Weight Decay for Specific Layers
Problem Statement
Apply weight decay only to weight parameters, not biases or normalization layers.
Background
Weight decay (L2 regularization) should typically only be applied to weight matrices, not bias terms or BatchNorm parameters. This is a common best practice.
Your Task
The starter code creates a model with Linear and BatchNorm layers. Separate the parameters into two groups: Linear weight matrices (which should get weight decay of 0.01) and everything else (no decay). Create an AdamW optimizer with these two parameter groups.
Output Format
Returns a dictionary with "decay_param_count", "no_decay_param_count", "decay_param_names", and "total_params".
Example:
None
{'decay_param_count': 2, 'no_decay_param_count': 4, 'decay_param_names': ['0.weight', '2.weight'], 'total_params': 6}- The function
selective_decay_test()creates aSequentialmodel with twoLinearlayers and oneBatchNorm1dlayer, resulting in 6 parameters: 2 weights and 2 biases from theLinearlayers, and 2 parameters from theBatchNorm1dlayer. - The parameters are then separated into two groups:
decay(weight parameters ofLinearlayers) andno_decay(bias parameters andBatchNormparameters), yielding 2 parameters in thedecaygroup and 4 parameters in theno_decaygroup. - The names of the parameters with weight decay are
0.weightand2.weight, corresponding to the weights of the twoLinearlayers. - The function returns a dictionary with the counts and names of parameters with and without weight decay, as well as the total parameter count, resulting in the output
{'decay_param_count': 2, 'no_decay_param_count': 4, 'decay_param_names': ['0.weight', '2.weight'], 'total_params': 6}.
Constraints:
- Weight decay only on Linear weight matrices
- No decay on biases and BatchNorm params
- Use AdamW optimizer
Background Knowledge
Introduction to Weight Decay
Weight decay, also known as L2 regularization, is a technique used to prevent overfitting in neural networks. It works by adding a penalty term to the loss function that is proportional to the magnitude of the model's weights. This encourages the model to use smaller weights, which can help to reduce overfitting. The penalty term is typically proportional to the square of the weight values, hence the name L2 regularization.
Parameter Groups in Optimizers
In PyTorch, optimizers can be used with multiple parameter groups. This allows for different hyperparameters, such as learning rate and weight decay, to be applied to different sets of parameters. For example, it's common to apply weight decay only to the weights of linear layers, while leaving the biases and batch normalization parameters unchanged. This is because weight decay can help to prevent overfitting in the linear layers, but may not be beneficial for the biases and batch normalization parameters.
AdamW Optimizer
The AdamW optimizer is a variant of the Adam optimizer that includes weight decay. It is similar to the Adam optimizer, but with an additional term that applies weight decay to the parameters. The AdamW optimizer is often used in deep learning models because it can help to prevent overfitting and improve the stability of the training process.
Algorithm/Approach
The general approach to solving this problem is to:
- Separate the model's parameters into two groups: one for parameters that should have weight decay applied, and one for parameters that should not.
- Create an AdamW optimizer with the desired hyperparameters (e.g. learning rate, weight decay) for each parameter group.
- Use the optimizer to update the model's parameters during training.
Continue the full explanation
You're reading the free preview. Unlock the complete walkthrough, the code editor, test runner and reference solution with Premium.
Editor locked
The code editor is locked for Pro problems. It is only available for free problems. Please upgrade to gain access to the code editor for all problems.