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)