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In [3]:

    
from __future__ import print_function
import torch



In [4]:

    
print(torch.__version__)



In [32]:

    
x = torch.Tensor(5,3)  # initialize as 0 or approxiamte to 0
print(x)









    



1.00000e-42 *
  0.0000  0.0000  0.0000
  0.0000  0.0000  0.0000
  0.0000  0.0000  0.0000
  0.0000  1.5975  0.0000
  0.0000  0.6208  0.0000
[torch.FloatTensor of size 5x3]



In [33]:

    
x = torch.rand(5,3)
print(x)









    



 0.5446  0.9017  0.3697
 0.2556  0.0482  0.2955
 0.5412  0.3691  0.4219
 0.0770  0.7549  0.3826
 0.4210  0.8101  0.5251
[torch.FloatTensor of size 5x3]



In [36]:

    
x.size()









    Out[36]:





torch.Size([5, 3])



In [37]:

    
y = torch.rand(5,3)
print(y)









    



 0.8818  0.1887  0.2476
 0.0571  0.8426  0.1903
 0.9361  0.9746  0.8263
 0.3841  0.9526  0.2364
 0.9489  0.0245  0.8846
[torch.FloatTensor of size 5x3]



In [38]:

    
x+y









    Out[38]:





 1.4264  1.0904  0.6173
 0.3127  0.8908  0.4858
 1.4772  1.3437  1.2482
 0.4611  1.7075  0.6189
 1.3699  0.8346  1.4098
[torch.FloatTensor of size 5x3]



In [40]:

    
torch.add(x,y)









    Out[40]:





 1.4264  1.0904  0.6173
 0.3127  0.8908  0.4858
 1.4772  1.3437  1.2482
 0.4611  1.7075  0.6189
 1.3699  0.8346  1.4098
[torch.FloatTensor of size 5x3]



In [41]:

    
x+y == torch.add(x,y)  # ele-wise









    Out[41]:





 1  1  1
 1  1  1
 1  1  1
 1  1  1
 1  1  1
[torch.ByteTensor of size 5x3]



In [45]:

    
result = torch.Tensor(5,3)
torch.add(x,y,out=result)









    Out[45]:





 1.4264  1.0904  0.6173
 0.3127  0.8908  0.4858
 1.4772  1.3437  1.2482
 0.4611  1.7075  0.6189
 1.3699  0.8346  1.4098
[torch.FloatTensor of size 5x3]



In [46]:

    
result









    Out[46]:





 1.4264  1.0904  0.6173
 0.3127  0.8908  0.4858
 1.4772  1.3437  1.2482
 0.4611  1.7075  0.6189
 1.3699  0.8346  1.4098
[torch.FloatTensor of size 5x3]



In [47]:

    
x.add_(y)  # x will be change









    Out[47]:





 1.4264  1.0904  0.6173
 0.3127  0.8908  0.4858
 1.4772  1.3437  1.2482
 0.4611  1.7075  0.6189
 1.3699  0.8346  1.4098
[torch.FloatTensor of size 5x3]



In [48]:

    
x.add_(y)









    Out[48]:





 2.3082  1.2790  0.8649
 0.3697  1.7335  0.6761
 2.4133  2.3183  2.0745
 0.8452  2.6600  0.8553
 2.3187  0.8592  2.2944
[torch.FloatTensor of size 5x3]

Any operation that mutates a tensor in-place is post-fixed with an For example: x.copy(y), x.t_(), will change x.



In [52]:

    
x[:1]  # equal to x[0:1] ,x[0]









    Out[52]:





 2.3082  1.2790  0.8649
[torch.FloatTensor of size 1x3]



In [53]:

    
x[:,1]









    Out[53]:





 1.2790
 1.7335
 2.3183
 2.6600
 0.8592
[torch.FloatTensor of size 5]



In [54]:

    
x[1]









    Out[54]:





 0.3697
 1.7335
 0.6761
[torch.FloatTensor of size 3]



In [57]:

    
a = torch.ones(5)
print(a)









    



 1
 1
 1
 1
 1
[torch.FloatTensor of size 5]



In [59]:

    
b = a.numpy()
print(b)









    



[ 1.  1.  1.  1.  1.]



In [60]:

    
a.add_(1)
print(a)
print(b)









    



 2
 2
 2
 2
 2
[torch.FloatTensor of size 5]

[ 2.  2.  2.  2.  2.]



In [62]:

    
import numpy as np
a = np.ones(5)
b = torch.from_numpy(a)
print(a)
print(b)









    



[ 1.  1.  1.  1.  1.]

 1
 1
 1
 1
 1
[torch.DoubleTensor of size 5]



In [72]:

    
# let us run this cell only if CUDA is available
if torch.cuda.is_available():
    print(111)
    x = x.cuda()
    y = y.cuda()
    print(x + y)









    



111

 3.1900  1.4677  1.1126
 0.4268  2.5761  0.8663
 3.3493  3.2929  2.9008
 1.2292  3.6126  1.0917
 3.2676  0.8837  3.1791
[torch.cuda.FloatTensor of size 5x3 (GPU 0)]



In [73]:



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