请写出使用attention-Unet模型分割乳腺超声图像的代码

使用attention-Unet模型分割乳腺超声图像的代码如下：from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate# Input Layer
inputs = Input(shape=(None, None, 1))# Attention U-Net
conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)# Attention Mechanism
attention_conv = Conv2D(1, 1, activation='sigmoid')(conv4)
up1 = UpSampling2D(size=(2,2))(attention_conv)
up1 = concatenate([conv3,up1], axis=3)
conv5 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(up1)
conv5 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)up2 = UpSampling2D(size=(2,2))(conv5)
up2 = concatenate([conv2,up2], axis=3)
conv6 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(up2)
conv6 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)up3 = UpSampling2D(size=(2,2))(conv6)
up3 = concatenate([conv1,up3], axis=3)
conv7 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(up3)
conv7 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)# Output Layer
outputs = Conv2D(1, 1, activation='sigmoid')(conv7)# Model Compilation
model = Model(inputs=[inputs], outputs=[outputs])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])