from torch import Tensor
时间: 2023-11-17 20:07:36 浏览: 58
The `Tensor` class is a multi-dimensional array in PyTorch that represents a mathematical concept of a tensor. It is similar to a NumPy array, but it can be used on a GPU for accelerated computing. A `Tensor` can be created from a Python list or a NumPy array, or initialized with random values. It can be used to perform mathematical operations such as addition, subtraction, multiplication, and division, and supports broadcasting and indexing. `Tensor` is a fundamental data type in PyTorch and is used extensively in deep learning.
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torch.tensor 和 torch.from_numpy
torch.tensor 和 torch.from_numpy 都可以用来将数据转换为 PyTorch 张量。
torch.tensor 可以接受各种类型的 Python 对象,包括列表、元组、NumPy 数组等,然后返回一个新的张量。例如:
```python
import torch
import numpy as np
# 从列表创建张量
a = torch.tensor([1, 2, 3])
print(a)
# 从NumPy数组创建张量
b = np.array([4, 5, 6])
c = torch.tensor(b)
print(c)
```
torch.from_numpy 则是专门用于将 NumPy 数组转换为张量的函数。与 torch.tensor 不同,torch.from_numpy 不会创建新的张量,而是直接使用输入的 NumPy 数组的内存来创建张量。例如:
```python
import torch
import numpy as np
# 创建NumPy数组
a = np.array([1, 2, 3])
# 从NumPy数组创建张量
b = torch.from_numpy(a)
print(b)
# 修改NumPy数组
a[0] = 4
# 查看张量内容
print(b)
```
在这个例子中,我们使用 torch.from_numpy 将 NumPy 数组 a 转换为张量 b,并将其打印出来。然后我们修改了 NumPy 数组 a 的第一个元素,再次打印张量 b,我们会发现张量 b 也发生了相应的改变,因为它们共享相同的内存。
torch.tensor()
torch.tensor() is a function in PyTorch that creates a new tensor with the specified data. It can take in various data types like lists, tuples, arrays, and other tensors, and returns a new tensor with the same data type as the input.
Example usage:
```
import torch
# create a new tensor from a list
my_list = [1, 2, 3]
my_tensor = torch.tensor(my_list)
# create a new tensor from a numpy array
import numpy as np
my_array = np.array([[1, 2], [3, 4]])
my_tensor2 = torch.tensor(my_array)
# create a new tensor from another tensor
my_tensor3 = torch.tensor([5, 6, 7])
my_tensor4 = torch.tensor(my_tensor3)
```