PyTorch get started：修订间差异
无编辑摘要 |
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= Concepts = | = Concepts = | ||
== | == Tensor（张量） == | ||
Tensors are a specialized data structure that are very similar to arrays and matrices. In PyTorch, we use tensors to encode the inputs and outputs of a model, as well as the model’s parameters. | Tensors are a specialized data structure that are very similar to arrays and matrices. In PyTorch, we use tensors to encode the inputs and outputs of a model, as well as the model’s parameters. | ||
2023年12月11日 (一) 04:36的版本
Installation
Conda Installation
conda create --name deeplearning python=3.11
conda activate deeplearning
python --version
// 3.11.5
Install pytorch
conda install pytorch::pytorch torchvision torchaudio -c pytorch
To verify:
import torch
x = torch.rand(5, 3)
print(x)
Output:
tensor([[0.2162, 0.2653, 0.6725],
[0.5371, 0.4180, 0.1353],
[0.3697, 0.5238, 0.0332],
[0.6179, 0.5008, 0.9435],
[0.1182, 0.3233, 0.9071]])
Concepts
Tensor（张量）
Tensors are a specialized data structure that are very similar to arrays and matrices. In PyTorch, we use tensors to encode the inputs and outputs of a model, as well as the model’s parameters.
Tensors are similar to NumPy’s ndarrays, except that tensors can run on GPUs or other hardware accelerators.