i have a batch of input vectors and i want to shuffle them before feeding to a nn.
i noticed to torch.randperm doesn’t have an option to generate multiple samples at once. in such a case, which is better in terms of computation time but also setbacks with learning:
my input is x with shape (batch_size,vec_size)
shuffled_indices = torch.empty(0,vec_size) for i in range(batch_size): shuffled_indices = torch.cat([shuffled_indices,torch.randperm(vec_size)]) x = x[shuffled_indices]
shuffled_indices = torch.randperm(vec_size).unsqueeze(0).repeat(batch_size,1) x=x[shuffled_indices]
notice that these are two different approaches. in one i use a loop to generate a batch of shuffled indices, in the other i just let all samples in the batch be shuffled in the same order. i’m trying to figure out if shuffling the entire batch in one order would add an unwanted bias to the batch, and if this is less significant than the time consumption of a for loop as in example 1.
also notice i assign the new order by simply getting the shuffled indices in the original tensor, but in torch this doesn’t give the desired outcome. so that’s another bonus question - how do you treat a multidimensional tensor as coordinates to get certain elements from another multidimensional tesnor?