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）

>>> import torch
>>> x = torch.arange(10)
>>> x
tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> x.shape
torch.Size([10])
>>> x.numel()
10
>>> X = x.reshape(3,4)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
RuntimeError: shape '[3, 4]' is invalid for input of size 10
>>> X = x.reshape(2,5) # or X = x.reshape(-1,5)
>>> X
tensor([[0, 1, 2, 3, 4],
        [5, 6, 7, 8, 9]])
>>> torch.zeros(2,3,4)
tensor([[[0., 0., 0., 0.],
         [0., 0., 0., 0.],
         [0., 0., 0., 0.]],

        [[0., 0., 0., 0.],
         [0., 0., 0., 0.],
         [0., 0., 0., 0.]]])
>>> torch.ones(2,3,4)
tensor([[[1., 1., 1., 1.],
         [1., 1., 1., 1.],
         [1., 1., 1., 1.]],

        [[1., 1., 1., 1.],
         [1., 1., 1., 1.],
         [1., 1., 1., 1.]]])
>>> torch.randn(3,4) #创建3x4的张量，其中每个值都从均值为0，标准差为1的正态分布中随机采样
tensor([[ 0.1182, -0.6975,  0.6529,  0.4547],
        [-0.6887,  0.1396,  1.1660,  0.0818],
        [-0.8471,  0.4265,  0.4753,  0.8336]])