如何在 Tensorflow 中更新 2D 张量的子集?

问题描述

我想更新值为 0 的二维张量中的索引.所以数据是一个二维张量，其第 2 行第 2 列索引值将被 0 替换.但是，我收到了类型错误.有人可以帮我吗?

I want to update an index in a 2D tensor with value 0. So data is a 2D tensor whose 2nd row 2nd column index value is to be replaced by 0. However, I am getting a type error. Can anyone help me with it?

TypeError: Input 'ref' of 'ScatterUpdate' Op 需要左值输入

data = tf.Variable([[1,2,3,4,5], [6,7,8,9,0], [1,2,3,4,5]])
data2 = tf.reshape(data, [-1])
sparse_update = tf.scatter_update(data2, tf.constant([7]), tf.constant([0]))
#data = tf.reshape(data, [N,S])
init_op = tf.initialize_all_variables()

sess = tf.Session()
sess.run([init_op])
print "Values before:", sess.run([data])
#sess.run([updated_data_subset])
print "Values after:", sess.run([sparse_update])

推荐答案

tf.scatter_update 只能应用于 Variable 类型.data 在你的代码中是一个 Variable，而 data2 不是，因为 tf.reshape 的返回类型是张量.

tf.scatter_update could only be applied to Variable type. data in your code IS a Variable, while data2 IS NOT, because the return type of tf.reshape is Tensor.

解决方案:

适用于 v1.0 之后的 tensorflow

data = tf.Variable([[1,2,3,4,5], [6,7,8,9,0], [1,2,3,4,5]])
row = tf.gather(data, 2)
new_row = tf.concat([row[:2], tf.constant([0]), row[3:]], axis=0)
sparse_update = tf.scatter_update(data, tf.constant(2), new_row)

适用于 v1.0 之前的 tensorflow

data = tf.Variable([[1,2,3,4,5], [6,7,8,9,0], [1,2,3,4,5]])
row = tf.gather(data, 2)
new_row = tf.concat(0, [row[:2], tf.constant([0]), row[3:]])
sparse_update = tf.scatter_update(data, tf.constant(2), new_row)

这篇关于如何在 Tensorflow 中更新 2D 张量的子集?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！