TensorFlow Regularization Techniques Explained

In TensorFlow, the following model regularization techniques can be used to prevent overfitting:

1. L1 regularization: By adding a penalty term of the L1 norm to the model’s loss function, the absolute values of the model weights are constrained, encouraging sparsity in the model parameters.
2. L2 regularization: It limits the sum of the squared weights of the model by adding an L2 norm penalty term to the model’s loss function, preventing the model parameters from becoming too large.
3. Dropout regularization: during training, randomly set some neurons’ outputs in the neural network to zero in order to reduce complexity and prevent overfitting.
4. Batch Normalization: By normalizing the inputs of each batch, the neural network ensures that the inputs to each layer remain relatively stable, which helps to accelerate the training process and improve the generalization ability of the model.
5. Early Stopping is a technique where the performance of the validation set is monitored during training, and the training is stopped when the validation set performance no longer improves, in order to prevent overfitting of the model.
6. Data augmentation: Increasing the diversity of the training data by applying random transformations such as rotation, flipping, and cropping, which helps to improve the model’s ability to generalize.

These regularization techniques can be used alone or in combination to improve the generalization ability and stability of the model.