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)