另一种解决方案是将权重作为附加的输出特征而不是输入特征。

One alternative solution is to pass weights as additional output features rather than input features.

完全没有权重相关的任何权重，权重仅出现在损失函数和 .fit（）调用中：

This keeps the model completely free of anything weights related, and the weights appear only in the loss function and the .fit() call:

import numpy as np
import tensorflow as tf
from tensorflow.keras import layers, losses, models

data_x = 2 * np.ones((7, 11, 15, 3), dtype=float)
data_y = 5 * np.ones((7, 9, 13, 5), dtype=float)

x = layers.Input(data_x.shape[1:])
y = layers.Conv2D(5, kernel_size=3)(x)
model = models.Model(inputs=x, outputs=y)


def loss(y_true, y_pred):
    (y_true, w) = tf.split(y_true, num_or_size_splits=[-1, 1], axis=-1)
    loss = tf.squeeze(w, axis=-1) * losses.mse(y_true, y_pred)

    tf.print(tf.math.reduce_mean(y_true), "== 5")
    tf.print(tf.math.reduce_mean(w), "== 3")

    return loss


model.compile(loss=loss)

data_w = 3 * np.ones((7, 9, 13, 1), dtype=float)
data_yw = np.concatenate((data_y, data_w), axis=-1)
model.fit(data_x, data_yw)

一个缺点仍然是，合并 y 和 numpy.stack（）中> w ，因此将进一步欣赏类似于TensorFlow的东西。

One drawback still is that you need to manipulate (potentially) large arrays when merging y and w in numpy.stack(), so anymore more TensorFlow-like will be appreciated.