我就废话不多说了,大家还是直接看代码吧~
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b = torch.zeros(( 3 , 2 , 6 , 6 ))
a = torch.zeros(( 3 , 2 , 1 , 1 ))
a.expand_as(b).size()
Out[ 32 ]: torch.Size([ 3 , 2 , 6 , 6 ])
a = torch.zeros(( 3 , 2 , 2 , 1 ))
a.expand_as(b).size()
Traceback (most recent call last):
File "/home/lart/.conda/envs/pt/lib/python3.6/site-packages/IPython/core/interactiveshell.py" , line 3267 , in run_code
exec (code_obj, self .user_global_ns, self .user_ns)
File "<ipython-input-34-972575f79e92>" , line 1 , in <module>
a.expand_as(b).size()
RuntimeError: The expanded size of the tensor ( 6 ) must match the existing size ( 2 ) at non - singleton dimension 2. Target sizes: [ 3 , 2 , 6 , 6 ]. Tensor sizes: [ 3 , 2 , 2 , 1 ]
a = torch.zeros(( 3 , 2 , 1 , 2 ))
a.expand_as(b).size()
Traceback (most recent call last):
File "/home/lart/.conda/envs/pt/lib/python3.6/site-packages/IPython/core/interactiveshell.py" , line 3267 , in run_code
exec (code_obj, self .user_global_ns, self .user_ns)
File "<ipython-input-36-972575f79e92>" , line 1 , in <module>
a.expand_as(b).size()
RuntimeError: The expanded size of the tensor ( 6 ) must match the existing size ( 2 ) at non - singleton dimension 3. Target sizes: [ 3 , 2 , 6 , 6 ]. Tensor sizes: [ 3 , 2 , 1 , 2 ]
a = torch.zeros(( 3 , 2 , 2 , 2 ))
a.expand_as(b).size()
Traceback (most recent call last):
File "/home/lart/.conda/envs/pt/lib/python3.6/site-packages/IPython/core/interactiveshell.py" , line 3267 , in run_code
exec (code_obj, self .user_global_ns, self .user_ns)
File "<ipython-input-38-972575f79e92>" , line 1 , in <module>
a.expand_as(b).size()
RuntimeError: The expanded size of the tensor ( 6 ) must match the existing size ( 2 ) at non - singleton dimension 3. Target sizes: [ 3 , 2 , 6 , 6 ]. Tensor sizes: [ 3 , 2 , 2 , 2 ]
a = torch.zeros(( 3 , 2 , 6 , 2 ))
a.expand_as(b).size()
Traceback (most recent call last):
File "/home/lart/.conda/envs/pt/lib/python3.6/site-packages/IPython/core/interactiveshell.py" , line 3267 , in run_code
exec (code_obj, self .user_global_ns, self .user_ns)
File "<ipython-input-40-972575f79e92>" , line 1 , in <module>
a.expand_as(b).size()
RuntimeError: The expanded size of the tensor ( 6 ) must match the existing size ( 2 ) at non - singleton dimension 3. Target sizes: [ 3 , 2 , 6 , 6 ]. Tensor sizes: [ 3 , 2 , 6 , 2 ]
a = torch.zeros(( 3 , 2 , 6 , 1 ))
a.expand_as(b).size()
Out[ 44 ]: torch.Size([ 3 , 2 , 6 , 6 ])
a = torch.zeros(( 3 , 2 , 1 , 6 ))
a.expand_as(b).size()
Out[ 46 ]: torch.Size([ 3 , 2 , 6 , 6 ])
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tensor.expand_as在这里用于扩展tensor到目标形状,常用的多是在H和W方向上的扩展。
假设目标形状为N, C, H, W,则要求tensor.size()=n, c, h, w(这里假设N,C不变):
1、h=w=1
2、h=1, w!=1
3、h!=1, w=1
补充:tensorflow 利用expand_dims和squeeze扩展和压缩tensor维度
在利用tensorflow进行文本挖掘工作的时候,经常涉及到维度扩展和压缩工作。
比如对文本进行embedding操作完成之后,若要进行卷积操作,就需要对embedded的向量扩展维度,将[batch_size, embedding_dims]扩展成为[batch_size, embedding_dims, 1],利用tf.expand_dims(input, -1)就可实现,反过来用squeeze(input, -1)或者tf.squeeze(input)也可以把最第三维去掉。
tf.expand_dims()
tf.squeeze()
tf.expand_dims()
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tf.expand_dims( input , axis = None , name = None , dim = None )
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在第axis位置增加一个维度.
给定张量输入,此操作在输入形状的维度索引轴处插入1的尺寸。 尺寸索引轴从零开始; 如果您指定轴的负数,则从最后向后计数。
如果要将批量维度添加到单个元素,则此操作非常有用。 例如,如果您有一个单一的形状[height,width,channels],您可以使用expand_dims(image,0)使其成为1个图像,这将使形状[1,高度,宽度,通道]。
例子
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# 't' is a tensor of shape [2]
shape(expand_dims(t, 0 )) = = > [ 1 , 2 ]
shape(expand_dims(t, 1 )) = = > [ 2 , 1 ]
shape(expand_dims(t, - 1 )) = = > [ 2 , 1 ]
# 't2' is a tensor of shape [2, 3, 5]
shape(expand_dims(t2, 0 )) = = > [ 1 , 2 , 3 , 5 ]
shape(expand_dims(t2, 2 )) = = > [ 2 , 3 , 1 , 5 ]
shape(expand_dims(t2, 3 )) = = > [ 2 , 3 , 5 , 1 ]
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tf.squeeze()
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tf.squeeze( input , axis = None , name = None , squeeze_dims = None )
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直接上例子
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# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
shape(squeeze(t)) = = > [ 2 , 3 ]
# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
shape(squeeze(t, [ 2 , 4 ])) = = > [ 1 , 2 , 3 , 1 ]
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以上为个人经验,希望能给大家一个参考,也希望大家多多支持服务器之家。如有错误或未考虑完全的地方,望不吝赐教。
原文链接:https://blog.csdn.net/P_LarT/article/details/90145088