如何使用Keras在神经网络回归中限制预测输出的总和(tensorflow)

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.constraints import Constraint

class SumConstraint(Constraint):
    def __init__(self, max_value):
        self.max_value = max_value

    def __call__(self, w):
        return tf.clip_by_value(w, 0, self.max_value)

model = keras.Sequential([
    layers.Dense(64, activation='relu', input_shape=(input_dim,), kernel_constraint=SumConstraint(1.0)),
    layers.Dense(64, activation='relu', kernel_constraint=SumConstraint(1.0)),
    layers.Dense(output_dim, kernel_constraint=SumConstraint(1.0))
])

model.compile(optimizer='adam', loss='mse')
model.fit(x_train, y_train, epochs=10, batch_size=32)