张量流中二维数组从最小值到最大值的排序 [英] sorting of 2d array min to max in tensorflow

问题描述

我有一个数组

x1 = tf.Variable([[0.51, 0.52, 0.53, 0.94, 0.35],
             [0.32, 0.72, 0.83, 0.74, 0.55],
             [0.23, 0.72, 0.63, 0.64, 0.35],
             [0.11, 0.02, 0.03, 0.14, 0.15],
             [0.01, 0.72, 0.73, 0.04, 0.75]],tf.float32)

我想对从最小到最大的每一行中的元素进行排序.有什么功能吗?

I want to sort the elements in each row from min to max. Is there any function for doing such ?

在这里的示例中，他们使用tf.nn.top_k 2d数组，我可以循环创建最大到最小.

In the example here they are using tf.nn.top_k2d array,using this I can loop to create the max to min.

def sort(instance):
   sorted = []
   rows = tf.shape(instance)[0]
   col = tf.shape(instance)[1]
   for i in range(rows.eval()):
       matrix.append([tf.gather(instance[i], tf.nn.top_k(instance[i], k=col.eval()).indices)])
   return matrix

是否有类似的事情可以找到最小值到最大值或如何反转每一行中的数组?

Is there any thing similar for finding the min to max or how to reverse the array in each row ?

推荐答案

按照@Yaroslav的建议，您可以只使用top_k值.

As suggested by @Yaroslav you can just use the top_k values.

a = tf.Variable([[0.51, 0.52, 0.53, 0.94, 0.35],
             [0.32, 0.72, 0.83, 0.74, 0.55],
             [0.23, 0.72, 0.63, 0.64, 0.35],
             [0.11, 0.02, 0.03, 0.14, 0.15],
             [0.01, 0.72, 0.73, 0.04, 0.75]],tf.float32)

row_size = a.get_shape().as_list()[-1]
top_k = tf.nn.top_k(-a, k=row_size)
sess.run(-top_k.values)

这为我打印

array([[ 0.34999999,  0.50999999,  0.51999998,  0.52999997,  0.94      ],
       [ 0.31999999,  0.55000001,  0.72000003,  0.74000001,  0.82999998],
       [ 0.23      ,  0.34999999,  0.63      ,  0.63999999,  0.72000003],
       [ 0.02      ,  0.03      ,  0.11      ,  0.14      ,  0.15000001],
       [ 0.01      ,  0.04      ,  0.72000003,  0.73000002,  0.75      ]], dtype=float32)

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