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The MLP client training code seems to assume that the input and output data / labels are identically ordered, and never checks that this is the case. Much of the time it is, but one can simply 'reorder' the entries of a Dataset – which should remain semantically equivalent because it's an unordered collection – and completely screw up one's training.
There's no particularly nice solution to this: we just have to traverse the ID list of one of the data sets and rearrange (a copy of) the other to match. Current fix in progress introduces a new flag (strict) to enable this, because it's not cheap.
The text was updated successfully, but these errors were encountered:
The MLP client training code seems to assume that the input and output data / labels are identically ordered, and never checks that this is the case. Much of the time it is, but one can simply 'reorder' the entries of a Dataset – which should remain semantically equivalent because it's an unordered collection – and completely screw up one's training.
There's no particularly nice solution to this: we just have to traverse the ID list of one of the data sets and rearrange (a copy of) the other to match. Current fix in progress introduces a new flag (
strict
) to enable this, because it's not cheap.The text was updated successfully, but these errors were encountered: