def __init__(self, input_dim): super(NeuralNet, self).__init__() # Define your neural network here # TODO: How to modify this model to achieve better performance? self.net = nn.Sequential( nn.Linear(input_dim, 64), #70是我调得最好的， 而且加层很容易过拟和 nn.ReLU(), nn.Linear(64, 1) ) # Mean squared error loss self.criterion = nn.MSELoss(reduction='mean')

To modify the `NeuralNet` model to achieve better performance, you can consider experimenting with the following modifications:

1. Increase the number of hidden layers: Adding more hidden layers can increase the model's capacity to learn complex patterns in the data. You can add additional `nn.Linear` layers with appropriate activation functions between them.

2. Adjust the number of hidden units in each layer: The number of hidden units determines the complexity and representational power of the neural network. Increasing the number of hidden units can potentially improve the model's ability to capture intricate relationships in the data. You can modify the `in_features` argument of `nn.Linear` to change the number of hidden units in a particular layer.

3. Try different activation functions: The ReLU activation function (`nn.ReLU`) is commonly used in neural networks, but experimenting with other activation functions such as `nn.LeakyReLU` or `nn.ELU` might yield better results for your specific task.

4. Implement regularization techniques: Regularization techniques like dropout or weight decay can help prevent overfitting and improve generalization. You can add dropout layers (`nn.Dropout`) after each hidden layer or apply weight decay using optimizer-specific parameters.

5. Adjust the learning rate and optimizer: The learning rate and choice of optimizer can significantly impact the model's convergence and performance. You can experiment with different learning rates and optimizers (e.g., Adam, RMSprop, SGD) to find the combination that works best for your specific task.

Remember to assess the impact of these modifications on both training and validation/test performance to ensure you're achieving better results without overfitting or sacrificing generalization. It may require some trial and error to find the optimal configuration for your specific problem.