Comments (2)
Hello,
We can find the weights by accessing the nets parameter of the solver object and then its NN field (for a FCNN object)(https://github.com/NeuroDiffGym/neurodiffeq/blob/master/neurodiffeq/networks.py#L6). This gives a torch.nn.Sequential object and its weights can easily be found out by indexing the layer and accessing weight field. For your given example,
solver_system.nets[0].NN[0].weight
The output I got when I ran the code:
Parameter containing:
tensor([[ 0.8768],
[-0.6987],
[-0.5978],
[ 0.3867],
[-0.3607],
[-0.6576],
[-0.4753],
[-0.1397],
[-0.6517],
[ 0.4632],
[-0.5898],
[-0.3324],
[ 0.8168],
[-0.8792],
[-0.7331],
[-0.1540],
[ 0.1956],
[ 0.7815],
[ 0.2859],
[-0.6865],
[ 0.8375],
[ 0.8586],
[-0.8746],
[-0.5371],
[-0.7475],
[ 0.4840],
[ 0.5717],
[-0.1432],
[ 0.2203],
[-0.3002],
[ 0.2296],
[-0.3601]], requires_grad=True)
from neurodiffeq.
Hi dear @shuheng-liu is there any way to find value of weights?
from neurodiffeq.
Related Issues (20)
- Is there a way to access the train/valid loss history for Solver1D, like for solve? HOT 2
- Using Special type activation function HOT 9
- Saving subclass of solvers HOT 1
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from neurodiffeq.