张量拼接_调整维度_切片

import tensorflow as tf

t1 = [[1, 2, 3], [4, 5, 6]]

t2 = [[7, 8, 9], [10, 11, 12]]

#按照第0维连接

t12_0 = tf.concat( [t1, t2],0)

#按照第1维连接

t12_1 = tf.concat([t1, t2],1)

with tf.Session() as sess:
    print(sess.run(t12_0))
    print(sess.run(t12_1))

输出:
---------------------
[[ 1  2  3]
 [ 4  5  6]
 [ 7  8  9]
 [10 11 12]]
[[ 1  2  3  7  8  9]
 [ 4  5  6 10 11 12]]
----------------------

x = tf.constant([1, 4])

y = tf.constant([2, 5])

z = tf.constant([3, 6])

tf.stack([x,y,z]) ==> [[1,4],[2,5],[3,6]]

tf.stack([x,y,z],axis=0) ==> [[1,4],[2,5],[3,6]]

tf.stack([x,y,z],axis=1) ==> [[1, 2, 3], [4, 5, 6]]

import tensorflow as tf

arr = tf.Variable(tf.truncated_normal([3,4,1,6,1], stddev=0.1))

sess = tf.Session()

sess.run(tf.global_variables_initializer())


print(sess.run(arr).shape)

print(sess.run(tf.squeeze(arr,[2,])).shape)

print(sess.run(tf.squeeze(arr,[2,4])).shape)

print(sess.run(tf.squeeze(arr)).shape)


输出:
----------------
(3, 4, 1, 6, 1)
(3, 4, 6, 1)
(3, 4, 6)
(3, 4, 6)
----------------

import tensorflow as tf

input = [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], [[5, 5, 5], [6, 6, 6]]]

slice_1 = tf.slice(input, [1, 0, 0], [1, 1, 3])

slice_2 = tf.slice(input, [1, 0, 0], [1, 2, 3])

slice_3 = tf.slice(input, [1, 0, 0], [2, 1, 3])

with tf.Session() as sess:
    print(sess.run(slice_1))
    print(sess.run(slice_2))
    print(sess.run(slice_3))

输出:
------------
[[[3 3 3]]]


[[[3 3 3]
  [4 4 4]]]


[[[3 3 3]]
 [[5 5 5]]]
------------

def strided_slice(
input_, begin, 
end, 
strides=None, 
begin_mask=0, 
end_mask=0, 
ellipsis_mask=0, 
new_axis_mask=0, 
shrink_axis_mask=0, 
var=None, 
name=None
):

import tensorflow as tf

# 来把输入变个型，可以看成3维的tensor，从外向为1,2,3维。
input= \
[
    [[1, 1, 1], 
     [2, 2, 2]
    ],
             
    [[3, 3, 3], 
     [4, 4, 4]
    ],
    
    [[5, 5, 5], 
     [6, 6, 6]
     ]
]

slice = tf.strided_slice(input, [0,0,0], [2,2,2], [1,2,1])

with tf.Session() as sess:
 print(sess.run(slice))

# start = [0,0,0] , end = [2,2,2], stride = [1,2,1]
# 求一个[start, end)的一个片段,注意end为开区间

# 第1维 start = 0 , end = 2, stride = 1, 所以取 0 , 1行，此时的输出

# [[[1, 1, 1], [2, 2, 2]],
# [[3, 3, 3], [4, 4, 4]]]

# 第2维时, start = 0 , end = 2 , stride = 2, 所以只能取0行,此时的输出

# [[[1, 1, 1]],
# [[3, 3, 3]]]

# 第3维的时候,start = 0, end = 2, stride = 1, 可以取0,1行,此时得到的就是最后的输出

# [[[1, 1]],
# [[3, 3]]]

# 整理之后最终的输出为:

# [[[1,1],[3,3]]]