1. 32位浮点型：torch.FloatTensor

a=torch.Tensor( [[2,3],[4,8],[7,9]], )
print "a:",a
print "a.size():",a.size()
print "a.dtype:",a.dtype

b=torch.FloatTensor( [[2,3],[4,8],[7,9]] )
print "b:",b
print "b.shape:",b.shape
print "b.dtype:",b.dtype

 

可以看出 torch.FloatTensor 是32位float类型，并且torch.Tensor默认的数据类型是32位float类型。

2. 64位浮点型：torch.DoubleTensor

b=torch.DoubleTensor( [[2,3],[4,8],[7,9]] )
print "b:",b
print "b.shape:",b.shape
print "b.dtype:",b.dtype

 

 3. 16位整型：torch.ShortTensor

b=torch.ShortTensor( [[2,3],[4,8],[7,9]] )
print "b:",b
print "b.shape:",b.shape
print "b.dtype:",b.dtype

4.  32位整型：torch.IntTensor

b=torch.IntTensor( [[2,3],[4,8],[7,9]] )
print "b:",b
print "b.shape:",b.shape
print "b.dtype:",b.dtype

5. 64位整型：torch.LongTensor

b=torch.LongTensor( [[2,3],[4,8],[7,9]] )
print "b:",b
print "b.shape:",b.shape
print "b.dtype:",b.dtype

6. 快速创建Tensor

（1） torch.zeros()

a=torch.zeros( size=(4,5),dtype=torch.float32 )
print a
print a.shape
print a.dtype

（2） torch.randn()

a=torch.randn( size=(4,5),dtype=torch.float32 )
print a
print a.shape
print a.dtype

7. Tensor索引方式，参考numpy

8. Tensor和numpy数组转换：

（1） Tensor转numpy，

a=torch.randn( size=(4,5),dtype=torch.float32 )
print a
print a.shape
print a.dtype

b= a.numpy()
print b
print b.shape
print b.dtype

（2） numpy转Tensor，

a=np.random.randn(4,3)
print a
print a.shape
print a.dtype

b=torch.from_numpy( a )
print b
print b.shape
print b.dtype

9.更改Tensor的数据类型，

a=torch.FloatTensor( (3,2) )
print a
print a.shape
print a.dtype

a.int()
print a
print a.shape
print a.dtype

10. GPU加速，如果pytorch支持GPU加速，可以加Tensor放到GPU执行，

if torch.cuda.is_available():
    a_cuda = a.cuda()