Pytorch基础语法学习
12月30日学习记录
学习了Pytorch的基本语法，包含以下几个
import torch as t
import numpy as np
t.size()
t.shape()
t.numel()
a = t.arange(0,6)
b = t.eye()
c = t.view()
a = t.squeeze()
b = t.unsqueeze()
c = t.resize_(1,3)
学这个基础理论感觉还是比较无聊的，觉得还是通过具体的项目来学习是比较有效的。
我在本地配置了Pytorch，网页上使用jupyter notebook，在使用之前需要进行一些环境的搭建，我配置的虚拟环境名称是pytorch，搭配的Anaconda，激活虚拟环境后，输入命令如下：
pip install ipykernel
python -m ipykernel install --user --name=pytorch运行第一行比较耗费时间，你需要做的就是安静等待。等环境配置完成后在命令行直接输入jupyter notebook，就可以直接在网页上运行python代码了。
关于Sequeeze的解释，直接贴一段代码，要注意的就是看清楚输入和输出。
import torch
x = torch.zeros(3, 2, 4, 1, 2, 1) # dimension of 3*2*4*1*2
print(x.size()) # torch.Size([3, 2, 4, 1, 2, 1])
print(x.shape)
# print(x)
y = torch.squeeze(x) # Returns a tensor with all the dimensions of input of size 1 removed.
print(y.size()) # torch.Size([3, 2, 4, 2])
print(y.shape)
z = torch.unsqueeze(y, dim=0) # Add a dimension of 1 in the 0th position
print(z.size()) # torch.Size([1, 3, 2, 4, 2])
print(z.shape)
z = torch.unsqueeze(y, dim=1) # Add a dimension of 1 in the 1st position
print(z.size()) # torch.Size([3, 1, 2, 4, 2])
print(z.shape)
z = torch.unsqueeze(y, dim=2) # Add a dimension of 1 in the 2nd position
print(z.size()) # torch.Size([3, 2, 1, 4, 2])
print(z.shape)
y = torch.squeeze(x, dim=0) # remove the 0th position of 1 (no 1)
print('dim=0', y.size()) # torch.Size([3, 2, 4, 1, 2, 1])
print('dim=0', y.shape)
y = torch.squeeze(x, dim=1) # remove the 1st position of 1 (no 1)
print('dim=1', y.size()) # torch.Size([3, 2, 4, 1, 2, 1])
print('dim=1', y.shape)
y = torch.squeeze(x, dim=2) # remove the 2nd position of 1 (no 1)
print('dim=2', y.size()) # torch.Size([3, 2, 4, 1, 2])
print('dim=2', y.shape)
y = torch.squeeze(x, dim=3) # remove the 3rd position of 1 (yes)
print('dim=3', y.size()) # torch.Size([3, 2, 4, 2])
print('dim=3', y.shape)
y = torch.squeeze(x, dim=4) # remove the 4th position of 1 (no 1)
print('dim=4', y.size()) # torch.Size([3, 2, 4, 1, 2, 1])
print('dim=4', y.shape)
y = torch.squeeze(x, dim=5) # remove the 5th position of 1 (yes)
print('dim=5', y.size()) # torch.Size([3, 2, 4, 1, 2])
print('dim=5', y.shape)
y = torch.squeeze(x, dim=6) # RuntimeError: Dimension out of range (expected to be in range of [-6, 5], but got 6)
print('dim=6', y.size())
print('dim=6', y.shape)