What are some effective regularization techniques in TensorFlow?

What are some effective regularization techniques in TensorFlow?

Some effective regularization techniques in TensorFlow include L1 and L2 regularization, dropout, and early stopping.

Can you explain how dropout works?

Dropout randomly sets a fraction of input units to 0 during training, which helps prevent overfitting.

How do I implement L2 regularization in my model?

You can implement L2 regularization by adding a regularization term to your loss function using the kernel_regularizer parameter in layers.

What is early stopping and how can I use it?

Early stopping monitors the validation loss and stops training when it starts to increase, preventing overfitting.