*Memos:
- My post explains requires_grad.
- My post explains how to set and get dtype.
- My post explains how to set and get device.
- My post explains how to set
keepdimargument. - My post explains how to set
outargument.
You can set requires_grad and get 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(). - Basically,
requires_grad(Optional-Default:False-Type:bool). - Basically,
requires_grad=must be used. - 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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