Deep-Learning-with-PyTorch

52 CHAPTER 3 It starts with a tensor
Under the hood, to checks whether the conversion is necessary and, if so, does it. The
dtype-named casting methods like float are shorthands for to, but the to method
can take additional arguments that we’ll discuss in section 3.9.
When mixing input types in operations, the inputs are converted to the larger type
automatically. Thus, if we want 32-bit computation, we need to make sure all our
inputs are (at most) 32-bit:
# In[51]:
points_64 = torch.rand(5, dtype=torch.double)
points_short = points_64.to(torch.short)
points_64 * points_short # works from PyTorch 1.3 onwards
# Out[51]:
tensor([0., 0., 0., 0., 0.], dtype=torch.float64)
3.6 The tensor API
At this point, we know what PyTorch tensors are and how they work under the hood.
Before we wrap up, it is worth taking a look at the tensor operations that PyTorch
offers. It would be of little use to list them all here. Instead, we’re going to get a general
feel for the API and establish a few directions on where to find things in the
online documentation at http://pytorch.org/docs.
First, the vast majority of operations on and between tensors are available in the
torch module and can also be called as methods of a tensor object. For instance, the
transpose function we encountered earlier can be used from the torch module
# In[71]:
a = torch.ones(3, 2)
a_t = torch.transpose(a, 0, 1)
a.shape, a_t.shape
# Out[71]:
(torch.Size([3, 2]), torch.Size([2, 3]))
or as a method of the a tensor:
# In[72]:
a = torch.ones(3, 2)
a_t = a.transpose(0, 1)
a.shape, a_t.shape
# Out[72]:
(torch.Size([3, 2]), torch.Size([2, 3]))
rand initializes the tensor elements to
random numbers between 0 and 1.
There is no difference between the two forms; they can be used interchangeably.
We mentioned the online docs earlier (http://pytorch.org/docs). They are
exhaustive and well organized, with the tensor operations divided into groups: