如何使用MSE创建一个Loss函数，该函数使用tf.where（）忽略某些元素

Question

下面是当前的函数。这里，它从MSE中删除y_true小于阈值（这里为0.1）的任何值

def my_loss(y_true,y_pred):
    loss = tf.square(y_true-y_pred)
    # if any y_true is less than a threshold (say 0.1) 
    # the element is removed from loss, and does not affect MSE
    loss = tf.where(y_true<0.1)
    # return mean of losses
    return tf.reduce_mean(loss)

这一个可以编译，但是网络没有很好地预测0。相反，我只想消除那些y_true和y_pred都小于某个阈值的值。这是因为它需要先学习如何预测0，然后在以后的训练中忽略这些点

然而，这并不编译

def my_better_loss(y_true,y_pred):
    loss = tf.square(y_true-y_pred)
    # remove all elements where BOTH y_true & y_pred < threshold
    loss = tf.where(y_true<0.1 and y_pred<0.1)
    # return mean of losses
    return tf.reduce_mean(loss)

它会导致以下错误

  (0) Invalid argument:  The second input must be a scalar, but it has shape [25,60,60]
         [[{{node replica_1/customMSE/cond/switch_pred/_51}}]]
  (1) Invalid argument:  The second input must be a scalar, but it has shape [25,60,60]
         [[{{node replica_1/customMSE/cond/switch_pred/_51}}]]
         [[customMSE/cond/Squeeze/_59]]
  (2) Invalid argument:  The second input must be a scalar, but it has shape [25,60,60]
         [[{{node replica_1/customMSE/cond/replica_1/customMSE/Less/_55}}]]
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_4715]

Function call stack:
train_function -> train_function -> train_function

编辑：

更具体地说。假设我们的阈值为0.5：

y_true = [0.3, 0.4, 0.6, 0.7]
y_pred = [0.2, 0.7, 0.5, 1]

然后，由于y_pred[0]和y_true[0]都小于阈值，因此损失函数将在删除第一个元素的情况下计算mse

# MSE would be computed between
y_true = [0.4, 0.6, 0.7]
#and
y_pred = [0.7, 0.5, 1]

Answer 1

如果在转换为图形模式的代码中使用python short-circuitand运算符，则大多数情况下会导致不良行为或错误，因为python short-circuitand运算符不能重载。要对张量执行元素和运算，请使用tf.math.logical_and

此外tf.where在这里是不必要的，而且速度可能较慢。首选掩蔽。示例代码：

@tf.function
def better_loss(y_true,y_pred):
  loss = tf.square(y_true - y_pred)
  # ignore elements where BOTH y_true & y_pred < 0.1
  mask = tf.cast(tf.logical_or(y_true >= 0.1, y_pred >= 0.1) ,tf.float32)
  loss *= mask
  return tf.reduce_sum(loss) / tf.reduce_sum(mask)