3D语义分割任务的Keras预处理

from keras import backend as K

def weighted_categorical_crossentropy(weights):
    weights = K.variable(weights)
    def loss(y_true, y_pred):
        y_true = K.cast(y_true, y_pred.dtype)
        return K.mean(-K.sum(y_true * K.log(y_pred + K.epsilon()) * weights, axis=-1))
    return loss

# 示例权重
class_weights = [1.0, 5.0, 10.0]  # 根据实际情况调整
model.compile(optimizer='adam', loss=weighted_categorical_crossentropy(class_weights))

from keras.preprocessing.image import ImageDataGenerator

datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)

train_generator = datagen.flow_from_directory(
    'path_to_data',
    target_size=(128, 128, 128),
    batch_size=4,
    class_mode='categorical',
    subset='training'
)

validation_generator = datagen.flow_from_directory(
    'path_to_data',
    target_size=(128, 128, 128),
    batch_size=4,
    class_mode='categorical',
    subset='validation'
)

from keras.layers import Dropout

model.add(Conv3D(64, kernel_size=(3, 3, 3), activation='relu', padding='same'))
model.add(Dropout(0.25))