我就廢話不多說了，大家還是直接看代碼吧~
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])
tensor.expand_as在這里用于擴展tensor到目標形狀，常用的多是在H和W方向上的擴展。
假設目標形狀為N, C, H, W，則要求tensor.size()=n, c, h, w(這里假設N，C不變)：
1、h=w=1
2、h=1, w!=1
3、h!=1, w=1
補充：tensorflow 利用expand_dims和squeeze擴展和壓縮tensor維度
在利用tensorflow進行文本挖掘工作的時候，經(jīng)常涉及到維度擴展和壓縮工作。
比如對文本進行embedding操作完成之后，若要進行卷積操作，就需要對embedded的向量擴展維度，將[batch_size, embedding_dims]擴展成為[batch_size, embedding_dims, 1]，利用tf.expand_dims(input, -1)就可實現(xiàn)，反過來用squeeze(input, -1)或者tf.squeeze（input）也可以把最第三維去掉。
tf.expand_dims()
tf.squeeze()
tf.expand_dims()
tf.expand_dims(input, axis=None, name=None, dim=None)
在第axis位置增加一個維度.
給定張量輸入，此操作在輸入形狀的維度索引軸處插入1的尺寸。 尺寸索引軸從零開始; 如果您指定軸的負數(shù)，則從最后向后計數(shù)。
如果要將批量維度添加到單個元素，則此操作非常有用。 例如，如果您有一個單一的形狀[height，width，channels]，您可以使用expand_dims（image，0）使其成為1個圖像，這將使形狀[1，高度，寬度，通道]。
例子
# '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]
tf.squeeze()
tf.squeeze(input, axis=None, name=None, squeeze_dims=None)
直接上例子
# '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]
以上為個人經(jīng)驗，希望能給大家一個參考，也希望大家多多支持腳本之家。如有錯誤或未考慮完全的地方，望不吝賜教。
您可能感興趣的文章:- PyTorch中Tensor的數(shù)據(jù)類型和運算的使用
- 對Pytorch中Tensor的各種池化操作解析
- 詳解PyTorch中Tensor的高階操作
- pytorch教程之Tensor的值及操作使用學習
