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from deligan.
Hi, I'm sorry I couldn't reply earlier.
Regarding the results. I'm not sure why you are getting such results. I just ran the code myself multiple times and my results seem pretty normal. Sometimes in the case of multiple gaussians the model does tend to oscillate between the results you show and the actual correct distribution but ultimately it does converge to the correct distribution once trained long enough. One thing you could try is learning rate decay if the problem persists in your case. But beyond that I'm not sure what's causing this issue in your case. But in the case of unimodal gaussian the behavior you show doesn't usually happen. I'm not sure what's causing it. I hope this was helpful.
from deligan.
Related Issues (8)
- Some questions about the project HOT 1
- Results in dg_mnist.py HOT 1
- fixed batch size HOT 3
- generating same sample HOT 1
- 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.')
- Latent Space HOT 5
- how to use cpu to train for deli_gan
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from deligan.