pytorch common operation in tensor

Pytorch recently started learning programming, which used to be a simple order tensor operations, to facilitate their investigation to take, if a better way to welcome the message.

1. tensor initialization

First, define the size tensor, denoted by size

# 支持多维的size
size = (dim1, dim2, ...)

Common initialization method

# a 返回大小为size的一个空tensor
a = torch.empty(size)

# b 返回大小为size的一个全为0的tensor
b = torch.zeros(size)
# b 返回大小和input tensor大小一样的一个全为0的tensor
b = torch.zeros_like(input)

# c 返回大小为size的一个全为1的tensor
c = torch.ones(size)
# c 返回大小和input tensor大小一样的一个全为1的tensor
c = torch.ones_like(input)

# d 返回大小为size的由[0,1)内的均匀分布随机数生成的一个tensor 
d = torch.rand(size)
# d 返回大小和input tensor大小一样的由[0,1)内的均匀分布随机数生成的一个tensor
d = torch.rand_like(input)

2. tensor splicing

Sometimes the need for stitching tensor according to one dimension, the operation is very simple

# 将tensor a和tensor b按照第dim_k维进行拼接
a = torch.cat((a, b), dim_k)

3. tensor file access

Training resulting tensor can be stored in files to facilitate subsequent reading, to avoid a time to spend time re-training
ideas for: tensor -> numpy -> save numpy -> load numpy -> tensor

 import numpy as np
 
 # step 1. ts_a为一个tensor，转化为numpy格式，存为np_a
 np_a = ts_a.numpy()
 # step 2. 存储numpy，文件名记录在变量file_embns中（后缀.npy）
 np.save(file_embns, np_embns)
 # step 3. 读取numpy，返回numpy格式，存为np_a
 np_a = np.load(file_embns)
 # step 4. np_a为一个numpy，转化为tensor格式，存为ts_a
 ts_a = torch.from_numpy(np_a)

4. tensor data types into the python

#Tensor ----> 单个Python数据，data为Tensor变量且只能为包含单个数据
data.item() 
#Tensor ----> Python list，data为Tensor变量，返回shape相同的可嵌套的list
data.tolist()

To be continued, updated ...