1..中文

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import tensorflow as tf
import numpy as np

input = tf.constant(np.random.rand(3,4))
k = 2
output = tf.nn.top_k(input, k)
with tf.Session() as sess:
    print(sess.run(input))
    print(sess.run(output))

[[ 0.98925872  0.15743092  0.76471106  0.5949957 ]
 [ 0.95766488  0.67846336  0.21058844  0.2644312 ]
 [ 0.65531991  0.61445187  0.65372938  0.88111084]]
TopKV2(values=array([[ 0.98925872,  0.76471106],
       [ 0.95766488,  0.67846336],
       [ 0.88111084,  0.65531991]]), indices=array([[0, 2],
       [0, 1],
       [3, 0]]))

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import tensorflow as tf
import numpy as np

input = tf.constant(np.random.rand(3,4), tf.float32)
k = 2   #targets对应的索引是否在最大的前k(2)个数据中
output = tf.nn.in_top_k(input, [3,3,3], k)
with tf.Session() as sess:
    print(sess.run(input))
    print(sess.run(output))

[[ 0.43401602  0.29302254  0.40603295  0.21894781]
 [ 0.77089119  0.95353228  0.04788217  0.37489092]
 [ 0.83710146  0.2505011   0.28791779  0.97788286]]
[False False  True]

2. 英文：

tf.nn.top_k(input, k, name=None)

Returns the values and indices of the k largest elements for each row.

 represents the j-th largest element in .

 gives the column index of the corresponding element,such that . If twoelements are equal, the lower-index element appears first.

Args:

• input: A Tensor. Must be one of the following types: float32, float64, int32, int64, uint8, int16, int8. A batch_size x classes tensor
• k: An int that is >= 1. Number of top elements to look for within each row
• name: A name for the operation (optional).

Returns:

A tuple of Tensor objects (values, indices).

• values: A Tensor. Has the same type as input. A batch_size x k tensor with the k largest elements for each row, sorted in descending order
• indices: A Tensor of type int32. A batch_size x k tensor with the index of each value within each row

tf.nn.in_top_k(predictions, targets, k, name=None)

Says whether the targets are in the top K predictions.

This outputs a batch_size bool array, an entry out[i] is true if theprediction for the target class is among the top k predictions amongall predictions for example i. Note that the behavior of InTopK differsfrom the TopK op in its handling of ties; if multiple classes have thesame prediction value and straddle the top-k boundary, all of thoseclasses are considered to be in the top k.

More formally, let

 be the predictions for all classes for example i,  be the target class for example i,  be the output for example i,

Args:

• predictions: A Tensor of type float32. A batch_size x classes tensor
• targets: A Tensor of type int32. A batch_size vector of class ids
• k: An int. Number of top elements to look at for computing precision
• name: A name for the operation (optional).

Returns:

A Tensor of type bool. Computed Precision at k as a bool Tensor