python之如何将 Tensorflows scatter_nd 与切片一起使用

我试图只优化变量的一部分。我找到了 this看似有用的答案。

但是我的变量是一个图像，我只想更改它的一部分，所以我试图将代码扩展到更多维度。这似乎工作正常:

import tensorflow as tf 
import tensorflow.contrib.opt as opt 
 
X = tf.Variable([[1.0, 2.0], [3.0, 4.0]]) 
 
# the next two lines need to change because 
# manually specifying the values is not feasible 
indexes = tf.constant([[0, 0], [1, 0]]) 
updates = [X[0, 0], X[1, 0]] 
 
part_X = tf.scatter_nd(indexes, updates, [2, 2]) 
X_2 = part_X + tf.stop_gradient(-part_X + X) 
Y = tf.constant([[2.5, -3.5], [5.5, -7.5]]) 
loss = tf.reduce_sum(tf.squared_difference(X_2, Y)) 
opt = opt.ScipyOptimizerInterface(loss, [X]) 
 
init = tf.global_variables_initializer() 
with tf.Session() as sess: 
    sess.run(init) 
    opt.minimize(sess) 
    print("X: {}".format(X.eval())) 

但是，由于我的图像尺寸和我想选择的区域要大得多，因此手动指定所有索引是不可行的。我想知道如何使用切片或范围分配来做到这一点。

请您参考如下方法:

你可以这样做:

import tensorflow as tf 
 
# Input with size (50, 100) 
X = tf.Variable([[0] * 100] * 50) 
# Selected slice 
row_start = 10 
row_end = 30 
col_start = 20 
col_end = 50 
# Make indices from meshgrid 
indexes = tf.meshgrid(tf.range(row_start, row_end), 
                      tf.range(col_start, col_end), indexing='ij') 
indexes = tf.stack(indexes, axis=-1) 
# Take slice 
updates = X[row_start:row_end, col_start:col_end] 
# Build tensor with "filtered" gradient 
part_X = tf.scatter_nd(indexes, updates, tf.shape(X)) 
X_2 = part_X + tf.stop_gradient(-part_X + X) 
# Continue as before...