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一 tf.concat( ) 函数–合并
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In [2]: a = tf.ones([4,35,8])
In [3]: b = tf.ones([2,35,8])
In [4]: c = tf.concat([a,b],axis=0)
In [5]: c.shape
Out[5]: TensorShape([6, 35, 8])
In [6]: a = tf.ones([4,32,8])
In [7]: b = tf.ones([4,3,8])
In [8]: c = tf.concat([a,b],axis=1)
In [9]: c.shape
Out[9]: TensorShape([4, 35, 8])
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二 tf.stack( ) 函数–数据的堆叠,创建新的维度
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In [2]: a = tf.ones([4,35,8])
In [3]: a.shape
Out[3]: TensorShape([4, 35, 8])
In [4]: b = tf.ones([4,35,8])
In [5]: b.shape
Out[5]: TensorShape([4, 35, 8])
In [6]: tf.concat([a,b],axis=-1).shape
Out[6]: TensorShape([4, 35, 16])
In [7]: tf.stack([a,b],axis=0).shape
Out[7]: TensorShape([2, 4, 35, 8])
In [8]: tf.stack([a,b],axis=3).shape
Out[8]: TensorShape([4, 35, 8, 2])
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三 tf.unstack( )函数–解堆叠
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In [16]: a = tf.ones([4,35,8])
In [17]: b = tf.ones([4,35,8])
In [18]: c = tf.stack([a,b],axis=0)
In [19]: a.shape,b.shape,c.shape
Out[19]: (TensorShape([4, 35, 8]), TensorShape([4, 35, 8]), TensorShape([2, 4, 35, 8]))
In [20]: aa,bb = tf.unstack(c,axis=0)
In [21]: aa.shape,bb.shape
Out[21]: (TensorShape([4, 35, 8]), TensorShape([4, 35, 8]))
In [22]: res = tf.unstack(c,axis=1)
In [23]: len(res)
Out[23]: 4
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四 tf.split( ) 函数
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In [16]: a = tf.ones([4,35,8])
In [17]: b = tf.ones([4,35,8])
In [18]: c = tf.stack([a,b],axis=0)
In [19]: a.shape,b.shape,c.shape
Out[19]: (TensorShape([4, 35, 8]), TensorShape([4, 35, 8]), TensorShape([2, 4, 35, 8]))
In [20]: aa,bb = tf.unstack(c,axis=0)
In [21]: aa.shape,bb.shape
Out[21]: (TensorShape([4, 35, 8]), TensorShape([4, 35, 8]))
In [22]: res = tf.unstack(c,axis=1)
In [23]: len(res)
Out[23]: 4
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以上这篇TensorFlow2.0:张量的合并与分割实例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/meijie2018_1/article/details/99439186