2023年12月11日 (一) 04:38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
tensors are also optimized for automatic differentiation（自动微分）

@@ 第36行： / 第36行： @@
 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.
+Tensors are similar to NumPy’s ndarrays, except that ：
+* tensors can run on GPUs or other hardware accelerators
+* tensors are also optimized for automatic differentiation（自动微分）
 [[Category:Deep Learning]]
 [[Category:PyTorch]]