The interesting point is that as you are using 2 rows of m to fill a colum of m_reshape, you are later on filling those “missing” colums with cat info creating this strange composition.Matlab's sparse matrix support may help you on this. That dimension corresponds to cat and basically has same effect. Obviously when dog image is finished, this is, when you have already used m it takes moves to m to keep taking pixels. As I mentioned previously, this is especially true with high-dimensional arrays and when we are. There is, of course, a small overhead for using the permute function, but in almost all practical cases it is best to include it. You are basically taking dogs to create the image on the top left and then dogs to create image in the top right. MATLAB includes a function called permute(), which is a generalization of the transpose function but for ND arrays. That’s why you can see that streching efect on the image. ![]() GPU Arrays Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox. For more information, see Run MATLAB Functions in Thread-Based Environment. The problem is that in m, in dimension 3 you have 1100 elements, meanwhile in dimension 2 of m_reshape you have 2200 elements, so it actually takes 2 rows from m to fill a row of m_reshape. This function fully supports thread-based environments. So it is filling the row 0 of m_reshape with colum pixels from m. So when you reshape it takes pixels from dimensions at the right and places them until filling new shape dimensions In this composition you have a BatchxImagesxrowsxcolums Ĭontiguous here mens 1-2, 2-3 even 1-2-3, but not 1-3 for example. ![]() All the elements of order must be unique. B has the same values of A but the order of the subscripts needed to access any particular element is rearranged as specified by order. If you want to reshape the ordering only remains for contiguous dimensions. B permute (A,order) rearranges the dimensions of A so that they are in the order specified by the vector order. You achieve what you want which is all the colums of image 1, all the colums of image 2 However if u properly order the dimensions ![]() So it takes the information of the image1, colum 1, then image2, colum 1 and so on. Here you are filling taking the info of one image and then the other because u set N at the right. If you permute and set dimensions before reshaping If u pay attention it 's resized to be fit in the desired shape What’s going on there? as you are reordering it’s getting the information in the original order which is, all colums of image 1, all rows of image 1, all colums of image 2, all rows of image 2 and so on. ![]() If you just reshape you get a wrong ordering m Function Declaration: function permutations permuteWhat(n,r) Return: a value representing the number of permutations Write a function that calculates. The cat cropped looks like that (that’s grayscale) I’m converting RGB images to gray and croping to have same size Im2 = np.mean(skio.imread('/home/jfm/Downloads/cat.jpg'),axis=2) Im1 = np.mean(skio.imread('/home/jfm/Downloads/dog.jpg'),axis=2) So lets see what happens if you reshape vs permute + reshape vs permute without paying attention So an example about how to apply view could be the following oneĪnd these tensor contains B batches of N images whose size is HxW and you want to make a montage of these images in a single one concatanating in the colums your outgoing dimension would be How to write disjoint cycles in matlab a and b are from S-16 permutation. That’s why this operation is different from 0 To write down the permutation in cycle notation, one proceeds as follows: 41. It takes numbers until it fills the dimensions. On the other hand, if you reshape you can see you are modifying the ordering because this is not rotating the cube but mapping in an ordered way from right to left. You are just rotating the tensor, but order is preserved
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