博客转载自：https://blog.csdn.net/davincil/article/details/77893185
函数原型：slice(input_, begin, size, name=None)
参数：
input：待切片的矩阵tensor。
begin：起始位置，表示从哪一个数据开始进行切片。这个起始位置从0开始。若input是一个n维的矩阵，则begin是一个长度为n的tensor。
size：切片的大小（尺寸），表示则起始位置开始获取每一维上的若干数据。是一个长度与begin相同的tensor。若size中第n个的数据为-1，则表示在该维度上，从起始位置开始的所有数据均被返回。
name：该操作的名称，是一个可选参数，默认为None。
对于一个n维的矩阵，需满足如下关系：
0 <= begin[i] <= begin[i] + size[i] <= Di for i in [0, n]
import tensorflow as tf # Tensorflow交互式会话
tf.InteractiveSession() # 定义5x5大小的一个矩阵变量
a = tf.Variable(tf.truncated_normal(shape=[5, 5], dtype=tf.float32)) # 进行切片操作，起始位置为[1,1]（从0开始），大小[2,2]
b = tf.slice(a, [1, 1], [2, 2]) # 同上
c = tf.Variable(tf.truncated_normal(shape=[2, 6, 5], dtype=tf.float32)) d = tf.slice(c, [0, 2, 3], [2, 3, 1]) # 全局变量初始化
tf.global_variables_initializer().run() # 输出
print("Example 01") print("the original matrix:\n", a.eval()) print("after being sliced:\n", b.eval()) print("Example 02") print("the original matrix:\n", c.eval()) print("after being sliced:\n", d.eval())
程序运行结果如下：（结果或有不同）
Example 01
the original matrix:
[[ 1.37798977 0.27846026 0.07193759 0.44368556 0.65868556]
[-0.57639289 -0.64335102 -0.62483543 0.38987917 0.29301718]
[ 0.18187736 0.11397317 1.85999572 -0.26037475 0.98114467]
[ 0.69557261 0.01183218 -0.27376401 -1.15162456 1.11336803]
[-0.66582751 -0.04991583 -1.58189285 0.98189503 -1.11317801]]
after being sliced:
[[-0.64335102 -0.62483543]
[ 0.11397317 1.85999572]]
Example 02
the original matrix:
[[[-0.44467756 -1.05340731 -0.32313645 -0.69316941 0.04659459]
[ 0.01275753 -0.11907347 1.70015264 0.60470396 -0.23756829]
[ 0.07424127 1.01376414 -1.15661514 -0.46597373 -1.82189155]
[-0.66635352 -0.34318891 0.49555108 0.13062055 -0.67137426]
[ 0.04240284 0.55397838 -0.09988129 -0.93551743 0.6810317 ]
[ 1.06745911 0.49900523 1.0482769 0.39871195 1.23199737]] [[ 1.22305858 -0.839634 0.63722724 -1.39846325 -0.04114933]
[-1.11448932 0.20783874 0.39737079 1.13769484 -0.09408376]
[-0.66636425 0.37878662 -0.32013494 -0.26526076 1.53422773]
[-0.55344075 0.23021726 0.10251451 0.08433547 1.19850338]
[ 1.73070538 -0.50309545 -0.52816319 -0.41802529 -1.52679396]
[-1.60076332 0.88759929 0.01327948 -0.7242741 -0.70737672]]]
after being sliced:
[[[-0.46597373]
[ 0.13062055]
[-0.93551743]] [[-0.26526076]
[ 0.08433547]
[-0.41802529]]]
2、TensorFlow矩阵链接操作：tf.concat函数
函数原型：concat(values, axis, name=”concat”)
参数：
values：需要链接的矩阵的集合，通常可以是一个list。
axis：需要进行链接的维度，若矩阵是n维的，则axis的取值为0~n-1。
name：名称，是一个可选参数。
import tensorflow as tf # Tensorflow交互式会话
tf.InteractiveSession() # 定义两个矩阵，大小为2x3x4
a = tf.Variable(tf.truncated_normal(shape=[2,3,4], dtype=tf.float32)) b = tf.Variable(tf.truncated_normal(shape=[2,3,4], dtype=tf.float32)) # 按照维度0链接
c1 = tf.concat([a, b], axis=0) # 按照维度1链接
c2 = tf.concat([a, b], axis=1) # 按照维度2链接
c3 = tf.concat([a, b], axis=2) # 初始化变量
tf.global_variables_initializer().run() # 输出
print("") print(c1) print(c1.eval()) print("") print(c2) print(c2.eval()) print("") print(c3) print(c3.eval())
程序运行结果如下：
Tensor("concat:0", shape=(4, 3, 4), dtype=float32)
[[[-0.08826777 1.92810595 -0.79408133 -0.34322619]
[-1.71443737 0.70375884 -0.78194672 -0.41254947]
[ 0.89348751 -0.08941202 0.70108914 0.64701825]]
[[ 1.50688016 0.45680258 -1.08100998 0.24127837]
[ 0.58221173 -1.41846514 -1.63450527 -0.41922286]
[ 0.48436531 -1.20013559 0.95647675 -0.03131635]]
[[-0.03254275 -1.8339541 -0.81978613 -1.25303519]
[-1.55067682 -0.37825376 -0.63578284 -0.83120823]
[ 0.09672505 -0.43550658 -0.31754431 -0.37109831]]
[[ 1.59722102 -0.32856748 -1.33017409 1.43195128]
[-0.58259052 -1.60538054 0.07504115 0.8916716 ]
[-1.23682356 -0.24931362 1.19812703 -0.81907171]]]
02
Tensor("concat_1:0", shape=(2, 6, 4), dtype=float32)
[[[-0.08826777 1.92810595 -0.79408133 -0.34322619]
[-1.71443737 0.70375884 -0.78194672 -0.41254947]
[ 0.89348751 -0.08941202 0.70108914 0.64701825]
[-0.03254275 -1.8339541 -0.81978613 -1.25303519]
[-1.55067682 -0.37825376 -0.63578284 -0.83120823]
[ 0.09672505 -0.43550658 -0.31754431 -0.37109831]]
[[ 1.50688016 0.45680258 -1.08100998 0.24127837]
[ 0.58221173 -1.41846514 -1.63450527 -0.41922286]
[ 0.48436531 -1.20013559 0.95647675 -0.03131635]
[ 1.59722102 -0.32856748 -1.33017409 1.43195128]
[-0.58259052 -1.60538054 0.07504115 0.8916716 ]
[-1.23682356 -0.24931362 1.19812703 -0.81907171]]]
03
Tensor("concat_2:0", shape=(2, 3, 8), dtype=float32)
[[[-0.08826777 1.92810595 -0.79408133 -0.34322619 -0.03254275 -1.8339541
-0.81978613 -1.25303519]
[-1.71443737 0.70375884 -0.78194672 -0.41254947 -1.55067682 -0.37825376
-0.63578284 -0.83120823]
[ 0.89348751 -0.08941202 0.70108914 0.64701825 0.09672505 -0.43550658
-0.31754431 -0.37109831]]
[[ 1.50688016 0.45680258 -1.08100998 0.24127837 1.59722102 -0.32856748
-1.33017409 1.43195128]
[ 0.58221173 -1.41846514 -1.63450527 -0.41922286 -0.58259052 -1.60538054
0.07504115 0.8916716 ]
[ 0.48436531 -1.20013559 0.95647675 -0.03131635 -1.23682356 -0.24931362
1.19812703 -0.81907171]]]