Code snippets are not showing up in AI assistant responses
In Adobe Acrobat I asked the AI assistant a question about how a particular machine learning method (described in the pdf) could be coded up.
The paper is here: [2412.10354] A Library for Learning Neural Operators
The prompt is this:
The response I got had no code scippet but it acted as if there was one and provided an explanation for the non visible code snippet. Here is what I got:
Certainly! Here is the code snippet for training a neural operator using the NeuralOperator library on pre-existing simulation grid data:
Explanation:
- Data Loading: The load_darcy_flow function loads the Darcy Flow dataset, which is used as an example of pre-existing simulation grid data.
- Model Definition: A FourierNeuralOperator model is defined with specified input and output dimensions, number of Fourier modes, and network width.
- Data Processor: A DataProcessor is defined to handle normalization and padding.
- Trainer Definition: A Trainer is set up with the model, data processor, training/validation/test data, loss function, optimizer, learning rate, number of epochs, batch size, and device (CPU/GPU).
- Training: The train method of the Trainer is called to train the model.
- Evaluation: The trained model is evaluated on the test data, and the test loss is printed.
This example provides a basic workflow for training a neural operator using the NeuralOperator library. Adjust the parameters and dataset loading as needed for your specific use case.
I then said "can you show the code snippet please, it did not appear in the previous response". It still failed to provide the code snippet and responded with this:
Certainly! Here is the code snippet for training a neural operator using the NeuralOperator library on pre-existing simulation grid data:
Explanation:
- Data Loading: The load_darcy_flow function loads the Darcy Flow dataset, which is used as an example of pre-existing simulation grid data.
- Model Definition: A FourierNeuralOperator model is defined with specified input and output dimensions, number of Fourier modes, and network width.
- Data Processor: A DataProcessor is defined to handle normalization and padding.
- Trainer Definition: A Trainer is set up with the model, data processor, training/validation/test data, loss function, optimizer, learning rate, number of epochs, batch size, and device (CPU/GPU).
- Training: The train method of the Trainer is called to train the model.
- Evaluation: The trained model is evaluated on the test data, and the test loss is printed.
This example provides a basic workflow for training a neural operator using the NeuralOperator library. Adjust the parameters and dataset loading as needed for your specific use case.
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