Question

这是我尝试做的最小例子：


import numpy as np
import tensorflow as tf

map = tf.placeholder(tf.float32)
xmin = tf.placeholder(tf.int32)
xmax = tf.placeholder(tf.int32)
ymin = tf.placeholder(tf.int32)
ymax = tf.placeholder(tf.int32)

post_operation_map = 2.0 * map + 1.0
post_operation_map[ymin:ymax, xmin:xmax] = np.nan
init = tf.global_variables_initializer()

with tf.Session() as sess:
    feed = {map:np.random.rand(200,200),
            xmin:20,
            xmax:40,
            ymin:20,
            ymax:40}
    sess.run(post_operation_map, feed_dict=feed)

代码失败，出现以下错误： TypeError: 'Tensor' object does not support item assignment。

可以对代码做出一些假设：

事先不知道地图的形状。
xmin, xmax, ymin, ymax的范围始终符合map.shape。

我该如何解决这个问题？我猜我需要使用tf.assign，但我不知道如何。

Answer 1

这应该可以解决问题：

import numpy as np
import tensorflow as tf

map = tf.placeholder(tf.float32)
xmin = tf.placeholder(tf.int32)
xmax = tf.placeholder(tf.int32)
ymin = tf.placeholder(tf.int32)
ymax = tf.placeholder(tf.int32)

post_operation_map = 2.0 * map + 1.0

# Fill block with nan
shape = tf.shape(post_operation_map)
dtype = post_operation_map.dtype
shape_x, shape_y = shape[0], shape[1]
x_range = tf.range(shape_x)[:, tf.newaxis]
y_range = tf.range(shape_y)[tf.newaxis, :]
mask = (xmin <= x_range) & (x_range < xmax) & (ymin <= y_range) & (y_range < ymax)
post_operation_map = tf.where(
    mask, tf.fill(shape, tf.constant(np.nan, dtype)), post_operation_map)

with tf.Session() as sess:
    feed = {map:np.random.rand(8, 6),
            xmin: 1,
            xmax: 4,
            ymin: 2,
            ymax: 5}
    print(sess.run(post_operation_map, feed_dict=feed))

输出：

[[ 2.50152206  1.01042879  2.88725328  1.27295971  2.99401283  1.84210801]
 [ 2.98338175  2.26357031         nan         nan         nan  2.68635511]
 [ 1.00461781  2.00605297         nan         nan         nan  2.16447353]
 [ 2.15073347  1.64699006         nan         nan         nan  1.97648919]
 [ 1.7709868   1.65353572  1.6698066   2.26957846  2.75840473  1.23831809]
 [ 1.51848006  1.45277226  1.46150732  1.08112144  2.87904882  2.62266874]
 [ 1.86656547  1.5177052   1.36731267  2.70582867  1.57994771  2.48001719]
 [ 1.89354372  2.88848639  1.49879098  1.36527407  1.47415829  2.95422626]]

tensorflow设置块在2d张量内到常数值

1 个答案: