from models import uNet
from tensorflow.python.keras.layers import Input
from lib.callbacks import getCallbacks
from lib.datahelper import loadDataFromCache
from lib.plotting import plotGraph
from lib.metrics import get_f1, iou_coef, dice_coef, dice_coef_loss
import configparser
config = configparser.ConfigParser()
config.read('config.ini')
croppedImageSize = int(config['Image']['croppedImageSize'])
X_train, X_valid, y_train, y_valid = loadDataFromCache()
input_img = Input((croppedImageSize, croppedImageSize, 1), name='imga')
uNetModel = uNet.get_unet(input_img,
n_filters=16,
dropout=0.05,
batchnorm=True)
uNetModel.compile(optimizer='adam',
loss="binary_crossentropy",
metrics=["accuracy", get_f1, iou_coef, dice_coef])
resultsuNet = uNetModel.fit(X_train, y_train, batch_size=64, epochs=30, callbacks=getCallbacks('./trainedModels/model-uNet.h5'),\
validation_data=(X_valid, y_valid))
plotGraph('UNET', resultsuNet)
from models import deepLab
from lib.callbacks import getCallbacks
from lib.datahelper import loadDataFromCache
from lib.plotting import plotGraph
from lib.metrics import get_f1, iou_coef, dice_coef, dice_coef_loss, hybrid_loss
import configparser
config = configparser.ConfigParser()
config.read('config.ini')
croppedImageSize = int(config['Image']['croppedImageSize'])
X_train, X_valid, y_train, y_valid = loadDataFromCache()
deeplab_model = deepLab.Deeplabv3((croppedImageSize, croppedImageSize, 1),
classes=1,
OS=16)
deeplab_model.compile(optimizer='adam',
loss=hybrid_loss,
metrics=["accuracy", get_f1, iou_coef, dice_coef])
resultsdlNet = deeplab_model.fit(X_train, y_train, batch_size=64, epochs=30, callbacks=getCallbacks('./trainedModels/model-deepLabv3p.h5'),\
validation_data=(X_valid, y_valid))
plotGraph('DeepLabv3p', resultsdlNet)