To use Tensorflow Unet in a project:
from tf_unet import unet, util, image_util #preparing data loading data_provider = image_util.ImageDataProvider("fishes/train/*.tif") #setup & training net = unet.Unet(layers=3, features_root=64, channels=1, n_class=2) trainer = unet.Trainer(net) path = trainer.train(data_provider, output_path, training_iters=32, epochs=100) #verification ... prediction = net.predict(path, data) unet.error_rate(prediction, util.crop_to_shape(label, prediction.shape)) img = util.combine_img_prediction(data, label, prediction) util.save_image(img, "prediction.jpg")
Keep track of the learning progress using Tensorboard. tf_unet automatically outputs relevant summaries.
More examples can be found in the Jupyter notebooks for a toy problem or for a RFI problem. Further code is stored in the scripts folder.