>>> x = torch.DoubleTensor([1, 1, 1])
>>> print(type(x)) # was torch.DoubleTensor
"<class 'torch.Tensor'>"
>>> print(x.type()) # OK: 'torch.DoubleTensor'
'torch.DoubleTensor'
>>> print(isinstance(x, torch.DoubleTensor)) # OK: True
True

>>> x = torch.ones(1)
>>> x.requires_grad #默認是False
False
>>> y = torch.ones(1)
>>> z = x + y
>>> # 顯然z的該屬性也是False
>>> z.requires_grad
False
>>> # 所有變量都不需要grad, 所以會出錯
>>> z.backward()
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn
>>>
>>> # 可以將`requires_grad`作為一個參數(shù), 構(gòu)造tensor
>>> w = torch.ones(1, requires_grad=True)
>>> w.requires_grad
True
>>> total = w + z
>>> total.requires_grad
True
>>> # 現(xiàn)在可以backward了
>>> total.backward()
>>> w.grad
tensor([ 1.])
>>> # x,y,z都是不需要梯度的,他們的grad也沒有計算
>>> z.grad == x.grad == y.grad == None
True

y = x.data # x需要進行autograd
# y和x是共享內(nèi)存的,但是這里y已經(jīng)不需要grad了, 
# 所以會導致本來需要計算梯度的x也沒有梯度可以計算.從而x不會得到更新!

y = x.data # x需要進行autograd
# y和x是共享內(nèi)存的,但是這里y已經(jīng)不需要grad了, 
# 所以會導致本來需要計算梯度的x也沒有梯度可以計算.從而x不會得到更新!

>>> x = torch.zeros(1, requires_grad=True)
>>> with torch.no_grad():
...   y = x * 2
>>> y.requires_grad
False
>>>
>>> is_train = False
>>> with torch.set_grad_enabled(is_train):
...   y = x * 2
>>> y.requires_grad
False
>>> torch.set_grad_enabled(True) # this can also be used as a function
>>> y = x * 2
>>> y.requires_grad
True
>>> torch.set_grad_enabled(False)
>>> y = x * 2
>>> y.requires_grad
False

>>> device = torch.device("cuda:1")
>>> x = torch.randn(3, 3, dtype=torch.float64, device=device)
tensor([[-0.6344, 0.8562, -1.2758],
    [ 0.8414, 1.7962, 1.0589],
    [-0.1369, -1.0462, -0.4373]], dtype=torch.float64, device='cuda:1')
>>> x.requires_grad # default is False
False
>>> x = torch.zeros(3, requires_grad=True)
>>> x.requires_grad
True

>>> device = torch.device("cuda:1")
>>> x = torch.randn(3, 3, dtype=torch.float64, device=device)
tensor([[-0.6344, 0.8562, -1.2758],
    [ 0.8414, 1.7962, 1.0589],
    [-0.1369, -1.0462, -0.4373]], dtype=torch.float64, device='cuda:1')
>>> x.requires_grad # default is False
False
>>> x = torch.zeros(3, requires_grad=True)
>>> x.requires_grad
True

# 從列表中, 創(chuàng)建tensor
>>> cuda = torch.device("cuda")
>>> torch.tensor([[1], [2], [3]], dtype=torch.half, device=cuda)
tensor([[ 1],
    [ 2],
    [ 3]], device='cuda:0')

>>> torch.tensor(1)        # 創(chuàng)建scalar
tensor(1)

 >>> x = torch.randn(3, dtype=torch.float64)
 >>> torch.zeros_like(x)
 tensor([ 0., 0., 0.], dtype=torch.float64)
 >>> torch.zeros_like(x, dtype=torch.int)
 tensor([ 0, 0, 0], dtype=torch.int32)

>>> x = torch.randn(3, dtype=torch.float64)
 >>> x.new_ones(2) # 屬性一致
 tensor([ 1., 1.], dtype=torch.float64)
 >>> x.new_ones(4, dtype=torch.int)
 tensor([ 1, 1, 1, 1], dtype=torch.int32)

# at beginning of the script
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

...

# then whenever you get a new Tensor or Module
# this won't copy if they are already on the desired device
input = data.to(device)
model = MyModule(...).to(device)

model = MyRNN()
 if use_cuda:
   model = model.cuda()

 # train
 total_loss = 0
 for input, target in train_loader:
   input, target = Variable(input), Variable(target)
   hidden = Variable(torch.zeros(*h_shape)) # init hidden
   if use_cuda:
     input, target, hidden = input.cuda(), target.cuda(), hidden.cuda()
   ... # get loss and optimize
   total_loss += loss.data[0]

 # evaluate
 for input, target in test_loader:
   input = Variable(input, volatile=True)
   if use_cuda:
     ...
   ...

 # torch.device object used throughout this script
 device = torch.device("cuda" if use_cuda else "cpu")

 model = MyRNN().to(device)

 # train
 total_loss = 0
 for input, target in train_loader:
   input, target = input.to(device), target.to(device)
   hidden = input.new_zeros(*h_shape) # has the same device & dtype as `input`
   ... # get loss and optimize
   total_loss += loss.item()      # get Python number from 1-element Tensor

 # evaluate
 with torch.no_grad():          # operations inside don't track history
   for input, target in test_loader:
     ...