tf.slice
slice(
input_,
begin,
size,
name=None
)
举例：
# 'input' is [[[1, 1, 1], [2, 2, 2]],
# [[3, 3, 3], [4, 4, 4]],
# [[5, 5, 5], [6, 6, 6]]]
tf.slice(input, [1, 0, 0], [1, 1, 3]) ==> [[[3, 3, 3]]]
tf.slice(input, [1, 0, 0], [1, 2, 3]) ==> [[[3, 3, 3],
[4, 4, 4]]]
tf.slice(input, [1, 0, 0], [2, 1, 3]) ==> [[[3, 3, 3]],
[[5, 5, 5]]]
第一个输出是[[[3, 3, 3]]]
我们分析一下,begin=[1, 0, 0]，第一个维度从第一个开始（0-based），第二三个维度从第0个开始。size=[1, 1, 3],即：
第一个维度从第一个开始取出来1个，也就是说取出来
[[[3, 3, 3], [4, 4, 4]]]
第二个维度从0开始取出了一个也就是[[[3, 3, 3]]]
第三个维度全取出来，即[[[3, 3, 3]]]
如果是tf.slice(input, [1, 0, 1], [1, 2, 2])呢？
根据同样的分析：
第一个维度取出一个就是[[[3, 3, 3], [4, 4, 4]]]
第二个维度从0开始取出来两个就是[[[3, 3, 3], [4, 4, 4]]]
第三个维度从1开始取出两个[[ 3, 3], [ 4, 4]]]
可以通过以下脚本验证：
import tensorflow as tf
pts=tf.constant([[[1, 1, 1], [2, 2, 2]],
[[3, 3, 3], [4, 4, 4]],
[[5, 5, 5], [6, 6, 6]]])
sess = tf.Session()
sess.run(tf.slice(pts,[1, 0, 1], [1, 2, 2]))
输出就是array([[[3, 3],
[4, 4]]])
当然为了，更明显，我们将原pts换一下数据：
pts=tf.constant([[[1, 2, 3], [4, 5, 6]],
[[7, 8, 9], [21, 22, 23]],
[[24, 25, 26], [27, 28, 29]]])
sess.run(tf.slice(pts,[1, 0, 1], [1, 2, 2]))
此时输出就是，array([[[ 8, 9],
[22, 23]]])
tf.expand_dims
在tensor’s shape中插入一个1
# 't' is a tensor of shape [2]
shape(expand_dims(t, 0)) ==> [1, 2]
shape(expand_dims(t, 1)) ==> [2, 1]
shape(expand_dims(t, -1)) ==> [2, 1]
# 't2' is a tensor of shape [2, 3, 5]
shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5]
shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5]
shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1]
tf.reduce_sum
Computes the sum of elements across dimensions of a tensor.
和np的sum一样啊，只不过参数多了一点，多了个keep_dim的参数，不过一般用不着。
# 'x' is [[1, 1, 1]
# [1, 1, 1]]
tf.reduce_sum(x) ==> 6
tf.reduce_sum(x, 0) ==> [2, 2, 2]
tf.reduce_sum(x, 1) ==> [3, 3]
tf.reduce_sum(x, 1, keep_dims=True) ==> [[3], [3]]
tf.reduce_sum(x, [0, 1]) ==> 6
tf.dynamic_partition
文档说的很麻烦，直接看例子吧：
# Scalar partitions.
partitions = 1
num_partitions = 2
data = [10, 20]
outputs[0] = [] # Empty with shape [0, 2]
outputs[1] = [[10, 20]]
# Vector partitions.
partitions = [0, 0, 1, 1, 0]
num_partitions = 2
data = [10, 20, 30, 40, 50]
outputs[0] = [10, 20, 50]
outputs[1] = [30, 40]
对于第一个标量的例子，partitions = 1就是指所有data都属于第一类。
第二个例子很好理解了。
顺便记录一下用到该函数的一段更新centroid的代码：
def update_centroids(samples, nearest_indices, n_clusters):
# Updates the centroid to be the mean of all samples associated with it.
nearest_indices = tf.to_int32(nearest_indices)
partitions = tf.dynamic_partition(samples, nearest_indices, n_clusters)
new_centroids = tf.concat([tf.expand_dims(tf.reduce_mean(partition, 0), 0) for partition in partitions], 0)
return new_centroids