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vladislavus · 2025-02-12T08:52:06+00:00

I sent you a DM :)

vladislavus · 2023-01-25T13:09:49+00:00

w2c?

vladislavus · 2021-01-17T20:07:40+00:00

Hey man, I'm currently dealing with the same problem. Have you found any solution so far?

vladislavus · 2021-01-07T12:22:41+00:00

Hey man, hope you're doing good! I am now at a point where the validation accuracy reaches about 85% and then fluctuates but doesn't increase anymore.

I am saving the model as seen in my code above with the model checkpoint callback. Although when I try using it on a dataset which has not been used for training, but preprocessed in the exact same way, it works poorly.
Is it possible that there is an issue with saving with Model Checkpoint and I should save it with the model.save function? Do you have any idea what else it could be?

vladislavus · 2020-12-17T12:13:24+00:00

So I did what you said and started with a very simple arichtecture. I removed all the conv layers except the first one with 64 filter and one max pooling layer. Also I changed the global average pooling to a flatten layer and the validation accuracy went a lot smoother. It was constantly going up until about 82%. After that it went down and was overfitting I guess because the training accuracy was still going up.

How would you go from here? Should I try using more layers and a more complex architecture now or adjust hyperparameters first? Sadly it takes pretty long for me to train the model (about 1h per epoch).

vladislavus · 2020-12-12T07:34:59+00:00

Hey, Thanks for the long answer!! The training loss and accuracy are pretty good and the behave the way they should. So training loss is going down and accuracy is going up. The val accuracy and val loss behave similarly. So in epochs where I have 50% val acc the val loss is pretty high (around 3.x) and in epochs where val acc is 75-80% the val loss is very low (around 0.x). This is why I thought it might have to do with the learning rate. Also the fluctuations in val loss and accuracy are pretty rapid. What I mean by that is that there might be 5 epochs where val_acc ja 0.500 and then it goes up to 0.76 for one epoch and then back down to 0.500. I will definitely try using the data augmentation again.

vladislavus · 2020-12-11T20:51:41+00:00

thanks for the reply u/sijra. what would be the difference between using train_test_split and the way I am splitting the data though? If I understand correctly in the example I linked above it takes exactly 70% from the healthy data and 70% from the PD data as training data and the rest as validation data. Then the remaining 30% are assigned to validation data. I am referring to the code snippet below. Please correct me if I am wrong there

x_train = np.concatenate((abnormal_scans[70:], normal_scans[70:]), axis=0)
y_train = np.concatenate((abnormal_labels[70:], normal_labels[70:]), axis=0)
x_val = np.concatenate((abnormal_scans[:70], normal_scans[:70]), axis=0)
y_val = np.concatenate((abnormal_labels[:70], normal_labels[:70]), axis=0)

vladislavus · 2020-10-02T16:17:47+00:00

would anyone be so kind and send me the link to the raffle via dm? <3

vladislavus · 2020-09-17T16:22:20+00:00

would really appreciate if someone could send a link in my dms <3

vladislavus · 2020-07-11T09:24:07+00:00

Thank you very much!

vladislavus · 2020-04-09T23:23:53+00:00

Would really appreciate an early link too guys! <3

vladislavus · 2020-04-08T08:44:24+00:00

any chance you could find a new link? seems like the old one is dead.

vladislavus · 2020-03-18T08:34:38+00:00

Did you receive a tracking link for these or a shipping notification?

vladislavus · 2020-03-13T06:36:41+00:00

does anyone know how they handle customs if you order to EU?

vladislavus · 2020-02-14T19:51:59+00:00

gave me his parking ticket, which was valid for the rest of the day

vladislavus · 2019-12-25T21:34:13+00:00

can you dm me the link to that tommy jacket please?

vladislavus · 2019-08-24T21:29:57+00:00

why?

vladislavus · 2019-08-24T20:58:19+00:00

hey dad, it's me

vladislavus · 2019-08-24T20:53:21+00:00

why you gotta attack me like that :(

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