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Noice!!

Well, you have to change your labels from being 1 long to 2 long. If your labels are True or 1 and False or 0, you will need to change them to [0, 1] and [1, 0]

I am getting an error when tried with 2 neurons and softmax activation func with binary crossentropy loss.

ValueError: logits and labels must have the same shape ((None, 2) vs (None, 1))

Doesn't matter. Softmax is just a multidimensional sigmoid. For binary classifications you can therefore use either 1 output and a sigmoid, or 2 outputs and a softmax. The only difference is that with a sigmoid you resolve the result as

is_fraud = result > 0.5

while with softmax you'd do

is_fraud = argmax(result) == 1

what about loss function I need to use?

yes, it is imbalanced

Do the positive classes make up 0.17% of your data? A softmax of a single neuron should be 1 always right?

get a used card (may be $200 ?), not brand new. PSU are not that expensive now (may be $50?), I used to have a 450W Silverstone 80+ Bronze with Ryzen5600x + RTX3060, it worked well.

Anyway, you will need a PSU for RTX2060 as well. RTX2060 & RTX3060 has almost same power requirement.

p40 for less than 150???

I've got an M40 and love it

2060 with 12g vram definitely better.

Tesla p40 is also a good choice，it is slower but 24g vram for less than 150$(modification required).

in my country both of them are near 300$ but 3060 is around 400$. If I want to upgrade to 3060, I have to upgrade my psu that cause me an extra cost!

how much is 3050 ?

I use A5000 and love it

My budget is limited and I can't buy 3060 :(

Used RTX3060 would be better, with 12GB VRAM too. It helped me greatly. what is your budget ? even new 3060 is around US$300 here.

2060. 3 times the tensor cores and 1.5x the bandwidth. 6 and 8gb really ain't much.

Well for softmax you need at least 2 neurons

Higher memory is generally better for deep learning tasks. Specially with the size of models now

Softmax activation function is usually used for multi categorical classification. Knowing that you are building a binary classification model, sigmoid is better for your application :)

EDIT : I've made a mistake. It is not used for multi categorical classification but to be able to classify multiple 'categories' from one input.

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I always use dask for this kind of situation. works perfectlyly

https://www.dask.org/get-started

Use a custom Dataset class.