“数组"Tensorflow中的检测 [英] "Array" detection in Tensorflow

问题描述

Tensorflow可以处理各种形状(大小)的输入吗?

Can Tensorflow handle inputs of varying shapes (sizes)?

我正在开发一种图像/形状识别器，它可以捕获 {x:#，y:#} 个位置的数组.

I'm developing a image/shape recognizer which captures an array of {x:#,y:#} positions.

例如，一个圆形可能看起来像这样

For example, a circle might look like this

[{"x":0.38,"y":0.32},{"x":0.33,"y":0.35},{"x":0.31,"y":0.4},{"x":0.31,"y":0.46},{"x":0.34,"y":0.51},{"x":0.39,"y":0.52},{"x":0.44,"y":0.51},{"x":0.47,"y":0.47},{"x":0.49,"y":0.42},{"x":0.47,"y":0.37},{"x":0.42,"y":0.34},{"x":0.37,"y":0.33}]

和一个像这样的正方形

[{"x":0.15,"y":0.19},{"x":0.15,"y":0.25},{"x":0.15,"y":0.31},{"x":0.15,"y":0.37},{"x":0.14,"y":0.42},{"x":0.14,"y":0.48},{"x":0.14,"y":0.53},{"x":0.14,"y":0.59},{"x":0.14,"y":0.64},{"x":0.2,"y":0.64},{"x":0.26,"y":0.64},{"x":0.31,"y":0.65},{"x":0.37,"y":0.65},{"x":0.43,"y":0.65},{"x":0.49,"y":0.65},{"x":0.54,"y":0.65},{"x":0.6,"y":0.65},{"x":0.65,"y":0.65},{"x":0.67,"y":0.6},{"x":0.68,"y":0.55},{"x":0.68,"y":0.5},{"x":0.68,"y":0.44},{"x":0.68,"y":0.38},{"x":0.68,"y":0.32},{"x":0.67,"y":0.27},{"x":0.67,"y":0.22},{"x":0.66,"y":0.17},{"x":0.61,"y":0.15},{"x":0.56,"y":0.13},{"x":0.51,"y":0.13},{"x":0.45,"y":0.13},{"x":0.39,"y":0.13},{"x":0.33,"y":0.13},{"x":0.27,"y":0.13},{"x":0.22,"y":0.14},{"x":0.17,"y":0.15}]

因为这些形状的长度可以变化，所以我想知道Tensorflow将如何处理它……据我所知，输入的形状"为需要始终保持相同的长度，对吧?

Because the length of these shapes can vary I was wondering how Tensorflow would handle it...as I understand it, the input "shape" needs to always be the same length, right?

推荐答案

是的，形状应该相同.但是，根据您的情况，可以通过将虚拟元素添加到长度不足的数组中来确保批量处理所有数组具有相同数量的元素.

Yes, the shape should be the same. But, in your case, you can make sure that for a batch, all the arrays have the same number of elements by adding dummy elements to those which fall short in length.

只需确保批次的形状相同即可.

Just make sure that for a batch, your shape is same.

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