Comments (1)
Hi,
Yeah, we faced that problem too sometimes with very small datasets, but some workarounds helped us resolve those issues.. for example decreasing the dimensionality of z, increasing the number of gaussians components and increasing the weight of the regularisation term.. But I agree, even after these workarounds sometimes it still was unstable and difficult to tune. Perhaps tuning these parameters might help?
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Related Issues (8)
- Some questions about the project HOT 1
- Mode Collapse for toy dataset? HOT 2
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- TypeError: ('An update must have the same type as the original shared variable (shared_var=W, shared_var.type=GpuArrayType<None>(float32, (False, True, False, False)), update_val=Elemwise{sub,no_inplace}.0, update_val.type=TensorType(float32, 4D)).', 'If the difference is related to the broadcast pattern, you can call the tensor.unbroadcast(var, axis_to_unbroadcast[, ...]) function to remove broadcastable dimensions.')
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