『TensorFlow』张量拼接_调整维度_切片
1、tf.concat
tf.concat的作用主要是将向量按指定维连起来，其余维度不变；而1.0版本以后，函数的用法变成：
t1 = [[1, 2, 3], [4, 5, 6]] t2 = [[7, 8, 9], [10, 11, 12]] #按照第0维连接 tf.concat( [t1, t2]，0) ==> [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]] #按照第1维连接 tf.concat([t1, t2]，1) ==> [[1, 2, 3, 7, 8, 9], [4, 5, 6, 10, 11, 12]]
作为参考合成神经网络输出的时候在深度方向（inception_v3）是数字3，[batch,heigh,width,depth]。
2、tf.stack
用法：stack(values, axis=0, name=”stack”):
“”“Stacks a list of rank-R tensors into one rank-(R+1) tensor.
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]]
tf.stack将一组R维张量变为R+1维张量。注意：tf.pack已经变成了tf.stack\3、tf.squeeze
数据降维，只裁剪等于1的维度
不指定维度则裁剪所有长度为1的维度
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()) sess.run(arr).shape # Out[12]: # (3, 4, 1, 6, 1) sess.run(tf.squeeze(arr,[2,])).shape # Out[17]: # (3, 4, 6, 1) sess.run(tf.squeeze(arr,[2,4])).shape # Out[16]: # (3, 4, 6) sess.run(tf.squeeze(arr)).shape # Out[19]: # (3, 4, 6)
3、tf.split
依照输入参数二的标量/向量有不同的行为：参数二为标量时，意为沿着axis等分为scalar份；向量时意为安装元素作为边界索引切分多份
def split(value, num_or_size_splits, axis=0, num=None, name="split"):
"""Splits a tensor into sub tensors.
If `num_or_size_splits` is an integer type, `num_split`, then splits `value`
along dimension `axis` into `num_split` smaller tensors.
Requires that `num_split` evenly divides `value.shape[axis]`.
If `num_or_size_splits` is not an integer type, it is presumed to be a Tensor
`size_splits`, then splits `value` into `len(size_splits)` pieces. The shape
of the `i`-th piece has the same size as the `value` except along dimension
`axis` where the size is `size_splits[i]`.
For example:
```python
# 'value' is a tensor with shape [5, 30]
# Split 'value' into 3 tensors with sizes [4, 15, 11] along dimension 1
split0, split1, split2 = tf.split(value, [4, 15, 11], 1)
tf.shape(split0) # [5, 4]
tf.shape(split1) # [5, 15]
tf.shape(split2) # [5, 11]
# Split 'value' into 3 tensors along dimension 1
split0, split1, split2 = tf.split(value, num_or_size_splits=3, axis=1)
tf.shape(split0) # [5, 10]
```
4、张量切片
tf.slice
解析：slice(input_, begin, size, name=None)：Extracts a slice from a tensor.
假设input为[[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], [[5, 5, 5], [6, 6, 6]]]，如下所示：
（1）tf.slice(input, [1, 0, 0], [1, 1, 3]) ==> [[[3, 3, 3]]]
（2）tf.slice(input, [1, 0, 0], [1, 2, 3]) ==> [[[3, 3, 3], [4, 4, 4]]]
（3）tf.slice(input, [1, 0, 0], [2, 1, 3]) ==> [[[3, 3, 3]], [[5, 5, 5]]]
tf.strided_slice(record_bytes, [0], [label_bytes]), tf.int32)
在看cifar10的例子的时候,必然会看到一个函数,官方给的文档注释长而晦涩,基本等于0.网上也有这个函数,但解释差劲或者基本没有解释,函数的原型是酱紫的.
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): """Extracts a strided slice from a tensor.'input'= [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], [[5, 5, 5], [6, 6, 6]]]来把输入变个型,可以看成3维的tensor,从外向为1,2,3维
[[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], [[5, 5, 5], [6, 6, 6]]]以tf.strided_slice(input, [0,0,0], [2,2,2], [1,2,1])调用为例,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]]]
类似代码如下：
import tensorflow as tf data = [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], [[5, 5, 5], [6, 6, 6]]] x = tf.strided_slice(data,[0,0,0],[1,1,1]) with tf.Session() as sess: print(sess.run(x))