Pytorch基础
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2022-06-26 11:30:43
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一 Pytorch安装
1. 依赖库
Pytorch配合N家的CUDA库可运行GPU加速,所以一般应先安装CUDA库
2.安装工具:pip 或 anaconda
pip install #安装地址由Pytorch官网来
pip install torchvision
二 Pytorch与Numpy对比
1.引入Pytorch与Numpy包
import torch
import numpy as np
注: Numpy包常简写做np
2. 产生随机数据
# convert numpy to tensor or vise versa
np_data = np.arange(6).reshape((2, 3))
torch_data = torch.from_numpy(np_data)
tensor2array = torch_data.numpy()
print(
'\nnumpy array:', np_data, # [[0 1 2], [3 4 5]]
'\ntorch tensor:', torch_data, # 0 1 2 \n 3 4 5 [torch.LongTensor of size 2x3]
'\ntensor to array:', tensor2array, # [[0 1 2], [3 4 5]]
)
- reshape函数:将Numpy数列转化至制定形式(2行3列)
- torch.from_numpy(np_data):将 Numpy 数列转换为 Tensor
- torch_data.numpy():将 Tensor 转换为 Numpy 数列
3. Pytorch函数
1. abs
data = [-1, -2, 1, 2]
tensor = torch.FloatTensor(data) # 32-bit floating point
print(
'\nabs',
'\nnumpy: ', np.abs(data), # [1 2 1 2]
'\ntorch: ', torch.abs(tensor) # [1 2 1 2]
)
2. sin
print(
'\nsin',
'\nnumpy: ', np.sin(data), # [-0.84147098 -0.90929743 0.84147098 0.90929743]
'\ntorch: ', torch.sin(tensor) # [-0.8415 -0.9093 0.8415 0.9093]
)
3. mean
print(
'\nmean',
'\nnumpy: ', np.mean(data), # 0.0
'\ntorch: ', torch.mean(tensor) # 0.0
)
4. matrix multiplication
data = [[1,2], [3,4]]
tensor = torch.FloatTensor(data) # 32-bit floating point
print(
'\nmatrix multiplication (matmul)',
'\nnumpy: ', np.matmul(data, data), # [[7, 10], [15, 22]]
'\ntorch: ', torch.mm(tensor, tensor) # [[7, 10], [15, 22]]
)
注:在 torch 中 dot() 函数表示矩阵点积,而非矩阵乘积
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