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Bookmark or Share- python - What does -1 mean in numpy reshape? - Stack OverflowWhen reshaping an array, the new shape must contain the same number of elements as the old shape, meaning the products of the two shapes' dimensions must be equal. When using a -1, the dimension corresponding to the -1 will be the product of the dimensions of the original array divided by the product of the dimensions given to reshape so as to ...
- python - What does .shape [] do in "for i in range (Y.shape [0 ...shape is a tuple that gives you an indication of the number of dimensions in the array. So in your case, since the index value of Y.shape[0] is 0, your are working along the first dimension of your array.
- PyTorch: How to get the shape of a Tensor as a list of intP.S.: Note that tensor.shape is an alias to tensor.size(), though tensor.shape is an attribute of the tensor in question whereas tensor.size() is a function. Share Improve this answer
- Difference between numpy.array shape (R, 1) and (R,)Shape n, expresses the shape of a 1D array with n items, and n, 1 the shape of a n-row x 1-column array. (R,) and (R,1) just add (useless) parentheses but still express respectively 1D and 2D array shapes, Parentheses around a tuple force the evaluation order and prevent it to be read as a list of values (e.g. in function calls). This tuple ...
- python - x.shape [0] vs x [0].shape in NumPy - Stack Overflowx is a 2D array, which can also be looked upon as an array of 1D arrays, having 10 rows and 1024 columns.x[0] is the first 1D sub-array which has 1024 elements (there are 10 such 1D sub-arrays in x), and x[0].shape gives the shape of that sub-array, which happens to be a 1-tuple - (1024, ).
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