object_for_each_prior = tf.constant([1 for i in range(8732)])
-><tf.Tensor: shape=(8732,), dtype=int32, numpy=array([1, 1, 1, ..., 1, 1, 1], dtype=int32)>
然后，如果我想获得职位1148,1149
prior_for_each_object = tf.constant([1148,1149])
object_for_each_prior[prior_for_each_object]
然后我遇到以下错误
TypeError: Only integers, slices (`:`), ellipsis (`...`), tf.newaxis (`None`) and scalar tf.int32/tf.int64 tensors are valid indices, got <tf.Tensor: shape=(2,), dtype=int32, numpy=array([1148, 1149], dtype=int32)>
如果我想通过索引获取张量数，应该如何处理？
答案 0 :(得分：0)
使用var correct = 0;
var totalQuestions = 3;
var finalScore = 0
function question() {
var answer1 = prompt('Question 1: what is 1 + 1?')
if (+answer1 === 2) {
correct += 1
};
var answer2 = prompt('Question 2: what is 2 + 3?')
if (+answer2 === 5) {
correct += 1
};
var answer3 = prompt('Question 3: what is 3 + 3?')
if (+answer3 === 6) {
correct += 1
};
finalScore = correct / totalQuestions
console.log(`your final score: ${finalScore}`);
}
question()函数索引张量。
例如：
tf.gather_nd请参阅this文档以进一步了解>>> object_for_each_prior = tf.constant([1 for i in range(8732)])
>>> prior_for_each_object = tf.gather_nd(object_for_each_prior, indices=[[1148], [1149]])
>>> prior_for_each_object
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([1, 1])>
>>> prior_for_each_object.numpy()
array([1, 1])
。