Attention-Guided Version of 2D UNet for Automatic Brain Tumor Segmentation

tf2模型代码：

import tensorflow.keras.backend as K
from tensorflow.keras.models import Model
from tensorflow.keras import Input
from tensorflow.keras.layers import Conv2D, PReLU, UpSampling2D, concatenate , Reshape, Dense, Permute, MaxPool2D
from tensorflow.keras.layers import GlobalAveragePooling2D, Activation, add, GaussianNoise, BatchNormalization, multiply
from tensorflow.keras.optimizers import SGD
from loss import custom_loss
K.set_image_data_format("channels_last")
 
 
 
def unet_model(input_shape, modified_unet=True, learning_rate=0.01, start_channel=64, 
               number_of_levels=3, inc_rate=2, output_channels=4, saved_model_dir=None):
    """
    Builds UNet model
    
    Parameters
    ----------
    input_shape : tuple
        Shape of the input data (height, width, channel)
    modified_unet : bool
        Whether to use modified UNet or the original UNet
    learning_rate : float
        Learning rat