如何有条件地将值分配给张量[蒙版损失函数]？

Question

I want to create a L2 loss function that ignores values (=> pixels) where the label has the value 0. The tensor batch[1] contains the labels while output is a tensor for the net output, both have a shape of (None,300,300,1) .

labels_mask = tf.identity(batch[1])
labels_mask[labels_mask > 0] = 1
loss = tf.reduce_sum(tf.square((output-batch[1])*labels_mask))/tf.reduce_sum(labels_mask)

My current code yields to TypeError: 'Tensor' object does not support item assignment (on the second line). What's the tensorflow-way to do this? I also tried to normalize the loss with tf.reduce_sum(labels_mask) , which I hope works like this.

Answer 1

If you wanted to write it that way, you would have to use Tensorflow's scatter method for assignment. Unfortunately, tensorflow doesn't really support boolean indexing either (the new boolean_select makes it possible, but annoying). It would be tricky to write and difficult to read.

You have two options that are less annoying:

1. Use labels_mask > 0 as a boolean mask and use Tensorflow's recent boolean_mask function. Maybe this is the more tensorflow way, because it invokes arbitrarily specific functions.
2. Cast labels_mask > 0 to float: tf.cast(labels_mask > 0, tf.float32) . Then, you can use it the way you wanted to in the final line of your code.

Answer 2

Here is an example how to apply boolean indexing and conditionally assign values to Variable:

a = tf.Variable(initial_value=[0, 0, 4, 6, 1, 2, 4, 0])
mask = tf.greater_equal(a, 2)  # [False False  True  True False  True  True False]
indexes = tf.where(mask)  # [[2] [3] [5] [6]], shape=(4, 1)
b = tf.scatter_update(a, mask, tf.constant(1500))

output:

[   0,    0, 1500, 1500,    1, 1500, 1500,    0]