'Handling operations with large tensors and memory usage in tensorflow
I'm trying to perform outer concatenation in Tensorflow by combining two 2D tensors into a third, so that two m by n tensors combine into an m by m by n^2 tensor. In the past when I've made a new tensor, I've used the entire data set and preallocated the space, so that here I would have a tensor of the total number of samples, S, times the other dimensions (S by m by m by n^2). This takes up too much memory. What are my options for processing an outer concatenation without overloading my RAM? Should I be making this an individual layer, for instance, and if so, how? Thank you for any advice provided.
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