You can set requires_grad with the functions which have requires_grad argument and get it with grad as shown below:
*Memos:
- I selected some popular
requires_gradargument functions such as tensor(), arange(), rand(), rand_like(), zeros(), zeros_like(), full(), full_like() and eye(). -
requires_grad(Optional-Default:False-Type:bool). - Basically,
requires_grad=is needed. -
My post explains
requires_gradand backward() withtensor().
tensor(). *My post explains tensor():
import torch
my_tensor = torch.tensor(data=7., requires_grad=True)
my_tensor, my_tensor.grad
# (tensor(7., requires_grad=True), None)
my_tensor.backward()
my_tensor, my_tensor.grad
# (tensor(7., requires_grad=True), tensor(1.))
arange(). *My post explains arange():
import torch
my_tensor = torch.arange(start=5, end=15, step=3, requires_grad=True)
my_tensor, my_tensor.grad
# (tensor([7.], requires_grad=True), None)
my_tensor.backward()
my_tensor, my_tensor.grad
# (tensor([7.], requires_grad=True), tensor([1.]))
rand(). *My post explains rand():
import torch
my_tensor = torch.rand(size=(1,), requires_grad=True)
my_tensor, my_tensor.grad
# (tensor([0.0030], requires_grad=True), None)
my_tensor.backward()
my_tensor, my_tensor.grad
# (tensor([0.0913], requires_grad=True), tensor([1.]))
rand_like(). *My post explains rand_like():
import torch
my_tensor = torch.rand_like(input=torch.tensor([7.]),
requires_grad=True)
my_tensor, my_tensor.grad
# (tensor([0.4687], requires_grad=True), None)
my_tensor.backward()
my_tensor, my_tensor.grad
# (tensor([0.4687], requires_grad=True), tensor([1.]))
zeros(). *My post explains zeros():
import torch
my_tensor = torch.zeros(size=(1,), requires_grad=True)
my_tensor, my_tensor.grad
# (tensor([0.], requires_grad=True), None)
my_tensor.backward()
my_tensor, my_tensor.grad
# (tensor([0.], requires_grad=True), tensor([1.]))
zeros_like(). *My post explains zeros_like():
import torch
my_tensor = torch.zeros_like(input=torch.tensor([7.]),
requires_grad=True)
my_tensor, my_tensor.grad
# (tensor([0.], requires_grad=True), None)
my_tensor.backward()
my_tensor, my_tensor.grad
# (tensor([0.], requires_grad=True), tensor([1.]))
full(). *My post explains full():
import torch
my_tensor = torch.full(size=(1,), fill_value=5., requires_grad=True)
my_tensor, my_tensor.grad
# (tensor([5.], requires_grad=True), None)
my_tensor.backward()
my_tensor, my_tensor.grad
# (tensor([5.], requires_grad=True), tensor([1.]))
full_like(). *My post explains full_like():
import torch
my_tensor = torch.full_like(input=torch.tensor([7.]),
fill_value=5.,
requires_grad=True)
my_tensor, my_tensor.grad
# (tensor([5.], requires_grad=True), None)
my_tensor.backward()
my_tensor, my_tensor.grad
# (tensor([5.], requires_grad=True), tensor([1.]))
eye(). *My post explains eye():
import torch
my_tensor = torch.eye(n=1, requires_grad=True)
my_tensor, my_tensor.grad
# (tensor([[1.]], requires_grad=True), None)
my_tensor.backward()
my_tensor, my_tensor.grad
# (tensor([[1.]], requires_grad=True), tensor([[1.]]))
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