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Fix empirical cross-correlation computation in the SSL example (#2679)

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I was using the normalized features za as both matrices, instead of za and zb.
I noticed this because the empirical cross-correlation matrix was symmetrical,
which is not supposed to.  It does not affect anything, as it was computed
properly in the loss.
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Adria Arrufat 2022-11-01 08:52:24 +09:00 committed by GitHub
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commit 7f06f6e185
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examples/dnn_self_supervised_learning_ex.cpp
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@ -283,7 +283,7 @@ try
beta = 0; beta = 0;
// Perform batch normalization on each feature representation, independently. // Perform batch normalization on each feature representation, independently.
tt::batch_normalize(eps, za_norm, means, invstds, 1, rms, rvs, za, gamma, beta); tt::batch_normalize(eps, za_norm, means, invstds, 1, rms, rvs, za, gamma, beta);
tt::batch_normalize(eps, zb_norm, means, invstds, 1, rms, rvs, za, gamma, beta); tt::batch_normalize(eps, zb_norm, means, invstds, 1, rms, rvs, zb, gamma, beta);
// Compute the empirical cross-correlation matrix between the features and // Compute the empirical cross-correlation matrix between the features and
// visualize it. // visualize it.
tt::gemm(0, eccm, 1, za_norm, true, zb_norm, false); tt::gemm(0, eccm, 1, za_norm, true, zb_norm, false);