Hi Chris,
Finding the optimal network architecture for a Generative Adversarial Network (GAN), especially with time sequence input, is an iterative and experimental process. There is no one-size-fits-all solution, and the architecture often depends on the specific characteristics of the data and the task at hand. Here are some general guidelines and steps you can take to refine your GAN architecture:
Refining the Discriminator Architecture
- Start Simple: Begin with a simpler architecture and gradually increase complexity. For time series data, you might start with one or two GRU layers before adding more.
- Balance Capacity: Ensure that the discriminator and generator are well-balanced in terms of capacity. If the discriminator is too powerful, it will easily distinguish real from fake data, and the generator won't learn effectively.
- Regularization: Use dropout and other regularization techniques to prevent overfitting, but don't overdo it as it might impair the learning of the discriminator.
- Batch Normalization: Consider adding batch normalization to stabilize learning and help with the gradient flow.
- Adjust Learning Rates: Sometimes, using different learning rates for the generator and discriminator can help balance the training process.
Dealing with the Error
The error you're encountering suggests that the network expects a specific mini-batch size during prediction based on how it was trained. In MATLAB, when using sequence networks like LSTM or GRU, the mini-batch size is typically flexible, but if you've configured your network to be stateful, it might expect a consistent mini-batch size.
To resolve this error, consider the following:
- Stateless Networks: Ensure your GRU layers are stateless. In MATLAB, stateless RNN layers do not maintain state between mini-batches, allowing for variable mini-batch sizes during prediction.
- Reset States: If your network is stateful, you may need to reset the states of the network before making predictions with a new mini-batch size.
- Consistent Mini-Batch Size: Try to use the same mini-batch size for training, validation, and testing. If this is not possible, you might need to retrain the network to be stateless or handle variable mini-batch sizes.
Note that, There is a typo in your code. It should be "Dropout" instead of "DropOut" when passing it as an argument to the "dropoutLayer". Here's the corrected line:
dropoutLayer(Dropout,"Name","dropout")
To learn more about GANs you can refer to the following documentation link:
