Use trained neural network for different initial conditions #811
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You can first restore the weights, and then compile and train the network. |
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Dear Dr. Lu Lu
I am working on a project which needs to use a trained neural network to solve ODE problems for different initial conditions. For example, I have a model which could correctly solve the Van der Pol equation with initial conditions y(0)=-2 and dy/dt=-2 at t=0. Now I want to solve the Van der Pol equation with initial conditions y(0)=-1 and dy/dt=-1 at t=0. How could I implement this in Deepxde. Does that mean I have to state new initial conditions and re-compile the model? Since I have re-complied the model, how could I make sure the weight matrix (e.g. dense/kernel:0 from model.print_model()) does not change? I have checked how to re-store the model but I think it can only predict the value based on the "old" initial conditions.
Best wishes
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