I’m working on an image classification project using this dataset. However, my validation accuracy is constantly stuck at exactly 20%.
I’ve tried changing epochs, optimizers, adding/decreasing layer complexity, batch_size, etc. I have a function that changes learning rate as the epochs change and I’m using data augmentation. I’m not sure what to do because my val_accuracy is not changing at all from 0.2. Any advice is greatly appreciated!
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Please go through the lectures and previous labs in the course so far. Here are some issues:
- For a multiclass classification,
sigmoid isn’t the correct activation to use for the output layer.
ImageDataGenerator encodes labels as integers and doesn’t one-hot encode them. The way you’ve done it is incorrect. Do fix the loss function to reflect the labels.
- Your learning rate schedule picks a very low rate for the optimizer beyond 10 iterations. I recommend removing the callback and let the optimizer learn with the default configuration.
- Leave the batch size at 32 since you have a lot of images.
Hello. Thank you for your help! I was able to correct these issues and my accuracy jumped to around 40%. Since this is still pretty low, I was wondering if you had any thoughts on what the foundational issues are with my program. I used a pre-existing cleaned dataset and I’ve tried a number of different epochs, optimizers, and loss functions to see if they make a difference. I’ve looked through the past lectures, and I’m still having trouble identifying what the core issue is. Do you have any thoughts on this?
Thank you so much for your help!
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