1. 大佬视频

1、PyTorch介绍与张量的创建

2. python 代码

#!/usr/bin/env python
# -*- coding:utf-8 -*-
# @FileName  :torch_learning01.py
# @Time      :2024/11/16 18:37
# @Author    :Jason Zhang
import torch
import numpy as nptorch.set_printoptions(sci_mode=False, precision=3)if __name__ == "__main__":run_code = 0data = [[1, 2], [3.0, 4]]data_tensor = torch.tensor(data)print(f"data={data},type(data)={type(data)}")print(f"data_tensor={data_tensor},type(data_tensor)={type(data_tensor)}")print(f"data_tensor.dtype={data_tensor.dtype}")data_numpy = np.array([[1, 2], [3, 4]])data_torch = torch.from_numpy(data_numpy)print(f"data_numpy={type(data_numpy)},type(data_torch)={type(data_torch)}")b = torch.arange(12, dtype=torch.float32).reshape((3, 4))b_ones_like = torch.ones_like(b)b_zeros_like = torch.zeros_like(b)b_rand_like = torch.rand_like(b)print(f"b=\n{b}")print(f"b_ones_like=\n{b_ones_like}")print(f"b_zeros_like=\n{b_zeros_like}")print(f"b_rand_like=\n{b_rand_like}")rand_shape23 = torch.rand((2, 3))print(f"rand_shape23=\n{rand_shape23}")ones_shape23 = torch.ones((2, 3))print(f"ones_shape23=\n{ones_shape23}")zeros_shape23 = torch.zeros((2, 3))print(f"zeros_shape23=\n{zeros_shape23}")zeros_shape23_dtype = zeros_shape23.dtypeprint(f"zeros_shape23_dtype={zeros_shape23_dtype}")zeros_shape23_shape = zeros_shape23.shapeprint(f"zeros_shape23_shape={zeros_shape23_shape}")zeros_shape23_device = zeros_shape23.deviceprint(f"zeros_shape23_device={zeros_shape23_device}")cuda_is_available = torch.cuda.is_available()print(f"cuda_is_available=\n{cuda_is_available}")zeros_shape23 = zeros_shape23.to('cuda')print(f"zeros_shape23_device={zeros_shape23.device}")np_23 = np.random.random((2, 3))np_23_is_tensor = torch.is_tensor(np_23)print(f"{np_23} is {np_23_is_tensor} torch.tensor")np_23_torch = torch.from_numpy(np_23)np_23_torch_is_tensor = torch.is_tensor(np_23_torch)print(f"{np_23_torch} is {np_23_torch_is_tensor} torch.tensor")one_tensor = torch.tensor(1.0)one_tensor_is_nonzero = torch.is_nonzero(one_tensor)print(f"one_tensor={one_tensor}")print(f"one_tensor_is_nonzero={one_tensor_is_nonzero}")rand_23 = torch.rand((2, 3))rand_23_numel = torch.numel(rand_23)print(f"{rand_23} is {rand_23_numel}")torch_arange = torch.arange(0, 5)print(f"torch_arange=\n{torch_arange}")#   torch_range = torch.range(start=0, end=5)#   print(f"torch_range=\n{torch_range}")torch_arange2 = torch.arange(1, 6) % 5print(f"torch_arange2=\n{torch_arange2}")for i in torch.arange(7):print(f"i={i}")torch_li = torch.linspace(1, 10, 5)print(f"torch_li={torch_li}")torch_eye5 = torch.eye(5)print(f"torch_eye5=\n{torch_eye5}")torch_eye24 = torch.eye(2, 4)print(f"torch_eye24=\n{torch_eye24}")torch_full_314 = torch.full([3, 4], 3.14)torch_ones_314 = 3.14 * torch.ones((3, 4))print(f"torch_full_314=\n{torch_full_314}")print(f"torch_ones_314=\n{torch_ones_314}")cat1 = torch.rand((2, 3))cat2 = torch.rand((2, 3))torch_cat12 = torch.cat((cat1, cat2), 0)torch_cat12_1 = torch.cat((cat1, cat2), 1)print(f"cat1=\n{cat1}")print(f"cat2=\n{cat2}")print(f"torch_cat12=\n{torch_cat12}")print(f"torch_cat12_1=\n{torch_cat12_1}")

• 结果：

data=[[1, 2], [3.0, 4]],type(data)=<class 'list'>
data_tensor=tensor([[1., 2.],[3., 4.]]),type(data_tensor)=<class 'torch.Tensor'>
data_tensor.dtype=torch.float32
data_numpy=<class 'numpy.ndarray'>,type(data_torch)=<class 'torch.Tensor'>
b=
tensor([[ 0.,  1.,  2.,  3.],[ 4.,  5.,  6.,  7.],[ 8.,  9., 10., 11.]])
b_ones_like=
tensor([[1., 1., 1., 1.],[1., 1., 1., 1.],[1., 1., 1., 1.]])
b_zeros_like=
tensor([[0., 0., 0., 0.],[0., 0., 0., 0.],[0., 0., 0., 0.]])
b_rand_like=
tensor([[0.334, 0.780, 0.210, 0.252],[0.235, 0.528, 0.647, 0.188],[0.991, 0.704, 0.177, 0.809]])
rand_shape23=
tensor([[0.377, 0.035, 0.664],[0.471, 0.954, 0.777]])
ones_shape23=
tensor([[1., 1., 1.],[1., 1., 1.]])
zeros_shape23=
tensor([[0., 0., 0.],[0., 0., 0.]])
zeros_shape23_dtype=torch.float32
zeros_shape23_shape=torch.Size([2, 3])
zeros_shape23_device=cpu
cuda_is_available=
True
zeros_shape23_device=cuda:0
[[0.99230163 0.0692696  0.80117093][0.73090885 0.51565045 0.52366436]] is False torch.tensor
tensor([[0.992, 0.069, 0.801],[0.731, 0.516, 0.524]], dtype=torch.float64) is True torch.tensor
one_tensor=1.0
one_tensor_is_nonzero=True
tensor([[0.445, 0.668, 0.124],[0.717, 0.891, 0.951]]) is 6
torch_arange=
tensor([0, 1, 2, 3, 4])
torch_arange2=
tensor([1, 2, 3, 4, 0])
i=0
i=1
i=2
i=3
i=4
i=5
i=6
torch_li=tensor([ 1.000,  3.250,  5.500,  7.750, 10.000])
torch_eye5=
tensor([[1., 0., 0., 0., 0.],[0., 1., 0., 0., 0.],[0., 0., 1., 0., 0.],[0., 0., 0., 1., 0.],[0., 0., 0., 0., 1.]])
torch_eye24=
tensor([[1., 0., 0., 0.],[0., 1., 0., 0.]])
torch_full_314=
tensor([[3.140, 3.140, 3.140, 3.140],[3.140, 3.140, 3.140, 3.140],[3.140, 3.140, 3.140, 3.140]])
torch_ones_314=
tensor([[3.140, 3.140, 3.140, 3.140],[3.140, 3.140, 3.140, 3.140],[3.140, 3.140, 3.140, 3.140]])
cat1=
tensor([[0.868, 0.323, 0.289],[0.773, 0.597, 0.806]])
cat2=
tensor([[0.522, 0.376, 0.304],[0.854, 0.298, 0.656]])
torch_cat12=
tensor([[0.868, 0.323, 0.289],[0.773, 0.597, 0.806],[0.522, 0.376, 0.304],[0.854, 0.298, 0.656]])
torch_cat12_1=
tensor([[0.868, 0.323, 0.289, 0.522, 0.376, 0.304],[0.773, 0.597, 0.806, 0.854, 0.298, 0.656]])