\n",
    "    o = (UpSampling2D((2, 2), data_format='channels_last'))(o)\n",
    "    o = (ZeroPadding2D((1, 1), data_format='channels_last'))(o)\n",
    "    o = (Conv2D(256, (3, 3), activation='relu',\n",
    "                padding='valid', data_format='channels_last'))(o)\n",
    "    o = (BatchNormalization())(o)\n",
    "\n",
    "    o = (UpSampling2D((2, 2), data_format='channels_last'))(o)\n",
    "    o = (ZeroPadding2D((1, 1), data_format='channels_last'))(o)\n",
    "    o = (Conv2D(128, (3, 3), activation='relu',\n",
    "                padding='valid', data_format='channels_last'))(o)\n",
    "    o = (BatchNormalization())(o)\n",
    "\n",
    "    o = (UpSampling2D((2, 2), data_format='channels_last'))(o)\n",
    "    o = (ZeroPadding2D((1, 1), data_format='channels_last'))(o)\n",
    "    o = (Conv2D(64, (3, 3), activation='relu',\n",
    "                padding='valid', data_format='channels_last'))(o)\n",
    "    o = (BatchNormalization())(o)\n",
    "\n",
    "    o = Conv2D(num_classes, (3, 3), padding='same',\n",
    "               data_format='channels_last')(o)\n",
    "    #o_shape = Model(img_input , o ).output_shape\n",
    "    #outputHeight = o_shape[2]\n",
    "    #outputWidth = o_shape[3]\n",
    "\n",
    "    #o = (Reshape((  -1  , outputHeight*outputWidth   )))(o)\n",
    "    #o = (Permute((2, 1)))(o)\n",
    "    o = (Activation('softmax'))(o)\n",
    "    model = Model(inputModel, o)\n",
    "    #model.outputWidth = outputWidth\n",
    "    #model.outputHeight = outputHeight\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building the custom model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
      "_________________________________________________________________\n",
      "block_init (Conv2D)          (None, 224, 224, 3)       12        \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "zero_padding2d_5 (ZeroPaddin (None, 16, 16, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "up_sampling2d_4 (UpSampling2 (None, 28, 28, 512)       0         \n",
      "_________________________________________________________________\n",
      "zero_padding2d_6 (ZeroPaddin (None, 30, 30, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 28, 28, 512)       2048      \n",
      "_________________________________________________________________\n",
      "up_sampling2d_5 (UpSampling2 (None, 56, 56, 512)       0         \n",
      "_________________________________________________________________\n",
      "zero_padding2d_7 (ZeroPaddin (None, 58, 58, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 56, 56, 256)       1179904   \n",
      "_________________________________________________________________\n",
      "batch_normalization_7 (Batch (None, 56, 56, 256)       1024      \n",
      "_________________________________________________________________\n",
      "up_sampling2d_6 (UpSampling2 (None, 112, 112, 256)     0         \n",
      "_________________________________________________________________\n",
      "zero_padding2d_8 (ZeroPaddin (None, 114, 114, 256)     0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 112, 112, 128)     295040    \n",
      "_________________________________________________________________\n",
      "batch_normalization_8 (Batch (None, 112, 112, 128)     512       \n",
      "_________________________________________________________________\n",
      "up_sampling2d_7 (UpSampling2 (None, 224, 224, 128)     0         \n",
      "_________________________________________________________________\n",
      "zero_padding2d_9 (ZeroPaddin (None, 226, 226, 128)     0         \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 224, 224, 64)      73792     \n",
      "_________________________________________________________________\n",
      "batch_normalization_9 (Batch (None, 224, 224, 64)      256       \n",
      "_________________________________________________________________\n",
      "conv2d_11 (Conv2D)           (None, 224, 224, 3)       1731      \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 224, 224, 3)       0         \n",
      "=================================================================\n",
      "Total params: 13,911,247\n",
      "Trainable params: 13,908,303\n",
      "Non-trainable params: 2,944\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "NUM_CLASSES = 3\n",
    "VGG_LEVEL = 3\n",
    "INPUT_HEIGHT = 224\n",
    "INPUT_WIDTH = 224\n",
    "INPUT_DEPTH = 3\n",
    "custom_segnet = CUSTOM_VGG16_SEGNET(num_classes=NUM_CLASSES, vgg_level=VGG_LEVEL, input_height=INPUT_HEIGHT, input_width=INPUT_WIDTH, input_depth=INPUT_DEPTH)\n",
    "custom_segnet.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining dice coefficients for model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dice_coef(y_true, y_pred):\n",
    "    y_true_f = K.flatten(y_true)\n",
    "    y_pred_f = K.flatten(y_pred)\n",
    "    intersection = K.sum(y_true_f * y_pred_f)\n",
    "    return (2. * intersection + smooth) / (K.sum(y_true_f*y_true_f) + K.sum(y_pred_f*y_pred_f) + smooth)\n",
    "\n",
    "\n",
    "def dice_coef_loss(y_true, y_pred):\n",
    "    return 1.-dice_coef(y_true, y_pred)\n",
    "smooth = 1.\n",
    "\n",
    "def tversky_loss(y_true, y_pred):\n",
    "    alpha = 0.5\n",
    "    beta  = 0.5\n",
    "    \n",
    "    ones = K.ones(K.shape(y_true))\n",
    "    p0 = y_pred      # proba that voxels are class i\n",
    "    p1 = ones-y_pred # proba that voxels are not class i\n",
    "    g0 = y_true\n",
    "    g1 = ones-y_true\n",
    "    \n",
    "    num = K.sum(p0*g0, (0,1,2,3))\n",
    "    den = num + alpha*K.sum(p0*g1,(0,1,2,3)) + beta*K.sum(p1*g0,(0,1,2,3))\n",
    "    \n",
    "    T = K.sum(num/den) # when summing over classes, T has dynamic range [0 Ncl]\n",
    "    \n",
    "    Ncl = K.cast(K.shape(y_true)[-1], 'float32')\n",
    "    return Ncl-T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "adam = tf.keras.optimizers.Adam(learning_rate=1e-6)\n",
    "custom_segnet.compile(optimizer=adam, loss=\"categorical_crossentropy\", metrics=[\"accuracy\",tversky_loss, dice_coef])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "tb = TensorBoard(log_dir='logs', write_graph=True)\n",
    "mc = ModelCheckpoint(mode='max', filepath='MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5', monitor='val_dice_coef', save_best_only='True', save_weights_only='True', verbose=1)\n",
    "es = EarlyStopping(mode='min', monitor='val_loss', patience=50, verbose=1)\n",
    "callbacks = [tb, mc]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train and save the SegNet model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size = 5\n",
    "steps_per_epoch = np.ceil(float(613) / float(batch_size))\n",
    "steps_per_epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_steps = np.floor(float(153) / float(batch_size))\n",
    "validation_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_epochs = 100\n",
    "# class weight = {bg, weed, crop}\n",
    "CLASS_WEIGHT = (0.0586, 0.0356, 0.9304)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 1.3152 - accuracy: 0.3635 - tversky_loss: 2.6716 - dice_coef: 0.4538\n",
      "Epoch 00001: val_dice_coef improved from -inf to 0.41443, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 42s 341ms/step - loss: 1.3151 - accuracy: 0.3634 - tversky_loss: 2.6715 - dice_coef: 0.4538 - val_loss: 1.2400 - val_accuracy: 0.1060 - val_tversky_loss: 2.7200 - val_dice_coef: 0.4144\n",
      "Epoch 2/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 1.1929 - accuracy: 0.4100 - tversky_loss: 2.6648 - dice_coef: 0.4773\n",
      "Epoch 00002: val_dice_coef improved from 0.41443 to 0.43727, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 324ms/step - loss: 1.1930 - accuracy: 0.4097 - tversky_loss: 2.6648 - dice_coef: 0.4772 - val_loss: 1.2213 - val_accuracy: 0.2191 - val_tversky_loss: 2.7004 - val_dice_coef: 0.4373\n",
      "Epoch 3/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 1.1409 - accuracy: 0.4348 - tversky_loss: 2.6629 - dice_coef: 0.4871\n",
      "Epoch 00003: val_dice_coef improved from 0.43727 to 0.48071, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 323ms/step - loss: 1.1410 - accuracy: 0.4344 - tversky_loss: 2.6629 - dice_coef: 0.4871 - val_loss: 1.1382 - val_accuracy: 0.3608 - val_tversky_loss: 2.6695 - val_dice_coef: 0.4807\n",
      "Epoch 4/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 1.1082 - accuracy: 0.4536 - tversky_loss: 2.6611 - dice_coef: 0.4943\n",
      "Epoch 00004: val_dice_coef improved from 0.48071 to 0.51170, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 323ms/step - loss: 1.1083 - accuracy: 0.4531 - tversky_loss: 2.6611 - dice_coef: 0.4943 - val_loss: 1.0722 - val_accuracy: 0.5217 - val_tversky_loss: 2.6494 - val_dice_coef: 0.5117\n",
      "Epoch 5/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 1.0849 - accuracy: 0.4742 - tversky_loss: 2.6591 - dice_coef: 0.5004\n",
      "Epoch 00005: val_dice_coef improved from 0.51170 to 0.52007, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 321ms/step - loss: 1.0850 - accuracy: 0.4737 - tversky_loss: 2.6591 - dice_coef: 0.5003 - val_loss: 1.0481 - val_accuracy: 0.5529 - val_tversky_loss: 2.6452 - val_dice_coef: 0.5201\n",
      "Epoch 6/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 1.0666 - accuracy: 0.4966 - tversky_loss: 2.6569 - dice_coef: 0.5059\n",
      "Epoch 00006: val_dice_coef improved from 0.52007 to 0.52161, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 324ms/step - loss: 1.0666 - accuracy: 0.4960 - tversky_loss: 2.6570 - dice_coef: 0.5058 - val_loss: 1.0375 - val_accuracy: 0.5622 - val_tversky_loss: 2.6455 - val_dice_coef: 0.5216\n",
      "Epoch 7/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 1.0511 - accuracy: 0.5168 - tversky_loss: 2.6545 - dice_coef: 0.5112\n",
      "Epoch 00007: val_dice_coef improved from 0.52161 to 0.52571, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 321ms/step - loss: 1.0512 - accuracy: 0.5162 - tversky_loss: 2.6545 - dice_coef: 0.5112 - val_loss: 1.0246 - val_accuracy: 0.5842 - val_tversky_loss: 2.6440 - val_dice_coef: 0.5257\n",
      "Epoch 8/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 1.0371 - accuracy: 0.5381 - tversky_loss: 2.6516 - dice_coef: 0.5167\n",
      "Epoch 00008: val_dice_coef improved from 0.52571 to 0.53007, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 326ms/step - loss: 1.0371 - accuracy: 0.5375 - tversky_loss: 2.6516 - dice_coef: 0.5167 - val_loss: 1.0119 - val_accuracy: 0.5916 - val_tversky_loss: 2.6418 - val_dice_coef: 0.5301\n",
      "Epoch 9/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 1.0231 - accuracy: 0.5607 - tversky_loss: 2.6481 - dice_coef: 0.5227\n",
      "Epoch 00009: val_dice_coef improved from 0.53007 to 0.53883, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 323ms/step - loss: 1.0232 - accuracy: 0.5602 - tversky_loss: 2.6481 - dice_coef: 0.5227 - val_loss: 0.9945 - val_accuracy: 0.6127 - val_tversky_loss: 2.6364 - val_dice_coef: 0.5388\n",
      "Epoch 10/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 1.0084 - accuracy: 0.5827 - tversky_loss: 2.6439 - dice_coef: 0.5296\n",
      "Epoch 00010: val_dice_coef improved from 0.53883 to 0.54639, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 326ms/step - loss: 1.0084 - accuracy: 0.5821 - tversky_loss: 2.6439 - dice_coef: 0.5295 - val_loss: 0.9769 - val_accuracy: 0.6250 - val_tversky_loss: 2.6318 - val_dice_coef: 0.5464\n",
      "Epoch 11/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.9922 - accuracy: 0.6054 - tversky_loss: 2.6388 - dice_coef: 0.5374\n",
      "Epoch 00011: val_dice_coef improved from 0.54639 to 0.56058, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 326ms/step - loss: 0.9922 - accuracy: 0.6048 - tversky_loss: 2.6388 - dice_coef: 0.5374 - val_loss: 0.9500 - val_accuracy: 0.6449 - val_tversky_loss: 2.6220 - val_dice_coef: 0.5606\n",
      "Epoch 12/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.9744 - accuracy: 0.6293 - tversky_loss: 2.6329 - dice_coef: 0.5464\n",
      "Epoch 00012: val_dice_coef improved from 0.56058 to 0.58041, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 323ms/step - loss: 0.9744 - accuracy: 0.6288 - tversky_loss: 2.6329 - dice_coef: 0.5464 - val_loss: 0.9131 - val_accuracy: 0.6773 - val_tversky_loss: 2.6081 - val_dice_coef: 0.5804\n",
      "Epoch 13/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.9553 - accuracy: 0.6556 - tversky_loss: 2.6263 - dice_coef: 0.5562\n",
      "Epoch 00013: val_dice_coef improved from 0.58041 to 0.59250, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 322ms/step - loss: 0.9554 - accuracy: 0.6550 - tversky_loss: 2.6263 - dice_coef: 0.5562 - val_loss: 0.8906 - val_accuracy: 0.7017 - val_tversky_loss: 2.5997 - val_dice_coef: 0.5925\n",
      "Epoch 14/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.9361 - accuracy: 0.6819 - tversky_loss: 2.6196 - dice_coef: 0.5661\n",
      "Epoch 00014: val_dice_coef improved from 0.59250 to 0.60341, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 323ms/step - loss: 0.9362 - accuracy: 0.6814 - tversky_loss: 2.6196 - dice_coef: 0.5661 - val_loss: 0.8709 - val_accuracy: 0.7275 - val_tversky_loss: 2.5921 - val_dice_coef: 0.6034\n",
      "Epoch 15/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.9173 - accuracy: 0.7048 - tversky_loss: 2.6129 - dice_coef: 0.5760\n",
      "Epoch 00015: val_dice_coef improved from 0.60341 to 0.61366, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 324ms/step - loss: 0.9173 - accuracy: 0.7043 - tversky_loss: 2.6129 - dice_coef: 0.5760 - val_loss: 0.8503 - val_accuracy: 0.7492 - val_tversky_loss: 2.5851 - val_dice_coef: 0.6137\n",
      "Epoch 16/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.8989 - accuracy: 0.7276 - tversky_loss: 2.6062 - dice_coef: 0.5858\n",
      "Epoch 00016: val_dice_coef improved from 0.61366 to 0.62445, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 319ms/step - loss: 0.8989 - accuracy: 0.7271 - tversky_loss: 2.6062 - dice_coef: 0.5858 - val_loss: 0.8311 - val_accuracy: 0.7722 - val_tversky_loss: 2.5774 - val_dice_coef: 0.6244\n",
      "Epoch 17/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.8810 - accuracy: 0.7498 - tversky_loss: 2.5996 - dice_coef: 0.5954\n",
      "Epoch 00017: val_dice_coef improved from 0.62445 to 0.63497, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "123/123 [==============================] - 40s 324ms/step - loss: 0.8811 - accuracy: 0.7493 - tversky_loss: 2.5996 - dice_coef: 0.5954 - val_loss: 0.8104 - val_accuracy: 0.7935 - val_tversky_loss: 2.5699 - val_dice_coef: 0.6350\n",
      "Epoch 18/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.8637 - accuracy: 0.7712 - tversky_loss: 2.5931 - dice_coef: 0.6048\n",
      "Epoch 00018: val_dice_coef improved from 0.63497 to 0.64811, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 318ms/step - loss: 0.8637 - accuracy: 0.7708 - tversky_loss: 2.5931 - dice_coef: 0.6048 - val_loss: 0.7894 - val_accuracy: 0.8227 - val_tversky_loss: 2.5603 - val_dice_coef: 0.6481\n",
      "Epoch 19/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.8470 - accuracy: 0.7918 - tversky_loss: 2.5868 - dice_coef: 0.6140\n",
      "Epoch 00019: val_dice_coef improved from 0.64811 to 0.65638, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 321ms/step - loss: 0.8470 - accuracy: 0.7914 - tversky_loss: 2.5868 - dice_coef: 0.6139 - val_loss: 0.7731 - val_accuracy: 0.8344 - val_tversky_loss: 2.5542 - val_dice_coef: 0.6564\n",
      "Epoch 20/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.8311 - accuracy: 0.8107 - tversky_loss: 2.5806 - dice_coef: 0.6227\n",
      "Epoch 00020: val_dice_coef improved from 0.65638 to 0.66461, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 323ms/step - loss: 0.8311 - accuracy: 0.8104 - tversky_loss: 2.5806 - dice_coef: 0.6227 - val_loss: 0.7585 - val_accuracy: 0.8518 - val_tversky_loss: 2.5482 - val_dice_coef: 0.6646\n",
      "Epoch 21/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.8161 - accuracy: 0.8281 - tversky_loss: 2.5747 - dice_coef: 0.6311\n",
      "Epoch 00021: val_dice_coef improved from 0.66461 to 0.67236, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 317ms/step - loss: 0.8161 - accuracy: 0.8278 - tversky_loss: 2.5747 - dice_coef: 0.6311 - val_loss: 0.7441 - val_accuracy: 0.8676 - val_tversky_loss: 2.5427 - val_dice_coef: 0.6724\n",
      "Epoch 22/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.8022 - accuracy: 0.8438 - tversky_loss: 2.5690 - dice_coef: 0.6390\n",
      "Epoch 00022: val_dice_coef improved from 0.67236 to 0.67989, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 315ms/step - loss: 0.8022 - accuracy: 0.8435 - tversky_loss: 2.5690 - dice_coef: 0.6389 - val_loss: 0.7322 - val_accuracy: 0.8833 - val_tversky_loss: 2.5370 - val_dice_coef: 0.6799\n",
      "Epoch 23/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.7895 - accuracy: 0.8583 - tversky_loss: 2.5638 - dice_coef: 0.6462\n",
      "Epoch 00023: val_dice_coef improved from 0.67989 to 0.68674, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 317ms/step - loss: 0.7895 - accuracy: 0.8581 - tversky_loss: 2.5638 - dice_coef: 0.6462 - val_loss: 0.7215 - val_accuracy: 0.8957 - val_tversky_loss: 2.5317 - val_dice_coef: 0.6867\n",
      "Epoch 24/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.7776 - accuracy: 0.8714 - tversky_loss: 2.5589 - dice_coef: 0.6530\n",
      "Epoch 00024: val_dice_coef improved from 0.68674 to 0.69193, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 325ms/step - loss: 0.7777 - accuracy: 0.8712 - tversky_loss: 2.5589 - dice_coef: 0.6530 - val_loss: 0.7119 - val_accuracy: 0.9058 - val_tversky_loss: 2.5279 - val_dice_coef: 0.6919\n",
      "Epoch 25/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.7667 - accuracy: 0.8831 - tversky_loss: 2.5542 - dice_coef: 0.6594\n",
      "Epoch 00025: val_dice_coef improved from 0.69193 to 0.69782, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 322ms/step - loss: 0.7667 - accuracy: 0.8829 - tversky_loss: 2.5542 - dice_coef: 0.6594 - val_loss: 0.7001 - val_accuracy: 0.9124 - val_tversky_loss: 2.5235 - val_dice_coef: 0.6978\n",
      "Epoch 26/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.7562 - accuracy: 0.8936 - tversky_loss: 2.5497 - dice_coef: 0.6655\n",
      "Epoch 00026: val_dice_coef improved from 0.69782 to 0.70335, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 322ms/step - loss: 0.7563 - accuracy: 0.8935 - tversky_loss: 2.5497 - dice_coef: 0.6655 - val_loss: 0.6921 - val_accuracy: 0.9217 - val_tversky_loss: 2.5191 - val_dice_coef: 0.7033\n",
      "Epoch 27/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.7468 - accuracy: 0.9034 - tversky_loss: 2.5456 - dice_coef: 0.6710\n",
      "Epoch 00027: val_dice_coef improved from 0.70335 to 0.70755, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 322ms/step - loss: 0.7468 - accuracy: 0.9033 - tversky_loss: 2.5456 - dice_coef: 0.6710 - val_loss: 0.6847 - val_accuracy: 0.9283 - val_tversky_loss: 2.5159 - val_dice_coef: 0.7076\n",
      "Epoch 28/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.7381 - accuracy: 0.9123 - tversky_loss: 2.5418 - dice_coef: 0.6761\n",
      "Epoch 00028: val_dice_coef improved from 0.70755 to 0.71121, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 320ms/step - loss: 0.7381 - accuracy: 0.9123 - tversky_loss: 2.5418 - dice_coef: 0.6761 - val_loss: 0.6790 - val_accuracy: 0.9347 - val_tversky_loss: 2.5130 - val_dice_coef: 0.7112\n",
      "Epoch 29/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.7296 - accuracy: 0.9205 - tversky_loss: 2.5380 - dice_coef: 0.6812\n",
      "Epoch 00029: val_dice_coef improved from 0.71121 to 0.71760, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 323ms/step - loss: 0.7297 - accuracy: 0.9205 - tversky_loss: 2.5380 - dice_coef: 0.6811 - val_loss: 0.6710 - val_accuracy: 0.9438 - val_tversky_loss: 2.5078 - val_dice_coef: 0.7176\n",
      "Epoch 30/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.7214 - accuracy: 0.9276 - tversky_loss: 2.5343 - dice_coef: 0.6861\n",
      "Epoch 00030: val_dice_coef improved from 0.71760 to 0.71921, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 325ms/step - loss: 0.7214 - accuracy: 0.9275 - tversky_loss: 2.5343 - dice_coef: 0.6860 - val_loss: 0.6673 - val_accuracy: 0.9470 - val_tversky_loss: 2.5067 - val_dice_coef: 0.7192\n",
      "Epoch 31/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.7141 - accuracy: 0.9333 - tversky_loss: 2.5309 - dice_coef: 0.6904\n",
      "Epoch 00031: val_dice_coef improved from 0.71921 to 0.72237, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 329ms/step - loss: 0.7141 - accuracy: 0.9333 - tversky_loss: 2.5309 - dice_coef: 0.6904 - val_loss: 0.6607 - val_accuracy: 0.9510 - val_tversky_loss: 2.5042 - val_dice_coef: 0.7224\n",
      "Epoch 32/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.7073 - accuracy: 0.9382 - tversky_loss: 2.5278 - dice_coef: 0.6945\n",
      "Epoch 00032: val_dice_coef improved from 0.72237 to 0.72364, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 323ms/step - loss: 0.7073 - accuracy: 0.9382 - tversky_loss: 2.5278 - dice_coef: 0.6945 - val_loss: 0.6578 - val_accuracy: 0.9521 - val_tversky_loss: 2.5033 - val_dice_coef: 0.7236\n",
      "Epoch 33/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.7011 - accuracy: 0.9424 - tversky_loss: 2.5248 - dice_coef: 0.6982\n",
      "Epoch 00033: val_dice_coef improved from 0.72364 to 0.72860, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 316ms/step - loss: 0.7011 - accuracy: 0.9424 - tversky_loss: 2.5248 - dice_coef: 0.6982 - val_loss: 0.6486 - val_accuracy: 0.9565 - val_tversky_loss: 2.4993 - val_dice_coef: 0.7286\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 34/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.6952 - accuracy: 0.9460 - tversky_loss: 2.5221 - dice_coef: 0.7018\n",
      "Epoch 00034: val_dice_coef improved from 0.72860 to 0.72986, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 317ms/step - loss: 0.6952 - accuracy: 0.9460 - tversky_loss: 2.5221 - dice_coef: 0.7017 - val_loss: 0.6463 - val_accuracy: 0.9578 - val_tversky_loss: 2.4984 - val_dice_coef: 0.7299\n",
      "Epoch 35/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.6898 - accuracy: 0.9487 - tversky_loss: 2.5194 - dice_coef: 0.7051\n",
      "Epoch 00035: val_dice_coef improved from 0.72986 to 0.73586, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 322ms/step - loss: 0.6898 - accuracy: 0.9487 - tversky_loss: 2.5194 - dice_coef: 0.7051 - val_loss: 0.6388 - val_accuracy: 0.9622 - val_tversky_loss: 2.4931 - val_dice_coef: 0.7359\n",
      "Epoch 36/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.6845 - accuracy: 0.9508 - tversky_loss: 2.5169 - dice_coef: 0.7083\n",
      "Epoch 00036: val_dice_coef improved from 0.73586 to 0.73779, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 325ms/step - loss: 0.6845 - accuracy: 0.9508 - tversky_loss: 2.5169 - dice_coef: 0.7083 - val_loss: 0.6353 - val_accuracy: 0.9638 - val_tversky_loss: 2.4916 - val_dice_coef: 0.7378\n",
      "Epoch 37/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.6796 - accuracy: 0.9526 - tversky_loss: 2.5144 - dice_coef: 0.7113\n",
      "Epoch 00037: val_dice_coef improved from 0.73779 to 0.73976, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 317ms/step - loss: 0.6796 - accuracy: 0.9526 - tversky_loss: 2.5144 - dice_coef: 0.7113 - val_loss: 0.6309 - val_accuracy: 0.9625 - val_tversky_loss: 2.4899 - val_dice_coef: 0.7398\n",
      "Epoch 38/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.6748 - accuracy: 0.9539 - tversky_loss: 2.5120 - dice_coef: 0.7142\n",
      "Epoch 00038: val_dice_coef improved from 0.73976 to 0.74253, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 322ms/step - loss: 0.6748 - accuracy: 0.9539 - tversky_loss: 2.5120 - dice_coef: 0.7142 - val_loss: 0.6270 - val_accuracy: 0.9633 - val_tversky_loss: 2.4875 - val_dice_coef: 0.7425\n",
      "Epoch 39/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.6701 - accuracy: 0.9547 - tversky_loss: 2.5096 - dice_coef: 0.7172\n",
      "Epoch 00039: val_dice_coef did not improve from 0.74253\n",
      "123/123 [==============================] - 39s 320ms/step - loss: 0.6701 - accuracy: 0.9547 - tversky_loss: 2.5096 - dice_coef: 0.7171 - val_loss: 0.6274 - val_accuracy: 0.9620 - val_tversky_loss: 2.4881 - val_dice_coef: 0.7419\n",
      "Epoch 40/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.6655 - accuracy: 0.9552 - tversky_loss: 2.5072 - dice_coef: 0.7200\n",
      "Epoch 00040: val_dice_coef improved from 0.74253 to 0.74528, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 324ms/step - loss: 0.6655 - accuracy: 0.9551 - tversky_loss: 2.5072 - dice_coef: 0.7200 - val_loss: 0.6231 - val_accuracy: 0.9649 - val_tversky_loss: 2.4852 - val_dice_coef: 0.7453\n",
      "Epoch 41/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.6611 - accuracy: 0.9554 - tversky_loss: 2.5049 - dice_coef: 0.7227\n",
      "Epoch 00041: val_dice_coef improved from 0.74528 to 0.74739, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 320ms/step - loss: 0.6611 - accuracy: 0.9554 - tversky_loss: 2.5049 - dice_coef: 0.7227 - val_loss: 0.6186 - val_accuracy: 0.9627 - val_tversky_loss: 2.4833 - val_dice_coef: 0.7474\n",
      "Epoch 42/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.6569 - accuracy: 0.9556 - tversky_loss: 2.5027 - dice_coef: 0.7254\n",
      "Epoch 00042: val_dice_coef improved from 0.74739 to 0.74862, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 323ms/step - loss: 0.6569 - accuracy: 0.9556 - tversky_loss: 2.5027 - dice_coef: 0.7254 - val_loss: 0.6169 - val_accuracy: 0.9621 - val_tversky_loss: 2.4822 - val_dice_coef: 0.7486\n",
      "Epoch 43/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.6527 - accuracy: 0.9556 - tversky_loss: 2.5005 - dice_coef: 0.7280\n",
      "Epoch 00043: val_dice_coef improved from 0.74862 to 0.75245, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 318ms/step - loss: 0.6527 - accuracy: 0.9556 - tversky_loss: 2.5005 - dice_coef: 0.7279 - val_loss: 0.6106 - val_accuracy: 0.9623 - val_tversky_loss: 2.4788 - val_dice_coef: 0.7525\n",
      "Epoch 44/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.6487 - accuracy: 0.9556 - tversky_loss: 2.4983 - dice_coef: 0.7305\n",
      "Epoch 00044: val_dice_coef improved from 0.75245 to 0.75376, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 324ms/step - loss: 0.6487 - accuracy: 0.9556 - tversky_loss: 2.4983 - dice_coef: 0.7305 - val_loss: 0.6087 - val_accuracy: 0.9612 - val_tversky_loss: 2.4776 - val_dice_coef: 0.7538\n",
      "Epoch 45/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.6446 - accuracy: 0.9555 - tversky_loss: 2.4960 - dice_coef: 0.7331\n",
      "Epoch 00045: val_dice_coef improved from 0.75376 to 0.75711, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 323ms/step - loss: 0.6446 - accuracy: 0.9555 - tversky_loss: 2.4960 - dice_coef: 0.7331 - val_loss: 0.6043 - val_accuracy: 0.9612 - val_tversky_loss: 2.4745 - val_dice_coef: 0.7571\n",
      "Epoch 46/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.6404 - accuracy: 0.9556 - tversky_loss: 2.4938 - dice_coef: 0.7357\n",
      "Epoch 00046: val_dice_coef improved from 0.75711 to 0.75908, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 325ms/step - loss: 0.6404 - accuracy: 0.9556 - tversky_loss: 2.4938 - dice_coef: 0.7357 - val_loss: 0.6001 - val_accuracy: 0.9613 - val_tversky_loss: 2.4727 - val_dice_coef: 0.7591\n",
      "Epoch 47/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.6364 - accuracy: 0.9558 - tversky_loss: 2.4916 - dice_coef: 0.7382\n",
      "Epoch 00047: val_dice_coef improved from 0.75908 to 0.76286, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 320ms/step - loss: 0.6364 - accuracy: 0.9557 - tversky_loss: 2.4916 - dice_coef: 0.7382 - val_loss: 0.5941 - val_accuracy: 0.9619 - val_tversky_loss: 2.4692 - val_dice_coef: 0.7629\n",
      "Epoch 48/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.6324 - accuracy: 0.9559 - tversky_loss: 2.4894 - dice_coef: 0.7407\n",
      "Epoch 00048: val_dice_coef improved from 0.76286 to 0.76656, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 323ms/step - loss: 0.6324 - accuracy: 0.9558 - tversky_loss: 2.4894 - dice_coef: 0.7406 - val_loss: 0.5867 - val_accuracy: 0.9621 - val_tversky_loss: 2.4659 - val_dice_coef: 0.7666\n",
      "Epoch 49/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.6285 - accuracy: 0.9560 - tversky_loss: 2.4872 - dice_coef: 0.7431\n",
      "Epoch 00049: val_dice_coef improved from 0.76656 to 0.77046, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 322ms/step - loss: 0.6285 - accuracy: 0.9560 - tversky_loss: 2.4872 - dice_coef: 0.7431 - val_loss: 0.5824 - val_accuracy: 0.9644 - val_tversky_loss: 2.4622 - val_dice_coef: 0.7705\n",
      "Epoch 50/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.6246 - accuracy: 0.9562 - tversky_loss: 2.4851 - dice_coef: 0.7455\n",
      "Epoch 00050: val_dice_coef improved from 0.77046 to 0.77085, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "123/123 [==============================] - 39s 320ms/step - loss: 0.6246 - accuracy: 0.9561 - tversky_loss: 2.4851 - dice_coef: 0.7455 - val_loss: 0.5805 - val_accuracy: 0.9628 - val_tversky_loss: 2.4618 - val_dice_coef: 0.7709\n",
      "Epoch 51/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.6207 - accuracy: 0.9563 - tversky_loss: 2.4828 - dice_coef: 0.7480\n",
      "Epoch 00051: val_dice_coef improved from 0.77085 to 0.77251, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 319ms/step - loss: 0.6207 - accuracy: 0.9563 - tversky_loss: 2.4828 - dice_coef: 0.7480 - val_loss: 0.5779 - val_accuracy: 0.9625 - val_tversky_loss: 2.4602 - val_dice_coef: 0.7725\n",
      "Epoch 52/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.6167 - accuracy: 0.9566 - tversky_loss: 2.4806 - dice_coef: 0.7504\n",
      "Epoch 00052: val_dice_coef improved from 0.77251 to 0.77414, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 320ms/step - loss: 0.6167 - accuracy: 0.9565 - tversky_loss: 2.4806 - dice_coef: 0.7504 - val_loss: 0.5745 - val_accuracy: 0.9626 - val_tversky_loss: 2.4588 - val_dice_coef: 0.7741\n",
      "Epoch 53/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.6129 - accuracy: 0.9568 - tversky_loss: 2.4785 - dice_coef: 0.7528\n",
      "Epoch 00053: val_dice_coef improved from 0.77414 to 0.77617, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 317ms/step - loss: 0.6129 - accuracy: 0.9567 - tversky_loss: 2.4785 - dice_coef: 0.7528 - val_loss: 0.5723 - val_accuracy: 0.9632 - val_tversky_loss: 2.4568 - val_dice_coef: 0.7762\n",
      "Epoch 54/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.6091 - accuracy: 0.9571 - tversky_loss: 2.4763 - dice_coef: 0.7552\n",
      "Epoch 00054: val_dice_coef improved from 0.77617 to 0.77953, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 320ms/step - loss: 0.6091 - accuracy: 0.9570 - tversky_loss: 2.4763 - dice_coef: 0.7552 - val_loss: 0.5676 - val_accuracy: 0.9648 - val_tversky_loss: 2.4535 - val_dice_coef: 0.7795\n",
      "Epoch 55/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.6051 - accuracy: 0.9574 - tversky_loss: 2.4740 - dice_coef: 0.7576\n",
      "Epoch 00055: val_dice_coef improved from 0.77953 to 0.78263, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 321ms/step - loss: 0.6051 - accuracy: 0.9573 - tversky_loss: 2.4740 - dice_coef: 0.7576 - val_loss: 0.5618 - val_accuracy: 0.9659 - val_tversky_loss: 2.4506 - val_dice_coef: 0.7826\n",
      "Epoch 56/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.6009 - accuracy: 0.9578 - tversky_loss: 2.4717 - dice_coef: 0.7602\n",
      "Epoch 00056: val_dice_coef improved from 0.78263 to 0.78271, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 315ms/step - loss: 0.6009 - accuracy: 0.9577 - tversky_loss: 2.4717 - dice_coef: 0.7602 - val_loss: 0.5603 - val_accuracy: 0.9635 - val_tversky_loss: 2.4505 - val_dice_coef: 0.7827\n",
      "Epoch 57/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5970 - accuracy: 0.9581 - tversky_loss: 2.4695 - dice_coef: 0.7626\n",
      "Epoch 00057: val_dice_coef improved from 0.78271 to 0.78518, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 320ms/step - loss: 0.5970 - accuracy: 0.9580 - tversky_loss: 2.4695 - dice_coef: 0.7626 - val_loss: 0.5575 - val_accuracy: 0.9643 - val_tversky_loss: 2.4481 - val_dice_coef: 0.7852\n",
      "Epoch 58/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5933 - accuracy: 0.9584 - tversky_loss: 2.4673 - dice_coef: 0.7649\n",
      "Epoch 00058: val_dice_coef improved from 0.78518 to 0.78576, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 319ms/step - loss: 0.5933 - accuracy: 0.9583 - tversky_loss: 2.4673 - dice_coef: 0.7649 - val_loss: 0.5565 - val_accuracy: 0.9635 - val_tversky_loss: 2.4475 - val_dice_coef: 0.7858\n",
      "Epoch 59/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5898 - accuracy: 0.9586 - tversky_loss: 2.4653 - dice_coef: 0.7671\n",
      "Epoch 00059: val_dice_coef improved from 0.78576 to 0.78710, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 320ms/step - loss: 0.5897 - accuracy: 0.9585 - tversky_loss: 2.4653 - dice_coef: 0.7671 - val_loss: 0.5548 - val_accuracy: 0.9634 - val_tversky_loss: 2.4461 - val_dice_coef: 0.7871\n",
      "Epoch 60/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5863 - accuracy: 0.9588 - tversky_loss: 2.4632 - dice_coef: 0.7692\n",
      "Epoch 00060: val_dice_coef improved from 0.78710 to 0.78910, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 319ms/step - loss: 0.5863 - accuracy: 0.9588 - tversky_loss: 2.4632 - dice_coef: 0.7692 - val_loss: 0.5540 - val_accuracy: 0.9652 - val_tversky_loss: 2.4442 - val_dice_coef: 0.7891\n",
      "Epoch 61/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5830 - accuracy: 0.9590 - tversky_loss: 2.4613 - dice_coef: 0.7713\n",
      "Epoch 00061: val_dice_coef improved from 0.78910 to 0.79124, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 320ms/step - loss: 0.5830 - accuracy: 0.9590 - tversky_loss: 2.4613 - dice_coef: 0.7713 - val_loss: 0.5497 - val_accuracy: 0.9647 - val_tversky_loss: 2.4420 - val_dice_coef: 0.7912\n",
      "Epoch 62/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5797 - accuracy: 0.9593 - tversky_loss: 2.4593 - dice_coef: 0.7734\n",
      "Epoch 00062: val_dice_coef improved from 0.79124 to 0.79267, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 321ms/step - loss: 0.5797 - accuracy: 0.9592 - tversky_loss: 2.4593 - dice_coef: 0.7734 - val_loss: 0.5461 - val_accuracy: 0.9647 - val_tversky_loss: 2.4406 - val_dice_coef: 0.7927\n",
      "Epoch 63/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5764 - accuracy: 0.9595 - tversky_loss: 2.4574 - dice_coef: 0.7754\n",
      "Epoch 00063: val_dice_coef improved from 0.79267 to 0.79298, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 323ms/step - loss: 0.5764 - accuracy: 0.9594 - tversky_loss: 2.4573 - dice_coef: 0.7754 - val_loss: 0.5452 - val_accuracy: 0.9635 - val_tversky_loss: 2.4402 - val_dice_coef: 0.7930\n",
      "Epoch 64/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5732 - accuracy: 0.9597 - tversky_loss: 2.4554 - dice_coef: 0.7774\n",
      "Epoch 00064: val_dice_coef improved from 0.79298 to 0.79338, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 318ms/step - loss: 0.5732 - accuracy: 0.9597 - tversky_loss: 2.4554 - dice_coef: 0.7774 - val_loss: 0.5440 - val_accuracy: 0.9630 - val_tversky_loss: 2.4399 - val_dice_coef: 0.7934\n",
      "Epoch 65/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5700 - accuracy: 0.9600 - tversky_loss: 2.4535 - dice_coef: 0.7794\n",
      "Epoch 00065: val_dice_coef improved from 0.79338 to 0.79370, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 322ms/step - loss: 0.5700 - accuracy: 0.9599 - tversky_loss: 2.4535 - dice_coef: 0.7794 - val_loss: 0.5435 - val_accuracy: 0.9625 - val_tversky_loss: 2.4395 - val_dice_coef: 0.7937\n",
      "Epoch 66/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5669 - accuracy: 0.9603 - tversky_loss: 2.4516 - dice_coef: 0.7814\n",
      "Epoch 00066: val_dice_coef improved from 0.79370 to 0.79570, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 322ms/step - loss: 0.5669 - accuracy: 0.9602 - tversky_loss: 2.4515 - dice_coef: 0.7814 - val_loss: 0.5426 - val_accuracy: 0.9642 - val_tversky_loss: 2.4375 - val_dice_coef: 0.7957\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 67/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5637 - accuracy: 0.9605 - tversky_loss: 2.4496 - dice_coef: 0.7834\n",
      "Epoch 00067: val_dice_coef improved from 0.79570 to 0.79711, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 324ms/step - loss: 0.5637 - accuracy: 0.9605 - tversky_loss: 2.4496 - dice_coef: 0.7833 - val_loss: 0.5402 - val_accuracy: 0.9636 - val_tversky_loss: 2.4360 - val_dice_coef: 0.7971\n",
      "Epoch 68/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5606 - accuracy: 0.9608 - tversky_loss: 2.4477 - dice_coef: 0.7853\n",
      "Epoch 00068: val_dice_coef improved from 0.79711 to 0.79816, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 326ms/step - loss: 0.5606 - accuracy: 0.9607 - tversky_loss: 2.4477 - dice_coef: 0.7853 - val_loss: 0.5375 - val_accuracy: 0.9631 - val_tversky_loss: 2.4349 - val_dice_coef: 0.7982\n",
      "Epoch 69/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5575 - accuracy: 0.9611 - tversky_loss: 2.4458 - dice_coef: 0.7872\n",
      "Epoch 00069: val_dice_coef improved from 0.79816 to 0.79837, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 322ms/step - loss: 0.5575 - accuracy: 0.9610 - tversky_loss: 2.4458 - dice_coef: 0.7872 - val_loss: 0.5380 - val_accuracy: 0.9629 - val_tversky_loss: 2.4347 - val_dice_coef: 0.7984\n",
      "Epoch 70/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5544 - accuracy: 0.9613 - tversky_loss: 2.4439 - dice_coef: 0.7892\n",
      "Epoch 00070: val_dice_coef improved from 0.79837 to 0.79885, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 321ms/step - loss: 0.5544 - accuracy: 0.9613 - tversky_loss: 2.4439 - dice_coef: 0.7892 - val_loss: 0.5369 - val_accuracy: 0.9624 - val_tversky_loss: 2.4341 - val_dice_coef: 0.7989\n",
      "Epoch 71/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5513 - accuracy: 0.9616 - tversky_loss: 2.4419 - dice_coef: 0.7911\n",
      "Epoch 00071: val_dice_coef improved from 0.79885 to 0.80296, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 323ms/step - loss: 0.5513 - accuracy: 0.9615 - tversky_loss: 2.4419 - dice_coef: 0.7911 - val_loss: 0.5299 - val_accuracy: 0.9655 - val_tversky_loss: 2.4301 - val_dice_coef: 0.8030\n",
      "Epoch 72/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5482 - accuracy: 0.9618 - tversky_loss: 2.4400 - dice_coef: 0.7930\n",
      "Epoch 00072: val_dice_coef improved from 0.80296 to 0.80362, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 319ms/step - loss: 0.5482 - accuracy: 0.9618 - tversky_loss: 2.4400 - dice_coef: 0.7930 - val_loss: 0.5291 - val_accuracy: 0.9637 - val_tversky_loss: 2.4293 - val_dice_coef: 0.8036\n",
      "Epoch 73/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5451 - accuracy: 0.9621 - tversky_loss: 2.4380 - dice_coef: 0.7950\n",
      "Epoch 00073: val_dice_coef improved from 0.80362 to 0.80486, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 322ms/step - loss: 0.5451 - accuracy: 0.9621 - tversky_loss: 2.4380 - dice_coef: 0.7950 - val_loss: 0.5274 - val_accuracy: 0.9642 - val_tversky_loss: 2.4280 - val_dice_coef: 0.8049\n",
      "Epoch 74/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5420 - accuracy: 0.9624 - tversky_loss: 2.4360 - dice_coef: 0.7969\n",
      "Epoch 00074: val_dice_coef improved from 0.80486 to 0.80691, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 320ms/step - loss: 0.5420 - accuracy: 0.9623 - tversky_loss: 2.4360 - dice_coef: 0.7969 - val_loss: 0.5241 - val_accuracy: 0.9643 - val_tversky_loss: 2.4259 - val_dice_coef: 0.8069\n",
      "Epoch 75/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5390 - accuracy: 0.9626 - tversky_loss: 2.4341 - dice_coef: 0.7988\n",
      "Epoch 00075: val_dice_coef improved from 0.80691 to 0.80883, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 320ms/step - loss: 0.5390 - accuracy: 0.9626 - tversky_loss: 2.4341 - dice_coef: 0.7988 - val_loss: 0.5211 - val_accuracy: 0.9643 - val_tversky_loss: 2.4238 - val_dice_coef: 0.8088\n",
      "Epoch 76/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5359 - accuracy: 0.9629 - tversky_loss: 2.4321 - dice_coef: 0.8008\n",
      "Epoch 00076: val_dice_coef improved from 0.80883 to 0.81168, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 317ms/step - loss: 0.5359 - accuracy: 0.9629 - tversky_loss: 2.4321 - dice_coef: 0.8008 - val_loss: 0.5175 - val_accuracy: 0.9650 - val_tversky_loss: 2.4208 - val_dice_coef: 0.8117\n",
      "Epoch 77/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5328 - accuracy: 0.9632 - tversky_loss: 2.4301 - dice_coef: 0.8026\n",
      "Epoch 00077: val_dice_coef did not improve from 0.81168\n",
      "123/123 [==============================] - 39s 316ms/step - loss: 0.5328 - accuracy: 0.9632 - tversky_loss: 2.4301 - dice_coef: 0.8026 - val_loss: 0.5183 - val_accuracy: 0.9644 - val_tversky_loss: 2.4215 - val_dice_coef: 0.8110\n",
      "Epoch 78/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5298 - accuracy: 0.9636 - tversky_loss: 2.4281 - dice_coef: 0.8046\n",
      "Epoch 00078: val_dice_coef improved from 0.81168 to 0.81200, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 320ms/step - loss: 0.5298 - accuracy: 0.9635 - tversky_loss: 2.4281 - dice_coef: 0.8046 - val_loss: 0.5172 - val_accuracy: 0.9646 - val_tversky_loss: 2.4203 - val_dice_coef: 0.8120\n",
      "Epoch 79/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5267 - accuracy: 0.9639 - tversky_loss: 2.4261 - dice_coef: 0.8065\n",
      "Epoch 00079: val_dice_coef improved from 0.81200 to 0.81390, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 322ms/step - loss: 0.5267 - accuracy: 0.9638 - tversky_loss: 2.4261 - dice_coef: 0.8065 - val_loss: 0.5131 - val_accuracy: 0.9641 - val_tversky_loss: 2.4183 - val_dice_coef: 0.8139\n",
      "Epoch 80/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5237 - accuracy: 0.9642 - tversky_loss: 2.4241 - dice_coef: 0.8084\n",
      "Epoch 00080: val_dice_coef improved from 0.81390 to 0.81548, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 318ms/step - loss: 0.5237 - accuracy: 0.9641 - tversky_loss: 2.4240 - dice_coef: 0.8084 - val_loss: 0.5132 - val_accuracy: 0.9657 - val_tversky_loss: 2.4166 - val_dice_coef: 0.8155\n",
      "Epoch 81/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5207 - accuracy: 0.9646 - tversky_loss: 2.4220 - dice_coef: 0.8103\n",
      "Epoch 00081: val_dice_coef improved from 0.81548 to 0.81744, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 319ms/step - loss: 0.5207 - accuracy: 0.9645 - tversky_loss: 2.4220 - dice_coef: 0.8103 - val_loss: 0.5088 - val_accuracy: 0.9646 - val_tversky_loss: 2.4143 - val_dice_coef: 0.8174\n",
      "Epoch 82/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5176 - accuracy: 0.9649 - tversky_loss: 2.4200 - dice_coef: 0.8122\n",
      "Epoch 00082: val_dice_coef improved from 0.81744 to 0.81917, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 315ms/step - loss: 0.5177 - accuracy: 0.9648 - tversky_loss: 2.4200 - dice_coef: 0.8122 - val_loss: 0.5065 - val_accuracy: 0.9647 - val_tversky_loss: 2.4124 - val_dice_coef: 0.8192\n",
      "Epoch 83/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5146 - accuracy: 0.9652 - tversky_loss: 2.4180 - dice_coef: 0.8140\n",
      "Epoch 00083: val_dice_coef improved from 0.81917 to 0.82167, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "123/123 [==============================] - 39s 317ms/step - loss: 0.5146 - accuracy: 0.9652 - tversky_loss: 2.4180 - dice_coef: 0.8140 - val_loss: 0.5016 - val_accuracy: 0.9648 - val_tversky_loss: 2.4097 - val_dice_coef: 0.8217\n",
      "Epoch 84/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5116 - accuracy: 0.9655 - tversky_loss: 2.4160 - dice_coef: 0.8159\n",
      "Epoch 00084: val_dice_coef improved from 0.82167 to 0.82363, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 318ms/step - loss: 0.5116 - accuracy: 0.9654 - tversky_loss: 2.4160 - dice_coef: 0.8159 - val_loss: 0.5003 - val_accuracy: 0.9651 - val_tversky_loss: 2.4074 - val_dice_coef: 0.8236\n",
      "Epoch 85/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5086 - accuracy: 0.9658 - tversky_loss: 2.4140 - dice_coef: 0.8178\n",
      "Epoch 00085: val_dice_coef improved from 0.82363 to 0.82675, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 316ms/step - loss: 0.5086 - accuracy: 0.9658 - tversky_loss: 2.4140 - dice_coef: 0.8178 - val_loss: 0.4961 - val_accuracy: 0.9661 - val_tversky_loss: 2.4039 - val_dice_coef: 0.8268\n",
      "Epoch 86/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5056 - accuracy: 0.9661 - tversky_loss: 2.4120 - dice_coef: 0.8196\n",
      "Epoch 00086: val_dice_coef did not improve from 0.82675\n",
      "123/123 [==============================] - 38s 313ms/step - loss: 0.5056 - accuracy: 0.9660 - tversky_loss: 2.4120 - dice_coef: 0.8196 - val_loss: 0.4970 - val_accuracy: 0.9651 - val_tversky_loss: 2.4041 - val_dice_coef: 0.8265\n",
      "Epoch 87/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.5027 - accuracy: 0.9664 - tversky_loss: 2.4100 - dice_coef: 0.8214\n",
      "Epoch 00087: val_dice_coef did not improve from 0.82675\n",
      "123/123 [==============================] - 39s 319ms/step - loss: 0.5027 - accuracy: 0.9663 - tversky_loss: 2.4100 - dice_coef: 0.8214 - val_loss: 0.4966 - val_accuracy: 0.9635 - val_tversky_loss: 2.4040 - val_dice_coef: 0.8264\n",
      "Epoch 88/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.4997 - accuracy: 0.9667 - tversky_loss: 2.4080 - dice_coef: 0.8232\n",
      "Epoch 00088: val_dice_coef improved from 0.82675 to 0.82694, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 38s 309ms/step - loss: 0.4997 - accuracy: 0.9667 - tversky_loss: 2.4080 - dice_coef: 0.8232 - val_loss: 0.4974 - val_accuracy: 0.9641 - val_tversky_loss: 2.4033 - val_dice_coef: 0.8269\n",
      "Epoch 89/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.4967 - accuracy: 0.9671 - tversky_loss: 2.4060 - dice_coef: 0.8250\n",
      "Epoch 00089: val_dice_coef improved from 0.82694 to 0.83037, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 322ms/step - loss: 0.4967 - accuracy: 0.9670 - tversky_loss: 2.4060 - dice_coef: 0.8250 - val_loss: 0.4913 - val_accuracy: 0.9651 - val_tversky_loss: 2.3996 - val_dice_coef: 0.8304\n",
      "Epoch 90/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.4938 - accuracy: 0.9673 - tversky_loss: 2.4040 - dice_coef: 0.8268\n",
      "Epoch 00090: val_dice_coef improved from 0.83037 to 0.83133, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 319ms/step - loss: 0.4938 - accuracy: 0.9672 - tversky_loss: 2.4039 - dice_coef: 0.8268 - val_loss: 0.4905 - val_accuracy: 0.9649 - val_tversky_loss: 2.3984 - val_dice_coef: 0.8313\n",
      "Epoch 91/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.4907 - accuracy: 0.9676 - tversky_loss: 2.4019 - dice_coef: 0.8287\n",
      "Epoch 00091: val_dice_coef improved from 0.83133 to 0.83261, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 318ms/step - loss: 0.4908 - accuracy: 0.9675 - tversky_loss: 2.4019 - dice_coef: 0.8287 - val_loss: 0.4912 - val_accuracy: 0.9666 - val_tversky_loss: 2.3971 - val_dice_coef: 0.8326\n",
      "Epoch 92/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.4877 - accuracy: 0.9679 - tversky_loss: 2.3998 - dice_coef: 0.8305\n",
      "Epoch 00092: val_dice_coef improved from 0.83261 to 0.83390, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 319ms/step - loss: 0.4877 - accuracy: 0.9678 - tversky_loss: 2.3998 - dice_coef: 0.8305 - val_loss: 0.4888 - val_accuracy: 0.9658 - val_tversky_loss: 2.3955 - val_dice_coef: 0.8339\n",
      "Epoch 93/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.4848 - accuracy: 0.9682 - tversky_loss: 2.3977 - dice_coef: 0.8323\n",
      "Epoch 00093: val_dice_coef did not improve from 0.83390\n",
      "123/123 [==============================] - 39s 316ms/step - loss: 0.4848 - accuracy: 0.9682 - tversky_loss: 2.3977 - dice_coef: 0.8323 - val_loss: 0.4903 - val_accuracy: 0.9648 - val_tversky_loss: 2.3963 - val_dice_coef: 0.8330\n",
      "Epoch 94/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.4818 - accuracy: 0.9685 - tversky_loss: 2.3957 - dice_coef: 0.8341\n",
      "Epoch 00094: val_dice_coef improved from 0.83390 to 0.83401, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 317ms/step - loss: 0.4818 - accuracy: 0.9684 - tversky_loss: 2.3957 - dice_coef: 0.8341 - val_loss: 0.4893 - val_accuracy: 0.9639 - val_tversky_loss: 2.3949 - val_dice_coef: 0.8340\n",
      "Epoch 95/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.4789 - accuracy: 0.9688 - tversky_loss: 2.3936 - dice_coef: 0.8359\n",
      "Epoch 00095: val_dice_coef improved from 0.83401 to 0.83669, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 319ms/step - loss: 0.4789 - accuracy: 0.9687 - tversky_loss: 2.3936 - dice_coef: 0.8359 - val_loss: 0.4834 - val_accuracy: 0.9648 - val_tversky_loss: 2.3920 - val_dice_coef: 0.8367\n",
      "Epoch 96/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.4759 - accuracy: 0.9691 - tversky_loss: 2.3915 - dice_coef: 0.8377\n",
      "Epoch 00096: val_dice_coef improved from 0.83669 to 0.83886, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 319ms/step - loss: 0.4760 - accuracy: 0.9690 - tversky_loss: 2.3915 - dice_coef: 0.8377 - val_loss: 0.4807 - val_accuracy: 0.9641 - val_tversky_loss: 2.3892 - val_dice_coef: 0.8389\n",
      "Epoch 97/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.4730 - accuracy: 0.9693 - tversky_loss: 2.3894 - dice_coef: 0.8395\n",
      "Epoch 00097: val_dice_coef improved from 0.83886 to 0.84128, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 319ms/step - loss: 0.4730 - accuracy: 0.9692 - tversky_loss: 2.3894 - dice_coef: 0.8395 - val_loss: 0.4786 - val_accuracy: 0.9661 - val_tversky_loss: 2.3866 - val_dice_coef: 0.8413\n",
      "Epoch 98/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.4701 - accuracy: 0.9696 - tversky_loss: 2.3872 - dice_coef: 0.8413\n",
      "Epoch 00098: val_dice_coef improved from 0.84128 to 0.84183, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 40s 322ms/step - loss: 0.4701 - accuracy: 0.9695 - tversky_loss: 2.3872 - dice_coef: 0.8413 - val_loss: 0.4789 - val_accuracy: 0.9649 - val_tversky_loss: 2.3857 - val_dice_coef: 0.8418\n",
      "Epoch 99/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.4671 - accuracy: 0.9699 - tversky_loss: 2.3850 - dice_coef: 0.8431\n",
      "Epoch 00099: val_dice_coef improved from 0.84183 to 0.84521, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n",
      "123/123 [==============================] - 39s 318ms/step - loss: 0.4671 - accuracy: 0.9698 - tversky_loss: 2.3850 - dice_coef: 0.8431 - val_loss: 0.4726 - val_accuracy: 0.9650 - val_tversky_loss: 2.3815 - val_dice_coef: 0.8452\n",
      "Epoch 100/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.4642 - accuracy: 0.9702 - tversky_loss: 2.3828 - dice_coef: 0.8449\n",
      "Epoch 00100: val_dice_coef improved from 0.84521 to 0.84639, saving model to MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "123/123 [==============================] - 40s 322ms/step - loss: 0.4642 - accuracy: 0.9701 - tversky_loss: 2.3828 - dice_coef: 0.8449 - val_loss: 0.4731 - val_accuracy: 0.9649 - val_tversky_loss: 2.3800 - val_dice_coef: 0.8464\n"
     ]
    }
   ],
   "source": [
    "# Train model\n",
    "result = custom_segnet.fit_generator(trainGen.generator(), steps_per_epoch=steps_per_epoch,\n",
    "                validation_data = valGen.generator(),\n",
    "                validation_steps = validation_steps, epochs=num_epochs, callbacks=callbacks, verbose=1)\n",
    "custom_segnet.save_weights(\"MODELS/weedmap_model_vgg16_segnet_EXP_TEST.h5\", overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "custom_segnet_test = CUSTOM_VGG16_SEGNET(num_classes=NUM_CLASSES, vgg_level=VGG_LEVEL, input_height=INPUT_HEIGHT, input_width=INPUT_WIDTH, input_depth=INPUT_DEPTH)\n",
    "custom_segnet_test.load_weights('MODELS/weedmap_model_vgg16_segnet_checkpoint_EXP_TEST.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show the loss/metric graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get actual number of epochs model was trained for\n",
    "N = len(result.history['loss'])\n",
    "\n",
    "#Plot the model evaluation history\n",
    "plt.style.use(\"ggplot\")\n",
    "fig = plt.figure(figsize=(20,8))\n",
    "\n",
    "fig.add_subplot(1,2,1)\n",
    "plt.title(\"Training Loss\")\n",
    "plt.plot(np.arange(0, N), result.history[\"loss\"], label=\"train_loss\")\n",
    "plt.plot(np.arange(0, N), result.history[\"val_loss\"], label=\"val_loss\")\n",
    "#plt.ylim(0, 1)\n",
    "plt.xlabel(\"Epoch #\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend(loc=\"lower left\")\n",
    "\n",
    "fig.add_subplot(1,2,2)\n",
    "plt.title(\"Training Dice Coef\")\n",
    "plt.plot(np.arange(0, N), result.history[\"dice_coef\"], label=\"train_dice_coef\")\n",
    "plt.plot(np.arange(0, N), result.history[\"val_dice_coef\"], label=\"val_dice_coef\")\n",
    "plt.xlabel(\"Epoch #\")\n",
    "plt.ylabel(\"Dice Coef\")\n",
    "plt.legend(loc=\"lower left\")\n",
    "plt.xlim(0, 100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test the model with test.df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create iterators for frames and masks\n",
    "TEST_HDF5 = Path.home() / Path(r\"WeedMapSemanticSegmentation/DATASETS/RedEdge/EXP4_RGB_BS5/test.hdf5\")\n",
    "testGen = HDF5DatasetGenerator(TEST_HDF5, 5, aug=None, preprocessors=[awp], classes=3, encoded=False)\n",
    "gen_test = testGen.generator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_TEST = list()\n",
    "Y_TEST = list()\n",
    "#Iterate over the train images while saving the frames and masks in appropriate folders\n",
    "for i, file in enumerate(range(0, 204, 5)):\n",
    "    #Convert tensors to numpy arrays\n",
    "    image_test, mask_test = next(gen_test)\n",
    "    #Convert numpy arrays to images\n",
    "    X_TEST.append(image_test/255.)\n",
    "    Y_TEST.append(mask_test)\n",
    "    if i%10 == 0:\n",
    "        print(\"[INFO] Iteration num: {}\".format(i))\n",
    "        i +=1\n",
    "\n",
    "X_TEST = np.concatenate(X_TEST, axis=0)\n",
    "Y_TEST = np.concatenate(Y_TEST, axis=0)\n",
    "Y_TEST_PRED = custom_segnet_test.predict(X_TEST)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.unique(Y_TEST)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(16, 14))\n",
    "plt.suptitle(\"PREDICTIONS IN THE TEST SET\", fontsize=25, fontweight=\"bold\")\n",
    "plt.grid(False)\n",
    "for i in range(5):\n",
    "    ax = fig.add_subplot(3, 5, i + 1)\n",
    "    plt.imshow(X_TEST[i+5])\n",
    "    plt.title(\"Image \" + str(i + 1))\n",
    "\n",
    "    ax = fig.add_subplot(3, 5, 5 + i + 1)\n",
    "    plt.imshow(Y_TEST[i+5])\n",
    "    plt.title(\"Ground Truth \" + str(i))\n",
    "\n",
    "    ax = fig.add_subplot(3, 5, i + 10 + 1)\n",
    "    plt.imshow(Y_TEST_PRED[i+5])\n",
    "    plt.title(\"Prediction\" + str(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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