0
$\begingroup$

I'm working on a classifier that uses a convolutional neural network. As part of this, the AdamOptimizer is used during gradient descent.

When I examine the results of training and testing, I'm finding significant overfitting given that the data available tends to be concentrated in several classes.

How can I reduce overfitting here? Ideas I've had, that I'm not sure are sound, or how they would work:

  • Change training data to make it more evenly distributed across classes
  • Modify the loss function in some way
  • Change the hyperparameters (learning rate?) for the Adam Optimizer
$\endgroup$
1
$\begingroup$

Have you tried adding dropout regularization? This is essentially removing a certain percentage of the neurons on every training iteration, resulting in a network less sensitive to the activations of specific neurons, therefore less fragile and sensitive to details in images in the training set.

It's perhaps explained better here: https://www.tensorflow.org/tutorials/layers. In the case of TensorFlow it's just a layer that you can stick between two arbitrary layers.

| cite | improve this answer | |
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.