About Training Burgers ? I wonder why the coef won't be trained during the second call of train (train without L1) #9
Description
We are trying to train the Burgers Equation:
In function train_deepmod, i wonder why the sparse_coef_vector won't be trained and changed in the second training (train with out L1) ?
It will be grateful if you could spare some time and help us figger out this problem, thank you !
def train_deepmod(model, data, target, optimizer, max_iterations, loss_func_args):
'''Performs full deepmod cycle: trains model, thresholds and trains again for unbiased estimate. Updates model in-place.'''
# Train first cycle and get prediction
train(model, data, target, optimizer, max_iterations, loss_func_args)
prediction, time_deriv_list, sparse_theta_list, coeff_vector_list = model(data)
# Threshold, set sparsity mask and coeff vector
sparse_coeff_vector_list, sparsity_mask_list = threshold(coeff_vector_list, sparse_theta_list, time_deriv_list)
model.fit.sparsity_mask = sparsity_mask_list
model.fit.coeff_vector = torch.nn.ParameterList(sparse_coeff_vector_list)
print()
print(sparse_coeff_vector_list)
print(sparsity_mask_list)
#Resetting optimizer for different shapes, train without l1
optimizer.param_groups[0]['params'] = model.parameters()
print() #empty line for correct printing
train(model, data, target, optimizer, max_iterations, dict(loss_func_args, **{'l1': 0.0})) # here! is the problem~we print out the coef vector that shows the problem
**`first call of train perfect result!`**
| Iteration | Progress | Time remaining | Cost | MSE | Reg | L1 |
-- Parameter containing:
tensor([[0.0325],
[0.9853],
[0.9293],
[0.5554],
[0.1535],
[0.0268],
[0.2549],
[0.7636],
[0.7127]], requires_grad=True)
0 0.00% 0s 7.02e-02 6.92e-02 1.41e-04 8.60e-04 -- Parameter containing:
tensor([[-0.0545],
[ 0.8102],
[ 1.0020],
[ 0.4142],
[-0.0333],
[ 0.0626],
[ 0.0997],
[ 0.5664],
[ 0.7545]], requires_grad=True)
100 10.00% 9s 1.16e-02 7.07e-03 4.41e-03 1.53e-04 -- Parameter containing:
.
.
.
[Parameter containing:
tensor([[ 0.1042],
[-0.8337]], requires_grad=True)]
[tensor([2, 4])]
**`the second call of train the coef vector won't change /(T o T)/~~`**
| Iteration | Progress | Time remaining | Cost | MSE | Reg | L1 |
-- Parameter containing:
tensor([[ 0.1042],
[-0.8337]], requires_grad=True)
0 0.00% 0s 1.67e-04 3.47e-05 1.33e-04 0.00e+00 -- Parameter containing:
tensor([[ 0.1042],
[-0.8337]], requires_grad=True)
100 10.00% 7s 1.67e-04 3.47e-05 1.33e-04 0.00e+00 -- Parameter containing:
tensor([[ 0.1042],
[-0.8337]], requires_grad=True)
200 20.00% 6s 1.67e-04 3.47e-05 1.33e-04 0.00e+00 -- Parameter containing:
tensor([[ 0.1042],
[-0.8337]], requires_grad=True)
300 30.00% 6s 1.67e-04 3.47e-05 1.33e-04 0.00e+00 -- Parameter containing:
tensor([[ 0.1042],
[-0.8337]], requires_grad=True)
400 40.00% 5s 1.67e-04 3.47e-05 1.33e-04 0.00e+00 -- Parameter containing:
tensor([[ 0.1042],
[-0.8337]], requires_grad=True)
500 50.00% 4s 1.67e-04 3.47e-05 1.33e-04 0.00e+00 -- Parameter containing:
tensor([[ 0.1042],
[-0.8337]], requires_grad=True)
600 60.00% 3s 1.67e-04 3.47e-05 1.33e-04 0.00e+00 -- Parameter containing:
tensor([[ 0.1042],
[-0.8337]], requires_grad=True)
700 70.00% 2s 1.67e-04 3.47e-05 1.33e-04 0.00e+00 -- Parameter containing:
tensor([[ 0.1042],
[-0.8337]], requires_grad=True)
800 80.00% 2s 1.67e-04 3.47e-05 1.33e-04 0.00e+00 -- Parameter containing:
tensor([[ 0.1042],
[-0.8337]], requires_grad=True)
900 90.00% 1s 1.67e-04 3.47e-05 1.33e-04 0.00e+00 -- Parameter containing:
tensor([[ 0.1042],
[-0.8337]], requires_grad=True)
1000 100.00% 0s 1.67e-04 3.47e-05 1.33e-04 0.00e+00
** [tensor([2, 4])]
** Parameter containing:
tensor([[ 0.1042],
[-0.8337]], requires_grad=True)
Process finished with exit code 0