Differences with torch.ones
torch.ones
torch.ones(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor
For more information, see torch.ones.
mindspore.ops.ones
mindspore.ops.ones(shape, dtype=dtype) -> Tensor
For more information, see mindspore.ops.ones.
Differences
PyTorch: Generate a Tensor of size *size
with a padding value of 1.
MindSpore: MindSpore API implements the same function as TensorFlow, and only the parameter names are different.
Categories |
Subcategories |
PyTorch |
MindSpore |
Difference |
---|---|---|---|---|
Parameters |
Parameter 1 |
size |
shape |
MindSpore supports input of int, tuple or Tensor type |
Parameter 2 |
out |
- |
Not involved |
|
Parameter 3 |
dtype |
dtype |
The parameter is consistent |
|
Parameter 4 |
layout |
- |
Not involved |
|
Parameter 5 |
device |
- |
Not involved |
|
Parameter 6 |
requires_grad |
- |
Not involved |
Code Example 1
The two APIs achieve the same function and have the same usage.
# PyTorch
import torch
from torch import tensor
output = torch.ones(2, 2, dtype=torch.float32)
print(output.numpy())
# [[1. 1.]
# [1. 1.]]
# MindSpore
import numpy as np
import mindspore
import mindspore.ops as ops
import mindspore as ms
from mindspore import Tensor
output = ops.ones((2, 2), dtype=ms.float32).asnumpy()
print(output)
# [[1. 1.]
# [1. 1.]]