def single_run(aug_sat, aug_gs, aug_blur, aug_rr, TEST_RUM_ONLY=False):
def augment_data_ext(x, y):
return data.augment_data_extended(x,
y,
saturation=aug_sat,
use_grayscale=aug_gs,
blur_amount=aug_blur,
num_random_rotations=aug_rr)
epochs = 150
if TEST_RUM_ONLY:
epochs = 1
crt_config_name = "_" + str(aug_sat) + "_" + str(aug_gs) + "_" + str(
aug_blur) + "_" + str(aug_rr)
# u_net_cross_entropy_augmented
model = unet.get_model(None, None, 3, do_compile=False)
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=[
'accuracy',
tf.keras.metrics.MeanIoU(num_classes=2), f1, f1_binary
])
model_name = 'u_net_cross_entropy_augmented_extended_3' + crt_config_name
cross_val(model,
model_name,
augment_data_func=augment_data_ext,
epochs=epochs)
def main():
from models import unet
# focal
from losses import focal
loss = focal.focal_loss
model_name = 'u_net_focal_loss_cross_training_ext_aug_5'
model = unet.get_model(None, None, 3, do_compile=False)
model.compile(
optimizer='adam',
loss=loss,
metrics=['accuracy',
tf.keras.metrics.MeanIoU(num_classes=2)])
cross_val(model, model_name)