Key hyperparameters in deep learning - Python Tutorial

- [Narrator] In deep learning, hyperparameters are external settings that define how a neural network is structured and trained. Unlike parameters which are learned during training, hyperparameters are set before training begins and have a significant impact on the model's ability to learn and generalize. Selecting the right type of parameters is crucial for ensuring efficient training, robust performance, and optimal internalization to unseen data. One of the most important type of parameters is the learning rate, which controls how much the model adjusts its weight during each training iteration. A learning rate that is too high can cause a model to overshoot the optimal values, leading to unstable training, while a learning rate that is too low, results in slow convergence and may trap the model in a suboptimal solution. It's common to start with a modest learning rate, such as 0.001, especially when using optimizers like Adam. This value serves as a stable baseline that often works well across a variety of tasks. From there, consider implementing a learning rate scheduler, such as step decay, cosine annealing, or a warmup schedule to adjust the learning rate dynamically as your training progresses. These schedulers can help you take larger steps early on, then refine those steps as you get closer to an optimal solution. Finally, conducting a quick search, such as testing rates between 0.0001 and 0.1 can help identify a sweet spot for your particular problem. Another critical hyperparameter is the batch size, which determines the number of training samples processed before updating the model's parameters. Smaller batch sizes result in noisier estimates of the gradient, but can help the model escape shallow local minima, and often lead to faster initial convergence. Larger batches can streamline computations, especially on modern GPUs, but might require careful adjustments of the learning rate, and can sometimes lead the model to a less ideal final solution. A good starting point is to pick a relatively moderate batch size between 32 and 256, balancing the available computational resources with stable training behavior. If your hardware is limited in memory, you may need to opt for smaller batches. Conversely, if you have ample GPU memory, you can try larger batches, but consider increasing the learning rates slightly to maintain a good training pace. As always, monitor performance and be prepared to iterate. The number of epochs is another hyperparameter, and it defines how many times the model passes through the entire training dataset during training. Too few epochs can result in underfitting, where the model fails to learn the data effectively, while too many epochs can lead to overfitting, where the model memorizes training data and fails to generalize. A typical starting range might be 10 to 50 epochs. But this is highly dependent on the complexity of your dataset and model. To prevent unnecessary training and overfitting, implement early stopping based on your validation loss or accuracy. If the validation metrics stop improving, that's a strong signal that you should halt training. Monitoring these validation metrics closely, and stopping when progress stalls, can save both time and computational resources. Hyperparameters also govern the architecture of the model. Decisions such as the number of layers and the number of neurons per layer, play a critical role in determining a model's capacity and the ability to capture complex patterns. Begin by starting simple. Try a shallow model and see how it performs. If the model underfits, meaning it's not capturing the complexity in your data, gradually increase depth or width and see if performance improves. When working on well-studied tasks, consider using well-known architectures, like ResNet for image classification, or BERT variants for natural language processing. These models have been extensively tested and provided a reliable starting framework. As the model grows in size, incorporate regularization methods like dropout, weight decay, or batch normalization to keep training stable and to prevent overfitting. One of the most popular and straightforward forms of regularization is dropout. The dropout rate defines a fraction of neurons randomly dropped or turned off during training. This prevents a network from relying too heavily on any one node, and encourages the network to learn more robust, generalizable patterns. A common range for dropout is between .1 and .5, depending on the layer type and complexity. If the model seems to underfit, lowering the dropout rates can help. Conversely, if the model appears to overfit, the dropout rates can be increased. Note that different layers may require different rates, so don't hesitate to experiment. We can also set regularization coefficients for L1 and L2 regularization. The key is to start with small coefficients and adjust, based on the model's tendency to overfit or under fit. The weight initialization method is also a crucial hyperparameter, as it affects the starting point of a training process and the model's ability to converge. Good initializations help ensure that signals propagate well and that gradients remain stable. Poor initialization can lead to vanishing or exploding gradients, making training difficult or impossible. Beyond initialization, the choice of optimizer is another key decision point. Optimizers dictate how the model updates its weights, based on the computed gradients during backpropagation. In terms of best practices for weight initialization, it's usually best to choose a well-established initialization method, like Xavier or He initialization for deep networks, as they both ensure that model weights are neither too large nor too small. For the optimizer, start with a commonly recommended option. Stochastic gradient descent is a widely used optimizer known for its simplicity and effectiveness, especially when combined with momentum to accelerate convergence. Adam, on the other hand, is an adaptive optimizer that adjusts the learning rates for each parameter dynamically, making it a popular choice for most deep learning tasks due to its robust performance. Finally, only fine-tune the weight initialization optimizer if you encounter convergence issues. If performance is stable, adjustments to learning rates and batch size often yield more significant improvements than optimizer tweaks. Hyperparameter tuning is a balancing act. The key is to always begin with known baselines or defaults. Once you have a stable training process, adjust one hyperparameter at a time and monitor validation metrics. By iteratively refining various hyperparameters, you'll eventually start to develop an intuition for how the various levers interact.