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Normalized loss parameter swtich #34

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Jul 28, 2023
Merged

Normalized loss parameter swtich #34

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46c5820
normalized_loss_stop switch
SuperSashka Jul 28, 2023
f9810d3
Example status quo
SuperSashka Jul 28, 2023
5f1538f
Update setup.py
SuperSashka Jul 28, 2023
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2 changes: 1 addition & 1 deletion examples/example_1d_navier_stokes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,7 @@

os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'

sys.path.pop()
#sys.path.pop()
sys.path.append(os.path.abspath(os.path.join(os.path.dirname( __file__ ), '..')))

from tedeous.input_preprocessing import Equation
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4 changes: 2 additions & 2 deletions examples/example_SODtest.py
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Expand Up @@ -14,9 +14,9 @@

os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'

sys.path.append('../')
#sys.path.append('../')

sys.path.pop()
#sys.path.pop()
sys.path.append(os.path.abspath(os.path.join(os.path.dirname( __file__ ), '..')))

from tedeous.input_preprocessing import Equation
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2 changes: 1 addition & 1 deletion setup.py
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Expand Up @@ -30,7 +30,7 @@ def get_requirements():

setup(
name = 'tedeous',
version= '0.3.0' ,
version= '0.3.1' ,
description = 'TEDEouS - Torch Exhaustive Differential Equations Solver. Differential equation solver, based on pytorch library',
long_description = 'Combine power of pytorch, numerical methods and math overall to conquer and solve ALL {O,P}DEs. There are some examples to provide a little insight to an operator form',
author = 'Alexander Hvatov',
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23 changes: 14 additions & 9 deletions tedeous/cache.py
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Expand Up @@ -132,7 +132,7 @@ def model_reform(init_model, model):


def cache_lookup(self, lambda_operator: float = 1., lambda_bound: float = 0.001,
nmodels: Union[int, None] = None, save_graph: bool = False, cache_verbose: bool = False) -> Tuple[dict, torch.Tensor]:
nmodels: Union[int, None] = None, save_graph: bool = False, cache_verbose: bool = False, return_normalized_loss: bool = False) -> Tuple[dict, torch.Tensor]:
"""
Looking for a saved cache.
Args:
Expand Down Expand Up @@ -193,7 +193,9 @@ def cache_lookup(self, lambda_operator: float = 1., lambda_bound: float = 0.001,
if best_checkpoint == {}:
best_checkpoint = None
min_loss = np.inf
return best_checkpoint, min_norm_loss
if return_normalized_loss:
min_loss=min_norm_loss
return best_checkpoint, min_loss

def save_model(self, prep_model: Any, state: dict, optimizer_state: dict, name: Union[str, None] = None):
"""
Expand Down Expand Up @@ -326,7 +328,7 @@ def cache_retrain(self, cache_checkpoint, cache_verbose: bool = False) -> Union[

def cache_nn(self, cache_dir: str, nmodels: Union[int, None], lambda_operator: float, lambda_bound: float,
cache_verbose: bool,model_randomize_parameter: Union[float, None],
cache_model: torch.nn.Sequential, ):
cache_model: torch.nn.Sequential, return_normalized_loss: bool = False):
"""
Restores the model from the cache and uses it for retraining.
Args:
Expand All @@ -345,7 +347,8 @@ def cache_nn(self, cache_dir: str, nmodels: Union[int, None], lambda_operator: f
cache_checkpoint, min_loss = self.cache_lookup(nmodels=nmodels,
cache_verbose=cache_verbose,
lambda_operator= lambda_operator,
lambda_bound=lambda_bound)
lambda_bound=lambda_bound,
return_normalized_loss = return_normalized_loss)

self.model, optimizer_state = self.cache_retrain(cache_checkpoint,
cache_verbose=cache_verbose)
Expand All @@ -356,7 +359,7 @@ def cache_nn(self, cache_dir: str, nmodels: Union[int, None], lambda_operator: f

def cache_mat(self, nmodels: Union[int, None],lambda_operator: float, lambda_bound: float,
cache_verbose: bool,model_randomize_parameter: Union[float, None],
cache_model: torch.nn.Sequential):
cache_model: torch.nn.Sequential, return_normalized_loss: bool = False):
"""
Restores the model from the cache and uses it for retraining.
Args:
Expand Down Expand Up @@ -389,7 +392,8 @@ def cache_mat(self, nmodels: Union[int, None],lambda_operator: float, lambda_bou
cache_dir=self.cache_dir,
nmodels=nmodels,
cache_verbose=cache_verbose,
lambda_bound=lambda_bound)
lambda_bound=lambda_bound,
return_normalized_loss=return_normalized_loss)
prepared_model, optimizer_state = model_cls.cache_retrain(
cache_checkpoint,
cache_verbose=cache_verbose)
Expand All @@ -411,7 +415,8 @@ def cache_mat(self, nmodels: Union[int, None],lambda_operator: float, lambda_bou

def cache(self, nmodels: Union[int, None],lambda_operator, lambda_bound: float,
cache_verbose: bool,model_randomize_parameter: Union[float, None],
cache_model: torch.nn.Sequential, ):
cache_model: torch.nn.Sequential,
return_normalized_loss: bool = False):
"""
Restores the model from the cache and uses it for retraining.
Args:
Expand All @@ -430,9 +435,9 @@ def cache(self, nmodels: Union[int, None],lambda_operator, lambda_bound: float,
if self.mode != 'mat':
return self.cache_nn(self.cache_dir, nmodels,lambda_operator, lambda_bound,
cache_verbose, model_randomize_parameter,
cache_model)
cache_model,return_normalized_loss=return_normalized_loss)
elif self.mode == 'mat':
return self.cache_mat(self.cache_dir, nmodels, lambda_operator, lambda_bound,
cache_verbose, model_randomize_parameter,
cache_model)
cache_model,return_normalized_loss=return_normalized_loss)

10 changes: 7 additions & 3 deletions tedeous/solver.py
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Expand Up @@ -222,7 +222,8 @@ def solve(self,lambda_operator: Union[float, list] = 1,lambda_bound: Union[float
patience: int = 5, loss_oscillation_window: int = 100,
no_improvement_patience: int = 1000, model_randomize_parameter: Union[int, float] = 0,
optimizer_mode: str = 'Adam', step_plot_print: Union[bool, int] = False,
step_plot_save: Union[bool, int] = False, image_save_dir: Union[str, None] = None, tol: float = 0,clear_cache: bool =False) -> Any:
step_plot_save: Union[bool, int] = False, image_save_dir: Union[str, None] = None, tol: float = 0,
clear_cache: bool =False, normalized_loss_stop: bool = False) -> Any:
"""
High-level interface for solving equations.

Expand Down Expand Up @@ -277,7 +278,7 @@ def solve(self,lambda_operator: Union[float, list] = 1,lambda_bound: Union[float
cache_verbose,
model_randomize_parameter,
cache_model,
)
return_normalized_loss=normalized_loss_stop)

Solution_class = Solution(self.grid, self.equal_cls,
self.model, self.mode, self.weak_form,
Expand Down Expand Up @@ -315,7 +316,10 @@ def closure():
tol=tol)

loss.backward()
cur_loss = loss_normalized.item()
if normalized_loss_stop:
cur_loss = loss_normalized.item()
else:
cur_loss = loss.item()
return loss

stop_dings = 0
Expand Down