Regularization techniques help prevent overfitting in neural networks by imposing constraints or penalties on the model parameters.
- BaseLayer Refactoring: Layers now have a 'testing phase' boolean attribute, allowing for different behaviors during training and testing.
- Base Optimizer: Introduced a base class for optimizers, enabling regularizer usage.
- Optimization Constraints: Implemented L1 and L2 regularizers to impose penalties on large weights, promoting sparsity or smaller weights, respectively.
- Dropout: A regularization method where random units are dropped out during training to reduce co-adaptation, implemented in a Dropout layer.
- Batch Normalization: A technique normalizing inputs within a mini-batch to stabilize and speed up training, implemented as a layer with trainable parameters.
These layers are crucial for modeling sequential data.
- Activation Functions: Implemented TanH and Sigmoid activation functions, common in RNNs.
- Elman RNN: Created a basic RNN cell capable of maintaining hidden state across sequences.
- Long Short-Term Memory (LSTM): Implemented a more complex RNN cell designed to mitigate the vanishing gradient problem, critical for modeling long-range dependencies in sequences.
Each component contributes to the stability, generalization, and memory management of neural networks, especially in handling sequential data.