I adapted the AlexNet architecture, preprocessed the images, augmented the images, used LeakyReLU activation function, utilized dropouts, and had tried adjusting a learning rate. However, these trials aren't improving my model's val_loss and val_categorical_accuracy. What should i do? Embedded are my model's fitting and history visualized =
This might occurs due to several reasons, the most common reasons,
The number of examples of the validation set are not big enough
A validation step at each epoch might be small
Validation set has examples not in the training set
Training set batch size not quite good compared with validation set size
Model biased against ( high bias ) some classes at training set and those classes more likely to be in the validation set.
... etc
You could check n - randomly chosen mislabeled examples from the validation set, usually is the best thing to do so.
Turns out i tweaked my model too much. I tried using purely AlexNet with ReLU function and it did the job, albeit too slow (175 s/epoch). I also didn't know that to have a layer (both Conv and FC layers) use an activation function, we must specify the function within the layer's parameter (before, i had used the.add after said layer and turns out it didn't register as the activation function of said layer therefore the layer didn't learn anything throughout the fitting process). I really need to learn more about building CNNs i guess.
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