Yurii

Thank you for reporting the issue. I looks like a bug, at least Levenberg-Marquardt algorithm doesn't work with mini-batches.

as a temporary solution you can either try to run ```python mdl1.fit(x, y, batch_size=None) ``` or modify events attribute before you do the training ```python from neupy.algorithms.base import Events optimizer...

> I have tried both options, but neither works. The error remains the same: division by zero I'm quite surprised that the second option didn't work. Do you get the...

Thank you for reporting the issue. It happens because of the bug that I didn't have time to fix yet. As a temporary workaround can you try the same solution...

Hi, I still have a lot of things to add and modify that has higher priority than moving SOM to tensorflow. I don't plan it any time soon.

@aserg24 You cannot train GNG on GPU. All algorithms with fixed architecture, except RBM, cannot be trained on GPU, since they were implemented using numpy library. I'll need to update...

Hi, did you consider reducing number of input samples? For example, you can randomly sample, for example, 5% of your data. In addition, your samples don't have to be fixed....

you need to do it yourself Solution 1 ```python data_sample = randomly_sample(data) for _ in range(200): sofm.train(data_sample, epochs=1) ``` Solution 2 ```python for _ in range(200): data_sample = randomly_sample(data) sofm.train(data_sample,...

Hi @sujithgangaraju, do you have to run it for 100 epochs? Can you use some sort of convergence criteria in order to avoid training it for that many epochs? For...

@sujithgangaraju > the cluster ranges which are calculating from min and max cluster. so all sequential ranges are looping over When you have large number of clusters adding/removing one or...