tf.concat中axis的含义是什么

tf.concat中axis的含义是什么？相信很多没有经验的人对此束手无策，为此本文总结了问题出现的原因和解决方法，通过这篇文章希望你能解决这个问题。

tensorflow中tf.concat的axis的使用我一直理解的比较模糊，这次做个笔记理下自己的思路。

importtensorflowastf
tf.enable_eager_execution()
importnumpyasnp

先生成两个矩阵m1, 和m2, 大小为两行三列

m1=np.random.rand(2,3)#m1.shape(2,3)
m1
>>array([[0.44529968,0.42451167,0.07463199],
[0.35787143,0.22926186,0.34583839]])
m2=np.random.rand(2,3)#m2.shape(2,3)
m2
>>array([[0.92811531,0.6180391,0.71969461],
[0.00564108,0.55381637,0.17155987]])

接下来采用tf.concat进行连接，简单来说，axis=0实际就是按行拼接，axis=1就是按列拼接

#axis=0
m3=tf.concat([m1,m2],axis=0)
m3
>>array([[0.44529968,0.42451167,0.07463199],
[0.35787143,0.22926186,0.34583839],
[0.92811531,0.6180391,0.71969461],
[0.00564108,0.55381637,0.17155987]])
m3.shape
>>(4,3)

#axis=1
m4=tf.concat([m1,m2],axis=1)
m4
>>array([[0.44529968,0.42451167,0.07463199,0.92811531,0.6180391,
0.71969461],
[0.35787143,0.22926186,0.34583839,0.00564108,0.55381637,
0.17155987]])
m4.shape
>>(2,6)

但这实际上这只有在我们的输入是二维矩阵时才可以这样理解。axis的实际含义是根据axis指定的维度进行连接，如矩阵m1的维度为(2,3), 那么axis=0就代表了第一个维度‘2'，因此，将m1和m2按照第一个维度进行连接，得到的新的矩阵就是将第一维度进行相加，其余维度不变，即维度变成了(4,3).

同理，axis=1时就是将矩阵的第二维度进行合并，其余维度不变，即维度变成了（2,6）。

接下来处理三个维度的数据，这也是我们在神经网络数据中经常要用到的，增加的一个维度通常代表了batch_size. 如下面的m5, batch_size=5, 可以理解为每个样本是个2*3的矩阵，一次将5个样本放在一起。

m5=np.random.rand(5,2,3)
m6=np.random.rand(5,2,3)
m5
>>array([[[0.04347217,0.03368232,0.36017024],
[0.74223151,0.06609717,0.38155531]],

[[0.50602728,0.355745,0.93379797],
[0.97572621,0.53745311,0.66461841]],

[[0.92832972,0.02441683,0.48436203],
[0.69651043,0.24194495,0.64623769]],

[[0.66667596,0.60053027,0.2970753],
[0.13281764,0.29326326,0.32393028]],

[[0.40892782,0.48516547,0.02298178],
[0.51239083,0.40151008,0.29913204]]])
m6
>>array([[[0.58001909,0.56925704,0.09798246],
[0.20841893,0.62683633,0.17923217]],

[[0.91216164,0.0200782,0.3986682],
[0.86687006,0.83730576,0.48443545]],

[[0.65641654,0.59786311,0.2055584],
[0.65391822,0.74093133,0.02416627]],

[[0.80778861,0.22644312,0.91610686],
[0.0789411,0.86955002,0.41437046]],

[[0.97821668,0.97118328,0.97714882],
[0.21543173,0.06964724,0.35360077]]])

在这种情况下，axis=0代表的第一个维度的含义就不再是之前认为的行的概念了，现在m5的第一维度的值是5，代表的是batch_size。仍然按照之前的理解，如果设置axis=0, axis=0就是将第一维度进行相加，其余维度不变，因此我们可以得到新的维度为（10,2,3）。

m7=tf.concat([m5,m6],axis=0)
m7
>>array([[[0.04347217,0.03368232,0.36017024],
[0.74223151,0.06609717,0.38155531]],

[[0.50602728,0.355745,0.93379797],
[0.97572621,0.53745311,0.66461841]],

[[0.92832972,0.02441683,0.48436203],
[0.69651043,0.24194495,0.64623769]],

[[0.66667596,0.60053027,0.2970753],
[0.13281764,0.29326326,0.32393028]],

[[0.40892782,0.48516547,0.02298178],
[0.51239083,0.40151008,0.29913204]],

[[0.58001909,0.56925704,0.09798246],
[0.20841893,0.62683633,0.17923217]],

[[0.91216164,0.0200782,0.3986682],
[0.86687006,0.83730576,0.48443545]],

[[0.65641654,0.59786311,0.2055584],
[0.65391822,0.74093133,0.02416627]],

[[0.80778861,0.22644312,0.91610686],
[0.0789411,0.86955002,0.41437046]],

[[0.97821668,0.97118328,0.97714882],
[0.21543173,0.06964724,0.35360077]]])
m7.shape
>>(10,2,3)

同理，也可以进行axis=1, axis=2的concat操作。

此外，axis的值也可以设置为负数，如axis=-1实际上就是指倒数第一个维度，如m5的倒数第一个维度的值就是‘3'。因此，axis=2的操作和axis=-1的操作是等价的。

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