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fix bug network

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This commit is contained in:
Dario Coscia
2023-11-13 12:25:40 +01:00
committed by Nicola Demo
parent ee39b39805
commit a9f14ac323
6 changed files with 127 additions and 80 deletions
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docs/source/_rst/tutorials/tutorial5/tutorial.rst
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@@ -13,8 +13,7 @@ First of all we import the modules needed for the tutorial. Importing
from scipy import io
import torch
from pina.model import FNO, FeedForward # let's import some models
from pina import Condition
from pina import LabelTensor
from pina import Condition, LabelTensor
from pina.solvers import SupervisedSolver
from pina.trainer import Trainer
from pina.problem import AbstractProblem
@@ -44,10 +43,10 @@ taken from the authors original reference.
data = io.loadmat("Data_Darcy.mat")
# extract data (we use only 100 data for train)
k_train = torch.tensor(data['k_train'], dtype=torch.float).unsqueeze(-1)
u_train = torch.tensor(data['u_train'], dtype=torch.float).unsqueeze(-1)
k_test = torch.tensor(data['k_test'], dtype=torch.float).unsqueeze(-1)
u_test= torch.tensor(data['u_test'], dtype=torch.float).unsqueeze(-1)
k_train = LabelTensor(torch.tensor(data['k_train'], dtype=torch.float).unsqueeze(-1), ['u0'])
u_train = LabelTensor(torch.tensor(data['u_train'], dtype=torch.float).unsqueeze(-1), ['u'])
k_test = LabelTensor(torch.tensor(data['k_test'], dtype=torch.float).unsqueeze(-1), ['u0'])
u_test= LabelTensor(torch.tensor(data['u_test'], dtype=torch.float).unsqueeze(-1), ['u'])
x = torch.tensor(data['x'], dtype=torch.float)[0]
y = torch.tensor(data['y'], dtype=torch.float)[0]
@@ -74,10 +73,10 @@ inheriting from ``AbstractProblem``.
.. code:: ipython3
class NeuralOperatorSolver(AbstractProblem):
input_variables = ['u_0']
output_variables = ['u']
conditions = {'data' : Condition(input_points=LabelTensor(k_train, input_variables),
output_points=LabelTensor(u_train, output_variables))}
input_variables = k_train.labels
output_variables = u_train.labels
conditions = {'data' : Condition(input_points=k_train,
output_points=u_train)}
# make problem
problem = NeuralOperatorSolver()
@@ -114,7 +113,7 @@ training using supervised learning.
.. parsed-literal::
Epoch 9: : 100it [00:00, 383.36it/s, v_num=36, mean_loss=0.108]
Epoch 9: : 100it [00:00, 357.28it/s, v_num=1, mean_loss=0.108]
.. parsed-literal::
@@ -123,7 +122,7 @@ training using supervised learning.
.. parsed-literal::
Epoch 9: : 100it [00:00, 380.57it/s, v_num=36, mean_loss=0.108]
Epoch 9: : 100it [00:00, 354.81it/s, v_num=1, mean_loss=0.108]
The final loss is pretty high… We can calculate the error by importing
@@ -137,10 +136,10 @@ The final loss is pretty high… We can calculate the error by importing
metric_err = LpLoss(relative=True)
err = float(metric_err(u_train.squeeze(-1), solver.models[0](k_train).squeeze(-1)).mean())*100
err = float(metric_err(u_train.squeeze(-1), solver.neural_net(k_train).squeeze(-1)).mean())*100
print(f'Final error training {err:.2f}%')
err = float(metric_err(u_test.squeeze(-1), solver.models[0](k_test).squeeze(-1)).mean())*100
err = float(metric_err(u_test.squeeze(-1), solver.neural_net(k_test).squeeze(-1)).mean())*100
print(f'Final error testing {err:.2f}%')
@@ -163,10 +162,10 @@ operator this approach is better suited, as we shall see.
projecting_net = torch.nn.Linear(24, 1)
model = FNO(lifting_net=lifting_net,
projecting_net=projecting_net,
n_modes=16,
n_modes=8,
dimensions=2,
inner_size=24,
padding=11)
padding=8)
# make solver
@@ -188,7 +187,7 @@ operator this approach is better suited, as we shall see.
.. parsed-literal::
Epoch 9: : 100it [00:04, 22.13it/s, v_num=37, mean_loss=0.000952]
Epoch 0: : 0it [00:00, ?it/s]Epoch 9: : 100it [00:02, 47.76it/s, v_num=4, mean_loss=0.00106]
.. parsed-literal::
@@ -197,7 +196,7 @@ operator this approach is better suited, as we shall see.
.. parsed-literal::
Epoch 9: : 100it [00:04, 22.07it/s, v_num=37, mean_loss=0.000952]
Epoch 9: : 100it [00:02, 47.65it/s, v_num=4, mean_loss=0.00106]
We can clearly see that the final loss is lower. Let’s see in testing..
@@ -207,17 +206,17 @@ training, when many data samples are used.
.. code:: ipython3
err = float(metric_err(u_train.squeeze(-1), solver.models[0](k_train).squeeze(-1)).mean())*100
err = float(metric_err(u_train.squeeze(-1), solver.neural_net(k_train).squeeze(-1)).mean())*100
print(f'Final error training {err:.2f}%')
err = float(metric_err(u_test.squeeze(-1), solver.models[0](k_test).squeeze(-1)).mean())*100
err = float(metric_err(u_test.squeeze(-1), solver.neural_net(k_test).squeeze(-1)).mean())*100
print(f'Final error testing {err:.2f}%')
.. parsed-literal::
Final error training 4.45%
Final error testing 4.91%
Final error training 4.83%
Final error testing 5.16%
As we can see the loss is way lower!