12/6/2023 0 Comments Numpy random permutationUse np.seed(2) before you do the shuffle. device will be the CPUįor CPU tensor types and the current CUDA device for CUDA tensor types. Random number generators are just mathematical functions which produce a series of numbers that seem random. Layout ( torch.layout, optional) – the desired layout of returned Tensor.ĭevice ( vice, optional) – the desired device of returned tensor.ĭefault: if None, uses the current device for the default tensor type (x) actually returns a new variable and the original data is not changed. In this case, you can use the numpy random.permutation() function. Permutation refers to the arrangement of elements in an array. Out ( Tensor, optional) – the output tensor.ĭtype ( torch.dtype, optional) – the desired data type of returned tensor. print(np.allclose(np.dot(a,b), np.identity(2))) The output of True tells you b is the. The Random module in NumPy helps make permutations of elements of an array. Generator ( torch.Generator, optional) – a pseudorandom number generator for sampling N ( int) – the upper bound (exclusive) Keyword Arguments : Returns a random permutation of integers from 0 to n - 1. randperm ( n, *, generator = None, out = None, dtype = torch.int64, layout = torch.strided, device = None, requires_grad = False, pin_memory = False ) → Tensor ¶ Shuffle means changing arrangement of elements in-place.
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