#Permute by row torch code#

The comparison between the two shows that the number of blocks and the number of rows in each block have changed Without changing every piece （ namely ） Under the premise of, Swap the rows and columns of each block （ The transpose of a two-dimensional matrix ） Change three ：0 And 1 In exchange for b = x.permute( 1, 0, 2) # Swap blocks and rows print(b) It is found that the matrix does not change at this time, It's still arranged in the same way as before Change 2 ：1 And 2 In exchange for b = x.permute( 0, 2, 1) # The rows and columns of each block are exchanged, That is, each piece does transpose behavior print(b)

#Permute by row torch code#

It's kind of like x,y,z To distinguish three coordinate dimensions, It's man-made The three-dimensional situation is explained to you directly with the following code Three dimensional situation Change one ： Do not change any parameters b = x.permute( 0, 1, 2) # Don't change dimensions print(b) permute（0,1） There is no change here, Same dimension as before If written permute（1,0） What you get is the transpose of the matrix If 3D is permute(0,1,2) 0 Represents a total of several dimensions ： In this case 0 Corresponding 3 Block matrix 1 Represents how many lines there are in each block ： In this case 1 Corresponding to each piece, there are 2 That's ok 2 Represents how many columns there are in each block ： In this case 2 Corresponding to each piece, there are 5 Column So it is 3 block 2 That's ok 5 Three dimensional matrix of columns these 0,1,2 There is no practical significance, Nor is it a numerical value, Just to identify the difference. Of course, the matrix must be at least two dimensions to use permute If it is two-dimensional ,dims They are 0 and 1 It can be written. That is to say 0 dimension, The first 1 Wei and so on It can also be understood as, The first 0 block, The first 1 And so on. Permute（dims） Parameters dims Substitute the dimension of the matrix into, Generally, the default is from 0 Start. Next, let's briefly introduce permute（） function I'll simply use 3 block 3*3 Figure example of laziness Then it's piled up, which is what we know as a three-dimensional matrix Such as (3,2,5), Express 3 block 2*5 Array of So let's follow the block, That's ok, Columns are easier to understand General handle （3,2,5） Interpreted as 3 dimension 2 That's ok 5 It's easy to get confused here Here, in order to prevent the coincidence of the same dimension value （ For example, a three-dimensional array （3,3,3） perhaps （2,4,4） etc. Print(x.size()) # Check the dimension of the array Copy code X = torch.linspace( 1, 30, steps= 30).view( 3, 2, 5) # Set a 3D array print(x) I'll explain it in combination with code and picturesįirst, create a small instance of a three-dimensional array import torch This paper only discusses the two-dimensional and three-dimensional permute usageĬurrent Attention One of the learning permute Functions don't make me understand This article has participated in 「 New people's creation ceremony 」 Activities, Start the road of nuggets creation together Preface Matrix = torch.This article has participated in 「 New people's creation ceremony 」 Activities, Start the road of nuggets creation together. In the following example, we shuffle 2nd and 3rd columns. Notice that the 1st and 2nd rows are shuffled. Print('Shuffled Matrix: ', matrix) Output Original Matrix: In the following example, we shuffle the 1st and 2nd rows. Print('Shuffled Matrix: ', matrix) Example 1 Shuffle the rows or columns of the matrix. In the followingĮxample we shuffle 1st and 2nd row.

Specify the row and column indices with shuffled indices. In all the following examples, the required Python library is torch. You could use the following steps to shuffle the rows or columns of a matrix. Let's take a couple of examples to have a better understanding of how it works. Then we just shuffle the index of these rows and make a slicing to find the To shuffle columns, we do slicing in the column indices.įor example, if we want to shuffle the 1st and 2nd rows of a 3☓3 matrix, If we want to shuffle rows, then we do slicing in the row indices. To shuffle rows or columns, we can use simple slicing and indexing as we do in Numpy. We can shuffle a row by another row and a column by another column. A matrix in PyTorch is a 2-dimension tensor having elements of the same dtype.